= prediction_threshold)\\n\\n for metric, function in scoring_functions.items():\\n if function[TYPE] == SCORE:\\n args = (y_true_binned, prediction_score)\\n elif function[TYPE] == BIN:\\n args = (y_true_binned, prediction)\\n elif function[TYPE] == BOTH:\\n args = (y_true_binned, prediction, prediction_score)\\n else:\\n raise ValueError(f\\\"TYPE = {function[TYPE]} not supported\\\")\\n try:\\n results[metric] = function[FUNCTION](*args, **function.get(KWARGS, {}))\\n except Exception as e:\\n print(e)\\n results[metric] = 1\\n if can_compute_enrichment_factor(results):\\n results[ENRICHMENT] = results[\\\"precision\\\"] / results[\\\"proportion_active\\\"]\\n return results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b253663ee7f51223c24e77cf41b27e8\",\n \"score\": \"0.48086765\",\n \"text\": \"def match(threshold, truths, priors):\\n # jaccard index\\n overlaps = jaccard(\\n truths,\\n priors\\n )\\n # (Bipartite Matching)\\n # [1,num_objects] best prior for each ground truth\\n best_prior_overlap, best_prior_idx = overlaps.max(1, keepdim=True)\\n # [1,num_priors] best ground truth for each prior\\n best_truth_overlap, best_truth_idx = overlaps.max(0, keepdim=True)\\n best_truth_idx.squeeze_(0)\\n best_truth_overlap.squeeze_(0)\\n best_prior_idx.squeeze_(1)\\n best_prior_overlap.squeeze_(1)\\n best_truth_overlap.index_fill_(0, best_prior_idx, 2) # ensure best prior\\n\\n # TODO refactor: index best_prior_idx with long tensor\\n # ensure every gt matches with its prior of max overlap\\n for j in range(best_prior_idx.size(0)):\\n best_truth_idx[best_prior_idx[j]] = j\\n matches = truths[best_truth_idx] # Shape: [num_priors,4]\\n # conf = labels[best_truth_idx] + 1 # Shape: [num_priors]\\n # conf[best_truth_overlap < threshold] = 0 # label as background\\n # loc = encode(matches, priors, variances)\\n # loc_t[idx] = loc # [num_priors,4] encoded offsets to learn\\n # conf_t[idx] = conf # [num_priors] top class label for each prior\\n print(f\\\"overlaps: {overlaps}\\\")\\n return matches\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b50b95e7cfd475e19a3b86b5311976e0\",\n \"score\": \"0.4800826\",\n \"text\": \"def transform_subtile_rank(\\n transform_func: Callable[[np.ndarray], np.ndarray],\\n rank: int,\\n layout: Tuple[int, int],\\n):\\n total_ranks = layout[0] * layout[1]\\n rank_array = np.arange(total_ranks).reshape(layout)\\n transformed_rank_array = transform_func(rank_array)\\n return rank_array[np.where(transformed_rank_array == rank)][0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71eb472cb3c6c8dc15cfeabee2246397\",\n \"score\": \"0.47967908\",\n \"text\": \"def compute_ranking(self, scores):\\n # sort k=range(...) in decreasing order of the netflows[k]\\n ranking = sorted(range(len(scores)), key=lambda k: scores[k],\\n reverse=True)\\n return ranking\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf93ab181e66320c0b5e8aaca84dc385\",\n \"score\": \"0.4793026\",\n \"text\": \"def calculate_ranking(experiment):\\n optimal_stats_wide = calculate_optimal_stats_wide(experiment, append_std=False)\\n ranking = optimal_stats_wide.apply(lambda row: _return_row_ranking(row[3:], SCORERS[row[2]]._sign), axis=1)\\n return pd.concat([optimal_stats_wide.iloc[:, :3], ranking], axis=1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20ea58217880b00f580a767d2b68191a\",\n \"score\": \"0.4789104\",\n \"text\": \"def ranking_eval(\\n model,\\n metrics,\\n train_set,\\n test_set,\\n val_set=None,\\n rating_threshold=1.0,\\n exclude_unknowns=True,\\n verbose=False,\\n props=None,\\n):\\n\\n if len(metrics) == 0:\\n return [], []\\n\\n avg_results = []\\n user_results = [{} for _ in enumerate(metrics)]\\n\\n gt_mat = test_set.csr_matrix\\n train_mat = train_set.csr_matrix\\n val_mat = None if val_set is None else val_set.csr_matrix\\n\\n def pos_items(csr_row):\\n return [\\n item_idx\\n for (item_idx, rating) in zip(csr_row.indices, csr_row.data)\\n if rating >= rating_threshold\\n ]\\n\\n for user_idx in tqdm.tqdm(test_set.user_indices, disable=not verbose, miniters=100):\\n test_pos_items = pos_items(gt_mat.getrow(user_idx))\\n if len(test_pos_items) == 0:\\n continue\\n\\n u_gt_pos = np.zeros(test_set.num_items, dtype='float')\\n u_gt_pos[test_pos_items] = 1\\n\\n val_pos_items = [] if val_mat is None else pos_items(val_mat.getrow(user_idx))\\n train_pos_items = (\\n []\\n if train_set.is_unk_user(user_idx)\\n else pos_items(train_mat.getrow(user_idx))\\n )\\n\\n u_gt_neg = np.ones(test_set.num_items, dtype='int')\\n u_gt_neg[test_pos_items + val_pos_items + train_pos_items] = 0\\n\\n item_indices = None if exclude_unknowns else np.arange(test_set.num_items)\\n item_rank, item_scores = model.rank(user_idx, item_indices)\\n\\n total_pi = 0.0\\n if props is not None:\\n for idx, e in enumerate(u_gt_pos):\\n if e > 0 and props[str(idx)] > 0:\\n u_gt_pos[idx] /= props[str(idx)]\\n total_pi += 1 / props[str(idx)]\\n\\n for i, mt in enumerate(metrics):\\n mt_score = mt.compute(\\n gt_pos=u_gt_pos,\\n gt_neg=u_gt_neg,\\n pd_rank=item_rank,\\n pd_scores=item_scores,\\n )\\n\\n user_results[i][user_idx] = mt_score\\n\\n # avg results of ranking metrics\\n for i, mt in enumerate(metrics):\\n avg_results.append(sum(user_results[i].values()) / len(user_results[i]))\\n\\n return avg_results, user_results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9fb2833206736e027598531cbcc8271b\",\n \"score\": \"0.47856644\",\n \"text\": \"def rank(self, X, Y):\\n def my_tokenizer(text):\\n initial_tokens = list(tokenize(text, lowercase=True, deacc=True))\\n filtered_tokens = [t for t in initial_tokens if len(t)>2]\\n return filtered_tokens\\n\\n tokenized_corpus = [my_tokenizer(y) for y in Y]\\n tokenized_query = my_tokenizer(X[0][0])\\n# dictionary = Dictionary(documents=tokenized_corpus)\\n# corpus = [dictionary.doc2bow(y) for y in tokenized_corpus]\\n# query = dictionary.doc2bow(tokenized_query)\\n\\n bm = BM25(tokenized_corpus)\\n average_idf = sum(map(lambda k: float(bm.idf[k]), bm.idf.keys())) / len(bm.idf.keys())\\n scores = bm.get_scores(tokenized_query, average_idf)\\n return scores\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2019407953eb771ed1ef78589e0ff08c\",\n \"score\": \"0.47831088\",\n \"text\": \"def mean_percentile_rank(dataset_users, model):\\r\\n users_k=np.zeros(len(dataset_users))\\r\\n for user_id in dataset_users:\\r\\n predictions = model.get_user_predictions(user_id)\\r\\n # last index is the movie name with the highest rating\\r\\n indexes = np.argsort(predictions)\\r\\n percentiles = np.zeros(len(indexes))\\r\\n # going over movies from last to first (highest rating to lowest)\\r\\n for position, movie_id in enumerate(indexes[::-1]):\\r\\n # the percentile is the place of that movie divided by the len of all movies\\r\\n percentile = 1 - (position / len(indexes))\\r\\n # the array percentiles indexes are the movies, the values are the percentile of the movie.\\r\\n percentiles[movie_id] = percentile\\r\\n\\r\\n ground_truth = create_ground_truth(dataset_users[user_id])\\r\\n\\r\\n # for each movie count in ground truth (size k) add that movie rank\\r\\n sum_percentiles = 0\\r\\n for index, item in enumerate(ground_truth):\\r\\n sum_percentiles += percentiles[item-1]\\r\\n\\r\\n # divide by number of movies in ground truth (k in our case)\\r\\n sum_percentiles = sum_percentiles / len(ground_truth)\\r\\n\\r\\n users_k[user_id-1] = sum_percentiles\\r\\n\\r\\n return np.mean(users_k)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"694ecb399297e6521ad7bd5d344acdb1\",\n \"score\": \"0.47766444\",\n \"text\": \"def interpretability_performance(scores, x_model, threshold=0.01):\\n\\n pr_score = []\\n roc_score = []\\n for j, score in enumerate(scores):\\n\\n # calculate information of ground truth\\n gt_info = np.log2(4) + np.sum(x_model[j] * np.log2(x_model[j] + 1e-10), axis=1)\\n\\n # set label if information is greater than 0\\n label = np.zeros(gt_info.shape)\\n label[gt_info > threshold] = 1\\n\\n # (don't evaluate over low info content motif positions)\\n index = np.where((gt_info > threshold) | (gt_info == np.min(gt_info)))[0]\\n\\n # precision recall metric\\n precision, recall, thresholds = precision_recall_curve(\\n label[index], score[index]\\n )\\n pr_score.append(auc(recall, precision))\\n\\n # roc curve\\n fpr, tpr, thresholds = roc_curve(label[index], score[index])\\n roc_score.append(auc(fpr, tpr))\\n\\n roc_score = np.array(roc_score)\\n pr_score = np.array(pr_score)\\n\\n return roc_score, pr_score\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f14d453c9d665715534c0b0e0a86fc91\",\n \"score\": \"0.47750986\",\n \"text\": \"def make_rank_func(weights):\\n def rank(matchinfo):\\n \\\"\\\"\\\"\\n `matchinfo` is defined as returning 32-bit unsigned integers in\\n machine byte order (see http://www.sqlite.org/fts3.html#matchinfo)\\n and `struct` defaults to machine byte order.\\n \\\"\\\"\\\"\\n bufsize = len(matchinfo) # Length in bytes.\\n matchinfo = [struct.unpack(b'I', matchinfo[i:i + 4])[0]\\n for i in range(0, bufsize, 4)]\\n it = iter(matchinfo[2:])\\n return sum(x[0] * w / x[1]\\n for x, w in zip(zip(it, it, it), weights)\\n if x[1])\\n return rank\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0256654cfbd465d6595cdd4707004bbf\",\n \"score\": \"0.47725248\",\n \"text\": \"def apply_topk(topk: float, weight: torch.Tensor, return_scale_factors=False):\\n # Retain only the topk weights, multiplying the rest by 0.\\n frac_to_zero = 1 - topk\\n with torch.no_grad():\\n flat_weight = weight.flatten()\\n # Want to convert it away from a special tensor, hence the float() call.\\n _, idx = flat_weight.float().abs().sort()\\n # @idx is a @special_tensors._SpecialTensor, but we need to convert it\\n # to a normal tensor for indexing to work properly.\\n idx = torch.tensor(idx, requires_grad=False)\\n f = int(frac_to_zero * weight.numel())\\n scale_factors = torch.ones_like(flat_weight, requires_grad=False)\\n scale_factors[idx[:f]] = 0\\n scale_factors = scale_factors.view_as(weight)\\n\\n ret = weight * scale_factors\\n\\n if return_scale_factors:\\n return ret, scale_factors\\n\\n return ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa2c7e8c54873065460c66289b8c3a4e\",\n \"score\": \"0.47604322\",\n \"text\": \"def L1_pruning(args, weight, prune_ratio):\\n percent = prune_ratio * 100\\n weight = weight.cpu().detach().numpy() # convert cpu tensor to numpy\\n shape = weight.shape\\n weight2d = weight.reshape(shape[0], -1)\\n shape2d = weight2d.shape\\n row_l1_norm = LA.norm(weight2d, 1, axis=1)\\n percentile = np.percentile(row_l1_norm, percent)\\n under_threshold = row_l1_norm < percentile\\n above_threshold = row_l1_norm > percentile\\n weight2d[under_threshold, :] = 0\\n above_threshold = above_threshold.astype(np.float32)\\n expand_above_threshold = np.zeros(shape2d, dtype=np.float32)\\n for i in range(shape2d[0]):\\n expand_above_threshold[i, :] = above_threshold[i]\\n weight = weight.reshape(shape)\\n expand_above_threshold = expand_above_threshold.reshape(shape)\\n return torch.from_numpy(expand_above_threshold).cuda(), torch.from_numpy(weight).cuda()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"593dead35288d15f7bbaccee1adb986b\",\n \"score\": \"0.47601005\",\n \"text\": \"def threshold_predictions(predictions, thr=0.999):\\n if thr is None:\\n return predictions[:]\\n\\n thresholded_preds = predictions[:]\\n low_values_indices = thresholded_preds <= thr\\n thresholded_preds[low_values_indices] = 0\\n low_values_indices = thresholded_preds > thr\\n thresholded_preds[low_values_indices] = 1\\n return thresholded_preds\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf90b5b2d8c9997d0c0fcfaf848de3d2\",\n \"score\": \"0.47554255\",\n \"text\": \"def topk_accuracy(scores, labels, ks, selected_class=None):\\n if selected_class is not None:\\n idx = labels == selected_class\\n scores = scores[idx]\\n labels = labels[idx]\\n rankings = scores.argsort()[:, ::-1]\\n # trim to max k to avoid extra computation\\n maxk = np.max(ks)\\n\\n # compute true positives in the top-maxk predictions\\n tp = rankings[:, :maxk] == labels.reshape(-1, 1)\\n\\n # trim to selected ks and compute accuracies\\n return [tp[:, :k].max(1).mean() for k in ks]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d262b361b4f9c7b802b756fa3efc52f\",\n \"score\": \"0.47515237\",\n \"text\": \"def textrank(\\n string: str,\\n model=None,\\n vectorizer=None,\\n top_k: int = 5,\\n atleast: int = 1,\\n stopwords=get_stopwords,\\n **kwargs,\\n):\\n stopwords = validator.validate_stopwords(stopwords)\\n\\n if not hasattr(model, 'fit_transform') and not hasattr(model, 'vectorize'):\\n raise ValueError(\\n 'model must have `fit_transform` or `vectorize` method'\\n )\\n\\n if top_k < 1:\\n raise ValueError('top_k must bigger than 0')\\n if atleast < 1:\\n raise ValueError('atleast must bigger than 0')\\n if not vectorizer:\\n auto_ngram = True\\n else:\\n auto_ngram = False\\n if not hasattr(vectorizer, 'fit'):\\n raise ValueError('vectorizer must have `fit` method')\\n if auto_ngram and not len(stopwords):\\n raise ValueError('insert stopwords if auto_ngram')\\n\\n if auto_ngram:\\n vocab = _auto_ngram(string, stopwords)\\n else:\\n vocab = _base(string, vectorizer=vectorizer, **kwargs)\\n\\n if hasattr(model, 'fit_transform'):\\n vectors = model.fit_transform(list(vocab.keys()))\\n if hasattr(model, 'vectorize'):\\n vectors = model.vectorize(list(vocab.keys()))\\n similar = cosine_similarity(vectors, vectors)\\n similar[similar >= 0.99999] = 0\\n scores = pagerank(similar, **kwargs)\\n total = sum(scores)\\n ranked_sentences = sorted(\\n [\\n (scores[i] / total, s)\\n for i, s in enumerate(vocab.keys())\\n if vocab[s] >= atleast\\n ],\\n reverse=True,\\n )\\n\\n return ranked_sentences[:top_k]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12ea60618ef70cf6a512774d06bff70a\",\n \"score\": \"0.4747656\",\n \"text\": \"def threshold_cost(\\n threshold: float,\\n actuals: pd.Series,\\n preds: pd.Series,\\n cost_matrix: np.ndarray,\\n) -> float:\\n cost_tn, cost_fp, cost_fn, cost_tp = cost_matrix.ravel()\\n bin_preds = np.where(preds >= threshold, 1, 0)\\n matrix = metrics.confusion_matrix(actuals, bin_preds)\\n tn, fp, fn, tp = matrix.ravel()\\n\\n # Punish the scores.\\n cost = (tp * cost_tp) + (tn * cost_tn) + (fn * cost_fn) + (fp * cost_fp)\\n\\n return cost\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1316385332f564c23efbb3590d3d8165\",\n \"score\": \"0.47407508\",\n \"text\": \"def patch_scoring(M, threshold=0.):\\r\\n # Cloning important\\r\\n A = M.clone()\\r\\n\\r\\n # Zero diagonal\\r\\n A.fill_diagonal_(0)\\r\\n\\r\\n # Make sure symmetric and non nul\\r\\n A[A < 0] = 0\\r\\n C = A + A.t()\\r\\n\\r\\n # Sort pixels by inverse degree\\r\\n cent = -torch.sum(A > threshold, dim=1).type(torch.float32)\\r\\n sel = torch.argsort(cent, descending=True)\\r\\n\\r\\n return sel, cent\",\n \"title\": \"\"\n },\n {\n \"docid\": \"293186c7dddcea683351ee4008536047\",\n \"score\": \"0.47376472\",\n \"text\": \"def predict(user_movie_pair,rating_matrix):\\n learning_matrix = create_learning_matrices2(rating_matrix,user_movie_pair)\\n nb_rank = np.sum(learning_matrix,axis=1)\\n for i in range(5):\\n learning_matrix[:,i] *= (i+1)\\n learning_matrix[:,i+4] *= (i+1)\\n total = np.sum(learning_matrix,axis=1)\\n return total/nb_rank\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8585e0cef6891d4345271cf3e99ae985\",\n \"score\": \"0.4732902\",\n \"text\": \"def threshold(\\n matrix : np.ndarray,\\n thresh : float\\n ) -> np.ndarray:\\n threshold_matrix = np.copy(matrix)\\n threshold_matrix[thresh > matrix] = 0\\n threshold_matrix[thresh <= matrix] = 1\\n return threshold_matrix\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08aed28c47742b90f76cf36584f28bc5\",\n \"score\": \"0.4730629\",\n \"text\": \"def feature_ranking(self, W):\\n mcfs_score = W.max(axis=1)\\n ranking = np.argsort(mcfs_score)[::-1]\\n return ranking[:self.d]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ece073f8cb2d14f0a37b0df391d6f2bf\",\n \"score\": \"0.472248\",\n \"text\": \"def rank_keyphrases(keyphrases, top_N_scores, M):\\n \\n scores = {keyphrase: sum([top_N_scores[token] / len(keyphrase) for token in keyphrase])\\n for keyphrase in keyphrases}\\n \\n top_M_scores = {k_p: score for k_p, score in sorted(scores.items(),\\n key = lambda item: item[1],\\n reverse = True)[:M]}\\n \\n return top_M_scores\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9928e6699a83316645f37f151566dcf\",\n \"score\": \"0.4718976\",\n \"text\": \"def low_rank_matrix_factorization(ratings, mask=None, num_features=15, regularization_amount=0.01):\\r\\n num_users, num_products = ratings.shape\\r\\n\\r\\n # If no mask is provided, consider all 'NaN' elements as missing and create a mask.\\r\\n if mask is None:\\r\\n mask = np.invert(np.isnan(ratings))\\r\\n\\r\\n # Replace NaN values with zero\\r\\n ratings = np.nan_to_num(ratings)\\r\\n\\r\\n # Create P and Q and fill with random numbers to start\\r\\n np.random.seed(0)\\r\\n P = np.random.randn(num_users, num_features)\\r\\n Q = np.random.randn(num_products, num_features)\\r\\n\\r\\n # Roll up P and Q into a contiguous array as fmin_cg expects\\r\\n initial = np.append(P.ravel(), Q.ravel())\\r\\n\\r\\n # Create an args array as fmin_cg expects\\r\\n args = (num_users, num_products, num_features, ratings, mask, regularization_amount)\\r\\n\\r\\n # Call fmin_cg to minimize the cost function and this find the best values for P and Q\\r\\n X = fmin_cg(cost, initial, fprime=gradient, args=args, maxiter=3000)\\r\\n\\r\\n # Unroll the new P and new Q arrays out of the contiguous array returned by fmin_cg\\r\\n nP = X[0:(num_users * num_features)].reshape(num_users, num_features)\\r\\n nQ = X[(num_users * num_features):].reshape(num_products, num_features)\\r\\n\\r\\n return nP, nQ.T\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f03bdb196226dca17f502163afad7a6\",\n \"score\": \"0.4714063\",\n \"text\": \"def determineTopKDocuments(self,scores,k):\\n candidateCounter = 0;\\n for i in sorted(scores, key=scores.get, reverse=True):\\n candidateCounter += 1;\\n print (\\\"Document : \\\" + self.docIDToFnameMap[i]);\\n if (candidateCounter == int(k)):\\n break;\\n print (\\\"candidateCounter\\\",candidateCounter);\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10dc43ba36382038e8f4b4806ed898ee\",\n \"score\": \"0.470868\",\n \"text\": \"def rank(ranked_list):\\n return sorted(ranked_list, key=lambda x: x[0])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f202c325d77e33e7b5f043d1b8cf14d2\",\n \"score\": \"0.4706679\",\n \"text\": \"def map_rank(traingnd, testgnd, hamming_rank):\\n numtrain, numtest = hamming_rank.shape\\n apall = np.zeros((numtrain, numtest))\\n aa = np.array([i+1 for i in range(numtrain)])\\n for i in range(numtest):\\n y = hamming_rank[:, i]\\n new_label = np.array([0 for j in range(numtrain)])\\n relevant_indices = (np.matmul(traingnd, testgnd[i, :].reshape((-1, 1))) > 0).reshape(-1)\\n new_label[relevant_indices] = 1\\n xx = np.cumsum(new_label[y])\\n x = xx * new_label[y]\\n p = x / aa\\n p = np.cumsum(p)\\n mask = (p != 0)\\n p[mask] = p[mask]/xx[mask]\\n apall[:, i] = p.copy()\\n mAP = np.mean(apall, axis=1)\\n return mAP\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4fc0d2dee1abc4b8af5d8fdf5077869b\",\n \"score\": \"0.46988523\",\n \"text\": \"def get_interesting_ranks(test_results):\\n rank_mat = test_results.get_rank_mat()\\n # Find rows which scored differently over the various configs FIXME: duplicated\\n isdiff_flags = [not np.all(row == row[0]) for row in rank_mat]\\n #diff_aids = ut.compress(test_results.qaids, isdiff_flags)\\n diff_rank = rank_mat.compress(isdiff_flags, axis=0)\\n diff_qxs = np.where(isdiff_flags)[0]\\n if False:\\n rankcategory = np.log(diff_rank + 1)\\n else:\\n rankcategory = diff_rank.copy()\\n rankcategory[diff_rank == 0] = 0\\n rankcategory[diff_rank > 0] = 1\\n rankcategory[diff_rank > 2] = 2\\n rankcategory[diff_rank > 5] = 3\\n rankcategory[diff_rank > 50] = 4\\n rankcategory[diff_rank > 100] = 5\\n row_rankcategory_std = np.std(rankcategory, axis=1)\\n row_rankcategory_mean = np.mean(rankcategory, axis=1)\\n import vtool as vt\\n row_sortx = vt.argsort_multiarray(\\n [row_rankcategory_std, row_rankcategory_mean], reverse=True)\\n interesting_qx_list = diff_qxs.take(row_sortx).tolist()\\n #print(\\\"INTERSETING MEASURE\\\")\\n #print(interesting_qx_list)\\n #print(row_rankcategory_std)\\n #print(ut.take(qaids, row_sortx))\\n #print(diff_rank.take(row_sortx, axis=0))\\n return interesting_qx_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af6954c236c6a337050b1393d2111a91\",\n \"score\": \"0.46978393\",\n \"text\": \"def gen_matrix_rank(size: MatSize, rank: int, max_denom: int = 1, max_val: int = 3):\\n A, piv = zeros(size[0], size[1]), nr.choice(rank, size[1])\\n for i in range(rank):\\n A[i,piv[i]] = 1\\n for j in range(piv[i]+1, size[1]):\\n A[i,j] = nr.choice([a for a in range(-1*max_val + 1, max_val)])\\n for i in range(size[0]):\\n j = nr.choice(size[0], min(size[0],3), replace=False)\\n for k in j:\\n if k!=i:\\n A = A.elementary_row_op(op=\\\"n->n+km\\\", row=i, row2=k, k=nr.choice(max_val)*nr.choice([-1,1]))\\n return A\",\n \"title\": \"\"\n },\n {\n \"docid\": \"344949ffc881a63b896ab62936ffbdd9\",\n \"score\": \"0.4694353\",\n \"text\": \"def threshold(col, thresh):\\n return 1.0 if col > thresh else 0.0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e8806cefd19fdc9aef8ddb37dd0860a\",\n \"score\": \"0.46933538\",\n \"text\": \"def test_position_rank (doc: Doc):\\n # given\\n position_rank = PositionRankFactory()\\n base_text_rank = BaseTextRankFactory()\\n\\n # when\\n processed_doc = position_rank(doc)\\n phrases = processed_doc._.phrases\\n comparison_doc = base_text_rank(doc)\\n comparison_phrases = comparison_doc._.phrases\\n\\n # then\\n assert set(p.rank for p in phrases) != set(p.rank for p in comparison_phrases)\\n\\n # The test article mentions `Chelsea` at the beginning of the\\n # article while it mentions `Shanghai Shenhua` anecdotally later\\n # in the article. However, with normal TextRank, `Shanghai\\n # Shenhua` is part of top 10 phrases and `Chelsea` isn't. With\\n # PositionRank, the situation is the opposite, which is the\\n # desired outcome when parsing a news article.\\n\\n assert \\\"Chelsea\\\" in [p.text for p in phrases[:10]]\\n assert \\\"Chelsea\\\" not in [p.text for p in comparison_phrases[:10]]\\n assert \\\"Shanghai Shenhua\\\" not in \\\";\\\".join(p.text for p in phrases[:10])\\n assert \\\"Shanghai Shenhua\\\" in \\\";\\\".join(p.text for p in comparison_phrases[:10])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"112f04f2474eedf7fac41e47ceb36166\",\n \"score\": \"0.4692125\",\n \"text\": \"def recall_curve(trial_ranks, top=None):\\n if not isinstance(trial_ranks, np.ndarray):\\n trial_ranks = np.array(trial_ranks)\\n\\n ret = np.zeros(len(trial_ranks))\\n if top is None:\\n for i in range(len(trial_ranks)):\\n ret[i] = np.sum(trial_ranks[:i] <= i) / (i+1)\\n else:\\n for i in range(len(trial_ranks)):\\n ret[i] = 1.0 * np.sum(trial_ranks[:i] < top) / top\\n return ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6248b43d7da03cff3cfecf7c6602c0ca\",\n \"score\": \"0.46913636\",\n \"text\": \"def rank(self, c, i):\\n\\n # go down the tree\\n node = self.root\\n\\n rank = i\\n while (node is not None) & (rank != 0):\\n\\n # rank in the bitstring for this node\\n rank_prev = rank\\n rank = node[1].rank(rank_prev)\\n\\n # check if c is less then pivot\\n if c < node[0]:\\n # we need the count of zeros\\n rank = rank_prev - rank\\n log.debug('Rank in node (left) (%c) = %d', node[0], rank)\\n node = node[2]\\n\\n # c is greater or equal to pivot\\n else:\\n log.debug('Rank in node (right) (%c) = %d', node[0], rank)\\n node = node[3]\\n\\n return rank\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06191884195bd8f9f29961ac5d6a3d23\",\n \"score\": \"0.46898106\",\n \"text\": \"def score(true_ranks: list, computed_ranks: list):\\n number_of_policies = len(true_ranks)\\n number_of_pairs = number_of_policies * (number_of_policies - 1) / 2\\n number_of_swaps = swap_count(true_ranks, computed_ranks)\\n return 1 - number_of_swaps / number_of_pairs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d17c53746d98a7b23b819b4914ebcdc7\",\n \"score\": \"0.4684393\",\n \"text\": \"def accuracy(scores, true_label, topk=(1,)):\\r\\n with torch.no_grad():\\r\\n maxk = max(topk)\\r\\n batch_size = true_label.size(0)\\r\\n\\r\\n _, pred_label = scores.topk(maxk, 1, True, True)\\r\\n pred_label = pred_label.t()\\r\\n correct = pred_label.eq(true_label.view(1, -1).expand_as(pred_label))\\r\\n\\r\\n result = []\\r\\n for k in topk:\\r\\n correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)\\r\\n result.append(correct_k.mul_(100.0 / batch_size))\\r\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"abc12c89829476e827eb089f6e0998ca\",\n \"score\": \"0.46758547\",\n \"text\": \"def rank_queries(predictions, qptr, max_documents=None, inverted=False):\\n\\n max_value = np.max(predictions)\\n # vector with lenght of each doclist\\n n = qptr[1:]-qptr[:-1]\\n if not max_documents:\\n max_documents = np.max(n)\\n\\n # the vector of documents is reshaped into a matrix\\n # with a document list on every row\\n ind = qptr[:-1,None] + np.arange(0,max_documents)[None,:]\\n ind = np.minimum(ind,qptr[1:,None]-1)\\n # warped is now a matrix of size n_queries x max_documents\\n warped = predictions[ind]\\n # every document that appears in a row but not in the query list\\n # (due to n_query_list < max_documents) gets the worst score in off all documents\\n # this makes sure they do not appear in the final ranking\\n warped[np.arange(max_documents)[None,:] >= n[:,None]] = max_value + 1\\n\\n # tiebreak sort uses numpy to rank every row in the matrix\\n # this is faster than ranking them by seperate calls\\n rankings = tiebreak_sort(warped)\\n if inverted:\\n inverted = invert_rankings(rankings,dtype=np.int32)\\n return inverted[np.arange(max_documents)[None,:] < n[:,None]]\\n\\n else:\\n return rankings[np.arange(max_documents)[None,:] < n[:,None]]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"62f2c6e56352d2686b468696cb36d2e9\",\n \"score\": \"0.46756983\",\n \"text\": \"def _rank_samples(fdatagrid):\\n ranks = np.zeros(fdatagrid.shape)\\n ncols_dim_image = np.asarray([range(fdatagrid.shape[i]) for i in range(len(fdatagrid.shape) - 1, 0, -1)])\\n tuples = list(itertools.product(*ncols_dim_image))\\n for t in tuples:\\n ranks.T[t] = rankdata(fdatagrid.data_matrix.T[t], method='max')\\n return ranks\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9332575e32756035e18c8f6e404521d3\",\n \"score\": \"0.467171\",\n \"text\": \"def process(img, upper, lower):\\n \\n image = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\\n gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)\\n \\n detects = []\\n \\n for index in range(len(ranks)):\\n \\n try: detects.append(classifiers[index].detectMultiScale(gray, scaleFactor=1.05, minNeighbors=4, maxSize=upper, minSize=lower))\\n except: detects.append([])\\n \\n circles = cv2.HoughCircles(gray, cv2.HOUGH_GRADIENT, 2.5, 25, minRadius=20, maxRadius=30)\\n \\n widths = []\\n heights = []\\n \\n out = [[], 0]\\n \\n index = 0\\n windows = []\\n \\n for found in detects[:-2]:\\n \\n out[0] += [ranks[index]] * len(detects[index])\\n index += 1\\n \\n for (x, y, w, h) in found: \\n \\n widths.append(w)\\n heights.append(h)\\n windows.append((x, y, w, h))\\n cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 5) \\n \\n for found in detects[-2:]: \\n \\n for (x, y, w, h) in found:\\n \\n if any([x + w / 2.0 >= xx \\n and x + w / 2.0 <= xx + ww \\n and y + h / 2.0 >= yy \\n and y + h / 2.0 <= yy + hh \\n for (xx, yy, ww, hh) in windows]): \\n \\n continue\\n\\n out[0] += [1]\\n widths.append(w)\\n heights.append(h)\\n\\n cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 5)\\n \\n if circles is not None: \\n \\n circles = np.round(circles[0, :]).astype(\\\"int\\\")\\n \\n for (x, y, r) in circles: \\n\\n if any([x >= xx \\n and x <= xx + ww \\n and y >= yy \\n and y <= yy + hh \\n for (xx, yy, ww, hh) in windows]): \\n\\n continue\\n\\n cv2.circle(image, (x, y), r, (255, 0, 255), 4)\\n out[1] += 1\\n \\n if widths and heights: \\n \\n upper = (max(widths) + 10, max(heights) + 6)\\n lower = (min(widths) - 10, min(heights) - 6)\\n \\n plot.imshow(image)\\n plot.show()\\n print(\\\"CURRENT:\\\", out[0], out[1])\\n \\n return out[0], out[1], upper, lower\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25eef21ba00edce620e04ea68181ec4e\",\n \"score\": \"0.46676886\",\n \"text\": \"def rank_n_accuracy(y_true, y_prob_mat, n=0.33):\\n n_classes = y_prob_mat.shape[1]\\n if n < 1:\\n # round to nearest int before casting\\n n = int(round(n_classes * n))\\n\\n # sort the rankings in descending order, then take the top n\\n rankings = np.argsort(-y_prob_mat)\\n rankings = rankings[:, :n]\\n\\n num_samples = len(y_true)\\n correct_sample_count = 0.0 # force floating point math\\n\\n for i in range(num_samples):\\n if y_true[i] in rankings[i, :]:\\n correct_sample_count += 1\\n\\n return old_div(correct_sample_count, num_samples)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32ff296f7d713e1aeac55ebdf1a4b83f\",\n \"score\": \"0.46652246\",\n \"text\": \"def find_rank(words, popularity_metric):\\n words_in_pop_list = [word for word in words if word in popularity_metric]\\n words_not_in_pop_list = [word for word in words if word not in popularity_metric]\\n df_in_list = pd.DataFrame(\\n [popularity_metric[word] for word in words_in_pop_list],\\n index=words_in_pop_list,\\n columns=['count']\\n )\\n\\n df_not_in_list = pd.DataFrame(\\n [0]*len(words_not_in_pop_list),\\n index=words_not_in_pop_list,\\n columns=['count']\\n )\\n\\n df = pd.concat([df_in_list, df_not_in_list])\\n df['rank'] = df['count'].rank(ascending=False)\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90782e301555af3b06546d53acc758bd\",\n \"score\": \"0.46630207\",\n \"text\": \"def rankingMatrix(probabilities):\\n ranking = np.zeros(shape=[probabilities.shape[0], probabilities.shape[1]])\\n probCopy = np.copy(probabilities)\\n for i in range(probabilities.shape[0]):\\n indexMost = 0\\n iteration = 1\\n while(sum(probCopy[i,:]) != 0):\\n for j in range(probabilities.shape[1]):\\n if probCopy[i,j] > probCopy[i,indexMost]:\\n indexMost = j\\n ranking[i, indexMost] = iteration\\n probCopy[i, indexMost] = 0\\n iteration += 1\\n \\n return ranking\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d07cee72cf18b638b70063f9df9852be\",\n \"score\": \"0.46618044\",\n \"text\": \"def knn_threshold(data, column, threshold=15, k=3):\\n from sklearn import neighbors\\n \\n def ids_centers_sizes(data):\\n dat = np.array([(i, data.latitude[data[column]==i].mean(), \\n data.longitude[data[column]==i].mean(),\\n (data[column]==i).sum()) \\n for i in set(list(data[column]))])\\n return dat[:,0], dat[:,1:-1].astype(float), dat[:,-1].astype(int)\\n\\n knn = neighbors.NearestNeighbors(n_neighbors=k)\\n while True:\\n ids, centers, sizes = ids_centers_sizes(data)\\n asrt = np.argsort(sizes)\\n \\n if sizes[asrt[0]] >= threshold:\\n break\\n \\n cids = np.copy(ids)\\n knn.fit(centers)\\n \\n for i in asrt:\\n if sizes[i] < threshold:\\n nearest = knn.kneighbors(centers[i])[1].flatten()\\n nearest = nearest[nearest != i]\\n sel = nearest[np.argmin(sizes[nearest])]\\n total_size = sizes[sel] + sizes[i]\\n data[column][data[column]==cids[i]] = cids[sel]\\n cids[cids==i] = cids[sel]\\n sizes[i] = total_size\\n sizes[sel] = total_size\\n \\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b80eb7d63db8b4bea10396cbaede314\",\n \"score\": \"0.46612287\",\n \"text\": \"def score_func(scores, predictions, inaction_score, perfect_score, thresh=0):\\n # Apply the threshold\\n predictions = (predictions > thresh).astype(int)\\n\\n # Get the actual score\\n actual_score = scores[:, 1][predictions == 1].sum() + scores[:, 0][predictions == 0].sum()\\n\\n # Get the normalized score\\n normalized_score = (actual_score - inaction_score) / (perfect_score - inaction_score)\\n\\n return normalized_score\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c3fd430802a352cb578ce50fcc691ea\",\n \"score\": \"0.46593824\",\n \"text\": \"def __call__(self, smiles, scores): \\n\\n fronts = self.getParetoFronts(scores)\\n\\n rank = []\\n # sort all fronts by crowding distance\\n for t, front in enumerate(fronts):\\n distance = np.zeros(len(front))\\n for i in range(scores.shape[1]):\\n # sort front small to large for value objective i\\n cpu_tensor = scores[front.cpu(), i]\\n order = cpu_tensor.argsort()\\n front = front[order]\\n # set distance value smallest and largest value objective i to large value\\n distance[order[0]] = 10 ** 4\\n distance[order[-1]] = 10 ** 4\\n # get all values of objective i in current front\\n m_values = [scores[j, i] for j in front]\\n # scale for crowding distance by difference between max and min of objective i in front\\n scale = max(m_values) - min(m_values)\\n if scale == 0:\\n scale = 1\\n # calculate crowding distance\\n for j in range(1, len(front) - 1):\\n distance[order[j]] += (scores[front[j + 1], i] - scores[front[j - 1], i]) / scale\\n # replace front by front sorted according to crowding distance\\n fronts[t] = front[np.argsort(distance)]\\n rank.extend(fronts[t].tolist())\\n return rank\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59ade0cb8dab59f5cce831efc5f36702\",\n \"score\": \"0.46563745\",\n \"text\": \"def tile_root_rank(self, rank: int) -> int:\\n return self.tile.total_ranks * (rank // self.tile.total_ranks)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee44ef462e8b20480e81b1827371ded3\",\n \"score\": \"0.46551478\",\n \"text\": \"def binarize(ratings, threshold=2.5):\\n ########################################################################\\n # Binarize the supplied ratings matrix. #\\n # #\\n # WARNING: Do not use self.ratings directly in this function. #\\n ########################################################################\\n\\n # The starter code returns a new matrix shaped like ratings but full of\\n # zeros.\\n binarized_ratings = np.copy(ratings)\\n for i in range(len(ratings)):\\n vec = ratings[i]\\n for j in range(len(vec)):\\n if vec[j] == 0:\\n continue\\n elif vec[j] > threshold:\\n binarized_ratings[i][j] = 1\\n elif vec[j] <= threshold:\\n binarized_ratings[i][j] = -1\\n\\n return binarized_ratings\\n\\n ########################################################################\\n # END OF YOUR CODE #\\n ########################################################################\\n return binarized_ratings\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4e9aef86c967898507ffeca39d4cb01\",\n \"score\": \"0.46529415\",\n \"text\": \"def check_ranking(ranking, mtable):\\n count_protected = 0\\n\\n # if the mtable has a different number elements than there are in the top docs return false\\n if len(ranking) != len(mtable):\\n raise ValueError(\\\"Number of documents in ranking and mtable length must be equal!\\\")\\n\\n # check number of protected element at each rank\\n for i, element in enumerate(ranking):\\n count_protected += 1 if element.is_protected else 0\\n if count_protected < mtable[i]:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c409604e50c6a668c2f39c3ed7f9ad4a\",\n \"score\": \"0.4642553\",\n \"text\": \"def sgrank(\\n doc,\\n *,\\n normalize=\\\"lemma\\\",\\n ngrams=(1, 2, 3, 4, 5, 6),\\n include_pos=(\\\"NOUN\\\", \\\"PROPN\\\", \\\"ADJ\\\"),\\n window_size=1500,\\n topn=10,\\n idf=None,\\n):\\n # validate / transform args\\n ngrams = utils.to_collection(ngrams, int, tuple)\\n include_pos = utils.to_collection(include_pos, str, set)\\n if window_size < 2:\\n raise ValueError(\\\"`window_size` must be >= 2\\\")\\n if isinstance(topn, float):\\n if not 0.0 < topn <= 1.0:\\n raise ValueError(\\n \\\"`topn` must be an int, or a float between 0.0 and 1.0\\\"\\n )\\n\\n n_toks = len(doc)\\n window_size = min(n_toks, window_size)\\n # bail out on (nearly) empty docs\\n if n_toks < 2:\\n return []\\n\\n candidates, candidate_counts = _get_candidates(doc, normalize, ngrams, include_pos)\\n # scale float topn based on total number of initial candidates\\n if isinstance(topn, float):\\n topn = int(round(len(candidate_counts) * topn))\\n candidates, unique_candidates = _prefilter_candidates(\\n candidates, candidate_counts, topn, idf)\\n\\n term_weights = _compute_term_weights(\\n candidates, candidate_counts, unique_candidates, n_toks, idf)\\n # filter terms to only those with positive weights\\n candidates = [cand for cand in candidates if term_weights[cand[0]] > 0]\\n edge_weights = _compute_edge_weights(candidates, term_weights, window_size, n_toks)\\n\\n # build the weighted directed graph from edges, rank nodes by pagerank\\n graph = nx.DiGraph()\\n graph.add_edges_from(edge_weights)\\n term_ranks = nx.pagerank_scipy(graph, alpha=0.85, weight=\\\"weight\\\")\\n sorted_term_ranks = sorted(\\n term_ranks.items(), key=operator.itemgetter(1, 0), reverse=True)\\n\\n return ke_utils.get_filtered_topn_terms(\\n sorted_term_ranks, topn, match_threshold=0.8)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb5a828abc63cdfeb24e4f804d07fe6b\",\n \"score\": \"0.46411014\",\n \"text\": \"def estimate_tucker_ranks(self, layer, compression_factor=0):\\n weights = layer.weight.data.numpy()\\n unfold_0 = tl.base.unfold(weights, 0)\\n unfold_1 = tl.base.unfold(weights, 1)\\n _, diag_0, _, _ = VBMF.EVBMF(unfold_0)\\n _, diag_1, _, _ = VBMF.EVBMF(unfold_1)\\n ranks = [diag_0.shape[0], diag_1.shape[1]]\\n\\n if compression_factor:\\n # Check if the VBMF ranks are small enough\\n ranks = choose_compression(\\n layer, ranks, compression_factor=compression_factor, flag='Tucker2')\\n\\n return ranks\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":674,"cells":{"query_id":{"kind":"string","value":"52af6ff21b973aa2cd9c9089aed1f599"},"query":{"kind":"string","value":"Save a dataset in a way that it's readable by load_dataset."},"positive_passages":{"kind":"list like","value":[{"docid":"ae1e677c9c29db4870dce2133ad9eba1","score":"0.0","text":"def save_dataset(filename, theta_vector, amp_dataset, ph_dataset, x_grid):\n\tto_save = np.concatenate((theta_vector, amp_dataset, ph_dataset), axis = 1)\n\ttemp_x_grid = np.zeros((1,to_save.shape[1]))\n\tK = int((to_save.shape[1]-3)/2)\n\ttemp_x_grid[0,3:3+k] = x_grid\n\tto_save = np.concatenate((temp_x_grid,to_save), axis = 0)\n\tq_max = np.max(theta_vector[:,0])\n\tspin_mag_max = np.max(np.abs(theta_vector[:,1:2]))\n\tx_step = x_grid[1]-x_grid[0]\n\tnp.savetxt(filename, to_save, header = \"# row: theta 3 | amp \"+str(amp_dataset.shape[1])+\"| ph \"+str(ph_dataset.shape[1])+\"\\n# N_grid = \"+str(x_grid.shape[0])+\" | f_step =\"+str(x_step)+\" | q_max = \"+str(q_max)+\" | spin_mag_max = \"+str(spin_mag_max), newline = '\\n')\n\treturn","title":""}],"string":"[\n {\n \"docid\": \"ae1e677c9c29db4870dce2133ad9eba1\",\n \"score\": \"0.0\",\n \"text\": \"def save_dataset(filename, theta_vector, amp_dataset, ph_dataset, x_grid):\\n\\tto_save = np.concatenate((theta_vector, amp_dataset, ph_dataset), axis = 1)\\n\\ttemp_x_grid = np.zeros((1,to_save.shape[1]))\\n\\tK = int((to_save.shape[1]-3)/2)\\n\\ttemp_x_grid[0,3:3+k] = x_grid\\n\\tto_save = np.concatenate((temp_x_grid,to_save), axis = 0)\\n\\tq_max = np.max(theta_vector[:,0])\\n\\tspin_mag_max = np.max(np.abs(theta_vector[:,1:2]))\\n\\tx_step = x_grid[1]-x_grid[0]\\n\\tnp.savetxt(filename, to_save, header = \\\"# row: theta 3 | amp \\\"+str(amp_dataset.shape[1])+\\\"| ph \\\"+str(ph_dataset.shape[1])+\\\"\\\\n# N_grid = \\\"+str(x_grid.shape[0])+\\\" | f_step =\\\"+str(x_step)+\\\" | q_max = \\\"+str(q_max)+\\\" | spin_mag_max = \\\"+str(spin_mag_max), newline = '\\\\n')\\n\\treturn\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"e208299ef2e05512fa69fd21543ef0cf","score":"0.7637095","text":"def _save_dataset(self):\n dataset = datastore.Resource.new(\n self.dataset_name,\n self._dataset_files,\n self._meta_data)\n repo = self.data_repo()\n repo.save(dataset, overwrite=True)","title":""},{"docid":"4cc779f78e0762ebf1ce68fa53bcab22","score":"0.74357975","text":"def save_dataset(dataset):\n logging.info(f\"Saving dataset {dataset}...\")\n try:\n dataset = json.loads(dataset)\n except json.decoder.JSONDecodeError:\n return\n\n dataset = dataset.rsplit(\"/\")[-1]\n dataset_path = f\"/tmp/data/{dataset}\"\n\n try:\n df = pd.read_csv(dataset_path, sep=None, engine=\"python\")\n platiagro.save_dataset(name=dataset, df=df)\n except (pd.errors.EmptyDataError, pd.errors.ParserError, UnicodeDecodeError, ValueError):\n content = open(dataset_path, \"rb\")\n platiagro.save_dataset(name=dataset, data=content)","title":""},{"docid":"e5c88b6e6d05add6aba8c963116248c4","score":"0.74094224","text":"def save_dataset(dataset: TablutDataset, path: Optional[str] = './'):\n with open(os.path.join(path, f'{dataset.name}.pkl'), 'wb') as f:\n pickle.dump(dataset, f)","title":""},{"docid":"ffea81906700acc0d3b2d1c28915fc07","score":"0.7367737","text":"def saveDataset(self, filename, datasets):\n with open(os.path.join(filename), 'wb') as handle:\n data = { # Warning: If adding something here, also modifying loadDataset\n 'word2index': self.word2index,\n 'index2word': self.index2word,\n 'index2vector': self.index2vector,\n 'datasets': datasets\n }\n pickle.dump(data, handle, -1) # Using the highest protocol available","title":""},{"docid":"f86e156671f9f9513cc31c6ec97160ce","score":"0.72513616","text":"def save_dataset(dataset):\n self.dataset_counter += 1\n self.saved_datasets.append(dataset)\n dataset_dir = self.datasets_directory_name + \"/training_dataset_{}.npy\".format(self.dataset_counter)\n dataset_path = Path(self.datasets_directory_name)\n try:\n dataset_path.rmdir()\n except OSError as e:\n print(f\"Error: {dataset_path} : {e.strerror}\")\n dataset_path.mkdir(exist_ok=True, parents=True)\n if os.path.exists(dataset_path) and self.dataset_counter == 1:\n shutil.rmtree(dataset_path)\n os.makedirs(dataset_path)\n with open(dataset_dir, 'wb') as f:\n np.save(f, dataset)\n print(\"[INFO]: Dataset was saved in the directory: \", dataset_path)","title":""},{"docid":"cee52fb6bab2b27167ecf9285269061a","score":"0.7239169","text":"def save(self):\n\n if self.path is None:\n raise ValueError('No path given to save the dataset.')\n\n self.save_at(self.path)","title":""},{"docid":"7bedc009f4111974b7ec2d1cf4cf7839","score":"0.71408814","text":"def SaveDataset(self, savepath_pickle=None):\n if savepath_pickle is None:\n savepath_pickle = self.GetDatasetFilePath()\n with open(savepath_pickle, \"wb\") as file:\n pickle.dump((self.sample_dict, self.path_list, self.set_dict, self.set_index), file)","title":""},{"docid":"f66c809f36781a6320b2b07ee4ff9afd","score":"0.7136119","text":"def save_encoded_dataset(dataset, filename):\n\n pickle.dump(dataset, open(filename, \"wb\"))","title":""},{"docid":"f4b1beb628e80d41a903616dc106428e","score":"0.7117572","text":"def save_dataset(self, path=None, add=True):\n self._assert_loaded(self._data, \"dataset\")\n # if not path, do DataLoader data_dir\n if not path:\n path = self.annotation_path\n\n os.makedirs(path, exist_ok=True)\n\n # save as json\n self._save_json_list(self._data, os.path.join(path, \"data.json\"), add=add, sub_annots=True)","title":""},{"docid":"48fd40f572f564a02c92aa0d550a52fd","score":"0.71117485","text":"def save(self, save_path): # todo: need interactionsDataset when saving?\n dump(self, save_path)","title":""},{"docid":"2006bb901a77089084c55cf5f8ea26b9","score":"0.7082088","text":"def saveDataset(self, filename):\n\n with open(os.path.join(filename), 'wb') as handle:\n data = {\n 'word2id': self.word2id,\n 'id2word': self.id2word,\n 'idCount': self.idCount,\n 'intent2id': self.intent2id,\n 'id2intent': self.id2intent,\n 'trainingSamples': self.trainingSamples,\n 'validationSamples': self.validationSamples,\n 'testSamples': self.testSamples,\n 'entities': self.entities_property,\n }\n pickle.dump(data, handle, -1)\n with open(self.data_path + \"/train.csv\", \"w\") as output:\n writer = csv.writer(output, lineterminator='\\n')\n writer.writerows(self.txtTrainingSamples)\n with open(self.data_path + \"/valid.csv\", \"w\") as output:\n writer = csv.writer(output, lineterminator='\\n')\n writer.writerows(self.txtValidationSamples)","title":""},{"docid":"6d4bdb422e81b3afbc74e2b92d976ca8","score":"0.6969148","text":"def save(self) -> None:\n create_dir(dataset_data_path)\n\n file_name = self.get_filename(self.name, self.dt)\n with open(file_name, 'wb') as f:\n pickle.dump(self, f)","title":""},{"docid":"8dca2543708c7155aae8ecbc4e26409e","score":"0.6887473","text":"def save(cls):\n cls._remove_old_datapoints()\n with open(cls._location, 'w') as fh:\n data = yaml.dump(cls._data, default_flow_style=False)\n fh.write(data)","title":""},{"docid":"fafaaa3ec321134fea0d4c06b272853e","score":"0.6887001","text":"def save_dataset(filename, dataset):\n\n with open(filename, mode='w', encoding='utf-8')as f:\n np.savetxt(filename, dataset)\n f.close()\n print(f\"{filename} saved successfully\")","title":""},{"docid":"0b24b760947b9412cfd32223f0946e59","score":"0.687435","text":"def save_dataset(self, save_filename, type='shuffled_data_array' ):\n\n if type == 'shuffled_data_array':\n\n np.save(save_filename,self.shuffled_data_array)\n\n np.save(('{}_labels'.format(save_filename)),self.shuffled_label_array)\n\n\n\n elif type == 'data_array':\n\n np.save(save_filename,self.data_array)\n\n np.save(('{}_labels'.format(save_filename)),self.label_array)\n\n\n\n elif type == 'xor_array' or type == 'and_array' or type == 'add_array':\n\n fn = 'self.{}_array'.format(type[0:2])\n\n fn_labels = 'self.{}_labels'.format(type[0:2])\n\n np.save(save_filename, fn)\n\n np.save(('{}_labels'.format(save_filename)),fn_labels)\n\n return","title":""},{"docid":"02f03dd376c7af2ba1a071f9e560fd9c","score":"0.6841852","text":"def save_dataset(self):\n\n import os\n from datetime import datetime\n\n timestamp = datetime.now()\n yearkey = \"yearkey={}\".format(timestamp.strftime(\"%Y\"))\n monthkey = \"monthkey={}\".format(timestamp.strftime(\"%m\"))\n daykey = \"daykey={}\".format(timestamp.strftime(\"%d\"))\n directory_to_save_processed_data_to = os.path.join(\n self.data_directory, \"processed\", yearkey, monthkey, daykey\n )\n if not os.path.exists(directory_to_save_processed_data_to):\n os.makedirs(directory_to_save_processed_data_to)\n\n self.data.to_parquet(\n os.path.join(directory_to_save_processed_data_to, \"data.parquet.gzip\"),\n compression=\"gzip\",\n )\n self.next(self.end)","title":""},{"docid":"35890761e1dbea961678b20ef230b45c","score":"0.6798494","text":"def save_dataset(self, filepath: str):\n np.save(filepath, self.all_sos_numpy)","title":""},{"docid":"b1433fc0f0714e8bb9f9fc9bd2d10ab0","score":"0.67621285","text":"def saveDataset(X_train, Y_train, X_test, Y_test, path):\n np.save(os.path.join(path, \"X_train.npy\"), X_train)\n np.save(os.path.join(path, \"Y_train.npy\"), Y_train)\n np.save(os.path.join(path, \"X_test.npy\"), X_test)\n np.save(os.path.join(path, \"Y_test.npy\"), Y_test)","title":""},{"docid":"2b0cbb864ec04c5c31603ffbffb6cd93","score":"0.6760361","text":"def save_to_pickle(dataset, fileloc):\n filename = fileloc + \".pickle\"\n with open(filename, 'wb') as f:\n pickle.dump(dataset, f)\n logger.info(\"Saved to {}\".format(filename))","title":""},{"docid":"f4c179adfce2e97fc750a981f00740d2","score":"0.6758912","text":"def save_dataset(self, dataset_file=None, force_overwrite=False):\n if dataset_file is not None:\n self.dataset_file = dataset_file\n\n # Initialize dict\n json_dataset = OrderedDict()\n\n # Save dataset info\n json_dataset['root_dir'] = self.root_dir\n json_dataset['dataset_type'] = self.dataset_type\n json_dataset['classes'] = list(self.name_to_class_info.values())\n json_dataset['images'] = list(self.image_infos.values())\n\n # Save dataset into json file\n if (not os.path.isfile(self.dataset_file)) or force_overwrite:\n print('Saving dataset as an annotation file, this can take a while')\n with open(self.dataset_file, 'w') as f:\n json.dump(json_dataset, f)\n print('Dataset saved')\n else:\n raise FileExistsError('Dataset not saved as it already exists, consider overwriting')","title":""},{"docid":"3bb9babcb5e0347ea0e529e663c70bde","score":"0.673526","text":"def write_dataset(self) -> list[str]:","title":""},{"docid":"7ae0a79cdc41b48bab4d50d026227349","score":"0.6731773","text":"def save_dataset(self, dataset_file=None, force_overwrite=False):\n if dataset_file is not None:\n self.dataset_file = dataset_file\n\n assert self.dataset_file is not None\n\n # Initialize dict\n json_dataset = OrderedDict()\n\n # Save dataset info\n json_dataset['root_dir'] = self.root_dir\n json_dataset['classes'] = list(self.name_to_class_info.keys())\n json_dataset['images'] = list(self.image_infos.values())\n json_dataset['annotations'] = list(self.ann_infos.values())\n\n # Save dataset into json file\n if (not os.path.isfile(self.dataset_file)) or force_overwrite:\n print('Saving dataset as an annotation file, this can take a while')\n with open(self.dataset_file, 'w') as f:\n json.dump(json_dataset, f)\n print('Dataset saved')\n else:\n raise FileExistsError('Dataset not saved as it already exists, consider overwriting')","title":""},{"docid":"539658809dd9ab621f6380de089c0177","score":"0.6696084","text":"def save_dataset(dataset, outfile):\n import h5py\n f = h5py.File(outfile, 'w')\n for key in dataset.keys():\n f.create_dataset(key, data=dataset[key])\n f.close()","title":""},{"docid":"2dfa68e4cbf5cc9c8e9f977c03040d61","score":"0.66912514","text":"def persist_data(self):\n self.workdir.mkdir(parents=True, exist_ok=True)\n ds2 = []\n for i, (ds, cand_f, ds_attrs) in enumerate(self._flat_datasets()):\n train, test = ds\n if isinstance(train, pd.DataFrame):\n fn = self.workdir / 'ds{}-train.parquet'.format(i+1)\n _logger.info('serializing to %s', fn)\n train.to_parquet(fn)\n train = fn\n if isinstance(test, pd.DataFrame):\n fn = self.workdir / 'ds{}-test.parquet'.format(i+1)\n _logger.info('serializing to %s', fn)\n test.to_parquet(fn)\n test = fn\n ds2.append(((train, test), cand_f, ds_attrs))\n self.datasets = ds2\n self._is_flat = True","title":""},{"docid":"b83b59004434bd8f3e17f25b19b5838c","score":"0.6667793","text":"def data_save(self, filename):\n\t\tfd = open(filename, \"wb\")\n\t\tfd.write(self.data)\n\t\tfd.close()","title":""},{"docid":"018add1572d08a029b1a7158919d9131","score":"0.666314","text":"def save(self, dataset: 'Dataset', dataset_uri: str, *, compute_digest: ComputeDigest = ComputeDigest.ALWAYS, preserve_metadata: bool = True) -> None:","title":""},{"docid":"8ecfb2d7c0c4c3ebb7d18dec8bd18944","score":"0.6654124","text":"def save_dataset(dataset, save_dir):\n # Create directory if it doesn't exist\n print(\"Saving in {}...\".format(save_dir))\n if not os.path.exists(save_dir):\n os.makedirs(save_dir)\n\n # Export the dataset\n with open(os.path.join(save_dir, 'sentences.txt'), 'w') as file_sentences:\n with open(os.path.join(save_dir, 'labels.txt'), 'w') as file_labels:\n for words, tags in dataset:\n file_sentences.write(\"{}\\n\".format(\" \".join(words)))\n file_labels.write(\"{}\\n\".format(\" \".join(tags)))\n print(\"- done.\")","title":""},{"docid":"8ecfb2d7c0c4c3ebb7d18dec8bd18944","score":"0.6654124","text":"def save_dataset(dataset, save_dir):\n # Create directory if it doesn't exist\n print(\"Saving in {}...\".format(save_dir))\n if not os.path.exists(save_dir):\n os.makedirs(save_dir)\n\n # Export the dataset\n with open(os.path.join(save_dir, 'sentences.txt'), 'w') as file_sentences:\n with open(os.path.join(save_dir, 'labels.txt'), 'w') as file_labels:\n for words, tags in dataset:\n file_sentences.write(\"{}\\n\".format(\" \".join(words)))\n file_labels.write(\"{}\\n\".format(\" \".join(tags)))\n print(\"- done.\")","title":""},{"docid":"cd12a4b52682693a939d5b6ce6c6e2b1","score":"0.6646873","text":"def save_datasets(datasets, file): \n with open(file, 'wb') as f:\n save(datasets, f)","title":""},{"docid":"7d56fa784544d6a2ad7597d5c01fe26a","score":"0.6637412","text":"def save(self):\n with open(self.filename, 'wb') as fp:\n pickle.dump(self.data, fp, pickle.HIGHEST_PROTOCOL)","title":""},{"docid":"9c1a70695432e6f9ebdb70c31caf4f90","score":"0.66245043","text":"def save_dataset(self, samples, labels, path):\n\n # Create the list structure of a dataset\n dataset = [samples, labels]\n\n # Save the dataset file in a pickle file\n with open(path + '.pckl', 'wb') as dataset_file:\n pickle.dump(dataset, dataset_file)","title":""},{"docid":"9c296c878726ffcb4eb8c2a390b8712d","score":"0.66019934","text":"def test_saveable_dataset():\n # build HCIDataset\n cube = np.zeros((5, 10, 10))\n angles = np.linspace(1, 2, 5)\n fwhm = 4 # test non-numpy type saving/loading\n\n ds = Dataset(cube=cube, angles=angles, fwhm=fwhm)\n\n # save\n fd, fn = tempfile.mkstemp(prefix=\"vip_\")\n ds.save(fn)\n\n # restore\n ds2 = Dataset.load(fn)\n\n # compare\n aarc(ds2.cube, cube)\n aarc(ds2.angles, angles)\n assert ds2.fwhm == fwhm\n\n # cleanup\n os.remove(fn)","title":""},{"docid":"d329dbc26a8ec3e8190f7538ea19a10a","score":"0.65863574","text":"def write_dataset(dataset, save_path):\n if not dataset:\n logging.info('No dataset to write.')\n return\n logging.info('Writing dataset to %s', save_path)\n for split_name, list_of_input_output_pairs in dataset.items():\n folder_name = os.path.join(save_path, split_name)\n if not os.path.exists(folder_name):\n os.makedirs(folder_name)\n encode_name = os.path.join(folder_name, '%s_encode.txt' % split_name)\n decode_name = os.path.join(folder_name, '%s_decode.txt' % split_name)\n with gfile.GFile(encode_name,\n 'w') as encode_f, gfile.GFile(decode_name,\n 'w') as decode_f:\n for pair in list_of_input_output_pairs:\n encode_f.write(pair[0] + '\\n')\n decode_f.write(pair[1] + '\\n')\n logging.info('Dataset written to %s', save_path)","title":""},{"docid":"d6f45f2f66946d1801a34db8776cf96b","score":"0.6560496","text":"def save(self, filename=None, verbose=False):\n if not filename:\n if self.name:\n filename = self.name\n else:\n print(\" [ERROR] - Must specify dataset.name [dataset.set_name('name')]\")\n print(\" or give filename [dataset.save(filename='filename')]\")\n hdf5_save.save_hdf_dataset(self, filename, verbose=verbose)","title":""},{"docid":"e054944a62bbf296902d61c5a41157ac","score":"0.6556137","text":"def save_data(self, data):","title":""},{"docid":"4afdb01e15302f79a6cc517d8f51e1f0","score":"0.6549905","text":"def save_data(self):\n np.savetxt(self.save_to, self.predicted, delimiter=\",\")","title":""},{"docid":"5f47511b87c05e21d215f7cab0656240","score":"0.65480536","text":"def save_dataset(self,mode=\"append new\"):\n # Create processed dataset folder if not existent\n if not os.path.exists(self.processed_dataset_folder):\n os.mkdir(self.processed_dataset_folder)\n\n # if dataset has not been saved before save\n dataset_full_path = f\"{self.processed_dataset_folder}{os.sep}{self.dataset_base_name}\"\n if not os.path.exists(dataset_full_path):\n pickle.dump(self.dataset, open(dataset_full_path,\"wb\"))\n return True\n\n if mode == \"override\": # Replace file\n pickle.dump(self.dataset, open(dataset_full_path,\"wb\"))\n return True\n elif mode == \"append new\": # Check if file contents in there and if not then append\n saved_dataset = pickle.load( open(dataset_full_path,\"rb\"))\n saved_dataset_processed_videos_set = set([row[\"Meta\"][\"VideoPath\"] for row in saved_dataset])\n current_dataset_processed_videos_set = set([row[\"Meta\"][\"VideoPath\"] for row in self.dataset])\n videos_processed_but_not_in_saved_dataset = current_dataset_processed_videos_set - saved_dataset_processed_videos_set\n\n if len(videos_processed_but_not_in_saved_dataset) > 0:\n additional_rows_to_save = [row for row in self.dataset if row[\"Meta\"][\"VideoPath\"] in videos_processed_but_not_in_saved_dataset]\n saved_dataset.extend(additional_rows_to_save)\n pickle.dump(saved_dataset, open(dataset_full_path,\"wb\"))\n return True\n return False","title":""},{"docid":"d53c5637e8f1c7854f8bf47e16545dd0","score":"0.65381706","text":"def write_dataset(dataset, save_path):\n if not dataset:\n logging.info('No dataset to write.')\n return\n logging.info(f'Writing dataset to {save_path}')\n for split_name, list_of_input_output_pairs in dataset.items():\n folder_name = os.path.join(save_path, split_name)\n if not os.path.exists(folder_name):\n os.makedirs(folder_name)\n encode_name = os.path.join(folder_name, f'{split_name}_encode.txt')\n decode_name = os.path.join(folder_name, f'{split_name}_decode.txt')\n skeleton_name = os.path.join(folder_name, f'{split_name}_skeleton.txt')\n with open(\n encode_name, 'w', encoding='utf8') as encode_f, open(\n decode_name, 'w', encoding='utf8') as decode_f, open(\n skeleton_name, 'w', encoding='utf8') as skeleton_f:\n for pair in list_of_input_output_pairs:\n encode_f.write(pair[0] + '\\n')\n decode_f.write(pair[1] + '\\n')\n skeleton_f.write(pair[2] + '\\n')\n logging.info(f'Dataset written to {save_path}')","title":""},{"docid":"0ddde7a8b0bb63b2b5d0216bb8cbad85","score":"0.6508642","text":"def save(self, filename):\n with open(filename, 'w') as fp:\n json.dump({\"image_size\": self.image_size,\n \"labels\": self.label_info,\n \"images\": self.image_info}, fp)\n utils.logger.info(\"The dataset has been saved into {}\".format(filename))","title":""},{"docid":"8f54c44b22efc7aaf40e75920e94b7ec","score":"0.6501132","text":"def saveData(self, data):","title":""},{"docid":"7e469438df6b586c8abd771b75782542","score":"0.6488153","text":"def save_mfcc_training_dataset(self):\n\n # Save the dataset as pickle\n with pathlib.Path('dataset.pkl').open('wb') as pfile:\n pickle.dump([(r.diffs, r.result_type) for r in self.res], pfile,\n protocol=pickle.HIGHEST_PROTOCOL)\n logging.getLogger(__name__).info(\"dataset.pkl has generated.\")","title":""},{"docid":"df87947f4111990f712114b009c36077","score":"0.647041","text":"def save_dataset(dataset, save_dir):\n\n print(f'Saving in {save_dir}...')\n if not os.path.exists(save_dir):\n os.makedirs(save_dir)\n\n with open(os.path.join(save_dir, 'sentences.txt'), 'w', encoding='utf-8') as file_sentences:\n with open(os.path.join(save_dir, 'indices.txt'), 'w', encoding='utf-8') as file_indices:\n for sentence, st_index, end_index in dataset:\n file_sentences.write(f'{sentence}\\n')\n file_indices.write(f'{str(st_index)} {str(end_index)}\\n')\n\n print(\"- done.\")","title":""},{"docid":"ec3b0a4722948edace9c276daf8f6b8b","score":"0.64498734","text":"def _save_dataset(filename, name, value, dtype=None):\n\n try:\n filename.create_dataset(name, data=value, maxshape=(None,), dtype=dtype)\n except RuntimeError:\n del filename[name]\n filename.create_dataset(name, data=value, maxshape=(None,), dtype=dtype)","title":""},{"docid":"c596aa6936a6d641fbed5e7e93d3ae31","score":"0.6447846","text":"def save_data(self):\r\n json.dump(self.data, open(self.filename, 'w'), indent=2)","title":""},{"docid":"87b6635f12c916bdf4cbc32fa7624d14","score":"0.6415625","text":"def save(self, filename='data.json'):\n with open(filename,'w') as f: \n json.dump(self.datadict,f)","title":""},{"docid":"9c44250c5ac1e877058182310833168d","score":"0.6409766","text":"def write(self, fname):\n numpy.save(fname, self.dataset)","title":""},{"docid":"4ab38983f55f551d5cb09a29ab5fe040","score":"0.63770056","text":"def savechanges(self):\n self.construct_name(force = True)\n if hasattr(self, 'clusterarray'):\n dataset = xr.Dataset({'clustidfield':self.clusterarray,self.array.name:self.array})\n else:\n dataset = self.array.to_dataset()\n # invoke the computation (if loading was lazy) and writing\n particular_encoding = {key : obs_netcdf_encoding[key] for key in dataset.keys()}\n dataset.to_netcdf(self.filepath, encoding = particular_encoding)\n #delattr(self, 'array')","title":""},{"docid":"7663110da61f79c8fbc9be557fb0bb4f","score":"0.6367028","text":"def save(\n filepath: str, dataset: Union[List[Frame], Dataset], nprocs: int = 0\n) -> None:\n if isinstance(dataset, Dataset):\n frames, config = dataset.frames, dataset.config\n else:\n frames, config = dataset, None\n\n if nprocs > 1:\n labels = pmap(dump, frames, nprocs)\n else:\n labels = list(map(dump, frames))\n\n with open(filepath, \"w\") as fp:\n json.dump(Dataset(frames=labels, config=config).dict(), fp, indent=2)","title":""},{"docid":"3f6d5d2f909984ab1e7d5e1d8af42b95","score":"0.63477445","text":"def save(self):\n entities = pandas.DataFrame(self.data)\n entities.to_csv(self.output_path, index_label=\"id\")\n self.logger.info(\"Initial custom data saved: {}\".format(self.output_path))","title":""},{"docid":"35aac372eeba883b5bb4e98eabd64216","score":"0.63380545","text":"def savePickle(path, dataset, protocol=2):\n f = open(path, 'wb')\n pickle.dump(dataset, f, protocol=2)\n f.close()\n return","title":""},{"docid":"00bc112f6e435929aceaff1795147ba2","score":"0.6337372","text":"def save_dataset(filename, train_labels=None, train_features=None, test_features=None, ids=None, feature_names=None):\n np.savez(filename, train_labels=train_labels, train_features=train_features, test_features=test_features, ids=ids, feature_names=feature_names)","title":""},{"docid":"35e9d7fe6b66f2689670791d56a25c7d","score":"0.63304573","text":"def save_data(data, filename):\n pass","title":""},{"docid":"87a808f54bbd868bf1ef675cef177bf3","score":"0.6328018","text":"def save_dataset(self, path, sep='\\t'):\n\n self._dataset.to_csv(path_or_buf=path, sep=sep, index=False)\n\n self.verboseprint(\"Dataset successfully saved at {}.\".format(path))\n logging.debug(\"Dataset saved at {}..\".format(path))","title":""},{"docid":"d0e8d2334736eb0118f7f589975ce2d7","score":"0.63111484","text":"def __save_in_database(self, dataset):\n\n if isinstance(dataset, list):\n for data in dataset:\n self.__save_in_database(data)\n else:\n self.save_into_database(dataset, None)","title":""},{"docid":"dbd0cba115ea4095c132b873f90dd688","score":"0.6308548","text":"def save_data_to_disk():\n global labelled_data\n with open(os.path.join(data_dir, filename), \"wb\") as f:\n np.savetxt(f, labelled_data, delimiter=\",\")","title":""},{"docid":"40baa2ec7ee0c74525f24dd3818c5817","score":"0.6305703","text":"def save_data(self, data):\n with open(self.fname, \"wb\") as file:\n pickle.dump(data, file)","title":""},{"docid":"ea3c9de5a047124f8ed555f9dd30aafa","score":"0.63037914","text":"def _write(self, featurized_dataset, dataset_name):\n dump_location = feature_set_location(dataset_name, self.__class__.__name__)\n joblib.dump(featurized_dataset, dump_location)","title":""},{"docid":"0d9ab93314ba8c1d872bfc2c5d50afe2","score":"0.63032645","text":"def save(cls, filepath, numpy_tuple_dataset):\n if not isinstance(numpy_tuple_dataset, NumpyTupleDataset):\n raise TypeError('numpy_tuple_dataset is not instance of '\n 'NumpyTupleDataset, got {}'\n .format(type(numpy_tuple_dataset)))\n np.savez(filepath, *numpy_tuple_dataset._datasets)\n print('Save {} done.'.format(filepath))","title":""},{"docid":"7fc946c9f352535a02ff70cd56b81922","score":"0.630007","text":"def save(self, save_dir):\n\n # Save params\n full_path = os.path.join(save_dir, 'dataset_params.pickle')\n with open(full_path, 'wb') as x:\n pickle.dump(self.params, x)\n\n # Each partition is persisted saved as a dataset index and color name pair\n # Dataset index can be used to re-create the partitons\n color_idx_lookup = {name: idx for idx, (_, _, name) in enumerate(self)}\n for partition, partition_path in [\n (self.train_set, 'train_partition.txt'),\n (self.cv_set, 'cv_partition.txt'),\n (self.test_set, 'test_partition.txt'),\n ]:\n full_path = os.path.join(save_dir, partition_path)\n color_names = ['{},{}'.format(color_idx_lookup[name], name) for _, _, name in partition]\n with open(full_path, 'w') as x:\n x.write('\\n'.join(color_names))","title":""},{"docid":"314f3f98dbd54460c07fd6df833cb058","score":"0.62917274","text":"def save(self, filename, **kwargs):\n\n save(self.ds, filename, **kwargs)","title":""},{"docid":"1f213dd16185287228cf0e16ff63b54e","score":"0.6291383","text":"def saveData(self, filename=None):\n if filename is None:\n filename = self.name + \".dat\"\n with open(filename, 'wb') as data:\n pickle.dump(self.items, data)","title":""},{"docid":"b8b32e843796f31568b45fb3a47b8f29","score":"0.62893254","text":"def save(self, filename, data):\n raise NotImplementedError()","title":""},{"docid":"a8aec74a063bd41a5f89b09050bed535","score":"0.6275846","text":"def save_data(self):\n libFile.write_json(self.datapath, {\"DeformerInfo\": self.data})","title":""},{"docid":"37799e9f3139ba6b2e3eb38fbc4c7e00","score":"0.6275192","text":"def save(self, filepath):\n f = h5py.File(filepath, \"w\")\n f.create_dataset(\"trial_type\", data=\"RankObservations\")\n f.create_dataset(\"stimulus_set\", data=self.stimulus_set)\n f.create_dataset(\"n_select\", data=self.n_select)\n f.create_dataset(\"is_ranked\", data=self.is_ranked)\n f.create_dataset(\"group_id\", data=self.group_id)\n f.create_dataset(\"agent_id\", data=self.agent_id)\n f.create_dataset(\"session_id\", data=self.session_id)\n f.create_dataset(\"weight\", data=self.weight)\n f.create_dataset(\"rt_ms\", data=self.rt_ms)\n f.close()","title":""},{"docid":"8567f474fb1db89a5b709a7d174c91d3","score":"0.6255518","text":"def save_data(self, filepath=None):\n if filepath is None:\n filepath = os.path.join(self.data_directory, 'df_classification_data.pickle')\n \n filetype = filepath.split('.')[-1]\n to_save = self.all_data[self.all_data[self.label_col].notna()]\n if filetype == 'pickle':\n to_save.to_pickle(filepath)\n elif filetype == 'csv':\n to_save.to_csv(filepath)\n elif filetype == 'json':\n to_save.to_json(filepath, orient='records', lines=True)","title":""},{"docid":"3c6c0683f720673113858a7505019ac7","score":"0.62554234","text":"def save(self, filepath):\n f = h5py.File(filepath, \"w\")\n f.create_dataset(\"trial_type\", data=\"RankDocket\")\n f.create_dataset(\"stimulus_set\", data=self.stimulus_set)\n f.create_dataset(\"n_select\", data=self.n_select)\n f.create_dataset(\"is_ranked\", data=self.is_ranked)\n f.close()","title":""},{"docid":"ec312bc200885ea10fb10d053cb27e5b","score":"0.62543446","text":"def save_training_data(self, data):\n pkl.dump(data, open('train_data.pkl', 'wb'))","title":""},{"docid":"76a758cff64f3817fe2c8751fb739178","score":"0.62477815","text":"def save_data(directory,dataset_name,dataset):\n \n if not os.path.exists(directory):\n os.makedirs(directory)\n \n print('Saving data...')\n np.save(os.path.join(directory,dataset_name),dataset)\n print(dataset_name,' saved at: ',os.path.join(directory,dataset_name))","title":""},{"docid":"a57c306aa40164d3240468f15e5cc226","score":"0.62472814","text":"def save(self):\n self._validate_path()\n self.data.save(self.filename)\n self.stored = True","title":""},{"docid":"6a929ba3f42a6c5c1412513d5f900c83","score":"0.62351054","text":"def save(self):\r\n os.system(\"mkdir DataSets\")\r\n numpy.save(\"DataSets\\\\\"+self.name+\"Frames\",self.FrameSet)\r\n numpy.save(\"DataSets\\\\\"+self.name+\"Inputs\",self.InputSet)","title":""},{"docid":"17d14c9b6dc8c77a0371d3e4533f4a69","score":"0.62292564","text":"def _save_data_on_disk(self):\n # create the training, validation and test arrays\n train_data = np.c_[self.X_train, self.y_train]\n valid_data = np.c_[self.X_valid, self.y_valid]\n test_data = np.c_[self.X_test, self.y_test]\n header_cols = self.housing.feature_names + [\"MedianHouseValue\"]\n header = \",\".join(header_cols)\n\n self.train_filepaths = self._save_to_multiple_csv_files(train_data, \"train\", header, n_parts=20)\n self.valid_filepaths = self._save_to_multiple_csv_files(valid_data, \"valid\", header, n_parts=10)\n self.test_filepaths = self._save_to_multiple_csv_files(test_data, \"test\", header, n_parts=10)","title":""},{"docid":"e317e867e6ae2df418d35a49c2145e5a","score":"0.6198814","text":"def save(self, filename=None):\n if filename is None:\n filename = self.filename\n with open(filename, 'wb') as f:\n f.write(self.data)","title":""},{"docid":"e6248170040d204c41971f5b4d0f27e4","score":"0.61897075","text":"def save_data(path, filename, data):\n train_data, val_data, test_data = data\n save_name = path / filename.split('.')[0]\n train_data.to_csv(f'{save_name}_train_set.csv')\n val_data.to_csv(f'{save_name}_val_set.csv')\n test_data.to_csv(f'{save_name}_test_set.csv')","title":""},{"docid":"c31933c85f6f34ec34a72ffe1048c12b","score":"0.61836815","text":"def save_data(self):\n data = AgentData(gridworld=self)\n pickle.dump(data, open(data.title + '.pkl', 'wb'))","title":""},{"docid":"2516dc600214f2d5ccf3d0529ee4ad80","score":"0.61756873","text":"def save_data(self, network):\n\n # save the current network\n self.network_path = \"{}/network_gen_{}.pt\".format(network_dir, self.cycle)\n torch.save(network, self.network_path)\n\n # dump the storage object to file\n with open(storage_path, 'wb') as output:\n pickle.dump(self, output, pickle.HIGHEST_PROTOCOL)","title":""},{"docid":"27d4a0566dc40bb177652c047c64228a","score":"0.61646855","text":"def save(self, ds):\n \n fspec = self.dataset.dsmap[ds]\n if fspec is None and self.directory is None: # file is not saved\n return\n if fspec is not None:\n fdir, base = os.path.dirname(fspec), os.path.basename(fspec)\n if self.suffix: # create file name with suffix preceding the extension\n parts = os.path.splitext(base)\n base = parts[0] + self.suffix + parts[1] # if no extension, parts[1] wil be empty string\n if self.directory: # substitute new location for original\n fdir = self.directory\n target = os.path.join(fdir, base)\n else:\n base = ds + os.path.extsep + \"sav\"\n target = os.path.join(self.directory, base)\n if not self.overwrite and os.path.exists(target):\n print(_(\"\"\"File %s already exists and will not be overwritten.\"\"\") % target)\n else:\n # this is already the active file\n spss.Submit(\"\"\"SAVE OUTFILE=\"%(target)s\".\"\"\" % locals())\n # file might already be gone\n try:\n if self.close and not ds == self.dataset.activefile:\n spss.Submit(\"DATASET CLOSE \" + ds) # file could remain open as active file\n except:\n pass","title":""},{"docid":"8eea44bb15fa0dd5cf2acee00ea300ce","score":"0.61642736","text":"def to_export(self):\n if self.data_file is not None:\n pickle.dump(self.data, open(self.data_file, \"wb\"))","title":""},{"docid":"265fa45dc293b07b267720c424aecd09","score":"0.615056","text":"def save_data_to_fiile(self):\n logger.warning(\"Only Use it for debug purposes!!!\")\n self.internationalstudies.to_csv(\"internationalstudies.csv\")\n self.drug_vocab_df.to_csv(\"drug_vocab.csv\")\n with open('all_US_studies_by_keyword.json', 'w', encoding='utf-8') as f:\n json.dump(self.all_US_studies_by_keyword, f, ensure_ascii=False, indent=4)","title":""},{"docid":"ec00575b039e2838d4402ecab3be9ac5","score":"0.6147158","text":"def _data_to_save(self):\n return self.data","title":""},{"docid":"1d63df6f56af1e7542b87438ae88a83f","score":"0.61453146","text":"def to_disk(self, ds_disk_path):\n self.ds.save_to_disk(ds_disk_path)","title":""},{"docid":"471b02133ccacfd3d852ae31ab9b0f1f","score":"0.614503","text":"def save(self, storePath, prefix, verbose=0):\n if self.datasets == []:\n print('no datasets to save. Generate datasets with gen_datasets() first.')\n \n print(f\"\\nSave {len(self.datasets)} datasettings into '{storePath}'\")\n if not os.path.isdir(storePath):\n os.makedirs(storePath)\n\n for i,d in enumerate(self.datasets):\n fname = prefix + str(i+1) + \".npy\"\n d.saveSets(storePath, fname)\n if verbose: print(fname)\n \n with open(os.path.join(storePath,'readme.md'), 'w') as f:\n f.write(f\"These datasets were generated from '{self.fileName}'\\n\")\n f.write(str(datetime.now()) + '\\n')","title":""},{"docid":"c2c6276be4ce74dd3d607e572ba4559d","score":"0.6137075","text":"def dump_and_load_dataset(self):\n self._init_qlib()\n self._prepare_calender_cache()\n dataset = init_instance_by_config(self.task[\"dataset\"])\n dataset_backtest = init_instance_by_config(self.task[\"dataset_backtest\"])\n\n ##=============dump dataset=============\n dataset.to_pickle(path=\"dataset.pkl\")\n dataset_backtest.to_pickle(path=\"dataset_backtest.pkl\")\n\n del dataset, dataset_backtest\n ##=============reload dataset=============\n with open(\"dataset.pkl\", \"rb\") as file_dataset:\n dataset = pickle.load(file_dataset)\n\n with open(\"dataset_backtest.pkl\", \"rb\") as file_dataset_backtest:\n dataset_backtest = pickle.load(file_dataset_backtest)\n\n self._prepare_calender_cache()\n ##=============reinit dataset=============\n dataset.config(\n handler_kwargs={\n \"start_time\": \"2021-01-19 00:00:00\",\n \"end_time\": \"2021-01-25 16:00:00\",\n },\n segments={\n \"test\": (\n \"2021-01-19 00:00:00\",\n \"2021-01-25 16:00:00\",\n ),\n },\n )\n dataset.setup_data(\n handler_kwargs={\n \"init_type\": DataHandlerLP.IT_LS,\n },\n )\n dataset_backtest.config(\n handler_kwargs={\n \"start_time\": \"2021-01-19 00:00:00\",\n \"end_time\": \"2021-01-25 16:00:00\",\n },\n segments={\n \"test\": (\n \"2021-01-19 00:00:00\",\n \"2021-01-25 16:00:00\",\n ),\n },\n )\n dataset_backtest.setup_data(handler_kwargs={})\n\n ##=============get data=============\n xtest = dataset.prepare(\"test\")\n backtest_test = dataset_backtest.prepare(\"test\")\n\n print(xtest, backtest_test)\n return","title":""},{"docid":"302084093b0c988ab6764987ec9a5a91","score":"0.6134747","text":"def write_dat(self):\n data = self.get_data()\n data.savetxt('{}.dat'.format(self.get_name()))","title":""},{"docid":"3d24882cc0abe7ed3f3a46fe27fb18f7","score":"0.6134005","text":"def save(self, path=None):\n if not path:\n path = './LMI-BACKUP'\n if not os.path.isdir(path):\n os.mkdir(path)\n today = datetime.datetime.today().strftime('%Y-%m-%d@%Hh-%Mm')\n path += f'/{today}'\n if not os.path.isdir(path):\n os.mkdir(path)\n else:\n if not os.path.isdir(path):\n os.mkdir(path)\n print(f'Saving to path: {path}')\n saved = []\n failed = []\n if server_uses_widgets(self.server):\n url_args = \"vocabulary,metadata,layer,widget\"\n else:\n url_args = \"metadata,layer\"\n for item in tqdm(self):\n if item['id'] not in saved:\n entity_type = item.get('type')\n if entity_type in ['Dataset', 'Table']:\n ds_id = item['id']\n else:\n ds_id = item['attributes']['dataset']\n try:\n url = f'{self.server}/v1/dataset/{ds_id}?includes={url_args}'\n r = requests.get(url)\n dataset_config = r.json()['data']\n except:\n failed.append(item)\n\n save_json = {\n \"id\": ds_id,\n \"type\": \"dataset\",\n \"server\": self.server,\n \"attributes\": dataset_config['attributes']\n }\n with open(f\"{path}/{ds_id}.json\", 'w') as fp:\n json.dump(save_json, fp)\n\n if len(failed) > 0:\n print(f'Some entities failed to save: {failed}')\n return failed\n print('Save complete!')","title":""},{"docid":"2e2d638bf83a24fcc914036690f8c760","score":"0.61270213","text":"def _dent_asset_save(self, datastore):\n data_buffer = io.BytesIO()\n np.save(data_buffer, self.data)\n data_header = tarfile.TarInfo(\"data\")\n data_header.size = data_buffer.getbuffer().nbytes\n data_buffer.seek(0)\n datastore.addfile(data_header, data_buffer)\n\n config_buffer = io.BytesIO()\n config_buffer.write(\n yaml.dump(\n {\n \"name\": self.name,\n \"directory\": self.directory,\n \"transform\": self._transform.tolist(),\n \"offset\": self.offset.tolist(),\n \"indices\": self.indices.tolist(),\n \"material_name\": self.material_name,\n }\n ).encode(\"ascii\")\n )\n config_buffer.flush()\n config_header = tarfile.TarInfo(\"config\")\n config_header.size = config_buffer.getbuffer().nbytes\n config_buffer.seek(0)\n datastore.addfile(config_header, config_buffer)","title":""},{"docid":"648da152253f95afa602bbbc4787408a","score":"0.61234426","text":"def write_dataset(self, path, data):\n self._h5file.create_dataset(path, data=data)","title":""},{"docid":"a58b6f4a21e3ff8379ccd13994cc7681","score":"0.6121262","text":"def _serialize_data(self, samples, losses):\n with open('train_samples.pkl', 'wb') as f:\n pkl.dump(samples, f)\n with open('train_loss.pkl', 'wb') as f:\n pkl.dump(losses, f)","title":""},{"docid":"4f8c5dcbda67b470276bedf9f5309ad2","score":"0.61147726","text":"def save(self):\n log.info(\"Writing to \", self.output_filepath)\n eKonf.save_data(self.df, self.output_filepath)","title":""},{"docid":"92c836b3d1dc2020cf0defcf67eef268","score":"0.61024535","text":"def fetch_and_save_data(self):\n self._fetch_ca_datset()\n if not os.path.exists(self.dataset_directory):\n self._save_data_on_disk()\n else:\n self._create_filepaths()","title":""},{"docid":"8097d66fe85273930d1355af9540a88d","score":"0.61008865","text":"def save_data(self, data, title=\"\"):\n\n\t\ttitle = title.replace(r\"\\\\\", \" -- \").replace(r\"/\", \" -- \").replace(\"\\\\\", \" -- \")[\n\t\t\t\t:-4] # Title tells both the set and the name of the file. Also crop out the extension .xml with the [:-4] command.\n\t\tnp.save(os.path.join(DataSources.forceplate_data, \"processed\", title),\n\t\t\t\tdata) # save as title without .xml, and no spaces on the end","title":""},{"docid":"b70e7be5544f5a63c5586cc851a7285c","score":"0.6100087","text":"def save_data(self, filepath: str) -> None:\n np.save(filepath, self._population_history)","title":""},{"docid":"756a0ad4616d5a5f74050e310650dd75","score":"0.60908103","text":"def save(self, filename: str):\n pd.to_pickle(self, filename)\n LOG.info(f\"Data successfully saved as {filename}!\")","title":""},{"docid":"ae71be0747280048ac911c47b088dce0","score":"0.608736","text":"def saveToDisk(self):\n out.info('Saving to disk (%s)\\n' % (self.filename))\n\n # Make sure they want to save\n if(not self.attrSaveable()):\n return\n\n # Get whatever the data is\n pyld = self.exportAttr(self.getAttr())\n\n # Write the file to disk, truncate if it exists\n try:\n with open(self.filename, 'wb') as output:\n pickle.dump(pyld, output)\n os.fsync(output.fileno())\n except Exception as e:\n out.err('Error writing to disk %s\\n' % (str(e)))\n\n try:\n with open(self.filename + \".yaml\", \"w\") as output:\n yaml.dump(pyld, output)\n except Exception as error:\n out.err(\"Error writing yaml file: {}\".format(error))","title":""},{"docid":"986edbb7909e73dce85e2ecac7093a48","score":"0.6077244","text":"def pickle_and_save_datasets(datasets, filename='plankton_data'):\n data_string = pickle.dumps(datasets)\n with gzip.open('data/{}.pkl.gz'.format(filename), 'wb') as f:\n f.write(data_string)","title":""},{"docid":"3f153b9cb844eaa20cd7a1000c395d23","score":"0.60747665","text":"def save_attributes(self, paths: af.DirectoryPaths):\r\n dataset_path = paths._files_path / \"dataset\"\r\n\r\n self.dataset.output_to_fits(\r\n data_path=dataset_path / \"data.fits\",\r\n noise_map_path=dataset_path / \"noise_map.fits\",\r\n pre_cti_data_path=dataset_path / \"pre_cti_data.fits\",\r\n overwrite=True,\r\n )\r\n self.dataset.layout.output_to_json(\r\n file_path=dataset_path / \"layout.json\",\r\n )\r\n self.dataset.mask.output_to_fits(\r\n file_path=dataset_path / \"mask.fits\", overwrite=True\r\n )\r\n\r\n if self.dataset_full is not None:\r\n dataset_path = paths._files_path / \"dataset_full\"\r\n\r\n self.dataset_full.output_to_fits(\r\n data_path=dataset_path / \"data.fits\",\r\n noise_map_path=dataset_path / \"noise_map.fits\",\r\n pre_cti_data_path=dataset_path / \"pre_cti_data.fits\",\r\n overwrite=True,\r\n )\r\n self.dataset_full.layout.output_to_json(\r\n file_path=dataset_path / \"layout.json\",\r\n )\r\n self.dataset_full.mask.output_to_fits(\r\n file_path=dataset_path / \"mask.fits\", overwrite=True\r\n )\r\n\r\n self.clocker.output_to_json(file_path=paths._files_path / \"clocker.json\")\r\n self.settings_cti.output_to_json(\r\n file_path=paths._files_path / \"settings_cti.json\"\r\n )","title":""},{"docid":"80144945f551fad8198d747e3e75d09a","score":"0.6074664","text":"def save(data, labels, dataset, metric='raw', overwrite=False):\n\n #Dataset filename\n dataFile = dataset.lower()+\"_\"+metric.lower()+\".npz\"\n labelsFile = dataset.lower()+\"_labels.npz\"\n\n #Full path to file\n dataFile_path = os.path.join(data_dir, dataFile)\n labelsFile_path = os.path.join(data_dir, labelsFile)\n\n #Check if Data directory exists\n if not os.path.exists(data_dir):\n os.makedirs(data_dir)\n\n #Save dataset and labels\n if os.path.isfile(dataFile_path) and not overwrite:\n print('Data file '+dataFile_path+' already exists. Not saving.')\n else:\n np.savez_compressed(dataFile_path,data=data)\n np.savez_compressed(labelsFile_path,labels=labels)","title":""},{"docid":"e4fe509157b6348d5e8ad50517a7e8d3","score":"0.607132","text":"def save(self, save_path=\"\"):\n save_data(self, save_path=save_path)","title":""},{"docid":"d752fe7436946cbb254e592fa0d57aec","score":"0.6066392","text":"def save(self, basename):\r\n if self.data_type == \"sesans\":\r\n np.savetxt(basename+\".dat\", np.array([self._data.x, self.theory()]).T)\r\n pass","title":""},{"docid":"78ed3c6adac78bed8172b3254cc83dcf","score":"0.6053453","text":"def save_data(self, data=None):\n self.data = data\n \n f = open(self.filename, 'w')\n \n for label, values in zip(data['labels'], data['values']):\n f.write(str(label))\n for i, value in enumerate(values):\n f.write(' ')\n f.write(str(i))\n f.write(':')\n f.write(str(value))\n f.write('\\n')\n \n f.close()\n \n return self","title":""},{"docid":"96fbe9601f48056eafc57bddf7a431e2","score":"0.60517126","text":"def _save_data(save_to_file: str, save_mode: str, metadata: GlobalMetadata) -> None:\n\n with open(save_to_file, mode=save_mode) as out:\n out.write(str(metadata.datamart_id))\n out.write(\"\\n\")\n out.write(json.dumps(metadata.value))\n out.write(\"\\n\")","title":""},{"docid":"b0c42c55074807fed6ab6bdcfb7a1b3f","score":"0.6049688","text":"def save(self):\n with open(self.file_path, 'w') as f:\n json.dump(self.data, f)","title":""}],"string":"[\n {\n \"docid\": \"e208299ef2e05512fa69fd21543ef0cf\",\n \"score\": \"0.7637095\",\n \"text\": \"def _save_dataset(self):\\n dataset = datastore.Resource.new(\\n self.dataset_name,\\n self._dataset_files,\\n self._meta_data)\\n repo = self.data_repo()\\n repo.save(dataset, overwrite=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4cc779f78e0762ebf1ce68fa53bcab22\",\n \"score\": \"0.74357975\",\n \"text\": \"def save_dataset(dataset):\\n logging.info(f\\\"Saving dataset {dataset}...\\\")\\n try:\\n dataset = json.loads(dataset)\\n except json.decoder.JSONDecodeError:\\n return\\n\\n dataset = dataset.rsplit(\\\"/\\\")[-1]\\n dataset_path = f\\\"/tmp/data/{dataset}\\\"\\n\\n try:\\n df = pd.read_csv(dataset_path, sep=None, engine=\\\"python\\\")\\n platiagro.save_dataset(name=dataset, df=df)\\n except (pd.errors.EmptyDataError, pd.errors.ParserError, UnicodeDecodeError, ValueError):\\n content = open(dataset_path, \\\"rb\\\")\\n platiagro.save_dataset(name=dataset, data=content)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5c88b6e6d05add6aba8c963116248c4\",\n \"score\": \"0.74094224\",\n \"text\": \"def save_dataset(dataset: TablutDataset, path: Optional[str] = './'):\\n with open(os.path.join(path, f'{dataset.name}.pkl'), 'wb') as f:\\n pickle.dump(dataset, f)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ffea81906700acc0d3b2d1c28915fc07\",\n \"score\": \"0.7367737\",\n \"text\": \"def saveDataset(self, filename, datasets):\\n with open(os.path.join(filename), 'wb') as handle:\\n data = { # Warning: If adding something here, also modifying loadDataset\\n 'word2index': self.word2index,\\n 'index2word': self.index2word,\\n 'index2vector': self.index2vector,\\n 'datasets': datasets\\n }\\n pickle.dump(data, handle, -1) # Using the highest protocol available\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f86e156671f9f9513cc31c6ec97160ce\",\n \"score\": \"0.72513616\",\n \"text\": \"def save_dataset(dataset):\\n self.dataset_counter += 1\\n self.saved_datasets.append(dataset)\\n dataset_dir = self.datasets_directory_name + \\\"/training_dataset_{}.npy\\\".format(self.dataset_counter)\\n dataset_path = Path(self.datasets_directory_name)\\n try:\\n dataset_path.rmdir()\\n except OSError as e:\\n print(f\\\"Error: {dataset_path} : {e.strerror}\\\")\\n dataset_path.mkdir(exist_ok=True, parents=True)\\n if os.path.exists(dataset_path) and self.dataset_counter == 1:\\n shutil.rmtree(dataset_path)\\n os.makedirs(dataset_path)\\n with open(dataset_dir, 'wb') as f:\\n np.save(f, dataset)\\n print(\\\"[INFO]: Dataset was saved in the directory: \\\", dataset_path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cee52fb6bab2b27167ecf9285269061a\",\n \"score\": \"0.7239169\",\n \"text\": \"def save(self):\\n\\n if self.path is None:\\n raise ValueError('No path given to save the dataset.')\\n\\n self.save_at(self.path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7bedc009f4111974b7ec2d1cf4cf7839\",\n \"score\": \"0.71408814\",\n \"text\": \"def SaveDataset(self, savepath_pickle=None):\\n if savepath_pickle is None:\\n savepath_pickle = self.GetDatasetFilePath()\\n with open(savepath_pickle, \\\"wb\\\") as file:\\n pickle.dump((self.sample_dict, self.path_list, self.set_dict, self.set_index), file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f66c809f36781a6320b2b07ee4ff9afd\",\n \"score\": \"0.7136119\",\n \"text\": \"def save_encoded_dataset(dataset, filename):\\n\\n pickle.dump(dataset, open(filename, \\\"wb\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4b1beb628e80d41a903616dc106428e\",\n \"score\": \"0.7117572\",\n \"text\": \"def save_dataset(self, path=None, add=True):\\n self._assert_loaded(self._data, \\\"dataset\\\")\\n # if not path, do DataLoader data_dir\\n if not path:\\n path = self.annotation_path\\n\\n os.makedirs(path, exist_ok=True)\\n\\n # save as json\\n self._save_json_list(self._data, os.path.join(path, \\\"data.json\\\"), add=add, sub_annots=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"48fd40f572f564a02c92aa0d550a52fd\",\n \"score\": \"0.71117485\",\n \"text\": \"def save(self, save_path): # todo: need interactionsDataset when saving?\\n dump(self, save_path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2006bb901a77089084c55cf5f8ea26b9\",\n \"score\": \"0.7082088\",\n \"text\": \"def saveDataset(self, filename):\\n\\n with open(os.path.join(filename), 'wb') as handle:\\n data = {\\n 'word2id': self.word2id,\\n 'id2word': self.id2word,\\n 'idCount': self.idCount,\\n 'intent2id': self.intent2id,\\n 'id2intent': self.id2intent,\\n 'trainingSamples': self.trainingSamples,\\n 'validationSamples': self.validationSamples,\\n 'testSamples': self.testSamples,\\n 'entities': self.entities_property,\\n }\\n pickle.dump(data, handle, -1)\\n with open(self.data_path + \\\"/train.csv\\\", \\\"w\\\") as output:\\n writer = csv.writer(output, lineterminator='\\\\n')\\n writer.writerows(self.txtTrainingSamples)\\n with open(self.data_path + \\\"/valid.csv\\\", \\\"w\\\") as output:\\n writer = csv.writer(output, lineterminator='\\\\n')\\n writer.writerows(self.txtValidationSamples)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d4bdb422e81b3afbc74e2b92d976ca8\",\n \"score\": \"0.6969148\",\n \"text\": \"def save(self) -> None:\\n create_dir(dataset_data_path)\\n\\n file_name = self.get_filename(self.name, self.dt)\\n with open(file_name, 'wb') as f:\\n pickle.dump(self, f)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8dca2543708c7155aae8ecbc4e26409e\",\n \"score\": \"0.6887473\",\n \"text\": \"def save(cls):\\n cls._remove_old_datapoints()\\n with open(cls._location, 'w') as fh:\\n data = yaml.dump(cls._data, default_flow_style=False)\\n fh.write(data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fafaaa3ec321134fea0d4c06b272853e\",\n \"score\": \"0.6887001\",\n \"text\": \"def save_dataset(filename, dataset):\\n\\n with open(filename, mode='w', encoding='utf-8')as f:\\n np.savetxt(filename, dataset)\\n f.close()\\n print(f\\\"{filename} saved successfully\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b24b760947b9412cfd32223f0946e59\",\n \"score\": \"0.687435\",\n \"text\": \"def save_dataset(self, save_filename, type='shuffled_data_array' ):\\n\\n if type == 'shuffled_data_array':\\n\\n np.save(save_filename,self.shuffled_data_array)\\n\\n np.save(('{}_labels'.format(save_filename)),self.shuffled_label_array)\\n\\n\\n\\n elif type == 'data_array':\\n\\n np.save(save_filename,self.data_array)\\n\\n np.save(('{}_labels'.format(save_filename)),self.label_array)\\n\\n\\n\\n elif type == 'xor_array' or type == 'and_array' or type == 'add_array':\\n\\n fn = 'self.{}_array'.format(type[0:2])\\n\\n fn_labels = 'self.{}_labels'.format(type[0:2])\\n\\n np.save(save_filename, fn)\\n\\n np.save(('{}_labels'.format(save_filename)),fn_labels)\\n\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02f03dd376c7af2ba1a071f9e560fd9c\",\n \"score\": \"0.6841852\",\n \"text\": \"def save_dataset(self):\\n\\n import os\\n from datetime import datetime\\n\\n timestamp = datetime.now()\\n yearkey = \\\"yearkey={}\\\".format(timestamp.strftime(\\\"%Y\\\"))\\n monthkey = \\\"monthkey={}\\\".format(timestamp.strftime(\\\"%m\\\"))\\n daykey = \\\"daykey={}\\\".format(timestamp.strftime(\\\"%d\\\"))\\n directory_to_save_processed_data_to = os.path.join(\\n self.data_directory, \\\"processed\\\", yearkey, monthkey, daykey\\n )\\n if not os.path.exists(directory_to_save_processed_data_to):\\n os.makedirs(directory_to_save_processed_data_to)\\n\\n self.data.to_parquet(\\n os.path.join(directory_to_save_processed_data_to, \\\"data.parquet.gzip\\\"),\\n compression=\\\"gzip\\\",\\n )\\n self.next(self.end)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35890761e1dbea961678b20ef230b45c\",\n \"score\": \"0.6798494\",\n \"text\": \"def save_dataset(self, filepath: str):\\n np.save(filepath, self.all_sos_numpy)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1433fc0f0714e8bb9f9fc9bd2d10ab0\",\n \"score\": \"0.67621285\",\n \"text\": \"def saveDataset(X_train, Y_train, X_test, Y_test, path):\\n np.save(os.path.join(path, \\\"X_train.npy\\\"), X_train)\\n np.save(os.path.join(path, \\\"Y_train.npy\\\"), Y_train)\\n np.save(os.path.join(path, \\\"X_test.npy\\\"), X_test)\\n np.save(os.path.join(path, \\\"Y_test.npy\\\"), Y_test)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b0cbb864ec04c5c31603ffbffb6cd93\",\n \"score\": \"0.6760361\",\n \"text\": \"def save_to_pickle(dataset, fileloc):\\n filename = fileloc + \\\".pickle\\\"\\n with open(filename, 'wb') as f:\\n pickle.dump(dataset, f)\\n logger.info(\\\"Saved to {}\\\".format(filename))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4c179adfce2e97fc750a981f00740d2\",\n \"score\": \"0.6758912\",\n \"text\": \"def save_dataset(self, dataset_file=None, force_overwrite=False):\\n if dataset_file is not None:\\n self.dataset_file = dataset_file\\n\\n # Initialize dict\\n json_dataset = OrderedDict()\\n\\n # Save dataset info\\n json_dataset['root_dir'] = self.root_dir\\n json_dataset['dataset_type'] = self.dataset_type\\n json_dataset['classes'] = list(self.name_to_class_info.values())\\n json_dataset['images'] = list(self.image_infos.values())\\n\\n # Save dataset into json file\\n if (not os.path.isfile(self.dataset_file)) or force_overwrite:\\n print('Saving dataset as an annotation file, this can take a while')\\n with open(self.dataset_file, 'w') as f:\\n json.dump(json_dataset, f)\\n print('Dataset saved')\\n else:\\n raise FileExistsError('Dataset not saved as it already exists, consider overwriting')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3bb9babcb5e0347ea0e529e663c70bde\",\n \"score\": \"0.673526\",\n \"text\": \"def write_dataset(self) -> list[str]:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ae0a79cdc41b48bab4d50d026227349\",\n \"score\": \"0.6731773\",\n \"text\": \"def save_dataset(self, dataset_file=None, force_overwrite=False):\\n if dataset_file is not None:\\n self.dataset_file = dataset_file\\n\\n assert self.dataset_file is not None\\n\\n # Initialize dict\\n json_dataset = OrderedDict()\\n\\n # Save dataset info\\n json_dataset['root_dir'] = self.root_dir\\n json_dataset['classes'] = list(self.name_to_class_info.keys())\\n json_dataset['images'] = list(self.image_infos.values())\\n json_dataset['annotations'] = list(self.ann_infos.values())\\n\\n # Save dataset into json file\\n if (not os.path.isfile(self.dataset_file)) or force_overwrite:\\n print('Saving dataset as an annotation file, this can take a while')\\n with open(self.dataset_file, 'w') as f:\\n json.dump(json_dataset, f)\\n print('Dataset saved')\\n else:\\n raise FileExistsError('Dataset not saved as it already exists, consider overwriting')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"539658809dd9ab621f6380de089c0177\",\n \"score\": \"0.6696084\",\n \"text\": \"def save_dataset(dataset, outfile):\\n import h5py\\n f = h5py.File(outfile, 'w')\\n for key in dataset.keys():\\n f.create_dataset(key, data=dataset[key])\\n f.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2dfa68e4cbf5cc9c8e9f977c03040d61\",\n \"score\": \"0.66912514\",\n \"text\": \"def persist_data(self):\\n self.workdir.mkdir(parents=True, exist_ok=True)\\n ds2 = []\\n for i, (ds, cand_f, ds_attrs) in enumerate(self._flat_datasets()):\\n train, test = ds\\n if isinstance(train, pd.DataFrame):\\n fn = self.workdir / 'ds{}-train.parquet'.format(i+1)\\n _logger.info('serializing to %s', fn)\\n train.to_parquet(fn)\\n train = fn\\n if isinstance(test, pd.DataFrame):\\n fn = self.workdir / 'ds{}-test.parquet'.format(i+1)\\n _logger.info('serializing to %s', fn)\\n test.to_parquet(fn)\\n test = fn\\n ds2.append(((train, test), cand_f, ds_attrs))\\n self.datasets = ds2\\n self._is_flat = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b83b59004434bd8f3e17f25b19b5838c\",\n \"score\": \"0.6667793\",\n \"text\": \"def data_save(self, filename):\\n\\t\\tfd = open(filename, \\\"wb\\\")\\n\\t\\tfd.write(self.data)\\n\\t\\tfd.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"018add1572d08a029b1a7158919d9131\",\n \"score\": \"0.666314\",\n \"text\": \"def save(self, dataset: 'Dataset', dataset_uri: str, *, compute_digest: ComputeDigest = ComputeDigest.ALWAYS, preserve_metadata: bool = True) -> None:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ecfb2d7c0c4c3ebb7d18dec8bd18944\",\n \"score\": \"0.6654124\",\n \"text\": \"def save_dataset(dataset, save_dir):\\n # Create directory if it doesn't exist\\n print(\\\"Saving in {}...\\\".format(save_dir))\\n if not os.path.exists(save_dir):\\n os.makedirs(save_dir)\\n\\n # Export the dataset\\n with open(os.path.join(save_dir, 'sentences.txt'), 'w') as file_sentences:\\n with open(os.path.join(save_dir, 'labels.txt'), 'w') as file_labels:\\n for words, tags in dataset:\\n file_sentences.write(\\\"{}\\\\n\\\".format(\\\" \\\".join(words)))\\n file_labels.write(\\\"{}\\\\n\\\".format(\\\" \\\".join(tags)))\\n print(\\\"- done.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ecfb2d7c0c4c3ebb7d18dec8bd18944\",\n \"score\": \"0.6654124\",\n \"text\": \"def save_dataset(dataset, save_dir):\\n # Create directory if it doesn't exist\\n print(\\\"Saving in {}...\\\".format(save_dir))\\n if not os.path.exists(save_dir):\\n os.makedirs(save_dir)\\n\\n # Export the dataset\\n with open(os.path.join(save_dir, 'sentences.txt'), 'w') as file_sentences:\\n with open(os.path.join(save_dir, 'labels.txt'), 'w') as file_labels:\\n for words, tags in dataset:\\n file_sentences.write(\\\"{}\\\\n\\\".format(\\\" \\\".join(words)))\\n file_labels.write(\\\"{}\\\\n\\\".format(\\\" \\\".join(tags)))\\n print(\\\"- done.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd12a4b52682693a939d5b6ce6c6e2b1\",\n \"score\": \"0.6646873\",\n \"text\": \"def save_datasets(datasets, file): \\n with open(file, 'wb') as f:\\n save(datasets, f)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d56fa784544d6a2ad7597d5c01fe26a\",\n \"score\": \"0.6637412\",\n \"text\": \"def save(self):\\n with open(self.filename, 'wb') as fp:\\n pickle.dump(self.data, fp, pickle.HIGHEST_PROTOCOL)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c1a70695432e6f9ebdb70c31caf4f90\",\n \"score\": \"0.66245043\",\n \"text\": \"def save_dataset(self, samples, labels, path):\\n\\n # Create the list structure of a dataset\\n dataset = [samples, labels]\\n\\n # Save the dataset file in a pickle file\\n with open(path + '.pckl', 'wb') as dataset_file:\\n pickle.dump(dataset, dataset_file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c296c878726ffcb4eb8c2a390b8712d\",\n \"score\": \"0.66019934\",\n \"text\": \"def test_saveable_dataset():\\n # build HCIDataset\\n cube = np.zeros((5, 10, 10))\\n angles = np.linspace(1, 2, 5)\\n fwhm = 4 # test non-numpy type saving/loading\\n\\n ds = Dataset(cube=cube, angles=angles, fwhm=fwhm)\\n\\n # save\\n fd, fn = tempfile.mkstemp(prefix=\\\"vip_\\\")\\n ds.save(fn)\\n\\n # restore\\n ds2 = Dataset.load(fn)\\n\\n # compare\\n aarc(ds2.cube, cube)\\n aarc(ds2.angles, angles)\\n assert ds2.fwhm == fwhm\\n\\n # cleanup\\n os.remove(fn)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d329dbc26a8ec3e8190f7538ea19a10a\",\n \"score\": \"0.65863574\",\n \"text\": \"def write_dataset(dataset, save_path):\\n if not dataset:\\n logging.info('No dataset to write.')\\n return\\n logging.info('Writing dataset to %s', save_path)\\n for split_name, list_of_input_output_pairs in dataset.items():\\n folder_name = os.path.join(save_path, split_name)\\n if not os.path.exists(folder_name):\\n os.makedirs(folder_name)\\n encode_name = os.path.join(folder_name, '%s_encode.txt' % split_name)\\n decode_name = os.path.join(folder_name, '%s_decode.txt' % split_name)\\n with gfile.GFile(encode_name,\\n 'w') as encode_f, gfile.GFile(decode_name,\\n 'w') as decode_f:\\n for pair in list_of_input_output_pairs:\\n encode_f.write(pair[0] + '\\\\n')\\n decode_f.write(pair[1] + '\\\\n')\\n logging.info('Dataset written to %s', save_path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d6f45f2f66946d1801a34db8776cf96b\",\n \"score\": \"0.6560496\",\n \"text\": \"def save(self, filename=None, verbose=False):\\n if not filename:\\n if self.name:\\n filename = self.name\\n else:\\n print(\\\" [ERROR] - Must specify dataset.name [dataset.set_name('name')]\\\")\\n print(\\\" or give filename [dataset.save(filename='filename')]\\\")\\n hdf5_save.save_hdf_dataset(self, filename, verbose=verbose)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e054944a62bbf296902d61c5a41157ac\",\n \"score\": \"0.6556137\",\n \"text\": \"def save_data(self, data):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4afdb01e15302f79a6cc517d8f51e1f0\",\n \"score\": \"0.6549905\",\n \"text\": \"def save_data(self):\\n np.savetxt(self.save_to, self.predicted, delimiter=\\\",\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f47511b87c05e21d215f7cab0656240\",\n \"score\": \"0.65480536\",\n \"text\": \"def save_dataset(self,mode=\\\"append new\\\"):\\n # Create processed dataset folder if not existent\\n if not os.path.exists(self.processed_dataset_folder):\\n os.mkdir(self.processed_dataset_folder)\\n\\n # if dataset has not been saved before save\\n dataset_full_path = f\\\"{self.processed_dataset_folder}{os.sep}{self.dataset_base_name}\\\"\\n if not os.path.exists(dataset_full_path):\\n pickle.dump(self.dataset, open(dataset_full_path,\\\"wb\\\"))\\n return True\\n\\n if mode == \\\"override\\\": # Replace file\\n pickle.dump(self.dataset, open(dataset_full_path,\\\"wb\\\"))\\n return True\\n elif mode == \\\"append new\\\": # Check if file contents in there and if not then append\\n saved_dataset = pickle.load( open(dataset_full_path,\\\"rb\\\"))\\n saved_dataset_processed_videos_set = set([row[\\\"Meta\\\"][\\\"VideoPath\\\"] for row in saved_dataset])\\n current_dataset_processed_videos_set = set([row[\\\"Meta\\\"][\\\"VideoPath\\\"] for row in self.dataset])\\n videos_processed_but_not_in_saved_dataset = current_dataset_processed_videos_set - saved_dataset_processed_videos_set\\n\\n if len(videos_processed_but_not_in_saved_dataset) > 0:\\n additional_rows_to_save = [row for row in self.dataset if row[\\\"Meta\\\"][\\\"VideoPath\\\"] in videos_processed_but_not_in_saved_dataset]\\n saved_dataset.extend(additional_rows_to_save)\\n pickle.dump(saved_dataset, open(dataset_full_path,\\\"wb\\\"))\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d53c5637e8f1c7854f8bf47e16545dd0\",\n \"score\": \"0.65381706\",\n \"text\": \"def write_dataset(dataset, save_path):\\n if not dataset:\\n logging.info('No dataset to write.')\\n return\\n logging.info(f'Writing dataset to {save_path}')\\n for split_name, list_of_input_output_pairs in dataset.items():\\n folder_name = os.path.join(save_path, split_name)\\n if not os.path.exists(folder_name):\\n os.makedirs(folder_name)\\n encode_name = os.path.join(folder_name, f'{split_name}_encode.txt')\\n decode_name = os.path.join(folder_name, f'{split_name}_decode.txt')\\n skeleton_name = os.path.join(folder_name, f'{split_name}_skeleton.txt')\\n with open(\\n encode_name, 'w', encoding='utf8') as encode_f, open(\\n decode_name, 'w', encoding='utf8') as decode_f, open(\\n skeleton_name, 'w', encoding='utf8') as skeleton_f:\\n for pair in list_of_input_output_pairs:\\n encode_f.write(pair[0] + '\\\\n')\\n decode_f.write(pair[1] + '\\\\n')\\n skeleton_f.write(pair[2] + '\\\\n')\\n logging.info(f'Dataset written to {save_path}')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ddde7a8b0bb63b2b5d0216bb8cbad85\",\n \"score\": \"0.6508642\",\n \"text\": \"def save(self, filename):\\n with open(filename, 'w') as fp:\\n json.dump({\\\"image_size\\\": self.image_size,\\n \\\"labels\\\": self.label_info,\\n \\\"images\\\": self.image_info}, fp)\\n utils.logger.info(\\\"The dataset has been saved into {}\\\".format(filename))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f54c44b22efc7aaf40e75920e94b7ec\",\n \"score\": \"0.6501132\",\n \"text\": \"def saveData(self, data):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e469438df6b586c8abd771b75782542\",\n \"score\": \"0.6488153\",\n \"text\": \"def save_mfcc_training_dataset(self):\\n\\n # Save the dataset as pickle\\n with pathlib.Path('dataset.pkl').open('wb') as pfile:\\n pickle.dump([(r.diffs, r.result_type) for r in self.res], pfile,\\n protocol=pickle.HIGHEST_PROTOCOL)\\n logging.getLogger(__name__).info(\\\"dataset.pkl has generated.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df87947f4111990f712114b009c36077\",\n \"score\": \"0.647041\",\n \"text\": \"def save_dataset(dataset, save_dir):\\n\\n print(f'Saving in {save_dir}...')\\n if not os.path.exists(save_dir):\\n os.makedirs(save_dir)\\n\\n with open(os.path.join(save_dir, 'sentences.txt'), 'w', encoding='utf-8') as file_sentences:\\n with open(os.path.join(save_dir, 'indices.txt'), 'w', encoding='utf-8') as file_indices:\\n for sentence, st_index, end_index in dataset:\\n file_sentences.write(f'{sentence}\\\\n')\\n file_indices.write(f'{str(st_index)} {str(end_index)}\\\\n')\\n\\n print(\\\"- done.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec3b0a4722948edace9c276daf8f6b8b\",\n \"score\": \"0.64498734\",\n \"text\": \"def _save_dataset(filename, name, value, dtype=None):\\n\\n try:\\n filename.create_dataset(name, data=value, maxshape=(None,), dtype=dtype)\\n except RuntimeError:\\n del filename[name]\\n filename.create_dataset(name, data=value, maxshape=(None,), dtype=dtype)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c596aa6936a6d641fbed5e7e93d3ae31\",\n \"score\": \"0.6447846\",\n \"text\": \"def save_data(self):\\r\\n json.dump(self.data, open(self.filename, 'w'), indent=2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87b6635f12c916bdf4cbc32fa7624d14\",\n \"score\": \"0.6415625\",\n \"text\": \"def save(self, filename='data.json'):\\n with open(filename,'w') as f: \\n json.dump(self.datadict,f)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c44250c5ac1e877058182310833168d\",\n \"score\": \"0.6409766\",\n \"text\": \"def write(self, fname):\\n numpy.save(fname, self.dataset)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ab38983f55f551d5cb09a29ab5fe040\",\n \"score\": \"0.63770056\",\n \"text\": \"def savechanges(self):\\n self.construct_name(force = True)\\n if hasattr(self, 'clusterarray'):\\n dataset = xr.Dataset({'clustidfield':self.clusterarray,self.array.name:self.array})\\n else:\\n dataset = self.array.to_dataset()\\n # invoke the computation (if loading was lazy) and writing\\n particular_encoding = {key : obs_netcdf_encoding[key] for key in dataset.keys()}\\n dataset.to_netcdf(self.filepath, encoding = particular_encoding)\\n #delattr(self, 'array')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7663110da61f79c8fbc9be557fb0bb4f\",\n \"score\": \"0.6367028\",\n \"text\": \"def save(\\n filepath: str, dataset: Union[List[Frame], Dataset], nprocs: int = 0\\n) -> None:\\n if isinstance(dataset, Dataset):\\n frames, config = dataset.frames, dataset.config\\n else:\\n frames, config = dataset, None\\n\\n if nprocs > 1:\\n labels = pmap(dump, frames, nprocs)\\n else:\\n labels = list(map(dump, frames))\\n\\n with open(filepath, \\\"w\\\") as fp:\\n json.dump(Dataset(frames=labels, config=config).dict(), fp, indent=2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f6d5d2f909984ab1e7d5e1d8af42b95\",\n \"score\": \"0.63477445\",\n \"text\": \"def save(self):\\n entities = pandas.DataFrame(self.data)\\n entities.to_csv(self.output_path, index_label=\\\"id\\\")\\n self.logger.info(\\\"Initial custom data saved: {}\\\".format(self.output_path))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35aac372eeba883b5bb4e98eabd64216\",\n \"score\": \"0.63380545\",\n \"text\": \"def savePickle(path, dataset, protocol=2):\\n f = open(path, 'wb')\\n pickle.dump(dataset, f, protocol=2)\\n f.close()\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00bc112f6e435929aceaff1795147ba2\",\n \"score\": \"0.6337372\",\n \"text\": \"def save_dataset(filename, train_labels=None, train_features=None, test_features=None, ids=None, feature_names=None):\\n np.savez(filename, train_labels=train_labels, train_features=train_features, test_features=test_features, ids=ids, feature_names=feature_names)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35e9d7fe6b66f2689670791d56a25c7d\",\n \"score\": \"0.63304573\",\n \"text\": \"def save_data(data, filename):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87a808f54bbd868bf1ef675cef177bf3\",\n \"score\": \"0.6328018\",\n \"text\": \"def save_dataset(self, path, sep='\\\\t'):\\n\\n self._dataset.to_csv(path_or_buf=path, sep=sep, index=False)\\n\\n self.verboseprint(\\\"Dataset successfully saved at {}.\\\".format(path))\\n logging.debug(\\\"Dataset saved at {}..\\\".format(path))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0e8d2334736eb0118f7f589975ce2d7\",\n \"score\": \"0.63111484\",\n \"text\": \"def __save_in_database(self, dataset):\\n\\n if isinstance(dataset, list):\\n for data in dataset:\\n self.__save_in_database(data)\\n else:\\n self.save_into_database(dataset, None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dbd0cba115ea4095c132b873f90dd688\",\n \"score\": \"0.6308548\",\n \"text\": \"def save_data_to_disk():\\n global labelled_data\\n with open(os.path.join(data_dir, filename), \\\"wb\\\") as f:\\n np.savetxt(f, labelled_data, delimiter=\\\",\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"40baa2ec7ee0c74525f24dd3818c5817\",\n \"score\": \"0.6305703\",\n \"text\": \"def save_data(self, data):\\n with open(self.fname, \\\"wb\\\") as file:\\n pickle.dump(data, file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea3c9de5a047124f8ed555f9dd30aafa\",\n \"score\": \"0.63037914\",\n \"text\": \"def _write(self, featurized_dataset, dataset_name):\\n dump_location = feature_set_location(dataset_name, self.__class__.__name__)\\n joblib.dump(featurized_dataset, dump_location)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d9ab93314ba8c1d872bfc2c5d50afe2\",\n \"score\": \"0.63032645\",\n \"text\": \"def save(cls, filepath, numpy_tuple_dataset):\\n if not isinstance(numpy_tuple_dataset, NumpyTupleDataset):\\n raise TypeError('numpy_tuple_dataset is not instance of '\\n 'NumpyTupleDataset, got {}'\\n .format(type(numpy_tuple_dataset)))\\n np.savez(filepath, *numpy_tuple_dataset._datasets)\\n print('Save {} done.'.format(filepath))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7fc946c9f352535a02ff70cd56b81922\",\n \"score\": \"0.630007\",\n \"text\": \"def save(self, save_dir):\\n\\n # Save params\\n full_path = os.path.join(save_dir, 'dataset_params.pickle')\\n with open(full_path, 'wb') as x:\\n pickle.dump(self.params, x)\\n\\n # Each partition is persisted saved as a dataset index and color name pair\\n # Dataset index can be used to re-create the partitons\\n color_idx_lookup = {name: idx for idx, (_, _, name) in enumerate(self)}\\n for partition, partition_path in [\\n (self.train_set, 'train_partition.txt'),\\n (self.cv_set, 'cv_partition.txt'),\\n (self.test_set, 'test_partition.txt'),\\n ]:\\n full_path = os.path.join(save_dir, partition_path)\\n color_names = ['{},{}'.format(color_idx_lookup[name], name) for _, _, name in partition]\\n with open(full_path, 'w') as x:\\n x.write('\\\\n'.join(color_names))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"314f3f98dbd54460c07fd6df833cb058\",\n \"score\": \"0.62917274\",\n \"text\": \"def save(self, filename, **kwargs):\\n\\n save(self.ds, filename, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f213dd16185287228cf0e16ff63b54e\",\n \"score\": \"0.6291383\",\n \"text\": \"def saveData(self, filename=None):\\n if filename is None:\\n filename = self.name + \\\".dat\\\"\\n with open(filename, 'wb') as data:\\n pickle.dump(self.items, data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8b32e843796f31568b45fb3a47b8f29\",\n \"score\": \"0.62893254\",\n \"text\": \"def save(self, filename, data):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8aec74a063bd41a5f89b09050bed535\",\n \"score\": \"0.6275846\",\n \"text\": \"def save_data(self):\\n libFile.write_json(self.datapath, {\\\"DeformerInfo\\\": self.data})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37799e9f3139ba6b2e3eb38fbc4c7e00\",\n \"score\": \"0.6275192\",\n \"text\": \"def save(self, filepath):\\n f = h5py.File(filepath, \\\"w\\\")\\n f.create_dataset(\\\"trial_type\\\", data=\\\"RankObservations\\\")\\n f.create_dataset(\\\"stimulus_set\\\", data=self.stimulus_set)\\n f.create_dataset(\\\"n_select\\\", data=self.n_select)\\n f.create_dataset(\\\"is_ranked\\\", data=self.is_ranked)\\n f.create_dataset(\\\"group_id\\\", data=self.group_id)\\n f.create_dataset(\\\"agent_id\\\", data=self.agent_id)\\n f.create_dataset(\\\"session_id\\\", data=self.session_id)\\n f.create_dataset(\\\"weight\\\", data=self.weight)\\n f.create_dataset(\\\"rt_ms\\\", data=self.rt_ms)\\n f.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8567f474fb1db89a5b709a7d174c91d3\",\n \"score\": \"0.6255518\",\n \"text\": \"def save_data(self, filepath=None):\\n if filepath is None:\\n filepath = os.path.join(self.data_directory, 'df_classification_data.pickle')\\n \\n filetype = filepath.split('.')[-1]\\n to_save = self.all_data[self.all_data[self.label_col].notna()]\\n if filetype == 'pickle':\\n to_save.to_pickle(filepath)\\n elif filetype == 'csv':\\n to_save.to_csv(filepath)\\n elif filetype == 'json':\\n to_save.to_json(filepath, orient='records', lines=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c6c0683f720673113858a7505019ac7\",\n \"score\": \"0.62554234\",\n \"text\": \"def save(self, filepath):\\n f = h5py.File(filepath, \\\"w\\\")\\n f.create_dataset(\\\"trial_type\\\", data=\\\"RankDocket\\\")\\n f.create_dataset(\\\"stimulus_set\\\", data=self.stimulus_set)\\n f.create_dataset(\\\"n_select\\\", data=self.n_select)\\n f.create_dataset(\\\"is_ranked\\\", data=self.is_ranked)\\n f.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec312bc200885ea10fb10d053cb27e5b\",\n \"score\": \"0.62543446\",\n \"text\": \"def save_training_data(self, data):\\n pkl.dump(data, open('train_data.pkl', 'wb'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76a758cff64f3817fe2c8751fb739178\",\n \"score\": \"0.62477815\",\n \"text\": \"def save_data(directory,dataset_name,dataset):\\n \\n if not os.path.exists(directory):\\n os.makedirs(directory)\\n \\n print('Saving data...')\\n np.save(os.path.join(directory,dataset_name),dataset)\\n print(dataset_name,' saved at: ',os.path.join(directory,dataset_name))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a57c306aa40164d3240468f15e5cc226\",\n \"score\": \"0.62472814\",\n \"text\": \"def save(self):\\n self._validate_path()\\n self.data.save(self.filename)\\n self.stored = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a929ba3f42a6c5c1412513d5f900c83\",\n \"score\": \"0.62351054\",\n \"text\": \"def save(self):\\r\\n os.system(\\\"mkdir DataSets\\\")\\r\\n numpy.save(\\\"DataSets\\\\\\\\\\\"+self.name+\\\"Frames\\\",self.FrameSet)\\r\\n numpy.save(\\\"DataSets\\\\\\\\\\\"+self.name+\\\"Inputs\\\",self.InputSet)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17d14c9b6dc8c77a0371d3e4533f4a69\",\n \"score\": \"0.62292564\",\n \"text\": \"def _save_data_on_disk(self):\\n # create the training, validation and test arrays\\n train_data = np.c_[self.X_train, self.y_train]\\n valid_data = np.c_[self.X_valid, self.y_valid]\\n test_data = np.c_[self.X_test, self.y_test]\\n header_cols = self.housing.feature_names + [\\\"MedianHouseValue\\\"]\\n header = \\\",\\\".join(header_cols)\\n\\n self.train_filepaths = self._save_to_multiple_csv_files(train_data, \\\"train\\\", header, n_parts=20)\\n self.valid_filepaths = self._save_to_multiple_csv_files(valid_data, \\\"valid\\\", header, n_parts=10)\\n self.test_filepaths = self._save_to_multiple_csv_files(test_data, \\\"test\\\", header, n_parts=10)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e317e867e6ae2df418d35a49c2145e5a\",\n \"score\": \"0.6198814\",\n \"text\": \"def save(self, filename=None):\\n if filename is None:\\n filename = self.filename\\n with open(filename, 'wb') as f:\\n f.write(self.data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e6248170040d204c41971f5b4d0f27e4\",\n \"score\": \"0.61897075\",\n \"text\": \"def save_data(path, filename, data):\\n train_data, val_data, test_data = data\\n save_name = path / filename.split('.')[0]\\n train_data.to_csv(f'{save_name}_train_set.csv')\\n val_data.to_csv(f'{save_name}_val_set.csv')\\n test_data.to_csv(f'{save_name}_test_set.csv')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c31933c85f6f34ec34a72ffe1048c12b\",\n \"score\": \"0.61836815\",\n \"text\": \"def save_data(self):\\n data = AgentData(gridworld=self)\\n pickle.dump(data, open(data.title + '.pkl', 'wb'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2516dc600214f2d5ccf3d0529ee4ad80\",\n \"score\": \"0.61756873\",\n \"text\": \"def save_data(self, network):\\n\\n # save the current network\\n self.network_path = \\\"{}/network_gen_{}.pt\\\".format(network_dir, self.cycle)\\n torch.save(network, self.network_path)\\n\\n # dump the storage object to file\\n with open(storage_path, 'wb') as output:\\n pickle.dump(self, output, pickle.HIGHEST_PROTOCOL)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"27d4a0566dc40bb177652c047c64228a\",\n \"score\": \"0.61646855\",\n \"text\": \"def save(self, ds):\\n \\n fspec = self.dataset.dsmap[ds]\\n if fspec is None and self.directory is None: # file is not saved\\n return\\n if fspec is not None:\\n fdir, base = os.path.dirname(fspec), os.path.basename(fspec)\\n if self.suffix: # create file name with suffix preceding the extension\\n parts = os.path.splitext(base)\\n base = parts[0] + self.suffix + parts[1] # if no extension, parts[1] wil be empty string\\n if self.directory: # substitute new location for original\\n fdir = self.directory\\n target = os.path.join(fdir, base)\\n else:\\n base = ds + os.path.extsep + \\\"sav\\\"\\n target = os.path.join(self.directory, base)\\n if not self.overwrite and os.path.exists(target):\\n print(_(\\\"\\\"\\\"File %s already exists and will not be overwritten.\\\"\\\"\\\") % target)\\n else:\\n # this is already the active file\\n spss.Submit(\\\"\\\"\\\"SAVE OUTFILE=\\\"%(target)s\\\".\\\"\\\"\\\" % locals())\\n # file might already be gone\\n try:\\n if self.close and not ds == self.dataset.activefile:\\n spss.Submit(\\\"DATASET CLOSE \\\" + ds) # file could remain open as active file\\n except:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8eea44bb15fa0dd5cf2acee00ea300ce\",\n \"score\": \"0.61642736\",\n \"text\": \"def to_export(self):\\n if self.data_file is not None:\\n pickle.dump(self.data, open(self.data_file, \\\"wb\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"265fa45dc293b07b267720c424aecd09\",\n \"score\": \"0.615056\",\n \"text\": \"def save_data_to_fiile(self):\\n logger.warning(\\\"Only Use it for debug purposes!!!\\\")\\n self.internationalstudies.to_csv(\\\"internationalstudies.csv\\\")\\n self.drug_vocab_df.to_csv(\\\"drug_vocab.csv\\\")\\n with open('all_US_studies_by_keyword.json', 'w', encoding='utf-8') as f:\\n json.dump(self.all_US_studies_by_keyword, f, ensure_ascii=False, indent=4)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec00575b039e2838d4402ecab3be9ac5\",\n \"score\": \"0.6147158\",\n \"text\": \"def _data_to_save(self):\\n return self.data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d63df6f56af1e7542b87438ae88a83f\",\n \"score\": \"0.61453146\",\n \"text\": \"def to_disk(self, ds_disk_path):\\n self.ds.save_to_disk(ds_disk_path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"471b02133ccacfd3d852ae31ab9b0f1f\",\n \"score\": \"0.614503\",\n \"text\": \"def save(self, storePath, prefix, verbose=0):\\n if self.datasets == []:\\n print('no datasets to save. Generate datasets with gen_datasets() first.')\\n \\n print(f\\\"\\\\nSave {len(self.datasets)} datasettings into '{storePath}'\\\")\\n if not os.path.isdir(storePath):\\n os.makedirs(storePath)\\n\\n for i,d in enumerate(self.datasets):\\n fname = prefix + str(i+1) + \\\".npy\\\"\\n d.saveSets(storePath, fname)\\n if verbose: print(fname)\\n \\n with open(os.path.join(storePath,'readme.md'), 'w') as f:\\n f.write(f\\\"These datasets were generated from '{self.fileName}'\\\\n\\\")\\n f.write(str(datetime.now()) + '\\\\n')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c2c6276be4ce74dd3d607e572ba4559d\",\n \"score\": \"0.6137075\",\n \"text\": \"def dump_and_load_dataset(self):\\n self._init_qlib()\\n self._prepare_calender_cache()\\n dataset = init_instance_by_config(self.task[\\\"dataset\\\"])\\n dataset_backtest = init_instance_by_config(self.task[\\\"dataset_backtest\\\"])\\n\\n ##=============dump dataset=============\\n dataset.to_pickle(path=\\\"dataset.pkl\\\")\\n dataset_backtest.to_pickle(path=\\\"dataset_backtest.pkl\\\")\\n\\n del dataset, dataset_backtest\\n ##=============reload dataset=============\\n with open(\\\"dataset.pkl\\\", \\\"rb\\\") as file_dataset:\\n dataset = pickle.load(file_dataset)\\n\\n with open(\\\"dataset_backtest.pkl\\\", \\\"rb\\\") as file_dataset_backtest:\\n dataset_backtest = pickle.load(file_dataset_backtest)\\n\\n self._prepare_calender_cache()\\n ##=============reinit dataset=============\\n dataset.config(\\n handler_kwargs={\\n \\\"start_time\\\": \\\"2021-01-19 00:00:00\\\",\\n \\\"end_time\\\": \\\"2021-01-25 16:00:00\\\",\\n },\\n segments={\\n \\\"test\\\": (\\n \\\"2021-01-19 00:00:00\\\",\\n \\\"2021-01-25 16:00:00\\\",\\n ),\\n },\\n )\\n dataset.setup_data(\\n handler_kwargs={\\n \\\"init_type\\\": DataHandlerLP.IT_LS,\\n },\\n )\\n dataset_backtest.config(\\n handler_kwargs={\\n \\\"start_time\\\": \\\"2021-01-19 00:00:00\\\",\\n \\\"end_time\\\": \\\"2021-01-25 16:00:00\\\",\\n },\\n segments={\\n \\\"test\\\": (\\n \\\"2021-01-19 00:00:00\\\",\\n \\\"2021-01-25 16:00:00\\\",\\n ),\\n },\\n )\\n dataset_backtest.setup_data(handler_kwargs={})\\n\\n ##=============get data=============\\n xtest = dataset.prepare(\\\"test\\\")\\n backtest_test = dataset_backtest.prepare(\\\"test\\\")\\n\\n print(xtest, backtest_test)\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"302084093b0c988ab6764987ec9a5a91\",\n \"score\": \"0.6134747\",\n \"text\": \"def write_dat(self):\\n data = self.get_data()\\n data.savetxt('{}.dat'.format(self.get_name()))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d24882cc0abe7ed3f3a46fe27fb18f7\",\n \"score\": \"0.6134005\",\n \"text\": \"def save(self, path=None):\\n if not path:\\n path = './LMI-BACKUP'\\n if not os.path.isdir(path):\\n os.mkdir(path)\\n today = datetime.datetime.today().strftime('%Y-%m-%d@%Hh-%Mm')\\n path += f'/{today}'\\n if not os.path.isdir(path):\\n os.mkdir(path)\\n else:\\n if not os.path.isdir(path):\\n os.mkdir(path)\\n print(f'Saving to path: {path}')\\n saved = []\\n failed = []\\n if server_uses_widgets(self.server):\\n url_args = \\\"vocabulary,metadata,layer,widget\\\"\\n else:\\n url_args = \\\"metadata,layer\\\"\\n for item in tqdm(self):\\n if item['id'] not in saved:\\n entity_type = item.get('type')\\n if entity_type in ['Dataset', 'Table']:\\n ds_id = item['id']\\n else:\\n ds_id = item['attributes']['dataset']\\n try:\\n url = f'{self.server}/v1/dataset/{ds_id}?includes={url_args}'\\n r = requests.get(url)\\n dataset_config = r.json()['data']\\n except:\\n failed.append(item)\\n\\n save_json = {\\n \\\"id\\\": ds_id,\\n \\\"type\\\": \\\"dataset\\\",\\n \\\"server\\\": self.server,\\n \\\"attributes\\\": dataset_config['attributes']\\n }\\n with open(f\\\"{path}/{ds_id}.json\\\", 'w') as fp:\\n json.dump(save_json, fp)\\n\\n if len(failed) > 0:\\n print(f'Some entities failed to save: {failed}')\\n return failed\\n print('Save complete!')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e2d638bf83a24fcc914036690f8c760\",\n \"score\": \"0.61270213\",\n \"text\": \"def _dent_asset_save(self, datastore):\\n data_buffer = io.BytesIO()\\n np.save(data_buffer, self.data)\\n data_header = tarfile.TarInfo(\\\"data\\\")\\n data_header.size = data_buffer.getbuffer().nbytes\\n data_buffer.seek(0)\\n datastore.addfile(data_header, data_buffer)\\n\\n config_buffer = io.BytesIO()\\n config_buffer.write(\\n yaml.dump(\\n {\\n \\\"name\\\": self.name,\\n \\\"directory\\\": self.directory,\\n \\\"transform\\\": self._transform.tolist(),\\n \\\"offset\\\": self.offset.tolist(),\\n \\\"indices\\\": self.indices.tolist(),\\n \\\"material_name\\\": self.material_name,\\n }\\n ).encode(\\\"ascii\\\")\\n )\\n config_buffer.flush()\\n config_header = tarfile.TarInfo(\\\"config\\\")\\n config_header.size = config_buffer.getbuffer().nbytes\\n config_buffer.seek(0)\\n datastore.addfile(config_header, config_buffer)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"648da152253f95afa602bbbc4787408a\",\n \"score\": \"0.61234426\",\n \"text\": \"def write_dataset(self, path, data):\\n self._h5file.create_dataset(path, data=data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a58b6f4a21e3ff8379ccd13994cc7681\",\n \"score\": \"0.6121262\",\n \"text\": \"def _serialize_data(self, samples, losses):\\n with open('train_samples.pkl', 'wb') as f:\\n pkl.dump(samples, f)\\n with open('train_loss.pkl', 'wb') as f:\\n pkl.dump(losses, f)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f8c5dcbda67b470276bedf9f5309ad2\",\n \"score\": \"0.61147726\",\n \"text\": \"def save(self):\\n log.info(\\\"Writing to \\\", self.output_filepath)\\n eKonf.save_data(self.df, self.output_filepath)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92c836b3d1dc2020cf0defcf67eef268\",\n \"score\": \"0.61024535\",\n \"text\": \"def fetch_and_save_data(self):\\n self._fetch_ca_datset()\\n if not os.path.exists(self.dataset_directory):\\n self._save_data_on_disk()\\n else:\\n self._create_filepaths()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8097d66fe85273930d1355af9540a88d\",\n \"score\": \"0.61008865\",\n \"text\": \"def save_data(self, data, title=\\\"\\\"):\\n\\n\\t\\ttitle = title.replace(r\\\"\\\\\\\\\\\", \\\" -- \\\").replace(r\\\"/\\\", \\\" -- \\\").replace(\\\"\\\\\\\\\\\", \\\" -- \\\")[\\n\\t\\t\\t\\t:-4] # Title tells both the set and the name of the file. Also crop out the extension .xml with the [:-4] command.\\n\\t\\tnp.save(os.path.join(DataSources.forceplate_data, \\\"processed\\\", title),\\n\\t\\t\\t\\tdata) # save as title without .xml, and no spaces on the end\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b70e7be5544f5a63c5586cc851a7285c\",\n \"score\": \"0.6100087\",\n \"text\": \"def save_data(self, filepath: str) -> None:\\n np.save(filepath, self._population_history)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"756a0ad4616d5a5f74050e310650dd75\",\n \"score\": \"0.60908103\",\n \"text\": \"def save(self, filename: str):\\n pd.to_pickle(self, filename)\\n LOG.info(f\\\"Data successfully saved as {filename}!\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae71be0747280048ac911c47b088dce0\",\n \"score\": \"0.608736\",\n \"text\": \"def saveToDisk(self):\\n out.info('Saving to disk (%s)\\\\n' % (self.filename))\\n\\n # Make sure they want to save\\n if(not self.attrSaveable()):\\n return\\n\\n # Get whatever the data is\\n pyld = self.exportAttr(self.getAttr())\\n\\n # Write the file to disk, truncate if it exists\\n try:\\n with open(self.filename, 'wb') as output:\\n pickle.dump(pyld, output)\\n os.fsync(output.fileno())\\n except Exception as e:\\n out.err('Error writing to disk %s\\\\n' % (str(e)))\\n\\n try:\\n with open(self.filename + \\\".yaml\\\", \\\"w\\\") as output:\\n yaml.dump(pyld, output)\\n except Exception as error:\\n out.err(\\\"Error writing yaml file: {}\\\".format(error))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"986edbb7909e73dce85e2ecac7093a48\",\n \"score\": \"0.6077244\",\n \"text\": \"def pickle_and_save_datasets(datasets, filename='plankton_data'):\\n data_string = pickle.dumps(datasets)\\n with gzip.open('data/{}.pkl.gz'.format(filename), 'wb') as f:\\n f.write(data_string)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f153b9cb844eaa20cd7a1000c395d23\",\n \"score\": \"0.60747665\",\n \"text\": \"def save_attributes(self, paths: af.DirectoryPaths):\\r\\n dataset_path = paths._files_path / \\\"dataset\\\"\\r\\n\\r\\n self.dataset.output_to_fits(\\r\\n data_path=dataset_path / \\\"data.fits\\\",\\r\\n noise_map_path=dataset_path / \\\"noise_map.fits\\\",\\r\\n pre_cti_data_path=dataset_path / \\\"pre_cti_data.fits\\\",\\r\\n overwrite=True,\\r\\n )\\r\\n self.dataset.layout.output_to_json(\\r\\n file_path=dataset_path / \\\"layout.json\\\",\\r\\n )\\r\\n self.dataset.mask.output_to_fits(\\r\\n file_path=dataset_path / \\\"mask.fits\\\", overwrite=True\\r\\n )\\r\\n\\r\\n if self.dataset_full is not None:\\r\\n dataset_path = paths._files_path / \\\"dataset_full\\\"\\r\\n\\r\\n self.dataset_full.output_to_fits(\\r\\n data_path=dataset_path / \\\"data.fits\\\",\\r\\n noise_map_path=dataset_path / \\\"noise_map.fits\\\",\\r\\n pre_cti_data_path=dataset_path / \\\"pre_cti_data.fits\\\",\\r\\n overwrite=True,\\r\\n )\\r\\n self.dataset_full.layout.output_to_json(\\r\\n file_path=dataset_path / \\\"layout.json\\\",\\r\\n )\\r\\n self.dataset_full.mask.output_to_fits(\\r\\n file_path=dataset_path / \\\"mask.fits\\\", overwrite=True\\r\\n )\\r\\n\\r\\n self.clocker.output_to_json(file_path=paths._files_path / \\\"clocker.json\\\")\\r\\n self.settings_cti.output_to_json(\\r\\n file_path=paths._files_path / \\\"settings_cti.json\\\"\\r\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80144945f551fad8198d747e3e75d09a\",\n \"score\": \"0.6074664\",\n \"text\": \"def save(data, labels, dataset, metric='raw', overwrite=False):\\n\\n #Dataset filename\\n dataFile = dataset.lower()+\\\"_\\\"+metric.lower()+\\\".npz\\\"\\n labelsFile = dataset.lower()+\\\"_labels.npz\\\"\\n\\n #Full path to file\\n dataFile_path = os.path.join(data_dir, dataFile)\\n labelsFile_path = os.path.join(data_dir, labelsFile)\\n\\n #Check if Data directory exists\\n if not os.path.exists(data_dir):\\n os.makedirs(data_dir)\\n\\n #Save dataset and labels\\n if os.path.isfile(dataFile_path) and not overwrite:\\n print('Data file '+dataFile_path+' already exists. Not saving.')\\n else:\\n np.savez_compressed(dataFile_path,data=data)\\n np.savez_compressed(labelsFile_path,labels=labels)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4fe509157b6348d5e8ad50517a7e8d3\",\n \"score\": \"0.607132\",\n \"text\": \"def save(self, save_path=\\\"\\\"):\\n save_data(self, save_path=save_path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d752fe7436946cbb254e592fa0d57aec\",\n \"score\": \"0.6066392\",\n \"text\": \"def save(self, basename):\\r\\n if self.data_type == \\\"sesans\\\":\\r\\n np.savetxt(basename+\\\".dat\\\", np.array([self._data.x, self.theory()]).T)\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78ed3c6adac78bed8172b3254cc83dcf\",\n \"score\": \"0.6053453\",\n \"text\": \"def save_data(self, data=None):\\n self.data = data\\n \\n f = open(self.filename, 'w')\\n \\n for label, values in zip(data['labels'], data['values']):\\n f.write(str(label))\\n for i, value in enumerate(values):\\n f.write(' ')\\n f.write(str(i))\\n f.write(':')\\n f.write(str(value))\\n f.write('\\\\n')\\n \\n f.close()\\n \\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"96fbe9601f48056eafc57bddf7a431e2\",\n \"score\": \"0.60517126\",\n \"text\": \"def _save_data(save_to_file: str, save_mode: str, metadata: GlobalMetadata) -> None:\\n\\n with open(save_to_file, mode=save_mode) as out:\\n out.write(str(metadata.datamart_id))\\n out.write(\\\"\\\\n\\\")\\n out.write(json.dumps(metadata.value))\\n out.write(\\\"\\\\n\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0c42c55074807fed6ab6bdcfb7a1b3f\",\n \"score\": \"0.6049688\",\n \"text\": \"def save(self):\\n with open(self.file_path, 'w') as f:\\n json.dump(self.data, f)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":675,"cells":{"query_id":{"kind":"string","value":"00c417c15b524e968fdfa3a8835f51cd"},"query":{"kind":"string","value":"Updates the config with data from the cache"},"positive_passages":{"kind":"list like","value":[{"docid":"513e60e2aad3bfe65b8444e2c411f1ae","score":"0.7814324","text":"async def update(self, data=None):\n if data is not None:\n self.cache.update(data)\n await self.api.update_config(self.cache)","title":""}],"string":"[\n {\n \"docid\": \"513e60e2aad3bfe65b8444e2c411f1ae\",\n \"score\": \"0.7814324\",\n \"text\": \"async def update(self, data=None):\\n if data is not None:\\n self.cache.update(data)\\n await self.api.update_config(self.cache)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"a75a38ce910e27ffeae5c022e27e18c4","score":"0.786449","text":"def update_cache(self):\n pass","title":""},{"docid":"d29532092cd1e14fedd9af2eb790a4c7","score":"0.7683715","text":"async def update(self):\n await self.bot.api.update_config(self.filter_default(self._cache))","title":""},{"docid":"c0e77216d25b736637d9269c8470c4c6","score":"0.7678912","text":"def update_cache(self):\n raise NotImplementedError","title":""},{"docid":"99fae66914559e99990685c4a833c1e9","score":"0.764164","text":"def _update_cache(self):\n pass","title":""},{"docid":"33cf0eff6b3c1b0328aa1f8f213b48bf","score":"0.7552436","text":"async def refresh(self):\n data = await self.api.get_config()\n self.cache.update(data)\n self.ready_event.set()","title":""},{"docid":"666547b788caaa7e49f18d9d2b122169","score":"0.7223693","text":"def update_cache(self):\n self._cache_updated = True","title":""},{"docid":"de5c902220ec1f758ae1e643adf03633","score":"0.71002644","text":"def update_cache(self):\n if not self.use_cache:\n with open(self.cache_name, \"w\") as file_cache:\n json.dump(self.cache, file_cache)","title":""},{"docid":"e3ccd88e4fccb03c579b559f6cdac4cc","score":"0.6993365","text":"async def update_config(self, ctx, data: dict):\n await self.db.find_one_and_update(\n {\"_id\": ctx.guild.id}, {\"$set\": data}, upsert=True\n )\n config = self._config_cache[ctx.guild.id]\n for key, value in data.items():\n config[key] = value\n\n self._config_cache[ctx.guild.id] = config","title":""},{"docid":"6a085e52b48fbe54f0869b119632702a","score":"0.6980284","text":"async def refresh(self) -> dict:\n for k, v in (await self.bot.api.get_config()).items():\n k = k.lower()\n if k in self.all_keys:\n self._cache[k] = v\n if not self.ready_event.is_set():\n self.ready_event.set()\n logger.info(\"Successfully fetched configurations from database.\")\n return self._cache","title":""},{"docid":"70e839d8cc555c935d0dc4386554d51b","score":"0.684675","text":"def _cache_server_config(self, data: dict) -> None:\n # Delete keys if they exist\n client = self._get_redis_client()\n client.delete(self.SERVER_CONFIG_CACHE_KEY)\n\n # Set data in cache. Any conversions here need to be reversed in the get_server_configuration()\n data['lfs_enabled'] = 'true' if data['lfs_enabled'] is True else 'false'\n client.hmset(self.SERVER_CONFIG_CACHE_KEY, data)","title":""},{"docid":"710ad8436d56822a628d102bc81f146a","score":"0.66896397","text":"def _cache_auth_config(self, data: dict) -> None:\n # Delete keys if they exist\n client = self._get_redis_client()\n client.delete(self.AUTH_CONFIG_CACHE_KEY)\n\n # Set data in cache\n client.hmset(self.AUTH_CONFIG_CACHE_KEY, data)","title":""},{"docid":"e75affc291afa5ec7017ee679556f2b7","score":"0.66419554","text":"async def update_cache(self) -> \"cache\":\n self._cache = await self.get(\"\") or dict()\n self.guild_minecraft_roles = {\n g: self._cache[\"guilds\"][g][\"role\"]\n for g in self._cache[\"guilds\"]\n if self._cache[\"guilds\"][g].get(\"role\")\n } if self._cache.get(\"guilds\") else dict()\n self.guild_server_ips = {\n g: self._cache[\"guilds\"][g][\"minecraft\"]\n for g in self._cache[\"guilds\"]\n if self._cache[\"guilds\"][g].get(\"minecraft\")\n } if self._cache.get(\"guilds\") else dict()\n\n self._requested = True\n return self._cache","title":""},{"docid":"5211e4c30dfbfba744fa4338be299af8","score":"0.65247273","text":"def update_cache(self):\n with open(self.cache_file, 'w') as cache:\n cache_values = list(self.cache.values())\n if len(cache_values) > self.cache_size:\n json.dump(cache_values[-self.cache_size:], cache, default=GeoInfo.to_json)\n else:\n json.dump(cache_values, cache, default=GeoInfo.to_json)","title":""},{"docid":"a4359d3691b5f64b60de6371539cfbcf","score":"0.64972913","text":"def update_from_cache(interface):\n\tcached = basedir.load_first_config(config_site, config_prog,\n\t\t\t\t\t 'interfaces', escape(interface.uri))\n\tif not cached:\n\t\treturn False\n\n\tinterface.reset()\n\tupdate(interface, cached)\n\tupdate_user_overrides(interface)\n\n\treturn True","title":""},{"docid":"6918ade82b059d7e395fc96fb5a6c9cb","score":"0.6476746","text":"def __cache_request (self, data):\n log.debug(\"Cache generated 'edit-config' request...\")\n # self.__last_request = data.full_copy()\n # Copy reference instead of full_copy to avoid overhead\n self.__last_request = data","title":""},{"docid":"a5f07b100ae3e62cefae1c81cb58fbdb","score":"0.64663553","text":"async def refresh(self):\n for ds in ['activedirectory', 'ldap', 'nis']:\n ds_state = await self.middleware.call(f'{ds}.get_state')\n if ds_state == 'HEALTHY':\n await self.middleware.call(f'{ds}.fill_cache', True)\n elif ds_state != 'DISABLED':\n self.logger.debug('Unable to refresh [%s] cache, state is: %s' % (ds, ds_state))\n await self.middleware.call('dscache.backup')","title":""},{"docid":"308a827d12b6130d7b365d9410e265a2","score":"0.64105165","text":"def update_config(self, config):\n return config","title":""},{"docid":"2a555dd408aa8e02194b6d45f2951fe1","score":"0.63837177","text":"def update(self, **kwargs):\n self.config.update(**kwargs)\n\n with open(self.path, \"w\") as buff:\n buff.write(json.dumps(self.config, sort_keys=True, indent=4))\n return self.config","title":""},{"docid":"d2e7aaae68359bf53aad6c3bbcdfb147","score":"0.6330594","text":"def _reloadConfig():\n\tbase.caches.clearCaches()\n\n\troot.loadUserVanity(root.ArchiveService)\n\tconfig.makeFallbackMeta(reload=True)\n\tconfig.loadConfig()\n\n\tbase.ui.notifyInfo(\"Cleared caches on SIGHUP\")","title":""},{"docid":"4b0b343ab1128c8455f0bc8e44a2c64b","score":"0.63123107","text":"def forcerefresh(self):\n #logging.info( \"loading settings from datastore to local cache\")\n qry=SettingStore.query()\n sets=qry.fetch(1000)# Return up to 1000 records\n self._lastloaded=datetime.utcnow()\n newsettings={}\n for set in sets:\n if set.enttype==\"int\":\n val=int(set.value)\n elif set.enttype==\"float\":\n val=float(set.value)\n elif set.enttype==\"boolean\":\n val=(set.value==True)\n elif set.enttype==\"string\":\n val=set.value\n else:\n val=json.loads(set.value)\n newsettings[set.keyname]=val\n self._settings=newsettings # replace the old settings\n #logging.info(\"Loaded new data into settings\")","title":""},{"docid":"fc1e7e157b80e807ab5440efe0a33cce","score":"0.6299515","text":"def update_config(self):\n raise NotImplemented(\"This function is not implemented in this subclass\")","title":""},{"docid":"0cd1d90c5068e7a8f452a2c61b690e79","score":"0.62661153","text":"def reload_cache():\n global _packages\n _packages = {}\n _load_package_config(True)","title":""},{"docid":"83506ebc4e60b9e4b7af4a8776a12ebe","score":"0.6240196","text":"def update_config(self):\n with open(cwd + '/config/configuration.pickle', 'wb') as f:\n pickle.dump(self.config, f, pickle.HIGHEST_PROTOCOL)\n print('config updated')\n return self.config","title":""},{"docid":"41846c41d24632bcf9a3e22ad92892c1","score":"0.6196341","text":"def update_config(self):\n \n self.config_handler.save_config()\n return True","title":""},{"docid":"637b4bf61754e8bb3041b36faed58721","score":"0.617664","text":"def populate_cache(cls):\n pass","title":""},{"docid":"703a6160adbb5542480b9f1b8b0d8946","score":"0.6171471","text":"def update(self, key, value):\n key = key.replace(\"\\n\", \"\")\n if key not in self.cache and len(self.cache) >= self.max_cache_size:\n self.remove_oldest()\n # print value\n self.cache[key] = {'date_accessed': datetime.datetime.now(),\n 'value': value}","title":""},{"docid":"c01effc6cdbb43dcc662c3129463d64d","score":"0.61697114","text":"async def update():\n try:\n async with aiohttp.ClientSession() as session:\n async with session.get(source_url) as response:\n global cache\n cache = await parse(await response.text())\n except ut.GetErrors as e:\n ut.note('Failed to GET: ' + source_url)","title":""},{"docid":"054980b11fe48b00e01ed7c70d78175e","score":"0.6148622","text":"def reload_cache_config(self):\r\n self._rest_inbound_socket.log.debug(\"RESTAPI ReloadCacheConfig with %s\" \\\r\n % str(request.form.items()))\r\n msg = \"ReloadCacheConfig Failed\"\r\n result = False\r\n\r\n try:\r\n cache_api_url = self.cache['url']\r\n except KeyError:\r\n msg = \"ReloadCacheConfig Failed -- CACHE_URL not found\"\r\n result = False\r\n return self.send_response(Success=result, Message=msg)\r\n\r\n try:\r\n req = HTTPRequest(auth_id=self.key, auth_token=self.secret)\r\n data = req.fetch_response(cache_api_url + '/ReloadConfig/', params={}, method='POST')\r\n res = json.loads(data)\r\n try:\r\n success = res['Success']\r\n msg = res['Message']\r\n except:\r\n success = False\r\n msg = \"unknown\"\r\n if success:\r\n msg = \"Plivo Cache Server config reloaded\"\r\n result = True\r\n self._rest_inbound_socket.log.info(\"ReloadCacheConfig Done\")\r\n else:\r\n raise Exception(msg)\r\n\r\n except Exception, e:\r\n msg = \"Plivo Cache Server config reload failed\"\r\n self._rest_inbound_socket.log.error(\"ReloadCacheConfig Failed -- %s\" % str(e))\r\n result = False\r\n\r\n return self.send_response(Success=result, Message=msg)","title":""},{"docid":"74adc18d40b3c29efa05b3bb511a1504","score":"0.6138389","text":"def create_new_cache(self):\n self.cache = {\"data\": {}, \"last_update\": None}","title":""},{"docid":"8af07cddf1b435f11048cb1b96a173e9","score":"0.61253875","text":"def reload(self):\n self._cache = dict(super().items())","title":""},{"docid":"e3c8f5a76c7b2c54b7333355f54a1456","score":"0.60885566","text":"async def do_update(self, data):\n old = await self.config()\n\n new = old.copy()\n new.update(data)\n\n verrors = ValidationErrors()\n\n servers = data.get('isns_servers') or []\n for server in servers:\n reg = RE_IP_PORT.search(server)\n if reg:\n ip = reg.group(1)\n if ip and ip[0] == '[' and ip[-1] == ']':\n ip = ip[1:-1]\n try:\n ipaddress.ip_address(ip)\n continue\n except ValueError:\n pass\n verrors.add('iscsiglobal_update.isns_servers', f'Server \"{server}\" is not a valid IP(:PORT)? tuple.')\n\n if verrors:\n raise verrors\n\n new['isns_servers'] = '\\n'.join(servers)\n\n await self._update_service(old, new)\n\n if old['alua'] != new['alua']:\n await self.middleware.call('service.start', 'ix-loader')\n\n return await self.config()","title":""},{"docid":"a6a14c621bdd34d04591ce73219cfc6c","score":"0.6077641","text":"def reload_config(self):\n\n pass","title":""},{"docid":"49b46056a2ec4e84ace98ea2e71674fa","score":"0.60755986","text":"def updated_config(self, **kwargs) -> None:\n state = {}\n for key, value in kwargs.items():\n state[key] = getattr(self, key)\n setattr(self, key, value)\n try:\n yield\n finally:\n for key, value in state.items():\n setattr(self, key, value)","title":""},{"docid":"c3bbddfd218ff6081edfd2cec3694903","score":"0.60317516","text":"def update_config():\n data_dir = os.path.dirname(os.path.realpath(__file__))\n data_dir = os.path.join(data_dir, '../data')\n cfg_file = os.path.join(data_dir, 'lydian.conf')\n get_configs().write_config_file(file_name=cfg_file)","title":""},{"docid":"15abea622d1a7c07eb4af14412800d21","score":"0.60176027","text":"def save_to_cache(self, data: dict) -> None:\n # Save the configuration, only if it doesn't already exist, to deal with possibility of multiple start up\n # processes all loading the configuration for the first time.\n did_set = self._get_redis_client().setnx(self.CLIENT_CONFIG_CACHE_KEY, json.dumps(data))\n\n if did_set:\n logger.info(\"Saved Client configuration to cache.\")\n else:\n logger.info(\"Skipping saving Client configuration to cache due to configuration that is already set.\")","title":""},{"docid":"ace5558df316c96a47fb7e54e7648cc8","score":"0.60073966","text":"def update_cfg(self,cfg):\n\n self._cfg = cfg","title":""},{"docid":"33c19ef205ccb96a315d78e1777b52b0","score":"0.5991477","text":"def __cache_topology (self, data):\n log.debug(\"Cache received 'get-config' response...\")\n # self.__last_virtualizer = data.full_copy()\n # Copy reference instead of full_copy to avoid overhead\n self.__last_virtualizer = data","title":""},{"docid":"e12d665c124a9dd233bc795c2626987e","score":"0.59874004","text":"def _read_cache(self):","title":""},{"docid":"715add55a7d5fbf229266baecda374a7","score":"0.5987221","text":"def load(self):\n try:\n self.update(json.loads(self.config.read())) # B\n except py.error.ENOENT:\n pass","title":""},{"docid":"85b4d75932032968429b0d836b842421","score":"0.59772277","text":"def refetch_auth_config(self):\n server_data = self._load_current_configuration()\n\n url = server_data['server'].get(\"auth_config_url\")\n if not url:\n # The auth_config_url wasn't persisted, so we assume gigantum hub format\n url = server_data['server']['base_url'] + \".well-known/auth.json\"\n\n # re-fetch\n try:\n response = requests.get(url)\n\n if response.status_code != 200:\n logger.error(f\"Failed to re-fetch auth configuration. No change applied: {response.status_code}\")\n return\n except Exception as err:\n logger.error(f\"Failed to re-fetch auth configuration. No change applied. Error: {err}\")\n return\n\n # All good, Parse and persist\n data = response.json()\n auth_config = dict_to_auth_config(data)\n\n server_data_file = self.get_server_config_file(server_data['server']['id'])\n server_data['auth'] = auth_config.to_dict()\n with open(server_data_file, 'wt') as f:\n json.dump(server_data, f, indent=2)\n\n # Reload the cache\n self._get_redis_client().delete(self.AUTH_CONFIG_CACHE_KEY)\n self.get_auth_configuration()","title":""},{"docid":"b64535720d15fe8abb88c384c96caa79","score":"0.5974665","text":"def copy_cache_to_data(self, cache):\n self.data.setdefault(cache[\"key\"], {}).update(cache[\"values\"])","title":""},{"docid":"63c77dc3dc116cede1907c06c1c5ed32","score":"0.5964161","text":"def __init__(self, config):\r\n self.cache = CacheManager(**parse_cache_config_options(config))","title":""},{"docid":"16e8cbf9f12275c2d4eb6e8140766b5f","score":"0.59638906","text":"def Set(self, cache_key, value):","title":""},{"docid":"5692b548ddeedb747d15846435196c22","score":"0.594958","text":"def reload_conf():\n global conf_cache\n conf_cache = None\n return get_config()","title":""},{"docid":"f97b28842fe19ec0f36a6da0f853b67f","score":"0.59491694","text":"def update(self):\n\t\ttry:\n\t\t\tfor key, val in self.data.items():\n\t\t\t\tif key == \"port\":\n\t\t\t\t\tself.SERVER_PORT = val\n\t\t\t\telif key == \"key\":\n\t\t\t\t\tself.KEY = val\n\t\t\t\telif key == \"max_conn\":\n\t\t\t\t\tself.MAX_CLIENTS = int(val)\n\t\t\t\telif key == \"close_conn\":\n\t\t\t\t\tself.DISCONNECT_CLIENT_TIME = int(val)\n\t\t\t\telif key == \"close_port\":\n\t\t\t\t\tself.DISCONNECT_SESSION_TIME = int(val)\n\t\t\t\telif key == \"pcap\":\n\t\t\t\t\tself.PCAP_TRAFFIC = to_bool(val)\n\t\t\t\telif key == \"unsecure\":\n\t\t\t\t\tself.ALLOW_UNSECURE_CONNECTION = to_bool(val)\n\t\t\t\telif key == \"max_log\":\n\t\t\t\t\tself.MAX_LOG = int(val)\n\t\t\t\telif key == \"username\":\n\t\t\t\t\tself.USERNAME = val\n\t\t\t\telif key == \"password\":\n\t\t\t\t\tself.PASSWORD = val\n\t\texcept:\n\t\t\tdebug.debug(\"[Error] Bad config file format.\", level=debug.ERROR)","title":""},{"docid":"667d5c5f69e44535558d7ec9bac41c9b","score":"0.5942972","text":"def refresh(self):\n self._data_cache = None","title":""},{"docid":"115857a06b22c5bf55719bda4b51729f","score":"0.59419745","text":"def _update(self):\n mod_time = os.stat(self._cnf_filename)[stat.ST_MTIME]\n if os.path.exists(self._cnf_filename) and self.last_updated != mod_time:\n self._conf_handle = ConfigParser()\n self._conf_handle.read(self._cnf_filename)\n self.last_updated = mod_time","title":""},{"docid":"9790f4001b411509e0e8a52e1ca4413a","score":"0.5940468","text":"def _write_cache(self):","title":""},{"docid":"1f327aa8589c2790b80bf9beb0844c3c","score":"0.5934752","text":"def _rebuild_cache(self):\n self.cache = {}\n self._build_cache_from_corpus()","title":""},{"docid":"1ba708f4e6ea218226a5d4c3db7f94f7","score":"0.5931864","text":"def load(self):\n\t\ttry:\n\t\t\tself.update(json.loads(self.config.read())) # B\n\t\texcept py.error.ENOENT:\n\t\t\tpass","title":""},{"docid":"51f70772142cd1c802baca9f6d8b6202","score":"0.5931201","text":"def update_config_cache(conf: Dict, changes: List[Change]) -> None:\n for c in changes:\n if isinstance(c, ChangeCreated):\n libyang.xpath_set(conf, c.xpath, c.value, after=c.after)\n elif isinstance(c, ChangeModified):\n libyang.xpath_set(conf, c.xpath, c.value)\n elif isinstance(c, ChangeMoved):\n libyang.xpath_move(conf, c.xpath, c.after)\n elif isinstance(c, ChangeDeleted):\n libyang.xpath_del(conf, c.xpath)","title":""},{"docid":"1535af025f4317767a8b72874d3b99f4","score":"0.5923526","text":"def update(self, key):\n if key not in self.cache and len(self.cache) >= self.max_cache_size:\n self.remove_oldest()\n \n self.cache[key] = {'date_stored': datetime.datetime.now()}","title":""},{"docid":"5d45b249bda43c63c92783e450924113","score":"0.5901761","text":"def load(self):\n with open(self.CACHE_DIR + self.file_name, 'r') as data_file: \n self.cache = json.load(data_file)","title":""},{"docid":"ffba25bee92c049178635ce3fa697e1e","score":"0.5889064","text":"def reload_configs(self):\n\n self.logger.debug('reloading configurations')\n\n alterations = {}\n\n string_keys = {\n 'name': 'name',\n 'admin_email': 'admin_email',\n 'currency': 'currency'\n }\n\n bool_keys = {\n 'login_suport': 'login_suport',\n 'ticket_suport': 'ticket_suport',\n 'default_welcome_msg': 'default_welcome_msg',\n 'close_apps': 'close_apps',\n 'use_logo': 'logo',\n 'use_background': 'background',\n 'inf_login': 'inf_login'\n }\n\n int_keys = {\n 'finish_action': 'finish_action',\n 'finish_action_time': 'finish_action_time',\n }\n\n for name in string_keys.keys():\n value = self.conf_client.get_string(string_keys[name])\n\n if (value != None and ((not(name in self.information)) or (name in self.information) and (self.information[name] != value))):\n self.information[name] = value\n alterations[name] = value\n\n for name in bool_keys.keys():\n value = self.conf_client.get_bool(bool_keys[name])\n\n if self.information[name] != value:\n self.information[name] = value\n alterations[name] = value\n\n for name in int_keys.keys():\n value = self.conf_client.get_int(int_keys[name])\n if (self.information[name] != value) and (value != None):\n self.information[name] = value\n alterations[name] = value\n\n value = self.conf_client.get_float('price_per_hour')\n if self.information['price.hour'] != value:\n self.information['price.hour'] = value\n alterations['price.hour'] = value\n\n if not self.information['default_welcome_msg']:\n\n value = self.conf_client.get_string('welcome_msg')\n\n if self.information.has_key('welcome_msg'):\n if self.information['welcome_msg'] != value:\n self.information['welcome_msg'] = value\n alterations['welcome_msg'] = value\n else:\n self.information['welcome_msg'] = value\n alterations['welcome_msg'] = value\n\n # close apps\n value = self.conf_client.get_string_list(\"close_apps_list\")\n if self.information['close_apps_list'] != value:\n self.information['close_apps_list'] = value\n alterations['close_apps_list'] = value\n\n value = self.conf_client.get_string('background_path')\n if self.common_background != value:\n self.common_background = value\n self.common_background_md5 = md5_cripto(open(self.common_background).read())\n self.instmachine_manager.update_common_backgroud(self.common_background_md5)\n\n value = self.conf_client.get_string('logo_path')\n if self.common_logo != value:\n self.common_logo = value\n self.common_logo_md5 = md5_cripto(open(self.common_logo).read())\n self.instmachine_manager.update_common_logo(self.common_logo_md5)\n\n if alterations:\n self.instmachine_manager.update_information(alterations)","title":""},{"docid":"4b59732357a7165895dc002303d0efde","score":"0.5880726","text":"def reset_cache(self):","title":""},{"docid":"3db346d7a816aa64fec11089a1dfd608","score":"0.5878871","text":"def cache(conan_api: ConanAPI, parser, *args):\n pass","title":""},{"docid":"95358def39e8da5696324e980f3a0fcf","score":"0.58723795","text":"def setCaching(self, val='True', **kwargs):\n \n pass","title":""},{"docid":"95358def39e8da5696324e980f3a0fcf","score":"0.58723795","text":"def setCaching(self, val='True', **kwargs):\n \n pass","title":""},{"docid":"95358def39e8da5696324e980f3a0fcf","score":"0.58723795","text":"def setCaching(self, val='True', **kwargs):\n \n pass","title":""},{"docid":"95358def39e8da5696324e980f3a0fcf","score":"0.58723795","text":"def setCaching(self, val='True', **kwargs):\n \n pass","title":""},{"docid":"95358def39e8da5696324e980f3a0fcf","score":"0.58723795","text":"def setCaching(self, val='True', **kwargs):\n \n pass","title":""},{"docid":"95358def39e8da5696324e980f3a0fcf","score":"0.58723795","text":"def setCaching(self, val='True', **kwargs):\n \n pass","title":""},{"docid":"95358def39e8da5696324e980f3a0fcf","score":"0.58723795","text":"def setCaching(self, val='True', **kwargs):\n \n pass","title":""},{"docid":"95358def39e8da5696324e980f3a0fcf","score":"0.5869458","text":"def setCaching(self, val='True', **kwargs):\n \n pass","title":""},{"docid":"95358def39e8da5696324e980f3a0fcf","score":"0.5869458","text":"def setCaching(self, val='True', **kwargs):\n \n pass","title":""},{"docid":"95358def39e8da5696324e980f3a0fcf","score":"0.5869458","text":"def setCaching(self, val='True', **kwargs):\n \n pass","title":""},{"docid":"95358def39e8da5696324e980f3a0fcf","score":"0.5869458","text":"def setCaching(self, val='True', **kwargs):\n \n pass","title":""},{"docid":"95358def39e8da5696324e980f3a0fcf","score":"0.5869458","text":"def setCaching(self, val='True', **kwargs):\n \n pass","title":""},{"docid":"42c896ec7df359ff715f801abed825b0","score":"0.5857698","text":"def reload_config(self):\n #self._client.reload_config()\n pass","title":""},{"docid":"ab94f30207484a1ac69e00f22d251fbf","score":"0.5842765","text":"def update(self, config_dict):\n self._update(config_dict, allow_new_keys=True)","title":""},{"docid":"d78b065a5dbc6323f0c1138178f7d63b","score":"0.5834923","text":"def load(self):\n filepath = Path(self.config).resolve()\n if filepath.exists():\n with open(self.config, 'r') as f:\n self.update(json.load(f))","title":""},{"docid":"1d5c6fcd88f2069fa90ed30b732f6162","score":"0.58169216","text":"def load_cache(self):\n try:\n with open(self.cache_name, \"r\") as file_cache:\n cache_content = file_cache.read()\n except FileNotFoundError:\n with open(self.cache_name, \"w\") as file_cache:\n cache_content = \"\"\n\n # transform cache to json object, handle cache case\n try:\n self.cache = json.loads(cache_content)\n except ValueError:\n self.cache = {\n \"last_mod\": \"1900-01-01 00:00:00.0\",\n \"current_weather\": {},\n \"forecast_weather\": {},\n }\n\n # validate last modification < 1 hour\n delta = datetime.now() - parse(self.cache[\"last_mod\"])\n delta_to_hour = (delta.days * 24) + (delta.seconds / 3600)\n self.use_cache = delta_to_hour < 1","title":""},{"docid":"23b53223472038f0d066ae95a02fd87f","score":"0.5808317","text":"def flush_cache(self):\n raise NotImplementedError()","title":""},{"docid":"e4ae86fc9257b79572f2b99782a6801e","score":"0.5798737","text":"def cache(self):\n\n pargs = ['cache']\n return self.hc.run_command(self, pargs)","title":""},{"docid":"71ef7e646f53120f1ea5c5e21c50edbf","score":"0.5788758","text":"def update_cache(self, query: QueryType, result: Iterator[BaseInterfaceModel]):","title":""},{"docid":"b902233a4e7fab0239edc34cafc3acfe","score":"0.57723135","text":"def update_cache(self, cat, img_path):\r\n try:\r\n shutil.copy(img_path, os.path.join(os.path.dirname(__file__), '..', '_cache_'))\r\n except (IOError, OSError) as ex:\r\n print(ex)\r\n print('ERROR in update_cache() ' +\r\n 'Cannot copy to _cache_ directory, make sure there ' +\r\n 'is enough space on disk')\r\n sys.exit(1)\r\n\r\n cache_img_path = os.path.join(os.path.dirname(__file__), '..', '_cache_',\r\n os.path.basename(img_path))\r\n\r\n zope.event.notify(CacheAddEvent(cat, cache_img_path))\r\n self.cache.append((cat, cache_img_path))","title":""},{"docid":"3ebf1b449eeb5f0f8a803211345e7c4d","score":"0.57661843","text":"def setCache(self, val='True', **kwargs):\n \n pass","title":""},{"docid":"3ebf1b449eeb5f0f8a803211345e7c4d","score":"0.57660276","text":"def setCache(self, val='True', **kwargs):\n \n pass","title":""},{"docid":"c6c98126bce2e0e1780f2d8fc5927fb6","score":"0.5758702","text":"def update(self, config, merge=False):\n\n self.config = {**self.config, **config} if merge else config\n self.dataset = dataset_model(self.config)\n return self","title":""},{"docid":"5da7b702af7fe7d33ec50105f9fe89e2","score":"0.5742303","text":"def update_config(self, update_dict, reload=False):\n self.config.update(update_dict)\n # set config temp_folder to wksp for brevity\n if \"temp_folder\" in update_dict.keys():\n self.config[\"wksp\"] = update_dict[\"temp_folder\"]\n self.validate_config()\n if reload:\n self.data = util.load_tables(self.config)","title":""},{"docid":"556966817bde09e6fad0a7a6a56f2e92","score":"0.5722837","text":"def _build_cache(self):\n print('Building cache...')\n assert self.cache == {}\n if self.data:\n print('loading data from json')\n self.load_saved_cache()\n else:\n print('from states')\n self._build_cache_from_corpus()\n self._save_cache()","title":""},{"docid":"9ac2e1ef6186cf332e2fc92f3cbcc8a5","score":"0.571785","text":"def update_configs():\n global blacklist_dict\n global whitelist_dict\n blacklist_dict = update_config_dict('blacklist.conf', blacklist_dict)\n whitelist_dict = update_config_dict('whitelist.conf', whitelist_dict)","title":""},{"docid":"6791a9925d2545cf9e16e5b43e235627","score":"0.57126206","text":"def use_cache(self,**kwargs):\n\t\tself.set_meta_file_name( kwargs.get(\"meta_file_name\",\"\"))\n\t\toverwrite_meta_by_centos = kwargs.get(\"overwrite_meta_by_centos\",False)\n\t\tself.store_allKeys = kwargs.get(\"store_allKeys\",False)\n\t\tself.ignore_invalid_mem = kwargs.get(\"ignore_invalid_mem\",False)\n\t\toverwrite_memory = kwargs.get(\"overwrite_memory\",False)\n\t\tset_to_be_valid = kwargs.get(\"set_to_be_valid\",False)\n\t\tignore_lock = kwargs.get(\"ignore_lock\",False)\n\t\tcheck_pref_is_short = kwargs.get(\"check_pref_is_short\",True)\n\t\tif not self.overwrite_prefix:\n\t\t\tself.overwrite_prefix = kwargs.get(\"overwrite_prefix\",False)\n\t\tloading_msg = kwargs.get(\"loading_msg\",\"\")\n\t\tparams = kwargs.get(\"params\",{}) # if you want to change params\n\t\tif \"params\" in kwargs:\n\t\t\tprint(\"[CM] params update these:\",params)\n\n\t\tif not os.path.exists(self.meta_file_abs_path):\n\t\t\tif self.my_platform!=\"centos\":\n\t\t\t\tr = requests.get(url=FILE_SERVER_GET+\"cache_meta\", params={\"meta_file_name\":self.meta_file_name})\n\t\t\t\tmeta= r.json() \n\t\t\t\t# TODO not using json, changed syncdir/file-server/app.py: get_file()\n\t\t\t\tif \"not-found\" in meta['status']:\n\t\t\t\t\traise Exception(\"Not exists? \"+self.meta_file_name)\n\t\t\t\tmeta.pop('status')\n\t\t\t\ttmpdir = os.sep.join(self.meta_file_abs_path.split(os.sep)[0:-1])\n\t\t\t\tif not os.path.exists(tmpdir): \n\t\t\t\t\tos.makedirs(tmpdir)\n\t\t\t\twith open(self.meta_file_abs_path, 'wb') as f: # TODO not using json\n\t\t\t\t\tjson.dump(meta, codecs.getwriter('utf-8')(f), ensure_ascii=False)\n\t\t\t\tif iprint: print(\"Copied from centos: \"+self.meta_file_abs_path)\n\t\t\telse:\n\t\t\t\traise Exception(\"Not exists? \"+self.meta_file_name)\n\t\telse: # already exists locally:\n\t\t\tif self.my_platform!=\"centos\" and overwrite_meta_by_centos:\n\t\t\t\tr = requests.get(url=FILE_SERVER_GET+\"cache_meta\", params={\"meta_file_name\":self.meta_file_name})\n\t\t\t\tmeta= r.json()\n\t\t\t\tif not \"not-found\" in meta['status']:\n\t\t\t\t\tmeta.pop('status')\n\t\t\t\t\twith open(self.meta_file_abs_path, 'wb') as f:# TODO not using json\n\t\t\t\t\t\tjson.dump(meta, codecs.getwriter('utf-8')(f), ensure_ascii=False)\n\t\t\t\t\tif iprint: print(\"Copied from centos: \"+self.meta_file_abs_path)\n\t\ttry: \n\t\t\tmeta = pickle.load(open(self.meta_file_abs_path, 'rb'))\n\t\texcept: # TODO not using json\n\t\t\tmeta = json.load(open(self.meta_file_abs_path, 'rb'))\n\t\tmeta[\"params\"]=pickle.loads(str(meta[\"params\"]))\n\t\tmeta[\"params\"].update(params)\n\n\t\tif self.overwrite_prefix:\n\t\t\tmeta[\"params\"]['overwrite_prefix'] = True\n\t\t\n\t\tmeta[\"params\"][\"overwrite_memory\"]=overwrite_memory\n\t\tmeta[\"params\"][\"ignore_lock\"]=ignore_lock\n\t\tif self.rt_servers: \n\t\t\tmeta[\"params\"][\"rt_servers\"]=True\n\t\tif self.overwrite_redis_servers:\n\t\t\tmeta[\"params\"][\"overwrite_servers\"]=True\n\n\t\tif set_to_be_valid:\n\t\t\tprint(\"force set to be valid !\")\n\t\t\tself.set_mem_valid( meta[\"params\"] )\n\t\t\treturn \n\t\tret = self.activate_mem( meta[\"params\"] )\n\t\tif (ret==\"invalid\" and not self.ignore_invalid_mem) or overwrite_memory:\n\t\t\tyield_func = dill.loads(meta[\"yield_func\"]) if \"yield_func\" in meta else None\n\t\t\tyield_args = meta[\"yield_args\"] if \"yield_args\" in meta else None\n\t\t\tif yield_func is None or yield_args is None:\n\t\t\t\tif iprint: print(\"Redo load_cache_file_into_mem() ...\")\n\t\t\t\tself.load_cache_file_into_mem( meta[\"cache_file_abs_path\"], meta[\"params\"],self.store_allKeys, msg = loading_msg)\n\t\t\telse:\n\t\t\t\tif iprint: print(\"RUN yield_func() \"+yield_args)\n\t\t\t\tkv_action_type = meta[\"kv_action_type\"] if \"kv_action_type\" in meta else None\n\t\t\t\tself.yield_file_into_mem( yield_func, yield_args, meta[\"params\"], kv_action_type=kv_action_type, msg = loading_msg )\n\t\t\tprint( self.activate_mem( meta[\"params\"] ) )\n\t\tself.meta = meta\n\t\tif check_pref_is_short:\n\t\t\tif len(self.mm.prefix)>4: # support more prefixes if > more.\n\t\t\t\tprint(self.mm.prefix)\n\t\t\t\tprint(\" :prefix too long, not overwrite_prefix?\")\n\t\t\t\tsys.exit(0) # You Edit","title":""},{"docid":"b49d7855cbe5a82a4960a4fdbccc18ee","score":"0.5708811","text":"def _flush_cache(self):\n self.__last_update = datetime.datetime(1970, 1, 1)","title":""},{"docid":"83092e50f0f37cae51f6928a20796ea1","score":"0.570268","text":"def update(self, settings):\n pass","title":""},{"docid":"31582ef7cf51573e1640da901ba3d5a6","score":"0.5700119","text":"def UpdateCache(self, prefix, results, partial_results):\n self.prefix = prefix\n self.results = results\n self.partial_results = partial_results\n self.timestamp = time.time()","title":""},{"docid":"d76f803b96f5aec84e1c9f1f4b3b11a2","score":"0.56960064","text":"def getCaching(self, **kwargs):\n \n pass","title":""},{"docid":"d76f803b96f5aec84e1c9f1f4b3b11a2","score":"0.56960064","text":"def getCaching(self, **kwargs):\n \n pass","title":""},{"docid":"d76f803b96f5aec84e1c9f1f4b3b11a2","score":"0.56960064","text":"def getCaching(self, **kwargs):\n \n pass","title":""},{"docid":"d76f803b96f5aec84e1c9f1f4b3b11a2","score":"0.56960064","text":"def getCaching(self, **kwargs):\n \n pass","title":""},{"docid":"d76f803b96f5aec84e1c9f1f4b3b11a2","score":"0.56960064","text":"def getCaching(self, **kwargs):\n \n pass","title":""},{"docid":"d76f803b96f5aec84e1c9f1f4b3b11a2","score":"0.56960064","text":"def getCaching(self, **kwargs):\n \n pass","title":""},{"docid":"d76f803b96f5aec84e1c9f1f4b3b11a2","score":"0.56950074","text":"def getCaching(self, **kwargs):\n \n pass","title":""},{"docid":"d76f803b96f5aec84e1c9f1f4b3b11a2","score":"0.56950074","text":"def getCaching(self, **kwargs):\n \n pass","title":""},{"docid":"d76f803b96f5aec84e1c9f1f4b3b11a2","score":"0.56950074","text":"def getCaching(self, **kwargs):\n \n pass","title":""},{"docid":"d76f803b96f5aec84e1c9f1f4b3b11a2","score":"0.56950074","text":"def getCaching(self, **kwargs):\n \n pass","title":""},{"docid":"d76f803b96f5aec84e1c9f1f4b3b11a2","score":"0.56950074","text":"def getCaching(self, **kwargs):\n \n pass","title":""},{"docid":"d76f803b96f5aec84e1c9f1f4b3b11a2","score":"0.56950074","text":"def getCaching(self, **kwargs):\n \n pass","title":""},{"docid":"4f9adade55c313a79b25bd2c29bb7ae8","score":"0.56866056","text":"def load(self):\n\t\ttry:\n\t\t\twith open(self.config, 'r') as f:\n\t\t\t\tself.update(json.loads(f.read()))\n\t\texcept errno.ENOENT:\n\t\t\tprint \"ENOENT error\"\n\t\t\tpass\n\t\texcept Exception as e:\n\t\t\tprint e","title":""},{"docid":"e68610b32df21910832c10c253c55f77","score":"0.5683001","text":"def update_cachegroups(self, cache_group_id=None, data=None):","title":""}],"string":"[\n {\n \"docid\": \"a75a38ce910e27ffeae5c022e27e18c4\",\n \"score\": \"0.786449\",\n \"text\": \"def update_cache(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d29532092cd1e14fedd9af2eb790a4c7\",\n \"score\": \"0.7683715\",\n \"text\": \"async def update(self):\\n await self.bot.api.update_config(self.filter_default(self._cache))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0e77216d25b736637d9269c8470c4c6\",\n \"score\": \"0.7678912\",\n \"text\": \"def update_cache(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99fae66914559e99990685c4a833c1e9\",\n \"score\": \"0.764164\",\n \"text\": \"def _update_cache(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33cf0eff6b3c1b0328aa1f8f213b48bf\",\n \"score\": \"0.7552436\",\n \"text\": \"async def refresh(self):\\n data = await self.api.get_config()\\n self.cache.update(data)\\n self.ready_event.set()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"666547b788caaa7e49f18d9d2b122169\",\n \"score\": \"0.7223693\",\n \"text\": \"def update_cache(self):\\n self._cache_updated = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de5c902220ec1f758ae1e643adf03633\",\n \"score\": \"0.71002644\",\n \"text\": \"def update_cache(self):\\n if not self.use_cache:\\n with open(self.cache_name, \\\"w\\\") as file_cache:\\n json.dump(self.cache, file_cache)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3ccd88e4fccb03c579b559f6cdac4cc\",\n \"score\": \"0.6993365\",\n \"text\": \"async def update_config(self, ctx, data: dict):\\n await self.db.find_one_and_update(\\n {\\\"_id\\\": ctx.guild.id}, {\\\"$set\\\": data}, upsert=True\\n )\\n config = self._config_cache[ctx.guild.id]\\n for key, value in data.items():\\n config[key] = value\\n\\n self._config_cache[ctx.guild.id] = config\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a085e52b48fbe54f0869b119632702a\",\n \"score\": \"0.6980284\",\n \"text\": \"async def refresh(self) -> dict:\\n for k, v in (await self.bot.api.get_config()).items():\\n k = k.lower()\\n if k in self.all_keys:\\n self._cache[k] = v\\n if not self.ready_event.is_set():\\n self.ready_event.set()\\n logger.info(\\\"Successfully fetched configurations from database.\\\")\\n return self._cache\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70e839d8cc555c935d0dc4386554d51b\",\n \"score\": \"0.684675\",\n \"text\": \"def _cache_server_config(self, data: dict) -> None:\\n # Delete keys if they exist\\n client = self._get_redis_client()\\n client.delete(self.SERVER_CONFIG_CACHE_KEY)\\n\\n # Set data in cache. Any conversions here need to be reversed in the get_server_configuration()\\n data['lfs_enabled'] = 'true' if data['lfs_enabled'] is True else 'false'\\n client.hmset(self.SERVER_CONFIG_CACHE_KEY, data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"710ad8436d56822a628d102bc81f146a\",\n \"score\": \"0.66896397\",\n \"text\": \"def _cache_auth_config(self, data: dict) -> None:\\n # Delete keys if they exist\\n client = self._get_redis_client()\\n client.delete(self.AUTH_CONFIG_CACHE_KEY)\\n\\n # Set data in cache\\n client.hmset(self.AUTH_CONFIG_CACHE_KEY, data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e75affc291afa5ec7017ee679556f2b7\",\n \"score\": \"0.66419554\",\n \"text\": \"async def update_cache(self) -> \\\"cache\\\":\\n self._cache = await self.get(\\\"\\\") or dict()\\n self.guild_minecraft_roles = {\\n g: self._cache[\\\"guilds\\\"][g][\\\"role\\\"]\\n for g in self._cache[\\\"guilds\\\"]\\n if self._cache[\\\"guilds\\\"][g].get(\\\"role\\\")\\n } if self._cache.get(\\\"guilds\\\") else dict()\\n self.guild_server_ips = {\\n g: self._cache[\\\"guilds\\\"][g][\\\"minecraft\\\"]\\n for g in self._cache[\\\"guilds\\\"]\\n if self._cache[\\\"guilds\\\"][g].get(\\\"minecraft\\\")\\n } if self._cache.get(\\\"guilds\\\") else dict()\\n\\n self._requested = True\\n return self._cache\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5211e4c30dfbfba744fa4338be299af8\",\n \"score\": \"0.65247273\",\n \"text\": \"def update_cache(self):\\n with open(self.cache_file, 'w') as cache:\\n cache_values = list(self.cache.values())\\n if len(cache_values) > self.cache_size:\\n json.dump(cache_values[-self.cache_size:], cache, default=GeoInfo.to_json)\\n else:\\n json.dump(cache_values, cache, default=GeoInfo.to_json)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4359d3691b5f64b60de6371539cfbcf\",\n \"score\": \"0.64972913\",\n \"text\": \"def update_from_cache(interface):\\n\\tcached = basedir.load_first_config(config_site, config_prog,\\n\\t\\t\\t\\t\\t 'interfaces', escape(interface.uri))\\n\\tif not cached:\\n\\t\\treturn False\\n\\n\\tinterface.reset()\\n\\tupdate(interface, cached)\\n\\tupdate_user_overrides(interface)\\n\\n\\treturn True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6918ade82b059d7e395fc96fb5a6c9cb\",\n \"score\": \"0.6476746\",\n \"text\": \"def __cache_request (self, data):\\n log.debug(\\\"Cache generated 'edit-config' request...\\\")\\n # self.__last_request = data.full_copy()\\n # Copy reference instead of full_copy to avoid overhead\\n self.__last_request = data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5f07b100ae3e62cefae1c81cb58fbdb\",\n \"score\": \"0.64663553\",\n \"text\": \"async def refresh(self):\\n for ds in ['activedirectory', 'ldap', 'nis']:\\n ds_state = await self.middleware.call(f'{ds}.get_state')\\n if ds_state == 'HEALTHY':\\n await self.middleware.call(f'{ds}.fill_cache', True)\\n elif ds_state != 'DISABLED':\\n self.logger.debug('Unable to refresh [%s] cache, state is: %s' % (ds, ds_state))\\n await self.middleware.call('dscache.backup')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"308a827d12b6130d7b365d9410e265a2\",\n \"score\": \"0.64105165\",\n \"text\": \"def update_config(self, config):\\n return config\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a555dd408aa8e02194b6d45f2951fe1\",\n \"score\": \"0.63837177\",\n \"text\": \"def update(self, **kwargs):\\n self.config.update(**kwargs)\\n\\n with open(self.path, \\\"w\\\") as buff:\\n buff.write(json.dumps(self.config, sort_keys=True, indent=4))\\n return self.config\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d2e7aaae68359bf53aad6c3bbcdfb147\",\n \"score\": \"0.6330594\",\n \"text\": \"def _reloadConfig():\\n\\tbase.caches.clearCaches()\\n\\n\\troot.loadUserVanity(root.ArchiveService)\\n\\tconfig.makeFallbackMeta(reload=True)\\n\\tconfig.loadConfig()\\n\\n\\tbase.ui.notifyInfo(\\\"Cleared caches on SIGHUP\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b0b343ab1128c8455f0bc8e44a2c64b\",\n \"score\": \"0.63123107\",\n \"text\": \"def forcerefresh(self):\\n #logging.info( \\\"loading settings from datastore to local cache\\\")\\n qry=SettingStore.query()\\n sets=qry.fetch(1000)# Return up to 1000 records\\n self._lastloaded=datetime.utcnow()\\n newsettings={}\\n for set in sets:\\n if set.enttype==\\\"int\\\":\\n val=int(set.value)\\n elif set.enttype==\\\"float\\\":\\n val=float(set.value)\\n elif set.enttype==\\\"boolean\\\":\\n val=(set.value==True)\\n elif set.enttype==\\\"string\\\":\\n val=set.value\\n else:\\n val=json.loads(set.value)\\n newsettings[set.keyname]=val\\n self._settings=newsettings # replace the old settings\\n #logging.info(\\\"Loaded new data into settings\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc1e7e157b80e807ab5440efe0a33cce\",\n \"score\": \"0.6299515\",\n \"text\": \"def update_config(self):\\n raise NotImplemented(\\\"This function is not implemented in this subclass\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0cd1d90c5068e7a8f452a2c61b690e79\",\n \"score\": \"0.62661153\",\n \"text\": \"def reload_cache():\\n global _packages\\n _packages = {}\\n _load_package_config(True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83506ebc4e60b9e4b7af4a8776a12ebe\",\n \"score\": \"0.6240196\",\n \"text\": \"def update_config(self):\\n with open(cwd + '/config/configuration.pickle', 'wb') as f:\\n pickle.dump(self.config, f, pickle.HIGHEST_PROTOCOL)\\n print('config updated')\\n return self.config\",\n \"title\": \"\"\n },\n {\n \"docid\": \"41846c41d24632bcf9a3e22ad92892c1\",\n \"score\": \"0.6196341\",\n \"text\": \"def update_config(self):\\n \\n self.config_handler.save_config()\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"637b4bf61754e8bb3041b36faed58721\",\n \"score\": \"0.617664\",\n \"text\": \"def populate_cache(cls):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"703a6160adbb5542480b9f1b8b0d8946\",\n \"score\": \"0.6171471\",\n \"text\": \"def update(self, key, value):\\n key = key.replace(\\\"\\\\n\\\", \\\"\\\")\\n if key not in self.cache and len(self.cache) >= self.max_cache_size:\\n self.remove_oldest()\\n # print value\\n self.cache[key] = {'date_accessed': datetime.datetime.now(),\\n 'value': value}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c01effc6cdbb43dcc662c3129463d64d\",\n \"score\": \"0.61697114\",\n \"text\": \"async def update():\\n try:\\n async with aiohttp.ClientSession() as session:\\n async with session.get(source_url) as response:\\n global cache\\n cache = await parse(await response.text())\\n except ut.GetErrors as e:\\n ut.note('Failed to GET: ' + source_url)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"054980b11fe48b00e01ed7c70d78175e\",\n \"score\": \"0.6148622\",\n \"text\": \"def reload_cache_config(self):\\r\\n self._rest_inbound_socket.log.debug(\\\"RESTAPI ReloadCacheConfig with %s\\\" \\\\\\r\\n % str(request.form.items()))\\r\\n msg = \\\"ReloadCacheConfig Failed\\\"\\r\\n result = False\\r\\n\\r\\n try:\\r\\n cache_api_url = self.cache['url']\\r\\n except KeyError:\\r\\n msg = \\\"ReloadCacheConfig Failed -- CACHE_URL not found\\\"\\r\\n result = False\\r\\n return self.send_response(Success=result, Message=msg)\\r\\n\\r\\n try:\\r\\n req = HTTPRequest(auth_id=self.key, auth_token=self.secret)\\r\\n data = req.fetch_response(cache_api_url + '/ReloadConfig/', params={}, method='POST')\\r\\n res = json.loads(data)\\r\\n try:\\r\\n success = res['Success']\\r\\n msg = res['Message']\\r\\n except:\\r\\n success = False\\r\\n msg = \\\"unknown\\\"\\r\\n if success:\\r\\n msg = \\\"Plivo Cache Server config reloaded\\\"\\r\\n result = True\\r\\n self._rest_inbound_socket.log.info(\\\"ReloadCacheConfig Done\\\")\\r\\n else:\\r\\n raise Exception(msg)\\r\\n\\r\\n except Exception, e:\\r\\n msg = \\\"Plivo Cache Server config reload failed\\\"\\r\\n self._rest_inbound_socket.log.error(\\\"ReloadCacheConfig Failed -- %s\\\" % str(e))\\r\\n result = False\\r\\n\\r\\n return self.send_response(Success=result, Message=msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74adc18d40b3c29efa05b3bb511a1504\",\n \"score\": \"0.6138389\",\n \"text\": \"def create_new_cache(self):\\n self.cache = {\\\"data\\\": {}, \\\"last_update\\\": None}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8af07cddf1b435f11048cb1b96a173e9\",\n \"score\": \"0.61253875\",\n \"text\": \"def reload(self):\\n self._cache = dict(super().items())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3c8f5a76c7b2c54b7333355f54a1456\",\n \"score\": \"0.60885566\",\n \"text\": \"async def do_update(self, data):\\n old = await self.config()\\n\\n new = old.copy()\\n new.update(data)\\n\\n verrors = ValidationErrors()\\n\\n servers = data.get('isns_servers') or []\\n for server in servers:\\n reg = RE_IP_PORT.search(server)\\n if reg:\\n ip = reg.group(1)\\n if ip and ip[0] == '[' and ip[-1] == ']':\\n ip = ip[1:-1]\\n try:\\n ipaddress.ip_address(ip)\\n continue\\n except ValueError:\\n pass\\n verrors.add('iscsiglobal_update.isns_servers', f'Server \\\"{server}\\\" is not a valid IP(:PORT)? tuple.')\\n\\n if verrors:\\n raise verrors\\n\\n new['isns_servers'] = '\\\\n'.join(servers)\\n\\n await self._update_service(old, new)\\n\\n if old['alua'] != new['alua']:\\n await self.middleware.call('service.start', 'ix-loader')\\n\\n return await self.config()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a6a14c621bdd34d04591ce73219cfc6c\",\n \"score\": \"0.6077641\",\n \"text\": \"def reload_config(self):\\n\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49b46056a2ec4e84ace98ea2e71674fa\",\n \"score\": \"0.60755986\",\n \"text\": \"def updated_config(self, **kwargs) -> None:\\n state = {}\\n for key, value in kwargs.items():\\n state[key] = getattr(self, key)\\n setattr(self, key, value)\\n try:\\n yield\\n finally:\\n for key, value in state.items():\\n setattr(self, key, value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3bbddfd218ff6081edfd2cec3694903\",\n \"score\": \"0.60317516\",\n \"text\": \"def update_config():\\n data_dir = os.path.dirname(os.path.realpath(__file__))\\n data_dir = os.path.join(data_dir, '../data')\\n cfg_file = os.path.join(data_dir, 'lydian.conf')\\n get_configs().write_config_file(file_name=cfg_file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15abea622d1a7c07eb4af14412800d21\",\n \"score\": \"0.60176027\",\n \"text\": \"def save_to_cache(self, data: dict) -> None:\\n # Save the configuration, only if it doesn't already exist, to deal with possibility of multiple start up\\n # processes all loading the configuration for the first time.\\n did_set = self._get_redis_client().setnx(self.CLIENT_CONFIG_CACHE_KEY, json.dumps(data))\\n\\n if did_set:\\n logger.info(\\\"Saved Client configuration to cache.\\\")\\n else:\\n logger.info(\\\"Skipping saving Client configuration to cache due to configuration that is already set.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ace5558df316c96a47fb7e54e7648cc8\",\n \"score\": \"0.60073966\",\n \"text\": \"def update_cfg(self,cfg):\\n\\n self._cfg = cfg\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33c19ef205ccb96a315d78e1777b52b0\",\n \"score\": \"0.5991477\",\n \"text\": \"def __cache_topology (self, data):\\n log.debug(\\\"Cache received 'get-config' response...\\\")\\n # self.__last_virtualizer = data.full_copy()\\n # Copy reference instead of full_copy to avoid overhead\\n self.__last_virtualizer = data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e12d665c124a9dd233bc795c2626987e\",\n \"score\": \"0.59874004\",\n \"text\": \"def _read_cache(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"715add55a7d5fbf229266baecda374a7\",\n \"score\": \"0.5987221\",\n \"text\": \"def load(self):\\n try:\\n self.update(json.loads(self.config.read())) # B\\n except py.error.ENOENT:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85b4d75932032968429b0d836b842421\",\n \"score\": \"0.59772277\",\n \"text\": \"def refetch_auth_config(self):\\n server_data = self._load_current_configuration()\\n\\n url = server_data['server'].get(\\\"auth_config_url\\\")\\n if not url:\\n # The auth_config_url wasn't persisted, so we assume gigantum hub format\\n url = server_data['server']['base_url'] + \\\".well-known/auth.json\\\"\\n\\n # re-fetch\\n try:\\n response = requests.get(url)\\n\\n if response.status_code != 200:\\n logger.error(f\\\"Failed to re-fetch auth configuration. No change applied: {response.status_code}\\\")\\n return\\n except Exception as err:\\n logger.error(f\\\"Failed to re-fetch auth configuration. No change applied. Error: {err}\\\")\\n return\\n\\n # All good, Parse and persist\\n data = response.json()\\n auth_config = dict_to_auth_config(data)\\n\\n server_data_file = self.get_server_config_file(server_data['server']['id'])\\n server_data['auth'] = auth_config.to_dict()\\n with open(server_data_file, 'wt') as f:\\n json.dump(server_data, f, indent=2)\\n\\n # Reload the cache\\n self._get_redis_client().delete(self.AUTH_CONFIG_CACHE_KEY)\\n self.get_auth_configuration()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b64535720d15fe8abb88c384c96caa79\",\n \"score\": \"0.5974665\",\n \"text\": \"def copy_cache_to_data(self, cache):\\n self.data.setdefault(cache[\\\"key\\\"], {}).update(cache[\\\"values\\\"])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63c77dc3dc116cede1907c06c1c5ed32\",\n \"score\": \"0.5964161\",\n \"text\": \"def __init__(self, config):\\r\\n self.cache = CacheManager(**parse_cache_config_options(config))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16e8cbf9f12275c2d4eb6e8140766b5f\",\n \"score\": \"0.59638906\",\n \"text\": \"def Set(self, cache_key, value):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5692b548ddeedb747d15846435196c22\",\n \"score\": \"0.594958\",\n \"text\": \"def reload_conf():\\n global conf_cache\\n conf_cache = None\\n return get_config()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f97b28842fe19ec0f36a6da0f853b67f\",\n \"score\": \"0.59491694\",\n \"text\": \"def update(self):\\n\\t\\ttry:\\n\\t\\t\\tfor key, val in self.data.items():\\n\\t\\t\\t\\tif key == \\\"port\\\":\\n\\t\\t\\t\\t\\tself.SERVER_PORT = val\\n\\t\\t\\t\\telif key == \\\"key\\\":\\n\\t\\t\\t\\t\\tself.KEY = val\\n\\t\\t\\t\\telif key == \\\"max_conn\\\":\\n\\t\\t\\t\\t\\tself.MAX_CLIENTS = int(val)\\n\\t\\t\\t\\telif key == \\\"close_conn\\\":\\n\\t\\t\\t\\t\\tself.DISCONNECT_CLIENT_TIME = int(val)\\n\\t\\t\\t\\telif key == \\\"close_port\\\":\\n\\t\\t\\t\\t\\tself.DISCONNECT_SESSION_TIME = int(val)\\n\\t\\t\\t\\telif key == \\\"pcap\\\":\\n\\t\\t\\t\\t\\tself.PCAP_TRAFFIC = to_bool(val)\\n\\t\\t\\t\\telif key == \\\"unsecure\\\":\\n\\t\\t\\t\\t\\tself.ALLOW_UNSECURE_CONNECTION = to_bool(val)\\n\\t\\t\\t\\telif key == \\\"max_log\\\":\\n\\t\\t\\t\\t\\tself.MAX_LOG = int(val)\\n\\t\\t\\t\\telif key == \\\"username\\\":\\n\\t\\t\\t\\t\\tself.USERNAME = val\\n\\t\\t\\t\\telif key == \\\"password\\\":\\n\\t\\t\\t\\t\\tself.PASSWORD = val\\n\\t\\texcept:\\n\\t\\t\\tdebug.debug(\\\"[Error] Bad config file format.\\\", level=debug.ERROR)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"667d5c5f69e44535558d7ec9bac41c9b\",\n \"score\": \"0.5942972\",\n \"text\": \"def refresh(self):\\n self._data_cache = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"115857a06b22c5bf55719bda4b51729f\",\n \"score\": \"0.59419745\",\n \"text\": \"def _update(self):\\n mod_time = os.stat(self._cnf_filename)[stat.ST_MTIME]\\n if os.path.exists(self._cnf_filename) and self.last_updated != mod_time:\\n self._conf_handle = ConfigParser()\\n self._conf_handle.read(self._cnf_filename)\\n self.last_updated = mod_time\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9790f4001b411509e0e8a52e1ca4413a\",\n \"score\": \"0.5940468\",\n \"text\": \"def _write_cache(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f327aa8589c2790b80bf9beb0844c3c\",\n \"score\": \"0.5934752\",\n \"text\": \"def _rebuild_cache(self):\\n self.cache = {}\\n self._build_cache_from_corpus()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ba708f4e6ea218226a5d4c3db7f94f7\",\n \"score\": \"0.5931864\",\n \"text\": \"def load(self):\\n\\t\\ttry:\\n\\t\\t\\tself.update(json.loads(self.config.read())) # B\\n\\t\\texcept py.error.ENOENT:\\n\\t\\t\\tpass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51f70772142cd1c802baca9f6d8b6202\",\n \"score\": \"0.5931201\",\n \"text\": \"def update_config_cache(conf: Dict, changes: List[Change]) -> None:\\n for c in changes:\\n if isinstance(c, ChangeCreated):\\n libyang.xpath_set(conf, c.xpath, c.value, after=c.after)\\n elif isinstance(c, ChangeModified):\\n libyang.xpath_set(conf, c.xpath, c.value)\\n elif isinstance(c, ChangeMoved):\\n libyang.xpath_move(conf, c.xpath, c.after)\\n elif isinstance(c, ChangeDeleted):\\n libyang.xpath_del(conf, c.xpath)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1535af025f4317767a8b72874d3b99f4\",\n \"score\": \"0.5923526\",\n \"text\": \"def update(self, key):\\n if key not in self.cache and len(self.cache) >= self.max_cache_size:\\n self.remove_oldest()\\n \\n self.cache[key] = {'date_stored': datetime.datetime.now()}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d45b249bda43c63c92783e450924113\",\n \"score\": \"0.5901761\",\n \"text\": \"def load(self):\\n with open(self.CACHE_DIR + self.file_name, 'r') as data_file: \\n self.cache = json.load(data_file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ffba25bee92c049178635ce3fa697e1e\",\n \"score\": \"0.5889064\",\n \"text\": \"def reload_configs(self):\\n\\n self.logger.debug('reloading configurations')\\n\\n alterations = {}\\n\\n string_keys = {\\n 'name': 'name',\\n 'admin_email': 'admin_email',\\n 'currency': 'currency'\\n }\\n\\n bool_keys = {\\n 'login_suport': 'login_suport',\\n 'ticket_suport': 'ticket_suport',\\n 'default_welcome_msg': 'default_welcome_msg',\\n 'close_apps': 'close_apps',\\n 'use_logo': 'logo',\\n 'use_background': 'background',\\n 'inf_login': 'inf_login'\\n }\\n\\n int_keys = {\\n 'finish_action': 'finish_action',\\n 'finish_action_time': 'finish_action_time',\\n }\\n\\n for name in string_keys.keys():\\n value = self.conf_client.get_string(string_keys[name])\\n\\n if (value != None and ((not(name in self.information)) or (name in self.information) and (self.information[name] != value))):\\n self.information[name] = value\\n alterations[name] = value\\n\\n for name in bool_keys.keys():\\n value = self.conf_client.get_bool(bool_keys[name])\\n\\n if self.information[name] != value:\\n self.information[name] = value\\n alterations[name] = value\\n\\n for name in int_keys.keys():\\n value = self.conf_client.get_int(int_keys[name])\\n if (self.information[name] != value) and (value != None):\\n self.information[name] = value\\n alterations[name] = value\\n\\n value = self.conf_client.get_float('price_per_hour')\\n if self.information['price.hour'] != value:\\n self.information['price.hour'] = value\\n alterations['price.hour'] = value\\n\\n if not self.information['default_welcome_msg']:\\n\\n value = self.conf_client.get_string('welcome_msg')\\n\\n if self.information.has_key('welcome_msg'):\\n if self.information['welcome_msg'] != value:\\n self.information['welcome_msg'] = value\\n alterations['welcome_msg'] = value\\n else:\\n self.information['welcome_msg'] = value\\n alterations['welcome_msg'] = value\\n\\n # close apps\\n value = self.conf_client.get_string_list(\\\"close_apps_list\\\")\\n if self.information['close_apps_list'] != value:\\n self.information['close_apps_list'] = value\\n alterations['close_apps_list'] = value\\n\\n value = self.conf_client.get_string('background_path')\\n if self.common_background != value:\\n self.common_background = value\\n self.common_background_md5 = md5_cripto(open(self.common_background).read())\\n self.instmachine_manager.update_common_backgroud(self.common_background_md5)\\n\\n value = self.conf_client.get_string('logo_path')\\n if self.common_logo != value:\\n self.common_logo = value\\n self.common_logo_md5 = md5_cripto(open(self.common_logo).read())\\n self.instmachine_manager.update_common_logo(self.common_logo_md5)\\n\\n if alterations:\\n self.instmachine_manager.update_information(alterations)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b59732357a7165895dc002303d0efde\",\n \"score\": \"0.5880726\",\n \"text\": \"def reset_cache(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3db346d7a816aa64fec11089a1dfd608\",\n \"score\": \"0.5878871\",\n \"text\": \"def cache(conan_api: ConanAPI, parser, *args):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95358def39e8da5696324e980f3a0fcf\",\n \"score\": \"0.58723795\",\n \"text\": \"def setCaching(self, val='True', **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95358def39e8da5696324e980f3a0fcf\",\n \"score\": \"0.58723795\",\n \"text\": \"def setCaching(self, val='True', **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95358def39e8da5696324e980f3a0fcf\",\n \"score\": \"0.58723795\",\n \"text\": \"def setCaching(self, val='True', **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95358def39e8da5696324e980f3a0fcf\",\n \"score\": \"0.58723795\",\n \"text\": \"def setCaching(self, val='True', **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95358def39e8da5696324e980f3a0fcf\",\n \"score\": \"0.58723795\",\n \"text\": \"def setCaching(self, val='True', **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95358def39e8da5696324e980f3a0fcf\",\n \"score\": \"0.58723795\",\n \"text\": \"def setCaching(self, val='True', **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95358def39e8da5696324e980f3a0fcf\",\n \"score\": \"0.58723795\",\n \"text\": \"def setCaching(self, val='True', **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95358def39e8da5696324e980f3a0fcf\",\n \"score\": \"0.5869458\",\n \"text\": \"def setCaching(self, val='True', **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95358def39e8da5696324e980f3a0fcf\",\n \"score\": \"0.5869458\",\n \"text\": \"def setCaching(self, val='True', **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95358def39e8da5696324e980f3a0fcf\",\n \"score\": \"0.5869458\",\n \"text\": \"def setCaching(self, val='True', **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95358def39e8da5696324e980f3a0fcf\",\n \"score\": \"0.5869458\",\n \"text\": \"def setCaching(self, val='True', **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95358def39e8da5696324e980f3a0fcf\",\n \"score\": \"0.5869458\",\n \"text\": \"def setCaching(self, val='True', **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42c896ec7df359ff715f801abed825b0\",\n \"score\": \"0.5857698\",\n \"text\": \"def reload_config(self):\\n #self._client.reload_config()\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab94f30207484a1ac69e00f22d251fbf\",\n \"score\": \"0.5842765\",\n \"text\": \"def update(self, config_dict):\\n self._update(config_dict, allow_new_keys=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d78b065a5dbc6323f0c1138178f7d63b\",\n \"score\": \"0.5834923\",\n \"text\": \"def load(self):\\n filepath = Path(self.config).resolve()\\n if filepath.exists():\\n with open(self.config, 'r') as f:\\n self.update(json.load(f))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d5c6fcd88f2069fa90ed30b732f6162\",\n \"score\": \"0.58169216\",\n \"text\": \"def load_cache(self):\\n try:\\n with open(self.cache_name, \\\"r\\\") as file_cache:\\n cache_content = file_cache.read()\\n except FileNotFoundError:\\n with open(self.cache_name, \\\"w\\\") as file_cache:\\n cache_content = \\\"\\\"\\n\\n # transform cache to json object, handle cache case\\n try:\\n self.cache = json.loads(cache_content)\\n except ValueError:\\n self.cache = {\\n \\\"last_mod\\\": \\\"1900-01-01 00:00:00.0\\\",\\n \\\"current_weather\\\": {},\\n \\\"forecast_weather\\\": {},\\n }\\n\\n # validate last modification < 1 hour\\n delta = datetime.now() - parse(self.cache[\\\"last_mod\\\"])\\n delta_to_hour = (delta.days * 24) + (delta.seconds / 3600)\\n self.use_cache = delta_to_hour < 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23b53223472038f0d066ae95a02fd87f\",\n \"score\": \"0.5808317\",\n \"text\": \"def flush_cache(self):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4ae86fc9257b79572f2b99782a6801e\",\n \"score\": \"0.5798737\",\n \"text\": \"def cache(self):\\n\\n pargs = ['cache']\\n return self.hc.run_command(self, pargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71ef7e646f53120f1ea5c5e21c50edbf\",\n \"score\": \"0.5788758\",\n \"text\": \"def update_cache(self, query: QueryType, result: Iterator[BaseInterfaceModel]):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b902233a4e7fab0239edc34cafc3acfe\",\n \"score\": \"0.57723135\",\n \"text\": \"def update_cache(self, cat, img_path):\\r\\n try:\\r\\n shutil.copy(img_path, os.path.join(os.path.dirname(__file__), '..', '_cache_'))\\r\\n except (IOError, OSError) as ex:\\r\\n print(ex)\\r\\n print('ERROR in update_cache() ' +\\r\\n 'Cannot copy to _cache_ directory, make sure there ' +\\r\\n 'is enough space on disk')\\r\\n sys.exit(1)\\r\\n\\r\\n cache_img_path = os.path.join(os.path.dirname(__file__), '..', '_cache_',\\r\\n os.path.basename(img_path))\\r\\n\\r\\n zope.event.notify(CacheAddEvent(cat, cache_img_path))\\r\\n self.cache.append((cat, cache_img_path))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ebf1b449eeb5f0f8a803211345e7c4d\",\n \"score\": \"0.57661843\",\n \"text\": \"def setCache(self, val='True', **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ebf1b449eeb5f0f8a803211345e7c4d\",\n \"score\": \"0.57660276\",\n \"text\": \"def setCache(self, val='True', **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6c98126bce2e0e1780f2d8fc5927fb6\",\n \"score\": \"0.5758702\",\n \"text\": \"def update(self, config, merge=False):\\n\\n self.config = {**self.config, **config} if merge else config\\n self.dataset = dataset_model(self.config)\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5da7b702af7fe7d33ec50105f9fe89e2\",\n \"score\": \"0.5742303\",\n \"text\": \"def update_config(self, update_dict, reload=False):\\n self.config.update(update_dict)\\n # set config temp_folder to wksp for brevity\\n if \\\"temp_folder\\\" in update_dict.keys():\\n self.config[\\\"wksp\\\"] = update_dict[\\\"temp_folder\\\"]\\n self.validate_config()\\n if reload:\\n self.data = util.load_tables(self.config)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"556966817bde09e6fad0a7a6a56f2e92\",\n \"score\": \"0.5722837\",\n \"text\": \"def _build_cache(self):\\n print('Building cache...')\\n assert self.cache == {}\\n if self.data:\\n print('loading data from json')\\n self.load_saved_cache()\\n else:\\n print('from states')\\n self._build_cache_from_corpus()\\n self._save_cache()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ac2e1ef6186cf332e2fc92f3cbcc8a5\",\n \"score\": \"0.571785\",\n \"text\": \"def update_configs():\\n global blacklist_dict\\n global whitelist_dict\\n blacklist_dict = update_config_dict('blacklist.conf', blacklist_dict)\\n whitelist_dict = update_config_dict('whitelist.conf', whitelist_dict)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6791a9925d2545cf9e16e5b43e235627\",\n \"score\": \"0.57126206\",\n \"text\": \"def use_cache(self,**kwargs):\\n\\t\\tself.set_meta_file_name( kwargs.get(\\\"meta_file_name\\\",\\\"\\\"))\\n\\t\\toverwrite_meta_by_centos = kwargs.get(\\\"overwrite_meta_by_centos\\\",False)\\n\\t\\tself.store_allKeys = kwargs.get(\\\"store_allKeys\\\",False)\\n\\t\\tself.ignore_invalid_mem = kwargs.get(\\\"ignore_invalid_mem\\\",False)\\n\\t\\toverwrite_memory = kwargs.get(\\\"overwrite_memory\\\",False)\\n\\t\\tset_to_be_valid = kwargs.get(\\\"set_to_be_valid\\\",False)\\n\\t\\tignore_lock = kwargs.get(\\\"ignore_lock\\\",False)\\n\\t\\tcheck_pref_is_short = kwargs.get(\\\"check_pref_is_short\\\",True)\\n\\t\\tif not self.overwrite_prefix:\\n\\t\\t\\tself.overwrite_prefix = kwargs.get(\\\"overwrite_prefix\\\",False)\\n\\t\\tloading_msg = kwargs.get(\\\"loading_msg\\\",\\\"\\\")\\n\\t\\tparams = kwargs.get(\\\"params\\\",{}) # if you want to change params\\n\\t\\tif \\\"params\\\" in kwargs:\\n\\t\\t\\tprint(\\\"[CM] params update these:\\\",params)\\n\\n\\t\\tif not os.path.exists(self.meta_file_abs_path):\\n\\t\\t\\tif self.my_platform!=\\\"centos\\\":\\n\\t\\t\\t\\tr = requests.get(url=FILE_SERVER_GET+\\\"cache_meta\\\", params={\\\"meta_file_name\\\":self.meta_file_name})\\n\\t\\t\\t\\tmeta= r.json() \\n\\t\\t\\t\\t# TODO not using json, changed syncdir/file-server/app.py: get_file()\\n\\t\\t\\t\\tif \\\"not-found\\\" in meta['status']:\\n\\t\\t\\t\\t\\traise Exception(\\\"Not exists? \\\"+self.meta_file_name)\\n\\t\\t\\t\\tmeta.pop('status')\\n\\t\\t\\t\\ttmpdir = os.sep.join(self.meta_file_abs_path.split(os.sep)[0:-1])\\n\\t\\t\\t\\tif not os.path.exists(tmpdir): \\n\\t\\t\\t\\t\\tos.makedirs(tmpdir)\\n\\t\\t\\t\\twith open(self.meta_file_abs_path, 'wb') as f: # TODO not using json\\n\\t\\t\\t\\t\\tjson.dump(meta, codecs.getwriter('utf-8')(f), ensure_ascii=False)\\n\\t\\t\\t\\tif iprint: print(\\\"Copied from centos: \\\"+self.meta_file_abs_path)\\n\\t\\t\\telse:\\n\\t\\t\\t\\traise Exception(\\\"Not exists? \\\"+self.meta_file_name)\\n\\t\\telse: # already exists locally:\\n\\t\\t\\tif self.my_platform!=\\\"centos\\\" and overwrite_meta_by_centos:\\n\\t\\t\\t\\tr = requests.get(url=FILE_SERVER_GET+\\\"cache_meta\\\", params={\\\"meta_file_name\\\":self.meta_file_name})\\n\\t\\t\\t\\tmeta= r.json()\\n\\t\\t\\t\\tif not \\\"not-found\\\" in meta['status']:\\n\\t\\t\\t\\t\\tmeta.pop('status')\\n\\t\\t\\t\\t\\twith open(self.meta_file_abs_path, 'wb') as f:# TODO not using json\\n\\t\\t\\t\\t\\t\\tjson.dump(meta, codecs.getwriter('utf-8')(f), ensure_ascii=False)\\n\\t\\t\\t\\t\\tif iprint: print(\\\"Copied from centos: \\\"+self.meta_file_abs_path)\\n\\t\\ttry: \\n\\t\\t\\tmeta = pickle.load(open(self.meta_file_abs_path, 'rb'))\\n\\t\\texcept: # TODO not using json\\n\\t\\t\\tmeta = json.load(open(self.meta_file_abs_path, 'rb'))\\n\\t\\tmeta[\\\"params\\\"]=pickle.loads(str(meta[\\\"params\\\"]))\\n\\t\\tmeta[\\\"params\\\"].update(params)\\n\\n\\t\\tif self.overwrite_prefix:\\n\\t\\t\\tmeta[\\\"params\\\"]['overwrite_prefix'] = True\\n\\t\\t\\n\\t\\tmeta[\\\"params\\\"][\\\"overwrite_memory\\\"]=overwrite_memory\\n\\t\\tmeta[\\\"params\\\"][\\\"ignore_lock\\\"]=ignore_lock\\n\\t\\tif self.rt_servers: \\n\\t\\t\\tmeta[\\\"params\\\"][\\\"rt_servers\\\"]=True\\n\\t\\tif self.overwrite_redis_servers:\\n\\t\\t\\tmeta[\\\"params\\\"][\\\"overwrite_servers\\\"]=True\\n\\n\\t\\tif set_to_be_valid:\\n\\t\\t\\tprint(\\\"force set to be valid !\\\")\\n\\t\\t\\tself.set_mem_valid( meta[\\\"params\\\"] )\\n\\t\\t\\treturn \\n\\t\\tret = self.activate_mem( meta[\\\"params\\\"] )\\n\\t\\tif (ret==\\\"invalid\\\" and not self.ignore_invalid_mem) or overwrite_memory:\\n\\t\\t\\tyield_func = dill.loads(meta[\\\"yield_func\\\"]) if \\\"yield_func\\\" in meta else None\\n\\t\\t\\tyield_args = meta[\\\"yield_args\\\"] if \\\"yield_args\\\" in meta else None\\n\\t\\t\\tif yield_func is None or yield_args is None:\\n\\t\\t\\t\\tif iprint: print(\\\"Redo load_cache_file_into_mem() ...\\\")\\n\\t\\t\\t\\tself.load_cache_file_into_mem( meta[\\\"cache_file_abs_path\\\"], meta[\\\"params\\\"],self.store_allKeys, msg = loading_msg)\\n\\t\\t\\telse:\\n\\t\\t\\t\\tif iprint: print(\\\"RUN yield_func() \\\"+yield_args)\\n\\t\\t\\t\\tkv_action_type = meta[\\\"kv_action_type\\\"] if \\\"kv_action_type\\\" in meta else None\\n\\t\\t\\t\\tself.yield_file_into_mem( yield_func, yield_args, meta[\\\"params\\\"], kv_action_type=kv_action_type, msg = loading_msg )\\n\\t\\t\\tprint( self.activate_mem( meta[\\\"params\\\"] ) )\\n\\t\\tself.meta = meta\\n\\t\\tif check_pref_is_short:\\n\\t\\t\\tif len(self.mm.prefix)>4: # support more prefixes if > more.\\n\\t\\t\\t\\tprint(self.mm.prefix)\\n\\t\\t\\t\\tprint(\\\" :prefix too long, not overwrite_prefix?\\\")\\n\\t\\t\\t\\tsys.exit(0) # You Edit\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b49d7855cbe5a82a4960a4fdbccc18ee\",\n \"score\": \"0.5708811\",\n \"text\": \"def _flush_cache(self):\\n self.__last_update = datetime.datetime(1970, 1, 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83092e50f0f37cae51f6928a20796ea1\",\n \"score\": \"0.570268\",\n \"text\": \"def update(self, settings):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31582ef7cf51573e1640da901ba3d5a6\",\n \"score\": \"0.5700119\",\n \"text\": \"def UpdateCache(self, prefix, results, partial_results):\\n self.prefix = prefix\\n self.results = results\\n self.partial_results = partial_results\\n self.timestamp = time.time()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d76f803b96f5aec84e1c9f1f4b3b11a2\",\n \"score\": \"0.56960064\",\n \"text\": \"def getCaching(self, **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d76f803b96f5aec84e1c9f1f4b3b11a2\",\n \"score\": \"0.56960064\",\n \"text\": \"def getCaching(self, **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d76f803b96f5aec84e1c9f1f4b3b11a2\",\n \"score\": \"0.56960064\",\n \"text\": \"def getCaching(self, **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d76f803b96f5aec84e1c9f1f4b3b11a2\",\n \"score\": \"0.56960064\",\n \"text\": \"def getCaching(self, **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d76f803b96f5aec84e1c9f1f4b3b11a2\",\n \"score\": \"0.56960064\",\n \"text\": \"def getCaching(self, **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d76f803b96f5aec84e1c9f1f4b3b11a2\",\n \"score\": \"0.56960064\",\n \"text\": \"def getCaching(self, **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d76f803b96f5aec84e1c9f1f4b3b11a2\",\n \"score\": \"0.56950074\",\n \"text\": \"def getCaching(self, **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d76f803b96f5aec84e1c9f1f4b3b11a2\",\n \"score\": \"0.56950074\",\n \"text\": \"def getCaching(self, **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d76f803b96f5aec84e1c9f1f4b3b11a2\",\n \"score\": \"0.56950074\",\n \"text\": \"def getCaching(self, **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d76f803b96f5aec84e1c9f1f4b3b11a2\",\n \"score\": \"0.56950074\",\n \"text\": \"def getCaching(self, **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d76f803b96f5aec84e1c9f1f4b3b11a2\",\n \"score\": \"0.56950074\",\n \"text\": \"def getCaching(self, **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d76f803b96f5aec84e1c9f1f4b3b11a2\",\n \"score\": \"0.56950074\",\n \"text\": \"def getCaching(self, **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f9adade55c313a79b25bd2c29bb7ae8\",\n \"score\": \"0.56866056\",\n \"text\": \"def load(self):\\n\\t\\ttry:\\n\\t\\t\\twith open(self.config, 'r') as f:\\n\\t\\t\\t\\tself.update(json.loads(f.read()))\\n\\t\\texcept errno.ENOENT:\\n\\t\\t\\tprint \\\"ENOENT error\\\"\\n\\t\\t\\tpass\\n\\t\\texcept Exception as e:\\n\\t\\t\\tprint e\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e68610b32df21910832c10c253c55f77\",\n \"score\": \"0.5683001\",\n \"text\": \"def update_cachegroups(self, cache_group_id=None, data=None):\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":676,"cells":{"query_id":{"kind":"string","value":"95d80d6dfef902c68f1348978cb4d026"},"query":{"kind":"string","value":"Read stock data (adjusted close) for given symbols from CSV files."},"positive_passages":{"kind":"list like","value":[{"docid":"85cde913e57d414b2527d791f792b1de","score":"0.6046349","text":"def get_data(symbols, dates):\r\n df = pd.DataFrame(index=dates)\r\n for symbol in symbols:\r\n df_temp = pd.read_csv(symbol_to_path(symbol), index_col='Date',\r\n parse_dates=True, usecols=['Date', 'Adj Close'], na_values=['nan'])\r\n df_temp = df_temp.rename(columns={'Adj Close': symbol})\r\n df = df.join(df_temp)\r\n df = df.dropna(subset=[\"IBM\"])\r\n return df","title":""}],"string":"[\n {\n \"docid\": \"85cde913e57d414b2527d791f792b1de\",\n \"score\": \"0.6046349\",\n \"text\": \"def get_data(symbols, dates):\\r\\n df = pd.DataFrame(index=dates)\\r\\n for symbol in symbols:\\r\\n df_temp = pd.read_csv(symbol_to_path(symbol), index_col='Date',\\r\\n parse_dates=True, usecols=['Date', 'Adj Close'], na_values=['nan'])\\r\\n df_temp = df_temp.rename(columns={'Adj Close': symbol})\\r\\n df = df.join(df_temp)\\r\\n df = df.dropna(subset=[\\\"IBM\\\"])\\r\\n return df\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"a8d8645fcc925ecee2412ed9a3db1768","score":"0.7471986","text":"def _open_csv(self):\n combined_index = None\n for symbol in self.symbol_list:\n path = self.csv_path + '/{}.csv'.format(symbol)\n self.symbol_data[symbol] = pd.read_csv(\n path, header=0, index_col=0, parse_dates=True,\n names = [\n 'datetime', 'high', 'low', 'open', 'close', 'volume', 'adj_close'\n ]\n )\n\n if combined_index is None:\n combined_index = self.symbol_data[symbol].index\n else:\n combined_index.union(self.symbol_data[symbol].index)\n\n self.latest_symbol_data[symbol] = []\n\n # todo time index combine\n for symbol in self.symbol_list:\n self.symbol_data[symbol] = self.symbol_data[symbol].reindex(\n index=combined_index, method='pad').iterrows()","title":""},{"docid":"cebdfe3e967af44cda092bcba193893a","score":"0.7000566","text":"def load(self, path):\n try:\n with open(join(path, self.name + \"_data.csv\"), 'r') as csvfile:\n reader = csv.reader(csvfile, delimiter=',', quotechar='\"')\n csvHeader = next(reader)\n for row in reader:\n try:\n currOpen = float(row[1])\n currClose = float(row[4])\n day = datetime.strptime(row[0], \"%Y-%m-%d\").strftime(\"%d.%m.%Y\")\n self.data[day] = (int(np.sign(currClose-currOpen)), currOpen)\n except ValueError:\n logging.warn(\"Could not interpret day \" + row[0])\n except Exception as ex:\n logging.error(\"Could not get the stock data. \" + ex)","title":""},{"docid":"aa81dd27973ddfc80be8cfc573767fd6","score":"0.69686425","text":"def _data_conversion_from_csv_files(self):\r\n\r\n combined_index = None\r\n for symbol in self.symbol_list:\r\n # Load the CSV file with no header information, indexed on date\r\n self.symbol_data[symbol] = pd.io.parsers.read_csv(\r\n os.path.join(self.csv_dir, \"%s.csv\" % symbol),\r\n header=0, index_col=0,\r\n names=[\"datetime\", \"open\", \"high\", \"low\", \"close\", \"adj_close\", \"volume\"]\r\n )\r\n\r\n # Combine the index to pad forward values\r\n if combined_index is None:\r\n combined_index = self.symbol_data[symbol].index\r\n else:\r\n combined_index.union(self.symbol_data[symbol].index)\r\n\r\n # Set the latest symbol_data to None\r\n self.latest_symbol_data[symbol] = []\r\n\r\n # Reindex the dataframes\r\n for symbol in self.symbol_list:\r\n self.symbol_data[symbol] = self.symbol_data[symbol].reindex(index=combined_index, method=\"pad\").iterrows()","title":""},{"docid":"99c87d792d6b9ffce8d7fe22020e8902","score":"0.6741852","text":"def read_csv(filename):\r\n # frame = pd.read_csv('C:/Users/tzagk/Downloads/Stocks/' + filename, dtype=size_dict, usecols=use_cols, header=0,\r\n # index_col=\"Date\", parse_dates=True)\r\n frame = pd.read_csv('C:/Users/tzagk/Downloads/Stocks/' + filename, dtype=size_dict, header=0,\r\n index_col=\"Date\", parse_dates=True)\r\n all_days = pd.date_range(frame.index.min(), frame.index.max(), freq='D')\r\n frame2 = frame.reindex(index = all_days, method = 'nearest')['Close'] \r\n return [filename, frame, frame.index[0]], [filename,frame2]","title":""},{"docid":"021e2d5e737135db4df84798ac8f8d14","score":"0.66182214","text":"def read_csv_file(self):\n try:\n with open('Lesson6_Data_Stocks.csv', 'r') as stock_file:\n reader = csv.reader(stock_file)\n next(reader)\n for row in reader:\n self.stocks[row[0]] = {'NO_SHARES': row[1]}\n except FileNotFoundError as e:\n print('File not found at the given location, Check the Path!')\n return self.stocks","title":""},{"docid":"e1d1eef665aa474c74f4f10780e078a7","score":"0.6386013","text":"def get_data(symbols, dates):\n df = pd.DataFrame(index=dates)\n if 'SPY' not in symbols: # add SPY for reference, if absent\n symbols.insert(0, 'SPY')\n\n for symbol in symbols:\n df_temp = pd.read_csv(\"data/{}.csv\".format(symbol), index_col='Date',\n parse_dates=True, usecols=['Date', 'Close'], na_values=['nan'])\n df_temp = df_temp.rename(columns={'Close': symbol})\n df = df.join(df_temp)\n if symbol == 'SPY': # drop dates SPY did not trade\n df = df.dropna(subset=[\"SPY\"])\n\n return df","title":""},{"docid":"c363f4c9a982b697c3635760e9e94e72","score":"0.6263173","text":"def get_weather_data(filename, dates, highs, lows, date_index, high_index, low_index):\n\n with open(filename) as f:\n reader = csv.reader(f)\n header_row = next(reader)\n\n\n # Get, dates, highs, lows from this file\n for row in reader:\n current_date = datetime.strptime(row[date_index], '%Y-%m-%d')\n try:\n high = int(row[high_index])\n low = int(row[low_index])\n except ValueError:\n print(f\"Missing data from {current_date}\")\n else:\n dates.append(current_date)\n highs.append(high)\n lows.append(low)","title":""},{"docid":"980c25a602a3c534b071696909b18188","score":"0.6246628","text":"def load_from_csv(self, symbol, path=''):\n if path == '':\n path = '../data/raw/{}.csv'.format(symbol)\n else:\n path = os.path.join(path, '{}.csv'.format(symbol))\n return pd.read_csv(path)","title":""},{"docid":"c06f0d9fda7b08cdb07c76cd25c46866","score":"0.6215058","text":"def load_base_stocks():\n\n stocks = {}\n\n with open(STOCKS_FILE) as d:\n data = csv.reader(d)\n\n for row in data:\n\n values = dict(zip(KEYS, row))\n _cast_values(values)\n stock = Stock(**values)\n\n stocks[stock.symbol] = stock\n\n return stocks","title":""},{"docid":"de52ce87548ca4e0e1d04367a9162c02","score":"0.61609524","text":"def read(csv='./tech.csv', metadata='./metadata.json'):\n\n metadata = pd.read_json(metadata)\n columns = metadata.datatable['columns']\n headings = [heading['name'] for heading in columns]\n stocks = pd.read_csv(\n filepath_or_buffer=csv, header=None, names=headings)\n\n return stocks","title":""},{"docid":"98359bce9097c6cd08a384ff2bc65d81","score":"0.6145918","text":"def _load_data_from_Yahoo_finance(self):\r\n\r\n combined_index = None\r\n for symbol in self.symbol_list:\r\n\r\n # download data from yfinance for symbol. This could be improved as yfinance can download several\r\n # symbols at the same time\r\n self.symbol_data[symbol] = yf.download(tickers=[symbol], start=self.start_date,\r\n end=self.end_date, interval=self.interval)\r\n\r\n # rename columns for consistency\r\n self.symbol_data[symbol].rename(columns={'Open': 'open',\r\n 'High': 'high',\r\n 'Low': 'low',\r\n 'Close': 'close',\r\n 'Adj Close': 'adj_close',\r\n 'Volume': 'volume'}, inplace=True)\r\n\r\n # rename index as well from 'Date' to 'datetime'\r\n self.symbol_data[symbol].index.name = 'datetime'\r\n\r\n # create returns column (used for some strategies)\r\n self.symbol_data[symbol]['returns'] = self.symbol_data[symbol][\"adj_close\"].pct_change() * 100.0\r\n\r\n # Combine the index to pad forward values\r\n if combined_index is None:\r\n combined_index = self.symbol_data[symbol].index\r\n else:\r\n combined_index.union(self.symbol_data[symbol].index)\r\n\r\n # Set the latest symbol_data to None\r\n self.latest_symbol_data[symbol] = []\r\n\r\n # Reindex the dataframes\r\n for symbol in self.symbol_list:\r\n self.symbol_data[symbol] = self.symbol_data[symbol].reindex(index=combined_index, method=\"pad\").iterrows()","title":""},{"docid":"b5a4c999b8d4992a563f69c8900a5ca7","score":"0.61391765","text":"def get_stock_data(stock_symbol):\n\n api_url = API_URL.replace('stock_symbol', stock_symbol)\n response = requests.get(api_url)\n if response.status_code == 200:\n decoded_content = response.content.decode('utf-8')\n csv_list = list(csv.reader(\n decoded_content.splitlines(), delimiter=','))\n close = csv_list[ROW][CLOSE_COLUMN]\n symbol = csv_list[ROW][SYMBOL_COLUMN]\n try:\n float(close)\n return f'{symbol} quote is ${close} per share'\n except:\n return None\n return None","title":""},{"docid":"ffdec9a7276578a41f5ffdd9163da0fa","score":"0.61227685","text":"def run(csv='./tech.csv', metadata='./metadata.json'):\n stocks = read(csv, metadata)\n print(\"STOCKS READ********************************************\")\n stocks = adjust(stocks)\n print(\"DATAFRAME COLUMNS ADDED********************************\")\n stocks = set_index(stocks)\n print(\"DATAFRAME INDEX SET************************************\")\n stocks = past(stocks)\n print(\"DATAFRAME FILLED OUT***********************************\\a\\a\")\n return stocks","title":""},{"docid":"2cce38414e94d76e0b9e9b7f3447d5a6","score":"0.60740167","text":"def get_data(symbols, dates):\n df = pd.DataFrame(index = dates)\n\n if 'SPY' not in symbols: # add SPY for reference, if absent\n symbols.insert(0, 'SPY')\n\n for symbol in symbols:\n\n dfTemp = pd.read_csv(symbol_to_path(symbol),\n index_col=\"Date\",\n parse_dates=True,\n usecols=['Date', 'Adj Close'],\n na_values=['nan'])\n\n dfTemp = dfTemp.rename(columns={'Adj Close' : symbol})\n df = df.join(dfTemp)\n\n if symbol == 'SPY': # drop dates SPY did not trade\n df = df.dropna(subset=[\"SPY\"])\n\n return df","title":""},{"docid":"be86e0c17e8087ab94ea6f54dc9aa3e2","score":"0.6033021","text":"def get_data(symbols, dates):\n df = pd.DataFrame(index=dates)\n if 'SPY' not in symbols: # add SPY for reference, if absent\n symbols.insert(0, 'SPY')\n \n for symbol in symbols:\n df_temp = pd.read_csv(symbol_to_path(symbol), index_col='Date',\n parse_dates=True, usecols=['Date', 'Adj Close'], na_values=['nan'])\n df_temp = df_temp.rename(columns={'Adj Close': symbol})\n df = df.join(df_temp)\n if symbol == 'SPY': # drop dates SPY did not trade\n df = df.dropna(subset=[\"SPY\"])\n return df","title":""},{"docid":"cd2d7064f5fd92db9bdedd81d293c7ab","score":"0.6030075","text":"def read_csv(filepath):\n symbols = []\n with open(filepath, 'rb') as csvfile:\n spamreader = csv.DictReader(csvfile, delimiter=',', quotechar='\"')\n for row in spamreader:\n symbols.append(row)\n return symbols","title":""},{"docid":"d00913eb61f40235634a5e6510f8db8a","score":"0.6020719","text":"def LoadDataFromCSV(csvFile):\r\n\tdata = pd.read_csv(csvFile)\r\n\tx = data.iloc[:, 0:315] # select columns 1 through end\r\n\ty = data.iloc[:, 315] # select column 0, the stock price\r\n\r\n\treturn x,y","title":""},{"docid":"92167a65083c44eb19259e448f09bb50","score":"0.6019517","text":"def get_yahoo_finance_data(symbol, start_date=None, end_date=None, remove_zero_volume=True):\n data_list = [('s', symbol)]\n if hasattr(start_date, 'split'):\n start_date = str2datetime(start_date)\n\n if hasattr(end_date, 'split'):\n end_date = str2datetime(end_date)\n\n if start_date:\n data_list.append(('a', start_date.month - 1))\n data_list.append(('b', start_date.day))\n data_list.append(('c', start_date.year))\n if end_date:\n data_list.append(('d', end_date.month - 1))\n data_list.append(('e', end_date.day))\n data_list.append(('f', end_date.year))\n data_list.append(('g', 'd'))\n data_list.append(('ignore', '.csv'))\n\n url = \"http://chart.finance.yahoo.com/table.csv\"\n stock_info = get(url=url, data_list=data_list)\n stock_data = StringIO(stock_info)\n stock_df = pd.read_csv(stock_data)\n stock_df['Date'] = stock_df['Date'].apply(lambda x: datetime.datetime.strptime(x, '%Y-%m-%d'))\n stock_df = stock_df.set_index('Date').sort_index()\n\n if remove_zero_volume:\n return stock_df[stock_df['Volume'] > 0]\n else:\n return stock_df","title":""},{"docid":"2bc6e86f7f63e952000799e3c8fa1ef5","score":"0.60183865","text":"def get_data(symbols, dates):\n df = pd.DataFrame(index=dates)\n if 'SPY' not in symbols: # add SPY for reference, if absent\n symbols.insert(0, 'SPY')\n\n for symbol in symbols:\n df_temp = pd.read_csv(symbol_to_path(symbol), index_col='Date',\n parse_dates=True, usecols=['Date', 'Adj Close'], na_values=['nan'])\n df_temp = df_temp.rename(columns={'Adj Close': symbol})\n df = df.join(df_temp)\n if symbol == 'SPY': # drop dates SPY did not trade\n df = df.dropna(subset=[\"SPY\"])\n\n return df","title":""},{"docid":"af11bb80d467de1a339224ba1cfa7972","score":"0.5999453","text":"def get_data(symbols, dates):\n df = pd.DataFrame(index=dates)\n if 'SPY' not in symbols: # Add SPY for reference, if absent\n symbols.insert(0, 'SPY')\n\n for symbol in symbols:\n df_temp = pd.read_csv(symbol_to_path(symbol),index_col='Date',\n parse_dates=True, usecols = [\"Date\", \"Adj Close\"], na_values = ['nan'])\n # Rename to prevent clash\n df_temp = df_temp.rename(columns={'Adj Close': symbol})\n df = df.join(df_temp) # use default how = 'left'\n\n if symbol == 'SPY': # drop dates on which SPY did not trade\n df = df.dropna(subset=[\"SPY\"])\n\n return df","title":""},{"docid":"c14378685d8bafdf8db2268e85fdaabc","score":"0.59816164","text":"def _parse_ticker_symbols(self):\n for files in os.listdir(path=self.directory):\n with open(self.directory+\"\\\\\"+files) as ticker_list:\n next(ticker_list) #skip header row\n for line in ticker_list:\n line = line[0:len(line)-1] #omit newline charachter\n pair = line.split(sep=\"\\t\")\n if re.search(\"^([A-z]{1,6}$)\",pair[0]) is None: #removes assets which are not common stocks from push to db.\n continue\n self.data[pair[0]]={\"name\":pair[1],\"lastUpdated\":date.today().isoformat(),\"sicCode\":None}","title":""},{"docid":"847db80f98a606660a931b2ebbac74b1","score":"0.59059405","text":"def load(csv_file):","title":""},{"docid":"de28705422e5cb800134800f859e40d4","score":"0.5905777","text":"def fetch_data_by_symbol():\n symbols = [\"crude oil\", \"xau\"]\n dataframes = []\n for symbol in symbols:\n search_result = investpy.search_quotes(text=symbol, n_results=1)\n dataframe = search_result.retrieve_historical_data(from_date='03/06/2012', to_date='15/07/2021')\n dataframe = dataframe.drop([\"Open\", \"High\", \"Low\", \"Volume\", \"Change Pct\"], axis=1)\n dataframe = dataframe.dropna()\n index_date = pd.date_range(\"2012-06-03\", \"2021-07-15\")\n dataframe = dataframe.reindex(index_date, fill_value=None)\n dataframe = dataframe.fillna(method='ffill')\n dataframe = dataframe.fillna(method='bfill')\n dataframe = dataframe.rename(columns={'Close': f'Close-{symbol}'})\n dataframes.append(dataframe)\n return pd.concat(dataframes, axis=1)","title":""},{"docid":"08c693a90d82d70c782a762e51ec3f25","score":"0.58816314","text":"def fetch_stock(ticker):\n fname = '%s.csv'%ticker\n url = 'http://ichart.finance.yahoo.com/table.csv?' +\\\n 's=%s&d=9&e=20&f=2007&g=d&a=0&b=29&c=1993&ignore=.csv'%ticker\n\n # the os.path module contains function for checking whether a file\n # exists\n if not os.path.exists(fname):\n urllib.urlretrieve(url, fname)\n r = mlab.csv2rec(fname)\n\n # note that the CSV file is sorted most recent date first, so you\n # will probably want to sort the record array so most recent date\n # is last\n r.sort()\n return r","title":""},{"docid":"a0d0984c796b330c2b1d385407fe4c45","score":"0.5836562","text":"def read_file(ticker):\r\n data = []\r\n file = open(ticker.lower()+'.csv')\r\n lines = file.readlines()\r\n print(lines)\r\n last_date = get_last_date(lines)\r\n curr_date = dt.datetime.now().date()\r\n if last_date < curr_date:\r\n print(\"it forkin ran!!!\")\r\n update_file()\r\n \r\n \"\"\"\r\n for line in file:\r\n line.strip('/n').split(',')\r\n data.append(line)\r\n \"\"\"","title":""},{"docid":"62f6c7e230fbd1ad6e01547d3c86b56e","score":"0.5776138","text":"def load_symbols():\n if DATA_SET:\n symbol_manifest = pd.read_csv(SYMBOL_MANIFEST, index_col=0, parse_dates=[7, 8, 9])\n symbol_manifest['MarketCap'] = symbol_manifest['MarketCap'].fillna(0.0)\n symbol_manifest.loc[symbol_manifest['Sector'].isnull(), 'Sector'] = 'n/a'\n symbol_manifest = symbol_manifest.rename(columns={'industry': 'Industry'})\n return symbol_manifest\n else:\n print NO_DATA_SET","title":""},{"docid":"8683a191f1c5ce39233d897e15316187","score":"0.57702637","text":"def getPrices(start_date,end_date,freq):\n\n\tquery = \"SELECT symbol FROM companyStaging\"\n\tsymbols =getQuery(query, '')\n\ti=0\n\n\tfor symbol in symbols:\n\t\ti= i + 1\n\t\ttotalNum = len(symbols)\n\t\tticker= ' '.join(symbol)\n\t\tlocalFilePath= globals['dataFilesPath'] + ticker + \".AX.csv\"\n\t\tyfURL = constructYFURL(ticker,start_date,end_date,freq)\n\t\tprint (str(i) + \" of \" + str(totalNum) + \" - \" + yfURL)\n\t\ttry:\n\t\t\tdownload(localFilePath,yfURL)\n\t\t\t#print(yfURL)\n\t\t# Add the column in csv file with header\n\t\t\tadd_column_in_csv(localFilePath, globals['dataFilesPath']+\"/1\" + ticker + \".csv\", lambda row, line_num: row.append('Symbol') if line_num == 1 else row.append(ticker))\n\t\t\tos.remove(localFilePath)\n\t\texcept:\n\t\t\tprint('download failed')","title":""},{"docid":"b7775e36f6f7695a287ab41c2b5ab655","score":"0.5693667","text":"def get_stock_data(ticker):\n path = os.path.join('data', 'stocks', ticker + '.csv')\n df_stock = pd.read_csv(path)\n df_stock = df_stock.assign(DifferenceRelative = df_stock['Close'] / df_stock['Open'] - 1) \n df_stock = df_stock.assign(Difference = df_stock['Close'] - df_stock['Open']) \n df_stock['Target'] = np.where(df_stock['Difference'] > 0, 1, 0)\n df_stock['Date'] = pd.to_datetime(df_stock['Date']).dt.date\n \n return df_stock","title":""},{"docid":"747a03e31e11107412370fc70e02384a","score":"0.5640498","text":"def read_data(start, end):\n df = fix_yahoo_finance.download(\"GLD\", start=\"2008-01-01\", end=\"2017-12-31\")\n df = df[[\"Close\"]].dropna()\n return df","title":""},{"docid":"eb178755f8b29a3b398ee86c879bc665","score":"0.56381935","text":"def __init__(self, events, csv_dir, symbol_list):\r\n\r\n self.events = events\r\n self.csv_dir = csv_dir\r\n self.symbol_list = symbol_list\r\n\r\n self.symbol_data = {}\r\n self.latest_symbol_data = {}\r\n self.continue_backtest = True\r\n self._data_conversion_from_csv_files()","title":""},{"docid":"372fbac03fa2ba2f91335cd6ddc18607","score":"0.563276","text":"def check_file():\n try:\n with open(\"stock.csv\", \"r\") as file:\n print(\"Items loaded from stock.csv\\n\")\n reader = csv.reader(file)\n for row in reader:\n ST.item_list.append(SIA.StockItem(row[0], row[1], row[2]))\n except IOError:\n print(\"Stock file not found! A new file will be created at end of session...\\n\")","title":""},{"docid":"30ba3337ca3c67efba4bfd0f9c1c1da7","score":"0.5616497","text":"def load_dataset(tweets_paths, prices_path, add_intercept=False):\n prices = pd.read_csv(prices_path, encoding = \"ISO-8859-1\", parse_dates = ['date'])\n final = pd.DataFrame()\n\n for tweets_path in tweets_paths:\n symbol = tweets_path[:tweets_path.find('/')].upper()\n tweets_path = \"initial_data/\" + tweets_path\n\n # Load Tweets\n tweets = pd.read_excel(tweets_path, sheet_name='Stream', encoding = \"ISO-8859-1\", parse_dates = ['Date'])\n tweets = tweets.drop(columns=['Hour', 'Tweet Id', 'User Name', 'Nickname', 'Bio'])\n aggregation_functions = {'Tweet content': 'sum', 'Date': 'first'}\n tweets = tweets.groupby(tweets['Date']).aggregate(aggregation_functions)\n tweets['date'] = tweets['Date']\n tweets = tweets.drop(columns=['Date'])\n\n # Load Prices\n specific_prices = prices.loc[prices['symbol'] == symbol]\n specific_prices = specific_prices.drop(columns=['open', 'low', 'high', 'volume'])\n next_day_prices = specific_prices.copy()\n next_day_prices = next_day_prices.drop(columns=['symbol'])\n next_day_prices['date'] -= pd.DateOffset(days=1)\n\n new_prices = specific_prices.merge(next_day_prices, how = 'inner', on = ['date'])\n new_prices['increase'] = np.where((new_prices['close_y'] - new_prices['close_x']) > 0, 1, 0) # For a certain date, increase represents whether or not the stock price increased between the current date's close and the next day's close\n specific_prices = new_prices.drop(columns=['close_y', 'close_x'])\n\n # Merge tweets and prices\n merged = tweets.merge(specific_prices, how = 'inner', on = ['date'])\n\n merged.to_csv(\"final_data/\" + symbol + \".csv\")\n\n final = final.append(merged)\n\n\n final.to_csv(\"final_data/compiled_data.csv\")\n\n return final","title":""},{"docid":"1bcaa14884acd27f2f27789cfbf0bb36","score":"0.5600141","text":"def import_stock_data(nr_days_of_history, nr_days_to_trade, start_date_str):\n \n # pick out the desired start and end dates\n dates = calculateDates(nr_days_of_history = nr_days_of_history, nr_days_to_trade = nr_days_to_trade, start_date_str = start_date_str)\n\n # list the files to be read\n #path = \"./data/full_history/*.csv\"\n path = \"../stock_data/data/full_history/*.csv\"\n all_files = np.array(glob.glob(path))\n\n # read in the data frames from the files\n li = read_in_as_list_of_dfs(all_files, dates['start_date'], dates['end_date'])\n\n # combine into a single data frame\n df = pd.concat(li, join='outer', axis=1)\n\n # clean the combined data frame\n df = clean_combined_df(df)\n\n return(df)","title":""},{"docid":"b42c41785a34c541e3863435cc4810d4","score":"0.55824095","text":"def _read_current_prices(self):\n for theItem in self.items:\n f = open(\"%s.csv\" % theItem.name)\n if theItem.has_previous_history():\n f.readline() #skip header\n line = f.readline()\n price = self._get_price_from_line(line)\n volume = self._get_volume_from_line(line)\n theItem.price_history = [price] + theItem.price_history\n theItem.operations_volume_history = [volume] + theItem.operations_volume_history\n else:\n lines = f.readlines()\n lines = lines[1:]\n prices = map( self._get_price_from_line, lines)\n volumes = map(self._get_volume_from_line, lines)\n theItem.price_history = prices\n theItem.operations_volume_history = volumes\n f.close()\n theItem.current_price = theItem.price_history[0]\n\n self.items_sorted_by_price = sorted(self.items, key = lambda x: x.current_price)","title":""},{"docid":"4c92a314f1a48db8fe73232200cda455","score":"0.5581322","text":"def import_stock(symbol, metric):\n\n df = pd.read_csv(symbol_to_path(symbol),\n index_col='Date', parse_dates=True,\n usecols=['Date', metric], na_values=['nan'])\n # Rename 'Metric' column to ticker symbol to prevent clash\n df_renamed = df.rename(columns={metric:symbol})\n\n return df_renamed","title":""},{"docid":"4c92a314f1a48db8fe73232200cda455","score":"0.5581322","text":"def import_stock(symbol, metric):\n\n df = pd.read_csv(symbol_to_path(symbol),\n index_col='Date', parse_dates=True,\n usecols=['Date', metric], na_values=['nan'])\n # Rename 'Metric' column to ticker symbol to prevent clash\n df_renamed = df.rename(columns={metric:symbol})\n\n return df_renamed","title":""},{"docid":"865c3c6977aa6701e3f609f67aa6c836","score":"0.55802244","text":"def parse_candles(self, candles, symbol) -> TickerCoin:\n import pandas as pd\n columns = ['time_open', 'open', 'high', 'low', 'close', 'volume', 'time_close', 'volume_quote_asset', 'n_trades', 'buy_base_asset_vol', 'buy_quote_asset_vol', 'ignore']\n df = pd.DataFrame(candles, columns=columns)\n\n\n # Convert dtypes\n df['time_open'] = pd.to_datetime(df['time_open'], unit='ms')\n df['time_close'] = pd.to_datetime(df['time_close'], unit='ms')\n cols_float = ['open', 'high', 'low', 'close', 'volume', 'volume_quote_asset', 'buy_base_asset_vol', 'buy_quote_asset_vol']\n df[cols_float] = df[cols_float].astype(float)\n\n # Chose what to save/drop\n drop_cols = ['time_close', 'volume_quote_asset', 'buy_base_asset_vol', 'buy_quote_asset_vol', 'ignore']\n rename_cols = {'time_open': 'time'}\n df = df.drop(columns=drop_cols)\n df = df.rename(columns=rename_cols)\n\n # df['symbol'] = symbol\n df.insert(loc=0, column='symbol', value=symbol)\n\n dfticker = TickerCoin(df)\n\n return dfticker","title":""},{"docid":"8793f0a11d40ef7982810b3d734a5bb3","score":"0.5579975","text":"def reading_data(path):\n out_1 = []\n out_2 = []\n try:\n with open(path, 'r') as csv_fd:\n file = csv.DictReader(csv_fd)\n for row in file:\n out_1.append(float(row['km']))\n out_2.append(float(row['price']))\n except:\n print('Fatal error when trying reading csv!')\n exit(1)\n return out_1, out_2","title":""},{"docid":"ecd6466378510c53adf3f16017289e99","score":"0.55788565","text":"def test_LoadCSV(self):\n EXPECTED_STOCKS = ['1AG', 'BNR', 'XNJ']\n loaded_stocks = self.all_stocks._all_stocks\n\n # Ensure only 3 stocks are loaded\n self.assertEqual(len(loaded_stocks.keys()), 3, \n \"StockCollection does not have 3 stocks in it.\")\n\n for stock_code in loaded_stocks.keys():\n self.assertIn(stock_code, EXPECTED_STOCKS, \n \"Loaded stock codes not correct. Got: \" + stock_code)\n\n # For each stock check it has 3 TradingData\n stock_obj = self.all_stocks.get_stock(stock_code)\n num_td = len(stock_obj._trading_data.keys())\n exp_num = 3\n self.assertEqual(num_td, exp_num, \n \"Stock: {} has {} TadingData, should have {}.\".format(\n stock_code, num_td, exp_num))","title":""},{"docid":"4052855c340696633eb3eaf588c655c8","score":"0.5577596","text":"def _parse_data_from_file(self, file_name):\n file_path = os.path.join(self.path, file_name)\n data_file = xlrd.open_workbook(file_path)\n worksheet = data_file.sheet_by_index(0)\n fin_data = []\n for row in xrange(self.offset, worksheet.nrows):\n row_data = worksheet.row(row)\n fin_data.append({\n 'stock_date': xlrd.xldate.xldate_as_datetime(row_data[0].value, data_file.datemode).utcnow(),\n 'stock_open_price': row_data[1].value,\n 'stock_high_price': row_data[2].value,\n 'stock_low_price': row_data[3].value,\n 'stock_close_price': row_data[4].value,\n 'stock_volume': row_data[5].value,\n 'stock_adj_close': row_data[6].value,\n 'stock_id': int(row_data[7].value)\n })\n return fin_data","title":""},{"docid":"121303a20b6ddf0058ec98198e024f22","score":"0.55765146","text":"def load_csv_file(self):\n f_stream = open(self.m_name, \"r\")\n f_text = f_stream.read()\n f_stream.close()\n rows = f_text.replace(\"\\r\",\"\").split(\"\\n\")\n\n\n\n for idx in range(self.m_numcols):\n self.m_datacols.append([])\n\n\n\n if len(rows) == self.m_headlen:\n return\n\n\n for item in rows[self.m_headlen:]:\n if item == \"\":\n continue\n cells = item.split(\",\")\n for col in range(len(cells)):\n if col == 0:\n self.m_datacols[col].\\\n append(csvd.string_to_datetime(cells[col]))\n else:\n try:\n self.m_datacols[col].append(float(cells[col]))\n except ( ValueError ):\n self.m_datacols[col].append(str(cells[col]))\n #store last date in the file\n try:\n self.m_last_init_date = self.m_datacols[0][-1]\n except ( IndexError ):\n pass","title":""},{"docid":"9762b772769bada8c58b223595c4f88f","score":"0.5559485","text":"def csvExtract(filename, country):\n \n rawdata = []\n filepath = 'data/stock/manual/' + filename\n file2 = open(filepath, 'rU')\n reader2 = csv.reader(file2)\n for row in reader2:\n rawdata.append(row)\n\n\tcurrentYear = rawdata[1][0][-2:]\n\tprices = []\n\tdata = []\n\tfor i in range(1, len(rawdata)):\n\t\tif rawdata[i][0][-2:] == currentYear:\n\t\t\tprices.append(float(rawdata[i][4]))\n\t\telse:\n\t\t\tdata.append([currentYear, float(sum(prices))/len(prices)])\n\t\t\tprices = []\n\t\t\tcurrentYear = rawdata[i][0][-2:]\n\t\t\tprices.append(float(rawdata[i][4]))\n\n\tcount = 0\n\tfor row in data:\n\t\tif count <= 12:\n\t\t\trow[0] = '20' + row[0]\n\t\t\tcount += 1\n\t\telif count >= 12:\n\t\t\trow[0] = '19' + row[0]\n\tdata = sorted(data)\n\n\twith open('data/stock/done/' + country + '.csv', 'w') as file1:\n\t\tfile1write = csv.writer(file1, delimiter = ',')\n\t\tfile1write.writerows(data)\n\n\tfile1.close()","title":""},{"docid":"51da6d67964ba889e02ff09336d999ee","score":"0.55501455","text":"def pd_parse_data(self, date_obj):\n url = self.get_url(date_obj)\n stocks_df = pd.read_csv(url, sep=r'\\s*,\\s*', engine='python')\n stocks_df = stocks_df[stocks_df['SERIES'] == 'EQ']\n stocks_df = stocks_df[['SYMBOL', p1, p2]]\n for name in self.stocks_dict.keys(): # get data for the stocks\n df = stocks_df[stocks_df['SYMBOL'] == name]\n if not df.empty:\n v1 = df[p1].values[0]\n v2 = df[p2].values[0]\n self.stocks_dict[name].append(v1 * v2)\n # pprint(self.stocks_dict)","title":""},{"docid":"3e713b53007d8c02fc0e8172c816c119","score":"0.5546367","text":"def read_values(csv_filename):\n start = time.process_time()\n data = pd.read_csv(csv_filename, parse_dates=True,\n date_parser=date_parser,\n index_col=[0])\n log.debug('csv import took %.2f seconds', time.process_time() - start)\n log.debug(dataframe_info(data))\n\n cols_to_keep = ['Open', 'Volume_(BTC)']\n log.info('keeping only the following column(s): ' + ','.join(cols_to_keep))\n for col_name in data.columns:\n if col_name not in cols_to_keep:\n data.drop(col_name, axis=1, inplace=True)\n log.debug(dataframe_info(data))\n\n log.info('removing NaN rows')\n data.dropna(inplace=True)\n log.debug(dataframe_info(data))\n\n return data","title":""},{"docid":"a5c49f0fd97851d9467bcbab03a5f867","score":"0.55251783","text":"def test_LoadCSV(self):\n EXPECTED_STOCKS = ['1AD', 'BNR', 'MIL']\n loaded_stocks = self.all_stocks._all_stocks\n\n # Ensure only 3 stocks are loaded\n self.assertEqual(len(loaded_stocks.keys()), 3, \n \"StockCollection does not have 3 stocks in it.\")\n\n for stock_code in loaded_stocks.keys():\n self.assertIn(stock_code, EXPECTED_STOCKS, \n \"Loaded stock codes not correct. Got: \" + stock_code)\n\n # For each stock check it has 5 TradingData\n stock_obj = self.all_stocks.get_stock(stock_code)\n num_td = len(stock_obj._trading_data.keys())\n exp_num = 5\n self.assertEqual(num_td, exp_num, \n \"Stock: {} has {} TadingData, should have {}.\".format(\n stock_code, num_td, exp_num))","title":""},{"docid":"5f80b4872901e2430271853b03c93052","score":"0.55162156","text":"def load_csv(self, name):\n loc = os.path.join(self.file_loc, name)\n with open(loc, 'r') as f:\n lines = []\n reader = csv.reader(f)\n for line in reader:\n # l = list(map(lambda x: float(x), line))\n lines.append(line)\n return lines","title":""},{"docid":"df4cf0e9e02d2424633ccc354f0d959e","score":"0.5502979","text":"def load_weather(self):\r\n self.w_df = pd.read_csv(\"682880.csv\",\r\n low_memory=False, \r\n usecols=[2,3,5,6,7])","title":""},{"docid":"6f206c845a75de69b9715e353c40d3cd","score":"0.5492445","text":"def addBarsFromCSV(self, instrument, path, timezone=None):\r\n\r\n if timezone is None:\r\n timezone = self.__timezone\r\n\r\n rowParser = GenericRowParser(self.__columnNames, self.__dateTimeFormat, self.getDailyBarTime(), self.getFrequency(), timezone)\r\n BarFeed.addBarsFromCSV(self, instrument, path, rowParser)\r\n\r\n if rowParser.barsHaveAdjClose():\r\n self.__haveAdjClose = True\r\n elif self.__haveAdjClose:\r\n raise Exception(\"Previous bars had adjusted close and these ones don't have.\")","title":""},{"docid":"9413c379ad40e55faa23dcef73ed5ab9","score":"0.5477521","text":"def readcsv(filename, **kargs):\r\n _kargs = {\r\n 'headers': True,\r\n 'transposed' : False,\r\n 'delimiter' : ';',\r\n 'quotechar' : '`'}\r\n _kargs.update(kargs)\r\n #TODO finish read from a csv file\r","title":""},{"docid":"57062c382ce2f8e1f20154cd6743941e","score":"0.54696983","text":"def get_max_close(symbol):\n df = pd.read_csv(\"data/{}.csv\".format(symbol)) #Read in data\n return df['Close'].max() #Compute and return max","title":""},{"docid":"f0dbff7da68ffa11671ba88c4f00215a","score":"0.54663837","text":"def read_data(symbol):\n \n with open(alphavantage_data_dir + symbol + '.json', 'r') as infile:\n data = json.load(infile)\n \n return data","title":""},{"docid":"dbfbbf201e38029f3659dd40bb50ebfd","score":"0.546602","text":"def parse_csv(filename):\n\n rows = []\n with codecs.open(filename, \"r\", \"utf-8\") as csvfile:\n reader = csv.reader(csvfile, delimiter=\",\")\n\n date_columns = range(8, 11)\n for row in reader:\n if not row or row[0].startswith(\"ZAVSIF\"):\n continue\n\n reformat_unit(row)\n reformat_dates(date_columns, row)\n rows.append(row)\n\n # sort by reporting date (the last date column)\n rows = sorted(rows, key=itemgetter(date_columns[-1]))\n return rows","title":""},{"docid":"dce17b8f6acc7b149811dec5cd576b36","score":"0.5464675","text":"def load_data_yahoo(num, return_file_path=False):\n \n paths = []\n for root, dirs, files in os.walk('data/Yahoo', topdown=False):\n for name in files:\n paths.append(os.path.join(root, name))\n\n file_path = paths[num]\n print(file_path)\n\n df = pd.read_csv(file_path)\n\n if 'is_anomaly' == df.columns[-1]:\n df['is_anomaly'].replace(to_replace=0, value=-1, inplace=True)\n else:\n df['anomaly'].replace(to_replace=0, value=-1, inplace=True)\n df.rename(columns={\"timestamps\": \"timestamp\"}, inplace=True)\n df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms')\n\n if return_file_path:\n return df, file_path\n else: \n return df","title":""},{"docid":"78910a6e73a41cceb0534a3156ae8d3b","score":"0.5459131","text":"def loadTradeData(self):\n\n signalMap = {'BUY': 1, 'HOLD': 0, 'SELL': -1, 0: 0, 1: 1, -1: -1}\n self.tradeDataList = pd.read_csv(self.filedir_trades)\n\n self.tradeDataList['signal'] = self.tradeDataList['signal'].map(signalMap)\n self.tradeDataList = self.tradeDataList.values.tolist()","title":""},{"docid":"04ee0367c8b538f5f05fff9ddb520cb0","score":"0.54565716","text":"def init_stock_data(self):\r\n stock_data = {}\r\n stock_data['stock_code'] = self._stock_code\r\n stock_symbol_file_name = self._save_flie_path + '%s.txt' % (str(self._stock_code))\r\n try:\r\n for line in open(stock_symbol_file_name):\r\n line = line.strip(\"\\n\")\r\n line = line.split(',')\r\n quary_data = self.assembly_quary_data(line)\r\n stock_data[str(line[1])] = quary_data\r\n finally:\r\n return stock_data","title":""},{"docid":"54dadb06e6048902d910eaa8633f90bd","score":"0.5449732","text":"def read_catalog_csv(self):\n\n data_types = {\n \"id\": str,\n \"name\": str,\n \"brand\": str,\n \"added_at\": str,\n \"is_active\": bool,\n \"category\": str,\n \"is_adult\": bool,\n \"is_shippable\": bool\n }\n\n df = pd.read_csv(CATALOG_FILE_PATH, dtype=data_types)\n # print(df.head(5))\n # print(df.dtypes)\n\n df['price'] = None\n\n df = df[(df.is_active == True) & (df.is_adult == False)]\n # print(df.head(5))\n\n self.data = df","title":""},{"docid":"82bc929650d8c2decc0e0107224af973","score":"0.54389256","text":"def get_quote_yahoo(symbols):\r\n if isinstance(symbols, compat.string_types):\r\n sym_list = symbols\r\n else:\r\n sym_list = '+'.join(symbols)\r\n\r\n # for codes see: http://www.gummy-stuff.org/Yahoo-data.htm\r\n request = ''.join(compat.itervalues(_yahoo_codes)) # code request string\r\n header = list(_yahoo_codes.keys())\r\n\r\n data = defaultdict(list)\r\n\r\n url_str = _YAHOO_QUOTE_URL + 's=%s&f=%s' % (sym_list, request)\r\n\r\n with urlopen(url_str) as url:\r\n lines = url.readlines()\r\n\r\n for line in lines:\r\n fields = line.decode('utf-8').strip().split(',')\r\n for i, field in enumerate(fields):\r\n if field[-2:] == '%\"':\r\n v = float(field.strip('\"%'))\r\n elif field[0] == '\"':\r\n v = field.strip('\"')\r\n else:\r\n try:\r\n v = float(field)\r\n except ValueError:\r\n v = np.nan\r\n data[header[i]].append(v)\r\n\r\n idx = data.pop('symbol')\r\n return DataFrame(data, index=idx)","title":""},{"docid":"a424a8dea6179991743ff28ee59dcda4","score":"0.54257447","text":"def get_price_from_yahoo(yahoo_fx_tickers, date):\n try:\n assert (isinstance(yahoo_fx_tickers, list) and isinstance(date, datetime.date))\n std_index = HistoricalIntradayPrices.common_intraday_tools.get_standardized_intraday_fx_dtindex(date)\n price_dat = pd.concat(map(Utilities.yahoo_import.get_intraday_price_data_of_single_ticker, yahoo_fx_tickers),\n ignore_index=True)\n price_dat = price_dat.applymap(float)\n price_dat.rename(columns={'Timestamp': 'Time'}, inplace=True)\n\n price_dat['Time'] = list(map(lambda t: pytz.utc.localize(datetime.datetime.utcfromtimestamp(t)), price_dat['Time']))\n price_dat['Time'] = list(map(Utilities.datetime_tools.truncate_to_next_minute, price_dat['Time']))\n\n price_dat = price_dat[['Close', 'High', 'Low', 'Open', 'Volume']+['Time']]\n price_dat = price_dat.groupby('Time').agg({\n 'Open': lambda l: l.iloc[0], 'Close': lambda l: l.iloc[-1], 'Low': min, 'High': max, 'Volume': sum})\n\n price_dat = price_dat[['Close', 'High', 'Low', 'Open', 'Volume']]\n price_dat = price_dat.reindex(index=std_index)\n price_dat.loc[:, 'Volume'] = price_dat['Volume'].fillna(0)\n price_dat.loc[:, 'Open'] = price_dat['Open'].fillna(0)\n price_dat.loc[:, 'Close'] = price_dat['Close'].fillna(method='ffill')\n\n def propagate_on_zero_open(t, field):\n if t['Open'] == 0:\n return [t[field]]*(len(t)-1)+[0]\n else:\n return t.values\n\n price_dat = price_dat.apply(lambda t: propagate_on_zero_open(t, 'Close'), axis=1)\n price_dat = price_dat.fillna(0).reset_index()\n\n assert (isinstance(price_dat, pd.DataFrame) and tuple(sorted(price_dat.columns)) == tuple(\n sorted(['Close', 'High', 'Low', 'Open', 'Time', 'Volume'])))\n\n logging.info('Yahoo price import and pandas enrich successful for: %s' % yahoo_fx_tickers)\n if price_dat['Close'].sum() == 0:\n return pd.DataFrame(None)\n return price_dat\n\n except AssertionError:\n logging.warning('Calling get_price_from_yahoo with wrong argument types')\n except Exception as err:\n logging.warning('Yahoo price import and pandas enrich failed for: %s with message %s' %\n (yahoo_fx_tickers, err))\n return pd.DataFrame(None)","title":""},{"docid":"979cf04209a86e117c71aafe5ee1ca61","score":"0.54220134","text":"def monthlyDataDownload(self):\n self.multiStockTrain = []\n self.multiStockTest = []\n with open('./resources/SnP500_his.csv', 'rb') as f:\n reader = csv.reader(f)\n ticker_list = list(reader)\n for tickers in ticker_list:\n tickerstring = tickers[0]\n s1 = singleStock(tickerstring, self.trainMonth1, 1, self.trainYear1,\n self.trainMonth2, 28, self.trainYear2, 'm')\n s1.loading()\n s1.Aclose.reverse()\n s1.Close.reverse()\n s1.Date.reverse()\n s1.Open.reverse()\n s1.High.reverse()\n s1.Low.reverse()\n self.multiStockTrain.append(s1)\n self.stockNum = len(self.multiStockTrain)\n\n\n return self.multiStockTrain[0]","title":""},{"docid":"182fa4490ca28ba32baf212feababa17","score":"0.54196775","text":"def read_csv(file_path, file_name):\n \n csvpath = os.path.join(file_path, file_name)\n profit_data = []\n total_profit = 0\n with open(csvpath, newline='') as csvfile:\n csvreader = csv.reader(csvfile, delimiter=',')\n next(csvfile)\n\n for row in csvreader:\n profit = float(row[1])\n profit_data.append([row[0], profit])\n total_profit += profit\n\n total_profit = round(total_profit, 2) \n \n return profit_data, total_profit","title":""},{"docid":"12dd0ed32a29dc72a53452d2c24f9c6b","score":"0.5404796","text":"def data_loader(self):\n\n # -- IMPORTS\n import csv\n from conversions import twh_conversion\n # -- DATA LOADING\n\n # Earthquake Data\n with open(self.earthFile) as f:\n reader = csv.reader(f)\n eq_header = next(reader)\n\n # Columns to be loaded, (1, 5, 14)\n\n # Oil Data\n with open(self.oilFile) as f:\n reader = csv.reader(f)\n\n oil_prod_amt, oil_prod_date = [], []\n for line in reader:\n if line[1] == 'USA':\n oil_prod_date.append(line[2])\n\n oil_prod = line[4]\n oil_prod_amt.append(twh_conversion(float(oil_prod)))\n\n # TEST\n # print(oil_prod_date)\n # print(oil_prod_amt)","title":""},{"docid":"50203404a9ebbab5259dba70a62da3a3","score":"0.5403184","text":"def read_market_file(path,input_file):\n _file = open(\"%s\\input\\%s\" % (path,input_file), \"r\")\n content_list = csv.reader(_file)\n return content_list,_file","title":""},{"docid":"d2abbd4f705f554826b93b7de71598c6","score":"0.53969216","text":"def import_csv(self, options):\n stock = self.env['stock.standard'].search([('id','=',options['active_id'])])\n csv_data = self.file\n file_type = self.file_name.split(\".\")\n if(self.location_id != False and self.picking_type_id != False and self.warehouse != False):\n if(file_type[1] == 'csv'):\n self.typee_format= 'csv'\n elif(file_type[1] == 'xlsx' or file_type[1] == 'xls'):\n self.typee_format= 'xls'\n # csv\n if(file_type[1] == 'csv' and self.typee_format== 'csv'):\n \n csv_data = base64.decodestring(self.file).decode(encoding='utf-8')\n self._addfromfile(csv_data.split('\\n'),stock)\n # xls\n \n elif((file_type[1] == 'xlsx' or file_type[1] == 'xls') and self.typee_format== 'xls'):\n \n file_data = self.file.decode('base64')\n wb = open_workbook(file_contents=file_data)\n newar = []\n data = []\n data_rows = []\n \n dataloaded=[]\n \n for s in wb.sheets():\n for row in range(s.nrows):\n dd = []\n for col in range(s.ncols):\n value = (s.cell(row, col).value)\n dd.append(value)\n data_rows.append(dd)\n \n for data_row in data_rows:\n if(isinstance(data_row[0], int) or isinstance(data_row[0], float)):\n ln_of_code = str(int(data_row[0]))\n # ln_of_code = len(str(int(data_row[0])))\n code = ln_of_code.zfill(8)\n \n data = self.env['product.product'].search([('name','=',data_row[1]),('code','=',code)])\n \n if(data):\n newar.append(data)\n for n in newar:\n pass\n for i in n:\n if(i.code != False and i.id != False):\n val = [0,0,{'product_id_code':i.code,\n 'product_id':i.id\n }]\n \n dataloaded.append(val)\n \n stock.write({'stock_standard_line':dataloaded})\n else:\n raise exceptions.Warning(_(\"Invalid Extension of File. Select Either .Xls or .CSV File\"))\n else:\n raise exceptions.Warning(_(\"Fill Picking Id, Location, WareHouse first\"))","title":""},{"docid":"6c8853f916bc3cf848bbe38e375ad442","score":"0.5394753","text":"def load_csv_file_opti(self):\n for idx in range(self.m_numcols):\n self.m_datacols.append([])\n\n f_stream = open(self.m_name, \"rb\")\n f_stream.seek(-2,2)\n while f_stream.read(1).decode(\"utf-8\") != \"\\n\":\n f_stream.seek(-2,1)\n line = f_stream.readline()\n if line.strip() == \"\":\n return\n str_line = line.decode(\"utf-8\")\n if str_line.find( ',' ) == self.m_header[-1][0] :\n return\n #print(len(line), len(str_line), str_line)\n cells = str_line.split(\",\")\n for col in range(len(cells)):\n if col == 0:\n self.m_datacols[col].append(csvd.string_to_datetime(cells[col]))\n else:\n self.m_datacols[col].append(float(cells[col]))\n f_stream.close()\n #store last date in the file\n try:\n self.m_last_init_date = self.m_datacols[0][-1]\n except ( IndexError ):\n pass","title":""},{"docid":"79032319f64b6c36bd023a206ff72054","score":"0.53896415","text":"async def ask_stock(session, asset):\n\n url = 'https://stooq.com/q/l/?s=%s&f=sd2t2ohlcv&h&e=csv' % (urllib.parse.quote(asset.lower()),)\n try:\n async with session.get(url) as response:\n if response.status != 200:\n print(\">>> finbot: WARNING unexpected HTTP code %s for symbol %s\" % (response.status_code, asset))\n return None, None\n else:\n reader = csv.DictReader((await response.text()).split('\\n'))\n try:\n row = next(reader)\n if row['Close'] == 'N/D':\n print(\">>> finbot: WARNING bad or unavailable stock symbol %s\" % asset)\n return None, None\n return row['Symbol'], row['Close']\n except StopIteration:\n print(\">>> finbot: WARNING empty data for symbol %s\" % asset)\n return None, None\n except Exception as e:\n print(\">>> finbot: WARNING exception while trying to get data for symbol %s: %s, %s\" % (asset, type(e).__name__, e.args))\n return None, None","title":""},{"docid":"2a697b28e6806408ca387220305ae1e4","score":"0.5383794","text":"def getIndexPrices():\n \n filename = 'data/stock/country_indexes.csv'\n \n indexes = {}\n \n csvfile = open(filename, 'rU')\n reader = csv.reader(csvfile)\n \n for row in reader:\n indexes[row[0]] = row[1:]\n countryCodes = {}\n csvfile2 = open('data/stock/country_codes.txt', 'rU')\n reader2 = csv.reader(csvfile2)\n for row in reader2:\n countryCodes[row[1].lower()] = row[0]\n \n csvfile2.close()\n \n for country in indexes.keys():\n countryCode = countryCodes[country.lower()]\n if indexes[country][1] == 'manual':\n csvExtract(indexes[country][2], countryCode)\n else:\n yahooExtract(indexes[country][1], countryCode)\n csvfile.close()","title":""},{"docid":"acc48366547752b1647ae80c6e8ab207","score":"0.5380555","text":"def fetch_btc_data(self):\n btc_abs_path = self.__get_absolut_path_to_data(self.__btc_csv_path)\n btc_data = pd.read_csv(btc_abs_path)\n btc_data = btc_data.dropna()\n btc_data['Position'] = btc_data['close'].diff().fillna(0.0)\n btc_data.loc[btc_data['Position'] > 0.0, 'Position'] = True\n btc_data.loc[btc_data['Position'] <= 0.0, 'Position'] = False\n btc_data['time'] = pd.to_datetime(btc_data['time'], unit='s')\n btc_data.insert(0, 'date', btc_data[\"time\"])\n btc_data = btc_data.set_index('time')\n return btc_data","title":""},{"docid":"6f31baf32c2896b2792a62fa6554fb6b","score":"0.53802884","text":"def download_histo_data_from_yahoo(self, date_from = datetime.date(datetime.datetime.now().year-1, datetime.datetime.now().month, datetime.datetime.now().day), date_to = datetime.date(datetime.datetime.now().year, datetime.datetime.now().month, datetime.datetime.now().day)):\n\n \n if self.is_valid:\n r = requests.get(\"http://ichart.finance.yahoo.com/table.csv\", params={'s' : self.symbol, 'a' : str(date_from.month-1), 'b' : str(date_from.day), 'c': str(date_from.year), 'd' : str(date_to.month-1), 'e' : str(date_to.day), 'f' : str(date_to.year), 'g' : 'd', 'ignore' : '.csv'})\n \n csv_data = r.content\n csv_data = csv_data.split('\\n')\n data_histo = []\n \n if (len(csv_data)>0): #check data are available\n for line in csv_data:\n if (len(line)>0): #sometimes, last line is empty\n data_histo.append(line.split(','))\n \n data_histo = [data_histo[0]] + data_histo[:1:-1] #headers + reverse timeserie\n \n #cast\n for i in range(1, len(data_histo)): #saute premiere ligne car header\n for j in range(1, len(data_histo[i])): #saute premiere colonne car date\n if utility.is_number(data_histo[i][j]):\n data_histo[i][j] = float(data_histo[i][j])\n \n self.histo_data = data_histo\n \n self.logs.append([datetime.datetime.now(), \"found histo datas on yahoo finance \" + str(len(csv_data)-1) + \" entries\"])","title":""},{"docid":"c8c77cfa2738a3bc32a6f3e08557303d","score":"0.53768027","text":"def readCSV(self):\n csv_files = [x for x in os.listdir(self.csv_dir)]\n csv_data = []\n for x in tqdm(csv_files):\n tmp = pd.read_csv(self.csv_dir + x, skiprows=[0])\n frame_numbers = tmp['Frame Number (Start)'].tolist()\n start_times = tmp['Start Time (seconds)'].tolist()\n length = tmp['Length (seconds)'].tolist()\n csv_data.append([x, frame_numbers, start_times, length])\n self.csv_data = csv_data","title":""},{"docid":"2e0e896ddb3bf1d413e8f70c3aef4e3e","score":"0.53737354","text":"def yahooExtract(ticker, country):\n \n rawdata = ystockquote.get_historical_prices(ticker, '1995-01-01', '2011-12-31')\n \n prices = {}\n for date in rawdata.keys():\n if date[0:4] in prices.keys():\n prices[date[0:4]].append(float(rawdata[date]['Close']))\n else:\n\t\t\tprices[date[0:4]] = []\n\t\t\tprices[date[0:4]].append(float(rawdata[date]['Close']))\n\n\tdata = []\n\tfor year in sorted(prices.keys()):\n\t\tdata.append([year, float(sum(prices[year]))/len(prices[year])])\n\n\twith open('data/stock/done/' + country + '.csv', 'w') as file1:\n\t\tfile1write = csv.writer(file1, delimiter = ',')\n\t\tfile1write.writerows(data)\n\n\tfile1.close()","title":""},{"docid":"1093cea5e516d639b2e32343105c6391","score":"0.53728276","text":"def __init__(self, filename=\"sp500.csv\",\n start=1950, end=2020,\n date_field=\"Date\",\n price_field=\"SP500\", # \"Real Price\" is inflation adjusted\n date_format=\"y-m-d\", ):\n input_file = filename\n source = open(filename, \"r\")\n\n # figure out which columns we want\n headers = source.readline()\n date_col = self.column(headers, date_field)\n price_col = self.column(headers, price_field)\n\n # figure out the date format\n delimiter = date_format[1]\n fields = date_format.split(delimiter)\n year_col = fields.index('y')\n if 'm' in fields:\n month_col = fields.index('m')\n if 'd' in fields:\n day_col = fields.index('d')\n\n # process the entire file\n prev = -1\n points = 0\n for line in source:\n fields = line.split(',')\n\n # make sure we have all of the expected data\n if (fields[date_col] == \"\" or fields[price_col] == \"\"):\n continue\n\n # extract the interesting fields\n price = float(fields[price_col])\n date = fields[date_col]\n date_fields = date.split(delimiter)\n year = int(date_fields[year_col])\n month = int(date_fields[month_col])\n if prev < 0:\n prev = price\n\n # see if this is within the requested range\n if year >= start and year <= end:\n appreciation = (price - prev)/prev\n tupple = (year, month, price)\n self.prices.append(tupple)\n prev = price\n points += 1\n\n source.close()","title":""},{"docid":"94a190a5942d9186c98aedaccfdb8a62","score":"0.53678477","text":"def get_historical_data(\n symbols,\n update = False\n):\n \n datas = []\n \n for symbol in symbols:\n if already_exists(symbol):\n if update:\n datas.append(\n download_data(symbol)\n )\n else:\n datas.append(\n read_data(symbol)\n )\n else:\n datas.append(\n download_data(symbol)\n )\n return datas","title":""},{"docid":"a7ad1e89b5c0baa49d9f3b264b6cd15f","score":"0.5358469","text":"def reader(self):\r\n\r\n # Read input data\r\n try:\r\n # catch errors with input arguments\r\n\r\n # Read file name given via standard input\r\n file_name = input()\r\n # Read number of bags\r\n self.bags = int(input())\r\n\r\n # For testing...\r\n # file_name = \"input.csv\"\r\n # self.bags = 0\r\n\r\n file = open(file_name, encoding=\"utf-8\")\r\n\r\n except FileNotFoundError as ex:\r\n print(ex)\r\n\r\n except ValueError as ex:\r\n print(\"Number of bags - wrong input parameter\")\r\n print(ex)\r\n\r\n else:\r\n reader = csv.reader(file)\r\n\r\n header = next(reader)\r\n for row in reader:\r\n try:\r\n # catch errors with wrong data format\r\n\r\n # row = [From, To, Departure date/time, Arrival date/time, Flight no, Price, Bags allowed,\r\n # Bag price]\r\n dep_airport = row[0]\r\n arr_airport = row[1]\r\n # USM,HKT,2019-05-11T06:25:00,2019-05-11T07:25:00,PV404,24,1,9\r\n dep_date_time = datetime.strptime(row[2], \"%Y-%m-%dT%H:%M:%S\")\r\n arr_date_time = datetime.strptime(row[3], \"%Y-%m-%dT%H:%M:%S\")\r\n flight_no = row[4]\r\n flight_price = int(row[5])\r\n bag_allowed = int(row[6])\r\n bag_price = int(row[7])\r\n\r\n except ValueError as ex:\r\n print(\"Wrong input file format - row skipped\")\r\n print(row)\r\n print(ex)\r\n print()\r\n\r\n except IndexError as ex:\r\n print(\"Wrong input file format - row skipped\")\r\n print(row)\r\n print(ex)\r\n print()\r\n\r\n else:\r\n self.data.append(\r\n [dep_airport, arr_airport, dep_date_time, arr_date_time, flight_no, flight_price, bag_allowed,\r\n bag_price])\r\n\r\n # Close file\r\n file.close()","title":""},{"docid":"222357b71cf6d09fd702c54172ca6709","score":"0.5351112","text":"def read_and_clean_data(self, file_name):\n\n assert file_name.endswith(\".csv\"), \"Please provide a csv file\"\n\n converters_dict = {\n \"Price\" : clean_string,\n \"Open\" : clean_string,\n \"High\" : clean_string,\n \"Low\" : clean_string,\n \"Vol.\" : clean_vol,\n \"Change %\": clean_change,\n }\n\n # read the csv file\n df = pd.read_csv(file_name,\n parse_dates=[\"Date\"],\n converters=converters_dict)\n\n df.sort_values(by=\"Date\", inplace=True)\n\n return df.reset_index(drop=True)","title":""},{"docid":"9e5ac6b8cdc48d3d6e63ecb88242b166","score":"0.5346863","text":"def get_closingPrice(fileloc, ticker):\n # Indices\n time_i = 2\n price_i = 3\n\n # Get the date\n fsplit = fileloc.split('_')\n date = fsplit[len(fsplit) - 1][:-4]\n formatted_date = '-'.join([date[:4], date[4:6], date[6:]])\n \n # 4:00 PM in unixtimestamp\n close_time = \"16:00\"\n pivot = time2unix(date, close_time)\n \n with open(fileloc, 'r') as finput:\n # Check if ticker is in file\n try:\n go_to_ticker(finput, ticker)\n except SystemExit:\n print(\"Ticker Does not exist\")\n return [formatted_date, None]\n \n price = -1\n\n for line in finput:\n if (line.startswith(\"H\")):\n if (price == -1):\n print(\"No trades found, alg did not past 4pm, \\\n and reached end of data\")\n break\n else:\n # Return the most recent price if we reach end of\n # data before 4pm\n return [formatted_date, price]\n elif (line.startswith(\"T\")):\n sline = line.split('|')\n if (float(sline[time_i]) > pivot):\n # If we reach 4pm, but there were no trades\n if (price == -1):\n print(\"Reached 4pm, but no trades occured\")\n break\n return [formatted_date, price]\n # If we are before 4pm, record price\n else:\n price = sline[price_i]\n # If quote line\n else:\n continue\n\n if (price == -1):\n return [formatted_date, None]\n # else:\n # f = open(\"helperfunc.txt\", \"a+\")\n # f.write(\"You fucked up on ticker \" + ticker + \"\\n\")\n # f.close()","title":""},{"docid":"205d2b9a47ed4acd1181af19425b8ff5","score":"0.534588","text":"def test_simple_case(self):\n loadCsv = sa.LoadCSV(TEST_FILES['march1.csv'], self.all_stocks)\n\n stocks_loaded = self.all_stocks._all_stocks.keys()\n self.assertEqual(len(stocks_loaded), 1910, \n 'LoadCSV should load correct number of stocks')","title":""},{"docid":"b53a5fa4da1d39d251c3029140e1ea1a","score":"0.53446454","text":"def readFrom_csv(self):\n try:\n with open('housing.csv', 'r') as data_csv:\n reader = data_csv.readlines()\n except FileNotFoundError or FileExistsError as e:\n print(str(e))\n return reader","title":""},{"docid":"ee2f42ed78e87d76cb333651d01c5b84","score":"0.5336711","text":"def _parse_csv(self):\n try:\n df = pd.read_csv(self.file_name)\n self.data = self._massage_and_clean(df)\n except IOError:\n print(\"Error reading file\")","title":""},{"docid":"0469afb415e8f720a02591fc05a7fbc4","score":"0.53349894","text":"def loop_csv_file(source_csv):\n import csv\n\n file_data = []\n with open(source_csv, \"rb\") as csvfile:\n file_reader = csv.reader(csvfile, delimiter=\",\", quotechar='\"')\n for row in file_reader:\n file_data.append(row)\n return file_data","title":""},{"docid":"d5503b25f1671d0219918faae2aa1705","score":"0.5328874","text":"def csv_reader(file_obj):\n reader = csv.reader(file_obj)\n i = 0\n prices = []\n time = []\n data = []\n for row in reader:\n if i >= 1:\n prices.append(float(row[-1]))\n time.append(row[-2][:4])\n data.append(row[-3])\n i += 1\n l = len(prices)\n i = 0\n max_dif = 0\n s_max_dif = 0\n index_max = 0\n index_by = 0\n list_index_by = []\n index_sell = 0\n list_index_sell = []\n for i in range(l-1):\n if i == index_max:\n s_max_dif += max_dif\n list_index_by.append(index_by)\n list_index_sell.append(index_sell)\n index_max = l - 1 - prices[::-1].index(max(prices[i+1:]))\n max_dif = 0\n index_by = 0\n index_sell = 0\n if max_dif < prices[index_max] - prices[i]:\n max_dif = prices[index_max] - prices[i]\n index_by = i\n index_sell = index_max\n else:\n if max_dif < prices[index_max] - prices[i]:\n max_dif = prices[index_max] - prices[i]\n index_by = i\n index_sell = index_max\n for j in range(len(list_index_by)):\n print(f\"by: {data[list_index_by[j]]} at {time[list_index_by[j]]}; sell: {data[list_index_sell[j]]} at {time[list_index_sell[j]]}\")\n print(f\"максмальный доход: {s_max_dif}\")","title":""},{"docid":"8d9aabae15a50d652145fd7f66605593","score":"0.5323603","text":"def _import_monthly_prices(exch, monthly_prices):\n stock = None\n prices_by_year = {}\n\n for monthly_price_raw in monthly_prices:\n close_price_raw = monthly_price_raw['close_price']\n close_price = Decimal(close_price_raw)\n\n if close_price != Decimal('0'):\n ticker_symbol = monthly_price_raw['ticker_symbol']\n\n if stock is not None and stock.ticker_symbol != ticker_symbol:\n create_stock_yearly_prices(stock, prices_by_year)\n prices_by_year = {}\n\n if stock is None or stock.ticker_symbol != ticker_symbol:\n stock = (Stock.query\n .filter_by(\n exchange=exch,\n ticker_symbol=ticker_symbol)\n .first())\n\n if not stock:\n raise click.BadParameter(\n 'No stock found for ticker symbol \"{0}\"'.format(\n ticker_symbol))\n\n close_at_raw = monthly_price_raw['close_at']\n close_at_year = int(close_at_raw.split('-')[0].lstrip('0'))\n\n if close_at_year not in prices_by_year:\n prices_by_year[close_at_year] = []\n\n prices_by_year[close_at_year].append(close_price)\n\n create_stock_yearly_prices(stock, prices_by_year)","title":""},{"docid":"15079b1569f7776ddbce80e238585353","score":"0.5318534","text":"def compile_data():\n with open('sp500tickers.pickle','rb') as f:\n tickers = pickle.load(f)\n\n main_df = pd.DataFrame()\n\n for count, ticker in enumerate(tickers):\n df = pd.read_csv('stock_dfs/{}.csv'.format(ticker))\n df.set_index('date', inplace=True)\n\n df.rename(columns={'adj_close': ticker}, inplace=True)\n df.drop(['open','high','low','close','volume'], axis=1, inplace=True)\n\n if main_df.empty:\n main_df = df\n else:\n main_df = main_df.join(df, how='outer')\n\n if count % 10 == 0:\n print(count)\n\n print(main_df.head())\n main_df.to_csv('sp500_joined_closes.csv')","title":""},{"docid":"82ad5e8e01fab8248c5283909c5e8170","score":"0.53020716","text":"def read(self, filename, fields=None, **read_csv):\n read_csv = {\n \"delimiter\": \"\\t\",\n # This should be the column where the date time string is,\n # in this case 0:\n \"parse_dates\": {\"time\": [0]},\n \"index_col\": 0,\n # \"header\": 1,\n }\n\n # Call the reading routine from the base class\n data = super(ShipMSM, self).read(filename, **read_csv)\n\n # Probably, these field names will change for each ship. So, look at\n # one CSV file and try to find those fields to rename them:\n data.rename({\n \"Weatherstation.PDWDA.Air_pressure\": \"air_pressure\",\n \"Weatherstation.PDWDA.Air_temperature\": \"air_temperature\",\n \"Weatherstation.PDWDA.Humidity\": \"humidity\",\n \"Weatherstation.PDWDA.Water_temperature\": \"water_temperature\",\n }, inplace=True)\n\n # Filter out error values. The error values might be different for each\n # ship. Adjust these lines then:\n data = data.isel(\n time=(data.air_temperature < 99) & (data.air_pressure > 500))\n\n if fields is not None:\n data = data[fields]\n\n return data.sortby(\"time\")","title":""},{"docid":"50c1f7be400ca91dfa6469ad73daa651","score":"0.528501","text":"def fromTickData(self, quote_file_name, trade_file_name, trading_day_YYYYMMDD, time_zone, pxmul, out_csv_file=None, extended_fields=False, overwrite_repo = False):\n\n if not overwrite_repo and out_csv_file is not None :\n # skip this if it exists\n fa = glob.glob(out_csv_file+'*')\n if len(fa) == 1:\n try:\n if os.stat(fa[0]).st_size > 1000:\n if extended_fields:\n # check number of columns to be more than BASE_COLS\n import subprocess\n BASE_COLS = 9 # utc,ohlc,vol,lpx,ltm,vbs\n gz = (fa[0][-3:]=='.gz')\n cmd_str = '%s %s | head -n 1'%('zcat' if gz else 'cat', fa[0])\n l = subprocess.check_output(cmd_str, shell=True).decode().strip().replace('\\n','')\n if len(l.split(',')) > BASE_COLS:\n print ('found ', fa[0], ' not writting')\n return []\n except Exception as e:\n print('problem checking the ' + fa[0] + ' overwriting it. error: ' + str(e))\n\n print ('getting quotes from %s'%quote_file_name)\n quote = td_parser.get_quote_tickdata(quote_file_name, time_zone=time_zone, px_multiplier=pxmul)\n print ('getting trades from %s'%trade_file_name)\n trade = td_parser.get_trade_tickdata(trade_file_name, time_zone=time_zone, px_multiplier=pxmul)\n start_utc, end_utc = self._get_utc(trading_day_YYYYMMDD)\n print ('generating bars on %s'%trading_day_YYYYMMDD)\n bar, colname = td_parser.daily_mts_bar(trade, quote, 1, start_utc, end_utc-start_utc, extended_fields = extended_fields)\n if self.barsec != 1 :\n bar = td_parser.mergeBar(bar, self.barsec)\n\n if out_csv_file is not None:\n print ('writting to %s'%out_csv_file)\n td_parser.saveCSV(bar, out_csv_file)\n return bar","title":""},{"docid":"c9a7f35f6a797f4eef43561f0358e717","score":"0.52802885","text":"def _read_csv(cls, input_file, quotechar=None):\n with open(input_file, \"r\", encoding=\"utf-8-sig\") as f:\n return list(csv.reader(f, delimiter=\",\", quotechar=quotechar))","title":""},{"docid":"5aa498bbca8f4f0dc6f9d51dcc4649d4","score":"0.5269067","text":"def read_data_from_csv(filename: str) -> List[Climate]:\n res = list()\n with open(filename, newline='') as csvfile:\n reader = csv.DictReader(csvfile, delimiter='\\t')\n for row in reader:\n climate = Climate(name=row[\"name\"], year=int(row[\"year\"]), value=float(row[\"value\"]))\n res.append(climate)\n\n return res","title":""},{"docid":"b8b338957d63e36f7ff43bcd328b4613","score":"0.5263532","text":"def read_data_file(sp_context, delimiter, file_path):\n\n raw_data = sp_context.textFile(file_path).cache()\n csv_data = raw_data.map(lambda line: line.split(delimiter)).cache()\n header = csv_data.first() # extract header\n raw_data = csv_data.filter(lambda x: x != header)\n print(raw_data.take(5))\n row_data = raw_data.map(lambda p: Row(\n number_of_orders=p[0],\n total_order_value=p[1],\n weeks_ago=p[2]\n ))\n\n return row_data","title":""},{"docid":"7cfc80a15ee44b614de7c5defe31f7e1","score":"0.52631974","text":"def read_csv(file_name: str) -> pd.DataFrame:\n df = pd.read_csv(file_name, encoding='Shift-JIS')\n df.columns = df.iloc[0]\n df.drop(df.index[[0, 1]], inplace=True)\n\n date_lst = df.values.tolist()\n date_lst = df.reset_index().values.tolist()\n columns = ['Date', 'Open', 'High', 'Low', 'Close', 'Volume', 'AdjClose']\n\n new_df = pd.DataFrame(date_lst, columns=columns)\n new_df = new_df.set_index('Date')\n new_df.index = pd.to_datetime(new_df.index, format='%Y-%m-%d')\n\n new_df[['High', 'Low', 'Open', 'Close', 'Volume', 'AdjClose']]\n return new_df","title":""},{"docid":"7305f659b8b6211dbdc0fa4f028f1578","score":"0.5259883","text":"def read_data(name):\r\n inputs=[]\r\n with open(name) as csvfile:\r\n readCSV = csv.reader(csvfile, delimiter=',')\r\n for row in readCSV:\r\n \r\n row[0] = float(row[0])\r\n row[1] = float(row[1])\r\n row[2] = int(row[2])\r\n \r\n inputs.append(row)\r\n \r\n return inputs","title":""},{"docid":"4fa1e27bf0f9415337a033e224c9a94f","score":"0.5259643","text":"def read_csv(csvfile):\n if not os.path.isfile(csvfile):\n raise RuntimeError(\"Invalid CSV file {0}\".format(csvfile))\n\n csvraw = list(csv.reader(open(csvfile, 'r')))\n\n if len(csvraw) == 0:\n raise RuntimeError(\"Empty CSV file {0}\".format(csvfile))\n\n for head_idx in range(len(csvraw)):\n # find first line which is not empty and not a comment\n # the comment line start with #\n if \"\" != \"\".join(csvraw[head_idx]).strip() and not csvraw[head_idx][0].strip().startswith(\"#\"):\n break\n\n if len(csvraw) == head_idx:\n raise RuntimeError(\"No data in CSV file {0}\".format(csvfile))\n\n if csvraw[head_idx][0].strip() in ['pv', 'PV']:\n csv_data = __read_csv_1(csvraw[head_idx:])\n else:\n csv_data = __read_csv_2(csvraw[head_idx:])\n\n return csv_data","title":""},{"docid":"cbe3fad7becaa8f91d21fb3b7f63ff6f","score":"0.52418756","text":"def read_cryptocurrencies_from_csv(filename=CSV_DEFAULT_FILENAME):\n try:\n crypto_df = pd.read_csv(filename)\n except FileNotFoundError:\n crypto_df = pd.DataFrame([], columns=CSV_COLUMNS)\n\n # set the index for faster access\n crypto_df.set_index(CSV_INDEX_COLUMN, inplace=True)\n\n return crypto_df","title":""},{"docid":"d1d22a9be2f8a2b235e58774bdeaed83","score":"0.523783","text":"def open_csv_file(csv_file_path):\n with open(csv_file_path, 'r', encoding='utf-8') as csv_file:\n reader = csv.reader(csv_file)\n next(reader)\n\n data = list()\n for row in reader:\n data.append(row)\n\n return data","title":""},{"docid":"00b1340730449738a181f4d565693709","score":"0.5234545","text":"def open_file(self):\n self.__data = []\n with open(self.__REL_PATH + '.' + self.__file_type) as data_csv: # open the file\n csv_reader_obj = csv.reader(data_csv, delimiter=',') # create the csv reader object\n for row in csv_reader_obj:\n self.__data.append(row)","title":""},{"docid":"845f7e938fe31fb7cef979898a6d5954","score":"0.5227001","text":"def read(self, session, analysisSession, path =None, compressed =False):\n\n if path is not None:\n self._path = path\n if self._path is None:\n return False\n\n model = Tracks_Track.MASTER\n for existingTrack in session.query(model).all():\n self.remainingTracks[existingTrack.uid] = existingTrack.fingerprint\n\n try:\n data = pd.read_csv(self._path)\n except Exception as err:\n self._writeError({\n 'message':'ERROR: Unable to read CSV file \"%s\"' % self._path,\n 'error':err })\n return\n\n if data is None:\n self._writeError({\n 'message':'ERROR: Failed to create CSV reader for file \"%s\"' % self._path })\n return\n\n for index, row in data.iterrows():\n # Skip any rows that don't start with the proper numeric index value, which\n # includes the header row (if it exists) with the column names\n try:\n index = int(row[0])\n except Exception:\n continue\n\n rowDict = dict()\n for column in Reflection.getReflectionList(TrackCsvColumnEnum):\n value = row[column.index]\n\n if value and StringUtils.isStringType(value) and not StringUtils.isTextType(value):\n # Try to decode the value into a unicode string using common codecs\n for codec in ['utf8', 'MacRoman', 'utf16']:\n try:\n decodedValue = value.decode(codec)\n if decodedValue:\n value = decodedValue\n break\n except Exception:\n continue\n\n try:\n # Check to see if the value is NaN, and if it is replace it with an empty\n # string to be ignored during import\n value = '' if np.isnan(value) else value\n except Exception:\n pass\n\n if value != '' or value is None:\n rowDict[column.name] = value\n\n self.fromSpreadsheetEntry(rowDict, session)\n\n for uid, fingerprint in DictUtils.iter(self.remainingTracks):\n # Iterate through the list of remaining tracks, which are tracks not found by the\n # importer. If the track is marked as custom (meaning it is not managed by the importer)\n # it is ignored. Otherwise, the track is deleted from the database as a track that no\n # longer exists.\n\n track = Tracks_Track.MASTER.getByUid(uid, session)\n if track.custom:\n continue\n\n Tracks_Track.removeTrack(track, analysisSession)\n self._logger.write('[REMOVED]: No longer exists \"%s\" (%s)' % (\n track.fingerprint, track.uid))\n\n session.flush()\n\n for track in self.created:\n self._logger.write('[CREATED]: \"%s\" (%s)' % (track.fingerprint, track.uid))\n\n return True","title":""},{"docid":"8ad58945f35324d51ea4c99b691d0b7a","score":"0.5226173","text":"def load_csv(self):\n raise NotImplementedError","title":""},{"docid":"3e1ae8623360dbabac91b69b06697b7b","score":"0.52219754","text":"def _ReadCsv(path):\n ret = []\n with open(path) as f:\n for line in f:\n parts = line.rstrip().split(',')\n if len(parts) == 2 and parts[0] != 'revision':\n ret.append((int(parts[0]), int(float(parts[1]))))\n return ret","title":""},{"docid":"340e882960eebb976fc5b122597499ad","score":"0.5220239","text":"def parse(indicator):\n\n # normalize if needed\n if indicator.endswith('.csv'):\n indicator = indicator[:4]\n\n # confirm or find real symbol\n symbol = parse_ticker(indicator)['symbol']\n\n # set path of data\n filename = symbol.upper() + '.csv'\n filepath = os.path.join(CACHE_DIR, filename)\n\n if not os.path.exists(filepath):\n raise CacheFileNotFound(\"Cache file doesn't exists: \", symbol)\n\n return read_data(filepath)","title":""},{"docid":"4db245c41bb51a0a441d52cb74399768","score":"0.52168983","text":"def load_stockpiles_df() -> pd.DataFrame:\r\n stockpile_df = pd.read_csv(\"Stockpiles.csv\").set_index('ISO Code')\r\n return stockpile_df","title":""},{"docid":"1edd68406375200280de81f8083b9096","score":"0.52142787","text":"def load_data_csv(self):\n data = []\n\n with open(self.csv_path, 'r') as rf:\n for line in rf.readlines()[1:]:\n audio_id, duration, wav, start, stop, spk_id = line.strip(\n ).split(',')\n data.append(\n meta_info(audio_id,\n float(duration), wav,\n int(start), int(stop), spk_id))\n if self.n_train_snts > 0:\n sample_num = min(self.n_train_snts, len(data))\n data = data[0:sample_num]\n\n return data","title":""},{"docid":"9943909591a5f949cb25671bb7a2e5a8","score":"0.52073073","text":"def loadDataFromFile(self, fileName):\n\t\t# dateList, openList, highList, lowList, closeList, volumeList, adjCloseList \n\t\t# unpack=True\n\t\treturn np.genfromtxt(fileName, dtype=None, delimiter=',', skiprows=1, usecols=(0,1,2,3,4,5,6))","title":""}],"string":"[\n {\n \"docid\": \"a8d8645fcc925ecee2412ed9a3db1768\",\n \"score\": \"0.7471986\",\n \"text\": \"def _open_csv(self):\\n combined_index = None\\n for symbol in self.symbol_list:\\n path = self.csv_path + '/{}.csv'.format(symbol)\\n self.symbol_data[symbol] = pd.read_csv(\\n path, header=0, index_col=0, parse_dates=True,\\n names = [\\n 'datetime', 'high', 'low', 'open', 'close', 'volume', 'adj_close'\\n ]\\n )\\n\\n if combined_index is None:\\n combined_index = self.symbol_data[symbol].index\\n else:\\n combined_index.union(self.symbol_data[symbol].index)\\n\\n self.latest_symbol_data[symbol] = []\\n\\n # todo time index combine\\n for symbol in self.symbol_list:\\n self.symbol_data[symbol] = self.symbol_data[symbol].reindex(\\n index=combined_index, method='pad').iterrows()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cebdfe3e967af44cda092bcba193893a\",\n \"score\": \"0.7000566\",\n \"text\": \"def load(self, path):\\n try:\\n with open(join(path, self.name + \\\"_data.csv\\\"), 'r') as csvfile:\\n reader = csv.reader(csvfile, delimiter=',', quotechar='\\\"')\\n csvHeader = next(reader)\\n for row in reader:\\n try:\\n currOpen = float(row[1])\\n currClose = float(row[4])\\n day = datetime.strptime(row[0], \\\"%Y-%m-%d\\\").strftime(\\\"%d.%m.%Y\\\")\\n self.data[day] = (int(np.sign(currClose-currOpen)), currOpen)\\n except ValueError:\\n logging.warn(\\\"Could not interpret day \\\" + row[0])\\n except Exception as ex:\\n logging.error(\\\"Could not get the stock data. \\\" + ex)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa81dd27973ddfc80be8cfc573767fd6\",\n \"score\": \"0.69686425\",\n \"text\": \"def _data_conversion_from_csv_files(self):\\r\\n\\r\\n combined_index = None\\r\\n for symbol in self.symbol_list:\\r\\n # Load the CSV file with no header information, indexed on date\\r\\n self.symbol_data[symbol] = pd.io.parsers.read_csv(\\r\\n os.path.join(self.csv_dir, \\\"%s.csv\\\" % symbol),\\r\\n header=0, index_col=0,\\r\\n names=[\\\"datetime\\\", \\\"open\\\", \\\"high\\\", \\\"low\\\", \\\"close\\\", \\\"adj_close\\\", \\\"volume\\\"]\\r\\n )\\r\\n\\r\\n # Combine the index to pad forward values\\r\\n if combined_index is None:\\r\\n combined_index = self.symbol_data[symbol].index\\r\\n else:\\r\\n combined_index.union(self.symbol_data[symbol].index)\\r\\n\\r\\n # Set the latest symbol_data to None\\r\\n self.latest_symbol_data[symbol] = []\\r\\n\\r\\n # Reindex the dataframes\\r\\n for symbol in self.symbol_list:\\r\\n self.symbol_data[symbol] = self.symbol_data[symbol].reindex(index=combined_index, method=\\\"pad\\\").iterrows()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99c87d792d6b9ffce8d7fe22020e8902\",\n \"score\": \"0.6741852\",\n \"text\": \"def read_csv(filename):\\r\\n # frame = pd.read_csv('C:/Users/tzagk/Downloads/Stocks/' + filename, dtype=size_dict, usecols=use_cols, header=0,\\r\\n # index_col=\\\"Date\\\", parse_dates=True)\\r\\n frame = pd.read_csv('C:/Users/tzagk/Downloads/Stocks/' + filename, dtype=size_dict, header=0,\\r\\n index_col=\\\"Date\\\", parse_dates=True)\\r\\n all_days = pd.date_range(frame.index.min(), frame.index.max(), freq='D')\\r\\n frame2 = frame.reindex(index = all_days, method = 'nearest')['Close'] \\r\\n return [filename, frame, frame.index[0]], [filename,frame2]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"021e2d5e737135db4df84798ac8f8d14\",\n \"score\": \"0.66182214\",\n \"text\": \"def read_csv_file(self):\\n try:\\n with open('Lesson6_Data_Stocks.csv', 'r') as stock_file:\\n reader = csv.reader(stock_file)\\n next(reader)\\n for row in reader:\\n self.stocks[row[0]] = {'NO_SHARES': row[1]}\\n except FileNotFoundError as e:\\n print('File not found at the given location, Check the Path!')\\n return self.stocks\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1d1eef665aa474c74f4f10780e078a7\",\n \"score\": \"0.6386013\",\n \"text\": \"def get_data(symbols, dates):\\n df = pd.DataFrame(index=dates)\\n if 'SPY' not in symbols: # add SPY for reference, if absent\\n symbols.insert(0, 'SPY')\\n\\n for symbol in symbols:\\n df_temp = pd.read_csv(\\\"data/{}.csv\\\".format(symbol), index_col='Date',\\n parse_dates=True, usecols=['Date', 'Close'], na_values=['nan'])\\n df_temp = df_temp.rename(columns={'Close': symbol})\\n df = df.join(df_temp)\\n if symbol == 'SPY': # drop dates SPY did not trade\\n df = df.dropna(subset=[\\\"SPY\\\"])\\n\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c363f4c9a982b697c3635760e9e94e72\",\n \"score\": \"0.6263173\",\n \"text\": \"def get_weather_data(filename, dates, highs, lows, date_index, high_index, low_index):\\n\\n with open(filename) as f:\\n reader = csv.reader(f)\\n header_row = next(reader)\\n\\n\\n # Get, dates, highs, lows from this file\\n for row in reader:\\n current_date = datetime.strptime(row[date_index], '%Y-%m-%d')\\n try:\\n high = int(row[high_index])\\n low = int(row[low_index])\\n except ValueError:\\n print(f\\\"Missing data from {current_date}\\\")\\n else:\\n dates.append(current_date)\\n highs.append(high)\\n lows.append(low)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"980c25a602a3c534b071696909b18188\",\n \"score\": \"0.6246628\",\n \"text\": \"def load_from_csv(self, symbol, path=''):\\n if path == '':\\n path = '../data/raw/{}.csv'.format(symbol)\\n else:\\n path = os.path.join(path, '{}.csv'.format(symbol))\\n return pd.read_csv(path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c06f0d9fda7b08cdb07c76cd25c46866\",\n \"score\": \"0.6215058\",\n \"text\": \"def load_base_stocks():\\n\\n stocks = {}\\n\\n with open(STOCKS_FILE) as d:\\n data = csv.reader(d)\\n\\n for row in data:\\n\\n values = dict(zip(KEYS, row))\\n _cast_values(values)\\n stock = Stock(**values)\\n\\n stocks[stock.symbol] = stock\\n\\n return stocks\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de52ce87548ca4e0e1d04367a9162c02\",\n \"score\": \"0.61609524\",\n \"text\": \"def read(csv='./tech.csv', metadata='./metadata.json'):\\n\\n metadata = pd.read_json(metadata)\\n columns = metadata.datatable['columns']\\n headings = [heading['name'] for heading in columns]\\n stocks = pd.read_csv(\\n filepath_or_buffer=csv, header=None, names=headings)\\n\\n return stocks\",\n \"title\": \"\"\n },\n {\n \"docid\": \"98359bce9097c6cd08a384ff2bc65d81\",\n \"score\": \"0.6145918\",\n \"text\": \"def _load_data_from_Yahoo_finance(self):\\r\\n\\r\\n combined_index = None\\r\\n for symbol in self.symbol_list:\\r\\n\\r\\n # download data from yfinance for symbol. This could be improved as yfinance can download several\\r\\n # symbols at the same time\\r\\n self.symbol_data[symbol] = yf.download(tickers=[symbol], start=self.start_date,\\r\\n end=self.end_date, interval=self.interval)\\r\\n\\r\\n # rename columns for consistency\\r\\n self.symbol_data[symbol].rename(columns={'Open': 'open',\\r\\n 'High': 'high',\\r\\n 'Low': 'low',\\r\\n 'Close': 'close',\\r\\n 'Adj Close': 'adj_close',\\r\\n 'Volume': 'volume'}, inplace=True)\\r\\n\\r\\n # rename index as well from 'Date' to 'datetime'\\r\\n self.symbol_data[symbol].index.name = 'datetime'\\r\\n\\r\\n # create returns column (used for some strategies)\\r\\n self.symbol_data[symbol]['returns'] = self.symbol_data[symbol][\\\"adj_close\\\"].pct_change() * 100.0\\r\\n\\r\\n # Combine the index to pad forward values\\r\\n if combined_index is None:\\r\\n combined_index = self.symbol_data[symbol].index\\r\\n else:\\r\\n combined_index.union(self.symbol_data[symbol].index)\\r\\n\\r\\n # Set the latest symbol_data to None\\r\\n self.latest_symbol_data[symbol] = []\\r\\n\\r\\n # Reindex the dataframes\\r\\n for symbol in self.symbol_list:\\r\\n self.symbol_data[symbol] = self.symbol_data[symbol].reindex(index=combined_index, method=\\\"pad\\\").iterrows()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5a4c999b8d4992a563f69c8900a5ca7\",\n \"score\": \"0.61391765\",\n \"text\": \"def get_stock_data(stock_symbol):\\n\\n api_url = API_URL.replace('stock_symbol', stock_symbol)\\n response = requests.get(api_url)\\n if response.status_code == 200:\\n decoded_content = response.content.decode('utf-8')\\n csv_list = list(csv.reader(\\n decoded_content.splitlines(), delimiter=','))\\n close = csv_list[ROW][CLOSE_COLUMN]\\n symbol = csv_list[ROW][SYMBOL_COLUMN]\\n try:\\n float(close)\\n return f'{symbol} quote is ${close} per share'\\n except:\\n return None\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ffdec9a7276578a41f5ffdd9163da0fa\",\n \"score\": \"0.61227685\",\n \"text\": \"def run(csv='./tech.csv', metadata='./metadata.json'):\\n stocks = read(csv, metadata)\\n print(\\\"STOCKS READ********************************************\\\")\\n stocks = adjust(stocks)\\n print(\\\"DATAFRAME COLUMNS ADDED********************************\\\")\\n stocks = set_index(stocks)\\n print(\\\"DATAFRAME INDEX SET************************************\\\")\\n stocks = past(stocks)\\n print(\\\"DATAFRAME FILLED OUT***********************************\\\\a\\\\a\\\")\\n return stocks\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2cce38414e94d76e0b9e9b7f3447d5a6\",\n \"score\": \"0.60740167\",\n \"text\": \"def get_data(symbols, dates):\\n df = pd.DataFrame(index = dates)\\n\\n if 'SPY' not in symbols: # add SPY for reference, if absent\\n symbols.insert(0, 'SPY')\\n\\n for symbol in symbols:\\n\\n dfTemp = pd.read_csv(symbol_to_path(symbol),\\n index_col=\\\"Date\\\",\\n parse_dates=True,\\n usecols=['Date', 'Adj Close'],\\n na_values=['nan'])\\n\\n dfTemp = dfTemp.rename(columns={'Adj Close' : symbol})\\n df = df.join(dfTemp)\\n\\n if symbol == 'SPY': # drop dates SPY did not trade\\n df = df.dropna(subset=[\\\"SPY\\\"])\\n\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be86e0c17e8087ab94ea6f54dc9aa3e2\",\n \"score\": \"0.6033021\",\n \"text\": \"def get_data(symbols, dates):\\n df = pd.DataFrame(index=dates)\\n if 'SPY' not in symbols: # add SPY for reference, if absent\\n symbols.insert(0, 'SPY')\\n \\n for symbol in symbols:\\n df_temp = pd.read_csv(symbol_to_path(symbol), index_col='Date',\\n parse_dates=True, usecols=['Date', 'Adj Close'], na_values=['nan'])\\n df_temp = df_temp.rename(columns={'Adj Close': symbol})\\n df = df.join(df_temp)\\n if symbol == 'SPY': # drop dates SPY did not trade\\n df = df.dropna(subset=[\\\"SPY\\\"])\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd2d7064f5fd92db9bdedd81d293c7ab\",\n \"score\": \"0.6030075\",\n \"text\": \"def read_csv(filepath):\\n symbols = []\\n with open(filepath, 'rb') as csvfile:\\n spamreader = csv.DictReader(csvfile, delimiter=',', quotechar='\\\"')\\n for row in spamreader:\\n symbols.append(row)\\n return symbols\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d00913eb61f40235634a5e6510f8db8a\",\n \"score\": \"0.6020719\",\n \"text\": \"def LoadDataFromCSV(csvFile):\\r\\n\\tdata = pd.read_csv(csvFile)\\r\\n\\tx = data.iloc[:, 0:315] # select columns 1 through end\\r\\n\\ty = data.iloc[:, 315] # select column 0, the stock price\\r\\n\\r\\n\\treturn x,y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92167a65083c44eb19259e448f09bb50\",\n \"score\": \"0.6019517\",\n \"text\": \"def get_yahoo_finance_data(symbol, start_date=None, end_date=None, remove_zero_volume=True):\\n data_list = [('s', symbol)]\\n if hasattr(start_date, 'split'):\\n start_date = str2datetime(start_date)\\n\\n if hasattr(end_date, 'split'):\\n end_date = str2datetime(end_date)\\n\\n if start_date:\\n data_list.append(('a', start_date.month - 1))\\n data_list.append(('b', start_date.day))\\n data_list.append(('c', start_date.year))\\n if end_date:\\n data_list.append(('d', end_date.month - 1))\\n data_list.append(('e', end_date.day))\\n data_list.append(('f', end_date.year))\\n data_list.append(('g', 'd'))\\n data_list.append(('ignore', '.csv'))\\n\\n url = \\\"http://chart.finance.yahoo.com/table.csv\\\"\\n stock_info = get(url=url, data_list=data_list)\\n stock_data = StringIO(stock_info)\\n stock_df = pd.read_csv(stock_data)\\n stock_df['Date'] = stock_df['Date'].apply(lambda x: datetime.datetime.strptime(x, '%Y-%m-%d'))\\n stock_df = stock_df.set_index('Date').sort_index()\\n\\n if remove_zero_volume:\\n return stock_df[stock_df['Volume'] > 0]\\n else:\\n return stock_df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2bc6e86f7f63e952000799e3c8fa1ef5\",\n \"score\": \"0.60183865\",\n \"text\": \"def get_data(symbols, dates):\\n df = pd.DataFrame(index=dates)\\n if 'SPY' not in symbols: # add SPY for reference, if absent\\n symbols.insert(0, 'SPY')\\n\\n for symbol in symbols:\\n df_temp = pd.read_csv(symbol_to_path(symbol), index_col='Date',\\n parse_dates=True, usecols=['Date', 'Adj Close'], na_values=['nan'])\\n df_temp = df_temp.rename(columns={'Adj Close': symbol})\\n df = df.join(df_temp)\\n if symbol == 'SPY': # drop dates SPY did not trade\\n df = df.dropna(subset=[\\\"SPY\\\"])\\n\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af11bb80d467de1a339224ba1cfa7972\",\n \"score\": \"0.5999453\",\n \"text\": \"def get_data(symbols, dates):\\n df = pd.DataFrame(index=dates)\\n if 'SPY' not in symbols: # Add SPY for reference, if absent\\n symbols.insert(0, 'SPY')\\n\\n for symbol in symbols:\\n df_temp = pd.read_csv(symbol_to_path(symbol),index_col='Date',\\n parse_dates=True, usecols = [\\\"Date\\\", \\\"Adj Close\\\"], na_values = ['nan'])\\n # Rename to prevent clash\\n df_temp = df_temp.rename(columns={'Adj Close': symbol})\\n df = df.join(df_temp) # use default how = 'left'\\n\\n if symbol == 'SPY': # drop dates on which SPY did not trade\\n df = df.dropna(subset=[\\\"SPY\\\"])\\n\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c14378685d8bafdf8db2268e85fdaabc\",\n \"score\": \"0.59816164\",\n \"text\": \"def _parse_ticker_symbols(self):\\n for files in os.listdir(path=self.directory):\\n with open(self.directory+\\\"\\\\\\\\\\\"+files) as ticker_list:\\n next(ticker_list) #skip header row\\n for line in ticker_list:\\n line = line[0:len(line)-1] #omit newline charachter\\n pair = line.split(sep=\\\"\\\\t\\\")\\n if re.search(\\\"^([A-z]{1,6}$)\\\",pair[0]) is None: #removes assets which are not common stocks from push to db.\\n continue\\n self.data[pair[0]]={\\\"name\\\":pair[1],\\\"lastUpdated\\\":date.today().isoformat(),\\\"sicCode\\\":None}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"847db80f98a606660a931b2ebbac74b1\",\n \"score\": \"0.59059405\",\n \"text\": \"def load(csv_file):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de28705422e5cb800134800f859e40d4\",\n \"score\": \"0.5905777\",\n \"text\": \"def fetch_data_by_symbol():\\n symbols = [\\\"crude oil\\\", \\\"xau\\\"]\\n dataframes = []\\n for symbol in symbols:\\n search_result = investpy.search_quotes(text=symbol, n_results=1)\\n dataframe = search_result.retrieve_historical_data(from_date='03/06/2012', to_date='15/07/2021')\\n dataframe = dataframe.drop([\\\"Open\\\", \\\"High\\\", \\\"Low\\\", \\\"Volume\\\", \\\"Change Pct\\\"], axis=1)\\n dataframe = dataframe.dropna()\\n index_date = pd.date_range(\\\"2012-06-03\\\", \\\"2021-07-15\\\")\\n dataframe = dataframe.reindex(index_date, fill_value=None)\\n dataframe = dataframe.fillna(method='ffill')\\n dataframe = dataframe.fillna(method='bfill')\\n dataframe = dataframe.rename(columns={'Close': f'Close-{symbol}'})\\n dataframes.append(dataframe)\\n return pd.concat(dataframes, axis=1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08c693a90d82d70c782a762e51ec3f25\",\n \"score\": \"0.58816314\",\n \"text\": \"def fetch_stock(ticker):\\n fname = '%s.csv'%ticker\\n url = 'http://ichart.finance.yahoo.com/table.csv?' +\\\\\\n 's=%s&d=9&e=20&f=2007&g=d&a=0&b=29&c=1993&ignore=.csv'%ticker\\n\\n # the os.path module contains function for checking whether a file\\n # exists\\n if not os.path.exists(fname):\\n urllib.urlretrieve(url, fname)\\n r = mlab.csv2rec(fname)\\n\\n # note that the CSV file is sorted most recent date first, so you\\n # will probably want to sort the record array so most recent date\\n # is last\\n r.sort()\\n return r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0d0984c796b330c2b1d385407fe4c45\",\n \"score\": \"0.5836562\",\n \"text\": \"def read_file(ticker):\\r\\n data = []\\r\\n file = open(ticker.lower()+'.csv')\\r\\n lines = file.readlines()\\r\\n print(lines)\\r\\n last_date = get_last_date(lines)\\r\\n curr_date = dt.datetime.now().date()\\r\\n if last_date < curr_date:\\r\\n print(\\\"it forkin ran!!!\\\")\\r\\n update_file()\\r\\n \\r\\n \\\"\\\"\\\"\\r\\n for line in file:\\r\\n line.strip('/n').split(',')\\r\\n data.append(line)\\r\\n \\\"\\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"62f6c7e230fbd1ad6e01547d3c86b56e\",\n \"score\": \"0.5776138\",\n \"text\": \"def load_symbols():\\n if DATA_SET:\\n symbol_manifest = pd.read_csv(SYMBOL_MANIFEST, index_col=0, parse_dates=[7, 8, 9])\\n symbol_manifest['MarketCap'] = symbol_manifest['MarketCap'].fillna(0.0)\\n symbol_manifest.loc[symbol_manifest['Sector'].isnull(), 'Sector'] = 'n/a'\\n symbol_manifest = symbol_manifest.rename(columns={'industry': 'Industry'})\\n return symbol_manifest\\n else:\\n print NO_DATA_SET\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8683a191f1c5ce39233d897e15316187\",\n \"score\": \"0.57702637\",\n \"text\": \"def getPrices(start_date,end_date,freq):\\n\\n\\tquery = \\\"SELECT symbol FROM companyStaging\\\"\\n\\tsymbols =getQuery(query, '')\\n\\ti=0\\n\\n\\tfor symbol in symbols:\\n\\t\\ti= i + 1\\n\\t\\ttotalNum = len(symbols)\\n\\t\\tticker= ' '.join(symbol)\\n\\t\\tlocalFilePath= globals['dataFilesPath'] + ticker + \\\".AX.csv\\\"\\n\\t\\tyfURL = constructYFURL(ticker,start_date,end_date,freq)\\n\\t\\tprint (str(i) + \\\" of \\\" + str(totalNum) + \\\" - \\\" + yfURL)\\n\\t\\ttry:\\n\\t\\t\\tdownload(localFilePath,yfURL)\\n\\t\\t\\t#print(yfURL)\\n\\t\\t# Add the column in csv file with header\\n\\t\\t\\tadd_column_in_csv(localFilePath, globals['dataFilesPath']+\\\"/1\\\" + ticker + \\\".csv\\\", lambda row, line_num: row.append('Symbol') if line_num == 1 else row.append(ticker))\\n\\t\\t\\tos.remove(localFilePath)\\n\\t\\texcept:\\n\\t\\t\\tprint('download failed')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7775e36f6f7695a287ab41c2b5ab655\",\n \"score\": \"0.5693667\",\n \"text\": \"def get_stock_data(ticker):\\n path = os.path.join('data', 'stocks', ticker + '.csv')\\n df_stock = pd.read_csv(path)\\n df_stock = df_stock.assign(DifferenceRelative = df_stock['Close'] / df_stock['Open'] - 1) \\n df_stock = df_stock.assign(Difference = df_stock['Close'] - df_stock['Open']) \\n df_stock['Target'] = np.where(df_stock['Difference'] > 0, 1, 0)\\n df_stock['Date'] = pd.to_datetime(df_stock['Date']).dt.date\\n \\n return df_stock\",\n \"title\": \"\"\n },\n {\n \"docid\": \"747a03e31e11107412370fc70e02384a\",\n \"score\": \"0.5640498\",\n \"text\": \"def read_data(start, end):\\n df = fix_yahoo_finance.download(\\\"GLD\\\", start=\\\"2008-01-01\\\", end=\\\"2017-12-31\\\")\\n df = df[[\\\"Close\\\"]].dropna()\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb178755f8b29a3b398ee86c879bc665\",\n \"score\": \"0.56381935\",\n \"text\": \"def __init__(self, events, csv_dir, symbol_list):\\r\\n\\r\\n self.events = events\\r\\n self.csv_dir = csv_dir\\r\\n self.symbol_list = symbol_list\\r\\n\\r\\n self.symbol_data = {}\\r\\n self.latest_symbol_data = {}\\r\\n self.continue_backtest = True\\r\\n self._data_conversion_from_csv_files()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"372fbac03fa2ba2f91335cd6ddc18607\",\n \"score\": \"0.563276\",\n \"text\": \"def check_file():\\n try:\\n with open(\\\"stock.csv\\\", \\\"r\\\") as file:\\n print(\\\"Items loaded from stock.csv\\\\n\\\")\\n reader = csv.reader(file)\\n for row in reader:\\n ST.item_list.append(SIA.StockItem(row[0], row[1], row[2]))\\n except IOError:\\n print(\\\"Stock file not found! A new file will be created at end of session...\\\\n\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30ba3337ca3c67efba4bfd0f9c1c1da7\",\n \"score\": \"0.5616497\",\n \"text\": \"def load_dataset(tweets_paths, prices_path, add_intercept=False):\\n prices = pd.read_csv(prices_path, encoding = \\\"ISO-8859-1\\\", parse_dates = ['date'])\\n final = pd.DataFrame()\\n\\n for tweets_path in tweets_paths:\\n symbol = tweets_path[:tweets_path.find('/')].upper()\\n tweets_path = \\\"initial_data/\\\" + tweets_path\\n\\n # Load Tweets\\n tweets = pd.read_excel(tweets_path, sheet_name='Stream', encoding = \\\"ISO-8859-1\\\", parse_dates = ['Date'])\\n tweets = tweets.drop(columns=['Hour', 'Tweet Id', 'User Name', 'Nickname', 'Bio'])\\n aggregation_functions = {'Tweet content': 'sum', 'Date': 'first'}\\n tweets = tweets.groupby(tweets['Date']).aggregate(aggregation_functions)\\n tweets['date'] = tweets['Date']\\n tweets = tweets.drop(columns=['Date'])\\n\\n # Load Prices\\n specific_prices = prices.loc[prices['symbol'] == symbol]\\n specific_prices = specific_prices.drop(columns=['open', 'low', 'high', 'volume'])\\n next_day_prices = specific_prices.copy()\\n next_day_prices = next_day_prices.drop(columns=['symbol'])\\n next_day_prices['date'] -= pd.DateOffset(days=1)\\n\\n new_prices = specific_prices.merge(next_day_prices, how = 'inner', on = ['date'])\\n new_prices['increase'] = np.where((new_prices['close_y'] - new_prices['close_x']) > 0, 1, 0) # For a certain date, increase represents whether or not the stock price increased between the current date's close and the next day's close\\n specific_prices = new_prices.drop(columns=['close_y', 'close_x'])\\n\\n # Merge tweets and prices\\n merged = tweets.merge(specific_prices, how = 'inner', on = ['date'])\\n\\n merged.to_csv(\\\"final_data/\\\" + symbol + \\\".csv\\\")\\n\\n final = final.append(merged)\\n\\n\\n final.to_csv(\\\"final_data/compiled_data.csv\\\")\\n\\n return final\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1bcaa14884acd27f2f27789cfbf0bb36\",\n \"score\": \"0.5600141\",\n \"text\": \"def import_stock_data(nr_days_of_history, nr_days_to_trade, start_date_str):\\n \\n # pick out the desired start and end dates\\n dates = calculateDates(nr_days_of_history = nr_days_of_history, nr_days_to_trade = nr_days_to_trade, start_date_str = start_date_str)\\n\\n # list the files to be read\\n #path = \\\"./data/full_history/*.csv\\\"\\n path = \\\"../stock_data/data/full_history/*.csv\\\"\\n all_files = np.array(glob.glob(path))\\n\\n # read in the data frames from the files\\n li = read_in_as_list_of_dfs(all_files, dates['start_date'], dates['end_date'])\\n\\n # combine into a single data frame\\n df = pd.concat(li, join='outer', axis=1)\\n\\n # clean the combined data frame\\n df = clean_combined_df(df)\\n\\n return(df)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b42c41785a34c541e3863435cc4810d4\",\n \"score\": \"0.55824095\",\n \"text\": \"def _read_current_prices(self):\\n for theItem in self.items:\\n f = open(\\\"%s.csv\\\" % theItem.name)\\n if theItem.has_previous_history():\\n f.readline() #skip header\\n line = f.readline()\\n price = self._get_price_from_line(line)\\n volume = self._get_volume_from_line(line)\\n theItem.price_history = [price] + theItem.price_history\\n theItem.operations_volume_history = [volume] + theItem.operations_volume_history\\n else:\\n lines = f.readlines()\\n lines = lines[1:]\\n prices = map( self._get_price_from_line, lines)\\n volumes = map(self._get_volume_from_line, lines)\\n theItem.price_history = prices\\n theItem.operations_volume_history = volumes\\n f.close()\\n theItem.current_price = theItem.price_history[0]\\n\\n self.items_sorted_by_price = sorted(self.items, key = lambda x: x.current_price)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c92a314f1a48db8fe73232200cda455\",\n \"score\": \"0.5581322\",\n \"text\": \"def import_stock(symbol, metric):\\n\\n df = pd.read_csv(symbol_to_path(symbol),\\n index_col='Date', parse_dates=True,\\n usecols=['Date', metric], na_values=['nan'])\\n # Rename 'Metric' column to ticker symbol to prevent clash\\n df_renamed = df.rename(columns={metric:symbol})\\n\\n return df_renamed\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c92a314f1a48db8fe73232200cda455\",\n \"score\": \"0.5581322\",\n \"text\": \"def import_stock(symbol, metric):\\n\\n df = pd.read_csv(symbol_to_path(symbol),\\n index_col='Date', parse_dates=True,\\n usecols=['Date', metric], na_values=['nan'])\\n # Rename 'Metric' column to ticker symbol to prevent clash\\n df_renamed = df.rename(columns={metric:symbol})\\n\\n return df_renamed\",\n \"title\": \"\"\n },\n {\n \"docid\": \"865c3c6977aa6701e3f609f67aa6c836\",\n \"score\": \"0.55802244\",\n \"text\": \"def parse_candles(self, candles, symbol) -> TickerCoin:\\n import pandas as pd\\n columns = ['time_open', 'open', 'high', 'low', 'close', 'volume', 'time_close', 'volume_quote_asset', 'n_trades', 'buy_base_asset_vol', 'buy_quote_asset_vol', 'ignore']\\n df = pd.DataFrame(candles, columns=columns)\\n\\n\\n # Convert dtypes\\n df['time_open'] = pd.to_datetime(df['time_open'], unit='ms')\\n df['time_close'] = pd.to_datetime(df['time_close'], unit='ms')\\n cols_float = ['open', 'high', 'low', 'close', 'volume', 'volume_quote_asset', 'buy_base_asset_vol', 'buy_quote_asset_vol']\\n df[cols_float] = df[cols_float].astype(float)\\n\\n # Chose what to save/drop\\n drop_cols = ['time_close', 'volume_quote_asset', 'buy_base_asset_vol', 'buy_quote_asset_vol', 'ignore']\\n rename_cols = {'time_open': 'time'}\\n df = df.drop(columns=drop_cols)\\n df = df.rename(columns=rename_cols)\\n\\n # df['symbol'] = symbol\\n df.insert(loc=0, column='symbol', value=symbol)\\n\\n dfticker = TickerCoin(df)\\n\\n return dfticker\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8793f0a11d40ef7982810b3d734a5bb3\",\n \"score\": \"0.5579975\",\n \"text\": \"def reading_data(path):\\n out_1 = []\\n out_2 = []\\n try:\\n with open(path, 'r') as csv_fd:\\n file = csv.DictReader(csv_fd)\\n for row in file:\\n out_1.append(float(row['km']))\\n out_2.append(float(row['price']))\\n except:\\n print('Fatal error when trying reading csv!')\\n exit(1)\\n return out_1, out_2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ecd6466378510c53adf3f16017289e99\",\n \"score\": \"0.55788565\",\n \"text\": \"def test_LoadCSV(self):\\n EXPECTED_STOCKS = ['1AG', 'BNR', 'XNJ']\\n loaded_stocks = self.all_stocks._all_stocks\\n\\n # Ensure only 3 stocks are loaded\\n self.assertEqual(len(loaded_stocks.keys()), 3, \\n \\\"StockCollection does not have 3 stocks in it.\\\")\\n\\n for stock_code in loaded_stocks.keys():\\n self.assertIn(stock_code, EXPECTED_STOCKS, \\n \\\"Loaded stock codes not correct. Got: \\\" + stock_code)\\n\\n # For each stock check it has 3 TradingData\\n stock_obj = self.all_stocks.get_stock(stock_code)\\n num_td = len(stock_obj._trading_data.keys())\\n exp_num = 3\\n self.assertEqual(num_td, exp_num, \\n \\\"Stock: {} has {} TadingData, should have {}.\\\".format(\\n stock_code, num_td, exp_num))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4052855c340696633eb3eaf588c655c8\",\n \"score\": \"0.5577596\",\n \"text\": \"def _parse_data_from_file(self, file_name):\\n file_path = os.path.join(self.path, file_name)\\n data_file = xlrd.open_workbook(file_path)\\n worksheet = data_file.sheet_by_index(0)\\n fin_data = []\\n for row in xrange(self.offset, worksheet.nrows):\\n row_data = worksheet.row(row)\\n fin_data.append({\\n 'stock_date': xlrd.xldate.xldate_as_datetime(row_data[0].value, data_file.datemode).utcnow(),\\n 'stock_open_price': row_data[1].value,\\n 'stock_high_price': row_data[2].value,\\n 'stock_low_price': row_data[3].value,\\n 'stock_close_price': row_data[4].value,\\n 'stock_volume': row_data[5].value,\\n 'stock_adj_close': row_data[6].value,\\n 'stock_id': int(row_data[7].value)\\n })\\n return fin_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"121303a20b6ddf0058ec98198e024f22\",\n \"score\": \"0.55765146\",\n \"text\": \"def load_csv_file(self):\\n f_stream = open(self.m_name, \\\"r\\\")\\n f_text = f_stream.read()\\n f_stream.close()\\n rows = f_text.replace(\\\"\\\\r\\\",\\\"\\\").split(\\\"\\\\n\\\")\\n\\n\\n\\n for idx in range(self.m_numcols):\\n self.m_datacols.append([])\\n\\n\\n\\n if len(rows) == self.m_headlen:\\n return\\n\\n\\n for item in rows[self.m_headlen:]:\\n if item == \\\"\\\":\\n continue\\n cells = item.split(\\\",\\\")\\n for col in range(len(cells)):\\n if col == 0:\\n self.m_datacols[col].\\\\\\n append(csvd.string_to_datetime(cells[col]))\\n else:\\n try:\\n self.m_datacols[col].append(float(cells[col]))\\n except ( ValueError ):\\n self.m_datacols[col].append(str(cells[col]))\\n #store last date in the file\\n try:\\n self.m_last_init_date = self.m_datacols[0][-1]\\n except ( IndexError ):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9762b772769bada8c58b223595c4f88f\",\n \"score\": \"0.5559485\",\n \"text\": \"def csvExtract(filename, country):\\n \\n rawdata = []\\n filepath = 'data/stock/manual/' + filename\\n file2 = open(filepath, 'rU')\\n reader2 = csv.reader(file2)\\n for row in reader2:\\n rawdata.append(row)\\n\\n\\tcurrentYear = rawdata[1][0][-2:]\\n\\tprices = []\\n\\tdata = []\\n\\tfor i in range(1, len(rawdata)):\\n\\t\\tif rawdata[i][0][-2:] == currentYear:\\n\\t\\t\\tprices.append(float(rawdata[i][4]))\\n\\t\\telse:\\n\\t\\t\\tdata.append([currentYear, float(sum(prices))/len(prices)])\\n\\t\\t\\tprices = []\\n\\t\\t\\tcurrentYear = rawdata[i][0][-2:]\\n\\t\\t\\tprices.append(float(rawdata[i][4]))\\n\\n\\tcount = 0\\n\\tfor row in data:\\n\\t\\tif count <= 12:\\n\\t\\t\\trow[0] = '20' + row[0]\\n\\t\\t\\tcount += 1\\n\\t\\telif count >= 12:\\n\\t\\t\\trow[0] = '19' + row[0]\\n\\tdata = sorted(data)\\n\\n\\twith open('data/stock/done/' + country + '.csv', 'w') as file1:\\n\\t\\tfile1write = csv.writer(file1, delimiter = ',')\\n\\t\\tfile1write.writerows(data)\\n\\n\\tfile1.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51da6d67964ba889e02ff09336d999ee\",\n \"score\": \"0.55501455\",\n \"text\": \"def pd_parse_data(self, date_obj):\\n url = self.get_url(date_obj)\\n stocks_df = pd.read_csv(url, sep=r'\\\\s*,\\\\s*', engine='python')\\n stocks_df = stocks_df[stocks_df['SERIES'] == 'EQ']\\n stocks_df = stocks_df[['SYMBOL', p1, p2]]\\n for name in self.stocks_dict.keys(): # get data for the stocks\\n df = stocks_df[stocks_df['SYMBOL'] == name]\\n if not df.empty:\\n v1 = df[p1].values[0]\\n v2 = df[p2].values[0]\\n self.stocks_dict[name].append(v1 * v2)\\n # pprint(self.stocks_dict)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e713b53007d8c02fc0e8172c816c119\",\n \"score\": \"0.5546367\",\n \"text\": \"def read_values(csv_filename):\\n start = time.process_time()\\n data = pd.read_csv(csv_filename, parse_dates=True,\\n date_parser=date_parser,\\n index_col=[0])\\n log.debug('csv import took %.2f seconds', time.process_time() - start)\\n log.debug(dataframe_info(data))\\n\\n cols_to_keep = ['Open', 'Volume_(BTC)']\\n log.info('keeping only the following column(s): ' + ','.join(cols_to_keep))\\n for col_name in data.columns:\\n if col_name not in cols_to_keep:\\n data.drop(col_name, axis=1, inplace=True)\\n log.debug(dataframe_info(data))\\n\\n log.info('removing NaN rows')\\n data.dropna(inplace=True)\\n log.debug(dataframe_info(data))\\n\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5c49f0fd97851d9467bcbab03a5f867\",\n \"score\": \"0.55251783\",\n \"text\": \"def test_LoadCSV(self):\\n EXPECTED_STOCKS = ['1AD', 'BNR', 'MIL']\\n loaded_stocks = self.all_stocks._all_stocks\\n\\n # Ensure only 3 stocks are loaded\\n self.assertEqual(len(loaded_stocks.keys()), 3, \\n \\\"StockCollection does not have 3 stocks in it.\\\")\\n\\n for stock_code in loaded_stocks.keys():\\n self.assertIn(stock_code, EXPECTED_STOCKS, \\n \\\"Loaded stock codes not correct. Got: \\\" + stock_code)\\n\\n # For each stock check it has 5 TradingData\\n stock_obj = self.all_stocks.get_stock(stock_code)\\n num_td = len(stock_obj._trading_data.keys())\\n exp_num = 5\\n self.assertEqual(num_td, exp_num, \\n \\\"Stock: {} has {} TadingData, should have {}.\\\".format(\\n stock_code, num_td, exp_num))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f80b4872901e2430271853b03c93052\",\n \"score\": \"0.55162156\",\n \"text\": \"def load_csv(self, name):\\n loc = os.path.join(self.file_loc, name)\\n with open(loc, 'r') as f:\\n lines = []\\n reader = csv.reader(f)\\n for line in reader:\\n # l = list(map(lambda x: float(x), line))\\n lines.append(line)\\n return lines\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df4cf0e9e02d2424633ccc354f0d959e\",\n \"score\": \"0.5502979\",\n \"text\": \"def load_weather(self):\\r\\n self.w_df = pd.read_csv(\\\"682880.csv\\\",\\r\\n low_memory=False, \\r\\n usecols=[2,3,5,6,7])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f206c845a75de69b9715e353c40d3cd\",\n \"score\": \"0.5492445\",\n \"text\": \"def addBarsFromCSV(self, instrument, path, timezone=None):\\r\\n\\r\\n if timezone is None:\\r\\n timezone = self.__timezone\\r\\n\\r\\n rowParser = GenericRowParser(self.__columnNames, self.__dateTimeFormat, self.getDailyBarTime(), self.getFrequency(), timezone)\\r\\n BarFeed.addBarsFromCSV(self, instrument, path, rowParser)\\r\\n\\r\\n if rowParser.barsHaveAdjClose():\\r\\n self.__haveAdjClose = True\\r\\n elif self.__haveAdjClose:\\r\\n raise Exception(\\\"Previous bars had adjusted close and these ones don't have.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9413c379ad40e55faa23dcef73ed5ab9\",\n \"score\": \"0.5477521\",\n \"text\": \"def readcsv(filename, **kargs):\\r\\n _kargs = {\\r\\n 'headers': True,\\r\\n 'transposed' : False,\\r\\n 'delimiter' : ';',\\r\\n 'quotechar' : '`'}\\r\\n _kargs.update(kargs)\\r\\n #TODO finish read from a csv file\\r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57062c382ce2f8e1f20154cd6743941e\",\n \"score\": \"0.54696983\",\n \"text\": \"def get_max_close(symbol):\\n df = pd.read_csv(\\\"data/{}.csv\\\".format(symbol)) #Read in data\\n return df['Close'].max() #Compute and return max\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0dbff7da68ffa11671ba88c4f00215a\",\n \"score\": \"0.54663837\",\n \"text\": \"def read_data(symbol):\\n \\n with open(alphavantage_data_dir + symbol + '.json', 'r') as infile:\\n data = json.load(infile)\\n \\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dbfbbf201e38029f3659dd40bb50ebfd\",\n \"score\": \"0.546602\",\n \"text\": \"def parse_csv(filename):\\n\\n rows = []\\n with codecs.open(filename, \\\"r\\\", \\\"utf-8\\\") as csvfile:\\n reader = csv.reader(csvfile, delimiter=\\\",\\\")\\n\\n date_columns = range(8, 11)\\n for row in reader:\\n if not row or row[0].startswith(\\\"ZAVSIF\\\"):\\n continue\\n\\n reformat_unit(row)\\n reformat_dates(date_columns, row)\\n rows.append(row)\\n\\n # sort by reporting date (the last date column)\\n rows = sorted(rows, key=itemgetter(date_columns[-1]))\\n return rows\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dce17b8f6acc7b149811dec5cd576b36\",\n \"score\": \"0.5464675\",\n \"text\": \"def load_data_yahoo(num, return_file_path=False):\\n \\n paths = []\\n for root, dirs, files in os.walk('data/Yahoo', topdown=False):\\n for name in files:\\n paths.append(os.path.join(root, name))\\n\\n file_path = paths[num]\\n print(file_path)\\n\\n df = pd.read_csv(file_path)\\n\\n if 'is_anomaly' == df.columns[-1]:\\n df['is_anomaly'].replace(to_replace=0, value=-1, inplace=True)\\n else:\\n df['anomaly'].replace(to_replace=0, value=-1, inplace=True)\\n df.rename(columns={\\\"timestamps\\\": \\\"timestamp\\\"}, inplace=True)\\n df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms')\\n\\n if return_file_path:\\n return df, file_path\\n else: \\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78910a6e73a41cceb0534a3156ae8d3b\",\n \"score\": \"0.5459131\",\n \"text\": \"def loadTradeData(self):\\n\\n signalMap = {'BUY': 1, 'HOLD': 0, 'SELL': -1, 0: 0, 1: 1, -1: -1}\\n self.tradeDataList = pd.read_csv(self.filedir_trades)\\n\\n self.tradeDataList['signal'] = self.tradeDataList['signal'].map(signalMap)\\n self.tradeDataList = self.tradeDataList.values.tolist()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04ee0367c8b538f5f05fff9ddb520cb0\",\n \"score\": \"0.54565716\",\n \"text\": \"def init_stock_data(self):\\r\\n stock_data = {}\\r\\n stock_data['stock_code'] = self._stock_code\\r\\n stock_symbol_file_name = self._save_flie_path + '%s.txt' % (str(self._stock_code))\\r\\n try:\\r\\n for line in open(stock_symbol_file_name):\\r\\n line = line.strip(\\\"\\\\n\\\")\\r\\n line = line.split(',')\\r\\n quary_data = self.assembly_quary_data(line)\\r\\n stock_data[str(line[1])] = quary_data\\r\\n finally:\\r\\n return stock_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54dadb06e6048902d910eaa8633f90bd\",\n \"score\": \"0.5449732\",\n \"text\": \"def read_catalog_csv(self):\\n\\n data_types = {\\n \\\"id\\\": str,\\n \\\"name\\\": str,\\n \\\"brand\\\": str,\\n \\\"added_at\\\": str,\\n \\\"is_active\\\": bool,\\n \\\"category\\\": str,\\n \\\"is_adult\\\": bool,\\n \\\"is_shippable\\\": bool\\n }\\n\\n df = pd.read_csv(CATALOG_FILE_PATH, dtype=data_types)\\n # print(df.head(5))\\n # print(df.dtypes)\\n\\n df['price'] = None\\n\\n df = df[(df.is_active == True) & (df.is_adult == False)]\\n # print(df.head(5))\\n\\n self.data = df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82bc929650d8c2decc0e0107224af973\",\n \"score\": \"0.54389256\",\n \"text\": \"def get_quote_yahoo(symbols):\\r\\n if isinstance(symbols, compat.string_types):\\r\\n sym_list = symbols\\r\\n else:\\r\\n sym_list = '+'.join(symbols)\\r\\n\\r\\n # for codes see: http://www.gummy-stuff.org/Yahoo-data.htm\\r\\n request = ''.join(compat.itervalues(_yahoo_codes)) # code request string\\r\\n header = list(_yahoo_codes.keys())\\r\\n\\r\\n data = defaultdict(list)\\r\\n\\r\\n url_str = _YAHOO_QUOTE_URL + 's=%s&f=%s' % (sym_list, request)\\r\\n\\r\\n with urlopen(url_str) as url:\\r\\n lines = url.readlines()\\r\\n\\r\\n for line in lines:\\r\\n fields = line.decode('utf-8').strip().split(',')\\r\\n for i, field in enumerate(fields):\\r\\n if field[-2:] == '%\\\"':\\r\\n v = float(field.strip('\\\"%'))\\r\\n elif field[0] == '\\\"':\\r\\n v = field.strip('\\\"')\\r\\n else:\\r\\n try:\\r\\n v = float(field)\\r\\n except ValueError:\\r\\n v = np.nan\\r\\n data[header[i]].append(v)\\r\\n\\r\\n idx = data.pop('symbol')\\r\\n return DataFrame(data, index=idx)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a424a8dea6179991743ff28ee59dcda4\",\n \"score\": \"0.54257447\",\n \"text\": \"def get_price_from_yahoo(yahoo_fx_tickers, date):\\n try:\\n assert (isinstance(yahoo_fx_tickers, list) and isinstance(date, datetime.date))\\n std_index = HistoricalIntradayPrices.common_intraday_tools.get_standardized_intraday_fx_dtindex(date)\\n price_dat = pd.concat(map(Utilities.yahoo_import.get_intraday_price_data_of_single_ticker, yahoo_fx_tickers),\\n ignore_index=True)\\n price_dat = price_dat.applymap(float)\\n price_dat.rename(columns={'Timestamp': 'Time'}, inplace=True)\\n\\n price_dat['Time'] = list(map(lambda t: pytz.utc.localize(datetime.datetime.utcfromtimestamp(t)), price_dat['Time']))\\n price_dat['Time'] = list(map(Utilities.datetime_tools.truncate_to_next_minute, price_dat['Time']))\\n\\n price_dat = price_dat[['Close', 'High', 'Low', 'Open', 'Volume']+['Time']]\\n price_dat = price_dat.groupby('Time').agg({\\n 'Open': lambda l: l.iloc[0], 'Close': lambda l: l.iloc[-1], 'Low': min, 'High': max, 'Volume': sum})\\n\\n price_dat = price_dat[['Close', 'High', 'Low', 'Open', 'Volume']]\\n price_dat = price_dat.reindex(index=std_index)\\n price_dat.loc[:, 'Volume'] = price_dat['Volume'].fillna(0)\\n price_dat.loc[:, 'Open'] = price_dat['Open'].fillna(0)\\n price_dat.loc[:, 'Close'] = price_dat['Close'].fillna(method='ffill')\\n\\n def propagate_on_zero_open(t, field):\\n if t['Open'] == 0:\\n return [t[field]]*(len(t)-1)+[0]\\n else:\\n return t.values\\n\\n price_dat = price_dat.apply(lambda t: propagate_on_zero_open(t, 'Close'), axis=1)\\n price_dat = price_dat.fillna(0).reset_index()\\n\\n assert (isinstance(price_dat, pd.DataFrame) and tuple(sorted(price_dat.columns)) == tuple(\\n sorted(['Close', 'High', 'Low', 'Open', 'Time', 'Volume'])))\\n\\n logging.info('Yahoo price import and pandas enrich successful for: %s' % yahoo_fx_tickers)\\n if price_dat['Close'].sum() == 0:\\n return pd.DataFrame(None)\\n return price_dat\\n\\n except AssertionError:\\n logging.warning('Calling get_price_from_yahoo with wrong argument types')\\n except Exception as err:\\n logging.warning('Yahoo price import and pandas enrich failed for: %s with message %s' %\\n (yahoo_fx_tickers, err))\\n return pd.DataFrame(None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"979cf04209a86e117c71aafe5ee1ca61\",\n \"score\": \"0.54220134\",\n \"text\": \"def monthlyDataDownload(self):\\n self.multiStockTrain = []\\n self.multiStockTest = []\\n with open('./resources/SnP500_his.csv', 'rb') as f:\\n reader = csv.reader(f)\\n ticker_list = list(reader)\\n for tickers in ticker_list:\\n tickerstring = tickers[0]\\n s1 = singleStock(tickerstring, self.trainMonth1, 1, self.trainYear1,\\n self.trainMonth2, 28, self.trainYear2, 'm')\\n s1.loading()\\n s1.Aclose.reverse()\\n s1.Close.reverse()\\n s1.Date.reverse()\\n s1.Open.reverse()\\n s1.High.reverse()\\n s1.Low.reverse()\\n self.multiStockTrain.append(s1)\\n self.stockNum = len(self.multiStockTrain)\\n\\n\\n return self.multiStockTrain[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"182fa4490ca28ba32baf212feababa17\",\n \"score\": \"0.54196775\",\n \"text\": \"def read_csv(file_path, file_name):\\n \\n csvpath = os.path.join(file_path, file_name)\\n profit_data = []\\n total_profit = 0\\n with open(csvpath, newline='') as csvfile:\\n csvreader = csv.reader(csvfile, delimiter=',')\\n next(csvfile)\\n\\n for row in csvreader:\\n profit = float(row[1])\\n profit_data.append([row[0], profit])\\n total_profit += profit\\n\\n total_profit = round(total_profit, 2) \\n \\n return profit_data, total_profit\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12dd0ed32a29dc72a53452d2c24f9c6b\",\n \"score\": \"0.5404796\",\n \"text\": \"def data_loader(self):\\n\\n # -- IMPORTS\\n import csv\\n from conversions import twh_conversion\\n # -- DATA LOADING\\n\\n # Earthquake Data\\n with open(self.earthFile) as f:\\n reader = csv.reader(f)\\n eq_header = next(reader)\\n\\n # Columns to be loaded, (1, 5, 14)\\n\\n # Oil Data\\n with open(self.oilFile) as f:\\n reader = csv.reader(f)\\n\\n oil_prod_amt, oil_prod_date = [], []\\n for line in reader:\\n if line[1] == 'USA':\\n oil_prod_date.append(line[2])\\n\\n oil_prod = line[4]\\n oil_prod_amt.append(twh_conversion(float(oil_prod)))\\n\\n # TEST\\n # print(oil_prod_date)\\n # print(oil_prod_amt)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50203404a9ebbab5259dba70a62da3a3\",\n \"score\": \"0.5403184\",\n \"text\": \"def read_market_file(path,input_file):\\n _file = open(\\\"%s\\\\input\\\\%s\\\" % (path,input_file), \\\"r\\\")\\n content_list = csv.reader(_file)\\n return content_list,_file\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d2abbd4f705f554826b93b7de71598c6\",\n \"score\": \"0.53969216\",\n \"text\": \"def import_csv(self, options):\\n stock = self.env['stock.standard'].search([('id','=',options['active_id'])])\\n csv_data = self.file\\n file_type = self.file_name.split(\\\".\\\")\\n if(self.location_id != False and self.picking_type_id != False and self.warehouse != False):\\n if(file_type[1] == 'csv'):\\n self.typee_format= 'csv'\\n elif(file_type[1] == 'xlsx' or file_type[1] == 'xls'):\\n self.typee_format= 'xls'\\n # csv\\n if(file_type[1] == 'csv' and self.typee_format== 'csv'):\\n \\n csv_data = base64.decodestring(self.file).decode(encoding='utf-8')\\n self._addfromfile(csv_data.split('\\\\n'),stock)\\n # xls\\n \\n elif((file_type[1] == 'xlsx' or file_type[1] == 'xls') and self.typee_format== 'xls'):\\n \\n file_data = self.file.decode('base64')\\n wb = open_workbook(file_contents=file_data)\\n newar = []\\n data = []\\n data_rows = []\\n \\n dataloaded=[]\\n \\n for s in wb.sheets():\\n for row in range(s.nrows):\\n dd = []\\n for col in range(s.ncols):\\n value = (s.cell(row, col).value)\\n dd.append(value)\\n data_rows.append(dd)\\n \\n for data_row in data_rows:\\n if(isinstance(data_row[0], int) or isinstance(data_row[0], float)):\\n ln_of_code = str(int(data_row[0]))\\n # ln_of_code = len(str(int(data_row[0])))\\n code = ln_of_code.zfill(8)\\n \\n data = self.env['product.product'].search([('name','=',data_row[1]),('code','=',code)])\\n \\n if(data):\\n newar.append(data)\\n for n in newar:\\n pass\\n for i in n:\\n if(i.code != False and i.id != False):\\n val = [0,0,{'product_id_code':i.code,\\n 'product_id':i.id\\n }]\\n \\n dataloaded.append(val)\\n \\n stock.write({'stock_standard_line':dataloaded})\\n else:\\n raise exceptions.Warning(_(\\\"Invalid Extension of File. Select Either .Xls or .CSV File\\\"))\\n else:\\n raise exceptions.Warning(_(\\\"Fill Picking Id, Location, WareHouse first\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c8853f916bc3cf848bbe38e375ad442\",\n \"score\": \"0.5394753\",\n \"text\": \"def load_csv_file_opti(self):\\n for idx in range(self.m_numcols):\\n self.m_datacols.append([])\\n\\n f_stream = open(self.m_name, \\\"rb\\\")\\n f_stream.seek(-2,2)\\n while f_stream.read(1).decode(\\\"utf-8\\\") != \\\"\\\\n\\\":\\n f_stream.seek(-2,1)\\n line = f_stream.readline()\\n if line.strip() == \\\"\\\":\\n return\\n str_line = line.decode(\\\"utf-8\\\")\\n if str_line.find( ',' ) == self.m_header[-1][0] :\\n return\\n #print(len(line), len(str_line), str_line)\\n cells = str_line.split(\\\",\\\")\\n for col in range(len(cells)):\\n if col == 0:\\n self.m_datacols[col].append(csvd.string_to_datetime(cells[col]))\\n else:\\n self.m_datacols[col].append(float(cells[col]))\\n f_stream.close()\\n #store last date in the file\\n try:\\n self.m_last_init_date = self.m_datacols[0][-1]\\n except ( IndexError ):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79032319f64b6c36bd023a206ff72054\",\n \"score\": \"0.53896415\",\n \"text\": \"async def ask_stock(session, asset):\\n\\n url = 'https://stooq.com/q/l/?s=%s&f=sd2t2ohlcv&h&e=csv' % (urllib.parse.quote(asset.lower()),)\\n try:\\n async with session.get(url) as response:\\n if response.status != 200:\\n print(\\\">>> finbot: WARNING unexpected HTTP code %s for symbol %s\\\" % (response.status_code, asset))\\n return None, None\\n else:\\n reader = csv.DictReader((await response.text()).split('\\\\n'))\\n try:\\n row = next(reader)\\n if row['Close'] == 'N/D':\\n print(\\\">>> finbot: WARNING bad or unavailable stock symbol %s\\\" % asset)\\n return None, None\\n return row['Symbol'], row['Close']\\n except StopIteration:\\n print(\\\">>> finbot: WARNING empty data for symbol %s\\\" % asset)\\n return None, None\\n except Exception as e:\\n print(\\\">>> finbot: WARNING exception while trying to get data for symbol %s: %s, %s\\\" % (asset, type(e).__name__, e.args))\\n return None, None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a697b28e6806408ca387220305ae1e4\",\n \"score\": \"0.5383794\",\n \"text\": \"def getIndexPrices():\\n \\n filename = 'data/stock/country_indexes.csv'\\n \\n indexes = {}\\n \\n csvfile = open(filename, 'rU')\\n reader = csv.reader(csvfile)\\n \\n for row in reader:\\n indexes[row[0]] = row[1:]\\n countryCodes = {}\\n csvfile2 = open('data/stock/country_codes.txt', 'rU')\\n reader2 = csv.reader(csvfile2)\\n for row in reader2:\\n countryCodes[row[1].lower()] = row[0]\\n \\n csvfile2.close()\\n \\n for country in indexes.keys():\\n countryCode = countryCodes[country.lower()]\\n if indexes[country][1] == 'manual':\\n csvExtract(indexes[country][2], countryCode)\\n else:\\n yahooExtract(indexes[country][1], countryCode)\\n csvfile.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"acc48366547752b1647ae80c6e8ab207\",\n \"score\": \"0.5380555\",\n \"text\": \"def fetch_btc_data(self):\\n btc_abs_path = self.__get_absolut_path_to_data(self.__btc_csv_path)\\n btc_data = pd.read_csv(btc_abs_path)\\n btc_data = btc_data.dropna()\\n btc_data['Position'] = btc_data['close'].diff().fillna(0.0)\\n btc_data.loc[btc_data['Position'] > 0.0, 'Position'] = True\\n btc_data.loc[btc_data['Position'] <= 0.0, 'Position'] = False\\n btc_data['time'] = pd.to_datetime(btc_data['time'], unit='s')\\n btc_data.insert(0, 'date', btc_data[\\\"time\\\"])\\n btc_data = btc_data.set_index('time')\\n return btc_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f31baf32c2896b2792a62fa6554fb6b\",\n \"score\": \"0.53802884\",\n \"text\": \"def download_histo_data_from_yahoo(self, date_from = datetime.date(datetime.datetime.now().year-1, datetime.datetime.now().month, datetime.datetime.now().day), date_to = datetime.date(datetime.datetime.now().year, datetime.datetime.now().month, datetime.datetime.now().day)):\\n\\n \\n if self.is_valid:\\n r = requests.get(\\\"http://ichart.finance.yahoo.com/table.csv\\\", params={'s' : self.symbol, 'a' : str(date_from.month-1), 'b' : str(date_from.day), 'c': str(date_from.year), 'd' : str(date_to.month-1), 'e' : str(date_to.day), 'f' : str(date_to.year), 'g' : 'd', 'ignore' : '.csv'})\\n \\n csv_data = r.content\\n csv_data = csv_data.split('\\\\n')\\n data_histo = []\\n \\n if (len(csv_data)>0): #check data are available\\n for line in csv_data:\\n if (len(line)>0): #sometimes, last line is empty\\n data_histo.append(line.split(','))\\n \\n data_histo = [data_histo[0]] + data_histo[:1:-1] #headers + reverse timeserie\\n \\n #cast\\n for i in range(1, len(data_histo)): #saute premiere ligne car header\\n for j in range(1, len(data_histo[i])): #saute premiere colonne car date\\n if utility.is_number(data_histo[i][j]):\\n data_histo[i][j] = float(data_histo[i][j])\\n \\n self.histo_data = data_histo\\n \\n self.logs.append([datetime.datetime.now(), \\\"found histo datas on yahoo finance \\\" + str(len(csv_data)-1) + \\\" entries\\\"])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8c77cfa2738a3bc32a6f3e08557303d\",\n \"score\": \"0.53768027\",\n \"text\": \"def readCSV(self):\\n csv_files = [x for x in os.listdir(self.csv_dir)]\\n csv_data = []\\n for x in tqdm(csv_files):\\n tmp = pd.read_csv(self.csv_dir + x, skiprows=[0])\\n frame_numbers = tmp['Frame Number (Start)'].tolist()\\n start_times = tmp['Start Time (seconds)'].tolist()\\n length = tmp['Length (seconds)'].tolist()\\n csv_data.append([x, frame_numbers, start_times, length])\\n self.csv_data = csv_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e0e896ddb3bf1d413e8f70c3aef4e3e\",\n \"score\": \"0.53737354\",\n \"text\": \"def yahooExtract(ticker, country):\\n \\n rawdata = ystockquote.get_historical_prices(ticker, '1995-01-01', '2011-12-31')\\n \\n prices = {}\\n for date in rawdata.keys():\\n if date[0:4] in prices.keys():\\n prices[date[0:4]].append(float(rawdata[date]['Close']))\\n else:\\n\\t\\t\\tprices[date[0:4]] = []\\n\\t\\t\\tprices[date[0:4]].append(float(rawdata[date]['Close']))\\n\\n\\tdata = []\\n\\tfor year in sorted(prices.keys()):\\n\\t\\tdata.append([year, float(sum(prices[year]))/len(prices[year])])\\n\\n\\twith open('data/stock/done/' + country + '.csv', 'w') as file1:\\n\\t\\tfile1write = csv.writer(file1, delimiter = ',')\\n\\t\\tfile1write.writerows(data)\\n\\n\\tfile1.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1093cea5e516d639b2e32343105c6391\",\n \"score\": \"0.53728276\",\n \"text\": \"def __init__(self, filename=\\\"sp500.csv\\\",\\n start=1950, end=2020,\\n date_field=\\\"Date\\\",\\n price_field=\\\"SP500\\\", # \\\"Real Price\\\" is inflation adjusted\\n date_format=\\\"y-m-d\\\", ):\\n input_file = filename\\n source = open(filename, \\\"r\\\")\\n\\n # figure out which columns we want\\n headers = source.readline()\\n date_col = self.column(headers, date_field)\\n price_col = self.column(headers, price_field)\\n\\n # figure out the date format\\n delimiter = date_format[1]\\n fields = date_format.split(delimiter)\\n year_col = fields.index('y')\\n if 'm' in fields:\\n month_col = fields.index('m')\\n if 'd' in fields:\\n day_col = fields.index('d')\\n\\n # process the entire file\\n prev = -1\\n points = 0\\n for line in source:\\n fields = line.split(',')\\n\\n # make sure we have all of the expected data\\n if (fields[date_col] == \\\"\\\" or fields[price_col] == \\\"\\\"):\\n continue\\n\\n # extract the interesting fields\\n price = float(fields[price_col])\\n date = fields[date_col]\\n date_fields = date.split(delimiter)\\n year = int(date_fields[year_col])\\n month = int(date_fields[month_col])\\n if prev < 0:\\n prev = price\\n\\n # see if this is within the requested range\\n if year >= start and year <= end:\\n appreciation = (price - prev)/prev\\n tupple = (year, month, price)\\n self.prices.append(tupple)\\n prev = price\\n points += 1\\n\\n source.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94a190a5942d9186c98aedaccfdb8a62\",\n \"score\": \"0.53678477\",\n \"text\": \"def get_historical_data(\\n symbols,\\n update = False\\n):\\n \\n datas = []\\n \\n for symbol in symbols:\\n if already_exists(symbol):\\n if update:\\n datas.append(\\n download_data(symbol)\\n )\\n else:\\n datas.append(\\n read_data(symbol)\\n )\\n else:\\n datas.append(\\n download_data(symbol)\\n )\\n return datas\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7ad1e89b5c0baa49d9f3b264b6cd15f\",\n \"score\": \"0.5358469\",\n \"text\": \"def reader(self):\\r\\n\\r\\n # Read input data\\r\\n try:\\r\\n # catch errors with input arguments\\r\\n\\r\\n # Read file name given via standard input\\r\\n file_name = input()\\r\\n # Read number of bags\\r\\n self.bags = int(input())\\r\\n\\r\\n # For testing...\\r\\n # file_name = \\\"input.csv\\\"\\r\\n # self.bags = 0\\r\\n\\r\\n file = open(file_name, encoding=\\\"utf-8\\\")\\r\\n\\r\\n except FileNotFoundError as ex:\\r\\n print(ex)\\r\\n\\r\\n except ValueError as ex:\\r\\n print(\\\"Number of bags - wrong input parameter\\\")\\r\\n print(ex)\\r\\n\\r\\n else:\\r\\n reader = csv.reader(file)\\r\\n\\r\\n header = next(reader)\\r\\n for row in reader:\\r\\n try:\\r\\n # catch errors with wrong data format\\r\\n\\r\\n # row = [From, To, Departure date/time, Arrival date/time, Flight no, Price, Bags allowed,\\r\\n # Bag price]\\r\\n dep_airport = row[0]\\r\\n arr_airport = row[1]\\r\\n # USM,HKT,2019-05-11T06:25:00,2019-05-11T07:25:00,PV404,24,1,9\\r\\n dep_date_time = datetime.strptime(row[2], \\\"%Y-%m-%dT%H:%M:%S\\\")\\r\\n arr_date_time = datetime.strptime(row[3], \\\"%Y-%m-%dT%H:%M:%S\\\")\\r\\n flight_no = row[4]\\r\\n flight_price = int(row[5])\\r\\n bag_allowed = int(row[6])\\r\\n bag_price = int(row[7])\\r\\n\\r\\n except ValueError as ex:\\r\\n print(\\\"Wrong input file format - row skipped\\\")\\r\\n print(row)\\r\\n print(ex)\\r\\n print()\\r\\n\\r\\n except IndexError as ex:\\r\\n print(\\\"Wrong input file format - row skipped\\\")\\r\\n print(row)\\r\\n print(ex)\\r\\n print()\\r\\n\\r\\n else:\\r\\n self.data.append(\\r\\n [dep_airport, arr_airport, dep_date_time, arr_date_time, flight_no, flight_price, bag_allowed,\\r\\n bag_price])\\r\\n\\r\\n # Close file\\r\\n file.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"222357b71cf6d09fd702c54172ca6709\",\n \"score\": \"0.5351112\",\n \"text\": \"def read_and_clean_data(self, file_name):\\n\\n assert file_name.endswith(\\\".csv\\\"), \\\"Please provide a csv file\\\"\\n\\n converters_dict = {\\n \\\"Price\\\" : clean_string,\\n \\\"Open\\\" : clean_string,\\n \\\"High\\\" : clean_string,\\n \\\"Low\\\" : clean_string,\\n \\\"Vol.\\\" : clean_vol,\\n \\\"Change %\\\": clean_change,\\n }\\n\\n # read the csv file\\n df = pd.read_csv(file_name,\\n parse_dates=[\\\"Date\\\"],\\n converters=converters_dict)\\n\\n df.sort_values(by=\\\"Date\\\", inplace=True)\\n\\n return df.reset_index(drop=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e5ac6b8cdc48d3d6e63ecb88242b166\",\n \"score\": \"0.5346863\",\n \"text\": \"def get_closingPrice(fileloc, ticker):\\n # Indices\\n time_i = 2\\n price_i = 3\\n\\n # Get the date\\n fsplit = fileloc.split('_')\\n date = fsplit[len(fsplit) - 1][:-4]\\n formatted_date = '-'.join([date[:4], date[4:6], date[6:]])\\n \\n # 4:00 PM in unixtimestamp\\n close_time = \\\"16:00\\\"\\n pivot = time2unix(date, close_time)\\n \\n with open(fileloc, 'r') as finput:\\n # Check if ticker is in file\\n try:\\n go_to_ticker(finput, ticker)\\n except SystemExit:\\n print(\\\"Ticker Does not exist\\\")\\n return [formatted_date, None]\\n \\n price = -1\\n\\n for line in finput:\\n if (line.startswith(\\\"H\\\")):\\n if (price == -1):\\n print(\\\"No trades found, alg did not past 4pm, \\\\\\n and reached end of data\\\")\\n break\\n else:\\n # Return the most recent price if we reach end of\\n # data before 4pm\\n return [formatted_date, price]\\n elif (line.startswith(\\\"T\\\")):\\n sline = line.split('|')\\n if (float(sline[time_i]) > pivot):\\n # If we reach 4pm, but there were no trades\\n if (price == -1):\\n print(\\\"Reached 4pm, but no trades occured\\\")\\n break\\n return [formatted_date, price]\\n # If we are before 4pm, record price\\n else:\\n price = sline[price_i]\\n # If quote line\\n else:\\n continue\\n\\n if (price == -1):\\n return [formatted_date, None]\\n # else:\\n # f = open(\\\"helperfunc.txt\\\", \\\"a+\\\")\\n # f.write(\\\"You fucked up on ticker \\\" + ticker + \\\"\\\\n\\\")\\n # f.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"205d2b9a47ed4acd1181af19425b8ff5\",\n \"score\": \"0.534588\",\n \"text\": \"def test_simple_case(self):\\n loadCsv = sa.LoadCSV(TEST_FILES['march1.csv'], self.all_stocks)\\n\\n stocks_loaded = self.all_stocks._all_stocks.keys()\\n self.assertEqual(len(stocks_loaded), 1910, \\n 'LoadCSV should load correct number of stocks')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b53a5fa4da1d39d251c3029140e1ea1a\",\n \"score\": \"0.53446454\",\n \"text\": \"def readFrom_csv(self):\\n try:\\n with open('housing.csv', 'r') as data_csv:\\n reader = data_csv.readlines()\\n except FileNotFoundError or FileExistsError as e:\\n print(str(e))\\n return reader\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee2f42ed78e87d76cb333651d01c5b84\",\n \"score\": \"0.5336711\",\n \"text\": \"def _parse_csv(self):\\n try:\\n df = pd.read_csv(self.file_name)\\n self.data = self._massage_and_clean(df)\\n except IOError:\\n print(\\\"Error reading file\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0469afb415e8f720a02591fc05a7fbc4\",\n \"score\": \"0.53349894\",\n \"text\": \"def loop_csv_file(source_csv):\\n import csv\\n\\n file_data = []\\n with open(source_csv, \\\"rb\\\") as csvfile:\\n file_reader = csv.reader(csvfile, delimiter=\\\",\\\", quotechar='\\\"')\\n for row in file_reader:\\n file_data.append(row)\\n return file_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5503b25f1671d0219918faae2aa1705\",\n \"score\": \"0.5328874\",\n \"text\": \"def csv_reader(file_obj):\\n reader = csv.reader(file_obj)\\n i = 0\\n prices = []\\n time = []\\n data = []\\n for row in reader:\\n if i >= 1:\\n prices.append(float(row[-1]))\\n time.append(row[-2][:4])\\n data.append(row[-3])\\n i += 1\\n l = len(prices)\\n i = 0\\n max_dif = 0\\n s_max_dif = 0\\n index_max = 0\\n index_by = 0\\n list_index_by = []\\n index_sell = 0\\n list_index_sell = []\\n for i in range(l-1):\\n if i == index_max:\\n s_max_dif += max_dif\\n list_index_by.append(index_by)\\n list_index_sell.append(index_sell)\\n index_max = l - 1 - prices[::-1].index(max(prices[i+1:]))\\n max_dif = 0\\n index_by = 0\\n index_sell = 0\\n if max_dif < prices[index_max] - prices[i]:\\n max_dif = prices[index_max] - prices[i]\\n index_by = i\\n index_sell = index_max\\n else:\\n if max_dif < prices[index_max] - prices[i]:\\n max_dif = prices[index_max] - prices[i]\\n index_by = i\\n index_sell = index_max\\n for j in range(len(list_index_by)):\\n print(f\\\"by: {data[list_index_by[j]]} at {time[list_index_by[j]]}; sell: {data[list_index_sell[j]]} at {time[list_index_sell[j]]}\\\")\\n print(f\\\"максмальный доход: {s_max_dif}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d9aabae15a50d652145fd7f66605593\",\n \"score\": \"0.5323603\",\n \"text\": \"def _import_monthly_prices(exch, monthly_prices):\\n stock = None\\n prices_by_year = {}\\n\\n for monthly_price_raw in monthly_prices:\\n close_price_raw = monthly_price_raw['close_price']\\n close_price = Decimal(close_price_raw)\\n\\n if close_price != Decimal('0'):\\n ticker_symbol = monthly_price_raw['ticker_symbol']\\n\\n if stock is not None and stock.ticker_symbol != ticker_symbol:\\n create_stock_yearly_prices(stock, prices_by_year)\\n prices_by_year = {}\\n\\n if stock is None or stock.ticker_symbol != ticker_symbol:\\n stock = (Stock.query\\n .filter_by(\\n exchange=exch,\\n ticker_symbol=ticker_symbol)\\n .first())\\n\\n if not stock:\\n raise click.BadParameter(\\n 'No stock found for ticker symbol \\\"{0}\\\"'.format(\\n ticker_symbol))\\n\\n close_at_raw = monthly_price_raw['close_at']\\n close_at_year = int(close_at_raw.split('-')[0].lstrip('0'))\\n\\n if close_at_year not in prices_by_year:\\n prices_by_year[close_at_year] = []\\n\\n prices_by_year[close_at_year].append(close_price)\\n\\n create_stock_yearly_prices(stock, prices_by_year)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15079b1569f7776ddbce80e238585353\",\n \"score\": \"0.5318534\",\n \"text\": \"def compile_data():\\n with open('sp500tickers.pickle','rb') as f:\\n tickers = pickle.load(f)\\n\\n main_df = pd.DataFrame()\\n\\n for count, ticker in enumerate(tickers):\\n df = pd.read_csv('stock_dfs/{}.csv'.format(ticker))\\n df.set_index('date', inplace=True)\\n\\n df.rename(columns={'adj_close': ticker}, inplace=True)\\n df.drop(['open','high','low','close','volume'], axis=1, inplace=True)\\n\\n if main_df.empty:\\n main_df = df\\n else:\\n main_df = main_df.join(df, how='outer')\\n\\n if count % 10 == 0:\\n print(count)\\n\\n print(main_df.head())\\n main_df.to_csv('sp500_joined_closes.csv')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82ad5e8e01fab8248c5283909c5e8170\",\n \"score\": \"0.53020716\",\n \"text\": \"def read(self, filename, fields=None, **read_csv):\\n read_csv = {\\n \\\"delimiter\\\": \\\"\\\\t\\\",\\n # This should be the column where the date time string is,\\n # in this case 0:\\n \\\"parse_dates\\\": {\\\"time\\\": [0]},\\n \\\"index_col\\\": 0,\\n # \\\"header\\\": 1,\\n }\\n\\n # Call the reading routine from the base class\\n data = super(ShipMSM, self).read(filename, **read_csv)\\n\\n # Probably, these field names will change for each ship. So, look at\\n # one CSV file and try to find those fields to rename them:\\n data.rename({\\n \\\"Weatherstation.PDWDA.Air_pressure\\\": \\\"air_pressure\\\",\\n \\\"Weatherstation.PDWDA.Air_temperature\\\": \\\"air_temperature\\\",\\n \\\"Weatherstation.PDWDA.Humidity\\\": \\\"humidity\\\",\\n \\\"Weatherstation.PDWDA.Water_temperature\\\": \\\"water_temperature\\\",\\n }, inplace=True)\\n\\n # Filter out error values. The error values might be different for each\\n # ship. Adjust these lines then:\\n data = data.isel(\\n time=(data.air_temperature < 99) & (data.air_pressure > 500))\\n\\n if fields is not None:\\n data = data[fields]\\n\\n return data.sortby(\\\"time\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50c1f7be400ca91dfa6469ad73daa651\",\n \"score\": \"0.528501\",\n \"text\": \"def fromTickData(self, quote_file_name, trade_file_name, trading_day_YYYYMMDD, time_zone, pxmul, out_csv_file=None, extended_fields=False, overwrite_repo = False):\\n\\n if not overwrite_repo and out_csv_file is not None :\\n # skip this if it exists\\n fa = glob.glob(out_csv_file+'*')\\n if len(fa) == 1:\\n try:\\n if os.stat(fa[0]).st_size > 1000:\\n if extended_fields:\\n # check number of columns to be more than BASE_COLS\\n import subprocess\\n BASE_COLS = 9 # utc,ohlc,vol,lpx,ltm,vbs\\n gz = (fa[0][-3:]=='.gz')\\n cmd_str = '%s %s | head -n 1'%('zcat' if gz else 'cat', fa[0])\\n l = subprocess.check_output(cmd_str, shell=True).decode().strip().replace('\\\\n','')\\n if len(l.split(',')) > BASE_COLS:\\n print ('found ', fa[0], ' not writting')\\n return []\\n except Exception as e:\\n print('problem checking the ' + fa[0] + ' overwriting it. error: ' + str(e))\\n\\n print ('getting quotes from %s'%quote_file_name)\\n quote = td_parser.get_quote_tickdata(quote_file_name, time_zone=time_zone, px_multiplier=pxmul)\\n print ('getting trades from %s'%trade_file_name)\\n trade = td_parser.get_trade_tickdata(trade_file_name, time_zone=time_zone, px_multiplier=pxmul)\\n start_utc, end_utc = self._get_utc(trading_day_YYYYMMDD)\\n print ('generating bars on %s'%trading_day_YYYYMMDD)\\n bar, colname = td_parser.daily_mts_bar(trade, quote, 1, start_utc, end_utc-start_utc, extended_fields = extended_fields)\\n if self.barsec != 1 :\\n bar = td_parser.mergeBar(bar, self.barsec)\\n\\n if out_csv_file is not None:\\n print ('writting to %s'%out_csv_file)\\n td_parser.saveCSV(bar, out_csv_file)\\n return bar\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9a7f35f6a797f4eef43561f0358e717\",\n \"score\": \"0.52802885\",\n \"text\": \"def _read_csv(cls, input_file, quotechar=None):\\n with open(input_file, \\\"r\\\", encoding=\\\"utf-8-sig\\\") as f:\\n return list(csv.reader(f, delimiter=\\\",\\\", quotechar=quotechar))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5aa498bbca8f4f0dc6f9d51dcc4649d4\",\n \"score\": \"0.5269067\",\n \"text\": \"def read_data_from_csv(filename: str) -> List[Climate]:\\n res = list()\\n with open(filename, newline='') as csvfile:\\n reader = csv.DictReader(csvfile, delimiter='\\\\t')\\n for row in reader:\\n climate = Climate(name=row[\\\"name\\\"], year=int(row[\\\"year\\\"]), value=float(row[\\\"value\\\"]))\\n res.append(climate)\\n\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8b338957d63e36f7ff43bcd328b4613\",\n \"score\": \"0.5263532\",\n \"text\": \"def read_data_file(sp_context, delimiter, file_path):\\n\\n raw_data = sp_context.textFile(file_path).cache()\\n csv_data = raw_data.map(lambda line: line.split(delimiter)).cache()\\n header = csv_data.first() # extract header\\n raw_data = csv_data.filter(lambda x: x != header)\\n print(raw_data.take(5))\\n row_data = raw_data.map(lambda p: Row(\\n number_of_orders=p[0],\\n total_order_value=p[1],\\n weeks_ago=p[2]\\n ))\\n\\n return row_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7cfc80a15ee44b614de7c5defe31f7e1\",\n \"score\": \"0.52631974\",\n \"text\": \"def read_csv(file_name: str) -> pd.DataFrame:\\n df = pd.read_csv(file_name, encoding='Shift-JIS')\\n df.columns = df.iloc[0]\\n df.drop(df.index[[0, 1]], inplace=True)\\n\\n date_lst = df.values.tolist()\\n date_lst = df.reset_index().values.tolist()\\n columns = ['Date', 'Open', 'High', 'Low', 'Close', 'Volume', 'AdjClose']\\n\\n new_df = pd.DataFrame(date_lst, columns=columns)\\n new_df = new_df.set_index('Date')\\n new_df.index = pd.to_datetime(new_df.index, format='%Y-%m-%d')\\n\\n new_df[['High', 'Low', 'Open', 'Close', 'Volume', 'AdjClose']]\\n return new_df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7305f659b8b6211dbdc0fa4f028f1578\",\n \"score\": \"0.5259883\",\n \"text\": \"def read_data(name):\\r\\n inputs=[]\\r\\n with open(name) as csvfile:\\r\\n readCSV = csv.reader(csvfile, delimiter=',')\\r\\n for row in readCSV:\\r\\n \\r\\n row[0] = float(row[0])\\r\\n row[1] = float(row[1])\\r\\n row[2] = int(row[2])\\r\\n \\r\\n inputs.append(row)\\r\\n \\r\\n return inputs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4fa1e27bf0f9415337a033e224c9a94f\",\n \"score\": \"0.5259643\",\n \"text\": \"def read_csv(csvfile):\\n if not os.path.isfile(csvfile):\\n raise RuntimeError(\\\"Invalid CSV file {0}\\\".format(csvfile))\\n\\n csvraw = list(csv.reader(open(csvfile, 'r')))\\n\\n if len(csvraw) == 0:\\n raise RuntimeError(\\\"Empty CSV file {0}\\\".format(csvfile))\\n\\n for head_idx in range(len(csvraw)):\\n # find first line which is not empty and not a comment\\n # the comment line start with #\\n if \\\"\\\" != \\\"\\\".join(csvraw[head_idx]).strip() and not csvraw[head_idx][0].strip().startswith(\\\"#\\\"):\\n break\\n\\n if len(csvraw) == head_idx:\\n raise RuntimeError(\\\"No data in CSV file {0}\\\".format(csvfile))\\n\\n if csvraw[head_idx][0].strip() in ['pv', 'PV']:\\n csv_data = __read_csv_1(csvraw[head_idx:])\\n else:\\n csv_data = __read_csv_2(csvraw[head_idx:])\\n\\n return csv_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cbe3fad7becaa8f91d21fb3b7f63ff6f\",\n \"score\": \"0.52418756\",\n \"text\": \"def read_cryptocurrencies_from_csv(filename=CSV_DEFAULT_FILENAME):\\n try:\\n crypto_df = pd.read_csv(filename)\\n except FileNotFoundError:\\n crypto_df = pd.DataFrame([], columns=CSV_COLUMNS)\\n\\n # set the index for faster access\\n crypto_df.set_index(CSV_INDEX_COLUMN, inplace=True)\\n\\n return crypto_df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1d22a9be2f8a2b235e58774bdeaed83\",\n \"score\": \"0.523783\",\n \"text\": \"def open_csv_file(csv_file_path):\\n with open(csv_file_path, 'r', encoding='utf-8') as csv_file:\\n reader = csv.reader(csv_file)\\n next(reader)\\n\\n data = list()\\n for row in reader:\\n data.append(row)\\n\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00b1340730449738a181f4d565693709\",\n \"score\": \"0.5234545\",\n \"text\": \"def open_file(self):\\n self.__data = []\\n with open(self.__REL_PATH + '.' + self.__file_type) as data_csv: # open the file\\n csv_reader_obj = csv.reader(data_csv, delimiter=',') # create the csv reader object\\n for row in csv_reader_obj:\\n self.__data.append(row)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"845f7e938fe31fb7cef979898a6d5954\",\n \"score\": \"0.5227001\",\n \"text\": \"def read(self, session, analysisSession, path =None, compressed =False):\\n\\n if path is not None:\\n self._path = path\\n if self._path is None:\\n return False\\n\\n model = Tracks_Track.MASTER\\n for existingTrack in session.query(model).all():\\n self.remainingTracks[existingTrack.uid] = existingTrack.fingerprint\\n\\n try:\\n data = pd.read_csv(self._path)\\n except Exception as err:\\n self._writeError({\\n 'message':'ERROR: Unable to read CSV file \\\"%s\\\"' % self._path,\\n 'error':err })\\n return\\n\\n if data is None:\\n self._writeError({\\n 'message':'ERROR: Failed to create CSV reader for file \\\"%s\\\"' % self._path })\\n return\\n\\n for index, row in data.iterrows():\\n # Skip any rows that don't start with the proper numeric index value, which\\n # includes the header row (if it exists) with the column names\\n try:\\n index = int(row[0])\\n except Exception:\\n continue\\n\\n rowDict = dict()\\n for column in Reflection.getReflectionList(TrackCsvColumnEnum):\\n value = row[column.index]\\n\\n if value and StringUtils.isStringType(value) and not StringUtils.isTextType(value):\\n # Try to decode the value into a unicode string using common codecs\\n for codec in ['utf8', 'MacRoman', 'utf16']:\\n try:\\n decodedValue = value.decode(codec)\\n if decodedValue:\\n value = decodedValue\\n break\\n except Exception:\\n continue\\n\\n try:\\n # Check to see if the value is NaN, and if it is replace it with an empty\\n # string to be ignored during import\\n value = '' if np.isnan(value) else value\\n except Exception:\\n pass\\n\\n if value != '' or value is None:\\n rowDict[column.name] = value\\n\\n self.fromSpreadsheetEntry(rowDict, session)\\n\\n for uid, fingerprint in DictUtils.iter(self.remainingTracks):\\n # Iterate through the list of remaining tracks, which are tracks not found by the\\n # importer. If the track is marked as custom (meaning it is not managed by the importer)\\n # it is ignored. Otherwise, the track is deleted from the database as a track that no\\n # longer exists.\\n\\n track = Tracks_Track.MASTER.getByUid(uid, session)\\n if track.custom:\\n continue\\n\\n Tracks_Track.removeTrack(track, analysisSession)\\n self._logger.write('[REMOVED]: No longer exists \\\"%s\\\" (%s)' % (\\n track.fingerprint, track.uid))\\n\\n session.flush()\\n\\n for track in self.created:\\n self._logger.write('[CREATED]: \\\"%s\\\" (%s)' % (track.fingerprint, track.uid))\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ad58945f35324d51ea4c99b691d0b7a\",\n \"score\": \"0.5226173\",\n \"text\": \"def load_csv(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e1ae8623360dbabac91b69b06697b7b\",\n \"score\": \"0.52219754\",\n \"text\": \"def _ReadCsv(path):\\n ret = []\\n with open(path) as f:\\n for line in f:\\n parts = line.rstrip().split(',')\\n if len(parts) == 2 and parts[0] != 'revision':\\n ret.append((int(parts[0]), int(float(parts[1]))))\\n return ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"340e882960eebb976fc5b122597499ad\",\n \"score\": \"0.5220239\",\n \"text\": \"def parse(indicator):\\n\\n # normalize if needed\\n if indicator.endswith('.csv'):\\n indicator = indicator[:4]\\n\\n # confirm or find real symbol\\n symbol = parse_ticker(indicator)['symbol']\\n\\n # set path of data\\n filename = symbol.upper() + '.csv'\\n filepath = os.path.join(CACHE_DIR, filename)\\n\\n if not os.path.exists(filepath):\\n raise CacheFileNotFound(\\\"Cache file doesn't exists: \\\", symbol)\\n\\n return read_data(filepath)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4db245c41bb51a0a441d52cb74399768\",\n \"score\": \"0.52168983\",\n \"text\": \"def load_stockpiles_df() -> pd.DataFrame:\\r\\n stockpile_df = pd.read_csv(\\\"Stockpiles.csv\\\").set_index('ISO Code')\\r\\n return stockpile_df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1edd68406375200280de81f8083b9096\",\n \"score\": \"0.52142787\",\n \"text\": \"def load_data_csv(self):\\n data = []\\n\\n with open(self.csv_path, 'r') as rf:\\n for line in rf.readlines()[1:]:\\n audio_id, duration, wav, start, stop, spk_id = line.strip(\\n ).split(',')\\n data.append(\\n meta_info(audio_id,\\n float(duration), wav,\\n int(start), int(stop), spk_id))\\n if self.n_train_snts > 0:\\n sample_num = min(self.n_train_snts, len(data))\\n data = data[0:sample_num]\\n\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9943909591a5f949cb25671bb7a2e5a8\",\n \"score\": \"0.52073073\",\n \"text\": \"def loadDataFromFile(self, fileName):\\n\\t\\t# dateList, openList, highList, lowList, closeList, volumeList, adjCloseList \\n\\t\\t# unpack=True\\n\\t\\treturn np.genfromtxt(fileName, dtype=None, delimiter=',', skiprows=1, usecols=(0,1,2,3,4,5,6))\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":677,"cells":{"query_id":{"kind":"string","value":"a5717931205c99dbc7e5b99b793f36a7"},"query":{"kind":"string","value":"Ova funkcija je poopcenje funkcije operator_kanbaza. Ona daje matricni prikaz linearnog operatora f u paru baza (bazaD2,bazaK2) ako je on zadan u paru baza (bazaD1,bazaK1). Ako bazaK1 nije eksplicitno navedena, podrazumijeva se da je bazaK1=bazaD1. Ako bazaK2 nije eksplicitno navedena, podrazumijeva se da je bazaK2=bazaD2."},"positive_passages":{"kind":"list like","value":[{"docid":"11a9ac321f1d945453fabe1b8393c2b6","score":"0.7151839","text":"def operator_baza(f,bazaD1,bazaD2,bazaK1=None,bazaK2=None,klasa='sympy'): \n if isfunction(f):\n #operator f je zadan formulom\n M = operator_kan(f,klasa)\n else:\n #operator f je zadan matricom u paru baza (bazaD1,bazaK1)\n M = f\n if bazaK1 == None: bazaK1 = bazaD1\n if bazaK2 == None: bazaK2 = bazaD2\n S = matrica_prijelaza(bazaD1,bazaD2,klasa)\n T = matrica_prijelaza(bazaK2,bazaK1,klasa)\n if klasa == 'sympy':\n return T*M*S\n elif klasa == 'numpy':\n return np.matmul(np.matmul(T,M),S)","title":""}],"string":"[\n {\n \"docid\": \"11a9ac321f1d945453fabe1b8393c2b6\",\n \"score\": \"0.7151839\",\n \"text\": \"def operator_baza(f,bazaD1,bazaD2,bazaK1=None,bazaK2=None,klasa='sympy'): \\n if isfunction(f):\\n #operator f je zadan formulom\\n M = operator_kan(f,klasa)\\n else:\\n #operator f je zadan matricom u paru baza (bazaD1,bazaK1)\\n M = f\\n if bazaK1 == None: bazaK1 = bazaD1\\n if bazaK2 == None: bazaK2 = bazaD2\\n S = matrica_prijelaza(bazaD1,bazaD2,klasa)\\n T = matrica_prijelaza(bazaK2,bazaK1,klasa)\\n if klasa == 'sympy':\\n return T*M*S\\n elif klasa == 'numpy':\\n return np.matmul(np.matmul(T,M),S)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"af7549cc5d18b9cf9d56a081cd3520b8","score":"0.7329589","text":"def operator_kanbaza(f,baza1,baza2=None,klasa='sympy'): \n if isfunction(f):\n #operator f je zadan formulom\n M = operator_kan(f,klasa)\n else:\n #operator f je zadan matricom u paru kanonskih baza\n M = f\n if klasa == 'sympy':\n S = sp.Matrix(baza1).T\n if S.det() == 0: return \"Error: prvi skup nije baza\"\n if baza2 == None:\n T = sp.Matrix(baza1).T\n else:\n T = sp.Matrix(baza2).T\n if T.det() == 0: return \"Error: drugi skup nije baza\"\n return T.inv()*M*S\n elif klasa == 'numpy':\n S = np.transpose(np.array(baza1))\n if np.abs(np.linalg.det(S)) < 1e-15: return \"Error: prvi skup nije baza\"\n if baza2 == None:\n T = np.transpose(np.array(baza1))\n else:\n T = np.transpose(np.array(baza2))\n if np.abs(np.linalg.det(T)) < 1e-15: return \"Error: drugi skup nije baza\"\n return np.matmul( np.matmul(np.linalg.inv(T), M), S)","title":""},{"docid":"18b1510b5cbc4819c5289f364d9616ea","score":"0.5638643","text":"def operator(self):","title":""},{"docid":"659288c0775c338c4c2a80237a24c876","score":"0.54506","text":"def AdministrarOperacio(operacio, a, b):\n if(mainLib.checkIfCanOperate(a, b)):\n if(operacio == \"+\"):\n op = \"Suma\"\n x = mainLib.sumaMatrius(a, b)\n elif(operacio == \"-\"):\n op = \"Resta\"\n x = mainLib.restaMatrius(a, b)\n else:\n op = \"Multiplicacio\"\n x = mainLib.multMatrius(a, b)\n\n print op + \" de A i B: \"\n print\n mainLib.mostraMatriu(a)\n print \" \" + operacio\n mainLib.mostraMatriu(b)\n print \" ||\"\n mainLib.mostraMatriu(x)\n\n else:\n print mainLib.whichMatrixIsEmpty(a, b)","title":""},{"docid":"e7a604825403906fea3ee86bcf2596b4","score":"0.5405991","text":"def operator(op,L_max,m,s):\n \n def ds(op):\n \"\"\"get s increment from the operator string\"\"\"\n if len(op)==1: return 0\n return int(op[1]+'1')\n\n root_half = np.sqrt(0.5)\n\n N = L_max-L_min(m,s)\n\n # identity\n if op == 'I':\n a,b = a_and_b(m,s)\n return jacobi.operator('I',N,a,b)\n\n # cosine multiplication\n if op == 'C':\n a,b = a_and_b(m,s)\n return jacobi.operator('J',N,a,b)\n\n # derivatives\n if (op == 'k+') or (op=='k-'):\n a,b,da,db = a_and_b(m,s,ds=ds(op))\n if (da== 1) and (db==-1): return jacobi.operator('C+',N ,a,b,rescale=-root_half)\n if (da==-1) and (db== 1): return jacobi.operator('C-',N ,a,b,rescale= root_half)\n if (da== 1) and (db== 1): return jacobi.operator('D+',N ,a,b,rescale=-root_half)[:-1,:]\n if (da==-1) and (db==-1): return jacobi.operator('D-',N+1,a,b,rescale= root_half)[:,:-1]\n\n # sine multiplication\n if (op == 'S+') or (op=='S-'):\n a,b,da,db = a_and_b(m,s,ds=ds(op))\n if (da== 1) and (db==-1):\n A = jacobi.operator('A+',N+1,a, b)\n B = jacobi.operator('B-',N+1,a+1,b)\n return B.dot(A)[:-1,:-1]\n if (da==-1) and (db== 1):\n A = jacobi.operator('A-',N+1,a, b)\n B = jacobi.operator('B+',N+1,a-1,b)\n return B.dot(A)[:-1,:-1]\n if (da== 1) and (db== 1):\n A = jacobi.operator('A+',N+1,a, b)\n B = jacobi.operator('B+',N+1,a+1,b)\n return (B.dot(A))[:-2,:-1]\n if (da==-1) and (db==-1):\n A = jacobi.operator('A-',N+2,a, b)\n B = jacobi.operator('B-',N+2,a-1,b)\n return (B.dot(A))[:-1,:-2]","title":""},{"docid":"f8416086a294220da01f163c7a47cb00","score":"0.5333984","text":"def AdministrarOperacioUnaMatriu(operacio, a, b):\n if(operacio == \"d\"):\n op = \"Determinant\"\n else:\n op = \"Trasposada\"\n\n aOrB = mainLib.askNumberOption(op +\" de A (1) o de B (2): \", 2)\n if (aOrB == 1):\n OperacioUnaMatriu(operacio, a, \"A\")\n else:\n OperacioUnaMatriu(operacio, b, \"B\")","title":""},{"docid":"bda4555c3c59dfc5e8b19de1fb6609df","score":"0.52813727","text":"def getOperator(mpo):\n return 0","title":""},{"docid":"4cb6f20aa8f7a8c2acd33a163bf105dc","score":"0.52739495","text":"def klik(self,w,k,PPM):\r\n if self._aktualizacja == True or self._grazakonczona == True:\r\n return\r\n self._aktualizacja = True\r\n if PPM == True:\r\n w = self._plansza.polePPM(w,k) \r\n else:\r\n w = self._plansza.poleLPM(w,k)\r\n if w == 1: #wygrana\r\n self._grazakonczona = True\r\n self._gui.wygrana()\r\n elif w == 2: #przegrana\r\n self._grazakonczona = True\r\n self._gui.przegrana()\r\n self._aktualizacja = False\r\n pass","title":""},{"docid":"83f7c715bb565bc65bee8bda0ae362ab","score":"0.520008","text":"def poleLPM(self,w,k,nie_klikaj_pytajnika=False):\r\n #pole w pozycji startowej\r\n if self._pola[w][k][0] % 7 == 0:\r\n if self._pola[w][k][1] == True: #bomba\r\n self._pola[w][k][0] = 6\r\n self._gui.ustaw_pole(w, k, '', self._pola[w][k][0],\r\n self._ilebomb - self._ileflag, self._pytajniki,\r\n self._wolnepola)\r\n return self.przegrana()\r\n else: \r\n self._pola[w][k][0] = 4 #ustawianie stanu\r\n ile = str(self.przegladnij_sasiadow(w,k))\r\n #uaktualnianie przycisku\r\n if ile == '0':\r\n ile = ''\r\n self.przeklikaj_sasiadow(w,k)\r\n self._wolnepola -= 1\r\n self._gui.ustaw_pole(w, k, ile, self._pola[w][k][0],\r\n self._ilebomb - self._ileflag, self._pytajniki,\r\n self._wolnepola)\r\n if self._wolnepola == 0 == self._pytajniki:\r\n return self.wygrana() \r\n #pole z flaga\r\n elif self._pola[w][k][0] % 7 == 1:\r\n #przyc lewy zablokowany\r\n pass\r\n \r\n \r\n #pole z pytajnikiem\r\n elif self._pola[w][k][0] % 7 == 2:\r\n #przy automatycznym odkrywaniu nie sprawdza flagi\r\n if nie_klikaj_pytajnika == False: \r\n self._pytajniki -= 1\r\n if self._pola[w][k][1] == True: #bomba\r\n self._pytajniki -= 1\r\n self._pola[w][k][0] = 6\r\n self._gui.ustaw_pole(w, k, '', self._pola[w][k][0],\r\n self._ilebomb - self._ileflag, self._pytajniki,\r\n self._wolnepola)\r\n return self.przegrana()\r\n \r\n #klik PPM - z autoodkrywaniem\r\n else:\r\n self._pola[w][k][0] = 4\r\n ile = str(self.przegladnij_sasiadow(w,k))\r\n #uaktualnianie przycisku\r\n if ile == '0':\r\n ile = ''\r\n self.przeklikaj_sasiadow(w,k)\r\n self._wolnepola -= 1\r\n self._gui.ustaw_pole(w, k, ile, self._pola[w][k][0],\r\n self._ilebomb - self._ileflag, self._pytajniki,\r\n self._wolnepola)\r\n if self._wolnepola == 0 == self._pytajniki:\r\n return self.wygrana() \r\n return 0 #status \"gramy dalej\"\r","title":""},{"docid":"b7f392b6eb2f2d20ca5a3967dec70241","score":"0.5185078","text":"def polePPM(self,w,k):\r\n #pole w pozycji startowej\r\n if self._pola[w][k][0] % 7 == 0:\r\n #ustaw stan z uwzglednieniem kodu xyzzy jesli to jest pole z bomba\r\n self._pola[w][k][0] = 1 + 7*self._uzytykod*self._pola[w][k][1]\r\n #aktualizacja zliczaczy\r\n self._ileflag += 1\r\n if self._pola[w][k][1] == True:\r\n self._flagzbomb += 1\r\n #ustawianie pola w interfejsie\r\n self._gui.ustaw_pole(w, k, \"\", self._pola[w][k][0],\r\n self._ilebomb - self._ileflag, self._pytajniki,\r\n self._wolnepola)\r\n #sprawdzanie czy nastapila wygrana\r\n if self._ilebomb == self._flagzbomb == self._ileflag and 0 == self._pytajniki:\r\n return self.wygrana()\r\n\r\n \r\n #pole z flaga\r\n elif self._pola[w][k][0] % 7 == 1:\r\n self._pola[w][k][0] = 2 + 7*self._uzytykod*self._pola[w][k][1]\r\n self._pytajniki += 1\r\n self._ileflag -= 1\r\n if self._pola[w][k][1] == True:\r\n self._flagzbomb -= 1\r\n self._gui.ustaw_pole(w, k, \"\", self._pola[w][k][0],\r\n self._ilebomb - self._ileflag, self._pytajniki,\r\n self._wolnepola)\r\n \r\n \r\n #pole z pytajnikiem\r\n elif self._pola[w][k][0] % 7 == 2:\r\n self._pytajniki -= 1\r\n self._pola[w][k][0] = 0+ 7*self._uzytykod*self._pola[w][k][1]\r\n self._gui.ustaw_pole(w, k, \"\", self._pola[w][k][0],\r\n self._ilebomb - self._ileflag, self._pytajniki,\r\n self._wolnepola)\r\n \r\n #wszystkie bomby wczesniej poprawnie oflagowane, brak zbednych flag, znika ostatni pytajnik\r\n if self._ilebomb == self._flagzbomb == self._ileflag and 0 == self._pytajniki:\r\n return self.wygrana()\r\n \r\n #wszystkie bez min byly klikniete LPM, ale byly rowniez pytajniki\r\n if self._wolnepola == 0 == self._pytajniki:\r\n return self.wygrana()\r\n \r\n return 0 #status \"gramy dalej\"\r","title":""},{"docid":"b334d51b06c52c97ede3a6c06e14d3b3","score":"0.5171027","text":"def same_quantum(self, a, b):\r\n return a.same_quantum(b)","title":""},{"docid":"b334d51b06c52c97ede3a6c06e14d3b3","score":"0.5171027","text":"def same_quantum(self, a, b):\r\n return a.same_quantum(b)","title":""},{"docid":"82d4a5ad8db163476da9cbd00602b275","score":"0.51175624","text":"def operator(self, values, idx_choosen):\n pass","title":""},{"docid":"246948c3d4b36ecf7941431d8a70a53a","score":"0.5031777","text":"def przegrana(self):\r\n for w in range(self._w):\r\n for k in range(self._k):\r\n if self._pola[w][k][0] %7 in [0]:\r\n #nieklikniete pole z bomba\r\n if self._pola[w][k][1] == True: #bomba\r\n self._pola[w][k][0] = 3 + self._uzytykod * 7 #ustawianie stanu\r\n self._gui.ustaw_pole(w, k, '', self._pola[w][k][0],\r\n self._ilebomb - self._flagzbomb, self._pytajniki,\r\n self._wolnepola)\r\n elif self._pola[w][k][0] %7 in [1]:\r\n #flaga postawiona w zlym miejscu\r\n if self._pola[w][k][1] == False: #bomba\r\n self._gui.ustaw_pole(w, k, '', 5,\r\n self._ilebomb - self._flagzbomb, self._pytajniki,\r\n self._wolnepola)\r\n elif self._pola[w][k][0] %7 in [2]:\r\n #odkrywanie pytajnika jezeli jest pod nim bomba\r\n if self._pola[w][k][1] == True: #bomba\r\n self._pola[w][k][0] = 3 + self._uzytykod * 7 #ustawianie stanu\r\n self._gui.ustaw_pole(w, k, '', self._pola[w][k][0],\r\n self._ilebomb - self._flagzbomb, self._pytajniki,\r\n self._wolnepola) \r\n return 2 #status przegranej\r","title":""},{"docid":"c40ba6cd6b941d5da25cb6f7d5fb2187","score":"0.4993907","text":"def PauliOperator(label):\n pauli = {\n 'I': np.matrix([[1, 0], [0, 1]]),\n 'Z': np.matrix([[1, 0], [0, -1]]),\n 'X': np.matrix([[0, 1], [1, 0]]),\n 'Y': np.matrix([[0, -1j], [1j, 0]])\n }\n\n operator = pauli[label[0]]\n for letter in label[1:]:\n operator = np.kron(operator, pauli[letter])\n\n return operator","title":""},{"docid":"be284642f1d7cbf8fd54ea8c4221d8b0","score":"0.4970203","text":"def test_operator_mapping(operand_a, operand_b, operator, expected_result):\n\n operator_func = OperatorUtils.get_operator(operator)\n\n assert operator_func(operand_a, operand_b) == expected_result","title":""},{"docid":"63fe42a54889ff40147928264b35afe3","score":"0.49599716","text":"def __or__(self, other: \"QuOperator\") -> \"QuOperator\":\n return self.tensor_product(other)","title":""},{"docid":"74a0c700148ca3b9363d11be29adc8f0","score":"0.49543437","text":"def test2_12_1_2(self):\n GA, e_1, e_2, e_3 = Ga.build('e*1|2|3')\n B = e_1 ^ (e_2 - e_3)\n self.assertEqual(e_1 ^ B, 0)\n self.assertNotEqual((e_1 + e_2) ^ B, 0)\n self.assertNotEqual((e_1 + e_2 + e_3) ^ B, 0)\n self.assertEqual((2 * e_1 - e_2 + e_3) ^ B, 0)","title":""},{"docid":"277e49f908efec3b393c80884e746cba","score":"0.49329183","text":"def test_two_qubits_merge_gate_subset(self):\n\n def qfunc():\n qml.CRX(0.1, wires=[0, 1])\n qml.CRX(0.2, wires=[0, 1])\n qml.RY(0.3, wires=[\"a\"])\n qml.RY(0.5, wires=[\"a\"])\n qml.RX(-0.5, wires=[2])\n qml.RX(0.2, wires=[2])\n qml.RZ(0.2, wires=[2])\n\n transformed_qfunc = merge_rotations(include_gates=[\"RX\", \"CRX\"])(qfunc)\n\n ops = qml.tape.make_qscript(transformed_qfunc)().operations\n\n names_expected = [\"CRX\", \"RY\", \"RY\", \"RX\", \"RZ\"]\n wires_expected = [Wires([0, 1]), Wires(\"a\"), Wires(\"a\"), Wires(2), Wires(2)]\n compare_operation_lists(ops, names_expected, wires_expected)\n\n assert qml.math.isclose(ops[0].parameters[0], 0.3)\n assert qml.math.isclose(ops[1].parameters[0], 0.3)\n assert qml.math.isclose(ops[2].parameters[0], 0.5)\n assert qml.math.isclose(ops[3].parameters[0], -0.3)","title":""},{"docid":"56bb84a5628b2b52976f4bd39950e155","score":"0.49108955","text":"def __init__(self, booleanos=[], operadores=[]):\r\n self.booleanos = (\"verdadero\", \"falso\")\r\n self.operadores = (\"-\", \"+\", \"*\", \"/\", \"&&\", \"||\", \">\", \"<\", \">=\", \"<=\", \"!=\", \"==\", \"=\")\r\n global debug\r\n debug = False","title":""},{"docid":"032a64e19220d5d1754793fa943a96d3","score":"0.4896869","text":"def binary_operator(operator: str, lhs: Formula, rhs: Formula) -> Formula:\r\n assert is_binary(operator)\r\n return Formula(operator, lhs, rhs)","title":""},{"docid":"27159cf346c0b0f6ab9c45aa0f05a8be","score":"0.48949242","text":"def operator_kan(f,klasa='sympy'): \n n = len(signature(f).parameters)\n if klasa == 'sympy':\n identiteta = sp.eye(n)\n kan = [list(identiteta.col(i)) for i in range(identiteta.cols)]\n slika = list(map(lambda t: f(*t), kan))\n matrica = sp.Matrix(slika).T\n elif klasa == 'numpy':\n kan = np.eye(n)\n slika = np.apply_along_axis(lambda t: f(*t),1,kan)\n matrica = np.transpose(slika)\n return matrica","title":""},{"docid":"2677d33b16434e816b08d2646d7e901b","score":"0.48808172","text":"def wygrana(self): \r\n #odkrywanie pozostałych pól \r\n for w in range(self._w):\r\n for k in range(self._k):\r\n if self._pola[w][k][0] %7 in [0]: \r\n if self._pola[w][k][1] == True: #bomba\r\n self._pola[w][k][0] = 3 + self._uzytykod * 7\r\n ile = ''\r\n else:\r\n self._pola[w][k][0] = 4 #ustawianie stanu\r\n ile = str(self.przegladnij_sasiadow(w,k))\r\n #uaktualnianie przycisku\r\n if ile == '0':\r\n ile = ''\r\n self._gui.ustaw_pole(w, k, ile, self._pola[w][k][0],0,0,0)\r\n \r\n return 1 #status wygranej\r","title":""},{"docid":"792faa135b8bcfb3b03a45810f7b6a05","score":"0.48751622","text":"def apply_operator_3(\n statevector1,\n statevector2,\n operator,\n A,\n B,\n C,\n ):\n\n N = (statevector1.shape[0]&-statevector1.shape[0]).bit_length()-1\n if A >= N: raise RuntimeError('A >= N')\n if B >= N: raise RuntimeError('B >= N')\n if C >= N: raise RuntimeError('C >= N')\n if A == B: raise RuntimeError('A == B')\n if A == C: raise RuntimeError('A == C')\n if B == C: raise RuntimeError('B == C')\n if operator.shape != (8,8): raise RuntimeError('3-body gate must be (8,8)')\n if statevector1.shape != (2**N,): raise RuntimeError('statevector1 should be (%d,) shape, is %r shape' % (2**N, statevector1.shape))\n if statevector2.shape != (2**N,): raise RuntimeError('statevector2 should be (%d,) shape, is %r shape' % (2**N, statevector2.shape))\n\n A2, B2, C2 = sorted((A, B, C))\n\n operator2 = np.reshape(operator, (2,2,2,2,2,2))\n\n bra_indices = 'ijk'\n ket_indices = 'lmn'\n bra_indices2 = ''.join([bra_indices[(A, B, C).index(_)] for _ in (A2, B2, C2)])\n ket_indices2 = ''.join([ket_indices[(A, B, C).index(_)] for _ in (A2, B2, C2)])\n \n operator2 = np.einsum('%s%s->%s%s' % (bra_indices, ket_indices, bra_indices2, ket_indices2), operator2)\n\n L = 2**(A2) # Left hangover\n M = 2**(B2-A2-1) # Middle1 hangover\n P = 2**(C2-B2-1) # Middle2 hangover\n R = 2**(N-C2-1) # Right hangover\n statevector1v = statevector1.view() \n statevector2v = statevector2.view()\n statevector1v.shape = (L,2,M,2,P,2,R)\n statevector2v.shape = (L,2,M,2,P,2,R)\n np.einsum('LlMmPnR,ijklmn->LiMjPkR', statevector1v, operator2, out=statevector2v)\n\n return statevector2, statevector1","title":""},{"docid":"b0cb36ed2e7d81d5733c74b5dbe8d64d","score":"0.48561287","text":"def apply_operator_2(\n statevector1,\n statevector2,\n operator,\n A,\n B,\n ):\n\n N = (statevector1.shape[0]&-statevector1.shape[0]).bit_length()-1\n if A >= N: raise RuntimeError('A >= N')\n if B >= N: raise RuntimeError('B >= N')\n if A == B: raise RuntimeError('A == B')\n if operator.shape != (4,4): raise RuntimeError('2-body gate must be (4,4)')\n if statevector1.shape != (2**N,): raise RuntimeError('statevector1 should be (%d,) shape, is %r shape' % (2**N, statevector1.shape))\n if statevector2.shape != (2**N,): raise RuntimeError('statevector2 should be (%d,) shape, is %r shape' % (2**N, statevector2.shape))\n\n operator2 = np.reshape(operator, (2,2,2,2))\n if A > B:\n A2, B2 = B, A\n operator2 = np.einsum('ijkl->jilk', operator2)\n else:\n A2, B2 = A, B\n\n L = 2**(A2) # Left hangover\n M = 2**(B2-A2-1) # Middle hangover\n R = 2**(N-B2-1) # Right hangover\n statevector1v = statevector1.view() \n statevector2v = statevector2.view()\n statevector1v.shape = (L,2,M,2,R)\n statevector2v.shape = (L,2,M,2,R)\n np.einsum('LkMlR,ijkl->LiMjR', statevector1v, operator2, out=statevector2v)\n\n return statevector2, statevector1","title":""},{"docid":"c18071e778eeab67d9130ad3a22199ac","score":"0.48549315","text":"def custo_par_aresta(distancias, aresta1, aresta2):\n return distancias[aresta1[0]][aresta1[1]] + distancias[aresta2[0]][aresta2[1]]","title":""},{"docid":"4fa2b9b5a6e78095a2bfd1954358b9b8","score":"0.48369998","text":"def get_operator_map(self):\n return self.operator_map","title":""},{"docid":"f73693990da3337c2205a0b9d4aff94e","score":"0.48365572","text":"def checkOperatorPrecedence(a,b):\n\tcheck={}\n\tcheck['(']=1\n\tcheck['*']=2\n\tcheck['/']=2\n\tcheck['-']=3\n\tcheck['+']=3\n\tif check[a] <= check[b]:\n\t\treturn 1\n\telse:\n\t\treturn 0","title":""},{"docid":"0985ce4fc97bd09f5153e8941618208a","score":"0.48317426","text":"def test2_12_1_3(self):\n GA, e_1, e_2, e_3 = Ga.build('e*1|2|3')\n a = e_1 + 2 * e_2\n b = -e_1 - e_2\n B = a ^ b\n self.assertEqual(B, 1 * (e_1 ^ e_2))","title":""},{"docid":"9fcbd6afeb60248618bc74bc53c79d27","score":"0.48085868","text":"def test_comparison_on_different_key_blocks(self):\n self.skipTest(\n \"The KeyBlock structure does not support the comparison operators.\"\n )","title":""},{"docid":"5856d33f0d0f0964d910bccbcec481b8","score":"0.47952715","text":"def eval(cls, a,b):\n if not (a and b): return S.Zero\n if a == b: return S.Zero\n if a.is_commutative or b.is_commutative:\n return S.Zero\n\n #\n # [A+B,C] -> [A,C] + [B,C]\n #\n a = a.expand()\n if isinstance(a,Add):\n return Add(*[cls(term,b) for term in a.args])\n b = b.expand()\n if isinstance(b,Add):\n return Add(*[cls(a,term) for term in b.args])\n\n #\n # [xA,yB] -> xy*[A,B]\n #\n c_part = []\n nc_part = []\n nc_part2 = []\n if isinstance(a,Mul):\n c_part,nc_part = split_commutative_parts(a)\n if isinstance(b,Mul):\n c_part2,nc_part2 = split_commutative_parts(b)\n c_part.extend(c_part2)\n if c_part:\n a = nc_part or [a]\n b = nc_part2 or [b]\n return Mul(*c_part)*cls(Mul(*a),Mul(*b))\n\n\n #\n # single second quantization operators\n #\n if isinstance(a, BosonicOperator) and isinstance(b, BosonicOperator):\n if isinstance(b,CreateBoson) and isinstance(a,AnnihilateBoson):\n return KroneckerDelta(a.state,b.state)\n if isinstance(a,CreateBoson) and isinstance(b,AnnihilateBoson):\n return S.NegativeOne*KroneckerDelta(a.state,b.state)\n else:\n return S.Zero\n if isinstance(a, FermionicOperator) and isinstance(b, FermionicOperator):\n return wicks(a*b)- wicks(b*a)\n\n #\n # Canonical ordering of arguments\n #\n if cmp(a, b) > 0:\n return S.NegativeOne*cls(b, a)","title":""},{"docid":"41851fb60fc066ff04c7c2e2f5872cd6","score":"0.47927785","text":"def compare(self, obj1, obj2):\n # Key o1 = (Key) obj1;\n # Key o2 = (Key) obj2;\n # if (o1.getNote() == KeyLetterEnum.BS && o1.getPosition() == KeyPositionEnum.getLastPosition(o2.getPosition())\n # && o2.getNote() == KeyLetterEnum.C) {\n # return 0;\n # }\n if (obj1.getNote() == kle.BS) and (obj1.getPosition() == kpe.getLastPosition(obj2.getPosition())) and (\n obj2.getNote() == kle.C):\n return 0\n\n # if (o1.getNote() == KeyLetterEnum.C\n # && o2.getPosition() == KeyPositionEnum.getNextPosition(o1.getPosition())\n # && o2.getNote() == KeyLetterEnum.B) {\n # return 0;\n # }\n if (obj1.getNote() == kle.C) and (obj2.getPosition() == kpe.getNextPosition(obj1.getPosition())) and (\n obj2.getNote() == kle.B):\n return 0\n\n # if (o1.getPosition().getPosition() > o2.getPosition().getPosition()) {\n # return 1;\n # }\n # if (o1.getPosition().getPosition() < o2.getPosition().getPosition()) {\n # return -1;\n # } else {\n # if (o1.getNote().getKeyNumber() == o2.getNote().getKeyNumber()) {\n # return 0;\n # } else if (o1.getNote().getKeyIdentity() < o2.getNote().getKeyIdentity()) {\n # return -1;\n # } else {\n # return 1;\n # }\n # }\n if (obj1.getPosition().getPosition() > obj2.getPosition().getPosition()):\n return 1\n elif obj2.getPosition().getPosition() < obj2.getPosition().getPosition():\n return -1\n else:\n if obj1.getNote().getKeyNumber() == obj2.getNote().getKeyNumber():\n return 0\n elif obj1.getNote().getKeyIdentity() < obj2.getNote().getKeyIdentity():\n return -1\n else:\n return 1","title":""},{"docid":"6e56a3ae04f61ff2c0f1d654ce269dc7","score":"0.4767549","text":"def makeBs2KstKstSameCharge(name,\n Kstsel,\n BMassWin,\n BVCHI2,\n BDOCA,\n BIPCHI2): \n \n _motherCuts = \"(VFASPF(VCHI2/VDOF) < %(BVCHI2)s) & (MIPCHI2DV(PRIMARY)< %(BIPCHI2)s)\"% locals()\n _combinationCut = \"(ADAMASS('B_s0') < %(BMassWin)s *MeV) & (AMAXDOCA('')< %(BDOCA)s *mm)\" % locals() \n\n _Bs = CombineParticles('_'+name)\n _Bs.DecayDescriptor = \"[B_s0 -> K*(892)0 K*(892)0]cc\"\n _Bs.CombinationCut = _combinationCut\n _Bs.MotherCut = _motherCuts\n\n _Bs.ReFitPVs = True\n\n _Bs.addTool( OfflineVertexFitter )\n# _Bs.VertexFitters.update( { \"\" : \"OfflineVertexFitter\"} )\n _Bs.ParticleCombiners.update( { \"\" : \"OfflineVertexFitter\"} ) # Fix for DaVinci v32r0 by A.Poluektov\n _Bs.OfflineVertexFitter.useResonanceVertex = False\n\n\n return Selection ( name,\n Algorithm = _Bs,\n RequiredSelections = [Kstsel])","title":""},{"docid":"24e8b039c74e0712addf16e9bf81d1ea","score":"0.47494054","text":"def set_k2D(Na,Nb,deltachia,deltachib): # notice Na as index should be Na-1\n k1D_a=set_k1D(Na,deltachia)\n k1D_b=set_k1D(Nb,deltachib)\n ka=np.zeros((Na,Nb), float)\n for i in range(Nb):\n ka[:,i]=k1D_a[:,0]\n kb=np.zeros((Na,Nb), float)\n for i in range(Na):\n kb[i,:]=k1D_b.T\n return np.asarray([ka, kb])\n\n #### -> for vk_indexset() -> for set_k2D()","title":""},{"docid":"917a7ff6c02ef586eb2dfdfc7481172c","score":"0.4740766","text":"def test_parens_for_sandwiched_single_operand_same_op_as_neighbors(self):\n b = self._bexpr_from_postfix_tokens(\n ['A', 'B', 'and',\n 'C',\n 'D', 'E', 'and',\n 'and', 'and'])\n self.assertEqual(\n b.symbols,\n ['A', 'B', 'C', 'D', 'E'])\n self.assertEqual(\n b.tokens,\n ['A', 'and', 'B', 'and', 'C', 'and', 'D', 'and', 'E'])\n self.assertEqual(\n b.postfix_tokens,\n ['A', 'B', 'and',\n 'C',\n 'D', 'E', 'and',\n 'and', 'and'])\n self.assertEqual(\n b.raw_expr,\n 'A and B and C and D and E')\n self.assertTrue(b.tree is not None)","title":""},{"docid":"f953a234e776f18b15f04b9076789d1a","score":"0.47368342","text":"def operator_eq(self, *args):\n return _vnl_matrix_fixedPython.vnl_matrix_fixedD_3_12_operator_eq(self, *args)","title":""},{"docid":"65003198493b8f2e2493e5ec05df4f50","score":"0.47259355","text":"def __init__(self,wier,kol,bomb,logika,gui):\r\n #referencje\r\n self._logika = logika\r\n self._gui = gui\r\n #rozmiar planszy\r\n self._w = wier #numeracja 1-15\r\n self._k = kol\r\n \r\n #ilosc bomb\r\n self._ilebomb = bomb\r\n # liczniki\r\n self._wolnepola = wier * kol - bomb\r\n self._ileflag = 0\r\n self._flagzbomb = 0\r\n self._pytajniki = 0\r\n #czy zostal uzyty kod - potrzebne przy aktualizacji statusow pol\r\n self._uzytykod = False\r\n \r\n '''Tworzenie macierzy pol [wiersz][kolumna][nr stanu, czy jest bomba].\r\n Lista stanow pol:\r\n 0- puste, 1- flaga, 2 - pytajnik, 3 - bomba // po xyzzy stan+=7\r\n 4 - klikniete bez bomby // tylko bez xyzzy\r\n 5 - zle postawiona flaga, 6 - wybuch // tylko bez xyzzy\r\n w sumie 7 + 4 = 11 stanow\r\n '''\r\n self._pola = [[[0,False] for j in range(kol)] for i in range(wier)]\r\n \r\n #losowanie pol\r\n licznik = self._ilebomb\r\n while 1:\r\n x = random.randint(0, self._w-1)\r\n y = random.randint(0, self._k-1)\r\n if self._pola[x][y][1] == False:\r\n self._pola[x][y][1] = True\r\n licznik -= 1\r\n if licznik == 0:\r\n break","title":""},{"docid":"a8c3ccaf3d610b1a6e3dc7a92bcb0c64","score":"0.47246957","text":"def test_parens_for_negated_leading_chained_operator_clause(self):\n b = self._bexpr_from_postfix_tokens(\n ['A', 'B', 'iff', '!',\n '0', '1', 'and',\n 'and'])\n self.assertEqual(\n b.symbols,\n ['A', 'B'])\n self.assertEqual(\n b.tokens,\n ['!', '(', 'A', 'iff', 'B', ')', 'and', '0', 'and', '1'])\n self.assertEqual(\n b.postfix_tokens,\n ['A', 'B', 'iff', '!',\n '0', '1', 'and',\n 'and'])\n self.assertEqual(b.raw_expr, '!(A iff B) and 0 and 1')\n self.assertTrue(b.tree is not None)","title":""},{"docid":"a8fc52adfa5fd764d9077ce87e938d3e","score":"0.47204044","text":"def _get_eq_op_ ( klass1 , klass2 ) :\n t = klass1 , klass2\n ops = _eq_ops_.get( t , None )\n if ops : return ops ## RETURN \n\n ## try to load the operators \n try :\n ops = Ostap.Math.EqualityOp ( klass1 , klass2 )\n _eq_ops_ [ t ] = ops\n return ops ## RETURN \n except TypeError:\n return None ## RETURN\n \n return None ## RETURN","title":""},{"docid":"d9046b95bd3ce0b86e2da0560c1a4b7c","score":"0.47179422","text":"def operator(self):\n return self._operator","title":""},{"docid":"1f74fdc5a8ae9f8d8d0852c1389e603f","score":"0.47171295","text":"def test_parens_for_leading_chained_operator_clause(self):\n b = self._bexpr_from_postfix_tokens(\n ['A', 'B', 'C', 'D', 'and', 'and', 'and',\n 'E',\n 'or'])\n self.assertEqual(\n b.symbols,\n ['A', 'B', 'C', 'D', 'E'])\n self.assertEqual(\n b.tokens,\n ['(', 'A', 'and', 'B', 'and', 'C', 'and', 'D', ')', 'or', 'E'])\n self.assertEqual(\n b.postfix_tokens,\n ['A', 'B', 'C', 'D', 'and', 'and', 'and',\n 'E',\n 'or'])\n self.assertEqual(\n b.raw_expr,\n '(A and B and C and D) or E')\n self.assertTrue(b.tree is not None)","title":""},{"docid":"d45dd47e8c274ff9de2aef1c303a60f7","score":"0.47143483","text":"def test2_12_2_9(self):\n for GA in [Ga('e*1|2'), Ga('e*1|2|3'), Ga('e*1|2|3|4')]: # , Ga('e*1|2|3|4|5')]:\n for k, l in product(range(GA.n + 1), range(GA.n + 1)):\n Ak = GA.mv('A', 'blade', k)\n Bl = GA.mv('B', 'blade', l)\n self.assertEqual(Ak ^ Bl, (-1) ** (k * l) * (Bl ^ Ak))","title":""},{"docid":"c284ce68e33e75bd58148d07b3736d0d","score":"0.47029236","text":"def operator_eq(self, *args):\n return _vnl_matrix_fixedPython.vnl_matrix_fixedD_2_6_operator_eq(self, *args)","title":""},{"docid":"3b0d4cd99bb9b9ba2e6650625ccde524","score":"0.46820453","text":"def demonstrate_logical_operators():","title":""},{"docid":"a6f652766248768b1a936e006e145696","score":"0.4679117","text":"def wyrownaj_liczbe_danych(dane, etykiety,slownik_etykiet_danych):\n lista_aktywnosci = list( slownik_etykiet_danych.keys() )\n lista_etykiet = list( etykiety )\n ksztalt_danych = dane.shape\n wystapienia = {}\n for aktywnosc in lista_aktywnosci:\n wystapienia[aktywnosc] = lista_etykiet.count(slownik_etykiet_danych[aktywnosc])\n liczby_wystapien_aktywnosci = list(wystapienia.values())\n minimalna_liczba_wystapien = min( liczby_wystapien_aktywnosci )\n\n print(\"AKTYWNOSCI W DANYCH WEJSCIOWYCH\")\n print(wystapienia)\n \n return_dane = np.zeros( ( len(slownik_etykiet_danych) * minimalna_liczba_wystapien,ksztalt_danych[1],ksztalt_danych[2] ) )\n return_etykiety = np.zeros( (len(slownik_etykiet_danych) * minimalna_liczba_wystapien,) )\n\n wektor_wystapien = np.zeros( ( len(slownik_etykiet_danych) , ) ) # tu bedziemy zliczac ile razy dana etykieta juz wystapila\n return_dane_licznik = 0\n \n for i in range(len(dane)):\n if wektor_wystapien[int(etykiety[i])] < minimalna_liczba_wystapien :\n wektor_wystapien[int(etykiety[i])] += 1\n return_dane[return_dane_licznik] = dane[i]\n return_etykiety[return_dane_licznik] = etykiety[i]\n return_dane_licznik += 1\n \n lista_etykiet = list( return_etykiety )\n wystapienia = {}\n for aktywnosc in lista_aktywnosci:\n wystapienia[aktywnosc] = lista_etykiet.count(slownik_etykiet_danych[aktywnosc])\n print(\"AKTYWNOSCI PO WYROWNANIU:\")\n print(wystapienia)\n\n return (return_dane,return_etykiety)","title":""},{"docid":"11f72a6dc60d5fd530943833b583dced","score":"0.46677864","text":"def operator_eq(self, *args):\n return _vnl_matrix_fixedPython.vnl_matrix_fixedD_2_2_operator_eq(self, *args)","title":""},{"docid":"70c2e0a9de1c08018645ac4b36237ee5","score":"0.4663572","text":"def test_multiple_dig_nop(self):\n game = self.test_mines.new_game(3,7,[[1,5],[2,4],(0,1)])\n game2 = self.test_mines.new_game(3,7,[[1,5],[2,4],(0,1)])\n\n game[\"mask\"][0][0]=True\n game2[\"mask\"][0][0]=True\n\n dig(game,0,0)\n\n for key in game.keys():\n self.assertEqual(game2[key], game[key])","title":""},{"docid":"6691420c5c49478cae619f6513340979","score":"0.46592966","text":"def test_parens_for_sandwiched_chained_operator_clause(self):\n b = self._bexpr_from_postfix_tokens(\n ['A',\n 'B', 'C', 'D', '&', '&',\n 'E',\n '->', '->'])\n self.assertEqual(\n b.symbols,\n ['A', 'B', 'C', 'D', 'E'])\n self.assertEqual(\n b.tokens,\n ['A', '->', '(', 'B', '&', 'C', '&', 'D', ')', '->', 'E'])\n self.assertEqual(\n b.postfix_tokens,\n ['A',\n 'B', 'C', 'D', '&', '&',\n 'E',\n '->', '->'])\n self.assertEqual(b.raw_expr, 'A -> (B & C & D) -> E')\n self.assertTrue(b.tree is not None)","title":""},{"docid":"4cf6fb357288802cd05cac976b50cb12","score":"0.46501648","text":"def contraction(a,b):\n if isinstance(b,FermionicOperator) and isinstance(a,FermionicOperator):\n if isinstance(a,AnnihilateFermion) and isinstance(b,CreateFermion):\n if b.state.assumptions0.get(\"below_fermi\"):\n return S.Zero\n if a.state.assumptions0.get(\"below_fermi\"):\n return S.Zero\n if b.state.assumptions0.get(\"above_fermi\"):\n return KroneckerDelta(a.state,b.state)\n if a.state.assumptions0.get(\"above_fermi\"):\n return KroneckerDelta(a.state,b.state)\n\n return (KroneckerDelta(a.state,b.state)*\n KroneckerDelta(b.state,Symbol('a',dummy=True,above_fermi=True)))\n if isinstance(b,AnnihilateFermion) and isinstance(a,CreateFermion):\n if b.state.assumptions0.get(\"above_fermi\"):\n return S.Zero\n if a.state.assumptions0.get(\"above_fermi\"):\n return S.Zero\n if b.state.assumptions0.get(\"below_fermi\"):\n return KroneckerDelta(a.state,b.state)\n if a.state.assumptions0.get(\"below_fermi\"):\n return KroneckerDelta(a.state,b.state)\n\n return (KroneckerDelta(a.state,b.state)*\n KroneckerDelta(b.state,Symbol('i',dummy=True,below_fermi=True)))\n\n # vanish if 2xAnnihilator or 2xCreator\n return S.Zero\n\n else:\n #not fermion operators\n t = ( isinstance(i,FermionicOperator) for i in (a,b) )\n raise ContractionAppliesOnlyToFermions(*t)","title":""},{"docid":"4e92e8fb408ba1f6109293c7042c9814","score":"0.46470928","text":"def operate(\n cls,\n value_1: CalculationValue,\n value_2: CalculationValue,\n value_1_calc: bool,\n value_2_calc: bool\n ) -> CalculationValue:\n l1, r1, l2, r2 = BinaryOperator.get_parens(value_1_calc, value_2_calc)\n\n if type(value_1) in (int, float):\n if type(value_2) in (int, float):\n return cls.operator(value_1, value_2)\n elif isinstance(value_2, Series):\n result = cls.operator(value_1, value_2)\n result.name = (\n f'{l1}{value_1}{r1} '\n f'{cls.symbol} '\n f'{l2}{value_2.name}{r2}'\n )\n return result\n elif isinstance(value_2, DataFrame):\n result = cls.operator(value_1, value_2)\n result.columns = [\n f'{l1}{value_1}{r1} {cls.symbol} {l2}{name_2}{r2}'\n for name_2 in value_2.columns\n ]\n return result\n else:\n raise TypeError(\n 'value_2 must be int, float, Series or DataFrame'\n )\n elif isinstance(value_1, Series):\n if type(value_2) in (int, float):\n result = cls.operator(value_1, value_2)\n result.name = (\n f'{l1}{value_1.name}{r1} '\n f'{cls.symbol} '\n f'{l2}{value_2}{r2}')\n return result\n elif isinstance(value_2, Series):\n result = cls.operator(value_1, value_2)\n result.name = (\n f'{l1}{value_1.name}{r1} '\n f'{cls.symbol} '\n f'{l2}{value_2.name}{r2}')\n return result\n elif isinstance(value_2, DataFrame):\n result = getattr(\n value_2, cls.pandas_op_name\n )(value_1, axis=0)\n result.columns = [\n f'{l1}{value_1.name}{r1} {cls.symbol} {l2}{column}{r2}'\n for column in value_2.columns\n ]\n return result\n else:\n raise TypeError(\n 'value_2 must be int, float, Series or DataFrame'\n )\n elif isinstance(value_1, DataFrame):\n if type(value_2) in (int, float):\n result = cls.operator(value_1, value_2)\n result.columns = [\n f'{l1}{column}{r1} {cls.symbol} {l2}{value_2}{r2}'\n for column in value_1.columns\n ]\n return result\n elif isinstance(value_2, Series):\n result = getattr(\n value_1, cls.pandas_op_name\n )(value_2, axis=0)\n result.columns = [\n f'{l1}{column}{r1} {cls.symbol} {l2}{value_2.name}{r2}'\n for column in value_1.columns\n ]\n return result\n elif isinstance(value_2, DataFrame):\n if value_1.shape[1] != value_2.shape[1]:\n raise ValueError(\n 'Can only operate 2 dataframes together with same '\n 'number of columns'\n )\n result = DataFrame.from_dict({\n f'{l1}{col_1}{r1} {cls.symbol} {l2}{col_2}{r2}':\n cls.operator(value_1[col_1], value_2[col_2])\n for col_1, col_2 in zip(value_1.columns, value_2.columns)\n })\n return result\n else:\n raise TypeError(\n 'value_2 must be int, float, Series or DataFrame'\n )\n else:\n raise TypeError(\n 'value_1 must be int, float, Series or DataFrame'\n )","title":""},{"docid":"fe37f36503495c794f3c851f8aaec2eb","score":"0.46409702","text":"def basic_op(operator, value1, value2):\n return eval(str(value1)+operator+str(value2))","title":""},{"docid":"b07abc88787af07e58b3ac7efa69fcb0","score":"0.4639607","text":"def is_left_associative(operator):\n\n return operator in \"+-*/\"","title":""},{"docid":"c14ac52a65303c405a1226cd4ba768e6","score":"0.46368247","text":"def setOverloadedOperator(self, isOverloadedOperator: bool) -> None:\n ...","title":""},{"docid":"8097a256ca8d5ed6199daa1103417b09","score":"0.46303564","text":"def xyzzy(self):\r\n self._uzytykod = True\r\n for w in range(self._w):\r\n for k in range(self._k):\r\n if self._pola[w][k][0] in [0,1,2,3]:\r\n if self._pola[w][k][1] == True: #bomba\r\n self._pola[w][k][0] += 7\r\n self._gui.ustaw_pole(w, k, '', self._pola[w][k][0],\r\n self._ilebomb - self._ileflag, self._pytajniki,\r\n self._wolnepola)","title":""},{"docid":"fa0cab431de23f571d24cda5b019b995","score":"0.4625291","text":"def b_operator(self, P):\r\n A, B, Q, R, beta = self.A, self.B, self.Q, self.R, self.beta\r\n S1 = Q + beta * dot(B.T, dot(P, B)) \r\n S2 = beta * dot(B.T, dot(P, A))\r\n S3 = beta * dot(A.T, dot(P, A))\r\n F = solve(S1, S2) \r\n new_P = R - dot(S2.T, solve(S1, S2)) + S3 \r\n return F, new_P","title":""},{"docid":"91ba95def0bd10fc1427b2dba35336e7","score":"0.46137527","text":"def operate (self, oper1, oper2):\n\n return None","title":""},{"docid":"abd9c27da68861c1280789ad126f6d03","score":"0.46124133","text":"def operationXOR(a, b):\n if(a==\"0\" and b==\"0\"):\n return \"0\"\n if(a==\"1\" and b==\"0\"):\n return \"1\"\n if(a==\"0\" and b==\"1\"):\n return \"1\"\n if(a==\"1\" and b==\"1\"):\n return \"0\"","title":""},{"docid":"6c68588d4ddec7371464dd0e5d6f136c","score":"0.4587624","text":"def optAlign( sel1, sel2 ):\n\tcmd.reset()\n\n\t# make the lists for holding coordinates\n\t# partial lists\n\tstored.sel1 = []\n\tstored.sel2 = []\n\t# full lists\n\tstored.mol1 = []\n\tstored.mol2 = []\n\n\t# now put the coordinates into a list\n\t# partials\n\n\t# -- REMOVE ALPHA CARBONS\n\tsel1 = sel1 + \" and N. CA\"\n\tsel2 = sel2 + \" and N. CA\"\n\t# -- REMOVE ALPHA CARBONS\n\n\tcmd.iterate_state(1, selector.process(sel1), \"stored.sel1.append([x,y,z])\")\n\tcmd.iterate_state(1, selector.process(sel2), \"stored.sel2.append([x,y,z])\")\n\t# full molecule\n\tmol1 = cmd.identify(sel1,1)[0][0]\n\tmol2 = cmd.identify(sel2,1)[0][0]\n\tcmd.iterate_state(1, mol1, \"stored.mol1.append([x,y,z])\")\n\tcmd.iterate_state(1, mol2, \"stored.mol2.append([x,y,z])\")\n\n\tK = kabsch()\n\tU, T1, T2, RMSD, c1, c2 = K.align(stored.sel1, stored.sel2, [])\n\n\tstored.mol2 = map(lambda v:[T2[0]+((v[0]*U[0][0])+(v[1]*U[1][0])+(v[2]*U[2][0])),T2[1]+((v[0]*U[0][1])+(v[1]*U[1][1])+(v[2]*U[2][1])),T2[2]+((v[0]*U[0][2])+(v[1]*U[1][2])+(v[2]*U[2][2]))],stored.mol2)\n\t#stored.mol1 = map(lambda v:[ v[0]+T1[0], v[1]+T1[1], v[2]+T1[2] ], stored.mol1)\n\tstored.mol1 = map(lambda v:[ v[0]+T1[0], v[1]+T1[1], v[2]+T1[2] ], stored.mol1)\n\n\tcmd.alter_state(1,mol1,\"(x,y,z)=stored.mol1.pop(0)\")\n\tcmd.alter_state(1,mol2,\"(x,y,z)=stored.mol2.pop(0)\")\n\tcmd.alter( 'all',\"segi=''\")\n\tcmd.alter('all', \"chain=''\")\n\tprint \"RMSD=%f\" % cmd.rms_cur(sel1, sel2)\n\tprint \"MY RMSD=%f\" % RMSD\n\tcmd.hide('everything')\n\tcmd.show('ribbon', sel1 + ' or ' + sel2)\n\tcmd.color('gray70', mol1 )\n\tcmd.color('paleyellow', mol2 )\n\tcmd.color('red', 'visible')\n\tcmd.show('ribbon', 'not visible')\n\tcmd.center('visible')\n\tcmd.orient()\n\tcmd.zoom('visible')","title":""},{"docid":"4f9f8c3aeb6ba4ce216a6f1cdf416c4b","score":"0.45873356","text":"def test_parens_for_sandwiched_negated_chained_operator_clause(self):\n b = self._bexpr_from_postfix_tokens(\n ['A',\n 'B', 'C', 'D', 'xor', 'xor', '~',\n 'E',\n '->', '->'])\n self.assertEqual(\n b.symbols,\n ['A', 'B', 'C', 'D', 'E'])\n self.assertEqual(\n b.tokens,\n ['A', '->', '~', '(', 'B', 'xor', 'C', 'xor', 'D', ')', '->', 'E'])\n self.assertEqual(\n b.postfix_tokens,\n ['A',\n 'B', 'C', 'D', 'xor', 'xor', '~',\n 'E',\n '->', '->'])\n self.assertEqual(b.raw_expr, 'A -> ~(B xor C xor D) -> E')\n self.assertTrue(b.tree is not None)","title":""},{"docid":"b7750b521ada02c0e8bd40b073c518be","score":"0.4569324","text":"def test_heat_rod1d_modes_BC1(self):\n # BC1\n solver = Rod1D(kappa=self.kappa, TL=self.T0, TR=self.T1, L=self.L,\n Nsum=self.Nsum, alpha1=1.0, beta1=0.0, alpha2=1.0,\n beta2=0.0)\n solver.modes_BC1()\n kn0 = [0, 1.57079633, 3.14159265]\n An0 = [0, 0, 0]\n Bn0 = [0, 4.45633841, -0.31830989]\n np.testing.assert_allclose(solver.kn, kn0)\n np.testing.assert_allclose(solver.An, An0)\n np.testing.assert_allclose(solver.Bn, Bn0)","title":""},{"docid":"2cb30dabc894c32284a46c310ffe90c0","score":"0.45673597","text":"def __or__(self, other):\n return self.binary(other, \"|\", is_operator=True, dimres=1, dimcheck=\"same\")","title":""},{"docid":"434998dc144619afedf3623209de5391","score":"0.45664072","text":"def opcions():\n print \"[1] Llegir matriu A\"\n print \"[2] Llegir matriu B\"\n print \"[3] Calcular A + B\"\n print \"[4] Calcular A - B\"\n print \"[5] Calcular A * B\"\n print \"[6] Calcular determinant\"\n print \"[7] Calcular trasposada\"\n print \"[8] Sortir\"","title":""},{"docid":"dbcdc1873d231e16de3e0384045e9280","score":"0.45625427","text":"def __eq__(self, operator):\n return int(self) != operator","title":""},{"docid":"78ebf2ef9af0d0c81b44035f5d057255","score":"0.45478916","text":"def kmers_binary_op(self, op, kmer_db1, kmer_db2, kmer_db_out,\n result_min_occs=1, result_max_occs=util.misc.MAX_INT32,\n result_counter_cap=DEFAULT_COUNTER_CAP,\n threads=None):\n kmer_db1, kmer_db2, kmer_db_out = map(self._kmer_db_name, (kmer_db1, kmer_db2, kmer_db_out))\n # db1_min_occs, db1_max_occs, db2_min_occs, db2_max_occs, db_out_min_occs, db_out_max_occs,\n self.execute(['simple', kmer_db1, kmer_db2, op, kmer_db_out,\n '-ci{}'.format(result_min_occs),\n '-cx{}'.format(result_max_occs),\n '-cs{}'.format(result_counter_cap)], threads=threads)\n _chk(self.is_kmer_db(kmer_db_out), 'kmer_binary_op: output not created')","title":""},{"docid":"9eafaa819934c3cf8f28434a14c0b219","score":"0.45444795","text":"def test_operator_shift(self, test_dag):\n # Unpack the fixture\n dag, (op1, op2, op3, op4) = test_dag\n # Arrange the operators with a Label in the middle\n op1 >> Label(\"Label 1\") >> op2\n op3 << Label(\"Label 2\") << op2 >> op4\n # Check that the DAG has the right edge info\n assert dag.get_edge_info(op1.task_id, op2.task_id) == {\"label\": \"Label 1\"}\n assert dag.get_edge_info(op2.task_id, op3.task_id) == {\"label\": \"Label 2\"}\n assert dag.get_edge_info(op2.task_id, op4.task_id) == {}","title":""},{"docid":"d9224101c23261a7ce9bd21437c52246","score":"0.45411834","text":"def operator_eq(self, *args):\n return _vnl_matrix_fixedPython.vnl_matrix_fixedD_2_3_operator_eq(self, *args)","title":""},{"docid":"1eda1cc91ce214d8a75ff0478cb1a6ad","score":"0.45113316","text":"def sıraKımde(self):\n\n if self.hamle % 2 == 0:\n return self.oyuncuDegis1()\n else:\n return self.oyuncuDegis2()","title":""},{"docid":"3c6ff994c7c2dabbe9cb291e786b5ecd","score":"0.45097646","text":"def main_8():\n\n n = 8\n stabs = \"ZIZZZIII IZZZIZII ZIIIZIZZ XXXIIIXI XXIXIIIX IIXIXXXI\"\n logops = \"IIXXIIII XIIIXIII ZZIIIIII IIZIIIZI\"\n code = Code(n, stabs, logops)\n code.check()\n\n #vs = list(code.get_encoded())\n v0 = code.get_encoded()\n v1 = code.logops[0] * v0\n v2 = code.logops[1] * v0\n v3 = code.logops[1] * v1\n\n for stab in code.stabs:\n assert stab*v0 == v0\n assert stab*v1 == v1\n assert stab*v2 == v2\n assert stab*v3 == v3\n\n assert v0 != v1\n\n # these are n-qubit operators:\n CZ = lambda i,j : Z.control(i-1, j-1, rank=n)\n CX = lambda i,j : X.control(i-1, j-1, rank=n)\n SWAP = lambda i,j : CX(i,j)*CX(j,i)\n\n #A = CZ(1,3)*CZ(4,5)*CZ(6,8)*CZ(2,7) # not a weak duality !\n\n for A in [\n #(I @ I @ I @ I @ I @ I @ I @ I)\n (I @ S @ I @ ~S @ S @ I @ ~S @ I)*CZ(1,6)*CZ(3,8), # logical S @ ~S\n #(S @ I @ ~S @ I @ I @ S @ I @ ~S)*CZ(2,5)*CZ(4,7), # logical S @ ~S\n #(I @ ~S @ I @ S @ ~S @ I @ S @ I)*CZ(1,6)*CZ(3,8), # logical ~S @ S\n #(S @ I @ I @ ~S @ I @ S @ ~S @ I)*CZ(2,5)*CZ(3,8), # logical S @ ~S\n #(H @ H @ H @ H @ H @ H @ H @ H) * SWAP(1,6) * SWAP(3,8), # fail....\n ]:\n\n #print(opstr(A))\n \n assert A*code.P == code.P*A\n #P1 = A*code.P*~A\n #assert(P1 == code.P)\n \n vs = [v0, v1, v2, v3]\n op = []\n for u in vs:\n u = A*u\n row = []\n for v in vs:\n r = u.dag() * v\n row.append(r)\n print(\"%.2f+%.2fj\"%(r.real, r.imag), end=\" \")\n print()\n op.append(row)\n \n op = numpy.array(op)\n #op.shape = (2,2,2,2)\n #print(op)\n\n print()","title":""},{"docid":"f68c803d96cc414fba054174dbb0db24","score":"0.45079932","text":"def cross(self, a1, a2):\n\n if len(a1) != len(self.alloy):\n raise ValueError('Wrong size of structure to optimize')\n if len(a1) != len(a2):\n raise ValueError('The two structures do not have the same length')\n\n N = len(self.alloy.numbers)\n cell = self.alloy.get_cell()\n pbc = self.alloy.get_pbc()\n atom_numbers = self.alloy.numbers\n pos = self.alloy.positions\n new_pos = np.zeros((N,3))\n\n # Doing order 1 crossover using ordering list\n order1 = list(a1.info['ordering'])\n order2 = list(a2.info['ordering'])\n # built-in list() function was used to avoid call-by-reference of \n # a2.info['ordering']\n new_order = list(np.zeros(N))\n\n rpi = -choice(range(N)) # initial point of random swath\n rpf = rpi + N/2 # final point of random swath\n\n # Possibilities of making swath from pos1\n if rpi < 0 and rpf < 0:\n # case 1\n new_order[rpi:rpf] = order1[rpi:rpf]\n elif rpi < 0 and rpf ==0:\n # case 2\n new_order[rpi:] = order1[rpi:]\n elif rpi < 0 and rpf > 0:\n # case 3\n new_order[rpi:] = order1[rpi:]\n new_order[:rpf] = order1[:rpf]\n elif rpi == 0 and rpf > 0:\n # case 4\n new_order[:rpf] = order1[:rpf]\n else:\n print \"else case ocurred!\"\n\n # Masking coordinates already selected from a1\n # 'del' is not compatible with array format\n for i in range(rpi,rpf):\n imask = np.where(np.array(order2) == new_order[i])[0][0]\n del order2[imask]\n\n Np = len(order2)\n\n # Fill in the rest of new_order list by with the rest of order2\n if rpi < 0 and rpf < 0:\n # case 1\n new_order[N+rpf:] = order2[Np+rpf:]\n new_order[:N+rpi] = order2[:Np+rpi]\n elif rpi < 0 and rpf ==0:\n # case 2\n new_order[:rpi] = order2\n elif rpi < 0 and rpf > 0:\n # case 3\n new_order[rpf:N+rpi] = order2\n elif rpi == 0 and rpf > 0:\n # case 4\n new_order[rpf:] = order2\n else:\n print \"else case ocurred!\"\n\n # Fill in new_pos array following the new_order list\n for i in range(N):\n new_pos[new_order[i]] = pos[i]\n\n alloy_offspring = Atoms(numbers=atom_numbers, positions=new_pos,\n pbc=pbc, cell=cell)\n alloy_offspring. info['ordering'] = new_order\n\n return alloy_offspring","title":""},{"docid":"bc187c7f7160f00a4f23c4582319f90d","score":"0.45015937","text":"def addOperators(self):\n return","title":""},{"docid":"b3702bb532fd22582e0ebe78442e99d1","score":"0.4493724","text":"def _check2_bitop(a0, a1):\n s0, s1 = len(a0), len(a1)\n sr = max(s0, s1)\n if (s0 != sr and s0 != 1) or (s1 != sr and s1 != 1):\n raise Exception(\"Incompatible argument sizes: %i and %i\" % (s0, s1))\n elif type(a0) is not type(a1) and type(a1) is not a0.MaskType: # noqa\n raise Exception(\"Type mismatch!\")\n ar = a0.empty_(sr if a0.Size == Dynamic else 0)\n return (ar, sr)","title":""},{"docid":"702d36dddcd8b44135719036ce52fdf6","score":"0.4484038","text":"def utworz_plansze(self,w,k,b):\r\n if self._aktualizacja == True:\r\n return False\r\n self._aktualizacja = True\r\n self._grazakonczona = False\r\n if hasattr(self, '_plansza') == True:\r\n del self._plansza\r\n self._plansza = Plansza(w,k,b,self,self._gui) #tworzenie planszy\r\n self._aktualizacja = False\r\n return True","title":""},{"docid":"00570d1f28d900b41bb2530c80196d86","score":"0.44770122","text":"def pan(a, b):\n ab = a * b\n t = str(a) + str(b) + str(ab)\n if '0' in t:\n return 0\n if len(t) == 9 == len(set(t)):\n return ab\n return 0","title":""},{"docid":"3245b05367263e98250455896d1bb37c","score":"0.44703317","text":"def test_operator_set(self, test_dag):\n # Unpack the fixture\n dag, (op1, op2, op3, op4) = test_dag\n # Arrange the operators with a Label in the middle\n op1.set_downstream(op2, Label(\"Label 1\"))\n op3.set_upstream(op2, Label(\"Label 2\"))\n op4.set_upstream(op2)\n # Check that the DAG has the right edge info\n assert dag.get_edge_info(op1.task_id, op2.task_id) == {\"label\": \"Label 1\"}\n assert dag.get_edge_info(op2.task_id, op3.task_id) == {\"label\": \"Label 2\"}\n assert dag.get_edge_info(op2.task_id, op4.task_id) == {}","title":""},{"docid":"c8b280a4717b8ae3d3a2dd08bdbeb9f0","score":"0.44652066","text":"def apply_operator_1(\n statevector1,\n statevector2,\n operator,\n A,\n ):\n\n N = (statevector1.shape[0]&-statevector1.shape[0]).bit_length()-1\n if A >= N: raise RuntimeError('A >= N')\n if operator.shape != (2,2): raise RuntimeError('1-body gate must be (2,2)')\n if statevector1.shape != (2**N,): raise RuntimeError('statevector1 should be (%d,) shape, is %r shape' % (2**N, statevector1.shape))\n if statevector2.shape != (2**N,): raise RuntimeError('statevector2 should be (%d,) shape, is %r shape' % (2**N, statevector2.shape))\n\n L = 2**(A) # Left hangover\n R = 2**(N-A-1) # Right hangover\n statevector1v = statevector1.view() \n statevector2v = statevector2.view()\n statevector1v.shape = (L,2,R)\n statevector2v.shape = (L,2,R)\n np.einsum('LjR,ij->LiR', statevector1v, operator, out=statevector2v)\n\n return statevector2, statevector1","title":""},{"docid":"05724ad7ea70f6c7782a73c43464c0fc","score":"0.446426","text":"def ketbra(self,state):\n result = Operator()\n for hv1 in self.hv:\n for hv2 in state.hv:\n result.add(hv1.ketbra(hv2))\n return result","title":""},{"docid":"75631e85afa28ff2ebccc0032409a930","score":"0.44622192","text":"def comprovaPUK(c,d,m):\n return c<5 and d!=m","title":""},{"docid":"319533272fd4abbf2443ab4a5e2d5d14","score":"0.4459272","text":"def bravyi_kitaev(operator, n_qubits=None):\n # Compute the number of qubits.\n from fermilib.utils import count_qubits\n if n_qubits is None:\n n_qubits = count_qubits(operator)\n if n_qubits < count_qubits(operator):\n raise ValueError('Invalid number of qubits specified.')\n\n # Compute transformed operator.\n transformed_operator = QubitOperator()\n for term in operator.terms:\n\n # Initialize identity matrix.\n coefficient = operator.terms[term]\n transformed_term = QubitOperator((), coefficient)\n\n # Build the Fenwick Tree\n fenwick_tree = FenwickTree(n_qubits)\n\n # Build the Bravyi-Kitaev transformed operators.\n for ladder_operator in term:\n index = ladder_operator[0]\n\n # Parity set. Set of nodes to apply Z to.\n parity_set = [node.index for node in\n fenwick_tree.get_parity_set(index)]\n\n # Update set. Set of ancestors to apply X to.\n ancestors = [node.index for node in\n fenwick_tree.get_update_set(index)]\n\n # The C(j) set.\n ancestor_children = [node.index for node in\n fenwick_tree.get_remainder_set(index)]\n\n # Switch between lowering/raising operators.\n d_coefficient = .5j\n if ladder_operator[1]:\n d_coefficient *= -1.\n\n # The fermion lowering operator is given by\n # a = (c+id)/2 where c, d are the majoranas.\n d_majorana_component = QubitOperator(\n (((ladder_operator[0], 'Y'),) +\n tuple((index, 'Z') for index in ancestor_children) +\n tuple((index, 'X') for index in ancestors)),\n d_coefficient)\n\n c_majorana_component = QubitOperator(\n (((ladder_operator[0], 'X'),) +\n tuple((index, 'Z') for index in parity_set) +\n tuple((index, 'X') for index in ancestors)),\n 0.5)\n\n # Update term.\n transformed_term *= c_majorana_component + d_majorana_component\n transformed_operator += transformed_term\n return transformed_operator","title":""},{"docid":"52b2f9c3087a06898a15ffbb58a7e255","score":"0.4454894","text":"def kartik_test(a, b):\n\n p_a = binomial_test(a.values, b.values)\n p_b = binomial_test(b.values, a.values)\n direction = np.zeros_like(p_a)\n direction[p_a < p_b] = 1\n direction[p_a > p_b] = -1\n p = np.minimum(p_a, p_b)\n q = multipletests(p, method='fdr_bh')[1]\n return pd.DataFrame(\n {'direction': direction,\n 'p': p,\n 'q': q}, index=a.columns)","title":""},{"docid":"30157b993d6ecdb14def0344942d06cf","score":"0.44480485","text":"def operator_eq(self, *args):\n return _vnl_matrix_fixedPython.vnl_matrix_fixedD_3_4_operator_eq(self, *args)","title":""},{"docid":"b737584aafcaf26c3f860de31b787a2b","score":"0.44441146","text":"def _expand_operators(self):\n return NO(self._remove_brackets)","title":""},{"docid":"252326e3e8e8cd047d00df8f60345e42","score":"0.44404167","text":"def is_operator(char):\n return char in OPERATORS","title":""},{"docid":"b1df92ab4b4322c1d3e6f748d7db43d3","score":"0.44399634","text":"def _setOperator(self, operator):\n index_list = self._getSelectedRowsIndex()\n if index_list:\n for index in index_list:\n oCombo = self.table.cellWidget(index.row(), 4)\n oCombo.setCurrentIndex(operator)\n else:\n for i in reversed(range(self.table.rowCount())):\n oCombo = self.table.cellWidget(i, 4)\n oCombo.setCurrentIndex(operator)","title":""},{"docid":"25ed6a845197256956de9d3100370e46","score":"0.4437378","text":"def matrica_prijelaza(baza1,baza2,klasa='sympy'):\n if klasa == 'sympy':\n m1 = sp.Matrix(baza1).T\n if m1.det() == 0: return \"Error: prvi skup nije baza\"\n m2 = sp.Matrix(baza2).T\n if m2.det() == 0: return \"Error: drugi skup nije baza\"\n if m1.shape != m2.shape: return \"Error: baze nisu iz istog vektorskog prostora\"\n return m1.inv()*m2\n elif klasa == 'numpy':\n m1 = np.transpose(np.array(baza1))\n if np.abs(np.linalg.det(m1)) < 1e-15: return \"Error: prvi skup nije baza\"\n m2 = np.transpose(np.array(baza2))\n if np.abs(np.linalg.det(m2)) < 1e-15: return \"Error: drugi skup nije baza\"\n if m1.shape != m2.shape: return \"Error: baze nisu iz istog vektorskog prostora\"\n return np.matmul(np.linalg.inv(m1), m2)","title":""},{"docid":"608729f5776723ef3a83b0fa00907447","score":"0.44357574","text":"def shortest_logicals(self, start_weight=None, delta=0, verbose=True, css=False):\n\n # if not self.logical_xs_reference and not self.logical_zs_reference:\n # self.eval(verbose=False)\n\n if start_weight is None:\n if self.dist is not None:\n start_weight = self.dist\n else:\n start_weight = 1\n\n end_weight = start_weight + delta\n\n if self.circuit is None:\n raise Exception('Must compile circuits first!')\n\n qudit_set = self.data_qubits\n\n if end_weight > len(qudit_set):\n end_weight = len(qudit_set)\n\n state = self.state\n found = self._dist_mode_smallest(state, qudit_set, css=css, verbose=False, start_len=start_weight,\n end_len=end_weight, list_ops=True)\n\n xs_labels = sorted(self.logical_xs_reference.keys())\n zs_labels = sorted(self.logical_zs_reference.keys())\n\n oplist = []\n\n if found:\n for paulis in found:\n # weight = len(paulis['X'] | paulis['Z'])\n\n op_product = []\n for xi, op_label in enumerate(xs_labels):\n if self.op_anticommute(paulis, self.logical_xs_reference[op_label]):\n op_product.append(zs_labels[xi])\n\n for zi, op_label in enumerate(zs_labels):\n if self.op_anticommute(paulis, self.logical_zs_reference[op_label]):\n op_product.append(xs_labels[zi])\n\n op_product = sorted(op_product)\n\n oplist.append({'X': paulis['X'], 'Z': paulis['Z'], 'equiv_ops': tuple(op_product)})\n\n if verbose:\n\n print('Reference Logical Operators:')\n print('\\nLogical Xs:')\n for op_label in xs_labels:\n op = self.logical_xs_reference[op_label]\n print(op_label, op)\n print('\\nLogical Zs:')\n for op_label in zs_labels:\n op = self.logical_zs_reference[op_label]\n print(op_label, op)\n\n print('\\nLogical Ops Found:\\n')\n for foundop in oplist:\n print('X - %s Z - %s Equiv Ops - %s' % (foundop['X'], foundop['Z'], foundop['equiv_ops']))\n\n return oplist, self.logical_xs_reference, self.logical_zs_reference","title":""},{"docid":"686ede47e5b5b04957fb41f12a8d12c8","score":"0.44339296","text":"def ds(op):\n if len(op)==1: return 0\n return int(op[1]+'1')","title":""},{"docid":"21c3ef04b214b1e888a7ceb63ce786a7","score":"0.44323573","text":"def g_function_cobra(self):\n if self.num_1 > self.num_2:\n return self.num_1 + self.num_2\n return self.num_1 + self.num_2","title":""},{"docid":"c3835c9c8a49248bdbc64faf0ac889cf","score":"0.44291458","text":"def _op_8_nest(self):\r\n self._optable_8[self._N]()","title":""},{"docid":"e2c8128d91095fe579cad4e2b68497fd","score":"0.4428731","text":"def Conditional__mul__(self, other):\n try:\n a = self.inf\n b = self.sup\n c = other.inf\n d = other.sup\n \n if a >= 0. and c >= 0.:\n return ia(a*c,b*d)\n elif a >= 0. and c<0.<d:\n return ia(b*c,b*d)\n elif a >= 0. and d <= 0.:\n return ia(b*c,a*d)\n elif a<0.<b and c>=0.:\n return ia(a*d,b*d) #corrected\n elif a<0.<b and d<=0.:\n return ia(b*c,a*c) # I say ad hansen says ac for sup #corrected# ia(b*d,a*d)- I agree with Eldon\n elif b<=0. and c>=0.:\n return ia(a*d,b*c)\n elif b<=0. and c<0.<d:\n return ia(a*d,a*c)\n elif b<=0. and d<=0.:\n return ia(b*d,a*c)\n elif a<0.<b and c<0.<d:\n return ia(min(b*c,a*d,a*c,b*d),max(b*c,a*d,a*c,b*d))\n #return ia(min(b*c,a*d),max(a*c,b*d))\n else:\n print 'error in multiplication'\n c1 = a*c\n c2 = a*d\n c3 = b*c\n c4 = b*d\n return ia(min(c1,c2,c3,c4),max(c1,c2,c3,c4))\n \n \n except:\n a = self.inf\n b = self.sup\n c = other\n d = other\n \n if a >= 0. and c >= 0.:\n return ia(a*c,b*d)\n elif a >= 0. and c<0.<d:\n return ia(b*c,b*d)\n elif a >= 0. and d <= 0.:\n return ia(b*c,a*d)\n elif a<0.<b and c>=0.:\n return ia(a*d,b*d) #corrected\n elif a<0.<b and d<=0.:\n return ia(b*c,a*c) # I say ad hansen says ac for sup #corrected# ia(b*d,a*d)- I agree with Eldon\n elif b<=0. and c>=0.:\n return ia(a*d,b*c)\n elif b<=0. and c<0.<d:\n return ia(a*d,a*c)\n elif b<=0. and d<=0.:\n return ia(b*d,a*c)\n elif a<0.<b and c<0.<d:\n return ia(min(b*c,a*d,a*c,b*d),max(b*c,a*d,a*c,b*d))\n #return ia(min(b*c,a*d),max(a*c,b*d))\n else:\n print 'error in multiplication'\n c1 = a*c\n c2 = a*d\n c3 = b*c\n c4 = b*d\n return ia(min(c1,c2,c3,c4),max(c1,c2,c3,c4))","title":""},{"docid":"09d18bceb93260af3312c09a0fd1ad0a","score":"0.44228566","text":"def diamond_bracket_operator(self, d):\n d = int(d) % self.__M.level()\n try:\n return self.__diamond_operator[d]\n except AttributeError:\n self.__diamond_operator = {}\n except KeyError:\n pass\n D = self.__M._diamond_operator_class()(self, d)\n self.__diamond_operator[d] = D\n return D","title":""},{"docid":"a2fc8be77084ef22852e1b79adecabef","score":"0.44221944","text":"def _q_multiply_shift_hexagon(op):\n x = op.args[0]\n y = op.args[1]\n fractional_bits = op.args[2]\n shift = op.args[3]\n\n # Don't use this intrinsic if we don't have a int32x32 vector\n # or if we are not multiplying q31 numbers\n if x.dtype != \"int32x32\" or fractional_bits.value != 31:\n return op\n\n # Case 1, shift is negative\n mul_e_1 = tvm.tir.call_llvm_intrin(\n op.dtype, \"llvm.hexagon.V6.vmpyewuh.128B\", tvm.tir.const(2, \"uint32\"), x, y\n )\n mul_o_1 = tvm.tir.call_llvm_intrin(\n op.dtype, \"llvm.hexagon.V6.vmpyowh.sacc.128B\", tvm.tir.const(3, \"uint32\"), mul_e_1, x, y\n )\n fixup = 1 << (-shift - 1)\n round_mul = mul_o_1 + fixup\n out_negative_shift = tvm.tir.call_llvm_intrin(\n op.dtype, \"llvm.hexagon.V6.vaslwv.128B\", tvm.tir.const(2, \"uint32\"), round_mul, shift\n )\n\n # Case 2, shift is positive\n x = x * (1 << (shift))\n mul_e_2 = tvm.tir.call_llvm_intrin(\n op.dtype, \"llvm.hexagon.V6.vmpyewuh.128B\", tvm.tir.const(2, \"uint32\"), x, y\n )\n mul_o_2 = tvm.tir.call_llvm_intrin(\n op.dtype, \"llvm.hexagon.V6.vmpyowh.rnd.sacc.128B\", tvm.tir.const(3, \"uint32\"), mul_e_2, x, y\n )\n\n # Select depending on the shift\n return tvm.tir.Select(shift < 0, out_negative_shift, mul_o_2)","title":""},{"docid":"283f5e4fd4fa97896569b204e88c5d9c","score":"0.44218627","text":"def print_ans(M1, M2):\n if M1 == M2:\n print(\"0\")\n else:\n print(\"1\")","title":""},{"docid":"c8d154c67ffddec1de0f7c32f4c340ce","score":"0.44174978","text":"def test2_12_1_1(self):\n GA, e_1, e_2, e_3 = Ga.build('e*1|2|3')\n self.assertEqual((e_1 + e_2) ^ (e_1 + e_3), (-e_1 ^ e_2) + (e_1 ^ e_3) + (e_2 ^ e_3))\n self.assertEqual((e_1 + e_2 + e_3) ^ (2 * e_1), -2 * (e_1 ^ e_2) - 2 * (e_1 ^ e_3))\n self.assertEqual((e_1 - e_2) ^ (e_1 - e_3), (e_1 ^ e_2) - (e_1 ^ e_3) + (e_2 ^ e_3))\n self.assertEqual(\n (e_1 + e_2) ^ (0.5 * e_1 + 2 * e_2 + 3 * e_3), 1.5 * (e_1 ^ e_2) + 3 * (e_1 ^ e_3) + 3 * (e_2 ^ e_3))\n self.assertEqual((e_1 ^ e_2) ^ (e_1 + e_3), (e_1 ^ e_2 ^ e_3))\n self.assertEqual((e_1 + e_2) ^ ((e_1 ^ e_2) + (e_2 ^ e_3)), (e_1 ^ e_2 ^ e_3))","title":""},{"docid":"45f179a393572ee5b40eec51cef01314","score":"0.4416586","text":"def verify_type_match(self, left, right, operator):\r\n \r\n # codigo para depurar\r\n global debug\r\n if debug :\r\n print('antes')\r\n print('l: ', left)\r\n print('r: ', right)\r\n print('op: ', operator)\r\n \r\n # se obtienen los codigos que representan a cada tipo\r\n l = self.get_val(left)\r\n r = self.get_val(right)\r\n op = self.get_val(operator)\r\n\r\n # codigo para depurar\r\n if debug :\r\n print('despues')\r\n print('l: ', l)\r\n print('r: ', r)\r\n print('op: ', op)\r\n\r\n # Se asigna un codigo a cada tipo\r\n ERROR, ENT, FLOAT, CADENA, CHAR, BOOL = 0, 1, 2, 3, 4, 5\r\n\r\n # Se inicializa el cubo semantico\r\n cube = (\r\n ((ENT, ENT, ENT, FLOAT, ERROR, ERROR, BOOL, BOOL, BOOL, BOOL, BOOL, BOOL, ENT),\r\n (FLOAT, FLOAT, FLOAT, FLOAT, ERROR, ERROR, BOOL, BOOL, BOOL, BOOL, BOOL, BOOL, ENT),\r\n (ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR),\r\n (ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR),\r\n (ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR),\r\n ),\r\n ((FLOAT, FLOAT, FLOAT, FLOAT, ERROR, ERROR, BOOL, BOOL, BOOL, BOOL, BOOL, BOOL, FLOAT),\r\n (FLOAT, FLOAT, FLOAT, FLOAT, ERROR, ERROR, BOOL, BOOL, BOOL, BOOL, BOOL, BOOL, FLOAT),\r\n (ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR),\r\n (ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR),\r\n (ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR),\r\n ),\r\n ((ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR),\r\n (ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR),\r\n (ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, BOOL, BOOL, CADENA),\r\n (ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR),\r\n (ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR)\r\n ),\r\n ((ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR),\r\n (ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR),\r\n (ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR),\r\n (ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, BOOL, BOOL, CHAR),\r\n (ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR),\r\n ),\r\n ((ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR),\r\n (ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR),\r\n (ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR),\r\n (ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR),\r\n (ERROR, ERROR, ERROR, ERROR, BOOL, BOOL, ERROR, ERROR, ERROR, ERROR, BOOL, BOOL, BOOL),\r\n ),\r\n )\r\n\r\n # Se guarda el resultado producido por el cubo\r\n result = cube[l][r][op]\r\n\r\n # Dependiendo del reusltado se regresa una cadena con el tipo resultante\r\n # o un -1 en caso de error de tipos.\r\n if result is 0:\r\n return -1 \r\n elif result is 1:\r\n return \"entero\"\r\n elif result is 2:\r\n return \"flotante\"\r\n elif result is 3:\r\n return \"cadena\"\r\n elif result is 4:\r\n return \"caracter\"\r\n elif result is 5:\r\n return \"bool\"","title":""},{"docid":"c6bb680ad0182c32d8ebb4e4d89e156f","score":"0.44119498","text":"def boolean(\n A: Union[ComponentOrReference, Tuple[ComponentOrReference, ...]],\n B: Union[ComponentOrReference, Tuple[ComponentOrReference, ...]],\n operation: str,\n precision: float = 1e-4,\n num_divisions: Union[int, Int2] = (1, 1),\n max_points: int = 4000,\n layer: LayerSpec = (1, 0),\n) -> Component:\n D = Component()\n A_polys = []\n B_polys = []\n A = list(A) if isinstance(A, (list, tuple)) else [A]\n B = list(B) if isinstance(B, (list, tuple)) else [B]\n\n for X, polys in ((A, A_polys), (B, B_polys)):\n for e in X:\n if isinstance(e, (Component, ComponentReference)):\n polys.extend(e.get_polygons())\n elif isinstance(e, Polygon):\n polys.extend(e.polygons)\n\n layer = gf.pdk.get_layer(layer)\n gds_layer, gds_datatype = _parse_layer(layer)\n\n operation = operation.lower().replace(\" \", \"\")\n if operation == \"a-b\":\n operation = \"not\"\n elif operation == \"b-a\":\n operation = \"not\"\n A_polys, B_polys = B_polys, A_polys\n elif operation == \"a+b\":\n operation = \"or\"\n elif operation not in [\"not\", \"and\", \"or\", \"xor\", \"a-b\", \"b-a\", \"a+b\"]:\n raise ValueError(\n \"gdsfactory.geometry.boolean() `operation` \"\n \"parameter not recognized, must be one of the \"\n \"following: 'not', 'and', 'or', 'xor', 'A-B', \"\n \"'B-A', 'A+B'\"\n )\n\n # Check for trivial solutions\n if (not A_polys or not B_polys) and operation != \"or\":\n if (\n operation != \"not\"\n and operation != \"and\"\n and operation == \"xor\"\n and not A_polys\n and not B_polys\n or operation != \"not\"\n and operation == \"and\"\n ):\n p = None\n elif operation != \"not\" and operation == \"xor\" and not A_polys:\n p = B_polys\n elif operation != \"not\" and operation == \"xor\":\n p = A_polys\n elif operation == \"not\":\n p = A_polys or None\n elif not A_polys and not B_polys:\n p = None\n elif all(np.array(num_divisions) == np.array([1, 1])):\n p = gdspy.boolean(\n operand1=A_polys,\n operand2=B_polys,\n operation=operation,\n precision=precision,\n max_points=max_points,\n layer=gds_layer,\n datatype=gds_datatype,\n )\n else:\n p = _boolean_polygons_parallel(\n polygons_A=A_polys,\n polygons_B=B_polys,\n num_divisions=num_divisions,\n operation=operation,\n precision=precision,\n )\n\n if p is not None:\n polygons = D.add_polygon(p, layer=layer)\n [\n polygon.fracture(max_points=max_points, precision=precision)\n for polygon in polygons\n ]\n return D","title":""},{"docid":"1f02dca0ab8c1db5ea5e0d874415c867","score":"0.44119266","text":"def crtaj_modul ( orMj , dX ):\n \"\"\"\n od = rel2abs ( origin , poc , smjer )\n do = rel2abs ( origin , kraj , smjer )\n mc.setBlocks ( od , do , blok_id , blok_dv )\n \"\"\"\n crtaj_kvadar ( orMj , [ 3 + dX , 0, 0 ] , [ 3 + dX , -2 , 2 ] , orSm , materijal , 2 ) # blok\n crtaj_kvadar ( orMj , [ 3 + dX , -2, 2 ] , [ 3 + dX , -2 , 2 ] , orSm , 0 , 0 ) # zrak\n crtaj_kvadar ( orMj , [ 3 + dX , -1, 1 ] , [ 3 + dX , -1 , 1 ] , orSm , 0 , 0 ) # zrak\n crtaj_kvadar ( orMj , [ 3 + dX , 0, 0 ] , [ 3 + dX , 0 , 0 ] , orSm , 0 , 0 ) # zrak\n crtaj_kvadar ( orMj , [ 3 + dX , -2, 0 ] , [ 3 + dX , -2 , 0 ] , orSm , 0 , 0 ) # zrak\n crtaj_redstonedust ( orMj , [ 3 + dX , -2, 2 ] , [ 3 + dX , -2 , 2 ] , orSm )\n crtaj_redstonedust ( orMj , [ 3 + dX , -1, 3 ] , [ 3 + dX , -1 , 3 ] , orSm )\n crtaj_redstonetorch ( orMj , [ 3 + dX , 1, 1 ] , orSm , \"desno\" ) \n crtaj_comparator ( orMj , [ 3 + dX , 0, 3 ] , [ 3 + dX , 0 , 3 ] , orSm , rel_smjer = \"lijevo\" )\n crtaj_repeater ( orMj , [ 3 + dX , -1, 1 ] , [ 3 + dX , -1 , 1 ] , orSm , rel_smjer = \"desno\" )\n \n crtaj_hopper ( orMj , [ 3 + dX , 1, 2 ] , [ 3 + dX , 1 , 3 ] , orSm , \"desno\" ) # dva doljnja\n crtaj_hopper ( orMj , [ 3 + dX , 1, 4 ] , [ 3 + dX , 1 , 4 ] , orSm , \"odmene\" ) # gornji\n \n sto = '{TransferCooldown:0,Items:[0:{Slot:0b,id:\"%s\",Count:4b,Damage:%ss,},1:{Slot:1b,id:\"%s\",Count:5b,Damage:%ss,},2:{Slot:2b,id:\"%s\",Count:5b,Damage:%ss,},3:{Slot:3b,id:\"%s\",Count:5b,Damage:%ss,},4:{Slot:4b,id:\"%s\",Count:5b,Damage:%ss,},],id:\"Hopper\",Lock:\"\",}' % ( popis [ dX ] [ 0 ] , popis [ dX ] [ 1 ] , popis [ dX ] [ 0 ] , popis [ dX ] [ 1 ] , popis [ dX ] [ 0 ] , popis [ dX ] [ 1 ] , popis [ dX ] [ 0 ] , popis [ dX ] [ 1 ] , popis [ dX ] [ 0 ] , popis [ dX ] [ 1 ] )\n bla = rel2abs ( orMj , ( 3 + dX , 1 , 3 ) , orSm )\n mc.postToChat(\"orginal: %s %s \" % ( dX , bla ) )\n time.sleep ( 1 )\n mc.setBlockWithNBT(bla,154,smjer_hoppera ( orSm , \"desno\") , sto ) #hopper gleda na istok \n bla = rel2abs ( orMj , ( 3 + dX , 1 , 2 ) , orSm )\n mc.postToChat(\"orginal: %s %s \" % ( dX , bla ) )\n time.sleep ( 1 )\n mc.setBlockWithNBT(bla,154,smjer_hoppera ( orSm , \"desno\") , sto ) #hopper gleda na istok \n #bla = rel2abs ( orMj , ( 3 + dX , 5 , 2 ) , orSm )\n #mc.setBlock(bla,53,4 ) #oak wood stairs naopako, gledaju na istok\n crtaj_stepenice ( orMj , ( 3 + dX , 5 , 2 ) , ( 3 + dX , 5 , 2 ) , orSm , blok_id = 53 , rel_smjer = \"desno\" , gore_dolje = \"da\" )\n\n \n kutija = 54\n tkutija = 146\n if ( int ( dX ) % 2 == 1 ) :\n kmat = kutija\n else :\n kmat = tkutija\n crtaj_kutiju ( orMj , [ 3 + dX , 2, 2 ] , [ 3 + dX , 3 , 2 ] , orSm , rel_smjer = \"meni\" , blok_id = kmat )\n crtaj_hopper ( orMj , [ 3 + dX , 3, 1 ] , [ 3 + dX , 3 , 0 ] , orSm , \"desno\" ) # hopper ispod kutije\n crtaj_kutiju ( orMj , [ 3 + dX , 4, 1 ] , [ 3 + dX , 5 , 0 ] , orSm , rel_smjer = \"meni\" , blok_id = kmat ) # dodatne kutije","title":""},{"docid":"833e060bbc82e213f266619f193cac80","score":"0.44113728","text":"def test_weight_for_valid_operator(self):\n self.assertTrue(type(self.instance.weight('*')) is IntType)","title":""},{"docid":"86e72879b685d99a34842bc065804d16","score":"0.4410812","text":"def test_batting_flags(batting):\n flag_cols = [\n 'g',\n 'g_dh',\n 'g_ph',\n 'g_pr'\n ]\n\n assert batting[flag_cols].min().min() == 0\n assert batting[flag_cols].max().max() == 1","title":""},{"docid":"e5515de56ece5c5f81b5757893cc31f6","score":"0.44100785","text":"def shift_operator(self,dd_):\n \n N_ = self.N\n aa = self.anihilation_operator()\n ad = self.creation_operator()\n \n # construct the Shift Operator\n Dd_large = numpy.zeros((N_,N_),dtype=numpy.float)\n Dd_large = dd_*(ad-aa)/numpy.sqrt(2.0)\n\n # Diagonalize and obtain transformation matrix\n A,S = numpy.linalg.eig(Dd_large)\n S1 = numpy.linalg.inv(S)\n \n # Exponentiate\n Dd_large = numpy.diag(numpy.exp(A))\n \n # Transform back and reduce to the lower number of states\n return numpy.real(numpy.dot(S,numpy.dot(Dd_large,S1)))","title":""},{"docid":"592e263302081fd9d99d999078f2dc78","score":"0.44087765","text":"def operator_eq(self, *args):\n return _vnl_matrix_fixedPython.vnl_matrix_fixedD_4_3_operator_eq(self, *args)","title":""},{"docid":"a971986501ed11ceaba23764587bbaae","score":"0.4408561","text":"def operar():\n a=[]\n b = []\n exit =False\n while not exit:\n print\n opcions()\n op = mainLib.askNumberOption(\"Choose an option: \", 8)\n print\n if(op == 1):\n a = AdministrarCrearMatriu(\"a\")\n elif(op == 2):\n b = AdministrarCrearMatriu(\"b\")\n elif (op == 3):\n AdministrarOperacio(\"+\",a,b)\n elif(op == 4):\n AdministrarOperacio(\"-\", a, b)\n elif (op == 5):\n AdministrarOperacio(\"*\", a, b)\n elif(op == 6):\n AdministrarOperacioUnaMatriu(\"d\",a,b)\n elif(op == 7):\n AdministrarOperacioUnaMatriu(\"t\",a,b)\n else:\n exit = True","title":""}],"string":"[\n {\n \"docid\": \"af7549cc5d18b9cf9d56a081cd3520b8\",\n \"score\": \"0.7329589\",\n \"text\": \"def operator_kanbaza(f,baza1,baza2=None,klasa='sympy'): \\n if isfunction(f):\\n #operator f je zadan formulom\\n M = operator_kan(f,klasa)\\n else:\\n #operator f je zadan matricom u paru kanonskih baza\\n M = f\\n if klasa == 'sympy':\\n S = sp.Matrix(baza1).T\\n if S.det() == 0: return \\\"Error: prvi skup nije baza\\\"\\n if baza2 == None:\\n T = sp.Matrix(baza1).T\\n else:\\n T = sp.Matrix(baza2).T\\n if T.det() == 0: return \\\"Error: drugi skup nije baza\\\"\\n return T.inv()*M*S\\n elif klasa == 'numpy':\\n S = np.transpose(np.array(baza1))\\n if np.abs(np.linalg.det(S)) < 1e-15: return \\\"Error: prvi skup nije baza\\\"\\n if baza2 == None:\\n T = np.transpose(np.array(baza1))\\n else:\\n T = np.transpose(np.array(baza2))\\n if np.abs(np.linalg.det(T)) < 1e-15: return \\\"Error: drugi skup nije baza\\\"\\n return np.matmul( np.matmul(np.linalg.inv(T), M), S)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18b1510b5cbc4819c5289f364d9616ea\",\n \"score\": \"0.5638643\",\n \"text\": \"def operator(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"659288c0775c338c4c2a80237a24c876\",\n \"score\": \"0.54506\",\n \"text\": \"def AdministrarOperacio(operacio, a, b):\\n if(mainLib.checkIfCanOperate(a, b)):\\n if(operacio == \\\"+\\\"):\\n op = \\\"Suma\\\"\\n x = mainLib.sumaMatrius(a, b)\\n elif(operacio == \\\"-\\\"):\\n op = \\\"Resta\\\"\\n x = mainLib.restaMatrius(a, b)\\n else:\\n op = \\\"Multiplicacio\\\"\\n x = mainLib.multMatrius(a, b)\\n\\n print op + \\\" de A i B: \\\"\\n print\\n mainLib.mostraMatriu(a)\\n print \\\" \\\" + operacio\\n mainLib.mostraMatriu(b)\\n print \\\" ||\\\"\\n mainLib.mostraMatriu(x)\\n\\n else:\\n print mainLib.whichMatrixIsEmpty(a, b)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7a604825403906fea3ee86bcf2596b4\",\n \"score\": \"0.5405991\",\n \"text\": \"def operator(op,L_max,m,s):\\n \\n def ds(op):\\n \\\"\\\"\\\"get s increment from the operator string\\\"\\\"\\\"\\n if len(op)==1: return 0\\n return int(op[1]+'1')\\n\\n root_half = np.sqrt(0.5)\\n\\n N = L_max-L_min(m,s)\\n\\n # identity\\n if op == 'I':\\n a,b = a_and_b(m,s)\\n return jacobi.operator('I',N,a,b)\\n\\n # cosine multiplication\\n if op == 'C':\\n a,b = a_and_b(m,s)\\n return jacobi.operator('J',N,a,b)\\n\\n # derivatives\\n if (op == 'k+') or (op=='k-'):\\n a,b,da,db = a_and_b(m,s,ds=ds(op))\\n if (da== 1) and (db==-1): return jacobi.operator('C+',N ,a,b,rescale=-root_half)\\n if (da==-1) and (db== 1): return jacobi.operator('C-',N ,a,b,rescale= root_half)\\n if (da== 1) and (db== 1): return jacobi.operator('D+',N ,a,b,rescale=-root_half)[:-1,:]\\n if (da==-1) and (db==-1): return jacobi.operator('D-',N+1,a,b,rescale= root_half)[:,:-1]\\n\\n # sine multiplication\\n if (op == 'S+') or (op=='S-'):\\n a,b,da,db = a_and_b(m,s,ds=ds(op))\\n if (da== 1) and (db==-1):\\n A = jacobi.operator('A+',N+1,a, b)\\n B = jacobi.operator('B-',N+1,a+1,b)\\n return B.dot(A)[:-1,:-1]\\n if (da==-1) and (db== 1):\\n A = jacobi.operator('A-',N+1,a, b)\\n B = jacobi.operator('B+',N+1,a-1,b)\\n return B.dot(A)[:-1,:-1]\\n if (da== 1) and (db== 1):\\n A = jacobi.operator('A+',N+1,a, b)\\n B = jacobi.operator('B+',N+1,a+1,b)\\n return (B.dot(A))[:-2,:-1]\\n if (da==-1) and (db==-1):\\n A = jacobi.operator('A-',N+2,a, b)\\n B = jacobi.operator('B-',N+2,a-1,b)\\n return (B.dot(A))[:-1,:-2]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8416086a294220da01f163c7a47cb00\",\n \"score\": \"0.5333984\",\n \"text\": \"def AdministrarOperacioUnaMatriu(operacio, a, b):\\n if(operacio == \\\"d\\\"):\\n op = \\\"Determinant\\\"\\n else:\\n op = \\\"Trasposada\\\"\\n\\n aOrB = mainLib.askNumberOption(op +\\\" de A (1) o de B (2): \\\", 2)\\n if (aOrB == 1):\\n OperacioUnaMatriu(operacio, a, \\\"A\\\")\\n else:\\n OperacioUnaMatriu(operacio, b, \\\"B\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bda4555c3c59dfc5e8b19de1fb6609df\",\n \"score\": \"0.52813727\",\n \"text\": \"def getOperator(mpo):\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4cb6f20aa8f7a8c2acd33a163bf105dc\",\n \"score\": \"0.52739495\",\n \"text\": \"def klik(self,w,k,PPM):\\r\\n if self._aktualizacja == True or self._grazakonczona == True:\\r\\n return\\r\\n self._aktualizacja = True\\r\\n if PPM == True:\\r\\n w = self._plansza.polePPM(w,k) \\r\\n else:\\r\\n w = self._plansza.poleLPM(w,k)\\r\\n if w == 1: #wygrana\\r\\n self._grazakonczona = True\\r\\n self._gui.wygrana()\\r\\n elif w == 2: #przegrana\\r\\n self._grazakonczona = True\\r\\n self._gui.przegrana()\\r\\n self._aktualizacja = False\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83f7c715bb565bc65bee8bda0ae362ab\",\n \"score\": \"0.520008\",\n \"text\": \"def poleLPM(self,w,k,nie_klikaj_pytajnika=False):\\r\\n #pole w pozycji startowej\\r\\n if self._pola[w][k][0] % 7 == 0:\\r\\n if self._pola[w][k][1] == True: #bomba\\r\\n self._pola[w][k][0] = 6\\r\\n self._gui.ustaw_pole(w, k, '', self._pola[w][k][0],\\r\\n self._ilebomb - self._ileflag, self._pytajniki,\\r\\n self._wolnepola)\\r\\n return self.przegrana()\\r\\n else: \\r\\n self._pola[w][k][0] = 4 #ustawianie stanu\\r\\n ile = str(self.przegladnij_sasiadow(w,k))\\r\\n #uaktualnianie przycisku\\r\\n if ile == '0':\\r\\n ile = ''\\r\\n self.przeklikaj_sasiadow(w,k)\\r\\n self._wolnepola -= 1\\r\\n self._gui.ustaw_pole(w, k, ile, self._pola[w][k][0],\\r\\n self._ilebomb - self._ileflag, self._pytajniki,\\r\\n self._wolnepola)\\r\\n if self._wolnepola == 0 == self._pytajniki:\\r\\n return self.wygrana() \\r\\n #pole z flaga\\r\\n elif self._pola[w][k][0] % 7 == 1:\\r\\n #przyc lewy zablokowany\\r\\n pass\\r\\n \\r\\n \\r\\n #pole z pytajnikiem\\r\\n elif self._pola[w][k][0] % 7 == 2:\\r\\n #przy automatycznym odkrywaniu nie sprawdza flagi\\r\\n if nie_klikaj_pytajnika == False: \\r\\n self._pytajniki -= 1\\r\\n if self._pola[w][k][1] == True: #bomba\\r\\n self._pytajniki -= 1\\r\\n self._pola[w][k][0] = 6\\r\\n self._gui.ustaw_pole(w, k, '', self._pola[w][k][0],\\r\\n self._ilebomb - self._ileflag, self._pytajniki,\\r\\n self._wolnepola)\\r\\n return self.przegrana()\\r\\n \\r\\n #klik PPM - z autoodkrywaniem\\r\\n else:\\r\\n self._pola[w][k][0] = 4\\r\\n ile = str(self.przegladnij_sasiadow(w,k))\\r\\n #uaktualnianie przycisku\\r\\n if ile == '0':\\r\\n ile = ''\\r\\n self.przeklikaj_sasiadow(w,k)\\r\\n self._wolnepola -= 1\\r\\n self._gui.ustaw_pole(w, k, ile, self._pola[w][k][0],\\r\\n self._ilebomb - self._ileflag, self._pytajniki,\\r\\n self._wolnepola)\\r\\n if self._wolnepola == 0 == self._pytajniki:\\r\\n return self.wygrana() \\r\\n return 0 #status \\\"gramy dalej\\\"\\r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7f392b6eb2f2d20ca5a3967dec70241\",\n \"score\": \"0.5185078\",\n \"text\": \"def polePPM(self,w,k):\\r\\n #pole w pozycji startowej\\r\\n if self._pola[w][k][0] % 7 == 0:\\r\\n #ustaw stan z uwzglednieniem kodu xyzzy jesli to jest pole z bomba\\r\\n self._pola[w][k][0] = 1 + 7*self._uzytykod*self._pola[w][k][1]\\r\\n #aktualizacja zliczaczy\\r\\n self._ileflag += 1\\r\\n if self._pola[w][k][1] == True:\\r\\n self._flagzbomb += 1\\r\\n #ustawianie pola w interfejsie\\r\\n self._gui.ustaw_pole(w, k, \\\"\\\", self._pola[w][k][0],\\r\\n self._ilebomb - self._ileflag, self._pytajniki,\\r\\n self._wolnepola)\\r\\n #sprawdzanie czy nastapila wygrana\\r\\n if self._ilebomb == self._flagzbomb == self._ileflag and 0 == self._pytajniki:\\r\\n return self.wygrana()\\r\\n\\r\\n \\r\\n #pole z flaga\\r\\n elif self._pola[w][k][0] % 7 == 1:\\r\\n self._pola[w][k][0] = 2 + 7*self._uzytykod*self._pola[w][k][1]\\r\\n self._pytajniki += 1\\r\\n self._ileflag -= 1\\r\\n if self._pola[w][k][1] == True:\\r\\n self._flagzbomb -= 1\\r\\n self._gui.ustaw_pole(w, k, \\\"\\\", self._pola[w][k][0],\\r\\n self._ilebomb - self._ileflag, self._pytajniki,\\r\\n self._wolnepola)\\r\\n \\r\\n \\r\\n #pole z pytajnikiem\\r\\n elif self._pola[w][k][0] % 7 == 2:\\r\\n self._pytajniki -= 1\\r\\n self._pola[w][k][0] = 0+ 7*self._uzytykod*self._pola[w][k][1]\\r\\n self._gui.ustaw_pole(w, k, \\\"\\\", self._pola[w][k][0],\\r\\n self._ilebomb - self._ileflag, self._pytajniki,\\r\\n self._wolnepola)\\r\\n \\r\\n #wszystkie bomby wczesniej poprawnie oflagowane, brak zbednych flag, znika ostatni pytajnik\\r\\n if self._ilebomb == self._flagzbomb == self._ileflag and 0 == self._pytajniki:\\r\\n return self.wygrana()\\r\\n \\r\\n #wszystkie bez min byly klikniete LPM, ale byly rowniez pytajniki\\r\\n if self._wolnepola == 0 == self._pytajniki:\\r\\n return self.wygrana()\\r\\n \\r\\n return 0 #status \\\"gramy dalej\\\"\\r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b334d51b06c52c97ede3a6c06e14d3b3\",\n \"score\": \"0.5171027\",\n \"text\": \"def same_quantum(self, a, b):\\r\\n return a.same_quantum(b)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b334d51b06c52c97ede3a6c06e14d3b3\",\n \"score\": \"0.5171027\",\n \"text\": \"def same_quantum(self, a, b):\\r\\n return a.same_quantum(b)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82d4a5ad8db163476da9cbd00602b275\",\n \"score\": \"0.51175624\",\n \"text\": \"def operator(self, values, idx_choosen):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"246948c3d4b36ecf7941431d8a70a53a\",\n \"score\": \"0.5031777\",\n \"text\": \"def przegrana(self):\\r\\n for w in range(self._w):\\r\\n for k in range(self._k):\\r\\n if self._pola[w][k][0] %7 in [0]:\\r\\n #nieklikniete pole z bomba\\r\\n if self._pola[w][k][1] == True: #bomba\\r\\n self._pola[w][k][0] = 3 + self._uzytykod * 7 #ustawianie stanu\\r\\n self._gui.ustaw_pole(w, k, '', self._pola[w][k][0],\\r\\n self._ilebomb - self._flagzbomb, self._pytajniki,\\r\\n self._wolnepola)\\r\\n elif self._pola[w][k][0] %7 in [1]:\\r\\n #flaga postawiona w zlym miejscu\\r\\n if self._pola[w][k][1] == False: #bomba\\r\\n self._gui.ustaw_pole(w, k, '', 5,\\r\\n self._ilebomb - self._flagzbomb, self._pytajniki,\\r\\n self._wolnepola)\\r\\n elif self._pola[w][k][0] %7 in [2]:\\r\\n #odkrywanie pytajnika jezeli jest pod nim bomba\\r\\n if self._pola[w][k][1] == True: #bomba\\r\\n self._pola[w][k][0] = 3 + self._uzytykod * 7 #ustawianie stanu\\r\\n self._gui.ustaw_pole(w, k, '', self._pola[w][k][0],\\r\\n self._ilebomb - self._flagzbomb, self._pytajniki,\\r\\n self._wolnepola) \\r\\n return 2 #status przegranej\\r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c40ba6cd6b941d5da25cb6f7d5fb2187\",\n \"score\": \"0.4993907\",\n \"text\": \"def PauliOperator(label):\\n pauli = {\\n 'I': np.matrix([[1, 0], [0, 1]]),\\n 'Z': np.matrix([[1, 0], [0, -1]]),\\n 'X': np.matrix([[0, 1], [1, 0]]),\\n 'Y': np.matrix([[0, -1j], [1j, 0]])\\n }\\n\\n operator = pauli[label[0]]\\n for letter in label[1:]:\\n operator = np.kron(operator, pauli[letter])\\n\\n return operator\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be284642f1d7cbf8fd54ea8c4221d8b0\",\n \"score\": \"0.4970203\",\n \"text\": \"def test_operator_mapping(operand_a, operand_b, operator, expected_result):\\n\\n operator_func = OperatorUtils.get_operator(operator)\\n\\n assert operator_func(operand_a, operand_b) == expected_result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63fe42a54889ff40147928264b35afe3\",\n \"score\": \"0.49599716\",\n \"text\": \"def __or__(self, other: \\\"QuOperator\\\") -> \\\"QuOperator\\\":\\n return self.tensor_product(other)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74a0c700148ca3b9363d11be29adc8f0\",\n \"score\": \"0.49543437\",\n \"text\": \"def test2_12_1_2(self):\\n GA, e_1, e_2, e_3 = Ga.build('e*1|2|3')\\n B = e_1 ^ (e_2 - e_3)\\n self.assertEqual(e_1 ^ B, 0)\\n self.assertNotEqual((e_1 + e_2) ^ B, 0)\\n self.assertNotEqual((e_1 + e_2 + e_3) ^ B, 0)\\n self.assertEqual((2 * e_1 - e_2 + e_3) ^ B, 0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"277e49f908efec3b393c80884e746cba\",\n \"score\": \"0.49329183\",\n \"text\": \"def test_two_qubits_merge_gate_subset(self):\\n\\n def qfunc():\\n qml.CRX(0.1, wires=[0, 1])\\n qml.CRX(0.2, wires=[0, 1])\\n qml.RY(0.3, wires=[\\\"a\\\"])\\n qml.RY(0.5, wires=[\\\"a\\\"])\\n qml.RX(-0.5, wires=[2])\\n qml.RX(0.2, wires=[2])\\n qml.RZ(0.2, wires=[2])\\n\\n transformed_qfunc = merge_rotations(include_gates=[\\\"RX\\\", \\\"CRX\\\"])(qfunc)\\n\\n ops = qml.tape.make_qscript(transformed_qfunc)().operations\\n\\n names_expected = [\\\"CRX\\\", \\\"RY\\\", \\\"RY\\\", \\\"RX\\\", \\\"RZ\\\"]\\n wires_expected = [Wires([0, 1]), Wires(\\\"a\\\"), Wires(\\\"a\\\"), Wires(2), Wires(2)]\\n compare_operation_lists(ops, names_expected, wires_expected)\\n\\n assert qml.math.isclose(ops[0].parameters[0], 0.3)\\n assert qml.math.isclose(ops[1].parameters[0], 0.3)\\n assert qml.math.isclose(ops[2].parameters[0], 0.5)\\n assert qml.math.isclose(ops[3].parameters[0], -0.3)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"56bb84a5628b2b52976f4bd39950e155\",\n \"score\": \"0.49108955\",\n \"text\": \"def __init__(self, booleanos=[], operadores=[]):\\r\\n self.booleanos = (\\\"verdadero\\\", \\\"falso\\\")\\r\\n self.operadores = (\\\"-\\\", \\\"+\\\", \\\"*\\\", \\\"/\\\", \\\"&&\\\", \\\"||\\\", \\\">\\\", \\\"<\\\", \\\">=\\\", \\\"<=\\\", \\\"!=\\\", \\\"==\\\", \\\"=\\\")\\r\\n global debug\\r\\n debug = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"032a64e19220d5d1754793fa943a96d3\",\n \"score\": \"0.4896869\",\n \"text\": \"def binary_operator(operator: str, lhs: Formula, rhs: Formula) -> Formula:\\r\\n assert is_binary(operator)\\r\\n return Formula(operator, lhs, rhs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"27159cf346c0b0f6ab9c45aa0f05a8be\",\n \"score\": \"0.48949242\",\n \"text\": \"def operator_kan(f,klasa='sympy'): \\n n = len(signature(f).parameters)\\n if klasa == 'sympy':\\n identiteta = sp.eye(n)\\n kan = [list(identiteta.col(i)) for i in range(identiteta.cols)]\\n slika = list(map(lambda t: f(*t), kan))\\n matrica = sp.Matrix(slika).T\\n elif klasa == 'numpy':\\n kan = np.eye(n)\\n slika = np.apply_along_axis(lambda t: f(*t),1,kan)\\n matrica = np.transpose(slika)\\n return matrica\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2677d33b16434e816b08d2646d7e901b\",\n \"score\": \"0.48808172\",\n \"text\": \"def wygrana(self): \\r\\n #odkrywanie pozostałych pól \\r\\n for w in range(self._w):\\r\\n for k in range(self._k):\\r\\n if self._pola[w][k][0] %7 in [0]: \\r\\n if self._pola[w][k][1] == True: #bomba\\r\\n self._pola[w][k][0] = 3 + self._uzytykod * 7\\r\\n ile = ''\\r\\n else:\\r\\n self._pola[w][k][0] = 4 #ustawianie stanu\\r\\n ile = str(self.przegladnij_sasiadow(w,k))\\r\\n #uaktualnianie przycisku\\r\\n if ile == '0':\\r\\n ile = ''\\r\\n self._gui.ustaw_pole(w, k, ile, self._pola[w][k][0],0,0,0)\\r\\n \\r\\n return 1 #status wygranej\\r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"792faa135b8bcfb3b03a45810f7b6a05\",\n \"score\": \"0.48751622\",\n \"text\": \"def apply_operator_3(\\n statevector1,\\n statevector2,\\n operator,\\n A,\\n B,\\n C,\\n ):\\n\\n N = (statevector1.shape[0]&-statevector1.shape[0]).bit_length()-1\\n if A >= N: raise RuntimeError('A >= N')\\n if B >= N: raise RuntimeError('B >= N')\\n if C >= N: raise RuntimeError('C >= N')\\n if A == B: raise RuntimeError('A == B')\\n if A == C: raise RuntimeError('A == C')\\n if B == C: raise RuntimeError('B == C')\\n if operator.shape != (8,8): raise RuntimeError('3-body gate must be (8,8)')\\n if statevector1.shape != (2**N,): raise RuntimeError('statevector1 should be (%d,) shape, is %r shape' % (2**N, statevector1.shape))\\n if statevector2.shape != (2**N,): raise RuntimeError('statevector2 should be (%d,) shape, is %r shape' % (2**N, statevector2.shape))\\n\\n A2, B2, C2 = sorted((A, B, C))\\n\\n operator2 = np.reshape(operator, (2,2,2,2,2,2))\\n\\n bra_indices = 'ijk'\\n ket_indices = 'lmn'\\n bra_indices2 = ''.join([bra_indices[(A, B, C).index(_)] for _ in (A2, B2, C2)])\\n ket_indices2 = ''.join([ket_indices[(A, B, C).index(_)] for _ in (A2, B2, C2)])\\n \\n operator2 = np.einsum('%s%s->%s%s' % (bra_indices, ket_indices, bra_indices2, ket_indices2), operator2)\\n\\n L = 2**(A2) # Left hangover\\n M = 2**(B2-A2-1) # Middle1 hangover\\n P = 2**(C2-B2-1) # Middle2 hangover\\n R = 2**(N-C2-1) # Right hangover\\n statevector1v = statevector1.view() \\n statevector2v = statevector2.view()\\n statevector1v.shape = (L,2,M,2,P,2,R)\\n statevector2v.shape = (L,2,M,2,P,2,R)\\n np.einsum('LlMmPnR,ijklmn->LiMjPkR', statevector1v, operator2, out=statevector2v)\\n\\n return statevector2, statevector1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0cb36ed2e7d81d5733c74b5dbe8d64d\",\n \"score\": \"0.48561287\",\n \"text\": \"def apply_operator_2(\\n statevector1,\\n statevector2,\\n operator,\\n A,\\n B,\\n ):\\n\\n N = (statevector1.shape[0]&-statevector1.shape[0]).bit_length()-1\\n if A >= N: raise RuntimeError('A >= N')\\n if B >= N: raise RuntimeError('B >= N')\\n if A == B: raise RuntimeError('A == B')\\n if operator.shape != (4,4): raise RuntimeError('2-body gate must be (4,4)')\\n if statevector1.shape != (2**N,): raise RuntimeError('statevector1 should be (%d,) shape, is %r shape' % (2**N, statevector1.shape))\\n if statevector2.shape != (2**N,): raise RuntimeError('statevector2 should be (%d,) shape, is %r shape' % (2**N, statevector2.shape))\\n\\n operator2 = np.reshape(operator, (2,2,2,2))\\n if A > B:\\n A2, B2 = B, A\\n operator2 = np.einsum('ijkl->jilk', operator2)\\n else:\\n A2, B2 = A, B\\n\\n L = 2**(A2) # Left hangover\\n M = 2**(B2-A2-1) # Middle hangover\\n R = 2**(N-B2-1) # Right hangover\\n statevector1v = statevector1.view() \\n statevector2v = statevector2.view()\\n statevector1v.shape = (L,2,M,2,R)\\n statevector2v.shape = (L,2,M,2,R)\\n np.einsum('LkMlR,ijkl->LiMjR', statevector1v, operator2, out=statevector2v)\\n\\n return statevector2, statevector1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c18071e778eeab67d9130ad3a22199ac\",\n \"score\": \"0.48549315\",\n \"text\": \"def custo_par_aresta(distancias, aresta1, aresta2):\\n return distancias[aresta1[0]][aresta1[1]] + distancias[aresta2[0]][aresta2[1]]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4fa2b9b5a6e78095a2bfd1954358b9b8\",\n \"score\": \"0.48369998\",\n \"text\": \"def get_operator_map(self):\\n return self.operator_map\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f73693990da3337c2205a0b9d4aff94e\",\n \"score\": \"0.48365572\",\n \"text\": \"def checkOperatorPrecedence(a,b):\\n\\tcheck={}\\n\\tcheck['(']=1\\n\\tcheck['*']=2\\n\\tcheck['/']=2\\n\\tcheck['-']=3\\n\\tcheck['+']=3\\n\\tif check[a] <= check[b]:\\n\\t\\treturn 1\\n\\telse:\\n\\t\\treturn 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0985ce4fc97bd09f5153e8941618208a\",\n \"score\": \"0.48317426\",\n \"text\": \"def test2_12_1_3(self):\\n GA, e_1, e_2, e_3 = Ga.build('e*1|2|3')\\n a = e_1 + 2 * e_2\\n b = -e_1 - e_2\\n B = a ^ b\\n self.assertEqual(B, 1 * (e_1 ^ e_2))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9fcbd6afeb60248618bc74bc53c79d27\",\n \"score\": \"0.48085868\",\n \"text\": \"def test_comparison_on_different_key_blocks(self):\\n self.skipTest(\\n \\\"The KeyBlock structure does not support the comparison operators.\\\"\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5856d33f0d0f0964d910bccbcec481b8\",\n \"score\": \"0.47952715\",\n \"text\": \"def eval(cls, a,b):\\n if not (a and b): return S.Zero\\n if a == b: return S.Zero\\n if a.is_commutative or b.is_commutative:\\n return S.Zero\\n\\n #\\n # [A+B,C] -> [A,C] + [B,C]\\n #\\n a = a.expand()\\n if isinstance(a,Add):\\n return Add(*[cls(term,b) for term in a.args])\\n b = b.expand()\\n if isinstance(b,Add):\\n return Add(*[cls(a,term) for term in b.args])\\n\\n #\\n # [xA,yB] -> xy*[A,B]\\n #\\n c_part = []\\n nc_part = []\\n nc_part2 = []\\n if isinstance(a,Mul):\\n c_part,nc_part = split_commutative_parts(a)\\n if isinstance(b,Mul):\\n c_part2,nc_part2 = split_commutative_parts(b)\\n c_part.extend(c_part2)\\n if c_part:\\n a = nc_part or [a]\\n b = nc_part2 or [b]\\n return Mul(*c_part)*cls(Mul(*a),Mul(*b))\\n\\n\\n #\\n # single second quantization operators\\n #\\n if isinstance(a, BosonicOperator) and isinstance(b, BosonicOperator):\\n if isinstance(b,CreateBoson) and isinstance(a,AnnihilateBoson):\\n return KroneckerDelta(a.state,b.state)\\n if isinstance(a,CreateBoson) and isinstance(b,AnnihilateBoson):\\n return S.NegativeOne*KroneckerDelta(a.state,b.state)\\n else:\\n return S.Zero\\n if isinstance(a, FermionicOperator) and isinstance(b, FermionicOperator):\\n return wicks(a*b)- wicks(b*a)\\n\\n #\\n # Canonical ordering of arguments\\n #\\n if cmp(a, b) > 0:\\n return S.NegativeOne*cls(b, a)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"41851fb60fc066ff04c7c2e2f5872cd6\",\n \"score\": \"0.47927785\",\n \"text\": \"def compare(self, obj1, obj2):\\n # Key o1 = (Key) obj1;\\n # Key o2 = (Key) obj2;\\n # if (o1.getNote() == KeyLetterEnum.BS && o1.getPosition() == KeyPositionEnum.getLastPosition(o2.getPosition())\\n # && o2.getNote() == KeyLetterEnum.C) {\\n # return 0;\\n # }\\n if (obj1.getNote() == kle.BS) and (obj1.getPosition() == kpe.getLastPosition(obj2.getPosition())) and (\\n obj2.getNote() == kle.C):\\n return 0\\n\\n # if (o1.getNote() == KeyLetterEnum.C\\n # && o2.getPosition() == KeyPositionEnum.getNextPosition(o1.getPosition())\\n # && o2.getNote() == KeyLetterEnum.B) {\\n # return 0;\\n # }\\n if (obj1.getNote() == kle.C) and (obj2.getPosition() == kpe.getNextPosition(obj1.getPosition())) and (\\n obj2.getNote() == kle.B):\\n return 0\\n\\n # if (o1.getPosition().getPosition() > o2.getPosition().getPosition()) {\\n # return 1;\\n # }\\n # if (o1.getPosition().getPosition() < o2.getPosition().getPosition()) {\\n # return -1;\\n # } else {\\n # if (o1.getNote().getKeyNumber() == o2.getNote().getKeyNumber()) {\\n # return 0;\\n # } else if (o1.getNote().getKeyIdentity() < o2.getNote().getKeyIdentity()) {\\n # return -1;\\n # } else {\\n # return 1;\\n # }\\n # }\\n if (obj1.getPosition().getPosition() > obj2.getPosition().getPosition()):\\n return 1\\n elif obj2.getPosition().getPosition() < obj2.getPosition().getPosition():\\n return -1\\n else:\\n if obj1.getNote().getKeyNumber() == obj2.getNote().getKeyNumber():\\n return 0\\n elif obj1.getNote().getKeyIdentity() < obj2.getNote().getKeyIdentity():\\n return -1\\n else:\\n return 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e56a3ae04f61ff2c0f1d654ce269dc7\",\n \"score\": \"0.4767549\",\n \"text\": \"def makeBs2KstKstSameCharge(name,\\n Kstsel,\\n BMassWin,\\n BVCHI2,\\n BDOCA,\\n BIPCHI2): \\n \\n _motherCuts = \\\"(VFASPF(VCHI2/VDOF) < %(BVCHI2)s) & (MIPCHI2DV(PRIMARY)< %(BIPCHI2)s)\\\"% locals()\\n _combinationCut = \\\"(ADAMASS('B_s0') < %(BMassWin)s *MeV) & (AMAXDOCA('')< %(BDOCA)s *mm)\\\" % locals() \\n\\n _Bs = CombineParticles('_'+name)\\n _Bs.DecayDescriptor = \\\"[B_s0 -> K*(892)0 K*(892)0]cc\\\"\\n _Bs.CombinationCut = _combinationCut\\n _Bs.MotherCut = _motherCuts\\n\\n _Bs.ReFitPVs = True\\n\\n _Bs.addTool( OfflineVertexFitter )\\n# _Bs.VertexFitters.update( { \\\"\\\" : \\\"OfflineVertexFitter\\\"} )\\n _Bs.ParticleCombiners.update( { \\\"\\\" : \\\"OfflineVertexFitter\\\"} ) # Fix for DaVinci v32r0 by A.Poluektov\\n _Bs.OfflineVertexFitter.useResonanceVertex = False\\n\\n\\n return Selection ( name,\\n Algorithm = _Bs,\\n RequiredSelections = [Kstsel])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24e8b039c74e0712addf16e9bf81d1ea\",\n \"score\": \"0.47494054\",\n \"text\": \"def set_k2D(Na,Nb,deltachia,deltachib): # notice Na as index should be Na-1\\n k1D_a=set_k1D(Na,deltachia)\\n k1D_b=set_k1D(Nb,deltachib)\\n ka=np.zeros((Na,Nb), float)\\n for i in range(Nb):\\n ka[:,i]=k1D_a[:,0]\\n kb=np.zeros((Na,Nb), float)\\n for i in range(Na):\\n kb[i,:]=k1D_b.T\\n return np.asarray([ka, kb])\\n\\n #### -> for vk_indexset() -> for set_k2D()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"917a7ff6c02ef586eb2dfdfc7481172c\",\n \"score\": \"0.4740766\",\n \"text\": \"def test_parens_for_sandwiched_single_operand_same_op_as_neighbors(self):\\n b = self._bexpr_from_postfix_tokens(\\n ['A', 'B', 'and',\\n 'C',\\n 'D', 'E', 'and',\\n 'and', 'and'])\\n self.assertEqual(\\n b.symbols,\\n ['A', 'B', 'C', 'D', 'E'])\\n self.assertEqual(\\n b.tokens,\\n ['A', 'and', 'B', 'and', 'C', 'and', 'D', 'and', 'E'])\\n self.assertEqual(\\n b.postfix_tokens,\\n ['A', 'B', 'and',\\n 'C',\\n 'D', 'E', 'and',\\n 'and', 'and'])\\n self.assertEqual(\\n b.raw_expr,\\n 'A and B and C and D and E')\\n self.assertTrue(b.tree is not None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f953a234e776f18b15f04b9076789d1a\",\n \"score\": \"0.47368342\",\n \"text\": \"def operator_eq(self, *args):\\n return _vnl_matrix_fixedPython.vnl_matrix_fixedD_3_12_operator_eq(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65003198493b8f2e2493e5ec05df4f50\",\n \"score\": \"0.47259355\",\n \"text\": \"def __init__(self,wier,kol,bomb,logika,gui):\\r\\n #referencje\\r\\n self._logika = logika\\r\\n self._gui = gui\\r\\n #rozmiar planszy\\r\\n self._w = wier #numeracja 1-15\\r\\n self._k = kol\\r\\n \\r\\n #ilosc bomb\\r\\n self._ilebomb = bomb\\r\\n # liczniki\\r\\n self._wolnepola = wier * kol - bomb\\r\\n self._ileflag = 0\\r\\n self._flagzbomb = 0\\r\\n self._pytajniki = 0\\r\\n #czy zostal uzyty kod - potrzebne przy aktualizacji statusow pol\\r\\n self._uzytykod = False\\r\\n \\r\\n '''Tworzenie macierzy pol [wiersz][kolumna][nr stanu, czy jest bomba].\\r\\n Lista stanow pol:\\r\\n 0- puste, 1- flaga, 2 - pytajnik, 3 - bomba // po xyzzy stan+=7\\r\\n 4 - klikniete bez bomby // tylko bez xyzzy\\r\\n 5 - zle postawiona flaga, 6 - wybuch // tylko bez xyzzy\\r\\n w sumie 7 + 4 = 11 stanow\\r\\n '''\\r\\n self._pola = [[[0,False] for j in range(kol)] for i in range(wier)]\\r\\n \\r\\n #losowanie pol\\r\\n licznik = self._ilebomb\\r\\n while 1:\\r\\n x = random.randint(0, self._w-1)\\r\\n y = random.randint(0, self._k-1)\\r\\n if self._pola[x][y][1] == False:\\r\\n self._pola[x][y][1] = True\\r\\n licznik -= 1\\r\\n if licznik == 0:\\r\\n break\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8c3ccaf3d610b1a6e3dc7a92bcb0c64\",\n \"score\": \"0.47246957\",\n \"text\": \"def test_parens_for_negated_leading_chained_operator_clause(self):\\n b = self._bexpr_from_postfix_tokens(\\n ['A', 'B', 'iff', '!',\\n '0', '1', 'and',\\n 'and'])\\n self.assertEqual(\\n b.symbols,\\n ['A', 'B'])\\n self.assertEqual(\\n b.tokens,\\n ['!', '(', 'A', 'iff', 'B', ')', 'and', '0', 'and', '1'])\\n self.assertEqual(\\n b.postfix_tokens,\\n ['A', 'B', 'iff', '!',\\n '0', '1', 'and',\\n 'and'])\\n self.assertEqual(b.raw_expr, '!(A iff B) and 0 and 1')\\n self.assertTrue(b.tree is not None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8fc52adfa5fd764d9077ce87e938d3e\",\n \"score\": \"0.47204044\",\n \"text\": \"def _get_eq_op_ ( klass1 , klass2 ) :\\n t = klass1 , klass2\\n ops = _eq_ops_.get( t , None )\\n if ops : return ops ## RETURN \\n\\n ## try to load the operators \\n try :\\n ops = Ostap.Math.EqualityOp ( klass1 , klass2 )\\n _eq_ops_ [ t ] = ops\\n return ops ## RETURN \\n except TypeError:\\n return None ## RETURN\\n \\n return None ## RETURN\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9046b95bd3ce0b86e2da0560c1a4b7c\",\n \"score\": \"0.47179422\",\n \"text\": \"def operator(self):\\n return self._operator\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f74fdc5a8ae9f8d8d0852c1389e603f\",\n \"score\": \"0.47171295\",\n \"text\": \"def test_parens_for_leading_chained_operator_clause(self):\\n b = self._bexpr_from_postfix_tokens(\\n ['A', 'B', 'C', 'D', 'and', 'and', 'and',\\n 'E',\\n 'or'])\\n self.assertEqual(\\n b.symbols,\\n ['A', 'B', 'C', 'D', 'E'])\\n self.assertEqual(\\n b.tokens,\\n ['(', 'A', 'and', 'B', 'and', 'C', 'and', 'D', ')', 'or', 'E'])\\n self.assertEqual(\\n b.postfix_tokens,\\n ['A', 'B', 'C', 'D', 'and', 'and', 'and',\\n 'E',\\n 'or'])\\n self.assertEqual(\\n b.raw_expr,\\n '(A and B and C and D) or E')\\n self.assertTrue(b.tree is not None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d45dd47e8c274ff9de2aef1c303a60f7\",\n \"score\": \"0.47143483\",\n \"text\": \"def test2_12_2_9(self):\\n for GA in [Ga('e*1|2'), Ga('e*1|2|3'), Ga('e*1|2|3|4')]: # , Ga('e*1|2|3|4|5')]:\\n for k, l in product(range(GA.n + 1), range(GA.n + 1)):\\n Ak = GA.mv('A', 'blade', k)\\n Bl = GA.mv('B', 'blade', l)\\n self.assertEqual(Ak ^ Bl, (-1) ** (k * l) * (Bl ^ Ak))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c284ce68e33e75bd58148d07b3736d0d\",\n \"score\": \"0.47029236\",\n \"text\": \"def operator_eq(self, *args):\\n return _vnl_matrix_fixedPython.vnl_matrix_fixedD_2_6_operator_eq(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b0d4cd99bb9b9ba2e6650625ccde524\",\n \"score\": \"0.46820453\",\n \"text\": \"def demonstrate_logical_operators():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a6f652766248768b1a936e006e145696\",\n \"score\": \"0.4679117\",\n \"text\": \"def wyrownaj_liczbe_danych(dane, etykiety,slownik_etykiet_danych):\\n lista_aktywnosci = list( slownik_etykiet_danych.keys() )\\n lista_etykiet = list( etykiety )\\n ksztalt_danych = dane.shape\\n wystapienia = {}\\n for aktywnosc in lista_aktywnosci:\\n wystapienia[aktywnosc] = lista_etykiet.count(slownik_etykiet_danych[aktywnosc])\\n liczby_wystapien_aktywnosci = list(wystapienia.values())\\n minimalna_liczba_wystapien = min( liczby_wystapien_aktywnosci )\\n\\n print(\\\"AKTYWNOSCI W DANYCH WEJSCIOWYCH\\\")\\n print(wystapienia)\\n \\n return_dane = np.zeros( ( len(slownik_etykiet_danych) * minimalna_liczba_wystapien,ksztalt_danych[1],ksztalt_danych[2] ) )\\n return_etykiety = np.zeros( (len(slownik_etykiet_danych) * minimalna_liczba_wystapien,) )\\n\\n wektor_wystapien = np.zeros( ( len(slownik_etykiet_danych) , ) ) # tu bedziemy zliczac ile razy dana etykieta juz wystapila\\n return_dane_licznik = 0\\n \\n for i in range(len(dane)):\\n if wektor_wystapien[int(etykiety[i])] < minimalna_liczba_wystapien :\\n wektor_wystapien[int(etykiety[i])] += 1\\n return_dane[return_dane_licznik] = dane[i]\\n return_etykiety[return_dane_licznik] = etykiety[i]\\n return_dane_licznik += 1\\n \\n lista_etykiet = list( return_etykiety )\\n wystapienia = {}\\n for aktywnosc in lista_aktywnosci:\\n wystapienia[aktywnosc] = lista_etykiet.count(slownik_etykiet_danych[aktywnosc])\\n print(\\\"AKTYWNOSCI PO WYROWNANIU:\\\")\\n print(wystapienia)\\n\\n return (return_dane,return_etykiety)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11f72a6dc60d5fd530943833b583dced\",\n \"score\": \"0.46677864\",\n \"text\": \"def operator_eq(self, *args):\\n return _vnl_matrix_fixedPython.vnl_matrix_fixedD_2_2_operator_eq(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70c2e0a9de1c08018645ac4b36237ee5\",\n \"score\": \"0.4663572\",\n \"text\": \"def test_multiple_dig_nop(self):\\n game = self.test_mines.new_game(3,7,[[1,5],[2,4],(0,1)])\\n game2 = self.test_mines.new_game(3,7,[[1,5],[2,4],(0,1)])\\n\\n game[\\\"mask\\\"][0][0]=True\\n game2[\\\"mask\\\"][0][0]=True\\n\\n dig(game,0,0)\\n\\n for key in game.keys():\\n self.assertEqual(game2[key], game[key])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6691420c5c49478cae619f6513340979\",\n \"score\": \"0.46592966\",\n \"text\": \"def test_parens_for_sandwiched_chained_operator_clause(self):\\n b = self._bexpr_from_postfix_tokens(\\n ['A',\\n 'B', 'C', 'D', '&', '&',\\n 'E',\\n '->', '->'])\\n self.assertEqual(\\n b.symbols,\\n ['A', 'B', 'C', 'D', 'E'])\\n self.assertEqual(\\n b.tokens,\\n ['A', '->', '(', 'B', '&', 'C', '&', 'D', ')', '->', 'E'])\\n self.assertEqual(\\n b.postfix_tokens,\\n ['A',\\n 'B', 'C', 'D', '&', '&',\\n 'E',\\n '->', '->'])\\n self.assertEqual(b.raw_expr, 'A -> (B & C & D) -> E')\\n self.assertTrue(b.tree is not None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4cf6fb357288802cd05cac976b50cb12\",\n \"score\": \"0.46501648\",\n \"text\": \"def contraction(a,b):\\n if isinstance(b,FermionicOperator) and isinstance(a,FermionicOperator):\\n if isinstance(a,AnnihilateFermion) and isinstance(b,CreateFermion):\\n if b.state.assumptions0.get(\\\"below_fermi\\\"):\\n return S.Zero\\n if a.state.assumptions0.get(\\\"below_fermi\\\"):\\n return S.Zero\\n if b.state.assumptions0.get(\\\"above_fermi\\\"):\\n return KroneckerDelta(a.state,b.state)\\n if a.state.assumptions0.get(\\\"above_fermi\\\"):\\n return KroneckerDelta(a.state,b.state)\\n\\n return (KroneckerDelta(a.state,b.state)*\\n KroneckerDelta(b.state,Symbol('a',dummy=True,above_fermi=True)))\\n if isinstance(b,AnnihilateFermion) and isinstance(a,CreateFermion):\\n if b.state.assumptions0.get(\\\"above_fermi\\\"):\\n return S.Zero\\n if a.state.assumptions0.get(\\\"above_fermi\\\"):\\n return S.Zero\\n if b.state.assumptions0.get(\\\"below_fermi\\\"):\\n return KroneckerDelta(a.state,b.state)\\n if a.state.assumptions0.get(\\\"below_fermi\\\"):\\n return KroneckerDelta(a.state,b.state)\\n\\n return (KroneckerDelta(a.state,b.state)*\\n KroneckerDelta(b.state,Symbol('i',dummy=True,below_fermi=True)))\\n\\n # vanish if 2xAnnihilator or 2xCreator\\n return S.Zero\\n\\n else:\\n #not fermion operators\\n t = ( isinstance(i,FermionicOperator) for i in (a,b) )\\n raise ContractionAppliesOnlyToFermions(*t)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e92e8fb408ba1f6109293c7042c9814\",\n \"score\": \"0.46470928\",\n \"text\": \"def operate(\\n cls,\\n value_1: CalculationValue,\\n value_2: CalculationValue,\\n value_1_calc: bool,\\n value_2_calc: bool\\n ) -> CalculationValue:\\n l1, r1, l2, r2 = BinaryOperator.get_parens(value_1_calc, value_2_calc)\\n\\n if type(value_1) in (int, float):\\n if type(value_2) in (int, float):\\n return cls.operator(value_1, value_2)\\n elif isinstance(value_2, Series):\\n result = cls.operator(value_1, value_2)\\n result.name = (\\n f'{l1}{value_1}{r1} '\\n f'{cls.symbol} '\\n f'{l2}{value_2.name}{r2}'\\n )\\n return result\\n elif isinstance(value_2, DataFrame):\\n result = cls.operator(value_1, value_2)\\n result.columns = [\\n f'{l1}{value_1}{r1} {cls.symbol} {l2}{name_2}{r2}'\\n for name_2 in value_2.columns\\n ]\\n return result\\n else:\\n raise TypeError(\\n 'value_2 must be int, float, Series or DataFrame'\\n )\\n elif isinstance(value_1, Series):\\n if type(value_2) in (int, float):\\n result = cls.operator(value_1, value_2)\\n result.name = (\\n f'{l1}{value_1.name}{r1} '\\n f'{cls.symbol} '\\n f'{l2}{value_2}{r2}')\\n return result\\n elif isinstance(value_2, Series):\\n result = cls.operator(value_1, value_2)\\n result.name = (\\n f'{l1}{value_1.name}{r1} '\\n f'{cls.symbol} '\\n f'{l2}{value_2.name}{r2}')\\n return result\\n elif isinstance(value_2, DataFrame):\\n result = getattr(\\n value_2, cls.pandas_op_name\\n )(value_1, axis=0)\\n result.columns = [\\n f'{l1}{value_1.name}{r1} {cls.symbol} {l2}{column}{r2}'\\n for column in value_2.columns\\n ]\\n return result\\n else:\\n raise TypeError(\\n 'value_2 must be int, float, Series or DataFrame'\\n )\\n elif isinstance(value_1, DataFrame):\\n if type(value_2) in (int, float):\\n result = cls.operator(value_1, value_2)\\n result.columns = [\\n f'{l1}{column}{r1} {cls.symbol} {l2}{value_2}{r2}'\\n for column in value_1.columns\\n ]\\n return result\\n elif isinstance(value_2, Series):\\n result = getattr(\\n value_1, cls.pandas_op_name\\n )(value_2, axis=0)\\n result.columns = [\\n f'{l1}{column}{r1} {cls.symbol} {l2}{value_2.name}{r2}'\\n for column in value_1.columns\\n ]\\n return result\\n elif isinstance(value_2, DataFrame):\\n if value_1.shape[1] != value_2.shape[1]:\\n raise ValueError(\\n 'Can only operate 2 dataframes together with same '\\n 'number of columns'\\n )\\n result = DataFrame.from_dict({\\n f'{l1}{col_1}{r1} {cls.symbol} {l2}{col_2}{r2}':\\n cls.operator(value_1[col_1], value_2[col_2])\\n for col_1, col_2 in zip(value_1.columns, value_2.columns)\\n })\\n return result\\n else:\\n raise TypeError(\\n 'value_2 must be int, float, Series or DataFrame'\\n )\\n else:\\n raise TypeError(\\n 'value_1 must be int, float, Series or DataFrame'\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe37f36503495c794f3c851f8aaec2eb\",\n \"score\": \"0.46409702\",\n \"text\": \"def basic_op(operator, value1, value2):\\n return eval(str(value1)+operator+str(value2))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b07abc88787af07e58b3ac7efa69fcb0\",\n \"score\": \"0.4639607\",\n \"text\": \"def is_left_associative(operator):\\n\\n return operator in \\\"+-*/\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c14ac52a65303c405a1226cd4ba768e6\",\n \"score\": \"0.46368247\",\n \"text\": \"def setOverloadedOperator(self, isOverloadedOperator: bool) -> None:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8097a256ca8d5ed6199daa1103417b09\",\n \"score\": \"0.46303564\",\n \"text\": \"def xyzzy(self):\\r\\n self._uzytykod = True\\r\\n for w in range(self._w):\\r\\n for k in range(self._k):\\r\\n if self._pola[w][k][0] in [0,1,2,3]:\\r\\n if self._pola[w][k][1] == True: #bomba\\r\\n self._pola[w][k][0] += 7\\r\\n self._gui.ustaw_pole(w, k, '', self._pola[w][k][0],\\r\\n self._ilebomb - self._ileflag, self._pytajniki,\\r\\n self._wolnepola)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa0cab431de23f571d24cda5b019b995\",\n \"score\": \"0.4625291\",\n \"text\": \"def b_operator(self, P):\\r\\n A, B, Q, R, beta = self.A, self.B, self.Q, self.R, self.beta\\r\\n S1 = Q + beta * dot(B.T, dot(P, B)) \\r\\n S2 = beta * dot(B.T, dot(P, A))\\r\\n S3 = beta * dot(A.T, dot(P, A))\\r\\n F = solve(S1, S2) \\r\\n new_P = R - dot(S2.T, solve(S1, S2)) + S3 \\r\\n return F, new_P\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91ba95def0bd10fc1427b2dba35336e7\",\n \"score\": \"0.46137527\",\n \"text\": \"def operate (self, oper1, oper2):\\n\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"abd9c27da68861c1280789ad126f6d03\",\n \"score\": \"0.46124133\",\n \"text\": \"def operationXOR(a, b):\\n if(a==\\\"0\\\" and b==\\\"0\\\"):\\n return \\\"0\\\"\\n if(a==\\\"1\\\" and b==\\\"0\\\"):\\n return \\\"1\\\"\\n if(a==\\\"0\\\" and b==\\\"1\\\"):\\n return \\\"1\\\"\\n if(a==\\\"1\\\" and b==\\\"1\\\"):\\n return \\\"0\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c68588d4ddec7371464dd0e5d6f136c\",\n \"score\": \"0.4587624\",\n \"text\": \"def optAlign( sel1, sel2 ):\\n\\tcmd.reset()\\n\\n\\t# make the lists for holding coordinates\\n\\t# partial lists\\n\\tstored.sel1 = []\\n\\tstored.sel2 = []\\n\\t# full lists\\n\\tstored.mol1 = []\\n\\tstored.mol2 = []\\n\\n\\t# now put the coordinates into a list\\n\\t# partials\\n\\n\\t# -- REMOVE ALPHA CARBONS\\n\\tsel1 = sel1 + \\\" and N. CA\\\"\\n\\tsel2 = sel2 + \\\" and N. CA\\\"\\n\\t# -- REMOVE ALPHA CARBONS\\n\\n\\tcmd.iterate_state(1, selector.process(sel1), \\\"stored.sel1.append([x,y,z])\\\")\\n\\tcmd.iterate_state(1, selector.process(sel2), \\\"stored.sel2.append([x,y,z])\\\")\\n\\t# full molecule\\n\\tmol1 = cmd.identify(sel1,1)[0][0]\\n\\tmol2 = cmd.identify(sel2,1)[0][0]\\n\\tcmd.iterate_state(1, mol1, \\\"stored.mol1.append([x,y,z])\\\")\\n\\tcmd.iterate_state(1, mol2, \\\"stored.mol2.append([x,y,z])\\\")\\n\\n\\tK = kabsch()\\n\\tU, T1, T2, RMSD, c1, c2 = K.align(stored.sel1, stored.sel2, [])\\n\\n\\tstored.mol2 = map(lambda v:[T2[0]+((v[0]*U[0][0])+(v[1]*U[1][0])+(v[2]*U[2][0])),T2[1]+((v[0]*U[0][1])+(v[1]*U[1][1])+(v[2]*U[2][1])),T2[2]+((v[0]*U[0][2])+(v[1]*U[1][2])+(v[2]*U[2][2]))],stored.mol2)\\n\\t#stored.mol1 = map(lambda v:[ v[0]+T1[0], v[1]+T1[1], v[2]+T1[2] ], stored.mol1)\\n\\tstored.mol1 = map(lambda v:[ v[0]+T1[0], v[1]+T1[1], v[2]+T1[2] ], stored.mol1)\\n\\n\\tcmd.alter_state(1,mol1,\\\"(x,y,z)=stored.mol1.pop(0)\\\")\\n\\tcmd.alter_state(1,mol2,\\\"(x,y,z)=stored.mol2.pop(0)\\\")\\n\\tcmd.alter( 'all',\\\"segi=''\\\")\\n\\tcmd.alter('all', \\\"chain=''\\\")\\n\\tprint \\\"RMSD=%f\\\" % cmd.rms_cur(sel1, sel2)\\n\\tprint \\\"MY RMSD=%f\\\" % RMSD\\n\\tcmd.hide('everything')\\n\\tcmd.show('ribbon', sel1 + ' or ' + sel2)\\n\\tcmd.color('gray70', mol1 )\\n\\tcmd.color('paleyellow', mol2 )\\n\\tcmd.color('red', 'visible')\\n\\tcmd.show('ribbon', 'not visible')\\n\\tcmd.center('visible')\\n\\tcmd.orient()\\n\\tcmd.zoom('visible')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f9f8c3aeb6ba4ce216a6f1cdf416c4b\",\n \"score\": \"0.45873356\",\n \"text\": \"def test_parens_for_sandwiched_negated_chained_operator_clause(self):\\n b = self._bexpr_from_postfix_tokens(\\n ['A',\\n 'B', 'C', 'D', 'xor', 'xor', '~',\\n 'E',\\n '->', '->'])\\n self.assertEqual(\\n b.symbols,\\n ['A', 'B', 'C', 'D', 'E'])\\n self.assertEqual(\\n b.tokens,\\n ['A', '->', '~', '(', 'B', 'xor', 'C', 'xor', 'D', ')', '->', 'E'])\\n self.assertEqual(\\n b.postfix_tokens,\\n ['A',\\n 'B', 'C', 'D', 'xor', 'xor', '~',\\n 'E',\\n '->', '->'])\\n self.assertEqual(b.raw_expr, 'A -> ~(B xor C xor D) -> E')\\n self.assertTrue(b.tree is not None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7750b521ada02c0e8bd40b073c518be\",\n \"score\": \"0.4569324\",\n \"text\": \"def test_heat_rod1d_modes_BC1(self):\\n # BC1\\n solver = Rod1D(kappa=self.kappa, TL=self.T0, TR=self.T1, L=self.L,\\n Nsum=self.Nsum, alpha1=1.0, beta1=0.0, alpha2=1.0,\\n beta2=0.0)\\n solver.modes_BC1()\\n kn0 = [0, 1.57079633, 3.14159265]\\n An0 = [0, 0, 0]\\n Bn0 = [0, 4.45633841, -0.31830989]\\n np.testing.assert_allclose(solver.kn, kn0)\\n np.testing.assert_allclose(solver.An, An0)\\n np.testing.assert_allclose(solver.Bn, Bn0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2cb30dabc894c32284a46c310ffe90c0\",\n \"score\": \"0.45673597\",\n \"text\": \"def __or__(self, other):\\n return self.binary(other, \\\"|\\\", is_operator=True, dimres=1, dimcheck=\\\"same\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"434998dc144619afedf3623209de5391\",\n \"score\": \"0.45664072\",\n \"text\": \"def opcions():\\n print \\\"[1] Llegir matriu A\\\"\\n print \\\"[2] Llegir matriu B\\\"\\n print \\\"[3] Calcular A + B\\\"\\n print \\\"[4] Calcular A - B\\\"\\n print \\\"[5] Calcular A * B\\\"\\n print \\\"[6] Calcular determinant\\\"\\n print \\\"[7] Calcular trasposada\\\"\\n print \\\"[8] Sortir\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dbcdc1873d231e16de3e0384045e9280\",\n \"score\": \"0.45625427\",\n \"text\": \"def __eq__(self, operator):\\n return int(self) != operator\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78ebf2ef9af0d0c81b44035f5d057255\",\n \"score\": \"0.45478916\",\n \"text\": \"def kmers_binary_op(self, op, kmer_db1, kmer_db2, kmer_db_out,\\n result_min_occs=1, result_max_occs=util.misc.MAX_INT32,\\n result_counter_cap=DEFAULT_COUNTER_CAP,\\n threads=None):\\n kmer_db1, kmer_db2, kmer_db_out = map(self._kmer_db_name, (kmer_db1, kmer_db2, kmer_db_out))\\n # db1_min_occs, db1_max_occs, db2_min_occs, db2_max_occs, db_out_min_occs, db_out_max_occs,\\n self.execute(['simple', kmer_db1, kmer_db2, op, kmer_db_out,\\n '-ci{}'.format(result_min_occs),\\n '-cx{}'.format(result_max_occs),\\n '-cs{}'.format(result_counter_cap)], threads=threads)\\n _chk(self.is_kmer_db(kmer_db_out), 'kmer_binary_op: output not created')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9eafaa819934c3cf8f28434a14c0b219\",\n \"score\": \"0.45444795\",\n \"text\": \"def test_operator_shift(self, test_dag):\\n # Unpack the fixture\\n dag, (op1, op2, op3, op4) = test_dag\\n # Arrange the operators with a Label in the middle\\n op1 >> Label(\\\"Label 1\\\") >> op2\\n op3 << Label(\\\"Label 2\\\") << op2 >> op4\\n # Check that the DAG has the right edge info\\n assert dag.get_edge_info(op1.task_id, op2.task_id) == {\\\"label\\\": \\\"Label 1\\\"}\\n assert dag.get_edge_info(op2.task_id, op3.task_id) == {\\\"label\\\": \\\"Label 2\\\"}\\n assert dag.get_edge_info(op2.task_id, op4.task_id) == {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9224101c23261a7ce9bd21437c52246\",\n \"score\": \"0.45411834\",\n \"text\": \"def operator_eq(self, *args):\\n return _vnl_matrix_fixedPython.vnl_matrix_fixedD_2_3_operator_eq(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1eda1cc91ce214d8a75ff0478cb1a6ad\",\n \"score\": \"0.45113316\",\n \"text\": \"def sıraKımde(self):\\n\\n if self.hamle % 2 == 0:\\n return self.oyuncuDegis1()\\n else:\\n return self.oyuncuDegis2()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c6ff994c7c2dabbe9cb291e786b5ecd\",\n \"score\": \"0.45097646\",\n \"text\": \"def main_8():\\n\\n n = 8\\n stabs = \\\"ZIZZZIII IZZZIZII ZIIIZIZZ XXXIIIXI XXIXIIIX IIXIXXXI\\\"\\n logops = \\\"IIXXIIII XIIIXIII ZZIIIIII IIZIIIZI\\\"\\n code = Code(n, stabs, logops)\\n code.check()\\n\\n #vs = list(code.get_encoded())\\n v0 = code.get_encoded()\\n v1 = code.logops[0] * v0\\n v2 = code.logops[1] * v0\\n v3 = code.logops[1] * v1\\n\\n for stab in code.stabs:\\n assert stab*v0 == v0\\n assert stab*v1 == v1\\n assert stab*v2 == v2\\n assert stab*v3 == v3\\n\\n assert v0 != v1\\n\\n # these are n-qubit operators:\\n CZ = lambda i,j : Z.control(i-1, j-1, rank=n)\\n CX = lambda i,j : X.control(i-1, j-1, rank=n)\\n SWAP = lambda i,j : CX(i,j)*CX(j,i)\\n\\n #A = CZ(1,3)*CZ(4,5)*CZ(6,8)*CZ(2,7) # not a weak duality !\\n\\n for A in [\\n #(I @ I @ I @ I @ I @ I @ I @ I)\\n (I @ S @ I @ ~S @ S @ I @ ~S @ I)*CZ(1,6)*CZ(3,8), # logical S @ ~S\\n #(S @ I @ ~S @ I @ I @ S @ I @ ~S)*CZ(2,5)*CZ(4,7), # logical S @ ~S\\n #(I @ ~S @ I @ S @ ~S @ I @ S @ I)*CZ(1,6)*CZ(3,8), # logical ~S @ S\\n #(S @ I @ I @ ~S @ I @ S @ ~S @ I)*CZ(2,5)*CZ(3,8), # logical S @ ~S\\n #(H @ H @ H @ H @ H @ H @ H @ H) * SWAP(1,6) * SWAP(3,8), # fail....\\n ]:\\n\\n #print(opstr(A))\\n \\n assert A*code.P == code.P*A\\n #P1 = A*code.P*~A\\n #assert(P1 == code.P)\\n \\n vs = [v0, v1, v2, v3]\\n op = []\\n for u in vs:\\n u = A*u\\n row = []\\n for v in vs:\\n r = u.dag() * v\\n row.append(r)\\n print(\\\"%.2f+%.2fj\\\"%(r.real, r.imag), end=\\\" \\\")\\n print()\\n op.append(row)\\n \\n op = numpy.array(op)\\n #op.shape = (2,2,2,2)\\n #print(op)\\n\\n print()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f68c803d96cc414fba054174dbb0db24\",\n \"score\": \"0.45079932\",\n \"text\": \"def cross(self, a1, a2):\\n\\n if len(a1) != len(self.alloy):\\n raise ValueError('Wrong size of structure to optimize')\\n if len(a1) != len(a2):\\n raise ValueError('The two structures do not have the same length')\\n\\n N = len(self.alloy.numbers)\\n cell = self.alloy.get_cell()\\n pbc = self.alloy.get_pbc()\\n atom_numbers = self.alloy.numbers\\n pos = self.alloy.positions\\n new_pos = np.zeros((N,3))\\n\\n # Doing order 1 crossover using ordering list\\n order1 = list(a1.info['ordering'])\\n order2 = list(a2.info['ordering'])\\n # built-in list() function was used to avoid call-by-reference of \\n # a2.info['ordering']\\n new_order = list(np.zeros(N))\\n\\n rpi = -choice(range(N)) # initial point of random swath\\n rpf = rpi + N/2 # final point of random swath\\n\\n # Possibilities of making swath from pos1\\n if rpi < 0 and rpf < 0:\\n # case 1\\n new_order[rpi:rpf] = order1[rpi:rpf]\\n elif rpi < 0 and rpf ==0:\\n # case 2\\n new_order[rpi:] = order1[rpi:]\\n elif rpi < 0 and rpf > 0:\\n # case 3\\n new_order[rpi:] = order1[rpi:]\\n new_order[:rpf] = order1[:rpf]\\n elif rpi == 0 and rpf > 0:\\n # case 4\\n new_order[:rpf] = order1[:rpf]\\n else:\\n print \\\"else case ocurred!\\\"\\n\\n # Masking coordinates already selected from a1\\n # 'del' is not compatible with array format\\n for i in range(rpi,rpf):\\n imask = np.where(np.array(order2) == new_order[i])[0][0]\\n del order2[imask]\\n\\n Np = len(order2)\\n\\n # Fill in the rest of new_order list by with the rest of order2\\n if rpi < 0 and rpf < 0:\\n # case 1\\n new_order[N+rpf:] = order2[Np+rpf:]\\n new_order[:N+rpi] = order2[:Np+rpi]\\n elif rpi < 0 and rpf ==0:\\n # case 2\\n new_order[:rpi] = order2\\n elif rpi < 0 and rpf > 0:\\n # case 3\\n new_order[rpf:N+rpi] = order2\\n elif rpi == 0 and rpf > 0:\\n # case 4\\n new_order[rpf:] = order2\\n else:\\n print \\\"else case ocurred!\\\"\\n\\n # Fill in new_pos array following the new_order list\\n for i in range(N):\\n new_pos[new_order[i]] = pos[i]\\n\\n alloy_offspring = Atoms(numbers=atom_numbers, positions=new_pos,\\n pbc=pbc, cell=cell)\\n alloy_offspring. info['ordering'] = new_order\\n\\n return alloy_offspring\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc187c7f7160f00a4f23c4582319f90d\",\n \"score\": \"0.45015937\",\n \"text\": \"def addOperators(self):\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3702bb532fd22582e0ebe78442e99d1\",\n \"score\": \"0.4493724\",\n \"text\": \"def _check2_bitop(a0, a1):\\n s0, s1 = len(a0), len(a1)\\n sr = max(s0, s1)\\n if (s0 != sr and s0 != 1) or (s1 != sr and s1 != 1):\\n raise Exception(\\\"Incompatible argument sizes: %i and %i\\\" % (s0, s1))\\n elif type(a0) is not type(a1) and type(a1) is not a0.MaskType: # noqa\\n raise Exception(\\\"Type mismatch!\\\")\\n ar = a0.empty_(sr if a0.Size == Dynamic else 0)\\n return (ar, sr)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"702d36dddcd8b44135719036ce52fdf6\",\n \"score\": \"0.4484038\",\n \"text\": \"def utworz_plansze(self,w,k,b):\\r\\n if self._aktualizacja == True:\\r\\n return False\\r\\n self._aktualizacja = True\\r\\n self._grazakonczona = False\\r\\n if hasattr(self, '_plansza') == True:\\r\\n del self._plansza\\r\\n self._plansza = Plansza(w,k,b,self,self._gui) #tworzenie planszy\\r\\n self._aktualizacja = False\\r\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00570d1f28d900b41bb2530c80196d86\",\n \"score\": \"0.44770122\",\n \"text\": \"def pan(a, b):\\n ab = a * b\\n t = str(a) + str(b) + str(ab)\\n if '0' in t:\\n return 0\\n if len(t) == 9 == len(set(t)):\\n return ab\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3245b05367263e98250455896d1bb37c\",\n \"score\": \"0.44703317\",\n \"text\": \"def test_operator_set(self, test_dag):\\n # Unpack the fixture\\n dag, (op1, op2, op3, op4) = test_dag\\n # Arrange the operators with a Label in the middle\\n op1.set_downstream(op2, Label(\\\"Label 1\\\"))\\n op3.set_upstream(op2, Label(\\\"Label 2\\\"))\\n op4.set_upstream(op2)\\n # Check that the DAG has the right edge info\\n assert dag.get_edge_info(op1.task_id, op2.task_id) == {\\\"label\\\": \\\"Label 1\\\"}\\n assert dag.get_edge_info(op2.task_id, op3.task_id) == {\\\"label\\\": \\\"Label 2\\\"}\\n assert dag.get_edge_info(op2.task_id, op4.task_id) == {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8b280a4717b8ae3d3a2dd08bdbeb9f0\",\n \"score\": \"0.44652066\",\n \"text\": \"def apply_operator_1(\\n statevector1,\\n statevector2,\\n operator,\\n A,\\n ):\\n\\n N = (statevector1.shape[0]&-statevector1.shape[0]).bit_length()-1\\n if A >= N: raise RuntimeError('A >= N')\\n if operator.shape != (2,2): raise RuntimeError('1-body gate must be (2,2)')\\n if statevector1.shape != (2**N,): raise RuntimeError('statevector1 should be (%d,) shape, is %r shape' % (2**N, statevector1.shape))\\n if statevector2.shape != (2**N,): raise RuntimeError('statevector2 should be (%d,) shape, is %r shape' % (2**N, statevector2.shape))\\n\\n L = 2**(A) # Left hangover\\n R = 2**(N-A-1) # Right hangover\\n statevector1v = statevector1.view() \\n statevector2v = statevector2.view()\\n statevector1v.shape = (L,2,R)\\n statevector2v.shape = (L,2,R)\\n np.einsum('LjR,ij->LiR', statevector1v, operator, out=statevector2v)\\n\\n return statevector2, statevector1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"05724ad7ea70f6c7782a73c43464c0fc\",\n \"score\": \"0.446426\",\n \"text\": \"def ketbra(self,state):\\n result = Operator()\\n for hv1 in self.hv:\\n for hv2 in state.hv:\\n result.add(hv1.ketbra(hv2))\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75631e85afa28ff2ebccc0032409a930\",\n \"score\": \"0.44622192\",\n \"text\": \"def comprovaPUK(c,d,m):\\n return c<5 and d!=m\",\n \"title\": \"\"\n },\n {\n \"docid\": \"319533272fd4abbf2443ab4a5e2d5d14\",\n \"score\": \"0.4459272\",\n \"text\": \"def bravyi_kitaev(operator, n_qubits=None):\\n # Compute the number of qubits.\\n from fermilib.utils import count_qubits\\n if n_qubits is None:\\n n_qubits = count_qubits(operator)\\n if n_qubits < count_qubits(operator):\\n raise ValueError('Invalid number of qubits specified.')\\n\\n # Compute transformed operator.\\n transformed_operator = QubitOperator()\\n for term in operator.terms:\\n\\n # Initialize identity matrix.\\n coefficient = operator.terms[term]\\n transformed_term = QubitOperator((), coefficient)\\n\\n # Build the Fenwick Tree\\n fenwick_tree = FenwickTree(n_qubits)\\n\\n # Build the Bravyi-Kitaev transformed operators.\\n for ladder_operator in term:\\n index = ladder_operator[0]\\n\\n # Parity set. Set of nodes to apply Z to.\\n parity_set = [node.index for node in\\n fenwick_tree.get_parity_set(index)]\\n\\n # Update set. Set of ancestors to apply X to.\\n ancestors = [node.index for node in\\n fenwick_tree.get_update_set(index)]\\n\\n # The C(j) set.\\n ancestor_children = [node.index for node in\\n fenwick_tree.get_remainder_set(index)]\\n\\n # Switch between lowering/raising operators.\\n d_coefficient = .5j\\n if ladder_operator[1]:\\n d_coefficient *= -1.\\n\\n # The fermion lowering operator is given by\\n # a = (c+id)/2 where c, d are the majoranas.\\n d_majorana_component = QubitOperator(\\n (((ladder_operator[0], 'Y'),) +\\n tuple((index, 'Z') for index in ancestor_children) +\\n tuple((index, 'X') for index in ancestors)),\\n d_coefficient)\\n\\n c_majorana_component = QubitOperator(\\n (((ladder_operator[0], 'X'),) +\\n tuple((index, 'Z') for index in parity_set) +\\n tuple((index, 'X') for index in ancestors)),\\n 0.5)\\n\\n # Update term.\\n transformed_term *= c_majorana_component + d_majorana_component\\n transformed_operator += transformed_term\\n return transformed_operator\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52b2f9c3087a06898a15ffbb58a7e255\",\n \"score\": \"0.4454894\",\n \"text\": \"def kartik_test(a, b):\\n\\n p_a = binomial_test(a.values, b.values)\\n p_b = binomial_test(b.values, a.values)\\n direction = np.zeros_like(p_a)\\n direction[p_a < p_b] = 1\\n direction[p_a > p_b] = -1\\n p = np.minimum(p_a, p_b)\\n q = multipletests(p, method='fdr_bh')[1]\\n return pd.DataFrame(\\n {'direction': direction,\\n 'p': p,\\n 'q': q}, index=a.columns)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30157b993d6ecdb14def0344942d06cf\",\n \"score\": \"0.44480485\",\n \"text\": \"def operator_eq(self, *args):\\n return _vnl_matrix_fixedPython.vnl_matrix_fixedD_3_4_operator_eq(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b737584aafcaf26c3f860de31b787a2b\",\n \"score\": \"0.44441146\",\n \"text\": \"def _expand_operators(self):\\n return NO(self._remove_brackets)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"252326e3e8e8cd047d00df8f60345e42\",\n \"score\": \"0.44404167\",\n \"text\": \"def is_operator(char):\\n return char in OPERATORS\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1df92ab4b4322c1d3e6f748d7db43d3\",\n \"score\": \"0.44399634\",\n \"text\": \"def _setOperator(self, operator):\\n index_list = self._getSelectedRowsIndex()\\n if index_list:\\n for index in index_list:\\n oCombo = self.table.cellWidget(index.row(), 4)\\n oCombo.setCurrentIndex(operator)\\n else:\\n for i in reversed(range(self.table.rowCount())):\\n oCombo = self.table.cellWidget(i, 4)\\n oCombo.setCurrentIndex(operator)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25ed6a845197256956de9d3100370e46\",\n \"score\": \"0.4437378\",\n \"text\": \"def matrica_prijelaza(baza1,baza2,klasa='sympy'):\\n if klasa == 'sympy':\\n m1 = sp.Matrix(baza1).T\\n if m1.det() == 0: return \\\"Error: prvi skup nije baza\\\"\\n m2 = sp.Matrix(baza2).T\\n if m2.det() == 0: return \\\"Error: drugi skup nije baza\\\"\\n if m1.shape != m2.shape: return \\\"Error: baze nisu iz istog vektorskog prostora\\\"\\n return m1.inv()*m2\\n elif klasa == 'numpy':\\n m1 = np.transpose(np.array(baza1))\\n if np.abs(np.linalg.det(m1)) < 1e-15: return \\\"Error: prvi skup nije baza\\\"\\n m2 = np.transpose(np.array(baza2))\\n if np.abs(np.linalg.det(m2)) < 1e-15: return \\\"Error: drugi skup nije baza\\\"\\n if m1.shape != m2.shape: return \\\"Error: baze nisu iz istog vektorskog prostora\\\"\\n return np.matmul(np.linalg.inv(m1), m2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"608729f5776723ef3a83b0fa00907447\",\n \"score\": \"0.44357574\",\n \"text\": \"def shortest_logicals(self, start_weight=None, delta=0, verbose=True, css=False):\\n\\n # if not self.logical_xs_reference and not self.logical_zs_reference:\\n # self.eval(verbose=False)\\n\\n if start_weight is None:\\n if self.dist is not None:\\n start_weight = self.dist\\n else:\\n start_weight = 1\\n\\n end_weight = start_weight + delta\\n\\n if self.circuit is None:\\n raise Exception('Must compile circuits first!')\\n\\n qudit_set = self.data_qubits\\n\\n if end_weight > len(qudit_set):\\n end_weight = len(qudit_set)\\n\\n state = self.state\\n found = self._dist_mode_smallest(state, qudit_set, css=css, verbose=False, start_len=start_weight,\\n end_len=end_weight, list_ops=True)\\n\\n xs_labels = sorted(self.logical_xs_reference.keys())\\n zs_labels = sorted(self.logical_zs_reference.keys())\\n\\n oplist = []\\n\\n if found:\\n for paulis in found:\\n # weight = len(paulis['X'] | paulis['Z'])\\n\\n op_product = []\\n for xi, op_label in enumerate(xs_labels):\\n if self.op_anticommute(paulis, self.logical_xs_reference[op_label]):\\n op_product.append(zs_labels[xi])\\n\\n for zi, op_label in enumerate(zs_labels):\\n if self.op_anticommute(paulis, self.logical_zs_reference[op_label]):\\n op_product.append(xs_labels[zi])\\n\\n op_product = sorted(op_product)\\n\\n oplist.append({'X': paulis['X'], 'Z': paulis['Z'], 'equiv_ops': tuple(op_product)})\\n\\n if verbose:\\n\\n print('Reference Logical Operators:')\\n print('\\\\nLogical Xs:')\\n for op_label in xs_labels:\\n op = self.logical_xs_reference[op_label]\\n print(op_label, op)\\n print('\\\\nLogical Zs:')\\n for op_label in zs_labels:\\n op = self.logical_zs_reference[op_label]\\n print(op_label, op)\\n\\n print('\\\\nLogical Ops Found:\\\\n')\\n for foundop in oplist:\\n print('X - %s Z - %s Equiv Ops - %s' % (foundop['X'], foundop['Z'], foundop['equiv_ops']))\\n\\n return oplist, self.logical_xs_reference, self.logical_zs_reference\",\n \"title\": \"\"\n },\n {\n \"docid\": \"686ede47e5b5b04957fb41f12a8d12c8\",\n \"score\": \"0.44339296\",\n \"text\": \"def ds(op):\\n if len(op)==1: return 0\\n return int(op[1]+'1')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21c3ef04b214b1e888a7ceb63ce786a7\",\n \"score\": \"0.44323573\",\n \"text\": \"def g_function_cobra(self):\\n if self.num_1 > self.num_2:\\n return self.num_1 + self.num_2\\n return self.num_1 + self.num_2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3835c9c8a49248bdbc64faf0ac889cf\",\n \"score\": \"0.44291458\",\n \"text\": \"def _op_8_nest(self):\\r\\n self._optable_8[self._N]()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2c8128d91095fe579cad4e2b68497fd\",\n \"score\": \"0.4428731\",\n \"text\": \"def Conditional__mul__(self, other):\\n try:\\n a = self.inf\\n b = self.sup\\n c = other.inf\\n d = other.sup\\n \\n if a >= 0. and c >= 0.:\\n return ia(a*c,b*d)\\n elif a >= 0. and c<0.<d:\\n return ia(b*c,b*d)\\n elif a >= 0. and d <= 0.:\\n return ia(b*c,a*d)\\n elif a<0.<b and c>=0.:\\n return ia(a*d,b*d) #corrected\\n elif a<0.<b and d<=0.:\\n return ia(b*c,a*c) # I say ad hansen says ac for sup #corrected# ia(b*d,a*d)- I agree with Eldon\\n elif b<=0. and c>=0.:\\n return ia(a*d,b*c)\\n elif b<=0. and c<0.<d:\\n return ia(a*d,a*c)\\n elif b<=0. and d<=0.:\\n return ia(b*d,a*c)\\n elif a<0.<b and c<0.<d:\\n return ia(min(b*c,a*d,a*c,b*d),max(b*c,a*d,a*c,b*d))\\n #return ia(min(b*c,a*d),max(a*c,b*d))\\n else:\\n print 'error in multiplication'\\n c1 = a*c\\n c2 = a*d\\n c3 = b*c\\n c4 = b*d\\n return ia(min(c1,c2,c3,c4),max(c1,c2,c3,c4))\\n \\n \\n except:\\n a = self.inf\\n b = self.sup\\n c = other\\n d = other\\n \\n if a >= 0. and c >= 0.:\\n return ia(a*c,b*d)\\n elif a >= 0. and c<0.<d:\\n return ia(b*c,b*d)\\n elif a >= 0. and d <= 0.:\\n return ia(b*c,a*d)\\n elif a<0.<b and c>=0.:\\n return ia(a*d,b*d) #corrected\\n elif a<0.<b and d<=0.:\\n return ia(b*c,a*c) # I say ad hansen says ac for sup #corrected# ia(b*d,a*d)- I agree with Eldon\\n elif b<=0. and c>=0.:\\n return ia(a*d,b*c)\\n elif b<=0. and c<0.<d:\\n return ia(a*d,a*c)\\n elif b<=0. and d<=0.:\\n return ia(b*d,a*c)\\n elif a<0.<b and c<0.<d:\\n return ia(min(b*c,a*d,a*c,b*d),max(b*c,a*d,a*c,b*d))\\n #return ia(min(b*c,a*d),max(a*c,b*d))\\n else:\\n print 'error in multiplication'\\n c1 = a*c\\n c2 = a*d\\n c3 = b*c\\n c4 = b*d\\n return ia(min(c1,c2,c3,c4),max(c1,c2,c3,c4))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09d18bceb93260af3312c09a0fd1ad0a\",\n \"score\": \"0.44228566\",\n \"text\": \"def diamond_bracket_operator(self, d):\\n d = int(d) % self.__M.level()\\n try:\\n return self.__diamond_operator[d]\\n except AttributeError:\\n self.__diamond_operator = {}\\n except KeyError:\\n pass\\n D = self.__M._diamond_operator_class()(self, d)\\n self.__diamond_operator[d] = D\\n return D\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2fc8be77084ef22852e1b79adecabef\",\n \"score\": \"0.44221944\",\n \"text\": \"def _q_multiply_shift_hexagon(op):\\n x = op.args[0]\\n y = op.args[1]\\n fractional_bits = op.args[2]\\n shift = op.args[3]\\n\\n # Don't use this intrinsic if we don't have a int32x32 vector\\n # or if we are not multiplying q31 numbers\\n if x.dtype != \\\"int32x32\\\" or fractional_bits.value != 31:\\n return op\\n\\n # Case 1, shift is negative\\n mul_e_1 = tvm.tir.call_llvm_intrin(\\n op.dtype, \\\"llvm.hexagon.V6.vmpyewuh.128B\\\", tvm.tir.const(2, \\\"uint32\\\"), x, y\\n )\\n mul_o_1 = tvm.tir.call_llvm_intrin(\\n op.dtype, \\\"llvm.hexagon.V6.vmpyowh.sacc.128B\\\", tvm.tir.const(3, \\\"uint32\\\"), mul_e_1, x, y\\n )\\n fixup = 1 << (-shift - 1)\\n round_mul = mul_o_1 + fixup\\n out_negative_shift = tvm.tir.call_llvm_intrin(\\n op.dtype, \\\"llvm.hexagon.V6.vaslwv.128B\\\", tvm.tir.const(2, \\\"uint32\\\"), round_mul, shift\\n )\\n\\n # Case 2, shift is positive\\n x = x * (1 << (shift))\\n mul_e_2 = tvm.tir.call_llvm_intrin(\\n op.dtype, \\\"llvm.hexagon.V6.vmpyewuh.128B\\\", tvm.tir.const(2, \\\"uint32\\\"), x, y\\n )\\n mul_o_2 = tvm.tir.call_llvm_intrin(\\n op.dtype, \\\"llvm.hexagon.V6.vmpyowh.rnd.sacc.128B\\\", tvm.tir.const(3, \\\"uint32\\\"), mul_e_2, x, y\\n )\\n\\n # Select depending on the shift\\n return tvm.tir.Select(shift < 0, out_negative_shift, mul_o_2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"283f5e4fd4fa97896569b204e88c5d9c\",\n \"score\": \"0.44218627\",\n \"text\": \"def print_ans(M1, M2):\\n if M1 == M2:\\n print(\\\"0\\\")\\n else:\\n print(\\\"1\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8d154c67ffddec1de0f7c32f4c340ce\",\n \"score\": \"0.44174978\",\n \"text\": \"def test2_12_1_1(self):\\n GA, e_1, e_2, e_3 = Ga.build('e*1|2|3')\\n self.assertEqual((e_1 + e_2) ^ (e_1 + e_3), (-e_1 ^ e_2) + (e_1 ^ e_3) + (e_2 ^ e_3))\\n self.assertEqual((e_1 + e_2 + e_3) ^ (2 * e_1), -2 * (e_1 ^ e_2) - 2 * (e_1 ^ e_3))\\n self.assertEqual((e_1 - e_2) ^ (e_1 - e_3), (e_1 ^ e_2) - (e_1 ^ e_3) + (e_2 ^ e_3))\\n self.assertEqual(\\n (e_1 + e_2) ^ (0.5 * e_1 + 2 * e_2 + 3 * e_3), 1.5 * (e_1 ^ e_2) + 3 * (e_1 ^ e_3) + 3 * (e_2 ^ e_3))\\n self.assertEqual((e_1 ^ e_2) ^ (e_1 + e_3), (e_1 ^ e_2 ^ e_3))\\n self.assertEqual((e_1 + e_2) ^ ((e_1 ^ e_2) + (e_2 ^ e_3)), (e_1 ^ e_2 ^ e_3))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45f179a393572ee5b40eec51cef01314\",\n \"score\": \"0.4416586\",\n \"text\": \"def verify_type_match(self, left, right, operator):\\r\\n \\r\\n # codigo para depurar\\r\\n global debug\\r\\n if debug :\\r\\n print('antes')\\r\\n print('l: ', left)\\r\\n print('r: ', right)\\r\\n print('op: ', operator)\\r\\n \\r\\n # se obtienen los codigos que representan a cada tipo\\r\\n l = self.get_val(left)\\r\\n r = self.get_val(right)\\r\\n op = self.get_val(operator)\\r\\n\\r\\n # codigo para depurar\\r\\n if debug :\\r\\n print('despues')\\r\\n print('l: ', l)\\r\\n print('r: ', r)\\r\\n print('op: ', op)\\r\\n\\r\\n # Se asigna un codigo a cada tipo\\r\\n ERROR, ENT, FLOAT, CADENA, CHAR, BOOL = 0, 1, 2, 3, 4, 5\\r\\n\\r\\n # Se inicializa el cubo semantico\\r\\n cube = (\\r\\n ((ENT, ENT, ENT, FLOAT, ERROR, ERROR, BOOL, BOOL, BOOL, BOOL, BOOL, BOOL, ENT),\\r\\n (FLOAT, FLOAT, FLOAT, FLOAT, ERROR, ERROR, BOOL, BOOL, BOOL, BOOL, BOOL, BOOL, ENT),\\r\\n (ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR),\\r\\n (ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR),\\r\\n (ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR),\\r\\n ),\\r\\n ((FLOAT, FLOAT, FLOAT, FLOAT, ERROR, ERROR, BOOL, BOOL, BOOL, BOOL, BOOL, BOOL, FLOAT),\\r\\n (FLOAT, FLOAT, FLOAT, FLOAT, ERROR, ERROR, BOOL, BOOL, BOOL, BOOL, BOOL, BOOL, FLOAT),\\r\\n (ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR),\\r\\n (ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR),\\r\\n (ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR),\\r\\n ),\\r\\n ((ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR),\\r\\n (ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR),\\r\\n (ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, BOOL, BOOL, CADENA),\\r\\n (ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR),\\r\\n (ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR)\\r\\n ),\\r\\n ((ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR),\\r\\n (ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR),\\r\\n (ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR),\\r\\n (ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, BOOL, BOOL, CHAR),\\r\\n (ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR),\\r\\n ),\\r\\n ((ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR),\\r\\n (ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR),\\r\\n (ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR),\\r\\n (ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR, ERROR),\\r\\n (ERROR, ERROR, ERROR, ERROR, BOOL, BOOL, ERROR, ERROR, ERROR, ERROR, BOOL, BOOL, BOOL),\\r\\n ),\\r\\n )\\r\\n\\r\\n # Se guarda el resultado producido por el cubo\\r\\n result = cube[l][r][op]\\r\\n\\r\\n # Dependiendo del reusltado se regresa una cadena con el tipo resultante\\r\\n # o un -1 en caso de error de tipos.\\r\\n if result is 0:\\r\\n return -1 \\r\\n elif result is 1:\\r\\n return \\\"entero\\\"\\r\\n elif result is 2:\\r\\n return \\\"flotante\\\"\\r\\n elif result is 3:\\r\\n return \\\"cadena\\\"\\r\\n elif result is 4:\\r\\n return \\\"caracter\\\"\\r\\n elif result is 5:\\r\\n return \\\"bool\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6bb680ad0182c32d8ebb4e4d89e156f\",\n \"score\": \"0.44119498\",\n \"text\": \"def boolean(\\n A: Union[ComponentOrReference, Tuple[ComponentOrReference, ...]],\\n B: Union[ComponentOrReference, Tuple[ComponentOrReference, ...]],\\n operation: str,\\n precision: float = 1e-4,\\n num_divisions: Union[int, Int2] = (1, 1),\\n max_points: int = 4000,\\n layer: LayerSpec = (1, 0),\\n) -> Component:\\n D = Component()\\n A_polys = []\\n B_polys = []\\n A = list(A) if isinstance(A, (list, tuple)) else [A]\\n B = list(B) if isinstance(B, (list, tuple)) else [B]\\n\\n for X, polys in ((A, A_polys), (B, B_polys)):\\n for e in X:\\n if isinstance(e, (Component, ComponentReference)):\\n polys.extend(e.get_polygons())\\n elif isinstance(e, Polygon):\\n polys.extend(e.polygons)\\n\\n layer = gf.pdk.get_layer(layer)\\n gds_layer, gds_datatype = _parse_layer(layer)\\n\\n operation = operation.lower().replace(\\\" \\\", \\\"\\\")\\n if operation == \\\"a-b\\\":\\n operation = \\\"not\\\"\\n elif operation == \\\"b-a\\\":\\n operation = \\\"not\\\"\\n A_polys, B_polys = B_polys, A_polys\\n elif operation == \\\"a+b\\\":\\n operation = \\\"or\\\"\\n elif operation not in [\\\"not\\\", \\\"and\\\", \\\"or\\\", \\\"xor\\\", \\\"a-b\\\", \\\"b-a\\\", \\\"a+b\\\"]:\\n raise ValueError(\\n \\\"gdsfactory.geometry.boolean() `operation` \\\"\\n \\\"parameter not recognized, must be one of the \\\"\\n \\\"following: 'not', 'and', 'or', 'xor', 'A-B', \\\"\\n \\\"'B-A', 'A+B'\\\"\\n )\\n\\n # Check for trivial solutions\\n if (not A_polys or not B_polys) and operation != \\\"or\\\":\\n if (\\n operation != \\\"not\\\"\\n and operation != \\\"and\\\"\\n and operation == \\\"xor\\\"\\n and not A_polys\\n and not B_polys\\n or operation != \\\"not\\\"\\n and operation == \\\"and\\\"\\n ):\\n p = None\\n elif operation != \\\"not\\\" and operation == \\\"xor\\\" and not A_polys:\\n p = B_polys\\n elif operation != \\\"not\\\" and operation == \\\"xor\\\":\\n p = A_polys\\n elif operation == \\\"not\\\":\\n p = A_polys or None\\n elif not A_polys and not B_polys:\\n p = None\\n elif all(np.array(num_divisions) == np.array([1, 1])):\\n p = gdspy.boolean(\\n operand1=A_polys,\\n operand2=B_polys,\\n operation=operation,\\n precision=precision,\\n max_points=max_points,\\n layer=gds_layer,\\n datatype=gds_datatype,\\n )\\n else:\\n p = _boolean_polygons_parallel(\\n polygons_A=A_polys,\\n polygons_B=B_polys,\\n num_divisions=num_divisions,\\n operation=operation,\\n precision=precision,\\n )\\n\\n if p is not None:\\n polygons = D.add_polygon(p, layer=layer)\\n [\\n polygon.fracture(max_points=max_points, precision=precision)\\n for polygon in polygons\\n ]\\n return D\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f02dca0ab8c1db5ea5e0d874415c867\",\n \"score\": \"0.44119266\",\n \"text\": \"def crtaj_modul ( orMj , dX ):\\n \\\"\\\"\\\"\\n od = rel2abs ( origin , poc , smjer )\\n do = rel2abs ( origin , kraj , smjer )\\n mc.setBlocks ( od , do , blok_id , blok_dv )\\n \\\"\\\"\\\"\\n crtaj_kvadar ( orMj , [ 3 + dX , 0, 0 ] , [ 3 + dX , -2 , 2 ] , orSm , materijal , 2 ) # blok\\n crtaj_kvadar ( orMj , [ 3 + dX , -2, 2 ] , [ 3 + dX , -2 , 2 ] , orSm , 0 , 0 ) # zrak\\n crtaj_kvadar ( orMj , [ 3 + dX , -1, 1 ] , [ 3 + dX , -1 , 1 ] , orSm , 0 , 0 ) # zrak\\n crtaj_kvadar ( orMj , [ 3 + dX , 0, 0 ] , [ 3 + dX , 0 , 0 ] , orSm , 0 , 0 ) # zrak\\n crtaj_kvadar ( orMj , [ 3 + dX , -2, 0 ] , [ 3 + dX , -2 , 0 ] , orSm , 0 , 0 ) # zrak\\n crtaj_redstonedust ( orMj , [ 3 + dX , -2, 2 ] , [ 3 + dX , -2 , 2 ] , orSm )\\n crtaj_redstonedust ( orMj , [ 3 + dX , -1, 3 ] , [ 3 + dX , -1 , 3 ] , orSm )\\n crtaj_redstonetorch ( orMj , [ 3 + dX , 1, 1 ] , orSm , \\\"desno\\\" ) \\n crtaj_comparator ( orMj , [ 3 + dX , 0, 3 ] , [ 3 + dX , 0 , 3 ] , orSm , rel_smjer = \\\"lijevo\\\" )\\n crtaj_repeater ( orMj , [ 3 + dX , -1, 1 ] , [ 3 + dX , -1 , 1 ] , orSm , rel_smjer = \\\"desno\\\" )\\n \\n crtaj_hopper ( orMj , [ 3 + dX , 1, 2 ] , [ 3 + dX , 1 , 3 ] , orSm , \\\"desno\\\" ) # dva doljnja\\n crtaj_hopper ( orMj , [ 3 + dX , 1, 4 ] , [ 3 + dX , 1 , 4 ] , orSm , \\\"odmene\\\" ) # gornji\\n \\n sto = '{TransferCooldown:0,Items:[0:{Slot:0b,id:\\\"%s\\\",Count:4b,Damage:%ss,},1:{Slot:1b,id:\\\"%s\\\",Count:5b,Damage:%ss,},2:{Slot:2b,id:\\\"%s\\\",Count:5b,Damage:%ss,},3:{Slot:3b,id:\\\"%s\\\",Count:5b,Damage:%ss,},4:{Slot:4b,id:\\\"%s\\\",Count:5b,Damage:%ss,},],id:\\\"Hopper\\\",Lock:\\\"\\\",}' % ( popis [ dX ] [ 0 ] , popis [ dX ] [ 1 ] , popis [ dX ] [ 0 ] , popis [ dX ] [ 1 ] , popis [ dX ] [ 0 ] , popis [ dX ] [ 1 ] , popis [ dX ] [ 0 ] , popis [ dX ] [ 1 ] , popis [ dX ] [ 0 ] , popis [ dX ] [ 1 ] )\\n bla = rel2abs ( orMj , ( 3 + dX , 1 , 3 ) , orSm )\\n mc.postToChat(\\\"orginal: %s %s \\\" % ( dX , bla ) )\\n time.sleep ( 1 )\\n mc.setBlockWithNBT(bla,154,smjer_hoppera ( orSm , \\\"desno\\\") , sto ) #hopper gleda na istok \\n bla = rel2abs ( orMj , ( 3 + dX , 1 , 2 ) , orSm )\\n mc.postToChat(\\\"orginal: %s %s \\\" % ( dX , bla ) )\\n time.sleep ( 1 )\\n mc.setBlockWithNBT(bla,154,smjer_hoppera ( orSm , \\\"desno\\\") , sto ) #hopper gleda na istok \\n #bla = rel2abs ( orMj , ( 3 + dX , 5 , 2 ) , orSm )\\n #mc.setBlock(bla,53,4 ) #oak wood stairs naopako, gledaju na istok\\n crtaj_stepenice ( orMj , ( 3 + dX , 5 , 2 ) , ( 3 + dX , 5 , 2 ) , orSm , blok_id = 53 , rel_smjer = \\\"desno\\\" , gore_dolje = \\\"da\\\" )\\n\\n \\n kutija = 54\\n tkutija = 146\\n if ( int ( dX ) % 2 == 1 ) :\\n kmat = kutija\\n else :\\n kmat = tkutija\\n crtaj_kutiju ( orMj , [ 3 + dX , 2, 2 ] , [ 3 + dX , 3 , 2 ] , orSm , rel_smjer = \\\"meni\\\" , blok_id = kmat )\\n crtaj_hopper ( orMj , [ 3 + dX , 3, 1 ] , [ 3 + dX , 3 , 0 ] , orSm , \\\"desno\\\" ) # hopper ispod kutije\\n crtaj_kutiju ( orMj , [ 3 + dX , 4, 1 ] , [ 3 + dX , 5 , 0 ] , orSm , rel_smjer = \\\"meni\\\" , blok_id = kmat ) # dodatne kutije\",\n \"title\": \"\"\n },\n {\n \"docid\": \"833e060bbc82e213f266619f193cac80\",\n \"score\": \"0.44113728\",\n \"text\": \"def test_weight_for_valid_operator(self):\\n self.assertTrue(type(self.instance.weight('*')) is IntType)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86e72879b685d99a34842bc065804d16\",\n \"score\": \"0.4410812\",\n \"text\": \"def test_batting_flags(batting):\\n flag_cols = [\\n 'g',\\n 'g_dh',\\n 'g_ph',\\n 'g_pr'\\n ]\\n\\n assert batting[flag_cols].min().min() == 0\\n assert batting[flag_cols].max().max() == 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5515de56ece5c5f81b5757893cc31f6\",\n \"score\": \"0.44100785\",\n \"text\": \"def shift_operator(self,dd_):\\n \\n N_ = self.N\\n aa = self.anihilation_operator()\\n ad = self.creation_operator()\\n \\n # construct the Shift Operator\\n Dd_large = numpy.zeros((N_,N_),dtype=numpy.float)\\n Dd_large = dd_*(ad-aa)/numpy.sqrt(2.0)\\n\\n # Diagonalize and obtain transformation matrix\\n A,S = numpy.linalg.eig(Dd_large)\\n S1 = numpy.linalg.inv(S)\\n \\n # Exponentiate\\n Dd_large = numpy.diag(numpy.exp(A))\\n \\n # Transform back and reduce to the lower number of states\\n return numpy.real(numpy.dot(S,numpy.dot(Dd_large,S1)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"592e263302081fd9d99d999078f2dc78\",\n \"score\": \"0.44087765\",\n \"text\": \"def operator_eq(self, *args):\\n return _vnl_matrix_fixedPython.vnl_matrix_fixedD_4_3_operator_eq(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a971986501ed11ceaba23764587bbaae\",\n \"score\": \"0.4408561\",\n \"text\": \"def operar():\\n a=[]\\n b = []\\n exit =False\\n while not exit:\\n print\\n opcions()\\n op = mainLib.askNumberOption(\\\"Choose an option: \\\", 8)\\n print\\n if(op == 1):\\n a = AdministrarCrearMatriu(\\\"a\\\")\\n elif(op == 2):\\n b = AdministrarCrearMatriu(\\\"b\\\")\\n elif (op == 3):\\n AdministrarOperacio(\\\"+\\\",a,b)\\n elif(op == 4):\\n AdministrarOperacio(\\\"-\\\", a, b)\\n elif (op == 5):\\n AdministrarOperacio(\\\"*\\\", a, b)\\n elif(op == 6):\\n AdministrarOperacioUnaMatriu(\\\"d\\\",a,b)\\n elif(op == 7):\\n AdministrarOperacioUnaMatriu(\\\"t\\\",a,b)\\n else:\\n exit = True\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":678,"cells":{"query_id":{"kind":"string","value":"a2ba58efdc9e91c122fb0243afe6f507"},"query":{"kind":"string","value":"Extract the course descriptions from the document. Write them to the appropriate Course records in the database."},"positive_passages":{"kind":"list like","value":[{"docid":"ba84b03c2eca5d2d3c535e6f92301885","score":"0.75976646","text":"def ExtractCourseDescriptions(document: Document, courses: List[Course]) -> None:\n\n courseDescriptionOn = False\n allparagraphs = []\n pertinentParagraphs = []\n number = 0\n pHeader = \"\"\n hIsCourseTitle = False\n partialTitle = []\n\n global courseNameStyles\n\n for p in document.paragraphs:\n\n if p.text.strip() != \"\":\n\n pText = p.text.strip()\n lastPos = len(pText)\n\n if pText[lastPos-1] == \":\":\n partialTitle.append(pText)\n else:\n if len(partialTitle) > 0:\n partialTitle.append(p.text.strip())\n\n number = number + 1\n paragraph = Paragraph()\n paragraph.setNumber(number)\n paragraph.setStyle(p.style.name)\n paragraph.setText(p.text.strip().rstrip(string.digits))\n allparagraphs.append(paragraph)\n\n for i, item in enumerate(allparagraphs):\n if item.getStyle() == constant.STYLE_BODY_TEXT:\n pPrev = allparagraphs[i-1]\n \n pHeader = \"\"\n if pPrev.getStyle() in courseNameStyles:\n number = number + 1\n p2 = Paragraph()\n p2.setNumber(number)\n p2.setStyle(pPrev.getStyle())\n p2.setText(pPrev.getText())\n paragraphs2.append(p2)\n pHeader = pPrev.getText().strip()\n if pHeader != \"\":\n hIsCourseTitle = HeaderIsCourseTitle(pHeader)\n\n if hIsCourseTitle:\n number = number + 1\n pc = Paragraph()\n pc.setNumber(number)\n pc.setStyle(item.getStyle())\n pc.setHeader(pHeader)\n pc.setText(item.getText())\n pertinentParagraphs.append(pc)\n\n associatedCourse = Course()\n currentCourseDescription = \"\"\n courseTitlePosition = -1\n\n for i, p in enumerate(pertinentParagraphs):\n #if p.getHeader().strip() != \"\":\n #print(\"paragraph header: {}\".format(p.getHeader().strip()))\n \n if p.getHeader().strip() != \"\" and courseDescriptionOn and i > courseTitlePosition:\n associatedCourse.setDescription(currentCourseDescription)\n courseDescriptionOn = False\n currentCourseDescription = \"\"\n courseTitlePosition = -1\n if p.getHeader().strip() != \"\" and not courseDescriptionOn:\n courseDescriptionOn = True\n associatedCourse = GetAssociatedCourse(courses, p.getHeader().replace(\"'\",\"\").strip())\n courseTitlePosition = i\n currentCourseDescription += p.getText().strip()\n if courseDescriptionOn and i > courseTitlePosition:\n currentCourseDescription += p.getText().strip()","title":""}],"string":"[\n {\n \"docid\": \"ba84b03c2eca5d2d3c535e6f92301885\",\n \"score\": \"0.75976646\",\n \"text\": \"def ExtractCourseDescriptions(document: Document, courses: List[Course]) -> None:\\n\\n courseDescriptionOn = False\\n allparagraphs = []\\n pertinentParagraphs = []\\n number = 0\\n pHeader = \\\"\\\"\\n hIsCourseTitle = False\\n partialTitle = []\\n\\n global courseNameStyles\\n\\n for p in document.paragraphs:\\n\\n if p.text.strip() != \\\"\\\":\\n\\n pText = p.text.strip()\\n lastPos = len(pText)\\n\\n if pText[lastPos-1] == \\\":\\\":\\n partialTitle.append(pText)\\n else:\\n if len(partialTitle) > 0:\\n partialTitle.append(p.text.strip())\\n\\n number = number + 1\\n paragraph = Paragraph()\\n paragraph.setNumber(number)\\n paragraph.setStyle(p.style.name)\\n paragraph.setText(p.text.strip().rstrip(string.digits))\\n allparagraphs.append(paragraph)\\n\\n for i, item in enumerate(allparagraphs):\\n if item.getStyle() == constant.STYLE_BODY_TEXT:\\n pPrev = allparagraphs[i-1]\\n \\n pHeader = \\\"\\\"\\n if pPrev.getStyle() in courseNameStyles:\\n number = number + 1\\n p2 = Paragraph()\\n p2.setNumber(number)\\n p2.setStyle(pPrev.getStyle())\\n p2.setText(pPrev.getText())\\n paragraphs2.append(p2)\\n pHeader = pPrev.getText().strip()\\n if pHeader != \\\"\\\":\\n hIsCourseTitle = HeaderIsCourseTitle(pHeader)\\n\\n if hIsCourseTitle:\\n number = number + 1\\n pc = Paragraph()\\n pc.setNumber(number)\\n pc.setStyle(item.getStyle())\\n pc.setHeader(pHeader)\\n pc.setText(item.getText())\\n pertinentParagraphs.append(pc)\\n\\n associatedCourse = Course()\\n currentCourseDescription = \\\"\\\"\\n courseTitlePosition = -1\\n\\n for i, p in enumerate(pertinentParagraphs):\\n #if p.getHeader().strip() != \\\"\\\":\\n #print(\\\"paragraph header: {}\\\".format(p.getHeader().strip()))\\n \\n if p.getHeader().strip() != \\\"\\\" and courseDescriptionOn and i > courseTitlePosition:\\n associatedCourse.setDescription(currentCourseDescription)\\n courseDescriptionOn = False\\n currentCourseDescription = \\\"\\\"\\n courseTitlePosition = -1\\n if p.getHeader().strip() != \\\"\\\" and not courseDescriptionOn:\\n courseDescriptionOn = True\\n associatedCourse = GetAssociatedCourse(courses, p.getHeader().replace(\\\"'\\\",\\\"\\\").strip())\\n courseTitlePosition = i\\n currentCourseDescription += p.getText().strip()\\n if courseDescriptionOn and i > courseTitlePosition:\\n currentCourseDescription += p.getText().strip()\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"3c0601b75ff7eab4168a97065371a8bf","score":"0.7158249","text":"def ExtractCourseAndDescription(firebase: firebase, document: Document, knowledgeAreas: List[KnowledgeArea], courses: List[Course], catalogId: str) -> None:\n\n global currentKnowledgeArea\n\n knowledgeAreaId = \"\"\n knowledgeAreaTitle = \"\"\n candidateId = \"\"\n partialTitle = []\n fulltitle = \"\"\n\n global courseNameStyles\n\n for p in document.paragraphs:\n if p.style.name == constant.STYLE_NORMAL:\n currentKnowledgeArea = getMatchingKnowledgeArea(p.text.strip())\n candidateId = currentKnowledgeArea.getId()\n\n if candidateId != \"\":\n knowledgeAreaId = str(candidateId)\n knowledgeAreaTitle = currentKnowledgeArea.getText()\n else:\n knowledgeAreaId = \"\"\n\n if knowledgeAreaId != \"\" and p.text.strip() != knowledgeAreaTitle and p.text.strip() != \"\":\n pText = p.text.strip()\n lastPos = len(pText)\n\n if not pText[lastPos-1].isdigit():\n partialTitle.append(p.text.strip())\n else:\n if len(partialTitle) > 0:\n partialTitle.append(p.text.strip())\n fulltitle = ' '.join(partialTitle)\n else:\n fulltitle = pText\n\n fulltitle = fulltitle.replace(\"*\", \"\")\n\n course = Course()\n course.setKnowledgeAreaId(knowledgeAreaId)\n course.setKnowledgeArea(currentKnowledgeArea.getText())\n course.setTOCEntry(p.text.strip())\n course.setTitle(fulltitle.rstrip(string.digits).strip())\n courses.append(course)\n partialTitle = []\n\n ExtractCourseDescriptions(document, courses)\n\n for course in courses:\n if course.getDescription().strip() == \"\":\n courseTitle = course.getTitle().strip()\n courseTitle = courseTitle.replace(\"New!\", \"\").replace(\"*\", \"\").replace(\"'\",\"\").strip()\n\n pNumber = 0\n takeDescription = 0\n descriptionElements = []\n\n for p in document.paragraphs:\n pNumber += 1\n \n if p.style.name in courseNameStyles and p.text != \"\":\n pText = p.text.replace(\"New! \", \"\").replace(\"*\", \"\").replace(\"'\",\"\").strip()\n if pText.__contains__(courseTitle) or courseTitle.__contains__(pText):\n takeDescription = 1\n else:\n takeDescription = 0\n\n if p.style.name == constant.STYLE_BODY_TEXT and takeDescription == 1:\n #print(\"taking {} as course description for course: {}\".format(p.text.strip(), course.getTitle()))\n descriptionElements.append(p.text.strip())\n\n fullDescription = course.getDescription()\n\n if len(descriptionElements) > 0:\n fullDescription = ' '.join(descriptionElements)\n\n course.setDescription(fullDescription)\n\n for course in courses:\n if course.getDescription().strip() == \"\":\n print(\"course: {} has no description\".format(course.getTitle()))\n\n if course.getKnowledgeArea() == course.getTitle():\n continue\n\n newCourse = {\n 'catalogid': catalogId,\n 'knowledgeareaid': course.getKnowledgeAreaId(),\n 'name': course.getTitle(),\n 'description': course.getDescription()\n }\n result = firebase.post('course', newCourse)\n course.setId(result)","title":""},{"docid":"a5602e3e4c7ec47b5d9ced790a9d9a27","score":"0.5897001","text":"def copy_course_description(self, courseevent_pk):\n courseevent = get_object_or_404(CourseEvent, pk=courseevent_pk)\n course = get_object_or_404(Course, pk=courseevent.course_id)\n\n courseevent = get_object_or_404(CourseEvent, pk=courseevent_pk)\n courseevent.target_group = course.target_group\n courseevent.excerpt = course.excerpt\n courseevent.text = course.text\n courseevent.prerequisites = course.prerequisites\n courseevent.project = course.project\n courseevent.save()\n\n logger.info(\"\"\"[%s] [course %s %s]:\n Beschreibung aus der Kursvorlage in den Kurs kopiert\"\"\"\n % (courseevent, course.id, course))\n return(HttpResponseRedirect(courseevent.get_absolute_url()))","title":""},{"docid":"cd0a830fd8fa1091526b29b84b6f6d9e","score":"0.5801686","text":"def get_description(self, course):\n if hasattr(course, 'programcourse') and course.programcourse.description:\n return course.programcourse.description\n return course.description","title":""},{"docid":"01b8a91b546cb01d2747b5365a103984","score":"0.57233155","text":"def get_courses(self) -> List[Dict[str, str]]:\n\n # Check if the catalog exists in the cache\n catalog_cache_path = (self._cache_dir / 'department_catalogs').with_suffix('.json')\n if catalog_cache_path.exists():\n with open(catalog_cache_path, 'r') as file:\n departments = json.load(file)\n else:\n departments = self._download_department_catalogs()\n with open(catalog_cache_path, 'w') as file:\n json.dump(departments, file)\n\n # Gather catalog HTML pages\n html_catalogs = [d['catalog_html'] for d in departments]\n\n department_map = {d['code']: d['name'] for d in departments}\n\n # First pass: Parse basic course information\n courses = []\n for catalog_html in html_catalogs:\n\n # Clean content\n catalog_html = catalog_html.replace(' ', ' ') # Non-breaking space\n\n soup = BeautifulSoup(catalog_html, 'lxml')\n courses_div = soup.find('div', attrs={'class': 'courses'})\n course_blocks = courses_div.find_all('div', attrs={'class': 'courseblock'})\n\n for block in course_blocks:\n paragraphs = block.find_all('p')\n\n # Parse department code, course number, and course title\n title_paragraph_text = paragraphs[0].text\n match = re.match(re.compile(r'^(.+?) (\\S+?)\\. (.+?)\\. (.+)?$'), title_paragraph_text)\n if not match:\n logging.warning(f'Failed to parse course title paragraph: {title_paragraph_text}')\n continue\n department_code = match.group(1)\n course_number = match.group(2)\n course_title = match.group(3)\n units_string = match.group(4)\n\n course = {\n 'department_code': department_code,\n 'department_name': department_map[department_code],\n 'number': course_number,\n 'title': course_title\n }\n\n # Parse units\n if units_string is not None:\n self._parse_units_string(course, units_string)\n else:\n logging.warning(f'No units found in title paragraph: \"{title_paragraph_text}\"')\n\n # Description\n course['description'] = paragraphs[1].text\n\n # Save remaining paragraphs (they will be parsed on the second pass)\n p = []\n for paragraph in paragraphs[2:]:\n lines = paragraph.text.splitlines()\n p.extend(lines)\n course['_paragraphs'] = p\n\n courses.append(course)\n\n valid_courses = {d['code']: [c['number'] for c in courses if c['department_code'] == d['code']] for d in departments}\n\n # Second pass: Parse extra paragraphs (prerequisites, restrictions, etc.)\n for course in courses:\n for paragraph in course['_paragraphs']:\n\n # Prerequisites\n match = re.match(r'^Prerequisite:\\s*(.+)$', paragraph)\n if match:\n self._parse_prerequisite_string(course, match.group(1), valid_courses=valid_courses)\n continue\n\n # Corequisite\n match = re.match(r'^Corequisite: (.+)$', paragraph)\n if match:\n self._parse_corequisite_string(course, match.group(1), valid_courses=valid_courses)\n continue\n\n # Prerequisite OR Corequisite\n match = re.match(r'^Prerequisite or corequisite: (.+)$', paragraph)\n if match:\n self._parse_prerequisite_or_corequisite_string(course, match.group(1), valid_courses=valid_courses)\n continue\n\n # Restrictions\n match = re.match(r'^Restriction:\\s*(.+)$', paragraph) # TODO: Parse?\n if match:\n course['restriction'] = match.group(1)\n continue\n\n # Same\n match = re.match(r'^Same as (.+)\\.$', paragraph)\n if match:\n self._parse_same_as_string(course, match.group(1)) # TODO: Validate course codes?\n continue\n\n # Concurrent\n match = re.match(r'^Concurrent with (.+)\\.$', paragraph)\n if match:\n course['concurrent'] = match.group(1) # TODO: Validate course codes and parse multiple\n continue\n\n # Repeatability\n match = re.match(r'^Repeatability:\\s*(.+)$', paragraph) # TODO: parse number?\n if match:\n course['repeatability'] = match.group(1)\n continue\n\n # Overlaps\n match = re.match(r'^Overlaps with (.+)\\.$', paragraph) # TODO: validate course codes and parse\n if match:\n course['overlap'] = match.group(1)\n continue\n\n # Grading Option\n match = re.match(r'^Grading Option: (.+)$', paragraph) # TODO: Parse?\n if match:\n course['grading_option'] = match.group(1)\n continue\n\n # Design units\n match = re.match(r'^\\(Design units: ((?:\\d+\\.\\d+|\\.\\d+|\\d+)(?:\\s*-\\s*(?:\\d+\\.\\d+|\\.\\d+|\\d+))?)\\)', paragraph)\n if match:\n course['design_units'] = match.group(1)\n continue\n\n # GE Category\n match = re.match(r'^\\((.+)\\)\\.?$', paragraph)\n if match:\n try:\n self._parse_ge_category_string(course, match.group(1))\n continue\n except Exception as e:\n logger.warning(f'Failed to parse potential GE category for {course[\"department_code\"]} {course[\"number\"]}: {e}')\n\n logger.warning(f'Unrecognized paragraph for course {course[\"department_code\"]} {course[\"number\"]}: \"{paragraph}\"')\n del course['_paragraphs']\n\n # names = [course['department_code'] + ' ' + course['number'] for course in courses]\n # print(names)\n #\n # # Extras\n # for course in courses:\n # if course['number'].upper().endswith('L'):\n # non = course['department_code'] + ' ' + course['number'][:-1]\n # if non in names:\n # print('POTENTIAL LAB:', course['department_code'] + ' ' + course['number'])\n # else:\n # print('BAMBOOZLE:', course['department_code'] + ' ' + course['number'], 'NON:', non)\n\n return courses","title":""},{"docid":"f2b2405b211045f5c0734f4b9e40dced","score":"0.559974","text":"def context_processor():\n sections = []\n for doc in DOCS:\n content = get_doc(doc)\n lines = content.split(\"\\n\")\n doc = {\n 'id': doc,\n 'title': lines[0].replace(\"#\", \"\").strip(),\n 'description': \"\\n\".join(lines[1:3]),\n 'body': content,\n }\n sections.append(doc)\n return {'sections': sections}","title":""},{"docid":"652b78f9baa59767606df70e08f274e5","score":"0.5530076","text":"def get_db_description_text(self):\n \n # sets description text in different languages\n # default value is English\n # sets header of devotion\n # sets description of type of lesson\n #name_text = bible_codes.db_info_description[\"en\"]\n try:\n name_text = bible_codes.db_info_description_title\n except Exception:\n print(\"unable to get internat.db_info_description for \" + self.lang_code)\n if (self.lesson_type == 'ad' or self.lesson_type == ''):\n version_text = bible_codes.db_info_description_version_adult\n else:\n if (self.lesson_type == 'ay'):\n version_text = version_text = bible_codes.db_info_description_version_youth\n else:\n version_text = \"\"\n description_text = \"{0} {1} {2}\".format(name_text, version_text, self.SS_year_inst.quarters_list_year[-1].get('id'))\n return description_text","title":""},{"docid":"ea7e9bbdfb864f925b5c8713c5b6e244","score":"0.55218595","text":"def preprocDescrip():\n # Load lists of descriptions\n concentrations = pickle.load(open(\"nlp/concentrations.p\", \"rb\"))\n descr = pickle.load(open(\"nlp/concentrationDescr.p\", \"rb\"))\n\n orgInfo = pickle.load(open(\"nlp/orgInfo.p\", \"rb\"))\n orgNames = pickle.load(open(\"nlp/orgNames.p\", \"rb\"))\n\n # Add org names to descriptions; remove the word 'Harvard'\n for i, org in enumerate(orgInfo):\n org = org.replace(\"harvard\", \"\")\n orgInfo[i] = str(orgNames[i]).lower() + \" \" + str(org).lower()\n\n # Calculate pairwise similarities\n orgInfoLists = tc.getSimilarityLists(orgInfo)\n concentrationLists = tc.getSimilarityLists(descr)\n\n # Store results for future retrieval\n pickle.dump(orgInfoLists, open(\"nlp/orgInfoLists.p\", \"wb\"))\n pickle.dump(concentrationLists, open(\"nlp/concentrationLists.p\", \"wb\"))","title":""},{"docid":"f09c5d3f360908b3e5e6869d150456c2","score":"0.5521627","text":"def course_attrs(self):\n\n TITLE_CLASS = \"PALEVEL0SECONDARY\"\n INFO_TABLE_CLASS = \"SSSGROUPBOXLTBLUEWBO\"\n INFO_BOX_CLASS = \"PSGROUPBOXNBO\"\n INFO_BOX_HEADER_CLASS = \"SSSGROUPBOXLTBLUE\"\n DESCRIPTION_CLASS = \"PSLONGEDITBOX\"\n\n EDITBOX_LABEL_CLASS = \"PSEDITBOXLABEL\"\n EDITBOX_DATA_CLASS = \"PSEDITBOX_DISPONLY\"\n DROPDOWN_LABEL_CLASS = \"PSDROPDOWNLABEL\"\n DROPDOWN_DATA_CLASS = \"PSDROPDOWNLIST_DISPONLY\"\n\n DESCRIPTION = \"Description\"\n COURSE_DETAIL = \"Course Detail\"\n COURSE_COMPS = \"Course Components\"\n ENROLL_INFO = \"Enrollment Information\"\n CEAB = \"CEAB Units\"\n\n KEYMAP = {\n \"Career\": \"career\",\n \"Typically Offered\": \"typically_offered\",\n \"Units\": \"units\",\n \"Grading Basis\": \"grading_basis\",\n \"Add Consent\": \"add_consent\",\n \"Drop Consent\": \"drop_consent\",\n \"Course Components\": \"course_components\",\n \"Enrollment Requirement\": \"enrollment_requirement\",\n }\n\n ret = {\n 'extra':{\n 'CEAB':{}\n }\n }\n\n # Get the title and number\n title = self.soup.find(\"span\", {\"class\": TITLE_CLASS})\n if not title:\n raise Exception(\"Could not find the course title to parse\")\n\n temp = self._clean_html(title.string)\n m = re.search('^([\\S]+)\\s+([\\S]+)\\s+-\\s+(.*)$', temp)\n if not m:\n raise Exception(\"Title found ({0}) didn't match regular expression\".format(temp))\n\n ret['basic'] = {\n 'title' : m.group(3),\n 'number' : m.group(2),\n 'description' : \"\"\n }\n\n # Blue table with info, enrollment, and description\n info_table = self.soup.find(\"table\", {\"class\": INFO_TABLE_CLASS})\n\n # Look through inner tables\n info_boxes = self.soup.find_all(\"table\", {\"class\": INFO_BOX_CLASS})\n for table in info_boxes:\n\n # Get the table type\n temp = table.find(\"td\", {\"class\": INFO_BOX_HEADER_CLASS})\n if not temp or not temp.string:\n # Nothing there\n continue\n\n box_title = temp.string\n\n # Process the description box\n if box_title == DESCRIPTION:\n desc_list = table.find(\"span\", {\"class\": DESCRIPTION_CLASS}).contents\n if desc_list:\n # If not x.string, it means it's a <br/> Tag\n ret['basic']['description'] = \"\\n\".join([x for x in desc_list if x.string])\n\n # Process the course details and enrollment info\n elif box_title in (COURSE_DETAIL, ENROLL_INFO):\n\n # Labels and values for \"Add/Drop Consent\" (enroll), \"Career\" (course), and \"Grading Basis\" (course)\n labels = table.find_all(\"label\", {\"class\": DROPDOWN_LABEL_CLASS})\n data = table.find_all(\"span\", {\"class\": DROPDOWN_DATA_CLASS})\n\n if box_title == ENROLL_INFO:\n # Labels and values for \"Typically Offered\", \"Enrollment Requirement\",\n labels += table.find_all(\"label\", {\"class\": EDITBOX_LABEL_CLASS})\n data += table.find_all(\"span\", {\"class\": EDITBOX_DATA_CLASS})\n\n # Add all the type -> value mappings to the ret dict\n for x in range(0, len(labels)):\n if labels[x].string in KEYMAP:\n ret['extra'][KEYMAP[labels[x].string]] = data[x].get_text()\n\n # Special case for course detail, \"Units\" and \"Course Components\"\n if box_title == COURSE_DETAIL:\n # Units and course components\n labels = table.find_all(\"label\", {\"class\": EDITBOX_LABEL_CLASS})\n data = table.find_all(\"span\", {\"class\": EDITBOX_DATA_CLASS})\n for x in range(0, len(labels)):\n if labels[x].string == COURSE_COMPS:\n # Last datafield, has multiple type -> value mappings\n comp_map = {}\n for i in range(x, len(data), 2):\n comp_map[data[i].string] = data[i+1].get_text()\n\n ret['extra'][KEYMAP[labels[x].string]] = comp_map\n break\n elif labels[x].string in KEYMAP:\n ret['extra'][KEYMAP[labels[x].string]] = data[x].get_text()\n\n # Process the CEAB information\n elif box_title == CEAB:\n\n labels = table.find_all(\"label\", {\"class\": EDITBOX_LABEL_CLASS})\n data = table.find_all(\"span\", {\"class\": EDITBOX_DATA_CLASS})\n\n for x in range(0, len(labels)):\n try:\n # Clean up the data\n temp = int(self._clean_html(data[x].string))\n except (TypeError, ValueError) as e:\n temp = 0\n\n # Add the data to the dict if it exists\n if labels[x].string:\n # Remove the last character of the label to remove the \":\"\n ret['extra']['CEAB'][labels[x].string[:-1]] = temp\n\n else:\n raise Exception('Encountered unexpected info_box with title: \"{0}\"'.format(box_title))\n\n return ret","title":""},{"docid":"7d5993b1c9d30fe4c4e6bdaecaea155d","score":"0.55084336","text":"def getCourses(self,course,link,abbrev):\n \t#navigates to course page\n \tcourse_map = {}\n\n \tpage = requests.get(BASE_URL + link,verify=False)\n \tsoup = BeautifulSoup(page.content)\n\n \t#finds all courses in major and puts them in a hashmap\n \tcoursePageLink = soup.find_all(\"li\")\n \tif coursePageLink:\n \t\tlink = str(coursePageLink[len(coursePageLink)-1])[54:-90]\n\n \tpage = requests.get(BASE_URL + link,verify=False)\n \tsoup = BeautifulSoup(page.content)\n \tregex = re.compile(r'<p><strong>(\\w+)')\n \tuniqueTup = []\n \tnormalTup = []\n \tcourseList = []\n\n \t#\n \tfor link in soup.find_all(\"p\"):\n\n \t\t#special cases\n \t\tif '<br/><br/>' in str(link):\n \t\t\t#tuple {major:[course_number,name]}\n \t\t\ttup = self.uniqueCases(link)\n \t\t\tif tup:\n \t\t\t\tuniqueTup = tup\n \t\t#standard case\n \t\telif re.match(regex,str(link)):\n \t\t\t#tuple {major:[course_number,name]}\n \t\t\ttup = normalCase = self.normalCase(link)\n \t\t\tif tup:\n \t\t\t\tnormalTup = tup\n \t\t#puts same major tuples under 1 major\n \t\tcourseTuple = uniqueTup + normalTup\n \t\tif courseTuple:\n \t\t\tcourseTuple[0] = courseTuple[0] + (\"Department: \" + abbrev,)\n \t\t\tcourseList.append(courseTuple)\n \treturn courseList","title":""},{"docid":"d22b234b0517aee1ffb2781dd5324987","score":"0.5499037","text":"def create_course_dict():\n wb = load_workbook('newcatalog.xlsx')\n catalog = wb.get_sheet_by_name('catalog')\n Course = namedtuple('Course', 'program, designation')\n CourseInfo = namedtuple('CourseInfo', 'credits, terms, prereqs')\n course_dict = {}\n for row in range(1, catalog.max_row + 1):\n key = Course(get_val(catalog, 'A', row), get_val(catalog, 'B', row))\n prereqs = tuple(tuple(get_split_course(prereq) for prereq in prereqs.split())\n for prereqs in none_split(get_val(catalog, 'E', row)))\n val = CourseInfo(get_val(catalog, 'C', row), tuple(get_val(catalog, 'D', row).split()), prereqs)\n course_dict[key] = val\n return course_dict","title":""},{"docid":"e32262f7ee3a30dd920a64891366d2b9","score":"0.5486802","text":"def load_descriptions(doc):\n mapping = dict()\n # process lines\n for line in doc.split('\\n'):\n # split line by white space\n tokens = line.split()\n if len(line) < 2:\n continue\n # take the first token as the image id, the rest as the description\n image_id, image_desc = tokens[0], tokens[1:]\n # remove filename from image id\n image_id = image_id.split('.')[0]\n # convert description tokens back to string\n image_desc = ' '.join(image_desc)\n # create the list if needed\n if image_id not in mapping:\n mapping[image_id] = list()\n # store description\n mapping[image_id].append(image_desc)\n return mapping # parse descriptions","title":""},{"docid":"9c0ac73637e8f0ed167be1abddecaac8","score":"0.54675364","text":"def __process_descriptions(self):\n while True:\n try:\n mp = self.catalog_description_queue.get()\n\n with self.mt_lock:\n self.catalog_description_progress.next()\n\n catalog_entry = mp.best_match\n catalog_desc = catalog_entry.description\n\n try:\n source_desc_obj = mp.program.descriptions.get(\n description_type=self.description_type_source_desc\n )\n source_desc = source_desc_obj.description if source_desc_obj.description else ''\n except ProgramDescription.DoesNotExist:\n source_desc = ''\n\n if self.force_desc_updates or not source_desc or source_desc != catalog_desc:\n # Sanitize/process the incoming catalog description if\n # we don't already have an existing original catalog\n # description to compare against, or if we do and it\n # changed since the last time it was imported\n # (or if we're forcing description updates)\n if catalog_entry.program_description_clean is None:\n catalog_entry.program_description_clean = self.__sanitize_description(\n description_str=catalog_desc,\n strip_tables=True\n )\n\n # Pass along to the curriculum queue next\n with self.mt_lock:\n self.catalog_curriculum_queue.put(mp)\n\n except Exception as e:\n logging.log(logging.ERROR, e)\n finally:\n self.catalog_description_queue.task_done()","title":""},{"docid":"cc30d81c48dcd7ffa4fd5eaf07a82637","score":"0.5457821","text":"def collect_doc(ori_doc):\n print('collecting original documents from DB list.')\n print(len(ori_doc), ' total documents are collected.')\n\n doc_name2txt = dict()\n for (doc_id, doc_body) in ori_doc:\n # repeated documentation is not allowed\n assert doc_id not in doc_name2txt\n doc_name2txt[doc_id] = doc_body\n return doc_name2txt","title":""},{"docid":"139d4b89e1c6197ed2ef9a1048e35c4c","score":"0.5442083","text":"def crawl_course(metadata: CourseInfo) -> CourseInfo:\n # Need to re-decode with utf-8, original is encoded with ISO-8859-1\n text: str = requests.get(\n \"http://class-qry.acad.ncku.edu.tw/syllabus/online_display.php\",\n params={\n \"syear\": metadata.year.zfill(4),\n \"sem\": metadata.semester,\n \"co_no\": metadata.course_id,\n \"class_code\": metadata.class_code if metadata.class_code != \"0\" else None\n }\n ).text.encode(\"ISO-8859-1\").decode(\"utf-8\")\n html: etree._Element = etree.HTML(text)\n\n title: etree._Element = html.xpath('//*[@id=\"header\"]/h1/div/span')[1]\n filtered = list(filter(lambda child: child.tag == \"br\", title.getchildren()))\n course_names = list(map(lambda child: child.tail.strip(), filtered))\n\n sidebar: etree._Element = html.xpath('//*[@id=\"sidebar\"]/div')[0]\n filtered = list(filter(lambda child: child.tag == \"span\", sidebar.getchildren()))\n information = list(map(lambda child: child.tail.strip(), filtered))\n\n return CourseInfo(**{\n \"course_name\": f\"{course_names[0]} {course_names[1]}\",\n \"department\": information[0],\n \"instructor\": information[1],\n \"year\": metadata.year,\n \"semester\": metadata.semester,\n \"serial_number\": information[4],\n \"attribute_code\": information[5],\n \"course_id\": metadata.course_id,\n \"class_code\": metadata.class_code,\n \"credit\": information[8],\n \"language\": information[9],\n \"files\": metadata.files,\n })","title":""},{"docid":"1eee7163c20f08148d4792abc8d8382b","score":"0.5434418","text":"def __process_course_tag(self, course_tag):\n course = Course()\n\n course.link = course_tag.find('a')['href']\n\n # Detect course ID\n expr = re.search('index_mobile\\.php\\?id=(\\d+)&code_departement=.*&mydate=([0-9/]+)', course.link)\n course.id = expr.group(1)\n course.date = datetime.datetime.strptime(expr.group(2), '%d/%m/%Y').date()\n\n for span_tag in course_tag.find_all('span'):\n\n assert(len(span_tag['class']) == 1)\n type = span_tag['class'][0]\n\n if type == 'image':\n # First field : time of beginning / end of the courses\n # <br><b>08:30<br>11:45</br>\n expr = re.search('<br/?><b>([0-9]{2}:[0-9]{2})<br>([0-9]{2}:[0-9]{2})</b', str(span_tag))\n if not expr:\n print(\"Unable to find hours in %s !\" % span_tag)\n continue\n\n time_format = '%H:%M'\n course.time_begin = datetime.datetime.strptime(expr.group(1), time_format).time()\n course.time_end = datetime.datetime.strptime(expr.group(2), time_format).time()\n elif type == 'comment':\n # Second field : department and place\n expr = re.search('(.*) : (.*)', span_tag.text)\n course.department = expr.group(1)\n course.place = expr.group(2)\n elif type == 'name':\n # Third field : name\n course.name = span_tag.text\n elif type == 'starcomment':\n # Fourth field : comment\n course.comment = span_tag.text\n elif type == 'arrow':\n # Nothing to do\n pass\n else:\n print(\"Unrecognized type : %s !\" % type)\n\n return course","title":""},{"docid":"2e8122fee62f4208b54a325a57548bc1","score":"0.53735864","text":"def insertDocument(self, details):\n details[\"Accreditation\"]=\"Not Evaluated\" \n details[\"Type\"]=\"Unknown\" \n\n if details[\"Category\"].lower().startswith(\"conference\"):\n details[\"ScanPath\"]=\"conferences/\"+details[\"ConferenceTitle\"].replace(' ', '_')+\"/publications/\"\n details[\"ScanFileName\"]=details[\"Title\"].replace(' ', '_')+\".pdf\"\n details[\"TableOfContentsPath\"]=\"conferences/\"+details[\"ConferenceTitle\"].replace(' ', '_')+\"/TOCs/\"\n details[\"PeerReviewPath\"]=\"conferences/\"+details[\"ConferenceTitle\"].replace(' ', '_')+\"/peerReviews/\"\n result=insertConferencePaper(self,details)\n \n elif details[\"Category\"].lower().startswith(\"journal\"):\n if \"Volume\" not in details:\n details[\"Volume\"]=None\n if \"Issue\" not in details:\n details[\"Issue\"]=None\n details[\"HIndex\"]=None\n details[\"ScanPath\"]=\"journals/\"+details[\"JournalTitle\"].replace(' ', '_')+\"/publications/\"\n details[\"ScanFileName\"]=details[\"Title\"].replace(' ', '_')+\".pdf\"\n details[\"TableOfContentsPath\"]=\"journals/\"+details[\"JournalTitle\"].replace(' ', '_')+\"/TOCs/\"\n details[\"PeerReviewPath\"]=\"journals/\"+details[\"JournalTitle\"].replace(' ', '_')+\"/peerReviews/\"\n result=insertJournalPaper(self,details)\n \n elif details[\"Category\"].lower().startswith(\"book\"):\n \n details[\"ScanPath\"]=\"books/\"+details[\"BookTitle\"].replace(' ', '_')+\"/publications/\"\n details[\"ScanFileName\"]=details[\"Title\"].replace(' ', '_')+\".pdf\"\n details[\"TableOfContentsPath\"]=\"books/\"+details[\"BookTitle\"].replace(' ', '_')+\"/TOCs/\"\n details[\"PeerReviewPath\"]=\"books/\"+details[\"BookTitle\"].replace(' ', '_')+\"/peerReviews/\"\n result=insertBookSection(self,details)\n\n return result","title":""},{"docid":"a34bbcb12c76ef2ac796e093dd76091d","score":"0.53501743","text":"def find_description(docs):\n summary = None\n filename = docs.get('meta.summary', 'SUMMARY').strip()\n if filename and _os.path.isfile(filename):\n fp = open(filename)\n try:\n try:\n summary = fp.read().strip().splitlines()[0].rstrip()\n except IndexError:\n summary = ''\n finally:\n fp.close()\n\n description = None\n filename = docs.get('meta.description', 'DESCRIPTION').strip()\n if filename and _os.path.isfile(filename):\n fp = open(filename)\n try:\n description = fp.read().rstrip()\n finally:\n fp.close()\n\n if summary is None and description:\n from docutils import core\n summary = core.publish_parts(\n source=description,\n source_path=filename,\n writer_name='html',\n )['title'].encode('utf-8')\n\n return summary, description","title":""},{"docid":"5628300a054c43e6ea3d5b036a1997f0","score":"0.5328472","text":"def fetch_description(self, given_description, given_words, left_margin, right_margin, num_of_results):\n # https://simply-python.com/2014/03/14/saving-output-of-nltk-text-concordance/\n # Instead of using nltk.word_tokenize which\n # hangs for a long time sometimes like when using the text from\n # \"url\":\"https://www.reddit.com/user/AutoModerator\"\n # The hanging comes from nltk/tokenize/__init__\n # line 94 : return tokenizer.tokenize(text)\n # I am avoiding this prt and I am only using the TreebankWordTokenizer\n tokens = self.treebank.tokenize(given_description)\n\n text = nltk.Text(tokens)\n c = nltk.ConcordanceIndex(tokens, key=lambda s: s.lower())\n concordance_txt = ([[text.tokens[list(map(lambda x: x-left_margin if (x-left_margin) > 0 else left_margin-x,\n [offset]))[0]:offset+right_margin+1]\n for offset in c.offsets(given_word)][:num_of_results]\n for given_word in given_words.split()])\n concordance_txt = itertools.chain(*concordance_txt)\n return '\\n'.join([''.join([x+' ' for x in con_sub]) for con_sub in concordance_txt])","title":""},{"docid":"30e4f9f1d8f34b0353c7fd7888b22f40","score":"0.52897835","text":"def test_export_description():\n desc = cds_list[1].export_description()\n assert desc == \"# origin_seq: scaffold_0 # strand: reverse # start: 1581 # end: 2864\"","title":""},{"docid":"84bdd069a9b1937cff12261a4d3b943a","score":"0.5288692","text":"def get_courses():\n return DB[COURSE_COLLECTION].find()","title":""},{"docid":"714bbc21287abecfeab9881af37d7a0c","score":"0.52808845","text":"def _commentsToDescription(self, parseResults):\n \n commentString = ''\n if self._KEY_COMMENT in parseResults.keys():\n commentString = parseResults[self._KEY_COMMENT]+\". \"\n # Remove the _KEY_COMMENTS from list of keys.\n parseResults.pop(self._KEY_COMMENT)\n \n if self._VALUE_COMMENT in parseResults.keys():\n commentString = commentString + parseResults[self._VALUE_COMMENT]\n parseResults.pop(self._VALUE_COMMENT)\n\n if len(commentString) == 0:\n return \n \n # Clean up the comment string, remove the // and extra spaces.\n commentString = re.sub(r'//', '', commentString)\n commentString = re.sub('\\s+', ' ',commentString)\n parseResults[self._DESCRIPTION] = commentString","title":""},{"docid":"379c97cf4f0c0597fb8cbe6965c27b5c","score":"0.5277338","text":"def extract(self):\n # undergraduate and comp3900 are parameters\n url = \"https://www.handbook.unsw.edu.au/{}/courses/2019/{}/\".format(self.study_level, self.course)\n url = requests.get(url)\n htmltext = url.text\n\n #read the html\n soup = BeautifulSoup(htmltext, 'html.parser')\n\n if soup.title.string:\n self.details[\"Title\"] = soup.title.string\n else:\n self.details[\"Title\"] = \"\"\n if soup.find(id=\"readMoreIntro\").div.p:\n self.details[\"Description\"] = soup.find(id=\"readMoreIntro\").div.p.string\n else:\n self.details[\"Description\"] = \"\"\n if soup.find_all('strong')[1]:\n self.details[\"Credit\"] = soup.find_all('strong')[1].string\n else:\n self.details[\"Credit\"] = \"\"\n if soup.find(id=\"readMoreSubjectConditions\"):\n self.details[\"Prerequisite\"] = soup.find(id=\"readMoreSubjectConditions\").div.div.string\n else:\n self.details[\"Prerequisite\"] = \"\"\n if soup.find(id=\"subject-outline\"):\n self.details[\"Course Outline\"] = soup.find(id=\"subject-outline\").div.a.attrs['href']\n else:\n self.details[\"Course Outline\"] = \"\"\n\n if soup.select('.o-attributes-table-item ')[0].a:\n self.details[\"Faculty\"] = soup.select('.o-attributes-table-item ')[0].a.string\n else:\n self.details[\"Faculty\"] = \"\"\n\n if soup.select('.o-attributes-table-item ')[1].a:\n self.details[\"School\"] = soup.select('.o-attributes-table-item ')[1].a.string\n else:\n self.details[\"School\"] = \"\"\n\n if soup.select('.o-attributes-table-item ')[3].p:\n self.details[\"Offering Terms\"] = soup.select('.o-attributes-table-item ')[3].p.string\n else:\n self.details[\"Offering Terms\"] = \"\"\n if soup.select('.o-attributes-table-item ')[4].p:\n self.details[\"Campus\"] = soup.select('.o-attributes-table-item ')[4].p.string.replace(\" \", \"\").strip()\n else:\n self.details[\"Campus\"] = \"\"\n\n if soup.select('.p-all-1')[0]:\n for value in soup.select('.p-all-1')[0].children:\n if (soup.select('.p-all-1')[0].index(value) == 3):\n self.details[\"PDF\"] = value.a.attrs['href']\n else:\n self.details[\"PDF\"] = \"\"\n\n if soup.select('.o-attributes-table-item ')[5].p:\n self.details[\"Indicative contact hours\"] = soup.select('.o-attributes-table-item ')[5].p.string\n else:\n self.details[\"Indicative contact hours\"] = \"\"\n if soup.select('.a-column-sm-12')[8].p:\n self.details[\"Commonwealth Supported Student\"] = soup.select('.a-column-sm-12')[8].p.string.strip()\n else:\n self.details[\"Commonwealth Supported Student\"] = \"\"\n if soup.select('.a-column-sm-12')[10].p:\n self.details[\"Domestic Student\"] = soup.select('.a-column-sm-12')[10].p.string.strip()\n else:\n self.details[\"Domestic Student\"] = \"\"\n if soup.select('.a-column-sm-12')[12].p:\n self.details[\"International Student\"] = soup.select('.a-column-sm-12')[12].p.string.strip()\n else:\n self.details[\"International Student\"] = \"\"","title":""},{"docid":"f3c2d89a31af1fdce89977df9b5cc353","score":"0.5242611","text":"def load_clean_descriptions(filename, dataset):\n\n doc = load_doc(filename)\n descriptions = {}\n for line in doc.split('\\n'):\n # Split line by white space.\n tokens = line.split()\n # Split id from description.\n image_id, image_desc = tokens[0], tokens[1:]\n # skip images not in the set.\n if image_id in dataset:\n # create list.\n if image_id not in descriptions:\n descriptions[image_id] = []\n # Wrap description in tokens.\n desc = 'startseq ' + ' '.join(image_desc) + ' endseq'\n descriptions[image_id].append(desc)\n return descriptions","title":""},{"docid":"24e7c4d31071574250e8b9b9e809c3a1","score":"0.5194507","text":"def _get_description(self):\n if self._generated:\n return self._title\n else:\n text = read_file(self.path.src).splitlines()\n if len(text) >= 4:\n desc_line = text[3]\n # Description begins with an AsciiDoc comment\n if desc_line[0:2] == '//':\n return desc_line[2:].strip()","title":""},{"docid":"7cc3979664f410b5639ae836e6d5a5f5","score":"0.51822454","text":"def set_description_and_comment(abstract_bibdocfiles, description, comment):\n for bibdocfile in abstract_bibdocfiles:\n bibdocfile.description = description\n bibdocfile.comment = comment","title":""},{"docid":"e53f1e2ca92345ae3c0bf7364ff9d87d","score":"0.5170063","text":"def get_interest_descriptions(db):\n failures = []\n\n for rec in db.records:\n if rec.is_interest() and not rec.has_field(\"DESC\"):\n failures.append(\"Missing description on {}\".format(rec))\n return failures","title":""},{"docid":"0d91de2921245258b44a7a814d787511","score":"0.5154081","text":"def get_courses(self):\n sitemap = 'http://www.golfadvisor.com/sitemap_courses-#.xml'\n pages = array(\n [sitemap.replace('#', str(index)) for index in range(1, 35)]\n )\n page_lists = array_split(pages, POOL_SIZE)\n with ProcessPoolExecutor() as extr:\n results = extr.map(self._get_course_pages, enumerate(page_lists))\n courses = array([link for links in results for link in links])\n self.courses = courses","title":""},{"docid":"4fe0f16a5d1795f42c79fa39249381cb","score":"0.51433355","text":"def _save_courses(self, category, page, courses):\n now = self._get_datetime_now()\n if not os.path.exists('files_{}'.format(now)):\n os.makedirs('files_{}'.format(now))\n out_file_name = 'files_{}/courses_{}_{}.csv'.format(\n now,\n category,\n page\n )\n csv_headers = list(filter(\n lambda property: not property.startswith('_'),\n dir(courses[0])))\n with open(out_file_name, mode='w+', encoding='utf-8', newline='') as f:\n writer = csv.writer(f)\n writer.writerow(csv_headers)\n for course in courses:\n row = [str(getattr(course, prop)) for prop in csv_headers]\n writer.writerow(row)","title":""},{"docid":"1489dc2b1301e4d7a62e4f4c927fa988","score":"0.5138065","text":"def load_courses(self):\n\n print('>> Loading Courses...')\n\n Course.objects.all().delete()\n\n # Load Course AIM Labels\n aim_label = {}\n\n with open(os.getcwd() + '/coursefinderapp/data/KISAIM.csv') as f:\n next(f) # Skip the header\n reader = csv.reader(f, skipinitialspace=True)\n aim_label = dict(reader)\n\n course_locs = self.load_course_locations()\n\n def handler(line):\n \"\"\"\n Translate each line of the csv in a Course object and associate with the\n its respective location.\n :param line: csv line\n :return: Course object\n \"\"\"\n\n c = Course(\n pubukprn=line[0],\n ukprn=line[1],\n kiscourseid=line[14],\n title=line[27].replace('\"', \"\"),\n url=line[4],\n distance=line[6],\n mode=line[15],\n aim=aim_label[line[32]],\n )\n\n loc_id = course_locs.get((c.pubukprn, c.ukprn, c.kiscourseid, c.mode), None)\n\n if loc_id is not None:\n c.location = Location.objects.get(ukprn=c.ukprn, locid=loc_id)\n\n return c\n\n courses = self._read_csv('/coursefinderapp/data/KISCOURSE.csv', handler=handler)\n Course.objects.bulk_create(courses)","title":""},{"docid":"bfb93adaa5a30d6a179d17b19bd7976a","score":"0.5123131","text":"def parse_course_overview(self, response):\n hxs = HtmlXPathSelector(response)\n xpath = '//table[contains(@class, \"courseBrochure\")]'\n loader = ItemLoader(CourseOverviewItem(), selector=hxs.select(xpath))\n loader.item['course'] = response.request.meta['course']\n loader.add_xpath('extent', 'tr[1]/td[2]', re=r'(\\d+(?:-\\d+)?)')\n loader.add_xpath('teaching_period', 'tr[2]/td[2]')\n loader.add_xpath('learning_outcomes', 'tr[3]/td[2]')\n loader.add_xpath('content', 'tr[4]/td[2]')\n loader.add_xpath('prerequisites', 'tr[5]/td[2]')\n loader.add_xpath('study_materials', 'tr[11]/td[2]')\n return loader.load_item()","title":""},{"docid":"a5775e30eea41cc62bad2d5c0bcff2bd","score":"0.50984627","text":"def description(doc=__doc__):\n print(doc)\n for line in doc.splitlines():\n return line.strip()","title":""},{"docid":"82dd849ccacca9e82eac364ede1530be","score":"0.5095845","text":"def fill_out_courses(results, new_keywords = None, student_major = None, student_interests = None):\n\n global conn\n\n cur = conn.cursor()\n courses = []\n\n for result in results:\n # Build Course object from current result\n result_course = Course.Course()\n result_course.department = result[17]\n result_course.course_num = result[2]\n result_course.id = result[13]\n result_course.name = result[16]\n result_course.term = result[19]\n result_course.credits = result[11]\n\n # Parse classroom/meeting times/description/prerequisites if defined\n if result[24] != None:\n res = parse_time_room(result[24])\n if res != None:\n result_course.classroom = res[0]\n result_course.time = res[1]\n if result[29] != None:\n result_course.description = result[29]\n if result[30] != None:\n result_course.prereqs = result[30]\n call_debug_print(result_course.prereqs)\n\n # Adding professor information based on id found in courses\n if result[21] != None:\n result_course.faculty_id = result[21]\n if '|' in result_course.faculty_id:\n prof_ids = result_course.faculty_id.split('|')\n query_str = \"SELECT * FROM professors WHERE id = \\'\"\n for id_num in prof_ids:\n query_str = query_str + str(int(id_num)) + \"' OR id = '\"\n query_str = query_str[:-10]\n cur.execute(query_str)\n names = cur.fetchall()\n result_course.faculty_id = \"\"\n for result in names:\n result_course.faculty_id = result_course.faculty_id + result[0] + \", \"\n result_course.faculty_name = result_course.faculty_name + result[1] + \", \"\n if result_course.faculty_id != \"\" and result_course.faculty_id != \"\":\n result_course.faculty_name = result_course.faculty_name[:-2]\n result_course.faculty_id = result_course.faculty_id[:-2]\n else:\n query_str = \"SELECT name FROM professors WHERE id = \\'\" \\\n + str(int(result_course.faculty_id)) + \"'\"\n cur.execute(query_str)\n name = cur.fetchall()\n if len(name) > 0 and len(name[0]) > 0:\n result_course.faculty_name = name[0][0]\n\n # Only runs in the case where this is called by query_by_keywords\n # Uses the new_keywords list in the query_by_keywords function\n if new_keywords is not None:\n relevance_list, weighted_relevance = calculate_course_relevance(result_course, new_keywords,\n student_major, student_interests)\n result_course.relevance = relevance_list\n result_course.weighted_score = weighted_relevance\n courses.append(result_course)\n\n return courses","title":""},{"docid":"691a638d42184e73fec23877a27a7f5b","score":"0.5080336","text":"def get_descriptions(programs, program_cache=None, nocattrans=0, quiet=0, slowdays=0):\n\n # This regexp tries to find details such as Genre, Acteurs, Jaar van Premiere etc.\n detail = re.compile('<li>[^<]*<strong>([\\w\\- ]+):</strong>(.*?)</li>', re.DOTALL)\n\n # These regexps find the main description area and lines of descriptive text in this area\n description = re.compile('<div id=\"prog-content\">(.*?)</div>',re.DOTALL)\n descrline = re.compile('<p>(.*?)</p>',re.DOTALL)\n\n # These regexps try to find the subgenre of the program, e.g. Fantasy-familiefilm, Comedyserie, \n # Woonprogramma, Culinair Programma etc.\n # descrtype searches for the subgenre in the description area, e.g.:\n # <strong>Woonprogramma</strong><p>Nance, Tooske, Ellemieke, Marlayne en Viktor helpen mensen...\n #\n # addprogtype searches for the subgenre in the special \"mijn TV agenda\" link, e.g.:\n # <a href=\"#perstvgids\" title=\"Plaats dit programma in mijn TV Agenda\" \n # onclick=\"addProg('10281755','Informatief','Woonprogramma.',event);return false;\">\n\n descrtype = re.compile('<strong>([^<]*)</strong>',re.DOTALL)\n addprogtype = re.compile(\"addProg\\(.*?,.*?,'(.*?)',.*?\\)\",re.DOTALL)\n\n # randomize detail requests\n nprograms = len(programs)\n fetch_order = list(range(0,nprograms))\n random.shuffle(fetch_order)\n\n counter = 0\n for i in fetch_order:\n counter += 1\n if programs[i]['offset'] >= slowdays:\n continue\n \n log('\\n(%3.0f%%) %s: %s ' % (100*float(counter)/float(nprograms), i, programs[i]['name']), quiet)\n\n # check the cache for this program's ID\n cached_program = program_cache.query(programs[i]['ID'])\n \n if (cached_program != None):\n log(' [cached]', quiet)\n # copy the cached information, except the start/end times, rating and clumping, \n # these may have changed.\n tstart = programs[i]['start-time']\n tstop = programs[i]['stop-time']\n try:\n clump = programs[i]['clumpidx']\n except LookupError:\n clump = False\n programs[i] = cached_program\n programs[i]['start-time'] = tstart\n programs[i]['stop-time'] = tstop\n if clump:\n programs[i]['clumpidx'] = clump\n continue\n\n # be nice to tvgids.nl\n time.sleep(random.randint(nice_time[0], nice_time[1]))\n\n # get the details page, and get all the detail nodes\n descriptions = ()\n details = ()\n try:\n log(' [normal fetch]', quiet)\n total = get_page(programs[i]['url'])\n details = detail.finditer(total)\n \n descrspan = description.search(total)\n if descrspan != None:\n descriptions = descrline.finditer(descrspan.group(1))\n else:\n log('Can not find program details on page\\n', quiet)\n descriptions = []\n \n except Exception as e:\n # if we cannot find the description page, \n # go to next in the loop\n log(' [fetch failed or timed out]', quiet)\n continue\n # define containers\n programs[i]['credits'] = {}\n programs[i]['video'] = {}\n\n # now parse the details\n programs[i]['details'] = []\n # First, we try to find the program type in the special \"mijn TV Agenda\" link, if not found there we\n # search for a type in the description section.\n # Note that this type is not the same as the generic genres (these are searched later on), \n # but a more descriptive one like \"Culinair programma\" \n # \n def add_details(program, details):\n details = filter_line(details)\n if len(details) == 0:\n return\n if len(program['details']) > 0 and program['details'][-1].lower() == details.lower():\n return\n program['details'].append(details)\n \n if 'subgenre' in programs[i]:\n add_details(programs[i], programs[i]['subgenre'])\n \n m = addprogtype.search(total)\n if m:\n add_details(programs[i], m.group(1).capitalize())\n \n m = descrtype.search(descrspan.group(1))\n if m:\n add_details(programs[i], m.group(1).capitalize())\n\n # Secondly, we add one or more lines of the program description that are present.\n for descript in descriptions:\n # descript is a re.Match object\n descr_html = descript.group(1)\n \n # Remove sponsored link from description if present.\n sponsor_pos = descr_html.rfind('<i>Gesponsorde link:</i>')\n if sponsor_pos > 0:\n descr_html = descr_html[0:sponsor_pos]\n if re.search('[Gg]een detailgegevens be(?:kend|schikbaar)', descr_html):\n descr_html = ''\n \n add_details(programs[i], descr_html)\n \n if len(programs[i]['details']) == 0:\n programs[i]['detail1'] = ''\n else:\n programs[i]['detail1'] = programs[i]['details'][0]\n \n # Finally, we check out all program details. These are generically denoted as:\n #\n # <li><strong>(TYPE):</strong>(CONTENT)</li> \n #\n # Some examples:\n #\n # <li><strong>Datum:</strong>16 oktober 2008</li>\n # <li><strong>Genre:</strong>Amusement</li>\n \n for d in details:\n ctype = d.group(1).strip().lower()\n content_asis = filter_line(d.group(2))\n content = filter_line(content_asis)\n \n if content == '':\n continue\n\n elif ctype == 'genre':\n\n # Fix detection of movies based on description as tvgids.nl sometimes \n # categorises a movie as e.g. \"Komedie\", \"Misdaadkomedie\", \"Detectivefilm\". \n genre = filter_line(content.title()) # Titlecase\n if nocattrans:\n programs[i]['genre'] = genre\n elif (programs[i]['detail1'].lower().find('film') != -1 \\\n or programs[i]['detail1'].lower().find('komedie') != -1)\\\n and programs[i]['detail1'].lower().find('tekenfilm') == -1 \\\n and programs[i]['detail1'].lower().find('animatiekomedie') == -1 \\\n and programs[i]['detail1'].lower().find('filmpje') == -1:\n programs[i]['genre'] = 'Film'\n else:\n try:\n programs[i]['genre'] = cattrans[genre.lower()].title()\n except LookupError:\n programs[i]['genre'] = genre\n\n # Parse persons and their roles for credit info\n elif ctype in roletrans:\n programs[i]['credits'][roletrans[ctype]] = []\n\n persons = content_asis.split(',');\n\n for name in persons:\n if name.find(':') != -1:\n name = name.split(':')[1]\n if name.find('-') != -1:\n name = name.split('-')[0]\n if name.find('e.a') != -1:\n name = name.split('e.a')[0]\n programs[i]['credits'][roletrans[ctype]].append(filter_line(name))\n\n elif ctype == 'bijzonderheden':\n if content.find('Breedbeeld') != -1:\n programs[i]['video']['breedbeeld'] = 1\n if content.find('Zwart') != -1: \n programs[i]['video']['blackwhite'] = 1\n if content.find('Teletekst') != -1: \n programs[i]['teletekst'] = 1\n if content.find('Stereo') != -1: \n programs[i]['stereo'] = 1\n elif ctype == 'url':\n programs[i]['infourl'] = filter_line(content)\n elif ctype not in programs[i]:\n # In unmatched cases, we still add the parsed type and content to the program details.\n # Some of these will lead to xmltv output during the xmlefy_programs step\n programs[i][filter_line(ctype)] = filter_line(content)\n\n # do not cache programming that is unknown at the time\n # of fetching.\n \n if programs[i]['name'].lower() != 'onbekend':\n program_cache.add(programs[i])\n\n log('\\ndone...\\n\\n', quiet)\n \n # done","title":""},{"docid":"46c6041fcc932cbe89678330c2def05a","score":"0.5073233","text":"def parse_description_page(self, response):\r\n house_ad = response.meta['house_ad']\r\n house_ad['description'] = \" \".join(response.xpath('//div[@class=\"mar_t_20\"]/p/text()').extract())\r\n yield house_ad","title":""},{"docid":"913d57ef889ca5d9fafd4753b200e6c1","score":"0.50694275","text":"def find_desc(self, cols):\n # go through the columns one at a time\n for i in range(len(cols)):\n # don't reuse columns already discovered\n if i == self.date or i == self.amt:\n continue\n\n # heuristic: descriptions are usually quoted\n s = cols[i]\n if s.startswith('\"') or s.startswith(\"'\"):\n if \"Reference\" in s: # known exception\n continue\n self.desc = i\n return True\n return True","title":""},{"docid":"e45b9bff5c0fa2a9c9e59f5dcff853bf","score":"0.5067044","text":"def scrape(args):\n\n\n import logging\n log = logging.getLogger(\"root\")\n log.info( \"Scraping RPI CS data.\" )\n\n\n # Generate a BeautifulSoup object.\n catalog_index_url = \"http://catalog.rpi.edu/content.php?filter[27]=CSCI&cur_cat_oid=13&navoid=313\"\n catalog_index = requests.get( catalog_index_url )\n soup = BeautifulSoup( catalog_index.text )\n\n # Identify the list of courses.\n course_list = soup.find_all(\n name=\"a\",\n onclick=re.compile(\"showCourse.*\"))\n\n # Select only the catoid and coid.\n catoid_re = re.compile(\"(?<=catoid=)\\d+\")\n coid_re = re.compile(\"(?<=coid=)\\d+\")\n\n # Piecewise course page url.\n course_url = \"http://catalog.rpi.edu/preview_course_nopop.php?catoid=%s&coid=%s\"\n\n # Fetch existing metadata objects from database.\n university = META.get(\"school\").get(\"name\")\n university = session.query(University)\\\n .filter(University.name==university)\\\n .first()\n departments = {department.abbreviation.lower() : department\n for department in session.query(Department)\\\n .filter(Department.university==university)\\\n .all()}\n\n # Identify relevant information for each course.\n prereqs = {}\n for course in course_list:\n\n # Generate metadata\n log.debug(course.text)\n full_title = re.compile(\"\\s+\").split(course.text)\n prefix = full_title[0]\n cnum = full_title[1]\n title = ' '.join(full_title[3:])\n title = title.replace(\"'\", \"\")\n\n # Identify coid to get description.\n href = course['href']\n catoid = catoid_re.search(href).group(0)\n coid = coid_re.search(href).group(0)\n\n # Generate a BeautifulSoup object of the course description.\n course_page = requests.get( course_url % (catoid, coid) )\n course_soup = BeautifulSoup( course_page.text )\n content = course_soup.find(class_=\"block_content\").hr.text\n\n # Clean up the description.\n description = content\n try:\n description = description[:description.index(\"Credit Hours\")]\n description = description[:description.index(\"When Offered\")]\n except:\n pass\n\n # Identify prerequisites\n # TODO: Match these up with their database entries.\n prereq_index = description.find(\"Prerequisit\")\n if prereq_index > -1:\n prereq_string = description[prereq_index:]\n description = description[:prereq_index]\n\n prereq_re = re.compile(\"\\w{2,4}\\s\\d{3}\")\n matches = re.findall(prereq_re, prereq_string)\n if len(matches) > 0:\n prereqs[\"%s %s\" % (prefix, cnum)] = matches\n\n # Clean the description string\n description_raw = description\n description = clean(description)\n if description is None:\n continue\n\n # Generate the appropriate course object.\n departments[prefix.lower()].courses.append(Course(\n number=cnum,\n title=title,\n description_raw=description_raw,\n description=description))\n\n log.info( \"Completed scraping.\" )","title":""},{"docid":"276bca30cabbd727e3ea49fdc300777e","score":"0.5064829","text":"def download_courses(courses):\n for course in courses:\n _pluradl(course)","title":""},{"docid":"060d37c628c4851e08e1b39aab32b2f9","score":"0.5061009","text":"def extract_document_text():\n all_document_pages = OfficialDocumentPage.objects.all()\n print(f'Beginning extraction...')\n\n for page in all_document_pages:\n # commenting out for now - print(f'Page id {page.id}')\n # Check if page body already has content, if so skip\n if len(page.body) > 0:\n continue\n if page.document and page.document.url:\n filename = page.document.url.split('documents')[1]\n print(f'reading {filename}')\n extracted_text = extract_text_from_url(page.document.url)\n # TODO: if the extracted_text is \"\", replace with message per content/OPO\n page.body = extracted_text\n page.save()\n else:\n print(f'Official Document Page with id {page.id} does not have a document')","title":""},{"docid":"af5f189a852872ea47102d52f5781ab9","score":"0.5059057","text":"def convert_to_osis(text: str, bookid: str = \"TEST\") -> Tuple[str, ...]:\n # ---------------------------------------------------------------------- #\n\n description: List[str] = []\n\n # ---------------------------------------------------------------------- #\n\n # preprocessing and special spacing\n text = c2o_preprocess(text)\n\n # split text into lines for processing\n lines = text.split(\"\\n\")\n\n # mark introduction endings...\n lines = markintroend(lines)\n\n for i in enumerate(lines):\n # identification\n lines[i[0]], description = c2o_identification(lines[i[0]], description)\n\n # character style formatting\n lines[i[0]] = c2o_noterefmarkers(lines[i[0]])\n lines[i[0]] = c2o_specialtext(lines[i[0]])\n\n # special features if present, and stray \\xt tags that were missed.\n lines[i[0]] = c2o_specialfeatures(lines[i[0]])\n\n # z tags if present\n lines[i[0]] = c2o_ztags(lines[i[0]])\n\n # paragraph style formatting.\n lines[i[0]] = c2o_titlepar(lines[i[0]], bookid)\n\n # process words of Jesus\n if r\"\\wj\" in text:\n lines = c2o_processwj2(lines)\n\n # postprocessing of poetry, lists, tables, and sections\n # to add missing tags and div's.\n lines = c2o_fixgroupings(lines)\n\n # process chapter/verse markers\n lines = c2o_chapverse(lines, bookid)\n\n # postprocessing to fix some issues that may be present\n lines = c2o_postprocess(lines)\n\n descriptiontext = \"\\n\".join(description)\n\n # rejoin lines after processing\n return \"\\n\".join([_ for _ in lines if _ != \"\"]), descriptiontext","title":""},{"docid":"e75dd4e4d0eb5f987ade021a06fff632","score":"0.50526416","text":"def updateCoursesCSV():\n with open(os.path.join(os.path.dirname(__file__), 'courses.csv'), 'w', newline='', encoding=\"UTF-8\") as file:\n n = 300\n writer = csv.writer(file, delimiter=',')\n i = 1\n courses = oisCourses.getNCourses(n, i)\n while len(courses) != 0:\n for c in courses:\n if 'title' in c:\n if 'et' in c['title']:\n t = Text(c['title']['et'].lower())\n t.tag_layer(['morph_analysis'])\n writer.writerow([\" \".join([x[0] for x in t.morph_analysis.lemma]), c['code']])\n elif 'en' in c['title']:\n t = Text(c['title']['en'].lower())\n t.tag_layer(['morph_analysis'])\n writer.writerow([\" \".join([x[0] for x in t.morph_analysis.lemma]), c['code']])\n i += n\n courses = oisCourses.getNCourses(n, i)","title":""},{"docid":"7780c8b2cd65375745f271f63826712a","score":"0.50322837","text":"def get_description_and_comment(bibdocfiles):\n description = None\n comment = None\n all_descriptions = [bibdocfile.get_description() for bibdocfile \\\n in bibdocfiles\n if bibdocfile.get_description() not in ['', None]]\n if len(all_descriptions) > 0:\n description = all_descriptions[0]\n\n all_comments = [bibdocfile.get_comment() for bibdocfile \\\n in bibdocfiles\n if bibdocfile.get_comment() not in ['', None]]\n if len(all_comments) > 0:\n comment = all_comments[0]\n\n return (description, comment)","title":""},{"docid":"8b0bc43156b2e55bd8b0b9b1c90c4672","score":"0.50264835","text":"def get_questionnaire_description(self, questionnaire_data, keys):\n keywords = self.get_description_keywords(keys)\n excerpt_data = collections.defaultdict(str)\n\n for keyword in keywords:\n for x in questionnaire_data.get(keyword.questiongroup, []):\n if x.get(keyword.question):\n for language, text in x[keyword.question].items():\n excerpt_data[language] += '{} '.format(text)\n return excerpt_data","title":""},{"docid":"1f22d2dc088fb1e01979c1937cae1dfe","score":"0.50097275","text":"def _parse_description(self, response):\n description = response.xpath('//div[@class=\"content\"]/div[contains(@class, \"field-name-field-event-\")]/descendant-or-self::*')\n descrip_str = ''\n for d in description:\n descrip_str = ' '.join([descrip_str, ' '.join(d.xpath('text()').extract())])\n descrip_str = ' '.join([descrip_str, ' '.join(d.xpath('a/@href').extract())])\n return descrip_str.strip()","title":""},{"docid":"65c0c0a15726a958dfd9962be03ea655","score":"0.5003737","text":"def record_story_chapters(self) -> None:\n\n id_regex = re.compile('chapter')\n new_line_regex = re.compile('\\n')\n chapters = self._soup.find_all('div', {'class': 'chapter', 'id': id_regex})\n\n for chapter in chapters:\n # todo: need to add author notes in as well\n chapter_object = Chapter()\n\n chap_name_container = chapter.find_all(True, {'class': 'chapter preface group'})[0]\n chap_name = chap_name_container.find_all('h3')[0].text\n chapter_object.name = new_line_regex.sub('', chap_name).strip()\n\n chap_text = chapter.find_all(True, {'class': 'userstuff'})[0]\n chapter_object.raw_body = chap_text.prettify()\n\n # remove the invisible heading\n heading = chapter.find_all('h3', {'class': ['landmark', 'heading']})\n for element in heading:\n element.decompose()\n chapter_object.processed_body = chap_text.prettify()\n chapter_object.word_count = len(chap_text.text.split())\n\n self._fanfic.add_chapter(chapter_object)","title":""},{"docid":"3a0479265dc892a8744d38e96c5ab930","score":"0.4978152","text":"def _parse_description(self, response):\n category_field = response.xpath(\n \"//div[contains(., 'Category:') and contains(@class, 'field-label')]\"\n )\n field_items = category_field.xpath(\"./following::div[contains(@class, 'field-items')]\")\n return ' '.join(\n field_items.xpath('.//p/text()').extract() +\n field_items.xpath('.//strong/text()').extract()\n ).strip()","title":""},{"docid":"af3583bf5c8d16410772762aff738e46","score":"0.49771658","text":"def read_docs() -> Generator[Doc, None, None]:\n yield from all_para(DOCS.normal)\n yield from wq_para(DOCS.csv)","title":""},{"docid":"87571a626bb2e6e0dc87f0dcc06400d8","score":"0.4975917","text":"def get_meta_data(self):\n # results = db.documents.find({\"url\": self.url})\n #\n # if len(results) > 0:\n # results = results[0]\n # document_id = results['_id']\n\n\n\n document_cursor = db.documents.find_one({\"url\": self.url})\n if document_cursor:\n document_id = document_cursor[\"_id\"]\n else:\n document_id = None\n if document_id:\n self.db_doc = db.documents.find_one({\"_id\": document_id})\n\n self.title = self.db_doc[\"title\"]\n self.authors = self.db_doc[\"authors\"]\n self.text = self.db_doc[\"text\"]\n self.published = self.db_doc[\"publish-date\"]\n # try:\n # self.images = self.db_doc[\"images\"]\n # except:\n # print(\"images for article \" + self.url + \" not collected from database\")\n\n try:\n self.summary = self.db_doc[\"summary\"]\n except:\n print(\"summary for article \" + self.url + \" not collected from database\")\n\n try:\n self.keywords = self.db_doc[\"keywords\"]\n except:\n print(\"keywords for article \" + self.url + \" not collected from database\")","title":""},{"docid":"803c4c0d28f19685353711f81fe448e7","score":"0.49539548","text":"def get_wiki_description(browser, city, wiki_collection):\n url = 'https://en.wikipedia.org/wiki/' + city.replace(' ', '_')\n try:\n browser.get(url)\n except:\n print(f'{city} NOT found on Wikipedia')\n summary = ''\n for i in range(1, 100):\n paragraphs = '//*[@id=\"mw-content-text\"]/div/p[' + str(i) + ']'\n try:\n summary += browser.find_element_by_xpath(paragraphs).text\n summary += '\\n'\n except:\n None\n wiki_dict = {'city': city, 'text': summary}\n wiki_collection.insert_one(wiki_dict)","title":""},{"docid":"c46a365b413d6fca939ba14b38ce122f","score":"0.4953722","text":"def __get_description(self, catalog_entry):\n desc = ''\n\n if catalog_entry.program_description_clean is not None:\n desc = catalog_entry.program_description_clean\n\n return desc","title":""},{"docid":"a16a095ba66b434600cbc9f382ea6198","score":"0.49533728","text":"def set_course_info(self):\n host = \"courses.uscden.net\"\n course_info_url = \"/d2l/le/manageCourses/widget/myCourses/\" + self.location_id + \"/ContentPartial?defaultLeftRightPixelLength=10&defaultTopBottomPixelLength=7\"\n\n # prepare post headers\n course_info_post_headers = {\n #'Host': ' courses.uscden.net',\n #'Connection': ' keep-alive',\n # 'Content-Length': ' 229',\n # 'Pragma': ' no-cache',\n # 'Cache-Control': ' no-cache',\n #'Origin': ' https://courses.uscden.net',\n #'User-Agent': ' Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_4)',\n 'Content-type': ' application/x-www-form-urlencoded',\n #'Accept': '*/*',\n #'Referer': 'https://courses.uscden.net/d2l/home/'+self.location_id,\n # 'Accept-Encoding': 'gzip, deflate, br',\n # 'Accept-Language': 'zh-CN,zh;q=0.8',\n 'Cookie': self.d2lSessionVal + '; ' + self.d2lSecureSessionVal\n }\n # prepare post data\n course_info_post_dict = {\n 'widgetId': '2',\n #'_d2l_prc$headingLevel': '3',\n #'_d2l_prc$scope': \"\",\n #'_d2l_prc$childScopeCounters': 'filtersData:0',\n #'_d2l_prc$hasActiveForm': 'false',\n #'filtersData$semesterId': 'All',\n #'isXhr': 'true',\n #'requestId': '2',\n 'd2l_referrer': self.xsrf\n }\n course_info_post_data = urllib.urlencode(course_info_post_dict)\n conn = httplib.HTTPSConnection(host, 443)\n conn.request(\"POST\", course_info_url, course_info_post_data, course_info_post_headers)\n resp = conn.getresponse()\n course_data = resp.read()\n self.term_list = Student.find_term_list(course_data)\n term_num = len(self.term_list)\n self.course_ou_list = [[]]*term_num\n self.course_list = [[]]*term_num\n if term_num == 0:\n return\n elif term_num == 1:\n stop_index = len(course_data)\n start_index = 0\n else:\n start_index = 0\n stop_index = re.compile(self.term_list[1]).search(course_data).start()\n\n for i in range(term_num):\n course_list_raw = re.compile(r'vui-link d2l-link \\S+ href\\=\\\\\\\"/d2l/lp/ouHome/home\\.d2l\\?ou=\\d+\\\\\\\" title\\=\\\\\\\"[^\"^\\\\]+').findall(course_data[start_index:stop_index])\n ou_list = []\n c_list = []\n for c in course_list_raw:\n ou_list.append(re.compile(r'ou=\\d+').search(c).group()[3:])\n c_list.append(re.compile(r'title\\=\\\\\\\"[^\"]+').search(c).group()[14:].replace('&', '&'))\n\n self.course_ou_list[i] = ou_list\n self.course_list[i] = c_list\n start_index = stop_index\n if i+2 < term_num:\n stop_index = re.compile(self.term_list[i+2]).search(course_data).start()\n else:\n stop_index = len(course_data)","title":""},{"docid":"a79924e4b513672156a4fcd0eb7459dc","score":"0.4949158","text":"def crawl_page(page, dept_iter, term_iter):\n tree = fromstring(page.content)\n num_courses = get_number_offered_for_term(tree)\n\n # iterates through a department during a specific term\n for i in range(1, num_courses + 1):\n course = Course(collect_dept(dept_iter), collect_nums(tree, i), collect_name(tree, i), collect_terms(term_iter),\n collect_reqs(tree, i), collect_period(tree, i), collect_professor(tree, i), collect_desc(tree, i))\n master_list.append(course)","title":""},{"docid":"8f7b36347f98feb6a17bcdab4d454fa3","score":"0.49249998","text":"def _generate_description(self):\n generations = []\n\n for j, templ in enumerate(self.template):\n result = []\n\n for key in templ:\n # get the text form from template object.\n item = key.generate_description(arg_index=0, templ_index=j)\n\n if not item:\n continue\n\n # Flatten if nested dict.\n if type(item) in [OrderedDict, dict]:\n val_list = list(values_of_nested_dict(item))\n result.extend(val_list)\n else:\n result.append(item)\n generations.append(\" \".join(result))\n\n return generations","title":""},{"docid":"5351e0026bc35ac92d8cc554caa89815","score":"0.49216616","text":"def _read_description(self, geocache_tree):\n\n description_short = \"\"\n if self.source == \"downloader\":\n description_short = geocache_tree.find(\".//{http://www.groundspeak.com/cache/1/0}short_description\").text\n elif self.source == \"geocaching.com\":\n description_short = geocache_tree.find(\".//{http://www.groundspeak.com/cache/1/0/1}short_description\").text\n if description_short:\n description_short = ownfunctions.replace_signs(description_short)\n else:\n description_short = \"\"\n\n description_long = \"\"\n if self.source == \"downloader\":\n description_long = geocache_tree.find(\".//{http://www.groundspeak.com/cache/1/0}long_description\").text\n elif self.source == \"geocaching.com\":\n description_long = geocache_tree.find(\".//{http://www.groundspeak.com/cache/1/0/1}long_description\").text\n if description_long:\n description_long = ownfunctions.replace_signs(description_long)\n else:\n description_long = \"\"\n\n return description_short + \"\\n\\n\" + description_long","title":""},{"docid":"2c6612107331ba2d7b8313053b20069f","score":"0.49178073","text":"def generate_documentation(self):\n information_translator = bkrdoc.analysis.GetInformation()\n for information in self.documentation_units:\n if information:\n self.phase_documentation_information.append(information_translator.get_information_from_facts(information))","title":""},{"docid":"8fd6f34f94b1688dbb1ce77ceb8b3796","score":"0.4910362","text":"def truncate_descriptions_task():\n articles = Article.objects.all()\n\n for article in articles:\n logger.info(\"Truncating Description for \" + article.title)\n\n article.description_truncated = truncatewords_html(article.description, 150)\n\n article.save()","title":""},{"docid":"7df42da4fb1268986e059e1fca811b52","score":"0.49091297","text":"def parse_course_data(filename):\r\n created_courses = {}\r\n with open(filename, 'r') as open_file:\r\n\r\n for line in open_file:\r\n line = line.strip()\r\n courses = line.split()\r\n if line != \"\" and len(courses) == 2:\r\n # If the course does not exist, create a new course\r\n if courses[1] not in created_courses:\r\n # Create Course object and store in dictionary\r\n created_courses[courses[1]] = Course(courses[1])\r\n if courses[0] not in created_courses:\r\n created_courses[courses[0]] = Course(courses[0])\r\n\r\n # If courses[0] is not a prereq of courses[1], add courses[0]\r\n # as a prereq to courses[1]\r\n if not created_courses[courses[0]].\\\r\n is_prereq(created_courses[courses[1]]):\r\n created_courses[courses[1]].\\\r\n add_prereq(created_courses[courses[0]])\r\n\r\n # Return the root course\r\n return get_root_course(created_courses)","title":""},{"docid":"180d088e1442715c629c6b13cd931b89","score":"0.49062607","text":"def cal_required_elective_courses_forstudent(self):\n path1:str=os.path.join(self._path, \"students.txt\")\n try:\n open_file1 = file_reader(path1, fields=3, sep=\"\\t\",header=True)\n for cwid,name,major in open_file1:\n self._students[cwid].add_courses(self.mainmajor[major].result_return_major())\n\n except ValueError as e:\n print(e)\n except FileNotFoundError as fe:\n print(fe)","title":""},{"docid":"6fabfd6de1117c0d02c7868ac6c94201","score":"0.49056607","text":"def __get_description_types(self):\n self.description_type_desc, created_desc = ProgramDescriptionType.objects.get_or_create(\n name='Catalog Description'\n )\n\n if created_desc:\n self.stdout.write(\n self.style.NOTICE(\n \"Created \\\"Catalog Description\\\" description type\"\n )\n )\n\n self.description_type_desc_full, created_desc_full = ProgramDescriptionType.objects.get_or_create(\n name='Full Catalog Description'\n )\n\n if created_desc_full:\n self.stdout.write(\n self.style.NOTICE(\n \"Created \\\"Full Catalog Description\\\" description type\"\n )\n )\n\n self.description_type_source_desc, created_source_desc = ProgramDescriptionType.objects.get_or_create(\n name='Source Catalog Description'\n )\n\n if created_source_desc:\n self.stdout.write(\n self.style.NOTICE(\n \"Created \\\"Source Catalog Description\\\" description type\"\n )\n )\n\n self.description_type_source_curriculum, created_source_curriculum = ProgramDescriptionType.objects.get_or_create(\n name='Source Catalog Curriculum'\n )\n\n if created_source_curriculum:\n self.stdout.write(\n self.style.NOTICE(\n \"Created \\\"Source Catalog Curriculum\\\" description type\"\n )\n )","title":""},{"docid":"bbe0e92e7e90c7f1f29fbbc2db863778","score":"0.4905648","text":"def get_courses(self, browser):\n logger.info(\"Checking pages on category {}\".format(self.name))\n for i in range(1, self.maxpages + 1):\n tries = 0\n done = False\n clean_courses = []\n while not done:\n if not os.path.exists(\"files_{}/courses_{}_{}.csv\".format(\n self._get_datetime_now(),\n self.name,\n i)\n ):\n try:\n browser.get(\"{}?p={}\".format(self.url, i))\n courses = browser.find_elements_by_class_name(\n \"browse-course-card--link--3KIkQ\"\n )\n for course in courses:\n url = course.get_attribute(\"href\")\n detail = course.find_element_by_class_name(\n \"course-card--large--1BVxY\"\n )\n name_course = detail.find_element_by_class_name(\n \"udlite-focus-visible-target\"\n ).text\n prize = detail.find_element_by_class_name(\n \"price-text--price-part--Tu6MH\"\n ).text.split(\"\\n\")[1]\n rank = detail.find_element_by_class_name(\n \"course-card--row--1OMjg\"\n ).find_element_by_class_name(\n \"star-rating--rating-number--3lVe8\"\n ).text\n c = Course(\n self.name,\n name_course,\n prize,\n url,\n rank\n )\n clean_courses.append(c)\n self._save_courses(self.name, i, clean_courses)\n done = True\n except NoSuchElementException:\n tries += 1\n logger.warning(\n 'Page {} on {} tried {}/5'.format(\n i,\n self.name,\n tries\n )\n )\n if tries >= 5:\n done = True\n logger.error('Page {} on {} failed.'.format(\n i,\n self.name\n )\n )\n else:\n done = True\n self.ready += 1\n browser.close()","title":""},{"docid":"7624b2c2800da4b643921f869f5a9bae","score":"0.48943287","text":"def update_description(self):\n exec_string = \"DELETE FROM info WHERE name='description'\" \n if DEBUG_LEVEL > 0:\n print (\"execute db : {0}\".format(exec_string))\n self.db_cursor.execute(exec_string)\n exec_string = \"\"\"INSERT INTO info VALUES ( 'description', '{0}' )\"\"\".format(self.get_db_description_text())\n if DEBUG_LEVEL > 0:\n print (\"execute db : {0}\".format(exec_string))\n self.db_cursor.execute(exec_string)","title":""},{"docid":"e538237b64b327b0b7cf9bd31c4e47e3","score":"0.48829094","text":"def read_descriptions(self):\r\n if not os.path.exists(self.descriptions_path):\r\n logger.warning(f'descriptions path does not exist.')\r\n self.descriptions = pd.DataFrame()\r\n else:\r\n self.descriptions = pd.read_excel(self.descriptions_path)\r\n self.descriptions = self.descriptions.set_index('Unnamed: 0', drop=True)","title":""},{"docid":"6fc41182ef098a8f8ab1a022467ffec0","score":"0.48779327","text":"def parse_courses(self):\n with open(self.data_dir + 'courses.csv', newline='') as csvfile:\n course_reader = csv.DictReader(csvfile)\n course_dict = dict()\n for course in course_reader:\n course_dict[course['id']] = {'name': course['name'], 'teacher': course['teacher']}\n return course_dict","title":""},{"docid":"65c59949da1dab84e9cc920e06970308","score":"0.48749024","text":"def test_get_course_for_section(self):\n pass","title":""},{"docid":"69a51e2ab27acd97e7657575ab7943a0","score":"0.48741475","text":"def description(self):\n lines = []\n for line in self.__doc__.split('\\n')[2:]:\n line = line.strip()\n if line:\n lines.append(line)\n return ' '.join(lines)","title":""},{"docid":"282ae5120449f2beb94d20354ae04318","score":"0.48621464","text":"def add_courses_to_calendar(self):\n for course in self.target_parser.courses:\n for meeting in course.meeting_times:\n self.ics_calendar.add_component(self.create_course(course, meeting))","title":""},{"docid":"dcb19fa1a96f80fe0b60bcc98b51e3fc","score":"0.48576903","text":"def coursetofile(self, filename):\n print('\\nWriting course to the file ' + filename)\n f = open(filename, \"w+\")\n f.write(self.title_full + \"\\n\")\n for section in self.sections:\n f.write(section.title + \"\\n\")\n for session in section.sessions:\n f.write(session.title + \"\\n\")\n f.write(session.content + \"\\n\")\n f.close()","title":""},{"docid":"d0375c53be35232c39beacffbe1aaa42","score":"0.48576567","text":"def get_description():\n desc = {}\n desc[\"data\"] = False\n desc[\n \"description\"\n ] = \"\"\"This application attempts to assess the\n effectiveness of a calendar day's rainfall based on where the rain fell\n in relation to a previous period of days departure from climatology. So\n for a given date and state, the areal coverage of daily precipitation\n at some given threshold is compared against the departure from climatology\n over some given number of days. The intention is to answer a question like\n how much of the rain on a given day fell on an area that needed it! The\n areal coverage percentages are relative to the given state.\n \"\"\"\n today = datetime.datetime.today() - datetime.timedelta(days=1)\n desc[\"arguments\"] = [\n dict(\n type=\"csector\", name=\"sector\", default=\"IA\", label=\"Select Sector:\"\n ),\n dict(\n type=\"date\",\n name=\"date\",\n default=today.strftime(\"%Y/%m/%d\"),\n label=\"Date:\",\n min=\"2011/01/01\",\n ),\n dict(\n type=\"int\",\n name=\"trailing\",\n default=31,\n label=\"Over how many trailing days to compute departures?\",\n ),\n dict(\n type=\"float\",\n name=\"threshold\",\n default=0.1,\n label=\"Date Precipitation Threshold (inch)\",\n ),\n ]\n return desc","title":""},{"docid":"77baa2d5123a53ce4c045045a4e59aeb","score":"0.48502633","text":"def get_course_summary(self, course_dict: Dict[str, str]) -> Tuple[Set[str], Set[str], Set[str]]:\n courses = set(\n [course for course, grade in course_dict.items() if grade in self.passing_grades])\n\n if courses.intersection(self._electives) == set():\n return courses, self._required.difference(courses), self._electives\n else:\n return courses, self._required.difference(courses), {}","title":""},{"docid":"0e12c9c486cb917772206e1de036a367","score":"0.4848594","text":"def collect_description_data(self, language):\n return [\n [tmf('label_task_title', u'Task title'), self.context.title],\n [tmf('label_deadline', u'Deadline'),\n api.portal.get_localized_time(str(self.context.deadline))],\n [tmf('label_text', u'Text'), self.context.text]\n ]","title":""},{"docid":"4f97ebbc0527ae2088b6037e83a87ba4","score":"0.4839508","text":"def get_description(self):","title":""},{"docid":"a0645d9a91825a5fdc3fc552cd14492d","score":"0.48346865","text":"def test_templates_organization_detail_meta_description_description(self):\n organization = OrganizationFactory()\n page = organization.extended_object\n\n # Add a description to the organization\n placeholder = organization.extended_object.placeholders.get(slot=\"description\")\n add_plugin(\n language=\"en\",\n placeholder=placeholder,\n plugin_type=\"CKEditorPlugin\",\n body=\" A <b>longer</b> organization description \",\n )\n page.publish(\"en\")\n\n url = organization.extended_object.get_absolute_url()\n response = self.client.get(url)\n self.assertEqual(response.status_code, 200)\n\n self.assertContains(\n response,\n '<meta name=\"description\" content=\"A longer organization description\" />',\n )\n\n # Add an excerpt to the organization\n placeholder = organization.extended_object.placeholders.get(slot=\"excerpt\")\n add_plugin(\n language=\"en\",\n placeholder=placeholder,\n plugin_type=\"PlainTextPlugin\",\n body=\" A longer organization excerpt \",\n )\n page.publish(\"en\")\n\n response = self.client.get(url)\n self.assertEqual(response.status_code, 200)\n\n self.assertContains(\n response,\n '<meta name=\"description\" content=\"A longer organization excerpt\" />',\n )","title":""},{"docid":"382cf1d548ca1c77825ae9f942029bf3","score":"0.48342228","text":"def __make_description_with_segments(self):\n\n html = []\n desc_result = self.resource.select.read(self.resource.type_name + \"_Description\",\n where=[[\"Description_ID\",\n \"=\",\n self.resource.description_id]]\n )\n values = desc_result.fetchone()\n station_1_id = values['Station_1_ID']\n station_2_id = values['Station_2_ID']\n connection_id = values['Connection_ID']\n\n\n html.append(\"<h2>Station A</h2>\")\n html.append(self.__make_generic_module(feature=None,\n table_name=self.resource.type_name + \"_Station_Description\",\n _id=(\"Station_ID\", station_1_id), dual=1))\n\n html.append(\"<h2>\" + str(self.resource.type_name) + \"</h2>\")\n html.append(self.__make_generic_module(feature=None,\n table_name=self.resource.type_name + \"_Connection_Description\",\n _id=(\"Connection_ID\", connection_id)))\n html.append(\"<h2>Station B</h2>\")\n html.append(self.__make_generic_module(feature=None,\n table_name=self.resource.type_name + \"_Station_Description\",\n _id=(\"Station_ID\", station_2_id), dual=2))\n\n return \"\".join(html)","title":""},{"docid":"be2a17665c9cac8ea9103c912052d232","score":"0.4829322","text":"def _extract_info(self, content, capturing_group):\n return re.match(self.configuration[\"COURSE_REGEX\"], content).group(\n capturing_group\n )","title":""},{"docid":"c810949def2b395b520142c4ff952bf8","score":"0.48208857","text":"def sort_document_properties():\n config = Config()\n\n LOGGER.info('Parsing documents')\n for collection in config.collections():\n LOGGER.info('Collection: %s', collection.name)\n for path, document in collection.documents():\n LOGGER.info(\"Writing fixed file %s\", path)\n with codecs.open(path, encoding='utf-8', mode='w') as f:\n f.write(object2jekyll(document, 'description'))\n LOGGER.warning('Done')","title":""},{"docid":"19cbc4f208feff641088407bad65f831","score":"0.4816295","text":"def descriptions(self, descriptions):\n\n self._descriptions = descriptions","title":""},{"docid":"19cbc4f208feff641088407bad65f831","score":"0.4816295","text":"def descriptions(self, descriptions):\n\n self._descriptions = descriptions","title":""},{"docid":"2d62dc8b188de63a7d6478d9bea414c2","score":"0.48134148","text":"def getEditions(TOChtml,docDic,language,date):\n sectionNames = [\"ANDERE BESLUITEN\",\"WETTEN, DECRETEN, ORDONNANTIES EN VERORDENINGEN\",\"OFFICIELE BERICHTEN\",\"AGENDA'S\",\"AUTRES ARRETES\",\"LOIS, DECRETS, ORDONNANCES ET REGLEMENTS\",\"AVIS OFFICIELS\",\"ORDRES DU JOUR\"]\n regionNames = ['VLAAMSE GEMEENSCHAP','DUITSTALIGE GEMEENSCHAP','WAALS GEWEST','BRUSSELS HOOFDSTEDELIJK GEWEST','FRANSE GEMEENSCHAP','VLAAMS GEWEST','REGION WALLONNE','REGION DE BRUXELLES-CAPITALE','COMMUNAUTE FLAMANDE',u'COMMUNAUTE FRAN\\xc7AISE','COMMUNAUTE GERMANOPHONE','REGION FLAMANDE',\"DEUTSCHSPRACHIGE GEMEINSCHAFT\",\"WALLONISCHE REGION\",\"DEUTSCHPRACHIGE GEMEINSCHAFT\"]\n levelNames = ['GEMEENSCHAPS- EN GEWESTREGERINGEN','GOUVERNEMENTS DE COMMUNAUTE ET DE REGION','GEMEINSCHAFTS- UND REGIONALREGIERUNGEN']\n editions = TOChtml.split(\"<a name=EDITION\")\n for edition in editions:\n if re.match(\"\\d>EDITI\",edition):\n curEdition = edition[0]\n else:\n curEdition = '0'\n regex = re.compile(\"<A name=(\\d+)></a>(.+?)<input type=submit name=numac value=(\\d+)\",re.DOTALL)\n articles = regex.findall(edition)\n curSection = 'NA'\n curRegion = 'NA'\n curSource = 'NA'\n curLevel = 'NA'\n curTitle = 'NA'\n url = 'NA'\n for article in articles:\n if (len(article) == 3):\n if article[0] == article[2]:\n numac = article[0]\n lines = stripHTMLTags(article[1])\n lines = lines.split('\\n')\n title = []\n section = []\n region = []\n level = []\n source = []\n for line in lines:\n line = line.strip()\n if (len(line) > 0) and (line[0] != '<'):\n if float(len(re.findall('[A-Z]',line)))/float(len(line))> 0.5: # if it is a Section title or source\n if line in sectionNames:\n section.append(line)\n elif line in regionNames:\n region.append(line)\n elif line in levelNames:\n level.append(line)\n else:\n source.append(line)\n else:\n title.append(line)\n if len(section) > 0:\n curSection = ' '.join(section)\n curRegion = 'NA'\n curSource = 'NA'\n if len(level) > 0:\n curLevel = ' '.join(level)\n curRegion = 'NA'\n curSource = 'NA'\n if len(region) > 0:\n curRegion = ' '.join(region)\n curSource = 'NA'\n if len(source) > 0:\n curSource = ' '.join(source)\n if len(title) > 0:\n curTitle = ' '.join(title)\n artID = date+'-'+str(numac)\n if not docDic.has_key(artID):\n docDic[artID] = {'nl':{},'fr':{},'du':{}}\n docDic[artID][language]['edition'] = curEdition\n docDic[artID][language]['section'] = curSection\n docDic[artID][language]['region'] = curRegion\n docDic[artID][language]['source'] = curSource\n docDic[artID][language]['title'] = curTitle\n docDic[artID][language]['article'] = getArticle(date, numac,language)\n url = str(\"http://www.ejustice.just.fgov.be/cgi/article_body.pl?\" +\"language=\"+language+\"&caller=summary&pub_date=\" + date + \"&numac=\" + numac)\n docDic[artID][language]['url'] = url\n curTitle = 'NA'\n url = 'NA'","title":""},{"docid":"729f420148a4616ccc25da8701325143","score":"0.48089322","text":"def scrape_course(self, course):\n print(course)\n\n department = course[:course.index(\" \")]\n course_code = course[course.index(\" \") + 1:]\n\n self.driver.get(\"https://thecriticalreview.org/search/\" + department + \"/\" + course_code)\n soup = BeautifulSoup(self.driver.page_source, 'lxml')\n\n course_review_info = {}\n course_review_info[\"courseCode\"] = course\n\n try:\n # no reviews for the course\n no_reviews = self.driver.find_element_by_css_selector(\"#unreturned_banner_message\")\n self.courses.append(course_review_info)\n except NoSuchElementException:\n course_rating = soup.find('div', class_=\"c_rating\")[\"data-num\"]\n course_review_info[\"courseRating\"] = course_rating\n\n course_data = soup.find('div', class_=\"review_data\")\n reviews = course_data[\"data-test-value\"]\n\n # demographic data\n course_review_info[\"freshmen\"] = course_data[\"data-frosh\"]\n course_review_info[\"sophomores\"] = course_data[\"data-soph\"]\n course_review_info[\"juniors\"] = course_data[\"data-jun\"]\n course_review_info[\"seniors\"] = course_data[\"data-sen\"]\n course_review_info[\"gradStudents\"] = course_data[\"data-grad\"]\n\n if reviews == \"\":\n self.courses.append(course_review_info)\n return\n\n reviews = json.loads(reviews)\n\n # hours spent on course\n if \"minhours\" in reviews:\n course_review_info[\"avgHrs\"] = self.avg(reviews[\"minhours\"])\n if \"maxhours\" in reviews:\n course_review_info[\"maxHrs\"] = self.avg(reviews[\"maxhours\"])\n\n # course feedback statistics\n if \"readings\" in reviews:\n course_review_info[\"readingsWorthwhile\"] = self.avg(reviews[\"readings\"])\n if \"class-materials\" in reviews:\n course_review_info[\"materialsUseful\"] = self.avg(reviews[\"class-materials\"])\n if \"difficult\" in reviews:\n course_review_info[\"difficult\"] = self.avg(reviews[\"difficult\"])\n if \"learned\" in reviews:\n course_review_info[\"learnedALot\"] = self.avg(reviews[\"learned\"])\n if \"loved\" in reviews:\n course_review_info[\"enjoyedCourse\"] = self.avg(reviews[\"loved\"])\n if \"grading-speed\" in reviews:\n course_review_info[\"timelyGrading\"] = self.avg(reviews[\"grading-speed\"])\n if \"grading-fairness\" in reviews:\n course_review_info[\"fairGrading\"] = self.avg(reviews[\"grading-fairness\"])\n\n # concentrator demographics\n if \"conc\" in reviews:\n course_review_info[\"concentratorYes\"] = self.count_elem(reviews[\"conc\"], \"C\")\n course_review_info[\"concentratorNo\"] = self.count_elem(reviews[\"conc\"], \"N\")\n course_review_info[\"concentratorMaybe\"] = self.count_elem(reviews[\"conc\"], \"D\")\n\n # taken as requirement?\n if \"requirement\" in reviews:\n course_review_info[\"requirementYes\"] = self.count_elem(reviews[\"requirement\"], \"Y\")\n course_review_info[\"requirementNo\"] = self.count_elem(reviews[\"requirement\"], \"N\")\n\n # expected grades\n if \"grade\" in reviews:\n course_review_info[\"expectedA\"] = self.count_elem(reviews[\"grade\"], \"A\")\n course_review_info[\"expectedB\"] = self.count_elem(reviews[\"grade\"], \"B\")\n course_review_info[\"expectedC\"] = self.count_elem(reviews[\"grade\"], \"C\")\n course_review_info[\"expectedS\"] = self.count_elem(reviews[\"grade\"], \"S\")\n course_review_info[\"expectedNC\"] = self.count_elem(reviews[\"grade\"], \"NC\")\n\n self.courses.append(course_review_info)","title":""},{"docid":"72c1fc14666f2a7ed3fc3717646458c8","score":"0.48037595","text":"def fetch_desc_costco(self):\n description = ''\n try:\n \t#Extract description from URL for costco\n link = self.product_link.replace(\"https://www.costco.com/\",\"\")\n for ch in link:\n if(ch!=\".\"):\n description += ch\n else:\n break\n description = description.replace('-',' ')\n except:\n description = ''\n return description","title":""},{"docid":"cf59ed3dad0a1903e1a6183aed5ff7c3","score":"0.4800775","text":"def getDescription(self, metadata):\n description = '== {{int:filedesc}} ==\\n'\n description += '{{Artwork|wikidata=%(item)s}}\\n' % metadata\n description += '\\n=={{int:license-header}}==\\n'\n description += '{{Yale Digital Dura-Europos Archive}}\\n'\n description += '{{Cc-zero}}\\n'\n description += '[[Category:Objects from Dura Europos in the Yale University Art Gallery]]\\n'\n return description","title":""},{"docid":"298155518858ea76b8613941f44817e3","score":"0.4797285","text":"def load_description(path_dir=PATH_ROOT, filename='DOCS.md'):\n with open(os.path.join(path_dir, filename)) as f:\n long_description = f.read()\n return long_description","title":""},{"docid":"97922e8004d07b7a9eb322a546e613b0","score":"0.4788354","text":"def _write_grid_description(self):\n logging.info(\"\\t\\t Writing echam6 grid description to generic file atmosphere.griddes...\")\n logging.info(\"\\t\\t * generatic griddes\")\n griddes = self.CDO.griddes(input=self.files[\"couple\"][\"atmosphere_file_for_ice\"].src)\n ofile = open(self.couple_dir+\"/griddes_file\", \"w\")\n ofile.write(\"\\n\".join(griddes))\n ofile.flush()\n self.files[\"couple\"][\"atmosphere_grid_description\"] = ComponentFile(src=ofile.name,\n dest=self.couple_dir+\"/atmosphere.griddes\")\n self._cleanup_list.append(ofile.name)\n logging.info(\"\\t\\t ...done!\")","title":""},{"docid":"96868f6eb0ee5481f56674a64aa1c4b0","score":"0.4762315","text":"def save(self, *args, **kwargs):\n if self.gen_description:\n self.description = strip_tags(self.description_from_content())\n super(MetaData, self).save(*args, **kwargs)","title":""},{"docid":"d679d338d0bf8a5bc2c3c56ad148d5fc","score":"0.47541976","text":"def get_college_dict(text_rows, url):\n college = {}\n for row in text_rows:\n i = 0\n key = \"\"\n info = \"\"\n for col in row:\n if i == 0:\n key = col\n else:\n info += col\n i += 1\n info = info.rstrip(' ')\n if key != \"\":\n college[key] = info\n try:\n college['Name'] = url.replace('_', ' ').split('/')[-1:][0]\n except:\n print \"Could not get Name of the College from URL\"\n sys.exit(2)\n\n return college","title":""},{"docid":"9c31d165665e5108f2e27da8af87ddc0","score":"0.47517684","text":"def test016b_description(self):\r\n w = self.doc['WR-P-E-J-0000000001.p.1.s.1.w.7']\r\n self.assertRaises( folia.NoDescription, w.description)","title":""},{"docid":"5e59463a2c0b7b7023b50e2e761b937e","score":"0.4748506","text":"def parse_docstr(docstr):\n\n # Indentation level ......................................................\n\n def indentation(line):\n \"\"\"\n Returns the index of the first non-space character\n\n Parameters\n ----------\n line : str\n String to find the indentation level from\n\n Returns\n -------\n Index of the first non-space character or None if there is no char\n\n \"\"\"\n\n strip = len(line.lstrip())\n\n if strip:\n return (len(line) - strip) // 4\n else:\n return None\n\n # Case with empty string .................................................\n\n if docstr is None or docstr.strip() == '':\n return \"No documentation found for this section\"\n\n html = ''\n\n # Split Lines ............................................................\n\n all_lines = docstr.split('\\n')\n\n # Finding minimum indent level ...........................................\n\n min_ = -1\n\n for line in all_lines:\n if line.strip() != '':\n i = indentation(line)\n if min_ == -1:\n min_ = i\n elif i < min_:\n min_ = i\n\n all_lines = [x[4 * min_:] for x in all_lines]\n\n # Parsing sections .......................................................\n\n all_sections = [] # List containing sections. Each section is a dict\n # with a name fiels and a content field\n\n i_l = len(all_lines) - 1\n last_content = i_l\n for line in all_lines[::-1]:\n if i_l:\n if all(x == '-' for x in line.strip()) and len(line.strip()) > 0:\n if indentation(line) == indentation(all_lines[i_l - 1]):\n if len(line.strip()) <= len(all_lines[i_l - 1].strip()):\n content = '\\n'.join(all_lines[i_l + 1:last_content])\n section = {'name': all_lines[i_l - 1].strip(),\n 'content': content}\n last_content = i_l - 1\n all_sections.append(section)\n i_l -= 1\n\n # If Description section is not explicit\n if last_content > 1 and \\\n ''.join([x.strip() for x in all_lines[:last_content]]) != '':\n content = '\\n'.join(all_lines[:last_content])\n section = {'name': 'Description',\n 'content': content}\n all_sections.append(section)\n\n # Parsing to HTML ........................................................\n\n for section in all_sections[::-1]:\n html += \"<h3>\" + section['name'] + \"</h3>\"\n\n # Parsing section content . . . . . . . . . . . . . . . . . . . . . .\n\n sect_lines = section['content'].split('\\n')\n last_indent = 0\n code = False # True if it's a code division \">>> foo(bar)\"\n list_ = False # True if there is a list in the docstring\n code_indent0 = 0\n list_indent0 = 0\n last_line_was_empty = False\n open_div = 0\n\n for i_l, line in enumerate(sect_lines):\n if line.strip() != '':\n # Finding indent evolution\n if indentation(line) > last_indent and not code:\n delta = indentation(line) - last_indent\n while delta > 0:\n html += '<div class=\"indent\">'\n open_div += 1\n delta -= 1\n\n elif indentation(line) < last_indent and not code and \\\n not list_ or code and \\\n indentation(\n line) < code_indent0 or list_ and not code:\n if code:\n html += \"</div>\"\n code = False\n if list_:\n if indentation(line) < list_indent0:\n html += \"</li></ul>\"\n list_indent0 = 0\n list_ = False\n for _ in range(last_indent - indentation(line)):\n html += '</div>'\n open_div -= 1\n\n content = line\n content = content.replace('&', '&')\n\n last_indent = indentation(line)\n\n if code:\n html += \"<br>\"\n\n # Code section\n if content.strip()[:3] == '>>>' and not code:\n code = True\n code_indent0 = indentation(line)\n if not last_line_was_empty:\n html += \"<br>\"\n html += '<div class=\"code\">'\n\n if code:\n content = content[4 * code_indent0:]\n content = content.replace(' ', ' ')\n else:\n content = line.strip()\n content = content.replace('<', '<').replace('>', '>')\n content = content.replace(\"'\", ''')\n content = content.replace('\"', \""\")\n\n # finding URLs\n urls = set(re.findall(\n r\"\"\"(?i)\\b((?:https?:(?:/{1,3}|[a-z0-9%])|[a-z0-9.\\-]+[.](?:com|net|org|edu|gov|mil|aero|asia|biz|cat|coop|info|int|jobs|mobi|museum|name|post|pro|tel|travel|xxx|ac|ad|ae|af|ag|ai|al|am|an|ao|aq|ar|as|at|au|aw|ax|az|ba|bb|bd|be|bf|bg|bh|bi|bj|bm|bn|bo|br|bs|bt|bv|bw|by|bz|ca|cc|cd|cf|cg|ch|ci|ck|cl|cm|cn|co|cr|cs|cu|cv|cx|cy|cz|dd|de|dj|dk|dm|do|dz|ec|ee|eg|eh|er|es|et|eu|fi|fj|fk|fm|fo|fr|ga|gb|gd|ge|gf|gg|gh|gi|gl|gm|gn|gp|gq|gr|gs|gt|gu|gw|gy|hk|hm|hn|hr|ht|hu|id|ie|il|im|in|io|iq|ir|is|it|je|jm|jo|jp|ke|kg|kh|ki|km|kn|kp|kr|kw|ky|kz|la|lb|lc|li|lk|lr|ls|lt|lu|lv|ly|ma|mc|md|me|mg|mh|mk|ml|mm|mn|mo|mp|mq|mr|ms|mt|mu|mv|mw|mx|my|mz|na|nc|ne|nf|ng|ni|nl|no|np|nr|nu|nz|om|pa|pe|pf|pg|ph|pk|pl|pm|pn|pr|ps|pt|pw|py|qa|re|ro|rs|ru|rw|sa|sb|sc|sd|se|sg|sh|si|sj|Ja|sk|sl|sm|sn|so|sr|ss|st|su|sv|sx|sy|sz|tc|td|tf|tg|th|tj|tk|tl|tm|tn|to|tp|tr|tt|tv|tw|tz|ua|ug|uk|us|uy|uz|va|vc|ve|vg|vi|vn|vu|wf|ws|ye|yt|yu|za|zm|zw)/)(?:[^\\s()<>{}\\[\\]]+|\\([^\\s()]*?\\([^\\s()]+\\)[^\\s()]*?\\)|\\([^\\s]+?\\))+(?:\\([^\\s()]*?\\([^\\s()]+\\)[^\\s()]*?\\)|\\([^\\s]+?\\)|[^\\s`!()\\[\\]{};:'\".,<>?«»“”‘’])|(?:(?<!@)[a-z0-9]+(?:[.\\-][a-z0-9]+)*[.](?:com|net|org|edu|gov|mil|aero|asia|biz|cat|coop|info|int|jobs|mobi|museum|name|post|pro|tel|travel|xxx|ac|ad|ae|af|ag|ai|al|am|an|ao|aq|ar|as|at|au|aw|ax|az|ba|bb|bd|be|bf|bg|bh|bi|bj|bm|bn|bo|br|bs|bt|bv|bw|by|bz|ca|cc|cd|cf|cg|ch|ci|ck|cl|cm|cn|co|cr|cs|cu|cv|cx|cy|cz|dd|de|dj|dk|dm|do|dz|ec|ee|eg|eh|er|es|et|eu|fi|fj|fk|fm|fo|fr|ga|gb|gd|ge|gf|gg|gh|gi|gl|gm|gn|gp|gq|gr|gs|gt|gu|gw|gy|hk|hm|hn|hr|ht|hu|id|ie|il|im|in|io|iq|ir|is|it|je|jm|jo|jp|ke|kg|kh|ki|km|kn|kp|kr|kw|ky|kz|la|lb|lc|li|lk|lr|ls|lt|lu|lv|ly|ma|mc|md|me|mg|mh|mk|ml|mm|mn|mo|mp|mq|mr|ms|mt|mu|mv|mw|mx|my|mz|na|nc|ne|nf|ng|ni|nl|no|np|nr|nu|nz|om|pa|pe|pf|pg|ph|pk|pl|pm|pn|pr|ps|pt|pw|qa|re|ro|rs|ru|rw|sa|sb|sc|sd|se|sg|sh|si|sj|Ja|sk|sl|sm|sn|so|sr|ss|st|su|sv|sx|sy|sz|tc|td|tf|tg|th|tj|tk|tl|tm|tn|to|tp|tr|tt|tv|tw|tz|ua|ug|uk|us|uy|uz|va|vc|ve|vg|vi|vn|vu|wf|ws|ye|yt|yu|za|zm|zw)\\b/?(?!@)))\"\"\",\n content))\n for url in urls:\n rep = '<a href=\"' + url + '\">' + url + \"</a>\"\n content = content.replace(url, rep)\n\n # List section\n if content[:2] in ['- ', '* '] and not code:\n content = content[2:]\n if not list_:\n html += '<ul>'\n list_ = True\n list_indent0 = indentation(line)\n else:\n html += \"</li>\"\n html += '<li>'\n\n # Bold before ':'\n if not code and \"</a>\" not in content:\n sp = content.split(':')\n if len(sp) > 1:\n content = \"<b>\" + sp[0] + \"</b>\" + ':' + \\\n ':'.join(sp[1:])\n\n # Adding content\n html += ' ' + content\n last_line_was_empty = False\n\n # Empty line\n else:\n if code:\n html += \"</div>\"\n code = False\n if list_:\n for _ in range(last_indent - list_indent0):\n html += \"</div>\"\n open_div -= 1\n last_indent = list_indent0\n html += \"</li></ul>\"\n list_ = False\n if i_l != 0:\n html += \"<br>\"\n last_line_was_empty = True\n\n # Closing section\n if code:\n html += \"</div>\"\n code = False\n if list_:\n html += \"</li></ul>\"\n list_ = False\n\n while open_div > 0:\n html += \"</div>\"\n open_div -= 1\n return html","title":""},{"docid":"147dbc4b8b90da3c14b40958a8178de9","score":"0.4743189","text":"def get_course_list(self, step_index, courses):\n response = self.browser.get(\"https://student.osiris.hro.nl:9021/osiris_student/OnderwijsZoekCursus.do\", params={\n \"event\": \"goto\",\n \"source\": \"OnderwijsZoekCursus\",\n \"value\": step_index + 1,\n \"size\": \"30\",\n \"partialTargets\": \"OnderwijsZoekCursus _uixState\",\n \"partial\": \"true\",\n \"requestToken\": self.get_request_token()\n })\n\n for index, row in enumerate(BeautifulSoup(response.text, \"lxml\").find(\"table\", {\"class\": \"OraTableContent\"}).find_all(\"tr\")):\n if not index == 0:\n course = self.get_course_info(course_row=row)\n courses.append(course)\n\n self.lock.acquire()\n print(\"[+] Scraped course metadata: %s (%s)\" % (course[\"korteNaamCursus\"], course[\"cursuscode\"]))\n self.lock.release()","title":""},{"docid":"d3ca67b182b130873ed6374312ff938e","score":"0.47429794","text":"def add_course(code, name, misc, descript):\n c = list(db.courses.find({\"code\": code}))\n if not c:\n course = {\"code\": code,\n \"name\": name,\n \"misc\": misc,\n \"description\": descript}\n db.courses.insert(course)\n return True\n print 'db add course'\n return False","title":""},{"docid":"e321840cf94f40f831c256d0270643b0","score":"0.47382024","text":"def test_course(self):\n course = CourseInstanceFactory()\n [res_course] = list(self.wrapper.courses())\n\n self.assertEqual(res_course.id, course.id)\n self.assertEqual(res_course.name, course.name)\n self.assertEqual(res_course.short_name, course.short_name)\n self.assertEqual(res_course.points, course.points)\n self.assertEqual(res_course.has_exam, course.has_exam)\n self.assertEqual(res_course.description, course.description)\n self.assertEqual(res_course.language, course.language)\n self.assertEqual(res_course.semester, course.semester.id)\n self.assertEqual(res_course.course_type, course.course_type.short_name)\n self.assertEqual(res_course.usos_kod, course.usos_kod)","title":""},{"docid":"981da650518f33b2becae61599f76b1b","score":"0.4735454","text":"def prepare(doc):\n doc.headers = [0, 0, 0, 0, 0, 0]\n doc.aliases = [\"\", \"\", \"\", \"\", \"\", \"\"]\n doc.information = {}\n doc.defined = {}\n\n if \"pandoc-numbering\" in doc.metadata.content and isinstance(\n doc.metadata.content[\"pandoc-numbering\"], MetaMap\n ):\n for category, definition in doc.metadata.content[\n \"pandoc-numbering\"\n ].content.items():\n if isinstance(definition, MetaMap):\n add_definition(category, definition, doc)\n\n doc.count = {}\n doc.collections = {}","title":""},{"docid":"cb5bdaccc9010bfcf914fcd7ed48068c","score":"0.47305542","text":"def describe(description):\n if \"experiment\" not in _eh:\n raise ValueError(\"Call flexp.setup first! flexp.describe after that\")\n with open(_eh[\"experiment\"].get_file_path(\"description.txt\"), \"wt\") as fx:\n print(description, file=fx)","title":""},{"docid":"f5b0dd6ae027a9534cf369473672a01f","score":"0.47261122","text":"def description_from_content(self):\n description = \"\"\n # Fall back to the title if description couldn't be determined.\n if not description:\n description = str(strip_tags(self.content))\n # Strip everything after the first block or sentence.\n ends = (\"</p>\", \"<br />\", \"<br/>\", \"<br>\", \"</ul>\",\n \"\\n\", \". \", \"! \", \"? \")\n for end in ends:\n pos = description.lower().find(end)\n if pos > -1:\n description = TagCloser(description[:pos]).html\n break\n else:\n description = truncatewords_html(description, 150)\n try:\n description = unicode(description)\n except NameError:\n pass # Python 3.\n return description","title":""},{"docid":"07725fea8405f335c47b19c80df9bb7f","score":"0.47248343","text":"def write_descr(self, f, design):\n bdt = time.localtime(design.design_attributes.metadata.updated_timestamp)\n datestr = time.strftime('%d %b %Y', bdt).lower()\n f.write('''\\\n$Descr A4 11700 8267\nencoding utf-8\nSheet 1 1\nTitle \"\"\nDate \"%s\"\nRev \"\"\nComp \"\"\nComment1 \"\"\nComment2 \"\"\nComment3 \"\"\nComment4 \"\"\n$EndDescr\n''' % (datestr,))","title":""},{"docid":"5bf2919b3ffb7af69aa3222e23efc597","score":"0.4722489","text":"def description(self):\n return self._book_dict['description']","title":""},{"docid":"166fbea4e4c9e947404c9201612cb082","score":"0.4722284","text":"def test_catalog(self):\n with Transaction():\n factory = self.root.manage_addProduct['silva.app.document']\n factory.manage_addDocument('document', 'Test Document')\n\n version = self.root.document.get_editable()\n save_editor_text(version.body, HTML_CATALOG, content=version)\n\n # Test appear in the title.\n self.assertItemsEqual(\n search(fulltext='Test'),\n ['/root/document/0'])\n\n # Catalog appear in the body text.\n self.assertItemsEqual(\n search(fulltext='catalog'),\n ['/root/document/0'])","title":""},{"docid":"3d44e826a28e6c8519b2eacf1fb2d0fb","score":"0.47211483","text":"def record_story_chapters(self) -> None:\n # get the chapters\n for chapter in self.chapter_list:\n time.sleep(self._chapter_sleep_time)\n chapter_object = Chapter()\n self.log_debug(\"Downloading chapter: \" + chapter['link'])\n self._update_soup(url=self._url[0:-1]+chapter['link'])\n chapter_text = \"\"\n chapter_count = 0\n story_tag = self._soup.find(id=\"storytextp\")\n for content in story_tag.find_all(['p', 'hr']):\n chapter_text += content.prettify()\n chapter_count += len(content.text.split())\n chapter_object.processed_body = chapter_text\n chapter_object.raw_body = self._soup.prettify()\n chapter_object.word_count = chapter_count\n chapter_object.name = chapter['name']\n self._fanfic.add_chapter(chapter_object)","title":""},{"docid":"d22cdbb3e976cf825990f8e15039e62d","score":"0.47167173","text":"def _getCourses(self):\n courses = defaultdict(list)\n with open(os.path.join(os.path.dirname(__file__), 'courses.csv'), encoding=\"UTF-8\") as file:\n reader = csv.reader(file)\n for line in reader:\n courses[line[0].strip()].append(line[1].strip())\n return courses","title":""},{"docid":"c89e022c52616bcbaecd97ee734ab935","score":"0.47144622","text":"def get_course_list(cls):\n\n cls.setup()\n\n course_list = []\n\n tree = html.fromstring(cls.__main_page)\n # Current semester courses\n courses_nodes = tree.get_element_by_id('navbar').find('li/ul').findall('li/a[@title]')\n # Previous courses\n courses_nodes += tree.get_element_by_id('navbar').find('.//ul[@class=\"dropdown-menu\"]')\\\n .findall('li/a[@title]')\n\n for course_node in courses_nodes:\n course = DataClasses.CourseInfo()\n course.name = course_node.text_content().split('(')[0].strip()\n course.semester = course_node.text_content().split(course.name)[1].strip()\n course.link = course_node.attrib['href']\n course_list.append(course)\n\n return course_list","title":""},{"docid":"a1f9d4bb0ef0c2d7cc46cc11e4697f2d","score":"0.4708553","text":"def saveContent(self):\n if not os.path.exists(self.contentFile):\n with open(self.translation.book_file, 'r') as f:\n lines = [line.strip() for line in f.readlines()]\n ch_cont = ' '.join(lines[self.firstLine-1:self.lastLine+1])\n start = 0\n new_lines = []\n for _, _, end in self.beads:\n new_lines.append(ch_cont[start:end])\n start = end\n ch_cont = '\\n\\n'.join(new_lines)\n with open(self.contentFile, 'w') as chf:\n chf.write(ch_cont)","title":""},{"docid":"e6eddfd62dee774785381eec12050614","score":"0.47085264","text":"def _generate_description(self):\n generations = []\n\n for j, templ in enumerate(self.template):\n result = []\n for i, key in enumerate(templ):\n key_type = type(key)\n arg_index = 0\n # arg_index 1 for StopCondition\n if (key_type in CONDIITON_TEMPLATES) and (len(self.args) > 1):\n arg_index = 1\n\n # get the text from template object\n item = key.generate_description(arg_index=arg_index, index=i, templ_index=j)\n\n if not item:\n continue\n # flatten nested dict\n if type(item) in [OrderedDict, dict]:\n val_list = list(values_of_nested_dict(item))\n result.extend(val_list)\n else:\n result.append(item)\n generations.append(\" \".join(result))\n\n return generations","title":""}],"string":"[\n {\n \"docid\": \"3c0601b75ff7eab4168a97065371a8bf\",\n \"score\": \"0.7158249\",\n \"text\": \"def ExtractCourseAndDescription(firebase: firebase, document: Document, knowledgeAreas: List[KnowledgeArea], courses: List[Course], catalogId: str) -> None:\\n\\n global currentKnowledgeArea\\n\\n knowledgeAreaId = \\\"\\\"\\n knowledgeAreaTitle = \\\"\\\"\\n candidateId = \\\"\\\"\\n partialTitle = []\\n fulltitle = \\\"\\\"\\n\\n global courseNameStyles\\n\\n for p in document.paragraphs:\\n if p.style.name == constant.STYLE_NORMAL:\\n currentKnowledgeArea = getMatchingKnowledgeArea(p.text.strip())\\n candidateId = currentKnowledgeArea.getId()\\n\\n if candidateId != \\\"\\\":\\n knowledgeAreaId = str(candidateId)\\n knowledgeAreaTitle = currentKnowledgeArea.getText()\\n else:\\n knowledgeAreaId = \\\"\\\"\\n\\n if knowledgeAreaId != \\\"\\\" and p.text.strip() != knowledgeAreaTitle and p.text.strip() != \\\"\\\":\\n pText = p.text.strip()\\n lastPos = len(pText)\\n\\n if not pText[lastPos-1].isdigit():\\n partialTitle.append(p.text.strip())\\n else:\\n if len(partialTitle) > 0:\\n partialTitle.append(p.text.strip())\\n fulltitle = ' '.join(partialTitle)\\n else:\\n fulltitle = pText\\n\\n fulltitle = fulltitle.replace(\\\"*\\\", \\\"\\\")\\n\\n course = Course()\\n course.setKnowledgeAreaId(knowledgeAreaId)\\n course.setKnowledgeArea(currentKnowledgeArea.getText())\\n course.setTOCEntry(p.text.strip())\\n course.setTitle(fulltitle.rstrip(string.digits).strip())\\n courses.append(course)\\n partialTitle = []\\n\\n ExtractCourseDescriptions(document, courses)\\n\\n for course in courses:\\n if course.getDescription().strip() == \\\"\\\":\\n courseTitle = course.getTitle().strip()\\n courseTitle = courseTitle.replace(\\\"New!\\\", \\\"\\\").replace(\\\"*\\\", \\\"\\\").replace(\\\"'\\\",\\\"\\\").strip()\\n\\n pNumber = 0\\n takeDescription = 0\\n descriptionElements = []\\n\\n for p in document.paragraphs:\\n pNumber += 1\\n \\n if p.style.name in courseNameStyles and p.text != \\\"\\\":\\n pText = p.text.replace(\\\"New! \\\", \\\"\\\").replace(\\\"*\\\", \\\"\\\").replace(\\\"'\\\",\\\"\\\").strip()\\n if pText.__contains__(courseTitle) or courseTitle.__contains__(pText):\\n takeDescription = 1\\n else:\\n takeDescription = 0\\n\\n if p.style.name == constant.STYLE_BODY_TEXT and takeDescription == 1:\\n #print(\\\"taking {} as course description for course: {}\\\".format(p.text.strip(), course.getTitle()))\\n descriptionElements.append(p.text.strip())\\n\\n fullDescription = course.getDescription()\\n\\n if len(descriptionElements) > 0:\\n fullDescription = ' '.join(descriptionElements)\\n\\n course.setDescription(fullDescription)\\n\\n for course in courses:\\n if course.getDescription().strip() == \\\"\\\":\\n print(\\\"course: {} has no description\\\".format(course.getTitle()))\\n\\n if course.getKnowledgeArea() == course.getTitle():\\n continue\\n\\n newCourse = {\\n 'catalogid': catalogId,\\n 'knowledgeareaid': course.getKnowledgeAreaId(),\\n 'name': course.getTitle(),\\n 'description': course.getDescription()\\n }\\n result = firebase.post('course', newCourse)\\n course.setId(result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5602e3e4c7ec47b5d9ced790a9d9a27\",\n \"score\": \"0.5897001\",\n \"text\": \"def copy_course_description(self, courseevent_pk):\\n courseevent = get_object_or_404(CourseEvent, pk=courseevent_pk)\\n course = get_object_or_404(Course, pk=courseevent.course_id)\\n\\n courseevent = get_object_or_404(CourseEvent, pk=courseevent_pk)\\n courseevent.target_group = course.target_group\\n courseevent.excerpt = course.excerpt\\n courseevent.text = course.text\\n courseevent.prerequisites = course.prerequisites\\n courseevent.project = course.project\\n courseevent.save()\\n\\n logger.info(\\\"\\\"\\\"[%s] [course %s %s]:\\n Beschreibung aus der Kursvorlage in den Kurs kopiert\\\"\\\"\\\"\\n % (courseevent, course.id, course))\\n return(HttpResponseRedirect(courseevent.get_absolute_url()))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd0a830fd8fa1091526b29b84b6f6d9e\",\n \"score\": \"0.5801686\",\n \"text\": \"def get_description(self, course):\\n if hasattr(course, 'programcourse') and course.programcourse.description:\\n return course.programcourse.description\\n return course.description\",\n \"title\": \"\"\n },\n {\n \"docid\": \"01b8a91b546cb01d2747b5365a103984\",\n \"score\": \"0.57233155\",\n \"text\": \"def get_courses(self) -> List[Dict[str, str]]:\\n\\n # Check if the catalog exists in the cache\\n catalog_cache_path = (self._cache_dir / 'department_catalogs').with_suffix('.json')\\n if catalog_cache_path.exists():\\n with open(catalog_cache_path, 'r') as file:\\n departments = json.load(file)\\n else:\\n departments = self._download_department_catalogs()\\n with open(catalog_cache_path, 'w') as file:\\n json.dump(departments, file)\\n\\n # Gather catalog HTML pages\\n html_catalogs = [d['catalog_html'] for d in departments]\\n\\n department_map = {d['code']: d['name'] for d in departments}\\n\\n # First pass: Parse basic course information\\n courses = []\\n for catalog_html in html_catalogs:\\n\\n # Clean content\\n catalog_html = catalog_html.replace(' ', ' ') # Non-breaking space\\n\\n soup = BeautifulSoup(catalog_html, 'lxml')\\n courses_div = soup.find('div', attrs={'class': 'courses'})\\n course_blocks = courses_div.find_all('div', attrs={'class': 'courseblock'})\\n\\n for block in course_blocks:\\n paragraphs = block.find_all('p')\\n\\n # Parse department code, course number, and course title\\n title_paragraph_text = paragraphs[0].text\\n match = re.match(re.compile(r'^(.+?) (\\\\S+?)\\\\. (.+?)\\\\. (.+)?$'), title_paragraph_text)\\n if not match:\\n logging.warning(f'Failed to parse course title paragraph: {title_paragraph_text}')\\n continue\\n department_code = match.group(1)\\n course_number = match.group(2)\\n course_title = match.group(3)\\n units_string = match.group(4)\\n\\n course = {\\n 'department_code': department_code,\\n 'department_name': department_map[department_code],\\n 'number': course_number,\\n 'title': course_title\\n }\\n\\n # Parse units\\n if units_string is not None:\\n self._parse_units_string(course, units_string)\\n else:\\n logging.warning(f'No units found in title paragraph: \\\"{title_paragraph_text}\\\"')\\n\\n # Description\\n course['description'] = paragraphs[1].text\\n\\n # Save remaining paragraphs (they will be parsed on the second pass)\\n p = []\\n for paragraph in paragraphs[2:]:\\n lines = paragraph.text.splitlines()\\n p.extend(lines)\\n course['_paragraphs'] = p\\n\\n courses.append(course)\\n\\n valid_courses = {d['code']: [c['number'] for c in courses if c['department_code'] == d['code']] for d in departments}\\n\\n # Second pass: Parse extra paragraphs (prerequisites, restrictions, etc.)\\n for course in courses:\\n for paragraph in course['_paragraphs']:\\n\\n # Prerequisites\\n match = re.match(r'^Prerequisite:\\\\s*(.+)$', paragraph)\\n if match:\\n self._parse_prerequisite_string(course, match.group(1), valid_courses=valid_courses)\\n continue\\n\\n # Corequisite\\n match = re.match(r'^Corequisite: (.+)$', paragraph)\\n if match:\\n self._parse_corequisite_string(course, match.group(1), valid_courses=valid_courses)\\n continue\\n\\n # Prerequisite OR Corequisite\\n match = re.match(r'^Prerequisite or corequisite: (.+)$', paragraph)\\n if match:\\n self._parse_prerequisite_or_corequisite_string(course, match.group(1), valid_courses=valid_courses)\\n continue\\n\\n # Restrictions\\n match = re.match(r'^Restriction:\\\\s*(.+)$', paragraph) # TODO: Parse?\\n if match:\\n course['restriction'] = match.group(1)\\n continue\\n\\n # Same\\n match = re.match(r'^Same as (.+)\\\\.$', paragraph)\\n if match:\\n self._parse_same_as_string(course, match.group(1)) # TODO: Validate course codes?\\n continue\\n\\n # Concurrent\\n match = re.match(r'^Concurrent with (.+)\\\\.$', paragraph)\\n if match:\\n course['concurrent'] = match.group(1) # TODO: Validate course codes and parse multiple\\n continue\\n\\n # Repeatability\\n match = re.match(r'^Repeatability:\\\\s*(.+)$', paragraph) # TODO: parse number?\\n if match:\\n course['repeatability'] = match.group(1)\\n continue\\n\\n # Overlaps\\n match = re.match(r'^Overlaps with (.+)\\\\.$', paragraph) # TODO: validate course codes and parse\\n if match:\\n course['overlap'] = match.group(1)\\n continue\\n\\n # Grading Option\\n match = re.match(r'^Grading Option: (.+)$', paragraph) # TODO: Parse?\\n if match:\\n course['grading_option'] = match.group(1)\\n continue\\n\\n # Design units\\n match = re.match(r'^\\\\(Design units: ((?:\\\\d+\\\\.\\\\d+|\\\\.\\\\d+|\\\\d+)(?:\\\\s*-\\\\s*(?:\\\\d+\\\\.\\\\d+|\\\\.\\\\d+|\\\\d+))?)\\\\)', paragraph)\\n if match:\\n course['design_units'] = match.group(1)\\n continue\\n\\n # GE Category\\n match = re.match(r'^\\\\((.+)\\\\)\\\\.?$', paragraph)\\n if match:\\n try:\\n self._parse_ge_category_string(course, match.group(1))\\n continue\\n except Exception as e:\\n logger.warning(f'Failed to parse potential GE category for {course[\\\"department_code\\\"]} {course[\\\"number\\\"]}: {e}')\\n\\n logger.warning(f'Unrecognized paragraph for course {course[\\\"department_code\\\"]} {course[\\\"number\\\"]}: \\\"{paragraph}\\\"')\\n del course['_paragraphs']\\n\\n # names = [course['department_code'] + ' ' + course['number'] for course in courses]\\n # print(names)\\n #\\n # # Extras\\n # for course in courses:\\n # if course['number'].upper().endswith('L'):\\n # non = course['department_code'] + ' ' + course['number'][:-1]\\n # if non in names:\\n # print('POTENTIAL LAB:', course['department_code'] + ' ' + course['number'])\\n # else:\\n # print('BAMBOOZLE:', course['department_code'] + ' ' + course['number'], 'NON:', non)\\n\\n return courses\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2b2405b211045f5c0734f4b9e40dced\",\n \"score\": \"0.559974\",\n \"text\": \"def context_processor():\\n sections = []\\n for doc in DOCS:\\n content = get_doc(doc)\\n lines = content.split(\\\"\\\\n\\\")\\n doc = {\\n 'id': doc,\\n 'title': lines[0].replace(\\\"#\\\", \\\"\\\").strip(),\\n 'description': \\\"\\\\n\\\".join(lines[1:3]),\\n 'body': content,\\n }\\n sections.append(doc)\\n return {'sections': sections}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"652b78f9baa59767606df70e08f274e5\",\n \"score\": \"0.5530076\",\n \"text\": \"def get_db_description_text(self):\\n \\n # sets description text in different languages\\n # default value is English\\n # sets header of devotion\\n # sets description of type of lesson\\n #name_text = bible_codes.db_info_description[\\\"en\\\"]\\n try:\\n name_text = bible_codes.db_info_description_title\\n except Exception:\\n print(\\\"unable to get internat.db_info_description for \\\" + self.lang_code)\\n if (self.lesson_type == 'ad' or self.lesson_type == ''):\\n version_text = bible_codes.db_info_description_version_adult\\n else:\\n if (self.lesson_type == 'ay'):\\n version_text = version_text = bible_codes.db_info_description_version_youth\\n else:\\n version_text = \\\"\\\"\\n description_text = \\\"{0} {1} {2}\\\".format(name_text, version_text, self.SS_year_inst.quarters_list_year[-1].get('id'))\\n return description_text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea7e9bbdfb864f925b5c8713c5b6e244\",\n \"score\": \"0.55218595\",\n \"text\": \"def preprocDescrip():\\n # Load lists of descriptions\\n concentrations = pickle.load(open(\\\"nlp/concentrations.p\\\", \\\"rb\\\"))\\n descr = pickle.load(open(\\\"nlp/concentrationDescr.p\\\", \\\"rb\\\"))\\n\\n orgInfo = pickle.load(open(\\\"nlp/orgInfo.p\\\", \\\"rb\\\"))\\n orgNames = pickle.load(open(\\\"nlp/orgNames.p\\\", \\\"rb\\\"))\\n\\n # Add org names to descriptions; remove the word 'Harvard'\\n for i, org in enumerate(orgInfo):\\n org = org.replace(\\\"harvard\\\", \\\"\\\")\\n orgInfo[i] = str(orgNames[i]).lower() + \\\" \\\" + str(org).lower()\\n\\n # Calculate pairwise similarities\\n orgInfoLists = tc.getSimilarityLists(orgInfo)\\n concentrationLists = tc.getSimilarityLists(descr)\\n\\n # Store results for future retrieval\\n pickle.dump(orgInfoLists, open(\\\"nlp/orgInfoLists.p\\\", \\\"wb\\\"))\\n pickle.dump(concentrationLists, open(\\\"nlp/concentrationLists.p\\\", \\\"wb\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f09c5d3f360908b3e5e6869d150456c2\",\n \"score\": \"0.5521627\",\n \"text\": \"def course_attrs(self):\\n\\n TITLE_CLASS = \\\"PALEVEL0SECONDARY\\\"\\n INFO_TABLE_CLASS = \\\"SSSGROUPBOXLTBLUEWBO\\\"\\n INFO_BOX_CLASS = \\\"PSGROUPBOXNBO\\\"\\n INFO_BOX_HEADER_CLASS = \\\"SSSGROUPBOXLTBLUE\\\"\\n DESCRIPTION_CLASS = \\\"PSLONGEDITBOX\\\"\\n\\n EDITBOX_LABEL_CLASS = \\\"PSEDITBOXLABEL\\\"\\n EDITBOX_DATA_CLASS = \\\"PSEDITBOX_DISPONLY\\\"\\n DROPDOWN_LABEL_CLASS = \\\"PSDROPDOWNLABEL\\\"\\n DROPDOWN_DATA_CLASS = \\\"PSDROPDOWNLIST_DISPONLY\\\"\\n\\n DESCRIPTION = \\\"Description\\\"\\n COURSE_DETAIL = \\\"Course Detail\\\"\\n COURSE_COMPS = \\\"Course Components\\\"\\n ENROLL_INFO = \\\"Enrollment Information\\\"\\n CEAB = \\\"CEAB Units\\\"\\n\\n KEYMAP = {\\n \\\"Career\\\": \\\"career\\\",\\n \\\"Typically Offered\\\": \\\"typically_offered\\\",\\n \\\"Units\\\": \\\"units\\\",\\n \\\"Grading Basis\\\": \\\"grading_basis\\\",\\n \\\"Add Consent\\\": \\\"add_consent\\\",\\n \\\"Drop Consent\\\": \\\"drop_consent\\\",\\n \\\"Course Components\\\": \\\"course_components\\\",\\n \\\"Enrollment Requirement\\\": \\\"enrollment_requirement\\\",\\n }\\n\\n ret = {\\n 'extra':{\\n 'CEAB':{}\\n }\\n }\\n\\n # Get the title and number\\n title = self.soup.find(\\\"span\\\", {\\\"class\\\": TITLE_CLASS})\\n if not title:\\n raise Exception(\\\"Could not find the course title to parse\\\")\\n\\n temp = self._clean_html(title.string)\\n m = re.search('^([\\\\S]+)\\\\s+([\\\\S]+)\\\\s+-\\\\s+(.*)$', temp)\\n if not m:\\n raise Exception(\\\"Title found ({0}) didn't match regular expression\\\".format(temp))\\n\\n ret['basic'] = {\\n 'title' : m.group(3),\\n 'number' : m.group(2),\\n 'description' : \\\"\\\"\\n }\\n\\n # Blue table with info, enrollment, and description\\n info_table = self.soup.find(\\\"table\\\", {\\\"class\\\": INFO_TABLE_CLASS})\\n\\n # Look through inner tables\\n info_boxes = self.soup.find_all(\\\"table\\\", {\\\"class\\\": INFO_BOX_CLASS})\\n for table in info_boxes:\\n\\n # Get the table type\\n temp = table.find(\\\"td\\\", {\\\"class\\\": INFO_BOX_HEADER_CLASS})\\n if not temp or not temp.string:\\n # Nothing there\\n continue\\n\\n box_title = temp.string\\n\\n # Process the description box\\n if box_title == DESCRIPTION:\\n desc_list = table.find(\\\"span\\\", {\\\"class\\\": DESCRIPTION_CLASS}).contents\\n if desc_list:\\n # If not x.string, it means it's a <br/> Tag\\n ret['basic']['description'] = \\\"\\\\n\\\".join([x for x in desc_list if x.string])\\n\\n # Process the course details and enrollment info\\n elif box_title in (COURSE_DETAIL, ENROLL_INFO):\\n\\n # Labels and values for \\\"Add/Drop Consent\\\" (enroll), \\\"Career\\\" (course), and \\\"Grading Basis\\\" (course)\\n labels = table.find_all(\\\"label\\\", {\\\"class\\\": DROPDOWN_LABEL_CLASS})\\n data = table.find_all(\\\"span\\\", {\\\"class\\\": DROPDOWN_DATA_CLASS})\\n\\n if box_title == ENROLL_INFO:\\n # Labels and values for \\\"Typically Offered\\\", \\\"Enrollment Requirement\\\",\\n labels += table.find_all(\\\"label\\\", {\\\"class\\\": EDITBOX_LABEL_CLASS})\\n data += table.find_all(\\\"span\\\", {\\\"class\\\": EDITBOX_DATA_CLASS})\\n\\n # Add all the type -> value mappings to the ret dict\\n for x in range(0, len(labels)):\\n if labels[x].string in KEYMAP:\\n ret['extra'][KEYMAP[labels[x].string]] = data[x].get_text()\\n\\n # Special case for course detail, \\\"Units\\\" and \\\"Course Components\\\"\\n if box_title == COURSE_DETAIL:\\n # Units and course components\\n labels = table.find_all(\\\"label\\\", {\\\"class\\\": EDITBOX_LABEL_CLASS})\\n data = table.find_all(\\\"span\\\", {\\\"class\\\": EDITBOX_DATA_CLASS})\\n for x in range(0, len(labels)):\\n if labels[x].string == COURSE_COMPS:\\n # Last datafield, has multiple type -> value mappings\\n comp_map = {}\\n for i in range(x, len(data), 2):\\n comp_map[data[i].string] = data[i+1].get_text()\\n\\n ret['extra'][KEYMAP[labels[x].string]] = comp_map\\n break\\n elif labels[x].string in KEYMAP:\\n ret['extra'][KEYMAP[labels[x].string]] = data[x].get_text()\\n\\n # Process the CEAB information\\n elif box_title == CEAB:\\n\\n labels = table.find_all(\\\"label\\\", {\\\"class\\\": EDITBOX_LABEL_CLASS})\\n data = table.find_all(\\\"span\\\", {\\\"class\\\": EDITBOX_DATA_CLASS})\\n\\n for x in range(0, len(labels)):\\n try:\\n # Clean up the data\\n temp = int(self._clean_html(data[x].string))\\n except (TypeError, ValueError) as e:\\n temp = 0\\n\\n # Add the data to the dict if it exists\\n if labels[x].string:\\n # Remove the last character of the label to remove the \\\":\\\"\\n ret['extra']['CEAB'][labels[x].string[:-1]] = temp\\n\\n else:\\n raise Exception('Encountered unexpected info_box with title: \\\"{0}\\\"'.format(box_title))\\n\\n return ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d5993b1c9d30fe4c4e6bdaecaea155d\",\n \"score\": \"0.55084336\",\n \"text\": \"def getCourses(self,course,link,abbrev):\\n \\t#navigates to course page\\n \\tcourse_map = {}\\n\\n \\tpage = requests.get(BASE_URL + link,verify=False)\\n \\tsoup = BeautifulSoup(page.content)\\n\\n \\t#finds all courses in major and puts them in a hashmap\\n \\tcoursePageLink = soup.find_all(\\\"li\\\")\\n \\tif coursePageLink:\\n \\t\\tlink = str(coursePageLink[len(coursePageLink)-1])[54:-90]\\n\\n \\tpage = requests.get(BASE_URL + link,verify=False)\\n \\tsoup = BeautifulSoup(page.content)\\n \\tregex = re.compile(r'<p><strong>(\\\\w+)')\\n \\tuniqueTup = []\\n \\tnormalTup = []\\n \\tcourseList = []\\n\\n \\t#\\n \\tfor link in soup.find_all(\\\"p\\\"):\\n\\n \\t\\t#special cases\\n \\t\\tif '<br/><br/>' in str(link):\\n \\t\\t\\t#tuple {major:[course_number,name]}\\n \\t\\t\\ttup = self.uniqueCases(link)\\n \\t\\t\\tif tup:\\n \\t\\t\\t\\tuniqueTup = tup\\n \\t\\t#standard case\\n \\t\\telif re.match(regex,str(link)):\\n \\t\\t\\t#tuple {major:[course_number,name]}\\n \\t\\t\\ttup = normalCase = self.normalCase(link)\\n \\t\\t\\tif tup:\\n \\t\\t\\t\\tnormalTup = tup\\n \\t\\t#puts same major tuples under 1 major\\n \\t\\tcourseTuple = uniqueTup + normalTup\\n \\t\\tif courseTuple:\\n \\t\\t\\tcourseTuple[0] = courseTuple[0] + (\\\"Department: \\\" + abbrev,)\\n \\t\\t\\tcourseList.append(courseTuple)\\n \\treturn courseList\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d22b234b0517aee1ffb2781dd5324987\",\n \"score\": \"0.5499037\",\n \"text\": \"def create_course_dict():\\n wb = load_workbook('newcatalog.xlsx')\\n catalog = wb.get_sheet_by_name('catalog')\\n Course = namedtuple('Course', 'program, designation')\\n CourseInfo = namedtuple('CourseInfo', 'credits, terms, prereqs')\\n course_dict = {}\\n for row in range(1, catalog.max_row + 1):\\n key = Course(get_val(catalog, 'A', row), get_val(catalog, 'B', row))\\n prereqs = tuple(tuple(get_split_course(prereq) for prereq in prereqs.split())\\n for prereqs in none_split(get_val(catalog, 'E', row)))\\n val = CourseInfo(get_val(catalog, 'C', row), tuple(get_val(catalog, 'D', row).split()), prereqs)\\n course_dict[key] = val\\n return course_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e32262f7ee3a30dd920a64891366d2b9\",\n \"score\": \"0.5486802\",\n \"text\": \"def load_descriptions(doc):\\n mapping = dict()\\n # process lines\\n for line in doc.split('\\\\n'):\\n # split line by white space\\n tokens = line.split()\\n if len(line) < 2:\\n continue\\n # take the first token as the image id, the rest as the description\\n image_id, image_desc = tokens[0], tokens[1:]\\n # remove filename from image id\\n image_id = image_id.split('.')[0]\\n # convert description tokens back to string\\n image_desc = ' '.join(image_desc)\\n # create the list if needed\\n if image_id not in mapping:\\n mapping[image_id] = list()\\n # store description\\n mapping[image_id].append(image_desc)\\n return mapping # parse descriptions\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c0ac73637e8f0ed167be1abddecaac8\",\n \"score\": \"0.54675364\",\n \"text\": \"def __process_descriptions(self):\\n while True:\\n try:\\n mp = self.catalog_description_queue.get()\\n\\n with self.mt_lock:\\n self.catalog_description_progress.next()\\n\\n catalog_entry = mp.best_match\\n catalog_desc = catalog_entry.description\\n\\n try:\\n source_desc_obj = mp.program.descriptions.get(\\n description_type=self.description_type_source_desc\\n )\\n source_desc = source_desc_obj.description if source_desc_obj.description else ''\\n except ProgramDescription.DoesNotExist:\\n source_desc = ''\\n\\n if self.force_desc_updates or not source_desc or source_desc != catalog_desc:\\n # Sanitize/process the incoming catalog description if\\n # we don't already have an existing original catalog\\n # description to compare against, or if we do and it\\n # changed since the last time it was imported\\n # (or if we're forcing description updates)\\n if catalog_entry.program_description_clean is None:\\n catalog_entry.program_description_clean = self.__sanitize_description(\\n description_str=catalog_desc,\\n strip_tables=True\\n )\\n\\n # Pass along to the curriculum queue next\\n with self.mt_lock:\\n self.catalog_curriculum_queue.put(mp)\\n\\n except Exception as e:\\n logging.log(logging.ERROR, e)\\n finally:\\n self.catalog_description_queue.task_done()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc30d81c48dcd7ffa4fd5eaf07a82637\",\n \"score\": \"0.5457821\",\n \"text\": \"def collect_doc(ori_doc):\\n print('collecting original documents from DB list.')\\n print(len(ori_doc), ' total documents are collected.')\\n\\n doc_name2txt = dict()\\n for (doc_id, doc_body) in ori_doc:\\n # repeated documentation is not allowed\\n assert doc_id not in doc_name2txt\\n doc_name2txt[doc_id] = doc_body\\n return doc_name2txt\",\n \"title\": \"\"\n },\n {\n \"docid\": \"139d4b89e1c6197ed2ef9a1048e35c4c\",\n \"score\": \"0.5442083\",\n \"text\": \"def crawl_course(metadata: CourseInfo) -> CourseInfo:\\n # Need to re-decode with utf-8, original is encoded with ISO-8859-1\\n text: str = requests.get(\\n \\\"http://class-qry.acad.ncku.edu.tw/syllabus/online_display.php\\\",\\n params={\\n \\\"syear\\\": metadata.year.zfill(4),\\n \\\"sem\\\": metadata.semester,\\n \\\"co_no\\\": metadata.course_id,\\n \\\"class_code\\\": metadata.class_code if metadata.class_code != \\\"0\\\" else None\\n }\\n ).text.encode(\\\"ISO-8859-1\\\").decode(\\\"utf-8\\\")\\n html: etree._Element = etree.HTML(text)\\n\\n title: etree._Element = html.xpath('//*[@id=\\\"header\\\"]/h1/div/span')[1]\\n filtered = list(filter(lambda child: child.tag == \\\"br\\\", title.getchildren()))\\n course_names = list(map(lambda child: child.tail.strip(), filtered))\\n\\n sidebar: etree._Element = html.xpath('//*[@id=\\\"sidebar\\\"]/div')[0]\\n filtered = list(filter(lambda child: child.tag == \\\"span\\\", sidebar.getchildren()))\\n information = list(map(lambda child: child.tail.strip(), filtered))\\n\\n return CourseInfo(**{\\n \\\"course_name\\\": f\\\"{course_names[0]} {course_names[1]}\\\",\\n \\\"department\\\": information[0],\\n \\\"instructor\\\": information[1],\\n \\\"year\\\": metadata.year,\\n \\\"semester\\\": metadata.semester,\\n \\\"serial_number\\\": information[4],\\n \\\"attribute_code\\\": information[5],\\n \\\"course_id\\\": metadata.course_id,\\n \\\"class_code\\\": metadata.class_code,\\n \\\"credit\\\": information[8],\\n \\\"language\\\": information[9],\\n \\\"files\\\": metadata.files,\\n })\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1eee7163c20f08148d4792abc8d8382b\",\n \"score\": \"0.5434418\",\n \"text\": \"def __process_course_tag(self, course_tag):\\n course = Course()\\n\\n course.link = course_tag.find('a')['href']\\n\\n # Detect course ID\\n expr = re.search('index_mobile\\\\.php\\\\?id=(\\\\d+)&code_departement=.*&mydate=([0-9/]+)', course.link)\\n course.id = expr.group(1)\\n course.date = datetime.datetime.strptime(expr.group(2), '%d/%m/%Y').date()\\n\\n for span_tag in course_tag.find_all('span'):\\n\\n assert(len(span_tag['class']) == 1)\\n type = span_tag['class'][0]\\n\\n if type == 'image':\\n # First field : time of beginning / end of the courses\\n # <br><b>08:30<br>11:45</br>\\n expr = re.search('<br/?><b>([0-9]{2}:[0-9]{2})<br>([0-9]{2}:[0-9]{2})</b', str(span_tag))\\n if not expr:\\n print(\\\"Unable to find hours in %s !\\\" % span_tag)\\n continue\\n\\n time_format = '%H:%M'\\n course.time_begin = datetime.datetime.strptime(expr.group(1), time_format).time()\\n course.time_end = datetime.datetime.strptime(expr.group(2), time_format).time()\\n elif type == 'comment':\\n # Second field : department and place\\n expr = re.search('(.*) : (.*)', span_tag.text)\\n course.department = expr.group(1)\\n course.place = expr.group(2)\\n elif type == 'name':\\n # Third field : name\\n course.name = span_tag.text\\n elif type == 'starcomment':\\n # Fourth field : comment\\n course.comment = span_tag.text\\n elif type == 'arrow':\\n # Nothing to do\\n pass\\n else:\\n print(\\\"Unrecognized type : %s !\\\" % type)\\n\\n return course\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e8122fee62f4208b54a325a57548bc1\",\n \"score\": \"0.53735864\",\n \"text\": \"def insertDocument(self, details):\\n details[\\\"Accreditation\\\"]=\\\"Not Evaluated\\\" \\n details[\\\"Type\\\"]=\\\"Unknown\\\" \\n\\n if details[\\\"Category\\\"].lower().startswith(\\\"conference\\\"):\\n details[\\\"ScanPath\\\"]=\\\"conferences/\\\"+details[\\\"ConferenceTitle\\\"].replace(' ', '_')+\\\"/publications/\\\"\\n details[\\\"ScanFileName\\\"]=details[\\\"Title\\\"].replace(' ', '_')+\\\".pdf\\\"\\n details[\\\"TableOfContentsPath\\\"]=\\\"conferences/\\\"+details[\\\"ConferenceTitle\\\"].replace(' ', '_')+\\\"/TOCs/\\\"\\n details[\\\"PeerReviewPath\\\"]=\\\"conferences/\\\"+details[\\\"ConferenceTitle\\\"].replace(' ', '_')+\\\"/peerReviews/\\\"\\n result=insertConferencePaper(self,details)\\n \\n elif details[\\\"Category\\\"].lower().startswith(\\\"journal\\\"):\\n if \\\"Volume\\\" not in details:\\n details[\\\"Volume\\\"]=None\\n if \\\"Issue\\\" not in details:\\n details[\\\"Issue\\\"]=None\\n details[\\\"HIndex\\\"]=None\\n details[\\\"ScanPath\\\"]=\\\"journals/\\\"+details[\\\"JournalTitle\\\"].replace(' ', '_')+\\\"/publications/\\\"\\n details[\\\"ScanFileName\\\"]=details[\\\"Title\\\"].replace(' ', '_')+\\\".pdf\\\"\\n details[\\\"TableOfContentsPath\\\"]=\\\"journals/\\\"+details[\\\"JournalTitle\\\"].replace(' ', '_')+\\\"/TOCs/\\\"\\n details[\\\"PeerReviewPath\\\"]=\\\"journals/\\\"+details[\\\"JournalTitle\\\"].replace(' ', '_')+\\\"/peerReviews/\\\"\\n result=insertJournalPaper(self,details)\\n \\n elif details[\\\"Category\\\"].lower().startswith(\\\"book\\\"):\\n \\n details[\\\"ScanPath\\\"]=\\\"books/\\\"+details[\\\"BookTitle\\\"].replace(' ', '_')+\\\"/publications/\\\"\\n details[\\\"ScanFileName\\\"]=details[\\\"Title\\\"].replace(' ', '_')+\\\".pdf\\\"\\n details[\\\"TableOfContentsPath\\\"]=\\\"books/\\\"+details[\\\"BookTitle\\\"].replace(' ', '_')+\\\"/TOCs/\\\"\\n details[\\\"PeerReviewPath\\\"]=\\\"books/\\\"+details[\\\"BookTitle\\\"].replace(' ', '_')+\\\"/peerReviews/\\\"\\n result=insertBookSection(self,details)\\n\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a34bbcb12c76ef2ac796e093dd76091d\",\n \"score\": \"0.53501743\",\n \"text\": \"def find_description(docs):\\n summary = None\\n filename = docs.get('meta.summary', 'SUMMARY').strip()\\n if filename and _os.path.isfile(filename):\\n fp = open(filename)\\n try:\\n try:\\n summary = fp.read().strip().splitlines()[0].rstrip()\\n except IndexError:\\n summary = ''\\n finally:\\n fp.close()\\n\\n description = None\\n filename = docs.get('meta.description', 'DESCRIPTION').strip()\\n if filename and _os.path.isfile(filename):\\n fp = open(filename)\\n try:\\n description = fp.read().rstrip()\\n finally:\\n fp.close()\\n\\n if summary is None and description:\\n from docutils import core\\n summary = core.publish_parts(\\n source=description,\\n source_path=filename,\\n writer_name='html',\\n )['title'].encode('utf-8')\\n\\n return summary, description\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5628300a054c43e6ea3d5b036a1997f0\",\n \"score\": \"0.5328472\",\n \"text\": \"def fetch_description(self, given_description, given_words, left_margin, right_margin, num_of_results):\\n # https://simply-python.com/2014/03/14/saving-output-of-nltk-text-concordance/\\n # Instead of using nltk.word_tokenize which\\n # hangs for a long time sometimes like when using the text from\\n # \\\"url\\\":\\\"https://www.reddit.com/user/AutoModerator\\\"\\n # The hanging comes from nltk/tokenize/__init__\\n # line 94 : return tokenizer.tokenize(text)\\n # I am avoiding this prt and I am only using the TreebankWordTokenizer\\n tokens = self.treebank.tokenize(given_description)\\n\\n text = nltk.Text(tokens)\\n c = nltk.ConcordanceIndex(tokens, key=lambda s: s.lower())\\n concordance_txt = ([[text.tokens[list(map(lambda x: x-left_margin if (x-left_margin) > 0 else left_margin-x,\\n [offset]))[0]:offset+right_margin+1]\\n for offset in c.offsets(given_word)][:num_of_results]\\n for given_word in given_words.split()])\\n concordance_txt = itertools.chain(*concordance_txt)\\n return '\\\\n'.join([''.join([x+' ' for x in con_sub]) for con_sub in concordance_txt])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30e4f9f1d8f34b0353c7fd7888b22f40\",\n \"score\": \"0.52897835\",\n \"text\": \"def test_export_description():\\n desc = cds_list[1].export_description()\\n assert desc == \\\"# origin_seq: scaffold_0 # strand: reverse # start: 1581 # end: 2864\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84bdd069a9b1937cff12261a4d3b943a\",\n \"score\": \"0.5288692\",\n \"text\": \"def get_courses():\\n return DB[COURSE_COLLECTION].find()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"714bbc21287abecfeab9881af37d7a0c\",\n \"score\": \"0.52808845\",\n \"text\": \"def _commentsToDescription(self, parseResults):\\n \\n commentString = ''\\n if self._KEY_COMMENT in parseResults.keys():\\n commentString = parseResults[self._KEY_COMMENT]+\\\". \\\"\\n # Remove the _KEY_COMMENTS from list of keys.\\n parseResults.pop(self._KEY_COMMENT)\\n \\n if self._VALUE_COMMENT in parseResults.keys():\\n commentString = commentString + parseResults[self._VALUE_COMMENT]\\n parseResults.pop(self._VALUE_COMMENT)\\n\\n if len(commentString) == 0:\\n return \\n \\n # Clean up the comment string, remove the // and extra spaces.\\n commentString = re.sub(r'//', '', commentString)\\n commentString = re.sub('\\\\s+', ' ',commentString)\\n parseResults[self._DESCRIPTION] = commentString\",\n \"title\": \"\"\n },\n {\n \"docid\": \"379c97cf4f0c0597fb8cbe6965c27b5c\",\n \"score\": \"0.5277338\",\n \"text\": \"def extract(self):\\n # undergraduate and comp3900 are parameters\\n url = \\\"https://www.handbook.unsw.edu.au/{}/courses/2019/{}/\\\".format(self.study_level, self.course)\\n url = requests.get(url)\\n htmltext = url.text\\n\\n #read the html\\n soup = BeautifulSoup(htmltext, 'html.parser')\\n\\n if soup.title.string:\\n self.details[\\\"Title\\\"] = soup.title.string\\n else:\\n self.details[\\\"Title\\\"] = \\\"\\\"\\n if soup.find(id=\\\"readMoreIntro\\\").div.p:\\n self.details[\\\"Description\\\"] = soup.find(id=\\\"readMoreIntro\\\").div.p.string\\n else:\\n self.details[\\\"Description\\\"] = \\\"\\\"\\n if soup.find_all('strong')[1]:\\n self.details[\\\"Credit\\\"] = soup.find_all('strong')[1].string\\n else:\\n self.details[\\\"Credit\\\"] = \\\"\\\"\\n if soup.find(id=\\\"readMoreSubjectConditions\\\"):\\n self.details[\\\"Prerequisite\\\"] = soup.find(id=\\\"readMoreSubjectConditions\\\").div.div.string\\n else:\\n self.details[\\\"Prerequisite\\\"] = \\\"\\\"\\n if soup.find(id=\\\"subject-outline\\\"):\\n self.details[\\\"Course Outline\\\"] = soup.find(id=\\\"subject-outline\\\").div.a.attrs['href']\\n else:\\n self.details[\\\"Course Outline\\\"] = \\\"\\\"\\n\\n if soup.select('.o-attributes-table-item ')[0].a:\\n self.details[\\\"Faculty\\\"] = soup.select('.o-attributes-table-item ')[0].a.string\\n else:\\n self.details[\\\"Faculty\\\"] = \\\"\\\"\\n\\n if soup.select('.o-attributes-table-item ')[1].a:\\n self.details[\\\"School\\\"] = soup.select('.o-attributes-table-item ')[1].a.string\\n else:\\n self.details[\\\"School\\\"] = \\\"\\\"\\n\\n if soup.select('.o-attributes-table-item ')[3].p:\\n self.details[\\\"Offering Terms\\\"] = soup.select('.o-attributes-table-item ')[3].p.string\\n else:\\n self.details[\\\"Offering Terms\\\"] = \\\"\\\"\\n if soup.select('.o-attributes-table-item ')[4].p:\\n self.details[\\\"Campus\\\"] = soup.select('.o-attributes-table-item ')[4].p.string.replace(\\\" \\\", \\\"\\\").strip()\\n else:\\n self.details[\\\"Campus\\\"] = \\\"\\\"\\n\\n if soup.select('.p-all-1')[0]:\\n for value in soup.select('.p-all-1')[0].children:\\n if (soup.select('.p-all-1')[0].index(value) == 3):\\n self.details[\\\"PDF\\\"] = value.a.attrs['href']\\n else:\\n self.details[\\\"PDF\\\"] = \\\"\\\"\\n\\n if soup.select('.o-attributes-table-item ')[5].p:\\n self.details[\\\"Indicative contact hours\\\"] = soup.select('.o-attributes-table-item ')[5].p.string\\n else:\\n self.details[\\\"Indicative contact hours\\\"] = \\\"\\\"\\n if soup.select('.a-column-sm-12')[8].p:\\n self.details[\\\"Commonwealth Supported Student\\\"] = soup.select('.a-column-sm-12')[8].p.string.strip()\\n else:\\n self.details[\\\"Commonwealth Supported Student\\\"] = \\\"\\\"\\n if soup.select('.a-column-sm-12')[10].p:\\n self.details[\\\"Domestic Student\\\"] = soup.select('.a-column-sm-12')[10].p.string.strip()\\n else:\\n self.details[\\\"Domestic Student\\\"] = \\\"\\\"\\n if soup.select('.a-column-sm-12')[12].p:\\n self.details[\\\"International Student\\\"] = soup.select('.a-column-sm-12')[12].p.string.strip()\\n else:\\n self.details[\\\"International Student\\\"] = \\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3c2d89a31af1fdce89977df9b5cc353\",\n \"score\": \"0.5242611\",\n \"text\": \"def load_clean_descriptions(filename, dataset):\\n\\n doc = load_doc(filename)\\n descriptions = {}\\n for line in doc.split('\\\\n'):\\n # Split line by white space.\\n tokens = line.split()\\n # Split id from description.\\n image_id, image_desc = tokens[0], tokens[1:]\\n # skip images not in the set.\\n if image_id in dataset:\\n # create list.\\n if image_id not in descriptions:\\n descriptions[image_id] = []\\n # Wrap description in tokens.\\n desc = 'startseq ' + ' '.join(image_desc) + ' endseq'\\n descriptions[image_id].append(desc)\\n return descriptions\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24e7c4d31071574250e8b9b9e809c3a1\",\n \"score\": \"0.5194507\",\n \"text\": \"def _get_description(self):\\n if self._generated:\\n return self._title\\n else:\\n text = read_file(self.path.src).splitlines()\\n if len(text) >= 4:\\n desc_line = text[3]\\n # Description begins with an AsciiDoc comment\\n if desc_line[0:2] == '//':\\n return desc_line[2:].strip()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7cc3979664f410b5639ae836e6d5a5f5\",\n \"score\": \"0.51822454\",\n \"text\": \"def set_description_and_comment(abstract_bibdocfiles, description, comment):\\n for bibdocfile in abstract_bibdocfiles:\\n bibdocfile.description = description\\n bibdocfile.comment = comment\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e53f1e2ca92345ae3c0bf7364ff9d87d\",\n \"score\": \"0.5170063\",\n \"text\": \"def get_interest_descriptions(db):\\n failures = []\\n\\n for rec in db.records:\\n if rec.is_interest() and not rec.has_field(\\\"DESC\\\"):\\n failures.append(\\\"Missing description on {}\\\".format(rec))\\n return failures\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d91de2921245258b44a7a814d787511\",\n \"score\": \"0.5154081\",\n \"text\": \"def get_courses(self):\\n sitemap = 'http://www.golfadvisor.com/sitemap_courses-#.xml'\\n pages = array(\\n [sitemap.replace('#', str(index)) for index in range(1, 35)]\\n )\\n page_lists = array_split(pages, POOL_SIZE)\\n with ProcessPoolExecutor() as extr:\\n results = extr.map(self._get_course_pages, enumerate(page_lists))\\n courses = array([link for links in results for link in links])\\n self.courses = courses\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4fe0f16a5d1795f42c79fa39249381cb\",\n \"score\": \"0.51433355\",\n \"text\": \"def _save_courses(self, category, page, courses):\\n now = self._get_datetime_now()\\n if not os.path.exists('files_{}'.format(now)):\\n os.makedirs('files_{}'.format(now))\\n out_file_name = 'files_{}/courses_{}_{}.csv'.format(\\n now,\\n category,\\n page\\n )\\n csv_headers = list(filter(\\n lambda property: not property.startswith('_'),\\n dir(courses[0])))\\n with open(out_file_name, mode='w+', encoding='utf-8', newline='') as f:\\n writer = csv.writer(f)\\n writer.writerow(csv_headers)\\n for course in courses:\\n row = [str(getattr(course, prop)) for prop in csv_headers]\\n writer.writerow(row)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1489dc2b1301e4d7a62e4f4c927fa988\",\n \"score\": \"0.5138065\",\n \"text\": \"def load_courses(self):\\n\\n print('>> Loading Courses...')\\n\\n Course.objects.all().delete()\\n\\n # Load Course AIM Labels\\n aim_label = {}\\n\\n with open(os.getcwd() + '/coursefinderapp/data/KISAIM.csv') as f:\\n next(f) # Skip the header\\n reader = csv.reader(f, skipinitialspace=True)\\n aim_label = dict(reader)\\n\\n course_locs = self.load_course_locations()\\n\\n def handler(line):\\n \\\"\\\"\\\"\\n Translate each line of the csv in a Course object and associate with the\\n its respective location.\\n :param line: csv line\\n :return: Course object\\n \\\"\\\"\\\"\\n\\n c = Course(\\n pubukprn=line[0],\\n ukprn=line[1],\\n kiscourseid=line[14],\\n title=line[27].replace('\\\"', \\\"\\\"),\\n url=line[4],\\n distance=line[6],\\n mode=line[15],\\n aim=aim_label[line[32]],\\n )\\n\\n loc_id = course_locs.get((c.pubukprn, c.ukprn, c.kiscourseid, c.mode), None)\\n\\n if loc_id is not None:\\n c.location = Location.objects.get(ukprn=c.ukprn, locid=loc_id)\\n\\n return c\\n\\n courses = self._read_csv('/coursefinderapp/data/KISCOURSE.csv', handler=handler)\\n Course.objects.bulk_create(courses)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bfb93adaa5a30d6a179d17b19bd7976a\",\n \"score\": \"0.5123131\",\n \"text\": \"def parse_course_overview(self, response):\\n hxs = HtmlXPathSelector(response)\\n xpath = '//table[contains(@class, \\\"courseBrochure\\\")]'\\n loader = ItemLoader(CourseOverviewItem(), selector=hxs.select(xpath))\\n loader.item['course'] = response.request.meta['course']\\n loader.add_xpath('extent', 'tr[1]/td[2]', re=r'(\\\\d+(?:-\\\\d+)?)')\\n loader.add_xpath('teaching_period', 'tr[2]/td[2]')\\n loader.add_xpath('learning_outcomes', 'tr[3]/td[2]')\\n loader.add_xpath('content', 'tr[4]/td[2]')\\n loader.add_xpath('prerequisites', 'tr[5]/td[2]')\\n loader.add_xpath('study_materials', 'tr[11]/td[2]')\\n return loader.load_item()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5775e30eea41cc62bad2d5c0bcff2bd\",\n \"score\": \"0.50984627\",\n \"text\": \"def description(doc=__doc__):\\n print(doc)\\n for line in doc.splitlines():\\n return line.strip()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82dd849ccacca9e82eac364ede1530be\",\n \"score\": \"0.5095845\",\n \"text\": \"def fill_out_courses(results, new_keywords = None, student_major = None, student_interests = None):\\n\\n global conn\\n\\n cur = conn.cursor()\\n courses = []\\n\\n for result in results:\\n # Build Course object from current result\\n result_course = Course.Course()\\n result_course.department = result[17]\\n result_course.course_num = result[2]\\n result_course.id = result[13]\\n result_course.name = result[16]\\n result_course.term = result[19]\\n result_course.credits = result[11]\\n\\n # Parse classroom/meeting times/description/prerequisites if defined\\n if result[24] != None:\\n res = parse_time_room(result[24])\\n if res != None:\\n result_course.classroom = res[0]\\n result_course.time = res[1]\\n if result[29] != None:\\n result_course.description = result[29]\\n if result[30] != None:\\n result_course.prereqs = result[30]\\n call_debug_print(result_course.prereqs)\\n\\n # Adding professor information based on id found in courses\\n if result[21] != None:\\n result_course.faculty_id = result[21]\\n if '|' in result_course.faculty_id:\\n prof_ids = result_course.faculty_id.split('|')\\n query_str = \\\"SELECT * FROM professors WHERE id = \\\\'\\\"\\n for id_num in prof_ids:\\n query_str = query_str + str(int(id_num)) + \\\"' OR id = '\\\"\\n query_str = query_str[:-10]\\n cur.execute(query_str)\\n names = cur.fetchall()\\n result_course.faculty_id = \\\"\\\"\\n for result in names:\\n result_course.faculty_id = result_course.faculty_id + result[0] + \\\", \\\"\\n result_course.faculty_name = result_course.faculty_name + result[1] + \\\", \\\"\\n if result_course.faculty_id != \\\"\\\" and result_course.faculty_id != \\\"\\\":\\n result_course.faculty_name = result_course.faculty_name[:-2]\\n result_course.faculty_id = result_course.faculty_id[:-2]\\n else:\\n query_str = \\\"SELECT name FROM professors WHERE id = \\\\'\\\" \\\\\\n + str(int(result_course.faculty_id)) + \\\"'\\\"\\n cur.execute(query_str)\\n name = cur.fetchall()\\n if len(name) > 0 and len(name[0]) > 0:\\n result_course.faculty_name = name[0][0]\\n\\n # Only runs in the case where this is called by query_by_keywords\\n # Uses the new_keywords list in the query_by_keywords function\\n if new_keywords is not None:\\n relevance_list, weighted_relevance = calculate_course_relevance(result_course, new_keywords,\\n student_major, student_interests)\\n result_course.relevance = relevance_list\\n result_course.weighted_score = weighted_relevance\\n courses.append(result_course)\\n\\n return courses\",\n \"title\": \"\"\n },\n {\n \"docid\": \"691a638d42184e73fec23877a27a7f5b\",\n \"score\": \"0.5080336\",\n \"text\": \"def get_descriptions(programs, program_cache=None, nocattrans=0, quiet=0, slowdays=0):\\n\\n # This regexp tries to find details such as Genre, Acteurs, Jaar van Premiere etc.\\n detail = re.compile('<li>[^<]*<strong>([\\\\w\\\\- ]+):</strong>(.*?)</li>', re.DOTALL)\\n\\n # These regexps find the main description area and lines of descriptive text in this area\\n description = re.compile('<div id=\\\"prog-content\\\">(.*?)</div>',re.DOTALL)\\n descrline = re.compile('<p>(.*?)</p>',re.DOTALL)\\n\\n # These regexps try to find the subgenre of the program, e.g. Fantasy-familiefilm, Comedyserie, \\n # Woonprogramma, Culinair Programma etc.\\n # descrtype searches for the subgenre in the description area, e.g.:\\n # <strong>Woonprogramma</strong><p>Nance, Tooske, Ellemieke, Marlayne en Viktor helpen mensen...\\n #\\n # addprogtype searches for the subgenre in the special \\\"mijn TV agenda\\\" link, e.g.:\\n # <a href=\\\"#perstvgids\\\" title=\\\"Plaats dit programma in mijn TV Agenda\\\" \\n # onclick=\\\"addProg('10281755','Informatief','Woonprogramma.',event);return false;\\\">\\n\\n descrtype = re.compile('<strong>([^<]*)</strong>',re.DOTALL)\\n addprogtype = re.compile(\\\"addProg\\\\(.*?,.*?,'(.*?)',.*?\\\\)\\\",re.DOTALL)\\n\\n # randomize detail requests\\n nprograms = len(programs)\\n fetch_order = list(range(0,nprograms))\\n random.shuffle(fetch_order)\\n\\n counter = 0\\n for i in fetch_order:\\n counter += 1\\n if programs[i]['offset'] >= slowdays:\\n continue\\n \\n log('\\\\n(%3.0f%%) %s: %s ' % (100*float(counter)/float(nprograms), i, programs[i]['name']), quiet)\\n\\n # check the cache for this program's ID\\n cached_program = program_cache.query(programs[i]['ID'])\\n \\n if (cached_program != None):\\n log(' [cached]', quiet)\\n # copy the cached information, except the start/end times, rating and clumping, \\n # these may have changed.\\n tstart = programs[i]['start-time']\\n tstop = programs[i]['stop-time']\\n try:\\n clump = programs[i]['clumpidx']\\n except LookupError:\\n clump = False\\n programs[i] = cached_program\\n programs[i]['start-time'] = tstart\\n programs[i]['stop-time'] = tstop\\n if clump:\\n programs[i]['clumpidx'] = clump\\n continue\\n\\n # be nice to tvgids.nl\\n time.sleep(random.randint(nice_time[0], nice_time[1]))\\n\\n # get the details page, and get all the detail nodes\\n descriptions = ()\\n details = ()\\n try:\\n log(' [normal fetch]', quiet)\\n total = get_page(programs[i]['url'])\\n details = detail.finditer(total)\\n \\n descrspan = description.search(total)\\n if descrspan != None:\\n descriptions = descrline.finditer(descrspan.group(1))\\n else:\\n log('Can not find program details on page\\\\n', quiet)\\n descriptions = []\\n \\n except Exception as e:\\n # if we cannot find the description page, \\n # go to next in the loop\\n log(' [fetch failed or timed out]', quiet)\\n continue\\n # define containers\\n programs[i]['credits'] = {}\\n programs[i]['video'] = {}\\n\\n # now parse the details\\n programs[i]['details'] = []\\n # First, we try to find the program type in the special \\\"mijn TV Agenda\\\" link, if not found there we\\n # search for a type in the description section.\\n # Note that this type is not the same as the generic genres (these are searched later on), \\n # but a more descriptive one like \\\"Culinair programma\\\" \\n # \\n def add_details(program, details):\\n details = filter_line(details)\\n if len(details) == 0:\\n return\\n if len(program['details']) > 0 and program['details'][-1].lower() == details.lower():\\n return\\n program['details'].append(details)\\n \\n if 'subgenre' in programs[i]:\\n add_details(programs[i], programs[i]['subgenre'])\\n \\n m = addprogtype.search(total)\\n if m:\\n add_details(programs[i], m.group(1).capitalize())\\n \\n m = descrtype.search(descrspan.group(1))\\n if m:\\n add_details(programs[i], m.group(1).capitalize())\\n\\n # Secondly, we add one or more lines of the program description that are present.\\n for descript in descriptions:\\n # descript is a re.Match object\\n descr_html = descript.group(1)\\n \\n # Remove sponsored link from description if present.\\n sponsor_pos = descr_html.rfind('<i>Gesponsorde link:</i>')\\n if sponsor_pos > 0:\\n descr_html = descr_html[0:sponsor_pos]\\n if re.search('[Gg]een detailgegevens be(?:kend|schikbaar)', descr_html):\\n descr_html = ''\\n \\n add_details(programs[i], descr_html)\\n \\n if len(programs[i]['details']) == 0:\\n programs[i]['detail1'] = ''\\n else:\\n programs[i]['detail1'] = programs[i]['details'][0]\\n \\n # Finally, we check out all program details. These are generically denoted as:\\n #\\n # <li><strong>(TYPE):</strong>(CONTENT)</li> \\n #\\n # Some examples:\\n #\\n # <li><strong>Datum:</strong>16 oktober 2008</li>\\n # <li><strong>Genre:</strong>Amusement</li>\\n \\n for d in details:\\n ctype = d.group(1).strip().lower()\\n content_asis = filter_line(d.group(2))\\n content = filter_line(content_asis)\\n \\n if content == '':\\n continue\\n\\n elif ctype == 'genre':\\n\\n # Fix detection of movies based on description as tvgids.nl sometimes \\n # categorises a movie as e.g. \\\"Komedie\\\", \\\"Misdaadkomedie\\\", \\\"Detectivefilm\\\". \\n genre = filter_line(content.title()) # Titlecase\\n if nocattrans:\\n programs[i]['genre'] = genre\\n elif (programs[i]['detail1'].lower().find('film') != -1 \\\\\\n or programs[i]['detail1'].lower().find('komedie') != -1)\\\\\\n and programs[i]['detail1'].lower().find('tekenfilm') == -1 \\\\\\n and programs[i]['detail1'].lower().find('animatiekomedie') == -1 \\\\\\n and programs[i]['detail1'].lower().find('filmpje') == -1:\\n programs[i]['genre'] = 'Film'\\n else:\\n try:\\n programs[i]['genre'] = cattrans[genre.lower()].title()\\n except LookupError:\\n programs[i]['genre'] = genre\\n\\n # Parse persons and their roles for credit info\\n elif ctype in roletrans:\\n programs[i]['credits'][roletrans[ctype]] = []\\n\\n persons = content_asis.split(',');\\n\\n for name in persons:\\n if name.find(':') != -1:\\n name = name.split(':')[1]\\n if name.find('-') != -1:\\n name = name.split('-')[0]\\n if name.find('e.a') != -1:\\n name = name.split('e.a')[0]\\n programs[i]['credits'][roletrans[ctype]].append(filter_line(name))\\n\\n elif ctype == 'bijzonderheden':\\n if content.find('Breedbeeld') != -1:\\n programs[i]['video']['breedbeeld'] = 1\\n if content.find('Zwart') != -1: \\n programs[i]['video']['blackwhite'] = 1\\n if content.find('Teletekst') != -1: \\n programs[i]['teletekst'] = 1\\n if content.find('Stereo') != -1: \\n programs[i]['stereo'] = 1\\n elif ctype == 'url':\\n programs[i]['infourl'] = filter_line(content)\\n elif ctype not in programs[i]:\\n # In unmatched cases, we still add the parsed type and content to the program details.\\n # Some of these will lead to xmltv output during the xmlefy_programs step\\n programs[i][filter_line(ctype)] = filter_line(content)\\n\\n # do not cache programming that is unknown at the time\\n # of fetching.\\n \\n if programs[i]['name'].lower() != 'onbekend':\\n program_cache.add(programs[i])\\n\\n log('\\\\ndone...\\\\n\\\\n', quiet)\\n \\n # done\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46c6041fcc932cbe89678330c2def05a\",\n \"score\": \"0.5073233\",\n \"text\": \"def parse_description_page(self, response):\\r\\n house_ad = response.meta['house_ad']\\r\\n house_ad['description'] = \\\" \\\".join(response.xpath('//div[@class=\\\"mar_t_20\\\"]/p/text()').extract())\\r\\n yield house_ad\",\n \"title\": \"\"\n },\n {\n \"docid\": \"913d57ef889ca5d9fafd4753b200e6c1\",\n \"score\": \"0.50694275\",\n \"text\": \"def find_desc(self, cols):\\n # go through the columns one at a time\\n for i in range(len(cols)):\\n # don't reuse columns already discovered\\n if i == self.date or i == self.amt:\\n continue\\n\\n # heuristic: descriptions are usually quoted\\n s = cols[i]\\n if s.startswith('\\\"') or s.startswith(\\\"'\\\"):\\n if \\\"Reference\\\" in s: # known exception\\n continue\\n self.desc = i\\n return True\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e45b9bff5c0fa2a9c9e59f5dcff853bf\",\n \"score\": \"0.5067044\",\n \"text\": \"def scrape(args):\\n\\n\\n import logging\\n log = logging.getLogger(\\\"root\\\")\\n log.info( \\\"Scraping RPI CS data.\\\" )\\n\\n\\n # Generate a BeautifulSoup object.\\n catalog_index_url = \\\"http://catalog.rpi.edu/content.php?filter[27]=CSCI&cur_cat_oid=13&navoid=313\\\"\\n catalog_index = requests.get( catalog_index_url )\\n soup = BeautifulSoup( catalog_index.text )\\n\\n # Identify the list of courses.\\n course_list = soup.find_all(\\n name=\\\"a\\\",\\n onclick=re.compile(\\\"showCourse.*\\\"))\\n\\n # Select only the catoid and coid.\\n catoid_re = re.compile(\\\"(?<=catoid=)\\\\d+\\\")\\n coid_re = re.compile(\\\"(?<=coid=)\\\\d+\\\")\\n\\n # Piecewise course page url.\\n course_url = \\\"http://catalog.rpi.edu/preview_course_nopop.php?catoid=%s&coid=%s\\\"\\n\\n # Fetch existing metadata objects from database.\\n university = META.get(\\\"school\\\").get(\\\"name\\\")\\n university = session.query(University)\\\\\\n .filter(University.name==university)\\\\\\n .first()\\n departments = {department.abbreviation.lower() : department\\n for department in session.query(Department)\\\\\\n .filter(Department.university==university)\\\\\\n .all()}\\n\\n # Identify relevant information for each course.\\n prereqs = {}\\n for course in course_list:\\n\\n # Generate metadata\\n log.debug(course.text)\\n full_title = re.compile(\\\"\\\\s+\\\").split(course.text)\\n prefix = full_title[0]\\n cnum = full_title[1]\\n title = ' '.join(full_title[3:])\\n title = title.replace(\\\"'\\\", \\\"\\\")\\n\\n # Identify coid to get description.\\n href = course['href']\\n catoid = catoid_re.search(href).group(0)\\n coid = coid_re.search(href).group(0)\\n\\n # Generate a BeautifulSoup object of the course description.\\n course_page = requests.get( course_url % (catoid, coid) )\\n course_soup = BeautifulSoup( course_page.text )\\n content = course_soup.find(class_=\\\"block_content\\\").hr.text\\n\\n # Clean up the description.\\n description = content\\n try:\\n description = description[:description.index(\\\"Credit Hours\\\")]\\n description = description[:description.index(\\\"When Offered\\\")]\\n except:\\n pass\\n\\n # Identify prerequisites\\n # TODO: Match these up with their database entries.\\n prereq_index = description.find(\\\"Prerequisit\\\")\\n if prereq_index > -1:\\n prereq_string = description[prereq_index:]\\n description = description[:prereq_index]\\n\\n prereq_re = re.compile(\\\"\\\\w{2,4}\\\\s\\\\d{3}\\\")\\n matches = re.findall(prereq_re, prereq_string)\\n if len(matches) > 0:\\n prereqs[\\\"%s %s\\\" % (prefix, cnum)] = matches\\n\\n # Clean the description string\\n description_raw = description\\n description = clean(description)\\n if description is None:\\n continue\\n\\n # Generate the appropriate course object.\\n departments[prefix.lower()].courses.append(Course(\\n number=cnum,\\n title=title,\\n description_raw=description_raw,\\n description=description))\\n\\n log.info( \\\"Completed scraping.\\\" )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"276bca30cabbd727e3ea49fdc300777e\",\n \"score\": \"0.5064829\",\n \"text\": \"def download_courses(courses):\\n for course in courses:\\n _pluradl(course)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"060d37c628c4851e08e1b39aab32b2f9\",\n \"score\": \"0.5061009\",\n \"text\": \"def extract_document_text():\\n all_document_pages = OfficialDocumentPage.objects.all()\\n print(f'Beginning extraction...')\\n\\n for page in all_document_pages:\\n # commenting out for now - print(f'Page id {page.id}')\\n # Check if page body already has content, if so skip\\n if len(page.body) > 0:\\n continue\\n if page.document and page.document.url:\\n filename = page.document.url.split('documents')[1]\\n print(f'reading {filename}')\\n extracted_text = extract_text_from_url(page.document.url)\\n # TODO: if the extracted_text is \\\"\\\", replace with message per content/OPO\\n page.body = extracted_text\\n page.save()\\n else:\\n print(f'Official Document Page with id {page.id} does not have a document')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af5f189a852872ea47102d52f5781ab9\",\n \"score\": \"0.5059057\",\n \"text\": \"def convert_to_osis(text: str, bookid: str = \\\"TEST\\\") -> Tuple[str, ...]:\\n # ---------------------------------------------------------------------- #\\n\\n description: List[str] = []\\n\\n # ---------------------------------------------------------------------- #\\n\\n # preprocessing and special spacing\\n text = c2o_preprocess(text)\\n\\n # split text into lines for processing\\n lines = text.split(\\\"\\\\n\\\")\\n\\n # mark introduction endings...\\n lines = markintroend(lines)\\n\\n for i in enumerate(lines):\\n # identification\\n lines[i[0]], description = c2o_identification(lines[i[0]], description)\\n\\n # character style formatting\\n lines[i[0]] = c2o_noterefmarkers(lines[i[0]])\\n lines[i[0]] = c2o_specialtext(lines[i[0]])\\n\\n # special features if present, and stray \\\\xt tags that were missed.\\n lines[i[0]] = c2o_specialfeatures(lines[i[0]])\\n\\n # z tags if present\\n lines[i[0]] = c2o_ztags(lines[i[0]])\\n\\n # paragraph style formatting.\\n lines[i[0]] = c2o_titlepar(lines[i[0]], bookid)\\n\\n # process words of Jesus\\n if r\\\"\\\\wj\\\" in text:\\n lines = c2o_processwj2(lines)\\n\\n # postprocessing of poetry, lists, tables, and sections\\n # to add missing tags and div's.\\n lines = c2o_fixgroupings(lines)\\n\\n # process chapter/verse markers\\n lines = c2o_chapverse(lines, bookid)\\n\\n # postprocessing to fix some issues that may be present\\n lines = c2o_postprocess(lines)\\n\\n descriptiontext = \\\"\\\\n\\\".join(description)\\n\\n # rejoin lines after processing\\n return \\\"\\\\n\\\".join([_ for _ in lines if _ != \\\"\\\"]), descriptiontext\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e75dd4e4d0eb5f987ade021a06fff632\",\n \"score\": \"0.50526416\",\n \"text\": \"def updateCoursesCSV():\\n with open(os.path.join(os.path.dirname(__file__), 'courses.csv'), 'w', newline='', encoding=\\\"UTF-8\\\") as file:\\n n = 300\\n writer = csv.writer(file, delimiter=',')\\n i = 1\\n courses = oisCourses.getNCourses(n, i)\\n while len(courses) != 0:\\n for c in courses:\\n if 'title' in c:\\n if 'et' in c['title']:\\n t = Text(c['title']['et'].lower())\\n t.tag_layer(['morph_analysis'])\\n writer.writerow([\\\" \\\".join([x[0] for x in t.morph_analysis.lemma]), c['code']])\\n elif 'en' in c['title']:\\n t = Text(c['title']['en'].lower())\\n t.tag_layer(['morph_analysis'])\\n writer.writerow([\\\" \\\".join([x[0] for x in t.morph_analysis.lemma]), c['code']])\\n i += n\\n courses = oisCourses.getNCourses(n, i)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7780c8b2cd65375745f271f63826712a\",\n \"score\": \"0.50322837\",\n \"text\": \"def get_description_and_comment(bibdocfiles):\\n description = None\\n comment = None\\n all_descriptions = [bibdocfile.get_description() for bibdocfile \\\\\\n in bibdocfiles\\n if bibdocfile.get_description() not in ['', None]]\\n if len(all_descriptions) > 0:\\n description = all_descriptions[0]\\n\\n all_comments = [bibdocfile.get_comment() for bibdocfile \\\\\\n in bibdocfiles\\n if bibdocfile.get_comment() not in ['', None]]\\n if len(all_comments) > 0:\\n comment = all_comments[0]\\n\\n return (description, comment)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b0bc43156b2e55bd8b0b9b1c90c4672\",\n \"score\": \"0.50264835\",\n \"text\": \"def get_questionnaire_description(self, questionnaire_data, keys):\\n keywords = self.get_description_keywords(keys)\\n excerpt_data = collections.defaultdict(str)\\n\\n for keyword in keywords:\\n for x in questionnaire_data.get(keyword.questiongroup, []):\\n if x.get(keyword.question):\\n for language, text in x[keyword.question].items():\\n excerpt_data[language] += '{} '.format(text)\\n return excerpt_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f22d2dc088fb1e01979c1937cae1dfe\",\n \"score\": \"0.50097275\",\n \"text\": \"def _parse_description(self, response):\\n description = response.xpath('//div[@class=\\\"content\\\"]/div[contains(@class, \\\"field-name-field-event-\\\")]/descendant-or-self::*')\\n descrip_str = ''\\n for d in description:\\n descrip_str = ' '.join([descrip_str, ' '.join(d.xpath('text()').extract())])\\n descrip_str = ' '.join([descrip_str, ' '.join(d.xpath('a/@href').extract())])\\n return descrip_str.strip()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65c0c0a15726a958dfd9962be03ea655\",\n \"score\": \"0.5003737\",\n \"text\": \"def record_story_chapters(self) -> None:\\n\\n id_regex = re.compile('chapter')\\n new_line_regex = re.compile('\\\\n')\\n chapters = self._soup.find_all('div', {'class': 'chapter', 'id': id_regex})\\n\\n for chapter in chapters:\\n # todo: need to add author notes in as well\\n chapter_object = Chapter()\\n\\n chap_name_container = chapter.find_all(True, {'class': 'chapter preface group'})[0]\\n chap_name = chap_name_container.find_all('h3')[0].text\\n chapter_object.name = new_line_regex.sub('', chap_name).strip()\\n\\n chap_text = chapter.find_all(True, {'class': 'userstuff'})[0]\\n chapter_object.raw_body = chap_text.prettify()\\n\\n # remove the invisible heading\\n heading = chapter.find_all('h3', {'class': ['landmark', 'heading']})\\n for element in heading:\\n element.decompose()\\n chapter_object.processed_body = chap_text.prettify()\\n chapter_object.word_count = len(chap_text.text.split())\\n\\n self._fanfic.add_chapter(chapter_object)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a0479265dc892a8744d38e96c5ab930\",\n \"score\": \"0.4978152\",\n \"text\": \"def _parse_description(self, response):\\n category_field = response.xpath(\\n \\\"//div[contains(., 'Category:') and contains(@class, 'field-label')]\\\"\\n )\\n field_items = category_field.xpath(\\\"./following::div[contains(@class, 'field-items')]\\\")\\n return ' '.join(\\n field_items.xpath('.//p/text()').extract() +\\n field_items.xpath('.//strong/text()').extract()\\n ).strip()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af3583bf5c8d16410772762aff738e46\",\n \"score\": \"0.49771658\",\n \"text\": \"def read_docs() -> Generator[Doc, None, None]:\\n yield from all_para(DOCS.normal)\\n yield from wq_para(DOCS.csv)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87571a626bb2e6e0dc87f0dcc06400d8\",\n \"score\": \"0.4975917\",\n \"text\": \"def get_meta_data(self):\\n # results = db.documents.find({\\\"url\\\": self.url})\\n #\\n # if len(results) > 0:\\n # results = results[0]\\n # document_id = results['_id']\\n\\n\\n\\n document_cursor = db.documents.find_one({\\\"url\\\": self.url})\\n if document_cursor:\\n document_id = document_cursor[\\\"_id\\\"]\\n else:\\n document_id = None\\n if document_id:\\n self.db_doc = db.documents.find_one({\\\"_id\\\": document_id})\\n\\n self.title = self.db_doc[\\\"title\\\"]\\n self.authors = self.db_doc[\\\"authors\\\"]\\n self.text = self.db_doc[\\\"text\\\"]\\n self.published = self.db_doc[\\\"publish-date\\\"]\\n # try:\\n # self.images = self.db_doc[\\\"images\\\"]\\n # except:\\n # print(\\\"images for article \\\" + self.url + \\\" not collected from database\\\")\\n\\n try:\\n self.summary = self.db_doc[\\\"summary\\\"]\\n except:\\n print(\\\"summary for article \\\" + self.url + \\\" not collected from database\\\")\\n\\n try:\\n self.keywords = self.db_doc[\\\"keywords\\\"]\\n except:\\n print(\\\"keywords for article \\\" + self.url + \\\" not collected from database\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"803c4c0d28f19685353711f81fe448e7\",\n \"score\": \"0.49539548\",\n \"text\": \"def get_wiki_description(browser, city, wiki_collection):\\n url = 'https://en.wikipedia.org/wiki/' + city.replace(' ', '_')\\n try:\\n browser.get(url)\\n except:\\n print(f'{city} NOT found on Wikipedia')\\n summary = ''\\n for i in range(1, 100):\\n paragraphs = '//*[@id=\\\"mw-content-text\\\"]/div/p[' + str(i) + ']'\\n try:\\n summary += browser.find_element_by_xpath(paragraphs).text\\n summary += '\\\\n'\\n except:\\n None\\n wiki_dict = {'city': city, 'text': summary}\\n wiki_collection.insert_one(wiki_dict)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c46a365b413d6fca939ba14b38ce122f\",\n \"score\": \"0.4953722\",\n \"text\": \"def __get_description(self, catalog_entry):\\n desc = ''\\n\\n if catalog_entry.program_description_clean is not None:\\n desc = catalog_entry.program_description_clean\\n\\n return desc\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a16a095ba66b434600cbc9f382ea6198\",\n \"score\": \"0.49533728\",\n \"text\": \"def set_course_info(self):\\n host = \\\"courses.uscden.net\\\"\\n course_info_url = \\\"/d2l/le/manageCourses/widget/myCourses/\\\" + self.location_id + \\\"/ContentPartial?defaultLeftRightPixelLength=10&defaultTopBottomPixelLength=7\\\"\\n\\n # prepare post headers\\n course_info_post_headers = {\\n #'Host': ' courses.uscden.net',\\n #'Connection': ' keep-alive',\\n # 'Content-Length': ' 229',\\n # 'Pragma': ' no-cache',\\n # 'Cache-Control': ' no-cache',\\n #'Origin': ' https://courses.uscden.net',\\n #'User-Agent': ' Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_4)',\\n 'Content-type': ' application/x-www-form-urlencoded',\\n #'Accept': '*/*',\\n #'Referer': 'https://courses.uscden.net/d2l/home/'+self.location_id,\\n # 'Accept-Encoding': 'gzip, deflate, br',\\n # 'Accept-Language': 'zh-CN,zh;q=0.8',\\n 'Cookie': self.d2lSessionVal + '; ' + self.d2lSecureSessionVal\\n }\\n # prepare post data\\n course_info_post_dict = {\\n 'widgetId': '2',\\n #'_d2l_prc$headingLevel': '3',\\n #'_d2l_prc$scope': \\\"\\\",\\n #'_d2l_prc$childScopeCounters': 'filtersData:0',\\n #'_d2l_prc$hasActiveForm': 'false',\\n #'filtersData$semesterId': 'All',\\n #'isXhr': 'true',\\n #'requestId': '2',\\n 'd2l_referrer': self.xsrf\\n }\\n course_info_post_data = urllib.urlencode(course_info_post_dict)\\n conn = httplib.HTTPSConnection(host, 443)\\n conn.request(\\\"POST\\\", course_info_url, course_info_post_data, course_info_post_headers)\\n resp = conn.getresponse()\\n course_data = resp.read()\\n self.term_list = Student.find_term_list(course_data)\\n term_num = len(self.term_list)\\n self.course_ou_list = [[]]*term_num\\n self.course_list = [[]]*term_num\\n if term_num == 0:\\n return\\n elif term_num == 1:\\n stop_index = len(course_data)\\n start_index = 0\\n else:\\n start_index = 0\\n stop_index = re.compile(self.term_list[1]).search(course_data).start()\\n\\n for i in range(term_num):\\n course_list_raw = re.compile(r'vui-link d2l-link \\\\S+ href\\\\=\\\\\\\\\\\\\\\"/d2l/lp/ouHome/home\\\\.d2l\\\\?ou=\\\\d+\\\\\\\\\\\\\\\" title\\\\=\\\\\\\\\\\\\\\"[^\\\"^\\\\\\\\]+').findall(course_data[start_index:stop_index])\\n ou_list = []\\n c_list = []\\n for c in course_list_raw:\\n ou_list.append(re.compile(r'ou=\\\\d+').search(c).group()[3:])\\n c_list.append(re.compile(r'title\\\\=\\\\\\\\\\\\\\\"[^\\\"]+').search(c).group()[14:].replace('&', '&'))\\n\\n self.course_ou_list[i] = ou_list\\n self.course_list[i] = c_list\\n start_index = stop_index\\n if i+2 < term_num:\\n stop_index = re.compile(self.term_list[i+2]).search(course_data).start()\\n else:\\n stop_index = len(course_data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a79924e4b513672156a4fcd0eb7459dc\",\n \"score\": \"0.4949158\",\n \"text\": \"def crawl_page(page, dept_iter, term_iter):\\n tree = fromstring(page.content)\\n num_courses = get_number_offered_for_term(tree)\\n\\n # iterates through a department during a specific term\\n for i in range(1, num_courses + 1):\\n course = Course(collect_dept(dept_iter), collect_nums(tree, i), collect_name(tree, i), collect_terms(term_iter),\\n collect_reqs(tree, i), collect_period(tree, i), collect_professor(tree, i), collect_desc(tree, i))\\n master_list.append(course)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f7b36347f98feb6a17bcdab4d454fa3\",\n \"score\": \"0.49249998\",\n \"text\": \"def _generate_description(self):\\n generations = []\\n\\n for j, templ in enumerate(self.template):\\n result = []\\n\\n for key in templ:\\n # get the text form from template object.\\n item = key.generate_description(arg_index=0, templ_index=j)\\n\\n if not item:\\n continue\\n\\n # Flatten if nested dict.\\n if type(item) in [OrderedDict, dict]:\\n val_list = list(values_of_nested_dict(item))\\n result.extend(val_list)\\n else:\\n result.append(item)\\n generations.append(\\\" \\\".join(result))\\n\\n return generations\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5351e0026bc35ac92d8cc554caa89815\",\n \"score\": \"0.49216616\",\n \"text\": \"def _read_description(self, geocache_tree):\\n\\n description_short = \\\"\\\"\\n if self.source == \\\"downloader\\\":\\n description_short = geocache_tree.find(\\\".//{http://www.groundspeak.com/cache/1/0}short_description\\\").text\\n elif self.source == \\\"geocaching.com\\\":\\n description_short = geocache_tree.find(\\\".//{http://www.groundspeak.com/cache/1/0/1}short_description\\\").text\\n if description_short:\\n description_short = ownfunctions.replace_signs(description_short)\\n else:\\n description_short = \\\"\\\"\\n\\n description_long = \\\"\\\"\\n if self.source == \\\"downloader\\\":\\n description_long = geocache_tree.find(\\\".//{http://www.groundspeak.com/cache/1/0}long_description\\\").text\\n elif self.source == \\\"geocaching.com\\\":\\n description_long = geocache_tree.find(\\\".//{http://www.groundspeak.com/cache/1/0/1}long_description\\\").text\\n if description_long:\\n description_long = ownfunctions.replace_signs(description_long)\\n else:\\n description_long = \\\"\\\"\\n\\n return description_short + \\\"\\\\n\\\\n\\\" + description_long\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c6612107331ba2d7b8313053b20069f\",\n \"score\": \"0.49178073\",\n \"text\": \"def generate_documentation(self):\\n information_translator = bkrdoc.analysis.GetInformation()\\n for information in self.documentation_units:\\n if information:\\n self.phase_documentation_information.append(information_translator.get_information_from_facts(information))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8fd6f34f94b1688dbb1ce77ceb8b3796\",\n \"score\": \"0.4910362\",\n \"text\": \"def truncate_descriptions_task():\\n articles = Article.objects.all()\\n\\n for article in articles:\\n logger.info(\\\"Truncating Description for \\\" + article.title)\\n\\n article.description_truncated = truncatewords_html(article.description, 150)\\n\\n article.save()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7df42da4fb1268986e059e1fca811b52\",\n \"score\": \"0.49091297\",\n \"text\": \"def parse_course_data(filename):\\r\\n created_courses = {}\\r\\n with open(filename, 'r') as open_file:\\r\\n\\r\\n for line in open_file:\\r\\n line = line.strip()\\r\\n courses = line.split()\\r\\n if line != \\\"\\\" and len(courses) == 2:\\r\\n # If the course does not exist, create a new course\\r\\n if courses[1] not in created_courses:\\r\\n # Create Course object and store in dictionary\\r\\n created_courses[courses[1]] = Course(courses[1])\\r\\n if courses[0] not in created_courses:\\r\\n created_courses[courses[0]] = Course(courses[0])\\r\\n\\r\\n # If courses[0] is not a prereq of courses[1], add courses[0]\\r\\n # as a prereq to courses[1]\\r\\n if not created_courses[courses[0]].\\\\\\r\\n is_prereq(created_courses[courses[1]]):\\r\\n created_courses[courses[1]].\\\\\\r\\n add_prereq(created_courses[courses[0]])\\r\\n\\r\\n # Return the root course\\r\\n return get_root_course(created_courses)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"180d088e1442715c629c6b13cd931b89\",\n \"score\": \"0.49062607\",\n \"text\": \"def cal_required_elective_courses_forstudent(self):\\n path1:str=os.path.join(self._path, \\\"students.txt\\\")\\n try:\\n open_file1 = file_reader(path1, fields=3, sep=\\\"\\\\t\\\",header=True)\\n for cwid,name,major in open_file1:\\n self._students[cwid].add_courses(self.mainmajor[major].result_return_major())\\n\\n except ValueError as e:\\n print(e)\\n except FileNotFoundError as fe:\\n print(fe)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6fabfd6de1117c0d02c7868ac6c94201\",\n \"score\": \"0.49056607\",\n \"text\": \"def __get_description_types(self):\\n self.description_type_desc, created_desc = ProgramDescriptionType.objects.get_or_create(\\n name='Catalog Description'\\n )\\n\\n if created_desc:\\n self.stdout.write(\\n self.style.NOTICE(\\n \\\"Created \\\\\\\"Catalog Description\\\\\\\" description type\\\"\\n )\\n )\\n\\n self.description_type_desc_full, created_desc_full = ProgramDescriptionType.objects.get_or_create(\\n name='Full Catalog Description'\\n )\\n\\n if created_desc_full:\\n self.stdout.write(\\n self.style.NOTICE(\\n \\\"Created \\\\\\\"Full Catalog Description\\\\\\\" description type\\\"\\n )\\n )\\n\\n self.description_type_source_desc, created_source_desc = ProgramDescriptionType.objects.get_or_create(\\n name='Source Catalog Description'\\n )\\n\\n if created_source_desc:\\n self.stdout.write(\\n self.style.NOTICE(\\n \\\"Created \\\\\\\"Source Catalog Description\\\\\\\" description type\\\"\\n )\\n )\\n\\n self.description_type_source_curriculum, created_source_curriculum = ProgramDescriptionType.objects.get_or_create(\\n name='Source Catalog Curriculum'\\n )\\n\\n if created_source_curriculum:\\n self.stdout.write(\\n self.style.NOTICE(\\n \\\"Created \\\\\\\"Source Catalog Curriculum\\\\\\\" description type\\\"\\n )\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bbe0e92e7e90c7f1f29fbbc2db863778\",\n \"score\": \"0.4905648\",\n \"text\": \"def get_courses(self, browser):\\n logger.info(\\\"Checking pages on category {}\\\".format(self.name))\\n for i in range(1, self.maxpages + 1):\\n tries = 0\\n done = False\\n clean_courses = []\\n while not done:\\n if not os.path.exists(\\\"files_{}/courses_{}_{}.csv\\\".format(\\n self._get_datetime_now(),\\n self.name,\\n i)\\n ):\\n try:\\n browser.get(\\\"{}?p={}\\\".format(self.url, i))\\n courses = browser.find_elements_by_class_name(\\n \\\"browse-course-card--link--3KIkQ\\\"\\n )\\n for course in courses:\\n url = course.get_attribute(\\\"href\\\")\\n detail = course.find_element_by_class_name(\\n \\\"course-card--large--1BVxY\\\"\\n )\\n name_course = detail.find_element_by_class_name(\\n \\\"udlite-focus-visible-target\\\"\\n ).text\\n prize = detail.find_element_by_class_name(\\n \\\"price-text--price-part--Tu6MH\\\"\\n ).text.split(\\\"\\\\n\\\")[1]\\n rank = detail.find_element_by_class_name(\\n \\\"course-card--row--1OMjg\\\"\\n ).find_element_by_class_name(\\n \\\"star-rating--rating-number--3lVe8\\\"\\n ).text\\n c = Course(\\n self.name,\\n name_course,\\n prize,\\n url,\\n rank\\n )\\n clean_courses.append(c)\\n self._save_courses(self.name, i, clean_courses)\\n done = True\\n except NoSuchElementException:\\n tries += 1\\n logger.warning(\\n 'Page {} on {} tried {}/5'.format(\\n i,\\n self.name,\\n tries\\n )\\n )\\n if tries >= 5:\\n done = True\\n logger.error('Page {} on {} failed.'.format(\\n i,\\n self.name\\n )\\n )\\n else:\\n done = True\\n self.ready += 1\\n browser.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7624b2c2800da4b643921f869f5a9bae\",\n \"score\": \"0.48943287\",\n \"text\": \"def update_description(self):\\n exec_string = \\\"DELETE FROM info WHERE name='description'\\\" \\n if DEBUG_LEVEL > 0:\\n print (\\\"execute db : {0}\\\".format(exec_string))\\n self.db_cursor.execute(exec_string)\\n exec_string = \\\"\\\"\\\"INSERT INTO info VALUES ( 'description', '{0}' )\\\"\\\"\\\".format(self.get_db_description_text())\\n if DEBUG_LEVEL > 0:\\n print (\\\"execute db : {0}\\\".format(exec_string))\\n self.db_cursor.execute(exec_string)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e538237b64b327b0b7cf9bd31c4e47e3\",\n \"score\": \"0.48829094\",\n \"text\": \"def read_descriptions(self):\\r\\n if not os.path.exists(self.descriptions_path):\\r\\n logger.warning(f'descriptions path does not exist.')\\r\\n self.descriptions = pd.DataFrame()\\r\\n else:\\r\\n self.descriptions = pd.read_excel(self.descriptions_path)\\r\\n self.descriptions = self.descriptions.set_index('Unnamed: 0', drop=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6fc41182ef098a8f8ab1a022467ffec0\",\n \"score\": \"0.48779327\",\n \"text\": \"def parse_courses(self):\\n with open(self.data_dir + 'courses.csv', newline='') as csvfile:\\n course_reader = csv.DictReader(csvfile)\\n course_dict = dict()\\n for course in course_reader:\\n course_dict[course['id']] = {'name': course['name'], 'teacher': course['teacher']}\\n return course_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65c59949da1dab84e9cc920e06970308\",\n \"score\": \"0.48749024\",\n \"text\": \"def test_get_course_for_section(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69a51e2ab27acd97e7657575ab7943a0\",\n \"score\": \"0.48741475\",\n \"text\": \"def description(self):\\n lines = []\\n for line in self.__doc__.split('\\\\n')[2:]:\\n line = line.strip()\\n if line:\\n lines.append(line)\\n return ' '.join(lines)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"282ae5120449f2beb94d20354ae04318\",\n \"score\": \"0.48621464\",\n \"text\": \"def add_courses_to_calendar(self):\\n for course in self.target_parser.courses:\\n for meeting in course.meeting_times:\\n self.ics_calendar.add_component(self.create_course(course, meeting))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dcb19fa1a96f80fe0b60bcc98b51e3fc\",\n \"score\": \"0.48576903\",\n \"text\": \"def coursetofile(self, filename):\\n print('\\\\nWriting course to the file ' + filename)\\n f = open(filename, \\\"w+\\\")\\n f.write(self.title_full + \\\"\\\\n\\\")\\n for section in self.sections:\\n f.write(section.title + \\\"\\\\n\\\")\\n for session in section.sessions:\\n f.write(session.title + \\\"\\\\n\\\")\\n f.write(session.content + \\\"\\\\n\\\")\\n f.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0375c53be35232c39beacffbe1aaa42\",\n \"score\": \"0.48576567\",\n \"text\": \"def get_description():\\n desc = {}\\n desc[\\\"data\\\"] = False\\n desc[\\n \\\"description\\\"\\n ] = \\\"\\\"\\\"This application attempts to assess the\\n effectiveness of a calendar day's rainfall based on where the rain fell\\n in relation to a previous period of days departure from climatology. So\\n for a given date and state, the areal coverage of daily precipitation\\n at some given threshold is compared against the departure from climatology\\n over some given number of days. The intention is to answer a question like\\n how much of the rain on a given day fell on an area that needed it! The\\n areal coverage percentages are relative to the given state.\\n \\\"\\\"\\\"\\n today = datetime.datetime.today() - datetime.timedelta(days=1)\\n desc[\\\"arguments\\\"] = [\\n dict(\\n type=\\\"csector\\\", name=\\\"sector\\\", default=\\\"IA\\\", label=\\\"Select Sector:\\\"\\n ),\\n dict(\\n type=\\\"date\\\",\\n name=\\\"date\\\",\\n default=today.strftime(\\\"%Y/%m/%d\\\"),\\n label=\\\"Date:\\\",\\n min=\\\"2011/01/01\\\",\\n ),\\n dict(\\n type=\\\"int\\\",\\n name=\\\"trailing\\\",\\n default=31,\\n label=\\\"Over how many trailing days to compute departures?\\\",\\n ),\\n dict(\\n type=\\\"float\\\",\\n name=\\\"threshold\\\",\\n default=0.1,\\n label=\\\"Date Precipitation Threshold (inch)\\\",\\n ),\\n ]\\n return desc\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77baa2d5123a53ce4c045045a4e59aeb\",\n \"score\": \"0.48502633\",\n \"text\": \"def get_course_summary(self, course_dict: Dict[str, str]) -> Tuple[Set[str], Set[str], Set[str]]:\\n courses = set(\\n [course for course, grade in course_dict.items() if grade in self.passing_grades])\\n\\n if courses.intersection(self._electives) == set():\\n return courses, self._required.difference(courses), self._electives\\n else:\\n return courses, self._required.difference(courses), {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e12c9c486cb917772206e1de036a367\",\n \"score\": \"0.4848594\",\n \"text\": \"def collect_description_data(self, language):\\n return [\\n [tmf('label_task_title', u'Task title'), self.context.title],\\n [tmf('label_deadline', u'Deadline'),\\n api.portal.get_localized_time(str(self.context.deadline))],\\n [tmf('label_text', u'Text'), self.context.text]\\n ]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f97ebbc0527ae2088b6037e83a87ba4\",\n \"score\": \"0.4839508\",\n \"text\": \"def get_description(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0645d9a91825a5fdc3fc552cd14492d\",\n \"score\": \"0.48346865\",\n \"text\": \"def test_templates_organization_detail_meta_description_description(self):\\n organization = OrganizationFactory()\\n page = organization.extended_object\\n\\n # Add a description to the organization\\n placeholder = organization.extended_object.placeholders.get(slot=\\\"description\\\")\\n add_plugin(\\n language=\\\"en\\\",\\n placeholder=placeholder,\\n plugin_type=\\\"CKEditorPlugin\\\",\\n body=\\\" A <b>longer</b> organization description \\\",\\n )\\n page.publish(\\\"en\\\")\\n\\n url = organization.extended_object.get_absolute_url()\\n response = self.client.get(url)\\n self.assertEqual(response.status_code, 200)\\n\\n self.assertContains(\\n response,\\n '<meta name=\\\"description\\\" content=\\\"A longer organization description\\\" />',\\n )\\n\\n # Add an excerpt to the organization\\n placeholder = organization.extended_object.placeholders.get(slot=\\\"excerpt\\\")\\n add_plugin(\\n language=\\\"en\\\",\\n placeholder=placeholder,\\n plugin_type=\\\"PlainTextPlugin\\\",\\n body=\\\" A longer organization excerpt \\\",\\n )\\n page.publish(\\\"en\\\")\\n\\n response = self.client.get(url)\\n self.assertEqual(response.status_code, 200)\\n\\n self.assertContains(\\n response,\\n '<meta name=\\\"description\\\" content=\\\"A longer organization excerpt\\\" />',\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"382cf1d548ca1c77825ae9f942029bf3\",\n \"score\": \"0.48342228\",\n \"text\": \"def __make_description_with_segments(self):\\n\\n html = []\\n desc_result = self.resource.select.read(self.resource.type_name + \\\"_Description\\\",\\n where=[[\\\"Description_ID\\\",\\n \\\"=\\\",\\n self.resource.description_id]]\\n )\\n values = desc_result.fetchone()\\n station_1_id = values['Station_1_ID']\\n station_2_id = values['Station_2_ID']\\n connection_id = values['Connection_ID']\\n\\n\\n html.append(\\\"<h2>Station A</h2>\\\")\\n html.append(self.__make_generic_module(feature=None,\\n table_name=self.resource.type_name + \\\"_Station_Description\\\",\\n _id=(\\\"Station_ID\\\", station_1_id), dual=1))\\n\\n html.append(\\\"<h2>\\\" + str(self.resource.type_name) + \\\"</h2>\\\")\\n html.append(self.__make_generic_module(feature=None,\\n table_name=self.resource.type_name + \\\"_Connection_Description\\\",\\n _id=(\\\"Connection_ID\\\", connection_id)))\\n html.append(\\\"<h2>Station B</h2>\\\")\\n html.append(self.__make_generic_module(feature=None,\\n table_name=self.resource.type_name + \\\"_Station_Description\\\",\\n _id=(\\\"Station_ID\\\", station_2_id), dual=2))\\n\\n return \\\"\\\".join(html)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be2a17665c9cac8ea9103c912052d232\",\n \"score\": \"0.4829322\",\n \"text\": \"def _extract_info(self, content, capturing_group):\\n return re.match(self.configuration[\\\"COURSE_REGEX\\\"], content).group(\\n capturing_group\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c810949def2b395b520142c4ff952bf8\",\n \"score\": \"0.48208857\",\n \"text\": \"def sort_document_properties():\\n config = Config()\\n\\n LOGGER.info('Parsing documents')\\n for collection in config.collections():\\n LOGGER.info('Collection: %s', collection.name)\\n for path, document in collection.documents():\\n LOGGER.info(\\\"Writing fixed file %s\\\", path)\\n with codecs.open(path, encoding='utf-8', mode='w') as f:\\n f.write(object2jekyll(document, 'description'))\\n LOGGER.warning('Done')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19cbc4f208feff641088407bad65f831\",\n \"score\": \"0.4816295\",\n \"text\": \"def descriptions(self, descriptions):\\n\\n self._descriptions = descriptions\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19cbc4f208feff641088407bad65f831\",\n \"score\": \"0.4816295\",\n \"text\": \"def descriptions(self, descriptions):\\n\\n self._descriptions = descriptions\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d62dc8b188de63a7d6478d9bea414c2\",\n \"score\": \"0.48134148\",\n \"text\": \"def getEditions(TOChtml,docDic,language,date):\\n sectionNames = [\\\"ANDERE BESLUITEN\\\",\\\"WETTEN, DECRETEN, ORDONNANTIES EN VERORDENINGEN\\\",\\\"OFFICIELE BERICHTEN\\\",\\\"AGENDA'S\\\",\\\"AUTRES ARRETES\\\",\\\"LOIS, DECRETS, ORDONNANCES ET REGLEMENTS\\\",\\\"AVIS OFFICIELS\\\",\\\"ORDRES DU JOUR\\\"]\\n regionNames = ['VLAAMSE GEMEENSCHAP','DUITSTALIGE GEMEENSCHAP','WAALS GEWEST','BRUSSELS HOOFDSTEDELIJK GEWEST','FRANSE GEMEENSCHAP','VLAAMS GEWEST','REGION WALLONNE','REGION DE BRUXELLES-CAPITALE','COMMUNAUTE FLAMANDE',u'COMMUNAUTE FRAN\\\\xc7AISE','COMMUNAUTE GERMANOPHONE','REGION FLAMANDE',\\\"DEUTSCHSPRACHIGE GEMEINSCHAFT\\\",\\\"WALLONISCHE REGION\\\",\\\"DEUTSCHPRACHIGE GEMEINSCHAFT\\\"]\\n levelNames = ['GEMEENSCHAPS- EN GEWESTREGERINGEN','GOUVERNEMENTS DE COMMUNAUTE ET DE REGION','GEMEINSCHAFTS- UND REGIONALREGIERUNGEN']\\n editions = TOChtml.split(\\\"<a name=EDITION\\\")\\n for edition in editions:\\n if re.match(\\\"\\\\d>EDITI\\\",edition):\\n curEdition = edition[0]\\n else:\\n curEdition = '0'\\n regex = re.compile(\\\"<A name=(\\\\d+)></a>(.+?)<input type=submit name=numac value=(\\\\d+)\\\",re.DOTALL)\\n articles = regex.findall(edition)\\n curSection = 'NA'\\n curRegion = 'NA'\\n curSource = 'NA'\\n curLevel = 'NA'\\n curTitle = 'NA'\\n url = 'NA'\\n for article in articles:\\n if (len(article) == 3):\\n if article[0] == article[2]:\\n numac = article[0]\\n lines = stripHTMLTags(article[1])\\n lines = lines.split('\\\\n')\\n title = []\\n section = []\\n region = []\\n level = []\\n source = []\\n for line in lines:\\n line = line.strip()\\n if (len(line) > 0) and (line[0] != '<'):\\n if float(len(re.findall('[A-Z]',line)))/float(len(line))> 0.5: # if it is a Section title or source\\n if line in sectionNames:\\n section.append(line)\\n elif line in regionNames:\\n region.append(line)\\n elif line in levelNames:\\n level.append(line)\\n else:\\n source.append(line)\\n else:\\n title.append(line)\\n if len(section) > 0:\\n curSection = ' '.join(section)\\n curRegion = 'NA'\\n curSource = 'NA'\\n if len(level) > 0:\\n curLevel = ' '.join(level)\\n curRegion = 'NA'\\n curSource = 'NA'\\n if len(region) > 0:\\n curRegion = ' '.join(region)\\n curSource = 'NA'\\n if len(source) > 0:\\n curSource = ' '.join(source)\\n if len(title) > 0:\\n curTitle = ' '.join(title)\\n artID = date+'-'+str(numac)\\n if not docDic.has_key(artID):\\n docDic[artID] = {'nl':{},'fr':{},'du':{}}\\n docDic[artID][language]['edition'] = curEdition\\n docDic[artID][language]['section'] = curSection\\n docDic[artID][language]['region'] = curRegion\\n docDic[artID][language]['source'] = curSource\\n docDic[artID][language]['title'] = curTitle\\n docDic[artID][language]['article'] = getArticle(date, numac,language)\\n url = str(\\\"http://www.ejustice.just.fgov.be/cgi/article_body.pl?\\\" +\\\"language=\\\"+language+\\\"&caller=summary&pub_date=\\\" + date + \\\"&numac=\\\" + numac)\\n docDic[artID][language]['url'] = url\\n curTitle = 'NA'\\n url = 'NA'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"729f420148a4616ccc25da8701325143\",\n \"score\": \"0.48089322\",\n \"text\": \"def scrape_course(self, course):\\n print(course)\\n\\n department = course[:course.index(\\\" \\\")]\\n course_code = course[course.index(\\\" \\\") + 1:]\\n\\n self.driver.get(\\\"https://thecriticalreview.org/search/\\\" + department + \\\"/\\\" + course_code)\\n soup = BeautifulSoup(self.driver.page_source, 'lxml')\\n\\n course_review_info = {}\\n course_review_info[\\\"courseCode\\\"] = course\\n\\n try:\\n # no reviews for the course\\n no_reviews = self.driver.find_element_by_css_selector(\\\"#unreturned_banner_message\\\")\\n self.courses.append(course_review_info)\\n except NoSuchElementException:\\n course_rating = soup.find('div', class_=\\\"c_rating\\\")[\\\"data-num\\\"]\\n course_review_info[\\\"courseRating\\\"] = course_rating\\n\\n course_data = soup.find('div', class_=\\\"review_data\\\")\\n reviews = course_data[\\\"data-test-value\\\"]\\n\\n # demographic data\\n course_review_info[\\\"freshmen\\\"] = course_data[\\\"data-frosh\\\"]\\n course_review_info[\\\"sophomores\\\"] = course_data[\\\"data-soph\\\"]\\n course_review_info[\\\"juniors\\\"] = course_data[\\\"data-jun\\\"]\\n course_review_info[\\\"seniors\\\"] = course_data[\\\"data-sen\\\"]\\n course_review_info[\\\"gradStudents\\\"] = course_data[\\\"data-grad\\\"]\\n\\n if reviews == \\\"\\\":\\n self.courses.append(course_review_info)\\n return\\n\\n reviews = json.loads(reviews)\\n\\n # hours spent on course\\n if \\\"minhours\\\" in reviews:\\n course_review_info[\\\"avgHrs\\\"] = self.avg(reviews[\\\"minhours\\\"])\\n if \\\"maxhours\\\" in reviews:\\n course_review_info[\\\"maxHrs\\\"] = self.avg(reviews[\\\"maxhours\\\"])\\n\\n # course feedback statistics\\n if \\\"readings\\\" in reviews:\\n course_review_info[\\\"readingsWorthwhile\\\"] = self.avg(reviews[\\\"readings\\\"])\\n if \\\"class-materials\\\" in reviews:\\n course_review_info[\\\"materialsUseful\\\"] = self.avg(reviews[\\\"class-materials\\\"])\\n if \\\"difficult\\\" in reviews:\\n course_review_info[\\\"difficult\\\"] = self.avg(reviews[\\\"difficult\\\"])\\n if \\\"learned\\\" in reviews:\\n course_review_info[\\\"learnedALot\\\"] = self.avg(reviews[\\\"learned\\\"])\\n if \\\"loved\\\" in reviews:\\n course_review_info[\\\"enjoyedCourse\\\"] = self.avg(reviews[\\\"loved\\\"])\\n if \\\"grading-speed\\\" in reviews:\\n course_review_info[\\\"timelyGrading\\\"] = self.avg(reviews[\\\"grading-speed\\\"])\\n if \\\"grading-fairness\\\" in reviews:\\n course_review_info[\\\"fairGrading\\\"] = self.avg(reviews[\\\"grading-fairness\\\"])\\n\\n # concentrator demographics\\n if \\\"conc\\\" in reviews:\\n course_review_info[\\\"concentratorYes\\\"] = self.count_elem(reviews[\\\"conc\\\"], \\\"C\\\")\\n course_review_info[\\\"concentratorNo\\\"] = self.count_elem(reviews[\\\"conc\\\"], \\\"N\\\")\\n course_review_info[\\\"concentratorMaybe\\\"] = self.count_elem(reviews[\\\"conc\\\"], \\\"D\\\")\\n\\n # taken as requirement?\\n if \\\"requirement\\\" in reviews:\\n course_review_info[\\\"requirementYes\\\"] = self.count_elem(reviews[\\\"requirement\\\"], \\\"Y\\\")\\n course_review_info[\\\"requirementNo\\\"] = self.count_elem(reviews[\\\"requirement\\\"], \\\"N\\\")\\n\\n # expected grades\\n if \\\"grade\\\" in reviews:\\n course_review_info[\\\"expectedA\\\"] = self.count_elem(reviews[\\\"grade\\\"], \\\"A\\\")\\n course_review_info[\\\"expectedB\\\"] = self.count_elem(reviews[\\\"grade\\\"], \\\"B\\\")\\n course_review_info[\\\"expectedC\\\"] = self.count_elem(reviews[\\\"grade\\\"], \\\"C\\\")\\n course_review_info[\\\"expectedS\\\"] = self.count_elem(reviews[\\\"grade\\\"], \\\"S\\\")\\n course_review_info[\\\"expectedNC\\\"] = self.count_elem(reviews[\\\"grade\\\"], \\\"NC\\\")\\n\\n self.courses.append(course_review_info)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72c1fc14666f2a7ed3fc3717646458c8\",\n \"score\": \"0.48037595\",\n \"text\": \"def fetch_desc_costco(self):\\n description = ''\\n try:\\n \\t#Extract description from URL for costco\\n link = self.product_link.replace(\\\"https://www.costco.com/\\\",\\\"\\\")\\n for ch in link:\\n if(ch!=\\\".\\\"):\\n description += ch\\n else:\\n break\\n description = description.replace('-',' ')\\n except:\\n description = ''\\n return description\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf59ed3dad0a1903e1a6183aed5ff7c3\",\n \"score\": \"0.4800775\",\n \"text\": \"def getDescription(self, metadata):\\n description = '== {{int:filedesc}} ==\\\\n'\\n description += '{{Artwork|wikidata=%(item)s}}\\\\n' % metadata\\n description += '\\\\n=={{int:license-header}}==\\\\n'\\n description += '{{Yale Digital Dura-Europos Archive}}\\\\n'\\n description += '{{Cc-zero}}\\\\n'\\n description += '[[Category:Objects from Dura Europos in the Yale University Art Gallery]]\\\\n'\\n return description\",\n \"title\": \"\"\n },\n {\n \"docid\": \"298155518858ea76b8613941f44817e3\",\n \"score\": \"0.4797285\",\n \"text\": \"def load_description(path_dir=PATH_ROOT, filename='DOCS.md'):\\n with open(os.path.join(path_dir, filename)) as f:\\n long_description = f.read()\\n return long_description\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97922e8004d07b7a9eb322a546e613b0\",\n \"score\": \"0.4788354\",\n \"text\": \"def _write_grid_description(self):\\n logging.info(\\\"\\\\t\\\\t Writing echam6 grid description to generic file atmosphere.griddes...\\\")\\n logging.info(\\\"\\\\t\\\\t * generatic griddes\\\")\\n griddes = self.CDO.griddes(input=self.files[\\\"couple\\\"][\\\"atmosphere_file_for_ice\\\"].src)\\n ofile = open(self.couple_dir+\\\"/griddes_file\\\", \\\"w\\\")\\n ofile.write(\\\"\\\\n\\\".join(griddes))\\n ofile.flush()\\n self.files[\\\"couple\\\"][\\\"atmosphere_grid_description\\\"] = ComponentFile(src=ofile.name,\\n dest=self.couple_dir+\\\"/atmosphere.griddes\\\")\\n self._cleanup_list.append(ofile.name)\\n logging.info(\\\"\\\\t\\\\t ...done!\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"96868f6eb0ee5481f56674a64aa1c4b0\",\n \"score\": \"0.4762315\",\n \"text\": \"def save(self, *args, **kwargs):\\n if self.gen_description:\\n self.description = strip_tags(self.description_from_content())\\n super(MetaData, self).save(*args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d679d338d0bf8a5bc2c3c56ad148d5fc\",\n \"score\": \"0.47541976\",\n \"text\": \"def get_college_dict(text_rows, url):\\n college = {}\\n for row in text_rows:\\n i = 0\\n key = \\\"\\\"\\n info = \\\"\\\"\\n for col in row:\\n if i == 0:\\n key = col\\n else:\\n info += col\\n i += 1\\n info = info.rstrip(' ')\\n if key != \\\"\\\":\\n college[key] = info\\n try:\\n college['Name'] = url.replace('_', ' ').split('/')[-1:][0]\\n except:\\n print \\\"Could not get Name of the College from URL\\\"\\n sys.exit(2)\\n\\n return college\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c31d165665e5108f2e27da8af87ddc0\",\n \"score\": \"0.47517684\",\n \"text\": \"def test016b_description(self):\\r\\n w = self.doc['WR-P-E-J-0000000001.p.1.s.1.w.7']\\r\\n self.assertRaises( folia.NoDescription, w.description)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e59463a2c0b7b7023b50e2e761b937e\",\n \"score\": \"0.4748506\",\n \"text\": \"def parse_docstr(docstr):\\n\\n # Indentation level ......................................................\\n\\n def indentation(line):\\n \\\"\\\"\\\"\\n Returns the index of the first non-space character\\n\\n Parameters\\n ----------\\n line : str\\n String to find the indentation level from\\n\\n Returns\\n -------\\n Index of the first non-space character or None if there is no char\\n\\n \\\"\\\"\\\"\\n\\n strip = len(line.lstrip())\\n\\n if strip:\\n return (len(line) - strip) // 4\\n else:\\n return None\\n\\n # Case with empty string .................................................\\n\\n if docstr is None or docstr.strip() == '':\\n return \\\"No documentation found for this section\\\"\\n\\n html = ''\\n\\n # Split Lines ............................................................\\n\\n all_lines = docstr.split('\\\\n')\\n\\n # Finding minimum indent level ...........................................\\n\\n min_ = -1\\n\\n for line in all_lines:\\n if line.strip() != '':\\n i = indentation(line)\\n if min_ == -1:\\n min_ = i\\n elif i < min_:\\n min_ = i\\n\\n all_lines = [x[4 * min_:] for x in all_lines]\\n\\n # Parsing sections .......................................................\\n\\n all_sections = [] # List containing sections. Each section is a dict\\n # with a name fiels and a content field\\n\\n i_l = len(all_lines) - 1\\n last_content = i_l\\n for line in all_lines[::-1]:\\n if i_l:\\n if all(x == '-' for x in line.strip()) and len(line.strip()) > 0:\\n if indentation(line) == indentation(all_lines[i_l - 1]):\\n if len(line.strip()) <= len(all_lines[i_l - 1].strip()):\\n content = '\\\\n'.join(all_lines[i_l + 1:last_content])\\n section = {'name': all_lines[i_l - 1].strip(),\\n 'content': content}\\n last_content = i_l - 1\\n all_sections.append(section)\\n i_l -= 1\\n\\n # If Description section is not explicit\\n if last_content > 1 and \\\\\\n ''.join([x.strip() for x in all_lines[:last_content]]) != '':\\n content = '\\\\n'.join(all_lines[:last_content])\\n section = {'name': 'Description',\\n 'content': content}\\n all_sections.append(section)\\n\\n # Parsing to HTML ........................................................\\n\\n for section in all_sections[::-1]:\\n html += \\\"<h3>\\\" + section['name'] + \\\"</h3>\\\"\\n\\n # Parsing section content . . . . . . . . . . . . . . . . . . . . . .\\n\\n sect_lines = section['content'].split('\\\\n')\\n last_indent = 0\\n code = False # True if it's a code division \\\">>> foo(bar)\\\"\\n list_ = False # True if there is a list in the docstring\\n code_indent0 = 0\\n list_indent0 = 0\\n last_line_was_empty = False\\n open_div = 0\\n\\n for i_l, line in enumerate(sect_lines):\\n if line.strip() != '':\\n # Finding indent evolution\\n if indentation(line) > last_indent and not code:\\n delta = indentation(line) - last_indent\\n while delta > 0:\\n html += '<div class=\\\"indent\\\">'\\n open_div += 1\\n delta -= 1\\n\\n elif indentation(line) < last_indent and not code and \\\\\\n not list_ or code and \\\\\\n indentation(\\n line) < code_indent0 or list_ and not code:\\n if code:\\n html += \\\"</div>\\\"\\n code = False\\n if list_:\\n if indentation(line) < list_indent0:\\n html += \\\"</li></ul>\\\"\\n list_indent0 = 0\\n list_ = False\\n for _ in range(last_indent - indentation(line)):\\n html += '</div>'\\n open_div -= 1\\n\\n content = line\\n content = content.replace('&', '&')\\n\\n last_indent = indentation(line)\\n\\n if code:\\n html += \\\"<br>\\\"\\n\\n # Code section\\n if content.strip()[:3] == '>>>' and not code:\\n code = True\\n code_indent0 = indentation(line)\\n if not last_line_was_empty:\\n html += \\\"<br>\\\"\\n html += '<div class=\\\"code\\\">'\\n\\n if code:\\n content = content[4 * code_indent0:]\\n content = content.replace(' ', ' ')\\n else:\\n content = line.strip()\\n content = content.replace('<', '<').replace('>', '>')\\n content = content.replace(\\\"'\\\", ''')\\n content = content.replace('\\\"', \\\""\\\")\\n\\n # finding URLs\\n urls = set(re.findall(\\n r\\\"\\\"\\\"(?i)\\\\b((?:https?:(?:/{1,3}|[a-z0-9%])|[a-z0-9.\\\\-]+[.](?:com|net|org|edu|gov|mil|aero|asia|biz|cat|coop|info|int|jobs|mobi|museum|name|post|pro|tel|travel|xxx|ac|ad|ae|af|ag|ai|al|am|an|ao|aq|ar|as|at|au|aw|ax|az|ba|bb|bd|be|bf|bg|bh|bi|bj|bm|bn|bo|br|bs|bt|bv|bw|by|bz|ca|cc|cd|cf|cg|ch|ci|ck|cl|cm|cn|co|cr|cs|cu|cv|cx|cy|cz|dd|de|dj|dk|dm|do|dz|ec|ee|eg|eh|er|es|et|eu|fi|fj|fk|fm|fo|fr|ga|gb|gd|ge|gf|gg|gh|gi|gl|gm|gn|gp|gq|gr|gs|gt|gu|gw|gy|hk|hm|hn|hr|ht|hu|id|ie|il|im|in|io|iq|ir|is|it|je|jm|jo|jp|ke|kg|kh|ki|km|kn|kp|kr|kw|ky|kz|la|lb|lc|li|lk|lr|ls|lt|lu|lv|ly|ma|mc|md|me|mg|mh|mk|ml|mm|mn|mo|mp|mq|mr|ms|mt|mu|mv|mw|mx|my|mz|na|nc|ne|nf|ng|ni|nl|no|np|nr|nu|nz|om|pa|pe|pf|pg|ph|pk|pl|pm|pn|pr|ps|pt|pw|py|qa|re|ro|rs|ru|rw|sa|sb|sc|sd|se|sg|sh|si|sj|Ja|sk|sl|sm|sn|so|sr|ss|st|su|sv|sx|sy|sz|tc|td|tf|tg|th|tj|tk|tl|tm|tn|to|tp|tr|tt|tv|tw|tz|ua|ug|uk|us|uy|uz|va|vc|ve|vg|vi|vn|vu|wf|ws|ye|yt|yu|za|zm|zw)/)(?:[^\\\\s()<>{}\\\\[\\\\]]+|\\\\([^\\\\s()]*?\\\\([^\\\\s()]+\\\\)[^\\\\s()]*?\\\\)|\\\\([^\\\\s]+?\\\\))+(?:\\\\([^\\\\s()]*?\\\\([^\\\\s()]+\\\\)[^\\\\s()]*?\\\\)|\\\\([^\\\\s]+?\\\\)|[^\\\\s`!()\\\\[\\\\]{};:'\\\".,<>?«»“”‘’])|(?:(?<!@)[a-z0-9]+(?:[.\\\\-][a-z0-9]+)*[.](?:com|net|org|edu|gov|mil|aero|asia|biz|cat|coop|info|int|jobs|mobi|museum|name|post|pro|tel|travel|xxx|ac|ad|ae|af|ag|ai|al|am|an|ao|aq|ar|as|at|au|aw|ax|az|ba|bb|bd|be|bf|bg|bh|bi|bj|bm|bn|bo|br|bs|bt|bv|bw|by|bz|ca|cc|cd|cf|cg|ch|ci|ck|cl|cm|cn|co|cr|cs|cu|cv|cx|cy|cz|dd|de|dj|dk|dm|do|dz|ec|ee|eg|eh|er|es|et|eu|fi|fj|fk|fm|fo|fr|ga|gb|gd|ge|gf|gg|gh|gi|gl|gm|gn|gp|gq|gr|gs|gt|gu|gw|gy|hk|hm|hn|hr|ht|hu|id|ie|il|im|in|io|iq|ir|is|it|je|jm|jo|jp|ke|kg|kh|ki|km|kn|kp|kr|kw|ky|kz|la|lb|lc|li|lk|lr|ls|lt|lu|lv|ly|ma|mc|md|me|mg|mh|mk|ml|mm|mn|mo|mp|mq|mr|ms|mt|mu|mv|mw|mx|my|mz|na|nc|ne|nf|ng|ni|nl|no|np|nr|nu|nz|om|pa|pe|pf|pg|ph|pk|pl|pm|pn|pr|ps|pt|pw|qa|re|ro|rs|ru|rw|sa|sb|sc|sd|se|sg|sh|si|sj|Ja|sk|sl|sm|sn|so|sr|ss|st|su|sv|sx|sy|sz|tc|td|tf|tg|th|tj|tk|tl|tm|tn|to|tp|tr|tt|tv|tw|tz|ua|ug|uk|us|uy|uz|va|vc|ve|vg|vi|vn|vu|wf|ws|ye|yt|yu|za|zm|zw)\\\\b/?(?!@)))\\\"\\\"\\\",\\n content))\\n for url in urls:\\n rep = '<a href=\\\"' + url + '\\\">' + url + \\\"</a>\\\"\\n content = content.replace(url, rep)\\n\\n # List section\\n if content[:2] in ['- ', '* '] and not code:\\n content = content[2:]\\n if not list_:\\n html += '<ul>'\\n list_ = True\\n list_indent0 = indentation(line)\\n else:\\n html += \\\"</li>\\\"\\n html += '<li>'\\n\\n # Bold before ':'\\n if not code and \\\"</a>\\\" not in content:\\n sp = content.split(':')\\n if len(sp) > 1:\\n content = \\\"<b>\\\" + sp[0] + \\\"</b>\\\" + ':' + \\\\\\n ':'.join(sp[1:])\\n\\n # Adding content\\n html += ' ' + content\\n last_line_was_empty = False\\n\\n # Empty line\\n else:\\n if code:\\n html += \\\"</div>\\\"\\n code = False\\n if list_:\\n for _ in range(last_indent - list_indent0):\\n html += \\\"</div>\\\"\\n open_div -= 1\\n last_indent = list_indent0\\n html += \\\"</li></ul>\\\"\\n list_ = False\\n if i_l != 0:\\n html += \\\"<br>\\\"\\n last_line_was_empty = True\\n\\n # Closing section\\n if code:\\n html += \\\"</div>\\\"\\n code = False\\n if list_:\\n html += \\\"</li></ul>\\\"\\n list_ = False\\n\\n while open_div > 0:\\n html += \\\"</div>\\\"\\n open_div -= 1\\n return html\",\n \"title\": \"\"\n },\n {\n \"docid\": \"147dbc4b8b90da3c14b40958a8178de9\",\n \"score\": \"0.4743189\",\n \"text\": \"def get_course_list(self, step_index, courses):\\n response = self.browser.get(\\\"https://student.osiris.hro.nl:9021/osiris_student/OnderwijsZoekCursus.do\\\", params={\\n \\\"event\\\": \\\"goto\\\",\\n \\\"source\\\": \\\"OnderwijsZoekCursus\\\",\\n \\\"value\\\": step_index + 1,\\n \\\"size\\\": \\\"30\\\",\\n \\\"partialTargets\\\": \\\"OnderwijsZoekCursus _uixState\\\",\\n \\\"partial\\\": \\\"true\\\",\\n \\\"requestToken\\\": self.get_request_token()\\n })\\n\\n for index, row in enumerate(BeautifulSoup(response.text, \\\"lxml\\\").find(\\\"table\\\", {\\\"class\\\": \\\"OraTableContent\\\"}).find_all(\\\"tr\\\")):\\n if not index == 0:\\n course = self.get_course_info(course_row=row)\\n courses.append(course)\\n\\n self.lock.acquire()\\n print(\\\"[+] Scraped course metadata: %s (%s)\\\" % (course[\\\"korteNaamCursus\\\"], course[\\\"cursuscode\\\"]))\\n self.lock.release()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3ca67b182b130873ed6374312ff938e\",\n \"score\": \"0.47429794\",\n \"text\": \"def add_course(code, name, misc, descript):\\n c = list(db.courses.find({\\\"code\\\": code}))\\n if not c:\\n course = {\\\"code\\\": code,\\n \\\"name\\\": name,\\n \\\"misc\\\": misc,\\n \\\"description\\\": descript}\\n db.courses.insert(course)\\n return True\\n print 'db add course'\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e321840cf94f40f831c256d0270643b0\",\n \"score\": \"0.47382024\",\n \"text\": \"def test_course(self):\\n course = CourseInstanceFactory()\\n [res_course] = list(self.wrapper.courses())\\n\\n self.assertEqual(res_course.id, course.id)\\n self.assertEqual(res_course.name, course.name)\\n self.assertEqual(res_course.short_name, course.short_name)\\n self.assertEqual(res_course.points, course.points)\\n self.assertEqual(res_course.has_exam, course.has_exam)\\n self.assertEqual(res_course.description, course.description)\\n self.assertEqual(res_course.language, course.language)\\n self.assertEqual(res_course.semester, course.semester.id)\\n self.assertEqual(res_course.course_type, course.course_type.short_name)\\n self.assertEqual(res_course.usos_kod, course.usos_kod)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"981da650518f33b2becae61599f76b1b\",\n \"score\": \"0.4735454\",\n \"text\": \"def prepare(doc):\\n doc.headers = [0, 0, 0, 0, 0, 0]\\n doc.aliases = [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"]\\n doc.information = {}\\n doc.defined = {}\\n\\n if \\\"pandoc-numbering\\\" in doc.metadata.content and isinstance(\\n doc.metadata.content[\\\"pandoc-numbering\\\"], MetaMap\\n ):\\n for category, definition in doc.metadata.content[\\n \\\"pandoc-numbering\\\"\\n ].content.items():\\n if isinstance(definition, MetaMap):\\n add_definition(category, definition, doc)\\n\\n doc.count = {}\\n doc.collections = {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb5bdaccc9010bfcf914fcd7ed48068c\",\n \"score\": \"0.47305542\",\n \"text\": \"def describe(description):\\n if \\\"experiment\\\" not in _eh:\\n raise ValueError(\\\"Call flexp.setup first! flexp.describe after that\\\")\\n with open(_eh[\\\"experiment\\\"].get_file_path(\\\"description.txt\\\"), \\\"wt\\\") as fx:\\n print(description, file=fx)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5b0dd6ae027a9534cf369473672a01f\",\n \"score\": \"0.47261122\",\n \"text\": \"def description_from_content(self):\\n description = \\\"\\\"\\n # Fall back to the title if description couldn't be determined.\\n if not description:\\n description = str(strip_tags(self.content))\\n # Strip everything after the first block or sentence.\\n ends = (\\\"</p>\\\", \\\"<br />\\\", \\\"<br/>\\\", \\\"<br>\\\", \\\"</ul>\\\",\\n \\\"\\\\n\\\", \\\". \\\", \\\"! \\\", \\\"? \\\")\\n for end in ends:\\n pos = description.lower().find(end)\\n if pos > -1:\\n description = TagCloser(description[:pos]).html\\n break\\n else:\\n description = truncatewords_html(description, 150)\\n try:\\n description = unicode(description)\\n except NameError:\\n pass # Python 3.\\n return description\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07725fea8405f335c47b19c80df9bb7f\",\n \"score\": \"0.47248343\",\n \"text\": \"def write_descr(self, f, design):\\n bdt = time.localtime(design.design_attributes.metadata.updated_timestamp)\\n datestr = time.strftime('%d %b %Y', bdt).lower()\\n f.write('''\\\\\\n$Descr A4 11700 8267\\nencoding utf-8\\nSheet 1 1\\nTitle \\\"\\\"\\nDate \\\"%s\\\"\\nRev \\\"\\\"\\nComp \\\"\\\"\\nComment1 \\\"\\\"\\nComment2 \\\"\\\"\\nComment3 \\\"\\\"\\nComment4 \\\"\\\"\\n$EndDescr\\n''' % (datestr,))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5bf2919b3ffb7af69aa3222e23efc597\",\n \"score\": \"0.4722489\",\n \"text\": \"def description(self):\\n return self._book_dict['description']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"166fbea4e4c9e947404c9201612cb082\",\n \"score\": \"0.4722284\",\n \"text\": \"def test_catalog(self):\\n with Transaction():\\n factory = self.root.manage_addProduct['silva.app.document']\\n factory.manage_addDocument('document', 'Test Document')\\n\\n version = self.root.document.get_editable()\\n save_editor_text(version.body, HTML_CATALOG, content=version)\\n\\n # Test appear in the title.\\n self.assertItemsEqual(\\n search(fulltext='Test'),\\n ['/root/document/0'])\\n\\n # Catalog appear in the body text.\\n self.assertItemsEqual(\\n search(fulltext='catalog'),\\n ['/root/document/0'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d44e826a28e6c8519b2eacf1fb2d0fb\",\n \"score\": \"0.47211483\",\n \"text\": \"def record_story_chapters(self) -> None:\\n # get the chapters\\n for chapter in self.chapter_list:\\n time.sleep(self._chapter_sleep_time)\\n chapter_object = Chapter()\\n self.log_debug(\\\"Downloading chapter: \\\" + chapter['link'])\\n self._update_soup(url=self._url[0:-1]+chapter['link'])\\n chapter_text = \\\"\\\"\\n chapter_count = 0\\n story_tag = self._soup.find(id=\\\"storytextp\\\")\\n for content in story_tag.find_all(['p', 'hr']):\\n chapter_text += content.prettify()\\n chapter_count += len(content.text.split())\\n chapter_object.processed_body = chapter_text\\n chapter_object.raw_body = self._soup.prettify()\\n chapter_object.word_count = chapter_count\\n chapter_object.name = chapter['name']\\n self._fanfic.add_chapter(chapter_object)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d22cdbb3e976cf825990f8e15039e62d\",\n \"score\": \"0.47167173\",\n \"text\": \"def _getCourses(self):\\n courses = defaultdict(list)\\n with open(os.path.join(os.path.dirname(__file__), 'courses.csv'), encoding=\\\"UTF-8\\\") as file:\\n reader = csv.reader(file)\\n for line in reader:\\n courses[line[0].strip()].append(line[1].strip())\\n return courses\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c89e022c52616bcbaecd97ee734ab935\",\n \"score\": \"0.47144622\",\n \"text\": \"def get_course_list(cls):\\n\\n cls.setup()\\n\\n course_list = []\\n\\n tree = html.fromstring(cls.__main_page)\\n # Current semester courses\\n courses_nodes = tree.get_element_by_id('navbar').find('li/ul').findall('li/a[@title]')\\n # Previous courses\\n courses_nodes += tree.get_element_by_id('navbar').find('.//ul[@class=\\\"dropdown-menu\\\"]')\\\\\\n .findall('li/a[@title]')\\n\\n for course_node in courses_nodes:\\n course = DataClasses.CourseInfo()\\n course.name = course_node.text_content().split('(')[0].strip()\\n course.semester = course_node.text_content().split(course.name)[1].strip()\\n course.link = course_node.attrib['href']\\n course_list.append(course)\\n\\n return course_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1f9d4bb0ef0c2d7cc46cc11e4697f2d\",\n \"score\": \"0.4708553\",\n \"text\": \"def saveContent(self):\\n if not os.path.exists(self.contentFile):\\n with open(self.translation.book_file, 'r') as f:\\n lines = [line.strip() for line in f.readlines()]\\n ch_cont = ' '.join(lines[self.firstLine-1:self.lastLine+1])\\n start = 0\\n new_lines = []\\n for _, _, end in self.beads:\\n new_lines.append(ch_cont[start:end])\\n start = end\\n ch_cont = '\\\\n\\\\n'.join(new_lines)\\n with open(self.contentFile, 'w') as chf:\\n chf.write(ch_cont)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e6eddfd62dee774785381eec12050614\",\n \"score\": \"0.47085264\",\n \"text\": \"def _generate_description(self):\\n generations = []\\n\\n for j, templ in enumerate(self.template):\\n result = []\\n for i, key in enumerate(templ):\\n key_type = type(key)\\n arg_index = 0\\n # arg_index 1 for StopCondition\\n if (key_type in CONDIITON_TEMPLATES) and (len(self.args) > 1):\\n arg_index = 1\\n\\n # get the text from template object\\n item = key.generate_description(arg_index=arg_index, index=i, templ_index=j)\\n\\n if not item:\\n continue\\n # flatten nested dict\\n if type(item) in [OrderedDict, dict]:\\n val_list = list(values_of_nested_dict(item))\\n result.extend(val_list)\\n else:\\n result.append(item)\\n generations.append(\\\" \\\".join(result))\\n\\n return generations\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":679,"cells":{"query_id":{"kind":"string","value":"10be4055dc44747101cc3d0847c103b0"},"query":{"kind":"string","value":"This is the main method of the tool."},"positive_passages":{"kind":"list like","value":[{"docid":"5987afff833f5856b1bc716afc9b2e8a","score":"0.0","text":"def main(self):\n if not os.path.isfile(os.path.join(self.project_directory, \"make_project.json\")):\n print(f\"Generating CProject in {self.project_directory}...\")\n self.normami(\"ami-makeproject\", [\"--rawfiletypes\", \"html,pdf,xml\", \"--omit\", \"template.xml\"])\n\n raw_project_contents = [os.path.join(self.project_directory, x) for x in os.listdir(self.project_directory)]\n project_contents = [x for x in raw_project_contents if os.path.isdir(x)]\n\n\n self.normami(\"ami-pdf\")\n\n self.normami(\"ami-filter\", [\"--small\", \"small\", \"--duplicate\", \"duplicate\", \"--monochrome\", \"monochrome\"])\n\n\n papers = []\n for ctree in project_contents:\n paper = Paper(ctree)\n papers.append(paper)\n\n pdf_images_dir = os.path.join(ctree, \"pdfimages\")\n try:\n imagedirs = [os.path.join(pdf_images_dir, x) for x in os.listdir(pdf_images_dir) if x.startswith(\"image.\")]\n except FileNotFoundError:\n # Most likely we've hit other dirs in the corpus, like .git\n continue\n for imagedir in imagedirs:\n skeleton = Skeleton(imagedir)\n plot = None\n if skeleton.likely_spss():\n os.rename(os.path.join(imagedir, \"lines.png\"),\n os.path.join(imagedir, \"spss.png\"))\n plot = SPSSForestPlot(imagedir, skeleton)\n elif skeleton.likely_stata():\n os.rename(os.path.join(imagedir, \"lines.png\"),\n os.path.join(imagedir, \"stata.png\"))\n plot = StataForestPlot(imagedir, skeleton)\n\n if not plot:\n continue\n\n try:\n plot.break_up_image()\n plot.process()\n except InvalidForestPlot:\n pass\n else:\n paper.plots.append(plot)\n plot.save()\n\n self.save_results(papers)","title":""}],"string":"[\n {\n \"docid\": \"5987afff833f5856b1bc716afc9b2e8a\",\n \"score\": \"0.0\",\n \"text\": \"def main(self):\\n if not os.path.isfile(os.path.join(self.project_directory, \\\"make_project.json\\\")):\\n print(f\\\"Generating CProject in {self.project_directory}...\\\")\\n self.normami(\\\"ami-makeproject\\\", [\\\"--rawfiletypes\\\", \\\"html,pdf,xml\\\", \\\"--omit\\\", \\\"template.xml\\\"])\\n\\n raw_project_contents = [os.path.join(self.project_directory, x) for x in os.listdir(self.project_directory)]\\n project_contents = [x for x in raw_project_contents if os.path.isdir(x)]\\n\\n\\n self.normami(\\\"ami-pdf\\\")\\n\\n self.normami(\\\"ami-filter\\\", [\\\"--small\\\", \\\"small\\\", \\\"--duplicate\\\", \\\"duplicate\\\", \\\"--monochrome\\\", \\\"monochrome\\\"])\\n\\n\\n papers = []\\n for ctree in project_contents:\\n paper = Paper(ctree)\\n papers.append(paper)\\n\\n pdf_images_dir = os.path.join(ctree, \\\"pdfimages\\\")\\n try:\\n imagedirs = [os.path.join(pdf_images_dir, x) for x in os.listdir(pdf_images_dir) if x.startswith(\\\"image.\\\")]\\n except FileNotFoundError:\\n # Most likely we've hit other dirs in the corpus, like .git\\n continue\\n for imagedir in imagedirs:\\n skeleton = Skeleton(imagedir)\\n plot = None\\n if skeleton.likely_spss():\\n os.rename(os.path.join(imagedir, \\\"lines.png\\\"),\\n os.path.join(imagedir, \\\"spss.png\\\"))\\n plot = SPSSForestPlot(imagedir, skeleton)\\n elif skeleton.likely_stata():\\n os.rename(os.path.join(imagedir, \\\"lines.png\\\"),\\n os.path.join(imagedir, \\\"stata.png\\\"))\\n plot = StataForestPlot(imagedir, skeleton)\\n\\n if not plot:\\n continue\\n\\n try:\\n plot.break_up_image()\\n plot.process()\\n except InvalidForestPlot:\\n pass\\n else:\\n paper.plots.append(plot)\\n plot.save()\\n\\n self.save_results(papers)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"ec021328057f10f8af523fff413bbe9a","score":"0.8747809","text":"def main ():","title":""},{"docid":"ec021328057f10f8af523fff413bbe9a","score":"0.8747809","text":"def main ():","title":""},{"docid":"1d4484b0529dbe0541b241834cb12a3e","score":"0.86891615","text":"def main():\n\t\tpass","title":""},{"docid":"08aa0c5cc07c5efe590ba89320b45dbc","score":"0.86536205","text":"def main():","title":""},{"docid":"08aa0c5cc07c5efe590ba89320b45dbc","score":"0.86536205","text":"def main():","title":""},{"docid":"08aa0c5cc07c5efe590ba89320b45dbc","score":"0.86536205","text":"def main():","title":""},{"docid":"08aa0c5cc07c5efe590ba89320b45dbc","score":"0.86536205","text":"def main():","title":""},{"docid":"08aa0c5cc07c5efe590ba89320b45dbc","score":"0.86536205","text":"def main():","title":""},{"docid":"08aa0c5cc07c5efe590ba89320b45dbc","score":"0.86536205","text":"def main():","title":""},{"docid":"08aa0c5cc07c5efe590ba89320b45dbc","score":"0.86536205","text":"def main():","title":""},{"docid":"08aa0c5cc07c5efe590ba89320b45dbc","score":"0.86536205","text":"def main():","title":""},{"docid":"08aa0c5cc07c5efe590ba89320b45dbc","score":"0.86536205","text":"def main():","title":""},{"docid":"08aa0c5cc07c5efe590ba89320b45dbc","score":"0.86536205","text":"def main():","title":""},{"docid":"08aa0c5cc07c5efe590ba89320b45dbc","score":"0.86536205","text":"def main():","title":""},{"docid":"08aa0c5cc07c5efe590ba89320b45dbc","score":"0.86536205","text":"def main():","title":""},{"docid":"08aa0c5cc07c5efe590ba89320b45dbc","score":"0.86536205","text":"def main():","title":""},{"docid":"08aa0c5cc07c5efe590ba89320b45dbc","score":"0.86536205","text":"def main():","title":""},{"docid":"08aa0c5cc07c5efe590ba89320b45dbc","score":"0.86536205","text":"def main():","title":""},{"docid":"08aa0c5cc07c5efe590ba89320b45dbc","score":"0.86536205","text":"def main():","title":""},{"docid":"08aa0c5cc07c5efe590ba89320b45dbc","score":"0.86536205","text":"def main():","title":""},{"docid":"08aa0c5cc07c5efe590ba89320b45dbc","score":"0.86536205","text":"def main():","title":""},{"docid":"08aa0c5cc07c5efe590ba89320b45dbc","score":"0.86536205","text":"def main():","title":""},{"docid":"08aa0c5cc07c5efe590ba89320b45dbc","score":"0.86536205","text":"def main():","title":""},{"docid":"08aa0c5cc07c5efe590ba89320b45dbc","score":"0.86536205","text":"def main():","title":""},{"docid":"08aa0c5cc07c5efe590ba89320b45dbc","score":"0.86536205","text":"def main():","title":""},{"docid":"08aa0c5cc07c5efe590ba89320b45dbc","score":"0.86536205","text":"def main():","title":""},{"docid":"08aa0c5cc07c5efe590ba89320b45dbc","score":"0.86536205","text":"def main():","title":""},{"docid":"82c54a749d7508e492693a3059faed30","score":"0.85027075","text":"def main():\n return","title":""},{"docid":"82c54a749d7508e492693a3059faed30","score":"0.85027075","text":"def main():\n return","title":""},{"docid":"07e76a2dea489b69c96fdd82461f8592","score":"0.84794545","text":"def main(self):\n pass","title":""},{"docid":"07e76a2dea489b69c96fdd82461f8592","score":"0.84794545","text":"def main(self):\n pass","title":""},{"docid":"cfc083a503ff24be9d8e1d2ad2fd1393","score":"0.844006","text":"def main(self):\n return","title":""},{"docid":"6a72f0523c7f4b19a3ff495c1baaa5c5","score":"0.8404597","text":"def main() -> None:","title":""},{"docid":"6a72f0523c7f4b19a3ff495c1baaa5c5","score":"0.8404597","text":"def main() -> None:","title":""},{"docid":"4301ce0ec56b79f3f0e020a93f26bdee","score":"0.8325753","text":"def main():\n \n pass","title":""},{"docid":"c8e238f9ea21384e3be28d52a1ee7939","score":"0.8275896","text":"def main():\n\tpass","title":""},{"docid":"59a11114512ac46a2a2d976a77d1c6d6","score":"0.8274263","text":"def main(self, *args):\n pass","title":""},{"docid":"59a11114512ac46a2a2d976a77d1c6d6","score":"0.8274263","text":"def main(self, *args):\n pass","title":""},{"docid":"d01bb886890ed36b0f4c3f9b6f39bbe6","score":"0.81955576","text":"def main():\r\n pass","title":""},{"docid":"d01bb886890ed36b0f4c3f9b6f39bbe6","score":"0.81955576","text":"def main():\r\n pass","title":""},{"docid":"1e8b2ebbe42aca7bf8565023e7af8326","score":"0.81616354","text":"def main(args=None):","title":""},{"docid":"a4873d5cb2b9c8e72f57155f6e9b8d43","score":"0.7982208","text":"def main(args):\n\treturn 0","title":""},{"docid":"f64ecdad4bada7fe6ab976b65fd314c4","score":"0.79688203","text":"def main():\n cli()","title":""},{"docid":"737edbcdda02609a89cb40f455a292fb","score":"0.7957253","text":"def main(args):\n pass","title":""},{"docid":"bc0d536b098278d704e13b8e318d6af5","score":"0.78789955","text":"def main():\n pass","title":""},{"docid":"bc0d536b098278d704e13b8e318d6af5","score":"0.78789955","text":"def main():\n pass","title":""},{"docid":"bc0d536b098278d704e13b8e318d6af5","score":"0.78789955","text":"def main():\n pass","title":""},{"docid":"bc0d536b098278d704e13b8e318d6af5","score":"0.78789955","text":"def main():\n pass","title":""},{"docid":"bc0d536b098278d704e13b8e318d6af5","score":"0.78789955","text":"def main():\n pass","title":""},{"docid":"bc0d536b098278d704e13b8e318d6af5","score":"0.78789955","text":"def main():\n pass","title":""},{"docid":"bc0d536b098278d704e13b8e318d6af5","score":"0.78789955","text":"def main():\n pass","title":""},{"docid":"bc0d536b098278d704e13b8e318d6af5","score":"0.78789955","text":"def main():\n pass","title":""},{"docid":"bc0d536b098278d704e13b8e318d6af5","score":"0.78789955","text":"def main():\n pass","title":""},{"docid":"bc0d536b098278d704e13b8e318d6af5","score":"0.78789955","text":"def main():\n pass","title":""},{"docid":"bc0d536b098278d704e13b8e318d6af5","score":"0.78789955","text":"def main():\n pass","title":""},{"docid":"bc0d536b098278d704e13b8e318d6af5","score":"0.78789955","text":"def main():\n pass","title":""},{"docid":"bc0d536b098278d704e13b8e318d6af5","score":"0.78789955","text":"def main():\n pass","title":""},{"docid":"bc0d536b098278d704e13b8e318d6af5","score":"0.78789955","text":"def main():\n pass","title":""},{"docid":"bc0d536b098278d704e13b8e318d6af5","score":"0.78789955","text":"def main():\n pass","title":""},{"docid":"bc0d536b098278d704e13b8e318d6af5","score":"0.78789955","text":"def main():\n pass","title":""},{"docid":"bc0d536b098278d704e13b8e318d6af5","score":"0.78789955","text":"def main():\n pass","title":""},{"docid":"bc0d536b098278d704e13b8e318d6af5","score":"0.78789955","text":"def main():\n pass","title":""},{"docid":"bc0d536b098278d704e13b8e318d6af5","score":"0.78789955","text":"def main():\n pass","title":""},{"docid":"bc0d536b098278d704e13b8e318d6af5","score":"0.78789955","text":"def main():\n pass","title":""},{"docid":"bc0d536b098278d704e13b8e318d6af5","score":"0.78789955","text":"def main():\n pass","title":""},{"docid":"bc0d536b098278d704e13b8e318d6af5","score":"0.78789955","text":"def main():\n pass","title":""},{"docid":"bc0d536b098278d704e13b8e318d6af5","score":"0.78789955","text":"def main():\n pass","title":""},{"docid":"bc0d536b098278d704e13b8e318d6af5","score":"0.78789955","text":"def main():\n pass","title":""},{"docid":"bc0d536b098278d704e13b8e318d6af5","score":"0.78789955","text":"def main():\n pass","title":""},{"docid":"bc0d536b098278d704e13b8e318d6af5","score":"0.78789955","text":"def main():\n pass","title":""},{"docid":"bc0d536b098278d704e13b8e318d6af5","score":"0.78789955","text":"def main():\n pass","title":""},{"docid":"bc0d536b098278d704e13b8e318d6af5","score":"0.78789955","text":"def main():\n pass","title":""},{"docid":"bc0d536b098278d704e13b8e318d6af5","score":"0.78789955","text":"def main():\n pass","title":""},{"docid":"566036b3a07ddd1af043005be3b82644","score":"0.78345406","text":"def main(args=None):\r\n pass","title":""},{"docid":"93e2db98c51fbce39b84a61c48c918ca","score":"0.78276795","text":"def main(args):\n return 0","title":""},{"docid":"a8550ef16b860a6cc3bad917dfb04f42","score":"0.78050315","text":"def main():\n ...","title":""},{"docid":"e302ae8a2be1a50834705897601e1c4f","score":"0.7797365","text":"def _main():\n\tpass","title":""},{"docid":"64a1a4261cea121e609f61fd60d0b36f","score":"0.7668843","text":"def main():\n return None","title":""},{"docid":"535c6e7d32028af3cc3d681a0804815d","score":"0.76315814","text":"def main(args=None):\n return 0","title":""},{"docid":"7d8c44a31b3fd929034986d163e95ff4","score":"0.75593626","text":"def main():\n sys.exit(CommandGenerator().run())","title":""},{"docid":"b816c59d61979d6c3de629bfe657acc3","score":"0.7548137","text":"def main():\n CLI().run(argv[1:])","title":""},{"docid":"04688cd98d9fcf835947139440a6440f","score":"0.7527326","text":"def main():\n\n pass\n\n return None","title":""},{"docid":"05b99850a4683aae56ee599491955a1b","score":"0.748265","text":"def _main() -> None:\n pass","title":""},{"docid":"68322a9750831c83f6f96d00feec8bfd","score":"0.74573714","text":"def cli():\n pass","title":""},{"docid":"68322a9750831c83f6f96d00feec8bfd","score":"0.74573714","text":"def cli():\n pass","title":""},{"docid":"68322a9750831c83f6f96d00feec8bfd","score":"0.74573714","text":"def cli():\n pass","title":""},{"docid":"68322a9750831c83f6f96d00feec8bfd","score":"0.74573714","text":"def cli():\n pass","title":""},{"docid":"5e4cec8ea848b3d798bca3b0fced58b1","score":"0.7456379","text":"def main(args):\n Run()","title":""},{"docid":"d3bc8d0ffe7ca1f67f6ed25dd23c3256","score":"0.74484986","text":"def main():\n return cli(obj={})","title":""},{"docid":"416ebef25c2ec8d1e62f66e4f8143a45","score":"0.7446385","text":"def cli():\n\tpass","title":""},{"docid":"f705cd79ef3e5c4359eff1300ac38c5b","score":"0.7443812","text":"def cli(ctx):","title":""},{"docid":"f705cd79ef3e5c4359eff1300ac38c5b","score":"0.7443812","text":"def cli(ctx):","title":""},{"docid":"f705cd79ef3e5c4359eff1300ac38c5b","score":"0.7443812","text":"def cli(ctx):","title":""},{"docid":"c9cb5e099e45669e7b63c3dbf25cf6a9","score":"0.74294496","text":"def cli():","title":""},{"docid":"c9cb5e099e45669e7b63c3dbf25cf6a9","score":"0.74294496","text":"def cli():","title":""},{"docid":"c9cb5e099e45669e7b63c3dbf25cf6a9","score":"0.74294496","text":"def cli():","title":""},{"docid":"c9cb5e099e45669e7b63c3dbf25cf6a9","score":"0.74294496","text":"def cli():","title":""},{"docid":"c9cb5e099e45669e7b63c3dbf25cf6a9","score":"0.74294496","text":"def cli():","title":""},{"docid":"c9cb5e099e45669e7b63c3dbf25cf6a9","score":"0.74294496","text":"def cli():","title":""},{"docid":"c9cb5e099e45669e7b63c3dbf25cf6a9","score":"0.74294496","text":"def cli():","title":""},{"docid":"c9cb5e099e45669e7b63c3dbf25cf6a9","score":"0.74294496","text":"def cli():","title":""}],"string":"[\n {\n \"docid\": \"ec021328057f10f8af523fff413bbe9a\",\n \"score\": \"0.8747809\",\n \"text\": \"def main ():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec021328057f10f8af523fff413bbe9a\",\n \"score\": \"0.8747809\",\n \"text\": \"def main ():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d4484b0529dbe0541b241834cb12a3e\",\n \"score\": \"0.86891615\",\n \"text\": \"def main():\\n\\t\\tpass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08aa0c5cc07c5efe590ba89320b45dbc\",\n \"score\": \"0.86536205\",\n \"text\": \"def main():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08aa0c5cc07c5efe590ba89320b45dbc\",\n \"score\": \"0.86536205\",\n \"text\": \"def main():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08aa0c5cc07c5efe590ba89320b45dbc\",\n \"score\": \"0.86536205\",\n \"text\": \"def main():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08aa0c5cc07c5efe590ba89320b45dbc\",\n \"score\": \"0.86536205\",\n \"text\": \"def main():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08aa0c5cc07c5efe590ba89320b45dbc\",\n \"score\": \"0.86536205\",\n \"text\": \"def main():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08aa0c5cc07c5efe590ba89320b45dbc\",\n \"score\": \"0.86536205\",\n \"text\": \"def main():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08aa0c5cc07c5efe590ba89320b45dbc\",\n \"score\": \"0.86536205\",\n \"text\": \"def main():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08aa0c5cc07c5efe590ba89320b45dbc\",\n \"score\": \"0.86536205\",\n \"text\": \"def main():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08aa0c5cc07c5efe590ba89320b45dbc\",\n \"score\": \"0.86536205\",\n \"text\": \"def main():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08aa0c5cc07c5efe590ba89320b45dbc\",\n \"score\": \"0.86536205\",\n \"text\": \"def main():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08aa0c5cc07c5efe590ba89320b45dbc\",\n \"score\": \"0.86536205\",\n \"text\": \"def main():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08aa0c5cc07c5efe590ba89320b45dbc\",\n \"score\": \"0.86536205\",\n \"text\": \"def main():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08aa0c5cc07c5efe590ba89320b45dbc\",\n \"score\": \"0.86536205\",\n \"text\": \"def main():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08aa0c5cc07c5efe590ba89320b45dbc\",\n \"score\": \"0.86536205\",\n \"text\": \"def main():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08aa0c5cc07c5efe590ba89320b45dbc\",\n \"score\": \"0.86536205\",\n \"text\": \"def main():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08aa0c5cc07c5efe590ba89320b45dbc\",\n \"score\": \"0.86536205\",\n \"text\": \"def main():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08aa0c5cc07c5efe590ba89320b45dbc\",\n \"score\": \"0.86536205\",\n \"text\": \"def main():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08aa0c5cc07c5efe590ba89320b45dbc\",\n \"score\": \"0.86536205\",\n \"text\": \"def main():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08aa0c5cc07c5efe590ba89320b45dbc\",\n \"score\": \"0.86536205\",\n \"text\": \"def main():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08aa0c5cc07c5efe590ba89320b45dbc\",\n \"score\": \"0.86536205\",\n \"text\": \"def main():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08aa0c5cc07c5efe590ba89320b45dbc\",\n \"score\": \"0.86536205\",\n \"text\": \"def main():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08aa0c5cc07c5efe590ba89320b45dbc\",\n \"score\": \"0.86536205\",\n \"text\": \"def main():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08aa0c5cc07c5efe590ba89320b45dbc\",\n \"score\": \"0.86536205\",\n \"text\": \"def main():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08aa0c5cc07c5efe590ba89320b45dbc\",\n \"score\": \"0.86536205\",\n \"text\": \"def main():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82c54a749d7508e492693a3059faed30\",\n \"score\": \"0.85027075\",\n \"text\": \"def main():\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82c54a749d7508e492693a3059faed30\",\n \"score\": \"0.85027075\",\n \"text\": \"def main():\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07e76a2dea489b69c96fdd82461f8592\",\n \"score\": \"0.84794545\",\n \"text\": \"def main(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07e76a2dea489b69c96fdd82461f8592\",\n \"score\": \"0.84794545\",\n \"text\": \"def main(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cfc083a503ff24be9d8e1d2ad2fd1393\",\n \"score\": \"0.844006\",\n \"text\": \"def main(self):\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a72f0523c7f4b19a3ff495c1baaa5c5\",\n \"score\": \"0.8404597\",\n \"text\": \"def main() -> None:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a72f0523c7f4b19a3ff495c1baaa5c5\",\n \"score\": \"0.8404597\",\n \"text\": \"def main() -> None:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4301ce0ec56b79f3f0e020a93f26bdee\",\n \"score\": \"0.8325753\",\n \"text\": \"def main():\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8e238f9ea21384e3be28d52a1ee7939\",\n \"score\": \"0.8275896\",\n \"text\": \"def main():\\n\\tpass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59a11114512ac46a2a2d976a77d1c6d6\",\n \"score\": \"0.8274263\",\n \"text\": \"def main(self, *args):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59a11114512ac46a2a2d976a77d1c6d6\",\n \"score\": \"0.8274263\",\n \"text\": \"def main(self, *args):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d01bb886890ed36b0f4c3f9b6f39bbe6\",\n \"score\": \"0.81955576\",\n \"text\": \"def main():\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d01bb886890ed36b0f4c3f9b6f39bbe6\",\n \"score\": \"0.81955576\",\n \"text\": \"def main():\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e8b2ebbe42aca7bf8565023e7af8326\",\n \"score\": \"0.81616354\",\n \"text\": \"def main(args=None):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4873d5cb2b9c8e72f57155f6e9b8d43\",\n \"score\": \"0.7982208\",\n \"text\": \"def main(args):\\n\\treturn 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f64ecdad4bada7fe6ab976b65fd314c4\",\n \"score\": \"0.79688203\",\n \"text\": \"def main():\\n cli()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"737edbcdda02609a89cb40f455a292fb\",\n \"score\": \"0.7957253\",\n \"text\": \"def main(args):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc0d536b098278d704e13b8e318d6af5\",\n \"score\": \"0.78789955\",\n \"text\": \"def main():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc0d536b098278d704e13b8e318d6af5\",\n \"score\": \"0.78789955\",\n \"text\": \"def main():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc0d536b098278d704e13b8e318d6af5\",\n \"score\": \"0.78789955\",\n \"text\": \"def main():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc0d536b098278d704e13b8e318d6af5\",\n \"score\": \"0.78789955\",\n \"text\": \"def main():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc0d536b098278d704e13b8e318d6af5\",\n \"score\": \"0.78789955\",\n \"text\": \"def main():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc0d536b098278d704e13b8e318d6af5\",\n \"score\": \"0.78789955\",\n \"text\": \"def main():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc0d536b098278d704e13b8e318d6af5\",\n \"score\": \"0.78789955\",\n \"text\": \"def main():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc0d536b098278d704e13b8e318d6af5\",\n \"score\": \"0.78789955\",\n \"text\": \"def main():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc0d536b098278d704e13b8e318d6af5\",\n \"score\": \"0.78789955\",\n \"text\": \"def main():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc0d536b098278d704e13b8e318d6af5\",\n \"score\": \"0.78789955\",\n \"text\": \"def main():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc0d536b098278d704e13b8e318d6af5\",\n \"score\": \"0.78789955\",\n \"text\": \"def main():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc0d536b098278d704e13b8e318d6af5\",\n \"score\": \"0.78789955\",\n \"text\": \"def main():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc0d536b098278d704e13b8e318d6af5\",\n \"score\": \"0.78789955\",\n \"text\": \"def main():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc0d536b098278d704e13b8e318d6af5\",\n \"score\": \"0.78789955\",\n \"text\": \"def main():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc0d536b098278d704e13b8e318d6af5\",\n \"score\": \"0.78789955\",\n \"text\": \"def main():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc0d536b098278d704e13b8e318d6af5\",\n \"score\": \"0.78789955\",\n \"text\": \"def main():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc0d536b098278d704e13b8e318d6af5\",\n \"score\": \"0.78789955\",\n \"text\": \"def main():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc0d536b098278d704e13b8e318d6af5\",\n \"score\": \"0.78789955\",\n \"text\": \"def main():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc0d536b098278d704e13b8e318d6af5\",\n \"score\": \"0.78789955\",\n \"text\": \"def main():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc0d536b098278d704e13b8e318d6af5\",\n \"score\": \"0.78789955\",\n \"text\": \"def main():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc0d536b098278d704e13b8e318d6af5\",\n \"score\": \"0.78789955\",\n \"text\": \"def main():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc0d536b098278d704e13b8e318d6af5\",\n \"score\": \"0.78789955\",\n \"text\": \"def main():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc0d536b098278d704e13b8e318d6af5\",\n \"score\": \"0.78789955\",\n \"text\": \"def main():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc0d536b098278d704e13b8e318d6af5\",\n \"score\": \"0.78789955\",\n \"text\": \"def main():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc0d536b098278d704e13b8e318d6af5\",\n \"score\": \"0.78789955\",\n \"text\": \"def main():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc0d536b098278d704e13b8e318d6af5\",\n \"score\": \"0.78789955\",\n \"text\": \"def main():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc0d536b098278d704e13b8e318d6af5\",\n \"score\": \"0.78789955\",\n \"text\": \"def main():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc0d536b098278d704e13b8e318d6af5\",\n \"score\": \"0.78789955\",\n \"text\": \"def main():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc0d536b098278d704e13b8e318d6af5\",\n \"score\": \"0.78789955\",\n \"text\": \"def main():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"566036b3a07ddd1af043005be3b82644\",\n \"score\": \"0.78345406\",\n \"text\": \"def main(args=None):\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93e2db98c51fbce39b84a61c48c918ca\",\n \"score\": \"0.78276795\",\n \"text\": \"def main(args):\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8550ef16b860a6cc3bad917dfb04f42\",\n \"score\": \"0.78050315\",\n \"text\": \"def main():\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e302ae8a2be1a50834705897601e1c4f\",\n \"score\": \"0.7797365\",\n \"text\": \"def _main():\\n\\tpass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64a1a4261cea121e609f61fd60d0b36f\",\n \"score\": \"0.7668843\",\n \"text\": \"def main():\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"535c6e7d32028af3cc3d681a0804815d\",\n \"score\": \"0.76315814\",\n \"text\": \"def main(args=None):\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d8c44a31b3fd929034986d163e95ff4\",\n \"score\": \"0.75593626\",\n \"text\": \"def main():\\n sys.exit(CommandGenerator().run())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b816c59d61979d6c3de629bfe657acc3\",\n \"score\": \"0.7548137\",\n \"text\": \"def main():\\n CLI().run(argv[1:])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04688cd98d9fcf835947139440a6440f\",\n \"score\": \"0.7527326\",\n \"text\": \"def main():\\n\\n pass\\n\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"05b99850a4683aae56ee599491955a1b\",\n \"score\": \"0.748265\",\n \"text\": \"def _main() -> None:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68322a9750831c83f6f96d00feec8bfd\",\n \"score\": \"0.74573714\",\n \"text\": \"def cli():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68322a9750831c83f6f96d00feec8bfd\",\n \"score\": \"0.74573714\",\n \"text\": \"def cli():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68322a9750831c83f6f96d00feec8bfd\",\n \"score\": \"0.74573714\",\n \"text\": \"def cli():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68322a9750831c83f6f96d00feec8bfd\",\n \"score\": \"0.74573714\",\n \"text\": \"def cli():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e4cec8ea848b3d798bca3b0fced58b1\",\n \"score\": \"0.7456379\",\n \"text\": \"def main(args):\\n Run()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3bc8d0ffe7ca1f67f6ed25dd23c3256\",\n \"score\": \"0.74484986\",\n \"text\": \"def main():\\n return cli(obj={})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"416ebef25c2ec8d1e62f66e4f8143a45\",\n \"score\": \"0.7446385\",\n \"text\": \"def cli():\\n\\tpass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f705cd79ef3e5c4359eff1300ac38c5b\",\n \"score\": \"0.7443812\",\n \"text\": \"def cli(ctx):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f705cd79ef3e5c4359eff1300ac38c5b\",\n \"score\": \"0.7443812\",\n \"text\": \"def cli(ctx):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f705cd79ef3e5c4359eff1300ac38c5b\",\n \"score\": \"0.7443812\",\n \"text\": \"def cli(ctx):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9cb5e099e45669e7b63c3dbf25cf6a9\",\n \"score\": \"0.74294496\",\n \"text\": \"def cli():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9cb5e099e45669e7b63c3dbf25cf6a9\",\n \"score\": \"0.74294496\",\n \"text\": \"def cli():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9cb5e099e45669e7b63c3dbf25cf6a9\",\n \"score\": \"0.74294496\",\n \"text\": \"def cli():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9cb5e099e45669e7b63c3dbf25cf6a9\",\n \"score\": \"0.74294496\",\n \"text\": \"def cli():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9cb5e099e45669e7b63c3dbf25cf6a9\",\n \"score\": \"0.74294496\",\n \"text\": \"def cli():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9cb5e099e45669e7b63c3dbf25cf6a9\",\n \"score\": \"0.74294496\",\n \"text\": \"def cli():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9cb5e099e45669e7b63c3dbf25cf6a9\",\n \"score\": \"0.74294496\",\n \"text\": \"def cli():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9cb5e099e45669e7b63c3dbf25cf6a9\",\n \"score\": \"0.74294496\",\n \"text\": \"def cli():\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":680,"cells":{"query_id":{"kind":"string","value":"c7cafb5f4313d1b5fa9cdfd206771bee"},"query":{"kind":"string","value":"Binary must wrap a string type"},"positive_passages":{"kind":"list like","value":[{"docid":"9ed9bd995a217fb464f9ddab65e1e4cd","score":"0.7420099","text":"def test_binary_force_string(self):\n with self.assertRaises(TypeError):\n Binary(2)","title":""}],"string":"[\n {\n \"docid\": \"9ed9bd995a217fb464f9ddab65e1e4cd\",\n \"score\": \"0.7420099\",\n \"text\": \"def test_binary_force_string(self):\\n with self.assertRaises(TypeError):\\n Binary(2)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"eea0267dc26b875303145a2447b0192b","score":"0.72017926","text":"def isbinary(s, params, ui, **kwargs):\n return s","title":""},{"docid":"5242fe89c082c090012e14c26675050c","score":"0.7061668","text":"def test_binary_converts_unicode(self):\n b = Binary('a')\n self.assertTrue(isinstance(b.value, six.binary_type))","title":""},{"docid":"cf85bb221bb0b88201946dcebef8ab1e","score":"0.68776315","text":"def test_binary_field(self):\n # Arrange\n byte_string = b'abcde\\x00\\x0f\\x00\\x0f\\x0a\\xcf\\xff'\n\n # System under test\n field = BinaryField(\"text\")\n\n # Act\n field_bytes = field.read(BytesIO(byte_string), context=None)\n\n # Assert\n self.assertEqual(field_bytes, byte_string)","title":""},{"docid":"69f05852d98b4288141c42438253c17e","score":"0.6781392","text":"def test_binary_bytes(self):\n self.make_table()\n data = {'a': 1, 'b': 2}\n self.dynamo.put_item('foobar', {'id': 'a',\n 'data': Binary(dumps(data))})\n item = list(self.dynamo.scan('foobar'))[0]\n self.assertEqual(loads(item['data'].value), data)","title":""},{"docid":"d2b69de01af9a9550d48ea5b3d6cf501","score":"0.6743652","text":"def is_byte_string(val: Any) -> bool:\n return not hasattr(val, 'encode')","title":""},{"docid":"ba7026c983f52df9243e6045ea29a148","score":"0.67091286","text":"def _binary(val):\n if isinstance(val, int) or isinstance(val, float):\n s = int(math.ceil(val.bit_length() / 8))\n return val.to_bytes(s, 'big')\n if isinstance(val, str):\n try:\n return decode(val)\n except Exception:\n return val.encode(\"utf-8\")\n if isinstance(val, bytes):\n return val\n raise TypeError(\"Byte serialization not supported\")","title":""},{"docid":"a66b79e59945e0eeeb5c9deeaf40570a","score":"0.6658232","text":"def Binary(value):\n if PY2:\n return Bytea(value)\n else:\n return value","title":""},{"docid":"5150f6352495f4c05ee073f09b48e8cb","score":"0.661736","text":"def Binary(x):\n if PY2:\n return bytearray(x)\n else:\n return bytes(x)","title":""},{"docid":"5f06d221d12f160cafc82db216db5970","score":"0.6566614","text":"def maybe_binstr(val):\n if not isinstance(val, six.string_types):\n return val\n return binstr(val)","title":""},{"docid":"55e272d3686314ea3a6062fec74ac12c","score":"0.65555143","text":"def test_str_param(self):\n request = DummyRequest()\n request.params = {'field': 'myfield'}\n field = param(request, 'field', type=bytes)\n self.assertEquals(field, b'myfield')\n self.assertTrue(isinstance(field, six.binary_type))","title":""},{"docid":"c126cabcc20eae54a7bf1faede064662","score":"0.6540289","text":"def parseByteString(string):\n if string is None : return None\n raise ApiError(api_error.API_ERROR_DATA_TYPE_NOT_SUPPORTED, db.ByteStringProperty)","title":""},{"docid":"e34aa966df4b4bb5c7061df0b1f43c42","score":"0.65340626","text":"def BINARY(self):","title":""},{"docid":"2992bdc51d62b409d6e0531d0f5a3a1b","score":"0.6477947","text":"def __call__(self, value):\r\n \r\n if not isinstance(value, unicode):\r\n try:\r\n unicode(value, self._encoding)\r\n except UnicodeError:\r\n errorMessage = \"The given binary string cannot be converted to unicode using the default encoding.\" + \\\r\n \"Please convert the string to unicode before.\"\r\n raise ValueError(errorMessage)\r\n except TypeError:\r\n raise ValueError(\"The value '%s' is no binary string.\" % str(value))","title":""},{"docid":"2a06a73e611b53c08ce9368ae146e98c","score":"0.64649063","text":"def test_string(self):\r\n\r\n\t\tself.n = bencode.ben_type(\"4:test\")\r\n\t\tself.assertEqual(self.n, str)","title":""},{"docid":"080b768bcd95c11eb552e64443b056ce","score":"0.6450479","text":"def is_binary_string(inp):\n # From https://stackoverflow.com/a/7392391\n return bool(inp.translate(None, TEXTCHARS))","title":""},{"docid":"e5687db72bf9ae6f4875bf5e60b34e93","score":"0.6426588","text":"def binstr(val):\n if isinstance(val, six.binary_type):\n return val\n if sys.version_info < (3, 0):\n return bytes(val)\n else:\n return bytes(val, 'utf8')","title":""},{"docid":"e10c14c3c5c5b0d1fa76a4efe73ac632","score":"0.64137053","text":"def ensure_bytes(v):\n\t\treturn v.encode(\"utf8\") if isinstance(v,str) else v","title":""},{"docid":"79558b70e015b5aaa0959b968c95d152","score":"0.6409564","text":"def _isBinaryString(bytes):\n\tnontext = bytes.translate(None, TEXTCHARS)\n\treturn bool(nontext)","title":""},{"docid":"6a470e9ec1584516c1fab0ee985cb641","score":"0.63404655","text":"async def binify(self, ctx: commands.Context, *message: str):\n if not message:\n response = \"Please include string to convert.\"\n elif set(\"\".join(message)).issubset([\"0\", \"1\", \" \"]) and len(\"\".join(message)) > 2:\n string = \"\".join(message)\n if len(string) % 8 != 0:\n response = \"Binary string contains partial byte.\"\n else:\n response = \"\"\n for i in range(0, len(string), 8):\n n = int(string[i:i+8], 2)\n if n >= 128:\n response = \"Character out of ascii range (0-127)\"\n break\n response += chr(n)\n else:\n response = \"\"\n for c in \" \".join(message).replace(\"&\", \"&\").replace(\"<\", \"<\").replace(\">\", \">\"):\n n = ord(c)\n if n >= 128:\n response = \"Character out of ascii range (0-127)\"\n break\n response += f\"{n:08b}\"\n\n await ctx.send(response)","title":""},{"docid":"82a2de68aa550f21eaa4dfbc4cb62007","score":"0.6296766","text":"def test_binary(self):\n self.assertEqual(self.sid_null_bin, sid.sid(self.sid_null).binary())\n self.assertEqual(self.sid_sample_bin,\n sid.sid(self.sid_sample).binary())","title":""},{"docid":"f5eda366edab8d7f22c57943d084c53c","score":"0.62784666","text":"def test_binary(self):\n self.make_table()\n self.dynamo.put_item('foobar', {'id': 'a', 'data': Binary('abc')})\n item = list(self.dynamo.scan('foobar'))[0]\n self.assertEqual(item['data'].value, b'abc')","title":""},{"docid":"9e585a359fd97e7e0b72cf493c51820f","score":"0.62733793","text":"def ensure_bytes( v ):\n\t\treturn v.encode(\"utf8\") if isinstance(v,unicode) else v","title":""},{"docid":"4c0c1c57f8099d79919a64c8a515dce3","score":"0.62726736","text":"def PlatformBytes(inputStr):\n\t\treturn PlatformString(inputStr)","title":""},{"docid":"8801a19226c43d112b157c52734b15ec","score":"0.62707","text":"def __init__(self, value: str):\n super(BuilderOp.BitString, self).__init__(type='BitString')\n self.value = value","title":""},{"docid":"958a81b333b9b00e14cdd83c3b292593","score":"0.62530494","text":"def test_bytes_param(self):\n request = DummyRequest()\n request.params = {'field': 'myfield'}\n field = param(request, 'field', type=bytes)\n self.assertEquals(field, b'myfield')\n self.assertTrue(isinstance(field, six.binary_type))","title":""},{"docid":"b9be128fa767f3b304b06909c4e48797","score":"0.6236167","text":"def unserialize(self, bytes, isBinary):","title":""},{"docid":"3dc6f4d722c62cc3ffaf9c21b2f4ff07","score":"0.61659217","text":"def ensure_string_type(obj):\n if isinstance(obj, bytes):\n return obj.decode()\n else:\n return str(obj)","title":""},{"docid":"42e0e7c2a2c58db621fbcf297efadd63","score":"0.6132326","text":"def test_binary(self):\n m = fd('joe', 2, '\\x00\\x01\\xff')\n self.assertEqual(m, Message('joe', 2, {\n 'line': '\\x00\\x01\\xff'.encode('base64'),\n 'encoding': 'base64',\n }))","title":""},{"docid":"8f15860be57991b9ae20c93b7de1846d","score":"0.61235255","text":"def test_init_binary(self):\n self.assertEqual(self.sid_null, str(\n sid.sid(self.sid_null_bin, sid.SID_BINARY)))\n self.assertEqual(self.sid_sample, str(\n sid.sid(self.sid_sample_bin, sid.SID_BINARY)))","title":""},{"docid":"d4c3cc8bf49239fd4299b10a04c8f9fd","score":"0.61196184","text":"def binary_encoding(string, encoding = 'utf-8'):\n try:\n return bytes(string, encoding)\n except TypeError: # We are in Python 2\n return str(string)","title":""},{"docid":"275fb4f95a7935f9e90b7ff959a59dc9","score":"0.6077308","text":"def test_bytes_preservation(self):\n data = {\n b'bytes_key': b'this is a byte array',\n 'unicode_key': 'unicode string!',\n }\n serializer = MsgpackSerializer()\n output = serializer.blob_to_dict(serializer.dict_to_blob(data))\n assert b'bytes_key' in output\n assert 'unicode_key' in output\n assert output[b'bytes_key'] == b'this is a byte array'\n assert output['unicode_key'] == 'unicode string!'","title":""},{"docid":"ae1486c1f4564bfdad974269914691a6","score":"0.6071919","text":"def IsBinaryBytes(self, bytes_str):\n return False\n trantab = str.maketrans(FileTypeChecker.ALLBYTES,\n FileTypeChecker.ALLBYTES,FileTypeChecker.TXTCHARS)\n print (trantab)\n nontext = bytes_str.translate(trantab.keys())\n return bool(nontext)","title":""},{"docid":"8a63d7b09c532f301200325eb40c29e8","score":"0.6019618","text":"def b(raw):\n return raw.encode(\"ascii\")","title":""},{"docid":"ed668d335e9a336876ed5e275079c54e","score":"0.60115725","text":"def testStringb(self):\n d = pdb.pdbdata('char[5]', 'abcde')\n self.assertEqual('abcde', str(d))\n o = pdb.unpack(d)\n self.assertEqual(o, 'abcde')","title":""},{"docid":"dde3888951a55ee35eb4ccaf4e35a2cc","score":"0.6000859","text":"def convert_to_bytes(_string: str) -> bytes:\n # print('input string: ', _string)\n # print(\"hold: \", _string.replace(b' ', b'').replace(b':', b''))\n # print('new string: ', binascii.unhexlify(_string.replace(b' ', b'').replace(b':', b'')))\n if type(_string) is str:\n return binascii.unhexlify(_string.replace(' ', '').replace(':', ''))\n elif type(_string) is bytes:\n return binascii.unhexlify(_string.replace(b' ', b'').replace(b':', b''))\n else:\n raise Exception(\"Use string or byte string\")","title":""},{"docid":"71e017b0fb0c6cf5dcae9262007147ad","score":"0.5997392","text":"def a2b_base64(data: str | bytes, /) -> bytes:","title":""},{"docid":"2d81c1717536d252676bc765a50c3b8d","score":"0.59918606","text":"def make_byte_string(s):\n if IS_PYTHON3 and isinstance(s, string_types):\n s = s.encode()\n return s","title":""},{"docid":"eaf8d3466829aa87e3588dd58a07cc00","score":"0.59788567","text":"def ensure_byte_strings():\n def convert_result_from_bytes(result):\n if isinstance(result, bytes):\n return result.decode('utf-8')\n if isinstance(result, list):\n return list(map(convert_result_from_bytes, result))\n if isinstance(result, tuple):\n return tuple(map(convert_result_from_bytes, result))\n return result\n def decorator(fn):\n @functools.wraps(fn)\n def wrapper(*args, **kwargs):\n newargs = []\n newkwargs = {}\n for arg in args:\n if isinstance(arg, str):\n newargs.append(bytes(arg, 'utf-8'))\n else:\n newargs.append(arg)\n for k, v in kwargs.items():\n if isinstance(v, str):\n newkwargs[k] = bytes(v, 'utf-8')\n else:\n newkwargs[k] = v\n newargs = tuple(newargs)\n return fn(*newargs, **newkwargs)\n return wrapper\n return decorator","title":""},{"docid":"16d7ec6b65a2cceada53744242cc6a5a","score":"0.5973994","text":"def testEncode(self):\n\n stream = BytesIO(b'')\n self.schema.encode(stream, {0x52A1:50})\n stream.seek(0)\n self.assertEqual(stream.getvalue(), b'\\x52\\xA1\\x81\\x32')","title":""},{"docid":"70d161b282f447bcdbc8ab59ce6648d1","score":"0.5971439","text":"def bytes_schema(self, schema: core_schema.BytesSchema) -> JsonSchemaValue:\n json_schema = {'type': 'string', 'format': 'binary'}\n self.update_with_validations(json_schema, schema, self.ValidationsMapping.bytes)\n return json_schema","title":""},{"docid":"e1caa98c4b8c78884a4ebbf7d584de84","score":"0.5967122","text":"def to_binary(value, encoding='utf-8'):\n if not value:\n return b''\n if isinstance(value, six.binary_type):\n return value\n if isinstance(value, six.text_type):\n return value.encode(encoding)\n return to_text(value).encode(encoding)","title":""},{"docid":"d12c10b75f029a146b2a7ed8a2a6c82e","score":"0.59585375","text":"def _deserialize_b(self, value):\n return base64.b64encode(value).decode('utf-8')","title":""},{"docid":"66fc8aaddb99f700c93b17691408c11c","score":"0.5942805","text":"def test_binary_equal(self):\n self.assertEqual(Binary('a'), Binary('a'))\n self.assertEqual(Binary('a'), b'a')\n self.assertFalse(Binary('a') != Binary('a'))","title":""},{"docid":"115d7fd398f1a630c00e0300d529271a","score":"0.59389806","text":"def test_str_json_body(self):\n request = DummyRequest()\n request.params = {}\n request.json_body = {'field': 'myfield'}\n request.headers = {'Content-Type': 'application/json'}\n field = param(request, 'field', type=bytes)\n self.assertEquals(field, b'myfield')\n self.assertTrue(isinstance(field, six.binary_type))","title":""},{"docid":"985cf41057490a2b3f4053a070bebfa6","score":"0.5937785","text":"def fix_test_case(block):\n if type(block) is str:\n block = bytes(block, encoding='utf-8')\n return block","title":""},{"docid":"e7fcdbe68d41c2638409bb09ea67e099","score":"0.5903928","text":"def test_binary_repr(self):\n self.assertEqual(repr(Binary('a')), 'Binary(%s)' % b'a')","title":""},{"docid":"492d1ed9bbb2445cdedcc5ced88e936f","score":"0.5893895","text":"def unhexlify(data: str | bytes, /) -> bytes:","title":""},{"docid":"74546d214f8797cc6cf2dee92bfc29da","score":"0.58908445","text":"def test_hash_tag_bytes():\n assert hash_tag_bytes(\"Value\", b\"123\") == \"25aa6da917ca4e568e859df039fb1ca184e65d57\"","title":""},{"docid":"a7f4dc5c9d003781233157c8cccdaef6","score":"0.58838505","text":"def test_get_prep_value_with_bytes(self):\n obj = Base64TestModel()\n value = obj._meta.get_field('field').get_prep_value(\n b'VGhpcyBpcyBhIHTDqXN0\\n')\n\n self.assertIs(type(value), str)\n self.assertEqual(value, 'VGhpcyBpcyBhIHTDqXN0\\n')","title":""},{"docid":"c83a7f26f6735c1d468f5af59b963453","score":"0.5876491","text":"def is_binary(data):\n if not data or not isinstance(data, (six.string_types, six.binary_type)):\n return False\n\n if isinstance(data, six.binary_type):\n if b'\\0' in data:\n return True\n elif str('\\0') in data:\n return True\n\n text_characters = ''.join([chr(x) for x in range(32, 127)] + list('\\n\\r\\t\\b'))\n\n # Get the non-text characters (map each character to itself then use the\n # 'remove' option to get rid of the text characters.)\n if six.PY3:\n if isinstance(data, six.binary_type):\n import sugar.utils.data\n nontext = data.translate(None, sugar.utils.data.encode(text_characters))\n else:\n trans = ''.maketrans('', '', text_characters)\n nontext = data.translate(trans)\n else:\n if isinstance(data, six.text_type):\n trans_args = ({ord(x): None for x in text_characters},)\n else:\n trans_args = (None, str(text_characters)) # future lint: blacklisted-function\n nontext = data.translate(*trans_args)\n\n # If more than 30% non-text characters, then\n # this is considered binary data\n if float(len(nontext)) / len(data) > 0.30:\n return True\n return False","title":""},{"docid":"65c8727e7914ffc91978695c338eb1dd","score":"0.5856668","text":"def test_string(self):\r\n\r\n\t\tself.n = bencode.encode_str(\"test\")\r\n\t\tself.assertEqual(self.n, \"4:test\")","title":""},{"docid":"d3dfce0ee5b6e47a6a227c1ac0109777","score":"0.58527464","text":"def test_convert_type_str(self):\n schema = {\n 'key': 'string'\n }\n record = {\n 'key': 100\n }\n assert_equal(ParserBase._convert_type(record, schema), True)\n assert_equal(record, {'key': '100'})","title":""},{"docid":"2ef402fb28cad8934454290732151ec7","score":"0.5852582","text":"def test_input_too_short(typ, spec, bs):\n\n protocol = BinaryProtocol()\n\n with pytest.raises(EndOfInputError) as exc_info:\n s = bytes(bytearray(bs))\n protocol.deserialize_value(typ, s)\n\n assert 'bytes but got' in str(exc_info)\n\n with pytest.raises(EndOfInputError) as exc_info:\n reader = protocol.reader(ReadBuffer(s))\n spec.read_from(reader)\n\n assert 'bytes but got' in str(exc_info)","title":""},{"docid":"4a4a167a3c25556304ee33cd6463955c","score":"0.584788","text":"def test_to_python_with_bytes(self):\n obj = Base64TestModel()\n value = obj._meta.get_field('field').to_python(\n b'VGhpcyBpcyBhIHRlc3Q=\\n')\n\n self.assertIs(type(value), Base64DecodedValue)\n self.assertEqual(value, b'This is a test')","title":""},{"docid":"16c103233ff4ba94680b7a685d79f37c","score":"0.5819751","text":"def serialize(self, buff):\n try:\n pass\n except struct.error as se: self._check_types(se)\n except TypeError as te: self._check_types(te)","title":""},{"docid":"f1fceeb677a41ec8a321e67d59fdf085","score":"0.5818131","text":"def is_bytes(obj):\n\treturn isinstance(obj, (bytes, bytearray))","title":""},{"docid":"0ea8913a529b803fee4ebdec7f1613fd","score":"0.5809857","text":"def test_object_with_id_binary_serialization() -> None:\n\n uid = UID(value=uuid.UUID(int=333779996850170035686993356951732753684))\n obj = ObjectWithID(id=uid)\n\n blob = (\n b\"\\n$syft.core.common.object.ObjectWithID\\x12\\x14\\n\\x12\\n\\x10\\xfb\\x1b\\xb0\"\n + b\"g[\\xb7LI\\xbe\\xce\\xe7\\x00\\xab\\n\\x15\\x14\"\n )\n\n assert sy.serialize(obj, to_bytes=True) == blob\n assert sy.serialize(obj, to_bytes=True) == blob\n assert sy.serialize(obj, to_bytes=True) == blob","title":""},{"docid":"cbf784e3a264f0d6697e2511936f1595","score":"0.58067","text":"def ensure_str(v):\n\t\treturn v.decode(\"utf8\") if isinstance(v,bytes) else v","title":""},{"docid":"6109fc19bc6624598fba982c13ef9c6f","score":"0.5803272","text":"def IsBinaryDataType(*args):\n return _DigitalMicrograph.IsBinaryDataType(*args)","title":""},{"docid":"50bde40eedc1a4f771a9b94e7c36f89e","score":"0.57741356","text":"def register_bitstring_types(connection):\r\n with closing(connection.cursor()) as cur:\r\n cur.execute(\"SELECT NULL::BIT\")\r\n bit_oid = cur.description[0].type_code\r\n cur.execute(\"SELECT NULL::VARBIT\")\r\n varbit_oid = cur.description[0].type_code\r\n bit_caster = ext.new_type((bit_oid, varbit_oid), 'BIT', cast_bits)\r\n ext.register_type(bit_caster, connection)","title":""},{"docid":"f75e16a5ff724d7c2989b391572b5a7a","score":"0.5751746","text":"def visit_Bytes(self, node):\n self.attr(node, 'content', [self.tokens.str], deps=('s',), default=node.s)","title":""},{"docid":"ffdd3e5916aa065c669f4c966299c3b6","score":"0.57433456","text":"def fix_binary_string(bin_str, num_bits):\n assert num_bits >= len(bin_str), \"num_bits is greater than the length of bin_str\"\n return '0'*(num_bits-len(bin_str))+bin_str","title":""},{"docid":"08044b0fb3960a94a059b111ab7b8cc1","score":"0.57409024","text":"def _bstr(s):\n if isinstance(s, unicode):\n s = s.encode('utf-8')\n elif not isinstance(s, str):\n s = str(s)\n return s","title":""},{"docid":"0bccb71e8079a9336e5ed3a2f608d0fd","score":"0.57385075","text":"def test_strings(self):\r\n\r\n\t\tself.n = bencode.encode(\"test\")\r\n\t\tself.assertEqual(self.n, \"4:test\")","title":""},{"docid":"1bd356b54ba78e4e8df8d3c9939d4471","score":"0.57316196","text":"def __init__(self, text, value, *args, **kwargs):\n str_value = (value and YES) or NO\n super(Binary, self).__init__(text, str_value, *args, **kwargs)","title":""},{"docid":"d5912ab4ea095add386a67b6072f64fb","score":"0.57289237","text":"def test_base64_str_representation(self):\n try:\n correct = '$base64_string = \"Test __str()__ call on YaraString w/ Base64 modifier.\" ' \\\n 'base64(!@#$%^&*(){}[].,|ABCDEFGHIJ\tLMNOPQRSTUVWXYZabcdefghijklmnopqrstu)'\n\n ys = YaraString(\"base64_string\", \"Test __str()__ call on YaraString w/ Base64 modifier.\",\n string_type=TEXT_TYPE,\n modifiers=[\n {\n \"keyword\": BASE64,\n \"data\": \"!@#$%^&*(){}[].,|ABCDEFGHIJ\\x09LMNOPQRSTUVWXYZabcdefghijklmnopqrstu\"\n }])\n\n self.assertEqual(str(ys), correct)\n except Exception as exc:\n self.fail(\"{}\".format(exc))","title":""},{"docid":"e404774965362d6638c95172c74b9fbb","score":"0.5723626","text":"def bsFromString(self, str):\n\n\t\tx = self.length - len(str)\n\t\tx = x // 2\n\t\tfor i in range(len(str)):\n\t\t\tif(str[i] == '1'):\n\t\t\t\tself.bits[x + i] = 1\n\t\t\telse:\n\t\t\t\tself.bits[x + i] = 0","title":""},{"docid":"a948d3a3a8d0560725eab7f79b7cffba","score":"0.5721702","text":"def test_query_must_be_bytes(self):\n with self.assertRaises(TypeError):\n utils.build_query_params('')","title":""},{"docid":"f5e14d67a5c947dbe460924b9967cad7","score":"0.5715057","text":"def is_string(data):\n return isinstance(data, str)","title":""},{"docid":"c39e4e39b2e46159771de4e002824b4d","score":"0.57147294","text":"def is_binary(self):\n return self._is_binary","title":""},{"docid":"9440473343ba740f8627af2378a3e31a","score":"0.5713475","text":"def PlatformBytes(inputStr):\n\t\tif isinstance(inputStr, bytes):\n\t\t\treturn inputStr\n\t\treturn inputStr.encode(\"UTF-8\")","title":""},{"docid":"3a73600246ed9e24f3dea532e4f7348b","score":"0.5713276","text":"def test__cast_to_bytes(self):\n self.assertEqual(\n bytes(RequestInsert(charset, errors, 0, 1, 0, 1, 2000, 30000)),\n binascii.unhexlify(\"0d0000001b00000000000000010000000000000003000000040100000004d00700000430750000\")\n )\n\n self.assertEqual(\n bytes(RequestInsert(charset, errors, 0, 1, 0, b\"AAA\", b\"BBBB\", b\"CCCCCC\")),\n binascii.unhexlify(\"0d0000001c0000000000000001000000000000000300000003414141044242424206434343434343\")\n )","title":""},{"docid":"4d2d86bf8b38baa14d95fecb617e3ff0","score":"0.570846","text":"def safe_encode(s: Optional[AnyStr]) -> Optional[bytes]:\n if isinstance(s, str):\n return s.encode(defenc)\n elif isinstance(s, bytes):\n return s\n elif s is None:\n return None\n else:\n raise TypeError(\"Expected bytes or text, but got %r\" % (s,))","title":""},{"docid":"acc05dee057031595529dcbab2745421","score":"0.5699245","text":"def binary_str(self):\n return f'{self.number:b}'","title":""},{"docid":"47c5f3caf979c66720a9dd84b48fa888","score":"0.56983876","text":"def __init__(self, value: bytes):\n self.value = value","title":""},{"docid":"2b5fb854a8dd9835760a27aa3f94c080","score":"0.56838495","text":"def s2b(s):\n if _py3:\n return s.encode('utf-8')\n else:\n return s","title":""},{"docid":"54bcbb0f0a90737d71a9ab0759eb4520","score":"0.5682309","text":"def is_binary(s): # DONT EDIT\n return s == '&' or s == '|' or s == '->'","title":""},{"docid":"3cc5557ff8cbd92978ee405269a2ca49","score":"0.5681747","text":"def is_binary(s: str) -> bool:\n return s == '&' or s == '|' or s == '->'","title":""},{"docid":"3cc5557ff8cbd92978ee405269a2ca49","score":"0.5681747","text":"def is_binary(s: str) -> bool:\n return s == '&' or s == '|' or s == '->'","title":""},{"docid":"28b0811a5806feba189e4f9ae6f40587","score":"0.56809175","text":"def _pack_value(self, x: Any) -> bytes:\n if type(x) is int:\n return self.TYPE_INT_PACKED + self._pack_int(x)\n elif type(x) is float:\n return self.TYPE_FLOAT_PACKED + self._pack_float(x)\n elif type(x) is str:\n return self._pack_text(x)\n elif type(x) is bool:\n return self.TYPE_BOOL_PACKED + self._pack_bool(x)\n else:\n raise Exception(\"Can't pack type\" + str(type(x)))","title":""},{"docid":"1cef023efb3de295d6818e60e433ff56","score":"0.5680797","text":"def test_typestring_conversion(self):\n conversion_tests = (\n (12, \"i|12\"),\n (12.4, \"f|12.4\"),\n (\"cowabunga\", \"s|cowabunga\"),\n )\n\n # Test conversion to typestring\n for _input, expected in conversion_tests:\n self.assertEqual(self.cog.redis._value_to_typestring(_input), expected)\n\n # Test conversion from typestrings\n for _input, expected in conversion_tests:\n self.assertEqual(self.cog.redis._value_from_typestring(expected), _input)\n\n # Test that exceptions are raised on invalid input\n with self.assertRaises(TypeError):\n self.cog.redis._value_to_typestring([\"internet\"])\n self.cog.redis._value_from_typestring(\"o|firedog\")","title":""},{"docid":"de416e6c2abb1fc810eba6f775f37f1a","score":"0.5674973","text":"def pack_string(string):\n data = string.encode('UTF-8') if isinstance(string, unicode) else str(string)\n return struct.pack('b', len(data)) + data","title":""},{"docid":"b2981a69cc0cef9a5efc254e0ac6ea0e","score":"0.56714255","text":"def _pack_string(instr):\n val = tools.ToBytes(instr)\n pad_len = align_int(len(val) + 1, FILENAME_ALIGN)\n return val + tools.GetBytes(0, pad_len - len(val))","title":""},{"docid":"652a50d51adaf1c2e187b68ef5890d7a","score":"0.5670707","text":"def test_query_accepts_bytes(self):\n utils.build_query_params(b'')","title":""},{"docid":"b25dc8fbce631a5a04554693082bd50d","score":"0.5668083","text":"def is_binary(s: str) -> bool:\n # return s == '&' or s == '|' or s == '->'\n # For Chapter 3:\n return s in {'&', '|', '->', '+', '<->', '-&', '-|'}","title":""},{"docid":"1463b68e4abf6c71c9615c91e7dea018","score":"0.5655611","text":"def key_bytes(key):\n from normality import stringify\n if isinstance(key, bytes):\n return key\n key = stringify(key) or \"\"\n return key.encode(\"utf-8\")","title":""},{"docid":"33e73f01505bebf572be6dc00deb4a68","score":"0.56469035","text":"def _is_string_array(restype):\n if isinstance(restype, WeldVec):\n if isinstance(restype.elem_type, WeldVec):\n if isinstance(restype.elem_type.elem_type, I8):\n return True\n return False","title":""},{"docid":"7de7b4890dc875d51ad5ff022618b7b6","score":"0.56468403","text":"def ispure(s):\n if s is None:\n return True\n if isinstance(s, (six.text_type, newstr)):\n return True\n if type(s) == bytes:\n try:\n s.decode('ascii')\n except UnicodeDecodeError:\n return False\n return True\n return False","title":""},{"docid":"26e45d2ee3395dc1934d426611e44c34","score":"0.56407607","text":"def test_types(self):\r\n fstr = _FakeString(\"fake string\")\r\n self.assertEqual(str(fstr), \"fake string\")\r\n self.assertEqual(bytes(fstr), b\"fake string\")\r\n if py3k:\r\n self.assertEqual(repr(fstr), \"'fake string'\")\r\n else:\r\n self.assertEqual(repr(fstr), b\"u'fake string'\")\r\n\r\n self.assertIsInstance(str(fstr), str)\r\n self.assertIsInstance(bytes(fstr), bytes)\r\n if py3k:\r\n self.assertIsInstance(repr(fstr), str)\r\n else:\r\n self.assertIsInstance(repr(fstr), bytes)","title":""},{"docid":"b67bd5a0e7cd62dae0b7d27534ba8d94","score":"0.56313056","text":"def test_convert_type_unicode_str(self):\n schema = {\n 'key': 'string'\n }\n record = {\n 'key': '\\ue82a'\n }\n assert_equal(ParserBase._convert_type(record, schema), True)\n assert_equal(record, {'key': '\\ue82a'})","title":""},{"docid":"5ce86445586eb42dd500d3cd00a820c4","score":"0.5625722","text":"def _a_encode_bytes(value, mapping):\n assert isinstance(value, (bytes, str)), \"VALUE has invalid type: %s\" % type(value)\n return str(len(value)).encode(\"UTF-8\"), b\"b\", cast_to_bin(value)","title":""},{"docid":"d8aec4bfc14b01d8ad0305c0592a8ddf","score":"0.56215763","text":"def __bytes__(self):\n return bytes([self.type * 2, 4]) + self.value.to_bytes(4, 'big')","title":""},{"docid":"4fdbe6cc9735c58a1fe63f750ebcc855","score":"0.56169206","text":"def transmit_encoded(plaintext: str) -> str:\n pass # <- implement your function","title":""},{"docid":"8fdff0b683448d84d291ef0b36fe9d2c","score":"0.5613103","text":"def unserialize(self, bytes):","title":""},{"docid":"8fdff0b683448d84d291ef0b36fe9d2c","score":"0.5613103","text":"def unserialize(self, bytes):","title":""},{"docid":"9acba63f90dec199819a7843af87ccdf","score":"0.5597181","text":"def _bytes(x): # pragma: no branch\n if sys.version > \"3\":\n return bytes(x, \"utf8\")\n else: # pragma: no cover\n return x.__bytes__()","title":""},{"docid":"aa346597e5f4d34a7c7eb42888d3b8a5","score":"0.55970603","text":"def test_solution() -> None:\n a = \"49276d206b696c6c696e6720796f757220627261696e206c696b65206120706f69736f6e6f7573206d757368726f6f6d\"\n b = b\"SSdtIGtpbGxpbmcgeW91ciBicmFpbiBsaWtlIGEgcG9pc29ub3VzIG11c2hyb29t\"\n assert encoding.hex_to_base64(a) == b","title":""},{"docid":"5f60d91a74345c7631600dfb384bd501","score":"0.5596351","text":"def _ops_binary_placeholder_sql(self, value):\n if value is not None:\n return '_binary %s'\n else:\n return '%s'","title":""},{"docid":"a810bdcba61e2e46707bdbdc77106af6","score":"0.55940217","text":"def test_string_length(self):\r\n\r\n\t\tself.n = bencode.decode_str(\"1:abc\")\r\n\t\tself.assertEqual(self.n, \"a\")","title":""},{"docid":"1c7a89e37f243904df7b374810aabe1c","score":"0.5593397","text":"def force_bytes(value):\n if IS_PY3:\n if isinstance(value, str):\n value = value.encode('utf-8', 'backslashreplace')\n else:\n if isinstance(value, unicode):\n value = value.encode('utf-8')\n\n return value","title":""}],"string":"[\n {\n \"docid\": \"eea0267dc26b875303145a2447b0192b\",\n \"score\": \"0.72017926\",\n \"text\": \"def isbinary(s, params, ui, **kwargs):\\n return s\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5242fe89c082c090012e14c26675050c\",\n \"score\": \"0.7061668\",\n \"text\": \"def test_binary_converts_unicode(self):\\n b = Binary('a')\\n self.assertTrue(isinstance(b.value, six.binary_type))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf85bb221bb0b88201946dcebef8ab1e\",\n \"score\": \"0.68776315\",\n \"text\": \"def test_binary_field(self):\\n # Arrange\\n byte_string = b'abcde\\\\x00\\\\x0f\\\\x00\\\\x0f\\\\x0a\\\\xcf\\\\xff'\\n\\n # System under test\\n field = BinaryField(\\\"text\\\")\\n\\n # Act\\n field_bytes = field.read(BytesIO(byte_string), context=None)\\n\\n # Assert\\n self.assertEqual(field_bytes, byte_string)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69f05852d98b4288141c42438253c17e\",\n \"score\": \"0.6781392\",\n \"text\": \"def test_binary_bytes(self):\\n self.make_table()\\n data = {'a': 1, 'b': 2}\\n self.dynamo.put_item('foobar', {'id': 'a',\\n 'data': Binary(dumps(data))})\\n item = list(self.dynamo.scan('foobar'))[0]\\n self.assertEqual(loads(item['data'].value), data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d2b69de01af9a9550d48ea5b3d6cf501\",\n \"score\": \"0.6743652\",\n \"text\": \"def is_byte_string(val: Any) -> bool:\\n return not hasattr(val, 'encode')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba7026c983f52df9243e6045ea29a148\",\n \"score\": \"0.67091286\",\n \"text\": \"def _binary(val):\\n if isinstance(val, int) or isinstance(val, float):\\n s = int(math.ceil(val.bit_length() / 8))\\n return val.to_bytes(s, 'big')\\n if isinstance(val, str):\\n try:\\n return decode(val)\\n except Exception:\\n return val.encode(\\\"utf-8\\\")\\n if isinstance(val, bytes):\\n return val\\n raise TypeError(\\\"Byte serialization not supported\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a66b79e59945e0eeeb5c9deeaf40570a\",\n \"score\": \"0.6658232\",\n \"text\": \"def Binary(value):\\n if PY2:\\n return Bytea(value)\\n else:\\n return value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5150f6352495f4c05ee073f09b48e8cb\",\n \"score\": \"0.661736\",\n \"text\": \"def Binary(x):\\n if PY2:\\n return bytearray(x)\\n else:\\n return bytes(x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f06d221d12f160cafc82db216db5970\",\n \"score\": \"0.6566614\",\n \"text\": \"def maybe_binstr(val):\\n if not isinstance(val, six.string_types):\\n return val\\n return binstr(val)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55e272d3686314ea3a6062fec74ac12c\",\n \"score\": \"0.65555143\",\n \"text\": \"def test_str_param(self):\\n request = DummyRequest()\\n request.params = {'field': 'myfield'}\\n field = param(request, 'field', type=bytes)\\n self.assertEquals(field, b'myfield')\\n self.assertTrue(isinstance(field, six.binary_type))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c126cabcc20eae54a7bf1faede064662\",\n \"score\": \"0.6540289\",\n \"text\": \"def parseByteString(string):\\n if string is None : return None\\n raise ApiError(api_error.API_ERROR_DATA_TYPE_NOT_SUPPORTED, db.ByteStringProperty)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e34aa966df4b4bb5c7061df0b1f43c42\",\n \"score\": \"0.65340626\",\n \"text\": \"def BINARY(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2992bdc51d62b409d6e0531d0f5a3a1b\",\n \"score\": \"0.6477947\",\n \"text\": \"def __call__(self, value):\\r\\n \\r\\n if not isinstance(value, unicode):\\r\\n try:\\r\\n unicode(value, self._encoding)\\r\\n except UnicodeError:\\r\\n errorMessage = \\\"The given binary string cannot be converted to unicode using the default encoding.\\\" + \\\\\\r\\n \\\"Please convert the string to unicode before.\\\"\\r\\n raise ValueError(errorMessage)\\r\\n except TypeError:\\r\\n raise ValueError(\\\"The value '%s' is no binary string.\\\" % str(value))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a06a73e611b53c08ce9368ae146e98c\",\n \"score\": \"0.64649063\",\n \"text\": \"def test_string(self):\\r\\n\\r\\n\\t\\tself.n = bencode.ben_type(\\\"4:test\\\")\\r\\n\\t\\tself.assertEqual(self.n, str)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"080b768bcd95c11eb552e64443b056ce\",\n \"score\": \"0.6450479\",\n \"text\": \"def is_binary_string(inp):\\n # From https://stackoverflow.com/a/7392391\\n return bool(inp.translate(None, TEXTCHARS))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5687db72bf9ae6f4875bf5e60b34e93\",\n \"score\": \"0.6426588\",\n \"text\": \"def binstr(val):\\n if isinstance(val, six.binary_type):\\n return val\\n if sys.version_info < (3, 0):\\n return bytes(val)\\n else:\\n return bytes(val, 'utf8')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e10c14c3c5c5b0d1fa76a4efe73ac632\",\n \"score\": \"0.64137053\",\n \"text\": \"def ensure_bytes(v):\\n\\t\\treturn v.encode(\\\"utf8\\\") if isinstance(v,str) else v\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79558b70e015b5aaa0959b968c95d152\",\n \"score\": \"0.6409564\",\n \"text\": \"def _isBinaryString(bytes):\\n\\tnontext = bytes.translate(None, TEXTCHARS)\\n\\treturn bool(nontext)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a470e9ec1584516c1fab0ee985cb641\",\n \"score\": \"0.63404655\",\n \"text\": \"async def binify(self, ctx: commands.Context, *message: str):\\n if not message:\\n response = \\\"Please include string to convert.\\\"\\n elif set(\\\"\\\".join(message)).issubset([\\\"0\\\", \\\"1\\\", \\\" \\\"]) and len(\\\"\\\".join(message)) > 2:\\n string = \\\"\\\".join(message)\\n if len(string) % 8 != 0:\\n response = \\\"Binary string contains partial byte.\\\"\\n else:\\n response = \\\"\\\"\\n for i in range(0, len(string), 8):\\n n = int(string[i:i+8], 2)\\n if n >= 128:\\n response = \\\"Character out of ascii range (0-127)\\\"\\n break\\n response += chr(n)\\n else:\\n response = \\\"\\\"\\n for c in \\\" \\\".join(message).replace(\\\"&\\\", \\\"&\\\").replace(\\\"<\\\", \\\"<\\\").replace(\\\">\\\", \\\">\\\"):\\n n = ord(c)\\n if n >= 128:\\n response = \\\"Character out of ascii range (0-127)\\\"\\n break\\n response += f\\\"{n:08b}\\\"\\n\\n await ctx.send(response)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82a2de68aa550f21eaa4dfbc4cb62007\",\n \"score\": \"0.6296766\",\n \"text\": \"def test_binary(self):\\n self.assertEqual(self.sid_null_bin, sid.sid(self.sid_null).binary())\\n self.assertEqual(self.sid_sample_bin,\\n sid.sid(self.sid_sample).binary())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5eda366edab8d7f22c57943d084c53c\",\n \"score\": \"0.62784666\",\n \"text\": \"def test_binary(self):\\n self.make_table()\\n self.dynamo.put_item('foobar', {'id': 'a', 'data': Binary('abc')})\\n item = list(self.dynamo.scan('foobar'))[0]\\n self.assertEqual(item['data'].value, b'abc')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e585a359fd97e7e0b72cf493c51820f\",\n \"score\": \"0.62733793\",\n \"text\": \"def ensure_bytes( v ):\\n\\t\\treturn v.encode(\\\"utf8\\\") if isinstance(v,unicode) else v\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c0c1c57f8099d79919a64c8a515dce3\",\n \"score\": \"0.62726736\",\n \"text\": \"def PlatformBytes(inputStr):\\n\\t\\treturn PlatformString(inputStr)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8801a19226c43d112b157c52734b15ec\",\n \"score\": \"0.62707\",\n \"text\": \"def __init__(self, value: str):\\n super(BuilderOp.BitString, self).__init__(type='BitString')\\n self.value = value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"958a81b333b9b00e14cdd83c3b292593\",\n \"score\": \"0.62530494\",\n \"text\": \"def test_bytes_param(self):\\n request = DummyRequest()\\n request.params = {'field': 'myfield'}\\n field = param(request, 'field', type=bytes)\\n self.assertEquals(field, b'myfield')\\n self.assertTrue(isinstance(field, six.binary_type))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9be128fa767f3b304b06909c4e48797\",\n \"score\": \"0.6236167\",\n \"text\": \"def unserialize(self, bytes, isBinary):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3dc6f4d722c62cc3ffaf9c21b2f4ff07\",\n \"score\": \"0.61659217\",\n \"text\": \"def ensure_string_type(obj):\\n if isinstance(obj, bytes):\\n return obj.decode()\\n else:\\n return str(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42e0e7c2a2c58db621fbcf297efadd63\",\n \"score\": \"0.6132326\",\n \"text\": \"def test_binary(self):\\n m = fd('joe', 2, '\\\\x00\\\\x01\\\\xff')\\n self.assertEqual(m, Message('joe', 2, {\\n 'line': '\\\\x00\\\\x01\\\\xff'.encode('base64'),\\n 'encoding': 'base64',\\n }))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f15860be57991b9ae20c93b7de1846d\",\n \"score\": \"0.61235255\",\n \"text\": \"def test_init_binary(self):\\n self.assertEqual(self.sid_null, str(\\n sid.sid(self.sid_null_bin, sid.SID_BINARY)))\\n self.assertEqual(self.sid_sample, str(\\n sid.sid(self.sid_sample_bin, sid.SID_BINARY)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4c3cc8bf49239fd4299b10a04c8f9fd\",\n \"score\": \"0.61196184\",\n \"text\": \"def binary_encoding(string, encoding = 'utf-8'):\\n try:\\n return bytes(string, encoding)\\n except TypeError: # We are in Python 2\\n return str(string)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"275fb4f95a7935f9e90b7ff959a59dc9\",\n \"score\": \"0.6077308\",\n \"text\": \"def test_bytes_preservation(self):\\n data = {\\n b'bytes_key': b'this is a byte array',\\n 'unicode_key': 'unicode string!',\\n }\\n serializer = MsgpackSerializer()\\n output = serializer.blob_to_dict(serializer.dict_to_blob(data))\\n assert b'bytes_key' in output\\n assert 'unicode_key' in output\\n assert output[b'bytes_key'] == b'this is a byte array'\\n assert output['unicode_key'] == 'unicode string!'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae1486c1f4564bfdad974269914691a6\",\n \"score\": \"0.6071919\",\n \"text\": \"def IsBinaryBytes(self, bytes_str):\\n return False\\n trantab = str.maketrans(FileTypeChecker.ALLBYTES,\\n FileTypeChecker.ALLBYTES,FileTypeChecker.TXTCHARS)\\n print (trantab)\\n nontext = bytes_str.translate(trantab.keys())\\n return bool(nontext)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a63d7b09c532f301200325eb40c29e8\",\n \"score\": \"0.6019618\",\n \"text\": \"def b(raw):\\n return raw.encode(\\\"ascii\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed668d335e9a336876ed5e275079c54e\",\n \"score\": \"0.60115725\",\n \"text\": \"def testStringb(self):\\n d = pdb.pdbdata('char[5]', 'abcde')\\n self.assertEqual('abcde', str(d))\\n o = pdb.unpack(d)\\n self.assertEqual(o, 'abcde')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dde3888951a55ee35eb4ccaf4e35a2cc\",\n \"score\": \"0.6000859\",\n \"text\": \"def convert_to_bytes(_string: str) -> bytes:\\n # print('input string: ', _string)\\n # print(\\\"hold: \\\", _string.replace(b' ', b'').replace(b':', b''))\\n # print('new string: ', binascii.unhexlify(_string.replace(b' ', b'').replace(b':', b'')))\\n if type(_string) is str:\\n return binascii.unhexlify(_string.replace(' ', '').replace(':', ''))\\n elif type(_string) is bytes:\\n return binascii.unhexlify(_string.replace(b' ', b'').replace(b':', b''))\\n else:\\n raise Exception(\\\"Use string or byte string\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71e017b0fb0c6cf5dcae9262007147ad\",\n \"score\": \"0.5997392\",\n \"text\": \"def a2b_base64(data: str | bytes, /) -> bytes:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d81c1717536d252676bc765a50c3b8d\",\n \"score\": \"0.59918606\",\n \"text\": \"def make_byte_string(s):\\n if IS_PYTHON3 and isinstance(s, string_types):\\n s = s.encode()\\n return s\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eaf8d3466829aa87e3588dd58a07cc00\",\n \"score\": \"0.59788567\",\n \"text\": \"def ensure_byte_strings():\\n def convert_result_from_bytes(result):\\n if isinstance(result, bytes):\\n return result.decode('utf-8')\\n if isinstance(result, list):\\n return list(map(convert_result_from_bytes, result))\\n if isinstance(result, tuple):\\n return tuple(map(convert_result_from_bytes, result))\\n return result\\n def decorator(fn):\\n @functools.wraps(fn)\\n def wrapper(*args, **kwargs):\\n newargs = []\\n newkwargs = {}\\n for arg in args:\\n if isinstance(arg, str):\\n newargs.append(bytes(arg, 'utf-8'))\\n else:\\n newargs.append(arg)\\n for k, v in kwargs.items():\\n if isinstance(v, str):\\n newkwargs[k] = bytes(v, 'utf-8')\\n else:\\n newkwargs[k] = v\\n newargs = tuple(newargs)\\n return fn(*newargs, **newkwargs)\\n return wrapper\\n return decorator\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16d7ec6b65a2cceada53744242cc6a5a\",\n \"score\": \"0.5973994\",\n \"text\": \"def testEncode(self):\\n\\n stream = BytesIO(b'')\\n self.schema.encode(stream, {0x52A1:50})\\n stream.seek(0)\\n self.assertEqual(stream.getvalue(), b'\\\\x52\\\\xA1\\\\x81\\\\x32')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70d161b282f447bcdbc8ab59ce6648d1\",\n \"score\": \"0.5971439\",\n \"text\": \"def bytes_schema(self, schema: core_schema.BytesSchema) -> JsonSchemaValue:\\n json_schema = {'type': 'string', 'format': 'binary'}\\n self.update_with_validations(json_schema, schema, self.ValidationsMapping.bytes)\\n return json_schema\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1caa98c4b8c78884a4ebbf7d584de84\",\n \"score\": \"0.5967122\",\n \"text\": \"def to_binary(value, encoding='utf-8'):\\n if not value:\\n return b''\\n if isinstance(value, six.binary_type):\\n return value\\n if isinstance(value, six.text_type):\\n return value.encode(encoding)\\n return to_text(value).encode(encoding)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d12c10b75f029a146b2a7ed8a2a6c82e\",\n \"score\": \"0.59585375\",\n \"text\": \"def _deserialize_b(self, value):\\n return base64.b64encode(value).decode('utf-8')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66fc8aaddb99f700c93b17691408c11c\",\n \"score\": \"0.5942805\",\n \"text\": \"def test_binary_equal(self):\\n self.assertEqual(Binary('a'), Binary('a'))\\n self.assertEqual(Binary('a'), b'a')\\n self.assertFalse(Binary('a') != Binary('a'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"115d7fd398f1a630c00e0300d529271a\",\n \"score\": \"0.59389806\",\n \"text\": \"def test_str_json_body(self):\\n request = DummyRequest()\\n request.params = {}\\n request.json_body = {'field': 'myfield'}\\n request.headers = {'Content-Type': 'application/json'}\\n field = param(request, 'field', type=bytes)\\n self.assertEquals(field, b'myfield')\\n self.assertTrue(isinstance(field, six.binary_type))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"985cf41057490a2b3f4053a070bebfa6\",\n \"score\": \"0.5937785\",\n \"text\": \"def fix_test_case(block):\\n if type(block) is str:\\n block = bytes(block, encoding='utf-8')\\n return block\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7fcdbe68d41c2638409bb09ea67e099\",\n \"score\": \"0.5903928\",\n \"text\": \"def test_binary_repr(self):\\n self.assertEqual(repr(Binary('a')), 'Binary(%s)' % b'a')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"492d1ed9bbb2445cdedcc5ced88e936f\",\n \"score\": \"0.5893895\",\n \"text\": \"def unhexlify(data: str | bytes, /) -> bytes:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74546d214f8797cc6cf2dee92bfc29da\",\n \"score\": \"0.58908445\",\n \"text\": \"def test_hash_tag_bytes():\\n assert hash_tag_bytes(\\\"Value\\\", b\\\"123\\\") == \\\"25aa6da917ca4e568e859df039fb1ca184e65d57\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7f4dc5c9d003781233157c8cccdaef6\",\n \"score\": \"0.58838505\",\n \"text\": \"def test_get_prep_value_with_bytes(self):\\n obj = Base64TestModel()\\n value = obj._meta.get_field('field').get_prep_value(\\n b'VGhpcyBpcyBhIHTDqXN0\\\\n')\\n\\n self.assertIs(type(value), str)\\n self.assertEqual(value, 'VGhpcyBpcyBhIHTDqXN0\\\\n')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c83a7f26f6735c1d468f5af59b963453\",\n \"score\": \"0.5876491\",\n \"text\": \"def is_binary(data):\\n if not data or not isinstance(data, (six.string_types, six.binary_type)):\\n return False\\n\\n if isinstance(data, six.binary_type):\\n if b'\\\\0' in data:\\n return True\\n elif str('\\\\0') in data:\\n return True\\n\\n text_characters = ''.join([chr(x) for x in range(32, 127)] + list('\\\\n\\\\r\\\\t\\\\b'))\\n\\n # Get the non-text characters (map each character to itself then use the\\n # 'remove' option to get rid of the text characters.)\\n if six.PY3:\\n if isinstance(data, six.binary_type):\\n import sugar.utils.data\\n nontext = data.translate(None, sugar.utils.data.encode(text_characters))\\n else:\\n trans = ''.maketrans('', '', text_characters)\\n nontext = data.translate(trans)\\n else:\\n if isinstance(data, six.text_type):\\n trans_args = ({ord(x): None for x in text_characters},)\\n else:\\n trans_args = (None, str(text_characters)) # future lint: blacklisted-function\\n nontext = data.translate(*trans_args)\\n\\n # If more than 30% non-text characters, then\\n # this is considered binary data\\n if float(len(nontext)) / len(data) > 0.30:\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65c8727e7914ffc91978695c338eb1dd\",\n \"score\": \"0.5856668\",\n \"text\": \"def test_string(self):\\r\\n\\r\\n\\t\\tself.n = bencode.encode_str(\\\"test\\\")\\r\\n\\t\\tself.assertEqual(self.n, \\\"4:test\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3dfce0ee5b6e47a6a227c1ac0109777\",\n \"score\": \"0.58527464\",\n \"text\": \"def test_convert_type_str(self):\\n schema = {\\n 'key': 'string'\\n }\\n record = {\\n 'key': 100\\n }\\n assert_equal(ParserBase._convert_type(record, schema), True)\\n assert_equal(record, {'key': '100'})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ef402fb28cad8934454290732151ec7\",\n \"score\": \"0.5852582\",\n \"text\": \"def test_input_too_short(typ, spec, bs):\\n\\n protocol = BinaryProtocol()\\n\\n with pytest.raises(EndOfInputError) as exc_info:\\n s = bytes(bytearray(bs))\\n protocol.deserialize_value(typ, s)\\n\\n assert 'bytes but got' in str(exc_info)\\n\\n with pytest.raises(EndOfInputError) as exc_info:\\n reader = protocol.reader(ReadBuffer(s))\\n spec.read_from(reader)\\n\\n assert 'bytes but got' in str(exc_info)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a4a167a3c25556304ee33cd6463955c\",\n \"score\": \"0.584788\",\n \"text\": \"def test_to_python_with_bytes(self):\\n obj = Base64TestModel()\\n value = obj._meta.get_field('field').to_python(\\n b'VGhpcyBpcyBhIHRlc3Q=\\\\n')\\n\\n self.assertIs(type(value), Base64DecodedValue)\\n self.assertEqual(value, b'This is a test')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16c103233ff4ba94680b7a685d79f37c\",\n \"score\": \"0.5819751\",\n \"text\": \"def serialize(self, buff):\\n try:\\n pass\\n except struct.error as se: self._check_types(se)\\n except TypeError as te: self._check_types(te)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1fceeb677a41ec8a321e67d59fdf085\",\n \"score\": \"0.5818131\",\n \"text\": \"def is_bytes(obj):\\n\\treturn isinstance(obj, (bytes, bytearray))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ea8913a529b803fee4ebdec7f1613fd\",\n \"score\": \"0.5809857\",\n \"text\": \"def test_object_with_id_binary_serialization() -> None:\\n\\n uid = UID(value=uuid.UUID(int=333779996850170035686993356951732753684))\\n obj = ObjectWithID(id=uid)\\n\\n blob = (\\n b\\\"\\\\n$syft.core.common.object.ObjectWithID\\\\x12\\\\x14\\\\n\\\\x12\\\\n\\\\x10\\\\xfb\\\\x1b\\\\xb0\\\"\\n + b\\\"g[\\\\xb7LI\\\\xbe\\\\xce\\\\xe7\\\\x00\\\\xab\\\\n\\\\x15\\\\x14\\\"\\n )\\n\\n assert sy.serialize(obj, to_bytes=True) == blob\\n assert sy.serialize(obj, to_bytes=True) == blob\\n assert sy.serialize(obj, to_bytes=True) == blob\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cbf784e3a264f0d6697e2511936f1595\",\n \"score\": \"0.58067\",\n \"text\": \"def ensure_str(v):\\n\\t\\treturn v.decode(\\\"utf8\\\") if isinstance(v,bytes) else v\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6109fc19bc6624598fba982c13ef9c6f\",\n \"score\": \"0.5803272\",\n \"text\": \"def IsBinaryDataType(*args):\\n return _DigitalMicrograph.IsBinaryDataType(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50bde40eedc1a4f771a9b94e7c36f89e\",\n \"score\": \"0.57741356\",\n \"text\": \"def register_bitstring_types(connection):\\r\\n with closing(connection.cursor()) as cur:\\r\\n cur.execute(\\\"SELECT NULL::BIT\\\")\\r\\n bit_oid = cur.description[0].type_code\\r\\n cur.execute(\\\"SELECT NULL::VARBIT\\\")\\r\\n varbit_oid = cur.description[0].type_code\\r\\n bit_caster = ext.new_type((bit_oid, varbit_oid), 'BIT', cast_bits)\\r\\n ext.register_type(bit_caster, connection)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f75e16a5ff724d7c2989b391572b5a7a\",\n \"score\": \"0.5751746\",\n \"text\": \"def visit_Bytes(self, node):\\n self.attr(node, 'content', [self.tokens.str], deps=('s',), default=node.s)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ffdd3e5916aa065c669f4c966299c3b6\",\n \"score\": \"0.57433456\",\n \"text\": \"def fix_binary_string(bin_str, num_bits):\\n assert num_bits >= len(bin_str), \\\"num_bits is greater than the length of bin_str\\\"\\n return '0'*(num_bits-len(bin_str))+bin_str\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08044b0fb3960a94a059b111ab7b8cc1\",\n \"score\": \"0.57409024\",\n \"text\": \"def _bstr(s):\\n if isinstance(s, unicode):\\n s = s.encode('utf-8')\\n elif not isinstance(s, str):\\n s = str(s)\\n return s\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0bccb71e8079a9336e5ed3a2f608d0fd\",\n \"score\": \"0.57385075\",\n \"text\": \"def test_strings(self):\\r\\n\\r\\n\\t\\tself.n = bencode.encode(\\\"test\\\")\\r\\n\\t\\tself.assertEqual(self.n, \\\"4:test\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1bd356b54ba78e4e8df8d3c9939d4471\",\n \"score\": \"0.57316196\",\n \"text\": \"def __init__(self, text, value, *args, **kwargs):\\n str_value = (value and YES) or NO\\n super(Binary, self).__init__(text, str_value, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5912ab4ea095add386a67b6072f64fb\",\n \"score\": \"0.57289237\",\n \"text\": \"def test_base64_str_representation(self):\\n try:\\n correct = '$base64_string = \\\"Test __str()__ call on YaraString w/ Base64 modifier.\\\" ' \\\\\\n 'base64(!@#$%^&*(){}[].,|ABCDEFGHIJ\\tLMNOPQRSTUVWXYZabcdefghijklmnopqrstu)'\\n\\n ys = YaraString(\\\"base64_string\\\", \\\"Test __str()__ call on YaraString w/ Base64 modifier.\\\",\\n string_type=TEXT_TYPE,\\n modifiers=[\\n {\\n \\\"keyword\\\": BASE64,\\n \\\"data\\\": \\\"!@#$%^&*(){}[].,|ABCDEFGHIJ\\\\x09LMNOPQRSTUVWXYZabcdefghijklmnopqrstu\\\"\\n }])\\n\\n self.assertEqual(str(ys), correct)\\n except Exception as exc:\\n self.fail(\\\"{}\\\".format(exc))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e404774965362d6638c95172c74b9fbb\",\n \"score\": \"0.5723626\",\n \"text\": \"def bsFromString(self, str):\\n\\n\\t\\tx = self.length - len(str)\\n\\t\\tx = x // 2\\n\\t\\tfor i in range(len(str)):\\n\\t\\t\\tif(str[i] == '1'):\\n\\t\\t\\t\\tself.bits[x + i] = 1\\n\\t\\t\\telse:\\n\\t\\t\\t\\tself.bits[x + i] = 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a948d3a3a8d0560725eab7f79b7cffba\",\n \"score\": \"0.5721702\",\n \"text\": \"def test_query_must_be_bytes(self):\\n with self.assertRaises(TypeError):\\n utils.build_query_params('')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5e14d67a5c947dbe460924b9967cad7\",\n \"score\": \"0.5715057\",\n \"text\": \"def is_string(data):\\n return isinstance(data, str)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c39e4e39b2e46159771de4e002824b4d\",\n \"score\": \"0.57147294\",\n \"text\": \"def is_binary(self):\\n return self._is_binary\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9440473343ba740f8627af2378a3e31a\",\n \"score\": \"0.5713475\",\n \"text\": \"def PlatformBytes(inputStr):\\n\\t\\tif isinstance(inputStr, bytes):\\n\\t\\t\\treturn inputStr\\n\\t\\treturn inputStr.encode(\\\"UTF-8\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a73600246ed9e24f3dea532e4f7348b\",\n \"score\": \"0.5713276\",\n \"text\": \"def test__cast_to_bytes(self):\\n self.assertEqual(\\n bytes(RequestInsert(charset, errors, 0, 1, 0, 1, 2000, 30000)),\\n binascii.unhexlify(\\\"0d0000001b00000000000000010000000000000003000000040100000004d00700000430750000\\\")\\n )\\n\\n self.assertEqual(\\n bytes(RequestInsert(charset, errors, 0, 1, 0, b\\\"AAA\\\", b\\\"BBBB\\\", b\\\"CCCCCC\\\")),\\n binascii.unhexlify(\\\"0d0000001c0000000000000001000000000000000300000003414141044242424206434343434343\\\")\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d2d86bf8b38baa14d95fecb617e3ff0\",\n \"score\": \"0.570846\",\n \"text\": \"def safe_encode(s: Optional[AnyStr]) -> Optional[bytes]:\\n if isinstance(s, str):\\n return s.encode(defenc)\\n elif isinstance(s, bytes):\\n return s\\n elif s is None:\\n return None\\n else:\\n raise TypeError(\\\"Expected bytes or text, but got %r\\\" % (s,))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"acc05dee057031595529dcbab2745421\",\n \"score\": \"0.5699245\",\n \"text\": \"def binary_str(self):\\n return f'{self.number:b}'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47c5f3caf979c66720a9dd84b48fa888\",\n \"score\": \"0.56983876\",\n \"text\": \"def __init__(self, value: bytes):\\n self.value = value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b5fb854a8dd9835760a27aa3f94c080\",\n \"score\": \"0.56838495\",\n \"text\": \"def s2b(s):\\n if _py3:\\n return s.encode('utf-8')\\n else:\\n return s\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54bcbb0f0a90737d71a9ab0759eb4520\",\n \"score\": \"0.5682309\",\n \"text\": \"def is_binary(s): # DONT EDIT\\n return s == '&' or s == '|' or s == '->'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3cc5557ff8cbd92978ee405269a2ca49\",\n \"score\": \"0.5681747\",\n \"text\": \"def is_binary(s: str) -> bool:\\n return s == '&' or s == '|' or s == '->'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3cc5557ff8cbd92978ee405269a2ca49\",\n \"score\": \"0.5681747\",\n \"text\": \"def is_binary(s: str) -> bool:\\n return s == '&' or s == '|' or s == '->'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28b0811a5806feba189e4f9ae6f40587\",\n \"score\": \"0.56809175\",\n \"text\": \"def _pack_value(self, x: Any) -> bytes:\\n if type(x) is int:\\n return self.TYPE_INT_PACKED + self._pack_int(x)\\n elif type(x) is float:\\n return self.TYPE_FLOAT_PACKED + self._pack_float(x)\\n elif type(x) is str:\\n return self._pack_text(x)\\n elif type(x) is bool:\\n return self.TYPE_BOOL_PACKED + self._pack_bool(x)\\n else:\\n raise Exception(\\\"Can't pack type\\\" + str(type(x)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1cef023efb3de295d6818e60e433ff56\",\n \"score\": \"0.5680797\",\n \"text\": \"def test_typestring_conversion(self):\\n conversion_tests = (\\n (12, \\\"i|12\\\"),\\n (12.4, \\\"f|12.4\\\"),\\n (\\\"cowabunga\\\", \\\"s|cowabunga\\\"),\\n )\\n\\n # Test conversion to typestring\\n for _input, expected in conversion_tests:\\n self.assertEqual(self.cog.redis._value_to_typestring(_input), expected)\\n\\n # Test conversion from typestrings\\n for _input, expected in conversion_tests:\\n self.assertEqual(self.cog.redis._value_from_typestring(expected), _input)\\n\\n # Test that exceptions are raised on invalid input\\n with self.assertRaises(TypeError):\\n self.cog.redis._value_to_typestring([\\\"internet\\\"])\\n self.cog.redis._value_from_typestring(\\\"o|firedog\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de416e6c2abb1fc810eba6f775f37f1a\",\n \"score\": \"0.5674973\",\n \"text\": \"def pack_string(string):\\n data = string.encode('UTF-8') if isinstance(string, unicode) else str(string)\\n return struct.pack('b', len(data)) + data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2981a69cc0cef9a5efc254e0ac6ea0e\",\n \"score\": \"0.56714255\",\n \"text\": \"def _pack_string(instr):\\n val = tools.ToBytes(instr)\\n pad_len = align_int(len(val) + 1, FILENAME_ALIGN)\\n return val + tools.GetBytes(0, pad_len - len(val))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"652a50d51adaf1c2e187b68ef5890d7a\",\n \"score\": \"0.5670707\",\n \"text\": \"def test_query_accepts_bytes(self):\\n utils.build_query_params(b'')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b25dc8fbce631a5a04554693082bd50d\",\n \"score\": \"0.5668083\",\n \"text\": \"def is_binary(s: str) -> bool:\\n # return s == '&' or s == '|' or s == '->'\\n # For Chapter 3:\\n return s in {'&', '|', '->', '+', '<->', '-&', '-|'}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1463b68e4abf6c71c9615c91e7dea018\",\n \"score\": \"0.5655611\",\n \"text\": \"def key_bytes(key):\\n from normality import stringify\\n if isinstance(key, bytes):\\n return key\\n key = stringify(key) or \\\"\\\"\\n return key.encode(\\\"utf-8\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33e73f01505bebf572be6dc00deb4a68\",\n \"score\": \"0.56469035\",\n \"text\": \"def _is_string_array(restype):\\n if isinstance(restype, WeldVec):\\n if isinstance(restype.elem_type, WeldVec):\\n if isinstance(restype.elem_type.elem_type, I8):\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7de7b4890dc875d51ad5ff022618b7b6\",\n \"score\": \"0.56468403\",\n \"text\": \"def ispure(s):\\n if s is None:\\n return True\\n if isinstance(s, (six.text_type, newstr)):\\n return True\\n if type(s) == bytes:\\n try:\\n s.decode('ascii')\\n except UnicodeDecodeError:\\n return False\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"26e45d2ee3395dc1934d426611e44c34\",\n \"score\": \"0.56407607\",\n \"text\": \"def test_types(self):\\r\\n fstr = _FakeString(\\\"fake string\\\")\\r\\n self.assertEqual(str(fstr), \\\"fake string\\\")\\r\\n self.assertEqual(bytes(fstr), b\\\"fake string\\\")\\r\\n if py3k:\\r\\n self.assertEqual(repr(fstr), \\\"'fake string'\\\")\\r\\n else:\\r\\n self.assertEqual(repr(fstr), b\\\"u'fake string'\\\")\\r\\n\\r\\n self.assertIsInstance(str(fstr), str)\\r\\n self.assertIsInstance(bytes(fstr), bytes)\\r\\n if py3k:\\r\\n self.assertIsInstance(repr(fstr), str)\\r\\n else:\\r\\n self.assertIsInstance(repr(fstr), bytes)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b67bd5a0e7cd62dae0b7d27534ba8d94\",\n \"score\": \"0.56313056\",\n \"text\": \"def test_convert_type_unicode_str(self):\\n schema = {\\n 'key': 'string'\\n }\\n record = {\\n 'key': '\\\\ue82a'\\n }\\n assert_equal(ParserBase._convert_type(record, schema), True)\\n assert_equal(record, {'key': '\\\\ue82a'})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ce86445586eb42dd500d3cd00a820c4\",\n \"score\": \"0.5625722\",\n \"text\": \"def _a_encode_bytes(value, mapping):\\n assert isinstance(value, (bytes, str)), \\\"VALUE has invalid type: %s\\\" % type(value)\\n return str(len(value)).encode(\\\"UTF-8\\\"), b\\\"b\\\", cast_to_bin(value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8aec4bfc14b01d8ad0305c0592a8ddf\",\n \"score\": \"0.56215763\",\n \"text\": \"def __bytes__(self):\\n return bytes([self.type * 2, 4]) + self.value.to_bytes(4, 'big')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4fdbe6cc9735c58a1fe63f750ebcc855\",\n \"score\": \"0.56169206\",\n \"text\": \"def transmit_encoded(plaintext: str) -> str:\\n pass # <- implement your function\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8fdff0b683448d84d291ef0b36fe9d2c\",\n \"score\": \"0.5613103\",\n \"text\": \"def unserialize(self, bytes):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8fdff0b683448d84d291ef0b36fe9d2c\",\n \"score\": \"0.5613103\",\n \"text\": \"def unserialize(self, bytes):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9acba63f90dec199819a7843af87ccdf\",\n \"score\": \"0.5597181\",\n \"text\": \"def _bytes(x): # pragma: no branch\\n if sys.version > \\\"3\\\":\\n return bytes(x, \\\"utf8\\\")\\n else: # pragma: no cover\\n return x.__bytes__()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa346597e5f4d34a7c7eb42888d3b8a5\",\n \"score\": \"0.55970603\",\n \"text\": \"def test_solution() -> None:\\n a = \\\"49276d206b696c6c696e6720796f757220627261696e206c696b65206120706f69736f6e6f7573206d757368726f6f6d\\\"\\n b = b\\\"SSdtIGtpbGxpbmcgeW91ciBicmFpbiBsaWtlIGEgcG9pc29ub3VzIG11c2hyb29t\\\"\\n assert encoding.hex_to_base64(a) == b\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f60d91a74345c7631600dfb384bd501\",\n \"score\": \"0.5596351\",\n \"text\": \"def _ops_binary_placeholder_sql(self, value):\\n if value is not None:\\n return '_binary %s'\\n else:\\n return '%s'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a810bdcba61e2e46707bdbdc77106af6\",\n \"score\": \"0.55940217\",\n \"text\": \"def test_string_length(self):\\r\\n\\r\\n\\t\\tself.n = bencode.decode_str(\\\"1:abc\\\")\\r\\n\\t\\tself.assertEqual(self.n, \\\"a\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c7a89e37f243904df7b374810aabe1c\",\n \"score\": \"0.5593397\",\n \"text\": \"def force_bytes(value):\\n if IS_PY3:\\n if isinstance(value, str):\\n value = value.encode('utf-8', 'backslashreplace')\\n else:\\n if isinstance(value, unicode):\\n value = value.encode('utf-8')\\n\\n return value\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":681,"cells":{"query_id":{"kind":"string","value":"1b26a7e2470731048736735d41e820ca"},"query":{"kind":"string","value":"centers the window to the screen"},"positive_passages":{"kind":"list like","value":[{"docid":"6622fe436845f7b3986af42688cfc35c","score":"0.8327689","text":"def _center_window(self):\r\n screen = QtGui.QDesktopWidget().screenGeometry()\r\n size = self.geometry()\r\n self.move(\r\n (screen.width() - size.width()) * 0.5,\r\n (screen.height() - size.height()) * 0.5\r\n )","title":""}],"string":"[\n {\n \"docid\": \"6622fe436845f7b3986af42688cfc35c\",\n \"score\": \"0.8327689\",\n \"text\": \"def _center_window(self):\\r\\n screen = QtGui.QDesktopWidget().screenGeometry()\\r\\n size = self.geometry()\\r\\n self.move(\\r\\n (screen.width() - size.width()) * 0.5,\\r\\n (screen.height() - size.height()) * 0.5\\r\\n )\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"0470869de0fe4c2dc345e28a653fcd8f","score":"0.8163197","text":"def center(self,win):\n win.update_idletasks()\n width = win.winfo_width()\n frm_width = win.winfo_rootx() - win.winfo_x()\n win_width = width + 2 * frm_width\n height = win.winfo_height()\n titlebar_height = win.winfo_rooty() - win.winfo_y()\n win_height = height + titlebar_height + frm_width\n x = win.winfo_screenwidth() // 2 - win_width // 2\n y = win.winfo_screenheight() // 2 - win_height // 2\n win.geometry('{}x{}+{}+{}'.format(width, height, x, y))\n win.deiconify()","title":""},{"docid":"7989c9a736b1a7bac945846d900d0030","score":"0.81152254","text":"def center(self):\n self.top.update_idletasks()\n w = self.top.winfo_screenwidth()\n h = self.top.winfo_screenheight()\n size = tuple(int(_) for _ in self.top.geometry().split('+')[0].split('x'))\n x = w/2 - size[0]/2\n y = h/2 - size[1]/2\n self.top.geometry(\"%dx%d+%d+%d\" % (size + (x, y)))\n return","title":""},{"docid":"269f4daf6f57d93af44737520be52128","score":"0.8040823","text":"def center(win):\n win.update_idletasks()\n width = win.winfo_width()\n frm_width = win.winfo_rootx() - win.winfo_x()\n win_width = width + 2 * frm_width\n height = win.winfo_height()\n titlebar_height = win.winfo_rooty() - win.winfo_y()\n win_height = height + titlebar_height + frm_width\n x = win.winfo_screenwidth() // 2 - win_width // 2\n y = win.winfo_screenheight() // 2 - win_height // 2\n win.geometry('{}x{}+{}+{}'.format(width, height, x, y))\n win.deiconify()","title":""},{"docid":"02e1510bda2656e2c42c0c0f8a7ee699","score":"0.7986522","text":"def makeWindowCenter(self):\n qtRectangle = self.frameGeometry()\n centerPoint = QDesktopWidget().availableGeometry().center()\n qtRectangle.moveCenter(centerPoint)\n self.move(qtRectangle.topLeft())","title":""},{"docid":"12a42b95583045c87ae3eb756f77d8e7","score":"0.7965381","text":"def center(win):\n window_height = 700\n window_width = 350\n\n screen_width = win.winfo_screenwidth()\n screen_height = win.winfo_screenheight()\n\n x_cordinate = int((screen_width/2) - (window_width/2))\n y_cordinate = int((screen_height/2) - (window_height/2))\n\n win.geometry(\"{}x{}+{}+{}\".format(window_width, window_height, x_cordinate, y_cordinate))","title":""},{"docid":"7bb3ba56ac54a8bb31e45fdd4e2a1c79","score":"0.794357","text":"def center_window_to_screen(self):\n desktop = QtWidgets.QApplication.desktop()\n cursor_pos = QtGui.QCursor.pos()\n desktop_number = desktop.screenNumber(cursor_pos)\n desktop_rect = desktop.screenGeometry(desktop_number)\n\n size = self.geometry()\n\n self.move(\n (desktop_rect.width() - size.width()) * 0.5 + desktop_rect.left(),\n (desktop_rect.height() - size.height()) * 0.5 + desktop_rect.top(),\n )","title":""},{"docid":"8186ea631fa7d764689461e8aae1f91e","score":"0.79214674","text":"def center_window(self):\n\n # if there is no main application then fall back to centering to the\n # screen that the mouse pointer is in\n parent = self.parent()\n if not parent:\n self.center_window_to_screen()\n return\n\n # get the main application size\n # center to that dimension\n parent_size = parent.geometry()\n size = self.geometry()\n\n left = (parent_size.width() - size.width()) * 0.5 + parent_size.left()\n top = (parent_size.height() - size.height()) * 0.5 + parent_size.top()\n\n left = max(0, left)\n top = max(0, top)\n\n self.move(left, top)","title":""},{"docid":"147eb2078b785d6620846c0627eb45c8","score":"0.7807315","text":"def center_widget(self) -> None:\n self.withdraw()\n self.update_idletasks()\n\n x = (self.winfo_screenwidth() - self.winfo_reqwidth())\n x /= 2\n y = (self.winfo_screenheight() - self.winfo_reqheight())\n y /= 2\n g = (\"+%d+%d\" % (x, y))\n\n self.geometry(g)\n self.deiconify()\n return","title":""},{"docid":"42200a08a775d4d4809d3c31e74889bb","score":"0.7756313","text":"def center(self):\n # geometry of the main window\n qr = self.frameGeometry()\n # center point of screen\n cp = QDesktopWidget().availableGeometry().center()\n # move rectangle's center point to screen's center point\n qr.moveCenter(cp)\n # top left of rectangle becomes top left of window centering it\n self.move(qr.topLeft())","title":""},{"docid":"87d50577c36ff2cfa23e7edca8c1711d","score":"0.75481683","text":"def _center(self):\n _frame = self.frameGeometry()\n # noinspection PyArgumentList\n _root = QApplication.desktop()\n _active_screen = _root.screenNumber(_root.cursor().pos())\n _center = _root.screenGeometry(_active_screen).center()\n _frame.moveCenter(_center)\n self.move(_frame.topLeft())","title":""},{"docid":"371012b5ceeecf752331c6cee8cd51f2","score":"0.7445819","text":"def center(self):\n qr = self.frameGeometry()\n cp = QDesktopWidget().availableGeometry().center() # get screen resolution and center point\n qr.moveCenter(cp) # \n self.move(qr.topLeft())","title":""},{"docid":"d02f3164df06f575e3e2f489bca8f86f","score":"0.74301237","text":"def align_window(self):\n self.parent.update()\n\n # get screen info\n screen_width = self.parent.winfo_screenwidth()\n screen_height = self.parent.winfo_screenheight()\n\n # get window info\n window_width = self.parent.winfo_width()\n window_height = self.parent.winfo_height()\n\n # determine position of the window\n x = screen_width - window_width/2 - 180\n y = screen_height/2 - window_height/2\n\n # move the window to determined position\n self.parent.geometry('+%d+%d' % (x, y))","title":""},{"docid":"e76197a9315d2cd00c67fb1f45031e48","score":"0.73379296","text":"def center(handle, w=500, h=450):\n sw = handle.winfo_screenwidth()\n sh = handle.winfo_screenheight()\n\n x = (sw - w) / 2\n y = (sh - h) / 2\n handle.geometry('%dx%d+%d+%d' % (w, h, x, y))\n handle.focus()","title":""},{"docid":"df6c6ae39fbb8dbb5665791cc37d7a2b","score":"0.7308243","text":"def center(widget,relx,rely):\n widget.withdraw() # Remain invisible while we fig+ure out the geometry \n widget.update_idletasks() # Actualize geometry information \n m_width = widget.winfo_screenwidth()\n m_height = widget.winfo_screenheight() \n w_width = widget.winfo_reqwidth() \n w_height = widget.winfo_reqheight()\n x = (m_width - w_width) * relx\n y = (m_height - w_height) * rely\n widget.geometry(\"+%d+%d\" % (x,y)) \n widget.deiconify()","title":""},{"docid":"70dde9aed48e875377bcfcad47acba10","score":"0.7297512","text":"def _center(self):\n qr = self.frameGeometry()\n cp = QtWidgets.QDesktopWidget().availableGeometry().center()\n qr.moveCenter(cp)\n self.move(qr.topLeft())","title":""},{"docid":"369c8b9530cc74f4002443cbb300e489","score":"0.72566235","text":"def center(self):\r\n self.showNormal()\r\n self.resize(QDesktopWidget().screenGeometry().width() // 1.25,\r\n QDesktopWidget().screenGeometry().height() // 1.25)\r\n qr = self.frameGeometry()\r\n qr.moveCenter(QDesktopWidget().availableGeometry().center())\r\n return self.move(qr.topLeft())","title":""},{"docid":"7fe6dcbdd46a00e26df77a92e7bae584","score":"0.72289217","text":"def center_window(window, resize=True):\n screen = QGuiApplication.screenAt(QCursor.pos())\n\n if resize:\n screen_size = screen.size()\n max_width = screen_size.width()\n max_height = screen_size.height()\n\n window.resize(max_width * 0.8, max_height * 0.8)\n\n window.move(screen.geometry().center() - window.frameGeometry().center())","title":""},{"docid":"25bb7977fec7d11fa007471e56a56a7e","score":"0.7194875","text":"def center(self):\n qr = self.frameGeometry()\n cp = QDesktopWidget().availableGeometry().center()\n qr.moveCenter(cp)\n self.move(qr.topLeft())","title":""},{"docid":"25bb7977fec7d11fa007471e56a56a7e","score":"0.7194875","text":"def center(self):\n qr = self.frameGeometry()\n cp = QDesktopWidget().availableGeometry().center()\n qr.moveCenter(cp)\n self.move(qr.topLeft())","title":""},{"docid":"25bb7977fec7d11fa007471e56a56a7e","score":"0.7194875","text":"def center(self):\n qr = self.frameGeometry()\n cp = QDesktopWidget().availableGeometry().center()\n qr.moveCenter(cp)\n self.move(qr.topLeft())","title":""},{"docid":"25bb7977fec7d11fa007471e56a56a7e","score":"0.7194875","text":"def center(self):\n qr = self.frameGeometry()\n cp = QDesktopWidget().availableGeometry().center()\n qr.moveCenter(cp)\n self.move(qr.topLeft())","title":""},{"docid":"fa855cec61d9050a6bc2654736866e2b","score":"0.7147751","text":"def center(self):\n self.translate(-1*self.com)","title":""},{"docid":"e9412a5089ccc42c202043ed03d01868","score":"0.71378535","text":"def center(self):\n\t\tqRect = self.frameGeometry()\n\t\tcenterPoint = QDesktopWidget().availableGeometry().center()\n\t\tqRect.moveCenter(centerPoint)\n\t\tself.move(qRect.topLeft())","title":""},{"docid":"9d5b14fb40a9bbf381dcac2ba60cfd93","score":"0.7021262","text":"def center_ball(self):\r\n\t\tself.centerx=self.screen_rect.centerx\r\n\t\tself.centery=self.screen_rect.centery","title":""},{"docid":"776829129fbd676c74b5fb205f077300","score":"0.6984075","text":"def setCenter(self, value):\n self._viewCenter = b2Vec2(*value)\n self._viewOffset = self._viewCenter - self.screenSize / 2\n self.window.graphicsView.centerOn(*self._viewCenter)","title":""},{"docid":"eef45dc7689335e3b622ef7a4bb49573","score":"0.6918894","text":"def center(master, win, width, height):\n # get screen width and height\n ws = master.winfo_screenwidth() # width of the screen\n hs = master.winfo_screenheight() # height of the screen\n\n # calculate x and y coordinates for the Tk root window\n x = (ws / 2) - (width / 2)\n y = (hs / 2) - (height / 2)\n\n return width, height, x, y","title":""},{"docid":"b95e1abcb1053ecb6644b7f1e93aff4f","score":"0.68476415","text":"def center_on_screen(widget):\r\n desktop = QtGui.QApplication.instance().desktop()\r\n rect = desktop.screenGeometry(QtGui.QCursor().pos())\r\n cy = rect.height()//2\r\n cx = rect.width()//2\r\n widget.move(cx - widget.width()//2, cy - widget.height()//2)","title":""},{"docid":"8da09214a9f5090dfa9e74e9f7d57656","score":"0.68216664","text":"def center_ship(self):\n\t\tself.center= self.screen_rect.centerx\n\t\tself.bottom= self.screen_rect.bottom","title":""},{"docid":"cb75846baa05e651057b7cf7fd49bc0a","score":"0.67556983","text":"def bringFrontCenter(self):\n\t\tif self.window:\n\t\t\tNSLog(\"window is present\")\n\t\t\tself.window.becomeMainWindow()\n\t\t\tself.window.center()\n\t\t\t# needed so the window can show over the loginwindow\n\t\t\tself.window.setCanBecomeVisibleWithoutLogin_(True)\n\t\t\tself.errorWindow.setCanBecomeVisibleWithoutLogin_(True)\n\t\t\tself.authSheetWindow.setCanBecomeVisibleWithoutLogin_(True)\n\t\t\tself.window.setLevel_(NSScreenSaverWindowLevel - 1)\n\t\t\tself.window.orderFrontRegardless()","title":""},{"docid":"89eb863d66f02897861eeac32e418d9d","score":"0.6749471","text":"def set_window_start(root):\n\tws = root.winfo_screenwidth() # gets width of screen\n\thw = root.winfo_screenheight() # gets height of screen\n\t\n\t# I want it to open in the bottom left\n\tx = 3.0*ws/4\n\ty = ws/3.0\n\t\n\t# set (width, height, x, y)\n\troot.geometry( '%dx%d+%d+%d' % (300, 300, x, y) )","title":""},{"docid":"819df5b06fefc33b272d262940fcc9c6","score":"0.6737455","text":"def sc_center(Window_size: tuple, Inverse: bool = False, \\\r\n center: tuple = ini_center, win: object = Window):\r\n if Inverse == False:\r\n win.size = Window_size\r\n variation_x = win.center[0] - center[0]\r\n variation_y = win.center[1] - center[1]\r\n win.left -= variation_x\r\n win.top -= variation_y\r\n \r\n if Inverse == True:\r\n variation_x = win.center[0] - center[0]\r\n variation_y = win.center[1] - center[1]\r\n win.left += variation_x\r\n win.top += variation_y\r\n win.size = Window_size","title":""},{"docid":"017fe70074d867c3461c416ea5ecc7ff","score":"0.6720759","text":"def center_ship(self):\r\n self.center = self.screen_rect.centerx","title":""},{"docid":"06febd4f073a0d937834f78f834e06f5","score":"0.6693505","text":"def position ( self, ui ):\n view = ui.view\n window = ui.control\n\n # Set up the default position of the window:\n parent = window.GetParent()\n if parent is None:\n window.Centre( wx.BOTH )\n else:\n position_near( parent, window, offset_y = -30 )\n \n # Calculate the correct width and height for the window:\n cur_width = window.winfo_width()\n cur_height = window.winfo_height()\n width = view.width\n height = view.height\n \n if width < 0.0:\n width = cur_width\n elif width <= 1.0:\n width = int( width * screen_dx )\n else:\n width = int( width )\n \n if height < 0.0:\n height = cur_height\n elif height <= 1.0:\n height = int( height * screen_dy )\n else:\n height = int( height )\n \n # Calculate the correct position for the window:\n x = view.x\n y = view.y\n \n if x < -99999.0:\n x = (screen_dx - width) / 2\n elif x <= -1.0:\n x = screen_dx - width + int( x ) + 1\n elif x < 0.0:\n x = screen_dx - width + int( x * screen_dx )\n elif x <= 1.0:\n x = int( x * screen_dx )\n else:\n x = int( x )\n \n if y < -99999.0:\n y = (screen_dy - height) / 2\n elif y <= -1.0:\n y = screen_dy - height + int( y ) + 1\n elif x < 0.0:\n y = screen_dy - height + int( y * screen_dy )\n elif y <= 1.0:\n y = int( y * screen_dy )\n else:\n y = int( y )\n \n # Position and size the window as requested:\n window.geometry( '%dx%d+%d+%d' % ( width, height, x, y ) )","title":""},{"docid":"7224f6c3a09e9cfcb151f31e30e2548a","score":"0.6666444","text":"def center_ship(self):\n\t\tself.center = self.screen_rect.centerx","title":""},{"docid":"c51981ade551ce3d92a2ad4ea44bd909","score":"0.6658568","text":"def update_window(self):\n screen_area = HOST_APP.proc_screen_workarea\n scale_factor = 1.0 / HOST_APP.proc_screen_scalefactor\n top = left = width = height = 0\n\n window_rect = revit.get_window_rectangle()\n\n # set width and height\n width = window_rect.Right - window_rect.Left\n height = self.user_height\n\n top = window_rect.Top\n # in maximized window, the top might be off the active screen\n # due to windows thicker window frames\n # lets cut the height and re-adjust the top\n top_diff = abs(screen_area.Top - top)\n if 10 > top_diff > 0 and top_diff < height:\n height -= top_diff\n top = screen_area.Top\n\n left = window_rect.Left\n # in maximized window, Left also might be off the active screen\n # due to windows thicker window frames\n # let's fix the width to accomodate the extra pixels as well\n left_diff = abs(screen_area.Left - left)\n if 10 > left_diff > 0 and left_diff < width:\n # deduct two times the left negative offset since this extra\n # offset happens on both left and right side\n width -= left_diff * 2\n left = screen_area.Left\n\n self.Top = top * scale_factor\n self.Left = left * scale_factor\n self.Width = width * scale_factor\n self.Height = height","title":""},{"docid":"293b7085262b8b8fc20c76b8ecec3298","score":"0.66558135","text":"def center_ship(self):\n self.center = self.screen_rect.centerx","title":""},{"docid":"293b7085262b8b8fc20c76b8ecec3298","score":"0.66558135","text":"def center_ship(self):\n self.center = self.screen_rect.centerx","title":""},{"docid":"293b7085262b8b8fc20c76b8ecec3298","score":"0.66558135","text":"def center_ship(self):\n self.center = self.screen_rect.centerx","title":""},{"docid":"fbc5e67a3d9f1aff8048198f786b8001","score":"0.66218907","text":"def place_window(self, width=400, height=300):\n w = width\n h = height\n\n # get screen width and height\n ws = self.winfo_screenwidth() # width of the screen\n hs = self.winfo_screenheight() # height of the screen\n\n x = (ws / 2) - (w / 2)\n y = (hs / 2) - (h / 2)\n\n # set the dimensions of the screen\n # and where it is placed\n self.geometry(\"%dx%d+%d+%d\" % (w, h, x, y))","title":""},{"docid":"d784b48449effe9dea7801dacd70c5cc","score":"0.6614368","text":"def update_center(self):\r\n \r\n pass","title":""},{"docid":"0a2036c3eca30ab636fdb2dcd0755eab","score":"0.6613791","text":"def updateCenter(self):\r\n self.viewOffset = self.viewCenter - self.screenSize/2\r\n\r\n self.debugDraw._setValues(self.viewZoom, self.viewCenter, self.viewOffset, self.screenSize.x, self.screenSize.y)","title":""},{"docid":"cf2f49fa2982edb87c8797ce929a6897","score":"0.65797716","text":"def center_mouse() -> None:\n rect = ui.active_window().rect\n center = (rect.x + round(rect.width / 2), rect.y + round(rect.height / 2))\n actions.mouse_move(*center)","title":""},{"docid":"5042901b8abfe0af229e4e9e761ca985","score":"0.65168935","text":"def Fullscreen(self):\n\t\tself.width = self.display_resolution.current_w\n\t\tself.height = self.display_resolution.current_h\n\n\t\tself.item1 = menuItem(\"minimize\",self.width, self.height)\n\t\tself.item2 = menuItem(\"start\",self.width, self.height - 2*self.item1.height)\n\t\tself.item3 = menuItem(\"quit\",self.width, self.height + 2*self.item1.height)\n\n\t\tself.boxy = Boxy()\n\t\tself.boxy = Boxy((255,255,255), self.item1.x - 2*self.boxy.width, centerHeight(self.item2.height, self.boxy.height) + self.item2.y)","title":""},{"docid":"142892e31fa0c29898c41d3abf3ba335","score":"0.650492","text":"def centrar(self, x, y):\n self.rect.center = (x,y)","title":""},{"docid":"132f432af86bbc508e85a84dabe708cd","score":"0.64783156","text":"def update_center(self):\n self._update(\"update_center\")","title":""},{"docid":"035faf8d51f20406e4bbad45aed154f2","score":"0.647459","text":"def move_window(self):","title":""},{"docid":"7cf2121bc8ba963d16ed8e1e3b72a7fc","score":"0.64682335","text":"def center_ship(self):\r\n\t\tself.center = self.screen_rect.centerx\r\n\t\t# ~ self.rect.midbottom = self.screen_rect.midbottom\r\n\t\t# ~ self.x = float(self.rect.x)\r","title":""},{"docid":"cd71756f72ff5c3ea8aba62ec99e5266","score":"0.6440379","text":"def update_dimensions(self):\n # get the window size\n window_rect = win32gui.GetWindowRect(self.hwnd)\n isFullScreen = win32gui.GetWindowRect(self.hwnd) == full_screen_rect\n self.isFullScreen = isFullScreen\n self.isTrueFullscreen = is_full_screen()\n self.w = window_rect[2] - window_rect[0]\n self.h = window_rect[3] - window_rect[1]\n\n if not isFullScreen:\n # account for the window border and titlebar and cut them off\n border_pixels = 8\n titlebar_pixels = 30\n self.w = self.w - (border_pixels * 2)\n self.h = self.h - titlebar_pixels - border_pixels\n self.cropped_x = border_pixels\n self.cropped_y = titlebar_pixels\n\n # images into actual screen positions\n self.offset_x = window_rect[0] + self.cropped_x\n self.offset_y = window_rect[1] + self.cropped_y\n\n else:\n self.offset_x, self.offset_y = 0, 0\n self.cropped_x, self.cropped_y = 0, 0","title":""},{"docid":"20043502f14d6ff55300211293dee7ff","score":"0.6421751","text":"def center_ship(self):\n\t\tself.rect.midbottom=self.screen_rect.midbottom\n\t\tself.x=float(self.rect.x)","title":""},{"docid":"7637cd8dd310dc4ad7a545c75bb8b0d1","score":"0.64083266","text":"def mwindow(self) -> None:\n self.setFixedSize(898, 422)\n self.setWindowTitle(\"Diabetes Prediction\")\n self.show()","title":""},{"docid":"f4ee51820918c900d7089d99163dfeb8","score":"0.6343348","text":"def center_ship(self):\n\t\tself.rect.midbottom = self.screen_rect.midbottom\n\t\tself.x = float(self.rect.x)","title":""},{"docid":"a22138d20e921b2b28a9fddc986ed1aa","score":"0.6323974","text":"def center_ship(self):\n self.rect.midbottom = self.screen_rect.midbottom\n self.x = float(self.rect.x)","title":""},{"docid":"a22138d20e921b2b28a9fddc986ed1aa","score":"0.6323974","text":"def center_ship(self):\n self.rect.midbottom = self.screen_rect.midbottom\n self.x = float(self.rect.x)","title":""},{"docid":"a22138d20e921b2b28a9fddc986ed1aa","score":"0.6323974","text":"def center_ship(self):\n self.rect.midbottom = self.screen_rect.midbottom\n self.x = float(self.rect.x)","title":""},{"docid":"a22138d20e921b2b28a9fddc986ed1aa","score":"0.6323974","text":"def center_ship(self):\n self.rect.midbottom = self.screen_rect.midbottom\n self.x = float(self.rect.x)","title":""},{"docid":"a22138d20e921b2b28a9fddc986ed1aa","score":"0.6323974","text":"def center_ship(self):\n self.rect.midbottom = self.screen_rect.midbottom\n self.x = float(self.rect.x)","title":""},{"docid":"37254e96a0ca94800ed64bbff3ec3e35","score":"0.6309343","text":"def center_view_on_player(self):\n self.view_left = self.player.center_x - self.screen_width / 2\n self.view_bottom = self.player.center_y - self.screen_height / 2\n self.update_viewport()\n self.scroll_viewport()","title":""},{"docid":"c7b8a991e51fd1bf757df595755e2e0f","score":"0.62983125","text":"def enlargeWindow(self):\n\t\t#\t\tNSLog(\"Main window size: {}\".format(self.window.frame().size))\n\t\twinRect = self.window.frame()\n\t\twinRect.size.height = 480\n\t\twinRect.origin.y = winRect.origin.y - 256\n\t\t#\t\tNSLog(\"Setting new main window size: {}\".format(winRect))\n\t\tself.window.setFrame_display_(winRect, True)","title":""},{"docid":"894e87f79182b72e4973662c3f11a7c2","score":"0.62930137","text":"def setupWindow(self):\n self.win = visual.Window(self.window_dims, allowGUI=False, fullscr=self.fullscreen, \n monitor='testMonitor', units='deg') \n self.win.setColor('black')\n self.win.flip()\n self.win.flip()","title":""},{"docid":"b7993ec81b773415fbff160fc0ba295a","score":"0.6288775","text":"def centerPage(self):\n top, bottom = self.yview()\n size = bottom - top\n if size==0: size=0.4\n middle = size * 0.4\n self.yview('moveto', middle)\n #print top, bottom\n #print middle\n return","title":""},{"docid":"3f02eae09c11c06ead9c05588945de66","score":"0.62675095","text":"def setCenter(self, newCenter):\n self.center = newCenter","title":""},{"docid":"22f098cc337aefbe2a7cbd4e9152196b","score":"0.6261226","text":"def center(self):\r\n\t\treturn self","title":""},{"docid":"ef770eb683b1d7eb7652da86c1ba61e9","score":"0.62596244","text":"def center(self, location: ghidra.program.util.ProgramLocation) -> None:\n ...","title":""},{"docid":"7f8ff8c99913c72f1bcb42ed6fe6cfab","score":"0.6228856","text":"def position_window(window):\n pos = QtGui.QCursor.pos()\n window.move(pos.x(), pos.y())","title":""},{"docid":"c8b74d323bb622f5ed3772eab740b5fa","score":"0.62265974","text":"def center(self):\n if self._mode == \"vertical\":\n margin = self._margins[0]\n gap = self._gaps[0]\n y = None\n if self._align == \"center\":\n x=\"auto\"\n elif self._align == \"left\":\n x=self._element.get_storer_rect().left + self._margins[0]\n elif self._align == \"right\":\n x=self._element.get_storer_rect().right - self._margins[0]\n elif self._mode == \"horizontal\":\n margin = self._margins[1]\n gap = self._gaps[1]\n x = None\n if self._align == \"center\":\n y=\"auto\"\n elif self._align == \"top\":\n y=self._element.get_storer_rect().top + self._margins[0]\n elif self._align == \"bottom\":\n y=self._element.get_storer_rect().bottom - self._margins[0]\n size=store(frame=self._element,\n elements=self._elements,\n mode=self._mode,\n x=x,\n y=y,\n margin=margin,\n gap=gap,\n align=self._align)","title":""},{"docid":"1054bb296adbfe9ace7edb2581942013","score":"0.6208886","text":"def draw(self, win, center):","title":""},{"docid":"24c9a2e106618fdc58d32be576711bcf","score":"0.62028426","text":"def display_win(self) -> None:\n\n self.top_panel.win_label.grid(row=0, columnspan=10)","title":""},{"docid":"b1f08957226628c87db15418d89f5643","score":"0.6200582","text":"def centre_on_screen(widget: QWidget):\n\n # Get the widget's frame size.\n widget_frame = widget.frameGeometry()\n\n # Get the centre of the screen.\n screen_centre = QDesktopWidget().availableGeometry().center()\n\n # Calculate where the frame should be placed.\n widget_frame.moveCenter(screen_centre)\n\n # Move the widget to the correct place.\n widget.move(widget_frame.topLeft())","title":""},{"docid":"10f666f06ecf333b9697225ec60897a7","score":"0.615979","text":"def wrap(self, screen_width, screen_height):\r\n #check if it is off the left side\r\n if self.center.x < 0:\r\n self.center.x = screen_width\r\n #check if it is off the right side\r\n if self.center.x > screen_width:\r\n self.center.x = 0\r\n #check if it is off the top\r\n if self.center.y > screen_height:\r\n self.center.y = 0\r\n #check if it is off the bottom\r\n if self.center.y < 0:\r\n self.center.y = screen_height","title":""},{"docid":"6bcbfe8e5921dfcd41a444af65208063","score":"0.6152299","text":"def center(self, surface, centerx=True, centery=True, xajout=0, yajout=0):\n\t\tif centerx and centery:\n\t\t\treturn surface.get_rect(centerx=self.screen.get_width() / 2 + xajout, centery=self.screen.get_height() / 2 + yajout)\n\t\tif centerx:\n\t\t\treturn surface.get_rect(centerx=self.screen.get_width() / 2 + xajout)\n\n\t\tif centery:\n\t\t\treturn surface.get_rect(centery=self.screen.get_width() / 2 + xajout)","title":""},{"docid":"4576a0ed37a7698571ffefa8fd99cd94","score":"0.6127658","text":"def LayoutDialog(self, centreFlags=wx.BOTH):","title":""},{"docid":"4d3671a52f03abf30be6a3927df9eb20","score":"0.61192656","text":"def collapseWindow(self):\n\t\t#\t\tNSLog(\"Main window size: {}\".format(self.window.frame().size))\n\t\twinRect = self.window.frame()\n\t\twinRect.size.height = 180\n\t\twinRect.origin.y = winRect.origin.y + (\n\t\t\t\tself.window.frame().size.height - 180 )\n\t\t\t\t#\t\tNSLog(\"Setting new main window size: {}\".format(winRect))\n\t\tself.window.setFrame_display_(winRect, True)","title":""},{"docid":"97926c64ee824f63a31c044d6e4126e3","score":"0.6070533","text":"def geometry(self):\n self.root.geometry(\n f\"{self.playwidth*self.boxsize}x{(self.playheight + 2)*self.boxsize}+400+150\")\n self.root.resizable(False, False)","title":""},{"docid":"f6725c71597f68ea5892ea28ad0f116a","score":"0.60663486","text":"def setWindowSize(self, x_size=800, y_size=600):\n \n self.window.resize(x_size, y_size)","title":""},{"docid":"30a44841bd6ca1fb736bb2e580c0ef44","score":"0.60618573","text":"def main(cls):\n\n tk.NoDefaultRoot()\n root = tk.Tk()\n app = cls(root)\n # auto frame resize\n app.grid(sticky=\"NSEW\")\n root.grid_columnconfigure(0, weight=1)\n root.grid_rowconfigure(0, weight=1)\n root.grid_columnconfigure(1, weight=1)\n root.grid_rowconfigure(1, weight=1)\n root.grid_columnconfigure(2, weight=1)\n root.grid_rowconfigure(2, weight=1)\n # make adjustable in x not y\n root.resizable(True, False)\n # run application until user exit\n root.mainloop()","title":""},{"docid":"90cf5b095735759888150d48960bbe34","score":"0.60437715","text":"def center(self, center):\n\n self._center = center","title":""},{"docid":"886ca718b27dba80b4a11dca9d2d35ab","score":"0.6043047","text":"def center(self, *args, **kwargs): # real signature unknown\n pass","title":""},{"docid":"205e5a85463cd27c6197dc8b834d6bfd","score":"0.5988926","text":"def reset_location(self, screen: pygame.Surface) -> None:\r\n self.rect.left = (screen.get_width() - self.rect.width) / 2\r\n self.rect.top = (screen.get_height() - self.rect.height) / 2","title":""},{"docid":"2bb2a30847998b804ba48a52a2b4d626","score":"0.59451467","text":"def start(self):\n self._root.grid_rowconfigure(0, weight=1)\n self._root.grid_columnconfigure(0, weight=1)\n\n center_frame = Frame(\n self._root,\n width=self._root_width,\n heigh=self._root_height - self._bottom_height,\n )\n\n bottom_frame = Frame(\n self._root,\n bg=\"black\",\n width=self._root_width,\n height=self._bottom_height,\n )\n\n center_frame.grid(\n row=0,\n column=0,\n sticky=\"nsew\"\n )\n\n bottom_frame.grid(\n row=1,\n column=0,\n sticky=\"nsew\"\n )\n\n bottom_frame.grid_rowconfigure(1)\n bottom_frame.grid_columnconfigure(\n [0, 1, 2, 3, 4, 5, 6, 7], minsize=(self._root_width/8))\n\n bottom_frame_headlines = [\"Intro\", \"Options\", \"Start\", \"Exit\"]\n commands = [lambda: self._show_intro_view(center_frame),\n lambda: self._show_options_view(center_frame),\n lambda: self._show_game_view(center_frame),\n self._quit]\n\n for j in range(0, len(bottom_frame_headlines)):\n button = Button(\n bottom_frame,\n text=bottom_frame_headlines[j],\n compound=\"center\",\n width=12,\n height=1,\n background=\"red\",\n activebackground=\"red\",\n font=self.fonts.small_text_font,\n command=commands[j]\n )\n\n button.grid(\n row=0,\n column=j,\n padx=12,\n pady=12\n )\n\n self._show_opening_view(center_frame)","title":""},{"docid":"0392d289092b31ad9c87556a5fe31a54","score":"0.5944433","text":"def set_relative_position(self):\n\n # Position window w/ respect to parent window\n mx, my = self.parent.GetTopLevelParent().GetScreenPosition()\n disp_geom = self.get_display(window=self.parent.GetTopLevelParent())\n dw = disp_geom[2]\n dxmin = disp_geom[0]\n dxmax = disp_geom[0] + dw\n dh = disp_geom[3]\n dymin = disp_geom[1]\n dymax = disp_geom[1] + dh\n\n if self.IsFrame():\n adj_x = -50\n adj_y = -50\n elif self.IsDialog():\n adj_x = int(dw * 0.025)\n adj_y = int(dh * 0.025)\n else:\n adj_x = 50\n adj_y = 50\n px = mx + adj_x\n py = my + adj_y\n\n ww, wh = self.GetSize()\n pxmax = px + ww\n pymax = py + wh\n\n # Calculate if window is going out of bounds, and adjust\n if pxmax >= dxmax:\n px = int((dxmin + dw - ww) * 0.9)\n if pymax >= dymax:\n py = int((dymin + dh - wh) * 0.9)\n if px <= dxmin:\n px = 25\n if py <= dymin:\n py = 25\n\n return px, py","title":""},{"docid":"8f4dbb231fc27b89e674e534b86e5d44","score":"0.59231","text":"def Minimized(self):\n\t\tself.width = 400\n\t\tself.height = 400\n\n\t\tself.item1 = menuItem(\"fullscreen\",self.width, self.height)\n\t\tself.item2 = menuItem(\"start\",self.width, self.height - 2*self.item1.height)\n\t\tself.item3 = menuItem(\"quit\",self.width, self.height + 2*self.item1.height)\n\n\t\tself.boxy = Boxy()\n\t\tself.boxy = Boxy((255,255,255), self.item1.x - 2*self.boxy.width, centerHeight(self.item2.height, self.boxy.height) + self.item2.y)","title":""},{"docid":"f570a60eae4ab0654eaae8990b6660c6","score":"0.5922452","text":"def updateCenter(self, x, y):\n self.Xcenter_old = self.Xcenter\n self.Ycenter_old = self.Ycenter\n self.Xcenter = x\n self.Ycenter = y","title":""},{"docid":"444579c2636bc48639514a8b52148b30","score":"0.59136033","text":"def center(self):\n return self.functional.center","title":""},{"docid":"d84a5dc7ce62649553f2f2b82b89b6da","score":"0.5893992","text":"def fulltonormal(self):\n if self.controlDock.FullNormSW.isChecked():\n self.MainWindow.showFullScreen()\n self.controlDock.setStretch(*(10,300)) # minimize control dock width\n else:\n self.MainWindow.showNormal()\n self.controlDock.setStretch(*(10,300)) # minimize control dock width","title":""},{"docid":"23ae16b3eaf5317888d150587f42609c","score":"0.58824813","text":"def center(self):\n return self._center","title":""},{"docid":"3c800d8734906e8ef484dd96aa4f6854","score":"0.58794665","text":"def initUI(self):\n \n self.resize(1024, 512)\n self.center()\n self.setWindowTitle('Py-Paint')\n\n self.createMenuBar() \n self.createToolBar()\n self.show()","title":""},{"docid":"574b5517593c60d987e14d16a271cff9","score":"0.58590764","text":"def set_center(self, center_x: int, center_y: int) -> None:\r\n self._x = center_x - self._image.get_width() // 2\r\n self._y = center_y - self._image.get_height() // 2","title":""},{"docid":"15fdbf611dd75250e5fa9274943d454b","score":"0.58475226","text":"def update(self):\n super().update()\n if self.center_x < LEFT_LIMIT:\n self.center_x = RIGHT_LIMIT\n if self.center_x > RIGHT_LIMIT:\n self.center_x = LEFT_LIMIT\n if self.center_y > TOP_LIMIT:\n self.center_y = BOTTOM_LIMIT\n if self.center_y < BOTTOM_LIMIT:\n self.center_y = TOP_LIMIT","title":""},{"docid":"4393c955b34a610783050d1cc4486909","score":"0.5829849","text":"def set_screen(self):\n self.screen = pygame.display.set_mode(self.size, pygame.RESIZABLE)","title":""},{"docid":"90f0740bddf78be51acb1e206ca54a13","score":"0.58262646","text":"def set_pos_root_button(self, instance):\n\n if self.anchor == \"right\":\n instance.y = dp(20)\n instance.x = Window.width - (dp(56) + dp(20))","title":""},{"docid":"6cfb7b2ce163c2782236d9577176a640","score":"0.58252335","text":"def setCentreCoordinates(self,xcenter,ycenter):\n self.x = xcenter\n self.y = ycenter","title":""},{"docid":"1e9cedc0d7819db57ffaf589ab568876","score":"0.58119756","text":"def render_to_window(self):\n window = self.window\n camera = self.camera\n\n y = camera.y\n x = camera.x\n\n x1 = x\n x2 = window.width + x\n y1 = y\n y2 = window.height + y\n\n glMatrixMode(GL_PROJECTION)\n glLoadIdentity()\n gluOrtho2D(x1, x2, y1, y2)\n\n glClear(GL_COLOR_BUFFER_BIT)\n glMatrixMode(GL_MODELVIEW)\n glLoadIdentity()\n\n self.draw_world()\n self.draw_hud()\n\n window.flip()","title":""},{"docid":"3396eb6fae01c0cd781c44167f042ddb","score":"0.5807685","text":"def _makeScreen(self):\n\n #Centering the display on screen (taken from pygame FAQ)\n os.environ['SDL_VIDEO_CENTERED'] = '1'\n\n #Starting pygame and setting up the display(game window)\n\n pygame.init()\n gamescreen = pygame.display.set_mode((RES_X, RES_Y))\n pygame.display.set_caption(CAPTION)\n \n return gamescreen","title":""},{"docid":"db386faf10c93eadf27bd745ea1d8b87","score":"0.5806194","text":"def main(location, rows, columns, padding, margin):\n\n window = get_active_window()\n width, height = get_new_size(columns, rows, padding)\n\n resize(window, width - margin * 2, height - margin * 2)\n\n left, right, top, bottom = get_border()\n\n r = location[0] * (height + top + bottom) + padding[0] + margin\n c = location[1] * (width + left + right) + padding[3] + margin\n\n move(window, r, c)","title":""},{"docid":"0ebae1f9805d26cbc74050259430e583","score":"0.57883084","text":"def center_inky(self):\n self.rect.centery = 250\n self.rect.centerx = 300\n self.centerx = self.rect.x\n self.rect.centerx = self.rect.x\n self.centery = self.rect.y\n self.rect.centery = self.rect.y","title":""},{"docid":"ac04d15cdd170866f11ac84cfa97e1f9","score":"0.5786315","text":"def center(self):\n return self.pos","title":""},{"docid":"7b3731be38e07d33f6edbd12c69bec3c","score":"0.5784496","text":"def show_main_screen(self):\n self.title_frame.pack(side=TOP)\n self.title_frame.pack_propagate(0)\n self.title.pack()\n self.intro_text.pack(side=TOP)\n self.buttons_frame.pack()\n self.select_button.pack(side=LEFT, padx=50)\n self.multi_button.pack(side=LEFT, padx=50)","title":""},{"docid":"d3024ab4d8e746468694bedfcd272f69","score":"0.5775383","text":"def resize(widthWindow, heightWindow):\r\n glMatrixMode(GL_PROJECTION)\r\n glLoadIdentity()\r\n glMatrixMode(GL_MODELVIEW)\r\n glLoadIdentity()","title":""},{"docid":"050ae135fd359b2acb55c92d62b45fe4","score":"0.57698506","text":"def frame_center(self):\n return self.frame.get_center()","title":""},{"docid":"ac6d306faee15e406690fd3eac54e14a","score":"0.5763159","text":"def display(self):\n self.window.clear()\n \n # If the screen is big enough print all relevant information\n if self.max_columns > 20 and self.max_rows > 10:\n self.print_buffer()\n self.print_title_bar()\n self.print_message()\n self.print_coords()\n self.print_menu()\n \n self.scroll() # Scroll the screen if needed\n self.set_cursor() # Set the cursor coordinates\n self.window.move(self.row, self.column) # Place the cursor on screen\n \n self.window.noutrefresh()","title":""}],"string":"[\n {\n \"docid\": \"0470869de0fe4c2dc345e28a653fcd8f\",\n \"score\": \"0.8163197\",\n \"text\": \"def center(self,win):\\n win.update_idletasks()\\n width = win.winfo_width()\\n frm_width = win.winfo_rootx() - win.winfo_x()\\n win_width = width + 2 * frm_width\\n height = win.winfo_height()\\n titlebar_height = win.winfo_rooty() - win.winfo_y()\\n win_height = height + titlebar_height + frm_width\\n x = win.winfo_screenwidth() // 2 - win_width // 2\\n y = win.winfo_screenheight() // 2 - win_height // 2\\n win.geometry('{}x{}+{}+{}'.format(width, height, x, y))\\n win.deiconify()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7989c9a736b1a7bac945846d900d0030\",\n \"score\": \"0.81152254\",\n \"text\": \"def center(self):\\n self.top.update_idletasks()\\n w = self.top.winfo_screenwidth()\\n h = self.top.winfo_screenheight()\\n size = tuple(int(_) for _ in self.top.geometry().split('+')[0].split('x'))\\n x = w/2 - size[0]/2\\n y = h/2 - size[1]/2\\n self.top.geometry(\\\"%dx%d+%d+%d\\\" % (size + (x, y)))\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"269f4daf6f57d93af44737520be52128\",\n \"score\": \"0.8040823\",\n \"text\": \"def center(win):\\n win.update_idletasks()\\n width = win.winfo_width()\\n frm_width = win.winfo_rootx() - win.winfo_x()\\n win_width = width + 2 * frm_width\\n height = win.winfo_height()\\n titlebar_height = win.winfo_rooty() - win.winfo_y()\\n win_height = height + titlebar_height + frm_width\\n x = win.winfo_screenwidth() // 2 - win_width // 2\\n y = win.winfo_screenheight() // 2 - win_height // 2\\n win.geometry('{}x{}+{}+{}'.format(width, height, x, y))\\n win.deiconify()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02e1510bda2656e2c42c0c0f8a7ee699\",\n \"score\": \"0.7986522\",\n \"text\": \"def makeWindowCenter(self):\\n qtRectangle = self.frameGeometry()\\n centerPoint = QDesktopWidget().availableGeometry().center()\\n qtRectangle.moveCenter(centerPoint)\\n self.move(qtRectangle.topLeft())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12a42b95583045c87ae3eb756f77d8e7\",\n \"score\": \"0.7965381\",\n \"text\": \"def center(win):\\n window_height = 700\\n window_width = 350\\n\\n screen_width = win.winfo_screenwidth()\\n screen_height = win.winfo_screenheight()\\n\\n x_cordinate = int((screen_width/2) - (window_width/2))\\n y_cordinate = int((screen_height/2) - (window_height/2))\\n\\n win.geometry(\\\"{}x{}+{}+{}\\\".format(window_width, window_height, x_cordinate, y_cordinate))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7bb3ba56ac54a8bb31e45fdd4e2a1c79\",\n \"score\": \"0.794357\",\n \"text\": \"def center_window_to_screen(self):\\n desktop = QtWidgets.QApplication.desktop()\\n cursor_pos = QtGui.QCursor.pos()\\n desktop_number = desktop.screenNumber(cursor_pos)\\n desktop_rect = desktop.screenGeometry(desktop_number)\\n\\n size = self.geometry()\\n\\n self.move(\\n (desktop_rect.width() - size.width()) * 0.5 + desktop_rect.left(),\\n (desktop_rect.height() - size.height()) * 0.5 + desktop_rect.top(),\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8186ea631fa7d764689461e8aae1f91e\",\n \"score\": \"0.79214674\",\n \"text\": \"def center_window(self):\\n\\n # if there is no main application then fall back to centering to the\\n # screen that the mouse pointer is in\\n parent = self.parent()\\n if not parent:\\n self.center_window_to_screen()\\n return\\n\\n # get the main application size\\n # center to that dimension\\n parent_size = parent.geometry()\\n size = self.geometry()\\n\\n left = (parent_size.width() - size.width()) * 0.5 + parent_size.left()\\n top = (parent_size.height() - size.height()) * 0.5 + parent_size.top()\\n\\n left = max(0, left)\\n top = max(0, top)\\n\\n self.move(left, top)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"147eb2078b785d6620846c0627eb45c8\",\n \"score\": \"0.7807315\",\n \"text\": \"def center_widget(self) -> None:\\n self.withdraw()\\n self.update_idletasks()\\n\\n x = (self.winfo_screenwidth() - self.winfo_reqwidth())\\n x /= 2\\n y = (self.winfo_screenheight() - self.winfo_reqheight())\\n y /= 2\\n g = (\\\"+%d+%d\\\" % (x, y))\\n\\n self.geometry(g)\\n self.deiconify()\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42200a08a775d4d4809d3c31e74889bb\",\n \"score\": \"0.7756313\",\n \"text\": \"def center(self):\\n # geometry of the main window\\n qr = self.frameGeometry()\\n # center point of screen\\n cp = QDesktopWidget().availableGeometry().center()\\n # move rectangle's center point to screen's center point\\n qr.moveCenter(cp)\\n # top left of rectangle becomes top left of window centering it\\n self.move(qr.topLeft())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87d50577c36ff2cfa23e7edca8c1711d\",\n \"score\": \"0.75481683\",\n \"text\": \"def _center(self):\\n _frame = self.frameGeometry()\\n # noinspection PyArgumentList\\n _root = QApplication.desktop()\\n _active_screen = _root.screenNumber(_root.cursor().pos())\\n _center = _root.screenGeometry(_active_screen).center()\\n _frame.moveCenter(_center)\\n self.move(_frame.topLeft())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"371012b5ceeecf752331c6cee8cd51f2\",\n \"score\": \"0.7445819\",\n \"text\": \"def center(self):\\n qr = self.frameGeometry()\\n cp = QDesktopWidget().availableGeometry().center() # get screen resolution and center point\\n qr.moveCenter(cp) # \\n self.move(qr.topLeft())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d02f3164df06f575e3e2f489bca8f86f\",\n \"score\": \"0.74301237\",\n \"text\": \"def align_window(self):\\n self.parent.update()\\n\\n # get screen info\\n screen_width = self.parent.winfo_screenwidth()\\n screen_height = self.parent.winfo_screenheight()\\n\\n # get window info\\n window_width = self.parent.winfo_width()\\n window_height = self.parent.winfo_height()\\n\\n # determine position of the window\\n x = screen_width - window_width/2 - 180\\n y = screen_height/2 - window_height/2\\n\\n # move the window to determined position\\n self.parent.geometry('+%d+%d' % (x, y))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e76197a9315d2cd00c67fb1f45031e48\",\n \"score\": \"0.73379296\",\n \"text\": \"def center(handle, w=500, h=450):\\n sw = handle.winfo_screenwidth()\\n sh = handle.winfo_screenheight()\\n\\n x = (sw - w) / 2\\n y = (sh - h) / 2\\n handle.geometry('%dx%d+%d+%d' % (w, h, x, y))\\n handle.focus()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df6c6ae39fbb8dbb5665791cc37d7a2b\",\n \"score\": \"0.7308243\",\n \"text\": \"def center(widget,relx,rely):\\n widget.withdraw() # Remain invisible while we fig+ure out the geometry \\n widget.update_idletasks() # Actualize geometry information \\n m_width = widget.winfo_screenwidth()\\n m_height = widget.winfo_screenheight() \\n w_width = widget.winfo_reqwidth() \\n w_height = widget.winfo_reqheight()\\n x = (m_width - w_width) * relx\\n y = (m_height - w_height) * rely\\n widget.geometry(\\\"+%d+%d\\\" % (x,y)) \\n widget.deiconify()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70dde9aed48e875377bcfcad47acba10\",\n \"score\": \"0.7297512\",\n \"text\": \"def _center(self):\\n qr = self.frameGeometry()\\n cp = QtWidgets.QDesktopWidget().availableGeometry().center()\\n qr.moveCenter(cp)\\n self.move(qr.topLeft())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"369c8b9530cc74f4002443cbb300e489\",\n \"score\": \"0.72566235\",\n \"text\": \"def center(self):\\r\\n self.showNormal()\\r\\n self.resize(QDesktopWidget().screenGeometry().width() // 1.25,\\r\\n QDesktopWidget().screenGeometry().height() // 1.25)\\r\\n qr = self.frameGeometry()\\r\\n qr.moveCenter(QDesktopWidget().availableGeometry().center())\\r\\n return self.move(qr.topLeft())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7fe6dcbdd46a00e26df77a92e7bae584\",\n \"score\": \"0.72289217\",\n \"text\": \"def center_window(window, resize=True):\\n screen = QGuiApplication.screenAt(QCursor.pos())\\n\\n if resize:\\n screen_size = screen.size()\\n max_width = screen_size.width()\\n max_height = screen_size.height()\\n\\n window.resize(max_width * 0.8, max_height * 0.8)\\n\\n window.move(screen.geometry().center() - window.frameGeometry().center())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25bb7977fec7d11fa007471e56a56a7e\",\n \"score\": \"0.7194875\",\n \"text\": \"def center(self):\\n qr = self.frameGeometry()\\n cp = QDesktopWidget().availableGeometry().center()\\n qr.moveCenter(cp)\\n self.move(qr.topLeft())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25bb7977fec7d11fa007471e56a56a7e\",\n \"score\": \"0.7194875\",\n \"text\": \"def center(self):\\n qr = self.frameGeometry()\\n cp = QDesktopWidget().availableGeometry().center()\\n qr.moveCenter(cp)\\n self.move(qr.topLeft())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25bb7977fec7d11fa007471e56a56a7e\",\n \"score\": \"0.7194875\",\n \"text\": \"def center(self):\\n qr = self.frameGeometry()\\n cp = QDesktopWidget().availableGeometry().center()\\n qr.moveCenter(cp)\\n self.move(qr.topLeft())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25bb7977fec7d11fa007471e56a56a7e\",\n \"score\": \"0.7194875\",\n \"text\": \"def center(self):\\n qr = self.frameGeometry()\\n cp = QDesktopWidget().availableGeometry().center()\\n qr.moveCenter(cp)\\n self.move(qr.topLeft())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa855cec61d9050a6bc2654736866e2b\",\n \"score\": \"0.7147751\",\n \"text\": \"def center(self):\\n self.translate(-1*self.com)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9412a5089ccc42c202043ed03d01868\",\n \"score\": \"0.71378535\",\n \"text\": \"def center(self):\\n\\t\\tqRect = self.frameGeometry()\\n\\t\\tcenterPoint = QDesktopWidget().availableGeometry().center()\\n\\t\\tqRect.moveCenter(centerPoint)\\n\\t\\tself.move(qRect.topLeft())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d5b14fb40a9bbf381dcac2ba60cfd93\",\n \"score\": \"0.7021262\",\n \"text\": \"def center_ball(self):\\r\\n\\t\\tself.centerx=self.screen_rect.centerx\\r\\n\\t\\tself.centery=self.screen_rect.centery\",\n \"title\": \"\"\n },\n {\n \"docid\": \"776829129fbd676c74b5fb205f077300\",\n \"score\": \"0.6984075\",\n \"text\": \"def setCenter(self, value):\\n self._viewCenter = b2Vec2(*value)\\n self._viewOffset = self._viewCenter - self.screenSize / 2\\n self.window.graphicsView.centerOn(*self._viewCenter)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eef45dc7689335e3b622ef7a4bb49573\",\n \"score\": \"0.6918894\",\n \"text\": \"def center(master, win, width, height):\\n # get screen width and height\\n ws = master.winfo_screenwidth() # width of the screen\\n hs = master.winfo_screenheight() # height of the screen\\n\\n # calculate x and y coordinates for the Tk root window\\n x = (ws / 2) - (width / 2)\\n y = (hs / 2) - (height / 2)\\n\\n return width, height, x, y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b95e1abcb1053ecb6644b7f1e93aff4f\",\n \"score\": \"0.68476415\",\n \"text\": \"def center_on_screen(widget):\\r\\n desktop = QtGui.QApplication.instance().desktop()\\r\\n rect = desktop.screenGeometry(QtGui.QCursor().pos())\\r\\n cy = rect.height()//2\\r\\n cx = rect.width()//2\\r\\n widget.move(cx - widget.width()//2, cy - widget.height()//2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8da09214a9f5090dfa9e74e9f7d57656\",\n \"score\": \"0.68216664\",\n \"text\": \"def center_ship(self):\\n\\t\\tself.center= self.screen_rect.centerx\\n\\t\\tself.bottom= self.screen_rect.bottom\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb75846baa05e651057b7cf7fd49bc0a\",\n \"score\": \"0.67556983\",\n \"text\": \"def bringFrontCenter(self):\\n\\t\\tif self.window:\\n\\t\\t\\tNSLog(\\\"window is present\\\")\\n\\t\\t\\tself.window.becomeMainWindow()\\n\\t\\t\\tself.window.center()\\n\\t\\t\\t# needed so the window can show over the loginwindow\\n\\t\\t\\tself.window.setCanBecomeVisibleWithoutLogin_(True)\\n\\t\\t\\tself.errorWindow.setCanBecomeVisibleWithoutLogin_(True)\\n\\t\\t\\tself.authSheetWindow.setCanBecomeVisibleWithoutLogin_(True)\\n\\t\\t\\tself.window.setLevel_(NSScreenSaverWindowLevel - 1)\\n\\t\\t\\tself.window.orderFrontRegardless()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89eb863d66f02897861eeac32e418d9d\",\n \"score\": \"0.6749471\",\n \"text\": \"def set_window_start(root):\\n\\tws = root.winfo_screenwidth() # gets width of screen\\n\\thw = root.winfo_screenheight() # gets height of screen\\n\\t\\n\\t# I want it to open in the bottom left\\n\\tx = 3.0*ws/4\\n\\ty = ws/3.0\\n\\t\\n\\t# set (width, height, x, y)\\n\\troot.geometry( '%dx%d+%d+%d' % (300, 300, x, y) )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"819df5b06fefc33b272d262940fcc9c6\",\n \"score\": \"0.6737455\",\n \"text\": \"def sc_center(Window_size: tuple, Inverse: bool = False, \\\\\\r\\n center: tuple = ini_center, win: object = Window):\\r\\n if Inverse == False:\\r\\n win.size = Window_size\\r\\n variation_x = win.center[0] - center[0]\\r\\n variation_y = win.center[1] - center[1]\\r\\n win.left -= variation_x\\r\\n win.top -= variation_y\\r\\n \\r\\n if Inverse == True:\\r\\n variation_x = win.center[0] - center[0]\\r\\n variation_y = win.center[1] - center[1]\\r\\n win.left += variation_x\\r\\n win.top += variation_y\\r\\n win.size = Window_size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"017fe70074d867c3461c416ea5ecc7ff\",\n \"score\": \"0.6720759\",\n \"text\": \"def center_ship(self):\\r\\n self.center = self.screen_rect.centerx\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06febd4f073a0d937834f78f834e06f5\",\n \"score\": \"0.6693505\",\n \"text\": \"def position ( self, ui ):\\n view = ui.view\\n window = ui.control\\n\\n # Set up the default position of the window:\\n parent = window.GetParent()\\n if parent is None:\\n window.Centre( wx.BOTH )\\n else:\\n position_near( parent, window, offset_y = -30 )\\n \\n # Calculate the correct width and height for the window:\\n cur_width = window.winfo_width()\\n cur_height = window.winfo_height()\\n width = view.width\\n height = view.height\\n \\n if width < 0.0:\\n width = cur_width\\n elif width <= 1.0:\\n width = int( width * screen_dx )\\n else:\\n width = int( width )\\n \\n if height < 0.0:\\n height = cur_height\\n elif height <= 1.0:\\n height = int( height * screen_dy )\\n else:\\n height = int( height )\\n \\n # Calculate the correct position for the window:\\n x = view.x\\n y = view.y\\n \\n if x < -99999.0:\\n x = (screen_dx - width) / 2\\n elif x <= -1.0:\\n x = screen_dx - width + int( x ) + 1\\n elif x < 0.0:\\n x = screen_dx - width + int( x * screen_dx )\\n elif x <= 1.0:\\n x = int( x * screen_dx )\\n else:\\n x = int( x )\\n \\n if y < -99999.0:\\n y = (screen_dy - height) / 2\\n elif y <= -1.0:\\n y = screen_dy - height + int( y ) + 1\\n elif x < 0.0:\\n y = screen_dy - height + int( y * screen_dy )\\n elif y <= 1.0:\\n y = int( y * screen_dy )\\n else:\\n y = int( y )\\n \\n # Position and size the window as requested:\\n window.geometry( '%dx%d+%d+%d' % ( width, height, x, y ) )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7224f6c3a09e9cfcb151f31e30e2548a\",\n \"score\": \"0.6666444\",\n \"text\": \"def center_ship(self):\\n\\t\\tself.center = self.screen_rect.centerx\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c51981ade551ce3d92a2ad4ea44bd909\",\n \"score\": \"0.6658568\",\n \"text\": \"def update_window(self):\\n screen_area = HOST_APP.proc_screen_workarea\\n scale_factor = 1.0 / HOST_APP.proc_screen_scalefactor\\n top = left = width = height = 0\\n\\n window_rect = revit.get_window_rectangle()\\n\\n # set width and height\\n width = window_rect.Right - window_rect.Left\\n height = self.user_height\\n\\n top = window_rect.Top\\n # in maximized window, the top might be off the active screen\\n # due to windows thicker window frames\\n # lets cut the height and re-adjust the top\\n top_diff = abs(screen_area.Top - top)\\n if 10 > top_diff > 0 and top_diff < height:\\n height -= top_diff\\n top = screen_area.Top\\n\\n left = window_rect.Left\\n # in maximized window, Left also might be off the active screen\\n # due to windows thicker window frames\\n # let's fix the width to accomodate the extra pixels as well\\n left_diff = abs(screen_area.Left - left)\\n if 10 > left_diff > 0 and left_diff < width:\\n # deduct two times the left negative offset since this extra\\n # offset happens on both left and right side\\n width -= left_diff * 2\\n left = screen_area.Left\\n\\n self.Top = top * scale_factor\\n self.Left = left * scale_factor\\n self.Width = width * scale_factor\\n self.Height = height\",\n \"title\": \"\"\n },\n {\n \"docid\": \"293b7085262b8b8fc20c76b8ecec3298\",\n \"score\": \"0.66558135\",\n \"text\": \"def center_ship(self):\\n self.center = self.screen_rect.centerx\",\n \"title\": \"\"\n },\n {\n \"docid\": \"293b7085262b8b8fc20c76b8ecec3298\",\n \"score\": \"0.66558135\",\n \"text\": \"def center_ship(self):\\n self.center = self.screen_rect.centerx\",\n \"title\": \"\"\n },\n {\n \"docid\": \"293b7085262b8b8fc20c76b8ecec3298\",\n \"score\": \"0.66558135\",\n \"text\": \"def center_ship(self):\\n self.center = self.screen_rect.centerx\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fbc5e67a3d9f1aff8048198f786b8001\",\n \"score\": \"0.66218907\",\n \"text\": \"def place_window(self, width=400, height=300):\\n w = width\\n h = height\\n\\n # get screen width and height\\n ws = self.winfo_screenwidth() # width of the screen\\n hs = self.winfo_screenheight() # height of the screen\\n\\n x = (ws / 2) - (w / 2)\\n y = (hs / 2) - (h / 2)\\n\\n # set the dimensions of the screen\\n # and where it is placed\\n self.geometry(\\\"%dx%d+%d+%d\\\" % (w, h, x, y))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d784b48449effe9dea7801dacd70c5cc\",\n \"score\": \"0.6614368\",\n \"text\": \"def update_center(self):\\r\\n \\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a2036c3eca30ab636fdb2dcd0755eab\",\n \"score\": \"0.6613791\",\n \"text\": \"def updateCenter(self):\\r\\n self.viewOffset = self.viewCenter - self.screenSize/2\\r\\n\\r\\n self.debugDraw._setValues(self.viewZoom, self.viewCenter, self.viewOffset, self.screenSize.x, self.screenSize.y)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf2f49fa2982edb87c8797ce929a6897\",\n \"score\": \"0.65797716\",\n \"text\": \"def center_mouse() -> None:\\n rect = ui.active_window().rect\\n center = (rect.x + round(rect.width / 2), rect.y + round(rect.height / 2))\\n actions.mouse_move(*center)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5042901b8abfe0af229e4e9e761ca985\",\n \"score\": \"0.65168935\",\n \"text\": \"def Fullscreen(self):\\n\\t\\tself.width = self.display_resolution.current_w\\n\\t\\tself.height = self.display_resolution.current_h\\n\\n\\t\\tself.item1 = menuItem(\\\"minimize\\\",self.width, self.height)\\n\\t\\tself.item2 = menuItem(\\\"start\\\",self.width, self.height - 2*self.item1.height)\\n\\t\\tself.item3 = menuItem(\\\"quit\\\",self.width, self.height + 2*self.item1.height)\\n\\n\\t\\tself.boxy = Boxy()\\n\\t\\tself.boxy = Boxy((255,255,255), self.item1.x - 2*self.boxy.width, centerHeight(self.item2.height, self.boxy.height) + self.item2.y)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"142892e31fa0c29898c41d3abf3ba335\",\n \"score\": \"0.650492\",\n \"text\": \"def centrar(self, x, y):\\n self.rect.center = (x,y)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"132f432af86bbc508e85a84dabe708cd\",\n \"score\": \"0.64783156\",\n \"text\": \"def update_center(self):\\n self._update(\\\"update_center\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"035faf8d51f20406e4bbad45aed154f2\",\n \"score\": \"0.647459\",\n \"text\": \"def move_window(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7cf2121bc8ba963d16ed8e1e3b72a7fc\",\n \"score\": \"0.64682335\",\n \"text\": \"def center_ship(self):\\r\\n\\t\\tself.center = self.screen_rect.centerx\\r\\n\\t\\t# ~ self.rect.midbottom = self.screen_rect.midbottom\\r\\n\\t\\t# ~ self.x = float(self.rect.x)\\r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd71756f72ff5c3ea8aba62ec99e5266\",\n \"score\": \"0.6440379\",\n \"text\": \"def update_dimensions(self):\\n # get the window size\\n window_rect = win32gui.GetWindowRect(self.hwnd)\\n isFullScreen = win32gui.GetWindowRect(self.hwnd) == full_screen_rect\\n self.isFullScreen = isFullScreen\\n self.isTrueFullscreen = is_full_screen()\\n self.w = window_rect[2] - window_rect[0]\\n self.h = window_rect[3] - window_rect[1]\\n\\n if not isFullScreen:\\n # account for the window border and titlebar and cut them off\\n border_pixels = 8\\n titlebar_pixels = 30\\n self.w = self.w - (border_pixels * 2)\\n self.h = self.h - titlebar_pixels - border_pixels\\n self.cropped_x = border_pixels\\n self.cropped_y = titlebar_pixels\\n\\n # images into actual screen positions\\n self.offset_x = window_rect[0] + self.cropped_x\\n self.offset_y = window_rect[1] + self.cropped_y\\n\\n else:\\n self.offset_x, self.offset_y = 0, 0\\n self.cropped_x, self.cropped_y = 0, 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20043502f14d6ff55300211293dee7ff\",\n \"score\": \"0.6421751\",\n \"text\": \"def center_ship(self):\\n\\t\\tself.rect.midbottom=self.screen_rect.midbottom\\n\\t\\tself.x=float(self.rect.x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7637cd8dd310dc4ad7a545c75bb8b0d1\",\n \"score\": \"0.64083266\",\n \"text\": \"def mwindow(self) -> None:\\n self.setFixedSize(898, 422)\\n self.setWindowTitle(\\\"Diabetes Prediction\\\")\\n self.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4ee51820918c900d7089d99163dfeb8\",\n \"score\": \"0.6343348\",\n \"text\": \"def center_ship(self):\\n\\t\\tself.rect.midbottom = self.screen_rect.midbottom\\n\\t\\tself.x = float(self.rect.x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a22138d20e921b2b28a9fddc986ed1aa\",\n \"score\": \"0.6323974\",\n \"text\": \"def center_ship(self):\\n self.rect.midbottom = self.screen_rect.midbottom\\n self.x = float(self.rect.x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a22138d20e921b2b28a9fddc986ed1aa\",\n \"score\": \"0.6323974\",\n \"text\": \"def center_ship(self):\\n self.rect.midbottom = self.screen_rect.midbottom\\n self.x = float(self.rect.x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a22138d20e921b2b28a9fddc986ed1aa\",\n \"score\": \"0.6323974\",\n \"text\": \"def center_ship(self):\\n self.rect.midbottom = self.screen_rect.midbottom\\n self.x = float(self.rect.x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a22138d20e921b2b28a9fddc986ed1aa\",\n \"score\": \"0.6323974\",\n \"text\": \"def center_ship(self):\\n self.rect.midbottom = self.screen_rect.midbottom\\n self.x = float(self.rect.x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a22138d20e921b2b28a9fddc986ed1aa\",\n \"score\": \"0.6323974\",\n \"text\": \"def center_ship(self):\\n self.rect.midbottom = self.screen_rect.midbottom\\n self.x = float(self.rect.x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37254e96a0ca94800ed64bbff3ec3e35\",\n \"score\": \"0.6309343\",\n \"text\": \"def center_view_on_player(self):\\n self.view_left = self.player.center_x - self.screen_width / 2\\n self.view_bottom = self.player.center_y - self.screen_height / 2\\n self.update_viewport()\\n self.scroll_viewport()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7b8a991e51fd1bf757df595755e2e0f\",\n \"score\": \"0.62983125\",\n \"text\": \"def enlargeWindow(self):\\n\\t\\t#\\t\\tNSLog(\\\"Main window size: {}\\\".format(self.window.frame().size))\\n\\t\\twinRect = self.window.frame()\\n\\t\\twinRect.size.height = 480\\n\\t\\twinRect.origin.y = winRect.origin.y - 256\\n\\t\\t#\\t\\tNSLog(\\\"Setting new main window size: {}\\\".format(winRect))\\n\\t\\tself.window.setFrame_display_(winRect, True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"894e87f79182b72e4973662c3f11a7c2\",\n \"score\": \"0.62930137\",\n \"text\": \"def setupWindow(self):\\n self.win = visual.Window(self.window_dims, allowGUI=False, fullscr=self.fullscreen, \\n monitor='testMonitor', units='deg') \\n self.win.setColor('black')\\n self.win.flip()\\n self.win.flip()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7993ec81b773415fbff160fc0ba295a\",\n \"score\": \"0.6288775\",\n \"text\": \"def centerPage(self):\\n top, bottom = self.yview()\\n size = bottom - top\\n if size==0: size=0.4\\n middle = size * 0.4\\n self.yview('moveto', middle)\\n #print top, bottom\\n #print middle\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f02eae09c11c06ead9c05588945de66\",\n \"score\": \"0.62675095\",\n \"text\": \"def setCenter(self, newCenter):\\n self.center = newCenter\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22f098cc337aefbe2a7cbd4e9152196b\",\n \"score\": \"0.6261226\",\n \"text\": \"def center(self):\\r\\n\\t\\treturn self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef770eb683b1d7eb7652da86c1ba61e9\",\n \"score\": \"0.62596244\",\n \"text\": \"def center(self, location: ghidra.program.util.ProgramLocation) -> None:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f8ff8c99913c72f1bcb42ed6fe6cfab\",\n \"score\": \"0.6228856\",\n \"text\": \"def position_window(window):\\n pos = QtGui.QCursor.pos()\\n window.move(pos.x(), pos.y())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8b74d323bb622f5ed3772eab740b5fa\",\n \"score\": \"0.62265974\",\n \"text\": \"def center(self):\\n if self._mode == \\\"vertical\\\":\\n margin = self._margins[0]\\n gap = self._gaps[0]\\n y = None\\n if self._align == \\\"center\\\":\\n x=\\\"auto\\\"\\n elif self._align == \\\"left\\\":\\n x=self._element.get_storer_rect().left + self._margins[0]\\n elif self._align == \\\"right\\\":\\n x=self._element.get_storer_rect().right - self._margins[0]\\n elif self._mode == \\\"horizontal\\\":\\n margin = self._margins[1]\\n gap = self._gaps[1]\\n x = None\\n if self._align == \\\"center\\\":\\n y=\\\"auto\\\"\\n elif self._align == \\\"top\\\":\\n y=self._element.get_storer_rect().top + self._margins[0]\\n elif self._align == \\\"bottom\\\":\\n y=self._element.get_storer_rect().bottom - self._margins[0]\\n size=store(frame=self._element,\\n elements=self._elements,\\n mode=self._mode,\\n x=x,\\n y=y,\\n margin=margin,\\n gap=gap,\\n align=self._align)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1054bb296adbfe9ace7edb2581942013\",\n \"score\": \"0.6208886\",\n \"text\": \"def draw(self, win, center):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24c9a2e106618fdc58d32be576711bcf\",\n \"score\": \"0.62028426\",\n \"text\": \"def display_win(self) -> None:\\n\\n self.top_panel.win_label.grid(row=0, columnspan=10)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1f08957226628c87db15418d89f5643\",\n \"score\": \"0.6200582\",\n \"text\": \"def centre_on_screen(widget: QWidget):\\n\\n # Get the widget's frame size.\\n widget_frame = widget.frameGeometry()\\n\\n # Get the centre of the screen.\\n screen_centre = QDesktopWidget().availableGeometry().center()\\n\\n # Calculate where the frame should be placed.\\n widget_frame.moveCenter(screen_centre)\\n\\n # Move the widget to the correct place.\\n widget.move(widget_frame.topLeft())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10f666f06ecf333b9697225ec60897a7\",\n \"score\": \"0.615979\",\n \"text\": \"def wrap(self, screen_width, screen_height):\\r\\n #check if it is off the left side\\r\\n if self.center.x < 0:\\r\\n self.center.x = screen_width\\r\\n #check if it is off the right side\\r\\n if self.center.x > screen_width:\\r\\n self.center.x = 0\\r\\n #check if it is off the top\\r\\n if self.center.y > screen_height:\\r\\n self.center.y = 0\\r\\n #check if it is off the bottom\\r\\n if self.center.y < 0:\\r\\n self.center.y = screen_height\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6bcbfe8e5921dfcd41a444af65208063\",\n \"score\": \"0.6152299\",\n \"text\": \"def center(self, surface, centerx=True, centery=True, xajout=0, yajout=0):\\n\\t\\tif centerx and centery:\\n\\t\\t\\treturn surface.get_rect(centerx=self.screen.get_width() / 2 + xajout, centery=self.screen.get_height() / 2 + yajout)\\n\\t\\tif centerx:\\n\\t\\t\\treturn surface.get_rect(centerx=self.screen.get_width() / 2 + xajout)\\n\\n\\t\\tif centery:\\n\\t\\t\\treturn surface.get_rect(centery=self.screen.get_width() / 2 + xajout)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4576a0ed37a7698571ffefa8fd99cd94\",\n \"score\": \"0.6127658\",\n \"text\": \"def LayoutDialog(self, centreFlags=wx.BOTH):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d3671a52f03abf30be6a3927df9eb20\",\n \"score\": \"0.61192656\",\n \"text\": \"def collapseWindow(self):\\n\\t\\t#\\t\\tNSLog(\\\"Main window size: {}\\\".format(self.window.frame().size))\\n\\t\\twinRect = self.window.frame()\\n\\t\\twinRect.size.height = 180\\n\\t\\twinRect.origin.y = winRect.origin.y + (\\n\\t\\t\\t\\tself.window.frame().size.height - 180 )\\n\\t\\t\\t\\t#\\t\\tNSLog(\\\"Setting new main window size: {}\\\".format(winRect))\\n\\t\\tself.window.setFrame_display_(winRect, True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97926c64ee824f63a31c044d6e4126e3\",\n \"score\": \"0.6070533\",\n \"text\": \"def geometry(self):\\n self.root.geometry(\\n f\\\"{self.playwidth*self.boxsize}x{(self.playheight + 2)*self.boxsize}+400+150\\\")\\n self.root.resizable(False, False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6725c71597f68ea5892ea28ad0f116a\",\n \"score\": \"0.60663486\",\n \"text\": \"def setWindowSize(self, x_size=800, y_size=600):\\n \\n self.window.resize(x_size, y_size)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30a44841bd6ca1fb736bb2e580c0ef44\",\n \"score\": \"0.60618573\",\n \"text\": \"def main(cls):\\n\\n tk.NoDefaultRoot()\\n root = tk.Tk()\\n app = cls(root)\\n # auto frame resize\\n app.grid(sticky=\\\"NSEW\\\")\\n root.grid_columnconfigure(0, weight=1)\\n root.grid_rowconfigure(0, weight=1)\\n root.grid_columnconfigure(1, weight=1)\\n root.grid_rowconfigure(1, weight=1)\\n root.grid_columnconfigure(2, weight=1)\\n root.grid_rowconfigure(2, weight=1)\\n # make adjustable in x not y\\n root.resizable(True, False)\\n # run application until user exit\\n root.mainloop()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90cf5b095735759888150d48960bbe34\",\n \"score\": \"0.60437715\",\n \"text\": \"def center(self, center):\\n\\n self._center = center\",\n \"title\": \"\"\n },\n {\n \"docid\": \"886ca718b27dba80b4a11dca9d2d35ab\",\n \"score\": \"0.6043047\",\n \"text\": \"def center(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"205e5a85463cd27c6197dc8b834d6bfd\",\n \"score\": \"0.5988926\",\n \"text\": \"def reset_location(self, screen: pygame.Surface) -> None:\\r\\n self.rect.left = (screen.get_width() - self.rect.width) / 2\\r\\n self.rect.top = (screen.get_height() - self.rect.height) / 2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2bb2a30847998b804ba48a52a2b4d626\",\n \"score\": \"0.59451467\",\n \"text\": \"def start(self):\\n self._root.grid_rowconfigure(0, weight=1)\\n self._root.grid_columnconfigure(0, weight=1)\\n\\n center_frame = Frame(\\n self._root,\\n width=self._root_width,\\n heigh=self._root_height - self._bottom_height,\\n )\\n\\n bottom_frame = Frame(\\n self._root,\\n bg=\\\"black\\\",\\n width=self._root_width,\\n height=self._bottom_height,\\n )\\n\\n center_frame.grid(\\n row=0,\\n column=0,\\n sticky=\\\"nsew\\\"\\n )\\n\\n bottom_frame.grid(\\n row=1,\\n column=0,\\n sticky=\\\"nsew\\\"\\n )\\n\\n bottom_frame.grid_rowconfigure(1)\\n bottom_frame.grid_columnconfigure(\\n [0, 1, 2, 3, 4, 5, 6, 7], minsize=(self._root_width/8))\\n\\n bottom_frame_headlines = [\\\"Intro\\\", \\\"Options\\\", \\\"Start\\\", \\\"Exit\\\"]\\n commands = [lambda: self._show_intro_view(center_frame),\\n lambda: self._show_options_view(center_frame),\\n lambda: self._show_game_view(center_frame),\\n self._quit]\\n\\n for j in range(0, len(bottom_frame_headlines)):\\n button = Button(\\n bottom_frame,\\n text=bottom_frame_headlines[j],\\n compound=\\\"center\\\",\\n width=12,\\n height=1,\\n background=\\\"red\\\",\\n activebackground=\\\"red\\\",\\n font=self.fonts.small_text_font,\\n command=commands[j]\\n )\\n\\n button.grid(\\n row=0,\\n column=j,\\n padx=12,\\n pady=12\\n )\\n\\n self._show_opening_view(center_frame)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0392d289092b31ad9c87556a5fe31a54\",\n \"score\": \"0.5944433\",\n \"text\": \"def set_relative_position(self):\\n\\n # Position window w/ respect to parent window\\n mx, my = self.parent.GetTopLevelParent().GetScreenPosition()\\n disp_geom = self.get_display(window=self.parent.GetTopLevelParent())\\n dw = disp_geom[2]\\n dxmin = disp_geom[0]\\n dxmax = disp_geom[0] + dw\\n dh = disp_geom[3]\\n dymin = disp_geom[1]\\n dymax = disp_geom[1] + dh\\n\\n if self.IsFrame():\\n adj_x = -50\\n adj_y = -50\\n elif self.IsDialog():\\n adj_x = int(dw * 0.025)\\n adj_y = int(dh * 0.025)\\n else:\\n adj_x = 50\\n adj_y = 50\\n px = mx + adj_x\\n py = my + adj_y\\n\\n ww, wh = self.GetSize()\\n pxmax = px + ww\\n pymax = py + wh\\n\\n # Calculate if window is going out of bounds, and adjust\\n if pxmax >= dxmax:\\n px = int((dxmin + dw - ww) * 0.9)\\n if pymax >= dymax:\\n py = int((dymin + dh - wh) * 0.9)\\n if px <= dxmin:\\n px = 25\\n if py <= dymin:\\n py = 25\\n\\n return px, py\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f4dbb231fc27b89e674e534b86e5d44\",\n \"score\": \"0.59231\",\n \"text\": \"def Minimized(self):\\n\\t\\tself.width = 400\\n\\t\\tself.height = 400\\n\\n\\t\\tself.item1 = menuItem(\\\"fullscreen\\\",self.width, self.height)\\n\\t\\tself.item2 = menuItem(\\\"start\\\",self.width, self.height - 2*self.item1.height)\\n\\t\\tself.item3 = menuItem(\\\"quit\\\",self.width, self.height + 2*self.item1.height)\\n\\n\\t\\tself.boxy = Boxy()\\n\\t\\tself.boxy = Boxy((255,255,255), self.item1.x - 2*self.boxy.width, centerHeight(self.item2.height, self.boxy.height) + self.item2.y)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f570a60eae4ab0654eaae8990b6660c6\",\n \"score\": \"0.5922452\",\n \"text\": \"def updateCenter(self, x, y):\\n self.Xcenter_old = self.Xcenter\\n self.Ycenter_old = self.Ycenter\\n self.Xcenter = x\\n self.Ycenter = y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"444579c2636bc48639514a8b52148b30\",\n \"score\": \"0.59136033\",\n \"text\": \"def center(self):\\n return self.functional.center\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d84a5dc7ce62649553f2f2b82b89b6da\",\n \"score\": \"0.5893992\",\n \"text\": \"def fulltonormal(self):\\n if self.controlDock.FullNormSW.isChecked():\\n self.MainWindow.showFullScreen()\\n self.controlDock.setStretch(*(10,300)) # minimize control dock width\\n else:\\n self.MainWindow.showNormal()\\n self.controlDock.setStretch(*(10,300)) # minimize control dock width\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23ae16b3eaf5317888d150587f42609c\",\n \"score\": \"0.58824813\",\n \"text\": \"def center(self):\\n return self._center\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c800d8734906e8ef484dd96aa4f6854\",\n \"score\": \"0.58794665\",\n \"text\": \"def initUI(self):\\n \\n self.resize(1024, 512)\\n self.center()\\n self.setWindowTitle('Py-Paint')\\n\\n self.createMenuBar() \\n self.createToolBar()\\n self.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"574b5517593c60d987e14d16a271cff9\",\n \"score\": \"0.58590764\",\n \"text\": \"def set_center(self, center_x: int, center_y: int) -> None:\\r\\n self._x = center_x - self._image.get_width() // 2\\r\\n self._y = center_y - self._image.get_height() // 2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15fdbf611dd75250e5fa9274943d454b\",\n \"score\": \"0.58475226\",\n \"text\": \"def update(self):\\n super().update()\\n if self.center_x < LEFT_LIMIT:\\n self.center_x = RIGHT_LIMIT\\n if self.center_x > RIGHT_LIMIT:\\n self.center_x = LEFT_LIMIT\\n if self.center_y > TOP_LIMIT:\\n self.center_y = BOTTOM_LIMIT\\n if self.center_y < BOTTOM_LIMIT:\\n self.center_y = TOP_LIMIT\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4393c955b34a610783050d1cc4486909\",\n \"score\": \"0.5829849\",\n \"text\": \"def set_screen(self):\\n self.screen = pygame.display.set_mode(self.size, pygame.RESIZABLE)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90f0740bddf78be51acb1e206ca54a13\",\n \"score\": \"0.58262646\",\n \"text\": \"def set_pos_root_button(self, instance):\\n\\n if self.anchor == \\\"right\\\":\\n instance.y = dp(20)\\n instance.x = Window.width - (dp(56) + dp(20))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6cfb7b2ce163c2782236d9577176a640\",\n \"score\": \"0.58252335\",\n \"text\": \"def setCentreCoordinates(self,xcenter,ycenter):\\n self.x = xcenter\\n self.y = ycenter\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e9cedc0d7819db57ffaf589ab568876\",\n \"score\": \"0.58119756\",\n \"text\": \"def render_to_window(self):\\n window = self.window\\n camera = self.camera\\n\\n y = camera.y\\n x = camera.x\\n\\n x1 = x\\n x2 = window.width + x\\n y1 = y\\n y2 = window.height + y\\n\\n glMatrixMode(GL_PROJECTION)\\n glLoadIdentity()\\n gluOrtho2D(x1, x2, y1, y2)\\n\\n glClear(GL_COLOR_BUFFER_BIT)\\n glMatrixMode(GL_MODELVIEW)\\n glLoadIdentity()\\n\\n self.draw_world()\\n self.draw_hud()\\n\\n window.flip()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3396eb6fae01c0cd781c44167f042ddb\",\n \"score\": \"0.5807685\",\n \"text\": \"def _makeScreen(self):\\n\\n #Centering the display on screen (taken from pygame FAQ)\\n os.environ['SDL_VIDEO_CENTERED'] = '1'\\n\\n #Starting pygame and setting up the display(game window)\\n\\n pygame.init()\\n gamescreen = pygame.display.set_mode((RES_X, RES_Y))\\n pygame.display.set_caption(CAPTION)\\n \\n return gamescreen\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db386faf10c93eadf27bd745ea1d8b87\",\n \"score\": \"0.5806194\",\n \"text\": \"def main(location, rows, columns, padding, margin):\\n\\n window = get_active_window()\\n width, height = get_new_size(columns, rows, padding)\\n\\n resize(window, width - margin * 2, height - margin * 2)\\n\\n left, right, top, bottom = get_border()\\n\\n r = location[0] * (height + top + bottom) + padding[0] + margin\\n c = location[1] * (width + left + right) + padding[3] + margin\\n\\n move(window, r, c)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ebae1f9805d26cbc74050259430e583\",\n \"score\": \"0.57883084\",\n \"text\": \"def center_inky(self):\\n self.rect.centery = 250\\n self.rect.centerx = 300\\n self.centerx = self.rect.x\\n self.rect.centerx = self.rect.x\\n self.centery = self.rect.y\\n self.rect.centery = self.rect.y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac04d15cdd170866f11ac84cfa97e1f9\",\n \"score\": \"0.5786315\",\n \"text\": \"def center(self):\\n return self.pos\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b3731be38e07d33f6edbd12c69bec3c\",\n \"score\": \"0.5784496\",\n \"text\": \"def show_main_screen(self):\\n self.title_frame.pack(side=TOP)\\n self.title_frame.pack_propagate(0)\\n self.title.pack()\\n self.intro_text.pack(side=TOP)\\n self.buttons_frame.pack()\\n self.select_button.pack(side=LEFT, padx=50)\\n self.multi_button.pack(side=LEFT, padx=50)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3024ab4d8e746468694bedfcd272f69\",\n \"score\": \"0.5775383\",\n \"text\": \"def resize(widthWindow, heightWindow):\\r\\n glMatrixMode(GL_PROJECTION)\\r\\n glLoadIdentity()\\r\\n glMatrixMode(GL_MODELVIEW)\\r\\n glLoadIdentity()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"050ae135fd359b2acb55c92d62b45fe4\",\n \"score\": \"0.57698506\",\n \"text\": \"def frame_center(self):\\n return self.frame.get_center()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac6d306faee15e406690fd3eac54e14a\",\n \"score\": \"0.5763159\",\n \"text\": \"def display(self):\\n self.window.clear()\\n \\n # If the screen is big enough print all relevant information\\n if self.max_columns > 20 and self.max_rows > 10:\\n self.print_buffer()\\n self.print_title_bar()\\n self.print_message()\\n self.print_coords()\\n self.print_menu()\\n \\n self.scroll() # Scroll the screen if needed\\n self.set_cursor() # Set the cursor coordinates\\n self.window.move(self.row, self.column) # Place the cursor on screen\\n \\n self.window.noutrefresh()\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":682,"cells":{"query_id":{"kind":"string","value":"495cf48c5e18e12b7bfaf87cc50f1428"},"query":{"kind":"string","value":"Wisdom AI engine. This extracts insights from documents and returns key points, abstracts, wordclouds and PDF viewer if possible."},"positive_passages":{"kind":"list like","value":[{"docid":"932e0ca123feee64a87a4865f30a383f","score":"0.5761071","text":"def wisdom(search_me, source, pdfurl, userid):\n ### source needs to be name of data source (\"arxiv\", \"google scholar\", \"doaj\")\n search_me = search_me.strip()\n # check if pdfurl has been found before\n pdf = db_summaries.find_one({\"url\": pdfurl})\n if pdf:\n text = pdf.get('text')\n summary = pdf.get('summary')\n topics = pdf.get('topics')\n # update in db if data is 1 days or older\n last_updated = datetime.utcnow() - pdf.get(\"last_updated\")\n last_updated_diff = last_updated.days\n if last_updated_diff > 1:\n search_term = db_search_terms.find_one({\"value\": search_me.lower()})\n search_id = search_term.get(\"_id\")\n data = {\"search_id\": search_id,\n \"url\": pdfurl, \"source\": source, \"text\": text,\n \"summary\": summary, \"topics\": topics, \"last_updated\": datetime.utcnow()}\n db_summaries.update({\"url\": pdfurl}, {\"$set\": data})\n else:\n pass\n else:\n text = wisdomaiengine.pdfdocumentextracter(pdfurl)\n summary = wisdomaiengine.summarisepdfdocument(text)\n topics = wisdomaiengine.wordcloud(search_me, text)\n if topics is None:\n topics = ['No Topics Found']\n # write data to arxiv collection\n search_term = db_search_terms.find_one({\"value\": search_me.lower()})\n search_id = search_term.get(\"_id\")\n data = {\"search_id\": search_id,\n \"url\": pdfurl,\n \"source\": source,\n \"text\": text,\n \"summary\": summary,\n \"topics\": topics,\n \"last_updated\": datetime.utcnow()}\n x = db_summaries.insert(data, check_keys=False)\n # return json\n summaryjson = jsonify(wisdomtopics=topics, wisdomsummary=summary)\n return summaryjson","title":""}],"string":"[\n {\n \"docid\": \"932e0ca123feee64a87a4865f30a383f\",\n \"score\": \"0.5761071\",\n \"text\": \"def wisdom(search_me, source, pdfurl, userid):\\n ### source needs to be name of data source (\\\"arxiv\\\", \\\"google scholar\\\", \\\"doaj\\\")\\n search_me = search_me.strip()\\n # check if pdfurl has been found before\\n pdf = db_summaries.find_one({\\\"url\\\": pdfurl})\\n if pdf:\\n text = pdf.get('text')\\n summary = pdf.get('summary')\\n topics = pdf.get('topics')\\n # update in db if data is 1 days or older\\n last_updated = datetime.utcnow() - pdf.get(\\\"last_updated\\\")\\n last_updated_diff = last_updated.days\\n if last_updated_diff > 1:\\n search_term = db_search_terms.find_one({\\\"value\\\": search_me.lower()})\\n search_id = search_term.get(\\\"_id\\\")\\n data = {\\\"search_id\\\": search_id,\\n \\\"url\\\": pdfurl, \\\"source\\\": source, \\\"text\\\": text,\\n \\\"summary\\\": summary, \\\"topics\\\": topics, \\\"last_updated\\\": datetime.utcnow()}\\n db_summaries.update({\\\"url\\\": pdfurl}, {\\\"$set\\\": data})\\n else:\\n pass\\n else:\\n text = wisdomaiengine.pdfdocumentextracter(pdfurl)\\n summary = wisdomaiengine.summarisepdfdocument(text)\\n topics = wisdomaiengine.wordcloud(search_me, text)\\n if topics is None:\\n topics = ['No Topics Found']\\n # write data to arxiv collection\\n search_term = db_search_terms.find_one({\\\"value\\\": search_me.lower()})\\n search_id = search_term.get(\\\"_id\\\")\\n data = {\\\"search_id\\\": search_id,\\n \\\"url\\\": pdfurl,\\n \\\"source\\\": source,\\n \\\"text\\\": text,\\n \\\"summary\\\": summary,\\n \\\"topics\\\": topics,\\n \\\"last_updated\\\": datetime.utcnow()}\\n x = db_summaries.insert(data, check_keys=False)\\n # return json\\n summaryjson = jsonify(wisdomtopics=topics, wisdomsummary=summary)\\n return summaryjson\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"d04f78c317274e362e0222600c00b016","score":"0.5694965","text":"def main():\n logging.basicConfig(level=logging.DEBUG)\n custom_embedding = True\n\n # Download embeddings'\n if custom_embedding:\n embedding_path = '../data/custom_embedding.pkl'\n embedding_index_path = '../data/custom_vocab_index.pkl'\n logging.info('Pulling custom embedding from: {}, and custom vocab from: {}'.format(embedding_path, embedding_index_path))\n embedding_matrix = pickle.load(open(embedding_path, 'rb'))\n embedding_index_lookup = pickle.load(open(embedding_index_path, 'rb'))\n\n else:\n logging.warning('Downloading embedding. If downloading for the first time, this make take 5-10 minutes.')\n embedding_url = 'https://s3.amazonaws.com/dl4j-distribution/GoogleNews-vectors-negative300.bin.gz'\n embedding_path = '~/nlp_example/'\n embedding_filename = 'GoogleNews-vectors-negative300.bin.gz'\n lib.download_file(embedding_url, embedding_path, embedding_filename)\n\n # Unpack embedding\n model = gensim.models.KeyedVectors.load_word2vec_format(embedding_path + '/' + embedding_filename, binary=True)\n embedding_matrix = model.syn0\n embedding_index_lookup = dict([(k, v.index) for k, v in model.vocab.items()])\n\n # Create thesaurus\n thesaurus = Thesaurus(embedding_matrix, embedding_index_lookup)\n\n # Find nearest neighbors for examples\n print(thesaurus.synonyms('day'))\n print(thesaurus.synonyms('top'))\n print(thesaurus.synonyms('bottom'))\n print(thesaurus.synonyms('cat'))\n print(thesaurus.synonyms('grown'))\n\n\n pass","title":""},{"docid":"5414d2cceb439a2c817b0dd22dec84e6","score":"0.5648464","text":"def analyse_presentation(pres_name:str, verbose=False) -> Dict[str, Any]:\n prs = Presentation(pres_name)\n if verbose:\n debug_dump(prs)\n (layouts_interactive, layouts) = count_layouts(prs)\n interaction_stars = min(layouts_interactive, 5)\n topic_stars = ([1,1,3,5,5,4,3,2,1]+[1]*100)[layouts[\"Section Header\"]]\n\n pres_properties = get_presentation_properties(prs)\n\n word_count = get_word_counts(prs.slides)\n words_per_slide = sum(word_count) / len(word_count)\n # ideal words/slide is 30-40 (5 stars)\n text_stars = calculate_text_stars(word_count)\n # print(\"word counts:\", word_count)\n\n # Create a list of warnings about very text-heavy slides\n heavy_warnings = []\n for slide, words in enumerate(word_count):\n if words > MAX_WORDS_PER_SLIDE:\n heavy_warnings.append(f\"WARNING: slide {slide} has {words} words!\")\n\n\n slides = get_slide_analytics(prs.slides)\n print(slides)\n result = {\n\n \"presentation_rating_stars_interaction\": interaction_stars,\n \"presentation_rating_stars_section\": topic_stars,\n \"presentation_rating_stars_accessibility\": 3, # not implemented yet!\n \"presentation_rating_stars_text\": text_stars,\n \"presentation_count_slide\": len(prs.slides),\n \"presentation_count_layout\": layouts, # dictionary that maps layout name to count\n \"presentation_total_words\": words_per_slide, # a float\n \"presentation_warning_text_heavy\": heavy_warnings, # a list of warning strings\n \"presentation_data_slides\": slides, # a list of slides and analytics\n \"filename\": pres_name, # TODO: strip any Path and just return file name?\n \"name\": \"ICT999\",\n \"description\": \"Introduction to ICT\"\n }\n\n return result","title":""},{"docid":"c0014dd9244d3b08e26a41c8651af12d","score":"0.5442001","text":"def run(self):\n self.tf_idf_scores = self.tf_idf()\n self.word_scores = self.create_word_score()\n self.keyword_scores = self.create_keyword_score()\n self.text_ranks = self.create_text_rank()\n self.pos_scores = {}\n self.z_area_scores = {}\n self.create_pos_and_area_score()\n self.all_scores = self.weight_distribution()\n self.all_scores_dict = self.get_all_scores_dict()\n return self.get_keywords()","title":""},{"docid":"403a0a54c67e379424a9698052881df0","score":"0.54035425","text":"def gather_documents(self):\n self.document_gatherer.gather_and_save_everything(Constants.path_cord, \n Constants.path_metadata, \n Constants.path_linked_documents,\n Constants.path_unlinked_documents,\n Constants.path_parsed_documents,\n Constants.path_all_documents)\n \n print(\"Done gathering documents.\")","title":""},{"docid":"d625ed60e5b26a3d637f1ce533c50d0d","score":"0.53915334","text":"def engine():\r\n document.add_heading('Engine details', 1)\r\n\r\n engine_metrics = ['customProperties','listenerPorts','autosaveInterval', 'tableFilesDirectory', 'genericUndoBufferMaxSize', 'documentTimeout','documentDirectory',\r\n 'allowDataLineage', 'qrsHttpNotificationPort', 'standardReload',\r\n 'workingSetSizeLoPct', 'workingSetSizeHiPct', 'workingSetSizeMode','cpuThrottlePercentage', 'maxCoreMaskPersisted', 'maxCoreMask',\r\n 'maxCoreMaskHiPersisted', 'maxCoreMaskHi','objectTimeLimitSec', 'exportTimeLimitSec', 'reloadTimeLimitSec',\r\n 'hyperCubeMemoryLimit', 'exportMemoryLimit', 'reloadMemoryLimit', 'createSearchIndexOnReloadEnabled', 'hostname',\r\n 'globalLogMinuteInterval','auditActivityLogVerbosity','auditSecurityLogVerbosity','serviceLogVerbosity','systemLogVerbosity','performanceLogVerbosity',\r\n 'qixPerformanceLogVerbosity','auditLogVerbosity','sessionLogVerbosity','trafficLogVerbosity']\r\n\r\n enginenodes = get_qlik_sense.get_engine()\r\n num_of_engines = len(enginenodes)\r\n num_of_engine_metrics = len(engine_metrics)\r\n table = document.add_table(rows=num_of_engine_metrics+1, cols=num_of_engines+1)\r\n table.style = 'Grid Table 1 Light Accent 1'\r\n row = table.rows[0]\r\n row.cells[0].text = 'Metric'\r\n for item in range(0, num_of_engines):\r\n row.cells[item+1].text = enginenodes[item][36]\r\n for item in range(num_of_engine_metrics):\r\n row = table.rows[item+1]\r\n row.cells[0].text = str(engine_metrics[item])\r\n for enginenode in range(num_of_engines):\r\n row.cells[enginenode+1].text = str(enginenodes[enginenode][item])\r\n\r\n document.add_page_break()","title":""},{"docid":"6cd69e819f3bb959089daf448964771b","score":"0.53757584","text":"def analyze(self, content):\n words = self.remove_stop_words(content)\n words = self.select_relevant_pos_words(words)\n concepts = [self.get_wiki_content(word) for word in words]\n self.create_wordcloud(concepts)","title":""},{"docid":"c2a7b6f5798d9a40ee7787daab82aa32","score":"0.52914554","text":"def main():\r\n\tlang = get_arguments()\r\n\twiki_analyzer(lang)","title":""},{"docid":"ae108b8342048aef56aaa979ce64d207","score":"0.524891","text":"def analyse(self):\n pass","title":""},{"docid":"ed0ae4bc0fa46fb9d01fdf38b5c22a1c","score":"0.5186837","text":"def analyse_document(dom, arguments):\n model = dom.getElementsByTagName(\"model\")[0]\n return analyse_model(model, arguments)","title":""},{"docid":"976be3cf0892e1e404a68421e4097787","score":"0.5137216","text":"def phonology(request):\n\n perspective_cid = request.params.get('perspective_client_id')\n perspective_oid = request.params.get('perspective_object_id')\n\n # Checking if we have limits on number of computed results.\n\n limit = (None if 'limit' not in request.params else\n int(request.params.get('limit')))\n\n limit_exception = (None if 'limit_exception' not in request.params else\n int(request.params.get('limit_exception')))\n\n limit_no_vowel = (None if 'limit_no_vowel' not in request.params else\n int(request.params.get('limit_no_vowel')))\n\n limit_result = (None if 'limit_result' not in request.params else\n int(request.params.get('limit_result')))\n\n # TODO: get perspective's translation and language it belongs to.\n\n # We get lexical entries of this perspective with markup'ed sounds.\n\n Sound = aliased(Entity, name = \"Sound\")\n PublishingSound = aliased(PublishingEntity, name = \"PublishingSound\")\n\n query = DBSession.query(LexicalEntry, Entity, Sound, PublishingEntity, PublishingSound).filter(and_(\n LexicalEntry.parent_client_id == perspective_cid,\n LexicalEntry.parent_object_id == perspective_oid,\n LexicalEntry.marked_for_deletion == False,\n Entity.parent_client_id == LexicalEntry.client_id,\n Entity.parent_object_id == LexicalEntry.object_id,\n Entity.marked_for_deletion == False,\n Entity.additional_metadata.contains({\"data_type\": \"praat markup\"}),\n PublishingEntity.client_id == Entity.client_id,\n PublishingEntity.object_id == Entity.object_id,\n PublishingEntity.published == True,\n PublishingEntity.accepted == True,\n Sound.client_id == Entity.self_client_id,\n Sound.object_id == Entity.self_object_id,\n Sound.marked_for_deletion == False,\n PublishingSound.client_id == Sound.client_id,\n PublishingSound.object_id == Sound.object_id,\n PublishingSound.published == True,\n PublishingSound.accepted == True))\n\n # We process these lexical entries in batches. Just in case, it seems that perspectives rarely have more\n # then several hundred such lexical entries.\n\n exception_counter = 0\n no_vowel_counter = 0\n result_list = list()\n\n for index, row in enumerate(query.yield_per(100)):\n\n markup_url = row.Entity.content\n sound_url = row.Sound.content\n\n cache_key = 'phonology:{0}:{1}:{2}:{3}'.format(\n row.Sound.client_id, row.Sound.object_id,\n row.Entity.client_id, row.Entity.object_id)\n\n # Checking if we have cached result for this pair of sound/markup.\n\n cache_result = CACHE.get(cache_key)\n\n if cache_result == 'no_vowel':\n\n log.debug(\n '{0} (LexicalEntry {1}/{2}, sound-Entity {3}/{4}, markup-Entity {5}/{6}) '\n '[CACHE {7}]: no vowels\\n{8}\\n{9}'.format(\n index,\n row.LexicalEntry.client_id, row.LexicalEntry.object_id,\n row.Sound.client_id, row.Sound.object_id,\n row.Entity.client_id, row.Entity.object_id,\n cache_key, markup_url, sound_url))\n\n no_vowel_counter += 1\n\n if (limit_no_vowel and no_vowel_counter >= limit_no_vowel or\n limit and index + 1 >= limit):\n break\n\n continue\n\n # If we have cached exception, we do the same as with absence of vowels, show its info and\n # continue.\n\n elif isinstance(cache_result, tuple) and cache_result[0] == 'exception':\n exception, traceback_string = cache_result[1:3]\n\n log.debug(\n '{0} (LexicalEntry {1}/{2}, sound-Entity {3}/{4}, markup-Entity {5}/{6}): '\n '[CACHE {7}]: exception\\n{8}\\n{9}'.format(\n index,\n row.LexicalEntry.client_id, row.LexicalEntry.object_id,\n row.Sound.client_id, row.Sound.object_id,\n row.Entity.client_id, row.Entity.object_id,\n cache_key, markup_url, sound_url))\n\n log.debug(traceback_string)\n\n exception_counter += 1\n\n if (limit_exception and exception_counter >= limit_exception or\n limit and index + 1 >= limit):\n break\n\n continue\n\n # If we actually have the result, we use it and continue.\n\n elif cache_result:\n\n result_string = '\\n'.join(\n 'tier {0} \\'{1}\\': {2}'.format(tier_number, tier_name,\n \n tier_result_seq_list if not isinstance(tier_result_seq_list, list) else\n tier_result_seq_list[0] if len(tier_result_seq_list) <= 1 else\n ''.join('\\n {0}'.format(tier_result) for tier_result in tier_result_seq_list))\n\n for tier_number, tier_name, tier_result_seq_list in cache_result)\n\n log.debug(\n '{0} (LexicalEntry {1}/{2}, sound-Entity {3}/{4}, markup-Entity {5}/{6}) '\n '[CACHE {7}]:\\n{8}\\n{9}\\n{10}'.format(\n index,\n row.LexicalEntry.client_id, row.LexicalEntry.object_id,\n row.Sound.client_id, row.Sound.object_id,\n row.Entity.client_id, row.Entity.object_id,\n cache_key, markup_url, sound_url, result_string))\n\n result_list.append(cache_result)\n\n if (limit_result and len(result_list) >= limit_result or\n limit and index + 1 >= limit):\n break\n\n continue\n\n try:\n # Getting markup, checking for each tier if it needs to be processed.\n\n markup_bytes = urllib.request.urlopen(urllib.parse.quote(markup_url, safe = '/:')).read()\n\n textgrid = pympi.Praat.TextGrid(xmax = 0)\n textgrid.from_file(\n io.BytesIO(markup_bytes),\n codec = chardet.detect(markup_bytes)['encoding'])\n\n tier_data_list = []\n vowel_flag = False\n\n for tier_number, tier_name in textgrid.get_tier_name_num():\n\n raw_interval_list = textgrid.get_tier(tier_number).get_all_intervals()\n raw_interval_seq_list = [[]]\n\n # Splitting interval sequence on empty intervals.\n\n for raw_index, interval in enumerate(raw_interval_list):\n\n if len(interval[2].strip()) <= 0:\n if len(raw_interval_seq_list[-1]) > 0:\n raw_interval_seq_list.append([])\n\n else:\n raw_interval_seq_list[-1].append((raw_index, interval))\n\n if len(raw_interval_seq_list[-1]) <= 0:\n del raw_interval_seq_list[-1]\n\n # Selecting interval sequences for analysis, checking if we have unusual markup.\n \n interval_seq_list = []\n interval_idx_to_raw_idx = dict()\n\n unusual_markup_flag = False\n unusual_markup_list = []\n\n for raw_interval_seq in raw_interval_seq_list:\n\n interval_seq_list.append([])\n interval_idx_to_raw_idx[len(interval_seq_list) - 1] = {}\n\n for partial_raw_index, (raw_index, interval) in enumerate(raw_interval_seq):\n\n interval_text = interval[2].strip()\n\n # Accepting interval if its text contains at least one vowel, and is short enough or\n # is a valid phonetic transcription.\n\n transcription_check = re.fullmatch(transcription_re, interval_text)\n\n if (len(interval_text) > 0 and\n any(character in vowel_set for character in interval_text) and\n (len(interval_text) <= 2 or transcription_check)):\n\n interval_seq_list[-1].append(interval)\n\n sequence_index = len(interval_seq_list) - 1\n interval_index = len(interval_seq_list[-1]) - 1\n\n interval_idx_to_raw_idx[(sequence_index, interval_index)] = raw_index\n interval_idx_to_raw_idx[sequence_index][interval_index] = partial_raw_index\n\n # Noting if the interval contains unusual (i.e. non-transcription) markup.\n\n elif not transcription_check:\n\n unusual_markup_flag = True\n unusual_markup_list.append((raw_index, interval))\n\n transcription_list = [text for begin, end, text in raw_interval_list]\n transcription = ''.join(transcription_list)\n\n selected_list = [text\n for interval_list in interval_seq_list\n for begin, end, text in interval_list]\n\n selected = ''.join(selected_list)\n\n # If we have intervals with unusual markup, we report them.\n\n if unusual_markup_flag:\n log.debug(\n '{0} (LexicalEntry {1}/{2}, sound-Entity {3}/{4}, markup-Entity {5}/{6}): '\n 'tier {7} \\'{8}\\' has interval(s) with unusual transcription text: '\n '{9} / {10}'.format(\n index,\n row.LexicalEntry.client_id, row.LexicalEntry.object_id,\n row.Sound.client_id, row.Sound.object_id,\n row.Entity.client_id, row.Entity.object_id,\n tier_number, tier_name, transcription, dict(unusual_markup_list)))\n\n # If the markup does not have any vowels, we note it and also report it.\n\n if all(character not in vowel_set for character in transcription):\n\n tier_data_list.append((tier_number, tier_name, 'no_vowel'))\n\n log.debug(\n '{0} (LexicalEntry {1}/{2}, sound-Entity {3}/{4}, markup-Entity {5}/{6}): '\n 'tier {7} \\'{8}\\' doesn\\'t have any vowel markup: {9}'.format(\n index,\n row.LexicalEntry.client_id, row.LexicalEntry.object_id,\n row.Sound.client_id, row.Sound.object_id,\n row.Entity.client_id, row.Entity.object_id,\n tier_number, tier_name, transcription_list))\n\n # It is also possible that while full transcription has vowels, intervals selected for\n # analysis do not. In that case we also note it and report it.\n\n elif not any(character in vowel_set for character in selected):\n\n tier_data_list.append((tier_number, tier_name, 'no_vowel_selected'))\n\n log.debug(\n '{0} (LexicalEntry {1}/{2}, sound-Entity {3}/{4}, markup-Entity {5}/{6}): '\n 'tier {7} \\'{8}\\' intervals to be processed don\\'t have any vowel markup: '\n 'markup {9}, selected {10}'.format(\n index,\n row.LexicalEntry.client_id, row.LexicalEntry.object_id,\n row.Sound.client_id, row.Sound.object_id,\n row.Entity.client_id, row.Entity.object_id,\n tier_number, tier_name,\n transcription_list, selected_list))\n\n # Otherwise we store tier data to be used during processing of the sound file.\n\n else:\n tier_data_list.append((tier_number, tier_name,\n (raw_interval_list, raw_interval_seq_list, interval_seq_list,\n interval_idx_to_raw_idx, transcription)))\n\n vowel_flag = True\n\n # If there are no tiers with vowel markup, we skip this sound-markup file altogether.\n\n if not vowel_flag:\n\n CACHE.set(cache_key, 'no_vowel')\n no_vowel_counter += 1\n\n if (limit_no_vowel and no_vowel_counter >= limit_no_vowel or\n limit and index + 1 >= limit):\n break\n\n continue\n\n # Otherwise we retrieve the sound file and analyse each vowel-containing markup.\n # Partially inspired by source code at scripts/convert_five_tiers.py:307.\n\n sound = None\n with tempfile.NamedTemporaryFile() as temp_file:\n\n sound_file = urllib.request.urlopen(urllib.parse.quote(sound_url, safe = '/:'))\n temp_file.write(sound_file.read())\n temp_file.flush()\n\n sound = AudioPraatLike(pydub.AudioSegment.from_wav(temp_file.name))\n\n tier_result_list = []\n\n for tier_number, tier_name, tier_data in tier_data_list:\n\n if tier_data == 'no_vowel' or tier_data == 'no_vowel_selected':\n tier_result_list.append((tier_number, tier_name, tier_data))\n continue\n\n # Analyzing vowel sounds of each interval sequence.\n\n (raw_interval_list, raw_interval_seq_list, interval_seq_list, interval_idx_to_raw_idx,\n transcription) = tier_data\n\n tier_result_list.append((tier_number, tier_name, []))\n\n for seq_index, (raw_interval_list, interval_list) in enumerate(zip(\n raw_interval_seq_list, interval_seq_list)):\n\n if len(interval_list) <= 0:\n continue\n\n (max_intensity_index, max_intensity, max_length_index, max_length) = \\\n find_max_interval_praat(sound, interval_list)\n\n max_intensity_interval = interval_list[max_intensity_index]\n max_length_interval = interval_list[max_length_index]\n\n max_intensity_f1_f2 = sound.get_interval_formants(*max_intensity_interval[:2])\n max_length_f1_f2 = sound.get_interval_formants(*max_length_interval[:2])\n\n # Compiling results.\n\n max_length_str = '{0} {1:.3f} [{2}]'.format(\n max_length_interval[2], max_length,\n len(''.join(text for index, (begin, end, text) in\n raw_interval_list[:interval_idx_to_raw_idx[seq_index][max_length_index]])))\n\n max_intensity_str = '{0} {1:.3f} [{2}]'.format(\n max_intensity_interval[2],\n max_intensity,\n len(''.join(text for index, (begin, end, text) in\n raw_interval_list[:interval_idx_to_raw_idx[seq_index][max_intensity_index]])))\n\n tier_result_list[-1][2].append([\n ''.join(text for index, (begin, end, text) in raw_interval_list),\n max_length_str,\n '{0:.3f}'.format(max_length_f1_f2[0]),\n '{0:.3f}'.format(max_length_f1_f2[1]),\n max_intensity_str,\n '{0:.3f}'.format(max_intensity_f1_f2[0]),\n '{0:.3f}'.format(max_intensity_f1_f2[1]),\n '+' if max_intensity_index == max_length_index else '-'])\n\n # Saving result.\n\n result_list.append(tier_result_list)\n CACHE.set(cache_key, tier_result_list)\n\n result_string = '\\n'.join(\n 'tier {0} \\'{1}\\': {2}'.format(tier_number, tier_name,\n \n tier_result_seq_list if not isinstance(tier_result_seq_list, list) else\n tier_result_seq_list[0] if len(tier_result_seq_list) <= 1 else\n ''.join('\\n {0}'.format(tier_result) for tier_result in tier_result_seq_list))\n\n for tier_number, tier_name, tier_result_seq_list in tier_result_list)\n\n log.debug(\n '{0} (LexicalEntry {1}/{2}, sound-Entity {3}/{4}, markup-Entity {5}/{6}):'\n '\\n{7}\\n{8}\\n{9}'.format(\n index,\n row.LexicalEntry.client_id, row.LexicalEntry.object_id,\n row.Sound.client_id, row.Sound.object_id,\n row.Entity.client_id, row.Entity.object_id,\n markup_url, sound_url, result_string))\n\n # Stopping earlier, if required.\n\n if (limit_result and len(result_list) >= limit_result or\n limit and index + 1 >= limit):\n break\n\n except Exception as exception:\n\n #\n # NOTE\n #\n # Exceptional situations encountered so far:\n #\n # 1. TextGrid file actually contains sound, and wav file actually contains textgrid markup.\n #\n # Perspective 330/4, LexicalEntry 330/7, sound-Entity 330/2328, markup-Entity 330/6934\n #\n # 2. Markup for one of the intervals contains a newline \"\\n\", and pympi fails to parse it.\n # Praat parses such files without problems.\n #\n # Perspective 330/4, LexicalEntry 330/20, sound-Entity 330/6297, markup-Entity 330/6967\n #\n\n log.debug(\n '{0} (LexicalEntry {1}/{2}, sound-Entity {3}/{4}, markup-Entity {5}/{6}): '\n 'exception\\n{7}\\n{8}'.format(\n index,\n row.LexicalEntry.client_id, row.LexicalEntry.object_id,\n row.Sound.client_id, row.Sound.object_id,\n row.Entity.client_id, row.Entity.object_id,\n markup_url, sound_url))\n\n # if we encountered an exception, we show its info and remember not to try offending\n # sound/markup pair again.\n\n traceback_string = ''.join(traceback.format_exception(\n exception, exception, exception.__traceback__))[:-1]\n\n log.debug(traceback_string)\n\n CACHE.set(cache_key, ('exception', exception,\n traceback_string.replace('Traceback', 'CACHEd traceback')))\n\n exception_counter += 1\n\n if (limit_exception and exception_counter >= limit_exception or\n limit and index + 1 >= limit):\n break\n\n log.debug('phonology {0}/{1}: {2} result{3}, {4} no vowels, {5} exceptions'.format(\n perspective_cid, perspective_oid,\n len(result_list), '' if len(result_list) == 1 else 's',\n no_vowel_counter, exception_counter))\n\n # If we have no results, we indicate the situation and also show number of failures and number of\n # markups with no vowels.\n\n if not result_list:\n request.response.status = HTTPPreconditionFailed.code\n\n return {\n \"error\": \"no markups for this query\",\n \"exception_counter\": exception_counter,\n \"no_vowel_counter\": no_vowel_counter}\n\n # Otherwise we create and then serve Excel file.\n\n excel_book = xlwt.Workbook(encoding = \"utf-8\")\n sheet = excel_book.add_sheet(\"Sheet 1\")\n\n sheet.write(0, 0, 'Transcription')\n sheet.write(0, 1, 'Longest (seconds) interval')\n sheet.write(0, 2, 'F1 (Hz)')\n sheet.write(0, 3, 'F2 (Hz)')\n sheet.write(0, 4, 'Highest intensity (dB) interval')\n sheet.write(0, 5, 'F1 (Hz)')\n sheet.write(0, 6, 'F2 (Hz)')\n sheet.write(0, 7, 'Coincidence')\n\n row_counter = 1\n\n for tier_result_list in result_list:\n for tier_number, tier_name, tier_result_seq_list in tier_result_list:\n\n if tier_result_seq_list == 'no_vowel':\n continue\n\n for tier_data in tier_result_seq_list:\n for index, tier_data_str in enumerate(tier_data):\n sheet.write(row_counter, index, tier_data_str)\n\n row_counter += 1\n\n # Formatting column widths.\n\n sheet.col(0).width = 24 * 256\n sheet.col(1).width = 24 * 256\n sheet.col(2).width = 12 * 256\n sheet.col(3).width = 12 * 256\n sheet.col(4).width = 24 * 256\n sheet.col(5).width = 12 * 256\n sheet.col(6).width = 12 * 256\n sheet.col(7).width = 12 * 256\n\n excel_stream = io.BytesIO()\n excel_book.save(excel_stream)\n excel_stream.seek(0)\n\n # See http://stackoverflow.com/questions/2937465/what-is-correct-content-type-for-excel-files for Excel\n # content-type.\n\n response = Response(content_type = 'application/vnd.ms-excel')\n\n response.app_iter = FileIter(excel_stream)\n response.headers['Content-Disposition'] = \"attachment; filename=phonology.xls\"\n\n return response","title":""},{"docid":"f6a4cdea62963b32f50ad71f7fa1cf1e","score":"0.5105812","text":"def extract_features(self):\n self.extract_features_static()\n self.extract_features_dynamic()","title":""},{"docid":"b5197cf37652e92e9c47a02f9d0cbe04","score":"0.5100792","text":"def main(keyword):\n fs_db = initFirestore()\n # keyword = \"whatshappeninginmyanmar\"\n all_posts = []\n\n twitterCrawler = TwitterCrawler(fs_db,keyword) \n cleaned_tweets = twitterCrawler.crawl() \n\n redditCrawler = RedditCrawler(fs_db, keyword) \n cleaned_submissions = redditCrawler.crawl() \n\n twitterCrawler.exportToDB(cleaned_tweets)\n redditCrawler.exportToDB(cleaned_submissions)\n\n crawler = Crawler()\n\n crawler.generateSentimentAnalysis(fs_db, cleaned_submissions, cleaned_tweets)\n wordcloud_img = crawler.generateWordCloud()\n\n # Send wordcloud to DB\n doc_ref = fs_db.collection(u'wordcloud').document('first')\n doc_ref.set({\n u'image': wordcloud_img\n })","title":""},{"docid":"166521861a8bb8d2e5ddb5b9a61e037d","score":"0.5074083","text":"def _prepare_analysis_input(self, documents):\n subdoc_to_doc_map = {}\n wordtype_to_number = {}\n number_to_wordtype = []\n wordtypes = {}\n \n # prevent duplicating work\n if os.path.exists(self.wordtype_file):\n return\n \n try:\n # First find singletons\n if self.remove_singletons:\n word_type_count_threshold = max(1, int(math.log(documents.count(), 10)) - 2)\n temp_word_type_counts = {}\n for doc_index, doc in enumerate(documents):\n tokens = self.tokenize(doc.get_content())\n for token, token_start in tokens:\n temp_word_type_counts[token] = temp_word_type_counts.setdefault(token, 0) + 1\n for word_type, count in temp_word_type_counts.iteritems(): # add singletons to stopword list\n if count <= word_type_count_threshold:\n self._excluded_words[word_type] = True\n with io.open(self.excluded_words_file, 'w', encoding='utf-8') as ex_f:\n ex_f.write(unicode(json.dumps(self._excluded_words)))\n \n haltwords = dict(self.stopwords)\n haltwords.update(self._excluded_words)\n # Second find bigrams, iterate through documents and train.\n if self.find_bigrams:\n from import_tool.analysis.bigram_finder import BigramFinder\n bigram_finder = BigramFinder(stopwords=haltwords)\n for doc_index, doc in enumerate(documents):\n bigram_finder.train(doc_index, self.tokenize(doc.get_content()))\n bigram_finder.print()\n \n # Third, we're going to stem words\n if self.stem_words:\n from import_tool.analysis.stemmer import Stemmer\n stemmer = Stemmer(self._working_dir, self.base_dir)\n \n # for each document tokenize and map tokens to numbers to avoid regex problems before passing data to Mallet\n with io.open(self.mallet_input_file, 'w', encoding='utf-8') as w:\n with io.open(self.start_index_file, 'w', encoding='utf-8') as w2:\n count = 0\n subcount = 0\n for doc_index, doc in enumerate(documents):\n doc_content = unicode(doc.get_content())\n count += 1\n subdocuments = self.create_subdocuments(doc_index, doc_content)\n token_start_index_offset = 0 # needed to make sure the start index remains correct once the document is re-merged\n for subdoc_name, subdoc_content in subdocuments:\n if subcount > 0:\n w2.write(u'\\n')\n subcount += 1\n subdoc_to_doc_map[subdoc_name] = doc_index\n tokens = self.tokenize(subdoc_content)\n \n if self.find_bigrams:\n tokens = bigram_finder.combine(tokens, subdoc_content)\n \n token_numbers = []\n token_start_indices = []\n only_tokens = []\n tokens_temp = []\n for tok, tok_start in tokens:\n only_tokens.append(tok)\n tokens_temp.append([tok, tok_start + token_start_index_offset])\n tokens = tokens_temp\n tokens_temp = None\n if self.stem_words:\n stemmed_tokens = stemmer.stem(only_tokens)\n else:\n stemmed_tokens = only_tokens\n for tup, tok_stem in zip(tokens, stemmed_tokens):\n tok, tok_start = tup\n wordtypes[tok] = True\n wordtypes[tok_stem] = True\n try:\n tok_num = wordtype_to_number[tok_stem]\n except:\n tok_num = len(wordtype_to_number)\n number_to_wordtype.append(tok_stem)\n wordtype_to_number[tok_stem] = tok_num\n token_numbers.append(unicode(tok_num))\n token_start_indices.append([tok, tok_start])\n text = u' '.join(token_numbers)\n w.write(u'{0} all {1}\\n'.format(subdoc_name, text))\n w2.write(unicode(json.dumps(token_start_indices)))\n token_start_index_offset += len(subdoc_content)\n for tok, tok_start in tokens:\n try:\n assert doc_content[tok_start:tok_start+len(tok)].lower() == tok.lower()\n except:\n print(tok_start)\n print(len(tok))\n print('\"'+doc_content[tok_start:tok_start+len(tok)].lower()+'\"')\n print('\"'+tok.lower()+'\"')\n raise\n if not count:\n raise Exception('No files processed.')\n # record which subdocuments belong to which documents\n with io.open(self.subdoc_to_doc_map_file, 'w', encoding='utf-8') as w:\n w.write(unicode(json.dumps(subdoc_to_doc_map)))\n with io.open(self.wordtype_to_number_file, 'w', encoding='utf-8') as w:\n w.write(unicode(json.dumps(wordtype_to_number)))\n with io.open(self.number_to_wordtype_file, 'w', encoding='utf-8') as w:\n w.write(unicode(json.dumps(number_to_wordtype)))\n with io.open(self.wordtype_file, 'w', encoding='utf-8') as w:\n w.write(unicode(json.dumps(wordtypes)))\n except: # cleanup\n self._cleanup(self.mallet_input_file)\n self._cleanup(self.subdoc_to_doc_map_file)\n self._cleanup(self.wordtype_to_number_file)\n self._cleanup(self.number_to_wordtype_file)\n self._cleanup(self.wordtype_file)\n self._cleanup(self.excluded_words_file)\n raise","title":""},{"docid":"86fa15b2609b8808c96c9a44223e9b6c","score":"0.5035346","text":"def main():\n LESSONS_PATH = os.path.join(LESSON_LOCATOR_DATA, LESSON_SETS[0])\n ORIGINAL_LESSONS_PATH = os.path.join(LESSONS_PATH, \"original\")\n ANNOTATED_LESSONS_PATH = os.path.join(LESSONS_PATH, \"annotated\")\n\n if not os.path.exists(ANNOTATED_LESSONS_PATH):\n os.mkdir(ANNOTATED_LESSONS_PATH)\n\n print(\"Scanning original lessons in %s...\" % ORIGINAL_LESSONS_PATH)\n\n for item in os.listdir(ORIGINAL_LESSONS_PATH):\n if item == \".DS_Store\": continue\n\n print(\" found: %s\" % item)\n\n item_path = os.path.join(ORIGINAL_LESSONS_PATH, item)\n\n lesson_number = None\n lesson_description = None\n mobj = re.search(r'^AY\\s+(\\d+)\\s*-\\s*(.+)\\.txt$', item)\n if mobj:\n lesson_number = mobj.group(1)\n lesson_description = mobj.group(2)\n\n print(\" number: %s\" % lesson_number)\n print(\" description: %s\" % lesson_description)\n\n lesson = dict()\n lesson['number'] = lesson_number\n lesson['description'] = lesson_description\n\n fh = open(item_path)\n lesson_raw_text = fh.read()\n fh.close()\n lesson_text = re.split(r'\\n', lesson_raw_text)\n# lesson_raw_text_reencoded = lesson_raw_text.decode('mac-roman').encode('utf-8')\n# lesson_text = re.split(r'\\n', lesson_raw_text_reencoded)\n\n lesson['text'] = lesson_text\n lesson['parsed'] = parseLesson(lesson_text)\n\n if lesson['parsed']['end_of_lesson'] is None:\n print(\" lesson has no 'end of lesson' marker\")\n\n lesson_json = json.dumps(lesson, indent=4)\n annotated_lesson_path = os.path.join(ANNOTATED_LESSONS_PATH, \"ay_%04d.json\" % int(lesson_number))\n fh = open(annotated_lesson_path, \"w\")\n fh.write(lesson_json)\n fh.close()\n\n else:\n print(\"ERROR: File name not understood: %s\" % item)\n\n return 0","title":""},{"docid":"a8088068221bd855f018d369c5d25ceb","score":"0.50167453","text":"def compute_wiki(self):\n\n self.__wiki_counts()\n self.__cross_wiki_counts()\n\n # Step 1: Calculate p(e|m) for wiki.\n print(\"Filtering candidates and calculating p(e|m) values for Wikipedia.\")\n for ent_mention in self.wiki_freq:\n if len(ent_mention) < 1:\n continue\n\n ent_wiki_names = sorted(\n self.wiki_freq[ent_mention].items(), key=lambda kv: kv[1], reverse=True\n )\n # Get the sum of at most 100 candidates, but less if less are available.\n total_count = np.sum([v for k, v in ent_wiki_names][:100])\n\n if total_count < 1:\n continue\n\n self.p_e_m[ent_mention] = {}\n\n for ent_name, count in ent_wiki_names:\n self.p_e_m[ent_mention][ent_name] = count / total_count\n\n if len(self.p_e_m[ent_mention]) >= 100:\n break\n\n del self.wiki_freq","title":""},{"docid":"9611488c4353d23bb3e08e0ca838baf8","score":"0.5009797","text":"def get_WS(w2v):\n # get set of MAX_NGRAM-grams in text\n lines = open(INFNAME_FORMAT.format(\"train\")).readlines() \\\n + open(INFNAME_FORMAT.format(\"test\")).readlines()\n raw = [process_line(l) for l in lines ]\n ngrams_in_data = set()\n for words in raw:\n for ngram in tweet_to_ngrams(words):\n ngrams_in_data.add(ngram)\n\n # load sentiment features from model\n clf_pipe = pickle.load(open(CLF_FNAME, 'rb')) # model\n\n vect = clf_pipe.best_estimator_.named_steps['vect']\n clf = clf_pipe.best_estimator_.named_steps['clf']\n\n features_to_sent_idx = vect.vocabulary_ # map from model features to sentiment index\n # currently, sentiment = 2 * (count_pos / (count_pos + count_neg)) - 1\n sentiments = clf.feature_count_[1,:] / np.sum(clf.feature_count_, axis=0) # in [0,1]\n sentiments = 2 * sentiments - 1 # rescale to [-1,1]\n\n features_to_sent = {feat: sentiments[idx] for (feat,idx) in features_to_sent_idx.items()}\n\n # build WS and ngram_idx_map for each MAX_NGRAM-gram in the text\n k = len(next(iter(w2v.values()))) # dimension of embedding\n WS = np.zeros(shape=(len(ngrams_in_data) + 1, k + MAX_NGRAM), dtype='float32')\n ngram_idx_map = {}\n\n index = 1 # first row is left 0, for padding in the cnn. This is also neutral sentiment.\n # For Vader Sentiment analysis\n# vader_analyzer = SentimentIntensityAnalyzer()\n\n\n for ngram in ngrams_in_data:\n ngram_idx_map[ngram] = index\n\n # set word embedding, note that unknown words already randomized in load_embedding \n words = ngram.split(' ')\n WS[index,:k] = w2v[words[-1]] # embedding of last word\n\n # set sentiment embedding\n for n in range(MAX_NGRAM): # for 1, 2, ... length ngrams\n sub_ngram = ' '.join(words[-1 - n:]) \n\n # Naive Bayes Sentiment feature --------------------------------\n sent = features_to_sent.get(sub_ngram, 0.0) # default to neutral 0\n # --------------------------------------------------------------\n\n# # TextBlob sentiment feature -----------------------------------\n# sent = TextBlob(sub_ngram).sentiment.polarity\n# # --------------------------------------------------------------\n\n# # Vader sentiment feature -------------------------------------\n# sent = vader_analyzer.polarity_scores(sub_ngram)['compound']\n# # -------------------------------------------------------------\n WS[index,k+n] = sent\n\n index += 1\n\n return WS, ngram_idx_map","title":""},{"docid":"094614d3bf2355fdc3c4f7e7041f0655","score":"0.4999468","text":"def workbench_scenarios():\n return [\n (\"Oppia Embedding\",\n \"\"\"<vertical_demo>\n <oppia oppiaid=\"0\" src=\"https://www.oppia.org\" width=\"700\" />\n </vertical_demo>\n \"\"\"),\n ]","title":""},{"docid":"4f999b0f59fddb0e1776ce03b69b5a8f","score":"0.4986965","text":"def extract_documents():\n client = MongoClient()\n conn = client.data\n coll = conn.germanwings\n\n query = {'text': {'$exists': 1}, 'exc': {'$exists': 0}}\n selection = {'text': 1, 'short_url': 1}\n for i, doc in enumerate(coll.find(query, selection)):\n short_url, text = tuple(doc[x] for x in (\"short_url\", \"text\"))\n print(\"Extracting {0} {1}\".format(i, short_url), file=stderr)\n filename = os.path.join(RAW_DIR, short_url)\n with open(filename, \"w\") as f:\n ascii = text.encode('ascii', 'ignore')\n f.write(ascii)","title":""},{"docid":"24c73e13f253f36b5593640e37a35998","score":"0.49765155","text":"def run():\n\n api = api_start()\n stonks = {}\n check_function = load_symbol_list()\n for obj in (\"comments\", \"submissions\"):\n for post in get_text(api, obj):\n if obj == \"comments\":\n full_text = post.body\n else: # obj == \"submissions\"\n full_text = post.title + post.selftext\n try:\n stonks = check_texts(\n full_text, post.author.name, stonks, check_function\n )\n except AttributeError:\n pass\n\n return stonks","title":""},{"docid":"8095ed8a35b084b91054a594ab0aff1a","score":"0.49734497","text":"def wps(model, data):\n with msg.loading(f\"Loading model '{model}'...\"):\n nlp = spacy.load(model)\n texts = (eg[\"text\"] for eg in srsly.read_jsonl(data))\n n_docs = 0\n n_words = 0\n start_time = timer()\n for doc in nlp.pipe(texts):\n n_docs += 1\n n_words += len(doc)\n end_time = timer()\n wps = int(n_words / (end_time - start_time))\n result = [\n (\"Docs\", f\"{n_docs:,}\"),\n (\"Words\", f\"{n_words:,}\"),\n (\"Words/s\", f\"{wps:,}\"),\n ]\n msg.table(result, widths=(7, 12), aligns=(\"l\", \"r\"))","title":""},{"docid":"4b0e78481ce7a6fad8056487980dcf05","score":"0.49670535","text":"def request_document(self):\n\t\tassert self.image is not None, \"image is not defined.\"\n\t\treturn {\"requests\":[{\n\t\t\t\"image\": {\"content\": self.image.base64},\n\t\t\t\"features\":[{\n\t\t\t\t\"type\": \"LANDMARK_DETECTION\",\n\t\t\t\t\"maxResults\": 1\n\t\t\t}]\n\t\t}]}","title":""},{"docid":"096e51a3b4435acc9ba293acae0c7e27","score":"0.49649388","text":"def evaluate():\n global dictionary, wv\n count = 0\n # To save the scores by distance and similarity\n scores = np.zeros(6)\n similar = np.zeros(6)\n itr = len(dictionary)\n logging.info('running evaluation for {0} samples'.format(itr))\n for key in dictionary:\n progress = (count / itr) * 100\n d = dictionary[key].split('resource/')\n d = [idx.split()[0].translate(table).lower() for idx in d[1:]]\n try:\n r = np.array(list(map(lambda x: wv.get_vector(x), d)),\n dtype=np.float32)\n except KeyError:\n itr -= 1\n continue\n if np.any(np.isnan(r)):\n itr -= 1\n continue\n else:\n if r.ndim == 2:\n try:\n # Mean of vector containing all word vectors\n # obtained from abstract.\n r = r.mean(axis=0).reshape(1, -1)\n \n # Obtain the vectors for the entity\n mean_vec = mean_encoder(dictionary[key])\n mean_vec = mean_vec.reshape(1, -1) / norm(mean_vec)\n mean_dist_vec = distance_encoder(dictionary[key])\n mean_dist_vec = mean_dist_vec.reshape(1, -1)\n mean_dist_vec = mean_dist_vec / norm(mean_dist_vec)\n title_vec = title_mean(key)\n title_vec = title_vec.reshape(1, -1) / norm(title_vec)\n abstract_vec = abstract_encoder(key)\n abstract_vec = abstract_vec.reshape(1, -1)\n abstract_vec = abstract_vec / norm(abstract_vec)\n random_vec = np.random.randn(100).reshape(1, -1)\n zero_vec = np.zeros(100).reshape(1, -1)\n \n # Score the entity vectors\n scores[0] += norm(r - mean_vec)\n scores[1] += norm(r - mean_dist_vec)\n scores[2] += norm(r - title_vec)\n scores[3] += norm(r - abstract_vec)\n scores[4] += norm(r - random_vec)\n scores[5] += norm(r - zero_vec)\n similar[0] += cosine_similarity(r, mean_vec)\n similar[1] += cosine_similarity(r, mean_dist_vec)\n similar[2] += cosine_similarity(r, title_vec)\n similar[3] += cosine_similarity(r, abstract_vec)\n similar[4] += cosine_similarity(r, random_vec)\n similar[5] += cosine_similarity(r, zero_vec)\n count += 1\n print(count, end='\\r')\n except (ValueError, KeyError) as _:\n itr -= 1\n continue\n else:\n itr -= 1\n continue\n # Normalize the scores to get a better\n # comparison against the baselines.\n scores = scores / norm(scores)\n similar = similar / norm(similar)\n print_summary(scores, similar)","title":""},{"docid":"19133503f4d039654bcf8787d017b0db","score":"0.49587277","text":"def visualize(self):\n # TODO\n #pyLDAvis.enable_notebook()\n #vis = pyLDAvis.gensim.prepare(self.lda_model, self.stemmed_corpus)\n return","title":""},{"docid":"21ae6d00632b3616ad16936719a6e07e","score":"0.49508405","text":"def run(self):\r\n history = self.extracter.load_user_history()\r\n self.plot_history(history)\r\n \r\n pp_history = self.analyser.preprocess_history(history)\r\n part_worths, attribute_importance, relative_importance = self.analyser.conjoint_analysis(pp_history)\r\n self.plot_analysis(part_worths, relative_importance)\r\n \r\n return history, pp_history, part_worths, relative_importance","title":""},{"docid":"b73cb825455fae6c04f57b8fa704ed31","score":"0.49432248","text":"def learn_models(self):\n\n influencers = self.influencers.infGroup\n\n self.complete_model = LanguageModel()\n self.influencer_models = { influencer: LanguageModel() for influencer in influencers }\n\n all_tweets = []\n # for influencer in tqdm(influencers, desc='Learning Models'):\n for influencer in influencers:\n tweets = [tweet for tweet in self.get_saved_tweets(influencer)]\n self.influencer_models[influencer].add_documents(tweets)\n all_tweets += tweets\n\n self.complete_model.add_documents(all_tweets)","title":""},{"docid":"2801359b261e4f2479acf6e4fc95b081","score":"0.494306","text":"def inference(self):\n for m, doc in enumerate(self.docs):\n # Be careful followings are views\n # So self.hoge will be change, when changing variant\n zs_j = self.zs_m_j[m]\n zk_j = self.zk_m_j[m]\n n_m_zs = self.n_m_zs[m]\n n_m_zk = self.n_m_zk[m]\n for j, t in enumerate(doc):\n # discount for n-th word t with topic z\n zs = zs_j[j]\n zk = zk_j[j]\n n_m_zs[zs] -= 1\n n_m_zk[zs, zk] -= 1\n self.n_zk_t[zk, t] -= 1\n self.n_zk[zk] -= 1\n\n # sampling topic new_z for t\n \"\"\"\n n_s = n_m_zs + self.alphas # mth doc, S vec\n p_s = n_s / np.sum(n_s)\n n_k = n_m_zk + self.alphask # mth doc, SxK matrix\n p_k = n_k / n_s.reshape(len(n_s), 1)\n n_v = self.n_zk_t[:, t] + self.beta\n p_v = n_v / (self.n_zk + self.beta)\n\n p_zsk = p_s.reshape(len(p_s), 1) * p_k * p_v # SxK matrix\n \"\"\"\n\n p_zsk = (n_m_zk + self.alphask) * self.n_zk_t[:, t] \\\n / (np.sum(n_m_zs + self.alphas) * self.n_zk)\n\n p_zs = np.sum(p_zsk, axis=1) / np.sum(p_zsk)\n p_zk = np.sum(p_zsk, axis=0) / np.sum(p_zsk)\n\n new_zs = np.random.multinomial(1, p_zs).argmax()\n new_zk = np.random.multinomial(1, p_zk).argmax()\n\n # print(\"arg\", np.argmax(p_s), np.argmax(p_k, axis=1),\n # np.argmax(p_k, axis=0), np.argmax(p_zk))\n # print('probs', p_s, p_zs)\n # print('probk', p_k, p_zk)\n # print('old', zs, zk)\n # print('new', new_zs, new_zk)\n\n # set z the new topic and increment counters\n zs_j[j] = new_zs\n zk_j[j] = new_zk\n n_m_zs[new_zs] += 1\n n_m_zk[new_zs, new_zk] += 1\n self.n_zk_t[new_zk, t] += 1\n self.n_zk[new_zk] += 1","title":""},{"docid":"4b7ecd53c19c14aa054a0489b52997ff","score":"0.49372828","text":"def emo_detect(text_list,language='English',method ='dimensional',output='data_frame',resolution = 'words_in_text', folder='',max_len=500000):\n \n # Dictionary containing file names of affect disctionaries:\n file_dict = {'english':{'dimensional':('english_anew_dict',),'discrete':('english_nstar_liwc_dict','english_star_liwc_dict')},\\\n 'german':{'dimensional':('german_anew_dict',),'discrete':('german_nstar_liwc_dict','german_star_liwc_dict')},\\\n 'chinese':{'discrete':('chinese_nstar_liwc_dict','chinese_star_liwc_dict')}}\n # Dictionary containing column names of data frames:\n colname_dict = {'english':{'dimensional':['PosVal', 'NegVal','Arousal','Dominance', 'PosCount','DetectCount','TokenCount','ValSq'],'discrete':['function', 'pronoun', 'ppron', 'i', 'we', 'you', 'shehe', 'they', 'ipron', 'article', 'prep', 'auxverb', 'adverb', 'conj', 'negate', 'verb', 'adj', 'compare', 'interrog', 'number', 'quant', 'affect', 'posemo', 'negemo', 'anx', 'anger', 'sad', 'social', 'family', 'friend', 'female', 'male', 'cogproc', 'insight', 'cause', 'discrep', 'tentat', 'certain', 'differ', 'percept', 'see', 'hear', 'feel', 'bio', 'body', 'health', 'sexual', 'ingest', 'drives', 'affiliation', 'achieve', 'power', 'reward', 'risk', 'focuspast', 'focuspresent', 'focusfuture', 'relativ', 'motion', 'space', 'time', 'work', 'leisure', 'home', 'money', 'relig', 'death', 'informal', 'swear', 'netspeak', 'assent', 'nonflu', 'filler','DetectCount','TokenCount']},\\\n 'german':{'dimensional':['PosVal', 'NegVal','Arousal','Dominance', 'PosCount','DetectCount','Imagine','Potency', 'DomPot_Count','TokenCount','ValSq'],'discrete':['Pronoun', 'I', 'We', 'Self', 'You', 'Other', 'Negate', 'Assent', 'Article', 'Preps', 'Number', 'Affect', 'Posemo', 'Posfeel', 'Optim', 'Negemo', 'Anx', 'Anger', 'Sad', 'Cogmech', 'Cause', 'Insight', 'Discrep', 'Inhib', 'Tentat', 'Certain', 'Senses', 'See', 'Hear', 'Feel', 'Social', 'Comm', 'Othref', 'Friends', 'Family', 'Humans', 'Time', 'Past', 'Present', 'Future', 'Space', 'Up', 'Down', 'Incl', 'Excl', 'Motion', 'Occup', 'School', 'Job', 'Achieve', 'Leisure', 'Home', 'Sports', 'TV', 'Music', 'Money', 'Metaph', 'Relig', 'Death', 'Physcal', 'Body', 'Sexual', 'Eating', 'Sleep', 'Groom', 'Swear', 'Nonfl', 'Fillers', 'Swiss', 'Ideo', 'Personalpronomina', 'Indefinitpronomina', 'AuxiliaryVerbs', 'Konjunktionen', 'Adverbien','Bedrohung', 'DetectCount','TokenCount']},\\\n 'chinese':{'discrete':['function', 'pronoun', 'ppron', 'i', 'we', 'you', 'shehe', 'they', 'youpl', 'ipron', 'prep', 'auxverb', 'adverb', 'conj', 'negate', 'quanunit', 'prepend', 'specart', 'tensem', 'focuspast', 'focuspresent', 'focusfuture', 'progm', 'particle', 'modal_pa', 'general_pa', 'compare', 'interrog', 'number', 'quant', 'affect', 'posemo', 'negemo', 'anx', 'anger', 'sad', 'social', 'family', 'friend', 'female', 'male', 'cogproc', 'insight', 'cause', 'discrep', 'tentat', 'certain', 'differ', 'percept', 'see', 'hear', 'feel', 'bio', 'body', 'health', 'sexual', 'ingest', 'drives', 'affiliation', 'achieve', 'power', 'reward', 'risk', 'relativ', 'motion', 'space', 'time', 'work', 'leisure', 'home', 'money', 'relig', 'death', 'informal', 'swear', 'netspeak', 'assent', 'nonflu', 'filler', 'DetectCount','TokenCount']}}\n # Normalize language and method parameters:\n language = language.lower()\n method = method.lower()\n #Initiate empty stemmer:\n stemmer = []\n # Counter:\n c = 0\n # Get the files to load from the file_dict:\n files = file_dict[language][method]\n # Get column names from col_dict:\n colnames = colname_dict[language][method]\n # Load files:\n # One dictionary (dimensional):\n if len(files) == 1:\n with open(folder + files[0],'rb') as f:\n dicts = pickle.load(f)\n # Length of affect vectors in dictionary:\n vec_len = len(list(dicts.values())[0])\n pos_list = 0\n # Two dictionaries (discrete):\n if len(files) == 2:\n with open(folder + files[0],'rb') as f:\n naster_disc_dict = pickle.load(f)\n with open(folder + files[1],'rb') as f:\n aster_disc_dict = pickle.load(f)\n dicts = (naster_disc_dict,aster_disc_dict)\n # Length of affect vectors in dictionary:\n vec_len = len(list(naster_disc_dict.values())[0])\n # Generate stemmer if german dimensional affect detection:\n if method == 'dimensional':\n if language == 'german':\n stemmer = SnowballStemmer('german')\n elif method == 'discrete':\n # List of stem lengths in the word stem dictionary:\n pos_list = list(reversed(list(aster_disc_dict.keys())))\n # Initiate vec_list if output is data_frame, and emo_mat if output is array:\n if output == 'data_frame': \n vec_list = []\n elif output == 'array':\n emo_mat = np.zeros([max_len,max_len,vec_len])\n # for resolution 'sentences_in_text' create an emo_mat with one additional layer for sentence counter\n if resolution == 'sentences_in_text':\n emo_mat = np.zeros([max_len,max_len,vec_len+1])\n\n # Iterate over texts in text_list:\n for c,text in enumerate(text_list):\n # Print counter every 10,000 texts:\n if c % 10000 == 0:\n print(c)\n # Ignore if it's not a text:\n if not isinstance(text,str):\n continue\n # Resolution words in text:\n if resolution == 'words_in_text':\n # create vector or array (depending on 'ouptut'):\n emo_thingy = text_detect(text=text,max_len=max_len,vec_len=vec_len,pos_list=pos_list,dicts=dicts,stemmer=stemmer,method = method,output = output)\n # Skip text if output of 'text_detect' is 0.\n if isinstance(emo_thingy, int):\n continue\n # Append emo_thingy to vec_list or add to emo_mat (depending on 'ouptut'):\n elif output == 'data_frame': \n vec_list.append(emo_thingy)\n elif output == 'array':\n emo_mat[emo_thingy[0],:emo_thingy[0]+1,:] += emo_thingy[1] \n elif resolution == 'words_in_sentence':\n sent_list = sentence_tokenize(text,language=language)\n for sent in sent_list:\n # create vector or array (depending on 'ouptut'):\n emo_thingy = text_detect(text=sent,max_len=max_len,vec_len=vec_len,pos_list=pos_list,dicts=dicts,stemmer=stemmer,method = method,output = output)\n # Skip text if output of 'text_detect' is 0.\n if isinstance(emo_thingy, int):\n continue\n # Append emo_thingy to vec_list or add to emo_mat (depending on 'ouptut'):\n elif output == 'data_frame': \n vec_list.append(emo_thingy)\n elif output == 'array':\n emo_mat[emo_thingy[0],:emo_thingy[0]+1,:] += emo_thingy[1] \n elif resolution == 'sentences_in_text':\n sent_list = sentence_tokenize(text,language=language)\n n_sent = len(sent_list)\n if (n_sent > max_len) or (n_sent == 0):\n continue\n n_sent = n_sent - 1\n for sc,sent in enumerate(sent_list):\n emo_thingy = text_detect(text=sent,max_len=10000,vec_len=vec_len,pos_list=pos_list,dicts=dicts,stemmer=stemmer,method = method,output = 'data_frame')\n if isinstance(emo_thingy, int):\n continue\n emo_thingy = np.append(emo_thingy,np.array([1]))\n emo_mat[n_sent,sc,:] += emo_thingy\n # return data frame or array, depending on 'output':\n if output == 'data_frame':\n return(pd.DataFrame(vec_list,columns=colnames))\n elif output == 'array':\n return(emo_mat)","title":""},{"docid":"8cf918980b856171a28d842d572d9096","score":"0.49365968","text":"def run(self):\r\n\r\n self.roi_analyzer = ROIAnalyzer(\r\n ini_path=self.config_path,\r\n data_path=self.outlier_corrected_dir,\r\n calculate_distances=True,\r\n settings=self.settings,\r\n )\r\n self.roi_analyzer.files_found = self.files_found\r\n self.all_shape_names = self.roi_analyzer.shape_names\r\n self.roi_analyzer.run()\r\n self.roi_analyzer.compute_framewise_distance_to_roi_centroids()\r\n self.roi_distances_dict = self.roi_analyzer.roi_centroid_distance\r\n self.roi_entries_df = self.roi_analyzer.detailed_df\r\n if self.roi_directing_viable:\r\n self.directing_analyzer.run()\r\n self.roi_direction_df = self.directing_analyzer.results_df\r\n\r\n self.data = {}\r\n for file_cnt, file_path in enumerate(self.features_files):\r\n _, self.video_name, _ = get_fn_ext(file_path)\r\n _, _, self.fps = self.read_video_info(video_name=self.video_name)\r\n data_df = read_df(file_path, self.file_type)\r\n self.out_df = deepcopy(data_df)\r\n self.__process_within_rois()\r\n self.__distance_to_roi_centroids()\r\n if self.roi_directing_viable:\r\n self.__process_directionality()\r\n self.data[self.video_name] = self.out_df","title":""},{"docid":"4ed2627a8180c954b6fecaf0d6fe506c","score":"0.49345893","text":"def detect_document(path):\n from google.cloud import vision\n client = vision.ImageAnnotatorClient()\n\n with io.open(path, 'rb') as image_file:\n content = image_file.read()\n\n image = vision.types.Image(content=content)\n\n response = client.document_text_detection(image=image)\n\n for page in response.full_text_annotation.pages:\n for block in page.blocks:\n #print('\\nBlock confidence: {}\\n'.format(block.confidence))\n for paragraph in block.paragraphs:\n for word in paragraph.words:\n word_text = ''.join([symbol.text for symbol in word.symbols])\n text.append(word_text.encode('utf-8'))\n #print(word_text)","title":""},{"docid":"8c9722f57ea37a8097212a4b00336cc0","score":"0.49288893","text":"def sentiment_aspects(docs: Iterable[tokens.Doc]) -> List[collections.Counter]:\n sent_dict_list = []\n start_time = time.time()\n\n for doc in docs:\n sent_dict = collections.Counter()\n for token in doc:\n # check if the word is an opinion word, then assign sentiment\n if token.text.lower() in _OPINION_WORDS:\n sentiment = 1 if token.text.lower() in _POS_WORDS else -1\n if (token.dep_ == \"advmod\"):\n # if target is an adverb modifier (i.e. pretty, highly, etc.)\n # but happens to be an opinion word, ignore and pass\n continue\n\n elif (token.dep_ == \"amod\"):\n sent_dict[token.head.text.lower()] += sentiment\n\n else:\n for child in token.children:\n # if there's a adj modifier (i.e. very, pretty, etc.) add\n # more weight to sentiment\n # This could be better updated for modifiers that either\n # positively or negatively emphasize\n if _is_opinion_mod(child):\n sentiment *= 1.5\n # check for negation words and flip the sign of sentiment\n if child.dep_ == \"neg\":\n sentiment *= -1\n for child in token.children:\n if (token.pos_ == \"VERB\") & (child.dep_ == \"dobj\"):\n # if verb, check if there's a direct object\n sent_dict[child.text.lower()] += sentiment\n # check for conjugates (a AND b), then add both to dictionary\n subchildren = []\n conj = 0\n for subchild in child.children:\n if subchild.text.lower() == \"and\": conj=1\n if (conj == 1) and (subchild.text.lower() != \"and\"):\n subchildren.append(subchild.text.lower())\n conj = 0\n for subchild in subchildren:\n sent_dict[subchild] += sentiment\n\n # check for negation\n for child in token.head.children:\n noun = \"\"\n if _is_opinion_mod(child):\n sentiment *= 1.5\n if (child.dep_ == \"neg\"):\n # check for negation words and flip the sign of sentiment\n sentiment *= -1\n\n # check for nouns\n for child in token.head.children:\n noun = \"\"\n if (child.pos_ == \"NOUN\") and (child.text not in sent_dict):\n noun = child.text.lower()\n # Check for compound nouns\n for subchild in child.children:\n if subchild.dep_ == \"compound\":\n noun = subchild.text.lower() + \" \" + noun\n sent_dict[noun] += sentiment\n sent_dict_list.append(collections.Counter(sent_dict))\n\n print(\"\\nFound aspects on {} reviews.\".format(len(sent_dict_list)))\n print(time.time() - start_time)\n return sent_dict_list","title":""},{"docid":"bb356c3bf2d1cbf9ef471575c497383c","score":"0.48967612","text":"def text_analytics(self):\n\n headers = {\n # Request headers\n 'Content-Type': 'application/json',\n 'Ocp-Apim-Subscription-Key': self.keys['text_analytics'],\n }\n \n sentiment_url = 'https://westus.api.cognitive.microsoft.com/text/analytics/v2.0/sentiment'\n \n raw_text = self.article_params['text']\n\n # Build post for sentiment\n try:\n sentences = tokenize.sent_tokenize(str(raw_text))\n content = []\n for i, sentence in enumerate(sentences):\n content.append({'id': str(i), 'language': 'en', 'text': sentence})\n body = json.dumps({\"documents\": content}).encode('utf-8')\n\n request = urllib.request.Request(sentiment_url, body, headers)\n response = urllib.request.urlopen(request)\n json_response = json.loads(response.read().decode('utf-8'))\n \n # A list of dictionaries, with each dictionary containing a sentence\n # sentiment score\n sentiments_list = json_response['documents']\n\n # Calculate the articles average sentiment from all the sentences\n cumulative_sentiment_score = 0\n for sent in sentiments_list:\n cumulative_sentiment_score += sent['score']\n avg_article_sentiment = cumulative_sentiment_score/len(sentiments_list)\n\n # Put article sentiments in bucket from 1 to 5, with 1 being very\n # negative and 5 being very positive\n if avg_article_sentiment < 0.2:\n sentiment = 1\n elif 0.2 <= avg_article_sentiment < 0.4:\n sentiment = 2\n elif 0.4 <= avg_article_sentiment < 0.6:\n sentiment = 3\n elif 0.6 <= avg_article_sentiment < 0.8:\n sentiment = 4\n else:\n sentiment = 5\n\n except Exception as e:\n print('Unable to process sentiment for article. Assuming '\n 'sentiment is neutral.')\n sentiment = 3\n\n return sentiment","title":""},{"docid":"35aea9116a20457b06626361b0a8c0a6","score":"0.4894925","text":"def main():\n try:\n res = requests.get('http://localhost:9200')\n pprint(json.loads(res.content.decode('utf-8')))\n except requests.exceptions.ConnectionError:\n print(\"ERROR: ELASTICSEARCH Server is not running!\")\n exit(-1)\n\n # scrapeAndSaveNewsArticles()\n # generateNewsDocsCSV() # may need to be modified based on how scrapeAndSave function file output\n if not es_client.indices.exists(index='huffpost_news_index'):\n print(\"PLEASE WAIT... LOADING DOCUMENTS INTO INVERTED INDEX\")\n indexDocsToES('huffpost_news_index')","title":""},{"docid":"9f4a186b81c2d93742075e9f6cf02bac","score":"0.48722687","text":"def forward(self, doc):\n out = torch.tensor([]).float().to(self.device)\n\n for i in range(len(doc)):\n sentences_raw = sentencesplit(cleantxt(doc[i]))\n sentences_ready = torch.tensor([]).float().to(self.device)\n for sentence in sentences_raw:\n sentence = sentence.split()\n if sentence == []:\n continue\n lookup_tensor = torch.tensor([]).long().to(self.device)\n for word in sentence:\n if word in self.embedd_dict:\n lookup_tensor = torch.cat((lookup_tensor,\n torch.LongTensor([self.embedd_dict[word]])), 0)\n else:\n lookup_tensor = torch.cat((lookup_tensor, torch.LongTensor([0])), 0)\n # Word embedding\n xw = self.word_embedding(lookup_tensor).view(1, -1, self.embedding_dim).to(self.device)\n # Word GRU\n self.hidden_gru_words = self.init_hidden_words()\n hw, self.hidden_gru_words = self.gru_word(xw, self.hidden_gru_words)\n # Word MLP\n uw = nn.Tanh()(self.MLP_word(hw)).to(self.device)\n # Word attention\n attention_score = torch.matmul(uw, self.attention_word).squeeze().to(self.device)\n attention_score = F.softmax(attention_score, dim=0).view(uw.size(0), uw.size(1), 1).to(self.device)\n scored_x = (hw * attention_score).to(self.device)\n s = torch.sum(scored_x, dim=1).to(self.device)\n #collecting sentences\n sentences_ready = torch.cat((sentences_ready, s), 0)\n # Sentence GRU\n if len(sentences_ready) == 0:\n out = torch.cat((out,\n torch.randn(1, self.number_cat).to(self.device)), 0).to(self.device)\n continue\n sentences_ready_gru = sentences_ready.view(1, -1, self.embedding_dim).to(self.device)\n self.hidden_gru_sentences = self.init_hidden_sentences()\n hs, self.hidden_gru_sentences = self.gru_sentence(torch.tensor(sentences_ready_gru), self.hidden_gru_sentences)\n # SENTENCE MLP\n us = nn.Tanh()(self.MLP_sentence(hs)).to(self.device)\n # Sentence attention\n attention_score = torch.matmul(us, self.attention_sentence).squeeze().to(self.device)\n attention_score = F.softmax(attention_score, dim=0).view(us.size(0), us.size(1), 1).to(self.device)\n scored_x = (hs * attention_score).to(self.device)\n v = torch.sum(scored_x, dim=1).to(self.device)\n # classification\n p = self.MLP_classification(v).to(self.device)\n out = torch.cat((out, p.float()), 0).float().to(self.device)\n return out","title":""},{"docid":"3f78117a3cf6cb34f721eab0266e6eeb","score":"0.4870776","text":"def test(self, filename: str, info_extractor: Optional[InfoExtractor]):\r\n if self.model is None:\r\n raise RatingModel.RatingModelError(\"model is not loaded or trained yet\")\r\n doc, _ = loadDocumentIntoSpacy(filename, self.parser, self.nlp)\r\n\r\n print(\"Getting rating...\")\r\n if self._type == \"fixed\":\r\n print(\"working on fixed model\")\r\n if self.keywords is None:\r\n raise RatingModel.RatingModelError(\"Keywords not found\")\r\n\r\n seen_chunks_words, all_tokens_chunks = getAllTokensAndChunks(doc)\r\n\r\n # scoring\r\n temp_out = self.__trainKMWM(list(seen_chunks_words), list(all_tokens_chunks), self.keywords)\r\n if temp_out is None:\r\n raise RatingModel.RatingModelError(\r\n \"Either parser cannot detect text or too few words in resume for analysis. Most usually the former.\"\r\n )\r\n km_scores, wm_scores = temp_out\r\n # average of km/wm scores for all keywords\r\n km_score = np.mean(km_scores)\r\n wm_score = np.mean(wm_scores)\r\n final_score = km_score * wm_score\r\n elif self._type == \"lda\":\r\n if self.lda is None or self.dictionary is None or self.top_k_words is None:\r\n raise RatingModel.RatingModelError(\"No LDA found\")\r\n\r\n seen_chunks_words, all_tokens_chunks = getAllTokensAndChunks(doc)\r\n seen_chunks_words, all_tokens_chunks = (\r\n list(seen_chunks_words),\r\n list(all_tokens_chunks),\r\n )\r\n\r\n # scoring\r\n new_seen_chunks_words = self.__keep_top_k_words(seen_chunks_words)\r\n bow = self.dictionary.doc2bow(new_seen_chunks_words)\r\n doc_distribution = np.array(\r\n [tup[1] for tup in self.lda.get_document_topics(bow=bow)]\r\n )\r\n # get keywords and weights\r\n keywords = []\r\n all_pair_scores = []\r\n all_topic_scores = []\r\n all_diff_scores = []\r\n # take top 5 topics\r\n for j in doc_distribution.argsort()[-5:][::-1]:\r\n topic_prob = doc_distribution[j]\r\n # take top 5 words for each topic\r\n st = self.lda.show_topic(topicid=j, topn=5)\r\n sum_st = np.sum(list(map(lambda x: x[1], st)))\r\n pair_scores = []\r\n for pair in st:\r\n keywords.append(pair[0])\r\n pair_scores.append(pair[1])\r\n all_pair_scores.append(np.array(pair_scores))\r\n all_topic_scores.append(np.array(topic_prob))\r\n\r\n all_pair_scores = np.array(all_pair_scores)\r\n norm_all_pair_scores = all_pair_scores.T / np.sum(all_pair_scores, axis=1)\r\n norm_all_topic_scores = all_topic_scores / np.sum(all_topic_scores)\r\n all_diff_scores = (norm_all_pair_scores * norm_all_topic_scores).flatten()\r\n weights = pd.Series(all_diff_scores, index=keywords)\r\n weights.sort_values(ascending=False, inplace=True)\r\n\r\n temp_out = self.__trainKMWM(seen_chunks_words, all_tokens_chunks, keywords)\r\n if temp_out is None:\r\n print(\r\n \"Either parser cannot detect text or too few words in resume for analysis. Most usually the former. Skip document.\"\r\n )\r\n km_scores, wm_scores = temp_out\r\n\r\n # average of km/wm scores for all keywords\r\n km_score = np.dot(weights.values, km_scores)\r\n wm_score = np.dot(weights.values, wm_scores)\r\n\r\n final_score = km_score * wm_score\r\n\r\n # max_score = self.model[\"score\"].iloc[0] - np.std(self.model[\"score\"])\r\n # min_score = self.model[\"score\"].iloc[-1]\r\n mean = np.mean(self.model[\"score\"])\r\n sd = np.std(self.model[\"score\"])\r\n\r\n rating = min(10, max(0, round(5 + (final_score-mean)/sd, 2)))\r\n if info_extractor is not None:\r\n print(\"-\" * 20)\r\n\r\n # info_extractor.extractFromFile(filename)\r\n output= info_extractor.extractFromFile(filename)\r\n\r\n print(\"output:----\",output)\r\n print(\"-\" * 20)\r\n print(\"Rating: %.1f\" % rating)\r\n # if info_extractor is not None:\r\n # print(\"info extractor is not working\")\r\n # env = os.environ\r\n # subprocess.call([sys.executable, filename], env=env)\r\n return output","title":""},{"docid":"935e065ed2e8ef1071fd6ea6c9fb8b2e","score":"0.48706353","text":"def collect_docs() -> Dict[str, Any]:\n docs = {}\n\n for integration_dir, meta in all_integrations():\n integration_name = integration_dir.name\n markdown = \"\"\n for readme in sorted(integration_dir.glob(\"*.md\")) + sorted(integration_dir.glob(\"README.md.jinja\")):\n markdown += readme.read_text(encoding=\"utf-8\")\n\n if not markdown:\n continue\n\n docs[integration_name] = {\n \"markdown\": process_markdown_for_app(markdown, integration_dir, uses_legacy_build(meta))\n }\n\n return docs","title":""},{"docid":"8cd00602ff5af8d15d5dd68f4ed79855","score":"0.48668104","text":"def get_slide_analytics_new(slides) -> List[int]:\n word_count = []\n for slide in slides:\n print(slide)\n words = 0\n for shape in slide.shapes:\n if not shape.has_text_frame:\n continue\n print(shape.name)\n for paragraph in shape.text_frame.paragraphs:\n for run in paragraph.runs:\n print(\" \" + run.text)\n words += len(run.text.split())\n word_count.append(words)\n return word_count","title":""},{"docid":"e4dd7429b51b3115c527e2a80a20b5d7","score":"0.48623803","text":"def create_embedding(self):\n self.embedding = []\n\n for index in range(1,self.args.window_size+1):\n print(\"\\nOptimization round: \" +str(index)+\"/\"+str(self.args.window_size)+\".\")\n print(\"Creating documents.\")\n clean_documents = self.walk_extracts(index)\n print(\"Fitting model.\")\n model = Word2Vec(clean_documents,\n size = self.args.dimensions,\n window = 1,\n min_count = self.args.min_count,\n sg = 1,\n workers = self.args.workers)\n\n new_embedding = self.get_embedding(model)\n self.embedding = self.embedding +[new_embedding]\n self.embedding = np.concatenate(self.embedding, axis = 1)","title":""},{"docid":"604fd95d4d3feaf292951f92b171aa21","score":"0.48512176","text":"def extract(self, documents):\n\n # Feature vector to return\n features = np.zeros((len(documents), len(self.idx_to_word)))\n\n # Raise an exception if 'extract' is called before 'preprocess'\n if len(self.word_to_idx) == 0 or len(self.idx_to_word) == 0:\n raise Exception(\"Dictionary not initialised.\")\n\n # Iterate over all documents\n for idx, doc in enumerate(documents):\n # Split the doc into a list of words\n words = extract_words(doc)\n\n # For each word\n for w in words:\n # Calculate it's frequency, however, keep in mind\n # that this word may not have been in the training\n # corpus. In that case, ignore the word.\n ''' YOUR CODE HERE '''\n try:\n features[idx][self.word_to_idx[w]] = words.count(w)\n except KeyError:\n pass\n\n ''' END CODE FOR THIS LOOP '''\n\n # Divide the vector by the total number of words in the document to\n # normalize the frequencies.\n ''' YOUR CODE HERE '''\n features[idx] = features[idx]/len(words)\n ''' END CODE FOR THIS LOOP '''\n\n return features","title":""},{"docid":"5830eddcc02787f888755402d0486b85","score":"0.48499954","text":"def environmental_science_news():\n\n return general_scraper(['http://mesva.univaq.it/?q=avvisi/cl-clm/52671'])","title":""},{"docid":"381a5db4c0d0ee2186d6415772225d77","score":"0.48452774","text":"def analyse() -> 'html':\n storeFileName = ''\n if request.method == 'POST':\n \n if 'file' not in request.files:\n return render_mainpageerror('No file selected')\n \n file = request.files['file']\n if file.filename == '':\n return render_mainpageerror('Empty file name Please select a File to upload')\n \n if file and not allowed_file(file.filename):\n storeFileName = filename = secure_filename(file.filename)\n file.save(os.path.join(app.config['UPLOAD_FOLDER'], filename))\n return render_mainpageerror('Not allowed file format')\n \n if ' ' in file.filename:\n return render_mainpageerror('Please remove withespace from file name before uploading')\n \n if file and allowed_file(file.filename):\n originalFilename = file.filename\n storeFileName = filename = secure_filename(file.filename)\n file.save(os.path.join(app.config['UPLOAD_FOLDER'], filename))\n \n \n if len(storeFileName) == 0:\n return render_template('mainpage.html',\n title='Word association')\n else:\n wordsfromfile = readFile.red_uploaded_file(storeFileName)\n #print(wordsfromfile, 'The ans from pdf')\n if wordsfromfile == 'STOP':\n return render_mainpageerror('Cant find Encoding on PDF document')\n size = get_store()\n if not check_fileName(size):\n \"\"\"Analyse first time building up JSON file\"\"\"\n keyWords = readFile.read_JSON()\n allJson = readFile.load_JSON()\n data = Counter(wordsfromfile)\n printResultsExluds = []\n container = {}\n container['size'] = size\n container['file'] = storeFileName\n toDB = []\n for k, v in sorted(data.items()):\n if k in keyWords:\n string = allJson[k][:3]\n toDB.append({k : v})\n toDB.append(string)\n else:\n value = str(v)\n keystr = k + '(' + value + ')'\n printResultsExluds.append(keystr)\n for w in printResultsExluds:\n toDB.append({w:'was not found in the associations list'})\n\n container['files'] = toDB \n js = json.dumps(container)\n print(js)\n c = get_client()\n c.insert(json.loads(js))\n print('insert')\n \"\"\"If analysed first time\"\"\"\n return pr(size, 'Analysed first time')\n else:\n \"\"\"If already Analysed\"\"\"\n return pr(size,'Already analysed')","title":""},{"docid":"77e666d9bf2f728844369dbba517797b","score":"0.48416984","text":"def develop_output():\n output_array = []\n for docs in db.get_collection(\"google_places\").find({}):\n docs[\"city\"] = docs[\"vicinity\"].split(\",\")[-1].strip()\n del docs['_id']\n\n for mg_doc in db.get_collection(\"michelin_guide\").find({}):\n del mg_doc['_id']\n\n if (fuzz.token_set_ratio(docs[\"vicinity\"], mg_doc[\"datasheets\"][0][\"address\"]) > 80 and\n fuzz.token_set_ratio(docs[\"name\"], mg_doc[\"datasheets\"][0][\"name\"]) > 80):\n docs[\"michelin_stars\"] = mg_doc[\"datasheets\"][0][\"michelin_stars\"]\n docs[\"michelin_mention\"] = True\n docs[\"michelin_description\"] = mg_doc[\"datasheets\"][0][\"description\"]\n docs[\"michelin_url\"] = mg_doc[\"datasheets\"][0][\"web\"]\n break\n\n else:\n docs[\"michelin_stars\"] = 0\n docs[\"michelin_mention\"] = False\n docs[\"michelin_description\"] = None\n docs[\"michelin_url\"] = None\n\n for yelp_doc in db.get_collection(\"Yelp\").find({}):\n del yelp_doc['_id']\n if (fuzz.token_set_ratio(docs[\"vicinity\"], yelp_doc[\"location\"][\"address1\"]) > 80 and\n fuzz.token_set_ratio(docs[\"name\"], yelp_doc[\"name\"]) > 80):\n docs[\"yelp_stars\"] = yelp_doc[\"rating\"]\n docs[\"yelp_url\"] = yelp_doc[\"url\"]\n break\n\n else:\n docs[\"yelp_stars\"] = None\n docs[\"yelp_url\"] = None\n\n \"\"\" The results in Zomato are nested in one document,\n so this for loop breaks them up so the break logic\n works better.\n \"\"\"\n clean_zomato_list = []\n for zom_doc in db.get_collection(\"zomato\").find({}):\n del zom_doc['_id']\n for restaurant in zom_doc[\"restaurants\"]:\n clean_zomato_list.append(restaurant)\n\n for restaurant in clean_zomato_list:\n if (fuzz.token_set_ratio(docs[\"vicinity\"], restaurant['restaurant'][\"location\"][\"address\"]) > 80 and\n fuzz.token_set_ratio(docs[\"name\"], restaurant['restaurant'][\"name\"]) > 80):\n docs[\"zomato_stars\"] = restaurant['restaurant']['user_rating']['aggregate_rating']\n docs[\"zomato_timings\"] = restaurant['restaurant']['timings']\n docs[\"zomato_avg_for_two\"] = restaurant['restaurant']['average_cost_for_two']\n docs[\"zomato_events\"] = restaurant['restaurant']['events_url']\n break\n\n else:\n docs[\"zomato_stars\"] = None\n docs[\"zomato_timings\"] = None\n docs[\"zomato_avg_for_two\"] = None\n docs[\"zomato_events\"] = None\n\n if docs not in output_array:\n output_array.append(docs)\n\n #db.get_collection(\"outputs\").delete_many({})\n db.get_collection(\"outputs\").insert_many(output_array)","title":""},{"docid":"4ea4663df0aea9df2d26cd986153b720","score":"0.48416552","text":"def benchmark(extract_size=800):\n random_file = random_html_file()\n with open(join(DATA_PATH, random_file), 'r') as f:\n html_string = f.read()\n\n # GOOSE\n try:\n g = Goose({'browser_user_agent': 'Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:61.0) Gecko/20100101 Firefox/61.0',\n 'enable_image_fetching': False})\n goose_article = g.extract(raw_html=html_string)\n goose_result = goose_article.cleaned_text\n except:\n goose_result = ' Goose error.'\n\n # EATIHT\n try:\n eatiht_result = eatiht.extract(html_string)\n except:\n eatiht_result = ' Eatiht error.'\n\n # DRAGNET\n\n try:\n dragnet_result = dragnet.extract_content(html_string)\n except Exception as e:\n dragnet_result = ' Dragnet error: ' + str(e)\n\n # LIBEXTRACT\n\n try:\n textnodes = list(libextract.api.extract(html_string))\n libextract_result = textnodes[0].text_content()\n except:\n libextract_result = ' Libextract error.'\n\n # BOILERPIPE (CanolaExtractor)\n\n try:\n extractor = Extractor(\n extractor='CanolaExtractor', html=html_string)\n boilerpipe_result = extractor.getText()\n except:\n boilerpipe_result = ' Boilerpipe error.'\n\n # NEWSPAPER\n\n try:\n article = Article('url')\n article.download(input_html=html_string)\n article.parse()\n print('Auteurs:', article.authors)\n print('Date de publication:', article.publish_date)\n newspaper_result = article.text\n except:\n newspaper_result = ' Newspaper error.'\n\n # JUSTEXT\n\n try:\n paragraphs = justext.justext(\n html_string, justext.get_stoplist(\"French\"))\n print('PARAGRAPHS')\n for p in paragraphs:\n if not p.is_boilerplate:\n print(p.text)\n justext_result = '\\n'.join(\n paragraph.text for paragraph in paragraphs if not paragraph.is_boilerplate)\n print('JUSTEXT_RESULT', justext_result)\n\n except Exception as e:\n justext_result = ' Justext error: ' + str(e)\n print(justext_result)\n\n # Results\n\n try:\n # finds the url associated with the file in a \"filename-url\" csv\n with open('./data/urls.csv', 'r') as csvfile:\n\n urls = dict((line['id'], line['url'])\n for line in csv.DictReader(csvfile))\n url = urls[random_file[:-5]]\n\n print('\\n\\n >>> URL n.' + random_file[:-5] + ' : ' + url)\n except:\n print('\\n\\n (URL of the html file not found. To print the associated URL, please provide a urls.csv file featuring filename & url in /data)')\n # webbrowser.open(url, autoraise=False)\n path = abspath('temp.html')\n local_url = 'file://' + path\n with open(path, 'w') as f:\n f.write(html_string)\n webbrowser.open(local_url)\n\n # print('\\n\\n /// GOOSE /// \\n')\n # print(goose_result[:extract_size])\n # print('\\n\\n /// EATIHT /// \\n')\n # print(eatiht_result[:extract_size])\n print('\\n ------ [[DRAGNET]] ------',\n len(dragnet_result), 'caractères\\n')\n print(dragnet_result[:extract_size] +\n '\\n...\\n' + dragnet_result[-extract_size:])\n print('\\n ------ [[NEWSPAPER]] ------',\n len(newspaper_result), 'caractères\\n')\n print(newspaper_result[:extract_size] +\n '\\n...\\n' + newspaper_result[-extract_size:])\n print('\\n ------ [[JUSTEXT]] ------',\n len(justext_result), 'caractères\\n')\n print(justext_result[:extract_size] +\n '\\n...\\n' + justext_result[-extract_size:])\n # print('\\n\\n /// LIBEXTRACT /// \\n')\n # print(libextract_result[:extract_size])\n # print('\\n\\n /// BOILERPIPE (CanolaExtractor) /// \\n\\n')\n # print(boilerpipe_result[:extract_size])\n # print('\\n\\n')\n return(url)","title":""},{"docid":"9092fc9ed9f2b354ab962190f27cb6f7","score":"0.48402354","text":"def problem3() -> None:\n try:\n print(\"Reading in the text files for each keyword...\")\n kw_df = collect_keyword_dataframe(collect_all=COLLECT_ALL) \n except FileNotFoundError:\n raise Exception(\"ERROR: The 'articles' folder was not found. Please \"\n \"ensure that the functions for problems 1 and 2 have \"\n \"been applied first.\")\n \n word2onehot, w2vp, matrices = word2vec_main(kw_df)\n sem_dist_mat = get_semantic_dist_matrix(\n w2vp.target_words, word2onehot, matrices\n )\n \n try:\n matrix2dataframe(w2vp.keywords, sem_dist_mat)\n except PermissionError:\n print(\"ERROR: Unable to write 'distance.xlsx'. Please ensure that \"\n \"this file is not open.\")\n \n # Functions for visualisations:\n reduce_and_plot_word_vectors(\n w2vp.keywords, [word2onehot[w] for w in w2vp.target_words], matrices\n )\n nearest_neighbours( \n w2vp, word2onehot, matrices \n )\n similarity_distributions(\n kw_df, w2vp, word2onehot, matrices\n )","title":""},{"docid":"86a45a0e06cc694a864568bc14c41dea","score":"0.48376784","text":"def run():\n logger.info(f\"Process started:\")\n logger.info(f\"Converting Glove file to Word2Vec format\")\n convert_to_word2vec.convert(\n \"./data/source/glove.6B.50d.txt\", \"./data/source/glove.6B.50d.w2vformat.txt\"\n )\n\n logger.info(f\"Extracting Click Stream data\")\n extract_click_stream_data()\n\n logger.info(\"Extracting Wiki articles\")\n extract_wiki_articles()\n\n logger.info(f\"Generating Clickstream dataset\")\n generate_datasets()\n\n logger.info(\"Tokenizing articles\")\n WikiArticlesTokenizer().process()\n\n logger.info(\"Creating dataset with Wiki Articles\")\n create_wiki_articles_dataset()","title":""},{"docid":"205c1a87d884508574f8ff796057d048","score":"0.48364243","text":"def main():\n loader = MicrosoftDataloader()\n train,dev,test = loader.getData()\n sentences = []\n\n # Collect all the training sentences\n for i,row in pd.concat((train,test)).iterrows():\n if isinstance(row[\"sentence1\"], basestring) and isinstance(row[\"sentence2\"], basestring):\n sentences.append(row[\"sentence1\"])\n sentences.append(row[\"sentence2\"])\n\n # Get the mapping between sentences and their cotext vectors\n mapped = get_sentence_to_context_map(sentences)\n\n # At this stage we have a map between every sentence and its context vector\n # However the JSON file must contain sentences in the same order as in the MSR data file\n data = []\n for i,sentence in enumerate(sentences):\n embedding = mapped[sentence]\n data.append({'index':i, 'embedding':embedding, 'text':sentence})\n\n # Write the sentences and embeddings to JSON\n # The array index should corrospond to the sentence #\n print \"Saving embedded sentences to: {0}\".format(EMBED_FILE)\n with open(EMBED_FILE,'w') as outfile:\n json.dump(data,outfile,indent=2)","title":""},{"docid":"0c00e716572836ad08706fbaa579d01c","score":"0.48353478","text":"def morpho_doc(doc):\n doc_text = doc.stripped\n mystem_analyzer.start()\n # new_morpho = mystem_analyzer.analyze(doc_text)\n new_morpho = mystem_analyzer.analyze(doc_text.replace('\\n',''))\n\n morpho_list = []\n\n for element in new_morpho: # разрезаем\n\n if is_sentence_end(element):\n morpho_list.append(element)\n else:\n\n line = element.get('text', '')\n\n space_len = 0\n\n word_start = -1\n word_len = 0\n\n symbol_number = -1\n for symbol in line:\n\n symbol_number+=1\n\n if symbol == \"'\" or symbol == '\"' or symbol == '»' or symbol == '«':\n\n if space_len > 0: # добавим пробелы\n\n cur_space = ' ' * space_len\n\n new_element = {'text': cur_space}\n if 'analysis' in element: new_element['analysis'] = element['analysis']\n morpho_list.append(new_element)\n\n space_len = 0\n\n elif word_start > -1: # добавим слово\n\n cur_word = line[word_start:(word_start + word_len)]\n\n new_element = {'text': cur_word}\n if 'analysis' in element: new_element['analysis'] = element['analysis']\n morpho_list.append(new_element)\n\n word_start = -1\n word_len = 0\n\n # добавим кавычку\n new_element = {'text': symbol}\n if 'analysis' in element: new_element['analysis'] = element['analysis']\n morpho_list.append(new_element)\n\n elif symbol == \" \":\n\n if word_start > -1: # добавим слово\n\n cur_word = line[word_start:(word_start + word_len)]\n\n new_element = {'text': cur_word}\n if 'analysis' in element: new_element['analysis'] = element['analysis']\n morpho_list.append(new_element)\n\n word_start = -1\n word_len = 0\n\n space_len += 1\n\n else:\n\n if space_len > 0: # добавим пробелы\n\n cur_space = ' ' * space_len\n\n new_element = {'text': cur_space}\n if 'analysis' in element: new_element['analysis'] = element['analysis']\n morpho_list.append(new_element)\n\n space_len = 0\n\n if word_start == -1:\n word_start = symbol_number\n word_len = 1\n else:\n word_len += 1\n\n if space_len > 0: # добавим пробелы\n\n cur_space = ' ' * space_len\n\n new_element = {'text': cur_space}\n if 'analysis' in element: new_element['analysis'] = element['analysis']\n\n morpho_list.append(new_element)\n\n elif word_start > -1: # добавим слово\n\n cur_word = line[word_start:(word_start + word_len)]\n\n new_element = {'text': cur_word}\n if 'analysis' in element: new_element['analysis'] = element['analysis']\n morpho_list.append(new_element)\n\n for i in range(len(morpho_list) - 1): # переставляем\n if i > 0:\n if morpho_list[i - 1]['text'] == ' ' and morpho_list[i]['text'] == '\"' and morpho_list[i + 1]['text'] == '\\\\s':\n morpho_list[i], morpho_list[i + 1] = morpho_list[i + 1], morpho_list[i]\n\n sentence_index = 0\n word_index = 0\n start_offset = 0\n\n for element in morpho_list: # нумеруем\n if is_sentence_end(element):\n if word_index != 0:\n sentence_index += 1\n word_index = 0\n else:\n line = element.get('text', '')\n line_len = len(line)\n\n if(line[0]!=' '):\n element['start_offset'] = start_offset\n element['end_offset'] = start_offset + line_len - 1\n element['word_index'] = word_index\n element['sentence_index'] = sentence_index\n\n word_index += 1\n start_offset += line_len\n\n doc.morpho = morpho_list\n mystem_analyzer.close()","title":""},{"docid":"c6d13864f711264db0bb546f4c2723f1","score":"0.4818834","text":"def _read_adv_embeddings(identity, target):\n embeddings_file = os.path.join(\n FLAGS.output_directory,\n identity,\n FLAGS.attack_type,\n target\n )\n embeddings_file = os.path.join(FLAGS.image_directory,\n identity,\n 'embeddings.h5')\n with h5py.File(embeddings_file, 'r') as f:\n return f['embeddings'][:].astype(np.float32)","title":""},{"docid":"429e4bfdb400d42f560067a1b0a86078","score":"0.48179644","text":"def get_wiki_content(self):\n url = \"https://fr.wikipedia.org/w/api.php?action=query&prop=extracts&exsentences=4&explaintext=&pageids={}&format=json\".format(self.page_id)\n self.page = str(self.page_id)\n self.response = requests.get(url)\n self.data = self.response.json()\n self.wiki_data = (self.data['query']['pages'][self.page]['extract'])\n return (self.wiki_data)","title":""},{"docid":"cad7e92437b5e1b930e423a581c70b19","score":"0.48148713","text":"def model_extract_document_embedding(self):\n input_ids = tf.keras.layers.Input(shape=(self.maxlen,), dtype=tf.int32, name=\"ids\")\n attention_mask = tf.keras.layers.Input(shape=(self.maxlen,), dtype=tf.int32, name=\"att\")\n token = tf.keras.layers.Input(shape=(self.maxlen,), dtype=tf.int32, name=\"tok\")\n\n # Embedding :\n if self.method_embedding == 'CamemBERT':\n Camembert_model = transformers.TFCamembertModel.from_pretrained(\"jplu/tf-camembert-base\")\n x = Camembert_model(input_ids, attention_mask=attention_mask, token_type_ids=token)\n elif self.method_embedding == 'FlauBERT':\n # lr = 0.00001\n Flaubert_model = transformers.TFFlaubertModel.from_pretrained(\"jplu/tf-flaubert-base-uncased\")\n x = Flaubert_model(input_ids, attention_mask=attention_mask, token_type_ids=token)\n elif self.method_embedding == 'XLM-RoBERTa':\n # lr = 0.00001\n XLMRoBERTa_model = transformers.TFXLMRobertaModel.from_pretrained(\"jplu/tf-xlm-roberta-base\")\n x = XLMRoBERTa_model(input_ids, attention_mask=attention_mask, token_type_ids=token)\n elif self.method_embedding == 'RoBERTa':\n # Experience Test path weights :\n PATH = '/kaggle/input/tf-roberta/'\n config = transformers.RobertaConfig.from_pretrained(PATH + 'config-roberta-base.json')\n Roberta_model = transformers.TFRobertaModel.from_pretrained(PATH + 'pretrained-roberta-base.h5',\n config=config)\n # Sinon :\n # Roberta_model = transformers.TFRobertaModel.from_pretrained('roberta-base')\n x = Roberta_model(input_ids, attention_mask=attention_mask, token_type_ids=token)\n elif self.method_embedding == 'BERT':\n BERT_model = transformers.TFBertModel.from_pretrained('bert-base-uncased')\n x = BERT_model(input_ids, attention_mask=attention_mask, token_type_ids=token)\n else:\n logger.critical(\"unknown embedding method name : '{}'\".format(self.method_embedding))\n\n # word vectors shape : (None, maxlen, 768)\n x = x[0]\n cls_token = x[:, 0, :]\n\n model = tf.keras.models.Model(inputs=[input_ids, attention_mask, token], outputs=cls_token)\n return model","title":""},{"docid":"73129f4013ee617835f66bf33515d190","score":"0.48147637","text":"def iter_documents(self):\n raise NotImplementedError","title":""},{"docid":"0584aa2acf6c3fbdc1c0d6e5b17ff798","score":"0.4809549","text":"def problem4() -> None:\n \n # Obtain the dataframe containing lines from the articles and the keyword\n # with which the article is associated.\n try:\n kw_df = collect_keyword_dataframe(collect_all=COLLECT_ALL) \n except FileNotFoundError:\n raise Exception(\"[ERROR] The 'articles' folder was not found. Please \"\n \"ensure that the functions for problems 1 and 2 have \"\n \"been applied first.\")\n \n keywords, corpus = separate_keywords_corpus(kw_df)\n \n # Complete the preprocessing for the corpus for comparison's sake\n w2vp = W2VPreprocessor(keywords, corpus)\n \n # Display the counts of sentences collected associated with each keyword\n keywords_countplot(kw_df)\n \n # Create displots for the lengths of sentences in the corpus\n displot_all_sentence_lengths(kw_df)\n displot_sentence_lengths_per_keyword(kw_df)\n \n # Visualise the semantic distances\n try:\n distances = read_in_distance_matrix()\n except FileNotFoundError:\n raise Exception(\"[ERROR] The distance matrix excel file was could not \"\n \"be found! Please ensure the methods for the \"\n \"preceding problems have been run first.\")\n visualise_distances(distances)\n \n # Visualise word frequency before and after preprocessing\n visualise_initial_most_frequent_words(kw_df)\n visualise_preprocessed_most_frequent_words(w2vp.corpus)","title":""},{"docid":"d1a10c0f30e43fde085d71e556d4c8dd","score":"0.4807212","text":"def speaker_spotting_try_system2(current_trial):\n # target model\n # record the model embedding vector \n # and model id\n model = {}\n model_id = current_trial['model_id'] \n model_embedding = models[current_trial['model_id']]\n model['mid'] = model_id\n model['embedding'] = model_embedding\n \n # where to look for this target\n try_with = current_trial['try_with']\n \n # precomputed embedding\n embeddings = precomputed(current_trial)\n \n # annotation of current file\n oracle_diarization = REFERENCE[current_trial['uri']].crop(current_trial['try_with'])\n \n # find index of first and last embedding fully included in 'try_with'\n indices = embeddings.sliding_window.crop(try_with, mode='strict')\n first, last = indices[0], indices[-1]\n onlineOracleClustering = clustering.OnlineOracleClustering(current_trial['uri'])\n start = embeddings.sliding_window[0].start\n data = np.zeros((len(embeddings.data), 1))\n for i, (window, _) in enumerate(embeddings):\n # make sure the current segment is in 'try_with'\n if i < first:\n start = window.end\n continue\n if i > last:\n break\n \n so_far = Segment(start, window.end)\n current_annotation = oracle_diarization.crop(so_far)\n score = 0.\n for segment, _, label in current_annotation.itertracks(label=True):\n example = {}\n example['label'] = label\n example['segment'] = segment\n example['embedding'] = embeddings.crop(segment, mode='center')\n example['indice'] = [i]\n # compute the distance with model\n example['distances'] = {}\n example['distances'][model['mid']] = list(cdist(example['embedding'], \n model['embedding'], \n metric='cosine').flatten())\n # update the online oracle clustering\n onlineOracleClustering.upadateCluster(example)\n if not onlineOracleClustering.empty():\n # compute the current score\n min_dist = min(onlineOracleClustering.modelDistance(model))\n score = max(score, 2-min_dist)\n data[i] = score\n start = window.end\n import pdb\n #pdb.set_trace()\n \n # transform scores to sliding window features\n data = data[first:last+1]\n sliding_window = SlidingWindow(start=embeddings.sliding_window[first].start,\n duration=embeddings.sliding_window.duration,\n step=embeddings.sliding_window.step)\n \n return SlidingWindowFeature(data, sliding_window)","title":""},{"docid":"7fb7839b49621951ed2f1a6da2cd10fc","score":"0.48055255","text":"def build_data(self):\n from desiutil.io import combine_dicts\n # Loop on exposures\n odict = {}\n for qanight in self.qa_nights:\n for qaexp in qanight.qa_exps:\n # Get the exposure dict\n idict = write_qa_exposure('foo', qaexp, ret_dict=True)\n odict = combine_dicts(odict, idict)\n # Finish\n self.data = odict","title":""},{"docid":"f2ec2642418c7cdf0bd5b65ab5d50cf4","score":"0.4800392","text":"def main() -> Dict[int, int]:\n # DOCUMENTS DIRECTORY\n # Path of the directory that contains the .txt documents. One .txt document by company. IT NEEDS TO BE \"/data\" when you upload it in data challenge platform. For test in local, you can modifiy to match your data path.\n if dev:\n documents_directory = \"../example_dataset/data\"\n else: \n documents_directory = \"/data\"\n\n path_to_files: List[str] = [os.path.join(documents_directory, file) for file in os.listdir(documents_directory)]\n assert len(path_to_files) == 10 # 10 files in documents directory\n path_to_files.sort() # Sort list of path file by alphabetical order to match ground truth annotations order : IT IS ESSENTIAL.\n\n # INITIALIZATION OF YOUR OBJECTS\n data_model = FormDataModel.from_json_file(\n os.path.join(os.path.dirname(__file__), \"resources\", \"data-model.json\")\n )\n country_referential = CountryReferential.from_csv(\n os.path.join(os.path.dirname(__file__), \"resources\", \"countries_code.csv\")\n )\n form_company_filling = FormCompanyFilling([\n BasicExtractor(\n question_ids=NOT_COUNTRY_QUESTIONS_NUMBERS,\n form_data_model=data_model,\n ),\n BasicCountryExtractor(\n question_ids=COUNTRY_QUESTIONS_NUMBERS,\n form_data_model=data_model,\n country_code_referential=country_referential,\n\n )\n ])\n\n # COMPUTE PREDICTION BY FILE (ie company)\n print(\"##################################\")\n print(\"RUNNING PREDICTION\")\n results: Dict[int, int] = {}\n start_time = time.time()\n for i, path in enumerate(path_to_files):\n start = time.time()\n print(f\"File : {path}\")\n with open(path, \"r\") as input_file:\n text = input_file.read()\n print(\"... Encoding ...\")\n start_encoding = time.time()\n embeddings = model.encode(prepare_sentences(text))\n # embeddings = []\n print(\"Successfully encoded\")\n print(\"Encoding time : \", time.time() - start_encoding)\n\n form_company_response = form_company_filling.fill(text, embeddings, model)\n form_company_response.sort_by_question_id() # ESSENTIAL : Sort the response by question number for each company\n for answer in form_company_response.answers:\n question_number = answer.question_id + i * 22 # ESSENTIAL : each company has 22 questions. Each question_number in results should be unique\n results[question_number] = answer.answer_id\n # gc.collect()\n print(\"File time :\", time.time()-start, '\\n')\n # CHECK FORMAT RESULTS IS DATACHALLENGE PLATFORM COMPATIBLE\n assert len(results) == len(path_to_files) * (len(COUNTRY_QUESTIONS_NUMBERS) + len(NOT_COUNTRY_QUESTIONS_NUMBERS))\n assert set(list(results.keys())) == {i for i in range(1,221)}\n print(\"Full Time\", time.time()-start_time)\n return results","title":""},{"docid":"3b2cfa7bf46142925deae1bf5c16630a","score":"0.47919524","text":"def corpora_stats(output):\n igFiles = []\n for root, directories, filenames in os.walk(output + \"/ig/\"):\n for filename in filenames:\n igFiles.append(os.path.join(root, filename))\n igFiles = filter(lambda x: \".txt\" in x, igFiles)\n words = []\n for file in igFiles:\n fileH = open(file, \"r\")\n words = words + fileH.read().split(\" \")\n print(\"Number of words in IG corpus: {}\".format(len(words)))\n print(\"Vocabulary size of IG corpus: {}\".format(len(set(words))))","title":""},{"docid":"0cf44b52206139aa58bd58914f0c86e8","score":"0.47894615","text":"def main():\n # Load the metadata file.\n\n # Compute audio average length.\n # audio_lengths()\n\n # Compute age.\n average_age()\n\n # Number of words and talking time.\n num_words()","title":""},{"docid":"ce416b27270034cd6ebdf65aba001f12","score":"0.47884974","text":"def run_module(self):\n info(\"Searching for cross site scripting (reflected)...\")\n\n # load in a list of lfi attach strings\n #self.attack_strings = self.main.db.get_wordlist(\n # self.info['wordlist_name'])\n\n self.attack_strings = ['<script>alert(1)</script>',\n '<img srx=\"x\" onerror=\"alert(1)>\"']\n\n # the search strings will be the attack strings themselves\n # because python will not interpret any javascript\n self.re_search_strings = self.attack_strings\n\n injectable_params = self._get_previous_results('HTMLParser')\n\n with concurrent.futures.ProcessPoolExecutor() as executor:\n results = executor.map(self._run_thread, injectable_params)\n\n final = []\n for r in results:\n final.extend(r)\n\n # save the results\n self._save_scan_results(final)","title":""},{"docid":"3070ac458fc5ec5216e0c4ee491376a4","score":"0.47828823","text":"def extract_features(data, stopwords=STOPWORDS):\n tags = set()\n docs = []\n for document in data:\n doc_data = dict()\n doc_data['pmid'] = document['sourceid']\n text = document['text']\n\n # Insert PubTator annotations inside abstract\n denotations = document['denotations']\n sorted_denotations = []\n for denotation in denotations:\n begin = denotation['span']['begin']\n end = denotation['span']['end']\n obj = denotation['obj']\n for c in punctuation:\n obj = obj.replace(c, '')\n tags.add(obj)\n doc_data[obj] = doc_data.get(obj,0)+1\n sorted_denotations.append([begin,end,obj])\n sorted_denotations.sort()\n sorted_denotations.reverse()\n for begin, end, obj in sorted_denotations:\n text = text[:begin] + obj + ' ' + text[end:]\n\n doc_data['text'] = clean_text(text, stopwords)\n docs.append(doc_data)\n\n return docs","title":""},{"docid":"6d2a91608f238f580391282fd94bd746","score":"0.4773797","text":"def passion_analyzer(text):\n\n\tlower_text = text.lower()\n\n\thashtag_scaling = 0.3\n\texclamation_scaling = 0.5\n\tuppercase_scaling = 0.2\n\n\n\tpassion_index = 0\n\n\tfor x in range(len(positive_words)):\n\t\tpassion_index += (lower_text.count(positive_words[x]))**2\n\tfor x in range(len(negative_words)):\n\t\tpassion_index -= (lower_text.count(negative_words[x]))**2\n\tif '!' in text:\n\t\tpassion_index *= exclamation_scaling * lower_text.count('!') + 1\n\tif '#' in text:\n\t\tpassion_index *= hashtag_scaling * lower_text.count('#') + 1\n\tpassion_index *= uppercase_scaling * sum(1 for c in text if c.isupper())\n\n\n\t\t\n\treturn math.sqrt(passion_index)","title":""},{"docid":"bd2ea745317b67984d2a6bd61347cc77","score":"0.47733462","text":"def insights(self):\r\n return insights.Insights(self)","title":""},{"docid":"decadcafbeea5086704d57080a838d03","score":"0.4761947","text":"def analysis():\n global prediction\n\n json_path = os.path.join(basedir, 'static', 'data', 'tmp_json')\n # csv_path = os.path.join(basedir, 'static', 'data', 'csv')\n # if not os.path.exists(csv_path):\n # os.mkdir(csv_path)\n\n if os.name == 'nt':\n audio_file = Path(os.path.join(json_path, 'audio_data.shlf.dir'))\n video_file = Path(os.path.join(json_path, 'facial_data.shlf.dir'))\n else:\n audio_file = Path(os.path.join(json_path, 'audio_data.shlf'))\n video_file = Path(os.path.join(json_path, 'facial_data.shlf'))\n\n # Files exists\n if audio_file.is_file() and video_file.is_file():\n with shelve.open(os.path.join(json_path, 'facial_data.shlf')) as shelf:\n emotion_data = shelf['emotion_data']\n microexpression_data = shelf['micro_expression_data']\n blink_data = shelf['blink_data']\n\n with shelve.open(os.path.join(json_path, 'audio_data.shlf')) as shelf:\n mean_energy = shelf['mean_energy']\n max_pitch_amp = shelf['max_pitch_amp']\n vowel_duration = shelf['vowel_duration']\n pitch_contour = shelf['pitch_contour']\n\n else:\n emotion_data = None\n microexpression_data = None\n blink_data = None\n mean_energy = None\n max_pitch_amp = None\n vowel_duration = None\n pitch_contour = None\n\n # Training Files (choose one)\n # soc_file = os.path.join(basedir, 'static', 'data', 'train_files', 'first_soc.txt')\n # niko_file = os.path.join(basedir, 'static', 'data', 'train_files', 'first_niko.txt')\n # vero_file = os.path.join(basedir, 'static', 'data', 'train_files', 'first_vero.txt')\n\n # txt_file = soc_file\n\n # train_data = []\n\n # for cases where one parameter has more elements\n # for i in range(min(len(blink_data), len(microexpression_data), len(mean_energy))):\n # train_data.append(0)\n\n # train_file = open(txt_file)\n\n # for line in train_file:\n # index1 = int((int(line[4]) * 600) + ((int(line[5]) * 60) + (int(line[7]) * 10) + int(line[8])) / 2)\n # index2 = int((int(line[10]) * 600) + ((int(line[11]) * 60) + (int(line[13]) * 10) + int(line[14])) / 2)\n # if line[0] == 'F':\n # train_data[index1] = 1\n # train_data[index2] = 1\n\n # with open(os.path.join(csv_path, 'train.csv'), 'w', newline='') as csv_file:\n # writer = csv.writer(csv_file)\n # writer.writerow(['Time Interval', 'Micro-expressions', 'Blinks',\n # 'Mean Energy', 'Max Pitch Amplitude', 'Vowel Duration', 'Fundamental Frequency',\n # 'False/True'])\n\n # # for cases where one parameter has more elements than another\n # for index in range(min(len(mean_energy), len(blink_data), len(microexpression_data))):\n # writer.writerow([index, microexpression_data[index], blink_data[index],\n # mean_energy[index], max_pitch_amp[index], vowel_duration[index], pitch_contour[index],\n # train_data[index]])\n\n # finalresults = [['Time Interval', 'Micro-expressions', 'Blinks',\n # 'Mean Energy', 'Max Pitch Amplitude', 'Vowel Duration', 'Fundamental Frequency' ]]\n final_results = []\n\n for index in range((min(len(mean_energy), len(blink_data), len(microexpression_data)))):\n final_results.append([microexpression_data[index], blink_data[index],\n mean_energy[index], max_pitch_amp[index], vowel_duration[index],\n pitch_contour[index]])\n\n prediction[0] = predict(final_results)\n\n return render_template('analysis.html', mean_energy=mean_energy, max_pitch_amp=max_pitch_amp,\n vowel_duration=vowel_duration, pitch_contour=pitch_contour, blink_data=blink_data,\n microexpression_data=microexpression_data, emotion_data=emotion_data)","title":""},{"docid":"dc92410a0520036c9ea03316608d2714","score":"0.4759545","text":"def sample_analyze_entities(text_content, fb2wiki):\n\n #client = language_v1.LanguageServiceClient()\n\n # text_content = 'California is a state.'\n\n # Available types: PLAIN_TEXT, HTML\n type_ = enums.Document.Type.PLAIN_TEXT\n\n # Optional. If not specified, the language is automatically detected.\n # For list of supported languages:\n # https://cloud.google.com/natural-language/docs/languages\n language = \"en\"\n document = {\"content\": text_content, \"type\": type_, \"language\": language}\n entities = []\n entity_map = {}\n\n # Available values: NONE, UTF8, UTF16, UTF32\n encoding_type = enums.EncodingType.UTF8\n\n response = client.analyze_entities(document, encoding_type=encoding_type)\n #print(text_content)\n # Loop through entitites returned from the API\n for entity in response.entities:\n #print(u\"Representative name for the entity: {}\".format(entity.name))\n # Get entity type, e.g. PERSON, LOCATION, ADDRESS, NUMBER, et al\n # print(u\"Entity type: {}\".format(enums.Entity.Type(entity.type).name))\n # Get the salience score associated with the entity in the [0, 1.0] range\n # print(u\"Salience score: {}\".format(entity.salience))\n # Loop over the metadata associated with entity. For many known entities,\n # the metadata is a Wikipedia URL (wikipedia_url) and Knowledge Graph MID (mid).\n # Some entity types may have additional metadata, e.g. ADDRESS entities\n # may have metadata for the address street_name, postal_code, et al.\n if entity.metadata['mid'] != '':\n mid = entity.metadata['mid'].strip(\"/\").replace('/','.')\n if mid in fb2wiki:\n wikiids = fb2wiki[mid]\n entities.extend(wikiids)\n #print(mid, wikiids)\n # for metadata_name, metadata_value in entity.metadata.items():\n # print(u\"{}: {}\".format(metadata_name, metadata_value))\n\n # Loop over the mentions of this entity in the input document.\n # The API currently supports proper noun mentions.\n for mention in entity.mentions:\n # print(u\"Mention text: {}\".format(mention.text.content))\n # # Get the mention type, e.g. PROPER for proper noun\n # print(\n # u\"Mention type: {}\".format(enums.EntityMention.Type(mention.type).name)\n # )\n #print(mention.text)\n mention_text = mention.text.content\n begin = mention.text.begin_offset\n end = mention.text.begin_offset + len(mention_text)\n #print(mention_text, begin, end)\n if begin in entity_map:\n entity_map[begin].extend([(end, wid) for wid in wikiids])\n else:\n entity_map[begin] = [(end, wid) for wid in wikiids]\n\n\n # Get the language of the text, which will be the same as\n # the language specified in the request or, if not specified,\n # the automatically-detected language.\n # print(u\"Language of the text: {}\".format(response.language))\n return entities, entity_map","title":""},{"docid":"a35cdeb822e74d2e45ff1f5ee19711e8","score":"0.47527993","text":"def _publish_results(self):\n\n doc = Document()\n date = get_stamp()\n\n labels = ExperimentTemplateBase.parameters_to_string(self._topology_parameters_list)\n\n title = 'Mutual Information labels vs ' + self._experiment_name\n self.plot_save(title,\n self._mutual_info,\n self._baseline_mutual_info,\n 'Norm. mutual information',\n labels, date, self._docs_folder, doc)\n\n title = 'Weak classifier accuracy labels vs ' + self._experiment_name\n self.plot_save(title,\n self._classifier_accuracy,\n self._baseline_classifier_accuracy,\n 'Classifier accuracy',\n labels, date, self._docs_folder, doc) #, smoothing_size=3)\n\n title = 'average delta'\n f = plot_multiple_runs(\n self._different_steps[0], # here the X axes are identical\n self._average_delta,\n title=title,\n ylabel='log(delta)',\n xlabel='steps',\n labels=labels\n )\n add_fig_to_doc(f, path.join(self._docs_folder, title), doc)\n\n title = 'average boosting duration'\n f = plot_multiple_runs(\n self._different_steps[0],\n self._average_boosting_dur,\n title=title,\n ylabel='duration',\n xlabel='steps',\n labels=labels\n )\n add_fig_to_doc(f, path.join(self._docs_folder, title), doc)\n\n doc.write_file(path.join(self._docs_folder, to_safe_name(self._complete_name() + date + \".html\")))\n\n print('done')","title":""},{"docid":"c1e768c23ac138268528027f134242d6","score":"0.47511122","text":"def do_analysis(ckpt, queries_type, entities_type, request):\n global currently_analyzing, results, d, analysis_user\n try:\n print(\"starting analysis!\")\n if entities_type == \"all\":\n print(\"using all entities detected!\")\n elif entities_type == \"uploaded\":\n print(\"using only entities specified in csv file!\")\n \n currently_analyzing = True\n analysis_user = request.user.username\n results = []\n proj_path = os.path.abspath(os.path.dirname(__file__)).split(\"FYP_Web_App\")[0]\n ckpt = proj_path + \"FewRel/checkpoint/\" + ckpt\n if d is None or d.ckpt_path != ckpt:\n d = DetectionFramework(ckpt_path=ckpt)\n if cancel_flag[0]:\n return\n d.clear_support_queries()\n if len([i for i in os.listdir(\"temp/relation_support_datasets\") if 'csv' in i and request.user.username in i]) == 0:\n raise ValueError(\"Please upload relation support dataset!\")\n \n d.load_support_files(\"temp/relation_support_datasets\", request.user.username)\n if queries_type == \"csv_option\":\n if not os.path.exists(\"temp/queries.csv\"):\n raise ValueError(\"Please upload query CSV dataset!\")\n d.load_queries_csv(\"temp/queries.csv\")\n \n elif queries_type == \"url_option\":\n if not os.path.exists(\"temp/url.txt\"):\n raise ValueError(\"Please specify news article url!\")\n with open(\"temp/url.txt\") as f:\n url = f.read()\n d.load_url(url)\n \n elif queries_type == \"txt_option\":\n d.load_text_files(os.path.abspath(\"temp/text_files\"))\n \n elif queries_type == \"ind_sentence_option\":\n ind_sentence = request.POST.get('ind_sent')\n d.load_ind_sentence(ind_sentence)\n \n elif queries_type == \"html_option\":\n d.load_html_file_queries(os.path.abspath(\"temp/html_files\"))\n \n if entities_type == \"uploaded\":\n d.trim_queries_based_on_entities_file(os.path.abspath(\"temp/entities_csv_file.csv\"))\n\n if cancel_flag[0]:\n return\n d.detect(rt_results=results, cancel_flag=cancel_flag)\n if cancel_flag[0]:\n return\n src=None\n if queries_type == \"csv_option\":\n src = \"queries_csv\"\n elif queries_type == \"txt_option\":\n src = \"queries_text_file\"\n elif queries_type == \"ind_sentence_option\":\n src = \"ind_sentence\"\n elif queries_type == \"url_option\":\n with open(\"temp/url.txt\") as f:\n src = f.read()\n elif queries_type == \"html_option\":\n src = \"html_files\"\n \n s = Source(source=src, user=request.user)\n s.save()\n for r in results:\n er = ExtractedRelation(sentence=r['sentence'],head=r['head'],tail=r['tail'],pred_relation=r['pred_relation'],sentiment=r['sent'],conf=r['conf'],ckpt=ckpt, source=s)\n er.save()\n except Exception as e:\n print(len(str(e)))\n print(str(e))\n errors.append(str(e))\n tb = traceback.format_exc()\n print(tb)\n finally:\n currently_analyzing = False\n analysis_user = None","title":""},{"docid":"a5fb22dc5cfe9f14d6eb38fe3934c88a","score":"0.47506815","text":"def extract_information(preprocessed_sentences):\n parsed = list(map(lambda sentence: nlp(sentence), preprocessed_sentences))\n\n quantities = list(filter(lambda sentence: eh.sentence_has_type(sentence, 'QUANTITY'), parsed))\n dates = list(filter(lambda sentence: eh.sentence_has_type(sentence, 'DATE'), parsed))\n\n hurricane_name = eh.extract_frequent_regex_match(parsed, '[Hh]urricane ([A-Z][a-z]+)').most_common(1)[0][0]\n hurricane_category = eh.extract_frequent_regex_match(parsed, '[Cc]ategory ([0-9]+)').most_common(1)[0][0]\n\n tropical_storm_name = eh.extract_frequent_regex_match(parsed, '[Tt]ropical [Ss]torm ([A-Z][a-z]+)').most_common(1)[0][0]\n formation_date, middle_month = extract_storm_timeline(dates, hurricane_name)\n\n preperation_info = extract_preparation_information(parsed)\n prep_gpes = preperation_info[0].most_common(3)\n\n restore_info = extract_restoration_information(parsed)\n\n landfall_info = extract_landfall_information(parsed)\n\n wind_info = extract_wind_information(quantities)\n rain_info = extract_rain_information(quantities)\n size_info = extract_size_information(parsed)\n\n # formation_info = extract_formation_info(parsed)\n death_info = extract_death_damages_info(parsed)\n\n print(constants.HURRICANE_SENTENCE.format(hurricane_name, middle_month, hurricane_category))\n print(constants.LANDFALL_SENTENCE.format(hurricane_name, landfall_info[2], landfall_info[3], landfall_info[0], landfall_info[1]))\n print(constants.WIND_SENTENCE.format(wind_info[0], wind_info[1], wind_info[2]))\n print(constants.RAIN_SENTENCE.format(hurricane_name, rain_info[1], rain_info[0], rain_info[2]))\n print(constants.FORMATION_SENTENCE.format(formation_date, tropical_storm_name))\n print(constants.PREPARATION_SENTENCE.format(prep_gpes[0][0], prep_gpes[1][0], prep_gpes[2][0], preperation_info[1].\n most_common(1)[0][0]))\n print(constants.SIZE_SENTENCE.format(size_info[0], size_info[1]))","title":""},{"docid":"8b0966fa977dfe08eafd5007f9d197d8","score":"0.4750112","text":"def inference(self):\n embeddings = self.process_speaker(speaker_speech_path=self.main_configs.SPEAKER_SPEECH_PATH)\n with open(self.main_configs.INPUT_TEXTS_PATH, \"r\") as file:\n texts = file.readlines()\n specs = self.synthesize_spectrograms(texts=texts, embeddings=embeddings)\n specs = specs[0]\n wav = self.generate_waveform(specs)\n return wav","title":""},{"docid":"1109a8c9c043b777d8ffe3c7da5c6437","score":"0.47493204","text":"def main(self, words_docs, cleaned_sentences, lang, model_dir, number_of_clusters, embedding_model, model_id):\n\t\ttry:\n\t\t\tif embedding_model == \"tfidf\": text_vector = self.create_tfidf_vectors(cleaned_sentences)\n\t\t\telif embedding_model == \"word2vec\": text_vector = self.create_w2v_vectors(words_docs)\n\t\t\tmodel, pred_dict = self.train_model(cleaned_sentences, text_vector, number_of_clusters, lang, model_id, model_dir)\n\t\t\tdf_dominant_topic = self.evaulate_clusters(pred_dict, model_dir)\n\n\t\texcept Exception as e:\n\t\t\tprint(\"\\n Error in main : \",e)\n\t\t\tprint(\"\\n Error details : \", traceback.format_exc())\n\n\t\treturn df_dominant_topic","title":""},{"docid":"350a25bc36f4975e5f26fd090758162e","score":"0.4746469","text":"def feature_extraction(args, what_roi):\n print(\"Start feature extraction...\")\n descr_dict = {}\n file_list = []\n for root, dirs, files in os.walk(args.imdir):\n for n, filename in enumerate(files):\n if n == 0:\n print(\"\\nCurrent directory: %s\" % root)\n filepath = os.path.join(root, filename)\n img = cv2.imread(filepath, cv2.IMREAD_COLOR)\n\n if type(img) != np.ndarray:\n sys.stderr.write(\"--> %d File %s is not an img: will be skipped.\\n\" % (n, filename))\n continue\n else:\n print(\"%d Image %s is being processed...\" % (n, filename))\n file_list.append(filename)\n\n img = apply_roi(args, what_roi, img)\n img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n ret, thresh = cv2.threshold(img_gray, 170, 255, cv2.THRESH_TRUNC)\n des = surf_keypoint_detection(thresh)\n descr_dict[filename] = des\n\n print(\"\\nAll images have been processed.\")\n return descr_dict, file_list","title":""},{"docid":"83f737bac1e245de9cc9cbb67328c603","score":"0.47404498","text":"def documents(self):\n from kitsune.wiki.models import Document\n\n return Document.objects.filter(documentimage__image=self)","title":""},{"docid":"36c67e8973d22370cdf488112cda2c18","score":"0.47379708","text":"def main():\n logging.basicConfig(level=logging.WARN)\n\n text = extract()\n text, char_indices, indices_char, x, y = transform(text)\n model(text, char_indices, indices_char, x, y)\n\n pass","title":""},{"docid":"3405afec9e0f9e933b1ab9fd89407f6e","score":"0.473772","text":"def run_extraction(self):\n self.background_estimator = ReflectedRegionsBackgroundEstimator(\n observations=self.observations, **self.config[\"background\"]\n )\n self.background_estimator.run()\n\n self.extraction = SpectrumExtraction(\n observations=self.observations,\n bkg_estimate=self.background_estimator.result,\n **self.config[\"extraction\"]\n )\n\n self.extraction.run()","title":""},{"docid":"87bc13b67079a664c76ca1d541f857c2","score":"0.47326425","text":"def getExcerpts(self, text, DICECodeResults):\n\t\t\"\"\" - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - \"\"\"\n\t\t\n\t\tdef getKernels(indices):\n\t\t\t\"\"\"\n\t\t\t\tgetKernels() is a sub-method that extracts strings from a doc-\n\t\t\t\tument using indices provided by the DICECodeResults data struc-\n\t\t\t\tture passed into this sub-method's parent method, getExcerpts().\n\t\t\t\tThis sub-method returns three strings.\n\n\t\t\t\tindices --> tuple containing indices in the document with text to extract.\n\t\t\t\"\"\"\n\n\t\t\ti = indices[0]\n\t\t\tj = indices[1]\n\n\t\t\th = i - self.scope\n\t\t\tk = j + self.scope\n\n\t\t\tif h < 0: h = 0\n\t\t\tif k > len(text): k = len(text)-1\n\n\t\t\treturn text[h:i].replace(\"\\n\", \"__\").replace(\"\\t\", \" \"), text[i:j].replace(\"\\n\", \"__\").replace(\"\\t\", \" \"), text[j:k].replace(\"\\n\", \"__\").replace(\"\\t\", \" \")\n\t\t\t#return \"|\"+text[h:i].replace(\"\\n\", \"__\").replace(\"\\t\", \" \")+\"|\", text[i:j].replace(\"\\n\", \"__\").replace(\"\\t\", \" \"), \"|\"+text[j:k].replace(\"\\n\", \"__\").replace(\"\\t\", \" \")+\"|\"\n\n\t\tdef getComboTerms(tuples):\n\t\t\t\"\"\"\n\t\t\t\tgetComboTerms() is a sub-method that combines search terms and \n\t\t\t\ttheir indices provided in the tuple parameter into a string with\n\t\t\t\tthe following structure: [(variant, index)]. This sub-method re-\n\t\t\t\tturns a string of that structure.\n\n\t\t\t\ttuples --> data structure containing the search term and index of the search term in the form of: (term, index)\n\t\t\t\"\"\"\t\t\t\n\t\t\t#return \"[{0}]\".format('; '.join([\"({0})\".format(','.join([text[indices[0]:indices[1]], str(indices[0])])) for indices in tuples]))\n\t\t\treturn \"{0}\".format('; '.join((\"{0}\".format(text[indices[0]:indices[1]]) for indices in tuples)))\n\n\t\tdef getProximity(tuples):\n\t\t\t\"\"\"\n\t\t\t\tgetProximity() is a sub-method that calculates the distance of the search terms provided in the tuple parameter. \n\t\t\t\tThis sub-method returns an absolute value integer.\n\n\t\t\t\ttuples:\tdata structure containing the search term and index of the search term in the form of: (term, index)\n\t\t\t\"\"\"\n\t\t\tsortedIndices = [indices for indices in tuples]\n\t\t\t#return abs(sortedIndices[0][1] - sortedIndices[-1][0])\n\t\t\treturn sortedIndices[-1][0] - sortedIndices[0][1] \n\n\t\t\"\"\" - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - \"\"\"\n\n\t\texcerptsResults = list()\t\t# NEW list to contain the expanded data structure provided by the DICECodeResults parameter\n\n\t\t# CALL THESE JUST ONCE BEFORE LOOP(S)\n\t\tappend = excerptsResults.append\n\t\tformat = str.format\n\t\t# - - - - - - - - - - - - - - - - - -\n\n\t\tfor row in DICECodeResults:\n\n\t\t\tDICECode \t\t= row[0]\t# (1) DICE code as specified in C:\\Users\\a5rjqzz\\Desktop\\Python\\files\\Types.gd\n\t\t\tTYPECode \t\t= row[1]\t# (2) Type code as specified in C:\\Users\\a5rjqzz\\Desktop\\Python\\files\\Types.gd\n\t\t\tCombo \t\t= False\t\t# (3) Boolean status of the presence of a combo term\n\t\t\tdocumentIndex \t= 0\t\t\t# (4) Index of this search term in the document\n\t\t\tindices \t\t= row[2]\t# (5) Indices of the search term and combo term if present\n\t\t\tproximity\t\t= 0\t\t\t# (6) Distance between search term and combo terms\n\n\t\t\tif type(row[2][0]) == type(tuple()):\n\t\t\t\tCombo = True\t# If the type of search term is a combo, this is true\n\n\t\t\t\tfor tuples in row[2]:\n\t\t\t\t\tindices \t\t\t\t\t\t= tuples[0]\t\t\t\t# (1) Location(s) of the search term in the tuple\n\t\t\t\t\tdocumentIndex \t\t\t\t\t= indices[0]\t\t\t# (2) Location of the search term in the document\n\t\t\t\t\tcomboTerms \t\t\t\t\t\t= getComboTerms(tuples)\t# (3) Multiple terms assigned to variable comboTerms\n\t\t\t\t\tproximity \t\t\t\t\t\t= getProximity(tuples)\t# (4) Proximity of combo terms if present\n\t\t\t\t\tkernelLeft, kernel, kernelRight = getKernels(indices)\t# (5) Left, center, and right kernels or excerpts\n\n\t\t\t\t\tappend([DICECode, TYPECode, Combo, documentIndex, kernelLeft, kernel, kernelRight, comboTerms, proximity])\n\n\t\t\telse:\n\t\t\t\tdocumentIndex \t\t\t\t\t= indices[0]\t\t\t\t\t\t\t\t\t# (1) Location of the search term in the document\n\t\t\t\tcomboTerms \t\t\t\t\t\t= format(\"[{0}]\", text[indices[0]:indices[1]])\t# (2) Single term assigned to variable comboTerms\n\t\t\t\tkernelLeft, kernel, kernelRight = getKernels(indices)\t\t\t\t\t\t\t# (3) Left, center, and right kernels or excerpts\n\n\t\t\t\tappend([DICECode, TYPECode, Combo, documentIndex, kernelLeft, kernel, kernelRight, comboTerms, proximity])\n\n\t\treturn excerptsResults","title":""},{"docid":"44f3996b20f8d1879a7cc17af9b1c6be","score":"0.47272265","text":"def _compute_expert_weights(self):\n # Shape = (num_nonzero, 1, num_binary).\n smooth_step_activations = self._smooth_step(self._z_logits)\n # Shape = (num_nonzero, num_experts).\n selector_outputs = tf.math.reduce_prod(\n tf.where(self._binary_codes, smooth_step_activations,\n 1 - smooth_step_activations),\n axis=2)\n # Weights for the single-expert selectors: shape = (num_nonzero, 1).\n selector_weights = tf.nn.softmax(self._w_logits, axis=0)\n expert_weights = tf.math.reduce_sum(\n selector_weights * selector_outputs, axis=0)\n\n return expert_weights, selector_outputs","title":""},{"docid":"6db70f1c5cbc29963fed521d462eda73","score":"0.47268128","text":"def display_wmd(google_snippets, my_snippets):\r\n for i in range(len(google_snippets)):\r\n print(i+1, '.', urls[i])\r\n print('Google Snippet:', google_snippets[i])\r\n print('Generated Snippet:', my_snippets[i])\r\n print(\"Word Mover's Distance:\", get_wmd_dist(google_snippets[i], my_snippets[i], model))\r\n print()","title":""},{"docid":"a07eef829c91f5c52fff772340bd9a01","score":"0.4724361","text":"def __init__(self):\n # Initialise class attributes (visibility ease)\n self.__corpus__ = None\n self.__pron_det_pos_words__ = None\n self.__triples_corpus__ = None\n self.__entities_in_doc__ = None\n self.__wvmodel__ = None\n \n # For purpose of parsing relation triplets later\n # Load pretrained embedding model\n #plog('Loading pretrained word embeddings. This will take some time to load...')\n #self.__wvmodel__ = api.load('fasttext-wiki-news-subwords-300')\n #plog('Pretrained word embeddings loaded!')","title":""},{"docid":"700e62bf908212d54d738f43d57281b8","score":"0.47217306","text":"def extract(self, documents):\n\n # Placeholder for return value.\n features = None\n\n # Create a numpy array of all zeros for storing frequencies.\n tf = np.zeros((len(documents), len(self.idx_to_word)))\n\n # Raise an exception if 'extract' is called before 'preprocess'\n if len(self.word_to_idx) == 0 or len(self.idx_to_word) == 0:\n raise Exception(\"Extractor not initialised.\")\n\n # For each document\n for idx, doc in enumerate(documents):\n # Split strig into a list of words\n words = extract_words(doc)\n\n # Calculate it's frequency, however, keep in mind\n # that this word may not have been in the training\n # corpus. In that case, ignore the word.\n for w in words:\n ''' YOUR CODE HERE '''\n try:\n tf[idx][self.word_to_idx[w]] = words.count(w)\n except KeyError:\n pass\n\n ''' END CODE FOR THIS LOOP '''\n\n # Divide the frequencies by the number of words in document.\n ''' YOUR CODE HERE '''\n tf[idx] = tf[idx]/len(words)\n ''' END CODE FOR THIS LOOP '''\n\n # Calculate the Tf-Idf features.\n features = tf * self.idf\n\n return features","title":""},{"docid":"f70ec6a6ce913ebf5cf9f65cfa079af5","score":"0.47216392","text":"def ingest_annotations(directory, annotation_type):\n in_golden = annotation_type == \"manual\"\n doc_type = None\n issue_number = None\n bid = None\n annotated_pages = []\n file_prefix = \"page\"\n try:\n bid = directory.split('/')[len(directory.split('/'))-1:][0]\n record = Metadata.objects(bid=bid).first()\n assert record is not None\n except Exception as e:\n bid,issue_number = directory.split('/')[len(directory.split('/'))-2:]\n record = Metadata.objects(bid=bid).first()\n try:\n doc_type = record[\"type_document\"]\n except Exception as e:\n doc_type = None\n logger.warning('The record for %s is not in MongoDB'%bid)\n try:\n page_numbers = {int(os.path.basename(fname).replace(\"page-\",\"\").split('.')[0]):os.path.basename(fname).split('.')[0] \n for fname in glob.glob(\"%s/*.ann\"%directory)}\n except Exception as e:\n page_numbers = {int(os.path.basename(fname).replace(\"image-\",\"\").split('.')[0]):os.path.basename(fname).split('.')[0] \n for fname in glob.glob(\"%s/*.ann\"%directory)}\n file_prefix = \"image\"\n # TODO: handle the exception of document not in the DB\n logger.info(\"Ingesting the annotations from directory \\\"%s\\\"\"%directory)\n if(issue_number != None):\n logger.info(\"Found document %s-%s [type=%s]\"%(bid,issue_number,doc_type))\n else:\n logger.info(\"Found document %s [type=%s]\"%(bid,doc_type))\n try:\n if(doc_type==\"journal\"):\n doc = LBDocument.objects(internal_id=\"%s-%s\"%(bid,issue_number)).first()\n if doc is None:\n doc = LBDocument.objects(internal_id=\"%s-%s\"%(bid, convert_issue_number(issue_number))).first()\n elif(doc_type==\"monograph\"):\n doc = LBDocument.objects(bid=bid).first()\n logger.info(\"%s has %i pages\"%(doc.internal_id, len(doc.pages)))\n for page_n in sorted(page_numbers):\n logger.debug(\"Reading in annotations for page %i from file %s/ %s\"%(page_n,directory,page_numbers[page_n]))\n entities_with_continuations = {}\n entities,relations = read_ann_file(page_numbers[page_n],directory)\n fulltext = codecs.open(\"%s/%s.txt\"%(directory,page_number2image_name(page_n, string=file_prefix)),'r', 'utf-8').read()\n line_breaks = find_newlines(fulltext)\n #logger.info(\"Found %i entities, %i relation in %s\"%(len(entities), len(relations), directory))\n doc_id = \"%s-%s-%s\"%(bid, issue_number, page_numbers[page_n])\n try:\n page = next((page for page in doc.pages if page.single_page_file_number==page_n))\n if(page[\"in_golden\"]==True):\n annotated_pages.append(page.id)\n logger.info(\"found %i entities in %s (p. %i)\"%(len(entities),doc_id,page_n))\n logger.info(\"found %i relations in %s (p. %i)\"%(len(relations.keys()),doc_id,page_n))\n \"\"\"\n Parse the `ContainedIN` relations and identify annotations that should be merged together.\n IDs of candidates for merging are stored in a dict(), e.g. {\"T1\":[\"T2\",\"T4\"]}\n \"\"\"\n entities_with_continuations = {}\n if len(relations.keys())>0:\n for relation_key in relations:\n args = relations[relation_key][\"arguments\"]\n if args[0] in entities_with_continuations:\n entities_with_continuations[args[0]].append(args[1])\n else:\n entities_with_continuations[args[0]] = [args[1]]\n logger.debug(\"(%s-%s) entities to be merged: %s\"%(doc_id,page_n,entities_with_continuations))\n \"\"\"\n Create the annotations (`entities` dict). \n Later they will be stored into the MongoDB\n \"\"\"\n for entity in entities:\n entities[entity][\"ingestion_timestamp\"] = datetime.utcnow()\n entities[entity][\"annotation_ingester_version\"] = __version__\n entities[entity][\"entity_type\"] = entities[entity][\"entity_type\"].lower( )\n entities[entity][\"filename\"] = \"%s/%s%s\"%(directory,page_numbers[page_n],\".ann\")\n if(doc_type==\"journal\"):\n entities[entity][\"bid\"] = bid\n entities[entity][\"pageid\"] = doc_id\n elif(doc_type==\"monograph\"):\n entities[entity][\"bid\"] = bid\n entities[entity][\"pageid\"] = \"%s-%s\"%(bid,page_numbers[page_n])\n entities[entity][\"container\"] = entities[entity][\"entity_type\"] in container_annotations\n # ref to page_id (from content_loader) ✓\n for position in entities[entity][\"positions\"]:\n line_number = find_linenumber_for_string(position[\"start\"],position[\"end\"], line_breaks)\n logger.debug(\"%s is found at line %s\"%(entity,line_number))\n position[\"line_n\"] = line_number\n position[\"page_id\"] = page.id\n positions_by_offset = sorted(entities[entity][\"positions\"]\n ,key=lambda position: position['start'])\n entities[entity][\"positions\"] = sorted(positions_by_offset\n , key=lambda position: Page.objects(id=position['page_id']).first().single_page_file_number)\n logger.debug(\"Annotations %s %s\"%(entity,entities[entity]))\n \"\"\"\n Now take the candidates for merging identified above and populate the annotations.\n Still nothing is saved into MongoDB at this stage.\n \"\"\"\n for ann_id in entities_with_continuations:\n try:\n logger.debug(\"Starting to merge SP and SF entities into meta-annotations (%s-%s)\"%(doc_id, page_n))\n logger.debug(\"%s will be merged with %s\"%(ann_id,\"+\".join(entities_with_continuations[ann_id])))\n top_entity_types = \"_\".join([entities[ann_id][\"entity_type\"]]+[entities[annid][\"entity_type\"] \n for annid in entities_with_continuations[ann_id]])\n logger.debug(\"%s\"%top_entity_types)\n new_entity = copy.deepcopy(entities)[ann_id] \n #container = True \n new_entity[\"ann_id\"] = \"%s+%s\"%(ann_id,\"+\".join(entities_with_continuations[ann_id]))\n new_entity[\"entity_type\"] = \"meta-annotation\"\n new_entity[\"top_entity_types\"] = top_entity_types\n new_entity[\"top_entities_ids\"] = [ann_id]\n new_entity[\"top_entities_ids\"] += [id for id in entities_with_continuations[ann_id]]\n fname = new_entity[\"filename\"]\n new_entity[\"filename\"] = [fname]\n for to_merge_id in entities_with_continuations[ann_id]:\n to_merge = dict(entities)[to_merge_id]\n new_entity[\"filename\"]+= [to_merge[\"filename\"]]\n new_entity[\"positions\"] = new_entity[\"positions\"] + to_merge[\"positions\"]\n positions_by_offset = sorted(new_entity[\"positions\"]\n ,key=lambda position: position['start'])\n new_entity[\"positions\"] = sorted(positions_by_offset\n ,key=lambda position: Page.objects(id=position['page_id']).first().single_page_file_number)\n new_entity[\"filename\"] = \", \".join(list(set(new_entity[\"filename\"])))\n surface_start = new_entity[\"positions\"][0][\"start\"]\n surface_end = new_entity[\"positions\"][-1][\"end\"]\n new_entity[\"surface\"] = fulltext[surface_start:surface_end]\n entities[new_entity[\"ann_id\"]] = new_entity\n logger.debug(new_entity)\n except Exception as e:\n logger.error(\"The merging of %s in (%s-%s) failed with error\\\"%s\\\"\"%(new_entity[\"ann_id\"],bid,page_n,e))\n \"\"\"\n Now all annotations will be stored into the MongoDB. \n And some specific fields (e.g. `top_entities`) are sorted, and annotations updated \n accordingly in the DB. \n \"\"\"\n try:\n annotations = []\n for entity in entities.values():\n annotation = Annotation(**entity)\n annotation.positions = [PagePosition(**position) for position in entity[\"positions\"]]\n annotation.save()\n annotations.append(annotation)\n page.annotations_ids = [] #TODO\n page.annotations_ids = annotations\n page.is_annotated = True\n page.save()\n logger.debug(\"Following annotations were inserted into MongoDB: %s\"%([annotation.id for annotation in annotations]))\n logger.info(\"%i annotations were inserted into MongoDB\"%len(annotations))\n except Exception as e:\n raise e\n containers = [annotation for annotation in annotations if annotation[\"container\"]] \n contained = [annotation for annotation in annotations if not annotation[\"container\"]]\n meta_annotations = [annotation for annotation in annotations if annotation[\"entity_type\"]==\"meta-annotation\"]\n logger.debug(\"meta annotations: %s\"%meta_annotations)\n \"\"\"\n Resolve the top entities in the meta-annotations: replace entity IDs with \n a reference to the annotation in the MongoDB.\n \"\"\"\n for annotation in meta_annotations:\n top_entities_ids = annotation[\"top_entities_ids\"]\n logger.debug('resolving top_entities')\n top_entities = [Annotation.objects(ann_id=ann_id, pageid=annotation.pageid).first() for ann_id in top_entities_ids]\n #top_entities = list([db_conn.annotations.find_one({\"ann_id\":ann_id,\"pageid\":annotation[\"pageid\"]}) for ann_id in top_entities_ids])\n logger.debug(\"Top entities before sorting %s\"%[ann.id for ann in top_entities])\n annotation[\"top_entities\"] = sort_annotations_by_offset(top_entities)\n logger.debug(\"Top entities after sorting %s\"%[ann.id for ann in top_entities])\n annotation[\"top_entities\"] = top_entities\n annotation.save()\n logger.debug(\"Updating meta-annotation: %s\"%annotation.id)\n \"\"\"\n Transform contains relations between entities into references between annotations \n in the MongoDB.\n \"\"\"\n for annotation in sort_annotations_by_offset(containers):\n if(len(annotation[\"positions\"]) > 1):\n start = annotation[\"positions\"][0][\"start\"]\n end = annotation[\"positions\"][len(annotation[\"positions\"])-1][\"end\"]\n else:\n start = annotation[\"positions\"][0][\"start\"]\n end = annotation[\"positions\"][0][\"end\"]\n annotation[\"contains\"] = []\n for contained_annotation in sort_annotations_by_offset(contained):\n if(len(contained_annotation[\"positions\"])>1):\n if(contained_annotation[\"positions\"][0][\"start\"] >= start\n and contained_annotation[\"positions\"][len(contained_annotation[\"positions\"])-1][\"end\"] <= end):\n annotation[\"contains\"].append(contained_annotation)\n logger.debug(\"[%s] Annotation %s (%s) contains %s (%s)\"%(\n doc_id\n ,annotation[\"ann_id\"]\n ,annotation[\"id\"]\n ,contained_annotation[\"ann_id\"]\n ,contained_annotation[\"id\"]))\n annotation.save()\n else:\n if(contained_annotation[\"positions\"][0][\"start\"] >= start\n and contained_annotation[\"positions\"][0][\"end\"] <= end):\n annotation[\"contains\"].append(contained_annotation)\n logger.debug(\"[%s] Annotation %s (%s) contains %s (%s)\"%(\n doc_id\n ,annotation[\"ann_id\"]\n ,annotation[\"id\"]\n ,contained_annotation[\"ann_id\"]\n ,contained_annotation[\"id\"]))\n annotation.save()\n else:\n page.is_annotated = False\n logger.info(\"%s was ignored because it's not in the golden set\"%doc_id)\n except StopIteration as e:\n logger.error(\"The annotations for %s-%s p. %i can't be ingested\"%(bid, issue_number, page_n))\n except Exception as e:\n logger.error(\"The annotations for %s-%s can't be ingested. Got error %s\"%(bid, issue_number, e))\n return annotated_pages","title":""},{"docid":"b1c3d4bd616d2f302eb79715a017de11","score":"0.4721362","text":"def main():\n\n\tst.title(\"Sentiment Analysis Emoji App\")\n\n\tactivities = [\"Sentiment\",\"Text Analysis on URL\",\"About\"]\n\tchoice = st.sidebar.selectbox(\"Choice\",activities)\n\n\tif choice == 'Sentiment':\n\t\tst.subheader(\"Sentiment Analysis\")\n\t\tst.write(emoji.emojize('Everyone :red_heart: Streamlit ',use_aliases=True))\n\t\traw_text = st.text_area(\"Enter Your Text\",\"Type Here\")\n\t\tif st.button(\"Analyze\"):\n\t\t\tblob = TextBlob(raw_text)\n\t\t\tresult = blob.sentiment.polarity\n\t\t\tif result > 0.0:\n\t\t\t\tcustom_emoji = ':smile:'\n\t\t\t\tst.write(emoji.emojize(custom_emoji,use_aliases=True))\n\t\t\telif result < 0.0:\n\t\t\t\tcustom_emoji = ':disappointed:'\n\t\t\t\tst.write(emoji.emojize(custom_emoji,use_aliases=True))\n\t\t\telse:\n\t\t\t\tst.write(emoji.emojize(':expressionless:',use_aliases=True))\n\t\t\tst.info(\"Polarity Score is:: {}\".format(result))\n\t\t\t\n\tif choice == 'Text Analysis on URL':\n\t\tst.subheader(\"Analysis on Text From URL\")\n\t\traw_url = st.text_input(\"Enter URL Here\",\"Type here\")\n\t\ttext_preview_length = st.slider(\"Length to Preview\",50,100)\n\t\tif st.button(\"Analyze\"):\n\t\t\tif raw_url != \"Type here\":\n\t\t\t\tresult = get_text(raw_url)\n\t\t\t\tblob = TextBlob(result)\n\t\t\t\tlen_of_full_text = len(result)\n\t\t\t\tlen_of_short_text = round(len(result)/text_preview_length)\n\t\t\t\tst.success(\"Length of Full Text::{}\".format(len_of_full_text))\n\t\t\t\tst.success(\"Length of Short Text::{}\".format(len_of_short_text))\n\t\t\t\tst.info(result[:len_of_short_text])\n\t\t\t\tc_sentences = [ sent for sent in blob.sentences ]\n\t\t\t\tc_sentiment = [sent.sentiment.polarity for sent in blob.sentences]\n\t\t\t\t\n\t\t\t\tnew_df = pd.DataFrame(zip(c_sentences,c_sentiment),columns=['Sentence','Sentiment'])\n\t\t\t\tst.dataframe(new_df)\n\n\tif choice == 'About':\n\t\tst.subheader(\"About:Sentiment Analysis Emoji App\")\n\t\tst.info(\"Built with Streamlit,Textblob and Emoji\")\n\t\tst.text(\"Jesse E.Agbe(JCharis\")\n\t\tst.text(\"Jesus Saves@JCharisTech\")","title":""},{"docid":"c7262f473392b5b603fc98c227ff6c8d","score":"0.47176358","text":"def run(self):\n\n self.log.debug('Reading wordlist %s', self.wordlists)\n\n # Test\n data = self.symfony.profiler.open(self.symfony.root)\n if data is None:\n self.log.info('The target does not support file preview')\n return\n\n # Enqueue\n with open(self.wordlists) as file:\n i = 0\n for i, line in enumerate(file, 1):\n url = self.symfony.profiler.url('open')\n params = {'line': 1, 'file': line.strip()}\n self.engine.queue.put(Request(method='GET', url=url, params=params))\n self.log.debug('Enqueued %d entries', i)\n\n self.engine.join()\n found = [response for response in self.engine.results if response.status_code != 404]\n files = [resp.request.params['file'] for resp in found]\n\n # Composer lookup\n composer = [file for file in files if file.endswith('composer.lock')]\n if composer:\n self.log.info(\"Found: %s, run 'symfony security:check' or submit it at %s\", composer[0], self.security_url)\n\n if not found:\n self.log.warning('Did not find any file')\n return\n\n # Save results\n for response in found:\n data = self.symfony.profiler.parse_file_preview(response.text)\n self.symfony.files[response.request.params['file']] = data\n\n self.log.warning('Found the following files:')\n for file in files:\n self.log.warning(' %s', file)","title":""},{"docid":"ca61f43be01711612262d6d843e3ae46","score":"0.47137287","text":"def biological_science_news():\n\n return general_scraper(['http://mesva.univaq.it/?q=avvisi/cl-clm/52672'])","title":""},{"docid":"f5e51157244b5b9fee98575a0590882d","score":"0.4712479","text":"def write():\n #with st.spinner(\"Loading Dashboard ...\"):\n #ast.shared.components.title_awesome(\"\")\n\n st.title('arXiv - Analytics')\n st.text(\"\")\n if st.checkbox('Most similar words in w2v'):\n user_input = st.text_input(\"Topic (please enter up to two keywords)\", 'Machine Learning')\n user_input = user_input.lower().replace(\" \", \"_\")\n st.text(\"\")\n number_of_similar_words = st.slider('Select a modulus', 3, 50)\n plot_similar_words(model, user_input, number_of_similar_words)\n st.pyplot()\n st.text(\"\")\n if st.checkbox('Word Cloud'):\n cluster = st.slider('Select a cluster', 0, 49)\n word_cloud_kmeans(cluster)\n st.pyplot()","title":""},{"docid":"efae1a4ea851905884fc047208f63737","score":"0.47093627","text":"def main():\n analyze_perturbations()","title":""},{"docid":"d4f5b9e8f3d1e131796e7ead6df986b6","score":"0.4708818","text":"def ALLEN_st_cells_1_movies(self):\n exp_dict = self.template_dataset()\n exp_dict = self.add_globals(exp_dict)\n exp_dict['experiment_name'] = 'ALLEN_st_cells_1_movies'\n exp_dict['only_process_n'] = None # MICHELE\n exp_dict['randomize_selection'] = True\n exp_dict['reference_image_key'] = {'proc_stimuli': 'image'}\n exp_dict['reference_label_key'] = {'neural_trace_trimmed': 'label'}\n exp_dict['rf_query'] = [{\n 'rf_coordinate_range': { # Get all cells\n 'x_min': 20,\n 'x_max': 30,\n 'y_min': 50,\n 'y_max': 60,\n },\n 'cre_line': 'Cux2',\n 'structure': 'VISp'}]\n exp_dict['cross_ref'] = 'rf_coordinate_range_and_stimuli'\n exp_dict['store_means'] = [\n 'image',\n 'label'\n ]\n exp_dict['deconv_method'] = 'OASIS'\n exp_dict['cv_split'] = {\n 'cv_split_single_stim': {\n 'target': 0,\n 'split': 0.95\n }\n }\n # exp_dict['cv_split'] = {\n # 'split_on_stim': 'natural_movie_two' # Specify train set\n # }\n exp_dict['neural_delay'] = [8, 13] # MS delay * 30fps for neural data\n exp_dict['slice_frames'] = 2 # 4 MICHELE\n exp_dict['st_conv'] = len(\n range(\n exp_dict['neural_delay'][0],\n exp_dict['neural_delay'][1]))\n exp_dict['grid_query'] = False # False = evaluate all neurons at once\n exp_dict['cc_repo_vars'] = {\n 'output_size': [1, 1],\n 'model_im_size': [152, 304, 1],\n 'loss_function': 'l2',\n 'score_metric': 'pearson',\n 'preprocess': 'resize'\n }\n exp_dict['weight_sharing'] = True\n return exp_dict","title":""},{"docid":"065651f7f50dca824f573e8e13fd54aa","score":"0.47043127","text":"def entity_recognition(text: str) -> spacy:\n nlp = spacy.load('en_core_web_sm')\n document = nlp(text)\n return document","title":""},{"docid":"4fddc26dc9e32773a8f058636e527888","score":"0.47029012","text":"def run_examples():\n\n for example in examples:\n\n print(str(example) + \" : \", end=\" \")\n try:\n t, smush = analyse(example, my_env)\n print(lookup(t, smush))\n # print(\"Smush\")\n # for k,v in smush.items():\n # print(f\"\\t{k} : {v}\")\n except (ParseError, InferenceError) as e:\n print(e)","title":""},{"docid":"c17e91b2e31287e2d3f226c28cd07bd0","score":"0.4702484","text":"def learn_visual_words(annots_df, taids, nWords, use_cache=USE_CACHE_WORDS):\n max_iters = 200\n flann_params = {}\n train_vecs_list = [pdh.ensure_values(vecs) for vecs in annots_df['vecs'][taids].values]\n train_vecs = np.vstack(train_vecs_list)\n print('Training %d word vocabulary with %d annots and %d descriptors' %\n (nWords, len(taids), len(train_vecs)))\n kwds = dict(max_iters=max_iters, use_cache=use_cache, appname='smk',\n flann_params=flann_params)\n _words = clustertool.cached_akmeans(train_vecs, nWords, **kwds)\n if WITH_PANDAS:\n # Pandasify\n wx_series = pdh.RangeIndex(len(_words), name='wx')\n #words = pd.DataFrame(_words, index=wx_series, columns=VEC_COLUMNS)\n words = pd.DataFrame(_words, index=wx_series)\n else:\n words = _words\n return words","title":""},{"docid":"2dd48456bcc504de6d6154321d62cd70","score":"0.46943823","text":"def _run_wiki_nogui(self):\n # start wikipedia page download\n self._log_print(msg_WHITE=\"Accessing Wikipedia...\")\n\n # download wikipedia page and track progress\n for message in self._get_preload_progress():\n if \"Searching for\" in message:\n print(f\"Searching for: {GREEN}{self.ALBUM}{RESET} by \"\n f\"{GREEN}{self.ALBUMARTIST}\")\n elif \"Using offline\" in message:\n self._log_print(msg_GREEN=\"Using offline cached page insted \"\n \"of web page\")\n elif \"Found at\" in message:\n self._log_print(msg_GREEN=\"Found at: \", msg_WHITE=self.url)\n else:\n self._log_print(msg_WHITE=message)\n\n # get error messages\n error_msg = self.get_wiki()\n if error_msg:\n self._log_print(msg_GREEN=error_msg)\n return\n\n if not we_are_frozen():\n # basic html textout for debug\n self.basic_out()\n\n # find release date\n self._log_print(msg_GREEN=\"Found release date:\",\n msg_WHITE=self.get_release_date())\n\n # find list of genres\n self._log_print(msg_GREEN=\"Found genre(s)\",\n msg_WHITE=\"\\n\".join(self.get_genres()))\n\n # get and print out page contents\n self._log_print(msg_GREEN=\"Found page contents\",\n msg_WHITE=\"\\n\".join(self.get_contents()))\n\n # extract track list\n self.get_tracks()\n\n # extract personel names\n self._log_print(msg_GREEN=\"Found aditional personel\")\n self.get_personnel()\n if not we_are_frozen():\n print(self.personnel_2_str())\n\n # extract writers, composers\n self._log_print(msg_GREEN=\"Found composers\",\n msg_WHITE=\"\\n\".join(flatten_set(self.get_composers())))\n\n if not we_are_frozen():\n # save to files\n self._log_print(msg_WHITE=\"Writing to disk\")\n self.disk_write()\n\n # print out found tracklist\n self._log_print(msg_GREEN=\"Found Track list(s)\")\n self.print_tracklist()\n\n # select genre\n if not self.GENRE:\n if not self.genres:\n print(CYAN + \"Input genre:\", end=\"\")\n self.genre = input()\n else:\n print(CYAN + \"Specify which genre you want to write: [1.]\")\n for i, gen in enumerate(self.genres, 1):\n print(f\"{i}. {gen}\")\n\n print(\"Input number:\", CYAN, end=\"\")\n index = input()\n try:\n index = int(index) - 1\n except ValueError:\n index = 0\n\n self.GENRE = self.genres[index]\n\n # decide what to do with artists\n print(CYAN + \"Do you want to assign artists to composers? ([y]/n)\",\n RESET, end=\" \")\n if to_bool(input()):\n self.merge_artist_composers()\n\n # decide if you want to find lyrics\n print(CYAN + \"\\nDo you want to find and save lyrics? ([y]/n): \" +\n RESET, end=\"\")\n\n # download lyrics\n self.save_lyrics(to_bool(input()))\n\n print(CYAN + \"Write data to ID3 tags? ([y]/n): \" + RESET, end=\"\")\n if to_bool(input()):\n if not self.write_tags():\n self._log_print(\n msg_WHITE=\"Cannot write tags because there are no \"\n \"coresponding files\")\n else:\n self._log_print(msg_GREEN=\"Done\")","title":""},{"docid":"cfc8b0bedfe5d8532ebec1c74a2dd5c2","score":"0.4692744","text":"def autoAnalyze(self):\n print(\"Perfoming full automatic analysis...\")\n t1=time.perf_counter()\n self.cleanUp()\n self.figure_rois()\n self.figure_roi_inspect_all()\n self.figure_dGoR_roi(showEach=False,saveAs=self.folderSave+\"/avg.png\")\n self.figure_dGoR_roi(showEach=True,saveAs=self.folderSave+\"/each.png\")\n self.index()\n print(\"analysis completed in %.02f sec\"%(time.perf_counter()-t1))","title":""},{"docid":"1073921c3e3389a456bb9b4b6f965094","score":"0.4691585","text":"def interrogator(corpus, \n search, \n query='any',\n show='w',\n exclude=False,\n excludemode='any',\n searchmode='all',\n dep_type='collapsed-ccprocessed-dependencies',\n case_sensitive=False,\n save=False,\n just_speakers=False,\n preserve_case=False,\n lemmatag=False,\n files_as_subcorpora=False,\n only_unique=False,\n random=False,\n only_format_match=False,\n multiprocess=False,\n spelling=False,\n regex_nonword_filter=r'[A-Za-z0-9:_]',\n gramsize=2,\n split_contractions=False,\n conc=False,\n maxconc=9999,\n window=4,\n no_closed=False,\n no_punct=True,\n whitelist=False,\n **kwargs\n ):\n\n # in case old kwarg is used\n conc = kwargs.get('do_concordancing', conc)\n\n # store kwargs and locs\n locs = locals().copy()\n locs.update(kwargs)\n locs.pop('kwargs', None)\n\n if isinstance(search, STRINGTYPE) and len(search) > 3:\n raise ValueError('search argument not recognised.')\n\n import codecs\n import signal\n import os\n from time import localtime, strftime\n from collections import Counter\n\n import corenlp_xml\n import pandas as pd\n from pandas import DataFrame, Series\n\n from corpkit.interrogation import Interrogation, Interrodict\n from corpkit.corpus import Datalist, Corpora, Corpus, File, Subcorpus\n from corpkit.process import (tregex_engine, get_deps, unsplitter, sanitise_dict, \n get_speakername, animator, filtermaker)\n from corpkit.other import as_regex\n from corpkit.dictionaries.word_transforms import wordlist, taglemma\n from corpkit.dictionaries.process_types import Wordlist\n from corpkit.build import check_jdk\n\n import re\n if regex_nonword_filter:\n is_a_word = re.compile(regex_nonword_filter)\n else:\n is_a_word = re.compile(r'.*')\n \n have_java = check_jdk()\n\n def signal_handler(signal, _):\n \"\"\"pause on ctrl+c, rather than just stop loop\"\"\" \n import signal\n import sys\n from time import localtime, strftime\n signal.signal(signal.SIGINT, original_sigint)\n thetime = strftime(\"%H:%M:%S\", localtime())\n INPUTFUNC('\\n\\n%s: Paused. Press any key to resume, or ctrl+c to quit.\\n' % thetime)\n time = strftime(\"%H:%M:%S\", localtime())\n print('%s: Interrogation resumed.\\n' % time)\n signal.signal(signal.SIGINT, signal_handler)\n\n def fix_show(show):\n \"\"\"lowercase anything in show and turn into list\"\"\"\n if isinstance(show, list):\n show = [i.lower() for i in show]\n elif isinstance(show, STRINGTYPE):\n show = show.lower()\n show = [show]\n\n # this little 'n' business is a hack: when ngramming,\n # n shows have their n stripped, so nw should be nw \n # so we know we're ngramming and so it's not empty.\n for index, val in enumerate(show):\n if val == 'n' or val == 'nw':\n show[index] = 'nw'\n elif val == 'b' or val == 'bw':\n show[index] = 'bw'\n elif val.endswith('pl'):\n show[index] = val.replace('pl', 'x')\n else:\n if len(val) == 2 and val.endswith('w'):\n show[index] = val[0]\n return show\n\n def fix_search(search):\n \"\"\"if search has nested dicts, remove them\"\"\"\n ends = ['w', 'l', 'i', 'n', 'f', 'p', 'x', 's']\n if not search:\n return\n if isinstance(search, STRINGTYPE):\n return search\n if search.get('t'):\n return search\n newsearch = {}\n for srch, pat in search.items():\n if len(srch) == 1 and srch in ends:\n srch = 'm%s' % srch\n if isinstance(pat, dict):\n for k, v in list(pat.items()):\n if k != 'w':\n newsearch[srch + k] = pat_format(v)\n else:\n newsearch[srch] = pat_format(v)\n else:\n newsearch[srch] = pat_format(pat)\n return newsearch\n\n def pat_format(pat):\n from corpkit.dictionaries.process_types import Wordlist\n import re\n if pat == 'any':\n return re.compile(r'.*')\n if isinstance(pat, Wordlist):\n pat = list(pat)\n if isinstance(pat, list):\n if all(isinstance(x, int) for x in pat):\n pat = [str(x) for x in pat]\n pat = filtermaker(pat, case_sensitive=case_sensitive, root=kwargs.get('root'))\n else:\n if isinstance(pat, int):\n return pat\n if isinstance(pat, re._pattern_type):\n return pat\n if case_sensitive:\n pat = re.compile(pat)\n else:\n pat = re.compile(pat, re.IGNORECASE)\n return pat\n\n def is_multiquery(corpus, search, query, just_speakers):\n \"\"\"determine if multiprocessing is needed\n do some retyping if need be as well\"\"\"\n is_mul = False\n from collections import OrderedDict\n #if hasattr(corpus, '__iter__'):\n # is_mul = True\n # so we can do search = 't', query = ['NP', 'VP']:\n from corpkit.dictionaries.process_types import Wordlist\n if isinstance(query, Wordlist):\n query = list(query)\n if isinstance(query, list):\n if query != list(search.values())[0] or len(list(search.keys())) > 1:\n query = {c.title(): c for c in query}\n if isinstance(query, (dict, OrderedDict)):\n is_mul = True\n if just_speakers:\n if just_speakers == 'each':\n is_mul = True\n just_speakers = ['each']\n if just_speakers == ['each']:\n is_mul = True\n elif isinstance(just_speakers, STRINGTYPE):\n is_mul = False\n just_speakers = [just_speakers]\n #import re\n #if isinstance(just_speakers, re._pattern_type):\n # is_mul = False\n if isinstance(just_speakers, list):\n if len(just_speakers) > 1:\n is_mul = True\n if isinstance(search, dict):\n if all(isinstance(i, dict) for i in list(search.values())):\n is_mul = True\n return is_mul, corpus, search, query, just_speakers\n\n def slow_tregex(sents, **dummy_args):\n \"\"\"do the speaker-specific version of tregex queries\"\"\"\n speakr = dummy_args.get('speaker', '')\n import os\n from corpkit.process import tregex_engine\n # first, put the relevant trees into temp file\n to_open = '\\n'.join(sent.parse_string.strip() for sent in sents \\\n if sent.parse_string is not None)\n q = list(search.values())[0]\n ops = ['-o', '-%s' % translated_option]\n concs = []\n res = tregex_engine(query=q, \n options=ops, \n corpus=to_open,\n root=root,\n preserve_case=True\n )\n if not no_conc:\n ops += ['-w', '-f']\n whole_res = tregex_engine(query=q, \n options=ops, \n corpus=to_open,\n root=root,\n preserve_case=True\n )\n for line in whole_res:\n line.insert(1, speakr) \n\n res = format_tregex(res)\n whole_res = format_tregex(whole_res, whole=True)\n concs = make_conc_lines_from_whole_mid(whole_res, res)\n\n if root:\n root.update()\n if countmode:\n if isinstance(res, int):\n return res, False\n else:\n return len(res), False\n else:\n return res, concs\n\n def get_stats(sents, **dummy_args):\n \"\"\"get a bunch of frequencies on interpersonal phenomena\"\"\"\n from collections import Counter\n statsmode_results = Counter() \n # first, put the relevant trees into temp file\n\n for sent in sents:\n statsmode_results['Sentences'] += 1\n deps = get_deps(sent, dep_type)\n numpass = len([x for x in deps.links if x.type.endswith('pass')])\n statsmode_results['Passives'] += numpass\n statsmode_results['Tokens'] += len(sent.tokens)\n words = [w.word for w in sent.tokens if w.word is not None and w.word.isalnum()]\n statsmode_results['Words'] += len(words)\n statsmode_results['Characters'] += len(''.join(words))\n\n to_open = '\\n'.join(s.parse_string.strip() for s in sents)\n\n from corpkit.dictionaries.process_types import processes\n from corpkit.other import as_regex\n tregex_qs = {'Imperative': r'ROOT < (/(S|SBAR)/ < (VP !< VBD !< VBG !$ NP !$ SBAR < NP !$-- S !$-- VP !$ VP)) !<< (/\\?/ !< __) !<<- /-R.B-/ !<<, /(?i)^(-l.b-|hi|hey|hello|oh|wow|thank|thankyou|thanks|welcome)$/',\n 'Open interrogative': r'ROOT < SBARQ <<- (/\\?/ !< __)', \n 'Closed interrogative': r'ROOT ( < (SQ < (NP $+ VP)) << (/\\?/ !< __) | < (/(S|SBAR)/ < (VP $+ NP)) <<- (/\\?/ !< __))',\n 'Unmodalised declarative': r'ROOT < (S < (/(NP|SBAR|VP)/ $+ (VP !< MD)))',\n 'Modalised declarative': r'ROOT < (S < (/(NP|SBAR|VP)/ $+ (VP < MD)))',\n 'Open class': r'/^(NN|JJ|VB|RB)/ < __',\n 'Closed class': r'__ !< __ !> /^(NN|JJ|VB|RB)/',\n 'Clauses': r'/^S/ < __',\n 'Interrogative': r'ROOT << (/\\?/ !< __)',\n 'Mental processes': r'VP > /^(S|ROOT)/ <+(VP) (VP <<# /%s/)' % \\\n as_regex(processes.mental, boundaries='w'),\n 'Verbal processes': r'VP > /^(S|ROOT)/ <+(VP) (VP <<# /%s/)' % \\\n as_regex(processes.verbal, boundaries='w'),\n 'Relational processes': r'VP > /^(S|ROOT)/ <+(VP) (VP <<# /%s/)' % \\\n as_regex(processes.relational, boundaries='w'),\n 'Verbless clause': r'/^S/ !<< /^VB.?/'}\n\n for name, q in sorted(tregex_qs.items()):\n res = tregex_engine(query=q, \n options=['-o', '-C'], \n corpus=to_open, \n root=root\n )\n statsmode_results[name] += int(res)\n if root:\n root.update()\n return statsmode_results, []\n\n def make_conc_lines_from_whole_mid(wholes,\n middle_column_result\n ):\n \"\"\"\n Create concordance line output from tregex output\n \"\"\"\n import re\n import os\n if not wholes and not middle_column_result:\n return []\n\n conc_lines = []\n # remove duplicates from results\n unique_wholes = []\n unique_middle_column_result = []\n duplicates = []\n for (f, sk, whole), mid in zip(wholes, middle_column_result):\n joined = '-join-'.join([f, sk, whole, mid])\n if joined not in duplicates:\n duplicates.append(joined)\n unique_wholes.append([f, sk, whole])\n unique_middle_column_result.append(mid)\n\n # split into start, middle and end, dealing with multiple occurrences\n for (f, sk, whole), mid in zip(unique_wholes, unique_middle_column_result):\n reg = re.compile(r'([^a-zA-Z0-9-]|^)(' + re.escape(mid) + r')([^a-zA-Z0-9-]|$)', \\\n re.IGNORECASE | re.UNICODE)\n offsets = [(m.start(), m.end()) for m in re.finditer(reg, whole)]\n for offstart, offend in offsets:\n start, middle, end = whole[0:offstart].strip(), whole[offstart:offend].strip(), \\\n whole[offend:].strip()\n conc_lines.append([os.path.basename(f), sk, start, middle, end])\n return conc_lines\n\n def uniquify(conc_lines):\n \"\"\"get unique concordance lines\"\"\"\n from collections import OrderedDict\n unique_lines = []\n checking = []\n for index, (_, speakr, start, middle, end) in enumerate(conc_lines):\n joined = ' '.join([speakr, start, 'MIDDLEHERE:', middle, ':MIDDLEHERE', end])\n if joined not in checking:\n unique_lines.append(conc_lines[index])\n checking.append(joined)\n return unique_lines\n\n def lemmatiser(list_of_words, tag):\n \"\"\"\n Take a list of unicode words and a tag and return a lemmatised list\n \"\"\"\n output = []\n for word in list_of_words:\n if translated_option.startswith('u'):\n word = taglemma.get(word.lower(), 'Other')\n else:\n word = wordlist.get(word, lmtzr.lemmatize(word, tag))\n if not preserve_case:\n word = word.lower()\n output.append(word)\n return output\n\n def tgrep_searcher(sents, search, show, conc, **kwargs):\n \"\"\"\n Use tgrep for constituency grammar search\n \"\"\"\n f = kwargs.get('filename')\n from corpkit.process import show_tree_as_per_option, tgrep\n out = []\n conc_output = []\n conc_out = []\n for sent in sents:\n sk = get_speakername(sent)\n results = tgrep(sent, search['t'])\n for res in results:\n out.append(show_tree_as_per_option(show, res, sent))\n if conc:\n lin = [f, sk, show_tree_as_per_option(show + ['whole'], res, sent)]\n conc_out.append(lin)\n\n if conc:\n conc_output = make_conc_lines_from_whole_mid(conc_out, out)\n return out, conc_output\n\n def gettag(query, lemmatag=False):\n \"\"\"\n Find tag for WordNet lemmatisation\n \"\"\"\n if lemmatag:\n return lemmatag\n\n tagdict = {'N': 'n',\n 'J': 'a',\n 'V': 'v',\n 'A': 'r',\n 'None': False,\n '': False,\n 'Off': False}\n\n # in case someone compiles the tregex query\n try:\n query = query.pattern\n except AttributeError:\n query = query\n \n\n qr = query.replace(r'\\w', '').replace(r'\\s', '').replace(r'\\b', '')\n firstletter = next((c for c in qr if c.isalpha()), 'n')\n return tagdict.get(firstletter.upper(), 'n')\n\n def format_tregex(results, whole=False):\n \"\"\"format tregex by show list\"\"\"\n import re\n\n if countmode:\n return results\n\n if not results:\n return\n\n done = []\n if whole:\n fnames, snames, results = zip(*results)\n\n if 'l' in show or 'x' in show:\n lemmata = lemmatiser(results, gettag(search.get('t'), lemmatag))\n else:\n lemmata = [None for i in results]\n for word, lemma in zip(results, lemmata):\n bits = []\n if exclude and exclude.get('w'):\n if len(list(exclude.keys())) == 1 or excludemode == 'any':\n if re.search(exclude.get('w'), word):\n continue\n if len(list(exclude.keys())) == 1 or excludemode == 'any':\n if re.search(exclude.get('l'), lemma):\n continue\n if len(list(exclude.keys())) == 1 or excludemode == 'any':\n if re.search(exclude.get('p'), word):\n continue\n if len(list(exclude.keys())) == 1 or excludemode == 'any':\n if re.search(exclude.get('x'), lemma):\n continue\n if exclude and excludemode == 'all':\n num_to_cause_exclude = len(list(exclude.keys()))\n current_num = 0\n if exclude.get('w'):\n if re.search(exclude.get('w'), word):\n current_num += 1\n if exclude.get('l'):\n if re.search(exclude.get('l'), lemma):\n current_num += 1\n if exclude.get('p'):\n if re.search(exclude.get('p'), word):\n current_num += 1\n if exclude.get('x'):\n if re.search(exclude.get('x'), lemma):\n current_num += 1 \n if current_num == num_to_cause_exclude:\n continue \n\n for i in show:\n if i == 't':\n bits.append(word)\n if i == 'l':\n bits.append(lemma)\n elif i == 'w':\n bits.append(word)\n elif i == 'p':\n bits.append(word)\n elif i == 'x':\n bits.append(lemma)\n joined = '/'.join(bits)\n done.append(joined)\n if whole:\n done = zip(fnames, snames, done)\n return done\n\n def tok_by_list(pattern, list_of_toks, concordancing=False, **kwargs):\n \"\"\"search for regex in plaintext corpora\"\"\"\n import re\n if isinstance(pattern, STRINGTYPE):\n pattern = [pattern]\n if not case_sensitive:\n pattern = [p.lower() for p in pattern]\n if not concordancing:\n if case_sensitive:\n matches = [m for m in list_of_toks if m in pattern]\n else:\n matches = [m for m in list_of_toks if m.lower() in pattern]\n else:\n matches = []\n for index, token in enumerate(list_of_toks):\n if token in pattern:\n if not split_contractions:\n match = [' '.join(t for t in unsplitter(list_of_toks[:index]))[-140:]]\n else:\n match = [' '.join(t for t in list_of_toks[:index])[-140:]]\n match.append(token)\n if not split_contractions:\n match.append(' '.join(t for t in unsplitter(list_of_toks[index + 1:]))[:140])\n else:\n match.append(' '.join(t for t in list_of_toks[index + 1:])[:140])\n\n matches.append(match)\n if countmode:\n return len(matches)\n else:\n return matches\n\n def tok_ngrams(pattern, list_of_toks, concordancing=False, split_contractions=True):\n import re\n result = []\n list_of_toks = [x for x in list_of_toks if re.search(regex_nonword_filter, x)]\n\n if not split_contractions:\n list_of_toks = unsplitter(list_of_toks)\n \n for i in range(len(list_of_toks)):\n try:\n the_gram = [list_of_toks[i+x] for x in range(gramsize)]\n if any(re.search(pattern, x) for x in the_gram):\n result.append(' '.join(the_gram))\n except IndexError:\n pass\n\n if countmode:\n return len(result)\n\n else:\n result = [i for i in result if result.count(i) > 1]\n return result\n\n def compiler(pattern):\n \"\"\"compile regex or fail gracefully\"\"\"\n if hasattr(pattern, 'pattern'):\n return pattern\n import re\n try:\n if case_sensitive:\n comped = re.compile(pattern)\n else:\n comped = re.compile(pattern, re.IGNORECASE)\n return comped\n except:\n import traceback\n import sys\n from time import localtime, strftime\n exc_type, exc_value, exc_traceback = sys.exc_info()\n lst = traceback.format_exception(exc_type, exc_value, exc_traceback)\n error_message = lst[-1]\n thetime = strftime(\"%H:%M:%S\", localtime())\n print('%s: Query %s' % (thetime, error_message))\n if root:\n return 'Bad query'\n else:\n raise ValueError('%s: Query %s' % (thetime, error_message))\n\n def tok_by_reg(pattern, list_of_toks, concordancing = False, **kwargs):\n \"\"\"search for regex in plaintext corpora\"\"\"\n import re\n comped = compiler(pattern)\n if comped == 'Bad query':\n return 'Bad query'\n if not concordancing:\n matches = [m for m in list_of_toks if re.search(comped, m)]\n else:\n matches = []\n for index, token in enumerate(list_of_toks):\n if re.search(comped, token):\n if not split_contractions:\n match = [' '.join(t for t in unsplitter(list_of_toks[:index]))[-140:]]\n else:\n match = [' '.join(t for t in list_of_toks[:index])[-140:]]\n match.append(re.search(comped, token).group(0))\n if not split_contractions:\n match.append(' '.join(t for t in unsplitter(list_of_toks[index + 1:]))[:140])\n else:\n match.append(' '.join(t for t in list_of_toks[index + 1:])[:140])\n matches.append(match)\n if countmode:\n return len(matches)\n else:\n return matches\n\n def determine_search_func(show):\n \"\"\"Figure out what search function we're using\"\"\"\n\n simple_tregex_mode = False\n statsmode = False\n tree_to_text = False\n\n if search.get('t') and not just_speakers and not kwargs.get('tgrep'):\n if have_java:\n simple_tregex_mode = True\n searcher = None\n else:\n searcher = tgrep_searcher\n optiontext = 'Searching parse trees'\n else:\n if datatype == 'plaintext':\n if any(i.endswith('n') for i in search.keys()):\n optiontext = 'n-grams via plaintext'\n raise NotImplementedError('Use a tokenised or parsed corpus for n-gramming.')\n #searcher = plaintext_ngram\n elif any(i.endswith('w') for i in search.keys()):\n if kwargs.get('regex', True):\n searcher = plaintext_regex_search\n else:\n searcher = plaintext_simple_search\n optiontext = 'Searching plaintext'\n else:\n raise ValueError(\"Plaintext search must be 'w' or 'n'.\")\n\n elif datatype == 'tokens':\n if any(i.endswith('n') for i in search.keys()):\n searcher = tok_ngrams\n optiontext = 'n-grams via tokens'\n elif any(i.endswith('w') for i in search.keys()):\n if kwargs.get('regex', True):\n searcher = tok_by_reg\n else:\n searcher = tok_by_list\n if isinstance(search.get('w'), (list, Wordlist)):\n searcher = tok_by_list\n optiontext = 'Searching tokens'\n only_parse = ['r', 'd', 'g', 'dl', 'gl', 'df', 'gf',\n 'dp', 'gp', 'f', 'd2', 'd2f', 'd2p', 'd2l']\n \n\n if datatype != 'parse' and any(i in only_parse for i in list(search.keys())):\n form = ', '.join(i for i in list(search.keys()) if i in only_parse)\n raise ValueError('Need parsed corpus to search with \"%s\" option(s).' % form)\n\n elif datatype == 'parse':\n if any(i.endswith('n') for i in search.keys()):\n search['w'] = search.pop('n')\n if not show_ngram:\n show = ['n']\n if any(i.endswith('t') for i in search.keys()):\n if have_java and not kwargs.get('tgrep'):\n searcher = slow_tregex\n else:\n searcher = tgrep_searcher\n optiontext = 'Searching parse trees'\n elif any(i.endswith('s') for i in search.keys()):\n searcher = get_stats\n statsmode = True\n optiontext = 'General statistics'\n elif any(i.endswith('r') for i in search.keys()):\n from corpkit.depsearch import dep_searcher\n searcher = dep_searcher\n optiontext = 'Distance from root'\n else:\n from corpkit.depsearch import dep_searcher\n searcher = dep_searcher\n optiontext = 'Dependency querying'\n \n # ngram mode for parsed data\n if show_ngram:\n optiontext = 'N-grams from parsed data'\n searcher = dep_searcher\n\n return searcher, optiontext, simple_tregex_mode, statsmode, tree_to_text\n\n def get_tregex_values():\n \"\"\"If using Tregex, set appropriate values\n\n - Check for valid query\n - Make 'any' query\n - Make list query\n \"\"\"\n\n translated_option = 't'\n if isinstance(search['t'], Wordlist):\n search['t'] = list(search['t'])\n q = tregex_engine(corpus=False,\n query=search.get('t'),\n options=['-t'],\n check_query=True,\n root=root,\n preserve_case=preserve_case\n )\n if q is False:\n if root:\n return 'Bad query', None\n else:\n return 'Bad query', None\n\n if isinstance(search['t'], list):\n regex = as_regex(search['t'], boundaries='line', case_sensitive=case_sensitive)\n else:\n regex = ''\n\n # listquery, anyquery, translated_option\n treg_dict = {'p': [r'__ < (/%s/ !< __)' % regex, r'__ < (/.?[A-Za-z0-9].?/ !< __)', 'u'],\n 'pl': [r'__ < (/%s/ !< __)' % regex, r'__ < (/.?[A-Za-z0-9].?/ !< __)', 'u'],\n 'x': [r'__ < (/%s/ !< __)' % regex, r'__ < (/.?[A-Za-z0-9].?/ !< __)', 'u'],\n 't': [r'__ < (/%s/ !< __)' % regex, r'__ < (/.?[A-Za-z0-9].?/ !< __)', 'o'],\n 'w': [r'/%s/ !< __' % regex, r'/.?[A-Za-z0-9].?/ !< __', 't'],\n 'c': [r'/%s/ !< __' % regex, r'/.?[A-Za-z0-9].?/ !< __', 'C'],\n 'l': [r'/%s/ !< __' % regex, r'/.?[A-Za-z0-9].?/ !< __', 't']\n }\n\n listq, anyq, translated_option = treg_dict.get(show[0].lower())\n if isinstance(search['t'], list):\n search['t'] = listq\n elif search['t'] == 'any': \n search['t'] = anyq\n return search['t'], translated_option\n\n def plaintext_regex_search(pattern, plaintext_data, concordancing=False, **kwargs):\n \"\"\"search for regex in plaintext corpora\n\n it searches over lines, so the user needs to be careful.\n \"\"\"\n import re\n if concordancing:\n pattern = r'(.{,140})\\b(' + pattern + r')\\b(.{,140})'\n compiled_pattern = compiler(pattern)\n if compiled_pattern == 'Bad query':\n return 'Bad query'\n matches = re.findall(compiled_pattern, plaintext_data)\n if concordancing:\n matches = [list(m) for m in matches]\n if not concordancing:\n for index, i in enumerate(matches):\n if isinstance(i, tuple):\n matches[index] = i[0]\n if countmode:\n return len(matches)\n else:\n return matches\n\n def correct_spelling(a_string):\n \"\"\"correct spelling within a string\"\"\"\n if not spelling:\n return a_string\n from corpkit.dictionaries.word_transforms import usa_convert\n if spelling.lower() == 'uk':\n usa_convert = {v: k for k, v in list(usa_convert.items())}\n bits = a_string.split('/')\n for index, i in enumerate(bits):\n converted = usa_convert.get(i.lower(), i)\n if i.islower() or preserve_case is False:\n converted = converted.lower()\n elif i.isupper() and preserve_case:\n converted = converted.upper()\n elif i.istitle() and preserve_case:\n converted = converted.title()\n bits[index] = converted\n r = '/'.join(bits)\n return r\n\n def plaintext_simple_search(pattern, plaintext_data, concordancing=False, **kwargs):\n \"\"\"search for tokens in plaintext corpora\"\"\"\n import re\n result = []\n if isinstance(pattern, STRINGTYPE):\n pattern = [pattern]\n for p in pattern:\n if concordancing:\n pat = r'(.{0,140})\\b(' + re.escape(p) + r')\\b(.{0,140})'\n pat = compiler(pat)\n if pat == 'Bad query':\n return 'Bad query'\n matches = re.findall(pat, plaintext_data)\n if concordancing:\n matches = [list(m) for m in matches]\n for i in matches:\n result.append(i)\n else: \n for m in range(len(matches)):\n result.append(p)\n return result\n\n def make_search_iterable(corpus):\n \"\"\"determine how to structure the corpus for interrogation\"\"\"\n \n # skip file definitions if they are not needed\n if simple_tregex_mode:\n if corpus.level in ['s', 'f']:\n return {(corpus.name, corpus.path): False}\n else:\n return {(os.path.basename(i), os.path.join(corpus.path, i)): False\n for i in os.listdir(corpus.path)\n if os.path.isdir(os.path.join(corpus.path, i))}\n\n if isinstance(corpus, Datalist):\n to_iterate_over = {}\n # it could be files or subcorpus objects\n if corpus[0].level == 's':\n if files_as_subcorpora:\n for subc in corpus:\n for f in subc.files:\n to_iterate_over[(f.name, f.path)] = [f]\n else:\n for subc in corpus:\n to_iterate_over[(subc.name, subc.path)] = subc.files\n elif corpus[0].level == 'f':\n for f in corpus:\n to_iterate_over[(f.name, f.path)] = [f]\n elif corpus.singlefile:\n to_iterate_over = {(corpus.name, corpus.path): [corpus]}\n elif not hasattr(corpus, 'subcorpora') or not corpus.subcorpora:\n # just files in a directory\n if files_as_subcorpora:\n to_iterate_over = {}\n for f in corpus.files:\n to_iterate_over[(f.name, f.path)] = [f]\n else:\n to_iterate_over = {(corpus.name, corpus.path): corpus.files}\n else:\n to_iterate_over = {}\n if files_as_subcorpora:\n # don't know if possible: has subcorpora but also .files\n if hasattr(corpus, 'files') and corpus.files is not None:\n for f in corpus.files:\n to_iterate_over[(f.name, f.path)] = [f]\n # has subcorpora with files in those\n elif hasattr(corpus, 'files') and corpus.files is None:\n for subc in corpus.subcorpora:\n for f in subc.files:\n to_iterate_over[(f.name, f.path)] = [f]\n else:\n if corpus[0].level == 's':\n for subcorpus in corpus:\n to_iterate_over[(subcorpus.name, subcorpus.path)] = subcorpus.files\n elif corpus[0].level == 'f':\n for f in corpus:\n to_iterate_over[(f.name, f.path)] = [f]\n else:\n for subcorpus in corpus.subcorpora:\n to_iterate_over[(subcorpus.name, subcorpus.path)] = subcorpus.files\n return to_iterate_over\n\n def welcome_printer(return_it=False):\n \"\"\"Print welcome message\"\"\"\n if no_conc:\n message = 'Interrogating'\n else:\n message = 'Interrogating and concordancing'\n if kwargs.get('printstatus', True):\n thetime = strftime(\"%H:%M:%S\", localtime())\n from corpkit.constants import transshow, transobjs\n sformat = '\\n'\n for k, v in search.items():\n if k == 't':\n dratt = ''\n else:\n dratt = transshow.get(k[-1], k[-1])\n drole = transobjs.get(k[0], k[0])\n if k == 't':\n drole = 'Trees'\n vform = getattr(v, 'pattern', v)\n sformat += ' %s %s: %s\\n' % (drole, dratt.lower(), vform)\n if search.get('s'):\n sformat = 'Features'\n welcome = ('\\n%s: %s %s ...\\n %s\\n ' \\\n 'Query: %s\\n %s corpus ... \\n' % \\\n (thetime, message, cname, optiontext, sformat, message))\n if return_it:\n return welcome\n else:\n print(welcome)\n\n def goodbye_printer(return_it=False, only_conc=False):\n \"\"\"Say goodbye before exiting\"\"\"\n if not kwargs.get('printstatus', True):\n return\n thetime = strftime(\"%H:%M:%S\", localtime())\n if only_conc:\n \n show_me = (thetime, len(conc_df))\n finalstring = '\\n\\n%s: Concordancing finished! %d results.' % show_me\n else:\n finalstring = '\\n\\n%s: Interrogation finished!' % thetime\n if countmode:\n finalstring += ' %d matches.' % tot\n else:\n dat = (numentries, total_total)\n finalstring += ' %d unique results, %d total occurrences.' % dat\n if return_it:\n return finalstring\n else:\n print(finalstring)\n\n\n def make_conc_obj_from_conclines(conc_results):\n \"\"\"\n Turn conclines into DataFrame\n \"\"\"\n from corpkit.interrogation import Concordance\n all_conc_lines = []\n for sc_name, resu in sorted(conc_results.items()):\n if only_unique:\n unique_results = uniquify(resu)\n else:\n unique_results = resu\n #make into series\n if PYTHON_VERSION == 2:\n pindex = 'c f s l m r'.encode('utf-8').split()\n else:\n pindex = 'c f s l m r'.split()\n for fname, spkr, start, word, end in unique_results:\n #spkr = str(spkr, errors = 'ignore')\n fname = os.path.basename(fname)\n ser = [sc_name, fname, spkr, start, word, end]\n all_conc_lines.append(Series(ser, index=pindex))\n\n if random:\n from random import shuffle\n shuffle(all_conc_lines)\n\n try:\n conc_df = pd.concat(all_conc_lines, axis=1).T\n if all(x == '' for x in list(conc_df['s'].values)):\n conc_df.drop('s', axis=1, inplace=True)\n \n if show_ngram or show_collocates:\n if not language_model:\n counted = Counter(conc_df['m'])\n indices = [l for l in list(conc_df.index) if counted[conc_df.ix[l]['m']] > 1] \n conc_df = conc_df.ix[indices]\n conc_df = conc_df.reset_index(drop=True)\n\n locs['corpus'] = corpus.name\n conc_df = Concordance(conc_df)\n try:\n conc_df.query = locs\n except AttributeError:\n pass\n return conc_df\n\n except ValueError:\n return\n\n def make_progress_bar():\n \"\"\"generate a progress bar\"\"\"\n\n if simple_tregex_mode:\n total_files = len(list(to_iterate_over.keys()))\n else:\n total_files = sum(len(x) for x in list(to_iterate_over.values()))\n\n par_args = {'printstatus': kwargs.get('printstatus', True),\n 'root': root, \n 'note': note,\n 'length': total_files,\n 'startnum': kwargs.get('startnum'),\n 'denom': kwargs.get('denominator', 1)}\n\n term = None\n if kwargs.get('paralleling', None) is not None:\n from blessings import Terminal\n term = Terminal()\n par_args['terminal'] = term\n par_args['linenum'] = kwargs.get('paralleling')\n\n if in_notebook:\n par_args['welcome_message'] = welcome_message\n\n outn = kwargs.get('outname', '')\n if outn:\n outn = outn + ': '\n\n tstr = '%s%d/%d' % (outn, current_iter, total_files)\n p = animator(None, None, init=True, tot_string=tstr, **par_args)\n tstr = '%s%d/%d' % (outn, current_iter + 1, total_files)\n animator(p, current_iter, tstr, **par_args)\n return p, outn, total_files, par_args\n\n # find out if using gui\n root = kwargs.get('root')\n note = kwargs.get('note')\n language_model = kwargs.get('language_model')\n\n # set up pause method\n original_sigint = signal.getsignal(signal.SIGINT)\n if kwargs.get('paralleling', None) is None:\n original_sigint = signal.getsignal(signal.SIGINT)\n signal.signal(signal.SIGINT, signal_handler)\n\n # find out about concordancing\n only_conc = False\n no_conc = False\n if conc is False:\n no_conc = True\n if isinstance(conc, str) and conc.lower() == 'only':\n only_conc = True\n no_conc = False\n numconc = 0\n\n # wipe non essential class attributes to not bloat query attrib\n if isinstance(corpus, Corpus):\n import copy\n corpus = copy.copy(corpus)\n for k, v in corpus.__dict__.items():\n if isinstance(v, (Interrogation, Interrodict)):\n corpus.__dict__.pop(k, None)\n\n # convert path to corpus object\n if not isinstance(corpus, (Corpus, Corpora, Subcorpus, File, Datalist)):\n if not multiprocess and not kwargs.get('outname'):\n corpus = Corpus(corpus, print_info=False)\n\n # figure out how the user has entered the query and show, and normalise\n from corpkit.process import searchfixer\n search = searchfixer(search, query)\n show = fix_show(show)\n \n show_ngram = any(x.startswith('n') for x in show)\n show_collocates = any(x.startswith('b') for x in show)\n\n # instantiate lemmatiser if need be\n if 'l' in show and isinstance(search, dict) and search.get('t'):\n from nltk.stem.wordnet import WordNetLemmatizer\n lmtzr = WordNetLemmatizer()\n\n # do multiprocessing if need be\n im, corpus, search, query, just_speakers = is_multiquery(corpus, search, query, just_speakers)\n\n # figure out if we can multiprocess the corpus\n if hasattr(corpus, '__iter__') and im:\n corpus = Corpus(corpus)\n if hasattr(corpus, '__iter__') and not im:\n im = True\n if isinstance(corpus, Corpora):\n im = True\n\n # split corpus if the user wants multiprocessing but no other iterable\n if not im and multiprocess:\n im = True\n corpus = corpus[:]\n\n search = fix_search(search)\n exclude = fix_search(exclude)\n\n # if it's already been through pmultiquery, don't do it again\n locs['search'] = search\n locs['query'] = query\n locs['just_speakers'] = just_speakers\n locs['corpus'] = corpus\n locs['multiprocess'] = multiprocess\n locs['print_info'] = kwargs.get('printstatus', True)\n\n # send to multiprocess function\n if im:\n signal.signal(signal.SIGINT, original_sigint)\n from corpkit.multiprocess import pmultiquery\n return pmultiquery(**locs)\n\n # get corpus metadata\n cname = corpus.name\n if isinstance(save, STRINGTYPE):\n savename = corpus.name + '-' + save\n if save is True:\n raise ValueError('save must be str, not bool.')\n\n\n datatype = getattr(corpus, 'datatype', 'parse')\n singlefile = getattr(corpus, 'singlefile', False)\n level = getattr(corpus, 'level', 'c')\n \n # store all results in here\n results = {}\n count_results = {}\n conc_results = {}\n\n # check if just counting, turn off conc if so\n countmode = 'c' in show\n if countmode:\n no_conc = True\n only_conc = False\n # where we are at in interrogation\n current_iter = 0\n\n # multiprocessing progress bar\n denom = kwargs.get('denominator', 1)\n startnum = kwargs.get('startnum', 0)\n\n # Determine the search function to be used #\n searcher, optiontext, simple_tregex_mode, statsmode, tree_to_text = determine_search_func(show)\n \n # no conc for statsmode\n if statsmode:\n no_conc = True\n only_conc = False\n conc = False\n\n # Set some Tregex-related values\n if search.get('t'):\n if show_ngram:\n raise ValueError(\"Can't search trees for n-grams---use a dependency search.\")\n query, translated_option = get_tregex_values()\n if query == 'Bad query' and translated_option is None:\n if root:\n return 'Bad query'\n else:\n return\n # more tregex options\n if tree_to_text:\n treg_q = r'ROOT << __'\n op = ['-o', '-t', '-w']\n elif simple_tregex_mode:\n treg_q = search['t']\n op = ['-o', '-' + translated_option]\n\n # make iterable object for corpus interrogation\n to_iterate_over = make_search_iterable(corpus)\n\n from traitlets import TraitError\n try:\n from ipywidgets import IntProgress\n\n _ = IntProgress(min=0, max=10, value=1)\n in_notebook = True\n except TraitError:\n in_notebook = False\n except ImportError:\n in_notebook = False\n\n # print welcome message\n welcome_message = welcome_printer(return_it=in_notebook)\n\n # create a progress bar\n p, outn, total_files, par_args = make_progress_bar()\n\n # Iterate over data, doing interrogations\n for (subcorpus_name, subcorpus_path), files in sorted(to_iterate_over.items()):\n\n # results for subcorpus go here\n conc_results[subcorpus_name] = []\n count_results[subcorpus_name] = []\n results[subcorpus_name] = Counter()\n\n # get either everything (tree_to_text) or the search['t'] query\n if tree_to_text or simple_tregex_mode:\n result = tregex_engine(query=treg_q,\n options=op,\n corpus=subcorpus_path,\n root=root,\n preserve_case=preserve_case\n )\n\n # format search results with slashes etc\n if not countmode and not tree_to_text:\n result = format_tregex(result)\n\n # if concordancing, do the query again with 'whole' sent and fname\n if not no_conc:\n ops = ['-w', '-f'] + op\n whole_result = tregex_engine(query=search['t'],\n options=ops,\n corpus=subcorpus_path,\n root=root,\n preserve_case=preserve_case\n )\n for line in whole_result:\n line.insert(1, '') \n\n # format match too depending on option\n if not only_format_match:\n whole_result = format_tregex(whole_result, whole=True)\n\n # make conc lines from conc results\n conc_result = make_conc_lines_from_whole_mid(whole_result, result)\n for lin in conc_result:\n if numconc < maxconc or not maxconc:\n conc_results[subcorpus_name].append(lin)\n numconc += 1\n\n # add matches to ongoing counts\n if countmode:\n count_results[subcorpus_name] += [result] \n else:\n result = Counter(result)\n results[subcorpus_name] += result\n\n # update progress bar\n current_iter += 1\n tstr = '%s%d/%d' % (outn, current_iter + 1, total_files)\n animator(p, current_iter, tstr, **par_args)\n\n # dependencies, plaintext, tokens, slow_tregex and tree_to_text\n if not simple_tregex_mode:\n for f in files:\n slow_treg_speaker_guess = kwargs.get('outname', False)\n if datatype == 'parse' and not tree_to_text:\n # right now, this is not using the File class's read() or document\n # methods. the reason is that there seem to be memory leaks. these\n # may have been fixed already though.\n try:\n from corenlp_xml import Document\n except ImportError:\n from corenlp_xml.document import Document\n with codecs.open(f.path, 'rb') as fo:\n data = fo.read()\n corenlp_xml = Document(data)\n #corenlp_xml = f.document\n if just_speakers:\n import re\n if isinstance(just_speakers, re._pattern_type):\n sents = [s for s in corenlp_xml.sentences if \\\n re.search(just_speakers, get_speakername(s))]\n else:\n sents = [s for s in corenlp_xml.sentences if get_speakername(s) in just_speakers]\n if len(just_speakers) == 1:\n slow_treg_speaker_guess = just_speakers[0]\n else:\n sents = corenlp_xml.sentences\n\n # get coreferences\n if kwargs.get('coref') or any(x.startswith('h') for x in show):\n if just_speakers:\n corefs = [i for i in corenlp_xml.coreferences if any(x == i.sentence for x in sents)]\n else:\n corefs = corenlp_xml.coreferences\n else:\n corefs = []\n \n corenlp_xml = None\n\n res, conc_res = searcher(sents, search=search, show=show,\n dep_type=dep_type,\n exclude=exclude,\n excludemode=excludemode,\n searchmode=searchmode,\n case_sensitive=case_sensitive,\n conc=conc,\n only_format_match=only_format_match,\n speaker=slow_treg_speaker_guess,\n gramsize=gramsize,\n no_punct=no_punct,\n no_closed=no_closed,\n whitelist=whitelist,\n split_contractions=split_contractions,\n window=window,\n filename=f.name,\n language_model=language_model,\n corefs=corefs,\n is_a_word=is_a_word,\n **kwargs\n )\n \n if res == 'Bad query':\n return 'Bad query'\n\n if datatype == 'tokens':\n import pickle\n with codecs.open(f.path, \"rb\") as fo:\n data = pickle.load(fo)\n elif datatype == 'plaintext' or tree_to_text:\n if tree_to_text:\n data = '\\n'.join(result)\n if not split_contractions:\n data = unsplitter(data)\n else:\n with codecs.open(f.path, 'rb', encoding='utf-8') as data:\n data = data.read()\n\n if datatype == 'tokens' or datatype == 'plaintext':\n\n query = list(search.values())[0]\n\n if not only_conc:\n res = searcher(query,\n data,\n split_contractions=split_contractions, \n concordancing=False\n )\n if res == 'Bad query':\n if root:\n return 'Bad query'\n if not no_conc:\n conc_res = searcher(query,\n data,\n split_contractions=split_contractions, \n concordancing=True\n )\n if conc_res == 'Bad query':\n if root:\n return 'Bad query'\n for line in conc_res:\n line.insert(0, '')\n\n if countmode:\n count_results[subcorpus_name] += [res]\n\n else:\n # add filename and do lowercasing for conc\n if not no_conc:\n for line in conc_res:\n if searcher != slow_tregex and searcher != tgrep_searcher:\n line.insert(0, f.name)\n else:\n line[0] = f.name\n if not preserve_case:\n line[3:] = [x.lower() for x in line[3:]]\n if spelling:\n line = [correct_spelling(b) for b in line]\n if numconc < maxconc or not maxconc:\n conc_results[subcorpus_name].append(line)\n numconc += 1\n\n # do lowercasing and spelling\n if not only_conc:\n if not preserve_case:\n if not statsmode:\n res = [i.lower() for i in res]\n\n if spelling:\n if not statsmode:\n res = [correct_spelling(r) for r in res]\n #if not statsmode:\n results[subcorpus_name] += Counter(res)\n #else:\n #results[subcorpus_name] += res\n\n # update progress bar\n current_iter += 1\n tstr = '%s%d/%d' % (outn, current_iter + 1, total_files)\n animator(p, current_iter, tstr, **par_args)\n\n # Get concordances into DataFrame, return if just conc\n if not no_conc:\n # fail on this line with typeerror if no results?\n conc_df = make_conc_obj_from_conclines(conc_results)\n\n if only_conc:\n locs = sanitise_dict(locs)\n try:\n conc_df.query = locs\n except AttributeError:\n return conc_df\n if save and not kwargs.get('outname'):\n print('\\n')\n conc_df.save(savename)\n goodbye_printer(only_conc=True)\n signal.signal(signal.SIGINT, original_sigint) \n return conc_df\n else:\n conc_df = None\n\n # Get interrogation into DataFrame\n if countmode:\n df = Series({k: sum(v) for k, v in sorted(count_results.items())})\n tot = df.sum()\n else:\n the_big_dict = {}\n unique_results = set(item for sublist in list(results.values()) for item in sublist)\n sortres = sorted(results.items(), key=lambda x: x[0])\n for word in unique_results:\n the_big_dict[word] = [subcorp_result[word] for _, subcorp_result in sortres]\n # turn master dict into dataframe, sorted\n df = DataFrame(the_big_dict, index=sorted(results.keys()))\n\n # for ngrams, remove hapaxes\n if show_ngram or show_collocates:\n if not language_model:\n df = df[[i for i in list(df.columns) if df[i].sum() > 1]]\n\n numentries = len(df.columns)\n tot = df.sum(axis=1)\n total_total = df.sum().sum()\n\n # turn df into series if all conditions met\n if not countmode:\n if level == 's' or singlefile:\n if not files_as_subcorpora:\n if not kwargs.get('df1_always_df'):\n df = Series(df.ix[0])\n df.sort_values(ascending=False, inplace=True)\n tot = df.sum()\n numentries = len(df.index)\n total_total = tot\n\n # turn data into DF for GUI if need be\n if isinstance(df, Series) and kwargs.get('df1_always_df'):\n total_total = df.sum()\n df = DataFrame(df)\n tot = Series(total_total, index=['Total'])\n\n # if we're doing files as subcorpora, we can remove the .txt.xml etc\n if isinstance(df, DataFrame) and files_as_subcorpora:\n cname = corpus.name.replace('-stripped', '').replace('-parsed', '')\n edits = [(r'(-[0-9][0-9][0-9])?\\.txt\\.xml', ''),\n (r'-%s(-stripped)?(-parsed)?' % cname, '')]\n from corpkit.editor import editor\n df = editor(df, replace_subcorpus_names=edits).results\n tot = df.sum(axis=1)\n total_total = df.sum().sum()\n\n # sort by total\n if isinstance(df, DataFrame):\n if not df.empty: \n df = df[list(df.sum().sort_values(ascending=False).index)]\n\n # make interrogation object\n locs['corpus'] = corpus.path\n locs = sanitise_dict(locs)\n interro = Interrogation(results=df, totals=tot, query=locs, concordance=conc_df)\n\n # save it\n if save and not kwargs.get('outname'):\n print('\\n')\n interro.save(savename)\n \n goodbye = goodbye_printer(return_it=in_notebook)\n if in_notebook:\n try:\n p.children[2].value = goodbye.replace('\\n', '')\n except AttributeError:\n pass\n signal.signal(signal.SIGINT, original_sigint)\n return interro","title":""},{"docid":"ca14bc835498c6cfdc4f79acaa23d9d3","score":"0.46904004","text":"def collecte_docs(self, chercheur, overwrite=False): # self,\n init = overwrite # If True, data persistence is lost when references are updated\n docs = hal.find_publications(chercheur[\"halId_s\"], \"authIdHal_s\")\n\n progress_recorder = ProgressRecorder(self)\n progress_recorder.set_progress(0, len(docs), description=\"récupération des données HAL\")\n # Insert documents collection\n for num, doc in enumerate(docs):\n doc[\"country_colaboration\"] = location_docs.generate_countrys_fields(doc)\n doc = doi_enrichissement.docs_enrichissement_doi(doc)\n if \"fr_abstract_s\" in doc.keys():\n if isinstance(doc[\"fr_abstract_s\"], list):\n doc[\"fr_abstract_s\"] = \"/n\".join(doc[\"fr_abstract_s\"])\n if len(doc[\"fr_abstract_s\"]) > 100:\n doc[\"fr_entites\"] = keyword_enrichissement.return_entities(\n doc[\"fr_abstract_s\"], \"fr\"\n )\n doc[\"fr_teeft_keywords\"] = keyword_enrichissement.keyword_from_teeft(\n doc[\"fr_abstract_s\"], \"fr\"\n )\n if \"en_abstract_s\" in doc.keys():\n if isinstance(doc[\"en_abstract_s\"], list):\n doc[\"en_abstract_s\"] = \"/n\".join(doc[\"en_abstract_s\"])\n if len(doc[\"en_abstract_s\"]) > 100:\n doc[\"en_entites\"] = keyword_enrichissement.return_entities(\n doc[\"en_abstract_s\"], \"en\"\n )\n doc[\"en_teeft_keywords\"] = keyword_enrichissement.keyword_from_teeft(\n doc[\"en_abstract_s\"], \"en\"\n )\n\n doc[\"_id\"] = doc[\"docid\"]\n doc[\"validated\"] = True\n\n doc[\"harvested_from\"] = \"researcher\"\n\n doc[\"harvested_from_ids\"] = []\n doc[\"harvested_from_label\"] = []\n\n #\n #\n # print(doc[\"authorship\"], doc ['authLastName_s'])\n\n if len(doc[\"authIdHal_s\"]) != len(doc[\"authLastName_s\"]):\n # print (\"elastichal.py : test d'autorat no good\")\n # test sur le nom complet...\n nom = [\n truc\n for truc in doc[\"authLastName_s\"]\n if chercheur[\"lastName\"].lower() in truc.lower()\n ] # pour les récemment mariés qui auraient un nom composé...\n # Après si 'lun des co-auteur porte le même nom...\n if len(nom) > 0:\n nom = nom[0].title()\n try:\n if doc[\"authLastName_s\"].index(nom) == 0: # premier\n doc[\"authorship\"] = [\n {\"authorship\": \"firstAuthor\", \"authIdHal_s\": chercheur[\"halId_s\"]}\n ]\n elif (\n doc[\"authLastName_s\"].index(nom) == len(doc[\"authLastName_s\"]) - 1\n ): # dernier\n doc[\"authorship\"] = [\n {\"authorship\": \"lastAuthor\", \"authIdHal_s\": chercheur[\"halId_s\"]}\n ]\n except ValueError:\n doc[\"authorship\"] = []\n else:\n doc[\"authorship\"] = []\n elif chercheur[\"halId_s\"] in doc[\"authIdHal_s\"]:\n if doc[\"authIdHal_s\"].index(chercheur[\"halId_s\"]) == 0:\n doc[\"authorship\"] = [\n {\"authorship\": \"firstAuthor\", \"authIdHal_s\": chercheur[\"halId_s\"]}\n ]\n elif (\n doc[\"authIdHal_s\"].index(chercheur[\"halId_s\"]) == len(doc[\"authIdHal_s\"]) - 1\n ): # dernier\n doc[\"authorship\"] = [\n {\"authorship\": \"lastAuthor\", \"authIdHal_s\": chercheur[\"halId_s\"]}\n ]\n else:\n doc[\"authorship\"] = []\n else:\n doc[\"authorship\"] = []\n\n doc[\"harvested_from_ids\"].append(chercheur[\"halId_s\"])\n\n # historique d'appartenance du docId\n # pour attribuer les bons docs aux chercheurs\n # harvet_history.append({'docid': doc['docid'], 'from': row['halId_s']})\n #\n # for h in harvet_history:\n # if h['docid'] == doc['docid']:\n # if h['from'] not in doc[\"harvested_from_ids\"]:\n # doc[\"harvested_from_ids\"].append(h['from'])\n\n doc[\"records\"] = []\n\n doc[\"MDS\"] = utils.calculate_mds(doc)\n\n try:\n should_be_open = utils.should_be_open(doc)\n if should_be_open == 1:\n doc[\"should_be_open\"] = True\n if should_be_open == -1:\n doc[\"should_be_open\"] = False\n\n if should_be_open == 1 or should_be_open == 2:\n doc[\"isOaExtra\"] = True\n elif should_be_open == -1:\n doc[\"isOaExtra\"] = False\n except IndexError:\n print(\"publicationDate_tdate error ?\")\n doc[\"Created\"] = datetime.datetime.now().isoformat()\n\n if not init: # récupération de l'existant pour ne pas écraser\n field = \"_id\"\n doc_param = esActions.scope_p(field, doc[\"_id\"])\n\n if not es.indices.exists(\n index=chercheur[\"structSirene\"]\n + \"-\"\n + chercheur[\"labHalId\"]\n + \"-researchers-\"\n + chercheur[\"ldapId\"]\n + \"-documents\"\n ): # -researchers\" + row[\"ldapId\"] + \"-documents\n print(\"exception \", chercheur[\"labHalId\"], chercheur[\"ldapId\"])\n\n res = es.search(\n index=chercheur[\"structSirene\"]\n + \"-\"\n + chercheur[\"labHalId\"]\n + \"-researchers-\"\n + chercheur[\"ldapId\"]\n + \"-documents\",\n body=doc_param,\n ) # -researchers\" + row[\"ldapId\"] + \"-documents\n\n if len(res[\"hits\"][\"hits\"]) > 0:\n doc[\"validated\"] = res[\"hits\"][\"hits\"][0][\"_source\"][\"validated\"]\n if \"authorship\" in res[\"hits\"][\"hits\"][0][\"_source\"]:\n doc[\"authorship\"] = res[\"hits\"][\"hits\"][0][\"_source\"][\"authorship\"]\n\n if (\n res[\"hits\"][\"hits\"][0][\"_source\"][\"modifiedDate_tdate\"]\n != doc[\"modifiedDate_tdate\"]\n ):\n doc[\"records\"].append(\n {\n \"beforeModifiedDate_tdate\": doc[\"modifiedDate_tdate\"],\n \"MDS\": res[\"hits\"][\"hits\"][0][\"_source\"][\"MDS\"],\n }\n )\n\n else:\n doc[\"validated\"] = True\n progress_recorder.set_progress(num, len(docs), description=\"(récolte)\")\n progress_recorder.set_progress(num, len(docs), description=\"(indexation)\")\n helpers.bulk(\n es,\n docs,\n index=chercheur[\"structSirene\"]\n + \"-\"\n + chercheur[\"labHalId\"]\n + \"-researchers-\"\n + chercheur[\"ldapId\"]\n + \"-documents\",\n refresh=\"wait_for\",\n )\n\n return chercheur # au cas où","title":""},{"docid":"552d8019a0ca35d82249c0f2c30e02ce","score":"0.46897706","text":"def interrogator(path,\n search,\n query = 'any', \n show = 'words',\n exclude = False,\n case_sensitive = False,\n lemmatise = False, \n titlefilter = False, \n lemmatag = False, \n spelling = False, \n phrases = False, \n dep_type = 'collapsed-ccprocessed-dependencies',\n quicksave = False,\n printstatus = True,\n root = False,\n df1_always_df = False,\n just_speakers = False,\n excludemode = 'any',\n searchmode = 'all',\n **kwargs):\n import corpkit\n from corpkit.other import add_corpkit_to_path\n from corpkit.other import tregex_engine\n from corpkit.other import add_nltk_data_to_nltk_path\n \n # some non-Python resources need to explicitly be added to path\n add_corpkit_to_path()\n\n import os\n import re\n import signal\n import gc\n\n import collections\n import warnings\n import nltk\n import numpy\n\n import pandas as pd\n from collections import Counter\n from time import localtime, strftime\n from pandas import DataFrame, Series\n\n from corpkit.tests import check_pytex, check_t_kinter\n from corpkit.textprogressbar import TextProgressBar\n\n import dictionaries\n from dictionaries.word_transforms import (wordlist, \n usa_convert, \n taglemma)\n\n # nltk data path for tokeniser/lemmatiser\n if 'nltk_data_path' in kwargs.keys():\n if kwargs['nltk_data_path'] not in nltk.data.path:\n nltk.data.path.append(kwargs['nltk_data_path'])\n locdir = '/Users/daniel/work/corpkit/nltk_data'\n if locdir not in nltk.data.path:\n nltk.data.path.append(locdir)\n\n # prefer ipython to python if the user has it\n try:\n from IPython.display import display, clear_output\n except ImportError:\n pass\n \n # check for gui, pythontex\n tk = check_t_kinter()\n have_python_tex = check_pytex()\n\n # multiprocessing progress bar\n if 'denominator' in kwargs.keys():\n denom = kwargs['denominator']\n else:\n denom = 1\n if 'startnum' in kwargs.keys():\n startnum = kwargs['startnum']\n else:\n startnum = 0\n\n # determine if multiquery\n is_multiquery = False\n if hasattr(path, '__iter__'):\n is_multiquery = True\n if 'postounts' in path[0]:\n spelling = 'UK'\n if type(query) == dict or type(query) == collections.OrderedDict:\n is_multiquery = True\n if just_speakers:\n if just_speakers == 'each':\n is_multiquery = True\n if type(just_speakers) == str:\n if just_speakers != 'each':\n just_speakers = [just_speakers]\n if type(just_speakers) == list:\n if len(just_speakers) > 1:\n is_multiquery = True\n\n # regex type\n retype = type(re.compile('hello, world'))\n\n # just for me: convert spelling automatically for bipolar\n if not is_multiquery:\n if 'postcounts' in path:\n spelling = 'UK'\n\n # don't print so much stdout in the GUI\n if root:\n shouldprint = False\n else:\n shouldprint = True\n\n # run pmultiquery if so\n if is_multiquery:\n from corpkit.multiprocess import pmultiquery\n d = { 'path': path, \n 'search': search,\n 'query': query,\n 'show': show,\n 'lemmatise': lemmatise, \n 'titlefilter': titlefilter, \n 'lemmatag': lemmatag, \n 'print_info': shouldprint, \n 'spelling': spelling, \n 'phrases': phrases, \n 'dep_type': dep_type, \n 'quicksave': quicksave, \n 'df1_always_df': df1_always_df,\n 'just_speakers': just_speakers, \n 'root': root,}\n \n if 'note' in kwargs.keys() and kwargs['note'] is not False:\n d['note'] = kwargs['note']\n\n if 'num_proc' in kwargs.keys():\n d['num_proc'] = kwargs['num_proc']\n\n return pmultiquery(**d)\n\n if 'paralleling' in kwargs.keys():\n paralleling = kwargs['paralleling']\n else:\n paralleling = False\n\n # multiple progress bars when multiprocessing\n par_args = {}\n if not root:\n from blessings import Terminal\n term = Terminal()\n par_args['terminal'] = term\n par_args['linenum'] = paralleling\n\n the_time_started = strftime(\"%Y-%m-%d %H:%M:%S\")\n \n # check if we are in ipython\n try:\n get_ipython().getoutput()\n except TypeError:\n have_ipython = True\n except NameError:\n import subprocess\n have_ipython = False\n\n def unsplitter(lst):\n \"\"\"unsplit contractions and apostophes from tokenised text\"\"\"\n unsplit = []\n for index, t in enumerate(lst):\n if index == 0 or index == len(lst) - 1:\n unsplit.append(t)\n continue\n if \"'\" in t and not t.endswith(\"'\"):\n rejoined = ''.join([lst[index - 1], t])\n unsplit.append(rejoined)\n else:\n if not \"'\" in lst[index + 1]:\n unsplit.append(t)\n return unsplit\n\n def animator(progbar, count, tot_string = False, linenum = False, terminal = False, \n init = False, length = False):\n \"\"\"animates progress bar in unique position in terminal\"\"\"\n if init:\n from textprogressbar import TextProgressBar\n return TextProgressBar(length, dirname = tot_string)\n if type(linenum) == int:\n with terminal.location(0, terminal.height - (linenum + 1)):\n if tot_string:\n progbar.animate(count, tot_string)\n else:\n progbar.animate(count)\n else:\n if tot_string:\n progbar.animate(count, tot_string)\n else:\n progbar.animate(count) \n\n def signal_handler(signal, frame):\n \"\"\"pause on ctrl+c, rather than just stop loop\"\"\" \n import signal\n import sys\n from time import localtime, strftime\n time = strftime(\"%H:%M:%S\", localtime())\n sel = raw_input('\\n\\n%s: Paused. Press return to resume, or type exit to quit: \\n' % time)\n if sel.startswith('e') or sel.startswith('E'):\n sys.exit(0)\n else:\n time = strftime(\"%H:%M:%S\", localtime())\n print '%s: Interrogation resumed.\\n' % time\n signal.signal(signal.SIGINT, signal_handler)\n \n def gettag(query, lemmatag = False):\n \"\"\"find tag for wordnet lemmatisation\"\"\"\n import re\n if lemmatag is False:\n tag = 'n' # same default as wordnet\n # attempt to find tag from tregex query\n tagfinder = re.compile(r'^[^A-Za-z]*([A-Za-z]*)')\n tagchecker = re.compile(r'^[A-Z]{1,4}$')\n treebank_tag = re.findall(tagfinder, query.replace(r'\\w', '').replace(r'\\s', '').replace(r'\\b', ''))\n if re.match(tagchecker, treebank_tag[0]):\n if treebank_tag[0].startswith('J'):\n tag = 'a'\n elif treebank_tag[0].startswith('V') or treebank_tag[0].startswith('M'):\n tag = 'v'\n elif treebank_tag[0].startswith('N'):\n tag = 'n'\n elif treebank_tag[0].startswith('R'):\n tag = 'r'\n elif lemmatag:\n tag = lemmatag\n tagchecker = re.compile(r'^[avrn]$')\n while not re.match(tagchecker, lemmatag):\n time = strftime(\"%H:%M:%S\", localtime())\n selection = raw_input('\\n%s: WordNet POS tag \"%s\" not recognised.\\n It must be:\\n\\n ' \\\n ' a: (adjective)' \\\n ' n: (noun)' \\\n ' r: (adverb)' \\\n ' v: (verb)\\n\\nYour selection: ' % (time, lemmatag))\n lemmatag = selection\n return tag\n \n def processwords(list_of_matches, lemmatag = False):\n \"\"\"normalise matches from interrogations\"\"\"\n list_of_matches = [w.lower() for w in list_of_matches]\n # remove nonwords, strip . to normalise \"dr.\"\n if translated_option != 'o' and translated_option != 'u':\n list_of_matches = [w.lstrip('.').rstrip('.') for w in list_of_matches if re.search(regex_nonword_filter, w)]\n \n list_of_matches.sort()\n \n # tokenise if multiword:\n if phrases and not n_gramming:\n from nltk import word_tokenize as word_tokenize\n list_of_matches = [word_tokenize(i) for i in list_of_matches]\n\n # this is just for plaintext ... should convert to unicode on file open\n if datatype == 'plaintext':\n try:\n list_of_matches = [unicode(w, errors = 'ignore') for w in list_of_matches]\n except TypeError:\n pass\n\n if not dependency and exclude and 'w' in exclude.keys():\n list_of_matches = [w for w in list_of_matches if not re.match(exclude['w'], w)]\n\n if lemmatise or 'l' in show:\n if not dependency:\n tag = gettag(query, lemmatag = lemmatag)\n lemmata = lemmatiser(list_of_matches, tag)\n tups = zip(list_of_matches, lemmata)\n res = []\n for w, l in tups:\n single_result = []\n if exclude and 'l' in exclude.keys():\n if re.match(exclude['l'], l):\n continue\n if 'w' in show:\n single_result.append(w)\n if 'l' in show:\n single_result.append(l)\n # bad fix:\n # this currently says, if pos in show, there must only be pos ...\n if 'p' in show:\n if lemmatise:\n single_result.append(l)\n else:\n single_result.append(w)\n\n single_result = '/'.join(single_result)\n res.append(single_result)\n list_of_matches = res\n\n if titlefilter and not dependency:\n list_of_matches = titlefilterer(list_of_matches)\n if spelling:\n list_of_matches = convert_spelling(list_of_matches, spelling = spelling)\n\n # use blacklist option in gui\n if 'blacklist' in kwargs.keys():\n stopwords = False\n if kwargs['blacklist'] is not False:\n if kwargs['blacklist'] is True:\n from dictionaries.stopwords import stopwords as my_stopwords\n stopwords = [i.lower() for i in my_stopwords]\n list_of_matches = [w for w in list_of_matches if w not in stopwords]\n else:\n if type(kwargs['blacklist']) == list:\n stopwords = [i.lower() for i in kwargs['blacklist']]\n list_of_matches = [w for w in list_of_matches if w not in stopwords]\n else:\n regexblacklist = re.compile(kwargs['blacklist'])\n list_of_matches = [w for w in list_of_matches if not re.search(regexblacklist, w)]\n\n #if not split_con:\n # list_of_matches = unsplitter(list_of_matches)\n \n # turn every result into a single string again if need be:\n if phrases:\n output = []\n for res in list_of_matches:\n joined = ' '.join(res)\n output.append(joined)\n return output\n else:\n return list_of_matches\n\n def lemmatiser(list_of_words, tag):\n \"\"\"take a list of unicode words and a tag and return a lemmatised list.\"\"\"\n \n output = []\n for entry in list_of_words:\n if phrases:\n # just get the rightmost word\n word = entry[-1]\n entry.pop()\n else:\n word = entry\n if translated_option.startswith('u'):\n if word in taglemma:\n word = taglemma[word]\n else:\n if word == 'x':\n word = 'Other'\n # only use wordnet lemmatiser when appropriate\n elif not dependency:\n if word in wordlist:\n word = wordlist[word]\n word = lmtzr.lemmatize(word, tag)\n # do the manual_lemmatisation\n else:\n if word in wordlist:\n word = wordlist[word]\n if phrases:\n entry.append(word)\n output.append(entry)\n else:\n output.append(word)\n return output\n\n def titlefilterer(list_of_matches):\n from dictionaries.wordlists import wordlists\n badwords = wordlists.titles + wordlists.closedclass\n output = []\n for result in list_of_matches:\n head = result[-1]\n non_head = len(result) - 1\n title_stripped = [token for token in result[:non_head] if token.rstrip('.') not in badwords]\n title_stripped.append(head)\n output.append(title_stripped)\n return output\n\n def convert_spelling(list_of_matches, spelling = 'US'):\n from dictionaries.word_transforms import usa_convert\n if spelling == 'UK':\n usa_convert = {v: k for k, v in usa_convert.items()}\n output = []\n for result in list_of_matches:\n if not phrases:\n result = result.split('/')\n for index, i in enumerate(result):\n try:\n result[index] = usa_convert[i]\n except KeyError:\n pass\n output.append('/'.join(result))\n return output\n\n def distancer(lks, lk):\n \"determine number of jumps to root\" \n c = 0\n # get the gov index, stop when it's zero\n root_found = False\n while not root_found:\n if c == 0:\n try:\n link_to_check = next(i for i in lks if i.dependent.idx == lk.id)\n except StopIteration:\n root_found = True\n break\n #link_to_check = lk\n gov_index = link_to_check.governor.idx\n if gov_index == 0:\n root_found = True\n else:\n if c > 29:\n root_found = True\n break\n link_to_check = [l for l in lks if l.dependent.idx == gov_index]\n if len(link_to_check) > 0:\n link_to_check = link_to_check[0]\n else:\n break\n c += 1\n if c < 30:\n return c\n\n def dep_searcher(sents):\n \"\"\"\n search corenlp dependency parse\n 1. search for 'search' keyword arg\n governor\n dependent\n function\n pos\n lemma\n word\n index\n\n 2. exclude entries if need be\n\n 3. return '/'-sep list of 'show' keyword arg:\n governor\n dependent\n function\n pos\n lemma\n word\n index\n distance\n \n ... or just return int count.\n \"\"\"\n \n result = []\n for s in sents:\n lks = []\n deps = get_deps(s, dep_type)\n tokens = s.tokens\n for opt, pat in search.items():\n pat = filtermaker(pat)\n if opt == 'g':\n for l in deps.links:\n if re.match(pat, l.governor.text):\n lks.append(s.get_token_by_id(l.dependent.idx))\n elif opt == 'd':\n for l in deps.links:\n if re.match(pat, l.dependent.text):\n lks.append(s.get_token_by_id(l.governor.idx))\n elif opt == 'f':\n for l in deps.links:\n if re.match(pat, l.type):\n lks.append(s.get_token_by_id(l.dependent.idx))\n elif opt == 'p':\n for tok in tokens:\n if re.match(pat, tok.pos):\n lks.append(tok)\n elif opt == 'l':\n for tok in tokens:\n if re.match(pat, tok.lemma):\n lks.append(tok)\n elif opt == 'w':\n for tok in tokens:\n if re.match(pat, tok.word):\n lks.append(tok)\n elif opt == 'i':\n for tok in tokens:\n if re.match(pat, str(tok.id)):\n lks.append(tok)\n\n # only return results if all conditions are met\n if searchmode == 'all':\n counted = Counter(lks)\n lks = [k for k, v in counted.items() if v >= len(search.keys())]\n\n lks = list(set([x for x in lks if re.search(regex_nonword_filter, x.word)]))\n\n if exclude is not False:\n to_remove = []\n for op, pat in exclude.items():\n pat = filtermaker(pat)\n for tok in lks:\n if op == 'g':\n for l in deps.links:\n if re.match(pat, l.governor.text):\n to_remove.append(s.get_token_by_id(l.governor.idx))\n elif op == 'd':\n for l in deps.links:\n if re.match(pat, l.dependent.text):\n to_remove.append(s.get_token_by_id(l.dependent.idx))\n elif op == 'f':\n for l in deps.links:\n if re.match(pat, l.type):\n to_remove.append(s.get_token_by_id(l.dependent.idx))\n elif op == 'p':\n for tok in tokens:\n if re.match(pat, tok.pos):\n to_remove.append(tok)\n elif op == 'l':\n for tok in tokens:\n if re.match(pat, tok.lemma):\n to_remove.append(tok)\n elif op == 'w':\n for tok in tokens:\n if re.match(pat, tok.word):\n to_remove.append(tok)\n elif op == 'i':\n for tok in tokens:\n if re.match(pat, str(tok.id)):\n to_remove.append(tok)\n\n if excludemode == 'all':\n counted = Counter(to_remove)\n to_remove = [k for k, v in counted.items() if v >= len(exclude.keys())]\n for i in to_remove:\n try:\n lks.remove(i)\n except ValueError:\n pass\n\n if only_count:\n result.append(len(lks))\n continue\n\n # figure out what to show\n for lk in lks:\n single_result = {}\n node = deps.get_node_by_idx(lk.id)\n\n if 'w' in show:\n single_result['w'] = 'none'\n if lemmatise:\n single_result['w'] = lk.lemma\n else:\n single_result['w'] = lk.word\n\n if 'l' in show:\n single_result['l'] = lk.lemma\n\n if 'p' in show:\n single_result['p'] = 'none'\n postag = lk.pos\n if lemmatise:\n if postag.lower() in taglemma.keys():\n single_result['p'] = taglemma[postag.lower()]\n else:\n single_result['p'] = postag.lower()\n else:\n single_result['p'] = postag\n if not single_result['p']:\n single_result['p'] == 'none'\n\n if 'f' in show:\n single_result['f'] = 'none'\n for i in deps.links:\n if i.dependent.idx == lk.id:\n single_result['f'] = i.type\n break\n if single_result['f'] == '':\n single_result['f'] = 'root'\n\n if 'g' in show:\n single_result['g'] = 'none'\n for i in deps.links:\n if i.dependent.idx == lk.id:\n if s.get_token_by_id(i.governor.idx):\n if lemmatise: \n single_result['g'] = s.get_token_by_id(i.governor.idx).lemma\n else:\n single_result['g'] = i.governor.text\n else:\n single_result['g'] = 'root'\n break\n\n if 'd' in show:\n single_result['d'] = 'none'\n for i in deps.links:\n if i.governor.idx == lk.id:\n if s.get_token_by_id(i.dependent.idx): \n if lemmatise:\n single_result['d'] = s.get_token_by_id(i.dependent.idx).lemma\n else:\n single_result['d'] = i.dependent.text\n break\n\n if 'r' in show:\n all_lks = [l for l in deps.links]\n distance = distancer(all_lks, lk)\n if distance:\n single_result['r'] = str(distance)\n else:\n single_result['r'] = '-1'\n\n if 'i' in show:\n single_result['i'] = str(lk.id)\n\n if not only_count:\n \n # add them in order\n out = []\n for i in show:\n out.append(single_result[i])\n\n result.append('/'.join(out))\n \n if 'c' in show:\n result = sum(result)\n\n return result\n\n def tok_by_list(pattern, list_of_toks):\n \"\"\"search for regex in plaintext corpora\"\"\"\n if type(pattern) == str:\n pattern = [pattern]\n result = []\n matches = [m for m in list_of_toks if m in pattern]\n for m in matches:\n result.append(m)\n return result\n\n def tok_ngrams(pattern, list_of_toks, split_contractions = True):\n from collections import Counter\n global gramsize\n import re\n ngrams = Counter()\n result = []\n # if it's not a compiled regex\n list_of_toks = [x for x in list_of_toks if re.search(regex_nonword_filter, x)]\n\n if not split_contractions:\n list_of_toks = unsplitter(list_of_toks)\n \n #list_of_toks = [x for x in list_of_toks if \"'\" not in x]\n for index, w in enumerate(list_of_toks):\n try:\n the_gram = [list_of_toks[index+x] for x in range(gramsize)]\n if not any(re.search(query, x) for x in the_gram):\n continue\n #if query != 'any':\n # if not any(re.search(query, w) is True for w in the_gram):\n # continue\n ngrams[' '.join(the_gram)] += 1\n except IndexError:\n pass\n # turn counter into list of results\n for k, v in ngrams.items():\n if v > 1:\n for i in range(v):\n result.append(k)\n return result\n\n def tok_by_reg(pattern, list_of_toks):\n \"\"\"search for regex in plaintext corpora\"\"\"\n try:\n comped = re.compile(pattern)\n except:\n import traceback\n import sys\n exc_type, exc_value, exc_traceback = sys.exc_info()\n lst = traceback.format_exception(exc_type, exc_value,\n exc_traceback)\n error_message = lst[-1]\n thetime = strftime(\"%H:%M:%S\", localtime())\n print '%s: Query %s' % (thetime, error_message)\n return 'Bad query'\n\n matches = [m for m in list_of_toks if re.search(comped, m)]\n\n return matches\n\n def plaintext_regex_search(pattern, plaintext_data):\n \"\"\"search for regex in plaintext corpora\"\"\"\n result = []\n #if not pattern.startswith(r'\\b') and not pattern.endswith(r'\\b'):\n #pattern = r'\\b' + pattern + '\\b'\n try:\n compiled_pattern = re.compile(pattern)\n except:\n import traceback\n import sys\n exc_type, exc_value, exc_traceback = sys.exc_info()\n lst = traceback.format_exception(exc_type, exc_value,\n exc_traceback)\n error_message = lst[-1]\n thetime = strftime(\"%H:%M:%S\", localtime())\n print '%s: Query %s' % (thetime, error_message)\n return 'Bad query'\n matches = re.findall(compiled_pattern, plaintext_data)\n for index, i in enumerate(matches):\n if type(i) == tuple:\n matches[index] = i[0]\n return matches\n\n def plaintext_simple_search(pattern, plaintext_data):\n \"\"\"search for tokens in plaintext corpora\"\"\"\n if type(pattern) == str:\n pattern = [pattern]\n result = []\n try:\n tmp = re.compile(pattern)\n except:\n import traceback\n import sys\n exc_type, exc_value, exc_traceback = sys.exc_info()\n lst = traceback.format_exception(exc_type, exc_value,\n exc_traceback)\n error_message = lst[-1]\n thetime = strftime(\"%H:%M:%S\", localtime())\n print '%s: Query %s' % (thetime, error_message)\n return 'Bad query'\n\n for p in pattern:\n if case_sensitive:\n pat = re.compile(r'\\b' + re.escape(p) + r'\\b')\n else:\n pat = re.compile(r'\\b' + re.escape(p) + r'\\b', re.IGNORECASE)\n if not any_plaintext_word:\n matches = re.findall(pat, plaintext_data)\n for m in range(len(matches)):\n result.append(p)\n else:\n for m in plaintext_data.split():\n result.append(m)\n return result\n\n def get_speaker_names_from_xml_corpus(path):\n import os\n import re\n from bs4 import BeautifulSoup\n names = []\n # parsing html with regular expression! :)\n speakid = re.compile(r'<speakername>[\\s\\n]*?([^\\s\\n]+)[\\s\\n]*?<.speakername>', re.MULTILINE)\n for (root, dirs, fs) in os.walk(path):\n for f in fs:\n with open(os.path.join(root, f), 'r') as fo:\n txt = fo.read()\n res = re.findall(speakid, txt)\n if res:\n res = [i.strip() for i in res]\n for i in res:\n if i not in names:\n names.append(i)\n return list(sorted(set(names)))\n\n def slow_tregex(sents):\n \"\"\"do the speaker-specific version of tregex queries\"\"\"\n import os\n import bs4\n # first, put the relevant trees into temp file\n if 'outname' in kwargs.keys():\n to_open = 'tmp-%s.txt' % kwargs['outname']\n else:\n to_open = 'tmp.txt'\n to_write = '\\n'.join([sent._parse_string.strip() for sent in sents if sent.parse_string is not None]).encode('utf-8', errors = 'ignore')\n with open(to_open, \"w\") as fo:\n fo.write(to_write)\n q = search.values()[0]\n res = tregex_engine(query = q, \n options = ['-o', '-%s' % translated_option], \n corpus = to_open,\n root = root)\n if root:\n root.update()\n os.remove(to_open)\n return res\n\n def get_deps(sentence, dep_type):\n if dep_type == 'basic-dependencies':\n return sentence.basic_dependencies\n if dep_type == 'collapsed-dependencies':\n return sentence.collapsed_dependencies\n if dep_type == 'collapsed-ccprocessed-dependencies':\n return sentence.collapsed_ccprocessed_dependencies\n\n def get_stats(sents):\n \"\"\"get a bunch of frequencies on interpersonal phenomena\"\"\"\n import os\n import re \n # first, put the relevant trees into temp file\n if 'outname' in kwargs.keys():\n to_open = 'tmp-%s.txt' % kwargs['outname']\n else:\n to_open = 'tmp.txt'\n with open(to_open, \"w\") as fo:\n for sent in sents:\n statsmode_results['Sentences'] += 1\n fo.write(sent.parse_string.rstrip().encode('utf-8', errors = 'ignore') + '\\n')\n deps = get_deps(sent, dep_type)\n numpass = len([x for x in deps.links if x.type.endswith('pass')])\n statsmode_results['Passives'] += numpass\n statsmode_results['Tokens'] += len(sent.tokens)\n statsmode_results['Words'] += len([w for w in sent.tokens if w.word.isalnum()])\n #statsmode_results['Unique words'] += len(set([w.word.lower() for w in sent.tokens if w.word.isalnum()]))\n #statsmode_results['Unique lemmata'] += len(set([w.lemma.lower() for w in sent.tokens if w.word.isalnum()]))\n\n # count moods via trees (/\\?/ !< __)\n from dictionaries.process_types import processes\n from corpkit.other import as_regex\n tregex_qs = {'Imperative': r'ROOT < (/(S|SBAR)/ < (VP !< VBD !< VBG !$ NP !$ SBAR < NP !$-- S !$-- VP !$ VP)) !<< (/\\?/ !< __) !<<- /-R.B-/ !<<, /(?i)^(-l.b-|hi|hey|hello|oh|wow|thank|thankyou|thanks|welcome)$/',\n #'Open interrogative': r'ROOT < SBARQ <<- (/\\?/ !< __)', \n #'Closed interrogative': r'ROOT ( < (SQ < (NP $+ VP)) << (/\\?/ !< __) | < (/(S|SBAR)/ < (VP $+ NP)) <<- (/\\?/ !< __))',\n 'Unmodalised declarative': r'ROOT < (S < (/(NP|SBAR|VP)/ $+ (VP !< MD)))',\n 'Modalised declarative': r'ROOT < (S < (/(NP|SBAR|VP)/ $+ (VP < MD)))',\n 'Open class words': r'/^(NN|JJ|VB|RB)/ < __',\n 'Closed class words': r'__ !< __ !> /^(NN|JJ|VB|RB)/',\n 'Clauses': r'/^S/ < __',\n 'Interrogative': r'ROOT << (/\\?/ !< __)',\n 'Mental processes': r'VP > /^(S|ROOT)/ <+(VP) (VP <<# /%s/)' % as_regex(processes.mental, boundaries = 'w'),\n 'Verbal processes': r'VP > /^(S|ROOT)/ <+(VP) (VP <<# /%s/)' % as_regex(processes.verbal, boundaries = 'w'),\n 'Relational processes': r'VP > /^(S|ROOT)/ <+(VP) (VP <<# /%s/)' % as_regex(processes.relational, boundaries = 'w')}\n\n for name, q in sorted(tregex_qs.items()):\n res = tregex_engine(query = q, \n options = ['-o', '-C'], \n corpus = to_open, \n root = root)\n statsmode_results[name] += int(res)\n global numdone\n numdone += 1\n if root:\n root.update()\n if not root:\n tot_string = str(numdone + 1) + '/' + str(total_files * len(tregex_qs.keys()))\n if 'outname' in kwargs.keys():\n tot_string = '%s: %s' % (kwargs['outname'], tot_string)\n animator(p, numdone, tot_string, **par_args)\n if 'note' in kwargs.keys() and kwargs['note'] is not False:\n kwargs['note'].progvar.set((numdone * 100.0 / (total_files * len(tregex_qs.keys())) / denom) + startnum)\n os.remove(to_open)\n\n def tabler(subcorpus_names, list_of_dicts, num_rows):\n \"\"\"make a word table showing num_rows results\"\"\"\n import pandas as pd\n cols = []\n for subcorp, data in zip(subcorpus_names, list_of_dicts):\n col = pd.Series([w for w, v in data.most_common(num_rows)], name = subcorp)\n cols.append(col)\n word_table = pd.concat(cols, axis = 1)\n return word_table\n\n # a few things are off by default:\n only_count = False\n using_tregex = False\n n_gramming = False\n dependency = False\n plaintext = False\n tokens = False\n statsmode = False\n split_con = True\n search_iterable = False\n\n # determine what kind of data the corpus is\n # this currently slows things down with huge corpora, \n # so judge from folder name first \n if type(path) == str and path.endswith('-parsed'):\n datatype = 'parse'\n elif type(path) == str and path.endswith('-tokenised'):\n datatype = 'tokens'\n else:\n from corpkit.other import determine_datatype\n datatype = determine_datatype(path)\n\n # some empty lists we'll need\n dicts = []\n allwords_list = []\n \n regex_nonword_filter = re.compile(\"[A-Za-z0-9:_]\")\n \n # fix up search\n if type(search) == str:\n search = search[0].lower()\n if not search.lower().startswith('t') and not search.lower().startswith('n') \\\n and datatype == 'parse':\n search_iterable = True\n if query == 'any':\n query = r'.*'\n search = {search: query}\n\n possb = ['d', 'g', 'i', 'c', 'a', 'p', 'l', 'w', 't', 'f']\n if not any(i in possb for i in search.keys()):\n raise ValueError('search argument \"%s\" unrecognised.' % search.keys())\n if len(search.keys()) > 1 and 't' in search.keys():\n raise ValueError('if \"t\" in search, it must be the only list item')\n\n # fix up exclude naming conventions, convert lists to regex\n fixed_exclude = {}\n if exclude:\n for k, v in exclude.items():\n if type(v) == list:\n from corpkit.other import as_regex\n v = as_regex(v, boundaries = 'l', case_sensitive = case_sensitive)\n if k != k.lower()[0]:\n fixed_exclude[k.lower()[0]] = v\n else:\n fixed_exclude[k] = v\n exclude = fixed_exclude\n\n if not search_iterable:\n query = search.values()[0]\n\n if type(show) == str or type(show) == unicode:\n show = [show.lower()[0]]\n\n for index, t in enumerate(show):\n show[index] = t.lower()[0]\n\n possb = ['d', 'g', 'i', 'c', 'a', 'p', 'l', 'w', 't', 'f']\n only_dep = ['d', 'g', 'i', 'a', 'f']\n if not any(i in possb for i in show):\n raise ValueError('show argument \"%s\" unrecognised.' % show)\n if len(show) > 1 and 'c' in show:\n raise ValueError('if \"c\" in show, it must be the only list item')\n if 't' in search.keys() and any(i in only_dep for i in show):\n raise ValueError('If searching trees, show can not include: %s' % ', '.join(only_dep))\n\n # Tregex option:\n translated_option = False\n from corpkit.other import as_regex\n \n if datatype == 'parse':\n if 't' in search.keys():\n using_tregex = True\n\n if datatype == 'plaintext':\n plaintext = True\n\n elif datatype == 'tokens':\n tokens = True\n\n\n if using_tregex:\n if 'p' in show:\n dep_funct = slow_tregex\n optiontext = 'Part-of-speech tags only.'\n translated_option = 'u'\n if type(query) == list:\n query = r'__ < (/%s/ !< __)' % as_regex(query, boundaries = 'line', case_sensitive = case_sensitive)\n if query == 'any':\n query = r'__ < (/.?[A-Za-z0-9].?/ !< __)'\n elif 't' in show:\n dep_funct = slow_tregex\n optiontext = 'Tags and words.'\n translated_option = 'o'\n if type(query) == list:\n query = r'__ < (/%s/ !< __)' % as_regex(query, boundaries = 'line', case_sensitive = case_sensitive)\n if query == 'any':\n query = r'__ < (/.?[A-Za-z0-9].?/ !< __)'\n elif 'w' in show:\n dep_funct = slow_tregex\n optiontext = 'Words only.'\n translated_option = 't'\n if type(query) == list:\n query = r'/%s/ !< __' % as_regex(query, boundaries = 'line', case_sensitive = case_sensitive)\n if query == 'any':\n query = r'/.?[A-Za-z0-9].?/ !< __'\n elif 'c' in show:\n dep_funct = slow_tregex\n count_results = {}\n only_count = True\n translated_option = 'C'\n optiontext = 'Counts only.'\n if type(query) == list:\n query = r'/%s/ !< __' % as_regex(query, boundaries = 'line', case_sensitive = case_sensitive)\n if query == 'any':\n query = r'/.?[A-Za-z0-9].?/ !< __'\n elif 'l' in show:\n dep_funct = slow_tregex\n translated_option = 't'\n optiontext = 'Words, lemmatised.'\n lemmatise = True\n if type(query) == list:\n query = r'/%s/ !< __' % as_regex(query, boundaries = 'line', case_sensitive = case_sensitive)\n if query == 'any':\n query = r'/.?[A-Za-z0-9].?/ !< __'\n\n elif datatype == 'plaintext':\n optiontext = 'Searching plaintext corpus'\n if 'regex' in kwargs.keys() and kwargs['regex'] is False:\n translated_option = 's'\n if query == 'any':\n any_plaintext_word = True\n else:\n any_plaintext_word = False\n else:\n translated_option = 'r'\n if query == 'any':\n query = r'[^\\s]+'\n if type(query) == list:\n query = as_regex(query, boundaries = 'line', case_sensitive = case_sensitive)\n \n elif datatype == 'tokens':\n if 'w' in search.keys():\n tokens = True\n if type(query) == list:\n translated_option = 'e'\n optiontext = 'Tokens via list.'\n dep_funct = tok_by_list\n else:\n translated_option = 'h'\n optiontext = 'Tokens via regular expression.'\n dep_funct = tok_by_reg\n if 'n' in search.keys():\n translated_option = 'j'\n tokens = True\n lemmatise = False\n optiontext = 'Get ngrams from tokens.'\n if query == 'any':\n query = r'.*'\n if type(query) == list:\n query = as_regex(query, boundaries = 'l', case_sensitive = case_sensitive)\n else:\n try:\n if not case_sensitive:\n query = re.compile(query, re.IGNORECASE)\n else:\n query = re.compile(query)\n except:\n import traceback\n import sys\n exc_type, exc_value, exc_traceback = sys.exc_info()\n lst = traceback.format_exception(exc_type, exc_value,\n exc_traceback)\n error_message = lst[-1]\n thetime = strftime(\"%H:%M:%S\", localtime())\n print '%s: Query %s' % (thetime, error_message)\n return 'Bad query'\n global gramsize\n if 'gramsize' in kwargs.keys():\n gramsize = kwargs['gramsize']\n else:\n gramsize = 2\n dep_funct = tok_ngrams\n\n elif datatype == 'parse':\n if 'n' not in search.keys() and 't' not in search.keys():\n translated_option = 'y'\n dependency = True\n optiontext = 'Dependency querying...'\n dep_funct = dep_searcher\n if 'c' in show:\n count_results = {}\n only_count = True\n\n if 's' in search.keys():\n translated_option = 'v'\n #using_tregex = True\n statsmode = True\n optiontext = 'Getting general stats.'\n dep_funct = get_stats\n if datatype != 'parse':\n print 'Need parsed corpus for this.'\n return\n\n # initialise nltk lemmatiser only once\n if lemmatise or ('l' in show and not dependency):\n from nltk.stem.wordnet import WordNetLemmatizer\n lmtzr=WordNetLemmatizer()\n\n if 'n' in search.keys():\n if datatype == 'parse':\n translated_option = 'n'\n using_tregex = True\n optiontext = 'n-grams only.'\n n_gramming = True\n if datatype == 'tokens':\n translated_option = 'j'\n using_tregex = False\n \n if type(query) == list:\n query = as_regex(query, boundaries = 'word', case_sensitive = case_sensitive)\n\n if dependency:\n if type(query) == list:\n query = as_regex(query, boundaries = 'line', case_sensitive = case_sensitive)\n #query = r'(?i)^(' + '|'.join(query) + r')$' \n if query == 'any':\n query = r'.*'\n\n # see if fast tregex can be done instead of temp file slow way\n can_do_fast = False\n if using_tregex:\n if just_speakers is False:\n if statsmode is False:\n can_do_fast = True\n\n if plaintext is True:\n try:\n if tregex_engine(corpus = os.path.join(path, os.listdir(path)[-1]), check_for_trees = True, root = root):\n if not root:\n decision = raw_input('\\nIt appears that your corpus contains parse trees. If using a plaintext search option, your counts will likely be inaccurate.\\n\\nHit enter to continue, or type \"exit\" to start again: ')\n if decision.startswith('e'):\n return\n else:\n time = strftime(\"%H:%M:%S\", localtime())\n print '%s: Corpus \"%s\" contains parse trees. Use \"Trees\" option.' % (time, os.path.basename(path))\n root.update()\n return False\n except:\n pass\n \n # if query is a special query, convert it:\n if query == 'any':\n if translated_option == 't' or translated_option == 'C':\n query = r'/.?[A-Za-z0-9].?/ !< __'\n if translated_option == 'u' or translated_option == 'o':\n query = r'__ < (/.?[A-Za-z0-9].?/ !< __)'\n if query == 'subjects':\n query = r'__ >># @NP'\n if query == 'processes':\n query = r'/VB.?/ >># ( VP >+(VP) (VP !> VP $ NP))'\n if query == 'modals':\n query = r'MD < __'\n if query == 'participants':\n query = r'/(NN|PRP|JJ).?/ >># (/(NP|ADJP)/ $ VP | > VP)'\n if query == 'entities':\n query = r'NP <# NNP'\n titlefilter = True\n\n # check that there's nothing in the quicksave path\n if quicksave:\n savedir = 'data/saved_interrogations'\n if not quicksave.endswith('.p'):\n quicksave = quicksave + '.p'\n fullpath = os.path.join(savedir, quicksave)\n if os.path.isfile(fullpath):\n # if the file exists, check if the query is pretty much the same\n from corpkit import load_result\n loaded = load_result(quicksave)\n if loaded.query['query'] == query and \\\n loaded.query['path'] == path and \\\n loaded.query['translated_option'] == translated_option and \\\n loaded.query['lemmatise'] == lemmatise and \\\n loaded.query['titlefilter'] == titlefilter and \\\n loaded.query['spelling'] == spelling and \\\n loaded.query['dep_type'] == dep_type and \\\n loaded.query['function'] == 'interrogator':\n dup_non_i = 'Duplicate'\n else:\n dup_non_i = 'Non-identical'\n\n while os.path.isfile(fullpath) and quicksave:\n dict_for_print = ' ' + '\\n '.join(sorted(['%s: %s' % (k, v) for k, v in loaded.query.items()])) + '\\n'\n time = strftime(\"%H:%M:%S\", localtime())\n selection = raw_input('\\n%s: %s interrogation found in %s:\\n\\n%s\\n' \\\n ' You have the following options:\\n\\n' \\\n ' a) save with a new name\\n' \\\n ' b) turn off \"quicksave\"\\n' \\\n ' c) return the results from %s\\n' \\\n ' d) delete %s\\n' \\\n ' e) Quickview %s and then decide\\n' \\\n ' f) exit\\n\\nYour selection: ' % (time, dup_non_i, fullpath, dict_for_print, fullpath, fullpath, fullpath))\n if 'a' in selection:\n sel = raw_input('\\nNew save name: ')\n quicksave = sel\n if not quicksave.endswith('.p'):\n quicksave = quicksave + '.p'\n fullpath = os.path.join(savedir, quicksave)\n elif 'b' in selection:\n quicksave = False\n elif 'c' in selection:\n return loaded\n elif 'd' in selection:\n os.remove(fullpath)\n elif 'e' in selection:\n print loaded.query\n print '\\n'\n try:\n print loaded.results\n except:\n print loaded.totals\n print '\\n'\n elif 'f' in selection:\n print ''\n return\n else:\n as_str = str(selection)\n print ' Choice \"%s\" not recognised.' % selection\n\n # titlefiltering only works with phrases, so turn it on\n if titlefilter:\n phrases = True\n\n def filtermaker(the_filter):\n if type(the_filter) == list:\n from other import as_regex\n the_filter = as_regex(the_filter, case_sensitive = case_sensitive)\n try:\n output = re.compile(the_filter)\n is_valid = True\n except:\n is_valid = False\n if root:\n import traceback\n import sys\n exc_type, exc_value, exc_traceback = sys.exc_info()\n lst = traceback.format_exception(exc_type, exc_value,\n exc_traceback)\n error_message = lst[-1]\n thetime = strftime(\"%H:%M:%S\", localtime())\n print '%s: Filter %s' % (thetime, error_message)\n return 'Bad query'\n \n while not is_valid:\n if root:\n time = strftime(\"%H:%M:%S\", localtime())\n print the_filter\n print '%s: Invalid the_filter regular expression.' % time\n return False\n time = strftime(\"%H:%M:%S\", localtime())\n selection = raw_input('\\n%s: filter regular expression \" %s \" contains an error. You can either:\\n\\n' \\\n ' a) rewrite it now\\n' \\\n ' b) exit\\n\\nYour selection: ' % (time, the_filter))\n if 'a' in selection:\n the_filter = raw_input('\\nNew regular expression: ')\n try:\n output = re.compile(r'\\b' + the_filter + r'\\b')\n is_valid = True\n except re.error:\n is_valid = False\n elif 'b' in selection:\n print ''\n return False\n return output\n\n # dependencies:\n # can't be phrases\n # check if regex valid\n # check if dep_type valid\n if dependency:\n if translated_option == 'v':\n names = get_speaker_names_from_xml_corpus(path)\n \n phrases = False\n \n allowed_dep_types = ['basic-dependencies', 'collapsed-dependencies', 'collapsed-ccprocessed-dependencies']\n \n # allow a b and c shorthand\n if dep_type == 'a':\n dep_type = allowed_dep_types[0]\n if dep_type == 'b':\n dep_type = allowed_dep_types[1]\n if dep_type == 'c':\n dep_type = allowed_dep_types[2]\n\n while dep_type not in allowed_dep_types:\n time = strftime(\"%H:%M:%S\", localtime())\n selection = raw_input('\\n%s: Dependency type \"%s\" not recognised. Must be one of:\\n\\n' \\\n ' a) basic-dependencies' \\\n ' b) collapsed-dependencies' \\\n ' c) collapsed-ccprocessed-dependencies\\n\\nYour selection: ' % (time, dep_type))\n if 'a' in selection:\n dep_type = allowed_dep_types[0]\n elif 'b' in selection:\n dep_type = allowed_dep_types[1]\n elif 'c' in selection:\n dep_type = allowed_dep_types[2]\n else:\n pass\n\n # get list of subcorpora and sort them ... user input if no corpus found\n got_corpus = False\n while got_corpus is False:\n try:\n sorted_dirs = [d for d in os.listdir(path) if os.path.isdir(os.path.join(path,d))]\n got_corpus = True\n except OSError:\n got_corpus = False\n time = strftime(\"%H:%M:%S\", localtime())\n selection = raw_input('\\n%s: Corpus directory not found: \" %s \". You can either:\\n\\n' \\\n ' a) enter a new corpus path\\n' \\\n ' b) exit\\n\\nYour selection: ' % (time, path))\n if 'a' in selection:\n path = raw_input('\\nNew corpus path: ')\n elif 'b' in selection:\n print ''\n return\n \n # treat as one large corpus if no subdirs found\n one_big_corpus = False\n if len(sorted_dirs) == 0:\n #warnings.warn('\\nNo subcorpora found in %s.\\nUsing %s as corpus dir.' % (path, path))\n one_big_corpus = True\n # fails if in wrong dir!\n sorted_dirs = [os.path.basename(path)]\n\n # numerically sort subcorpora if the first can be an int\n # could improve now with is_number, all\n else:\n try:\n check = int(sorted_dirs[0])\n sorted_dirs.sort(key=int)\n except:\n pass\n\n # if doing dependencies, make list of all files, and a progress bar\n if dependency or plaintext or tokens or can_do_fast is False:\n all_files = []\n for d in sorted_dirs:\n if not one_big_corpus:\n subcorpus = os.path.join(path, d)\n else:\n subcorpus = path\n if dependency:\n files = [f for f in os.listdir(subcorpus) if f.endswith('.xml')]\n else:\n files = [f for f in os.listdir(subcorpus) if not f.startswith('.')]\n \n # skip files not containing speakers...\n if just_speakers:\n rem = []\n for f in files:\n fp = os.path.join(subcorpus, f)\n data = open(fp, 'r').read()\n if any('<speakername>' + name in data for name in just_speakers):\n rem.append(f)\n files = rem\n\n all_files.append([d, files])\n total_files = len([item for sublist in all_files for item in sublist[1]])\n sorted_dirs = all_files\n c = 0\n if not root:\n tstr = False\n if 'outname' in kwargs.keys():\n if dependency or plaintext or tokens:\n tstr = '%s: %d/%d' % (kwargs['outname'], 0, total_files)\n else:\n tstr = '%s: %d/%d' % (kwargs['outname'], 0, len(sorted_dirs))\n if translated_option != 'v':\n p = animator(None, None, init = True, tot_string = tstr, length = total_files, **par_args)\n #p = TextProgressBar(total_files)\n else:\n p = animator(None, None, init = True, tot_string = tstr, length = total_files * 10, **par_args)\n #p = TextProgressBar(total_files * 10)\n \n # if tregex, make progress bar for each dir\n else:\n if not root:\n tstr = False\n if 'outname' in kwargs.keys():\n tstr = '%s: %d/%d' % (kwargs['outname'], 0, len(sorted_dirs))\n p = animator(None, None, tot_string = tstr, init = True, length = len(sorted_dirs), **par_args)\n\n # loop through each subcorpus\n subcorpus_names = []\n\n # check for valid query. so ugly.\n if using_tregex:\n if query:\n if not n_gramming:\n q = search.values()[0]\n query = tregex_engine(corpus = False, query = q, options = ['-t'], check_query = True, root = root)\n if query is False:\n if root:\n return 'Bad query'\n else:\n return\n \n else:\n if dependency or translated_option == 'r' or translated_option == 'h':\n is_valid = True\n try:\n if translated_option == 'r':\n if type(query) == str:\n if query.startswith(r'\\b'):\n query = query[2:]\n if query.endswith(r'\\b'):\n query = query[:-2]\n if case_sensitive:\n regex = re.compile(r'\\b' + query + r'\\b')\n else:\n regex = re.compile(r'\\b' + query + r'\\b', re.IGNORECASE)\n else:\n regex = query\n else:\n if case_sensitive:\n regex = re.compile(query)\n else:\n regex = re.compile(query, re.IGNORECASE)\n is_valid = True\n except re.error:\n is_valid = False\n if root:\n import traceback\n import sys\n exc_type, exc_value, exc_traceback = sys.exc_info()\n lst = traceback.format_exception(exc_type, exc_value,\n exc_traceback)\n error_message = lst[-1]\n thetime = strftime(\"%H:%M:%S\", localtime())\n print '%s: Query %s' % (thetime, error_message)\n return \"Bad query\"\n while not is_valid:\n time = strftime(\"%H:%M:%S\", localtime())\n if root:\n time = strftime(\"%H:%M:%S\", localtime())\n print '%s: Regular expression in query contains an error.' % time\n return 'Bad query'\n selection = raw_input('\\n%s: Regular expression \" %s \" contains an error. You can either:\\n\\n' \\\n ' a) rewrite it now\\n' \\\n ' b) exit\\n\\nYour selection: ' % (time, query))\n if 'a' in selection:\n query = raw_input('\\nNew regular expression: ')\n try:\n if case_sensitive:\n regex = re.compile(r'\\b' + query + r'\\b')\n else:\n regex = re.compile(r'\\b' + query + r'\\b', re.IGNORECASE)\n is_valid = True\n except re.error:\n is_valid = False\n elif 'b' in selection:\n print ''\n return\n\n #print list nicely\n if type(query) == list:\n qtext = ', '.join(query)\n elif type(query) == str or type(query) == unicode:\n qtext = query\n else:\n qtext = 'regex'\n\n global skipped_sents\n skipped_sents = 0\n\n # begin interrogation\n time = strftime(\"%H:%M:%S\", localtime())\n if printstatus:\n print (\"\\n%s: Beginning corpus interrogation: %s\" \\\n \"\\n Query: '%s'\\n %s\" \\\n \"\\n Interrogating corpus ... \\n\" % (time, os.path.basename(path), qtext, optiontext) )\n if root:\n print '%s: Interrogating corpus ...' % time\n if root and tk:\n root.update()\n\n global numdone\n numdone = 0\n\n for index, d in enumerate(sorted_dirs):\n if using_tregex or n_gramming:\n if can_do_fast or n_gramming:\n subcorpus_name = d\n subcorpus_names.append(subcorpus_name)\n if not root:\n if paralleling is not False:\n tstr = '%s: %d/%d' % (kwargs['outname'], index + 1, len(sorted_dirs))\n else:\n tstr = False\n animator(p, index, tstr, **par_args)\n #animator(p, index, **par_args)\n #p.animate(index)\n if root and tk:\n time = strftime(\"%H:%M:%S\", localtime())\n if not one_big_corpus:\n print '%s: Interrogating subcorpus: %s' % (time, subcorpus_name)\n else:\n print '%s: Interrogating corpus ... ' % time\n root.update()\n if 'note' in kwargs.keys() and kwargs['note'] is not False:\n kwargs['note'].progvar.set(((index + 1) * 100.0 / len(sorted_dirs) / denom) + startnum)\n # get path to corpus/subcorpus\n if len(sorted_dirs) == 1:\n subcorpus = path\n else:\n subcorpus = os.path.join(path,subcorpus_name)\n \n if n_gramming:\n result = []\n if 'split_contractions' in kwargs.keys():\n if kwargs['split_contractions'] is True:\n split_con = True\n elif kwargs['split_contractions'] is False:\n split_con = False\n from corpkit.keys import ngrams\n if 'blacklist' in kwargs.keys():\n the_blacklist = kwargs['blacklist']\n else:\n the_blacklist = False\n if 'gramsize' in kwargs.keys():\n gramsz = kwargs['gramsize']\n else:\n gramsz = 2\n\n spindle_out = ngrams(subcorpus, reference_corpus = False, \n blacklist = the_blacklist,\n printstatus = False, \n clear = False, \n lemmatise = lemmatise, \n split_contractions = split_con, \n whitelist = query,\n gramsize = gramsz\n )\n for w in list(spindle_out.index):\n if query != 'any':\n if re.search(query, w):\n for _ in range(spindle_out[w]):\n result.append(w)\n else:\n for _ in range(spindle_out[w]):\n result.append(w)\n\n #if tregex, search\n else:\n if not statsmode:\n op = ['-o', '-' + translated_option]\n q = search.values()[0]\n result = tregex_engine(query = q, options = op, \n corpus = subcorpus, root = root)\n if result is False:\n return\n \n # if just counting matches, just \n # add subcorpus name and count...\n if only_count:\n count_results[d] = result\n continue\n\n # for dependencies, d[0] is the subcorpus name \n # and d[1] is its file list ... \n\n elif dependency or plaintext or tokens or statsmode or can_do_fast is False:\n #if not root:\n #p.animate(-1, str(0) + '/' + str(total_files))\n from collections import Counter\n statsmode_results = Counter({'Sentences': 0, 'Passives': 0, 'Tokens': 0})\n subcorpus_name = d[0]\n subcorpus_names.append(subcorpus_name)\n fileset = d[1]\n #for f in read_files:\n result = []\n for f in fileset:\n result_from_file = None\n # pass the x/y argument for more updates \n if not root and translated_option != 'v':\n tot_string = str(c + 1) + '/' + str(total_files)\n if 'outname' in kwargs.keys():\n tot_string = '%s: %s' % (kwargs['outname'], tot_string)\n animator(p, c, tot_string, **par_args)\n #p.animate((c), tot_string)\n if root and tk and not statsmode:\n root.update()\n if 'note' in kwargs.keys() and kwargs['note'] is not False:\n kwargs['note'].progvar.set((((c + 1) * 100.0 / total_files) / denom) + startnum)\n time = strftime(\"%H:%M:%S\", localtime())\n if not one_big_corpus:\n print '%s: Interrogating subcorpus: %s' % (time, subcorpus_name)\n else:\n print '%s: Interrogating corpus ...' % (time)\n c += 1\n if one_big_corpus:\n filepath = os.path.join(path, f)\n else:\n filepath = os.path.join(path, subcorpus_name, f)\n if dependency or can_do_fast is False:\n if not plaintext and not tokens:\n with open(filepath, \"rb\") as text:\n data = text.read()\n from corenlp_xml.document import Document\n try:\n corenlp_xml = Document(data)\n except:\n print 'Could not read file: %s' % filepath\n continue\n #corenlp_xml = Beautifulcorenlp_xml(data, parse_only=justsents) \n if just_speakers: \n sents = [s for s in corenlp_xml.sentences if s.speakername in just_speakers]\n if not sents:\n continue\n #sents = [s for s in corenlp_xml.find_all('sentence') \\\n #if s.speakername.text.strip() in just_speakers]\n else:\n sents = corenlp_xml.sentences\n # run whichever function has been called\n if translated_option == 'y':\n result_from_file = dep_searcher(sents)\n else:\n result_from_file = dep_funct(sents)\n if only_count:\n count_results[subcorpus_name] = result_from_file\n\n # memory problems\n corenlp_xml = None\n data = None\n gc.collect()\n\n if plaintext:\n with open(filepath, \"rb\") as text:\n data = text.read()\n if translated_option == 'r':\n result_from_file = plaintext_regex_search(regex, data)\n if translated_option == 's':\n result_from_file = plaintext_simple_search(query, data)\n if tokens:\n import pickle\n data = pickle.load(open(filepath, \"rb\"))\n #print data\n if translated_option == 'h':\n result_from_file = tok_by_reg(regex, data)\n if translated_option == 'e':\n result_from_file = tok_by_list(query, data)\n if translated_option == 'j':\n split_con = False\n if 'split_contractions' in kwargs.keys():\n if kwargs['split_contractions'] is True:\n split_con = True\n result_from_file = tok_ngrams(query, data, split_contractions = split_con)\n \n if result_from_file:\n if not statsmode and not only_count:\n for entry in result_from_file:\n result.append(entry)\n\n if not statsmode and 'c' not in show:\n result.sort()\n\n # lowercaseing, encoding, lemmatisation, \n # titlewords removal, usa_english, etc.\n if not statsmode:\n processed_result = processwords(result, lemmatag = lemmatag)\n \n if not statsmode:\n allwords_list.append(processed_result)\n dicts.append(Counter(processed_result))\n if statsmode:\n dicts.append(statsmode_results)\n allwords_list.append([w for w in statsmode_results.keys()])\n\n if not plaintext:\n if not root:\n if paralleling is not False:\n if dependency or plaintext or tokens or can_do_fast is False:\n tstr = '%s: %d/%d' % (kwargs['outname'], total_files, total_files)\n else:\n tstr = '%s: %d/%d' % (kwargs['outname'], len(sorted_dirs), len(sorted_dirs))\n\n else:\n tstr = False\n animator(p, len(sorted_dirs), tot_string = tstr, **par_args)\n\n #p.animate(len(sorted_dirs))\n if 'note' in kwargs.keys() and kwargs['note'] is not False:\n kwargs['note'].progvar.set((100 / denom + startnum))\n if root and tk:\n root.update()\n\n else:\n # weird float div by 0 zero error here for plaintext\n try:\n if not root:\n if translated_option != 'v':\n if paralleling is not False:\n animator(p, total_files, kwargs['outname'], **par_args)\n else:\n animator(p, total_files, **par_args)\n #p.animate(total_files)\n\n else:\n if paralleling is not False:\n animator(p, total_files * 10, kwargs['outname'], **par_args)\n else:\n animator(p, total_files * 10, **par_args)\n #p.animate(total_files * 10)\n if 'note' in kwargs.keys() and kwargs['note'] is not False:\n kwargs['note'].progvar.set((100 / denom + startnum))\n except:\n pass\n\n if root and tk:\n root.update()\n\n if not have_ipython and not root and not tk:\n print '\\n'\n \n # if only counting, get total total and finish up:\n if only_count:\n stotals = pd.Series(count_results)\n stotals.name = 'Total' \n outputnames = collections.namedtuple('interrogation', ['query', 'totals'])\n the_time_ended = strftime(\"%Y-%m-%d %H:%M:%S\")\n # add option to named tuple\n the_options = {'path': path,\n 'search': search,\n 'show': show,\n 'function': 'interrogator',\n 'datatype': stotals.dtype,\n 'query': query,\n 'exclude': exclude,\n 'lemmatise': lemmatise,\n 'titlefilter': titlefilter,\n 'lemmatag': lemmatag,\n 'spelling': spelling,\n 'phrases': phrases,\n 'dep_type': dep_type,\n 'quicksave': quicksave,\n 'time_started': the_time_started,\n 'time_ended': the_time_ended}\n\n try:\n the_options['translated_option'] = translated_option\n except:\n the_options['translated_options'] = translated_options\n\n output = outputnames(the_options, stotals)\n if 'outname' in kwargs:\n stotals.name = kwargs['outname']\n return stotals\n if have_ipython:\n clear_output()\n if quicksave:\n if stotals.sum() > 0:\n from other import save_result\n save_result(output, quicksave)\n \n if printstatus:\n time = strftime(\"%H:%M:%S\", localtime())\n print '%s: Interrogation finished! %d total occurrences.' % (time, stotals.sum())\n if not tk:\n print ''\n\n return output\n\n # flatten and sort master list, in order to make a list of unique words\n allwords = [item for sublist in allwords_list for item in sublist]\n allwords.sort()\n unique_words = set(allwords)\n\n #make master reference_corpus\n the_big_dict = {}\n\n # calculate results\n # for every unique entry, find out how many times it appears per subcorpus\n for word in unique_words:\n the_big_dict[word] = [each_dict[word] for each_dict in dicts]\n \n # turn master dict into dataframe, sorted\n df = DataFrame(the_big_dict, index = subcorpus_names)\n\n if one_big_corpus:\n df = df.T.sort(list(df.T.columns)[0], ascending = False).T\n\n try:\n if not one_big_corpus:\n df.ix['Total'] = df.sum()\n tot = df.ix['Total']\n df = df[tot.argsort()[::-1]]\n df = df.drop('Total', axis = 0)\n except:\n pass\n\n # make totals branch\n stotals = df.sum(axis = 1)\n stotals.name = 'Total'\n\n # make result into series if only one subcorpus\n if one_big_corpus and not df1_always_df:\n try:\n df = df.ix[subcorpus_names[0]]\n except:\n pass\n df.sort(ascending = False)\n\n # if numerical colnames, sort numerically\n if show == ['r'] or show == ['i']:\n intcols = sorted([int(c) for c in list(df.columns)])\n df.columns = [str(c) for c in intcols]\n\n # add sort info for tk\n if tk:\n df = df.T\n df['tkintertable-order'] = pd.Series([index for index, data in enumerate(list(df.index))], index = list(df.index))\n df = df.T\n \n # print skipped sent information for distance_mode\n if printstatus and 'r' in show and skipped_sents > 0:\n print '\\n %d sentences over 99 words skipped.\\n' % skipped_sents\n \n #make results into named tuple\n # add option to named tuple\n the_time_ended = strftime(\"%Y-%m-%d %H:%M:%S\")\n the_options = {'path': path,\n 'search': search,\n 'show': show,\n 'datatype': df.iloc[0].dtype,\n 'query': query,\n 'lemmatise': lemmatise,\n 'titlefilter': titlefilter,\n 'lemmatag': lemmatag,\n 'function': 'interrogator',\n 'spelling': spelling,\n 'exclude': exclude,\n 'phrases': phrases,\n 'dep_type': dep_type,\n 'quicksave': quicksave,\n 'time_started': the_time_started,\n 'time_ended': the_time_ended}\n\n try:\n the_options['translated_option'] = translated_option\n except:\n the_options['translated_options'] = translated_options\n\n outputnames = collections.namedtuple('interrogation', ['query', 'results', 'totals'])\n output = outputnames(the_options, df, stotals)\n\n if type(paralleling) == int:\n return (kwargs['outname'], df, stotals)\n \n if have_ipython:\n clear_output()\n\n # warnings if nothing generated...\n if not one_big_corpus and not df1_always_df:\n num_diff_results = len(list(df.columns))\n elif df1_always_df and not one_big_corpus:\n num_diff_results = len(list(df.columns))\n elif not df1_always_df and one_big_corpus:\n num_diff_results = len(list(df.index))\n elif df1_always_df and one_big_corpus:\n num_diff_results = len(list(df.columns))\n\n if num_diff_results == 0:\n if not root:\n print ''\n warnings.warn('No results produced. Maybe your query needs work.')\n else:\n time = strftime(\"%H:%M:%S\", localtime())\n print '%s: Interrogation produced no results, sorry.' % time\n return False\n\n if stotals.sum() == 0:\n if not root:\n print ''\n warnings.warn('No totals produced. Maybe your query needs work.')\n else:\n time = strftime(\"%H:%M:%S\", localtime())\n print '%s: Interrogation produced no results, sorry.' % time\n return False\n\n time = strftime(\"%H:%M:%S\", localtime())\n if printstatus:\n print '%s: Interrogation finished! %d unique results, %d total.' % (time, num_diff_results, stotals.sum())\n if not tk:\n print ''\n\n if quicksave:\n if stotals.sum() > 0 and num_diff_results > 0:\n from other import save_result\n save_result(output, quicksave)\n\n return output","title":""},{"docid":"d6c25ddac5290f858c0f4d0bae72b436","score":"0.4681151","text":"def find_corpora_visualizations():\n\n # build a quick map of visualizations, so that we can reference by annis label.\n vis_map = dict()\n for vis in HtmlVisualizationFormat.objects.all():\n vis_map[vis.slug] = vis\n\n for corpus in Corpus.objects.all():\n # get the list of all the visualizations already loaded for this corpus.\n already_have = set()\n for one_fmt in corpus.html_visualization_formats.all():\n already_have.add(one_fmt.slug)\n\n url_fmt = \"https://corpling.uis.georgetown.edu/annis-service/annis/query/resolver/{0}/NULL/node\"\n url_to_fetch = url_fmt.format(corpus.annis_corpus_name)\n res = request.urlopen(url_to_fetch)\n root = ET.fromstring(res.read())\n xpath = \"./resolverEntry[visType='htmldoc']/mappings/entry/value\"\n added = False\n for one_node in root.findall(xpath):\n vis_slug = one_node.text\n if vis_slug not in already_have:\n corpus.html_visualization_formats.add(vis_map[vis_slug])\n added = True\n\n # If we added any visualizations, save them now\n if added:\n corpus.save()","title":""},{"docid":"f9b8dbc964c6dab7f81072eb1f1e3fb7","score":"0.46805918","text":"def analyze_entities(text_content):\n # Set connection to client as variable\n client = language_v1.LanguageServiceClient()\n\n # Set type_ to read PLAIN_TEXT\n type_ = language_v1.Document.Type.PLAIN_TEXT\n\n # specify language & set document variable\n '''\n https://cloud.google.com/natural-language/docs/languages\n if language is not set, it will be detected.\n '''\n lang = \"en\"\n document = {\"content\": text_content, \"type_\": type_, \"language\": lang}\n\n # Set Encoding type to UTF8\n encoding_type = language_v1.EncodingType.UTF8\n\n # Pass in client request with defined specifications\n response = client.analyze_entities(request = {'document': document, 'encoding_type': encoding_type})\n\n # Loop through entitites returned from the API\n for entity in response.entities:\n print(u\"Representative name for the entity: {}\".format(entity.name))\n\n # Get entity type (PERSON, LOCATION, ADDRESS, NUMBER, etc)\n print(u\"Entity type: {}\".format(language_v1.Entity.Type(entity.type_).name))\n\n # Get salience score in [0, 1.0] range\n print(u\"Salience score: {}\".format(entity.salience))\n\n \n # Loop over the metadata associated with entity. \n '''\n Many known entities have a wiki (wikipedia_url) and Knowledge Graph MID (mid)/\n (Knowledge Graph is used by google to show widgets of condensed info from multiple sources)\n\n Some entity types may also have additional metadata\n e.g. ADDRESS entities may have metadata for the address street_name, postal_code, etc\n ''' \n for metadata_name, metadata_value in entity.metadata.items():\n print(u\"{}: {}\".format(metadata_name, metadata_value))\n \n # Loop over the mentions of entity from input document.\n '''\n API also supports proper noun mentions.\n # of appearances in given text will affect an entity's salience score.\n '''\n for mention in entity.mentions:\n print(u\"Mention text: {}\".format(mention.text.content))\n\n # Get mention type, e.g. PROPER for proper noun\n print(\n u\"Mention type: {}\".format(language_v1.EntityMention.Type(mention.type_).name)\n )\n\n # outputs the language, useful if language needs to be automatically detected.\n print(u\"Language of the text: {}\".format(response.language))","title":""},{"docid":"bc98f23142f5966cda19dd9b4b5c74ca","score":"0.46783927","text":"def showcase():\n\tfrom PIL import Image\n\tfrom PIL import ImageFont\n\tfrom PIL import ImageDraw\n\n\t# Optional: Varied loading process for showcases, when not done at the end of training\n\t# directory = \"results/dirname\"\n\t# checkpoint_path = directory + \"/50000.pth\"\n\t# checkpoint = torch.load(checkpoint_path)\n\t# epoch = checkpoint['epoch']\n\t\"\"\"\n\tfrom collections import OrderedDict\n\tnew_state_dict = OrderedDict()\n\tfor k, v in checkpoint['state_dict'].items():\n\t\t# name = k[7:] # remove `module.`\n\t\tname = k.replace(\".module\", \"\") # removing ‘.moldule’ from key\n\t\tnew_state_dict[name] = v\n\t# load params\n\tmodel.load_state_dict(new_state_dict)\n\n\toptimizer.load_state_dict(checkpoint['optimizer'])\n\tprint(\"Loaded checkpoint '{}' (epoch {})\".format(checkpoint_path, checkpoint['epoch']))\n\t\"\"\"\n\tos.makedirs(directory + \"/showcase\", exist_ok=True)\n\n\tglobal decoder_nat_loss, decoder_syn_loss, KLD_syn_loss, KLD_nat_loss, regressor_nat, regressor_syn\n\n\tactual_showcase(False, False)\n\treset_loss_sums()\n\tactual_showcase(True, False)\n\treset_loss_sums()\n\tactual_showcase(False, True)\n\treset_loss_sums()\n\tactual_showcase(True, True)","title":""},{"docid":"f8fc840b87112fb50c87543c7bc75ec0","score":"0.4677465","text":"def read_document(self):\n words = self.word_runner()\n word = \"press space to start\"\n orp_ind = 13\n try:\n while True:\n time.sleep(60 / self.wpm)\n\n if self.is_reading:\n word = next(words)\n orp_ind = int(self.orp_index(word))\n\n yield (word, orp_ind)\n except StopIteration:\n pass\n finally:\n del words","title":""},{"docid":"1e71b5860b51c1b20ff775ff4753becb","score":"0.46771982","text":"def deep_dream_of_extreme_control(FLAGS,model,input_images=[],num_iterations=10,step_size=0.1):\n if len(input_images) == 0:\n # use predefined images\n img_dir='/esat/opal/kkelchte/docker_home/pilot_data/visualization_images'\n input_images=sorted([img_dir+'/'+f for f in os.listdir(img_dir)])\n\n print(\"[tools.py]: extracting deep dream maps of {0} in {1}\".format([os.path.basename(i) for i in input_images], os.path.dirname(input_images[0])))\n \n # experts=np.asarray([[k]*(FLAGS.action_quantity if FLAGS.discrete else 1) for v in sorted(model.factor_offsets.values()) for k in model.factor_offsets.keys() if model.factor_offsets[k]==v]).flatten()\n\n inputs = load_images(input_images, model.input_size[1:])\n \n # collect gradients for output endpoint of evaluation model\n grads={}\n with tf.device('/cpu:0'):\n output_tensor = model.endpoints['eval']['outputs']\n for i in range(output_tensor.shape[1].value):\n layer_loss = output_tensor[:,i]\n gradients = tf.gradients(layer_loss, model.inputs)[0]\n gradients /= (tf.sqrt(tf.reduce_mean(tf.square(gradients))) + 1e-5)\n grads[output_tensor.name+'_'+str(i)]=gradients\n\n\n # apply gradient ascent for all outputs and each input image\n # if number of outputs ==1 apply gradient descent for contrast\n if len(grads.keys())== 1:\n opposite_results={}\n else:\n opposite_results=None\n\n import copy\n results = {}\n for gk in grads.keys(): \n results[gk]=copy.deepcopy(inputs)\n if isinstance(opposite_results,dict): opposite_results[gk]=copy.deepcopy(inputs)\n\n for step in range(num_iterations):\n if step%10==0: print \"{0} step: {1}\".format(time.ctime(), step)\n for i,gk in enumerate(sorted(grads.keys())):\n results[gk] += step_size * model.sess.run(grads[gk], {model.inputs: results[gk]})\n if isinstance(opposite_results,dict):\n opposite_results[gk] -= step_size * model.sess.run(grads[gk], {model.inputs: opposite_results[gk]})\n\n # Normalize results within 0:1 range\n clean_results={}\n for gk in results.keys():\n clean_results[gk]=[]\n for i in range(results[gk].shape[0]):\n clean_results[gk].append(deprocess_image(results[gk][i], one_channel=True))\n # results[gk][i]=deprocess_image(results[gk][i], one_channel=True)\n if isinstance(opposite_results,dict):\n opposite_results[gk][i]=deprocess_image(opposite_results[gk][i])\n\n # combine adjust input images in one overview image\n # one column for each input image\n # one row with each extreme control for separate and difference images\n num_rows=1+len(results.keys())\n fig, axes = plt.subplots(num_rows ,min(len(input_images),5),figsize=(23, 4*(len(grads.keys())+1)))\n # fig, axes = plt.subplots(num_rows ,min(len(input_images),5),figsize=(23, 4*(len(grads.keys())+1)))\n # add original images in first row\n for i in range(axes.shape[1]):\n axes[0, i].set_title(os.path.basename(input_images[i]).split('.')[0])\n axes[0, i].imshow(matplotlibprove(inputs[i]), cmap='inferno')\n axes[0, i].axis('off')\n\n # add for each filter the modified input\n row_index=1\n for gk in sorted(results.keys()):\n for i in range(axes.shape[1]):\n # print gk\n # axes[row_index, i].set_title('Grad Asc: '+gk.split('/')[1]+'/'+gk[-1]) \n axes[row_index, i].set_title('Grad Asc: '+gk)\n # axes[row_index, i].set_title(experts[row_index-1])\n\n axes[row_index, i].imshow(np.concatenate((inputs[i],np.expand_dims(clean_results[gk][i],axis=2)), axis=2), cmap='inferno')\n # axes[row_index, i].imshow(matplotlibprove(results[gk][i]), cmap='inferno')\n axes[row_index, i].axis('off')\n row_index+=1\n # In cas of continouos controls: visualize the gradient descent and difference\n # if isinstance(opposite_results,dict):\n # for gk in opposite_results.keys():\n # for i in range(axes.shape[1]):\n # # axes[row_index, i].set_title('Grad Desc: '+gk.split('/')[1]) \n # axes[row_index, i].set_title('Grad Desc: '+gk) \n # axes[row_index, i].imshow(matplotlibprove(opposite_results[gk][i]), cmap='inferno')\n # axes[row_index, i].axis('off')\n # row_index+=1\n \n # # add difference\n # for gk in opposite_results.keys():\n # for i in range(axes.shape[1]):\n # # axes[row_index, i].set_title('Diff: '+gk.split('/')[1]) \n # axes[row_index, i].set_title('Diff: '+gk) \n # axes[row_index, i].imshow(matplotlibprove(deprocess_image((opposite_results[gk][i]-results[gk][i])**2)), cmap='inferno')\n # axes[row_index, i].axis('off')\n # row_index+=1\n # else:\n # # add difference between 2 exteme actions\n # gk_left=sorted(results.keys())[0]\n # gk_right=sorted(results.keys())[-1]\n # for i in range(axes.shape[1]):\n # # axes[row_index, i].set_title('Diff : '+gk.split('/')[1]) \n # axes[row_index, i].set_title('Diff : '+gk) \n # axes[row_index, i].imshow(matplotlibprove(deprocess_image((results[gk_left][i]-results[gk_right][i])**2)), cmap='inferno')\n # axes[row_index, i].axis('off')\n # row_index+=1\n \n \n plt.savefig(FLAGS.summary_dir+FLAGS.log_tag+'/control_dream_maps.jpg',bbox_inches='tight')\n # plt.show()","title":""},{"docid":"a1211ae4f48a93630006ec87da1038d6","score":"0.4672397","text":"def wiki_analyzer(language: str) -> None:\r\n\trunning = True\r\n\tdbase = database.WikiDatabase(f'{language}wikidb')\r\n\r\n\tsource_buffer = []\r\n\ttarget_buffer = []\r\n\tpaths_buffer = []\r\n\r\n\tlast_time = time.time()\r\n\tpaths_added = 0\r\n\r\n\ttry:\r\n\t\twhile running:\r\n\t\t\r\n\t\t\tsources, targets, all_paths = analyze_path(dbase)\r\n\t\t\tsource_buffer.extend(sources)\r\n\t\t\ttarget_buffer.extend(targets)\r\n\t\t\tpaths_buffer.extend(all_paths)\r\n\t\t\tpaths_added += 2\r\n\r\n\t\t\tif len(source_buffer) >= BUFFER_SIZE:\r\n\t\t\t\tdbase.dump_statistics(source_buffer, target_buffer, paths_buffer)\r\n\t\t\t\tsource_buffer = []\r\n\t\t\t\ttarget_buffer = []\r\n\t\t\t\tpaths_buffer = []\r\n\r\n\t\t\tif select.select([sys.stdin], [], [], 0.0)[0]:\r\n\t\t\t\tusr_input = input()\r\n\t\t\t\tif usr_input.lower() == 'q' or usr_input.lower() == 'quit':\r\n\t\t\t\t\trunning = False\r\n\t\t\t\t\tif source_buffer:\r\n\t\t\t\t\t\tdbase.dump_statistics(source_buffer, target_buffer, paths_buffer)\r\n\r\n\t\t\tif time.time() - last_time > 15:\r\n\t\t\t\td_t = time.time() - last_time\r\n\t\t\t\tpaths_per_min = round(paths_added/d_t * 60)\r\n\t\t\t\tprint(f\"\\r{paths_per_min} paths / min \", end='')\r\n\t\t\t\tpaths_added = 0\r\n\t\t\t\tlast_time = time.time()\r\n\tfinally:\r\n\t\tif running: \r\n\t\t\tdbase.dump_statistics(source_buffer, target_buffer, paths_buffer)","title":""},{"docid":"1d6e331c27062a0f9d40ad67ab7987c2","score":"0.4666665","text":"def run_demo():\n while True:\n embeddings = beer_emb.embed_doc(input(\"Test if words are beer-related: \"),\n word_filter=False)\n for word_vec in embeddings:\n print(is_beer_related(word_vec))","title":""},{"docid":"f434f6961f529bac8a8952d09c4673b0","score":"0.46660876","text":"def Run_Extraction(self):\n\n # print the summary of the model\n print(self.ww_model.model.summary(), end=\"\\n\\n\", flush=True)\n # open an audio data stream\n self.stream = self.p.open(format=self.format, channels=self.channels,\n rate=self.rate, input=True,\n frames_per_buffer=self.chunk)\n\n act_count = 0\n\n while True:\n\n # reads chunk of audio\n data = self.stream.read(self.chunk)\n\n # appends chunk to frame list\n self.frames.append(data)\n\n # begins making predictions after the first\n # 2.5 seconds of audio is read\n if (len(self.frames) > 19):\n\n prediction = self.Prediction()\n\n # if the predictions is larger than the defined confidence\n if (prediction > self.confidence):\n\n # increment the activation counter\n act_count += 1\n\n # if the number of consecutive activations\n # exceeds the activation value\n if(act_count >= self.activations):\n\n # print out \"nimbus\"\n print(\" << nimbus >> \", end=\" \", flush=True)\n\n # reset activation count\n act_count = 0\n\n self.False_Activation()\n\n self.frames = self.frames[18:]\n\n if (self.false_counts >= self.false_count):\n self.Retrain_Model()\n\n # if prediction falls below the confidence level\n else:\n\n # reset the activation count\n act_count = 0\n\n if not(self.print_pred):\n # output nothing to the stream\n print(\"-\", end=\"\", flush=True)\n\n # window the data stream\n self.frames = self.frames[1:]","title":""},{"docid":"cb03b00deff42713f9827aa01b7eb32e","score":"0.46658754","text":"def main(args):\n corpus = read_corpus(args, verbose=True)\n for k in sorted(corpus, key=educe.stac.id_to_path):\n doc = corpus[k]\n print(\"========== %s ============\" % k)\n print()\n if args.edges:\n dialogues = sorted_first_widest(filter(is_dialogue, doc.units))\n if dialogues:\n d_first = dialogues[0]\n print(annotate_doc(doc, span=d_first.text_span()))\n if len(dialogues) > 1:\n d_last = dialogues[-1]\n txt = annotate_doc(doc, span=d_last.text_span())\n print(\"...\\n\")\n print(txt.encode('utf-8'))\n else:\n print(annotate_doc(doc).encode('utf-8'))\n print()","title":""}],"string":"[\n {\n \"docid\": \"d04f78c317274e362e0222600c00b016\",\n \"score\": \"0.5694965\",\n \"text\": \"def main():\\n logging.basicConfig(level=logging.DEBUG)\\n custom_embedding = True\\n\\n # Download embeddings'\\n if custom_embedding:\\n embedding_path = '../data/custom_embedding.pkl'\\n embedding_index_path = '../data/custom_vocab_index.pkl'\\n logging.info('Pulling custom embedding from: {}, and custom vocab from: {}'.format(embedding_path, embedding_index_path))\\n embedding_matrix = pickle.load(open(embedding_path, 'rb'))\\n embedding_index_lookup = pickle.load(open(embedding_index_path, 'rb'))\\n\\n else:\\n logging.warning('Downloading embedding. If downloading for the first time, this make take 5-10 minutes.')\\n embedding_url = 'https://s3.amazonaws.com/dl4j-distribution/GoogleNews-vectors-negative300.bin.gz'\\n embedding_path = '~/nlp_example/'\\n embedding_filename = 'GoogleNews-vectors-negative300.bin.gz'\\n lib.download_file(embedding_url, embedding_path, embedding_filename)\\n\\n # Unpack embedding\\n model = gensim.models.KeyedVectors.load_word2vec_format(embedding_path + '/' + embedding_filename, binary=True)\\n embedding_matrix = model.syn0\\n embedding_index_lookup = dict([(k, v.index) for k, v in model.vocab.items()])\\n\\n # Create thesaurus\\n thesaurus = Thesaurus(embedding_matrix, embedding_index_lookup)\\n\\n # Find nearest neighbors for examples\\n print(thesaurus.synonyms('day'))\\n print(thesaurus.synonyms('top'))\\n print(thesaurus.synonyms('bottom'))\\n print(thesaurus.synonyms('cat'))\\n print(thesaurus.synonyms('grown'))\\n\\n\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5414d2cceb439a2c817b0dd22dec84e6\",\n \"score\": \"0.5648464\",\n \"text\": \"def analyse_presentation(pres_name:str, verbose=False) -> Dict[str, Any]:\\n prs = Presentation(pres_name)\\n if verbose:\\n debug_dump(prs)\\n (layouts_interactive, layouts) = count_layouts(prs)\\n interaction_stars = min(layouts_interactive, 5)\\n topic_stars = ([1,1,3,5,5,4,3,2,1]+[1]*100)[layouts[\\\"Section Header\\\"]]\\n\\n pres_properties = get_presentation_properties(prs)\\n\\n word_count = get_word_counts(prs.slides)\\n words_per_slide = sum(word_count) / len(word_count)\\n # ideal words/slide is 30-40 (5 stars)\\n text_stars = calculate_text_stars(word_count)\\n # print(\\\"word counts:\\\", word_count)\\n\\n # Create a list of warnings about very text-heavy slides\\n heavy_warnings = []\\n for slide, words in enumerate(word_count):\\n if words > MAX_WORDS_PER_SLIDE:\\n heavy_warnings.append(f\\\"WARNING: slide {slide} has {words} words!\\\")\\n\\n\\n slides = get_slide_analytics(prs.slides)\\n print(slides)\\n result = {\\n\\n \\\"presentation_rating_stars_interaction\\\": interaction_stars,\\n \\\"presentation_rating_stars_section\\\": topic_stars,\\n \\\"presentation_rating_stars_accessibility\\\": 3, # not implemented yet!\\n \\\"presentation_rating_stars_text\\\": text_stars,\\n \\\"presentation_count_slide\\\": len(prs.slides),\\n \\\"presentation_count_layout\\\": layouts, # dictionary that maps layout name to count\\n \\\"presentation_total_words\\\": words_per_slide, # a float\\n \\\"presentation_warning_text_heavy\\\": heavy_warnings, # a list of warning strings\\n \\\"presentation_data_slides\\\": slides, # a list of slides and analytics\\n \\\"filename\\\": pres_name, # TODO: strip any Path and just return file name?\\n \\\"name\\\": \\\"ICT999\\\",\\n \\\"description\\\": \\\"Introduction to ICT\\\"\\n }\\n\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0014dd9244d3b08e26a41c8651af12d\",\n \"score\": \"0.5442001\",\n \"text\": \"def run(self):\\n self.tf_idf_scores = self.tf_idf()\\n self.word_scores = self.create_word_score()\\n self.keyword_scores = self.create_keyword_score()\\n self.text_ranks = self.create_text_rank()\\n self.pos_scores = {}\\n self.z_area_scores = {}\\n self.create_pos_and_area_score()\\n self.all_scores = self.weight_distribution()\\n self.all_scores_dict = self.get_all_scores_dict()\\n return self.get_keywords()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"403a0a54c67e379424a9698052881df0\",\n \"score\": \"0.54035425\",\n \"text\": \"def gather_documents(self):\\n self.document_gatherer.gather_and_save_everything(Constants.path_cord, \\n Constants.path_metadata, \\n Constants.path_linked_documents,\\n Constants.path_unlinked_documents,\\n Constants.path_parsed_documents,\\n Constants.path_all_documents)\\n \\n print(\\\"Done gathering documents.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d625ed60e5b26a3d637f1ce533c50d0d\",\n \"score\": \"0.53915334\",\n \"text\": \"def engine():\\r\\n document.add_heading('Engine details', 1)\\r\\n\\r\\n engine_metrics = ['customProperties','listenerPorts','autosaveInterval', 'tableFilesDirectory', 'genericUndoBufferMaxSize', 'documentTimeout','documentDirectory',\\r\\n 'allowDataLineage', 'qrsHttpNotificationPort', 'standardReload',\\r\\n 'workingSetSizeLoPct', 'workingSetSizeHiPct', 'workingSetSizeMode','cpuThrottlePercentage', 'maxCoreMaskPersisted', 'maxCoreMask',\\r\\n 'maxCoreMaskHiPersisted', 'maxCoreMaskHi','objectTimeLimitSec', 'exportTimeLimitSec', 'reloadTimeLimitSec',\\r\\n 'hyperCubeMemoryLimit', 'exportMemoryLimit', 'reloadMemoryLimit', 'createSearchIndexOnReloadEnabled', 'hostname',\\r\\n 'globalLogMinuteInterval','auditActivityLogVerbosity','auditSecurityLogVerbosity','serviceLogVerbosity','systemLogVerbosity','performanceLogVerbosity',\\r\\n 'qixPerformanceLogVerbosity','auditLogVerbosity','sessionLogVerbosity','trafficLogVerbosity']\\r\\n\\r\\n enginenodes = get_qlik_sense.get_engine()\\r\\n num_of_engines = len(enginenodes)\\r\\n num_of_engine_metrics = len(engine_metrics)\\r\\n table = document.add_table(rows=num_of_engine_metrics+1, cols=num_of_engines+1)\\r\\n table.style = 'Grid Table 1 Light Accent 1'\\r\\n row = table.rows[0]\\r\\n row.cells[0].text = 'Metric'\\r\\n for item in range(0, num_of_engines):\\r\\n row.cells[item+1].text = enginenodes[item][36]\\r\\n for item in range(num_of_engine_metrics):\\r\\n row = table.rows[item+1]\\r\\n row.cells[0].text = str(engine_metrics[item])\\r\\n for enginenode in range(num_of_engines):\\r\\n row.cells[enginenode+1].text = str(enginenodes[enginenode][item])\\r\\n\\r\\n document.add_page_break()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6cd69e819f3bb959089daf448964771b\",\n \"score\": \"0.53757584\",\n \"text\": \"def analyze(self, content):\\n words = self.remove_stop_words(content)\\n words = self.select_relevant_pos_words(words)\\n concepts = [self.get_wiki_content(word) for word in words]\\n self.create_wordcloud(concepts)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c2a7b6f5798d9a40ee7787daab82aa32\",\n \"score\": \"0.52914554\",\n \"text\": \"def main():\\r\\n\\tlang = get_arguments()\\r\\n\\twiki_analyzer(lang)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae108b8342048aef56aaa979ce64d207\",\n \"score\": \"0.524891\",\n \"text\": \"def analyse(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed0ae4bc0fa46fb9d01fdf38b5c22a1c\",\n \"score\": \"0.5186837\",\n \"text\": \"def analyse_document(dom, arguments):\\n model = dom.getElementsByTagName(\\\"model\\\")[0]\\n return analyse_model(model, arguments)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"976be3cf0892e1e404a68421e4097787\",\n \"score\": \"0.5137216\",\n \"text\": \"def phonology(request):\\n\\n perspective_cid = request.params.get('perspective_client_id')\\n perspective_oid = request.params.get('perspective_object_id')\\n\\n # Checking if we have limits on number of computed results.\\n\\n limit = (None if 'limit' not in request.params else\\n int(request.params.get('limit')))\\n\\n limit_exception = (None if 'limit_exception' not in request.params else\\n int(request.params.get('limit_exception')))\\n\\n limit_no_vowel = (None if 'limit_no_vowel' not in request.params else\\n int(request.params.get('limit_no_vowel')))\\n\\n limit_result = (None if 'limit_result' not in request.params else\\n int(request.params.get('limit_result')))\\n\\n # TODO: get perspective's translation and language it belongs to.\\n\\n # We get lexical entries of this perspective with markup'ed sounds.\\n\\n Sound = aliased(Entity, name = \\\"Sound\\\")\\n PublishingSound = aliased(PublishingEntity, name = \\\"PublishingSound\\\")\\n\\n query = DBSession.query(LexicalEntry, Entity, Sound, PublishingEntity, PublishingSound).filter(and_(\\n LexicalEntry.parent_client_id == perspective_cid,\\n LexicalEntry.parent_object_id == perspective_oid,\\n LexicalEntry.marked_for_deletion == False,\\n Entity.parent_client_id == LexicalEntry.client_id,\\n Entity.parent_object_id == LexicalEntry.object_id,\\n Entity.marked_for_deletion == False,\\n Entity.additional_metadata.contains({\\\"data_type\\\": \\\"praat markup\\\"}),\\n PublishingEntity.client_id == Entity.client_id,\\n PublishingEntity.object_id == Entity.object_id,\\n PublishingEntity.published == True,\\n PublishingEntity.accepted == True,\\n Sound.client_id == Entity.self_client_id,\\n Sound.object_id == Entity.self_object_id,\\n Sound.marked_for_deletion == False,\\n PublishingSound.client_id == Sound.client_id,\\n PublishingSound.object_id == Sound.object_id,\\n PublishingSound.published == True,\\n PublishingSound.accepted == True))\\n\\n # We process these lexical entries in batches. Just in case, it seems that perspectives rarely have more\\n # then several hundred such lexical entries.\\n\\n exception_counter = 0\\n no_vowel_counter = 0\\n result_list = list()\\n\\n for index, row in enumerate(query.yield_per(100)):\\n\\n markup_url = row.Entity.content\\n sound_url = row.Sound.content\\n\\n cache_key = 'phonology:{0}:{1}:{2}:{3}'.format(\\n row.Sound.client_id, row.Sound.object_id,\\n row.Entity.client_id, row.Entity.object_id)\\n\\n # Checking if we have cached result for this pair of sound/markup.\\n\\n cache_result = CACHE.get(cache_key)\\n\\n if cache_result == 'no_vowel':\\n\\n log.debug(\\n '{0} (LexicalEntry {1}/{2}, sound-Entity {3}/{4}, markup-Entity {5}/{6}) '\\n '[CACHE {7}]: no vowels\\\\n{8}\\\\n{9}'.format(\\n index,\\n row.LexicalEntry.client_id, row.LexicalEntry.object_id,\\n row.Sound.client_id, row.Sound.object_id,\\n row.Entity.client_id, row.Entity.object_id,\\n cache_key, markup_url, sound_url))\\n\\n no_vowel_counter += 1\\n\\n if (limit_no_vowel and no_vowel_counter >= limit_no_vowel or\\n limit and index + 1 >= limit):\\n break\\n\\n continue\\n\\n # If we have cached exception, we do the same as with absence of vowels, show its info and\\n # continue.\\n\\n elif isinstance(cache_result, tuple) and cache_result[0] == 'exception':\\n exception, traceback_string = cache_result[1:3]\\n\\n log.debug(\\n '{0} (LexicalEntry {1}/{2}, sound-Entity {3}/{4}, markup-Entity {5}/{6}): '\\n '[CACHE {7}]: exception\\\\n{8}\\\\n{9}'.format(\\n index,\\n row.LexicalEntry.client_id, row.LexicalEntry.object_id,\\n row.Sound.client_id, row.Sound.object_id,\\n row.Entity.client_id, row.Entity.object_id,\\n cache_key, markup_url, sound_url))\\n\\n log.debug(traceback_string)\\n\\n exception_counter += 1\\n\\n if (limit_exception and exception_counter >= limit_exception or\\n limit and index + 1 >= limit):\\n break\\n\\n continue\\n\\n # If we actually have the result, we use it and continue.\\n\\n elif cache_result:\\n\\n result_string = '\\\\n'.join(\\n 'tier {0} \\\\'{1}\\\\': {2}'.format(tier_number, tier_name,\\n \\n tier_result_seq_list if not isinstance(tier_result_seq_list, list) else\\n tier_result_seq_list[0] if len(tier_result_seq_list) <= 1 else\\n ''.join('\\\\n {0}'.format(tier_result) for tier_result in tier_result_seq_list))\\n\\n for tier_number, tier_name, tier_result_seq_list in cache_result)\\n\\n log.debug(\\n '{0} (LexicalEntry {1}/{2}, sound-Entity {3}/{4}, markup-Entity {5}/{6}) '\\n '[CACHE {7}]:\\\\n{8}\\\\n{9}\\\\n{10}'.format(\\n index,\\n row.LexicalEntry.client_id, row.LexicalEntry.object_id,\\n row.Sound.client_id, row.Sound.object_id,\\n row.Entity.client_id, row.Entity.object_id,\\n cache_key, markup_url, sound_url, result_string))\\n\\n result_list.append(cache_result)\\n\\n if (limit_result and len(result_list) >= limit_result or\\n limit and index + 1 >= limit):\\n break\\n\\n continue\\n\\n try:\\n # Getting markup, checking for each tier if it needs to be processed.\\n\\n markup_bytes = urllib.request.urlopen(urllib.parse.quote(markup_url, safe = '/:')).read()\\n\\n textgrid = pympi.Praat.TextGrid(xmax = 0)\\n textgrid.from_file(\\n io.BytesIO(markup_bytes),\\n codec = chardet.detect(markup_bytes)['encoding'])\\n\\n tier_data_list = []\\n vowel_flag = False\\n\\n for tier_number, tier_name in textgrid.get_tier_name_num():\\n\\n raw_interval_list = textgrid.get_tier(tier_number).get_all_intervals()\\n raw_interval_seq_list = [[]]\\n\\n # Splitting interval sequence on empty intervals.\\n\\n for raw_index, interval in enumerate(raw_interval_list):\\n\\n if len(interval[2].strip()) <= 0:\\n if len(raw_interval_seq_list[-1]) > 0:\\n raw_interval_seq_list.append([])\\n\\n else:\\n raw_interval_seq_list[-1].append((raw_index, interval))\\n\\n if len(raw_interval_seq_list[-1]) <= 0:\\n del raw_interval_seq_list[-1]\\n\\n # Selecting interval sequences for analysis, checking if we have unusual markup.\\n \\n interval_seq_list = []\\n interval_idx_to_raw_idx = dict()\\n\\n unusual_markup_flag = False\\n unusual_markup_list = []\\n\\n for raw_interval_seq in raw_interval_seq_list:\\n\\n interval_seq_list.append([])\\n interval_idx_to_raw_idx[len(interval_seq_list) - 1] = {}\\n\\n for partial_raw_index, (raw_index, interval) in enumerate(raw_interval_seq):\\n\\n interval_text = interval[2].strip()\\n\\n # Accepting interval if its text contains at least one vowel, and is short enough or\\n # is a valid phonetic transcription.\\n\\n transcription_check = re.fullmatch(transcription_re, interval_text)\\n\\n if (len(interval_text) > 0 and\\n any(character in vowel_set for character in interval_text) and\\n (len(interval_text) <= 2 or transcription_check)):\\n\\n interval_seq_list[-1].append(interval)\\n\\n sequence_index = len(interval_seq_list) - 1\\n interval_index = len(interval_seq_list[-1]) - 1\\n\\n interval_idx_to_raw_idx[(sequence_index, interval_index)] = raw_index\\n interval_idx_to_raw_idx[sequence_index][interval_index] = partial_raw_index\\n\\n # Noting if the interval contains unusual (i.e. non-transcription) markup.\\n\\n elif not transcription_check:\\n\\n unusual_markup_flag = True\\n unusual_markup_list.append((raw_index, interval))\\n\\n transcription_list = [text for begin, end, text in raw_interval_list]\\n transcription = ''.join(transcription_list)\\n\\n selected_list = [text\\n for interval_list in interval_seq_list\\n for begin, end, text in interval_list]\\n\\n selected = ''.join(selected_list)\\n\\n # If we have intervals with unusual markup, we report them.\\n\\n if unusual_markup_flag:\\n log.debug(\\n '{0} (LexicalEntry {1}/{2}, sound-Entity {3}/{4}, markup-Entity {5}/{6}): '\\n 'tier {7} \\\\'{8}\\\\' has interval(s) with unusual transcription text: '\\n '{9} / {10}'.format(\\n index,\\n row.LexicalEntry.client_id, row.LexicalEntry.object_id,\\n row.Sound.client_id, row.Sound.object_id,\\n row.Entity.client_id, row.Entity.object_id,\\n tier_number, tier_name, transcription, dict(unusual_markup_list)))\\n\\n # If the markup does not have any vowels, we note it and also report it.\\n\\n if all(character not in vowel_set for character in transcription):\\n\\n tier_data_list.append((tier_number, tier_name, 'no_vowel'))\\n\\n log.debug(\\n '{0} (LexicalEntry {1}/{2}, sound-Entity {3}/{4}, markup-Entity {5}/{6}): '\\n 'tier {7} \\\\'{8}\\\\' doesn\\\\'t have any vowel markup: {9}'.format(\\n index,\\n row.LexicalEntry.client_id, row.LexicalEntry.object_id,\\n row.Sound.client_id, row.Sound.object_id,\\n row.Entity.client_id, row.Entity.object_id,\\n tier_number, tier_name, transcription_list))\\n\\n # It is also possible that while full transcription has vowels, intervals selected for\\n # analysis do not. In that case we also note it and report it.\\n\\n elif not any(character in vowel_set for character in selected):\\n\\n tier_data_list.append((tier_number, tier_name, 'no_vowel_selected'))\\n\\n log.debug(\\n '{0} (LexicalEntry {1}/{2}, sound-Entity {3}/{4}, markup-Entity {5}/{6}): '\\n 'tier {7} \\\\'{8}\\\\' intervals to be processed don\\\\'t have any vowel markup: '\\n 'markup {9}, selected {10}'.format(\\n index,\\n row.LexicalEntry.client_id, row.LexicalEntry.object_id,\\n row.Sound.client_id, row.Sound.object_id,\\n row.Entity.client_id, row.Entity.object_id,\\n tier_number, tier_name,\\n transcription_list, selected_list))\\n\\n # Otherwise we store tier data to be used during processing of the sound file.\\n\\n else:\\n tier_data_list.append((tier_number, tier_name,\\n (raw_interval_list, raw_interval_seq_list, interval_seq_list,\\n interval_idx_to_raw_idx, transcription)))\\n\\n vowel_flag = True\\n\\n # If there are no tiers with vowel markup, we skip this sound-markup file altogether.\\n\\n if not vowel_flag:\\n\\n CACHE.set(cache_key, 'no_vowel')\\n no_vowel_counter += 1\\n\\n if (limit_no_vowel and no_vowel_counter >= limit_no_vowel or\\n limit and index + 1 >= limit):\\n break\\n\\n continue\\n\\n # Otherwise we retrieve the sound file and analyse each vowel-containing markup.\\n # Partially inspired by source code at scripts/convert_five_tiers.py:307.\\n\\n sound = None\\n with tempfile.NamedTemporaryFile() as temp_file:\\n\\n sound_file = urllib.request.urlopen(urllib.parse.quote(sound_url, safe = '/:'))\\n temp_file.write(sound_file.read())\\n temp_file.flush()\\n\\n sound = AudioPraatLike(pydub.AudioSegment.from_wav(temp_file.name))\\n\\n tier_result_list = []\\n\\n for tier_number, tier_name, tier_data in tier_data_list:\\n\\n if tier_data == 'no_vowel' or tier_data == 'no_vowel_selected':\\n tier_result_list.append((tier_number, tier_name, tier_data))\\n continue\\n\\n # Analyzing vowel sounds of each interval sequence.\\n\\n (raw_interval_list, raw_interval_seq_list, interval_seq_list, interval_idx_to_raw_idx,\\n transcription) = tier_data\\n\\n tier_result_list.append((tier_number, tier_name, []))\\n\\n for seq_index, (raw_interval_list, interval_list) in enumerate(zip(\\n raw_interval_seq_list, interval_seq_list)):\\n\\n if len(interval_list) <= 0:\\n continue\\n\\n (max_intensity_index, max_intensity, max_length_index, max_length) = \\\\\\n find_max_interval_praat(sound, interval_list)\\n\\n max_intensity_interval = interval_list[max_intensity_index]\\n max_length_interval = interval_list[max_length_index]\\n\\n max_intensity_f1_f2 = sound.get_interval_formants(*max_intensity_interval[:2])\\n max_length_f1_f2 = sound.get_interval_formants(*max_length_interval[:2])\\n\\n # Compiling results.\\n\\n max_length_str = '{0} {1:.3f} [{2}]'.format(\\n max_length_interval[2], max_length,\\n len(''.join(text for index, (begin, end, text) in\\n raw_interval_list[:interval_idx_to_raw_idx[seq_index][max_length_index]])))\\n\\n max_intensity_str = '{0} {1:.3f} [{2}]'.format(\\n max_intensity_interval[2],\\n max_intensity,\\n len(''.join(text for index, (begin, end, text) in\\n raw_interval_list[:interval_idx_to_raw_idx[seq_index][max_intensity_index]])))\\n\\n tier_result_list[-1][2].append([\\n ''.join(text for index, (begin, end, text) in raw_interval_list),\\n max_length_str,\\n '{0:.3f}'.format(max_length_f1_f2[0]),\\n '{0:.3f}'.format(max_length_f1_f2[1]),\\n max_intensity_str,\\n '{0:.3f}'.format(max_intensity_f1_f2[0]),\\n '{0:.3f}'.format(max_intensity_f1_f2[1]),\\n '+' if max_intensity_index == max_length_index else '-'])\\n\\n # Saving result.\\n\\n result_list.append(tier_result_list)\\n CACHE.set(cache_key, tier_result_list)\\n\\n result_string = '\\\\n'.join(\\n 'tier {0} \\\\'{1}\\\\': {2}'.format(tier_number, tier_name,\\n \\n tier_result_seq_list if not isinstance(tier_result_seq_list, list) else\\n tier_result_seq_list[0] if len(tier_result_seq_list) <= 1 else\\n ''.join('\\\\n {0}'.format(tier_result) for tier_result in tier_result_seq_list))\\n\\n for tier_number, tier_name, tier_result_seq_list in tier_result_list)\\n\\n log.debug(\\n '{0} (LexicalEntry {1}/{2}, sound-Entity {3}/{4}, markup-Entity {5}/{6}):'\\n '\\\\n{7}\\\\n{8}\\\\n{9}'.format(\\n index,\\n row.LexicalEntry.client_id, row.LexicalEntry.object_id,\\n row.Sound.client_id, row.Sound.object_id,\\n row.Entity.client_id, row.Entity.object_id,\\n markup_url, sound_url, result_string))\\n\\n # Stopping earlier, if required.\\n\\n if (limit_result and len(result_list) >= limit_result or\\n limit and index + 1 >= limit):\\n break\\n\\n except Exception as exception:\\n\\n #\\n # NOTE\\n #\\n # Exceptional situations encountered so far:\\n #\\n # 1. TextGrid file actually contains sound, and wav file actually contains textgrid markup.\\n #\\n # Perspective 330/4, LexicalEntry 330/7, sound-Entity 330/2328, markup-Entity 330/6934\\n #\\n # 2. Markup for one of the intervals contains a newline \\\"\\\\n\\\", and pympi fails to parse it.\\n # Praat parses such files without problems.\\n #\\n # Perspective 330/4, LexicalEntry 330/20, sound-Entity 330/6297, markup-Entity 330/6967\\n #\\n\\n log.debug(\\n '{0} (LexicalEntry {1}/{2}, sound-Entity {3}/{4}, markup-Entity {5}/{6}): '\\n 'exception\\\\n{7}\\\\n{8}'.format(\\n index,\\n row.LexicalEntry.client_id, row.LexicalEntry.object_id,\\n row.Sound.client_id, row.Sound.object_id,\\n row.Entity.client_id, row.Entity.object_id,\\n markup_url, sound_url))\\n\\n # if we encountered an exception, we show its info and remember not to try offending\\n # sound/markup pair again.\\n\\n traceback_string = ''.join(traceback.format_exception(\\n exception, exception, exception.__traceback__))[:-1]\\n\\n log.debug(traceback_string)\\n\\n CACHE.set(cache_key, ('exception', exception,\\n traceback_string.replace('Traceback', 'CACHEd traceback')))\\n\\n exception_counter += 1\\n\\n if (limit_exception and exception_counter >= limit_exception or\\n limit and index + 1 >= limit):\\n break\\n\\n log.debug('phonology {0}/{1}: {2} result{3}, {4} no vowels, {5} exceptions'.format(\\n perspective_cid, perspective_oid,\\n len(result_list), '' if len(result_list) == 1 else 's',\\n no_vowel_counter, exception_counter))\\n\\n # If we have no results, we indicate the situation and also show number of failures and number of\\n # markups with no vowels.\\n\\n if not result_list:\\n request.response.status = HTTPPreconditionFailed.code\\n\\n return {\\n \\\"error\\\": \\\"no markups for this query\\\",\\n \\\"exception_counter\\\": exception_counter,\\n \\\"no_vowel_counter\\\": no_vowel_counter}\\n\\n # Otherwise we create and then serve Excel file.\\n\\n excel_book = xlwt.Workbook(encoding = \\\"utf-8\\\")\\n sheet = excel_book.add_sheet(\\\"Sheet 1\\\")\\n\\n sheet.write(0, 0, 'Transcription')\\n sheet.write(0, 1, 'Longest (seconds) interval')\\n sheet.write(0, 2, 'F1 (Hz)')\\n sheet.write(0, 3, 'F2 (Hz)')\\n sheet.write(0, 4, 'Highest intensity (dB) interval')\\n sheet.write(0, 5, 'F1 (Hz)')\\n sheet.write(0, 6, 'F2 (Hz)')\\n sheet.write(0, 7, 'Coincidence')\\n\\n row_counter = 1\\n\\n for tier_result_list in result_list:\\n for tier_number, tier_name, tier_result_seq_list in tier_result_list:\\n\\n if tier_result_seq_list == 'no_vowel':\\n continue\\n\\n for tier_data in tier_result_seq_list:\\n for index, tier_data_str in enumerate(tier_data):\\n sheet.write(row_counter, index, tier_data_str)\\n\\n row_counter += 1\\n\\n # Formatting column widths.\\n\\n sheet.col(0).width = 24 * 256\\n sheet.col(1).width = 24 * 256\\n sheet.col(2).width = 12 * 256\\n sheet.col(3).width = 12 * 256\\n sheet.col(4).width = 24 * 256\\n sheet.col(5).width = 12 * 256\\n sheet.col(6).width = 12 * 256\\n sheet.col(7).width = 12 * 256\\n\\n excel_stream = io.BytesIO()\\n excel_book.save(excel_stream)\\n excel_stream.seek(0)\\n\\n # See http://stackoverflow.com/questions/2937465/what-is-correct-content-type-for-excel-files for Excel\\n # content-type.\\n\\n response = Response(content_type = 'application/vnd.ms-excel')\\n\\n response.app_iter = FileIter(excel_stream)\\n response.headers['Content-Disposition'] = \\\"attachment; filename=phonology.xls\\\"\\n\\n return response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6a4cdea62963b32f50ad71f7fa1cf1e\",\n \"score\": \"0.5105812\",\n \"text\": \"def extract_features(self):\\n self.extract_features_static()\\n self.extract_features_dynamic()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5197cf37652e92e9c47a02f9d0cbe04\",\n \"score\": \"0.5100792\",\n \"text\": \"def main(keyword):\\n fs_db = initFirestore()\\n # keyword = \\\"whatshappeninginmyanmar\\\"\\n all_posts = []\\n\\n twitterCrawler = TwitterCrawler(fs_db,keyword) \\n cleaned_tweets = twitterCrawler.crawl() \\n\\n redditCrawler = RedditCrawler(fs_db, keyword) \\n cleaned_submissions = redditCrawler.crawl() \\n\\n twitterCrawler.exportToDB(cleaned_tweets)\\n redditCrawler.exportToDB(cleaned_submissions)\\n\\n crawler = Crawler()\\n\\n crawler.generateSentimentAnalysis(fs_db, cleaned_submissions, cleaned_tweets)\\n wordcloud_img = crawler.generateWordCloud()\\n\\n # Send wordcloud to DB\\n doc_ref = fs_db.collection(u'wordcloud').document('first')\\n doc_ref.set({\\n u'image': wordcloud_img\\n })\",\n \"title\": \"\"\n },\n {\n \"docid\": \"166521861a8bb8d2e5ddb5b9a61e037d\",\n \"score\": \"0.5074083\",\n \"text\": \"def _prepare_analysis_input(self, documents):\\n subdoc_to_doc_map = {}\\n wordtype_to_number = {}\\n number_to_wordtype = []\\n wordtypes = {}\\n \\n # prevent duplicating work\\n if os.path.exists(self.wordtype_file):\\n return\\n \\n try:\\n # First find singletons\\n if self.remove_singletons:\\n word_type_count_threshold = max(1, int(math.log(documents.count(), 10)) - 2)\\n temp_word_type_counts = {}\\n for doc_index, doc in enumerate(documents):\\n tokens = self.tokenize(doc.get_content())\\n for token, token_start in tokens:\\n temp_word_type_counts[token] = temp_word_type_counts.setdefault(token, 0) + 1\\n for word_type, count in temp_word_type_counts.iteritems(): # add singletons to stopword list\\n if count <= word_type_count_threshold:\\n self._excluded_words[word_type] = True\\n with io.open(self.excluded_words_file, 'w', encoding='utf-8') as ex_f:\\n ex_f.write(unicode(json.dumps(self._excluded_words)))\\n \\n haltwords = dict(self.stopwords)\\n haltwords.update(self._excluded_words)\\n # Second find bigrams, iterate through documents and train.\\n if self.find_bigrams:\\n from import_tool.analysis.bigram_finder import BigramFinder\\n bigram_finder = BigramFinder(stopwords=haltwords)\\n for doc_index, doc in enumerate(documents):\\n bigram_finder.train(doc_index, self.tokenize(doc.get_content()))\\n bigram_finder.print()\\n \\n # Third, we're going to stem words\\n if self.stem_words:\\n from import_tool.analysis.stemmer import Stemmer\\n stemmer = Stemmer(self._working_dir, self.base_dir)\\n \\n # for each document tokenize and map tokens to numbers to avoid regex problems before passing data to Mallet\\n with io.open(self.mallet_input_file, 'w', encoding='utf-8') as w:\\n with io.open(self.start_index_file, 'w', encoding='utf-8') as w2:\\n count = 0\\n subcount = 0\\n for doc_index, doc in enumerate(documents):\\n doc_content = unicode(doc.get_content())\\n count += 1\\n subdocuments = self.create_subdocuments(doc_index, doc_content)\\n token_start_index_offset = 0 # needed to make sure the start index remains correct once the document is re-merged\\n for subdoc_name, subdoc_content in subdocuments:\\n if subcount > 0:\\n w2.write(u'\\\\n')\\n subcount += 1\\n subdoc_to_doc_map[subdoc_name] = doc_index\\n tokens = self.tokenize(subdoc_content)\\n \\n if self.find_bigrams:\\n tokens = bigram_finder.combine(tokens, subdoc_content)\\n \\n token_numbers = []\\n token_start_indices = []\\n only_tokens = []\\n tokens_temp = []\\n for tok, tok_start in tokens:\\n only_tokens.append(tok)\\n tokens_temp.append([tok, tok_start + token_start_index_offset])\\n tokens = tokens_temp\\n tokens_temp = None\\n if self.stem_words:\\n stemmed_tokens = stemmer.stem(only_tokens)\\n else:\\n stemmed_tokens = only_tokens\\n for tup, tok_stem in zip(tokens, stemmed_tokens):\\n tok, tok_start = tup\\n wordtypes[tok] = True\\n wordtypes[tok_stem] = True\\n try:\\n tok_num = wordtype_to_number[tok_stem]\\n except:\\n tok_num = len(wordtype_to_number)\\n number_to_wordtype.append(tok_stem)\\n wordtype_to_number[tok_stem] = tok_num\\n token_numbers.append(unicode(tok_num))\\n token_start_indices.append([tok, tok_start])\\n text = u' '.join(token_numbers)\\n w.write(u'{0} all {1}\\\\n'.format(subdoc_name, text))\\n w2.write(unicode(json.dumps(token_start_indices)))\\n token_start_index_offset += len(subdoc_content)\\n for tok, tok_start in tokens:\\n try:\\n assert doc_content[tok_start:tok_start+len(tok)].lower() == tok.lower()\\n except:\\n print(tok_start)\\n print(len(tok))\\n print('\\\"'+doc_content[tok_start:tok_start+len(tok)].lower()+'\\\"')\\n print('\\\"'+tok.lower()+'\\\"')\\n raise\\n if not count:\\n raise Exception('No files processed.')\\n # record which subdocuments belong to which documents\\n with io.open(self.subdoc_to_doc_map_file, 'w', encoding='utf-8') as w:\\n w.write(unicode(json.dumps(subdoc_to_doc_map)))\\n with io.open(self.wordtype_to_number_file, 'w', encoding='utf-8') as w:\\n w.write(unicode(json.dumps(wordtype_to_number)))\\n with io.open(self.number_to_wordtype_file, 'w', encoding='utf-8') as w:\\n w.write(unicode(json.dumps(number_to_wordtype)))\\n with io.open(self.wordtype_file, 'w', encoding='utf-8') as w:\\n w.write(unicode(json.dumps(wordtypes)))\\n except: # cleanup\\n self._cleanup(self.mallet_input_file)\\n self._cleanup(self.subdoc_to_doc_map_file)\\n self._cleanup(self.wordtype_to_number_file)\\n self._cleanup(self.number_to_wordtype_file)\\n self._cleanup(self.wordtype_file)\\n self._cleanup(self.excluded_words_file)\\n raise\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86fa15b2609b8808c96c9a44223e9b6c\",\n \"score\": \"0.5035346\",\n \"text\": \"def main():\\n LESSONS_PATH = os.path.join(LESSON_LOCATOR_DATA, LESSON_SETS[0])\\n ORIGINAL_LESSONS_PATH = os.path.join(LESSONS_PATH, \\\"original\\\")\\n ANNOTATED_LESSONS_PATH = os.path.join(LESSONS_PATH, \\\"annotated\\\")\\n\\n if not os.path.exists(ANNOTATED_LESSONS_PATH):\\n os.mkdir(ANNOTATED_LESSONS_PATH)\\n\\n print(\\\"Scanning original lessons in %s...\\\" % ORIGINAL_LESSONS_PATH)\\n\\n for item in os.listdir(ORIGINAL_LESSONS_PATH):\\n if item == \\\".DS_Store\\\": continue\\n\\n print(\\\" found: %s\\\" % item)\\n\\n item_path = os.path.join(ORIGINAL_LESSONS_PATH, item)\\n\\n lesson_number = None\\n lesson_description = None\\n mobj = re.search(r'^AY\\\\s+(\\\\d+)\\\\s*-\\\\s*(.+)\\\\.txt$', item)\\n if mobj:\\n lesson_number = mobj.group(1)\\n lesson_description = mobj.group(2)\\n\\n print(\\\" number: %s\\\" % lesson_number)\\n print(\\\" description: %s\\\" % lesson_description)\\n\\n lesson = dict()\\n lesson['number'] = lesson_number\\n lesson['description'] = lesson_description\\n\\n fh = open(item_path)\\n lesson_raw_text = fh.read()\\n fh.close()\\n lesson_text = re.split(r'\\\\n', lesson_raw_text)\\n# lesson_raw_text_reencoded = lesson_raw_text.decode('mac-roman').encode('utf-8')\\n# lesson_text = re.split(r'\\\\n', lesson_raw_text_reencoded)\\n\\n lesson['text'] = lesson_text\\n lesson['parsed'] = parseLesson(lesson_text)\\n\\n if lesson['parsed']['end_of_lesson'] is None:\\n print(\\\" lesson has no 'end of lesson' marker\\\")\\n\\n lesson_json = json.dumps(lesson, indent=4)\\n annotated_lesson_path = os.path.join(ANNOTATED_LESSONS_PATH, \\\"ay_%04d.json\\\" % int(lesson_number))\\n fh = open(annotated_lesson_path, \\\"w\\\")\\n fh.write(lesson_json)\\n fh.close()\\n\\n else:\\n print(\\\"ERROR: File name not understood: %s\\\" % item)\\n\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8088068221bd855f018d369c5d25ceb\",\n \"score\": \"0.50167453\",\n \"text\": \"def compute_wiki(self):\\n\\n self.__wiki_counts()\\n self.__cross_wiki_counts()\\n\\n # Step 1: Calculate p(e|m) for wiki.\\n print(\\\"Filtering candidates and calculating p(e|m) values for Wikipedia.\\\")\\n for ent_mention in self.wiki_freq:\\n if len(ent_mention) < 1:\\n continue\\n\\n ent_wiki_names = sorted(\\n self.wiki_freq[ent_mention].items(), key=lambda kv: kv[1], reverse=True\\n )\\n # Get the sum of at most 100 candidates, but less if less are available.\\n total_count = np.sum([v for k, v in ent_wiki_names][:100])\\n\\n if total_count < 1:\\n continue\\n\\n self.p_e_m[ent_mention] = {}\\n\\n for ent_name, count in ent_wiki_names:\\n self.p_e_m[ent_mention][ent_name] = count / total_count\\n\\n if len(self.p_e_m[ent_mention]) >= 100:\\n break\\n\\n del self.wiki_freq\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9611488c4353d23bb3e08e0ca838baf8\",\n \"score\": \"0.5009797\",\n \"text\": \"def get_WS(w2v):\\n # get set of MAX_NGRAM-grams in text\\n lines = open(INFNAME_FORMAT.format(\\\"train\\\")).readlines() \\\\\\n + open(INFNAME_FORMAT.format(\\\"test\\\")).readlines()\\n raw = [process_line(l) for l in lines ]\\n ngrams_in_data = set()\\n for words in raw:\\n for ngram in tweet_to_ngrams(words):\\n ngrams_in_data.add(ngram)\\n\\n # load sentiment features from model\\n clf_pipe = pickle.load(open(CLF_FNAME, 'rb')) # model\\n\\n vect = clf_pipe.best_estimator_.named_steps['vect']\\n clf = clf_pipe.best_estimator_.named_steps['clf']\\n\\n features_to_sent_idx = vect.vocabulary_ # map from model features to sentiment index\\n # currently, sentiment = 2 * (count_pos / (count_pos + count_neg)) - 1\\n sentiments = clf.feature_count_[1,:] / np.sum(clf.feature_count_, axis=0) # in [0,1]\\n sentiments = 2 * sentiments - 1 # rescale to [-1,1]\\n\\n features_to_sent = {feat: sentiments[idx] for (feat,idx) in features_to_sent_idx.items()}\\n\\n # build WS and ngram_idx_map for each MAX_NGRAM-gram in the text\\n k = len(next(iter(w2v.values()))) # dimension of embedding\\n WS = np.zeros(shape=(len(ngrams_in_data) + 1, k + MAX_NGRAM), dtype='float32')\\n ngram_idx_map = {}\\n\\n index = 1 # first row is left 0, for padding in the cnn. This is also neutral sentiment.\\n # For Vader Sentiment analysis\\n# vader_analyzer = SentimentIntensityAnalyzer()\\n\\n\\n for ngram in ngrams_in_data:\\n ngram_idx_map[ngram] = index\\n\\n # set word embedding, note that unknown words already randomized in load_embedding \\n words = ngram.split(' ')\\n WS[index,:k] = w2v[words[-1]] # embedding of last word\\n\\n # set sentiment embedding\\n for n in range(MAX_NGRAM): # for 1, 2, ... length ngrams\\n sub_ngram = ' '.join(words[-1 - n:]) \\n\\n # Naive Bayes Sentiment feature --------------------------------\\n sent = features_to_sent.get(sub_ngram, 0.0) # default to neutral 0\\n # --------------------------------------------------------------\\n\\n# # TextBlob sentiment feature -----------------------------------\\n# sent = TextBlob(sub_ngram).sentiment.polarity\\n# # --------------------------------------------------------------\\n\\n# # Vader sentiment feature -------------------------------------\\n# sent = vader_analyzer.polarity_scores(sub_ngram)['compound']\\n# # -------------------------------------------------------------\\n WS[index,k+n] = sent\\n\\n index += 1\\n\\n return WS, ngram_idx_map\",\n \"title\": \"\"\n },\n {\n \"docid\": \"094614d3bf2355fdc3c4f7e7041f0655\",\n \"score\": \"0.4999468\",\n \"text\": \"def workbench_scenarios():\\n return [\\n (\\\"Oppia Embedding\\\",\\n \\\"\\\"\\\"<vertical_demo>\\n <oppia oppiaid=\\\"0\\\" src=\\\"https://www.oppia.org\\\" width=\\\"700\\\" />\\n </vertical_demo>\\n \\\"\\\"\\\"),\\n ]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f999b0f59fddb0e1776ce03b69b5a8f\",\n \"score\": \"0.4986965\",\n \"text\": \"def extract_documents():\\n client = MongoClient()\\n conn = client.data\\n coll = conn.germanwings\\n\\n query = {'text': {'$exists': 1}, 'exc': {'$exists': 0}}\\n selection = {'text': 1, 'short_url': 1}\\n for i, doc in enumerate(coll.find(query, selection)):\\n short_url, text = tuple(doc[x] for x in (\\\"short_url\\\", \\\"text\\\"))\\n print(\\\"Extracting {0} {1}\\\".format(i, short_url), file=stderr)\\n filename = os.path.join(RAW_DIR, short_url)\\n with open(filename, \\\"w\\\") as f:\\n ascii = text.encode('ascii', 'ignore')\\n f.write(ascii)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24c73e13f253f36b5593640e37a35998\",\n \"score\": \"0.49765155\",\n \"text\": \"def run():\\n\\n api = api_start()\\n stonks = {}\\n check_function = load_symbol_list()\\n for obj in (\\\"comments\\\", \\\"submissions\\\"):\\n for post in get_text(api, obj):\\n if obj == \\\"comments\\\":\\n full_text = post.body\\n else: # obj == \\\"submissions\\\"\\n full_text = post.title + post.selftext\\n try:\\n stonks = check_texts(\\n full_text, post.author.name, stonks, check_function\\n )\\n except AttributeError:\\n pass\\n\\n return stonks\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8095ed8a35b084b91054a594ab0aff1a\",\n \"score\": \"0.49734497\",\n \"text\": \"def wps(model, data):\\n with msg.loading(f\\\"Loading model '{model}'...\\\"):\\n nlp = spacy.load(model)\\n texts = (eg[\\\"text\\\"] for eg in srsly.read_jsonl(data))\\n n_docs = 0\\n n_words = 0\\n start_time = timer()\\n for doc in nlp.pipe(texts):\\n n_docs += 1\\n n_words += len(doc)\\n end_time = timer()\\n wps = int(n_words / (end_time - start_time))\\n result = [\\n (\\\"Docs\\\", f\\\"{n_docs:,}\\\"),\\n (\\\"Words\\\", f\\\"{n_words:,}\\\"),\\n (\\\"Words/s\\\", f\\\"{wps:,}\\\"),\\n ]\\n msg.table(result, widths=(7, 12), aligns=(\\\"l\\\", \\\"r\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b0e78481ce7a6fad8056487980dcf05\",\n \"score\": \"0.49670535\",\n \"text\": \"def request_document(self):\\n\\t\\tassert self.image is not None, \\\"image is not defined.\\\"\\n\\t\\treturn {\\\"requests\\\":[{\\n\\t\\t\\t\\\"image\\\": {\\\"content\\\": self.image.base64},\\n\\t\\t\\t\\\"features\\\":[{\\n\\t\\t\\t\\t\\\"type\\\": \\\"LANDMARK_DETECTION\\\",\\n\\t\\t\\t\\t\\\"maxResults\\\": 1\\n\\t\\t\\t}]\\n\\t\\t}]}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"096e51a3b4435acc9ba293acae0c7e27\",\n \"score\": \"0.49649388\",\n \"text\": \"def evaluate():\\n global dictionary, wv\\n count = 0\\n # To save the scores by distance and similarity\\n scores = np.zeros(6)\\n similar = np.zeros(6)\\n itr = len(dictionary)\\n logging.info('running evaluation for {0} samples'.format(itr))\\n for key in dictionary:\\n progress = (count / itr) * 100\\n d = dictionary[key].split('resource/')\\n d = [idx.split()[0].translate(table).lower() for idx in d[1:]]\\n try:\\n r = np.array(list(map(lambda x: wv.get_vector(x), d)),\\n dtype=np.float32)\\n except KeyError:\\n itr -= 1\\n continue\\n if np.any(np.isnan(r)):\\n itr -= 1\\n continue\\n else:\\n if r.ndim == 2:\\n try:\\n # Mean of vector containing all word vectors\\n # obtained from abstract.\\n r = r.mean(axis=0).reshape(1, -1)\\n \\n # Obtain the vectors for the entity\\n mean_vec = mean_encoder(dictionary[key])\\n mean_vec = mean_vec.reshape(1, -1) / norm(mean_vec)\\n mean_dist_vec = distance_encoder(dictionary[key])\\n mean_dist_vec = mean_dist_vec.reshape(1, -1)\\n mean_dist_vec = mean_dist_vec / norm(mean_dist_vec)\\n title_vec = title_mean(key)\\n title_vec = title_vec.reshape(1, -1) / norm(title_vec)\\n abstract_vec = abstract_encoder(key)\\n abstract_vec = abstract_vec.reshape(1, -1)\\n abstract_vec = abstract_vec / norm(abstract_vec)\\n random_vec = np.random.randn(100).reshape(1, -1)\\n zero_vec = np.zeros(100).reshape(1, -1)\\n \\n # Score the entity vectors\\n scores[0] += norm(r - mean_vec)\\n scores[1] += norm(r - mean_dist_vec)\\n scores[2] += norm(r - title_vec)\\n scores[3] += norm(r - abstract_vec)\\n scores[4] += norm(r - random_vec)\\n scores[5] += norm(r - zero_vec)\\n similar[0] += cosine_similarity(r, mean_vec)\\n similar[1] += cosine_similarity(r, mean_dist_vec)\\n similar[2] += cosine_similarity(r, title_vec)\\n similar[3] += cosine_similarity(r, abstract_vec)\\n similar[4] += cosine_similarity(r, random_vec)\\n similar[5] += cosine_similarity(r, zero_vec)\\n count += 1\\n print(count, end='\\\\r')\\n except (ValueError, KeyError) as _:\\n itr -= 1\\n continue\\n else:\\n itr -= 1\\n continue\\n # Normalize the scores to get a better\\n # comparison against the baselines.\\n scores = scores / norm(scores)\\n similar = similar / norm(similar)\\n print_summary(scores, similar)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19133503f4d039654bcf8787d017b0db\",\n \"score\": \"0.49587277\",\n \"text\": \"def visualize(self):\\n # TODO\\n #pyLDAvis.enable_notebook()\\n #vis = pyLDAvis.gensim.prepare(self.lda_model, self.stemmed_corpus)\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21ae6d00632b3616ad16936719a6e07e\",\n \"score\": \"0.49508405\",\n \"text\": \"def run(self):\\r\\n history = self.extracter.load_user_history()\\r\\n self.plot_history(history)\\r\\n \\r\\n pp_history = self.analyser.preprocess_history(history)\\r\\n part_worths, attribute_importance, relative_importance = self.analyser.conjoint_analysis(pp_history)\\r\\n self.plot_analysis(part_worths, relative_importance)\\r\\n \\r\\n return history, pp_history, part_worths, relative_importance\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b73cb825455fae6c04f57b8fa704ed31\",\n \"score\": \"0.49432248\",\n \"text\": \"def learn_models(self):\\n\\n influencers = self.influencers.infGroup\\n\\n self.complete_model = LanguageModel()\\n self.influencer_models = { influencer: LanguageModel() for influencer in influencers }\\n\\n all_tweets = []\\n # for influencer in tqdm(influencers, desc='Learning Models'):\\n for influencer in influencers:\\n tweets = [tweet for tweet in self.get_saved_tweets(influencer)]\\n self.influencer_models[influencer].add_documents(tweets)\\n all_tweets += tweets\\n\\n self.complete_model.add_documents(all_tweets)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2801359b261e4f2479acf6e4fc95b081\",\n \"score\": \"0.494306\",\n \"text\": \"def inference(self):\\n for m, doc in enumerate(self.docs):\\n # Be careful followings are views\\n # So self.hoge will be change, when changing variant\\n zs_j = self.zs_m_j[m]\\n zk_j = self.zk_m_j[m]\\n n_m_zs = self.n_m_zs[m]\\n n_m_zk = self.n_m_zk[m]\\n for j, t in enumerate(doc):\\n # discount for n-th word t with topic z\\n zs = zs_j[j]\\n zk = zk_j[j]\\n n_m_zs[zs] -= 1\\n n_m_zk[zs, zk] -= 1\\n self.n_zk_t[zk, t] -= 1\\n self.n_zk[zk] -= 1\\n\\n # sampling topic new_z for t\\n \\\"\\\"\\\"\\n n_s = n_m_zs + self.alphas # mth doc, S vec\\n p_s = n_s / np.sum(n_s)\\n n_k = n_m_zk + self.alphask # mth doc, SxK matrix\\n p_k = n_k / n_s.reshape(len(n_s), 1)\\n n_v = self.n_zk_t[:, t] + self.beta\\n p_v = n_v / (self.n_zk + self.beta)\\n\\n p_zsk = p_s.reshape(len(p_s), 1) * p_k * p_v # SxK matrix\\n \\\"\\\"\\\"\\n\\n p_zsk = (n_m_zk + self.alphask) * self.n_zk_t[:, t] \\\\\\n / (np.sum(n_m_zs + self.alphas) * self.n_zk)\\n\\n p_zs = np.sum(p_zsk, axis=1) / np.sum(p_zsk)\\n p_zk = np.sum(p_zsk, axis=0) / np.sum(p_zsk)\\n\\n new_zs = np.random.multinomial(1, p_zs).argmax()\\n new_zk = np.random.multinomial(1, p_zk).argmax()\\n\\n # print(\\\"arg\\\", np.argmax(p_s), np.argmax(p_k, axis=1),\\n # np.argmax(p_k, axis=0), np.argmax(p_zk))\\n # print('probs', p_s, p_zs)\\n # print('probk', p_k, p_zk)\\n # print('old', zs, zk)\\n # print('new', new_zs, new_zk)\\n\\n # set z the new topic and increment counters\\n zs_j[j] = new_zs\\n zk_j[j] = new_zk\\n n_m_zs[new_zs] += 1\\n n_m_zk[new_zs, new_zk] += 1\\n self.n_zk_t[new_zk, t] += 1\\n self.n_zk[new_zk] += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b7ecd53c19c14aa054a0489b52997ff\",\n \"score\": \"0.49372828\",\n \"text\": \"def emo_detect(text_list,language='English',method ='dimensional',output='data_frame',resolution = 'words_in_text', folder='',max_len=500000):\\n \\n # Dictionary containing file names of affect disctionaries:\\n file_dict = {'english':{'dimensional':('english_anew_dict',),'discrete':('english_nstar_liwc_dict','english_star_liwc_dict')},\\\\\\n 'german':{'dimensional':('german_anew_dict',),'discrete':('german_nstar_liwc_dict','german_star_liwc_dict')},\\\\\\n 'chinese':{'discrete':('chinese_nstar_liwc_dict','chinese_star_liwc_dict')}}\\n # Dictionary containing column names of data frames:\\n colname_dict = {'english':{'dimensional':['PosVal', 'NegVal','Arousal','Dominance', 'PosCount','DetectCount','TokenCount','ValSq'],'discrete':['function', 'pronoun', 'ppron', 'i', 'we', 'you', 'shehe', 'they', 'ipron', 'article', 'prep', 'auxverb', 'adverb', 'conj', 'negate', 'verb', 'adj', 'compare', 'interrog', 'number', 'quant', 'affect', 'posemo', 'negemo', 'anx', 'anger', 'sad', 'social', 'family', 'friend', 'female', 'male', 'cogproc', 'insight', 'cause', 'discrep', 'tentat', 'certain', 'differ', 'percept', 'see', 'hear', 'feel', 'bio', 'body', 'health', 'sexual', 'ingest', 'drives', 'affiliation', 'achieve', 'power', 'reward', 'risk', 'focuspast', 'focuspresent', 'focusfuture', 'relativ', 'motion', 'space', 'time', 'work', 'leisure', 'home', 'money', 'relig', 'death', 'informal', 'swear', 'netspeak', 'assent', 'nonflu', 'filler','DetectCount','TokenCount']},\\\\\\n 'german':{'dimensional':['PosVal', 'NegVal','Arousal','Dominance', 'PosCount','DetectCount','Imagine','Potency', 'DomPot_Count','TokenCount','ValSq'],'discrete':['Pronoun', 'I', 'We', 'Self', 'You', 'Other', 'Negate', 'Assent', 'Article', 'Preps', 'Number', 'Affect', 'Posemo', 'Posfeel', 'Optim', 'Negemo', 'Anx', 'Anger', 'Sad', 'Cogmech', 'Cause', 'Insight', 'Discrep', 'Inhib', 'Tentat', 'Certain', 'Senses', 'See', 'Hear', 'Feel', 'Social', 'Comm', 'Othref', 'Friends', 'Family', 'Humans', 'Time', 'Past', 'Present', 'Future', 'Space', 'Up', 'Down', 'Incl', 'Excl', 'Motion', 'Occup', 'School', 'Job', 'Achieve', 'Leisure', 'Home', 'Sports', 'TV', 'Music', 'Money', 'Metaph', 'Relig', 'Death', 'Physcal', 'Body', 'Sexual', 'Eating', 'Sleep', 'Groom', 'Swear', 'Nonfl', 'Fillers', 'Swiss', 'Ideo', 'Personalpronomina', 'Indefinitpronomina', 'AuxiliaryVerbs', 'Konjunktionen', 'Adverbien','Bedrohung', 'DetectCount','TokenCount']},\\\\\\n 'chinese':{'discrete':['function', 'pronoun', 'ppron', 'i', 'we', 'you', 'shehe', 'they', 'youpl', 'ipron', 'prep', 'auxverb', 'adverb', 'conj', 'negate', 'quanunit', 'prepend', 'specart', 'tensem', 'focuspast', 'focuspresent', 'focusfuture', 'progm', 'particle', 'modal_pa', 'general_pa', 'compare', 'interrog', 'number', 'quant', 'affect', 'posemo', 'negemo', 'anx', 'anger', 'sad', 'social', 'family', 'friend', 'female', 'male', 'cogproc', 'insight', 'cause', 'discrep', 'tentat', 'certain', 'differ', 'percept', 'see', 'hear', 'feel', 'bio', 'body', 'health', 'sexual', 'ingest', 'drives', 'affiliation', 'achieve', 'power', 'reward', 'risk', 'relativ', 'motion', 'space', 'time', 'work', 'leisure', 'home', 'money', 'relig', 'death', 'informal', 'swear', 'netspeak', 'assent', 'nonflu', 'filler', 'DetectCount','TokenCount']}}\\n # Normalize language and method parameters:\\n language = language.lower()\\n method = method.lower()\\n #Initiate empty stemmer:\\n stemmer = []\\n # Counter:\\n c = 0\\n # Get the files to load from the file_dict:\\n files = file_dict[language][method]\\n # Get column names from col_dict:\\n colnames = colname_dict[language][method]\\n # Load files:\\n # One dictionary (dimensional):\\n if len(files) == 1:\\n with open(folder + files[0],'rb') as f:\\n dicts = pickle.load(f)\\n # Length of affect vectors in dictionary:\\n vec_len = len(list(dicts.values())[0])\\n pos_list = 0\\n # Two dictionaries (discrete):\\n if len(files) == 2:\\n with open(folder + files[0],'rb') as f:\\n naster_disc_dict = pickle.load(f)\\n with open(folder + files[1],'rb') as f:\\n aster_disc_dict = pickle.load(f)\\n dicts = (naster_disc_dict,aster_disc_dict)\\n # Length of affect vectors in dictionary:\\n vec_len = len(list(naster_disc_dict.values())[0])\\n # Generate stemmer if german dimensional affect detection:\\n if method == 'dimensional':\\n if language == 'german':\\n stemmer = SnowballStemmer('german')\\n elif method == 'discrete':\\n # List of stem lengths in the word stem dictionary:\\n pos_list = list(reversed(list(aster_disc_dict.keys())))\\n # Initiate vec_list if output is data_frame, and emo_mat if output is array:\\n if output == 'data_frame': \\n vec_list = []\\n elif output == 'array':\\n emo_mat = np.zeros([max_len,max_len,vec_len])\\n # for resolution 'sentences_in_text' create an emo_mat with one additional layer for sentence counter\\n if resolution == 'sentences_in_text':\\n emo_mat = np.zeros([max_len,max_len,vec_len+1])\\n\\n # Iterate over texts in text_list:\\n for c,text in enumerate(text_list):\\n # Print counter every 10,000 texts:\\n if c % 10000 == 0:\\n print(c)\\n # Ignore if it's not a text:\\n if not isinstance(text,str):\\n continue\\n # Resolution words in text:\\n if resolution == 'words_in_text':\\n # create vector or array (depending on 'ouptut'):\\n emo_thingy = text_detect(text=text,max_len=max_len,vec_len=vec_len,pos_list=pos_list,dicts=dicts,stemmer=stemmer,method = method,output = output)\\n # Skip text if output of 'text_detect' is 0.\\n if isinstance(emo_thingy, int):\\n continue\\n # Append emo_thingy to vec_list or add to emo_mat (depending on 'ouptut'):\\n elif output == 'data_frame': \\n vec_list.append(emo_thingy)\\n elif output == 'array':\\n emo_mat[emo_thingy[0],:emo_thingy[0]+1,:] += emo_thingy[1] \\n elif resolution == 'words_in_sentence':\\n sent_list = sentence_tokenize(text,language=language)\\n for sent in sent_list:\\n # create vector or array (depending on 'ouptut'):\\n emo_thingy = text_detect(text=sent,max_len=max_len,vec_len=vec_len,pos_list=pos_list,dicts=dicts,stemmer=stemmer,method = method,output = output)\\n # Skip text if output of 'text_detect' is 0.\\n if isinstance(emo_thingy, int):\\n continue\\n # Append emo_thingy to vec_list or add to emo_mat (depending on 'ouptut'):\\n elif output == 'data_frame': \\n vec_list.append(emo_thingy)\\n elif output == 'array':\\n emo_mat[emo_thingy[0],:emo_thingy[0]+1,:] += emo_thingy[1] \\n elif resolution == 'sentences_in_text':\\n sent_list = sentence_tokenize(text,language=language)\\n n_sent = len(sent_list)\\n if (n_sent > max_len) or (n_sent == 0):\\n continue\\n n_sent = n_sent - 1\\n for sc,sent in enumerate(sent_list):\\n emo_thingy = text_detect(text=sent,max_len=10000,vec_len=vec_len,pos_list=pos_list,dicts=dicts,stemmer=stemmer,method = method,output = 'data_frame')\\n if isinstance(emo_thingy, int):\\n continue\\n emo_thingy = np.append(emo_thingy,np.array([1]))\\n emo_mat[n_sent,sc,:] += emo_thingy\\n # return data frame or array, depending on 'output':\\n if output == 'data_frame':\\n return(pd.DataFrame(vec_list,columns=colnames))\\n elif output == 'array':\\n return(emo_mat)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8cf918980b856171a28d842d572d9096\",\n \"score\": \"0.49365968\",\n \"text\": \"def run(self):\\r\\n\\r\\n self.roi_analyzer = ROIAnalyzer(\\r\\n ini_path=self.config_path,\\r\\n data_path=self.outlier_corrected_dir,\\r\\n calculate_distances=True,\\r\\n settings=self.settings,\\r\\n )\\r\\n self.roi_analyzer.files_found = self.files_found\\r\\n self.all_shape_names = self.roi_analyzer.shape_names\\r\\n self.roi_analyzer.run()\\r\\n self.roi_analyzer.compute_framewise_distance_to_roi_centroids()\\r\\n self.roi_distances_dict = self.roi_analyzer.roi_centroid_distance\\r\\n self.roi_entries_df = self.roi_analyzer.detailed_df\\r\\n if self.roi_directing_viable:\\r\\n self.directing_analyzer.run()\\r\\n self.roi_direction_df = self.directing_analyzer.results_df\\r\\n\\r\\n self.data = {}\\r\\n for file_cnt, file_path in enumerate(self.features_files):\\r\\n _, self.video_name, _ = get_fn_ext(file_path)\\r\\n _, _, self.fps = self.read_video_info(video_name=self.video_name)\\r\\n data_df = read_df(file_path, self.file_type)\\r\\n self.out_df = deepcopy(data_df)\\r\\n self.__process_within_rois()\\r\\n self.__distance_to_roi_centroids()\\r\\n if self.roi_directing_viable:\\r\\n self.__process_directionality()\\r\\n self.data[self.video_name] = self.out_df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ed2627a8180c954b6fecaf0d6fe506c\",\n \"score\": \"0.49345893\",\n \"text\": \"def detect_document(path):\\n from google.cloud import vision\\n client = vision.ImageAnnotatorClient()\\n\\n with io.open(path, 'rb') as image_file:\\n content = image_file.read()\\n\\n image = vision.types.Image(content=content)\\n\\n response = client.document_text_detection(image=image)\\n\\n for page in response.full_text_annotation.pages:\\n for block in page.blocks:\\n #print('\\\\nBlock confidence: {}\\\\n'.format(block.confidence))\\n for paragraph in block.paragraphs:\\n for word in paragraph.words:\\n word_text = ''.join([symbol.text for symbol in word.symbols])\\n text.append(word_text.encode('utf-8'))\\n #print(word_text)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c9722f57ea37a8097212a4b00336cc0\",\n \"score\": \"0.49288893\",\n \"text\": \"def sentiment_aspects(docs: Iterable[tokens.Doc]) -> List[collections.Counter]:\\n sent_dict_list = []\\n start_time = time.time()\\n\\n for doc in docs:\\n sent_dict = collections.Counter()\\n for token in doc:\\n # check if the word is an opinion word, then assign sentiment\\n if token.text.lower() in _OPINION_WORDS:\\n sentiment = 1 if token.text.lower() in _POS_WORDS else -1\\n if (token.dep_ == \\\"advmod\\\"):\\n # if target is an adverb modifier (i.e. pretty, highly, etc.)\\n # but happens to be an opinion word, ignore and pass\\n continue\\n\\n elif (token.dep_ == \\\"amod\\\"):\\n sent_dict[token.head.text.lower()] += sentiment\\n\\n else:\\n for child in token.children:\\n # if there's a adj modifier (i.e. very, pretty, etc.) add\\n # more weight to sentiment\\n # This could be better updated for modifiers that either\\n # positively or negatively emphasize\\n if _is_opinion_mod(child):\\n sentiment *= 1.5\\n # check for negation words and flip the sign of sentiment\\n if child.dep_ == \\\"neg\\\":\\n sentiment *= -1\\n for child in token.children:\\n if (token.pos_ == \\\"VERB\\\") & (child.dep_ == \\\"dobj\\\"):\\n # if verb, check if there's a direct object\\n sent_dict[child.text.lower()] += sentiment\\n # check for conjugates (a AND b), then add both to dictionary\\n subchildren = []\\n conj = 0\\n for subchild in child.children:\\n if subchild.text.lower() == \\\"and\\\": conj=1\\n if (conj == 1) and (subchild.text.lower() != \\\"and\\\"):\\n subchildren.append(subchild.text.lower())\\n conj = 0\\n for subchild in subchildren:\\n sent_dict[subchild] += sentiment\\n\\n # check for negation\\n for child in token.head.children:\\n noun = \\\"\\\"\\n if _is_opinion_mod(child):\\n sentiment *= 1.5\\n if (child.dep_ == \\\"neg\\\"):\\n # check for negation words and flip the sign of sentiment\\n sentiment *= -1\\n\\n # check for nouns\\n for child in token.head.children:\\n noun = \\\"\\\"\\n if (child.pos_ == \\\"NOUN\\\") and (child.text not in sent_dict):\\n noun = child.text.lower()\\n # Check for compound nouns\\n for subchild in child.children:\\n if subchild.dep_ == \\\"compound\\\":\\n noun = subchild.text.lower() + \\\" \\\" + noun\\n sent_dict[noun] += sentiment\\n sent_dict_list.append(collections.Counter(sent_dict))\\n\\n print(\\\"\\\\nFound aspects on {} reviews.\\\".format(len(sent_dict_list)))\\n print(time.time() - start_time)\\n return sent_dict_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb356c3bf2d1cbf9ef471575c497383c\",\n \"score\": \"0.48967612\",\n \"text\": \"def text_analytics(self):\\n\\n headers = {\\n # Request headers\\n 'Content-Type': 'application/json',\\n 'Ocp-Apim-Subscription-Key': self.keys['text_analytics'],\\n }\\n \\n sentiment_url = 'https://westus.api.cognitive.microsoft.com/text/analytics/v2.0/sentiment'\\n \\n raw_text = self.article_params['text']\\n\\n # Build post for sentiment\\n try:\\n sentences = tokenize.sent_tokenize(str(raw_text))\\n content = []\\n for i, sentence in enumerate(sentences):\\n content.append({'id': str(i), 'language': 'en', 'text': sentence})\\n body = json.dumps({\\\"documents\\\": content}).encode('utf-8')\\n\\n request = urllib.request.Request(sentiment_url, body, headers)\\n response = urllib.request.urlopen(request)\\n json_response = json.loads(response.read().decode('utf-8'))\\n \\n # A list of dictionaries, with each dictionary containing a sentence\\n # sentiment score\\n sentiments_list = json_response['documents']\\n\\n # Calculate the articles average sentiment from all the sentences\\n cumulative_sentiment_score = 0\\n for sent in sentiments_list:\\n cumulative_sentiment_score += sent['score']\\n avg_article_sentiment = cumulative_sentiment_score/len(sentiments_list)\\n\\n # Put article sentiments in bucket from 1 to 5, with 1 being very\\n # negative and 5 being very positive\\n if avg_article_sentiment < 0.2:\\n sentiment = 1\\n elif 0.2 <= avg_article_sentiment < 0.4:\\n sentiment = 2\\n elif 0.4 <= avg_article_sentiment < 0.6:\\n sentiment = 3\\n elif 0.6 <= avg_article_sentiment < 0.8:\\n sentiment = 4\\n else:\\n sentiment = 5\\n\\n except Exception as e:\\n print('Unable to process sentiment for article. Assuming '\\n 'sentiment is neutral.')\\n sentiment = 3\\n\\n return sentiment\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35aea9116a20457b06626361b0a8c0a6\",\n \"score\": \"0.4894925\",\n \"text\": \"def main():\\n try:\\n res = requests.get('http://localhost:9200')\\n pprint(json.loads(res.content.decode('utf-8')))\\n except requests.exceptions.ConnectionError:\\n print(\\\"ERROR: ELASTICSEARCH Server is not running!\\\")\\n exit(-1)\\n\\n # scrapeAndSaveNewsArticles()\\n # generateNewsDocsCSV() # may need to be modified based on how scrapeAndSave function file output\\n if not es_client.indices.exists(index='huffpost_news_index'):\\n print(\\\"PLEASE WAIT... LOADING DOCUMENTS INTO INVERTED INDEX\\\")\\n indexDocsToES('huffpost_news_index')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f4a186b81c2d93742075e9f6cf02bac\",\n \"score\": \"0.48722687\",\n \"text\": \"def forward(self, doc):\\n out = torch.tensor([]).float().to(self.device)\\n\\n for i in range(len(doc)):\\n sentences_raw = sentencesplit(cleantxt(doc[i]))\\n sentences_ready = torch.tensor([]).float().to(self.device)\\n for sentence in sentences_raw:\\n sentence = sentence.split()\\n if sentence == []:\\n continue\\n lookup_tensor = torch.tensor([]).long().to(self.device)\\n for word in sentence:\\n if word in self.embedd_dict:\\n lookup_tensor = torch.cat((lookup_tensor,\\n torch.LongTensor([self.embedd_dict[word]])), 0)\\n else:\\n lookup_tensor = torch.cat((lookup_tensor, torch.LongTensor([0])), 0)\\n # Word embedding\\n xw = self.word_embedding(lookup_tensor).view(1, -1, self.embedding_dim).to(self.device)\\n # Word GRU\\n self.hidden_gru_words = self.init_hidden_words()\\n hw, self.hidden_gru_words = self.gru_word(xw, self.hidden_gru_words)\\n # Word MLP\\n uw = nn.Tanh()(self.MLP_word(hw)).to(self.device)\\n # Word attention\\n attention_score = torch.matmul(uw, self.attention_word).squeeze().to(self.device)\\n attention_score = F.softmax(attention_score, dim=0).view(uw.size(0), uw.size(1), 1).to(self.device)\\n scored_x = (hw * attention_score).to(self.device)\\n s = torch.sum(scored_x, dim=1).to(self.device)\\n #collecting sentences\\n sentences_ready = torch.cat((sentences_ready, s), 0)\\n # Sentence GRU\\n if len(sentences_ready) == 0:\\n out = torch.cat((out,\\n torch.randn(1, self.number_cat).to(self.device)), 0).to(self.device)\\n continue\\n sentences_ready_gru = sentences_ready.view(1, -1, self.embedding_dim).to(self.device)\\n self.hidden_gru_sentences = self.init_hidden_sentences()\\n hs, self.hidden_gru_sentences = self.gru_sentence(torch.tensor(sentences_ready_gru), self.hidden_gru_sentences)\\n # SENTENCE MLP\\n us = nn.Tanh()(self.MLP_sentence(hs)).to(self.device)\\n # Sentence attention\\n attention_score = torch.matmul(us, self.attention_sentence).squeeze().to(self.device)\\n attention_score = F.softmax(attention_score, dim=0).view(us.size(0), us.size(1), 1).to(self.device)\\n scored_x = (hs * attention_score).to(self.device)\\n v = torch.sum(scored_x, dim=1).to(self.device)\\n # classification\\n p = self.MLP_classification(v).to(self.device)\\n out = torch.cat((out, p.float()), 0).float().to(self.device)\\n return out\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f78117a3cf6cb34f721eab0266e6eeb\",\n \"score\": \"0.4870776\",\n \"text\": \"def test(self, filename: str, info_extractor: Optional[InfoExtractor]):\\r\\n if self.model is None:\\r\\n raise RatingModel.RatingModelError(\\\"model is not loaded or trained yet\\\")\\r\\n doc, _ = loadDocumentIntoSpacy(filename, self.parser, self.nlp)\\r\\n\\r\\n print(\\\"Getting rating...\\\")\\r\\n if self._type == \\\"fixed\\\":\\r\\n print(\\\"working on fixed model\\\")\\r\\n if self.keywords is None:\\r\\n raise RatingModel.RatingModelError(\\\"Keywords not found\\\")\\r\\n\\r\\n seen_chunks_words, all_tokens_chunks = getAllTokensAndChunks(doc)\\r\\n\\r\\n # scoring\\r\\n temp_out = self.__trainKMWM(list(seen_chunks_words), list(all_tokens_chunks), self.keywords)\\r\\n if temp_out is None:\\r\\n raise RatingModel.RatingModelError(\\r\\n \\\"Either parser cannot detect text or too few words in resume for analysis. Most usually the former.\\\"\\r\\n )\\r\\n km_scores, wm_scores = temp_out\\r\\n # average of km/wm scores for all keywords\\r\\n km_score = np.mean(km_scores)\\r\\n wm_score = np.mean(wm_scores)\\r\\n final_score = km_score * wm_score\\r\\n elif self._type == \\\"lda\\\":\\r\\n if self.lda is None or self.dictionary is None or self.top_k_words is None:\\r\\n raise RatingModel.RatingModelError(\\\"No LDA found\\\")\\r\\n\\r\\n seen_chunks_words, all_tokens_chunks = getAllTokensAndChunks(doc)\\r\\n seen_chunks_words, all_tokens_chunks = (\\r\\n list(seen_chunks_words),\\r\\n list(all_tokens_chunks),\\r\\n )\\r\\n\\r\\n # scoring\\r\\n new_seen_chunks_words = self.__keep_top_k_words(seen_chunks_words)\\r\\n bow = self.dictionary.doc2bow(new_seen_chunks_words)\\r\\n doc_distribution = np.array(\\r\\n [tup[1] for tup in self.lda.get_document_topics(bow=bow)]\\r\\n )\\r\\n # get keywords and weights\\r\\n keywords = []\\r\\n all_pair_scores = []\\r\\n all_topic_scores = []\\r\\n all_diff_scores = []\\r\\n # take top 5 topics\\r\\n for j in doc_distribution.argsort()[-5:][::-1]:\\r\\n topic_prob = doc_distribution[j]\\r\\n # take top 5 words for each topic\\r\\n st = self.lda.show_topic(topicid=j, topn=5)\\r\\n sum_st = np.sum(list(map(lambda x: x[1], st)))\\r\\n pair_scores = []\\r\\n for pair in st:\\r\\n keywords.append(pair[0])\\r\\n pair_scores.append(pair[1])\\r\\n all_pair_scores.append(np.array(pair_scores))\\r\\n all_topic_scores.append(np.array(topic_prob))\\r\\n\\r\\n all_pair_scores = np.array(all_pair_scores)\\r\\n norm_all_pair_scores = all_pair_scores.T / np.sum(all_pair_scores, axis=1)\\r\\n norm_all_topic_scores = all_topic_scores / np.sum(all_topic_scores)\\r\\n all_diff_scores = (norm_all_pair_scores * norm_all_topic_scores).flatten()\\r\\n weights = pd.Series(all_diff_scores, index=keywords)\\r\\n weights.sort_values(ascending=False, inplace=True)\\r\\n\\r\\n temp_out = self.__trainKMWM(seen_chunks_words, all_tokens_chunks, keywords)\\r\\n if temp_out is None:\\r\\n print(\\r\\n \\\"Either parser cannot detect text or too few words in resume for analysis. Most usually the former. Skip document.\\\"\\r\\n )\\r\\n km_scores, wm_scores = temp_out\\r\\n\\r\\n # average of km/wm scores for all keywords\\r\\n km_score = np.dot(weights.values, km_scores)\\r\\n wm_score = np.dot(weights.values, wm_scores)\\r\\n\\r\\n final_score = km_score * wm_score\\r\\n\\r\\n # max_score = self.model[\\\"score\\\"].iloc[0] - np.std(self.model[\\\"score\\\"])\\r\\n # min_score = self.model[\\\"score\\\"].iloc[-1]\\r\\n mean = np.mean(self.model[\\\"score\\\"])\\r\\n sd = np.std(self.model[\\\"score\\\"])\\r\\n\\r\\n rating = min(10, max(0, round(5 + (final_score-mean)/sd, 2)))\\r\\n if info_extractor is not None:\\r\\n print(\\\"-\\\" * 20)\\r\\n\\r\\n # info_extractor.extractFromFile(filename)\\r\\n output= info_extractor.extractFromFile(filename)\\r\\n\\r\\n print(\\\"output:----\\\",output)\\r\\n print(\\\"-\\\" * 20)\\r\\n print(\\\"Rating: %.1f\\\" % rating)\\r\\n # if info_extractor is not None:\\r\\n # print(\\\"info extractor is not working\\\")\\r\\n # env = os.environ\\r\\n # subprocess.call([sys.executable, filename], env=env)\\r\\n return output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"935e065ed2e8ef1071fd6ea6c9fb8b2e\",\n \"score\": \"0.48706353\",\n \"text\": \"def collect_docs() -> Dict[str, Any]:\\n docs = {}\\n\\n for integration_dir, meta in all_integrations():\\n integration_name = integration_dir.name\\n markdown = \\\"\\\"\\n for readme in sorted(integration_dir.glob(\\\"*.md\\\")) + sorted(integration_dir.glob(\\\"README.md.jinja\\\")):\\n markdown += readme.read_text(encoding=\\\"utf-8\\\")\\n\\n if not markdown:\\n continue\\n\\n docs[integration_name] = {\\n \\\"markdown\\\": process_markdown_for_app(markdown, integration_dir, uses_legacy_build(meta))\\n }\\n\\n return docs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8cd00602ff5af8d15d5dd68f4ed79855\",\n \"score\": \"0.48668104\",\n \"text\": \"def get_slide_analytics_new(slides) -> List[int]:\\n word_count = []\\n for slide in slides:\\n print(slide)\\n words = 0\\n for shape in slide.shapes:\\n if not shape.has_text_frame:\\n continue\\n print(shape.name)\\n for paragraph in shape.text_frame.paragraphs:\\n for run in paragraph.runs:\\n print(\\\" \\\" + run.text)\\n words += len(run.text.split())\\n word_count.append(words)\\n return word_count\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4dd7429b51b3115c527e2a80a20b5d7\",\n \"score\": \"0.48623803\",\n \"text\": \"def create_embedding(self):\\n self.embedding = []\\n\\n for index in range(1,self.args.window_size+1):\\n print(\\\"\\\\nOptimization round: \\\" +str(index)+\\\"/\\\"+str(self.args.window_size)+\\\".\\\")\\n print(\\\"Creating documents.\\\")\\n clean_documents = self.walk_extracts(index)\\n print(\\\"Fitting model.\\\")\\n model = Word2Vec(clean_documents,\\n size = self.args.dimensions,\\n window = 1,\\n min_count = self.args.min_count,\\n sg = 1,\\n workers = self.args.workers)\\n\\n new_embedding = self.get_embedding(model)\\n self.embedding = self.embedding +[new_embedding]\\n self.embedding = np.concatenate(self.embedding, axis = 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"604fd95d4d3feaf292951f92b171aa21\",\n \"score\": \"0.48512176\",\n \"text\": \"def extract(self, documents):\\n\\n # Feature vector to return\\n features = np.zeros((len(documents), len(self.idx_to_word)))\\n\\n # Raise an exception if 'extract' is called before 'preprocess'\\n if len(self.word_to_idx) == 0 or len(self.idx_to_word) == 0:\\n raise Exception(\\\"Dictionary not initialised.\\\")\\n\\n # Iterate over all documents\\n for idx, doc in enumerate(documents):\\n # Split the doc into a list of words\\n words = extract_words(doc)\\n\\n # For each word\\n for w in words:\\n # Calculate it's frequency, however, keep in mind\\n # that this word may not have been in the training\\n # corpus. In that case, ignore the word.\\n ''' YOUR CODE HERE '''\\n try:\\n features[idx][self.word_to_idx[w]] = words.count(w)\\n except KeyError:\\n pass\\n\\n ''' END CODE FOR THIS LOOP '''\\n\\n # Divide the vector by the total number of words in the document to\\n # normalize the frequencies.\\n ''' YOUR CODE HERE '''\\n features[idx] = features[idx]/len(words)\\n ''' END CODE FOR THIS LOOP '''\\n\\n return features\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5830eddcc02787f888755402d0486b85\",\n \"score\": \"0.48499954\",\n \"text\": \"def environmental_science_news():\\n\\n return general_scraper(['http://mesva.univaq.it/?q=avvisi/cl-clm/52671'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"381a5db4c0d0ee2186d6415772225d77\",\n \"score\": \"0.48452774\",\n \"text\": \"def analyse() -> 'html':\\n storeFileName = ''\\n if request.method == 'POST':\\n \\n if 'file' not in request.files:\\n return render_mainpageerror('No file selected')\\n \\n file = request.files['file']\\n if file.filename == '':\\n return render_mainpageerror('Empty file name Please select a File to upload')\\n \\n if file and not allowed_file(file.filename):\\n storeFileName = filename = secure_filename(file.filename)\\n file.save(os.path.join(app.config['UPLOAD_FOLDER'], filename))\\n return render_mainpageerror('Not allowed file format')\\n \\n if ' ' in file.filename:\\n return render_mainpageerror('Please remove withespace from file name before uploading')\\n \\n if file and allowed_file(file.filename):\\n originalFilename = file.filename\\n storeFileName = filename = secure_filename(file.filename)\\n file.save(os.path.join(app.config['UPLOAD_FOLDER'], filename))\\n \\n \\n if len(storeFileName) == 0:\\n return render_template('mainpage.html',\\n title='Word association')\\n else:\\n wordsfromfile = readFile.red_uploaded_file(storeFileName)\\n #print(wordsfromfile, 'The ans from pdf')\\n if wordsfromfile == 'STOP':\\n return render_mainpageerror('Cant find Encoding on PDF document')\\n size = get_store()\\n if not check_fileName(size):\\n \\\"\\\"\\\"Analyse first time building up JSON file\\\"\\\"\\\"\\n keyWords = readFile.read_JSON()\\n allJson = readFile.load_JSON()\\n data = Counter(wordsfromfile)\\n printResultsExluds = []\\n container = {}\\n container['size'] = size\\n container['file'] = storeFileName\\n toDB = []\\n for k, v in sorted(data.items()):\\n if k in keyWords:\\n string = allJson[k][:3]\\n toDB.append({k : v})\\n toDB.append(string)\\n else:\\n value = str(v)\\n keystr = k + '(' + value + ')'\\n printResultsExluds.append(keystr)\\n for w in printResultsExluds:\\n toDB.append({w:'was not found in the associations list'})\\n\\n container['files'] = toDB \\n js = json.dumps(container)\\n print(js)\\n c = get_client()\\n c.insert(json.loads(js))\\n print('insert')\\n \\\"\\\"\\\"If analysed first time\\\"\\\"\\\"\\n return pr(size, 'Analysed first time')\\n else:\\n \\\"\\\"\\\"If already Analysed\\\"\\\"\\\"\\n return pr(size,'Already analysed')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77e666d9bf2f728844369dbba517797b\",\n \"score\": \"0.48416984\",\n \"text\": \"def develop_output():\\n output_array = []\\n for docs in db.get_collection(\\\"google_places\\\").find({}):\\n docs[\\\"city\\\"] = docs[\\\"vicinity\\\"].split(\\\",\\\")[-1].strip()\\n del docs['_id']\\n\\n for mg_doc in db.get_collection(\\\"michelin_guide\\\").find({}):\\n del mg_doc['_id']\\n\\n if (fuzz.token_set_ratio(docs[\\\"vicinity\\\"], mg_doc[\\\"datasheets\\\"][0][\\\"address\\\"]) > 80 and\\n fuzz.token_set_ratio(docs[\\\"name\\\"], mg_doc[\\\"datasheets\\\"][0][\\\"name\\\"]) > 80):\\n docs[\\\"michelin_stars\\\"] = mg_doc[\\\"datasheets\\\"][0][\\\"michelin_stars\\\"]\\n docs[\\\"michelin_mention\\\"] = True\\n docs[\\\"michelin_description\\\"] = mg_doc[\\\"datasheets\\\"][0][\\\"description\\\"]\\n docs[\\\"michelin_url\\\"] = mg_doc[\\\"datasheets\\\"][0][\\\"web\\\"]\\n break\\n\\n else:\\n docs[\\\"michelin_stars\\\"] = 0\\n docs[\\\"michelin_mention\\\"] = False\\n docs[\\\"michelin_description\\\"] = None\\n docs[\\\"michelin_url\\\"] = None\\n\\n for yelp_doc in db.get_collection(\\\"Yelp\\\").find({}):\\n del yelp_doc['_id']\\n if (fuzz.token_set_ratio(docs[\\\"vicinity\\\"], yelp_doc[\\\"location\\\"][\\\"address1\\\"]) > 80 and\\n fuzz.token_set_ratio(docs[\\\"name\\\"], yelp_doc[\\\"name\\\"]) > 80):\\n docs[\\\"yelp_stars\\\"] = yelp_doc[\\\"rating\\\"]\\n docs[\\\"yelp_url\\\"] = yelp_doc[\\\"url\\\"]\\n break\\n\\n else:\\n docs[\\\"yelp_stars\\\"] = None\\n docs[\\\"yelp_url\\\"] = None\\n\\n \\\"\\\"\\\" The results in Zomato are nested in one document,\\n so this for loop breaks them up so the break logic\\n works better.\\n \\\"\\\"\\\"\\n clean_zomato_list = []\\n for zom_doc in db.get_collection(\\\"zomato\\\").find({}):\\n del zom_doc['_id']\\n for restaurant in zom_doc[\\\"restaurants\\\"]:\\n clean_zomato_list.append(restaurant)\\n\\n for restaurant in clean_zomato_list:\\n if (fuzz.token_set_ratio(docs[\\\"vicinity\\\"], restaurant['restaurant'][\\\"location\\\"][\\\"address\\\"]) > 80 and\\n fuzz.token_set_ratio(docs[\\\"name\\\"], restaurant['restaurant'][\\\"name\\\"]) > 80):\\n docs[\\\"zomato_stars\\\"] = restaurant['restaurant']['user_rating']['aggregate_rating']\\n docs[\\\"zomato_timings\\\"] = restaurant['restaurant']['timings']\\n docs[\\\"zomato_avg_for_two\\\"] = restaurant['restaurant']['average_cost_for_two']\\n docs[\\\"zomato_events\\\"] = restaurant['restaurant']['events_url']\\n break\\n\\n else:\\n docs[\\\"zomato_stars\\\"] = None\\n docs[\\\"zomato_timings\\\"] = None\\n docs[\\\"zomato_avg_for_two\\\"] = None\\n docs[\\\"zomato_events\\\"] = None\\n\\n if docs not in output_array:\\n output_array.append(docs)\\n\\n #db.get_collection(\\\"outputs\\\").delete_many({})\\n db.get_collection(\\\"outputs\\\").insert_many(output_array)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ea4663df0aea9df2d26cd986153b720\",\n \"score\": \"0.48416552\",\n \"text\": \"def benchmark(extract_size=800):\\n random_file = random_html_file()\\n with open(join(DATA_PATH, random_file), 'r') as f:\\n html_string = f.read()\\n\\n # GOOSE\\n try:\\n g = Goose({'browser_user_agent': 'Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:61.0) Gecko/20100101 Firefox/61.0',\\n 'enable_image_fetching': False})\\n goose_article = g.extract(raw_html=html_string)\\n goose_result = goose_article.cleaned_text\\n except:\\n goose_result = ' Goose error.'\\n\\n # EATIHT\\n try:\\n eatiht_result = eatiht.extract(html_string)\\n except:\\n eatiht_result = ' Eatiht error.'\\n\\n # DRAGNET\\n\\n try:\\n dragnet_result = dragnet.extract_content(html_string)\\n except Exception as e:\\n dragnet_result = ' Dragnet error: ' + str(e)\\n\\n # LIBEXTRACT\\n\\n try:\\n textnodes = list(libextract.api.extract(html_string))\\n libextract_result = textnodes[0].text_content()\\n except:\\n libextract_result = ' Libextract error.'\\n\\n # BOILERPIPE (CanolaExtractor)\\n\\n try:\\n extractor = Extractor(\\n extractor='CanolaExtractor', html=html_string)\\n boilerpipe_result = extractor.getText()\\n except:\\n boilerpipe_result = ' Boilerpipe error.'\\n\\n # NEWSPAPER\\n\\n try:\\n article = Article('url')\\n article.download(input_html=html_string)\\n article.parse()\\n print('Auteurs:', article.authors)\\n print('Date de publication:', article.publish_date)\\n newspaper_result = article.text\\n except:\\n newspaper_result = ' Newspaper error.'\\n\\n # JUSTEXT\\n\\n try:\\n paragraphs = justext.justext(\\n html_string, justext.get_stoplist(\\\"French\\\"))\\n print('PARAGRAPHS')\\n for p in paragraphs:\\n if not p.is_boilerplate:\\n print(p.text)\\n justext_result = '\\\\n'.join(\\n paragraph.text for paragraph in paragraphs if not paragraph.is_boilerplate)\\n print('JUSTEXT_RESULT', justext_result)\\n\\n except Exception as e:\\n justext_result = ' Justext error: ' + str(e)\\n print(justext_result)\\n\\n # Results\\n\\n try:\\n # finds the url associated with the file in a \\\"filename-url\\\" csv\\n with open('./data/urls.csv', 'r') as csvfile:\\n\\n urls = dict((line['id'], line['url'])\\n for line in csv.DictReader(csvfile))\\n url = urls[random_file[:-5]]\\n\\n print('\\\\n\\\\n >>> URL n.' + random_file[:-5] + ' : ' + url)\\n except:\\n print('\\\\n\\\\n (URL of the html file not found. To print the associated URL, please provide a urls.csv file featuring filename & url in /data)')\\n # webbrowser.open(url, autoraise=False)\\n path = abspath('temp.html')\\n local_url = 'file://' + path\\n with open(path, 'w') as f:\\n f.write(html_string)\\n webbrowser.open(local_url)\\n\\n # print('\\\\n\\\\n /// GOOSE /// \\\\n')\\n # print(goose_result[:extract_size])\\n # print('\\\\n\\\\n /// EATIHT /// \\\\n')\\n # print(eatiht_result[:extract_size])\\n print('\\\\n ------ [[DRAGNET]] ------',\\n len(dragnet_result), 'caractères\\\\n')\\n print(dragnet_result[:extract_size] +\\n '\\\\n...\\\\n' + dragnet_result[-extract_size:])\\n print('\\\\n ------ [[NEWSPAPER]] ------',\\n len(newspaper_result), 'caractères\\\\n')\\n print(newspaper_result[:extract_size] +\\n '\\\\n...\\\\n' + newspaper_result[-extract_size:])\\n print('\\\\n ------ [[JUSTEXT]] ------',\\n len(justext_result), 'caractères\\\\n')\\n print(justext_result[:extract_size] +\\n '\\\\n...\\\\n' + justext_result[-extract_size:])\\n # print('\\\\n\\\\n /// LIBEXTRACT /// \\\\n')\\n # print(libextract_result[:extract_size])\\n # print('\\\\n\\\\n /// BOILERPIPE (CanolaExtractor) /// \\\\n\\\\n')\\n # print(boilerpipe_result[:extract_size])\\n # print('\\\\n\\\\n')\\n return(url)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9092fc9ed9f2b354ab962190f27cb6f7\",\n \"score\": \"0.48402354\",\n \"text\": \"def problem3() -> None:\\n try:\\n print(\\\"Reading in the text files for each keyword...\\\")\\n kw_df = collect_keyword_dataframe(collect_all=COLLECT_ALL) \\n except FileNotFoundError:\\n raise Exception(\\\"ERROR: The 'articles' folder was not found. Please \\\"\\n \\\"ensure that the functions for problems 1 and 2 have \\\"\\n \\\"been applied first.\\\")\\n \\n word2onehot, w2vp, matrices = word2vec_main(kw_df)\\n sem_dist_mat = get_semantic_dist_matrix(\\n w2vp.target_words, word2onehot, matrices\\n )\\n \\n try:\\n matrix2dataframe(w2vp.keywords, sem_dist_mat)\\n except PermissionError:\\n print(\\\"ERROR: Unable to write 'distance.xlsx'. Please ensure that \\\"\\n \\\"this file is not open.\\\")\\n \\n # Functions for visualisations:\\n reduce_and_plot_word_vectors(\\n w2vp.keywords, [word2onehot[w] for w in w2vp.target_words], matrices\\n )\\n nearest_neighbours( \\n w2vp, word2onehot, matrices \\n )\\n similarity_distributions(\\n kw_df, w2vp, word2onehot, matrices\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86a45a0e06cc694a864568bc14c41dea\",\n \"score\": \"0.48376784\",\n \"text\": \"def run():\\n logger.info(f\\\"Process started:\\\")\\n logger.info(f\\\"Converting Glove file to Word2Vec format\\\")\\n convert_to_word2vec.convert(\\n \\\"./data/source/glove.6B.50d.txt\\\", \\\"./data/source/glove.6B.50d.w2vformat.txt\\\"\\n )\\n\\n logger.info(f\\\"Extracting Click Stream data\\\")\\n extract_click_stream_data()\\n\\n logger.info(\\\"Extracting Wiki articles\\\")\\n extract_wiki_articles()\\n\\n logger.info(f\\\"Generating Clickstream dataset\\\")\\n generate_datasets()\\n\\n logger.info(\\\"Tokenizing articles\\\")\\n WikiArticlesTokenizer().process()\\n\\n logger.info(\\\"Creating dataset with Wiki Articles\\\")\\n create_wiki_articles_dataset()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"205c1a87d884508574f8ff796057d048\",\n \"score\": \"0.48364243\",\n \"text\": \"def main():\\n loader = MicrosoftDataloader()\\n train,dev,test = loader.getData()\\n sentences = []\\n\\n # Collect all the training sentences\\n for i,row in pd.concat((train,test)).iterrows():\\n if isinstance(row[\\\"sentence1\\\"], basestring) and isinstance(row[\\\"sentence2\\\"], basestring):\\n sentences.append(row[\\\"sentence1\\\"])\\n sentences.append(row[\\\"sentence2\\\"])\\n\\n # Get the mapping between sentences and their cotext vectors\\n mapped = get_sentence_to_context_map(sentences)\\n\\n # At this stage we have a map between every sentence and its context vector\\n # However the JSON file must contain sentences in the same order as in the MSR data file\\n data = []\\n for i,sentence in enumerate(sentences):\\n embedding = mapped[sentence]\\n data.append({'index':i, 'embedding':embedding, 'text':sentence})\\n\\n # Write the sentences and embeddings to JSON\\n # The array index should corrospond to the sentence #\\n print \\\"Saving embedded sentences to: {0}\\\".format(EMBED_FILE)\\n with open(EMBED_FILE,'w') as outfile:\\n json.dump(data,outfile,indent=2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c00e716572836ad08706fbaa579d01c\",\n \"score\": \"0.48353478\",\n \"text\": \"def morpho_doc(doc):\\n doc_text = doc.stripped\\n mystem_analyzer.start()\\n # new_morpho = mystem_analyzer.analyze(doc_text)\\n new_morpho = mystem_analyzer.analyze(doc_text.replace('\\\\n',''))\\n\\n morpho_list = []\\n\\n for element in new_morpho: # разрезаем\\n\\n if is_sentence_end(element):\\n morpho_list.append(element)\\n else:\\n\\n line = element.get('text', '')\\n\\n space_len = 0\\n\\n word_start = -1\\n word_len = 0\\n\\n symbol_number = -1\\n for symbol in line:\\n\\n symbol_number+=1\\n\\n if symbol == \\\"'\\\" or symbol == '\\\"' or symbol == '»' or symbol == '«':\\n\\n if space_len > 0: # добавим пробелы\\n\\n cur_space = ' ' * space_len\\n\\n new_element = {'text': cur_space}\\n if 'analysis' in element: new_element['analysis'] = element['analysis']\\n morpho_list.append(new_element)\\n\\n space_len = 0\\n\\n elif word_start > -1: # добавим слово\\n\\n cur_word = line[word_start:(word_start + word_len)]\\n\\n new_element = {'text': cur_word}\\n if 'analysis' in element: new_element['analysis'] = element['analysis']\\n morpho_list.append(new_element)\\n\\n word_start = -1\\n word_len = 0\\n\\n # добавим кавычку\\n new_element = {'text': symbol}\\n if 'analysis' in element: new_element['analysis'] = element['analysis']\\n morpho_list.append(new_element)\\n\\n elif symbol == \\\" \\\":\\n\\n if word_start > -1: # добавим слово\\n\\n cur_word = line[word_start:(word_start + word_len)]\\n\\n new_element = {'text': cur_word}\\n if 'analysis' in element: new_element['analysis'] = element['analysis']\\n morpho_list.append(new_element)\\n\\n word_start = -1\\n word_len = 0\\n\\n space_len += 1\\n\\n else:\\n\\n if space_len > 0: # добавим пробелы\\n\\n cur_space = ' ' * space_len\\n\\n new_element = {'text': cur_space}\\n if 'analysis' in element: new_element['analysis'] = element['analysis']\\n morpho_list.append(new_element)\\n\\n space_len = 0\\n\\n if word_start == -1:\\n word_start = symbol_number\\n word_len = 1\\n else:\\n word_len += 1\\n\\n if space_len > 0: # добавим пробелы\\n\\n cur_space = ' ' * space_len\\n\\n new_element = {'text': cur_space}\\n if 'analysis' in element: new_element['analysis'] = element['analysis']\\n\\n morpho_list.append(new_element)\\n\\n elif word_start > -1: # добавим слово\\n\\n cur_word = line[word_start:(word_start + word_len)]\\n\\n new_element = {'text': cur_word}\\n if 'analysis' in element: new_element['analysis'] = element['analysis']\\n morpho_list.append(new_element)\\n\\n for i in range(len(morpho_list) - 1): # переставляем\\n if i > 0:\\n if morpho_list[i - 1]['text'] == ' ' and morpho_list[i]['text'] == '\\\"' and morpho_list[i + 1]['text'] == '\\\\\\\\s':\\n morpho_list[i], morpho_list[i + 1] = morpho_list[i + 1], morpho_list[i]\\n\\n sentence_index = 0\\n word_index = 0\\n start_offset = 0\\n\\n for element in morpho_list: # нумеруем\\n if is_sentence_end(element):\\n if word_index != 0:\\n sentence_index += 1\\n word_index = 0\\n else:\\n line = element.get('text', '')\\n line_len = len(line)\\n\\n if(line[0]!=' '):\\n element['start_offset'] = start_offset\\n element['end_offset'] = start_offset + line_len - 1\\n element['word_index'] = word_index\\n element['sentence_index'] = sentence_index\\n\\n word_index += 1\\n start_offset += line_len\\n\\n doc.morpho = morpho_list\\n mystem_analyzer.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6d13864f711264db0bb546f4c2723f1\",\n \"score\": \"0.4818834\",\n \"text\": \"def _read_adv_embeddings(identity, target):\\n embeddings_file = os.path.join(\\n FLAGS.output_directory,\\n identity,\\n FLAGS.attack_type,\\n target\\n )\\n embeddings_file = os.path.join(FLAGS.image_directory,\\n identity,\\n 'embeddings.h5')\\n with h5py.File(embeddings_file, 'r') as f:\\n return f['embeddings'][:].astype(np.float32)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"429e4bfdb400d42f560067a1b0a86078\",\n \"score\": \"0.48179644\",\n \"text\": \"def get_wiki_content(self):\\n url = \\\"https://fr.wikipedia.org/w/api.php?action=query&prop=extracts&exsentences=4&explaintext=&pageids={}&format=json\\\".format(self.page_id)\\n self.page = str(self.page_id)\\n self.response = requests.get(url)\\n self.data = self.response.json()\\n self.wiki_data = (self.data['query']['pages'][self.page]['extract'])\\n return (self.wiki_data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cad7e92437b5e1b930e423a581c70b19\",\n \"score\": \"0.48148713\",\n \"text\": \"def model_extract_document_embedding(self):\\n input_ids = tf.keras.layers.Input(shape=(self.maxlen,), dtype=tf.int32, name=\\\"ids\\\")\\n attention_mask = tf.keras.layers.Input(shape=(self.maxlen,), dtype=tf.int32, name=\\\"att\\\")\\n token = tf.keras.layers.Input(shape=(self.maxlen,), dtype=tf.int32, name=\\\"tok\\\")\\n\\n # Embedding :\\n if self.method_embedding == 'CamemBERT':\\n Camembert_model = transformers.TFCamembertModel.from_pretrained(\\\"jplu/tf-camembert-base\\\")\\n x = Camembert_model(input_ids, attention_mask=attention_mask, token_type_ids=token)\\n elif self.method_embedding == 'FlauBERT':\\n # lr = 0.00001\\n Flaubert_model = transformers.TFFlaubertModel.from_pretrained(\\\"jplu/tf-flaubert-base-uncased\\\")\\n x = Flaubert_model(input_ids, attention_mask=attention_mask, token_type_ids=token)\\n elif self.method_embedding == 'XLM-RoBERTa':\\n # lr = 0.00001\\n XLMRoBERTa_model = transformers.TFXLMRobertaModel.from_pretrained(\\\"jplu/tf-xlm-roberta-base\\\")\\n x = XLMRoBERTa_model(input_ids, attention_mask=attention_mask, token_type_ids=token)\\n elif self.method_embedding == 'RoBERTa':\\n # Experience Test path weights :\\n PATH = '/kaggle/input/tf-roberta/'\\n config = transformers.RobertaConfig.from_pretrained(PATH + 'config-roberta-base.json')\\n Roberta_model = transformers.TFRobertaModel.from_pretrained(PATH + 'pretrained-roberta-base.h5',\\n config=config)\\n # Sinon :\\n # Roberta_model = transformers.TFRobertaModel.from_pretrained('roberta-base')\\n x = Roberta_model(input_ids, attention_mask=attention_mask, token_type_ids=token)\\n elif self.method_embedding == 'BERT':\\n BERT_model = transformers.TFBertModel.from_pretrained('bert-base-uncased')\\n x = BERT_model(input_ids, attention_mask=attention_mask, token_type_ids=token)\\n else:\\n logger.critical(\\\"unknown embedding method name : '{}'\\\".format(self.method_embedding))\\n\\n # word vectors shape : (None, maxlen, 768)\\n x = x[0]\\n cls_token = x[:, 0, :]\\n\\n model = tf.keras.models.Model(inputs=[input_ids, attention_mask, token], outputs=cls_token)\\n return model\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73129f4013ee617835f66bf33515d190\",\n \"score\": \"0.48147637\",\n \"text\": \"def iter_documents(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0584aa2acf6c3fbdc1c0d6e5b17ff798\",\n \"score\": \"0.4809549\",\n \"text\": \"def problem4() -> None:\\n \\n # Obtain the dataframe containing lines from the articles and the keyword\\n # with which the article is associated.\\n try:\\n kw_df = collect_keyword_dataframe(collect_all=COLLECT_ALL) \\n except FileNotFoundError:\\n raise Exception(\\\"[ERROR] The 'articles' folder was not found. Please \\\"\\n \\\"ensure that the functions for problems 1 and 2 have \\\"\\n \\\"been applied first.\\\")\\n \\n keywords, corpus = separate_keywords_corpus(kw_df)\\n \\n # Complete the preprocessing for the corpus for comparison's sake\\n w2vp = W2VPreprocessor(keywords, corpus)\\n \\n # Display the counts of sentences collected associated with each keyword\\n keywords_countplot(kw_df)\\n \\n # Create displots for the lengths of sentences in the corpus\\n displot_all_sentence_lengths(kw_df)\\n displot_sentence_lengths_per_keyword(kw_df)\\n \\n # Visualise the semantic distances\\n try:\\n distances = read_in_distance_matrix()\\n except FileNotFoundError:\\n raise Exception(\\\"[ERROR] The distance matrix excel file was could not \\\"\\n \\\"be found! Please ensure the methods for the \\\"\\n \\\"preceding problems have been run first.\\\")\\n visualise_distances(distances)\\n \\n # Visualise word frequency before and after preprocessing\\n visualise_initial_most_frequent_words(kw_df)\\n visualise_preprocessed_most_frequent_words(w2vp.corpus)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1a10c0f30e43fde085d71e556d4c8dd\",\n \"score\": \"0.4807212\",\n \"text\": \"def speaker_spotting_try_system2(current_trial):\\n # target model\\n # record the model embedding vector \\n # and model id\\n model = {}\\n model_id = current_trial['model_id'] \\n model_embedding = models[current_trial['model_id']]\\n model['mid'] = model_id\\n model['embedding'] = model_embedding\\n \\n # where to look for this target\\n try_with = current_trial['try_with']\\n \\n # precomputed embedding\\n embeddings = precomputed(current_trial)\\n \\n # annotation of current file\\n oracle_diarization = REFERENCE[current_trial['uri']].crop(current_trial['try_with'])\\n \\n # find index of first and last embedding fully included in 'try_with'\\n indices = embeddings.sliding_window.crop(try_with, mode='strict')\\n first, last = indices[0], indices[-1]\\n onlineOracleClustering = clustering.OnlineOracleClustering(current_trial['uri'])\\n start = embeddings.sliding_window[0].start\\n data = np.zeros((len(embeddings.data), 1))\\n for i, (window, _) in enumerate(embeddings):\\n # make sure the current segment is in 'try_with'\\n if i < first:\\n start = window.end\\n continue\\n if i > last:\\n break\\n \\n so_far = Segment(start, window.end)\\n current_annotation = oracle_diarization.crop(so_far)\\n score = 0.\\n for segment, _, label in current_annotation.itertracks(label=True):\\n example = {}\\n example['label'] = label\\n example['segment'] = segment\\n example['embedding'] = embeddings.crop(segment, mode='center')\\n example['indice'] = [i]\\n # compute the distance with model\\n example['distances'] = {}\\n example['distances'][model['mid']] = list(cdist(example['embedding'], \\n model['embedding'], \\n metric='cosine').flatten())\\n # update the online oracle clustering\\n onlineOracleClustering.upadateCluster(example)\\n if not onlineOracleClustering.empty():\\n # compute the current score\\n min_dist = min(onlineOracleClustering.modelDistance(model))\\n score = max(score, 2-min_dist)\\n data[i] = score\\n start = window.end\\n import pdb\\n #pdb.set_trace()\\n \\n # transform scores to sliding window features\\n data = data[first:last+1]\\n sliding_window = SlidingWindow(start=embeddings.sliding_window[first].start,\\n duration=embeddings.sliding_window.duration,\\n step=embeddings.sliding_window.step)\\n \\n return SlidingWindowFeature(data, sliding_window)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7fb7839b49621951ed2f1a6da2cd10fc\",\n \"score\": \"0.48055255\",\n \"text\": \"def build_data(self):\\n from desiutil.io import combine_dicts\\n # Loop on exposures\\n odict = {}\\n for qanight in self.qa_nights:\\n for qaexp in qanight.qa_exps:\\n # Get the exposure dict\\n idict = write_qa_exposure('foo', qaexp, ret_dict=True)\\n odict = combine_dicts(odict, idict)\\n # Finish\\n self.data = odict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2ec2642418c7cdf0bd5b65ab5d50cf4\",\n \"score\": \"0.4800392\",\n \"text\": \"def main() -> Dict[int, int]:\\n # DOCUMENTS DIRECTORY\\n # Path of the directory that contains the .txt documents. One .txt document by company. IT NEEDS TO BE \\\"/data\\\" when you upload it in data challenge platform. For test in local, you can modifiy to match your data path.\\n if dev:\\n documents_directory = \\\"../example_dataset/data\\\"\\n else: \\n documents_directory = \\\"/data\\\"\\n\\n path_to_files: List[str] = [os.path.join(documents_directory, file) for file in os.listdir(documents_directory)]\\n assert len(path_to_files) == 10 # 10 files in documents directory\\n path_to_files.sort() # Sort list of path file by alphabetical order to match ground truth annotations order : IT IS ESSENTIAL.\\n\\n # INITIALIZATION OF YOUR OBJECTS\\n data_model = FormDataModel.from_json_file(\\n os.path.join(os.path.dirname(__file__), \\\"resources\\\", \\\"data-model.json\\\")\\n )\\n country_referential = CountryReferential.from_csv(\\n os.path.join(os.path.dirname(__file__), \\\"resources\\\", \\\"countries_code.csv\\\")\\n )\\n form_company_filling = FormCompanyFilling([\\n BasicExtractor(\\n question_ids=NOT_COUNTRY_QUESTIONS_NUMBERS,\\n form_data_model=data_model,\\n ),\\n BasicCountryExtractor(\\n question_ids=COUNTRY_QUESTIONS_NUMBERS,\\n form_data_model=data_model,\\n country_code_referential=country_referential,\\n\\n )\\n ])\\n\\n # COMPUTE PREDICTION BY FILE (ie company)\\n print(\\\"##################################\\\")\\n print(\\\"RUNNING PREDICTION\\\")\\n results: Dict[int, int] = {}\\n start_time = time.time()\\n for i, path in enumerate(path_to_files):\\n start = time.time()\\n print(f\\\"File : {path}\\\")\\n with open(path, \\\"r\\\") as input_file:\\n text = input_file.read()\\n print(\\\"... Encoding ...\\\")\\n start_encoding = time.time()\\n embeddings = model.encode(prepare_sentences(text))\\n # embeddings = []\\n print(\\\"Successfully encoded\\\")\\n print(\\\"Encoding time : \\\", time.time() - start_encoding)\\n\\n form_company_response = form_company_filling.fill(text, embeddings, model)\\n form_company_response.sort_by_question_id() # ESSENTIAL : Sort the response by question number for each company\\n for answer in form_company_response.answers:\\n question_number = answer.question_id + i * 22 # ESSENTIAL : each company has 22 questions. Each question_number in results should be unique\\n results[question_number] = answer.answer_id\\n # gc.collect()\\n print(\\\"File time :\\\", time.time()-start, '\\\\n')\\n # CHECK FORMAT RESULTS IS DATACHALLENGE PLATFORM COMPATIBLE\\n assert len(results) == len(path_to_files) * (len(COUNTRY_QUESTIONS_NUMBERS) + len(NOT_COUNTRY_QUESTIONS_NUMBERS))\\n assert set(list(results.keys())) == {i for i in range(1,221)}\\n print(\\\"Full Time\\\", time.time()-start_time)\\n return results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b2cfa7bf46142925deae1bf5c16630a\",\n \"score\": \"0.47919524\",\n \"text\": \"def corpora_stats(output):\\n igFiles = []\\n for root, directories, filenames in os.walk(output + \\\"/ig/\\\"):\\n for filename in filenames:\\n igFiles.append(os.path.join(root, filename))\\n igFiles = filter(lambda x: \\\".txt\\\" in x, igFiles)\\n words = []\\n for file in igFiles:\\n fileH = open(file, \\\"r\\\")\\n words = words + fileH.read().split(\\\" \\\")\\n print(\\\"Number of words in IG corpus: {}\\\".format(len(words)))\\n print(\\\"Vocabulary size of IG corpus: {}\\\".format(len(set(words))))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0cf44b52206139aa58bd58914f0c86e8\",\n \"score\": \"0.47894615\",\n \"text\": \"def main():\\n # Load the metadata file.\\n\\n # Compute audio average length.\\n # audio_lengths()\\n\\n # Compute age.\\n average_age()\\n\\n # Number of words and talking time.\\n num_words()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce416b27270034cd6ebdf65aba001f12\",\n \"score\": \"0.47884974\",\n \"text\": \"def run_module(self):\\n info(\\\"Searching for cross site scripting (reflected)...\\\")\\n\\n # load in a list of lfi attach strings\\n #self.attack_strings = self.main.db.get_wordlist(\\n # self.info['wordlist_name'])\\n\\n self.attack_strings = ['<script>alert(1)</script>',\\n '<img srx=\\\"x\\\" onerror=\\\"alert(1)>\\\"']\\n\\n # the search strings will be the attack strings themselves\\n # because python will not interpret any javascript\\n self.re_search_strings = self.attack_strings\\n\\n injectable_params = self._get_previous_results('HTMLParser')\\n\\n with concurrent.futures.ProcessPoolExecutor() as executor:\\n results = executor.map(self._run_thread, injectable_params)\\n\\n final = []\\n for r in results:\\n final.extend(r)\\n\\n # save the results\\n self._save_scan_results(final)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3070ac458fc5ec5216e0c4ee491376a4\",\n \"score\": \"0.47828823\",\n \"text\": \"def extract_features(data, stopwords=STOPWORDS):\\n tags = set()\\n docs = []\\n for document in data:\\n doc_data = dict()\\n doc_data['pmid'] = document['sourceid']\\n text = document['text']\\n\\n # Insert PubTator annotations inside abstract\\n denotations = document['denotations']\\n sorted_denotations = []\\n for denotation in denotations:\\n begin = denotation['span']['begin']\\n end = denotation['span']['end']\\n obj = denotation['obj']\\n for c in punctuation:\\n obj = obj.replace(c, '')\\n tags.add(obj)\\n doc_data[obj] = doc_data.get(obj,0)+1\\n sorted_denotations.append([begin,end,obj])\\n sorted_denotations.sort()\\n sorted_denotations.reverse()\\n for begin, end, obj in sorted_denotations:\\n text = text[:begin] + obj + ' ' + text[end:]\\n\\n doc_data['text'] = clean_text(text, stopwords)\\n docs.append(doc_data)\\n\\n return docs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d2a91608f238f580391282fd94bd746\",\n \"score\": \"0.4773797\",\n \"text\": \"def passion_analyzer(text):\\n\\n\\tlower_text = text.lower()\\n\\n\\thashtag_scaling = 0.3\\n\\texclamation_scaling = 0.5\\n\\tuppercase_scaling = 0.2\\n\\n\\n\\tpassion_index = 0\\n\\n\\tfor x in range(len(positive_words)):\\n\\t\\tpassion_index += (lower_text.count(positive_words[x]))**2\\n\\tfor x in range(len(negative_words)):\\n\\t\\tpassion_index -= (lower_text.count(negative_words[x]))**2\\n\\tif '!' in text:\\n\\t\\tpassion_index *= exclamation_scaling * lower_text.count('!') + 1\\n\\tif '#' in text:\\n\\t\\tpassion_index *= hashtag_scaling * lower_text.count('#') + 1\\n\\tpassion_index *= uppercase_scaling * sum(1 for c in text if c.isupper())\\n\\n\\n\\t\\t\\n\\treturn math.sqrt(passion_index)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd2ea745317b67984d2a6bd61347cc77\",\n \"score\": \"0.47733462\",\n \"text\": \"def insights(self):\\r\\n return insights.Insights(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"decadcafbeea5086704d57080a838d03\",\n \"score\": \"0.4761947\",\n \"text\": \"def analysis():\\n global prediction\\n\\n json_path = os.path.join(basedir, 'static', 'data', 'tmp_json')\\n # csv_path = os.path.join(basedir, 'static', 'data', 'csv')\\n # if not os.path.exists(csv_path):\\n # os.mkdir(csv_path)\\n\\n if os.name == 'nt':\\n audio_file = Path(os.path.join(json_path, 'audio_data.shlf.dir'))\\n video_file = Path(os.path.join(json_path, 'facial_data.shlf.dir'))\\n else:\\n audio_file = Path(os.path.join(json_path, 'audio_data.shlf'))\\n video_file = Path(os.path.join(json_path, 'facial_data.shlf'))\\n\\n # Files exists\\n if audio_file.is_file() and video_file.is_file():\\n with shelve.open(os.path.join(json_path, 'facial_data.shlf')) as shelf:\\n emotion_data = shelf['emotion_data']\\n microexpression_data = shelf['micro_expression_data']\\n blink_data = shelf['blink_data']\\n\\n with shelve.open(os.path.join(json_path, 'audio_data.shlf')) as shelf:\\n mean_energy = shelf['mean_energy']\\n max_pitch_amp = shelf['max_pitch_amp']\\n vowel_duration = shelf['vowel_duration']\\n pitch_contour = shelf['pitch_contour']\\n\\n else:\\n emotion_data = None\\n microexpression_data = None\\n blink_data = None\\n mean_energy = None\\n max_pitch_amp = None\\n vowel_duration = None\\n pitch_contour = None\\n\\n # Training Files (choose one)\\n # soc_file = os.path.join(basedir, 'static', 'data', 'train_files', 'first_soc.txt')\\n # niko_file = os.path.join(basedir, 'static', 'data', 'train_files', 'first_niko.txt')\\n # vero_file = os.path.join(basedir, 'static', 'data', 'train_files', 'first_vero.txt')\\n\\n # txt_file = soc_file\\n\\n # train_data = []\\n\\n # for cases where one parameter has more elements\\n # for i in range(min(len(blink_data), len(microexpression_data), len(mean_energy))):\\n # train_data.append(0)\\n\\n # train_file = open(txt_file)\\n\\n # for line in train_file:\\n # index1 = int((int(line[4]) * 600) + ((int(line[5]) * 60) + (int(line[7]) * 10) + int(line[8])) / 2)\\n # index2 = int((int(line[10]) * 600) + ((int(line[11]) * 60) + (int(line[13]) * 10) + int(line[14])) / 2)\\n # if line[0] == 'F':\\n # train_data[index1] = 1\\n # train_data[index2] = 1\\n\\n # with open(os.path.join(csv_path, 'train.csv'), 'w', newline='') as csv_file:\\n # writer = csv.writer(csv_file)\\n # writer.writerow(['Time Interval', 'Micro-expressions', 'Blinks',\\n # 'Mean Energy', 'Max Pitch Amplitude', 'Vowel Duration', 'Fundamental Frequency',\\n # 'False/True'])\\n\\n # # for cases where one parameter has more elements than another\\n # for index in range(min(len(mean_energy), len(blink_data), len(microexpression_data))):\\n # writer.writerow([index, microexpression_data[index], blink_data[index],\\n # mean_energy[index], max_pitch_amp[index], vowel_duration[index], pitch_contour[index],\\n # train_data[index]])\\n\\n # finalresults = [['Time Interval', 'Micro-expressions', 'Blinks',\\n # 'Mean Energy', 'Max Pitch Amplitude', 'Vowel Duration', 'Fundamental Frequency' ]]\\n final_results = []\\n\\n for index in range((min(len(mean_energy), len(blink_data), len(microexpression_data)))):\\n final_results.append([microexpression_data[index], blink_data[index],\\n mean_energy[index], max_pitch_amp[index], vowel_duration[index],\\n pitch_contour[index]])\\n\\n prediction[0] = predict(final_results)\\n\\n return render_template('analysis.html', mean_energy=mean_energy, max_pitch_amp=max_pitch_amp,\\n vowel_duration=vowel_duration, pitch_contour=pitch_contour, blink_data=blink_data,\\n microexpression_data=microexpression_data, emotion_data=emotion_data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc92410a0520036c9ea03316608d2714\",\n \"score\": \"0.4759545\",\n \"text\": \"def sample_analyze_entities(text_content, fb2wiki):\\n\\n #client = language_v1.LanguageServiceClient()\\n\\n # text_content = 'California is a state.'\\n\\n # Available types: PLAIN_TEXT, HTML\\n type_ = enums.Document.Type.PLAIN_TEXT\\n\\n # Optional. If not specified, the language is automatically detected.\\n # For list of supported languages:\\n # https://cloud.google.com/natural-language/docs/languages\\n language = \\\"en\\\"\\n document = {\\\"content\\\": text_content, \\\"type\\\": type_, \\\"language\\\": language}\\n entities = []\\n entity_map = {}\\n\\n # Available values: NONE, UTF8, UTF16, UTF32\\n encoding_type = enums.EncodingType.UTF8\\n\\n response = client.analyze_entities(document, encoding_type=encoding_type)\\n #print(text_content)\\n # Loop through entitites returned from the API\\n for entity in response.entities:\\n #print(u\\\"Representative name for the entity: {}\\\".format(entity.name))\\n # Get entity type, e.g. PERSON, LOCATION, ADDRESS, NUMBER, et al\\n # print(u\\\"Entity type: {}\\\".format(enums.Entity.Type(entity.type).name))\\n # Get the salience score associated with the entity in the [0, 1.0] range\\n # print(u\\\"Salience score: {}\\\".format(entity.salience))\\n # Loop over the metadata associated with entity. For many known entities,\\n # the metadata is a Wikipedia URL (wikipedia_url) and Knowledge Graph MID (mid).\\n # Some entity types may have additional metadata, e.g. ADDRESS entities\\n # may have metadata for the address street_name, postal_code, et al.\\n if entity.metadata['mid'] != '':\\n mid = entity.metadata['mid'].strip(\\\"/\\\").replace('/','.')\\n if mid in fb2wiki:\\n wikiids = fb2wiki[mid]\\n entities.extend(wikiids)\\n #print(mid, wikiids)\\n # for metadata_name, metadata_value in entity.metadata.items():\\n # print(u\\\"{}: {}\\\".format(metadata_name, metadata_value))\\n\\n # Loop over the mentions of this entity in the input document.\\n # The API currently supports proper noun mentions.\\n for mention in entity.mentions:\\n # print(u\\\"Mention text: {}\\\".format(mention.text.content))\\n # # Get the mention type, e.g. PROPER for proper noun\\n # print(\\n # u\\\"Mention type: {}\\\".format(enums.EntityMention.Type(mention.type).name)\\n # )\\n #print(mention.text)\\n mention_text = mention.text.content\\n begin = mention.text.begin_offset\\n end = mention.text.begin_offset + len(mention_text)\\n #print(mention_text, begin, end)\\n if begin in entity_map:\\n entity_map[begin].extend([(end, wid) for wid in wikiids])\\n else:\\n entity_map[begin] = [(end, wid) for wid in wikiids]\\n\\n\\n # Get the language of the text, which will be the same as\\n # the language specified in the request or, if not specified,\\n # the automatically-detected language.\\n # print(u\\\"Language of the text: {}\\\".format(response.language))\\n return entities, entity_map\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a35cdeb822e74d2e45ff1f5ee19711e8\",\n \"score\": \"0.47527993\",\n \"text\": \"def _publish_results(self):\\n\\n doc = Document()\\n date = get_stamp()\\n\\n labels = ExperimentTemplateBase.parameters_to_string(self._topology_parameters_list)\\n\\n title = 'Mutual Information labels vs ' + self._experiment_name\\n self.plot_save(title,\\n self._mutual_info,\\n self._baseline_mutual_info,\\n 'Norm. mutual information',\\n labels, date, self._docs_folder, doc)\\n\\n title = 'Weak classifier accuracy labels vs ' + self._experiment_name\\n self.plot_save(title,\\n self._classifier_accuracy,\\n self._baseline_classifier_accuracy,\\n 'Classifier accuracy',\\n labels, date, self._docs_folder, doc) #, smoothing_size=3)\\n\\n title = 'average delta'\\n f = plot_multiple_runs(\\n self._different_steps[0], # here the X axes are identical\\n self._average_delta,\\n title=title,\\n ylabel='log(delta)',\\n xlabel='steps',\\n labels=labels\\n )\\n add_fig_to_doc(f, path.join(self._docs_folder, title), doc)\\n\\n title = 'average boosting duration'\\n f = plot_multiple_runs(\\n self._different_steps[0],\\n self._average_boosting_dur,\\n title=title,\\n ylabel='duration',\\n xlabel='steps',\\n labels=labels\\n )\\n add_fig_to_doc(f, path.join(self._docs_folder, title), doc)\\n\\n doc.write_file(path.join(self._docs_folder, to_safe_name(self._complete_name() + date + \\\".html\\\")))\\n\\n print('done')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1e768c23ac138268528027f134242d6\",\n \"score\": \"0.47511122\",\n \"text\": \"def do_analysis(ckpt, queries_type, entities_type, request):\\n global currently_analyzing, results, d, analysis_user\\n try:\\n print(\\\"starting analysis!\\\")\\n if entities_type == \\\"all\\\":\\n print(\\\"using all entities detected!\\\")\\n elif entities_type == \\\"uploaded\\\":\\n print(\\\"using only entities specified in csv file!\\\")\\n \\n currently_analyzing = True\\n analysis_user = request.user.username\\n results = []\\n proj_path = os.path.abspath(os.path.dirname(__file__)).split(\\\"FYP_Web_App\\\")[0]\\n ckpt = proj_path + \\\"FewRel/checkpoint/\\\" + ckpt\\n if d is None or d.ckpt_path != ckpt:\\n d = DetectionFramework(ckpt_path=ckpt)\\n if cancel_flag[0]:\\n return\\n d.clear_support_queries()\\n if len([i for i in os.listdir(\\\"temp/relation_support_datasets\\\") if 'csv' in i and request.user.username in i]) == 0:\\n raise ValueError(\\\"Please upload relation support dataset!\\\")\\n \\n d.load_support_files(\\\"temp/relation_support_datasets\\\", request.user.username)\\n if queries_type == \\\"csv_option\\\":\\n if not os.path.exists(\\\"temp/queries.csv\\\"):\\n raise ValueError(\\\"Please upload query CSV dataset!\\\")\\n d.load_queries_csv(\\\"temp/queries.csv\\\")\\n \\n elif queries_type == \\\"url_option\\\":\\n if not os.path.exists(\\\"temp/url.txt\\\"):\\n raise ValueError(\\\"Please specify news article url!\\\")\\n with open(\\\"temp/url.txt\\\") as f:\\n url = f.read()\\n d.load_url(url)\\n \\n elif queries_type == \\\"txt_option\\\":\\n d.load_text_files(os.path.abspath(\\\"temp/text_files\\\"))\\n \\n elif queries_type == \\\"ind_sentence_option\\\":\\n ind_sentence = request.POST.get('ind_sent')\\n d.load_ind_sentence(ind_sentence)\\n \\n elif queries_type == \\\"html_option\\\":\\n d.load_html_file_queries(os.path.abspath(\\\"temp/html_files\\\"))\\n \\n if entities_type == \\\"uploaded\\\":\\n d.trim_queries_based_on_entities_file(os.path.abspath(\\\"temp/entities_csv_file.csv\\\"))\\n\\n if cancel_flag[0]:\\n return\\n d.detect(rt_results=results, cancel_flag=cancel_flag)\\n if cancel_flag[0]:\\n return\\n src=None\\n if queries_type == \\\"csv_option\\\":\\n src = \\\"queries_csv\\\"\\n elif queries_type == \\\"txt_option\\\":\\n src = \\\"queries_text_file\\\"\\n elif queries_type == \\\"ind_sentence_option\\\":\\n src = \\\"ind_sentence\\\"\\n elif queries_type == \\\"url_option\\\":\\n with open(\\\"temp/url.txt\\\") as f:\\n src = f.read()\\n elif queries_type == \\\"html_option\\\":\\n src = \\\"html_files\\\"\\n \\n s = Source(source=src, user=request.user)\\n s.save()\\n for r in results:\\n er = ExtractedRelation(sentence=r['sentence'],head=r['head'],tail=r['tail'],pred_relation=r['pred_relation'],sentiment=r['sent'],conf=r['conf'],ckpt=ckpt, source=s)\\n er.save()\\n except Exception as e:\\n print(len(str(e)))\\n print(str(e))\\n errors.append(str(e))\\n tb = traceback.format_exc()\\n print(tb)\\n finally:\\n currently_analyzing = False\\n analysis_user = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5fb22dc5cfe9f14d6eb38fe3934c88a\",\n \"score\": \"0.47506815\",\n \"text\": \"def extract_information(preprocessed_sentences):\\n parsed = list(map(lambda sentence: nlp(sentence), preprocessed_sentences))\\n\\n quantities = list(filter(lambda sentence: eh.sentence_has_type(sentence, 'QUANTITY'), parsed))\\n dates = list(filter(lambda sentence: eh.sentence_has_type(sentence, 'DATE'), parsed))\\n\\n hurricane_name = eh.extract_frequent_regex_match(parsed, '[Hh]urricane ([A-Z][a-z]+)').most_common(1)[0][0]\\n hurricane_category = eh.extract_frequent_regex_match(parsed, '[Cc]ategory ([0-9]+)').most_common(1)[0][0]\\n\\n tropical_storm_name = eh.extract_frequent_regex_match(parsed, '[Tt]ropical [Ss]torm ([A-Z][a-z]+)').most_common(1)[0][0]\\n formation_date, middle_month = extract_storm_timeline(dates, hurricane_name)\\n\\n preperation_info = extract_preparation_information(parsed)\\n prep_gpes = preperation_info[0].most_common(3)\\n\\n restore_info = extract_restoration_information(parsed)\\n\\n landfall_info = extract_landfall_information(parsed)\\n\\n wind_info = extract_wind_information(quantities)\\n rain_info = extract_rain_information(quantities)\\n size_info = extract_size_information(parsed)\\n\\n # formation_info = extract_formation_info(parsed)\\n death_info = extract_death_damages_info(parsed)\\n\\n print(constants.HURRICANE_SENTENCE.format(hurricane_name, middle_month, hurricane_category))\\n print(constants.LANDFALL_SENTENCE.format(hurricane_name, landfall_info[2], landfall_info[3], landfall_info[0], landfall_info[1]))\\n print(constants.WIND_SENTENCE.format(wind_info[0], wind_info[1], wind_info[2]))\\n print(constants.RAIN_SENTENCE.format(hurricane_name, rain_info[1], rain_info[0], rain_info[2]))\\n print(constants.FORMATION_SENTENCE.format(formation_date, tropical_storm_name))\\n print(constants.PREPARATION_SENTENCE.format(prep_gpes[0][0], prep_gpes[1][0], prep_gpes[2][0], preperation_info[1].\\n most_common(1)[0][0]))\\n print(constants.SIZE_SENTENCE.format(size_info[0], size_info[1]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b0966fa977dfe08eafd5007f9d197d8\",\n \"score\": \"0.4750112\",\n \"text\": \"def inference(self):\\n embeddings = self.process_speaker(speaker_speech_path=self.main_configs.SPEAKER_SPEECH_PATH)\\n with open(self.main_configs.INPUT_TEXTS_PATH, \\\"r\\\") as file:\\n texts = file.readlines()\\n specs = self.synthesize_spectrograms(texts=texts, embeddings=embeddings)\\n specs = specs[0]\\n wav = self.generate_waveform(specs)\\n return wav\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1109a8c9c043b777d8ffe3c7da5c6437\",\n \"score\": \"0.47493204\",\n \"text\": \"def main(self, words_docs, cleaned_sentences, lang, model_dir, number_of_clusters, embedding_model, model_id):\\n\\t\\ttry:\\n\\t\\t\\tif embedding_model == \\\"tfidf\\\": text_vector = self.create_tfidf_vectors(cleaned_sentences)\\n\\t\\t\\telif embedding_model == \\\"word2vec\\\": text_vector = self.create_w2v_vectors(words_docs)\\n\\t\\t\\tmodel, pred_dict = self.train_model(cleaned_sentences, text_vector, number_of_clusters, lang, model_id, model_dir)\\n\\t\\t\\tdf_dominant_topic = self.evaulate_clusters(pred_dict, model_dir)\\n\\n\\t\\texcept Exception as e:\\n\\t\\t\\tprint(\\\"\\\\n Error in main : \\\",e)\\n\\t\\t\\tprint(\\\"\\\\n Error details : \\\", traceback.format_exc())\\n\\n\\t\\treturn df_dominant_topic\",\n \"title\": \"\"\n },\n {\n \"docid\": \"350a25bc36f4975e5f26fd090758162e\",\n \"score\": \"0.4746469\",\n \"text\": \"def feature_extraction(args, what_roi):\\n print(\\\"Start feature extraction...\\\")\\n descr_dict = {}\\n file_list = []\\n for root, dirs, files in os.walk(args.imdir):\\n for n, filename in enumerate(files):\\n if n == 0:\\n print(\\\"\\\\nCurrent directory: %s\\\" % root)\\n filepath = os.path.join(root, filename)\\n img = cv2.imread(filepath, cv2.IMREAD_COLOR)\\n\\n if type(img) != np.ndarray:\\n sys.stderr.write(\\\"--> %d File %s is not an img: will be skipped.\\\\n\\\" % (n, filename))\\n continue\\n else:\\n print(\\\"%d Image %s is being processed...\\\" % (n, filename))\\n file_list.append(filename)\\n\\n img = apply_roi(args, what_roi, img)\\n img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\\n ret, thresh = cv2.threshold(img_gray, 170, 255, cv2.THRESH_TRUNC)\\n des = surf_keypoint_detection(thresh)\\n descr_dict[filename] = des\\n\\n print(\\\"\\\\nAll images have been processed.\\\")\\n return descr_dict, file_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83f737bac1e245de9cc9cbb67328c603\",\n \"score\": \"0.47404498\",\n \"text\": \"def documents(self):\\n from kitsune.wiki.models import Document\\n\\n return Document.objects.filter(documentimage__image=self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36c67e8973d22370cdf488112cda2c18\",\n \"score\": \"0.47379708\",\n \"text\": \"def main():\\n logging.basicConfig(level=logging.WARN)\\n\\n text = extract()\\n text, char_indices, indices_char, x, y = transform(text)\\n model(text, char_indices, indices_char, x, y)\\n\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3405afec9e0f9e933b1ab9fd89407f6e\",\n \"score\": \"0.473772\",\n \"text\": \"def run_extraction(self):\\n self.background_estimator = ReflectedRegionsBackgroundEstimator(\\n observations=self.observations, **self.config[\\\"background\\\"]\\n )\\n self.background_estimator.run()\\n\\n self.extraction = SpectrumExtraction(\\n observations=self.observations,\\n bkg_estimate=self.background_estimator.result,\\n **self.config[\\\"extraction\\\"]\\n )\\n\\n self.extraction.run()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87bc13b67079a664c76ca1d541f857c2\",\n \"score\": \"0.47326425\",\n \"text\": \"def getExcerpts(self, text, DICECodeResults):\\n\\t\\t\\\"\\\"\\\" - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - \\\"\\\"\\\"\\n\\t\\t\\n\\t\\tdef getKernels(indices):\\n\\t\\t\\t\\\"\\\"\\\"\\n\\t\\t\\t\\tgetKernels() is a sub-method that extracts strings from a doc-\\n\\t\\t\\t\\tument using indices provided by the DICECodeResults data struc-\\n\\t\\t\\t\\tture passed into this sub-method's parent method, getExcerpts().\\n\\t\\t\\t\\tThis sub-method returns three strings.\\n\\n\\t\\t\\t\\tindices --> tuple containing indices in the document with text to extract.\\n\\t\\t\\t\\\"\\\"\\\"\\n\\n\\t\\t\\ti = indices[0]\\n\\t\\t\\tj = indices[1]\\n\\n\\t\\t\\th = i - self.scope\\n\\t\\t\\tk = j + self.scope\\n\\n\\t\\t\\tif h < 0: h = 0\\n\\t\\t\\tif k > len(text): k = len(text)-1\\n\\n\\t\\t\\treturn text[h:i].replace(\\\"\\\\n\\\", \\\"__\\\").replace(\\\"\\\\t\\\", \\\" \\\"), text[i:j].replace(\\\"\\\\n\\\", \\\"__\\\").replace(\\\"\\\\t\\\", \\\" \\\"), text[j:k].replace(\\\"\\\\n\\\", \\\"__\\\").replace(\\\"\\\\t\\\", \\\" \\\")\\n\\t\\t\\t#return \\\"|\\\"+text[h:i].replace(\\\"\\\\n\\\", \\\"__\\\").replace(\\\"\\\\t\\\", \\\" \\\")+\\\"|\\\", text[i:j].replace(\\\"\\\\n\\\", \\\"__\\\").replace(\\\"\\\\t\\\", \\\" \\\"), \\\"|\\\"+text[j:k].replace(\\\"\\\\n\\\", \\\"__\\\").replace(\\\"\\\\t\\\", \\\" \\\")+\\\"|\\\"\\n\\n\\t\\tdef getComboTerms(tuples):\\n\\t\\t\\t\\\"\\\"\\\"\\n\\t\\t\\t\\tgetComboTerms() is a sub-method that combines search terms and \\n\\t\\t\\t\\ttheir indices provided in the tuple parameter into a string with\\n\\t\\t\\t\\tthe following structure: [(variant, index)]. This sub-method re-\\n\\t\\t\\t\\tturns a string of that structure.\\n\\n\\t\\t\\t\\ttuples --> data structure containing the search term and index of the search term in the form of: (term, index)\\n\\t\\t\\t\\\"\\\"\\\"\\t\\t\\t\\n\\t\\t\\t#return \\\"[{0}]\\\".format('; '.join([\\\"({0})\\\".format(','.join([text[indices[0]:indices[1]], str(indices[0])])) for indices in tuples]))\\n\\t\\t\\treturn \\\"{0}\\\".format('; '.join((\\\"{0}\\\".format(text[indices[0]:indices[1]]) for indices in tuples)))\\n\\n\\t\\tdef getProximity(tuples):\\n\\t\\t\\t\\\"\\\"\\\"\\n\\t\\t\\t\\tgetProximity() is a sub-method that calculates the distance of the search terms provided in the tuple parameter. \\n\\t\\t\\t\\tThis sub-method returns an absolute value integer.\\n\\n\\t\\t\\t\\ttuples:\\tdata structure containing the search term and index of the search term in the form of: (term, index)\\n\\t\\t\\t\\\"\\\"\\\"\\n\\t\\t\\tsortedIndices = [indices for indices in tuples]\\n\\t\\t\\t#return abs(sortedIndices[0][1] - sortedIndices[-1][0])\\n\\t\\t\\treturn sortedIndices[-1][0] - sortedIndices[0][1] \\n\\n\\t\\t\\\"\\\"\\\" - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - \\\"\\\"\\\"\\n\\n\\t\\texcerptsResults = list()\\t\\t# NEW list to contain the expanded data structure provided by the DICECodeResults parameter\\n\\n\\t\\t# CALL THESE JUST ONCE BEFORE LOOP(S)\\n\\t\\tappend = excerptsResults.append\\n\\t\\tformat = str.format\\n\\t\\t# - - - - - - - - - - - - - - - - - -\\n\\n\\t\\tfor row in DICECodeResults:\\n\\n\\t\\t\\tDICECode \\t\\t= row[0]\\t# (1) DICE code as specified in C:\\\\Users\\\\a5rjqzz\\\\Desktop\\\\Python\\\\files\\\\Types.gd\\n\\t\\t\\tTYPECode \\t\\t= row[1]\\t# (2) Type code as specified in C:\\\\Users\\\\a5rjqzz\\\\Desktop\\\\Python\\\\files\\\\Types.gd\\n\\t\\t\\tCombo \\t\\t= False\\t\\t# (3) Boolean status of the presence of a combo term\\n\\t\\t\\tdocumentIndex \\t= 0\\t\\t\\t# (4) Index of this search term in the document\\n\\t\\t\\tindices \\t\\t= row[2]\\t# (5) Indices of the search term and combo term if present\\n\\t\\t\\tproximity\\t\\t= 0\\t\\t\\t# (6) Distance between search term and combo terms\\n\\n\\t\\t\\tif type(row[2][0]) == type(tuple()):\\n\\t\\t\\t\\tCombo = True\\t# If the type of search term is a combo, this is true\\n\\n\\t\\t\\t\\tfor tuples in row[2]:\\n\\t\\t\\t\\t\\tindices \\t\\t\\t\\t\\t\\t= tuples[0]\\t\\t\\t\\t# (1) Location(s) of the search term in the tuple\\n\\t\\t\\t\\t\\tdocumentIndex \\t\\t\\t\\t\\t= indices[0]\\t\\t\\t# (2) Location of the search term in the document\\n\\t\\t\\t\\t\\tcomboTerms \\t\\t\\t\\t\\t\\t= getComboTerms(tuples)\\t# (3) Multiple terms assigned to variable comboTerms\\n\\t\\t\\t\\t\\tproximity \\t\\t\\t\\t\\t\\t= getProximity(tuples)\\t# (4) Proximity of combo terms if present\\n\\t\\t\\t\\t\\tkernelLeft, kernel, kernelRight = getKernels(indices)\\t# (5) Left, center, and right kernels or excerpts\\n\\n\\t\\t\\t\\t\\tappend([DICECode, TYPECode, Combo, documentIndex, kernelLeft, kernel, kernelRight, comboTerms, proximity])\\n\\n\\t\\t\\telse:\\n\\t\\t\\t\\tdocumentIndex \\t\\t\\t\\t\\t= indices[0]\\t\\t\\t\\t\\t\\t\\t\\t\\t# (1) Location of the search term in the document\\n\\t\\t\\t\\tcomboTerms \\t\\t\\t\\t\\t\\t= format(\\\"[{0}]\\\", text[indices[0]:indices[1]])\\t# (2) Single term assigned to variable comboTerms\\n\\t\\t\\t\\tkernelLeft, kernel, kernelRight = getKernels(indices)\\t\\t\\t\\t\\t\\t\\t# (3) Left, center, and right kernels or excerpts\\n\\n\\t\\t\\t\\tappend([DICECode, TYPECode, Combo, documentIndex, kernelLeft, kernel, kernelRight, comboTerms, proximity])\\n\\n\\t\\treturn excerptsResults\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44f3996b20f8d1879a7cc17af9b1c6be\",\n \"score\": \"0.47272265\",\n \"text\": \"def _compute_expert_weights(self):\\n # Shape = (num_nonzero, 1, num_binary).\\n smooth_step_activations = self._smooth_step(self._z_logits)\\n # Shape = (num_nonzero, num_experts).\\n selector_outputs = tf.math.reduce_prod(\\n tf.where(self._binary_codes, smooth_step_activations,\\n 1 - smooth_step_activations),\\n axis=2)\\n # Weights for the single-expert selectors: shape = (num_nonzero, 1).\\n selector_weights = tf.nn.softmax(self._w_logits, axis=0)\\n expert_weights = tf.math.reduce_sum(\\n selector_weights * selector_outputs, axis=0)\\n\\n return expert_weights, selector_outputs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6db70f1c5cbc29963fed521d462eda73\",\n \"score\": \"0.47268128\",\n \"text\": \"def display_wmd(google_snippets, my_snippets):\\r\\n for i in range(len(google_snippets)):\\r\\n print(i+1, '.', urls[i])\\r\\n print('Google Snippet:', google_snippets[i])\\r\\n print('Generated Snippet:', my_snippets[i])\\r\\n print(\\\"Word Mover's Distance:\\\", get_wmd_dist(google_snippets[i], my_snippets[i], model))\\r\\n print()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a07eef829c91f5c52fff772340bd9a01\",\n \"score\": \"0.4724361\",\n \"text\": \"def __init__(self):\\n # Initialise class attributes (visibility ease)\\n self.__corpus__ = None\\n self.__pron_det_pos_words__ = None\\n self.__triples_corpus__ = None\\n self.__entities_in_doc__ = None\\n self.__wvmodel__ = None\\n \\n # For purpose of parsing relation triplets later\\n # Load pretrained embedding model\\n #plog('Loading pretrained word embeddings. This will take some time to load...')\\n #self.__wvmodel__ = api.load('fasttext-wiki-news-subwords-300')\\n #plog('Pretrained word embeddings loaded!')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"700e62bf908212d54d738f43d57281b8\",\n \"score\": \"0.47217306\",\n \"text\": \"def extract(self, documents):\\n\\n # Placeholder for return value.\\n features = None\\n\\n # Create a numpy array of all zeros for storing frequencies.\\n tf = np.zeros((len(documents), len(self.idx_to_word)))\\n\\n # Raise an exception if 'extract' is called before 'preprocess'\\n if len(self.word_to_idx) == 0 or len(self.idx_to_word) == 0:\\n raise Exception(\\\"Extractor not initialised.\\\")\\n\\n # For each document\\n for idx, doc in enumerate(documents):\\n # Split strig into a list of words\\n words = extract_words(doc)\\n\\n # Calculate it's frequency, however, keep in mind\\n # that this word may not have been in the training\\n # corpus. In that case, ignore the word.\\n for w in words:\\n ''' YOUR CODE HERE '''\\n try:\\n tf[idx][self.word_to_idx[w]] = words.count(w)\\n except KeyError:\\n pass\\n\\n ''' END CODE FOR THIS LOOP '''\\n\\n # Divide the frequencies by the number of words in document.\\n ''' YOUR CODE HERE '''\\n tf[idx] = tf[idx]/len(words)\\n ''' END CODE FOR THIS LOOP '''\\n\\n # Calculate the Tf-Idf features.\\n features = tf * self.idf\\n\\n return features\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f70ec6a6ce913ebf5cf9f65cfa079af5\",\n \"score\": \"0.47216392\",\n \"text\": \"def ingest_annotations(directory, annotation_type):\\n in_golden = annotation_type == \\\"manual\\\"\\n doc_type = None\\n issue_number = None\\n bid = None\\n annotated_pages = []\\n file_prefix = \\\"page\\\"\\n try:\\n bid = directory.split('/')[len(directory.split('/'))-1:][0]\\n record = Metadata.objects(bid=bid).first()\\n assert record is not None\\n except Exception as e:\\n bid,issue_number = directory.split('/')[len(directory.split('/'))-2:]\\n record = Metadata.objects(bid=bid).first()\\n try:\\n doc_type = record[\\\"type_document\\\"]\\n except Exception as e:\\n doc_type = None\\n logger.warning('The record for %s is not in MongoDB'%bid)\\n try:\\n page_numbers = {int(os.path.basename(fname).replace(\\\"page-\\\",\\\"\\\").split('.')[0]):os.path.basename(fname).split('.')[0] \\n for fname in glob.glob(\\\"%s/*.ann\\\"%directory)}\\n except Exception as e:\\n page_numbers = {int(os.path.basename(fname).replace(\\\"image-\\\",\\\"\\\").split('.')[0]):os.path.basename(fname).split('.')[0] \\n for fname in glob.glob(\\\"%s/*.ann\\\"%directory)}\\n file_prefix = \\\"image\\\"\\n # TODO: handle the exception of document not in the DB\\n logger.info(\\\"Ingesting the annotations from directory \\\\\\\"%s\\\\\\\"\\\"%directory)\\n if(issue_number != None):\\n logger.info(\\\"Found document %s-%s [type=%s]\\\"%(bid,issue_number,doc_type))\\n else:\\n logger.info(\\\"Found document %s [type=%s]\\\"%(bid,doc_type))\\n try:\\n if(doc_type==\\\"journal\\\"):\\n doc = LBDocument.objects(internal_id=\\\"%s-%s\\\"%(bid,issue_number)).first()\\n if doc is None:\\n doc = LBDocument.objects(internal_id=\\\"%s-%s\\\"%(bid, convert_issue_number(issue_number))).first()\\n elif(doc_type==\\\"monograph\\\"):\\n doc = LBDocument.objects(bid=bid).first()\\n logger.info(\\\"%s has %i pages\\\"%(doc.internal_id, len(doc.pages)))\\n for page_n in sorted(page_numbers):\\n logger.debug(\\\"Reading in annotations for page %i from file %s/ %s\\\"%(page_n,directory,page_numbers[page_n]))\\n entities_with_continuations = {}\\n entities,relations = read_ann_file(page_numbers[page_n],directory)\\n fulltext = codecs.open(\\\"%s/%s.txt\\\"%(directory,page_number2image_name(page_n, string=file_prefix)),'r', 'utf-8').read()\\n line_breaks = find_newlines(fulltext)\\n #logger.info(\\\"Found %i entities, %i relation in %s\\\"%(len(entities), len(relations), directory))\\n doc_id = \\\"%s-%s-%s\\\"%(bid, issue_number, page_numbers[page_n])\\n try:\\n page = next((page for page in doc.pages if page.single_page_file_number==page_n))\\n if(page[\\\"in_golden\\\"]==True):\\n annotated_pages.append(page.id)\\n logger.info(\\\"found %i entities in %s (p. %i)\\\"%(len(entities),doc_id,page_n))\\n logger.info(\\\"found %i relations in %s (p. %i)\\\"%(len(relations.keys()),doc_id,page_n))\\n \\\"\\\"\\\"\\n Parse the `ContainedIN` relations and identify annotations that should be merged together.\\n IDs of candidates for merging are stored in a dict(), e.g. {\\\"T1\\\":[\\\"T2\\\",\\\"T4\\\"]}\\n \\\"\\\"\\\"\\n entities_with_continuations = {}\\n if len(relations.keys())>0:\\n for relation_key in relations:\\n args = relations[relation_key][\\\"arguments\\\"]\\n if args[0] in entities_with_continuations:\\n entities_with_continuations[args[0]].append(args[1])\\n else:\\n entities_with_continuations[args[0]] = [args[1]]\\n logger.debug(\\\"(%s-%s) entities to be merged: %s\\\"%(doc_id,page_n,entities_with_continuations))\\n \\\"\\\"\\\"\\n Create the annotations (`entities` dict). \\n Later they will be stored into the MongoDB\\n \\\"\\\"\\\"\\n for entity in entities:\\n entities[entity][\\\"ingestion_timestamp\\\"] = datetime.utcnow()\\n entities[entity][\\\"annotation_ingester_version\\\"] = __version__\\n entities[entity][\\\"entity_type\\\"] = entities[entity][\\\"entity_type\\\"].lower( )\\n entities[entity][\\\"filename\\\"] = \\\"%s/%s%s\\\"%(directory,page_numbers[page_n],\\\".ann\\\")\\n if(doc_type==\\\"journal\\\"):\\n entities[entity][\\\"bid\\\"] = bid\\n entities[entity][\\\"pageid\\\"] = doc_id\\n elif(doc_type==\\\"monograph\\\"):\\n entities[entity][\\\"bid\\\"] = bid\\n entities[entity][\\\"pageid\\\"] = \\\"%s-%s\\\"%(bid,page_numbers[page_n])\\n entities[entity][\\\"container\\\"] = entities[entity][\\\"entity_type\\\"] in container_annotations\\n # ref to page_id (from content_loader) ✓\\n for position in entities[entity][\\\"positions\\\"]:\\n line_number = find_linenumber_for_string(position[\\\"start\\\"],position[\\\"end\\\"], line_breaks)\\n logger.debug(\\\"%s is found at line %s\\\"%(entity,line_number))\\n position[\\\"line_n\\\"] = line_number\\n position[\\\"page_id\\\"] = page.id\\n positions_by_offset = sorted(entities[entity][\\\"positions\\\"]\\n ,key=lambda position: position['start'])\\n entities[entity][\\\"positions\\\"] = sorted(positions_by_offset\\n , key=lambda position: Page.objects(id=position['page_id']).first().single_page_file_number)\\n logger.debug(\\\"Annotations %s %s\\\"%(entity,entities[entity]))\\n \\\"\\\"\\\"\\n Now take the candidates for merging identified above and populate the annotations.\\n Still nothing is saved into MongoDB at this stage.\\n \\\"\\\"\\\"\\n for ann_id in entities_with_continuations:\\n try:\\n logger.debug(\\\"Starting to merge SP and SF entities into meta-annotations (%s-%s)\\\"%(doc_id, page_n))\\n logger.debug(\\\"%s will be merged with %s\\\"%(ann_id,\\\"+\\\".join(entities_with_continuations[ann_id])))\\n top_entity_types = \\\"_\\\".join([entities[ann_id][\\\"entity_type\\\"]]+[entities[annid][\\\"entity_type\\\"] \\n for annid in entities_with_continuations[ann_id]])\\n logger.debug(\\\"%s\\\"%top_entity_types)\\n new_entity = copy.deepcopy(entities)[ann_id] \\n #container = True \\n new_entity[\\\"ann_id\\\"] = \\\"%s+%s\\\"%(ann_id,\\\"+\\\".join(entities_with_continuations[ann_id]))\\n new_entity[\\\"entity_type\\\"] = \\\"meta-annotation\\\"\\n new_entity[\\\"top_entity_types\\\"] = top_entity_types\\n new_entity[\\\"top_entities_ids\\\"] = [ann_id]\\n new_entity[\\\"top_entities_ids\\\"] += [id for id in entities_with_continuations[ann_id]]\\n fname = new_entity[\\\"filename\\\"]\\n new_entity[\\\"filename\\\"] = [fname]\\n for to_merge_id in entities_with_continuations[ann_id]:\\n to_merge = dict(entities)[to_merge_id]\\n new_entity[\\\"filename\\\"]+= [to_merge[\\\"filename\\\"]]\\n new_entity[\\\"positions\\\"] = new_entity[\\\"positions\\\"] + to_merge[\\\"positions\\\"]\\n positions_by_offset = sorted(new_entity[\\\"positions\\\"]\\n ,key=lambda position: position['start'])\\n new_entity[\\\"positions\\\"] = sorted(positions_by_offset\\n ,key=lambda position: Page.objects(id=position['page_id']).first().single_page_file_number)\\n new_entity[\\\"filename\\\"] = \\\", \\\".join(list(set(new_entity[\\\"filename\\\"])))\\n surface_start = new_entity[\\\"positions\\\"][0][\\\"start\\\"]\\n surface_end = new_entity[\\\"positions\\\"][-1][\\\"end\\\"]\\n new_entity[\\\"surface\\\"] = fulltext[surface_start:surface_end]\\n entities[new_entity[\\\"ann_id\\\"]] = new_entity\\n logger.debug(new_entity)\\n except Exception as e:\\n logger.error(\\\"The merging of %s in (%s-%s) failed with error\\\\\\\"%s\\\\\\\"\\\"%(new_entity[\\\"ann_id\\\"],bid,page_n,e))\\n \\\"\\\"\\\"\\n Now all annotations will be stored into the MongoDB. \\n And some specific fields (e.g. `top_entities`) are sorted, and annotations updated \\n accordingly in the DB. \\n \\\"\\\"\\\"\\n try:\\n annotations = []\\n for entity in entities.values():\\n annotation = Annotation(**entity)\\n annotation.positions = [PagePosition(**position) for position in entity[\\\"positions\\\"]]\\n annotation.save()\\n annotations.append(annotation)\\n page.annotations_ids = [] #TODO\\n page.annotations_ids = annotations\\n page.is_annotated = True\\n page.save()\\n logger.debug(\\\"Following annotations were inserted into MongoDB: %s\\\"%([annotation.id for annotation in annotations]))\\n logger.info(\\\"%i annotations were inserted into MongoDB\\\"%len(annotations))\\n except Exception as e:\\n raise e\\n containers = [annotation for annotation in annotations if annotation[\\\"container\\\"]] \\n contained = [annotation for annotation in annotations if not annotation[\\\"container\\\"]]\\n meta_annotations = [annotation for annotation in annotations if annotation[\\\"entity_type\\\"]==\\\"meta-annotation\\\"]\\n logger.debug(\\\"meta annotations: %s\\\"%meta_annotations)\\n \\\"\\\"\\\"\\n Resolve the top entities in the meta-annotations: replace entity IDs with \\n a reference to the annotation in the MongoDB.\\n \\\"\\\"\\\"\\n for annotation in meta_annotations:\\n top_entities_ids = annotation[\\\"top_entities_ids\\\"]\\n logger.debug('resolving top_entities')\\n top_entities = [Annotation.objects(ann_id=ann_id, pageid=annotation.pageid).first() for ann_id in top_entities_ids]\\n #top_entities = list([db_conn.annotations.find_one({\\\"ann_id\\\":ann_id,\\\"pageid\\\":annotation[\\\"pageid\\\"]}) for ann_id in top_entities_ids])\\n logger.debug(\\\"Top entities before sorting %s\\\"%[ann.id for ann in top_entities])\\n annotation[\\\"top_entities\\\"] = sort_annotations_by_offset(top_entities)\\n logger.debug(\\\"Top entities after sorting %s\\\"%[ann.id for ann in top_entities])\\n annotation[\\\"top_entities\\\"] = top_entities\\n annotation.save()\\n logger.debug(\\\"Updating meta-annotation: %s\\\"%annotation.id)\\n \\\"\\\"\\\"\\n Transform contains relations between entities into references between annotations \\n in the MongoDB.\\n \\\"\\\"\\\"\\n for annotation in sort_annotations_by_offset(containers):\\n if(len(annotation[\\\"positions\\\"]) > 1):\\n start = annotation[\\\"positions\\\"][0][\\\"start\\\"]\\n end = annotation[\\\"positions\\\"][len(annotation[\\\"positions\\\"])-1][\\\"end\\\"]\\n else:\\n start = annotation[\\\"positions\\\"][0][\\\"start\\\"]\\n end = annotation[\\\"positions\\\"][0][\\\"end\\\"]\\n annotation[\\\"contains\\\"] = []\\n for contained_annotation in sort_annotations_by_offset(contained):\\n if(len(contained_annotation[\\\"positions\\\"])>1):\\n if(contained_annotation[\\\"positions\\\"][0][\\\"start\\\"] >= start\\n and contained_annotation[\\\"positions\\\"][len(contained_annotation[\\\"positions\\\"])-1][\\\"end\\\"] <= end):\\n annotation[\\\"contains\\\"].append(contained_annotation)\\n logger.debug(\\\"[%s] Annotation %s (%s) contains %s (%s)\\\"%(\\n doc_id\\n ,annotation[\\\"ann_id\\\"]\\n ,annotation[\\\"id\\\"]\\n ,contained_annotation[\\\"ann_id\\\"]\\n ,contained_annotation[\\\"id\\\"]))\\n annotation.save()\\n else:\\n if(contained_annotation[\\\"positions\\\"][0][\\\"start\\\"] >= start\\n and contained_annotation[\\\"positions\\\"][0][\\\"end\\\"] <= end):\\n annotation[\\\"contains\\\"].append(contained_annotation)\\n logger.debug(\\\"[%s] Annotation %s (%s) contains %s (%s)\\\"%(\\n doc_id\\n ,annotation[\\\"ann_id\\\"]\\n ,annotation[\\\"id\\\"]\\n ,contained_annotation[\\\"ann_id\\\"]\\n ,contained_annotation[\\\"id\\\"]))\\n annotation.save()\\n else:\\n page.is_annotated = False\\n logger.info(\\\"%s was ignored because it's not in the golden set\\\"%doc_id)\\n except StopIteration as e:\\n logger.error(\\\"The annotations for %s-%s p. %i can't be ingested\\\"%(bid, issue_number, page_n))\\n except Exception as e:\\n logger.error(\\\"The annotations for %s-%s can't be ingested. Got error %s\\\"%(bid, issue_number, e))\\n return annotated_pages\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1c3d4bd616d2f302eb79715a017de11\",\n \"score\": \"0.4721362\",\n \"text\": \"def main():\\n\\n\\tst.title(\\\"Sentiment Analysis Emoji App\\\")\\n\\n\\tactivities = [\\\"Sentiment\\\",\\\"Text Analysis on URL\\\",\\\"About\\\"]\\n\\tchoice = st.sidebar.selectbox(\\\"Choice\\\",activities)\\n\\n\\tif choice == 'Sentiment':\\n\\t\\tst.subheader(\\\"Sentiment Analysis\\\")\\n\\t\\tst.write(emoji.emojize('Everyone :red_heart: Streamlit ',use_aliases=True))\\n\\t\\traw_text = st.text_area(\\\"Enter Your Text\\\",\\\"Type Here\\\")\\n\\t\\tif st.button(\\\"Analyze\\\"):\\n\\t\\t\\tblob = TextBlob(raw_text)\\n\\t\\t\\tresult = blob.sentiment.polarity\\n\\t\\t\\tif result > 0.0:\\n\\t\\t\\t\\tcustom_emoji = ':smile:'\\n\\t\\t\\t\\tst.write(emoji.emojize(custom_emoji,use_aliases=True))\\n\\t\\t\\telif result < 0.0:\\n\\t\\t\\t\\tcustom_emoji = ':disappointed:'\\n\\t\\t\\t\\tst.write(emoji.emojize(custom_emoji,use_aliases=True))\\n\\t\\t\\telse:\\n\\t\\t\\t\\tst.write(emoji.emojize(':expressionless:',use_aliases=True))\\n\\t\\t\\tst.info(\\\"Polarity Score is:: {}\\\".format(result))\\n\\t\\t\\t\\n\\tif choice == 'Text Analysis on URL':\\n\\t\\tst.subheader(\\\"Analysis on Text From URL\\\")\\n\\t\\traw_url = st.text_input(\\\"Enter URL Here\\\",\\\"Type here\\\")\\n\\t\\ttext_preview_length = st.slider(\\\"Length to Preview\\\",50,100)\\n\\t\\tif st.button(\\\"Analyze\\\"):\\n\\t\\t\\tif raw_url != \\\"Type here\\\":\\n\\t\\t\\t\\tresult = get_text(raw_url)\\n\\t\\t\\t\\tblob = TextBlob(result)\\n\\t\\t\\t\\tlen_of_full_text = len(result)\\n\\t\\t\\t\\tlen_of_short_text = round(len(result)/text_preview_length)\\n\\t\\t\\t\\tst.success(\\\"Length of Full Text::{}\\\".format(len_of_full_text))\\n\\t\\t\\t\\tst.success(\\\"Length of Short Text::{}\\\".format(len_of_short_text))\\n\\t\\t\\t\\tst.info(result[:len_of_short_text])\\n\\t\\t\\t\\tc_sentences = [ sent for sent in blob.sentences ]\\n\\t\\t\\t\\tc_sentiment = [sent.sentiment.polarity for sent in blob.sentences]\\n\\t\\t\\t\\t\\n\\t\\t\\t\\tnew_df = pd.DataFrame(zip(c_sentences,c_sentiment),columns=['Sentence','Sentiment'])\\n\\t\\t\\t\\tst.dataframe(new_df)\\n\\n\\tif choice == 'About':\\n\\t\\tst.subheader(\\\"About:Sentiment Analysis Emoji App\\\")\\n\\t\\tst.info(\\\"Built with Streamlit,Textblob and Emoji\\\")\\n\\t\\tst.text(\\\"Jesse E.Agbe(JCharis\\\")\\n\\t\\tst.text(\\\"Jesus Saves@JCharisTech\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7262f473392b5b603fc98c227ff6c8d\",\n \"score\": \"0.47176358\",\n \"text\": \"def run(self):\\n\\n self.log.debug('Reading wordlist %s', self.wordlists)\\n\\n # Test\\n data = self.symfony.profiler.open(self.symfony.root)\\n if data is None:\\n self.log.info('The target does not support file preview')\\n return\\n\\n # Enqueue\\n with open(self.wordlists) as file:\\n i = 0\\n for i, line in enumerate(file, 1):\\n url = self.symfony.profiler.url('open')\\n params = {'line': 1, 'file': line.strip()}\\n self.engine.queue.put(Request(method='GET', url=url, params=params))\\n self.log.debug('Enqueued %d entries', i)\\n\\n self.engine.join()\\n found = [response for response in self.engine.results if response.status_code != 404]\\n files = [resp.request.params['file'] for resp in found]\\n\\n # Composer lookup\\n composer = [file for file in files if file.endswith('composer.lock')]\\n if composer:\\n self.log.info(\\\"Found: %s, run 'symfony security:check' or submit it at %s\\\", composer[0], self.security_url)\\n\\n if not found:\\n self.log.warning('Did not find any file')\\n return\\n\\n # Save results\\n for response in found:\\n data = self.symfony.profiler.parse_file_preview(response.text)\\n self.symfony.files[response.request.params['file']] = data\\n\\n self.log.warning('Found the following files:')\\n for file in files:\\n self.log.warning(' %s', file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca61f43be01711612262d6d843e3ae46\",\n \"score\": \"0.47137287\",\n \"text\": \"def biological_science_news():\\n\\n return general_scraper(['http://mesva.univaq.it/?q=avvisi/cl-clm/52672'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5e51157244b5b9fee98575a0590882d\",\n \"score\": \"0.4712479\",\n \"text\": \"def write():\\n #with st.spinner(\\\"Loading Dashboard ...\\\"):\\n #ast.shared.components.title_awesome(\\\"\\\")\\n\\n st.title('arXiv - Analytics')\\n st.text(\\\"\\\")\\n if st.checkbox('Most similar words in w2v'):\\n user_input = st.text_input(\\\"Topic (please enter up to two keywords)\\\", 'Machine Learning')\\n user_input = user_input.lower().replace(\\\" \\\", \\\"_\\\")\\n st.text(\\\"\\\")\\n number_of_similar_words = st.slider('Select a modulus', 3, 50)\\n plot_similar_words(model, user_input, number_of_similar_words)\\n st.pyplot()\\n st.text(\\\"\\\")\\n if st.checkbox('Word Cloud'):\\n cluster = st.slider('Select a cluster', 0, 49)\\n word_cloud_kmeans(cluster)\\n st.pyplot()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"efae1a4ea851905884fc047208f63737\",\n \"score\": \"0.47093627\",\n \"text\": \"def main():\\n analyze_perturbations()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4f5b9e8f3d1e131796e7ead6df986b6\",\n \"score\": \"0.4708818\",\n \"text\": \"def ALLEN_st_cells_1_movies(self):\\n exp_dict = self.template_dataset()\\n exp_dict = self.add_globals(exp_dict)\\n exp_dict['experiment_name'] = 'ALLEN_st_cells_1_movies'\\n exp_dict['only_process_n'] = None # MICHELE\\n exp_dict['randomize_selection'] = True\\n exp_dict['reference_image_key'] = {'proc_stimuli': 'image'}\\n exp_dict['reference_label_key'] = {'neural_trace_trimmed': 'label'}\\n exp_dict['rf_query'] = [{\\n 'rf_coordinate_range': { # Get all cells\\n 'x_min': 20,\\n 'x_max': 30,\\n 'y_min': 50,\\n 'y_max': 60,\\n },\\n 'cre_line': 'Cux2',\\n 'structure': 'VISp'}]\\n exp_dict['cross_ref'] = 'rf_coordinate_range_and_stimuli'\\n exp_dict['store_means'] = [\\n 'image',\\n 'label'\\n ]\\n exp_dict['deconv_method'] = 'OASIS'\\n exp_dict['cv_split'] = {\\n 'cv_split_single_stim': {\\n 'target': 0,\\n 'split': 0.95\\n }\\n }\\n # exp_dict['cv_split'] = {\\n # 'split_on_stim': 'natural_movie_two' # Specify train set\\n # }\\n exp_dict['neural_delay'] = [8, 13] # MS delay * 30fps for neural data\\n exp_dict['slice_frames'] = 2 # 4 MICHELE\\n exp_dict['st_conv'] = len(\\n range(\\n exp_dict['neural_delay'][0],\\n exp_dict['neural_delay'][1]))\\n exp_dict['grid_query'] = False # False = evaluate all neurons at once\\n exp_dict['cc_repo_vars'] = {\\n 'output_size': [1, 1],\\n 'model_im_size': [152, 304, 1],\\n 'loss_function': 'l2',\\n 'score_metric': 'pearson',\\n 'preprocess': 'resize'\\n }\\n exp_dict['weight_sharing'] = True\\n return exp_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"065651f7f50dca824f573e8e13fd54aa\",\n \"score\": \"0.47043127\",\n \"text\": \"def entity_recognition(text: str) -> spacy:\\n nlp = spacy.load('en_core_web_sm')\\n document = nlp(text)\\n return document\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4fddc26dc9e32773a8f058636e527888\",\n \"score\": \"0.47029012\",\n \"text\": \"def run_examples():\\n\\n for example in examples:\\n\\n print(str(example) + \\\" : \\\", end=\\\" \\\")\\n try:\\n t, smush = analyse(example, my_env)\\n print(lookup(t, smush))\\n # print(\\\"Smush\\\")\\n # for k,v in smush.items():\\n # print(f\\\"\\\\t{k} : {v}\\\")\\n except (ParseError, InferenceError) as e:\\n print(e)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c17e91b2e31287e2d3f226c28cd07bd0\",\n \"score\": \"0.4702484\",\n \"text\": \"def learn_visual_words(annots_df, taids, nWords, use_cache=USE_CACHE_WORDS):\\n max_iters = 200\\n flann_params = {}\\n train_vecs_list = [pdh.ensure_values(vecs) for vecs in annots_df['vecs'][taids].values]\\n train_vecs = np.vstack(train_vecs_list)\\n print('Training %d word vocabulary with %d annots and %d descriptors' %\\n (nWords, len(taids), len(train_vecs)))\\n kwds = dict(max_iters=max_iters, use_cache=use_cache, appname='smk',\\n flann_params=flann_params)\\n _words = clustertool.cached_akmeans(train_vecs, nWords, **kwds)\\n if WITH_PANDAS:\\n # Pandasify\\n wx_series = pdh.RangeIndex(len(_words), name='wx')\\n #words = pd.DataFrame(_words, index=wx_series, columns=VEC_COLUMNS)\\n words = pd.DataFrame(_words, index=wx_series)\\n else:\\n words = _words\\n return words\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2dd48456bcc504de6d6154321d62cd70\",\n \"score\": \"0.46943823\",\n \"text\": \"def _run_wiki_nogui(self):\\n # start wikipedia page download\\n self._log_print(msg_WHITE=\\\"Accessing Wikipedia...\\\")\\n\\n # download wikipedia page and track progress\\n for message in self._get_preload_progress():\\n if \\\"Searching for\\\" in message:\\n print(f\\\"Searching for: {GREEN}{self.ALBUM}{RESET} by \\\"\\n f\\\"{GREEN}{self.ALBUMARTIST}\\\")\\n elif \\\"Using offline\\\" in message:\\n self._log_print(msg_GREEN=\\\"Using offline cached page insted \\\"\\n \\\"of web page\\\")\\n elif \\\"Found at\\\" in message:\\n self._log_print(msg_GREEN=\\\"Found at: \\\", msg_WHITE=self.url)\\n else:\\n self._log_print(msg_WHITE=message)\\n\\n # get error messages\\n error_msg = self.get_wiki()\\n if error_msg:\\n self._log_print(msg_GREEN=error_msg)\\n return\\n\\n if not we_are_frozen():\\n # basic html textout for debug\\n self.basic_out()\\n\\n # find release date\\n self._log_print(msg_GREEN=\\\"Found release date:\\\",\\n msg_WHITE=self.get_release_date())\\n\\n # find list of genres\\n self._log_print(msg_GREEN=\\\"Found genre(s)\\\",\\n msg_WHITE=\\\"\\\\n\\\".join(self.get_genres()))\\n\\n # get and print out page contents\\n self._log_print(msg_GREEN=\\\"Found page contents\\\",\\n msg_WHITE=\\\"\\\\n\\\".join(self.get_contents()))\\n\\n # extract track list\\n self.get_tracks()\\n\\n # extract personel names\\n self._log_print(msg_GREEN=\\\"Found aditional personel\\\")\\n self.get_personnel()\\n if not we_are_frozen():\\n print(self.personnel_2_str())\\n\\n # extract writers, composers\\n self._log_print(msg_GREEN=\\\"Found composers\\\",\\n msg_WHITE=\\\"\\\\n\\\".join(flatten_set(self.get_composers())))\\n\\n if not we_are_frozen():\\n # save to files\\n self._log_print(msg_WHITE=\\\"Writing to disk\\\")\\n self.disk_write()\\n\\n # print out found tracklist\\n self._log_print(msg_GREEN=\\\"Found Track list(s)\\\")\\n self.print_tracklist()\\n\\n # select genre\\n if not self.GENRE:\\n if not self.genres:\\n print(CYAN + \\\"Input genre:\\\", end=\\\"\\\")\\n self.genre = input()\\n else:\\n print(CYAN + \\\"Specify which genre you want to write: [1.]\\\")\\n for i, gen in enumerate(self.genres, 1):\\n print(f\\\"{i}. {gen}\\\")\\n\\n print(\\\"Input number:\\\", CYAN, end=\\\"\\\")\\n index = input()\\n try:\\n index = int(index) - 1\\n except ValueError:\\n index = 0\\n\\n self.GENRE = self.genres[index]\\n\\n # decide what to do with artists\\n print(CYAN + \\\"Do you want to assign artists to composers? ([y]/n)\\\",\\n RESET, end=\\\" \\\")\\n if to_bool(input()):\\n self.merge_artist_composers()\\n\\n # decide if you want to find lyrics\\n print(CYAN + \\\"\\\\nDo you want to find and save lyrics? ([y]/n): \\\" +\\n RESET, end=\\\"\\\")\\n\\n # download lyrics\\n self.save_lyrics(to_bool(input()))\\n\\n print(CYAN + \\\"Write data to ID3 tags? ([y]/n): \\\" + RESET, end=\\\"\\\")\\n if to_bool(input()):\\n if not self.write_tags():\\n self._log_print(\\n msg_WHITE=\\\"Cannot write tags because there are no \\\"\\n \\\"coresponding files\\\")\\n else:\\n self._log_print(msg_GREEN=\\\"Done\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cfc8b0bedfe5d8532ebec1c74a2dd5c2\",\n \"score\": \"0.4692744\",\n \"text\": \"def autoAnalyze(self):\\n print(\\\"Perfoming full automatic analysis...\\\")\\n t1=time.perf_counter()\\n self.cleanUp()\\n self.figure_rois()\\n self.figure_roi_inspect_all()\\n self.figure_dGoR_roi(showEach=False,saveAs=self.folderSave+\\\"/avg.png\\\")\\n self.figure_dGoR_roi(showEach=True,saveAs=self.folderSave+\\\"/each.png\\\")\\n self.index()\\n print(\\\"analysis completed in %.02f sec\\\"%(time.perf_counter()-t1))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1073921c3e3389a456bb9b4b6f965094\",\n \"score\": \"0.4691585\",\n \"text\": \"def interrogator(corpus, \\n search, \\n query='any',\\n show='w',\\n exclude=False,\\n excludemode='any',\\n searchmode='all',\\n dep_type='collapsed-ccprocessed-dependencies',\\n case_sensitive=False,\\n save=False,\\n just_speakers=False,\\n preserve_case=False,\\n lemmatag=False,\\n files_as_subcorpora=False,\\n only_unique=False,\\n random=False,\\n only_format_match=False,\\n multiprocess=False,\\n spelling=False,\\n regex_nonword_filter=r'[A-Za-z0-9:_]',\\n gramsize=2,\\n split_contractions=False,\\n conc=False,\\n maxconc=9999,\\n window=4,\\n no_closed=False,\\n no_punct=True,\\n whitelist=False,\\n **kwargs\\n ):\\n\\n # in case old kwarg is used\\n conc = kwargs.get('do_concordancing', conc)\\n\\n # store kwargs and locs\\n locs = locals().copy()\\n locs.update(kwargs)\\n locs.pop('kwargs', None)\\n\\n if isinstance(search, STRINGTYPE) and len(search) > 3:\\n raise ValueError('search argument not recognised.')\\n\\n import codecs\\n import signal\\n import os\\n from time import localtime, strftime\\n from collections import Counter\\n\\n import corenlp_xml\\n import pandas as pd\\n from pandas import DataFrame, Series\\n\\n from corpkit.interrogation import Interrogation, Interrodict\\n from corpkit.corpus import Datalist, Corpora, Corpus, File, Subcorpus\\n from corpkit.process import (tregex_engine, get_deps, unsplitter, sanitise_dict, \\n get_speakername, animator, filtermaker)\\n from corpkit.other import as_regex\\n from corpkit.dictionaries.word_transforms import wordlist, taglemma\\n from corpkit.dictionaries.process_types import Wordlist\\n from corpkit.build import check_jdk\\n\\n import re\\n if regex_nonword_filter:\\n is_a_word = re.compile(regex_nonword_filter)\\n else:\\n is_a_word = re.compile(r'.*')\\n \\n have_java = check_jdk()\\n\\n def signal_handler(signal, _):\\n \\\"\\\"\\\"pause on ctrl+c, rather than just stop loop\\\"\\\"\\\" \\n import signal\\n import sys\\n from time import localtime, strftime\\n signal.signal(signal.SIGINT, original_sigint)\\n thetime = strftime(\\\"%H:%M:%S\\\", localtime())\\n INPUTFUNC('\\\\n\\\\n%s: Paused. Press any key to resume, or ctrl+c to quit.\\\\n' % thetime)\\n time = strftime(\\\"%H:%M:%S\\\", localtime())\\n print('%s: Interrogation resumed.\\\\n' % time)\\n signal.signal(signal.SIGINT, signal_handler)\\n\\n def fix_show(show):\\n \\\"\\\"\\\"lowercase anything in show and turn into list\\\"\\\"\\\"\\n if isinstance(show, list):\\n show = [i.lower() for i in show]\\n elif isinstance(show, STRINGTYPE):\\n show = show.lower()\\n show = [show]\\n\\n # this little 'n' business is a hack: when ngramming,\\n # n shows have their n stripped, so nw should be nw \\n # so we know we're ngramming and so it's not empty.\\n for index, val in enumerate(show):\\n if val == 'n' or val == 'nw':\\n show[index] = 'nw'\\n elif val == 'b' or val == 'bw':\\n show[index] = 'bw'\\n elif val.endswith('pl'):\\n show[index] = val.replace('pl', 'x')\\n else:\\n if len(val) == 2 and val.endswith('w'):\\n show[index] = val[0]\\n return show\\n\\n def fix_search(search):\\n \\\"\\\"\\\"if search has nested dicts, remove them\\\"\\\"\\\"\\n ends = ['w', 'l', 'i', 'n', 'f', 'p', 'x', 's']\\n if not search:\\n return\\n if isinstance(search, STRINGTYPE):\\n return search\\n if search.get('t'):\\n return search\\n newsearch = {}\\n for srch, pat in search.items():\\n if len(srch) == 1 and srch in ends:\\n srch = 'm%s' % srch\\n if isinstance(pat, dict):\\n for k, v in list(pat.items()):\\n if k != 'w':\\n newsearch[srch + k] = pat_format(v)\\n else:\\n newsearch[srch] = pat_format(v)\\n else:\\n newsearch[srch] = pat_format(pat)\\n return newsearch\\n\\n def pat_format(pat):\\n from corpkit.dictionaries.process_types import Wordlist\\n import re\\n if pat == 'any':\\n return re.compile(r'.*')\\n if isinstance(pat, Wordlist):\\n pat = list(pat)\\n if isinstance(pat, list):\\n if all(isinstance(x, int) for x in pat):\\n pat = [str(x) for x in pat]\\n pat = filtermaker(pat, case_sensitive=case_sensitive, root=kwargs.get('root'))\\n else:\\n if isinstance(pat, int):\\n return pat\\n if isinstance(pat, re._pattern_type):\\n return pat\\n if case_sensitive:\\n pat = re.compile(pat)\\n else:\\n pat = re.compile(pat, re.IGNORECASE)\\n return pat\\n\\n def is_multiquery(corpus, search, query, just_speakers):\\n \\\"\\\"\\\"determine if multiprocessing is needed\\n do some retyping if need be as well\\\"\\\"\\\"\\n is_mul = False\\n from collections import OrderedDict\\n #if hasattr(corpus, '__iter__'):\\n # is_mul = True\\n # so we can do search = 't', query = ['NP', 'VP']:\\n from corpkit.dictionaries.process_types import Wordlist\\n if isinstance(query, Wordlist):\\n query = list(query)\\n if isinstance(query, list):\\n if query != list(search.values())[0] or len(list(search.keys())) > 1:\\n query = {c.title(): c for c in query}\\n if isinstance(query, (dict, OrderedDict)):\\n is_mul = True\\n if just_speakers:\\n if just_speakers == 'each':\\n is_mul = True\\n just_speakers = ['each']\\n if just_speakers == ['each']:\\n is_mul = True\\n elif isinstance(just_speakers, STRINGTYPE):\\n is_mul = False\\n just_speakers = [just_speakers]\\n #import re\\n #if isinstance(just_speakers, re._pattern_type):\\n # is_mul = False\\n if isinstance(just_speakers, list):\\n if len(just_speakers) > 1:\\n is_mul = True\\n if isinstance(search, dict):\\n if all(isinstance(i, dict) for i in list(search.values())):\\n is_mul = True\\n return is_mul, corpus, search, query, just_speakers\\n\\n def slow_tregex(sents, **dummy_args):\\n \\\"\\\"\\\"do the speaker-specific version of tregex queries\\\"\\\"\\\"\\n speakr = dummy_args.get('speaker', '')\\n import os\\n from corpkit.process import tregex_engine\\n # first, put the relevant trees into temp file\\n to_open = '\\\\n'.join(sent.parse_string.strip() for sent in sents \\\\\\n if sent.parse_string is not None)\\n q = list(search.values())[0]\\n ops = ['-o', '-%s' % translated_option]\\n concs = []\\n res = tregex_engine(query=q, \\n options=ops, \\n corpus=to_open,\\n root=root,\\n preserve_case=True\\n )\\n if not no_conc:\\n ops += ['-w', '-f']\\n whole_res = tregex_engine(query=q, \\n options=ops, \\n corpus=to_open,\\n root=root,\\n preserve_case=True\\n )\\n for line in whole_res:\\n line.insert(1, speakr) \\n\\n res = format_tregex(res)\\n whole_res = format_tregex(whole_res, whole=True)\\n concs = make_conc_lines_from_whole_mid(whole_res, res)\\n\\n if root:\\n root.update()\\n if countmode:\\n if isinstance(res, int):\\n return res, False\\n else:\\n return len(res), False\\n else:\\n return res, concs\\n\\n def get_stats(sents, **dummy_args):\\n \\\"\\\"\\\"get a bunch of frequencies on interpersonal phenomena\\\"\\\"\\\"\\n from collections import Counter\\n statsmode_results = Counter() \\n # first, put the relevant trees into temp file\\n\\n for sent in sents:\\n statsmode_results['Sentences'] += 1\\n deps = get_deps(sent, dep_type)\\n numpass = len([x for x in deps.links if x.type.endswith('pass')])\\n statsmode_results['Passives'] += numpass\\n statsmode_results['Tokens'] += len(sent.tokens)\\n words = [w.word for w in sent.tokens if w.word is not None and w.word.isalnum()]\\n statsmode_results['Words'] += len(words)\\n statsmode_results['Characters'] += len(''.join(words))\\n\\n to_open = '\\\\n'.join(s.parse_string.strip() for s in sents)\\n\\n from corpkit.dictionaries.process_types import processes\\n from corpkit.other import as_regex\\n tregex_qs = {'Imperative': r'ROOT < (/(S|SBAR)/ < (VP !< VBD !< VBG !$ NP !$ SBAR < NP !$-- S !$-- VP !$ VP)) !<< (/\\\\?/ !< __) !<<- /-R.B-/ !<<, /(?i)^(-l.b-|hi|hey|hello|oh|wow|thank|thankyou|thanks|welcome)$/',\\n 'Open interrogative': r'ROOT < SBARQ <<- (/\\\\?/ !< __)', \\n 'Closed interrogative': r'ROOT ( < (SQ < (NP $+ VP)) << (/\\\\?/ !< __) | < (/(S|SBAR)/ < (VP $+ NP)) <<- (/\\\\?/ !< __))',\\n 'Unmodalised declarative': r'ROOT < (S < (/(NP|SBAR|VP)/ $+ (VP !< MD)))',\\n 'Modalised declarative': r'ROOT < (S < (/(NP|SBAR|VP)/ $+ (VP < MD)))',\\n 'Open class': r'/^(NN|JJ|VB|RB)/ < __',\\n 'Closed class': r'__ !< __ !> /^(NN|JJ|VB|RB)/',\\n 'Clauses': r'/^S/ < __',\\n 'Interrogative': r'ROOT << (/\\\\?/ !< __)',\\n 'Mental processes': r'VP > /^(S|ROOT)/ <+(VP) (VP <<# /%s/)' % \\\\\\n as_regex(processes.mental, boundaries='w'),\\n 'Verbal processes': r'VP > /^(S|ROOT)/ <+(VP) (VP <<# /%s/)' % \\\\\\n as_regex(processes.verbal, boundaries='w'),\\n 'Relational processes': r'VP > /^(S|ROOT)/ <+(VP) (VP <<# /%s/)' % \\\\\\n as_regex(processes.relational, boundaries='w'),\\n 'Verbless clause': r'/^S/ !<< /^VB.?/'}\\n\\n for name, q in sorted(tregex_qs.items()):\\n res = tregex_engine(query=q, \\n options=['-o', '-C'], \\n corpus=to_open, \\n root=root\\n )\\n statsmode_results[name] += int(res)\\n if root:\\n root.update()\\n return statsmode_results, []\\n\\n def make_conc_lines_from_whole_mid(wholes,\\n middle_column_result\\n ):\\n \\\"\\\"\\\"\\n Create concordance line output from tregex output\\n \\\"\\\"\\\"\\n import re\\n import os\\n if not wholes and not middle_column_result:\\n return []\\n\\n conc_lines = []\\n # remove duplicates from results\\n unique_wholes = []\\n unique_middle_column_result = []\\n duplicates = []\\n for (f, sk, whole), mid in zip(wholes, middle_column_result):\\n joined = '-join-'.join([f, sk, whole, mid])\\n if joined not in duplicates:\\n duplicates.append(joined)\\n unique_wholes.append([f, sk, whole])\\n unique_middle_column_result.append(mid)\\n\\n # split into start, middle and end, dealing with multiple occurrences\\n for (f, sk, whole), mid in zip(unique_wholes, unique_middle_column_result):\\n reg = re.compile(r'([^a-zA-Z0-9-]|^)(' + re.escape(mid) + r')([^a-zA-Z0-9-]|$)', \\\\\\n re.IGNORECASE | re.UNICODE)\\n offsets = [(m.start(), m.end()) for m in re.finditer(reg, whole)]\\n for offstart, offend in offsets:\\n start, middle, end = whole[0:offstart].strip(), whole[offstart:offend].strip(), \\\\\\n whole[offend:].strip()\\n conc_lines.append([os.path.basename(f), sk, start, middle, end])\\n return conc_lines\\n\\n def uniquify(conc_lines):\\n \\\"\\\"\\\"get unique concordance lines\\\"\\\"\\\"\\n from collections import OrderedDict\\n unique_lines = []\\n checking = []\\n for index, (_, speakr, start, middle, end) in enumerate(conc_lines):\\n joined = ' '.join([speakr, start, 'MIDDLEHERE:', middle, ':MIDDLEHERE', end])\\n if joined not in checking:\\n unique_lines.append(conc_lines[index])\\n checking.append(joined)\\n return unique_lines\\n\\n def lemmatiser(list_of_words, tag):\\n \\\"\\\"\\\"\\n Take a list of unicode words and a tag and return a lemmatised list\\n \\\"\\\"\\\"\\n output = []\\n for word in list_of_words:\\n if translated_option.startswith('u'):\\n word = taglemma.get(word.lower(), 'Other')\\n else:\\n word = wordlist.get(word, lmtzr.lemmatize(word, tag))\\n if not preserve_case:\\n word = word.lower()\\n output.append(word)\\n return output\\n\\n def tgrep_searcher(sents, search, show, conc, **kwargs):\\n \\\"\\\"\\\"\\n Use tgrep for constituency grammar search\\n \\\"\\\"\\\"\\n f = kwargs.get('filename')\\n from corpkit.process import show_tree_as_per_option, tgrep\\n out = []\\n conc_output = []\\n conc_out = []\\n for sent in sents:\\n sk = get_speakername(sent)\\n results = tgrep(sent, search['t'])\\n for res in results:\\n out.append(show_tree_as_per_option(show, res, sent))\\n if conc:\\n lin = [f, sk, show_tree_as_per_option(show + ['whole'], res, sent)]\\n conc_out.append(lin)\\n\\n if conc:\\n conc_output = make_conc_lines_from_whole_mid(conc_out, out)\\n return out, conc_output\\n\\n def gettag(query, lemmatag=False):\\n \\\"\\\"\\\"\\n Find tag for WordNet lemmatisation\\n \\\"\\\"\\\"\\n if lemmatag:\\n return lemmatag\\n\\n tagdict = {'N': 'n',\\n 'J': 'a',\\n 'V': 'v',\\n 'A': 'r',\\n 'None': False,\\n '': False,\\n 'Off': False}\\n\\n # in case someone compiles the tregex query\\n try:\\n query = query.pattern\\n except AttributeError:\\n query = query\\n \\n\\n qr = query.replace(r'\\\\w', '').replace(r'\\\\s', '').replace(r'\\\\b', '')\\n firstletter = next((c for c in qr if c.isalpha()), 'n')\\n return tagdict.get(firstletter.upper(), 'n')\\n\\n def format_tregex(results, whole=False):\\n \\\"\\\"\\\"format tregex by show list\\\"\\\"\\\"\\n import re\\n\\n if countmode:\\n return results\\n\\n if not results:\\n return\\n\\n done = []\\n if whole:\\n fnames, snames, results = zip(*results)\\n\\n if 'l' in show or 'x' in show:\\n lemmata = lemmatiser(results, gettag(search.get('t'), lemmatag))\\n else:\\n lemmata = [None for i in results]\\n for word, lemma in zip(results, lemmata):\\n bits = []\\n if exclude and exclude.get('w'):\\n if len(list(exclude.keys())) == 1 or excludemode == 'any':\\n if re.search(exclude.get('w'), word):\\n continue\\n if len(list(exclude.keys())) == 1 or excludemode == 'any':\\n if re.search(exclude.get('l'), lemma):\\n continue\\n if len(list(exclude.keys())) == 1 or excludemode == 'any':\\n if re.search(exclude.get('p'), word):\\n continue\\n if len(list(exclude.keys())) == 1 or excludemode == 'any':\\n if re.search(exclude.get('x'), lemma):\\n continue\\n if exclude and excludemode == 'all':\\n num_to_cause_exclude = len(list(exclude.keys()))\\n current_num = 0\\n if exclude.get('w'):\\n if re.search(exclude.get('w'), word):\\n current_num += 1\\n if exclude.get('l'):\\n if re.search(exclude.get('l'), lemma):\\n current_num += 1\\n if exclude.get('p'):\\n if re.search(exclude.get('p'), word):\\n current_num += 1\\n if exclude.get('x'):\\n if re.search(exclude.get('x'), lemma):\\n current_num += 1 \\n if current_num == num_to_cause_exclude:\\n continue \\n\\n for i in show:\\n if i == 't':\\n bits.append(word)\\n if i == 'l':\\n bits.append(lemma)\\n elif i == 'w':\\n bits.append(word)\\n elif i == 'p':\\n bits.append(word)\\n elif i == 'x':\\n bits.append(lemma)\\n joined = '/'.join(bits)\\n done.append(joined)\\n if whole:\\n done = zip(fnames, snames, done)\\n return done\\n\\n def tok_by_list(pattern, list_of_toks, concordancing=False, **kwargs):\\n \\\"\\\"\\\"search for regex in plaintext corpora\\\"\\\"\\\"\\n import re\\n if isinstance(pattern, STRINGTYPE):\\n pattern = [pattern]\\n if not case_sensitive:\\n pattern = [p.lower() for p in pattern]\\n if not concordancing:\\n if case_sensitive:\\n matches = [m for m in list_of_toks if m in pattern]\\n else:\\n matches = [m for m in list_of_toks if m.lower() in pattern]\\n else:\\n matches = []\\n for index, token in enumerate(list_of_toks):\\n if token in pattern:\\n if not split_contractions:\\n match = [' '.join(t for t in unsplitter(list_of_toks[:index]))[-140:]]\\n else:\\n match = [' '.join(t for t in list_of_toks[:index])[-140:]]\\n match.append(token)\\n if not split_contractions:\\n match.append(' '.join(t for t in unsplitter(list_of_toks[index + 1:]))[:140])\\n else:\\n match.append(' '.join(t for t in list_of_toks[index + 1:])[:140])\\n\\n matches.append(match)\\n if countmode:\\n return len(matches)\\n else:\\n return matches\\n\\n def tok_ngrams(pattern, list_of_toks, concordancing=False, split_contractions=True):\\n import re\\n result = []\\n list_of_toks = [x for x in list_of_toks if re.search(regex_nonword_filter, x)]\\n\\n if not split_contractions:\\n list_of_toks = unsplitter(list_of_toks)\\n \\n for i in range(len(list_of_toks)):\\n try:\\n the_gram = [list_of_toks[i+x] for x in range(gramsize)]\\n if any(re.search(pattern, x) for x in the_gram):\\n result.append(' '.join(the_gram))\\n except IndexError:\\n pass\\n\\n if countmode:\\n return len(result)\\n\\n else:\\n result = [i for i in result if result.count(i) > 1]\\n return result\\n\\n def compiler(pattern):\\n \\\"\\\"\\\"compile regex or fail gracefully\\\"\\\"\\\"\\n if hasattr(pattern, 'pattern'):\\n return pattern\\n import re\\n try:\\n if case_sensitive:\\n comped = re.compile(pattern)\\n else:\\n comped = re.compile(pattern, re.IGNORECASE)\\n return comped\\n except:\\n import traceback\\n import sys\\n from time import localtime, strftime\\n exc_type, exc_value, exc_traceback = sys.exc_info()\\n lst = traceback.format_exception(exc_type, exc_value, exc_traceback)\\n error_message = lst[-1]\\n thetime = strftime(\\\"%H:%M:%S\\\", localtime())\\n print('%s: Query %s' % (thetime, error_message))\\n if root:\\n return 'Bad query'\\n else:\\n raise ValueError('%s: Query %s' % (thetime, error_message))\\n\\n def tok_by_reg(pattern, list_of_toks, concordancing = False, **kwargs):\\n \\\"\\\"\\\"search for regex in plaintext corpora\\\"\\\"\\\"\\n import re\\n comped = compiler(pattern)\\n if comped == 'Bad query':\\n return 'Bad query'\\n if not concordancing:\\n matches = [m for m in list_of_toks if re.search(comped, m)]\\n else:\\n matches = []\\n for index, token in enumerate(list_of_toks):\\n if re.search(comped, token):\\n if not split_contractions:\\n match = [' '.join(t for t in unsplitter(list_of_toks[:index]))[-140:]]\\n else:\\n match = [' '.join(t for t in list_of_toks[:index])[-140:]]\\n match.append(re.search(comped, token).group(0))\\n if not split_contractions:\\n match.append(' '.join(t for t in unsplitter(list_of_toks[index + 1:]))[:140])\\n else:\\n match.append(' '.join(t for t in list_of_toks[index + 1:])[:140])\\n matches.append(match)\\n if countmode:\\n return len(matches)\\n else:\\n return matches\\n\\n def determine_search_func(show):\\n \\\"\\\"\\\"Figure out what search function we're using\\\"\\\"\\\"\\n\\n simple_tregex_mode = False\\n statsmode = False\\n tree_to_text = False\\n\\n if search.get('t') and not just_speakers and not kwargs.get('tgrep'):\\n if have_java:\\n simple_tregex_mode = True\\n searcher = None\\n else:\\n searcher = tgrep_searcher\\n optiontext = 'Searching parse trees'\\n else:\\n if datatype == 'plaintext':\\n if any(i.endswith('n') for i in search.keys()):\\n optiontext = 'n-grams via plaintext'\\n raise NotImplementedError('Use a tokenised or parsed corpus for n-gramming.')\\n #searcher = plaintext_ngram\\n elif any(i.endswith('w') for i in search.keys()):\\n if kwargs.get('regex', True):\\n searcher = plaintext_regex_search\\n else:\\n searcher = plaintext_simple_search\\n optiontext = 'Searching plaintext'\\n else:\\n raise ValueError(\\\"Plaintext search must be 'w' or 'n'.\\\")\\n\\n elif datatype == 'tokens':\\n if any(i.endswith('n') for i in search.keys()):\\n searcher = tok_ngrams\\n optiontext = 'n-grams via tokens'\\n elif any(i.endswith('w') for i in search.keys()):\\n if kwargs.get('regex', True):\\n searcher = tok_by_reg\\n else:\\n searcher = tok_by_list\\n if isinstance(search.get('w'), (list, Wordlist)):\\n searcher = tok_by_list\\n optiontext = 'Searching tokens'\\n only_parse = ['r', 'd', 'g', 'dl', 'gl', 'df', 'gf',\\n 'dp', 'gp', 'f', 'd2', 'd2f', 'd2p', 'd2l']\\n \\n\\n if datatype != 'parse' and any(i in only_parse for i in list(search.keys())):\\n form = ', '.join(i for i in list(search.keys()) if i in only_parse)\\n raise ValueError('Need parsed corpus to search with \\\"%s\\\" option(s).' % form)\\n\\n elif datatype == 'parse':\\n if any(i.endswith('n') for i in search.keys()):\\n search['w'] = search.pop('n')\\n if not show_ngram:\\n show = ['n']\\n if any(i.endswith('t') for i in search.keys()):\\n if have_java and not kwargs.get('tgrep'):\\n searcher = slow_tregex\\n else:\\n searcher = tgrep_searcher\\n optiontext = 'Searching parse trees'\\n elif any(i.endswith('s') for i in search.keys()):\\n searcher = get_stats\\n statsmode = True\\n optiontext = 'General statistics'\\n elif any(i.endswith('r') for i in search.keys()):\\n from corpkit.depsearch import dep_searcher\\n searcher = dep_searcher\\n optiontext = 'Distance from root'\\n else:\\n from corpkit.depsearch import dep_searcher\\n searcher = dep_searcher\\n optiontext = 'Dependency querying'\\n \\n # ngram mode for parsed data\\n if show_ngram:\\n optiontext = 'N-grams from parsed data'\\n searcher = dep_searcher\\n\\n return searcher, optiontext, simple_tregex_mode, statsmode, tree_to_text\\n\\n def get_tregex_values():\\n \\\"\\\"\\\"If using Tregex, set appropriate values\\n\\n - Check for valid query\\n - Make 'any' query\\n - Make list query\\n \\\"\\\"\\\"\\n\\n translated_option = 't'\\n if isinstance(search['t'], Wordlist):\\n search['t'] = list(search['t'])\\n q = tregex_engine(corpus=False,\\n query=search.get('t'),\\n options=['-t'],\\n check_query=True,\\n root=root,\\n preserve_case=preserve_case\\n )\\n if q is False:\\n if root:\\n return 'Bad query', None\\n else:\\n return 'Bad query', None\\n\\n if isinstance(search['t'], list):\\n regex = as_regex(search['t'], boundaries='line', case_sensitive=case_sensitive)\\n else:\\n regex = ''\\n\\n # listquery, anyquery, translated_option\\n treg_dict = {'p': [r'__ < (/%s/ !< __)' % regex, r'__ < (/.?[A-Za-z0-9].?/ !< __)', 'u'],\\n 'pl': [r'__ < (/%s/ !< __)' % regex, r'__ < (/.?[A-Za-z0-9].?/ !< __)', 'u'],\\n 'x': [r'__ < (/%s/ !< __)' % regex, r'__ < (/.?[A-Za-z0-9].?/ !< __)', 'u'],\\n 't': [r'__ < (/%s/ !< __)' % regex, r'__ < (/.?[A-Za-z0-9].?/ !< __)', 'o'],\\n 'w': [r'/%s/ !< __' % regex, r'/.?[A-Za-z0-9].?/ !< __', 't'],\\n 'c': [r'/%s/ !< __' % regex, r'/.?[A-Za-z0-9].?/ !< __', 'C'],\\n 'l': [r'/%s/ !< __' % regex, r'/.?[A-Za-z0-9].?/ !< __', 't']\\n }\\n\\n listq, anyq, translated_option = treg_dict.get(show[0].lower())\\n if isinstance(search['t'], list):\\n search['t'] = listq\\n elif search['t'] == 'any': \\n search['t'] = anyq\\n return search['t'], translated_option\\n\\n def plaintext_regex_search(pattern, plaintext_data, concordancing=False, **kwargs):\\n \\\"\\\"\\\"search for regex in plaintext corpora\\n\\n it searches over lines, so the user needs to be careful.\\n \\\"\\\"\\\"\\n import re\\n if concordancing:\\n pattern = r'(.{,140})\\\\b(' + pattern + r')\\\\b(.{,140})'\\n compiled_pattern = compiler(pattern)\\n if compiled_pattern == 'Bad query':\\n return 'Bad query'\\n matches = re.findall(compiled_pattern, plaintext_data)\\n if concordancing:\\n matches = [list(m) for m in matches]\\n if not concordancing:\\n for index, i in enumerate(matches):\\n if isinstance(i, tuple):\\n matches[index] = i[0]\\n if countmode:\\n return len(matches)\\n else:\\n return matches\\n\\n def correct_spelling(a_string):\\n \\\"\\\"\\\"correct spelling within a string\\\"\\\"\\\"\\n if not spelling:\\n return a_string\\n from corpkit.dictionaries.word_transforms import usa_convert\\n if spelling.lower() == 'uk':\\n usa_convert = {v: k for k, v in list(usa_convert.items())}\\n bits = a_string.split('/')\\n for index, i in enumerate(bits):\\n converted = usa_convert.get(i.lower(), i)\\n if i.islower() or preserve_case is False:\\n converted = converted.lower()\\n elif i.isupper() and preserve_case:\\n converted = converted.upper()\\n elif i.istitle() and preserve_case:\\n converted = converted.title()\\n bits[index] = converted\\n r = '/'.join(bits)\\n return r\\n\\n def plaintext_simple_search(pattern, plaintext_data, concordancing=False, **kwargs):\\n \\\"\\\"\\\"search for tokens in plaintext corpora\\\"\\\"\\\"\\n import re\\n result = []\\n if isinstance(pattern, STRINGTYPE):\\n pattern = [pattern]\\n for p in pattern:\\n if concordancing:\\n pat = r'(.{0,140})\\\\b(' + re.escape(p) + r')\\\\b(.{0,140})'\\n pat = compiler(pat)\\n if pat == 'Bad query':\\n return 'Bad query'\\n matches = re.findall(pat, plaintext_data)\\n if concordancing:\\n matches = [list(m) for m in matches]\\n for i in matches:\\n result.append(i)\\n else: \\n for m in range(len(matches)):\\n result.append(p)\\n return result\\n\\n def make_search_iterable(corpus):\\n \\\"\\\"\\\"determine how to structure the corpus for interrogation\\\"\\\"\\\"\\n \\n # skip file definitions if they are not needed\\n if simple_tregex_mode:\\n if corpus.level in ['s', 'f']:\\n return {(corpus.name, corpus.path): False}\\n else:\\n return {(os.path.basename(i), os.path.join(corpus.path, i)): False\\n for i in os.listdir(corpus.path)\\n if os.path.isdir(os.path.join(corpus.path, i))}\\n\\n if isinstance(corpus, Datalist):\\n to_iterate_over = {}\\n # it could be files or subcorpus objects\\n if corpus[0].level == 's':\\n if files_as_subcorpora:\\n for subc in corpus:\\n for f in subc.files:\\n to_iterate_over[(f.name, f.path)] = [f]\\n else:\\n for subc in corpus:\\n to_iterate_over[(subc.name, subc.path)] = subc.files\\n elif corpus[0].level == 'f':\\n for f in corpus:\\n to_iterate_over[(f.name, f.path)] = [f]\\n elif corpus.singlefile:\\n to_iterate_over = {(corpus.name, corpus.path): [corpus]}\\n elif not hasattr(corpus, 'subcorpora') or not corpus.subcorpora:\\n # just files in a directory\\n if files_as_subcorpora:\\n to_iterate_over = {}\\n for f in corpus.files:\\n to_iterate_over[(f.name, f.path)] = [f]\\n else:\\n to_iterate_over = {(corpus.name, corpus.path): corpus.files}\\n else:\\n to_iterate_over = {}\\n if files_as_subcorpora:\\n # don't know if possible: has subcorpora but also .files\\n if hasattr(corpus, 'files') and corpus.files is not None:\\n for f in corpus.files:\\n to_iterate_over[(f.name, f.path)] = [f]\\n # has subcorpora with files in those\\n elif hasattr(corpus, 'files') and corpus.files is None:\\n for subc in corpus.subcorpora:\\n for f in subc.files:\\n to_iterate_over[(f.name, f.path)] = [f]\\n else:\\n if corpus[0].level == 's':\\n for subcorpus in corpus:\\n to_iterate_over[(subcorpus.name, subcorpus.path)] = subcorpus.files\\n elif corpus[0].level == 'f':\\n for f in corpus:\\n to_iterate_over[(f.name, f.path)] = [f]\\n else:\\n for subcorpus in corpus.subcorpora:\\n to_iterate_over[(subcorpus.name, subcorpus.path)] = subcorpus.files\\n return to_iterate_over\\n\\n def welcome_printer(return_it=False):\\n \\\"\\\"\\\"Print welcome message\\\"\\\"\\\"\\n if no_conc:\\n message = 'Interrogating'\\n else:\\n message = 'Interrogating and concordancing'\\n if kwargs.get('printstatus', True):\\n thetime = strftime(\\\"%H:%M:%S\\\", localtime())\\n from corpkit.constants import transshow, transobjs\\n sformat = '\\\\n'\\n for k, v in search.items():\\n if k == 't':\\n dratt = ''\\n else:\\n dratt = transshow.get(k[-1], k[-1])\\n drole = transobjs.get(k[0], k[0])\\n if k == 't':\\n drole = 'Trees'\\n vform = getattr(v, 'pattern', v)\\n sformat += ' %s %s: %s\\\\n' % (drole, dratt.lower(), vform)\\n if search.get('s'):\\n sformat = 'Features'\\n welcome = ('\\\\n%s: %s %s ...\\\\n %s\\\\n ' \\\\\\n 'Query: %s\\\\n %s corpus ... \\\\n' % \\\\\\n (thetime, message, cname, optiontext, sformat, message))\\n if return_it:\\n return welcome\\n else:\\n print(welcome)\\n\\n def goodbye_printer(return_it=False, only_conc=False):\\n \\\"\\\"\\\"Say goodbye before exiting\\\"\\\"\\\"\\n if not kwargs.get('printstatus', True):\\n return\\n thetime = strftime(\\\"%H:%M:%S\\\", localtime())\\n if only_conc:\\n \\n show_me = (thetime, len(conc_df))\\n finalstring = '\\\\n\\\\n%s: Concordancing finished! %d results.' % show_me\\n else:\\n finalstring = '\\\\n\\\\n%s: Interrogation finished!' % thetime\\n if countmode:\\n finalstring += ' %d matches.' % tot\\n else:\\n dat = (numentries, total_total)\\n finalstring += ' %d unique results, %d total occurrences.' % dat\\n if return_it:\\n return finalstring\\n else:\\n print(finalstring)\\n\\n\\n def make_conc_obj_from_conclines(conc_results):\\n \\\"\\\"\\\"\\n Turn conclines into DataFrame\\n \\\"\\\"\\\"\\n from corpkit.interrogation import Concordance\\n all_conc_lines = []\\n for sc_name, resu in sorted(conc_results.items()):\\n if only_unique:\\n unique_results = uniquify(resu)\\n else:\\n unique_results = resu\\n #make into series\\n if PYTHON_VERSION == 2:\\n pindex = 'c f s l m r'.encode('utf-8').split()\\n else:\\n pindex = 'c f s l m r'.split()\\n for fname, spkr, start, word, end in unique_results:\\n #spkr = str(spkr, errors = 'ignore')\\n fname = os.path.basename(fname)\\n ser = [sc_name, fname, spkr, start, word, end]\\n all_conc_lines.append(Series(ser, index=pindex))\\n\\n if random:\\n from random import shuffle\\n shuffle(all_conc_lines)\\n\\n try:\\n conc_df = pd.concat(all_conc_lines, axis=1).T\\n if all(x == '' for x in list(conc_df['s'].values)):\\n conc_df.drop('s', axis=1, inplace=True)\\n \\n if show_ngram or show_collocates:\\n if not language_model:\\n counted = Counter(conc_df['m'])\\n indices = [l for l in list(conc_df.index) if counted[conc_df.ix[l]['m']] > 1] \\n conc_df = conc_df.ix[indices]\\n conc_df = conc_df.reset_index(drop=True)\\n\\n locs['corpus'] = corpus.name\\n conc_df = Concordance(conc_df)\\n try:\\n conc_df.query = locs\\n except AttributeError:\\n pass\\n return conc_df\\n\\n except ValueError:\\n return\\n\\n def make_progress_bar():\\n \\\"\\\"\\\"generate a progress bar\\\"\\\"\\\"\\n\\n if simple_tregex_mode:\\n total_files = len(list(to_iterate_over.keys()))\\n else:\\n total_files = sum(len(x) for x in list(to_iterate_over.values()))\\n\\n par_args = {'printstatus': kwargs.get('printstatus', True),\\n 'root': root, \\n 'note': note,\\n 'length': total_files,\\n 'startnum': kwargs.get('startnum'),\\n 'denom': kwargs.get('denominator', 1)}\\n\\n term = None\\n if kwargs.get('paralleling', None) is not None:\\n from blessings import Terminal\\n term = Terminal()\\n par_args['terminal'] = term\\n par_args['linenum'] = kwargs.get('paralleling')\\n\\n if in_notebook:\\n par_args['welcome_message'] = welcome_message\\n\\n outn = kwargs.get('outname', '')\\n if outn:\\n outn = outn + ': '\\n\\n tstr = '%s%d/%d' % (outn, current_iter, total_files)\\n p = animator(None, None, init=True, tot_string=tstr, **par_args)\\n tstr = '%s%d/%d' % (outn, current_iter + 1, total_files)\\n animator(p, current_iter, tstr, **par_args)\\n return p, outn, total_files, par_args\\n\\n # find out if using gui\\n root = kwargs.get('root')\\n note = kwargs.get('note')\\n language_model = kwargs.get('language_model')\\n\\n # set up pause method\\n original_sigint = signal.getsignal(signal.SIGINT)\\n if kwargs.get('paralleling', None) is None:\\n original_sigint = signal.getsignal(signal.SIGINT)\\n signal.signal(signal.SIGINT, signal_handler)\\n\\n # find out about concordancing\\n only_conc = False\\n no_conc = False\\n if conc is False:\\n no_conc = True\\n if isinstance(conc, str) and conc.lower() == 'only':\\n only_conc = True\\n no_conc = False\\n numconc = 0\\n\\n # wipe non essential class attributes to not bloat query attrib\\n if isinstance(corpus, Corpus):\\n import copy\\n corpus = copy.copy(corpus)\\n for k, v in corpus.__dict__.items():\\n if isinstance(v, (Interrogation, Interrodict)):\\n corpus.__dict__.pop(k, None)\\n\\n # convert path to corpus object\\n if not isinstance(corpus, (Corpus, Corpora, Subcorpus, File, Datalist)):\\n if not multiprocess and not kwargs.get('outname'):\\n corpus = Corpus(corpus, print_info=False)\\n\\n # figure out how the user has entered the query and show, and normalise\\n from corpkit.process import searchfixer\\n search = searchfixer(search, query)\\n show = fix_show(show)\\n \\n show_ngram = any(x.startswith('n') for x in show)\\n show_collocates = any(x.startswith('b') for x in show)\\n\\n # instantiate lemmatiser if need be\\n if 'l' in show and isinstance(search, dict) and search.get('t'):\\n from nltk.stem.wordnet import WordNetLemmatizer\\n lmtzr = WordNetLemmatizer()\\n\\n # do multiprocessing if need be\\n im, corpus, search, query, just_speakers = is_multiquery(corpus, search, query, just_speakers)\\n\\n # figure out if we can multiprocess the corpus\\n if hasattr(corpus, '__iter__') and im:\\n corpus = Corpus(corpus)\\n if hasattr(corpus, '__iter__') and not im:\\n im = True\\n if isinstance(corpus, Corpora):\\n im = True\\n\\n # split corpus if the user wants multiprocessing but no other iterable\\n if not im and multiprocess:\\n im = True\\n corpus = corpus[:]\\n\\n search = fix_search(search)\\n exclude = fix_search(exclude)\\n\\n # if it's already been through pmultiquery, don't do it again\\n locs['search'] = search\\n locs['query'] = query\\n locs['just_speakers'] = just_speakers\\n locs['corpus'] = corpus\\n locs['multiprocess'] = multiprocess\\n locs['print_info'] = kwargs.get('printstatus', True)\\n\\n # send to multiprocess function\\n if im:\\n signal.signal(signal.SIGINT, original_sigint)\\n from corpkit.multiprocess import pmultiquery\\n return pmultiquery(**locs)\\n\\n # get corpus metadata\\n cname = corpus.name\\n if isinstance(save, STRINGTYPE):\\n savename = corpus.name + '-' + save\\n if save is True:\\n raise ValueError('save must be str, not bool.')\\n\\n\\n datatype = getattr(corpus, 'datatype', 'parse')\\n singlefile = getattr(corpus, 'singlefile', False)\\n level = getattr(corpus, 'level', 'c')\\n \\n # store all results in here\\n results = {}\\n count_results = {}\\n conc_results = {}\\n\\n # check if just counting, turn off conc if so\\n countmode = 'c' in show\\n if countmode:\\n no_conc = True\\n only_conc = False\\n # where we are at in interrogation\\n current_iter = 0\\n\\n # multiprocessing progress bar\\n denom = kwargs.get('denominator', 1)\\n startnum = kwargs.get('startnum', 0)\\n\\n # Determine the search function to be used #\\n searcher, optiontext, simple_tregex_mode, statsmode, tree_to_text = determine_search_func(show)\\n \\n # no conc for statsmode\\n if statsmode:\\n no_conc = True\\n only_conc = False\\n conc = False\\n\\n # Set some Tregex-related values\\n if search.get('t'):\\n if show_ngram:\\n raise ValueError(\\\"Can't search trees for n-grams---use a dependency search.\\\")\\n query, translated_option = get_tregex_values()\\n if query == 'Bad query' and translated_option is None:\\n if root:\\n return 'Bad query'\\n else:\\n return\\n # more tregex options\\n if tree_to_text:\\n treg_q = r'ROOT << __'\\n op = ['-o', '-t', '-w']\\n elif simple_tregex_mode:\\n treg_q = search['t']\\n op = ['-o', '-' + translated_option]\\n\\n # make iterable object for corpus interrogation\\n to_iterate_over = make_search_iterable(corpus)\\n\\n from traitlets import TraitError\\n try:\\n from ipywidgets import IntProgress\\n\\n _ = IntProgress(min=0, max=10, value=1)\\n in_notebook = True\\n except TraitError:\\n in_notebook = False\\n except ImportError:\\n in_notebook = False\\n\\n # print welcome message\\n welcome_message = welcome_printer(return_it=in_notebook)\\n\\n # create a progress bar\\n p, outn, total_files, par_args = make_progress_bar()\\n\\n # Iterate over data, doing interrogations\\n for (subcorpus_name, subcorpus_path), files in sorted(to_iterate_over.items()):\\n\\n # results for subcorpus go here\\n conc_results[subcorpus_name] = []\\n count_results[subcorpus_name] = []\\n results[subcorpus_name] = Counter()\\n\\n # get either everything (tree_to_text) or the search['t'] query\\n if tree_to_text or simple_tregex_mode:\\n result = tregex_engine(query=treg_q,\\n options=op,\\n corpus=subcorpus_path,\\n root=root,\\n preserve_case=preserve_case\\n )\\n\\n # format search results with slashes etc\\n if not countmode and not tree_to_text:\\n result = format_tregex(result)\\n\\n # if concordancing, do the query again with 'whole' sent and fname\\n if not no_conc:\\n ops = ['-w', '-f'] + op\\n whole_result = tregex_engine(query=search['t'],\\n options=ops,\\n corpus=subcorpus_path,\\n root=root,\\n preserve_case=preserve_case\\n )\\n for line in whole_result:\\n line.insert(1, '') \\n\\n # format match too depending on option\\n if not only_format_match:\\n whole_result = format_tregex(whole_result, whole=True)\\n\\n # make conc lines from conc results\\n conc_result = make_conc_lines_from_whole_mid(whole_result, result)\\n for lin in conc_result:\\n if numconc < maxconc or not maxconc:\\n conc_results[subcorpus_name].append(lin)\\n numconc += 1\\n\\n # add matches to ongoing counts\\n if countmode:\\n count_results[subcorpus_name] += [result] \\n else:\\n result = Counter(result)\\n results[subcorpus_name] += result\\n\\n # update progress bar\\n current_iter += 1\\n tstr = '%s%d/%d' % (outn, current_iter + 1, total_files)\\n animator(p, current_iter, tstr, **par_args)\\n\\n # dependencies, plaintext, tokens, slow_tregex and tree_to_text\\n if not simple_tregex_mode:\\n for f in files:\\n slow_treg_speaker_guess = kwargs.get('outname', False)\\n if datatype == 'parse' and not tree_to_text:\\n # right now, this is not using the File class's read() or document\\n # methods. the reason is that there seem to be memory leaks. these\\n # may have been fixed already though.\\n try:\\n from corenlp_xml import Document\\n except ImportError:\\n from corenlp_xml.document import Document\\n with codecs.open(f.path, 'rb') as fo:\\n data = fo.read()\\n corenlp_xml = Document(data)\\n #corenlp_xml = f.document\\n if just_speakers:\\n import re\\n if isinstance(just_speakers, re._pattern_type):\\n sents = [s for s in corenlp_xml.sentences if \\\\\\n re.search(just_speakers, get_speakername(s))]\\n else:\\n sents = [s for s in corenlp_xml.sentences if get_speakername(s) in just_speakers]\\n if len(just_speakers) == 1:\\n slow_treg_speaker_guess = just_speakers[0]\\n else:\\n sents = corenlp_xml.sentences\\n\\n # get coreferences\\n if kwargs.get('coref') or any(x.startswith('h') for x in show):\\n if just_speakers:\\n corefs = [i for i in corenlp_xml.coreferences if any(x == i.sentence for x in sents)]\\n else:\\n corefs = corenlp_xml.coreferences\\n else:\\n corefs = []\\n \\n corenlp_xml = None\\n\\n res, conc_res = searcher(sents, search=search, show=show,\\n dep_type=dep_type,\\n exclude=exclude,\\n excludemode=excludemode,\\n searchmode=searchmode,\\n case_sensitive=case_sensitive,\\n conc=conc,\\n only_format_match=only_format_match,\\n speaker=slow_treg_speaker_guess,\\n gramsize=gramsize,\\n no_punct=no_punct,\\n no_closed=no_closed,\\n whitelist=whitelist,\\n split_contractions=split_contractions,\\n window=window,\\n filename=f.name,\\n language_model=language_model,\\n corefs=corefs,\\n is_a_word=is_a_word,\\n **kwargs\\n )\\n \\n if res == 'Bad query':\\n return 'Bad query'\\n\\n if datatype == 'tokens':\\n import pickle\\n with codecs.open(f.path, \\\"rb\\\") as fo:\\n data = pickle.load(fo)\\n elif datatype == 'plaintext' or tree_to_text:\\n if tree_to_text:\\n data = '\\\\n'.join(result)\\n if not split_contractions:\\n data = unsplitter(data)\\n else:\\n with codecs.open(f.path, 'rb', encoding='utf-8') as data:\\n data = data.read()\\n\\n if datatype == 'tokens' or datatype == 'plaintext':\\n\\n query = list(search.values())[0]\\n\\n if not only_conc:\\n res = searcher(query,\\n data,\\n split_contractions=split_contractions, \\n concordancing=False\\n )\\n if res == 'Bad query':\\n if root:\\n return 'Bad query'\\n if not no_conc:\\n conc_res = searcher(query,\\n data,\\n split_contractions=split_contractions, \\n concordancing=True\\n )\\n if conc_res == 'Bad query':\\n if root:\\n return 'Bad query'\\n for line in conc_res:\\n line.insert(0, '')\\n\\n if countmode:\\n count_results[subcorpus_name] += [res]\\n\\n else:\\n # add filename and do lowercasing for conc\\n if not no_conc:\\n for line in conc_res:\\n if searcher != slow_tregex and searcher != tgrep_searcher:\\n line.insert(0, f.name)\\n else:\\n line[0] = f.name\\n if not preserve_case:\\n line[3:] = [x.lower() for x in line[3:]]\\n if spelling:\\n line = [correct_spelling(b) for b in line]\\n if numconc < maxconc or not maxconc:\\n conc_results[subcorpus_name].append(line)\\n numconc += 1\\n\\n # do lowercasing and spelling\\n if not only_conc:\\n if not preserve_case:\\n if not statsmode:\\n res = [i.lower() for i in res]\\n\\n if spelling:\\n if not statsmode:\\n res = [correct_spelling(r) for r in res]\\n #if not statsmode:\\n results[subcorpus_name] += Counter(res)\\n #else:\\n #results[subcorpus_name] += res\\n\\n # update progress bar\\n current_iter += 1\\n tstr = '%s%d/%d' % (outn, current_iter + 1, total_files)\\n animator(p, current_iter, tstr, **par_args)\\n\\n # Get concordances into DataFrame, return if just conc\\n if not no_conc:\\n # fail on this line with typeerror if no results?\\n conc_df = make_conc_obj_from_conclines(conc_results)\\n\\n if only_conc:\\n locs = sanitise_dict(locs)\\n try:\\n conc_df.query = locs\\n except AttributeError:\\n return conc_df\\n if save and not kwargs.get('outname'):\\n print('\\\\n')\\n conc_df.save(savename)\\n goodbye_printer(only_conc=True)\\n signal.signal(signal.SIGINT, original_sigint) \\n return conc_df\\n else:\\n conc_df = None\\n\\n # Get interrogation into DataFrame\\n if countmode:\\n df = Series({k: sum(v) for k, v in sorted(count_results.items())})\\n tot = df.sum()\\n else:\\n the_big_dict = {}\\n unique_results = set(item for sublist in list(results.values()) for item in sublist)\\n sortres = sorted(results.items(), key=lambda x: x[0])\\n for word in unique_results:\\n the_big_dict[word] = [subcorp_result[word] for _, subcorp_result in sortres]\\n # turn master dict into dataframe, sorted\\n df = DataFrame(the_big_dict, index=sorted(results.keys()))\\n\\n # for ngrams, remove hapaxes\\n if show_ngram or show_collocates:\\n if not language_model:\\n df = df[[i for i in list(df.columns) if df[i].sum() > 1]]\\n\\n numentries = len(df.columns)\\n tot = df.sum(axis=1)\\n total_total = df.sum().sum()\\n\\n # turn df into series if all conditions met\\n if not countmode:\\n if level == 's' or singlefile:\\n if not files_as_subcorpora:\\n if not kwargs.get('df1_always_df'):\\n df = Series(df.ix[0])\\n df.sort_values(ascending=False, inplace=True)\\n tot = df.sum()\\n numentries = len(df.index)\\n total_total = tot\\n\\n # turn data into DF for GUI if need be\\n if isinstance(df, Series) and kwargs.get('df1_always_df'):\\n total_total = df.sum()\\n df = DataFrame(df)\\n tot = Series(total_total, index=['Total'])\\n\\n # if we're doing files as subcorpora, we can remove the .txt.xml etc\\n if isinstance(df, DataFrame) and files_as_subcorpora:\\n cname = corpus.name.replace('-stripped', '').replace('-parsed', '')\\n edits = [(r'(-[0-9][0-9][0-9])?\\\\.txt\\\\.xml', ''),\\n (r'-%s(-stripped)?(-parsed)?' % cname, '')]\\n from corpkit.editor import editor\\n df = editor(df, replace_subcorpus_names=edits).results\\n tot = df.sum(axis=1)\\n total_total = df.sum().sum()\\n\\n # sort by total\\n if isinstance(df, DataFrame):\\n if not df.empty: \\n df = df[list(df.sum().sort_values(ascending=False).index)]\\n\\n # make interrogation object\\n locs['corpus'] = corpus.path\\n locs = sanitise_dict(locs)\\n interro = Interrogation(results=df, totals=tot, query=locs, concordance=conc_df)\\n\\n # save it\\n if save and not kwargs.get('outname'):\\n print('\\\\n')\\n interro.save(savename)\\n \\n goodbye = goodbye_printer(return_it=in_notebook)\\n if in_notebook:\\n try:\\n p.children[2].value = goodbye.replace('\\\\n', '')\\n except AttributeError:\\n pass\\n signal.signal(signal.SIGINT, original_sigint)\\n return interro\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca14bc835498c6cfdc4f79acaa23d9d3\",\n \"score\": \"0.46904004\",\n \"text\": \"def collecte_docs(self, chercheur, overwrite=False): # self,\\n init = overwrite # If True, data persistence is lost when references are updated\\n docs = hal.find_publications(chercheur[\\\"halId_s\\\"], \\\"authIdHal_s\\\")\\n\\n progress_recorder = ProgressRecorder(self)\\n progress_recorder.set_progress(0, len(docs), description=\\\"récupération des données HAL\\\")\\n # Insert documents collection\\n for num, doc in enumerate(docs):\\n doc[\\\"country_colaboration\\\"] = location_docs.generate_countrys_fields(doc)\\n doc = doi_enrichissement.docs_enrichissement_doi(doc)\\n if \\\"fr_abstract_s\\\" in doc.keys():\\n if isinstance(doc[\\\"fr_abstract_s\\\"], list):\\n doc[\\\"fr_abstract_s\\\"] = \\\"/n\\\".join(doc[\\\"fr_abstract_s\\\"])\\n if len(doc[\\\"fr_abstract_s\\\"]) > 100:\\n doc[\\\"fr_entites\\\"] = keyword_enrichissement.return_entities(\\n doc[\\\"fr_abstract_s\\\"], \\\"fr\\\"\\n )\\n doc[\\\"fr_teeft_keywords\\\"] = keyword_enrichissement.keyword_from_teeft(\\n doc[\\\"fr_abstract_s\\\"], \\\"fr\\\"\\n )\\n if \\\"en_abstract_s\\\" in doc.keys():\\n if isinstance(doc[\\\"en_abstract_s\\\"], list):\\n doc[\\\"en_abstract_s\\\"] = \\\"/n\\\".join(doc[\\\"en_abstract_s\\\"])\\n if len(doc[\\\"en_abstract_s\\\"]) > 100:\\n doc[\\\"en_entites\\\"] = keyword_enrichissement.return_entities(\\n doc[\\\"en_abstract_s\\\"], \\\"en\\\"\\n )\\n doc[\\\"en_teeft_keywords\\\"] = keyword_enrichissement.keyword_from_teeft(\\n doc[\\\"en_abstract_s\\\"], \\\"en\\\"\\n )\\n\\n doc[\\\"_id\\\"] = doc[\\\"docid\\\"]\\n doc[\\\"validated\\\"] = True\\n\\n doc[\\\"harvested_from\\\"] = \\\"researcher\\\"\\n\\n doc[\\\"harvested_from_ids\\\"] = []\\n doc[\\\"harvested_from_label\\\"] = []\\n\\n #\\n #\\n # print(doc[\\\"authorship\\\"], doc ['authLastName_s'])\\n\\n if len(doc[\\\"authIdHal_s\\\"]) != len(doc[\\\"authLastName_s\\\"]):\\n # print (\\\"elastichal.py : test d'autorat no good\\\")\\n # test sur le nom complet...\\n nom = [\\n truc\\n for truc in doc[\\\"authLastName_s\\\"]\\n if chercheur[\\\"lastName\\\"].lower() in truc.lower()\\n ] # pour les récemment mariés qui auraient un nom composé...\\n # Après si 'lun des co-auteur porte le même nom...\\n if len(nom) > 0:\\n nom = nom[0].title()\\n try:\\n if doc[\\\"authLastName_s\\\"].index(nom) == 0: # premier\\n doc[\\\"authorship\\\"] = [\\n {\\\"authorship\\\": \\\"firstAuthor\\\", \\\"authIdHal_s\\\": chercheur[\\\"halId_s\\\"]}\\n ]\\n elif (\\n doc[\\\"authLastName_s\\\"].index(nom) == len(doc[\\\"authLastName_s\\\"]) - 1\\n ): # dernier\\n doc[\\\"authorship\\\"] = [\\n {\\\"authorship\\\": \\\"lastAuthor\\\", \\\"authIdHal_s\\\": chercheur[\\\"halId_s\\\"]}\\n ]\\n except ValueError:\\n doc[\\\"authorship\\\"] = []\\n else:\\n doc[\\\"authorship\\\"] = []\\n elif chercheur[\\\"halId_s\\\"] in doc[\\\"authIdHal_s\\\"]:\\n if doc[\\\"authIdHal_s\\\"].index(chercheur[\\\"halId_s\\\"]) == 0:\\n doc[\\\"authorship\\\"] = [\\n {\\\"authorship\\\": \\\"firstAuthor\\\", \\\"authIdHal_s\\\": chercheur[\\\"halId_s\\\"]}\\n ]\\n elif (\\n doc[\\\"authIdHal_s\\\"].index(chercheur[\\\"halId_s\\\"]) == len(doc[\\\"authIdHal_s\\\"]) - 1\\n ): # dernier\\n doc[\\\"authorship\\\"] = [\\n {\\\"authorship\\\": \\\"lastAuthor\\\", \\\"authIdHal_s\\\": chercheur[\\\"halId_s\\\"]}\\n ]\\n else:\\n doc[\\\"authorship\\\"] = []\\n else:\\n doc[\\\"authorship\\\"] = []\\n\\n doc[\\\"harvested_from_ids\\\"].append(chercheur[\\\"halId_s\\\"])\\n\\n # historique d'appartenance du docId\\n # pour attribuer les bons docs aux chercheurs\\n # harvet_history.append({'docid': doc['docid'], 'from': row['halId_s']})\\n #\\n # for h in harvet_history:\\n # if h['docid'] == doc['docid']:\\n # if h['from'] not in doc[\\\"harvested_from_ids\\\"]:\\n # doc[\\\"harvested_from_ids\\\"].append(h['from'])\\n\\n doc[\\\"records\\\"] = []\\n\\n doc[\\\"MDS\\\"] = utils.calculate_mds(doc)\\n\\n try:\\n should_be_open = utils.should_be_open(doc)\\n if should_be_open == 1:\\n doc[\\\"should_be_open\\\"] = True\\n if should_be_open == -1:\\n doc[\\\"should_be_open\\\"] = False\\n\\n if should_be_open == 1 or should_be_open == 2:\\n doc[\\\"isOaExtra\\\"] = True\\n elif should_be_open == -1:\\n doc[\\\"isOaExtra\\\"] = False\\n except IndexError:\\n print(\\\"publicationDate_tdate error ?\\\")\\n doc[\\\"Created\\\"] = datetime.datetime.now().isoformat()\\n\\n if not init: # récupération de l'existant pour ne pas écraser\\n field = \\\"_id\\\"\\n doc_param = esActions.scope_p(field, doc[\\\"_id\\\"])\\n\\n if not es.indices.exists(\\n index=chercheur[\\\"structSirene\\\"]\\n + \\\"-\\\"\\n + chercheur[\\\"labHalId\\\"]\\n + \\\"-researchers-\\\"\\n + chercheur[\\\"ldapId\\\"]\\n + \\\"-documents\\\"\\n ): # -researchers\\\" + row[\\\"ldapId\\\"] + \\\"-documents\\n print(\\\"exception \\\", chercheur[\\\"labHalId\\\"], chercheur[\\\"ldapId\\\"])\\n\\n res = es.search(\\n index=chercheur[\\\"structSirene\\\"]\\n + \\\"-\\\"\\n + chercheur[\\\"labHalId\\\"]\\n + \\\"-researchers-\\\"\\n + chercheur[\\\"ldapId\\\"]\\n + \\\"-documents\\\",\\n body=doc_param,\\n ) # -researchers\\\" + row[\\\"ldapId\\\"] + \\\"-documents\\n\\n if len(res[\\\"hits\\\"][\\\"hits\\\"]) > 0:\\n doc[\\\"validated\\\"] = res[\\\"hits\\\"][\\\"hits\\\"][0][\\\"_source\\\"][\\\"validated\\\"]\\n if \\\"authorship\\\" in res[\\\"hits\\\"][\\\"hits\\\"][0][\\\"_source\\\"]:\\n doc[\\\"authorship\\\"] = res[\\\"hits\\\"][\\\"hits\\\"][0][\\\"_source\\\"][\\\"authorship\\\"]\\n\\n if (\\n res[\\\"hits\\\"][\\\"hits\\\"][0][\\\"_source\\\"][\\\"modifiedDate_tdate\\\"]\\n != doc[\\\"modifiedDate_tdate\\\"]\\n ):\\n doc[\\\"records\\\"].append(\\n {\\n \\\"beforeModifiedDate_tdate\\\": doc[\\\"modifiedDate_tdate\\\"],\\n \\\"MDS\\\": res[\\\"hits\\\"][\\\"hits\\\"][0][\\\"_source\\\"][\\\"MDS\\\"],\\n }\\n )\\n\\n else:\\n doc[\\\"validated\\\"] = True\\n progress_recorder.set_progress(num, len(docs), description=\\\"(récolte)\\\")\\n progress_recorder.set_progress(num, len(docs), description=\\\"(indexation)\\\")\\n helpers.bulk(\\n es,\\n docs,\\n index=chercheur[\\\"structSirene\\\"]\\n + \\\"-\\\"\\n + chercheur[\\\"labHalId\\\"]\\n + \\\"-researchers-\\\"\\n + chercheur[\\\"ldapId\\\"]\\n + \\\"-documents\\\",\\n refresh=\\\"wait_for\\\",\\n )\\n\\n return chercheur # au cas où\",\n \"title\": \"\"\n },\n {\n \"docid\": \"552d8019a0ca35d82249c0f2c30e02ce\",\n \"score\": \"0.46897706\",\n \"text\": \"def interrogator(path,\\n search,\\n query = 'any', \\n show = 'words',\\n exclude = False,\\n case_sensitive = False,\\n lemmatise = False, \\n titlefilter = False, \\n lemmatag = False, \\n spelling = False, \\n phrases = False, \\n dep_type = 'collapsed-ccprocessed-dependencies',\\n quicksave = False,\\n printstatus = True,\\n root = False,\\n df1_always_df = False,\\n just_speakers = False,\\n excludemode = 'any',\\n searchmode = 'all',\\n **kwargs):\\n import corpkit\\n from corpkit.other import add_corpkit_to_path\\n from corpkit.other import tregex_engine\\n from corpkit.other import add_nltk_data_to_nltk_path\\n \\n # some non-Python resources need to explicitly be added to path\\n add_corpkit_to_path()\\n\\n import os\\n import re\\n import signal\\n import gc\\n\\n import collections\\n import warnings\\n import nltk\\n import numpy\\n\\n import pandas as pd\\n from collections import Counter\\n from time import localtime, strftime\\n from pandas import DataFrame, Series\\n\\n from corpkit.tests import check_pytex, check_t_kinter\\n from corpkit.textprogressbar import TextProgressBar\\n\\n import dictionaries\\n from dictionaries.word_transforms import (wordlist, \\n usa_convert, \\n taglemma)\\n\\n # nltk data path for tokeniser/lemmatiser\\n if 'nltk_data_path' in kwargs.keys():\\n if kwargs['nltk_data_path'] not in nltk.data.path:\\n nltk.data.path.append(kwargs['nltk_data_path'])\\n locdir = '/Users/daniel/work/corpkit/nltk_data'\\n if locdir not in nltk.data.path:\\n nltk.data.path.append(locdir)\\n\\n # prefer ipython to python if the user has it\\n try:\\n from IPython.display import display, clear_output\\n except ImportError:\\n pass\\n \\n # check for gui, pythontex\\n tk = check_t_kinter()\\n have_python_tex = check_pytex()\\n\\n # multiprocessing progress bar\\n if 'denominator' in kwargs.keys():\\n denom = kwargs['denominator']\\n else:\\n denom = 1\\n if 'startnum' in kwargs.keys():\\n startnum = kwargs['startnum']\\n else:\\n startnum = 0\\n\\n # determine if multiquery\\n is_multiquery = False\\n if hasattr(path, '__iter__'):\\n is_multiquery = True\\n if 'postounts' in path[0]:\\n spelling = 'UK'\\n if type(query) == dict or type(query) == collections.OrderedDict:\\n is_multiquery = True\\n if just_speakers:\\n if just_speakers == 'each':\\n is_multiquery = True\\n if type(just_speakers) == str:\\n if just_speakers != 'each':\\n just_speakers = [just_speakers]\\n if type(just_speakers) == list:\\n if len(just_speakers) > 1:\\n is_multiquery = True\\n\\n # regex type\\n retype = type(re.compile('hello, world'))\\n\\n # just for me: convert spelling automatically for bipolar\\n if not is_multiquery:\\n if 'postcounts' in path:\\n spelling = 'UK'\\n\\n # don't print so much stdout in the GUI\\n if root:\\n shouldprint = False\\n else:\\n shouldprint = True\\n\\n # run pmultiquery if so\\n if is_multiquery:\\n from corpkit.multiprocess import pmultiquery\\n d = { 'path': path, \\n 'search': search,\\n 'query': query,\\n 'show': show,\\n 'lemmatise': lemmatise, \\n 'titlefilter': titlefilter, \\n 'lemmatag': lemmatag, \\n 'print_info': shouldprint, \\n 'spelling': spelling, \\n 'phrases': phrases, \\n 'dep_type': dep_type, \\n 'quicksave': quicksave, \\n 'df1_always_df': df1_always_df,\\n 'just_speakers': just_speakers, \\n 'root': root,}\\n \\n if 'note' in kwargs.keys() and kwargs['note'] is not False:\\n d['note'] = kwargs['note']\\n\\n if 'num_proc' in kwargs.keys():\\n d['num_proc'] = kwargs['num_proc']\\n\\n return pmultiquery(**d)\\n\\n if 'paralleling' in kwargs.keys():\\n paralleling = kwargs['paralleling']\\n else:\\n paralleling = False\\n\\n # multiple progress bars when multiprocessing\\n par_args = {}\\n if not root:\\n from blessings import Terminal\\n term = Terminal()\\n par_args['terminal'] = term\\n par_args['linenum'] = paralleling\\n\\n the_time_started = strftime(\\\"%Y-%m-%d %H:%M:%S\\\")\\n \\n # check if we are in ipython\\n try:\\n get_ipython().getoutput()\\n except TypeError:\\n have_ipython = True\\n except NameError:\\n import subprocess\\n have_ipython = False\\n\\n def unsplitter(lst):\\n \\\"\\\"\\\"unsplit contractions and apostophes from tokenised text\\\"\\\"\\\"\\n unsplit = []\\n for index, t in enumerate(lst):\\n if index == 0 or index == len(lst) - 1:\\n unsplit.append(t)\\n continue\\n if \\\"'\\\" in t and not t.endswith(\\\"'\\\"):\\n rejoined = ''.join([lst[index - 1], t])\\n unsplit.append(rejoined)\\n else:\\n if not \\\"'\\\" in lst[index + 1]:\\n unsplit.append(t)\\n return unsplit\\n\\n def animator(progbar, count, tot_string = False, linenum = False, terminal = False, \\n init = False, length = False):\\n \\\"\\\"\\\"animates progress bar in unique position in terminal\\\"\\\"\\\"\\n if init:\\n from textprogressbar import TextProgressBar\\n return TextProgressBar(length, dirname = tot_string)\\n if type(linenum) == int:\\n with terminal.location(0, terminal.height - (linenum + 1)):\\n if tot_string:\\n progbar.animate(count, tot_string)\\n else:\\n progbar.animate(count)\\n else:\\n if tot_string:\\n progbar.animate(count, tot_string)\\n else:\\n progbar.animate(count) \\n\\n def signal_handler(signal, frame):\\n \\\"\\\"\\\"pause on ctrl+c, rather than just stop loop\\\"\\\"\\\" \\n import signal\\n import sys\\n from time import localtime, strftime\\n time = strftime(\\\"%H:%M:%S\\\", localtime())\\n sel = raw_input('\\\\n\\\\n%s: Paused. Press return to resume, or type exit to quit: \\\\n' % time)\\n if sel.startswith('e') or sel.startswith('E'):\\n sys.exit(0)\\n else:\\n time = strftime(\\\"%H:%M:%S\\\", localtime())\\n print '%s: Interrogation resumed.\\\\n' % time\\n signal.signal(signal.SIGINT, signal_handler)\\n \\n def gettag(query, lemmatag = False):\\n \\\"\\\"\\\"find tag for wordnet lemmatisation\\\"\\\"\\\"\\n import re\\n if lemmatag is False:\\n tag = 'n' # same default as wordnet\\n # attempt to find tag from tregex query\\n tagfinder = re.compile(r'^[^A-Za-z]*([A-Za-z]*)')\\n tagchecker = re.compile(r'^[A-Z]{1,4}$')\\n treebank_tag = re.findall(tagfinder, query.replace(r'\\\\w', '').replace(r'\\\\s', '').replace(r'\\\\b', ''))\\n if re.match(tagchecker, treebank_tag[0]):\\n if treebank_tag[0].startswith('J'):\\n tag = 'a'\\n elif treebank_tag[0].startswith('V') or treebank_tag[0].startswith('M'):\\n tag = 'v'\\n elif treebank_tag[0].startswith('N'):\\n tag = 'n'\\n elif treebank_tag[0].startswith('R'):\\n tag = 'r'\\n elif lemmatag:\\n tag = lemmatag\\n tagchecker = re.compile(r'^[avrn]$')\\n while not re.match(tagchecker, lemmatag):\\n time = strftime(\\\"%H:%M:%S\\\", localtime())\\n selection = raw_input('\\\\n%s: WordNet POS tag \\\"%s\\\" not recognised.\\\\n It must be:\\\\n\\\\n ' \\\\\\n ' a: (adjective)' \\\\\\n ' n: (noun)' \\\\\\n ' r: (adverb)' \\\\\\n ' v: (verb)\\\\n\\\\nYour selection: ' % (time, lemmatag))\\n lemmatag = selection\\n return tag\\n \\n def processwords(list_of_matches, lemmatag = False):\\n \\\"\\\"\\\"normalise matches from interrogations\\\"\\\"\\\"\\n list_of_matches = [w.lower() for w in list_of_matches]\\n # remove nonwords, strip . to normalise \\\"dr.\\\"\\n if translated_option != 'o' and translated_option != 'u':\\n list_of_matches = [w.lstrip('.').rstrip('.') for w in list_of_matches if re.search(regex_nonword_filter, w)]\\n \\n list_of_matches.sort()\\n \\n # tokenise if multiword:\\n if phrases and not n_gramming:\\n from nltk import word_tokenize as word_tokenize\\n list_of_matches = [word_tokenize(i) for i in list_of_matches]\\n\\n # this is just for plaintext ... should convert to unicode on file open\\n if datatype == 'plaintext':\\n try:\\n list_of_matches = [unicode(w, errors = 'ignore') for w in list_of_matches]\\n except TypeError:\\n pass\\n\\n if not dependency and exclude and 'w' in exclude.keys():\\n list_of_matches = [w for w in list_of_matches if not re.match(exclude['w'], w)]\\n\\n if lemmatise or 'l' in show:\\n if not dependency:\\n tag = gettag(query, lemmatag = lemmatag)\\n lemmata = lemmatiser(list_of_matches, tag)\\n tups = zip(list_of_matches, lemmata)\\n res = []\\n for w, l in tups:\\n single_result = []\\n if exclude and 'l' in exclude.keys():\\n if re.match(exclude['l'], l):\\n continue\\n if 'w' in show:\\n single_result.append(w)\\n if 'l' in show:\\n single_result.append(l)\\n # bad fix:\\n # this currently says, if pos in show, there must only be pos ...\\n if 'p' in show:\\n if lemmatise:\\n single_result.append(l)\\n else:\\n single_result.append(w)\\n\\n single_result = '/'.join(single_result)\\n res.append(single_result)\\n list_of_matches = res\\n\\n if titlefilter and not dependency:\\n list_of_matches = titlefilterer(list_of_matches)\\n if spelling:\\n list_of_matches = convert_spelling(list_of_matches, spelling = spelling)\\n\\n # use blacklist option in gui\\n if 'blacklist' in kwargs.keys():\\n stopwords = False\\n if kwargs['blacklist'] is not False:\\n if kwargs['blacklist'] is True:\\n from dictionaries.stopwords import stopwords as my_stopwords\\n stopwords = [i.lower() for i in my_stopwords]\\n list_of_matches = [w for w in list_of_matches if w not in stopwords]\\n else:\\n if type(kwargs['blacklist']) == list:\\n stopwords = [i.lower() for i in kwargs['blacklist']]\\n list_of_matches = [w for w in list_of_matches if w not in stopwords]\\n else:\\n regexblacklist = re.compile(kwargs['blacklist'])\\n list_of_matches = [w for w in list_of_matches if not re.search(regexblacklist, w)]\\n\\n #if not split_con:\\n # list_of_matches = unsplitter(list_of_matches)\\n \\n # turn every result into a single string again if need be:\\n if phrases:\\n output = []\\n for res in list_of_matches:\\n joined = ' '.join(res)\\n output.append(joined)\\n return output\\n else:\\n return list_of_matches\\n\\n def lemmatiser(list_of_words, tag):\\n \\\"\\\"\\\"take a list of unicode words and a tag and return a lemmatised list.\\\"\\\"\\\"\\n \\n output = []\\n for entry in list_of_words:\\n if phrases:\\n # just get the rightmost word\\n word = entry[-1]\\n entry.pop()\\n else:\\n word = entry\\n if translated_option.startswith('u'):\\n if word in taglemma:\\n word = taglemma[word]\\n else:\\n if word == 'x':\\n word = 'Other'\\n # only use wordnet lemmatiser when appropriate\\n elif not dependency:\\n if word in wordlist:\\n word = wordlist[word]\\n word = lmtzr.lemmatize(word, tag)\\n # do the manual_lemmatisation\\n else:\\n if word in wordlist:\\n word = wordlist[word]\\n if phrases:\\n entry.append(word)\\n output.append(entry)\\n else:\\n output.append(word)\\n return output\\n\\n def titlefilterer(list_of_matches):\\n from dictionaries.wordlists import wordlists\\n badwords = wordlists.titles + wordlists.closedclass\\n output = []\\n for result in list_of_matches:\\n head = result[-1]\\n non_head = len(result) - 1\\n title_stripped = [token for token in result[:non_head] if token.rstrip('.') not in badwords]\\n title_stripped.append(head)\\n output.append(title_stripped)\\n return output\\n\\n def convert_spelling(list_of_matches, spelling = 'US'):\\n from dictionaries.word_transforms import usa_convert\\n if spelling == 'UK':\\n usa_convert = {v: k for k, v in usa_convert.items()}\\n output = []\\n for result in list_of_matches:\\n if not phrases:\\n result = result.split('/')\\n for index, i in enumerate(result):\\n try:\\n result[index] = usa_convert[i]\\n except KeyError:\\n pass\\n output.append('/'.join(result))\\n return output\\n\\n def distancer(lks, lk):\\n \\\"determine number of jumps to root\\\" \\n c = 0\\n # get the gov index, stop when it's zero\\n root_found = False\\n while not root_found:\\n if c == 0:\\n try:\\n link_to_check = next(i for i in lks if i.dependent.idx == lk.id)\\n except StopIteration:\\n root_found = True\\n break\\n #link_to_check = lk\\n gov_index = link_to_check.governor.idx\\n if gov_index == 0:\\n root_found = True\\n else:\\n if c > 29:\\n root_found = True\\n break\\n link_to_check = [l for l in lks if l.dependent.idx == gov_index]\\n if len(link_to_check) > 0:\\n link_to_check = link_to_check[0]\\n else:\\n break\\n c += 1\\n if c < 30:\\n return c\\n\\n def dep_searcher(sents):\\n \\\"\\\"\\\"\\n search corenlp dependency parse\\n 1. search for 'search' keyword arg\\n governor\\n dependent\\n function\\n pos\\n lemma\\n word\\n index\\n\\n 2. exclude entries if need be\\n\\n 3. return '/'-sep list of 'show' keyword arg:\\n governor\\n dependent\\n function\\n pos\\n lemma\\n word\\n index\\n distance\\n \\n ... or just return int count.\\n \\\"\\\"\\\"\\n \\n result = []\\n for s in sents:\\n lks = []\\n deps = get_deps(s, dep_type)\\n tokens = s.tokens\\n for opt, pat in search.items():\\n pat = filtermaker(pat)\\n if opt == 'g':\\n for l in deps.links:\\n if re.match(pat, l.governor.text):\\n lks.append(s.get_token_by_id(l.dependent.idx))\\n elif opt == 'd':\\n for l in deps.links:\\n if re.match(pat, l.dependent.text):\\n lks.append(s.get_token_by_id(l.governor.idx))\\n elif opt == 'f':\\n for l in deps.links:\\n if re.match(pat, l.type):\\n lks.append(s.get_token_by_id(l.dependent.idx))\\n elif opt == 'p':\\n for tok in tokens:\\n if re.match(pat, tok.pos):\\n lks.append(tok)\\n elif opt == 'l':\\n for tok in tokens:\\n if re.match(pat, tok.lemma):\\n lks.append(tok)\\n elif opt == 'w':\\n for tok in tokens:\\n if re.match(pat, tok.word):\\n lks.append(tok)\\n elif opt == 'i':\\n for tok in tokens:\\n if re.match(pat, str(tok.id)):\\n lks.append(tok)\\n\\n # only return results if all conditions are met\\n if searchmode == 'all':\\n counted = Counter(lks)\\n lks = [k for k, v in counted.items() if v >= len(search.keys())]\\n\\n lks = list(set([x for x in lks if re.search(regex_nonword_filter, x.word)]))\\n\\n if exclude is not False:\\n to_remove = []\\n for op, pat in exclude.items():\\n pat = filtermaker(pat)\\n for tok in lks:\\n if op == 'g':\\n for l in deps.links:\\n if re.match(pat, l.governor.text):\\n to_remove.append(s.get_token_by_id(l.governor.idx))\\n elif op == 'd':\\n for l in deps.links:\\n if re.match(pat, l.dependent.text):\\n to_remove.append(s.get_token_by_id(l.dependent.idx))\\n elif op == 'f':\\n for l in deps.links:\\n if re.match(pat, l.type):\\n to_remove.append(s.get_token_by_id(l.dependent.idx))\\n elif op == 'p':\\n for tok in tokens:\\n if re.match(pat, tok.pos):\\n to_remove.append(tok)\\n elif op == 'l':\\n for tok in tokens:\\n if re.match(pat, tok.lemma):\\n to_remove.append(tok)\\n elif op == 'w':\\n for tok in tokens:\\n if re.match(pat, tok.word):\\n to_remove.append(tok)\\n elif op == 'i':\\n for tok in tokens:\\n if re.match(pat, str(tok.id)):\\n to_remove.append(tok)\\n\\n if excludemode == 'all':\\n counted = Counter(to_remove)\\n to_remove = [k for k, v in counted.items() if v >= len(exclude.keys())]\\n for i in to_remove:\\n try:\\n lks.remove(i)\\n except ValueError:\\n pass\\n\\n if only_count:\\n result.append(len(lks))\\n continue\\n\\n # figure out what to show\\n for lk in lks:\\n single_result = {}\\n node = deps.get_node_by_idx(lk.id)\\n\\n if 'w' in show:\\n single_result['w'] = 'none'\\n if lemmatise:\\n single_result['w'] = lk.lemma\\n else:\\n single_result['w'] = lk.word\\n\\n if 'l' in show:\\n single_result['l'] = lk.lemma\\n\\n if 'p' in show:\\n single_result['p'] = 'none'\\n postag = lk.pos\\n if lemmatise:\\n if postag.lower() in taglemma.keys():\\n single_result['p'] = taglemma[postag.lower()]\\n else:\\n single_result['p'] = postag.lower()\\n else:\\n single_result['p'] = postag\\n if not single_result['p']:\\n single_result['p'] == 'none'\\n\\n if 'f' in show:\\n single_result['f'] = 'none'\\n for i in deps.links:\\n if i.dependent.idx == lk.id:\\n single_result['f'] = i.type\\n break\\n if single_result['f'] == '':\\n single_result['f'] = 'root'\\n\\n if 'g' in show:\\n single_result['g'] = 'none'\\n for i in deps.links:\\n if i.dependent.idx == lk.id:\\n if s.get_token_by_id(i.governor.idx):\\n if lemmatise: \\n single_result['g'] = s.get_token_by_id(i.governor.idx).lemma\\n else:\\n single_result['g'] = i.governor.text\\n else:\\n single_result['g'] = 'root'\\n break\\n\\n if 'd' in show:\\n single_result['d'] = 'none'\\n for i in deps.links:\\n if i.governor.idx == lk.id:\\n if s.get_token_by_id(i.dependent.idx): \\n if lemmatise:\\n single_result['d'] = s.get_token_by_id(i.dependent.idx).lemma\\n else:\\n single_result['d'] = i.dependent.text\\n break\\n\\n if 'r' in show:\\n all_lks = [l for l in deps.links]\\n distance = distancer(all_lks, lk)\\n if distance:\\n single_result['r'] = str(distance)\\n else:\\n single_result['r'] = '-1'\\n\\n if 'i' in show:\\n single_result['i'] = str(lk.id)\\n\\n if not only_count:\\n \\n # add them in order\\n out = []\\n for i in show:\\n out.append(single_result[i])\\n\\n result.append('/'.join(out))\\n \\n if 'c' in show:\\n result = sum(result)\\n\\n return result\\n\\n def tok_by_list(pattern, list_of_toks):\\n \\\"\\\"\\\"search for regex in plaintext corpora\\\"\\\"\\\"\\n if type(pattern) == str:\\n pattern = [pattern]\\n result = []\\n matches = [m for m in list_of_toks if m in pattern]\\n for m in matches:\\n result.append(m)\\n return result\\n\\n def tok_ngrams(pattern, list_of_toks, split_contractions = True):\\n from collections import Counter\\n global gramsize\\n import re\\n ngrams = Counter()\\n result = []\\n # if it's not a compiled regex\\n list_of_toks = [x for x in list_of_toks if re.search(regex_nonword_filter, x)]\\n\\n if not split_contractions:\\n list_of_toks = unsplitter(list_of_toks)\\n \\n #list_of_toks = [x for x in list_of_toks if \\\"'\\\" not in x]\\n for index, w in enumerate(list_of_toks):\\n try:\\n the_gram = [list_of_toks[index+x] for x in range(gramsize)]\\n if not any(re.search(query, x) for x in the_gram):\\n continue\\n #if query != 'any':\\n # if not any(re.search(query, w) is True for w in the_gram):\\n # continue\\n ngrams[' '.join(the_gram)] += 1\\n except IndexError:\\n pass\\n # turn counter into list of results\\n for k, v in ngrams.items():\\n if v > 1:\\n for i in range(v):\\n result.append(k)\\n return result\\n\\n def tok_by_reg(pattern, list_of_toks):\\n \\\"\\\"\\\"search for regex in plaintext corpora\\\"\\\"\\\"\\n try:\\n comped = re.compile(pattern)\\n except:\\n import traceback\\n import sys\\n exc_type, exc_value, exc_traceback = sys.exc_info()\\n lst = traceback.format_exception(exc_type, exc_value,\\n exc_traceback)\\n error_message = lst[-1]\\n thetime = strftime(\\\"%H:%M:%S\\\", localtime())\\n print '%s: Query %s' % (thetime, error_message)\\n return 'Bad query'\\n\\n matches = [m for m in list_of_toks if re.search(comped, m)]\\n\\n return matches\\n\\n def plaintext_regex_search(pattern, plaintext_data):\\n \\\"\\\"\\\"search for regex in plaintext corpora\\\"\\\"\\\"\\n result = []\\n #if not pattern.startswith(r'\\\\b') and not pattern.endswith(r'\\\\b'):\\n #pattern = r'\\\\b' + pattern + '\\\\b'\\n try:\\n compiled_pattern = re.compile(pattern)\\n except:\\n import traceback\\n import sys\\n exc_type, exc_value, exc_traceback = sys.exc_info()\\n lst = traceback.format_exception(exc_type, exc_value,\\n exc_traceback)\\n error_message = lst[-1]\\n thetime = strftime(\\\"%H:%M:%S\\\", localtime())\\n print '%s: Query %s' % (thetime, error_message)\\n return 'Bad query'\\n matches = re.findall(compiled_pattern, plaintext_data)\\n for index, i in enumerate(matches):\\n if type(i) == tuple:\\n matches[index] = i[0]\\n return matches\\n\\n def plaintext_simple_search(pattern, plaintext_data):\\n \\\"\\\"\\\"search for tokens in plaintext corpora\\\"\\\"\\\"\\n if type(pattern) == str:\\n pattern = [pattern]\\n result = []\\n try:\\n tmp = re.compile(pattern)\\n except:\\n import traceback\\n import sys\\n exc_type, exc_value, exc_traceback = sys.exc_info()\\n lst = traceback.format_exception(exc_type, exc_value,\\n exc_traceback)\\n error_message = lst[-1]\\n thetime = strftime(\\\"%H:%M:%S\\\", localtime())\\n print '%s: Query %s' % (thetime, error_message)\\n return 'Bad query'\\n\\n for p in pattern:\\n if case_sensitive:\\n pat = re.compile(r'\\\\b' + re.escape(p) + r'\\\\b')\\n else:\\n pat = re.compile(r'\\\\b' + re.escape(p) + r'\\\\b', re.IGNORECASE)\\n if not any_plaintext_word:\\n matches = re.findall(pat, plaintext_data)\\n for m in range(len(matches)):\\n result.append(p)\\n else:\\n for m in plaintext_data.split():\\n result.append(m)\\n return result\\n\\n def get_speaker_names_from_xml_corpus(path):\\n import os\\n import re\\n from bs4 import BeautifulSoup\\n names = []\\n # parsing html with regular expression! :)\\n speakid = re.compile(r'<speakername>[\\\\s\\\\n]*?([^\\\\s\\\\n]+)[\\\\s\\\\n]*?<.speakername>', re.MULTILINE)\\n for (root, dirs, fs) in os.walk(path):\\n for f in fs:\\n with open(os.path.join(root, f), 'r') as fo:\\n txt = fo.read()\\n res = re.findall(speakid, txt)\\n if res:\\n res = [i.strip() for i in res]\\n for i in res:\\n if i not in names:\\n names.append(i)\\n return list(sorted(set(names)))\\n\\n def slow_tregex(sents):\\n \\\"\\\"\\\"do the speaker-specific version of tregex queries\\\"\\\"\\\"\\n import os\\n import bs4\\n # first, put the relevant trees into temp file\\n if 'outname' in kwargs.keys():\\n to_open = 'tmp-%s.txt' % kwargs['outname']\\n else:\\n to_open = 'tmp.txt'\\n to_write = '\\\\n'.join([sent._parse_string.strip() for sent in sents if sent.parse_string is not None]).encode('utf-8', errors = 'ignore')\\n with open(to_open, \\\"w\\\") as fo:\\n fo.write(to_write)\\n q = search.values()[0]\\n res = tregex_engine(query = q, \\n options = ['-o', '-%s' % translated_option], \\n corpus = to_open,\\n root = root)\\n if root:\\n root.update()\\n os.remove(to_open)\\n return res\\n\\n def get_deps(sentence, dep_type):\\n if dep_type == 'basic-dependencies':\\n return sentence.basic_dependencies\\n if dep_type == 'collapsed-dependencies':\\n return sentence.collapsed_dependencies\\n if dep_type == 'collapsed-ccprocessed-dependencies':\\n return sentence.collapsed_ccprocessed_dependencies\\n\\n def get_stats(sents):\\n \\\"\\\"\\\"get a bunch of frequencies on interpersonal phenomena\\\"\\\"\\\"\\n import os\\n import re \\n # first, put the relevant trees into temp file\\n if 'outname' in kwargs.keys():\\n to_open = 'tmp-%s.txt' % kwargs['outname']\\n else:\\n to_open = 'tmp.txt'\\n with open(to_open, \\\"w\\\") as fo:\\n for sent in sents:\\n statsmode_results['Sentences'] += 1\\n fo.write(sent.parse_string.rstrip().encode('utf-8', errors = 'ignore') + '\\\\n')\\n deps = get_deps(sent, dep_type)\\n numpass = len([x for x in deps.links if x.type.endswith('pass')])\\n statsmode_results['Passives'] += numpass\\n statsmode_results['Tokens'] += len(sent.tokens)\\n statsmode_results['Words'] += len([w for w in sent.tokens if w.word.isalnum()])\\n #statsmode_results['Unique words'] += len(set([w.word.lower() for w in sent.tokens if w.word.isalnum()]))\\n #statsmode_results['Unique lemmata'] += len(set([w.lemma.lower() for w in sent.tokens if w.word.isalnum()]))\\n\\n # count moods via trees (/\\\\?/ !< __)\\n from dictionaries.process_types import processes\\n from corpkit.other import as_regex\\n tregex_qs = {'Imperative': r'ROOT < (/(S|SBAR)/ < (VP !< VBD !< VBG !$ NP !$ SBAR < NP !$-- S !$-- VP !$ VP)) !<< (/\\\\?/ !< __) !<<- /-R.B-/ !<<, /(?i)^(-l.b-|hi|hey|hello|oh|wow|thank|thankyou|thanks|welcome)$/',\\n #'Open interrogative': r'ROOT < SBARQ <<- (/\\\\?/ !< __)', \\n #'Closed interrogative': r'ROOT ( < (SQ < (NP $+ VP)) << (/\\\\?/ !< __) | < (/(S|SBAR)/ < (VP $+ NP)) <<- (/\\\\?/ !< __))',\\n 'Unmodalised declarative': r'ROOT < (S < (/(NP|SBAR|VP)/ $+ (VP !< MD)))',\\n 'Modalised declarative': r'ROOT < (S < (/(NP|SBAR|VP)/ $+ (VP < MD)))',\\n 'Open class words': r'/^(NN|JJ|VB|RB)/ < __',\\n 'Closed class words': r'__ !< __ !> /^(NN|JJ|VB|RB)/',\\n 'Clauses': r'/^S/ < __',\\n 'Interrogative': r'ROOT << (/\\\\?/ !< __)',\\n 'Mental processes': r'VP > /^(S|ROOT)/ <+(VP) (VP <<# /%s/)' % as_regex(processes.mental, boundaries = 'w'),\\n 'Verbal processes': r'VP > /^(S|ROOT)/ <+(VP) (VP <<# /%s/)' % as_regex(processes.verbal, boundaries = 'w'),\\n 'Relational processes': r'VP > /^(S|ROOT)/ <+(VP) (VP <<# /%s/)' % as_regex(processes.relational, boundaries = 'w')}\\n\\n for name, q in sorted(tregex_qs.items()):\\n res = tregex_engine(query = q, \\n options = ['-o', '-C'], \\n corpus = to_open, \\n root = root)\\n statsmode_results[name] += int(res)\\n global numdone\\n numdone += 1\\n if root:\\n root.update()\\n if not root:\\n tot_string = str(numdone + 1) + '/' + str(total_files * len(tregex_qs.keys()))\\n if 'outname' in kwargs.keys():\\n tot_string = '%s: %s' % (kwargs['outname'], tot_string)\\n animator(p, numdone, tot_string, **par_args)\\n if 'note' in kwargs.keys() and kwargs['note'] is not False:\\n kwargs['note'].progvar.set((numdone * 100.0 / (total_files * len(tregex_qs.keys())) / denom) + startnum)\\n os.remove(to_open)\\n\\n def tabler(subcorpus_names, list_of_dicts, num_rows):\\n \\\"\\\"\\\"make a word table showing num_rows results\\\"\\\"\\\"\\n import pandas as pd\\n cols = []\\n for subcorp, data in zip(subcorpus_names, list_of_dicts):\\n col = pd.Series([w for w, v in data.most_common(num_rows)], name = subcorp)\\n cols.append(col)\\n word_table = pd.concat(cols, axis = 1)\\n return word_table\\n\\n # a few things are off by default:\\n only_count = False\\n using_tregex = False\\n n_gramming = False\\n dependency = False\\n plaintext = False\\n tokens = False\\n statsmode = False\\n split_con = True\\n search_iterable = False\\n\\n # determine what kind of data the corpus is\\n # this currently slows things down with huge corpora, \\n # so judge from folder name first \\n if type(path) == str and path.endswith('-parsed'):\\n datatype = 'parse'\\n elif type(path) == str and path.endswith('-tokenised'):\\n datatype = 'tokens'\\n else:\\n from corpkit.other import determine_datatype\\n datatype = determine_datatype(path)\\n\\n # some empty lists we'll need\\n dicts = []\\n allwords_list = []\\n \\n regex_nonword_filter = re.compile(\\\"[A-Za-z0-9:_]\\\")\\n \\n # fix up search\\n if type(search) == str:\\n search = search[0].lower()\\n if not search.lower().startswith('t') and not search.lower().startswith('n') \\\\\\n and datatype == 'parse':\\n search_iterable = True\\n if query == 'any':\\n query = r'.*'\\n search = {search: query}\\n\\n possb = ['d', 'g', 'i', 'c', 'a', 'p', 'l', 'w', 't', 'f']\\n if not any(i in possb for i in search.keys()):\\n raise ValueError('search argument \\\"%s\\\" unrecognised.' % search.keys())\\n if len(search.keys()) > 1 and 't' in search.keys():\\n raise ValueError('if \\\"t\\\" in search, it must be the only list item')\\n\\n # fix up exclude naming conventions, convert lists to regex\\n fixed_exclude = {}\\n if exclude:\\n for k, v in exclude.items():\\n if type(v) == list:\\n from corpkit.other import as_regex\\n v = as_regex(v, boundaries = 'l', case_sensitive = case_sensitive)\\n if k != k.lower()[0]:\\n fixed_exclude[k.lower()[0]] = v\\n else:\\n fixed_exclude[k] = v\\n exclude = fixed_exclude\\n\\n if not search_iterable:\\n query = search.values()[0]\\n\\n if type(show) == str or type(show) == unicode:\\n show = [show.lower()[0]]\\n\\n for index, t in enumerate(show):\\n show[index] = t.lower()[0]\\n\\n possb = ['d', 'g', 'i', 'c', 'a', 'p', 'l', 'w', 't', 'f']\\n only_dep = ['d', 'g', 'i', 'a', 'f']\\n if not any(i in possb for i in show):\\n raise ValueError('show argument \\\"%s\\\" unrecognised.' % show)\\n if len(show) > 1 and 'c' in show:\\n raise ValueError('if \\\"c\\\" in show, it must be the only list item')\\n if 't' in search.keys() and any(i in only_dep for i in show):\\n raise ValueError('If searching trees, show can not include: %s' % ', '.join(only_dep))\\n\\n # Tregex option:\\n translated_option = False\\n from corpkit.other import as_regex\\n \\n if datatype == 'parse':\\n if 't' in search.keys():\\n using_tregex = True\\n\\n if datatype == 'plaintext':\\n plaintext = True\\n\\n elif datatype == 'tokens':\\n tokens = True\\n\\n\\n if using_tregex:\\n if 'p' in show:\\n dep_funct = slow_tregex\\n optiontext = 'Part-of-speech tags only.'\\n translated_option = 'u'\\n if type(query) == list:\\n query = r'__ < (/%s/ !< __)' % as_regex(query, boundaries = 'line', case_sensitive = case_sensitive)\\n if query == 'any':\\n query = r'__ < (/.?[A-Za-z0-9].?/ !< __)'\\n elif 't' in show:\\n dep_funct = slow_tregex\\n optiontext = 'Tags and words.'\\n translated_option = 'o'\\n if type(query) == list:\\n query = r'__ < (/%s/ !< __)' % as_regex(query, boundaries = 'line', case_sensitive = case_sensitive)\\n if query == 'any':\\n query = r'__ < (/.?[A-Za-z0-9].?/ !< __)'\\n elif 'w' in show:\\n dep_funct = slow_tregex\\n optiontext = 'Words only.'\\n translated_option = 't'\\n if type(query) == list:\\n query = r'/%s/ !< __' % as_regex(query, boundaries = 'line', case_sensitive = case_sensitive)\\n if query == 'any':\\n query = r'/.?[A-Za-z0-9].?/ !< __'\\n elif 'c' in show:\\n dep_funct = slow_tregex\\n count_results = {}\\n only_count = True\\n translated_option = 'C'\\n optiontext = 'Counts only.'\\n if type(query) == list:\\n query = r'/%s/ !< __' % as_regex(query, boundaries = 'line', case_sensitive = case_sensitive)\\n if query == 'any':\\n query = r'/.?[A-Za-z0-9].?/ !< __'\\n elif 'l' in show:\\n dep_funct = slow_tregex\\n translated_option = 't'\\n optiontext = 'Words, lemmatised.'\\n lemmatise = True\\n if type(query) == list:\\n query = r'/%s/ !< __' % as_regex(query, boundaries = 'line', case_sensitive = case_sensitive)\\n if query == 'any':\\n query = r'/.?[A-Za-z0-9].?/ !< __'\\n\\n elif datatype == 'plaintext':\\n optiontext = 'Searching plaintext corpus'\\n if 'regex' in kwargs.keys() and kwargs['regex'] is False:\\n translated_option = 's'\\n if query == 'any':\\n any_plaintext_word = True\\n else:\\n any_plaintext_word = False\\n else:\\n translated_option = 'r'\\n if query == 'any':\\n query = r'[^\\\\s]+'\\n if type(query) == list:\\n query = as_regex(query, boundaries = 'line', case_sensitive = case_sensitive)\\n \\n elif datatype == 'tokens':\\n if 'w' in search.keys():\\n tokens = True\\n if type(query) == list:\\n translated_option = 'e'\\n optiontext = 'Tokens via list.'\\n dep_funct = tok_by_list\\n else:\\n translated_option = 'h'\\n optiontext = 'Tokens via regular expression.'\\n dep_funct = tok_by_reg\\n if 'n' in search.keys():\\n translated_option = 'j'\\n tokens = True\\n lemmatise = False\\n optiontext = 'Get ngrams from tokens.'\\n if query == 'any':\\n query = r'.*'\\n if type(query) == list:\\n query = as_regex(query, boundaries = 'l', case_sensitive = case_sensitive)\\n else:\\n try:\\n if not case_sensitive:\\n query = re.compile(query, re.IGNORECASE)\\n else:\\n query = re.compile(query)\\n except:\\n import traceback\\n import sys\\n exc_type, exc_value, exc_traceback = sys.exc_info()\\n lst = traceback.format_exception(exc_type, exc_value,\\n exc_traceback)\\n error_message = lst[-1]\\n thetime = strftime(\\\"%H:%M:%S\\\", localtime())\\n print '%s: Query %s' % (thetime, error_message)\\n return 'Bad query'\\n global gramsize\\n if 'gramsize' in kwargs.keys():\\n gramsize = kwargs['gramsize']\\n else:\\n gramsize = 2\\n dep_funct = tok_ngrams\\n\\n elif datatype == 'parse':\\n if 'n' not in search.keys() and 't' not in search.keys():\\n translated_option = 'y'\\n dependency = True\\n optiontext = 'Dependency querying...'\\n dep_funct = dep_searcher\\n if 'c' in show:\\n count_results = {}\\n only_count = True\\n\\n if 's' in search.keys():\\n translated_option = 'v'\\n #using_tregex = True\\n statsmode = True\\n optiontext = 'Getting general stats.'\\n dep_funct = get_stats\\n if datatype != 'parse':\\n print 'Need parsed corpus for this.'\\n return\\n\\n # initialise nltk lemmatiser only once\\n if lemmatise or ('l' in show and not dependency):\\n from nltk.stem.wordnet import WordNetLemmatizer\\n lmtzr=WordNetLemmatizer()\\n\\n if 'n' in search.keys():\\n if datatype == 'parse':\\n translated_option = 'n'\\n using_tregex = True\\n optiontext = 'n-grams only.'\\n n_gramming = True\\n if datatype == 'tokens':\\n translated_option = 'j'\\n using_tregex = False\\n \\n if type(query) == list:\\n query = as_regex(query, boundaries = 'word', case_sensitive = case_sensitive)\\n\\n if dependency:\\n if type(query) == list:\\n query = as_regex(query, boundaries = 'line', case_sensitive = case_sensitive)\\n #query = r'(?i)^(' + '|'.join(query) + r')$' \\n if query == 'any':\\n query = r'.*'\\n\\n # see if fast tregex can be done instead of temp file slow way\\n can_do_fast = False\\n if using_tregex:\\n if just_speakers is False:\\n if statsmode is False:\\n can_do_fast = True\\n\\n if plaintext is True:\\n try:\\n if tregex_engine(corpus = os.path.join(path, os.listdir(path)[-1]), check_for_trees = True, root = root):\\n if not root:\\n decision = raw_input('\\\\nIt appears that your corpus contains parse trees. If using a plaintext search option, your counts will likely be inaccurate.\\\\n\\\\nHit enter to continue, or type \\\"exit\\\" to start again: ')\\n if decision.startswith('e'):\\n return\\n else:\\n time = strftime(\\\"%H:%M:%S\\\", localtime())\\n print '%s: Corpus \\\"%s\\\" contains parse trees. Use \\\"Trees\\\" option.' % (time, os.path.basename(path))\\n root.update()\\n return False\\n except:\\n pass\\n \\n # if query is a special query, convert it:\\n if query == 'any':\\n if translated_option == 't' or translated_option == 'C':\\n query = r'/.?[A-Za-z0-9].?/ !< __'\\n if translated_option == 'u' or translated_option == 'o':\\n query = r'__ < (/.?[A-Za-z0-9].?/ !< __)'\\n if query == 'subjects':\\n query = r'__ >># @NP'\\n if query == 'processes':\\n query = r'/VB.?/ >># ( VP >+(VP) (VP !> VP $ NP))'\\n if query == 'modals':\\n query = r'MD < __'\\n if query == 'participants':\\n query = r'/(NN|PRP|JJ).?/ >># (/(NP|ADJP)/ $ VP | > VP)'\\n if query == 'entities':\\n query = r'NP <# NNP'\\n titlefilter = True\\n\\n # check that there's nothing in the quicksave path\\n if quicksave:\\n savedir = 'data/saved_interrogations'\\n if not quicksave.endswith('.p'):\\n quicksave = quicksave + '.p'\\n fullpath = os.path.join(savedir, quicksave)\\n if os.path.isfile(fullpath):\\n # if the file exists, check if the query is pretty much the same\\n from corpkit import load_result\\n loaded = load_result(quicksave)\\n if loaded.query['query'] == query and \\\\\\n loaded.query['path'] == path and \\\\\\n loaded.query['translated_option'] == translated_option and \\\\\\n loaded.query['lemmatise'] == lemmatise and \\\\\\n loaded.query['titlefilter'] == titlefilter and \\\\\\n loaded.query['spelling'] == spelling and \\\\\\n loaded.query['dep_type'] == dep_type and \\\\\\n loaded.query['function'] == 'interrogator':\\n dup_non_i = 'Duplicate'\\n else:\\n dup_non_i = 'Non-identical'\\n\\n while os.path.isfile(fullpath) and quicksave:\\n dict_for_print = ' ' + '\\\\n '.join(sorted(['%s: %s' % (k, v) for k, v in loaded.query.items()])) + '\\\\n'\\n time = strftime(\\\"%H:%M:%S\\\", localtime())\\n selection = raw_input('\\\\n%s: %s interrogation found in %s:\\\\n\\\\n%s\\\\n' \\\\\\n ' You have the following options:\\\\n\\\\n' \\\\\\n ' a) save with a new name\\\\n' \\\\\\n ' b) turn off \\\"quicksave\\\"\\\\n' \\\\\\n ' c) return the results from %s\\\\n' \\\\\\n ' d) delete %s\\\\n' \\\\\\n ' e) Quickview %s and then decide\\\\n' \\\\\\n ' f) exit\\\\n\\\\nYour selection: ' % (time, dup_non_i, fullpath, dict_for_print, fullpath, fullpath, fullpath))\\n if 'a' in selection:\\n sel = raw_input('\\\\nNew save name: ')\\n quicksave = sel\\n if not quicksave.endswith('.p'):\\n quicksave = quicksave + '.p'\\n fullpath = os.path.join(savedir, quicksave)\\n elif 'b' in selection:\\n quicksave = False\\n elif 'c' in selection:\\n return loaded\\n elif 'd' in selection:\\n os.remove(fullpath)\\n elif 'e' in selection:\\n print loaded.query\\n print '\\\\n'\\n try:\\n print loaded.results\\n except:\\n print loaded.totals\\n print '\\\\n'\\n elif 'f' in selection:\\n print ''\\n return\\n else:\\n as_str = str(selection)\\n print ' Choice \\\"%s\\\" not recognised.' % selection\\n\\n # titlefiltering only works with phrases, so turn it on\\n if titlefilter:\\n phrases = True\\n\\n def filtermaker(the_filter):\\n if type(the_filter) == list:\\n from other import as_regex\\n the_filter = as_regex(the_filter, case_sensitive = case_sensitive)\\n try:\\n output = re.compile(the_filter)\\n is_valid = True\\n except:\\n is_valid = False\\n if root:\\n import traceback\\n import sys\\n exc_type, exc_value, exc_traceback = sys.exc_info()\\n lst = traceback.format_exception(exc_type, exc_value,\\n exc_traceback)\\n error_message = lst[-1]\\n thetime = strftime(\\\"%H:%M:%S\\\", localtime())\\n print '%s: Filter %s' % (thetime, error_message)\\n return 'Bad query'\\n \\n while not is_valid:\\n if root:\\n time = strftime(\\\"%H:%M:%S\\\", localtime())\\n print the_filter\\n print '%s: Invalid the_filter regular expression.' % time\\n return False\\n time = strftime(\\\"%H:%M:%S\\\", localtime())\\n selection = raw_input('\\\\n%s: filter regular expression \\\" %s \\\" contains an error. You can either:\\\\n\\\\n' \\\\\\n ' a) rewrite it now\\\\n' \\\\\\n ' b) exit\\\\n\\\\nYour selection: ' % (time, the_filter))\\n if 'a' in selection:\\n the_filter = raw_input('\\\\nNew regular expression: ')\\n try:\\n output = re.compile(r'\\\\b' + the_filter + r'\\\\b')\\n is_valid = True\\n except re.error:\\n is_valid = False\\n elif 'b' in selection:\\n print ''\\n return False\\n return output\\n\\n # dependencies:\\n # can't be phrases\\n # check if regex valid\\n # check if dep_type valid\\n if dependency:\\n if translated_option == 'v':\\n names = get_speaker_names_from_xml_corpus(path)\\n \\n phrases = False\\n \\n allowed_dep_types = ['basic-dependencies', 'collapsed-dependencies', 'collapsed-ccprocessed-dependencies']\\n \\n # allow a b and c shorthand\\n if dep_type == 'a':\\n dep_type = allowed_dep_types[0]\\n if dep_type == 'b':\\n dep_type = allowed_dep_types[1]\\n if dep_type == 'c':\\n dep_type = allowed_dep_types[2]\\n\\n while dep_type not in allowed_dep_types:\\n time = strftime(\\\"%H:%M:%S\\\", localtime())\\n selection = raw_input('\\\\n%s: Dependency type \\\"%s\\\" not recognised. Must be one of:\\\\n\\\\n' \\\\\\n ' a) basic-dependencies' \\\\\\n ' b) collapsed-dependencies' \\\\\\n ' c) collapsed-ccprocessed-dependencies\\\\n\\\\nYour selection: ' % (time, dep_type))\\n if 'a' in selection:\\n dep_type = allowed_dep_types[0]\\n elif 'b' in selection:\\n dep_type = allowed_dep_types[1]\\n elif 'c' in selection:\\n dep_type = allowed_dep_types[2]\\n else:\\n pass\\n\\n # get list of subcorpora and sort them ... user input if no corpus found\\n got_corpus = False\\n while got_corpus is False:\\n try:\\n sorted_dirs = [d for d in os.listdir(path) if os.path.isdir(os.path.join(path,d))]\\n got_corpus = True\\n except OSError:\\n got_corpus = False\\n time = strftime(\\\"%H:%M:%S\\\", localtime())\\n selection = raw_input('\\\\n%s: Corpus directory not found: \\\" %s \\\". You can either:\\\\n\\\\n' \\\\\\n ' a) enter a new corpus path\\\\n' \\\\\\n ' b) exit\\\\n\\\\nYour selection: ' % (time, path))\\n if 'a' in selection:\\n path = raw_input('\\\\nNew corpus path: ')\\n elif 'b' in selection:\\n print ''\\n return\\n \\n # treat as one large corpus if no subdirs found\\n one_big_corpus = False\\n if len(sorted_dirs) == 0:\\n #warnings.warn('\\\\nNo subcorpora found in %s.\\\\nUsing %s as corpus dir.' % (path, path))\\n one_big_corpus = True\\n # fails if in wrong dir!\\n sorted_dirs = [os.path.basename(path)]\\n\\n # numerically sort subcorpora if the first can be an int\\n # could improve now with is_number, all\\n else:\\n try:\\n check = int(sorted_dirs[0])\\n sorted_dirs.sort(key=int)\\n except:\\n pass\\n\\n # if doing dependencies, make list of all files, and a progress bar\\n if dependency or plaintext or tokens or can_do_fast is False:\\n all_files = []\\n for d in sorted_dirs:\\n if not one_big_corpus:\\n subcorpus = os.path.join(path, d)\\n else:\\n subcorpus = path\\n if dependency:\\n files = [f for f in os.listdir(subcorpus) if f.endswith('.xml')]\\n else:\\n files = [f for f in os.listdir(subcorpus) if not f.startswith('.')]\\n \\n # skip files not containing speakers...\\n if just_speakers:\\n rem = []\\n for f in files:\\n fp = os.path.join(subcorpus, f)\\n data = open(fp, 'r').read()\\n if any('<speakername>' + name in data for name in just_speakers):\\n rem.append(f)\\n files = rem\\n\\n all_files.append([d, files])\\n total_files = len([item for sublist in all_files for item in sublist[1]])\\n sorted_dirs = all_files\\n c = 0\\n if not root:\\n tstr = False\\n if 'outname' in kwargs.keys():\\n if dependency or plaintext or tokens:\\n tstr = '%s: %d/%d' % (kwargs['outname'], 0, total_files)\\n else:\\n tstr = '%s: %d/%d' % (kwargs['outname'], 0, len(sorted_dirs))\\n if translated_option != 'v':\\n p = animator(None, None, init = True, tot_string = tstr, length = total_files, **par_args)\\n #p = TextProgressBar(total_files)\\n else:\\n p = animator(None, None, init = True, tot_string = tstr, length = total_files * 10, **par_args)\\n #p = TextProgressBar(total_files * 10)\\n \\n # if tregex, make progress bar for each dir\\n else:\\n if not root:\\n tstr = False\\n if 'outname' in kwargs.keys():\\n tstr = '%s: %d/%d' % (kwargs['outname'], 0, len(sorted_dirs))\\n p = animator(None, None, tot_string = tstr, init = True, length = len(sorted_dirs), **par_args)\\n\\n # loop through each subcorpus\\n subcorpus_names = []\\n\\n # check for valid query. so ugly.\\n if using_tregex:\\n if query:\\n if not n_gramming:\\n q = search.values()[0]\\n query = tregex_engine(corpus = False, query = q, options = ['-t'], check_query = True, root = root)\\n if query is False:\\n if root:\\n return 'Bad query'\\n else:\\n return\\n \\n else:\\n if dependency or translated_option == 'r' or translated_option == 'h':\\n is_valid = True\\n try:\\n if translated_option == 'r':\\n if type(query) == str:\\n if query.startswith(r'\\\\b'):\\n query = query[2:]\\n if query.endswith(r'\\\\b'):\\n query = query[:-2]\\n if case_sensitive:\\n regex = re.compile(r'\\\\b' + query + r'\\\\b')\\n else:\\n regex = re.compile(r'\\\\b' + query + r'\\\\b', re.IGNORECASE)\\n else:\\n regex = query\\n else:\\n if case_sensitive:\\n regex = re.compile(query)\\n else:\\n regex = re.compile(query, re.IGNORECASE)\\n is_valid = True\\n except re.error:\\n is_valid = False\\n if root:\\n import traceback\\n import sys\\n exc_type, exc_value, exc_traceback = sys.exc_info()\\n lst = traceback.format_exception(exc_type, exc_value,\\n exc_traceback)\\n error_message = lst[-1]\\n thetime = strftime(\\\"%H:%M:%S\\\", localtime())\\n print '%s: Query %s' % (thetime, error_message)\\n return \\\"Bad query\\\"\\n while not is_valid:\\n time = strftime(\\\"%H:%M:%S\\\", localtime())\\n if root:\\n time = strftime(\\\"%H:%M:%S\\\", localtime())\\n print '%s: Regular expression in query contains an error.' % time\\n return 'Bad query'\\n selection = raw_input('\\\\n%s: Regular expression \\\" %s \\\" contains an error. You can either:\\\\n\\\\n' \\\\\\n ' a) rewrite it now\\\\n' \\\\\\n ' b) exit\\\\n\\\\nYour selection: ' % (time, query))\\n if 'a' in selection:\\n query = raw_input('\\\\nNew regular expression: ')\\n try:\\n if case_sensitive:\\n regex = re.compile(r'\\\\b' + query + r'\\\\b')\\n else:\\n regex = re.compile(r'\\\\b' + query + r'\\\\b', re.IGNORECASE)\\n is_valid = True\\n except re.error:\\n is_valid = False\\n elif 'b' in selection:\\n print ''\\n return\\n\\n #print list nicely\\n if type(query) == list:\\n qtext = ', '.join(query)\\n elif type(query) == str or type(query) == unicode:\\n qtext = query\\n else:\\n qtext = 'regex'\\n\\n global skipped_sents\\n skipped_sents = 0\\n\\n # begin interrogation\\n time = strftime(\\\"%H:%M:%S\\\", localtime())\\n if printstatus:\\n print (\\\"\\\\n%s: Beginning corpus interrogation: %s\\\" \\\\\\n \\\"\\\\n Query: '%s'\\\\n %s\\\" \\\\\\n \\\"\\\\n Interrogating corpus ... \\\\n\\\" % (time, os.path.basename(path), qtext, optiontext) )\\n if root:\\n print '%s: Interrogating corpus ...' % time\\n if root and tk:\\n root.update()\\n\\n global numdone\\n numdone = 0\\n\\n for index, d in enumerate(sorted_dirs):\\n if using_tregex or n_gramming:\\n if can_do_fast or n_gramming:\\n subcorpus_name = d\\n subcorpus_names.append(subcorpus_name)\\n if not root:\\n if paralleling is not False:\\n tstr = '%s: %d/%d' % (kwargs['outname'], index + 1, len(sorted_dirs))\\n else:\\n tstr = False\\n animator(p, index, tstr, **par_args)\\n #animator(p, index, **par_args)\\n #p.animate(index)\\n if root and tk:\\n time = strftime(\\\"%H:%M:%S\\\", localtime())\\n if not one_big_corpus:\\n print '%s: Interrogating subcorpus: %s' % (time, subcorpus_name)\\n else:\\n print '%s: Interrogating corpus ... ' % time\\n root.update()\\n if 'note' in kwargs.keys() and kwargs['note'] is not False:\\n kwargs['note'].progvar.set(((index + 1) * 100.0 / len(sorted_dirs) / denom) + startnum)\\n # get path to corpus/subcorpus\\n if len(sorted_dirs) == 1:\\n subcorpus = path\\n else:\\n subcorpus = os.path.join(path,subcorpus_name)\\n \\n if n_gramming:\\n result = []\\n if 'split_contractions' in kwargs.keys():\\n if kwargs['split_contractions'] is True:\\n split_con = True\\n elif kwargs['split_contractions'] is False:\\n split_con = False\\n from corpkit.keys import ngrams\\n if 'blacklist' in kwargs.keys():\\n the_blacklist = kwargs['blacklist']\\n else:\\n the_blacklist = False\\n if 'gramsize' in kwargs.keys():\\n gramsz = kwargs['gramsize']\\n else:\\n gramsz = 2\\n\\n spindle_out = ngrams(subcorpus, reference_corpus = False, \\n blacklist = the_blacklist,\\n printstatus = False, \\n clear = False, \\n lemmatise = lemmatise, \\n split_contractions = split_con, \\n whitelist = query,\\n gramsize = gramsz\\n )\\n for w in list(spindle_out.index):\\n if query != 'any':\\n if re.search(query, w):\\n for _ in range(spindle_out[w]):\\n result.append(w)\\n else:\\n for _ in range(spindle_out[w]):\\n result.append(w)\\n\\n #if tregex, search\\n else:\\n if not statsmode:\\n op = ['-o', '-' + translated_option]\\n q = search.values()[0]\\n result = tregex_engine(query = q, options = op, \\n corpus = subcorpus, root = root)\\n if result is False:\\n return\\n \\n # if just counting matches, just \\n # add subcorpus name and count...\\n if only_count:\\n count_results[d] = result\\n continue\\n\\n # for dependencies, d[0] is the subcorpus name \\n # and d[1] is its file list ... \\n\\n elif dependency or plaintext or tokens or statsmode or can_do_fast is False:\\n #if not root:\\n #p.animate(-1, str(0) + '/' + str(total_files))\\n from collections import Counter\\n statsmode_results = Counter({'Sentences': 0, 'Passives': 0, 'Tokens': 0})\\n subcorpus_name = d[0]\\n subcorpus_names.append(subcorpus_name)\\n fileset = d[1]\\n #for f in read_files:\\n result = []\\n for f in fileset:\\n result_from_file = None\\n # pass the x/y argument for more updates \\n if not root and translated_option != 'v':\\n tot_string = str(c + 1) + '/' + str(total_files)\\n if 'outname' in kwargs.keys():\\n tot_string = '%s: %s' % (kwargs['outname'], tot_string)\\n animator(p, c, tot_string, **par_args)\\n #p.animate((c), tot_string)\\n if root and tk and not statsmode:\\n root.update()\\n if 'note' in kwargs.keys() and kwargs['note'] is not False:\\n kwargs['note'].progvar.set((((c + 1) * 100.0 / total_files) / denom) + startnum)\\n time = strftime(\\\"%H:%M:%S\\\", localtime())\\n if not one_big_corpus:\\n print '%s: Interrogating subcorpus: %s' % (time, subcorpus_name)\\n else:\\n print '%s: Interrogating corpus ...' % (time)\\n c += 1\\n if one_big_corpus:\\n filepath = os.path.join(path, f)\\n else:\\n filepath = os.path.join(path, subcorpus_name, f)\\n if dependency or can_do_fast is False:\\n if not plaintext and not tokens:\\n with open(filepath, \\\"rb\\\") as text:\\n data = text.read()\\n from corenlp_xml.document import Document\\n try:\\n corenlp_xml = Document(data)\\n except:\\n print 'Could not read file: %s' % filepath\\n continue\\n #corenlp_xml = Beautifulcorenlp_xml(data, parse_only=justsents) \\n if just_speakers: \\n sents = [s for s in corenlp_xml.sentences if s.speakername in just_speakers]\\n if not sents:\\n continue\\n #sents = [s for s in corenlp_xml.find_all('sentence') \\\\\\n #if s.speakername.text.strip() in just_speakers]\\n else:\\n sents = corenlp_xml.sentences\\n # run whichever function has been called\\n if translated_option == 'y':\\n result_from_file = dep_searcher(sents)\\n else:\\n result_from_file = dep_funct(sents)\\n if only_count:\\n count_results[subcorpus_name] = result_from_file\\n\\n # memory problems\\n corenlp_xml = None\\n data = None\\n gc.collect()\\n\\n if plaintext:\\n with open(filepath, \\\"rb\\\") as text:\\n data = text.read()\\n if translated_option == 'r':\\n result_from_file = plaintext_regex_search(regex, data)\\n if translated_option == 's':\\n result_from_file = plaintext_simple_search(query, data)\\n if tokens:\\n import pickle\\n data = pickle.load(open(filepath, \\\"rb\\\"))\\n #print data\\n if translated_option == 'h':\\n result_from_file = tok_by_reg(regex, data)\\n if translated_option == 'e':\\n result_from_file = tok_by_list(query, data)\\n if translated_option == 'j':\\n split_con = False\\n if 'split_contractions' in kwargs.keys():\\n if kwargs['split_contractions'] is True:\\n split_con = True\\n result_from_file = tok_ngrams(query, data, split_contractions = split_con)\\n \\n if result_from_file:\\n if not statsmode and not only_count:\\n for entry in result_from_file:\\n result.append(entry)\\n\\n if not statsmode and 'c' not in show:\\n result.sort()\\n\\n # lowercaseing, encoding, lemmatisation, \\n # titlewords removal, usa_english, etc.\\n if not statsmode:\\n processed_result = processwords(result, lemmatag = lemmatag)\\n \\n if not statsmode:\\n allwords_list.append(processed_result)\\n dicts.append(Counter(processed_result))\\n if statsmode:\\n dicts.append(statsmode_results)\\n allwords_list.append([w for w in statsmode_results.keys()])\\n\\n if not plaintext:\\n if not root:\\n if paralleling is not False:\\n if dependency or plaintext or tokens or can_do_fast is False:\\n tstr = '%s: %d/%d' % (kwargs['outname'], total_files, total_files)\\n else:\\n tstr = '%s: %d/%d' % (kwargs['outname'], len(sorted_dirs), len(sorted_dirs))\\n\\n else:\\n tstr = False\\n animator(p, len(sorted_dirs), tot_string = tstr, **par_args)\\n\\n #p.animate(len(sorted_dirs))\\n if 'note' in kwargs.keys() and kwargs['note'] is not False:\\n kwargs['note'].progvar.set((100 / denom + startnum))\\n if root and tk:\\n root.update()\\n\\n else:\\n # weird float div by 0 zero error here for plaintext\\n try:\\n if not root:\\n if translated_option != 'v':\\n if paralleling is not False:\\n animator(p, total_files, kwargs['outname'], **par_args)\\n else:\\n animator(p, total_files, **par_args)\\n #p.animate(total_files)\\n\\n else:\\n if paralleling is not False:\\n animator(p, total_files * 10, kwargs['outname'], **par_args)\\n else:\\n animator(p, total_files * 10, **par_args)\\n #p.animate(total_files * 10)\\n if 'note' in kwargs.keys() and kwargs['note'] is not False:\\n kwargs['note'].progvar.set((100 / denom + startnum))\\n except:\\n pass\\n\\n if root and tk:\\n root.update()\\n\\n if not have_ipython and not root and not tk:\\n print '\\\\n'\\n \\n # if only counting, get total total and finish up:\\n if only_count:\\n stotals = pd.Series(count_results)\\n stotals.name = 'Total' \\n outputnames = collections.namedtuple('interrogation', ['query', 'totals'])\\n the_time_ended = strftime(\\\"%Y-%m-%d %H:%M:%S\\\")\\n # add option to named tuple\\n the_options = {'path': path,\\n 'search': search,\\n 'show': show,\\n 'function': 'interrogator',\\n 'datatype': stotals.dtype,\\n 'query': query,\\n 'exclude': exclude,\\n 'lemmatise': lemmatise,\\n 'titlefilter': titlefilter,\\n 'lemmatag': lemmatag,\\n 'spelling': spelling,\\n 'phrases': phrases,\\n 'dep_type': dep_type,\\n 'quicksave': quicksave,\\n 'time_started': the_time_started,\\n 'time_ended': the_time_ended}\\n\\n try:\\n the_options['translated_option'] = translated_option\\n except:\\n the_options['translated_options'] = translated_options\\n\\n output = outputnames(the_options, stotals)\\n if 'outname' in kwargs:\\n stotals.name = kwargs['outname']\\n return stotals\\n if have_ipython:\\n clear_output()\\n if quicksave:\\n if stotals.sum() > 0:\\n from other import save_result\\n save_result(output, quicksave)\\n \\n if printstatus:\\n time = strftime(\\\"%H:%M:%S\\\", localtime())\\n print '%s: Interrogation finished! %d total occurrences.' % (time, stotals.sum())\\n if not tk:\\n print ''\\n\\n return output\\n\\n # flatten and sort master list, in order to make a list of unique words\\n allwords = [item for sublist in allwords_list for item in sublist]\\n allwords.sort()\\n unique_words = set(allwords)\\n\\n #make master reference_corpus\\n the_big_dict = {}\\n\\n # calculate results\\n # for every unique entry, find out how many times it appears per subcorpus\\n for word in unique_words:\\n the_big_dict[word] = [each_dict[word] for each_dict in dicts]\\n \\n # turn master dict into dataframe, sorted\\n df = DataFrame(the_big_dict, index = subcorpus_names)\\n\\n if one_big_corpus:\\n df = df.T.sort(list(df.T.columns)[0], ascending = False).T\\n\\n try:\\n if not one_big_corpus:\\n df.ix['Total'] = df.sum()\\n tot = df.ix['Total']\\n df = df[tot.argsort()[::-1]]\\n df = df.drop('Total', axis = 0)\\n except:\\n pass\\n\\n # make totals branch\\n stotals = df.sum(axis = 1)\\n stotals.name = 'Total'\\n\\n # make result into series if only one subcorpus\\n if one_big_corpus and not df1_always_df:\\n try:\\n df = df.ix[subcorpus_names[0]]\\n except:\\n pass\\n df.sort(ascending = False)\\n\\n # if numerical colnames, sort numerically\\n if show == ['r'] or show == ['i']:\\n intcols = sorted([int(c) for c in list(df.columns)])\\n df.columns = [str(c) for c in intcols]\\n\\n # add sort info for tk\\n if tk:\\n df = df.T\\n df['tkintertable-order'] = pd.Series([index for index, data in enumerate(list(df.index))], index = list(df.index))\\n df = df.T\\n \\n # print skipped sent information for distance_mode\\n if printstatus and 'r' in show and skipped_sents > 0:\\n print '\\\\n %d sentences over 99 words skipped.\\\\n' % skipped_sents\\n \\n #make results into named tuple\\n # add option to named tuple\\n the_time_ended = strftime(\\\"%Y-%m-%d %H:%M:%S\\\")\\n the_options = {'path': path,\\n 'search': search,\\n 'show': show,\\n 'datatype': df.iloc[0].dtype,\\n 'query': query,\\n 'lemmatise': lemmatise,\\n 'titlefilter': titlefilter,\\n 'lemmatag': lemmatag,\\n 'function': 'interrogator',\\n 'spelling': spelling,\\n 'exclude': exclude,\\n 'phrases': phrases,\\n 'dep_type': dep_type,\\n 'quicksave': quicksave,\\n 'time_started': the_time_started,\\n 'time_ended': the_time_ended}\\n\\n try:\\n the_options['translated_option'] = translated_option\\n except:\\n the_options['translated_options'] = translated_options\\n\\n outputnames = collections.namedtuple('interrogation', ['query', 'results', 'totals'])\\n output = outputnames(the_options, df, stotals)\\n\\n if type(paralleling) == int:\\n return (kwargs['outname'], df, stotals)\\n \\n if have_ipython:\\n clear_output()\\n\\n # warnings if nothing generated...\\n if not one_big_corpus and not df1_always_df:\\n num_diff_results = len(list(df.columns))\\n elif df1_always_df and not one_big_corpus:\\n num_diff_results = len(list(df.columns))\\n elif not df1_always_df and one_big_corpus:\\n num_diff_results = len(list(df.index))\\n elif df1_always_df and one_big_corpus:\\n num_diff_results = len(list(df.columns))\\n\\n if num_diff_results == 0:\\n if not root:\\n print ''\\n warnings.warn('No results produced. Maybe your query needs work.')\\n else:\\n time = strftime(\\\"%H:%M:%S\\\", localtime())\\n print '%s: Interrogation produced no results, sorry.' % time\\n return False\\n\\n if stotals.sum() == 0:\\n if not root:\\n print ''\\n warnings.warn('No totals produced. Maybe your query needs work.')\\n else:\\n time = strftime(\\\"%H:%M:%S\\\", localtime())\\n print '%s: Interrogation produced no results, sorry.' % time\\n return False\\n\\n time = strftime(\\\"%H:%M:%S\\\", localtime())\\n if printstatus:\\n print '%s: Interrogation finished! %d unique results, %d total.' % (time, num_diff_results, stotals.sum())\\n if not tk:\\n print ''\\n\\n if quicksave:\\n if stotals.sum() > 0 and num_diff_results > 0:\\n from other import save_result\\n save_result(output, quicksave)\\n\\n return output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d6c25ddac5290f858c0f4d0bae72b436\",\n \"score\": \"0.4681151\",\n \"text\": \"def find_corpora_visualizations():\\n\\n # build a quick map of visualizations, so that we can reference by annis label.\\n vis_map = dict()\\n for vis in HtmlVisualizationFormat.objects.all():\\n vis_map[vis.slug] = vis\\n\\n for corpus in Corpus.objects.all():\\n # get the list of all the visualizations already loaded for this corpus.\\n already_have = set()\\n for one_fmt in corpus.html_visualization_formats.all():\\n already_have.add(one_fmt.slug)\\n\\n url_fmt = \\\"https://corpling.uis.georgetown.edu/annis-service/annis/query/resolver/{0}/NULL/node\\\"\\n url_to_fetch = url_fmt.format(corpus.annis_corpus_name)\\n res = request.urlopen(url_to_fetch)\\n root = ET.fromstring(res.read())\\n xpath = \\\"./resolverEntry[visType='htmldoc']/mappings/entry/value\\\"\\n added = False\\n for one_node in root.findall(xpath):\\n vis_slug = one_node.text\\n if vis_slug not in already_have:\\n corpus.html_visualization_formats.add(vis_map[vis_slug])\\n added = True\\n\\n # If we added any visualizations, save them now\\n if added:\\n corpus.save()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9b8dbc964c6dab7f81072eb1f1e3fb7\",\n \"score\": \"0.46805918\",\n \"text\": \"def analyze_entities(text_content):\\n # Set connection to client as variable\\n client = language_v1.LanguageServiceClient()\\n\\n # Set type_ to read PLAIN_TEXT\\n type_ = language_v1.Document.Type.PLAIN_TEXT\\n\\n # specify language & set document variable\\n '''\\n https://cloud.google.com/natural-language/docs/languages\\n if language is not set, it will be detected.\\n '''\\n lang = \\\"en\\\"\\n document = {\\\"content\\\": text_content, \\\"type_\\\": type_, \\\"language\\\": lang}\\n\\n # Set Encoding type to UTF8\\n encoding_type = language_v1.EncodingType.UTF8\\n\\n # Pass in client request with defined specifications\\n response = client.analyze_entities(request = {'document': document, 'encoding_type': encoding_type})\\n\\n # Loop through entitites returned from the API\\n for entity in response.entities:\\n print(u\\\"Representative name for the entity: {}\\\".format(entity.name))\\n\\n # Get entity type (PERSON, LOCATION, ADDRESS, NUMBER, etc)\\n print(u\\\"Entity type: {}\\\".format(language_v1.Entity.Type(entity.type_).name))\\n\\n # Get salience score in [0, 1.0] range\\n print(u\\\"Salience score: {}\\\".format(entity.salience))\\n\\n \\n # Loop over the metadata associated with entity. \\n '''\\n Many known entities have a wiki (wikipedia_url) and Knowledge Graph MID (mid)/\\n (Knowledge Graph is used by google to show widgets of condensed info from multiple sources)\\n\\n Some entity types may also have additional metadata\\n e.g. ADDRESS entities may have metadata for the address street_name, postal_code, etc\\n ''' \\n for metadata_name, metadata_value in entity.metadata.items():\\n print(u\\\"{}: {}\\\".format(metadata_name, metadata_value))\\n \\n # Loop over the mentions of entity from input document.\\n '''\\n API also supports proper noun mentions.\\n # of appearances in given text will affect an entity's salience score.\\n '''\\n for mention in entity.mentions:\\n print(u\\\"Mention text: {}\\\".format(mention.text.content))\\n\\n # Get mention type, e.g. PROPER for proper noun\\n print(\\n u\\\"Mention type: {}\\\".format(language_v1.EntityMention.Type(mention.type_).name)\\n )\\n\\n # outputs the language, useful if language needs to be automatically detected.\\n print(u\\\"Language of the text: {}\\\".format(response.language))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc98f23142f5966cda19dd9b4b5c74ca\",\n \"score\": \"0.46783927\",\n \"text\": \"def showcase():\\n\\tfrom PIL import Image\\n\\tfrom PIL import ImageFont\\n\\tfrom PIL import ImageDraw\\n\\n\\t# Optional: Varied loading process for showcases, when not done at the end of training\\n\\t# directory = \\\"results/dirname\\\"\\n\\t# checkpoint_path = directory + \\\"/50000.pth\\\"\\n\\t# checkpoint = torch.load(checkpoint_path)\\n\\t# epoch = checkpoint['epoch']\\n\\t\\\"\\\"\\\"\\n\\tfrom collections import OrderedDict\\n\\tnew_state_dict = OrderedDict()\\n\\tfor k, v in checkpoint['state_dict'].items():\\n\\t\\t# name = k[7:] # remove `module.`\\n\\t\\tname = k.replace(\\\".module\\\", \\\"\\\") # removing ‘.moldule’ from key\\n\\t\\tnew_state_dict[name] = v\\n\\t# load params\\n\\tmodel.load_state_dict(new_state_dict)\\n\\n\\toptimizer.load_state_dict(checkpoint['optimizer'])\\n\\tprint(\\\"Loaded checkpoint '{}' (epoch {})\\\".format(checkpoint_path, checkpoint['epoch']))\\n\\t\\\"\\\"\\\"\\n\\tos.makedirs(directory + \\\"/showcase\\\", exist_ok=True)\\n\\n\\tglobal decoder_nat_loss, decoder_syn_loss, KLD_syn_loss, KLD_nat_loss, regressor_nat, regressor_syn\\n\\n\\tactual_showcase(False, False)\\n\\treset_loss_sums()\\n\\tactual_showcase(True, False)\\n\\treset_loss_sums()\\n\\tactual_showcase(False, True)\\n\\treset_loss_sums()\\n\\tactual_showcase(True, True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8fc840b87112fb50c87543c7bc75ec0\",\n \"score\": \"0.4677465\",\n \"text\": \"def read_document(self):\\n words = self.word_runner()\\n word = \\\"press space to start\\\"\\n orp_ind = 13\\n try:\\n while True:\\n time.sleep(60 / self.wpm)\\n\\n if self.is_reading:\\n word = next(words)\\n orp_ind = int(self.orp_index(word))\\n\\n yield (word, orp_ind)\\n except StopIteration:\\n pass\\n finally:\\n del words\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e71b5860b51c1b20ff775ff4753becb\",\n \"score\": \"0.46771982\",\n \"text\": \"def deep_dream_of_extreme_control(FLAGS,model,input_images=[],num_iterations=10,step_size=0.1):\\n if len(input_images) == 0:\\n # use predefined images\\n img_dir='/esat/opal/kkelchte/docker_home/pilot_data/visualization_images'\\n input_images=sorted([img_dir+'/'+f for f in os.listdir(img_dir)])\\n\\n print(\\\"[tools.py]: extracting deep dream maps of {0} in {1}\\\".format([os.path.basename(i) for i in input_images], os.path.dirname(input_images[0])))\\n \\n # experts=np.asarray([[k]*(FLAGS.action_quantity if FLAGS.discrete else 1) for v in sorted(model.factor_offsets.values()) for k in model.factor_offsets.keys() if model.factor_offsets[k]==v]).flatten()\\n\\n inputs = load_images(input_images, model.input_size[1:])\\n \\n # collect gradients for output endpoint of evaluation model\\n grads={}\\n with tf.device('/cpu:0'):\\n output_tensor = model.endpoints['eval']['outputs']\\n for i in range(output_tensor.shape[1].value):\\n layer_loss = output_tensor[:,i]\\n gradients = tf.gradients(layer_loss, model.inputs)[0]\\n gradients /= (tf.sqrt(tf.reduce_mean(tf.square(gradients))) + 1e-5)\\n grads[output_tensor.name+'_'+str(i)]=gradients\\n\\n\\n # apply gradient ascent for all outputs and each input image\\n # if number of outputs ==1 apply gradient descent for contrast\\n if len(grads.keys())== 1:\\n opposite_results={}\\n else:\\n opposite_results=None\\n\\n import copy\\n results = {}\\n for gk in grads.keys(): \\n results[gk]=copy.deepcopy(inputs)\\n if isinstance(opposite_results,dict): opposite_results[gk]=copy.deepcopy(inputs)\\n\\n for step in range(num_iterations):\\n if step%10==0: print \\\"{0} step: {1}\\\".format(time.ctime(), step)\\n for i,gk in enumerate(sorted(grads.keys())):\\n results[gk] += step_size * model.sess.run(grads[gk], {model.inputs: results[gk]})\\n if isinstance(opposite_results,dict):\\n opposite_results[gk] -= step_size * model.sess.run(grads[gk], {model.inputs: opposite_results[gk]})\\n\\n # Normalize results within 0:1 range\\n clean_results={}\\n for gk in results.keys():\\n clean_results[gk]=[]\\n for i in range(results[gk].shape[0]):\\n clean_results[gk].append(deprocess_image(results[gk][i], one_channel=True))\\n # results[gk][i]=deprocess_image(results[gk][i], one_channel=True)\\n if isinstance(opposite_results,dict):\\n opposite_results[gk][i]=deprocess_image(opposite_results[gk][i])\\n\\n # combine adjust input images in one overview image\\n # one column for each input image\\n # one row with each extreme control for separate and difference images\\n num_rows=1+len(results.keys())\\n fig, axes = plt.subplots(num_rows ,min(len(input_images),5),figsize=(23, 4*(len(grads.keys())+1)))\\n # fig, axes = plt.subplots(num_rows ,min(len(input_images),5),figsize=(23, 4*(len(grads.keys())+1)))\\n # add original images in first row\\n for i in range(axes.shape[1]):\\n axes[0, i].set_title(os.path.basename(input_images[i]).split('.')[0])\\n axes[0, i].imshow(matplotlibprove(inputs[i]), cmap='inferno')\\n axes[0, i].axis('off')\\n\\n # add for each filter the modified input\\n row_index=1\\n for gk in sorted(results.keys()):\\n for i in range(axes.shape[1]):\\n # print gk\\n # axes[row_index, i].set_title('Grad Asc: '+gk.split('/')[1]+'/'+gk[-1]) \\n axes[row_index, i].set_title('Grad Asc: '+gk)\\n # axes[row_index, i].set_title(experts[row_index-1])\\n\\n axes[row_index, i].imshow(np.concatenate((inputs[i],np.expand_dims(clean_results[gk][i],axis=2)), axis=2), cmap='inferno')\\n # axes[row_index, i].imshow(matplotlibprove(results[gk][i]), cmap='inferno')\\n axes[row_index, i].axis('off')\\n row_index+=1\\n # In cas of continouos controls: visualize the gradient descent and difference\\n # if isinstance(opposite_results,dict):\\n # for gk in opposite_results.keys():\\n # for i in range(axes.shape[1]):\\n # # axes[row_index, i].set_title('Grad Desc: '+gk.split('/')[1]) \\n # axes[row_index, i].set_title('Grad Desc: '+gk) \\n # axes[row_index, i].imshow(matplotlibprove(opposite_results[gk][i]), cmap='inferno')\\n # axes[row_index, i].axis('off')\\n # row_index+=1\\n \\n # # add difference\\n # for gk in opposite_results.keys():\\n # for i in range(axes.shape[1]):\\n # # axes[row_index, i].set_title('Diff: '+gk.split('/')[1]) \\n # axes[row_index, i].set_title('Diff: '+gk) \\n # axes[row_index, i].imshow(matplotlibprove(deprocess_image((opposite_results[gk][i]-results[gk][i])**2)), cmap='inferno')\\n # axes[row_index, i].axis('off')\\n # row_index+=1\\n # else:\\n # # add difference between 2 exteme actions\\n # gk_left=sorted(results.keys())[0]\\n # gk_right=sorted(results.keys())[-1]\\n # for i in range(axes.shape[1]):\\n # # axes[row_index, i].set_title('Diff : '+gk.split('/')[1]) \\n # axes[row_index, i].set_title('Diff : '+gk) \\n # axes[row_index, i].imshow(matplotlibprove(deprocess_image((results[gk_left][i]-results[gk_right][i])**2)), cmap='inferno')\\n # axes[row_index, i].axis('off')\\n # row_index+=1\\n \\n \\n plt.savefig(FLAGS.summary_dir+FLAGS.log_tag+'/control_dream_maps.jpg',bbox_inches='tight')\\n # plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1211ae4f48a93630006ec87da1038d6\",\n \"score\": \"0.4672397\",\n \"text\": \"def wiki_analyzer(language: str) -> None:\\r\\n\\trunning = True\\r\\n\\tdbase = database.WikiDatabase(f'{language}wikidb')\\r\\n\\r\\n\\tsource_buffer = []\\r\\n\\ttarget_buffer = []\\r\\n\\tpaths_buffer = []\\r\\n\\r\\n\\tlast_time = time.time()\\r\\n\\tpaths_added = 0\\r\\n\\r\\n\\ttry:\\r\\n\\t\\twhile running:\\r\\n\\t\\t\\r\\n\\t\\t\\tsources, targets, all_paths = analyze_path(dbase)\\r\\n\\t\\t\\tsource_buffer.extend(sources)\\r\\n\\t\\t\\ttarget_buffer.extend(targets)\\r\\n\\t\\t\\tpaths_buffer.extend(all_paths)\\r\\n\\t\\t\\tpaths_added += 2\\r\\n\\r\\n\\t\\t\\tif len(source_buffer) >= BUFFER_SIZE:\\r\\n\\t\\t\\t\\tdbase.dump_statistics(source_buffer, target_buffer, paths_buffer)\\r\\n\\t\\t\\t\\tsource_buffer = []\\r\\n\\t\\t\\t\\ttarget_buffer = []\\r\\n\\t\\t\\t\\tpaths_buffer = []\\r\\n\\r\\n\\t\\t\\tif select.select([sys.stdin], [], [], 0.0)[0]:\\r\\n\\t\\t\\t\\tusr_input = input()\\r\\n\\t\\t\\t\\tif usr_input.lower() == 'q' or usr_input.lower() == 'quit':\\r\\n\\t\\t\\t\\t\\trunning = False\\r\\n\\t\\t\\t\\t\\tif source_buffer:\\r\\n\\t\\t\\t\\t\\t\\tdbase.dump_statistics(source_buffer, target_buffer, paths_buffer)\\r\\n\\r\\n\\t\\t\\tif time.time() - last_time > 15:\\r\\n\\t\\t\\t\\td_t = time.time() - last_time\\r\\n\\t\\t\\t\\tpaths_per_min = round(paths_added/d_t * 60)\\r\\n\\t\\t\\t\\tprint(f\\\"\\\\r{paths_per_min} paths / min \\\", end='')\\r\\n\\t\\t\\t\\tpaths_added = 0\\r\\n\\t\\t\\t\\tlast_time = time.time()\\r\\n\\tfinally:\\r\\n\\t\\tif running: \\r\\n\\t\\t\\tdbase.dump_statistics(source_buffer, target_buffer, paths_buffer)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d6e331c27062a0f9d40ad67ab7987c2\",\n \"score\": \"0.4666665\",\n \"text\": \"def run_demo():\\n while True:\\n embeddings = beer_emb.embed_doc(input(\\\"Test if words are beer-related: \\\"),\\n word_filter=False)\\n for word_vec in embeddings:\\n print(is_beer_related(word_vec))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f434f6961f529bac8a8952d09c4673b0\",\n \"score\": \"0.46660876\",\n \"text\": \"def Run_Extraction(self):\\n\\n # print the summary of the model\\n print(self.ww_model.model.summary(), end=\\\"\\\\n\\\\n\\\", flush=True)\\n # open an audio data stream\\n self.stream = self.p.open(format=self.format, channels=self.channels,\\n rate=self.rate, input=True,\\n frames_per_buffer=self.chunk)\\n\\n act_count = 0\\n\\n while True:\\n\\n # reads chunk of audio\\n data = self.stream.read(self.chunk)\\n\\n # appends chunk to frame list\\n self.frames.append(data)\\n\\n # begins making predictions after the first\\n # 2.5 seconds of audio is read\\n if (len(self.frames) > 19):\\n\\n prediction = self.Prediction()\\n\\n # if the predictions is larger than the defined confidence\\n if (prediction > self.confidence):\\n\\n # increment the activation counter\\n act_count += 1\\n\\n # if the number of consecutive activations\\n # exceeds the activation value\\n if(act_count >= self.activations):\\n\\n # print out \\\"nimbus\\\"\\n print(\\\" << nimbus >> \\\", end=\\\" \\\", flush=True)\\n\\n # reset activation count\\n act_count = 0\\n\\n self.False_Activation()\\n\\n self.frames = self.frames[18:]\\n\\n if (self.false_counts >= self.false_count):\\n self.Retrain_Model()\\n\\n # if prediction falls below the confidence level\\n else:\\n\\n # reset the activation count\\n act_count = 0\\n\\n if not(self.print_pred):\\n # output nothing to the stream\\n print(\\\"-\\\", end=\\\"\\\", flush=True)\\n\\n # window the data stream\\n self.frames = self.frames[1:]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb03b00deff42713f9827aa01b7eb32e\",\n \"score\": \"0.46658754\",\n \"text\": \"def main(args):\\n corpus = read_corpus(args, verbose=True)\\n for k in sorted(corpus, key=educe.stac.id_to_path):\\n doc = corpus[k]\\n print(\\\"========== %s ============\\\" % k)\\n print()\\n if args.edges:\\n dialogues = sorted_first_widest(filter(is_dialogue, doc.units))\\n if dialogues:\\n d_first = dialogues[0]\\n print(annotate_doc(doc, span=d_first.text_span()))\\n if len(dialogues) > 1:\\n d_last = dialogues[-1]\\n txt = annotate_doc(doc, span=d_last.text_span())\\n print(\\\"...\\\\n\\\")\\n print(txt.encode('utf-8'))\\n else:\\n print(annotate_doc(doc).encode('utf-8'))\\n print()\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":683,"cells":{"query_id":{"kind":"string","value":"8e011d0b414fc36e0eedeb9448e99c0d"},"query":{"kind":"string","value":"returns a dict with some joint info"},"positive_passages":{"kind":"list like","value":[{"docid":"8972d83bad2dffd5c842b188e5eca22d","score":"0.7336723","text":"def _get_joint_info(self, body_id, joint_id):\n # todo: make joint_info a class so we don't have to memorise the keys\n info = self._p.getJointInfo(body_id, joint_id)\n joint_info = {\n 'id': info[0],\n 'link_name': info[12].decode(\"utf-8\"),\n 'joint_name': info[1].decode(\"utf-8\"),\n 'type': self.JOINT_TYPES[info[2]],\n 'friction': info[7],\n 'lower_limit': info[8],\n 'upper limit': info[9],\n 'max_force': info[10],\n 'max_velocity': info[11],\n 'joint_axis': info[13],\n 'parent_pos': info[14],\n 'parent_orn': info[15]\n }\n return joint_info","title":""}],"string":"[\n {\n \"docid\": \"8972d83bad2dffd5c842b188e5eca22d\",\n \"score\": \"0.7336723\",\n \"text\": \"def _get_joint_info(self, body_id, joint_id):\\n # todo: make joint_info a class so we don't have to memorise the keys\\n info = self._p.getJointInfo(body_id, joint_id)\\n joint_info = {\\n 'id': info[0],\\n 'link_name': info[12].decode(\\\"utf-8\\\"),\\n 'joint_name': info[1].decode(\\\"utf-8\\\"),\\n 'type': self.JOINT_TYPES[info[2]],\\n 'friction': info[7],\\n 'lower_limit': info[8],\\n 'upper limit': info[9],\\n 'max_force': info[10],\\n 'max_velocity': info[11],\\n 'joint_axis': info[13],\\n 'parent_pos': info[14],\\n 'parent_orn': info[15]\\n }\\n return joint_info\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"d062645d5feee624eb9b8bbb5e39aa28","score":"0.7694279","text":"def joint_dict(self):\n self.check_joint_names()\n return dict(zip(self.joint_names, self.joint_values))","title":""},{"docid":"711b5a3870f4e152646742d8714a6112","score":"0.6767711","text":"def _get_joint_positions(self) -> dict:\n return self.joint_positions","title":""},{"docid":"9453c6c77ce8e2672c0a22d7ff2958d7","score":"0.6716907","text":"def get_joints(id):\n # initialize the dictionaries:\n r={}\n p={}\n real_w = {}\n for key in handler.keys():\n r[key] = skel_cap.get_joint_position(id,handler[key])# -> [str,str,str,float]\n # Convert to projective\n p[key] = convert2projective(r[key])\n # Convert the data in the original dictonary to format\n real_w[key] = [ float(r[key].point[0]),float(r[key].point[1]),\n float(r[key].point[2]),r[key].confidence]\n # confidences:\n return p, real_w","title":""},{"docid":"3617ab53669c02df20184b825c6ea366","score":"0.6657556","text":"def joints(self):\n if self.floating_platform is None: return {}\n joints_dict = {}\n for member in self.floating_platform[\"joints\"]:\n if \"cylindrical\" in member and member[\"cylindrical\"]:\n r = member[\"location\"][0]\n theta = member[\"location\"][1]\n x = r * cos(theta)\n y = r * sin(theta)\n z = member[\"location\"][2]\n joints_dict[member[\"name\"]] = np.array([x, y, z])\n else:\n joints_dict[member[\"name\"]] = np.array(member[\"location\"])\n for member in self.floating_platform[\"members\"]:\n joint1 = joints_dict[member[\"joint1\"]]\n joint2 = joints_dict[member[\"joint2\"]]\n direction = joint2 - joint1\n if \"axial_joints\" in member:\n for axial_joint in member[\"axial_joints\"]:\n grid = axial_joint[\"grid\"]\n axial_cartesian = joint1 + direction * grid\n joints_dict[axial_joint[\"name\"]] = axial_cartesian\n return joints_dict","title":""},{"docid":"d162a8e7f986f9e8b56800168169adf9","score":"0.6567416","text":"def get_params(self):\n return {'joints_actor': dict()}","title":""},{"docid":"185005d67656e0bf3ccc680b1d239ebe","score":"0.64702004","text":"def _collect_information(self):\n for i in range(self.num_joints):\n joint = Joint(\n bc=self.bc,\n body_id=self.body_id,\n joint_index=i,\n max_force=self.max_force,\n max_velocity=self.max_velocity\n )\n self.joint_list.append(joint)\n self.joint_dict[joint.name] = joint\n if joint.controllable and not joint.name.startswith('jointfix') \\\n and not joint.name.startswith('ignore'):\n self.motor_list.append(joint)\n self.motor_dict[joint.name] = joint\n else:\n joint.disable_motor()\n link = Link(\n bc=self.bc,\n name=joint.child_link_name,\n body_id=self.body_id,\n link_index=i\n )\n self.link_list.append(link)\n self.link_dict[link.name] = link\n\n # if link matches the name of the whole robot, then take it as root\n if joint.child_link_name == self.robot_name:\n self.root_link = link\n\n if i == 0 and self.root_link is None:\n link = Link(\n bc=self.bc,\n name=self.robot_name.encode('utf-8'),\n body_id=self.body_id,\n link_index=-1\n )\n print('Root Link:', link.name) if self.debug else None\n self.root_link = link\n self.link_list.append(link)\n self.link_dict[link.name] = link","title":""},{"docid":"e960a7c3528dfff58a462b8eda2881f3","score":"0.62016517","text":"def type_dict(self):\n self.check_joint_names()\n return dict(zip(self.joint_names, self.joint_types))","title":""},{"docid":"164a5edc86f6c0c2b9078a545cf8620d","score":"0.60796785","text":"def join_info(self):\n return self._join_info","title":""},{"docid":"74d76037a88835f1086953621d354a51","score":"0.59992784","text":"def get_dict(self):\n _dict = {\n \"linked\": self._linked,\n \"actor_id\": self._actor_id,\n \"link_type\": self._link_type,\n \"linked_activity\": self.linked_activity.get_dict(),\n \"link_weight\": self._link_weight,\n }\n if self._extra is not None:\n _dict[\"extra\"] = self._extra\n return _dict","title":""},{"docid":"6d6fd251251df7a282aca6890bf97377","score":"0.59644425","text":"def _joints(self):\n raise NotImplementedError","title":""},{"docid":"7582cfa11ad2b8802650bbc5f32a5954","score":"0.58877295","text":"def convert_info(self):\n subject_id= \"subject/{}\".format(self.subject_id)\n subject_name = self.subject_name\n subject_dict ={\n \"id\" :subject_id,\n \"nodeName\": subject_name,\n \"type\": \"subject\"\n }\n\n return subject_dict","title":""},{"docid":"ebf87994ec33aa3784a585bae4c38bc3","score":"0.58164215","text":"def __dict__(self):\n return {\n \"join\": self.join, \"spectate\": self.spectate, \"match\": self.match\n }","title":""},{"docid":"c850eb189582904fa959c1c2b566b0ff","score":"0.58135253","text":"def joint_values(self):\n return self._joint_values","title":""},{"docid":"aa03363b850c7feda750c450dc817a40","score":"0.5809639","text":"def joint_names(self):\n return self._joint_names[self.name]","title":""},{"docid":"8bbe4f0f8379dcbcf38c43167191872d","score":"0.57585543","text":"def get_joint_arrays(id):\n # initialize the dictionaries:\n r={}\n p={}\n real_w = {}\n real_list = []\n proj_list = []\n conf_list =[]\n for key in handler.keys():\n r[key] = skel_cap.get_joint_position(id,handler[key])# -> [str,str,str,float]\n # Convert to projective\n p[key] = convert2projective(r[key])\n # Convert the data in the original dictonary to format\n real_w[key] = [ float(r[key].point[0]),float(r[key].point[1]),\n float(r[key].point[2])]#,r[key].confidence]\n #convert to list\n conf_list.append(r[key].confidence)\n proj_list.append(p[key])\n real_list.append(real_w[key])\n # convert to array\n confidences = (np.array(conf_list)).reshape(15,1)\n proj_coords = (np.array(proj_list)).reshape(15,3)\n real_coords = (np.array(real_list)).reshape(15,3)\n return proj_coords, real_coords, confidences","title":""},{"docid":"c364bc4a80fc86465c9f3b39f195e0de","score":"0.57168806","text":"def info(self):\n info = None\n\n if self.is_relation:\n pairs = self._property.local_remote_pairs\n if len(pairs):\n for pair in reversed(pairs):\n for col in pair:\n if col.table in self._property.parent.tables and not col.primary_key:\n return getattr(col, 'info', None)\n elif col.table in self._property.mapper.tables:\n if col.primary_key:\n if self._property.direction == MANYTOMANY:\n return getattr(col, 'info', None)\n else:\n parent_info = getattr(col, 'info', {})\n info = {}\n for k, v in parent_info.items():\n if k.startswith('backref_'):\n info[k[8:]] = v\n return info\n else:\n try:\n col = getattr(self.model.__table__.c, self.key)\n except AttributeError:\n return {}\n else:\n return getattr(col, 'info', None)\n return {}","title":""},{"docid":"2284e7e650736db7d2d846565a97849b","score":"0.5706495","text":"def determine_joint_names(self):\n self.joint_names = joint_names(self.prefix)","title":""},{"docid":"6603563f3cc8cbf32480f6268ac52d21","score":"0.56856775","text":"def get_joint_values(self):\n return self.group.get_current_joint_values()","title":""},{"docid":"74bcd4be1ff7f1eebe1e8e813d4eff60","score":"0.5661835","text":"def joint_names(self):\n return self.arm.joint_names()","title":""},{"docid":"422da9e97ba4553ecee07573b274664e","score":"0.56460065","text":"def Joints(self):\r\n self.link.check_connection()\r\n command = 'G_Thetas'\r\n self.link.send_line(command)\r\n self.link.send_item(self)\r\n joints = self.link.rec_array()\r\n self.link.check_status()\r\n return joints","title":""},{"docid":"555b1d76c90032faf0457132e115a125","score":"0.5609154","text":"def joint_names(self):\n return self._joint_names","title":""},{"docid":"555b1d76c90032faf0457132e115a125","score":"0.5609154","text":"def joint_names(self):\n return self._joint_names","title":""},{"docid":"9a99b5ba3cc85cf93b2503acfd5dbb8d","score":"0.5587697","text":"def get_protein_dict(self):\n protein_dict = {\"Protein_name\": [self.name], \"CAZy_family\": [self.family]}\n\n if len(self.ec) == 0:\n protein_dict[\"EC#\"] = [np.nan]\n elif len(self.ec) == 1:\n protein_dict[\"EC#\"] = self.ec\n else:\n ec_string = \"\\n\".join(self.ec)\n protein_dict[\"EC#\"] = [ec_string]\n\n protein_dict[\"Source_organism\"] = [self.source]\n\n if type(self.links) is dict:\n for database in [\"GenBank\", \"UniProt\", \"PDB/3D\"]:\n try:\n if len(self.links[database]) == 1:\n protein_dict[database] = self.links[database]\n else:\n accession_string = \",\\n\".join(self.links[database])\n protein_dict[database] = [accession_string]\n except KeyError:\n protein_dict[database] = [np.nan]\n else:\n for database in [\"GenBank\", \"UniProt\", \"PDB/3D\"]:\n protein_dict[database] = [np.nan]\n return protein_dict","title":""},{"docid":"79c27084874bf203883c36472a5f414a","score":"0.55684775","text":"def relationship_dicts() -> dict:\n parent = {\n 'VariationParent': {\n 'Identifiers': {\n 'MarketplaceASIN': {\n 'ASIN': {\n 'value': 'TickleStick'\n }\n }\n }\n }\n\n }\n\n children = {\n 'VariationChildren': [{\n 'Identifiers': {\n 'MarketplaceASIN': {\n 'ASIN': {\n 'value': 'TickleStick'\n }\n }\n }\n },\n {\n 'Identifiers': {\n 'MarketplaceASIN': {\n 'ASIN': {\n 'value': 'Geni'\n }\n }\n }\n }]\n\n }\n\n return {'parent': parent, 'children': children}","title":""},{"docid":"01166a642a57859af8b80d3a9f9a82ad","score":"0.5558503","text":"def joint_names(self) -> List[str]:\n return [jnt.name for jnt in self.urdf.joints]","title":""},{"docid":"1d3397d8b970d055867e3888fffbeaed","score":"0.5551889","text":"def get_info(self) -> _InfoDict:","title":""},{"docid":"1d3397d8b970d055867e3888fffbeaed","score":"0.5551889","text":"def get_info(self) -> _InfoDict:","title":""},{"docid":"1d3397d8b970d055867e3888fffbeaed","score":"0.5551889","text":"def get_info(self) -> _InfoDict:","title":""},{"docid":"23d734b780a86aa41084bf906b315da8","score":"0.55495477","text":"def get_attributes(self):\n retdict = {}\n if self.name:\n retdict['name'] = self.name\n if self.rule:\n retdict['rule'] = self.rule\n retdict['id'] = str(self.id)\n retdict['junction'] = str(self.road_type)\n retdict['length'] = str(self.planview.get_total_length())\n return retdict","title":""},{"docid":"d88c62900d967d80d222acf24ac36f2d","score":"0.5528877","text":"def get_organism_dictionary():\n\n \"\"\"Assume this means dictionaries within a dictionary\"\"\"","title":""},{"docid":"9b7488a67b7f25c85f651a0e11fd43eb","score":"0.552584","text":"def _get_info(self):\n\n return {}","title":""},{"docid":"333ef79d7258a4f4a806ba0b3d9e7b69","score":"0.5476746","text":"def update_joints(self):\n\n self.primary_joints = {\n 'hip': self.hip,\n 'shoulder': self.shoulder,\n 'right_elbow': (self.shoulder[0] + 85, self.right_elbow_pos),\n 'left_elbow': (self.shoulder[0] - 85, self.left_elbow_pos),\n 'right_knee': (self.hip[0] * 1.25, self.hip[1] * self.right_knee_pos),\n 'left_knee': (self.hip[0] * 0.75, self.hip[1] * self.left_knee_pos),\n }\n\n\n self.secondary_joints = {\n 'right_hand': (self.primary_joints['right_elbow'][0] + 20, self.primary_joints['right_elbow'][1] + self.right_hand_pos),\n 'left_hand': (self.primary_joints['left_elbow'][0] - 20, self.primary_joints['left_elbow'][1] + self.left_hand_pos),\n 'right_foot': (self.primary_joints['right_knee'][0] + 10, self.primary_joints['right_knee'][1] + self.right_foot_pos),\n 'left_foot': (self.primary_joints['left_knee'][0] - 10, self.primary_joints['left_knee'][1] + self.left_foot_pos),\n }","title":""},{"docid":"b96c7bb98c9691b667aaf4f75cbdfd3e","score":"0.5447122","text":"def get_details(self):\n detail_dict = {\"platform\": self.platform,\n \"src\": self.src,\n \"channel\": self.channel,\n \"genre\": self.genre,\n \"type\": self.type,\n \"id\": self.id}\n return detail_dict","title":""},{"docid":"0741286de05bf159a051cf6eae83ae59","score":"0.54389894","text":"def build_relationshipdict(self, white=True):\n rels = get_initial_queryset(\"Journal\").relationships().written_by(self.obj)\n if white:\n rels = rels.white()\n else:\n rels = rels.black()\n relsdict = {}\n for rel in rels:\n if rel.db_receivers_objects.all():\n name = rel.db_receivers_objects.all()[0].key.lower()\n relslist = relsdict.get(name, [])\n relslist.append(rel)\n relsdict[name] = relslist\n if white:\n self._white_relationships = relsdict\n else:\n self._black_relationships = relsdict\n return relsdict","title":""},{"docid":"3ffd040974ee03fe2393fac1bb079099","score":"0.5438322","text":"def _read_joint_data(self, jdata):\r\n for joint in jdata.get_Joint():\r\n jdata = JointData(self.componentsdict[joint.get_AssembledComponentInstanceID()],\r\n [self.componentsdict[constrainedtocomp.get_ComponentInstanceID()] for constrainedtocomp in\r\n joint.get_ConstrainedToComponents().get_ConstrainedToComponent()],\r\n joint.get_Type())\r\n jdata.location = Vec3(\r\n [float(x) for x in joint.get_GlobalCoordinateSystem().get_Location().get_ArrayValue().split(\";\")])\r\n jdata.orientation = Vec3(\r\n [float(x) for x in joint.get_GlobalCoordinateSystem().get_Orientation().get_ArrayValue().split(\";\")])\r\n jdata.id = joint.get_ID()\r\n self.joints.append(jdata)","title":""},{"docid":"69e439fd21fb96a89f6767d31e514dc4","score":"0.5434439","text":"def as_linkage(self):\n return {'data': {'type': self.type_, 'id': self.id_}}","title":""},{"docid":"8854b6a2e28dc2239ef6c89215789e28","score":"0.5419667","text":"async def get_dict(self) -> dict:\n return {\n \"booster\": self.booster.id,\n \"target\": self.target.id\n }","title":""},{"docid":"dc51fa9c378a7f565542675c4b800afc","score":"0.54082257","text":"def joint_community(self):\n return self._joint_community","title":""},{"docid":"e622cd8cf777aee1ac6f8e8b3162b1dc","score":"0.53909075","text":"def to_joint_trajectory_msg(self):\n joint_trajectory_msg = trajectory_msg.JointTrajectory()\n\n joint_trajectory_msg.joint_names = [joint.name for joint in self.joints]\n\n timestamps = self.get_unique_timestamps()\n for timestamp in timestamps:\n joint_trajectory_point = trajectory_msg.JointTrajectoryPoint()\n joint_trajectory_point.time_from_start = rospy.Duration.from_sec(timestamp)\n\n for joint in self.joints:\n interpolated_setpoint = joint.get_interpolated_setpoint(timestamp)\n if interpolated_setpoint.time != timestamp:\n rospy.logwarn('Time mismatch in joint {jn} at timestamp {ts}, '\n 'got time {ti}'.format(jn=joint.name, ts=timestamp, ti=interpolated_setpoint.time))\n\n joint_trajectory_point.positions.append(interpolated_setpoint.position)\n joint_trajectory_point.velocities.append(interpolated_setpoint.velocity)\n\n joint_trajectory_msg.points.append(joint_trajectory_point)\n\n return joint_trajectory_msg","title":""},{"docid":"66846692a88a4e49bb8b536def65a373","score":"0.53876406","text":"def joint(*args, **kwargs):\n pass","title":""},{"docid":"fe870ca61b0dc04e2448539db7fb5039","score":"0.5379347","text":"def to_dict(self):\n to_dict = dict()\n to_dict['id'] = self.id\n if self.disqualified:\n to_dict['disqualified'] = self.disqualified\n to_dict['summit'] = self.summit.id\n to_dict['saddle'] = self.saddle.id\n return to_dict","title":""},{"docid":"62756e38bc589f3396ae11139fa3cca0","score":"0.53661394","text":"def to_dict(self) -> Dict[str, Any]:\n return {\n \"satisfied\": self.satisfied,\n \"satisfied_at\": self.satisfied_at,\n \"annotation\": self.annotation.to_dict(),\n \"children\": [c.to_dict() for c in self.children],\n }","title":""},{"docid":"80bd23b32f6f567dc218861795c06a1b","score":"0.5366131","text":"def serialize(self):\n error, resp = RelationshipStatus().get_most_recent(self.id_relacion)\n return {\n 'id': self.id_relacion,\n 'usuario': self.usuario.correo_electronico,\n 'amigo': self.usuario_amigo.correo_electronico,\n 'estado': resp.estado_solicitud.estado_solicitud\n }","title":""},{"docid":"180ec04ff95986b4aadab99032a23731","score":"0.536113","text":"def _to_dict(self):\n _dict = {}\n if hasattr(self, 'translation') and self.translation is not None:\n _dict['translation'] = self.translation\n if hasattr(self, 'part_of_speech') and self.part_of_speech is not None:\n _dict['part_of_speech'] = self.part_of_speech\n return _dict","title":""},{"docid":"eeeaa94a3cbfe481786809b7bf9f6971","score":"0.5354963","text":"def to_dict(self):\n\n info_dict = {'p1': self.p1, 'p2': self.p2,\n 'surface': self.surface.name,\n 'tournament_name': self.tournament_name,\n 'date': self.date}\n\n # Flatten additional info if it exists\n if self.additional_info is not None:\n info_dict.update(flatten_nested_dict(self.additional_info, ''))\n\n if self.tournament_round is not None:\n info_dict['round'] = self.tournament_round.name\n else:\n info_dict['round'] = None\n\n return info_dict","title":""},{"docid":"7b8b5b1a21b4625d18172b7c7dd58124","score":"0.53501695","text":"def get_info(self, observation):\n info = {}\n return info","title":""},{"docid":"2625b54efc021486b16108dcc3a7871d","score":"0.53472024","text":"def __getitem__(self, joint_id):\n return self._joints[joint_id]","title":""},{"docid":"bb9e51f481a0c1fdf185d5ba00436f6f","score":"0.53373337","text":"def __iter__(self):\n return self._joints.itervalues()","title":""},{"docid":"0be0acf4d25270f2a774ba831066743a","score":"0.5335678","text":"def GetJoin(self):","title":""},{"docid":"eed4fe1279a8a109a279efaa60af282b","score":"0.5335058","text":"def get_info(self):\n return {}","title":""},{"docid":"c96437a799aeb6b76c44f51a3a245242","score":"0.5334832","text":"def serialize(self):\n return {\n 'pk' : self.pk,\n 'tax_id': self.tax_id,\n # This is an example how to deal with Many2Many relations\n 'description' : self.description,\n 'Org_name':self.Org_name\n }","title":""},{"docid":"137341abce9bd73f2f5957f971b9cf5f","score":"0.5334062","text":"def get_info(self):\n return {\n 'name': self.name,\n 'actions': self.actions,\n 'gamma': self.gamma,\n 'gravity': self.gravity,\n 'masscart': self.masscart,\n 'masspole': self.masspole,\n 'total_mass': self.total_mass,\n 'length': self.length,\n 'polemass_length': self.polemass_length,\n 'force_mag': self.force_mag,\n 'tau': self.tau,\n 'kinematics_integrator': self.kinematics_integrator,\n 'theta_threshold_radians': self.theta_threshold_radians,\n 'x_threshold': self.x_threshold\n }","title":""},{"docid":"30dae037fcc072ed4af41197d5ef0c49","score":"0.53264934","text":"def _prepare_info_dict(self, joint_agent_action_info, mdp_infos):\n # Get the agent action info, that could contain info about action probs, or other\n # custom user defined information\n env_info = {\n \"agent_infos\": [\n joint_agent_action_info[agent_idx]\n for agent_idx in range(self.mdp.num_players)\n ]\n }\n # TODO: This can be further simplified by having all the mdp_infos copied over to the env_infos automatically\n env_info[\"sparse_r_by_agent\"] = mdp_infos[\"sparse_reward_by_agent\"]\n env_info[\"shaped_r_by_agent\"] = mdp_infos[\"shaped_reward_by_agent\"]\n env_info[\"phi_s\"] = (\n mdp_infos[\"phi_s\"] if \"phi_s\" in mdp_infos else None\n )\n env_info[\"phi_s_prime\"] = (\n mdp_infos[\"phi_s_prime\"] if \"phi_s_prime\" in mdp_infos else None\n )\n return env_info","title":""},{"docid":"ea1cca6174d417a90e294ca23d456d6d","score":"0.53198475","text":"def _create_info_dictionary(self, obs):\n return {}","title":""},{"docid":"cdf56b2892cf33d8bfb071bda2a4c9ff","score":"0.53118724","text":"def _dictme(self):\r\n\r\n return {\"name\": self._name,\r\n \"stats\": self._stats,\r\n \"lvl\": self._lvl,\r\n \"race\": str(self._race),\r\n \"char_class\": str(self._chclass),\r\n \"background\": self._background,\r\n \"profs\": list(self._profs.keys())\r\n }","title":""},{"docid":"59e600d4e5c6e37364919daa4b63313b","score":"0.5302283","text":"def jointgroup(self):\n return util.buf_to_npy(self._ptr.contents.jointgroup, (6,))","title":""},{"docid":"3383d182b5f63bfa92512bd39f5768b8","score":"0.52996635","text":"def getJoinDict(url,referenceSetId=0):\n\tdef defaultList():\n\t\treturn defaultdict(list)\n\tjoinDict=defaultdict(defaultList)\n\turl+='/joins/search'\n\treq={\n \"length\": None, \n \"pageSize\": 100, \n \"pageToken\": '0', \n \"referenceSetId\": str(referenceSetId), \n \"sequenceId\": '', \n \"start\": None, \n \"strand\": None, \n \"variantSetId\": ''\n\t}\n\tnextPageToken=True\n\theader={'Content-Type':'application/json'}\n\twhile nextPageToken:\n\t\tres=requests.post(url,data=json.dumps(req),headers=header)\n\t\tthePage=json.loads(res.text)\n\t\tnextPageToken=thePage['nextPageToken']\n\t\treq['pageToken']=nextPageToken\n\t\tjoins=thePage['joins']\n\t\tfor join in joins:\n\t\t\tside1=join['side1']\n\t\t\tside2=join['side2']\n\t\t\tbase1=side1['base']\n\t\t\tbase2=side2['base']\n\t\t\tseq1=base1['sequenceId']\n\t\t\tseq2=base2['sequenceId']\n\t\t\tpos1=int(base1['position'])\n\t\t\tpos2=int(base2['position'])\n\t\t\tstrand1=side1['strand']\n\t\t\tstrand2=side2['strand']\n\t\t\tjoinDict[seq1][(pos1,strand1)].append((seq2,pos2,strand2))\n\t\t\tjoinDict[seq2][(pos2,strand2)].append((seq1,pos1,strand1))\n\treturn joinDict","title":""},{"docid":"11a1ab9901e6f822f89f4ea6d81d182e","score":"0.52772593","text":"def get_dictionary(self):\n settlement_dict = {}\n settlement_dict[\"road_id\"] = self.id\n settlement_dict[\"road_row\"] = self.location[0]\n settlement_dict[\"road_column\"] = self.location[1]\n settlement_dict[\"road_color\"] = self.color\n if self.ownedBy is not None:\n settlement_dict[\"road_ownedBy\"] = self.ownedBy.id\n if self.attached_settlement is not None:\n settlement_dict[\"attached_settlement\"] = self.attached_settlement.id\n return settlement_dict","title":""},{"docid":"877c5a7bcef4b374b3b83b6d23344987","score":"0.52753085","text":"def final_position(self):\n return {joint.name: joint.setpoints[-1].position for joint in self.joints}","title":""},{"docid":"c4a448f6964950f9f7fe7d4b4d0af700","score":"0.5273507","text":"def _to_dict(self):\n _dict = {}\n if hasattr(self, 'external_id') and self.external_id is not None:\n _dict['externalId'] = self.external_id\n if hasattr(self, 'document_id') and self.document_id is not None:\n _dict['documentId'] = self.document_id\n if hasattr(self, 'parent_document_id') and self.parent_document_id is not None:\n _dict['parentDocumentId'] = self.parent_document_id\n if hasattr(self, 'publication_name') and self.publication_name is not None:\n _dict['publicationName'] = self.publication_name\n if hasattr(self, 'publication_date') and self.publication_date is not None:\n _dict['publicationDate'] = self.publication_date\n if hasattr(self, 'publication_url') and self.publication_url is not None:\n _dict['publicationURL'] = self.publication_url\n if hasattr(self, 'authors') and self.authors is not None:\n _dict['authors'] = self.authors\n if hasattr(self, 'title') and self.title is not None:\n _dict['title'] = self.title\n if hasattr(self, 'medline_license') and self.medline_license is not None:\n _dict['medlineLicense'] = self.medline_license\n if hasattr(self, 'href_pub_med') and self.href_pub_med is not None:\n _dict['hrefPubMed'] = self.href_pub_med\n if hasattr(self, 'pdf_url') and self.pdf_url is not None:\n _dict['pdfUrl'] = self.pdf_url\n if hasattr(self, 'reference_url') and self.reference_url is not None:\n _dict['referenceUrl'] = self.reference_url\n if hasattr(self, 'highlighted_title') and self.highlighted_title is not None:\n _dict['highlightedTitle'] = self.highlighted_title\n if hasattr(self, 'highlighted_abstract') and self.highlighted_abstract is not None:\n _dict['highlightedAbstract'] = self.highlighted_abstract\n if hasattr(self, 'highlighted_body') and self.highlighted_body is not None:\n _dict['highlightedBody'] = self.highlighted_body\n if hasattr(self, 'highlighted_sections') and self.highlighted_sections is not None:\n _dict['highlightedSections'] = ({k : v._to_dict() for k, v in\n self.highlighted_sections.items()})\n if hasattr(self, 'passages') and self.passages is not None:\n _dict['passages'] = self.passages\n if hasattr(self, 'annotations') and self.annotations is not None:\n _dict['annotations'] = {k : v._to_dict() for k, v in self.annotations.items()}\n return _dict","title":""},{"docid":"465637611f9f736ae0bae5fedbe10ea2","score":"0.526984","text":"def get_joint_name(self):\n return self.joint_name","title":""},{"docid":"6188f2df0044d5a4cc24ca8e1a020357","score":"0.5267064","text":"def classifier_info(self):\n return {\n 'classname': self.classname,\n 'subject': self.subject,\n 'roc': self.roc,\n 'auc': self.auc,\n 'params': json.dumps(self.params),\n }","title":""},{"docid":"17a549efbcdd1c126c61b0a153d3dc9a","score":"0.5262656","text":"def json_data(self):\n return {\n \"type\": self.type,\n \"recruiter\": self.recruiter_id,\n \"assignment_id\": self.assignment_id,\n \"hit_id\": self.hit_id,\n \"mode\": self.mode,\n \"end_time\": self.end_time,\n \"base_pay\": self.base_pay,\n \"bonus\": self.bonus,\n \"status\": self.status,\n \"object_type\": \"Participant\",\n \"entry_information\": self.entry_information,\n }","title":""},{"docid":"0a1bcf4f5689b5319c73539510f330f6","score":"0.52610385","text":"def _known_persons(self, home_id: str) -> dict[str, dict]:\n return {pid: p for pid, p in self.persons[home_id].items() if \"pseudo\" in p}","title":""},{"docid":"291ec2704b719fa5d2a789e1908d5f14","score":"0.52527004","text":"def JointAccount(self):\n return super(FContactRegulatoryInfo, self).JointAccount()","title":""},{"docid":"36e7ac51947e604798085b8121f1b1e5","score":"0.52463794","text":"def joint_array_to_dict(vel_torque_array, limb):\n\n return dict(itertools.izip(limb.joint_names(), vel_torque_array))","title":""},{"docid":"4784ff9bee5067a8d2ed7a9a2db52780","score":"0.5244284","text":"def serialize(self):\n\t\treturn {\n\t\t\t'notice_id' : self.notice_id,\n\t\t\t'timestamp' : self.timestamp,\n\t\t\t'message' : self.message,\n\t\t\t'c_id' : self.c_id\n\t\t\t# This is an example how to deal with Many2Many relations\n\t\t#\t'many2many' : self.serialize_many2many\n\t\t}","title":""},{"docid":"76632e4065eb6cf729e4027ab411c0a4","score":"0.524409","text":"def convert_book_info(self):\n\n book_id = \"book/{}\".format(self.books.book_id)\n book_title = self.books.title\n subjects = self.books.subjects\n print(subjects)\n book_dict ={\n \"id\" :book_id,\n \"nodeName\": book_title,\n \"type\": \"book\",\n \"subjects\": []\n }\n\n return book_dict","title":""},{"docid":"408654f17dd7687231850c185f010b17","score":"0.5231306","text":"def qtrobot_joints_callback(self, msg):\n self.qtrobot_joint_dictionary = dict(zip(msg.name, msg.position))","title":""},{"docid":"69ea2d6b02948daa38a8b27d26d2d257","score":"0.52244484","text":"def get_additional_info(self) -> dict:\n return {}","title":""},{"docid":"03a1f4a5e018277f9927273afc5b5107","score":"0.5223652","text":"def get_info(self):\n info = {\n 'name': self.name,\n 'model': self.model,\n 'syringe_diameter': self.diameter,\n 'rate': self.rate\n }\n return info","title":""},{"docid":"23bf576946779dc34ff7fc6faffeabd4","score":"0.5217067","text":"def to_dict(self):\n rep = super(Person, self).to_dict()\n rep.update({\n 'identifier': self.get('identifier', None),\n 'name': self.name,\n 'email': self.email,\n })\n return rep","title":""},{"docid":"f929e67261ad6167ac05e9c7bc1905c4","score":"0.5217003","text":"def serialize(self):\n\t\treturn {\n\t\t\t'enrolls_id' : self.enrolls_id,\n\t\t\t'student_id' : self.student_id,\n\t\t\t'course_id' : self.course_id\n\t\t\t# This is an example how to deal with Many2Many relations\n\t\t\t#\t'many2many' : self.serialize_many2many\n\t\t}","title":""},{"docid":"83f39aeb2d2e9ce694ff89d2923ac8c0","score":"0.5211111","text":"def _extract_info(self, infos, domain):\n ids = []\n residues = infos[domain]['residues']\n # the residues are necessarily in the same chain, so :\n chain = residues[0].get_parent().get_id()\n for residue in residues:\n number = str(residue.get_id()[1])\n if number not in ids:\n ids.append(number)\n return {'chain': chain, 'ids': ids}","title":""},{"docid":"5638ea690f583d18769c933ac7a6787e","score":"0.5210504","text":"def joint_type(self):\n return self._joint_type","title":""},{"docid":"77c283cf4889c65cb2ba9e57fb0e4255","score":"0.5210123","text":"def getTransformationMatrices(self):\n transformationMatrices = {}\n # TODO modify from here\n # Hint: the output should be a dictionary with joint names as keys and\n # their corresponding homogeneous transformation matrices as values.\n return transformationMatrices","title":""},{"docid":"172f110a538b236726848b9e5910a6fa","score":"0.52030563","text":"def info(self):\n if self.name == \"\":\n return {}\n else:\n return {\"name\" : self.name}","title":""},{"docid":"944b0d031eb5b090d84d288a43fd888f","score":"0.5201102","text":"def get_info(message):\n try:\n from_info = message.get('From', 'No sender provided')\n to_info = message.get('To', 'No recipient provided')\n date_info = message.get('Date', 'No date provided')\n subj_info = message.get('Subject', 'No subject provided')\n except:\n print('Didnt provide correct email object instance')\n raise\n return {'From':from_info, 'To':to_info ,'Date':date_info, 'Subject':subj_info }","title":""},{"docid":"fd0322522c50de936c9cddec8f3effa3","score":"0.5191566","text":"def _to_dict(self):\n _dict = {}\n if hasattr(self, 'qualifier_id') and self.qualifier_id is not None:\n _dict['qualifier_id'] = self.qualifier_id\n if hasattr(self, 'qualifier_name') and self.qualifier_name is not None:\n _dict['qualifier_name'] = self.qualifier_name\n return _dict","title":""},{"docid":"bd0b5d04fffbef9a480349cc3ae6e8b9","score":"0.51772","text":"def export_relationship_json(rel: Relationship) -> Dict:\n return {\"node_a\": rel.node_a.node_id, \"relationship_type\": rel.relationship_type, \"node_b\": rel.node_b.node_id,\n \"properties\": rel.properties}","title":""},{"docid":"8cb66cb5ce4edafaac5658a651f776d0","score":"0.5176936","text":"def get_entity(self):\n if self.sender_id:\n return {'user_id': self.sender_id}\n return {'name': self.name}","title":""},{"docid":"e13878f3958f0dcdf4e1f9f609f5188d","score":"0.5172545","text":"def get_breach_details():","title":""},{"docid":"1528e1cebce18657d064dd15d1102a71","score":"0.5170092","text":"def getInfo(self):\n return {}","title":""},{"docid":"038c5cc0433079592e5d407b5d2b02c5","score":"0.51697385","text":"def serialize(self):\n return {\n 'id' : self.id,\n 'sender_id' : self.sender_id,\n 'recipient_id' : self.recipient_id,\n 'sender_first_name' : self.sender_first_name,\n 'sender_last_name' : self.sender_last_name,\n 'sender_picture' : self.sender_picture,\n 'recipient_first_name' : self.recipient_first_name,\n 'recipient_last_name' : self.recipient_last_name,\n 'sent_status' : self.sent_status,\n 'message' : self.message,\n 'time_sent' : self.time_sent,\n 'conversation_id' : self.conversation_id\n \n }","title":""},{"docid":"3f8a7c8bceb17dd4e3291df451b9deb2","score":"0.5168953","text":"def to_json_dict(self) -> dict:\r\n\r\n return {'id_time' : self.id_tine, 'id_user' : self.id_user,\r\n 'access_mode' : self.access_mode,\r\n 'attributes' : self.attributes.to_json_dict(),\r\n 'attachments' : self.attachments.to_json_dict(),\r\n 'is_finished' : self.is_finished}","title":""},{"docid":"fa6ebf8bae06447ffa6ea3239ba4af26","score":"0.51628435","text":"def get_attributes(self):\n retdict = {}\n retdict['entityRef'] = self.target\n retdict['freespace'] = convert_bool(self.freespace)\n retdict['continuous'] = convert_bool(self.continuous)\n if self.distance:\n retdict['distance'] = str(self.distance)\n return retdict","title":""},{"docid":"fde6c1f6846b9ca827eb5aeb430fab00","score":"0.5162007","text":"def relations(relate_soup):\n lis = []\n for r in relate_soup.find_all('tr'):\n relation = {}\n types = r.find_all('td')\n \n if len(types)==4:\n relation['name'] = types[0].text.strip()\n relation['type'] = types[1].text.strip()\n relation['year'] = types[2].text.strip()\n relation['location'] = types[3].text.strip()\n lis.append(relation)\n return lis","title":""},{"docid":"bc62631ebfda8c39c1b193cab8d05744","score":"0.5161577","text":"def joints(self) -> npt.NDArray[np.float64]:\n return self._joints","title":""},{"docid":"4930f6531a8b6972f7d10f204a6613d3","score":"0.5157041","text":"def _get_join_desired(self):\n return self.__join_desired","title":""},{"docid":"ed5c2a7fb771688ca0d40e9b350b22c0","score":"0.51545626","text":"def get_details(self):\n return {}","title":""},{"docid":"80c3b33460f841b67917bb39f0ac6847","score":"0.51529557","text":"def _to_dict(self):\n return {\n \"hookup_type\": self.hookup_type,\n \"corr_index\": self.corr_index,\n \"all_pols\": self.all_pols,\n \"redirect_part_types\": self.redirect_part_types,\n \"single_pol_labeled_parts\": self.single_pol_labeled_parts,\n \"full_connection_path\": self.full_connection_path,\n }","title":""},{"docid":"33f251c444deef95f8c56e70480cdb95","score":"0.51513135","text":"def get_joint_index(self):\n return self.joint_index","title":""},{"docid":"61f634d387369d7de08c709432ec9091","score":"0.51467377","text":"def get_dict(self):\n para_dict = {\n \"id\": self.id,\n \"news_id\": self.news_id,\n \"type\": self.type,\n \"title\": self.title,\n \"publish_time\": self.publish_time,\n \"content\": self.content,\n \"img_name\": self.img_name,\n \"audio_name\": self.audio_name,\n \"has_img\": self.has_img,\n \"has_audio\": self.has_audio,\n \"dangos\": self.dangos\n }\n\n return para_dict","title":""},{"docid":"7523395f3c565d7ff9dd7b0af96d1f81","score":"0.5145694","text":"def get_infos(self):\n infos = dict()\n infos[\"dataset\"] = self.dataset_name\n infos[\"task\"] = \"harmony_sep\"\n infos[\"licenses\"] = [mdb_license]\n return infos","title":""},{"docid":"ec2c0ebe6d69b174f1ad4bc536314b19","score":"0.5141187","text":"def dictionary(self):\n return {\n \"name\": self.name, \"agency\": self.agency, \"mission\": self.type_of_mission,\n \"year_launched\": self.year_launched}","title":""},{"docid":"cd24214198d90e08997c285f8bc68664","score":"0.5140088","text":"def get_dict():","title":""},{"docid":"aef4509c432c822f7a228247d67af651","score":"0.51353806","text":"def as_linkage(self):\n return {'data': None}","title":""},{"docid":"6a31e9f29c1ff0de423dc8f9dc445027","score":"0.5133731","text":"def convert_info(self):\n\n author_dict = {\n \"id\" : \"author/{}\".format(self.author_id),\n \"nodeName\": self.name,\n \"type\": \"author\"\n }\n\n return author_dict","title":""},{"docid":"2d7f55b5586cb0728c9abb6a8e63a17a","score":"0.5127704","text":"def get_info(self):\n ret = {}\n ret[\"isSequence\"] = self.isSequence\n ret[\"maxSequenceLength\"] = self.maxSequenceLength\n ret[\"avgSequenceLength\"] = self.avgSequenceLength\n ret[\"nAttrs\"] = self.nAttrs\n ret[\"nFeatures\"] = self.nFeatures\n ret[\"nInstances\"] = self.nInstances\n ret[\"targetType\"] = self.targetType\n ret[\"nClasses\"] = self.nClasses\n ret[\"targetClasses\"] = self.targetClasses\n ret[\"features\"] = self.features\n ret[\"target\"] = self.target\n return ret","title":""},{"docid":"b79ee125065e6369b6a364f27a592dc2","score":"0.5127179","text":"def print_information(self) -> None:\n print('---' * 20)\n print(f' Joint name: \\t \\t{self.name}')\n print(f' Joint type: \\t \\t{self.type}')\n print(f' Joint index: \\t \\t{self.index}')\n print(f' Body index: \\t \\t{self.body_id}')\n print(f' maximum force: \\t \\t{self.max_force}')","title":""}],"string":"[\n {\n \"docid\": \"d062645d5feee624eb9b8bbb5e39aa28\",\n \"score\": \"0.7694279\",\n \"text\": \"def joint_dict(self):\\n self.check_joint_names()\\n return dict(zip(self.joint_names, self.joint_values))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"711b5a3870f4e152646742d8714a6112\",\n \"score\": \"0.6767711\",\n \"text\": \"def _get_joint_positions(self) -> dict:\\n return self.joint_positions\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9453c6c77ce8e2672c0a22d7ff2958d7\",\n \"score\": \"0.6716907\",\n \"text\": \"def get_joints(id):\\n # initialize the dictionaries:\\n r={}\\n p={}\\n real_w = {}\\n for key in handler.keys():\\n r[key] = skel_cap.get_joint_position(id,handler[key])# -> [str,str,str,float]\\n # Convert to projective\\n p[key] = convert2projective(r[key])\\n # Convert the data in the original dictonary to format\\n real_w[key] = [ float(r[key].point[0]),float(r[key].point[1]),\\n float(r[key].point[2]),r[key].confidence]\\n # confidences:\\n return p, real_w\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3617ab53669c02df20184b825c6ea366\",\n \"score\": \"0.6657556\",\n \"text\": \"def joints(self):\\n if self.floating_platform is None: return {}\\n joints_dict = {}\\n for member in self.floating_platform[\\\"joints\\\"]:\\n if \\\"cylindrical\\\" in member and member[\\\"cylindrical\\\"]:\\n r = member[\\\"location\\\"][0]\\n theta = member[\\\"location\\\"][1]\\n x = r * cos(theta)\\n y = r * sin(theta)\\n z = member[\\\"location\\\"][2]\\n joints_dict[member[\\\"name\\\"]] = np.array([x, y, z])\\n else:\\n joints_dict[member[\\\"name\\\"]] = np.array(member[\\\"location\\\"])\\n for member in self.floating_platform[\\\"members\\\"]:\\n joint1 = joints_dict[member[\\\"joint1\\\"]]\\n joint2 = joints_dict[member[\\\"joint2\\\"]]\\n direction = joint2 - joint1\\n if \\\"axial_joints\\\" in member:\\n for axial_joint in member[\\\"axial_joints\\\"]:\\n grid = axial_joint[\\\"grid\\\"]\\n axial_cartesian = joint1 + direction * grid\\n joints_dict[axial_joint[\\\"name\\\"]] = axial_cartesian\\n return joints_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d162a8e7f986f9e8b56800168169adf9\",\n \"score\": \"0.6567416\",\n \"text\": \"def get_params(self):\\n return {'joints_actor': dict()}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"185005d67656e0bf3ccc680b1d239ebe\",\n \"score\": \"0.64702004\",\n \"text\": \"def _collect_information(self):\\n for i in range(self.num_joints):\\n joint = Joint(\\n bc=self.bc,\\n body_id=self.body_id,\\n joint_index=i,\\n max_force=self.max_force,\\n max_velocity=self.max_velocity\\n )\\n self.joint_list.append(joint)\\n self.joint_dict[joint.name] = joint\\n if joint.controllable and not joint.name.startswith('jointfix') \\\\\\n and not joint.name.startswith('ignore'):\\n self.motor_list.append(joint)\\n self.motor_dict[joint.name] = joint\\n else:\\n joint.disable_motor()\\n link = Link(\\n bc=self.bc,\\n name=joint.child_link_name,\\n body_id=self.body_id,\\n link_index=i\\n )\\n self.link_list.append(link)\\n self.link_dict[link.name] = link\\n\\n # if link matches the name of the whole robot, then take it as root\\n if joint.child_link_name == self.robot_name:\\n self.root_link = link\\n\\n if i == 0 and self.root_link is None:\\n link = Link(\\n bc=self.bc,\\n name=self.robot_name.encode('utf-8'),\\n body_id=self.body_id,\\n link_index=-1\\n )\\n print('Root Link:', link.name) if self.debug else None\\n self.root_link = link\\n self.link_list.append(link)\\n self.link_dict[link.name] = link\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e960a7c3528dfff58a462b8eda2881f3\",\n \"score\": \"0.62016517\",\n \"text\": \"def type_dict(self):\\n self.check_joint_names()\\n return dict(zip(self.joint_names, self.joint_types))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"164a5edc86f6c0c2b9078a545cf8620d\",\n \"score\": \"0.60796785\",\n \"text\": \"def join_info(self):\\n return self._join_info\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74d76037a88835f1086953621d354a51\",\n \"score\": \"0.59992784\",\n \"text\": \"def get_dict(self):\\n _dict = {\\n \\\"linked\\\": self._linked,\\n \\\"actor_id\\\": self._actor_id,\\n \\\"link_type\\\": self._link_type,\\n \\\"linked_activity\\\": self.linked_activity.get_dict(),\\n \\\"link_weight\\\": self._link_weight,\\n }\\n if self._extra is not None:\\n _dict[\\\"extra\\\"] = self._extra\\n return _dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d6fd251251df7a282aca6890bf97377\",\n \"score\": \"0.59644425\",\n \"text\": \"def _joints(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7582cfa11ad2b8802650bbc5f32a5954\",\n \"score\": \"0.58877295\",\n \"text\": \"def convert_info(self):\\n subject_id= \\\"subject/{}\\\".format(self.subject_id)\\n subject_name = self.subject_name\\n subject_dict ={\\n \\\"id\\\" :subject_id,\\n \\\"nodeName\\\": subject_name,\\n \\\"type\\\": \\\"subject\\\"\\n }\\n\\n return subject_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ebf87994ec33aa3784a585bae4c38bc3\",\n \"score\": \"0.58164215\",\n \"text\": \"def __dict__(self):\\n return {\\n \\\"join\\\": self.join, \\\"spectate\\\": self.spectate, \\\"match\\\": self.match\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c850eb189582904fa959c1c2b566b0ff\",\n \"score\": \"0.58135253\",\n \"text\": \"def joint_values(self):\\n return self._joint_values\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa03363b850c7feda750c450dc817a40\",\n \"score\": \"0.5809639\",\n \"text\": \"def joint_names(self):\\n return self._joint_names[self.name]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8bbe4f0f8379dcbcf38c43167191872d\",\n \"score\": \"0.57585543\",\n \"text\": \"def get_joint_arrays(id):\\n # initialize the dictionaries:\\n r={}\\n p={}\\n real_w = {}\\n real_list = []\\n proj_list = []\\n conf_list =[]\\n for key in handler.keys():\\n r[key] = skel_cap.get_joint_position(id,handler[key])# -> [str,str,str,float]\\n # Convert to projective\\n p[key] = convert2projective(r[key])\\n # Convert the data in the original dictonary to format\\n real_w[key] = [ float(r[key].point[0]),float(r[key].point[1]),\\n float(r[key].point[2])]#,r[key].confidence]\\n #convert to list\\n conf_list.append(r[key].confidence)\\n proj_list.append(p[key])\\n real_list.append(real_w[key])\\n # convert to array\\n confidences = (np.array(conf_list)).reshape(15,1)\\n proj_coords = (np.array(proj_list)).reshape(15,3)\\n real_coords = (np.array(real_list)).reshape(15,3)\\n return proj_coords, real_coords, confidences\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c364bc4a80fc86465c9f3b39f195e0de\",\n \"score\": \"0.57168806\",\n \"text\": \"def info(self):\\n info = None\\n\\n if self.is_relation:\\n pairs = self._property.local_remote_pairs\\n if len(pairs):\\n for pair in reversed(pairs):\\n for col in pair:\\n if col.table in self._property.parent.tables and not col.primary_key:\\n return getattr(col, 'info', None)\\n elif col.table in self._property.mapper.tables:\\n if col.primary_key:\\n if self._property.direction == MANYTOMANY:\\n return getattr(col, 'info', None)\\n else:\\n parent_info = getattr(col, 'info', {})\\n info = {}\\n for k, v in parent_info.items():\\n if k.startswith('backref_'):\\n info[k[8:]] = v\\n return info\\n else:\\n try:\\n col = getattr(self.model.__table__.c, self.key)\\n except AttributeError:\\n return {}\\n else:\\n return getattr(col, 'info', None)\\n return {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2284e7e650736db7d2d846565a97849b\",\n \"score\": \"0.5706495\",\n \"text\": \"def determine_joint_names(self):\\n self.joint_names = joint_names(self.prefix)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6603563f3cc8cbf32480f6268ac52d21\",\n \"score\": \"0.56856775\",\n \"text\": \"def get_joint_values(self):\\n return self.group.get_current_joint_values()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74bcd4be1ff7f1eebe1e8e813d4eff60\",\n \"score\": \"0.5661835\",\n \"text\": \"def joint_names(self):\\n return self.arm.joint_names()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"422da9e97ba4553ecee07573b274664e\",\n \"score\": \"0.56460065\",\n \"text\": \"def Joints(self):\\r\\n self.link.check_connection()\\r\\n command = 'G_Thetas'\\r\\n self.link.send_line(command)\\r\\n self.link.send_item(self)\\r\\n joints = self.link.rec_array()\\r\\n self.link.check_status()\\r\\n return joints\",\n \"title\": \"\"\n },\n {\n \"docid\": \"555b1d76c90032faf0457132e115a125\",\n \"score\": \"0.5609154\",\n \"text\": \"def joint_names(self):\\n return self._joint_names\",\n \"title\": \"\"\n },\n {\n \"docid\": \"555b1d76c90032faf0457132e115a125\",\n \"score\": \"0.5609154\",\n \"text\": \"def joint_names(self):\\n return self._joint_names\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a99b5ba3cc85cf93b2503acfd5dbb8d\",\n \"score\": \"0.5587697\",\n \"text\": \"def get_protein_dict(self):\\n protein_dict = {\\\"Protein_name\\\": [self.name], \\\"CAZy_family\\\": [self.family]}\\n\\n if len(self.ec) == 0:\\n protein_dict[\\\"EC#\\\"] = [np.nan]\\n elif len(self.ec) == 1:\\n protein_dict[\\\"EC#\\\"] = self.ec\\n else:\\n ec_string = \\\"\\\\n\\\".join(self.ec)\\n protein_dict[\\\"EC#\\\"] = [ec_string]\\n\\n protein_dict[\\\"Source_organism\\\"] = [self.source]\\n\\n if type(self.links) is dict:\\n for database in [\\\"GenBank\\\", \\\"UniProt\\\", \\\"PDB/3D\\\"]:\\n try:\\n if len(self.links[database]) == 1:\\n protein_dict[database] = self.links[database]\\n else:\\n accession_string = \\\",\\\\n\\\".join(self.links[database])\\n protein_dict[database] = [accession_string]\\n except KeyError:\\n protein_dict[database] = [np.nan]\\n else:\\n for database in [\\\"GenBank\\\", \\\"UniProt\\\", \\\"PDB/3D\\\"]:\\n protein_dict[database] = [np.nan]\\n return protein_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79c27084874bf203883c36472a5f414a\",\n \"score\": \"0.55684775\",\n \"text\": \"def relationship_dicts() -> dict:\\n parent = {\\n 'VariationParent': {\\n 'Identifiers': {\\n 'MarketplaceASIN': {\\n 'ASIN': {\\n 'value': 'TickleStick'\\n }\\n }\\n }\\n }\\n\\n }\\n\\n children = {\\n 'VariationChildren': [{\\n 'Identifiers': {\\n 'MarketplaceASIN': {\\n 'ASIN': {\\n 'value': 'TickleStick'\\n }\\n }\\n }\\n },\\n {\\n 'Identifiers': {\\n 'MarketplaceASIN': {\\n 'ASIN': {\\n 'value': 'Geni'\\n }\\n }\\n }\\n }]\\n\\n }\\n\\n return {'parent': parent, 'children': children}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"01166a642a57859af8b80d3a9f9a82ad\",\n \"score\": \"0.5558503\",\n \"text\": \"def joint_names(self) -> List[str]:\\n return [jnt.name for jnt in self.urdf.joints]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d3397d8b970d055867e3888fffbeaed\",\n \"score\": \"0.5551889\",\n \"text\": \"def get_info(self) -> _InfoDict:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d3397d8b970d055867e3888fffbeaed\",\n \"score\": \"0.5551889\",\n \"text\": \"def get_info(self) -> _InfoDict:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d3397d8b970d055867e3888fffbeaed\",\n \"score\": \"0.5551889\",\n \"text\": \"def get_info(self) -> _InfoDict:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23d734b780a86aa41084bf906b315da8\",\n \"score\": \"0.55495477\",\n \"text\": \"def get_attributes(self):\\n retdict = {}\\n if self.name:\\n retdict['name'] = self.name\\n if self.rule:\\n retdict['rule'] = self.rule\\n retdict['id'] = str(self.id)\\n retdict['junction'] = str(self.road_type)\\n retdict['length'] = str(self.planview.get_total_length())\\n return retdict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d88c62900d967d80d222acf24ac36f2d\",\n \"score\": \"0.5528877\",\n \"text\": \"def get_organism_dictionary():\\n\\n \\\"\\\"\\\"Assume this means dictionaries within a dictionary\\\"\\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b7488a67b7f25c85f651a0e11fd43eb\",\n \"score\": \"0.552584\",\n \"text\": \"def _get_info(self):\\n\\n return {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"333ef79d7258a4f4a806ba0b3d9e7b69\",\n \"score\": \"0.5476746\",\n \"text\": \"def update_joints(self):\\n\\n self.primary_joints = {\\n 'hip': self.hip,\\n 'shoulder': self.shoulder,\\n 'right_elbow': (self.shoulder[0] + 85, self.right_elbow_pos),\\n 'left_elbow': (self.shoulder[0] - 85, self.left_elbow_pos),\\n 'right_knee': (self.hip[0] * 1.25, self.hip[1] * self.right_knee_pos),\\n 'left_knee': (self.hip[0] * 0.75, self.hip[1] * self.left_knee_pos),\\n }\\n\\n\\n self.secondary_joints = {\\n 'right_hand': (self.primary_joints['right_elbow'][0] + 20, self.primary_joints['right_elbow'][1] + self.right_hand_pos),\\n 'left_hand': (self.primary_joints['left_elbow'][0] - 20, self.primary_joints['left_elbow'][1] + self.left_hand_pos),\\n 'right_foot': (self.primary_joints['right_knee'][0] + 10, self.primary_joints['right_knee'][1] + self.right_foot_pos),\\n 'left_foot': (self.primary_joints['left_knee'][0] - 10, self.primary_joints['left_knee'][1] + self.left_foot_pos),\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b96c7bb98c9691b667aaf4f75cbdfd3e\",\n \"score\": \"0.5447122\",\n \"text\": \"def get_details(self):\\n detail_dict = {\\\"platform\\\": self.platform,\\n \\\"src\\\": self.src,\\n \\\"channel\\\": self.channel,\\n \\\"genre\\\": self.genre,\\n \\\"type\\\": self.type,\\n \\\"id\\\": self.id}\\n return detail_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0741286de05bf159a051cf6eae83ae59\",\n \"score\": \"0.54389894\",\n \"text\": \"def build_relationshipdict(self, white=True):\\n rels = get_initial_queryset(\\\"Journal\\\").relationships().written_by(self.obj)\\n if white:\\n rels = rels.white()\\n else:\\n rels = rels.black()\\n relsdict = {}\\n for rel in rels:\\n if rel.db_receivers_objects.all():\\n name = rel.db_receivers_objects.all()[0].key.lower()\\n relslist = relsdict.get(name, [])\\n relslist.append(rel)\\n relsdict[name] = relslist\\n if white:\\n self._white_relationships = relsdict\\n else:\\n self._black_relationships = relsdict\\n return relsdict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ffd040974ee03fe2393fac1bb079099\",\n \"score\": \"0.5438322\",\n \"text\": \"def _read_joint_data(self, jdata):\\r\\n for joint in jdata.get_Joint():\\r\\n jdata = JointData(self.componentsdict[joint.get_AssembledComponentInstanceID()],\\r\\n [self.componentsdict[constrainedtocomp.get_ComponentInstanceID()] for constrainedtocomp in\\r\\n joint.get_ConstrainedToComponents().get_ConstrainedToComponent()],\\r\\n joint.get_Type())\\r\\n jdata.location = Vec3(\\r\\n [float(x) for x in joint.get_GlobalCoordinateSystem().get_Location().get_ArrayValue().split(\\\";\\\")])\\r\\n jdata.orientation = Vec3(\\r\\n [float(x) for x in joint.get_GlobalCoordinateSystem().get_Orientation().get_ArrayValue().split(\\\";\\\")])\\r\\n jdata.id = joint.get_ID()\\r\\n self.joints.append(jdata)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69e439fd21fb96a89f6767d31e514dc4\",\n \"score\": \"0.5434439\",\n \"text\": \"def as_linkage(self):\\n return {'data': {'type': self.type_, 'id': self.id_}}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8854b6a2e28dc2239ef6c89215789e28\",\n \"score\": \"0.5419667\",\n \"text\": \"async def get_dict(self) -> dict:\\n return {\\n \\\"booster\\\": self.booster.id,\\n \\\"target\\\": self.target.id\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc51fa9c378a7f565542675c4b800afc\",\n \"score\": \"0.54082257\",\n \"text\": \"def joint_community(self):\\n return self._joint_community\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e622cd8cf777aee1ac6f8e8b3162b1dc\",\n \"score\": \"0.53909075\",\n \"text\": \"def to_joint_trajectory_msg(self):\\n joint_trajectory_msg = trajectory_msg.JointTrajectory()\\n\\n joint_trajectory_msg.joint_names = [joint.name for joint in self.joints]\\n\\n timestamps = self.get_unique_timestamps()\\n for timestamp in timestamps:\\n joint_trajectory_point = trajectory_msg.JointTrajectoryPoint()\\n joint_trajectory_point.time_from_start = rospy.Duration.from_sec(timestamp)\\n\\n for joint in self.joints:\\n interpolated_setpoint = joint.get_interpolated_setpoint(timestamp)\\n if interpolated_setpoint.time != timestamp:\\n rospy.logwarn('Time mismatch in joint {jn} at timestamp {ts}, '\\n 'got time {ti}'.format(jn=joint.name, ts=timestamp, ti=interpolated_setpoint.time))\\n\\n joint_trajectory_point.positions.append(interpolated_setpoint.position)\\n joint_trajectory_point.velocities.append(interpolated_setpoint.velocity)\\n\\n joint_trajectory_msg.points.append(joint_trajectory_point)\\n\\n return joint_trajectory_msg\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66846692a88a4e49bb8b536def65a373\",\n \"score\": \"0.53876406\",\n \"text\": \"def joint(*args, **kwargs):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe870ca61b0dc04e2448539db7fb5039\",\n \"score\": \"0.5379347\",\n \"text\": \"def to_dict(self):\\n to_dict = dict()\\n to_dict['id'] = self.id\\n if self.disqualified:\\n to_dict['disqualified'] = self.disqualified\\n to_dict['summit'] = self.summit.id\\n to_dict['saddle'] = self.saddle.id\\n return to_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"62756e38bc589f3396ae11139fa3cca0\",\n \"score\": \"0.53661394\",\n \"text\": \"def to_dict(self) -> Dict[str, Any]:\\n return {\\n \\\"satisfied\\\": self.satisfied,\\n \\\"satisfied_at\\\": self.satisfied_at,\\n \\\"annotation\\\": self.annotation.to_dict(),\\n \\\"children\\\": [c.to_dict() for c in self.children],\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80bd23b32f6f567dc218861795c06a1b\",\n \"score\": \"0.5366131\",\n \"text\": \"def serialize(self):\\n error, resp = RelationshipStatus().get_most_recent(self.id_relacion)\\n return {\\n 'id': self.id_relacion,\\n 'usuario': self.usuario.correo_electronico,\\n 'amigo': self.usuario_amigo.correo_electronico,\\n 'estado': resp.estado_solicitud.estado_solicitud\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"180ec04ff95986b4aadab99032a23731\",\n \"score\": \"0.536113\",\n \"text\": \"def _to_dict(self):\\n _dict = {}\\n if hasattr(self, 'translation') and self.translation is not None:\\n _dict['translation'] = self.translation\\n if hasattr(self, 'part_of_speech') and self.part_of_speech is not None:\\n _dict['part_of_speech'] = self.part_of_speech\\n return _dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eeeaa94a3cbfe481786809b7bf9f6971\",\n \"score\": \"0.5354963\",\n \"text\": \"def to_dict(self):\\n\\n info_dict = {'p1': self.p1, 'p2': self.p2,\\n 'surface': self.surface.name,\\n 'tournament_name': self.tournament_name,\\n 'date': self.date}\\n\\n # Flatten additional info if it exists\\n if self.additional_info is not None:\\n info_dict.update(flatten_nested_dict(self.additional_info, ''))\\n\\n if self.tournament_round is not None:\\n info_dict['round'] = self.tournament_round.name\\n else:\\n info_dict['round'] = None\\n\\n return info_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b8b5b1a21b4625d18172b7c7dd58124\",\n \"score\": \"0.53501695\",\n \"text\": \"def get_info(self, observation):\\n info = {}\\n return info\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2625b54efc021486b16108dcc3a7871d\",\n \"score\": \"0.53472024\",\n \"text\": \"def __getitem__(self, joint_id):\\n return self._joints[joint_id]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb9e51f481a0c1fdf185d5ba00436f6f\",\n \"score\": \"0.53373337\",\n \"text\": \"def __iter__(self):\\n return self._joints.itervalues()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0be0acf4d25270f2a774ba831066743a\",\n \"score\": \"0.5335678\",\n \"text\": \"def GetJoin(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eed4fe1279a8a109a279efaa60af282b\",\n \"score\": \"0.5335058\",\n \"text\": \"def get_info(self):\\n return {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c96437a799aeb6b76c44f51a3a245242\",\n \"score\": \"0.5334832\",\n \"text\": \"def serialize(self):\\n return {\\n 'pk' : self.pk,\\n 'tax_id': self.tax_id,\\n # This is an example how to deal with Many2Many relations\\n 'description' : self.description,\\n 'Org_name':self.Org_name\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"137341abce9bd73f2f5957f971b9cf5f\",\n \"score\": \"0.5334062\",\n \"text\": \"def get_info(self):\\n return {\\n 'name': self.name,\\n 'actions': self.actions,\\n 'gamma': self.gamma,\\n 'gravity': self.gravity,\\n 'masscart': self.masscart,\\n 'masspole': self.masspole,\\n 'total_mass': self.total_mass,\\n 'length': self.length,\\n 'polemass_length': self.polemass_length,\\n 'force_mag': self.force_mag,\\n 'tau': self.tau,\\n 'kinematics_integrator': self.kinematics_integrator,\\n 'theta_threshold_radians': self.theta_threshold_radians,\\n 'x_threshold': self.x_threshold\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30dae037fcc072ed4af41197d5ef0c49\",\n \"score\": \"0.53264934\",\n \"text\": \"def _prepare_info_dict(self, joint_agent_action_info, mdp_infos):\\n # Get the agent action info, that could contain info about action probs, or other\\n # custom user defined information\\n env_info = {\\n \\\"agent_infos\\\": [\\n joint_agent_action_info[agent_idx]\\n for agent_idx in range(self.mdp.num_players)\\n ]\\n }\\n # TODO: This can be further simplified by having all the mdp_infos copied over to the env_infos automatically\\n env_info[\\\"sparse_r_by_agent\\\"] = mdp_infos[\\\"sparse_reward_by_agent\\\"]\\n env_info[\\\"shaped_r_by_agent\\\"] = mdp_infos[\\\"shaped_reward_by_agent\\\"]\\n env_info[\\\"phi_s\\\"] = (\\n mdp_infos[\\\"phi_s\\\"] if \\\"phi_s\\\" in mdp_infos else None\\n )\\n env_info[\\\"phi_s_prime\\\"] = (\\n mdp_infos[\\\"phi_s_prime\\\"] if \\\"phi_s_prime\\\" in mdp_infos else None\\n )\\n return env_info\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea1cca6174d417a90e294ca23d456d6d\",\n \"score\": \"0.53198475\",\n \"text\": \"def _create_info_dictionary(self, obs):\\n return {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cdf56b2892cf33d8bfb071bda2a4c9ff\",\n \"score\": \"0.53118724\",\n \"text\": \"def _dictme(self):\\r\\n\\r\\n return {\\\"name\\\": self._name,\\r\\n \\\"stats\\\": self._stats,\\r\\n \\\"lvl\\\": self._lvl,\\r\\n \\\"race\\\": str(self._race),\\r\\n \\\"char_class\\\": str(self._chclass),\\r\\n \\\"background\\\": self._background,\\r\\n \\\"profs\\\": list(self._profs.keys())\\r\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59e600d4e5c6e37364919daa4b63313b\",\n \"score\": \"0.5302283\",\n \"text\": \"def jointgroup(self):\\n return util.buf_to_npy(self._ptr.contents.jointgroup, (6,))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3383d182b5f63bfa92512bd39f5768b8\",\n \"score\": \"0.52996635\",\n \"text\": \"def getJoinDict(url,referenceSetId=0):\\n\\tdef defaultList():\\n\\t\\treturn defaultdict(list)\\n\\tjoinDict=defaultdict(defaultList)\\n\\turl+='/joins/search'\\n\\treq={\\n \\\"length\\\": None, \\n \\\"pageSize\\\": 100, \\n \\\"pageToken\\\": '0', \\n \\\"referenceSetId\\\": str(referenceSetId), \\n \\\"sequenceId\\\": '', \\n \\\"start\\\": None, \\n \\\"strand\\\": None, \\n \\\"variantSetId\\\": ''\\n\\t}\\n\\tnextPageToken=True\\n\\theader={'Content-Type':'application/json'}\\n\\twhile nextPageToken:\\n\\t\\tres=requests.post(url,data=json.dumps(req),headers=header)\\n\\t\\tthePage=json.loads(res.text)\\n\\t\\tnextPageToken=thePage['nextPageToken']\\n\\t\\treq['pageToken']=nextPageToken\\n\\t\\tjoins=thePage['joins']\\n\\t\\tfor join in joins:\\n\\t\\t\\tside1=join['side1']\\n\\t\\t\\tside2=join['side2']\\n\\t\\t\\tbase1=side1['base']\\n\\t\\t\\tbase2=side2['base']\\n\\t\\t\\tseq1=base1['sequenceId']\\n\\t\\t\\tseq2=base2['sequenceId']\\n\\t\\t\\tpos1=int(base1['position'])\\n\\t\\t\\tpos2=int(base2['position'])\\n\\t\\t\\tstrand1=side1['strand']\\n\\t\\t\\tstrand2=side2['strand']\\n\\t\\t\\tjoinDict[seq1][(pos1,strand1)].append((seq2,pos2,strand2))\\n\\t\\t\\tjoinDict[seq2][(pos2,strand2)].append((seq1,pos1,strand1))\\n\\treturn joinDict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11a1ab9901e6f822f89f4ea6d81d182e\",\n \"score\": \"0.52772593\",\n \"text\": \"def get_dictionary(self):\\n settlement_dict = {}\\n settlement_dict[\\\"road_id\\\"] = self.id\\n settlement_dict[\\\"road_row\\\"] = self.location[0]\\n settlement_dict[\\\"road_column\\\"] = self.location[1]\\n settlement_dict[\\\"road_color\\\"] = self.color\\n if self.ownedBy is not None:\\n settlement_dict[\\\"road_ownedBy\\\"] = self.ownedBy.id\\n if self.attached_settlement is not None:\\n settlement_dict[\\\"attached_settlement\\\"] = self.attached_settlement.id\\n return settlement_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"877c5a7bcef4b374b3b83b6d23344987\",\n \"score\": \"0.52753085\",\n \"text\": \"def final_position(self):\\n return {joint.name: joint.setpoints[-1].position for joint in self.joints}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4a448f6964950f9f7fe7d4b4d0af700\",\n \"score\": \"0.5273507\",\n \"text\": \"def _to_dict(self):\\n _dict = {}\\n if hasattr(self, 'external_id') and self.external_id is not None:\\n _dict['externalId'] = self.external_id\\n if hasattr(self, 'document_id') and self.document_id is not None:\\n _dict['documentId'] = self.document_id\\n if hasattr(self, 'parent_document_id') and self.parent_document_id is not None:\\n _dict['parentDocumentId'] = self.parent_document_id\\n if hasattr(self, 'publication_name') and self.publication_name is not None:\\n _dict['publicationName'] = self.publication_name\\n if hasattr(self, 'publication_date') and self.publication_date is not None:\\n _dict['publicationDate'] = self.publication_date\\n if hasattr(self, 'publication_url') and self.publication_url is not None:\\n _dict['publicationURL'] = self.publication_url\\n if hasattr(self, 'authors') and self.authors is not None:\\n _dict['authors'] = self.authors\\n if hasattr(self, 'title') and self.title is not None:\\n _dict['title'] = self.title\\n if hasattr(self, 'medline_license') and self.medline_license is not None:\\n _dict['medlineLicense'] = self.medline_license\\n if hasattr(self, 'href_pub_med') and self.href_pub_med is not None:\\n _dict['hrefPubMed'] = self.href_pub_med\\n if hasattr(self, 'pdf_url') and self.pdf_url is not None:\\n _dict['pdfUrl'] = self.pdf_url\\n if hasattr(self, 'reference_url') and self.reference_url is not None:\\n _dict['referenceUrl'] = self.reference_url\\n if hasattr(self, 'highlighted_title') and self.highlighted_title is not None:\\n _dict['highlightedTitle'] = self.highlighted_title\\n if hasattr(self, 'highlighted_abstract') and self.highlighted_abstract is not None:\\n _dict['highlightedAbstract'] = self.highlighted_abstract\\n if hasattr(self, 'highlighted_body') and self.highlighted_body is not None:\\n _dict['highlightedBody'] = self.highlighted_body\\n if hasattr(self, 'highlighted_sections') and self.highlighted_sections is not None:\\n _dict['highlightedSections'] = ({k : v._to_dict() for k, v in\\n self.highlighted_sections.items()})\\n if hasattr(self, 'passages') and self.passages is not None:\\n _dict['passages'] = self.passages\\n if hasattr(self, 'annotations') and self.annotations is not None:\\n _dict['annotations'] = {k : v._to_dict() for k, v in self.annotations.items()}\\n return _dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"465637611f9f736ae0bae5fedbe10ea2\",\n \"score\": \"0.526984\",\n \"text\": \"def get_joint_name(self):\\n return self.joint_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6188f2df0044d5a4cc24ca8e1a020357\",\n \"score\": \"0.5267064\",\n \"text\": \"def classifier_info(self):\\n return {\\n 'classname': self.classname,\\n 'subject': self.subject,\\n 'roc': self.roc,\\n 'auc': self.auc,\\n 'params': json.dumps(self.params),\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17a549efbcdd1c126c61b0a153d3dc9a\",\n \"score\": \"0.5262656\",\n \"text\": \"def json_data(self):\\n return {\\n \\\"type\\\": self.type,\\n \\\"recruiter\\\": self.recruiter_id,\\n \\\"assignment_id\\\": self.assignment_id,\\n \\\"hit_id\\\": self.hit_id,\\n \\\"mode\\\": self.mode,\\n \\\"end_time\\\": self.end_time,\\n \\\"base_pay\\\": self.base_pay,\\n \\\"bonus\\\": self.bonus,\\n \\\"status\\\": self.status,\\n \\\"object_type\\\": \\\"Participant\\\",\\n \\\"entry_information\\\": self.entry_information,\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a1bcf4f5689b5319c73539510f330f6\",\n \"score\": \"0.52610385\",\n \"text\": \"def _known_persons(self, home_id: str) -> dict[str, dict]:\\n return {pid: p for pid, p in self.persons[home_id].items() if \\\"pseudo\\\" in p}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"291ec2704b719fa5d2a789e1908d5f14\",\n \"score\": \"0.52527004\",\n \"text\": \"def JointAccount(self):\\n return super(FContactRegulatoryInfo, self).JointAccount()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36e7ac51947e604798085b8121f1b1e5\",\n \"score\": \"0.52463794\",\n \"text\": \"def joint_array_to_dict(vel_torque_array, limb):\\n\\n return dict(itertools.izip(limb.joint_names(), vel_torque_array))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4784ff9bee5067a8d2ed7a9a2db52780\",\n \"score\": \"0.5244284\",\n \"text\": \"def serialize(self):\\n\\t\\treturn {\\n\\t\\t\\t'notice_id' : self.notice_id,\\n\\t\\t\\t'timestamp' : self.timestamp,\\n\\t\\t\\t'message' : self.message,\\n\\t\\t\\t'c_id' : self.c_id\\n\\t\\t\\t# This is an example how to deal with Many2Many relations\\n\\t\\t#\\t'many2many' : self.serialize_many2many\\n\\t\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76632e4065eb6cf729e4027ab411c0a4\",\n \"score\": \"0.524409\",\n \"text\": \"def convert_book_info(self):\\n\\n book_id = \\\"book/{}\\\".format(self.books.book_id)\\n book_title = self.books.title\\n subjects = self.books.subjects\\n print(subjects)\\n book_dict ={\\n \\\"id\\\" :book_id,\\n \\\"nodeName\\\": book_title,\\n \\\"type\\\": \\\"book\\\",\\n \\\"subjects\\\": []\\n }\\n\\n return book_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"408654f17dd7687231850c185f010b17\",\n \"score\": \"0.5231306\",\n \"text\": \"def qtrobot_joints_callback(self, msg):\\n self.qtrobot_joint_dictionary = dict(zip(msg.name, msg.position))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69ea2d6b02948daa38a8b27d26d2d257\",\n \"score\": \"0.52244484\",\n \"text\": \"def get_additional_info(self) -> dict:\\n return {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03a1f4a5e018277f9927273afc5b5107\",\n \"score\": \"0.5223652\",\n \"text\": \"def get_info(self):\\n info = {\\n 'name': self.name,\\n 'model': self.model,\\n 'syringe_diameter': self.diameter,\\n 'rate': self.rate\\n }\\n return info\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23bf576946779dc34ff7fc6faffeabd4\",\n \"score\": \"0.5217067\",\n \"text\": \"def to_dict(self):\\n rep = super(Person, self).to_dict()\\n rep.update({\\n 'identifier': self.get('identifier', None),\\n 'name': self.name,\\n 'email': self.email,\\n })\\n return rep\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f929e67261ad6167ac05e9c7bc1905c4\",\n \"score\": \"0.5217003\",\n \"text\": \"def serialize(self):\\n\\t\\treturn {\\n\\t\\t\\t'enrolls_id' : self.enrolls_id,\\n\\t\\t\\t'student_id' : self.student_id,\\n\\t\\t\\t'course_id' : self.course_id\\n\\t\\t\\t# This is an example how to deal with Many2Many relations\\n\\t\\t\\t#\\t'many2many' : self.serialize_many2many\\n\\t\\t}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83f39aeb2d2e9ce694ff89d2923ac8c0\",\n \"score\": \"0.5211111\",\n \"text\": \"def _extract_info(self, infos, domain):\\n ids = []\\n residues = infos[domain]['residues']\\n # the residues are necessarily in the same chain, so :\\n chain = residues[0].get_parent().get_id()\\n for residue in residues:\\n number = str(residue.get_id()[1])\\n if number not in ids:\\n ids.append(number)\\n return {'chain': chain, 'ids': ids}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5638ea690f583d18769c933ac7a6787e\",\n \"score\": \"0.5210504\",\n \"text\": \"def joint_type(self):\\n return self._joint_type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77c283cf4889c65cb2ba9e57fb0e4255\",\n \"score\": \"0.5210123\",\n \"text\": \"def getTransformationMatrices(self):\\n transformationMatrices = {}\\n # TODO modify from here\\n # Hint: the output should be a dictionary with joint names as keys and\\n # their corresponding homogeneous transformation matrices as values.\\n return transformationMatrices\",\n \"title\": \"\"\n },\n {\n \"docid\": \"172f110a538b236726848b9e5910a6fa\",\n \"score\": \"0.52030563\",\n \"text\": \"def info(self):\\n if self.name == \\\"\\\":\\n return {}\\n else:\\n return {\\\"name\\\" : self.name}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"944b0d031eb5b090d84d288a43fd888f\",\n \"score\": \"0.5201102\",\n \"text\": \"def get_info(message):\\n try:\\n from_info = message.get('From', 'No sender provided')\\n to_info = message.get('To', 'No recipient provided')\\n date_info = message.get('Date', 'No date provided')\\n subj_info = message.get('Subject', 'No subject provided')\\n except:\\n print('Didnt provide correct email object instance')\\n raise\\n return {'From':from_info, 'To':to_info ,'Date':date_info, 'Subject':subj_info }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd0322522c50de936c9cddec8f3effa3\",\n \"score\": \"0.5191566\",\n \"text\": \"def _to_dict(self):\\n _dict = {}\\n if hasattr(self, 'qualifier_id') and self.qualifier_id is not None:\\n _dict['qualifier_id'] = self.qualifier_id\\n if hasattr(self, 'qualifier_name') and self.qualifier_name is not None:\\n _dict['qualifier_name'] = self.qualifier_name\\n return _dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd0b5d04fffbef9a480349cc3ae6e8b9\",\n \"score\": \"0.51772\",\n \"text\": \"def export_relationship_json(rel: Relationship) -> Dict:\\n return {\\\"node_a\\\": rel.node_a.node_id, \\\"relationship_type\\\": rel.relationship_type, \\\"node_b\\\": rel.node_b.node_id,\\n \\\"properties\\\": rel.properties}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8cb66cb5ce4edafaac5658a651f776d0\",\n \"score\": \"0.5176936\",\n \"text\": \"def get_entity(self):\\n if self.sender_id:\\n return {'user_id': self.sender_id}\\n return {'name': self.name}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e13878f3958f0dcdf4e1f9f609f5188d\",\n \"score\": \"0.5172545\",\n \"text\": \"def get_breach_details():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1528e1cebce18657d064dd15d1102a71\",\n \"score\": \"0.5170092\",\n \"text\": \"def getInfo(self):\\n return {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"038c5cc0433079592e5d407b5d2b02c5\",\n \"score\": \"0.51697385\",\n \"text\": \"def serialize(self):\\n return {\\n 'id' : self.id,\\n 'sender_id' : self.sender_id,\\n 'recipient_id' : self.recipient_id,\\n 'sender_first_name' : self.sender_first_name,\\n 'sender_last_name' : self.sender_last_name,\\n 'sender_picture' : self.sender_picture,\\n 'recipient_first_name' : self.recipient_first_name,\\n 'recipient_last_name' : self.recipient_last_name,\\n 'sent_status' : self.sent_status,\\n 'message' : self.message,\\n 'time_sent' : self.time_sent,\\n 'conversation_id' : self.conversation_id\\n \\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f8a7c8bceb17dd4e3291df451b9deb2\",\n \"score\": \"0.5168953\",\n \"text\": \"def to_json_dict(self) -> dict:\\r\\n\\r\\n return {'id_time' : self.id_tine, 'id_user' : self.id_user,\\r\\n 'access_mode' : self.access_mode,\\r\\n 'attributes' : self.attributes.to_json_dict(),\\r\\n 'attachments' : self.attachments.to_json_dict(),\\r\\n 'is_finished' : self.is_finished}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa6ebf8bae06447ffa6ea3239ba4af26\",\n \"score\": \"0.51628435\",\n \"text\": \"def get_attributes(self):\\n retdict = {}\\n retdict['entityRef'] = self.target\\n retdict['freespace'] = convert_bool(self.freespace)\\n retdict['continuous'] = convert_bool(self.continuous)\\n if self.distance:\\n retdict['distance'] = str(self.distance)\\n return retdict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fde6c1f6846b9ca827eb5aeb430fab00\",\n \"score\": \"0.5162007\",\n \"text\": \"def relations(relate_soup):\\n lis = []\\n for r in relate_soup.find_all('tr'):\\n relation = {}\\n types = r.find_all('td')\\n \\n if len(types)==4:\\n relation['name'] = types[0].text.strip()\\n relation['type'] = types[1].text.strip()\\n relation['year'] = types[2].text.strip()\\n relation['location'] = types[3].text.strip()\\n lis.append(relation)\\n return lis\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc62631ebfda8c39c1b193cab8d05744\",\n \"score\": \"0.5161577\",\n \"text\": \"def joints(self) -> npt.NDArray[np.float64]:\\n return self._joints\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4930f6531a8b6972f7d10f204a6613d3\",\n \"score\": \"0.5157041\",\n \"text\": \"def _get_join_desired(self):\\n return self.__join_desired\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed5c2a7fb771688ca0d40e9b350b22c0\",\n \"score\": \"0.51545626\",\n \"text\": \"def get_details(self):\\n return {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80c3b33460f841b67917bb39f0ac6847\",\n \"score\": \"0.51529557\",\n \"text\": \"def _to_dict(self):\\n return {\\n \\\"hookup_type\\\": self.hookup_type,\\n \\\"corr_index\\\": self.corr_index,\\n \\\"all_pols\\\": self.all_pols,\\n \\\"redirect_part_types\\\": self.redirect_part_types,\\n \\\"single_pol_labeled_parts\\\": self.single_pol_labeled_parts,\\n \\\"full_connection_path\\\": self.full_connection_path,\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33f251c444deef95f8c56e70480cdb95\",\n \"score\": \"0.51513135\",\n \"text\": \"def get_joint_index(self):\\n return self.joint_index\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61f634d387369d7de08c709432ec9091\",\n \"score\": \"0.51467377\",\n \"text\": \"def get_dict(self):\\n para_dict = {\\n \\\"id\\\": self.id,\\n \\\"news_id\\\": self.news_id,\\n \\\"type\\\": self.type,\\n \\\"title\\\": self.title,\\n \\\"publish_time\\\": self.publish_time,\\n \\\"content\\\": self.content,\\n \\\"img_name\\\": self.img_name,\\n \\\"audio_name\\\": self.audio_name,\\n \\\"has_img\\\": self.has_img,\\n \\\"has_audio\\\": self.has_audio,\\n \\\"dangos\\\": self.dangos\\n }\\n\\n return para_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7523395f3c565d7ff9dd7b0af96d1f81\",\n \"score\": \"0.5145694\",\n \"text\": \"def get_infos(self):\\n infos = dict()\\n infos[\\\"dataset\\\"] = self.dataset_name\\n infos[\\\"task\\\"] = \\\"harmony_sep\\\"\\n infos[\\\"licenses\\\"] = [mdb_license]\\n return infos\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec2c0ebe6d69b174f1ad4bc536314b19\",\n \"score\": \"0.5141187\",\n \"text\": \"def dictionary(self):\\n return {\\n \\\"name\\\": self.name, \\\"agency\\\": self.agency, \\\"mission\\\": self.type_of_mission,\\n \\\"year_launched\\\": self.year_launched}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd24214198d90e08997c285f8bc68664\",\n \"score\": \"0.5140088\",\n \"text\": \"def get_dict():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aef4509c432c822f7a228247d67af651\",\n \"score\": \"0.51353806\",\n \"text\": \"def as_linkage(self):\\n return {'data': None}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a31e9f29c1ff0de423dc8f9dc445027\",\n \"score\": \"0.5133731\",\n \"text\": \"def convert_info(self):\\n\\n author_dict = {\\n \\\"id\\\" : \\\"author/{}\\\".format(self.author_id),\\n \\\"nodeName\\\": self.name,\\n \\\"type\\\": \\\"author\\\"\\n }\\n\\n return author_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d7f55b5586cb0728c9abb6a8e63a17a\",\n \"score\": \"0.5127704\",\n \"text\": \"def get_info(self):\\n ret = {}\\n ret[\\\"isSequence\\\"] = self.isSequence\\n ret[\\\"maxSequenceLength\\\"] = self.maxSequenceLength\\n ret[\\\"avgSequenceLength\\\"] = self.avgSequenceLength\\n ret[\\\"nAttrs\\\"] = self.nAttrs\\n ret[\\\"nFeatures\\\"] = self.nFeatures\\n ret[\\\"nInstances\\\"] = self.nInstances\\n ret[\\\"targetType\\\"] = self.targetType\\n ret[\\\"nClasses\\\"] = self.nClasses\\n ret[\\\"targetClasses\\\"] = self.targetClasses\\n ret[\\\"features\\\"] = self.features\\n ret[\\\"target\\\"] = self.target\\n return ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b79ee125065e6369b6a364f27a592dc2\",\n \"score\": \"0.5127179\",\n \"text\": \"def print_information(self) -> None:\\n print('---' * 20)\\n print(f' Joint name: \\\\t \\\\t{self.name}')\\n print(f' Joint type: \\\\t \\\\t{self.type}')\\n print(f' Joint index: \\\\t \\\\t{self.index}')\\n print(f' Body index: \\\\t \\\\t{self.body_id}')\\n print(f' maximum force: \\\\t \\\\t{self.max_force}')\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":684,"cells":{"query_id":{"kind":"string","value":"5d53dc63cf45f2b3db50f8910018ee32"},"query":{"kind":"string","value":"Initialize with the column names (in a XiboEvent tuple) use in Xibo JSON responses."},"positive_passages":{"kind":"list like","value":[{"docid":"4b51f78cbba9b28efcdfefb61709a950","score":"0.58089435","text":"def __init__(self, column_names):\n self.column_names = column_names","title":""}],"string":"[\n {\n \"docid\": \"4b51f78cbba9b28efcdfefb61709a950\",\n \"score\": \"0.58089435\",\n \"text\": \"def __init__(self, column_names):\\n self.column_names = column_names\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"b91406418d4f7587da0fc1f8a3998de4","score":"0.5988165","text":"def json_to_xibo_event(self, json_event):\n return XiboEvent(\n xibo_id=json_event[self.column_names.xibo_id],\n meetup_id=json_event[self.column_names.meetup_id],\n name=json_event[self.column_names.name],\n location=json_event[self.column_names.location],\n start_time=json_event[self.column_names.start_time],\n end_time=json_event[self.column_names.end_time],\n )","title":""},{"docid":"9fe58cf205025650616deb803c0b3281","score":"0.5880382","text":"def event_to_columns(self, event):\n return {\n self.column_ids.meetup_id: event.meetup_id,\n self.column_ids.name: event.name,\n self.column_ids.location: event.location,\n self.column_ids.start_time: event.start_time,\n self.column_ids.end_time: event.end_time\n }","title":""},{"docid":"6fa46088315790b24ff4a2ea42e2ae13","score":"0.55893785","text":"def __init__(self, metadata_columns, tag_columns):\n self.data = None\n self.metadata_columns = metadata_columns\n self.tag_columns = tag_columns","title":""},{"docid":"2c93d6b4a297340b664fd4328b8b2608","score":"0.53798157","text":"def initColumnHeaders():","title":""},{"docid":"37035d405ff17e55c796d5c2c68b99ce","score":"0.5357916","text":"def __init__(self, column_ids):\n self.column_ids = column_ids","title":""},{"docid":"0fd4658c4d564e0ea5d8d9bc2002c9c4","score":"0.5304528","text":"def __init__(self, event=None):\n if event is not None:\n for key, val in list(event.__dict__.items()):\n setattr(self, key, val)","title":""},{"docid":"3e001a4881aeda1663693e07ebb37473","score":"0.52718776","text":"def _parse_event(self, response):\n data = {\n '_type': 'event',\n 'name': self._parse_name(response),\n 'event_description': self._parse_description(response),\n 'start': self._parse_start(response),\n 'end': self._parse_end(response),\n 'all_day': self._parse_all_day(response),\n 'location': self._parse_location(response),\n 'documents': self._parse_documents(response),\n 'sources': self._parse_sources(response)\n }\n data['id'] = self._generate_id(data)\n data['status'] = self._generate_status(data)\n data['classification'] = self._parse_classification(data['name'])\n return data","title":""},{"docid":"6b4e7e8e177186c328c5c8edd4974448","score":"0.52701914","text":"def get_event_columns(self):\n if not self.dwca_event_columns:\n self.dwca_event_columns = [\n 'id', \n 'eventID', \n 'parentEventID', \n 'type', \n 'samplingProtocol', \n 'sampleSizeValue', \n 'sampleSizeUnit', \n 'samplingEffort', \n 'eventDate', \n 'eventTime', \n 'startDayOfYear', \n 'endDayOfYear', \n 'year', \n 'month', \n 'day', \n 'verbatimEventDate', \n 'habitat', \n 'fieldNumber', \n 'fieldNotes', \n 'eventRemarks', \n 'locationID', \n 'country', \n 'countryCode', \n 'county', \n 'municipality', \n 'locality', \n 'verbatimLocality', \n 'waterBody', \n 'verbatimDepth', \n 'minimumDepthInMeters', \n 'maximumDepthInMeters', \n 'decimalLatitude', \n 'decimalLongitude', \n 'geodeticDatum', \n 'license', \n 'rightsHolder', \n 'accessRights', \n 'bibliographicCitation', \n 'references', \n 'institutionID', \n 'datasetID', \n 'institutionCode', \n 'datasetName', \n 'ownerInstitutionCode', \n 'dataGeneralizations', # (ex: aggregerad över storleksklass...)\n 'dynamicProperties', \n ]\n #\n return self.dwca_event_columns","title":""},{"docid":"c52a3d711d4f061f53175be4fac982d3","score":"0.52676564","text":"def __create_message__(cls, row):\n message = dict()\n message['eventtype'] = cls.EVENT_TYPE\n message['type'] = row.pop('state', None)\n inner_obj = dict()\n for key, value in cls.DB_MAP.items():\n inner_obj[key] = row.pop(value)\n\n message[cls.INNER_OBJ] = cls.__clean_dict__(inner_obj)\n message = cls.__clean_dict__(message)\n return message","title":""},{"docid":"8e2e08613fff835239210831346302ac","score":"0.52657056","text":"def _populate_from_database(self, row):\n\n (name, message, button, mode, \n groups, start, end, author, created_at) = row\n\n self['name'] = name\n self['message'] = message\n self['button'] = button\n self['mode'] = mode\n self['groups'] = json.loads(groups) if groups else None\n self['start'] = from_utimestamp(start)\n self['end'] = from_utimestamp(end)\n self['author'] = author\n self['created_at'] = from_utimestamp(created_at)","title":""},{"docid":"3a26b03e217949446268368964e9e1d2","score":"0.5224331","text":"def __init__(self):\n self._map1 = {\n \"CIRC\" : self.circ_status_event,\n \"STREAM\" : self.stream_status_event,\n \"ORCONN\" : self.or_conn_status_event,\n \"STREAM_BW\" : self.stream_bw_event,\n \"BW\" : self.bandwidth_event,\n \"DEBUG\" : self.msg_event,\n \"INFO\" : self.msg_event,\n \"NOTICE\" : self.msg_event,\n \"WARN\" : self.msg_event,\n \"ERR\" : self.msg_event,\n \"NEWDESC\" : self.new_desc_event,\n \"ADDRMAP\" : self.address_mapped_event,\n \"NS\" : self.ns_event,\n \"STATUS_GENERAL\" : self.general_status_event,\n \"STATUS_CLIENT\" : self.client_status_event,\n \"STATUS_SERVER\" : self.server_status_event,\n \"ORCIRCUIT\" : self.orcircuit_event,\n \"NEWCONSENSUS\" : self.new_consensus_event,\n \"TOKEN_LEVELS\" : self.token_level_event\n }","title":""},{"docid":"3b6dee8d464df1e0b99e3233fe8f77c0","score":"0.51967037","text":"def _init_column_names():\n mapping = {}\n names = []\n\n index = 1\n while index < 256:\n name = _colnum_string(index)\n names.append(name)\n mapping[name] = index\n index += 1\n return names, mapping","title":""},{"docid":"f53e3234f705e656da34736ba81c8c76","score":"0.5186084","text":"def _data_events(self, object):\n\t\t# Builds an event list that gives for each event:\n\t\t# - Gramps ID\\n\"\n\t\t# - The event name\n\t\t# - The event date\n\t\t# - The event date in ISO format (sortable)\n\t\t# - The event place index (in table 'P'), -1 if none\n\t\t# - The event description\n\t\t# - The event text and notes (including event reference notes)\n\t\t# - A list of the event media index, in the form:\n\t\t# - media index (in table 'M')\n\t\t# - media thumbnail path\n\t\t# - [x1, y1, x2, y2] of the media reference\n\t\t# - notes of the media reference\n\t\t# - list of the media reference source citations index (in table 'C')\\n\"\n\t\t# - A list of the event source citations index (in table 'C')\n\t\tevent_ref_list = object.get_event_ref_list()\n\t\tif not event_ref_list: return(\"\")\n\t\trows = []\n\t\tfor event_ref in event_ref_list:\n\t\t\tif (event_ref.ref not in self.obj_dict[Event]): continue\n\t\t\tevent = self.database.get_event_from_handle(event_ref.ref)\n\t\t\tif (not event): continue\n\t\t\ttrow = \"\\t[\"\n\t\t\tevt_type = str(event.get_type())\n\t\t\tevent_role = event_ref.get_role()\n\t\t\tif (event_role != EventRoleType.PRIMARY and event_role != EventRoleType.FAMILY):\n\t\t\t\tevt_type += \" (%s)\" % event_role\n\t\t\tplace_index = -1\n\t\t\tplace_handle = event.get_place_handle()\n\t\t\tif (place_handle and (place_handle in self.obj_dict[Place])):\n\t\t\t\tplace_index = self.obj_dict[Place][place_handle][OBJDICT_INDEX]\n\t\t\tevt_desc = event.get_description()\n\t\t\ttrow += \"\\\"\" + self.obj_dict[Event][event_ref.ref][OBJDICT_GID] + \"\\\",\"\n\t\t\ttrow += \"\\\"\" + script_escape(html_escape(evt_type)) + \"\\\",\"\n\t\t\tevt_date = format_date(event.get_date_object())\n\t\t\ttrow += \"\\\"\" + script_escape(html_escape(evt_date)) + \"\\\",\"\n\t\t\tevt_date = format_date(event.get_date_object(), True)\n\t\t\ttrow += \"\\\"\" + script_escape(html_escape(evt_date)) + \"\\\",\"\n\t\t\ttrow += str(place_index) + \",\"\n\t\t\tif (evt_desc is None): evt_desc = \"\"\n\t\t\ttrow += \"\\\"\" + script_escape(html_escape(evt_desc)) + \"\\\",\"\n\t\t\t# Get event notes\n\t\t\tnotelist = event.get_note_list()\n\t\t\tnotelist.extend(event_ref.get_note_list())\n\t\t\tattrlist = event.get_attribute_list()\n\t\t\tattrlist.extend(event_ref.get_attribute_list())\n\t\t\ttrow += \"\\\"\" + script_escape(self.get_notes_attributes_text(notelist, attrlist)) + \"\\\",\"\n\t\t\t# Get event media\n\t\t\ttrow += self._data_media_reference_index(event)\n\t\t\ttrow += \",\"\n\t\t\t# Get event sources\n\t\t\tcitationlist = event.get_citation_list()\n\t\t\tcitationlist.extend(event_ref.get_citation_list())\n\t\t\tfor attr in attrlist: citationlist.extend(attr.get_citation_list())\n\t\t\ttrow += self._data_source_citation_index_from_list(citationlist)\n\t\t\t#\n\t\t\ttrow += \"]\"\n\t\t\trows.append(trow)\n\t\treturn(\",\\n\".join(rows))","title":""},{"docid":"3b907638876dee9732352a59351036d8","score":"0.5179667","text":"def initialize(self):\n self.__as_columns.clear()\n \n db_map = self.__aggregate.getProofInstance().getDatabaseMap(self.getDBName())\n table_map = db_map.getTable(self.__table_name)\n column_maps = table_map.getColumns()\n for column_map in column_maps:\n column_name = column_map.getFullyQualifiedName()\n self.__as_columns[column_name] = column_name\n\n self.__initialized = 1","title":""},{"docid":"ff383e535b7f2d0abffb5d856f45af65","score":"0.51698637","text":"def json_to_column_ids(self, json_columns):\n heading_to_id_map = {\n column[\"heading\"]:\n \"dataSetColumnId_{}\".format(column[\"dataSetColumnId\"])\n for column in json_columns\n }\n heading_to_id_map[self.column_names.xibo_id] = None\n return self.json_to_xibo_event(heading_to_id_map)","title":""},{"docid":"d621bf8463ee4adc4e6db807d0dd35c7","score":"0.513667","text":"def __init__(self, events, database, symbol_list):\n self.events = events\n self.database = database\n self.symbol_list = symbol_list\n\n self.symbol_data = {}\n self.latest_symbol_data = {}\n self.continue_backtest = True\n self.bar_index = 0\n self.all_data_dic = {} # access data in list form for testing\n\n self._open_convert_database_data()","title":""},{"docid":"556411b20c977e1a15b99e65e98724f5","score":"0.5135141","text":"def __init__(self, source, data_column, label_columns):\n self._source = source\n self._data_column = data_column\n self._label_columns = label_columns","title":""},{"docid":"c392fe47ce9273a24396ab8ec6de90b4","score":"0.5060727","text":"def __init__(self, **kwargs):\n super().__init__(**kwargs)\n self._label_column_name = None\n self._numeric_columns = None\n self._cat_columns = None","title":""},{"docid":"fb2380ba54b7ce37ccd546b7d61f3043","score":"0.5059268","text":"def __init__(self, init_example, **kwargs):\n super().__init__(init_example, **kwargs)\n self.event_mat = init_example['events'][None]","title":""},{"docid":"53d42a3dc49e6b2b68e839d5ae76ad56","score":"0.5048306","text":"def __init__(self):\n event_data_stream = events.EventDataStream()\n\n super(FieldFormattingHelper, self).__init__()\n self._callback_functions = {}\n self._event_data_stream_field_names = event_data_stream.GetAttributeNames()\n self._event_tag_field_names = []\n\n for field_name, callback_name in self._FIELD_FORMAT_CALLBACKS.items():\n if callback_name == '_FormatTag':\n self._event_tag_field_names.append(field_name)\n else:\n self._callback_functions[field_name] = getattr(\n self, callback_name, None)","title":""},{"docid":"ef855f557aea52947c1211326cf0dbe3","score":"0.5042969","text":"def events_to_dataframe(events):\n columns = [\"id\", \"name\", \"source\", \"time\", \"description\"]\n data = {column: list() for column in columns}\n for event in events:\n data[\"id\"].append(event.get_id())\n data[\"name\"].append(event.get_name())\n data[\"source\"].append(event.get_source())\n data[\"time\"].append(event.get_time())\n data[\"description\"].append(event.get_description())\n return pd.DataFrame(data)","title":""},{"docid":"fb3984eba4945e2793a0d918fc8882c8","score":"0.5035369","text":"def extract_new_event_data(table_name, response):\n output = {}\n context = {}\n\n output['Status'] = response.get('status', '')\n output['Message'] = response.get('message', '')\n output['Incident key'] = response.get('dedup_key', '')\n\n context['Status'] = output['Status']\n context['Message'] = output['Message']\n context['incident_key'] = output['Incident key']\n\n return {\n 'Type': entryTypes['note'],\n 'Contents': response,\n 'ContentsFormat': formats['json'],\n 'ReadableContentsFormat': formats['markdown'],\n 'HumanReadable': tableToMarkdown(table_name, output),\n 'EntryContext': {\n 'PagerDuty.Event(val.incident_key==obj.dedup_key)': context,\n 'Event.ID(val.ID==obj.dedup_key)': context['incident_key']\n }\n }","title":""},{"docid":"d57800ca714283669f7ae4fc0c162373","score":"0.5033141","text":"def __init__(self, eventdata_data):\n self.events = eventdata_data","title":""},{"docid":"4f5a8ae66ba289320e896ab610cb3f26","score":"0.50146925","text":"def __init__(self, *args, **kwargs):\n\n args = list(args)\n name, type_ = self._extract_name_and_type(args, kwargs)\n kwargs.update(name=name, type_=self._column_type)\n\n super().__init__(*args, **kwargs)","title":""},{"docid":"db3bc598712d408f6e8d19a47f7b4a61","score":"0.50042796","text":"def __init__(self, event_type):\n if (isinstance(event_type, str)):\n self.event_type = self.EVENT_TYPES_REVERSE[event_type]\n else:\n self.event_type = event_type\n self.err_str = None\n self.data = None","title":""},{"docid":"1eac21066ef32d41680a8afe1ee61721","score":"0.50023246","text":"def _convert_event(self, event):\n\n doc = {\"h\": event.hostname,\n \"ts\": event.timestamp}\n if event.args:\n doc[\"fields\"] = event.args\n return doc","title":""},{"docid":"3f3ea996e27cdc926f8fa7e5e4a7bb88","score":"0.49914545","text":"def __init__(self, columns=None):\n self._columns = {}\n self._reindex = False\n columns = columns or self._table.GetColumns()\n for c in columns:\n if not isinstance(c, schema.IndexTermsColumn):\n self._columns[c.name] = c.NewInstance()","title":""},{"docid":"8d06545e910bbededd45bda6b3b5263f","score":"0.49783707","text":"def __init__(self, col_index=default_record_layout, *args, **kwargs):\n self.col_index = col_index","title":""},{"docid":"04055d5624c222fc2ed2e7eb27282a98","score":"0.49726656","text":"def __init__(self, data, name):\r\n self.data = data\r\n self.name = name\r\n self.descriptions = event_descriptions","title":""},{"docid":"f7a4da247735ed2c1f23b972cddc3209","score":"0.49467462","text":"def build_labeled_event_trigger(x):\n event_trigger = {\n 'id': x.trigger['id'],\n 'event_type_probs': x.event_type_probs\n }\n x['event_triggers'] = [event_trigger]\n return x","title":""},{"docid":"f531560851f0ef163a5892cfc8d307cc","score":"0.49383092","text":"def __init__(__self__, *,\n columns: Optional[Sequence[str]] = None):\n if columns is not None:\n pulumi.set(__self__, \"columns\", columns)","title":""},{"docid":"f531560851f0ef163a5892cfc8d307cc","score":"0.49383092","text":"def __init__(__self__, *,\n columns: Optional[Sequence[str]] = None):\n if columns is not None:\n pulumi.set(__self__, \"columns\", columns)","title":""},{"docid":"c383daf21affd89fdc7dce054daa5884","score":"0.49186832","text":"def __init__(self, events):\n self.events = events","title":""},{"docid":"b2d41786980211e451f80abff91be16b","score":"0.49103194","text":"def __init__(self, *args, **kargs):\n super(_EventObjectJSONDecoder, self).__init__(\n *args, object_hook=self._ConvertDictToObject, **kargs)","title":""},{"docid":"4f95d2a3a693d006b52a64e1c69128cb","score":"0.49047938","text":"def __init__(self, mds, fs, es, headers, name, handler_registry=None,\n handler_override=None):\n from .broker import Broker\n self.fs = fs\n db = Broker(mds, fs)\n events = db.get_events(headers, [name], fill=False)\n\n self._datum_uids = [event.data[name] for event in events\n if name in event.data]\n self._len = len(self._datum_uids)\n first_uid = self._datum_uids[0]\n if handler_override is None:\n self.handler_registry = handler_registry\n else:\n # mock a handler registry\n self.handler_registry = defaultdict(lambda: handler_override)\n with self.fs.handler_context(self.handler_registry) as fs:\n example_frame = fs.get_datum(first_uid)\n # Try to duck-type as a numpy array, but fall back as a general\n # Python object.\n try:\n self._dtype = example_frame.dtype\n except AttributeError:\n self._dtype = type(example_frame)\n try:\n self._shape = example_frame.shape\n except AttributeError:\n self._shape = None # as in, unknown","title":""},{"docid":"5d54b0c36c9091b70858b0b4d825728e","score":"0.4904374","text":"def __init__(self, source):\n # init data from known format\n ColumnData.__init__(self, source,\n header=['onsets', 'durations', 'intensities'],\n sep=None, dtype=float)","title":""},{"docid":"70018d8c4b60118ce152068c30bc8baa","score":"0.48975858","text":"def _parse_loxone_message(self, message):\n event_dict = {}\n if self._current_message_typ == 0:\n event_dict = message\n elif self._current_message_typ == 1:\n pass\n elif self._current_message_typ == 2:\n length = len(message)\n num = length / 24\n start = 0\n end = 24\n for _ in range(int(num)):\n packet = message[start:end]\n event_uuid = uuid.UUID(bytes_le=packet[0:16])\n fields = event_uuid.urn.replace(\"urn:uuid:\", \"\").split(\"-\")\n uuidstr = f\"{fields[0]}-{fields[1]}-{fields[2]}-{fields[3]}{fields[4]}\"\n value = unpack(\"d\", packet[16:24])[0]\n event_dict[uuidstr] = value\n start += 24\n end += 24\n elif self._current_message_typ == 3:\n start = 0\n\n def get_text(message, start, offset):\n first = start\n second = start + offset\n event_uuid = uuid.UUID(bytes_le=message[first:second])\n first += offset\n second += offset\n\n icon_uuid_fields = event_uuid.urn.replace(\"urn:uuid:\", \"\").split(\"-\")\n uuidstr = \"{}-{}-{}-{}{}\".format(\n icon_uuid_fields[0],\n icon_uuid_fields[1],\n icon_uuid_fields[2],\n icon_uuid_fields[3],\n icon_uuid_fields[4],\n )\n\n icon_uuid = uuid.UUID(bytes_le=message[first:second])\n icon_uuid_fields = icon_uuid.urn.replace(\"urn:uuid:\", \"\").split(\"-\")\n\n first = second\n second += 4\n\n text_length = unpack(\"<I\", message[first:second])[0]\n\n first = second\n second = first + text_length\n message_str = unpack(f\"{text_length}s\", message[first:second])[0]\n start += (floor((4 + text_length + 16 + 16 - 1) / 4) + 1) * 4\n event_dict[uuidstr] = message_str.decode(\"utf-8\")\n return start\n\n while start < len(message):\n start = get_text(message, start, 16)\n\n elif self._current_message_typ == 6:\n event_dict[\"keep_alive\"] = \"received\"\n else:\n self._current_message_typ = 7\n return event_dict","title":""},{"docid":"8d8e6f19ea8d4c3df0cdaf56c19accbc","score":"0.4883175","text":"def __init__(self, columns: Optional[DatasetSchema] = None):\n self.columns = columns\n self.data = list()\n self.index = list()\n self.dtypes = object","title":""},{"docid":"1b70ea09a4009eefa683b453933c1848","score":"0.4880899","text":"def reformat_events(self, columns, one_timestamp):\n temp_data = list()\n log_df = self.log.to_dict('records')\n key = 'end_timestamp' if one_timestamp else 'start_timestamp'\n log_df = sorted(log_df, key=lambda x: (x['caseid'], key))\n for key, group in itertools.groupby(log_df, key=lambda x: x['caseid']):\n trace = list(group)\n temp_dict = dict()\n for x in columns:\n serie = [y[x] for y in trace]\n if x == 'ac_index':\n serie.insert(0, self.ac_index[('start')])\n serie.append(self.ac_index[('end')])\n elif x == 'rl_index':\n serie.insert(0, self.rl_index[('start')])\n serie.append(self.rl_index[('end')])\n else:\n serie.insert(0, 0)\n serie.append(0)\n temp_dict = {**{x: serie}, **temp_dict}\n temp_dict = {**{'caseid': key}, **temp_dict}\n temp_data.append(temp_dict)\n return temp_data","title":""},{"docid":"0ef843eb99825ae53c8bd5fd082c862a","score":"0.48724988","text":"def __init__(self, fields):\n self.field_names = (str(field) for field in fields)","title":""},{"docid":"db480c616154a456f611bee81a84a694","score":"0.48669606","text":"def get_column_map(self, X):\n\n if not isinstance(X, pd.DataFrame):\n raise TypeError(\"X should be a pd.DataFrame\")\n\n new_columns = [\"column_\" + str(n) for n in range(0, X.shape[1])]\n\n self.column_map = dict(zip(X.columns, new_columns))","title":""},{"docid":"a9ade0ce4a0363d5ed9bd36894faecfb","score":"0.48584414","text":"def __init__(self, data=None, annotation_metadata=None, sandbox=None):\n # TODO(ejhumphrey@nyu.edu): We may want to subclass list here to turn\n # 'data' into a special container with convenience methods to more\n # easily unpack sparse events, among other things.\n if data is None:\n data = list()\n if annotation_metadata is None:\n annotation_metadata = AnnotationMetadata()\n if sandbox is None:\n sandbox = JSONType()\n self.data = self.__parse_data__(data)\n self.annotation_metadata = AnnotationMetadata(**annotation_metadata)\n self.sandbox = JSONType(**sandbox)","title":""},{"docid":"1f369e867cd0ab1e29b75590c9768216","score":"0.4847639","text":"def __init__(self, event_type=None, data=None):\n self._type = event_type\n self._data = data","title":""},{"docid":"c0be8de13187e01ce2b046bb4273f1bd","score":"0.4846005","text":"def __init__(self, *args, **kwds):\n if args or kwds:\n super(InverseResponse, self).__init__(*args, **kwds)\n #message fields cannot be None, assign default values for those that are\n if self.event is None:\n self.event = 0\n if self.angles is None:\n self.angles = []\n else:\n self.event = 0\n self.angles = []","title":""},{"docid":"5f33fde51b8bb8dd4380325af9bf169f","score":"0.48278165","text":"def create_deft_table_json_mappings():\n mappings = list()\n mappings.append(JsonColumnMapping(columnName=\"rownumber\", jsonPath=\"$.rownumber\", cslDataType=\"int\"))\n mappings.append(JsonColumnMapping(columnName=\"rowguid\", jsonPath=\"$.rowguid\", cslDataType=\"string\"))\n mappings.append(JsonColumnMapping(columnName=\"xdouble\", jsonPath=\"$.xdouble\", cslDataType=\"real\"))\n mappings.append(JsonColumnMapping(columnName=\"xfloat\", jsonPath=\"$.xfloat\", cslDataType=\"real\"))\n mappings.append(JsonColumnMapping(columnName=\"xbool\", jsonPath=\"$.xbool\", cslDataType=\"bool\"))\n mappings.append(JsonColumnMapping(columnName=\"xint16\", jsonPath=\"$.xint16\", cslDataType=\"int\"))\n mappings.append(JsonColumnMapping(columnName=\"xint32\", jsonPath=\"$.xint32\", cslDataType=\"int\"))\n mappings.append(JsonColumnMapping(columnName=\"xint64\", jsonPath=\"$.xint64\", cslDataType=\"long\"))\n mappings.append(JsonColumnMapping(columnName=\"xuint8\", jsonPath=\"$.xuint8\", cslDataType=\"long\"))\n mappings.append(JsonColumnMapping(columnName=\"xuint16\", jsonPath=\"$.xuint16\", cslDataType=\"long\"))\n mappings.append(JsonColumnMapping(columnName=\"xuint32\", jsonPath=\"$.xuint32\", cslDataType=\"long\"))\n mappings.append(JsonColumnMapping(columnName=\"xuint64\", jsonPath=\"$.xuint64\", cslDataType=\"long\"))\n mappings.append(JsonColumnMapping(columnName=\"xdate\", jsonPath=\"$.xdate\", cslDataType=\"datetime\"))\n mappings.append(JsonColumnMapping(columnName=\"xsmalltext\", jsonPath=\"$.xsmalltext\", cslDataType=\"string\"))\n mappings.append(JsonColumnMapping(columnName=\"xtext\", jsonPath=\"$.xtext\", cslDataType=\"string\"))\n mappings.append(JsonColumnMapping(columnName=\"xnumberAsText\", jsonPath=\"$.xnumberAsText\", cslDataType=\"string\"))\n mappings.append(JsonColumnMapping(columnName=\"xtime\", jsonPath=\"$.xtime\", cslDataType=\"timespan\"))\n mappings.append(JsonColumnMapping(columnName=\"xtextWithNulls\", jsonPath=\"$.xtextWithNulls\", cslDataType=\"string\"))\n mappings.append(JsonColumnMapping(columnName=\"xdynamicWithNulls\", jsonPath=\"$.xdynamicWithNulls\", cslDataType=\"dynamic\"))\n return mappings","title":""},{"docid":"617c29551cdaf5b44f087e0b3fea5e36","score":"0.48263848","text":"def get_events(sqlx, sqlx_count):\n ap = db_create_connection()\n ap.sqlx = sqlx_count\n ap.op = 'count'\n try:\n obj_count = {\"count\": -1}\n ap = db_exec_cur(ap)\n for t in ap.result:\n obj_count[\"count\"] = t[0]\n\n ap.sqlx = sqlx\n ap.op = 'get_data'\n db_exec_cur(ap)\n except Exception as e:\n ms1 = \"error reading the database\" + str(e)\n print ms1\n raise \"error reading the database\"\n\n rslt_list = convert_tuple(ap, obj_count)\n db_close_con(ap)\n return rslt_list","title":""},{"docid":"d1c9d28f24d0df568c4a1f4d45236683","score":"0.48244342","text":"def sample_metadata(sample):\n select = [\n chart_table.currentWeekPosition,\n chart_table.previousWeekPosition,\n chart_table.peakPosition,\n chart_table.Artist,\n #chart_table.entryDate,\n #chart_table.entryPosition,\n ]\n results = db.session.query(*select).all()\n\n # dic viewing rows of columns\n sample_metadata = {}\n for result in results:\n sample_metadata[\"Current Week Position \"] = result[0]\n sample_metadata[\"Previous Week Position \"] = result[1]\n sample_metadata[\"Peak Position \"] = result[2]\n #sample_metadata[\" Artist \"] = result[3]\n #sample_metadata[\"entryDate\"] = result[4]\n #sample_metadata[\"entryPosition\"] = result[5]\n\n print(sample_metadata)\n return jsonify(sample_metadata)","title":""},{"docid":"0a09bbd111337c1ee7acfebcf96a5562","score":"0.48241213","text":"def _initialize_column_rows(self):\n data_source = self.data_source_scan.data_source\n self.sql = data_source.sql_get_table_columns(self.table.table_name)\n self.sql = self.data_source_scan.scan.jinja_resolve(self.sql)\n\n self.fetchall()","title":""},{"docid":"7eab99b014c879f3cffda49622fa8fb5","score":"0.48206058","text":"def __init__(self, event_type, data=None):\n self._type = event_type\n self._data = data","title":""},{"docid":"33cadac38e158f265be7dbd289354023","score":"0.48056328","text":"def apigw_event():\n return [\n {\n \"headers\": {\n \"Accept-Language\": \"en-US,en;q=0.8\",\n \"X-Forwarded-For\": \"127.0.0.1, 127.0.0.2\",\n \"Accept\": \"text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8\",\n },\n \"pathParameters\": {\n \"id\": \"id_01\",\n \"user_id\": \"user_01\",\n },\n \"httpMethod\": \"GET\",\n },\n {\n \"headers\": {\n \"Accept-Language\": \"en-US,en;q=0.8\",\n \"X-Forwarded-For\": \"127.0.0.1, 127.0.0.2\",\n \"Accept\": \"text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8\",\n },\n \"httpMethod\": \"POST\",\n \"body\": \"{\\\"id\\\":\\\"id_02\\\", \\\"user_id\\\":\\\"user_02\\\", \\\"body\\\":\\\"message_02\\\"}\",\n },\n {\n \"headers\": {\n \"Accept-Language\": \"en-US,en;q=0.8\",\n \"X-Forwarded-For\": \"127.0.0.1, 127.0.0.2\",\n \"Accept\": \"text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8\"\n },\n \"pathParameters\": {\n \"id\": \"id_02\",\n \"user_id\": \"user_02\"\n },\n \"httpMethod\": \"DELETE\",\n }\n ]","title":""},{"docid":"2713175732bd705462b25e799a0d162d","score":"0.47925967","text":"def __init__(self, **kwargs):\n super().__init__()\n self.column_name = {}\n self.column_name['water_body'] = {'internal': 'MS_CD',\n 'display': 'WATERBODY_NAME'}\n \n self.column_name['type_area'] = {'internal': 'TYPE_AREA_CODE', \n 'display': 'TYPE_AREA_NAME'}\n \n self.column_name['water_district'] = {'internal': 'WATER_DISTRICT_CODE', \n 'display': 'WATER_DISTRICT_NAME'}\n \n \n #TODO Add Parametermapping for water body names\n #TODO Map against .lower() letters \n if kwargs:\n self.load_water_body_match(**kwargs)","title":""},{"docid":"c04d0b55c15c771160f04452cf8a336b","score":"0.4789773","text":"def __init__(self, input_json):\n self.id = input_json.get('id')\n self.tstamp = input_json.get('tstamp')\n self.schemaname = input_json.get('schemaname')\n self.operation = input_json.get('operation')\n self.who = input_json.get('who')\n self.new_val = input_json.get('new_val')\n self.old_val = input_json.get('old_val')\n self.tabname = input_json.get('tabname')","title":""},{"docid":"024861791e77fa79c9d107235ff0652f","score":"0.47877645","text":"def __init__(__self__, *,\n events: Sequence['outputs.EventResponse'],\n state: str):\n pulumi.set(__self__, \"events\", events)\n pulumi.set(__self__, \"state\", state)","title":""},{"docid":"18c1ec90dc16d1ba149322cfcafd0609","score":"0.47723952","text":"def __init__(self, jsondict=None):\n \n super(DiagnosticOrderItemEvent, self).__init__(jsondict)","title":""},{"docid":"c907b14328d564e8a5e5416d9933b135","score":"0.47677287","text":"def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n\n def string_casting(data: any):\n \"\"\"\n Closure to cast the data to string\n :param data: any\n Column value\n :return: str\n Parsed column value\n \"\"\"\n try:\n if data:\n return str(data)\n else:\n return data\n except Exception:\n logging.error('\\n')\n logging.error('~' * 100)\n logging.exception(\"String casting exception:\")\n logging.error('~' * 100)\n raise StringParsingException(\"Column value - {} could not be casted into String.\".format(data))\n\n self.add_func(string_casting)","title":""},{"docid":"7485a2f89c9dcc6fd1fc70af9689993a","score":"0.47654918","text":"def initEvents():\n global PLAYER, CURRENT_MAP, EVENTS, MAPS, SOUNDS\n for i in EVENTS.e:\n EVENTS[i].data[\"player\"] = PLAYER\n EVENTS[i].data[\"cmap\"] = CURRENT_MAP\n EVENTS[i].data['setter'] = setMap \n EVENTS[i].data['setChar'] = setChar\n EVENTS[i].data['exit'] = Exit\n EVENTS[i].data['sounds'] = SOUNDS\n EVENTS[i].data['globmaps'] = MAPS","title":""},{"docid":"4d362755317471c977468e89767e570e","score":"0.47465035","text":"def parse_raw_events(self, events):\n events = json.loads(events) or None\n if events:\n events = (OmegleEvent(self.id,\n ev[0],\n None if len(ev) == 1 else ev[1])\n for ev in events)\n\n return events","title":""},{"docid":"1d61d20bbc0b7199ccf2b7275e298d8a","score":"0.47428414","text":"def parse_events(self,response):","title":""},{"docid":"220e9a6d4777dd12e19b68be74ba5a2c","score":"0.47350973","text":"def __init__(self, options, columns):\n super(DatadogForeignDataWrapper, self).__init__(options, columns)\n initialize(\n api_key=options['api_key'],\n app_key=options['app_key']\n )\n self.columns = columns","title":""},{"docid":"d14879aea552d7c3b459a45af49d18e8","score":"0.47228545","text":"def __init__(self, x, y, independent_columns, dependent_column):\n\n self.x = pd.DataFrame(x)\n self.y = pd.DataFrame(y)\n self.independent_columns = independent_columns\n self.dependent_column = dependent_column","title":""},{"docid":"f9fb537bae44ea54e6491004e23d1322","score":"0.47167116","text":"def _init_dict(self, data):\n if not self._columns:\n self._columns = list(data.keys())\n\n # Filter values by defined columns\n columns = (\n to_list(values)\n for column, values in data.items()\n if column in self._columns\n )\n\n # Convert columns to rows\n self._data = [list(row) for row in zip_longest(*columns)]\n\n self._index = self._index or list(range(len(self._data)))","title":""},{"docid":"c2f496d515391bcc209216a9ae71b233","score":"0.4716067","text":"def __init__(self,house=None, api_type=None, api_name=None, schema=None, entry_columns=None):\n self.house = house\n self.api_type = api_type\n self.api_name = api_name\n self.schema = schema\n\n\n self.table = ('_').join([self.house, self.api_type, self.api_name])\n self.conn = utils.connect_sqlalchemy()\n\n self.entry_structure = dict.fromkeys(entry_columns)","title":""},{"docid":"2a4903d2e8e7968e3b3ccab23ee5f476","score":"0.47156122","text":"def __init__(self, *args, **kwargs):\n super(DnsUniqueResponse, self).__init__(*args, **kwargs)\n\n self._records = list()\n self._process_records()","title":""},{"docid":"e799c8353f805af4b6d00df9b98c1653","score":"0.47143358","text":"def make_event(self):\n event_dict = {}\n quote = \"\"\n while quote == \"\":\n aut, quote = self.get_quote()\n event_dict[\"author\"] = aut\n event_dict[\"quote\"] = quote\n return event_dict","title":""},{"docid":"73d3cfc2efa359fe8d1ba0dc778692c0","score":"0.4713355","text":"def __init__(__self__, *,\n count: int,\n first_timestamp: str,\n last_timestamp: str,\n message: str,\n name: str,\n type: str):\n pulumi.set(__self__, \"count\", count)\n pulumi.set(__self__, \"first_timestamp\", first_timestamp)\n pulumi.set(__self__, \"last_timestamp\", last_timestamp)\n pulumi.set(__self__, \"message\", message)\n pulumi.set(__self__, \"name\", name)\n pulumi.set(__self__, \"type\", type)","title":""},{"docid":"a7a30c7780885ed5d7289dbda009ca2f","score":"0.47081393","text":"def __init__(self, name):\n # these items must match the columns above\n # if they're not created above, they won't be stored to the DB\n\n self.name = name","title":""},{"docid":"53715b788f87abe66ffd0a5d4548c3d9","score":"0.47060105","text":"def __init__(self, row):\n\n if row == None:\n return\n\n self.id = row['id']\n self.owner = row['owner']\n self.created = row['created']\n self.reply_to_user = row['reply_to_user']\n self.reply_to_tweet = row['reply_to_tweet']\n self.geo = row['geo']\n self.place_name = None\n self.source = None\n self.contents = None\n self.yyyymm = 0 # weirdly set to 0 on init.\n \n if row['place_name'] != None:\n self.place_name = row['place_name']\n\n self.place_box = row['place_box']\n \n if row['source'] != None:\n self.source = row['source']\n\n if row['contents'] != None:\n self.contents = row['contents']\n \n if row['yyyymm'] != None:\n self.yyyymm = row['yyyymm']","title":""},{"docid":"518992cb645a820edae890999a13d75b","score":"0.47015494","text":"def __init__(self, row, cols):\n self.field_dict = {}\n for c in range(len(cols)):\n self.field_dict[cols[c]] = row[c]","title":""},{"docid":"edea8576948777c699d403e22eac21e3","score":"0.46917585","text":"def get_event(event):\n if isinstance(event, str):\n name = event\n else:\n try:\n name = event.name\n except AttributeError:\n raise ValueError(\"Cannot get event. Use a string.\")\n db = Data(DB_BLAZE)\n selection = odo(db.event[db.event.name==name], pd.DataFrame)\n if selection.empty:\n raise ValueError(\"Event does not exist.\")\n else:\n event = dict(selection.loc[0])\n #event = collections.namedtuple('Event', event.keys())(**event)\n event = Event(**event)\n return event","title":""},{"docid":"fa3ab3ff24d24ba7de7ea998dc9c1c0c","score":"0.46899942","text":"def __init__(self, req_id, location, time, event):\n self.req_id = req_id\n self.location = location\n self.time = time\n self.event = event","title":""},{"docid":"ee24f77d2bed03f0f90fc0b3569b1567","score":"0.46879146","text":"def __create_column_name_mapping__(self, columns):\n mapping = {'names': {}, 'numbers': {},\n 'header': { 'separator': self.separator}}\n\n argn = 0\n for token in columns:\n mapping['names'][token] = argn\n mapping['numbers'][argn] = token\n argn += 1\n\n self._column_names = mapping['names']\n self.column_nums = mapping['numbers']\n\n if not self._out_column_names_set:\n self._out_column_names = self._column_names.keys()\n\n self._mapping = mapping\n return mapping","title":""},{"docid":"37e2a3729fe3843c08be8721bd51400d","score":"0.4683065","text":"def add_column_names(self, request):\n model_obj = None\n org = self.get_organization(request, return_obj=True)\n inventory_pk = request.query_params.get('inventory_pk')\n inventory_type = request.query_params.get('inventory_type', 'property')\n if inventory_type in ['property', 'propertystate']:\n if not inventory_pk:\n model_obj = PropertyState.objects.filter(\n organization=org\n ).order_by('-id').first()\n try:\n model_obj = PropertyState.objects.get(id=inventory_pk)\n except PropertyState.DoesNotExist:\n pass\n elif inventory_type in ['taxlot', 'taxlotstate']:\n if not inventory_pk:\n model_obj = TaxLotState.objects.filter(\n organization=org\n ).order_by('-id').first()\n else:\n try:\n model_obj = TaxLotState.objects.get(id=inventory_pk)\n inventory_type = 'taxlotstate'\n except TaxLotState.DoesNotExist:\n pass\n else:\n msg = \"{} is not a valid inventory type\".format(inventory_type)\n raise ParseError(msg)\n if not model_obj:\n msg = \"No {} was found matching {}\".format(\n inventory_type, inventory_pk\n )\n raise NotFound(msg)\n Column.save_column_names(model_obj)\n\n columns = Column.objects.filter(\n organization=model_obj.organization,\n table_name=model_obj.__class__.__name__,\n is_extra_data=True,\n\n )\n columns = ColumnSerializer(columns, many=True)\n return Response(columns.data, status=status.HTTP_200_OK)","title":""},{"docid":"bec2fdba68756c84db2a39467f6b8adb","score":"0.46814585","text":"def _populate_from_database(self, row):\n\n (record_id, name, user, agreed_at) = row\n\n self['record_id'] = record_id\n self['message_name'] = name\n self['agreed_by'] = user\n self['agreed_at'] = from_utimestamp(agreed_at)","title":""},{"docid":"e6dd36a0c0f13e0559d215c4116acfb6","score":"0.46798655","text":"def __init__(self, x=None, y=None, fold_column='', blending_avg=True, inflection_point=3, smoothing=1):\n\n self._teColumns = x\n self._responseColumnName = y\n self._foldColumnName = fold_column\n self._blending = blending_avg\n self._inflectionPoint = inflection_point\n self._smoothing = smoothing","title":""},{"docid":"85791cb4ca1e09d072aa5dc903369219","score":"0.4676823","text":"def __init__(self, message, status=None, event_status=None, events=None):\n self.status = status\n self.event_status = event_status\n self.events = events\n super().__init__(self, message)","title":""},{"docid":"b65fa6d4855864214fe624fa2c407167","score":"0.46701154","text":"def emit_basic(data):\n #~ pass\n res = {}\n i = 0\n if 'uuid' in data:\n data = {0:data}\n if 'uuid' in data[data.keys()[0]]:\n data = {0:data}\n for idx1 in data:\n logger.debug('Basic event idx1 :%s,%s' % (idx1,data[idx1]))\n if idx1 not in res:\n res[idx1]={}\n for idx2 in data[idx1]:\n logger.debug('Basic event idx2 :%s,%s' % (idx2,data[idx1][idx2]))\n res[idx1][idx2] = data[idx1][idx2]\n logger.debug(u'Basic event :%s', res)\n self.socketio.emit('my basics response',\n {'data':res},\n namespace='/janitoo')","title":""},{"docid":"bd08c3274924405aa7480f0899e25fe2","score":"0.46612448","text":"def _json_object_hook(dat):\n return namedtuple('X', dat.keys())(*dat.values())","title":""},{"docid":"ba540ddbdd51ea164d85b9f3b4b66154","score":"0.46515137","text":"def __init__(self,test=False,remote=False):\n self._test = test\n self.dbh = DBHandler(league=LEAGUES[0],test=self._test,remote=remote)\n self.data = {_l:{_y:(None,None) for _y in YEARS} for _l in LEAGUES}\n self._indces = {_l:0 for _l in LEAGUES}","title":""},{"docid":"85d18835daeaf3460a228c8b966a7c26","score":"0.46498805","text":"def __init__(self, row):\n \n self.id = row['id']\n \n self.screen_name = None\n self.name = None\n self.lang = None\n self.location = None\n self.private = None\n \n if row['screen_name'] != None:\n # I don't think I need the single_unescape here, the python library \n # handles it; but in theory if I correctly escape thing and unescape\n # things all the time--it should be fine.\n self.screen_name = row['screen_name']\n\n if row['name'] != None:\n self.name = row['name']\n\n if row['lang'] != None:\n self.lang = row['lang']\n\n if row['location'] != None:\n self.location = row['location']\n\n if row['private'] != None:\n self.private = row['private']\n\n self.friends = row['friends']","title":""},{"docid":"d43a2c77b5ca36b90f91e58ee131012f","score":"0.46496642","text":"def __init__(self, num_columns):\n\n self.row_values = []\n self.num_columns = int(num_columns)","title":""},{"docid":"55d2927057e0adfa328d29c57df4b3c1","score":"0.4648","text":"def event_names(self):\n pass","title":""},{"docid":"cac80d9fb89be12c87c014fa8cf371f4","score":"0.46443748","text":"def __init__(self, *args, **kwargs):\n super(DnsResponse, self).__init__(*args, **kwargs)\n self._process_records()","title":""},{"docid":"266214551c4cbf23f0c47624f4701480","score":"0.46403676","text":"def _init_empty(self):\n self._data = [[None for _ in self._columns] for _ in self._index]","title":""},{"docid":"039d8356db5ff32889c4e696ff7ced98","score":"0.46364927","text":"def __init__(self):\n self.hits = {}\n self.timestamp_arr = []","title":""},{"docid":"7a3f0c0975a95c7bc5e6abf1e2473e47","score":"0.46327257","text":"def test_json_columns(self):\n\n class MyTable(Table):\n column_a = JSON()\n column_b = JSONB()\n\n self.assertEqual(\n MyTable._meta.json_columns, [MyTable.column_a, MyTable.column_b]\n )","title":""},{"docid":"69c0396e7ad7180d315f9ea4b1cea260","score":"0.4631868","text":"def __init__(self, *args, **kwds):\n if args or kwds:\n super(mpu_valuesResponse, self).__init__(*args, **kwds)\n #message fields cannot be None, assign default values for those that are\n if self.ax is None:\n self.ax = 0.\n if self.ay is None:\n self.ay = 0.\n if self.az is None:\n self.az = 0.\n else:\n self.ax = 0.\n self.ay = 0.\n self.az = 0.","title":""},{"docid":"cafdf92001be20639b59c28ebdc571b7","score":"0.4631288","text":"def __init__(\n self,\n response: dict\n ):\n self.__more = read_value(\n \"more\", response, bool, True)\n self.__total_count = read_value(\n \"totalCount\", response, int, True)\n self.__filtered_count = read_value(\n \"filteredCount\", response, int, True)\n self.__items = read_value(\n \"items\", response, Row, True)","title":""},{"docid":"8b59ff681ac08c7d193accaa60e5dc08","score":"0.4620476","text":"def __init__(self):\n\n self._column_dict = {\"UI\": [self._USER, self._ITEM],\n \"UIR\": [self._USER, self._ITEM, self._RATING],\n \"UIT\": [self._USER, self._ITEM, self._TIME],\n \"UIRT\": [self._USER, self._ITEM, self._RATING, self._TIME]}\n self._column_name = None\n self._config = OrderedDict()\n self.all_data = None\n self.train_data = None\n self.valid_data = None\n self.test_data = None\n self.user2id = None\n self.item2id = None\n self._dir_path = None\n self._data_name = \"\"\n self._split_manner = \"\"\n self._user_min = 0\n self._item_min = 0","title":""},{"docid":"d94b2698c08a68be9684d8a34e42c7f0","score":"0.46192908","text":"def __init__(self, records):\n self._data = [record.data for record in records]","title":""},{"docid":"b96841bd8ecb0daaadb2c03d92c462a6","score":"0.46117195","text":"def __init__(self):\n super(AggHandler, self).__init__() # makes sure variables from super \"__init__\" are also inherited\n self.timestamp_loc = {'start': 1, 'end': 9}\n self.transtype_loc = 9\n self.volume_loc = {'short':{'start':19, 'end': 25}, 'long':{'start':19, 'end':28}}\n self.contra_id_loc = {'short':{'start':34, 'end': 43}, 'long': {'start':38, 'end':47}}\n self.passive_id_loc = {'start': 10, 'end': 19}\n self.traderef_loc = {'short':{'start':25, 'end': 34}, 'long': {'start':29, 'end':38}}\n self.reset_cache()","title":""},{"docid":"b26e42edab1a5b72991e902d9ec8a3c9","score":"0.46102205","text":"def __init__(\n self,\n pre_event: list = [],\n event: list = [],\n post_event: list = [],\n title: str = '',\n event_type: EventType = EventType.UNDEFINED,\n event_detail: EventDetail = EventDetail(),\n ) -> None:\n self.pre_event: list = pre_event\n self.event: list = event\n self.post_event: list = post_event\n self.title = title\n self.event_type: EventType = event_type\n self.event_detail: EventDetailType = event_detail","title":""},{"docid":"3fd6d0b0dd67de7848312dcb8846c93a","score":"0.46096426","text":"def __init__(self, commandsetresponse):\n self.metrics = {Metric(i).uniquetuple: Metric(i).value for i in commandsetresponse.parsed_message}\n self.executed_at = commandsetresponse.executed_at\n self.host = commandsetresponse.device","title":""},{"docid":"52ce965b73f05182ec52e483795e69a4","score":"0.4606544","text":"def __init__(self, sample_dict: dict, sample_ordinal: int, illumina_naming: bool=False, sample_barcode_column: Optional[str]=None):\n self.__dict: dict = sample_dict\n self.__sample_ordinal: int = sample_ordinal\n self.__illumina_naming: bool = illumina_naming\n self.__sample_barcode_column: str = sample_barcode_column\n assert sample_dict['sample_name'], \"'sample_name' not in the sample dictionary\"\n assert sample_dict['sample_id'], \"'sample_id' not in the sample dictionary\"\n assert 0 < self.__sample_ordinal, f\"sample_ordinal must be greater than zero, was {sample_ordinal}\"","title":""},{"docid":"fc7c35b3b3fceaa594e64044090ad0d1","score":"0.46021602","text":"def __init__(self, fields, ignore_empty=False):\n\n self.field_names = (str(field) for field in fields)","title":""},{"docid":"868061b649c3e8fb70bb46f2d5997d24","score":"0.4600528","text":"def __init__(self, *args, **kwds):\n if args or kwds:\n super(Event, self).__init__(*args, **kwds)\n #message fields cannot be None, assign default values for those that are\n if self.type is None:\n self.type = 0\n if self.ball1 is None:\n self.ball1 = billiards_msgs.msg.BallState()\n if self.ball2 is None:\n self.ball2 = billiards_msgs.msg.BallState()\n if self.string_rep is None:\n self.string_rep = ''\n else:\n self.type = 0\n self.ball1 = billiards_msgs.msg.BallState()\n self.ball2 = billiards_msgs.msg.BallState()\n self.string_rep = ''","title":""},{"docid":"a2ced6db26e3bd4921ff0bca6d8b3698","score":"0.45960116","text":"def __init__(self, read_data_events=None, write_data_events=None, activity_tracker_crn=None):\n self.read_data_events = read_data_events\n self.write_data_events = write_data_events\n self.activity_tracker_crn = activity_tracker_crn","title":""},{"docid":"29f06b9a961d014db62d0431838a80a1","score":"0.45852238","text":"def __init__(self):\n self.keys = {}\n self.timestamps = {}","title":""},{"docid":"8040cc31a768a3fc2078241cb1bba3a2","score":"0.45846277","text":"def header(self):\n return dict(\n [\n (key, value)\n for key, value in self.__dict__.items()\n if key not in [\"tseries\", \"dseries\"]\n ]\n )","title":""},{"docid":"8f22259c630adccf7c6bacd9e72b1da9","score":"0.45820498","text":"def __init__(self, **dataset):\n for key, value in dataset.items():\n insertion_information = asarray(value)\n setattr(self, key, insertion_information)","title":""},{"docid":"7e07e784ac45daf63f02fccfe53099d8","score":"0.45713317","text":"def events():\n is_empty = False\n\n con = connect_database()\n\n sql_query = \"SELECT * FROM prithvidb.disastersFinal ORDER BY date\"\n\n cursor = con.cursor()\n cursor.execute(sql_query)\n data = cursor.fetchall()\n if len(data) == 0:\n is_empty = True\n cursor.close()\n\n result = []\n\n if data is not None:\n app.logger.info(\"Success\")\n for row in data:\n row_data = dict()\n # app.logger.info(row)\n row_data['date'] = row[1]\n row_data['title'] = row[2]\n row_data['description'] = row[3]\n row_data['country'] = row[4]\n row_data['city'] = row[5]\n row_data['latitude'] = row[6]\n row_data['longitude'] = row[7]\n row_data['name'] = row[8]\n result.append(row_data)\n else:\n error = \"No user could be found\"\n return json.dumps(result)","title":""}],"string":"[\n {\n \"docid\": \"b91406418d4f7587da0fc1f8a3998de4\",\n \"score\": \"0.5988165\",\n \"text\": \"def json_to_xibo_event(self, json_event):\\n return XiboEvent(\\n xibo_id=json_event[self.column_names.xibo_id],\\n meetup_id=json_event[self.column_names.meetup_id],\\n name=json_event[self.column_names.name],\\n location=json_event[self.column_names.location],\\n start_time=json_event[self.column_names.start_time],\\n end_time=json_event[self.column_names.end_time],\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9fe58cf205025650616deb803c0b3281\",\n \"score\": \"0.5880382\",\n \"text\": \"def event_to_columns(self, event):\\n return {\\n self.column_ids.meetup_id: event.meetup_id,\\n self.column_ids.name: event.name,\\n self.column_ids.location: event.location,\\n self.column_ids.start_time: event.start_time,\\n self.column_ids.end_time: event.end_time\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6fa46088315790b24ff4a2ea42e2ae13\",\n \"score\": \"0.55893785\",\n \"text\": \"def __init__(self, metadata_columns, tag_columns):\\n self.data = None\\n self.metadata_columns = metadata_columns\\n self.tag_columns = tag_columns\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c93d6b4a297340b664fd4328b8b2608\",\n \"score\": \"0.53798157\",\n \"text\": \"def initColumnHeaders():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37035d405ff17e55c796d5c2c68b99ce\",\n \"score\": \"0.5357916\",\n \"text\": \"def __init__(self, column_ids):\\n self.column_ids = column_ids\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fd4658c4d564e0ea5d8d9bc2002c9c4\",\n \"score\": \"0.5304528\",\n \"text\": \"def __init__(self, event=None):\\n if event is not None:\\n for key, val in list(event.__dict__.items()):\\n setattr(self, key, val)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e001a4881aeda1663693e07ebb37473\",\n \"score\": \"0.52718776\",\n \"text\": \"def _parse_event(self, response):\\n data = {\\n '_type': 'event',\\n 'name': self._parse_name(response),\\n 'event_description': self._parse_description(response),\\n 'start': self._parse_start(response),\\n 'end': self._parse_end(response),\\n 'all_day': self._parse_all_day(response),\\n 'location': self._parse_location(response),\\n 'documents': self._parse_documents(response),\\n 'sources': self._parse_sources(response)\\n }\\n data['id'] = self._generate_id(data)\\n data['status'] = self._generate_status(data)\\n data['classification'] = self._parse_classification(data['name'])\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b4e7e8e177186c328c5c8edd4974448\",\n \"score\": \"0.52701914\",\n \"text\": \"def get_event_columns(self):\\n if not self.dwca_event_columns:\\n self.dwca_event_columns = [\\n 'id', \\n 'eventID', \\n 'parentEventID', \\n 'type', \\n 'samplingProtocol', \\n 'sampleSizeValue', \\n 'sampleSizeUnit', \\n 'samplingEffort', \\n 'eventDate', \\n 'eventTime', \\n 'startDayOfYear', \\n 'endDayOfYear', \\n 'year', \\n 'month', \\n 'day', \\n 'verbatimEventDate', \\n 'habitat', \\n 'fieldNumber', \\n 'fieldNotes', \\n 'eventRemarks', \\n 'locationID', \\n 'country', \\n 'countryCode', \\n 'county', \\n 'municipality', \\n 'locality', \\n 'verbatimLocality', \\n 'waterBody', \\n 'verbatimDepth', \\n 'minimumDepthInMeters', \\n 'maximumDepthInMeters', \\n 'decimalLatitude', \\n 'decimalLongitude', \\n 'geodeticDatum', \\n 'license', \\n 'rightsHolder', \\n 'accessRights', \\n 'bibliographicCitation', \\n 'references', \\n 'institutionID', \\n 'datasetID', \\n 'institutionCode', \\n 'datasetName', \\n 'ownerInstitutionCode', \\n 'dataGeneralizations', # (ex: aggregerad över storleksklass...)\\n 'dynamicProperties', \\n ]\\n #\\n return self.dwca_event_columns\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c52a3d711d4f061f53175be4fac982d3\",\n \"score\": \"0.52676564\",\n \"text\": \"def __create_message__(cls, row):\\n message = dict()\\n message['eventtype'] = cls.EVENT_TYPE\\n message['type'] = row.pop('state', None)\\n inner_obj = dict()\\n for key, value in cls.DB_MAP.items():\\n inner_obj[key] = row.pop(value)\\n\\n message[cls.INNER_OBJ] = cls.__clean_dict__(inner_obj)\\n message = cls.__clean_dict__(message)\\n return message\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e2e08613fff835239210831346302ac\",\n \"score\": \"0.52657056\",\n \"text\": \"def _populate_from_database(self, row):\\n\\n (name, message, button, mode, \\n groups, start, end, author, created_at) = row\\n\\n self['name'] = name\\n self['message'] = message\\n self['button'] = button\\n self['mode'] = mode\\n self['groups'] = json.loads(groups) if groups else None\\n self['start'] = from_utimestamp(start)\\n self['end'] = from_utimestamp(end)\\n self['author'] = author\\n self['created_at'] = from_utimestamp(created_at)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a26b03e217949446268368964e9e1d2\",\n \"score\": \"0.5224331\",\n \"text\": \"def __init__(self):\\n self._map1 = {\\n \\\"CIRC\\\" : self.circ_status_event,\\n \\\"STREAM\\\" : self.stream_status_event,\\n \\\"ORCONN\\\" : self.or_conn_status_event,\\n \\\"STREAM_BW\\\" : self.stream_bw_event,\\n \\\"BW\\\" : self.bandwidth_event,\\n \\\"DEBUG\\\" : self.msg_event,\\n \\\"INFO\\\" : self.msg_event,\\n \\\"NOTICE\\\" : self.msg_event,\\n \\\"WARN\\\" : self.msg_event,\\n \\\"ERR\\\" : self.msg_event,\\n \\\"NEWDESC\\\" : self.new_desc_event,\\n \\\"ADDRMAP\\\" : self.address_mapped_event,\\n \\\"NS\\\" : self.ns_event,\\n \\\"STATUS_GENERAL\\\" : self.general_status_event,\\n \\\"STATUS_CLIENT\\\" : self.client_status_event,\\n \\\"STATUS_SERVER\\\" : self.server_status_event,\\n \\\"ORCIRCUIT\\\" : self.orcircuit_event,\\n \\\"NEWCONSENSUS\\\" : self.new_consensus_event,\\n \\\"TOKEN_LEVELS\\\" : self.token_level_event\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b6dee8d464df1e0b99e3233fe8f77c0\",\n \"score\": \"0.51967037\",\n \"text\": \"def _init_column_names():\\n mapping = {}\\n names = []\\n\\n index = 1\\n while index < 256:\\n name = _colnum_string(index)\\n names.append(name)\\n mapping[name] = index\\n index += 1\\n return names, mapping\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f53e3234f705e656da34736ba81c8c76\",\n \"score\": \"0.5186084\",\n \"text\": \"def _data_events(self, object):\\n\\t\\t# Builds an event list that gives for each event:\\n\\t\\t# - Gramps ID\\\\n\\\"\\n\\t\\t# - The event name\\n\\t\\t# - The event date\\n\\t\\t# - The event date in ISO format (sortable)\\n\\t\\t# - The event place index (in table 'P'), -1 if none\\n\\t\\t# - The event description\\n\\t\\t# - The event text and notes (including event reference notes)\\n\\t\\t# - A list of the event media index, in the form:\\n\\t\\t# - media index (in table 'M')\\n\\t\\t# - media thumbnail path\\n\\t\\t# - [x1, y1, x2, y2] of the media reference\\n\\t\\t# - notes of the media reference\\n\\t\\t# - list of the media reference source citations index (in table 'C')\\\\n\\\"\\n\\t\\t# - A list of the event source citations index (in table 'C')\\n\\t\\tevent_ref_list = object.get_event_ref_list()\\n\\t\\tif not event_ref_list: return(\\\"\\\")\\n\\t\\trows = []\\n\\t\\tfor event_ref in event_ref_list:\\n\\t\\t\\tif (event_ref.ref not in self.obj_dict[Event]): continue\\n\\t\\t\\tevent = self.database.get_event_from_handle(event_ref.ref)\\n\\t\\t\\tif (not event): continue\\n\\t\\t\\ttrow = \\\"\\\\t[\\\"\\n\\t\\t\\tevt_type = str(event.get_type())\\n\\t\\t\\tevent_role = event_ref.get_role()\\n\\t\\t\\tif (event_role != EventRoleType.PRIMARY and event_role != EventRoleType.FAMILY):\\n\\t\\t\\t\\tevt_type += \\\" (%s)\\\" % event_role\\n\\t\\t\\tplace_index = -1\\n\\t\\t\\tplace_handle = event.get_place_handle()\\n\\t\\t\\tif (place_handle and (place_handle in self.obj_dict[Place])):\\n\\t\\t\\t\\tplace_index = self.obj_dict[Place][place_handle][OBJDICT_INDEX]\\n\\t\\t\\tevt_desc = event.get_description()\\n\\t\\t\\ttrow += \\\"\\\\\\\"\\\" + self.obj_dict[Event][event_ref.ref][OBJDICT_GID] + \\\"\\\\\\\",\\\"\\n\\t\\t\\ttrow += \\\"\\\\\\\"\\\" + script_escape(html_escape(evt_type)) + \\\"\\\\\\\",\\\"\\n\\t\\t\\tevt_date = format_date(event.get_date_object())\\n\\t\\t\\ttrow += \\\"\\\\\\\"\\\" + script_escape(html_escape(evt_date)) + \\\"\\\\\\\",\\\"\\n\\t\\t\\tevt_date = format_date(event.get_date_object(), True)\\n\\t\\t\\ttrow += \\\"\\\\\\\"\\\" + script_escape(html_escape(evt_date)) + \\\"\\\\\\\",\\\"\\n\\t\\t\\ttrow += str(place_index) + \\\",\\\"\\n\\t\\t\\tif (evt_desc is None): evt_desc = \\\"\\\"\\n\\t\\t\\ttrow += \\\"\\\\\\\"\\\" + script_escape(html_escape(evt_desc)) + \\\"\\\\\\\",\\\"\\n\\t\\t\\t# Get event notes\\n\\t\\t\\tnotelist = event.get_note_list()\\n\\t\\t\\tnotelist.extend(event_ref.get_note_list())\\n\\t\\t\\tattrlist = event.get_attribute_list()\\n\\t\\t\\tattrlist.extend(event_ref.get_attribute_list())\\n\\t\\t\\ttrow += \\\"\\\\\\\"\\\" + script_escape(self.get_notes_attributes_text(notelist, attrlist)) + \\\"\\\\\\\",\\\"\\n\\t\\t\\t# Get event media\\n\\t\\t\\ttrow += self._data_media_reference_index(event)\\n\\t\\t\\ttrow += \\\",\\\"\\n\\t\\t\\t# Get event sources\\n\\t\\t\\tcitationlist = event.get_citation_list()\\n\\t\\t\\tcitationlist.extend(event_ref.get_citation_list())\\n\\t\\t\\tfor attr in attrlist: citationlist.extend(attr.get_citation_list())\\n\\t\\t\\ttrow += self._data_source_citation_index_from_list(citationlist)\\n\\t\\t\\t#\\n\\t\\t\\ttrow += \\\"]\\\"\\n\\t\\t\\trows.append(trow)\\n\\t\\treturn(\\\",\\\\n\\\".join(rows))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b907638876dee9732352a59351036d8\",\n \"score\": \"0.5179667\",\n \"text\": \"def initialize(self):\\n self.__as_columns.clear()\\n \\n db_map = self.__aggregate.getProofInstance().getDatabaseMap(self.getDBName())\\n table_map = db_map.getTable(self.__table_name)\\n column_maps = table_map.getColumns()\\n for column_map in column_maps:\\n column_name = column_map.getFullyQualifiedName()\\n self.__as_columns[column_name] = column_name\\n\\n self.__initialized = 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff383e535b7f2d0abffb5d856f45af65\",\n \"score\": \"0.51698637\",\n \"text\": \"def json_to_column_ids(self, json_columns):\\n heading_to_id_map = {\\n column[\\\"heading\\\"]:\\n \\\"dataSetColumnId_{}\\\".format(column[\\\"dataSetColumnId\\\"])\\n for column in json_columns\\n }\\n heading_to_id_map[self.column_names.xibo_id] = None\\n return self.json_to_xibo_event(heading_to_id_map)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d621bf8463ee4adc4e6db807d0dd35c7\",\n \"score\": \"0.513667\",\n \"text\": \"def __init__(self, events, database, symbol_list):\\n self.events = events\\n self.database = database\\n self.symbol_list = symbol_list\\n\\n self.symbol_data = {}\\n self.latest_symbol_data = {}\\n self.continue_backtest = True\\n self.bar_index = 0\\n self.all_data_dic = {} # access data in list form for testing\\n\\n self._open_convert_database_data()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"556411b20c977e1a15b99e65e98724f5\",\n \"score\": \"0.5135141\",\n \"text\": \"def __init__(self, source, data_column, label_columns):\\n self._source = source\\n self._data_column = data_column\\n self._label_columns = label_columns\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c392fe47ce9273a24396ab8ec6de90b4\",\n \"score\": \"0.5060727\",\n \"text\": \"def __init__(self, **kwargs):\\n super().__init__(**kwargs)\\n self._label_column_name = None\\n self._numeric_columns = None\\n self._cat_columns = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb2380ba54b7ce37ccd546b7d61f3043\",\n \"score\": \"0.5059268\",\n \"text\": \"def __init__(self, init_example, **kwargs):\\n super().__init__(init_example, **kwargs)\\n self.event_mat = init_example['events'][None]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53d42a3dc49e6b2b68e839d5ae76ad56\",\n \"score\": \"0.5048306\",\n \"text\": \"def __init__(self):\\n event_data_stream = events.EventDataStream()\\n\\n super(FieldFormattingHelper, self).__init__()\\n self._callback_functions = {}\\n self._event_data_stream_field_names = event_data_stream.GetAttributeNames()\\n self._event_tag_field_names = []\\n\\n for field_name, callback_name in self._FIELD_FORMAT_CALLBACKS.items():\\n if callback_name == '_FormatTag':\\n self._event_tag_field_names.append(field_name)\\n else:\\n self._callback_functions[field_name] = getattr(\\n self, callback_name, None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef855f557aea52947c1211326cf0dbe3\",\n \"score\": \"0.5042969\",\n \"text\": \"def events_to_dataframe(events):\\n columns = [\\\"id\\\", \\\"name\\\", \\\"source\\\", \\\"time\\\", \\\"description\\\"]\\n data = {column: list() for column in columns}\\n for event in events:\\n data[\\\"id\\\"].append(event.get_id())\\n data[\\\"name\\\"].append(event.get_name())\\n data[\\\"source\\\"].append(event.get_source())\\n data[\\\"time\\\"].append(event.get_time())\\n data[\\\"description\\\"].append(event.get_description())\\n return pd.DataFrame(data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb3984eba4945e2793a0d918fc8882c8\",\n \"score\": \"0.5035369\",\n \"text\": \"def extract_new_event_data(table_name, response):\\n output = {}\\n context = {}\\n\\n output['Status'] = response.get('status', '')\\n output['Message'] = response.get('message', '')\\n output['Incident key'] = response.get('dedup_key', '')\\n\\n context['Status'] = output['Status']\\n context['Message'] = output['Message']\\n context['incident_key'] = output['Incident key']\\n\\n return {\\n 'Type': entryTypes['note'],\\n 'Contents': response,\\n 'ContentsFormat': formats['json'],\\n 'ReadableContentsFormat': formats['markdown'],\\n 'HumanReadable': tableToMarkdown(table_name, output),\\n 'EntryContext': {\\n 'PagerDuty.Event(val.incident_key==obj.dedup_key)': context,\\n 'Event.ID(val.ID==obj.dedup_key)': context['incident_key']\\n }\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d57800ca714283669f7ae4fc0c162373\",\n \"score\": \"0.5033141\",\n \"text\": \"def __init__(self, eventdata_data):\\n self.events = eventdata_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f5a8ae66ba289320e896ab610cb3f26\",\n \"score\": \"0.50146925\",\n \"text\": \"def __init__(self, *args, **kwargs):\\n\\n args = list(args)\\n name, type_ = self._extract_name_and_type(args, kwargs)\\n kwargs.update(name=name, type_=self._column_type)\\n\\n super().__init__(*args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db3bc598712d408f6e8d19a47f7b4a61\",\n \"score\": \"0.50042796\",\n \"text\": \"def __init__(self, event_type):\\n if (isinstance(event_type, str)):\\n self.event_type = self.EVENT_TYPES_REVERSE[event_type]\\n else:\\n self.event_type = event_type\\n self.err_str = None\\n self.data = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1eac21066ef32d41680a8afe1ee61721\",\n \"score\": \"0.50023246\",\n \"text\": \"def _convert_event(self, event):\\n\\n doc = {\\\"h\\\": event.hostname,\\n \\\"ts\\\": event.timestamp}\\n if event.args:\\n doc[\\\"fields\\\"] = event.args\\n return doc\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f3ea996e27cdc926f8fa7e5e4a7bb88\",\n \"score\": \"0.49914545\",\n \"text\": \"def __init__(self, columns=None):\\n self._columns = {}\\n self._reindex = False\\n columns = columns or self._table.GetColumns()\\n for c in columns:\\n if not isinstance(c, schema.IndexTermsColumn):\\n self._columns[c.name] = c.NewInstance()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d06545e910bbededd45bda6b3b5263f\",\n \"score\": \"0.49783707\",\n \"text\": \"def __init__(self, col_index=default_record_layout, *args, **kwargs):\\n self.col_index = col_index\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04055d5624c222fc2ed2e7eb27282a98\",\n \"score\": \"0.49726656\",\n \"text\": \"def __init__(self, data, name):\\r\\n self.data = data\\r\\n self.name = name\\r\\n self.descriptions = event_descriptions\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7a4da247735ed2c1f23b972cddc3209\",\n \"score\": \"0.49467462\",\n \"text\": \"def build_labeled_event_trigger(x):\\n event_trigger = {\\n 'id': x.trigger['id'],\\n 'event_type_probs': x.event_type_probs\\n }\\n x['event_triggers'] = [event_trigger]\\n return x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f531560851f0ef163a5892cfc8d307cc\",\n \"score\": \"0.49383092\",\n \"text\": \"def __init__(__self__, *,\\n columns: Optional[Sequence[str]] = None):\\n if columns is not None:\\n pulumi.set(__self__, \\\"columns\\\", columns)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f531560851f0ef163a5892cfc8d307cc\",\n \"score\": \"0.49383092\",\n \"text\": \"def __init__(__self__, *,\\n columns: Optional[Sequence[str]] = None):\\n if columns is not None:\\n pulumi.set(__self__, \\\"columns\\\", columns)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c383daf21affd89fdc7dce054daa5884\",\n \"score\": \"0.49186832\",\n \"text\": \"def __init__(self, events):\\n self.events = events\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2d41786980211e451f80abff91be16b\",\n \"score\": \"0.49103194\",\n \"text\": \"def __init__(self, *args, **kargs):\\n super(_EventObjectJSONDecoder, self).__init__(\\n *args, object_hook=self._ConvertDictToObject, **kargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f95d2a3a693d006b52a64e1c69128cb\",\n \"score\": \"0.49047938\",\n \"text\": \"def __init__(self, mds, fs, es, headers, name, handler_registry=None,\\n handler_override=None):\\n from .broker import Broker\\n self.fs = fs\\n db = Broker(mds, fs)\\n events = db.get_events(headers, [name], fill=False)\\n\\n self._datum_uids = [event.data[name] for event in events\\n if name in event.data]\\n self._len = len(self._datum_uids)\\n first_uid = self._datum_uids[0]\\n if handler_override is None:\\n self.handler_registry = handler_registry\\n else:\\n # mock a handler registry\\n self.handler_registry = defaultdict(lambda: handler_override)\\n with self.fs.handler_context(self.handler_registry) as fs:\\n example_frame = fs.get_datum(first_uid)\\n # Try to duck-type as a numpy array, but fall back as a general\\n # Python object.\\n try:\\n self._dtype = example_frame.dtype\\n except AttributeError:\\n self._dtype = type(example_frame)\\n try:\\n self._shape = example_frame.shape\\n except AttributeError:\\n self._shape = None # as in, unknown\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d54b0c36c9091b70858b0b4d825728e\",\n \"score\": \"0.4904374\",\n \"text\": \"def __init__(self, source):\\n # init data from known format\\n ColumnData.__init__(self, source,\\n header=['onsets', 'durations', 'intensities'],\\n sep=None, dtype=float)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70018d8c4b60118ce152068c30bc8baa\",\n \"score\": \"0.48975858\",\n \"text\": \"def _parse_loxone_message(self, message):\\n event_dict = {}\\n if self._current_message_typ == 0:\\n event_dict = message\\n elif self._current_message_typ == 1:\\n pass\\n elif self._current_message_typ == 2:\\n length = len(message)\\n num = length / 24\\n start = 0\\n end = 24\\n for _ in range(int(num)):\\n packet = message[start:end]\\n event_uuid = uuid.UUID(bytes_le=packet[0:16])\\n fields = event_uuid.urn.replace(\\\"urn:uuid:\\\", \\\"\\\").split(\\\"-\\\")\\n uuidstr = f\\\"{fields[0]}-{fields[1]}-{fields[2]}-{fields[3]}{fields[4]}\\\"\\n value = unpack(\\\"d\\\", packet[16:24])[0]\\n event_dict[uuidstr] = value\\n start += 24\\n end += 24\\n elif self._current_message_typ == 3:\\n start = 0\\n\\n def get_text(message, start, offset):\\n first = start\\n second = start + offset\\n event_uuid = uuid.UUID(bytes_le=message[first:second])\\n first += offset\\n second += offset\\n\\n icon_uuid_fields = event_uuid.urn.replace(\\\"urn:uuid:\\\", \\\"\\\").split(\\\"-\\\")\\n uuidstr = \\\"{}-{}-{}-{}{}\\\".format(\\n icon_uuid_fields[0],\\n icon_uuid_fields[1],\\n icon_uuid_fields[2],\\n icon_uuid_fields[3],\\n icon_uuid_fields[4],\\n )\\n\\n icon_uuid = uuid.UUID(bytes_le=message[first:second])\\n icon_uuid_fields = icon_uuid.urn.replace(\\\"urn:uuid:\\\", \\\"\\\").split(\\\"-\\\")\\n\\n first = second\\n second += 4\\n\\n text_length = unpack(\\\"<I\\\", message[first:second])[0]\\n\\n first = second\\n second = first + text_length\\n message_str = unpack(f\\\"{text_length}s\\\", message[first:second])[0]\\n start += (floor((4 + text_length + 16 + 16 - 1) / 4) + 1) * 4\\n event_dict[uuidstr] = message_str.decode(\\\"utf-8\\\")\\n return start\\n\\n while start < len(message):\\n start = get_text(message, start, 16)\\n\\n elif self._current_message_typ == 6:\\n event_dict[\\\"keep_alive\\\"] = \\\"received\\\"\\n else:\\n self._current_message_typ = 7\\n return event_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d8e6f19ea8d4c3df0cdaf56c19accbc\",\n \"score\": \"0.4883175\",\n \"text\": \"def __init__(self, columns: Optional[DatasetSchema] = None):\\n self.columns = columns\\n self.data = list()\\n self.index = list()\\n self.dtypes = object\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b70ea09a4009eefa683b453933c1848\",\n \"score\": \"0.4880899\",\n \"text\": \"def reformat_events(self, columns, one_timestamp):\\n temp_data = list()\\n log_df = self.log.to_dict('records')\\n key = 'end_timestamp' if one_timestamp else 'start_timestamp'\\n log_df = sorted(log_df, key=lambda x: (x['caseid'], key))\\n for key, group in itertools.groupby(log_df, key=lambda x: x['caseid']):\\n trace = list(group)\\n temp_dict = dict()\\n for x in columns:\\n serie = [y[x] for y in trace]\\n if x == 'ac_index':\\n serie.insert(0, self.ac_index[('start')])\\n serie.append(self.ac_index[('end')])\\n elif x == 'rl_index':\\n serie.insert(0, self.rl_index[('start')])\\n serie.append(self.rl_index[('end')])\\n else:\\n serie.insert(0, 0)\\n serie.append(0)\\n temp_dict = {**{x: serie}, **temp_dict}\\n temp_dict = {**{'caseid': key}, **temp_dict}\\n temp_data.append(temp_dict)\\n return temp_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ef843eb99825ae53c8bd5fd082c862a\",\n \"score\": \"0.48724988\",\n \"text\": \"def __init__(self, fields):\\n self.field_names = (str(field) for field in fields)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db480c616154a456f611bee81a84a694\",\n \"score\": \"0.48669606\",\n \"text\": \"def get_column_map(self, X):\\n\\n if not isinstance(X, pd.DataFrame):\\n raise TypeError(\\\"X should be a pd.DataFrame\\\")\\n\\n new_columns = [\\\"column_\\\" + str(n) for n in range(0, X.shape[1])]\\n\\n self.column_map = dict(zip(X.columns, new_columns))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a9ade0ce4a0363d5ed9bd36894faecfb\",\n \"score\": \"0.48584414\",\n \"text\": \"def __init__(self, data=None, annotation_metadata=None, sandbox=None):\\n # TODO(ejhumphrey@nyu.edu): We may want to subclass list here to turn\\n # 'data' into a special container with convenience methods to more\\n # easily unpack sparse events, among other things.\\n if data is None:\\n data = list()\\n if annotation_metadata is None:\\n annotation_metadata = AnnotationMetadata()\\n if sandbox is None:\\n sandbox = JSONType()\\n self.data = self.__parse_data__(data)\\n self.annotation_metadata = AnnotationMetadata(**annotation_metadata)\\n self.sandbox = JSONType(**sandbox)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f369e867cd0ab1e29b75590c9768216\",\n \"score\": \"0.4847639\",\n \"text\": \"def __init__(self, event_type=None, data=None):\\n self._type = event_type\\n self._data = data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0be8de13187e01ce2b046bb4273f1bd\",\n \"score\": \"0.4846005\",\n \"text\": \"def __init__(self, *args, **kwds):\\n if args or kwds:\\n super(InverseResponse, self).__init__(*args, **kwds)\\n #message fields cannot be None, assign default values for those that are\\n if self.event is None:\\n self.event = 0\\n if self.angles is None:\\n self.angles = []\\n else:\\n self.event = 0\\n self.angles = []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f33fde51b8bb8dd4380325af9bf169f\",\n \"score\": \"0.48278165\",\n \"text\": \"def create_deft_table_json_mappings():\\n mappings = list()\\n mappings.append(JsonColumnMapping(columnName=\\\"rownumber\\\", jsonPath=\\\"$.rownumber\\\", cslDataType=\\\"int\\\"))\\n mappings.append(JsonColumnMapping(columnName=\\\"rowguid\\\", jsonPath=\\\"$.rowguid\\\", cslDataType=\\\"string\\\"))\\n mappings.append(JsonColumnMapping(columnName=\\\"xdouble\\\", jsonPath=\\\"$.xdouble\\\", cslDataType=\\\"real\\\"))\\n mappings.append(JsonColumnMapping(columnName=\\\"xfloat\\\", jsonPath=\\\"$.xfloat\\\", cslDataType=\\\"real\\\"))\\n mappings.append(JsonColumnMapping(columnName=\\\"xbool\\\", jsonPath=\\\"$.xbool\\\", cslDataType=\\\"bool\\\"))\\n mappings.append(JsonColumnMapping(columnName=\\\"xint16\\\", jsonPath=\\\"$.xint16\\\", cslDataType=\\\"int\\\"))\\n mappings.append(JsonColumnMapping(columnName=\\\"xint32\\\", jsonPath=\\\"$.xint32\\\", cslDataType=\\\"int\\\"))\\n mappings.append(JsonColumnMapping(columnName=\\\"xint64\\\", jsonPath=\\\"$.xint64\\\", cslDataType=\\\"long\\\"))\\n mappings.append(JsonColumnMapping(columnName=\\\"xuint8\\\", jsonPath=\\\"$.xuint8\\\", cslDataType=\\\"long\\\"))\\n mappings.append(JsonColumnMapping(columnName=\\\"xuint16\\\", jsonPath=\\\"$.xuint16\\\", cslDataType=\\\"long\\\"))\\n mappings.append(JsonColumnMapping(columnName=\\\"xuint32\\\", jsonPath=\\\"$.xuint32\\\", cslDataType=\\\"long\\\"))\\n mappings.append(JsonColumnMapping(columnName=\\\"xuint64\\\", jsonPath=\\\"$.xuint64\\\", cslDataType=\\\"long\\\"))\\n mappings.append(JsonColumnMapping(columnName=\\\"xdate\\\", jsonPath=\\\"$.xdate\\\", cslDataType=\\\"datetime\\\"))\\n mappings.append(JsonColumnMapping(columnName=\\\"xsmalltext\\\", jsonPath=\\\"$.xsmalltext\\\", cslDataType=\\\"string\\\"))\\n mappings.append(JsonColumnMapping(columnName=\\\"xtext\\\", jsonPath=\\\"$.xtext\\\", cslDataType=\\\"string\\\"))\\n mappings.append(JsonColumnMapping(columnName=\\\"xnumberAsText\\\", jsonPath=\\\"$.xnumberAsText\\\", cslDataType=\\\"string\\\"))\\n mappings.append(JsonColumnMapping(columnName=\\\"xtime\\\", jsonPath=\\\"$.xtime\\\", cslDataType=\\\"timespan\\\"))\\n mappings.append(JsonColumnMapping(columnName=\\\"xtextWithNulls\\\", jsonPath=\\\"$.xtextWithNulls\\\", cslDataType=\\\"string\\\"))\\n mappings.append(JsonColumnMapping(columnName=\\\"xdynamicWithNulls\\\", jsonPath=\\\"$.xdynamicWithNulls\\\", cslDataType=\\\"dynamic\\\"))\\n return mappings\",\n \"title\": \"\"\n },\n {\n \"docid\": \"617c29551cdaf5b44f087e0b3fea5e36\",\n \"score\": \"0.48263848\",\n \"text\": \"def get_events(sqlx, sqlx_count):\\n ap = db_create_connection()\\n ap.sqlx = sqlx_count\\n ap.op = 'count'\\n try:\\n obj_count = {\\\"count\\\": -1}\\n ap = db_exec_cur(ap)\\n for t in ap.result:\\n obj_count[\\\"count\\\"] = t[0]\\n\\n ap.sqlx = sqlx\\n ap.op = 'get_data'\\n db_exec_cur(ap)\\n except Exception as e:\\n ms1 = \\\"error reading the database\\\" + str(e)\\n print ms1\\n raise \\\"error reading the database\\\"\\n\\n rslt_list = convert_tuple(ap, obj_count)\\n db_close_con(ap)\\n return rslt_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1c9d28f24d0df568c4a1f4d45236683\",\n \"score\": \"0.48244342\",\n \"text\": \"def sample_metadata(sample):\\n select = [\\n chart_table.currentWeekPosition,\\n chart_table.previousWeekPosition,\\n chart_table.peakPosition,\\n chart_table.Artist,\\n #chart_table.entryDate,\\n #chart_table.entryPosition,\\n ]\\n results = db.session.query(*select).all()\\n\\n # dic viewing rows of columns\\n sample_metadata = {}\\n for result in results:\\n sample_metadata[\\\"Current Week Position \\\"] = result[0]\\n sample_metadata[\\\"Previous Week Position \\\"] = result[1]\\n sample_metadata[\\\"Peak Position \\\"] = result[2]\\n #sample_metadata[\\\" Artist \\\"] = result[3]\\n #sample_metadata[\\\"entryDate\\\"] = result[4]\\n #sample_metadata[\\\"entryPosition\\\"] = result[5]\\n\\n print(sample_metadata)\\n return jsonify(sample_metadata)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a09bbd111337c1ee7acfebcf96a5562\",\n \"score\": \"0.48241213\",\n \"text\": \"def _initialize_column_rows(self):\\n data_source = self.data_source_scan.data_source\\n self.sql = data_source.sql_get_table_columns(self.table.table_name)\\n self.sql = self.data_source_scan.scan.jinja_resolve(self.sql)\\n\\n self.fetchall()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7eab99b014c879f3cffda49622fa8fb5\",\n \"score\": \"0.48206058\",\n \"text\": \"def __init__(self, event_type, data=None):\\n self._type = event_type\\n self._data = data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33cadac38e158f265be7dbd289354023\",\n \"score\": \"0.48056328\",\n \"text\": \"def apigw_event():\\n return [\\n {\\n \\\"headers\\\": {\\n \\\"Accept-Language\\\": \\\"en-US,en;q=0.8\\\",\\n \\\"X-Forwarded-For\\\": \\\"127.0.0.1, 127.0.0.2\\\",\\n \\\"Accept\\\": \\\"text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8\\\",\\n },\\n \\\"pathParameters\\\": {\\n \\\"id\\\": \\\"id_01\\\",\\n \\\"user_id\\\": \\\"user_01\\\",\\n },\\n \\\"httpMethod\\\": \\\"GET\\\",\\n },\\n {\\n \\\"headers\\\": {\\n \\\"Accept-Language\\\": \\\"en-US,en;q=0.8\\\",\\n \\\"X-Forwarded-For\\\": \\\"127.0.0.1, 127.0.0.2\\\",\\n \\\"Accept\\\": \\\"text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8\\\",\\n },\\n \\\"httpMethod\\\": \\\"POST\\\",\\n \\\"body\\\": \\\"{\\\\\\\"id\\\\\\\":\\\\\\\"id_02\\\\\\\", \\\\\\\"user_id\\\\\\\":\\\\\\\"user_02\\\\\\\", \\\\\\\"body\\\\\\\":\\\\\\\"message_02\\\\\\\"}\\\",\\n },\\n {\\n \\\"headers\\\": {\\n \\\"Accept-Language\\\": \\\"en-US,en;q=0.8\\\",\\n \\\"X-Forwarded-For\\\": \\\"127.0.0.1, 127.0.0.2\\\",\\n \\\"Accept\\\": \\\"text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8\\\"\\n },\\n \\\"pathParameters\\\": {\\n \\\"id\\\": \\\"id_02\\\",\\n \\\"user_id\\\": \\\"user_02\\\"\\n },\\n \\\"httpMethod\\\": \\\"DELETE\\\",\\n }\\n ]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2713175732bd705462b25e799a0d162d\",\n \"score\": \"0.47925967\",\n \"text\": \"def __init__(self, **kwargs):\\n super().__init__()\\n self.column_name = {}\\n self.column_name['water_body'] = {'internal': 'MS_CD',\\n 'display': 'WATERBODY_NAME'}\\n \\n self.column_name['type_area'] = {'internal': 'TYPE_AREA_CODE', \\n 'display': 'TYPE_AREA_NAME'}\\n \\n self.column_name['water_district'] = {'internal': 'WATER_DISTRICT_CODE', \\n 'display': 'WATER_DISTRICT_NAME'}\\n \\n \\n #TODO Add Parametermapping for water body names\\n #TODO Map against .lower() letters \\n if kwargs:\\n self.load_water_body_match(**kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c04d0b55c15c771160f04452cf8a336b\",\n \"score\": \"0.4789773\",\n \"text\": \"def __init__(self, input_json):\\n self.id = input_json.get('id')\\n self.tstamp = input_json.get('tstamp')\\n self.schemaname = input_json.get('schemaname')\\n self.operation = input_json.get('operation')\\n self.who = input_json.get('who')\\n self.new_val = input_json.get('new_val')\\n self.old_val = input_json.get('old_val')\\n self.tabname = input_json.get('tabname')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"024861791e77fa79c9d107235ff0652f\",\n \"score\": \"0.47877645\",\n \"text\": \"def __init__(__self__, *,\\n events: Sequence['outputs.EventResponse'],\\n state: str):\\n pulumi.set(__self__, \\\"events\\\", events)\\n pulumi.set(__self__, \\\"state\\\", state)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18c1ec90dc16d1ba149322cfcafd0609\",\n \"score\": \"0.47723952\",\n \"text\": \"def __init__(self, jsondict=None):\\n \\n super(DiagnosticOrderItemEvent, self).__init__(jsondict)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c907b14328d564e8a5e5416d9933b135\",\n \"score\": \"0.47677287\",\n \"text\": \"def __init__(self, *args, **kwargs):\\n super().__init__(*args, **kwargs)\\n\\n def string_casting(data: any):\\n \\\"\\\"\\\"\\n Closure to cast the data to string\\n :param data: any\\n Column value\\n :return: str\\n Parsed column value\\n \\\"\\\"\\\"\\n try:\\n if data:\\n return str(data)\\n else:\\n return data\\n except Exception:\\n logging.error('\\\\n')\\n logging.error('~' * 100)\\n logging.exception(\\\"String casting exception:\\\")\\n logging.error('~' * 100)\\n raise StringParsingException(\\\"Column value - {} could not be casted into String.\\\".format(data))\\n\\n self.add_func(string_casting)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7485a2f89c9dcc6fd1fc70af9689993a\",\n \"score\": \"0.47654918\",\n \"text\": \"def initEvents():\\n global PLAYER, CURRENT_MAP, EVENTS, MAPS, SOUNDS\\n for i in EVENTS.e:\\n EVENTS[i].data[\\\"player\\\"] = PLAYER\\n EVENTS[i].data[\\\"cmap\\\"] = CURRENT_MAP\\n EVENTS[i].data['setter'] = setMap \\n EVENTS[i].data['setChar'] = setChar\\n EVENTS[i].data['exit'] = Exit\\n EVENTS[i].data['sounds'] = SOUNDS\\n EVENTS[i].data['globmaps'] = MAPS\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d362755317471c977468e89767e570e\",\n \"score\": \"0.47465035\",\n \"text\": \"def parse_raw_events(self, events):\\n events = json.loads(events) or None\\n if events:\\n events = (OmegleEvent(self.id,\\n ev[0],\\n None if len(ev) == 1 else ev[1])\\n for ev in events)\\n\\n return events\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d61d20bbc0b7199ccf2b7275e298d8a\",\n \"score\": \"0.47428414\",\n \"text\": \"def parse_events(self,response):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"220e9a6d4777dd12e19b68be74ba5a2c\",\n \"score\": \"0.47350973\",\n \"text\": \"def __init__(self, options, columns):\\n super(DatadogForeignDataWrapper, self).__init__(options, columns)\\n initialize(\\n api_key=options['api_key'],\\n app_key=options['app_key']\\n )\\n self.columns = columns\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d14879aea552d7c3b459a45af49d18e8\",\n \"score\": \"0.47228545\",\n \"text\": \"def __init__(self, x, y, independent_columns, dependent_column):\\n\\n self.x = pd.DataFrame(x)\\n self.y = pd.DataFrame(y)\\n self.independent_columns = independent_columns\\n self.dependent_column = dependent_column\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9fb537bae44ea54e6491004e23d1322\",\n \"score\": \"0.47167116\",\n \"text\": \"def _init_dict(self, data):\\n if not self._columns:\\n self._columns = list(data.keys())\\n\\n # Filter values by defined columns\\n columns = (\\n to_list(values)\\n for column, values in data.items()\\n if column in self._columns\\n )\\n\\n # Convert columns to rows\\n self._data = [list(row) for row in zip_longest(*columns)]\\n\\n self._index = self._index or list(range(len(self._data)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c2f496d515391bcc209216a9ae71b233\",\n \"score\": \"0.4716067\",\n \"text\": \"def __init__(self,house=None, api_type=None, api_name=None, schema=None, entry_columns=None):\\n self.house = house\\n self.api_type = api_type\\n self.api_name = api_name\\n self.schema = schema\\n\\n\\n self.table = ('_').join([self.house, self.api_type, self.api_name])\\n self.conn = utils.connect_sqlalchemy()\\n\\n self.entry_structure = dict.fromkeys(entry_columns)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a4903d2e8e7968e3b3ccab23ee5f476\",\n \"score\": \"0.47156122\",\n \"text\": \"def __init__(self, *args, **kwargs):\\n super(DnsUniqueResponse, self).__init__(*args, **kwargs)\\n\\n self._records = list()\\n self._process_records()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e799c8353f805af4b6d00df9b98c1653\",\n \"score\": \"0.47143358\",\n \"text\": \"def make_event(self):\\n event_dict = {}\\n quote = \\\"\\\"\\n while quote == \\\"\\\":\\n aut, quote = self.get_quote()\\n event_dict[\\\"author\\\"] = aut\\n event_dict[\\\"quote\\\"] = quote\\n return event_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73d3cfc2efa359fe8d1ba0dc778692c0\",\n \"score\": \"0.4713355\",\n \"text\": \"def __init__(__self__, *,\\n count: int,\\n first_timestamp: str,\\n last_timestamp: str,\\n message: str,\\n name: str,\\n type: str):\\n pulumi.set(__self__, \\\"count\\\", count)\\n pulumi.set(__self__, \\\"first_timestamp\\\", first_timestamp)\\n pulumi.set(__self__, \\\"last_timestamp\\\", last_timestamp)\\n pulumi.set(__self__, \\\"message\\\", message)\\n pulumi.set(__self__, \\\"name\\\", name)\\n pulumi.set(__self__, \\\"type\\\", type)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7a30c7780885ed5d7289dbda009ca2f\",\n \"score\": \"0.47081393\",\n \"text\": \"def __init__(self, name):\\n # these items must match the columns above\\n # if they're not created above, they won't be stored to the DB\\n\\n self.name = name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53715b788f87abe66ffd0a5d4548c3d9\",\n \"score\": \"0.47060105\",\n \"text\": \"def __init__(self, row):\\n\\n if row == None:\\n return\\n\\n self.id = row['id']\\n self.owner = row['owner']\\n self.created = row['created']\\n self.reply_to_user = row['reply_to_user']\\n self.reply_to_tweet = row['reply_to_tweet']\\n self.geo = row['geo']\\n self.place_name = None\\n self.source = None\\n self.contents = None\\n self.yyyymm = 0 # weirdly set to 0 on init.\\n \\n if row['place_name'] != None:\\n self.place_name = row['place_name']\\n\\n self.place_box = row['place_box']\\n \\n if row['source'] != None:\\n self.source = row['source']\\n\\n if row['contents'] != None:\\n self.contents = row['contents']\\n \\n if row['yyyymm'] != None:\\n self.yyyymm = row['yyyymm']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"518992cb645a820edae890999a13d75b\",\n \"score\": \"0.47015494\",\n \"text\": \"def __init__(self, row, cols):\\n self.field_dict = {}\\n for c in range(len(cols)):\\n self.field_dict[cols[c]] = row[c]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"edea8576948777c699d403e22eac21e3\",\n \"score\": \"0.46917585\",\n \"text\": \"def get_event(event):\\n if isinstance(event, str):\\n name = event\\n else:\\n try:\\n name = event.name\\n except AttributeError:\\n raise ValueError(\\\"Cannot get event. Use a string.\\\")\\n db = Data(DB_BLAZE)\\n selection = odo(db.event[db.event.name==name], pd.DataFrame)\\n if selection.empty:\\n raise ValueError(\\\"Event does not exist.\\\")\\n else:\\n event = dict(selection.loc[0])\\n #event = collections.namedtuple('Event', event.keys())(**event)\\n event = Event(**event)\\n return event\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa3ab3ff24d24ba7de7ea998dc9c1c0c\",\n \"score\": \"0.46899942\",\n \"text\": \"def __init__(self, req_id, location, time, event):\\n self.req_id = req_id\\n self.location = location\\n self.time = time\\n self.event = event\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee24f77d2bed03f0f90fc0b3569b1567\",\n \"score\": \"0.46879146\",\n \"text\": \"def __create_column_name_mapping__(self, columns):\\n mapping = {'names': {}, 'numbers': {},\\n 'header': { 'separator': self.separator}}\\n\\n argn = 0\\n for token in columns:\\n mapping['names'][token] = argn\\n mapping['numbers'][argn] = token\\n argn += 1\\n\\n self._column_names = mapping['names']\\n self.column_nums = mapping['numbers']\\n\\n if not self._out_column_names_set:\\n self._out_column_names = self._column_names.keys()\\n\\n self._mapping = mapping\\n return mapping\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37e2a3729fe3843c08be8721bd51400d\",\n \"score\": \"0.4683065\",\n \"text\": \"def add_column_names(self, request):\\n model_obj = None\\n org = self.get_organization(request, return_obj=True)\\n inventory_pk = request.query_params.get('inventory_pk')\\n inventory_type = request.query_params.get('inventory_type', 'property')\\n if inventory_type in ['property', 'propertystate']:\\n if not inventory_pk:\\n model_obj = PropertyState.objects.filter(\\n organization=org\\n ).order_by('-id').first()\\n try:\\n model_obj = PropertyState.objects.get(id=inventory_pk)\\n except PropertyState.DoesNotExist:\\n pass\\n elif inventory_type in ['taxlot', 'taxlotstate']:\\n if not inventory_pk:\\n model_obj = TaxLotState.objects.filter(\\n organization=org\\n ).order_by('-id').first()\\n else:\\n try:\\n model_obj = TaxLotState.objects.get(id=inventory_pk)\\n inventory_type = 'taxlotstate'\\n except TaxLotState.DoesNotExist:\\n pass\\n else:\\n msg = \\\"{} is not a valid inventory type\\\".format(inventory_type)\\n raise ParseError(msg)\\n if not model_obj:\\n msg = \\\"No {} was found matching {}\\\".format(\\n inventory_type, inventory_pk\\n )\\n raise NotFound(msg)\\n Column.save_column_names(model_obj)\\n\\n columns = Column.objects.filter(\\n organization=model_obj.organization,\\n table_name=model_obj.__class__.__name__,\\n is_extra_data=True,\\n\\n )\\n columns = ColumnSerializer(columns, many=True)\\n return Response(columns.data, status=status.HTTP_200_OK)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bec2fdba68756c84db2a39467f6b8adb\",\n \"score\": \"0.46814585\",\n \"text\": \"def _populate_from_database(self, row):\\n\\n (record_id, name, user, agreed_at) = row\\n\\n self['record_id'] = record_id\\n self['message_name'] = name\\n self['agreed_by'] = user\\n self['agreed_at'] = from_utimestamp(agreed_at)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e6dd36a0c0f13e0559d215c4116acfb6\",\n \"score\": \"0.46798655\",\n \"text\": \"def __init__(self, x=None, y=None, fold_column='', blending_avg=True, inflection_point=3, smoothing=1):\\n\\n self._teColumns = x\\n self._responseColumnName = y\\n self._foldColumnName = fold_column\\n self._blending = blending_avg\\n self._inflectionPoint = inflection_point\\n self._smoothing = smoothing\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85791cb4ca1e09d072aa5dc903369219\",\n \"score\": \"0.4676823\",\n \"text\": \"def __init__(self, message, status=None, event_status=None, events=None):\\n self.status = status\\n self.event_status = event_status\\n self.events = events\\n super().__init__(self, message)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b65fa6d4855864214fe624fa2c407167\",\n \"score\": \"0.46701154\",\n \"text\": \"def emit_basic(data):\\n #~ pass\\n res = {}\\n i = 0\\n if 'uuid' in data:\\n data = {0:data}\\n if 'uuid' in data[data.keys()[0]]:\\n data = {0:data}\\n for idx1 in data:\\n logger.debug('Basic event idx1 :%s,%s' % (idx1,data[idx1]))\\n if idx1 not in res:\\n res[idx1]={}\\n for idx2 in data[idx1]:\\n logger.debug('Basic event idx2 :%s,%s' % (idx2,data[idx1][idx2]))\\n res[idx1][idx2] = data[idx1][idx2]\\n logger.debug(u'Basic event :%s', res)\\n self.socketio.emit('my basics response',\\n {'data':res},\\n namespace='/janitoo')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd08c3274924405aa7480f0899e25fe2\",\n \"score\": \"0.46612448\",\n \"text\": \"def _json_object_hook(dat):\\n return namedtuple('X', dat.keys())(*dat.values())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba540ddbdd51ea164d85b9f3b4b66154\",\n \"score\": \"0.46515137\",\n \"text\": \"def __init__(self,test=False,remote=False):\\n self._test = test\\n self.dbh = DBHandler(league=LEAGUES[0],test=self._test,remote=remote)\\n self.data = {_l:{_y:(None,None) for _y in YEARS} for _l in LEAGUES}\\n self._indces = {_l:0 for _l in LEAGUES}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85d18835daeaf3460a228c8b966a7c26\",\n \"score\": \"0.46498805\",\n \"text\": \"def __init__(self, row):\\n \\n self.id = row['id']\\n \\n self.screen_name = None\\n self.name = None\\n self.lang = None\\n self.location = None\\n self.private = None\\n \\n if row['screen_name'] != None:\\n # I don't think I need the single_unescape here, the python library \\n # handles it; but in theory if I correctly escape thing and unescape\\n # things all the time--it should be fine.\\n self.screen_name = row['screen_name']\\n\\n if row['name'] != None:\\n self.name = row['name']\\n\\n if row['lang'] != None:\\n self.lang = row['lang']\\n\\n if row['location'] != None:\\n self.location = row['location']\\n\\n if row['private'] != None:\\n self.private = row['private']\\n\\n self.friends = row['friends']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d43a2c77b5ca36b90f91e58ee131012f\",\n \"score\": \"0.46496642\",\n \"text\": \"def __init__(self, num_columns):\\n\\n self.row_values = []\\n self.num_columns = int(num_columns)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55d2927057e0adfa328d29c57df4b3c1\",\n \"score\": \"0.4648\",\n \"text\": \"def event_names(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cac80d9fb89be12c87c014fa8cf371f4\",\n \"score\": \"0.46443748\",\n \"text\": \"def __init__(self, *args, **kwargs):\\n super(DnsResponse, self).__init__(*args, **kwargs)\\n self._process_records()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"266214551c4cbf23f0c47624f4701480\",\n \"score\": \"0.46403676\",\n \"text\": \"def _init_empty(self):\\n self._data = [[None for _ in self._columns] for _ in self._index]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"039d8356db5ff32889c4e696ff7ced98\",\n \"score\": \"0.46364927\",\n \"text\": \"def __init__(self):\\n self.hits = {}\\n self.timestamp_arr = []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a3f0c0975a95c7bc5e6abf1e2473e47\",\n \"score\": \"0.46327257\",\n \"text\": \"def test_json_columns(self):\\n\\n class MyTable(Table):\\n column_a = JSON()\\n column_b = JSONB()\\n\\n self.assertEqual(\\n MyTable._meta.json_columns, [MyTable.column_a, MyTable.column_b]\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69c0396e7ad7180d315f9ea4b1cea260\",\n \"score\": \"0.4631868\",\n \"text\": \"def __init__(self, *args, **kwds):\\n if args or kwds:\\n super(mpu_valuesResponse, self).__init__(*args, **kwds)\\n #message fields cannot be None, assign default values for those that are\\n if self.ax is None:\\n self.ax = 0.\\n if self.ay is None:\\n self.ay = 0.\\n if self.az is None:\\n self.az = 0.\\n else:\\n self.ax = 0.\\n self.ay = 0.\\n self.az = 0.\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cafdf92001be20639b59c28ebdc571b7\",\n \"score\": \"0.4631288\",\n \"text\": \"def __init__(\\n self,\\n response: dict\\n ):\\n self.__more = read_value(\\n \\\"more\\\", response, bool, True)\\n self.__total_count = read_value(\\n \\\"totalCount\\\", response, int, True)\\n self.__filtered_count = read_value(\\n \\\"filteredCount\\\", response, int, True)\\n self.__items = read_value(\\n \\\"items\\\", response, Row, True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b59ff681ac08c7d193accaa60e5dc08\",\n \"score\": \"0.4620476\",\n \"text\": \"def __init__(self):\\n\\n self._column_dict = {\\\"UI\\\": [self._USER, self._ITEM],\\n \\\"UIR\\\": [self._USER, self._ITEM, self._RATING],\\n \\\"UIT\\\": [self._USER, self._ITEM, self._TIME],\\n \\\"UIRT\\\": [self._USER, self._ITEM, self._RATING, self._TIME]}\\n self._column_name = None\\n self._config = OrderedDict()\\n self.all_data = None\\n self.train_data = None\\n self.valid_data = None\\n self.test_data = None\\n self.user2id = None\\n self.item2id = None\\n self._dir_path = None\\n self._data_name = \\\"\\\"\\n self._split_manner = \\\"\\\"\\n self._user_min = 0\\n self._item_min = 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d94b2698c08a68be9684d8a34e42c7f0\",\n \"score\": \"0.46192908\",\n \"text\": \"def __init__(self, records):\\n self._data = [record.data for record in records]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b96841bd8ecb0daaadb2c03d92c462a6\",\n \"score\": \"0.46117195\",\n \"text\": \"def __init__(self):\\n super(AggHandler, self).__init__() # makes sure variables from super \\\"__init__\\\" are also inherited\\n self.timestamp_loc = {'start': 1, 'end': 9}\\n self.transtype_loc = 9\\n self.volume_loc = {'short':{'start':19, 'end': 25}, 'long':{'start':19, 'end':28}}\\n self.contra_id_loc = {'short':{'start':34, 'end': 43}, 'long': {'start':38, 'end':47}}\\n self.passive_id_loc = {'start': 10, 'end': 19}\\n self.traderef_loc = {'short':{'start':25, 'end': 34}, 'long': {'start':29, 'end':38}}\\n self.reset_cache()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b26e42edab1a5b72991e902d9ec8a3c9\",\n \"score\": \"0.46102205\",\n \"text\": \"def __init__(\\n self,\\n pre_event: list = [],\\n event: list = [],\\n post_event: list = [],\\n title: str = '',\\n event_type: EventType = EventType.UNDEFINED,\\n event_detail: EventDetail = EventDetail(),\\n ) -> None:\\n self.pre_event: list = pre_event\\n self.event: list = event\\n self.post_event: list = post_event\\n self.title = title\\n self.event_type: EventType = event_type\\n self.event_detail: EventDetailType = event_detail\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3fd6d0b0dd67de7848312dcb8846c93a\",\n \"score\": \"0.46096426\",\n \"text\": \"def __init__(self, commandsetresponse):\\n self.metrics = {Metric(i).uniquetuple: Metric(i).value for i in commandsetresponse.parsed_message}\\n self.executed_at = commandsetresponse.executed_at\\n self.host = commandsetresponse.device\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52ce965b73f05182ec52e483795e69a4\",\n \"score\": \"0.4606544\",\n \"text\": \"def __init__(self, sample_dict: dict, sample_ordinal: int, illumina_naming: bool=False, sample_barcode_column: Optional[str]=None):\\n self.__dict: dict = sample_dict\\n self.__sample_ordinal: int = sample_ordinal\\n self.__illumina_naming: bool = illumina_naming\\n self.__sample_barcode_column: str = sample_barcode_column\\n assert sample_dict['sample_name'], \\\"'sample_name' not in the sample dictionary\\\"\\n assert sample_dict['sample_id'], \\\"'sample_id' not in the sample dictionary\\\"\\n assert 0 < self.__sample_ordinal, f\\\"sample_ordinal must be greater than zero, was {sample_ordinal}\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc7c35b3b3fceaa594e64044090ad0d1\",\n \"score\": \"0.46021602\",\n \"text\": \"def __init__(self, fields, ignore_empty=False):\\n\\n self.field_names = (str(field) for field in fields)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"868061b649c3e8fb70bb46f2d5997d24\",\n \"score\": \"0.4600528\",\n \"text\": \"def __init__(self, *args, **kwds):\\n if args or kwds:\\n super(Event, self).__init__(*args, **kwds)\\n #message fields cannot be None, assign default values for those that are\\n if self.type is None:\\n self.type = 0\\n if self.ball1 is None:\\n self.ball1 = billiards_msgs.msg.BallState()\\n if self.ball2 is None:\\n self.ball2 = billiards_msgs.msg.BallState()\\n if self.string_rep is None:\\n self.string_rep = ''\\n else:\\n self.type = 0\\n self.ball1 = billiards_msgs.msg.BallState()\\n self.ball2 = billiards_msgs.msg.BallState()\\n self.string_rep = ''\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2ced6db26e3bd4921ff0bca6d8b3698\",\n \"score\": \"0.45960116\",\n \"text\": \"def __init__(self, read_data_events=None, write_data_events=None, activity_tracker_crn=None):\\n self.read_data_events = read_data_events\\n self.write_data_events = write_data_events\\n self.activity_tracker_crn = activity_tracker_crn\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29f06b9a961d014db62d0431838a80a1\",\n \"score\": \"0.45852238\",\n \"text\": \"def __init__(self):\\n self.keys = {}\\n self.timestamps = {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8040cc31a768a3fc2078241cb1bba3a2\",\n \"score\": \"0.45846277\",\n \"text\": \"def header(self):\\n return dict(\\n [\\n (key, value)\\n for key, value in self.__dict__.items()\\n if key not in [\\\"tseries\\\", \\\"dseries\\\"]\\n ]\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f22259c630adccf7c6bacd9e72b1da9\",\n \"score\": \"0.45820498\",\n \"text\": \"def __init__(self, **dataset):\\n for key, value in dataset.items():\\n insertion_information = asarray(value)\\n setattr(self, key, insertion_information)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e07e784ac45daf63f02fccfe53099d8\",\n \"score\": \"0.45713317\",\n \"text\": \"def events():\\n is_empty = False\\n\\n con = connect_database()\\n\\n sql_query = \\\"SELECT * FROM prithvidb.disastersFinal ORDER BY date\\\"\\n\\n cursor = con.cursor()\\n cursor.execute(sql_query)\\n data = cursor.fetchall()\\n if len(data) == 0:\\n is_empty = True\\n cursor.close()\\n\\n result = []\\n\\n if data is not None:\\n app.logger.info(\\\"Success\\\")\\n for row in data:\\n row_data = dict()\\n # app.logger.info(row)\\n row_data['date'] = row[1]\\n row_data['title'] = row[2]\\n row_data['description'] = row[3]\\n row_data['country'] = row[4]\\n row_data['city'] = row[5]\\n row_data['latitude'] = row[6]\\n row_data['longitude'] = row[7]\\n row_data['name'] = row[8]\\n result.append(row_data)\\n else:\\n error = \\\"No user could be found\\\"\\n return json.dumps(result)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":685,"cells":{"query_id":{"kind":"string","value":"9567da02ab3490901b639507bf63db42"},"query":{"kind":"string","value":"Returns a list of locales found in the \"locales\" property of the manifest. This will convert locales found in the SHORTER_LANGUAGES setting to their full locale. It will also remove locales not found in AMO_LANGUAGES."},"positive_passages":{"kind":"list like","value":[{"docid":"b3e587f2da19952e54367eb789e2dd40","score":"0.6775279","text":"def get_supported_locales(manifest):\n return sorted(filter(None, map(find_language, set(\n manifest.get('locales', {}).keys()))))","title":""}],"string":"[\n {\n \"docid\": \"b3e587f2da19952e54367eb789e2dd40\",\n \"score\": \"0.6775279\",\n \"text\": \"def get_supported_locales(manifest):\\n return sorted(filter(None, map(find_language, set(\\n manifest.get('locales', {}).keys()))))\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"c7eeb0b6810a336325fe917618b5dcf9","score":"0.74035686","text":"def get_locales(app: Sphinx) -> List[str]:\n # Manually configured list of locales\n sitemap_locales: Optional[List[str]] = app.builder.config.sitemap_locales\n if sitemap_locales:\n # special value to add nothing -> use primary language only\n if sitemap_locales == [None]:\n return []\n\n # otherwise, add each locale\n return [locale for locale in sitemap_locales]\n\n # Or autodetect locales\n locales = []\n for locale_dir in app.builder.config.locale_dirs:\n locale_dir = os.path.join(app.confdir, locale_dir)\n if os.path.isdir(locale_dir):\n for locale in os.listdir(locale_dir):\n if os.path.isdir(os.path.join(locale_dir, locale)):\n locales.append(locale)\n return locales","title":""},{"docid":"7c2f34c990e34f7b0561f72ee92e445d","score":"0.60102385","text":"def discover_resources():\n locale_discovery_paths = list(settings.TRANZ_LOCALE_PATHS)\n if settings.TRANZ_SEARCH_LOCALE_IN_APPS:\n locale_discovery_paths += [os.path.join(app.path, settings.TRANZ_DIR_NAME) for app in list(apps.app_configs.values())]\n \n APP_LANGUAGES = [l[0] for l in settings.TRANZ_LANGUAGES]\n\n resources = []\n for path in locale_discovery_paths:\n if not os.path.isdir(path):\n continue\n\n # Try to match direct children or discovery paths\n for file in os.listdir(path):\n if os.path.isfile(os.path.join(path, file)):\n try:\n domain, lang, format = file.split('.')\n except ValueError as e:\n continue\n resources.append((format, os.path.join(path, file), lang, domain))\n \n \n # Try to match django's LC_MESSAGES directories\n if settings.TRANZ_REPLACE_DJANGO_TRANSLATIONS:\n for lang in APP_LANGUAGES:\n if os.path.isdir(os.path.join(path, lang)):\n LC_MESSAGES_PATH = os.path.join(path, lang, 'LC_MESSAGES')\n if os.path.isdir(LC_MESSAGES_PATH):\n for file in os.listdir(LC_MESSAGES_PATH):\n try:\n domain, format = file.split('.')\n except ValueError as e:\n continue\n resources.append((format, os.path.join(LC_MESSAGES_PATH, file), lang, domain))\n return resources","title":""},{"docid":"6ae3fb7a33fd6a008941607e1e77abe3","score":"0.5900377","text":"def submission_locales(self):\n return self._submission_locales","title":""},{"docid":"ac68e878272a6ee481b4187ed32129b2","score":"0.5781434","text":"def get_all_locale():\n wb = openpyxl.load_workbook(__matrix_file_name__)\n sheet = wb[\"RYI\"]\n\n res = []\n for index in range(4, 35):\n locale = sheet[\"B{}\".format(index)].value.split()[0]\n res.append(locale)\n\n return res","title":""},{"docid":"4abe104d5dd647ac97f4b89e7230fb1d","score":"0.57366514","text":"def parse_locales(paths):\n\n\tif not paths:\n\t\traise ValueError(\"Invalid argument: no paths were passed.\")\n\n\treturn set([parse_locale(path) for path in paths])","title":""},{"docid":"19b9a8e3cb01b0ff5fc13c7ae5e99982","score":"0.5657871","text":"def interface_locales(self):\n return self._interface_locales","title":""},{"docid":"02f4e2537c49240e2b03799638be48ea","score":"0.5627225","text":"def regularize_locales(locales):\n locales = [regularize_locale(loc) for loc in locales]\n locales_set = set(locales)\n for loc in locales:\n yield loc\n parts = loc.split('_')\n if len(parts) > 1 and parts[0] not in locales_set:\n # Insert \"fr\" after \"fr_fr\" if it's not somewhere in the list\n yield parts[0]\n alias = ALIASES.get(loc)\n if alias and alias not in locales_set:\n # Insert \"fr_fr\" after \"fr\" if it's not somewhere in the list\n yield alias\n if 'en' not in locales_set and 'en_us' not in locales_set:\n yield 'en'\n yield 'en_us'","title":""},{"docid":"16d43ff401058c5f2e8ec48dcf083b83","score":"0.5538066","text":"def locales(ctx):\n # https://docs.djangoproject.com/en/dev/ref/django-admin/#django-admin-makemessages\n \"\"\"\n python manage.py makemessages -v 3 --no-wrap --ignore \".*\" --locale=pl_PL\n python manage.py compilemessages -v 3\n \"\"\"\n tmp = ROOT_DIR / \".tmp\"\n if not tmp.exists():\n os.makedirs(str(tmp))\n locale = ROOT_DIR / \"src\" / \"locale\"\n if not locale.exists():\n os.makedirs(str(locale))\n # http://babel.edgewall.org/wiki/BabelDjango\n pybabel = str(VENV_BIN / 'pybabel')\n ctx.run(pybabel + \" extract -F locale/babel.cfg -o locale/django.pot --no-wrap --sort-output .\")\n # create locales firs\n # pybabel init -D django -i locale/django.pot -d locale -l es\n # http://babel.edgewall.org/wiki/BabelDjango#CreatingandUpdatingTranslationsCatalogs\n # ctx.run(pybabel + \" update -D django -i locale/django.pot -d locale --ignore-obsolete\")\n ctx.run(pybabel + \" update -D django -i locale/django.pot -d locale --previous --no-wrap\")\n ctx.run(pybabel + \" compile -D django -d locale --statistics\")\n log.info(\"JavaScript locales\")\n # ctx.run(pybabel + \" update -D djangojs -i locale/djangojs.pot -d locale --previous --no-wrap\")\n ctx.run(\"django-admin makemessages -d djangojs -i static -i node_modules\")\n ctx.run(pybabel + \" compile -D djangojs -d locale --statistics\")","title":""},{"docid":"8cf42f69eb6352d8aaf8d51d5d0eaaa2","score":"0.5526054","text":"def locale_resources(self, locale):\n resources = []\n project_files = self.get_or_set_project_files(locale.code)\n\n for resource in self.vcs_project.db_project.resources.all():\n absolute_resource_path = os.path.join(\n self.vcs_project.source_directory_path,\n resource.path,\n )\n\n if project_files.match(absolute_resource_path):\n resources.append(resource)\n\n return resources","title":""},{"docid":"f881f0110cd5024ed4c3ca358857b5a8","score":"0.5444359","text":"def get_localizations(self):\n localizations = []\n for glat in self.haldane_lattices:\n localizations.append(glat.get_localization())\n\n return localizations","title":""},{"docid":"0fc3416ca41e00704adea508153c7958","score":"0.540855","text":"def get_available_languages(domain):\r\n if domain in _AVAILABLE_LANGUAGES:\r\n return copy.copy(_AVAILABLE_LANGUAGES[domain])\r\n\r\n localedir = '%s_LOCALEDIR' % domain.upper()\r\n find = lambda x: gettext.find(domain,\r\n localedir=os.environ.get(localedir),\r\n languages=[x])\r\n\r\n # NOTE(mrodden): en_US should always be available (and first in case\r\n # order matters) since our in-line message strings are en_US\r\n language_list = ['en_US']\r\n # NOTE(luisg): Babel <1.0 used a function called list(), which was\r\n # renamed to locale_identifiers() in >=1.0, the requirements master list\r\n # requires >=0.9.6, uncapped, so defensively work with both. We can remove\r\n # this check when the master list updates to >=1.0, and update all projects\r\n list_identifiers = (getattr(localedata, 'list', None) or\r\n getattr(localedata, 'locale_identifiers'))\r\n locale_identifiers = list_identifiers()\r\n\r\n for i in locale_identifiers:\r\n if find(i) is not None:\r\n language_list.append(i)\r\n\r\n # NOTE(luisg): Babel>=1.0,<1.3 has a bug where some OpenStack supported\r\n # locales (e.g. 'zh_CN', and 'zh_TW') aren't supported even though they\r\n # are perfectly legitimate locales:\r\n # https://github.com/mitsuhiko/babel/issues/37\r\n # In Babel 1.3 they fixed the bug and they support these locales, but\r\n # they are still not explicitly \"listed\" by locale_identifiers().\r\n # That is why we add the locales here explicitly if necessary so that\r\n # they are listed as supported.\r\n aliases = {'zh': 'zh_CN',\r\n 'zh_Hant_HK': 'zh_HK',\r\n 'zh_Hant': 'zh_TW',\r\n 'fil': 'tl_PH'}\r\n for (locale, alias) in six.iteritems(aliases):\r\n if locale in language_list and alias not in language_list:\r\n language_list.append(alias)\r\n\r\n _AVAILABLE_LANGUAGES[domain] = language_list\r\n return copy.copy(language_list)","title":""},{"docid":"28a92844e27af4433511216282afe0bf","score":"0.53916764","text":"def list_translations(dirname='locale'):\n if not os.path.isdir(dirname):\n return []\n\n result = []\n for folder in sorted(os.listdir(dirname)):\n if os.path.isdir(os.path.join(dirname, folder, 'LC_MESSAGES')):\n result.append(Locale.parse(folder))\n\n return result","title":""},{"docid":"898393fb9181012607099ab8a0f798d0","score":"0.53290695","text":"def getlocalenames(self):\n if rootdir:\n o = gui.rootrun('chroot %s locale -a' % rootdir)[1]\n else:\n o = Popen(['locale', '-a'], stdout=PIPE).communicate()[0]\n ll = [ l for l in o.splitlines() if '.' in l ]\n return ['C', 'POSIX'] + ll","title":""},{"docid":"0f2ee6c7bcc3323b6b02d92fedb8bf37","score":"0.5278709","text":"def list_languages():\n logger.info(\"retrieving list of supported languages\")\n return [lang.to_dict() for lang in installation.languages]","title":""},{"docid":"411b711418162e4e8e423f01a8311886","score":"0.5230266","text":"def ordered_locales(request):\n\n accept_language = get_accept_language(request)\n langs = accept_language.split(\",\")\n pairs = map( lambda(lang): lang.split(\";\"), langs)\n\n for pair in pairs:\n if len(pair) < 2: pair.append(1)\n else: pair[1] = float(pair[1][2:])\n pairs.sort(key=lambda(x):x[1], reverse=True)\n\n return [lang for (lang,val) in pairs if val > 0.0]","title":""},{"docid":"468535c7e7d5aafa90b517d27678c6b1","score":"0.52168137","text":"def langs(self) -> list[str]:\n return list(self._translations.keys())","title":""},{"docid":"c4f5ffab6cb4c3fd0c4a1234cbdfa243","score":"0.5147565","text":"def _get_test_locales(config):\n return eval(config.getoption(\"nl\")) if is_multilocale_arkouda() else 1","title":""},{"docid":"5b4af42c6dccda2ea5b54f33e7b80c03","score":"0.5137169","text":"def released_languages_list(self):\n if not self.released_languages.strip():\n return []\n\n languages = [lang.lower().strip() for lang in self.released_languages.split(',')]\n # Put in alphabetical order\n languages.sort()\n return languages","title":""},{"docid":"99a090f6616d7d5a5f6d035f7e9a2d8a","score":"0.51151115","text":"def locale_path_locales(self, repo_checkout_path):\n locale_path_locales = {}\n\n for locale in self.db_project.locales.all():\n locale_directory = self.locale_directory_paths[locale.code]\n path = locale_directory[len(repo_checkout_path) :].lstrip(os.sep)\n path = os.path.join(path, \"\") # Ensure the path ends with os.sep\n locale_path_locales[path] = locale\n\n return locale_path_locales","title":""},{"docid":"ee0b6d30bf35b8accab51f9b4bcc365b","score":"0.50602275","text":"def findTranslationResources():\n\n\ttranslations_dir = QtCore.QDir(':/')\n\tfile_names = translations_dir.entryList(['*.qm'],\n\t\tQtCore.QDir.Files, QtCore.QDir.Name)\n\n\ttranslator = QtCore.QTranslator()\n\n\ttranslation_files = []\n\tfor file_name in file_names:\n\t\tresource_path = translations_dir.filePath(file_name)\n\n\t\ttry:\n\t\t\tlocale = _parseTranslationFileName(str(file_name))\n\t\texcept:\n\t\t\t# theoretically, this should not happen, because these files \n\t\t\t# have already passed through findTranslationFiles()\n\t\t\twarning(\"Failed to parse translation file name: \" + file_name)\n\t\t\tcontinue\n\n\t\tif not translator.load(resource_path):\n\t\t\twarning(\"Failed to load translation file \" + resource_path)\n\t\t\tcontinue\n\n\t\tlocale_name, full_name = _getLocaleInfo(locale)\n\n\t\t# do not include default locale to list, we do not want to show it to user\n\t\t# it will be used only as a backup one\n\t\tif locale_name == _QT_UNKNOWN_LOCALE:\n\t\t\tcontinue\n\n\t\ttranslation_files.append((locale_name, full_name, resource_path))\n\n\treturn translation_files","title":""},{"docid":"2797cdbc5afd29c56928def62bf73f2a","score":"0.5051008","text":"def get_languages(self):\n global GENGO_LANGUAGE_CACHE\n if not GENGO_LANGUAGE_CACHE:\n resp = self.gengo_api.getServiceLanguages()\n GENGO_LANGUAGE_CACHE = tuple(\n [item['lc'] for item in resp['response']])\n return GENGO_LANGUAGE_CACHE","title":""},{"docid":"173dd799b4a1a88a3263f83ce54d1972","score":"0.5037178","text":"def get_languages(self):\n return self._data.get_data(const.LANGUAGES_COLLECTION)","title":""},{"docid":"1e66e4297e1b4215f4a020b572013aa6","score":"0.503531","text":"def get_slang_abbreviations():\n with open(get_root_directory()+'/resources/twitter_slang_abbreviations.json') as data_file:\n return json.load(data_file)","title":""},{"docid":"b36a03eba590e30d686fc6112bf4f9bb","score":"0.50228155","text":"def _get_languages(self):\r\n LOGGER.log()\r\n gtk_lang_mgr = gtksourceview2.language_manager_get_default()\r\n language_ids = gtk_lang_mgr.get_language_ids()\r\n language_names = []\r\n for language_id in language_ids:\r\n language = gtk_lang_mgr.get_language(language_id)\r\n language_names.append(language.get_name())\r\n language_names.sort(lambda a, b:\r\n cmp(a.lower(), b.lower()))\r\n return language_names","title":""},{"docid":"83a10795cb06800ba71d8a4e424d41f3","score":"0.5002563","text":"def supported_languages(self) -> list[str]:","title":""},{"docid":"64ba48589172e3de8af030f931846647","score":"0.50003403","text":"def getTranslatedThemeList(self, category=None):\n pre = \"theme_\"\n themes = [ (_(pre+t), t) for t in self.getThemes() ]\n \n return themes","title":""},{"docid":"f5db54f7b92bee22e8e1f8f19eb3a102","score":"0.49863845","text":"def get_mapping_languages(self) -> List[str]:\n return sorted(lang for _, lang in self.cfg[\"mapping\"].items())","title":""},{"docid":"83e8ebb1b115a295e3ae4f31974be6fa","score":"0.49761966","text":"def get_available_languages():\n _set_environment()\n proc = subprocess.Popen([TESSERACT_CMD, \"--list-langs\"],\n startupinfo=g_subprocess_startup_info,\n creationflags=g_creation_flags,\n stdout=subprocess.PIPE,\n stderr=subprocess.STDOUT)\n langs = proc.stdout.read().decode('utf-8').splitlines(False)\n ret = proc.wait()\n if ret != 0:\n raise TesseractError(ret, \"unable to get languages\")\n\n return [lang for lang in langs if lang and lang[-1] != ':']","title":""},{"docid":"48149a715a8e25c9e9beb8d0ee4b2480","score":"0.49359855","text":"def get_translation_list(obj, language_codes=[lang['code'] for lang in settings.PARLER_LANGUAGES[None]]):\n prefetched_translations = getattr(obj, 'translation_list', [])\n filtered_prefetched = [\n translation for translation in prefetched_translations if translation.language_code in language_codes\n ]\n return filtered_prefetched if prefetched_translations else obj.translations.filter(language_code__in=language_codes)","title":""},{"docid":"79a7bda2cb146f6ecfacfb4bfffd9ce2","score":"0.4918901","text":"def ADDITIONAL_LANGUAGES(self) -> Iterable[str]:\n return self._setting(\n \"ADDITIONAL_LANGUAGES\", self.config[\"additional_languages\"]\n )","title":""},{"docid":"5ea62ac62cbc7d56e16fd0db6e3da884","score":"0.48982581","text":"def languages(self) -> set[str]:\n langs = set()\n for cat in self.entry_page.categories:\n if (m := DASH_LANG_SEARCH(cat)) and (lang := LANG_ABBREV_MAP.get(m.group(1))):\n langs.add(lang)\n break\n return langs","title":""},{"docid":"21f8b41900cf0ebcc66381afb60aa0f2","score":"0.48617068","text":"def langs(cls):\n return cls._langs","title":""},{"docid":"921d037d8efa8843abe9d72cb279e08c","score":"0.48374712","text":"def languages(self):\n if not self._languages:\n # query API only the first time\n self._languages = self.get_languages()\n return self._languages","title":""},{"docid":"b9f7e0677792e6ad99e5f24f32720b52","score":"0.48260513","text":"def monster_language_set(data):\n languages = set()\n for m in data:\n languages.update([x.strip() for x in m[\"Languages\"].split(\",\")])\n return languages","title":""},{"docid":"7b775d5acb15d705dda67041b0fdb9e1","score":"0.48153034","text":"def get_available_languages():\n pass","title":""},{"docid":"dcd43e1d1d67bfe446574aba56a9d96f","score":"0.47988316","text":"def supported_languages(self) -> list[str]:\n return [\"en\"]","title":""},{"docid":"c33d85380325b9ba9fabc7b7e610e774","score":"0.4771139","text":"def realm_names(self):\n return sorted(self._realms)","title":""},{"docid":"9bb3849a3dc981c05195b6b3ab968be3","score":"0.4760957","text":"def get_languages(self):\n languages = cache.get(self.PAGE_LANGUAGES_KEY % (self.id))\n if languages:\n return languages\n\n languages = [c['language'] for\n c in Content.objects.filter(page=self,\n type=\"slug\").values('language')]\n languages = list(set(languages)) # remove duplicates\n languages.sort()\n cache.set(self.PAGE_LANGUAGES_KEY % (self.id), languages)\n return languages","title":""},{"docid":"e9b31ef160f8f215c66dad0978c45271","score":"0.4718545","text":"def get_languages(self):\r\n if self._languages:\r\n return self._languages\r\n self._languages = cache.get(self.PAGE_LANGUAGES_KEY % (self.id))\r\n if self._languages is not None:\r\n return self._languages\r\n\r\n languages = [c['language'] for\r\n c in Content.objects.filter(page=self,\r\n type=\"slug\").values('language')]\r\n # remove duplicates\r\n languages = list(set(languages))\r\n languages.sort()\r\n cache.set(self.PAGE_LANGUAGES_KEY % (self.id), languages)\r\n self._languages = languages\r\n return languages","title":""},{"docid":"d15f3e4606fefb752190146427cdae71","score":"0.46740437","text":"def locale(self) -> str | None:\n settings = self.app.settings\n\n locale = settings.i18n.locale_negotiator(self.app.locales, self)\n\n return locale or self.app.default_locale","title":""},{"docid":"258dad49294b10163e12099f0f59ee39","score":"0.46649918","text":"def get_language_list(client, db_name):\n dbo = client[db_name]\n distinct_lang = dbo.tweets.distinct(\"lang\")\n return unicode_to_utf(distinct_lang)","title":""},{"docid":"275e0aedb414f63988ac2b10d563046c","score":"0.46614388","text":"def locale_codes (self) :\n codes = self.get_user_locale_codes ()\n if not codes :\n codes = self.__super.locale_codes\n return codes","title":""},{"docid":"4fc084dd4a56df251818f427efb2c9da","score":"0.46555066","text":"def list_message_files(suffix=\".po\"):\n _files = glob(\"locale/*\" + suffix)\n _list = []\n for _file in _files:\n # basename (without extension) is a locale name\n _locale = os.path.splitext(os.path.basename(_file))[0]\n _list.append((_file, os.path.join(\n \"share\", \"locale\", _locale, \"LC_MESSAGES\", \"roundup.mo\")))\n return _list","title":""},{"docid":"9a171b1fc9ebc6f1da52a13e6c4d5840","score":"0.46511155","text":"def test_empty_target_locale(self):\n for addon in Addon.objects.all():\n addon.target_locale = ''\n addon.save()\n response = self.client.get(self.url, follow=True)\n eq_(response.status_code, 200)\n eq_(response.context['locales'], [])","title":""},{"docid":"f9ed332aa7e5c75dd037e2ac30a9aceb","score":"0.46353132","text":"def get_locale() :\n return app.config.get('LANGUAGE')","title":""},{"docid":"4237276968868a362f8a66160ba823a8","score":"0.46096516","text":"def available_domains(self) -> list:\r\n\r\n response = requests.get(\"https://api4.temp-mail.org/request/domains/format/json\", headers={\"User-Agent\": \"okhttp/3.12.6\"})\r\n domains = json.loads(response.text)\r\n \r\n return list(domains)","title":""},{"docid":"a30adba10f5259116802ed65996b125d","score":"0.45978656","text":"def list_resources() -> List[str]:\n return []","title":""},{"docid":"014072c33a315b14854cfc2d5e083edb","score":"0.45900485","text":"def get_all_manga(self):\n results = []\n for name in self.manga.keys():\n manga = self.get_manga(name)\n if not manga or not manga[2]: continue\n results.append(manga)\n return results","title":""},{"docid":"715fd5a61ea1af5aa35a43bd08b55c3c","score":"0.4580036","text":"def find_pos(lang, include_djangos = False, include_rosetta = False):\n \n paths = []\n \n # project/locale\n parts = settings.SETTINGS_MODULE.split('.')\n project = __import__(parts[0], {}, {}, [])\n paths.append(os.path.join(os.path.dirname(project.__file__), 'locale'))\n \n # django/locale\n if include_djangos:\n paths.append(os.path.join(os.path.dirname(sys.modules[settings.__module__].__file__), 'locale'))\n \n # settings \n for localepath in settings.LOCALE_PATHS:\n if os.path.isdir(localepath):\n paths.append(localepath)\n \n # project/app/locale\n for appname in settings.INSTALLED_APPS:\n \n if 'rosetta' == appname and include_rosetta == False:\n continue\n \n if rosetta_settings.EXCLUDED_APPLICATIONS and appname in rosetta_settings.EXCLUDED_APPLICATIONS:\n continue\n \n p = appname.rfind('.')\n if p >= 0:\n app = getattr(__import__(appname[:p], {}, {}, [appname[p+1:]]), appname[p+1:])\n else:\n app = __import__(appname, {}, {}, [])\n\n apppath = os.path.join(os.path.dirname(app.__file__), 'locale')\n\n if os.path.isdir(apppath):\n paths.append(apppath)\n \n ret = set()\n rx=re.compile(r'(\\w+)/../\\1')\n langs = (lang,)\n if u'-' in lang:\n _l,_c = map(lambda x:x.lower(),lang.split(u'-'))\n langs += (u'%s_%s' %(_l, _c), u'%s_%s' %(_l, _c.upper()), )\n elif u'_' in lang:\n _l,_c = map(lambda x:x.lower(),lang.split(u'_'))\n langs += (u'%s-%s' %(_l, _c), u'%s-%s' %(_l, _c.upper()), )\n \n for path in paths:\n for lang_ in langs:\n dirname = rx.sub(r'\\1', '%s/%s/LC_MESSAGES/' %(path,lang_))\n for fn in ('django.po','djangojs.po',):\n if os.path.isfile(dirname+fn):\n ret.add(os.path.abspath(dirname+fn))\n return list(ret)","title":""},{"docid":"aee5d3e87db8c9ce0d11dc0a80d74a5b","score":"0.45782062","text":"def getSupportedLanguages():\n\tlangs = []\n\tfor fullName, (ivoId,descr) in SUPPORTED_LANGUAGES.iteritems():\n\t\ttry:\n\t\t\tname, version = fullName.split(\"-\", 1)\n\t\texcept ValueError: \n\t\t\t# fullName has no version info, there must be at least one entry\n\t\t\t# that includes a version, so skip this one.\n\t\t\tcontinue\n\t\tlangs.append((name, version, descr, ivoId))\n\treturn langs","title":""},{"docid":"2c84139a392d0384d520ed6bdb77d2ce","score":"0.4571465","text":"def list_languages():\n load_languages()\n langs = []\n visited = set([])\n for lg in language_map:\n L = language_map[lg]\n if L.code:\n if not L.code in visited:\n langs.append(L)\n visited.add(L.code)\n if L.code_iso3:\n if not L.code_iso3 in visited:\n langs.append(L)\n visited.add(L.code_iso3)\n return langs","title":""},{"docid":"284475dd42bbb2f3572125483dac599c","score":"0.45637026","text":"def months():\n x = None\n return [x for x in calendar.month_name]","title":""},{"docid":"b48ec1dec1d5b8fa56a731658ea009ed","score":"0.45559436","text":"def interpreterStrings(self):\n if self.interpreterclass():\n return sorted(self.interpreterclass().getDefaultConfig()['translations'])\n return []","title":""},{"docid":"b48ec1dec1d5b8fa56a731658ea009ed","score":"0.45559436","text":"def interpreterStrings(self):\n if self.interpreterclass():\n return sorted(self.interpreterclass().getDefaultConfig()['translations'])\n return []","title":""},{"docid":"630b5e7cb7ac337565f6b767020670d5","score":"0.454772","text":"def obtener_monedas(self):\n\n tabla = self.derivado_generico.flujos_valorizados[[\"ID\",\"ActivoPasivo\", \"Fecha\", \"FechaFixing\", \"FechaFlujo\", \"FechaPago\", \"Flujo\", \"ValorPresenteMonFlujo\", \"Moneda\", \"MonedaBase\"]]\n monedas = tabla[\"Moneda\"]\n arreglo_monedas = []\n for i in range(len(monedas)):\n\n moneda = monedas[i]\n if moneda in arreglo_monedas: continue\n arreglo_monedas.append(moneda)\n\n return arreglo_monedas","title":""},{"docid":"b5d40bb035046db69162da52a158bc73","score":"0.4544738","text":"def all():\n return PLUGINS.values()","title":""},{"docid":"f718746fe6b1180c5dff1a37b5fa3223","score":"0.45395854","text":"def locale_filter(project):\n # a silly example: display only Locales whose code starts with the \n # second letter of the project label\n return Locale.objects.filter(code__istartswith=project.label[1])","title":""},{"docid":"eb9a595d86e9c466c1dc3b29f2d8bb7e","score":"0.45247325","text":"def month_all_foo():\n all_month = [\n 'Январь', 'Февраль', 'Март', 'Апрель',\n 'Май', 'Июнь', 'Июль', 'Август',\n 'Сентябрь', 'Октябрь', 'Ноябрь', 'Декабрь'\n ]\n return all_month","title":""},{"docid":"8670cbc17514795a0fdee600640b89a3","score":"0.45229542","text":"def listall(self):\n if self._cache_allmedia is None:\n self._cache_allmedia = []\n for collection in self.collections:\n self._cache_allmedia.extend(collection.listall())\n # else returns the cache value\n return self._cache_allmedia","title":""},{"docid":"bec70a3efa2c83d3fde7c49ddd649bd2","score":"0.4517889","text":"def supportedLanguages(self):\n return [\"de\", \"en\", \"es\", \"fr\", \"it\", \"ja\", \"pt\", \"ru\", ]","title":""},{"docid":"b1409b783cd214f06c75c03470391d6d","score":"0.45082396","text":"def all_manifests(request):\r\n \r\n return _phas(request, smart_only=True)","title":""},{"docid":"88916e9fd30042f6bfb235a75ca5bb0b","score":"0.4497977","text":"def beta_languages_list(self):\n if not self.beta_languages.strip():\n return []\n\n languages = [lang.lower().strip() for lang in self.beta_languages.split(',')]\n # Put in alphabetical order\n languages.sort()\n return languages","title":""},{"docid":"8d72a980d7df2fdcd1e8c9690a1436bd","score":"0.44638142","text":"def get_locale():\n return request.accept_languages.best_match(current_app.config['LANGUAGES'])","title":""},{"docid":"8a2d77aeb6d9ad2dfe6d108dd5af6b03","score":"0.44614086","text":"def registered_manga(self):\n return sorted(self.manga.keys())","title":""},{"docid":"8ce53372d1b14428acc8a6d066d336e9","score":"0.44600862","text":"def build_bot_locales(\n self,\n bot_id: str,\n bot_locale_ids: List[str],\n bot_version: str = DRAFT_VERSION,\n max_concurrent_builds: int = 5,\n ) -> None:\n self._bot_manager.build_bot_locales(\n bot_id=bot_id,\n bot_locale_ids=bot_locale_ids,\n bot_version=bot_version,\n max_concurrent_builds=max_concurrent_builds,\n )","title":""},{"docid":"227241ae37f80892f0501e38cfeb61f2","score":"0.44542634","text":"def test_missing_locale(self):\n wa = dict(self.locales)['wa']\n eq_(wa.display, 'Walloon Language Pack (wa)')\n eq_(wa.native, '')","title":""},{"docid":"e7792195b156a1cf3353245b7d5c3c8c","score":"0.4453551","text":"def list_languages():\n translate_client = translate.Client()\n\n results = translate_client.get_languages()\n\n for language in results:\n print(u'{name} ({language})'.format(**language))","title":""},{"docid":"3f1a8b3911940aaf1231d135640965a5","score":"0.44452986","text":"def changed_locales_files(self):\n files = {}\n\n # VCS changes\n repos = self.db_project.translation_repositories()\n if self.repo_locales:\n repos = repos.filter(pk__in=self.repo_locales.keys())\n\n for repo in repos:\n if repo.multi_locale:\n locales = (\n self.repo_locales[repo.pk]\n if self.repo_locales\n else self.db_project.locales.all()\n )\n for locale in locales:\n changed_files = get_changed_files(\n repo.type,\n repo.locale_checkout_path(locale),\n repo.get_last_synced_revisions(locale.code),\n )[0]\n\n for path in changed_files:\n files.setdefault(path, []).append(locale)\n else:\n changed_files = get_changed_files(\n repo.type, repo.checkout_path, repo.get_last_synced_revisions()\n )[0]\n\n log.info(\n \"Changed files in {} repository, all: {}\".format(\n self.db_project, changed_files\n )\n )\n\n # Include only relevant (localizable) files\n if self.configuration:\n files = self.get_relevant_files_with_config(changed_files)\n else:\n files = self.get_relevant_files_without_config(\n changed_files, self.locale_path_locales(repo.checkout_path)\n )\n\n log.info(\n \"Changed files in {} repository, relevant for enabled locales: {}\".format(\n self.db_project, files\n )\n )\n\n # DB changes\n vcs = files\n db = self.db_project.changed_resources(self.now)\n for path in set(list(vcs.keys()) + list(db.keys())):\n if path in vcs and path in db:\n vcs[path] = set(list(vcs[path]) + list(db[path]))\n\n else:\n vcs[path] = vcs[path] if path in vcs else db[path]\n\n return files","title":""},{"docid":"55dde4d91abaa326f50554d2d61f1edb","score":"0.44370285","text":"def get_all_org_units_from_mo(self) -> list:\n logger.info(\"Fetching all org units from OS2mo\")\n query = gql(\n \"\"\"\n query OrgUnitQuery {\n org_units {\n objects {\n current {\n uuid\n org_unit_level {\n name\n }\n name\n }\n }\n }\n }\n \"\"\"\n )\n\n r = self.gql_client.execute(query)\n\n units = [o[\"current\"] for o in r[\"org_units\"][\"objects\"]]\n # filter by org_unit_level if configured in settings\n if org_unit_levels := self.settings.integrations_kle_xlsx_org_unit_levels:\n units = [\n o for o in units if o[\"org_unit_level\"].get(\"name\") in org_unit_levels\n ]\n logger.info(\"Found {} units\".format(len(units)))\n return units","title":""},{"docid":"20d32e4f201db992d607638c15dddf62","score":"0.44329232","text":"def test_getWindowsLocale(self):\n self.assertEqual(l10n.getWindowsLocale(\"en_EN.UTF-8\"), \"english\")","title":""},{"docid":"6ae28ee980f31cef586387ea8a8b747d","score":"0.4432367","text":"def build_loaders_for_all_locales():\n for locale in supported_locales:\n with open(\"m32cmploader_{}.js\".format(locale), \"w+\") as f:\n f.write(preppy.getModule(\"cmp_loader.prep\").get(locale))","title":""},{"docid":"82f4ec748d7760c4158e2663d8e7bd36","score":"0.4426602","text":"def get_locale_msgs(nestData):\n msgs = None\n locale = nestData.get_locale()\n print \"locale = \", locale\n try:\n if locale:\n with open('config_' + locale + '.json') as jsonfile:\n msgs = json.loads(jsonfile.read())\n except Exception as ex:\n print \"Error trying to open configuration file for locale: \", locale, ex\n\n if not msgs:\n try:\n with open('config.json') as jsonfile:\n msgs = json.loads(jsonfile.read())\n except Exception as ex1:\n print \"Error trying to open config.json: \", ex1\n\n if not msgs:\n msgs = {}\n if \"labels\" not in msgs:\n msgs[\"labels\"] = {}\n if \"fa-icons\" not in msgs:\n msgs[\"fa-icons\"] = {}\n return msgs","title":""},{"docid":"c1bb6b090af2174da74c85d0f79882a4","score":"0.44257262","text":"def _list(self):\n conn = self._connect()\n try:\n names = conn.listDefinedDomains()\n except libvirt.libvirtError:\n raise CuckooMachineError(\"Cannot list domains\")\n finally:\n self._disconnect(conn)\n return names","title":""},{"docid":"27f4fdd267966d2b68eb7cf0d85609fc","score":"0.44242364","text":"def supportedLanguages(self):\n return [\"ar\", \"be\", \"bg\", \"bs\", \"ca\", \"cs\", \"da\", \"de\", \"el\", \"en\",\n \"es\", \"et\", \"fi\", \"fr\", \"ga\", \"gl\", \"hi\", \"hr\", \"hu\", \"id\",\n \"is\", \"it\", \"iw\", \"ja\", \"ka\", \"ko\", \"lt\", \"lv\", \"mk\", \"mt\",\n \"nl\", \"no\", \"pl\", \"pt\", \"ro\", \"ru\", \"sk\", \"sl\", \"sq\", \"sr\",\n \"sv\", \"th\", \"tl\", \"tr\", \"uk\", \"vi\", \"zh-CN\", \"zh-TW\",\n ]","title":""},{"docid":"20bef12eba86253350e228449b6c41c9","score":"0.44210565","text":"def get_theme_list(self):\n themes = [theme.replace('.scienv_theme', '') for theme in os.listdir(\n self._config['General']['themes_path'])]\n return themes","title":""},{"docid":"9c081877e61833fb709543c211001579","score":"0.44178656","text":"def _get_domain_list(self):\n return reduce(operator.add, [device.get_configured_domains() for device in self.devices])","title":""},{"docid":"d7c412e499b4bd0c3656800cdb033b39","score":"0.4417432","text":"def pick_locale(request):\n\n available_parm = request.get(\"available\")\n if not available_parm:\n available_parm = \"none\"\n available = available_parm.split(\",\")\n preferred = ordered_locales(request)\n\n best = pick(available, preferred)\n return best","title":""},{"docid":"06cded6642373374f63402f59e68df4e","score":"0.44165623","text":"def fetch_languages(self):\n\n cds_url = TRANSIFEX_CDS_URLS['FETCH_AVAILABLE_LANGUAGES']\n languages = []\n\n try:\n last_response_status = 202\n while last_response_status == 202:\n response = requests.get(\n self.host + cds_url,\n headers=self._get_headers(),\n )\n last_response_status = response.status_code\n\n if not response.ok:\n logger.error(\n 'Error retrieving languages from CDS: `{}`'.format(\n response.reason\n )\n )\n response.raise_for_status()\n\n json_content = response.json()\n languages = json_content['data']\n\n except (KeyError, ValueError):\n # Compatibility with python2.7 where `JSONDecodeError` doesn't\n # exist\n logger.error(\n 'Error retrieving languages from CDS: Malformed response')\n except requests.ConnectionError:\n logger.error(\n 'Error retrieving languages from CDS: ConnectionError')\n except Exception as e:\n logger.error('Error retrieving languages from CDS: UnknownError '\n '(`{}`)'.format(str(e)))\n\n return languages","title":""},{"docid":"58ef9194e6846d80e6f37a024d7ea01b","score":"0.4413733","text":"def locale(self):\n return self._locale","title":""},{"docid":"99b1142e2dce1afc7eefead33742f322","score":"0.44072366","text":"def langs(self, cache=True):\n try:\n if not self.cache['languages'] and cache:\n # data = urlencode({'key': self.api_key, 'ui' : self.ui})\n data = urlencode({'key': self.api_key, 'ui': self.ui})\n result = urlopen(self.api_urls['langs'] % data).read()\n # self.cache['languages'] = loads(result.decode(\"utf-8\"))['dirs']\n self.cache['languages'] = loads(result.decode(\"utf-8\"))\n except IOError:\n raise YandexTranslateException(self.error_codes[503])\n except ValueError:\n raise YandexTranslateException(result)\n return self.cache['languages']","title":""},{"docid":"7fa37a62c70699e7f309bc496f2b160e","score":"0.43963456","text":"def test_null_target_locale(self):\n for addon in Addon.objects.all():\n addon.target_locale = None\n addon.save()\n response = self.client.get(self.url, follow=True)\n eq_(response.status_code, 200)\n eq_(response.context['locales'], [])","title":""},{"docid":"5b083dda265a01d5761412b7e6077ed2","score":"0.4396338","text":"def get_languages():\r\n\r\n url = \"http://accent.gmu.edu/browse_language.php\"\r\n html = get(url)\r\n soup = BeautifulSoup(html.content, 'html.parser')\r\n languages = []\r\n language_lists = soup.findAll('ul', attrs={'class': 'languagelist'})\r\n for ul in language_lists:\r\n for li in ul.findAll('li'):\r\n languages.append(li.text)\r\n return languages","title":""},{"docid":"3cbc6edcb3d08a196e270f8f711393cc","score":"0.43886054","text":"def artist_name_translation_list(self):\n return self._artist_name_translation_list","title":""},{"docid":"28b107d02928592138b142de500ecf18","score":"0.43792656","text":"def test_sorting(self):\n displays = [locale.display for lang, locale in self.locales]\n eq_(displays, sorted(displays))","title":""},{"docid":"c3d4cce6c8c08444d54aeb700ec13540","score":"0.4378617","text":"def topodic_supported_languages():\n return settings.LANGUAGE_DICT","title":""},{"docid":"08305dfa8b57985c1884bc191c321884","score":"0.43781117","text":"def languages(self):\n if self.tool is None:\n return []\n\n bound = self.tool.getLanguageBindings()\n current = bound[0]\n\n def merge(lang, info):\n info[\"code\"] = lang\n if lang == current:\n info['selected'] = True\n else:\n info['selected'] = False\n return info\n\n languages = [merge(lang, info) for (lang, info) in\n self.tool.getAvailableLanguageInformation().items()\n if info[\"selected\"]]\n\n # sort supported languages by index in portal_languages tool\n supported_langs = self.tool.getSupportedLanguages()\n\n def index(info):\n try:\n return supported_langs.index(info[\"code\"])\n except ValueError:\n return len(supported_langs)\n\n return sorted(languages, key=index)","title":""},{"docid":"3bc84a436b4e7d8b9be6239e61f8a822","score":"0.4377666","text":"def idndl_DownlevelGetLocaleScripts(jitter):\n ret_ad, args = jitter.func_args_stdcall([\"lpLocaleName\", \"lpScripts\", \"cchScripts\"])\n raise RuntimeError('API not implemented')\n jitter.func_ret_stdcall(ret_ad, ret_value)","title":""},{"docid":"1968d94ba370fa7ff834ab42f4d470bf","score":"0.43686083","text":"def get_supported_language_names(self) -> typing.Iterable[str]:\n return [LanguageClassLoader.to_language_name(s) for s in self._ln_loader.config.sections()]","title":""},{"docid":"a3f292799849960bf344fcc794461d47","score":"0.4364841","text":"def extended_managers(self):\n return self.properties.get('ExtendedManagers', StringCollection())","title":""},{"docid":"f176e33f7e46ae0ecc75cfdf55dadbfa","score":"0.43625548","text":"def getTranslatedSoundStringList(self, category=None):\n sound_list = \", \".join( [ _(\"lang_\" + s) for s in self.getSounds()]) \n\n return sound_list","title":""},{"docid":"ca580efa90b985f4586af91464af56ec","score":"0.43622023","text":"def getAvailableReportersVocab(self):\n members = self.getReporters()\n return DisplayList([(m.UID(), m.Title()) for m in members])","title":""},{"docid":"f677170e9a668d68143f2f3daf85758f","score":"0.43580335","text":"def get_official_abbreviations():\n with open(get_root_directory()+'/resources/official_abbreviations.json') as data_file:\n return json.load(data_file)","title":""},{"docid":"ad679bd993a1a86a4109f6fc2f2706b7","score":"0.43578392","text":"def get_providers() -> List[CalendarProvider]:\r\n return [x.value for x in CalendarProviders]","title":""},{"docid":"e6ac1b8337ed73f1b0a4a45a1d3c30f0","score":"0.4356482","text":"def get_supported_languages(translate_client, verbose=False):\n results = translate_client.get_languages()\n if verbose:\n for language in results:\n print(u\"{name} ({language})\".format(**language))\n return [r[\"language\"] for r in results]","title":""},{"docid":"c73e90a4ea69c2d62e64d9062419c15f","score":"0.43560612","text":"def ls_arrs(self):\r\n return self.conf.ls_arrs(self.dir)","title":""},{"docid":"08dfd97d9e976fbb97a5c5f951701a77","score":"0.43514547","text":"def supported_languages(self):\n return SUPPORTED_LANGUAGES","title":""},{"docid":"fb0f98a673fe12f3fdfa1f850c7e1075","score":"0.43497854","text":"def get_language_choices():\r\n DEFAULT_LANGUAGES = (\r\n (\"bash\", \"Bash/Shell\"),\r\n (\"css\", \"CSS\"),\r\n (\"diff\", \"diff\"),\r\n (\"html\", \"HTML\"),\r\n (\"javascript\", \"Javascript\"),\r\n (\"json\", \"JSON\"),\r\n (\"python\", \"Python\"),\r\n (\"scss\", \"SCSS\"),\r\n (\"yaml\", \"YAML\"),\r\n )\r\n\r\n return getattr(settings, \"WAGTAIL_CODE_BLOCK_LANGUAGES\", DEFAULT_LANGUAGES)","title":""},{"docid":"e2b8290a300dc51fa45eb3bfe0af973d","score":"0.43403623","text":"def get_all_domains(self):\n return self.domains.values()","title":""},{"docid":"1b04a5e8d61d33c9a2453c5feab8143a","score":"0.43337968","text":"def locale_directory_paths(self):\n locale_directory_paths = {}\n parent_directories = set()\n\n for locale in self.locales:\n try:\n if self.configuration:\n locale_directory_paths[locale.code] = self.configuration.l10n_base\n else:\n locale_directory_paths[locale.code] = locale_directory_path(\n self.checkout_path,\n locale.code,\n parent_directories,\n )\n parent_directory = get_parent_directory(\n locale_directory_paths[locale.code]\n )\n\n except OSError:\n if not self.db_project.has_multi_locale_repositories:\n source_directory = self.source_directory_path\n parent_directory = get_parent_directory(source_directory)\n\n locale_code = locale.code\n if uses_undercore_as_separator(parent_directory):\n locale_code = locale_code.replace(\"-\", \"_\")\n\n locale_directory = os.path.join(parent_directory, locale_code)\n\n # For asymmetric formats, create empty folder\n if is_asymmetric_resource(next(self.relative_resource_paths())):\n os.makedirs(locale_directory)\n\n # For other formats, copy resources from source directory\n else:\n shutil.copytree(source_directory, locale_directory)\n\n for root, dirnames, filenames in os.walk(locale_directory):\n for filename in filenames:\n path = os.path.join(root, filename)\n if is_resource(filename):\n os.rename(path, source_to_locale_path(path))\n else:\n os.remove(path)\n\n locale_directory_paths[locale.code] = locale_directory\n\n else:\n raise MissingLocaleDirectoryError(\n f\"Directory for locale `{locale.code}` not found\"\n )\n\n parent_directories.add(parent_directory)\n\n return locale_directory_paths","title":""},{"docid":"fe3ca67aaeadd7b2c5e7eae397c744b1","score":"0.4326779","text":"def clashing_months(self) -> typing.Optional[typing.List[date]]:\n if (\n not self.preflight\n or \"months\" not in self.preflight\n or \"format_version\" not in self.preflight\n or self.preflight[\"format_version\"] != self.PREFLIGHT_FORMAT_VERSION\n ):\n return None\n\n # get actual orgnizations\n organizations_with_names = self.organizations_from_data()\n if any(e[0] is None for e in organizations_with_names):\n # Unable to resolve organization from data => can determine whether\n # there are clashing data present\n return None\n organizations = [e[1] for e in organizations_with_names] or [self.organization]\n\n # preflight was performed\n return sorted(\n {\n e.date\n for e in ImportBatch.objects.filter(\n report_type=self.report_type,\n platform=self.platform,\n organization__in=organizations,\n date__in=[e for e in self.preflight[\"months\"]],\n )\n }\n )","title":""}],"string":"[\n {\n \"docid\": \"c7eeb0b6810a336325fe917618b5dcf9\",\n \"score\": \"0.74035686\",\n \"text\": \"def get_locales(app: Sphinx) -> List[str]:\\n # Manually configured list of locales\\n sitemap_locales: Optional[List[str]] = app.builder.config.sitemap_locales\\n if sitemap_locales:\\n # special value to add nothing -> use primary language only\\n if sitemap_locales == [None]:\\n return []\\n\\n # otherwise, add each locale\\n return [locale for locale in sitemap_locales]\\n\\n # Or autodetect locales\\n locales = []\\n for locale_dir in app.builder.config.locale_dirs:\\n locale_dir = os.path.join(app.confdir, locale_dir)\\n if os.path.isdir(locale_dir):\\n for locale in os.listdir(locale_dir):\\n if os.path.isdir(os.path.join(locale_dir, locale)):\\n locales.append(locale)\\n return locales\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c2f34c990e34f7b0561f72ee92e445d\",\n \"score\": \"0.60102385\",\n \"text\": \"def discover_resources():\\n locale_discovery_paths = list(settings.TRANZ_LOCALE_PATHS)\\n if settings.TRANZ_SEARCH_LOCALE_IN_APPS:\\n locale_discovery_paths += [os.path.join(app.path, settings.TRANZ_DIR_NAME) for app in list(apps.app_configs.values())]\\n \\n APP_LANGUAGES = [l[0] for l in settings.TRANZ_LANGUAGES]\\n\\n resources = []\\n for path in locale_discovery_paths:\\n if not os.path.isdir(path):\\n continue\\n\\n # Try to match direct children or discovery paths\\n for file in os.listdir(path):\\n if os.path.isfile(os.path.join(path, file)):\\n try:\\n domain, lang, format = file.split('.')\\n except ValueError as e:\\n continue\\n resources.append((format, os.path.join(path, file), lang, domain))\\n \\n \\n # Try to match django's LC_MESSAGES directories\\n if settings.TRANZ_REPLACE_DJANGO_TRANSLATIONS:\\n for lang in APP_LANGUAGES:\\n if os.path.isdir(os.path.join(path, lang)):\\n LC_MESSAGES_PATH = os.path.join(path, lang, 'LC_MESSAGES')\\n if os.path.isdir(LC_MESSAGES_PATH):\\n for file in os.listdir(LC_MESSAGES_PATH):\\n try:\\n domain, format = file.split('.')\\n except ValueError as e:\\n continue\\n resources.append((format, os.path.join(LC_MESSAGES_PATH, file), lang, domain))\\n return resources\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ae3fb7a33fd6a008941607e1e77abe3\",\n \"score\": \"0.5900377\",\n \"text\": \"def submission_locales(self):\\n return self._submission_locales\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac68e878272a6ee481b4187ed32129b2\",\n \"score\": \"0.5781434\",\n \"text\": \"def get_all_locale():\\n wb = openpyxl.load_workbook(__matrix_file_name__)\\n sheet = wb[\\\"RYI\\\"]\\n\\n res = []\\n for index in range(4, 35):\\n locale = sheet[\\\"B{}\\\".format(index)].value.split()[0]\\n res.append(locale)\\n\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4abe104d5dd647ac97f4b89e7230fb1d\",\n \"score\": \"0.57366514\",\n \"text\": \"def parse_locales(paths):\\n\\n\\tif not paths:\\n\\t\\traise ValueError(\\\"Invalid argument: no paths were passed.\\\")\\n\\n\\treturn set([parse_locale(path) for path in paths])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19b9a8e3cb01b0ff5fc13c7ae5e99982\",\n \"score\": \"0.5657871\",\n \"text\": \"def interface_locales(self):\\n return self._interface_locales\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02f4e2537c49240e2b03799638be48ea\",\n \"score\": \"0.5627225\",\n \"text\": \"def regularize_locales(locales):\\n locales = [regularize_locale(loc) for loc in locales]\\n locales_set = set(locales)\\n for loc in locales:\\n yield loc\\n parts = loc.split('_')\\n if len(parts) > 1 and parts[0] not in locales_set:\\n # Insert \\\"fr\\\" after \\\"fr_fr\\\" if it's not somewhere in the list\\n yield parts[0]\\n alias = ALIASES.get(loc)\\n if alias and alias not in locales_set:\\n # Insert \\\"fr_fr\\\" after \\\"fr\\\" if it's not somewhere in the list\\n yield alias\\n if 'en' not in locales_set and 'en_us' not in locales_set:\\n yield 'en'\\n yield 'en_us'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16d43ff401058c5f2e8ec48dcf083b83\",\n \"score\": \"0.5538066\",\n \"text\": \"def locales(ctx):\\n # https://docs.djangoproject.com/en/dev/ref/django-admin/#django-admin-makemessages\\n \\\"\\\"\\\"\\n python manage.py makemessages -v 3 --no-wrap --ignore \\\".*\\\" --locale=pl_PL\\n python manage.py compilemessages -v 3\\n \\\"\\\"\\\"\\n tmp = ROOT_DIR / \\\".tmp\\\"\\n if not tmp.exists():\\n os.makedirs(str(tmp))\\n locale = ROOT_DIR / \\\"src\\\" / \\\"locale\\\"\\n if not locale.exists():\\n os.makedirs(str(locale))\\n # http://babel.edgewall.org/wiki/BabelDjango\\n pybabel = str(VENV_BIN / 'pybabel')\\n ctx.run(pybabel + \\\" extract -F locale/babel.cfg -o locale/django.pot --no-wrap --sort-output .\\\")\\n # create locales firs\\n # pybabel init -D django -i locale/django.pot -d locale -l es\\n # http://babel.edgewall.org/wiki/BabelDjango#CreatingandUpdatingTranslationsCatalogs\\n # ctx.run(pybabel + \\\" update -D django -i locale/django.pot -d locale --ignore-obsolete\\\")\\n ctx.run(pybabel + \\\" update -D django -i locale/django.pot -d locale --previous --no-wrap\\\")\\n ctx.run(pybabel + \\\" compile -D django -d locale --statistics\\\")\\n log.info(\\\"JavaScript locales\\\")\\n # ctx.run(pybabel + \\\" update -D djangojs -i locale/djangojs.pot -d locale --previous --no-wrap\\\")\\n ctx.run(\\\"django-admin makemessages -d djangojs -i static -i node_modules\\\")\\n ctx.run(pybabel + \\\" compile -D djangojs -d locale --statistics\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8cf42f69eb6352d8aaf8d51d5d0eaaa2\",\n \"score\": \"0.5526054\",\n \"text\": \"def locale_resources(self, locale):\\n resources = []\\n project_files = self.get_or_set_project_files(locale.code)\\n\\n for resource in self.vcs_project.db_project.resources.all():\\n absolute_resource_path = os.path.join(\\n self.vcs_project.source_directory_path,\\n resource.path,\\n )\\n\\n if project_files.match(absolute_resource_path):\\n resources.append(resource)\\n\\n return resources\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f881f0110cd5024ed4c3ca358857b5a8\",\n \"score\": \"0.5444359\",\n \"text\": \"def get_localizations(self):\\n localizations = []\\n for glat in self.haldane_lattices:\\n localizations.append(glat.get_localization())\\n\\n return localizations\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fc3416ca41e00704adea508153c7958\",\n \"score\": \"0.540855\",\n \"text\": \"def get_available_languages(domain):\\r\\n if domain in _AVAILABLE_LANGUAGES:\\r\\n return copy.copy(_AVAILABLE_LANGUAGES[domain])\\r\\n\\r\\n localedir = '%s_LOCALEDIR' % domain.upper()\\r\\n find = lambda x: gettext.find(domain,\\r\\n localedir=os.environ.get(localedir),\\r\\n languages=[x])\\r\\n\\r\\n # NOTE(mrodden): en_US should always be available (and first in case\\r\\n # order matters) since our in-line message strings are en_US\\r\\n language_list = ['en_US']\\r\\n # NOTE(luisg): Babel <1.0 used a function called list(), which was\\r\\n # renamed to locale_identifiers() in >=1.0, the requirements master list\\r\\n # requires >=0.9.6, uncapped, so defensively work with both. We can remove\\r\\n # this check when the master list updates to >=1.0, and update all projects\\r\\n list_identifiers = (getattr(localedata, 'list', None) or\\r\\n getattr(localedata, 'locale_identifiers'))\\r\\n locale_identifiers = list_identifiers()\\r\\n\\r\\n for i in locale_identifiers:\\r\\n if find(i) is not None:\\r\\n language_list.append(i)\\r\\n\\r\\n # NOTE(luisg): Babel>=1.0,<1.3 has a bug where some OpenStack supported\\r\\n # locales (e.g. 'zh_CN', and 'zh_TW') aren't supported even though they\\r\\n # are perfectly legitimate locales:\\r\\n # https://github.com/mitsuhiko/babel/issues/37\\r\\n # In Babel 1.3 they fixed the bug and they support these locales, but\\r\\n # they are still not explicitly \\\"listed\\\" by locale_identifiers().\\r\\n # That is why we add the locales here explicitly if necessary so that\\r\\n # they are listed as supported.\\r\\n aliases = {'zh': 'zh_CN',\\r\\n 'zh_Hant_HK': 'zh_HK',\\r\\n 'zh_Hant': 'zh_TW',\\r\\n 'fil': 'tl_PH'}\\r\\n for (locale, alias) in six.iteritems(aliases):\\r\\n if locale in language_list and alias not in language_list:\\r\\n language_list.append(alias)\\r\\n\\r\\n _AVAILABLE_LANGUAGES[domain] = language_list\\r\\n return copy.copy(language_list)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28a92844e27af4433511216282afe0bf\",\n \"score\": \"0.53916764\",\n \"text\": \"def list_translations(dirname='locale'):\\n if not os.path.isdir(dirname):\\n return []\\n\\n result = []\\n for folder in sorted(os.listdir(dirname)):\\n if os.path.isdir(os.path.join(dirname, folder, 'LC_MESSAGES')):\\n result.append(Locale.parse(folder))\\n\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"898393fb9181012607099ab8a0f798d0\",\n \"score\": \"0.53290695\",\n \"text\": \"def getlocalenames(self):\\n if rootdir:\\n o = gui.rootrun('chroot %s locale -a' % rootdir)[1]\\n else:\\n o = Popen(['locale', '-a'], stdout=PIPE).communicate()[0]\\n ll = [ l for l in o.splitlines() if '.' in l ]\\n return ['C', 'POSIX'] + ll\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f2ee6c7bcc3323b6b02d92fedb8bf37\",\n \"score\": \"0.5278709\",\n \"text\": \"def list_languages():\\n logger.info(\\\"retrieving list of supported languages\\\")\\n return [lang.to_dict() for lang in installation.languages]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"411b711418162e4e8e423f01a8311886\",\n \"score\": \"0.5230266\",\n \"text\": \"def ordered_locales(request):\\n\\n accept_language = get_accept_language(request)\\n langs = accept_language.split(\\\",\\\")\\n pairs = map( lambda(lang): lang.split(\\\";\\\"), langs)\\n\\n for pair in pairs:\\n if len(pair) < 2: pair.append(1)\\n else: pair[1] = float(pair[1][2:])\\n pairs.sort(key=lambda(x):x[1], reverse=True)\\n\\n return [lang for (lang,val) in pairs if val > 0.0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"468535c7e7d5aafa90b517d27678c6b1\",\n \"score\": \"0.52168137\",\n \"text\": \"def langs(self) -> list[str]:\\n return list(self._translations.keys())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4f5ffab6cb4c3fd0c4a1234cbdfa243\",\n \"score\": \"0.5147565\",\n \"text\": \"def _get_test_locales(config):\\n return eval(config.getoption(\\\"nl\\\")) if is_multilocale_arkouda() else 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b4af42c6dccda2ea5b54f33e7b80c03\",\n \"score\": \"0.5137169\",\n \"text\": \"def released_languages_list(self):\\n if not self.released_languages.strip():\\n return []\\n\\n languages = [lang.lower().strip() for lang in self.released_languages.split(',')]\\n # Put in alphabetical order\\n languages.sort()\\n return languages\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99a090f6616d7d5a5f6d035f7e9a2d8a\",\n \"score\": \"0.51151115\",\n \"text\": \"def locale_path_locales(self, repo_checkout_path):\\n locale_path_locales = {}\\n\\n for locale in self.db_project.locales.all():\\n locale_directory = self.locale_directory_paths[locale.code]\\n path = locale_directory[len(repo_checkout_path) :].lstrip(os.sep)\\n path = os.path.join(path, \\\"\\\") # Ensure the path ends with os.sep\\n locale_path_locales[path] = locale\\n\\n return locale_path_locales\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee0b6d30bf35b8accab51f9b4bcc365b\",\n \"score\": \"0.50602275\",\n \"text\": \"def findTranslationResources():\\n\\n\\ttranslations_dir = QtCore.QDir(':/')\\n\\tfile_names = translations_dir.entryList(['*.qm'],\\n\\t\\tQtCore.QDir.Files, QtCore.QDir.Name)\\n\\n\\ttranslator = QtCore.QTranslator()\\n\\n\\ttranslation_files = []\\n\\tfor file_name in file_names:\\n\\t\\tresource_path = translations_dir.filePath(file_name)\\n\\n\\t\\ttry:\\n\\t\\t\\tlocale = _parseTranslationFileName(str(file_name))\\n\\t\\texcept:\\n\\t\\t\\t# theoretically, this should not happen, because these files \\n\\t\\t\\t# have already passed through findTranslationFiles()\\n\\t\\t\\twarning(\\\"Failed to parse translation file name: \\\" + file_name)\\n\\t\\t\\tcontinue\\n\\n\\t\\tif not translator.load(resource_path):\\n\\t\\t\\twarning(\\\"Failed to load translation file \\\" + resource_path)\\n\\t\\t\\tcontinue\\n\\n\\t\\tlocale_name, full_name = _getLocaleInfo(locale)\\n\\n\\t\\t# do not include default locale to list, we do not want to show it to user\\n\\t\\t# it will be used only as a backup one\\n\\t\\tif locale_name == _QT_UNKNOWN_LOCALE:\\n\\t\\t\\tcontinue\\n\\n\\t\\ttranslation_files.append((locale_name, full_name, resource_path))\\n\\n\\treturn translation_files\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2797cdbc5afd29c56928def62bf73f2a\",\n \"score\": \"0.5051008\",\n \"text\": \"def get_languages(self):\\n global GENGO_LANGUAGE_CACHE\\n if not GENGO_LANGUAGE_CACHE:\\n resp = self.gengo_api.getServiceLanguages()\\n GENGO_LANGUAGE_CACHE = tuple(\\n [item['lc'] for item in resp['response']])\\n return GENGO_LANGUAGE_CACHE\",\n \"title\": \"\"\n },\n {\n \"docid\": \"173dd799b4a1a88a3263f83ce54d1972\",\n \"score\": \"0.5037178\",\n \"text\": \"def get_languages(self):\\n return self._data.get_data(const.LANGUAGES_COLLECTION)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e66e4297e1b4215f4a020b572013aa6\",\n \"score\": \"0.503531\",\n \"text\": \"def get_slang_abbreviations():\\n with open(get_root_directory()+'/resources/twitter_slang_abbreviations.json') as data_file:\\n return json.load(data_file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b36a03eba590e30d686fc6112bf4f9bb\",\n \"score\": \"0.50228155\",\n \"text\": \"def _get_languages(self):\\r\\n LOGGER.log()\\r\\n gtk_lang_mgr = gtksourceview2.language_manager_get_default()\\r\\n language_ids = gtk_lang_mgr.get_language_ids()\\r\\n language_names = []\\r\\n for language_id in language_ids:\\r\\n language = gtk_lang_mgr.get_language(language_id)\\r\\n language_names.append(language.get_name())\\r\\n language_names.sort(lambda a, b:\\r\\n cmp(a.lower(), b.lower()))\\r\\n return language_names\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83a10795cb06800ba71d8a4e424d41f3\",\n \"score\": \"0.5002563\",\n \"text\": \"def supported_languages(self) -> list[str]:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64ba48589172e3de8af030f931846647\",\n \"score\": \"0.50003403\",\n \"text\": \"def getTranslatedThemeList(self, category=None):\\n pre = \\\"theme_\\\"\\n themes = [ (_(pre+t), t) for t in self.getThemes() ]\\n \\n return themes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5db54f7b92bee22e8e1f8f19eb3a102\",\n \"score\": \"0.49863845\",\n \"text\": \"def get_mapping_languages(self) -> List[str]:\\n return sorted(lang for _, lang in self.cfg[\\\"mapping\\\"].items())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83e8ebb1b115a295e3ae4f31974be6fa\",\n \"score\": \"0.49761966\",\n \"text\": \"def get_available_languages():\\n _set_environment()\\n proc = subprocess.Popen([TESSERACT_CMD, \\\"--list-langs\\\"],\\n startupinfo=g_subprocess_startup_info,\\n creationflags=g_creation_flags,\\n stdout=subprocess.PIPE,\\n stderr=subprocess.STDOUT)\\n langs = proc.stdout.read().decode('utf-8').splitlines(False)\\n ret = proc.wait()\\n if ret != 0:\\n raise TesseractError(ret, \\\"unable to get languages\\\")\\n\\n return [lang for lang in langs if lang and lang[-1] != ':']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"48149a715a8e25c9e9beb8d0ee4b2480\",\n \"score\": \"0.49359855\",\n \"text\": \"def get_translation_list(obj, language_codes=[lang['code'] for lang in settings.PARLER_LANGUAGES[None]]):\\n prefetched_translations = getattr(obj, 'translation_list', [])\\n filtered_prefetched = [\\n translation for translation in prefetched_translations if translation.language_code in language_codes\\n ]\\n return filtered_prefetched if prefetched_translations else obj.translations.filter(language_code__in=language_codes)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79a7bda2cb146f6ecfacfb4bfffd9ce2\",\n \"score\": \"0.4918901\",\n \"text\": \"def ADDITIONAL_LANGUAGES(self) -> Iterable[str]:\\n return self._setting(\\n \\\"ADDITIONAL_LANGUAGES\\\", self.config[\\\"additional_languages\\\"]\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ea62ac62cbc7d56e16fd0db6e3da884\",\n \"score\": \"0.48982581\",\n \"text\": \"def languages(self) -> set[str]:\\n langs = set()\\n for cat in self.entry_page.categories:\\n if (m := DASH_LANG_SEARCH(cat)) and (lang := LANG_ABBREV_MAP.get(m.group(1))):\\n langs.add(lang)\\n break\\n return langs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21f8b41900cf0ebcc66381afb60aa0f2\",\n \"score\": \"0.48617068\",\n \"text\": \"def langs(cls):\\n return cls._langs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"921d037d8efa8843abe9d72cb279e08c\",\n \"score\": \"0.48374712\",\n \"text\": \"def languages(self):\\n if not self._languages:\\n # query API only the first time\\n self._languages = self.get_languages()\\n return self._languages\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9f7e0677792e6ad99e5f24f32720b52\",\n \"score\": \"0.48260513\",\n \"text\": \"def monster_language_set(data):\\n languages = set()\\n for m in data:\\n languages.update([x.strip() for x in m[\\\"Languages\\\"].split(\\\",\\\")])\\n return languages\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b775d5acb15d705dda67041b0fdb9e1\",\n \"score\": \"0.48153034\",\n \"text\": \"def get_available_languages():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dcd43e1d1d67bfe446574aba56a9d96f\",\n \"score\": \"0.47988316\",\n \"text\": \"def supported_languages(self) -> list[str]:\\n return [\\\"en\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c33d85380325b9ba9fabc7b7e610e774\",\n \"score\": \"0.4771139\",\n \"text\": \"def realm_names(self):\\n return sorted(self._realms)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9bb3849a3dc981c05195b6b3ab968be3\",\n \"score\": \"0.4760957\",\n \"text\": \"def get_languages(self):\\n languages = cache.get(self.PAGE_LANGUAGES_KEY % (self.id))\\n if languages:\\n return languages\\n\\n languages = [c['language'] for\\n c in Content.objects.filter(page=self,\\n type=\\\"slug\\\").values('language')]\\n languages = list(set(languages)) # remove duplicates\\n languages.sort()\\n cache.set(self.PAGE_LANGUAGES_KEY % (self.id), languages)\\n return languages\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9b31ef160f8f215c66dad0978c45271\",\n \"score\": \"0.4718545\",\n \"text\": \"def get_languages(self):\\r\\n if self._languages:\\r\\n return self._languages\\r\\n self._languages = cache.get(self.PAGE_LANGUAGES_KEY % (self.id))\\r\\n if self._languages is not None:\\r\\n return self._languages\\r\\n\\r\\n languages = [c['language'] for\\r\\n c in Content.objects.filter(page=self,\\r\\n type=\\\"slug\\\").values('language')]\\r\\n # remove duplicates\\r\\n languages = list(set(languages))\\r\\n languages.sort()\\r\\n cache.set(self.PAGE_LANGUAGES_KEY % (self.id), languages)\\r\\n self._languages = languages\\r\\n return languages\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d15f3e4606fefb752190146427cdae71\",\n \"score\": \"0.46740437\",\n \"text\": \"def locale(self) -> str | None:\\n settings = self.app.settings\\n\\n locale = settings.i18n.locale_negotiator(self.app.locales, self)\\n\\n return locale or self.app.default_locale\",\n \"title\": \"\"\n },\n {\n \"docid\": \"258dad49294b10163e12099f0f59ee39\",\n \"score\": \"0.46649918\",\n \"text\": \"def get_language_list(client, db_name):\\n dbo = client[db_name]\\n distinct_lang = dbo.tweets.distinct(\\\"lang\\\")\\n return unicode_to_utf(distinct_lang)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"275e0aedb414f63988ac2b10d563046c\",\n \"score\": \"0.46614388\",\n \"text\": \"def locale_codes (self) :\\n codes = self.get_user_locale_codes ()\\n if not codes :\\n codes = self.__super.locale_codes\\n return codes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4fc084dd4a56df251818f427efb2c9da\",\n \"score\": \"0.46555066\",\n \"text\": \"def list_message_files(suffix=\\\".po\\\"):\\n _files = glob(\\\"locale/*\\\" + suffix)\\n _list = []\\n for _file in _files:\\n # basename (without extension) is a locale name\\n _locale = os.path.splitext(os.path.basename(_file))[0]\\n _list.append((_file, os.path.join(\\n \\\"share\\\", \\\"locale\\\", _locale, \\\"LC_MESSAGES\\\", \\\"roundup.mo\\\")))\\n return _list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a171b1fc9ebc6f1da52a13e6c4d5840\",\n \"score\": \"0.46511155\",\n \"text\": \"def test_empty_target_locale(self):\\n for addon in Addon.objects.all():\\n addon.target_locale = ''\\n addon.save()\\n response = self.client.get(self.url, follow=True)\\n eq_(response.status_code, 200)\\n eq_(response.context['locales'], [])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9ed332aa7e5c75dd037e2ac30a9aceb\",\n \"score\": \"0.46353132\",\n \"text\": \"def get_locale() :\\n return app.config.get('LANGUAGE')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4237276968868a362f8a66160ba823a8\",\n \"score\": \"0.46096516\",\n \"text\": \"def available_domains(self) -> list:\\r\\n\\r\\n response = requests.get(\\\"https://api4.temp-mail.org/request/domains/format/json\\\", headers={\\\"User-Agent\\\": \\\"okhttp/3.12.6\\\"})\\r\\n domains = json.loads(response.text)\\r\\n \\r\\n return list(domains)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a30adba10f5259116802ed65996b125d\",\n \"score\": \"0.45978656\",\n \"text\": \"def list_resources() -> List[str]:\\n return []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"014072c33a315b14854cfc2d5e083edb\",\n \"score\": \"0.45900485\",\n \"text\": \"def get_all_manga(self):\\n results = []\\n for name in self.manga.keys():\\n manga = self.get_manga(name)\\n if not manga or not manga[2]: continue\\n results.append(manga)\\n return results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"715fd5a61ea1af5aa35a43bd08b55c3c\",\n \"score\": \"0.4580036\",\n \"text\": \"def find_pos(lang, include_djangos = False, include_rosetta = False):\\n \\n paths = []\\n \\n # project/locale\\n parts = settings.SETTINGS_MODULE.split('.')\\n project = __import__(parts[0], {}, {}, [])\\n paths.append(os.path.join(os.path.dirname(project.__file__), 'locale'))\\n \\n # django/locale\\n if include_djangos:\\n paths.append(os.path.join(os.path.dirname(sys.modules[settings.__module__].__file__), 'locale'))\\n \\n # settings \\n for localepath in settings.LOCALE_PATHS:\\n if os.path.isdir(localepath):\\n paths.append(localepath)\\n \\n # project/app/locale\\n for appname in settings.INSTALLED_APPS:\\n \\n if 'rosetta' == appname and include_rosetta == False:\\n continue\\n \\n if rosetta_settings.EXCLUDED_APPLICATIONS and appname in rosetta_settings.EXCLUDED_APPLICATIONS:\\n continue\\n \\n p = appname.rfind('.')\\n if p >= 0:\\n app = getattr(__import__(appname[:p], {}, {}, [appname[p+1:]]), appname[p+1:])\\n else:\\n app = __import__(appname, {}, {}, [])\\n\\n apppath = os.path.join(os.path.dirname(app.__file__), 'locale')\\n\\n if os.path.isdir(apppath):\\n paths.append(apppath)\\n \\n ret = set()\\n rx=re.compile(r'(\\\\w+)/../\\\\1')\\n langs = (lang,)\\n if u'-' in lang:\\n _l,_c = map(lambda x:x.lower(),lang.split(u'-'))\\n langs += (u'%s_%s' %(_l, _c), u'%s_%s' %(_l, _c.upper()), )\\n elif u'_' in lang:\\n _l,_c = map(lambda x:x.lower(),lang.split(u'_'))\\n langs += (u'%s-%s' %(_l, _c), u'%s-%s' %(_l, _c.upper()), )\\n \\n for path in paths:\\n for lang_ in langs:\\n dirname = rx.sub(r'\\\\1', '%s/%s/LC_MESSAGES/' %(path,lang_))\\n for fn in ('django.po','djangojs.po',):\\n if os.path.isfile(dirname+fn):\\n ret.add(os.path.abspath(dirname+fn))\\n return list(ret)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aee5d3e87db8c9ce0d11dc0a80d74a5b\",\n \"score\": \"0.45782062\",\n \"text\": \"def getSupportedLanguages():\\n\\tlangs = []\\n\\tfor fullName, (ivoId,descr) in SUPPORTED_LANGUAGES.iteritems():\\n\\t\\ttry:\\n\\t\\t\\tname, version = fullName.split(\\\"-\\\", 1)\\n\\t\\texcept ValueError: \\n\\t\\t\\t# fullName has no version info, there must be at least one entry\\n\\t\\t\\t# that includes a version, so skip this one.\\n\\t\\t\\tcontinue\\n\\t\\tlangs.append((name, version, descr, ivoId))\\n\\treturn langs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c84139a392d0384d520ed6bdb77d2ce\",\n \"score\": \"0.4571465\",\n \"text\": \"def list_languages():\\n load_languages()\\n langs = []\\n visited = set([])\\n for lg in language_map:\\n L = language_map[lg]\\n if L.code:\\n if not L.code in visited:\\n langs.append(L)\\n visited.add(L.code)\\n if L.code_iso3:\\n if not L.code_iso3 in visited:\\n langs.append(L)\\n visited.add(L.code_iso3)\\n return langs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"284475dd42bbb2f3572125483dac599c\",\n \"score\": \"0.45637026\",\n \"text\": \"def months():\\n x = None\\n return [x for x in calendar.month_name]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b48ec1dec1d5b8fa56a731658ea009ed\",\n \"score\": \"0.45559436\",\n \"text\": \"def interpreterStrings(self):\\n if self.interpreterclass():\\n return sorted(self.interpreterclass().getDefaultConfig()['translations'])\\n return []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b48ec1dec1d5b8fa56a731658ea009ed\",\n \"score\": \"0.45559436\",\n \"text\": \"def interpreterStrings(self):\\n if self.interpreterclass():\\n return sorted(self.interpreterclass().getDefaultConfig()['translations'])\\n return []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"630b5e7cb7ac337565f6b767020670d5\",\n \"score\": \"0.454772\",\n \"text\": \"def obtener_monedas(self):\\n\\n tabla = self.derivado_generico.flujos_valorizados[[\\\"ID\\\",\\\"ActivoPasivo\\\", \\\"Fecha\\\", \\\"FechaFixing\\\", \\\"FechaFlujo\\\", \\\"FechaPago\\\", \\\"Flujo\\\", \\\"ValorPresenteMonFlujo\\\", \\\"Moneda\\\", \\\"MonedaBase\\\"]]\\n monedas = tabla[\\\"Moneda\\\"]\\n arreglo_monedas = []\\n for i in range(len(monedas)):\\n\\n moneda = monedas[i]\\n if moneda in arreglo_monedas: continue\\n arreglo_monedas.append(moneda)\\n\\n return arreglo_monedas\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5d40bb035046db69162da52a158bc73\",\n \"score\": \"0.4544738\",\n \"text\": \"def all():\\n return PLUGINS.values()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f718746fe6b1180c5dff1a37b5fa3223\",\n \"score\": \"0.45395854\",\n \"text\": \"def locale_filter(project):\\n # a silly example: display only Locales whose code starts with the \\n # second letter of the project label\\n return Locale.objects.filter(code__istartswith=project.label[1])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb9a595d86e9c466c1dc3b29f2d8bb7e\",\n \"score\": \"0.45247325\",\n \"text\": \"def month_all_foo():\\n all_month = [\\n 'Январь', 'Февраль', 'Март', 'Апрель',\\n 'Май', 'Июнь', 'Июль', 'Август',\\n 'Сентябрь', 'Октябрь', 'Ноябрь', 'Декабрь'\\n ]\\n return all_month\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8670cbc17514795a0fdee600640b89a3\",\n \"score\": \"0.45229542\",\n \"text\": \"def listall(self):\\n if self._cache_allmedia is None:\\n self._cache_allmedia = []\\n for collection in self.collections:\\n self._cache_allmedia.extend(collection.listall())\\n # else returns the cache value\\n return self._cache_allmedia\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bec70a3efa2c83d3fde7c49ddd649bd2\",\n \"score\": \"0.4517889\",\n \"text\": \"def supportedLanguages(self):\\n return [\\\"de\\\", \\\"en\\\", \\\"es\\\", \\\"fr\\\", \\\"it\\\", \\\"ja\\\", \\\"pt\\\", \\\"ru\\\", ]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1409b783cd214f06c75c03470391d6d\",\n \"score\": \"0.45082396\",\n \"text\": \"def all_manifests(request):\\r\\n \\r\\n return _phas(request, smart_only=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88916e9fd30042f6bfb235a75ca5bb0b\",\n \"score\": \"0.4497977\",\n \"text\": \"def beta_languages_list(self):\\n if not self.beta_languages.strip():\\n return []\\n\\n languages = [lang.lower().strip() for lang in self.beta_languages.split(',')]\\n # Put in alphabetical order\\n languages.sort()\\n return languages\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d72a980d7df2fdcd1e8c9690a1436bd\",\n \"score\": \"0.44638142\",\n \"text\": \"def get_locale():\\n return request.accept_languages.best_match(current_app.config['LANGUAGES'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a2d77aeb6d9ad2dfe6d108dd5af6b03\",\n \"score\": \"0.44614086\",\n \"text\": \"def registered_manga(self):\\n return sorted(self.manga.keys())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ce53372d1b14428acc8a6d066d336e9\",\n \"score\": \"0.44600862\",\n \"text\": \"def build_bot_locales(\\n self,\\n bot_id: str,\\n bot_locale_ids: List[str],\\n bot_version: str = DRAFT_VERSION,\\n max_concurrent_builds: int = 5,\\n ) -> None:\\n self._bot_manager.build_bot_locales(\\n bot_id=bot_id,\\n bot_locale_ids=bot_locale_ids,\\n bot_version=bot_version,\\n max_concurrent_builds=max_concurrent_builds,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"227241ae37f80892f0501e38cfeb61f2\",\n \"score\": \"0.44542634\",\n \"text\": \"def test_missing_locale(self):\\n wa = dict(self.locales)['wa']\\n eq_(wa.display, 'Walloon Language Pack (wa)')\\n eq_(wa.native, '')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7792195b156a1cf3353245b7d5c3c8c\",\n \"score\": \"0.4453551\",\n \"text\": \"def list_languages():\\n translate_client = translate.Client()\\n\\n results = translate_client.get_languages()\\n\\n for language in results:\\n print(u'{name} ({language})'.format(**language))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f1a8b3911940aaf1231d135640965a5\",\n \"score\": \"0.44452986\",\n \"text\": \"def changed_locales_files(self):\\n files = {}\\n\\n # VCS changes\\n repos = self.db_project.translation_repositories()\\n if self.repo_locales:\\n repos = repos.filter(pk__in=self.repo_locales.keys())\\n\\n for repo in repos:\\n if repo.multi_locale:\\n locales = (\\n self.repo_locales[repo.pk]\\n if self.repo_locales\\n else self.db_project.locales.all()\\n )\\n for locale in locales:\\n changed_files = get_changed_files(\\n repo.type,\\n repo.locale_checkout_path(locale),\\n repo.get_last_synced_revisions(locale.code),\\n )[0]\\n\\n for path in changed_files:\\n files.setdefault(path, []).append(locale)\\n else:\\n changed_files = get_changed_files(\\n repo.type, repo.checkout_path, repo.get_last_synced_revisions()\\n )[0]\\n\\n log.info(\\n \\\"Changed files in {} repository, all: {}\\\".format(\\n self.db_project, changed_files\\n )\\n )\\n\\n # Include only relevant (localizable) files\\n if self.configuration:\\n files = self.get_relevant_files_with_config(changed_files)\\n else:\\n files = self.get_relevant_files_without_config(\\n changed_files, self.locale_path_locales(repo.checkout_path)\\n )\\n\\n log.info(\\n \\\"Changed files in {} repository, relevant for enabled locales: {}\\\".format(\\n self.db_project, files\\n )\\n )\\n\\n # DB changes\\n vcs = files\\n db = self.db_project.changed_resources(self.now)\\n for path in set(list(vcs.keys()) + list(db.keys())):\\n if path in vcs and path in db:\\n vcs[path] = set(list(vcs[path]) + list(db[path]))\\n\\n else:\\n vcs[path] = vcs[path] if path in vcs else db[path]\\n\\n return files\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55dde4d91abaa326f50554d2d61f1edb\",\n \"score\": \"0.44370285\",\n \"text\": \"def get_all_org_units_from_mo(self) -> list:\\n logger.info(\\\"Fetching all org units from OS2mo\\\")\\n query = gql(\\n \\\"\\\"\\\"\\n query OrgUnitQuery {\\n org_units {\\n objects {\\n current {\\n uuid\\n org_unit_level {\\n name\\n }\\n name\\n }\\n }\\n }\\n }\\n \\\"\\\"\\\"\\n )\\n\\n r = self.gql_client.execute(query)\\n\\n units = [o[\\\"current\\\"] for o in r[\\\"org_units\\\"][\\\"objects\\\"]]\\n # filter by org_unit_level if configured in settings\\n if org_unit_levels := self.settings.integrations_kle_xlsx_org_unit_levels:\\n units = [\\n o for o in units if o[\\\"org_unit_level\\\"].get(\\\"name\\\") in org_unit_levels\\n ]\\n logger.info(\\\"Found {} units\\\".format(len(units)))\\n return units\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20d32e4f201db992d607638c15dddf62\",\n \"score\": \"0.44329232\",\n \"text\": \"def test_getWindowsLocale(self):\\n self.assertEqual(l10n.getWindowsLocale(\\\"en_EN.UTF-8\\\"), \\\"english\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ae28ee980f31cef586387ea8a8b747d\",\n \"score\": \"0.4432367\",\n \"text\": \"def build_loaders_for_all_locales():\\n for locale in supported_locales:\\n with open(\\\"m32cmploader_{}.js\\\".format(locale), \\\"w+\\\") as f:\\n f.write(preppy.getModule(\\\"cmp_loader.prep\\\").get(locale))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82f4ec748d7760c4158e2663d8e7bd36\",\n \"score\": \"0.4426602\",\n \"text\": \"def get_locale_msgs(nestData):\\n msgs = None\\n locale = nestData.get_locale()\\n print \\\"locale = \\\", locale\\n try:\\n if locale:\\n with open('config_' + locale + '.json') as jsonfile:\\n msgs = json.loads(jsonfile.read())\\n except Exception as ex:\\n print \\\"Error trying to open configuration file for locale: \\\", locale, ex\\n\\n if not msgs:\\n try:\\n with open('config.json') as jsonfile:\\n msgs = json.loads(jsonfile.read())\\n except Exception as ex1:\\n print \\\"Error trying to open config.json: \\\", ex1\\n\\n if not msgs:\\n msgs = {}\\n if \\\"labels\\\" not in msgs:\\n msgs[\\\"labels\\\"] = {}\\n if \\\"fa-icons\\\" not in msgs:\\n msgs[\\\"fa-icons\\\"] = {}\\n return msgs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1bb6b090af2174da74c85d0f79882a4\",\n \"score\": \"0.44257262\",\n \"text\": \"def _list(self):\\n conn = self._connect()\\n try:\\n names = conn.listDefinedDomains()\\n except libvirt.libvirtError:\\n raise CuckooMachineError(\\\"Cannot list domains\\\")\\n finally:\\n self._disconnect(conn)\\n return names\",\n \"title\": \"\"\n },\n {\n \"docid\": \"27f4fdd267966d2b68eb7cf0d85609fc\",\n \"score\": \"0.44242364\",\n \"text\": \"def supportedLanguages(self):\\n return [\\\"ar\\\", \\\"be\\\", \\\"bg\\\", \\\"bs\\\", \\\"ca\\\", \\\"cs\\\", \\\"da\\\", \\\"de\\\", \\\"el\\\", \\\"en\\\",\\n \\\"es\\\", \\\"et\\\", \\\"fi\\\", \\\"fr\\\", \\\"ga\\\", \\\"gl\\\", \\\"hi\\\", \\\"hr\\\", \\\"hu\\\", \\\"id\\\",\\n \\\"is\\\", \\\"it\\\", \\\"iw\\\", \\\"ja\\\", \\\"ka\\\", \\\"ko\\\", \\\"lt\\\", \\\"lv\\\", \\\"mk\\\", \\\"mt\\\",\\n \\\"nl\\\", \\\"no\\\", \\\"pl\\\", \\\"pt\\\", \\\"ro\\\", \\\"ru\\\", \\\"sk\\\", \\\"sl\\\", \\\"sq\\\", \\\"sr\\\",\\n \\\"sv\\\", \\\"th\\\", \\\"tl\\\", \\\"tr\\\", \\\"uk\\\", \\\"vi\\\", \\\"zh-CN\\\", \\\"zh-TW\\\",\\n ]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20bef12eba86253350e228449b6c41c9\",\n \"score\": \"0.44210565\",\n \"text\": \"def get_theme_list(self):\\n themes = [theme.replace('.scienv_theme', '') for theme in os.listdir(\\n self._config['General']['themes_path'])]\\n return themes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c081877e61833fb709543c211001579\",\n \"score\": \"0.44178656\",\n \"text\": \"def _get_domain_list(self):\\n return reduce(operator.add, [device.get_configured_domains() for device in self.devices])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7c412e499b4bd0c3656800cdb033b39\",\n \"score\": \"0.4417432\",\n \"text\": \"def pick_locale(request):\\n\\n available_parm = request.get(\\\"available\\\")\\n if not available_parm:\\n available_parm = \\\"none\\\"\\n available = available_parm.split(\\\",\\\")\\n preferred = ordered_locales(request)\\n\\n best = pick(available, preferred)\\n return best\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06cded6642373374f63402f59e68df4e\",\n \"score\": \"0.44165623\",\n \"text\": \"def fetch_languages(self):\\n\\n cds_url = TRANSIFEX_CDS_URLS['FETCH_AVAILABLE_LANGUAGES']\\n languages = []\\n\\n try:\\n last_response_status = 202\\n while last_response_status == 202:\\n response = requests.get(\\n self.host + cds_url,\\n headers=self._get_headers(),\\n )\\n last_response_status = response.status_code\\n\\n if not response.ok:\\n logger.error(\\n 'Error retrieving languages from CDS: `{}`'.format(\\n response.reason\\n )\\n )\\n response.raise_for_status()\\n\\n json_content = response.json()\\n languages = json_content['data']\\n\\n except (KeyError, ValueError):\\n # Compatibility with python2.7 where `JSONDecodeError` doesn't\\n # exist\\n logger.error(\\n 'Error retrieving languages from CDS: Malformed response')\\n except requests.ConnectionError:\\n logger.error(\\n 'Error retrieving languages from CDS: ConnectionError')\\n except Exception as e:\\n logger.error('Error retrieving languages from CDS: UnknownError '\\n '(`{}`)'.format(str(e)))\\n\\n return languages\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58ef9194e6846d80e6f37a024d7ea01b\",\n \"score\": \"0.4413733\",\n \"text\": \"def locale(self):\\n return self._locale\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99b1142e2dce1afc7eefead33742f322\",\n \"score\": \"0.44072366\",\n \"text\": \"def langs(self, cache=True):\\n try:\\n if not self.cache['languages'] and cache:\\n # data = urlencode({'key': self.api_key, 'ui' : self.ui})\\n data = urlencode({'key': self.api_key, 'ui': self.ui})\\n result = urlopen(self.api_urls['langs'] % data).read()\\n # self.cache['languages'] = loads(result.decode(\\\"utf-8\\\"))['dirs']\\n self.cache['languages'] = loads(result.decode(\\\"utf-8\\\"))\\n except IOError:\\n raise YandexTranslateException(self.error_codes[503])\\n except ValueError:\\n raise YandexTranslateException(result)\\n return self.cache['languages']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7fa37a62c70699e7f309bc496f2b160e\",\n \"score\": \"0.43963456\",\n \"text\": \"def test_null_target_locale(self):\\n for addon in Addon.objects.all():\\n addon.target_locale = None\\n addon.save()\\n response = self.client.get(self.url, follow=True)\\n eq_(response.status_code, 200)\\n eq_(response.context['locales'], [])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b083dda265a01d5761412b7e6077ed2\",\n \"score\": \"0.4396338\",\n \"text\": \"def get_languages():\\r\\n\\r\\n url = \\\"http://accent.gmu.edu/browse_language.php\\\"\\r\\n html = get(url)\\r\\n soup = BeautifulSoup(html.content, 'html.parser')\\r\\n languages = []\\r\\n language_lists = soup.findAll('ul', attrs={'class': 'languagelist'})\\r\\n for ul in language_lists:\\r\\n for li in ul.findAll('li'):\\r\\n languages.append(li.text)\\r\\n return languages\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3cbc6edcb3d08a196e270f8f711393cc\",\n \"score\": \"0.43886054\",\n \"text\": \"def artist_name_translation_list(self):\\n return self._artist_name_translation_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28b107d02928592138b142de500ecf18\",\n \"score\": \"0.43792656\",\n \"text\": \"def test_sorting(self):\\n displays = [locale.display for lang, locale in self.locales]\\n eq_(displays, sorted(displays))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3d4cce6c8c08444d54aeb700ec13540\",\n \"score\": \"0.4378617\",\n \"text\": \"def topodic_supported_languages():\\n return settings.LANGUAGE_DICT\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08305dfa8b57985c1884bc191c321884\",\n \"score\": \"0.43781117\",\n \"text\": \"def languages(self):\\n if self.tool is None:\\n return []\\n\\n bound = self.tool.getLanguageBindings()\\n current = bound[0]\\n\\n def merge(lang, info):\\n info[\\\"code\\\"] = lang\\n if lang == current:\\n info['selected'] = True\\n else:\\n info['selected'] = False\\n return info\\n\\n languages = [merge(lang, info) for (lang, info) in\\n self.tool.getAvailableLanguageInformation().items()\\n if info[\\\"selected\\\"]]\\n\\n # sort supported languages by index in portal_languages tool\\n supported_langs = self.tool.getSupportedLanguages()\\n\\n def index(info):\\n try:\\n return supported_langs.index(info[\\\"code\\\"])\\n except ValueError:\\n return len(supported_langs)\\n\\n return sorted(languages, key=index)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3bc84a436b4e7d8b9be6239e61f8a822\",\n \"score\": \"0.4377666\",\n \"text\": \"def idndl_DownlevelGetLocaleScripts(jitter):\\n ret_ad, args = jitter.func_args_stdcall([\\\"lpLocaleName\\\", \\\"lpScripts\\\", \\\"cchScripts\\\"])\\n raise RuntimeError('API not implemented')\\n jitter.func_ret_stdcall(ret_ad, ret_value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1968d94ba370fa7ff834ab42f4d470bf\",\n \"score\": \"0.43686083\",\n \"text\": \"def get_supported_language_names(self) -> typing.Iterable[str]:\\n return [LanguageClassLoader.to_language_name(s) for s in self._ln_loader.config.sections()]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3f292799849960bf344fcc794461d47\",\n \"score\": \"0.4364841\",\n \"text\": \"def extended_managers(self):\\n return self.properties.get('ExtendedManagers', StringCollection())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f176e33f7e46ae0ecc75cfdf55dadbfa\",\n \"score\": \"0.43625548\",\n \"text\": \"def getTranslatedSoundStringList(self, category=None):\\n sound_list = \\\", \\\".join( [ _(\\\"lang_\\\" + s) for s in self.getSounds()]) \\n\\n return sound_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca580efa90b985f4586af91464af56ec\",\n \"score\": \"0.43622023\",\n \"text\": \"def getAvailableReportersVocab(self):\\n members = self.getReporters()\\n return DisplayList([(m.UID(), m.Title()) for m in members])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f677170e9a668d68143f2f3daf85758f\",\n \"score\": \"0.43580335\",\n \"text\": \"def get_official_abbreviations():\\n with open(get_root_directory()+'/resources/official_abbreviations.json') as data_file:\\n return json.load(data_file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad679bd993a1a86a4109f6fc2f2706b7\",\n \"score\": \"0.43578392\",\n \"text\": \"def get_providers() -> List[CalendarProvider]:\\r\\n return [x.value for x in CalendarProviders]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e6ac1b8337ed73f1b0a4a45a1d3c30f0\",\n \"score\": \"0.4356482\",\n \"text\": \"def get_supported_languages(translate_client, verbose=False):\\n results = translate_client.get_languages()\\n if verbose:\\n for language in results:\\n print(u\\\"{name} ({language})\\\".format(**language))\\n return [r[\\\"language\\\"] for r in results]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c73e90a4ea69c2d62e64d9062419c15f\",\n \"score\": \"0.43560612\",\n \"text\": \"def ls_arrs(self):\\r\\n return self.conf.ls_arrs(self.dir)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08dfd97d9e976fbb97a5c5f951701a77\",\n \"score\": \"0.43514547\",\n \"text\": \"def supported_languages(self):\\n return SUPPORTED_LANGUAGES\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb0f98a673fe12f3fdfa1f850c7e1075\",\n \"score\": \"0.43497854\",\n \"text\": \"def get_language_choices():\\r\\n DEFAULT_LANGUAGES = (\\r\\n (\\\"bash\\\", \\\"Bash/Shell\\\"),\\r\\n (\\\"css\\\", \\\"CSS\\\"),\\r\\n (\\\"diff\\\", \\\"diff\\\"),\\r\\n (\\\"html\\\", \\\"HTML\\\"),\\r\\n (\\\"javascript\\\", \\\"Javascript\\\"),\\r\\n (\\\"json\\\", \\\"JSON\\\"),\\r\\n (\\\"python\\\", \\\"Python\\\"),\\r\\n (\\\"scss\\\", \\\"SCSS\\\"),\\r\\n (\\\"yaml\\\", \\\"YAML\\\"),\\r\\n )\\r\\n\\r\\n return getattr(settings, \\\"WAGTAIL_CODE_BLOCK_LANGUAGES\\\", DEFAULT_LANGUAGES)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2b8290a300dc51fa45eb3bfe0af973d\",\n \"score\": \"0.43403623\",\n \"text\": \"def get_all_domains(self):\\n return self.domains.values()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b04a5e8d61d33c9a2453c5feab8143a\",\n \"score\": \"0.43337968\",\n \"text\": \"def locale_directory_paths(self):\\n locale_directory_paths = {}\\n parent_directories = set()\\n\\n for locale in self.locales:\\n try:\\n if self.configuration:\\n locale_directory_paths[locale.code] = self.configuration.l10n_base\\n else:\\n locale_directory_paths[locale.code] = locale_directory_path(\\n self.checkout_path,\\n locale.code,\\n parent_directories,\\n )\\n parent_directory = get_parent_directory(\\n locale_directory_paths[locale.code]\\n )\\n\\n except OSError:\\n if not self.db_project.has_multi_locale_repositories:\\n source_directory = self.source_directory_path\\n parent_directory = get_parent_directory(source_directory)\\n\\n locale_code = locale.code\\n if uses_undercore_as_separator(parent_directory):\\n locale_code = locale_code.replace(\\\"-\\\", \\\"_\\\")\\n\\n locale_directory = os.path.join(parent_directory, locale_code)\\n\\n # For asymmetric formats, create empty folder\\n if is_asymmetric_resource(next(self.relative_resource_paths())):\\n os.makedirs(locale_directory)\\n\\n # For other formats, copy resources from source directory\\n else:\\n shutil.copytree(source_directory, locale_directory)\\n\\n for root, dirnames, filenames in os.walk(locale_directory):\\n for filename in filenames:\\n path = os.path.join(root, filename)\\n if is_resource(filename):\\n os.rename(path, source_to_locale_path(path))\\n else:\\n os.remove(path)\\n\\n locale_directory_paths[locale.code] = locale_directory\\n\\n else:\\n raise MissingLocaleDirectoryError(\\n f\\\"Directory for locale `{locale.code}` not found\\\"\\n )\\n\\n parent_directories.add(parent_directory)\\n\\n return locale_directory_paths\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe3ca67aaeadd7b2c5e7eae397c744b1\",\n \"score\": \"0.4326779\",\n \"text\": \"def clashing_months(self) -> typing.Optional[typing.List[date]]:\\n if (\\n not self.preflight\\n or \\\"months\\\" not in self.preflight\\n or \\\"format_version\\\" not in self.preflight\\n or self.preflight[\\\"format_version\\\"] != self.PREFLIGHT_FORMAT_VERSION\\n ):\\n return None\\n\\n # get actual orgnizations\\n organizations_with_names = self.organizations_from_data()\\n if any(e[0] is None for e in organizations_with_names):\\n # Unable to resolve organization from data => can determine whether\\n # there are clashing data present\\n return None\\n organizations = [e[1] for e in organizations_with_names] or [self.organization]\\n\\n # preflight was performed\\n return sorted(\\n {\\n e.date\\n for e in ImportBatch.objects.filter(\\n report_type=self.report_type,\\n platform=self.platform,\\n organization__in=organizations,\\n date__in=[e for e in self.preflight[\\\"months\\\"]],\\n )\\n }\\n )\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":686,"cells":{"query_id":{"kind":"string","value":"0514c3e3f00ed75fc520a4e64741f14f"},"query":{"kind":"string","value":"Renders a template from the given template source string with the given context. Template variables will be autoescaped."},"positive_passages":{"kind":"list like","value":[{"docid":"a770a3bef0b5686c817732c498e625f6","score":"0.8366585","text":"def render_template_string(source, **context):\n ctx = _app_ctx_stack.top\n ctx.app.update_template_context(context)\n return _render(ctx.app.jinja_env.from_string(source), context, ctx.app)","title":""}],"string":"[\n {\n \"docid\": \"a770a3bef0b5686c817732c498e625f6\",\n \"score\": \"0.8366585\",\n \"text\": \"def render_template_string(source, **context):\\n ctx = _app_ctx_stack.top\\n ctx.app.update_template_context(context)\\n return _render(ctx.app.jinja_env.from_string(source), context, ctx.app)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"2591aad30c9c871ce95371d363072d1e","score":"0.8500779","text":"def render_template_string(source, **context):\n ctx = stack.top\n lookup = _lookup(ctx.app)\n template = Template(source, lookup=_lookup(ctx.app), **lookup.template_args)\n return _render(template, context, ctx.app)","title":""},{"docid":"a015558512ae75e88f118dbe614b433e","score":"0.774367","text":"def render(self, template_string, context={}):\n try:\n t = template.Template(template_string)\n c = template.Context(context)\n return t.render(c)\n except Exception, e:\n return e","title":""},{"docid":"0eafb67497567658857cbbca433a13a1","score":"0.74834913","text":"def render(text, context):\n return Template(text).render(context)","title":""},{"docid":"0f37767455aa3ef260766b47bba8bd4e","score":"0.7334508","text":"def render(self, template_str: str, context: (dict, None)) -> str:\n if context is None:\n context = self.context\n t = self._env.from_string(template_str)\n return t.render(context)","title":""},{"docid":"73a668ba9ee559f7619a6286f9e727e3","score":"0.7262923","text":"def render_template_to_string(self, template, context):\r\n return self.lookup.from_string(template).render(**context)","title":""},{"docid":"9bd6406d3f4fbc0d702248d40f4c7aab","score":"0.7225489","text":"def render_template(process, template_string, context):\n from resolwe.flow.managers import manager\n\n # Get the appropriate expression engine. If none is defined, do not evaluate\n # any expressions.\n expression_engine = process.requirements.get(\"expression-engine\", None)\n if not expression_engine:\n return template_string\n\n return manager.get_expression_engine(expression_engine).evaluate_block(\n template_string, context\n )","title":""},{"docid":"347e04caad1dc357e14b8a02dd6732bd","score":"0.722019","text":"def render_template(self, template_string):\n original_autoescape = self.context.autoescape\n self.context.autoescape = False\n\n template = Template(\"\")\n template_debug = getattr(\n settings, \"TEMPLATE_DEBUG\", template.engine.debug if hasattr(template, \"engine\") else False\n )\n if template_debug is True:\n origin = Origin(template_string)\n else:\n origin = None\n\n template.nodelist = self.compile_string(template_string, origin, template_debug)\n\n rendered = template.render(self.context)\n self.context.autoescape = original_autoescape\n return rendered","title":""},{"docid":"8b9f4cc476e5b4b0aab14a45b3361a3a","score":"0.71682847","text":"def render_template(self, _filename, **context):\r\n template = self.environment.get_template(_filename)\r\n return template.render_unicode(**context)","title":""},{"docid":"33a96759c3797eea318e2dc866eaf9cb","score":"0.71345305","text":"def render_from_string(template: str, ctx: Context) -> str:\n template = Template(template)\n return template.render(ctx)","title":""},{"docid":"6f912dd17f5ab42b0e0e77e85d926a0a","score":"0.7085872","text":"def _render_string_from_template(\n source: str, template: _BaseInstallationTemplate\n) -> str:\n # TODO: we could use a while loop or recursive function to render the template until\n # there are no jinja-specific things. At this point, we support one level of\n # nesting.\n n_renders = 0\n max_renders = 20\n\n err = (\n \"A template included in this renderer raised an error. Please check the\"\n \" template definition. A required argument might not be included in the\"\n \" required arguments part of the template. Variables in the template should\"\n \" start with `self.`.\"\n )\n\n # Render the string again. This is sometimes necessary because some defaults in the\n # template are rendered as {{ self.X }}. These defaults need to be rendered again.\n\n while (\n _jinja_env.variable_start_string in source\n and _jinja_env.variable_end_string in source\n ):\n source = source.replace(\"self.\", \"template.\")\n tmpl = _jinja_env.from_string(source)\n try:\n source = tmpl.render(template=template)\n except jinja2.exceptions.UndefinedError as e:\n raise RendererError(err) from e\n n_renders += 1\n\n if n_renders > max_renders:\n raise RendererError(\n f\"reached maximum rendering iterations ({max_renders}). Templates\"\n f\" should not nest variables more than {max_renders} times.\"\n )\n return source","title":""},{"docid":"648c29585ddd6bb2292ce23c335eb606","score":"0.70501155","text":"def render_template(self, _filename, **context):\r\n return self.environment.get_template(_filename).render(**context)","title":""},{"docid":"5225976f5bf500b0ff313cb44cfe5977","score":"0.70279455","text":"def render_template_to_string(self, template, context):\r\n\r\n context = context.update(self.filters)\r\n\r\n return Template(template).render(**context)","title":""},{"docid":"89c8169fbe0f615c5b3582dd4f0ba4de","score":"0.69808406","text":"def render_template(template, **kwargs):\n if template is None or not isinstance(template, basestring):\n return template\n tmpl = _get_environment().from_string(template)\n ctx = dict(kwargs)\n if global_context is not None:\n for k, v in global_context.iteritems():\n if not k in ctx:\n ctx[k] = v\n # expose the global context\n ctx['_ctx'] = global_context\n if 'self' in ctx:\n del ctx['self']\n try:\n return tmpl.render(**ctx)\n except TemplateSyntaxError as err:\n raise RenderError(template, err.message, line=err.lineno)","title":""},{"docid":"90c7fd7858f045df59d08cba92720317","score":"0.69634664","text":"def render(template, context, template_dir=\"templates\"):\n j2env = Environment(loader=FileSystemLoader(template_dir))\n\n tpl = j2env.get_template(template)\n\n return tpl.render(context)","title":""},{"docid":"5e5e87fc7c77e90c3c3d443ac311ecf8","score":"0.68901604","text":"def render_to_string(template_name, context):\n f = open(settings.PLSQL_TEMPLATE_DIR + '/' + template_name)\n template_string = f.read()\n f.close()\n\n t = Template(template_string, template_string)\n return t.render(context)","title":""},{"docid":"abd6a0c5fefa51b56e45aefa549df2bd","score":"0.68708706","text":"def render_template(template, **context):\n if ':' not in template:\n template = '%s:%s' % (request.package, template)\n return jinja_env.get_template(template).render(context)","title":""},{"docid":"0b71038c9e116f603838a6ebc2dc3d4e","score":"0.6852936","text":"def render_template(template_name, context={}):\n \n path = os.path.join(os.path.dirname(__file__), 'templates', template_name)\n return template.render(path, context)","title":""},{"docid":"2bd970858079f8dee89e5a2075531f7b","score":"0.68046623","text":"def render(charm_dir, source, context):\n templates_dir = os.path.join(charm_dir, 'templates')\n template_env = Environment(loader=FileSystemLoader(templates_dir))\n\n try:\n template = template_env.get_template(source)\n except exceptions.TemplateNotFound as e:\n logger.error('Could not load template %s from %s.' %\n (source, templates_dir))\n raise e\n return template.render(context)","title":""},{"docid":"7b8d23c2bd57c6c45704ce6be9e54e8b","score":"0.68041766","text":"def render_template(template_name, **context):\n ctx = stack.top\n return _render(_lookup(ctx.app).get_template(template_name),\n context, ctx.app)","title":""},{"docid":"5c58b96dbf471fff73e438d854519db4","score":"0.6776268","text":"def _render(template, context, app):\n app.update_template_context(context)\n try:\n rv = template.render_unicode(**context)\n template_rendered.send(app, template=template, context=context)\n return rv\n except:\n translated = TemplateError(template)\n raise translated","title":""},{"docid":"3f4b9513e740f73db1c03d979526a005","score":"0.67737067","text":"def render_template(self, template_path, context={}):\n template_str = self.load_resource(template_path)\n return Template(template_str).render(Context(context))","title":""},{"docid":"edbc06a93a201c5dfc476c03adbb3400","score":"0.67288864","text":"def render_template(template, context):\n for key, value in context.iteritems():\n template = template.replace('{{ %s }}' % (key,), value)\n return template","title":""},{"docid":"1c81d9467257d4ac56d374411817eced","score":"0.6688212","text":"def render_template(self, template_name, output_name, context):\r\n context['striphtml'] = striphtml\r\n template = self.lookup.get_template(template_name)\r\n data = template.render_unicode(**context)\r\n if output_name is not None:\r\n makedirs(os.path.dirname(output_name))\r\n with open(output_name, 'w+') as output:\r\n output.write(data)\r\n return data","title":""},{"docid":"34bcc21ccca1219e7ca38523596bf115","score":"0.6605197","text":"def render_template(self, template_name, output_name, context):\r\n raise NotImplementedError()","title":""},{"docid":"d41f469f1f76fbbe9b3b983200990041","score":"0.6602932","text":"def render(request, template, context):\n ctx = RequestContext(request, context)\n return render_to_response(template, ctx)","title":""},{"docid":"0fc772dce5f52691fa6e1668d92e610a","score":"0.6592469","text":"def render(template, **context):\n return render_response(template, context)","title":""},{"docid":"9ab7aa16c0d690df68425548f45a7ef1","score":"0.6583847","text":"def render_str(self, *template, **kw):\n tmp = JINJA_ENV.get_template(*template)\n return tmp.render(**kw)","title":""},{"docid":"fb80040988830a32af5ba5f22e9493aa","score":"0.65768784","text":"def render_template(template_file, **context):\n t_env = get_template_env('templates')\n template = t_env.get_template(template_file)\n body = template.render(**context or {})\n return body","title":""},{"docid":"6104a01a8f2545752b6e05836470eb9f","score":"0.65703696","text":"def _interpolate_str(\n data: str, context: Dict[str, Any], strict: bool = True\n) -> RenderedString:\n # Render the given string as a Django template with the given context.\n template = Template(data)\n template_context = Context(context, autoescape=False)\n rendered_string = RenderedString(template.render(template_context))\n\n # Extract a dict of variables used to render the string.\n rendered_context = {\n var: template_context.get(var, NOT_PROVIDED)\n for var in frozenset(\n v.filter_expression.var.lookups[0]\n for v in template.nodelist\n if isinstance(v, VariableNode)\n )\n }\n\n # Attach the render context to the string.\n rendered_string.__context__ = rendered_context\n\n missing_variables = frozenset(\n k for k, v in rendered_context.items() if v is NOT_PROVIDED\n )\n if strict and missing_variables:\n raise LookupError(\n f\"The template references variables that were not in the context \"\n f'provided: {\", \".join(missing_variables)}'\n )\n\n return rendered_string","title":""},{"docid":"935008cf3c13f96af87356adb922d37a","score":"0.6564538","text":"def render_template(out, name, context, templates_dir, prefix=None):\n\n # support \"::\" syntax\n pp = [tenjin.PrefixedLinePreprocessor(prefix=prefix)\n if prefix else tenjin.PrefixedLinePreprocessor()]\n # disable HTML escaping\n template_globals = {\"to_str\": str, \"escape\": str}\n if templates_dir:\n engine = TemplateEngine(path=[templates_dir], pp=pp, cache=False)\n else:\n engine = TemplateEngine(pp=pp, cache=False)\n out.write(engine.render(name, context, template_globals))\n if 'KD_DICT' in context:\n return context['KD_DICT']\n if 'P4TBL_TYPES' in context:\n return context['P4TBL_TYPES']\n else:\n return None","title":""},{"docid":"c6c91d4b2c0a27b962911b9852294b95","score":"0.6560027","text":"def render_to_string(template_name, context=None, request=None,\r\n processors=None):\r\n context = dict(context or {})\r\n if request is not None:\r\n context['request'] = request\r\n for processor in chain(get_standard_processors(), processors or ()):\r\n context.update(processor(request))\r\n return get_template(template_name).render(context)","title":""},{"docid":"51c6b29d48abd64ebf5febd4799c058f","score":"0.6551095","text":"def render_template_to_string(self, template, context):\r\n raise NotImplementedError()","title":""},{"docid":"f08ec913ff12add5f8152ff7c9b8b7f9","score":"0.6512847","text":"def render_template(self, template_file, **context) -> str:\n if not self.template_dir: raise InsufficientError('template_dir')\n try: \n with open(Path(self.template_dir)/template_file, 'r') as f:\n template:Template = Template(f.read())\n return template.render(context)\n except FileNotFoundError: raise TemplateNotFoundError(template_file)\n except exceptions.UndefinedError: raise Jinja2ContextDataError","title":""},{"docid":"1198088871e9f64196d46307c077b79c","score":"0.65125775","text":"def _render(template, context, app):\n\n before_render_template.send(app, template=template, context=context)\n rv = template.render(context)\n template_rendered.send(app, template=template, context=context)\n return rv","title":""},{"docid":"2a4ba2129529b076af1fe4fa76bf41e1","score":"0.64339757","text":"def _render(self, context):\n if not context:\n context = Context({})\n\n plain = self.template_plain.render(context)\n html = self.template_html.render(context)\n css = get_template(self.template_style).render(Context({}))\n\n p = Pynliner()\n html = p.from_string(html).with_cssString(css).run()\n\n return plain, html","title":""},{"docid":"59b62f38b09a4d02a8b2b835178f1aa4","score":"0.6406535","text":"def render_template_with_globals(template, args):\n args = fill_template_arguments(args)\n debug('Rendering {} with args: {}'.format(template, args))\n return render_template(template, **args)","title":""},{"docid":"1c76c93b75703a9cf070cddaf608e7da","score":"0.6400795","text":"def render(template_name, context={}):\n template = myenv.get_template(template_name)\n context['user'] = get_user()\n context['path'] = cherrypy.request.path_info\n context['params'] = cherrypy.request.params\n return template.render(context)","title":""},{"docid":"aff98d638dfbb556b3716c50c2fab03c","score":"0.63886666","text":"def render_str(self, template, **params):\n t = JINJA_ENV.get_template(template)\n return t.render(params)","title":""},{"docid":"03785d27a1ceb854ca3a56528ce23931","score":"0.63627714","text":"def render_str(template, **params):\n t = JINJA_ENV.get_template(template)\n return t.render(params)","title":""},{"docid":"7876a6de1d41e75c7d8ed7ba1adb41f6","score":"0.6349783","text":"def render(self, context):\n try:\n template = self.template.resolve(context)\n # Does this quack like a Template?\n if not callable(getattr(template, 'render', None)):\n # If not, we'll try our cache, and get_template()\n template_name = template\n cache = context.render_context.dicts[0].setdefault(self, {})\n template = cache.get(template_name)\n if template is None:\n template = context.template.engine.get_template(template_name)\n cache[template_name] = template\n # Use the base.Template of a backends.django.Template.\n elif hasattr(template, 'template'):\n template = template.template\n values = {\n name: var.resolve(context)\n for name, var in self.extra_context.items()\n }\n if self.isolated_context:\n return template.render(context.new(values))\n with context.push(**values):\n return template.render(context)\n except Exception as e:\n if context.template.engine.debug:\n raise\n template_name = getattr(context, 'template_name', None) or 'unknown'\n warnings.warn(\n \"Rendering {%% include '%s' %%} raised %s. In Django 2.1, \"\n \"this exception will be raised rather than silenced and \"\n \"rendered as an empty string.\" %\n (template_name, e.__class__.__name__),\n RemovedInDjango21Warning,\n )\n logger.warning(\n \"Exception raised while rendering {%% include %%} for \"\n \"template '%s'. Empty string rendered instead.\",\n template_name,\n exc_info=True,\n )\n return ''","title":""},{"docid":"c2547c14902b5c626cd62cb0a3511787","score":"0.6347726","text":"def render_str(template, **params):\n t = jinja_env.get_template(template)\n return t.render(params)","title":""},{"docid":"32dec2cb212a8ff0a093da4dc25f76b1","score":"0.63275397","text":"def render_str(self, template, **params):\n templ = jinja_env.get_template(template)\n return templ.render(params)","title":""},{"docid":"035646f2e1a265afd52ddb35ace6c20f","score":"0.6326421","text":"def render_filter(ctx, template):\n if isinstance(template, basestring):\n template = ctx.environment.from_string(template)\n return template.render(ctx)","title":""},{"docid":"3104d1fe5af67f1c9f68dff95c73d95a","score":"0.6316073","text":"def render_body(template, context):\n for key, value in context.iteritems():\n template = template.replace('{{ %s }}' % key, value)\n return template","title":""},{"docid":"9089eb52191d6da8561798849a121fd2","score":"0.63057363","text":"def render_template(self, template_name, output_name, context):\r\n if jinja2 is None:\r\n req_missing(['jinja2'], 'use this theme')\r\n template = self.lookup.get_template(template_name)\r\n output = template.render(**context)\r\n if output_name is not None:\r\n makedirs(os.path.dirname(output_name))\r\n with open(output_name, 'w+') as output:\r\n output.write(output.encode('utf8'))\r\n return output","title":""},{"docid":"d58483926e6356482bbc371f740136e0","score":"0.62967765","text":"def render_template(\n searchpath: Path,\n template_name: str,\n context: Dict[str, Any],\n extension: str,\n autoescape: bool = True,\n keep_trailing_newline: bool = False,\n) -> str:\n import jinja2\n\n template_loader = jinja2.FileSystemLoader(searchpath=searchpath)\n template_env = jinja2.Environment(\n loader=template_loader,\n undefined=jinja2.StrictUndefined,\n autoescape=autoescape,\n keep_trailing_newline=keep_trailing_newline,\n )\n template = template_env.get_template(f\"{template_name}_TEMPLATE{extension}.jinja2\")\n content: str = template.render(context)\n return content","title":""},{"docid":"b8b9b618f43a1b23b763aee6e6848f07","score":"0.628353","text":"def render(self, **context):\n template = JinjaTemplate(self.get_content())\n return template.render(**context)","title":""},{"docid":"3582dd4fc5f98e7183b06c9921d72f3b","score":"0.6250179","text":"def render(template_name, **kwargs):\r\n with open(template_name) as f:\r\n template = Template(f.read())\r\n return template.render(**kwargs)","title":""},{"docid":"fe16f26c2d4bddac69e43d3ea6af479b","score":"0.62370026","text":"def render_template(self, name, **ctx):\n tmpl = self.get_template(name)\n return tmpl.render(ctx)","title":""},{"docid":"0c9f6f3003022bd31ce361f1a0653f66","score":"0.62352145","text":"def render(self, template=None, context=None, encoding=None):\n template = template or self.template\n context = context or self.context\n\n template = self.render_sections(template, context)\n result = self.render_tags(template, context)\n if encoding is not None:\n result = result.encode(encoding)\n return result","title":""},{"docid":"051856278a0e4d25425631ac1f3c71ee","score":"0.6229935","text":"def render_context(self, context, *args, **kwargs):\r\n if getattr(context, '_with_template', None) is None:\r\n context._with_template = self\r\n runtime._render_context(self, \r\n self.callable_, \r\n context, \r\n *args, \r\n **kwargs)","title":""},{"docid":"9cf62eb638fe4f4f05606c1d5d6fe34d","score":"0.6182085","text":"def include(self, template_name, **kwargs):\n frame = sys._getframe(1)\n locals = frame.f_locals\n globals = frame.f_globals\n context = locals[\"_context\"].copy()\n context.update(kwargs)\n template = self.get_template(template_name, context, globals)\n return template.render(context, globals, _buf=locals[\"_buf\"])","title":""},{"docid":"4afca3aa29d60bb5945acb025893dbd4","score":"0.6169488","text":"def render_template(template=None, context=None,\n method=None, string=None):\n kajiki = current_app.extensions['kajiki']\n method = kajiki._method_for(template, method)\n template = generate_template(template, context, method, string)\n # TODO kajiki has no arguments for the serializer\n #render_args = dict(method=kajiki.methods[method]['serializer'])\n #if 'doctype' in kajiki.methods[method]:\n # render_args['doctype'] = kajiki.methods[method]['doctype']\n #return template.render(**render_args)\n return template.render()","title":""},{"docid":"1f473c3e91f689a1814c9eef19827c1d","score":"0.61638755","text":"def check_template_tag(self, template_string: str, context: dict, result_string: str) -> None:\n t = Template(template_string)\n c = Context(context)\n output = t.render(c)\n self.assertEqual(output, result_string)","title":""},{"docid":"4a40c80b6114e508b33596942dd485eb","score":"0.61482924","text":"def render(self, template, **kw): \n render_out = \"\"\n jinja_env = self.app.config.get(\"jinja_env\")\n if jinja_env:\n t = jinja_env.get_template(template)\n render_out = t.render(kw)\n self.response.out.write(render_out)","title":""},{"docid":"cd8af326978fb812beedac7473398a9e","score":"0.61314666","text":"def render_email(template, context=None):\n if context is None:\n context = {}\n email_body = render_jinja2_template(template, context)\n return email_body","title":""},{"docid":"cc2546b8d00fe3c695455667d185579a","score":"0.61176956","text":"def render_template(cls, template_name: str, **kwargs) -> str:\n template_filename: str = \"{}.tmp\".format(template_name)\n template = cls.env.get_template(template_filename)\n return template.render(kwargs['input_data'])","title":""},{"docid":"32df02fd23a0825fbff625132b084641","score":"0.6117059","text":"def render_template(template_name_or_list, **context):\n ctx = _app_ctx_stack.top\n ctx.app.update_template_context(context)\n return _render(\n ctx.app.jinja_env.get_or_select_template(template_name_or_list),\n context,\n ctx.app,\n )","title":""},{"docid":"1407eb8f5f05f061d06032aef7bd8c3a","score":"0.6114893","text":"def render_and_write(template_name, context, output_name, output_dir):\n\n template = templates_env.get_template(template_name)\n f = open(path.join(output_dir, output_name), \"w\")\n f.write(template.render(**context))\n f.close()","title":""},{"docid":"f255e2720129c505d4bdc6285915990f","score":"0.61102927","text":"def _render_template(\n self,\n mako_template_filepath: str,\n context: dict,\n translator: Translator\n ) -> str:\n try:\n template = Template(filename=mako_template_filepath)\n return template.render(\n _=translator.get_translation,\n config=self.config,\n **context\n )\n except Exception as exc:\n logger.exception(self, 'Failed to render email template: {}'.format(exc.__str__()))\n raise EmailTemplateError('Failed to render email template: {}'.format(exc.__str__()))","title":""},{"docid":"c3ce590a89a49cdbbb8818fec833c8c2","score":"0.6107454","text":"def render_template(template, dirs = [ ], context = None, **kwargs):\n dirs = sequencify(dirs)\n if PATH[\"TEMPLATES\"] not in dirs:\n dirs.append(PATH[\"TEMPLATES\"])\n\n dirs = [osp.abspath(dir_) for dir_ in dirs]\n\n logger.info(\"Searching for templates within directories: %s\" % dirs)\n\n path = None\n for dir_ in dirs:\n temp = osp.join(dir_, template)\n if osp.exists(temp):\n path = temp\n break\n \n if not path:\n raise TemplateNotFoundError(\"Template %s not found.\" % template)\n \n string = read(path)\n rendered = string\n\n if not context:\n context = kwargs\n\n if context:\n template = Template(string)\n rendered = template.substitute(context)\n \n return rendered","title":""},{"docid":"5da6a4d98552f41635d04f122af46184","score":"0.60734165","text":"def render_to_string(template_path: str, ctx: Context) -> str:\n template = loader.get_template(template_path)\n return template.render(ctx)","title":""},{"docid":"b2819a830cb5664f3875f2e2d528389f","score":"0.6047136","text":"def _render(tpl, ctx, req):\n return render_to_response(tpl, ctx, RequestContext(req))","title":""},{"docid":"2d7a4ba402fc250cd85678028ab2696b","score":"0.6034613","text":"def render_template(src: Path, dst: Path, context: dict[str, Any]) -> None:\n dst.parent.mkdir(parents=True, exist_ok=True)\n\n with src.open() as src_fd, dst.open(\"w\") as dst_fd:\n template = DottedIDsTemplate(src_fd.read())\n dst_fd.write(template.substitute(context))","title":""},{"docid":"a882c36c2eb04afa041d6aa002fdb349","score":"0.6016755","text":"def render_tags(self, template, context):\n while 1:\n match = self.tag_re.search(template)\n if match is None:\n break\n\n tag, tag_type, tag_name = match.group(0, 1, 2)\n tag_name = tag_name.strip()\n try:\n func = modifiers[tag_type]\n replacement = func(self, tag_name, context)\n template = template.replace(tag, replacement)\n except (SyntaxError, KeyError):\n return u\"{{invalid template}}\"\n\n return template","title":""},{"docid":"b1c304b9f441b7f293a350704788c8a0","score":"0.60144174","text":"def render_template(self, **data):\n return self.get_template().format(**data)","title":""},{"docid":"4ba954284abae6a27d1241c5681b22eb","score":"0.6013257","text":"def render_email(context, text_template, html_template=None):\n try:\n t = loader.get_template(text_template)\n text_content = t.render(context)\n except TemplateDoesNotExist:\n text_content = None\n\n if html_template:\n try:\n html_content = Pynliner().from_string(\n render_to_string(html_template, context)).run()\n except TemplateDoesNotExist:\n html_content = None\n else:\n html_content = None\n\n if not text_content and not html_content:\n #print \"None of the following two templates are found: %s and %s\" % (text_template, html_template)\n raise NoContent(\"None of the following two templates are found: %s and %s\" % (text_template, html_template))\n\n return text_content, html_content","title":""},{"docid":"7ef0bcc8f9a45fba0aa2675c192a5c4c","score":"0.5996987","text":"def handlebars_template(package, name, context):\n\n handlebars_check_context(context)\n\n package_name = package.replace(\"-\", \"_\")\n function_name = name.replace(\"-\", \"_\")\n\n # Enable for debugging\n if False:\n logging.info(\"Rendering template %s.%s with context: %s\" %\n (package, name, api.jsonify.jsonify(context)))\n\n module_name = (\"compiled_templates.%s_package.%s\"\n % (package_name, function_name))\n\n function = None\n\n if App.is_dev_server:\n # In dev mode, load all templates dynamically\n function = handlebars_dynamic_load(package, name)\n\n else:\n # In production mode, dynamically load the compiled template module and\n # find the function\n\n if not module_name in sys.modules:\n try:\n __import__(module_name)\n except ImportError:\n logging.info(\"Import error: %s\" % traceback.format_exc())\n\n if module_name in sys.modules:\n function = getattr(sys.modules[module_name], function_name)\n\n if function:\n try:\n ret = function(context,\n helpers=handlebars_helpers,\n partials=handlebars_partials)\n return u\"\".join(ret)\n except:\n logging.error(\"Exception running Handlebars template: %s\"\n % traceback.format_exc())\n return u\"\"\n else:\n return u\"\"","title":""},{"docid":"3c2d9419beac6f0ccc8bf349ff62acac","score":"0.5990973","text":"def render(self, template, **kw):\r\n t = JINJA_ENV.get_template(template)\r\n self.response.write(t.render(kw))","title":""},{"docid":"078653ed86a49646688d14b95ffba395","score":"0.59891635","text":"def render_template(\n template: str, destination: str, context: Dict[str, Any]) -> None:\n template_file = os.path.join(charm_dir(), 'templates', template)\n with open(template_file) as f:\n template_object = Template(f.read())\n\n with open(destination, 'w') as f:\n f.write(juju_header())\n f.write(template_object.render(context))\n os.chmod(destination, 0o444)","title":""},{"docid":"9f2e44b0396835eb61feaa32985b7624","score":"0.5973079","text":"def text_template(text, variables):\n template = string.Template(text)\n return template.safe_substitute(variables)","title":""},{"docid":"18f895a66a2e75d8122306225e5c33bc","score":"0.5933755","text":"def render(self, template, **kw):\n self.write(self.render_str(template, **kw))","title":""},{"docid":"18f895a66a2e75d8122306225e5c33bc","score":"0.5933755","text":"def render(self, template, **kw):\n self.write(self.render_str(template, **kw))","title":""},{"docid":"18f895a66a2e75d8122306225e5c33bc","score":"0.5933755","text":"def render(self, template, **kw):\n self.write(self.render_str(template, **kw))","title":""},{"docid":"c5aadb562830e00c0f6529950ec69b61","score":"0.59312475","text":"def render_template(email, member=None):\n\n f = open(\"./template/template.html\", \"r\")\n html = f.read()\n f.close()\n\n text = \"\"\n\n for line in email.text.split(\"\\n\"):\n text += \"<p>%s</p>\" % line\n\n if member is not None:\n # Replace variables with personalized values for each member\n text = text.replace(\"{{first}}\", member.first).replace(\"{{last}}\", member.last).replace(\"{{email}}\", member.email)\n\n html = html.replace(\"{{content}}\", text)\n\n f = open(\"./template/template.css\", \"r\")\n css = f.read()\n f.close()\n\n return Pynliner().from_string(html).with_cssString(css).run()","title":""},{"docid":"ebd7bd359aeae876d51e8b8d9d2cbf0d","score":"0.5930599","text":"def substitute(self, _str, ctx):\n if isinstance(_str, str) or isinstance(_str, unicode):\n _str = Template(_str).safe_substitute(ctx)\n return _str","title":""},{"docid":"73b2516a2b677e9f6413d9340f073724","score":"0.59170884","text":"def render_str(self, template, **params):\n t = jinja_env.get_template(template)\n params['username'] = self.username\n return t.render(params)","title":""},{"docid":"487571eb4aa47b9569cb795f843aee1a","score":"0.5910275","text":"def compile_template_to_string(\n template: Path,\n context: Context,\n shell_command_working_directory: Optional[Path] = None,\n) -> str:\n if not shell_command_working_directory:\n shell_command_working_directory = template.parent\n\n env = jinja_environment(\n templates_folder=template.parent,\n shell_command_working_directory=shell_command_working_directory,\n )\n jinja_template = env.get_template(name=template.name)\n\n return jinja_template.render(context)","title":""},{"docid":"809faa721ef9f9f8ca8e6eecf6dc443c","score":"0.5877433","text":"def render(self, *template, **kw):\n self.write(self.render_str(*template, **kw))","title":""},{"docid":"67ccbc45d8280debb030cedd5b10dcc0","score":"0.58703214","text":"def _render_template(self):\n template = templates.Template.from_file(\n os.path.join(\n self.project.directory, \n 'templates',\n self.template_name)\n )\n contents = {\n 'content':self.markup(),\n }\n # add additional headers from the source into template context\n contents.update(self.page)\n return template.render(contents)","title":""},{"docid":"1800e0948eb8044f7d80156d8d571011","score":"0.5851413","text":"def render(self, template):\n return re.sub(r'\\$\\$|\\${\\w+}', self._render_sub, template)","title":""},{"docid":"2e3763cb7eb7c4eca591d34e9191c636","score":"0.5844247","text":"def template(ctx: Context, path: \"string\"):\n jinja_env = _get_template_engine(ctx)\n template_path = _extend_path(ctx, path)\n if template_path in tcache:\n template = tcache[template_path]\n else:\n template = jinja_env.get_template(template_path)\n tcache[template_path] = template\n\n resolver = ctx.get_resolver()\n\n try:\n out = template.render({\"{{resolver\": resolver})\n return out\n except UndefinedError as e:\n raise NotFoundException(ctx.owner, None, e.message)","title":""},{"docid":"6e2cf5320a0949dac73ea9b982de71be","score":"0.58427966","text":"def EvaluateTemplate(template, env, escape=True):\n for key, val in env.items():\n if escape and not key.endswith(\"_unescaped\"):\n val = cgi.escape(val)\n template = template.replace('{{%s}}' % key, val)\n return template","title":""},{"docid":"d8093edc88f306e5dc4c95141895888e","score":"0.582872","text":"def render(template, **params):\n return re.sub(r'{{\\s*([^}\\s]+)\\s*}}',\n lambda match: str(params.get(match.group(1), match.group(0))),\n template)","title":""},{"docid":"71f584ab8db6160f2b60ee37e6e7739f","score":"0.5822259","text":"def rstjinja(app, docname, source):\n # Make sure we're outputting HTML\n if app.builder.format != \"html\":\n return\n src = source[0]\n rendered = app.builder.templates.render_string(src, app.config.html_context)\n source[0] = rendered","title":""},{"docid":"32348fcf3387e0f180b45c1f3bd3e026","score":"0.58207905","text":"def render_email_template( template, **context ):\n\n subject, body = render_to_string( template, context ).split( '\\n', 1 )\n return subject.strip(), body","title":""},{"docid":"7a3d3eddd67ac0ee5850f4c59f5bfa7b","score":"0.5817247","text":"def from_string(self, source, globals=None, template_class=None):\r\n globals = self.make_globals(globals)\r\n cls = template_class or self.template_class\r\n return cls.from_code(self, self.compile(source), globals, None)","title":""},{"docid":"ec7ba75b381a7085d5a8f9b2684c4b2b","score":"0.5808121","text":"def renderfile(tpl, path, context):\n file_h = open(path, 'w')\n file_h.write(tpl.render(context))\n file_h.close()","title":""},{"docid":"df10b36aab515c36a493de3dbd825b3f","score":"0.58006716","text":"def render_template(template_file, template_vars):\n templateLoader = jinja2.FileSystemLoader(searchpath=\"./templates/\")\n template_env = jinja2.Environment(loader=templateLoader)\n template = template_env.get_template(template_file)\n return template.render( template_vars )","title":""},{"docid":"20e14542ebdc05ab47534887ccd14bf9","score":"0.5797771","text":"def render_to_response(self, context):\n return TemplateResponse(\n request=self.request, template=self.get_template_names(), context=context\n )","title":""},{"docid":"d7eb993c4077c2525cbd4d9519da7800","score":"0.5795039","text":"def render_template(\n template: str,\n request: 'CoreRequest',\n content: dict[str, Any],\n suppress_global_variables: bool = True\n) -> str:\n\n if suppress_global_variables == 'infer':\n suppress_global_variables = template.startswith('mail_')\n\n registry = request.app.config.template_engine_registry\n page_template = registry._template_loaders['.pt'][template]\n\n variables = get_default_vars(\n request, content, suppress_global_variables=suppress_global_variables)\n\n return page_template.render(**variables)","title":""},{"docid":"7e4a85d52eaa6a97c2a4b91f783ed911","score":"0.5780865","text":"def render_template(text, **context_args):\n template = Template(\"{% load bootstrap3 %}\" + text)\n if not 'form' in context_args:\n context_args['form'] = TestForm()\n return template.render(Context(context_args))","title":""},{"docid":"05456eac42feb6620109966140d30fa8","score":"0.5766187","text":"def render(self, template, **params):\n self.write(self.render_str(template,\n current_user = self.get_current_user(), **params))","title":""},{"docid":"7fafb027cf1e439c3f24d04308a42bab","score":"0.5762089","text":"def __call__(self, template_name, template_vars, **kwargs):\n config = tg.config._current_obj()\n\n # Gets template format from content type or from config options\n format = kwargs.get('format')\n if not format:\n format = self.format_for_content_type.get(tg.response.content_type)\n if not format:\n format = config.get('templating.chameleon.genshi.format')\n if not format:\n format = config.get('templating.genshi.method')\n if not format or format not in ('xml', 'text'):\n format = 'xml'\n\n def render_template():\n template = self.load_template(template_name, format=format)\n return literal(template.render(**template_vars))\n\n return cached_template(template_name, render_template,\n ns_options=('doctype', 'method'), **kwargs)","title":""},{"docid":"c646fc053b655bc00c60e5db4bfee703","score":"0.5760994","text":"def compile(self, source, name=None, filename=None, raw=False,\r\n defer_init=False):\r\n source_hint = None\r\n try:\r\n if isinstance(source, basestring):\r\n source_hint = source\r\n source = self._parse(source, name, filename)\r\n if self.optimized:\r\n source = optimize(source, self)\r\n source = self._generate(source, name, filename,\r\n defer_init=defer_init)\r\n if raw:\r\n return source\r\n if filename is None:\r\n filename = '<template>'\r\n else:\r\n filename = _encode_filename(filename)\r\n return self._compile(source, filename)\r\n except TemplateSyntaxError:\r\n exc_info = sys.exc_info()\r\n self.handle_exception(exc_info, source_hint=source)","title":""},{"docid":"d4b2550dddab8e6ce4c66ac855c6e2bd","score":"0.57590085","text":"def _exec_template(callable_, context, args=None, kwargs=None):\r\n template = context._with_template\r\n if template is not None and \\\r\n (template.format_exceptions or template.error_handler):\r\n error = None\r\n try:\r\n callable_(context, *args, **kwargs)\r\n except Exception, e:\r\n _render_error(template, context, e)\r\n except: \r\n e = sys.exc_info()[0]\r\n _render_error(template, context, e)\r\n else:\r\n callable_(context, *args, **kwargs)","title":""},{"docid":"5a8791f85ee75c2e9c457867b9e13164","score":"0.5756796","text":"def render_template_text(email, member=None):\n\n text = email.text\n\n # Add extra newlines in plaintext emails\n text = text.replace(\"\\n\", \"\\n\\n\")\n\n # Replace anchor tags with plaintext links\n for anchor in re.finditer(\"<a href=\\\"([^\\\"]+)\\\">([^<]+)<\\/a>\", text):\n url = anchor.group(1)\n content = anchor.group(2)\n text = text.replace(\"<a href=\\\"%s\\\">%s</a>\" % (url, content), \"%s (%s)\" % (content, url))\n\n if member is not None:\n # Replace variables with personalized values for each member\n text = text.replace(\"{{first}}\", member.first).replace(\"{{last}}\", member.last).replace(\"{{email}}\", member.email)\n\n if \"<br>\" in text or \"<br/>\" in text or \"<br />\" in text:\n # Replace HTML newlines with plaintext newlines\n text = text.replace(\"<br>\", \"\\n\").replace(\"<br/>\", \"\\n\").replace(\"<br />\", \"\\n\")\n\n return text","title":""},{"docid":"77465f32502bb5ca75429ca33174a884","score":"0.5743868","text":"def process_template(self, template_text, data_dict=None):\n if data_dict is None:\n data_dict = {}\n else:\n assert isinstance(data_dict, dict)\n data_dict = dict(data_dict)\n assert \"algo\" not in data_dict, \"algo already set by user data\"\n data_dict[\"algo\"] = self\n\n template = jinja2.Template(template_text)\n return template.render(data_dict)","title":""},{"docid":"e6b2a4fc618839fa102fa8466272ee57","score":"0.57370764","text":"def test_context(self):\r\n collection = lookup.TemplateLookup()\r\n\r\n collection.put_string('main.html', \"\"\"\r\n <%namespace name=\"comp\" file=\"defs.html\"/>\r\n\r\n this is main. ${comp.def1()}\r\n ${comp.def2(\"there\")}\r\n\"\"\")\r\n\r\n collection.put_string('defs.html', \"\"\"\r\n <%def name=\"def1()\">\r\n def1: x is ${x}\r\n </%def>\r\n\r\n <%def name=\"def2(x)\">\r\n def2: x is ${x}\r\n </%def>\r\n\"\"\")\r\n\r\n assert flatten_result(collection.get_template('main.html').render(x=\"context x\")) == \"this is main. def1: x is context x def2: x is there\"","title":""},{"docid":"08838bc6bb3c5d059ba8c0079ec334dd","score":"0.5736208","text":"def render_template(data, template_name, filters=None):\n env = Environment(loader=FileSystemLoader(''))\n\n if filters is not None:\n for key, value in filters.iteritems():\n env.filters[key] = value\n\n template = env.get_template(template_name)\n return template.render(feed=data).encode('utf-8')","title":""}],"string":"[\n {\n \"docid\": \"2591aad30c9c871ce95371d363072d1e\",\n \"score\": \"0.8500779\",\n \"text\": \"def render_template_string(source, **context):\\n ctx = stack.top\\n lookup = _lookup(ctx.app)\\n template = Template(source, lookup=_lookup(ctx.app), **lookup.template_args)\\n return _render(template, context, ctx.app)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a015558512ae75e88f118dbe614b433e\",\n \"score\": \"0.774367\",\n \"text\": \"def render(self, template_string, context={}):\\n try:\\n t = template.Template(template_string)\\n c = template.Context(context)\\n return t.render(c)\\n except Exception, e:\\n return e\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0eafb67497567658857cbbca433a13a1\",\n \"score\": \"0.74834913\",\n \"text\": \"def render(text, context):\\n return Template(text).render(context)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f37767455aa3ef260766b47bba8bd4e\",\n \"score\": \"0.7334508\",\n \"text\": \"def render(self, template_str: str, context: (dict, None)) -> str:\\n if context is None:\\n context = self.context\\n t = self._env.from_string(template_str)\\n return t.render(context)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73a668ba9ee559f7619a6286f9e727e3\",\n \"score\": \"0.7262923\",\n \"text\": \"def render_template_to_string(self, template, context):\\r\\n return self.lookup.from_string(template).render(**context)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9bd6406d3f4fbc0d702248d40f4c7aab\",\n \"score\": \"0.7225489\",\n \"text\": \"def render_template(process, template_string, context):\\n from resolwe.flow.managers import manager\\n\\n # Get the appropriate expression engine. If none is defined, do not evaluate\\n # any expressions.\\n expression_engine = process.requirements.get(\\\"expression-engine\\\", None)\\n if not expression_engine:\\n return template_string\\n\\n return manager.get_expression_engine(expression_engine).evaluate_block(\\n template_string, context\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"347e04caad1dc357e14b8a02dd6732bd\",\n \"score\": \"0.722019\",\n \"text\": \"def render_template(self, template_string):\\n original_autoescape = self.context.autoescape\\n self.context.autoescape = False\\n\\n template = Template(\\\"\\\")\\n template_debug = getattr(\\n settings, \\\"TEMPLATE_DEBUG\\\", template.engine.debug if hasattr(template, \\\"engine\\\") else False\\n )\\n if template_debug is True:\\n origin = Origin(template_string)\\n else:\\n origin = None\\n\\n template.nodelist = self.compile_string(template_string, origin, template_debug)\\n\\n rendered = template.render(self.context)\\n self.context.autoescape = original_autoescape\\n return rendered\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b9f4cc476e5b4b0aab14a45b3361a3a\",\n \"score\": \"0.71682847\",\n \"text\": \"def render_template(self, _filename, **context):\\r\\n template = self.environment.get_template(_filename)\\r\\n return template.render_unicode(**context)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33a96759c3797eea318e2dc866eaf9cb\",\n \"score\": \"0.71345305\",\n \"text\": \"def render_from_string(template: str, ctx: Context) -> str:\\n template = Template(template)\\n return template.render(ctx)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f912dd17f5ab42b0e0e77e85d926a0a\",\n \"score\": \"0.7085872\",\n \"text\": \"def _render_string_from_template(\\n source: str, template: _BaseInstallationTemplate\\n) -> str:\\n # TODO: we could use a while loop or recursive function to render the template until\\n # there are no jinja-specific things. At this point, we support one level of\\n # nesting.\\n n_renders = 0\\n max_renders = 20\\n\\n err = (\\n \\\"A template included in this renderer raised an error. Please check the\\\"\\n \\\" template definition. A required argument might not be included in the\\\"\\n \\\" required arguments part of the template. Variables in the template should\\\"\\n \\\" start with `self.`.\\\"\\n )\\n\\n # Render the string again. This is sometimes necessary because some defaults in the\\n # template are rendered as {{ self.X }}. These defaults need to be rendered again.\\n\\n while (\\n _jinja_env.variable_start_string in source\\n and _jinja_env.variable_end_string in source\\n ):\\n source = source.replace(\\\"self.\\\", \\\"template.\\\")\\n tmpl = _jinja_env.from_string(source)\\n try:\\n source = tmpl.render(template=template)\\n except jinja2.exceptions.UndefinedError as e:\\n raise RendererError(err) from e\\n n_renders += 1\\n\\n if n_renders > max_renders:\\n raise RendererError(\\n f\\\"reached maximum rendering iterations ({max_renders}). Templates\\\"\\n f\\\" should not nest variables more than {max_renders} times.\\\"\\n )\\n return source\",\n \"title\": \"\"\n },\n {\n \"docid\": \"648c29585ddd6bb2292ce23c335eb606\",\n \"score\": \"0.70501155\",\n \"text\": \"def render_template(self, _filename, **context):\\r\\n return self.environment.get_template(_filename).render(**context)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5225976f5bf500b0ff313cb44cfe5977\",\n \"score\": \"0.70279455\",\n \"text\": \"def render_template_to_string(self, template, context):\\r\\n\\r\\n context = context.update(self.filters)\\r\\n\\r\\n return Template(template).render(**context)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89c8169fbe0f615c5b3582dd4f0ba4de\",\n \"score\": \"0.69808406\",\n \"text\": \"def render_template(template, **kwargs):\\n if template is None or not isinstance(template, basestring):\\n return template\\n tmpl = _get_environment().from_string(template)\\n ctx = dict(kwargs)\\n if global_context is not None:\\n for k, v in global_context.iteritems():\\n if not k in ctx:\\n ctx[k] = v\\n # expose the global context\\n ctx['_ctx'] = global_context\\n if 'self' in ctx:\\n del ctx['self']\\n try:\\n return tmpl.render(**ctx)\\n except TemplateSyntaxError as err:\\n raise RenderError(template, err.message, line=err.lineno)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90c7fd7858f045df59d08cba92720317\",\n \"score\": \"0.69634664\",\n \"text\": \"def render(template, context, template_dir=\\\"templates\\\"):\\n j2env = Environment(loader=FileSystemLoader(template_dir))\\n\\n tpl = j2env.get_template(template)\\n\\n return tpl.render(context)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e5e87fc7c77e90c3c3d443ac311ecf8\",\n \"score\": \"0.68901604\",\n \"text\": \"def render_to_string(template_name, context):\\n f = open(settings.PLSQL_TEMPLATE_DIR + '/' + template_name)\\n template_string = f.read()\\n f.close()\\n\\n t = Template(template_string, template_string)\\n return t.render(context)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"abd6a0c5fefa51b56e45aefa549df2bd\",\n \"score\": \"0.68708706\",\n \"text\": \"def render_template(template, **context):\\n if ':' not in template:\\n template = '%s:%s' % (request.package, template)\\n return jinja_env.get_template(template).render(context)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b71038c9e116f603838a6ebc2dc3d4e\",\n \"score\": \"0.6852936\",\n \"text\": \"def render_template(template_name, context={}):\\n \\n path = os.path.join(os.path.dirname(__file__), 'templates', template_name)\\n return template.render(path, context)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2bd970858079f8dee89e5a2075531f7b\",\n \"score\": \"0.68046623\",\n \"text\": \"def render(charm_dir, source, context):\\n templates_dir = os.path.join(charm_dir, 'templates')\\n template_env = Environment(loader=FileSystemLoader(templates_dir))\\n\\n try:\\n template = template_env.get_template(source)\\n except exceptions.TemplateNotFound as e:\\n logger.error('Could not load template %s from %s.' %\\n (source, templates_dir))\\n raise e\\n return template.render(context)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b8d23c2bd57c6c45704ce6be9e54e8b\",\n \"score\": \"0.68041766\",\n \"text\": \"def render_template(template_name, **context):\\n ctx = stack.top\\n return _render(_lookup(ctx.app).get_template(template_name),\\n context, ctx.app)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c58b96dbf471fff73e438d854519db4\",\n \"score\": \"0.6776268\",\n \"text\": \"def _render(template, context, app):\\n app.update_template_context(context)\\n try:\\n rv = template.render_unicode(**context)\\n template_rendered.send(app, template=template, context=context)\\n return rv\\n except:\\n translated = TemplateError(template)\\n raise translated\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f4b9513e740f73db1c03d979526a005\",\n \"score\": \"0.67737067\",\n \"text\": \"def render_template(self, template_path, context={}):\\n template_str = self.load_resource(template_path)\\n return Template(template_str).render(Context(context))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"edbc06a93a201c5dfc476c03adbb3400\",\n \"score\": \"0.67288864\",\n \"text\": \"def render_template(template, context):\\n for key, value in context.iteritems():\\n template = template.replace('{{ %s }}' % (key,), value)\\n return template\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c81d9467257d4ac56d374411817eced\",\n \"score\": \"0.6688212\",\n \"text\": \"def render_template(self, template_name, output_name, context):\\r\\n context['striphtml'] = striphtml\\r\\n template = self.lookup.get_template(template_name)\\r\\n data = template.render_unicode(**context)\\r\\n if output_name is not None:\\r\\n makedirs(os.path.dirname(output_name))\\r\\n with open(output_name, 'w+') as output:\\r\\n output.write(data)\\r\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34bcc21ccca1219e7ca38523596bf115\",\n \"score\": \"0.6605197\",\n \"text\": \"def render_template(self, template_name, output_name, context):\\r\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d41f469f1f76fbbe9b3b983200990041\",\n \"score\": \"0.6602932\",\n \"text\": \"def render(request, template, context):\\n ctx = RequestContext(request, context)\\n return render_to_response(template, ctx)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fc772dce5f52691fa6e1668d92e610a\",\n \"score\": \"0.6592469\",\n \"text\": \"def render(template, **context):\\n return render_response(template, context)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ab7aa16c0d690df68425548f45a7ef1\",\n \"score\": \"0.6583847\",\n \"text\": \"def render_str(self, *template, **kw):\\n tmp = JINJA_ENV.get_template(*template)\\n return tmp.render(**kw)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb80040988830a32af5ba5f22e9493aa\",\n \"score\": \"0.65768784\",\n \"text\": \"def render_template(template_file, **context):\\n t_env = get_template_env('templates')\\n template = t_env.get_template(template_file)\\n body = template.render(**context or {})\\n return body\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6104a01a8f2545752b6e05836470eb9f\",\n \"score\": \"0.65703696\",\n \"text\": \"def _interpolate_str(\\n data: str, context: Dict[str, Any], strict: bool = True\\n) -> RenderedString:\\n # Render the given string as a Django template with the given context.\\n template = Template(data)\\n template_context = Context(context, autoescape=False)\\n rendered_string = RenderedString(template.render(template_context))\\n\\n # Extract a dict of variables used to render the string.\\n rendered_context = {\\n var: template_context.get(var, NOT_PROVIDED)\\n for var in frozenset(\\n v.filter_expression.var.lookups[0]\\n for v in template.nodelist\\n if isinstance(v, VariableNode)\\n )\\n }\\n\\n # Attach the render context to the string.\\n rendered_string.__context__ = rendered_context\\n\\n missing_variables = frozenset(\\n k for k, v in rendered_context.items() if v is NOT_PROVIDED\\n )\\n if strict and missing_variables:\\n raise LookupError(\\n f\\\"The template references variables that were not in the context \\\"\\n f'provided: {\\\", \\\".join(missing_variables)}'\\n )\\n\\n return rendered_string\",\n \"title\": \"\"\n },\n {\n \"docid\": \"935008cf3c13f96af87356adb922d37a\",\n \"score\": \"0.6564538\",\n \"text\": \"def render_template(out, name, context, templates_dir, prefix=None):\\n\\n # support \\\"::\\\" syntax\\n pp = [tenjin.PrefixedLinePreprocessor(prefix=prefix)\\n if prefix else tenjin.PrefixedLinePreprocessor()]\\n # disable HTML escaping\\n template_globals = {\\\"to_str\\\": str, \\\"escape\\\": str}\\n if templates_dir:\\n engine = TemplateEngine(path=[templates_dir], pp=pp, cache=False)\\n else:\\n engine = TemplateEngine(pp=pp, cache=False)\\n out.write(engine.render(name, context, template_globals))\\n if 'KD_DICT' in context:\\n return context['KD_DICT']\\n if 'P4TBL_TYPES' in context:\\n return context['P4TBL_TYPES']\\n else:\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6c91d4b2c0a27b962911b9852294b95\",\n \"score\": \"0.6560027\",\n \"text\": \"def render_to_string(template_name, context=None, request=None,\\r\\n processors=None):\\r\\n context = dict(context or {})\\r\\n if request is not None:\\r\\n context['request'] = request\\r\\n for processor in chain(get_standard_processors(), processors or ()):\\r\\n context.update(processor(request))\\r\\n return get_template(template_name).render(context)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51c6b29d48abd64ebf5febd4799c058f\",\n \"score\": \"0.6551095\",\n \"text\": \"def render_template_to_string(self, template, context):\\r\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f08ec913ff12add5f8152ff7c9b8b7f9\",\n \"score\": \"0.6512847\",\n \"text\": \"def render_template(self, template_file, **context) -> str:\\n if not self.template_dir: raise InsufficientError('template_dir')\\n try: \\n with open(Path(self.template_dir)/template_file, 'r') as f:\\n template:Template = Template(f.read())\\n return template.render(context)\\n except FileNotFoundError: raise TemplateNotFoundError(template_file)\\n except exceptions.UndefinedError: raise Jinja2ContextDataError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1198088871e9f64196d46307c077b79c\",\n \"score\": \"0.65125775\",\n \"text\": \"def _render(template, context, app):\\n\\n before_render_template.send(app, template=template, context=context)\\n rv = template.render(context)\\n template_rendered.send(app, template=template, context=context)\\n return rv\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a4ba2129529b076af1fe4fa76bf41e1\",\n \"score\": \"0.64339757\",\n \"text\": \"def _render(self, context):\\n if not context:\\n context = Context({})\\n\\n plain = self.template_plain.render(context)\\n html = self.template_html.render(context)\\n css = get_template(self.template_style).render(Context({}))\\n\\n p = Pynliner()\\n html = p.from_string(html).with_cssString(css).run()\\n\\n return plain, html\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59b62f38b09a4d02a8b2b835178f1aa4\",\n \"score\": \"0.6406535\",\n \"text\": \"def render_template_with_globals(template, args):\\n args = fill_template_arguments(args)\\n debug('Rendering {} with args: {}'.format(template, args))\\n return render_template(template, **args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c76c93b75703a9cf070cddaf608e7da\",\n \"score\": \"0.6400795\",\n \"text\": \"def render(template_name, context={}):\\n template = myenv.get_template(template_name)\\n context['user'] = get_user()\\n context['path'] = cherrypy.request.path_info\\n context['params'] = cherrypy.request.params\\n return template.render(context)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aff98d638dfbb556b3716c50c2fab03c\",\n \"score\": \"0.63886666\",\n \"text\": \"def render_str(self, template, **params):\\n t = JINJA_ENV.get_template(template)\\n return t.render(params)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03785d27a1ceb854ca3a56528ce23931\",\n \"score\": \"0.63627714\",\n \"text\": \"def render_str(template, **params):\\n t = JINJA_ENV.get_template(template)\\n return t.render(params)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7876a6de1d41e75c7d8ed7ba1adb41f6\",\n \"score\": \"0.6349783\",\n \"text\": \"def render(self, context):\\n try:\\n template = self.template.resolve(context)\\n # Does this quack like a Template?\\n if not callable(getattr(template, 'render', None)):\\n # If not, we'll try our cache, and get_template()\\n template_name = template\\n cache = context.render_context.dicts[0].setdefault(self, {})\\n template = cache.get(template_name)\\n if template is None:\\n template = context.template.engine.get_template(template_name)\\n cache[template_name] = template\\n # Use the base.Template of a backends.django.Template.\\n elif hasattr(template, 'template'):\\n template = template.template\\n values = {\\n name: var.resolve(context)\\n for name, var in self.extra_context.items()\\n }\\n if self.isolated_context:\\n return template.render(context.new(values))\\n with context.push(**values):\\n return template.render(context)\\n except Exception as e:\\n if context.template.engine.debug:\\n raise\\n template_name = getattr(context, 'template_name', None) or 'unknown'\\n warnings.warn(\\n \\\"Rendering {%% include '%s' %%} raised %s. In Django 2.1, \\\"\\n \\\"this exception will be raised rather than silenced and \\\"\\n \\\"rendered as an empty string.\\\" %\\n (template_name, e.__class__.__name__),\\n RemovedInDjango21Warning,\\n )\\n logger.warning(\\n \\\"Exception raised while rendering {%% include %%} for \\\"\\n \\\"template '%s'. Empty string rendered instead.\\\",\\n template_name,\\n exc_info=True,\\n )\\n return ''\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c2547c14902b5c626cd62cb0a3511787\",\n \"score\": \"0.6347726\",\n \"text\": \"def render_str(template, **params):\\n t = jinja_env.get_template(template)\\n return t.render(params)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32dec2cb212a8ff0a093da4dc25f76b1\",\n \"score\": \"0.63275397\",\n \"text\": \"def render_str(self, template, **params):\\n templ = jinja_env.get_template(template)\\n return templ.render(params)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"035646f2e1a265afd52ddb35ace6c20f\",\n \"score\": \"0.6326421\",\n \"text\": \"def render_filter(ctx, template):\\n if isinstance(template, basestring):\\n template = ctx.environment.from_string(template)\\n return template.render(ctx)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3104d1fe5af67f1c9f68dff95c73d95a\",\n \"score\": \"0.6316073\",\n \"text\": \"def render_body(template, context):\\n for key, value in context.iteritems():\\n template = template.replace('{{ %s }}' % key, value)\\n return template\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9089eb52191d6da8561798849a121fd2\",\n \"score\": \"0.63057363\",\n \"text\": \"def render_template(self, template_name, output_name, context):\\r\\n if jinja2 is None:\\r\\n req_missing(['jinja2'], 'use this theme')\\r\\n template = self.lookup.get_template(template_name)\\r\\n output = template.render(**context)\\r\\n if output_name is not None:\\r\\n makedirs(os.path.dirname(output_name))\\r\\n with open(output_name, 'w+') as output:\\r\\n output.write(output.encode('utf8'))\\r\\n return output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d58483926e6356482bbc371f740136e0\",\n \"score\": \"0.62967765\",\n \"text\": \"def render_template(\\n searchpath: Path,\\n template_name: str,\\n context: Dict[str, Any],\\n extension: str,\\n autoescape: bool = True,\\n keep_trailing_newline: bool = False,\\n) -> str:\\n import jinja2\\n\\n template_loader = jinja2.FileSystemLoader(searchpath=searchpath)\\n template_env = jinja2.Environment(\\n loader=template_loader,\\n undefined=jinja2.StrictUndefined,\\n autoescape=autoescape,\\n keep_trailing_newline=keep_trailing_newline,\\n )\\n template = template_env.get_template(f\\\"{template_name}_TEMPLATE{extension}.jinja2\\\")\\n content: str = template.render(context)\\n return content\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8b9b618f43a1b23b763aee6e6848f07\",\n \"score\": \"0.628353\",\n \"text\": \"def render(self, **context):\\n template = JinjaTemplate(self.get_content())\\n return template.render(**context)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3582dd4fc5f98e7183b06c9921d72f3b\",\n \"score\": \"0.6250179\",\n \"text\": \"def render(template_name, **kwargs):\\r\\n with open(template_name) as f:\\r\\n template = Template(f.read())\\r\\n return template.render(**kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe16f26c2d4bddac69e43d3ea6af479b\",\n \"score\": \"0.62370026\",\n \"text\": \"def render_template(self, name, **ctx):\\n tmpl = self.get_template(name)\\n return tmpl.render(ctx)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c9f6f3003022bd31ce361f1a0653f66\",\n \"score\": \"0.62352145\",\n \"text\": \"def render(self, template=None, context=None, encoding=None):\\n template = template or self.template\\n context = context or self.context\\n\\n template = self.render_sections(template, context)\\n result = self.render_tags(template, context)\\n if encoding is not None:\\n result = result.encode(encoding)\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"051856278a0e4d25425631ac1f3c71ee\",\n \"score\": \"0.6229935\",\n \"text\": \"def render_context(self, context, *args, **kwargs):\\r\\n if getattr(context, '_with_template', None) is None:\\r\\n context._with_template = self\\r\\n runtime._render_context(self, \\r\\n self.callable_, \\r\\n context, \\r\\n *args, \\r\\n **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9cf62eb638fe4f4f05606c1d5d6fe34d\",\n \"score\": \"0.6182085\",\n \"text\": \"def include(self, template_name, **kwargs):\\n frame = sys._getframe(1)\\n locals = frame.f_locals\\n globals = frame.f_globals\\n context = locals[\\\"_context\\\"].copy()\\n context.update(kwargs)\\n template = self.get_template(template_name, context, globals)\\n return template.render(context, globals, _buf=locals[\\\"_buf\\\"])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4afca3aa29d60bb5945acb025893dbd4\",\n \"score\": \"0.6169488\",\n \"text\": \"def render_template(template=None, context=None,\\n method=None, string=None):\\n kajiki = current_app.extensions['kajiki']\\n method = kajiki._method_for(template, method)\\n template = generate_template(template, context, method, string)\\n # TODO kajiki has no arguments for the serializer\\n #render_args = dict(method=kajiki.methods[method]['serializer'])\\n #if 'doctype' in kajiki.methods[method]:\\n # render_args['doctype'] = kajiki.methods[method]['doctype']\\n #return template.render(**render_args)\\n return template.render()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f473c3e91f689a1814c9eef19827c1d\",\n \"score\": \"0.61638755\",\n \"text\": \"def check_template_tag(self, template_string: str, context: dict, result_string: str) -> None:\\n t = Template(template_string)\\n c = Context(context)\\n output = t.render(c)\\n self.assertEqual(output, result_string)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a40c80b6114e508b33596942dd485eb\",\n \"score\": \"0.61482924\",\n \"text\": \"def render(self, template, **kw): \\n render_out = \\\"\\\"\\n jinja_env = self.app.config.get(\\\"jinja_env\\\")\\n if jinja_env:\\n t = jinja_env.get_template(template)\\n render_out = t.render(kw)\\n self.response.out.write(render_out)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd8af326978fb812beedac7473398a9e\",\n \"score\": \"0.61314666\",\n \"text\": \"def render_email(template, context=None):\\n if context is None:\\n context = {}\\n email_body = render_jinja2_template(template, context)\\n return email_body\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc2546b8d00fe3c695455667d185579a\",\n \"score\": \"0.61176956\",\n \"text\": \"def render_template(cls, template_name: str, **kwargs) -> str:\\n template_filename: str = \\\"{}.tmp\\\".format(template_name)\\n template = cls.env.get_template(template_filename)\\n return template.render(kwargs['input_data'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32df02fd23a0825fbff625132b084641\",\n \"score\": \"0.6117059\",\n \"text\": \"def render_template(template_name_or_list, **context):\\n ctx = _app_ctx_stack.top\\n ctx.app.update_template_context(context)\\n return _render(\\n ctx.app.jinja_env.get_or_select_template(template_name_or_list),\\n context,\\n ctx.app,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1407eb8f5f05f061d06032aef7bd8c3a\",\n \"score\": \"0.6114893\",\n \"text\": \"def render_and_write(template_name, context, output_name, output_dir):\\n\\n template = templates_env.get_template(template_name)\\n f = open(path.join(output_dir, output_name), \\\"w\\\")\\n f.write(template.render(**context))\\n f.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f255e2720129c505d4bdc6285915990f\",\n \"score\": \"0.61102927\",\n \"text\": \"def _render_template(\\n self,\\n mako_template_filepath: str,\\n context: dict,\\n translator: Translator\\n ) -> str:\\n try:\\n template = Template(filename=mako_template_filepath)\\n return template.render(\\n _=translator.get_translation,\\n config=self.config,\\n **context\\n )\\n except Exception as exc:\\n logger.exception(self, 'Failed to render email template: {}'.format(exc.__str__()))\\n raise EmailTemplateError('Failed to render email template: {}'.format(exc.__str__()))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3ce590a89a49cdbbb8818fec833c8c2\",\n \"score\": \"0.6107454\",\n \"text\": \"def render_template(template, dirs = [ ], context = None, **kwargs):\\n dirs = sequencify(dirs)\\n if PATH[\\\"TEMPLATES\\\"] not in dirs:\\n dirs.append(PATH[\\\"TEMPLATES\\\"])\\n\\n dirs = [osp.abspath(dir_) for dir_ in dirs]\\n\\n logger.info(\\\"Searching for templates within directories: %s\\\" % dirs)\\n\\n path = None\\n for dir_ in dirs:\\n temp = osp.join(dir_, template)\\n if osp.exists(temp):\\n path = temp\\n break\\n \\n if not path:\\n raise TemplateNotFoundError(\\\"Template %s not found.\\\" % template)\\n \\n string = read(path)\\n rendered = string\\n\\n if not context:\\n context = kwargs\\n\\n if context:\\n template = Template(string)\\n rendered = template.substitute(context)\\n \\n return rendered\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5da6a4d98552f41635d04f122af46184\",\n \"score\": \"0.60734165\",\n \"text\": \"def render_to_string(template_path: str, ctx: Context) -> str:\\n template = loader.get_template(template_path)\\n return template.render(ctx)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2819a830cb5664f3875f2e2d528389f\",\n \"score\": \"0.6047136\",\n \"text\": \"def _render(tpl, ctx, req):\\n return render_to_response(tpl, ctx, RequestContext(req))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d7a4ba402fc250cd85678028ab2696b\",\n \"score\": \"0.6034613\",\n \"text\": \"def render_template(src: Path, dst: Path, context: dict[str, Any]) -> None:\\n dst.parent.mkdir(parents=True, exist_ok=True)\\n\\n with src.open() as src_fd, dst.open(\\\"w\\\") as dst_fd:\\n template = DottedIDsTemplate(src_fd.read())\\n dst_fd.write(template.substitute(context))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a882c36c2eb04afa041d6aa002fdb349\",\n \"score\": \"0.6016755\",\n \"text\": \"def render_tags(self, template, context):\\n while 1:\\n match = self.tag_re.search(template)\\n if match is None:\\n break\\n\\n tag, tag_type, tag_name = match.group(0, 1, 2)\\n tag_name = tag_name.strip()\\n try:\\n func = modifiers[tag_type]\\n replacement = func(self, tag_name, context)\\n template = template.replace(tag, replacement)\\n except (SyntaxError, KeyError):\\n return u\\\"{{invalid template}}\\\"\\n\\n return template\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1c304b9f441b7f293a350704788c8a0\",\n \"score\": \"0.60144174\",\n \"text\": \"def render_template(self, **data):\\n return self.get_template().format(**data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ba954284abae6a27d1241c5681b22eb\",\n \"score\": \"0.6013257\",\n \"text\": \"def render_email(context, text_template, html_template=None):\\n try:\\n t = loader.get_template(text_template)\\n text_content = t.render(context)\\n except TemplateDoesNotExist:\\n text_content = None\\n\\n if html_template:\\n try:\\n html_content = Pynliner().from_string(\\n render_to_string(html_template, context)).run()\\n except TemplateDoesNotExist:\\n html_content = None\\n else:\\n html_content = None\\n\\n if not text_content and not html_content:\\n #print \\\"None of the following two templates are found: %s and %s\\\" % (text_template, html_template)\\n raise NoContent(\\\"None of the following two templates are found: %s and %s\\\" % (text_template, html_template))\\n\\n return text_content, html_content\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ef0bcc8f9a45fba0aa2675c192a5c4c\",\n \"score\": \"0.5996987\",\n \"text\": \"def handlebars_template(package, name, context):\\n\\n handlebars_check_context(context)\\n\\n package_name = package.replace(\\\"-\\\", \\\"_\\\")\\n function_name = name.replace(\\\"-\\\", \\\"_\\\")\\n\\n # Enable for debugging\\n if False:\\n logging.info(\\\"Rendering template %s.%s with context: %s\\\" %\\n (package, name, api.jsonify.jsonify(context)))\\n\\n module_name = (\\\"compiled_templates.%s_package.%s\\\"\\n % (package_name, function_name))\\n\\n function = None\\n\\n if App.is_dev_server:\\n # In dev mode, load all templates dynamically\\n function = handlebars_dynamic_load(package, name)\\n\\n else:\\n # In production mode, dynamically load the compiled template module and\\n # find the function\\n\\n if not module_name in sys.modules:\\n try:\\n __import__(module_name)\\n except ImportError:\\n logging.info(\\\"Import error: %s\\\" % traceback.format_exc())\\n\\n if module_name in sys.modules:\\n function = getattr(sys.modules[module_name], function_name)\\n\\n if function:\\n try:\\n ret = function(context,\\n helpers=handlebars_helpers,\\n partials=handlebars_partials)\\n return u\\\"\\\".join(ret)\\n except:\\n logging.error(\\\"Exception running Handlebars template: %s\\\"\\n % traceback.format_exc())\\n return u\\\"\\\"\\n else:\\n return u\\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c2d9419beac6f0ccc8bf349ff62acac\",\n \"score\": \"0.5990973\",\n \"text\": \"def render(self, template, **kw):\\r\\n t = JINJA_ENV.get_template(template)\\r\\n self.response.write(t.render(kw))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"078653ed86a49646688d14b95ffba395\",\n \"score\": \"0.59891635\",\n \"text\": \"def render_template(\\n template: str, destination: str, context: Dict[str, Any]) -> None:\\n template_file = os.path.join(charm_dir(), 'templates', template)\\n with open(template_file) as f:\\n template_object = Template(f.read())\\n\\n with open(destination, 'w') as f:\\n f.write(juju_header())\\n f.write(template_object.render(context))\\n os.chmod(destination, 0o444)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f2e44b0396835eb61feaa32985b7624\",\n \"score\": \"0.5973079\",\n \"text\": \"def text_template(text, variables):\\n template = string.Template(text)\\n return template.safe_substitute(variables)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18f895a66a2e75d8122306225e5c33bc\",\n \"score\": \"0.5933755\",\n \"text\": \"def render(self, template, **kw):\\n self.write(self.render_str(template, **kw))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18f895a66a2e75d8122306225e5c33bc\",\n \"score\": \"0.5933755\",\n \"text\": \"def render(self, template, **kw):\\n self.write(self.render_str(template, **kw))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18f895a66a2e75d8122306225e5c33bc\",\n \"score\": \"0.5933755\",\n \"text\": \"def render(self, template, **kw):\\n self.write(self.render_str(template, **kw))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5aadb562830e00c0f6529950ec69b61\",\n \"score\": \"0.59312475\",\n \"text\": \"def render_template(email, member=None):\\n\\n f = open(\\\"./template/template.html\\\", \\\"r\\\")\\n html = f.read()\\n f.close()\\n\\n text = \\\"\\\"\\n\\n for line in email.text.split(\\\"\\\\n\\\"):\\n text += \\\"<p>%s</p>\\\" % line\\n\\n if member is not None:\\n # Replace variables with personalized values for each member\\n text = text.replace(\\\"{{first}}\\\", member.first).replace(\\\"{{last}}\\\", member.last).replace(\\\"{{email}}\\\", member.email)\\n\\n html = html.replace(\\\"{{content}}\\\", text)\\n\\n f = open(\\\"./template/template.css\\\", \\\"r\\\")\\n css = f.read()\\n f.close()\\n\\n return Pynliner().from_string(html).with_cssString(css).run()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ebd7bd359aeae876d51e8b8d9d2cbf0d\",\n \"score\": \"0.5930599\",\n \"text\": \"def substitute(self, _str, ctx):\\n if isinstance(_str, str) or isinstance(_str, unicode):\\n _str = Template(_str).safe_substitute(ctx)\\n return _str\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73b2516a2b677e9f6413d9340f073724\",\n \"score\": \"0.59170884\",\n \"text\": \"def render_str(self, template, **params):\\n t = jinja_env.get_template(template)\\n params['username'] = self.username\\n return t.render(params)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"487571eb4aa47b9569cb795f843aee1a\",\n \"score\": \"0.5910275\",\n \"text\": \"def compile_template_to_string(\\n template: Path,\\n context: Context,\\n shell_command_working_directory: Optional[Path] = None,\\n) -> str:\\n if not shell_command_working_directory:\\n shell_command_working_directory = template.parent\\n\\n env = jinja_environment(\\n templates_folder=template.parent,\\n shell_command_working_directory=shell_command_working_directory,\\n )\\n jinja_template = env.get_template(name=template.name)\\n\\n return jinja_template.render(context)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"809faa721ef9f9f8ca8e6eecf6dc443c\",\n \"score\": \"0.5877433\",\n \"text\": \"def render(self, *template, **kw):\\n self.write(self.render_str(*template, **kw))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67ccbc45d8280debb030cedd5b10dcc0\",\n \"score\": \"0.58703214\",\n \"text\": \"def _render_template(self):\\n template = templates.Template.from_file(\\n os.path.join(\\n self.project.directory, \\n 'templates',\\n self.template_name)\\n )\\n contents = {\\n 'content':self.markup(),\\n }\\n # add additional headers from the source into template context\\n contents.update(self.page)\\n return template.render(contents)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1800e0948eb8044f7d80156d8d571011\",\n \"score\": \"0.5851413\",\n \"text\": \"def render(self, template):\\n return re.sub(r'\\\\$\\\\$|\\\\${\\\\w+}', self._render_sub, template)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e3763cb7eb7c4eca591d34e9191c636\",\n \"score\": \"0.5844247\",\n \"text\": \"def template(ctx: Context, path: \\\"string\\\"):\\n jinja_env = _get_template_engine(ctx)\\n template_path = _extend_path(ctx, path)\\n if template_path in tcache:\\n template = tcache[template_path]\\n else:\\n template = jinja_env.get_template(template_path)\\n tcache[template_path] = template\\n\\n resolver = ctx.get_resolver()\\n\\n try:\\n out = template.render({\\\"{{resolver\\\": resolver})\\n return out\\n except UndefinedError as e:\\n raise NotFoundException(ctx.owner, None, e.message)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e2cf5320a0949dac73ea9b982de71be\",\n \"score\": \"0.58427966\",\n \"text\": \"def EvaluateTemplate(template, env, escape=True):\\n for key, val in env.items():\\n if escape and not key.endswith(\\\"_unescaped\\\"):\\n val = cgi.escape(val)\\n template = template.replace('{{%s}}' % key, val)\\n return template\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8093edc88f306e5dc4c95141895888e\",\n \"score\": \"0.582872\",\n \"text\": \"def render(template, **params):\\n return re.sub(r'{{\\\\s*([^}\\\\s]+)\\\\s*}}',\\n lambda match: str(params.get(match.group(1), match.group(0))),\\n template)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71f584ab8db6160f2b60ee37e6e7739f\",\n \"score\": \"0.5822259\",\n \"text\": \"def rstjinja(app, docname, source):\\n # Make sure we're outputting HTML\\n if app.builder.format != \\\"html\\\":\\n return\\n src = source[0]\\n rendered = app.builder.templates.render_string(src, app.config.html_context)\\n source[0] = rendered\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32348fcf3387e0f180b45c1f3bd3e026\",\n \"score\": \"0.58207905\",\n \"text\": \"def render_email_template( template, **context ):\\n\\n subject, body = render_to_string( template, context ).split( '\\\\n', 1 )\\n return subject.strip(), body\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a3d3eddd67ac0ee5850f4c59f5bfa7b\",\n \"score\": \"0.5817247\",\n \"text\": \"def from_string(self, source, globals=None, template_class=None):\\r\\n globals = self.make_globals(globals)\\r\\n cls = template_class or self.template_class\\r\\n return cls.from_code(self, self.compile(source), globals, None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec7ba75b381a7085d5a8f9b2684c4b2b\",\n \"score\": \"0.5808121\",\n \"text\": \"def renderfile(tpl, path, context):\\n file_h = open(path, 'w')\\n file_h.write(tpl.render(context))\\n file_h.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df10b36aab515c36a493de3dbd825b3f\",\n \"score\": \"0.58006716\",\n \"text\": \"def render_template(template_file, template_vars):\\n templateLoader = jinja2.FileSystemLoader(searchpath=\\\"./templates/\\\")\\n template_env = jinja2.Environment(loader=templateLoader)\\n template = template_env.get_template(template_file)\\n return template.render( template_vars )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20e14542ebdc05ab47534887ccd14bf9\",\n \"score\": \"0.5797771\",\n \"text\": \"def render_to_response(self, context):\\n return TemplateResponse(\\n request=self.request, template=self.get_template_names(), context=context\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7eb993c4077c2525cbd4d9519da7800\",\n \"score\": \"0.5795039\",\n \"text\": \"def render_template(\\n template: str,\\n request: 'CoreRequest',\\n content: dict[str, Any],\\n suppress_global_variables: bool = True\\n) -> str:\\n\\n if suppress_global_variables == 'infer':\\n suppress_global_variables = template.startswith('mail_')\\n\\n registry = request.app.config.template_engine_registry\\n page_template = registry._template_loaders['.pt'][template]\\n\\n variables = get_default_vars(\\n request, content, suppress_global_variables=suppress_global_variables)\\n\\n return page_template.render(**variables)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e4a85d52eaa6a97c2a4b91f783ed911\",\n \"score\": \"0.5780865\",\n \"text\": \"def render_template(text, **context_args):\\n template = Template(\\\"{% load bootstrap3 %}\\\" + text)\\n if not 'form' in context_args:\\n context_args['form'] = TestForm()\\n return template.render(Context(context_args))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"05456eac42feb6620109966140d30fa8\",\n \"score\": \"0.5766187\",\n \"text\": \"def render(self, template, **params):\\n self.write(self.render_str(template,\\n current_user = self.get_current_user(), **params))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7fafb027cf1e439c3f24d04308a42bab\",\n \"score\": \"0.5762089\",\n \"text\": \"def __call__(self, template_name, template_vars, **kwargs):\\n config = tg.config._current_obj()\\n\\n # Gets template format from content type or from config options\\n format = kwargs.get('format')\\n if not format:\\n format = self.format_for_content_type.get(tg.response.content_type)\\n if not format:\\n format = config.get('templating.chameleon.genshi.format')\\n if not format:\\n format = config.get('templating.genshi.method')\\n if not format or format not in ('xml', 'text'):\\n format = 'xml'\\n\\n def render_template():\\n template = self.load_template(template_name, format=format)\\n return literal(template.render(**template_vars))\\n\\n return cached_template(template_name, render_template,\\n ns_options=('doctype', 'method'), **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c646fc053b655bc00c60e5db4bfee703\",\n \"score\": \"0.5760994\",\n \"text\": \"def compile(self, source, name=None, filename=None, raw=False,\\r\\n defer_init=False):\\r\\n source_hint = None\\r\\n try:\\r\\n if isinstance(source, basestring):\\r\\n source_hint = source\\r\\n source = self._parse(source, name, filename)\\r\\n if self.optimized:\\r\\n source = optimize(source, self)\\r\\n source = self._generate(source, name, filename,\\r\\n defer_init=defer_init)\\r\\n if raw:\\r\\n return source\\r\\n if filename is None:\\r\\n filename = '<template>'\\r\\n else:\\r\\n filename = _encode_filename(filename)\\r\\n return self._compile(source, filename)\\r\\n except TemplateSyntaxError:\\r\\n exc_info = sys.exc_info()\\r\\n self.handle_exception(exc_info, source_hint=source)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4b2550dddab8e6ce4c66ac855c6e2bd\",\n \"score\": \"0.57590085\",\n \"text\": \"def _exec_template(callable_, context, args=None, kwargs=None):\\r\\n template = context._with_template\\r\\n if template is not None and \\\\\\r\\n (template.format_exceptions or template.error_handler):\\r\\n error = None\\r\\n try:\\r\\n callable_(context, *args, **kwargs)\\r\\n except Exception, e:\\r\\n _render_error(template, context, e)\\r\\n except: \\r\\n e = sys.exc_info()[0]\\r\\n _render_error(template, context, e)\\r\\n else:\\r\\n callable_(context, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a8791f85ee75c2e9c457867b9e13164\",\n \"score\": \"0.5756796\",\n \"text\": \"def render_template_text(email, member=None):\\n\\n text = email.text\\n\\n # Add extra newlines in plaintext emails\\n text = text.replace(\\\"\\\\n\\\", \\\"\\\\n\\\\n\\\")\\n\\n # Replace anchor tags with plaintext links\\n for anchor in re.finditer(\\\"<a href=\\\\\\\"([^\\\\\\\"]+)\\\\\\\">([^<]+)<\\\\/a>\\\", text):\\n url = anchor.group(1)\\n content = anchor.group(2)\\n text = text.replace(\\\"<a href=\\\\\\\"%s\\\\\\\">%s</a>\\\" % (url, content), \\\"%s (%s)\\\" % (content, url))\\n\\n if member is not None:\\n # Replace variables with personalized values for each member\\n text = text.replace(\\\"{{first}}\\\", member.first).replace(\\\"{{last}}\\\", member.last).replace(\\\"{{email}}\\\", member.email)\\n\\n if \\\"<br>\\\" in text or \\\"<br/>\\\" in text or \\\"<br />\\\" in text:\\n # Replace HTML newlines with plaintext newlines\\n text = text.replace(\\\"<br>\\\", \\\"\\\\n\\\").replace(\\\"<br/>\\\", \\\"\\\\n\\\").replace(\\\"<br />\\\", \\\"\\\\n\\\")\\n\\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77465f32502bb5ca75429ca33174a884\",\n \"score\": \"0.5743868\",\n \"text\": \"def process_template(self, template_text, data_dict=None):\\n if data_dict is None:\\n data_dict = {}\\n else:\\n assert isinstance(data_dict, dict)\\n data_dict = dict(data_dict)\\n assert \\\"algo\\\" not in data_dict, \\\"algo already set by user data\\\"\\n data_dict[\\\"algo\\\"] = self\\n\\n template = jinja2.Template(template_text)\\n return template.render(data_dict)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e6b2a4fc618839fa102fa8466272ee57\",\n \"score\": \"0.57370764\",\n \"text\": \"def test_context(self):\\r\\n collection = lookup.TemplateLookup()\\r\\n\\r\\n collection.put_string('main.html', \\\"\\\"\\\"\\r\\n <%namespace name=\\\"comp\\\" file=\\\"defs.html\\\"/>\\r\\n\\r\\n this is main. ${comp.def1()}\\r\\n ${comp.def2(\\\"there\\\")}\\r\\n\\\"\\\"\\\")\\r\\n\\r\\n collection.put_string('defs.html', \\\"\\\"\\\"\\r\\n <%def name=\\\"def1()\\\">\\r\\n def1: x is ${x}\\r\\n </%def>\\r\\n\\r\\n <%def name=\\\"def2(x)\\\">\\r\\n def2: x is ${x}\\r\\n </%def>\\r\\n\\\"\\\"\\\")\\r\\n\\r\\n assert flatten_result(collection.get_template('main.html').render(x=\\\"context x\\\")) == \\\"this is main. def1: x is context x def2: x is there\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08838bc6bb3c5d059ba8c0079ec334dd\",\n \"score\": \"0.5736208\",\n \"text\": \"def render_template(data, template_name, filters=None):\\n env = Environment(loader=FileSystemLoader(''))\\n\\n if filters is not None:\\n for key, value in filters.iteritems():\\n env.filters[key] = value\\n\\n template = env.get_template(template_name)\\n return template.render(feed=data).encode('utf-8')\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":687,"cells":{"query_id":{"kind":"string","value":"6287c1dd2a1e7937b08a68ddf5349771"},"query":{"kind":"string","value":"T.__new__(S, ...) > a new object with type S, a subtype of T"},"positive_passages":{"kind":"list like","value":[{"docid":"4cd774e88eb43b6786dfb6dbd212399c","score":"0.72122014","text":"def __new__(S, *more): # real signature unknown; restored from __doc__\r\n pass","title":""}],"string":"[\n {\n \"docid\": \"4cd774e88eb43b6786dfb6dbd212399c\",\n \"score\": \"0.72122014\",\n \"text\": \"def __new__(S, *more): # real signature unknown; restored from __doc__\\r\\n pass\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"4fb50b5679d0bc2205d77fa7704c8195","score":"0.74194497","text":"def __new__(self,S, ):\n pass","title":""},{"docid":"4fb50b5679d0bc2205d77fa7704c8195","score":"0.74194497","text":"def __new__(self,S, ):\n pass","title":""},{"docid":"4fb50b5679d0bc2205d77fa7704c8195","score":"0.74194497","text":"def __new__(self,S, ):\n pass","title":""},{"docid":"4fb50b5679d0bc2205d77fa7704c8195","score":"0.74194497","text":"def __new__(self,S, ):\n pass","title":""},{"docid":"4fb50b5679d0bc2205d77fa7704c8195","score":"0.74194497","text":"def __new__(self,S, ):\n pass","title":""},{"docid":"4fb50b5679d0bc2205d77fa7704c8195","score":"0.74194497","text":"def __new__(self,S, ):\n pass","title":""},{"docid":"4fb50b5679d0bc2205d77fa7704c8195","score":"0.74194497","text":"def __new__(self,S, ):\n pass","title":""},{"docid":"4fb50b5679d0bc2205d77fa7704c8195","score":"0.74194497","text":"def __new__(self,S, ):\n pass","title":""},{"docid":"4fb50b5679d0bc2205d77fa7704c8195","score":"0.74194497","text":"def __new__(self,S, ):\n pass","title":""},{"docid":"4fb50b5679d0bc2205d77fa7704c8195","score":"0.74194497","text":"def __new__(self,S, ):\n pass","title":""},{"docid":"4fb50b5679d0bc2205d77fa7704c8195","score":"0.74194497","text":"def __new__(self,S, ):\n pass","title":""},{"docid":"4fb50b5679d0bc2205d77fa7704c8195","score":"0.74194497","text":"def __new__(self,S, ):\n pass","title":""},{"docid":"4fb50b5679d0bc2205d77fa7704c8195","score":"0.74194497","text":"def __new__(self,S, ):\n pass","title":""},{"docid":"4fb50b5679d0bc2205d77fa7704c8195","score":"0.74194497","text":"def __new__(self,S, ):\n pass","title":""},{"docid":"4fb50b5679d0bc2205d77fa7704c8195","score":"0.74194497","text":"def __new__(self,S, ):\n pass","title":""},{"docid":"4fb50b5679d0bc2205d77fa7704c8195","score":"0.74194497","text":"def __new__(self,S, ):\n pass","title":""},{"docid":"4fb50b5679d0bc2205d77fa7704c8195","score":"0.74194497","text":"def __new__(self,S, ):\n pass","title":""},{"docid":"4fb50b5679d0bc2205d77fa7704c8195","score":"0.74194497","text":"def __new__(self,S, ):\n pass","title":""},{"docid":"4fb50b5679d0bc2205d77fa7704c8195","score":"0.74194497","text":"def __new__(self,S, ):\n pass","title":""},{"docid":"4fb50b5679d0bc2205d77fa7704c8195","score":"0.74194497","text":"def __new__(self,S, ):\n pass","title":""},{"docid":"4fb50b5679d0bc2205d77fa7704c8195","score":"0.74194497","text":"def __new__(self,S, ):\n pass","title":""},{"docid":"3843fb29d325d0e9a5ccde53d9af8436","score":"0.7382608","text":"def __new__(S, *more): # real signature unknown; restored from __doc__\n pass","title":""},{"docid":"3843fb29d325d0e9a5ccde53d9af8436","score":"0.7382608","text":"def __new__(S, *more): # real signature unknown; restored from __doc__\n pass","title":""},{"docid":"3843fb29d325d0e9a5ccde53d9af8436","score":"0.7382608","text":"def __new__(S, *more): # real signature unknown; restored from __doc__\n pass","title":""},{"docid":"3843fb29d325d0e9a5ccde53d9af8436","score":"0.7382608","text":"def __new__(S, *more): # real signature unknown; restored from __doc__\n pass","title":""},{"docid":"84aa418b6c50688106f5e1f5c04561bd","score":"0.7366072","text":"def _new_object(cls):\n return cls.__new__(cls)","title":""},{"docid":"84aa418b6c50688106f5e1f5c04561bd","score":"0.7366072","text":"def _new_object(cls):\n return cls.__new__(cls)","title":""},{"docid":"0ca538c0ae200206717a9486704074a6","score":"0.7291962","text":"def new(self, t, **attrs):\n\n if 'recurse' in attrs:\n attrs['recurse'].update(self.attributes)\n else:\n attrs['recurse'] = self.attributes\n\n attrs.setdefault('parent', self)\n\n # instantiate an instance if we're given a type\n if not(istype(t) or isinstance(t,generic)):\n raise error.TypeError(self, 'base.new', message='%r is not a ptype class'% t.__class__)\n\n # if it's a type, then instantiate it\n if istype(t):\n t = t(**attrs)\n\n # if already instantiated, then update it's attributes\n elif isinstance(t,generic):\n t.update_attributes(**attrs)\n\n # give the instance a default name\n t.__name__ = attrs.get('__name__', hex(id(t)) )\n return t","title":""},{"docid":"a453149e7ab3d88db00f8da5f8f78338","score":"0.7284284","text":"def __new__(cls):\n return object.__new__(cls)","title":""},{"docid":"a453149e7ab3d88db00f8da5f8f78338","score":"0.7284284","text":"def __new__(cls):\n return object.__new__(cls)","title":""},{"docid":"600b65c4dd051d7d3fbab32dd7b1ae98","score":"0.72610724","text":"def __newobj__(cls, *args):\n return cls.__new__(cls, *args)","title":""},{"docid":"4ed5c9722106eee1fdf63563e15eab4c","score":"0.72600895","text":"def __new__(S, *more): # real signature unknown; restored from __doc__\n pass","title":""},{"docid":"4ed5c9722106eee1fdf63563e15eab4c","score":"0.72600895","text":"def __new__(S, *more): # real signature unknown; restored from __doc__\n pass","title":""},{"docid":"4ed5c9722106eee1fdf63563e15eab4c","score":"0.72600895","text":"def __new__(S, *more): # real signature unknown; restored from __doc__\n pass","title":""},{"docid":"4ed5c9722106eee1fdf63563e15eab4c","score":"0.72600895","text":"def __new__(S, *more): # real signature unknown; restored from __doc__\n pass","title":""},{"docid":"4ed5c9722106eee1fdf63563e15eab4c","score":"0.72600895","text":"def __new__(S, *more): # real signature unknown; restored from __doc__\n pass","title":""},{"docid":"4ed5c9722106eee1fdf63563e15eab4c","score":"0.72600895","text":"def __new__(S, *more): # real signature unknown; restored from __doc__\n pass","title":""},{"docid":"4ed5c9722106eee1fdf63563e15eab4c","score":"0.72600895","text":"def __new__(S, *more): # real signature unknown; restored from __doc__\n pass","title":""},{"docid":"4ed5c9722106eee1fdf63563e15eab4c","score":"0.72600895","text":"def __new__(S, *more): # real signature unknown; restored from __doc__\n pass","title":""},{"docid":"4ed5c9722106eee1fdf63563e15eab4c","score":"0.72600895","text":"def __new__(S, *more): # real signature unknown; restored from __doc__\n pass","title":""},{"docid":"4ed5c9722106eee1fdf63563e15eab4c","score":"0.72600895","text":"def __new__(S, *more): # real signature unknown; restored from __doc__\n pass","title":""},{"docid":"4ed5c9722106eee1fdf63563e15eab4c","score":"0.72600895","text":"def __new__(S, *more): # real signature unknown; restored from __doc__\n pass","title":""},{"docid":"4ed5c9722106eee1fdf63563e15eab4c","score":"0.72600895","text":"def __new__(S, *more): # real signature unknown; restored from __doc__\n pass","title":""},{"docid":"4ed5c9722106eee1fdf63563e15eab4c","score":"0.72600895","text":"def __new__(S, *more): # real signature unknown; restored from __doc__\n pass","title":""},{"docid":"4ed5c9722106eee1fdf63563e15eab4c","score":"0.72600895","text":"def __new__(S, *more): # real signature unknown; restored from __doc__\n pass","title":""},{"docid":"4ed5c9722106eee1fdf63563e15eab4c","score":"0.72600895","text":"def __new__(S, *more): # real signature unknown; restored from __doc__\n pass","title":""},{"docid":"4ed5c9722106eee1fdf63563e15eab4c","score":"0.72600895","text":"def __new__(S, *more): # real signature unknown; restored from __doc__\n pass","title":""},{"docid":"4ed5c9722106eee1fdf63563e15eab4c","score":"0.72600895","text":"def __new__(S, *more): # real signature unknown; restored from __doc__\n pass","title":""},{"docid":"4ed5c9722106eee1fdf63563e15eab4c","score":"0.72600895","text":"def __new__(S, *more): # real signature unknown; restored from __doc__\n pass","title":""},{"docid":"4ed5c9722106eee1fdf63563e15eab4c","score":"0.72600895","text":"def __new__(S, *more): # real signature unknown; restored from __doc__\n pass","title":""},{"docid":"4ed5c9722106eee1fdf63563e15eab4c","score":"0.72600895","text":"def __new__(S, *more): # real signature unknown; restored from __doc__\n pass","title":""},{"docid":"4ed5c9722106eee1fdf63563e15eab4c","score":"0.72600895","text":"def __new__(S, *more): # real signature unknown; restored from __doc__\n pass","title":""},{"docid":"4ed5c9722106eee1fdf63563e15eab4c","score":"0.72600895","text":"def __new__(S, *more): # real signature unknown; restored from __doc__\n pass","title":""},{"docid":"4ed5c9722106eee1fdf63563e15eab4c","score":"0.72600895","text":"def __new__(S, *more): # real signature unknown; restored from __doc__\n pass","title":""},{"docid":"4ed5c9722106eee1fdf63563e15eab4c","score":"0.72600895","text":"def __new__(S, *more): # real signature unknown; restored from __doc__\n pass","title":""},{"docid":"4ed5c9722106eee1fdf63563e15eab4c","score":"0.72600895","text":"def __new__(S, *more): # real signature unknown; restored from __doc__\n pass","title":""},{"docid":"4ed5c9722106eee1fdf63563e15eab4c","score":"0.72600895","text":"def __new__(S, *more): # real signature unknown; restored from __doc__\n pass","title":""},{"docid":"4ed5c9722106eee1fdf63563e15eab4c","score":"0.72600895","text":"def __new__(S, *more): # real signature unknown; restored from __doc__\n pass","title":""},{"docid":"4ed5c9722106eee1fdf63563e15eab4c","score":"0.72600895","text":"def __new__(S, *more): # real signature unknown; restored from __doc__\n pass","title":""},{"docid":"4ed5c9722106eee1fdf63563e15eab4c","score":"0.72600895","text":"def __new__(S, *more): # real signature unknown; restored from __doc__\n pass","title":""},{"docid":"4ed5c9722106eee1fdf63563e15eab4c","score":"0.72600895","text":"def __new__(S, *more): # real signature unknown; restored from __doc__\n pass","title":""},{"docid":"4ed5c9722106eee1fdf63563e15eab4c","score":"0.72600895","text":"def __new__(S, *more): # real signature unknown; restored from __doc__\n pass","title":""},{"docid":"4ed5c9722106eee1fdf63563e15eab4c","score":"0.72600895","text":"def __new__(S, *more): # real signature unknown; restored from __doc__\n pass","title":""},{"docid":"4ed5c9722106eee1fdf63563e15eab4c","score":"0.72600895","text":"def __new__(S, *more): # real signature unknown; restored from __doc__\n pass","title":""},{"docid":"87940601e25435f4264d51c2d912cd54","score":"0.7111726","text":"def __new__(cls, *args, **kwargs) -> None:\n return object.__new__(cls)","title":""},{"docid":"7d3fe66ade8448e03c921da394780ab2","score":"0.7001853","text":"def __new__(cls, *more):\r\n pass","title":""},{"docid":"2733724a81729db4b9c2ba785677a254","score":"0.69560206","text":"def __new__(cls, *args, **kwargs):\n obj = object.__new__(cls)\n obj.__init__(*args, **kwargs)\n return obj()","title":""},{"docid":"0b25d781608a505fa11b511e1c5a53c6","score":"0.6847844","text":"def __new__(cls, *args, **kwargs):\n\t\tif cls != type(cls._inst):\n\t\t\tcls._inst = object.__new__(cls, *args, **kwargs)\n\t\treturn cls._inst","title":""},{"docid":"22660530c5d8ab09984c7997a39a7a6a","score":"0.6818508","text":"def __new__(metacls, nom, bases, dict):\n print(metacls, nom)\n return type.__new__(metacls, nom, bases, dict)","title":""},{"docid":"d1b686bcf11d1209814ad5910c4213c8","score":"0.67791396","text":"def new_instance_of_same_type(parent):\n instance = type(parent).__new__(type(parent))\n instance.__init__()\n return instance","title":""},{"docid":"ec006e0c7be72e9929c1168c2783a42b","score":"0.67152846","text":"def __basicnew__(self):\n return self.__new__(self)","title":""},{"docid":"f28f1d277624952bf336eee9458e8b07","score":"0.66792864","text":"def __new__(cls, *na, **kwna):\n if not cls.is_initialized():\n cls._instances[cls] = super().__new__(cls)\n return cls._instances[cls]","title":""},{"docid":"33f985d7d74f27ab690601037ed51383","score":"0.66685194","text":"def __new__(cls, *args, **kwargs):\n raise NotImplementedError","title":""},{"docid":"1e56279f8f4a17ef7f5c19aa884b0405","score":"0.664382","text":"def __new__(cls, *args, **kwargs): # type: ignore\n if cls not in cls._instances:\n cls._instances[cls] = object.__new__(cls, *args, **kwargs)\n return cls._instances[cls]","title":""},{"docid":"ec8211b49baad98d656be4eb4678ef74","score":"0.66253453","text":"def __new__(cls):\n return cls","title":""},{"docid":"47f8d874943fad63102963dcc9b0a072","score":"0.6620953","text":"def __new__(*args, **kwargs): # real signature unknown\n pass","title":""},{"docid":"47f8d874943fad63102963dcc9b0a072","score":"0.6620953","text":"def __new__(*args, **kwargs): # real signature unknown\n pass","title":""},{"docid":"47f8d874943fad63102963dcc9b0a072","score":"0.6620953","text":"def __new__(*args, **kwargs): # real signature unknown\n pass","title":""},{"docid":"47f8d874943fad63102963dcc9b0a072","score":"0.6620953","text":"def __new__(*args, **kwargs): # real signature unknown\n pass","title":""},{"docid":"47f8d874943fad63102963dcc9b0a072","score":"0.6620953","text":"def __new__(*args, **kwargs): # real signature unknown\n pass","title":""},{"docid":"47f8d874943fad63102963dcc9b0a072","score":"0.6620953","text":"def __new__(*args, **kwargs): # real signature unknown\n pass","title":""},{"docid":"47f8d874943fad63102963dcc9b0a072","score":"0.6620953","text":"def __new__(*args, **kwargs): # real signature unknown\n pass","title":""},{"docid":"47f8d874943fad63102963dcc9b0a072","score":"0.6620953","text":"def __new__(*args, **kwargs): # real signature unknown\n pass","title":""},{"docid":"47f8d874943fad63102963dcc9b0a072","score":"0.6620953","text":"def __new__(*args, **kwargs): # real signature unknown\n pass","title":""},{"docid":"47f8d874943fad63102963dcc9b0a072","score":"0.6620953","text":"def __new__(*args, **kwargs): # real signature unknown\n pass","title":""},{"docid":"47f8d874943fad63102963dcc9b0a072","score":"0.6620953","text":"def __new__(*args, **kwargs): # real signature unknown\n pass","title":""},{"docid":"47f8d874943fad63102963dcc9b0a072","score":"0.6620953","text":"def __new__(*args, **kwargs): # real signature unknown\n pass","title":""},{"docid":"47f8d874943fad63102963dcc9b0a072","score":"0.6620953","text":"def __new__(*args, **kwargs): # real signature unknown\n pass","title":""},{"docid":"47f8d874943fad63102963dcc9b0a072","score":"0.6620953","text":"def __new__(*args, **kwargs): # real signature unknown\n pass","title":""},{"docid":"47f8d874943fad63102963dcc9b0a072","score":"0.6620953","text":"def __new__(*args, **kwargs): # real signature unknown\n pass","title":""},{"docid":"47f8d874943fad63102963dcc9b0a072","score":"0.6620953","text":"def __new__(*args, **kwargs): # real signature unknown\n pass","title":""}],"string":"[\n {\n \"docid\": \"4fb50b5679d0bc2205d77fa7704c8195\",\n \"score\": \"0.74194497\",\n \"text\": \"def __new__(self,S, ):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4fb50b5679d0bc2205d77fa7704c8195\",\n \"score\": \"0.74194497\",\n \"text\": \"def __new__(self,S, ):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4fb50b5679d0bc2205d77fa7704c8195\",\n \"score\": \"0.74194497\",\n \"text\": \"def __new__(self,S, ):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4fb50b5679d0bc2205d77fa7704c8195\",\n \"score\": \"0.74194497\",\n \"text\": \"def __new__(self,S, ):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4fb50b5679d0bc2205d77fa7704c8195\",\n \"score\": \"0.74194497\",\n \"text\": \"def __new__(self,S, ):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4fb50b5679d0bc2205d77fa7704c8195\",\n \"score\": \"0.74194497\",\n \"text\": \"def __new__(self,S, ):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4fb50b5679d0bc2205d77fa7704c8195\",\n \"score\": \"0.74194497\",\n \"text\": \"def __new__(self,S, ):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4fb50b5679d0bc2205d77fa7704c8195\",\n \"score\": \"0.74194497\",\n \"text\": \"def __new__(self,S, ):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4fb50b5679d0bc2205d77fa7704c8195\",\n \"score\": \"0.74194497\",\n \"text\": \"def __new__(self,S, ):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4fb50b5679d0bc2205d77fa7704c8195\",\n \"score\": \"0.74194497\",\n \"text\": \"def __new__(self,S, ):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4fb50b5679d0bc2205d77fa7704c8195\",\n \"score\": \"0.74194497\",\n \"text\": \"def __new__(self,S, ):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4fb50b5679d0bc2205d77fa7704c8195\",\n \"score\": \"0.74194497\",\n \"text\": \"def __new__(self,S, ):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4fb50b5679d0bc2205d77fa7704c8195\",\n \"score\": \"0.74194497\",\n \"text\": \"def __new__(self,S, ):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4fb50b5679d0bc2205d77fa7704c8195\",\n \"score\": \"0.74194497\",\n \"text\": \"def __new__(self,S, ):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4fb50b5679d0bc2205d77fa7704c8195\",\n \"score\": \"0.74194497\",\n \"text\": \"def __new__(self,S, ):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4fb50b5679d0bc2205d77fa7704c8195\",\n \"score\": \"0.74194497\",\n \"text\": \"def __new__(self,S, ):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4fb50b5679d0bc2205d77fa7704c8195\",\n \"score\": \"0.74194497\",\n \"text\": \"def __new__(self,S, ):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4fb50b5679d0bc2205d77fa7704c8195\",\n \"score\": \"0.74194497\",\n \"text\": \"def __new__(self,S, ):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4fb50b5679d0bc2205d77fa7704c8195\",\n \"score\": \"0.74194497\",\n \"text\": \"def __new__(self,S, ):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4fb50b5679d0bc2205d77fa7704c8195\",\n \"score\": \"0.74194497\",\n \"text\": \"def __new__(self,S, ):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4fb50b5679d0bc2205d77fa7704c8195\",\n \"score\": \"0.74194497\",\n \"text\": \"def __new__(self,S, ):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3843fb29d325d0e9a5ccde53d9af8436\",\n \"score\": \"0.7382608\",\n \"text\": \"def __new__(S, *more): # real signature unknown; restored from __doc__\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3843fb29d325d0e9a5ccde53d9af8436\",\n \"score\": \"0.7382608\",\n \"text\": \"def __new__(S, *more): # real signature unknown; restored from __doc__\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3843fb29d325d0e9a5ccde53d9af8436\",\n \"score\": \"0.7382608\",\n \"text\": \"def __new__(S, *more): # real signature unknown; restored from __doc__\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3843fb29d325d0e9a5ccde53d9af8436\",\n \"score\": \"0.7382608\",\n \"text\": \"def __new__(S, *more): # real signature unknown; restored from __doc__\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84aa418b6c50688106f5e1f5c04561bd\",\n \"score\": \"0.7366072\",\n \"text\": \"def _new_object(cls):\\n return cls.__new__(cls)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84aa418b6c50688106f5e1f5c04561bd\",\n \"score\": \"0.7366072\",\n \"text\": \"def _new_object(cls):\\n return cls.__new__(cls)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ca538c0ae200206717a9486704074a6\",\n \"score\": \"0.7291962\",\n \"text\": \"def new(self, t, **attrs):\\n\\n if 'recurse' in attrs:\\n attrs['recurse'].update(self.attributes)\\n else:\\n attrs['recurse'] = self.attributes\\n\\n attrs.setdefault('parent', self)\\n\\n # instantiate an instance if we're given a type\\n if not(istype(t) or isinstance(t,generic)):\\n raise error.TypeError(self, 'base.new', message='%r is not a ptype class'% t.__class__)\\n\\n # if it's a type, then instantiate it\\n if istype(t):\\n t = t(**attrs)\\n\\n # if already instantiated, then update it's attributes\\n elif isinstance(t,generic):\\n t.update_attributes(**attrs)\\n\\n # give the instance a default name\\n t.__name__ = attrs.get('__name__', hex(id(t)) )\\n return t\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a453149e7ab3d88db00f8da5f8f78338\",\n \"score\": \"0.7284284\",\n \"text\": \"def __new__(cls):\\n return object.__new__(cls)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a453149e7ab3d88db00f8da5f8f78338\",\n \"score\": \"0.7284284\",\n \"text\": \"def __new__(cls):\\n return object.__new__(cls)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"600b65c4dd051d7d3fbab32dd7b1ae98\",\n \"score\": \"0.72610724\",\n \"text\": \"def __newobj__(cls, *args):\\n return cls.__new__(cls, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ed5c9722106eee1fdf63563e15eab4c\",\n \"score\": \"0.72600895\",\n \"text\": \"def __new__(S, *more): # real signature unknown; restored from __doc__\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ed5c9722106eee1fdf63563e15eab4c\",\n \"score\": \"0.72600895\",\n \"text\": \"def __new__(S, *more): # real signature unknown; restored from __doc__\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ed5c9722106eee1fdf63563e15eab4c\",\n \"score\": \"0.72600895\",\n \"text\": \"def __new__(S, *more): # real signature unknown; restored from __doc__\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ed5c9722106eee1fdf63563e15eab4c\",\n \"score\": \"0.72600895\",\n \"text\": \"def __new__(S, *more): # real signature unknown; restored from __doc__\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ed5c9722106eee1fdf63563e15eab4c\",\n \"score\": \"0.72600895\",\n \"text\": \"def __new__(S, *more): # real signature unknown; restored from __doc__\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ed5c9722106eee1fdf63563e15eab4c\",\n \"score\": \"0.72600895\",\n \"text\": \"def __new__(S, *more): # real signature unknown; restored from __doc__\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ed5c9722106eee1fdf63563e15eab4c\",\n \"score\": \"0.72600895\",\n \"text\": \"def __new__(S, *more): # real signature unknown; restored from __doc__\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ed5c9722106eee1fdf63563e15eab4c\",\n \"score\": \"0.72600895\",\n \"text\": \"def __new__(S, *more): # real signature unknown; restored from __doc__\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ed5c9722106eee1fdf63563e15eab4c\",\n \"score\": \"0.72600895\",\n \"text\": \"def __new__(S, *more): # real signature unknown; restored from __doc__\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ed5c9722106eee1fdf63563e15eab4c\",\n \"score\": \"0.72600895\",\n \"text\": \"def __new__(S, *more): # real signature unknown; restored from __doc__\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ed5c9722106eee1fdf63563e15eab4c\",\n \"score\": \"0.72600895\",\n \"text\": \"def __new__(S, *more): # real signature unknown; restored from __doc__\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ed5c9722106eee1fdf63563e15eab4c\",\n \"score\": \"0.72600895\",\n \"text\": \"def __new__(S, *more): # real signature unknown; restored from __doc__\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ed5c9722106eee1fdf63563e15eab4c\",\n \"score\": \"0.72600895\",\n \"text\": \"def __new__(S, *more): # real signature unknown; restored from __doc__\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ed5c9722106eee1fdf63563e15eab4c\",\n \"score\": \"0.72600895\",\n \"text\": \"def __new__(S, *more): # real signature unknown; restored from __doc__\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ed5c9722106eee1fdf63563e15eab4c\",\n \"score\": \"0.72600895\",\n \"text\": \"def __new__(S, *more): # real signature unknown; restored from __doc__\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ed5c9722106eee1fdf63563e15eab4c\",\n \"score\": \"0.72600895\",\n \"text\": \"def __new__(S, *more): # real signature unknown; restored from __doc__\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ed5c9722106eee1fdf63563e15eab4c\",\n \"score\": \"0.72600895\",\n \"text\": \"def __new__(S, *more): # real signature unknown; restored from __doc__\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ed5c9722106eee1fdf63563e15eab4c\",\n \"score\": \"0.72600895\",\n \"text\": \"def __new__(S, *more): # real signature unknown; restored from __doc__\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ed5c9722106eee1fdf63563e15eab4c\",\n \"score\": \"0.72600895\",\n \"text\": \"def __new__(S, *more): # real signature unknown; restored from __doc__\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ed5c9722106eee1fdf63563e15eab4c\",\n \"score\": \"0.72600895\",\n \"text\": \"def __new__(S, *more): # real signature unknown; restored from __doc__\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ed5c9722106eee1fdf63563e15eab4c\",\n \"score\": \"0.72600895\",\n \"text\": \"def __new__(S, *more): # real signature unknown; restored from __doc__\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ed5c9722106eee1fdf63563e15eab4c\",\n \"score\": \"0.72600895\",\n \"text\": \"def __new__(S, *more): # real signature unknown; restored from __doc__\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ed5c9722106eee1fdf63563e15eab4c\",\n \"score\": \"0.72600895\",\n \"text\": \"def __new__(S, *more): # real signature unknown; restored from __doc__\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ed5c9722106eee1fdf63563e15eab4c\",\n \"score\": \"0.72600895\",\n \"text\": \"def __new__(S, *more): # real signature unknown; restored from __doc__\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ed5c9722106eee1fdf63563e15eab4c\",\n \"score\": \"0.72600895\",\n \"text\": \"def __new__(S, *more): # real signature unknown; restored from __doc__\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ed5c9722106eee1fdf63563e15eab4c\",\n \"score\": \"0.72600895\",\n \"text\": \"def __new__(S, *more): # real signature unknown; restored from __doc__\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ed5c9722106eee1fdf63563e15eab4c\",\n \"score\": \"0.72600895\",\n \"text\": \"def __new__(S, *more): # real signature unknown; restored from __doc__\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ed5c9722106eee1fdf63563e15eab4c\",\n \"score\": \"0.72600895\",\n \"text\": \"def __new__(S, *more): # real signature unknown; restored from __doc__\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ed5c9722106eee1fdf63563e15eab4c\",\n \"score\": \"0.72600895\",\n \"text\": \"def __new__(S, *more): # real signature unknown; restored from __doc__\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ed5c9722106eee1fdf63563e15eab4c\",\n \"score\": \"0.72600895\",\n \"text\": \"def __new__(S, *more): # real signature unknown; restored from __doc__\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ed5c9722106eee1fdf63563e15eab4c\",\n \"score\": \"0.72600895\",\n \"text\": \"def __new__(S, *more): # real signature unknown; restored from __doc__\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ed5c9722106eee1fdf63563e15eab4c\",\n \"score\": \"0.72600895\",\n \"text\": \"def __new__(S, *more): # real signature unknown; restored from __doc__\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ed5c9722106eee1fdf63563e15eab4c\",\n \"score\": \"0.72600895\",\n \"text\": \"def __new__(S, *more): # real signature unknown; restored from __doc__\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87940601e25435f4264d51c2d912cd54\",\n \"score\": \"0.7111726\",\n \"text\": \"def __new__(cls, *args, **kwargs) -> None:\\n return object.__new__(cls)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d3fe66ade8448e03c921da394780ab2\",\n \"score\": \"0.7001853\",\n \"text\": \"def __new__(cls, *more):\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2733724a81729db4b9c2ba785677a254\",\n \"score\": \"0.69560206\",\n \"text\": \"def __new__(cls, *args, **kwargs):\\n obj = object.__new__(cls)\\n obj.__init__(*args, **kwargs)\\n return obj()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b25d781608a505fa11b511e1c5a53c6\",\n \"score\": \"0.6847844\",\n \"text\": \"def __new__(cls, *args, **kwargs):\\n\\t\\tif cls != type(cls._inst):\\n\\t\\t\\tcls._inst = object.__new__(cls, *args, **kwargs)\\n\\t\\treturn cls._inst\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22660530c5d8ab09984c7997a39a7a6a\",\n \"score\": \"0.6818508\",\n \"text\": \"def __new__(metacls, nom, bases, dict):\\n print(metacls, nom)\\n return type.__new__(metacls, nom, bases, dict)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1b686bcf11d1209814ad5910c4213c8\",\n \"score\": \"0.67791396\",\n \"text\": \"def new_instance_of_same_type(parent):\\n instance = type(parent).__new__(type(parent))\\n instance.__init__()\\n return instance\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec006e0c7be72e9929c1168c2783a42b\",\n \"score\": \"0.67152846\",\n \"text\": \"def __basicnew__(self):\\n return self.__new__(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f28f1d277624952bf336eee9458e8b07\",\n \"score\": \"0.66792864\",\n \"text\": \"def __new__(cls, *na, **kwna):\\n if not cls.is_initialized():\\n cls._instances[cls] = super().__new__(cls)\\n return cls._instances[cls]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33f985d7d74f27ab690601037ed51383\",\n \"score\": \"0.66685194\",\n \"text\": \"def __new__(cls, *args, **kwargs):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e56279f8f4a17ef7f5c19aa884b0405\",\n \"score\": \"0.664382\",\n \"text\": \"def __new__(cls, *args, **kwargs): # type: ignore\\n if cls not in cls._instances:\\n cls._instances[cls] = object.__new__(cls, *args, **kwargs)\\n return cls._instances[cls]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec8211b49baad98d656be4eb4678ef74\",\n \"score\": \"0.66253453\",\n \"text\": \"def __new__(cls):\\n return cls\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47f8d874943fad63102963dcc9b0a072\",\n \"score\": \"0.6620953\",\n \"text\": \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47f8d874943fad63102963dcc9b0a072\",\n \"score\": \"0.6620953\",\n \"text\": \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47f8d874943fad63102963dcc9b0a072\",\n \"score\": \"0.6620953\",\n \"text\": \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47f8d874943fad63102963dcc9b0a072\",\n \"score\": \"0.6620953\",\n \"text\": \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47f8d874943fad63102963dcc9b0a072\",\n \"score\": \"0.6620953\",\n \"text\": \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47f8d874943fad63102963dcc9b0a072\",\n \"score\": \"0.6620953\",\n \"text\": \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47f8d874943fad63102963dcc9b0a072\",\n \"score\": \"0.6620953\",\n \"text\": \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47f8d874943fad63102963dcc9b0a072\",\n \"score\": \"0.6620953\",\n \"text\": \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47f8d874943fad63102963dcc9b0a072\",\n \"score\": \"0.6620953\",\n \"text\": \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47f8d874943fad63102963dcc9b0a072\",\n \"score\": \"0.6620953\",\n \"text\": \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47f8d874943fad63102963dcc9b0a072\",\n \"score\": \"0.6620953\",\n \"text\": \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47f8d874943fad63102963dcc9b0a072\",\n \"score\": \"0.6620953\",\n \"text\": \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47f8d874943fad63102963dcc9b0a072\",\n \"score\": \"0.6620953\",\n \"text\": \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47f8d874943fad63102963dcc9b0a072\",\n \"score\": \"0.6620953\",\n \"text\": \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47f8d874943fad63102963dcc9b0a072\",\n \"score\": \"0.6620953\",\n \"text\": \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47f8d874943fad63102963dcc9b0a072\",\n \"score\": \"0.6620953\",\n \"text\": \"def __new__(*args, **kwargs): # real signature unknown\\n pass\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":688,"cells":{"query_id":{"kind":"string","value":"b5590739bfefe0c7424bd86d5dede73b"},"query":{"kind":"string","value":"returns a connection to mechanical turk."},"positive_passages":{"kind":"list like","value":[{"docid":"3b946ba33bb556f7b8bda8e254aea043","score":"0.79187876","text":"def get_mt_conn(sandbox=settings.SANDBOX):\n if sandbox:\n host=\"mechanicalturk.sandbox.amazonaws.com\"\n else:\n host=\"mechanicalturk.amazonaws.com\"\n\n return connection.MTurkConnection(\n aws_access_key_id=settings.aws_id,\n aws_secret_access_key=settings.aws_secret,\n host=host)","title":""}],"string":"[\n {\n \"docid\": \"3b946ba33bb556f7b8bda8e254aea043\",\n \"score\": \"0.79187876\",\n \"text\": \"def get_mt_conn(sandbox=settings.SANDBOX):\\n if sandbox:\\n host=\\\"mechanicalturk.sandbox.amazonaws.com\\\"\\n else:\\n host=\\\"mechanicalturk.amazonaws.com\\\"\\n\\n return connection.MTurkConnection(\\n aws_access_key_id=settings.aws_id,\\n aws_secret_access_key=settings.aws_secret,\\n host=host)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"56a5a7204653cff73a19f5b0b1ac1780","score":"0.627893","text":"def connection():\n return get_connection()","title":""},{"docid":"c29a8ed3dbdcb9193cd9222e6b99893b","score":"0.6158203","text":"def _connect(self):\n return httplib.HTTPConnection(self._host, self._port)","title":""},{"docid":"3536ef050714fc24c3a51b4e3e47b3a1","score":"0.61508757","text":"def remote_connection(self):\n return DriverRemoteConnection(self.endpoint,'g')","title":""},{"docid":"ea4b4310c7c8224cfa8fb9ed30aafa5d","score":"0.6127698","text":"def __get_tg_connection___(config: TgcliConfiguration, graph_name: Optional[str] = None) -> TigerGraphConnection:\n return TigerGraphConnection(\n host=config.server,\n username=config.username,\n password=config.password,\n clientVersion=config.client_version,\n graphname=graph_name or '',\n restppPort=config.restpp_port,\n gsPort=config.gs_port,\n apiToken='',\n useCert=config.use_auth\n )","title":""},{"docid":"a1919717fa4acfa293e9f2b7304b099f","score":"0.611027","text":"def get_connection():\n pass","title":""},{"docid":"c4e95a23eced284c110c543e1f8af436","score":"0.6091545","text":"def bot_connection():\n return BotConnection('dummybot', 'http://dummybot')","title":""},{"docid":"8c33542b21ef72c67f61771dd2ecf9af","score":"0.6083142","text":"def get_conn(self):\n http_authorized = self._authorize()\n return build(\n 'dataflow', 'v1b3', http=http_authorized, cache_discovery=False)","title":""},{"docid":"4143d72a156531e2856e4e121ad85448","score":"0.60658413","text":"def connect(self):\n self.wrdsconn = wrds.Connection()\n self.conn = self.wrdsconn.connect()\n return self.wrdsconn","title":""},{"docid":"785ceaf1326da26e85971bf3c7a8ae99","score":"0.59902596","text":"def get_connection(self):\n return self.conn","title":""},{"docid":"e5e344d04fb18a0123bad93b80fc1c59","score":"0.59759575","text":"def connection(self):\n return self.connection","title":""},{"docid":"1ddc2b2ea4bfcf4a866b105fcdab8953","score":"0.5972781","text":"def connect(self):\n conn = pika.BlockingConnection(\n pika.ConnectionParameters(\n host=self.settings.host,\n credentials=pika.credentials.PlainCredentials(\n username=self.settings.credentials.username,\n password=self.settings.credentials.password)))\n return conn","title":""},{"docid":"654e24bd55ec03901ac9ff593fd70f15","score":"0.5970635","text":"def get_connection(self):\n return self.connection","title":""},{"docid":"89a6ce69cffae26ea401dc9f9ef69a0a","score":"0.59662926","text":"def connect(self):\n # TODO: sockets, etc.\n host = self._connection_options.get('host', DEFAULT_HOST)\n port = self._connection_options.get('port', DEFAULT_PORT)\n self.connection = Tyrant(host=host, port=port)","title":""},{"docid":"b10f42e12613f4a92f2c6792821af459","score":"0.5954163","text":"def connect_reddit(self):\r\n lg.debug('CointipBot::connect_reddit(): connecting to Reddit...')\r\n\r\n conn = praw.Reddit(user_agent = self.conf.reddit.auth.user)\r\n conn.login(self.conf.reddit.auth.user, self.conf.reddit.auth.password)\r\n\r\n lg.info(\"CointipBot::connect_reddit(): logged in to Reddit as %s\", self.conf.reddit.auth.user)\r\n return conn","title":""},{"docid":"d65e9cb550e3306a9cbda4e6db471c36","score":"0.5935847","text":"def _Conn(self):\n cls_name = '{scheme}Connection'.format(scheme=self.scheme.upper())\n return getattr(http.client, cls_name)","title":""},{"docid":"af580b2348f91ff2dc1b44a61cafe6bf","score":"0.59355676","text":"def conn(self):\n client = RiakClient(\n protocol=self.conf.get(\"protocol\"),\n http_port=self.conf.get(\"port\"),\n host=self.conf.get(\"host\"))\n\n conn = client.bucket(self.conf.get(\"db\"))\n conn.enable_search()\n return conn","title":""},{"docid":"0f662151f658966cbf276f4dc9412293","score":"0.59076524","text":"def connection(self):\n\t\treturn self._connection","title":""},{"docid":"40fad0c19bd62bd037720d99ec96780d","score":"0.5906515","text":"def get_connection():\n return Connection.from_environment()","title":""},{"docid":"21efa182d470e392d9bb33f30eec02d0","score":"0.5885212","text":"def connect(self):\n self._client.session = self._client.create_session()\n return self._client.session","title":""},{"docid":"02f519b9ed1ca4a3e47ad06b49b91881","score":"0.58223546","text":"def connection(self):\n # if there isn't an open connection, open one...\n if self._connection is None:\n self._connection = self.connect_fn(**self.connect_kwargs)\n\n return self._connection","title":""},{"docid":"855bc012cfd747aff473afe3e2ffd44c","score":"0.5804371","text":"def connect(self):\n return self","title":""},{"docid":"d6986303c49abf47402eaa10bd9ce32b","score":"0.58029765","text":"def _getURLOpener(self):\n return HTTPConnection(self['host'])","title":""},{"docid":"5132131444313abce79e07eaa2d32c87","score":"0.5773046","text":"def get_connection(self, **options):\n options.setdefault('authentication', NTLM)\n options.setdefault('user', self.username)\n options.setdefault('password', self.password)\n return Connection(self.server, **options)","title":""},{"docid":"f23e162f26487042d2091c4a1d0b7ede","score":"0.57689136","text":"def getConnection():","title":""},{"docid":"b436acf3bcabaa034337e34a7a7d475e","score":"0.573757","text":"def get_connection(self):\n return self.__r, self.__conn","title":""},{"docid":"f88dbf7ed53fd0d1751762afc0d6f23f","score":"0.5724949","text":"def connect(self):\n print \"Connecting to \" + str(self._url)\n return pika.SelectConnection(pika.URLParameters(self._url),\n self.on_connection_open,\n stop_ioloop_on_close=False)","title":""},{"docid":"e47a8f83dbb4f6a18118faeceb071c8e","score":"0.57212406","text":"def getMQTTConnection(self):\n # Return the internal AWSIoTMQTTClient instance\n return self._AWSIoTMQTTClient","title":""},{"docid":"1f6f8dd0ea57eb6b0a3c77f429af48ff","score":"0.57123935","text":"def _getURLOpener(self):\n key, cert = self.getKeyCert()\n return HTTPSConnection(self['host'], key_file=key, cert_file=cert)","title":""},{"docid":"d4ce3d503c63326c2dcea71401fdc324","score":"0.571099","text":"def connection(self):\n return self._connection","title":""},{"docid":"d4ce3d503c63326c2dcea71401fdc324","score":"0.571099","text":"def connection(self):\n return self._connection","title":""},{"docid":"d5744e067ed87f752820b6df1e5aad87","score":"0.56917465","text":"def _get_connection(self):\n return self._acquire()","title":""},{"docid":"a1fc5c741e5d657a71bc8ae25f8009f6","score":"0.56911755","text":"def connect(self):\n logger.debug('Connecting to %s', self._url)\n return pika.SelectConnection(parameters=pika.URLParameters(self._url),\n on_open_callback=self.on_connection_open)","title":""},{"docid":"13c15f37579431b6229a8c20b9a0a7b2","score":"0.56787","text":"def con(backend):\n return backend.connection","title":""},{"docid":"dcb21b11689091fc3a2bd825b818ae5b","score":"0.5646509","text":"def make_connection(self):\n connection = None\n channel = None\n try:\n connection = pika.BlockingConnection(self.connection_params)\n channel = connection.channel()\n except:\n print(\"Failed to make connection\")\n return connection, channel","title":""},{"docid":"b9b8e9b0c4ea1f2bed1d7110d3781d38","score":"0.56456053","text":"def get_tg_connection(config: TgcliConfiguration,\n graph_name: Optional[str] = None,\n clean_init: bool = False) -> TigerGraphConnection:\n conn = __get_tg_connection___(config, graph_name)\n # Manually download dependencies and set flags in the underlying TigerGraphConnection\n # This gives more flexibility/control into what and where we download dependencies to\n init_dependencies(config, conn, clean_init=clean_init)\n conn.gsqlInitiated = True\n conn.downloadCert = False\n conn.downloadJar = False\n conn.jarLocation = get_jar_folder(config).expanduser().__str__()\n conn.certLocation = get_cert_filepath(config).expanduser().__str__()\n # Still call init for other dependencies (self.url) - but dependencies will not be downloaded\n conn.initGsql(conn.jarLocation, conn.certLocation)\n if config.use_auth and graph_name:\n # Get secret for the graph, if provided\n # TODO: Consider a class function for adding and retrieving secrets\n secret = config.secrets.get(graph_name, None)\n if not secret or clean_init:\n print(f\"Creating new secret for graph {graph_name} and saving to configuration.\")\n conn.graphname = graph_name\n secret = conn.createSecret()\n if not secret:\n raise ValueError(f\"Could not create a secret for the connection to graph {graph_name}.\")\n # Also save the new config\n config.secrets[graph_name] = secret\n upsert_config(config)\n # Finally, get the token\n conn.getToken(secret=secret)\n return conn","title":""},{"docid":"9e1b6090dc3bf039f11c059cff02c1bd","score":"0.5643282","text":"def connect(self):\n LOGGER.info('Connecting to %s', self._url)\n return pika.TornadoConnection(pika.URLParameters(self._url),\n self.on_connection_open,\n on_open_error_callback=self.on_open_error)","title":""},{"docid":"dc275b1b2fd580c4df1bec91d41a6739","score":"0.56326294","text":"def _getURLOpener(self):\n return HTTPSConnection(self[\"host\"])","title":""},{"docid":"d14e3932a277ee8e1d5bb634b492d723","score":"0.560438","text":"def get_connection(name: str = None) -> Connection:\n return get_engine(name).connect()","title":""},{"docid":"c9cfcdd05cb0352cde214d1ae715c754","score":"0.5597934","text":"def _connect_to_bdd(self):\n redis_co = redis.Redis(\\\n host=self._host,\n port=self._port,\n password=self._password)\n\n return redis_co","title":""},{"docid":"ac24cb40f3f1c5612accdfcc84150cec","score":"0.55907065","text":"def connect(self):\n self.message(\"connect\")\n logger.info(\"going to connect to connection!!\", extra=self.extra)\n logger.debug(str(self.connection), extra=self.extra)\n return self.connection.connect() # will create connection.connection","title":""},{"docid":"8dca176d8682b33d198c37a51c5b5b60","score":"0.5586824","text":"def _getURLOpener(self):\n return HTTPSConnection(self['host'])","title":""},{"docid":"87a9954f7f678816c470a91e615829d1","score":"0.55579937","text":"def connect(self):\n LOGGER.info('Connecting to %s', self._url)\n return pika.SelectConnection(pika.URLParameters(self._url),\n on_open_callback=self.on_connection_open,\n on_open_error_callback=None,\n on_close_callback=self.on_connection_closed,\n stop_ioloop_on_close=False)","title":""},{"docid":"ca220ca3e152b5df27ed5bc5cbe3896f","score":"0.5544147","text":"def createConnection(self):\n\n conn = \"\"\n context = ssl._create_unverified_context()\n if self.type == \"https\":\n conn = httplib.HTTPSConnection(self.ip, context=context)\n sock = socket.create_connection(\n (conn.host, conn.port), conn.timeout, conn.source_address)\n conn.sock = ssl.wrap_socket(sock, conn.key_file, conn.cert_file,\n ssl_version=ssl.PROTOCOL_TLSv1)\n elif self.type == \"ssh\":\n conn = SSHConnection(self.ip, self.username, self.password)\n elif self.type == \"scp\":\n transport = paramiko.Transport((self.ip, 22))\n transport.connect(username=\"root\", password=self.password)\n conn = paramiko.SFTPClient.from_transport(transport)\n elif self.type == \"expect\":\n conn = ExpectConnection(self.ip, self.username, self.password)\n else:\n conn = httplib.HTTPConnection(self.ip)\n return conn","title":""},{"docid":"121deb2178c7f4e14128b323a5f28a18","score":"0.55438393","text":"def _connect() -> Connection:\n if entry.options.get(CONF_UNAUTHENTICATED_MODE):\n _LOGGER.debug(\"Connecting in unauthenticated mode, reduced feature set\")\n connection = Connection(url, timeout=CONNECTION_TIMEOUT)\n else:\n _LOGGER.debug(\"Connecting in authenticated mode, full feature set\")\n username = entry.data.get(CONF_USERNAME) or \"\"\n password = entry.data.get(CONF_PASSWORD) or \"\"\n connection = Connection(\n url, username=username, password=password, timeout=CONNECTION_TIMEOUT\n )\n return connection","title":""},{"docid":"41716fc9078a9e7e784509e88fb83ca2","score":"0.5524809","text":"def get_backend():\n return Connection()","title":""},{"docid":"41716fc9078a9e7e784509e88fb83ca2","score":"0.5524809","text":"def get_backend():\n return Connection()","title":""},{"docid":"41716fc9078a9e7e784509e88fb83ca2","score":"0.5524809","text":"def get_backend():\n return Connection()","title":""},{"docid":"853e3b25d8c7a93de2fdd933e252f1ed","score":"0.55162114","text":"async def _get_connection(self):\n if self._connection is None:\n self._pool = await self._get_pool()\n self._connection = await self._pool.acquire()\n\n return self._connection","title":""},{"docid":"996a46683674887c775ba2e2cf83a33e","score":"0.55141294","text":"def getConnection(**cParms) :\n if len(cParms.keys()) == 0 : \n return getConnectionJacuzzi().newConnection(**_defaultConnectionParms)\n else : \n return getConnectionJacuzzi().newConnection(**cParms)","title":""},{"docid":"51919e318b88bbb5b86b0f9459a98fd7","score":"0.5505527","text":"def connect(hostname, securely):\n if securely:\n https_connection = httplib.HTTPSConnection(hostname)\n return https_connection\n else:\n http_connection = httplib.HTTPConnection(hostname)\n return http_connection","title":""},{"docid":"d03ff662e5efe38834616e697a0c448b","score":"0.54912704","text":"def get_connection(self):\n return vertica_python.connect(**self.conn_info)","title":""},{"docid":"a55a8fef119d748b9fb0242d71e16e64","score":"0.54886323","text":"def conn(self):\n return os_conn.Connection(verify=self.verify, **self.os_auth_args)","title":""},{"docid":"2b17c98513cae1dc59eb4c48983f329b","score":"0.5481048","text":"def connect(self):\n self.connection, self.channel = self.__connect(self.user, self.password, self.host, self.port,\n self.url_parameters)","title":""},{"docid":"fec30c87354d4c13392f8e69e4cb768d","score":"0.54759896","text":"def get_rethink_connection(props):\n\n\trethink_conn = r.connect(\n\t\thost=props.get(\"RETHINKDB\", \"RETHINK_HOST\"),\n\t\tport=props.get(\"RETHINKDB\", \"RETHINK_PORT\"),\n\t\tdb=props.get(\"RETHINKDB\", \"RETHINK_DB\"),\n\t\tuser=props.get(\"RETHINKDB\", \"RETHINK_USER\"),\n\t\tpassword=props.get(\"RETHINKDB\", \"RETHINK_PASSWORD\"),\n\t\ttimeout=int(props.get(\"RETHINKDB\", \"RETHINK_TIMEOUT\")),\n\t)\n\treturn rethink_conn","title":""},{"docid":"8400ffead37b52ae849e744ab74325c8","score":"0.54647315","text":"def _connect(self) -> ClientLibrary:\n with contextlib.redirect_stderr(io.StringIO()):\n return ClientLibrary(url=self.ip, username=self.username,\n password=self.password, ssl_verify=False)","title":""},{"docid":"6517734f2d07d05b6e4cd3ddf596a370","score":"0.5463275","text":"def open_connection():","title":""},{"docid":"7634129384d7eac220f182da1becdb79","score":"0.5456022","text":"def getConnection(self):\n raise NotImplementedError(\"Shell.getConnection()\")","title":""},{"docid":"ae3de7866e94ea20cc903ee3efaa1c09","score":"0.54367614","text":"def get_conn(self):\n if self.pentaho_cli:\n return self.pentaho_cli\n\n self.pentaho_cli = self.PentahoCarteClient(\n host=self.connection.host,\n port=self.connection.port,\n rep=self.extras.get('rep'),\n username=self.connection.login,\n password=self.connection.password,\n carte_username=self.extras.get('carte_username'),\n carte_password=self.extras.get('carte_password'),\n level=self.level)\n\n return self.pentaho_cli","title":""},{"docid":"02ba2849ed025aa80e89af08882f8e45","score":"0.5436113","text":"def make_connection(self):\r\n exchange_class = getattr(ccxt, self.name)\r\n exch = exchange_class({\r\n 'enableRateLimit': True,\r\n })\r\n exch.load_markets()\r\n return exch","title":""},{"docid":"e27cbb8ffaa0c1a27b6fbcae437759b4","score":"0.54358673","text":"def solid_connection(self):\n\t\treturn connect(self._dbapi,\n\t\t\tself._maxusage, self._setsession, *self._args, **self._kwargs)","title":""},{"docid":"2794ff242d6e41c73eafb7c96dccc6f1","score":"0.54164785","text":"def get_connection(self):\n logging.info(\"[%s] Connecting to %s\", self._consumer_name, str(self._brokers))\n conn = stomp.Connection(host_and_ports=self._brokers)\n conn.set_listener(self._consumer_name, self._listener)\n conn.start()\n conn.connect(self._user, self._passcode, wait=True)\n # Give the connection threads a little breather\n time.sleep(0.5)\n return conn","title":""},{"docid":"226213234bb545d658b19c96c62d18f8","score":"0.5407084","text":"def getconn(self):\n return self._conn","title":""},{"docid":"47f4fd6edebbb84805b3907e80acef4d","score":"0.5404162","text":"def get_connection(self):\n\t\tfrom pymongo import MongoClient\n\n\t\tif self._connection is None:\n\t\t\tself._connection = MongoClient(host=self.url, max_pool_size=10)\n\n\t\treturn self._connection","title":""},{"docid":"cb8298055a4140ad45c0067574942e08","score":"0.5395113","text":"def connection(self):\n # we have to keep this reference here,\n # if we just return self.engine.raw_connection(),\n # any cursor from that connection will fail\n # doing: engine.raw_connection().cursor().execute('') fails!\n if self._connection is None:\n self._connection = self.engine.raw_connection()\n\n # if a task or product calls client.connection.close(), we have to\n # re-open the connection\n if not self._connection.is_valid:\n self._connection = self.engine.raw_connection()\n\n return self._connection","title":""},{"docid":"0bfec17a452c7c53a1c0aceb93b5ce72","score":"0.53897625","text":"def establish_connection(self):","title":""},{"docid":"9bf886ae84870a408ee0afea2d7e7cf7","score":"0.53859836","text":"def getConnection(self):\n return connection['MirrDatabase']","title":""},{"docid":"41365c28fbcbcd7d1d7511c3997bf4a7","score":"0.53846216","text":"def make_twitter_connection( self ):\n connection = TwitterConnection( self.credentials_file )\n self.conn = connection.connection","title":""},{"docid":"44b0d66948d57320ffd233e8d628a059","score":"0.5383322","text":"def connect(self, keepAliveIntervalSecond=600):\n self._load_callbacks()\n return self._AWSIoTMQTTClient.connect(keepAliveIntervalSecond)","title":""},{"docid":"3c77f14325d3cebee814416c3bbbe644","score":"0.53798884","text":"def get_connection(self, conn_handle):\n return self.conn_manager.get_connection(conn_handle)","title":""},{"docid":"b2c27029fe61bff7a968bede64a0aa62","score":"0.5378017","text":"def get_connection(region='eu-west-1'):\n return connect_to_region(region)","title":""},{"docid":"43d05f5fd60252924535ef2bd087ef5e","score":"0.5373982","text":"def connect(self):\n log.debug('Attempting connection to Accumulo proxy ...')\n log.debug('Connection args: \"%s:%s@%s:%s\"' %\n (self.user, '********', self.host, self.port))\n try:\n self.connection = pyaccumulo.Accumulo(host=self.host,\n port=int(self.port),\n user=self.user,\n password=self.password)\n self.meta.connection = self.connection\n self.meta_search.connection = self.connection\n self.image.connection = self.connection\n self.thumb.connection = self.connection\n self.audit.connection = self.connection\n self.gdelt.connection = self.connection\n except (TTransportException,\n AccumuloSecurityException) as err:\n log.error('Connection error: \"%s\"' % err)\n\n return self.connection","title":""},{"docid":"4ef4888223dfde7340f04e98e9acccd7","score":"0.53689206","text":"def get_connection(self, ishttps=False, cookie=None, verbose=False):\r\n if self.__certificate is not None:\r\n if verbose:\r\n print(\"------>Cert\")\r\n return self.get_connection_secure(verbose)\r\n else:\r\n if verbose:\r\n print(\"------>Cookie\")\r\n return httplib.HTTPSConnection(self._ConnectionHandler__connHost,\r\n self._ConnectionHandler__connPort)","title":""},{"docid":"54131b4eddc1bef2853df22d30845c78","score":"0.5362701","text":"def get_connection(self):\n\n try:\n db = getattr(g, '_database', None)\n if db is None:\n db = g._database = sqlite3.connect('burgers.db')\n return db\n except:\n # if we cant connect lets throw a 500 with an error message\n abort(500, 'Issue Connecting to the database')","title":""},{"docid":"49add38642c530b07feb0d552ad44221","score":"0.53365016","text":"def get_conn():\n if not hasattr(g, 'redis_client'):\n g.redis_client = RedisClient()\n return g.redis_client","title":""},{"docid":"680a7c1e1bf3d7d3bda71c894c332d4e","score":"0.5328758","text":"def connect(self):\n return self","title":""},{"docid":"e946d14bf0c51993ac7238387121ca72","score":"0.53257626","text":"def __get_connection(self, connection_dict):\n return connect(\n db=connection_dict['db_name'],\n username=connection_dict['user'],\n password=connection_dict['password'],\n host=connection_dict['host'],\n port=connection_dict['port']\n )","title":""},{"docid":"338d8778ed5115b5df1be3abbee016af","score":"0.53096926","text":"def _get_connection(self):\n try:\n if self._ncc_connection and self._ncc_connection.connected:\n return self._ncc_connection\n else:\n self._ncc_connection = manager.connect(\n host=self._host_ip, port=self._host_ssh_port,\n username=self._username, password=self._password,\n device_params={'name': \"csr\"}, timeout=self._timeout)\n if not self._itfcs_enabled:\n self._itfcs_enabled = self._enable_itfcs(\n self._ncc_connection)\n return self._ncc_connection\n except Exception as e:\n conn_params = {'host': self._host_ip, 'port': self._host_ssh_port,\n 'user': self._username,\n 'timeout': self._timeout, 'reason': e.message}\n raise cfg_exc.ConnectionException(**conn_params)","title":""},{"docid":"302cec3aa12d843ce8f9fd39b0a68de7","score":"0.5306083","text":"def connect(self):\n return BlockingConnection(\n parameters=ConnectionParameters(\n host=self.host,\n port=self.port,\n virtual_host=self.vhost,\n credentials=PlainCredentials(\n username=self.user,\n password=self.passwd,\n )\n )\n )","title":""},{"docid":"2bbcff104042e916ebfe176a53c2df26","score":"0.52994365","text":"def get_connection():\n\n client = pymongo.MongoClient(CONFIG[\"database\"])\n return client.hitmen","title":""},{"docid":"962e22a8a7bd30d60b2300bae0bd5cdc","score":"0.5298408","text":"def conn():\n return __context__[\"netmiko_device\"].get(\"connection\")","title":""},{"docid":"41832f195ce19b9faaddd30c887ef8fe","score":"0.52926916","text":"def request_connection():\n\n # If there is no connection in the global (\"g\") variable, try to establish it\n if not hasattr(g, \"conn\"):\n g.conn = None\n try:\n g.conn = sqlite3.connect(DATABASE)\n except Exception as e:\n print(e)\n\n return g.conn","title":""},{"docid":"ef4344c304ebad40a1175b27d4b44d4c","score":"0.5285611","text":"def createConnection(self):\n uri = self.reader.createConnection()\n return Pyro.core.getAttrProxyForURI(uri)","title":""},{"docid":"65e5076aefae8f2cdac187ab092a95e6","score":"0.5269692","text":"def connect(config_file=qcs.default_filename, section='info', remember_me=False, remember_me_always=False):\n # Retrieve login credentials.\n conf = qcconf.QualysConnectConfig(filename=config_file, section=section, remember_me=remember_me,\n remember_me_always=remember_me_always)\n connect = qcconn.QGConnector(conf.get_auth(),\n conf.get_hostname(),\n conf.proxies,\n conf.max_retries)\n logger.info(\"Finished building connector.\")\n return connect","title":""},{"docid":"4f519480a8c27a392be67578b7e55e5b","score":"0.5266888","text":"def connect(*args, **kwargs):\n return Connection.connect(*args, **kwargs)","title":""},{"docid":"14d41aadbfd88e7bcc1f87ad669ad5d8","score":"0.5255701","text":"def _connect(self):\n server = self._server\n port = self._port\n headers = self._headers\n ssl_enabled = self._ssl_enabled\n proxy_server, proxy_port, proxy_auth = self._get_proxy_config()\n\n if proxy_server and proxy_port:\n if ssl_enabled:\n context = self._get_ssl_context()\n self._conn = http.client.HTTPSConnection(\n proxy_server, proxy_port, context=context\n )\n else:\n self._conn = http.client.HTTPConnection(proxy_server, proxy_port)\n\n tunnel_headers = None\n if proxy_auth:\n tunnel_headers = {\"Proxy-Authorization\": proxy_auth}\n\n self._conn.set_tunnel(server, port, headers=tunnel_headers)\n else:\n if ssl_enabled:\n context = self._get_ssl_context()\n self._conn = http.client.HTTPSConnection(server, port, context=context)\n else:\n self._conn = http.client.HTTPConnection(server, port)\n\n self._conn.putrequest(\"POST\", self._url)\n self._conn.putheader(\"Transfer-Encoding\", \"chunked\")\n for header in headers:\n self._conn.putheader(header, headers[header])\n self._conn.endheaders()\n\n # Set blocking to False prevents recv\n # from blocking while waiting for a response.\n self._conn.sock.setblocking(False)\n self._bytes = b\"\"\n self._reset_retries()\n time.sleep(0.5)","title":""},{"docid":"251e745130aad12529f99a6c4a812bda","score":"0.52496","text":"def post_get_connection(\n self, response: repositories.Connection\n ) -> repositories.Connection:\n return response","title":""},{"docid":"f3c008bcd4905e9794ed8cd759904cda","score":"0.52410686","text":"def _get_session(self) -> requests.Session:\n session = requests.Session()\n retry = rq_retry.Retry(\n total=self.retries_number,\n read=self.retries_number,\n connect=self.retries_number,\n backoff_factor=self.backoff_factor,\n status_forcelist=self.status_forcelist,\n )\n adapter = rq_adapt.HTTPAdapter(max_retries=retry)\n session.mount(\"https://\", adapter)\n return session","title":""},{"docid":"db944c32a46d21a6402048805b0cebf8","score":"0.5240774","text":"def open_connection(self):\n connection = tcp.TCPConnection(config.CONF['vim-api']['rpc_host'],\n config.CONF['vim-api']['rpc_port'])\n connection.connect(config.CONF['vim']['rpc_host'],\n config.CONF['vim']['rpc_port'])\n self._connections.append(connection)\n return connection","title":""},{"docid":"79fa9459a1bdee50bb1ae4dc87286e33","score":"0.5233544","text":"def establish_connection(self):\n conninfo = self.client\n for name, default_value in self.default_connection_params.items():\n if not getattr(conninfo, name, None):\n setattr(conninfo, name, default_value)\n if conninfo.ssl:\n conninfo.qpid_transport = 'ssl'\n conninfo.transport_options['ssl_keyfile'] = conninfo.ssl[\n 'keyfile']\n conninfo.transport_options['ssl_certfile'] = conninfo.ssl[\n 'certfile']\n conninfo.transport_options['ssl_trustfile'] = conninfo.ssl[\n 'ca_certs']\n if conninfo.ssl['cert_reqs'] == ssl.CERT_REQUIRED:\n conninfo.transport_options['ssl_skip_hostname_check'] = False\n else:\n conninfo.transport_options['ssl_skip_hostname_check'] = True\n else:\n conninfo.qpid_transport = 'tcp'\n\n credentials = {}\n if conninfo.login_method is None:\n if conninfo.userid is not None and conninfo.password is not None:\n sasl_mech = 'PLAIN'\n credentials['username'] = conninfo.userid\n credentials['password'] = conninfo.password\n elif conninfo.userid is None and conninfo.password is not None:\n raise Exception(\n 'Password configured but no username. SASL PLAIN '\n 'requires a username when using a password.')\n elif conninfo.userid is not None and conninfo.password is None:\n raise Exception(\n 'Username configured but no password. SASL PLAIN '\n 'requires a password when using a username.')\n else:\n sasl_mech = 'ANONYMOUS'\n else:\n sasl_mech = conninfo.login_method\n if conninfo.userid is not None:\n credentials['username'] = conninfo.userid\n\n opts = {\n 'host': conninfo.hostname,\n 'port': conninfo.port,\n 'sasl_mechanisms': sasl_mech,\n 'timeout': conninfo.connect_timeout,\n 'transport': conninfo.qpid_transport\n }\n\n opts.update(credentials)\n opts.update(conninfo.transport_options)\n\n conn = self.Connection(**opts)\n conn.client = self.client\n self.session = conn.get_qpid_connection().session()\n self.session.set_message_received_notify_handler(\n self._qpid_message_ready_handler\n )\n conn.get_qpid_connection().set_async_exception_notify_handler(\n self._qpid_async_exception_notify_handler\n )\n self.session.set_async_exception_notify_handler(\n self._qpid_async_exception_notify_handler\n )\n return conn","title":""},{"docid":"358c7306625b06d2a4a24e3aa96e7dcf","score":"0.5217251","text":"def connect(*args, **kwargs):\n return Connection(args, kwargs)","title":""},{"docid":"bf1d9bb1baff67401275d80504f2e7b4","score":"0.52161855","text":"def connect_server(self) -> rpc.RPCSession:\n tracker = self.connect_tracker()\n session: rpc.RPCSession = tracker.request(\n key=self.tracker_key,\n priority=self.session_priority,\n session_timeout=self.session_timeout_sec,\n )\n return session","title":""},{"docid":"d7a74737e101619910201bd1fa0686ed","score":"0.52113956","text":"def get_client(self):\n return self.session","title":""},{"docid":"771964ad6b85edd071234fdc2be90619","score":"0.51924706","text":"async def _connect(self):\n logger.debug(\"connecting to the stream\")\n await self.client.setup\n if self.session is None:\n self.session = self.client._session\n kwargs = await self.client.headers.prepare_request(**self.kwargs)\n request = self.client.error_handler(self.session.request)\n\n return await request(timeout=0, **kwargs)","title":""},{"docid":"93843003c088dd5d18f12abef3194e56","score":"0.5192259","text":"def connect(hostname, username, password):\n conn = BlitzGateway(username, password,\n host=hostname, secure=True)\n conn.connect()\n conn.c.enableKeepAlive(60)\n return conn","title":""},{"docid":"4590052c41063f9f5a2c63bc5072d646","score":"0.51867414","text":"def connection(self, context=None):\n if context is None:\n context = ssl.SSLContext(ssl.PROTOCOL_TLS)\n context.check_hostname = False\n context.verify_mode = ssl.CERT_NONE\n\n address = (self.args.host, self.args.port)\n with socket.create_connection(address, timeout=5) as sock:\n with context.wrap_socket(sock, server_hostname=self.args.host) as ssock:\n yield ssock","title":""},{"docid":"92cec4f398f40733bbc00da42a838add","score":"0.5182721","text":"async def open_connection(self) -> _BaseConnector:\n connector = self.connector\n if connector is None:\n raise HekrAPIException('No connector attached to device')\n\n if not connector.is_connected:\n await connector.open_connection()\n\n return connector","title":""},{"docid":"53f1e1f16aad6c0ad2a581c357916cdc","score":"0.5178644","text":"def get_connection(self):\n return sqlite3.connect(self.database)","title":""},{"docid":"b2301e98f7801319ff39b021fde35517","score":"0.51771706","text":"def connect(cls, url: Optional[str] = None) -> \"YubiHsm\":\n return cls(get_backend(url))","title":""},{"docid":"49bb63a97ecd309e79718a3ab9e12650","score":"0.51625913","text":"def connect(self):\n\n if self.getConfig().initDone is False:\n logging.debug('connecting without config init')\n raise ConfigError(self.__class__.__name__, Error.errDict['initBad']['errCode'], Error.errDict['initBad']['errMsg'])\n\n headers = {'user-agent': self.userAgent}\n formData = {'client_id': self.getConfig().getClientID(),\n 'client_secret': self.getConfig().getClientSecret(),\n 'grant_type': self.getConfig().getGrantType(),\n 'code': self.getConfig().getAuthorizationCode(),\n 'redirect_uri': self.getConfig().getRedirectURL()}\n r = requests.post(self.getConfig().getTokenServerURL(),\n headers=(headers),\n cert=(self.getConfig().getSSLCertPath(),\n self.getConfig().getSSLKeyPath()),\n data=(formData),\n verify=(self.getConfig().getSSLCertPath()))\n logging.debug(r.status_code)\n logging.debug(r.json())\n if (r.status_code == requests.codes.ok):\n self.connection['status'] = 'connected'\n self.connection['token'] = r.json()['access_token']\n self.connection['expires'] = datetime.datetime.now() + datetime.timedelta(0, r.json()['expires_in'], 0)\n if self.getSessionState() == '':\n self.setSessionState(str(uuid.uuid1()))\n else:\n raise ConnectError(self.__class__.__name__, str(r.status_code), 'Unable to connect to ADP')","title":""},{"docid":"d28faf5e70ae0468852309db28d485a3","score":"0.5158046","text":"def __GetConnection(self, transaction):\r\n self.__connection_lock.acquire()\r\n request_tx = transaction and transaction.handle()\r\n if request_tx == 0:\r\n request_tx = None\r\n if request_tx != self.__current_transaction:\r\n raise apiproxy_errors.ApplicationError(\r\n datastore_pb.Error.BAD_REQUEST,\r\n 'Only one concurrent transaction per thread is permitted.')\r\n return self.__connection","title":""}],"string":"[\n {\n \"docid\": \"56a5a7204653cff73a19f5b0b1ac1780\",\n \"score\": \"0.627893\",\n \"text\": \"def connection():\\n return get_connection()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c29a8ed3dbdcb9193cd9222e6b99893b\",\n \"score\": \"0.6158203\",\n \"text\": \"def _connect(self):\\n return httplib.HTTPConnection(self._host, self._port)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3536ef050714fc24c3a51b4e3e47b3a1\",\n \"score\": \"0.61508757\",\n \"text\": \"def remote_connection(self):\\n return DriverRemoteConnection(self.endpoint,'g')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea4b4310c7c8224cfa8fb9ed30aafa5d\",\n \"score\": \"0.6127698\",\n \"text\": \"def __get_tg_connection___(config: TgcliConfiguration, graph_name: Optional[str] = None) -> TigerGraphConnection:\\n return TigerGraphConnection(\\n host=config.server,\\n username=config.username,\\n password=config.password,\\n clientVersion=config.client_version,\\n graphname=graph_name or '',\\n restppPort=config.restpp_port,\\n gsPort=config.gs_port,\\n apiToken='',\\n useCert=config.use_auth\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1919717fa4acfa293e9f2b7304b099f\",\n \"score\": \"0.611027\",\n \"text\": \"def get_connection():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4e95a23eced284c110c543e1f8af436\",\n \"score\": \"0.6091545\",\n \"text\": \"def bot_connection():\\n return BotConnection('dummybot', 'http://dummybot')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c33542b21ef72c67f61771dd2ecf9af\",\n \"score\": \"0.6083142\",\n \"text\": \"def get_conn(self):\\n http_authorized = self._authorize()\\n return build(\\n 'dataflow', 'v1b3', http=http_authorized, cache_discovery=False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4143d72a156531e2856e4e121ad85448\",\n \"score\": \"0.60658413\",\n \"text\": \"def connect(self):\\n self.wrdsconn = wrds.Connection()\\n self.conn = self.wrdsconn.connect()\\n return self.wrdsconn\",\n \"title\": \"\"\n },\n {\n \"docid\": \"785ceaf1326da26e85971bf3c7a8ae99\",\n \"score\": \"0.59902596\",\n \"text\": \"def get_connection(self):\\n return self.conn\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5e344d04fb18a0123bad93b80fc1c59\",\n \"score\": \"0.59759575\",\n \"text\": \"def connection(self):\\n return self.connection\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ddc2b2ea4bfcf4a866b105fcdab8953\",\n \"score\": \"0.5972781\",\n \"text\": \"def connect(self):\\n conn = pika.BlockingConnection(\\n pika.ConnectionParameters(\\n host=self.settings.host,\\n credentials=pika.credentials.PlainCredentials(\\n username=self.settings.credentials.username,\\n password=self.settings.credentials.password)))\\n return conn\",\n \"title\": \"\"\n },\n {\n \"docid\": \"654e24bd55ec03901ac9ff593fd70f15\",\n \"score\": \"0.5970635\",\n \"text\": \"def get_connection(self):\\n return self.connection\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89a6ce69cffae26ea401dc9f9ef69a0a\",\n \"score\": \"0.59662926\",\n \"text\": \"def connect(self):\\n # TODO: sockets, etc.\\n host = self._connection_options.get('host', DEFAULT_HOST)\\n port = self._connection_options.get('port', DEFAULT_PORT)\\n self.connection = Tyrant(host=host, port=port)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b10f42e12613f4a92f2c6792821af459\",\n \"score\": \"0.5954163\",\n \"text\": \"def connect_reddit(self):\\r\\n lg.debug('CointipBot::connect_reddit(): connecting to Reddit...')\\r\\n\\r\\n conn = praw.Reddit(user_agent = self.conf.reddit.auth.user)\\r\\n conn.login(self.conf.reddit.auth.user, self.conf.reddit.auth.password)\\r\\n\\r\\n lg.info(\\\"CointipBot::connect_reddit(): logged in to Reddit as %s\\\", self.conf.reddit.auth.user)\\r\\n return conn\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d65e9cb550e3306a9cbda4e6db471c36\",\n \"score\": \"0.5935847\",\n \"text\": \"def _Conn(self):\\n cls_name = '{scheme}Connection'.format(scheme=self.scheme.upper())\\n return getattr(http.client, cls_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af580b2348f91ff2dc1b44a61cafe6bf\",\n \"score\": \"0.59355676\",\n \"text\": \"def conn(self):\\n client = RiakClient(\\n protocol=self.conf.get(\\\"protocol\\\"),\\n http_port=self.conf.get(\\\"port\\\"),\\n host=self.conf.get(\\\"host\\\"))\\n\\n conn = client.bucket(self.conf.get(\\\"db\\\"))\\n conn.enable_search()\\n return conn\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f662151f658966cbf276f4dc9412293\",\n \"score\": \"0.59076524\",\n \"text\": \"def connection(self):\\n\\t\\treturn self._connection\",\n \"title\": \"\"\n },\n {\n \"docid\": \"40fad0c19bd62bd037720d99ec96780d\",\n \"score\": \"0.5906515\",\n \"text\": \"def get_connection():\\n return Connection.from_environment()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21efa182d470e392d9bb33f30eec02d0\",\n \"score\": \"0.5885212\",\n \"text\": \"def connect(self):\\n self._client.session = self._client.create_session()\\n return self._client.session\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02f519b9ed1ca4a3e47ad06b49b91881\",\n \"score\": \"0.58223546\",\n \"text\": \"def connection(self):\\n # if there isn't an open connection, open one...\\n if self._connection is None:\\n self._connection = self.connect_fn(**self.connect_kwargs)\\n\\n return self._connection\",\n \"title\": \"\"\n },\n {\n \"docid\": \"855bc012cfd747aff473afe3e2ffd44c\",\n \"score\": \"0.5804371\",\n \"text\": \"def connect(self):\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d6986303c49abf47402eaa10bd9ce32b\",\n \"score\": \"0.58029765\",\n \"text\": \"def _getURLOpener(self):\\n return HTTPConnection(self['host'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5132131444313abce79e07eaa2d32c87\",\n \"score\": \"0.5773046\",\n \"text\": \"def get_connection(self, **options):\\n options.setdefault('authentication', NTLM)\\n options.setdefault('user', self.username)\\n options.setdefault('password', self.password)\\n return Connection(self.server, **options)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f23e162f26487042d2091c4a1d0b7ede\",\n \"score\": \"0.57689136\",\n \"text\": \"def getConnection():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b436acf3bcabaa034337e34a7a7d475e\",\n \"score\": \"0.573757\",\n \"text\": \"def get_connection(self):\\n return self.__r, self.__conn\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f88dbf7ed53fd0d1751762afc0d6f23f\",\n \"score\": \"0.5724949\",\n \"text\": \"def connect(self):\\n print \\\"Connecting to \\\" + str(self._url)\\n return pika.SelectConnection(pika.URLParameters(self._url),\\n self.on_connection_open,\\n stop_ioloop_on_close=False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e47a8f83dbb4f6a18118faeceb071c8e\",\n \"score\": \"0.57212406\",\n \"text\": \"def getMQTTConnection(self):\\n # Return the internal AWSIoTMQTTClient instance\\n return self._AWSIoTMQTTClient\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f6f8dd0ea57eb6b0a3c77f429af48ff\",\n \"score\": \"0.57123935\",\n \"text\": \"def _getURLOpener(self):\\n key, cert = self.getKeyCert()\\n return HTTPSConnection(self['host'], key_file=key, cert_file=cert)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4ce3d503c63326c2dcea71401fdc324\",\n \"score\": \"0.571099\",\n \"text\": \"def connection(self):\\n return self._connection\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4ce3d503c63326c2dcea71401fdc324\",\n \"score\": \"0.571099\",\n \"text\": \"def connection(self):\\n return self._connection\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5744e067ed87f752820b6df1e5aad87\",\n \"score\": \"0.56917465\",\n \"text\": \"def _get_connection(self):\\n return self._acquire()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1fc5c741e5d657a71bc8ae25f8009f6\",\n \"score\": \"0.56911755\",\n \"text\": \"def connect(self):\\n logger.debug('Connecting to %s', self._url)\\n return pika.SelectConnection(parameters=pika.URLParameters(self._url),\\n on_open_callback=self.on_connection_open)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13c15f37579431b6229a8c20b9a0a7b2\",\n \"score\": \"0.56787\",\n \"text\": \"def con(backend):\\n return backend.connection\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dcb21b11689091fc3a2bd825b818ae5b\",\n \"score\": \"0.5646509\",\n \"text\": \"def make_connection(self):\\n connection = None\\n channel = None\\n try:\\n connection = pika.BlockingConnection(self.connection_params)\\n channel = connection.channel()\\n except:\\n print(\\\"Failed to make connection\\\")\\n return connection, channel\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9b8e9b0c4ea1f2bed1d7110d3781d38\",\n \"score\": \"0.56456053\",\n \"text\": \"def get_tg_connection(config: TgcliConfiguration,\\n graph_name: Optional[str] = None,\\n clean_init: bool = False) -> TigerGraphConnection:\\n conn = __get_tg_connection___(config, graph_name)\\n # Manually download dependencies and set flags in the underlying TigerGraphConnection\\n # This gives more flexibility/control into what and where we download dependencies to\\n init_dependencies(config, conn, clean_init=clean_init)\\n conn.gsqlInitiated = True\\n conn.downloadCert = False\\n conn.downloadJar = False\\n conn.jarLocation = get_jar_folder(config).expanduser().__str__()\\n conn.certLocation = get_cert_filepath(config).expanduser().__str__()\\n # Still call init for other dependencies (self.url) - but dependencies will not be downloaded\\n conn.initGsql(conn.jarLocation, conn.certLocation)\\n if config.use_auth and graph_name:\\n # Get secret for the graph, if provided\\n # TODO: Consider a class function for adding and retrieving secrets\\n secret = config.secrets.get(graph_name, None)\\n if not secret or clean_init:\\n print(f\\\"Creating new secret for graph {graph_name} and saving to configuration.\\\")\\n conn.graphname = graph_name\\n secret = conn.createSecret()\\n if not secret:\\n raise ValueError(f\\\"Could not create a secret for the connection to graph {graph_name}.\\\")\\n # Also save the new config\\n config.secrets[graph_name] = secret\\n upsert_config(config)\\n # Finally, get the token\\n conn.getToken(secret=secret)\\n return conn\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e1b6090dc3bf039f11c059cff02c1bd\",\n \"score\": \"0.5643282\",\n \"text\": \"def connect(self):\\n LOGGER.info('Connecting to %s', self._url)\\n return pika.TornadoConnection(pika.URLParameters(self._url),\\n self.on_connection_open,\\n on_open_error_callback=self.on_open_error)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc275b1b2fd580c4df1bec91d41a6739\",\n \"score\": \"0.56326294\",\n \"text\": \"def _getURLOpener(self):\\n return HTTPSConnection(self[\\\"host\\\"])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d14e3932a277ee8e1d5bb634b492d723\",\n \"score\": \"0.560438\",\n \"text\": \"def get_connection(name: str = None) -> Connection:\\n return get_engine(name).connect()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9cfcdd05cb0352cde214d1ae715c754\",\n \"score\": \"0.5597934\",\n \"text\": \"def _connect_to_bdd(self):\\n redis_co = redis.Redis(\\\\\\n host=self._host,\\n port=self._port,\\n password=self._password)\\n\\n return redis_co\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac24cb40f3f1c5612accdfcc84150cec\",\n \"score\": \"0.55907065\",\n \"text\": \"def connect(self):\\n self.message(\\\"connect\\\")\\n logger.info(\\\"going to connect to connection!!\\\", extra=self.extra)\\n logger.debug(str(self.connection), extra=self.extra)\\n return self.connection.connect() # will create connection.connection\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8dca176d8682b33d198c37a51c5b5b60\",\n \"score\": \"0.5586824\",\n \"text\": \"def _getURLOpener(self):\\n return HTTPSConnection(self['host'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87a9954f7f678816c470a91e615829d1\",\n \"score\": \"0.55579937\",\n \"text\": \"def connect(self):\\n LOGGER.info('Connecting to %s', self._url)\\n return pika.SelectConnection(pika.URLParameters(self._url),\\n on_open_callback=self.on_connection_open,\\n on_open_error_callback=None,\\n on_close_callback=self.on_connection_closed,\\n stop_ioloop_on_close=False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca220ca3e152b5df27ed5bc5cbe3896f\",\n \"score\": \"0.5544147\",\n \"text\": \"def createConnection(self):\\n\\n conn = \\\"\\\"\\n context = ssl._create_unverified_context()\\n if self.type == \\\"https\\\":\\n conn = httplib.HTTPSConnection(self.ip, context=context)\\n sock = socket.create_connection(\\n (conn.host, conn.port), conn.timeout, conn.source_address)\\n conn.sock = ssl.wrap_socket(sock, conn.key_file, conn.cert_file,\\n ssl_version=ssl.PROTOCOL_TLSv1)\\n elif self.type == \\\"ssh\\\":\\n conn = SSHConnection(self.ip, self.username, self.password)\\n elif self.type == \\\"scp\\\":\\n transport = paramiko.Transport((self.ip, 22))\\n transport.connect(username=\\\"root\\\", password=self.password)\\n conn = paramiko.SFTPClient.from_transport(transport)\\n elif self.type == \\\"expect\\\":\\n conn = ExpectConnection(self.ip, self.username, self.password)\\n else:\\n conn = httplib.HTTPConnection(self.ip)\\n return conn\",\n \"title\": \"\"\n },\n {\n \"docid\": \"121deb2178c7f4e14128b323a5f28a18\",\n \"score\": \"0.55438393\",\n \"text\": \"def _connect() -> Connection:\\n if entry.options.get(CONF_UNAUTHENTICATED_MODE):\\n _LOGGER.debug(\\\"Connecting in unauthenticated mode, reduced feature set\\\")\\n connection = Connection(url, timeout=CONNECTION_TIMEOUT)\\n else:\\n _LOGGER.debug(\\\"Connecting in authenticated mode, full feature set\\\")\\n username = entry.data.get(CONF_USERNAME) or \\\"\\\"\\n password = entry.data.get(CONF_PASSWORD) or \\\"\\\"\\n connection = Connection(\\n url, username=username, password=password, timeout=CONNECTION_TIMEOUT\\n )\\n return connection\",\n \"title\": \"\"\n },\n {\n \"docid\": \"41716fc9078a9e7e784509e88fb83ca2\",\n \"score\": \"0.5524809\",\n \"text\": \"def get_backend():\\n return Connection()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"41716fc9078a9e7e784509e88fb83ca2\",\n \"score\": \"0.5524809\",\n \"text\": \"def get_backend():\\n return Connection()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"41716fc9078a9e7e784509e88fb83ca2\",\n \"score\": \"0.5524809\",\n \"text\": \"def get_backend():\\n return Connection()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"853e3b25d8c7a93de2fdd933e252f1ed\",\n \"score\": \"0.55162114\",\n \"text\": \"async def _get_connection(self):\\n if self._connection is None:\\n self._pool = await self._get_pool()\\n self._connection = await self._pool.acquire()\\n\\n return self._connection\",\n \"title\": \"\"\n },\n {\n \"docid\": \"996a46683674887c775ba2e2cf83a33e\",\n \"score\": \"0.55141294\",\n \"text\": \"def getConnection(**cParms) :\\n if len(cParms.keys()) == 0 : \\n return getConnectionJacuzzi().newConnection(**_defaultConnectionParms)\\n else : \\n return getConnectionJacuzzi().newConnection(**cParms)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51919e318b88bbb5b86b0f9459a98fd7\",\n \"score\": \"0.5505527\",\n \"text\": \"def connect(hostname, securely):\\n if securely:\\n https_connection = httplib.HTTPSConnection(hostname)\\n return https_connection\\n else:\\n http_connection = httplib.HTTPConnection(hostname)\\n return http_connection\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d03ff662e5efe38834616e697a0c448b\",\n \"score\": \"0.54912704\",\n \"text\": \"def get_connection(self):\\n return vertica_python.connect(**self.conn_info)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a55a8fef119d748b9fb0242d71e16e64\",\n \"score\": \"0.54886323\",\n \"text\": \"def conn(self):\\n return os_conn.Connection(verify=self.verify, **self.os_auth_args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b17c98513cae1dc59eb4c48983f329b\",\n \"score\": \"0.5481048\",\n \"text\": \"def connect(self):\\n self.connection, self.channel = self.__connect(self.user, self.password, self.host, self.port,\\n self.url_parameters)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fec30c87354d4c13392f8e69e4cb768d\",\n \"score\": \"0.54759896\",\n \"text\": \"def get_rethink_connection(props):\\n\\n\\trethink_conn = r.connect(\\n\\t\\thost=props.get(\\\"RETHINKDB\\\", \\\"RETHINK_HOST\\\"),\\n\\t\\tport=props.get(\\\"RETHINKDB\\\", \\\"RETHINK_PORT\\\"),\\n\\t\\tdb=props.get(\\\"RETHINKDB\\\", \\\"RETHINK_DB\\\"),\\n\\t\\tuser=props.get(\\\"RETHINKDB\\\", \\\"RETHINK_USER\\\"),\\n\\t\\tpassword=props.get(\\\"RETHINKDB\\\", \\\"RETHINK_PASSWORD\\\"),\\n\\t\\ttimeout=int(props.get(\\\"RETHINKDB\\\", \\\"RETHINK_TIMEOUT\\\")),\\n\\t)\\n\\treturn rethink_conn\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8400ffead37b52ae849e744ab74325c8\",\n \"score\": \"0.54647315\",\n \"text\": \"def _connect(self) -> ClientLibrary:\\n with contextlib.redirect_stderr(io.StringIO()):\\n return ClientLibrary(url=self.ip, username=self.username,\\n password=self.password, ssl_verify=False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6517734f2d07d05b6e4cd3ddf596a370\",\n \"score\": \"0.5463275\",\n \"text\": \"def open_connection():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7634129384d7eac220f182da1becdb79\",\n \"score\": \"0.5456022\",\n \"text\": \"def getConnection(self):\\n raise NotImplementedError(\\\"Shell.getConnection()\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae3de7866e94ea20cc903ee3efaa1c09\",\n \"score\": \"0.54367614\",\n \"text\": \"def get_conn(self):\\n if self.pentaho_cli:\\n return self.pentaho_cli\\n\\n self.pentaho_cli = self.PentahoCarteClient(\\n host=self.connection.host,\\n port=self.connection.port,\\n rep=self.extras.get('rep'),\\n username=self.connection.login,\\n password=self.connection.password,\\n carte_username=self.extras.get('carte_username'),\\n carte_password=self.extras.get('carte_password'),\\n level=self.level)\\n\\n return self.pentaho_cli\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02ba2849ed025aa80e89af08882f8e45\",\n \"score\": \"0.5436113\",\n \"text\": \"def make_connection(self):\\r\\n exchange_class = getattr(ccxt, self.name)\\r\\n exch = exchange_class({\\r\\n 'enableRateLimit': True,\\r\\n })\\r\\n exch.load_markets()\\r\\n return exch\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e27cbb8ffaa0c1a27b6fbcae437759b4\",\n \"score\": \"0.54358673\",\n \"text\": \"def solid_connection(self):\\n\\t\\treturn connect(self._dbapi,\\n\\t\\t\\tself._maxusage, self._setsession, *self._args, **self._kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2794ff242d6e41c73eafb7c96dccc6f1\",\n \"score\": \"0.54164785\",\n \"text\": \"def get_connection(self):\\n logging.info(\\\"[%s] Connecting to %s\\\", self._consumer_name, str(self._brokers))\\n conn = stomp.Connection(host_and_ports=self._brokers)\\n conn.set_listener(self._consumer_name, self._listener)\\n conn.start()\\n conn.connect(self._user, self._passcode, wait=True)\\n # Give the connection threads a little breather\\n time.sleep(0.5)\\n return conn\",\n \"title\": \"\"\n },\n {\n \"docid\": \"226213234bb545d658b19c96c62d18f8\",\n \"score\": \"0.5407084\",\n \"text\": \"def getconn(self):\\n return self._conn\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47f4fd6edebbb84805b3907e80acef4d\",\n \"score\": \"0.5404162\",\n \"text\": \"def get_connection(self):\\n\\t\\tfrom pymongo import MongoClient\\n\\n\\t\\tif self._connection is None:\\n\\t\\t\\tself._connection = MongoClient(host=self.url, max_pool_size=10)\\n\\n\\t\\treturn self._connection\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb8298055a4140ad45c0067574942e08\",\n \"score\": \"0.5395113\",\n \"text\": \"def connection(self):\\n # we have to keep this reference here,\\n # if we just return self.engine.raw_connection(),\\n # any cursor from that connection will fail\\n # doing: engine.raw_connection().cursor().execute('') fails!\\n if self._connection is None:\\n self._connection = self.engine.raw_connection()\\n\\n # if a task or product calls client.connection.close(), we have to\\n # re-open the connection\\n if not self._connection.is_valid:\\n self._connection = self.engine.raw_connection()\\n\\n return self._connection\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0bfec17a452c7c53a1c0aceb93b5ce72\",\n \"score\": \"0.53897625\",\n \"text\": \"def establish_connection(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9bf886ae84870a408ee0afea2d7e7cf7\",\n \"score\": \"0.53859836\",\n \"text\": \"def getConnection(self):\\n return connection['MirrDatabase']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"41365c28fbcbcd7d1d7511c3997bf4a7\",\n \"score\": \"0.53846216\",\n \"text\": \"def make_twitter_connection( self ):\\n connection = TwitterConnection( self.credentials_file )\\n self.conn = connection.connection\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44b0d66948d57320ffd233e8d628a059\",\n \"score\": \"0.5383322\",\n \"text\": \"def connect(self, keepAliveIntervalSecond=600):\\n self._load_callbacks()\\n return self._AWSIoTMQTTClient.connect(keepAliveIntervalSecond)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c77f14325d3cebee814416c3bbbe644\",\n \"score\": \"0.53798884\",\n \"text\": \"def get_connection(self, conn_handle):\\n return self.conn_manager.get_connection(conn_handle)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2c27029fe61bff7a968bede64a0aa62\",\n \"score\": \"0.5378017\",\n \"text\": \"def get_connection(region='eu-west-1'):\\n return connect_to_region(region)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43d05f5fd60252924535ef2bd087ef5e\",\n \"score\": \"0.5373982\",\n \"text\": \"def connect(self):\\n log.debug('Attempting connection to Accumulo proxy ...')\\n log.debug('Connection args: \\\"%s:%s@%s:%s\\\"' %\\n (self.user, '********', self.host, self.port))\\n try:\\n self.connection = pyaccumulo.Accumulo(host=self.host,\\n port=int(self.port),\\n user=self.user,\\n password=self.password)\\n self.meta.connection = self.connection\\n self.meta_search.connection = self.connection\\n self.image.connection = self.connection\\n self.thumb.connection = self.connection\\n self.audit.connection = self.connection\\n self.gdelt.connection = self.connection\\n except (TTransportException,\\n AccumuloSecurityException) as err:\\n log.error('Connection error: \\\"%s\\\"' % err)\\n\\n return self.connection\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ef4888223dfde7340f04e98e9acccd7\",\n \"score\": \"0.53689206\",\n \"text\": \"def get_connection(self, ishttps=False, cookie=None, verbose=False):\\r\\n if self.__certificate is not None:\\r\\n if verbose:\\r\\n print(\\\"------>Cert\\\")\\r\\n return self.get_connection_secure(verbose)\\r\\n else:\\r\\n if verbose:\\r\\n print(\\\"------>Cookie\\\")\\r\\n return httplib.HTTPSConnection(self._ConnectionHandler__connHost,\\r\\n self._ConnectionHandler__connPort)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54131b4eddc1bef2853df22d30845c78\",\n \"score\": \"0.5362701\",\n \"text\": \"def get_connection(self):\\n\\n try:\\n db = getattr(g, '_database', None)\\n if db is None:\\n db = g._database = sqlite3.connect('burgers.db')\\n return db\\n except:\\n # if we cant connect lets throw a 500 with an error message\\n abort(500, 'Issue Connecting to the database')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49add38642c530b07feb0d552ad44221\",\n \"score\": \"0.53365016\",\n \"text\": \"def get_conn():\\n if not hasattr(g, 'redis_client'):\\n g.redis_client = RedisClient()\\n return g.redis_client\",\n \"title\": \"\"\n },\n {\n \"docid\": \"680a7c1e1bf3d7d3bda71c894c332d4e\",\n \"score\": \"0.5328758\",\n \"text\": \"def connect(self):\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e946d14bf0c51993ac7238387121ca72\",\n \"score\": \"0.53257626\",\n \"text\": \"def __get_connection(self, connection_dict):\\n return connect(\\n db=connection_dict['db_name'],\\n username=connection_dict['user'],\\n password=connection_dict['password'],\\n host=connection_dict['host'],\\n port=connection_dict['port']\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"338d8778ed5115b5df1be3abbee016af\",\n \"score\": \"0.53096926\",\n \"text\": \"def _get_connection(self):\\n try:\\n if self._ncc_connection and self._ncc_connection.connected:\\n return self._ncc_connection\\n else:\\n self._ncc_connection = manager.connect(\\n host=self._host_ip, port=self._host_ssh_port,\\n username=self._username, password=self._password,\\n device_params={'name': \\\"csr\\\"}, timeout=self._timeout)\\n if not self._itfcs_enabled:\\n self._itfcs_enabled = self._enable_itfcs(\\n self._ncc_connection)\\n return self._ncc_connection\\n except Exception as e:\\n conn_params = {'host': self._host_ip, 'port': self._host_ssh_port,\\n 'user': self._username,\\n 'timeout': self._timeout, 'reason': e.message}\\n raise cfg_exc.ConnectionException(**conn_params)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"302cec3aa12d843ce8f9fd39b0a68de7\",\n \"score\": \"0.5306083\",\n \"text\": \"def connect(self):\\n return BlockingConnection(\\n parameters=ConnectionParameters(\\n host=self.host,\\n port=self.port,\\n virtual_host=self.vhost,\\n credentials=PlainCredentials(\\n username=self.user,\\n password=self.passwd,\\n )\\n )\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2bbcff104042e916ebfe176a53c2df26\",\n \"score\": \"0.52994365\",\n \"text\": \"def get_connection():\\n\\n client = pymongo.MongoClient(CONFIG[\\\"database\\\"])\\n return client.hitmen\",\n \"title\": \"\"\n },\n {\n \"docid\": \"962e22a8a7bd30d60b2300bae0bd5cdc\",\n \"score\": \"0.5298408\",\n \"text\": \"def conn():\\n return __context__[\\\"netmiko_device\\\"].get(\\\"connection\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"41832f195ce19b9faaddd30c887ef8fe\",\n \"score\": \"0.52926916\",\n \"text\": \"def request_connection():\\n\\n # If there is no connection in the global (\\\"g\\\") variable, try to establish it\\n if not hasattr(g, \\\"conn\\\"):\\n g.conn = None\\n try:\\n g.conn = sqlite3.connect(DATABASE)\\n except Exception as e:\\n print(e)\\n\\n return g.conn\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef4344c304ebad40a1175b27d4b44d4c\",\n \"score\": \"0.5285611\",\n \"text\": \"def createConnection(self):\\n uri = self.reader.createConnection()\\n return Pyro.core.getAttrProxyForURI(uri)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65e5076aefae8f2cdac187ab092a95e6\",\n \"score\": \"0.5269692\",\n \"text\": \"def connect(config_file=qcs.default_filename, section='info', remember_me=False, remember_me_always=False):\\n # Retrieve login credentials.\\n conf = qcconf.QualysConnectConfig(filename=config_file, section=section, remember_me=remember_me,\\n remember_me_always=remember_me_always)\\n connect = qcconn.QGConnector(conf.get_auth(),\\n conf.get_hostname(),\\n conf.proxies,\\n conf.max_retries)\\n logger.info(\\\"Finished building connector.\\\")\\n return connect\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f519480a8c27a392be67578b7e55e5b\",\n \"score\": \"0.5266888\",\n \"text\": \"def connect(*args, **kwargs):\\n return Connection.connect(*args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14d41aadbfd88e7bcc1f87ad669ad5d8\",\n \"score\": \"0.5255701\",\n \"text\": \"def _connect(self):\\n server = self._server\\n port = self._port\\n headers = self._headers\\n ssl_enabled = self._ssl_enabled\\n proxy_server, proxy_port, proxy_auth = self._get_proxy_config()\\n\\n if proxy_server and proxy_port:\\n if ssl_enabled:\\n context = self._get_ssl_context()\\n self._conn = http.client.HTTPSConnection(\\n proxy_server, proxy_port, context=context\\n )\\n else:\\n self._conn = http.client.HTTPConnection(proxy_server, proxy_port)\\n\\n tunnel_headers = None\\n if proxy_auth:\\n tunnel_headers = {\\\"Proxy-Authorization\\\": proxy_auth}\\n\\n self._conn.set_tunnel(server, port, headers=tunnel_headers)\\n else:\\n if ssl_enabled:\\n context = self._get_ssl_context()\\n self._conn = http.client.HTTPSConnection(server, port, context=context)\\n else:\\n self._conn = http.client.HTTPConnection(server, port)\\n\\n self._conn.putrequest(\\\"POST\\\", self._url)\\n self._conn.putheader(\\\"Transfer-Encoding\\\", \\\"chunked\\\")\\n for header in headers:\\n self._conn.putheader(header, headers[header])\\n self._conn.endheaders()\\n\\n # Set blocking to False prevents recv\\n # from blocking while waiting for a response.\\n self._conn.sock.setblocking(False)\\n self._bytes = b\\\"\\\"\\n self._reset_retries()\\n time.sleep(0.5)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"251e745130aad12529f99a6c4a812bda\",\n \"score\": \"0.52496\",\n \"text\": \"def post_get_connection(\\n self, response: repositories.Connection\\n ) -> repositories.Connection:\\n return response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3c008bcd4905e9794ed8cd759904cda\",\n \"score\": \"0.52410686\",\n \"text\": \"def _get_session(self) -> requests.Session:\\n session = requests.Session()\\n retry = rq_retry.Retry(\\n total=self.retries_number,\\n read=self.retries_number,\\n connect=self.retries_number,\\n backoff_factor=self.backoff_factor,\\n status_forcelist=self.status_forcelist,\\n )\\n adapter = rq_adapt.HTTPAdapter(max_retries=retry)\\n session.mount(\\\"https://\\\", adapter)\\n return session\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db944c32a46d21a6402048805b0cebf8\",\n \"score\": \"0.5240774\",\n \"text\": \"def open_connection(self):\\n connection = tcp.TCPConnection(config.CONF['vim-api']['rpc_host'],\\n config.CONF['vim-api']['rpc_port'])\\n connection.connect(config.CONF['vim']['rpc_host'],\\n config.CONF['vim']['rpc_port'])\\n self._connections.append(connection)\\n return connection\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79fa9459a1bdee50bb1ae4dc87286e33\",\n \"score\": \"0.5233544\",\n \"text\": \"def establish_connection(self):\\n conninfo = self.client\\n for name, default_value in self.default_connection_params.items():\\n if not getattr(conninfo, name, None):\\n setattr(conninfo, name, default_value)\\n if conninfo.ssl:\\n conninfo.qpid_transport = 'ssl'\\n conninfo.transport_options['ssl_keyfile'] = conninfo.ssl[\\n 'keyfile']\\n conninfo.transport_options['ssl_certfile'] = conninfo.ssl[\\n 'certfile']\\n conninfo.transport_options['ssl_trustfile'] = conninfo.ssl[\\n 'ca_certs']\\n if conninfo.ssl['cert_reqs'] == ssl.CERT_REQUIRED:\\n conninfo.transport_options['ssl_skip_hostname_check'] = False\\n else:\\n conninfo.transport_options['ssl_skip_hostname_check'] = True\\n else:\\n conninfo.qpid_transport = 'tcp'\\n\\n credentials = {}\\n if conninfo.login_method is None:\\n if conninfo.userid is not None and conninfo.password is not None:\\n sasl_mech = 'PLAIN'\\n credentials['username'] = conninfo.userid\\n credentials['password'] = conninfo.password\\n elif conninfo.userid is None and conninfo.password is not None:\\n raise Exception(\\n 'Password configured but no username. SASL PLAIN '\\n 'requires a username when using a password.')\\n elif conninfo.userid is not None and conninfo.password is None:\\n raise Exception(\\n 'Username configured but no password. SASL PLAIN '\\n 'requires a password when using a username.')\\n else:\\n sasl_mech = 'ANONYMOUS'\\n else:\\n sasl_mech = conninfo.login_method\\n if conninfo.userid is not None:\\n credentials['username'] = conninfo.userid\\n\\n opts = {\\n 'host': conninfo.hostname,\\n 'port': conninfo.port,\\n 'sasl_mechanisms': sasl_mech,\\n 'timeout': conninfo.connect_timeout,\\n 'transport': conninfo.qpid_transport\\n }\\n\\n opts.update(credentials)\\n opts.update(conninfo.transport_options)\\n\\n conn = self.Connection(**opts)\\n conn.client = self.client\\n self.session = conn.get_qpid_connection().session()\\n self.session.set_message_received_notify_handler(\\n self._qpid_message_ready_handler\\n )\\n conn.get_qpid_connection().set_async_exception_notify_handler(\\n self._qpid_async_exception_notify_handler\\n )\\n self.session.set_async_exception_notify_handler(\\n self._qpid_async_exception_notify_handler\\n )\\n return conn\",\n \"title\": \"\"\n },\n {\n \"docid\": \"358c7306625b06d2a4a24e3aa96e7dcf\",\n \"score\": \"0.5217251\",\n \"text\": \"def connect(*args, **kwargs):\\n return Connection(args, kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf1d9bb1baff67401275d80504f2e7b4\",\n \"score\": \"0.52161855\",\n \"text\": \"def connect_server(self) -> rpc.RPCSession:\\n tracker = self.connect_tracker()\\n session: rpc.RPCSession = tracker.request(\\n key=self.tracker_key,\\n priority=self.session_priority,\\n session_timeout=self.session_timeout_sec,\\n )\\n return session\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7a74737e101619910201bd1fa0686ed\",\n \"score\": \"0.52113956\",\n \"text\": \"def get_client(self):\\n return self.session\",\n \"title\": \"\"\n },\n {\n \"docid\": \"771964ad6b85edd071234fdc2be90619\",\n \"score\": \"0.51924706\",\n \"text\": \"async def _connect(self):\\n logger.debug(\\\"connecting to the stream\\\")\\n await self.client.setup\\n if self.session is None:\\n self.session = self.client._session\\n kwargs = await self.client.headers.prepare_request(**self.kwargs)\\n request = self.client.error_handler(self.session.request)\\n\\n return await request(timeout=0, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93843003c088dd5d18f12abef3194e56\",\n \"score\": \"0.5192259\",\n \"text\": \"def connect(hostname, username, password):\\n conn = BlitzGateway(username, password,\\n host=hostname, secure=True)\\n conn.connect()\\n conn.c.enableKeepAlive(60)\\n return conn\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4590052c41063f9f5a2c63bc5072d646\",\n \"score\": \"0.51867414\",\n \"text\": \"def connection(self, context=None):\\n if context is None:\\n context = ssl.SSLContext(ssl.PROTOCOL_TLS)\\n context.check_hostname = False\\n context.verify_mode = ssl.CERT_NONE\\n\\n address = (self.args.host, self.args.port)\\n with socket.create_connection(address, timeout=5) as sock:\\n with context.wrap_socket(sock, server_hostname=self.args.host) as ssock:\\n yield ssock\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92cec4f398f40733bbc00da42a838add\",\n \"score\": \"0.5182721\",\n \"text\": \"async def open_connection(self) -> _BaseConnector:\\n connector = self.connector\\n if connector is None:\\n raise HekrAPIException('No connector attached to device')\\n\\n if not connector.is_connected:\\n await connector.open_connection()\\n\\n return connector\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53f1e1f16aad6c0ad2a581c357916cdc\",\n \"score\": \"0.5178644\",\n \"text\": \"def get_connection(self):\\n return sqlite3.connect(self.database)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2301e98f7801319ff39b021fde35517\",\n \"score\": \"0.51771706\",\n \"text\": \"def connect(cls, url: Optional[str] = None) -> \\\"YubiHsm\\\":\\n return cls(get_backend(url))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49bb63a97ecd309e79718a3ab9e12650\",\n \"score\": \"0.51625913\",\n \"text\": \"def connect(self):\\n\\n if self.getConfig().initDone is False:\\n logging.debug('connecting without config init')\\n raise ConfigError(self.__class__.__name__, Error.errDict['initBad']['errCode'], Error.errDict['initBad']['errMsg'])\\n\\n headers = {'user-agent': self.userAgent}\\n formData = {'client_id': self.getConfig().getClientID(),\\n 'client_secret': self.getConfig().getClientSecret(),\\n 'grant_type': self.getConfig().getGrantType(),\\n 'code': self.getConfig().getAuthorizationCode(),\\n 'redirect_uri': self.getConfig().getRedirectURL()}\\n r = requests.post(self.getConfig().getTokenServerURL(),\\n headers=(headers),\\n cert=(self.getConfig().getSSLCertPath(),\\n self.getConfig().getSSLKeyPath()),\\n data=(formData),\\n verify=(self.getConfig().getSSLCertPath()))\\n logging.debug(r.status_code)\\n logging.debug(r.json())\\n if (r.status_code == requests.codes.ok):\\n self.connection['status'] = 'connected'\\n self.connection['token'] = r.json()['access_token']\\n self.connection['expires'] = datetime.datetime.now() + datetime.timedelta(0, r.json()['expires_in'], 0)\\n if self.getSessionState() == '':\\n self.setSessionState(str(uuid.uuid1()))\\n else:\\n raise ConnectError(self.__class__.__name__, str(r.status_code), 'Unable to connect to ADP')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d28faf5e70ae0468852309db28d485a3\",\n \"score\": \"0.5158046\",\n \"text\": \"def __GetConnection(self, transaction):\\r\\n self.__connection_lock.acquire()\\r\\n request_tx = transaction and transaction.handle()\\r\\n if request_tx == 0:\\r\\n request_tx = None\\r\\n if request_tx != self.__current_transaction:\\r\\n raise apiproxy_errors.ApplicationError(\\r\\n datastore_pb.Error.BAD_REQUEST,\\r\\n 'Only one concurrent transaction per thread is permitted.')\\r\\n return self.__connection\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":689,"cells":{"query_id":{"kind":"string","value":"9b337da8305531e87506f09a76a8b1cf"},"query":{"kind":"string","value":"Receives a message from the CCM using the configured adapter"},"positive_passages":{"kind":"list like","value":[{"docid":"13b97c92b7ab12f1f249fbb976afc713","score":"0.73328966","text":"def receive_message_from_ccm(self) -> str:\n with hw_lock:\n return self.ccm_adapter.receive_message_with_stop_byte()","title":""}],"string":"[\n {\n \"docid\": \"13b97c92b7ab12f1f249fbb976afc713\",\n \"score\": \"0.73328966\",\n \"text\": \"def receive_message_from_ccm(self) -> str:\\n with hw_lock:\\n return self.ccm_adapter.receive_message_with_stop_byte()\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"8c6cd8c20e92b52dfd75e49af8253aa7","score":"0.7003274","text":"def receive_message(self):\n pass","title":""},{"docid":"586a9036a9863930cd60d2c5f50d037a","score":"0.6642486","text":"def rcv_message(self, message):\n pass","title":""},{"docid":"1c961b58bd64ade3bf545d7a8d81ba51","score":"0.6423138","text":"def receive_data(self):\n c_message = utils.receive_data(self.active_socket, 'encrypted message')\n print('received encrypted message:\\n{c_m}'.format(c_m=base64.b64encode(c_message)))\n print('decrypting message ...\\n')\n message = utils.aes_decrypt(self.cipher, c_message)\n print('do you want to write the message to a file ? if so input a \"file:absolute_file_path\" (example = '\n 'file:/etc/my_file.txt) if not type no\\n')\n choice = input('input your choice: ')\n if choice[:5] == 'file:':\n utils.write_to_file(message, choice[5:])\n else:\n print('decrypted message: {message}'.format(message=message.decode()))\n self.received_messages.append(message.decode())","title":""},{"docid":"dd5a479fd9ad746f230bada9262dffc1","score":"0.63875616","text":"async def recv_message(self):\n ...","title":""},{"docid":"b619eb17ff87f506702d59cdebb42589","score":"0.6357205","text":"def receiveAndroid(self):\n try:\n msg = self.client_sock.recv(1024)\n dispatcher.send(message=msg, signal=ts.ANDROID_SIGNAL, sender=ts.ANDROID_SENDER)\n self.sendAndroid(\"This message has been received: \" + msg)\n # logging.info(\"Received \" + msg)\n return msg\n except BluetoothError:\n logging.info(\"Bluetooth Error\")\n self.connect()","title":""},{"docid":"a35fb1577414e35798b91fb33b0b20f4","score":"0.63511354","text":"def receive_message(self, message: bytes):\r\n schc_message = SCHCParser.from_bytes(self.sm.protocol, message)\r\n if isinstance(schc_message, SCHCAck):\r\n return self.receive_schc_ack(schc_message)\r\n elif isinstance(schc_message, SCHCReceiverAbort):\r\n return self.receive_schc_receiver_abort(schc_message)\r\n else:\r\n raise ValueError(\"Bytes received could not be decoded\")","title":""},{"docid":"505095bf9efa5bef0de9e96a0371bf44","score":"0.6346035","text":"def __receive_message_callback(self, message, counter):\n b = message.get_bytearray()\n size = len(b)\n config.logging.warning('FirebaseTest: __receive_message_callback - Message Received: \\n'\n ' Data: [{0}]'.format(b[:size].decode('utf-8')))\n self._execute_commands(b[:size].decode('utf-8'))\n return IoTHubMessageDispositionResult.ACCEPTED","title":""},{"docid":"4d3426538bdb08e104bef77d754df46b","score":"0.63265383","text":"def message_received(self, msg):","title":""},{"docid":"1543518f4d1d432d3d6eb6760c8af93f","score":"0.62314844","text":"def _receive_message(self):\n\n return self.connection.getresponse().read().decode()","title":""},{"docid":"380e4a7e57081b1d8c883aa89e01151d","score":"0.62233067","text":"def consume(self, msg):\n pass","title":""},{"docid":"05dbf10fc55d1adbb51fba04673a02ed","score":"0.6222334","text":"def receive_msg(self, msg):\n return None","title":""},{"docid":"ab2339715ee1c23b49e25d4ee81afa54","score":"0.6130102","text":"def _receive(self, msg, msgID):\n raise NotImplementedError(\"The method '_receive' has to \"\n 'be implemented.')","title":""},{"docid":"3fbcbade1a4b6a4aaef371d7fd859d56","score":"0.6121717","text":"def receive(self):\n AMOEBA_RECEIVE_DEBUG=0\n if AMOEBA_RECEIVE_DEBUG:\n print \"Receive message.\"\n # Retrieve the received data from the server and process the strings.","title":""},{"docid":"64b73eb3d030e8b9b4919a55f8c2e4f3","score":"0.6078694","text":"def test_peer_to_peer_receive_short(self):\r\n self.can_messages = [\r\n (TestECU.MsgType.CANRX, 0x00DC0201, [1, 2, 3, 4, 5, 6, 7, 8], 0.0), # TP.CM RTS\r\n ]\r\n\r\n self.pdus = [\r\n (TestECU.MsgType.PDU, 56320, [1, 2, 3, 4, 5, 6, 7, 8], 0),\r\n ]\r\n\r\n self.ecu.subscribe(self._on_message)\r\n self._inject_messages_into_ecu()\r\n # wait until all messages are processed asynchronously\r\n while len(self.pdus)>0:\r\n time.sleep(0.500)\r\n # wait for final processing \r\n time.sleep(0.100)\r\n self.ecu.unsubscribe(self._on_message)","title":""},{"docid":"04ae75e9a7d4cfb4025045b5c344c4c9","score":"0.6002246","text":"def receive(self) -> FBSPMessage:\n msg = self.channel.receive(self.timeout)\n if isinstance(msg, ErrorMessage):\n raise self.protocol.exception_for(msg)\n if msg is TIMEOUT:\n raise TimeoutError()\n if msg is INVALID:\n raise Error(\"Invalid response from service\")\n if msg.msg_type is MsgType.CLOSE:\n raise Error(\"Connection closed by service\")\n return msg","title":""},{"docid":"262092244c27897b26fa0ce1fc6ea053","score":"0.59878373","text":"def recieve():\n\n while 1:\n msg_len = client.recv(HEADER).decode(FORMAT)\n if msg_len:\n msg_len = int(msg_len)\n msg = client.recv(msg_len).decode(FORMAT)\n if msg == DC_CMD:\n break\n if msg != \"\":\n chat.display_message(msg)","title":""},{"docid":"3e93a651b82d8b4b24e459ba8a9db22f","score":"0.59755003","text":"def messageReceived(self, message):\r\n self.sendMessage(message)","title":""},{"docid":"0d55edd0a89211f8b631d58bbd766cfa","score":"0.59641314","text":"async def on_message_received(_msg: \"api.ChatMessage\"):","title":""},{"docid":"62a79038957d8091f05ceb6e4f5c70fc","score":"0.5932269","text":"def on_message(self, message, transport):","title":""},{"docid":"c64118beb04b40b16a946b6cdb57c5dc","score":"0.5929551","text":"def message_arrived(self, msg):\n self.buffer += msg\n #is the whole message here?\n msgLen, msgData = Basic.read_short(self.buffer)\n if len(msgData) >= msgLen:\n msgData = msgData[:msgLen]\n #we just discard the rest of the cell, two messages are never packed in the same cell currently\n self.buffer = \"\"\n #what type of message is this?\n msgType, msgData = Basic.read_byte(msgData)\n #ok, now handle that message:\n for msgName in MESSAGE_CODES.keys():\n if msgType == MESSAGE_CODES[msgName]:\n #if we don't know how to handle this message, just close the circuit\n if msgName not in self.messageHandlers:\n log_msg(\"Remote request for %s, which we do not know how to handle\" % (msgName), 1)\n self.close()\n return\n #get the handler:\n handler = self.messageHandlers[msgName]\n #get the handler function:\n funcName = \"handle_%s\" % (msgName)\n if not hasattr(handler, funcName):\n raise Exception(\"%s cannot handle %s payment message?\" % (handler, msgName))\n f = getattr(handler, funcName)\n f(msgData)\n return\n #uhh, not sure how to handle this message:\n raise Exception(\"Unknown message type for payment message: %s\" % (msgType))","title":""},{"docid":"38a3144e88b5d934fd9de435ca5863cf","score":"0.59223104","text":"def receive_request_message(self):\n raise NotImplementedError()","title":""},{"docid":"1806ae014a6b056f38d860d29511c9f0","score":"0.5918319","text":"def receive_message(self):\n\n if self._request.client_terminated:\n raise BadOperationException(\n 'Requested receive_message after receiving a closing '\n 'handshake')\n\n while True:\n \n \n \n frame_type_str = self.receive_bytes(1)\n frame_type = ord(frame_type_str)\n if (frame_type & 0x80) == 0x80:\n \n \n length = self._read_payload_length_hixie75()\n if length > 0:\n _ = self.receive_bytes(length)\n \n \n if not self._enable_closing_handshake:\n continue\n\n if frame_type == 0xFF and length == 0:\n self._request.client_terminated = True\n\n if self._request.server_terminated:\n self._logger.debug(\n 'Received ack for server-initiated closing '\n 'handshake')\n return None\n\n self._logger.debug(\n 'Received client-initiated closing handshake')\n\n self._send_closing_handshake()\n self._logger.debug(\n 'Sent ack for client-initiated closing handshake')\n return None\n else:\n \n bytes = self._read_until('\\xff')\n \n \n \n message = bytes.decode('utf-8', 'replace')\n if frame_type == 0x00:\n return message","title":""},{"docid":"cd68d90ef4eb23ec5e08c31a7d9171e4","score":"0.5913723","text":"def receive(self, packet):\n super(Messenger, self).receive(packet)\n\n if packet.data['tk'] == TrnsKind.message:\n if packet.data['pk'] == PcktKind.ack: # more\n self.acked = True\n self.another() # continue message\n elif packet.data['pk'] == PcktKind.resend: # resend\n self.acked = True\n self.resend() # resend missed segments\n elif packet.data['pk'] == PcktKind.done: # completed\n self.acked = True\n self.complete()\n elif packet.data['pk'] == PcktKind.nack: # rejected\n self.reject()","title":""},{"docid":"b827062d0b2801d3ff2c3bfc98eddc3b","score":"0.5911782","text":"def c2_listener_callback(self, msg):\n self.channel_two_data = msg.data","title":""},{"docid":"a1b8f3b5299df0a848c2f2fbd58484c9","score":"0.5896654","text":"def receive(self, message):\n raise NotImplemented()","title":""},{"docid":"ac8f592c5806606f6e0d9a05ff5eeaa2","score":"0.58768207","text":"def receive(self):\n self.__print_verbose(\"Waiting for message\")\n msg = self.f.readline()\n if self.echo:\n self.__print_verbose(\"Received message: \\\"\" + msg +\"\\\"\")\n else:\n self.__print_verbose(\"Received message\")\n return msg","title":""},{"docid":"df0e9157b058028fc3029284127dad62","score":"0.58523536","text":"def receiveMessage(self, msg):\r\n super(MessagingGateway, self).receiveMessage(msg)\r\n self.distributeMessage(msg, self.getId())","title":""},{"docid":"33fda0e3cbf0338354068d4d71489f25","score":"0.5851399","text":"def _receive(self, raw_bytes, ancdata, src_addr):\n raise NotImplementedError","title":""},{"docid":"e71ce4cf75e7077d00b6af805ab6e7c7","score":"0.58372515","text":"def _get_receive_message(self):\n return self.__receive_message","title":""},{"docid":"ef2fd1df2838b638e3e6d9902b646545","score":"0.58371794","text":"def test_broadcast_receive_short(self):\r\n self.can_messages = [\r\n (TestECU.MsgType.CANRX, 0x00FEB201, [1, 2, 3, 4, 5, 6, 7, 8], 0.0), \r\n ]\r\n\r\n self.pdus = [\r\n (TestECU.MsgType.PDU, 65202, [1, 2, 3, 4, 5, 6, 7, 8]),\r\n ]\r\n\r\n self.ecu.subscribe(self._on_message)\r\n self._inject_messages_into_ecu()\r\n # wait until all messages are processed asynchronously\r\n while len(self.pdus)>0:\r\n time.sleep(0.500)\r\n # wait for final processing \r\n time.sleep(0.100)\r\n self.ecu.unsubscribe(self._on_message)","title":""},{"docid":"853f945589d241335d75fb04a2f62cfc","score":"0.5826855","text":"def receive(self):\n pass","title":""},{"docid":"7cfce78dbfc69eaf48234390349260f6","score":"0.5818358","text":"def receive(self, packet):\n super(Messengent, self).receive(packet)\n\n # resent message\n if packet.data['tk'] == TrnsKind.message:\n if packet.data['pk'] == PcktKind.message:\n self.message()\n elif packet.data['pk'] == PcktKind.nack: # rejected\n self.reject()","title":""},{"docid":"754d590d3924985dc65d237436439808","score":"0.5808071","text":"def receive(self, message):\n body = [b.decode() for t, b in message.bodies('text/plain')][0]\n self.process_update(message.to, body)","title":""},{"docid":"e7e2681514f337c80a6a12db9c697964","score":"0.5807718","text":"def data_received(self, message):\n pass","title":""},{"docid":"88d36849c4fc30963fef41223cbe28fc","score":"0.58055055","text":"def receive(self):","title":""},{"docid":"fec975a84d9977310d6de48d75fd3173","score":"0.5766006","text":"def receive_message(self) -> NetMessage:\n\n header = self.sock.recv(struct.calcsize(MSG_HEADER_FORMAT))\n msg_type, msg_len = struct.unpack_from(MSG_HEADER_FORMAT, header, 0)\n payload = self.sock.recv(msg_len)\n typed_message = NetMessage(msg_type)\n self.message_callbacks[typed_message](payload)\n return typed_message","title":""},{"docid":"4c3616f0047bf9f4d6d43d61fed7e2b6","score":"0.57630056","text":"def MsgRead (self, Rcvid, Buffer, Bytes, Offset = 0):\n\treturn self._MsgRead (Rcvid, Buffer, Bytes, Offset)","title":""},{"docid":"db842e1a2dd7520de813175109771f1f","score":"0.57459533","text":"def recv(self) -> BatsimMessage:\n if not self.is_connected:\n raise SystemError(\"Connection not opened.\")\n\n return self.__socket.recv_json(object_hook=BatsimMessageDecoder())","title":""},{"docid":"5dc65855ad0f2da3d6ba3c1b6a3e5fc3","score":"0.5738059","text":"def _on_mqtt_message(self, client, userdata, msg):\n logging.debug(f\"RCV: {msg.topic}: {str(msg.payload)}\")\n payload = json.loads(msg.payload)\n self._on_message(msg.topic, payload)","title":""},{"docid":"cb301f3a6cce950b0aeffcb228285633","score":"0.571993","text":"def read(self):\n # We wait for a message (and its size)\n data_len = self._rfxcom.read()\n hex_data_len = binascii.hexlify(data_len)\n int_data_len = int(hex_data_len, 16)\n print(\"----------------------------\")\n self._log.debug(\"----------------------------\")\n print(\"LENGTH = %s\" % int_data_len)\n self._log.debug(\"LENGTH = %s\" % int_data_len)\n\n if int_data_len != 0:\n # We read data\n data = self._rfxcom.read(int_data_len)\n hex_data = binascii.hexlify(data)\n print(\"DATA = %s\" % hex_data)\n self._log.debug(\"DATA = %s\" % hex_data)\n\n # Process data\n self._process_received_data(hex_data)","title":""},{"docid":"449ae5167395368062ed12429200c9e6","score":"0.571637","text":"def send_message_to_ccm(self, message: str):\n with hw_lock:\n self.logger.debug(f\" PI --> CCM: {message}\")\n self.ccm_adapter.send_message(message)","title":""},{"docid":"6e0738608a2d5c9bdbc1b2c5317dd27e","score":"0.5704182","text":"def callback_message(self, conn, mess):\n\n # Prepare to handle either private chats or group chats\n type = mess.getType()\n jid = mess.getFrom()\n props = mess.getProperties()\n text = mess.getBody()","title":""},{"docid":"b213c943d54f4355adf4bf09328f5877","score":"0.5692245","text":"def extract_message(tcp_buffer):\r\n pass","title":""},{"docid":"967942d059a45cc3f987ae220d06b85a","score":"0.56857777","text":"def decode_message(self, message):\n # Current readings request\n if message == \"CR\":\n print(\"Send Current Readings\")\n current_reading = self.get_reading()\n if current_reading:\n self.send_current_readings(current_reading)\n # Previous stored readings request\n elif message == \"NA\":\n print(\"Get Network Activity\")\n activity = self.get_network_activity()\n if activity:\n self.send_network_activity(activity)\n # Plot data request\n elif message == \"PD\":\n print(\"Send Plot Data\")\n plot_data = self.get_plot_data()\n if plot_data:\n self.send_plot_data(plot_data)\n # Unknown request\n else:\n print(\"Unsupported message, nothing to do!\")","title":""},{"docid":"f0e931a6df75731713f83b1dc5e78bc3","score":"0.5682126","text":"def receive(self):\n while True:\n data = self._conn.recv(1024)\n if data: break\n DEBUG and print(\"Received message: \" + data.decode('utf-8'))\n return data","title":""},{"docid":"a9036cfd8423098367427765fe3f0714","score":"0.5679466","text":"def receive(self):\n try:\n self._last_reply = self._last_reply.decode()\n except UnicodeDecodeError:\n raise PLConnectionProtocolError(f\"Can't decode device reply!\\n {self._last_reply}\")\n return LabDeviceReply(body=self._last_reply, parameters=self._last_reply_headers, content_type=\"json\")","title":""},{"docid":"95cb42b3227595e270264dd252ec86ce","score":"0.5668115","text":"async def recv(self) -> T:\n if self._listener is None and self._messages.empty():\n raise exceptions.ChannelClosedError(\n \"the channel is closed and no residual message exist\"\n )\n\n loop = asyncio.get_event_loop()\n waiter: Future[T] = loop.create_future()\n\n def _release_waiter(f: Union[Future[T], Future[None]]) -> None:\n if waiter.done():\n return\n elif (exc := f.exception()) :\n waiter.set_exception(exc)\n elif (res := f.result()) :\n waiter.set_result(res)\n else:\n waiter.set_exception(\n exceptions.ChannelClosedError(\"the channel is closed\")\n )\n\n if self._listener:\n self._listener.add_done_callback(_release_waiter)\n waiter.add_done_callback(\n partial(self._listener.remove_done_callback, _release_waiter)\n )\n\n getter = asyncio.create_task(self._messages.get())\n getter.add_done_callback(_release_waiter)\n try:\n return await waiter\n finally:\n getter.cancel()","title":""},{"docid":"1ce900faa0b3fb345628108148dec31a","score":"0.565768","text":"def receive(self, message):\n\n self.messages.append(message)\n\n if message.type == MessageTypes.PA_BLOCKS:\n print \"replying blocks\"\n reply = Message(MessageTypes.PA_BLOCKS_REPLY,\n self.public_key,\n self.interactions.get_blocks())\n self.interactions.add_blocks(message.payload)\n self.interface.send(message.sender, reply)\n if message.type == MessageTypes.PA_BLOCKS_REPLY:\n print \"sending score\"\n self.interactions.add_blocks(message.payload)\n reply = Message(MessageTypes.PA_SCORE,\n self.public_key,\n True)\n self.interface.send(message.sender, reply)\n self.endorsements.append(Endorsement([self.public_key, message.sender, True]))\n if message.type == MessageTypes.PA_SCORE:\n print \"replying score\"\n reply = Message(MessageTypes.PA_SCORE_REPLY,\n self.public_key,\n True)\n self.endorsements.append(Endorsement([self.public_key, message.sender, True]))\n self.interface.send(message.sender, reply)\n if message.type == MessageTypes.CHAIN:\n reply = Message(MessageTypes.CHAIN_REPLY,\n self.public_key,\n self.chain)\n self.interface.send(message.sender, reply)\n if message.type == MessageTypes.CHAIN_REPLY:\n self.interactions.add_blocks(message.payload.get_blocks())","title":""},{"docid":"43222a76175ff0eb67b3a466e024da14","score":"0.56537455","text":"async def fetch_message(self, channel: SupportsInt, message: SupportsInt) -> Dict[str, Any]:\n return await self.request(Route(\n 'GET', '/channels/{channel_id}/messages/{message_id}',\n channel_id=int(channel), message_id=int(message)\n ))","title":""},{"docid":"60da68bc8910fefca393f93340b1c7b4","score":"0.56529784","text":"def receive(self,msgQueue, callbackF):\n cParams = pika.ConnectionParameters(host=self.host)\n connection = pika.BlockingConnection(cParams)\n channel = connection.channel()\n\n channel.queue_declare(queue=msgQueue) # ensure queue created\n channel.basic_consume(callbackF, queue=msgQueue, no_ack=True)\n channel.start_consuming()\n # will only be reached once channel stops consuming (assumed in callback)\n connection.close()","title":""},{"docid":"dbdf022702f906189bcf2c45e1334926","score":"0.5637638","text":"def _message_callback(mqttc, userdata, msg):\n callback(msg.topic, msg.payload.decode(\"utf-8\"), msg.qos)","title":""},{"docid":"753bd139a774944ba9899d3298541707","score":"0.56216216","text":"def on_message(self, message):\n print(\"Received\",message)","title":""},{"docid":"6ee78fbe54ae4defaf534e1e60692454","score":"0.5619653","text":"def channel_message(self, message_type, channel, data):\n pass","title":""},{"docid":"d2586ecd517be62ab9783241322393bc","score":"0.5618884","text":"def onMessage(self, payload, isBinary):\n self.factory.communicate(self, payload, isBinary)","title":""},{"docid":"b2d10881afea3260524a5407a3535c5e","score":"0.5614722","text":"async def receive(self, payload_enc: Union[str, bytes]) -> InboundMessage:\n if self._check_relay_context:\n await self.handle_relay_context(payload_enc)\n self._check_relay_context = False\n\n message = await self.parse_inbound(payload_enc)\n self.receive_inbound(message)\n return message","title":""},{"docid":"eec4e2bab89e097a6f3cd45a747fef51","score":"0.5613303","text":"def on_accept_client(self, channel: Channel, msg: HelloMessage) -> None:","title":""},{"docid":"dd6b7d75a38d43507137476ca5b06e01","score":"0.56121206","text":"def message_received(self, new_message):\n pass","title":""},{"docid":"3254eb89994bb80fb5c8bb65545bb930","score":"0.561203","text":"def receive_messages(self) -> None:\n header = self.client_socket.recv(struct.calcsize(MSG_HEADER_FORMAT))\n msg_type, msg_len = struct.unpack_from(MSG_HEADER_FORMAT, header, 0)\n payload = self.client_socket.recv(msg_len)\n self.message_callbacks[NetMessage(msg_type)](payload)","title":""},{"docid":"5ece0d043535e979ae6596524542a89e","score":"0.56118125","text":"def recv_msg(self, decode=True):\n data = self.recv_all(self.sock, self.data_size)\n if decode:\n return data.decode('utf-8')\n return data","title":""},{"docid":"b0a4e4cb7fc8f6328f145f82b7ce2585","score":"0.5611682","text":"def __receive_message(self, return_pattern=None, multiline=False):\n try:\n # checking if a connection is there\n if self.__connection is not None:\n # if multiple lines are expected, keep reading lines until no more lines come in\n if multiline:\n answer = \"\"\n while True:\n line = self.__connection.readline()\n if line:\n answer += line.decode(self.standard_encoding)\n else:\n break\n # if just one line is expected, just read one line (faster than always waiting for the timeout)\n else:\n answer = self.__connection.readline()\n # decoding the answer using the standard settings from __init__\n answer = answer.decode(self.standard_encoding)\n\n # if the user wants a code check performed\n if return_pattern is not None:\n if type(return_pattern).__name__ != \"SRE_Pattern\":\n raise ValueError(\n \"The return code you specified was not a valid regular expression: {0}\".format(return_pattern)\n )\n\n if re.fullmatch(pattern=return_pattern, string=answer):\n # if the answer matches the desired pattern return the read value\n return re.match(pattern=return_pattern, string=answer).groups()\n else:\n self.logger.critical(\n \"Value Error. Serial device did not return correct return code. Send: \\\"{0}\\\". \"\n \"Received: \\\"{1}\\\".\".format(return_pattern, answer)\n )\n if self.__soft_fail_for_testing:\n return answer.strip()\n else:\n raise ValueError(\n \"Value Error. Serial device did not return correct return code. Send: \\\"{0}\\\". \"\n \"Received: \\\"{1}\\\".\".format(return_pattern, answer)\n )\n else:\n # if no return code checking was requested, just return the read value\n return answer.strip()\n else:\n raise Exception(\"Error. No connection to the device\")\n except Exception as e:\n # This is not very elegant, but lets the user know that the device failed while retaining the error\n # information\n raise Exception(\"Serial device message receive failed. Error Message: {0}\\n{1}\".format(e, e.__traceback__))","title":""},{"docid":"85008ed6dc214544af856a1a74f413d8","score":"0.5609495","text":"def munch(self, unused):\n self._channel.add_on_cancel_callback(self.cancel_channel)\n self._consumer_tag = self._channel.basic_consume(self._process_message)","title":""},{"docid":"cb7b51f89276e598e606fd0ce546c9d5","score":"0.56038445","text":"def _received_message_handler(self, channel, method, properties, body):\n pass","title":""},{"docid":"a14438dbfcab1d95235b2c86650059cf","score":"0.55976534","text":"async def receive(self, text_data=None, bytes_data=None):\n text_data_json = json.loads(text_data)\n message = text_data_json['message']\n\n # Send message to room group\n await self.channel_layer.group_send(\n self.room_group_name,\n {\n 'type': 'chat_message',\n 'message': message,\n }\n )","title":""},{"docid":"f1f8e25c69f52107ea8acb8ba2166030","score":"0.5596549","text":"def processReceivedMessage(iTag, clsName, msgID, msg): #@NoSelf","title":""},{"docid":"24ca69ae9dd7d572335204d03a0b6e68","score":"0.55940956","text":"def receive(self, message):\n\n try:\n # Ensure the messae decodes\n message = loads(message)\n\n # Make sure some basic stuctures are here\n originator = message[\"from\"]\n tx, action = message['Tx'], message['action']\n idx, deps, new_obj, data = tx\n\n except Exception as e:\n raise Exception(\"Badly formatted messages: %s\" % str(e))\n\n # Ignore messages we sent\n if self.name == originator:\n return\n \n if not idx == packageTx(data, deps, len(new_obj))[0]:\n raise Exception(\"Invalid transaction.\")\n\n if not self._within_TX(tx):\n if action == \"process\":\n # We are going to re-route the message on a correct channel\n msg = dumps({ \"action\":\"process\", \"from\":self.name, \"Tx\":tx })\n self.send(tx, msg)\n return\n else:\n raise Exception(\"Transaction not of interest.\")\n\n if action == \"vote\":\n # We process an incoming vote.\n n, l, v = message['vote']\n vote = (n, tuple(l), v)\n self.RLogger.info(\"Receive vote (%s) for %s (%s)\" % (v, idx[:6], self.name))\n \n if vote not in self.pending_vote[idx]:\n self.pending_vote[idx].add( vote )\n self.process(tx)\n \n if action == \"commit\":\n # We process an incoming commit.\n yesno = message['yesno']\n self.RLogger.info(\"Receive commit (%s) for %s (%s)\" % (yesno ,idx[:6], self.name))\n\n if yesno:\n self.do_commit_yes(tx)\n else:\n self.commit_no.add(idx)\n\n if action == \"process\":\n # We process a request\n self.process(tx)","title":""},{"docid":"68168949c66b1c88e69521f0c153f83f","score":"0.5591507","text":"def receive(self, node, message):\n\t\tif self.recv: self.recv(node, pickle.loads(message))","title":""},{"docid":"916d9907df5467e0865d8040011e350b","score":"0.5574089","text":"def on_receive(self, message, from_jid, otr_state):\n pass","title":""},{"docid":"839e7bea280e0091919ad06c61302458","score":"0.55709845","text":"async def recv_message(self):\n return await asyncio.sleep(1e10)","title":""},{"docid":"4a9b068e181e6e8d882f05f4785409a0","score":"0.55663013","text":"def _receive(self, _unused_frame, queue, control_center):\n\n log.info(\" [*] Waiting for Messages. To exit press CTRL+C\")\n\n self.channels[queue].basic_consume(queue=queue, on_message_callback=control_center.received_message_handler)","title":""},{"docid":"ec225a6af1012c51cc61d4d191befa75","score":"0.5563348","text":"def dispatch_msg(self, plugin, message):\n self.__run_plugin(plugin, \"recv_msg\", message)","title":""},{"docid":"4fe638da3b3688859bcb118f441e8135","score":"0.5562309","text":"def handle_message(self, msg, rcvtime):\n raise NotImplementedError","title":""},{"docid":"ee71f825bad1937e204d3142d9fb2b45","score":"0.5556639","text":"def receive_message(self):\n message = None\n properties = None\n while not self.done:\n # Process message queue events, returning as soon as possible\n self.server_connection.process_data_events(time_limit=0)\n\n method, properties, body = self.server_channel.basic_get(queue=self.response_server_queue,\n no_ack=True)\n # Return as soon as we get a valid message\n if method is not None:\n print(method, properties, body)\n message = json.loads(body.decode('utf-8'))\n logger.info('Received a message: {} | {}'.format(body, properties.reply_to))\n break\n\n return message, properties","title":""},{"docid":"cbaeedd359cd1e03ec2f4ce076ed77ab","score":"0.5553836","text":"def messageReceived(self, ammsgtype, msg):\n\t#print \"serial.messageReceived - %s\" % msg.tostring()\n if ammsgtype in self.__handlers:\n for handler in self.__handlers[ammsgtype]:\n handler(ammsgtype, msg[:])\n else:\n self.__logger.debug(msg.tostring())","title":""},{"docid":"82e371c7f077e0146d70e89e35a04f8c","score":"0.55513245","text":"async def listen(self):\n\n while True:\n if not self.connected:\n # sleep and hope the checker fixes us\n await asyncio.sleep(5)\n continue\n\n data = await self.read_one_message()\n if data is None:\n await asyncio.sleep(1)\n continue\n\n mtype = self.find_balboa_mtype(data)\n if mtype is None:\n self.log.error(f\"Spa sent an unknown message: {data.hex()}\")\n await asyncio.sleep(0.1)\n continue\n if mtype == BMTR_MOD_IDENT_RESP:\n self.parse_module_identification(data)\n await asyncio.sleep(0.1)\n continue\n if mtype == BMTR_STATUS_UPDATE:\n await self.parse_status_update(data)\n await asyncio.sleep(0.1)\n continue\n if mtype == BMTR_DEVICE_CONFIG_RESP:\n self.parse_device_configuration(data)\n await asyncio.sleep(0.1)\n continue\n if mtype == BMTR_SYS_INFO_RESP:\n self.parse_system_information(data)\n await asyncio.sleep(0.1)\n continue\n if mtype == BMTR_SETUP_PARAMS_RESP:\n self.parse_setup_parameters(data)\n await asyncio.sleep(0.1)\n continue\n if mtype == BMTR_FILTER_INFO_RESP:\n self.parse_filter_cycle_info(data)\n await asyncio.sleep(0.1)\n continue\n self.log.error(\"Unhandled mtype {0}\".format(mtype))","title":""},{"docid":"36bf39777f80d1339cabd83aadc4f0e9","score":"0.5551283","text":"def sms_receive(self) -> Optional[dict]:\n ...","title":""},{"docid":"0b8b38f07cf3f90ab19a4c48a697b3f3","score":"0.554542","text":"def receive(self):\n buffer = self.cnt.get_buffer_and_flush()\n for m in buffer:\n self.messages.append(m)","title":""},{"docid":"4fca0deca003e941070970e746d1aa88","score":"0.5529326","text":"def tcp_message(self, flow: mitmproxy.tcp.TCPFlow):","title":""},{"docid":"daa0e736e62df75557bd0889afbbe291","score":"0.5512056","text":"def dispatch_msg(self, plugin, message):\n self.__run_plugin(plugin, message.audit_name, \"recv_msg\", message)","title":""},{"docid":"636680db535e96e63531f91db6829e7c","score":"0.55093056","text":"def got_message(self, address, message):\n self.callback(message)\n # print(message)\n return \"DONE\"","title":""},{"docid":"5c4ace76e52155089a3ff2727725732f","score":"0.5507662","text":"def recv(self):\n logger.debug(\"LinkReceiver receiving message.\")\n \n # Keep trying until we get something\n while True:\n recv_data = self._recv()\n if recv_data is not None:\n return recv_data","title":""},{"docid":"ecf96e3aaf194e4721e7ee691335f140","score":"0.5503975","text":"def handle_call(self, context, connection):\n print(\"Received message: %s\" % context.message)","title":""},{"docid":"a557aae9874442022b83c803f8c62d39","score":"0.54957265","text":"def c1_listener_callback(self, msg):\n self.channel_one_data = msg.data","title":""},{"docid":"e27f90269d2dc525f2ebe62533fe0011","score":"0.54953676","text":"def handleMessage(self, message):","title":""},{"docid":"77f34777b6faed7a74d75e098fb1a3be","score":"0.5492488","text":"def on_msg(mqttc, obj, msg):\n # print(msg.topic + \" \" + str(msg.qos) + \" \" + str(msg.payload))\n LOG.debug(\"got message\")\n payload = msg.payload.decode(\"UTF-8\")\n topic = msg.topic\n LOG.debug(\"Message topic : %s\" % topic)","title":""},{"docid":"c4e0a7b379037c42dbfb5060a0c539d1","score":"0.54916763","text":"def mqtt_message(self):\n time.sleep(1)\n return self.message","title":""},{"docid":"15b349dddc8baebd5b07c1f0966d7609","score":"0.549045","text":"def test_broadcast_receive_long(self):\r\n self.can_messages = [\r\n (TestECU.MsgType.CANRX, 0x00ECFF01, [32, 20, 0, 3, 255, 0xB0, 0xFE, 0], 0.0), # TP.CM BAM (to global Address)\r\n (TestECU.MsgType.CANRX, 0x00EBFF01, [1, 1, 2, 3, 4, 5, 6, 7], 0.0), # TP.DT 1\r\n (TestECU.MsgType.CANRX, 0x00EBFF01, [2, 1, 2, 3, 4, 5, 6, 7], 0.0), # TP.DT 2\r\n (TestECU.MsgType.CANRX, 0x00EBFF01, [3, 1, 2, 3, 4, 5, 6, 255], 0.0), # TP.DT 3\r\n ]\r\n\r\n self.pdus = [\r\n (TestECU.MsgType.PDU, 65200, [1, 2, 3, 4, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7, 1, 2, 3, 4, 5, 6]),\r\n ]\r\n\r\n self.ecu.subscribe(self._on_message)\r\n self._inject_messages_into_ecu()\r\n # wait until all messages are processed asynchronously\r\n while len(self.pdus)>0:\r\n time.sleep(0.500)\r\n # wait for final processing \r\n time.sleep(0.100)\r\n self.ecu.unsubscribe(self._on_message)","title":""},{"docid":"2257298e838d5b35ca1fc043269858e4","score":"0.5489604","text":"def msg(self, msg):\n if self.simulator:\n message_string = \"{}\".format(msg)\n self.com.send(message_string.encode())\n resp = ''\n if '?' in msg:\n resp = self.com.recv(BUFF_SIZE).decode()\n return resp\n\n else:\n # Writes message\n message_string = \"{}\\r\\n\".format(msg).encode()\n\n # write(message_string)\n self.com.write(message_string)\n\n # Reads response if queried\n resp = ''\n if \"?\" in msg:\n resp = self.com.readline()\n resp = str(resp[:-2], 'utf-8') # Strips terminating chars\n if not resp:\n raise TimeoutError(\"Device timed out\")\n\n # Must wait 10 ms before sending another command\n time.sleep(.01)\n\n return resp","title":""},{"docid":"2d360ddaddf7133b8317c974faf48c60","score":"0.5483794","text":"def handle_message(self, payload):\n assert isinstance(payload, dict)\n if 'getToken' in payload:\n token = channel.create_channel(self.client_id)\n self.send({'token': token})\n elif 'pickup' in payload:\n pickup_id = payload['pickup']\n memcache_key = _PICKUP_MEMCACHE_PREFIX + pickup_id\n results_get_pickup = yield memcache.Client().get_multi_async(\n [memcache_key])\n pickup_data = results_get_pickup.get(memcache_key)\n if pickup_data is not None:\n for service, messages_for_service in pickup_data.iteritems():\n for message in messages_for_service:\n self.send(message, service=service)\n else:\n logging.warning('Memcache read failure.')\n else: logging.error('Unrecognized system service message.')","title":""},{"docid":"6b798b3af11ba9f3d15de282a793d788","score":"0.5477442","text":"def process_received_message(self, message):\n self.log.debug('Received \"%s\"', message)\n\n try:\n cmd, kwargs = bcp.decode_command_string(message)\n self.receive_queue.put((cmd, kwargs))\n except ValueError:\n self.log.error(\"DECODE BCP ERROR. Message: %s\", message)\n raise","title":""},{"docid":"cf55405139c42b5244321789642a33af","score":"0.54757184","text":"def message(self, user, channel, message):\n pass","title":""},{"docid":"62f7cd8370563af7fac48d838c97e207","score":"0.54728395","text":"def receive_data(self):\n return self.message_queue.get(True, 5)\n #return self.message_queue.get_nowait()","title":""},{"docid":"bc18a79e72e810c3a2edcd48f1939667","score":"0.54724187","text":"def recv(self, *args, **kwargs):\n return self.connection.recv(*args, **kwargs)","title":""},{"docid":"b9eb88cb32743abd2b664a3d54fd77b4","score":"0.5469767","text":"def on_message(self, service, msg):\n pass","title":""},{"docid":"53c1beeda9bf9a6838e71d17447052be","score":"0.546417","text":"def receive(self):\n\n logging.info(\"transceiver: start receiving\")\n # request extra packet at start so there is always one ready\n self.xcvr.request_audio_output()\n while 1:\n\n if not self.command_queue.empty():\n break\n\n request = None\n if not self.request_queue.empty():\n request = self.request_queue.get()\n if request == \"frequency\" : \n self.xcvr.request_frequency()\n elif request == \"mode\" : \n self.xcvr.request_mode()\n elif request == \"tx\" : \n self.xcvr.request_TX()\n self.xcvr.request_audio_output()\n data = self.xcvr.get_audio()\n self.player.stdin.write(data)\n\n if request is not None:\n if request == \"frequency\" : \n self.frequency = self.xcvr.get_frequency()\n self.response_queue.put(self.frequency)\n elif request == \"mode\" : \n mode = self.xcvr.get_mode()\n self.response_queue.put(mode)\n if mode != self.mode:\n self.mode = mode\n break\n elif request == \"tx\" : \n tx_rx = self.xcvr.get_TX()\n self.response_queue.put(tx_rx)\n if tx_rx != self.tx_rx:\n self.tx_rx = tx_rx\n break\n\n # when complete, play the extra packet\n data = self.xcvr.get_audio() # process extra request once finished\n self.player.stdin.write(data)\n logging.info(\"transceiver: stop receiving\")","title":""},{"docid":"b769d54988d108b754d46a9f81c37383","score":"0.545968","text":"def receive_msg():\n\n for msg in messager.listen():\n messages.append(msg)","title":""},{"docid":"b769d54988d108b754d46a9f81c37383","score":"0.545968","text":"def receive_msg():\n\n for msg in messager.listen():\n messages.append(msg)","title":""},{"docid":"e31fe1dba11f8a55e93d85bc8b26b353","score":"0.5456528","text":"def test_get_message(self):\n headers = { \n 'Accept': 'application/json',\n 'LoginRequired': 'special-key',\n }\n response = self.client.open(\n '/api/v1/channels/{channel_id}/messages/{message_id}'.format(channel_id=56, message_id=56),\n method='GET',\n headers=headers)\n self.assert200(response,\n 'Response body is : ' + response.data.decode('utf-8'))","title":""},{"docid":"cab724291c29799a58123fd842a693aa","score":"0.5455665","text":"async def receive_message(reader: typing_classes.StreamReader) -> str:\n data = await reader.read(1024)\n return data.decode(errors='ignore')","title":""},{"docid":"de30b4d7d5ae564f3cee8a358ca6c4a4","score":"0.5454776","text":"def received_msg(account, sender, message, conv, flags):\n\n obj = bus.get_object(\"im.pidgin.purple.PurpleService\", \"/im/pidgin/purple/PurpleObject\")\n purple = dbus.Interface(obj, \"im.pidgin.purple.PurpleInterface\")\n\n pmesg = purple.PurpleMarkupStripHtml(message)\n unread = purple.PurpleConversationGetData(conv, \"unseen-count\")\n\n type = purple.PurpleConversationGetType(conv)\n\n if unread or True:\n if type == 1: # That's PURPLE_CONV_TYPE_IM\n g.notify(\"Received IM message\", purple.PurpleConversationGetTitle(conv), pmesg)\n else:\n g.notify(\"Received Chat message\", sender, pmesg)","title":""},{"docid":"8928138262fd5ba28960d6080a3439f2","score":"0.5454483","text":"def lbm_rcv_deliver(self, rcv, msg):\n self.app_rcv_callback(rcv, msg, self.app_clientd)","title":""}],"string":"[\n {\n \"docid\": \"8c6cd8c20e92b52dfd75e49af8253aa7\",\n \"score\": \"0.7003274\",\n \"text\": \"def receive_message(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"586a9036a9863930cd60d2c5f50d037a\",\n \"score\": \"0.6642486\",\n \"text\": \"def rcv_message(self, message):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c961b58bd64ade3bf545d7a8d81ba51\",\n \"score\": \"0.6423138\",\n \"text\": \"def receive_data(self):\\n c_message = utils.receive_data(self.active_socket, 'encrypted message')\\n print('received encrypted message:\\\\n{c_m}'.format(c_m=base64.b64encode(c_message)))\\n print('decrypting message ...\\\\n')\\n message = utils.aes_decrypt(self.cipher, c_message)\\n print('do you want to write the message to a file ? if so input a \\\"file:absolute_file_path\\\" (example = '\\n 'file:/etc/my_file.txt) if not type no\\\\n')\\n choice = input('input your choice: ')\\n if choice[:5] == 'file:':\\n utils.write_to_file(message, choice[5:])\\n else:\\n print('decrypted message: {message}'.format(message=message.decode()))\\n self.received_messages.append(message.decode())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd5a479fd9ad746f230bada9262dffc1\",\n \"score\": \"0.63875616\",\n \"text\": \"async def recv_message(self):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b619eb17ff87f506702d59cdebb42589\",\n \"score\": \"0.6357205\",\n \"text\": \"def receiveAndroid(self):\\n try:\\n msg = self.client_sock.recv(1024)\\n dispatcher.send(message=msg, signal=ts.ANDROID_SIGNAL, sender=ts.ANDROID_SENDER)\\n self.sendAndroid(\\\"This message has been received: \\\" + msg)\\n # logging.info(\\\"Received \\\" + msg)\\n return msg\\n except BluetoothError:\\n logging.info(\\\"Bluetooth Error\\\")\\n self.connect()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a35fb1577414e35798b91fb33b0b20f4\",\n \"score\": \"0.63511354\",\n \"text\": \"def receive_message(self, message: bytes):\\r\\n schc_message = SCHCParser.from_bytes(self.sm.protocol, message)\\r\\n if isinstance(schc_message, SCHCAck):\\r\\n return self.receive_schc_ack(schc_message)\\r\\n elif isinstance(schc_message, SCHCReceiverAbort):\\r\\n return self.receive_schc_receiver_abort(schc_message)\\r\\n else:\\r\\n raise ValueError(\\\"Bytes received could not be decoded\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"505095bf9efa5bef0de9e96a0371bf44\",\n \"score\": \"0.6346035\",\n \"text\": \"def __receive_message_callback(self, message, counter):\\n b = message.get_bytearray()\\n size = len(b)\\n config.logging.warning('FirebaseTest: __receive_message_callback - Message Received: \\\\n'\\n ' Data: [{0}]'.format(b[:size].decode('utf-8')))\\n self._execute_commands(b[:size].decode('utf-8'))\\n return IoTHubMessageDispositionResult.ACCEPTED\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d3426538bdb08e104bef77d754df46b\",\n \"score\": \"0.63265383\",\n \"text\": \"def message_received(self, msg):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1543518f4d1d432d3d6eb6760c8af93f\",\n \"score\": \"0.62314844\",\n \"text\": \"def _receive_message(self):\\n\\n return self.connection.getresponse().read().decode()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"380e4a7e57081b1d8c883aa89e01151d\",\n \"score\": \"0.62233067\",\n \"text\": \"def consume(self, msg):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"05dbf10fc55d1adbb51fba04673a02ed\",\n \"score\": \"0.6222334\",\n \"text\": \"def receive_msg(self, msg):\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab2339715ee1c23b49e25d4ee81afa54\",\n \"score\": \"0.6130102\",\n \"text\": \"def _receive(self, msg, msgID):\\n raise NotImplementedError(\\\"The method '_receive' has to \\\"\\n 'be implemented.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3fbcbade1a4b6a4aaef371d7fd859d56\",\n \"score\": \"0.6121717\",\n \"text\": \"def receive(self):\\n AMOEBA_RECEIVE_DEBUG=0\\n if AMOEBA_RECEIVE_DEBUG:\\n print \\\"Receive message.\\\"\\n # Retrieve the received data from the server and process the strings.\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64b73eb3d030e8b9b4919a55f8c2e4f3\",\n \"score\": \"0.6078694\",\n \"text\": \"def test_peer_to_peer_receive_short(self):\\r\\n self.can_messages = [\\r\\n (TestECU.MsgType.CANRX, 0x00DC0201, [1, 2, 3, 4, 5, 6, 7, 8], 0.0), # TP.CM RTS\\r\\n ]\\r\\n\\r\\n self.pdus = [\\r\\n (TestECU.MsgType.PDU, 56320, [1, 2, 3, 4, 5, 6, 7, 8], 0),\\r\\n ]\\r\\n\\r\\n self.ecu.subscribe(self._on_message)\\r\\n self._inject_messages_into_ecu()\\r\\n # wait until all messages are processed asynchronously\\r\\n while len(self.pdus)>0:\\r\\n time.sleep(0.500)\\r\\n # wait for final processing \\r\\n time.sleep(0.100)\\r\\n self.ecu.unsubscribe(self._on_message)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04ae75e9a7d4cfb4025045b5c344c4c9\",\n \"score\": \"0.6002246\",\n \"text\": \"def receive(self) -> FBSPMessage:\\n msg = self.channel.receive(self.timeout)\\n if isinstance(msg, ErrorMessage):\\n raise self.protocol.exception_for(msg)\\n if msg is TIMEOUT:\\n raise TimeoutError()\\n if msg is INVALID:\\n raise Error(\\\"Invalid response from service\\\")\\n if msg.msg_type is MsgType.CLOSE:\\n raise Error(\\\"Connection closed by service\\\")\\n return msg\",\n \"title\": \"\"\n },\n {\n \"docid\": \"262092244c27897b26fa0ce1fc6ea053\",\n \"score\": \"0.59878373\",\n \"text\": \"def recieve():\\n\\n while 1:\\n msg_len = client.recv(HEADER).decode(FORMAT)\\n if msg_len:\\n msg_len = int(msg_len)\\n msg = client.recv(msg_len).decode(FORMAT)\\n if msg == DC_CMD:\\n break\\n if msg != \\\"\\\":\\n chat.display_message(msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e93a651b82d8b4b24e459ba8a9db22f\",\n \"score\": \"0.59755003\",\n \"text\": \"def messageReceived(self, message):\\r\\n self.sendMessage(message)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d55edd0a89211f8b631d58bbd766cfa\",\n \"score\": \"0.59641314\",\n \"text\": \"async def on_message_received(_msg: \\\"api.ChatMessage\\\"):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"62a79038957d8091f05ceb6e4f5c70fc\",\n \"score\": \"0.5932269\",\n \"text\": \"def on_message(self, message, transport):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c64118beb04b40b16a946b6cdb57c5dc\",\n \"score\": \"0.5929551\",\n \"text\": \"def message_arrived(self, msg):\\n self.buffer += msg\\n #is the whole message here?\\n msgLen, msgData = Basic.read_short(self.buffer)\\n if len(msgData) >= msgLen:\\n msgData = msgData[:msgLen]\\n #we just discard the rest of the cell, two messages are never packed in the same cell currently\\n self.buffer = \\\"\\\"\\n #what type of message is this?\\n msgType, msgData = Basic.read_byte(msgData)\\n #ok, now handle that message:\\n for msgName in MESSAGE_CODES.keys():\\n if msgType == MESSAGE_CODES[msgName]:\\n #if we don't know how to handle this message, just close the circuit\\n if msgName not in self.messageHandlers:\\n log_msg(\\\"Remote request for %s, which we do not know how to handle\\\" % (msgName), 1)\\n self.close()\\n return\\n #get the handler:\\n handler = self.messageHandlers[msgName]\\n #get the handler function:\\n funcName = \\\"handle_%s\\\" % (msgName)\\n if not hasattr(handler, funcName):\\n raise Exception(\\\"%s cannot handle %s payment message?\\\" % (handler, msgName))\\n f = getattr(handler, funcName)\\n f(msgData)\\n return\\n #uhh, not sure how to handle this message:\\n raise Exception(\\\"Unknown message type for payment message: %s\\\" % (msgType))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38a3144e88b5d934fd9de435ca5863cf\",\n \"score\": \"0.59223104\",\n \"text\": \"def receive_request_message(self):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1806ae014a6b056f38d860d29511c9f0\",\n \"score\": \"0.5918319\",\n \"text\": \"def receive_message(self):\\n\\n if self._request.client_terminated:\\n raise BadOperationException(\\n 'Requested receive_message after receiving a closing '\\n 'handshake')\\n\\n while True:\\n \\n \\n \\n frame_type_str = self.receive_bytes(1)\\n frame_type = ord(frame_type_str)\\n if (frame_type & 0x80) == 0x80:\\n \\n \\n length = self._read_payload_length_hixie75()\\n if length > 0:\\n _ = self.receive_bytes(length)\\n \\n \\n if not self._enable_closing_handshake:\\n continue\\n\\n if frame_type == 0xFF and length == 0:\\n self._request.client_terminated = True\\n\\n if self._request.server_terminated:\\n self._logger.debug(\\n 'Received ack for server-initiated closing '\\n 'handshake')\\n return None\\n\\n self._logger.debug(\\n 'Received client-initiated closing handshake')\\n\\n self._send_closing_handshake()\\n self._logger.debug(\\n 'Sent ack for client-initiated closing handshake')\\n return None\\n else:\\n \\n bytes = self._read_until('\\\\xff')\\n \\n \\n \\n message = bytes.decode('utf-8', 'replace')\\n if frame_type == 0x00:\\n return message\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd68d90ef4eb23ec5e08c31a7d9171e4\",\n \"score\": \"0.5913723\",\n \"text\": \"def receive(self, packet):\\n super(Messenger, self).receive(packet)\\n\\n if packet.data['tk'] == TrnsKind.message:\\n if packet.data['pk'] == PcktKind.ack: # more\\n self.acked = True\\n self.another() # continue message\\n elif packet.data['pk'] == PcktKind.resend: # resend\\n self.acked = True\\n self.resend() # resend missed segments\\n elif packet.data['pk'] == PcktKind.done: # completed\\n self.acked = True\\n self.complete()\\n elif packet.data['pk'] == PcktKind.nack: # rejected\\n self.reject()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b827062d0b2801d3ff2c3bfc98eddc3b\",\n \"score\": \"0.5911782\",\n \"text\": \"def c2_listener_callback(self, msg):\\n self.channel_two_data = msg.data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1b8f3b5299df0a848c2f2fbd58484c9\",\n \"score\": \"0.5896654\",\n \"text\": \"def receive(self, message):\\n raise NotImplemented()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac8f592c5806606f6e0d9a05ff5eeaa2\",\n \"score\": \"0.58768207\",\n \"text\": \"def receive(self):\\n self.__print_verbose(\\\"Waiting for message\\\")\\n msg = self.f.readline()\\n if self.echo:\\n self.__print_verbose(\\\"Received message: \\\\\\\"\\\" + msg +\\\"\\\\\\\"\\\")\\n else:\\n self.__print_verbose(\\\"Received message\\\")\\n return msg\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df0e9157b058028fc3029284127dad62\",\n \"score\": \"0.58523536\",\n \"text\": \"def receiveMessage(self, msg):\\r\\n super(MessagingGateway, self).receiveMessage(msg)\\r\\n self.distributeMessage(msg, self.getId())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33fda0e3cbf0338354068d4d71489f25\",\n \"score\": \"0.5851399\",\n \"text\": \"def _receive(self, raw_bytes, ancdata, src_addr):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e71ce4cf75e7077d00b6af805ab6e7c7\",\n \"score\": \"0.58372515\",\n \"text\": \"def _get_receive_message(self):\\n return self.__receive_message\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef2fd1df2838b638e3e6d9902b646545\",\n \"score\": \"0.58371794\",\n \"text\": \"def test_broadcast_receive_short(self):\\r\\n self.can_messages = [\\r\\n (TestECU.MsgType.CANRX, 0x00FEB201, [1, 2, 3, 4, 5, 6, 7, 8], 0.0), \\r\\n ]\\r\\n\\r\\n self.pdus = [\\r\\n (TestECU.MsgType.PDU, 65202, [1, 2, 3, 4, 5, 6, 7, 8]),\\r\\n ]\\r\\n\\r\\n self.ecu.subscribe(self._on_message)\\r\\n self._inject_messages_into_ecu()\\r\\n # wait until all messages are processed asynchronously\\r\\n while len(self.pdus)>0:\\r\\n time.sleep(0.500)\\r\\n # wait for final processing \\r\\n time.sleep(0.100)\\r\\n self.ecu.unsubscribe(self._on_message)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"853f945589d241335d75fb04a2f62cfc\",\n \"score\": \"0.5826855\",\n \"text\": \"def receive(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7cfce78dbfc69eaf48234390349260f6\",\n \"score\": \"0.5818358\",\n \"text\": \"def receive(self, packet):\\n super(Messengent, self).receive(packet)\\n\\n # resent message\\n if packet.data['tk'] == TrnsKind.message:\\n if packet.data['pk'] == PcktKind.message:\\n self.message()\\n elif packet.data['pk'] == PcktKind.nack: # rejected\\n self.reject()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"754d590d3924985dc65d237436439808\",\n \"score\": \"0.5808071\",\n \"text\": \"def receive(self, message):\\n body = [b.decode() for t, b in message.bodies('text/plain')][0]\\n self.process_update(message.to, body)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7e2681514f337c80a6a12db9c697964\",\n \"score\": \"0.5807718\",\n \"text\": \"def data_received(self, message):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88d36849c4fc30963fef41223cbe28fc\",\n \"score\": \"0.58055055\",\n \"text\": \"def receive(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fec975a84d9977310d6de48d75fd3173\",\n \"score\": \"0.5766006\",\n \"text\": \"def receive_message(self) -> NetMessage:\\n\\n header = self.sock.recv(struct.calcsize(MSG_HEADER_FORMAT))\\n msg_type, msg_len = struct.unpack_from(MSG_HEADER_FORMAT, header, 0)\\n payload = self.sock.recv(msg_len)\\n typed_message = NetMessage(msg_type)\\n self.message_callbacks[typed_message](payload)\\n return typed_message\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c3616f0047bf9f4d6d43d61fed7e2b6\",\n \"score\": \"0.57630056\",\n \"text\": \"def MsgRead (self, Rcvid, Buffer, Bytes, Offset = 0):\\n\\treturn self._MsgRead (Rcvid, Buffer, Bytes, Offset)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db842e1a2dd7520de813175109771f1f\",\n \"score\": \"0.57459533\",\n \"text\": \"def recv(self) -> BatsimMessage:\\n if not self.is_connected:\\n raise SystemError(\\\"Connection not opened.\\\")\\n\\n return self.__socket.recv_json(object_hook=BatsimMessageDecoder())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5dc65855ad0f2da3d6ba3c1b6a3e5fc3\",\n \"score\": \"0.5738059\",\n \"text\": \"def _on_mqtt_message(self, client, userdata, msg):\\n logging.debug(f\\\"RCV: {msg.topic}: {str(msg.payload)}\\\")\\n payload = json.loads(msg.payload)\\n self._on_message(msg.topic, payload)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb301f3a6cce950b0aeffcb228285633\",\n \"score\": \"0.571993\",\n \"text\": \"def read(self):\\n # We wait for a message (and its size)\\n data_len = self._rfxcom.read()\\n hex_data_len = binascii.hexlify(data_len)\\n int_data_len = int(hex_data_len, 16)\\n print(\\\"----------------------------\\\")\\n self._log.debug(\\\"----------------------------\\\")\\n print(\\\"LENGTH = %s\\\" % int_data_len)\\n self._log.debug(\\\"LENGTH = %s\\\" % int_data_len)\\n\\n if int_data_len != 0:\\n # We read data\\n data = self._rfxcom.read(int_data_len)\\n hex_data = binascii.hexlify(data)\\n print(\\\"DATA = %s\\\" % hex_data)\\n self._log.debug(\\\"DATA = %s\\\" % hex_data)\\n\\n # Process data\\n self._process_received_data(hex_data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"449ae5167395368062ed12429200c9e6\",\n \"score\": \"0.571637\",\n \"text\": \"def send_message_to_ccm(self, message: str):\\n with hw_lock:\\n self.logger.debug(f\\\" PI --> CCM: {message}\\\")\\n self.ccm_adapter.send_message(message)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e0738608a2d5c9bdbc1b2c5317dd27e\",\n \"score\": \"0.5704182\",\n \"text\": \"def callback_message(self, conn, mess):\\n\\n # Prepare to handle either private chats or group chats\\n type = mess.getType()\\n jid = mess.getFrom()\\n props = mess.getProperties()\\n text = mess.getBody()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b213c943d54f4355adf4bf09328f5877\",\n \"score\": \"0.5692245\",\n \"text\": \"def extract_message(tcp_buffer):\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"967942d059a45cc3f987ae220d06b85a\",\n \"score\": \"0.56857777\",\n \"text\": \"def decode_message(self, message):\\n # Current readings request\\n if message == \\\"CR\\\":\\n print(\\\"Send Current Readings\\\")\\n current_reading = self.get_reading()\\n if current_reading:\\n self.send_current_readings(current_reading)\\n # Previous stored readings request\\n elif message == \\\"NA\\\":\\n print(\\\"Get Network Activity\\\")\\n activity = self.get_network_activity()\\n if activity:\\n self.send_network_activity(activity)\\n # Plot data request\\n elif message == \\\"PD\\\":\\n print(\\\"Send Plot Data\\\")\\n plot_data = self.get_plot_data()\\n if plot_data:\\n self.send_plot_data(plot_data)\\n # Unknown request\\n else:\\n print(\\\"Unsupported message, nothing to do!\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0e931a6df75731713f83b1dc5e78bc3\",\n \"score\": \"0.5682126\",\n \"text\": \"def receive(self):\\n while True:\\n data = self._conn.recv(1024)\\n if data: break\\n DEBUG and print(\\\"Received message: \\\" + data.decode('utf-8'))\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a9036cfd8423098367427765fe3f0714\",\n \"score\": \"0.5679466\",\n \"text\": \"def receive(self):\\n try:\\n self._last_reply = self._last_reply.decode()\\n except UnicodeDecodeError:\\n raise PLConnectionProtocolError(f\\\"Can't decode device reply!\\\\n {self._last_reply}\\\")\\n return LabDeviceReply(body=self._last_reply, parameters=self._last_reply_headers, content_type=\\\"json\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95cb42b3227595e270264dd252ec86ce\",\n \"score\": \"0.5668115\",\n \"text\": \"async def recv(self) -> T:\\n if self._listener is None and self._messages.empty():\\n raise exceptions.ChannelClosedError(\\n \\\"the channel is closed and no residual message exist\\\"\\n )\\n\\n loop = asyncio.get_event_loop()\\n waiter: Future[T] = loop.create_future()\\n\\n def _release_waiter(f: Union[Future[T], Future[None]]) -> None:\\n if waiter.done():\\n return\\n elif (exc := f.exception()) :\\n waiter.set_exception(exc)\\n elif (res := f.result()) :\\n waiter.set_result(res)\\n else:\\n waiter.set_exception(\\n exceptions.ChannelClosedError(\\\"the channel is closed\\\")\\n )\\n\\n if self._listener:\\n self._listener.add_done_callback(_release_waiter)\\n waiter.add_done_callback(\\n partial(self._listener.remove_done_callback, _release_waiter)\\n )\\n\\n getter = asyncio.create_task(self._messages.get())\\n getter.add_done_callback(_release_waiter)\\n try:\\n return await waiter\\n finally:\\n getter.cancel()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ce900faa0b3fb345628108148dec31a\",\n \"score\": \"0.565768\",\n \"text\": \"def receive(self, message):\\n\\n self.messages.append(message)\\n\\n if message.type == MessageTypes.PA_BLOCKS:\\n print \\\"replying blocks\\\"\\n reply = Message(MessageTypes.PA_BLOCKS_REPLY,\\n self.public_key,\\n self.interactions.get_blocks())\\n self.interactions.add_blocks(message.payload)\\n self.interface.send(message.sender, reply)\\n if message.type == MessageTypes.PA_BLOCKS_REPLY:\\n print \\\"sending score\\\"\\n self.interactions.add_blocks(message.payload)\\n reply = Message(MessageTypes.PA_SCORE,\\n self.public_key,\\n True)\\n self.interface.send(message.sender, reply)\\n self.endorsements.append(Endorsement([self.public_key, message.sender, True]))\\n if message.type == MessageTypes.PA_SCORE:\\n print \\\"replying score\\\"\\n reply = Message(MessageTypes.PA_SCORE_REPLY,\\n self.public_key,\\n True)\\n self.endorsements.append(Endorsement([self.public_key, message.sender, True]))\\n self.interface.send(message.sender, reply)\\n if message.type == MessageTypes.CHAIN:\\n reply = Message(MessageTypes.CHAIN_REPLY,\\n self.public_key,\\n self.chain)\\n self.interface.send(message.sender, reply)\\n if message.type == MessageTypes.CHAIN_REPLY:\\n self.interactions.add_blocks(message.payload.get_blocks())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43222a76175ff0eb67b3a466e024da14\",\n \"score\": \"0.56537455\",\n \"text\": \"async def fetch_message(self, channel: SupportsInt, message: SupportsInt) -> Dict[str, Any]:\\n return await self.request(Route(\\n 'GET', '/channels/{channel_id}/messages/{message_id}',\\n channel_id=int(channel), message_id=int(message)\\n ))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60da68bc8910fefca393f93340b1c7b4\",\n \"score\": \"0.56529784\",\n \"text\": \"def receive(self,msgQueue, callbackF):\\n cParams = pika.ConnectionParameters(host=self.host)\\n connection = pika.BlockingConnection(cParams)\\n channel = connection.channel()\\n\\n channel.queue_declare(queue=msgQueue) # ensure queue created\\n channel.basic_consume(callbackF, queue=msgQueue, no_ack=True)\\n channel.start_consuming()\\n # will only be reached once channel stops consuming (assumed in callback)\\n connection.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dbdf022702f906189bcf2c45e1334926\",\n \"score\": \"0.5637638\",\n \"text\": \"def _message_callback(mqttc, userdata, msg):\\n callback(msg.topic, msg.payload.decode(\\\"utf-8\\\"), msg.qos)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"753bd139a774944ba9899d3298541707\",\n \"score\": \"0.56216216\",\n \"text\": \"def on_message(self, message):\\n print(\\\"Received\\\",message)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ee78fbe54ae4defaf534e1e60692454\",\n \"score\": \"0.5619653\",\n \"text\": \"def channel_message(self, message_type, channel, data):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d2586ecd517be62ab9783241322393bc\",\n \"score\": \"0.5618884\",\n \"text\": \"def onMessage(self, payload, isBinary):\\n self.factory.communicate(self, payload, isBinary)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2d10881afea3260524a5407a3535c5e\",\n \"score\": \"0.5614722\",\n \"text\": \"async def receive(self, payload_enc: Union[str, bytes]) -> InboundMessage:\\n if self._check_relay_context:\\n await self.handle_relay_context(payload_enc)\\n self._check_relay_context = False\\n\\n message = await self.parse_inbound(payload_enc)\\n self.receive_inbound(message)\\n return message\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eec4e2bab89e097a6f3cd45a747fef51\",\n \"score\": \"0.5613303\",\n \"text\": \"def on_accept_client(self, channel: Channel, msg: HelloMessage) -> None:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd6b7d75a38d43507137476ca5b06e01\",\n \"score\": \"0.56121206\",\n \"text\": \"def message_received(self, new_message):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3254eb89994bb80fb5c8bb65545bb930\",\n \"score\": \"0.561203\",\n \"text\": \"def receive_messages(self) -> None:\\n header = self.client_socket.recv(struct.calcsize(MSG_HEADER_FORMAT))\\n msg_type, msg_len = struct.unpack_from(MSG_HEADER_FORMAT, header, 0)\\n payload = self.client_socket.recv(msg_len)\\n self.message_callbacks[NetMessage(msg_type)](payload)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ece0d043535e979ae6596524542a89e\",\n \"score\": \"0.56118125\",\n \"text\": \"def recv_msg(self, decode=True):\\n data = self.recv_all(self.sock, self.data_size)\\n if decode:\\n return data.decode('utf-8')\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0a4e4cb7fc8f6328f145f82b7ce2585\",\n \"score\": \"0.5611682\",\n \"text\": \"def __receive_message(self, return_pattern=None, multiline=False):\\n try:\\n # checking if a connection is there\\n if self.__connection is not None:\\n # if multiple lines are expected, keep reading lines until no more lines come in\\n if multiline:\\n answer = \\\"\\\"\\n while True:\\n line = self.__connection.readline()\\n if line:\\n answer += line.decode(self.standard_encoding)\\n else:\\n break\\n # if just one line is expected, just read one line (faster than always waiting for the timeout)\\n else:\\n answer = self.__connection.readline()\\n # decoding the answer using the standard settings from __init__\\n answer = answer.decode(self.standard_encoding)\\n\\n # if the user wants a code check performed\\n if return_pattern is not None:\\n if type(return_pattern).__name__ != \\\"SRE_Pattern\\\":\\n raise ValueError(\\n \\\"The return code you specified was not a valid regular expression: {0}\\\".format(return_pattern)\\n )\\n\\n if re.fullmatch(pattern=return_pattern, string=answer):\\n # if the answer matches the desired pattern return the read value\\n return re.match(pattern=return_pattern, string=answer).groups()\\n else:\\n self.logger.critical(\\n \\\"Value Error. Serial device did not return correct return code. Send: \\\\\\\"{0}\\\\\\\". \\\"\\n \\\"Received: \\\\\\\"{1}\\\\\\\".\\\".format(return_pattern, answer)\\n )\\n if self.__soft_fail_for_testing:\\n return answer.strip()\\n else:\\n raise ValueError(\\n \\\"Value Error. Serial device did not return correct return code. Send: \\\\\\\"{0}\\\\\\\". \\\"\\n \\\"Received: \\\\\\\"{1}\\\\\\\".\\\".format(return_pattern, answer)\\n )\\n else:\\n # if no return code checking was requested, just return the read value\\n return answer.strip()\\n else:\\n raise Exception(\\\"Error. No connection to the device\\\")\\n except Exception as e:\\n # This is not very elegant, but lets the user know that the device failed while retaining the error\\n # information\\n raise Exception(\\\"Serial device message receive failed. Error Message: {0}\\\\n{1}\\\".format(e, e.__traceback__))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85008ed6dc214544af856a1a74f413d8\",\n \"score\": \"0.5609495\",\n \"text\": \"def munch(self, unused):\\n self._channel.add_on_cancel_callback(self.cancel_channel)\\n self._consumer_tag = self._channel.basic_consume(self._process_message)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb7b51f89276e598e606fd0ce546c9d5\",\n \"score\": \"0.56038445\",\n \"text\": \"def _received_message_handler(self, channel, method, properties, body):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a14438dbfcab1d95235b2c86650059cf\",\n \"score\": \"0.55976534\",\n \"text\": \"async def receive(self, text_data=None, bytes_data=None):\\n text_data_json = json.loads(text_data)\\n message = text_data_json['message']\\n\\n # Send message to room group\\n await self.channel_layer.group_send(\\n self.room_group_name,\\n {\\n 'type': 'chat_message',\\n 'message': message,\\n }\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1f8e25c69f52107ea8acb8ba2166030\",\n \"score\": \"0.5596549\",\n \"text\": \"def processReceivedMessage(iTag, clsName, msgID, msg): #@NoSelf\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24ca69ae9dd7d572335204d03a0b6e68\",\n \"score\": \"0.55940956\",\n \"text\": \"def receive(self, message):\\n\\n try:\\n # Ensure the messae decodes\\n message = loads(message)\\n\\n # Make sure some basic stuctures are here\\n originator = message[\\\"from\\\"]\\n tx, action = message['Tx'], message['action']\\n idx, deps, new_obj, data = tx\\n\\n except Exception as e:\\n raise Exception(\\\"Badly formatted messages: %s\\\" % str(e))\\n\\n # Ignore messages we sent\\n if self.name == originator:\\n return\\n \\n if not idx == packageTx(data, deps, len(new_obj))[0]:\\n raise Exception(\\\"Invalid transaction.\\\")\\n\\n if not self._within_TX(tx):\\n if action == \\\"process\\\":\\n # We are going to re-route the message on a correct channel\\n msg = dumps({ \\\"action\\\":\\\"process\\\", \\\"from\\\":self.name, \\\"Tx\\\":tx })\\n self.send(tx, msg)\\n return\\n else:\\n raise Exception(\\\"Transaction not of interest.\\\")\\n\\n if action == \\\"vote\\\":\\n # We process an incoming vote.\\n n, l, v = message['vote']\\n vote = (n, tuple(l), v)\\n self.RLogger.info(\\\"Receive vote (%s) for %s (%s)\\\" % (v, idx[:6], self.name))\\n \\n if vote not in self.pending_vote[idx]:\\n self.pending_vote[idx].add( vote )\\n self.process(tx)\\n \\n if action == \\\"commit\\\":\\n # We process an incoming commit.\\n yesno = message['yesno']\\n self.RLogger.info(\\\"Receive commit (%s) for %s (%s)\\\" % (yesno ,idx[:6], self.name))\\n\\n if yesno:\\n self.do_commit_yes(tx)\\n else:\\n self.commit_no.add(idx)\\n\\n if action == \\\"process\\\":\\n # We process a request\\n self.process(tx)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68168949c66b1c88e69521f0c153f83f\",\n \"score\": \"0.5591507\",\n \"text\": \"def receive(self, node, message):\\n\\t\\tif self.recv: self.recv(node, pickle.loads(message))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"916d9907df5467e0865d8040011e350b\",\n \"score\": \"0.5574089\",\n \"text\": \"def on_receive(self, message, from_jid, otr_state):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"839e7bea280e0091919ad06c61302458\",\n \"score\": \"0.55709845\",\n \"text\": \"async def recv_message(self):\\n return await asyncio.sleep(1e10)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a9b068e181e6e8d882f05f4785409a0\",\n \"score\": \"0.55663013\",\n \"text\": \"def _receive(self, _unused_frame, queue, control_center):\\n\\n log.info(\\\" [*] Waiting for Messages. To exit press CTRL+C\\\")\\n\\n self.channels[queue].basic_consume(queue=queue, on_message_callback=control_center.received_message_handler)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec225a6af1012c51cc61d4d191befa75\",\n \"score\": \"0.5563348\",\n \"text\": \"def dispatch_msg(self, plugin, message):\\n self.__run_plugin(plugin, \\\"recv_msg\\\", message)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4fe638da3b3688859bcb118f441e8135\",\n \"score\": \"0.5562309\",\n \"text\": \"def handle_message(self, msg, rcvtime):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee71f825bad1937e204d3142d9fb2b45\",\n \"score\": \"0.5556639\",\n \"text\": \"def receive_message(self):\\n message = None\\n properties = None\\n while not self.done:\\n # Process message queue events, returning as soon as possible\\n self.server_connection.process_data_events(time_limit=0)\\n\\n method, properties, body = self.server_channel.basic_get(queue=self.response_server_queue,\\n no_ack=True)\\n # Return as soon as we get a valid message\\n if method is not None:\\n print(method, properties, body)\\n message = json.loads(body.decode('utf-8'))\\n logger.info('Received a message: {} | {}'.format(body, properties.reply_to))\\n break\\n\\n return message, properties\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cbaeedd359cd1e03ec2f4ce076ed77ab\",\n \"score\": \"0.5553836\",\n \"text\": \"def messageReceived(self, ammsgtype, msg):\\n\\t#print \\\"serial.messageReceived - %s\\\" % msg.tostring()\\n if ammsgtype in self.__handlers:\\n for handler in self.__handlers[ammsgtype]:\\n handler(ammsgtype, msg[:])\\n else:\\n self.__logger.debug(msg.tostring())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82e371c7f077e0146d70e89e35a04f8c\",\n \"score\": \"0.55513245\",\n \"text\": \"async def listen(self):\\n\\n while True:\\n if not self.connected:\\n # sleep and hope the checker fixes us\\n await asyncio.sleep(5)\\n continue\\n\\n data = await self.read_one_message()\\n if data is None:\\n await asyncio.sleep(1)\\n continue\\n\\n mtype = self.find_balboa_mtype(data)\\n if mtype is None:\\n self.log.error(f\\\"Spa sent an unknown message: {data.hex()}\\\")\\n await asyncio.sleep(0.1)\\n continue\\n if mtype == BMTR_MOD_IDENT_RESP:\\n self.parse_module_identification(data)\\n await asyncio.sleep(0.1)\\n continue\\n if mtype == BMTR_STATUS_UPDATE:\\n await self.parse_status_update(data)\\n await asyncio.sleep(0.1)\\n continue\\n if mtype == BMTR_DEVICE_CONFIG_RESP:\\n self.parse_device_configuration(data)\\n await asyncio.sleep(0.1)\\n continue\\n if mtype == BMTR_SYS_INFO_RESP:\\n self.parse_system_information(data)\\n await asyncio.sleep(0.1)\\n continue\\n if mtype == BMTR_SETUP_PARAMS_RESP:\\n self.parse_setup_parameters(data)\\n await asyncio.sleep(0.1)\\n continue\\n if mtype == BMTR_FILTER_INFO_RESP:\\n self.parse_filter_cycle_info(data)\\n await asyncio.sleep(0.1)\\n continue\\n self.log.error(\\\"Unhandled mtype {0}\\\".format(mtype))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36bf39777f80d1339cabd83aadc4f0e9\",\n \"score\": \"0.5551283\",\n \"text\": \"def sms_receive(self) -> Optional[dict]:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b8b38f07cf3f90ab19a4c48a697b3f3\",\n \"score\": \"0.554542\",\n \"text\": \"def receive(self):\\n buffer = self.cnt.get_buffer_and_flush()\\n for m in buffer:\\n self.messages.append(m)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4fca0deca003e941070970e746d1aa88\",\n \"score\": \"0.5529326\",\n \"text\": \"def tcp_message(self, flow: mitmproxy.tcp.TCPFlow):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"daa0e736e62df75557bd0889afbbe291\",\n \"score\": \"0.5512056\",\n \"text\": \"def dispatch_msg(self, plugin, message):\\n self.__run_plugin(plugin, message.audit_name, \\\"recv_msg\\\", message)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"636680db535e96e63531f91db6829e7c\",\n \"score\": \"0.55093056\",\n \"text\": \"def got_message(self, address, message):\\n self.callback(message)\\n # print(message)\\n return \\\"DONE\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c4ace76e52155089a3ff2727725732f\",\n \"score\": \"0.5507662\",\n \"text\": \"def recv(self):\\n logger.debug(\\\"LinkReceiver receiving message.\\\")\\n \\n # Keep trying until we get something\\n while True:\\n recv_data = self._recv()\\n if recv_data is not None:\\n return recv_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ecf96e3aaf194e4721e7ee691335f140\",\n \"score\": \"0.5503975\",\n \"text\": \"def handle_call(self, context, connection):\\n print(\\\"Received message: %s\\\" % context.message)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a557aae9874442022b83c803f8c62d39\",\n \"score\": \"0.54957265\",\n \"text\": \"def c1_listener_callback(self, msg):\\n self.channel_one_data = msg.data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e27f90269d2dc525f2ebe62533fe0011\",\n \"score\": \"0.54953676\",\n \"text\": \"def handleMessage(self, message):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77f34777b6faed7a74d75e098fb1a3be\",\n \"score\": \"0.5492488\",\n \"text\": \"def on_msg(mqttc, obj, msg):\\n # print(msg.topic + \\\" \\\" + str(msg.qos) + \\\" \\\" + str(msg.payload))\\n LOG.debug(\\\"got message\\\")\\n payload = msg.payload.decode(\\\"UTF-8\\\")\\n topic = msg.topic\\n LOG.debug(\\\"Message topic : %s\\\" % topic)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4e0a7b379037c42dbfb5060a0c539d1\",\n \"score\": \"0.54916763\",\n \"text\": \"def mqtt_message(self):\\n time.sleep(1)\\n return self.message\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15b349dddc8baebd5b07c1f0966d7609\",\n \"score\": \"0.549045\",\n \"text\": \"def test_broadcast_receive_long(self):\\r\\n self.can_messages = [\\r\\n (TestECU.MsgType.CANRX, 0x00ECFF01, [32, 20, 0, 3, 255, 0xB0, 0xFE, 0], 0.0), # TP.CM BAM (to global Address)\\r\\n (TestECU.MsgType.CANRX, 0x00EBFF01, [1, 1, 2, 3, 4, 5, 6, 7], 0.0), # TP.DT 1\\r\\n (TestECU.MsgType.CANRX, 0x00EBFF01, [2, 1, 2, 3, 4, 5, 6, 7], 0.0), # TP.DT 2\\r\\n (TestECU.MsgType.CANRX, 0x00EBFF01, [3, 1, 2, 3, 4, 5, 6, 255], 0.0), # TP.DT 3\\r\\n ]\\r\\n\\r\\n self.pdus = [\\r\\n (TestECU.MsgType.PDU, 65200, [1, 2, 3, 4, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7, 1, 2, 3, 4, 5, 6]),\\r\\n ]\\r\\n\\r\\n self.ecu.subscribe(self._on_message)\\r\\n self._inject_messages_into_ecu()\\r\\n # wait until all messages are processed asynchronously\\r\\n while len(self.pdus)>0:\\r\\n time.sleep(0.500)\\r\\n # wait for final processing \\r\\n time.sleep(0.100)\\r\\n self.ecu.unsubscribe(self._on_message)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2257298e838d5b35ca1fc043269858e4\",\n \"score\": \"0.5489604\",\n \"text\": \"def msg(self, msg):\\n if self.simulator:\\n message_string = \\\"{}\\\".format(msg)\\n self.com.send(message_string.encode())\\n resp = ''\\n if '?' in msg:\\n resp = self.com.recv(BUFF_SIZE).decode()\\n return resp\\n\\n else:\\n # Writes message\\n message_string = \\\"{}\\\\r\\\\n\\\".format(msg).encode()\\n\\n # write(message_string)\\n self.com.write(message_string)\\n\\n # Reads response if queried\\n resp = ''\\n if \\\"?\\\" in msg:\\n resp = self.com.readline()\\n resp = str(resp[:-2], 'utf-8') # Strips terminating chars\\n if not resp:\\n raise TimeoutError(\\\"Device timed out\\\")\\n\\n # Must wait 10 ms before sending another command\\n time.sleep(.01)\\n\\n return resp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d360ddaddf7133b8317c974faf48c60\",\n \"score\": \"0.5483794\",\n \"text\": \"def handle_message(self, payload):\\n assert isinstance(payload, dict)\\n if 'getToken' in payload:\\n token = channel.create_channel(self.client_id)\\n self.send({'token': token})\\n elif 'pickup' in payload:\\n pickup_id = payload['pickup']\\n memcache_key = _PICKUP_MEMCACHE_PREFIX + pickup_id\\n results_get_pickup = yield memcache.Client().get_multi_async(\\n [memcache_key])\\n pickup_data = results_get_pickup.get(memcache_key)\\n if pickup_data is not None:\\n for service, messages_for_service in pickup_data.iteritems():\\n for message in messages_for_service:\\n self.send(message, service=service)\\n else:\\n logging.warning('Memcache read failure.')\\n else: logging.error('Unrecognized system service message.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b798b3af11ba9f3d15de282a793d788\",\n \"score\": \"0.5477442\",\n \"text\": \"def process_received_message(self, message):\\n self.log.debug('Received \\\"%s\\\"', message)\\n\\n try:\\n cmd, kwargs = bcp.decode_command_string(message)\\n self.receive_queue.put((cmd, kwargs))\\n except ValueError:\\n self.log.error(\\\"DECODE BCP ERROR. Message: %s\\\", message)\\n raise\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf55405139c42b5244321789642a33af\",\n \"score\": \"0.54757184\",\n \"text\": \"def message(self, user, channel, message):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"62f7cd8370563af7fac48d838c97e207\",\n \"score\": \"0.54728395\",\n \"text\": \"def receive_data(self):\\n return self.message_queue.get(True, 5)\\n #return self.message_queue.get_nowait()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc18a79e72e810c3a2edcd48f1939667\",\n \"score\": \"0.54724187\",\n \"text\": \"def recv(self, *args, **kwargs):\\n return self.connection.recv(*args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9eb88cb32743abd2b664a3d54fd77b4\",\n \"score\": \"0.5469767\",\n \"text\": \"def on_message(self, service, msg):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53c1beeda9bf9a6838e71d17447052be\",\n \"score\": \"0.546417\",\n \"text\": \"def receive(self):\\n\\n logging.info(\\\"transceiver: start receiving\\\")\\n # request extra packet at start so there is always one ready\\n self.xcvr.request_audio_output()\\n while 1:\\n\\n if not self.command_queue.empty():\\n break\\n\\n request = None\\n if not self.request_queue.empty():\\n request = self.request_queue.get()\\n if request == \\\"frequency\\\" : \\n self.xcvr.request_frequency()\\n elif request == \\\"mode\\\" : \\n self.xcvr.request_mode()\\n elif request == \\\"tx\\\" : \\n self.xcvr.request_TX()\\n self.xcvr.request_audio_output()\\n data = self.xcvr.get_audio()\\n self.player.stdin.write(data)\\n\\n if request is not None:\\n if request == \\\"frequency\\\" : \\n self.frequency = self.xcvr.get_frequency()\\n self.response_queue.put(self.frequency)\\n elif request == \\\"mode\\\" : \\n mode = self.xcvr.get_mode()\\n self.response_queue.put(mode)\\n if mode != self.mode:\\n self.mode = mode\\n break\\n elif request == \\\"tx\\\" : \\n tx_rx = self.xcvr.get_TX()\\n self.response_queue.put(tx_rx)\\n if tx_rx != self.tx_rx:\\n self.tx_rx = tx_rx\\n break\\n\\n # when complete, play the extra packet\\n data = self.xcvr.get_audio() # process extra request once finished\\n self.player.stdin.write(data)\\n logging.info(\\\"transceiver: stop receiving\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b769d54988d108b754d46a9f81c37383\",\n \"score\": \"0.545968\",\n \"text\": \"def receive_msg():\\n\\n for msg in messager.listen():\\n messages.append(msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b769d54988d108b754d46a9f81c37383\",\n \"score\": \"0.545968\",\n \"text\": \"def receive_msg():\\n\\n for msg in messager.listen():\\n messages.append(msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e31fe1dba11f8a55e93d85bc8b26b353\",\n \"score\": \"0.5456528\",\n \"text\": \"def test_get_message(self):\\n headers = { \\n 'Accept': 'application/json',\\n 'LoginRequired': 'special-key',\\n }\\n response = self.client.open(\\n '/api/v1/channels/{channel_id}/messages/{message_id}'.format(channel_id=56, message_id=56),\\n method='GET',\\n headers=headers)\\n self.assert200(response,\\n 'Response body is : ' + response.data.decode('utf-8'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cab724291c29799a58123fd842a693aa\",\n \"score\": \"0.5455665\",\n \"text\": \"async def receive_message(reader: typing_classes.StreamReader) -> str:\\n data = await reader.read(1024)\\n return data.decode(errors='ignore')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de30b4d7d5ae564f3cee8a358ca6c4a4\",\n \"score\": \"0.5454776\",\n \"text\": \"def received_msg(account, sender, message, conv, flags):\\n\\n obj = bus.get_object(\\\"im.pidgin.purple.PurpleService\\\", \\\"/im/pidgin/purple/PurpleObject\\\")\\n purple = dbus.Interface(obj, \\\"im.pidgin.purple.PurpleInterface\\\")\\n\\n pmesg = purple.PurpleMarkupStripHtml(message)\\n unread = purple.PurpleConversationGetData(conv, \\\"unseen-count\\\")\\n\\n type = purple.PurpleConversationGetType(conv)\\n\\n if unread or True:\\n if type == 1: # That's PURPLE_CONV_TYPE_IM\\n g.notify(\\\"Received IM message\\\", purple.PurpleConversationGetTitle(conv), pmesg)\\n else:\\n g.notify(\\\"Received Chat message\\\", sender, pmesg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8928138262fd5ba28960d6080a3439f2\",\n \"score\": \"0.5454483\",\n \"text\": \"def lbm_rcv_deliver(self, rcv, msg):\\n self.app_rcv_callback(rcv, msg, self.app_clientd)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":690,"cells":{"query_id":{"kind":"string","value":"94d97e50552b686245c4cf9ada41c177"},"query":{"kind":"string","value":"Permission Checking Function to be used as a Dependency for API endpoints. This is used as a helper. This will either return a User object to the calling method if the user meets the authentication requirements, or it will raise a CredentialException and prevent the method that depends on this from continuing."},"positive_passages":{"kind":"list like","value":[{"docid":"70d2872c3c88f205c82a40f0a7ad2ad2","score":"0.0","text":"def current_user_researcher(token: str = Depends(oauth2_scheme)):\n user = get_current_user(token)\n if not any(role in [Roles.admin.name, Roles.researcher.name] for role in user.roles):\n raise CredentialException()\n\n return user","title":""}],"string":"[\n {\n \"docid\": \"70d2872c3c88f205c82a40f0a7ad2ad2\",\n \"score\": \"0.0\",\n \"text\": \"def current_user_researcher(token: str = Depends(oauth2_scheme)):\\n user = get_current_user(token)\\n if not any(role in [Roles.admin.name, Roles.researcher.name] for role in user.roles):\\n raise CredentialException()\\n\\n return user\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"5e224920ce0bc70b58729d9fc529e91d","score":"0.7034291","text":"def check_auth(func):\n\n @wraps(func)\n def invoke(self, *args, **kwargs):\n try:\n self.user = self.state.get_user()\n if self.user is None:\n print(\"Authentication is needed, run spock auth\")\n return\n return func(self, *args, **kwargs)\n except (tk.Forbidden, tk.NotFound) as e:\n # TODO better error messages\n print(e)\n\n return invoke","title":""},{"docid":"9c183ffd7b906a8c05f0f231f81b6324","score":"0.6600555","text":"def authorized_access(func):\n @wraps(func)\n def decor(*args, **kwargs):\n print(get_current_user())\n if get_current_user() is None:\n return unauthorized_handler()\n elif not get_current_user().authorized:\n return abort(403)\n else:\n return func(*args, **kwargs)\n\n return decor","title":""},{"docid":"fd9e443a33c3b891c19039d102f52b2f","score":"0.64821035","text":"def authorization_required(func):\n @functools.wraps(func)\n def decorated_function(*pa, **ka): # pylint: disable=missing-docstring\n if auth.is_authorized(ndb.Key(urlsafe=ka['key'])):\n return func(*pa, **ka)\n if not auth.is_logged_in():\n return abort(401)\n return abort(403)\n\n return decorated_function","title":""},{"docid":"cd463cc1d707007b256213b66d9c8904","score":"0.64415056","text":"def required(method):\n @functools.wraps(method)\n def inner_method(self, *args, **kwargs):\n if not self.current_user:\n raise tornado.web.HTTPError(401)\n return method(self, *args, **kwargs)\n return inner_method","title":""},{"docid":"abc910645dcc40e9d1560fc0a525fe33","score":"0.6417043","text":"def user_required(f):\n def decorator(*args, **kwargs):\n if not g.user:\n abort(401)\n return f(*args, **kwargs)\n return decorator","title":""},{"docid":"94a50332767a575f570154b464d8eed7","score":"0.63966864","text":"def permission_required(permission):\n def decorator(f):\n @wraps(f)\n def decorated_function(*args, **kwargs):\n if not current_user.is_authenticated:\n abort(403)\n if not current_user.can(permission, current_user.curr_conf):\n abort(403)\n return f(*args, **kwargs)\n return decorated_function\n return decorator","title":""},{"docid":"1039840cfb67ea036663b1b2462cf129","score":"0.6382413","text":"def authorize(func):\n def func_wrapper(*args, **kwargs):\n \"\"\"Wrap the function.\"\"\"\n user = endpoints.get_current_user()\n if not user:\n raise endpoints.UnauthorizedException(\"Authorization required.\")\n return func(*args, **kwargs)\n return func_wrapper","title":""},{"docid":"6f86255f20bc57edb3370291c40b3811","score":"0.6320725","text":"def auth_required():\n\n def decorator(endpoints_method):\n @wraps(endpoints_method)\n def validate(*args, **kwargs):\n \"\"\" Validates the User ID and token sent in an endpoints request.\"\"\"\n\n # Get the headers from the self arg\n headers = args[0].request_state.headers\n token = headers.get('Token')\n if headers.get('User-Id') is None:\n raise endpoints.UnauthorizedException(MESSAGE_UNAUTHORIZED)\n\n user_id = int(headers.get('User-Id'))\n user, timestamp = User.get_by_auth_token(user_id, token)\n if user:\n return endpoints_method(*args, **kwargs)\n else:\n logging.warn('Invalid API request! User ID:%s, token:%s'\n %(user_id, token))\n raise endpoints.UnauthorizedException(MESSAGE_UNAUTHORIZED)\n return validate\n return decorator","title":""},{"docid":"0349a07186161c0a71e50c532d092f0f","score":"0.630414","text":"def _check_auth(func):\n @functools.wraps(func)\n def deco(*args, **kwargs):\n if 'Authorization' in request.headers:\n try:\n scheme, creds = request.headers['Authorization'].split(\n None, 1\n )\n except ValueError:\n return '', 401\n else:\n if scheme == 'Bearer':\n user = User.verify_auth_token(creds)\n if user is not None:\n g.user = user\n return func(*args, **kwargs)\n return '', 401\n return deco","title":""},{"docid":"fd8e5c0d88e8fb022b045e6d2d774e44","score":"0.6302787","text":"def admin_credentials_required(f):\n @wraps(f)\n def decorated(*args, **kwargs):\n if 'username' not in request.headers or 'password' not in request.headers:\n return abort(401)\n if not ldapuser.authenticate(request.headers['username'], request.headers['password']):\n return abort(403)\n if not ldapuser.is_api_authorized(request.headers['username']):\n return abort(403)\n return f(*args, **kwargs)\n return decorated","title":""},{"docid":"a6a4039a26761beeec593737fe030535","score":"0.6287337","text":"def requires_auth(self, f):\r\n @wraps(f)\r\n def decorated(*args, **kwargs):\r\n auth = request.authorization\r\n if not auth or not self.check_auth(auth.username, auth.password):\r\n return self.authenticate()\r\n return f(*args, **kwargs)\r\n return decorated","title":""},{"docid":"208fefd390f0cdc23b1fc5870703a1e8","score":"0.6263697","text":"def local_user_required(decorated_function):\n def check_for_local_user(self, *kw, **kwargs):\n try:\n current_user = users.get_current_user()\n user = get_user_by_google_id(current_user.user_id())\n if not user:\n raise Exception\n except:\n self.abort(401)\n else:\n decorated_function(self, *kw, **kwargs)\n return check_for_local_user","title":""},{"docid":"bc693f93f343f5a0b8d2025626db9a95","score":"0.6183728","text":"def requires_authentication(func):\n def endpoint_wrapper(*args, **kwargs):\n try:\n header = connexion.request.headers['Authorization']\n User.authenticate(header)\n except KeyError:\n logger.info(\"Missing 'Authorization' header\")\n raise AuthException(\"Missing 'Authorization' header\")\n except AuthenticationFailure as e:\n logger.info(\"Unable to authenticate: {}\".format(e))\n raise AuthException(\"Resource requires authentication\")\n else:\n return func(*args, **kwargs)\n\n return endpoint_wrapper","title":""},{"docid":"a9bf9b2cebb9b16cdb7ff1619229446d","score":"0.61732227","text":"def check_auth(self, request):\n\n if not request.user.is_authenticated():\n from django.core.exceptions import PermissionDenied\n raise PermissionDenied","title":""},{"docid":"a9bf9b2cebb9b16cdb7ff1619229446d","score":"0.61732227","text":"def check_auth(self, request):\n\n if not request.user.is_authenticated():\n from django.core.exceptions import PermissionDenied\n raise PermissionDenied","title":""},{"docid":"a9bf9b2cebb9b16cdb7ff1619229446d","score":"0.61732227","text":"def check_auth(self, request):\n\n if not request.user.is_authenticated():\n from django.core.exceptions import PermissionDenied\n raise PermissionDenied","title":""},{"docid":"664a22a4980590a182a4bc2541f3f186","score":"0.6165246","text":"def authenticated(method):\n @functools.wraps(method)\n def wrapper(self, *args, **kwargs):\n if not self.current_user: \n raise HTTPError(401)\n return method(self, *args, **kwargs)\n return wrapper","title":""},{"docid":"4a95fe246588bbcae5023ab97beaac59","score":"0.6143246","text":"def _auth_required_healthpro_or_config_admin(func):\n\n def wrapped(*args, **kwargs):\n if not is_config_admin(app_util.get_oauth_id()):\n _, user_info = get_validated_user_info()\n if not HEALTHPRO in user_info.get(\"roles\", []):\n logging.warning(\"User has roles {}, but HEALTHPRO or admin is required\".format(user_info.get(\"roles\")))\n raise Forbidden()\n return func(*args, **kwargs)\n\n return wrapped","title":""},{"docid":"bbdbdfa1559f574b9fdd0e0053769e53","score":"0.6111663","text":"def require_permission(permission):\n def handler(f, *args, **kwargs):\n request = args[0]\n if check_permission(request, request.current_user, permission):\n return f(*args, **kwargs)\n elif request.current_user:\n raise HTTPForbidden()\n else:\n raise HTTPFound(request.route_url('user.login', _query={'redirect': encode_route(request)}))\n return decorator(handler)","title":""},{"docid":"16e31dc169c8638440859b248e1702c1","score":"0.61005646","text":"def require_context(f):\n\n def wrapper(*args, **kwargs):\n if not is_admin_context(args[0]) and not is_user_context(args[0]):\n raise exception.NotAuthorized()\n return f(*args, **kwargs)\n\n return wrapper","title":""},{"docid":"00ec2e2b9cb34ebe00620a63d20b8048","score":"0.6080321","text":"def require_authentication():\n if not current_user.is_authenticated:\n return current_app.login_manager.unauthorized()","title":""},{"docid":"fd376234770beaa4d7ccb211694c74c1","score":"0.6043147","text":"def login_required(func):\n @wraps(func)\n def decor(*args, **kwargs):\n if get_current_user() is None:\n return unauthorized_handler()\n else:\n return func(*args, **kwargs)\n\n return decor","title":""},{"docid":"81087392ac3ba9f7146145cb87852d79","score":"0.6038824","text":"def auth_required(f):\n\n @wraps(f)\n async def _wrapper(request):\n if 'auth' not in request:\n raise HTTPUnauthorized()\n return await f(request)\n\n return _wrapper","title":""},{"docid":"690bfe268358413070982277e8cc2b16","score":"0.6031002","text":"async def check(self, req: Request, handler: Callable, client):\n if client.administrator or client.permissions[self.permission.value]:\n return\n\n raise HTTPForbidden(text=\"Not permitted\")","title":""},{"docid":"0acc64c6851d439d7c4204987a0f9843","score":"0.6015531","text":"def check_ldap_access(view_func):\n def _decorated_view (request, pw_pk=None, **kwargs):\n if not pw_pk or check_authorization(pw_pk, request.user.username):\n return view_func(request, pw_pk, **kwargs)\n elif check_authorization(pw_pk, request.user.username) == None:\n return view_func(request, None, **kwargs) \n else:\n raise PermissionDenied(str(get_ldap_groups(request.user.username))+request.user.username)\n return wraps(view_func)(_decorated_view)","title":""},{"docid":"e89c01d0aec7d23e69a52504401dd066","score":"0.5991843","text":"def session_oriented_request(self, func, *args, **kwargs):\n try:\n session = self.check_authorization()\n user = get_user(session.user)\n\n if user is None:\n return responses.client_error(404, 'User not found')\n\n return func(user, *args, **kwargs)\n except AuthException as ae:\n return responses.client_error(401, '{}'.format(ae))","title":""},{"docid":"1933ecdf77a950c0c67577884966bc1b","score":"0.59741074","text":"def api_credentials_required(f):\n @wraps(f)\n def decorated(*args, **kwargs):\n if 'id' in request.headers and 'token' in request.headers:\n if not tokens.verify(request.headers['id'], request.headers['token']):\n return abort(403)\n if tokens.is_instance(request.headers['id']):\n if 'instance_id' in kwargs:\n instance = aws.get_instance(kwargs['instance_id'])\n if not instance:\n return abort(400)\n client_ip = request.environ.get('HTTP_X_REAL_IP', request.remote_addr)\n if instance.private_ip_address != client_ip:\n return abort(403)\n return f(*args, **kwargs)\n\n if 'username' in request.headers and 'password' in request.headers:\n if not ldapuser.authenticate(request.headers['username'], request.headers['password']):\n return abort(403)\n if not ldapuser.is_api_authorized(request.headers['username']):\n return abort(403)\n return f(*args, **kwargs)\n\n return abort(401)\n\n return decorated","title":""},{"docid":"506438b679fbfdde6a949cab44586763","score":"0.5949683","text":"def _validate_authorization(context):\n AUTHORIZE(context)\n return context.elevated()","title":""},{"docid":"79812fe61a797b58d6967c2e6468ea2a","score":"0.59406054","text":"def requires_login(f):\n @wraps(f)\n def decorated(*args, **kwargs):\n if g.user is None:\n return access_denied()\n return f(*args, **kwargs)\n return decorated","title":""},{"docid":"93d256654a930de698e8f8538c3483a2","score":"0.5940354","text":"def authenticated(method):\n @functools.wraps(method)\n def wrapper(self, *args, **kwargs):\n if not self.current_user:\n logging.error(\"User not authorized: {}\".format(self.current_user))\n raise HTTPError(401)\n return method(self, *args, **kwargs)\n return wrapper","title":""},{"docid":"6809dae3bd3c6051563ae62d91091fb8","score":"0.5909586","text":"def authenticated(method):\r\n @functools.wraps(method)\r\n def wrapper(self, *args, **kwargs):\r\n if not self.current_user:\r\n if self.request.method in (\"GET\", \"HEAD\"):\r\n self.redirect(\"/\")\r\n return\r\n self.abort(403)\r\n return method(self, *args, **kwargs)\r\n return wrapper","title":""},{"docid":"b4d76201127532935ecfde847caaaa9a","score":"0.5895598","text":"def wrap(request):\n token = request.token\n if token is not None:\n user_type = token.user.role.name\n if user_type in user_types:\n return func(request)\n else:\n raise HTTPForbidden(\"You can not access this route\")\n else:\n raise HTTPForbidden(\"Need token for access\")","title":""},{"docid":"f323651b4f73f44c03dc32facf452a8f","score":"0.58904517","text":"def is_accessible(self):\n return auth.check()","title":""},{"docid":"64e07618fa76b10986aea0de46066886","score":"0.5880261","text":"def __get_user_permissions(request):\r\n auth_func = getattr(settings, 'AMF_AUTH_FUNC')\r\n if hasattr(request, 'amfcredentials'):\r\n username = request.amfcredentials.get('username', None)\r\n password = request.amfcredentials.get('password', None)\r\n else:\r\n username = None\r\n password = None\r\n if isinstance(auth_func, FunctionType):\r\n result = auth_func(request, username, password)\r\n elif isinstance(auth_func, StringTypes):\r\n func = amf.utils.get_func(auth_func)\r\n result = func(request, username, password)\r\n\r\n if isinstance(result, (StringTypes, ListType, TupleType)):\r\n return result\r\n else:\r\n return ()","title":""},{"docid":"063288c19eb91ff85f8a8e8825f7f51a","score":"0.5871366","text":"def requires_authn(): # noqa: D202\n\n def wrapper(fn):\n @wraps(fn)\n def decorator(*args, **kwargs):\n check_authn(request)\n return fn(*args, **kwargs)\n\n return decorator\n\n return wrapper","title":""},{"docid":"39394503a311be399512a17ff3886f5e","score":"0.5844865","text":"def check_permissions(self, request):\n obj = (\n hasattr(self, 'get_controlled_object') and self.get_controlled_object() or\n hasattr(self, 'get_object') and self.get_object() or getattr(self, 'object', None)\n )\n user = request.user\n\n # Get the permissions to check\n perms = self.get_required_permissions(self)\n\n # Check permissions\n has_permissions = self.perform_permissions_check(user, obj, perms)\n\n if not has_permissions and not user.is_authenticated:\n return HttpResponseRedirect('{}?{}={}'.format(\n resolve_url(self.login_url),\n self.redirect_field_name,\n quote(request.get_full_path())\n ))\n elif not has_permissions:\n raise PermissionDenied","title":""},{"docid":"89c1e34e33b6705e0f0a9e96873cf2b5","score":"0.5838557","text":"def auth_required(handler_method):\n\n def check_auth(self, *args):\n root_url = Apputil.Url.get_root_url(self)\n redirect_url = Social.Gapi.get_redirect_uri(root_url)\n\n userinfo = _load_session_userinfo(self)\n\n logging.info(userinfo)\n\n if userinfo is None:\n return self.redirect(redirect_url)\n\n\n self.userid = Core.Session.load_session_userid(self)\n self.credentials = userinfo.credentials\n\n self.mirror_service = Apputil.Service.create('mirror', 'v1', self.credentials)\n\n if self.credentials:\n try:\n self.credentials.refresh(httplib2.Http())\n return handler_method(self, *args)\n except AccessTokenRefreshError:\n # Access has been revoked.\n Core.Session.store_userid(self, '')\n credentials_entity = userinfo.credentials\n if credentials_entity:\n credentials_entity.delete()\n\n self.redirect(redirect_url)\n\n return check_auth","title":""},{"docid":"146020e8617698fa76d769a8ec8f9bae","score":"0.5824737","text":"def check_if_user_linked(provider_identifier, func, *args, **kwargs):\n\n if current_user.is_authenticated:\n if getattr(current_user, provider_identifier):\n return func(*args, **kwargs)\n else:\n return abort(403)\n\n else:\n return abort(401)","title":""},{"docid":"17807e9f22433da0eceb8a073641062d","score":"0.58089256","text":"async def check_permission(request: web.Request, permission: Union[str, enum.Enum],\n context: Any = None) -> None:\n\n await check_authorized(request)\n allowed = await permits(request, permission, context)\n if not allowed:\n raise web.HTTPForbidden(reason=\"User does not have '{}' permission\".format(permission))","title":""},{"docid":"d6b7dac13a60f412b30e4be27b38f197","score":"0.58044344","text":"def authenticate(func):\n @wraps(func)\n def with_authentication(self, *args):\n user_id = self.get_argument('userId', None)\n pwd = self.get_argument('pwd', None)\n\n if None in (user_id, pwd):\n log.warning(\"Authentication failed! Missing auth parameters\")\n return self.error({\"message\": \"Authentication failed! Missing auth parameters\"}, 401)\n\n claro_user = self.settings['config']['claro/v1/user']['value']\n claro_pass = self.settings['config']['claro/v1/password']['value']\n\n if claro_user != user_id or claro_pass != pwd:\n log.error(\"Could not authenticate such a combination: userId {0} and pwd {1}\".format(user_id, pwd))\n return self.error({\"message\": \"Could not authenticate! Wrong userId/pwd combination\"}, 401)\n\n return func(self, *args)\n return with_authentication","title":""},{"docid":"c253cad6c4681ddee550853c7902269c","score":"0.58027345","text":"def requires_auth(f):\n\n @wraps(f)\n def decorated(*decorator_args, **decorator_kwargs):\n auth = request.authorization\n\n if args.enable_auth:\n if not auth or not verify_authentication(auth.username, auth.password):\n response = Response(\"Need basic auth to request the resources\", 401, {\n \"WWW-Authenticate\": '\"Basic realm=\"Login Required\"'\n })\n return response\n\n return f(*decorator_args, **decorator_kwargs)\n\n return decorated","title":""},{"docid":"4c18347ee35ed7558eb8729321c069bc","score":"0.5783026","text":"def perm_check(dummy): # pragma: no cover\n def wrap_outer(func):\n def wrap_inner(*args, **kargs):\n # get the argument frrom the func (method route(cls, request)\n kclass = args[0]\n request = args[1]\n\n # count number of permisson passed\n passed_count = 0\n flag_pass = False\n flag_login_required = False\n for perm in kclass.x_perm:\n if perm == 'login':\n flag_login_required = True\n if Utils.has_perm(request, perm):\n passed_count = passed_count + 1\n\n # determine pass or not\n if len(kclass.x_perm) == passed_count:\n flag_pass = True\n\n # handle failed permission\n if flag_pass == False:\n if flag_login_required:\n result = {'return':'302',\n 'msg':'Please login to continue',\n 'redirect_url':'/login/' }\n return kclass()._render(request, result)\n else:\n result = {'return':'302',\n 'msg':'Sorry, you do not have permission to continue',\n 'redirect_url':'/'}\n return kclass()._render(request, result)\n # handle passed permission\n return func(*args, **kargs)\n return wrap_inner\n return wrap_outer","title":""},{"docid":"f40725d2259e30628b97bbf42db159ce","score":"0.5781028","text":"def UserRequired(handler):\n def checkLogin(self, *args, **kwargs):\n auth = self.auth\n if not auth.get_user_by_session():\n self.redirect(self.uri_for('recruiterLogin'), abort=True)\n else:\n return handler(self, *args, **kwargs)\n return checkLogin","title":""},{"docid":"7282b26766954256db3a31237877f3b2","score":"0.57790387","text":"def require_auth(f):\n\n @wraps(f)\n def wrapped(*args, **kwargs):\n request = args[0]\n if not request.user or not request.user.is_authenticated:\n return redirect('/admin/login')\n else:\n return f(*args, **kwargs)\n\n return wrapped","title":""},{"docid":"f4f4d5ee7e0a427ef8165df402f1aeaf","score":"0.5778319","text":"def user_required(handler):\n def check_login(self, *args, **kwargs):\n auth = self.auth\n if not auth.get_user_by_session():\n self.redirect(self.uri_for('login'), abort=True)\n else:\n return handler(self, *args, **kwargs)\n\n return check_login","title":""},{"docid":"ec4a6d5c5637835f46fa2c623ba0a589","score":"0.57674855","text":"def admin_required(func):\n @functools.wraps(func)\n def decorated_function(*pa, **ka): # pylint: disable=missing-docstring\n if auth.is_admin():\n return func(*pa, **ka)\n if not auth.is_logged_in():\n return abort(401)\n return abort(403)\n\n return decorated_function","title":""},{"docid":"9ecf09da57e965947198d0e2d92d523a","score":"0.57638514","text":"def CheckAccess(self, handler, username):\n handler_name = handler.__class__.__name__\n\n if (self.api_groups and\n not self.CheckPermissions(username, self.api_access)):\n stats.STATS.IncrementCounter(\"grr_api_auth_fail\",\n fields=[handler_name, username])\n raise access_control.UnauthorizedAccess(\n \"User %s not in groups %s, authorized for %s API access.\" % (\n username, self.api_groups, self.api_access))\n\n if handler_name in self.ACLedHandlers():\n if not self.CheckPermissions(username, handler_name):\n stats.STATS.IncrementCounter(\"grr_api_auth_fail\",\n fields=[handler_name, username])\n raise access_control.UnauthorizedAccess(\n \"User %s not authorized for handler %s.\" % (\n username, handler_name))\n elif not handler.enabled_by_default:\n raise access_control.UnauthorizedAccess(\n \"%s has enabled_by_default=False and no explicit ACL set. Add ACL\"\n \" to ACL list (see API.HandlerACLFile config option) to use \"\n \"this API\" % handler)\n\n logging.debug(\"Authorizing %s for API %s\", username, handler_name)\n stats.STATS.IncrementCounter(\"grr_api_auth_success\",\n fields=[handler_name, username])","title":""},{"docid":"e5f3c177a107f9ade06f0a8eb8f76193","score":"0.57541656","text":"def admin_only(func):\n\n @functools.wraps(func)\n def wraps(*args, **kwargs):\n if slack_blueprint.config['DEBUG']:\n return func(*args, **kwargs)\n slack_token = slack_blueprint.config['SLACK_OAUTH_TOKEN']\n if not slack_token:\n flask.abort(401)\n slack = slacker.Slacker(slack_token)\n user_id = flask.request.form['user_id']\n flask.current_app.logger.info(f'[access]: performing admin check...')\n try:\n info = slack.users.info(user_id)\n except slacker.Error:\n flask.current_app.logger.info(f'[access]: {user_id} not found')\n flask.abort(403)\n if info.body['user']['is_admin']:\n return func(*args, **kwargs)\n flask.current_app.logger.error('[access]: failed admin-only test')\n flask.abort(403)\n\n return wraps","title":""},{"docid":"71ce77a8a7e677ea18fc31fe3fb7bc51","score":"0.5752249","text":"def auth_required(role_allowed_list):\n\n if not role_allowed_list:\n raise AssertionError(\"Can't call auth_required with empty role_allowed_list.\")\n\n if not isinstance(role_allowed_list, list):\n role_allowed_list = [role_allowed_list]\n\n def auth_required_wrapper(func):\n def wrapped(*args, **kwargs):\n appid = GAE_PROJECT\n request.log_record = base_api.log_api_request()\n # Only enforce HTTPS and auth for external requests; requests made for data generation\n # are allowed through (when enabled).\n acceptable_hosts = (\"None\", \"testbed-test\", \"testapp\", \"localhost\", \"127.0.0.1\")\n # logging.info(str(request.headers))\n if not is_self_request():\n if request.scheme.lower() != \"https\" and appid not in acceptable_hosts:\n raise Unauthorized(f\"HTTPS is required for {appid}\", www_authenticate='Bearer realm=\"rdr\"')\n check_auth(role_allowed_list)\n request.logged = False\n result = func(*args, **kwargs)\n if request.logged is False:\n try:\n base_api.log_api_request(log=request.log_record)\n except RuntimeError:\n # Unittests don't always setup a valid flask request context.\n pass\n return result\n\n return wrapped\n\n return auth_required_wrapper","title":""},{"docid":"6046ec6c668f464503a4c46e1ea84261","score":"0.5748685","text":"async def __call__(self, request: Request):\n checked = False\n for permission_class in self.permissions_classes:\n checked = await permission_class(request=request).check_permission()\n if checked:\n break\n\n if not checked:\n raise HTTPException(status_code=self.status_code, detail={\"detail\": self.error_msg})","title":""},{"docid":"670f6e89b529b78b9bb1bc3c2c056dff","score":"0.57462764","text":"def _auth_advice(*args, **kwargs):\n token = self._auth_context.current_token\n\n if token is None:\n raise AuthMissingTokenError(\n \"Denied access to a protected service: Method was \"\n \"called outside of an Authorization Context?\"\n )\n\n self._auth_service.check_permission(\n access_token=token,\n in_domain=domain,\n to_execute=method_name,\n args=args,\n kwargs=kwargs\n )\n\n return wrapped_f(*args, **kwargs)","title":""},{"docid":"46e6a1057420f547a3948f15f5781d89","score":"0.57433856","text":"def require_user(func):\n def decorated(self, *args, **kwargs):\n if not self.user:\n self.redirect(users.create_login_url(self.request.url))\n else:\n return func(self, *args, **kwargs)\n return decorated","title":""},{"docid":"8d6510a8538b8e58a503c2bf8562525b","score":"0.5741873","text":"def requires_authentication(func):\n\n @wraps(func)\n def wrapper(*args, **kwargs):\n authenticator = current_app.authenticator\n authentic, status, message = authenticator.authenticate(request)\n if not authentic:\n _message = json.dumps(dict(errors=dict(mauth=[message])))\n return Response(response=_message, status=status, mimetype=\"application/json\")\n return func(*args, **kwargs)\n\n return wrapper","title":""},{"docid":"f5d11787e5bc194976e0e85e05b612b5","score":"0.5739391","text":"def permission_required(perm, login_url=None, raise_exception=False):\n def check_perms(user):\n # First check if the user has the permission (even anon users)\n if user.has_perm(perm):\n return True\n # In case the 403 handler should be called raise the exception\n if raise_exception:\n try:\n app_label, codename = perm.split('.')\n permission = Permission.objects.get(content_type__app_label=app_label, codename=codename)\n except (ObjectDoesNotExist, ValueError):\n permission = perm\n user.permission_error = permission\n raise PermissionDenied\n # As the last resort, show the login form\n return False\n return user_passes_test(check_perms, login_url=login_url)","title":""},{"docid":"ce74f305b41af98542bdadebd2fb864d","score":"0.57391584","text":"def abort_if_no_google_user(decorated_function):\n def check_current_user(self, *kw, **kwargs):\n current_user = users.get_current_user()\n if not current_user:\n self.abort(401)\n else:\n decorated_function(self, *kw, **kwargs)\n return check_current_user","title":""},{"docid":"cd9e06b783f23f29186e2a3870cc3990","score":"0.57304513","text":"def user_required(handler):\r\n def check_login(self, *args, **kwargs):\r\n auth = self.auth\r\n if not auth.get_user_by_session():\r\n self.redirect(self.uri_for('login'), abort=True)\r\n else:\r\n return handler(self, *args, **kwargs)\r\n\r\n return check_login","title":""},{"docid":"7c30c9166b41b91975984b56deac1e61","score":"0.5723275","text":"def require_auth(f, self = None, *args, **kw):\n\n if not self.sid:\n raise NotLoggedIn\n\n return f(self, *args, **kw)","title":""},{"docid":"0b00621708363e97543aaea45bb5f71b","score":"0.5713961","text":"def test_it_should_raise_a_PermissionError_if_user_does_not_have_permission(self, current_user):\n func = Mock()\n current_user.has_permission.return_value = False\n current_user.is_admin = False\n current_user.return_value = current_user\n decorated_func = permission(func, permission=\"permission\")\n\n with pytest.raises(PermissionError):\n decorated_func()","title":""},{"docid":"0b7bc055a4c6fc708beaa0786ce0b0af","score":"0.570744","text":"def test_user_does_not_need_permission(self, current_user):\n func = Mock()\n current_user.return_value = current_user\n decorated_func = permission(func)\n\n assert decorated_func().is_called","title":""},{"docid":"1693e4b1307d7b17be2c38eb35b7e32b","score":"0.5705757","text":"def service_permission(view_func):\n @wraps(view_func, assigned=available_attrs(view_func))\n def _wrapped_view(request, *args, **kwargs):\n # if we get here, user has scrooge permission\n if (\n (\n 'service' in kwargs and\n has_permission_to_service(request.user, kwargs['service'])\n ) or\n 'service' not in kwargs\n ):\n return view_func(request, *args, **kwargs)\n return HttpResponseForbidden(\n json.dumps({'message': 'No permission to service'}),\n content_type=\"application/json\"\n )\n return login_required(_wrapped_view)","title":""},{"docid":"72b2053eb9d9aa129aea92df54627ad7","score":"0.5698636","text":"def helper_auth_helper(request):\n if request.user and request.user.is_authenticated:\n return None\n # source, http://djangosnippets.org/snippets/243/\n if \"HTTP_AUTHORIZATION\" in request.META:\n auth = request.headers[\"Authorization\"].split()\n if len(auth) == 2 and auth[0].lower() == \"basic\":\n uname, passwd = (\n base64.b64decode(auth[1].encode(\"utf-8\")).decode(\"utf-8\").split(\":\")\n )\n user = authenticate(username=uname, password=passwd)\n if user:\n request.user = user\n return None\n\n return HttpResponseNotAuthorized()","title":""},{"docid":"d48a6e2ffe8186453bd4ee6894b503e8","score":"0.5694516","text":"def can_handle_filterquery(func):\n def wrapper(self, request, **kwargs):\n obj = self.get_object()\n is_owner = obj.user == self.request.user\n can_handle = obj.for_everyone and obj.has_global_perm(self.request.user)\n if not is_owner and not can_handle:\n raise PermissionDenied\n\n return func(self, request, **kwargs)\n return wrapper","title":""},{"docid":"d0e15feec485c2eb7d74ac8e3a9e99a3","score":"0.5689806","text":"def login_required(func):\n @functools.wraps(func)\n def decorated_function(*pa, **ka): # pylint: disable=missing-docstring\n if auth.is_logged_in():\n return func(*pa, **ka)\n return abort(401)\n\n return decorated_function","title":""},{"docid":"3694a1c1b2c34d4f83baeca8f5ccab4c","score":"0.5689007","text":"def requires_auth(f):\n @wraps(f)\n def wrapper(*args, **kwds):\n if 'uid' not in session:\n return login(\"You are not logged in yet. Please log in\")\n return f(*args, **kwds)\n return wrapper","title":""},{"docid":"18a5f62370543658432ce13df93782a8","score":"0.5688777","text":"def check_user_access(**kwargs):\n su = kwargs.get(\"su\", False)\n rules = kwargs.get(\"rules\", [])\n match_all = kwargs.get(\"match_all\", False)\n read_required = kwargs.get(\"read_required\", False)\n write_required = kwargs.get(\"write_required\", False)\n cls = kwargs.get(\"cls\", Rest)\n \n # no rules applied for super user (no need to execute rules)\n if su: return\n # no read/write access required at all (no need to execute rules)\n if not read_required and not write_required: return\n \n r = RulesEngine.execute_rule(rules)\n # local user should always be returned in rule, if not then allow\n # exception to occur. Also, all rule_dn's passed to execute_rule\n # must have r/w attribute per user returned\n match_count = 0\n for dn in rules:\n access = r[dn][\"users\"][g.user.username]\n if read_required and write_required:\n if access[\"write\"] and access[\"read\"]: match_count+=1\n elif match_all: abort(403, MSG_403)\n elif read_required:\n if access[\"read\"]: match_count+=1\n elif match_all: abort(403, MSG_403)\n elif write_required:\n if access[\"write\"]: match_count+=1\n elif match_all: abort(403, MSG_403)\n\n # check if any rules were matched\n if match_count == 0:\n abort(403, MSG_403)","title":""},{"docid":"cec496ee4c5734d2c662ab95f23a8ca3","score":"0.56752","text":"def auth_required(function):\n\n def wrap(request, *args, **kwargs):\n if request.user.is_authenticated:\n return function(request, *args, **kwargs)\n\n resp = HttpResponse()\n resp.status_code = 401\n return resp\n\n return wrap","title":""},{"docid":"cddc3d21bac4d0f14c7b56f8ec6c06b8","score":"0.56547445","text":"def requires_auth(f):\n\n @wraps(f)\n def decorated(*args, **kwargs):\n # return f(*args, **kwargs)\n self = args[0]\n request = args[1]\n if six.PY3:\n token = request.args.get(b'token', [b''])[0].decode(\"utf8\")\n else:\n token = str(request.args.get('token', [''])[0])\n if self.config['token'] is None or token == self.config['token']:\n return f(*args, **kwargs)\n request.setResponseCode(401)\n return 'unauthorized'\n\n return decorated","title":""},{"docid":"88e9310993c82d8541f71eaf94e0386d","score":"0.5654406","text":"def check_if_user_not_linked(provider_identifier, func, *args, **kwargs):\n\n if current_user.is_authenticated:\n if not getattr(current_user, provider_identifier):\n return func(*args, **kwargs)\n else:\n return abort(403)\n\n else:\n return abort(401)","title":""},{"docid":"18b1e4ea635e0c940c27a65e22b02e97","score":"0.56495607","text":"def xmlauthenticated(method):\n \n def inner(*args, **kwargs):\n if args[0].current_user is None:\n try:\n raise404(args[0], \"Access forbidden\")\n except ForbiddenOperationException as e:\n pass\n return\n else:\n return method(*args, **kwargs)\n return inner","title":""},{"docid":"30b52a7bc4cefa6047b66d2a13c15cfa","score":"0.56485206","text":"def require_login(definition: Callable) -> Callable:\n\n @functools.wraps(definition)\n def wrapper(state: State, *args, **kwargs) -> flask.Response:\n if state.user is None: # If No User Exists In State\n flask.abort(403)\n else:\n return definition(*args, **kwargs, state=state) # Continue Request\n\n return wrapper","title":""},{"docid":"10ef830457f0392a45aae5804cab992e","score":"0.5641129","text":"def _authenticate(self, request):\n\n def anonymous_access(exc_obj):\n \"\"\" Check whether anonymous access is allowed. \"\"\"\n if not request.user and not self.allow_anonymous:\n raise exc_obj\n\n request.user = None\n if self.authentication:\n try:\n request.user = self.authentication.authenticate(request)\n except Unauthorized, exc:\n anonymous_access(exc)\n else:\n anonymous_access(Forbidden())\n return request.user","title":""},{"docid":"15a65d9fceb039a4c11b252d76e5c92c","score":"0.5638501","text":"def authenticate(self, handler, data=None):\n userdict = yield super().authenticate(handler, data)\n denylist = os.environ.get('GITHUB_ORGANIZATION_DENYLIST')\n if denylist:\n self.log.debug(\"Denylist `%s` found.\" % denylist)\n denylist = denylist.split(',')\n denied = yield self._check_denylist(userdict, denylist)\n if denied:\n self.log.warning(\"Rejecting user: denylisted\")\n userdict = None\n return userdict","title":""},{"docid":"261fbaa3390dd85814fc5e37f08f0ea8","score":"0.5632094","text":"def privilege_required(view_func):\n def _decorated_view(request, machine_name, **kwargs):\n if _is_privileged_on(request, machine_name):\n return view_func(request, machine_name, **kwargs)\n else:\n raise PermissionDenied\n return wraps(view_func)(_decorated_view)","title":""},{"docid":"b972b8da2ace9151179b39c4f62a5da2","score":"0.5623965","text":"def _get_auth_safe_user(\n self,\n request: 'TracimRequest',\n ) -> User:\n app_config = request.registry.settings['CFG']\n uapi = UserApi(None, session=request.dbsession, config=app_config)\n login = ''\n try:\n login = request.authenticated_userid\n if not login:\n raise UserNotFoundInTracimRequest('You request a current user but the context not permit to found one') # nopep8\n user = uapi.get_one(login)\n if not user.is_active:\n raise UserAuthenticatedIsNotActive('User {} is not active'.format(login))\n except (UserDoesNotExist, UserNotFoundInTracimRequest) as exc:\n raise NotAuthenticated('User {} not found'.format(login)) from exc\n return user","title":""},{"docid":"ff9301ed2477d78fdf8f2ef4fa15cd12","score":"0.5601441","text":"def http_auth(func):\n @wraps(func)\n def inner(request, *args, **kwargs):\n result = __http_auth_helper(request)\n if result is not None:\n return result\n return func(request, *args, **kwargs)\n return inner","title":""},{"docid":"59eef6ab037351aca6df5df49acd2841","score":"0.5600946","text":"def get_authenticated_user(self, auth_func, realm):\n try:\n auth = self.request.headers.get('Authorization')\n \n if auth == None: return self._request_auth(realm)\n if not auth.startswith('Basic '): return self._request_auth(realm)\n \n auth_decoded = base64.decodestring(auth[6:])\n username, password = auth_decoded.split(':', 1)\n \n if auth_func(self, realm, username, password):\n self._current_user = username\n return True\n else:\n return self._request_auth(realm)\n except Exception, e:\n return self._request_auth(realm)","title":""},{"docid":"4b77562199f407806fecbafe4cc83728","score":"0.5593828","text":"def _special_Trusted(self, request, name, dowhat, rightsdict):\n if (request.user.name == name and\n request.user.auth_method in request.cfg.auth_methods_trusted):\n return rightsdict.get(dowhat)\n return None","title":""},{"docid":"2540dedef3b39496ac968ac4743b422a","score":"0.55937165","text":"def _validate_authorization(context, action):\n #We want to use the more granular version, but can't until it exists\n AUTHORIZE(context, action=action)\n return context.elevated()","title":""},{"docid":"fb810051923c44c7726a16aba5291ea6","score":"0.55935144","text":"def validate(*args, **kwargs):\n\n # Get the headers from the self arg\n headers = args[0].request_state.headers\n token = headers.get('Token')\n if headers.get('User-Id') is None:\n raise endpoints.UnauthorizedException(MESSAGE_UNAUTHORIZED)\n\n user_id = int(headers.get('User-Id'))\n user, timestamp = User.get_by_auth_token(user_id, token)\n if user:\n return endpoints_method(*args, **kwargs)\n else:\n logging.warn('Invalid API request! User ID:%s, token:%s'\n %(user_id, token))\n raise endpoints.UnauthorizedException(MESSAGE_UNAUTHORIZED)","title":""},{"docid":"b98589612c4b1750897b75db9f005c99","score":"0.55931556","text":"def permissions_required(app_label, login_url=None, raise_exception=False):\n def check_perms(user):\n # First check if the user has the permission (even anon users)\n if user.has_module_perms(app_label):\n return True\n # In case the 403 handler should be called raise the exception\n if raise_exception:\n user.permission_error = app_label\n raise PermissionDenied\n # As the last resort, show the login form\n return False\n\n return user_passes_test(check_perms, login_url=login_url)","title":""},{"docid":"600144fd3f5625fd30047464f024f2c4","score":"0.5591398","text":"def login_required(f):\n def decorator(*args, **kwargs):\n if not current_user.is_authenticated:\n abort(401)\n return f(*args, **kwargs)\n return decorator","title":""},{"docid":"c074381197bc17c810a71bae0dab05a0","score":"0.5590082","text":"def authenticate(self, **kwargs):\n authenticated = False\n if 'password' and 'username' in kwargs:\n try:\n user = TaskerUser.objects.get_by_natural_key(\n kwargs.get('username'))\n authenticated = user.check_password(kwargs.get('password'))\n except TaskerUser.DoesNotExist:\n pass\n elif 'google_user' in kwargs:\n google_user = kwargs.get('google_user')\n email = google_user['email']\n try:\n user = TaskerUser.objects.get_by_natural_key(email)\n authenticated = True\n except TaskerUser.DoesNotExist:\n email = google_user['email']\n try:\n user = TaskerUser.objects.get_by_natural_key(email)\n authenticated = True\n except TaskerUser.DoesNotExist:\n pass\n if authenticated:\n return user\n return None","title":""},{"docid":"4c61841bfa3da2370fd955ada28736ea","score":"0.5589802","text":"def _check_basic_auth (self, user, password):\n if self._is_basic_auth_enabled is False: return True\n\n try:\n return self.basic_auth_function(self, user, password)\n except TypeError:\n return self.basic_auth_function(user, password)","title":""},{"docid":"96b0b326964489b2b46f65677b838a6a","score":"0.5589744","text":"def requires_auth(f):\n @functools.wraps(f)\n def decorated(*args, **kwargs):\n\n potential_user_id = BaseController().is_logged_in()\n \n if not potential_user_id:\n return redirect(\"/login\", code=302)\n\n request.user_id = potential_user_id\n return f(*args, **kwargs)\n\n return decorated","title":""},{"docid":"1897ba7745e99f68995f290c0f6693f4","score":"0.55829257","text":"async def __call__(self, request: Request):\n credentials: HTTPAuthorizationCredentials = await super().__call__(request)\n credentials = credentials.credentials\n if not credentials:\n raise HTTPException(status_code=403, detail=AuthState.NO_TOKEN)\n\n try:\n token_data = jwt.decode(credentials, Server.JWT_SECRET)\n except JWTError:\n raise HTTPException(status_code=403, detail=AuthState.INVALID_TOKEN.value)\n\n user_id, token_salt = token_data[\"id\"], token_data[\"salt\"]\n\n user_state = await request.state.db_conn.fetchrow(\n \"SELECT is_banned, is_mod, key_salt FROM users WHERE user_id = $1\",\n int(user_id)\n )\n\n # Handle bad scenarios\n if user_state is None or user_state[\"key_salt\"] != token_salt:\n raise HTTPException(status_code=403, detail=AuthState.INVALID_TOKEN.value)\n elif user_state[\"is_banned\"]:\n raise HTTPException(status_code=403, detail=AuthState.BANNED.value)\n elif self.is_mod_endpoint and not user_state[\"is_mod\"]:\n raise HTTPException(status_code=403, detail=AuthState.NEEDS_MODERATOR.value)\n\n request.state.user_id = int(user_id)\n return credentials","title":""},{"docid":"4d33e68e2049b70e9c1b250ff0db18e5","score":"0.55694336","text":"def check_user_permissions(self, user, method, status, url, *args):\n if (user):\n self.client.login(username=user, password='password')\n\n if (method == 'get'):\n response = self.client.get(url, format='json')\n elif (method == 'post'):\n response = self.client.post(url, args[0], format='json')\n elif (method == 'put'):\n response = self.client.put(url, args[0], format='json')\n elif (method == 'delete'):\n response = self.client.delete(url, format='json')\n\n self.assertEqual(response.status_code, status)","title":""},{"docid":"564bb62b641f28c3aa60d9fe2fdd8fc6","score":"0.55652887","text":"def admin_required(func):\n @wraps(func)\n def decorated_view(*args, **kwargs):\n if not users.is_current_user_admin():\n raise exceptions.MethodNotAllowed('Admin credentials required.')\n return func(*args, **kwargs)\n return decorated_view","title":""},{"docid":"d50f72a47505787e139ef4899e2ad4d7","score":"0.5563556","text":"def user_required(handler):\n\n def check_login(self, *args, **kwargs):\n \"\"\"\n If handler has no login_url specified invoke a 403 error\n \"\"\"\n if self.request.query_string != '':\n query_string = '?' + self.request.query_string\n else:\n query_string = ''\n\n continue_url = self.request.path_url + query_string\n login_url = self.uri_for('login', **{'continue': continue_url})\n\n try:\n user = self.auth.get_user_by_session()\n if not user:\n try:\n self.redirect(login_url, abort=True)\n except (AttributeError, KeyError), e:\n self.abort(403)\n except AttributeError, e:\n # avoid AttributeError when the session was delete from the server\n logging.error(e)\n self.user.unset_session()\n self.redirect(login_url)\n\n return handler(self, *args, **kwargs)\n return check_login","title":""},{"docid":"3786d87c21418424b921cc176ef2ba45","score":"0.5563208","text":"def administrator(method):\n @functools.wraps(method)\n def wrapper(self, *args, **kwargs):\n if not self.current_user:\n if self.request.method == \"GET\":\n self.redirect(self.get_login_url())\n return\n raise tornado.web.HTTPError(403)\n elif not self.current_user.administrator:\n if self.request.method == \"GET\":\n self.redirect(\"/\")\n return\n raise tornado.web.HTTPError(403)\n else:\n return method(self, *args, **kwargs)\n return wrapper","title":""},{"docid":"d7ddbdb3b819025e3fbf8a291025f20c","score":"0.5543244","text":"def superuser_permission(view_func):\n @wraps(view_func, assigned=available_attrs(view_func))\n def _wrapped_view(request, *args, **kwargs):\n if request.user.is_superuser:\n return view_func(request, *args, **kwargs)\n return HttpResponseForbidden(\n json.dumps({'message': 'No permission to service'}),\n content_type=\"application/json\"\n )\n return login_required(_wrapped_view)","title":""},{"docid":"949aac04f4b355d4c02237b73cfc6e50","score":"0.5542497","text":"def authorization(func):\n def wrapper(*args, **kwargs):\n if 'Authorization' not in args[0].headers:\n response = error_handler(error_status=401, message=f'Security token is missing!')\n else:\n response = func(*args, **kwargs)\n return response\n return wrapper","title":""},{"docid":"806cc471ae62c3f73229680d3259043c","score":"0.55357605","text":"def auth_token_required(fn):\r\n\r\n @wraps(fn)\r\n def decorated(*args, **kwargs):\r\n if _check_token():\r\n return fn(*args, **kwargs)\r\n return _get_unauthorized_response()\r\n return decorated","title":""},{"docid":"1ae8c2cb6c4210f778d60c0f952834b5","score":"0.5535683","text":"def wrapper(*args, **kwargs):\n if not _is_admin():\n abort(403, message='Only admins can do this')\n\n return fn(*args, **kwargs)","title":""},{"docid":"837034616a13848ea50ec9be3d73fc67","score":"0.5534459","text":"def has_access(func):\n @wraps(func)\n def _wrapper(bot, update, conf, *args, **kwargs):\n try:\n user = update.message.from_user\n id = int(user['id'])\n if id in conf[\"white_list\"]:\n return func(bot, update, conf=conf, *args, **kwargs)\n update.message.reply_text(\"You don't have access to me yet.\")\n except Exception as e:\n update.message.reply_text(\"Error occur in has_access: \" + str(e))\n return ConversationHandler.END\n return _wrapper","title":""},{"docid":"55456d9ee8d1b4da3a2844abf7dc211a","score":"0.5519779","text":"def authorizeRequireWarp(fn):\n @wraps(fn)\n def wrapper(self, session, *args, **kwargs):\n try:\n if SessionManager.Check(session) is True:\n return fn(self, session, *args, **kwargs)\n else:\n return False, CController.Unauthorized(session)\n except Exception as e:\n print \"Exception in COrgan: %s\" % str(e)\n return False, e\n return wrapper","title":""},{"docid":"5de27c3687c0909d2d8c9187af9e0b51","score":"0.5511475","text":"def isOwner():\n def wrapper(func):\n @wraps(func)\n def decorator(*args, **kwargs):\n current_user = User.query.get(get_jwt_identity())\n if current_user:\n if current_user.id == int(kwargs['id']):\n return func(*args, **kwargs)\n return abort(401)\n return abort(401)\n return decorator\n return wrapper","title":""},{"docid":"cb9ff2a8a781203479484fcd63d5120f","score":"0.5503048","text":"def RequireAdministrator(func):\n\t@wraps(func)\n\tdef f(request, *args, **kwargs):\n\t\tif 'Administrator' not in request.session['MHL_Users']:\n\t\t\treturn err403(request)\n\n\t\treturn func(request, *args, **kwargs)\n\n\treturn f","title":""},{"docid":"a14ce9ca125173a4e8a48fc4efe09e0b","score":"0.55030423","text":"def superuser_required(func):\n @wraps(func)\n def wrapper(request, *args, **kwargs):\n user = getCurrentUser(request)\n if not user.getUserpermission('superuser'):\n return util.ctrl.infoMsg(_(\"您不具有{}权限\").format(_(\"超级管理员\")))\n return func(request, *args, **kwargs)\n return wrapper","title":""},{"docid":"e5e0da68fc98969c0945e1dde6e89fb9","score":"0.55025524","text":"def _check_credentials(\n self, user: Optional[TUser], handle: str, password: str\n ) -> TUser:\n if not user or not user.check_password(password):\n raise AuthenticationFailure(\n self._strings.wrong_email\n if \"@\" in handle\n else self._strings.wrong_username\n )\n\n if self._require_activation and not user.is_activated:\n raise AuthenticationFailure(self._strings.inactive_account)\n return user","title":""},{"docid":"317a7efee883d520f7770631e97b152a","score":"0.5497476","text":"def require_login(func):\n @wraps(func)\n def wrapper(*args, **kwargs):\n if _has_logged_in():\n return func(*args, **kwargs)\n else:\n # Forbidden, login invalid or non existant.\n # TODO: Redirect to login page.\n abort(403)\n return wrapper","title":""},{"docid":"645ad852b7e1118d13065488a0df3ae6","score":"0.5497418","text":"def basic_http_auth(func):\n\n @wraps(func)\n def _inner(request, *args, **kwargs):\n result = helper_auth_helper(request)\n if result is not None:\n return result\n return func(request, *args, **kwargs)\n\n return _inner","title":""}],"string":"[\n {\n \"docid\": \"5e224920ce0bc70b58729d9fc529e91d\",\n \"score\": \"0.7034291\",\n \"text\": \"def check_auth(func):\\n\\n @wraps(func)\\n def invoke(self, *args, **kwargs):\\n try:\\n self.user = self.state.get_user()\\n if self.user is None:\\n print(\\\"Authentication is needed, run spock auth\\\")\\n return\\n return func(self, *args, **kwargs)\\n except (tk.Forbidden, tk.NotFound) as e:\\n # TODO better error messages\\n print(e)\\n\\n return invoke\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c183ffd7b906a8c05f0f231f81b6324\",\n \"score\": \"0.6600555\",\n \"text\": \"def authorized_access(func):\\n @wraps(func)\\n def decor(*args, **kwargs):\\n print(get_current_user())\\n if get_current_user() is None:\\n return unauthorized_handler()\\n elif not get_current_user().authorized:\\n return abort(403)\\n else:\\n return func(*args, **kwargs)\\n\\n return decor\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd9e443a33c3b891c19039d102f52b2f\",\n \"score\": \"0.64821035\",\n \"text\": \"def authorization_required(func):\\n @functools.wraps(func)\\n def decorated_function(*pa, **ka): # pylint: disable=missing-docstring\\n if auth.is_authorized(ndb.Key(urlsafe=ka['key'])):\\n return func(*pa, **ka)\\n if not auth.is_logged_in():\\n return abort(401)\\n return abort(403)\\n\\n return decorated_function\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd463cc1d707007b256213b66d9c8904\",\n \"score\": \"0.64415056\",\n \"text\": \"def required(method):\\n @functools.wraps(method)\\n def inner_method(self, *args, **kwargs):\\n if not self.current_user:\\n raise tornado.web.HTTPError(401)\\n return method(self, *args, **kwargs)\\n return inner_method\",\n \"title\": \"\"\n },\n {\n \"docid\": \"abc910645dcc40e9d1560fc0a525fe33\",\n \"score\": \"0.6417043\",\n \"text\": \"def user_required(f):\\n def decorator(*args, **kwargs):\\n if not g.user:\\n abort(401)\\n return f(*args, **kwargs)\\n return decorator\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94a50332767a575f570154b464d8eed7\",\n \"score\": \"0.63966864\",\n \"text\": \"def permission_required(permission):\\n def decorator(f):\\n @wraps(f)\\n def decorated_function(*args, **kwargs):\\n if not current_user.is_authenticated:\\n abort(403)\\n if not current_user.can(permission, current_user.curr_conf):\\n abort(403)\\n return f(*args, **kwargs)\\n return decorated_function\\n return decorator\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1039840cfb67ea036663b1b2462cf129\",\n \"score\": \"0.6382413\",\n \"text\": \"def authorize(func):\\n def func_wrapper(*args, **kwargs):\\n \\\"\\\"\\\"Wrap the function.\\\"\\\"\\\"\\n user = endpoints.get_current_user()\\n if not user:\\n raise endpoints.UnauthorizedException(\\\"Authorization required.\\\")\\n return func(*args, **kwargs)\\n return func_wrapper\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f86255f20bc57edb3370291c40b3811\",\n \"score\": \"0.6320725\",\n \"text\": \"def auth_required():\\n\\n def decorator(endpoints_method):\\n @wraps(endpoints_method)\\n def validate(*args, **kwargs):\\n \\\"\\\"\\\" Validates the User ID and token sent in an endpoints request.\\\"\\\"\\\"\\n\\n # Get the headers from the self arg\\n headers = args[0].request_state.headers\\n token = headers.get('Token')\\n if headers.get('User-Id') is None:\\n raise endpoints.UnauthorizedException(MESSAGE_UNAUTHORIZED)\\n\\n user_id = int(headers.get('User-Id'))\\n user, timestamp = User.get_by_auth_token(user_id, token)\\n if user:\\n return endpoints_method(*args, **kwargs)\\n else:\\n logging.warn('Invalid API request! User ID:%s, token:%s'\\n %(user_id, token))\\n raise endpoints.UnauthorizedException(MESSAGE_UNAUTHORIZED)\\n return validate\\n return decorator\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0349a07186161c0a71e50c532d092f0f\",\n \"score\": \"0.630414\",\n \"text\": \"def _check_auth(func):\\n @functools.wraps(func)\\n def deco(*args, **kwargs):\\n if 'Authorization' in request.headers:\\n try:\\n scheme, creds = request.headers['Authorization'].split(\\n None, 1\\n )\\n except ValueError:\\n return '', 401\\n else:\\n if scheme == 'Bearer':\\n user = User.verify_auth_token(creds)\\n if user is not None:\\n g.user = user\\n return func(*args, **kwargs)\\n return '', 401\\n return deco\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd8e5c0d88e8fb022b045e6d2d774e44\",\n \"score\": \"0.6302787\",\n \"text\": \"def admin_credentials_required(f):\\n @wraps(f)\\n def decorated(*args, **kwargs):\\n if 'username' not in request.headers or 'password' not in request.headers:\\n return abort(401)\\n if not ldapuser.authenticate(request.headers['username'], request.headers['password']):\\n return abort(403)\\n if not ldapuser.is_api_authorized(request.headers['username']):\\n return abort(403)\\n return f(*args, **kwargs)\\n return decorated\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a6a4039a26761beeec593737fe030535\",\n \"score\": \"0.6287337\",\n \"text\": \"def requires_auth(self, f):\\r\\n @wraps(f)\\r\\n def decorated(*args, **kwargs):\\r\\n auth = request.authorization\\r\\n if not auth or not self.check_auth(auth.username, auth.password):\\r\\n return self.authenticate()\\r\\n return f(*args, **kwargs)\\r\\n return decorated\",\n \"title\": \"\"\n },\n {\n \"docid\": \"208fefd390f0cdc23b1fc5870703a1e8\",\n \"score\": \"0.6263697\",\n \"text\": \"def local_user_required(decorated_function):\\n def check_for_local_user(self, *kw, **kwargs):\\n try:\\n current_user = users.get_current_user()\\n user = get_user_by_google_id(current_user.user_id())\\n if not user:\\n raise Exception\\n except:\\n self.abort(401)\\n else:\\n decorated_function(self, *kw, **kwargs)\\n return check_for_local_user\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc693f93f343f5a0b8d2025626db9a95\",\n \"score\": \"0.6183728\",\n \"text\": \"def requires_authentication(func):\\n def endpoint_wrapper(*args, **kwargs):\\n try:\\n header = connexion.request.headers['Authorization']\\n User.authenticate(header)\\n except KeyError:\\n logger.info(\\\"Missing 'Authorization' header\\\")\\n raise AuthException(\\\"Missing 'Authorization' header\\\")\\n except AuthenticationFailure as e:\\n logger.info(\\\"Unable to authenticate: {}\\\".format(e))\\n raise AuthException(\\\"Resource requires authentication\\\")\\n else:\\n return func(*args, **kwargs)\\n\\n return endpoint_wrapper\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a9bf9b2cebb9b16cdb7ff1619229446d\",\n \"score\": \"0.61732227\",\n \"text\": \"def check_auth(self, request):\\n\\n if not request.user.is_authenticated():\\n from django.core.exceptions import PermissionDenied\\n raise PermissionDenied\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a9bf9b2cebb9b16cdb7ff1619229446d\",\n \"score\": \"0.61732227\",\n \"text\": \"def check_auth(self, request):\\n\\n if not request.user.is_authenticated():\\n from django.core.exceptions import PermissionDenied\\n raise PermissionDenied\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a9bf9b2cebb9b16cdb7ff1619229446d\",\n \"score\": \"0.61732227\",\n \"text\": \"def check_auth(self, request):\\n\\n if not request.user.is_authenticated():\\n from django.core.exceptions import PermissionDenied\\n raise PermissionDenied\",\n \"title\": \"\"\n },\n {\n \"docid\": \"664a22a4980590a182a4bc2541f3f186\",\n \"score\": \"0.6165246\",\n \"text\": \"def authenticated(method):\\n @functools.wraps(method)\\n def wrapper(self, *args, **kwargs):\\n if not self.current_user: \\n raise HTTPError(401)\\n return method(self, *args, **kwargs)\\n return wrapper\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a95fe246588bbcae5023ab97beaac59\",\n \"score\": \"0.6143246\",\n \"text\": \"def _auth_required_healthpro_or_config_admin(func):\\n\\n def wrapped(*args, **kwargs):\\n if not is_config_admin(app_util.get_oauth_id()):\\n _, user_info = get_validated_user_info()\\n if not HEALTHPRO in user_info.get(\\\"roles\\\", []):\\n logging.warning(\\\"User has roles {}, but HEALTHPRO or admin is required\\\".format(user_info.get(\\\"roles\\\")))\\n raise Forbidden()\\n return func(*args, **kwargs)\\n\\n return wrapped\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bbdbdfa1559f574b9fdd0e0053769e53\",\n \"score\": \"0.6111663\",\n \"text\": \"def require_permission(permission):\\n def handler(f, *args, **kwargs):\\n request = args[0]\\n if check_permission(request, request.current_user, permission):\\n return f(*args, **kwargs)\\n elif request.current_user:\\n raise HTTPForbidden()\\n else:\\n raise HTTPFound(request.route_url('user.login', _query={'redirect': encode_route(request)}))\\n return decorator(handler)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16e31dc169c8638440859b248e1702c1\",\n \"score\": \"0.61005646\",\n \"text\": \"def require_context(f):\\n\\n def wrapper(*args, **kwargs):\\n if not is_admin_context(args[0]) and not is_user_context(args[0]):\\n raise exception.NotAuthorized()\\n return f(*args, **kwargs)\\n\\n return wrapper\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00ec2e2b9cb34ebe00620a63d20b8048\",\n \"score\": \"0.6080321\",\n \"text\": \"def require_authentication():\\n if not current_user.is_authenticated:\\n return current_app.login_manager.unauthorized()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd376234770beaa4d7ccb211694c74c1\",\n \"score\": \"0.6043147\",\n \"text\": \"def login_required(func):\\n @wraps(func)\\n def decor(*args, **kwargs):\\n if get_current_user() is None:\\n return unauthorized_handler()\\n else:\\n return func(*args, **kwargs)\\n\\n return decor\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81087392ac3ba9f7146145cb87852d79\",\n \"score\": \"0.6038824\",\n \"text\": \"def auth_required(f):\\n\\n @wraps(f)\\n async def _wrapper(request):\\n if 'auth' not in request:\\n raise HTTPUnauthorized()\\n return await f(request)\\n\\n return _wrapper\",\n \"title\": \"\"\n },\n {\n \"docid\": \"690bfe268358413070982277e8cc2b16\",\n \"score\": \"0.6031002\",\n \"text\": \"async def check(self, req: Request, handler: Callable, client):\\n if client.administrator or client.permissions[self.permission.value]:\\n return\\n\\n raise HTTPForbidden(text=\\\"Not permitted\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0acc64c6851d439d7c4204987a0f9843\",\n \"score\": \"0.6015531\",\n \"text\": \"def check_ldap_access(view_func):\\n def _decorated_view (request, pw_pk=None, **kwargs):\\n if not pw_pk or check_authorization(pw_pk, request.user.username):\\n return view_func(request, pw_pk, **kwargs)\\n elif check_authorization(pw_pk, request.user.username) == None:\\n return view_func(request, None, **kwargs) \\n else:\\n raise PermissionDenied(str(get_ldap_groups(request.user.username))+request.user.username)\\n return wraps(view_func)(_decorated_view)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e89c01d0aec7d23e69a52504401dd066\",\n \"score\": \"0.5991843\",\n \"text\": \"def session_oriented_request(self, func, *args, **kwargs):\\n try:\\n session = self.check_authorization()\\n user = get_user(session.user)\\n\\n if user is None:\\n return responses.client_error(404, 'User not found')\\n\\n return func(user, *args, **kwargs)\\n except AuthException as ae:\\n return responses.client_error(401, '{}'.format(ae))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1933ecdf77a950c0c67577884966bc1b\",\n \"score\": \"0.59741074\",\n \"text\": \"def api_credentials_required(f):\\n @wraps(f)\\n def decorated(*args, **kwargs):\\n if 'id' in request.headers and 'token' in request.headers:\\n if not tokens.verify(request.headers['id'], request.headers['token']):\\n return abort(403)\\n if tokens.is_instance(request.headers['id']):\\n if 'instance_id' in kwargs:\\n instance = aws.get_instance(kwargs['instance_id'])\\n if not instance:\\n return abort(400)\\n client_ip = request.environ.get('HTTP_X_REAL_IP', request.remote_addr)\\n if instance.private_ip_address != client_ip:\\n return abort(403)\\n return f(*args, **kwargs)\\n\\n if 'username' in request.headers and 'password' in request.headers:\\n if not ldapuser.authenticate(request.headers['username'], request.headers['password']):\\n return abort(403)\\n if not ldapuser.is_api_authorized(request.headers['username']):\\n return abort(403)\\n return f(*args, **kwargs)\\n\\n return abort(401)\\n\\n return decorated\",\n \"title\": \"\"\n },\n {\n \"docid\": \"506438b679fbfdde6a949cab44586763\",\n \"score\": \"0.5949683\",\n \"text\": \"def _validate_authorization(context):\\n AUTHORIZE(context)\\n return context.elevated()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79812fe61a797b58d6967c2e6468ea2a\",\n \"score\": \"0.59406054\",\n \"text\": \"def requires_login(f):\\n @wraps(f)\\n def decorated(*args, **kwargs):\\n if g.user is None:\\n return access_denied()\\n return f(*args, **kwargs)\\n return decorated\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93d256654a930de698e8f8538c3483a2\",\n \"score\": \"0.5940354\",\n \"text\": \"def authenticated(method):\\n @functools.wraps(method)\\n def wrapper(self, *args, **kwargs):\\n if not self.current_user:\\n logging.error(\\\"User not authorized: {}\\\".format(self.current_user))\\n raise HTTPError(401)\\n return method(self, *args, **kwargs)\\n return wrapper\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6809dae3bd3c6051563ae62d91091fb8\",\n \"score\": \"0.5909586\",\n \"text\": \"def authenticated(method):\\r\\n @functools.wraps(method)\\r\\n def wrapper(self, *args, **kwargs):\\r\\n if not self.current_user:\\r\\n if self.request.method in (\\\"GET\\\", \\\"HEAD\\\"):\\r\\n self.redirect(\\\"/\\\")\\r\\n return\\r\\n self.abort(403)\\r\\n return method(self, *args, **kwargs)\\r\\n return wrapper\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b4d76201127532935ecfde847caaaa9a\",\n \"score\": \"0.5895598\",\n \"text\": \"def wrap(request):\\n token = request.token\\n if token is not None:\\n user_type = token.user.role.name\\n if user_type in user_types:\\n return func(request)\\n else:\\n raise HTTPForbidden(\\\"You can not access this route\\\")\\n else:\\n raise HTTPForbidden(\\\"Need token for access\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f323651b4f73f44c03dc32facf452a8f\",\n \"score\": \"0.58904517\",\n \"text\": \"def is_accessible(self):\\n return auth.check()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64e07618fa76b10986aea0de46066886\",\n \"score\": \"0.5880261\",\n \"text\": \"def __get_user_permissions(request):\\r\\n auth_func = getattr(settings, 'AMF_AUTH_FUNC')\\r\\n if hasattr(request, 'amfcredentials'):\\r\\n username = request.amfcredentials.get('username', None)\\r\\n password = request.amfcredentials.get('password', None)\\r\\n else:\\r\\n username = None\\r\\n password = None\\r\\n if isinstance(auth_func, FunctionType):\\r\\n result = auth_func(request, username, password)\\r\\n elif isinstance(auth_func, StringTypes):\\r\\n func = amf.utils.get_func(auth_func)\\r\\n result = func(request, username, password)\\r\\n\\r\\n if isinstance(result, (StringTypes, ListType, TupleType)):\\r\\n return result\\r\\n else:\\r\\n return ()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"063288c19eb91ff85f8a8e8825f7f51a\",\n \"score\": \"0.5871366\",\n \"text\": \"def requires_authn(): # noqa: D202\\n\\n def wrapper(fn):\\n @wraps(fn)\\n def decorator(*args, **kwargs):\\n check_authn(request)\\n return fn(*args, **kwargs)\\n\\n return decorator\\n\\n return wrapper\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39394503a311be399512a17ff3886f5e\",\n \"score\": \"0.5844865\",\n \"text\": \"def check_permissions(self, request):\\n obj = (\\n hasattr(self, 'get_controlled_object') and self.get_controlled_object() or\\n hasattr(self, 'get_object') and self.get_object() or getattr(self, 'object', None)\\n )\\n user = request.user\\n\\n # Get the permissions to check\\n perms = self.get_required_permissions(self)\\n\\n # Check permissions\\n has_permissions = self.perform_permissions_check(user, obj, perms)\\n\\n if not has_permissions and not user.is_authenticated:\\n return HttpResponseRedirect('{}?{}={}'.format(\\n resolve_url(self.login_url),\\n self.redirect_field_name,\\n quote(request.get_full_path())\\n ))\\n elif not has_permissions:\\n raise PermissionDenied\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89c1e34e33b6705e0f0a9e96873cf2b5\",\n \"score\": \"0.5838557\",\n \"text\": \"def auth_required(handler_method):\\n\\n def check_auth(self, *args):\\n root_url = Apputil.Url.get_root_url(self)\\n redirect_url = Social.Gapi.get_redirect_uri(root_url)\\n\\n userinfo = _load_session_userinfo(self)\\n\\n logging.info(userinfo)\\n\\n if userinfo is None:\\n return self.redirect(redirect_url)\\n\\n\\n self.userid = Core.Session.load_session_userid(self)\\n self.credentials = userinfo.credentials\\n\\n self.mirror_service = Apputil.Service.create('mirror', 'v1', self.credentials)\\n\\n if self.credentials:\\n try:\\n self.credentials.refresh(httplib2.Http())\\n return handler_method(self, *args)\\n except AccessTokenRefreshError:\\n # Access has been revoked.\\n Core.Session.store_userid(self, '')\\n credentials_entity = userinfo.credentials\\n if credentials_entity:\\n credentials_entity.delete()\\n\\n self.redirect(redirect_url)\\n\\n return check_auth\",\n \"title\": \"\"\n },\n {\n \"docid\": \"146020e8617698fa76d769a8ec8f9bae\",\n \"score\": \"0.5824737\",\n \"text\": \"def check_if_user_linked(provider_identifier, func, *args, **kwargs):\\n\\n if current_user.is_authenticated:\\n if getattr(current_user, provider_identifier):\\n return func(*args, **kwargs)\\n else:\\n return abort(403)\\n\\n else:\\n return abort(401)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17807e9f22433da0eceb8a073641062d\",\n \"score\": \"0.58089256\",\n \"text\": \"async def check_permission(request: web.Request, permission: Union[str, enum.Enum],\\n context: Any = None) -> None:\\n\\n await check_authorized(request)\\n allowed = await permits(request, permission, context)\\n if not allowed:\\n raise web.HTTPForbidden(reason=\\\"User does not have '{}' permission\\\".format(permission))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d6b7dac13a60f412b30e4be27b38f197\",\n \"score\": \"0.58044344\",\n \"text\": \"def authenticate(func):\\n @wraps(func)\\n def with_authentication(self, *args):\\n user_id = self.get_argument('userId', None)\\n pwd = self.get_argument('pwd', None)\\n\\n if None in (user_id, pwd):\\n log.warning(\\\"Authentication failed! Missing auth parameters\\\")\\n return self.error({\\\"message\\\": \\\"Authentication failed! Missing auth parameters\\\"}, 401)\\n\\n claro_user = self.settings['config']['claro/v1/user']['value']\\n claro_pass = self.settings['config']['claro/v1/password']['value']\\n\\n if claro_user != user_id or claro_pass != pwd:\\n log.error(\\\"Could not authenticate such a combination: userId {0} and pwd {1}\\\".format(user_id, pwd))\\n return self.error({\\\"message\\\": \\\"Could not authenticate! Wrong userId/pwd combination\\\"}, 401)\\n\\n return func(self, *args)\\n return with_authentication\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c253cad6c4681ddee550853c7902269c\",\n \"score\": \"0.58027345\",\n \"text\": \"def requires_auth(f):\\n\\n @wraps(f)\\n def decorated(*decorator_args, **decorator_kwargs):\\n auth = request.authorization\\n\\n if args.enable_auth:\\n if not auth or not verify_authentication(auth.username, auth.password):\\n response = Response(\\\"Need basic auth to request the resources\\\", 401, {\\n \\\"WWW-Authenticate\\\": '\\\"Basic realm=\\\"Login Required\\\"'\\n })\\n return response\\n\\n return f(*decorator_args, **decorator_kwargs)\\n\\n return decorated\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c18347ee35ed7558eb8729321c069bc\",\n \"score\": \"0.5783026\",\n \"text\": \"def perm_check(dummy): # pragma: no cover\\n def wrap_outer(func):\\n def wrap_inner(*args, **kargs):\\n # get the argument frrom the func (method route(cls, request)\\n kclass = args[0]\\n request = args[1]\\n\\n # count number of permisson passed\\n passed_count = 0\\n flag_pass = False\\n flag_login_required = False\\n for perm in kclass.x_perm:\\n if perm == 'login':\\n flag_login_required = True\\n if Utils.has_perm(request, perm):\\n passed_count = passed_count + 1\\n\\n # determine pass or not\\n if len(kclass.x_perm) == passed_count:\\n flag_pass = True\\n\\n # handle failed permission\\n if flag_pass == False:\\n if flag_login_required:\\n result = {'return':'302',\\n 'msg':'Please login to continue',\\n 'redirect_url':'/login/' }\\n return kclass()._render(request, result)\\n else:\\n result = {'return':'302',\\n 'msg':'Sorry, you do not have permission to continue',\\n 'redirect_url':'/'}\\n return kclass()._render(request, result)\\n # handle passed permission\\n return func(*args, **kargs)\\n return wrap_inner\\n return wrap_outer\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f40725d2259e30628b97bbf42db159ce\",\n \"score\": \"0.5781028\",\n \"text\": \"def UserRequired(handler):\\n def checkLogin(self, *args, **kwargs):\\n auth = self.auth\\n if not auth.get_user_by_session():\\n self.redirect(self.uri_for('recruiterLogin'), abort=True)\\n else:\\n return handler(self, *args, **kwargs)\\n return checkLogin\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7282b26766954256db3a31237877f3b2\",\n \"score\": \"0.57790387\",\n \"text\": \"def require_auth(f):\\n\\n @wraps(f)\\n def wrapped(*args, **kwargs):\\n request = args[0]\\n if not request.user or not request.user.is_authenticated:\\n return redirect('/admin/login')\\n else:\\n return f(*args, **kwargs)\\n\\n return wrapped\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4f4d5ee7e0a427ef8165df402f1aeaf\",\n \"score\": \"0.5778319\",\n \"text\": \"def user_required(handler):\\n def check_login(self, *args, **kwargs):\\n auth = self.auth\\n if not auth.get_user_by_session():\\n self.redirect(self.uri_for('login'), abort=True)\\n else:\\n return handler(self, *args, **kwargs)\\n\\n return check_login\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec4a6d5c5637835f46fa2c623ba0a589\",\n \"score\": \"0.57674855\",\n \"text\": \"def admin_required(func):\\n @functools.wraps(func)\\n def decorated_function(*pa, **ka): # pylint: disable=missing-docstring\\n if auth.is_admin():\\n return func(*pa, **ka)\\n if not auth.is_logged_in():\\n return abort(401)\\n return abort(403)\\n\\n return decorated_function\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ecf09da57e965947198d0e2d92d523a\",\n \"score\": \"0.57638514\",\n \"text\": \"def CheckAccess(self, handler, username):\\n handler_name = handler.__class__.__name__\\n\\n if (self.api_groups and\\n not self.CheckPermissions(username, self.api_access)):\\n stats.STATS.IncrementCounter(\\\"grr_api_auth_fail\\\",\\n fields=[handler_name, username])\\n raise access_control.UnauthorizedAccess(\\n \\\"User %s not in groups %s, authorized for %s API access.\\\" % (\\n username, self.api_groups, self.api_access))\\n\\n if handler_name in self.ACLedHandlers():\\n if not self.CheckPermissions(username, handler_name):\\n stats.STATS.IncrementCounter(\\\"grr_api_auth_fail\\\",\\n fields=[handler_name, username])\\n raise access_control.UnauthorizedAccess(\\n \\\"User %s not authorized for handler %s.\\\" % (\\n username, handler_name))\\n elif not handler.enabled_by_default:\\n raise access_control.UnauthorizedAccess(\\n \\\"%s has enabled_by_default=False and no explicit ACL set. Add ACL\\\"\\n \\\" to ACL list (see API.HandlerACLFile config option) to use \\\"\\n \\\"this API\\\" % handler)\\n\\n logging.debug(\\\"Authorizing %s for API %s\\\", username, handler_name)\\n stats.STATS.IncrementCounter(\\\"grr_api_auth_success\\\",\\n fields=[handler_name, username])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5f3c177a107f9ade06f0a8eb8f76193\",\n \"score\": \"0.57541656\",\n \"text\": \"def admin_only(func):\\n\\n @functools.wraps(func)\\n def wraps(*args, **kwargs):\\n if slack_blueprint.config['DEBUG']:\\n return func(*args, **kwargs)\\n slack_token = slack_blueprint.config['SLACK_OAUTH_TOKEN']\\n if not slack_token:\\n flask.abort(401)\\n slack = slacker.Slacker(slack_token)\\n user_id = flask.request.form['user_id']\\n flask.current_app.logger.info(f'[access]: performing admin check...')\\n try:\\n info = slack.users.info(user_id)\\n except slacker.Error:\\n flask.current_app.logger.info(f'[access]: {user_id} not found')\\n flask.abort(403)\\n if info.body['user']['is_admin']:\\n return func(*args, **kwargs)\\n flask.current_app.logger.error('[access]: failed admin-only test')\\n flask.abort(403)\\n\\n return wraps\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71ce77a8a7e677ea18fc31fe3fb7bc51\",\n \"score\": \"0.5752249\",\n \"text\": \"def auth_required(role_allowed_list):\\n\\n if not role_allowed_list:\\n raise AssertionError(\\\"Can't call auth_required with empty role_allowed_list.\\\")\\n\\n if not isinstance(role_allowed_list, list):\\n role_allowed_list = [role_allowed_list]\\n\\n def auth_required_wrapper(func):\\n def wrapped(*args, **kwargs):\\n appid = GAE_PROJECT\\n request.log_record = base_api.log_api_request()\\n # Only enforce HTTPS and auth for external requests; requests made for data generation\\n # are allowed through (when enabled).\\n acceptable_hosts = (\\\"None\\\", \\\"testbed-test\\\", \\\"testapp\\\", \\\"localhost\\\", \\\"127.0.0.1\\\")\\n # logging.info(str(request.headers))\\n if not is_self_request():\\n if request.scheme.lower() != \\\"https\\\" and appid not in acceptable_hosts:\\n raise Unauthorized(f\\\"HTTPS is required for {appid}\\\", www_authenticate='Bearer realm=\\\"rdr\\\"')\\n check_auth(role_allowed_list)\\n request.logged = False\\n result = func(*args, **kwargs)\\n if request.logged is False:\\n try:\\n base_api.log_api_request(log=request.log_record)\\n except RuntimeError:\\n # Unittests don't always setup a valid flask request context.\\n pass\\n return result\\n\\n return wrapped\\n\\n return auth_required_wrapper\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6046ec6c668f464503a4c46e1ea84261\",\n \"score\": \"0.5748685\",\n \"text\": \"async def __call__(self, request: Request):\\n checked = False\\n for permission_class in self.permissions_classes:\\n checked = await permission_class(request=request).check_permission()\\n if checked:\\n break\\n\\n if not checked:\\n raise HTTPException(status_code=self.status_code, detail={\\\"detail\\\": self.error_msg})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"670f6e89b529b78b9bb1bc3c2c056dff\",\n \"score\": \"0.57462764\",\n \"text\": \"def _auth_advice(*args, **kwargs):\\n token = self._auth_context.current_token\\n\\n if token is None:\\n raise AuthMissingTokenError(\\n \\\"Denied access to a protected service: Method was \\\"\\n \\\"called outside of an Authorization Context?\\\"\\n )\\n\\n self._auth_service.check_permission(\\n access_token=token,\\n in_domain=domain,\\n to_execute=method_name,\\n args=args,\\n kwargs=kwargs\\n )\\n\\n return wrapped_f(*args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46e6a1057420f547a3948f15f5781d89\",\n \"score\": \"0.57433856\",\n \"text\": \"def require_user(func):\\n def decorated(self, *args, **kwargs):\\n if not self.user:\\n self.redirect(users.create_login_url(self.request.url))\\n else:\\n return func(self, *args, **kwargs)\\n return decorated\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d6510a8538b8e58a503c2bf8562525b\",\n \"score\": \"0.5741873\",\n \"text\": \"def requires_authentication(func):\\n\\n @wraps(func)\\n def wrapper(*args, **kwargs):\\n authenticator = current_app.authenticator\\n authentic, status, message = authenticator.authenticate(request)\\n if not authentic:\\n _message = json.dumps(dict(errors=dict(mauth=[message])))\\n return Response(response=_message, status=status, mimetype=\\\"application/json\\\")\\n return func(*args, **kwargs)\\n\\n return wrapper\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5d11787e5bc194976e0e85e05b612b5\",\n \"score\": \"0.5739391\",\n \"text\": \"def permission_required(perm, login_url=None, raise_exception=False):\\n def check_perms(user):\\n # First check if the user has the permission (even anon users)\\n if user.has_perm(perm):\\n return True\\n # In case the 403 handler should be called raise the exception\\n if raise_exception:\\n try:\\n app_label, codename = perm.split('.')\\n permission = Permission.objects.get(content_type__app_label=app_label, codename=codename)\\n except (ObjectDoesNotExist, ValueError):\\n permission = perm\\n user.permission_error = permission\\n raise PermissionDenied\\n # As the last resort, show the login form\\n return False\\n return user_passes_test(check_perms, login_url=login_url)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce74f305b41af98542bdadebd2fb864d\",\n \"score\": \"0.57391584\",\n \"text\": \"def abort_if_no_google_user(decorated_function):\\n def check_current_user(self, *kw, **kwargs):\\n current_user = users.get_current_user()\\n if not current_user:\\n self.abort(401)\\n else:\\n decorated_function(self, *kw, **kwargs)\\n return check_current_user\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd9e06b783f23f29186e2a3870cc3990\",\n \"score\": \"0.57304513\",\n \"text\": \"def user_required(handler):\\r\\n def check_login(self, *args, **kwargs):\\r\\n auth = self.auth\\r\\n if not auth.get_user_by_session():\\r\\n self.redirect(self.uri_for('login'), abort=True)\\r\\n else:\\r\\n return handler(self, *args, **kwargs)\\r\\n\\r\\n return check_login\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c30c9166b41b91975984b56deac1e61\",\n \"score\": \"0.5723275\",\n \"text\": \"def require_auth(f, self = None, *args, **kw):\\n\\n if not self.sid:\\n raise NotLoggedIn\\n\\n return f(self, *args, **kw)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b00621708363e97543aaea45bb5f71b\",\n \"score\": \"0.5713961\",\n \"text\": \"def test_it_should_raise_a_PermissionError_if_user_does_not_have_permission(self, current_user):\\n func = Mock()\\n current_user.has_permission.return_value = False\\n current_user.is_admin = False\\n current_user.return_value = current_user\\n decorated_func = permission(func, permission=\\\"permission\\\")\\n\\n with pytest.raises(PermissionError):\\n decorated_func()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b7bc055a4c6fc708beaa0786ce0b0af\",\n \"score\": \"0.570744\",\n \"text\": \"def test_user_does_not_need_permission(self, current_user):\\n func = Mock()\\n current_user.return_value = current_user\\n decorated_func = permission(func)\\n\\n assert decorated_func().is_called\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1693e4b1307d7b17be2c38eb35b7e32b\",\n \"score\": \"0.5705757\",\n \"text\": \"def service_permission(view_func):\\n @wraps(view_func, assigned=available_attrs(view_func))\\n def _wrapped_view(request, *args, **kwargs):\\n # if we get here, user has scrooge permission\\n if (\\n (\\n 'service' in kwargs and\\n has_permission_to_service(request.user, kwargs['service'])\\n ) or\\n 'service' not in kwargs\\n ):\\n return view_func(request, *args, **kwargs)\\n return HttpResponseForbidden(\\n json.dumps({'message': 'No permission to service'}),\\n content_type=\\\"application/json\\\"\\n )\\n return login_required(_wrapped_view)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72b2053eb9d9aa129aea92df54627ad7\",\n \"score\": \"0.5698636\",\n \"text\": \"def helper_auth_helper(request):\\n if request.user and request.user.is_authenticated:\\n return None\\n # source, http://djangosnippets.org/snippets/243/\\n if \\\"HTTP_AUTHORIZATION\\\" in request.META:\\n auth = request.headers[\\\"Authorization\\\"].split()\\n if len(auth) == 2 and auth[0].lower() == \\\"basic\\\":\\n uname, passwd = (\\n base64.b64decode(auth[1].encode(\\\"utf-8\\\")).decode(\\\"utf-8\\\").split(\\\":\\\")\\n )\\n user = authenticate(username=uname, password=passwd)\\n if user:\\n request.user = user\\n return None\\n\\n return HttpResponseNotAuthorized()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d48a6e2ffe8186453bd4ee6894b503e8\",\n \"score\": \"0.5694516\",\n \"text\": \"def can_handle_filterquery(func):\\n def wrapper(self, request, **kwargs):\\n obj = self.get_object()\\n is_owner = obj.user == self.request.user\\n can_handle = obj.for_everyone and obj.has_global_perm(self.request.user)\\n if not is_owner and not can_handle:\\n raise PermissionDenied\\n\\n return func(self, request, **kwargs)\\n return wrapper\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0e15feec485c2eb7d74ac8e3a9e99a3\",\n \"score\": \"0.5689806\",\n \"text\": \"def login_required(func):\\n @functools.wraps(func)\\n def decorated_function(*pa, **ka): # pylint: disable=missing-docstring\\n if auth.is_logged_in():\\n return func(*pa, **ka)\\n return abort(401)\\n\\n return decorated_function\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3694a1c1b2c34d4f83baeca8f5ccab4c\",\n \"score\": \"0.5689007\",\n \"text\": \"def requires_auth(f):\\n @wraps(f)\\n def wrapper(*args, **kwds):\\n if 'uid' not in session:\\n return login(\\\"You are not logged in yet. Please log in\\\")\\n return f(*args, **kwds)\\n return wrapper\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18a5f62370543658432ce13df93782a8\",\n \"score\": \"0.5688777\",\n \"text\": \"def check_user_access(**kwargs):\\n su = kwargs.get(\\\"su\\\", False)\\n rules = kwargs.get(\\\"rules\\\", [])\\n match_all = kwargs.get(\\\"match_all\\\", False)\\n read_required = kwargs.get(\\\"read_required\\\", False)\\n write_required = kwargs.get(\\\"write_required\\\", False)\\n cls = kwargs.get(\\\"cls\\\", Rest)\\n \\n # no rules applied for super user (no need to execute rules)\\n if su: return\\n # no read/write access required at all (no need to execute rules)\\n if not read_required and not write_required: return\\n \\n r = RulesEngine.execute_rule(rules)\\n # local user should always be returned in rule, if not then allow\\n # exception to occur. Also, all rule_dn's passed to execute_rule\\n # must have r/w attribute per user returned\\n match_count = 0\\n for dn in rules:\\n access = r[dn][\\\"users\\\"][g.user.username]\\n if read_required and write_required:\\n if access[\\\"write\\\"] and access[\\\"read\\\"]: match_count+=1\\n elif match_all: abort(403, MSG_403)\\n elif read_required:\\n if access[\\\"read\\\"]: match_count+=1\\n elif match_all: abort(403, MSG_403)\\n elif write_required:\\n if access[\\\"write\\\"]: match_count+=1\\n elif match_all: abort(403, MSG_403)\\n\\n # check if any rules were matched\\n if match_count == 0:\\n abort(403, MSG_403)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cec496ee4c5734d2c662ab95f23a8ca3\",\n \"score\": \"0.56752\",\n \"text\": \"def auth_required(function):\\n\\n def wrap(request, *args, **kwargs):\\n if request.user.is_authenticated:\\n return function(request, *args, **kwargs)\\n\\n resp = HttpResponse()\\n resp.status_code = 401\\n return resp\\n\\n return wrap\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cddc3d21bac4d0f14c7b56f8ec6c06b8\",\n \"score\": \"0.56547445\",\n \"text\": \"def requires_auth(f):\\n\\n @wraps(f)\\n def decorated(*args, **kwargs):\\n # return f(*args, **kwargs)\\n self = args[0]\\n request = args[1]\\n if six.PY3:\\n token = request.args.get(b'token', [b''])[0].decode(\\\"utf8\\\")\\n else:\\n token = str(request.args.get('token', [''])[0])\\n if self.config['token'] is None or token == self.config['token']:\\n return f(*args, **kwargs)\\n request.setResponseCode(401)\\n return 'unauthorized'\\n\\n return decorated\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88e9310993c82d8541f71eaf94e0386d\",\n \"score\": \"0.5654406\",\n \"text\": \"def check_if_user_not_linked(provider_identifier, func, *args, **kwargs):\\n\\n if current_user.is_authenticated:\\n if not getattr(current_user, provider_identifier):\\n return func(*args, **kwargs)\\n else:\\n return abort(403)\\n\\n else:\\n return abort(401)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18b1e4ea635e0c940c27a65e22b02e97\",\n \"score\": \"0.56495607\",\n \"text\": \"def xmlauthenticated(method):\\n \\n def inner(*args, **kwargs):\\n if args[0].current_user is None:\\n try:\\n raise404(args[0], \\\"Access forbidden\\\")\\n except ForbiddenOperationException as e:\\n pass\\n return\\n else:\\n return method(*args, **kwargs)\\n return inner\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30b52a7bc4cefa6047b66d2a13c15cfa\",\n \"score\": \"0.56485206\",\n \"text\": \"def require_login(definition: Callable) -> Callable:\\n\\n @functools.wraps(definition)\\n def wrapper(state: State, *args, **kwargs) -> flask.Response:\\n if state.user is None: # If No User Exists In State\\n flask.abort(403)\\n else:\\n return definition(*args, **kwargs, state=state) # Continue Request\\n\\n return wrapper\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10ef830457f0392a45aae5804cab992e\",\n \"score\": \"0.5641129\",\n \"text\": \"def _authenticate(self, request):\\n\\n def anonymous_access(exc_obj):\\n \\\"\\\"\\\" Check whether anonymous access is allowed. \\\"\\\"\\\"\\n if not request.user and not self.allow_anonymous:\\n raise exc_obj\\n\\n request.user = None\\n if self.authentication:\\n try:\\n request.user = self.authentication.authenticate(request)\\n except Unauthorized, exc:\\n anonymous_access(exc)\\n else:\\n anonymous_access(Forbidden())\\n return request.user\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15a65d9fceb039a4c11b252d76e5c92c\",\n \"score\": \"0.5638501\",\n \"text\": \"def authenticate(self, handler, data=None):\\n userdict = yield super().authenticate(handler, data)\\n denylist = os.environ.get('GITHUB_ORGANIZATION_DENYLIST')\\n if denylist:\\n self.log.debug(\\\"Denylist `%s` found.\\\" % denylist)\\n denylist = denylist.split(',')\\n denied = yield self._check_denylist(userdict, denylist)\\n if denied:\\n self.log.warning(\\\"Rejecting user: denylisted\\\")\\n userdict = None\\n return userdict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"261fbaa3390dd85814fc5e37f08f0ea8\",\n \"score\": \"0.5632094\",\n \"text\": \"def privilege_required(view_func):\\n def _decorated_view(request, machine_name, **kwargs):\\n if _is_privileged_on(request, machine_name):\\n return view_func(request, machine_name, **kwargs)\\n else:\\n raise PermissionDenied\\n return wraps(view_func)(_decorated_view)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b972b8da2ace9151179b39c4f62a5da2\",\n \"score\": \"0.5623965\",\n \"text\": \"def _get_auth_safe_user(\\n self,\\n request: 'TracimRequest',\\n ) -> User:\\n app_config = request.registry.settings['CFG']\\n uapi = UserApi(None, session=request.dbsession, config=app_config)\\n login = ''\\n try:\\n login = request.authenticated_userid\\n if not login:\\n raise UserNotFoundInTracimRequest('You request a current user but the context not permit to found one') # nopep8\\n user = uapi.get_one(login)\\n if not user.is_active:\\n raise UserAuthenticatedIsNotActive('User {} is not active'.format(login))\\n except (UserDoesNotExist, UserNotFoundInTracimRequest) as exc:\\n raise NotAuthenticated('User {} not found'.format(login)) from exc\\n return user\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff9301ed2477d78fdf8f2ef4fa15cd12\",\n \"score\": \"0.5601441\",\n \"text\": \"def http_auth(func):\\n @wraps(func)\\n def inner(request, *args, **kwargs):\\n result = __http_auth_helper(request)\\n if result is not None:\\n return result\\n return func(request, *args, **kwargs)\\n return inner\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59eef6ab037351aca6df5df49acd2841\",\n \"score\": \"0.5600946\",\n \"text\": \"def get_authenticated_user(self, auth_func, realm):\\n try:\\n auth = self.request.headers.get('Authorization')\\n \\n if auth == None: return self._request_auth(realm)\\n if not auth.startswith('Basic '): return self._request_auth(realm)\\n \\n auth_decoded = base64.decodestring(auth[6:])\\n username, password = auth_decoded.split(':', 1)\\n \\n if auth_func(self, realm, username, password):\\n self._current_user = username\\n return True\\n else:\\n return self._request_auth(realm)\\n except Exception, e:\\n return self._request_auth(realm)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b77562199f407806fecbafe4cc83728\",\n \"score\": \"0.5593828\",\n \"text\": \"def _special_Trusted(self, request, name, dowhat, rightsdict):\\n if (request.user.name == name and\\n request.user.auth_method in request.cfg.auth_methods_trusted):\\n return rightsdict.get(dowhat)\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2540dedef3b39496ac968ac4743b422a\",\n \"score\": \"0.55937165\",\n \"text\": \"def _validate_authorization(context, action):\\n #We want to use the more granular version, but can't until it exists\\n AUTHORIZE(context, action=action)\\n return context.elevated()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb810051923c44c7726a16aba5291ea6\",\n \"score\": \"0.55935144\",\n \"text\": \"def validate(*args, **kwargs):\\n\\n # Get the headers from the self arg\\n headers = args[0].request_state.headers\\n token = headers.get('Token')\\n if headers.get('User-Id') is None:\\n raise endpoints.UnauthorizedException(MESSAGE_UNAUTHORIZED)\\n\\n user_id = int(headers.get('User-Id'))\\n user, timestamp = User.get_by_auth_token(user_id, token)\\n if user:\\n return endpoints_method(*args, **kwargs)\\n else:\\n logging.warn('Invalid API request! User ID:%s, token:%s'\\n %(user_id, token))\\n raise endpoints.UnauthorizedException(MESSAGE_UNAUTHORIZED)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b98589612c4b1750897b75db9f005c99\",\n \"score\": \"0.55931556\",\n \"text\": \"def permissions_required(app_label, login_url=None, raise_exception=False):\\n def check_perms(user):\\n # First check if the user has the permission (even anon users)\\n if user.has_module_perms(app_label):\\n return True\\n # In case the 403 handler should be called raise the exception\\n if raise_exception:\\n user.permission_error = app_label\\n raise PermissionDenied\\n # As the last resort, show the login form\\n return False\\n\\n return user_passes_test(check_perms, login_url=login_url)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"600144fd3f5625fd30047464f024f2c4\",\n \"score\": \"0.5591398\",\n \"text\": \"def login_required(f):\\n def decorator(*args, **kwargs):\\n if not current_user.is_authenticated:\\n abort(401)\\n return f(*args, **kwargs)\\n return decorator\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c074381197bc17c810a71bae0dab05a0\",\n \"score\": \"0.5590082\",\n \"text\": \"def authenticate(self, **kwargs):\\n authenticated = False\\n if 'password' and 'username' in kwargs:\\n try:\\n user = TaskerUser.objects.get_by_natural_key(\\n kwargs.get('username'))\\n authenticated = user.check_password(kwargs.get('password'))\\n except TaskerUser.DoesNotExist:\\n pass\\n elif 'google_user' in kwargs:\\n google_user = kwargs.get('google_user')\\n email = google_user['email']\\n try:\\n user = TaskerUser.objects.get_by_natural_key(email)\\n authenticated = True\\n except TaskerUser.DoesNotExist:\\n email = google_user['email']\\n try:\\n user = TaskerUser.objects.get_by_natural_key(email)\\n authenticated = True\\n except TaskerUser.DoesNotExist:\\n pass\\n if authenticated:\\n return user\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c61841bfa3da2370fd955ada28736ea\",\n \"score\": \"0.5589802\",\n \"text\": \"def _check_basic_auth (self, user, password):\\n if self._is_basic_auth_enabled is False: return True\\n\\n try:\\n return self.basic_auth_function(self, user, password)\\n except TypeError:\\n return self.basic_auth_function(user, password)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"96b0b326964489b2b46f65677b838a6a\",\n \"score\": \"0.5589744\",\n \"text\": \"def requires_auth(f):\\n @functools.wraps(f)\\n def decorated(*args, **kwargs):\\n\\n potential_user_id = BaseController().is_logged_in()\\n \\n if not potential_user_id:\\n return redirect(\\\"/login\\\", code=302)\\n\\n request.user_id = potential_user_id\\n return f(*args, **kwargs)\\n\\n return decorated\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1897ba7745e99f68995f290c0f6693f4\",\n \"score\": \"0.55829257\",\n \"text\": \"async def __call__(self, request: Request):\\n credentials: HTTPAuthorizationCredentials = await super().__call__(request)\\n credentials = credentials.credentials\\n if not credentials:\\n raise HTTPException(status_code=403, detail=AuthState.NO_TOKEN)\\n\\n try:\\n token_data = jwt.decode(credentials, Server.JWT_SECRET)\\n except JWTError:\\n raise HTTPException(status_code=403, detail=AuthState.INVALID_TOKEN.value)\\n\\n user_id, token_salt = token_data[\\\"id\\\"], token_data[\\\"salt\\\"]\\n\\n user_state = await request.state.db_conn.fetchrow(\\n \\\"SELECT is_banned, is_mod, key_salt FROM users WHERE user_id = $1\\\",\\n int(user_id)\\n )\\n\\n # Handle bad scenarios\\n if user_state is None or user_state[\\\"key_salt\\\"] != token_salt:\\n raise HTTPException(status_code=403, detail=AuthState.INVALID_TOKEN.value)\\n elif user_state[\\\"is_banned\\\"]:\\n raise HTTPException(status_code=403, detail=AuthState.BANNED.value)\\n elif self.is_mod_endpoint and not user_state[\\\"is_mod\\\"]:\\n raise HTTPException(status_code=403, detail=AuthState.NEEDS_MODERATOR.value)\\n\\n request.state.user_id = int(user_id)\\n return credentials\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d33e68e2049b70e9c1b250ff0db18e5\",\n \"score\": \"0.55694336\",\n \"text\": \"def check_user_permissions(self, user, method, status, url, *args):\\n if (user):\\n self.client.login(username=user, password='password')\\n\\n if (method == 'get'):\\n response = self.client.get(url, format='json')\\n elif (method == 'post'):\\n response = self.client.post(url, args[0], format='json')\\n elif (method == 'put'):\\n response = self.client.put(url, args[0], format='json')\\n elif (method == 'delete'):\\n response = self.client.delete(url, format='json')\\n\\n self.assertEqual(response.status_code, status)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"564bb62b641f28c3aa60d9fe2fdd8fc6\",\n \"score\": \"0.55652887\",\n \"text\": \"def admin_required(func):\\n @wraps(func)\\n def decorated_view(*args, **kwargs):\\n if not users.is_current_user_admin():\\n raise exceptions.MethodNotAllowed('Admin credentials required.')\\n return func(*args, **kwargs)\\n return decorated_view\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d50f72a47505787e139ef4899e2ad4d7\",\n \"score\": \"0.5563556\",\n \"text\": \"def user_required(handler):\\n\\n def check_login(self, *args, **kwargs):\\n \\\"\\\"\\\"\\n If handler has no login_url specified invoke a 403 error\\n \\\"\\\"\\\"\\n if self.request.query_string != '':\\n query_string = '?' + self.request.query_string\\n else:\\n query_string = ''\\n\\n continue_url = self.request.path_url + query_string\\n login_url = self.uri_for('login', **{'continue': continue_url})\\n\\n try:\\n user = self.auth.get_user_by_session()\\n if not user:\\n try:\\n self.redirect(login_url, abort=True)\\n except (AttributeError, KeyError), e:\\n self.abort(403)\\n except AttributeError, e:\\n # avoid AttributeError when the session was delete from the server\\n logging.error(e)\\n self.user.unset_session()\\n self.redirect(login_url)\\n\\n return handler(self, *args, **kwargs)\\n return check_login\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3786d87c21418424b921cc176ef2ba45\",\n \"score\": \"0.5563208\",\n \"text\": \"def administrator(method):\\n @functools.wraps(method)\\n def wrapper(self, *args, **kwargs):\\n if not self.current_user:\\n if self.request.method == \\\"GET\\\":\\n self.redirect(self.get_login_url())\\n return\\n raise tornado.web.HTTPError(403)\\n elif not self.current_user.administrator:\\n if self.request.method == \\\"GET\\\":\\n self.redirect(\\\"/\\\")\\n return\\n raise tornado.web.HTTPError(403)\\n else:\\n return method(self, *args, **kwargs)\\n return wrapper\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7ddbdb3b819025e3fbf8a291025f20c\",\n \"score\": \"0.5543244\",\n \"text\": \"def superuser_permission(view_func):\\n @wraps(view_func, assigned=available_attrs(view_func))\\n def _wrapped_view(request, *args, **kwargs):\\n if request.user.is_superuser:\\n return view_func(request, *args, **kwargs)\\n return HttpResponseForbidden(\\n json.dumps({'message': 'No permission to service'}),\\n content_type=\\\"application/json\\\"\\n )\\n return login_required(_wrapped_view)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"949aac04f4b355d4c02237b73cfc6e50\",\n \"score\": \"0.5542497\",\n \"text\": \"def authorization(func):\\n def wrapper(*args, **kwargs):\\n if 'Authorization' not in args[0].headers:\\n response = error_handler(error_status=401, message=f'Security token is missing!')\\n else:\\n response = func(*args, **kwargs)\\n return response\\n return wrapper\",\n \"title\": \"\"\n },\n {\n \"docid\": \"806cc471ae62c3f73229680d3259043c\",\n \"score\": \"0.55357605\",\n \"text\": \"def auth_token_required(fn):\\r\\n\\r\\n @wraps(fn)\\r\\n def decorated(*args, **kwargs):\\r\\n if _check_token():\\r\\n return fn(*args, **kwargs)\\r\\n return _get_unauthorized_response()\\r\\n return decorated\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ae8c2cb6c4210f778d60c0f952834b5\",\n \"score\": \"0.5535683\",\n \"text\": \"def wrapper(*args, **kwargs):\\n if not _is_admin():\\n abort(403, message='Only admins can do this')\\n\\n return fn(*args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"837034616a13848ea50ec9be3d73fc67\",\n \"score\": \"0.5534459\",\n \"text\": \"def has_access(func):\\n @wraps(func)\\n def _wrapper(bot, update, conf, *args, **kwargs):\\n try:\\n user = update.message.from_user\\n id = int(user['id'])\\n if id in conf[\\\"white_list\\\"]:\\n return func(bot, update, conf=conf, *args, **kwargs)\\n update.message.reply_text(\\\"You don't have access to me yet.\\\")\\n except Exception as e:\\n update.message.reply_text(\\\"Error occur in has_access: \\\" + str(e))\\n return ConversationHandler.END\\n return _wrapper\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55456d9ee8d1b4da3a2844abf7dc211a\",\n \"score\": \"0.5519779\",\n \"text\": \"def authorizeRequireWarp(fn):\\n @wraps(fn)\\n def wrapper(self, session, *args, **kwargs):\\n try:\\n if SessionManager.Check(session) is True:\\n return fn(self, session, *args, **kwargs)\\n else:\\n return False, CController.Unauthorized(session)\\n except Exception as e:\\n print \\\"Exception in COrgan: %s\\\" % str(e)\\n return False, e\\n return wrapper\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5de27c3687c0909d2d8c9187af9e0b51\",\n \"score\": \"0.5511475\",\n \"text\": \"def isOwner():\\n def wrapper(func):\\n @wraps(func)\\n def decorator(*args, **kwargs):\\n current_user = User.query.get(get_jwt_identity())\\n if current_user:\\n if current_user.id == int(kwargs['id']):\\n return func(*args, **kwargs)\\n return abort(401)\\n return abort(401)\\n return decorator\\n return wrapper\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb9ff2a8a781203479484fcd63d5120f\",\n \"score\": \"0.5503048\",\n \"text\": \"def RequireAdministrator(func):\\n\\t@wraps(func)\\n\\tdef f(request, *args, **kwargs):\\n\\t\\tif 'Administrator' not in request.session['MHL_Users']:\\n\\t\\t\\treturn err403(request)\\n\\n\\t\\treturn func(request, *args, **kwargs)\\n\\n\\treturn f\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a14ce9ca125173a4e8a48fc4efe09e0b\",\n \"score\": \"0.55030423\",\n \"text\": \"def superuser_required(func):\\n @wraps(func)\\n def wrapper(request, *args, **kwargs):\\n user = getCurrentUser(request)\\n if not user.getUserpermission('superuser'):\\n return util.ctrl.infoMsg(_(\\\"您不具有{}权限\\\").format(_(\\\"超级管理员\\\")))\\n return func(request, *args, **kwargs)\\n return wrapper\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5e0da68fc98969c0945e1dde6e89fb9\",\n \"score\": \"0.55025524\",\n \"text\": \"def _check_credentials(\\n self, user: Optional[TUser], handle: str, password: str\\n ) -> TUser:\\n if not user or not user.check_password(password):\\n raise AuthenticationFailure(\\n self._strings.wrong_email\\n if \\\"@\\\" in handle\\n else self._strings.wrong_username\\n )\\n\\n if self._require_activation and not user.is_activated:\\n raise AuthenticationFailure(self._strings.inactive_account)\\n return user\",\n \"title\": \"\"\n },\n {\n \"docid\": \"317a7efee883d520f7770631e97b152a\",\n \"score\": \"0.5497476\",\n \"text\": \"def require_login(func):\\n @wraps(func)\\n def wrapper(*args, **kwargs):\\n if _has_logged_in():\\n return func(*args, **kwargs)\\n else:\\n # Forbidden, login invalid or non existant.\\n # TODO: Redirect to login page.\\n abort(403)\\n return wrapper\",\n \"title\": \"\"\n },\n {\n \"docid\": \"645ad852b7e1118d13065488a0df3ae6\",\n \"score\": \"0.5497418\",\n \"text\": \"def basic_http_auth(func):\\n\\n @wraps(func)\\n def _inner(request, *args, **kwargs):\\n result = helper_auth_helper(request)\\n if result is not None:\\n return result\\n return func(request, *args, **kwargs)\\n\\n return _inner\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":691,"cells":{"query_id":{"kind":"string","value":"814d29215dd48fa3003c1a2a39a7b4ff"},"query":{"kind":"string","value":"Looks up the UID of the resource with the given name."},"positive_passages":{"kind":"list like","value":[{"docid":"7ccbfb8989ccbd49b774db442cfa0174","score":"0.8512743","text":"def resourceUIDForName(self, name):\n uid = self._db_value_for_sql(\"select UID from RESOURCE where NAME = :1\", name)\n\n return uid","title":""}],"string":"[\n {\n \"docid\": \"7ccbfb8989ccbd49b774db442cfa0174\",\n \"score\": \"0.8512743\",\n \"text\": \"def resourceUIDForName(self, name):\\n uid = self._db_value_for_sql(\\\"select UID from RESOURCE where NAME = :1\\\", name)\\n\\n return uid\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"22c43a1d43961de77cb6605b3291cf35","score":"0.6511902","text":"def resource_uid(self) -> Optional[str]:\n return pulumi.get(self, \"resource_uid\")","title":""},{"docid":"bd16aa5bb834eff63cf6559219c3e5ea","score":"0.65088683","text":"def _get_uid(self, name):\n if name.startswith(\"http\"):\n name = self._get_path_from_url(name)\n\n if not name.startswith(self.storage_dir):\n return name\n\n uid = path.dirname(name)\n uid = path.relpath(uid, start=self.storage_dir)\n return uid","title":""},{"docid":"7e4700dcd3c51083415bfb4a93ce47d8","score":"0.63126373","text":"def get_by_name(self, name):\n if not name:\n raise TypeError('name must be a non empty string.')\n\n uid = self._name_uid_map.get(name)\n return self.get(uid)","title":""},{"docid":"80c5d982b67a87365519f6edebfb79f2","score":"0.62900656","text":"def resourceExists(self, name):\n uid = self._db_value_for_sql(\"select UID from RESOURCE where NAME = :1\", name)\n return uid is not None","title":""},{"docid":"808824b59148320eeb5b431fcdf0e707","score":"0.628813","text":"def _lookupUid(self, short_uid):","title":""},{"docid":"af59f42b118ebc6d0a74e3fad386d1e1","score":"0.6165063","text":"def resourceNameForUID(self, uid):\n result = None\n\n for name in self.resourceNamesForUID(uid):\n assert result is None, \"More than one resource with UID %s in address book collection %r\" % (uid, self)\n result = name\n\n return result","title":""},{"docid":"2395f4950f18a3656a8e4801cdb9e8ff","score":"0.6115621","text":"def get_uid(self):\n\t\tresult, data = self.connection.uid('search', None, \"ALL\")\n\t\tif result == \"OK\":\n\t\t\tself.uid = (data[0]).split()\n\t\t\tprint(\"[ i ] UIDs fetched.\")\n\t\telse:\n\t\t\tprint(\"[ e ] Searching for UIDs failed.\")","title":""},{"docid":"d4a86f8782fb0839b53449419106f330","score":"0.60574347","text":"def _nameToUID(self, name):\n return name.rsplit(\".\", 1)[0]","title":""},{"docid":"b9cd2b58e15eaa1c42d1485869549c3b","score":"0.59734124","text":"def _match_name(self, uids: Iterable) -> Optional[str]:\n for uid in uids:\n if parseaddr(uid)[0] == self.provided_name:\n return uid","title":""},{"docid":"0ef31118ddd85bead69e7a65b6642a85","score":"0.59275097","text":"def get_resource_id(self, resource, name):\n response = self._request.get(resource, params={'links': 'false', 'query': name})\n if not response[resource]:\n return None\n if len(response[resource]) > 1:\n raise Dhis2ApiQueryError('Query returned multiple results')\n return response[resource][0]['id']","title":""},{"docid":"e4bac146f5b50fea845eec6a70249ec7","score":"0.58771193","text":"def get_uid():\n with uid_lock:\n out = next(uid)\n return out","title":""},{"docid":"3b928f9c1708f4e1da0e182658a471f2","score":"0.5778452","text":"def uid(self) -> Optional[str]:\n return pulumi.get(self, \"uid\")","title":""},{"docid":"3b928f9c1708f4e1da0e182658a471f2","score":"0.5778452","text":"def uid(self) -> Optional[str]:\n return pulumi.get(self, \"uid\")","title":""},{"docid":"3b928f9c1708f4e1da0e182658a471f2","score":"0.5778452","text":"def uid(self) -> Optional[str]:\n return pulumi.get(self, \"uid\")","title":""},{"docid":"3b928f9c1708f4e1da0e182658a471f2","score":"0.5778452","text":"def uid(self) -> Optional[str]:\n return pulumi.get(self, \"uid\")","title":""},{"docid":"de3ef9111a5a0f839647ce44c12b9d10","score":"0.572037","text":"def resolve_uid(uid):\n site = get_site()\n if site is None:\n return\n catalog = getToolByName(site, 'portal_catalog', None)\n if catalog is None:\n return\n result = catalog.unrestrictedSearchResults(UID=uid)\n if len(result) != 1:\n return\n return result[0]._unrestrictedGetObject()","title":""},{"docid":"a2024e61892b6a4ee115c2772592169f","score":"0.57159597","text":"def get_uid(self, idx):\n try:\n return self.__uid_cache[idx]\n except KeyError:\n return self[idx].uid","title":""},{"docid":"daaa143455b31a5d30e2194122f15b4f","score":"0.5692868","text":"def get_or_generate_uid(file_name: str, generator: callable, validator: [callable, None]):\n full_path = os.path.join(get_uid_path(), file_name)\n uid = None\n if os.path.exists(full_path):\n with open(full_path, 'r') as file:\n uid = file.readline().strip()\n\n if uid is not None and (validator is not None and not validator(uid)):\n uid = None\n\n if uid is None:\n uid = generator()\n save_uid_to_file(full_path, uid)\n return uid","title":""},{"docid":"caf32cf3c075b5b075abdd43fcd9f3c9","score":"0.5673893","text":"def resource_id(self):\n\t\treturn self.name","title":""},{"docid":"4a14dbf3aa274bbe68bed063d14088ad","score":"0.5669674","text":"def name(self) -> str:\n uid_string = str(self)\n if uid_string in UID_dictionary:\n return UID_dictionary[self][0]\n\n return uid_string","title":""},{"docid":"d92e92b43ce90c20d1b7a5d6a3239c4c","score":"0.5653038","text":"def getUID(self):\n uid = \"%s_%s_%s_%s\" % (self.res_sid, self.resid, self.chain_id, self.model_id)\n return uid","title":""},{"docid":"04f2b4888f7478884ecd2c88087931d1","score":"0.5642538","text":"def uid(self) -> str:\n return pulumi.get(self, \"uid\")","title":""},{"docid":"d2dcd55f000a5d6c3fa75f1b8fc78155","score":"0.56291854","text":"def uid(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"uid\")","title":""},{"docid":"9d31f9273d459d1ae3c91c0b7a1de10b","score":"0.5614781","text":"def lookup_name(name):\n resp = rx.get('/rxcui.json', params=dict(name=name))\n resp.raise_for_status()\n\n data = resp.json()\n return extract_cui(data)","title":""},{"docid":"51edd26527de2a839f754577e05629c5","score":"0.56011206","text":"def get_id_for_name(self, name):\n client = MongoClient(MONGO_HOST, 27017)\n coll = client[MONGO_DBNAME]['signature']\n resp = coll.find({\"name\": name}).next()\n oid = str(resp.get(\"_id\"))\n return oid","title":""},{"docid":"331fca09de3fdd0d89c97faa6048d8a0","score":"0.5601014","text":"def __format_id_from_name(self, name):\n\n query = Query(\"format\", \"id\")\n query.where(query.Condition(\"name\", name))\n rows = query.execute(self.cursor).fetchall()\n return rows[0].id if rows else None","title":""},{"docid":"e60bf14ba074ef633f1aac4d361416ec","score":"0.5573876","text":"def uid(self) -> int:\n return pulumi.get(self, \"uid\")","title":""},{"docid":"e60bf14ba074ef633f1aac4d361416ec","score":"0.5573876","text":"def uid(self) -> int:\n return pulumi.get(self, \"uid\")","title":""},{"docid":"9ed7faeddb6ba6abb3162e0e1626d0bb","score":"0.55551934","text":"def uid(self):\n\n return self.metadata[0]","title":""},{"docid":"29bad0aa5d80ced3831a1e59cd142cdf","score":"0.5533407","text":"def avatar_for_name(name=None):\n avatars = avatars_by_name()\n if name in avatars:\n return avatars[name]\n \n return all_avatars()[0]","title":""},{"docid":"ae10603100564df7b731f87e9d07e1c7","score":"0.55214924","text":"def _fetch_register(self, name: str) -> int:\n with open(os.path.join(self.statedir, name), \"r\") as f:\n return int(f.read())","title":""},{"docid":"f4217f7e735ad2811fe3a3302d79842b","score":"0.55143017","text":"def GetUserid(self):\n if self.scmRef == None or self.ref == None or \\\n self.ver == None or self.type == None:\n raise KeyError, \"getuid: Params can't be empty\"\n\n if self.debug:\n print \"GetUserID(%s, %s, %s)\" % \\\n (self.scmRef, self.ver, self.type)\n\n try:\n userid = \\\n self.db[self.scmRef][self.ref][self.ver][self.type]['misc'][0]\n except ( IndexError, KeyError):\n userid = self.userid\n except:\n raise\n\n if self.debug:\n print \"GetUserid: %s\" % userid\n\n return string.strip(userid)","title":""},{"docid":"ba79ec5e9649c41de81a2b8072874008","score":"0.5513815","text":"def uid(self):\n self._uid = getattr(self, \"_uid\", unicode(uuid.uuid4().hex))\n return self._uid","title":""},{"docid":"50391a2ae318443df3b32fb11647d064","score":"0.5499823","text":"def find_resourceid_by_name_or_id(resource_client, name_or_id):\n try:\n # Try to return an uuid\n return str(uuid.UUID(name_or_id))\n except ValueError:\n # Not an uuid => assume it is resource name\n pass\n\n resources = resource_client.list()\n candidate_ids = [r['id'] for r in resources if r.get('name') == name_or_id]\n if not candidate_ids:\n raise exceptions.ResourceNotFound(\n 'Could not find resource with name \"%s\"' % name_or_id)\n elif len(candidate_ids) > 1:\n str_ids = ','.join(candidate_ids)\n raise exceptions.NoUniqueMatch(\n 'Multiple resources with name \"%s\": %s' % (name_or_id, str_ids))\n return candidate_ids[0]","title":""},{"docid":"14b8f2669ccf939f8183bbf2e5312b31","score":"0.54843956","text":"def get_res_by_name(self, name):\n for res in self.resources:\n if res.name == name:\n return res\n return None","title":""},{"docid":"07815e8e739e92c2cf1d4e2e2f7102a0","score":"0.54808587","text":"def from_name(self, name):\n if name not in self._lookup:\n raise ValueError('Unkown name {} in registry'.format(name))\n return self._lookup[name]","title":""},{"docid":"5e02a5b4aca376971561e574eafa86a2","score":"0.5473876","text":"def getUserId(request, searchName):\n if not searchName:\n return None\n cfg = request.cfg\n try:\n _name2id = cfg._name2id\n except AttributeError:\n arena = 'user'\n key = 'name2id'\n cache = caching.CacheEntry(request, arena, key)\n try:\n _name2id = pickle.loads(cache.content())\n except (pickle.UnpicklingError, IOError, EOFError, ValueError):\n _name2id = {}\n cfg._name2id = _name2id\n id = _name2id.get(searchName, None)\n if id is None:\n for userid in getUserList(request):\n name = User(request, id=userid).name\n _name2id[name] = userid\n cfg._name2id = _name2id\n arena = 'user'\n key = 'name2id'\n cache = caching.CacheEntry(request, arena, key)\n cache.update(pickle.dumps(_name2id, PICKLE_PROTOCOL))\n id = _name2id.get(searchName, None)\n return id","title":""},{"docid":"ec5aa89179643410573bb78d4ec6a452","score":"0.54694355","text":"def uid(self):\n return self.__dict__.get('id')","title":""},{"docid":"0eae1f89b7ff4e70d19c80ce5df7b991","score":"0.5454802","text":"def get_image_id(self, name):\n if name.startswith(\"sha256:\"):\n return name[7:]\n image = self.get_image(name)\n if not image:\n return\n return image[\"id\"]","title":""},{"docid":"4bc789a080c9e6385ee0434f6cf443e6","score":"0.5448324","text":"def getUID(self):\n return self.uid","title":""},{"docid":"f966a42b639f5d8bc951adb2f4d144dd","score":"0.54480475","text":"def get_uid(prefix=''):\n _uid_prefixes[prefix] += 1\n return _uid_prefixes[prefix]","title":""},{"docid":"b0bc584418fa16f9036bdb92151db333","score":"0.5398736","text":"def get_sensor_id_by_name(self, name):\n sensors = self.get_sensor()\n for sensor_id in sensors:\n if PY3K:\n if name == sensors[sensor_id][\"name\"]:\n return sensor_id\n else:\n if name.decode(\"utf-8\") == sensors[sensor_id][\"name\"]:\n return sensor_id\n return False","title":""},{"docid":"33dcf5fccfdbdc4b31e647f3e928e439","score":"0.5386785","text":"def name(self) -> str:\n return parseaddr(self.uid)[0]","title":""},{"docid":"b536a2b0f59e2c2159c8bc327c32087d","score":"0.5367091","text":"def get_ref_by_name(self, name_str):\n\t\tuser_dict = self.get_user_dict_by_name(name_str)\n\t\tif user_dict:\n\t\t\treturn user_dict['ref']\n\t\telse:\n\t\t\treturn None","title":""},{"docid":"9873c8b2e054f1389e4822f2650c5561","score":"0.5353032","text":"def uid(self) -> str:\n return typing.cast(\n str,\n self._properties.get(\"uid\"),\n )","title":""},{"docid":"9873c8b2e054f1389e4822f2650c5561","score":"0.5353032","text":"def uid(self) -> str:\n return typing.cast(\n str,\n self._properties.get(\"uid\"),\n )","title":""},{"docid":"9873c8b2e054f1389e4822f2650c5561","score":"0.5353032","text":"def uid(self) -> str:\n return typing.cast(\n str,\n self._properties.get(\"uid\"),\n )","title":""},{"docid":"9c3c3bbbb52adc21041ba1bfebd8abac","score":"0.53486484","text":"def uid(self) -> Optional[str]:\n return self.__uid","title":""},{"docid":"11da66e6ab081c7cdb521677905c782d","score":"0.5337882","text":"def getUser(name):","title":""},{"docid":"43602a2b149932d47487094947ddb482","score":"0.5333861","text":"def id_from_name(uname_raw: str):\n uname_low: str = uname_raw.lower()\n response = requests.get(\n \"https://api.mojang.com/users/profiles/minecraft/{}\".format(uname_low)\n )\n log.f(\"WLME_RESP\", str(response))\n if response.status_code == 200:\n return {\"code\": response.status_code, \"udat\": response.json()}\n else:\n return {\"code\": response.status_code}","title":""},{"docid":"0a906cc5d219648489ec8b446d34d18f","score":"0.5323687","text":"def lookup(self, name):\n return self._name_to_spec.get(name, None)","title":""},{"docid":"7f9cce8bd678f8abd1b56e19bee54cd5","score":"0.53209287","text":"def get_uid(self):\n dat = self._handle.controlRead(Panda.REQUEST_IN, 0xc3, 0, 0, 12)\n return binascii.hexlify(dat).decode()","title":""},{"docid":"c85907d06bc3481af8dafa3ed35786b9","score":"0.53176194","text":"def getUID(self):\r\n return self.id","title":""},{"docid":"37d9a2bdffc1223298670875ca5f43ec","score":"0.5313008","text":"def fetchName(self, name, pwonly, decrypt, fulldecrypt):\n\t\tret = self.find(name, nameOnly=True)\n\t\tif not ret:\n\t\t\treturn\n\t\tif len(ret) > 1:\n\t\t\tlog.error(\"found more than 1 record, only returning the first.\")\n\t\tuuid = ret[0]['uuid']\n\t\treturn self.fetchUUID(uuid, pwonly, decrypt, fulldecrypt)","title":""},{"docid":"96282b58f8328b10f1442d16d8752fea","score":"0.5308342","text":"def get_resource(name, decode=True):\n # get the resource using relative names\n with open(os.path.join(_pwd, name), 'rb') as f:\n resource = f.read()\n\n # make sure we return it as a string if asked\n if decode and hasattr(resource, 'decode'):\n return resource.decode('utf-8')\n\n return resource","title":""},{"docid":"0d2de678dd016aca72d7adeaaa79f0d2","score":"0.53070873","text":"def uid(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"uid\")","title":""},{"docid":"0d2de678dd016aca72d7adeaaa79f0d2","score":"0.53070873","text":"def uid(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"uid\")","title":""},{"docid":"0d2de678dd016aca72d7adeaaa79f0d2","score":"0.53070873","text":"def uid(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"uid\")","title":""},{"docid":"89e23ddf58072ae5da604b0fbcd450a0","score":"0.5299741","text":"def _findId(self, name):\n isinstance(name, str)\n\n for key_id in list(self.filenames.keys()):\n # data with title\n if self.filenames[key_id].name:\n input = self.filenames[key_id].name\n # data without title\n else:\n input = str(key_id)\n if name in input:\n return key_id","title":""},{"docid":"cce1863b16d563a09cb86014456b4482","score":"0.5275912","text":"def findUser(self, name):\n name = name.lower()\n ul = self._getUserlist()\n udi = dict(zip([u.name for u in ul], ul))\n cname = None\n for n in udi.keys():\n if name in n:\n if cname: return None #ambiguous!!\n cname = n\n if cname: return udi[cname]\n else: return None","title":""},{"docid":"6c09866794796dd8d40defa98b5e640c","score":"0.52732617","text":"def _get_uid_attr(self, ldap_userfolder):\n uid_attr = ldap_userfolder._uid_attr\n schema_dicts = ldap_userfolder.getSchemaDict()\n for schema_map in schema_dicts:\n if uid_attr == schema_map['ldap_name']:\n return schema_map['public_name']\n return uid_attr","title":""},{"docid":"c700595a317f90df1f1246d734ca25da","score":"0.52654034","text":"def getuid(): # type: ignore\n return 0","title":""},{"docid":"4c4d3ac3c0e90699737789302ccf5cb7","score":"0.5258746","text":"def name_to_id(self, obj_name):\n comp_map = self.get_comp_map()\n\n res = self.redis.hget(comp_map, obj_name)\n\n if res is not None:\n return res\n\n redir_map = self.get_redir_map()\n if redir_map:\n return self.redis.hget(redir_map, obj_name)","title":""},{"docid":"440059df1cccfbad3baaa78ea75ce362","score":"0.5256874","text":"def get_node_by_name(self, name):\n for res in self.resources:\n for node in res.nodes:\n if node.name == name:\n return node\n return None","title":""},{"docid":"90f4210057524d28a925310179ea3687","score":"0.52474374","text":"def uid(self):\n return self._uid","title":""},{"docid":"90f4210057524d28a925310179ea3687","score":"0.52474374","text":"def uid(self):\n return self._uid","title":""},{"docid":"90f4210057524d28a925310179ea3687","score":"0.52474374","text":"def uid(self):\n return self._uid","title":""},{"docid":"90f4210057524d28a925310179ea3687","score":"0.52474374","text":"def uid(self):\n return self._uid","title":""},{"docid":"90f4210057524d28a925310179ea3687","score":"0.52474374","text":"def uid(self):\n return self._uid","title":""},{"docid":"90f4210057524d28a925310179ea3687","score":"0.52474374","text":"def uid(self):\n return self._uid","title":""},{"docid":"cf25c51e21a3b8cd752c4e42745148cd","score":"0.523516","text":"def get_resource_from_uri(self, uri: str):\n pat = self.oidch.get_new_pat()\n resource_reg_endpoint = self.wkh.get(TYPE_UMA_V2, KEY_UMA_V2_RESOURCE_REGISTRATION_ENDPOINT)\n #r=self.mongo.get_id_from_uri(icon_uri)\n #if not r: return False\n resources = self.get_resources()\n for resource_id in resources:\n data = resource.read(pat, resource_reg_endpoint, resource_id, self.verify)\n print(data)\n if \"icon_uri\" in data and data[\"icon_uri\"] in uri:\n return data[\"_id\"]\n return None","title":""},{"docid":"0de1f12c168b0df92bdc96a3805d7aa9","score":"0.5232988","text":"def _resolve_id(self, id):\n return self._lookupUid(id) if len(id) < 10 else id","title":""},{"docid":"56e6ab3b00c42aaf84fcb712352c8e00","score":"0.52116","text":"def _match_uid(self, uids: Iterable) -> Optional[str]:\n return self.provided_id if self.provided_id in uids else None","title":""},{"docid":"ad08cffdc09dd4ac42e1e712db3a76ec","score":"0.5196318","text":"def _find_resource_by_id_or_name(id_or_name, resource_list,\n resource_type_name):\n search_result_by_id = [res for res in resource_list\n if res.id == id_or_name]\n if search_result_by_id:\n return search_result_by_id[0]\n else:\n # try to find resource by name\n search_result_by_name = [res for res in resource_list\n if res.name == id_or_name]\n match_count = len(search_result_by_name)\n if match_count > 1:\n message = (\"Ambiguous {0} name '{1}'. Found more than one \"\n \"{0} for this name in Manila.\"\n ).format(resource_type_name, id_or_name)\n raise exceptions.NoUniqueMatch(message)\n elif match_count == 1:\n return search_result_by_name[0]\n else:\n raise heat_exception.EntityNotFound(entity=resource_type_name,\n name=id_or_name)","title":""},{"docid":"308d626c79052e54aa57c2d63e981684","score":"0.5190388","text":"def _lookup_nsid(self, username):\n find_resp = self.api.people.findByUsername(username=username, format=\"parsed-json\")\n nsid = None\n if find_resp[\"stat\"] == \"ok\":\n nsid = find_resp[\"user\"][\"nsid\"]\n log.debug(\"Looking up username %s returned %s\", username, nsid)\n return nsid","title":""},{"docid":"012cde7994eabdfc45701399fa874ef6","score":"0.5184868","text":"def uid(self):\n if not self._uid:\n self._uid = uuid4()\n return self._uid","title":""},{"docid":"d5797af7e151ec1d307086df70936dd0","score":"0.51786214","text":"def uid(self):\n return self._id","title":""},{"docid":"2f78b1570d0443131a6ec2dd619ce6fa","score":"0.515076","text":"def owner_lookup(self, name, rtype='ANY'):\n return self._request(FROM_OWNER_URL.format(value=name, rtype=rtype))","title":""},{"docid":"993fa9c5ec513baf8e56d738f4641aac","score":"0.5140477","text":"def uid(self) -> str:\n return self.signing_key[\"uid\"]","title":""},{"docid":"041d61ae1251659360af10c6c9cd8b1b","score":"0.513205","text":"def get_unit_guid(self, name):\n unit = next((unit for unit in self.units() if unit['name'].find(name) >= 0), None)\n return None if unit is None else unit['unitGuid']","title":""},{"docid":"e2a9a30ef7fa54aeb7d4669673f9c8d6","score":"0.51276284","text":"def get_resource_string(manager, resource_name):","title":""},{"docid":"e2a9a30ef7fa54aeb7d4669673f9c8d6","score":"0.51276284","text":"def get_resource_string(manager, resource_name):","title":""},{"docid":"38a24d8785c0eaa37573099980c9e9a8","score":"0.5092694","text":"def _uuid_find(context, host, name_label):\n for i in objects.InstanceList.get_by_host(context, host):\n if i.name == name_label:\n return i.uuid\n return None","title":""},{"docid":"fff2651be6cc6bd45e8f8bdf5074f32e","score":"0.50889","text":"def get_rs_id_by_name(name, response_sets):\n\n for rs in response_sets:\n if rs['name'] == name:\n return rs","title":""},{"docid":"18d2caf8c5727c724773faaea6fb5ea1","score":"0.5084684","text":"def lookupUser(conn, uid):\n curs = conn.cursor(MySQLdb.cursors.DictCursor)\n curs.execute('''select * from user where username = %s''', [uid])\n return curs.fetchone()","title":""},{"docid":"d988ae903e6488eaeaf0e117f2819dc7","score":"0.50842035","text":"def get_rv(self, name):\r\n if name in self._name_lookup:\r\n return self._name_lookup[name]\r\n return None","title":""},{"docid":"707512f47bcf675d558e31db4b759f7b","score":"0.50782555","text":"def get_system_id(self, name):\n for k, v in self.nodes_iter(data=True):\n if 'name' in v and v['name'].lower() == name.lower():\n return k","title":""},{"docid":"e2ee30c9f9b0c3e71a79b3ced9215dde","score":"0.50745374","text":"def __init__(self, name: str, uid: str):\n\n self.uid = uid\n self.name = name","title":""},{"docid":"5506f6bc160101f8f5699413aa0b07ae","score":"0.50717187","text":"def insert_and_get_uid(self, username):\n uid = -1\n\n with sqlite.connect(dbfile) as conn:\n cur = conn.cursor()\n q1 = safe_unicode(u\"INSERT OR IGNORE INTO user VALUES (null, ?);\")\n cur.execute(q1, [username])\n conn.commit()\n\n q2 = safe_unicode(u\"SELECT uid FROM user WHERE name = ?;\")\n res = cur.execute(q2, [username])\n uid = res.fetchone()[0]\n return uid","title":""},{"docid":"c89418a274b3703f8825022a3585e82d","score":"0.50656986","text":"def _testNameFromId(self, name):\n m = self.idpat.match(name)\n if m is None:\n return None\n\n id_ = int(m.groups()[0])\n\n self.loadIds()\n # Translate to test's real ID\n try:\n return self.ids[id_]\n except KeyError:\n return None","title":""},{"docid":"a9e3d52baf0db68609800e6b102eda13","score":"0.5058789","text":"def get_uid(self, cached_robot):\n return cached_robot.attr[\"uid\"]","title":""},{"docid":"50ce15db50cd47eaa08c32c9e1af0464","score":"0.50444424","text":"def get_entry_uid(self, entry_data):\n dl_url = self.get_entry_download_url(entry_data)\n if not dl_url:\n raise NotImplementedError('%s must implement get_entry_uid '\n 'method' % self.__class__.__name__)\n return md5(self.__class__.__name__ + dl_url).hexdigest()","title":""},{"docid":"3a860e4c4af4e4d4f5ede7f32a72c198","score":"0.5041299","text":"def resource(self, name):\n if name in self.resources:\n res = self.resources[name]\n else:\n url = self.url + '/' + name.strip('/') + '/'\n res = RestResource(url)\n self.resources[name] = res\n return res","title":""},{"docid":"fb9b01b5d7d2e429bc8641077e185aca","score":"0.50398725","text":"def resource_id(self) -> str:\n return pulumi.get(self, \"resource_id\")","title":""},{"docid":"fb9b01b5d7d2e429bc8641077e185aca","score":"0.50398725","text":"def resource_id(self) -> str:\n return pulumi.get(self, \"resource_id\")","title":""},{"docid":"fb9b01b5d7d2e429bc8641077e185aca","score":"0.50398725","text":"def resource_id(self) -> str:\n return pulumi.get(self, \"resource_id\")","title":""},{"docid":"fb9b01b5d7d2e429bc8641077e185aca","score":"0.50398725","text":"def resource_id(self) -> str:\n return pulumi.get(self, \"resource_id\")","title":""},{"docid":"fb9b01b5d7d2e429bc8641077e185aca","score":"0.50398725","text":"def resource_id(self) -> str:\n return pulumi.get(self, \"resource_id\")","title":""},{"docid":"fb9b01b5d7d2e429bc8641077e185aca","score":"0.50398725","text":"def resource_id(self) -> str:\n return pulumi.get(self, \"resource_id\")","title":""},{"docid":"fb9b01b5d7d2e429bc8641077e185aca","score":"0.50398725","text":"def resource_id(self) -> str:\n return pulumi.get(self, \"resource_id\")","title":""}],"string":"[\n {\n \"docid\": \"22c43a1d43961de77cb6605b3291cf35\",\n \"score\": \"0.6511902\",\n \"text\": \"def resource_uid(self) -> Optional[str]:\\n return pulumi.get(self, \\\"resource_uid\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd16aa5bb834eff63cf6559219c3e5ea\",\n \"score\": \"0.65088683\",\n \"text\": \"def _get_uid(self, name):\\n if name.startswith(\\\"http\\\"):\\n name = self._get_path_from_url(name)\\n\\n if not name.startswith(self.storage_dir):\\n return name\\n\\n uid = path.dirname(name)\\n uid = path.relpath(uid, start=self.storage_dir)\\n return uid\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e4700dcd3c51083415bfb4a93ce47d8\",\n \"score\": \"0.63126373\",\n \"text\": \"def get_by_name(self, name):\\n if not name:\\n raise TypeError('name must be a non empty string.')\\n\\n uid = self._name_uid_map.get(name)\\n return self.get(uid)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80c5d982b67a87365519f6edebfb79f2\",\n \"score\": \"0.62900656\",\n \"text\": \"def resourceExists(self, name):\\n uid = self._db_value_for_sql(\\\"select UID from RESOURCE where NAME = :1\\\", name)\\n return uid is not None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"808824b59148320eeb5b431fcdf0e707\",\n \"score\": \"0.628813\",\n \"text\": \"def _lookupUid(self, short_uid):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af59f42b118ebc6d0a74e3fad386d1e1\",\n \"score\": \"0.6165063\",\n \"text\": \"def resourceNameForUID(self, uid):\\n result = None\\n\\n for name in self.resourceNamesForUID(uid):\\n assert result is None, \\\"More than one resource with UID %s in address book collection %r\\\" % (uid, self)\\n result = name\\n\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2395f4950f18a3656a8e4801cdb9e8ff\",\n \"score\": \"0.6115621\",\n \"text\": \"def get_uid(self):\\n\\t\\tresult, data = self.connection.uid('search', None, \\\"ALL\\\")\\n\\t\\tif result == \\\"OK\\\":\\n\\t\\t\\tself.uid = (data[0]).split()\\n\\t\\t\\tprint(\\\"[ i ] UIDs fetched.\\\")\\n\\t\\telse:\\n\\t\\t\\tprint(\\\"[ e ] Searching for UIDs failed.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4a86f8782fb0839b53449419106f330\",\n \"score\": \"0.60574347\",\n \"text\": \"def _nameToUID(self, name):\\n return name.rsplit(\\\".\\\", 1)[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9cd2b58e15eaa1c42d1485869549c3b\",\n \"score\": \"0.59734124\",\n \"text\": \"def _match_name(self, uids: Iterable) -> Optional[str]:\\n for uid in uids:\\n if parseaddr(uid)[0] == self.provided_name:\\n return uid\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ef31118ddd85bead69e7a65b6642a85\",\n \"score\": \"0.59275097\",\n \"text\": \"def get_resource_id(self, resource, name):\\n response = self._request.get(resource, params={'links': 'false', 'query': name})\\n if not response[resource]:\\n return None\\n if len(response[resource]) > 1:\\n raise Dhis2ApiQueryError('Query returned multiple results')\\n return response[resource][0]['id']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4bac146f5b50fea845eec6a70249ec7\",\n \"score\": \"0.58771193\",\n \"text\": \"def get_uid():\\n with uid_lock:\\n out = next(uid)\\n return out\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b928f9c1708f4e1da0e182658a471f2\",\n \"score\": \"0.5778452\",\n \"text\": \"def uid(self) -> Optional[str]:\\n return pulumi.get(self, \\\"uid\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b928f9c1708f4e1da0e182658a471f2\",\n \"score\": \"0.5778452\",\n \"text\": \"def uid(self) -> Optional[str]:\\n return pulumi.get(self, \\\"uid\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b928f9c1708f4e1da0e182658a471f2\",\n \"score\": \"0.5778452\",\n \"text\": \"def uid(self) -> Optional[str]:\\n return pulumi.get(self, \\\"uid\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b928f9c1708f4e1da0e182658a471f2\",\n \"score\": \"0.5778452\",\n \"text\": \"def uid(self) -> Optional[str]:\\n return pulumi.get(self, \\\"uid\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de3ef9111a5a0f839647ce44c12b9d10\",\n \"score\": \"0.572037\",\n \"text\": \"def resolve_uid(uid):\\n site = get_site()\\n if site is None:\\n return\\n catalog = getToolByName(site, 'portal_catalog', None)\\n if catalog is None:\\n return\\n result = catalog.unrestrictedSearchResults(UID=uid)\\n if len(result) != 1:\\n return\\n return result[0]._unrestrictedGetObject()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2024e61892b6a4ee115c2772592169f\",\n \"score\": \"0.57159597\",\n \"text\": \"def get_uid(self, idx):\\n try:\\n return self.__uid_cache[idx]\\n except KeyError:\\n return self[idx].uid\",\n \"title\": \"\"\n },\n {\n \"docid\": \"daaa143455b31a5d30e2194122f15b4f\",\n \"score\": \"0.5692868\",\n \"text\": \"def get_or_generate_uid(file_name: str, generator: callable, validator: [callable, None]):\\n full_path = os.path.join(get_uid_path(), file_name)\\n uid = None\\n if os.path.exists(full_path):\\n with open(full_path, 'r') as file:\\n uid = file.readline().strip()\\n\\n if uid is not None and (validator is not None and not validator(uid)):\\n uid = None\\n\\n if uid is None:\\n uid = generator()\\n save_uid_to_file(full_path, uid)\\n return uid\",\n \"title\": \"\"\n },\n {\n \"docid\": \"caf32cf3c075b5b075abdd43fcd9f3c9\",\n \"score\": \"0.5673893\",\n \"text\": \"def resource_id(self):\\n\\t\\treturn self.name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a14dbf3aa274bbe68bed063d14088ad\",\n \"score\": \"0.5669674\",\n \"text\": \"def name(self) -> str:\\n uid_string = str(self)\\n if uid_string in UID_dictionary:\\n return UID_dictionary[self][0]\\n\\n return uid_string\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d92e92b43ce90c20d1b7a5d6a3239c4c\",\n \"score\": \"0.5653038\",\n \"text\": \"def getUID(self):\\n uid = \\\"%s_%s_%s_%s\\\" % (self.res_sid, self.resid, self.chain_id, self.model_id)\\n return uid\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04f2b4888f7478884ecd2c88087931d1\",\n \"score\": \"0.5642538\",\n \"text\": \"def uid(self) -> str:\\n return pulumi.get(self, \\\"uid\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d2dcd55f000a5d6c3fa75f1b8fc78155\",\n \"score\": \"0.56291854\",\n \"text\": \"def uid(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"uid\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d31f9273d459d1ae3c91c0b7a1de10b\",\n \"score\": \"0.5614781\",\n \"text\": \"def lookup_name(name):\\n resp = rx.get('/rxcui.json', params=dict(name=name))\\n resp.raise_for_status()\\n\\n data = resp.json()\\n return extract_cui(data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51edd26527de2a839f754577e05629c5\",\n \"score\": \"0.56011206\",\n \"text\": \"def get_id_for_name(self, name):\\n client = MongoClient(MONGO_HOST, 27017)\\n coll = client[MONGO_DBNAME]['signature']\\n resp = coll.find({\\\"name\\\": name}).next()\\n oid = str(resp.get(\\\"_id\\\"))\\n return oid\",\n \"title\": \"\"\n },\n {\n \"docid\": \"331fca09de3fdd0d89c97faa6048d8a0\",\n \"score\": \"0.5601014\",\n \"text\": \"def __format_id_from_name(self, name):\\n\\n query = Query(\\\"format\\\", \\\"id\\\")\\n query.where(query.Condition(\\\"name\\\", name))\\n rows = query.execute(self.cursor).fetchall()\\n return rows[0].id if rows else None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e60bf14ba074ef633f1aac4d361416ec\",\n \"score\": \"0.5573876\",\n \"text\": \"def uid(self) -> int:\\n return pulumi.get(self, \\\"uid\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e60bf14ba074ef633f1aac4d361416ec\",\n \"score\": \"0.5573876\",\n \"text\": \"def uid(self) -> int:\\n return pulumi.get(self, \\\"uid\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ed7faeddb6ba6abb3162e0e1626d0bb\",\n \"score\": \"0.55551934\",\n \"text\": \"def uid(self):\\n\\n return self.metadata[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29bad0aa5d80ced3831a1e59cd142cdf\",\n \"score\": \"0.5533407\",\n \"text\": \"def avatar_for_name(name=None):\\n avatars = avatars_by_name()\\n if name in avatars:\\n return avatars[name]\\n \\n return all_avatars()[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae10603100564df7b731f87e9d07e1c7\",\n \"score\": \"0.55214924\",\n \"text\": \"def _fetch_register(self, name: str) -> int:\\n with open(os.path.join(self.statedir, name), \\\"r\\\") as f:\\n return int(f.read())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4217f7e735ad2811fe3a3302d79842b\",\n \"score\": \"0.55143017\",\n \"text\": \"def GetUserid(self):\\n if self.scmRef == None or self.ref == None or \\\\\\n self.ver == None or self.type == None:\\n raise KeyError, \\\"getuid: Params can't be empty\\\"\\n\\n if self.debug:\\n print \\\"GetUserID(%s, %s, %s)\\\" % \\\\\\n (self.scmRef, self.ver, self.type)\\n\\n try:\\n userid = \\\\\\n self.db[self.scmRef][self.ref][self.ver][self.type]['misc'][0]\\n except ( IndexError, KeyError):\\n userid = self.userid\\n except:\\n raise\\n\\n if self.debug:\\n print \\\"GetUserid: %s\\\" % userid\\n\\n return string.strip(userid)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba79ec5e9649c41de81a2b8072874008\",\n \"score\": \"0.5513815\",\n \"text\": \"def uid(self):\\n self._uid = getattr(self, \\\"_uid\\\", unicode(uuid.uuid4().hex))\\n return self._uid\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50391a2ae318443df3b32fb11647d064\",\n \"score\": \"0.5499823\",\n \"text\": \"def find_resourceid_by_name_or_id(resource_client, name_or_id):\\n try:\\n # Try to return an uuid\\n return str(uuid.UUID(name_or_id))\\n except ValueError:\\n # Not an uuid => assume it is resource name\\n pass\\n\\n resources = resource_client.list()\\n candidate_ids = [r['id'] for r in resources if r.get('name') == name_or_id]\\n if not candidate_ids:\\n raise exceptions.ResourceNotFound(\\n 'Could not find resource with name \\\"%s\\\"' % name_or_id)\\n elif len(candidate_ids) > 1:\\n str_ids = ','.join(candidate_ids)\\n raise exceptions.NoUniqueMatch(\\n 'Multiple resources with name \\\"%s\\\": %s' % (name_or_id, str_ids))\\n return candidate_ids[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14b8f2669ccf939f8183bbf2e5312b31\",\n \"score\": \"0.54843956\",\n \"text\": \"def get_res_by_name(self, name):\\n for res in self.resources:\\n if res.name == name:\\n return res\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07815e8e739e92c2cf1d4e2e2f7102a0\",\n \"score\": \"0.54808587\",\n \"text\": \"def from_name(self, name):\\n if name not in self._lookup:\\n raise ValueError('Unkown name {} in registry'.format(name))\\n return self._lookup[name]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e02a5b4aca376971561e574eafa86a2\",\n \"score\": \"0.5473876\",\n \"text\": \"def getUserId(request, searchName):\\n if not searchName:\\n return None\\n cfg = request.cfg\\n try:\\n _name2id = cfg._name2id\\n except AttributeError:\\n arena = 'user'\\n key = 'name2id'\\n cache = caching.CacheEntry(request, arena, key)\\n try:\\n _name2id = pickle.loads(cache.content())\\n except (pickle.UnpicklingError, IOError, EOFError, ValueError):\\n _name2id = {}\\n cfg._name2id = _name2id\\n id = _name2id.get(searchName, None)\\n if id is None:\\n for userid in getUserList(request):\\n name = User(request, id=userid).name\\n _name2id[name] = userid\\n cfg._name2id = _name2id\\n arena = 'user'\\n key = 'name2id'\\n cache = caching.CacheEntry(request, arena, key)\\n cache.update(pickle.dumps(_name2id, PICKLE_PROTOCOL))\\n id = _name2id.get(searchName, None)\\n return id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec5aa89179643410573bb78d4ec6a452\",\n \"score\": \"0.54694355\",\n \"text\": \"def uid(self):\\n return self.__dict__.get('id')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0eae1f89b7ff4e70d19c80ce5df7b991\",\n \"score\": \"0.5454802\",\n \"text\": \"def get_image_id(self, name):\\n if name.startswith(\\\"sha256:\\\"):\\n return name[7:]\\n image = self.get_image(name)\\n if not image:\\n return\\n return image[\\\"id\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4bc789a080c9e6385ee0434f6cf443e6\",\n \"score\": \"0.5448324\",\n \"text\": \"def getUID(self):\\n return self.uid\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f966a42b639f5d8bc951adb2f4d144dd\",\n \"score\": \"0.54480475\",\n \"text\": \"def get_uid(prefix=''):\\n _uid_prefixes[prefix] += 1\\n return _uid_prefixes[prefix]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0bc584418fa16f9036bdb92151db333\",\n \"score\": \"0.5398736\",\n \"text\": \"def get_sensor_id_by_name(self, name):\\n sensors = self.get_sensor()\\n for sensor_id in sensors:\\n if PY3K:\\n if name == sensors[sensor_id][\\\"name\\\"]:\\n return sensor_id\\n else:\\n if name.decode(\\\"utf-8\\\") == sensors[sensor_id][\\\"name\\\"]:\\n return sensor_id\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33dcf5fccfdbdc4b31e647f3e928e439\",\n \"score\": \"0.5386785\",\n \"text\": \"def name(self) -> str:\\n return parseaddr(self.uid)[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b536a2b0f59e2c2159c8bc327c32087d\",\n \"score\": \"0.5367091\",\n \"text\": \"def get_ref_by_name(self, name_str):\\n\\t\\tuser_dict = self.get_user_dict_by_name(name_str)\\n\\t\\tif user_dict:\\n\\t\\t\\treturn user_dict['ref']\\n\\t\\telse:\\n\\t\\t\\treturn None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9873c8b2e054f1389e4822f2650c5561\",\n \"score\": \"0.5353032\",\n \"text\": \"def uid(self) -> str:\\n return typing.cast(\\n str,\\n self._properties.get(\\\"uid\\\"),\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9873c8b2e054f1389e4822f2650c5561\",\n \"score\": \"0.5353032\",\n \"text\": \"def uid(self) -> str:\\n return typing.cast(\\n str,\\n self._properties.get(\\\"uid\\\"),\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9873c8b2e054f1389e4822f2650c5561\",\n \"score\": \"0.5353032\",\n \"text\": \"def uid(self) -> str:\\n return typing.cast(\\n str,\\n self._properties.get(\\\"uid\\\"),\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c3c3bbbb52adc21041ba1bfebd8abac\",\n \"score\": \"0.53486484\",\n \"text\": \"def uid(self) -> Optional[str]:\\n return self.__uid\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11da66e6ab081c7cdb521677905c782d\",\n \"score\": \"0.5337882\",\n \"text\": \"def getUser(name):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43602a2b149932d47487094947ddb482\",\n \"score\": \"0.5333861\",\n \"text\": \"def id_from_name(uname_raw: str):\\n uname_low: str = uname_raw.lower()\\n response = requests.get(\\n \\\"https://api.mojang.com/users/profiles/minecraft/{}\\\".format(uname_low)\\n )\\n log.f(\\\"WLME_RESP\\\", str(response))\\n if response.status_code == 200:\\n return {\\\"code\\\": response.status_code, \\\"udat\\\": response.json()}\\n else:\\n return {\\\"code\\\": response.status_code}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a906cc5d219648489ec8b446d34d18f\",\n \"score\": \"0.5323687\",\n \"text\": \"def lookup(self, name):\\n return self._name_to_spec.get(name, None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f9cce8bd678f8abd1b56e19bee54cd5\",\n \"score\": \"0.53209287\",\n \"text\": \"def get_uid(self):\\n dat = self._handle.controlRead(Panda.REQUEST_IN, 0xc3, 0, 0, 12)\\n return binascii.hexlify(dat).decode()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c85907d06bc3481af8dafa3ed35786b9\",\n \"score\": \"0.53176194\",\n \"text\": \"def getUID(self):\\r\\n return self.id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37d9a2bdffc1223298670875ca5f43ec\",\n \"score\": \"0.5313008\",\n \"text\": \"def fetchName(self, name, pwonly, decrypt, fulldecrypt):\\n\\t\\tret = self.find(name, nameOnly=True)\\n\\t\\tif not ret:\\n\\t\\t\\treturn\\n\\t\\tif len(ret) > 1:\\n\\t\\t\\tlog.error(\\\"found more than 1 record, only returning the first.\\\")\\n\\t\\tuuid = ret[0]['uuid']\\n\\t\\treturn self.fetchUUID(uuid, pwonly, decrypt, fulldecrypt)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"96282b58f8328b10f1442d16d8752fea\",\n \"score\": \"0.5308342\",\n \"text\": \"def get_resource(name, decode=True):\\n # get the resource using relative names\\n with open(os.path.join(_pwd, name), 'rb') as f:\\n resource = f.read()\\n\\n # make sure we return it as a string if asked\\n if decode and hasattr(resource, 'decode'):\\n return resource.decode('utf-8')\\n\\n return resource\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d2de678dd016aca72d7adeaaa79f0d2\",\n \"score\": \"0.53070873\",\n \"text\": \"def uid(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"uid\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d2de678dd016aca72d7adeaaa79f0d2\",\n \"score\": \"0.53070873\",\n \"text\": \"def uid(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"uid\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d2de678dd016aca72d7adeaaa79f0d2\",\n \"score\": \"0.53070873\",\n \"text\": \"def uid(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"uid\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89e23ddf58072ae5da604b0fbcd450a0\",\n \"score\": \"0.5299741\",\n \"text\": \"def _findId(self, name):\\n isinstance(name, str)\\n\\n for key_id in list(self.filenames.keys()):\\n # data with title\\n if self.filenames[key_id].name:\\n input = self.filenames[key_id].name\\n # data without title\\n else:\\n input = str(key_id)\\n if name in input:\\n return key_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cce1863b16d563a09cb86014456b4482\",\n \"score\": \"0.5275912\",\n \"text\": \"def findUser(self, name):\\n name = name.lower()\\n ul = self._getUserlist()\\n udi = dict(zip([u.name for u in ul], ul))\\n cname = None\\n for n in udi.keys():\\n if name in n:\\n if cname: return None #ambiguous!!\\n cname = n\\n if cname: return udi[cname]\\n else: return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c09866794796dd8d40defa98b5e640c\",\n \"score\": \"0.52732617\",\n \"text\": \"def _get_uid_attr(self, ldap_userfolder):\\n uid_attr = ldap_userfolder._uid_attr\\n schema_dicts = ldap_userfolder.getSchemaDict()\\n for schema_map in schema_dicts:\\n if uid_attr == schema_map['ldap_name']:\\n return schema_map['public_name']\\n return uid_attr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c700595a317f90df1f1246d734ca25da\",\n \"score\": \"0.52654034\",\n \"text\": \"def getuid(): # type: ignore\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c4d3ac3c0e90699737789302ccf5cb7\",\n \"score\": \"0.5258746\",\n \"text\": \"def name_to_id(self, obj_name):\\n comp_map = self.get_comp_map()\\n\\n res = self.redis.hget(comp_map, obj_name)\\n\\n if res is not None:\\n return res\\n\\n redir_map = self.get_redir_map()\\n if redir_map:\\n return self.redis.hget(redir_map, obj_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"440059df1cccfbad3baaa78ea75ce362\",\n \"score\": \"0.5256874\",\n \"text\": \"def get_node_by_name(self, name):\\n for res in self.resources:\\n for node in res.nodes:\\n if node.name == name:\\n return node\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90f4210057524d28a925310179ea3687\",\n \"score\": \"0.52474374\",\n \"text\": \"def uid(self):\\n return self._uid\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90f4210057524d28a925310179ea3687\",\n \"score\": \"0.52474374\",\n \"text\": \"def uid(self):\\n return self._uid\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90f4210057524d28a925310179ea3687\",\n \"score\": \"0.52474374\",\n \"text\": \"def uid(self):\\n return self._uid\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90f4210057524d28a925310179ea3687\",\n \"score\": \"0.52474374\",\n \"text\": \"def uid(self):\\n return self._uid\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90f4210057524d28a925310179ea3687\",\n \"score\": \"0.52474374\",\n \"text\": \"def uid(self):\\n return self._uid\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90f4210057524d28a925310179ea3687\",\n \"score\": \"0.52474374\",\n \"text\": \"def uid(self):\\n return self._uid\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf25c51e21a3b8cd752c4e42745148cd\",\n \"score\": \"0.523516\",\n \"text\": \"def get_resource_from_uri(self, uri: str):\\n pat = self.oidch.get_new_pat()\\n resource_reg_endpoint = self.wkh.get(TYPE_UMA_V2, KEY_UMA_V2_RESOURCE_REGISTRATION_ENDPOINT)\\n #r=self.mongo.get_id_from_uri(icon_uri)\\n #if not r: return False\\n resources = self.get_resources()\\n for resource_id in resources:\\n data = resource.read(pat, resource_reg_endpoint, resource_id, self.verify)\\n print(data)\\n if \\\"icon_uri\\\" in data and data[\\\"icon_uri\\\"] in uri:\\n return data[\\\"_id\\\"]\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0de1f12c168b0df92bdc96a3805d7aa9\",\n \"score\": \"0.5232988\",\n \"text\": \"def _resolve_id(self, id):\\n return self._lookupUid(id) if len(id) < 10 else id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"56e6ab3b00c42aaf84fcb712352c8e00\",\n \"score\": \"0.52116\",\n \"text\": \"def _match_uid(self, uids: Iterable) -> Optional[str]:\\n return self.provided_id if self.provided_id in uids else None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad08cffdc09dd4ac42e1e712db3a76ec\",\n \"score\": \"0.5196318\",\n \"text\": \"def _find_resource_by_id_or_name(id_or_name, resource_list,\\n resource_type_name):\\n search_result_by_id = [res for res in resource_list\\n if res.id == id_or_name]\\n if search_result_by_id:\\n return search_result_by_id[0]\\n else:\\n # try to find resource by name\\n search_result_by_name = [res for res in resource_list\\n if res.name == id_or_name]\\n match_count = len(search_result_by_name)\\n if match_count > 1:\\n message = (\\\"Ambiguous {0} name '{1}'. Found more than one \\\"\\n \\\"{0} for this name in Manila.\\\"\\n ).format(resource_type_name, id_or_name)\\n raise exceptions.NoUniqueMatch(message)\\n elif match_count == 1:\\n return search_result_by_name[0]\\n else:\\n raise heat_exception.EntityNotFound(entity=resource_type_name,\\n name=id_or_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"308d626c79052e54aa57c2d63e981684\",\n \"score\": \"0.5190388\",\n \"text\": \"def _lookup_nsid(self, username):\\n find_resp = self.api.people.findByUsername(username=username, format=\\\"parsed-json\\\")\\n nsid = None\\n if find_resp[\\\"stat\\\"] == \\\"ok\\\":\\n nsid = find_resp[\\\"user\\\"][\\\"nsid\\\"]\\n log.debug(\\\"Looking up username %s returned %s\\\", username, nsid)\\n return nsid\",\n \"title\": \"\"\n },\n {\n \"docid\": \"012cde7994eabdfc45701399fa874ef6\",\n \"score\": \"0.5184868\",\n \"text\": \"def uid(self):\\n if not self._uid:\\n self._uid = uuid4()\\n return self._uid\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5797af7e151ec1d307086df70936dd0\",\n \"score\": \"0.51786214\",\n \"text\": \"def uid(self):\\n return self._id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f78b1570d0443131a6ec2dd619ce6fa\",\n \"score\": \"0.515076\",\n \"text\": \"def owner_lookup(self, name, rtype='ANY'):\\n return self._request(FROM_OWNER_URL.format(value=name, rtype=rtype))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"993fa9c5ec513baf8e56d738f4641aac\",\n \"score\": \"0.5140477\",\n \"text\": \"def uid(self) -> str:\\n return self.signing_key[\\\"uid\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"041d61ae1251659360af10c6c9cd8b1b\",\n \"score\": \"0.513205\",\n \"text\": \"def get_unit_guid(self, name):\\n unit = next((unit for unit in self.units() if unit['name'].find(name) >= 0), None)\\n return None if unit is None else unit['unitGuid']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2a9a30ef7fa54aeb7d4669673f9c8d6\",\n \"score\": \"0.51276284\",\n \"text\": \"def get_resource_string(manager, resource_name):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2a9a30ef7fa54aeb7d4669673f9c8d6\",\n \"score\": \"0.51276284\",\n \"text\": \"def get_resource_string(manager, resource_name):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38a24d8785c0eaa37573099980c9e9a8\",\n \"score\": \"0.5092694\",\n \"text\": \"def _uuid_find(context, host, name_label):\\n for i in objects.InstanceList.get_by_host(context, host):\\n if i.name == name_label:\\n return i.uuid\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fff2651be6cc6bd45e8f8bdf5074f32e\",\n \"score\": \"0.50889\",\n \"text\": \"def get_rs_id_by_name(name, response_sets):\\n\\n for rs in response_sets:\\n if rs['name'] == name:\\n return rs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18d2caf8c5727c724773faaea6fb5ea1\",\n \"score\": \"0.5084684\",\n \"text\": \"def lookupUser(conn, uid):\\n curs = conn.cursor(MySQLdb.cursors.DictCursor)\\n curs.execute('''select * from user where username = %s''', [uid])\\n return curs.fetchone()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d988ae903e6488eaeaf0e117f2819dc7\",\n \"score\": \"0.50842035\",\n \"text\": \"def get_rv(self, name):\\r\\n if name in self._name_lookup:\\r\\n return self._name_lookup[name]\\r\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"707512f47bcf675d558e31db4b759f7b\",\n \"score\": \"0.50782555\",\n \"text\": \"def get_system_id(self, name):\\n for k, v in self.nodes_iter(data=True):\\n if 'name' in v and v['name'].lower() == name.lower():\\n return k\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2ee30c9f9b0c3e71a79b3ced9215dde\",\n \"score\": \"0.50745374\",\n \"text\": \"def __init__(self, name: str, uid: str):\\n\\n self.uid = uid\\n self.name = name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5506f6bc160101f8f5699413aa0b07ae\",\n \"score\": \"0.50717187\",\n \"text\": \"def insert_and_get_uid(self, username):\\n uid = -1\\n\\n with sqlite.connect(dbfile) as conn:\\n cur = conn.cursor()\\n q1 = safe_unicode(u\\\"INSERT OR IGNORE INTO user VALUES (null, ?);\\\")\\n cur.execute(q1, [username])\\n conn.commit()\\n\\n q2 = safe_unicode(u\\\"SELECT uid FROM user WHERE name = ?;\\\")\\n res = cur.execute(q2, [username])\\n uid = res.fetchone()[0]\\n return uid\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c89418a274b3703f8825022a3585e82d\",\n \"score\": \"0.50656986\",\n \"text\": \"def _testNameFromId(self, name):\\n m = self.idpat.match(name)\\n if m is None:\\n return None\\n\\n id_ = int(m.groups()[0])\\n\\n self.loadIds()\\n # Translate to test's real ID\\n try:\\n return self.ids[id_]\\n except KeyError:\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a9e3d52baf0db68609800e6b102eda13\",\n \"score\": \"0.5058789\",\n \"text\": \"def get_uid(self, cached_robot):\\n return cached_robot.attr[\\\"uid\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50ce15db50cd47eaa08c32c9e1af0464\",\n \"score\": \"0.50444424\",\n \"text\": \"def get_entry_uid(self, entry_data):\\n dl_url = self.get_entry_download_url(entry_data)\\n if not dl_url:\\n raise NotImplementedError('%s must implement get_entry_uid '\\n 'method' % self.__class__.__name__)\\n return md5(self.__class__.__name__ + dl_url).hexdigest()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a860e4c4af4e4d4f5ede7f32a72c198\",\n \"score\": \"0.5041299\",\n \"text\": \"def resource(self, name):\\n if name in self.resources:\\n res = self.resources[name]\\n else:\\n url = self.url + '/' + name.strip('/') + '/'\\n res = RestResource(url)\\n self.resources[name] = res\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb9b01b5d7d2e429bc8641077e185aca\",\n \"score\": \"0.50398725\",\n \"text\": \"def resource_id(self) -> str:\\n return pulumi.get(self, \\\"resource_id\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb9b01b5d7d2e429bc8641077e185aca\",\n \"score\": \"0.50398725\",\n \"text\": \"def resource_id(self) -> str:\\n return pulumi.get(self, \\\"resource_id\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb9b01b5d7d2e429bc8641077e185aca\",\n \"score\": \"0.50398725\",\n \"text\": \"def resource_id(self) -> str:\\n return pulumi.get(self, \\\"resource_id\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb9b01b5d7d2e429bc8641077e185aca\",\n \"score\": \"0.50398725\",\n \"text\": \"def resource_id(self) -> str:\\n return pulumi.get(self, \\\"resource_id\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb9b01b5d7d2e429bc8641077e185aca\",\n \"score\": \"0.50398725\",\n \"text\": \"def resource_id(self) -> str:\\n return pulumi.get(self, \\\"resource_id\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb9b01b5d7d2e429bc8641077e185aca\",\n \"score\": \"0.50398725\",\n \"text\": \"def resource_id(self) -> str:\\n return pulumi.get(self, \\\"resource_id\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb9b01b5d7d2e429bc8641077e185aca\",\n \"score\": \"0.50398725\",\n \"text\": \"def resource_id(self) -> str:\\n return pulumi.get(self, \\\"resource_id\\\")\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":692,"cells":{"query_id":{"kind":"string","value":"7fedc36119e96a11cbfecfce4483d863"},"query":{"kind":"string","value":"Creates a view that allows ANY logged in user to submit a field trip request but checks to make sure the admin is accepting requests first."},"positive_passages":{"kind":"list like","value":[{"docid":"d808caee7bdabd7b0bddaebc979f5bd0","score":"0.66285205","text":"def create(request):\n admin_option = AdminOption.objects.get()\n if not admin_option.window_open:\n return HttpResponse(\"New field trip requests have been disabled.\")\n\n title = \"Submit a Field Trip Request\"\n cards = (\n (\"General Information\", FORMS + 'general.html'),\n (\"Transportation\", FORMS + 'transportation.html'),\n (\"Funding\", FORMS + 'funding.html'),\n (\"Curriculum\", FORMS + 'curriculum.html'),\n )\n buttons = (\n ('submit', \"Submit\"),\n ('draft', \"Save as Draft\"),\n )\n ChaperoneFormSet = inlineformset_factory(FieldTrip, Chaperone, extra=1,\n form=ChaperoneForm)\n if request.method == 'POST':\n field_trip = FieldTrip(submitter=request.user)\n # status defaults to IN_PROGRESS, make it DRAFT for a draft\n if 'draft' in request.POST:\n field_trip.status = FieldTrip.DRAFT\n form = CreateForm(request.POST, request.FILES, instance=field_trip)\n chaperones = ChaperoneFormSet(request.POST, instance=field_trip)\n if form.is_valid() and chaperones.is_valid():\n form.save()\n chaperones.save()\n return redirect('field_trips:index')\n else:\n chaperones = ChaperoneFormSet()\n form = CreateForm()\n return render(request, 'field_trips/show_cards.html', {\n 'title': title,\n 'cards': cards,\n 'buttons': buttons,\n 'form': form,\n 'chaperones': chaperones,\n 'enctype': \"multipart/form-data\",\n 'action': reverse('field_trips:create'),\n 'admin_option': admin_option,\n })","title":""}],"string":"[\n {\n \"docid\": \"d808caee7bdabd7b0bddaebc979f5bd0\",\n \"score\": \"0.66285205\",\n \"text\": \"def create(request):\\n admin_option = AdminOption.objects.get()\\n if not admin_option.window_open:\\n return HttpResponse(\\\"New field trip requests have been disabled.\\\")\\n\\n title = \\\"Submit a Field Trip Request\\\"\\n cards = (\\n (\\\"General Information\\\", FORMS + 'general.html'),\\n (\\\"Transportation\\\", FORMS + 'transportation.html'),\\n (\\\"Funding\\\", FORMS + 'funding.html'),\\n (\\\"Curriculum\\\", FORMS + 'curriculum.html'),\\n )\\n buttons = (\\n ('submit', \\\"Submit\\\"),\\n ('draft', \\\"Save as Draft\\\"),\\n )\\n ChaperoneFormSet = inlineformset_factory(FieldTrip, Chaperone, extra=1,\\n form=ChaperoneForm)\\n if request.method == 'POST':\\n field_trip = FieldTrip(submitter=request.user)\\n # status defaults to IN_PROGRESS, make it DRAFT for a draft\\n if 'draft' in request.POST:\\n field_trip.status = FieldTrip.DRAFT\\n form = CreateForm(request.POST, request.FILES, instance=field_trip)\\n chaperones = ChaperoneFormSet(request.POST, instance=field_trip)\\n if form.is_valid() and chaperones.is_valid():\\n form.save()\\n chaperones.save()\\n return redirect('field_trips:index')\\n else:\\n chaperones = ChaperoneFormSet()\\n form = CreateForm()\\n return render(request, 'field_trips/show_cards.html', {\\n 'title': title,\\n 'cards': cards,\\n 'buttons': buttons,\\n 'form': form,\\n 'chaperones': chaperones,\\n 'enctype': \\\"multipart/form-data\\\",\\n 'action': reverse('field_trips:create'),\\n 'admin_option': admin_option,\\n })\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"34a30345c20e0a3d5e61f19441df8845","score":"0.6473721","text":"def home():\n\n form = TripForm()\n \n if form.validate_on_submit():\n \n if validate_dates(form.start_date_time.data, form.end_date_time.data, form):\n \n return render_template('create_trip.html', form=form)\n\n if validate_number_of_people(form.number_of_people.data):\n\n form.number_of_people.errors = [\"Please enter at least 1 person\"]\n\n return render_template('create_trip.html', form=form)\n\n # check if user is logged in if not log them in as a guest user\n \n if 'user_id' not in session:\n User.log_in_as_guest()\n\n \n trip = Trip(start_date_time=form.start_date_time.data,\n end_date_time=form.end_date_time.data,\n number_of_people=form.number_of_people.data,\n name=form.name.data,\n user_id=session.get('user_id')\n )\n \n db.session.add(trip)\n db.session.commit()\n\n session['trip_id'] = trip.id\n\n return redirect(url_for('select_meals', trip_id=trip.id))\n \n return render_template('create_trip.html', form=form)","title":""},{"docid":"11820e2a784e3f55cabea6763cdaae97","score":"0.6250833","text":"def user_trips(request):\n # extremely necessary to make the golobal vaiables\n # global display1\n # global display2\n global locations\n global matching_tlist\n tform1 = TripCreationForm1(request.user)\n tform2 = TripCreationForm2()\n tmform = TripModifyForm(request.user)\n lis = []\n for o, dis in matching_tlist:\n d = {}\n d[\"trip_name\"] = o.trip_id\n d[\"trip_date\"] = o.start_time\n d[\"trip_participants\"] = [x.username for x in o.participants.all()]\n d[\"disable\"] = dis\n d[\"trip_locations\"] = [\n x.location.location_name for x in TripPoint.objects.filter(trip=o)]\n lis.append(d)\n req_lis = Notification.objects.filter(\n user_to=request.user, notif_type=\"Trip Related\", resolved=\"No\")\n display1 = len(locations) != 0\n display2 = len(lis) != 0\n\n tlist = Trip.objects.filter(participants__in=[request.user])\n for t in tlist:\n # and (datetime.datetime.now()+datetime.timedelta(hours=5.5)-j.start_time.replace(tzinfo=utc)).days>10)\n if(t.start_time < (datetime.datetime.now()+datetime.timedelta(hours=5.5)).replace(tzinfo=utc)):\n if(t.posted):\n t.closed = True\n t.save()\n\n return render(request, 'info/trips.html', {\"tform1\": tform1, \"tform2\": tform2, \"tmform\": tmform,\n \"display1\": display1, \"display2\": display2, \"lis\": lis, \"locations\": locations,\n \"req_lis\": req_lis, \"tlist\": tlist})","title":""},{"docid":"090407cebc8e21af4c863e281f240ec4","score":"0.61548936","text":"def trip_request_add_handler(request):\n pprint(request.POST)\n global matching_tlist\n n = len(matching_tlist)\n user = request.user\n print(n)\n index = 0\n for i in range(n):\n if(\"submit\"+str(i) in request.POST):\n index = i\n print(\"index\", index)\n trp = matching_tlist[index][0]\n description = \"User \"+user.username+\" wants to be added to the \" + \\\n trp.trip_id+\" in which you are a participant.\"\n title = \"Trip Add request\"\n # for ut in trp.participants.all():\n Notification.objects.create(user_to=trp.leader, user_from=user, title=title, description=description,\n notif_type=\"Trip Related\", creation_time=datetime.datetime.now()+datetime.timedelta(hours=5.5),\n travel_id=trp.trip_id)\n matching_jlist[index][1] = True\n return redirect(\"/user_trips/\")","title":""},{"docid":"37b8dae6a16b4fde1251a4fe16cc1220","score":"0.59939307","text":"def post(self, request, *args, **kwargs):\n c = self.getContext(request)\n if 'choice' not in request.POST:\n c.update({\n 'error': True,\n 'message': 'No one was selected'\n })\n return render_to_response('display_message.html', c)\n tutor_name = request.POST['choice'].split('^?^')\n c.update({ 'firstname': tutor_name[0], 'lastname': tutor_name[1], 'username': tutor_name[2], 'specific_request': True })\n c.update({ \n 'times': range(1,24),\n 'days': ['Mon','Tues','Wed','Thurs','Fri','Sat','Sun']\n })\n return render_to_response('request_help.html',c)","title":""},{"docid":"9c906c53aa964d8f345f8d803aa47f20","score":"0.59727734","text":"def create(request):","title":""},{"docid":"b24f9e345d203cf3da1ebe2d1d1df526","score":"0.58876973","text":"def trip_creation_handler2(request):\n # function to handle the creation of new trip\n global locations\n pprint(locations)\n pprint(request.POST)\n trip_name = request.POST.get(\"trip_name\")\n trip_date = request.POST.get(\"travel_date\")\n cotravel_number = request.POST.get(\"cotravel_number\")\n source = request.POST.get(\"source\")\n duration = request.POST.get(\"duration\")\n expected_budget = request.POST.get(\"expected_budget\")\n trip_info = request.POST.get(\"trip_info\")\n\n trp = Trip(trip_id=trip_name, start_time=parser.parse(trip_date),\n source=source, cotravel_number=cotravel_number, duration=duration, expected_budget=expected_budget,\n trip_info=trip_info, leader=request.user)\n trp.save()\n trp.participants.add(request.user)\n # for each checkpoint create a new trip point with foreignkey to location and the trip objects\n for i, x in enumerate(locations):\n loc = LocationPoint.objects.get(\n location_name=x[\"location\"], user=request.user, location_type=\"Trip Point\")\n TripPoint.objects.create(location=loc, trip=trp)\n locations = []\n return redirect(\"/user_trips/\")","title":""},{"docid":"9c3d3e9c22aa87484c485af03b10f769","score":"0.5838591","text":"def get(self, request, *args, **kwargs):\n c = self.getContext(request) \n is_tutor = False\n if tutor(c['user'].username):\n is_tutor = True\n c.update({'tutor': is_tutor})\n tutee_list = Tutee.objects.all()\n requests = Request.objects.all()\n help_requests = []\n for tutee in tutee_list:\n for req in requests:\n if tutee.user.username == req.user and req.user != c['user'].username:\n if not req.accepted_by:\n help_requests.append(req)\n \n c.update(csrf(request))\n c.update({'help_requests': help_requests})\n return render_to_response('claim_tutee.html', c)","title":""},{"docid":"0dd84a9eb01d218f5047fd36da5ec442","score":"0.5813315","text":"def onflight(request):\n\n # Authentication check\n if not request.user.is_authenticated():\n # TODO redirect to error page\n return redirect(reverse('index'))\n\n user = request.user\n\n sia_user = get_object_or_404(SIAUser, user=user)\n return render(request, 'users/on-flight.html', {'SiaUser':sia_user})","title":""},{"docid":"c8bf2c8a119f844f584cdc2729223166","score":"0.5789913","text":"def get(self, request, *args, **kwargs):\n c = RequestContext(request)\n c.update(csrf(request))\n if c['user'].is_superuser:\n return redirect('/promote_user/', permanant=True)\n \n is_tutor = False\n if tutor(c['user'].username):\n is_tutor = True\n\n requests = Request.objects.all()\n pending_requests = []\n my_tutors = []\n if is_tutor:\n my_tutees = []\n for req in requests:\n if req.user == c['user'].username:\n if not req.accepted_by:\n pending_requests.append(req)\n else:\n my_tutors.append((Tutee.objects.get(user__username=req.accepted_by), req))\n elif is_tutor and req.accepted_by == c['user'].username:\n my_tutees.append((Tutee.objects.get(user__username=req.user), req))\n\n c.update(csrf(request))\n c.update({'pending_requests': pending_requests})\n c.update({'my_tutors': my_tutors})\n c.update({'tutor': is_tutor})\n if is_tutor:\n c.update({'my_tutees': my_tutees})\n return render_to_response('profile.html', c)","title":""},{"docid":"67ac31aa35b321fa6f0b1e7be751644f","score":"0.5786391","text":"def denied_add():\n messages.error(request, \"You don't have permission to create topologies.\")\n return HttpResponseRedirect('/login/')","title":""},{"docid":"e51ce1763db0292fcfe32cbd41d23b1e","score":"0.5765464","text":"def new_ticket(request, *arg):\n if request.method == 'POST':\n form = TicketForm(request.POST)\n if form.is_valid():\n t = Ticket()\n t.ticket_type = form.cleaned_data['ticket_type']\n t.project = form.cleaned_data['project']\n t.component = form.cleaned_data['component']\n t.summary = form.cleaned_data['summary']\n t.description = form.cleaned_data['description']\n t.created_by = request.user\n t.updated_by = request.user\n # workflow related stuff\n ticket_type = t.ticket_type\n wm = WorkflowManager()\n wm.workflow = ticket_type.workflow\n wm.created_by = request.user\n wm.save()\n r = Role.objects.get(id=settings.ROLE_SUBMITTER)\n p = Participant()\n p.user = request.user\n p.role = r\n p.workflowmanager=wm\n p.save()\n t.workflow_manager=wm\n t.save()\n wm.start(p)\n # Generates a confirmation email to send to the new user\n current_site = Site.objects.get_current() \n site_name = current_site.name\n domain = current_site.domain\n tplt = loader.get_template(settings.NEW_TICKET_EMAIL_TEMPLATE)\n c = {\n 'email': request.user.email,\n 'domain': domain,\n 'site_name': site_name,\n 'user': request.user,\n 'protocol': settings.REGISTRATION_USE_HTTPS and 'https' or 'http',\n 'ticket': t,\n }\n send_mail(_(\"Confirmation of new ticket on IssueTracker\"),\n tplt.render(Context(c)), None, [request.user.email])\n \n request.user.message_set.create(message=_(\"The ticket has been\"\\\n \" successfully created.\"))\n return HttpResponseRedirect('/ticket/%d'%t.id)\n else:\n form = TicketForm()\n c = RequestContext(request, {'form': form})\n return render_to_response('new.html', c)","title":""},{"docid":"f9de72b64e34113d105db135061cac5d","score":"0.5754959","text":"def get(self, request):\n if request.user.is_authenticated():\n return self.redirect_to(request)\n else:\n return render(request, 'account/signup.html', {'form': self.form()})","title":""},{"docid":"20851bfd02b455b3643f59c1122d68cf","score":"0.5754119","text":"def get(self, request):\n if request.user.status == 2:\n return redirect('orders:pickup_service')\n else:\n return super().get(request)","title":""},{"docid":"e6caf7263eecd8a03f00f1c60a0a949e","score":"0.5731373","text":"def validate_trip():\n\n email = session.get('email')\n user = User.query.filter_by(email=email).first()\n trip_name = request.form.get('trip-name')\n start_date = request.form.get('start-date')\n end_date = request.form.get('end-date')\n\n new_trip = Trip(creator_id=user.user_id,\n trip_name=trip_name,\n start_date=start_date,\n end_date=end_date)\n\n db.session.add(new_trip)\n\n db.session.commit()\n\n return redirect(url_for('trip_itinerary',\n trip_name=new_trip.trip_name, \n trip_id=new_trip.trip_id))","title":""},{"docid":"85ea1b84eee129c3638cf22bcbc1a38d","score":"0.5671864","text":"def create(self,request):# create function is called when a http post request is made to the ViewSet\n\n return ObtainAuthToken().post(request)","title":""},{"docid":"08d1f6fe285eedcd9c99ff813199d95f","score":"0.56620723","text":"def new_request_widget(self):\n if self.user_id == 0:\n self.login.click()\n if self.user_id == 0:\n return\n new_request_form = ui.NewRequest(self)\n new_request_form.move(get_popup_pos(self, new_request_form))\n new_request_form.user_id = self.user_id\n new_request_form.request.setFocus()\n new_request_form.exec_()","title":""},{"docid":"a4e7bea2a630bd478d6a762a41642f75","score":"0.5627527","text":"def questionnaire(request):\n if request.method == \"POST\":\n # If is valid\n if not request.user.is_superuser:\n set_user_step(request.user, step=3, phase=1)\n return http.HttpResponseRedirect('/phase1/step3/')\n\n return http.HttpResponseBadRequest(\"Only accesible with POST\")","title":""},{"docid":"5fe75ff2da849df7fa2224978dcd31e4","score":"0.56189126","text":"def post(self, request, *args, **kwargs):\n c = self.getContext(request)\n validate = validate_help_request(request.POST)\n if validate is not None:\n c.update(validate)\n c.update(csrf(request))\n return render_to_response('request_help.html', c)\n\n fc = request.POST['for_class']\n desc = request.POST['description']\n d = request.POST['day']\n t = request.POST['time']\n users_requests = Request.objects.filter(user=c['user'].username)\n valid = True\n \"\"\"\n If the user has ten requests, then they are not able to create a new one.\n If the user has already requested help for a specific class then they are not able to request help for that class again\n \"\"\"\n if len(users_requests) < 11:\n for req in users_requests:\n if req.for_class == fc:\n c.update({'request_exists_error': 'A help request for this class already exists'})\n valid = False\n else:\n valid = True\n elif len(users_requests) > 10:\n c.update({'too_many_error': 'You have too many open requests'})\n valid = False\n\n if valid:\n helprequest = Request(user=c['user'].username, first_name=c['user'].first_name, last_name=c['user'].last_name, for_class=fc, description=desc, days=d, time=t)\n \"\"\"\n if a tutor is being specifically requested then they will recieve an email informing them of the request\n \"\"\"\n if 'specific_request' in request.POST:\n helprequest.requested = request.POST['requested']\n helprequest.save()\n target = Tutee.objects.get(user__username=request.POST['requested']).user\n emailer = emails()\n message = 'You have been requested as a tutor by ' +\\\n c['user'].first_name + ' ' + c['user'].last_name +\\\n '. Log in to RPI Tutor Time to view their request.'\n emailer.send_email(target, message, 'Tutor Request')\n else:\n helprequest.save()\n c.update({\n 'error': False,\n 'message': 'Request successfully submitted.'\n })\n return render_to_response('display_message.html', c)\n\n return render_to_response('request_help.html', c)","title":""},{"docid":"69c59ca5c155c48f96160ae758760e06","score":"0.56134325","text":"def login_required(view):\n @functools.wraps(view)\n def wrapped_view(**kwags):\n if g.user is None:\n # import pdb; pdb.set_trace()\n abort(404)\n return view(**kwags)\n return wrapped_view","title":""},{"docid":"057f2be48aed0d8c3615532e742a7833","score":"0.5605659","text":"def get(self, request):\n today = datetime.date.today()\n random_string = hashlib.md5(bytes(str(random.random()).encode('utf-8'))).hexdigest()\n user_id = today.strftime('lmdemo-%y%m%d-')\n user_id += random_string[:3]\n\n initial_form = {\n 'lm_url': '/',\n 'first_name': 'Test',\n 'last_name': 'Example',\n 'user_id': user_id,\n 'email': '{}@example.com'.format(user_id),\n 'postal_code': '63303',\n 'endpoint': loginbypass.PROD_ENDPOINT,\n 'key': settings.LOGIN_BYPASS_KEY,\n }\n form = forms.LoginBypassForm(initial=initial_form)\n context = {'form': form}\n return render(request, self.template_name, context)","title":""},{"docid":"1d63e3ddd34926b93d7150372fb06513","score":"0.5597213","text":"def create_view(self):\n if flask.request.method == \"POST\":\n try:\n self.start_task()\n flash(\"The task was successfully started.\", \"success\")\n except Exception as e:\n current_app.logger.exception(e)\n flash(\"An error occurred while starting the task.\", \"error\")\n return redirect(request.path) # Redirect after POST\n\n return_url = get_redirect_target() or self.get_url(\".index_view\")\n\n return self.render(\n \"cds_ils_admin/create_task.html\", return_url=return_url\n )","title":""},{"docid":"cadb06ee4bbee3c5c67cde8a06320fd1","score":"0.558085","text":"def show_create_page():\n\n return render_template('user_form.html')","title":""},{"docid":"52df3e9d53b6a9222a7ca6b5829a4155","score":"0.5577324","text":"def plans(request):\n if request.user.is_authenticated:\n if request.method == \"POST\":\n try:\n start_date = request.POST['start_date']\n start_time = request.POST['start_time']\n end_date = request.POST['end_date']\n end_time = request.POST['end_time']\n title = request.POST['title']\n start_datetime = pytz.utc.localize(datetime.combine(\n datetime.strptime(start_date, \"%Y-%m-%d\"),\n datetime.strptime(start_time, \"%H:%M\").time()))\n end_datetime = pytz.utc.localize(datetime.combine(\n datetime.strptime(end_date, \"%Y-%m-%d\"),\n datetime.strptime(end_time, \"%H:%M\").time()))\n\n Plan.objects.create(\n user=request.user,\n start_time=start_datetime,\n end_time=end_datetime,\n title=title)\n message = {\n 'mtype': \"success\",\n 'text': \"Pomyślnie dodano do bazy danych\",\n 'bold': \"Sukces!\"\n }\n\n return render(request, 'polls/add_plans.html', {'message': message})\n except Exception as ex:\n message = {\n 'mtype': \"danger\",\n 'text': \"Nie udało się dodać planów do bazy danych\",\n 'bold': \"Błąd!\"\n }\n print(ex)\n return render(request, 'polls/add_plans.html', {'message': message})\n return render(request, 'polls/add_plans.html')\n return render(request,\n 'polls/user_profile.html',\n {'error_message': \"Musisz być zalogowany!\"})","title":""},{"docid":"152da2b87c003c66224a55e443d82f1d","score":"0.5573352","text":"def user(request):\n logging_user = LoginUser.get_login_user()\n if not logging_user.is_view_right():\n return\n auth_type = None\n type_form = GacoiForm(\"typeForm\", \"/user/\", \"POST\")\n\n keu_form = LagForm()\n keu_form.set_title(\"User List\")\n type_form.set_view(\"id,user_name,login_name,address,phone,email,course,gender,roles,teacher_id\")\n type_form.set_key(\"id\")\n\n if logging_user.is_add_right():\n type_form.set_insert(\"user_name,login_name,address, phone,course,email,gender,roles,teacher_id\")\n if logging_user.is_update_right():\n type_form.set_update(\"user_name,login_name,address, phone,course,email,gender,roles,teacher_id\")\n if logging_user.is_delete_right():\n type_form.set_option_deletable(True)\n\n type_form.set_type('password', GacoiFormFieldType.Password)\n type_form.set_required('user_name,login_name')\n type_form.set_type('updated_datetime', GacoiFormFieldType.DateTime)\n type_form.set_type('created_datetime', GacoiFormFieldType.DateTime)\n\n dept_id = AuthMetaTypeDefine.Department.code\n genders = UserLogic.get_genders_dict()\n teachers = UserLogic.get_teachers_dict()\n roles = UserLogic.get_roles_dict()\n type_form.get_field(\"gender\").set_drop_down_list_values(genders)\n type_form.get_field(\"teacher_id\").set_drop_down_list_values(teachers)\n type_form.get_field(\"roles\").set_drop_down_list_values(roles)\n type_form.get_field(\"course\").set_multi_choices(\"select id,name from course id\",\n \"select c.id from course c inner join base_user_course u \"\n \"on u.course_id = c.id where u.user_id = [id]\", 1)\n\n type_form.set_search('user_name,login_name, address,gender,roles,teacher_id')\n type_form.set_order('id,user_name,login_name,address,teacher_id,roles')\n type_form.init(request)\n if logging_user.is_update_right():\n type_form.add_inline_user_button(InlineUserButton(\"Change password\", \"change_password\",\n action=\"do_change_password([id])\"))\n\n reset_id = None\n reset_password = False\n\n # Check request\n # if typeForm_action = reset_password -> handle request password\n params = request.POST\n\n if params.get('typeForm_action') == 'reset_password':\n reset_id = params.get('reset_id')\n # transaction.set_autocommit(False)\n try:\n user_changed = User.objects.get(id=reset_id)\n user_changed.password = UserLogic.hash_password(params.get('new_password', None))\n user_changed.save()\n reset_password = False\n except ObjectDoesNotExist:\n # transaction.rollback()\n print(\"aaa\")\n\n # transaction.commit()\n\n # Check type_form.get_inline_user_button_return_value() return change_password button to open modal\n if logging_user.is_update_right():\n if \"change_password\" == type_form.get_inline_user_button_return_value():\n reset_id = type_form.get_key_value(\"id\")\n reset_password = True\n\n # call to edit in form view, if not: edit in grid view\n # type_form.set_option_update_in_form_view(True)\n\n if type_form.is_action(GacoiFormAction.DeleteDone):\n if logging_user.is_delete_right():\n try:\n user_deleted = User.objects.get(pk=type_form.get_key_value(\"id\"))\n user_deleted.delete()\n LogOperation.log(LogModule.User, LogType.Delete, LogResult.Success, type_form.get_key_value(\"id\"))\n except ObjectDoesNotExist:\n LogOperation.log(LogModule.User, LogType.Delete, LogResult.Fail, type_form.get_key_value(\"id\"))\n insert = False\n update = False\n if type_form.is_action(GacoiFormAction.InsertStart):\n keu_form.set_title(\"Create New User\")\n insert = True\n if type_form.is_action(GacoiFormAction.UpdateStart):\n update = True\n # Insert\n if type_form.is_action(GacoiFormAction.InsertDone):\n try:\n User.objects.get(login_name=type_form.get_field('login_name').get_value_blank2none())\n error_message = \"The login user is exist!\"\n type_form.set_action(GacoiFormAction.InsertStart)\n type_form.set_error_message(error_message)\n LogOperation.log(LogModule.User, LogType.Insert, LogResult.Fail)\n except ObjectDoesNotExist:\n # transaction.set_autocommit(False)\n try:\n role = type_form.get_field('roles').get_value_blank2none()\n teacher_id = type_form.get_field('teacher_id').get_value_blank2none()\n user_courses = type_form.get_field('course').get_multi_choices_selected_values()\n if int(role) != UserRoles.Student.code:\n teacher_id = None\n user_new = UserLogic.create_user(type_form.get_field('user_name').get_value_blank2none(),\n type_form.get_field('login_name').get_value_blank2none(),\n type_form.get_field('gender').get_value_blank2none(),\n role,\n type_form.get_field('phone').get_value_blank2none(),\n type_form.get_field('email').get_value_blank2none(),\n type_form.get_field('address').get_value_blank2none(),\n teacher_id,\n password=type_form.get_field('password').get_value_blank2none())\n UserLogic.store_user_courses(user_new.id, user_courses)\n type_form.set_action(1000)\n\n except ObjectDoesNotExist as e:\n\n # transaction.rollback()\n LogOperation.log(LogModule.User, LogType.Insert, LogResult.Fail, None, e)\n\n # transaction.commit()\n insert = False\n return HttpResponseRedirect(\"/user\")\n\n if type_form.is_action(GacoiFormAction.UpdateDone):\n today = datetime.datetime.today()\n # transaction.set_autocommit(False)\n try:\n user_updated = User.objects.get(id=type_form.get_key_value('id'))\n print(user_updated.login_name)\n new_user_name = type_form.get_field('user_name').get_value_blank2none()\n new_login_name = type_form.get_field('login_name').get_value_blank2none()\n # if user_updated.user_name != new_user_name or user_updated.login_name != new_login_name:\n user_updated.user_name = new_user_name\n user_updated.login_name = new_login_name\n user_updated.updated_datetime = today\n role = type_form.get_field('roles').get_value_blank2none()\n teacher_id = type_form.get_field('teacher_id').get_value_blank2none()\n user_courses = type_form.get_field('course').get_multi_choices_selected_values()\n if int(role) != UserRoles.Student.code:\n teacher_id = None\n user_updated.gender = type_form.get_field('gender').get_value_blank2none()\n user_updated.roles = role\n user_updated.phone = type_form.get_field('phone').get_value_blank2none()\n user_updated.email = type_form.get_field('email').get_value_blank2none()\n user_updated.address = type_form.get_field('address').get_value_blank2none()\n user_updated.teacher_id = teacher_id\n\n type_form.get_field('roles').get_value_blank2none()\n user_updated.save()\n UserLogic.update_user_courses(user_updated.id, user_courses)\n\n LogOperation.log(LogModule.User, LogType.Update, LogResult.Success, type_form.get_key_value('id'))\n except ObjectDoesNotExist:\n # transaction.rollback()\n LogOperation.log(LogModule.User, LogType.Update, LogResult.Fail)\n # transaction.commit()\n update = False\n type_form.set_paging_value(10)\n\n # data = User.objects.all().values_list('id',flat=False)\n data = User.objects.all()\n # Order\n if type_form.order_field:\n if type_form.order_type == 'desc':\n data = data.order_by(\"-\" + type_form.order_field)\n else:\n data = data.order_by(type_form.order_field)\n\n # Search user_name,login_name\n search = type_form.get_field(\"user_name\").get_search_value()\n if search is not None and search != '':\n data = data.filter(user_name__contains=search)\n search = type_form.get_field(\"login_name\").get_search_value()\n if search is not None and search != '':\n data = data.filter(login_name__contains=search)\n search = type_form.get_field(\"roles\").get_search_value()\n if search:\n l = list(User.objects.filter(roles__in=search).values_list('id', flat=True))\n data = data.filter(id__in=l)\n search = type_form.get_field(\"gender\").get_search_value()\n if search:\n l = list(User.objects.filter(gender__in=search).values_list('id', flat=True))\n data = data.filter(id__in=l)\n\n type_form.set_form_data(data)\n # type_form.set_form_model(User)\n type_form.set_caption([\"id,user_name,login_name,address,phone,email,gender,roles, teacher_id, course\",\n \"ID,Username,Login name,Address, Phone, Email, Gender,Roles, Teacher, Course\"])\n info_form = None\n context = { # \"select id,name,email,password from user\"\n 'authType': auth_type,\n 'user': logging_user,\n 'infoForm': info_form,\n 'typeForm': type_form,\n 'keuForm': keu_form,\n 'screen_name': ScreenName.User,\n 'reset_password': reset_password,\n 'update': update,\n 'insert': insert,\n 'reset_id': reset_id,\n }\n\n return render(request, 'user.html', context)","title":""},{"docid":"013138d001bdc09426a8a84089b87ee5","score":"0.5567037","text":"def test_create_regular(self):\n self.client.force_authenticate(user=self.regular_user)\n response = self.client.post(_get_list_url(1), submission_data(self.regular_user))\n self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN)","title":""},{"docid":"b1afd3575e3c44f26bb4c57a869500de","score":"0.5560713","text":"def tickets_view_one(request, pk):\n # get ticket details\n ticket = get_object_or_404(Ticket, pk=pk)\n # increase number of views by 1\n ticket.views += 1\n ticket.save()\n # filter comments on specific ticket\n comments = Comment.objects.filter(ticket_id=ticket.pk)\n # filter upvotes on specific ticket by user ID\n upvotes = Upvote.objects.filter(ticket_id=ticket.pk).values(\"user_id\")\n voters = [vote[\"user_id\"] for vote in upvotes]\n donors = User.objects.filter(id__in=voters)\n # render drop-down list for superusers to update status\n ticket_status_list = TicketStatus.objects.all()\n tkt_status = ticket.ticket_status.id\n # POST methods\n if request.method == \"POST\":\n comment_form = CommentForm(request.POST)\n if comment_form.is_valid():\n comment_form.instance.commenter = request.user\n comment_form.instance.ticket = ticket\n comment_form.save()\n # remove two ticket views to avoid duplicates on POST\n ticket.views -= 2\n ticket.save()\n messages.success(\n request, f\"Comment successfully added!\")\n return redirect(tickets_view_one, ticket.pk)\n else:\n comment_form = CommentForm()\n donation_form = DonationForm()\n context = {\n \"comment_form\": comment_form,\n \"comments\": comments,\n \"donation_form\": donation_form,\n \"donors\": donors,\n \"publishable\": settings.STRIPE_PUBLISHABLE,\n \"ticket\": ticket,\n \"ticket_status_list\": ticket_status_list,\n \"tkt_status\": tkt_status,\n \"voters\": voters,\n }\n return render(request, \"tickets_view_one.html\", context)","title":""},{"docid":"a5e87b6147606310816795316cd2cd9f","score":"0.5551106","text":"def post(self, request, *args, **kwargs):\n poll = self.get_object()\n\n if request.user.id != poll.user.id:\n raise PermissionDenied(\"You can not create choice for this poll.\")\n\n return super().post(request, *args, **kwargs) # pylint: disable=no-member","title":""},{"docid":"9ae82132ff4ca7b1410c3fd77247c009","score":"0.55449873","text":"def trip_creation_handler1(request):\n global locations\n if(\"add_checkpoint\" in request.POST):\n pprint(request.POST)\n d = {}\n d[\"location\"] = request.POST.get(\"location\")\n locations.append(d)\n return redirect(\"/user_trips/\")","title":""},{"docid":"286e0637132874f32ec62bbdbec0d5d3","score":"0.5543584","text":"def get(self, request, user_id):\n user = User.objects.get(pk=request.user.id)\n volunteer_user = User.objects.get(pk=user_id)\n if user.id == volunteer_user.id:\n volunteer = get_object_or_404(Volunteer_User_Add_Ons, user=user_id)\n infoForm = volunteerSignupForm(instance=volunteer)\n\n return render(\n request,\n self.url_volunteer_edit,\n {\n 'infoForm': infoForm,\n 'user_id': volunteer.user.id,\n 'volunteer': volunteer\n }\n )\n else:\n return redirect(self.login_url)","title":""},{"docid":"4abd47fd823fb7c057a2cb8156f64866","score":"0.5537861","text":"def hoteliers_only(view):\n def new_view(request, *args, **kwargs):\n response = view(request, *args, **kwargs)\n id = request.user.id\n my_user = Hotelier.objects.get(pk=id)\n if my_user != None:\n return response\n # TODO: Add your own forbidden view\n return render(request, 'thanks.html', {'message': 'شما مجاز به انجام این تغییرات نیستید.', 'redir' : '/simorgh/home/'})\n\n return new_view","title":""},{"docid":"e3f21d26efe8164e5acee4c06a619dca","score":"0.5534656","text":"def request_access(request):\n context = {}\n if request.method == 'POST':\n context.update(request.POST)\n context['name'] = request.POST.get('name')\n context['email_address'] = request.POST.get('email_address')\n context['username'] = request.POST.get('username')\n context['affiliation'] = request.POST.get('affiliation')\n context['reason'] = request.POST.get('reason')\n\n if not request.POST.get('name'):\n context['error'] = 'name is required'\n elif not request.POST.get('username'):\n context['error'] = 'username is required'\n elif not request.POST.get('email_address'):\n context['error'] = 'email address is required.'\n elif not request.POST.get('reason'):\n context['error'] = 'A reason is required'\n if not context.get('error'):\n msg = \"\"\"User Requesting Access to Reactors Service\\n\n Name:{}\n username: {}\n email address: {}\n affiliation: {}\n reason: {}\"\"\".format(context['name'],\n context['username'],\n context['email_address'],\n context.get('affiliation'),\n context['reason'])\n # write the request to a local file as backup:\n with open(os.path.join(settings.REACTORS_REQUESTS_DIR, context['username']), 'w+') as f:\n f.write(msg)\n # attempt to notify\n try:\n send_mail(msg,\n \"do-not-reply@reactors.tacc.cloud\",\n settings.REACTORS_EMAIL_LIST)\n except Exception as e:\n print(\"Error sending email for context: {}\".format(context))\n context['success'] = \"You request has been submitted! You will hear back from TACC stuff soon.\"\n return render(request, 'abaco/request_access.html', context, content_type='text/html')","title":""},{"docid":"751d5244512674429a573251c4bb4d4d","score":"0.5526437","text":"def onflightfr(request):\n \n # Authentication check\n if not request.user.is_authenticated():\n # TODO redirect to error page\n return redirect(reverse('index'))\n\n user = request.user\n\n sia_user = get_object_or_404(SIAUser, user=user)\n return render(request, 'users/on-flight-fr.html', {'SiaUser':sia_user})","title":""},{"docid":"2aa10841b1582990c7f7036e79c87d8a","score":"0.55042714","text":"def customer_login_required(view):\n\n @functools.wraps(view)\n def wrapped_view(**kwargs):\n if session.get('user_type') != 'customer':\n return jsonify({'status': 'failed',\n 'msg': 'Operation not permitted.'})\n return view(**kwargs)\n\n return wrapped_view","title":""},{"docid":"241dca1a0c3ece54e6e94a41fe9ef234","score":"0.55041355","text":"def show_pickup_analyzer():\n #Check that user is logged in\n if 'user' in session:\n user = User.query.get(session['user']['username'])\n teams = user.teams\n return render_template('pickup-analyzer.html', user=user, teams=teams, cats=categories)\n else:\n flash('Please log in first.')\n return redirect(url_for('show_login_form'))","title":""},{"docid":"f9c6b1d3a9aa02ffb1cb6cf707e0d057","score":"0.54990375","text":"def login_required(view):\n @ wraps(view)\n def wrapped_view(**kwargs):\n db = get_db()\n if g.user is None:\n\n return redirect(url_for(\"login\", next=request.url))\n if db.execute(\"\"\"SELECT isBanned FROM users WHERE user_id = ? \"\"\", (g.user,)).fetchone()[\"isBanned\"] == 1:\n return redirect(url_for(\"login\"))\n return view(**kwargs)\n return wrapped_view","title":""},{"docid":"70e2f2a4887e3c643a932dec058adc16","score":"0.54793507","text":"def residence_add(request):\n if request.method == 'POST':\n form = ResidenceForm(request.POST)\n if form.is_valid():\n home = form.save(commit=False)\n home.user = request.user # The logged user\n home.save()\n return redirect('homepage')\n else:\n form = ResidenceForm()\n return render(request, 'myapp/residence/residence_add.html', {\n 'form': form,\n 'user': request.user\n })","title":""},{"docid":"4c5405c1259d0a6135f7447b2455f7ed","score":"0.54747885","text":"def add_patient(request):\n form = PatientForm()\n print('Getting the form')\n if request.method==\"POST\":\n form = PatientForm(request.POST)\n rai_list = ['age',\n 'snf',\n 'nephrologist',\n 'chf',\n 'sob',\n 'cancer',\n 'weight_loss',\n 'appetite',\n 'memory',\n 'mobility',\n 'eating',\n 'toileting',\n 'hygiene']\n features = [int(form.data[r]) for r in rai_list]\n \n\n \n rai = utils.get_rai(form)\n\n\n if form.is_valid():\n data = form.save(commit=False)\n data.created_by = request.user\n data.save()\n \n print(request.user)\n return render (request, \n 'show_rai.html', \n context = {'rai':rai,\n 'Age':form.data['age'],\n 'FirstName': form.data['first_name'],\n 'LastName': form.data['last_name'],\n 'MiddleInit': form.data['middle_initial'],\n 'SSN':form.data['SSN']})\n\n form = PatientForm()\n else:\t\t\n form=PatientForm\n return render(request, 'add_patient.html',{'form':form})","title":""},{"docid":"4d9a2e8895051d3bbaa68325ceb9c8cf","score":"0.5472861","text":"def journey_creation_handler2(request):\n # function to handle the creation of new jounrey\n global checkpoints\n # form = JourneyCreationForm1(request.POST)\n # if form.is_valid():\n pprint(checkpoints)\n pprint(request.POST)\n journey_name = request.POST.get(\"journey_name\")\n journey_date = request.POST.get(\"travel_date\")\n cotravel_number = request.POST.get(\"cotravel_number\")\n jrny = Journey(journey_id=journey_name, start_time=parser.parse(journey_date),\n source=checkpoints[0][\"checkpointA\"], destination=checkpoints[-1][\"checkpointB\"],\n cotravel_number=cotravel_number)\n jrny.save()\n # save the journey and the sole participant of the journey is the request user\n jrny.participants.add(request.user)\n\n # for each checkpoint create a new journe point with foreignkey to location and the journey objects\n for i, x in enumerate(checkpoints):\n loc = LocationPoint.objects.get(\n location_name=x[\"checkpointA\"], user=request.user, location_type=\"Journey Point\")\n JourneyPoint.objects.create(\n location=loc, transport=x[\"means\"], point_id=i, journey=jrny)\n if(len(checkpoints) != 0):\n loc = LocationPoint.objects.get(\n location_name=checkpoints[-1][\"checkpointB\"], user=request.user, location_type=\"Journey Point\")\n JourneyPoint.objects.create(\n location=loc, transport=x[\"means\"], point_id=len(checkpoints), journey=jrny)\n checkpoints = []\n return redirect(\"/user_journeys/\")","title":""},{"docid":"85f036be1088d98ef307d2da6c9b03aa","score":"0.5469385","text":"def create(self, request, *args, **kwargs):\n if not request.user.is_superuser:\n raise exceptions.NotFound\n\n return super().create(request, *args, **kwargs)","title":""},{"docid":"bfc6cd42bd424dcadb879965772268b3","score":"0.5464898","text":"def instructor(request):\n\n # get all lesson sets, display\n if request.method == 'POST':\n # Will this include where the data is updated? Such as selecting different visuals to interact with?\n if user_auth_inst(request):\n # Take instructor to data view\n if lesson_set_auth(request):\n return redirect(\"/tutor/tutor\")\n else:\n return redirect(\"accounts:profile\")\n else:\n return redirect(\"/accounts/settings\")\n else:\n\n return render(request, \"data_analysis/visual.html\")","title":""},{"docid":"d5ac9ebd0bd2f55f99ed73f7079bc737","score":"0.54510874","text":"def post(self, request, *args, **kwargs):\n c = self.getContext(request)\n is_tutor = False\n if tutor(c['user'].username):\n is_tutor = True\n c.update({'tutor': is_tutor})\n msg = 'Good news, your request has been accepted by ' +\\\n c['user'].first_name + \" \" + c['user'].last_name +\\\n \". They should be contacting you directly shortly\"\n request_user_and_id = request.POST['choice'].split('?^?')\n requests = Request.objects.all()\n for req in requests:\n if req.user == request_user_and_id[0]:\n if req.id == int(request_user_and_id[1]):\n req.accepted_by = c['user'].username\n req.save()\n usr = Tutee.objects.get(user__username=req.user).user\n c.update({'target': req.user})\n #Email the student\n emailer = emails()\n emailer.send_email(usr, msg, \"Good News\")\n \n c.update(csrf(request))\n return render_to_response('email_tutee.html', c)","title":""},{"docid":"51010e31b102db3425a4dfa28e91e371","score":"0.5438009","text":"def trip_request_resolve_handler(request):\n req_lis = Notification.objects.filter(\n user_to=request.user, notif_type=\"Trip Related\", resolved=\"No\")\n print(request.POST)\n index = 0\n typ = 0\n for i in range(len(req_lis)):\n if(\"accept\"+str(i) in request.POST):\n index = i\n typ = 1\n elif(\"reject\"+str(i) in request.POST):\n index = i\n typ = 0\n print(index, typ)\n req = req_lis[index]\n if(typ == 0):\n # typ 0 means the request is rejected.\n # create a new notification meant for all the partiipants and the request user\n req.resolved = \"Request rejected by \"+request.user.username\n trp_id = req.travel_id\n print(trp_id)\n trp = Trip.objects.get(participants__in=[request.user], trip_id=trp_id)\n\n desc = request.user.username+\" rejected the request by \"+req.user_from.username\n for par in trp.participants.all():\n Notification.objects.create(user_to=par, user_from=request.user, title=\"Trip add request rejected\", description=desc,\n notif_type=\"Trip Related\", creation_time=datetime.datetime.now()+datetime.timedelta(hours=5.5), resolved=\"Yes\")\n Notification.objects.create(user_to=req.user_from, user_from=request.user, title=\"Journey add request rejected\", description=desc,\n notif_type=\"Journey Related\", creation_time=datetime.datetime.now()+datetime.timedelta(hours=5.5),\n resolved=\"Yes\")\n if(typ == 1):\n # typ 1 means the request is rejected.\n # create a new notification meant for all the partiipants and the request user\n req.resolved = \"Request accepted by \"+request.user.username\n print(req.description.split())\n trp_id = req.travel_id\n print(trp_id)\n trp = Trip.objects.get(participants__in=[request.user], trip_id=trp_id)\n\n trp.participants.add(req.user_from)\n desc = request.user.username+\" accepted the request by \"+req.user_from.username\n for par in jrny.participants.all():\n Notification.objects.create(user_to=par, user_from=request.user, title=\"Trip add request accepted\", description=desc,\n notif_type=\"Journey Related\", creation_time=datetime.datetime.now()+datetime.timedelta(hours=5.5),\n resolved=\"Yes\")\n req.save()\n return redirect(\"/user_trips/\")","title":""},{"docid":"a073faf23c82ae9426ad206fff18dd59","score":"0.54373556","text":"def dispatch_request(self, **kwargs):\n not_authorized = self.pre_tests(**kwargs)\n\n if not_authorized:\n return not_authorized\n\n to_redirect = 0\n\n self.set_choices(**kwargs)\n\n if helpers.really_submitted(self.create_form):\n self.handle_create(**kwargs)\n\n elif helpers.really_submitted(self.process_form):\n to_redirect = self.handle_process(**kwargs)\n\n #TODO: maybe not the cleanest way to do it!!\n if to_redirect == 0 or to_redirect is None:\n self.reset_fields()\n\n return render_template(self.template,\n create_form=self.create_form,\n process_form=self.process_form,\n **self.template_kwargs)\n else:\n return redirect(to_redirect)","title":""},{"docid":"dec6d521a9717dc4453718b7247a6a20","score":"0.5433963","text":"def dispatch_assignent_post_request(request):\n\n return create_new_homework(request)","title":""},{"docid":"2c047b4d0c5deabb3530c48ab646fab1","score":"0.5433689","text":"def show_create_user_form():\n\n return render_template(\"create_user_form.html\")","title":""},{"docid":"de2503a629885b0d4219f03c6453b269","score":"0.5425061","text":"def add_view(self, request, form_url='', extra_context=None):\n data = request.GET.copy()\n\n origin_types = TblOriginType.objects.all()\n if len(origin_types) > 0:\n data['origin_type'] = origin_types[len(origin_types)-1]\n\n environmental_ids = TblEnvironmental.objects.all()\n if len(environmental_ids) > 0:\n data['environmentalid'] = environmental_ids[len(environmental_ids)-1]\n \n taxonomy_ids = TblTaxonomy.objects.all()\n if len(taxonomy_ids) > 0:\n data['taxonomyid'] = taxonomy_ids[len(taxonomy_ids)-1]\n\n request.GET = data\n return super().add_view(request, form_url=form_url, extra_context=extra_context)","title":""},{"docid":"a5414096496e75485277b253ac942a64","score":"0.54199666","text":"def test_options_user(self):\n request = self.factory.get(reverse('planet:options'))\n request.user = self.user\n response = OptionsView.as_view()(request)\n self.assertEqual(response.status_code, 200)","title":""},{"docid":"b5fc56e9aaf0f28128d8a20085f704a1","score":"0.5411256","text":"def show_add_user_form():\n\n return render_template (\"new_user.html\")","title":""},{"docid":"9f0b384f4bde6e16666a43f1e90709e5","score":"0.5405932","text":"def candidate_register(request):\n if request.user.is_authenticated:\n return redirect(\"index\")\n else:\n if request.method == 'POST':\n logic.candidate_register_logic(request)\n return redirect(\"index\")\n else:\n skills = Skill.objects.all()\n return render(request,'candidate/login_signup.html',{\"skills\":skills})","title":""},{"docid":"acf6dbc843d171c5c468b2b24ddd9eaa","score":"0.5396369","text":"def agent_login_required(view):\n\n @functools.wraps(view)\n def wrapped_view(**kwargs):\n if session.get('user_type') != 'agent':\n return jsonify({'status': 'failed',\n 'msg': 'Operation not permitted.'})\n return view(**kwargs)\n\n return wrapped_view","title":""},{"docid":"fa934ebaaba98a05256eb6b23dcbe6a5","score":"0.5386511","text":"def sign_up_view(request):\n if request.method == 'POST':\n form = ProjectPlannerUserCreationForm(request.POST)\n if form.is_valid():\n form.save()\n # log in the user\n accounts_user = form.save()\n login(request, accounts_user)\n\n # Message\n messages.success(request, 'Welcome!')\n\n return redirect('project:index')\n else:\n # Message\n messages.warning(request, 'Some fields are not correct!')\n else:\n form = ProjectPlannerUserCreationForm()\n\n context = {\n 'form': form\n }\n\n return render(request, 'accounts/signUp.html', context)","title":""},{"docid":"fa9ab1d77fda619867dbcfaa5705c298","score":"0.5375764","text":"def add(request):\n\n if datetime.now() > datetime.strptime(settings.DEADLINE_SUBMISSION, '%Y-%m-%d %H:%M'):\n return redirect('/')\n else:\n # save form\n if request.method == 'POST':\n new_entry = Entry()\n entryform = EntryForm(request.POST, request.FILES, instance=new_entry)\n if entryform.is_valid():\n if request.user.is_authenticated():\n new_entry.user = request.user\n entryform.save()\n vis_thumbnail(new_entry.screenshot)\n return render_to_response('submission/thankyou.html', locals(), context_instance=RequestContext(request))\n # show empty form\n else:\n user = request.user\n entryform = EntryForm()\n return render_to_response('submission/add.html', locals(), context_instance=RequestContext(request))","title":""},{"docid":"9780a2142149d141bbff62048bd05538","score":"0.53741014","text":"def show_add_form():\n return render_template('newuser.html')","title":""},{"docid":"66148209a2aa0aee601c0fd10bcf05cb","score":"0.5363834","text":"def staff_login_required(view):\n\n @functools.wraps(view)\n def wrapped_view(**kwargs):\n print(session)\n if session.get('user_type') != 'staff':\n return jsonify({'status': 'failed',\n 'msg': 'Operation not permitted.'})\n return view(**kwargs)\n\n return wrapped_view","title":""},{"docid":"a93e38e2a807db60aca04bcdc89bced9","score":"0.5361074","text":"def set_tasks(request):\n user = request.user\n form = CreateTaskForm(user=request.user)\n\n # Users can only set tasks for tables they are part of\n tables = CafeTable.objects.filter(\n university=user.university,\n table_id__in=user.cafe_table_ids.values_list('table_id', flat=True))\n form.fields['table_id'].queryset = tables\n\n # reset number of tasks set today if necessary\n if user.tasks_set_today >= 2 and \\\n user.next_possible_set == datetime.date.today():\n user.tasks_set_today = 0\n user.save()\n\n # students can set max 2 tasks per day (to avoid spamming)\n if user.tasks_set_today >= 2 and not user.is_staff:\n return redirect(\"dashboard\")\n\n context = {'form': form, 'num_users': get_number_current_users()}\n\n if request.method == 'POST':\n # create task with provided data\n form = CreateTaskForm(request.POST, user=request.user)\n if form.is_valid():\n task_name = form.cleaned_data.get('task_name')\n table_id = form.cleaned_data.get('table_id')\n task_content = form.cleaned_data.get('task_content')\n points = form.cleaned_data.get('points')\n recurrence_interval = form.cleaned_data.get('recurrence_interval')\n max_repeats = form.cleaned_data.get('max_repeats')\n Task.objects.create( # removed task =\n task_name=task_name,\n created_by=user,\n table_id=table_id,\n task_content=task_content,\n points=points,\n recurrence_interval=recurrence_interval,\n max_repeats=max_repeats\n )\n user.tasks_set_today += 1\n user.save()\n\n # Add notification\n task_text = user.first_name + user.last_name + \" has added '\" + \\\n task_name + \"' as a new task - go check it out!\"\n notification = Notification(table_id=table_id, notification_type=3,\n text_preview=task_text)\n notification.save()\n\n # if student, redirect if can't set more tasks now\n if user.tasks_set_today >= 2 and not user.is_staff:\n user.next_possible_set = datetime.date.today() + \\\n datetime.timedelta(days=1)\n user.save()\n return redirect(\"dashboard\")\n\n else:\n context[\"CreateTaskForm\"] = form\n else:\n context[\"CreateTaskForm\"] = form\n\n # by default, tasks are not recurring\n form.fields['recurrence_interval'].initial = \"n\"\n return render(request, 'set_tasks.html', context)","title":""},{"docid":"cbb92d218481c2941900bb68bb87d8e1","score":"0.53457695","text":"def tasks():\n # if user reached route via GET (as by clicking a link or via redirect)\n if request.method == \"GET\":\n # get tasks of current user and pass to template\n cur_user_tasks = select_task(session[\"user_id\"])\n return render_template(\"tasks.html\", tasks = cur_user_tasks)\n \n # else if user reached route via POST (as by submitting a form via POST)\n elif request.method == \"POST\":\n # delete completed tasks\n delete_task(session[\"user_id\"])\n return redirect(url_for(\"tasks\"))","title":""},{"docid":"e0698c9b2edb9940969beaf0ccdf97dd","score":"0.53387177","text":"def add_operator(request, t):\n try:\n messages = None\n admin = get_operator_by_username(request.session['username'])\n params = {\n 'rao': get_attributes_RAO(),\n 'is_admin': is_admin(request.session['username']),\n 'active_operator': admin\n }\n form = ()\n if request.method == 'POST':\n if 'add_operator' not in request.POST:\n form = NewOperatorForm(request.POST)\n else:\n form = NewOperatorPinForm(request.POST)\n\n if form.is_valid():\n\n if 'add_operator' not in request.POST:\n params['operator'] = True\n return render(request, settings.TEMPLATE_URL_AGENCY + 'add_operator.html',\n {'params': params, 'token': t, 'form': form})\n elif admin and not admin.signStatus:\n return HttpResponseRedirect(reverse('agency:logout_agency'))\n else:\n params['operator'] = False\n result, pin = create_operator(request.session['username'], request.POST)\n if result == StatusCode.OK.value:\n params = {\n 'username': request.session['username'],\n 'operator': request.POST.get('fiscalNumber').upper(),\n 'is_admin': is_admin(request.session['username']),\n 'timestamp': datetime.datetime.strftime(datetime.datetime.utcnow(), '%Y-%m-%d %H:%M')\n }\n request.session['pin'] = pin\n t = signing.dumps(params)\n LOG.info(\"admin: {}, operator: {} - Operatore creato con successo\".format(\n request.session['username'], request.POST.get('fiscalNumber').upper()),\n extra=set_client_ip(request))\n return HttpResponseRedirect(reverse('agency:list_operator', kwargs={'t': t, 'page': 1}))\n elif result == StatusCode.ERROR.value:\n messages = display_alert(AlertType.DANGER,\n \"Si è verificato un problema durante l'inserimento del nuovo operatore.\"\n \" Esiste già un operatore con questa email e/o codice fiscale.\")\n elif result == StatusCode.SIGN_NOT_AVAILABLE.value:\n messages = display_alert(AlertType.DANGER,\n \"Pin errato per la terza volta. Il tuo PIN non è più valido.\")\n elif result == StatusCode.UNAUTHORIZED.value:\n messages = display_alert(AlertType.DANGER, \"Il PIN inserito è errato.\")\n else:\n messages = display_alert(AlertType.DANGER,\n \"Si è verificato un problema durante l'inserimento del nuovo operatore.\"\n \" Invio della mail non riuscito.\")\n\n return render(request, settings.TEMPLATE_URL_AGENCY + 'add_operator.html',\n {'params': params, 'token': t, 'form': form, 'messages': messages})\n except Exception as e:\n LOG.error(\"Exception: {}\".format(str(e)), extra=set_client_ip(request))\n params = {\n 'rao': get_attributes_RAO(),\n 'is_admin': is_admin(request.session['username']),\n 'active_operator': get_operator_by_username(request.session['username'])\n }\n return render(request, settings.TEMPLATE_URL_AGENCY + 'error.html',\n {\"statusCode\": StatusCode.EXC.value, 'params': params,\n \"message\": \"Errore durante l'inserimento nuovo operatore\"})","title":""},{"docid":"fefb3802f746641f2e8cd19de6320459","score":"0.53360593","text":"def create(self, request, *args, **kwargs):\n response = self.lib.create_trip(request.user, request_to_dict(request))\n if (response['error']):\n return rc.BAD_REQUEST\n elif (response['trip']):\n return response['trip']","title":""},{"docid":"d3d55dc6fcde6af1f2f0fda9d6450d52","score":"0.5322102","text":"def login_not_required(view):\n setattr(view, LOGIN_NOT_REQUIRED_MARKER, True)\n return view","title":""},{"docid":"888c484e48be828ad1ae45563f319a06","score":"0.5321672","text":"def form_valid(self, form):\n form.instance.user = self.request.user\n form.instance.issue = bug_or_feature(self)\n return super(TicketCreateView, self).form_valid(form)","title":""},{"docid":"9525759e43f08c5e81f36bd18428090a","score":"0.53206843","text":"def checkAccess(self):\n self.mutator.taskFromKwargs(comments=True, work_submissions=True)\n self.data.is_visible = self.check.isTaskVisible()\n\n if task_logic.updateTaskStatus(self.data.task):\n # The task logic updated the status of the task since the deadline passed\n # and the GAE task was late to run. Reload the page.\n raise RedirectRequest('')\n\n if self.request.method == 'POST':\n # Access checks for the different forms on this page. Note that there\n # are no elif clauses because one could add multiple GET params :).\n self.check.isProfileActive()\n\n if 'reply' in self.data.GET:\n # checks for posting comments\n # valid tasks and profile are already checked.\n self.check.isBeforeAllWorkStopped()\n self.check.isCommentingAllowed()\n\n if 'submit_work' in self.data.GET:\n self.check.isBeforeAllWorkStopped()\n if not task_logic.canSubmitWork(self.data.task, self.data.profile):\n self.check.fail(DEF_NOT_ALLOWED_TO_UPLOAD_WORK)\n\n if 'button' in self.data.GET:\n # check for any of the buttons\n button_name = self._buttonName()\n\n buttons = {}\n TaskInformation(self.data).setButtonControls(buttons)\n if not buttons.get(button_name):\n self.check.fail(DEF_NOT_ALLOWED_TO_OPERATE_BUTTON % button_name)\n\n if 'send_for_review' in self.data.GET:\n self.check.isBeforeAllWorkStopped()\n if not task_logic.isOwnerOfTask(self.data.task, self.data.profile) or \\\n not self.data.work_submissions:\n self.check.fail(DEF_CANT_SEND_FOR_REVIEW)\n\n if 'delete_submission' in self.data.GET:\n self.check.isBeforeAllWorkStopped()\n id = self._submissionId()\n work = GCIWorkSubmission.get_by_id(id, parent=self.data.task)\n\n if not work:\n self.check.fail(DEF_NO_WORK_FOUND %id)\n\n time_expired = work.submitted_on - datetime.datetime.now()\n if work.user.key() != self.data.user.key() or \\\n time_expired > task_logic.DELETE_EXPIRATION:\n self.check.fail(DEF_NOT_ALLOWED_TO_DELETE)","title":""},{"docid":"f82efd90a272c120d130f50fa16fa763","score":"0.5315018","text":"def addCar(request):\n if 'user_type' in request.session.keys():\n if request.session.get('user_type') == 1:\n return render(request, 'addCar.html')\n else:\n return HttpResponseForbidden()\n else:\n return redirect('login')","title":""},{"docid":"03ab43ca5fe2a3b83290df5bbcee6e01","score":"0.53149116","text":"def canCreate(self, request):\n return False","title":""},{"docid":"4835ecd887134dc82ec4d1b8824ebd0b","score":"0.5306381","text":"def create_user(self, request, *args, **kwargs):\n return self.create(request, *args, **kwargs)","title":""},{"docid":"dabe37ee3cc2de0c3c86ee65e7d2db45","score":"0.53038955","text":"def create(self, request, *args, **kwargs):\n return super().create(request, *args, **kwargs)","title":""},{"docid":"c93313e21128ee4716fc07ae092bc38c","score":"0.5301878","text":"def allow(self, user, action):\n if action == 'view':\n return True\n else:\n return False","title":""},{"docid":"5579ebe20458e78b103955434e693cd9","score":"0.529623","text":"def isNotCompetentToAuthenticate(request):","title":""},{"docid":"751fd3d37ac51672d1be06352d806e40","score":"0.529372","text":"def new(request):\n\treturn render(request, 'semi_restful/add.html')","title":""},{"docid":"c3f8276ee454fc74888d031a81a8b90a","score":"0.5291223","text":"def post(self, request):\n context = {\n 'signed_in_volunteers': VolunteerLog.logged_in_volunteers_objects.all(),\n 'log_sign_in_form': LogSignInForm\n }\n if request.method == 'POST':\n log_sign_out_form = LogSignOutForm(request.POST)\n if log_sign_out_form.is_valid():\n log_sign_out_form.save()\n return super(VolunteerSignInView, self).form_valid(log_sign_out_form)\n\n if request.method == 'POST' and not log_sign_out_form.is_valid():\n log_sign_in_form = LogSignInForm(request.POST)\n if log_sign_in_form.is_valid():\n log_sign_in_form.save()\n return render(request, self.template_name, context)\n else:\n messages.error(request, \"Please correct the errors below and resubmit.\")\n context['log_sign_in_form'] = log_sign_in_form\n return render(request, self.template_name, context)","title":""},{"docid":"e3cf5086ff32730417e9d87c44acc7df","score":"0.5285659","text":"def topology_access_check(request, callee, action, **kwargs):\n\n def denied():\n \"\"\"Generate an error message and redirect if we try do something to a\n topology we're not allowed to\"\"\"\n messages.error(request, \"Either this topology doesn't exist or you don't \"\n \"have permission to %s it.\" % action)\n return HttpResponseRedirect('/login/')\n\n def denied_add():\n \"\"\"Generate an error message and redirect if we try to create a topology\n and are not allowed to\"\"\"\n messages.error(request, \"You don't have permission to create topologies.\")\n return HttpResponseRedirect('/login/')\n\n \n # If we're trying to add a template, don't need to get the template itself\n if action == \"add\":\n if permissions.allowed_topology_access_create(request.user):\n return callee(request)\n else:\n return denied_add()\n\n else:\n\n # Try getting the template - if it doesn't exist, show the same message\n # as for permission denied. If we don't have a \"tid\" argument, django\n # will show an internal error, which is what we want.\n tid = int(kwargs[\"tid\"])\n kwargs[\"tid\"] = tid\n try :\n topo = db.Topology.objects.get(pk=tid)\n except db.Topology.DoesNotExist:\n return denied()\n\n if action == \"use\":\n # See if there is an HTTP GET token - if there is, try to use the token\n # method for authentication\n try:\n token = request.GET[\"token\"]\n except KeyError:\n pass\n else:\n # See if the token is valid\n user = crypto.validate_token(token)\n if user != None and permissions.allowed_topology_access_use(user, topo):\n request.user = user\n return callee(request, topo=topo, **kwargs)\n if permissions.allowed_topology_access_use(request.user, topo):\n return callee(request, topo=topo, **kwargs)\n else:\n return denied()\n\n elif action == \"change\":\n if permissions.allowed_topology_access_change(request.user, topo):\n return callee(request, topo=topo, **kwargs)\n else:\n return denied()\n elif action == \"delete\":\n if permissions.allowed_topology_access_delete(request.user, topo):\n return callee(request, topo=topo, **kwargs)\n else:\n return denied()\n else:\n raise ValueError(\"Unknown action: %s\" % options[\"action\"])","title":""},{"docid":"4cadcc43d8373a9636811caf391ee728","score":"0.52789825","text":"def createlisting(request):\n\t# Check if user submitted a new listing post\n\tif request.method == \"POST\":\n\n\t\tform = NewListingForm(request.POST)\n\t\t# Validate the form\n\t\tif form.is_valid():\n\t\t\tlisting = form.save(commit=False) # allow us to change fields manually\n\t\t\t# Add information that wasn't on user form\n\t\t\tlisting.current_price = form.cleaned_data[\"starting_bid\"]\n\t\t\t#listing.current_price = 0.0\n\t\t\tlisting.user = request.user\n\t\t\n\t\t# Save the listing to the Database\n\t\tlisting.save()\n\t\t# Return User to New Listing\n\t\treturn HttpResponseRedirect(reverse(\"listing\", args=(listing.id,)))\n\telse:\n\t\treturn render(request, \"auctions/createlisting.html\", {\"form\": NewListingForm()})","title":""},{"docid":"51c8105bb6ba8aff252af65752efaf46","score":"0.5276149","text":"def show_new_user_form():\n return render_template('add_user_form.html')","title":""},{"docid":"b37c2d9133b8896585b13f4746c34b70","score":"0.5267732","text":"def create_ticket(request, ticket_type):\n if request.method == \"POST\":\n form = TicketForm(request.POST)\n if form.is_valid():\n form.save()\n messages.success(request, \"Ticket added\")\n if ticket_type == \"FEATURE\":\n return redirect(all_features)\n else:\n return redirect(all_bugs)\n else:\n form = TicketForm()\n form.fields[\"ticket_type\"].initial = ticket_type\n return render(request, 'ticketform.html', {'form': form})","title":""},{"docid":"4d5c536d79aaf059cd621f8aab6c0a4d","score":"0.52633303","text":"def get(self, request):\n if request.user.is_authenticated and not request.user.is_verified:\n return render(request, 'account/signup_done.html')\n return redirect('home')","title":""},{"docid":"2c59f41e3ae926cf90b9ff7627f6a4da","score":"0.526257","text":"def __call__(self):\n # aq_inner is needed in some cases like in the portlet renderers\n # where the context itself is a portlet renderer and it's not on the\n # acquisition chain leading to the portal root.\n # If you are unsure what this means always use context.aq_inner\n context = self.context.aq_inner\n form = self.request.form\n self.userid = form.get(\"userid\", \"\")\n self.loginid = form.get(\"loginid\", \"\")\n self.user_edit = form.get(\"form.button.edit\", None) is not None\n self.user_activate = form.get(\"form.button.activate\", None) is not None\n self.user_deactivate = form.get(\n \"form.button.deactivate\", None) is not None\n\n if not self.userid:\n return self.index()\n\n if self.user_edit:\n edit_view = \"%s/user_edit_view?userid=%s\" % (\n self.context.absolute_url(), self.userid)\n self.request.response.redirect(edit_view)\n\n portal_membership = getToolByName(context, 'portal_membership')\n\n if portal_membership.isAnonymousUser():\n raise Unauthorized('You need to login to access this page.')\n\n current_user = portal_membership.getAuthenticatedMember()\n user_groups = current_user.getGroups()\n sm = getSecurityManager()\n portal = getUtility(ISiteRoot)\n\n # user_management_list = context.get_management_dict()\n manage_by_group = context.get_manage_by_groups()\n manage_all = context.get_manage_all()\n\n # TODO: \"checking for * should be done in get_manage_by_groups\"\n if sm.checkPermission(ManagePortal, portal) and not manage_by_group:\n # show all for adminstratior/manager\n manage_by_group = [manage_all]\n\n if not manage_by_group:\n raise Unauthorized(UNAUTHORISED_ERROR)\n\n user = portal_membership.getMemberById(self.userid)\n if not user:\n context.plone_utils.addPortalMessage(\n _(u'This user does not exists.'))\n return self.index()\n\n user_groups = user.getGroups()\n same_groups = user_groups\n if manage_all not in manage_by_group:\n # TODO((ivan) limit the search instead of doing it after that\n same_groups = set(manage_by_group) & set(user_groups)\n if not same_groups:\n raise Unauthorized(UNAUTHORISED_ERROR)\n\n self.user_fullname = user.getProperty('fullname', '')\n self.user_email = user.getProperty('email', '')\n approved_by = user.getProperty('approved_by', '')\n self.user_approved_by = context.get_user_name(\n approved_by)\n self.user_approved_date = user.getProperty('approved_date', '')\n last_updated_by = user.getProperty('last_updated_by', '')\n self.user_last_updated_by = context.get_user_name(\n last_updated_by)\n self.user_last_updated_date = user.getProperty('last_updated_date', '')\n self.user_status = context.get_status(user)\n self.user_is_active = context.is_active(user)\n # display the groups based from the login user management list\n group_names = context.get_groups_title(same_groups)\n self.user_group = \", \".join(\n [group_name[\"group_title\"] for group_name in group_names])\n\n if self.user_activate:\n context.user_activate(self.userid, self.request)\n\n profile_view = \"%s/user_profile_view?userid=%s\" % (\n self.context.absolute_url(), self.userid)\n self.request.response.redirect(profile_view)\n\n if self.user_deactivate:\n context.user_deactivate(self.userid)\n\n profile_view = \"%s/user_profile_view?userid=%s\" % (\n self.context.absolute_url(), self.userid)\n self.request.response.redirect(profile_view)\n\n return self.index()","title":""},{"docid":"64f784f505cdbf2c353f89c4151ca79f","score":"0.52621067","text":"def trip_itinerary(trip_name, trip_id):\n \n email = session.get('email')\n user = User.query.filter_by(email=email).first()\n users = User.query.filter(User.email!=email).all()\n trip = Trip.query.filter_by(trip_id=trip_id).first()\n\n return render_template('itinerary.html',\n trip_name=trip_name,\n trip_id=trip_id,\n trip=trip,\n username=user.username,\n users=users)","title":""},{"docid":"0e4cb222be15ae7423c55b127040c9f4","score":"0.5257335","text":"def home_signup_view(request:Request, email:str = Form(...)):\n\n airtable_client = Airtable(\n base_id = AIRTABLE_BASE_ID,\n api_key = AIRTABLE_API_KEY,\n table_name = AIRTABLE_TABLE_NAME,\n )\n\n # to send email to airtable\n did_send = airtable_client.create_records({\"email\":email})\n return templates.TemplateResponse(\"home.html\",{\"request\":request, \"submitted_email\":email, \"did_send\":did_send})","title":""},{"docid":"87e1f9786a4c9efd33e97b98bb7aa81f","score":"0.52547777","text":"def tickets_new_feature(request):\n if request.method == \"POST\":\n ticket_form = TicketForm(request.POST)\n donation_form = DonationForm(request.POST)\n if ticket_form.is_valid() and donation_form.is_valid():\n # amount to pay / donate\n donation_amount = 0\n donation_amount += int(request.POST.get(\"donation_amount\"))\n try:\n # build Stripe payment\n token = request.POST[\"stripeToken\"]\n customer = stripe.Charge.create(\n amount=int(donation_amount * 100),\n currency=\"EUR\",\n description=(\n request.user.email +\n \" (\" + request.user.get_full_name() + \")\"),\n source=token,\n )\n except stripe.error.CardError:\n # Stripe payment error\n messages.error(request, f\"Your card was declined!\")\n # authorization is valid - payment successful\n if customer.paid:\n ticket_form.instance.author = request.user\n ticket_form.instance.ticket_type_id = 2\n ticket_form.instance.total_donations = donation_amount\n # update user Profile with additional donation amount\n get_user_donations = Profile.objects.values_list(\n \"total_donated\", flat=True).get(user_id=request.user.id)\n new_user_donations = get_user_donations + donation_amount\n Profile.objects.filter(\n user_id=request.user.id).update(\n total_donated=new_user_donations)\n # update ticket status to In Progress if €100 goal is achieved\n if donation_amount >= int(100):\n ticket_form.instance.ticket_status_id = 2\n else:\n ticket_form.instance.ticket_status_id = 1\n new_ticket = ticket_form.save()\n new_ticket_id = new_ticket.pk\n messages.success(\n request, f\"Thank you for your Feature Request!\\\n €{donation_amount} was charged to your card.\")\n return redirect(tickets_view_one, new_ticket_id)\n else:\n messages.error(request, f\"Unable to take payment!\")\n else:\n messages.error(request, f\"There was an error. Please try again.\")\n else:\n ticket_form = TicketForm()\n donation_form = DonationForm()\n context = {\n \"donation_form\": donation_form,\n \"ticket_form\": ticket_form,\n \"publishable\": settings.STRIPE_PUBLISHABLE\n }\n return render(request, \"tickets_new_feature.html\", context)","title":""},{"docid":"77df534e230304382a17d6b64b34905c","score":"0.52546364","text":"def before_request():\n g.user = current_user","title":""},{"docid":"f38f0acf2569f2adef9f69a1e8305274","score":"0.5254325","text":"def vouch_required(f):\n @login_required\n @wraps(f)\n def wrapped(request, *args, **kwargs):\n if request.user.get_profile().is_vouched:\n return f(request, *args, **kwargs)\n else:\n log.warning('vouch_required forbidding access')\n return HttpResponseForbidden(_('You must be vouched to do this.'))\n\n return wrapped","title":""},{"docid":"9d56c55b292834e2d79c1009c4813918","score":"0.52485853","text":"def new_food_entry_form(user_id):\n\t\n\treturn render_template(\"entry.html\", user_id=user_id)","title":""},{"docid":"44966277b7e240553f5bc4381193f23b","score":"0.5245977","text":"def test_func(self):\n\n todo = self.get_object()\n return self.request.user.is_superuser or \\\n self.request.user == todo.assigned_user","title":""},{"docid":"0a8e417dcb45cd61a48ed67dc518567f","score":"0.5244312","text":"def create_task_form(self,req):\r\n\t\ttemp_set=loader.get_template(\"form_set.html\")\r\n\t\tset_form=task_write()\r\n\t\tcontext={\r\n\t\t\t\"form\":set_form,\r\n\t\t\t\"title\":u\"Authorization Setup\",\r\n\t\t\t\"act_cn\":u\"Authorization Management\",\r\n\t\t\t\"act\":\"authset\",\r\n\t\t\t}\r\n\t\trt=temp_set.render(context,req)\r\n\t\treturn rt","title":""},{"docid":"f4551a4ecb5421c1b161a404fdf27681","score":"0.5242026","text":"def login_router(request):\n\n user = request.user\n\n # Checks if an AllSkills model is attached to the user\n # If not create and send to edit page\n try:\n user = user.allskills\n except AttributeError:\n models.AllSkills.objects.create(\n user_id=user.pk\n )\n\n # Redirect to the main edit page\n return redirect('profiles:edit')\n\n else:\n # redirect to main profile page\n return redirect('profiles:profile', pk=request.user.pk)","title":""},{"docid":"b7f9a95a0d7a27fc246ac6c8d5991503","score":"0.5235917","text":"def shared(request):\n print(request.method)\n if request.method == \"POST\":\n set_user_step(request.user, step=4, phase=1)\n result = 'success'\n json_response = {'result': result}\n return http.JsonResponse(json_response)\n\n return http.HttpResponseBadRequest(\"Only accesible with POST\")","title":""},{"docid":"442e9ff84517371839822f77d41d2a57","score":"0.5216749","text":"def main(request):\n\n if request.user.is_authenticated:\n return redirect(\"question\")\n else:\n return redirect(\"login\")","title":""},{"docid":"966c6a282b653994b33a2d74ed999101","score":"0.52164596","text":"def test_view_uses_correct_template(self):\n self.client.login(username=\"john.doe\", password=\"XVAP11!0$\")\n response = self.client.get(reverse(\"meal-add\"))\n self.assertEqual(response.status_code, 200)\n self.assertTemplateUsed(response, \"meals/meal_form.html\")","title":""},{"docid":"8dde170298c7585c4318c3b2cb303907","score":"0.52153194","text":"def add_single_view(self, request, form_url='', extra_context=None):\n return self.add_view(\n request, form_url=form_url, extra_context=extra_context)","title":""},{"docid":"194b028581b1132edb8149ac5a44e8da","score":"0.521503","text":"def dashboard():\n\n client_form = AddClientForm(prefix='client_form')\n prop_form = AddPropertyForm(prefix='prop_form')\n showing_form = AddShowingForm(prefix='showing_form')\n showings = query.getShowingByUser(current_user.username)\n table = query.getClientsForUser(current_user.username)\n\n if prop_form.validate_on_submit() and prop_form.submit.data:\n query.createProperty({\n 'List_Price': prop_form.list_price.data,\n 'Location': prop_form.location.data,\n 'Trend_Link': prop_form.trend_link.data})\n\n return redirect(\"dashboard\")\n\n if showing_form.validate_on_submit() and showing_form.submit.data:\n \n r = query.createShowing({\n 'client_id': showing_form.client_id.data,\n 'Property_ID': showing_form.Property_ID.data,\n 'Feedback': showing_form.Feedback.data,\n 'Rating': showing_form.Rating.data})\n print(r)\n\n showings = query.getShowingByUser(current_user.username)\n\n return redirect(\"dashboard\")\n\n # on submit re-render template using form data and query\n if client_form.validate_on_submit() and client_form.submit.data:\n\n query.createClient(\n {\n \"first_name\": client_form.first_name.data,\n \"last_name\": client_form.last_name.data,\n \"email\": client_form.email.data,\n \"phone\": client_form.phone.data,\n \"user_id\": current_user.id,\n }\n )\n\n table = query.getClientsForUser(current_user.username)\n\n return redirect(\"dashboard\")\n\n return render_template(\n \"home/dashboard.html\",\n title=\"Dashboard\",\n table=table,\n user=current_user,\n showings=showings,\n showing_form=showing_form,\n client_form=client_form,\n prop_form=prop_form,\n )","title":""},{"docid":"dfdd30fbabecd233635b20197681a729","score":"0.52150214","text":"def login_required(view):\n def view_wrapper(request, *args, **kwargs):\n if request.user.is_authenticated:\n return view(request, *args, **kwargs)\n else:\n if request.is_ajax():\n return JsonResponse({'error': _(u'Debes estar logueado.')}, status=400)\n else:\n path = request.get_full_path()\n return redirect_to_login(path)\n return view_wrapper","title":""},{"docid":"d3ac053c38a9d0cdfce3cc9ea28d9d7b","score":"0.5206036","text":"def plan_view(request, plan_id):\n\n plan = Plan.objects.get(pk=plan_id)\n\n if request.user==plan.user:\n motes = plan.motes.all()\n return {'plan': plan, 'motes': motes,}\n else:\n return redirect('login')","title":""},{"docid":"6d00f31420d4523019a4218ad23f6d33","score":"0.52053094","text":"def view(self, user, financing, *args):\n if user.is_anonymous or user.is_client:\n return False\n\n if user.is_administrator:\n return True\n\n if user.is_manager:\n return True\n\n if user.is_advisor:\n return True\n\n return self.admin_permission(user, financing, *args)","title":""},{"docid":"652b785db101bc9a7e11ddae86875afe","score":"0.520398","text":"def create(self, request):\n\n\t\treturn ObtainAuthToken().post(request)","title":""},{"docid":"4454f383ca75e348bf7ed7f2a2707f88","score":"0.5203732","text":"def __init__(self, *args, **kwargs):\n self.user = kwargs.pop('user')\n self.draw = kwargs.pop('draw')\n super(BuyTicketsForm, self).__init__(*args, **kwargs)","title":""},{"docid":"7de9557587be1cb02d0322356f05e65e","score":"0.52032346","text":"def protected_view(request):\n return render(request, 'bands/protected.html', {'current_user': request.user})","title":""},{"docid":"d563c679a14a0b955ac40e5daeeea3ed","score":"0.5195981","text":"def show_new_user_form():\n\n return render_template('users/new_user.html')","title":""},{"docid":"b5d0307b5ea8cae318c54d98cd5b9a9b","score":"0.5190887","text":"def request_add_handler(request):\n pprint(request.POST)\n global matching_jlist\n n = len(matching_jlist)\n user = request.user\n print(n)\n index = 0\n for i in range(n):\n if(\"submit\"+str(i) in request.POST):\n index = i\n print(\"index\", index)\n jrny = matching_jlist[index][0]\n description = \"User \"+user.username+\" wants to be added to the \" + \\\n jrny.journey_id+\" in which you are a participant.\"\n title = \"Journey Add request\"\n for ut in jrny.participants.all():\n Notification.objects.create(user_to=ut, user_from=user, title=title, description=description,\n notif_type=\"Journey Related\", creation_time=datetime.datetime.now()+datetime.timedelta(hours=5.5),\n travel_id=jrny.journey_id)\n matching_jlist[index][1] = True\n return redirect(\"/user_journeys/\")","title":""},{"docid":"4d366c6b0f8b3efbe38ebde5c749dd98","score":"0.5189515","text":"def test_add_flight_functionality(self):\n with self.subTest('Test normal user cannot add flight.'):\n self.client.force_authenticate(user=self.normal_user)\n response = self.client.post(self.url, data=self.data, format='json')\n self.assertEqual(response.status_code, 403)\n self.assertEqual(response.data['error'], 'PermissionDenied')\n self.assertEqual(response.data['error_description'],\n 'You do not have permission to perform this action.')\n\n with self.subTest('Test a staff can add flight.'):\n self.client.force_authenticate(user=self.staff_user)\n response = self.client.post(self.url, data=self.data, format='json')\n self.assertEqual(response.status_code, 201)\n self.assertEqual(response.data['name'], 'FLIGHT VALERIA')\n self.assertEqual(Flight.objects.all().count(), 1)\n\n with self.subTest('Test a super user can add flight.'):\n self.data['name'] = 'flight1'\n self.client.force_authenticate(user=self.super_user)\n response = self.client.post(self.url, data=self.data, format='json')\n self.assertEqual(response.status_code, 201)\n self.assertEqual(response.data['name'], 'FLIGHT1')\n self.assertEqual(Location.objects.all().count(), 2)","title":""},{"docid":"1bbce2b702c17f914f37dabd1c1dfd8d","score":"0.51842207","text":"def admin_login_required(view):\n\n @functools.wraps(view)\n def wrapped_view(**kwargs):\n if session.get('user_type') != 'admin':\n return json\n return view(**kwargs)\n\n return wrapped_view","title":""},{"docid":"e23a66c0b804869ef276b1106c07f5e4","score":"0.51601136","text":"def post(self, request: HttpRequest, *args, **kwargs) -> Any:\n\n self.set_application_and_task()\n\n self.application.cleared_by.add(self.request.user)\n\n if self.kwargs[\"case_type\"] == \"import\":\n view_name = \"Search Import Applications\"\n else:\n view_name = \"Search Certificate Applications\"\n\n messages.success(request, f\"Case cleared, it can still be viewed in the {view_name} page.\")\n\n return redirect(reverse(\"workbasket\"))","title":""}],"string":"[\n {\n \"docid\": \"34a30345c20e0a3d5e61f19441df8845\",\n \"score\": \"0.6473721\",\n \"text\": \"def home():\\n\\n form = TripForm()\\n \\n if form.validate_on_submit():\\n \\n if validate_dates(form.start_date_time.data, form.end_date_time.data, form):\\n \\n return render_template('create_trip.html', form=form)\\n\\n if validate_number_of_people(form.number_of_people.data):\\n\\n form.number_of_people.errors = [\\\"Please enter at least 1 person\\\"]\\n\\n return render_template('create_trip.html', form=form)\\n\\n # check if user is logged in if not log them in as a guest user\\n \\n if 'user_id' not in session:\\n User.log_in_as_guest()\\n\\n \\n trip = Trip(start_date_time=form.start_date_time.data,\\n end_date_time=form.end_date_time.data,\\n number_of_people=form.number_of_people.data,\\n name=form.name.data,\\n user_id=session.get('user_id')\\n )\\n \\n db.session.add(trip)\\n db.session.commit()\\n\\n session['trip_id'] = trip.id\\n\\n return redirect(url_for('select_meals', trip_id=trip.id))\\n \\n return render_template('create_trip.html', form=form)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11820e2a784e3f55cabea6763cdaae97\",\n \"score\": \"0.6250833\",\n \"text\": \"def user_trips(request):\\n # extremely necessary to make the golobal vaiables\\n # global display1\\n # global display2\\n global locations\\n global matching_tlist\\n tform1 = TripCreationForm1(request.user)\\n tform2 = TripCreationForm2()\\n tmform = TripModifyForm(request.user)\\n lis = []\\n for o, dis in matching_tlist:\\n d = {}\\n d[\\\"trip_name\\\"] = o.trip_id\\n d[\\\"trip_date\\\"] = o.start_time\\n d[\\\"trip_participants\\\"] = [x.username for x in o.participants.all()]\\n d[\\\"disable\\\"] = dis\\n d[\\\"trip_locations\\\"] = [\\n x.location.location_name for x in TripPoint.objects.filter(trip=o)]\\n lis.append(d)\\n req_lis = Notification.objects.filter(\\n user_to=request.user, notif_type=\\\"Trip Related\\\", resolved=\\\"No\\\")\\n display1 = len(locations) != 0\\n display2 = len(lis) != 0\\n\\n tlist = Trip.objects.filter(participants__in=[request.user])\\n for t in tlist:\\n # and (datetime.datetime.now()+datetime.timedelta(hours=5.5)-j.start_time.replace(tzinfo=utc)).days>10)\\n if(t.start_time < (datetime.datetime.now()+datetime.timedelta(hours=5.5)).replace(tzinfo=utc)):\\n if(t.posted):\\n t.closed = True\\n t.save()\\n\\n return render(request, 'info/trips.html', {\\\"tform1\\\": tform1, \\\"tform2\\\": tform2, \\\"tmform\\\": tmform,\\n \\\"display1\\\": display1, \\\"display2\\\": display2, \\\"lis\\\": lis, \\\"locations\\\": locations,\\n \\\"req_lis\\\": req_lis, \\\"tlist\\\": tlist})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"090407cebc8e21af4c863e281f240ec4\",\n \"score\": \"0.61548936\",\n \"text\": \"def trip_request_add_handler(request):\\n pprint(request.POST)\\n global matching_tlist\\n n = len(matching_tlist)\\n user = request.user\\n print(n)\\n index = 0\\n for i in range(n):\\n if(\\\"submit\\\"+str(i) in request.POST):\\n index = i\\n print(\\\"index\\\", index)\\n trp = matching_tlist[index][0]\\n description = \\\"User \\\"+user.username+\\\" wants to be added to the \\\" + \\\\\\n trp.trip_id+\\\" in which you are a participant.\\\"\\n title = \\\"Trip Add request\\\"\\n # for ut in trp.participants.all():\\n Notification.objects.create(user_to=trp.leader, user_from=user, title=title, description=description,\\n notif_type=\\\"Trip Related\\\", creation_time=datetime.datetime.now()+datetime.timedelta(hours=5.5),\\n travel_id=trp.trip_id)\\n matching_jlist[index][1] = True\\n return redirect(\\\"/user_trips/\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37b8dae6a16b4fde1251a4fe16cc1220\",\n \"score\": \"0.59939307\",\n \"text\": \"def post(self, request, *args, **kwargs):\\n c = self.getContext(request)\\n if 'choice' not in request.POST:\\n c.update({\\n 'error': True,\\n 'message': 'No one was selected'\\n })\\n return render_to_response('display_message.html', c)\\n tutor_name = request.POST['choice'].split('^?^')\\n c.update({ 'firstname': tutor_name[0], 'lastname': tutor_name[1], 'username': tutor_name[2], 'specific_request': True })\\n c.update({ \\n 'times': range(1,24),\\n 'days': ['Mon','Tues','Wed','Thurs','Fri','Sat','Sun']\\n })\\n return render_to_response('request_help.html',c)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c906c53aa964d8f345f8d803aa47f20\",\n \"score\": \"0.59727734\",\n \"text\": \"def create(request):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b24f9e345d203cf3da1ebe2d1d1df526\",\n \"score\": \"0.58876973\",\n \"text\": \"def trip_creation_handler2(request):\\n # function to handle the creation of new trip\\n global locations\\n pprint(locations)\\n pprint(request.POST)\\n trip_name = request.POST.get(\\\"trip_name\\\")\\n trip_date = request.POST.get(\\\"travel_date\\\")\\n cotravel_number = request.POST.get(\\\"cotravel_number\\\")\\n source = request.POST.get(\\\"source\\\")\\n duration = request.POST.get(\\\"duration\\\")\\n expected_budget = request.POST.get(\\\"expected_budget\\\")\\n trip_info = request.POST.get(\\\"trip_info\\\")\\n\\n trp = Trip(trip_id=trip_name, start_time=parser.parse(trip_date),\\n source=source, cotravel_number=cotravel_number, duration=duration, expected_budget=expected_budget,\\n trip_info=trip_info, leader=request.user)\\n trp.save()\\n trp.participants.add(request.user)\\n # for each checkpoint create a new trip point with foreignkey to location and the trip objects\\n for i, x in enumerate(locations):\\n loc = LocationPoint.objects.get(\\n location_name=x[\\\"location\\\"], user=request.user, location_type=\\\"Trip Point\\\")\\n TripPoint.objects.create(location=loc, trip=trp)\\n locations = []\\n return redirect(\\\"/user_trips/\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c3d3e9c22aa87484c485af03b10f769\",\n \"score\": \"0.5838591\",\n \"text\": \"def get(self, request, *args, **kwargs):\\n c = self.getContext(request) \\n is_tutor = False\\n if tutor(c['user'].username):\\n is_tutor = True\\n c.update({'tutor': is_tutor})\\n tutee_list = Tutee.objects.all()\\n requests = Request.objects.all()\\n help_requests = []\\n for tutee in tutee_list:\\n for req in requests:\\n if tutee.user.username == req.user and req.user != c['user'].username:\\n if not req.accepted_by:\\n help_requests.append(req)\\n \\n c.update(csrf(request))\\n c.update({'help_requests': help_requests})\\n return render_to_response('claim_tutee.html', c)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0dd84a9eb01d218f5047fd36da5ec442\",\n \"score\": \"0.5813315\",\n \"text\": \"def onflight(request):\\n\\n # Authentication check\\n if not request.user.is_authenticated():\\n # TODO redirect to error page\\n return redirect(reverse('index'))\\n\\n user = request.user\\n\\n sia_user = get_object_or_404(SIAUser, user=user)\\n return render(request, 'users/on-flight.html', {'SiaUser':sia_user})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8bf2c8a119f844f584cdc2729223166\",\n \"score\": \"0.5789913\",\n \"text\": \"def get(self, request, *args, **kwargs):\\n c = RequestContext(request)\\n c.update(csrf(request))\\n if c['user'].is_superuser:\\n return redirect('/promote_user/', permanant=True)\\n \\n is_tutor = False\\n if tutor(c['user'].username):\\n is_tutor = True\\n\\n requests = Request.objects.all()\\n pending_requests = []\\n my_tutors = []\\n if is_tutor:\\n my_tutees = []\\n for req in requests:\\n if req.user == c['user'].username:\\n if not req.accepted_by:\\n pending_requests.append(req)\\n else:\\n my_tutors.append((Tutee.objects.get(user__username=req.accepted_by), req))\\n elif is_tutor and req.accepted_by == c['user'].username:\\n my_tutees.append((Tutee.objects.get(user__username=req.user), req))\\n\\n c.update(csrf(request))\\n c.update({'pending_requests': pending_requests})\\n c.update({'my_tutors': my_tutors})\\n c.update({'tutor': is_tutor})\\n if is_tutor:\\n c.update({'my_tutees': my_tutees})\\n return render_to_response('profile.html', c)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67ac31aa35b321fa6f0b1e7be751644f\",\n \"score\": \"0.5786391\",\n \"text\": \"def denied_add():\\n messages.error(request, \\\"You don't have permission to create topologies.\\\")\\n return HttpResponseRedirect('/login/')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e51ce1763db0292fcfe32cbd41d23b1e\",\n \"score\": \"0.5765464\",\n \"text\": \"def new_ticket(request, *arg):\\n if request.method == 'POST':\\n form = TicketForm(request.POST)\\n if form.is_valid():\\n t = Ticket()\\n t.ticket_type = form.cleaned_data['ticket_type']\\n t.project = form.cleaned_data['project']\\n t.component = form.cleaned_data['component']\\n t.summary = form.cleaned_data['summary']\\n t.description = form.cleaned_data['description']\\n t.created_by = request.user\\n t.updated_by = request.user\\n # workflow related stuff\\n ticket_type = t.ticket_type\\n wm = WorkflowManager()\\n wm.workflow = ticket_type.workflow\\n wm.created_by = request.user\\n wm.save()\\n r = Role.objects.get(id=settings.ROLE_SUBMITTER)\\n p = Participant()\\n p.user = request.user\\n p.role = r\\n p.workflowmanager=wm\\n p.save()\\n t.workflow_manager=wm\\n t.save()\\n wm.start(p)\\n # Generates a confirmation email to send to the new user\\n current_site = Site.objects.get_current() \\n site_name = current_site.name\\n domain = current_site.domain\\n tplt = loader.get_template(settings.NEW_TICKET_EMAIL_TEMPLATE)\\n c = {\\n 'email': request.user.email,\\n 'domain': domain,\\n 'site_name': site_name,\\n 'user': request.user,\\n 'protocol': settings.REGISTRATION_USE_HTTPS and 'https' or 'http',\\n 'ticket': t,\\n }\\n send_mail(_(\\\"Confirmation of new ticket on IssueTracker\\\"),\\n tplt.render(Context(c)), None, [request.user.email])\\n \\n request.user.message_set.create(message=_(\\\"The ticket has been\\\"\\\\\\n \\\" successfully created.\\\"))\\n return HttpResponseRedirect('/ticket/%d'%t.id)\\n else:\\n form = TicketForm()\\n c = RequestContext(request, {'form': form})\\n return render_to_response('new.html', c)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9de72b64e34113d105db135061cac5d\",\n \"score\": \"0.5754959\",\n \"text\": \"def get(self, request):\\n if request.user.is_authenticated():\\n return self.redirect_to(request)\\n else:\\n return render(request, 'account/signup.html', {'form': self.form()})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20851bfd02b455b3643f59c1122d68cf\",\n \"score\": \"0.5754119\",\n \"text\": \"def get(self, request):\\n if request.user.status == 2:\\n return redirect('orders:pickup_service')\\n else:\\n return super().get(request)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e6caf7263eecd8a03f00f1c60a0a949e\",\n \"score\": \"0.5731373\",\n \"text\": \"def validate_trip():\\n\\n email = session.get('email')\\n user = User.query.filter_by(email=email).first()\\n trip_name = request.form.get('trip-name')\\n start_date = request.form.get('start-date')\\n end_date = request.form.get('end-date')\\n\\n new_trip = Trip(creator_id=user.user_id,\\n trip_name=trip_name,\\n start_date=start_date,\\n end_date=end_date)\\n\\n db.session.add(new_trip)\\n\\n db.session.commit()\\n\\n return redirect(url_for('trip_itinerary',\\n trip_name=new_trip.trip_name, \\n trip_id=new_trip.trip_id))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85ea1b84eee129c3638cf22bcbc1a38d\",\n \"score\": \"0.5671864\",\n \"text\": \"def create(self,request):# create function is called when a http post request is made to the ViewSet\\n\\n return ObtainAuthToken().post(request)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08d1f6fe285eedcd9c99ff813199d95f\",\n \"score\": \"0.56620723\",\n \"text\": \"def new_request_widget(self):\\n if self.user_id == 0:\\n self.login.click()\\n if self.user_id == 0:\\n return\\n new_request_form = ui.NewRequest(self)\\n new_request_form.move(get_popup_pos(self, new_request_form))\\n new_request_form.user_id = self.user_id\\n new_request_form.request.setFocus()\\n new_request_form.exec_()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4e7bea2a630bd478d6a762a41642f75\",\n \"score\": \"0.5627527\",\n \"text\": \"def questionnaire(request):\\n if request.method == \\\"POST\\\":\\n # If is valid\\n if not request.user.is_superuser:\\n set_user_step(request.user, step=3, phase=1)\\n return http.HttpResponseRedirect('/phase1/step3/')\\n\\n return http.HttpResponseBadRequest(\\\"Only accesible with POST\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5fe75ff2da849df7fa2224978dcd31e4\",\n \"score\": \"0.56189126\",\n \"text\": \"def post(self, request, *args, **kwargs):\\n c = self.getContext(request)\\n validate = validate_help_request(request.POST)\\n if validate is not None:\\n c.update(validate)\\n c.update(csrf(request))\\n return render_to_response('request_help.html', c)\\n\\n fc = request.POST['for_class']\\n desc = request.POST['description']\\n d = request.POST['day']\\n t = request.POST['time']\\n users_requests = Request.objects.filter(user=c['user'].username)\\n valid = True\\n \\\"\\\"\\\"\\n If the user has ten requests, then they are not able to create a new one.\\n If the user has already requested help for a specific class then they are not able to request help for that class again\\n \\\"\\\"\\\"\\n if len(users_requests) < 11:\\n for req in users_requests:\\n if req.for_class == fc:\\n c.update({'request_exists_error': 'A help request for this class already exists'})\\n valid = False\\n else:\\n valid = True\\n elif len(users_requests) > 10:\\n c.update({'too_many_error': 'You have too many open requests'})\\n valid = False\\n\\n if valid:\\n helprequest = Request(user=c['user'].username, first_name=c['user'].first_name, last_name=c['user'].last_name, for_class=fc, description=desc, days=d, time=t)\\n \\\"\\\"\\\"\\n if a tutor is being specifically requested then they will recieve an email informing them of the request\\n \\\"\\\"\\\"\\n if 'specific_request' in request.POST:\\n helprequest.requested = request.POST['requested']\\n helprequest.save()\\n target = Tutee.objects.get(user__username=request.POST['requested']).user\\n emailer = emails()\\n message = 'You have been requested as a tutor by ' +\\\\\\n c['user'].first_name + ' ' + c['user'].last_name +\\\\\\n '. Log in to RPI Tutor Time to view their request.'\\n emailer.send_email(target, message, 'Tutor Request')\\n else:\\n helprequest.save()\\n c.update({\\n 'error': False,\\n 'message': 'Request successfully submitted.'\\n })\\n return render_to_response('display_message.html', c)\\n\\n return render_to_response('request_help.html', c)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69c59ca5c155c48f96160ae758760e06\",\n \"score\": \"0.56134325\",\n \"text\": \"def login_required(view):\\n @functools.wraps(view)\\n def wrapped_view(**kwags):\\n if g.user is None:\\n # import pdb; pdb.set_trace()\\n abort(404)\\n return view(**kwags)\\n return wrapped_view\",\n \"title\": \"\"\n },\n {\n \"docid\": \"057f2be48aed0d8c3615532e742a7833\",\n \"score\": \"0.5605659\",\n \"text\": \"def get(self, request):\\n today = datetime.date.today()\\n random_string = hashlib.md5(bytes(str(random.random()).encode('utf-8'))).hexdigest()\\n user_id = today.strftime('lmdemo-%y%m%d-')\\n user_id += random_string[:3]\\n\\n initial_form = {\\n 'lm_url': '/',\\n 'first_name': 'Test',\\n 'last_name': 'Example',\\n 'user_id': user_id,\\n 'email': '{}@example.com'.format(user_id),\\n 'postal_code': '63303',\\n 'endpoint': loginbypass.PROD_ENDPOINT,\\n 'key': settings.LOGIN_BYPASS_KEY,\\n }\\n form = forms.LoginBypassForm(initial=initial_form)\\n context = {'form': form}\\n return render(request, self.template_name, context)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d63e3ddd34926b93d7150372fb06513\",\n \"score\": \"0.5597213\",\n \"text\": \"def create_view(self):\\n if flask.request.method == \\\"POST\\\":\\n try:\\n self.start_task()\\n flash(\\\"The task was successfully started.\\\", \\\"success\\\")\\n except Exception as e:\\n current_app.logger.exception(e)\\n flash(\\\"An error occurred while starting the task.\\\", \\\"error\\\")\\n return redirect(request.path) # Redirect after POST\\n\\n return_url = get_redirect_target() or self.get_url(\\\".index_view\\\")\\n\\n return self.render(\\n \\\"cds_ils_admin/create_task.html\\\", return_url=return_url\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cadb06ee4bbee3c5c67cde8a06320fd1\",\n \"score\": \"0.558085\",\n \"text\": \"def show_create_page():\\n\\n return render_template('user_form.html')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52df3e9d53b6a9222a7ca6b5829a4155\",\n \"score\": \"0.5577324\",\n \"text\": \"def plans(request):\\n if request.user.is_authenticated:\\n if request.method == \\\"POST\\\":\\n try:\\n start_date = request.POST['start_date']\\n start_time = request.POST['start_time']\\n end_date = request.POST['end_date']\\n end_time = request.POST['end_time']\\n title = request.POST['title']\\n start_datetime = pytz.utc.localize(datetime.combine(\\n datetime.strptime(start_date, \\\"%Y-%m-%d\\\"),\\n datetime.strptime(start_time, \\\"%H:%M\\\").time()))\\n end_datetime = pytz.utc.localize(datetime.combine(\\n datetime.strptime(end_date, \\\"%Y-%m-%d\\\"),\\n datetime.strptime(end_time, \\\"%H:%M\\\").time()))\\n\\n Plan.objects.create(\\n user=request.user,\\n start_time=start_datetime,\\n end_time=end_datetime,\\n title=title)\\n message = {\\n 'mtype': \\\"success\\\",\\n 'text': \\\"Pomyślnie dodano do bazy danych\\\",\\n 'bold': \\\"Sukces!\\\"\\n }\\n\\n return render(request, 'polls/add_plans.html', {'message': message})\\n except Exception as ex:\\n message = {\\n 'mtype': \\\"danger\\\",\\n 'text': \\\"Nie udało się dodać planów do bazy danych\\\",\\n 'bold': \\\"Błąd!\\\"\\n }\\n print(ex)\\n return render(request, 'polls/add_plans.html', {'message': message})\\n return render(request, 'polls/add_plans.html')\\n return render(request,\\n 'polls/user_profile.html',\\n {'error_message': \\\"Musisz być zalogowany!\\\"})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"152da2b87c003c66224a55e443d82f1d\",\n \"score\": \"0.5573352\",\n \"text\": \"def user(request):\\n logging_user = LoginUser.get_login_user()\\n if not logging_user.is_view_right():\\n return\\n auth_type = None\\n type_form = GacoiForm(\\\"typeForm\\\", \\\"/user/\\\", \\\"POST\\\")\\n\\n keu_form = LagForm()\\n keu_form.set_title(\\\"User List\\\")\\n type_form.set_view(\\\"id,user_name,login_name,address,phone,email,course,gender,roles,teacher_id\\\")\\n type_form.set_key(\\\"id\\\")\\n\\n if logging_user.is_add_right():\\n type_form.set_insert(\\\"user_name,login_name,address, phone,course,email,gender,roles,teacher_id\\\")\\n if logging_user.is_update_right():\\n type_form.set_update(\\\"user_name,login_name,address, phone,course,email,gender,roles,teacher_id\\\")\\n if logging_user.is_delete_right():\\n type_form.set_option_deletable(True)\\n\\n type_form.set_type('password', GacoiFormFieldType.Password)\\n type_form.set_required('user_name,login_name')\\n type_form.set_type('updated_datetime', GacoiFormFieldType.DateTime)\\n type_form.set_type('created_datetime', GacoiFormFieldType.DateTime)\\n\\n dept_id = AuthMetaTypeDefine.Department.code\\n genders = UserLogic.get_genders_dict()\\n teachers = UserLogic.get_teachers_dict()\\n roles = UserLogic.get_roles_dict()\\n type_form.get_field(\\\"gender\\\").set_drop_down_list_values(genders)\\n type_form.get_field(\\\"teacher_id\\\").set_drop_down_list_values(teachers)\\n type_form.get_field(\\\"roles\\\").set_drop_down_list_values(roles)\\n type_form.get_field(\\\"course\\\").set_multi_choices(\\\"select id,name from course id\\\",\\n \\\"select c.id from course c inner join base_user_course u \\\"\\n \\\"on u.course_id = c.id where u.user_id = [id]\\\", 1)\\n\\n type_form.set_search('user_name,login_name, address,gender,roles,teacher_id')\\n type_form.set_order('id,user_name,login_name,address,teacher_id,roles')\\n type_form.init(request)\\n if logging_user.is_update_right():\\n type_form.add_inline_user_button(InlineUserButton(\\\"Change password\\\", \\\"change_password\\\",\\n action=\\\"do_change_password([id])\\\"))\\n\\n reset_id = None\\n reset_password = False\\n\\n # Check request\\n # if typeForm_action = reset_password -> handle request password\\n params = request.POST\\n\\n if params.get('typeForm_action') == 'reset_password':\\n reset_id = params.get('reset_id')\\n # transaction.set_autocommit(False)\\n try:\\n user_changed = User.objects.get(id=reset_id)\\n user_changed.password = UserLogic.hash_password(params.get('new_password', None))\\n user_changed.save()\\n reset_password = False\\n except ObjectDoesNotExist:\\n # transaction.rollback()\\n print(\\\"aaa\\\")\\n\\n # transaction.commit()\\n\\n # Check type_form.get_inline_user_button_return_value() return change_password button to open modal\\n if logging_user.is_update_right():\\n if \\\"change_password\\\" == type_form.get_inline_user_button_return_value():\\n reset_id = type_form.get_key_value(\\\"id\\\")\\n reset_password = True\\n\\n # call to edit in form view, if not: edit in grid view\\n # type_form.set_option_update_in_form_view(True)\\n\\n if type_form.is_action(GacoiFormAction.DeleteDone):\\n if logging_user.is_delete_right():\\n try:\\n user_deleted = User.objects.get(pk=type_form.get_key_value(\\\"id\\\"))\\n user_deleted.delete()\\n LogOperation.log(LogModule.User, LogType.Delete, LogResult.Success, type_form.get_key_value(\\\"id\\\"))\\n except ObjectDoesNotExist:\\n LogOperation.log(LogModule.User, LogType.Delete, LogResult.Fail, type_form.get_key_value(\\\"id\\\"))\\n insert = False\\n update = False\\n if type_form.is_action(GacoiFormAction.InsertStart):\\n keu_form.set_title(\\\"Create New User\\\")\\n insert = True\\n if type_form.is_action(GacoiFormAction.UpdateStart):\\n update = True\\n # Insert\\n if type_form.is_action(GacoiFormAction.InsertDone):\\n try:\\n User.objects.get(login_name=type_form.get_field('login_name').get_value_blank2none())\\n error_message = \\\"The login user is exist!\\\"\\n type_form.set_action(GacoiFormAction.InsertStart)\\n type_form.set_error_message(error_message)\\n LogOperation.log(LogModule.User, LogType.Insert, LogResult.Fail)\\n except ObjectDoesNotExist:\\n # transaction.set_autocommit(False)\\n try:\\n role = type_form.get_field('roles').get_value_blank2none()\\n teacher_id = type_form.get_field('teacher_id').get_value_blank2none()\\n user_courses = type_form.get_field('course').get_multi_choices_selected_values()\\n if int(role) != UserRoles.Student.code:\\n teacher_id = None\\n user_new = UserLogic.create_user(type_form.get_field('user_name').get_value_blank2none(),\\n type_form.get_field('login_name').get_value_blank2none(),\\n type_form.get_field('gender').get_value_blank2none(),\\n role,\\n type_form.get_field('phone').get_value_blank2none(),\\n type_form.get_field('email').get_value_blank2none(),\\n type_form.get_field('address').get_value_blank2none(),\\n teacher_id,\\n password=type_form.get_field('password').get_value_blank2none())\\n UserLogic.store_user_courses(user_new.id, user_courses)\\n type_form.set_action(1000)\\n\\n except ObjectDoesNotExist as e:\\n\\n # transaction.rollback()\\n LogOperation.log(LogModule.User, LogType.Insert, LogResult.Fail, None, e)\\n\\n # transaction.commit()\\n insert = False\\n return HttpResponseRedirect(\\\"/user\\\")\\n\\n if type_form.is_action(GacoiFormAction.UpdateDone):\\n today = datetime.datetime.today()\\n # transaction.set_autocommit(False)\\n try:\\n user_updated = User.objects.get(id=type_form.get_key_value('id'))\\n print(user_updated.login_name)\\n new_user_name = type_form.get_field('user_name').get_value_blank2none()\\n new_login_name = type_form.get_field('login_name').get_value_blank2none()\\n # if user_updated.user_name != new_user_name or user_updated.login_name != new_login_name:\\n user_updated.user_name = new_user_name\\n user_updated.login_name = new_login_name\\n user_updated.updated_datetime = today\\n role = type_form.get_field('roles').get_value_blank2none()\\n teacher_id = type_form.get_field('teacher_id').get_value_blank2none()\\n user_courses = type_form.get_field('course').get_multi_choices_selected_values()\\n if int(role) != UserRoles.Student.code:\\n teacher_id = None\\n user_updated.gender = type_form.get_field('gender').get_value_blank2none()\\n user_updated.roles = role\\n user_updated.phone = type_form.get_field('phone').get_value_blank2none()\\n user_updated.email = type_form.get_field('email').get_value_blank2none()\\n user_updated.address = type_form.get_field('address').get_value_blank2none()\\n user_updated.teacher_id = teacher_id\\n\\n type_form.get_field('roles').get_value_blank2none()\\n user_updated.save()\\n UserLogic.update_user_courses(user_updated.id, user_courses)\\n\\n LogOperation.log(LogModule.User, LogType.Update, LogResult.Success, type_form.get_key_value('id'))\\n except ObjectDoesNotExist:\\n # transaction.rollback()\\n LogOperation.log(LogModule.User, LogType.Update, LogResult.Fail)\\n # transaction.commit()\\n update = False\\n type_form.set_paging_value(10)\\n\\n # data = User.objects.all().values_list('id',flat=False)\\n data = User.objects.all()\\n # Order\\n if type_form.order_field:\\n if type_form.order_type == 'desc':\\n data = data.order_by(\\\"-\\\" + type_form.order_field)\\n else:\\n data = data.order_by(type_form.order_field)\\n\\n # Search user_name,login_name\\n search = type_form.get_field(\\\"user_name\\\").get_search_value()\\n if search is not None and search != '':\\n data = data.filter(user_name__contains=search)\\n search = type_form.get_field(\\\"login_name\\\").get_search_value()\\n if search is not None and search != '':\\n data = data.filter(login_name__contains=search)\\n search = type_form.get_field(\\\"roles\\\").get_search_value()\\n if search:\\n l = list(User.objects.filter(roles__in=search).values_list('id', flat=True))\\n data = data.filter(id__in=l)\\n search = type_form.get_field(\\\"gender\\\").get_search_value()\\n if search:\\n l = list(User.objects.filter(gender__in=search).values_list('id', flat=True))\\n data = data.filter(id__in=l)\\n\\n type_form.set_form_data(data)\\n # type_form.set_form_model(User)\\n type_form.set_caption([\\\"id,user_name,login_name,address,phone,email,gender,roles, teacher_id, course\\\",\\n \\\"ID,Username,Login name,Address, Phone, Email, Gender,Roles, Teacher, Course\\\"])\\n info_form = None\\n context = { # \\\"select id,name,email,password from user\\\"\\n 'authType': auth_type,\\n 'user': logging_user,\\n 'infoForm': info_form,\\n 'typeForm': type_form,\\n 'keuForm': keu_form,\\n 'screen_name': ScreenName.User,\\n 'reset_password': reset_password,\\n 'update': update,\\n 'insert': insert,\\n 'reset_id': reset_id,\\n }\\n\\n return render(request, 'user.html', context)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"013138d001bdc09426a8a84089b87ee5\",\n \"score\": \"0.5567037\",\n \"text\": \"def test_create_regular(self):\\n self.client.force_authenticate(user=self.regular_user)\\n response = self.client.post(_get_list_url(1), submission_data(self.regular_user))\\n self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1afd3575e3c44f26bb4c57a869500de\",\n \"score\": \"0.5560713\",\n \"text\": \"def tickets_view_one(request, pk):\\n # get ticket details\\n ticket = get_object_or_404(Ticket, pk=pk)\\n # increase number of views by 1\\n ticket.views += 1\\n ticket.save()\\n # filter comments on specific ticket\\n comments = Comment.objects.filter(ticket_id=ticket.pk)\\n # filter upvotes on specific ticket by user ID\\n upvotes = Upvote.objects.filter(ticket_id=ticket.pk).values(\\\"user_id\\\")\\n voters = [vote[\\\"user_id\\\"] for vote in upvotes]\\n donors = User.objects.filter(id__in=voters)\\n # render drop-down list for superusers to update status\\n ticket_status_list = TicketStatus.objects.all()\\n tkt_status = ticket.ticket_status.id\\n # POST methods\\n if request.method == \\\"POST\\\":\\n comment_form = CommentForm(request.POST)\\n if comment_form.is_valid():\\n comment_form.instance.commenter = request.user\\n comment_form.instance.ticket = ticket\\n comment_form.save()\\n # remove two ticket views to avoid duplicates on POST\\n ticket.views -= 2\\n ticket.save()\\n messages.success(\\n request, f\\\"Comment successfully added!\\\")\\n return redirect(tickets_view_one, ticket.pk)\\n else:\\n comment_form = CommentForm()\\n donation_form = DonationForm()\\n context = {\\n \\\"comment_form\\\": comment_form,\\n \\\"comments\\\": comments,\\n \\\"donation_form\\\": donation_form,\\n \\\"donors\\\": donors,\\n \\\"publishable\\\": settings.STRIPE_PUBLISHABLE,\\n \\\"ticket\\\": ticket,\\n \\\"ticket_status_list\\\": ticket_status_list,\\n \\\"tkt_status\\\": tkt_status,\\n \\\"voters\\\": voters,\\n }\\n return render(request, \\\"tickets_view_one.html\\\", context)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5e87b6147606310816795316cd2cd9f\",\n \"score\": \"0.5551106\",\n \"text\": \"def post(self, request, *args, **kwargs):\\n poll = self.get_object()\\n\\n if request.user.id != poll.user.id:\\n raise PermissionDenied(\\\"You can not create choice for this poll.\\\")\\n\\n return super().post(request, *args, **kwargs) # pylint: disable=no-member\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ae82132ff4ca7b1410c3fd77247c009\",\n \"score\": \"0.55449873\",\n \"text\": \"def trip_creation_handler1(request):\\n global locations\\n if(\\\"add_checkpoint\\\" in request.POST):\\n pprint(request.POST)\\n d = {}\\n d[\\\"location\\\"] = request.POST.get(\\\"location\\\")\\n locations.append(d)\\n return redirect(\\\"/user_trips/\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"286e0637132874f32ec62bbdbec0d5d3\",\n \"score\": \"0.5543584\",\n \"text\": \"def get(self, request, user_id):\\n user = User.objects.get(pk=request.user.id)\\n volunteer_user = User.objects.get(pk=user_id)\\n if user.id == volunteer_user.id:\\n volunteer = get_object_or_404(Volunteer_User_Add_Ons, user=user_id)\\n infoForm = volunteerSignupForm(instance=volunteer)\\n\\n return render(\\n request,\\n self.url_volunteer_edit,\\n {\\n 'infoForm': infoForm,\\n 'user_id': volunteer.user.id,\\n 'volunteer': volunteer\\n }\\n )\\n else:\\n return redirect(self.login_url)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4abd47fd823fb7c057a2cb8156f64866\",\n \"score\": \"0.5537861\",\n \"text\": \"def hoteliers_only(view):\\n def new_view(request, *args, **kwargs):\\n response = view(request, *args, **kwargs)\\n id = request.user.id\\n my_user = Hotelier.objects.get(pk=id)\\n if my_user != None:\\n return response\\n # TODO: Add your own forbidden view\\n return render(request, 'thanks.html', {'message': 'شما مجاز به انجام این تغییرات نیستید.', 'redir' : '/simorgh/home/'})\\n\\n return new_view\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3f21d26efe8164e5acee4c06a619dca\",\n \"score\": \"0.5534656\",\n \"text\": \"def request_access(request):\\n context = {}\\n if request.method == 'POST':\\n context.update(request.POST)\\n context['name'] = request.POST.get('name')\\n context['email_address'] = request.POST.get('email_address')\\n context['username'] = request.POST.get('username')\\n context['affiliation'] = request.POST.get('affiliation')\\n context['reason'] = request.POST.get('reason')\\n\\n if not request.POST.get('name'):\\n context['error'] = 'name is required'\\n elif not request.POST.get('username'):\\n context['error'] = 'username is required'\\n elif not request.POST.get('email_address'):\\n context['error'] = 'email address is required.'\\n elif not request.POST.get('reason'):\\n context['error'] = 'A reason is required'\\n if not context.get('error'):\\n msg = \\\"\\\"\\\"User Requesting Access to Reactors Service\\\\n\\n Name:{}\\n username: {}\\n email address: {}\\n affiliation: {}\\n reason: {}\\\"\\\"\\\".format(context['name'],\\n context['username'],\\n context['email_address'],\\n context.get('affiliation'),\\n context['reason'])\\n # write the request to a local file as backup:\\n with open(os.path.join(settings.REACTORS_REQUESTS_DIR, context['username']), 'w+') as f:\\n f.write(msg)\\n # attempt to notify\\n try:\\n send_mail(msg,\\n \\\"do-not-reply@reactors.tacc.cloud\\\",\\n settings.REACTORS_EMAIL_LIST)\\n except Exception as e:\\n print(\\\"Error sending email for context: {}\\\".format(context))\\n context['success'] = \\\"You request has been submitted! You will hear back from TACC stuff soon.\\\"\\n return render(request, 'abaco/request_access.html', context, content_type='text/html')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"751d5244512674429a573251c4bb4d4d\",\n \"score\": \"0.5526437\",\n \"text\": \"def onflightfr(request):\\n \\n # Authentication check\\n if not request.user.is_authenticated():\\n # TODO redirect to error page\\n return redirect(reverse('index'))\\n\\n user = request.user\\n\\n sia_user = get_object_or_404(SIAUser, user=user)\\n return render(request, 'users/on-flight-fr.html', {'SiaUser':sia_user})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2aa10841b1582990c7f7036e79c87d8a\",\n \"score\": \"0.55042714\",\n \"text\": \"def customer_login_required(view):\\n\\n @functools.wraps(view)\\n def wrapped_view(**kwargs):\\n if session.get('user_type') != 'customer':\\n return jsonify({'status': 'failed',\\n 'msg': 'Operation not permitted.'})\\n return view(**kwargs)\\n\\n return wrapped_view\",\n \"title\": \"\"\n },\n {\n \"docid\": \"241dca1a0c3ece54e6e94a41fe9ef234\",\n \"score\": \"0.55041355\",\n \"text\": \"def show_pickup_analyzer():\\n #Check that user is logged in\\n if 'user' in session:\\n user = User.query.get(session['user']['username'])\\n teams = user.teams\\n return render_template('pickup-analyzer.html', user=user, teams=teams, cats=categories)\\n else:\\n flash('Please log in first.')\\n return redirect(url_for('show_login_form'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9c6b1d3a9aa02ffb1cb6cf707e0d057\",\n \"score\": \"0.54990375\",\n \"text\": \"def login_required(view):\\n @ wraps(view)\\n def wrapped_view(**kwargs):\\n db = get_db()\\n if g.user is None:\\n\\n return redirect(url_for(\\\"login\\\", next=request.url))\\n if db.execute(\\\"\\\"\\\"SELECT isBanned FROM users WHERE user_id = ? \\\"\\\"\\\", (g.user,)).fetchone()[\\\"isBanned\\\"] == 1:\\n return redirect(url_for(\\\"login\\\"))\\n return view(**kwargs)\\n return wrapped_view\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70e2f2a4887e3c643a932dec058adc16\",\n \"score\": \"0.54793507\",\n \"text\": \"def residence_add(request):\\n if request.method == 'POST':\\n form = ResidenceForm(request.POST)\\n if form.is_valid():\\n home = form.save(commit=False)\\n home.user = request.user # The logged user\\n home.save()\\n return redirect('homepage')\\n else:\\n form = ResidenceForm()\\n return render(request, 'myapp/residence/residence_add.html', {\\n 'form': form,\\n 'user': request.user\\n })\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c5405c1259d0a6135f7447b2455f7ed\",\n \"score\": \"0.54747885\",\n \"text\": \"def add_patient(request):\\n form = PatientForm()\\n print('Getting the form')\\n if request.method==\\\"POST\\\":\\n form = PatientForm(request.POST)\\n rai_list = ['age',\\n 'snf',\\n 'nephrologist',\\n 'chf',\\n 'sob',\\n 'cancer',\\n 'weight_loss',\\n 'appetite',\\n 'memory',\\n 'mobility',\\n 'eating',\\n 'toileting',\\n 'hygiene']\\n features = [int(form.data[r]) for r in rai_list]\\n \\n\\n \\n rai = utils.get_rai(form)\\n\\n\\n if form.is_valid():\\n data = form.save(commit=False)\\n data.created_by = request.user\\n data.save()\\n \\n print(request.user)\\n return render (request, \\n 'show_rai.html', \\n context = {'rai':rai,\\n 'Age':form.data['age'],\\n 'FirstName': form.data['first_name'],\\n 'LastName': form.data['last_name'],\\n 'MiddleInit': form.data['middle_initial'],\\n 'SSN':form.data['SSN']})\\n\\n form = PatientForm()\\n else:\\t\\t\\n form=PatientForm\\n return render(request, 'add_patient.html',{'form':form})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d9a2e8895051d3bbaa68325ceb9c8cf\",\n \"score\": \"0.5472861\",\n \"text\": \"def journey_creation_handler2(request):\\n # function to handle the creation of new jounrey\\n global checkpoints\\n # form = JourneyCreationForm1(request.POST)\\n # if form.is_valid():\\n pprint(checkpoints)\\n pprint(request.POST)\\n journey_name = request.POST.get(\\\"journey_name\\\")\\n journey_date = request.POST.get(\\\"travel_date\\\")\\n cotravel_number = request.POST.get(\\\"cotravel_number\\\")\\n jrny = Journey(journey_id=journey_name, start_time=parser.parse(journey_date),\\n source=checkpoints[0][\\\"checkpointA\\\"], destination=checkpoints[-1][\\\"checkpointB\\\"],\\n cotravel_number=cotravel_number)\\n jrny.save()\\n # save the journey and the sole participant of the journey is the request user\\n jrny.participants.add(request.user)\\n\\n # for each checkpoint create a new journe point with foreignkey to location and the journey objects\\n for i, x in enumerate(checkpoints):\\n loc = LocationPoint.objects.get(\\n location_name=x[\\\"checkpointA\\\"], user=request.user, location_type=\\\"Journey Point\\\")\\n JourneyPoint.objects.create(\\n location=loc, transport=x[\\\"means\\\"], point_id=i, journey=jrny)\\n if(len(checkpoints) != 0):\\n loc = LocationPoint.objects.get(\\n location_name=checkpoints[-1][\\\"checkpointB\\\"], user=request.user, location_type=\\\"Journey Point\\\")\\n JourneyPoint.objects.create(\\n location=loc, transport=x[\\\"means\\\"], point_id=len(checkpoints), journey=jrny)\\n checkpoints = []\\n return redirect(\\\"/user_journeys/\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85f036be1088d98ef307d2da6c9b03aa\",\n \"score\": \"0.5469385\",\n \"text\": \"def create(self, request, *args, **kwargs):\\n if not request.user.is_superuser:\\n raise exceptions.NotFound\\n\\n return super().create(request, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bfc6cd42bd424dcadb879965772268b3\",\n \"score\": \"0.5464898\",\n \"text\": \"def instructor(request):\\n\\n # get all lesson sets, display\\n if request.method == 'POST':\\n # Will this include where the data is updated? Such as selecting different visuals to interact with?\\n if user_auth_inst(request):\\n # Take instructor to data view\\n if lesson_set_auth(request):\\n return redirect(\\\"/tutor/tutor\\\")\\n else:\\n return redirect(\\\"accounts:profile\\\")\\n else:\\n return redirect(\\\"/accounts/settings\\\")\\n else:\\n\\n return render(request, \\\"data_analysis/visual.html\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5ac9ebd0bd2f55f99ed73f7079bc737\",\n \"score\": \"0.54510874\",\n \"text\": \"def post(self, request, *args, **kwargs):\\n c = self.getContext(request)\\n is_tutor = False\\n if tutor(c['user'].username):\\n is_tutor = True\\n c.update({'tutor': is_tutor})\\n msg = 'Good news, your request has been accepted by ' +\\\\\\n c['user'].first_name + \\\" \\\" + c['user'].last_name +\\\\\\n \\\". They should be contacting you directly shortly\\\"\\n request_user_and_id = request.POST['choice'].split('?^?')\\n requests = Request.objects.all()\\n for req in requests:\\n if req.user == request_user_and_id[0]:\\n if req.id == int(request_user_and_id[1]):\\n req.accepted_by = c['user'].username\\n req.save()\\n usr = Tutee.objects.get(user__username=req.user).user\\n c.update({'target': req.user})\\n #Email the student\\n emailer = emails()\\n emailer.send_email(usr, msg, \\\"Good News\\\")\\n \\n c.update(csrf(request))\\n return render_to_response('email_tutee.html', c)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51010e31b102db3425a4dfa28e91e371\",\n \"score\": \"0.5438009\",\n \"text\": \"def trip_request_resolve_handler(request):\\n req_lis = Notification.objects.filter(\\n user_to=request.user, notif_type=\\\"Trip Related\\\", resolved=\\\"No\\\")\\n print(request.POST)\\n index = 0\\n typ = 0\\n for i in range(len(req_lis)):\\n if(\\\"accept\\\"+str(i) in request.POST):\\n index = i\\n typ = 1\\n elif(\\\"reject\\\"+str(i) in request.POST):\\n index = i\\n typ = 0\\n print(index, typ)\\n req = req_lis[index]\\n if(typ == 0):\\n # typ 0 means the request is rejected.\\n # create a new notification meant for all the partiipants and the request user\\n req.resolved = \\\"Request rejected by \\\"+request.user.username\\n trp_id = req.travel_id\\n print(trp_id)\\n trp = Trip.objects.get(participants__in=[request.user], trip_id=trp_id)\\n\\n desc = request.user.username+\\\" rejected the request by \\\"+req.user_from.username\\n for par in trp.participants.all():\\n Notification.objects.create(user_to=par, user_from=request.user, title=\\\"Trip add request rejected\\\", description=desc,\\n notif_type=\\\"Trip Related\\\", creation_time=datetime.datetime.now()+datetime.timedelta(hours=5.5), resolved=\\\"Yes\\\")\\n Notification.objects.create(user_to=req.user_from, user_from=request.user, title=\\\"Journey add request rejected\\\", description=desc,\\n notif_type=\\\"Journey Related\\\", creation_time=datetime.datetime.now()+datetime.timedelta(hours=5.5),\\n resolved=\\\"Yes\\\")\\n if(typ == 1):\\n # typ 1 means the request is rejected.\\n # create a new notification meant for all the partiipants and the request user\\n req.resolved = \\\"Request accepted by \\\"+request.user.username\\n print(req.description.split())\\n trp_id = req.travel_id\\n print(trp_id)\\n trp = Trip.objects.get(participants__in=[request.user], trip_id=trp_id)\\n\\n trp.participants.add(req.user_from)\\n desc = request.user.username+\\\" accepted the request by \\\"+req.user_from.username\\n for par in jrny.participants.all():\\n Notification.objects.create(user_to=par, user_from=request.user, title=\\\"Trip add request accepted\\\", description=desc,\\n notif_type=\\\"Journey Related\\\", creation_time=datetime.datetime.now()+datetime.timedelta(hours=5.5),\\n resolved=\\\"Yes\\\")\\n req.save()\\n return redirect(\\\"/user_trips/\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a073faf23c82ae9426ad206fff18dd59\",\n \"score\": \"0.54373556\",\n \"text\": \"def dispatch_request(self, **kwargs):\\n not_authorized = self.pre_tests(**kwargs)\\n\\n if not_authorized:\\n return not_authorized\\n\\n to_redirect = 0\\n\\n self.set_choices(**kwargs)\\n\\n if helpers.really_submitted(self.create_form):\\n self.handle_create(**kwargs)\\n\\n elif helpers.really_submitted(self.process_form):\\n to_redirect = self.handle_process(**kwargs)\\n\\n #TODO: maybe not the cleanest way to do it!!\\n if to_redirect == 0 or to_redirect is None:\\n self.reset_fields()\\n\\n return render_template(self.template,\\n create_form=self.create_form,\\n process_form=self.process_form,\\n **self.template_kwargs)\\n else:\\n return redirect(to_redirect)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dec6d521a9717dc4453718b7247a6a20\",\n \"score\": \"0.5433963\",\n \"text\": \"def dispatch_assignent_post_request(request):\\n\\n return create_new_homework(request)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c047b4d0c5deabb3530c48ab646fab1\",\n \"score\": \"0.5433689\",\n \"text\": \"def show_create_user_form():\\n\\n return render_template(\\\"create_user_form.html\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de2503a629885b0d4219f03c6453b269\",\n \"score\": \"0.5425061\",\n \"text\": \"def add_view(self, request, form_url='', extra_context=None):\\n data = request.GET.copy()\\n\\n origin_types = TblOriginType.objects.all()\\n if len(origin_types) > 0:\\n data['origin_type'] = origin_types[len(origin_types)-1]\\n\\n environmental_ids = TblEnvironmental.objects.all()\\n if len(environmental_ids) > 0:\\n data['environmentalid'] = environmental_ids[len(environmental_ids)-1]\\n \\n taxonomy_ids = TblTaxonomy.objects.all()\\n if len(taxonomy_ids) > 0:\\n data['taxonomyid'] = taxonomy_ids[len(taxonomy_ids)-1]\\n\\n request.GET = data\\n return super().add_view(request, form_url=form_url, extra_context=extra_context)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5414096496e75485277b253ac942a64\",\n \"score\": \"0.54199666\",\n \"text\": \"def test_options_user(self):\\n request = self.factory.get(reverse('planet:options'))\\n request.user = self.user\\n response = OptionsView.as_view()(request)\\n self.assertEqual(response.status_code, 200)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5fc56e9aaf0f28128d8a20085f704a1\",\n \"score\": \"0.5411256\",\n \"text\": \"def show_add_user_form():\\n\\n return render_template (\\\"new_user.html\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f0b384f4bde6e16666a43f1e90709e5\",\n \"score\": \"0.5405932\",\n \"text\": \"def candidate_register(request):\\n if request.user.is_authenticated:\\n return redirect(\\\"index\\\")\\n else:\\n if request.method == 'POST':\\n logic.candidate_register_logic(request)\\n return redirect(\\\"index\\\")\\n else:\\n skills = Skill.objects.all()\\n return render(request,'candidate/login_signup.html',{\\\"skills\\\":skills})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"acf6dbc843d171c5c468b2b24ddd9eaa\",\n \"score\": \"0.5396369\",\n \"text\": \"def agent_login_required(view):\\n\\n @functools.wraps(view)\\n def wrapped_view(**kwargs):\\n if session.get('user_type') != 'agent':\\n return jsonify({'status': 'failed',\\n 'msg': 'Operation not permitted.'})\\n return view(**kwargs)\\n\\n return wrapped_view\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa934ebaaba98a05256eb6b23dcbe6a5\",\n \"score\": \"0.5386511\",\n \"text\": \"def sign_up_view(request):\\n if request.method == 'POST':\\n form = ProjectPlannerUserCreationForm(request.POST)\\n if form.is_valid():\\n form.save()\\n # log in the user\\n accounts_user = form.save()\\n login(request, accounts_user)\\n\\n # Message\\n messages.success(request, 'Welcome!')\\n\\n return redirect('project:index')\\n else:\\n # Message\\n messages.warning(request, 'Some fields are not correct!')\\n else:\\n form = ProjectPlannerUserCreationForm()\\n\\n context = {\\n 'form': form\\n }\\n\\n return render(request, 'accounts/signUp.html', context)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa9ab1d77fda619867dbcfaa5705c298\",\n \"score\": \"0.5375764\",\n \"text\": \"def add(request):\\n\\n if datetime.now() > datetime.strptime(settings.DEADLINE_SUBMISSION, '%Y-%m-%d %H:%M'):\\n return redirect('/')\\n else:\\n # save form\\n if request.method == 'POST':\\n new_entry = Entry()\\n entryform = EntryForm(request.POST, request.FILES, instance=new_entry)\\n if entryform.is_valid():\\n if request.user.is_authenticated():\\n new_entry.user = request.user\\n entryform.save()\\n vis_thumbnail(new_entry.screenshot)\\n return render_to_response('submission/thankyou.html', locals(), context_instance=RequestContext(request))\\n # show empty form\\n else:\\n user = request.user\\n entryform = EntryForm()\\n return render_to_response('submission/add.html', locals(), context_instance=RequestContext(request))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9780a2142149d141bbff62048bd05538\",\n \"score\": \"0.53741014\",\n \"text\": \"def show_add_form():\\n return render_template('newuser.html')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66148209a2aa0aee601c0fd10bcf05cb\",\n \"score\": \"0.5363834\",\n \"text\": \"def staff_login_required(view):\\n\\n @functools.wraps(view)\\n def wrapped_view(**kwargs):\\n print(session)\\n if session.get('user_type') != 'staff':\\n return jsonify({'status': 'failed',\\n 'msg': 'Operation not permitted.'})\\n return view(**kwargs)\\n\\n return wrapped_view\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a93e38e2a807db60aca04bcdc89bced9\",\n \"score\": \"0.5361074\",\n \"text\": \"def set_tasks(request):\\n user = request.user\\n form = CreateTaskForm(user=request.user)\\n\\n # Users can only set tasks for tables they are part of\\n tables = CafeTable.objects.filter(\\n university=user.university,\\n table_id__in=user.cafe_table_ids.values_list('table_id', flat=True))\\n form.fields['table_id'].queryset = tables\\n\\n # reset number of tasks set today if necessary\\n if user.tasks_set_today >= 2 and \\\\\\n user.next_possible_set == datetime.date.today():\\n user.tasks_set_today = 0\\n user.save()\\n\\n # students can set max 2 tasks per day (to avoid spamming)\\n if user.tasks_set_today >= 2 and not user.is_staff:\\n return redirect(\\\"dashboard\\\")\\n\\n context = {'form': form, 'num_users': get_number_current_users()}\\n\\n if request.method == 'POST':\\n # create task with provided data\\n form = CreateTaskForm(request.POST, user=request.user)\\n if form.is_valid():\\n task_name = form.cleaned_data.get('task_name')\\n table_id = form.cleaned_data.get('table_id')\\n task_content = form.cleaned_data.get('task_content')\\n points = form.cleaned_data.get('points')\\n recurrence_interval = form.cleaned_data.get('recurrence_interval')\\n max_repeats = form.cleaned_data.get('max_repeats')\\n Task.objects.create( # removed task =\\n task_name=task_name,\\n created_by=user,\\n table_id=table_id,\\n task_content=task_content,\\n points=points,\\n recurrence_interval=recurrence_interval,\\n max_repeats=max_repeats\\n )\\n user.tasks_set_today += 1\\n user.save()\\n\\n # Add notification\\n task_text = user.first_name + user.last_name + \\\" has added '\\\" + \\\\\\n task_name + \\\"' as a new task - go check it out!\\\"\\n notification = Notification(table_id=table_id, notification_type=3,\\n text_preview=task_text)\\n notification.save()\\n\\n # if student, redirect if can't set more tasks now\\n if user.tasks_set_today >= 2 and not user.is_staff:\\n user.next_possible_set = datetime.date.today() + \\\\\\n datetime.timedelta(days=1)\\n user.save()\\n return redirect(\\\"dashboard\\\")\\n\\n else:\\n context[\\\"CreateTaskForm\\\"] = form\\n else:\\n context[\\\"CreateTaskForm\\\"] = form\\n\\n # by default, tasks are not recurring\\n form.fields['recurrence_interval'].initial = \\\"n\\\"\\n return render(request, 'set_tasks.html', context)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cbb92d218481c2941900bb68bb87d8e1\",\n \"score\": \"0.53457695\",\n \"text\": \"def tasks():\\n # if user reached route via GET (as by clicking a link or via redirect)\\n if request.method == \\\"GET\\\":\\n # get tasks of current user and pass to template\\n cur_user_tasks = select_task(session[\\\"user_id\\\"])\\n return render_template(\\\"tasks.html\\\", tasks = cur_user_tasks)\\n \\n # else if user reached route via POST (as by submitting a form via POST)\\n elif request.method == \\\"POST\\\":\\n # delete completed tasks\\n delete_task(session[\\\"user_id\\\"])\\n return redirect(url_for(\\\"tasks\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0698c9b2edb9940969beaf0ccdf97dd\",\n \"score\": \"0.53387177\",\n \"text\": \"def add_operator(request, t):\\n try:\\n messages = None\\n admin = get_operator_by_username(request.session['username'])\\n params = {\\n 'rao': get_attributes_RAO(),\\n 'is_admin': is_admin(request.session['username']),\\n 'active_operator': admin\\n }\\n form = ()\\n if request.method == 'POST':\\n if 'add_operator' not in request.POST:\\n form = NewOperatorForm(request.POST)\\n else:\\n form = NewOperatorPinForm(request.POST)\\n\\n if form.is_valid():\\n\\n if 'add_operator' not in request.POST:\\n params['operator'] = True\\n return render(request, settings.TEMPLATE_URL_AGENCY + 'add_operator.html',\\n {'params': params, 'token': t, 'form': form})\\n elif admin and not admin.signStatus:\\n return HttpResponseRedirect(reverse('agency:logout_agency'))\\n else:\\n params['operator'] = False\\n result, pin = create_operator(request.session['username'], request.POST)\\n if result == StatusCode.OK.value:\\n params = {\\n 'username': request.session['username'],\\n 'operator': request.POST.get('fiscalNumber').upper(),\\n 'is_admin': is_admin(request.session['username']),\\n 'timestamp': datetime.datetime.strftime(datetime.datetime.utcnow(), '%Y-%m-%d %H:%M')\\n }\\n request.session['pin'] = pin\\n t = signing.dumps(params)\\n LOG.info(\\\"admin: {}, operator: {} - Operatore creato con successo\\\".format(\\n request.session['username'], request.POST.get('fiscalNumber').upper()),\\n extra=set_client_ip(request))\\n return HttpResponseRedirect(reverse('agency:list_operator', kwargs={'t': t, 'page': 1}))\\n elif result == StatusCode.ERROR.value:\\n messages = display_alert(AlertType.DANGER,\\n \\\"Si è verificato un problema durante l'inserimento del nuovo operatore.\\\"\\n \\\" Esiste già un operatore con questa email e/o codice fiscale.\\\")\\n elif result == StatusCode.SIGN_NOT_AVAILABLE.value:\\n messages = display_alert(AlertType.DANGER,\\n \\\"Pin errato per la terza volta. Il tuo PIN non è più valido.\\\")\\n elif result == StatusCode.UNAUTHORIZED.value:\\n messages = display_alert(AlertType.DANGER, \\\"Il PIN inserito è errato.\\\")\\n else:\\n messages = display_alert(AlertType.DANGER,\\n \\\"Si è verificato un problema durante l'inserimento del nuovo operatore.\\\"\\n \\\" Invio della mail non riuscito.\\\")\\n\\n return render(request, settings.TEMPLATE_URL_AGENCY + 'add_operator.html',\\n {'params': params, 'token': t, 'form': form, 'messages': messages})\\n except Exception as e:\\n LOG.error(\\\"Exception: {}\\\".format(str(e)), extra=set_client_ip(request))\\n params = {\\n 'rao': get_attributes_RAO(),\\n 'is_admin': is_admin(request.session['username']),\\n 'active_operator': get_operator_by_username(request.session['username'])\\n }\\n return render(request, settings.TEMPLATE_URL_AGENCY + 'error.html',\\n {\\\"statusCode\\\": StatusCode.EXC.value, 'params': params,\\n \\\"message\\\": \\\"Errore durante l'inserimento nuovo operatore\\\"})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fefb3802f746641f2e8cd19de6320459\",\n \"score\": \"0.53360593\",\n \"text\": \"def create(self, request, *args, **kwargs):\\n response = self.lib.create_trip(request.user, request_to_dict(request))\\n if (response['error']):\\n return rc.BAD_REQUEST\\n elif (response['trip']):\\n return response['trip']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3d55dc6fcde6af1f2f0fda9d6450d52\",\n \"score\": \"0.5322102\",\n \"text\": \"def login_not_required(view):\\n setattr(view, LOGIN_NOT_REQUIRED_MARKER, True)\\n return view\",\n \"title\": \"\"\n },\n {\n \"docid\": \"888c484e48be828ad1ae45563f319a06\",\n \"score\": \"0.5321672\",\n \"text\": \"def form_valid(self, form):\\n form.instance.user = self.request.user\\n form.instance.issue = bug_or_feature(self)\\n return super(TicketCreateView, self).form_valid(form)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9525759e43f08c5e81f36bd18428090a\",\n \"score\": \"0.53206843\",\n \"text\": \"def checkAccess(self):\\n self.mutator.taskFromKwargs(comments=True, work_submissions=True)\\n self.data.is_visible = self.check.isTaskVisible()\\n\\n if task_logic.updateTaskStatus(self.data.task):\\n # The task logic updated the status of the task since the deadline passed\\n # and the GAE task was late to run. Reload the page.\\n raise RedirectRequest('')\\n\\n if self.request.method == 'POST':\\n # Access checks for the different forms on this page. Note that there\\n # are no elif clauses because one could add multiple GET params :).\\n self.check.isProfileActive()\\n\\n if 'reply' in self.data.GET:\\n # checks for posting comments\\n # valid tasks and profile are already checked.\\n self.check.isBeforeAllWorkStopped()\\n self.check.isCommentingAllowed()\\n\\n if 'submit_work' in self.data.GET:\\n self.check.isBeforeAllWorkStopped()\\n if not task_logic.canSubmitWork(self.data.task, self.data.profile):\\n self.check.fail(DEF_NOT_ALLOWED_TO_UPLOAD_WORK)\\n\\n if 'button' in self.data.GET:\\n # check for any of the buttons\\n button_name = self._buttonName()\\n\\n buttons = {}\\n TaskInformation(self.data).setButtonControls(buttons)\\n if not buttons.get(button_name):\\n self.check.fail(DEF_NOT_ALLOWED_TO_OPERATE_BUTTON % button_name)\\n\\n if 'send_for_review' in self.data.GET:\\n self.check.isBeforeAllWorkStopped()\\n if not task_logic.isOwnerOfTask(self.data.task, self.data.profile) or \\\\\\n not self.data.work_submissions:\\n self.check.fail(DEF_CANT_SEND_FOR_REVIEW)\\n\\n if 'delete_submission' in self.data.GET:\\n self.check.isBeforeAllWorkStopped()\\n id = self._submissionId()\\n work = GCIWorkSubmission.get_by_id(id, parent=self.data.task)\\n\\n if not work:\\n self.check.fail(DEF_NO_WORK_FOUND %id)\\n\\n time_expired = work.submitted_on - datetime.datetime.now()\\n if work.user.key() != self.data.user.key() or \\\\\\n time_expired > task_logic.DELETE_EXPIRATION:\\n self.check.fail(DEF_NOT_ALLOWED_TO_DELETE)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f82efd90a272c120d130f50fa16fa763\",\n \"score\": \"0.5315018\",\n \"text\": \"def addCar(request):\\n if 'user_type' in request.session.keys():\\n if request.session.get('user_type') == 1:\\n return render(request, 'addCar.html')\\n else:\\n return HttpResponseForbidden()\\n else:\\n return redirect('login')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03ab43ca5fe2a3b83290df5bbcee6e01\",\n \"score\": \"0.53149116\",\n \"text\": \"def canCreate(self, request):\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4835ecd887134dc82ec4d1b8824ebd0b\",\n \"score\": \"0.5306381\",\n \"text\": \"def create_user(self, request, *args, **kwargs):\\n return self.create(request, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dabe37ee3cc2de0c3c86ee65e7d2db45\",\n \"score\": \"0.53038955\",\n \"text\": \"def create(self, request, *args, **kwargs):\\n return super().create(request, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c93313e21128ee4716fc07ae092bc38c\",\n \"score\": \"0.5301878\",\n \"text\": \"def allow(self, user, action):\\n if action == 'view':\\n return True\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5579ebe20458e78b103955434e693cd9\",\n \"score\": \"0.529623\",\n \"text\": \"def isNotCompetentToAuthenticate(request):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"751fd3d37ac51672d1be06352d806e40\",\n \"score\": \"0.529372\",\n \"text\": \"def new(request):\\n\\treturn render(request, 'semi_restful/add.html')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3f8276ee454fc74888d031a81a8b90a\",\n \"score\": \"0.5291223\",\n \"text\": \"def post(self, request):\\n context = {\\n 'signed_in_volunteers': VolunteerLog.logged_in_volunteers_objects.all(),\\n 'log_sign_in_form': LogSignInForm\\n }\\n if request.method == 'POST':\\n log_sign_out_form = LogSignOutForm(request.POST)\\n if log_sign_out_form.is_valid():\\n log_sign_out_form.save()\\n return super(VolunteerSignInView, self).form_valid(log_sign_out_form)\\n\\n if request.method == 'POST' and not log_sign_out_form.is_valid():\\n log_sign_in_form = LogSignInForm(request.POST)\\n if log_sign_in_form.is_valid():\\n log_sign_in_form.save()\\n return render(request, self.template_name, context)\\n else:\\n messages.error(request, \\\"Please correct the errors below and resubmit.\\\")\\n context['log_sign_in_form'] = log_sign_in_form\\n return render(request, self.template_name, context)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3cf5086ff32730417e9d87c44acc7df\",\n \"score\": \"0.5285659\",\n \"text\": \"def topology_access_check(request, callee, action, **kwargs):\\n\\n def denied():\\n \\\"\\\"\\\"Generate an error message and redirect if we try do something to a\\n topology we're not allowed to\\\"\\\"\\\"\\n messages.error(request, \\\"Either this topology doesn't exist or you don't \\\"\\n \\\"have permission to %s it.\\\" % action)\\n return HttpResponseRedirect('/login/')\\n\\n def denied_add():\\n \\\"\\\"\\\"Generate an error message and redirect if we try to create a topology\\n and are not allowed to\\\"\\\"\\\"\\n messages.error(request, \\\"You don't have permission to create topologies.\\\")\\n return HttpResponseRedirect('/login/')\\n\\n \\n # If we're trying to add a template, don't need to get the template itself\\n if action == \\\"add\\\":\\n if permissions.allowed_topology_access_create(request.user):\\n return callee(request)\\n else:\\n return denied_add()\\n\\n else:\\n\\n # Try getting the template - if it doesn't exist, show the same message\\n # as for permission denied. If we don't have a \\\"tid\\\" argument, django\\n # will show an internal error, which is what we want.\\n tid = int(kwargs[\\\"tid\\\"])\\n kwargs[\\\"tid\\\"] = tid\\n try :\\n topo = db.Topology.objects.get(pk=tid)\\n except db.Topology.DoesNotExist:\\n return denied()\\n\\n if action == \\\"use\\\":\\n # See if there is an HTTP GET token - if there is, try to use the token\\n # method for authentication\\n try:\\n token = request.GET[\\\"token\\\"]\\n except KeyError:\\n pass\\n else:\\n # See if the token is valid\\n user = crypto.validate_token(token)\\n if user != None and permissions.allowed_topology_access_use(user, topo):\\n request.user = user\\n return callee(request, topo=topo, **kwargs)\\n if permissions.allowed_topology_access_use(request.user, topo):\\n return callee(request, topo=topo, **kwargs)\\n else:\\n return denied()\\n\\n elif action == \\\"change\\\":\\n if permissions.allowed_topology_access_change(request.user, topo):\\n return callee(request, topo=topo, **kwargs)\\n else:\\n return denied()\\n elif action == \\\"delete\\\":\\n if permissions.allowed_topology_access_delete(request.user, topo):\\n return callee(request, topo=topo, **kwargs)\\n else:\\n return denied()\\n else:\\n raise ValueError(\\\"Unknown action: %s\\\" % options[\\\"action\\\"])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4cadcc43d8373a9636811caf391ee728\",\n \"score\": \"0.52789825\",\n \"text\": \"def createlisting(request):\\n\\t# Check if user submitted a new listing post\\n\\tif request.method == \\\"POST\\\":\\n\\n\\t\\tform = NewListingForm(request.POST)\\n\\t\\t# Validate the form\\n\\t\\tif form.is_valid():\\n\\t\\t\\tlisting = form.save(commit=False) # allow us to change fields manually\\n\\t\\t\\t# Add information that wasn't on user form\\n\\t\\t\\tlisting.current_price = form.cleaned_data[\\\"starting_bid\\\"]\\n\\t\\t\\t#listing.current_price = 0.0\\n\\t\\t\\tlisting.user = request.user\\n\\t\\t\\n\\t\\t# Save the listing to the Database\\n\\t\\tlisting.save()\\n\\t\\t# Return User to New Listing\\n\\t\\treturn HttpResponseRedirect(reverse(\\\"listing\\\", args=(listing.id,)))\\n\\telse:\\n\\t\\treturn render(request, \\\"auctions/createlisting.html\\\", {\\\"form\\\": NewListingForm()})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51c8105bb6ba8aff252af65752efaf46\",\n \"score\": \"0.5276149\",\n \"text\": \"def show_new_user_form():\\n return render_template('add_user_form.html')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b37c2d9133b8896585b13f4746c34b70\",\n \"score\": \"0.5267732\",\n \"text\": \"def create_ticket(request, ticket_type):\\n if request.method == \\\"POST\\\":\\n form = TicketForm(request.POST)\\n if form.is_valid():\\n form.save()\\n messages.success(request, \\\"Ticket added\\\")\\n if ticket_type == \\\"FEATURE\\\":\\n return redirect(all_features)\\n else:\\n return redirect(all_bugs)\\n else:\\n form = TicketForm()\\n form.fields[\\\"ticket_type\\\"].initial = ticket_type\\n return render(request, 'ticketform.html', {'form': form})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d5c536d79aaf059cd621f8aab6c0a4d\",\n \"score\": \"0.52633303\",\n \"text\": \"def get(self, request):\\n if request.user.is_authenticated and not request.user.is_verified:\\n return render(request, 'account/signup_done.html')\\n return redirect('home')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c59f41e3ae926cf90b9ff7627f6a4da\",\n \"score\": \"0.526257\",\n \"text\": \"def __call__(self):\\n # aq_inner is needed in some cases like in the portlet renderers\\n # where the context itself is a portlet renderer and it's not on the\\n # acquisition chain leading to the portal root.\\n # If you are unsure what this means always use context.aq_inner\\n context = self.context.aq_inner\\n form = self.request.form\\n self.userid = form.get(\\\"userid\\\", \\\"\\\")\\n self.loginid = form.get(\\\"loginid\\\", \\\"\\\")\\n self.user_edit = form.get(\\\"form.button.edit\\\", None) is not None\\n self.user_activate = form.get(\\\"form.button.activate\\\", None) is not None\\n self.user_deactivate = form.get(\\n \\\"form.button.deactivate\\\", None) is not None\\n\\n if not self.userid:\\n return self.index()\\n\\n if self.user_edit:\\n edit_view = \\\"%s/user_edit_view?userid=%s\\\" % (\\n self.context.absolute_url(), self.userid)\\n self.request.response.redirect(edit_view)\\n\\n portal_membership = getToolByName(context, 'portal_membership')\\n\\n if portal_membership.isAnonymousUser():\\n raise Unauthorized('You need to login to access this page.')\\n\\n current_user = portal_membership.getAuthenticatedMember()\\n user_groups = current_user.getGroups()\\n sm = getSecurityManager()\\n portal = getUtility(ISiteRoot)\\n\\n # user_management_list = context.get_management_dict()\\n manage_by_group = context.get_manage_by_groups()\\n manage_all = context.get_manage_all()\\n\\n # TODO: \\\"checking for * should be done in get_manage_by_groups\\\"\\n if sm.checkPermission(ManagePortal, portal) and not manage_by_group:\\n # show all for adminstratior/manager\\n manage_by_group = [manage_all]\\n\\n if not manage_by_group:\\n raise Unauthorized(UNAUTHORISED_ERROR)\\n\\n user = portal_membership.getMemberById(self.userid)\\n if not user:\\n context.plone_utils.addPortalMessage(\\n _(u'This user does not exists.'))\\n return self.index()\\n\\n user_groups = user.getGroups()\\n same_groups = user_groups\\n if manage_all not in manage_by_group:\\n # TODO((ivan) limit the search instead of doing it after that\\n same_groups = set(manage_by_group) & set(user_groups)\\n if not same_groups:\\n raise Unauthorized(UNAUTHORISED_ERROR)\\n\\n self.user_fullname = user.getProperty('fullname', '')\\n self.user_email = user.getProperty('email', '')\\n approved_by = user.getProperty('approved_by', '')\\n self.user_approved_by = context.get_user_name(\\n approved_by)\\n self.user_approved_date = user.getProperty('approved_date', '')\\n last_updated_by = user.getProperty('last_updated_by', '')\\n self.user_last_updated_by = context.get_user_name(\\n last_updated_by)\\n self.user_last_updated_date = user.getProperty('last_updated_date', '')\\n self.user_status = context.get_status(user)\\n self.user_is_active = context.is_active(user)\\n # display the groups based from the login user management list\\n group_names = context.get_groups_title(same_groups)\\n self.user_group = \\\", \\\".join(\\n [group_name[\\\"group_title\\\"] for group_name in group_names])\\n\\n if self.user_activate:\\n context.user_activate(self.userid, self.request)\\n\\n profile_view = \\\"%s/user_profile_view?userid=%s\\\" % (\\n self.context.absolute_url(), self.userid)\\n self.request.response.redirect(profile_view)\\n\\n if self.user_deactivate:\\n context.user_deactivate(self.userid)\\n\\n profile_view = \\\"%s/user_profile_view?userid=%s\\\" % (\\n self.context.absolute_url(), self.userid)\\n self.request.response.redirect(profile_view)\\n\\n return self.index()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64f784f505cdbf2c353f89c4151ca79f\",\n \"score\": \"0.52621067\",\n \"text\": \"def trip_itinerary(trip_name, trip_id):\\n \\n email = session.get('email')\\n user = User.query.filter_by(email=email).first()\\n users = User.query.filter(User.email!=email).all()\\n trip = Trip.query.filter_by(trip_id=trip_id).first()\\n\\n return render_template('itinerary.html',\\n trip_name=trip_name,\\n trip_id=trip_id,\\n trip=trip,\\n username=user.username,\\n users=users)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e4cb222be15ae7423c55b127040c9f4\",\n \"score\": \"0.5257335\",\n \"text\": \"def home_signup_view(request:Request, email:str = Form(...)):\\n\\n airtable_client = Airtable(\\n base_id = AIRTABLE_BASE_ID,\\n api_key = AIRTABLE_API_KEY,\\n table_name = AIRTABLE_TABLE_NAME,\\n )\\n\\n # to send email to airtable\\n did_send = airtable_client.create_records({\\\"email\\\":email})\\n return templates.TemplateResponse(\\\"home.html\\\",{\\\"request\\\":request, \\\"submitted_email\\\":email, \\\"did_send\\\":did_send})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87e1f9786a4c9efd33e97b98bb7aa81f\",\n \"score\": \"0.52547777\",\n \"text\": \"def tickets_new_feature(request):\\n if request.method == \\\"POST\\\":\\n ticket_form = TicketForm(request.POST)\\n donation_form = DonationForm(request.POST)\\n if ticket_form.is_valid() and donation_form.is_valid():\\n # amount to pay / donate\\n donation_amount = 0\\n donation_amount += int(request.POST.get(\\\"donation_amount\\\"))\\n try:\\n # build Stripe payment\\n token = request.POST[\\\"stripeToken\\\"]\\n customer = stripe.Charge.create(\\n amount=int(donation_amount * 100),\\n currency=\\\"EUR\\\",\\n description=(\\n request.user.email +\\n \\\" (\\\" + request.user.get_full_name() + \\\")\\\"),\\n source=token,\\n )\\n except stripe.error.CardError:\\n # Stripe payment error\\n messages.error(request, f\\\"Your card was declined!\\\")\\n # authorization is valid - payment successful\\n if customer.paid:\\n ticket_form.instance.author = request.user\\n ticket_form.instance.ticket_type_id = 2\\n ticket_form.instance.total_donations = donation_amount\\n # update user Profile with additional donation amount\\n get_user_donations = Profile.objects.values_list(\\n \\\"total_donated\\\", flat=True).get(user_id=request.user.id)\\n new_user_donations = get_user_donations + donation_amount\\n Profile.objects.filter(\\n user_id=request.user.id).update(\\n total_donated=new_user_donations)\\n # update ticket status to In Progress if €100 goal is achieved\\n if donation_amount >= int(100):\\n ticket_form.instance.ticket_status_id = 2\\n else:\\n ticket_form.instance.ticket_status_id = 1\\n new_ticket = ticket_form.save()\\n new_ticket_id = new_ticket.pk\\n messages.success(\\n request, f\\\"Thank you for your Feature Request!\\\\\\n €{donation_amount} was charged to your card.\\\")\\n return redirect(tickets_view_one, new_ticket_id)\\n else:\\n messages.error(request, f\\\"Unable to take payment!\\\")\\n else:\\n messages.error(request, f\\\"There was an error. Please try again.\\\")\\n else:\\n ticket_form = TicketForm()\\n donation_form = DonationForm()\\n context = {\\n \\\"donation_form\\\": donation_form,\\n \\\"ticket_form\\\": ticket_form,\\n \\\"publishable\\\": settings.STRIPE_PUBLISHABLE\\n }\\n return render(request, \\\"tickets_new_feature.html\\\", context)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77df534e230304382a17d6b64b34905c\",\n \"score\": \"0.52546364\",\n \"text\": \"def before_request():\\n g.user = current_user\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f38f0acf2569f2adef9f69a1e8305274\",\n \"score\": \"0.5254325\",\n \"text\": \"def vouch_required(f):\\n @login_required\\n @wraps(f)\\n def wrapped(request, *args, **kwargs):\\n if request.user.get_profile().is_vouched:\\n return f(request, *args, **kwargs)\\n else:\\n log.warning('vouch_required forbidding access')\\n return HttpResponseForbidden(_('You must be vouched to do this.'))\\n\\n return wrapped\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d56c55b292834e2d79c1009c4813918\",\n \"score\": \"0.52485853\",\n \"text\": \"def new_food_entry_form(user_id):\\n\\t\\n\\treturn render_template(\\\"entry.html\\\", user_id=user_id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44966277b7e240553f5bc4381193f23b\",\n \"score\": \"0.5245977\",\n \"text\": \"def test_func(self):\\n\\n todo = self.get_object()\\n return self.request.user.is_superuser or \\\\\\n self.request.user == todo.assigned_user\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a8e417dcb45cd61a48ed67dc518567f\",\n \"score\": \"0.5244312\",\n \"text\": \"def create_task_form(self,req):\\r\\n\\t\\ttemp_set=loader.get_template(\\\"form_set.html\\\")\\r\\n\\t\\tset_form=task_write()\\r\\n\\t\\tcontext={\\r\\n\\t\\t\\t\\\"form\\\":set_form,\\r\\n\\t\\t\\t\\\"title\\\":u\\\"Authorization Setup\\\",\\r\\n\\t\\t\\t\\\"act_cn\\\":u\\\"Authorization Management\\\",\\r\\n\\t\\t\\t\\\"act\\\":\\\"authset\\\",\\r\\n\\t\\t\\t}\\r\\n\\t\\trt=temp_set.render(context,req)\\r\\n\\t\\treturn rt\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4551a4ecb5421c1b161a404fdf27681\",\n \"score\": \"0.5242026\",\n \"text\": \"def login_router(request):\\n\\n user = request.user\\n\\n # Checks if an AllSkills model is attached to the user\\n # If not create and send to edit page\\n try:\\n user = user.allskills\\n except AttributeError:\\n models.AllSkills.objects.create(\\n user_id=user.pk\\n )\\n\\n # Redirect to the main edit page\\n return redirect('profiles:edit')\\n\\n else:\\n # redirect to main profile page\\n return redirect('profiles:profile', pk=request.user.pk)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7f9a95a0d7a27fc246ac6c8d5991503\",\n \"score\": \"0.5235917\",\n \"text\": \"def shared(request):\\n print(request.method)\\n if request.method == \\\"POST\\\":\\n set_user_step(request.user, step=4, phase=1)\\n result = 'success'\\n json_response = {'result': result}\\n return http.JsonResponse(json_response)\\n\\n return http.HttpResponseBadRequest(\\\"Only accesible with POST\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"442e9ff84517371839822f77d41d2a57\",\n \"score\": \"0.5216749\",\n \"text\": \"def main(request):\\n\\n if request.user.is_authenticated:\\n return redirect(\\\"question\\\")\\n else:\\n return redirect(\\\"login\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"966c6a282b653994b33a2d74ed999101\",\n \"score\": \"0.52164596\",\n \"text\": \"def test_view_uses_correct_template(self):\\n self.client.login(username=\\\"john.doe\\\", password=\\\"XVAP11!0$\\\")\\n response = self.client.get(reverse(\\\"meal-add\\\"))\\n self.assertEqual(response.status_code, 200)\\n self.assertTemplateUsed(response, \\\"meals/meal_form.html\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8dde170298c7585c4318c3b2cb303907\",\n \"score\": \"0.52153194\",\n \"text\": \"def add_single_view(self, request, form_url='', extra_context=None):\\n return self.add_view(\\n request, form_url=form_url, extra_context=extra_context)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"194b028581b1132edb8149ac5a44e8da\",\n \"score\": \"0.521503\",\n \"text\": \"def dashboard():\\n\\n client_form = AddClientForm(prefix='client_form')\\n prop_form = AddPropertyForm(prefix='prop_form')\\n showing_form = AddShowingForm(prefix='showing_form')\\n showings = query.getShowingByUser(current_user.username)\\n table = query.getClientsForUser(current_user.username)\\n\\n if prop_form.validate_on_submit() and prop_form.submit.data:\\n query.createProperty({\\n 'List_Price': prop_form.list_price.data,\\n 'Location': prop_form.location.data,\\n 'Trend_Link': prop_form.trend_link.data})\\n\\n return redirect(\\\"dashboard\\\")\\n\\n if showing_form.validate_on_submit() and showing_form.submit.data:\\n \\n r = query.createShowing({\\n 'client_id': showing_form.client_id.data,\\n 'Property_ID': showing_form.Property_ID.data,\\n 'Feedback': showing_form.Feedback.data,\\n 'Rating': showing_form.Rating.data})\\n print(r)\\n\\n showings = query.getShowingByUser(current_user.username)\\n\\n return redirect(\\\"dashboard\\\")\\n\\n # on submit re-render template using form data and query\\n if client_form.validate_on_submit() and client_form.submit.data:\\n\\n query.createClient(\\n {\\n \\\"first_name\\\": client_form.first_name.data,\\n \\\"last_name\\\": client_form.last_name.data,\\n \\\"email\\\": client_form.email.data,\\n \\\"phone\\\": client_form.phone.data,\\n \\\"user_id\\\": current_user.id,\\n }\\n )\\n\\n table = query.getClientsForUser(current_user.username)\\n\\n return redirect(\\\"dashboard\\\")\\n\\n return render_template(\\n \\\"home/dashboard.html\\\",\\n title=\\\"Dashboard\\\",\\n table=table,\\n user=current_user,\\n showings=showings,\\n showing_form=showing_form,\\n client_form=client_form,\\n prop_form=prop_form,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dfdd30fbabecd233635b20197681a729\",\n \"score\": \"0.52150214\",\n \"text\": \"def login_required(view):\\n def view_wrapper(request, *args, **kwargs):\\n if request.user.is_authenticated:\\n return view(request, *args, **kwargs)\\n else:\\n if request.is_ajax():\\n return JsonResponse({'error': _(u'Debes estar logueado.')}, status=400)\\n else:\\n path = request.get_full_path()\\n return redirect_to_login(path)\\n return view_wrapper\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3ac053c38a9d0cdfce3cc9ea28d9d7b\",\n \"score\": \"0.5206036\",\n \"text\": \"def plan_view(request, plan_id):\\n\\n plan = Plan.objects.get(pk=plan_id)\\n\\n if request.user==plan.user:\\n motes = plan.motes.all()\\n return {'plan': plan, 'motes': motes,}\\n else:\\n return redirect('login')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d00f31420d4523019a4218ad23f6d33\",\n \"score\": \"0.52053094\",\n \"text\": \"def view(self, user, financing, *args):\\n if user.is_anonymous or user.is_client:\\n return False\\n\\n if user.is_administrator:\\n return True\\n\\n if user.is_manager:\\n return True\\n\\n if user.is_advisor:\\n return True\\n\\n return self.admin_permission(user, financing, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"652b785db101bc9a7e11ddae86875afe\",\n \"score\": \"0.520398\",\n \"text\": \"def create(self, request):\\n\\n\\t\\treturn ObtainAuthToken().post(request)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4454f383ca75e348bf7ed7f2a2707f88\",\n \"score\": \"0.5203732\",\n \"text\": \"def __init__(self, *args, **kwargs):\\n self.user = kwargs.pop('user')\\n self.draw = kwargs.pop('draw')\\n super(BuyTicketsForm, self).__init__(*args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7de9557587be1cb02d0322356f05e65e\",\n \"score\": \"0.52032346\",\n \"text\": \"def protected_view(request):\\n return render(request, 'bands/protected.html', {'current_user': request.user})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d563c679a14a0b955ac40e5daeeea3ed\",\n \"score\": \"0.5195981\",\n \"text\": \"def show_new_user_form():\\n\\n return render_template('users/new_user.html')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5d0307b5ea8cae318c54d98cd5b9a9b\",\n \"score\": \"0.5190887\",\n \"text\": \"def request_add_handler(request):\\n pprint(request.POST)\\n global matching_jlist\\n n = len(matching_jlist)\\n user = request.user\\n print(n)\\n index = 0\\n for i in range(n):\\n if(\\\"submit\\\"+str(i) in request.POST):\\n index = i\\n print(\\\"index\\\", index)\\n jrny = matching_jlist[index][0]\\n description = \\\"User \\\"+user.username+\\\" wants to be added to the \\\" + \\\\\\n jrny.journey_id+\\\" in which you are a participant.\\\"\\n title = \\\"Journey Add request\\\"\\n for ut in jrny.participants.all():\\n Notification.objects.create(user_to=ut, user_from=user, title=title, description=description,\\n notif_type=\\\"Journey Related\\\", creation_time=datetime.datetime.now()+datetime.timedelta(hours=5.5),\\n travel_id=jrny.journey_id)\\n matching_jlist[index][1] = True\\n return redirect(\\\"/user_journeys/\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d366c6b0f8b3efbe38ebde5c749dd98\",\n \"score\": \"0.5189515\",\n \"text\": \"def test_add_flight_functionality(self):\\n with self.subTest('Test normal user cannot add flight.'):\\n self.client.force_authenticate(user=self.normal_user)\\n response = self.client.post(self.url, data=self.data, format='json')\\n self.assertEqual(response.status_code, 403)\\n self.assertEqual(response.data['error'], 'PermissionDenied')\\n self.assertEqual(response.data['error_description'],\\n 'You do not have permission to perform this action.')\\n\\n with self.subTest('Test a staff can add flight.'):\\n self.client.force_authenticate(user=self.staff_user)\\n response = self.client.post(self.url, data=self.data, format='json')\\n self.assertEqual(response.status_code, 201)\\n self.assertEqual(response.data['name'], 'FLIGHT VALERIA')\\n self.assertEqual(Flight.objects.all().count(), 1)\\n\\n with self.subTest('Test a super user can add flight.'):\\n self.data['name'] = 'flight1'\\n self.client.force_authenticate(user=self.super_user)\\n response = self.client.post(self.url, data=self.data, format='json')\\n self.assertEqual(response.status_code, 201)\\n self.assertEqual(response.data['name'], 'FLIGHT1')\\n self.assertEqual(Location.objects.all().count(), 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1bbce2b702c17f914f37dabd1c1dfd8d\",\n \"score\": \"0.51842207\",\n \"text\": \"def admin_login_required(view):\\n\\n @functools.wraps(view)\\n def wrapped_view(**kwargs):\\n if session.get('user_type') != 'admin':\\n return json\\n return view(**kwargs)\\n\\n return wrapped_view\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e23a66c0b804869ef276b1106c07f5e4\",\n \"score\": \"0.51601136\",\n \"text\": \"def post(self, request: HttpRequest, *args, **kwargs) -> Any:\\n\\n self.set_application_and_task()\\n\\n self.application.cleared_by.add(self.request.user)\\n\\n if self.kwargs[\\\"case_type\\\"] == \\\"import\\\":\\n view_name = \\\"Search Import Applications\\\"\\n else:\\n view_name = \\\"Search Certificate Applications\\\"\\n\\n messages.success(request, f\\\"Case cleared, it can still be viewed in the {view_name} page.\\\")\\n\\n return redirect(reverse(\\\"workbasket\\\"))\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":693,"cells":{"query_id":{"kind":"string","value":"99d3bdd659d55dca74810a5a89293664"},"query":{"kind":"string","value":"Write a pd.DataFrame to a google sheets sheet"},"positive_passages":{"kind":"list like","value":[{"docid":"eaa125b9edb84e9ad4fed7d55fc2e99d","score":"0.63241166","text":"def write_df(wks, df, row, col):\n\n # header row\n for j, col_name in enumerate(df.columns):\n if 'AOL' in col_name:\n wks.update_cell(row+1, col+j+1, 'AOL')\n wks.update_cell(row+2, col+j+1, col_name.replace('AOL', ''))\n elif 'Keen' in col_name:\n wks.update_cell(row+1, col+j+1, 'Keen')\n wks.update_cell(row+2, col+j+1, col_name.replace('Keen', ''))\n else:\n wks.update_cell(row+2, col+j+1, col_name)\n # data\n for i, row_of_data in df.iterrows():\n for j, col_name in enumerate(df.columns):\n wks.update_cell(row+3+i, col+j+1, row_of_data[j])","title":""}],"string":"[\n {\n \"docid\": \"eaa125b9edb84e9ad4fed7d55fc2e99d\",\n \"score\": \"0.63241166\",\n \"text\": \"def write_df(wks, df, row, col):\\n\\n # header row\\n for j, col_name in enumerate(df.columns):\\n if 'AOL' in col_name:\\n wks.update_cell(row+1, col+j+1, 'AOL')\\n wks.update_cell(row+2, col+j+1, col_name.replace('AOL', ''))\\n elif 'Keen' in col_name:\\n wks.update_cell(row+1, col+j+1, 'Keen')\\n wks.update_cell(row+2, col+j+1, col_name.replace('Keen', ''))\\n else:\\n wks.update_cell(row+2, col+j+1, col_name)\\n # data\\n for i, row_of_data in df.iterrows():\\n for j, col_name in enumerate(df.columns):\\n wks.update_cell(row+3+i, col+j+1, row_of_data[j])\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"f2cbcdddc55b0d33b6dba497953238b6","score":"0.7028948","text":"def write_google_sheet(results_dict, row=2, name='results_layers', sheet_name='run1'):\n # Google API stuff\n scope = [\"https://www.googleapis.com/auth/drive\"]\n creds = ServiceAccountCredentials.from_json_keyfile_name('config/gcp-credentials.json', scope)\n client = gspread.authorize(creds)\n sheet = client.open(name).worksheet(sheet_name)\n sheet.update(f'B{row}:D{row}', [list(results_dict.values())])","title":""},{"docid":"78602bd0955a460b27245fc8415e5a3a","score":"0.6928508","text":"def write_sheet(writer, name, df, index=False):\n if index:\n df = df.reset_index()\n df.to_excel(writer, name, index=False)\n for i, col in enumerate(df.columns):\n if df.dtypes[col].name.startswith((\"float\", \"int\")):\n width = len(str(col)) + 2\n else:\n width = (\n max([df[col].map(lambda x: len(str(x or \"None\"))).max(), len(col)]) + 2\n )\n writer.sheets[name].set_column(i, i, width) # assumes xlsxwriter as engine","title":""},{"docid":"256e4ad6fd9f80ade2a6a4d0a44a951d","score":"0.69066215","text":"def _xlsheet(writer, df, sheet_name, **kwargs):\r\n\r\n df.to_excel(writer, sheet_name, **kwargs)\r\n #tweak column width\r\n for i in range(df.shape[1]):\r\n writer.sheets[sheet_name].set_column(i, i+1, len(df.columns[i]) + 4)","title":""},{"docid":"eefd16ba6ef9b81eb9b0a1cdeb078a2a","score":"0.661218","text":"def write_file(df, file_name):\n writer = pd.ExcelWriter(f\"{file_name}\", engine=\"openpyxl\")\n df.to_excel(writer, sheet_name=\"Sheet1\", startrow=1)\n writer.save()","title":""},{"docid":"482c9dbb15055c9e7f453ac116d14f7c","score":"0.65816694","text":"def save_sheet(svc_sheet, spreadsheet_id, tab_name, header, records):\n if len(records) == 0:\n return\n\n count_columns = len(header)\n count_rows = len(records) + 1\n workbook = svc_sheet.open_by_key(spreadsheet_id)\n sheets = dict()\n for sheet in workbook.worksheets():\n sheets[sheet.title] = sheet\n\n if tab_name not in sheets:\n worksheet = workbook.add_worksheet(tab_name, count_rows, count_columns)\n\n else:\n worksheet = sheets[tab_name]\n\n worksheet.resize(rows=count_rows, cols=count_columns)\n range_text = 'A1:{}'.format(rowcol_to_a1(count_rows, count_columns))\n logging.info('accessing range {}'.format(range_text))\n cells = worksheet.range(range_text)\n for cell in cells:\n count_row = cell.row - 1\n count_col = cell.col - 1\n field = header[count_col]\n if count_row == 0:\n cell.value = field\n\n else:\n row_data = records[count_row - 1]\n cell.value = row_data[field]\n\n worksheet.update_cells(cells)","title":""},{"docid":"0377b94a25e49272918533b5a48ad464","score":"0.64087117","text":"def write_sheet(writer, tables, id_name, ligand_name):\n\tfor table in tables:\n\t\ttable_id = table.get('id')\n\t\tif table_id == id_name:\n\t\t\ttable_df = pd.concat(pd.read_html(table.prettify()))\n\t\t\tdrop_list = [i for i in table_df.index if i%2!=0]\n\t\t\ttable_df = table_df.drop(drop_list, axis=0)\n\t\t\tdrop_col = [\"Unnamed: \"+str(i) for i in range(1, 10)]\n\t\t\ttable_df = table_df.drop(drop_col, axis=1)\n\t\t\ttable_df.to_excel(writer, sheet_name=id_name, index=False)\n\t\t\tprint(\"Sheet name \"+ligand_name+\" is add\")","title":""},{"docid":"7343d3fff603e2f33dbaf85c80e46764","score":"0.6363107","text":"def save_styled_dataframe(\n filepath_with_ext: str,\n sheet_name_to_styler: Dict[str, Any],\n ) -> None:\n with pd.ExcelWriter(filepath_with_ext) as excel_writer:\n for sheet_name, styler_obj in sheet_name_to_styler.items():\n styler_obj.to_excel(\n excel_writer=excel_writer,\n sheet_name=sheet_name,\n index=False,\n engine='xlsxwriter',\n )\n return None","title":""},{"docid":"0cc79258ffe88e4964b9eb467f5155e9","score":"0.63454306","text":"def write_exel(df, output_name):\n # create excel writer object\n writer = pd.ExcelWriter('covid_data.xlsx')\n # write dataframe to excel\n covid_data.to_excel(writer)\n # save the excel\n writer.save()\n print('DataFrame is written successfully to Excel File.')","title":""},{"docid":"61cd408ea60be3df2ad61d27e0c4db45","score":"0.63329643","text":"def write_output_excel(excel_book_path, output_dict, sheetname):\n book = xw.Book(excel_book_path)\n sheet = book.sheets[sheetname]\n sheet.range(\"D12\").options(index=False, header=False).value = pd.DataFrame(output_dict)","title":""},{"docid":"bcb3506fdd897e62e7b2b860095989b5","score":"0.6283218","text":"def generate_excel_effectiveness(dataframe_list, sheet_name_list, filename, project_io):\n with closing(BytesIO()) as output:\n writer = pd.ExcelWriter(output, engine='xlsxwriter', options={'remove_timezone': True})\n workbook = writer.book\n data_format1 = workbook.add_format({'bg_color': '#BBCCE2'})\n data_format2 = workbook.add_format({'bg_color': '#DEE6EF'})\n\n format_header = workbook.add_format({'text_wrap': True})\n\n workbook.formats[0].set_font_size(15)\n for df, name in zip(dataframe_list, sheet_name_list):\n df.to_excel(writer, sheet_name=name)\n worksheet = writer.sheets[name]\n worksheet.set_row(0, 20, format_header)\n worksheet.set_column('A:A', 5)\n worksheet.set_column('B:D', 30)\n for row in range(1, len(df) + 1, 2):\n worksheet.set_row(row, cell_format=data_format1)\n worksheet.set_row(row + 1, cell_format=data_format2)\n writer.save()\n if project_io is not None:\n project_io.save_data(filename, output.getvalue(), overwrite=True)\n else:\n with open(filename, 'wb') as out:\n out.write(output.getvalue())","title":""},{"docid":"8787549f8820474d8210f1d94fb7b74e","score":"0.6259219","text":"def send_to_gsheet(service):\n sorted_elo_player = sorted(ALL_PLAYERS.values(), key=operator.attrgetter('elo'))\n sorted_elo_player.reverse()\n all_players_gsheet_format = []\n rank = 1\n longest_line = 0\n for player in sorted_elo_player:\n rank_string = str(rank)\n if len(player.tournaments) == 1:\n evolution = 0\n else:\n evolution = compute_evolution(player)\n if evolution > 0:\n rank_string += \"(+\"+str(evolution)+\")\"\n elif evolution < 0:\n rank_string += (\"(\"+str(evolution)+\")\")\n else:\n rank_string += (\"(=)\")\n rank += 1\n player_line = []\n player_line.append(rank_string)\n player_line.append(player.name)\n player_line.append(str(player.elo).replace(\".\", \",\"))\n player_line.append(len(player.tournaments))\n player_line.append(pretty_dict(player.elo_history).replace(\".\", \",\"))\n\n if len(player_line) > longest_line:\n longest_line = len(player_line)\n all_players_gsheet_format.append(player_line)\n\n rank_range = RANK_SHEET_NAME+\"!A1:\"+str(column_to_letter(longest_line))+str(len(all_players_gsheet_format))\n\n body = {\n 'values': all_players_gsheet_format\n }\n\n clear_sheet(service, ELO_SPREADSHEET_ID, RANK_SHEET_NAME)\n result = service.spreadsheets().values().update(spreadsheetId=ELO_SPREADSHEET_ID,\n range=rank_range,\n valueInputOption=\"RAW\",\n body=body).execute()\n logger.info(\"%s cells updated.\", result.get('updatedCells'))","title":""},{"docid":"d492b17a949346070cb2edf10c93fb37","score":"0.61341095","text":"def append_df_to_excel(filename, df, sheet_name='Sheet1', startrow=None,\r\n truncate_sheet=False, \r\n **to_excel_kwargs):\r\n\r\n\r\n # ignore [engine] parameter if it was passed\r\n if 'engine' in to_excel_kwargs:\r\n to_excel_kwargs.pop('engine')\r\n\r\n writer = pd.ExcelWriter(filename, engine='openpyxl')\r\n\r\n try:\r\n FileNotFoundError\r\n except NameError:\r\n FileNotFoundError = IOError\r\n\r\n\r\n try:\r\n # try to open an existing workbook\r\n writer.book = load_workbook(filename)\r\n\r\n # get the last row in the existing Excel sheet\r\n # if it was not specified explicitly\r\n if startrow is None and sheet_name in writer.book.sheetnames:\r\n startrow = writer.book[sheet_name].max_row\r\n\r\n # copy existing sheets\r\n writer.sheets = {ws.title:ws for ws in writer.book.worksheets}\r\n except FileNotFoundError:\r\n print (\"File not found\")# file does not exist yet, we will create it\r\n pass\r\n\r\n if startrow is None:\r\n startrow = 0\r\n\r\n # write out the new sheet\r\n df.to_excel(writer, sheet_name, startrow=startrow, **to_excel_kwargs)\r\n\r\n # save the workbook\r\n try:\r\n writer.save()\r\n except:\r\n print(\"Close the target file\")\r\n return","title":""},{"docid":"c417120e645c8679eead9e9fc5440590","score":"0.6112243","text":"def to_excel(self, workbook, sheet_name=\"Sheet1\", startrow=0, startcol=0):\n # import pdb\n # pdb.set_trace()\n excel_writer = pd.ExcelWriter(workbook.filename, engine=\"xlsxwriter\")\n excel_writer.book = workbook\n excel_writer.sheets = workbook.sheetnames\n self.data = self.data[self.column_order]\n self.styler.data = self.styler.data[self.column_order]\n new_index, indexer = self.styler.columns.reindex(self.column_order)\n self.styler.columns = new_index\n for styler_rule in self.styler_rules:\n if \"map\" in styler_rule:\n styler = self.styler[styler_rule[\"map\"]]\n else:\n styler = self.styler\n styler.applymap(\n styler_rule[\"func\"],\n subset=styler_rule[\"subset\"] if \"subset\" in styler_rule else None,\n )\n self.styler.to_excel(\n excel_writer,\n sheet_name=sheet_name,\n startrow=startrow,\n startcol=startcol,\n **self.params\n )\n workbook = excel_writer.book\n if self.params[\"index\"]:\n col_add = self.index_width\n else:\n col_add = 0\n if self.params[\"header\"]:\n row_add = self.header_height\n else:\n row_add = 0\n worksheet = workbook.get_worksheet_by_name(sheet_name)\n if self.conditional_formatters:\n # import pdb\n # pdb.set_trace()\n for format_rule in self.conditional_formatters:\n if format_rule[\"style\"]:\n format_obj = workbook.add_format(format_rule[\"style\"])\n format_rule[\"options\"].update({\"format\": format_obj})\n if format_rule[\"cols\"] is not None:\n for col in format_rule[\"cols\"]:\n col_index = list(self.data.columns).index(col) + col_add\n worksheet.conditional_format(\n row_add,\n col_index,\n row_add + len(self.data),\n col_index,\n options=format_rule[\"options\"],\n )\n if format_rule[\"rows\"] is not None:\n for row in format_rule[\"rows\"]:\n row_index = list(self.data.index).index(row) + row_add\n worksheet.conditional_format(\n row_index,\n col_add,\n row_index,\n len(self.data.columns) + col_add,\n options=format_rule[\"options\"],\n )\n if format_rule[\"index\"] is not None:\n # import pdb\n # pdb.set_trace()\n col_start_index = 0 + startcol\n if format_rule[\"index\"] is True:\n col_range = range(\n col_start_index,\n col_start_index + len(self.data.index.names),\n )\n elif isinstance(format_rule[\"index\"], list):\n col_range = [\n (col_start_index + level) for level in format_rule[\"index\"]\n ]\n else:\n continue\n for col_index in col_range:\n worksheet.conditional_format(\n 0,\n col_index,\n len(self.data),\n col_index,\n options=format_rule[\"options\"],\n )\n if format_rule[\"header\"] is not None:\n row_start_index = 0 + startrow\n if format_rule[\"header\"] is True:\n row_range = range(\n row_start_index,\n row_start_index + len(self.data.columns.names),\n )\n elif isinstance(format_rule[\"header\"], list):\n row_range = [\n (row_start_index + level) for level in format_rule[\"header\"]\n ]\n else:\n continue\n for row_index in row_range:\n worksheet.conditional_format(\n row_index,\n 0,\n row_index,\n len(self.data.columns),\n options=format_rule[\"options\"],\n )\n if format_rule[\"bool_map\"] is not None:\n # TODO: Finish this\n if format_rule[\"col_wise\"]:\n for col in format_rule[\"cols\"]:\n col_index = list(self.data.columns).index(col) + col_add\n worksheet.conditional_format(\n 1,\n col_index,\n len(self.data),\n col_index,\n options=format_rule[\"options\"],\n )\n else:\n for row in format_rule[\"rows\"]:\n row_index = list(self.data.index).index(row) + row_add\n worksheet.conditional_format(\n row_index,\n 0,\n row_index,\n len(self.data.columns),\n options=format_rule[\"options\"],\n )\n col_rules = [rule for rule in self.cell_formatters if rule[\"cols\"] is not None]\n row_rules = [rule for rule in self.cell_formatters if rule[\"rows\"] is not None]\n bool_rules = [\n rule for rule in self.cell_formatters if rule[\"bool_map\"] is not None\n ]\n index_rules = [rule for rule in self.cell_formatters if rule[\"index\"]]\n header_rules = [rule for rule in self.cell_formatters if rule[\"header\"]]\n if col_rules:\n for col in set(flatten_list([x[\"cols\"] for x in col_rules])) & set(\n list(self.data.columns)\n ):\n all_rules_for_col = [x for x in col_rules if col in x[\"cols\"]]\n combined_style = {}\n combined_options = {}\n for each_rule in all_rules_for_col:\n combined_style.update(each_rule[\"style\"])\n if each_rule[\"options\"] is not None:\n combined_options.update(each_rule[\"options\"])\n width_val, style = process_col_style(\n combined_style, self.data[col], self.styler\n )\n col_index = list(self.data.columns).index(col) + col_add + startcol\n style_format = workbook.add_format(style)\n worksheet.set_column(\n col_index,\n col_index,\n width=width_val,\n cell_format=style_format,\n options=combined_options,\n )\n if row_rules:\n row_vals = list(set(flatten_list([x[\"rows\"] for x in row_rules])))\n # import pdb\n # pdb.set_trace()\n if str(self.data.index.dtype) != \"int64\":\n int_rows = [x for x in row_vals if isinstance(x, int)]\n int_row_names = [self.data.index[x] for x in int_rows]\n row_vals = set(\n [x for x in row_vals if not isinstance(x, int)] + int_row_names\n ) & set(self.data.index)\n for row in row_vals:\n all_rules_for_row = [\n x\n for x in row_rules\n if row in x[\"rows\"] or list(self.data.index).index(row) in x[\"rows\"]\n ]\n combined_style = {}\n combined_options = {}\n for each_rule in all_rules_for_row:\n combined_style.update(each_rule[\"style\"])\n if each_rule[\"options\"] is not None:\n combined_options.update(each_rule[\"options\"])\n height_val, style = process_row_style(\n combined_style, self.data.loc[row], self.styler\n )\n row_index = list(self.data.index).index(row) + row_add + startrow\n style_format = workbook.add_format(style)\n worksheet.set_row(\n row_index,\n height=height_val,\n cell_format=style_format,\n options=combined_options,\n )\n if self.params[\"index\"] and index_rules:\n # import pdb\n # pdb.set_trace()\n if not isinstance(self.data.index, pd.MultiIndex):\n width_val, style, options = create_index_format(\n index_rules, self.data.index\n )\n col_index = 0 + startcol\n style_format = workbook.add_format(style)\n worksheet.set_column(\n col_index,\n col_index,\n width=width_val,\n cell_format=style_format,\n options=options,\n )\n else:\n for level in range(0, len(self.data.index.names)):\n all_rules_for_index = [\n x\n for x in index_rules\n if level in x[\"index\"] or x[\"index\"] is True\n ]\n width_val, style, options = create_index_format(\n all_rules_for_index, self.data.index.get_level_values(level)\n )\n col_index = level + startcol\n style_format = workbook.add_format(style)\n worksheet.set_column(\n col_index,\n col_index,\n width=width_val,\n cell_format=style_format,\n options=options,\n )\n if self.params[\"header\"] and header_rules:\n # import pdb\n # pdb.set_trace()\n if not isinstance(self.data.columns, pd.MultiIndex):\n height_val, style, options = create_header_format(\n header_rules, self.data.columns\n )\n row_index = 0 + startrow\n style_format = workbook.add_format(style)\n worksheet.set_row(\n row_index,\n height=height_val,\n cell_format=style_format,\n options=options,\n )\n else:\n for level in range(0, len(self.data.columns.names)):\n all_rules_for_header = [\n x\n for x in header_rules\n if level in x[\"header\"] or x[\"header\"] is True\n ]\n height_val, style, options = create_index_format(\n all_rules_for_header, self.data.columns.get_level_values(level)\n )\n row_index = level + startrow\n style_format = workbook.add_format(style)\n worksheet.set_row(\n row_index,\n height=height_val,\n cell_format=style_format,\n options=options,\n )\n if bool_rules:\n pass\n # TODO: Finish this\n # excel_writer.save()\n # excel_writer.close()","title":""},{"docid":"9bf40cf076d1b36619eb79204177b504","score":"0.6102703","text":"def append_df_to_excel(filename, df, sheet_name, startrow=0,truncate_sheet=False,**to_excel_kwargs):\n\n\n # ignore [engine] parameter if it was passed\n if 'engine' in to_excel_kwargs:\n to_excel_kwargs.pop('engine')\n\n writer = pd.ExcelWriter(filename, engine='openpyxl')\n\n # Python 2.x: define [FileNotFoundError] exception if it doesn't exist\n try:\n FileNotFoundError\n except NameError:\n FileNotFoundError = IOError\n\n\n try:\n # try to open an existing workbook\n writer.book = load_workbook(filename)\n\n # get the last row in the existing Excel sheet\n # if it was not specified explicitly\n if startrow is None and sheet_name in writer.book.sheetnames:\n startrow = writer.book[sheet_name].max_row\n\n # truncate sheet\n if truncate_sheet and sheet_name in writer.book.sheetnames:\n # index of [sheet_name] sheet\n idx = writer.book.sheetnames.index(sheet_name)\n # remove [sheet_name]\n writer.book.remove(writer.book.worksheets[idx])\n # create an empty sheet [sheet_name] using old index\n writer.book.create_sheet(sheet_name, idx)\n\n # copy existing sheets\n writer.sheets = {ws.title:ws for ws in writer.book.worksheets}\n except FileNotFoundError:\n # file does not exist yet, we will create it\n pass\n\n if startrow is None:\n startrow = 0\n\n # write out the new sheet\n df.to_excel(writer, sheet_name, startrow=startrow, **to_excel_kwargs)\n ws = writer.book[sheet_name]\n for column_cells in ws.columns:\n length = max(map(lambda cell: len(str(cell.value)) if cell.value else 0, column_cells))\n ws.column_dimensions[column_cells[0].column_letter].width = length+3\n # save the workbook\n writer.save()","title":""},{"docid":"28a242f432037c09848321fa74f853ca","score":"0.60957867","text":"def generate_excel_measure(dataframe_list, sheet_name_list, filename, project_io):\n with closing(BytesIO()) as output:\n writer = pd.ExcelWriter(output, engine='xlsxwriter', options={'remove_timezone': True})\n workbook = writer.book\n data_format1 = workbook.add_format({'bg_color': '#BBCCE2'})\n data_format2 = workbook.add_format({'bg_color': '#DEE6EF'})\n\n format_header = workbook.add_format({'text_wrap': True})\n\n workbook.formats[0].set_font_size(15)\n for df, name in zip(dataframe_list, sheet_name_list):\n df.to_excel(writer, sheet_name=name)\n worksheet = writer.sheets[name]\n worksheet.set_row(0, 30, format_header)\n worksheet.set_column('A:A', 5)\n worksheet.set_column('B:B', 30)\n worksheet.set_column('C:C', 30)\n worksheet.set_column('D:D', 15)\n worksheet.set_column('F:G', 35)\n worksheet.set_column('H:AH', 20)\n for row in range(1, len(df) + 1, 2):\n worksheet.set_row(row, cell_format=data_format1)\n worksheet.set_row(row + 1, cell_format=data_format2)\n writer.save()\n if project_io is not None:\n project_io.save_data(filename, output.getvalue(), overwrite=True)\n else:\n with open(filename, 'wb') as out:\n out.write(output.getvalue())","title":""},{"docid":"bdc52290fb473595c31cc3dbea88385e","score":"0.6046522","text":"def write_excel_df(save_file_path: str,\n df_list: List, sheet_name_list: List):\n writer = pd.ExcelWriter(save_file_path, engine='xlsxwriter')\n # Write each dataframe to a different worksheet\n assert len(df_list) == len(sheet_name_list)\n for index in range(len(df_list)):\n df_list[index].to_excel(writer, sheet_name=sheet_name_list[index])\n # Close the Pandas Excel writer and output the Excel file.\n writer.save()\n return","title":""},{"docid":"66e553c1e866d8bc58ea30574aff37d9","score":"0.60190165","text":"def save_large_df_to_excel(df, file_path, sheet_prefix='page'):\n\n def _split_file_path():\n \"\"\"Returns a list representing the file path containing the file.\"\"\"\n if '/' in file_path:\n # Split file path into a list\n path_parts = file_path.split('/')\n # Take all except last entry and append together\n file_dir = '/'.join(path_parts[:-1])\n file_name = path_parts[-1]\n\n return file_dir, file_name\n else:\n # Otherwise, return current directory as '.'\n return '.', file_path\n\n\n file_dir, file_name = _split_file_path()\n # Prevents overwriting if file already exists\n if file_name in os.listdir(file_dir):\n raise ValueError('Filename {} already exists'.format(file_name))\n writer = pd.ExcelWriter(file_path, engine='xlsxwriter')\n\n i = 0\n while True:\n # Remove one row to leave space for header\n max_num_rows = 2**20 - 1\n # Subselected DataFrame to write as a single sheet\n temp_df = df.iloc[max_num_rows*i: max_num_rows*(i+1)]\n\n # Terminate if we have reached the end of the DataFrame\n if temp_df.shape[0] == 0:\n break\n\n # Save sheet to excel file\n temp_df.to_excel(writer, sheet_name='{}_{}'.format(sheet_prefix, i))\n i += 1\n\n writer.save()","title":""},{"docid":"803f84dc291b5fc63f5b33dc08da6220","score":"0.6018584","text":"def send_to_gs(df, wks_name):\n\n d2g.upload(df,\n spreadsheet_key,\n wks_name,\n credentials=credentials,\n row_names=True)","title":""},{"docid":"ce8937d80ee49a96e0035ddef6e6fede","score":"0.6017467","text":"def save_dict_xlsx(data_dict: Dict[Any, Any], save_location: str, sheet_name: str) -> None:\n pd_df = pd.DataFrame.from_dict(data_dict, orient=\"index\")\n\n with pd.ExcelWriter(save_location, engine=\"xlsxwriter\") as writer: # pylint: disable=abstract-class-instantiated\n for n, df in (pd_df).items():\n df.to_excel(writer, sheet_name=sheet_name)","title":""},{"docid":"2b89a258259a56c6995b575754f255ce","score":"0.5974723","text":"def list_to_spreadsheet(spreadsheet_name, column_names, mylist):\n records = [dict(zip(column_names, row)) for row in mylist]\n dataframe = pandas.DataFrame.from_records(\n records, index=column_names[0], columns=column_names\n )\n dataframe.to_excel(writer, sheet_name=spreadsheet_name)","title":""},{"docid":"ee90ff375db44056af145452f293aa70","score":"0.5937404","text":"def append_df_to_excel(filename, df, sheet_name='Sheet1', startrow=None,\r\n truncate_sheet=False, \r\n **to_excel_kwargs):\r\n from openpyxl import load_workbook\r\n\r\n import pandas as pd\r\n\r\n # ignore [engine] parameter if it was passed\r\n if 'engine' in to_excel_kwargs:\r\n to_excel_kwargs.pop('engine')\r\n\r\n writer = pd.ExcelWriter(filename, engine='openpyxl')\r\n\r\n # Python 2.x: define [FileNotFoundError] exception if it doesn't exist \r\n try:\r\n FileNotFoundError\r\n except NameError:\r\n FileNotFoundError = IOError\r\n\r\n\r\n try:\r\n # try to open an existing workbook\r\n writer.book = load_workbook(filename)\r\n\r\n # get the last row in the existing Excel sheet\r\n # if it was not specified explicitly\r\n if startrow is None and sheet_name in writer.book.sheetnames:\r\n startrow = writer.book[sheet_name].max_row\r\n\r\n # truncate sheet\r\n if truncate_sheet and sheet_name in writer.book.sheetnames:\r\n # index of [sheet_name] sheet\r\n idx = writer.book.sheetnames.index(sheet_name)\r\n # remove [sheet_name]\r\n writer.book.remove(writer.book.worksheets[idx])\r\n # create an empty sheet [sheet_name] using old index\r\n writer.book.create_sheet(sheet_name, idx)\r\n\r\n # copy existing sheets\r\n writer.sheets = {ws.title:ws for ws in writer.book.worksheets}\r\n except FileNotFoundError:\r\n # file does not exist yet, we will create it\r\n pass\r\n\r\n if startrow is None:\r\n startrow = 0\r\n\r\n # write out the new sheet\r\n df.to_excel(writer, sheet_name, startrow=startrow, **to_excel_kwargs)\r\n\r\n # save the workbook\r\n writer.save()","title":""},{"docid":"093a902d626edf70cd431a2b64705baa","score":"0.5931121","text":"def to_excel(self):\r\n raise NotImplementedError()","title":""},{"docid":"d70cf37bab07a7e56ca22281eb9174c9","score":"0.5927741","text":"def append_df_to_excel(filename, df, sheet_name='Sheet1', startrow=None,\n startcol=None, truncate_sheet=False, \n **to_excel_kwargs):\n from openpyxl import load_workbook\n\n import pandas as pd\n\n # ignore [engine] parameter if it was passed\n if 'engine' in to_excel_kwargs:\n to_excel_kwargs.pop('engine')\n\n writer = pd.ExcelWriter(filename, engine='openpyxl')\n\n # Python 2.x: define [FileNotFoundError] exception if it doesn't exist \n try:\n FileNotFoundError\n except NameError:\n FileNotFoundError = IOError\n\n\n try:\n # try to open an existing workbook\n writer.book = load_workbook(filename)\n\n # get the last row in the existing Excel sheet\n # if it was not specified explicitly\n if startrow is None and sheet_name in writer.book.sheetnames:\n startrow = writer.book[sheet_name].max_row\n # if startcol is None and sheet_name in writer.book.sheetnames:\n # startcol = writer.book[sheet_name].max_col\n # truncate sheet\n if truncate_sheet and sheet_name in writer.book.sheetnames:\n # index of [sheet_name] sheet\n idx = writer.book.sheetnames.index(sheet_name)\n # remove [sheet_name]\n writer.book.remove(writer.book.worksheets[idx])\n # create an empty sheet [sheet_name] using old index\n writer.book.create_sheet(sheet_name, idx)\n\n # copy existing sheets\n writer.sheets = {ws.title:ws for ws in writer.book.worksheets}\n except FileNotFoundError:\n # file does not exist yet, we will create it\n pass\n\n if startrow is None:\n startrow = 0\n if startcol is None:\n startcol = 0\n\n # write out the new sheet\n df.to_excel(writer, sheet_name, startrow=startrow, startcol=startcol, **to_excel_kwargs)\n\n # save the workbook\n writer.save()","title":""},{"docid":"21f1698790968ba9a0f551c30c69b498","score":"0.592436","text":"def saveSpreadSheet(self):\r\n try:\r\n self.wb.save(self.fullFilePath)\r\n logger.info('In module ' + __name__ \r\n + '. saveSpreadSheet.')\r\n except Exception as e:\r\n print('ExcelWriter.saveSpreadSheet: ' + str(e)) \r\n logger.error('ExcelWriter.saveSpreadSheet: ' + str(e))","title":""},{"docid":"933ef31907dee5effbf16e13207f5bef","score":"0.59137565","text":"def data_to_xls(path, data, sheet_name):\n df = pd.DataFrame(data, index=[0]).T\n df.to_excel(path, sheet_name=sheet_name)","title":""},{"docid":"3a911c35e3a4a7ac7e55e637e2d9a57b","score":"0.5850379","text":"def Test():\n\n # https://docs.google.com/spreadsheets/d/1CR_8w8ZYeu8gD9X3UwwtuZA2djj5jCpNSFycNSXM_GY/edit#gid=1608909088\n # setup\n\n service = GetService()\n # - preface as upload.GetService() if use in another file\n\n # doc: db_adwords\n spreadsheetId = \"1CR_8w8ZYeu8gD9X3UwwtuZA2djj5jCpNSFycNSXM_GY\"\n\n # READ\n read_sheet = \"Sheet2\" # please make sure this exists first\n read_data = GetGSheetsRange(service, spreadsheetId, read_sheet, quiet=False)\n print len(read_data)\n\n # WRITE\n write_sheet = \"Sheet1\"\n newValues = [[\"a\", \"bbbb\", \"ccc\"], [\"235235\"], [\"325235235\", \"dgdfg\", \"fdgfdg\"]]\n OverwriteCells(service, spreadsheetId, write_sheet, newValues, quiet=False)\n\n # DELETE\n DeleteSheetId = 1608909088\n DeleteGSheetsRange(service, spreadsheetId, DeleteSheetId, 0, 3, quiet=False)","title":""},{"docid":"8edbb3584cae0c4ffa59d1409b2345be","score":"0.584908","text":"def append_df_to_excel(filename, df, sheet_name='Sheet1', startrow=None,\n truncate_sheet=False, \n **to_excel_kwargs):\n from openpyxl import load_workbook\n\n import pandas as pd\n\n # ignore [engine] parameter if it was passed\n if 'engine' in to_excel_kwargs:\n to_excel_kwargs.pop('engine')\n\n writer = pd.ExcelWriter(filename, engine='openpyxl')\n\n # Python 2.x: define [FileNotFoundError] exception if it doesn't exist \n try:\n FileNotFoundError\n except NameError:\n FileNotFoundError = IOError\n\n\n try:\n # try to open an existing workbook\n writer.book = load_workbook(filename)\n\n # get the last row in the existing Excel sheet\n # if it was not specified explicitly\n if startrow is None and sheet_name in writer.book.sheetnames:\n startrow = writer.book[sheet_name].max_row\n\n # truncate sheet\n if truncate_sheet and sheet_name in writer.book.sheetnames:\n # index of [sheet_name] sheet\n idx = writer.book.sheetnames.index(sheet_name)\n # remove [sheet_name]\n writer.book.remove(writer.book.worksheets[idx])\n # create an empty sheet [sheet_name] using old index\n writer.book.create_sheet(sheet_name, idx)\n\n # copy existing sheets\n writer.sheets = {ws.title:ws for ws in writer.book.worksheets}\n except FileNotFoundError:\n # file does not exist yet, we will create it\n pass\n\n if startrow is None:\n startrow = 0\n\n # write out the new sheet\n df.to_excel(writer, sheet_name, startrow=startrow, **to_excel_kwargs)\n\n # save the workbook\n writer.save()","title":""},{"docid":"4eb6c084084054d7b8a99bc49dc2a0d2","score":"0.5847653","text":"def excel(Df, index=True):\n\n writer = pd.ExcelWriter(r'C:\\Users\\Administrator\\PycharmProjects\\Stock\\Output\\Output.xlsx', engine='xlsxwriter')\n Df.to_excel(writer, sheet_name='Sheet1', index= index)","title":""},{"docid":"5692c43f10c52a25bf5de3b02a9673b6","score":"0.5833753","text":"def save_as_xlsx(df, file, index=True, **to_excel_kwargs):\n\n def localize(df):\n \"\"\"\n Remove tz from datetime cols since Excel doesn't allow\n \"\"\"\n tz_cols = df.select_dtypes(include=[\"datetimetz\"]).columns\n for tz_col in tz_cols:\n df[tz_col] = df[tz_col].dt.tz_localize(None)\n return df\n\n import xlsxwriter\n\n df = df.copy()\n df = localize(df)\n with pd.ExcelWriter(\n file,\n engine=\"xlsxwriter\",\n date_format=\"m/d/yyy\",\n datetime_format=\"m/d/yyy h:mmAM/PM\",\n ) as writer:\n df.to_excel(writer, index=index, **to_excel_kwargs)","title":""},{"docid":"95dad6f82acdfb6b791e83e8e00e1790","score":"0.5817795","text":"def append_df_to_excel(filename, df, sheet_name='Sheet1', startrow=None,\n truncate_sheet=False,\n **to_excel_kwargs):\n\n # ignore [engine] parameter if it was passed\n if 'engine' in to_excel_kwargs:\n to_excel_kwargs.pop('engine')\n\n writer = pd.ExcelWriter(filename, engine='openpyxl')\n\n try:\n # try to open an existing workbook\n writer.book = load_workbook(filename)\n\n # get the last row in the existing Excel sheet\n # if it was not specified explicitly\n if startrow is None and sheet_name in writer.book.sheetnames:\n startrow = writer.book[sheet_name].max_row\n\n # truncate sheet\n if truncate_sheet and sheet_name in writer.book.sheetnames:\n # index of [sheet_name] sheet\n idx = writer.book.sheetnames.index(sheet_name)\n # remove [sheet_name]\n writer.book.remove(writer.book.worksheets[idx])\n # create an empty sheet [sheet_name] using old index\n writer.book.create_sheet(sheet_name, idx)\n\n # copy existing sheets\n writer.sheets = {ws.title:ws for ws in writer.book.worksheets}\n except FileNotFoundError:\n # file does not exist yet, we will create it\n pass\n\n if startrow is None:\n startrow = 0\n\n # write out the new sheet\n df.to_excel(writer, sheet_name, startrow=startrow, **to_excel_kwargs)\n\n # save the workbook\n writer.save()","title":""},{"docid":"e2bd06eccf5ab13d8e9d3d835029849c","score":"0.57952166","text":"def WriteGSheetsRange(\n service, spreadsheetId, range_name, values, value_input_option=\"RAW\", quiet=True\n):\n body = {\"values\": values}\n result = (\n service.spreadsheets()\n .values()\n .update(\n spreadsheetId=spreadsheetId,\n range=range_name,\n valueInputOption=value_input_option,\n body=body,\n )\n .execute()\n )\n\n if not quiet:\n print \" WRITE RESULT\"\n for k, v in result.iteritems():\n print \" %s: %s\" % (k, v)\n print\n \"\"\"\n # example\n spreadsheetId: 1d9Cb9-_-XTYHevDHUfVvQAZjJY9EN6yeZmEcR4DsFVw\n updatedRange: tb_staging_camp_day!A1:O630\n updatedCells: 9450\n updatedRows: 630\n updatedColumns: 15\n \"\"\"","title":""},{"docid":"5e66375247f6a897a78555151e6b1838","score":"0.5775305","text":"def save_to_file(out_folder, animal_name, dataframe):\n file_name = \"%s.xls\" % (animal_name)\n file_name = os.path.join(out_folder, file_name)\n\n\n cols = dataframe.columns.tolist()\n #print ('original:', cols)\n\n #neworder=[11, 0, 17, 19, 15, 18, 21, 5, 4, 20, 3, 16, 1, 12, 7, 14, 6, 8, 2, 10, 9, 13]\n #cols = [cols[i] for i in neworder]\n #print ('reorderd:', cols)\n #neworder = [[]]\n neworder = ['date','animal','program','duration','trials','hit','miss','false','withold','hit_rate','false_positive_rate','dprime','recall','precision','F_score','500 Hz', '1000 Hz','2000 Hz','4000 Hz', '8000 Hz', '16000 Hz', '32000 Hz']\n\n #cols = list(neworder.columns.values)\n dataframe = dataframe[neworder]\n\n\n\n# df = df[['f','f']]\n# cols = list(df.columns.values)\n\n\n dataframe.to_csv(file_name, sep='\\t', index=False)\n return","title":""},{"docid":"2b23817b6ce8b42c15f784340d94116e","score":"0.575602","text":"def write_xls(table_2Dlist, path, sheet_name, data_line=None, start_from_row1=False):\n workbook = xlwt.Workbook(encoding=sys.stdout.encoding)\n sheet = workbook.add_sheet(sheet_name)\n if data_line != None:\n for index, value in enumerate(data_line):\n sheet.write(0, index, value)\n if start_from_row1 == False:\n for row in range(len(table_2Dlist)):\n for col in range(len(table_2Dlist[row])):\n sheet.write(row+1, col, table_2Dlist[row][col])\n else:\n for row in range(len(table_2Dlist)):\n if row == 0:\n pass\n else:\n for col in range(len(table_2Dlist[row])):\n sheet.write(row+1, col, table_2Dlist[row][col])\n else:\n for row in range(len(table_2Dlist)):\n for col in range(len(table_2Dlist[row])):\n sheet.write(row, col, table_2Dlist[row][col])\n workbook.save(path)","title":""},{"docid":"99d53caa93d338aae767269f6852c5a8","score":"0.5753641","text":"def export_scatter_diagram(self, filename):\n\n writer = pd.ExcelWriter(filename, engine=\"xlsxwriter\")\n\n # Seastates sheet\n self.df_ss.to_excel(writer, sheet_name=\"Sea States\", na_rep=\"N/A\", float_format=\"%.2f\")\n ws = writer.sheets[\"Sea States\"]\n ws.set_column(\"A:A\", 11)\n # wb = writer.book\n # fmt = wb.add_format({'num_format': '0.00'})\n # ws.set_column('B:Y', None, fmt)\n\n # Seascatter sheet\n # Replace zeros with blanks\n df_scatter = self.df_scatter.replace({0: \"\"})\n df_scatter.to_excel(writer, sheet_name=\"Sea Scatter Diagram\", float_format=\"%.2f\")\n ws.set_column(\"A:A\", 18)\n writer.save()","title":""},{"docid":"e3c2ab4f8c7e83a2cde2573e974a483f","score":"0.572457","text":"def __generate_sheet__(self, app, xls_doc):\n # Create the worksheet\n sheet = xls_doc.add_sheet(app)\n \n # Create header style\n hdr_font = xlwt.Font()\n hdr_font.name = 'Times New Roman'\n hdr_font.colour_index = 2\n hdr_font.bold = True\n hdr_style = xlwt.XFStyle()\n hdr_style.font = hdr_font\n \n # Create header\n sheet.write(0, 0, \"Count\")\n sheet.write(0, 1, \"DB Name\")\n sheet.write(0, 2, \"DB ID\")\n sheet.write(0, 3, \"GO Name\")\n sheet.write(0, 4, \"GO ID\")\n sheet.write(0, 5, \"DB Link\")\n sheet.write(0, 6, \"GO Link\")\n \n # Write a row to the spreadsheet for each row in iprstatsdata\n length = self.cache.get_match_length(app)\n for r in range(length):\n row = self.cache.get_one_row(app, r)\n if row:\n sheet.write(r+1, 0, str(row[1])) # Count\n sheet.write(r+1, 1, row[0]) # DB Name\n sheet.write(r+1, 2, row[3]) # DB ID\n sheet.write(r+1, 3, row[4]) # GO Name\n sheet.write(r+1, 4, row[2]) # GO ID\n sheet.write(r+1, 5, self.cache.get_url(app, r)) # DB URL\n sheet.write(r+1, 6, self.cache.get_url(app, r, True))#GO URL\n if len(row) == 7:\n sheet.write(r, 7, row[6]) # GO Definition\n else:\n break","title":""},{"docid":"3bb0b535c4b0b29b7dc6fb8a131ee981","score":"0.5717932","text":"def hstFailuresToExcel(df, sheet):\n\n sheet = openSheet(sheet)\n\n failure_cells = ['D17', 'D18', 'D19', 'D20', 'D21', 'D22', 'D23', 'D24', 'D25', 'D26', 'D27', 'D28', 'D29', 'D30',\n 'D31', 'D32', 'D33', 'D34', 'D35']\n failures = df['Failure'].dropna().tolist()\n for i in range(len(failures)):\n sheet.range(failure_cells[i]).value = failures[i]","title":""},{"docid":"8331960356cc03f22b609bbfecb7590e","score":"0.5711396","text":"def save_df(write_df, outputfile):\n print('Saving File....')\n if outputfile.split('.')[-1] == 'csv':\n write_df.to_csv(outputfile)\n\n elif outputfile.split('.')[-1] == 'xls' or outputfile.split('.')[-1] == 'xlsx':\n writer = pd.ExcelWriter(outputfile)\n write_df.to_excel(writer,'Result')\n writer.save()\n else:\n print('csv or excel needs to be selected for file {}!'.format(outputfile))\n raise NotImplementedError\n write_df.to_clipboard()\n print('...done!')","title":""},{"docid":"be585cf10696b87075b8f27848f983b6","score":"0.5699932","text":"def xlsave(output_file, frames, sheets, **kwargs):\r\n\r\n with pd.ExcelWriter(\r\n output_file,\r\n options = {\r\n 'remove_timezone': True,\r\n },\r\n ) as writer:\r\n _l.info(f'{me()} saving to file {output_file} ...')\r\n #if only one sheet:\r\n if isinstance(sheets, str):\r\n _xlsheet(writer, frames, sheets, **kwargs)\r\n #if multiple sheets\r\n else:\r\n for f, s in zip(frames, sheets):\r\n _xlsheet(writer, f, s, **kwargs)\r\n _l.info(f'{me()} done')","title":""},{"docid":"910bb5610f9971b92df06e942514e4b3","score":"0.569453","text":"def write_to_excel(dest_fullpath:str, dfs_map:dict, include_index:bool=True)->None:\n with pd.ExcelWriter(dest_fullpath) as writer:\n for k, df in dfs_map.items():\n df.to_excel(writer, sheet_name=k, index=include_index)","title":""},{"docid":"6839f9f4a8fa4ed796ea5d74d6219a19","score":"0.5687497","text":"def export_dataframe(self, dataframe, filename, sep = ','):\n\t\tdataframe.to_excel(filename, index = False)","title":""},{"docid":"0eb125fe416312607ed17881d2b21f9a","score":"0.56730133","text":"def _create_bom_worksheet(df, writer, header_format):\n df.to_excel(\n writer,\n sheet_name='bom',\n index=False,\n header=False,\n startrow=1\n )\n\n worksheet = writer.sheets['bom']\n for col_num, value in enumerate(df.columns.values):\n worksheet.write(0, col_num, value, header_format)\n\n worksheet.set_column(0, 0, 16) # Assembly\n worksheet.set_column(1, 1, 26) # Assembly_Name\n worksheet.set_column(5, 5, 16) # Dwg_No\n worksheet.set_column(6, 6, 34) # Component","title":""},{"docid":"6fde5d0e01e97d68cfba7f5ea4f15eee","score":"0.5660677","text":"def write_to_sheet(sheet, row, col, exec_time, size, graph, path):\n sheet.write(0, 0, \"Expanded States\")\n sheet.write(0, 1, \"Solution Size\")\n sheet.write(0, 2, \"Time\")\n sheet.write(0, 3, \"Puzzle Size\")\n sheet.write(row, col, graph.expanded_states())\n if path is not None:\n sheet.write(row, col + 1, len(path))\n else:\n sheet.write(row, col + 1, \"Failed\")\n sheet.write(row, col + 2, exec_time)\n sheet.write(row, col + 3, size)","title":""},{"docid":"dd864ea0d7ff343ce51ee078edd77628","score":"0.5633066","text":"def output_data(self, path):\n self.df = self.treat_data(self.df)\n self.df.to_excel(path, index=False, header=True)","title":""},{"docid":"c580c6664e5964f95832d7599f7f0d72","score":"0.5631117","text":"def _create_indented_bom_worksheet(df, writer, header_format):\n df.to_excel(\n writer,\n sheet_name='indented_bom',\n index=False,\n header=False,\n startrow=1,\n )\n\n worksheet = writer.sheets['indented_bom']\n for col_num, value in enumerate(df.columns.values):\n worksheet.write(0, col_num, value, header_format)\n\n worksheet.set_column(2, 2, 21) # Dwg_No\n worksheet.set_column(3, 3, 60) # Component","title":""},{"docid":"703bb7dad47b570920adce15b167cdf3","score":"0.5628814","text":"def write_xls(data, file_name):\r\n try:\r\n book = xlwt.Workbook()\r\n sheet1 = book.add_sheet(\"Jul-15\")\r\n\r\n for num in range(len(data)):\r\n row = sheet1.row(num)\r\n vals = data[num]\r\n for index, col in enumerate(vals):\r\n row.write(index, col)\r\n xls_name = '%s/%s.xls' % (MEDIA_ROOT, file_name)\r\n book.save(xls_name)\r\n except Exception, e:\r\n raise e","title":""},{"docid":"acd8e8bd77e107629deaa7cde8c57687","score":"0.56226164","text":"def write_excel(sheets, sheet_names):\n wb = xlwt.Workbook()\n for sheet, sheet_name in zip(sheets, sheet_names):\n ws = wb.add_sheet(sheet_name)\n for row_num, row in enumerate(sheet):\n for col_num, cell in enumerate(row):\n try:\n as_float = float(cell)\n ws.write(row_num, col_num, as_float) # write number to excel\n except Exception, e:\n ws.write(row_num, col_num, cell) # write without changing to number\n wb.save('migrants.xls')","title":""},{"docid":"0f8f834e72a853fd420181f4d5a10ebf","score":"0.56202","text":"def update(service):\n\n # Call the Sheets API\n # Compute a timestamp and pass the first two arguments\n values = [[1]]\n body = {'values': values}\n result = service.spreadsheets().values().update(spreadsheetId=SPREADSHEET_ID,\n range=RANGE_NAME,\n # How the input data should be interpreted.\n valueInputOption='USER_ENTERED',\n # How the input data should be inserted.\n # insertDataOption='INSERT_ROWS'\n body=body\n ).execute()","title":""},{"docid":"404bfd7abf9bcb03d2fadd8a104ea8bc","score":"0.5614299","text":"def _create_import_file_worksheet(df, writer, header_format):\n df.to_excel(\n writer,\n sheet_name='import',\n index=False,\n startrow=1,\n header=False\n )\n\n worksheet = writer.sheets['import']\n for col_num, value in enumerate(df.columns.values):\n worksheet.write(0, col_num, value, header_format)\n\n # worksheet.set_column(0, 0, 16) # Assembly\n # worksheet.set_column(1, 1, 26) # Assembly_Name","title":""},{"docid":"f3b9a078a0cf72a23899207be645f889","score":"0.56134135","text":"def sendToExcel(studies, sheet, provider):\n\n # excel sheet passed in from previous functions\n sheet = openSheet(sheet)\n\n # mapping for spreadsheet locations\n kgw_cells = {'PSG': 'K12', 'EEG': 'K13', 'ETCO2': 'K14', 'SPLIT': 'K15', 'HST': 'K16', 'MSLT': 'K17', 'MWT': 'K18',\n 'OAT': 'K19', 'Inspire': 'K20', 'PAP': 'K21', 'NAP': 'K22', 'FHST': 'K23', 'NS': 'K24'}\n mb_cells = {'PSG': 'P12', 'EEG': 'P13', 'ETCO2': 'P14', 'SPLIT': 'P15', 'HST': 'P16', 'MSLT': 'P17', 'MWT': 'P18',\n 'OAT': 'P19', 'Inspire': 'P20', 'PAP': 'P21', 'NAP': 'P22', 'FHST': 'P23', 'NS': 'P24'}\n qr_cells = {'PSG': 'U12', 'EEG': 'U13', 'ETCO2': 'U14', 'SPLIT': 'U15', 'HST': 'U16', 'MSLT': 'U17', 'MWT': 'U18',\n 'OAT': 'U19', 'Inspire': 'U20', 'PAP': 'U21', 'NAP': 'U22', 'FHST': 'U23', 'NS': 'U24'}\n jf_cells = {'PSG': 'AA12', 'EEG': 'AA13', 'ETCO2': 'AA14', 'SPLIT': 'AA15', 'HST': 'AA16', 'MSLT': 'AA17',\n 'MWT': 'AA18', 'OAT': 'AA19', 'Inspire': 'AA20', 'PAP': 'AA21', 'NAP': 'AA22', 'FHST': 'AA23', 'NS': 'AA24'}\n dr_cells = {'PSG': 'AG12', 'EEG': 'AG13', 'ETCO2': 'AG14', 'SPLIT': 'AG15', 'HST': 'AG16', 'MSLT': 'AG17',\n 'MWT': 'AG18', 'OAT': 'AG19', 'Inspire': 'AG20', 'PAP': 'AG21', 'NAP': 'AG22', 'FHST': 'AG23', 'NS': 'AG24'}\n\n # assign correct cell mapping by provider\n if provider == 'kgw':\n cells = kgw_cells\n elif provider == 'mb':\n cells = mb_cells\n elif provider == 'qr':\n cells = qr_cells\n elif provider == 'jf':\n cells = jf_cells\n elif provider == 'dr':\n cells = dr_cells\n else:\n print('Provider {} is unknown'.format(provider))\n return\n \n if studies[0] == 'PSG':\n sheet.range(cells['PSG']).value = studies[1]\n elif 'EEG' in studies[0]:\n sheet.range(cells['EEG']).value = studies[1]\n elif 'EtCO2' in studies[0]:\n sheet.range(cells['ETCO2']).value = studies[1]\n elif 'Split' in studies[0]:\n sheet.range(cells['SPLIT']).value = studies[1]\n elif studies[0] == 'HST':\n sheet.range(cells['HST']).value = studies[1]\n elif 'MSLT' in studies[0]:\n sheet.range(cells['MSLT']).value = studies[1]\n elif 'MWT' in studies[0]:\n sheet.range(cells['MWT']).value = studies[1]\n elif 'OAT' in studies[0]:\n sheet.range(cells['OAT']).value = studies[1]\n elif studies[0] == 'PAP':\n sheet.range(cells['PAP']).value = studies[1]\n elif studies[0] == 'PAP Nap':\n sheet.range(cells['NAP']).value = studies[1]\n elif studies[0] == 'Failed HST':\n sheet.range(cells['FHST']).value = studies[1]\n elif studies[0] == 'Failed in lab':\n sheet.range(cells['NS']).value = studies[1]\n elif studies[0] == 'Inspire':\n sheet.range(cells['Inspire']).value = studies[1]\n elif 'Other' in studies[0]:\n print(studies[0], studies[1])\n else:\n print('Study type, {} is not indexed'.format(studies[0]))\n return","title":""},{"docid":"cea8111b49f1c6cec99d3f767902cb4a","score":"0.561059","text":"def put_data(self, google_sheet_id, sheet_a1_notation, rows, value_input_option=\"USER_ENTERED\", overwrite_or_append=\"OVERWRITE\"):\n with googleapiclient.discovery.build('sheets', 'v4', credentials=self.creds) as service:\n if overwrite_or_append == \"OVERWRITE\": # default\n service.spreadsheets().values().clear(spreadsheetId=google_sheet_id,\n range=sheet_a1_notation,\n body={}).execute()\n\n body = {\"values\": rows}\n return service.spreadsheets().values().append(\n spreadsheetId=google_sheet_id,\n range=sheet_a1_notation,\n valueInputOption=value_input_option,\n body=body).execute()","title":""},{"docid":"8c5956e70bd5debe82ccc768860e8689","score":"0.5608833","text":"def writeto_excel(cls,path,names,excelname):\n\n df = pd.DataFrame.from_dict(data={'ImageName': [i for i in names.keys()], 'EmpName': [i for i in names.values()]},orient='columns')\n print(f'[INFO] Save in - {path}')\n \n fullpath = os.path.join(path,excelname)\n if os.path.isfile(fullpath):\n os.remove(fullpath)\n\n writer = pd.ExcelWriter(path +'data.xlsx')\n # writer = pd.ExcelWriter('test.xlsx', engine='xlsxwriter')\n df.to_excel(writer,sheet_name='test1',index=False)\n writer.save()","title":""},{"docid":"71df5d4cc7d9f4b9864b3bbc96501fb9","score":"0.5601026","text":"def OverwriteCells(service, spreadsheetId, sheet, newValues, quiet=True):\n print \"Sheet: %s\" % sheet\n print \"Command: Upload to sheets, overwriting data\"\n try:\n print \"Reading existing values...\"\n preview = 3\n existingValues = GetGSheetsRange(\n service, spreadsheetId, sheet, preview, quiet=quiet\n )\n print \"Emptying table...\"\n emptyValues = MakeEmptyTable(existingValues)\n WriteGSheetsRange(service, spreadsheetId, sheet, emptyValues, quiet=quiet)\n print \"Uploading new data...\"\n WriteGSheetsRange(\n service,\n spreadsheetId,\n sheet,\n newValues,\n value_input_option=\"USER_ENTERED\",\n quiet=quiet,\n )\n print\n print \"Done.\"\n\n except google_errors.HttpError as h:\n print \"ERROR\"\n print \" - Http connection to Google Sheets failed!\"\n print \" - %s\" % h\n print\n print","title":""},{"docid":"f53cb7fb9e7e9587b9048453a6018b98","score":"0.55847424","text":"def write_xlsx(outs, *args, **kwargs):\n\tdf = pd.DataFrame(outs)\n\tdf.to_excel(\"output.xlsx\")\n\treturn","title":""},{"docid":"b2a43077029ba2b0fd750261b5f91709","score":"0.5560496","text":"def upload_to_goole_sheets(df_loc: str) -> None:\n # must have your custom creds\n credentials = ServiceAccountCredentials.from_json_keyfile_name(\n \"gdrive_creds.json\", scope\n )\n client = gspread.authorize(credentials)\n spreadsheet = client.open(\"dc_crash_bot\") # Name of spread sheet\n\n with open(df_loc, \"r\") as file_obj:\n content = file_obj.read()\n client.import_csv(spreadsheet.id, data=content)","title":""},{"docid":"b2a43077029ba2b0fd750261b5f91709","score":"0.5560496","text":"def upload_to_goole_sheets(df_loc: str) -> None:\n # must have your custom creds\n credentials = ServiceAccountCredentials.from_json_keyfile_name(\n \"gdrive_creds.json\", scope\n )\n client = gspread.authorize(credentials)\n spreadsheet = client.open(\"dc_crash_bot\") # Name of spread sheet\n\n with open(df_loc, \"r\") as file_obj:\n content = file_obj.read()\n client.import_csv(spreadsheet.id, data=content)","title":""},{"docid":"5813f267a04e48761cf5fa4bd90b763c","score":"0.555385","text":"def _create_new_revision_worksheet(df, writer, header_format):\n df.to_excel(\n writer,\n sheet_name='new_revision',\n index=False,\n startrow=1,\n header=False\n )\n\n worksheet = writer.sheets['new_revision']\n for col_num, value in enumerate(df.columns.values):\n worksheet.write(0, col_num, value, header_format)\n\n worksheet.set_column(0, 0, 16) # Assembly\n worksheet.set_column(1, 1, 26) # Assembly_Name\n worksheet.set_column(2, 1, 15) # Revision","title":""},{"docid":"a96905cfbe210adbb7ea0ed89fa49416","score":"0.55521977","text":"def write(self, values, spreadsheet_id, cell, sheet=None, row_major=True, raw=False):\n self._spreadsheets.values().update(\n spreadsheetId=spreadsheet_id,\n range=self._get_range(sheet, cell, spreadsheet_id),\n valueInputOption='RAW' if raw else 'USER_ENTERED',\n body={\n 'values': self._values(values),\n 'majorDimension': 'ROWS' if row_major else 'COLUMNS',\n },\n ).execute()","title":""},{"docid":"202354bac8caab1a4c9cdd03f738ec55","score":"0.5541308","text":"def write_to_excel(data_matrix, filename):\n wb = Workbook()\n ws = wb.create_sheet(\"data\", 0)\n \n for row in data_matrix:\n ws.append(row)\n \n save_file = filename + '.xlsx'\n wb.save( save_file )","title":""},{"docid":"a23bb2bda49b49b9298a6b30c012e9bc","score":"0.5529477","text":"def save_results(output):\r\n # extension = '.xlsx'\r\n # if filename.lower().endswith('.xlsx'):\r\n # output_file = os.path.join(output, filename)\r\n # else:\r\n # output_file = os.path.join(output, filename+extension)\r\n for key, value in output_df.iteritems():\r\n output_file = os.path.join(output, key)\r\n writer = pd.ExcelWriter(output_file, engine='xlsxwriter')\r\n value.to_excel(writer)\r\n writer.save()","title":""},{"docid":"acd6f6b3e142b8e980cd54d17ed1ba0a","score":"0.5527286","text":"def __write_xlsx(self, data_f, name):\n file_path = os.path.join(self.__get_file_path(), self.__get_file_name() + \"_\" + name)\n # Github open ticket for the abstract method\n writer = pd.ExcelWriter('%s.xlsx' % file_path, engine='xlsxwriter')\n data_f.to_excel(writer, sheet_name=name)\n writer.save()","title":""},{"docid":"3515a98e30034850fd351980c60dace6","score":"0.55242693","text":"def update_worksheet(data, sheet_name):\n print(f'Updating {sheet_name} worksheet...{newline}')\n updated_worksheet = SHEET.worksheet(sheet_name)\n updated_worksheet.append_row(data)\n print(f'{sheet_name} worksheet updated successfully!{newline}')","title":""},{"docid":"2a6bddcee0299d534df260f0aa6600dc","score":"0.5509846","text":"def write_result(header, grouped_data_items, num_sheets, highlight_cols=None):\n\n header = [\"Номер групи\"] + header\n if highlight_cols is None:\n highlight_cols = []\n\n filename = 'formatted_{:%Y-%m-%d_%H:%M:%S}.xlsx'.format(datetime.now())\n print('Writing to XLSX workbook \"{}\"'.format(filename))\n workbook = xlsxwriter.Workbook(filename)\n\n groups_per_sheet = len(grouped_data_items) // num_sheets\n for sheet_num in range(num_sheets):\n worksheet = workbook.add_worksheet('Book{}'.format(sheet_num + 1))\n # Pagination bounds\n lower_bound = groups_per_sheet * sheet_num\n if sheet_num == num_sheets - 1:\n upper_bound = None\n else:\n upper_bound = groups_per_sheet * (sheet_num + 1)\n sheet_groups = grouped_data_items[lower_bound:upper_bound]\n\n row_pointer = 0 # Current row in the worksheet\n worksheet.write_row(row_pointer, 0, header)\n for group_num, rows in enumerate(sheet_groups):\n for row_num, row in enumerate(rows):\n row_pointer += 1\n prev_group = \"\"\n\n for col, cell in enumerate([group_num] + row):\n # Here we are using number in front of header of current\n # column to determine if we are still in the same group\n new_group_started = True\n if len(header) > col:\n m = re.match(\"(\\d+)\", header[col])\n if m:\n if m.group(1) == prev_group:\n new_group_started = False\n else:\n prev_group = m.group(1)\n\n orig_col = col - 1\n format = workbook.add_format()\n # Highlight columns that contain non-matching cells\n if orig_col in highlight_cols:\n if any([x[orig_col] != cell for i, x in enumerate(rows) if i != row_num]):\n format.set_bg_color('red')\n # Handle the group borders\n format.set_border_color('#DDDDDD')\n format.set_border(1)\n\n if new_group_started:\n format.set_left_color('black')\n\n if row_num == 0:\n format.set_top_color('black')\n elif row_num == len(rows) - 1:\n format.set_bottom_color('black')\n\n # Filenames might sometimes be detected as numbers and we don't want this\n if col == COL_FILENAME:\n worksheet.write_string(row_pointer, col, cell, format)\n else:\n worksheet.write(row_pointer, col, cell, format)\n\n # Disabled for now\n # # Add an empty row between the groups\n # row_pointer += 1\n # worksheet.write_row(row_pointer, 0, [])\n print('Wrote {} rows for sheet {}'.format(row_pointer + 1, sheet_num))\n\n workbook.close()","title":""},{"docid":"90020d289fa0394def3ec56edac70671","score":"0.5501802","text":"def create_sheet(self, name):\n return ODSSheetWriter(self.native_book, None, name)","title":""},{"docid":"005e54560235314bcecf0d0622c632d8","score":"0.54850245","text":"def export_list(self, sheet):\n if self.groupby:\n idx = self.fields.index(self.groupby)\n groups = []\n uniquekeys = []\n queryset = sorted(self.data, key=lambda x: x[idx])\n for key, group in groupby(queryset, key=lambda x: x[idx]):\n groups.append(list(group))\n uniquekeys.append(key)\n row = 1\n for grouper, group in zip(uniquekeys, groups):\n sheet.write(row, 0, u'', styles['data'])\n sheet.write(row + 1, 0, grouper, styles['subheader'])\n row += 2\n for line in group:\n col = 0\n for val in line:\n self.write_cell_data(sheet, (row, col), val)\n col += 1\n row += 1\n else:\n row = 1\n for line in self.data:\n col = 0\n for val in line:\n self.write_cell_data(sheet, (row, col), val)\n col += 1\n row += 1","title":""},{"docid":"8e3af6fdf857b9f948d85ee4bdf89d31","score":"0.54808885","text":"def getExcel(f):\n return pd.ExcelWriter(f)","title":""},{"docid":"b03056eec848a8cfb8ebed16e67461fa","score":"0.5475697","text":"def to_excel(self, excel_writer='output.xlsx', sheet_name='Sheet1', *, protect_sheet=False,\n right_to_left=False, columns_to_hide=None, add_filters=False, replace_sheet=False,\n auto_fit=None, **kwargs):\n save = kwargs.pop('save', isinstance(excel_writer, str))\n\n # pandas.to_excel defaults\n index = kwargs.pop('index', True)\n header = kwargs.pop('header', True)\n columns = kwargs.pop('columns', None)\n engine = kwargs.pop('engine', 'openpyxl')\n startcol = kwargs.pop('startcol', 0)\n startrow = headerrow = kwargs.pop('startrow', 0)\n\n index_label = kwargs.get('index_label', self.index.name)\n\n if self._table_args and not header:\n raise ValueError('Cannot format as table without headers.')\n\n if columns is not None:\n return self.loc[:, columns].to_excel(\n excel_writer=excel_writer, sheet_name=sheet_name, protect_sheet=protect_sheet,\n right_to_left=right_to_left, columns_to_hide=columns_to_hide, add_filters=add_filters,\n replace_sheet=replace_sheet, auto_fit=auto_fit, header=header, index=index,\n startcol=startcol, startrow=startrow, engine=engine, save=save, **kwargs\n )\n\n if isinstance(excel_writer, str):\n excel_writer = self.ExcelWriter(excel_writer)\n elif 'openpyxl' not in excel_writer.engine:\n raise ValueError('Engine for excel_writer must be openpyxl.')\n\n if _os.path.splitext(excel_writer.path)[1] not in excel_writer.supported_extensions:\n raise ValueError(\n 'Unsupported file extension {}. Use {}.'.format(\n _os.path.splitext(excel_writer.path)[1], '/'.join(excel_writer.supported_extensions)\n )\n )\n\n if replace_sheet:\n if sheet_name in excel_writer.book:\n del excel_writer.book[sheet_name]\n if sheet_name in excel_writer.sheets:\n del excel_writer.sheets[sheet_name]\n\n self.dataframe.to_excel(\n excel_writer, sheet_name=sheet_name, engine=engine, header=header,\n index=index, startcol=startcol, startrow=startrow, columns=columns, **kwargs\n )\n\n book = excel_writer.book\n sheet = book[sheet_name]\n sheet.sheet_view.rightToLeft = right_to_left\n\n # add named styles. Rename any whose name is already taken within book.\n renamed_styles = self._add_named_styles(book)\n\n if auto_fit is not None and auto_fit is not False:\n if auto_fit is True:\n auto_fit = self.columns\n self.auto_fit(auto_fit, index=index, include_header=bool(header))\n\n # index styles\n if index:\n current_cell = sheet.cell(row=startrow + 1, column=startcol + 1)\n if header and not index_label and self._table_args:\n index_label = 'index'\n current_cell.value = index_label # Otherwise formatting as table will auto give it a ColumnX name.\n if header: # TODO: Style this cell properly\n current_cell.style = self._header_styles.iat[0]\n offset = 2 if header else 1\n for row_index, index_style in enumerate(self._index_styles.iteritems()):\n index_value, style = index_style\n current_cell = sheet.cell(row=row_index + startrow + offset, column=startcol + 1)\n current_cell.style = renamed_styles.get(style, style)\n # set index width\n sheet.column_dimensions[self.get_column_letter(startcol)].width = self._index_width\n # adjust startcol for added index column\n startcol += 1\n\n # header styles\n if header:\n for col_index, col_style in enumerate(self._header_styles.iteritems()):\n col_name, style = col_style\n current_cell = sheet.cell(row=startrow + 1, column=col_index + startcol + 1)\n current_cell.style = renamed_styles.get(style, style)\n # set header height\n sheet.row_dimensions[startrow + 1].height = self.header_height\n # adjust startrow for header row\n startrow += 1\n\n # data styles\n for col_index, col_series in enumerate(self._styleframe.iteritems()):\n col_name, column = col_series\n for row_index, index_style in enumerate(column.iteritems()):\n index_value, style = index_style\n current_cell = sheet.cell(row=row_index + startrow + 1, column=col_index + startcol + 1)\n current_cell.style = renamed_styles.get(style, style)\n\n # add any hyperlinks\n if self._hyperlinks is not None:\n for col_index, col_series in enumerate(self._hyperlinks.iteritems()):\n col_name, column = col_series\n\n try:\n col_index = self.columns.get_loc(col_name)\n except KeyError: # col_name not in dataframe\n if index and col_name in (index_label, self.index.name, 'index'):\n col_index = -1\n else:\n continue\n\n for row_index, index_link in enumerate(column.iteritems()):\n index_value, hyperlink = index_link\n if isinstance(hyperlink, _Hyperlink):\n # make sure each cell has a unique Hyperlink obj to receive the ref of that cell\n hyperlink = _copy(hyperlink)\n\n current_cell = sheet.cell(row=row_index + startrow + 1, column=col_index + startcol + 1)\n # openpyxl assigns cell ref to hyperlink\n current_cell.hyperlink = hyperlink\n\n # set column widths\n for col_index, column_width in enumerate(self._column_widths.iteritems()):\n column, width = column_width\n column_letter = self.get_column_letter(col_index, startcol=startcol)\n sheet.column_dimensions[column_letter].width = width\n\n # set row heights\n for row_index, row_height in enumerate(self._row_heights.iteritems()):\n row, height = row_height\n sheet.row_dimensions[startrow + row_index + 1].height = height\n\n # format as table if needed\n if self._table_args:\n tables = {tbl.name for sht in book.worksheets for tbl in sht._tables}\n\n rows = None if not self.dataframe.empty else (0, 1)\n self._table_args['ref'] = self._get_range_as_str(\n row_index=rows, startcol=startcol, startrow=headerrow, index=index\n )\n\n if 'name' in self._table_args:\n self._table_args['displayName'] = self._table_args.pop('name')\n if self._table_args.get('displayName', None) in tables:\n raise ValueError('Table name \"{}\" already exists in book.'.format(self._table_args['displayName']))\n\n if 'displayName' not in self._table_args:\n # find next available table name\n for i in _count(1):\n if 'Table{}'.format(i) not in tables:\n tbl = _table.Table(\n displayName='Table{}'.format(i),\n **self._table_args\n )\n break\n else:\n tbl = _table.Table(\n **self._table_args\n )\n\n sheet.add_table(tbl)\n\n elif add_filters:\n sheet.auto_filter.ref = self._get_range_as_str(\n row_index=0, startcol=startcol, startrow=headerrow, index=index\n )\n\n # Hide columns\n if columns_to_hide:\n if isinstance(columns_to_hide, (str, int)):\n column_letter = self.get_column_letter(columns_to_hide, startcol=startcol)\n sheet.column_dimensions[column_letter].hidden = True\n else:\n for column in columns_to_hide:\n column_letter = self.get_column_letter(column, startcol=startcol)\n sheet.column_dimensions[column_letter].hidden = True\n\n # Protect sheet\n if protect_sheet:\n sheet.protection.autoFilter = False\n sheet.protection.enable()\n\n if save:\n excel_writer.save()\n\n return excel_writer","title":""},{"docid":"d117f0ba4b793b01d4cfb49dbca8761e","score":"0.5475479","text":"def fill_xls(ws, row, task_title, milestone, start_date, end_date, estimated_hours, qa, _developer, priority, _type,\n description):\n ws.write(row, 0, task_title)\n ws.write(row, 1, milestone)\n ws.write(row, 2, start_date)\n ws.write(row, 3, end_date)\n ws.write(row, 4, estimated_hours)\n ws.write(row, 5, qa)\n ws.write(row, 6, _developer)\n ws.write(row, 7, priority)\n ws.write(row, 8, _type)\n ws.col(9).width = 100 * 256\n ws.write(row, 9, description.encode('utf-8').replace(\"\\r\\n\", \"\").strip())","title":""},{"docid":"dbe4d4315ba3009a6859dd6474b182c4","score":"0.54749334","text":"def write_excel(filename, sheetnames, arrays):\n if len(sheetnames) != len(arrays):\n raise IndexError(\"Array and sheet number must be equal.\")\n \n book = xlwt.Workbook()\n \n for name, array in zip(sheetnames, arrays):\n sheet = book.add_sheet(name)\n cols, rows = array.shape\n \n for row in range(rows):\n for col in range(cols):\n sheet.write(row, col, array[col][row])\n \n book.save(filename)","title":""},{"docid":"a8b5761fc33d5831f01f1e613998bedf","score":"0.54558617","text":"def save_excel(self, file):\n if not file.endswith('.xlsx'):\n file = file + '.xlsx'\n df = pd.DataFrame(self._items, columns=['DOI'])\n df.to_excel(file)","title":""},{"docid":"bc8ddb688c107d1c406b28972c8cf94b","score":"0.54516715","text":"def create_doc(title):\n\n spreadsheet_body = {\n 'properties': {\n 'locale': 'zh_CN',\n 'timeZone': 'Etc/GMT',\n 'autoRecalc': 'ON_CHANGE',\n 'title': title\n },\n }\n return service.spreadsheets().create(body = spreadsheet_body).execute()","title":""},{"docid":"1119a6786ae6317fa2dede14acea2a1b","score":"0.544675","text":"def InsertGSheetsRows(\n service, spreadsheetId, range_name, values, value_input_option=\"RAW\", quiet=True\n):\n body = {\"values\": values}\n result = (\n service.spreadsheets()\n .values()\n .append(\n spreadsheetId=spreadsheetId,\n range=range_name,\n valueInputOption=value_input_option,\n body=body,\n )\n .execute()\n )\n print result","title":""},{"docid":"5c47401fc4029867494317494e0f9174","score":"0.5435767","text":"def get_Data_temblor(idsheet, rangesheet):\n credentials = get_credentials()\n http = credentials.authorize(httplib2.Http())\n discoveryUrl = ('https://sheets.googleapis.com/$discovery/rest?'\n 'version=v4')\n service = discovery.build('sheets', 'v4', http=http, discoveryServiceUrl=discoveryUrl)\n# spreadsheetId = 'Necesidades de Zonas Afectadas y Albergues '\n spreadsheetId = 'Centros de Acopio'\n #####DAÑOS Y DERRUMBES VERIFICADOS\n #####Para descargar otras páginas cambiar el nombre en el campo range\n result = service.spreadsheets().values().get(\n spreadsheetId=idsheet,\n range=rangesheet).execute()\n values = result.get('values', [])\n return pd.DataFrame(values)","title":""},{"docid":"01d33f342016389413e0957bb66237fc","score":"0.54277146","text":"def _save_values(self, nm):\n try:\n result = self.sheet.values().get(\n spreadsheetId=self.sheet_id,\n range=nm).execute()\n values = result.get('values', [])\n df = pd.DataFrame(\n values[4:], columns=values[3])\n df.to_csv(\n 'data/Canada-{}.csv'.format(nm),\n index=False)\n except:\n print(\"Retrieving {} failed.\".format(nm))","title":""},{"docid":"c01547aa9dce285db91bf405caa9e215","score":"0.5426704","text":"def save_report_file(assembly, bom, indented_bom, ebom, prev):\n writer = pd.ExcelWriter(assembly + '.xlsx', engine='xlsxwriter')\n workbook = writer.book\n header_format = workbook.add_format({\n 'bold': True,\n 'text_wrap': False,\n 'fg_color': '#DCE6F1',\n 'border': 1\n })\n\n _create_bom_worksheet(bom, writer, header_format)\n _create_indented_bom_worksheet(indented_bom, writer, header_format)\n _create_import_file_worksheet(ebom, writer, header_format)\n _create_new_revision_worksheet(prev, writer, header_format)\n\n writer.close()","title":""},{"docid":"08bfcc9ad3d5d2bae36b6cbe1469a98c","score":"0.54227155","text":"def save_excel(self, file):\n if not file.endswith('.xlsx'):\n file = file + '.xlsx'\n df = pd.DataFrame(self._items, columns=self.field_names)\n df.to_excel(file)","title":""},{"docid":"dc4f5ac48ce66fdbd5298200b1c57742","score":"0.54178506","text":"def gs(credentials, title, output_path):\n\n con = create_database(output_path)\n\n loader = simplesqlite.loader.GoogleSheetsTableLoader()\n loader.source = credentials\n loader.title = title\n\n for tabledata in loader.load():\n click.echo(\n \"convert '%s' to '%s' table\" % (title, tabledata.table_name))\n con.create_table_from_tabledata(tabledata)\n\n return 0","title":""},{"docid":"8eee5ff2e2dfb0baaede0560768c0ecf","score":"0.54152787","text":"def export_plot_data_to_excel(self):\n\n all_srs = self.plot_settings.axis1_series_list + self.plot_settings.axis2_series_list\n\n # Collate all time series to dataframe\n df_ts_list = []\n populated_srs = (srs for srs in all_srs if len(srs.y) > 0)\n for srs in populated_srs:\n col = self._create_label(srs)\n df = pd.DataFrame(srs.y, index=srs.x, columns=[col])\n df_ts_list.append(df)\n\n y = srs.y_filt\n if len(y) > 0:\n col = self._create_label(srs, filtered=True)\n df = pd.DataFrame(y, index=srs.x, columns=[col])\n df_ts_list.append(df)\n\n # Collate all psd series to dataframe\n df_psd_list = []\n populated_srs = (srs for srs in all_srs if len(srs.pxx) > 0)\n for srs in populated_srs:\n col = self._create_label(srs)\n df = pd.DataFrame(srs.pxx, index=srs.freq, columns=[col])\n df_psd_list.append(df)\n\n pxx = srs.pxx_filt\n if len(pxx) > 0:\n col = self._create_label(srs, filtered=True)\n df = pd.DataFrame(pxx, index=srs.freq, columns=[col])\n df_psd_list.append(df)\n\n # Concatenate to sheet dataframe\n df_ts: pd.DataFrame\n df_ts = pd.concat(df_ts_list, axis=1, sort=False)\n df_ts.index.name = \"Time (s)\"\n df_psd = pd.concat(df_psd_list, axis=1, sort=False)\n df_psd.index.name = \"Freq (Hz)\"\n\n # Write to Excel\n filename, _ = QtWidgets.QFileDialog.getSaveFileName(\n self, caption=\"Export Plot Data\", filter=\"Excel File (*.xlsx)\"\n )\n if filename:\n writer = pd.ExcelWriter(filename, engine=\"xlsxwriter\")\n wb = writer.book\n fmt1 = wb.add_format({\"text_wrap\": True, \"bold\": True})\n fmt2 = wb.add_format({\"num_format\": \"0.000E+00\"})\n\n # Time Series sheet\n df_ts.to_excel(writer, sheet_name=\"Time Series\")\n ws = writer.sheets[\"Time Series\"]\n ws.set_column(1, len(df_ts.columns), 12, fmt2)\n\n # Need to rewrite column names to add desired format\n for i, col in enumerate(df_ts.columns):\n ws.write(0, i + 1, col, fmt1)\n\n # PSD sheet\n df_psd.to_excel(writer, sheet_name=\"PSD\")\n ws = writer.sheets[\"PSD\"]\n ws.set_column(1, len(df_psd.columns), 12, fmt2)\n\n # Need to rewrite column names to add desired format\n for i, col in enumerate(df_psd.columns):\n ws.write(0, i + 1, col, fmt1)\n\n writer.save()\n msg = \"Plot data exported successfully.\"\n QtWidgets.QMessageBox.information(self, \"Export Plot Data\", msg)","title":""},{"docid":"b2a494b3bb6b423ad7e85594fa6a25c5","score":"0.54057837","text":"def toExcel(fname,tables):\r\n writer = pd.ExcelWriter(fname)\r\n for k,v in tables.items():\r\n # Make sure the order of columns complies the specs\r\n record = [r for r in V4.records if r.__class__.__name__.upper()==k]\r\n if len(record)==0:\r\n print(\"Ignore exporting table %s: No such record type exists.\" %k)\r\n else:\r\n columns = [field[0] for field in record[0].fieldMap]\r\n v.to_excel(writer,sheet_name=k,columns=columns,index=False,na_rep=\"N/A\")\r\n writer.save()","title":""},{"docid":"900e1abdf76864c909b42e6d3e90893c","score":"0.54052615","text":"def save(self, worksheet, workbook, startrow=0, startcol=0):\n if self.name and self.show_title:\n write_title(self.name, worksheet, workbook, startrow, startcol, self.width)\n startrow += 1\n self.to_excel(workbook, worksheet.name, startrow=startrow, startcol=startcol)\n return workbook.get_worksheet_by_name(worksheet.name)","title":""},{"docid":"b83887975b4d69d609d3298d95402e2a","score":"0.5397788","text":"def update_worksheet(data,worksheet):\n print(f\"Updating {worksheet} worksheet...\\n\")\n worksheet_to_update = SHEET.worksheet(worksheet)\n worksheet_to_update.append_row(data)\n print(f\"{worksheet} worksheet updated!\")","title":""},{"docid":"59abed42a585384f2eca9ce05a3ff0a9","score":"0.53878915","text":"def export_df(df: pd.DataFrame, export_path: str,\r\n export_format: str = \"pickle\",\r\n hdf_key: Optional[str] = None\r\n ) -> None:\r\n _, ext = os.path.splitext(export_path)z\r\n use_ext = '.' + export_format if len(ext) == 0 else ''\r\n if export_format == \"html\":\r\n color_dict = {\r\n 'POSITIVE': 'limegreen',\r\n 'NEGATIVE': 'red',\r\n 'NEUTRAL': 'lightgrey',\r\n 'MIXED': 'yellow'\r\n }\r\n spk_dict = {\r\n 'ch_0': '#F0F8FF',\r\n 'spk_0': '#F0F8FF',\r\n 'ch_1': '#FFF8DC',\r\n 'spk_1': '#FFF8DC'\r\n }\r\n df.set_index(\r\n ['transcript', 'recording', 'speaker', 'index']\r\n ).to_html(export_path + use_ext, encoding='utf-8',\r\n formatters={\r\n 'pred_sent': lambda sent: f'<span style=\"background-color:{color_dict[sent]}\">{sent}</span>',\r\n 'speaker': lambda speaker: f'<span style=\"background-color:{spk_dict[speaker]}\">{speaker}</span>'\r\n }, escape=False)\r\n elif export_format == \"csv\":\r\n df.to_csv(export_path + use_ext)\r\n elif export_format == \"json\":\r\n df.to_json(export_path + use_ext)\r\n elif export_format == \"parquet\":\r\n df.to_parquet(export_path + use_ext)\r\n elif export_format == \"pickle\":\r\n df.to_pickle(export_path + use_ext)\r\n elif export_format == \"excel\":\r\n if len(use_ext) > 0:\r\n use_ext = '.xlsx'\r\n df.to_excel(export_path + use_ext, index=False)\r\n elif export_format == \"hdf\":\r\n assert hdf_key is not None, \"Parameter hdf_key must be informed if export format is hdf.\"\r\n df.to_hdf(export_path + use_ext, hdf_key)\r\n else:\r\n raise ValueError(f\"Unknown export format: {export_format}\")","title":""},{"docid":"86fee2d337aaac1a263aea1dced745ef","score":"0.5387516","text":"def _create_spreadsheet(self, ws_name: str, columns: List[str]) -> None:\n ws = self.workbook.create_sheet(ws_name)\n self.workbook.active = ws\n ws.append(columns)\n self.workbook.save(self.wb_name)","title":""},{"docid":"1009060490d1e796dfb6313590e8848a","score":"0.5387063","text":"def make_worksheet(self, sheet_name, query_results, **kwargs):\n try:\n self.workbook_builder.add_worksheet(\n sheet_name=sheet_name,\n field_names=query_results.field_names,\n sheet_data=query_results.result_data,\n **kwargs\n )\n except:\n error = \"Unable to add worksheet {}\".format(sheet_name)\n self.logger.exception(error)","title":""},{"docid":"6664e94d2265df84fe471f9a4543c9ad","score":"0.5382799","text":"def export_excel(table, file_name, attributes):\r\n \r\n writer = pd.ExcelWriter(file_name, engine='xlsxwriter')\r\n table.to_excel(writer, index=False, header=False, startrow=1,\r\n sheet_name = 'Sheet1')\r\n num_rows, num_cols = table.shape\r\n num_cols -= 1\r\n workbook = writer.book\r\n worksheet = writer.sheets['Sheet1']\r\n \r\n # Add attribute labels: Row 1 merge (b-d), (e-g), etc basiac\r\n alphabet = string.ascii_uppercase\r\n\r\n offset = 1\r\n width = 3\r\n row = 1\r\n \r\n merge_format = workbook.add_format({\r\n 'bold': 1,\r\n 'border': 1,\r\n 'align': 'center',\r\n 'valign': 'vcenter'})\r\n \r\n for i, attribute in enumerate(attributes):\r\n start_col = offset + i*width\r\n end_col = start_col + width - 1\r\n positions = \"{0}{2}:{1}{2}\".format(alphabet[start_col],\r\n alphabet[end_col], row)\r\n \r\n\r\n worksheet.merge_range(positions, attribute, merge_format)\r\n\r\n writer.save()","title":""},{"docid":"9d57cc42f8e4fe93df0d9a84580a1a17","score":"0.5378535","text":"def excel(self): \n nombres =pandas.read_excel('C:/Videojuego/Proyecto/Usuarios/Nombres.xlsx',header = None)\n nombres = list(nombres.iloc[:,0])\n nombres.append(self.text2)\n clave=nombres.pop(0)\n dic={clave:nombres}\n\n df = pandas.DataFrame(dic)\n df =df[['Nombres']]\n writer =ExcelWriter('C:/Videojuego/Proyecto/Usuarios/Nombres.xlsx') \n df.to_excel(writer,sheet_name='Alumnos',index=False)\n writer.save()\n\n df2 = pandas.DataFrame({'Juego':[\"----------\"],'Estatus':[\"----------\"],'Tiempo':[\"----------\"],'Real':[\"----------\"],'Ideal':[\"----------\"],'Fecha':[\"----------\"]})\n df2=df2[['Juego','Estatus','Tiempo','Real','Ideal','Fecha']]\n writer = ExcelWriter('C:/Videojuego/Proyecto/Usuarios/Alumnos/'+self.text2+'.xlsx')\n df2.to_excel(writer,sheet_name=self.text2,index=False)\n writer.save()","title":""},{"docid":"0939f485d27b8f78aca961be7ecddb6f","score":"0.5359601","text":"def write_cell_data(self, sheet, pos, val):\n row, col = pos\n if isinstance(val, (date, datetime)):\n sheet.write(row, col, label=val, style=styles['date'])\n else:\n sheet.write(row, col, label=val, style=styles['data'])","title":""},{"docid":"00cfdd4549accbbf00151cc8943187cc","score":"0.53470063","text":"def tableFormat(sheetData, sheetName, wbook):\n # Nothing to format, so return.\n if sheetData.shape[0] == 0:\n return\n sheet = wbook.sheets[sheetName]\n sheet.freeze_panes(1, 0)\n # Set the autofilter for the sheet.\n sheet.autofilter(0, 0, sheetData.shape[0], sheetData.shape[1]-1)\n # Set document formatting.\n docFormat = wbook.book.add_format({'font': 'Calibri', 'font_size': 11})\n acctFormat = wbook.book.add_format({'font': 'Calibri', 'font_size': 11, 'num_format': 44})\n commaFormat = wbook.book.add_format({'font': 'Calibri', 'font_size': 11, 'num_format': 3})\n newFormat = wbook.book.add_format({'font': 'Calibri', 'font_size': 11, 'bg_color': 'yellow'})\n movedFormat = wbook.book.add_format({'font': 'Calibri', 'font_size': 11, 'bg_color': '#FF9900'})\n # Format and fit each column.\n i = 0\n # Columns which get shrunk down in reports.\n hideCols = ['Technology', 'Excel Part Link', 'Report Part Nbr Link', 'MFG Part Description',\n 'Focus', 'Part Class Name', 'Vendor ID', 'Invoice Detail Nbr', 'Assigned Account Rep',\n 'Recipient', 'DKLI Report Date', 'Invoice Date Group', 'Comments', 'Sales Channel']\n coreCols = ['Must Contact', 'End Product', 'How Contacted', 'Information for Digikey']\n for col in sheetData.columns:\n acctCols = ['Unit Price', 'Invoiced Dollars']\n if col in acctCols:\n formatting = acctFormat\n elif col == 'Quantity':\n formatting = commaFormat\n else:\n formatting = docFormat\n maxWidth = max(len(str(val)) for val in sheetData[col].values)\n # Set maximum column width at 50.\n maxWidth = min(maxWidth, 50)\n if col in hideCols:\n maxWidth = 0\n elif col in coreCols:\n maxWidth = 25\n sheet.set_column(i, i, maxWidth+0.8, formatting)\n i += 1\n # Highlight new root customer and moved city rows.\n try:\n for row in sheetData.index:\n ind = str(sheetData.loc[row, 'TAARCOM Comments']).lower().rstrip() == 'individual'\n no_root_cust = sheetData.loc[row, 'Root Customer..'] == ''\n if ind or no_root_cust:\n continue\n root_cust_loc = int(np.where(sheetData.columns == 'Root Customer..')[0])\n city_loc = int(np.where(sheetData.columns == 'City on Acct List')[0])\n if sheetData.loc[row, 'New T-Cust'] == 'Y':\n sheet.write(row + 1, root_cust_loc, sheetData.loc[row, 'Root Customer..'], newFormat)\n elif not sheetData.loc[row, 'City on Acct List']:\n pass\n elif sheetData.loc[row, 'Customer City'] not in sheetData.loc[row, 'City on Acct List'].split(', '):\n sheet.write(row + 1, root_cust_loc, sheetData.loc[row, 'Root Customer..'], movedFormat)\n sheet.write(row + 1, city_loc, sheetData.loc[row, 'City on Acct List'], movedFormat)\n except KeyError:\n print('Error locating Sales and/or City on Acct List columns.\\n'\n 'Unable to highlight without these columns.\\n---')","title":""},{"docid":"8f24d0a7e51812a100d1a19aac8a1043","score":"0.53447926","text":"def export_to_pdf(whpa, export_path):\n wb = xlwt.Workbook()\n ws = wb.add_sheet(whpa)\n\n header = Header(*range(0, len(Header._fields)))\n\n for field, index in header._asdict().items():\n ws.write(0, index, field)\n\n with psql.connect(\"dbname=gwi_csi user=eric\") as conn:\n with conn.cursor() as cur:\n cur.execute('SELECT facility_id, facility_name, whpa_id, address,\\\n phone, property_type, facility_type, facility_status,\\\n csi_zone, \"date\", comments, location, urls, issues\\\n from whpa_report WHERE facility_id LIKE %s',\n (\"%{}%\".format(whpa),))\n\n row_index = 1\n\n for row in cur.fetchall():\n facility = Facility(*row)\n\n for field, value in facility._asdict().items():\n if field == 'location':\n x = float(facility.location.split(' ')[0].lstrip('POINT('))\n y = float(facility.location.split(' ')[1].rstrip(')'))\n\n ws.write(row_index, header.X, x)\n ws.write(row_index, header.Y, y)\n elif field == 'issues' and facility.issues is not None:\n ws.write(row_index, header.issues, \", \".join(facility.issues))\n else:\n ws.write(row_index, getattr(header, field), value)\n row_index += 1\n\n wb.save(os.path.join(export_path, \"{}.xlsx\".format(whpa)))","title":""},{"docid":"1f42799db991eecb9748aa6323344809","score":"0.53431857","text":"def upload_as_gsheet(file_to_upload, filename): \r\n drive_service = initialize_drive_service()\r\n file_found = False\r\n \r\n try:\r\n extension = file_to_upload.split(\".\")[-1]\r\n \r\n file_metadata = {\r\n 'name' : filename,\r\n 'mimeType' : 'application/vnd.google-apps.spreadsheet'\r\n }\r\n \r\n if (extension == 'xls'):\r\n media = MediaFileUpload('%s' % (file_to_upload),\r\n mimetype='application/vnd.ms-excel',\r\n resumable=True)\r\n file_found = True\r\n elif (extension == 'xlsx'):\r\n media = MediaFileUpload('%s' % (file_to_upload),\r\n mimetype='application/vnd.openxmlformats-officedocument.spreadsheetml.sheet',\r\n resumable=True)\r\n file_found = True\r\n else:\r\n pass \r\n except IndexError:\r\n pass\r\n \r\n if (file_found == True):\r\n file = drive_service.files().create(body=file_metadata,\r\n media_body=media,\r\n fields='id').execute()\r\n log_file().info(\"Source Compiler.log\", \"File \\\"{0}\\\" uploaded as: {1} (ID: {2}).\".format(file_to_upload, filename, file.get('id')))\r\n print(\"File \\\"{0}\\\" uploaded as: {1} (ID: {2}).\".format(file_to_upload, filename, file.get('id'))) \r\n else:\r\n log_file().error(\"Source Compiler.log\", \"Invalid file name or extension. Provide full file name with .xls or .xlsx extensions.\")\r\n print(\"Invalid file name or extension. Provide full file name with .xls or .xlsx extensions.\")","title":""},{"docid":"2e847e36617b1ec476406e82f662fbe2","score":"0.53355503","text":"def create_main_tables_structure(ws, df, date_str):\r\n ws['A1']=\"Data is provided for reference purposes only. Data is the property of PV InfoLink.\\nUsers should respect the legitimate rights for use based on the principle of integrity.\"\r\n ws['A1'].alignment=Alignment(wrap_text=True)\r\n ws.merge_cells('A1:D1')\r\n ws['A2']=\"Please do not modify this sheet manually. Use a copy for playing with the data\"\r\n ws.merge_cells('A2:D2')\r\n ws['A3']=df.columns[-3]+\", \"+ws.title\r\n ws['A3'].font=Font(bold=True)\r\n ws.merge_cells('A3:D3')\r\n for r in dataframe_to_rows(df[[\"Category\", \"Item\", \"Unit\", \"High Price\"]], index=False, header=True):\r\n ws.append(r)\r\n ws.cell(row=ws.max_row+2, column=1, value=df.columns[-1]+\", \"+ws.title)\r\n ws.cell(row=ws.max_row, column=1).font=Font(bold=True)\r\n ws.merge_cells(start_row=ws.max_row, start_column=1, end_row=ws.max_row, end_column=ws.max_column)\r\n for r in dataframe_to_rows(df[[\"Category\", \"Item\", \"Unit\", \"Average Price\"]], index=False, header=True):\r\n ws.append(r)\r\n ws.cell(row=ws.max_row+2, column=1, value=df.columns[-2]+\", \"+ws.title)\r\n ws.cell(row=ws.max_row, column=1).font=Font(bold=True)\r\n ws.merge_cells(start_row=ws.max_row, start_column=1, end_row=ws.max_row, end_column=ws.max_column)\r\n for r in dataframe_to_rows(df[[\"Category\", \"Item\", \"Unit\", \"Low Price\"]], index=False, header=True):\r\n ws.append(r)\r\n\r\n ws.cell(row=ws.max_row+2, column=1).value='*The quote of mono wafers is low resistivity product.'\r\n ws.merge_cells(start_row=ws.max_row, start_column=1, end_row=ws.max_row, end_column=ws.max_column)\r\n ws.cell(row=ws.max_row+1, column=1).value='**Mono-Si wafer quotes are based on those of 180µm. Prices of thinner ones are calculated with formula.'\r\n ws.merge_cells(start_row=ws.max_row, start_column=1, end_row=ws.max_row, end_column=ws.max_column)\r\n ws.cell(row=ws.max_row+1, column=1).value='***US and Indian module prices showed on the PV InfoLink website is after-tax price (punitive tariffs). Others are FOB price.'\r\n ws.merge_cells(start_row=ws.max_row, start_column=1, end_row=ws.max_row, end_column=ws.max_column)\r\n\r\n swap_headers(ws, \"Low Price\", date_str)\r\n swap_headers(ws, \"Average Price\", date_str)\r\n swap_headers(ws, \"High Price\", date_str)\r\n ws.column_dimensions['B'].width=51\r\n ws.column_dimensions['A'].width=16\r\n ws.column_dimensions['D'].width=10\r\n ws.row_dimensions[1].height=30","title":""},{"docid":"5ee69d526e0731440c03eff2c10a0eaf","score":"0.53336","text":"async def spreadsheet(ctx,*,request : str=''):\n await ctx.send(r\"https://docs.google.com/spreadsheets/d/11ZXN22CTmItPMVduRdpEwf6wR9KRMJR7Ro9HensEWSk/edit#gid=656420900\")","title":""},{"docid":"38eef9c9098476f332c511ef6042d32b","score":"0.53316915","text":"def save_dataframe(df, filename):\n df.to_csv(filename, header=True, index=True, index_label=False, mode='wb',\n encoding='utf-8', na_rep='NaN', sep='|')\n print \"DataFrame Saved to:\\n-->\\033[1;36m{}\\033[0m\\n\".format(filename)","title":""},{"docid":"1d794bfb31cc731373cdd1798728d10e","score":"0.533051","text":"def write_rows(self, sheet_range, values=[]):\n body = {\n 'values': values\n }\n result = self.service.spreadsheets().values().append(\n spreadsheetId=self.spreadsheet_id,\n range=sheet_range,\n valueInputOption='USER_ENTERED',\n insertDataOption='INSERT_ROWS',\n body=body).execute()\n return result","title":""},{"docid":"3e12938908cc62c01c3c4a3ea2081ac9","score":"0.5330464","text":"def export_qs(self, sheet):\n opts = self.data.model._meta\n row = 4\n for item in self.data:\n col = 0\n for name in self.fields:\n val = getattr(item, name, '')\n self.write_cell_data(sheet, (row, col), val)\n col += 1\n row += 1","title":""},{"docid":"f060c38e3752d6c8d03a8ceba4655cb4","score":"0.5324046","text":"def to_xls(scraped_data):\n book = xlwt.Workbook()\n sheet = book.add_sheet(\"labs\")\n\n data = [COL_HEADERS] + to_lists(scraped_data)\n\n for i, data_row in enumerate(data):\n sheet_row = sheet.row(i)\n for j, item in enumerate(data_row):\n sheet_row.write(j, item)\n\n book.save(FILEPATH_TEMPLATE.format(\"xls\"))","title":""},{"docid":"af880beb5950a3ab6e3f49b0273f016d","score":"0.5315864","text":"def export_inventory_to_excel(inventory, day):\r\n\r\n df = pd.DataFrame(inventory)\r\n writer = pd.ExcelWriter(f'inventory_{day}.xlsx', engine='xlsxwriter')\r\n df.to_excel(writer, sheet_name='Sheet1', startrow=1, header=False)\r\n workbook = writer.book\r\n worksheet = writer.sheets['Sheet1']\r\n header = ['Product Name', 'Count', 'Expiration Date']\r\n header_format = workbook.add_format({\r\n 'bold': True,\r\n 'text_wrap': True,\r\n 'valign': 'top',\r\n 'fg_color': '#D7E4BC',\r\n 'border': 1})\r\n for col_num, value in enumerate(header):\r\n worksheet.write(0, col_num + 1, value, header_format)\r\n writer.save()","title":""},{"docid":"4553a70c20c9d6419d0c556130d7ce06","score":"0.5311012","text":"def add_to_worksheet(worksheet, data):\n print(f'Updating {worksheet} worksheet...\\n')\n worksheet_to_update = SHEET.worksheet(worksheet)\n worksheet_to_update.append_row(data)\n print(f'{worksheet} worksheet updated successfully.\\n')","title":""},{"docid":"5a5444b140b762536c7b10ab8676bf1b","score":"0.52888787","text":"def df_to_excel(self, df, filename, **kwargs):\n if filename.startswith('/'):\n filename = filename[1:]\n if (\n 'index' not in kwargs\n ): # make the default index=False if not specified in input\n kwargs = dict({'index': False}, **kwargs)\n return df.to_excel(self.filepath(filename), **kwargs)","title":""},{"docid":"469f76923658fc90a75e63e4787ed69f","score":"0.5274583","text":"def _save(tables, writer, sheet_name='Sheet1', n_row=1):\n for t in tables:\n label = t.columns.name\n t.to_excel(writer, sheet_name, \n startrow=n_row, index_label=label)\n n_row += len(t.index) + 2\n \n return n_row","title":""},{"docid":"ef4530a1cccaab30ecd88c61ff362744","score":"0.52736664","text":"def new_excel(seatNo, ID, gender, first_name, last_name, seatName, filename):\r\n wb = xlwt.Workbook(encoding='utf-8')\r\n sheet1 = wb.add_sheet('Sheet1')\r\n\r\n row = 0\r\n sheet1.write(row, 0, u\"ลำดับ\".encode('utf-8'))\r\n sheet1.write(row, 1, u\"รหัสนิสิต\".encode('utf-8'))\r\n # sheet1.write(row, 2, u\"คำนำหน้า\".encode('utf-8'))\r\n sheet1.write(row, 2, u\"ชื่อ - นามสกุล\".encode('utf-8'))\r\n sheet1.write(row, 3, u\"เลขที่นั่ง\".encode('utf-8'))\r\n for row in range(0, len(ID)):\r\n sheet1.write(row+1, 0, seatNo[row])\r\n sheet1.write(row+1, 1, ID[row])\r\n sheet1.write(row+1, 2, gender[row] + first_name[row] + \" \" + last_name[row])\r\n sheet1.write(row+1, 3, seatName[row])\r\n\r\n wb.save(filename + '_new.xls')","title":""}],"string":"[\n {\n \"docid\": \"f2cbcdddc55b0d33b6dba497953238b6\",\n \"score\": \"0.7028948\",\n \"text\": \"def write_google_sheet(results_dict, row=2, name='results_layers', sheet_name='run1'):\\n # Google API stuff\\n scope = [\\\"https://www.googleapis.com/auth/drive\\\"]\\n creds = ServiceAccountCredentials.from_json_keyfile_name('config/gcp-credentials.json', scope)\\n client = gspread.authorize(creds)\\n sheet = client.open(name).worksheet(sheet_name)\\n sheet.update(f'B{row}:D{row}', [list(results_dict.values())])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78602bd0955a460b27245fc8415e5a3a\",\n \"score\": \"0.6928508\",\n \"text\": \"def write_sheet(writer, name, df, index=False):\\n if index:\\n df = df.reset_index()\\n df.to_excel(writer, name, index=False)\\n for i, col in enumerate(df.columns):\\n if df.dtypes[col].name.startswith((\\\"float\\\", \\\"int\\\")):\\n width = len(str(col)) + 2\\n else:\\n width = (\\n max([df[col].map(lambda x: len(str(x or \\\"None\\\"))).max(), len(col)]) + 2\\n )\\n writer.sheets[name].set_column(i, i, width) # assumes xlsxwriter as engine\",\n \"title\": \"\"\n },\n {\n \"docid\": \"256e4ad6fd9f80ade2a6a4d0a44a951d\",\n \"score\": \"0.69066215\",\n \"text\": \"def _xlsheet(writer, df, sheet_name, **kwargs):\\r\\n\\r\\n df.to_excel(writer, sheet_name, **kwargs)\\r\\n #tweak column width\\r\\n for i in range(df.shape[1]):\\r\\n writer.sheets[sheet_name].set_column(i, i+1, len(df.columns[i]) + 4)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eefd16ba6ef9b81eb9b0a1cdeb078a2a\",\n \"score\": \"0.661218\",\n \"text\": \"def write_file(df, file_name):\\n writer = pd.ExcelWriter(f\\\"{file_name}\\\", engine=\\\"openpyxl\\\")\\n df.to_excel(writer, sheet_name=\\\"Sheet1\\\", startrow=1)\\n writer.save()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"482c9dbb15055c9e7f453ac116d14f7c\",\n \"score\": \"0.65816694\",\n \"text\": \"def save_sheet(svc_sheet, spreadsheet_id, tab_name, header, records):\\n if len(records) == 0:\\n return\\n\\n count_columns = len(header)\\n count_rows = len(records) + 1\\n workbook = svc_sheet.open_by_key(spreadsheet_id)\\n sheets = dict()\\n for sheet in workbook.worksheets():\\n sheets[sheet.title] = sheet\\n\\n if tab_name not in sheets:\\n worksheet = workbook.add_worksheet(tab_name, count_rows, count_columns)\\n\\n else:\\n worksheet = sheets[tab_name]\\n\\n worksheet.resize(rows=count_rows, cols=count_columns)\\n range_text = 'A1:{}'.format(rowcol_to_a1(count_rows, count_columns))\\n logging.info('accessing range {}'.format(range_text))\\n cells = worksheet.range(range_text)\\n for cell in cells:\\n count_row = cell.row - 1\\n count_col = cell.col - 1\\n field = header[count_col]\\n if count_row == 0:\\n cell.value = field\\n\\n else:\\n row_data = records[count_row - 1]\\n cell.value = row_data[field]\\n\\n worksheet.update_cells(cells)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0377b94a25e49272918533b5a48ad464\",\n \"score\": \"0.64087117\",\n \"text\": \"def write_sheet(writer, tables, id_name, ligand_name):\\n\\tfor table in tables:\\n\\t\\ttable_id = table.get('id')\\n\\t\\tif table_id == id_name:\\n\\t\\t\\ttable_df = pd.concat(pd.read_html(table.prettify()))\\n\\t\\t\\tdrop_list = [i for i in table_df.index if i%2!=0]\\n\\t\\t\\ttable_df = table_df.drop(drop_list, axis=0)\\n\\t\\t\\tdrop_col = [\\\"Unnamed: \\\"+str(i) for i in range(1, 10)]\\n\\t\\t\\ttable_df = table_df.drop(drop_col, axis=1)\\n\\t\\t\\ttable_df.to_excel(writer, sheet_name=id_name, index=False)\\n\\t\\t\\tprint(\\\"Sheet name \\\"+ligand_name+\\\" is add\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7343d3fff603e2f33dbaf85c80e46764\",\n \"score\": \"0.6363107\",\n \"text\": \"def save_styled_dataframe(\\n filepath_with_ext: str,\\n sheet_name_to_styler: Dict[str, Any],\\n ) -> None:\\n with pd.ExcelWriter(filepath_with_ext) as excel_writer:\\n for sheet_name, styler_obj in sheet_name_to_styler.items():\\n styler_obj.to_excel(\\n excel_writer=excel_writer,\\n sheet_name=sheet_name,\\n index=False,\\n engine='xlsxwriter',\\n )\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0cc79258ffe88e4964b9eb467f5155e9\",\n \"score\": \"0.63454306\",\n \"text\": \"def write_exel(df, output_name):\\n # create excel writer object\\n writer = pd.ExcelWriter('covid_data.xlsx')\\n # write dataframe to excel\\n covid_data.to_excel(writer)\\n # save the excel\\n writer.save()\\n print('DataFrame is written successfully to Excel File.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61cd408ea60be3df2ad61d27e0c4db45\",\n \"score\": \"0.63329643\",\n \"text\": \"def write_output_excel(excel_book_path, output_dict, sheetname):\\n book = xw.Book(excel_book_path)\\n sheet = book.sheets[sheetname]\\n sheet.range(\\\"D12\\\").options(index=False, header=False).value = pd.DataFrame(output_dict)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bcb3506fdd897e62e7b2b860095989b5\",\n \"score\": \"0.6283218\",\n \"text\": \"def generate_excel_effectiveness(dataframe_list, sheet_name_list, filename, project_io):\\n with closing(BytesIO()) as output:\\n writer = pd.ExcelWriter(output, engine='xlsxwriter', options={'remove_timezone': True})\\n workbook = writer.book\\n data_format1 = workbook.add_format({'bg_color': '#BBCCE2'})\\n data_format2 = workbook.add_format({'bg_color': '#DEE6EF'})\\n\\n format_header = workbook.add_format({'text_wrap': True})\\n\\n workbook.formats[0].set_font_size(15)\\n for df, name in zip(dataframe_list, sheet_name_list):\\n df.to_excel(writer, sheet_name=name)\\n worksheet = writer.sheets[name]\\n worksheet.set_row(0, 20, format_header)\\n worksheet.set_column('A:A', 5)\\n worksheet.set_column('B:D', 30)\\n for row in range(1, len(df) + 1, 2):\\n worksheet.set_row(row, cell_format=data_format1)\\n worksheet.set_row(row + 1, cell_format=data_format2)\\n writer.save()\\n if project_io is not None:\\n project_io.save_data(filename, output.getvalue(), overwrite=True)\\n else:\\n with open(filename, 'wb') as out:\\n out.write(output.getvalue())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8787549f8820474d8210f1d94fb7b74e\",\n \"score\": \"0.6259219\",\n \"text\": \"def send_to_gsheet(service):\\n sorted_elo_player = sorted(ALL_PLAYERS.values(), key=operator.attrgetter('elo'))\\n sorted_elo_player.reverse()\\n all_players_gsheet_format = []\\n rank = 1\\n longest_line = 0\\n for player in sorted_elo_player:\\n rank_string = str(rank)\\n if len(player.tournaments) == 1:\\n evolution = 0\\n else:\\n evolution = compute_evolution(player)\\n if evolution > 0:\\n rank_string += \\\"(+\\\"+str(evolution)+\\\")\\\"\\n elif evolution < 0:\\n rank_string += (\\\"(\\\"+str(evolution)+\\\")\\\")\\n else:\\n rank_string += (\\\"(=)\\\")\\n rank += 1\\n player_line = []\\n player_line.append(rank_string)\\n player_line.append(player.name)\\n player_line.append(str(player.elo).replace(\\\".\\\", \\\",\\\"))\\n player_line.append(len(player.tournaments))\\n player_line.append(pretty_dict(player.elo_history).replace(\\\".\\\", \\\",\\\"))\\n\\n if len(player_line) > longest_line:\\n longest_line = len(player_line)\\n all_players_gsheet_format.append(player_line)\\n\\n rank_range = RANK_SHEET_NAME+\\\"!A1:\\\"+str(column_to_letter(longest_line))+str(len(all_players_gsheet_format))\\n\\n body = {\\n 'values': all_players_gsheet_format\\n }\\n\\n clear_sheet(service, ELO_SPREADSHEET_ID, RANK_SHEET_NAME)\\n result = service.spreadsheets().values().update(spreadsheetId=ELO_SPREADSHEET_ID,\\n range=rank_range,\\n valueInputOption=\\\"RAW\\\",\\n body=body).execute()\\n logger.info(\\\"%s cells updated.\\\", result.get('updatedCells'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d492b17a949346070cb2edf10c93fb37\",\n \"score\": \"0.61341095\",\n \"text\": \"def append_df_to_excel(filename, df, sheet_name='Sheet1', startrow=None,\\r\\n truncate_sheet=False, \\r\\n **to_excel_kwargs):\\r\\n\\r\\n\\r\\n # ignore [engine] parameter if it was passed\\r\\n if 'engine' in to_excel_kwargs:\\r\\n to_excel_kwargs.pop('engine')\\r\\n\\r\\n writer = pd.ExcelWriter(filename, engine='openpyxl')\\r\\n\\r\\n try:\\r\\n FileNotFoundError\\r\\n except NameError:\\r\\n FileNotFoundError = IOError\\r\\n\\r\\n\\r\\n try:\\r\\n # try to open an existing workbook\\r\\n writer.book = load_workbook(filename)\\r\\n\\r\\n # get the last row in the existing Excel sheet\\r\\n # if it was not specified explicitly\\r\\n if startrow is None and sheet_name in writer.book.sheetnames:\\r\\n startrow = writer.book[sheet_name].max_row\\r\\n\\r\\n # copy existing sheets\\r\\n writer.sheets = {ws.title:ws for ws in writer.book.worksheets}\\r\\n except FileNotFoundError:\\r\\n print (\\\"File not found\\\")# file does not exist yet, we will create it\\r\\n pass\\r\\n\\r\\n if startrow is None:\\r\\n startrow = 0\\r\\n\\r\\n # write out the new sheet\\r\\n df.to_excel(writer, sheet_name, startrow=startrow, **to_excel_kwargs)\\r\\n\\r\\n # save the workbook\\r\\n try:\\r\\n writer.save()\\r\\n except:\\r\\n print(\\\"Close the target file\\\")\\r\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c417120e645c8679eead9e9fc5440590\",\n \"score\": \"0.6112243\",\n \"text\": \"def to_excel(self, workbook, sheet_name=\\\"Sheet1\\\", startrow=0, startcol=0):\\n # import pdb\\n # pdb.set_trace()\\n excel_writer = pd.ExcelWriter(workbook.filename, engine=\\\"xlsxwriter\\\")\\n excel_writer.book = workbook\\n excel_writer.sheets = workbook.sheetnames\\n self.data = self.data[self.column_order]\\n self.styler.data = self.styler.data[self.column_order]\\n new_index, indexer = self.styler.columns.reindex(self.column_order)\\n self.styler.columns = new_index\\n for styler_rule in self.styler_rules:\\n if \\\"map\\\" in styler_rule:\\n styler = self.styler[styler_rule[\\\"map\\\"]]\\n else:\\n styler = self.styler\\n styler.applymap(\\n styler_rule[\\\"func\\\"],\\n subset=styler_rule[\\\"subset\\\"] if \\\"subset\\\" in styler_rule else None,\\n )\\n self.styler.to_excel(\\n excel_writer,\\n sheet_name=sheet_name,\\n startrow=startrow,\\n startcol=startcol,\\n **self.params\\n )\\n workbook = excel_writer.book\\n if self.params[\\\"index\\\"]:\\n col_add = self.index_width\\n else:\\n col_add = 0\\n if self.params[\\\"header\\\"]:\\n row_add = self.header_height\\n else:\\n row_add = 0\\n worksheet = workbook.get_worksheet_by_name(sheet_name)\\n if self.conditional_formatters:\\n # import pdb\\n # pdb.set_trace()\\n for format_rule in self.conditional_formatters:\\n if format_rule[\\\"style\\\"]:\\n format_obj = workbook.add_format(format_rule[\\\"style\\\"])\\n format_rule[\\\"options\\\"].update({\\\"format\\\": format_obj})\\n if format_rule[\\\"cols\\\"] is not None:\\n for col in format_rule[\\\"cols\\\"]:\\n col_index = list(self.data.columns).index(col) + col_add\\n worksheet.conditional_format(\\n row_add,\\n col_index,\\n row_add + len(self.data),\\n col_index,\\n options=format_rule[\\\"options\\\"],\\n )\\n if format_rule[\\\"rows\\\"] is not None:\\n for row in format_rule[\\\"rows\\\"]:\\n row_index = list(self.data.index).index(row) + row_add\\n worksheet.conditional_format(\\n row_index,\\n col_add,\\n row_index,\\n len(self.data.columns) + col_add,\\n options=format_rule[\\\"options\\\"],\\n )\\n if format_rule[\\\"index\\\"] is not None:\\n # import pdb\\n # pdb.set_trace()\\n col_start_index = 0 + startcol\\n if format_rule[\\\"index\\\"] is True:\\n col_range = range(\\n col_start_index,\\n col_start_index + len(self.data.index.names),\\n )\\n elif isinstance(format_rule[\\\"index\\\"], list):\\n col_range = [\\n (col_start_index + level) for level in format_rule[\\\"index\\\"]\\n ]\\n else:\\n continue\\n for col_index in col_range:\\n worksheet.conditional_format(\\n 0,\\n col_index,\\n len(self.data),\\n col_index,\\n options=format_rule[\\\"options\\\"],\\n )\\n if format_rule[\\\"header\\\"] is not None:\\n row_start_index = 0 + startrow\\n if format_rule[\\\"header\\\"] is True:\\n row_range = range(\\n row_start_index,\\n row_start_index + len(self.data.columns.names),\\n )\\n elif isinstance(format_rule[\\\"header\\\"], list):\\n row_range = [\\n (row_start_index + level) for level in format_rule[\\\"header\\\"]\\n ]\\n else:\\n continue\\n for row_index in row_range:\\n worksheet.conditional_format(\\n row_index,\\n 0,\\n row_index,\\n len(self.data.columns),\\n options=format_rule[\\\"options\\\"],\\n )\\n if format_rule[\\\"bool_map\\\"] is not None:\\n # TODO: Finish this\\n if format_rule[\\\"col_wise\\\"]:\\n for col in format_rule[\\\"cols\\\"]:\\n col_index = list(self.data.columns).index(col) + col_add\\n worksheet.conditional_format(\\n 1,\\n col_index,\\n len(self.data),\\n col_index,\\n options=format_rule[\\\"options\\\"],\\n )\\n else:\\n for row in format_rule[\\\"rows\\\"]:\\n row_index = list(self.data.index).index(row) + row_add\\n worksheet.conditional_format(\\n row_index,\\n 0,\\n row_index,\\n len(self.data.columns),\\n options=format_rule[\\\"options\\\"],\\n )\\n col_rules = [rule for rule in self.cell_formatters if rule[\\\"cols\\\"] is not None]\\n row_rules = [rule for rule in self.cell_formatters if rule[\\\"rows\\\"] is not None]\\n bool_rules = [\\n rule for rule in self.cell_formatters if rule[\\\"bool_map\\\"] is not None\\n ]\\n index_rules = [rule for rule in self.cell_formatters if rule[\\\"index\\\"]]\\n header_rules = [rule for rule in self.cell_formatters if rule[\\\"header\\\"]]\\n if col_rules:\\n for col in set(flatten_list([x[\\\"cols\\\"] for x in col_rules])) & set(\\n list(self.data.columns)\\n ):\\n all_rules_for_col = [x for x in col_rules if col in x[\\\"cols\\\"]]\\n combined_style = {}\\n combined_options = {}\\n for each_rule in all_rules_for_col:\\n combined_style.update(each_rule[\\\"style\\\"])\\n if each_rule[\\\"options\\\"] is not None:\\n combined_options.update(each_rule[\\\"options\\\"])\\n width_val, style = process_col_style(\\n combined_style, self.data[col], self.styler\\n )\\n col_index = list(self.data.columns).index(col) + col_add + startcol\\n style_format = workbook.add_format(style)\\n worksheet.set_column(\\n col_index,\\n col_index,\\n width=width_val,\\n cell_format=style_format,\\n options=combined_options,\\n )\\n if row_rules:\\n row_vals = list(set(flatten_list([x[\\\"rows\\\"] for x in row_rules])))\\n # import pdb\\n # pdb.set_trace()\\n if str(self.data.index.dtype) != \\\"int64\\\":\\n int_rows = [x for x in row_vals if isinstance(x, int)]\\n int_row_names = [self.data.index[x] for x in int_rows]\\n row_vals = set(\\n [x for x in row_vals if not isinstance(x, int)] + int_row_names\\n ) & set(self.data.index)\\n for row in row_vals:\\n all_rules_for_row = [\\n x\\n for x in row_rules\\n if row in x[\\\"rows\\\"] or list(self.data.index).index(row) in x[\\\"rows\\\"]\\n ]\\n combined_style = {}\\n combined_options = {}\\n for each_rule in all_rules_for_row:\\n combined_style.update(each_rule[\\\"style\\\"])\\n if each_rule[\\\"options\\\"] is not None:\\n combined_options.update(each_rule[\\\"options\\\"])\\n height_val, style = process_row_style(\\n combined_style, self.data.loc[row], self.styler\\n )\\n row_index = list(self.data.index).index(row) + row_add + startrow\\n style_format = workbook.add_format(style)\\n worksheet.set_row(\\n row_index,\\n height=height_val,\\n cell_format=style_format,\\n options=combined_options,\\n )\\n if self.params[\\\"index\\\"] and index_rules:\\n # import pdb\\n # pdb.set_trace()\\n if not isinstance(self.data.index, pd.MultiIndex):\\n width_val, style, options = create_index_format(\\n index_rules, self.data.index\\n )\\n col_index = 0 + startcol\\n style_format = workbook.add_format(style)\\n worksheet.set_column(\\n col_index,\\n col_index,\\n width=width_val,\\n cell_format=style_format,\\n options=options,\\n )\\n else:\\n for level in range(0, len(self.data.index.names)):\\n all_rules_for_index = [\\n x\\n for x in index_rules\\n if level in x[\\\"index\\\"] or x[\\\"index\\\"] is True\\n ]\\n width_val, style, options = create_index_format(\\n all_rules_for_index, self.data.index.get_level_values(level)\\n )\\n col_index = level + startcol\\n style_format = workbook.add_format(style)\\n worksheet.set_column(\\n col_index,\\n col_index,\\n width=width_val,\\n cell_format=style_format,\\n options=options,\\n )\\n if self.params[\\\"header\\\"] and header_rules:\\n # import pdb\\n # pdb.set_trace()\\n if not isinstance(self.data.columns, pd.MultiIndex):\\n height_val, style, options = create_header_format(\\n header_rules, self.data.columns\\n )\\n row_index = 0 + startrow\\n style_format = workbook.add_format(style)\\n worksheet.set_row(\\n row_index,\\n height=height_val,\\n cell_format=style_format,\\n options=options,\\n )\\n else:\\n for level in range(0, len(self.data.columns.names)):\\n all_rules_for_header = [\\n x\\n for x in header_rules\\n if level in x[\\\"header\\\"] or x[\\\"header\\\"] is True\\n ]\\n height_val, style, options = create_index_format(\\n all_rules_for_header, self.data.columns.get_level_values(level)\\n )\\n row_index = level + startrow\\n style_format = workbook.add_format(style)\\n worksheet.set_row(\\n row_index,\\n height=height_val,\\n cell_format=style_format,\\n options=options,\\n )\\n if bool_rules:\\n pass\\n # TODO: Finish this\\n # excel_writer.save()\\n # excel_writer.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9bf40cf076d1b36619eb79204177b504\",\n \"score\": \"0.6102703\",\n \"text\": \"def append_df_to_excel(filename, df, sheet_name, startrow=0,truncate_sheet=False,**to_excel_kwargs):\\n\\n\\n # ignore [engine] parameter if it was passed\\n if 'engine' in to_excel_kwargs:\\n to_excel_kwargs.pop('engine')\\n\\n writer = pd.ExcelWriter(filename, engine='openpyxl')\\n\\n # Python 2.x: define [FileNotFoundError] exception if it doesn't exist\\n try:\\n FileNotFoundError\\n except NameError:\\n FileNotFoundError = IOError\\n\\n\\n try:\\n # try to open an existing workbook\\n writer.book = load_workbook(filename)\\n\\n # get the last row in the existing Excel sheet\\n # if it was not specified explicitly\\n if startrow is None and sheet_name in writer.book.sheetnames:\\n startrow = writer.book[sheet_name].max_row\\n\\n # truncate sheet\\n if truncate_sheet and sheet_name in writer.book.sheetnames:\\n # index of [sheet_name] sheet\\n idx = writer.book.sheetnames.index(sheet_name)\\n # remove [sheet_name]\\n writer.book.remove(writer.book.worksheets[idx])\\n # create an empty sheet [sheet_name] using old index\\n writer.book.create_sheet(sheet_name, idx)\\n\\n # copy existing sheets\\n writer.sheets = {ws.title:ws for ws in writer.book.worksheets}\\n except FileNotFoundError:\\n # file does not exist yet, we will create it\\n pass\\n\\n if startrow is None:\\n startrow = 0\\n\\n # write out the new sheet\\n df.to_excel(writer, sheet_name, startrow=startrow, **to_excel_kwargs)\\n ws = writer.book[sheet_name]\\n for column_cells in ws.columns:\\n length = max(map(lambda cell: len(str(cell.value)) if cell.value else 0, column_cells))\\n ws.column_dimensions[column_cells[0].column_letter].width = length+3\\n # save the workbook\\n writer.save()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28a242f432037c09848321fa74f853ca\",\n \"score\": \"0.60957867\",\n \"text\": \"def generate_excel_measure(dataframe_list, sheet_name_list, filename, project_io):\\n with closing(BytesIO()) as output:\\n writer = pd.ExcelWriter(output, engine='xlsxwriter', options={'remove_timezone': True})\\n workbook = writer.book\\n data_format1 = workbook.add_format({'bg_color': '#BBCCE2'})\\n data_format2 = workbook.add_format({'bg_color': '#DEE6EF'})\\n\\n format_header = workbook.add_format({'text_wrap': True})\\n\\n workbook.formats[0].set_font_size(15)\\n for df, name in zip(dataframe_list, sheet_name_list):\\n df.to_excel(writer, sheet_name=name)\\n worksheet = writer.sheets[name]\\n worksheet.set_row(0, 30, format_header)\\n worksheet.set_column('A:A', 5)\\n worksheet.set_column('B:B', 30)\\n worksheet.set_column('C:C', 30)\\n worksheet.set_column('D:D', 15)\\n worksheet.set_column('F:G', 35)\\n worksheet.set_column('H:AH', 20)\\n for row in range(1, len(df) + 1, 2):\\n worksheet.set_row(row, cell_format=data_format1)\\n worksheet.set_row(row + 1, cell_format=data_format2)\\n writer.save()\\n if project_io is not None:\\n project_io.save_data(filename, output.getvalue(), overwrite=True)\\n else:\\n with open(filename, 'wb') as out:\\n out.write(output.getvalue())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bdc52290fb473595c31cc3dbea88385e\",\n \"score\": \"0.6046522\",\n \"text\": \"def write_excel_df(save_file_path: str,\\n df_list: List, sheet_name_list: List):\\n writer = pd.ExcelWriter(save_file_path, engine='xlsxwriter')\\n # Write each dataframe to a different worksheet\\n assert len(df_list) == len(sheet_name_list)\\n for index in range(len(df_list)):\\n df_list[index].to_excel(writer, sheet_name=sheet_name_list[index])\\n # Close the Pandas Excel writer and output the Excel file.\\n writer.save()\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66e553c1e866d8bc58ea30574aff37d9\",\n \"score\": \"0.60190165\",\n \"text\": \"def save_large_df_to_excel(df, file_path, sheet_prefix='page'):\\n\\n def _split_file_path():\\n \\\"\\\"\\\"Returns a list representing the file path containing the file.\\\"\\\"\\\"\\n if '/' in file_path:\\n # Split file path into a list\\n path_parts = file_path.split('/')\\n # Take all except last entry and append together\\n file_dir = '/'.join(path_parts[:-1])\\n file_name = path_parts[-1]\\n\\n return file_dir, file_name\\n else:\\n # Otherwise, return current directory as '.'\\n return '.', file_path\\n\\n\\n file_dir, file_name = _split_file_path()\\n # Prevents overwriting if file already exists\\n if file_name in os.listdir(file_dir):\\n raise ValueError('Filename {} already exists'.format(file_name))\\n writer = pd.ExcelWriter(file_path, engine='xlsxwriter')\\n\\n i = 0\\n while True:\\n # Remove one row to leave space for header\\n max_num_rows = 2**20 - 1\\n # Subselected DataFrame to write as a single sheet\\n temp_df = df.iloc[max_num_rows*i: max_num_rows*(i+1)]\\n\\n # Terminate if we have reached the end of the DataFrame\\n if temp_df.shape[0] == 0:\\n break\\n\\n # Save sheet to excel file\\n temp_df.to_excel(writer, sheet_name='{}_{}'.format(sheet_prefix, i))\\n i += 1\\n\\n writer.save()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"803f84dc291b5fc63f5b33dc08da6220\",\n \"score\": \"0.6018584\",\n \"text\": \"def send_to_gs(df, wks_name):\\n\\n d2g.upload(df,\\n spreadsheet_key,\\n wks_name,\\n credentials=credentials,\\n row_names=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce8937d80ee49a96e0035ddef6e6fede\",\n \"score\": \"0.6017467\",\n \"text\": \"def save_dict_xlsx(data_dict: Dict[Any, Any], save_location: str, sheet_name: str) -> None:\\n pd_df = pd.DataFrame.from_dict(data_dict, orient=\\\"index\\\")\\n\\n with pd.ExcelWriter(save_location, engine=\\\"xlsxwriter\\\") as writer: # pylint: disable=abstract-class-instantiated\\n for n, df in (pd_df).items():\\n df.to_excel(writer, sheet_name=sheet_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b89a258259a56c6995b575754f255ce\",\n \"score\": \"0.5974723\",\n \"text\": \"def list_to_spreadsheet(spreadsheet_name, column_names, mylist):\\n records = [dict(zip(column_names, row)) for row in mylist]\\n dataframe = pandas.DataFrame.from_records(\\n records, index=column_names[0], columns=column_names\\n )\\n dataframe.to_excel(writer, sheet_name=spreadsheet_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee90ff375db44056af145452f293aa70\",\n \"score\": \"0.5937404\",\n \"text\": \"def append_df_to_excel(filename, df, sheet_name='Sheet1', startrow=None,\\r\\n truncate_sheet=False, \\r\\n **to_excel_kwargs):\\r\\n from openpyxl import load_workbook\\r\\n\\r\\n import pandas as pd\\r\\n\\r\\n # ignore [engine] parameter if it was passed\\r\\n if 'engine' in to_excel_kwargs:\\r\\n to_excel_kwargs.pop('engine')\\r\\n\\r\\n writer = pd.ExcelWriter(filename, engine='openpyxl')\\r\\n\\r\\n # Python 2.x: define [FileNotFoundError] exception if it doesn't exist \\r\\n try:\\r\\n FileNotFoundError\\r\\n except NameError:\\r\\n FileNotFoundError = IOError\\r\\n\\r\\n\\r\\n try:\\r\\n # try to open an existing workbook\\r\\n writer.book = load_workbook(filename)\\r\\n\\r\\n # get the last row in the existing Excel sheet\\r\\n # if it was not specified explicitly\\r\\n if startrow is None and sheet_name in writer.book.sheetnames:\\r\\n startrow = writer.book[sheet_name].max_row\\r\\n\\r\\n # truncate sheet\\r\\n if truncate_sheet and sheet_name in writer.book.sheetnames:\\r\\n # index of [sheet_name] sheet\\r\\n idx = writer.book.sheetnames.index(sheet_name)\\r\\n # remove [sheet_name]\\r\\n writer.book.remove(writer.book.worksheets[idx])\\r\\n # create an empty sheet [sheet_name] using old index\\r\\n writer.book.create_sheet(sheet_name, idx)\\r\\n\\r\\n # copy existing sheets\\r\\n writer.sheets = {ws.title:ws for ws in writer.book.worksheets}\\r\\n except FileNotFoundError:\\r\\n # file does not exist yet, we will create it\\r\\n pass\\r\\n\\r\\n if startrow is None:\\r\\n startrow = 0\\r\\n\\r\\n # write out the new sheet\\r\\n df.to_excel(writer, sheet_name, startrow=startrow, **to_excel_kwargs)\\r\\n\\r\\n # save the workbook\\r\\n writer.save()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"093a902d626edf70cd431a2b64705baa\",\n \"score\": \"0.5931121\",\n \"text\": \"def to_excel(self):\\r\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d70cf37bab07a7e56ca22281eb9174c9\",\n \"score\": \"0.5927741\",\n \"text\": \"def append_df_to_excel(filename, df, sheet_name='Sheet1', startrow=None,\\n startcol=None, truncate_sheet=False, \\n **to_excel_kwargs):\\n from openpyxl import load_workbook\\n\\n import pandas as pd\\n\\n # ignore [engine] parameter if it was passed\\n if 'engine' in to_excel_kwargs:\\n to_excel_kwargs.pop('engine')\\n\\n writer = pd.ExcelWriter(filename, engine='openpyxl')\\n\\n # Python 2.x: define [FileNotFoundError] exception if it doesn't exist \\n try:\\n FileNotFoundError\\n except NameError:\\n FileNotFoundError = IOError\\n\\n\\n try:\\n # try to open an existing workbook\\n writer.book = load_workbook(filename)\\n\\n # get the last row in the existing Excel sheet\\n # if it was not specified explicitly\\n if startrow is None and sheet_name in writer.book.sheetnames:\\n startrow = writer.book[sheet_name].max_row\\n # if startcol is None and sheet_name in writer.book.sheetnames:\\n # startcol = writer.book[sheet_name].max_col\\n # truncate sheet\\n if truncate_sheet and sheet_name in writer.book.sheetnames:\\n # index of [sheet_name] sheet\\n idx = writer.book.sheetnames.index(sheet_name)\\n # remove [sheet_name]\\n writer.book.remove(writer.book.worksheets[idx])\\n # create an empty sheet [sheet_name] using old index\\n writer.book.create_sheet(sheet_name, idx)\\n\\n # copy existing sheets\\n writer.sheets = {ws.title:ws for ws in writer.book.worksheets}\\n except FileNotFoundError:\\n # file does not exist yet, we will create it\\n pass\\n\\n if startrow is None:\\n startrow = 0\\n if startcol is None:\\n startcol = 0\\n\\n # write out the new sheet\\n df.to_excel(writer, sheet_name, startrow=startrow, startcol=startcol, **to_excel_kwargs)\\n\\n # save the workbook\\n writer.save()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21f1698790968ba9a0f551c30c69b498\",\n \"score\": \"0.592436\",\n \"text\": \"def saveSpreadSheet(self):\\r\\n try:\\r\\n self.wb.save(self.fullFilePath)\\r\\n logger.info('In module ' + __name__ \\r\\n + '. saveSpreadSheet.')\\r\\n except Exception as e:\\r\\n print('ExcelWriter.saveSpreadSheet: ' + str(e)) \\r\\n logger.error('ExcelWriter.saveSpreadSheet: ' + str(e))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"933ef31907dee5effbf16e13207f5bef\",\n \"score\": \"0.59137565\",\n \"text\": \"def data_to_xls(path, data, sheet_name):\\n df = pd.DataFrame(data, index=[0]).T\\n df.to_excel(path, sheet_name=sheet_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a911c35e3a4a7ac7e55e637e2d9a57b\",\n \"score\": \"0.5850379\",\n \"text\": \"def Test():\\n\\n # https://docs.google.com/spreadsheets/d/1CR_8w8ZYeu8gD9X3UwwtuZA2djj5jCpNSFycNSXM_GY/edit#gid=1608909088\\n # setup\\n\\n service = GetService()\\n # - preface as upload.GetService() if use in another file\\n\\n # doc: db_adwords\\n spreadsheetId = \\\"1CR_8w8ZYeu8gD9X3UwwtuZA2djj5jCpNSFycNSXM_GY\\\"\\n\\n # READ\\n read_sheet = \\\"Sheet2\\\" # please make sure this exists first\\n read_data = GetGSheetsRange(service, spreadsheetId, read_sheet, quiet=False)\\n print len(read_data)\\n\\n # WRITE\\n write_sheet = \\\"Sheet1\\\"\\n newValues = [[\\\"a\\\", \\\"bbbb\\\", \\\"ccc\\\"], [\\\"235235\\\"], [\\\"325235235\\\", \\\"dgdfg\\\", \\\"fdgfdg\\\"]]\\n OverwriteCells(service, spreadsheetId, write_sheet, newValues, quiet=False)\\n\\n # DELETE\\n DeleteSheetId = 1608909088\\n DeleteGSheetsRange(service, spreadsheetId, DeleteSheetId, 0, 3, quiet=False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8edbb3584cae0c4ffa59d1409b2345be\",\n \"score\": \"0.584908\",\n \"text\": \"def append_df_to_excel(filename, df, sheet_name='Sheet1', startrow=None,\\n truncate_sheet=False, \\n **to_excel_kwargs):\\n from openpyxl import load_workbook\\n\\n import pandas as pd\\n\\n # ignore [engine] parameter if it was passed\\n if 'engine' in to_excel_kwargs:\\n to_excel_kwargs.pop('engine')\\n\\n writer = pd.ExcelWriter(filename, engine='openpyxl')\\n\\n # Python 2.x: define [FileNotFoundError] exception if it doesn't exist \\n try:\\n FileNotFoundError\\n except NameError:\\n FileNotFoundError = IOError\\n\\n\\n try:\\n # try to open an existing workbook\\n writer.book = load_workbook(filename)\\n\\n # get the last row in the existing Excel sheet\\n # if it was not specified explicitly\\n if startrow is None and sheet_name in writer.book.sheetnames:\\n startrow = writer.book[sheet_name].max_row\\n\\n # truncate sheet\\n if truncate_sheet and sheet_name in writer.book.sheetnames:\\n # index of [sheet_name] sheet\\n idx = writer.book.sheetnames.index(sheet_name)\\n # remove [sheet_name]\\n writer.book.remove(writer.book.worksheets[idx])\\n # create an empty sheet [sheet_name] using old index\\n writer.book.create_sheet(sheet_name, idx)\\n\\n # copy existing sheets\\n writer.sheets = {ws.title:ws for ws in writer.book.worksheets}\\n except FileNotFoundError:\\n # file does not exist yet, we will create it\\n pass\\n\\n if startrow is None:\\n startrow = 0\\n\\n # write out the new sheet\\n df.to_excel(writer, sheet_name, startrow=startrow, **to_excel_kwargs)\\n\\n # save the workbook\\n writer.save()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4eb6c084084054d7b8a99bc49dc2a0d2\",\n \"score\": \"0.5847653\",\n \"text\": \"def excel(Df, index=True):\\n\\n writer = pd.ExcelWriter(r'C:\\\\Users\\\\Administrator\\\\PycharmProjects\\\\Stock\\\\Output\\\\Output.xlsx', engine='xlsxwriter')\\n Df.to_excel(writer, sheet_name='Sheet1', index= index)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5692c43f10c52a25bf5de3b02a9673b6\",\n \"score\": \"0.5833753\",\n \"text\": \"def save_as_xlsx(df, file, index=True, **to_excel_kwargs):\\n\\n def localize(df):\\n \\\"\\\"\\\"\\n Remove tz from datetime cols since Excel doesn't allow\\n \\\"\\\"\\\"\\n tz_cols = df.select_dtypes(include=[\\\"datetimetz\\\"]).columns\\n for tz_col in tz_cols:\\n df[tz_col] = df[tz_col].dt.tz_localize(None)\\n return df\\n\\n import xlsxwriter\\n\\n df = df.copy()\\n df = localize(df)\\n with pd.ExcelWriter(\\n file,\\n engine=\\\"xlsxwriter\\\",\\n date_format=\\\"m/d/yyy\\\",\\n datetime_format=\\\"m/d/yyy h:mmAM/PM\\\",\\n ) as writer:\\n df.to_excel(writer, index=index, **to_excel_kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95dad6f82acdfb6b791e83e8e00e1790\",\n \"score\": \"0.5817795\",\n \"text\": \"def append_df_to_excel(filename, df, sheet_name='Sheet1', startrow=None,\\n truncate_sheet=False,\\n **to_excel_kwargs):\\n\\n # ignore [engine] parameter if it was passed\\n if 'engine' in to_excel_kwargs:\\n to_excel_kwargs.pop('engine')\\n\\n writer = pd.ExcelWriter(filename, engine='openpyxl')\\n\\n try:\\n # try to open an existing workbook\\n writer.book = load_workbook(filename)\\n\\n # get the last row in the existing Excel sheet\\n # if it was not specified explicitly\\n if startrow is None and sheet_name in writer.book.sheetnames:\\n startrow = writer.book[sheet_name].max_row\\n\\n # truncate sheet\\n if truncate_sheet and sheet_name in writer.book.sheetnames:\\n # index of [sheet_name] sheet\\n idx = writer.book.sheetnames.index(sheet_name)\\n # remove [sheet_name]\\n writer.book.remove(writer.book.worksheets[idx])\\n # create an empty sheet [sheet_name] using old index\\n writer.book.create_sheet(sheet_name, idx)\\n\\n # copy existing sheets\\n writer.sheets = {ws.title:ws for ws in writer.book.worksheets}\\n except FileNotFoundError:\\n # file does not exist yet, we will create it\\n pass\\n\\n if startrow is None:\\n startrow = 0\\n\\n # write out the new sheet\\n df.to_excel(writer, sheet_name, startrow=startrow, **to_excel_kwargs)\\n\\n # save the workbook\\n writer.save()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2bd06eccf5ab13d8e9d3d835029849c\",\n \"score\": \"0.57952166\",\n \"text\": \"def WriteGSheetsRange(\\n service, spreadsheetId, range_name, values, value_input_option=\\\"RAW\\\", quiet=True\\n):\\n body = {\\\"values\\\": values}\\n result = (\\n service.spreadsheets()\\n .values()\\n .update(\\n spreadsheetId=spreadsheetId,\\n range=range_name,\\n valueInputOption=value_input_option,\\n body=body,\\n )\\n .execute()\\n )\\n\\n if not quiet:\\n print \\\" WRITE RESULT\\\"\\n for k, v in result.iteritems():\\n print \\\" %s: %s\\\" % (k, v)\\n print\\n \\\"\\\"\\\"\\n # example\\n spreadsheetId: 1d9Cb9-_-XTYHevDHUfVvQAZjJY9EN6yeZmEcR4DsFVw\\n updatedRange: tb_staging_camp_day!A1:O630\\n updatedCells: 9450\\n updatedRows: 630\\n updatedColumns: 15\\n \\\"\\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e66375247f6a897a78555151e6b1838\",\n \"score\": \"0.5775305\",\n \"text\": \"def save_to_file(out_folder, animal_name, dataframe):\\n file_name = \\\"%s.xls\\\" % (animal_name)\\n file_name = os.path.join(out_folder, file_name)\\n\\n\\n cols = dataframe.columns.tolist()\\n #print ('original:', cols)\\n\\n #neworder=[11, 0, 17, 19, 15, 18, 21, 5, 4, 20, 3, 16, 1, 12, 7, 14, 6, 8, 2, 10, 9, 13]\\n #cols = [cols[i] for i in neworder]\\n #print ('reorderd:', cols)\\n #neworder = [[]]\\n neworder = ['date','animal','program','duration','trials','hit','miss','false','withold','hit_rate','false_positive_rate','dprime','recall','precision','F_score','500 Hz', '1000 Hz','2000 Hz','4000 Hz', '8000 Hz', '16000 Hz', '32000 Hz']\\n\\n #cols = list(neworder.columns.values)\\n dataframe = dataframe[neworder]\\n\\n\\n\\n# df = df[['f','f']]\\n# cols = list(df.columns.values)\\n\\n\\n dataframe.to_csv(file_name, sep='\\\\t', index=False)\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b23817b6ce8b42c15f784340d94116e\",\n \"score\": \"0.575602\",\n \"text\": \"def write_xls(table_2Dlist, path, sheet_name, data_line=None, start_from_row1=False):\\n workbook = xlwt.Workbook(encoding=sys.stdout.encoding)\\n sheet = workbook.add_sheet(sheet_name)\\n if data_line != None:\\n for index, value in enumerate(data_line):\\n sheet.write(0, index, value)\\n if start_from_row1 == False:\\n for row in range(len(table_2Dlist)):\\n for col in range(len(table_2Dlist[row])):\\n sheet.write(row+1, col, table_2Dlist[row][col])\\n else:\\n for row in range(len(table_2Dlist)):\\n if row == 0:\\n pass\\n else:\\n for col in range(len(table_2Dlist[row])):\\n sheet.write(row+1, col, table_2Dlist[row][col])\\n else:\\n for row in range(len(table_2Dlist)):\\n for col in range(len(table_2Dlist[row])):\\n sheet.write(row, col, table_2Dlist[row][col])\\n workbook.save(path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99d53caa93d338aae767269f6852c5a8\",\n \"score\": \"0.5753641\",\n \"text\": \"def export_scatter_diagram(self, filename):\\n\\n writer = pd.ExcelWriter(filename, engine=\\\"xlsxwriter\\\")\\n\\n # Seastates sheet\\n self.df_ss.to_excel(writer, sheet_name=\\\"Sea States\\\", na_rep=\\\"N/A\\\", float_format=\\\"%.2f\\\")\\n ws = writer.sheets[\\\"Sea States\\\"]\\n ws.set_column(\\\"A:A\\\", 11)\\n # wb = writer.book\\n # fmt = wb.add_format({'num_format': '0.00'})\\n # ws.set_column('B:Y', None, fmt)\\n\\n # Seascatter sheet\\n # Replace zeros with blanks\\n df_scatter = self.df_scatter.replace({0: \\\"\\\"})\\n df_scatter.to_excel(writer, sheet_name=\\\"Sea Scatter Diagram\\\", float_format=\\\"%.2f\\\")\\n ws.set_column(\\\"A:A\\\", 18)\\n writer.save()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3c2ab4f8c7e83a2cde2573e974a483f\",\n \"score\": \"0.572457\",\n \"text\": \"def __generate_sheet__(self, app, xls_doc):\\n # Create the worksheet\\n sheet = xls_doc.add_sheet(app)\\n \\n # Create header style\\n hdr_font = xlwt.Font()\\n hdr_font.name = 'Times New Roman'\\n hdr_font.colour_index = 2\\n hdr_font.bold = True\\n hdr_style = xlwt.XFStyle()\\n hdr_style.font = hdr_font\\n \\n # Create header\\n sheet.write(0, 0, \\\"Count\\\")\\n sheet.write(0, 1, \\\"DB Name\\\")\\n sheet.write(0, 2, \\\"DB ID\\\")\\n sheet.write(0, 3, \\\"GO Name\\\")\\n sheet.write(0, 4, \\\"GO ID\\\")\\n sheet.write(0, 5, \\\"DB Link\\\")\\n sheet.write(0, 6, \\\"GO Link\\\")\\n \\n # Write a row to the spreadsheet for each row in iprstatsdata\\n length = self.cache.get_match_length(app)\\n for r in range(length):\\n row = self.cache.get_one_row(app, r)\\n if row:\\n sheet.write(r+1, 0, str(row[1])) # Count\\n sheet.write(r+1, 1, row[0]) # DB Name\\n sheet.write(r+1, 2, row[3]) # DB ID\\n sheet.write(r+1, 3, row[4]) # GO Name\\n sheet.write(r+1, 4, row[2]) # GO ID\\n sheet.write(r+1, 5, self.cache.get_url(app, r)) # DB URL\\n sheet.write(r+1, 6, self.cache.get_url(app, r, True))#GO URL\\n if len(row) == 7:\\n sheet.write(r, 7, row[6]) # GO Definition\\n else:\\n break\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3bb0b535c4b0b29b7dc6fb8a131ee981\",\n \"score\": \"0.5717932\",\n \"text\": \"def hstFailuresToExcel(df, sheet):\\n\\n sheet = openSheet(sheet)\\n\\n failure_cells = ['D17', 'D18', 'D19', 'D20', 'D21', 'D22', 'D23', 'D24', 'D25', 'D26', 'D27', 'D28', 'D29', 'D30',\\n 'D31', 'D32', 'D33', 'D34', 'D35']\\n failures = df['Failure'].dropna().tolist()\\n for i in range(len(failures)):\\n sheet.range(failure_cells[i]).value = failures[i]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8331960356cc03f22b609bbfecb7590e\",\n \"score\": \"0.5711396\",\n \"text\": \"def save_df(write_df, outputfile):\\n print('Saving File....')\\n if outputfile.split('.')[-1] == 'csv':\\n write_df.to_csv(outputfile)\\n\\n elif outputfile.split('.')[-1] == 'xls' or outputfile.split('.')[-1] == 'xlsx':\\n writer = pd.ExcelWriter(outputfile)\\n write_df.to_excel(writer,'Result')\\n writer.save()\\n else:\\n print('csv or excel needs to be selected for file {}!'.format(outputfile))\\n raise NotImplementedError\\n write_df.to_clipboard()\\n print('...done!')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be585cf10696b87075b8f27848f983b6\",\n \"score\": \"0.5699932\",\n \"text\": \"def xlsave(output_file, frames, sheets, **kwargs):\\r\\n\\r\\n with pd.ExcelWriter(\\r\\n output_file,\\r\\n options = {\\r\\n 'remove_timezone': True,\\r\\n },\\r\\n ) as writer:\\r\\n _l.info(f'{me()} saving to file {output_file} ...')\\r\\n #if only one sheet:\\r\\n if isinstance(sheets, str):\\r\\n _xlsheet(writer, frames, sheets, **kwargs)\\r\\n #if multiple sheets\\r\\n else:\\r\\n for f, s in zip(frames, sheets):\\r\\n _xlsheet(writer, f, s, **kwargs)\\r\\n _l.info(f'{me()} done')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"910bb5610f9971b92df06e942514e4b3\",\n \"score\": \"0.569453\",\n \"text\": \"def write_to_excel(dest_fullpath:str, dfs_map:dict, include_index:bool=True)->None:\\n with pd.ExcelWriter(dest_fullpath) as writer:\\n for k, df in dfs_map.items():\\n df.to_excel(writer, sheet_name=k, index=include_index)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6839f9f4a8fa4ed796ea5d74d6219a19\",\n \"score\": \"0.5687497\",\n \"text\": \"def export_dataframe(self, dataframe, filename, sep = ','):\\n\\t\\tdataframe.to_excel(filename, index = False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0eb125fe416312607ed17881d2b21f9a\",\n \"score\": \"0.56730133\",\n \"text\": \"def _create_bom_worksheet(df, writer, header_format):\\n df.to_excel(\\n writer,\\n sheet_name='bom',\\n index=False,\\n header=False,\\n startrow=1\\n )\\n\\n worksheet = writer.sheets['bom']\\n for col_num, value in enumerate(df.columns.values):\\n worksheet.write(0, col_num, value, header_format)\\n\\n worksheet.set_column(0, 0, 16) # Assembly\\n worksheet.set_column(1, 1, 26) # Assembly_Name\\n worksheet.set_column(5, 5, 16) # Dwg_No\\n worksheet.set_column(6, 6, 34) # Component\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6fde5d0e01e97d68cfba7f5ea4f15eee\",\n \"score\": \"0.5660677\",\n \"text\": \"def write_to_sheet(sheet, row, col, exec_time, size, graph, path):\\n sheet.write(0, 0, \\\"Expanded States\\\")\\n sheet.write(0, 1, \\\"Solution Size\\\")\\n sheet.write(0, 2, \\\"Time\\\")\\n sheet.write(0, 3, \\\"Puzzle Size\\\")\\n sheet.write(row, col, graph.expanded_states())\\n if path is not None:\\n sheet.write(row, col + 1, len(path))\\n else:\\n sheet.write(row, col + 1, \\\"Failed\\\")\\n sheet.write(row, col + 2, exec_time)\\n sheet.write(row, col + 3, size)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd864ea0d7ff343ce51ee078edd77628\",\n \"score\": \"0.5633066\",\n \"text\": \"def output_data(self, path):\\n self.df = self.treat_data(self.df)\\n self.df.to_excel(path, index=False, header=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c580c6664e5964f95832d7599f7f0d72\",\n \"score\": \"0.5631117\",\n \"text\": \"def _create_indented_bom_worksheet(df, writer, header_format):\\n df.to_excel(\\n writer,\\n sheet_name='indented_bom',\\n index=False,\\n header=False,\\n startrow=1,\\n )\\n\\n worksheet = writer.sheets['indented_bom']\\n for col_num, value in enumerate(df.columns.values):\\n worksheet.write(0, col_num, value, header_format)\\n\\n worksheet.set_column(2, 2, 21) # Dwg_No\\n worksheet.set_column(3, 3, 60) # Component\",\n \"title\": \"\"\n },\n {\n \"docid\": \"703bb7dad47b570920adce15b167cdf3\",\n \"score\": \"0.5628814\",\n \"text\": \"def write_xls(data, file_name):\\r\\n try:\\r\\n book = xlwt.Workbook()\\r\\n sheet1 = book.add_sheet(\\\"Jul-15\\\")\\r\\n\\r\\n for num in range(len(data)):\\r\\n row = sheet1.row(num)\\r\\n vals = data[num]\\r\\n for index, col in enumerate(vals):\\r\\n row.write(index, col)\\r\\n xls_name = '%s/%s.xls' % (MEDIA_ROOT, file_name)\\r\\n book.save(xls_name)\\r\\n except Exception, e:\\r\\n raise e\",\n \"title\": \"\"\n },\n {\n \"docid\": \"acd8e8bd77e107629deaa7cde8c57687\",\n \"score\": \"0.56226164\",\n \"text\": \"def write_excel(sheets, sheet_names):\\n wb = xlwt.Workbook()\\n for sheet, sheet_name in zip(sheets, sheet_names):\\n ws = wb.add_sheet(sheet_name)\\n for row_num, row in enumerate(sheet):\\n for col_num, cell in enumerate(row):\\n try:\\n as_float = float(cell)\\n ws.write(row_num, col_num, as_float) # write number to excel\\n except Exception, e:\\n ws.write(row_num, col_num, cell) # write without changing to number\\n wb.save('migrants.xls')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f8f834e72a853fd420181f4d5a10ebf\",\n \"score\": \"0.56202\",\n \"text\": \"def update(service):\\n\\n # Call the Sheets API\\n # Compute a timestamp and pass the first two arguments\\n values = [[1]]\\n body = {'values': values}\\n result = service.spreadsheets().values().update(spreadsheetId=SPREADSHEET_ID,\\n range=RANGE_NAME,\\n # How the input data should be interpreted.\\n valueInputOption='USER_ENTERED',\\n # How the input data should be inserted.\\n # insertDataOption='INSERT_ROWS'\\n body=body\\n ).execute()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"404bfd7abf9bcb03d2fadd8a104ea8bc\",\n \"score\": \"0.5614299\",\n \"text\": \"def _create_import_file_worksheet(df, writer, header_format):\\n df.to_excel(\\n writer,\\n sheet_name='import',\\n index=False,\\n startrow=1,\\n header=False\\n )\\n\\n worksheet = writer.sheets['import']\\n for col_num, value in enumerate(df.columns.values):\\n worksheet.write(0, col_num, value, header_format)\\n\\n # worksheet.set_column(0, 0, 16) # Assembly\\n # worksheet.set_column(1, 1, 26) # Assembly_Name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3b9a078a0cf72a23899207be645f889\",\n \"score\": \"0.56134135\",\n \"text\": \"def sendToExcel(studies, sheet, provider):\\n\\n # excel sheet passed in from previous functions\\n sheet = openSheet(sheet)\\n\\n # mapping for spreadsheet locations\\n kgw_cells = {'PSG': 'K12', 'EEG': 'K13', 'ETCO2': 'K14', 'SPLIT': 'K15', 'HST': 'K16', 'MSLT': 'K17', 'MWT': 'K18',\\n 'OAT': 'K19', 'Inspire': 'K20', 'PAP': 'K21', 'NAP': 'K22', 'FHST': 'K23', 'NS': 'K24'}\\n mb_cells = {'PSG': 'P12', 'EEG': 'P13', 'ETCO2': 'P14', 'SPLIT': 'P15', 'HST': 'P16', 'MSLT': 'P17', 'MWT': 'P18',\\n 'OAT': 'P19', 'Inspire': 'P20', 'PAP': 'P21', 'NAP': 'P22', 'FHST': 'P23', 'NS': 'P24'}\\n qr_cells = {'PSG': 'U12', 'EEG': 'U13', 'ETCO2': 'U14', 'SPLIT': 'U15', 'HST': 'U16', 'MSLT': 'U17', 'MWT': 'U18',\\n 'OAT': 'U19', 'Inspire': 'U20', 'PAP': 'U21', 'NAP': 'U22', 'FHST': 'U23', 'NS': 'U24'}\\n jf_cells = {'PSG': 'AA12', 'EEG': 'AA13', 'ETCO2': 'AA14', 'SPLIT': 'AA15', 'HST': 'AA16', 'MSLT': 'AA17',\\n 'MWT': 'AA18', 'OAT': 'AA19', 'Inspire': 'AA20', 'PAP': 'AA21', 'NAP': 'AA22', 'FHST': 'AA23', 'NS': 'AA24'}\\n dr_cells = {'PSG': 'AG12', 'EEG': 'AG13', 'ETCO2': 'AG14', 'SPLIT': 'AG15', 'HST': 'AG16', 'MSLT': 'AG17',\\n 'MWT': 'AG18', 'OAT': 'AG19', 'Inspire': 'AG20', 'PAP': 'AG21', 'NAP': 'AG22', 'FHST': 'AG23', 'NS': 'AG24'}\\n\\n # assign correct cell mapping by provider\\n if provider == 'kgw':\\n cells = kgw_cells\\n elif provider == 'mb':\\n cells = mb_cells\\n elif provider == 'qr':\\n cells = qr_cells\\n elif provider == 'jf':\\n cells = jf_cells\\n elif provider == 'dr':\\n cells = dr_cells\\n else:\\n print('Provider {} is unknown'.format(provider))\\n return\\n \\n if studies[0] == 'PSG':\\n sheet.range(cells['PSG']).value = studies[1]\\n elif 'EEG' in studies[0]:\\n sheet.range(cells['EEG']).value = studies[1]\\n elif 'EtCO2' in studies[0]:\\n sheet.range(cells['ETCO2']).value = studies[1]\\n elif 'Split' in studies[0]:\\n sheet.range(cells['SPLIT']).value = studies[1]\\n elif studies[0] == 'HST':\\n sheet.range(cells['HST']).value = studies[1]\\n elif 'MSLT' in studies[0]:\\n sheet.range(cells['MSLT']).value = studies[1]\\n elif 'MWT' in studies[0]:\\n sheet.range(cells['MWT']).value = studies[1]\\n elif 'OAT' in studies[0]:\\n sheet.range(cells['OAT']).value = studies[1]\\n elif studies[0] == 'PAP':\\n sheet.range(cells['PAP']).value = studies[1]\\n elif studies[0] == 'PAP Nap':\\n sheet.range(cells['NAP']).value = studies[1]\\n elif studies[0] == 'Failed HST':\\n sheet.range(cells['FHST']).value = studies[1]\\n elif studies[0] == 'Failed in lab':\\n sheet.range(cells['NS']).value = studies[1]\\n elif studies[0] == 'Inspire':\\n sheet.range(cells['Inspire']).value = studies[1]\\n elif 'Other' in studies[0]:\\n print(studies[0], studies[1])\\n else:\\n print('Study type, {} is not indexed'.format(studies[0]))\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cea8111b49f1c6cec99d3f767902cb4a\",\n \"score\": \"0.561059\",\n \"text\": \"def put_data(self, google_sheet_id, sheet_a1_notation, rows, value_input_option=\\\"USER_ENTERED\\\", overwrite_or_append=\\\"OVERWRITE\\\"):\\n with googleapiclient.discovery.build('sheets', 'v4', credentials=self.creds) as service:\\n if overwrite_or_append == \\\"OVERWRITE\\\": # default\\n service.spreadsheets().values().clear(spreadsheetId=google_sheet_id,\\n range=sheet_a1_notation,\\n body={}).execute()\\n\\n body = {\\\"values\\\": rows}\\n return service.spreadsheets().values().append(\\n spreadsheetId=google_sheet_id,\\n range=sheet_a1_notation,\\n valueInputOption=value_input_option,\\n body=body).execute()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c5956e70bd5debe82ccc768860e8689\",\n \"score\": \"0.5608833\",\n \"text\": \"def writeto_excel(cls,path,names,excelname):\\n\\n df = pd.DataFrame.from_dict(data={'ImageName': [i for i in names.keys()], 'EmpName': [i for i in names.values()]},orient='columns')\\n print(f'[INFO] Save in - {path}')\\n \\n fullpath = os.path.join(path,excelname)\\n if os.path.isfile(fullpath):\\n os.remove(fullpath)\\n\\n writer = pd.ExcelWriter(path +'data.xlsx')\\n # writer = pd.ExcelWriter('test.xlsx', engine='xlsxwriter')\\n df.to_excel(writer,sheet_name='test1',index=False)\\n writer.save()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71df5d4cc7d9f4b9864b3bbc96501fb9\",\n \"score\": \"0.5601026\",\n \"text\": \"def OverwriteCells(service, spreadsheetId, sheet, newValues, quiet=True):\\n print \\\"Sheet: %s\\\" % sheet\\n print \\\"Command: Upload to sheets, overwriting data\\\"\\n try:\\n print \\\"Reading existing values...\\\"\\n preview = 3\\n existingValues = GetGSheetsRange(\\n service, spreadsheetId, sheet, preview, quiet=quiet\\n )\\n print \\\"Emptying table...\\\"\\n emptyValues = MakeEmptyTable(existingValues)\\n WriteGSheetsRange(service, spreadsheetId, sheet, emptyValues, quiet=quiet)\\n print \\\"Uploading new data...\\\"\\n WriteGSheetsRange(\\n service,\\n spreadsheetId,\\n sheet,\\n newValues,\\n value_input_option=\\\"USER_ENTERED\\\",\\n quiet=quiet,\\n )\\n print\\n print \\\"Done.\\\"\\n\\n except google_errors.HttpError as h:\\n print \\\"ERROR\\\"\\n print \\\" - Http connection to Google Sheets failed!\\\"\\n print \\\" - %s\\\" % h\\n print\\n print\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f53cb7fb9e7e9587b9048453a6018b98\",\n \"score\": \"0.55847424\",\n \"text\": \"def write_xlsx(outs, *args, **kwargs):\\n\\tdf = pd.DataFrame(outs)\\n\\tdf.to_excel(\\\"output.xlsx\\\")\\n\\treturn\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2a43077029ba2b0fd750261b5f91709\",\n \"score\": \"0.5560496\",\n \"text\": \"def upload_to_goole_sheets(df_loc: str) -> None:\\n # must have your custom creds\\n credentials = ServiceAccountCredentials.from_json_keyfile_name(\\n \\\"gdrive_creds.json\\\", scope\\n )\\n client = gspread.authorize(credentials)\\n spreadsheet = client.open(\\\"dc_crash_bot\\\") # Name of spread sheet\\n\\n with open(df_loc, \\\"r\\\") as file_obj:\\n content = file_obj.read()\\n client.import_csv(spreadsheet.id, data=content)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2a43077029ba2b0fd750261b5f91709\",\n \"score\": \"0.5560496\",\n \"text\": \"def upload_to_goole_sheets(df_loc: str) -> None:\\n # must have your custom creds\\n credentials = ServiceAccountCredentials.from_json_keyfile_name(\\n \\\"gdrive_creds.json\\\", scope\\n )\\n client = gspread.authorize(credentials)\\n spreadsheet = client.open(\\\"dc_crash_bot\\\") # Name of spread sheet\\n\\n with open(df_loc, \\\"r\\\") as file_obj:\\n content = file_obj.read()\\n client.import_csv(spreadsheet.id, data=content)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5813f267a04e48761cf5fa4bd90b763c\",\n \"score\": \"0.555385\",\n \"text\": \"def _create_new_revision_worksheet(df, writer, header_format):\\n df.to_excel(\\n writer,\\n sheet_name='new_revision',\\n index=False,\\n startrow=1,\\n header=False\\n )\\n\\n worksheet = writer.sheets['new_revision']\\n for col_num, value in enumerate(df.columns.values):\\n worksheet.write(0, col_num, value, header_format)\\n\\n worksheet.set_column(0, 0, 16) # Assembly\\n worksheet.set_column(1, 1, 26) # Assembly_Name\\n worksheet.set_column(2, 1, 15) # Revision\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a96905cfbe210adbb7ea0ed89fa49416\",\n \"score\": \"0.55521977\",\n \"text\": \"def write(self, values, spreadsheet_id, cell, sheet=None, row_major=True, raw=False):\\n self._spreadsheets.values().update(\\n spreadsheetId=spreadsheet_id,\\n range=self._get_range(sheet, cell, spreadsheet_id),\\n valueInputOption='RAW' if raw else 'USER_ENTERED',\\n body={\\n 'values': self._values(values),\\n 'majorDimension': 'ROWS' if row_major else 'COLUMNS',\\n },\\n ).execute()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"202354bac8caab1a4c9cdd03f738ec55\",\n \"score\": \"0.5541308\",\n \"text\": \"def write_to_excel(data_matrix, filename):\\n wb = Workbook()\\n ws = wb.create_sheet(\\\"data\\\", 0)\\n \\n for row in data_matrix:\\n ws.append(row)\\n \\n save_file = filename + '.xlsx'\\n wb.save( save_file )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a23bb2bda49b49b9298a6b30c012e9bc\",\n \"score\": \"0.5529477\",\n \"text\": \"def save_results(output):\\r\\n # extension = '.xlsx'\\r\\n # if filename.lower().endswith('.xlsx'):\\r\\n # output_file = os.path.join(output, filename)\\r\\n # else:\\r\\n # output_file = os.path.join(output, filename+extension)\\r\\n for key, value in output_df.iteritems():\\r\\n output_file = os.path.join(output, key)\\r\\n writer = pd.ExcelWriter(output_file, engine='xlsxwriter')\\r\\n value.to_excel(writer)\\r\\n writer.save()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"acd6f6b3e142b8e980cd54d17ed1ba0a\",\n \"score\": \"0.5527286\",\n \"text\": \"def __write_xlsx(self, data_f, name):\\n file_path = os.path.join(self.__get_file_path(), self.__get_file_name() + \\\"_\\\" + name)\\n # Github open ticket for the abstract method\\n writer = pd.ExcelWriter('%s.xlsx' % file_path, engine='xlsxwriter')\\n data_f.to_excel(writer, sheet_name=name)\\n writer.save()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3515a98e30034850fd351980c60dace6\",\n \"score\": \"0.55242693\",\n \"text\": \"def update_worksheet(data, sheet_name):\\n print(f'Updating {sheet_name} worksheet...{newline}')\\n updated_worksheet = SHEET.worksheet(sheet_name)\\n updated_worksheet.append_row(data)\\n print(f'{sheet_name} worksheet updated successfully!{newline}')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a6bddcee0299d534df260f0aa6600dc\",\n \"score\": \"0.5509846\",\n \"text\": \"def write_result(header, grouped_data_items, num_sheets, highlight_cols=None):\\n\\n header = [\\\"Номер групи\\\"] + header\\n if highlight_cols is None:\\n highlight_cols = []\\n\\n filename = 'formatted_{:%Y-%m-%d_%H:%M:%S}.xlsx'.format(datetime.now())\\n print('Writing to XLSX workbook \\\"{}\\\"'.format(filename))\\n workbook = xlsxwriter.Workbook(filename)\\n\\n groups_per_sheet = len(grouped_data_items) // num_sheets\\n for sheet_num in range(num_sheets):\\n worksheet = workbook.add_worksheet('Book{}'.format(sheet_num + 1))\\n # Pagination bounds\\n lower_bound = groups_per_sheet * sheet_num\\n if sheet_num == num_sheets - 1:\\n upper_bound = None\\n else:\\n upper_bound = groups_per_sheet * (sheet_num + 1)\\n sheet_groups = grouped_data_items[lower_bound:upper_bound]\\n\\n row_pointer = 0 # Current row in the worksheet\\n worksheet.write_row(row_pointer, 0, header)\\n for group_num, rows in enumerate(sheet_groups):\\n for row_num, row in enumerate(rows):\\n row_pointer += 1\\n prev_group = \\\"\\\"\\n\\n for col, cell in enumerate([group_num] + row):\\n # Here we are using number in front of header of current\\n # column to determine if we are still in the same group\\n new_group_started = True\\n if len(header) > col:\\n m = re.match(\\\"(\\\\d+)\\\", header[col])\\n if m:\\n if m.group(1) == prev_group:\\n new_group_started = False\\n else:\\n prev_group = m.group(1)\\n\\n orig_col = col - 1\\n format = workbook.add_format()\\n # Highlight columns that contain non-matching cells\\n if orig_col in highlight_cols:\\n if any([x[orig_col] != cell for i, x in enumerate(rows) if i != row_num]):\\n format.set_bg_color('red')\\n # Handle the group borders\\n format.set_border_color('#DDDDDD')\\n format.set_border(1)\\n\\n if new_group_started:\\n format.set_left_color('black')\\n\\n if row_num == 0:\\n format.set_top_color('black')\\n elif row_num == len(rows) - 1:\\n format.set_bottom_color('black')\\n\\n # Filenames might sometimes be detected as numbers and we don't want this\\n if col == COL_FILENAME:\\n worksheet.write_string(row_pointer, col, cell, format)\\n else:\\n worksheet.write(row_pointer, col, cell, format)\\n\\n # Disabled for now\\n # # Add an empty row between the groups\\n # row_pointer += 1\\n # worksheet.write_row(row_pointer, 0, [])\\n print('Wrote {} rows for sheet {}'.format(row_pointer + 1, sheet_num))\\n\\n workbook.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90020d289fa0394def3ec56edac70671\",\n \"score\": \"0.5501802\",\n \"text\": \"def create_sheet(self, name):\\n return ODSSheetWriter(self.native_book, None, name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"005e54560235314bcecf0d0622c632d8\",\n \"score\": \"0.54850245\",\n \"text\": \"def export_list(self, sheet):\\n if self.groupby:\\n idx = self.fields.index(self.groupby)\\n groups = []\\n uniquekeys = []\\n queryset = sorted(self.data, key=lambda x: x[idx])\\n for key, group in groupby(queryset, key=lambda x: x[idx]):\\n groups.append(list(group))\\n uniquekeys.append(key)\\n row = 1\\n for grouper, group in zip(uniquekeys, groups):\\n sheet.write(row, 0, u'', styles['data'])\\n sheet.write(row + 1, 0, grouper, styles['subheader'])\\n row += 2\\n for line in group:\\n col = 0\\n for val in line:\\n self.write_cell_data(sheet, (row, col), val)\\n col += 1\\n row += 1\\n else:\\n row = 1\\n for line in self.data:\\n col = 0\\n for val in line:\\n self.write_cell_data(sheet, (row, col), val)\\n col += 1\\n row += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e3af6fdf857b9f948d85ee4bdf89d31\",\n \"score\": \"0.54808885\",\n \"text\": \"def getExcel(f):\\n return pd.ExcelWriter(f)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b03056eec848a8cfb8ebed16e67461fa\",\n \"score\": \"0.5475697\",\n \"text\": \"def to_excel(self, excel_writer='output.xlsx', sheet_name='Sheet1', *, protect_sheet=False,\\n right_to_left=False, columns_to_hide=None, add_filters=False, replace_sheet=False,\\n auto_fit=None, **kwargs):\\n save = kwargs.pop('save', isinstance(excel_writer, str))\\n\\n # pandas.to_excel defaults\\n index = kwargs.pop('index', True)\\n header = kwargs.pop('header', True)\\n columns = kwargs.pop('columns', None)\\n engine = kwargs.pop('engine', 'openpyxl')\\n startcol = kwargs.pop('startcol', 0)\\n startrow = headerrow = kwargs.pop('startrow', 0)\\n\\n index_label = kwargs.get('index_label', self.index.name)\\n\\n if self._table_args and not header:\\n raise ValueError('Cannot format as table without headers.')\\n\\n if columns is not None:\\n return self.loc[:, columns].to_excel(\\n excel_writer=excel_writer, sheet_name=sheet_name, protect_sheet=protect_sheet,\\n right_to_left=right_to_left, columns_to_hide=columns_to_hide, add_filters=add_filters,\\n replace_sheet=replace_sheet, auto_fit=auto_fit, header=header, index=index,\\n startcol=startcol, startrow=startrow, engine=engine, save=save, **kwargs\\n )\\n\\n if isinstance(excel_writer, str):\\n excel_writer = self.ExcelWriter(excel_writer)\\n elif 'openpyxl' not in excel_writer.engine:\\n raise ValueError('Engine for excel_writer must be openpyxl.')\\n\\n if _os.path.splitext(excel_writer.path)[1] not in excel_writer.supported_extensions:\\n raise ValueError(\\n 'Unsupported file extension {}. Use {}.'.format(\\n _os.path.splitext(excel_writer.path)[1], '/'.join(excel_writer.supported_extensions)\\n )\\n )\\n\\n if replace_sheet:\\n if sheet_name in excel_writer.book:\\n del excel_writer.book[sheet_name]\\n if sheet_name in excel_writer.sheets:\\n del excel_writer.sheets[sheet_name]\\n\\n self.dataframe.to_excel(\\n excel_writer, sheet_name=sheet_name, engine=engine, header=header,\\n index=index, startcol=startcol, startrow=startrow, columns=columns, **kwargs\\n )\\n\\n book = excel_writer.book\\n sheet = book[sheet_name]\\n sheet.sheet_view.rightToLeft = right_to_left\\n\\n # add named styles. Rename any whose name is already taken within book.\\n renamed_styles = self._add_named_styles(book)\\n\\n if auto_fit is not None and auto_fit is not False:\\n if auto_fit is True:\\n auto_fit = self.columns\\n self.auto_fit(auto_fit, index=index, include_header=bool(header))\\n\\n # index styles\\n if index:\\n current_cell = sheet.cell(row=startrow + 1, column=startcol + 1)\\n if header and not index_label and self._table_args:\\n index_label = 'index'\\n current_cell.value = index_label # Otherwise formatting as table will auto give it a ColumnX name.\\n if header: # TODO: Style this cell properly\\n current_cell.style = self._header_styles.iat[0]\\n offset = 2 if header else 1\\n for row_index, index_style in enumerate(self._index_styles.iteritems()):\\n index_value, style = index_style\\n current_cell = sheet.cell(row=row_index + startrow + offset, column=startcol + 1)\\n current_cell.style = renamed_styles.get(style, style)\\n # set index width\\n sheet.column_dimensions[self.get_column_letter(startcol)].width = self._index_width\\n # adjust startcol for added index column\\n startcol += 1\\n\\n # header styles\\n if header:\\n for col_index, col_style in enumerate(self._header_styles.iteritems()):\\n col_name, style = col_style\\n current_cell = sheet.cell(row=startrow + 1, column=col_index + startcol + 1)\\n current_cell.style = renamed_styles.get(style, style)\\n # set header height\\n sheet.row_dimensions[startrow + 1].height = self.header_height\\n # adjust startrow for header row\\n startrow += 1\\n\\n # data styles\\n for col_index, col_series in enumerate(self._styleframe.iteritems()):\\n col_name, column = col_series\\n for row_index, index_style in enumerate(column.iteritems()):\\n index_value, style = index_style\\n current_cell = sheet.cell(row=row_index + startrow + 1, column=col_index + startcol + 1)\\n current_cell.style = renamed_styles.get(style, style)\\n\\n # add any hyperlinks\\n if self._hyperlinks is not None:\\n for col_index, col_series in enumerate(self._hyperlinks.iteritems()):\\n col_name, column = col_series\\n\\n try:\\n col_index = self.columns.get_loc(col_name)\\n except KeyError: # col_name not in dataframe\\n if index and col_name in (index_label, self.index.name, 'index'):\\n col_index = -1\\n else:\\n continue\\n\\n for row_index, index_link in enumerate(column.iteritems()):\\n index_value, hyperlink = index_link\\n if isinstance(hyperlink, _Hyperlink):\\n # make sure each cell has a unique Hyperlink obj to receive the ref of that cell\\n hyperlink = _copy(hyperlink)\\n\\n current_cell = sheet.cell(row=row_index + startrow + 1, column=col_index + startcol + 1)\\n # openpyxl assigns cell ref to hyperlink\\n current_cell.hyperlink = hyperlink\\n\\n # set column widths\\n for col_index, column_width in enumerate(self._column_widths.iteritems()):\\n column, width = column_width\\n column_letter = self.get_column_letter(col_index, startcol=startcol)\\n sheet.column_dimensions[column_letter].width = width\\n\\n # set row heights\\n for row_index, row_height in enumerate(self._row_heights.iteritems()):\\n row, height = row_height\\n sheet.row_dimensions[startrow + row_index + 1].height = height\\n\\n # format as table if needed\\n if self._table_args:\\n tables = {tbl.name for sht in book.worksheets for tbl in sht._tables}\\n\\n rows = None if not self.dataframe.empty else (0, 1)\\n self._table_args['ref'] = self._get_range_as_str(\\n row_index=rows, startcol=startcol, startrow=headerrow, index=index\\n )\\n\\n if 'name' in self._table_args:\\n self._table_args['displayName'] = self._table_args.pop('name')\\n if self._table_args.get('displayName', None) in tables:\\n raise ValueError('Table name \\\"{}\\\" already exists in book.'.format(self._table_args['displayName']))\\n\\n if 'displayName' not in self._table_args:\\n # find next available table name\\n for i in _count(1):\\n if 'Table{}'.format(i) not in tables:\\n tbl = _table.Table(\\n displayName='Table{}'.format(i),\\n **self._table_args\\n )\\n break\\n else:\\n tbl = _table.Table(\\n **self._table_args\\n )\\n\\n sheet.add_table(tbl)\\n\\n elif add_filters:\\n sheet.auto_filter.ref = self._get_range_as_str(\\n row_index=0, startcol=startcol, startrow=headerrow, index=index\\n )\\n\\n # Hide columns\\n if columns_to_hide:\\n if isinstance(columns_to_hide, (str, int)):\\n column_letter = self.get_column_letter(columns_to_hide, startcol=startcol)\\n sheet.column_dimensions[column_letter].hidden = True\\n else:\\n for column in columns_to_hide:\\n column_letter = self.get_column_letter(column, startcol=startcol)\\n sheet.column_dimensions[column_letter].hidden = True\\n\\n # Protect sheet\\n if protect_sheet:\\n sheet.protection.autoFilter = False\\n sheet.protection.enable()\\n\\n if save:\\n excel_writer.save()\\n\\n return excel_writer\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d117f0ba4b793b01d4cfb49dbca8761e\",\n \"score\": \"0.5475479\",\n \"text\": \"def fill_xls(ws, row, task_title, milestone, start_date, end_date, estimated_hours, qa, _developer, priority, _type,\\n description):\\n ws.write(row, 0, task_title)\\n ws.write(row, 1, milestone)\\n ws.write(row, 2, start_date)\\n ws.write(row, 3, end_date)\\n ws.write(row, 4, estimated_hours)\\n ws.write(row, 5, qa)\\n ws.write(row, 6, _developer)\\n ws.write(row, 7, priority)\\n ws.write(row, 8, _type)\\n ws.col(9).width = 100 * 256\\n ws.write(row, 9, description.encode('utf-8').replace(\\\"\\\\r\\\\n\\\", \\\"\\\").strip())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dbe4d4315ba3009a6859dd6474b182c4\",\n \"score\": \"0.54749334\",\n \"text\": \"def write_excel(filename, sheetnames, arrays):\\n if len(sheetnames) != len(arrays):\\n raise IndexError(\\\"Array and sheet number must be equal.\\\")\\n \\n book = xlwt.Workbook()\\n \\n for name, array in zip(sheetnames, arrays):\\n sheet = book.add_sheet(name)\\n cols, rows = array.shape\\n \\n for row in range(rows):\\n for col in range(cols):\\n sheet.write(row, col, array[col][row])\\n \\n book.save(filename)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8b5761fc33d5831f01f1e613998bedf\",\n \"score\": \"0.54558617\",\n \"text\": \"def save_excel(self, file):\\n if not file.endswith('.xlsx'):\\n file = file + '.xlsx'\\n df = pd.DataFrame(self._items, columns=['DOI'])\\n df.to_excel(file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc8ddb688c107d1c406b28972c8cf94b\",\n \"score\": \"0.54516715\",\n \"text\": \"def create_doc(title):\\n\\n spreadsheet_body = {\\n 'properties': {\\n 'locale': 'zh_CN',\\n 'timeZone': 'Etc/GMT',\\n 'autoRecalc': 'ON_CHANGE',\\n 'title': title\\n },\\n }\\n return service.spreadsheets().create(body = spreadsheet_body).execute()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1119a6786ae6317fa2dede14acea2a1b\",\n \"score\": \"0.544675\",\n \"text\": \"def InsertGSheetsRows(\\n service, spreadsheetId, range_name, values, value_input_option=\\\"RAW\\\", quiet=True\\n):\\n body = {\\\"values\\\": values}\\n result = (\\n service.spreadsheets()\\n .values()\\n .append(\\n spreadsheetId=spreadsheetId,\\n range=range_name,\\n valueInputOption=value_input_option,\\n body=body,\\n )\\n .execute()\\n )\\n print result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c47401fc4029867494317494e0f9174\",\n \"score\": \"0.5435767\",\n \"text\": \"def get_Data_temblor(idsheet, rangesheet):\\n credentials = get_credentials()\\n http = credentials.authorize(httplib2.Http())\\n discoveryUrl = ('https://sheets.googleapis.com/$discovery/rest?'\\n 'version=v4')\\n service = discovery.build('sheets', 'v4', http=http, discoveryServiceUrl=discoveryUrl)\\n# spreadsheetId = 'Necesidades de Zonas Afectadas y Albergues '\\n spreadsheetId = 'Centros de Acopio'\\n #####DAÑOS Y DERRUMBES VERIFICADOS\\n #####Para descargar otras páginas cambiar el nombre en el campo range\\n result = service.spreadsheets().values().get(\\n spreadsheetId=idsheet,\\n range=rangesheet).execute()\\n values = result.get('values', [])\\n return pd.DataFrame(values)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"01d33f342016389413e0957bb66237fc\",\n \"score\": \"0.54277146\",\n \"text\": \"def _save_values(self, nm):\\n try:\\n result = self.sheet.values().get(\\n spreadsheetId=self.sheet_id,\\n range=nm).execute()\\n values = result.get('values', [])\\n df = pd.DataFrame(\\n values[4:], columns=values[3])\\n df.to_csv(\\n 'data/Canada-{}.csv'.format(nm),\\n index=False)\\n except:\\n print(\\\"Retrieving {} failed.\\\".format(nm))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c01547aa9dce285db91bf405caa9e215\",\n \"score\": \"0.5426704\",\n \"text\": \"def save_report_file(assembly, bom, indented_bom, ebom, prev):\\n writer = pd.ExcelWriter(assembly + '.xlsx', engine='xlsxwriter')\\n workbook = writer.book\\n header_format = workbook.add_format({\\n 'bold': True,\\n 'text_wrap': False,\\n 'fg_color': '#DCE6F1',\\n 'border': 1\\n })\\n\\n _create_bom_worksheet(bom, writer, header_format)\\n _create_indented_bom_worksheet(indented_bom, writer, header_format)\\n _create_import_file_worksheet(ebom, writer, header_format)\\n _create_new_revision_worksheet(prev, writer, header_format)\\n\\n writer.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08bfcc9ad3d5d2bae36b6cbe1469a98c\",\n \"score\": \"0.54227155\",\n \"text\": \"def save_excel(self, file):\\n if not file.endswith('.xlsx'):\\n file = file + '.xlsx'\\n df = pd.DataFrame(self._items, columns=self.field_names)\\n df.to_excel(file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc4f5ac48ce66fdbd5298200b1c57742\",\n \"score\": \"0.54178506\",\n \"text\": \"def gs(credentials, title, output_path):\\n\\n con = create_database(output_path)\\n\\n loader = simplesqlite.loader.GoogleSheetsTableLoader()\\n loader.source = credentials\\n loader.title = title\\n\\n for tabledata in loader.load():\\n click.echo(\\n \\\"convert '%s' to '%s' table\\\" % (title, tabledata.table_name))\\n con.create_table_from_tabledata(tabledata)\\n\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8eee5ff2e2dfb0baaede0560768c0ecf\",\n \"score\": \"0.54152787\",\n \"text\": \"def export_plot_data_to_excel(self):\\n\\n all_srs = self.plot_settings.axis1_series_list + self.plot_settings.axis2_series_list\\n\\n # Collate all time series to dataframe\\n df_ts_list = []\\n populated_srs = (srs for srs in all_srs if len(srs.y) > 0)\\n for srs in populated_srs:\\n col = self._create_label(srs)\\n df = pd.DataFrame(srs.y, index=srs.x, columns=[col])\\n df_ts_list.append(df)\\n\\n y = srs.y_filt\\n if len(y) > 0:\\n col = self._create_label(srs, filtered=True)\\n df = pd.DataFrame(y, index=srs.x, columns=[col])\\n df_ts_list.append(df)\\n\\n # Collate all psd series to dataframe\\n df_psd_list = []\\n populated_srs = (srs for srs in all_srs if len(srs.pxx) > 0)\\n for srs in populated_srs:\\n col = self._create_label(srs)\\n df = pd.DataFrame(srs.pxx, index=srs.freq, columns=[col])\\n df_psd_list.append(df)\\n\\n pxx = srs.pxx_filt\\n if len(pxx) > 0:\\n col = self._create_label(srs, filtered=True)\\n df = pd.DataFrame(pxx, index=srs.freq, columns=[col])\\n df_psd_list.append(df)\\n\\n # Concatenate to sheet dataframe\\n df_ts: pd.DataFrame\\n df_ts = pd.concat(df_ts_list, axis=1, sort=False)\\n df_ts.index.name = \\\"Time (s)\\\"\\n df_psd = pd.concat(df_psd_list, axis=1, sort=False)\\n df_psd.index.name = \\\"Freq (Hz)\\\"\\n\\n # Write to Excel\\n filename, _ = QtWidgets.QFileDialog.getSaveFileName(\\n self, caption=\\\"Export Plot Data\\\", filter=\\\"Excel File (*.xlsx)\\\"\\n )\\n if filename:\\n writer = pd.ExcelWriter(filename, engine=\\\"xlsxwriter\\\")\\n wb = writer.book\\n fmt1 = wb.add_format({\\\"text_wrap\\\": True, \\\"bold\\\": True})\\n fmt2 = wb.add_format({\\\"num_format\\\": \\\"0.000E+00\\\"})\\n\\n # Time Series sheet\\n df_ts.to_excel(writer, sheet_name=\\\"Time Series\\\")\\n ws = writer.sheets[\\\"Time Series\\\"]\\n ws.set_column(1, len(df_ts.columns), 12, fmt2)\\n\\n # Need to rewrite column names to add desired format\\n for i, col in enumerate(df_ts.columns):\\n ws.write(0, i + 1, col, fmt1)\\n\\n # PSD sheet\\n df_psd.to_excel(writer, sheet_name=\\\"PSD\\\")\\n ws = writer.sheets[\\\"PSD\\\"]\\n ws.set_column(1, len(df_psd.columns), 12, fmt2)\\n\\n # Need to rewrite column names to add desired format\\n for i, col in enumerate(df_psd.columns):\\n ws.write(0, i + 1, col, fmt1)\\n\\n writer.save()\\n msg = \\\"Plot data exported successfully.\\\"\\n QtWidgets.QMessageBox.information(self, \\\"Export Plot Data\\\", msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2a494b3bb6b423ad7e85594fa6a25c5\",\n \"score\": \"0.54057837\",\n \"text\": \"def toExcel(fname,tables):\\r\\n writer = pd.ExcelWriter(fname)\\r\\n for k,v in tables.items():\\r\\n # Make sure the order of columns complies the specs\\r\\n record = [r for r in V4.records if r.__class__.__name__.upper()==k]\\r\\n if len(record)==0:\\r\\n print(\\\"Ignore exporting table %s: No such record type exists.\\\" %k)\\r\\n else:\\r\\n columns = [field[0] for field in record[0].fieldMap]\\r\\n v.to_excel(writer,sheet_name=k,columns=columns,index=False,na_rep=\\\"N/A\\\")\\r\\n writer.save()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"900e1abdf76864c909b42e6d3e90893c\",\n \"score\": \"0.54052615\",\n \"text\": \"def save(self, worksheet, workbook, startrow=0, startcol=0):\\n if self.name and self.show_title:\\n write_title(self.name, worksheet, workbook, startrow, startcol, self.width)\\n startrow += 1\\n self.to_excel(workbook, worksheet.name, startrow=startrow, startcol=startcol)\\n return workbook.get_worksheet_by_name(worksheet.name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b83887975b4d69d609d3298d95402e2a\",\n \"score\": \"0.5397788\",\n \"text\": \"def update_worksheet(data,worksheet):\\n print(f\\\"Updating {worksheet} worksheet...\\\\n\\\")\\n worksheet_to_update = SHEET.worksheet(worksheet)\\n worksheet_to_update.append_row(data)\\n print(f\\\"{worksheet} worksheet updated!\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59abed42a585384f2eca9ce05a3ff0a9\",\n \"score\": \"0.53878915\",\n \"text\": \"def export_df(df: pd.DataFrame, export_path: str,\\r\\n export_format: str = \\\"pickle\\\",\\r\\n hdf_key: Optional[str] = None\\r\\n ) -> None:\\r\\n _, ext = os.path.splitext(export_path)z\\r\\n use_ext = '.' + export_format if len(ext) == 0 else ''\\r\\n if export_format == \\\"html\\\":\\r\\n color_dict = {\\r\\n 'POSITIVE': 'limegreen',\\r\\n 'NEGATIVE': 'red',\\r\\n 'NEUTRAL': 'lightgrey',\\r\\n 'MIXED': 'yellow'\\r\\n }\\r\\n spk_dict = {\\r\\n 'ch_0': '#F0F8FF',\\r\\n 'spk_0': '#F0F8FF',\\r\\n 'ch_1': '#FFF8DC',\\r\\n 'spk_1': '#FFF8DC'\\r\\n }\\r\\n df.set_index(\\r\\n ['transcript', 'recording', 'speaker', 'index']\\r\\n ).to_html(export_path + use_ext, encoding='utf-8',\\r\\n formatters={\\r\\n 'pred_sent': lambda sent: f'<span style=\\\"background-color:{color_dict[sent]}\\\">{sent}</span>',\\r\\n 'speaker': lambda speaker: f'<span style=\\\"background-color:{spk_dict[speaker]}\\\">{speaker}</span>'\\r\\n }, escape=False)\\r\\n elif export_format == \\\"csv\\\":\\r\\n df.to_csv(export_path + use_ext)\\r\\n elif export_format == \\\"json\\\":\\r\\n df.to_json(export_path + use_ext)\\r\\n elif export_format == \\\"parquet\\\":\\r\\n df.to_parquet(export_path + use_ext)\\r\\n elif export_format == \\\"pickle\\\":\\r\\n df.to_pickle(export_path + use_ext)\\r\\n elif export_format == \\\"excel\\\":\\r\\n if len(use_ext) > 0:\\r\\n use_ext = '.xlsx'\\r\\n df.to_excel(export_path + use_ext, index=False)\\r\\n elif export_format == \\\"hdf\\\":\\r\\n assert hdf_key is not None, \\\"Parameter hdf_key must be informed if export format is hdf.\\\"\\r\\n df.to_hdf(export_path + use_ext, hdf_key)\\r\\n else:\\r\\n raise ValueError(f\\\"Unknown export format: {export_format}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86fee2d337aaac1a263aea1dced745ef\",\n \"score\": \"0.5387516\",\n \"text\": \"def _create_spreadsheet(self, ws_name: str, columns: List[str]) -> None:\\n ws = self.workbook.create_sheet(ws_name)\\n self.workbook.active = ws\\n ws.append(columns)\\n self.workbook.save(self.wb_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1009060490d1e796dfb6313590e8848a\",\n \"score\": \"0.5387063\",\n \"text\": \"def make_worksheet(self, sheet_name, query_results, **kwargs):\\n try:\\n self.workbook_builder.add_worksheet(\\n sheet_name=sheet_name,\\n field_names=query_results.field_names,\\n sheet_data=query_results.result_data,\\n **kwargs\\n )\\n except:\\n error = \\\"Unable to add worksheet {}\\\".format(sheet_name)\\n self.logger.exception(error)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6664e94d2265df84fe471f9a4543c9ad\",\n \"score\": \"0.5382799\",\n \"text\": \"def export_excel(table, file_name, attributes):\\r\\n \\r\\n writer = pd.ExcelWriter(file_name, engine='xlsxwriter')\\r\\n table.to_excel(writer, index=False, header=False, startrow=1,\\r\\n sheet_name = 'Sheet1')\\r\\n num_rows, num_cols = table.shape\\r\\n num_cols -= 1\\r\\n workbook = writer.book\\r\\n worksheet = writer.sheets['Sheet1']\\r\\n \\r\\n # Add attribute labels: Row 1 merge (b-d), (e-g), etc basiac\\r\\n alphabet = string.ascii_uppercase\\r\\n\\r\\n offset = 1\\r\\n width = 3\\r\\n row = 1\\r\\n \\r\\n merge_format = workbook.add_format({\\r\\n 'bold': 1,\\r\\n 'border': 1,\\r\\n 'align': 'center',\\r\\n 'valign': 'vcenter'})\\r\\n \\r\\n for i, attribute in enumerate(attributes):\\r\\n start_col = offset + i*width\\r\\n end_col = start_col + width - 1\\r\\n positions = \\\"{0}{2}:{1}{2}\\\".format(alphabet[start_col],\\r\\n alphabet[end_col], row)\\r\\n \\r\\n\\r\\n worksheet.merge_range(positions, attribute, merge_format)\\r\\n\\r\\n writer.save()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d57cc42f8e4fe93df0d9a84580a1a17\",\n \"score\": \"0.5378535\",\n \"text\": \"def excel(self): \\n nombres =pandas.read_excel('C:/Videojuego/Proyecto/Usuarios/Nombres.xlsx',header = None)\\n nombres = list(nombres.iloc[:,0])\\n nombres.append(self.text2)\\n clave=nombres.pop(0)\\n dic={clave:nombres}\\n\\n df = pandas.DataFrame(dic)\\n df =df[['Nombres']]\\n writer =ExcelWriter('C:/Videojuego/Proyecto/Usuarios/Nombres.xlsx') \\n df.to_excel(writer,sheet_name='Alumnos',index=False)\\n writer.save()\\n\\n df2 = pandas.DataFrame({'Juego':[\\\"----------\\\"],'Estatus':[\\\"----------\\\"],'Tiempo':[\\\"----------\\\"],'Real':[\\\"----------\\\"],'Ideal':[\\\"----------\\\"],'Fecha':[\\\"----------\\\"]})\\n df2=df2[['Juego','Estatus','Tiempo','Real','Ideal','Fecha']]\\n writer = ExcelWriter('C:/Videojuego/Proyecto/Usuarios/Alumnos/'+self.text2+'.xlsx')\\n df2.to_excel(writer,sheet_name=self.text2,index=False)\\n writer.save()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0939f485d27b8f78aca961be7ecddb6f\",\n \"score\": \"0.5359601\",\n \"text\": \"def write_cell_data(self, sheet, pos, val):\\n row, col = pos\\n if isinstance(val, (date, datetime)):\\n sheet.write(row, col, label=val, style=styles['date'])\\n else:\\n sheet.write(row, col, label=val, style=styles['data'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00cfdd4549accbbf00151cc8943187cc\",\n \"score\": \"0.53470063\",\n \"text\": \"def tableFormat(sheetData, sheetName, wbook):\\n # Nothing to format, so return.\\n if sheetData.shape[0] == 0:\\n return\\n sheet = wbook.sheets[sheetName]\\n sheet.freeze_panes(1, 0)\\n # Set the autofilter for the sheet.\\n sheet.autofilter(0, 0, sheetData.shape[0], sheetData.shape[1]-1)\\n # Set document formatting.\\n docFormat = wbook.book.add_format({'font': 'Calibri', 'font_size': 11})\\n acctFormat = wbook.book.add_format({'font': 'Calibri', 'font_size': 11, 'num_format': 44})\\n commaFormat = wbook.book.add_format({'font': 'Calibri', 'font_size': 11, 'num_format': 3})\\n newFormat = wbook.book.add_format({'font': 'Calibri', 'font_size': 11, 'bg_color': 'yellow'})\\n movedFormat = wbook.book.add_format({'font': 'Calibri', 'font_size': 11, 'bg_color': '#FF9900'})\\n # Format and fit each column.\\n i = 0\\n # Columns which get shrunk down in reports.\\n hideCols = ['Technology', 'Excel Part Link', 'Report Part Nbr Link', 'MFG Part Description',\\n 'Focus', 'Part Class Name', 'Vendor ID', 'Invoice Detail Nbr', 'Assigned Account Rep',\\n 'Recipient', 'DKLI Report Date', 'Invoice Date Group', 'Comments', 'Sales Channel']\\n coreCols = ['Must Contact', 'End Product', 'How Contacted', 'Information for Digikey']\\n for col in sheetData.columns:\\n acctCols = ['Unit Price', 'Invoiced Dollars']\\n if col in acctCols:\\n formatting = acctFormat\\n elif col == 'Quantity':\\n formatting = commaFormat\\n else:\\n formatting = docFormat\\n maxWidth = max(len(str(val)) for val in sheetData[col].values)\\n # Set maximum column width at 50.\\n maxWidth = min(maxWidth, 50)\\n if col in hideCols:\\n maxWidth = 0\\n elif col in coreCols:\\n maxWidth = 25\\n sheet.set_column(i, i, maxWidth+0.8, formatting)\\n i += 1\\n # Highlight new root customer and moved city rows.\\n try:\\n for row in sheetData.index:\\n ind = str(sheetData.loc[row, 'TAARCOM Comments']).lower().rstrip() == 'individual'\\n no_root_cust = sheetData.loc[row, 'Root Customer..'] == ''\\n if ind or no_root_cust:\\n continue\\n root_cust_loc = int(np.where(sheetData.columns == 'Root Customer..')[0])\\n city_loc = int(np.where(sheetData.columns == 'City on Acct List')[0])\\n if sheetData.loc[row, 'New T-Cust'] == 'Y':\\n sheet.write(row + 1, root_cust_loc, sheetData.loc[row, 'Root Customer..'], newFormat)\\n elif not sheetData.loc[row, 'City on Acct List']:\\n pass\\n elif sheetData.loc[row, 'Customer City'] not in sheetData.loc[row, 'City on Acct List'].split(', '):\\n sheet.write(row + 1, root_cust_loc, sheetData.loc[row, 'Root Customer..'], movedFormat)\\n sheet.write(row + 1, city_loc, sheetData.loc[row, 'City on Acct List'], movedFormat)\\n except KeyError:\\n print('Error locating Sales and/or City on Acct List columns.\\\\n'\\n 'Unable to highlight without these columns.\\\\n---')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f24d0a7e51812a100d1a19aac8a1043\",\n \"score\": \"0.53447926\",\n \"text\": \"def export_to_pdf(whpa, export_path):\\n wb = xlwt.Workbook()\\n ws = wb.add_sheet(whpa)\\n\\n header = Header(*range(0, len(Header._fields)))\\n\\n for field, index in header._asdict().items():\\n ws.write(0, index, field)\\n\\n with psql.connect(\\\"dbname=gwi_csi user=eric\\\") as conn:\\n with conn.cursor() as cur:\\n cur.execute('SELECT facility_id, facility_name, whpa_id, address,\\\\\\n phone, property_type, facility_type, facility_status,\\\\\\n csi_zone, \\\"date\\\", comments, location, urls, issues\\\\\\n from whpa_report WHERE facility_id LIKE %s',\\n (\\\"%{}%\\\".format(whpa),))\\n\\n row_index = 1\\n\\n for row in cur.fetchall():\\n facility = Facility(*row)\\n\\n for field, value in facility._asdict().items():\\n if field == 'location':\\n x = float(facility.location.split(' ')[0].lstrip('POINT('))\\n y = float(facility.location.split(' ')[1].rstrip(')'))\\n\\n ws.write(row_index, header.X, x)\\n ws.write(row_index, header.Y, y)\\n elif field == 'issues' and facility.issues is not None:\\n ws.write(row_index, header.issues, \\\", \\\".join(facility.issues))\\n else:\\n ws.write(row_index, getattr(header, field), value)\\n row_index += 1\\n\\n wb.save(os.path.join(export_path, \\\"{}.xlsx\\\".format(whpa)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f42799db991eecb9748aa6323344809\",\n \"score\": \"0.53431857\",\n \"text\": \"def upload_as_gsheet(file_to_upload, filename): \\r\\n drive_service = initialize_drive_service()\\r\\n file_found = False\\r\\n \\r\\n try:\\r\\n extension = file_to_upload.split(\\\".\\\")[-1]\\r\\n \\r\\n file_metadata = {\\r\\n 'name' : filename,\\r\\n 'mimeType' : 'application/vnd.google-apps.spreadsheet'\\r\\n }\\r\\n \\r\\n if (extension == 'xls'):\\r\\n media = MediaFileUpload('%s' % (file_to_upload),\\r\\n mimetype='application/vnd.ms-excel',\\r\\n resumable=True)\\r\\n file_found = True\\r\\n elif (extension == 'xlsx'):\\r\\n media = MediaFileUpload('%s' % (file_to_upload),\\r\\n mimetype='application/vnd.openxmlformats-officedocument.spreadsheetml.sheet',\\r\\n resumable=True)\\r\\n file_found = True\\r\\n else:\\r\\n pass \\r\\n except IndexError:\\r\\n pass\\r\\n \\r\\n if (file_found == True):\\r\\n file = drive_service.files().create(body=file_metadata,\\r\\n media_body=media,\\r\\n fields='id').execute()\\r\\n log_file().info(\\\"Source Compiler.log\\\", \\\"File \\\\\\\"{0}\\\\\\\" uploaded as: {1} (ID: {2}).\\\".format(file_to_upload, filename, file.get('id')))\\r\\n print(\\\"File \\\\\\\"{0}\\\\\\\" uploaded as: {1} (ID: {2}).\\\".format(file_to_upload, filename, file.get('id'))) \\r\\n else:\\r\\n log_file().error(\\\"Source Compiler.log\\\", \\\"Invalid file name or extension. Provide full file name with .xls or .xlsx extensions.\\\")\\r\\n print(\\\"Invalid file name or extension. Provide full file name with .xls or .xlsx extensions.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e847e36617b1ec476406e82f662fbe2\",\n \"score\": \"0.53355503\",\n \"text\": \"def create_main_tables_structure(ws, df, date_str):\\r\\n ws['A1']=\\\"Data is provided for reference purposes only. Data is the property of PV InfoLink.\\\\nUsers should respect the legitimate rights for use based on the principle of integrity.\\\"\\r\\n ws['A1'].alignment=Alignment(wrap_text=True)\\r\\n ws.merge_cells('A1:D1')\\r\\n ws['A2']=\\\"Please do not modify this sheet manually. Use a copy for playing with the data\\\"\\r\\n ws.merge_cells('A2:D2')\\r\\n ws['A3']=df.columns[-3]+\\\", \\\"+ws.title\\r\\n ws['A3'].font=Font(bold=True)\\r\\n ws.merge_cells('A3:D3')\\r\\n for r in dataframe_to_rows(df[[\\\"Category\\\", \\\"Item\\\", \\\"Unit\\\", \\\"High Price\\\"]], index=False, header=True):\\r\\n ws.append(r)\\r\\n ws.cell(row=ws.max_row+2, column=1, value=df.columns[-1]+\\\", \\\"+ws.title)\\r\\n ws.cell(row=ws.max_row, column=1).font=Font(bold=True)\\r\\n ws.merge_cells(start_row=ws.max_row, start_column=1, end_row=ws.max_row, end_column=ws.max_column)\\r\\n for r in dataframe_to_rows(df[[\\\"Category\\\", \\\"Item\\\", \\\"Unit\\\", \\\"Average Price\\\"]], index=False, header=True):\\r\\n ws.append(r)\\r\\n ws.cell(row=ws.max_row+2, column=1, value=df.columns[-2]+\\\", \\\"+ws.title)\\r\\n ws.cell(row=ws.max_row, column=1).font=Font(bold=True)\\r\\n ws.merge_cells(start_row=ws.max_row, start_column=1, end_row=ws.max_row, end_column=ws.max_column)\\r\\n for r in dataframe_to_rows(df[[\\\"Category\\\", \\\"Item\\\", \\\"Unit\\\", \\\"Low Price\\\"]], index=False, header=True):\\r\\n ws.append(r)\\r\\n\\r\\n ws.cell(row=ws.max_row+2, column=1).value='*The quote of mono wafers is low resistivity product.'\\r\\n ws.merge_cells(start_row=ws.max_row, start_column=1, end_row=ws.max_row, end_column=ws.max_column)\\r\\n ws.cell(row=ws.max_row+1, column=1).value='**Mono-Si wafer quotes are based on those of 180µm. Prices of thinner ones are calculated with formula.'\\r\\n ws.merge_cells(start_row=ws.max_row, start_column=1, end_row=ws.max_row, end_column=ws.max_column)\\r\\n ws.cell(row=ws.max_row+1, column=1).value='***US and Indian module prices showed on the PV InfoLink website is after-tax price (punitive tariffs). Others are FOB price.'\\r\\n ws.merge_cells(start_row=ws.max_row, start_column=1, end_row=ws.max_row, end_column=ws.max_column)\\r\\n\\r\\n swap_headers(ws, \\\"Low Price\\\", date_str)\\r\\n swap_headers(ws, \\\"Average Price\\\", date_str)\\r\\n swap_headers(ws, \\\"High Price\\\", date_str)\\r\\n ws.column_dimensions['B'].width=51\\r\\n ws.column_dimensions['A'].width=16\\r\\n ws.column_dimensions['D'].width=10\\r\\n ws.row_dimensions[1].height=30\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ee69d526e0731440c03eff2c10a0eaf\",\n \"score\": \"0.53336\",\n \"text\": \"async def spreadsheet(ctx,*,request : str=''):\\n await ctx.send(r\\\"https://docs.google.com/spreadsheets/d/11ZXN22CTmItPMVduRdpEwf6wR9KRMJR7Ro9HensEWSk/edit#gid=656420900\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38eef9c9098476f332c511ef6042d32b\",\n \"score\": \"0.53316915\",\n \"text\": \"def save_dataframe(df, filename):\\n df.to_csv(filename, header=True, index=True, index_label=False, mode='wb',\\n encoding='utf-8', na_rep='NaN', sep='|')\\n print \\\"DataFrame Saved to:\\\\n-->\\\\033[1;36m{}\\\\033[0m\\\\n\\\".format(filename)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d794bfb31cc731373cdd1798728d10e\",\n \"score\": \"0.533051\",\n \"text\": \"def write_rows(self, sheet_range, values=[]):\\n body = {\\n 'values': values\\n }\\n result = self.service.spreadsheets().values().append(\\n spreadsheetId=self.spreadsheet_id,\\n range=sheet_range,\\n valueInputOption='USER_ENTERED',\\n insertDataOption='INSERT_ROWS',\\n body=body).execute()\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e12938908cc62c01c3c4a3ea2081ac9\",\n \"score\": \"0.5330464\",\n \"text\": \"def export_qs(self, sheet):\\n opts = self.data.model._meta\\n row = 4\\n for item in self.data:\\n col = 0\\n for name in self.fields:\\n val = getattr(item, name, '')\\n self.write_cell_data(sheet, (row, col), val)\\n col += 1\\n row += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f060c38e3752d6c8d03a8ceba4655cb4\",\n \"score\": \"0.5324046\",\n \"text\": \"def to_xls(scraped_data):\\n book = xlwt.Workbook()\\n sheet = book.add_sheet(\\\"labs\\\")\\n\\n data = [COL_HEADERS] + to_lists(scraped_data)\\n\\n for i, data_row in enumerate(data):\\n sheet_row = sheet.row(i)\\n for j, item in enumerate(data_row):\\n sheet_row.write(j, item)\\n\\n book.save(FILEPATH_TEMPLATE.format(\\\"xls\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af880beb5950a3ab6e3f49b0273f016d\",\n \"score\": \"0.5315864\",\n \"text\": \"def export_inventory_to_excel(inventory, day):\\r\\n\\r\\n df = pd.DataFrame(inventory)\\r\\n writer = pd.ExcelWriter(f'inventory_{day}.xlsx', engine='xlsxwriter')\\r\\n df.to_excel(writer, sheet_name='Sheet1', startrow=1, header=False)\\r\\n workbook = writer.book\\r\\n worksheet = writer.sheets['Sheet1']\\r\\n header = ['Product Name', 'Count', 'Expiration Date']\\r\\n header_format = workbook.add_format({\\r\\n 'bold': True,\\r\\n 'text_wrap': True,\\r\\n 'valign': 'top',\\r\\n 'fg_color': '#D7E4BC',\\r\\n 'border': 1})\\r\\n for col_num, value in enumerate(header):\\r\\n worksheet.write(0, col_num + 1, value, header_format)\\r\\n writer.save()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4553a70c20c9d6419d0c556130d7ce06\",\n \"score\": \"0.5311012\",\n \"text\": \"def add_to_worksheet(worksheet, data):\\n print(f'Updating {worksheet} worksheet...\\\\n')\\n worksheet_to_update = SHEET.worksheet(worksheet)\\n worksheet_to_update.append_row(data)\\n print(f'{worksheet} worksheet updated successfully.\\\\n')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a5444b140b762536c7b10ab8676bf1b\",\n \"score\": \"0.52888787\",\n \"text\": \"def df_to_excel(self, df, filename, **kwargs):\\n if filename.startswith('/'):\\n filename = filename[1:]\\n if (\\n 'index' not in kwargs\\n ): # make the default index=False if not specified in input\\n kwargs = dict({'index': False}, **kwargs)\\n return df.to_excel(self.filepath(filename), **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"469f76923658fc90a75e63e4787ed69f\",\n \"score\": \"0.5274583\",\n \"text\": \"def _save(tables, writer, sheet_name='Sheet1', n_row=1):\\n for t in tables:\\n label = t.columns.name\\n t.to_excel(writer, sheet_name, \\n startrow=n_row, index_label=label)\\n n_row += len(t.index) + 2\\n \\n return n_row\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef4530a1cccaab30ecd88c61ff362744\",\n \"score\": \"0.52736664\",\n \"text\": \"def new_excel(seatNo, ID, gender, first_name, last_name, seatName, filename):\\r\\n wb = xlwt.Workbook(encoding='utf-8')\\r\\n sheet1 = wb.add_sheet('Sheet1')\\r\\n\\r\\n row = 0\\r\\n sheet1.write(row, 0, u\\\"ลำดับ\\\".encode('utf-8'))\\r\\n sheet1.write(row, 1, u\\\"รหัสนิสิต\\\".encode('utf-8'))\\r\\n # sheet1.write(row, 2, u\\\"คำนำหน้า\\\".encode('utf-8'))\\r\\n sheet1.write(row, 2, u\\\"ชื่อ - นามสกุล\\\".encode('utf-8'))\\r\\n sheet1.write(row, 3, u\\\"เลขที่นั่ง\\\".encode('utf-8'))\\r\\n for row in range(0, len(ID)):\\r\\n sheet1.write(row+1, 0, seatNo[row])\\r\\n sheet1.write(row+1, 1, ID[row])\\r\\n sheet1.write(row+1, 2, gender[row] + first_name[row] + \\\" \\\" + last_name[row])\\r\\n sheet1.write(row+1, 3, seatName[row])\\r\\n\\r\\n wb.save(filename + '_new.xls')\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":694,"cells":{"query_id":{"kind":"string","value":"9410c012f0a34a67a772c66287a39474"},"query":{"kind":"string","value":"moves item identified by key to the head of the list"},"positive_passages":{"kind":"list like","value":[{"docid":"82f0a5cf6411760d15775794141626bc","score":"0.814508","text":"def movehead(self, key):\n #remove from old position in list\n n = self.d[key]\n n.next.prev = n.prev\n n.prev.next = n.next\n #put in front position of list\n self.l.next.prev = n\n n.next = self.l.next \n n.prev = self.l\n self.l.next = n","title":""}],"string":"[\n {\n \"docid\": \"82f0a5cf6411760d15775794141626bc\",\n \"score\": \"0.814508\",\n \"text\": \"def movehead(self, key):\\n #remove from old position in list\\n n = self.d[key]\\n n.next.prev = n.prev\\n n.prev.next = n.next\\n #put in front position of list\\n self.l.next.prev = n\\n n.next = self.l.next \\n n.prev = self.l\\n self.l.next = n\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"67072f43b5e8d065011b2c282e4bfc4b","score":"0.67528284","text":"def change_key(self, key, newkey):\n if key in self.list:\n self.list.remove(key)\n self.list.append(newkey)\n self.sort()\n else:\n raise KeyError('No such key')","title":""},{"docid":"9c42b964b276d53263b68eafbd0d83ef","score":"0.6639715","text":"def enqueue(self, key):\n self.list.append(key)\n self.sort()","title":""},{"docid":"def28f2e110ad711e55f7e5d2702a478","score":"0.65499777","text":"def insert_front(self, key):\n new_node = Node(key)\n if self.head:\n self.head.previous = new_node\n new_node.next = self.head\n self.head = new_node","title":""},{"docid":"e70c3b23b8a8948d8a5a26469e922eee","score":"0.64309484","text":"def remove(self, key):\n ptr = self.head\n if ptr is not None:\n if ptr.data == key:\n self.head = ptr.next\n ptr = None\n return\n while ptr:\n if ptr.data == key:\n break\n prev = ptr\n ptr = ptr.next\n if not ptr:\n return \n prev.next = ptr.next\n ptr.next = None","title":""},{"docid":"e107d0e308f6c3dda44367433ee108e4","score":"0.6384707","text":"def change_key(self, item, key):\n index = item._index\n if (self._list[index]._key == item._key and\n self._list[index]._value == item._value):\n self._list[index]._key = key\n self._repair_heap_up(index)\n self._repair_heap_down(index)","title":""},{"docid":"28cd21badddfcd92da2b3a50b74ab783","score":"0.6366439","text":"def inc(self, key):\n cur = self.dt[key] if key in self.dt else self.head\n cur.keys.discard(key)\n\n if cur.cnt + 1 == cur.next.cnt:\n new = cur.next\n else:\n new = ListNode(key, cur.cnt + 1)\n self.insert(cur, new)\n\n self.dt[key] = new\n if not cur.keys and cur.cnt != 0:\n cur.pre.next, cur.next.pre = cur.next, cur.pre","title":""},{"docid":"c9939238bec7bd86c85717335e9d235c","score":"0.6358132","text":"def insert(self, key):\n new_node = Node(key)\n self.size += 1\n if not self.head:\n self.head = new_node\n return\n tmp = self.head\n new_node.next = tmp\n self.head = new_node","title":""},{"docid":"b78e80ff3d6236d444bf2597e791a4a9","score":"0.6307552","text":"def ListLeftPush(self, key, value):\n if key in self.data:\n self.data[key].insert(0, value)\n else:\n self.data[key] = [value]\n\n return Operation(success=len(self.data[key]))","title":""},{"docid":"13aad791916436a1902759f16f0bc7db","score":"0.6242076","text":"def move_to_start(self, key):\n super().move_to_end(key, last=False)","title":""},{"docid":"925c2feb33d3fe5eafc422c2b99dbb70","score":"0.6210368","text":"def put(self, key, value):\n # If the key already exists in the self.map dict,\n # replace the key-value pair stored in the linked list. \n # If the key does not exist, add this key-value pair to self.list. If\n # self.map is over capacity, evict the least recent used key-value.\n # Also be sure to update the LRU order in self.list.\n\n if key in self.map:\n self.list.remove(self.map[key])\n new_node = ListNode((key, value))\n self.list.prepend(new_node) # Add new node to order list\n self.map[key] = new_node # Add new node to hash table\n\n # If hash table overflows, evict the LRU key-value.\n if len(self.map) > self.capacity:\n new_node = self.head.next\n self.list.remove(new_node) # Pop node that stores key-value pair from order list\n self.map.pop(new_node[0], None) # Pop key-value pair from hash table","title":""},{"docid":"aaa99bf523f03549a86de938e8130994","score":"0.6200396","text":"def remove(self, key: int) -> None:\n index=key%self.size\n if self.a[index] != None:\n tempHead = self.a[index]\n if tempHead.key == key:\n self.a[index] = tempHead.next\n else:\n prevNode,current_Node = tempHead, tempHead.next\n while current_Node:\n if current_Node.key == key:\n prevNode.next = current_Node.next\n break\n prevNode = prevNode.next\n current_Node = current_Node.next\n \n else:\n return","title":""},{"docid":"d86678b34d4d4389087645e0eafe98cc","score":"0.62001115","text":"def remove(self, key):\n hashKey = self.hash(key)\n head = self.table[hashKey]\n if head is None:\n return\n last = None\n if head.key == key:\n self.table[hashKey] = head.next\n return\n while head is not None:\n if head.key == key:\n last.next = head.next\n return\n last = head\n head = head.next","title":""},{"docid":"2831581e8edfd4d1c4bca93c85e9fbbf","score":"0.6199767","text":"def add_front(self, key, value):\r\n new_node = SLNode(key, value)\r\n new_node.next = self.head\r\n self.head = new_node\r\n self.size = self.size + 1","title":""},{"docid":"ae25dc1a948d4f705378040597905831","score":"0.6171246","text":"def inc(self, key: str) -> None:\n if key not in self.memo:\n self.memo[key] = self.add(self.head, key)\n else:\n node = self.memo[key]\n self.memo[key] = self.add(node, key)\n node.data.remove(key)\n if not node.data:\n node.pre.next, node.next.pre = node.next, node.pre\n node.next = node.pre = None","title":""},{"docid":"417b4bfe2562690f79c8105bb9418e82","score":"0.61354256","text":"def __setitem__(self, key, newValue):\n list.__setitem__(self, key - self.start, newValue)","title":""},{"docid":"1bda2f7dd2fb7bbbe31f33b6aa308c44","score":"0.6091285","text":"def remove(self, key: str) -> None:\n # get the list at hash location\n list_ = self._get_list(key)\n list_.remove(key)\n self.size -= 1","title":""},{"docid":"90fc6c1e9a908e1ccf0208d54a817981","score":"0.6082191","text":"def add_to_start(self, key, value):\n self[key] = value # adding\n self.move_to_start(key) # moving","title":""},{"docid":"a2b6981f2124c7ec9219912aa17dd75d","score":"0.60670775","text":"def _remove_from_list(self, item, list_key):\n with self.mutable(list_key) as l:\n if item in l:\n l.remove(item)","title":""},{"docid":"2caed29444929368776511c5aab179e2","score":"0.60443157","text":"def put(self, key: int, value: int) -> None:\n \n index = key%self.size\n if(self.a[index]==None):\n self.a[index] = ListNode(key,value)\n else:\n current_Node = self.a[index]\n #update value of head node to required key\n while True:\n if current_Node.key == key:\n current_Node.val = value\n return\n if current_Node.next == None:\n break\n current_Node = current_Node.next\n \n current_Node.next = ListNode(key,value)","title":""},{"docid":"a6f08561c4c3b5ded5b765d78aaa1d64","score":"0.6027231","text":"def remove(self, key: int) -> None:\n data = self.data\n\n\n cell = data[self.hash(key)]\n if cell:\n if cell.key == key:\n data[self.hash(key)] = cell.next\n self.n -= 1\n return \n cur = cell\n while cur:\n if cur.next and cur.next.key == key:\n cur.next = cur.next.next\n self.n -=1\n break\n cur = cur.next\n return\n else:\n return","title":""},{"docid":"3a5b48e3fc31ad6c9d4ed2c1a6c2e000","score":"0.5985992","text":"def _add_to_list(self, item, list_key):\n with self.mutable(list_key) as l:\n l.append(item)","title":""},{"docid":"b3bfe81ec4139a9c5f4ade577090f9da","score":"0.5975116","text":"def enqueue(self, key):\n element = Element(key, None)\n if self.empty():\n self.head = element\n else:\n self.tail.pointer = element\n self.tail = element","title":""},{"docid":"1d5f2c70f549a1674721b317ab8ae231","score":"0.5974976","text":"def __setitem__(self, key, value):\n self._list[key] = value\n return None","title":""},{"docid":"3afc178dc71371ca54b36b45ef85a515","score":"0.597273","text":"def removeByKey(self, key):\n current=self.head\n\n while current != None:\n if current.data[0] == key:\n self.remove(current.data)\n return\n\n current=current.next\n \n raise KeyError(\"Value not present\")","title":""},{"docid":"cc4ab1cf3b91327f02e3703c348c3583","score":"0.5965164","text":"def delete(self, key):\n # find hash index:\n index = self.hash_index(key)\n # if current is None, print key not found:\n if self.storage[index] is None:\n return\n else:\n # set previous and current variables:\n previous = None\n current = self.storage[index]\n # while there's more than 1 node in the linked list:\n while current.next is not None:\n # if keys match:\n if current.key == key:\n # if key is first node:\n if previous is None:\n # change pointer from first node to next:\n self.storage[index] = current.next\n else:\n # skip over node so previous points to next:\n previous.next = current.next\n # decrease count\n self.counter -= 1\n break\n else:\n # keep moving down the list looking for matching key\n previous = current\n current = current.next\n # if only 1 item in the list and keys match, replace with None\n if previous is None and current.key == key:\n self.storage[index] = None\n self.counter -= 1\n if (self.counter/self.capacity) >= .7:\n self.resize(new_capacity=self.capacity*2)\n return","title":""},{"docid":"903c404450235e480f6be7df4cd3f9ba","score":"0.5952744","text":"def insert(self, item, key):\n\n index = len(self.nodes)\n self.nodes.append((item, key))\n\n while index > 0:\n parent = self._parent(index)\n if self.nodes[parent][1] > key:\n self.nodes[parent], self.nodes[index] = \\\n self.nodes[index], self.nodes[parent]\n index = parent","title":""},{"docid":"bea12c718df9faaa5c0770e66dbcca68","score":"0.5937095","text":"def append(self, key):\n new_node = Node(key)\n if self.head is None:\n self.head = new_node\n else:\n current_node = self.head\n while current_node.next:\n current_node = current_node.next\n current_node.next = new_node\n new_node.previous = current_node","title":""},{"docid":"6066ddf177bd4d30a509c9ad8d31cbb4","score":"0.5899931","text":"def remove(self, key):\n # your code here\n prev, curr = self._linear_search(key)\n if curr is None:\n value = None\n else:\n value = curr._value\n if prev is None:\n self._front = curr._next\n else:\n prev._next = curr._next\n \n return value","title":""},{"docid":"c91cf7a2db64069d6f26464e2b4d59c9","score":"0.58926606","text":"def remove(self, key):\n temp=self.head.next\n prev=self.head\n while(temp.next!=None):\n if(temp.data==key):\n return key\n else:\n temp=temp.next\n prev=prev.next\n return None","title":""},{"docid":"9c82e4e46b46627f835c277dddb4524b","score":"0.5888624","text":"def remove(self, key):\n if(self.head == None):\n return 'Empty LinkedList'\n if(self.head.data == key):\n self.head = self.head.next\n return 'Done'\n prev = self.head\n temp = self.head.next\n while(temp!=None):\n if(temp.data == key):\n prev.next = temp.next\n return 'Done'\n prev = temp\n temp = temp.next\n return 'Key not found'","title":""},{"docid":"ee94e045035d1267c55c48caa11a57af","score":"0.5881477","text":"def replace_head(input_list):\n input_list[0]=42\n return input_list","title":""},{"docid":"d736392ea712bd6c528f854207190d72","score":"0.5881373","text":"def unshift(self, value): # O(1)\n self._items.unshift(value)","title":""},{"docid":"3c89bcbcd6f26a1d44016508d8785a3f","score":"0.58781224","text":"def put(self, key, item):\n if key is not None and item is not None:\n last = len(self.all_keys) - 1\n self.all_keys.append(key)\n self.cache_data[key] = item\n if len(self.cache_data) > BaseCaching.MAX_ITEMS:\n removing = self.all_keys.pop(last)\n del self.cache_data[removing]\n print(\"DISCARD: {}\".format(removing))","title":""},{"docid":"b6fc092df7daffa5a25b7434559aaac4","score":"0.5876702","text":"def _adjust_entries_in_queue(self,key): ## internal method \n if key in self._q:\n self._q.remove(key)\n self._q.appendleft(key)\n else:\n self._q.appendleft(key)","title":""},{"docid":"d0cb599f650ca4e47ab36f944b6580f6","score":"0.58751494","text":"def put(self, key: int, value: int) -> None:\n hash_id=self.get_index(key)\n if self.hashmap[hash_id] == None:\n self.hashmap[hash_id]=Listnode((key,value))\n else:\n current=self.hashmap[hash_id]\n while current:\n k,v =current.key_value\n if k == key:\n current.key_value = (key,value)\n return\n if current.next == None:\n current.next = Listnode((key, value))\n return\n else:\n current=current.next\n return","title":""},{"docid":"b35985da4eeeada58df01a2e82a1590d","score":"0.58726424","text":"def _put(self, node, key, index):\n if node is None:\n node = Node()\n if index == len(key):\n return node\n order = ord(key[index])\n node.next[order] = self._put(node.next[order], key, index+1)\n node.size += 1\n return node","title":""},{"docid":"fef541a5882ce810736de371016c6a98","score":"0.58448285","text":"def insert_item_before_item(self, item, data):\t\t\n\t\t\n\n\t\tif item == self.head.data:\n\t\t\tvalue = Node(data)\n\t\t\tvalue.next = self.head\n\t\t\tself.head = value\n\t\t\treturn \n\n\n\t\tvalue = Node(data)\n\t\tstart = self.head\n\t\twhile start.next != None:\n\t\t\tif start.next.data == item:\n\t\t\t\tbreak\n\t\t\tstart = start.next\n\t\tif start.next == None:\n\t\t\tprint(item,\"is not in this list\")\n\t\telse:\n\t\t\tvalue.next = start.next\n\t\t\tstart.next = value","title":""},{"docid":"2605d3d44c09743dcaa3de29ed8a56a2","score":"0.5843531","text":"def insert_before(self, key, next_node):\n new_node = Node(key)\n if next_node is None:\n print (\"Next node is None. Did nothing.\")\n return \n new_node.previous = next_node.previous\n new_node.next = next_node\n next_node.previous = new_node\n if new_node.previous:\n new_node.previous.next = new_node","title":""},{"docid":"212554791e72660a5de415d5f9794218","score":"0.58359325","text":"def __getitem__(self, key):\n if key in self.index:\n node = self.index[key]\n node.remove()\n node.setnext(self.head)\n return self.index[key].value\n return None","title":""},{"docid":"5f3fa6483355ba3fdcdd7f6759032854","score":"0.58045965","text":"def delete(self, key):\n current_node = self.head\n previous_node = None\n while current_node:\n if current_node.key == key:\n if previous_node:\n previous_node.next = current_node.next\n else:\n self.head = current_node.next\n if current_node.next:\n current_node.next.previous = previous_node\n return current_node\n previous_node = current_node\n current_node = current_node.next\n return current_node","title":""},{"docid":"37b5f58ab69793831abb605b6c669dfc","score":"0.5793984","text":"def appendleft(self, key, value):\n n = node()\n n.key = key\n n.value = value\n #add n to front of list\n self.l.next.prev = n\n n.next = self.l.next \n n.prev = self.l\n self.l.next = n\n #add n to the map\n self.d[key] = n","title":""},{"docid":"2de8ffc0bcbc09c85ba2e63c8e4fc71a","score":"0.57900405","text":"def append(self, key):\n new_node = Node(key)\n self.size += 1\n if not self.head:\n self.head = new_node\n return\n\n tmp = self.head\n while tmp.next:\n tmp = tmp.next\n tmp.next = new_node","title":""},{"docid":"a33acb2f7ff213d7570e0dd58cfff488","score":"0.57731175","text":"def __delitem__(self, key):\n self.pop(self.pop(key))","title":""},{"docid":"f959c33df369c4597f2e054a27e4db4c","score":"0.57515085","text":"def __delitem__(self, key: str) -> None:\n del_key = key.lower().encode(\"latin-1\")\n\n pop_indexes = []\n for idx, (item_key, item_value) in enumerate(self._list):\n if item_key == del_key:\n pop_indexes.append(idx)\n\n for idx in reversed(pop_indexes):\n del self._list[idx]","title":""},{"docid":"c4f12370809f4fb84b8513ff663d9ef2","score":"0.5717696","text":"def put(self, key, item):\n if key is None or item is None:\n pass\n if key and item:\n self.cache_data[key] = item\n if key not in self.LRUkeys:\n self.LRUkeys.append(key)\n else:\n self.LRUkeys.append(self.LRUkeys.pop(self.LRUkeys.index(key)))\n if len(self.LRUkeys) > BaseCaching.MAX_ITEMS:\n discard = self.LRUkeys.pop(0)\n del self.cache_data[discard]\n print(\"DISCARD: {}\".format(discard))","title":""},{"docid":"671b81c85b7a63288d965949c14c3db2","score":"0.57173526","text":"def __delitem__(self, key):\n if self.is_empty():\n raise KeyError(\"Unable to delete item: key is missing!\")\n\n if not isinstance(key, type(self.contents[0].key)):\n raise TypeError(\"Unable to delete item: key of incorrect type!\")\n\n (boolean, current, counter) = self._retrieve(key)\n if not boolean:\n raise KeyError(\"Unable to delete item: key is missing!\")\n\n # If there are multiple items with the same searchkey, removing only one of those will suffice.\n if len(current.contents[counter].node_content) > 1:\n del current.contents[counter].node_content[0] # deleting first item in node_content\n return\n\n if current._no_children():\n # The node with the given key will have to be deleted.\n del current.contents[counter]\n if current.parent is not None:\n # If the (sub)tree is an empty leaf, the fix-algorithm will be called.\n if current.is_empty():\n current._fix()\n\n # If we're dealing with an internal node, then it must swap positions with it's inorder successor.\n else:\n inord_succ = current.children[counter+1]._inorder_successor()\n # The inorder successor is in the left TreeItem, always, that is the whole essence of the inorder successor.\n current.contents[counter] = inord_succ.contents[0]\n del inord_succ.contents[0]\n if inord_succ.is_empty(): # If the inorder successor's node is empty after deleting, the fix algorithm\n inord_succ._fix() # must be called.\n return","title":""},{"docid":"5dfd5ebe720dbac74c0aa9a00c39e6d0","score":"0.57126516","text":"def delete_by_key(self, key):\n\n current = self.head\n previous = None\n found = False\n\n while current and not found:\n if current.data == key and current == self.head:\n found = True\n self.head = current.next_node\n elif current.data == key:\n found = True\n previous.next_node = current.next_node\n else:\n previous = current\n current = current.next_node\n return current","title":""},{"docid":"90ef553c3dcb9b5f43796712ced2eecd","score":"0.5711856","text":"def __getitem__(self, key):\n rv = self._mapping[key]\n if self._queue[-1] != key:\n self._remove(key)\n self._append(key)\n return rv","title":""},{"docid":"bc0287f09a699d6397ac7f7bd5b2b726","score":"0.5691216","text":"def remove(self, key):\r\n i = key % self.base\r\n if self.keyList[i] != 0:\r\n # 为0时证明key根本不在，不用考虑，所以只考虑不为0时的\r\n root = self.keyList[i]\r\n # 根节点\r\n if root.key == key:\r\n # 第一个点就是目标点\r\n if root.next:\r\n # 如果存在下一个节点，将keyList直接链接到下一个点上\r\n self.keyList[i] = root.next\r\n else:\r\n # 不存在下一个节点，即这个链表只有目标点时，重置链表\r\n self.keyList[i] = 0\r\n else:\r\n while root.next:\r\n if root.next.key == key:\r\n # 找到目标点\r\n root.next = root.next.next\r\n break\r\n root = root.next","title":""},{"docid":"8d19d272e00a045be7611e5c5a386495","score":"0.56843966","text":"def insert(self, key, value):\n index = len(self._list)\n item = PriorityQueueItem(key, value, index)\n self._list.append(item)\n self._repair_heap_up(index)","title":""},{"docid":"0075ce38d7a614b18392836531ec62cd","score":"0.5678694","text":"def remove(self, key: int) -> None:\n \n \n hc = hash(key)\n index = self.getCompressed(hc)\n \n head = self.buckets[index]\n \n prev = None\n \n while head is not None:\n \n if head.key==key:\n \n # do \n \n if prev is None:\n self.buckets[index] = head.next\n else:\n prev.next = head.next\n self.count-=1\n \n prev = head\n head = head.next","title":""},{"docid":"7d4db56317c78093849fc6b2733d8ad0","score":"0.56628597","text":"def __delitem__(self, key):\n if key in self.index:\n node = self.index[key]\n if node == self.tail:\n self.tail = node.prev\n if node == self.head:\n self.head = node.successor\n del self.index[key]\n self.count -= 1\n node.remove()\n self.checksize()","title":""},{"docid":"8e84ff78e5e3400a820cda6a9c56a248","score":"0.5650674","text":"def insertBefore(list, nextItem, x):\n\n pass","title":""},{"docid":"5757772e85c73176f600c66ef6a25f4b","score":"0.5650055","text":"def insert(self, key, id_):\n self.A.append((key, id_))\n self._siftup(len(self.A) - 1)","title":""},{"docid":"8e9a83708be74bf0d2ab6e98d7324d2a","score":"0.5638734","text":"def __delitem__(self, key):\n n = self.d[key]\n n.next.prev = n.prev\n n.prev.next = n.next\n del self.d[key]","title":""},{"docid":"0f8cbbb55b4d22fb341630e6411b952d","score":"0.563836","text":"def unshift(self, item):\n\n # create new node. forward to head's next. back to head\n new = Node(item, succeeding=self.head.succeeding, previous=self.head)\n self.cursor = self.head.succeeding\n self.head.succeeding.previous = new\n self.head.succeeding = new\n self._length += 1","title":""},{"docid":"81fb245179f80d45d46343f7c3a226c0","score":"0.56381696","text":"def remove(self, key):\n index = key % self.size\n if self.hashlist[index]:\n singlenode = self.hashlist[index]\n prenode = singlenode\n if key == singlenode.key:\n self.hashlist[index] = singlenode.next\n return\n singlenode = singlenode.next\n while singlenode:\n if key == singlenode.key:\n prenode.next = singlenode.next\n return\n prenode = singlenode\n singlenode = singlenode.next\n\n\n\n # Your MyHashMap object will be instantiated and called as such:\n # obj = MyHashMap()\n # obj.put(key,value)\n # param_2 = obj.get(key)\n # obj.remove(key)","title":""},{"docid":"db547f98caadaadac19f4bf19cde3137","score":"0.5613023","text":"def remove(self, key: int) -> None:\n i = self.index(key)\n if not self.nodes[i]:\n return\n prev = self.find(self.nodes[i], key)\n if not prev.next:\n return\n else:\n prev.next = prev.next.next\n return","title":""},{"docid":"b52d99b20b19d437844f05c5171ee4b7","score":"0.55961514","text":"def put(self, key: str, value: object) -> None:\n # get the list at hash location\n list_ = self._get_list(key)\n\n # if key exists, replace value\n node = list_.contains(key)\n if node is not None:\n node.value = value\n return\n\n # key does not yet exist, add key/value pair\n list_.insert(key, value)\n self.size += 1","title":""},{"docid":"eeb4875e92c6e5807d8908baab10b7ce","score":"0.5570143","text":"def __delitem__(self, k):\n h = self.head\n while h.next:\n if h.next.key == k:\n h.next = h.next.next\n self.n -= 1\n return\n h = h.next\n raise KeyError(k)","title":""},{"docid":"fae8e772033bc69d12352f78d6da67e5","score":"0.5554949","text":"def prepend(self, item):\n self.insert(0, item)","title":""},{"docid":"2ec70a40ff1f466e2e00e247152c1eec","score":"0.5539818","text":"def remove(self, key):\n ll = self.hashtable[self._hash_key(key)]\n if ll is None:\n raise TypeError\n temp = ll.head\n if ll.head.key is key:\n temp = ll.head\n ll.head = ll.head.next\n return temp.val\n while ll:\n if ll.head.next.key is key:\n temp = ll.head.next\n ll.head.next = ll.head.next.next\n return temp.val\n if ll.head is None:\n self.hashtable[self._hash_key(key)] = None\n raise TypeError","title":""},{"docid":"f1d8f0cbb35ab4cb01b05a4edc16c2d1","score":"0.55357224","text":"def set(self, _key, _value, **kw):\n if kw:\n _value = _options_header_vkw(_value, kw)\n _key = _unicodify_header_value(_key)\n _value = _unicodify_header_value(_value)\n self._validate_value(_value)\n if not self._list:\n self._list.append((_key, _value))\n return\n listiter = iter(self._list)\n ikey = _key.lower()\n for idx, (old_key, _old_value) in enumerate(listiter):\n if old_key.lower() == ikey:\n # replace first occurrence\n self._list[idx] = (_key, _value)\n break\n else:\n self._list.append((_key, _value))\n return\n self._list[idx + 1 :] = [t for t in listiter if t[0].lower() != ikey]","title":""},{"docid":"9e9186b44eaec30633cc183b1919bf8b","score":"0.55349404","text":"def remove(self, key: int) -> None:\n hash_id=self.get_index(key)\n if self.hashmap[hash_id] == None:\n return\n else:\n current=self.hashmap[hash_id]\n prev=None\n while current:\n k,v =current.key_value\n if k == key:\n if prev == None:\n self.hashmap[hash_id]=None\n return\n else:\n prev.next=current.next\n return\n else:\n prev=current\n current=current.next\n return","title":""},{"docid":"cae190aa6fa0d127b86ee74d7bda300c","score":"0.5534382","text":"def push_key(self,key):\n return _ldns.ldns_key_list_push_key(self,key)","title":""},{"docid":"09fefad60bba70775cd634e63d81cbaa","score":"0.5530455","text":"def insert(self, key) -> None:\n self.heap_size += 1\n self.A.append(-math.inf)\n self.increase_key(self.heap_size, key)","title":""},{"docid":"a72116d3ef57213e1c0a87f0288f227f","score":"0.5528654","text":"def __delitem__(self, key):\n self.pop(key)","title":""},{"docid":"34134b3a4d0231765fc74442fca690d8","score":"0.55276364","text":"def put(self, key, item):\n if key and item:\n self.cache_data[key] = item\n if len(self.cache_data) > BaseCaching.MAX_ITEMS:\n last_key = self.stk.pop()\n print(\"DISCARD: {}\".format(last_key))\n del self.cache_data[last_key]\n self.stk.append(key)","title":""},{"docid":"fffb72515f81164b3e916fba39109c16","score":"0.5525205","text":"def delete(self, key):\n # Your code here\n keyHash = self.hash_index(key)\n cur = self.capacity[keyHash]\n if cur == None:\n print('warning Key not found')\n # if key is head\n if cur.key == key:\n cur.value = None\n cur = cur.next\n return cur\n else: # key not head\n while cur != None:\n prev = cur\n #see if keys matchs\n cur = cur.next\n if cur.key == key:\n #set prev next to cur.next, to skip over current\n prev.next = cur.next\n #self.get_load_factor()\n return cur.value\n \n #set current to next\n \n #otherwise check the next node","title":""},{"docid":"77b2d824e18ec8c8d331fec1d2372dbf","score":"0.5521774","text":"def put(self, key, item):\n if key is not None and item is not None:\n if key in self.cache_data:\n self.cache_data[key] = item\n self.frequency[key] += 1\n self.lfu_order.remove(key)\n else:\n if len(self.cache_data) >= self.MAX_ITEMS:\n min_value = min(self.frequency.values())\n min_keys = [k for k in self.frequency\n if self.frequency[k] == min_value]\n for i in range(len(self.lfu_order)):\n if self.lfu_order[i] in min_keys:\n break\n del self.cache_data[self.lfu_order[i]]\n del self.frequency[self.lfu_order[i]]\n print(\"DISCARD:\", self.lfu_order[i])\n self.lfu_order.pop(i)\n self.cache_data[key] = item\n self.frequency[key] = 1\n self.lfu_order.append(key)","title":""},{"docid":"ee58fcbe0e18eaa8e72197820e062ab4","score":"0.54907763","text":"def remove(self, key):\r\n if self.head is None:\r\n return False\r\n if self.head.key == key:\r\n self.head = self.head.next\r\n self.size = self.size - 1\r\n return True\r\n cur = self.head.next\r\n prev = self.head\r\n while cur is not None:\r\n if cur.key == key:\r\n prev.next = cur.next\r\n self.size = self.size - 1\r\n return True\r\n prev = cur\r\n cur = cur.next\r\n return False","title":""},{"docid":"dfc7747db410ad327397dd27616b664b","score":"0.5484711","text":"def __setitem__(self, key, value):\n self.insert(key, value)","title":""},{"docid":"fe571cf74644ec17c60236bf08cd5eb0","score":"0.54834","text":"def move_object_to_top(id):","title":""},{"docid":"d5aad2e411522af5ec616503c20eb4f6","score":"0.54734606","text":"def dec(self, key):\n cur = self.dt[key]\n cur.keys.discard(key)\n\n if cur.cnt - 1 == cur.pre.cnt:\n new = cur.pre\n else:\n new = ListNode(key, cur.cnt - 1)\n self.insert(cur.pre, new)\n\n self.dt[key] = new\n if not cur.keys and cur.cnt != 0:\n cur.pre.next, cur.next.pre = cur.next, cur.pre","title":""},{"docid":"595591804ca0d10665b8784ca05caa83","score":"0.5471652","text":"def update(self, key):\n if key == pg.K_w or key == pg.K_UP:\n self.prev_item()\n elif key == pg.K_s or key == pg.K_DOWN:\n self.next_item()\n elif key == pg.K_RETURN or key == pg.K_f:\n self.choose_item()","title":""},{"docid":"ee9c13ddd98a1504efd4b4680ad6e24e","score":"0.54651386","text":"def remove(self, key):\n pass","title":""},{"docid":"9654c5c0f1a311aa650c8733cd058da7","score":"0.54557216","text":"def __setitem__(self, key, value):\n node = None\n if key in self.index:\n node = self.index[key]\n node.remove()\n node.setnext(self.head)\n self.head = node\n node.value = value\n else:\n node = Node(key, value)\n self.index[key] = node\n if not self.head:\n self.tail = node\n node.setnext(self.head)\n self.head = node\n self.count += 1\n self.checksize()","title":""},{"docid":"23c87d9a2cd44b70bd900de63bb680bf","score":"0.54512745","text":"def __setitem__(self, key, value):\n position = self.hash(key)\n count = 0\n\n\n if self.array[position] == None:\n linklist = List()\n pair = (key,value)\n linklist.insert(count,pair)\n head = linklist\n set = (count+1,head) #(number_of_items,pointer_to_linklist)\n self.array[position] = set\n\n return\n\n elif self.array[position]!= None:\n copy = self.array[position][1].head\n\n\n while copy!= None:\n if copy.item[0] == key:\n new_tuple = (key,value)\n copy.item = new_tuple\n return\n\n copy = copy.next\n\n\n self.array[position][1].insert(self.array[position][0],(key,value))\n new_tuple = (self.array[position][0]+1,self.array[position][1])\n self.array[position] = new_tuple\n self.collisions+=1\n\n return","title":""},{"docid":"7a7edf279809d8ef6e52a7a58036f5cc","score":"0.54457074","text":"def put(self, key, value):\n index = key % self.size\n if not self.hashlist[index]:\n singlenode = Node(key, value)\n self.hashlist[index] = singlenode\n else:\n singlenode = self.hashlist[index]\n while singlenode.next:\n if key == singlenode.key:\n singlenode.val = value\n return\n singlenode = singlenode.next\n if key == singlenode.key:\n singlenode.val = value\n return\n singlenode.next = Node(key, value)","title":""},{"docid":"599848974fef85c4dab2c02796cd0810","score":"0.54426867","text":"def pop(self, key, default=...):\n ...","title":""},{"docid":"d4df67cfe7d6eb708a76e057e499f225","score":"0.5435662","text":"def pop_first(self):\n\t\tif self.head == None:\n\t\t\tprint(\"list has no element\")\n\t\t\treturn \n\t\tself.head = self.head.next","title":""},{"docid":"241d98ae4fcd6feb4c56255138a79366","score":"0.54346967","text":"def put(self, key, value):\n node = Node(key, value)\n hashKey = self.hash(key)\n head = self.table[hashKey]\n if head is None:\n self.table[hashKey] = node\n return\n last = None\n while head is not None:\n if(head.key == key):\n head.value = value\n return\n last = head\n head = head.next\n last.next = node","title":""},{"docid":"5ff8de1a15420d6b599167060178660e","score":"0.54343766","text":"def shift_left(my_list):\r\n #my_list = []\r\n new_list = my_list[1:]\r\n new_list.insert(2,my_list[0])\r\n return new_list","title":""},{"docid":"364156383e293b7a6ad9defbe164c522","score":"0.5433465","text":"def __setitem__(self, k, o):\n try:\n (age, item) = self.items[k]\n del self.age_to_items[age]\n except KeyError:\n pass\n\n self._set_newest(k, o)\n\n if len(self.items) > self.size:\n # Maximum number of keys reached\n # Delete the last recently used key\n while self.oldest not in self.age_to_items:\n self.oldest += 1\n\n del self.items[self.age_to_items.pop(self.oldest)]","title":""},{"docid":"043ec5618c9a0fbaa7f3f7afd453abc4","score":"0.5432182","text":"def put(self, key, item):\n if key and item:\n self.cache_data[key] = item\n if len(self.cache_data.keys()) <= self.MAX_ITEMS:\n pass\n else:\n del self.cache_data[self.last_key]\n print(f'DISCARD: {self.last_key}')\n self.last_key = key","title":""},{"docid":"8b359e6f0b1a108ca333555d889a2d6a","score":"0.54296947","text":"def sort(self, key):\r\n self.list_places = sorted(self.list_places, key=attrgetter(key, \"name\"))","title":""},{"docid":"9608066f928e0c91cee07292de5c4a3e","score":"0.5416643","text":"def insert(self, key, value):\n\n if key in self.hash_map:\n node = self.hash_map[key]\n node.value = value\n\n if self.head != node:\n self.remove(node)\n self.set_head(node)\n else:\n new_node = QNode(key, value)\n if self.current_size == self.capacity:\n del self.hash_map[self.end.key]\n self.remove(self.end)\n self.set_head(new_node)\n self.hash_map[key] = new_node","title":""},{"docid":"c60e2f0c850e3160267b515d7da1a8d3","score":"0.5410061","text":"def insert_item_after_item(self, item, data):\n\t\tvalue = Node(data)\n\t\tstart = self.head\n\t\twhile start != None:\n\t\t\tif start.data == item:\n\t\t\t\tbreak\n\t\t\tstart = start.next\n\n\t\tif start == None:\n\t\t\tprint(item, \"is not in this list\")\n\t\telse:\n\t\t\tvalue.next = start.next\n\t\t\tstart.next = value\n\t\t\treturn \t\t\n\t\t\t# value.next = start.next\n\t\t\t# start.next = value","title":""},{"docid":"7afcf72b7ab794358df4970f0a00bb13","score":"0.54066145","text":"def __setitem__(self, key, item):","title":""},{"docid":"80b15888d56a9f2149c5db5761bac2f1","score":"0.5404773","text":"def put(self, key, item):\n if key and item:\n if key in self.cache_data:\n self.queue.remove(key)\n elif len(self.cache_data) >= self.MAX_ITEMS:\n popped = self.queue.popleft()\n del self.cache_data[popped]\n print(\"DISCARD: \" + str(popped))\n self.queue.append(key)\n self.cache_data[key] = item","title":""},{"docid":"1929c5bf39edcdee1df2bce4a33538dc","score":"0.54008895","text":"def put(self, key, item):\n if key is None or item is None:\n return\n keys_list = self.cache_data.keys()\n if len(keys_list) == super().MAX_ITEMS:\n if key in keys_list:\n del self.cache_data[key]\n else:\n last_key = list(keys_list)[-1]\n print(f'DISCARD: {last_key}')\n del self.cache_data[last_key]\n self.cache_data[key] = item","title":""},{"docid":"d8b345acdf0276910bb90522843c8f12","score":"0.5395307","text":"def put(self, key, value):\n index = self._get_index(key)\n head = self._bucket_list[index]\n while head is not None:\n if head.key == key:\n head.value = value\n return\n head = head.next\n self._size += 1\n head = self._bucket_list[index]\n new_node = Node(key, value)\n new_node.next = head\n self._bucket_list[index] = new_node\n if self._should_expand():\n self.expand()","title":""},{"docid":"394fc4ceb62cf37962fd73a39d6d4ca4","score":"0.5394903","text":"def put(self, key: int, value: int) -> None:\n index = key % self.MOD\n values = self.ht[index]\n for i, v in enumerate(values):\n if key == v[0]:\n values[i] = (key, value)\n break\n else:\n values.append((key, value))","title":""},{"docid":"e4b0f25ed98a8abe8e2fd4820d7a0c0d","score":"0.5386447","text":"def remove(self, item):\t\t\n\t\tif self.head == None:\n\t\t\tprint(\"No item in this list\")\n\t\t\treturn \n\n\t\tif self.head.data == item:\n\t\t\tself.head = self.head.next\n\t\t\treturn\n\n\t\tstart = self.head\n\t\twhile start.next != None:\n\t\t\tif start.next.data == item:\n\t\t\t\tbreak \n\t\t\tstart = start.next\n\t\tif start.next == None:\n\t\t\tprint(item, \"is not present in this list\")\n\t\telse:\n\t\t\tstart.next = start.next.next","title":""},{"docid":"7195ccec227b70b353680f434f97cca2","score":"0.5383614","text":"def remove(self, key):\n raise NotImplementedError('Method from abstract implementation')","title":""},{"docid":"499e1eb9e2406f36ffd4f8e0c2dcb05e","score":"0.5374034","text":"def add(self, key, value):\n index = self.hash(key)\n if self.array[index] is not None:\n for value in self.array[index]:\n if value[0] == key:\n value[1] = value\n break\n else:\n self.array[index].append([key, value])\n\n else:\n self.array[index] = []\n self.array[index].append([key, value])","title":""},{"docid":"ab4f717a412c8ad285e740fd12d734d6","score":"0.5363762","text":"def add(self, key, value):\n self._data.append(self._Item(key, value))\n self._upheap(len(self._data) - 1) # upheap newly added position","title":""},{"docid":"abdd4cd5417a265fea0cf40a019685fc","score":"0.53632635","text":"def set(self, key, value):\n # If key is in map, update the value and move node to front of queue\n evict = None\n if key in self.map:\n # Update value of key\n if isinstance(self.map[key][0], set):\n self.map[key][0].add(value)\n else:\n self.map[key][0] = value\n node = self.map[key][1]\n # Update Queue using the following steps:\n # Update tail if current node is tail and there is more than one element in cache\n if self._tail == node and node.prev != self._dummy_head:\n self._tail = node.prev\n # Extract node from current position in Queue\n node.prev.next = node.next\n if node.next:\n node.next.prev = node.prev\n # Modify node's prev and next references\n node.prev, node.next = self._dummy_head, self._dummy_head.next\n # Transplant node into beginning of Queue\n if self._dummy_head.next is not None:\n self._dummy_head.next.prev = node\n self._dummy_head.next = node\n return None\n elif self._size == self._capacity:\n # Evict LRU element using key of LRU\n lru = self._tail\n evict = (lru.data, self.map[lru.data][0])\n self.map.pop(lru.data)\n self._tail = lru.prev if lru.prev != self._dummy_head else None\n lru.prev.next = None\n self._size -= 1\n\n node = ListNode(key, self._dummy_head, self._dummy_head.next)\n if self._dummy_head.next is not None:\n self._dummy_head.next.prev = node\n self._dummy_head.next = node\n if self._tail is None:\n self._tail = node\n self.map[key] = [value, node]\n self._size += 1\n return evict","title":""},{"docid":"6e59409f484f43b4df5428f8fe8a2bc2","score":"0.53602254","text":"def __setitem__(self, key, lumi):\n try:\n oldLumi = sorted(self.eventsPerLumi.keys())[key] # Extract the lumi from the sorted list\n del self.eventsPerLumi[oldLumi] # Delete it and add the new one\n except IndexError:\n pass\n self.appendLumi(lumi)","title":""},{"docid":"80277a93cec72990c66cf62aa4d8826e","score":"0.5342852","text":"def remove_key(self, key):\n for index, element in enumerate(self.heap): # pragma: no cover\n if element[0] == key:\n last_element = self.heap.pop()\n self.heap[index] = last_element\n self._build_push_down_heapify(index)\n return self.heap\n return self.heap","title":""}],"string":"[\n {\n \"docid\": \"67072f43b5e8d065011b2c282e4bfc4b\",\n \"score\": \"0.67528284\",\n \"text\": \"def change_key(self, key, newkey):\\n if key in self.list:\\n self.list.remove(key)\\n self.list.append(newkey)\\n self.sort()\\n else:\\n raise KeyError('No such key')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c42b964b276d53263b68eafbd0d83ef\",\n \"score\": \"0.6639715\",\n \"text\": \"def enqueue(self, key):\\n self.list.append(key)\\n self.sort()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"def28f2e110ad711e55f7e5d2702a478\",\n \"score\": \"0.65499777\",\n \"text\": \"def insert_front(self, key):\\n new_node = Node(key)\\n if self.head:\\n self.head.previous = new_node\\n new_node.next = self.head\\n self.head = new_node\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e70c3b23b8a8948d8a5a26469e922eee\",\n \"score\": \"0.64309484\",\n \"text\": \"def remove(self, key):\\n ptr = self.head\\n if ptr is not None:\\n if ptr.data == key:\\n self.head = ptr.next\\n ptr = None\\n return\\n while ptr:\\n if ptr.data == key:\\n break\\n prev = ptr\\n ptr = ptr.next\\n if not ptr:\\n return \\n prev.next = ptr.next\\n ptr.next = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e107d0e308f6c3dda44367433ee108e4\",\n \"score\": \"0.6384707\",\n \"text\": \"def change_key(self, item, key):\\n index = item._index\\n if (self._list[index]._key == item._key and\\n self._list[index]._value == item._value):\\n self._list[index]._key = key\\n self._repair_heap_up(index)\\n self._repair_heap_down(index)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28cd21badddfcd92da2b3a50b74ab783\",\n \"score\": \"0.6366439\",\n \"text\": \"def inc(self, key):\\n cur = self.dt[key] if key in self.dt else self.head\\n cur.keys.discard(key)\\n\\n if cur.cnt + 1 == cur.next.cnt:\\n new = cur.next\\n else:\\n new = ListNode(key, cur.cnt + 1)\\n self.insert(cur, new)\\n\\n self.dt[key] = new\\n if not cur.keys and cur.cnt != 0:\\n cur.pre.next, cur.next.pre = cur.next, cur.pre\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9939238bec7bd86c85717335e9d235c\",\n \"score\": \"0.6358132\",\n \"text\": \"def insert(self, key):\\n new_node = Node(key)\\n self.size += 1\\n if not self.head:\\n self.head = new_node\\n return\\n tmp = self.head\\n new_node.next = tmp\\n self.head = new_node\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b78e80ff3d6236d444bf2597e791a4a9\",\n \"score\": \"0.6307552\",\n \"text\": \"def ListLeftPush(self, key, value):\\n if key in self.data:\\n self.data[key].insert(0, value)\\n else:\\n self.data[key] = [value]\\n\\n return Operation(success=len(self.data[key]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13aad791916436a1902759f16f0bc7db\",\n \"score\": \"0.6242076\",\n \"text\": \"def move_to_start(self, key):\\n super().move_to_end(key, last=False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"925c2feb33d3fe5eafc422c2b99dbb70\",\n \"score\": \"0.6210368\",\n \"text\": \"def put(self, key, value):\\n # If the key already exists in the self.map dict,\\n # replace the key-value pair stored in the linked list. \\n # If the key does not exist, add this key-value pair to self.list. If\\n # self.map is over capacity, evict the least recent used key-value.\\n # Also be sure to update the LRU order in self.list.\\n\\n if key in self.map:\\n self.list.remove(self.map[key])\\n new_node = ListNode((key, value))\\n self.list.prepend(new_node) # Add new node to order list\\n self.map[key] = new_node # Add new node to hash table\\n\\n # If hash table overflows, evict the LRU key-value.\\n if len(self.map) > self.capacity:\\n new_node = self.head.next\\n self.list.remove(new_node) # Pop node that stores key-value pair from order list\\n self.map.pop(new_node[0], None) # Pop key-value pair from hash table\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aaa99bf523f03549a86de938e8130994\",\n \"score\": \"0.6200396\",\n \"text\": \"def remove(self, key: int) -> None:\\n index=key%self.size\\n if self.a[index] != None:\\n tempHead = self.a[index]\\n if tempHead.key == key:\\n self.a[index] = tempHead.next\\n else:\\n prevNode,current_Node = tempHead, tempHead.next\\n while current_Node:\\n if current_Node.key == key:\\n prevNode.next = current_Node.next\\n break\\n prevNode = prevNode.next\\n current_Node = current_Node.next\\n \\n else:\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d86678b34d4d4389087645e0eafe98cc\",\n \"score\": \"0.62001115\",\n \"text\": \"def remove(self, key):\\n hashKey = self.hash(key)\\n head = self.table[hashKey]\\n if head is None:\\n return\\n last = None\\n if head.key == key:\\n self.table[hashKey] = head.next\\n return\\n while head is not None:\\n if head.key == key:\\n last.next = head.next\\n return\\n last = head\\n head = head.next\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2831581e8edfd4d1c4bca93c85e9fbbf\",\n \"score\": \"0.6199767\",\n \"text\": \"def add_front(self, key, value):\\r\\n new_node = SLNode(key, value)\\r\\n new_node.next = self.head\\r\\n self.head = new_node\\r\\n self.size = self.size + 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae25dc1a948d4f705378040597905831\",\n \"score\": \"0.6171246\",\n \"text\": \"def inc(self, key: str) -> None:\\n if key not in self.memo:\\n self.memo[key] = self.add(self.head, key)\\n else:\\n node = self.memo[key]\\n self.memo[key] = self.add(node, key)\\n node.data.remove(key)\\n if not node.data:\\n node.pre.next, node.next.pre = node.next, node.pre\\n node.next = node.pre = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"417b4bfe2562690f79c8105bb9418e82\",\n \"score\": \"0.61354256\",\n \"text\": \"def __setitem__(self, key, newValue):\\n list.__setitem__(self, key - self.start, newValue)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1bda2f7dd2fb7bbbe31f33b6aa308c44\",\n \"score\": \"0.6091285\",\n \"text\": \"def remove(self, key: str) -> None:\\n # get the list at hash location\\n list_ = self._get_list(key)\\n list_.remove(key)\\n self.size -= 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90fc6c1e9a908e1ccf0208d54a817981\",\n \"score\": \"0.6082191\",\n \"text\": \"def add_to_start(self, key, value):\\n self[key] = value # adding\\n self.move_to_start(key) # moving\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2b6981f2124c7ec9219912aa17dd75d\",\n \"score\": \"0.60670775\",\n \"text\": \"def _remove_from_list(self, item, list_key):\\n with self.mutable(list_key) as l:\\n if item in l:\\n l.remove(item)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2caed29444929368776511c5aab179e2\",\n \"score\": \"0.60443157\",\n \"text\": \"def put(self, key: int, value: int) -> None:\\n \\n index = key%self.size\\n if(self.a[index]==None):\\n self.a[index] = ListNode(key,value)\\n else:\\n current_Node = self.a[index]\\n #update value of head node to required key\\n while True:\\n if current_Node.key == key:\\n current_Node.val = value\\n return\\n if current_Node.next == None:\\n break\\n current_Node = current_Node.next\\n \\n current_Node.next = ListNode(key,value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a6f08561c4c3b5ded5b765d78aaa1d64\",\n \"score\": \"0.6027231\",\n \"text\": \"def remove(self, key: int) -> None:\\n data = self.data\\n\\n\\n cell = data[self.hash(key)]\\n if cell:\\n if cell.key == key:\\n data[self.hash(key)] = cell.next\\n self.n -= 1\\n return \\n cur = cell\\n while cur:\\n if cur.next and cur.next.key == key:\\n cur.next = cur.next.next\\n self.n -=1\\n break\\n cur = cur.next\\n return\\n else:\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a5b48e3fc31ad6c9d4ed2c1a6c2e000\",\n \"score\": \"0.5985992\",\n \"text\": \"def _add_to_list(self, item, list_key):\\n with self.mutable(list_key) as l:\\n l.append(item)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3bfe81ec4139a9c5f4ade577090f9da\",\n \"score\": \"0.5975116\",\n \"text\": \"def enqueue(self, key):\\n element = Element(key, None)\\n if self.empty():\\n self.head = element\\n else:\\n self.tail.pointer = element\\n self.tail = element\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d5f2c70f549a1674721b317ab8ae231\",\n \"score\": \"0.5974976\",\n \"text\": \"def __setitem__(self, key, value):\\n self._list[key] = value\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3afc178dc71371ca54b36b45ef85a515\",\n \"score\": \"0.597273\",\n \"text\": \"def removeByKey(self, key):\\n current=self.head\\n\\n while current != None:\\n if current.data[0] == key:\\n self.remove(current.data)\\n return\\n\\n current=current.next\\n \\n raise KeyError(\\\"Value not present\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc4ab1cf3b91327f02e3703c348c3583\",\n \"score\": \"0.5965164\",\n \"text\": \"def delete(self, key):\\n # find hash index:\\n index = self.hash_index(key)\\n # if current is None, print key not found:\\n if self.storage[index] is None:\\n return\\n else:\\n # set previous and current variables:\\n previous = None\\n current = self.storage[index]\\n # while there's more than 1 node in the linked list:\\n while current.next is not None:\\n # if keys match:\\n if current.key == key:\\n # if key is first node:\\n if previous is None:\\n # change pointer from first node to next:\\n self.storage[index] = current.next\\n else:\\n # skip over node so previous points to next:\\n previous.next = current.next\\n # decrease count\\n self.counter -= 1\\n break\\n else:\\n # keep moving down the list looking for matching key\\n previous = current\\n current = current.next\\n # if only 1 item in the list and keys match, replace with None\\n if previous is None and current.key == key:\\n self.storage[index] = None\\n self.counter -= 1\\n if (self.counter/self.capacity) >= .7:\\n self.resize(new_capacity=self.capacity*2)\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"903c404450235e480f6be7df4cd3f9ba\",\n \"score\": \"0.5952744\",\n \"text\": \"def insert(self, item, key):\\n\\n index = len(self.nodes)\\n self.nodes.append((item, key))\\n\\n while index > 0:\\n parent = self._parent(index)\\n if self.nodes[parent][1] > key:\\n self.nodes[parent], self.nodes[index] = \\\\\\n self.nodes[index], self.nodes[parent]\\n index = parent\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bea12c718df9faaa5c0770e66dbcca68\",\n \"score\": \"0.5937095\",\n \"text\": \"def append(self, key):\\n new_node = Node(key)\\n if self.head is None:\\n self.head = new_node\\n else:\\n current_node = self.head\\n while current_node.next:\\n current_node = current_node.next\\n current_node.next = new_node\\n new_node.previous = current_node\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6066ddf177bd4d30a509c9ad8d31cbb4\",\n \"score\": \"0.5899931\",\n \"text\": \"def remove(self, key):\\n # your code here\\n prev, curr = self._linear_search(key)\\n if curr is None:\\n value = None\\n else:\\n value = curr._value\\n if prev is None:\\n self._front = curr._next\\n else:\\n prev._next = curr._next\\n \\n return value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c91cf7a2db64069d6f26464e2b4d59c9\",\n \"score\": \"0.58926606\",\n \"text\": \"def remove(self, key):\\n temp=self.head.next\\n prev=self.head\\n while(temp.next!=None):\\n if(temp.data==key):\\n return key\\n else:\\n temp=temp.next\\n prev=prev.next\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c82e4e46b46627f835c277dddb4524b\",\n \"score\": \"0.5888624\",\n \"text\": \"def remove(self, key):\\n if(self.head == None):\\n return 'Empty LinkedList'\\n if(self.head.data == key):\\n self.head = self.head.next\\n return 'Done'\\n prev = self.head\\n temp = self.head.next\\n while(temp!=None):\\n if(temp.data == key):\\n prev.next = temp.next\\n return 'Done'\\n prev = temp\\n temp = temp.next\\n return 'Key not found'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee94e045035d1267c55c48caa11a57af\",\n \"score\": \"0.5881477\",\n \"text\": \"def replace_head(input_list):\\n input_list[0]=42\\n return input_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d736392ea712bd6c528f854207190d72\",\n \"score\": \"0.5881373\",\n \"text\": \"def unshift(self, value): # O(1)\\n self._items.unshift(value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c89bcbcd6f26a1d44016508d8785a3f\",\n \"score\": \"0.58781224\",\n \"text\": \"def put(self, key, item):\\n if key is not None and item is not None:\\n last = len(self.all_keys) - 1\\n self.all_keys.append(key)\\n self.cache_data[key] = item\\n if len(self.cache_data) > BaseCaching.MAX_ITEMS:\\n removing = self.all_keys.pop(last)\\n del self.cache_data[removing]\\n print(\\\"DISCARD: {}\\\".format(removing))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6fc092df7daffa5a25b7434559aaac4\",\n \"score\": \"0.5876702\",\n \"text\": \"def _adjust_entries_in_queue(self,key): ## internal method \\n if key in self._q:\\n self._q.remove(key)\\n self._q.appendleft(key)\\n else:\\n self._q.appendleft(key)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0cb599f650ca4e47ab36f944b6580f6\",\n \"score\": \"0.58751494\",\n \"text\": \"def put(self, key: int, value: int) -> None:\\n hash_id=self.get_index(key)\\n if self.hashmap[hash_id] == None:\\n self.hashmap[hash_id]=Listnode((key,value))\\n else:\\n current=self.hashmap[hash_id]\\n while current:\\n k,v =current.key_value\\n if k == key:\\n current.key_value = (key,value)\\n return\\n if current.next == None:\\n current.next = Listnode((key, value))\\n return\\n else:\\n current=current.next\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b35985da4eeeada58df01a2e82a1590d\",\n \"score\": \"0.58726424\",\n \"text\": \"def _put(self, node, key, index):\\n if node is None:\\n node = Node()\\n if index == len(key):\\n return node\\n order = ord(key[index])\\n node.next[order] = self._put(node.next[order], key, index+1)\\n node.size += 1\\n return node\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fef541a5882ce810736de371016c6a98\",\n \"score\": \"0.58448285\",\n \"text\": \"def insert_item_before_item(self, item, data):\\t\\t\\n\\t\\t\\n\\n\\t\\tif item == self.head.data:\\n\\t\\t\\tvalue = Node(data)\\n\\t\\t\\tvalue.next = self.head\\n\\t\\t\\tself.head = value\\n\\t\\t\\treturn \\n\\n\\n\\t\\tvalue = Node(data)\\n\\t\\tstart = self.head\\n\\t\\twhile start.next != None:\\n\\t\\t\\tif start.next.data == item:\\n\\t\\t\\t\\tbreak\\n\\t\\t\\tstart = start.next\\n\\t\\tif start.next == None:\\n\\t\\t\\tprint(item,\\\"is not in this list\\\")\\n\\t\\telse:\\n\\t\\t\\tvalue.next = start.next\\n\\t\\t\\tstart.next = value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2605d3d44c09743dcaa3de29ed8a56a2\",\n \"score\": \"0.5843531\",\n \"text\": \"def insert_before(self, key, next_node):\\n new_node = Node(key)\\n if next_node is None:\\n print (\\\"Next node is None. Did nothing.\\\")\\n return \\n new_node.previous = next_node.previous\\n new_node.next = next_node\\n next_node.previous = new_node\\n if new_node.previous:\\n new_node.previous.next = new_node\",\n \"title\": \"\"\n },\n {\n \"docid\": \"212554791e72660a5de415d5f9794218\",\n \"score\": \"0.58359325\",\n \"text\": \"def __getitem__(self, key):\\n if key in self.index:\\n node = self.index[key]\\n node.remove()\\n node.setnext(self.head)\\n return self.index[key].value\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f3fa6483355ba3fdcdd7f6759032854\",\n \"score\": \"0.58045965\",\n \"text\": \"def delete(self, key):\\n current_node = self.head\\n previous_node = None\\n while current_node:\\n if current_node.key == key:\\n if previous_node:\\n previous_node.next = current_node.next\\n else:\\n self.head = current_node.next\\n if current_node.next:\\n current_node.next.previous = previous_node\\n return current_node\\n previous_node = current_node\\n current_node = current_node.next\\n return current_node\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37b5f58ab69793831abb605b6c669dfc\",\n \"score\": \"0.5793984\",\n \"text\": \"def appendleft(self, key, value):\\n n = node()\\n n.key = key\\n n.value = value\\n #add n to front of list\\n self.l.next.prev = n\\n n.next = self.l.next \\n n.prev = self.l\\n self.l.next = n\\n #add n to the map\\n self.d[key] = n\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2de8ffc0bcbc09c85ba2e63c8e4fc71a\",\n \"score\": \"0.57900405\",\n \"text\": \"def append(self, key):\\n new_node = Node(key)\\n self.size += 1\\n if not self.head:\\n self.head = new_node\\n return\\n\\n tmp = self.head\\n while tmp.next:\\n tmp = tmp.next\\n tmp.next = new_node\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a33acb2f7ff213d7570e0dd58cfff488\",\n \"score\": \"0.57731175\",\n \"text\": \"def __delitem__(self, key):\\n self.pop(self.pop(key))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f959c33df369c4597f2e054a27e4db4c\",\n \"score\": \"0.57515085\",\n \"text\": \"def __delitem__(self, key: str) -> None:\\n del_key = key.lower().encode(\\\"latin-1\\\")\\n\\n pop_indexes = []\\n for idx, (item_key, item_value) in enumerate(self._list):\\n if item_key == del_key:\\n pop_indexes.append(idx)\\n\\n for idx in reversed(pop_indexes):\\n del self._list[idx]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4f12370809f4fb84b8513ff663d9ef2\",\n \"score\": \"0.5717696\",\n \"text\": \"def put(self, key, item):\\n if key is None or item is None:\\n pass\\n if key and item:\\n self.cache_data[key] = item\\n if key not in self.LRUkeys:\\n self.LRUkeys.append(key)\\n else:\\n self.LRUkeys.append(self.LRUkeys.pop(self.LRUkeys.index(key)))\\n if len(self.LRUkeys) > BaseCaching.MAX_ITEMS:\\n discard = self.LRUkeys.pop(0)\\n del self.cache_data[discard]\\n print(\\\"DISCARD: {}\\\".format(discard))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"671b81c85b7a63288d965949c14c3db2\",\n \"score\": \"0.57173526\",\n \"text\": \"def __delitem__(self, key):\\n if self.is_empty():\\n raise KeyError(\\\"Unable to delete item: key is missing!\\\")\\n\\n if not isinstance(key, type(self.contents[0].key)):\\n raise TypeError(\\\"Unable to delete item: key of incorrect type!\\\")\\n\\n (boolean, current, counter) = self._retrieve(key)\\n if not boolean:\\n raise KeyError(\\\"Unable to delete item: key is missing!\\\")\\n\\n # If there are multiple items with the same searchkey, removing only one of those will suffice.\\n if len(current.contents[counter].node_content) > 1:\\n del current.contents[counter].node_content[0] # deleting first item in node_content\\n return\\n\\n if current._no_children():\\n # The node with the given key will have to be deleted.\\n del current.contents[counter]\\n if current.parent is not None:\\n # If the (sub)tree is an empty leaf, the fix-algorithm will be called.\\n if current.is_empty():\\n current._fix()\\n\\n # If we're dealing with an internal node, then it must swap positions with it's inorder successor.\\n else:\\n inord_succ = current.children[counter+1]._inorder_successor()\\n # The inorder successor is in the left TreeItem, always, that is the whole essence of the inorder successor.\\n current.contents[counter] = inord_succ.contents[0]\\n del inord_succ.contents[0]\\n if inord_succ.is_empty(): # If the inorder successor's node is empty after deleting, the fix algorithm\\n inord_succ._fix() # must be called.\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5dfd5ebe720dbac74c0aa9a00c39e6d0\",\n \"score\": \"0.57126516\",\n \"text\": \"def delete_by_key(self, key):\\n\\n current = self.head\\n previous = None\\n found = False\\n\\n while current and not found:\\n if current.data == key and current == self.head:\\n found = True\\n self.head = current.next_node\\n elif current.data == key:\\n found = True\\n previous.next_node = current.next_node\\n else:\\n previous = current\\n current = current.next_node\\n return current\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90ef553c3dcb9b5f43796712ced2eecd\",\n \"score\": \"0.5711856\",\n \"text\": \"def __getitem__(self, key):\\n rv = self._mapping[key]\\n if self._queue[-1] != key:\\n self._remove(key)\\n self._append(key)\\n return rv\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc0287f09a699d6397ac7f7bd5b2b726\",\n \"score\": \"0.5691216\",\n \"text\": \"def remove(self, key):\\r\\n i = key % self.base\\r\\n if self.keyList[i] != 0:\\r\\n # 为0时证明key根本不在，不用考虑，所以只考虑不为0时的\\r\\n root = self.keyList[i]\\r\\n # 根节点\\r\\n if root.key == key:\\r\\n # 第一个点就是目标点\\r\\n if root.next:\\r\\n # 如果存在下一个节点，将keyList直接链接到下一个点上\\r\\n self.keyList[i] = root.next\\r\\n else:\\r\\n # 不存在下一个节点，即这个链表只有目标点时，重置链表\\r\\n self.keyList[i] = 0\\r\\n else:\\r\\n while root.next:\\r\\n if root.next.key == key:\\r\\n # 找到目标点\\r\\n root.next = root.next.next\\r\\n break\\r\\n root = root.next\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d19d272e00a045be7611e5c5a386495\",\n \"score\": \"0.56843966\",\n \"text\": \"def insert(self, key, value):\\n index = len(self._list)\\n item = PriorityQueueItem(key, value, index)\\n self._list.append(item)\\n self._repair_heap_up(index)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0075ce38d7a614b18392836531ec62cd\",\n \"score\": \"0.5678694\",\n \"text\": \"def remove(self, key: int) -> None:\\n \\n \\n hc = hash(key)\\n index = self.getCompressed(hc)\\n \\n head = self.buckets[index]\\n \\n prev = None\\n \\n while head is not None:\\n \\n if head.key==key:\\n \\n # do \\n \\n if prev is None:\\n self.buckets[index] = head.next\\n else:\\n prev.next = head.next\\n self.count-=1\\n \\n prev = head\\n head = head.next\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d4db56317c78093849fc6b2733d8ad0\",\n \"score\": \"0.56628597\",\n \"text\": \"def __delitem__(self, key):\\n if key in self.index:\\n node = self.index[key]\\n if node == self.tail:\\n self.tail = node.prev\\n if node == self.head:\\n self.head = node.successor\\n del self.index[key]\\n self.count -= 1\\n node.remove()\\n self.checksize()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e84ff78e5e3400a820cda6a9c56a248\",\n \"score\": \"0.5650674\",\n \"text\": \"def insertBefore(list, nextItem, x):\\n\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5757772e85c73176f600c66ef6a25f4b\",\n \"score\": \"0.5650055\",\n \"text\": \"def insert(self, key, id_):\\n self.A.append((key, id_))\\n self._siftup(len(self.A) - 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e9a83708be74bf0d2ab6e98d7324d2a\",\n \"score\": \"0.5638734\",\n \"text\": \"def __delitem__(self, key):\\n n = self.d[key]\\n n.next.prev = n.prev\\n n.prev.next = n.next\\n del self.d[key]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f8cbbb55b4d22fb341630e6411b952d\",\n \"score\": \"0.563836\",\n \"text\": \"def unshift(self, item):\\n\\n # create new node. forward to head's next. back to head\\n new = Node(item, succeeding=self.head.succeeding, previous=self.head)\\n self.cursor = self.head.succeeding\\n self.head.succeeding.previous = new\\n self.head.succeeding = new\\n self._length += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81fb245179f80d45d46343f7c3a226c0\",\n \"score\": \"0.56381696\",\n \"text\": \"def remove(self, key):\\n index = key % self.size\\n if self.hashlist[index]:\\n singlenode = self.hashlist[index]\\n prenode = singlenode\\n if key == singlenode.key:\\n self.hashlist[index] = singlenode.next\\n return\\n singlenode = singlenode.next\\n while singlenode:\\n if key == singlenode.key:\\n prenode.next = singlenode.next\\n return\\n prenode = singlenode\\n singlenode = singlenode.next\\n\\n\\n\\n # Your MyHashMap object will be instantiated and called as such:\\n # obj = MyHashMap()\\n # obj.put(key,value)\\n # param_2 = obj.get(key)\\n # obj.remove(key)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db547f98caadaadac19f4bf19cde3137\",\n \"score\": \"0.5613023\",\n \"text\": \"def remove(self, key: int) -> None:\\n i = self.index(key)\\n if not self.nodes[i]:\\n return\\n prev = self.find(self.nodes[i], key)\\n if not prev.next:\\n return\\n else:\\n prev.next = prev.next.next\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b52d99b20b19d437844f05c5171ee4b7\",\n \"score\": \"0.55961514\",\n \"text\": \"def put(self, key: str, value: object) -> None:\\n # get the list at hash location\\n list_ = self._get_list(key)\\n\\n # if key exists, replace value\\n node = list_.contains(key)\\n if node is not None:\\n node.value = value\\n return\\n\\n # key does not yet exist, add key/value pair\\n list_.insert(key, value)\\n self.size += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eeb4875e92c6e5807d8908baab10b7ce\",\n \"score\": \"0.5570143\",\n \"text\": \"def __delitem__(self, k):\\n h = self.head\\n while h.next:\\n if h.next.key == k:\\n h.next = h.next.next\\n self.n -= 1\\n return\\n h = h.next\\n raise KeyError(k)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fae8e772033bc69d12352f78d6da67e5\",\n \"score\": \"0.5554949\",\n \"text\": \"def prepend(self, item):\\n self.insert(0, item)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ec70a40ff1f466e2e00e247152c1eec\",\n \"score\": \"0.5539818\",\n \"text\": \"def remove(self, key):\\n ll = self.hashtable[self._hash_key(key)]\\n if ll is None:\\n raise TypeError\\n temp = ll.head\\n if ll.head.key is key:\\n temp = ll.head\\n ll.head = ll.head.next\\n return temp.val\\n while ll:\\n if ll.head.next.key is key:\\n temp = ll.head.next\\n ll.head.next = ll.head.next.next\\n return temp.val\\n if ll.head is None:\\n self.hashtable[self._hash_key(key)] = None\\n raise TypeError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1d8f0cbb35ab4cb01b05a4edc16c2d1\",\n \"score\": \"0.55357224\",\n \"text\": \"def set(self, _key, _value, **kw):\\n if kw:\\n _value = _options_header_vkw(_value, kw)\\n _key = _unicodify_header_value(_key)\\n _value = _unicodify_header_value(_value)\\n self._validate_value(_value)\\n if not self._list:\\n self._list.append((_key, _value))\\n return\\n listiter = iter(self._list)\\n ikey = _key.lower()\\n for idx, (old_key, _old_value) in enumerate(listiter):\\n if old_key.lower() == ikey:\\n # replace first occurrence\\n self._list[idx] = (_key, _value)\\n break\\n else:\\n self._list.append((_key, _value))\\n return\\n self._list[idx + 1 :] = [t for t in listiter if t[0].lower() != ikey]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e9186b44eaec30633cc183b1919bf8b\",\n \"score\": \"0.55349404\",\n \"text\": \"def remove(self, key: int) -> None:\\n hash_id=self.get_index(key)\\n if self.hashmap[hash_id] == None:\\n return\\n else:\\n current=self.hashmap[hash_id]\\n prev=None\\n while current:\\n k,v =current.key_value\\n if k == key:\\n if prev == None:\\n self.hashmap[hash_id]=None\\n return\\n else:\\n prev.next=current.next\\n return\\n else:\\n prev=current\\n current=current.next\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cae190aa6fa0d127b86ee74d7bda300c\",\n \"score\": \"0.5534382\",\n \"text\": \"def push_key(self,key):\\n return _ldns.ldns_key_list_push_key(self,key)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09fefad60bba70775cd634e63d81cbaa\",\n \"score\": \"0.5530455\",\n \"text\": \"def insert(self, key) -> None:\\n self.heap_size += 1\\n self.A.append(-math.inf)\\n self.increase_key(self.heap_size, key)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a72116d3ef57213e1c0a87f0288f227f\",\n \"score\": \"0.5528654\",\n \"text\": \"def __delitem__(self, key):\\n self.pop(key)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34134b3a4d0231765fc74442fca690d8\",\n \"score\": \"0.55276364\",\n \"text\": \"def put(self, key, item):\\n if key and item:\\n self.cache_data[key] = item\\n if len(self.cache_data) > BaseCaching.MAX_ITEMS:\\n last_key = self.stk.pop()\\n print(\\\"DISCARD: {}\\\".format(last_key))\\n del self.cache_data[last_key]\\n self.stk.append(key)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fffb72515f81164b3e916fba39109c16\",\n \"score\": \"0.5525205\",\n \"text\": \"def delete(self, key):\\n # Your code here\\n keyHash = self.hash_index(key)\\n cur = self.capacity[keyHash]\\n if cur == None:\\n print('warning Key not found')\\n # if key is head\\n if cur.key == key:\\n cur.value = None\\n cur = cur.next\\n return cur\\n else: # key not head\\n while cur != None:\\n prev = cur\\n #see if keys matchs\\n cur = cur.next\\n if cur.key == key:\\n #set prev next to cur.next, to skip over current\\n prev.next = cur.next\\n #self.get_load_factor()\\n return cur.value\\n \\n #set current to next\\n \\n #otherwise check the next node\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77b2d824e18ec8c8d331fec1d2372dbf\",\n \"score\": \"0.5521774\",\n \"text\": \"def put(self, key, item):\\n if key is not None and item is not None:\\n if key in self.cache_data:\\n self.cache_data[key] = item\\n self.frequency[key] += 1\\n self.lfu_order.remove(key)\\n else:\\n if len(self.cache_data) >= self.MAX_ITEMS:\\n min_value = min(self.frequency.values())\\n min_keys = [k for k in self.frequency\\n if self.frequency[k] == min_value]\\n for i in range(len(self.lfu_order)):\\n if self.lfu_order[i] in min_keys:\\n break\\n del self.cache_data[self.lfu_order[i]]\\n del self.frequency[self.lfu_order[i]]\\n print(\\\"DISCARD:\\\", self.lfu_order[i])\\n self.lfu_order.pop(i)\\n self.cache_data[key] = item\\n self.frequency[key] = 1\\n self.lfu_order.append(key)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee58fcbe0e18eaa8e72197820e062ab4\",\n \"score\": \"0.54907763\",\n \"text\": \"def remove(self, key):\\r\\n if self.head is None:\\r\\n return False\\r\\n if self.head.key == key:\\r\\n self.head = self.head.next\\r\\n self.size = self.size - 1\\r\\n return True\\r\\n cur = self.head.next\\r\\n prev = self.head\\r\\n while cur is not None:\\r\\n if cur.key == key:\\r\\n prev.next = cur.next\\r\\n self.size = self.size - 1\\r\\n return True\\r\\n prev = cur\\r\\n cur = cur.next\\r\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dfc7747db410ad327397dd27616b664b\",\n \"score\": \"0.5484711\",\n \"text\": \"def __setitem__(self, key, value):\\n self.insert(key, value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe571cf74644ec17c60236bf08cd5eb0\",\n \"score\": \"0.54834\",\n \"text\": \"def move_object_to_top(id):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5aad2e411522af5ec616503c20eb4f6\",\n \"score\": \"0.54734606\",\n \"text\": \"def dec(self, key):\\n cur = self.dt[key]\\n cur.keys.discard(key)\\n\\n if cur.cnt - 1 == cur.pre.cnt:\\n new = cur.pre\\n else:\\n new = ListNode(key, cur.cnt - 1)\\n self.insert(cur.pre, new)\\n\\n self.dt[key] = new\\n if not cur.keys and cur.cnt != 0:\\n cur.pre.next, cur.next.pre = cur.next, cur.pre\",\n \"title\": \"\"\n },\n {\n \"docid\": \"595591804ca0d10665b8784ca05caa83\",\n \"score\": \"0.5471652\",\n \"text\": \"def update(self, key):\\n if key == pg.K_w or key == pg.K_UP:\\n self.prev_item()\\n elif key == pg.K_s or key == pg.K_DOWN:\\n self.next_item()\\n elif key == pg.K_RETURN or key == pg.K_f:\\n self.choose_item()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee9c13ddd98a1504efd4b4680ad6e24e\",\n \"score\": \"0.54651386\",\n \"text\": \"def remove(self, key):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9654c5c0f1a311aa650c8733cd058da7\",\n \"score\": \"0.54557216\",\n \"text\": \"def __setitem__(self, key, value):\\n node = None\\n if key in self.index:\\n node = self.index[key]\\n node.remove()\\n node.setnext(self.head)\\n self.head = node\\n node.value = value\\n else:\\n node = Node(key, value)\\n self.index[key] = node\\n if not self.head:\\n self.tail = node\\n node.setnext(self.head)\\n self.head = node\\n self.count += 1\\n self.checksize()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23c87d9a2cd44b70bd900de63bb680bf\",\n \"score\": \"0.54512745\",\n \"text\": \"def __setitem__(self, key, value):\\n position = self.hash(key)\\n count = 0\\n\\n\\n if self.array[position] == None:\\n linklist = List()\\n pair = (key,value)\\n linklist.insert(count,pair)\\n head = linklist\\n set = (count+1,head) #(number_of_items,pointer_to_linklist)\\n self.array[position] = set\\n\\n return\\n\\n elif self.array[position]!= None:\\n copy = self.array[position][1].head\\n\\n\\n while copy!= None:\\n if copy.item[0] == key:\\n new_tuple = (key,value)\\n copy.item = new_tuple\\n return\\n\\n copy = copy.next\\n\\n\\n self.array[position][1].insert(self.array[position][0],(key,value))\\n new_tuple = (self.array[position][0]+1,self.array[position][1])\\n self.array[position] = new_tuple\\n self.collisions+=1\\n\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a7edf279809d8ef6e52a7a58036f5cc\",\n \"score\": \"0.54457074\",\n \"text\": \"def put(self, key, value):\\n index = key % self.size\\n if not self.hashlist[index]:\\n singlenode = Node(key, value)\\n self.hashlist[index] = singlenode\\n else:\\n singlenode = self.hashlist[index]\\n while singlenode.next:\\n if key == singlenode.key:\\n singlenode.val = value\\n return\\n singlenode = singlenode.next\\n if key == singlenode.key:\\n singlenode.val = value\\n return\\n singlenode.next = Node(key, value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"599848974fef85c4dab2c02796cd0810\",\n \"score\": \"0.54426867\",\n \"text\": \"def pop(self, key, default=...):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4df67cfe7d6eb708a76e057e499f225\",\n \"score\": \"0.5435662\",\n \"text\": \"def pop_first(self):\\n\\t\\tif self.head == None:\\n\\t\\t\\tprint(\\\"list has no element\\\")\\n\\t\\t\\treturn \\n\\t\\tself.head = self.head.next\",\n \"title\": \"\"\n },\n {\n \"docid\": \"241d98ae4fcd6feb4c56255138a79366\",\n \"score\": \"0.54346967\",\n \"text\": \"def put(self, key, value):\\n node = Node(key, value)\\n hashKey = self.hash(key)\\n head = self.table[hashKey]\\n if head is None:\\n self.table[hashKey] = node\\n return\\n last = None\\n while head is not None:\\n if(head.key == key):\\n head.value = value\\n return\\n last = head\\n head = head.next\\n last.next = node\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ff8de1a15420d6b599167060178660e\",\n \"score\": \"0.54343766\",\n \"text\": \"def shift_left(my_list):\\r\\n #my_list = []\\r\\n new_list = my_list[1:]\\r\\n new_list.insert(2,my_list[0])\\r\\n return new_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"364156383e293b7a6ad9defbe164c522\",\n \"score\": \"0.5433465\",\n \"text\": \"def __setitem__(self, k, o):\\n try:\\n (age, item) = self.items[k]\\n del self.age_to_items[age]\\n except KeyError:\\n pass\\n\\n self._set_newest(k, o)\\n\\n if len(self.items) > self.size:\\n # Maximum number of keys reached\\n # Delete the last recently used key\\n while self.oldest not in self.age_to_items:\\n self.oldest += 1\\n\\n del self.items[self.age_to_items.pop(self.oldest)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"043ec5618c9a0fbaa7f3f7afd453abc4\",\n \"score\": \"0.5432182\",\n \"text\": \"def put(self, key, item):\\n if key and item:\\n self.cache_data[key] = item\\n if len(self.cache_data.keys()) <= self.MAX_ITEMS:\\n pass\\n else:\\n del self.cache_data[self.last_key]\\n print(f'DISCARD: {self.last_key}')\\n self.last_key = key\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b359e6f0b1a108ca333555d889a2d6a\",\n \"score\": \"0.54296947\",\n \"text\": \"def sort(self, key):\\r\\n self.list_places = sorted(self.list_places, key=attrgetter(key, \\\"name\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9608066f928e0c91cee07292de5c4a3e\",\n \"score\": \"0.5416643\",\n \"text\": \"def insert(self, key, value):\\n\\n if key in self.hash_map:\\n node = self.hash_map[key]\\n node.value = value\\n\\n if self.head != node:\\n self.remove(node)\\n self.set_head(node)\\n else:\\n new_node = QNode(key, value)\\n if self.current_size == self.capacity:\\n del self.hash_map[self.end.key]\\n self.remove(self.end)\\n self.set_head(new_node)\\n self.hash_map[key] = new_node\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c60e2f0c850e3160267b515d7da1a8d3\",\n \"score\": \"0.5410061\",\n \"text\": \"def insert_item_after_item(self, item, data):\\n\\t\\tvalue = Node(data)\\n\\t\\tstart = self.head\\n\\t\\twhile start != None:\\n\\t\\t\\tif start.data == item:\\n\\t\\t\\t\\tbreak\\n\\t\\t\\tstart = start.next\\n\\n\\t\\tif start == None:\\n\\t\\t\\tprint(item, \\\"is not in this list\\\")\\n\\t\\telse:\\n\\t\\t\\tvalue.next = start.next\\n\\t\\t\\tstart.next = value\\n\\t\\t\\treturn \\t\\t\\n\\t\\t\\t# value.next = start.next\\n\\t\\t\\t# start.next = value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7afcf72b7ab794358df4970f0a00bb13\",\n \"score\": \"0.54066145\",\n \"text\": \"def __setitem__(self, key, item):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80b15888d56a9f2149c5db5761bac2f1\",\n \"score\": \"0.5404773\",\n \"text\": \"def put(self, key, item):\\n if key and item:\\n if key in self.cache_data:\\n self.queue.remove(key)\\n elif len(self.cache_data) >= self.MAX_ITEMS:\\n popped = self.queue.popleft()\\n del self.cache_data[popped]\\n print(\\\"DISCARD: \\\" + str(popped))\\n self.queue.append(key)\\n self.cache_data[key] = item\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1929c5bf39edcdee1df2bce4a33538dc\",\n \"score\": \"0.54008895\",\n \"text\": \"def put(self, key, item):\\n if key is None or item is None:\\n return\\n keys_list = self.cache_data.keys()\\n if len(keys_list) == super().MAX_ITEMS:\\n if key in keys_list:\\n del self.cache_data[key]\\n else:\\n last_key = list(keys_list)[-1]\\n print(f'DISCARD: {last_key}')\\n del self.cache_data[last_key]\\n self.cache_data[key] = item\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8b345acdf0276910bb90522843c8f12\",\n \"score\": \"0.5395307\",\n \"text\": \"def put(self, key, value):\\n index = self._get_index(key)\\n head = self._bucket_list[index]\\n while head is not None:\\n if head.key == key:\\n head.value = value\\n return\\n head = head.next\\n self._size += 1\\n head = self._bucket_list[index]\\n new_node = Node(key, value)\\n new_node.next = head\\n self._bucket_list[index] = new_node\\n if self._should_expand():\\n self.expand()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"394fc4ceb62cf37962fd73a39d6d4ca4\",\n \"score\": \"0.5394903\",\n \"text\": \"def put(self, key: int, value: int) -> None:\\n index = key % self.MOD\\n values = self.ht[index]\\n for i, v in enumerate(values):\\n if key == v[0]:\\n values[i] = (key, value)\\n break\\n else:\\n values.append((key, value))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4b0f25ed98a8abe8e2fd4820d7a0c0d\",\n \"score\": \"0.5386447\",\n \"text\": \"def remove(self, item):\\t\\t\\n\\t\\tif self.head == None:\\n\\t\\t\\tprint(\\\"No item in this list\\\")\\n\\t\\t\\treturn \\n\\n\\t\\tif self.head.data == item:\\n\\t\\t\\tself.head = self.head.next\\n\\t\\t\\treturn\\n\\n\\t\\tstart = self.head\\n\\t\\twhile start.next != None:\\n\\t\\t\\tif start.next.data == item:\\n\\t\\t\\t\\tbreak \\n\\t\\t\\tstart = start.next\\n\\t\\tif start.next == None:\\n\\t\\t\\tprint(item, \\\"is not present in this list\\\")\\n\\t\\telse:\\n\\t\\t\\tstart.next = start.next.next\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7195ccec227b70b353680f434f97cca2\",\n \"score\": \"0.5383614\",\n \"text\": \"def remove(self, key):\\n raise NotImplementedError('Method from abstract implementation')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"499e1eb9e2406f36ffd4f8e0c2dcb05e\",\n \"score\": \"0.5374034\",\n \"text\": \"def add(self, key, value):\\n index = self.hash(key)\\n if self.array[index] is not None:\\n for value in self.array[index]:\\n if value[0] == key:\\n value[1] = value\\n break\\n else:\\n self.array[index].append([key, value])\\n\\n else:\\n self.array[index] = []\\n self.array[index].append([key, value])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab4f717a412c8ad285e740fd12d734d6\",\n \"score\": \"0.5363762\",\n \"text\": \"def add(self, key, value):\\n self._data.append(self._Item(key, value))\\n self._upheap(len(self._data) - 1) # upheap newly added position\",\n \"title\": \"\"\n },\n {\n \"docid\": \"abdd4cd5417a265fea0cf40a019685fc\",\n \"score\": \"0.53632635\",\n \"text\": \"def set(self, key, value):\\n # If key is in map, update the value and move node to front of queue\\n evict = None\\n if key in self.map:\\n # Update value of key\\n if isinstance(self.map[key][0], set):\\n self.map[key][0].add(value)\\n else:\\n self.map[key][0] = value\\n node = self.map[key][1]\\n # Update Queue using the following steps:\\n # Update tail if current node is tail and there is more than one element in cache\\n if self._tail == node and node.prev != self._dummy_head:\\n self._tail = node.prev\\n # Extract node from current position in Queue\\n node.prev.next = node.next\\n if node.next:\\n node.next.prev = node.prev\\n # Modify node's prev and next references\\n node.prev, node.next = self._dummy_head, self._dummy_head.next\\n # Transplant node into beginning of Queue\\n if self._dummy_head.next is not None:\\n self._dummy_head.next.prev = node\\n self._dummy_head.next = node\\n return None\\n elif self._size == self._capacity:\\n # Evict LRU element using key of LRU\\n lru = self._tail\\n evict = (lru.data, self.map[lru.data][0])\\n self.map.pop(lru.data)\\n self._tail = lru.prev if lru.prev != self._dummy_head else None\\n lru.prev.next = None\\n self._size -= 1\\n\\n node = ListNode(key, self._dummy_head, self._dummy_head.next)\\n if self._dummy_head.next is not None:\\n self._dummy_head.next.prev = node\\n self._dummy_head.next = node\\n if self._tail is None:\\n self._tail = node\\n self.map[key] = [value, node]\\n self._size += 1\\n return evict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e59409f484f43b4df5428f8fe8a2bc2\",\n \"score\": \"0.53602254\",\n \"text\": \"def __setitem__(self, key, lumi):\\n try:\\n oldLumi = sorted(self.eventsPerLumi.keys())[key] # Extract the lumi from the sorted list\\n del self.eventsPerLumi[oldLumi] # Delete it and add the new one\\n except IndexError:\\n pass\\n self.appendLumi(lumi)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80277a93cec72990c66cf62aa4d8826e\",\n \"score\": \"0.5342852\",\n \"text\": \"def remove_key(self, key):\\n for index, element in enumerate(self.heap): # pragma: no cover\\n if element[0] == key:\\n last_element = self.heap.pop()\\n self.heap[index] = last_element\\n self._build_push_down_heapify(index)\\n return self.heap\\n return self.heap\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":695,"cells":{"query_id":{"kind":"string","value":"fe32bdc2e08352e3b214f2ef83cc9a05"},"query":{"kind":"string","value":"Return vector of distances (from vector a to each vector in matrix B)."},"positive_passages":{"kind":"list like","value":[{"docid":"f90a260f3c1a7dec66483c160d36697c","score":"0.74742806","text":"def distance(self, a, B):\n return np.linalg.norm(a-B, axis=1, ord=2)","title":""}],"string":"[\n {\n \"docid\": \"f90a260f3c1a7dec66483c160d36697c\",\n \"score\": \"0.74742806\",\n \"text\": \"def distance(self, a, B):\\n return np.linalg.norm(a-B, axis=1, ord=2)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"b999fcf1356d308e2fae0525e6491457","score":"0.6998436","text":"def calculate_distance_list(rA, rB):\n squared_sum = 0\n for dim in range(len(rA)):\n squared_sum += (rA[dim] - rB[dim])**2\n \n distance = np.sqrt(squared_sum)\n return distance","title":""},{"docid":"751f44a0928531d323338f05df29c154","score":"0.698104","text":"def euclidean_distance(a, b):\n n = a.shape[0]\n m = b.shape[0]\n result = np.empty((n, m), dtype='float32')\n\n for i in range(n):\n result[i] = np.linalg.norm(b - a[i], axis=1)\n\n return result","title":""},{"docid":"b18ecd513476f97773edb60cb8236b67","score":"0.69747764","text":"def compute_distance(A, B):\n if len(A) != len(B) or len(A[1]) != len(B[1]):\n raise IndexError\n n = len(A)\n m = len(A[1])\n distance = 0\n for i in range(n):\n for j in range(m):\n distance += pow((A[i][j] - B[i][j]), 2)\n return distance","title":""},{"docid":"ac14e43c04de1d8c5c30e2872ea3d249","score":"0.6846432","text":"def calc_distance(A, B):\n dist = np.linalg.norm(B - A)\n return dist","title":""},{"docid":"f0330e4b708a55ecd098fa2a52c9bf7a","score":"0.67587334","text":"def distance(a: np.ndarray, b: np.ndarray) -> float:\n return np.linalg.norm( np.subtract(a , b))","title":""},{"docid":"f0025bc9ebf2972bfb1935c5693e3a85","score":"0.66584617","text":"def calculate_distance(rA, rB):\n dist_vec = (rA-rB)\n distance = np.linalg.norm(dist_vec)\n return distance","title":""},{"docid":"2618c4156e371b7e28fe193adc676348","score":"0.66153526","text":"def distance(a, b):\n return norme_vecteur(difference_vecteur(a, b))","title":""},{"docid":"7e3bbb893ed8f22acfbc50231c647003","score":"0.64958817","text":"def getDistancia(A, B):\n return math.sqrt(((A[0] - B[0]) ** 2) + ((A[1] - B[1]) ** 2))","title":""},{"docid":"7a07b9e88e6e9da8d2ebb6f4384d988e","score":"0.64586884","text":"def pairwise_euclidean_distances(A, B):\n with tf.variable_scope('pairwise_euclidean_dist'):\n # squared norms of each row in A and B\n na = tf.reduce_sum(tf.square(A), 2)\n nb = tf.reduce_sum(tf.square(B), 2)\n\n # na as a row and nb as a co\"lumn vectors\n na = tf.expand_dims(na, 2)\n nb = tf.expand_dims(nb, 1)\n\n # return pairwise euclidead difference matrix\n D = tf.reduce_mean(tf.reduce_sum(tf.reduce_sum(tf.sqrt(tf.maximum(na - 2 * tf.matmul(A, B, False, True) + nb, 0.0)),axis=1), axis=1))\n return D","title":""},{"docid":"df2d115ce1582708c6347ddd738dcc71","score":"0.6414005","text":"def dist(iter_a, iter_b):\r\n return math.sqrt(sum((a - b)**2 for a, b in zip_longest(iter_a, iter_b,\r\n fillvalue=0)))","title":""},{"docid":"7efe188629f9a2ec89b43b037780bcf2","score":"0.63694096","text":"def distance_nodes(self,a,b):\n distance = np.linalg.norm(np.array(a) - np.array(b))\n return distance","title":""},{"docid":"5829c82155685e0af0a00ff73385bbbe","score":"0.63446283","text":"def distance(a, b):\n sum = 0\n if len(a) == len(b):\n for i, a_i in enumerate(a):\n diff = a_i - b[i]\n sum += diff * diff\n return sum\n return 0","title":""},{"docid":"f77dc008fa17d5c206d9cfad7efc46af","score":"0.6327542","text":"def distance(a, b):\n [a_tonnetz, b_tonnetz] = [_to_tonnetz(x) for x in [a, b]]\n return np.linalg.norm(b_tonnetz - a_tonnetz)","title":""},{"docid":"56cd42a42894b7a6242ead174cd4f7e2","score":"0.6306227","text":"def dist_euclidean(A, B):\n return np.linalg.norm(A - B)","title":""},{"docid":"47260e8564f54d4254d1761b8464048f","score":"0.629432","text":"def distance(a, b):\n\n return ((a[0] - b[0])**2 + (a[1] - b[1])**2)**0.5","title":""},{"docid":"2895144164bbbc9d90c7f6f2dabbec86","score":"0.62916136","text":"def distances(a, b):\n\n # a[i] and b[j]\n # set up an empty matrix output[i][j] with extra row on top and column to the left\n output = [[(0, None) for x in range(len(b) + 1)] for y in range(len(a) + 1)]\n\n # fill in the top row\n for i in range(len(a)):\n output[i + 1][0] = (i + 1, Operation.DELETED)\n\n # fill in the first column\n for j in range(len(b)):\n output[0][j + 1] = (j + 1, Operation.INSERTED)\n\n # navigate through the output matrix\n for i in range(len(a)):\n for j in range(len(b)):\n # calculate costs for deletion, insertion and substitution\n deletion = (output[i][j + 1][0] + 1, Operation.DELETED)\n insertion = (output[i + 1][j][0] + 1, Operation.INSERTED)\n substitution = (output[i][j][0] + (0 if a[i] == b[j] else 1), Operation.SUBSTITUTED)\n\n # choose based on lowest cost\n if insertion[0] <= deletion[0] and insertion[0] <= substitution[0]:\n output[i + 1][j + 1] = insertion\n elif deletion[0] <= substitution[0]:\n output[i + 1][j + 1] = deletion\n else:\n output[i + 1][j + 1] = substitution\n\n return output","title":""},{"docid":"accf79f739fe5010b07688cc42271b5c","score":"0.62498426","text":"def dist(A, B) :\n n = len(A.data)\n Block.comparisons += 1\n\n D = A.data - B.data\n return np.sum(D**2) # // n**2 - (np.sum(D)**2 // n**2) )","title":""},{"docid":"ae4d608acb619b54c86e3bc69c1b8662","score":"0.6209211","text":"def dist(self, a, b):\n return math.sqrt(sum([(a[i] - b[i])**2 for i in range(3)]))","title":""},{"docid":"4d2079160dc5af00b696c21816bff921","score":"0.620893","text":"def _distance(self, a, b):\n _sum = 0\n if (not a) or (not b):\n return 1\n for d in xrange(self._dim):\n difference_sq = (a.flist[d].iweight - b.flist[d].iweight) ** 2\n _sum += difference_sq\n return sqrt(_sum)","title":""},{"docid":"c70c2c3ee1f8a40a8befb76dd55d00a6","score":"0.61898935","text":"def calculate_distances(train: np.ndarray, test: np.ndarray) -> np.ndarray:\n vector_diff = test[:, np.newaxis] - train\n distances = np.linalg.norm(vector_diff, ord=2, axis=2)\n return distances","title":""},{"docid":"ce9ae8d751122823a0aaf82b364dbe44","score":"0.6180827","text":"def get_dist(self, a, b):\n if self.cl is True:\n return math.sqrt(((a - b).T * (a - b)).todense())\n else:\n return (a.T * b).todense()","title":""},{"docid":"965ef0ef43bfb1102cac1fbb1ee8e34d","score":"0.6180034","text":"def calculate_distance(rA, rB):\n \n d = rA - rB\n dist = np.linalg.norm(d)\n return dist","title":""},{"docid":"32efcfa42bf8202e1b840133810cb6b7","score":"0.6155911","text":"def get_distances(list_of_locs:np.ndarray, point:np.ndarray) -> np.ndarray:\n return np.linalg.norm(list_of_locs - point, ord=2, axis=1)","title":""},{"docid":"e6eea85ad83cad598746328c9d6da344","score":"0.614864","text":"def distance(a, b):\n\n vector = vector_by(a,b)\n _distance = absolute(vector)\n return _distance","title":""},{"docid":"99998ea6ae0931638b6c38ea19806064","score":"0.6130537","text":"def calculateDistance(vector1,vector2):\n calc_sum = 0.0\n pairs = zip(vector1,vector2)\n for pair in pairs:\n calc_sum = calc_sum + ( pair[0] - pair[1] ) ** 2\n return calc_sum ** 0.5","title":""},{"docid":"906a6c58cc13f19b83b5a3d5be3b494c","score":"0.61228305","text":"def dist(a, b):\n if a.shape != b.shape:\n raise ValueError\n return np.sqrt(np.sum(np.square(a - b)))","title":""},{"docid":"888d41beb653cb261780928282362fdc","score":"0.6085567","text":"def dist(positions):\n return array([abs(array(positions) - i) for i in positions])","title":""},{"docid":"ecb450d6b1f4472a89a7fa98372d2ee3","score":"0.60848695","text":"def compute_distances_two_loops(self, X):\n num_test = X.shape[0]\n num_train = self.X_train.shape[0]\n dists = np.zeros((num_test, num_train))\n for i in xrange(num_test):\n for j in xrange(num_train):\n dists[i, j] = np.linalg.norm(X[i] - self.X_train[j])\n return dists","title":""},{"docid":"b9c1cb7d2fd198e60f058d11d6c4815c","score":"0.60709256","text":"def distance(a, b):\n n, m = len(a), len(b)\n if n > m:\n # Make sure n <= m, to use O(min(n, m)) space\n a, b = b, a\n n, m = m, n\n\n current_row = range(n + 1) # Keep current and previous row, not entire matrix\n for i in range(1, m + 1):\n previous_row, current_row = current_row, [i] + [0] * n\n for j in range(1, n + 1):\n add, delete, change = previous_row[j] + 1, current_row[j - 1] + 1, previous_row[j - 1]\n if a[j - 1] != b[i - 1]:\n change += 1\n current_row[j] = min(add, delete, change)\n\n return current_row[n]","title":""},{"docid":"69aae99e54acd3cdda662bcf5f280e50","score":"0.6053983","text":"def euclidean_distance(a, b):\n return(sqrt(sum([(a_i - b_i)**2 for a_i, b_i in zip(a, b)])))","title":""},{"docid":"27264f67409e25c2f8f51ec3bbbbcc57","score":"0.6053733","text":"def compute_euclidean_distance(vector_a, vector_b):\r\n return np.linalg.norm(vector_a - vector_b)","title":""},{"docid":"c07d097f6feac5a0e1b04450c71a13f5","score":"0.6016696","text":"def euclidean_distances(X, Y):\r\n\r\n M = len(X)\r\n if Y.ndim == 1:\r\n N = 1\r\n D = np.zeros((M,N))\r\n\r\n for m in range(0,M):\r\n D[m] = np.linalg.norm(X[m] - Y)\r\n else:\r\n N = len(Y)\r\n D = np.zeros((M,N))\r\n\r\n for m in range(0,M):\r\n for n in range(0,N):\r\n D[m,n] = np.linalg.norm(X[m] - Y[n])\r\n\r\n return D","title":""},{"docid":"c527171be66fb083db6499383a90aac4","score":"0.6016545","text":"def edist(A, B):\n B_transpose = tf.transpose(B) # (DxM)\n A_norm = tf.reduce_sum(A ** 2, axis=1, keepdims=True) # (Nx1)\n B_norm = tf.reduce_sum(B_transpose ** 2, axis=0, keepdims=True) # (1xM)\n A_dot_B = tf.matmul(A, B_transpose) # (NxM)\n dist = tf.sqrt(A_norm + B_norm - 2 * A_dot_B) # (NxM)\n return dist","title":""},{"docid":"308a1c36befae803a1f5a804485fcfaf","score":"0.59987545","text":"def distances(self):\n dists = list()\n for p0, p1 in self.edges():\n dists.append(distance(p0, p1))\n return np.array(dists)","title":""},{"docid":"5555ab08faba01d4776c0c967fac8d65","score":"0.59825456","text":"def distance(a, b):\n return norm(subs3(a, b))","title":""},{"docid":"2a9e53000725e4777a4b5b6056da83b6","score":"0.59783965","text":"def distance_xy(A):\n d_ = np.sqrt(np.sum((A[1:,:] - A[:-1,:])**2, axis=1))\n d = [0]\n for a in d_:\n d.append(d[-1]+a)\n return np.array(d)","title":""},{"docid":"9038eda52b41d394fb5d762a7bc8377e","score":"0.5967414","text":"def euclidean_distances(X, Y):\n\n M = X.shape[0]\n N = Y.shape[0]\n D = np.zeros([M, N])\n\n for i in range(M):\n for j in range(N):\n D[i, j] = np.sqrt(np.dot(X[i] - Y[j], X[i] - Y[j]))\n\n return D","title":""},{"docid":"d08285dff2863d78124e306b81a0a347","score":"0.5966403","text":"def batched_pairwise_l2_distance(A, B):\n batch = tf.shape(A)[0]\n row_norms_A = tf.math.reduce_sum(tf.square(A), axis=-1)\n row_norms_A = tf.reshape(row_norms_A, [batch, -1, 1]) # Column vector.\n\n row_norms_B = tf.math.reduce_sum(tf.square(B), axis=-1)\n row_norms_B = tf.reshape(row_norms_B, [batch, 1, -1]) # Row vector.\n\n dist = row_norms_A - 2. * tf.matmul(A, B, transpose_b=True) + row_norms_B\n return tf.math.maximum(dist, 0.)","title":""},{"docid":"52d311520e7a5729489701d1e67f4421","score":"0.596113","text":"def matrix_subtraction(a, b):\n c = [[0 for y in range(len(a[0]))] for x in range(len(a))]\n for i in range(len(a)):\n for j in range(len(a[0])):\n c[i][j] = a[i][j] - b[i][j]\n return c","title":""},{"docid":"3e70fe5dc1aba3f9f30be98680ad36db","score":"0.5939913","text":"def distance(first, second):\n return sqrt(sum(((first - second) ** 2).ravel()))","title":""},{"docid":"7a3d4c5db520737ef5a4a7ae2976b72d","score":"0.59383345","text":"def pairwise_euclidean_distances( coord_array_a, coord_array_b ):\n N = len(coord_array_a)\n M = len(coord_array_b)\n assert coord_array_a.shape[-1] == coord_array_b.shape[-1]\n ndim = coord_array_a.shape[-1]\n\n distances = numpy.zeros((N,M), dtype=numpy.float32)\n for i in range(ndim):\n tmp = numpy.empty_like(distances) # force float32\n pairwise_subtracted = numpy.subtract.outer(coord_array_a[:,i], coord_array_b[:,i], out=tmp)\n squared = numpy.power(pairwise_subtracted, 2, out=pairwise_subtracted)\n distances[:] += squared\n\n numpy.sqrt(distances, out=distances)\n return distances","title":""},{"docid":"6311c505ed6123bcc29a15ee5fd8364b","score":"0.593795","text":"def dist_numba(A, B, p):\n sum = 0\n for i in prange(A.shape[0]):\n for j in prange(A.shape[1]):\n sum += abs(A[i, j] - B[i, j]) ** p\n return sum ** (1 / p)","title":""},{"docid":"2ba6900881ece7a66bb40934ce97b3ca","score":"0.5934452","text":"def euclidean_distance(vects):\n x, y = vects\n return K.sqrt(K.sum(K.square(x - y), axis=1, keepdims=True))","title":""},{"docid":"629b9b7e1daafa6b976b49d7b58580d8","score":"0.5930885","text":"def distance(vector1, vector2):\n return np.linalg.norm(vector1 - vector2)","title":""},{"docid":"a72e8f5f3c3ed77103dccd231a2a1e38","score":"0.59288937","text":"def distance_to(self, vector):\n # pythagoras in 3D\n dist = 0.0\n for i, j in zip(self, vector):\n dist += (i - j)**2\n return dist**0.5","title":""},{"docid":"5b53d330753cc75ec1317fbc19b5bcc2","score":"0.5922461","text":"def vector_distance(v1, v2):\n return math.sqrt(sum([(x1-x2)*(x1-x2) for x1, x2 in zip(v1, v2)]))","title":""},{"docid":"4f467ce24862481d3dfb85dfcb0d4283","score":"0.5914176","text":"def distance(self):\n costs = self.calculate_costs()\n print(cols[:self.N])\n #M1 = nx.to_numpy_matrix(self.g1)\n #M2 = nx.to_numpy_matrix(self.g2)\n \n self.Mindices = cols[:self.N]\n return np.sum(costs)","title":""},{"docid":"a0a1cb4e7d0bf558c597e4c9dd07ce58","score":"0.59123456","text":"def pairwise_distances(a: torch.Tensor, b: torch.Tensor, p=2):\n\n if len(a.shape) != 3:\n raise ValueError(\"Invalid shape for a. Must be [m, n, d] but got\", a.shape)\n if len(b.shape) != 3:\n raise ValueError(\"Invalid shape for a. Must be [m, n, d] but got\", b.shape)\n return (a.unsqueeze(2) - b.unsqueeze(1)).abs().pow(p).sum(3)","title":""},{"docid":"a0a1cb4e7d0bf558c597e4c9dd07ce58","score":"0.59123456","text":"def pairwise_distances(a: torch.Tensor, b: torch.Tensor, p=2):\n\n if len(a.shape) != 3:\n raise ValueError(\"Invalid shape for a. Must be [m, n, d] but got\", a.shape)\n if len(b.shape) != 3:\n raise ValueError(\"Invalid shape for a. Must be [m, n, d] but got\", b.shape)\n return (a.unsqueeze(2) - b.unsqueeze(1)).abs().pow(p).sum(3)","title":""},{"docid":"efd2264c774ad977c48bf888c4045348","score":"0.59114164","text":"def euclidean_distance(vectors):\n # Todo: Possibly Euclidean distance is also not useful, but angular distance is needed for high-dimensional space\n\n # unpack the vectors into separate lists\n (featsA, featsB) = vectors\n # compute the sum of squared distances between the vectors\n sum_squared = K.sum(K.square(featsA - featsB), axis=1, keepdims=True)\n # return the euclidean distance between the vectors\n return K.sqrt(K.maximum(sum_squared, K.epsilon()))","title":""},{"docid":"dcc26fb00e78e7891e5d656cb2ae8e73","score":"0.59098953","text":"def min_distances(A, B, distances):\n assert A[0].size == A[1].size\n assert B[0].size == B[1].size\n assert A[0].size > 0 and B[0].size > 0\n for i in range(A[0].size):\n distances[i] = euclidean_distance(A[0][i], A[1][i], B[0][0], B[1][0])\n for j in range(B[0].size):\n d = euclidean_distance(A[0][i], A[1][i], B[0][j], B[1][j])\n if d < distances[i]:\n distances[i] = d","title":""},{"docid":"4acaf2c7d8562ca1dc82fc64c3c3a7f9","score":"0.5896878","text":"def distance(a, b):\n dx = abs(a[0] - b[0])\n x = min(dx, abs(A - dx))\n \n dy = abs(a[1] - b[1])\n y = min(dy, abs(B - dy))\n \n dz = abs(a[2] - b[2])\n z = min(dz, abs(C - dz))\n \n return sp.sqrt(x**2 + y**2 + z**2)","title":""},{"docid":"cab4cb30e05bd265b2cf1b23235cd489","score":"0.589407","text":"def distM(self, x):\n\n result = np.zeros((len(x), len(x)))\n for i in range(len(x)):\n for j in range(len(x)):\n result[i,j] = euclid_distance(x[i],x[j])\n return result","title":""},{"docid":"63980fa1e70d05424c29b2765c1cba0e","score":"0.58916867","text":"def distance(vector):\n\n return np.sqrt(np.sum(np.square(vector)))","title":""},{"docid":"e9d6aba03a6409f9ab13897c9e147ec0","score":"0.58781874","text":"def euclidian_distance(vector_a, vector_b):\r\n\r\n return math.sqrt(sum([(vector_a - vector_b) ** 2 for vector_a, vector_b in zip(vector_a, vector_b)]))","title":""},{"docid":"552c5484cbf2bd029cff8c9cfd619ab1","score":"0.58758956","text":"def distance(a, b):\n if a is None or b is None:\n raise Exception(\"Error: Neither a nor b can be none.\")\n elif len(a) != len(b):\n raise Exception(\"Error: a and b should have the same dimension\")\n sum = 0\n for index in range(len(a)):\n sum += (a[index] - b[index])**2\n return round(math.sqrt(sum), 2)","title":""},{"docid":"291403ba8924d35391970c414c7b437e","score":"0.58720523","text":"def dist_cpu(A, B, p):\n sum = 0\n for i in range(A.shape[0]):\n for j in range(A.shape[1]):\n sum += abs(A[i,j] - B[i,j])**p\n return sum**(1/p)","title":""},{"docid":"d6e3c5226f662a83c855cc98a27b1b9c","score":"0.5867506","text":"def distance_point_point(a, b):\n ab = subtract_vectors(b, a)\n return length_vector(ab)","title":""},{"docid":"83ff10c9abe0bea86301139fe92228d6","score":"0.58656734","text":"def squared_dist(A):\r\n expanded_a = tf.expand_dims(A, 1)\r\n expanded_b = tf.expand_dims(A, 0)\r\n distances = tf.reduce_mean(tf.squared_difference(expanded_a, expanded_b), 2)\r\n return distances","title":""},{"docid":"91bffd7513ad7e4e6421e4726dc240a7","score":"0.5826428","text":"def euclidean_distance(a, b):\n return(\n sqrt(\n sum(\n [(a_i - b_i)**2 for a_i, b_i in zip(a, b)]\n )\n )\n )","title":""},{"docid":"8359c323b358d437c8b6582bbf0c61c7","score":"0.58201265","text":"def distance(vec1, vec2):\n return np.dot(vec1, vec2)/(np.linalg.norm(vec1)*np.linalg.norm(vec2))","title":""},{"docid":"7a742570f410803c6f7203f5e51b3a9c","score":"0.58172035","text":"def distance_matrix_l1(x, y):\n m = pairwise_difference_vector_batch(x, y)\n \n return m.abs().sum(2)","title":""},{"docid":"c38aa5733c8bbc7db559fb796c8b7595","score":"0.58076125","text":"def pairwise_edist(A, B, keepdims=False):\n A_norm = tf.reduce_sum(A ** 2, axis=1, keepdims=keepdims)\n B_norm = tf.reduce_sum(B ** 2, axis=1, keepdims=keepdims)\n pw_A_dot_B = tf.reduce_sum(tf.multiply(A, B), axis=1, keepdims=keepdims)\n dist = tf.sqrt(A_norm + B_norm - 2 * pw_A_dot_B)\n return dist","title":""},{"docid":"0192c9f75ddc5c46e9e4d54f26fe63c1","score":"0.57975763","text":"def eucli_distance(a, b):\n if not len(a) == len(b):\n raise ValueError(\"a and b must be of the same size\")\n return math.sqrt(np.power(a-b, 2).sum())","title":""},{"docid":"9014ccfd35f9867413835af60b74b16f","score":"0.5791496","text":"def euclidian_distance(a, b):\r\n return np.linalg.norm(a - b, 2)","title":""},{"docid":"4ab65ebff8ae1427f9c809936e2d0c1a","score":"0.578901","text":"def distance_tensor(self, X: torch.Tensor, Nbrs: torch.Tensor,\n box_size: Union[torch.Tensor, None], B: int, N: int,\n M: int, d: int) -> torch.Tensor:\n if box_size is not None and len(box_size) != d:\n raise ValueError(\"Length of `box_size` must be equal to `d`\")\n\n flat_neighbors = torch.reshape(Nbrs, (-1, N * M))\n neighbor_coords = torch.stack(\n [X[b, flat_neighbors[b]] for b in range(B)])\n neighbor_coords = torch.reshape(neighbor_coords, (-1, N, M, d))\n D = neighbor_coords - torch.unsqueeze(X, 2)\n if box_size is not None:\n box_size = torch.reshape(box_size, (1, 1, 1, d))\n D -= torch.round(D / box_size) * box_size\n\n return D","title":""},{"docid":"e715c4f8b6d31937ea20dcbd4f94d0ef","score":"0.5784734","text":"def dist(vec1, vec2):\n return np.linalg.norm(vec1-vec2)","title":""},{"docid":"6e7a46f3ec24a18f46a92c8d45f4e3e2","score":"0.57786006","text":"def get_distance(a, b):\n return abs(a[0] - b[0]) + abs(a[1] - b[1])","title":""},{"docid":"80cd72af8d92189a8d76ed495f22d379","score":"0.57643646","text":"def distance_matrix(self, D: torch.Tensor) -> torch.Tensor:\n return torch.sqrt(torch.sum(torch.mul(D, D), 3))","title":""},{"docid":"7d24374af0a8cff8f3b27c0e8ba9682a","score":"0.57495534","text":"def squared_dist(A):\n expanded_a = tf.expand_dims(A, 1)\n expanded_b = tf.expand_dims(A, 0)\n distances = tf.reduce_mean(tf.math.squared_difference(expanded_a, expanded_b), 2)\n return distances","title":""},{"docid":"13d635838a5fe52238dc23b885f5f748","score":"0.5749381","text":"def calc_dist(first, second):\n\tdistance = 0\n\tfor x in range(1,len(first)):\n\t\tdistance += pow(first[x] - second[x], 2)\n\treturn math.sqrt(distance)","title":""},{"docid":"0e17633d4606fe821c38c34e4c5ff465","score":"0.57466865","text":"def measure_distance(self, vector_1:pd.Series, vector_2:pd.Series) -> float:","title":""},{"docid":"168a87b75d58bbdd383a07264a419f95","score":"0.57386404","text":"def cosine_distances(X, Y):\r\n M = len(X)\r\n if Y.ndim == 1:\r\n N = 1\r\n D = np.zeros((M,N))\r\n\r\n for m in range(0,M):\r\n dot = np.dot(X[m], Y)\r\n Ux = np.linalg.norm(X[m])\r\n Uy = np.linalg.norm(Y)\r\n\r\n s = dot / (Ux * Uy)\r\n\r\n D[m] = 1 - s\r\n else:\r\n N = len(Y)\r\n D = np.zeros((M,N))\r\n\r\n for m in range(0,M):\r\n for n in range(0,N):\r\n dot = np.dot(X[m], Y[n])\r\n Ux = np.linalg.norm(X[m])\r\n Uy = np.linalg.norm(Y[n])\r\n\r\n s = dot / (Ux * Uy)\r\n\r\n D[m,n] = 1 - s\r\n\r\n return D","title":""},{"docid":"734d7a4f8c195018fbafb699bd846b73","score":"0.5736716","text":"def pairwise_siamese_dist(self, A, B):\n with tf.compat.v1.variable_scope(\"pair_siam_dist\"), tf.device(\n self.next_device()):\n xx = tf.expand_dims(A, -1)\n xx = tf.tile(xx, tf.stack([1, 1, tf.shape(B)[0]]))\n\n yy = tf.expand_dims(B, -1)\n yy = tf.tile(yy, tf.stack([1, 1, tf.shape(A)[0]]))\n yy = tf.transpose(yy, perm=[2, 1, 0])\n zz = tf.reduce_sum(tf.abs(xx - yy) * tf.exp(self.alpha), 1)\n return zz","title":""},{"docid":"551d1ba8eeea8f02079ea6aca3ea1151","score":"0.57265234","text":"def distance_matrix_l2(x, y):\n m = pairwise_difference_vector_batch(x, y)\n \n return m.pow(2).sum(2).sqrt().squeeze()","title":""},{"docid":"097f14e07636252efd3eda83fe8ca62c","score":"0.5726431","text":"def difference_vecteur(a, b):\n \n return [a[0] - b[0], a[1] - b[1]]","title":""},{"docid":"b1e826acf3518a1b12d18567b6fb0f10","score":"0.5721511","text":"def beam_distances(self):\n dist = np.zeros(len(self.beams))\n for n, beam in enumerate(self.beams):\n dist[n] = beam.total_distance()\n return dist","title":""},{"docid":"01355bb6f218f6b0b73ae4d767cbb581","score":"0.57180476","text":"def euclidean_distances(X):\n\n Y = X\n X = _transpose_on_first_two_axes(X)\n\n XX = K.expand_dims(K.sum(K.square(X), axis=1), 0)\n YY = _transpose_on_first_two_axes(XX)\n\n distance = _dot(X, Y)\n distance_shapes = K.int_shape(distance)\n distance *= -2\n distance += XX\n distance += YY\n distance = K.maximum(distance,\n K.constant(0, dtype=distance.dtype))\n distance._keras_shape = distance_shapes\n return distance","title":""},{"docid":"f703c1930aae94c96b52941f46401944","score":"0.5696638","text":"def dist_sq(a, b):\n return sum([(xa - xb) ** 2 for xa, xb in zip(a, b)])","title":""},{"docid":"29d2595d8a3f9070debcded043368517","score":"0.56800634","text":"def get_distances(centroid, points):\n return np.linalg.norm(points - centroid, axis=1)","title":""},{"docid":"b8b6c08872e988f5f58dcc051676d898","score":"0.56785434","text":"def squared_euclidean_distance(a, b):\n result = numpy.float32(0.0)\n for i in range(len(a)):\n result += (a[i] - b[i])**2\n return result","title":""},{"docid":"82d7114065e54fbcfec6f9548a4eaad2","score":"0.5678065","text":"def cosine_distances(X, Y):\n\n M = X.shape[0]\n N = Y.shape[0]\n D = np.zeros([M, N])\n\n for i in range(M):\n for j in range(N):\n n_1 = np.sqrt(np.dot(X[i], X[i]))\n n_2 = np.sqrt(np.dot(Y[j], Y[j]))\n # This yield the cosine value of the two vectors, which range from 1 to -1\n D[i, j] = np.dot(X[i], Y[j]) / (n_1 * n_2)\n\n # Larger value (closer to 1) means the two vectors have similar direction, so they\n # should have smaller distance\n return 1 - D","title":""},{"docid":"4e09d3d7171f5f847a8fb073ad2caee5","score":"0.56737864","text":"def calc_dis(point_list1, point_list2):\r\n y = []\r\n for i in range(point_list1.__len__()):\r\n y.append(point_list2)\r\n y = np.array(y)\r\n x=[]\r\n for i in range(point_list2.__len__()):\r\n x.append(point_list1)\r\n x = np.array(x)\r\n x = x.transpose(1, 0, 2)\r\n distance_matrix = np.linalg.norm(np.array(x) - np.array(y), axis=2)\r\n return distance_matrix","title":""},{"docid":"23d73f5120348cd47fdcda47ed685a06","score":"0.5671712","text":"def pairwise_l2_distance(A, B):\n row_norms_A = tf.math.reduce_sum(tf.square(A), axis=1)\n row_norms_A = tf.reshape(row_norms_A, [-1, 1]) # Column vector.\n\n row_norms_B = tf.math.reduce_sum(tf.square(B), axis=1)\n row_norms_B = tf.reshape(row_norms_B, [1, -1]) # Row vector.\n\n dist = row_norms_A - 2. * tf.matmul(A, B, transpose_b=True) + row_norms_B\n return tf.math.maximum(dist, 0.)","title":""},{"docid":"3f17710c371f07fe87682760e442a93a","score":"0.5666328","text":"def dist(self, b):\n distance = sqrt((b.x - self.x) ** 2 + (b.y - self.y) ** 2)\n return distance","title":""},{"docid":"e045377b5fe9fe603cf5c878cd888650","score":"0.5661958","text":"def euclidian(A,B):\n return np.linalg.norm(A-B)","title":""},{"docid":"c69b1355759f7189fe1275de58b23d24","score":"0.5659475","text":"def euclidean_distance(vector1, vector2):\n z = 0\n for (v1, v2) in zip(vector1, vector2):\n z += (v1 - v2) ** 2\n return math.sqrt(z)","title":""},{"docid":"72533805c3077426cc35e00448bb4427","score":"0.5652093","text":"def distance(a, b):\r\n x1, y1 = a[0], a[1]\r\n x2, y2 = b[0], b[1]\r\n return round(math.sqrt((x1-x2)**2 + (y1-y2)**2))","title":""},{"docid":"169227d4de47447b759a496fd88d9c12","score":"0.56466854","text":"def calculate_distance(lhs, rhs):\n return numpy.linalg.norm((numpy.array(lhs) - numpy.array(rhs)))","title":""},{"docid":"f160015286ab4aec62b3d1d51740807f","score":"0.56271565","text":"def produit_vectoriel(a, b):\n \n return a[0] * b[1] - a[1] * b[0]","title":""},{"docid":"1e58157569d33d5c2b7429807c98d3b4","score":"0.5619837","text":"def subtractMatrices(matrixA, matrixB):\n return map(lambda i: map(lambda x, y: x - y, matrixA[i], matrixB[i]),\n xrange(len(matrixA)))","title":""},{"docid":"08a2c9fa437d258abda07054c6b13c8e","score":"0.5617198","text":"def compute_distances_one_loop(self, X):\n num_test = X.shape[0]\n num_train = self.X_train.shape[0]\n dists = np.zeros((num_test, num_train))\n for i in xrange(num_test):\n dists[i] = np.linalg.norm(self.X_train - X[i], axis=1)\n return dists","title":""},{"docid":"5acedc0b30bd5fe1cf2fffbca00cdfac","score":"0.5617071","text":"def dist(a, b):\n from math import sqrt\n return sqrt((a.y - b.y)**2 + (a.x - b.x)**2)","title":""},{"docid":"f87baaf1bac246cedae642c3769fdca2","score":"0.5614947","text":"def matrix_dist(X, Y):\n return numpy.sqrt((X[:, 0] - Y[:, 0]) ** 2 + (X[:, 1] - Y[:, 1]) ** 2)","title":""},{"docid":"118d6a4851403b31e82ca97d3618e4a0","score":"0.56115663","text":"def dist(a, b):\n dx = a.x - b.x\n dy = a.y - b.y\n return math.sqrt(dx * dx + dy * dy)","title":""},{"docid":"1a24f020b9ccb54ff82918465e039ec3","score":"0.56030965","text":"def distance(a,b):\n n, m = len(a), len(b)\n if n > m:\n # Make sure n <= m, to use O(min(n,m)) space\n a,b = b,a\n n,m = m,n\n current = range(n+1)\n for i in range(1,m+1):\n previous, current = current, [i]+[0]*n\n for j in range(1,n+1):\n add, delete = previous[j]+1, current[j-1]+1\n change = previous[j-1]\n if a[j-1] != b[i-1]:\n change = change + 1\n current[j] = min(add, delete, change)\n return current[n]","title":""},{"docid":"3e121742de1a96883b303cc1643f5a50","score":"0.5577346","text":"def distance_matrix(self) -> np.ndarray:\n # TODO check handling of unknown distances\n G = self.directed\n selected_edges = [(u, v) for u, v, d in G.edges(data=True) if DIST in d]\n return (\n nx.to_numpy_array(\n nx.to_undirected(G.edge_subgraph(selected_edges)), weight=DIST\n )\n ** 2\n )","title":""},{"docid":"3f27ad03f8c3aafa703ef861cb3d073d","score":"0.55584216","text":"def _distance(feature0, feature1):\n return np.power(feature0 - feature1, 2).sum()","title":""},{"docid":"664d949332985e1f628edeacfdf679d0","score":"0.5557304","text":"def distance_point_point_sqrd(a, b):\n ab = subtract_vectors(b, a)\n return length_vector_sqrd(ab)","title":""},{"docid":"2e82a00de70d0ce824591f74a24cd60b","score":"0.5555838","text":"def eucledian_norm(vec_a, vec_b):\n distance = vec_a - vec_b\n # return np.sqrt(np.sum(np.square(distance)))\n # return np.sqrt(np.einsum('ij, ij->i', distance, distance))\n return np.linalg.norm(distance, ord = 'fro', axis = 1)","title":""}],"string":"[\n {\n \"docid\": \"b999fcf1356d308e2fae0525e6491457\",\n \"score\": \"0.6998436\",\n \"text\": \"def calculate_distance_list(rA, rB):\\n squared_sum = 0\\n for dim in range(len(rA)):\\n squared_sum += (rA[dim] - rB[dim])**2\\n \\n distance = np.sqrt(squared_sum)\\n return distance\",\n \"title\": \"\"\n },\n {\n \"docid\": \"751f44a0928531d323338f05df29c154\",\n \"score\": \"0.698104\",\n \"text\": \"def euclidean_distance(a, b):\\n n = a.shape[0]\\n m = b.shape[0]\\n result = np.empty((n, m), dtype='float32')\\n\\n for i in range(n):\\n result[i] = np.linalg.norm(b - a[i], axis=1)\\n\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b18ecd513476f97773edb60cb8236b67\",\n \"score\": \"0.69747764\",\n \"text\": \"def compute_distance(A, B):\\n if len(A) != len(B) or len(A[1]) != len(B[1]):\\n raise IndexError\\n n = len(A)\\n m = len(A[1])\\n distance = 0\\n for i in range(n):\\n for j in range(m):\\n distance += pow((A[i][j] - B[i][j]), 2)\\n return distance\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac14e43c04de1d8c5c30e2872ea3d249\",\n \"score\": \"0.6846432\",\n \"text\": \"def calc_distance(A, B):\\n dist = np.linalg.norm(B - A)\\n return dist\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0330e4b708a55ecd098fa2a52c9bf7a\",\n \"score\": \"0.67587334\",\n \"text\": \"def distance(a: np.ndarray, b: np.ndarray) -> float:\\n return np.linalg.norm( np.subtract(a , b))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0025bc9ebf2972bfb1935c5693e3a85\",\n \"score\": \"0.66584617\",\n \"text\": \"def calculate_distance(rA, rB):\\n dist_vec = (rA-rB)\\n distance = np.linalg.norm(dist_vec)\\n return distance\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2618c4156e371b7e28fe193adc676348\",\n \"score\": \"0.66153526\",\n \"text\": \"def distance(a, b):\\n return norme_vecteur(difference_vecteur(a, b))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e3bbb893ed8f22acfbc50231c647003\",\n \"score\": \"0.64958817\",\n \"text\": \"def getDistancia(A, B):\\n return math.sqrt(((A[0] - B[0]) ** 2) + ((A[1] - B[1]) ** 2))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a07b9e88e6e9da8d2ebb6f4384d988e\",\n \"score\": \"0.64586884\",\n \"text\": \"def pairwise_euclidean_distances(A, B):\\n with tf.variable_scope('pairwise_euclidean_dist'):\\n # squared norms of each row in A and B\\n na = tf.reduce_sum(tf.square(A), 2)\\n nb = tf.reduce_sum(tf.square(B), 2)\\n\\n # na as a row and nb as a co\\\"lumn vectors\\n na = tf.expand_dims(na, 2)\\n nb = tf.expand_dims(nb, 1)\\n\\n # return pairwise euclidead difference matrix\\n D = tf.reduce_mean(tf.reduce_sum(tf.reduce_sum(tf.sqrt(tf.maximum(na - 2 * tf.matmul(A, B, False, True) + nb, 0.0)),axis=1), axis=1))\\n return D\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df2d115ce1582708c6347ddd738dcc71\",\n \"score\": \"0.6414005\",\n \"text\": \"def dist(iter_a, iter_b):\\r\\n return math.sqrt(sum((a - b)**2 for a, b in zip_longest(iter_a, iter_b,\\r\\n fillvalue=0)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7efe188629f9a2ec89b43b037780bcf2\",\n \"score\": \"0.63694096\",\n \"text\": \"def distance_nodes(self,a,b):\\n distance = np.linalg.norm(np.array(a) - np.array(b))\\n return distance\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5829c82155685e0af0a00ff73385bbbe\",\n \"score\": \"0.63446283\",\n \"text\": \"def distance(a, b):\\n sum = 0\\n if len(a) == len(b):\\n for i, a_i in enumerate(a):\\n diff = a_i - b[i]\\n sum += diff * diff\\n return sum\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f77dc008fa17d5c206d9cfad7efc46af\",\n \"score\": \"0.6327542\",\n \"text\": \"def distance(a, b):\\n [a_tonnetz, b_tonnetz] = [_to_tonnetz(x) for x in [a, b]]\\n return np.linalg.norm(b_tonnetz - a_tonnetz)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"56cd42a42894b7a6242ead174cd4f7e2\",\n \"score\": \"0.6306227\",\n \"text\": \"def dist_euclidean(A, B):\\n return np.linalg.norm(A - B)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47260e8564f54d4254d1761b8464048f\",\n \"score\": \"0.629432\",\n \"text\": \"def distance(a, b):\\n\\n return ((a[0] - b[0])**2 + (a[1] - b[1])**2)**0.5\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2895144164bbbc9d90c7f6f2dabbec86\",\n \"score\": \"0.62916136\",\n \"text\": \"def distances(a, b):\\n\\n # a[i] and b[j]\\n # set up an empty matrix output[i][j] with extra row on top and column to the left\\n output = [[(0, None) for x in range(len(b) + 1)] for y in range(len(a) + 1)]\\n\\n # fill in the top row\\n for i in range(len(a)):\\n output[i + 1][0] = (i + 1, Operation.DELETED)\\n\\n # fill in the first column\\n for j in range(len(b)):\\n output[0][j + 1] = (j + 1, Operation.INSERTED)\\n\\n # navigate through the output matrix\\n for i in range(len(a)):\\n for j in range(len(b)):\\n # calculate costs for deletion, insertion and substitution\\n deletion = (output[i][j + 1][0] + 1, Operation.DELETED)\\n insertion = (output[i + 1][j][0] + 1, Operation.INSERTED)\\n substitution = (output[i][j][0] + (0 if a[i] == b[j] else 1), Operation.SUBSTITUTED)\\n\\n # choose based on lowest cost\\n if insertion[0] <= deletion[0] and insertion[0] <= substitution[0]:\\n output[i + 1][j + 1] = insertion\\n elif deletion[0] <= substitution[0]:\\n output[i + 1][j + 1] = deletion\\n else:\\n output[i + 1][j + 1] = substitution\\n\\n return output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"accf79f739fe5010b07688cc42271b5c\",\n \"score\": \"0.62498426\",\n \"text\": \"def dist(A, B) :\\n n = len(A.data)\\n Block.comparisons += 1\\n\\n D = A.data - B.data\\n return np.sum(D**2) # // n**2 - (np.sum(D)**2 // n**2) )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae4d608acb619b54c86e3bc69c1b8662\",\n \"score\": \"0.6209211\",\n \"text\": \"def dist(self, a, b):\\n return math.sqrt(sum([(a[i] - b[i])**2 for i in range(3)]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d2079160dc5af00b696c21816bff921\",\n \"score\": \"0.620893\",\n \"text\": \"def _distance(self, a, b):\\n _sum = 0\\n if (not a) or (not b):\\n return 1\\n for d in xrange(self._dim):\\n difference_sq = (a.flist[d].iweight - b.flist[d].iweight) ** 2\\n _sum += difference_sq\\n return sqrt(_sum)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c70c2c3ee1f8a40a8befb76dd55d00a6\",\n \"score\": \"0.61898935\",\n \"text\": \"def calculate_distances(train: np.ndarray, test: np.ndarray) -> np.ndarray:\\n vector_diff = test[:, np.newaxis] - train\\n distances = np.linalg.norm(vector_diff, ord=2, axis=2)\\n return distances\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce9ae8d751122823a0aaf82b364dbe44\",\n \"score\": \"0.6180827\",\n \"text\": \"def get_dist(self, a, b):\\n if self.cl is True:\\n return math.sqrt(((a - b).T * (a - b)).todense())\\n else:\\n return (a.T * b).todense()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"965ef0ef43bfb1102cac1fbb1ee8e34d\",\n \"score\": \"0.6180034\",\n \"text\": \"def calculate_distance(rA, rB):\\n \\n d = rA - rB\\n dist = np.linalg.norm(d)\\n return dist\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32efcfa42bf8202e1b840133810cb6b7\",\n \"score\": \"0.6155911\",\n \"text\": \"def get_distances(list_of_locs:np.ndarray, point:np.ndarray) -> np.ndarray:\\n return np.linalg.norm(list_of_locs - point, ord=2, axis=1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e6eea85ad83cad598746328c9d6da344\",\n \"score\": \"0.614864\",\n \"text\": \"def distance(a, b):\\n\\n vector = vector_by(a,b)\\n _distance = absolute(vector)\\n return _distance\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99998ea6ae0931638b6c38ea19806064\",\n \"score\": \"0.6130537\",\n \"text\": \"def calculateDistance(vector1,vector2):\\n calc_sum = 0.0\\n pairs = zip(vector1,vector2)\\n for pair in pairs:\\n calc_sum = calc_sum + ( pair[0] - pair[1] ) ** 2\\n return calc_sum ** 0.5\",\n \"title\": \"\"\n },\n {\n \"docid\": \"906a6c58cc13f19b83b5a3d5be3b494c\",\n \"score\": \"0.61228305\",\n \"text\": \"def dist(a, b):\\n if a.shape != b.shape:\\n raise ValueError\\n return np.sqrt(np.sum(np.square(a - b)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"888d41beb653cb261780928282362fdc\",\n \"score\": \"0.6085567\",\n \"text\": \"def dist(positions):\\n return array([abs(array(positions) - i) for i in positions])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ecb450d6b1f4472a89a7fa98372d2ee3\",\n \"score\": \"0.60848695\",\n \"text\": \"def compute_distances_two_loops(self, X):\\n num_test = X.shape[0]\\n num_train = self.X_train.shape[0]\\n dists = np.zeros((num_test, num_train))\\n for i in xrange(num_test):\\n for j in xrange(num_train):\\n dists[i, j] = np.linalg.norm(X[i] - self.X_train[j])\\n return dists\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9c1cb7d2fd198e60f058d11d6c4815c\",\n \"score\": \"0.60709256\",\n \"text\": \"def distance(a, b):\\n n, m = len(a), len(b)\\n if n > m:\\n # Make sure n <= m, to use O(min(n, m)) space\\n a, b = b, a\\n n, m = m, n\\n\\n current_row = range(n + 1) # Keep current and previous row, not entire matrix\\n for i in range(1, m + 1):\\n previous_row, current_row = current_row, [i] + [0] * n\\n for j in range(1, n + 1):\\n add, delete, change = previous_row[j] + 1, current_row[j - 1] + 1, previous_row[j - 1]\\n if a[j - 1] != b[i - 1]:\\n change += 1\\n current_row[j] = min(add, delete, change)\\n\\n return current_row[n]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69aae99e54acd3cdda662bcf5f280e50\",\n \"score\": \"0.6053983\",\n \"text\": \"def euclidean_distance(a, b):\\n return(sqrt(sum([(a_i - b_i)**2 for a_i, b_i in zip(a, b)])))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"27264f67409e25c2f8f51ec3bbbbcc57\",\n \"score\": \"0.6053733\",\n \"text\": \"def compute_euclidean_distance(vector_a, vector_b):\\r\\n return np.linalg.norm(vector_a - vector_b)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c07d097f6feac5a0e1b04450c71a13f5\",\n \"score\": \"0.6016696\",\n \"text\": \"def euclidean_distances(X, Y):\\r\\n\\r\\n M = len(X)\\r\\n if Y.ndim == 1:\\r\\n N = 1\\r\\n D = np.zeros((M,N))\\r\\n\\r\\n for m in range(0,M):\\r\\n D[m] = np.linalg.norm(X[m] - Y)\\r\\n else:\\r\\n N = len(Y)\\r\\n D = np.zeros((M,N))\\r\\n\\r\\n for m in range(0,M):\\r\\n for n in range(0,N):\\r\\n D[m,n] = np.linalg.norm(X[m] - Y[n])\\r\\n\\r\\n return D\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c527171be66fb083db6499383a90aac4\",\n \"score\": \"0.6016545\",\n \"text\": \"def edist(A, B):\\n B_transpose = tf.transpose(B) # (DxM)\\n A_norm = tf.reduce_sum(A ** 2, axis=1, keepdims=True) # (Nx1)\\n B_norm = tf.reduce_sum(B_transpose ** 2, axis=0, keepdims=True) # (1xM)\\n A_dot_B = tf.matmul(A, B_transpose) # (NxM)\\n dist = tf.sqrt(A_norm + B_norm - 2 * A_dot_B) # (NxM)\\n return dist\",\n \"title\": \"\"\n },\n {\n \"docid\": \"308a1c36befae803a1f5a804485fcfaf\",\n \"score\": \"0.59987545\",\n \"text\": \"def distances(self):\\n dists = list()\\n for p0, p1 in self.edges():\\n dists.append(distance(p0, p1))\\n return np.array(dists)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5555ab08faba01d4776c0c967fac8d65\",\n \"score\": \"0.59825456\",\n \"text\": \"def distance(a, b):\\n return norm(subs3(a, b))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a9e53000725e4777a4b5b6056da83b6\",\n \"score\": \"0.59783965\",\n \"text\": \"def distance_xy(A):\\n d_ = np.sqrt(np.sum((A[1:,:] - A[:-1,:])**2, axis=1))\\n d = [0]\\n for a in d_:\\n d.append(d[-1]+a)\\n return np.array(d)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9038eda52b41d394fb5d762a7bc8377e\",\n \"score\": \"0.5967414\",\n \"text\": \"def euclidean_distances(X, Y):\\n\\n M = X.shape[0]\\n N = Y.shape[0]\\n D = np.zeros([M, N])\\n\\n for i in range(M):\\n for j in range(N):\\n D[i, j] = np.sqrt(np.dot(X[i] - Y[j], X[i] - Y[j]))\\n\\n return D\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d08285dff2863d78124e306b81a0a347\",\n \"score\": \"0.5966403\",\n \"text\": \"def batched_pairwise_l2_distance(A, B):\\n batch = tf.shape(A)[0]\\n row_norms_A = tf.math.reduce_sum(tf.square(A), axis=-1)\\n row_norms_A = tf.reshape(row_norms_A, [batch, -1, 1]) # Column vector.\\n\\n row_norms_B = tf.math.reduce_sum(tf.square(B), axis=-1)\\n row_norms_B = tf.reshape(row_norms_B, [batch, 1, -1]) # Row vector.\\n\\n dist = row_norms_A - 2. * tf.matmul(A, B, transpose_b=True) + row_norms_B\\n return tf.math.maximum(dist, 0.)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52d311520e7a5729489701d1e67f4421\",\n \"score\": \"0.596113\",\n \"text\": \"def matrix_subtraction(a, b):\\n c = [[0 for y in range(len(a[0]))] for x in range(len(a))]\\n for i in range(len(a)):\\n for j in range(len(a[0])):\\n c[i][j] = a[i][j] - b[i][j]\\n return c\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e70fe5dc1aba3f9f30be98680ad36db\",\n \"score\": \"0.5939913\",\n \"text\": \"def distance(first, second):\\n return sqrt(sum(((first - second) ** 2).ravel()))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a3d4c5db520737ef5a4a7ae2976b72d\",\n \"score\": \"0.59383345\",\n \"text\": \"def pairwise_euclidean_distances( coord_array_a, coord_array_b ):\\n N = len(coord_array_a)\\n M = len(coord_array_b)\\n assert coord_array_a.shape[-1] == coord_array_b.shape[-1]\\n ndim = coord_array_a.shape[-1]\\n\\n distances = numpy.zeros((N,M), dtype=numpy.float32)\\n for i in range(ndim):\\n tmp = numpy.empty_like(distances) # force float32\\n pairwise_subtracted = numpy.subtract.outer(coord_array_a[:,i], coord_array_b[:,i], out=tmp)\\n squared = numpy.power(pairwise_subtracted, 2, out=pairwise_subtracted)\\n distances[:] += squared\\n\\n numpy.sqrt(distances, out=distances)\\n return distances\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6311c505ed6123bcc29a15ee5fd8364b\",\n \"score\": \"0.593795\",\n \"text\": \"def dist_numba(A, B, p):\\n sum = 0\\n for i in prange(A.shape[0]):\\n for j in prange(A.shape[1]):\\n sum += abs(A[i, j] - B[i, j]) ** p\\n return sum ** (1 / p)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ba6900881ece7a66bb40934ce97b3ca\",\n \"score\": \"0.5934452\",\n \"text\": \"def euclidean_distance(vects):\\n x, y = vects\\n return K.sqrt(K.sum(K.square(x - y), axis=1, keepdims=True))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"629b9b7e1daafa6b976b49d7b58580d8\",\n \"score\": \"0.5930885\",\n \"text\": \"def distance(vector1, vector2):\\n return np.linalg.norm(vector1 - vector2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a72e8f5f3c3ed77103dccd231a2a1e38\",\n \"score\": \"0.59288937\",\n \"text\": \"def distance_to(self, vector):\\n # pythagoras in 3D\\n dist = 0.0\\n for i, j in zip(self, vector):\\n dist += (i - j)**2\\n return dist**0.5\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b53d330753cc75ec1317fbc19b5bcc2\",\n \"score\": \"0.5922461\",\n \"text\": \"def vector_distance(v1, v2):\\n return math.sqrt(sum([(x1-x2)*(x1-x2) for x1, x2 in zip(v1, v2)]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f467ce24862481d3dfb85dfcb0d4283\",\n \"score\": \"0.5914176\",\n \"text\": \"def distance(self):\\n costs = self.calculate_costs()\\n print(cols[:self.N])\\n #M1 = nx.to_numpy_matrix(self.g1)\\n #M2 = nx.to_numpy_matrix(self.g2)\\n \\n self.Mindices = cols[:self.N]\\n return np.sum(costs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0a1cb4e7d0bf558c597e4c9dd07ce58\",\n \"score\": \"0.59123456\",\n \"text\": \"def pairwise_distances(a: torch.Tensor, b: torch.Tensor, p=2):\\n\\n if len(a.shape) != 3:\\n raise ValueError(\\\"Invalid shape for a. Must be [m, n, d] but got\\\", a.shape)\\n if len(b.shape) != 3:\\n raise ValueError(\\\"Invalid shape for a. Must be [m, n, d] but got\\\", b.shape)\\n return (a.unsqueeze(2) - b.unsqueeze(1)).abs().pow(p).sum(3)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0a1cb4e7d0bf558c597e4c9dd07ce58\",\n \"score\": \"0.59123456\",\n \"text\": \"def pairwise_distances(a: torch.Tensor, b: torch.Tensor, p=2):\\n\\n if len(a.shape) != 3:\\n raise ValueError(\\\"Invalid shape for a. Must be [m, n, d] but got\\\", a.shape)\\n if len(b.shape) != 3:\\n raise ValueError(\\\"Invalid shape for a. Must be [m, n, d] but got\\\", b.shape)\\n return (a.unsqueeze(2) - b.unsqueeze(1)).abs().pow(p).sum(3)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"efd2264c774ad977c48bf888c4045348\",\n \"score\": \"0.59114164\",\n \"text\": \"def euclidean_distance(vectors):\\n # Todo: Possibly Euclidean distance is also not useful, but angular distance is needed for high-dimensional space\\n\\n # unpack the vectors into separate lists\\n (featsA, featsB) = vectors\\n # compute the sum of squared distances between the vectors\\n sum_squared = K.sum(K.square(featsA - featsB), axis=1, keepdims=True)\\n # return the euclidean distance between the vectors\\n return K.sqrt(K.maximum(sum_squared, K.epsilon()))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dcc26fb00e78e7891e5d656cb2ae8e73\",\n \"score\": \"0.59098953\",\n \"text\": \"def min_distances(A, B, distances):\\n assert A[0].size == A[1].size\\n assert B[0].size == B[1].size\\n assert A[0].size > 0 and B[0].size > 0\\n for i in range(A[0].size):\\n distances[i] = euclidean_distance(A[0][i], A[1][i], B[0][0], B[1][0])\\n for j in range(B[0].size):\\n d = euclidean_distance(A[0][i], A[1][i], B[0][j], B[1][j])\\n if d < distances[i]:\\n distances[i] = d\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4acaf2c7d8562ca1dc82fc64c3c3a7f9\",\n \"score\": \"0.5896878\",\n \"text\": \"def distance(a, b):\\n dx = abs(a[0] - b[0])\\n x = min(dx, abs(A - dx))\\n \\n dy = abs(a[1] - b[1])\\n y = min(dy, abs(B - dy))\\n \\n dz = abs(a[2] - b[2])\\n z = min(dz, abs(C - dz))\\n \\n return sp.sqrt(x**2 + y**2 + z**2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cab4cb30e05bd265b2cf1b23235cd489\",\n \"score\": \"0.589407\",\n \"text\": \"def distM(self, x):\\n\\n result = np.zeros((len(x), len(x)))\\n for i in range(len(x)):\\n for j in range(len(x)):\\n result[i,j] = euclid_distance(x[i],x[j])\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63980fa1e70d05424c29b2765c1cba0e\",\n \"score\": \"0.58916867\",\n \"text\": \"def distance(vector):\\n\\n return np.sqrt(np.sum(np.square(vector)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9d6aba03a6409f9ab13897c9e147ec0\",\n \"score\": \"0.58781874\",\n \"text\": \"def euclidian_distance(vector_a, vector_b):\\r\\n\\r\\n return math.sqrt(sum([(vector_a - vector_b) ** 2 for vector_a, vector_b in zip(vector_a, vector_b)]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"552c5484cbf2bd029cff8c9cfd619ab1\",\n \"score\": \"0.58758956\",\n \"text\": \"def distance(a, b):\\n if a is None or b is None:\\n raise Exception(\\\"Error: Neither a nor b can be none.\\\")\\n elif len(a) != len(b):\\n raise Exception(\\\"Error: a and b should have the same dimension\\\")\\n sum = 0\\n for index in range(len(a)):\\n sum += (a[index] - b[index])**2\\n return round(math.sqrt(sum), 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"291403ba8924d35391970c414c7b437e\",\n \"score\": \"0.58720523\",\n \"text\": \"def dist_cpu(A, B, p):\\n sum = 0\\n for i in range(A.shape[0]):\\n for j in range(A.shape[1]):\\n sum += abs(A[i,j] - B[i,j])**p\\n return sum**(1/p)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d6e3c5226f662a83c855cc98a27b1b9c\",\n \"score\": \"0.5867506\",\n \"text\": \"def distance_point_point(a, b):\\n ab = subtract_vectors(b, a)\\n return length_vector(ab)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83ff10c9abe0bea86301139fe92228d6\",\n \"score\": \"0.58656734\",\n \"text\": \"def squared_dist(A):\\r\\n expanded_a = tf.expand_dims(A, 1)\\r\\n expanded_b = tf.expand_dims(A, 0)\\r\\n distances = tf.reduce_mean(tf.squared_difference(expanded_a, expanded_b), 2)\\r\\n return distances\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91bffd7513ad7e4e6421e4726dc240a7\",\n \"score\": \"0.5826428\",\n \"text\": \"def euclidean_distance(a, b):\\n return(\\n sqrt(\\n sum(\\n [(a_i - b_i)**2 for a_i, b_i in zip(a, b)]\\n )\\n )\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8359c323b358d437c8b6582bbf0c61c7\",\n \"score\": \"0.58201265\",\n \"text\": \"def distance(vec1, vec2):\\n return np.dot(vec1, vec2)/(np.linalg.norm(vec1)*np.linalg.norm(vec2))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a742570f410803c6f7203f5e51b3a9c\",\n \"score\": \"0.58172035\",\n \"text\": \"def distance_matrix_l1(x, y):\\n m = pairwise_difference_vector_batch(x, y)\\n \\n return m.abs().sum(2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c38aa5733c8bbc7db559fb796c8b7595\",\n \"score\": \"0.58076125\",\n \"text\": \"def pairwise_edist(A, B, keepdims=False):\\n A_norm = tf.reduce_sum(A ** 2, axis=1, keepdims=keepdims)\\n B_norm = tf.reduce_sum(B ** 2, axis=1, keepdims=keepdims)\\n pw_A_dot_B = tf.reduce_sum(tf.multiply(A, B), axis=1, keepdims=keepdims)\\n dist = tf.sqrt(A_norm + B_norm - 2 * pw_A_dot_B)\\n return dist\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0192c9f75ddc5c46e9e4d54f26fe63c1\",\n \"score\": \"0.57975763\",\n \"text\": \"def eucli_distance(a, b):\\n if not len(a) == len(b):\\n raise ValueError(\\\"a and b must be of the same size\\\")\\n return math.sqrt(np.power(a-b, 2).sum())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9014ccfd35f9867413835af60b74b16f\",\n \"score\": \"0.5791496\",\n \"text\": \"def euclidian_distance(a, b):\\r\\n return np.linalg.norm(a - b, 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ab65ebff8ae1427f9c809936e2d0c1a\",\n \"score\": \"0.578901\",\n \"text\": \"def distance_tensor(self, X: torch.Tensor, Nbrs: torch.Tensor,\\n box_size: Union[torch.Tensor, None], B: int, N: int,\\n M: int, d: int) -> torch.Tensor:\\n if box_size is not None and len(box_size) != d:\\n raise ValueError(\\\"Length of `box_size` must be equal to `d`\\\")\\n\\n flat_neighbors = torch.reshape(Nbrs, (-1, N * M))\\n neighbor_coords = torch.stack(\\n [X[b, flat_neighbors[b]] for b in range(B)])\\n neighbor_coords = torch.reshape(neighbor_coords, (-1, N, M, d))\\n D = neighbor_coords - torch.unsqueeze(X, 2)\\n if box_size is not None:\\n box_size = torch.reshape(box_size, (1, 1, 1, d))\\n D -= torch.round(D / box_size) * box_size\\n\\n return D\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e715c4f8b6d31937ea20dcbd4f94d0ef\",\n \"score\": \"0.5784734\",\n \"text\": \"def dist(vec1, vec2):\\n return np.linalg.norm(vec1-vec2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e7a46f3ec24a18f46a92c8d45f4e3e2\",\n \"score\": \"0.57786006\",\n \"text\": \"def get_distance(a, b):\\n return abs(a[0] - b[0]) + abs(a[1] - b[1])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80cd72af8d92189a8d76ed495f22d379\",\n \"score\": \"0.57643646\",\n \"text\": \"def distance_matrix(self, D: torch.Tensor) -> torch.Tensor:\\n return torch.sqrt(torch.sum(torch.mul(D, D), 3))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d24374af0a8cff8f3b27c0e8ba9682a\",\n \"score\": \"0.57495534\",\n \"text\": \"def squared_dist(A):\\n expanded_a = tf.expand_dims(A, 1)\\n expanded_b = tf.expand_dims(A, 0)\\n distances = tf.reduce_mean(tf.math.squared_difference(expanded_a, expanded_b), 2)\\n return distances\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13d635838a5fe52238dc23b885f5f748\",\n \"score\": \"0.5749381\",\n \"text\": \"def calc_dist(first, second):\\n\\tdistance = 0\\n\\tfor x in range(1,len(first)):\\n\\t\\tdistance += pow(first[x] - second[x], 2)\\n\\treturn math.sqrt(distance)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e17633d4606fe821c38c34e4c5ff465\",\n \"score\": \"0.57466865\",\n \"text\": \"def measure_distance(self, vector_1:pd.Series, vector_2:pd.Series) -> float:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"168a87b75d58bbdd383a07264a419f95\",\n \"score\": \"0.57386404\",\n \"text\": \"def cosine_distances(X, Y):\\r\\n M = len(X)\\r\\n if Y.ndim == 1:\\r\\n N = 1\\r\\n D = np.zeros((M,N))\\r\\n\\r\\n for m in range(0,M):\\r\\n dot = np.dot(X[m], Y)\\r\\n Ux = np.linalg.norm(X[m])\\r\\n Uy = np.linalg.norm(Y)\\r\\n\\r\\n s = dot / (Ux * Uy)\\r\\n\\r\\n D[m] = 1 - s\\r\\n else:\\r\\n N = len(Y)\\r\\n D = np.zeros((M,N))\\r\\n\\r\\n for m in range(0,M):\\r\\n for n in range(0,N):\\r\\n dot = np.dot(X[m], Y[n])\\r\\n Ux = np.linalg.norm(X[m])\\r\\n Uy = np.linalg.norm(Y[n])\\r\\n\\r\\n s = dot / (Ux * Uy)\\r\\n\\r\\n D[m,n] = 1 - s\\r\\n\\r\\n return D\",\n \"title\": \"\"\n },\n {\n \"docid\": \"734d7a4f8c195018fbafb699bd846b73\",\n \"score\": \"0.5736716\",\n \"text\": \"def pairwise_siamese_dist(self, A, B):\\n with tf.compat.v1.variable_scope(\\\"pair_siam_dist\\\"), tf.device(\\n self.next_device()):\\n xx = tf.expand_dims(A, -1)\\n xx = tf.tile(xx, tf.stack([1, 1, tf.shape(B)[0]]))\\n\\n yy = tf.expand_dims(B, -1)\\n yy = tf.tile(yy, tf.stack([1, 1, tf.shape(A)[0]]))\\n yy = tf.transpose(yy, perm=[2, 1, 0])\\n zz = tf.reduce_sum(tf.abs(xx - yy) * tf.exp(self.alpha), 1)\\n return zz\",\n \"title\": \"\"\n },\n {\n \"docid\": \"551d1ba8eeea8f02079ea6aca3ea1151\",\n \"score\": \"0.57265234\",\n \"text\": \"def distance_matrix_l2(x, y):\\n m = pairwise_difference_vector_batch(x, y)\\n \\n return m.pow(2).sum(2).sqrt().squeeze()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"097f14e07636252efd3eda83fe8ca62c\",\n \"score\": \"0.5726431\",\n \"text\": \"def difference_vecteur(a, b):\\n \\n return [a[0] - b[0], a[1] - b[1]]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1e826acf3518a1b12d18567b6fb0f10\",\n \"score\": \"0.5721511\",\n \"text\": \"def beam_distances(self):\\n dist = np.zeros(len(self.beams))\\n for n, beam in enumerate(self.beams):\\n dist[n] = beam.total_distance()\\n return dist\",\n \"title\": \"\"\n },\n {\n \"docid\": \"01355bb6f218f6b0b73ae4d767cbb581\",\n \"score\": \"0.57180476\",\n \"text\": \"def euclidean_distances(X):\\n\\n Y = X\\n X = _transpose_on_first_two_axes(X)\\n\\n XX = K.expand_dims(K.sum(K.square(X), axis=1), 0)\\n YY = _transpose_on_first_two_axes(XX)\\n\\n distance = _dot(X, Y)\\n distance_shapes = K.int_shape(distance)\\n distance *= -2\\n distance += XX\\n distance += YY\\n distance = K.maximum(distance,\\n K.constant(0, dtype=distance.dtype))\\n distance._keras_shape = distance_shapes\\n return distance\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f703c1930aae94c96b52941f46401944\",\n \"score\": \"0.5696638\",\n \"text\": \"def dist_sq(a, b):\\n return sum([(xa - xb) ** 2 for xa, xb in zip(a, b)])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29d2595d8a3f9070debcded043368517\",\n \"score\": \"0.56800634\",\n \"text\": \"def get_distances(centroid, points):\\n return np.linalg.norm(points - centroid, axis=1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8b6c08872e988f5f58dcc051676d898\",\n \"score\": \"0.56785434\",\n \"text\": \"def squared_euclidean_distance(a, b):\\n result = numpy.float32(0.0)\\n for i in range(len(a)):\\n result += (a[i] - b[i])**2\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82d7114065e54fbcfec6f9548a4eaad2\",\n \"score\": \"0.5678065\",\n \"text\": \"def cosine_distances(X, Y):\\n\\n M = X.shape[0]\\n N = Y.shape[0]\\n D = np.zeros([M, N])\\n\\n for i in range(M):\\n for j in range(N):\\n n_1 = np.sqrt(np.dot(X[i], X[i]))\\n n_2 = np.sqrt(np.dot(Y[j], Y[j]))\\n # This yield the cosine value of the two vectors, which range from 1 to -1\\n D[i, j] = np.dot(X[i], Y[j]) / (n_1 * n_2)\\n\\n # Larger value (closer to 1) means the two vectors have similar direction, so they\\n # should have smaller distance\\n return 1 - D\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e09d3d7171f5f847a8fb073ad2caee5\",\n \"score\": \"0.56737864\",\n \"text\": \"def calc_dis(point_list1, point_list2):\\r\\n y = []\\r\\n for i in range(point_list1.__len__()):\\r\\n y.append(point_list2)\\r\\n y = np.array(y)\\r\\n x=[]\\r\\n for i in range(point_list2.__len__()):\\r\\n x.append(point_list1)\\r\\n x = np.array(x)\\r\\n x = x.transpose(1, 0, 2)\\r\\n distance_matrix = np.linalg.norm(np.array(x) - np.array(y), axis=2)\\r\\n return distance_matrix\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23d73f5120348cd47fdcda47ed685a06\",\n \"score\": \"0.5671712\",\n \"text\": \"def pairwise_l2_distance(A, B):\\n row_norms_A = tf.math.reduce_sum(tf.square(A), axis=1)\\n row_norms_A = tf.reshape(row_norms_A, [-1, 1]) # Column vector.\\n\\n row_norms_B = tf.math.reduce_sum(tf.square(B), axis=1)\\n row_norms_B = tf.reshape(row_norms_B, [1, -1]) # Row vector.\\n\\n dist = row_norms_A - 2. * tf.matmul(A, B, transpose_b=True) + row_norms_B\\n return tf.math.maximum(dist, 0.)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f17710c371f07fe87682760e442a93a\",\n \"score\": \"0.5666328\",\n \"text\": \"def dist(self, b):\\n distance = sqrt((b.x - self.x) ** 2 + (b.y - self.y) ** 2)\\n return distance\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e045377b5fe9fe603cf5c878cd888650\",\n \"score\": \"0.5661958\",\n \"text\": \"def euclidian(A,B):\\n return np.linalg.norm(A-B)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c69b1355759f7189fe1275de58b23d24\",\n \"score\": \"0.5659475\",\n \"text\": \"def euclidean_distance(vector1, vector2):\\n z = 0\\n for (v1, v2) in zip(vector1, vector2):\\n z += (v1 - v2) ** 2\\n return math.sqrt(z)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72533805c3077426cc35e00448bb4427\",\n \"score\": \"0.5652093\",\n \"text\": \"def distance(a, b):\\r\\n x1, y1 = a[0], a[1]\\r\\n x2, y2 = b[0], b[1]\\r\\n return round(math.sqrt((x1-x2)**2 + (y1-y2)**2))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"169227d4de47447b759a496fd88d9c12\",\n \"score\": \"0.56466854\",\n \"text\": \"def calculate_distance(lhs, rhs):\\n return numpy.linalg.norm((numpy.array(lhs) - numpy.array(rhs)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f160015286ab4aec62b3d1d51740807f\",\n \"score\": \"0.56271565\",\n \"text\": \"def produit_vectoriel(a, b):\\n \\n return a[0] * b[1] - a[1] * b[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e58157569d33d5c2b7429807c98d3b4\",\n \"score\": \"0.5619837\",\n \"text\": \"def subtractMatrices(matrixA, matrixB):\\n return map(lambda i: map(lambda x, y: x - y, matrixA[i], matrixB[i]),\\n xrange(len(matrixA)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08a2c9fa437d258abda07054c6b13c8e\",\n \"score\": \"0.5617198\",\n \"text\": \"def compute_distances_one_loop(self, X):\\n num_test = X.shape[0]\\n num_train = self.X_train.shape[0]\\n dists = np.zeros((num_test, num_train))\\n for i in xrange(num_test):\\n dists[i] = np.linalg.norm(self.X_train - X[i], axis=1)\\n return dists\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5acedc0b30bd5fe1cf2fffbca00cdfac\",\n \"score\": \"0.5617071\",\n \"text\": \"def dist(a, b):\\n from math import sqrt\\n return sqrt((a.y - b.y)**2 + (a.x - b.x)**2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f87baaf1bac246cedae642c3769fdca2\",\n \"score\": \"0.5614947\",\n \"text\": \"def matrix_dist(X, Y):\\n return numpy.sqrt((X[:, 0] - Y[:, 0]) ** 2 + (X[:, 1] - Y[:, 1]) ** 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"118d6a4851403b31e82ca97d3618e4a0\",\n \"score\": \"0.56115663\",\n \"text\": \"def dist(a, b):\\n dx = a.x - b.x\\n dy = a.y - b.y\\n return math.sqrt(dx * dx + dy * dy)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a24f020b9ccb54ff82918465e039ec3\",\n \"score\": \"0.56030965\",\n \"text\": \"def distance(a,b):\\n n, m = len(a), len(b)\\n if n > m:\\n # Make sure n <= m, to use O(min(n,m)) space\\n a,b = b,a\\n n,m = m,n\\n current = range(n+1)\\n for i in range(1,m+1):\\n previous, current = current, [i]+[0]*n\\n for j in range(1,n+1):\\n add, delete = previous[j]+1, current[j-1]+1\\n change = previous[j-1]\\n if a[j-1] != b[i-1]:\\n change = change + 1\\n current[j] = min(add, delete, change)\\n return current[n]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e121742de1a96883b303cc1643f5a50\",\n \"score\": \"0.5577346\",\n \"text\": \"def distance_matrix(self) -> np.ndarray:\\n # TODO check handling of unknown distances\\n G = self.directed\\n selected_edges = [(u, v) for u, v, d in G.edges(data=True) if DIST in d]\\n return (\\n nx.to_numpy_array(\\n nx.to_undirected(G.edge_subgraph(selected_edges)), weight=DIST\\n )\\n ** 2\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f27ad03f8c3aafa703ef861cb3d073d\",\n \"score\": \"0.55584216\",\n \"text\": \"def _distance(feature0, feature1):\\n return np.power(feature0 - feature1, 2).sum()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"664d949332985e1f628edeacfdf679d0\",\n \"score\": \"0.5557304\",\n \"text\": \"def distance_point_point_sqrd(a, b):\\n ab = subtract_vectors(b, a)\\n return length_vector_sqrd(ab)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e82a00de70d0ce824591f74a24cd60b\",\n \"score\": \"0.5555838\",\n \"text\": \"def eucledian_norm(vec_a, vec_b):\\n distance = vec_a - vec_b\\n # return np.sqrt(np.sum(np.square(distance)))\\n # return np.sqrt(np.einsum('ij, ij->i', distance, distance))\\n return np.linalg.norm(distance, ord = 'fro', axis = 1)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":696,"cells":{"query_id":{"kind":"string","value":"e3681f8e80d992b6a48d659329f3e97f"},"query":{"kind":"string","value":"@ param query_embedds = (n, d) @ param target_embedds = (n, d) @ param img_ids = (n,)"},"positive_passages":{"kind":"list like","value":[{"docid":"5aac2fbdaeacbcc9c51d58942cf4ec1f","score":"0.6189335","text":"def ranking(query_embedds, target_embedds, img_ids):\n\n cos_sim = torch.mm(query_embedds,target_embedds.T)/ \\\n torch.mm(query_embedds.norm(2, dim=1, keepdim=True),\n target_embedds.norm(2, dim=1, keepdim=True).T)\n _, idx = torch.topk(cos_sim, len(query_embedds)//100, dim=1)\n top20 = idx.cpu().numpy()\n img_ids = np.array(img_ids)\n count = 0\n with open('answer.csv', 'w') as f:\n f.write(\"Descritpion_ID,Top_20_Image_IDs\\n\")\n for i, img_id in enumerate(img_ids):\n top_imgs = img_ids[top20[i]]\n top_imgs_str = \" \".join(list(top_imgs))\n text_id = img_id.split(\".\")[0]+\".txt\"\n f.write(text_id+\",\"+top_imgs_str+\"\\n\")\n if img_id in list(top_imgs):\n count+=1\n print(\"count\", count)","title":""}],"string":"[\n {\n \"docid\": \"5aac2fbdaeacbcc9c51d58942cf4ec1f\",\n \"score\": \"0.6189335\",\n \"text\": \"def ranking(query_embedds, target_embedds, img_ids):\\n\\n cos_sim = torch.mm(query_embedds,target_embedds.T)/ \\\\\\n torch.mm(query_embedds.norm(2, dim=1, keepdim=True),\\n target_embedds.norm(2, dim=1, keepdim=True).T)\\n _, idx = torch.topk(cos_sim, len(query_embedds)//100, dim=1)\\n top20 = idx.cpu().numpy()\\n img_ids = np.array(img_ids)\\n count = 0\\n with open('answer.csv', 'w') as f:\\n f.write(\\\"Descritpion_ID,Top_20_Image_IDs\\\\n\\\")\\n for i, img_id in enumerate(img_ids):\\n top_imgs = img_ids[top20[i]]\\n top_imgs_str = \\\" \\\".join(list(top_imgs))\\n text_id = img_id.split(\\\".\\\")[0]+\\\".txt\\\"\\n f.write(text_id+\\\",\\\"+top_imgs_str+\\\"\\\\n\\\")\\n if img_id in list(top_imgs):\\n count+=1\\n print(\\\"count\\\", count)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"2a0d8c3f6444f00756c4da15a4161f82","score":"0.6125822","text":"def get_images(k):\n\n keys = np.linspace(0, 99, 50)\n values = np.random.randn(50, 50)\n image_embeddings = dict(zip(keys, values))\n\n # with open(\"image_embeddings.pickle\", \"rb\") as f:\n # image_embeddings = pickle.load(f)\n\n with open(\"url.pickle\", \"rb\") as urls:\n url = pickle.load(urls)\n\n # GETTING THE SIMILAR IDS\n caption_embed = get_query.query() # embedded text\n caption_embed = fu.normalize(caption_embed)\n ids, embeddings = (shit.split(image_embeddings))\n for embed in embeddings:\n embed.reshape((50, 1))\n\n embed_values = []\n for e in embeddings:\n embed_values.append((caption_embed @ e).data)\n embed_values = np.array(embed_values)\n\n max_values = np.argsort(embed_values, axis=0)[:k].flatten()\n similar_image_ids = []\n\n for m in max_values:\n similar_image_ids.append(ids[m])\n # END ID ACQUISITION\n\n image_links = []\n for img_id in similar_image_ids:\n image_links.append(url[img_id])\n\n for i in image_links:\n response = requests.get(i)\n img = Image.open(BytesIO(response.content))\n\n return similar_image_ids","title":""},{"docid":"e22e73abe8ddacf0792bf8c06e485f86","score":"0.5829802","text":"def query(self, images):\n if self.buffer_size == 0:\n return images\n return_images = []\n for image in images:\n image = torch.unsqueeze(image.data, 0)\n if self.img_num < self.buffer_size:\n self.img_num = self.img_num + 1\n self.image_buffer.append(image)\n return_images.append(image)\n else:\n use_buffer = np.random.random() < self.buffer_ratio\n if use_buffer:\n random_id = np.random.randint(0, self.buffer_size)\n image_tmp = self.image_buffer[random_id].clone()\n self.image_buffer[random_id] = image\n return_images.append(image_tmp)\n else:\n return_images.append(image)\n return_images = torch.cat(return_images, 0)\n return return_images","title":""},{"docid":"4c692ea5aa1ea036507e41f5991b7013","score":"0.5704483","text":"def query(self, images):\n if self.pool_size == 0:\n return images\n return_images = []\n for image in images:\n image = torch.unsqueeze(image.data, 0)\n if self.num_imgs < self.pool_size:\n self.num_imgs = self.num_imgs + 1\n self.images.append(image)\n return_images.append(image)\n else:\n p = random.uniform(0, 1)\n if p > 0.5:\n random_id = random.randint(0, self.pool_size - 1)\n tmp = self.images[random_id].clone()\n self.images[random_id] = image\n return_images.append(tmp)\n else:\n return_images.append(image)\n return_images = torch.cat(return_images, 0)\n return return_images","title":""},{"docid":"4305676d81fddbf49b61c17aaa65a782","score":"0.56069136","text":"def handle_flikr_embed(session,post_dict):\n logging.debug(\"Processing flikr embed\")\n # Extract image link from post dict\n thumbnail_link = post_dict[\"thumbnail_url\"]\n # Download videos if there are any\n media_id_list = download_image_link(session,thumbnail_link)\n logging.debug(\"Finished downloading flikr embed\")\n return media_id_list","title":""},{"docid":"2a8d15e4f2fa1496522854b4f6d7adfe","score":"0.5513861","text":"def pack_query_data(people_imgs):\n uids = list(range(-len(people_imgs), 0))\n pids = list(range(-len(people_imgs), 0))\n imgs = None\n\n for person_img in people_imgs:\n pil_img = Image.fromarray(cv2.cvtColor(person_img, cv2.COLOR_BGR2RGB))\n img = TimingRecorder.transformer(pil_img)\n img = img.view(1, 3, 256, 128)\n if imgs is None:\n imgs = img\n else:\n imgs = torch.cat((imgs, img), dim=0)\n\n return uids, pids, imgs","title":""},{"docid":"26426e7880382deaafb6dc63fcbf4ba3","score":"0.55134726","text":"def _get_multiple_evidences_predictions_normal(self, query_ids: torch.Tensor,\n query_contents: torch.Tensor,\n query_lens: np.ndarray,\n query_sources: torch.Tensor,\n evd_doc_ids: torch.Tensor,\n evd_doc_contents: torch.Tensor,\n evd_docs_lens: np.ndarray,\n evd_sources: torch.Tensor,\n labels: np.ndarray,\n n: int, **kargs) -> torch.Tensor:\n evd_count_per_query = kargs[KeyWordSettings.EvidenceCountPerQuery] # (B, )\n assert evd_doc_ids.size() == evd_docs_lens.shape\n assert query_ids.size(0) == evd_doc_ids.size(0)\n assert query_lens.shape == labels.size()\n assert query_contents.size(0) == evd_doc_contents.size(0) # = batch_size\n _, L = query_contents.size()\n batch_size = query_ids.size(0)\n # prunning at this step to remove padding\\\n e_lens, e_conts, q_conts, q_lens = [], [], [], []\n expaned_labels = []\n for evd_cnt, q_cont, q_len, evd_lens, evd_doc_cont, label in \\\n zip(evd_count_per_query, query_contents, query_lens, evd_docs_lens, evd_doc_contents, labels):\n evd_cnt = int(torch_utils.cpu(evd_cnt).detach().numpy())\n e_lens.extend(list(evd_lens[:evd_cnt]))\n e_conts.append(evd_doc_cont[:evd_cnt, :]) # stacking later\n q_lens.extend([q_len] * evd_cnt)\n q_conts.append(q_cont.unsqueeze(0).expand(evd_cnt, L))\n expaned_labels.extend([int(torch_utils.cpu(label).detach().numpy())] * evd_cnt)\n # concat\n e_conts = torch.cat(e_conts, dim = 0) # (n1 + n2 + ..., R)\n e_lens = np.array(e_lens) # (n1 + n2 + ..., )\n q_conts = torch.cat(q_conts, dim = 0) # (n1 + n2 + ..., R)\n q_lens = np.array(q_lens)\n assert q_conts.size(0) == q_lens.shape[0] == e_conts.size(0) == e_lens.shape[0]\n\n d_new_indices, d_old_indices = torch_utils.get_sorted_index_and_reverse_index(e_lens)\n e_lens = my_utils.gpu(torch.from_numpy(e_lens), self._use_cuda)\n q_new_indices, q_restoring_indices = torch_utils.get_sorted_index_and_reverse_index(query_lens)\n query_lens = my_utils.gpu(torch.from_numpy(query_lens), self._use_cuda)\n # query_lens = my_utils.gpu(torch.from_numpy(query_lens), self._use_cuda)\n additional_paramters = {\n KeyWordSettings.Query_lens: query_lens,\n KeyWordSettings.Doc_lens: evd_docs_lens,\n KeyWordSettings.DocLensIndices: (d_new_indices, d_old_indices, e_lens),\n KeyWordSettings.QueryLensIndices: (q_new_indices, q_restoring_indices, query_lens),\n KeyWordSettings.QuerySources: query_sources,\n KeyWordSettings.DocSources: evd_sources,\n KeyWordSettings.TempLabel: labels,\n KeyWordSettings.DocContentNoPaddingEvidence: e_conts,\n KeyWordSettings.QueryContentNoPaddingEvidence: q_conts,\n KeyWordSettings.EvidenceCountPerQuery: evd_count_per_query,\n KeyWordSettings.FIXED_NUM_EVIDENCES: n\n }\n predictions = self._net(query_contents, evd_doc_contents, **additional_paramters) # (B, )\n # labels.unsqueeze(-1).expand(batch_size, n).reshape(batch_size * n)\n # labels = torch_utils.gpu(torch.from_numpy(np.array(expaned_labels)), self._use_cuda)\n # print(\"Labels: \", labels)\n # mask = (evd_doc_ids >= 0).view(batch_size * n).float()\n return self._loss_func(predictions, labels.float())","title":""},{"docid":"f6844efe7ac0d2f70fb8945117d175ff","score":"0.54689616","text":"def get_image_list(releaseid):\n ...","title":""},{"docid":"c36dbba6bc9e1aeb9366f525170254c5","score":"0.5445434","text":"def query(self, images):\n if self.pool_size == 0:\n return images\n return_images = []\n for image in images:\n if self.num_imgs < self.pool_size:\n self.num_imgs += 1\n self.images.append(image)\n return_images.append(image)\n else:\n p = np.random.rand()\n if p > 0.5:\n random_id = np.random.randint(0, self.pool_size)\n tmp = self.images[random_id].copy()\n self.images[random_id] = image\n return_images.append(tmp)\n else:\n return_images.append(image)\n return np.array(return_images)","title":""},{"docid":"c16af1b378e0e96b52c0f5b88773092e","score":"0.54368937","text":"def selectRows(sentEmbeds, idxs):\n results = []\n for idx in idxs:\n if idx == -1:\n results.append(torch.zeros((1, 2 * configuration['kiperwasser']['rnnUnitNum']), dtype=dtype).to(device))\n else:\n results.append(sentEmbeds[idx].view(1, -1).to(device))\n return torch.cat(results)","title":""},{"docid":"6fbf994d8832957b36ce0c53c704befc","score":"0.5318698","text":"def getImgIds(self, quesIds=[], quesTypes=[], ansTypes=[]):\n quesIds = quesIds if type(quesIds) == list else [quesIds]\n quesTypes = quesTypes if type(quesTypes) == list else [quesTypes]\n ansTypes = ansTypes if type(ansTypes) == list else [ansTypes]\n\n if len(quesIds) == len(quesTypes) == len(ansTypes) == 0:\n anns = self.dataset['annotations']\n else:\n if not len(quesIds) == 0:\n anns = sum([self.qa[quesId] for quesId in quesIds if quesId in self.qa], [])\n else:\n anns = self.dataset['annotations']\n anns = anns if len(quesTypes) == 0 else [\n ann for ann in anns if ann['question_type'] in quesTypes]\n anns = anns if len(ansTypes) == 0 else [\n ann for ann in anns if ann['answer_type'] in ansTypes]\n ids = [ann['image_id'] for ann in anns]\n return ids","title":""},{"docid":"35dcf57bd7045a81857eb964b2f9a9d4","score":"0.53042674","text":"def query(self, images):\r\n if self.pool_size == 0: # if the buffer size is 0, do nothing\r\n return images\r\n return_images = []\r\n for image in images:\r\n image = torch.unsqueeze(image.data, 0)\r\n if self.num_imgs < self.pool_size: # if the buffer is not full; keep inserting current images to the buffer\r\n self.num_imgs = self.num_imgs + 1\r\n self.images.append(image)\r\n return_images.append(image)\r\n else:\r\n p = random.uniform(0, 1)\r\n if p > 0.5: # by 50% chance, the buffer will return a previously stored image, and insert the current image into the buffer\r\n random_id = random.randint(0, self.pool_size - 1) # randint is inclusive\r\n tmp = self.images[random_id].clone()\r\n self.images[random_id] = image\r\n return_images.append(tmp)\r\n else: # by another 50% chance, the buffer will return the current image\r\n return_images.append(image)\r\n return_images = torch.cat(return_images, 0) # collect all the images and return\r\n return return_images","title":""},{"docid":"a601f179348f7ceb0465b44616276175","score":"0.5228106","text":"def update_images(self):","title":""},{"docid":"3443bad0ccfb4a22cd49606fda782f03","score":"0.5216751","text":"def loadImgs(self, ids=[]):\n if type(ids) == list:\n return [self.imgs[id] for id in ids]\n elif type(ids) == int:\n return [self.imgs[ids]]","title":""},{"docid":"5a0d7b609fcfa2106f3be5660bcbd2df","score":"0.52092993","text":"def run(conn, params):\n images = []\n\n if params.get(\"Data_Type\") == 'Dataset':\n for dsId in params[\"IDs\"]:\n dataset = conn.getObject(\"Dataset\", dsId)\n if dataset:\n for image in dataset.listChildren():\n images.append(image)\n\n if len(images) == 0:\n return None\n\n for image in images:\n print(\"---- Processing image\", image.id)\n return images","title":""},{"docid":"cab34cf6a80e977703a0613d3b9d2d85","score":"0.51977825","text":"def conjunction(images):\n pass","title":""},{"docid":"7fee612bb560f597bf3ad8325b0fdd02","score":"0.5192922","text":"def query(self, images):\n if self.pool_size == 0: # if the buffer size is 0, do nothing\n return images\n\n return_images = []\n\n for image in images:\n image = torch.unsqueeze(image.data, 0)\n if self.num_imgs < self.pool_size: # if the buffer is not full; keep inserting current images to the buffer\n self.num_imgs = self.num_imgs + 1\n self.images.append(image)\n return_images.append(image)\n else:\n p = random.uniform(0, 1)\n if p > 0.5: # by 50% chance, the buffer will return a previously stored image, and insert the current image into the buffer\n random_id = random.randint(\n 0, self.pool_size - 1) # randint is inclusive\n tmp = self.images[random_id].clone()\n self.images[random_id] = image\n return_images.append(tmp)\n else: # by another 50% chance, the buffer will return the current image\n return_images.append(image)\n # collect all the images and return\n return_images = torch.cat(return_images, 0)\n return return_images","title":""},{"docid":"c3f9929a1405b247e339098908966709","score":"0.509739","text":"def call(self, inputs):\n batch_size, h, w = tf.shape(inputs)[0], tf.shape(inputs)[1], tf.shape(inputs)[2]\n i = tf.range(w)\n j = tf.range(h)\n x_emb = self.col_embed(i)\n y_emb = self.row_embed(j)\n single_img_emb = tf.concat([\n tf.tile(x_emb[None], (h, 1, 1)),\n tf.tile(y_emb[:, None], (1, w, 1)),\n ],\n axis=-1)\n\n batch_emb = tf.tile(single_img_emb[None], (batch_size, 1, 1, 1))\n return batch_emb","title":""},{"docid":"cbedda7c25a4e5719821d0fc14ee2b00","score":"0.50831044","text":"def update_imageset_ids(experiments, reflections):\n # input a list of ordered matching experiments and reflection tables.\n\n next_iset_id = 0\n imagesets_found = OrderedSet()\n for expt, table in zip(experiments, reflections):\n if \"imageset_id\" in table:\n assert len(set(table[\"imageset_id\"])) == 1\n iset = expt.imageset\n if iset not in imagesets_found:\n imagesets_found.add(iset)\n table[\"imageset_id\"] = flex.int(table.size(), next_iset_id)\n next_iset_id += 1\n else:\n iset_id = imagesets_found.index(iset)\n table[\"imageset_id\"] = flex.int(table.size(), iset_id)\n return reflections","title":""},{"docid":"7dd6ea39d2a705595f93509f91dd5810","score":"0.50658953","text":"def processImages(self, imsq):\n pass","title":""},{"docid":"b27466e9652a4b147e79196ab24e143b","score":"0.50580966","text":"def _embed(embedders, xg, mode=None):\n\n if mode is not None:\n for e in embedders:\n e.mode = mode\n\n return torch.cat([e(xg_j) for e, xg_j in zip(embedders, xg.T)], dim=1)","title":""},{"docid":"62675314977c217eac9e8f1f6331494b","score":"0.50363404","text":"def generate_batch(images_embeddings, indexed_captions, batch_size, max_len=None):\n\n # added replace=False to avoid repetitions\n batch_indexes = np.random.choice(len(images_embeddings), batch_size, replace=False)\n batch_image_embeddings = images_embeddings[batch_indexes]\n captions_of_batch_images = indexed_captions[batch_indexes].tolist()\n\n # a little brutto\n batch_captions = []\n captions_batch_indexes = np.random.choice(5, batch_size)\n for i,captions in enumerate(captions_of_batch_images):\n batch_captions += [captions[captions_batch_indexes[i]]]\n\n\n batch_captions_matrix = batch_captions_to_matrix(batch_captions, 0, max_len)\n\n return {decoder.img_embeds: batch_image_embeddings,\n decoder.sentences: batch_captions_matrix}","title":""},{"docid":"71cd25755e41e3e0aec5ffeecfca45cc","score":"0.50174147","text":"def disjunction(images):\n pass","title":""},{"docid":"437acf87f30c64065fe387e2228ee1d3","score":"0.5012557","text":"def queue_image(self, image_id):","title":""},{"docid":"a3a5b9fd3fe746eb865a40f04ed92156","score":"0.50075275","text":"def getQuesIds(self, imgIds=[], quesTypes=[], ansTypes=[]):\n imgIds = imgIds if type(imgIds) == list else [imgIds]\n quesTypes = quesTypes if type(quesTypes) == list else [quesTypes]\n ansTypes = ansTypes if type(ansTypes) == list else [ansTypes]\n\n if len(imgIds) == len(quesTypes) == len(ansTypes) == 0:\n anns = self.dataset['annotations']\n else:\n if not len(imgIds) == 0:\n anns = sum([self.imgToQA[imgId] for imgId in imgIds if imgId in self.imgToQA], [])\n else:\n anns = self.dataset['annotations']\n anns = anns if len(quesTypes) == 0 else [\n ann for ann in anns if ann['question_type'] in quesTypes]\n anns = anns if len(ansTypes) == 0 else [\n ann for ann in anns if ann['answer_type'] in ansTypes]\n ids = [ann['question_id'] for ann in anns]\n return ids","title":""},{"docid":"63d09bb32009c7c86361258db6b4be89","score":"0.49977264","text":"def build_image_embeddings(self):\n\n # parameter initialization\n batch_norm_params = {\n \"is_training\": False,\n \"trainable\": False,\n # decay for the moving averages\n \"decay\": 0.9997,\n # epsilon to prevent 0s in variance\n \"epsilon\": 0.001,\n # collection containing the moving mean and moving variance\n \"variables_collections\": {\n \"beta\": None,\n \"gamma\": None,\n \"moving_mean\": [\"moving_vars\"],\n \"moving_variance\": [\"moving_vars\"],\n }\n }\n\n stddev = 0.1,\n dropout_keep_prob = 0.8\n\n with tf.variable_scope(\"InceptionV3\", \"InceptionV3\", [self.images]) as scope:\n with slim.arg_scope(\n [slim.conv2d, slim.fully_connected],\n weights_regularizer=None,\n trainable=False):\n with slim.arg_scope(\n [slim.conv2d],\n weights_initializer=tf.truncated_normal_initializer(stddev=stddev),\n activation_fn=tf.nn.relu,\n normalizer_fn=slim.batch_norm,\n normalizer_params=batch_norm_params):\n net, end_points = inception_v3_base(self.images, scope=scope)\n with tf.variable_scope(\"logits\"):\n shape = net.get_shape()\n net = slim.avg_pool2d(net, shape[1:3], padding=\"VALID\", scope=\"pool\")\n net = slim.dropout(\n net,\n keep_prob=dropout_keep_prob,\n is_training=False,\n scope=\"dropout\")\n net = slim.flatten(net, scope=\"flatten\")\n\n # add summaries\n for v in end_points.values():\n tf.contrib.layers.summaries.summarize_activation(v)\n\n self.inception_variables = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope=\"InceptionV3\")\n\n # map inception output(net) into embedding space\n with tf.variable_scope(\"image_embedding\") as scope:\n image_embeddings = tf.contrib.layers.fully_connected(\n inputs=net,\n num_outputs=self.embedding_size,\n activation_fn=None,\n weights_initializer=self.initializer,\n biases_initializer=None,\n scope=scope)\n\n # save the embedding size in the graph\n tf.constant(self.embedding_size, name=\"embedding_size\")\n\n self.image_embeddings = image_embeddings","title":""},{"docid":"fee9a2c8e53e8b89649db295c889603b","score":"0.49957043","text":"def compile_embedding(self, ids):\n pass","title":""},{"docid":"5aa722c7bbe3ba0105cde528ed3c6e38","score":"0.49870318","text":"def prepare_queries(self, queries: List[Instances], mask):\n for x in queries:\n x.pred_boxes = Boxes(x.pred_boxes.tensor.detach())\n x.scores = x.scores.detach()\n x.features = x.features.detach()\n \n bs, vis_dim, box_dim = len(queries), queries[0].features.shape[-1], self.hidden_dim\n device = queries[0].features.device\n \n num_queries = max([len(x) for x in queries]) * self.num_duplicates\n\n query_masks = torch.ones((num_queries, num_queries, bs), dtype=torch.bool, device=device)\n query_vis_embs = torch.zeros((num_queries, bs, vis_dim), dtype=torch.float32, device=device)\n query_box_embs = torch.zeros((num_queries, bs, box_dim), dtype=torch.float32, device=device)\n query_boxes = torch.zeros((num_queries, bs, 4), dtype=torch.float32, device=device)\n query_classes = torch.full((num_queries, bs), -1, dtype=torch.int64, device=device)\n query_scores = torch.zeros((num_queries, bs), dtype=torch.float32, device=device)\n for i, x in enumerate(queries):\n size = len(x) * self.num_duplicates\n\n query_vis_embs[:size, i, :] = x.features.repeat(1, self.num_duplicates).view(-1, vis_dim)\n \n box = x.pred_boxes.tensor\n query_boxes[:size, i, :] = box.repeat(1, self.num_duplicates).view(-1, 4)\n \n box_embs_x = self.box_encoding(x.image_size, x.pred_boxes.tensor.clone())\n query_box_embs[:size, i, :] = box_embs_x.repeat(1, self.num_duplicates).view(-1, box_dim)\n\n classes_x = x.pred_classes.unsqueeze(-1).repeat(1, self.num_duplicates).view(-1)\n query_classes[:size, i] = classes_x\n\n scores_x = x.scores.unsqueeze(-1).repeat(1, self.num_duplicates).view(-1)\n query_scores[:size, i] = scores_x\n\n for q in range(max([len(x) for x in queries])):\n stx = q * self.num_duplicates\n end = (q + 1) * self.num_duplicates\n query_masks[stx:end, stx:end, :] = False\n\n query_vis_embs = self.query_norm(self.query_proj(query_vis_embs))\n query_instance = Instances(image_size=tuple(mask.shape[-2:]))\n query_instance.query_feat = query_vis_embs + query_box_embs\n query_instance.query_boxes = query_boxes\n query_instance.query_masks = query_masks\n query_instance.query_classes = query_classes\n query_instance.query_scores = query_scores\n\n return query_instance","title":""},{"docid":"6666ab477e0848306c535fb2bda95a3c","score":"0.49816033","text":"def handle_imgur_videos(session,post_dict):# NEW TABLES\n logging.debug(\"Processing imgur videos\")\n # Extract video links from post dict\n imgur_urls = []\n video_items = post_dict[\"player\"]\n for video_item in video_items:\n embed_code = video_item[\"embed_code\"]\n # u'<iframe class=\"imgur-embed\" width=\"100%\" height=\"720\" frameborder=\"0\" src=\"http://i.imgur.com/wSBlRyv.gifv#embed\"></iframe>'\n # http://i.imgur.com/wSBlRyv.gifv\n if embed_code:\n # Process links so YT-DL can understand them\n logging.debug(\"embed_code: \"+repr(embed_code))\n embed_url_regex =\"\"\"src=[\"']([^?\"'#]+)\"\"\"\n embed_url_search = re.search(embed_url_regex, embed_code, re.IGNORECASE|re.DOTALL)\n if embed_url_search:\n embed_url = embed_url_search.group(1)\n imgur_urls.append(embed_url)\n continue\n\n logging.debug(\"imgur_urls: \"+repr(imgur_urls))\n\n # Download videos if there are any\n media_id_list = run_yt_dl_multiple(\n session = session,\n download_urls = imgur_urls,\n extractor_used=\"video_handlers.handle_imgur_videos()\",\n )\n logging.debug(\"Finished downloading imgur_video embeds\")\n return media_id_list","title":""},{"docid":"ff96acb17ad79439fb15f971a9951f4a","score":"0.497084","text":"def get_batch(self):\r\n\r\n indices = np.random.choice(self.dataset_len, self.batch_size) # Sample non-repeated indices\r\n img_batch, hair_tags, eye_tags, face_tags, glass_tags = [], [], [], [], []\r\n for i in indices:\r\n img, hair_tag, eye_tag, face_tag, glass_tag = self.dataset.get_item(i)\r\n img_batch.append(img.unsqueeze(0))\r\n #print(img.shape)\r\n hair_tags.append(hair_tag.unsqueeze(0))\r\n #print(hair_tag.shape)\r\n #print(hair_tag.unsqueeze(0).shape) #torch.Size([1, 6])\r\n #input()\r\n eye_tags.append(eye_tag.unsqueeze(0)) #unsqueeze dim-0 for later concatenation\r\n face_tags.append(face_tag.unsqueeze(0))\r\n glass_tags.append(glass_tag.unsqueeze(0))\r\n img_batch = torch.cat(img_batch, 0)\r\n #print(img_batch)\r\n #print(img_batch.shape) #torch.Size([batch_size, 3, 128, 128])\r\n hair_tags = torch.cat(hair_tags, 0)\r\n #print(hair_tags)\r\n #print(hair_tags.shape) #torch.Size([128, 6])\r\n eye_tags = torch.cat(eye_tags, 0)\r\n #print(eye_tags.shape) #torch.Size([128, 4])\r\n face_tags= torch.cat(face_tags, 0)\r\n #print(face_tags.shape) #torch.Size([128, 3])\r\n glass_tags= torch.cat(glass_tags, 0)\r\n #print(glass_tags.shape) #torch.Size([128, 2])\r\n #input()\r\n return img_batch, hair_tags, eye_tags, face_tags, glass_tags","title":""},{"docid":"99f9715d6d6b0be63dea1c88e49d7b2a","score":"0.49688542","text":"def pull(self, queries):\n self.start_queue()\n self.results = []\n threads = []\n self.info(\"Saving images in: %s\" % self.path)\n for query in queries:\n self.info(\"Searching for \\\"%s\\\" images...\" % query)\n start = 0\n while start < self.max_results:\n api_url = self.make_api_url(query, start)\n if self.is_url_in_history(api_url):\n self.info(\"Already have %s\" % api_url)\n start += 4 # Normal number of results.\n continue\n self.put_url_in_history(api_url)\n data = self.http_get(api_url)\n if data:\n msg = json.loads(data)\n if msg['responseStatus'] == 200 and msg['responseData']:\n # Good response.\n newresults = msg['responseData']['results']\n if not len(newresults):\n self.warn(\"No results in response: %s\" % msg)\n time.sleep(3)\n break\n self.results += newresults\n start += len(newresults)\n # Retrieve.\n for result in newresults:\n image_url = result['unescapedUrl']\n image_id = self.make_hash(image_url)\n image_path = self.get_std_image_path(image_id)\n if 'dogdrip.net/' in image_url:\n # Exceptionally slow domain. :-)\n self.info(\"Skipping %s\" % image_url)\n if self.is_url_in_history(image_url):\n # Seen it already.\n self.info(\"Already have %s\" % image_url)\n else:\n # Put URL in history, to avoid repeats.\n self.put_url_in_history(image_url)\n # Join queue to retrieve image content.\n self.q.put(image_url)\n else:\n # Bad response.\n details = msg.get('responseDetails', '')\n if details.startswith('qps rate exceeded'):\n self.warn('Google just complained about ' +\\\n 'too many queries per second. Pausing...')\n time.sleep(15)\n else:\n self.warn(\"Response not OK: %s\" % msg)\n time.sleep(3)\n break\n else:\n # No data in response.\n self.warn(\"No data from %s\" % api_url)\n time.sleep(3)\n break\n # Be nice to Google.\n time.sleep(1.5)\n # Wait for workers to finish processing the queue.\n self.q.join()\n # Summarise results.\n std_count = len(self.get_paths('.std'))\n if not std_count:\n self.warn(\"No images found.\")\n self.info(\"There are %d images.\" % std_count)","title":""},{"docid":"109e3f5b2d4c5cdc9382f971f70c4f28","score":"0.49677628","text":"def embed(\n docs: Union[DocumentArray, DocumentArrayMemmap],\n embed_model: AnyDNN,\n device: str = 'cpu',\n) -> None:\n fm = get_framework(embed_model)\n globals()[f'_set_embeddings_{fm}'](docs, embed_model, device)","title":""},{"docid":"e6f1f8c5da25e46c88b1f684d56c0865","score":"0.49635997","text":"def get_concat_embeddings(self, word_id_batch: torch.Tensor,\r\n tag_id_batch: torch.Tensor,\r\n deprel_id_batch: torch.Tensor) -> torch.Tensor:\r\n ##****BEGIN YOUR CODE****\r\n word_id_batch_temp = self.word_embed(word_id_batch)\r\n reshape_word = self.reshape_embedded(word_id_batch_temp)\r\n tag_id_batch_temp = self.tag_embed(tag_id_batch)\r\n reshape_tag = self.reshape_embedded(tag_id_batch_temp)\r\n deprel_id_batch_temp = self.deprel_embed(deprel_id_batch)\r\n reshape_deprel = self.reshape_embedded(deprel_id_batch_temp)\r\n x = torch.cat([reshape_word, reshape_tag, reshape_deprel], -1)\r\n ##****END YOUR CODE****\r\n return x","title":""},{"docid":"b4264b3b2f9b502c5cab62e83a87888a","score":"0.49584693","text":"def all(ctx):\n print(\"Pushing all images\")","title":""},{"docid":"89b538720820a544b67f4cd3db4c4fbb","score":"0.49465153","text":"def _filter_images(self):\n self.new_images = []\n self.total_new_images = 0\n for image_id in self.new_image_ids:\n self.total_new_images += 1\n self.new_images.append(self.images[image_id])","title":""},{"docid":"c2e397fb48d52c84acfcf795c6ded174","score":"0.4944695","text":"def loadImgs(self, ids=[]):\n if _isArrayLike(ids):\n return [self.imgs[id] for id in ids]\n elif type(ids) == int:\n return [self.imgs[ids]]","title":""},{"docid":"1f5b7c42e61cda7b06067c5b64b241a4","score":"0.49264753","text":"def get_imgs_by_ids(img_ids, coco, c, path_to_imgs):\n imgs = coco.loadImgs(img_ids)\n X = np.empty((c.batch_size, c.img_height, c.img_width, 3), dtype=np.float)\n for i, img in enumerate(imgs):\n path = os.path.join(path_to_imgs, img['file_name'])\n X[i] = image.img_to_array(image.load_img(path,\n target_size=[c.img_height, c.img_width]))/127.5 - 1\n return X","title":""},{"docid":"70a9c0f45f8717a559e9826ae97f2c1f","score":"0.49138683","text":"def extract_references(cls, img, query_size, container_size):\n\n num_containers_row = int(np.floor(img.shape[0] / container_size))\n num_containers_col = int(np.floor(img.shape[1] / container_size))\n\n windows = np.zeros((num_containers_row * num_containers_col * 4, query_size, query_size))\n win_idx = 0\n\n mean_all, std_all = cls.moments(img, query_size)\n\n for y_contain in range(1, num_containers_row+1):\n for x_contain in range(1, num_containers_col+1):\n temp = img[(y_contain - 1) * container_size: min(img.shape[0],\n y_contain * container_size),\n (x_contain - 1) * container_size: min(img.shape[1],\n x_contain * container_size)]\n\n mean_contain = mean_all[(y_contain - 1) * container_size + query_size - 1: min(\n mean_all.shape[0] - query_size,\n (y_contain - 1) * container_size + container_size),\n (x_contain - 1) * container_size + query_size - 1:min(\n mean_all.shape[1] - query_size,\n (x_contain - 1) * container_size + container_size)]\n\n std_contain = std_all[\n (y_contain - 1) * container_size + query_size - 1:min(\n mean_all.shape[0] - query_size, (\n y_contain - 1) * container_size + container_size),\n (x_contain - 1) * container_size + query_size - 1:min(\n mean_all.shape[1] - query_size, (\n x_contain - 1) * container_size + container_size)]\n\n y, x = np.where(mean_contain == mean_contain.max())\n if np.prod(y.shape) == 1:\n windows[win_idx, :, :] = temp[int(y):int(y + query_size),\n int(x):int(x + query_size)]\n win_idx = win_idx + 1\n \n y, x = np.where(mean_contain == mean_contain.min())\n if np.prod(y.shape) == 1:\n windows[win_idx, :, :] = temp[int(y):int(y + query_size),\n int(x):int(x + query_size)]\n win_idx = win_idx + 1\n \n y, x = np.where(std_contain == std_contain.max())\n if np.prod(y.shape) == 1:\n windows[win_idx, :, :] = temp[int(y):int(y + query_size),\n int(x):int(x + query_size)]\n win_idx = win_idx + 1\n \n y, x = np.where(std_contain == std_contain.min())\n if np.prod(y.shape) == 1:\n windows[win_idx, :, :] = temp[int(y):int(y + query_size),\n int(x):int(x + query_size)]\n win_idx = win_idx + 1\n\n return windows.copy()","title":""},{"docid":"bcf109783809ee5f22c5af9187f6dbfa","score":"0.48999664","text":"def test_embed_list_in_object(self):\n\n obj_id = self.get_list_with_min_size(1)[0].id\n\n # Make sure that this object has a list attached to it.\n resp = self.client.get(reverse(self.api_details_url, kwargs={'pk': obj_id}), {'embed': ','.join([self.embedded_list_ids])})\n self.assertGreater(len(resp.data[self.embedded_list_ids]), 0)\n\n # Make sure the list is of ids integers:\n for obj in resp.data[self.embedded_list_ids]:\n self.assertIsInstance(obj, int)\n\n # Get the object with the embedded list\n resp = self.client.get(reverse(\n self.api_details_url, kwargs={'pk': obj_id}\n ) + '?embed=%s' % self.embedded_obj_p)\n self.assertGreater(len(resp.data[self.embedded_obj_p]), 0)\n\n # Make sure the list is now embedded\n for obj in resp.data[self.embedded_obj_p]:\n self.assertIn('id', obj)","title":""},{"docid":"7ba4e2c6c944021c1fbe91260b313f2f","score":"0.48985377","text":"def create_position_ids_from_inputs_embeds(self, inputs_embeds):\n input_shape = inputs_embeds.size()[:-1]\n sequence_length = input_shape[1]\n\n position_ids = torch.arange(\n self.padding_idx + 1,\n sequence_length + self.padding_idx + 1,\n dtype=torch.long,\n device=inputs_embeds.device,\n )\n return position_ids.unsqueeze(0).expand(input_shape)","title":""},{"docid":"c8295a19c5cfd8a02c9d630818b03b7e","score":"0.48903325","text":"def fetch_images(self):\n ids = [i.id for i in self]\n if len(ids) == 0:\n return {}\n nodes = Item.objects.hfilter(\n {'_class': 'product'}).extra(\n where=[\"(content_item.data -> 'legacy_id') IN (%s)\"\n % ', '.join('%s' for x in ids)],\n params=map(unicode, ids)).hselect(['legacy_id'])\n match = (len(nodes) == self.count())\n ids_table = {}\n for obj in self:\n obj.node = [n for n in nodes if int(n.legacy_id) == obj.id][0] \\\n if match else obj.node\n ids_table.update({obj.node.id: obj.id})\n\n images_qs = Item().graph.get_images(ids=ids_table.keys()).extra(\n select={'item_id': 'T4.from_item_id'})\n for i in images_qs:\n for obj in self:\n if obj.id == ids_table[i.item_id]:\n obj.image = i\n return self","title":""},{"docid":"437ec11d77e8ed5373a670c27df486e5","score":"0.4886353","text":"def forward_test(self, imgs):","title":""},{"docid":"54225281ac3652dc8b54063698d2544d","score":"0.48729935","text":"def compile_embedding(self, ids):\n to_concat = [ids, np.transpose(self.W), np.transpose(np.dot(self.H, self.T))]\n return np.concatenate(to_concat, axis=1)","title":""},{"docid":"fa7207329dfb276bce7c90db2726125b","score":"0.48617682","text":"def showimages(images):\n for image in images:\n showimage(image)","title":""},{"docid":"a25ea4030fa895c7765408b8a7962af7","score":"0.48496383","text":"async def embeds(self, ctx, search: int = None):\n await self.do_purge(ctx, search, lambda e: len(e.embeds))","title":""},{"docid":"52e33b94fb1af054ad5d77863974d750","score":"0.48362797","text":"def make_prediction(image_ids, model, word_to_ind, ind_to_word):\n predictions = []\n vgg_model = preprocess_images.load_prebuilt_model()\n maxlen = model.input_shape[1][1]\n for ii in image_ids:\n prediction = []\n print('Predicting image: ' + str(ii))\n example = image.load_img(ii, target_size=(224, 224))\n example_arr = image.img_to_array(example, dtype='float32')\n example_arr = np.expand_dims(example_arr, axis=0)\n example_arr = preprocess_input(example_arr)\n example_features = vgg_model.predict(example_arr)\n example_features = np.array(example_features).reshape(-1, 4096)\n \n start_string = ['*start*']\n start_ind = list(map(word_to_ind.get, start_string))\n for i in range(maxlen):\n start_seq = pad_sequences([start_ind], maxlen)\n yhat = model.predict([example_features, start_seq])\n yhat = np.argmax(yhat)\n if ind_to_word[yhat] == '*end*':\n break\n prediction.append(ind_to_word[yhat])\n start_ind.append(yhat)\n predictions.append(prediction)\n return predictions","title":""},{"docid":"a3842b8fe5c34232e17c540c9d6a92ce","score":"0.48355153","text":"def compute_image_embeddings(model_name, images, device, return_tensor=False):\n model = SentenceTransformer(model_name, device=device)\n if return_tensor:\n embeddings = model.encode(images, device=device, convert_to_tensor=True).cpu()\n else:\n embeddings = model.encode(images, device=device, convert_to_numpy=True)\n return embeddings","title":""},{"docid":"1d14886ff377badb8c0f2ccb5abb9fb6","score":"0.4835186","text":"def get_current_images_id(hparams, dataset_name):\n\n train_file = hparams[dataset_name]\n image_dir = os.path.join(hparams['root'], train_file)\n caption_file_path = get_cleaned_captions_path(hparams, train_file)\n img_size = hparams[\"image_size\"]\n transform_pipeline = transforms.Compose([CenteringPad(),\n transforms.Resize(\n (img_size, img_size)),\n transforms.ToTensor()])\n\n coco_train_set = dset.CocoDetection(root=image_dir,\n annFile=caption_file_path,\n transform=transform_pipeline\n )\n train_loader = torch.utils.data.DataLoader(\n coco_train_set, batch_size=hparams[\"batch_size\"])\n break_training_loop_percentage = hparams[\"break_training_loop_percentage\"]\n break_training_loop_idx = max(\n int(len(train_loader) * break_training_loop_percentage / 100) - 1, 0)\n\n img_list = []\n for idx, sample in enumerate(tqdm(train_loader)):\n img_list.extend(sample[1][0][\"image_id\"].tolist())\n if idx == break_training_loop_idx:\n break\n return img_list","title":""},{"docid":"b772d023feb5cfd7d91a51f18602c751","score":"0.48272422","text":"def process_embed(embed_items=None,\n embed_tracks=None,\n embed_metadata=None):\n\n result = None\n\n embed = ''\n if embed_items:\n embed = 'items'\n if embed_tracks:\n if embed != '':\n embed += ','\n embed += 'tracks'\n if embed_metadata:\n if embed != '':\n embed += ','\n embed += 'metadata'\n\n if embed != '':\n result = embed\n\n return result","title":""},{"docid":"7ff37ad5cd9b377f01ce8d6878d099b5","score":"0.47936964","text":"def interactImages(images, titles=None, subplotShape=None, figsize=None):\n #determine view\n #fixed dimension\n dimensions = np.arange(3)\n fixedDimensionWidget = widgets.IntSlider(min=0, max=dimensions[-1], value=0)\n \n #determine position on fixed dimension\n # for example z\n \n positionWidget = widgets.IntSlider(min=0)\n \n #updates\n def updatePositionWidget(*args):\n positionWidget.max = pet_scan.IMAGE_SHAPE[fixedDimensionWidget.value] -1\n positionWidget.description = pet_scan.IMAGE_DIMENSION_TITLES[fixedDimensionWidget.value]\n \n fixedDimensionWidget.observe(updatePositionWidget, 'value')\n updatePositionWidget()\n \n def f(fixDim, fixPos):\n #print(fixedDims)\n #determine image axes from fixdim\n dimensions = np.arange(3)\n hDim,vDim = dimensions[dimensions != fixDim]\n \n viewNDImages(images=images, fixedDimensions=[fixDim], fixedValues=[fixPos],\n subplotShape=subplotShape, titles = titles,\n axisLabels=[pet_scan.IMAGE_DIMENSION_TITLES[hDim],pet_scan.IMAGE_DIMENSION_TITLES[vDim]],\n figsize=figsize)\n \n interact(f, fixDim=fixedDimensionWidget, fixPos=positionWidget);","title":""},{"docid":"2c40ebec4ac67575a2f5c95bd1066937","score":"0.477987","text":"def slides(self, req):\n\tlength = req.GET.get('length', 10)\n\t\n\n slides = []\n\n # FIXME(nmg): should catch exception if any\n queries = self.db.get_veg_slides()\n\n for query in queries:\n item = {\n 'id': str(query['_id']),\n 'name': query['name'],\n 'photo': query['photo'],\n 'price': query['price'],\n 'mprice': query['mprice'],\n 'size': query['size']\n }\n slides.append(item)\n\n return HttpResponse(slides)","title":""},{"docid":"d8177cdcb0592feec54339f76cf97ac9","score":"0.47709528","text":"def wildbook_signal_imgsetid_list(ibs, imgsetid_list=None,\n set_shipped_flag=True,\n open_url_on_complete=True,\n wb_target=None, dryrun=False):\n try:\n wb_url = ibs.get_wildbook_base_url(wb_target)\n except IOError:\n print('[ibs.wildbook_signal_imgsetid_list] Caught IOError, returning None')\n return None\n\n try:\n ibs.assert_ia_available_for_wb(wb_target)\n except Exception:\n pass\n\n if imgsetid_list is None:\n imgsetid_list = ibs.get_valid_imgsetids()\n\n # Check to make sure imagesets are ok:\n for imgsetid in imgsetid_list:\n # First, check if imageset can be pushed\n aid_list = ibs.get_imageset_aids(imgsetid)\n assert len(aid_list) > 0, (\n 'ImageSet imgsetid=%r cannot be shipped with0 annots' % (imgsetid,))\n unknown_flags = ibs.is_aid_unknown(aid_list)\n unnamed_aid_list = ut.compress(aid_list, unknown_flags)\n unnamed_ok_aid_list = ibs.filter_annots_general(\n unnamed_aid_list,\n minqual='ok',\n )\n nUnnamedOk = sum(unnamed_ok_aid_list)\n assert nUnnamedOk == 0, (\n ('ImageSet imgsetid=%r1 cannot be shipped becuase '\n 'annotation(s) %r with an identifiable quality have '\n 'not been named') % (imgsetid, unnamed_ok_aid_list, ))\n\n # Call Wildbook url to signal update\n print('[ibs.wildbook_signal_imgsetid_list] ship imgsetid_list = %r to wildbook' % (\n imgsetid_list, ))\n imageset_uuid_list = ibs.get_imageset_uuid(imgsetid_list)\n print('[ibs.wildbook_signal_imgsetid_list] ship imgset_uuid_list = %r to wildbook' % (\n imageset_uuid_list, ))\n\n url = wb_url + '/ia'\n dbname = ibs.db.get_db_init_uuid()\n occur_url_fmt = (wb_url + '/occurrence.jsp?number={uuid}&dbname={dbname}')\n #enc_url_fmt = (wb_url + '/encounters/encounter.jsp?number={uuid}')\n\n # Check and push 'done' imagesets\n status_list = []\n for imgsetid, imageset_uuid in zip(imgsetid_list, imageset_uuid_list):\n print('[_send] URL=%r' % (url, ))\n json_payload = {'resolver': {'fromIAImageSet': str(imageset_uuid) }}\n if dryrun:\n status = False\n else:\n response = requests.post(url, json=json_payload)\n status = response.status_code == 200\n print('response = %r' % (response,))\n if set_shipped_flag:\n ibs.set_imageset_shipped_flags([imgsetid], [status])\n if status and open_url_on_complete:\n view_occur_url = occur_url_fmt.format(uuid=imageset_uuid, dbname=dbname)\n _browser = ut.get_prefered_browser(PREFERED_BROWSER)\n _browser.open_new_tab(view_occur_url)\n status_list.append(status)\n\n try:\n ibs.update_special_imagesets()\n ibs.notify_observers()\n except:\n pass\n\n return status_list","title":""},{"docid":"5978b424ec3226a8ad4d89ff6e7b4a15","score":"0.4765014","text":"def zfturbo_convert_targets_from_dataloader(self, targets, image_ids):\n for img_id, t in zip(image_ids, targets):\n\n for box, label in zip(t[\"boxes\"].numpy(), t[\"labels\"].numpy()):\n self.true_image_id_arr.append(img_id)\n self.true_label_arr.append(label)\n self.true_x_min_arr.append(box[0])\n self.true_x_max_arr.append(box[2])\n self.true_y_min_arr.append(box[1])\n self.true_y_max_arr.append(box[3])","title":""},{"docid":"945e83496bc1ca9498ff7d2e5347b0f7","score":"0.4762966","text":"def merge_images(sources, targets, opts, k=10):\n _, _, h, w = sources.shape\n row = int(np.sqrt(opts.batch_size))\n merged = np.zeros([3, row * h, row * w * 2])\n for idx, (s, t) in enumerate(zip(sources, targets)):\n i = idx // row\n j = idx % row\n merged[:, i * h : (i + 1) * h, (j * 2) * h : (j * 2 + 1) * h] = s\n merged[:, i * h : (i + 1) * h, (j * 2 + 1) * h : (j * 2 + 2) * h] = t\n return merged.transpose(1, 2, 0)","title":""},{"docid":"37572145bd7f92d5af541da7abd038af","score":"0.47581768","text":"async def dog(self, ctx):\r\n url = \"https://api.thedogapi.com/v1/images/search\"\r\n response = urlopen(url)\r\n result = json.loads(response.read())\r\n actualData = result[0]\r\n url = actualData[\"url\"]\r\n embed = discord.Embed(color=random.randint(1, 0xfffff))\r\n \r\n embed.set_image(url=url)\r\n embed.set_footer(text=f\"Requested by {ctx.author}\", icon_url=ctx.author.avatar_url)\r\n return await ctx.send(embed=embed)","title":""},{"docid":"16d74c7bdc9c202eceff5bf7376e371a","score":"0.47462964","text":"def preprocess_image(self, batched_inputs):\n images = [self.normalizer(x[\"image\"].to(self.device)) for x in batched_inputs]\n\n# images = ImageList.from_tensors(images, self.size_divisibility)\n images = ImageList.from_tensors(images, 32)\n\n images_whwh = list()\n for bi in batched_inputs:\n h, w = bi[\"image\"].shape[-2:]\n images_whwh.append(torch.tensor([w, h, w, h], dtype=torch.float32, device=self.device))\n images_whwh = torch.stack(images_whwh)\n\n return images, images_whwh","title":""},{"docid":"56d518c8ae03ca4e43f86a63e3837285","score":"0.47427243","text":"def fetch_images(self, response):\n pass","title":""},{"docid":"e5108fccdf4ade0e82ce74706a7369e0","score":"0.47369584","text":"def create_pids2idxs(data_source):\n pid2imgs = {}\n for idx, (img, target, _) in enumerate(data_source.imgs):\n if target not in pid2imgs:\n pid2imgs[target] = [idx]\n else:\n pid2imgs[target].append(idx)\n return pid2imgs","title":""},{"docid":"bcd934d0fee6c7970daa89fdbb6c2bf7","score":"0.47367933","text":"def compute_embeddings(images):\n\n # Creates graph from saved GraphDef.\n create_graph()\n filename_to_emb = {}\n config = tf.ConfigProto(device_count = {'GPU': 0})\n bar = progressbar.ProgressBar(widgets=[progressbar.Bar('=', '[', ']'), ' ', progressbar.Percentage()])\n with tf.Session(config=config) as sess:\n i = 0\n for image in bar(images):\n if not tf.gfile.Exists(image):\n tf.logging.fatal('File does not exist %s', image) \n image_data = tf.gfile.FastGFile(image, 'rb').read()\n # Some useful tensors:\n # 'softmax:0': A tensor containing the normalized prediction across\n # 1000 labels.\n # 'pool_3:0': A tensor containing the next-to-last layer containing 2048\n # float description of the image.\n # 'DecodeJpeg/contents:0': A tensor containing a string providing JPEG\n # encoding of the image.\n # Runs the softmax tensor by feeding the image_data as input to the graph.\n softmax_tensor = sess.graph.get_tensor_by_name('softmax:0')\n embedding_tensor = sess.graph.get_tensor_by_name('pool_3:0')\n embedding = sess.run(embedding_tensor,\n {'DecodeJpeg/contents:0': image_data})\n filename_to_emb[image] = embedding.reshape(2048)\n i += 1\n # print(image, i, len(images))\n return filename_to_emb","title":""},{"docid":"0b5766afdc9114e55c6ae90ea297dbc9","score":"0.47360665","text":"def _get_embeddings(self, input_ids):\n # embedding_matrix = self.model.transformer.wte.weight\n embedding_matrix = self.model_embeddings\n\n vocab_size = embedding_matrix.shape[0]\n one_hot_tensor = self.to(_one_hot(input_ids, vocab_size))\n\n token_ids_tensor_one_hot = one_hot_tensor.clone().requires_grad_(True)\n # token_ids_tensor_one_hot.requires_grad_(True)\n\n inputs_embeds = torch.matmul(token_ids_tensor_one_hot, embedding_matrix)\n return inputs_embeds, token_ids_tensor_one_hot","title":""},{"docid":"0de5bffa8a5d9b236f2ce7cf95223f02","score":"0.47346297","text":"def preprocess_batch_imgs(self,generated_content):\n\n # check the largest image width and word len in the batch\n pil_images = [img for img, word in generated_content]\n max_width = max([img.size[0] for img in pil_images])\n max_word_len_batch = max([len(word) for img, word in generated_content])\n\n\n # expand img with to mod 4_ds so that the maxpoolings wil result into\n # well defined integer length for the mapped tdist dimension (\"new width\")\n if max_width % 4 == 0:\n img_w = max_width\n else:\n img_w = max_width + 4 - (max_width % 4)\n\n #augment batch images\n for batch_ind in range(self.batch_size):\n\n # pad the image width with to the largest (fixed) image width\n pil_img = pil_images[batch_ind]\n width, height = pil_img.size\n\n new_img = Image.new(pil_img.mode, (img_w, self.img_h), (255,255,255))\n new_img.paste(pil_img, ((img_w - width) // 2, 0))\n\n # convert to numpy array\n img_arr = np.array(new_img)\n \n #some additional augmentation\n img_arr = self.keras_augmentor.random_transform(img_arr)\n\n # scale with 255 so that the values are between 0 and 1\n # and save to batch, also transpose because the \"time axis\" is width\n generated_content[batch_ind][0] = img_arr.transpose((1,0,2)) / 255\n\n return generated_content, img_w, max_word_len_batch","title":""},{"docid":"cad859d122fe1f8c23b5d88dc4f99f67","score":"0.4732007","text":"def update_image_canvas_multi(indices, data, source, max_images=25):\n n_images = len(indices)\n filenames = data['path'].iloc[indices]\n if n_images > max_images:\n filenames = filenames[:max_images - 1]\n images = [imread(fn) for fn in filenames]\n if n_images > max_images:\n # from the My First Pixel Art (TM) School of Design\n dotdotdot = np.full((7, 7, 4), 255, dtype=np.uint8)\n dotdotdot[3, 1::2, :3] = 0\n images.append(dotdotdot)\n sidelen = ceil(sqrt(min(n_images, max_images)))\n step_size = 1 / sidelen\n grid_points = np.arange(0, 1 - step_size/2, step_size)\n start_xs, start_ys = np.meshgrid(grid_points, grid_points, indexing='ij')\n n_rows = len(images)\n step_sizes = np.full(n_rows, step_size)\n source.data = {'image': images, 'x': start_xs.ravel()[:n_rows],\n 'y': start_ys.ravel()[:n_rows],\n 'dx': step_sizes, 'dy': step_sizes}\n print(source.data)","title":""},{"docid":"55d59fb7dd4bd9036c703f2e7e056b61","score":"0.47269878","text":"def compile_embedding(self, ids):\n to_concat = [ids, np.transpose(self.W), self.T.transpose().dot(self.H.transpose())]\n return np.concatenate(to_concat, axis=1)","title":""},{"docid":"d94b86d20817e2f5da2f4aee98a2088c","score":"0.47199464","text":"def compute_contextualized_embeddings(self, sent_w_ids, sent_p_ids):\n\n sent_length = len(sent_p_ids)\n \n sentence_w_embeddings = self.w_embeddings(sent_w_ids) # shape = len of sent (+2) , w_embedding_dim\n sentence_p_embeddings = self.p_embeddings(sent_p_ids) # shape = len of sent (+2) , p_embedding_dim\n\n # embedding for one word is concatenation of the word form embedding and the pos embedding\n sentence_embeddings = torch.cat( [ sentence_w_embeddings, sentence_p_embeddings ], 1 )\n\n # re-set to 0 the hidden vectors (TODO: other initialization?)\n if self.gru_or_lstm == 'lstm':\n # init hidden and cell states h0 c0\n self.rnn_hidden = self.init_lstm_hidden_and_cell()\n else:\n # init hidden state h0\n self.rnn_hidden = self.init_gru_hidden()\n \n #print(\"SENTENCE_EMBEDDINGS.SHAPE\", sentence_embeddings.shape)\n #print(\"INPUT SHAPE TO RNN:\", sentence_embeddings.view(len(sent_length, 1, -1).shape)\n #print(\"RNN_HIDDEN.shape:\", self.rnn_hidden_and_cell[0].shape)\n\n # rnn takes the batch size as second dimension (here batch size=1)\n # rnn_hidden_seq = the output(hidden) vectors at each position in sequence\n # rnn_hidden = if lstm: the output and cell vectors at the last position\n # if gru: the output vector at the last position\n rnn_hidden_seq, self.rnn_hidden = self.rnn(sentence_embeddings.view(sent_length, 1, -1),\n self.rnn_hidden)\n #print(\"LSTM_OUT.shape\", lstm_out.shape)\n #print(\"LSTM_OUT output shape\", lstm_out.view(len(sent_length), -1).shape)\n\n return rnn_hidden_seq.view(sent_length, -1)","title":""},{"docid":"5786e99304dd361a79ca4d230b7ff4dc","score":"0.47181153","text":"def get_images(self):\n \"\"\"Returns a list of all the images used. The indices\n in the layout refer to sprites in the list returned by\n this function\"\"\"\n pass","title":""},{"docid":"3b7db5a269498eebeb76c33b2d3311c8","score":"0.4713153","text":"def get_relevant_imgs(img_lst, img_map, indices, distances,k, form=\"list\", rank=True, img_dir=None):\n df_lst = []\n for img in img_lst:\n df_lst.append(get_similar_imgs(img, img_map, indices, distances, k, img_dir=img_dir))\n \n df = pd.concat(df_lst)\n if rank:\n df = df.sort_values(\"dist\")\n else:\n df = df.sample(k)\n if form == \"list\":\n return df.head(k)[\"img\"].values\n elif form == \"df\":\n return df.head(k)","title":""},{"docid":"b80ec583f7fd29fdea7bc4a1db3c2e9c","score":"0.471242","text":"def embedding_lookup_unique(params, ids, name=None):\n with ops.name_scope(name, \"EmbeddingLookupUnique\", [params, ids]):\n ids = ops.convert_to_tensor(ids)\n shape = array_ops.shape(ids)\n ids_flat = array_ops.reshape(\n ids, math_ops.reduce_prod(shape, keep_dims=True))\n unique_ids, idx = array_ops.unique(ids_flat)\n unique_embeddings = embedding_ops.embedding_lookup(params, unique_ids)\n embeds_flat = array_ops.gather(unique_embeddings, idx)\n embed_shape = array_ops.concat(\n [shape, array_ops.shape(unique_embeddings)[1:]], 0)\n embeds = array_ops.reshape(embeds_flat, embed_shape)\n embeds.set_shape(ids.get_shape().concatenate(\n unique_embeddings.get_shape()[1:]))\n return embeds","title":""},{"docid":"2e19bfdf2259484153178fbf63f1407a","score":"0.46960354","text":"def get_images(self):\n pass","title":""},{"docid":"2a0f64d8eea4fe8bec303882f9da7776","score":"0.46924624","text":"async def images(request: Request):\n recommendation_request = RecommendationRequest(**request.raw_args)\n response = await selector.get_recommendations(app.client_session,\n recommendation_request)\n return json(response.json, status=response.status)","title":""},{"docid":"e43500af3356291694915dd671b04e16","score":"0.4683429","text":"def create_image_pairs(self, input_images, input_actions):\n\timages = []\n\tactions = []\n\tfuture_images = []\n\tfuture_actions = []\n\tids = []\n\tfuture_ids = []\n\tif len(input_images) < 1 or len(input_actions) < 1:\n\t return [images, actions, future_images, future_actions, ids, future_ids]\n\t\n\t# specify the length of the action sequence based on the maximally possible delta_t\n\tdelta_t = self.min_time_difference\n\tif self.random_time:\n\t delta_t = self.max_time_difference\n\taction_sequence_length = delta_t * len(input_actions[0])\n\timage_sequence_length = delta_t * input_images[0].shape[2] \n\t\n\t# create present-future image/action pairs\n\tfor i in range(len(input_images)):\t \n\t # select time delta\n\t if self.random_time:\n\t\tmax_time_difference = min(len(input_images) - i, self.max_time_difference)\n\t\tif max_time_difference <= self.min_time_difference \\\n\t\t\tor max_time_difference < 1:\n\t\t continue\n\t\tdelta_t = np.random.randint(self.min_time_difference, max_time_difference)\n\t else:\n\t\tif i + delta_t >= len(input_images):\n\t\t break\n\t images.append(input_images[i])\n\t actions.append(input_actions[i])\n\t future_images.append(input_images[i+delta_t])\n\t future_actions.append(input_actions[i+delta_t])\n\t ids.append(i+self.batch_num*self.batch_size)\n\t future_ids.append(i+delta_t+self.batch_num*self.batch_size)","title":""},{"docid":"65afb84fa6dc6bb020c9cf7a7250557b","score":"0.46827105","text":"def download_galleries(driver, gallery_id_list, num_galleries):\n for num, id in enumerate(gallery_id_list, 1):\n # Only want the first N galleries specified by user\n if num > num_galleries:\n return\n\n # Make a direct link to the gallery\n gallery_link = 'https://imgur.com/gallery/' + id\n\n # Go to the gallery_link page\n driver.get(gallery_link)\n\n # Large galleries will have a \"load more images\" button\n load_more_images_button = driver.find_elements_by_xpath(\"\"\"//a[contains(@class, 'post-loadall')]\"\"\")\n\n # If there is a \"Load More Images\" button, then we must switch to the gallery's grid view\n # so the entire gallery is loaded. Otherwise, we can use the default gallery view.\n if load_more_images_button:\n use_grid_view(driver, id, num)\n else:\n use_normal_view(driver, num)","title":""},{"docid":"f48c75d7cdec57f7ef00c29c6dd28548","score":"0.46815458","text":"def load_imgs(self, ids):\n return self._load_helper(self.imgs, ids)","title":""},{"docid":"5448286e9efc61d561e56ae305761b81","score":"0.46776003","text":"def get_queued_images(self):\r\n raise NotImplementedError","title":""},{"docid":"f1c6341c877c963c0a7e5c1c5eee8545","score":"0.4675306","text":"def handle_flash_embed(session,post_dict):#TODO FIXME\n logging.debug(\"Processing flash embed\")\n logging.warning(\"handle_flash_embed() is not finished yet. FIX IT!\")#TODO FIXME\n \"\"\" u'player': [{u'embed_code': u'<embed width=\"250\" height=\"291\" align=\"middle\" pluginspage=\"http://www.adobe.com/go/getflashplayer\" type=\"application/x-shockwave-flash\" allowfullscreen=\"false\" allowscriptaccess=\"sameDomain\" name=\"xdft\" bgcolor=\"#000000\" scale=\"noscale\" quality=\"high\" menu=\"false\" src=\"http://www.najle.com/idaft/idaft/xdft.swf\">',\n u'width': 250},\n {u'embed_code': u'<embed width=\"400\" height=\"466\" align=\"middle\" pluginspage=\"http://www.adobe.com/go/getflashplayer\" type=\"application/x-shockwave-flash\" allowfullscreen=\"false\" allowscriptaccess=\"sameDomain\" name=\"xdft\" bgcolor=\"#000000\" scale=\"noscale\" quality=\"high\" menu=\"false\" src=\"http://www.najle.com/idaft/idaft/xdft.swf\">',\n u'width': 400},\n {u'embed_code': u'<embed width=\"500\" height=\"582\" align=\"middle\" pluginspage=\"http://www.adobe.com/go/getflashplayer\" type=\"application/x-shockwave-flash\" allowfullscreen=\"false\" allowscriptaccess=\"sameDomain\" name=\"xdft\" bgcolor=\"#000000\" scale=\"noscale\" quality=\"high\" menu=\"false\" src=\"http://www.najle.com/idaft/idaft/xdft.swf\">',\n u'width': 500}],\"\"\"\n # Extract video links from post dict\n found_links = []\n video_items = post_dict[\"player\"]\n for video_item in video_items:\n embed_code = video_item[\"embed_code\"]\n #\n if embed_code:\n # Find links in the field\n field_links = link_handlers.find_links(embed_code)\n found_links += field_links\n continue\n logging.debug(\"handle_flash_embed() found_links: \"+repr(found_links))\n\n # Remove duplicate links\n links = uniquify(found_links)\n\n media_id_list = []\n # Choose which links to save\n for link in links:\n # If link ends in .swf\n if \".swf\" in link[-4:]:\n media_id_list += download_image_link(session,link)\n continue\n # If link ends in .flv\n elif \".flv\" in link[-4:]:\n media_id_list += download_image_link(session,link)\n continue\n continue\n\n return media_id_list","title":""},{"docid":"c4f74a44a5911a768227ce1c7872bd16","score":"0.46719703","text":"def main(*args):\n if len(args) >= 1:\n image_source_id = bson.objectid.ObjectId(args[0])\n\n config = global_conf.load_global_config('config.yml')\n db_client = database.client.DatabaseClient(config=config)\n\n image_source = None\n s_image_source = db_client.image_source_collection.find_one({'_id': image_source_id})\n if s_image_source is not None:\n image_source = db_client.deserialize_entity(s_image_source)\n del s_image_source\n\n if image_source is not None:\n image_source.begin()\n while not image_source.is_complete():\n image, _ = image_source.get_next_image()\n debug_img = image.data[:, :, ::-1].copy()\n for obj in image.metadata.labelled_objects:\n x, y, w, h = obj.bounding_box\n cv2.rectangle(debug_img, (x, y), (x + w, y + h), (0, 0, 255), 2)\n\n text_label = str(obj.class_names[0])\n (retval, baseLine) = cv2.getTextSize(text_label, cv2.FONT_HERSHEY_COMPLEX, 1, 1)\n text_org = (x, y - 0)\n\n cv2.rectangle(debug_img, (text_org[0] - 5, text_org[1] + baseLine - 5),\n (text_org[0] + retval[0] + 5, text_org[1] - retval[1] - 5), (0, 0, 0), 2)\n cv2.rectangle(debug_img, (text_org[0] - 5, text_org[1] + baseLine - 5),\n (text_org[0] + retval[0] + 5, text_org[1] - retval[1] - 5), (255, 255, 255), -1)\n cv2.putText(debug_img, text_label, text_org, cv2.FONT_HERSHEY_DUPLEX, 1, (0, 0, 0), 1)\n\n cv2.imshow('debug', debug_img)\n cv2.waitKey(0)","title":""},{"docid":"560fa680efd3836987a15f68557e5a71","score":"0.46693918","text":"def batch_pad(data_dict,ids):\n batch_info = {}\n b_fp = torch.tensor(())\n b_dfpdX = torch.tensor(())\n b_e_mask = torch.tensor(())\n b_e = torch.tensor(())\n b_f = torch.tensor(())\n\n # Find the largest image in the batch\n N_max = 0\n all_atoms = torch.tensor(())\n for ID in ids:\n N_atoms, N_element = data_dict[ID]['e_mask'].shape\n all_atoms = torch.cat((all_atoms,torch.tensor(N_atoms).float().view(1,1)))\n if N_atoms > N_max:\n N_max = N_atoms\n N_sym = data_dict[ID]['fp'].shape[1]\n\n # Loop through the ids to batch the values\n for ID in ids:\n pad_fp = torch.zeros(N_max,N_sym)\n pad_dfpdX = torch.zeros(N_max,N_sym,N_max,3)\n pad_e_mask = torch.zeros(N_max,N_element)\n pad_f = torch.zeros(N_max,3)\n fp = data_dict[ID]['fp']\n dfpdX = data_dict[ID]['dfpdX']\n e_mask = data_dict[ID]['e_mask']\n pad_fp[:fp.shape[0],:fp.shape[1]] = fp\n pad_dfpdX[:dfpdX.shape[0],:dfpdX.shape[1],:dfpdX.shape[2],:] = dfpdX\n pad_e_mask[:e_mask.shape[0],:e_mask.shape[1]] = e_mask\n pad_f[:fp.shape[0],:] = data_dict[ID]['f'] \n b_fp = torch.cat((b_fp,pad_fp))\n b_dfpdX = torch.cat((b_dfpdX,pad_dfpdX))\n b_e_mask = torch.cat((b_e_mask,pad_e_mask))\n b_e = torch.cat((b_e,data_dict[ID]['e'].view(1,1)),dim=0)\n b_f = torch.cat((b_f,pad_f))\n\n # Update the output dictionary\n batch_info.update({'N_atoms':all_atoms})\n batch_info.update({'b_fp':b_fp.view(len(ids),N_max,N_sym)})\n batch_info.update({'b_dfpdX':b_dfpdX.view(len(ids),N_max,N_sym,N_max,3)})\n batch_info.update({'b_e_mask':b_e_mask.view(len(ids),N_max,N_element)})\n batch_info.update({'b_e':b_e})\n batch_info.update({'b_f':b_f.view(len(ids),N_max,3)})\n return batch_info","title":""},{"docid":"e43b5925594eab6f5f3eff9c3796fe00","score":"0.4665801","text":"def get_true_captions(img_ids, coco):\n batch_captions = []\n for img_id in img_ids:\n image_metadata = coco.imgToAnns[img_id]\n # Here, we compute the image metadata\n #print(\"Image metadata is {}\".format(image_metadata))\n captions = []\n for metadata in image_metadata:\n captions.append(metadata['caption'].lower())\n batch_captions.append(captions)\n return batch_captions","title":""},{"docid":"2947ebef2f28671ca4cab0f9856cebd0","score":"0.46594942","text":"def handle_dailymotion_videos(session,post_dict):\n logging.debug(\"Processing dailymotion video\")\n # Extract video links from post dict\n video_urls = []\n video_items = post_dict[\"player\"]\n for video_item in video_items:\n embed_code = video_item[\"embed_code\"]\n # u'embed_code': u'<iframe src=\"https://www.dailymotion.com/embed/video/x2msryd\" width=\"250\" height=\"139\" frameborder=\"0\" allowfullscreen></iframe>'\n # https://www.dailymotion.com/embed/video/x2msryd\n if embed_code:\n # Process links so YT-DL can understand them\n logging.debug(\"embed_code: \"+repr(embed_code))\n embed_url_regex =\"\"\"src=[\"']([^?\"'#]+)\"\"\"\n embed_url_search = re.search(embed_url_regex, embed_code, re.IGNORECASE|re.DOTALL)\n if embed_url_search:\n embed_url = embed_url_search.group(1)\n video_urls.append(embed_url)\n continue\n logging.debug(\"video_urls: \"+repr(video_urls))\n\n # Download videos if there are any\n media_id_list = run_yt_dl_multiple(\n session = session,\n download_urls = video_urls,\n extractor_used=\"video_handlers.handle_dailymotion_videos()\",\n )\n\n logging.debug(\"Finished downloading dailymotion embeds\")\n return media_id_list","title":""},{"docid":"c227bbc1238334453a77e67cd7660e0e","score":"0.4656213","text":"def setHelpEmbedThumbnails():\n for levelSection in botCommands.helpSectionEmbeds:\n for helpSection in levelSection.values():\n for embed in helpSection:\n embed.set_thumbnail(url=botState.client.user.avatar_url_as(size=64))","title":""},{"docid":"33ce80f1bf81423b7379d77e5a97b831","score":"0.4655339","text":"def add_images_to_visual(self, visual_id, images=[]):\n for image in images:\n logger.info(\"Adding image %s to visual %d\" % (image, visual_id))\n image_buffer = self._load_file(image)\n return self._post(\"projects/visuals/%d/images/\" % visual_id,\n files={'image': image_buffer})","title":""},{"docid":"5336d6eabcf2ad7d1e70261c7a6fe426","score":"0.46519732","text":"def handle_inline(inline_query):\n if not nekowat.is_allowed(inline_query.from_user.id):\n nekowat.answer_inline_query(inline_query.id, [])\n return\n\n # Normalize expression\n expression = inline_query.query.lower().strip()\n\n if not expression:\n # Get all images\n wats = nekowat.get_all_wats()\n\n else:\n # Get by expression\n wats = nekowat.get_wats_by_expression(expression)\n\n try:\n responses = []\n\n for index, wat in enumerate(wats):\n r = telebot.types.InlineQueryResultCachedPhoto(\n str(index),\n # Get smallest file for inline reply\n wat['file_ids'][0],\n parse_mode='' # Workaround for Telegram API error\n )\n\n responses.append(r)\n\n nekowat.answer_inline_query(inline_query.id, responses)\n\n except Exception as e:\n print(e)","title":""},{"docid":"2eb13699da3751bd8ed7ed1093635c96","score":"0.46486753","text":"def detection_wanted_img(path_wanted,path_image,portrait,l_reso,l_seuil):\n print(\"calcul des embeddings à rechercher\")\n res=convert_embd(path_wanted)\n list_emb,list_name_wanted=res[0],res[1]\n print(\"calcul terminé\")\n list_emb=np.asarray(list_emb)\n list_name_wanted=np.asarray(list_name_wanted)\n detection_wanted(list_emb,list_name_wanted,path_image,portrait,l_reso,l_seuil)","title":""},{"docid":"255c0a3c0fc2021ebc041ad4c8c10171","score":"0.46456274","text":"def get_example(id):\n c = get_chunk(id // aug_single_chunk_size)\n return c[0][id % aug_single_chunk_size].reshape((ImageChannels*aug_image_side**2)), c[1][id % aug_single_chunk_size]","title":""},{"docid":"6db4b7f19262e4107771040e81754350","score":"0.4644313","text":"def GetQAofImage(id=61512):\n page = 1\n next = '/api/v0/image/' + str(id) + '/qa?page=' + str(page)\n qas = []\n image_map = {}\n while True:\n data = utils.RetrieveData(next)\n for d in data['results']:\n if d['image'] not in image_map:\n image_map[d['image']] = GetImageData(id=d['image'])\n qas.extend(utils.ParseQA(data['results'], image_map))\n if data['next'] is None:\n break\n page += 1\n next = '/api/v0/image/' + str(id) + '/qa?page=' + str(page)\n return qas","title":""},{"docid":"e76ef9f4b53da57548cd606270e2b42f","score":"0.4642653","text":"def embedplays(params):\n # set up default values\n default_from, default_to, yesterday, _ = make_default_times()\n\n # get params\n try:\n series = \"onionstudios\"\n from_date = params.get(\"from\", [default_from])[0]\n to_date = params.get(\"to\", [default_to])[0]\n group_by = params.get(\"group_by\", [DEFAULT_GROUP_BY])[0]\n except Exception as e:\n LOGGER.exception(e)\n return json.dumps({\"error\": e.message}), \"500 Internal Error\"\n\n # check the cache\n cache_key = \"{}:{}:{}:{}:{}:{}\".format(memcached_prefix, \"embedplays.json\", series, from_date, to_date, group_by)\n try:\n data = MEMCACHED_CLIENT.get(cache_key)\n if data:\n return data, \"200 OK\"\n except Exception as e:\n LOGGER.exception(e)\n\n # parse from date\n from_date = parse_datetime(from_date)\n if from_date is None:\n LOGGER.error(\"could not parse 'from'\")\n return json.dumps({\"error\": \"could not parse 'from'\"}), \"400 Bad Request\"\n\n # parse to date\n to_date = parse_datetime(to_date)\n if to_date is None:\n LOGGER.error(\"could not parse 'to'\")\n return json.dumps({\"error\": \"could not parse 'to'\"}), \"400 Bad Request\"\n\n # influx will only keep non-aggregated data for a day, so if the from param is beyond that point\n # we need to update the series name to use the rolled up values\n rollup_query = False\n if from_date < yesterday:\n series = update_series(series, \"embedplays\")\n rollup_query = True\n\n # format times\n from_date = format_datetime(from_date)\n to_date = format_datetime(to_date)\n\n # build out the query\n if not rollup_query:\n query = \"SELECT sum(value) as value \" \\\n \"FROM {series} \" \\\n \"WHERE time > '{from_date}' \" \\\n \"AND time < '{to_date}' \" \\\n \"AND event =~ /^embedplay$/ \" \\\n \"GROUP BY time({group_by}) \" \\\n \"fill(0);\"\n else:\n query = \"SELECT sum(value) as value \" \\\n \"FROM {series} \" \\\n \"WHERE time > '{from_date}' \" \\\n \"AND time < '{to_date}' \" \\\n \"GROUP BY time({group_by}) \" \\\n \"fill(0);\"\n args = {\"series\": series, \"from_date\": from_date, \"to_date\": to_date, \"group_by\": group_by}\n\n # send the request\n try:\n res = INFLUXDB_CLIENT.query(query.format(**args))\n\n # capture errors and send them back along with the query (for inspection/debugging)\n except Exception as e:\n LOGGER.exception(e)\n return json.dumps({\"error\": e.message, \"query\": query.format(**args)}), \"500 Internal Error\"\n\n # build the response object\n response = flatten_response(res)\n res = json.dumps(response)\n\n # cache the response\n try:\n MEMCACHED_CLIENT.set(cache_key, res, time=MEMCACHED_EXPIRATION)\n except Exception as e:\n LOGGER.exception(e)\n\n return res, \"200 OK\"","title":""},{"docid":"5f890c7a86c30ef8bdba91e46f3abe82","score":"0.4639542","text":"def getPictures(base_url, sizes):\n pictures = []\n widths = []\n names = {}\n for name in sizes:\n width = sizes[name][0]\n widths.append(width)\n names[str(width)] = name\n widths.sort()\n noscript = \"\"\n previous = None\n for width in widths:\n name = names[str(width)]\n if width >= 128 and not noscript:\n noscript = base_url + name\n previous = width\n continue\n if not previous:\n continue\n media = \"(min-width: %spx)\" % (previous + 1)\n# media = \"(min-width: %spx)\" % width\n# media = \"(max-width: %spx)\" % width\n image = {'src': base_url + name, 'media': media}\n pictures.append(image)\n previous = width\n media = \"(min-width: %spx)\" % (widths[-1] + 1)\n image = {'src': base_url[:-1], 'media': media}\n pictures.append(image)\n\n #try to find a width > 128px for the noscript image\n return pictures, noscript","title":""},{"docid":"c9591abb4f30cc5e8feb027fd8ad4f26","score":"0.46377063","text":"def __woo_upload_metadata(self):\n for item_id in self.active_item_ids:\n self.woo.try_command('upload_images', item_id)","title":""},{"docid":"0e7c69f82972dec8d2f18d44603b04fc","score":"0.46374556","text":"def get_multiple_image(curid):\n\tapi_url = 'https://en.wikipedia.org/w/api.php?action=query&prop=revisions&rvprop=content&format=json&pageids='+str(curid)+'&rvsection=0'\n\tresult = _rget(api_url)[u'query'][u'pages']\n\tr = result[unicode(curid)][u'revisions'][0][u'*']\n\twikicode = mwparserfromhell.parse(r)\n\ttemplates = wikicode.filter_templates()\n\tbox = {}\n\tfor template in templates:\n\t\tname = template.name.lstrip().rstrip().lower()\n\t\tif 'image' in name:\n\t\t\tbox_ = {}\n\t\t\tfor param in template.params:\n\t\t\t\tkey = drop_comments(param.name).strip().lower().replace(' ','_')\n\t\t\t\tvalue = drop_comments(param.value).strip()\n\t\t\t\tbox_[key] = value\n\t\t\tbox['image'] = box_\n\t\t\tbreak #Grab only the first one\n\treturn box","title":""},{"docid":"98783ff50dde8dd0e83dc5b01dba0ddf","score":"0.46356267","text":"def handle_kaltura_videos(session,post_dict):#TODO FIXME\n logging.debug(\"Processing kaltura video\")\n # Extract video links from post dict\n video_urls = []\n video_items = post_dict[\"player\"]\n for video_item in video_items:\n embed_code = video_item[\"embed_code\"]\n # ...data=\"http://www.kaltura.com/index.php/kwidget/wid/0_jzs5tgmh/uiconf_id/7342382\">...\n # http://www.kaltura.com/index.php/kwidget/wid/0_jzs5tgmh/uiconf_id/7342382\n if embed_code:\n # Process links so YT-DL can understand them\n logging.debug(\"handle_kaltura_videos() embed_code: \"+repr(embed_code))\n embed_url_regex =\"\"\"(?:http://www.)?kaltura.com/index.php/kwidget/wid/[^\"'<>]+\"\"\"\n embed_url_search = re.search(embed_url_regex, embed_code, re.IGNORECASE|re.DOTALL)\n if embed_url_search:\n video_url = embed_url_search.group(0)\n video_urls.append(video_url)\n continue\n logging.debug(\"handle_kaltura_videos() video_urls: \"+repr(video_urls))\n\n # Download videos if there are any\n media_id_list = run_yt_dl_multiple(\n session = session,\n download_urls = video_urls,\n extractor_used=\"video_handlers.handle_kaltura_videos()\",\n )\n logging.debug(\"Finished downloading kaltura embeds\")\n return media_id_list","title":""},{"docid":"527b9b6d0c7dc5bd490b55107abebcb2","score":"0.46338552","text":"def encode_img(self,\n images: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:\n device = images.device\n x = self.vision_encoder(images)[0]\n image_embeds = self.ln_vision(x).to(device)\n image_atts = torch.ones(\n image_embeds.size()[:-1], dtype=torch.long).to(device)\n\n query_tokens = self.query_tokens.expand(image_embeds.shape[0], -1, -1)\n query_output = self.q_former.bert(\n query_embeds=query_tokens,\n encoder_hidden_states=image_embeds,\n encoder_attention_mask=image_atts,\n return_dict=True,\n )\n\n inputs_llama = self.llama_proj(query_output.last_hidden_state)\n atts_llama = torch.ones(\n inputs_llama.size()[:-1], dtype=torch.long).to(images.device)\n return inputs_llama, atts_llama","title":""},{"docid":"f70668f69c033880fc324f7d7f924523","score":"0.4633523","text":"def plot_pruned_seqs(model_output, inputs, n_logged_samples=3, max_seq_len=None):\n assert \"images\" in model_output.dense_rec # need pruned predicted images of SH-Pred model\n if inputs is not None:\n batch, n_gt_imgs, channels, res, _ = inputs.traj_seq.shape\n else:\n batch = len(model_output.dense_rec.images)\n assert batch == 1 # con currently only handle batch size 1\n n_gt_imgs, channels, res, _ = model_output.dense_rec.images[0].shape\n MAX_SEQ_LEN = int(n_gt_imgs * 1.5) if not max_seq_len else max_seq_len\n\n im_height = 2 * res\n im_width = (MAX_SEQ_LEN+1) * res\n im = np.asarray(0.7 * np.ones((n_logged_samples, im_height, im_width, 3)), dtype=np.float32)\n pred_imgs = list(map(imgtensor2np, model_output.dense_rec.images[:n_logged_samples]))\n max_len = min(n_gt_imgs, MAX_SEQ_LEN)\n for b in range(n_logged_samples):\n if pred_imgs[b] is None: continue\n seq_len = min(pred_imgs[b].shape[0], MAX_SEQ_LEN)\n max_len = max(max_len, seq_len)\n im[b, -res:, res:(seq_len+1)*res] = pred_imgs[b][:seq_len].transpose(2, 0, 3, 1).reshape(res, seq_len*res, channels)\n if inputs is not None:\n im[:, :res, :(n_gt_imgs*res)] = imgtensor2np(inputs.traj_seq, n_logged_samples).transpose(0, 3, 1, 4, 2)\\\n .reshape(n_logged_samples, res, n_gt_imgs*res, channels)\n if \"actions\" in model_output.dense_rec \\\n and model_output.dense_rec.actions is not None \\\n and (True in [a is not None for a in model_output.dense_rec.actions]) \\\n and (inputs is None or inputs.actions.shape[-1] == 2):\n ac_im = np.asarray(0.7 * np.ones((n_logged_samples, res, im_width, 3)), dtype=np.float32)\n pred_ac = list(map(tensor2np, model_output.dense_rec.actions[:n_logged_samples]))\n for b in range(n_logged_samples):\n if pred_ac[b] is None: continue\n seq_len = min(pred_ac[b].shape[0], MAX_SEQ_LEN)\n ac_im[b, :, :seq_len*res] = batch_action2img(pred_ac[b][None, :seq_len], res, channels).transpose(0, 2, 1, 3, 4)\\\n .reshape(res, seq_len*res, channels)\n im = np.concatenate((im, ac_im), axis=1)\n\n # prepare GIF version\n gif_imgs = np.swapaxes(im.reshape(n_logged_samples, im.shape[1], MAX_SEQ_LEN+1, res, channels), 0, 2)[:max_len+1] \\\n .reshape(max_len+1, im.shape[1], res * n_logged_samples, channels)\n\n return im, gif_imgs","title":""},{"docid":"8c54fed09598be2964aae56673c1e44e","score":"0.46313572","text":"def parse_fewshot_anno(self, query: dict, support_list: List) -> dict:\n # prepare n shots examples\n shots = random.sample(support_list, self.num_shots)\n\n # append image path for n shots\n img_path = [shot['img_path'] for shot in shots]\n img_path.append(query['img_path'])\n query['img_path'] = img_path\n\n query['shots'] = [\n dict(\n question=item['question'],\n answer=item['gt_answer'][0],\n ) for item in shots\n ]\n return query","title":""},{"docid":"e229cc38993d00b972a9bc0fd50b3c6f","score":"0.46307558","text":"async def search_images(self, **kwargs):\n\n params = {\n 'breed_id': kwargs.pop(\"breed_id\", \"\"),\n 'category_ids': kwargs.pop(\"category_ids\", \"\"),\n 'format': kwargs.pop(\"format\", \"\"),\n 'limit': kwargs.pop(\"limit\", 1),\n 'mime_types': kwargs.pop(\"mime_types\", \"\"),\n 'order': kwargs.pop(\"order\", \"RANDOM\"),\n 'size': kwargs.pop(\"size\", \"med\"),\n 'page': kwargs.pop(\"page\", 0),\n }\n\n url = f'{BASE_URL}/images/search'\n\n images = await self.api_get_session(url, params)\n images = [Image(**image) for image in images]\n return images","title":""},{"docid":"7293401296aec8aa177ef92054ed2c40","score":"0.46290153","text":"def beam_search_batch(self, src_id, max_decode_length):\n\t\tpass","title":""},{"docid":"cf26fb747e81d58dabe00acc80b4859e","score":"0.4619074","text":"def images(self, **query):\n if 'limit' not in query.keys():\n query[\"limit\"] = 1000\n return list(self._list(_image.Image, **query))","title":""},{"docid":"221e7db9aa6d90102f21e2581e0349fd","score":"0.4618204","text":"def embed(self, input, batchsize):\n if self.prm[\"wo_tok\"]:\n # emb: [seq_len*nbatch, char_hid]\n emb = self.drop(self.char_encoder(input[\"char\"]))\n # emb: [seq_len, nbatch, char_hid]\n if batchsize is not None:\n emb = emb.reshape(-1, batchsize, emb.shape[-1])\n else:\n emb = emb.reshape(input[\"word\"].shape[0], input[\"word\"].shape[1], -1)\n elif self.prm[\"wo_char\"]:\n # emb: [seq_len, nbatch, tok_emb]\n emb = self.drop(self.word_encoder(input[\"word\"]))\n elif self.prm[\"wo_tok\"] and self.prm[\"wo_char\"]:\n # At least one embedding layer is required.\n assert(False)\n else:\n # emb: [seq_len, nbatch, tok_emb]\n emb_word = self.drop(self.word_encoder(input[\"word\"]))\n # emb: [seq_len*nbatch, char_hid]\n emb_char = self.drop(self.char_encoder(input[\"char\"]))\n # emb: [seq_len, nbatch, char_hid]\n emb_char = emb_char.reshape(input[\"word\"].shape[0], input[\"word\"].shape[1], -1)\n # emb: [seq_len, nbatch, tok_emb + char_hid]\n emb = torch.cat([emb_word, emb_char], dim=2)\n return emb","title":""},{"docid":"0c9fcf47c55227001f30b26cf2f54e2e","score":"0.46140903","text":"def get_similar_imgs(img_name, img_map, indices, distances,k, form =\"df\",img_dir=None):\n index = get_key(img_map, img_name)\n distances = distances[index][1:k+1]\n \n if img_dir == None:\n images = [x for x in indices[index]]\n else: \n images = [img_dir+x for x in indices[index]] \n \n images = images[1:k+1]\n \n if form == \"df\":\n return pd.DataFrame({\"img\": images,\"dist\": distances})\n elif form == \"list\": \n return images","title":""},{"docid":"e696a6114c4d79b6bf659778d3a9ba22","score":"0.46125808","text":"def attack(self, imgs, targets):\n r = []\n print('go up to', imgs.shape[0])\n for i in range(0, imgs.shape[0], self.batch_size):\n print('tick', i)\n # print(\"imgs[i:i + self.batch_size]\", imgs[i:i + self.batch_size])\n # print(\"targets\", targets)\n r.extend(self.attack_batch(\n imgs[i:i + self.batch_size], targets))\n return np.array(r)","title":""},{"docid":"fecf5e13cd785cb5a966ebf9ea07e75b","score":"0.4611163","text":"def parse_fewshot_anno(self, query: dict, support_list: List) -> dict:\n # prepare n shots examples\n shots = random.sample(support_list, self.num_shots)\n\n # append image path for n shots\n img_path = [shot['img_path'] for shot in shots]\n img_path.append(query['img_path'])\n query['img_path'] = img_path\n\n query['shots'] = [dict(caption=item['gt_caption']) for item in shots]\n return query","title":""},{"docid":"580b4ef695c411f550b2c57524a29ae6","score":"0.46107417","text":"def DisplayImages(self) -> LayerFilterDisplayImages:","title":""},{"docid":"731c3c12b58e2092c84150a0090c8265","score":"0.4608333","text":"def build_image_embedding(self):\r\n\r\n with tf.variable_scope(\"D_\"):\r\n with tf.variable_scope(\"image_embedding\") as scope:\r\n image_embeddings = tf.contrib.layers.fully_connected(\r\n inputs=self.inception_output,\r\n num_outputs=self.config.embedding_size,\r\n activation_fn=None,\r\n weights_initializer=self.initializer,\r\n biases_initializer=None,\r\n scope=scope)\r\n\r\n self.image_embeddings = image_embeddings","title":""}],"string":"[\n {\n \"docid\": \"2a0d8c3f6444f00756c4da15a4161f82\",\n \"score\": \"0.6125822\",\n \"text\": \"def get_images(k):\\n\\n keys = np.linspace(0, 99, 50)\\n values = np.random.randn(50, 50)\\n image_embeddings = dict(zip(keys, values))\\n\\n # with open(\\\"image_embeddings.pickle\\\", \\\"rb\\\") as f:\\n # image_embeddings = pickle.load(f)\\n\\n with open(\\\"url.pickle\\\", \\\"rb\\\") as urls:\\n url = pickle.load(urls)\\n\\n # GETTING THE SIMILAR IDS\\n caption_embed = get_query.query() # embedded text\\n caption_embed = fu.normalize(caption_embed)\\n ids, embeddings = (shit.split(image_embeddings))\\n for embed in embeddings:\\n embed.reshape((50, 1))\\n\\n embed_values = []\\n for e in embeddings:\\n embed_values.append((caption_embed @ e).data)\\n embed_values = np.array(embed_values)\\n\\n max_values = np.argsort(embed_values, axis=0)[:k].flatten()\\n similar_image_ids = []\\n\\n for m in max_values:\\n similar_image_ids.append(ids[m])\\n # END ID ACQUISITION\\n\\n image_links = []\\n for img_id in similar_image_ids:\\n image_links.append(url[img_id])\\n\\n for i in image_links:\\n response = requests.get(i)\\n img = Image.open(BytesIO(response.content))\\n\\n return similar_image_ids\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e22e73abe8ddacf0792bf8c06e485f86\",\n \"score\": \"0.5829802\",\n \"text\": \"def query(self, images):\\n if self.buffer_size == 0:\\n return images\\n return_images = []\\n for image in images:\\n image = torch.unsqueeze(image.data, 0)\\n if self.img_num < self.buffer_size:\\n self.img_num = self.img_num + 1\\n self.image_buffer.append(image)\\n return_images.append(image)\\n else:\\n use_buffer = np.random.random() < self.buffer_ratio\\n if use_buffer:\\n random_id = np.random.randint(0, self.buffer_size)\\n image_tmp = self.image_buffer[random_id].clone()\\n self.image_buffer[random_id] = image\\n return_images.append(image_tmp)\\n else:\\n return_images.append(image)\\n return_images = torch.cat(return_images, 0)\\n return return_images\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c692ea5aa1ea036507e41f5991b7013\",\n \"score\": \"0.5704483\",\n \"text\": \"def query(self, images):\\n if self.pool_size == 0:\\n return images\\n return_images = []\\n for image in images:\\n image = torch.unsqueeze(image.data, 0)\\n if self.num_imgs < self.pool_size:\\n self.num_imgs = self.num_imgs + 1\\n self.images.append(image)\\n return_images.append(image)\\n else:\\n p = random.uniform(0, 1)\\n if p > 0.5:\\n random_id = random.randint(0, self.pool_size - 1)\\n tmp = self.images[random_id].clone()\\n self.images[random_id] = image\\n return_images.append(tmp)\\n else:\\n return_images.append(image)\\n return_images = torch.cat(return_images, 0)\\n return return_images\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4305676d81fddbf49b61c17aaa65a782\",\n \"score\": \"0.56069136\",\n \"text\": \"def handle_flikr_embed(session,post_dict):\\n logging.debug(\\\"Processing flikr embed\\\")\\n # Extract image link from post dict\\n thumbnail_link = post_dict[\\\"thumbnail_url\\\"]\\n # Download videos if there are any\\n media_id_list = download_image_link(session,thumbnail_link)\\n logging.debug(\\\"Finished downloading flikr embed\\\")\\n return media_id_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a8d15e4f2fa1496522854b4f6d7adfe\",\n \"score\": \"0.5513861\",\n \"text\": \"def pack_query_data(people_imgs):\\n uids = list(range(-len(people_imgs), 0))\\n pids = list(range(-len(people_imgs), 0))\\n imgs = None\\n\\n for person_img in people_imgs:\\n pil_img = Image.fromarray(cv2.cvtColor(person_img, cv2.COLOR_BGR2RGB))\\n img = TimingRecorder.transformer(pil_img)\\n img = img.view(1, 3, 256, 128)\\n if imgs is None:\\n imgs = img\\n else:\\n imgs = torch.cat((imgs, img), dim=0)\\n\\n return uids, pids, imgs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"26426e7880382deaafb6dc63fcbf4ba3\",\n \"score\": \"0.55134726\",\n \"text\": \"def _get_multiple_evidences_predictions_normal(self, query_ids: torch.Tensor,\\n query_contents: torch.Tensor,\\n query_lens: np.ndarray,\\n query_sources: torch.Tensor,\\n evd_doc_ids: torch.Tensor,\\n evd_doc_contents: torch.Tensor,\\n evd_docs_lens: np.ndarray,\\n evd_sources: torch.Tensor,\\n labels: np.ndarray,\\n n: int, **kargs) -> torch.Tensor:\\n evd_count_per_query = kargs[KeyWordSettings.EvidenceCountPerQuery] # (B, )\\n assert evd_doc_ids.size() == evd_docs_lens.shape\\n assert query_ids.size(0) == evd_doc_ids.size(0)\\n assert query_lens.shape == labels.size()\\n assert query_contents.size(0) == evd_doc_contents.size(0) # = batch_size\\n _, L = query_contents.size()\\n batch_size = query_ids.size(0)\\n # prunning at this step to remove padding\\\\\\n e_lens, e_conts, q_conts, q_lens = [], [], [], []\\n expaned_labels = []\\n for evd_cnt, q_cont, q_len, evd_lens, evd_doc_cont, label in \\\\\\n zip(evd_count_per_query, query_contents, query_lens, evd_docs_lens, evd_doc_contents, labels):\\n evd_cnt = int(torch_utils.cpu(evd_cnt).detach().numpy())\\n e_lens.extend(list(evd_lens[:evd_cnt]))\\n e_conts.append(evd_doc_cont[:evd_cnt, :]) # stacking later\\n q_lens.extend([q_len] * evd_cnt)\\n q_conts.append(q_cont.unsqueeze(0).expand(evd_cnt, L))\\n expaned_labels.extend([int(torch_utils.cpu(label).detach().numpy())] * evd_cnt)\\n # concat\\n e_conts = torch.cat(e_conts, dim = 0) # (n1 + n2 + ..., R)\\n e_lens = np.array(e_lens) # (n1 + n2 + ..., )\\n q_conts = torch.cat(q_conts, dim = 0) # (n1 + n2 + ..., R)\\n q_lens = np.array(q_lens)\\n assert q_conts.size(0) == q_lens.shape[0] == e_conts.size(0) == e_lens.shape[0]\\n\\n d_new_indices, d_old_indices = torch_utils.get_sorted_index_and_reverse_index(e_lens)\\n e_lens = my_utils.gpu(torch.from_numpy(e_lens), self._use_cuda)\\n q_new_indices, q_restoring_indices = torch_utils.get_sorted_index_and_reverse_index(query_lens)\\n query_lens = my_utils.gpu(torch.from_numpy(query_lens), self._use_cuda)\\n # query_lens = my_utils.gpu(torch.from_numpy(query_lens), self._use_cuda)\\n additional_paramters = {\\n KeyWordSettings.Query_lens: query_lens,\\n KeyWordSettings.Doc_lens: evd_docs_lens,\\n KeyWordSettings.DocLensIndices: (d_new_indices, d_old_indices, e_lens),\\n KeyWordSettings.QueryLensIndices: (q_new_indices, q_restoring_indices, query_lens),\\n KeyWordSettings.QuerySources: query_sources,\\n KeyWordSettings.DocSources: evd_sources,\\n KeyWordSettings.TempLabel: labels,\\n KeyWordSettings.DocContentNoPaddingEvidence: e_conts,\\n KeyWordSettings.QueryContentNoPaddingEvidence: q_conts,\\n KeyWordSettings.EvidenceCountPerQuery: evd_count_per_query,\\n KeyWordSettings.FIXED_NUM_EVIDENCES: n\\n }\\n predictions = self._net(query_contents, evd_doc_contents, **additional_paramters) # (B, )\\n # labels.unsqueeze(-1).expand(batch_size, n).reshape(batch_size * n)\\n # labels = torch_utils.gpu(torch.from_numpy(np.array(expaned_labels)), self._use_cuda)\\n # print(\\\"Labels: \\\", labels)\\n # mask = (evd_doc_ids >= 0).view(batch_size * n).float()\\n return self._loss_func(predictions, labels.float())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6844efe7ac0d2f70fb8945117d175ff\",\n \"score\": \"0.54689616\",\n \"text\": \"def get_image_list(releaseid):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c36dbba6bc9e1aeb9366f525170254c5\",\n \"score\": \"0.5445434\",\n \"text\": \"def query(self, images):\\n if self.pool_size == 0:\\n return images\\n return_images = []\\n for image in images:\\n if self.num_imgs < self.pool_size:\\n self.num_imgs += 1\\n self.images.append(image)\\n return_images.append(image)\\n else:\\n p = np.random.rand()\\n if p > 0.5:\\n random_id = np.random.randint(0, self.pool_size)\\n tmp = self.images[random_id].copy()\\n self.images[random_id] = image\\n return_images.append(tmp)\\n else:\\n return_images.append(image)\\n return np.array(return_images)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c16af1b378e0e96b52c0f5b88773092e\",\n \"score\": \"0.54368937\",\n \"text\": \"def selectRows(sentEmbeds, idxs):\\n results = []\\n for idx in idxs:\\n if idx == -1:\\n results.append(torch.zeros((1, 2 * configuration['kiperwasser']['rnnUnitNum']), dtype=dtype).to(device))\\n else:\\n results.append(sentEmbeds[idx].view(1, -1).to(device))\\n return torch.cat(results)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6fbf994d8832957b36ce0c53c704befc\",\n \"score\": \"0.5318698\",\n \"text\": \"def getImgIds(self, quesIds=[], quesTypes=[], ansTypes=[]):\\n quesIds = quesIds if type(quesIds) == list else [quesIds]\\n quesTypes = quesTypes if type(quesTypes) == list else [quesTypes]\\n ansTypes = ansTypes if type(ansTypes) == list else [ansTypes]\\n\\n if len(quesIds) == len(quesTypes) == len(ansTypes) == 0:\\n anns = self.dataset['annotations']\\n else:\\n if not len(quesIds) == 0:\\n anns = sum([self.qa[quesId] for quesId in quesIds if quesId in self.qa], [])\\n else:\\n anns = self.dataset['annotations']\\n anns = anns if len(quesTypes) == 0 else [\\n ann for ann in anns if ann['question_type'] in quesTypes]\\n anns = anns if len(ansTypes) == 0 else [\\n ann for ann in anns if ann['answer_type'] in ansTypes]\\n ids = [ann['image_id'] for ann in anns]\\n return ids\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35dcf57bd7045a81857eb964b2f9a9d4\",\n \"score\": \"0.53042674\",\n \"text\": \"def query(self, images):\\r\\n if self.pool_size == 0: # if the buffer size is 0, do nothing\\r\\n return images\\r\\n return_images = []\\r\\n for image in images:\\r\\n image = torch.unsqueeze(image.data, 0)\\r\\n if self.num_imgs < self.pool_size: # if the buffer is not full; keep inserting current images to the buffer\\r\\n self.num_imgs = self.num_imgs + 1\\r\\n self.images.append(image)\\r\\n return_images.append(image)\\r\\n else:\\r\\n p = random.uniform(0, 1)\\r\\n if p > 0.5: # by 50% chance, the buffer will return a previously stored image, and insert the current image into the buffer\\r\\n random_id = random.randint(0, self.pool_size - 1) # randint is inclusive\\r\\n tmp = self.images[random_id].clone()\\r\\n self.images[random_id] = image\\r\\n return_images.append(tmp)\\r\\n else: # by another 50% chance, the buffer will return the current image\\r\\n return_images.append(image)\\r\\n return_images = torch.cat(return_images, 0) # collect all the images and return\\r\\n return return_images\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a601f179348f7ceb0465b44616276175\",\n \"score\": \"0.5228106\",\n \"text\": \"def update_images(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3443bad0ccfb4a22cd49606fda782f03\",\n \"score\": \"0.5216751\",\n \"text\": \"def loadImgs(self, ids=[]):\\n if type(ids) == list:\\n return [self.imgs[id] for id in ids]\\n elif type(ids) == int:\\n return [self.imgs[ids]]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a0d7b609fcfa2106f3be5660bcbd2df\",\n \"score\": \"0.52092993\",\n \"text\": \"def run(conn, params):\\n images = []\\n\\n if params.get(\\\"Data_Type\\\") == 'Dataset':\\n for dsId in params[\\\"IDs\\\"]:\\n dataset = conn.getObject(\\\"Dataset\\\", dsId)\\n if dataset:\\n for image in dataset.listChildren():\\n images.append(image)\\n\\n if len(images) == 0:\\n return None\\n\\n for image in images:\\n print(\\\"---- Processing image\\\", image.id)\\n return images\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cab34cf6a80e977703a0613d3b9d2d85\",\n \"score\": \"0.51977825\",\n \"text\": \"def conjunction(images):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7fee612bb560f597bf3ad8325b0fdd02\",\n \"score\": \"0.5192922\",\n \"text\": \"def query(self, images):\\n if self.pool_size == 0: # if the buffer size is 0, do nothing\\n return images\\n\\n return_images = []\\n\\n for image in images:\\n image = torch.unsqueeze(image.data, 0)\\n if self.num_imgs < self.pool_size: # if the buffer is not full; keep inserting current images to the buffer\\n self.num_imgs = self.num_imgs + 1\\n self.images.append(image)\\n return_images.append(image)\\n else:\\n p = random.uniform(0, 1)\\n if p > 0.5: # by 50% chance, the buffer will return a previously stored image, and insert the current image into the buffer\\n random_id = random.randint(\\n 0, self.pool_size - 1) # randint is inclusive\\n tmp = self.images[random_id].clone()\\n self.images[random_id] = image\\n return_images.append(tmp)\\n else: # by another 50% chance, the buffer will return the current image\\n return_images.append(image)\\n # collect all the images and return\\n return_images = torch.cat(return_images, 0)\\n return return_images\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3f9929a1405b247e339098908966709\",\n \"score\": \"0.509739\",\n \"text\": \"def call(self, inputs):\\n batch_size, h, w = tf.shape(inputs)[0], tf.shape(inputs)[1], tf.shape(inputs)[2]\\n i = tf.range(w)\\n j = tf.range(h)\\n x_emb = self.col_embed(i)\\n y_emb = self.row_embed(j)\\n single_img_emb = tf.concat([\\n tf.tile(x_emb[None], (h, 1, 1)),\\n tf.tile(y_emb[:, None], (1, w, 1)),\\n ],\\n axis=-1)\\n\\n batch_emb = tf.tile(single_img_emb[None], (batch_size, 1, 1, 1))\\n return batch_emb\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cbedda7c25a4e5719821d0fc14ee2b00\",\n \"score\": \"0.50831044\",\n \"text\": \"def update_imageset_ids(experiments, reflections):\\n # input a list of ordered matching experiments and reflection tables.\\n\\n next_iset_id = 0\\n imagesets_found = OrderedSet()\\n for expt, table in zip(experiments, reflections):\\n if \\\"imageset_id\\\" in table:\\n assert len(set(table[\\\"imageset_id\\\"])) == 1\\n iset = expt.imageset\\n if iset not in imagesets_found:\\n imagesets_found.add(iset)\\n table[\\\"imageset_id\\\"] = flex.int(table.size(), next_iset_id)\\n next_iset_id += 1\\n else:\\n iset_id = imagesets_found.index(iset)\\n table[\\\"imageset_id\\\"] = flex.int(table.size(), iset_id)\\n return reflections\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7dd6ea39d2a705595f93509f91dd5810\",\n \"score\": \"0.50658953\",\n \"text\": \"def processImages(self, imsq):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b27466e9652a4b147e79196ab24e143b\",\n \"score\": \"0.50580966\",\n \"text\": \"def _embed(embedders, xg, mode=None):\\n\\n if mode is not None:\\n for e in embedders:\\n e.mode = mode\\n\\n return torch.cat([e(xg_j) for e, xg_j in zip(embedders, xg.T)], dim=1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"62675314977c217eac9e8f1f6331494b\",\n \"score\": \"0.50363404\",\n \"text\": \"def generate_batch(images_embeddings, indexed_captions, batch_size, max_len=None):\\n\\n # added replace=False to avoid repetitions\\n batch_indexes = np.random.choice(len(images_embeddings), batch_size, replace=False)\\n batch_image_embeddings = images_embeddings[batch_indexes]\\n captions_of_batch_images = indexed_captions[batch_indexes].tolist()\\n\\n # a little brutto\\n batch_captions = []\\n captions_batch_indexes = np.random.choice(5, batch_size)\\n for i,captions in enumerate(captions_of_batch_images):\\n batch_captions += [captions[captions_batch_indexes[i]]]\\n\\n\\n batch_captions_matrix = batch_captions_to_matrix(batch_captions, 0, max_len)\\n\\n return {decoder.img_embeds: batch_image_embeddings,\\n decoder.sentences: batch_captions_matrix}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71cd25755e41e3e0aec5ffeecfca45cc\",\n \"score\": \"0.50174147\",\n \"text\": \"def disjunction(images):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"437acf87f30c64065fe387e2228ee1d3\",\n \"score\": \"0.5012557\",\n \"text\": \"def queue_image(self, image_id):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3a5b9fd3fe746eb865a40f04ed92156\",\n \"score\": \"0.50075275\",\n \"text\": \"def getQuesIds(self, imgIds=[], quesTypes=[], ansTypes=[]):\\n imgIds = imgIds if type(imgIds) == list else [imgIds]\\n quesTypes = quesTypes if type(quesTypes) == list else [quesTypes]\\n ansTypes = ansTypes if type(ansTypes) == list else [ansTypes]\\n\\n if len(imgIds) == len(quesTypes) == len(ansTypes) == 0:\\n anns = self.dataset['annotations']\\n else:\\n if not len(imgIds) == 0:\\n anns = sum([self.imgToQA[imgId] for imgId in imgIds if imgId in self.imgToQA], [])\\n else:\\n anns = self.dataset['annotations']\\n anns = anns if len(quesTypes) == 0 else [\\n ann for ann in anns if ann['question_type'] in quesTypes]\\n anns = anns if len(ansTypes) == 0 else [\\n ann for ann in anns if ann['answer_type'] in ansTypes]\\n ids = [ann['question_id'] for ann in anns]\\n return ids\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63d09bb32009c7c86361258db6b4be89\",\n \"score\": \"0.49977264\",\n \"text\": \"def build_image_embeddings(self):\\n\\n # parameter initialization\\n batch_norm_params = {\\n \\\"is_training\\\": False,\\n \\\"trainable\\\": False,\\n # decay for the moving averages\\n \\\"decay\\\": 0.9997,\\n # epsilon to prevent 0s in variance\\n \\\"epsilon\\\": 0.001,\\n # collection containing the moving mean and moving variance\\n \\\"variables_collections\\\": {\\n \\\"beta\\\": None,\\n \\\"gamma\\\": None,\\n \\\"moving_mean\\\": [\\\"moving_vars\\\"],\\n \\\"moving_variance\\\": [\\\"moving_vars\\\"],\\n }\\n }\\n\\n stddev = 0.1,\\n dropout_keep_prob = 0.8\\n\\n with tf.variable_scope(\\\"InceptionV3\\\", \\\"InceptionV3\\\", [self.images]) as scope:\\n with slim.arg_scope(\\n [slim.conv2d, slim.fully_connected],\\n weights_regularizer=None,\\n trainable=False):\\n with slim.arg_scope(\\n [slim.conv2d],\\n weights_initializer=tf.truncated_normal_initializer(stddev=stddev),\\n activation_fn=tf.nn.relu,\\n normalizer_fn=slim.batch_norm,\\n normalizer_params=batch_norm_params):\\n net, end_points = inception_v3_base(self.images, scope=scope)\\n with tf.variable_scope(\\\"logits\\\"):\\n shape = net.get_shape()\\n net = slim.avg_pool2d(net, shape[1:3], padding=\\\"VALID\\\", scope=\\\"pool\\\")\\n net = slim.dropout(\\n net,\\n keep_prob=dropout_keep_prob,\\n is_training=False,\\n scope=\\\"dropout\\\")\\n net = slim.flatten(net, scope=\\\"flatten\\\")\\n\\n # add summaries\\n for v in end_points.values():\\n tf.contrib.layers.summaries.summarize_activation(v)\\n\\n self.inception_variables = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope=\\\"InceptionV3\\\")\\n\\n # map inception output(net) into embedding space\\n with tf.variable_scope(\\\"image_embedding\\\") as scope:\\n image_embeddings = tf.contrib.layers.fully_connected(\\n inputs=net,\\n num_outputs=self.embedding_size,\\n activation_fn=None,\\n weights_initializer=self.initializer,\\n biases_initializer=None,\\n scope=scope)\\n\\n # save the embedding size in the graph\\n tf.constant(self.embedding_size, name=\\\"embedding_size\\\")\\n\\n self.image_embeddings = image_embeddings\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fee9a2c8e53e8b89649db295c889603b\",\n \"score\": \"0.49957043\",\n \"text\": \"def compile_embedding(self, ids):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5aa722c7bbe3ba0105cde528ed3c6e38\",\n \"score\": \"0.49870318\",\n \"text\": \"def prepare_queries(self, queries: List[Instances], mask):\\n for x in queries:\\n x.pred_boxes = Boxes(x.pred_boxes.tensor.detach())\\n x.scores = x.scores.detach()\\n x.features = x.features.detach()\\n \\n bs, vis_dim, box_dim = len(queries), queries[0].features.shape[-1], self.hidden_dim\\n device = queries[0].features.device\\n \\n num_queries = max([len(x) for x in queries]) * self.num_duplicates\\n\\n query_masks = torch.ones((num_queries, num_queries, bs), dtype=torch.bool, device=device)\\n query_vis_embs = torch.zeros((num_queries, bs, vis_dim), dtype=torch.float32, device=device)\\n query_box_embs = torch.zeros((num_queries, bs, box_dim), dtype=torch.float32, device=device)\\n query_boxes = torch.zeros((num_queries, bs, 4), dtype=torch.float32, device=device)\\n query_classes = torch.full((num_queries, bs), -1, dtype=torch.int64, device=device)\\n query_scores = torch.zeros((num_queries, bs), dtype=torch.float32, device=device)\\n for i, x in enumerate(queries):\\n size = len(x) * self.num_duplicates\\n\\n query_vis_embs[:size, i, :] = x.features.repeat(1, self.num_duplicates).view(-1, vis_dim)\\n \\n box = x.pred_boxes.tensor\\n query_boxes[:size, i, :] = box.repeat(1, self.num_duplicates).view(-1, 4)\\n \\n box_embs_x = self.box_encoding(x.image_size, x.pred_boxes.tensor.clone())\\n query_box_embs[:size, i, :] = box_embs_x.repeat(1, self.num_duplicates).view(-1, box_dim)\\n\\n classes_x = x.pred_classes.unsqueeze(-1).repeat(1, self.num_duplicates).view(-1)\\n query_classes[:size, i] = classes_x\\n\\n scores_x = x.scores.unsqueeze(-1).repeat(1, self.num_duplicates).view(-1)\\n query_scores[:size, i] = scores_x\\n\\n for q in range(max([len(x) for x in queries])):\\n stx = q * self.num_duplicates\\n end = (q + 1) * self.num_duplicates\\n query_masks[stx:end, stx:end, :] = False\\n\\n query_vis_embs = self.query_norm(self.query_proj(query_vis_embs))\\n query_instance = Instances(image_size=tuple(mask.shape[-2:]))\\n query_instance.query_feat = query_vis_embs + query_box_embs\\n query_instance.query_boxes = query_boxes\\n query_instance.query_masks = query_masks\\n query_instance.query_classes = query_classes\\n query_instance.query_scores = query_scores\\n\\n return query_instance\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6666ab477e0848306c535fb2bda95a3c\",\n \"score\": \"0.49816033\",\n \"text\": \"def handle_imgur_videos(session,post_dict):# NEW TABLES\\n logging.debug(\\\"Processing imgur videos\\\")\\n # Extract video links from post dict\\n imgur_urls = []\\n video_items = post_dict[\\\"player\\\"]\\n for video_item in video_items:\\n embed_code = video_item[\\\"embed_code\\\"]\\n # u'<iframe class=\\\"imgur-embed\\\" width=\\\"100%\\\" height=\\\"720\\\" frameborder=\\\"0\\\" src=\\\"http://i.imgur.com/wSBlRyv.gifv#embed\\\"></iframe>'\\n # http://i.imgur.com/wSBlRyv.gifv\\n if embed_code:\\n # Process links so YT-DL can understand them\\n logging.debug(\\\"embed_code: \\\"+repr(embed_code))\\n embed_url_regex =\\\"\\\"\\\"src=[\\\"']([^?\\\"'#]+)\\\"\\\"\\\"\\n embed_url_search = re.search(embed_url_regex, embed_code, re.IGNORECASE|re.DOTALL)\\n if embed_url_search:\\n embed_url = embed_url_search.group(1)\\n imgur_urls.append(embed_url)\\n continue\\n\\n logging.debug(\\\"imgur_urls: \\\"+repr(imgur_urls))\\n\\n # Download videos if there are any\\n media_id_list = run_yt_dl_multiple(\\n session = session,\\n download_urls = imgur_urls,\\n extractor_used=\\\"video_handlers.handle_imgur_videos()\\\",\\n )\\n logging.debug(\\\"Finished downloading imgur_video embeds\\\")\\n return media_id_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff96acb17ad79439fb15f971a9951f4a\",\n \"score\": \"0.497084\",\n \"text\": \"def get_batch(self):\\r\\n\\r\\n indices = np.random.choice(self.dataset_len, self.batch_size) # Sample non-repeated indices\\r\\n img_batch, hair_tags, eye_tags, face_tags, glass_tags = [], [], [], [], []\\r\\n for i in indices:\\r\\n img, hair_tag, eye_tag, face_tag, glass_tag = self.dataset.get_item(i)\\r\\n img_batch.append(img.unsqueeze(0))\\r\\n #print(img.shape)\\r\\n hair_tags.append(hair_tag.unsqueeze(0))\\r\\n #print(hair_tag.shape)\\r\\n #print(hair_tag.unsqueeze(0).shape) #torch.Size([1, 6])\\r\\n #input()\\r\\n eye_tags.append(eye_tag.unsqueeze(0)) #unsqueeze dim-0 for later concatenation\\r\\n face_tags.append(face_tag.unsqueeze(0))\\r\\n glass_tags.append(glass_tag.unsqueeze(0))\\r\\n img_batch = torch.cat(img_batch, 0)\\r\\n #print(img_batch)\\r\\n #print(img_batch.shape) #torch.Size([batch_size, 3, 128, 128])\\r\\n hair_tags = torch.cat(hair_tags, 0)\\r\\n #print(hair_tags)\\r\\n #print(hair_tags.shape) #torch.Size([128, 6])\\r\\n eye_tags = torch.cat(eye_tags, 0)\\r\\n #print(eye_tags.shape) #torch.Size([128, 4])\\r\\n face_tags= torch.cat(face_tags, 0)\\r\\n #print(face_tags.shape) #torch.Size([128, 3])\\r\\n glass_tags= torch.cat(glass_tags, 0)\\r\\n #print(glass_tags.shape) #torch.Size([128, 2])\\r\\n #input()\\r\\n return img_batch, hair_tags, eye_tags, face_tags, glass_tags\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99f9715d6d6b0be63dea1c88e49d7b2a\",\n \"score\": \"0.49688542\",\n \"text\": \"def pull(self, queries):\\n self.start_queue()\\n self.results = []\\n threads = []\\n self.info(\\\"Saving images in: %s\\\" % self.path)\\n for query in queries:\\n self.info(\\\"Searching for \\\\\\\"%s\\\\\\\" images...\\\" % query)\\n start = 0\\n while start < self.max_results:\\n api_url = self.make_api_url(query, start)\\n if self.is_url_in_history(api_url):\\n self.info(\\\"Already have %s\\\" % api_url)\\n start += 4 # Normal number of results.\\n continue\\n self.put_url_in_history(api_url)\\n data = self.http_get(api_url)\\n if data:\\n msg = json.loads(data)\\n if msg['responseStatus'] == 200 and msg['responseData']:\\n # Good response.\\n newresults = msg['responseData']['results']\\n if not len(newresults):\\n self.warn(\\\"No results in response: %s\\\" % msg)\\n time.sleep(3)\\n break\\n self.results += newresults\\n start += len(newresults)\\n # Retrieve.\\n for result in newresults:\\n image_url = result['unescapedUrl']\\n image_id = self.make_hash(image_url)\\n image_path = self.get_std_image_path(image_id)\\n if 'dogdrip.net/' in image_url:\\n # Exceptionally slow domain. :-)\\n self.info(\\\"Skipping %s\\\" % image_url)\\n if self.is_url_in_history(image_url):\\n # Seen it already.\\n self.info(\\\"Already have %s\\\" % image_url)\\n else:\\n # Put URL in history, to avoid repeats.\\n self.put_url_in_history(image_url)\\n # Join queue to retrieve image content.\\n self.q.put(image_url)\\n else:\\n # Bad response.\\n details = msg.get('responseDetails', '')\\n if details.startswith('qps rate exceeded'):\\n self.warn('Google just complained about ' +\\\\\\n 'too many queries per second. Pausing...')\\n time.sleep(15)\\n else:\\n self.warn(\\\"Response not OK: %s\\\" % msg)\\n time.sleep(3)\\n break\\n else:\\n # No data in response.\\n self.warn(\\\"No data from %s\\\" % api_url)\\n time.sleep(3)\\n break\\n # Be nice to Google.\\n time.sleep(1.5)\\n # Wait for workers to finish processing the queue.\\n self.q.join()\\n # Summarise results.\\n std_count = len(self.get_paths('.std'))\\n if not std_count:\\n self.warn(\\\"No images found.\\\")\\n self.info(\\\"There are %d images.\\\" % std_count)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"109e3f5b2d4c5cdc9382f971f70c4f28\",\n \"score\": \"0.49677628\",\n \"text\": \"def embed(\\n docs: Union[DocumentArray, DocumentArrayMemmap],\\n embed_model: AnyDNN,\\n device: str = 'cpu',\\n) -> None:\\n fm = get_framework(embed_model)\\n globals()[f'_set_embeddings_{fm}'](docs, embed_model, device)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e6f1f8c5da25e46c88b1f684d56c0865\",\n \"score\": \"0.49635997\",\n \"text\": \"def get_concat_embeddings(self, word_id_batch: torch.Tensor,\\r\\n tag_id_batch: torch.Tensor,\\r\\n deprel_id_batch: torch.Tensor) -> torch.Tensor:\\r\\n ##****BEGIN YOUR CODE****\\r\\n word_id_batch_temp = self.word_embed(word_id_batch)\\r\\n reshape_word = self.reshape_embedded(word_id_batch_temp)\\r\\n tag_id_batch_temp = self.tag_embed(tag_id_batch)\\r\\n reshape_tag = self.reshape_embedded(tag_id_batch_temp)\\r\\n deprel_id_batch_temp = self.deprel_embed(deprel_id_batch)\\r\\n reshape_deprel = self.reshape_embedded(deprel_id_batch_temp)\\r\\n x = torch.cat([reshape_word, reshape_tag, reshape_deprel], -1)\\r\\n ##****END YOUR CODE****\\r\\n return x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b4264b3b2f9b502c5cab62e83a87888a\",\n \"score\": \"0.49584693\",\n \"text\": \"def all(ctx):\\n print(\\\"Pushing all images\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89b538720820a544b67f4cd3db4c4fbb\",\n \"score\": \"0.49465153\",\n \"text\": \"def _filter_images(self):\\n self.new_images = []\\n self.total_new_images = 0\\n for image_id in self.new_image_ids:\\n self.total_new_images += 1\\n self.new_images.append(self.images[image_id])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c2e397fb48d52c84acfcf795c6ded174\",\n \"score\": \"0.4944695\",\n \"text\": \"def loadImgs(self, ids=[]):\\n if _isArrayLike(ids):\\n return [self.imgs[id] for id in ids]\\n elif type(ids) == int:\\n return [self.imgs[ids]]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f5b7c42e61cda7b06067c5b64b241a4\",\n \"score\": \"0.49264753\",\n \"text\": \"def get_imgs_by_ids(img_ids, coco, c, path_to_imgs):\\n imgs = coco.loadImgs(img_ids)\\n X = np.empty((c.batch_size, c.img_height, c.img_width, 3), dtype=np.float)\\n for i, img in enumerate(imgs):\\n path = os.path.join(path_to_imgs, img['file_name'])\\n X[i] = image.img_to_array(image.load_img(path,\\n target_size=[c.img_height, c.img_width]))/127.5 - 1\\n return X\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70a9c0f45f8717a559e9826ae97f2c1f\",\n \"score\": \"0.49138683\",\n \"text\": \"def extract_references(cls, img, query_size, container_size):\\n\\n num_containers_row = int(np.floor(img.shape[0] / container_size))\\n num_containers_col = int(np.floor(img.shape[1] / container_size))\\n\\n windows = np.zeros((num_containers_row * num_containers_col * 4, query_size, query_size))\\n win_idx = 0\\n\\n mean_all, std_all = cls.moments(img, query_size)\\n\\n for y_contain in range(1, num_containers_row+1):\\n for x_contain in range(1, num_containers_col+1):\\n temp = img[(y_contain - 1) * container_size: min(img.shape[0],\\n y_contain * container_size),\\n (x_contain - 1) * container_size: min(img.shape[1],\\n x_contain * container_size)]\\n\\n mean_contain = mean_all[(y_contain - 1) * container_size + query_size - 1: min(\\n mean_all.shape[0] - query_size,\\n (y_contain - 1) * container_size + container_size),\\n (x_contain - 1) * container_size + query_size - 1:min(\\n mean_all.shape[1] - query_size,\\n (x_contain - 1) * container_size + container_size)]\\n\\n std_contain = std_all[\\n (y_contain - 1) * container_size + query_size - 1:min(\\n mean_all.shape[0] - query_size, (\\n y_contain - 1) * container_size + container_size),\\n (x_contain - 1) * container_size + query_size - 1:min(\\n mean_all.shape[1] - query_size, (\\n x_contain - 1) * container_size + container_size)]\\n\\n y, x = np.where(mean_contain == mean_contain.max())\\n if np.prod(y.shape) == 1:\\n windows[win_idx, :, :] = temp[int(y):int(y + query_size),\\n int(x):int(x + query_size)]\\n win_idx = win_idx + 1\\n \\n y, x = np.where(mean_contain == mean_contain.min())\\n if np.prod(y.shape) == 1:\\n windows[win_idx, :, :] = temp[int(y):int(y + query_size),\\n int(x):int(x + query_size)]\\n win_idx = win_idx + 1\\n \\n y, x = np.where(std_contain == std_contain.max())\\n if np.prod(y.shape) == 1:\\n windows[win_idx, :, :] = temp[int(y):int(y + query_size),\\n int(x):int(x + query_size)]\\n win_idx = win_idx + 1\\n \\n y, x = np.where(std_contain == std_contain.min())\\n if np.prod(y.shape) == 1:\\n windows[win_idx, :, :] = temp[int(y):int(y + query_size),\\n int(x):int(x + query_size)]\\n win_idx = win_idx + 1\\n\\n return windows.copy()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bcf109783809ee5f22c5af9187f6dbfa\",\n \"score\": \"0.48999664\",\n \"text\": \"def test_embed_list_in_object(self):\\n\\n obj_id = self.get_list_with_min_size(1)[0].id\\n\\n # Make sure that this object has a list attached to it.\\n resp = self.client.get(reverse(self.api_details_url, kwargs={'pk': obj_id}), {'embed': ','.join([self.embedded_list_ids])})\\n self.assertGreater(len(resp.data[self.embedded_list_ids]), 0)\\n\\n # Make sure the list is of ids integers:\\n for obj in resp.data[self.embedded_list_ids]:\\n self.assertIsInstance(obj, int)\\n\\n # Get the object with the embedded list\\n resp = self.client.get(reverse(\\n self.api_details_url, kwargs={'pk': obj_id}\\n ) + '?embed=%s' % self.embedded_obj_p)\\n self.assertGreater(len(resp.data[self.embedded_obj_p]), 0)\\n\\n # Make sure the list is now embedded\\n for obj in resp.data[self.embedded_obj_p]:\\n self.assertIn('id', obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ba4e2c6c944021c1fbe91260b313f2f\",\n \"score\": \"0.48985377\",\n \"text\": \"def create_position_ids_from_inputs_embeds(self, inputs_embeds):\\n input_shape = inputs_embeds.size()[:-1]\\n sequence_length = input_shape[1]\\n\\n position_ids = torch.arange(\\n self.padding_idx + 1,\\n sequence_length + self.padding_idx + 1,\\n dtype=torch.long,\\n device=inputs_embeds.device,\\n )\\n return position_ids.unsqueeze(0).expand(input_shape)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8295a19c5cfd8a02c9d630818b03b7e\",\n \"score\": \"0.48903325\",\n \"text\": \"def fetch_images(self):\\n ids = [i.id for i in self]\\n if len(ids) == 0:\\n return {}\\n nodes = Item.objects.hfilter(\\n {'_class': 'product'}).extra(\\n where=[\\\"(content_item.data -> 'legacy_id') IN (%s)\\\"\\n % ', '.join('%s' for x in ids)],\\n params=map(unicode, ids)).hselect(['legacy_id'])\\n match = (len(nodes) == self.count())\\n ids_table = {}\\n for obj in self:\\n obj.node = [n for n in nodes if int(n.legacy_id) == obj.id][0] \\\\\\n if match else obj.node\\n ids_table.update({obj.node.id: obj.id})\\n\\n images_qs = Item().graph.get_images(ids=ids_table.keys()).extra(\\n select={'item_id': 'T4.from_item_id'})\\n for i in images_qs:\\n for obj in self:\\n if obj.id == ids_table[i.item_id]:\\n obj.image = i\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"437ec11d77e8ed5373a670c27df486e5\",\n \"score\": \"0.4886353\",\n \"text\": \"def forward_test(self, imgs):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54225281ac3652dc8b54063698d2544d\",\n \"score\": \"0.48729935\",\n \"text\": \"def compile_embedding(self, ids):\\n to_concat = [ids, np.transpose(self.W), np.transpose(np.dot(self.H, self.T))]\\n return np.concatenate(to_concat, axis=1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa7207329dfb276bce7c90db2726125b\",\n \"score\": \"0.48617682\",\n \"text\": \"def showimages(images):\\n for image in images:\\n showimage(image)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a25ea4030fa895c7765408b8a7962af7\",\n \"score\": \"0.48496383\",\n \"text\": \"async def embeds(self, ctx, search: int = None):\\n await self.do_purge(ctx, search, lambda e: len(e.embeds))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52e33b94fb1af054ad5d77863974d750\",\n \"score\": \"0.48362797\",\n \"text\": \"def make_prediction(image_ids, model, word_to_ind, ind_to_word):\\n predictions = []\\n vgg_model = preprocess_images.load_prebuilt_model()\\n maxlen = model.input_shape[1][1]\\n for ii in image_ids:\\n prediction = []\\n print('Predicting image: ' + str(ii))\\n example = image.load_img(ii, target_size=(224, 224))\\n example_arr = image.img_to_array(example, dtype='float32')\\n example_arr = np.expand_dims(example_arr, axis=0)\\n example_arr = preprocess_input(example_arr)\\n example_features = vgg_model.predict(example_arr)\\n example_features = np.array(example_features).reshape(-1, 4096)\\n \\n start_string = ['*start*']\\n start_ind = list(map(word_to_ind.get, start_string))\\n for i in range(maxlen):\\n start_seq = pad_sequences([start_ind], maxlen)\\n yhat = model.predict([example_features, start_seq])\\n yhat = np.argmax(yhat)\\n if ind_to_word[yhat] == '*end*':\\n break\\n prediction.append(ind_to_word[yhat])\\n start_ind.append(yhat)\\n predictions.append(prediction)\\n return predictions\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3842b8fe5c34232e17c540c9d6a92ce\",\n \"score\": \"0.48355153\",\n \"text\": \"def compute_image_embeddings(model_name, images, device, return_tensor=False):\\n model = SentenceTransformer(model_name, device=device)\\n if return_tensor:\\n embeddings = model.encode(images, device=device, convert_to_tensor=True).cpu()\\n else:\\n embeddings = model.encode(images, device=device, convert_to_numpy=True)\\n return embeddings\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d14886ff377badb8c0f2ccb5abb9fb6\",\n \"score\": \"0.4835186\",\n \"text\": \"def get_current_images_id(hparams, dataset_name):\\n\\n train_file = hparams[dataset_name]\\n image_dir = os.path.join(hparams['root'], train_file)\\n caption_file_path = get_cleaned_captions_path(hparams, train_file)\\n img_size = hparams[\\\"image_size\\\"]\\n transform_pipeline = transforms.Compose([CenteringPad(),\\n transforms.Resize(\\n (img_size, img_size)),\\n transforms.ToTensor()])\\n\\n coco_train_set = dset.CocoDetection(root=image_dir,\\n annFile=caption_file_path,\\n transform=transform_pipeline\\n )\\n train_loader = torch.utils.data.DataLoader(\\n coco_train_set, batch_size=hparams[\\\"batch_size\\\"])\\n break_training_loop_percentage = hparams[\\\"break_training_loop_percentage\\\"]\\n break_training_loop_idx = max(\\n int(len(train_loader) * break_training_loop_percentage / 100) - 1, 0)\\n\\n img_list = []\\n for idx, sample in enumerate(tqdm(train_loader)):\\n img_list.extend(sample[1][0][\\\"image_id\\\"].tolist())\\n if idx == break_training_loop_idx:\\n break\\n return img_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b772d023feb5cfd7d91a51f18602c751\",\n \"score\": \"0.48272422\",\n \"text\": \"def process_embed(embed_items=None,\\n embed_tracks=None,\\n embed_metadata=None):\\n\\n result = None\\n\\n embed = ''\\n if embed_items:\\n embed = 'items'\\n if embed_tracks:\\n if embed != '':\\n embed += ','\\n embed += 'tracks'\\n if embed_metadata:\\n if embed != '':\\n embed += ','\\n embed += 'metadata'\\n\\n if embed != '':\\n result = embed\\n\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ff37ad5cd9b377f01ce8d6878d099b5\",\n \"score\": \"0.47936964\",\n \"text\": \"def interactImages(images, titles=None, subplotShape=None, figsize=None):\\n #determine view\\n #fixed dimension\\n dimensions = np.arange(3)\\n fixedDimensionWidget = widgets.IntSlider(min=0, max=dimensions[-1], value=0)\\n \\n #determine position on fixed dimension\\n # for example z\\n \\n positionWidget = widgets.IntSlider(min=0)\\n \\n #updates\\n def updatePositionWidget(*args):\\n positionWidget.max = pet_scan.IMAGE_SHAPE[fixedDimensionWidget.value] -1\\n positionWidget.description = pet_scan.IMAGE_DIMENSION_TITLES[fixedDimensionWidget.value]\\n \\n fixedDimensionWidget.observe(updatePositionWidget, 'value')\\n updatePositionWidget()\\n \\n def f(fixDim, fixPos):\\n #print(fixedDims)\\n #determine image axes from fixdim\\n dimensions = np.arange(3)\\n hDim,vDim = dimensions[dimensions != fixDim]\\n \\n viewNDImages(images=images, fixedDimensions=[fixDim], fixedValues=[fixPos],\\n subplotShape=subplotShape, titles = titles,\\n axisLabels=[pet_scan.IMAGE_DIMENSION_TITLES[hDim],pet_scan.IMAGE_DIMENSION_TITLES[vDim]],\\n figsize=figsize)\\n \\n interact(f, fixDim=fixedDimensionWidget, fixPos=positionWidget);\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c40ebec4ac67575a2f5c95bd1066937\",\n \"score\": \"0.477987\",\n \"text\": \"def slides(self, req):\\n\\tlength = req.GET.get('length', 10)\\n\\t\\n\\n slides = []\\n\\n # FIXME(nmg): should catch exception if any\\n queries = self.db.get_veg_slides()\\n\\n for query in queries:\\n item = {\\n 'id': str(query['_id']),\\n 'name': query['name'],\\n 'photo': query['photo'],\\n 'price': query['price'],\\n 'mprice': query['mprice'],\\n 'size': query['size']\\n }\\n slides.append(item)\\n\\n return HttpResponse(slides)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8177cdcb0592feec54339f76cf97ac9\",\n \"score\": \"0.47709528\",\n \"text\": \"def wildbook_signal_imgsetid_list(ibs, imgsetid_list=None,\\n set_shipped_flag=True,\\n open_url_on_complete=True,\\n wb_target=None, dryrun=False):\\n try:\\n wb_url = ibs.get_wildbook_base_url(wb_target)\\n except IOError:\\n print('[ibs.wildbook_signal_imgsetid_list] Caught IOError, returning None')\\n return None\\n\\n try:\\n ibs.assert_ia_available_for_wb(wb_target)\\n except Exception:\\n pass\\n\\n if imgsetid_list is None:\\n imgsetid_list = ibs.get_valid_imgsetids()\\n\\n # Check to make sure imagesets are ok:\\n for imgsetid in imgsetid_list:\\n # First, check if imageset can be pushed\\n aid_list = ibs.get_imageset_aids(imgsetid)\\n assert len(aid_list) > 0, (\\n 'ImageSet imgsetid=%r cannot be shipped with0 annots' % (imgsetid,))\\n unknown_flags = ibs.is_aid_unknown(aid_list)\\n unnamed_aid_list = ut.compress(aid_list, unknown_flags)\\n unnamed_ok_aid_list = ibs.filter_annots_general(\\n unnamed_aid_list,\\n minqual='ok',\\n )\\n nUnnamedOk = sum(unnamed_ok_aid_list)\\n assert nUnnamedOk == 0, (\\n ('ImageSet imgsetid=%r1 cannot be shipped becuase '\\n 'annotation(s) %r with an identifiable quality have '\\n 'not been named') % (imgsetid, unnamed_ok_aid_list, ))\\n\\n # Call Wildbook url to signal update\\n print('[ibs.wildbook_signal_imgsetid_list] ship imgsetid_list = %r to wildbook' % (\\n imgsetid_list, ))\\n imageset_uuid_list = ibs.get_imageset_uuid(imgsetid_list)\\n print('[ibs.wildbook_signal_imgsetid_list] ship imgset_uuid_list = %r to wildbook' % (\\n imageset_uuid_list, ))\\n\\n url = wb_url + '/ia'\\n dbname = ibs.db.get_db_init_uuid()\\n occur_url_fmt = (wb_url + '/occurrence.jsp?number={uuid}&dbname={dbname}')\\n #enc_url_fmt = (wb_url + '/encounters/encounter.jsp?number={uuid}')\\n\\n # Check and push 'done' imagesets\\n status_list = []\\n for imgsetid, imageset_uuid in zip(imgsetid_list, imageset_uuid_list):\\n print('[_send] URL=%r' % (url, ))\\n json_payload = {'resolver': {'fromIAImageSet': str(imageset_uuid) }}\\n if dryrun:\\n status = False\\n else:\\n response = requests.post(url, json=json_payload)\\n status = response.status_code == 200\\n print('response = %r' % (response,))\\n if set_shipped_flag:\\n ibs.set_imageset_shipped_flags([imgsetid], [status])\\n if status and open_url_on_complete:\\n view_occur_url = occur_url_fmt.format(uuid=imageset_uuid, dbname=dbname)\\n _browser = ut.get_prefered_browser(PREFERED_BROWSER)\\n _browser.open_new_tab(view_occur_url)\\n status_list.append(status)\\n\\n try:\\n ibs.update_special_imagesets()\\n ibs.notify_observers()\\n except:\\n pass\\n\\n return status_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5978b424ec3226a8ad4d89ff6e7b4a15\",\n \"score\": \"0.4765014\",\n \"text\": \"def zfturbo_convert_targets_from_dataloader(self, targets, image_ids):\\n for img_id, t in zip(image_ids, targets):\\n\\n for box, label in zip(t[\\\"boxes\\\"].numpy(), t[\\\"labels\\\"].numpy()):\\n self.true_image_id_arr.append(img_id)\\n self.true_label_arr.append(label)\\n self.true_x_min_arr.append(box[0])\\n self.true_x_max_arr.append(box[2])\\n self.true_y_min_arr.append(box[1])\\n self.true_y_max_arr.append(box[3])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"945e83496bc1ca9498ff7d2e5347b0f7\",\n \"score\": \"0.4762966\",\n \"text\": \"def merge_images(sources, targets, opts, k=10):\\n _, _, h, w = sources.shape\\n row = int(np.sqrt(opts.batch_size))\\n merged = np.zeros([3, row * h, row * w * 2])\\n for idx, (s, t) in enumerate(zip(sources, targets)):\\n i = idx // row\\n j = idx % row\\n merged[:, i * h : (i + 1) * h, (j * 2) * h : (j * 2 + 1) * h] = s\\n merged[:, i * h : (i + 1) * h, (j * 2 + 1) * h : (j * 2 + 2) * h] = t\\n return merged.transpose(1, 2, 0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37572145bd7f92d5af541da7abd038af\",\n \"score\": \"0.47581768\",\n \"text\": \"async def dog(self, ctx):\\r\\n url = \\\"https://api.thedogapi.com/v1/images/search\\\"\\r\\n response = urlopen(url)\\r\\n result = json.loads(response.read())\\r\\n actualData = result[0]\\r\\n url = actualData[\\\"url\\\"]\\r\\n embed = discord.Embed(color=random.randint(1, 0xfffff))\\r\\n \\r\\n embed.set_image(url=url)\\r\\n embed.set_footer(text=f\\\"Requested by {ctx.author}\\\", icon_url=ctx.author.avatar_url)\\r\\n return await ctx.send(embed=embed)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16d74c7bdc9c202eceff5bf7376e371a\",\n \"score\": \"0.47462964\",\n \"text\": \"def preprocess_image(self, batched_inputs):\\n images = [self.normalizer(x[\\\"image\\\"].to(self.device)) for x in batched_inputs]\\n\\n# images = ImageList.from_tensors(images, self.size_divisibility)\\n images = ImageList.from_tensors(images, 32)\\n\\n images_whwh = list()\\n for bi in batched_inputs:\\n h, w = bi[\\\"image\\\"].shape[-2:]\\n images_whwh.append(torch.tensor([w, h, w, h], dtype=torch.float32, device=self.device))\\n images_whwh = torch.stack(images_whwh)\\n\\n return images, images_whwh\",\n \"title\": \"\"\n },\n {\n \"docid\": \"56d518c8ae03ca4e43f86a63e3837285\",\n \"score\": \"0.47427243\",\n \"text\": \"def fetch_images(self, response):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5108fccdf4ade0e82ce74706a7369e0\",\n \"score\": \"0.47369584\",\n \"text\": \"def create_pids2idxs(data_source):\\n pid2imgs = {}\\n for idx, (img, target, _) in enumerate(data_source.imgs):\\n if target not in pid2imgs:\\n pid2imgs[target] = [idx]\\n else:\\n pid2imgs[target].append(idx)\\n return pid2imgs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bcd934d0fee6c7970daa89fdbb6c2bf7\",\n \"score\": \"0.47367933\",\n \"text\": \"def compute_embeddings(images):\\n\\n # Creates graph from saved GraphDef.\\n create_graph()\\n filename_to_emb = {}\\n config = tf.ConfigProto(device_count = {'GPU': 0})\\n bar = progressbar.ProgressBar(widgets=[progressbar.Bar('=', '[', ']'), ' ', progressbar.Percentage()])\\n with tf.Session(config=config) as sess:\\n i = 0\\n for image in bar(images):\\n if not tf.gfile.Exists(image):\\n tf.logging.fatal('File does not exist %s', image) \\n image_data = tf.gfile.FastGFile(image, 'rb').read()\\n # Some useful tensors:\\n # 'softmax:0': A tensor containing the normalized prediction across\\n # 1000 labels.\\n # 'pool_3:0': A tensor containing the next-to-last layer containing 2048\\n # float description of the image.\\n # 'DecodeJpeg/contents:0': A tensor containing a string providing JPEG\\n # encoding of the image.\\n # Runs the softmax tensor by feeding the image_data as input to the graph.\\n softmax_tensor = sess.graph.get_tensor_by_name('softmax:0')\\n embedding_tensor = sess.graph.get_tensor_by_name('pool_3:0')\\n embedding = sess.run(embedding_tensor,\\n {'DecodeJpeg/contents:0': image_data})\\n filename_to_emb[image] = embedding.reshape(2048)\\n i += 1\\n # print(image, i, len(images))\\n return filename_to_emb\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b5766afdc9114e55c6ae90ea297dbc9\",\n \"score\": \"0.47360665\",\n \"text\": \"def _get_embeddings(self, input_ids):\\n # embedding_matrix = self.model.transformer.wte.weight\\n embedding_matrix = self.model_embeddings\\n\\n vocab_size = embedding_matrix.shape[0]\\n one_hot_tensor = self.to(_one_hot(input_ids, vocab_size))\\n\\n token_ids_tensor_one_hot = one_hot_tensor.clone().requires_grad_(True)\\n # token_ids_tensor_one_hot.requires_grad_(True)\\n\\n inputs_embeds = torch.matmul(token_ids_tensor_one_hot, embedding_matrix)\\n return inputs_embeds, token_ids_tensor_one_hot\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0de5bffa8a5d9b236f2ce7cf95223f02\",\n \"score\": \"0.47346297\",\n \"text\": \"def preprocess_batch_imgs(self,generated_content):\\n\\n # check the largest image width and word len in the batch\\n pil_images = [img for img, word in generated_content]\\n max_width = max([img.size[0] for img in pil_images])\\n max_word_len_batch = max([len(word) for img, word in generated_content])\\n\\n\\n # expand img with to mod 4_ds so that the maxpoolings wil result into\\n # well defined integer length for the mapped tdist dimension (\\\"new width\\\")\\n if max_width % 4 == 0:\\n img_w = max_width\\n else:\\n img_w = max_width + 4 - (max_width % 4)\\n\\n #augment batch images\\n for batch_ind in range(self.batch_size):\\n\\n # pad the image width with to the largest (fixed) image width\\n pil_img = pil_images[batch_ind]\\n width, height = pil_img.size\\n\\n new_img = Image.new(pil_img.mode, (img_w, self.img_h), (255,255,255))\\n new_img.paste(pil_img, ((img_w - width) // 2, 0))\\n\\n # convert to numpy array\\n img_arr = np.array(new_img)\\n \\n #some additional augmentation\\n img_arr = self.keras_augmentor.random_transform(img_arr)\\n\\n # scale with 255 so that the values are between 0 and 1\\n # and save to batch, also transpose because the \\\"time axis\\\" is width\\n generated_content[batch_ind][0] = img_arr.transpose((1,0,2)) / 255\\n\\n return generated_content, img_w, max_word_len_batch\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cad859d122fe1f8c23b5d88dc4f99f67\",\n \"score\": \"0.4732007\",\n \"text\": \"def update_image_canvas_multi(indices, data, source, max_images=25):\\n n_images = len(indices)\\n filenames = data['path'].iloc[indices]\\n if n_images > max_images:\\n filenames = filenames[:max_images - 1]\\n images = [imread(fn) for fn in filenames]\\n if n_images > max_images:\\n # from the My First Pixel Art (TM) School of Design\\n dotdotdot = np.full((7, 7, 4), 255, dtype=np.uint8)\\n dotdotdot[3, 1::2, :3] = 0\\n images.append(dotdotdot)\\n sidelen = ceil(sqrt(min(n_images, max_images)))\\n step_size = 1 / sidelen\\n grid_points = np.arange(0, 1 - step_size/2, step_size)\\n start_xs, start_ys = np.meshgrid(grid_points, grid_points, indexing='ij')\\n n_rows = len(images)\\n step_sizes = np.full(n_rows, step_size)\\n source.data = {'image': images, 'x': start_xs.ravel()[:n_rows],\\n 'y': start_ys.ravel()[:n_rows],\\n 'dx': step_sizes, 'dy': step_sizes}\\n print(source.data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55d59fb7dd4bd9036c703f2e7e056b61\",\n \"score\": \"0.47269878\",\n \"text\": \"def compile_embedding(self, ids):\\n to_concat = [ids, np.transpose(self.W), self.T.transpose().dot(self.H.transpose())]\\n return np.concatenate(to_concat, axis=1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d94b86d20817e2f5da2f4aee98a2088c\",\n \"score\": \"0.47199464\",\n \"text\": \"def compute_contextualized_embeddings(self, sent_w_ids, sent_p_ids):\\n\\n sent_length = len(sent_p_ids)\\n \\n sentence_w_embeddings = self.w_embeddings(sent_w_ids) # shape = len of sent (+2) , w_embedding_dim\\n sentence_p_embeddings = self.p_embeddings(sent_p_ids) # shape = len of sent (+2) , p_embedding_dim\\n\\n # embedding for one word is concatenation of the word form embedding and the pos embedding\\n sentence_embeddings = torch.cat( [ sentence_w_embeddings, sentence_p_embeddings ], 1 )\\n\\n # re-set to 0 the hidden vectors (TODO: other initialization?)\\n if self.gru_or_lstm == 'lstm':\\n # init hidden and cell states h0 c0\\n self.rnn_hidden = self.init_lstm_hidden_and_cell()\\n else:\\n # init hidden state h0\\n self.rnn_hidden = self.init_gru_hidden()\\n \\n #print(\\\"SENTENCE_EMBEDDINGS.SHAPE\\\", sentence_embeddings.shape)\\n #print(\\\"INPUT SHAPE TO RNN:\\\", sentence_embeddings.view(len(sent_length, 1, -1).shape)\\n #print(\\\"RNN_HIDDEN.shape:\\\", self.rnn_hidden_and_cell[0].shape)\\n\\n # rnn takes the batch size as second dimension (here batch size=1)\\n # rnn_hidden_seq = the output(hidden) vectors at each position in sequence\\n # rnn_hidden = if lstm: the output and cell vectors at the last position\\n # if gru: the output vector at the last position\\n rnn_hidden_seq, self.rnn_hidden = self.rnn(sentence_embeddings.view(sent_length, 1, -1),\\n self.rnn_hidden)\\n #print(\\\"LSTM_OUT.shape\\\", lstm_out.shape)\\n #print(\\\"LSTM_OUT output shape\\\", lstm_out.view(len(sent_length), -1).shape)\\n\\n return rnn_hidden_seq.view(sent_length, -1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5786e99304dd361a79ca4d230b7ff4dc\",\n \"score\": \"0.47181153\",\n \"text\": \"def get_images(self):\\n \\\"\\\"\\\"Returns a list of all the images used. The indices\\n in the layout refer to sprites in the list returned by\\n this function\\\"\\\"\\\"\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b7db5a269498eebeb76c33b2d3311c8\",\n \"score\": \"0.4713153\",\n \"text\": \"def get_relevant_imgs(img_lst, img_map, indices, distances,k, form=\\\"list\\\", rank=True, img_dir=None):\\n df_lst = []\\n for img in img_lst:\\n df_lst.append(get_similar_imgs(img, img_map, indices, distances, k, img_dir=img_dir))\\n \\n df = pd.concat(df_lst)\\n if rank:\\n df = df.sort_values(\\\"dist\\\")\\n else:\\n df = df.sample(k)\\n if form == \\\"list\\\":\\n return df.head(k)[\\\"img\\\"].values\\n elif form == \\\"df\\\":\\n return df.head(k)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b80ec583f7fd29fdea7bc4a1db3c2e9c\",\n \"score\": \"0.471242\",\n \"text\": \"def embedding_lookup_unique(params, ids, name=None):\\n with ops.name_scope(name, \\\"EmbeddingLookupUnique\\\", [params, ids]):\\n ids = ops.convert_to_tensor(ids)\\n shape = array_ops.shape(ids)\\n ids_flat = array_ops.reshape(\\n ids, math_ops.reduce_prod(shape, keep_dims=True))\\n unique_ids, idx = array_ops.unique(ids_flat)\\n unique_embeddings = embedding_ops.embedding_lookup(params, unique_ids)\\n embeds_flat = array_ops.gather(unique_embeddings, idx)\\n embed_shape = array_ops.concat(\\n [shape, array_ops.shape(unique_embeddings)[1:]], 0)\\n embeds = array_ops.reshape(embeds_flat, embed_shape)\\n embeds.set_shape(ids.get_shape().concatenate(\\n unique_embeddings.get_shape()[1:]))\\n return embeds\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e19bfdf2259484153178fbf63f1407a\",\n \"score\": \"0.46960354\",\n \"text\": \"def get_images(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a0f64d8eea4fe8bec303882f9da7776\",\n \"score\": \"0.46924624\",\n \"text\": \"async def images(request: Request):\\n recommendation_request = RecommendationRequest(**request.raw_args)\\n response = await selector.get_recommendations(app.client_session,\\n recommendation_request)\\n return json(response.json, status=response.status)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e43500af3356291694915dd671b04e16\",\n \"score\": \"0.4683429\",\n \"text\": \"def create_image_pairs(self, input_images, input_actions):\\n\\timages = []\\n\\tactions = []\\n\\tfuture_images = []\\n\\tfuture_actions = []\\n\\tids = []\\n\\tfuture_ids = []\\n\\tif len(input_images) < 1 or len(input_actions) < 1:\\n\\t return [images, actions, future_images, future_actions, ids, future_ids]\\n\\t\\n\\t# specify the length of the action sequence based on the maximally possible delta_t\\n\\tdelta_t = self.min_time_difference\\n\\tif self.random_time:\\n\\t delta_t = self.max_time_difference\\n\\taction_sequence_length = delta_t * len(input_actions[0])\\n\\timage_sequence_length = delta_t * input_images[0].shape[2] \\n\\t\\n\\t# create present-future image/action pairs\\n\\tfor i in range(len(input_images)):\\t \\n\\t # select time delta\\n\\t if self.random_time:\\n\\t\\tmax_time_difference = min(len(input_images) - i, self.max_time_difference)\\n\\t\\tif max_time_difference <= self.min_time_difference \\\\\\n\\t\\t\\tor max_time_difference < 1:\\n\\t\\t continue\\n\\t\\tdelta_t = np.random.randint(self.min_time_difference, max_time_difference)\\n\\t else:\\n\\t\\tif i + delta_t >= len(input_images):\\n\\t\\t break\\n\\t images.append(input_images[i])\\n\\t actions.append(input_actions[i])\\n\\t future_images.append(input_images[i+delta_t])\\n\\t future_actions.append(input_actions[i+delta_t])\\n\\t ids.append(i+self.batch_num*self.batch_size)\\n\\t future_ids.append(i+delta_t+self.batch_num*self.batch_size)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65afb84fa6dc6bb020c9cf7a7250557b\",\n \"score\": \"0.46827105\",\n \"text\": \"def download_galleries(driver, gallery_id_list, num_galleries):\\n for num, id in enumerate(gallery_id_list, 1):\\n # Only want the first N galleries specified by user\\n if num > num_galleries:\\n return\\n\\n # Make a direct link to the gallery\\n gallery_link = 'https://imgur.com/gallery/' + id\\n\\n # Go to the gallery_link page\\n driver.get(gallery_link)\\n\\n # Large galleries will have a \\\"load more images\\\" button\\n load_more_images_button = driver.find_elements_by_xpath(\\\"\\\"\\\"//a[contains(@class, 'post-loadall')]\\\"\\\"\\\")\\n\\n # If there is a \\\"Load More Images\\\" button, then we must switch to the gallery's grid view\\n # so the entire gallery is loaded. Otherwise, we can use the default gallery view.\\n if load_more_images_button:\\n use_grid_view(driver, id, num)\\n else:\\n use_normal_view(driver, num)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f48c75d7cdec57f7ef00c29c6dd28548\",\n \"score\": \"0.46815458\",\n \"text\": \"def load_imgs(self, ids):\\n return self._load_helper(self.imgs, ids)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5448286e9efc61d561e56ae305761b81\",\n \"score\": \"0.46776003\",\n \"text\": \"def get_queued_images(self):\\r\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1c6341c877c963c0a7e5c1c5eee8545\",\n \"score\": \"0.4675306\",\n \"text\": \"def handle_flash_embed(session,post_dict):#TODO FIXME\\n logging.debug(\\\"Processing flash embed\\\")\\n logging.warning(\\\"handle_flash_embed() is not finished yet. FIX IT!\\\")#TODO FIXME\\n \\\"\\\"\\\" u'player': [{u'embed_code': u'<embed width=\\\"250\\\" height=\\\"291\\\" align=\\\"middle\\\" pluginspage=\\\"http://www.adobe.com/go/getflashplayer\\\" type=\\\"application/x-shockwave-flash\\\" allowfullscreen=\\\"false\\\" allowscriptaccess=\\\"sameDomain\\\" name=\\\"xdft\\\" bgcolor=\\\"#000000\\\" scale=\\\"noscale\\\" quality=\\\"high\\\" menu=\\\"false\\\" src=\\\"http://www.najle.com/idaft/idaft/xdft.swf\\\">',\\n u'width': 250},\\n {u'embed_code': u'<embed width=\\\"400\\\" height=\\\"466\\\" align=\\\"middle\\\" pluginspage=\\\"http://www.adobe.com/go/getflashplayer\\\" type=\\\"application/x-shockwave-flash\\\" allowfullscreen=\\\"false\\\" allowscriptaccess=\\\"sameDomain\\\" name=\\\"xdft\\\" bgcolor=\\\"#000000\\\" scale=\\\"noscale\\\" quality=\\\"high\\\" menu=\\\"false\\\" src=\\\"http://www.najle.com/idaft/idaft/xdft.swf\\\">',\\n u'width': 400},\\n {u'embed_code': u'<embed width=\\\"500\\\" height=\\\"582\\\" align=\\\"middle\\\" pluginspage=\\\"http://www.adobe.com/go/getflashplayer\\\" type=\\\"application/x-shockwave-flash\\\" allowfullscreen=\\\"false\\\" allowscriptaccess=\\\"sameDomain\\\" name=\\\"xdft\\\" bgcolor=\\\"#000000\\\" scale=\\\"noscale\\\" quality=\\\"high\\\" menu=\\\"false\\\" src=\\\"http://www.najle.com/idaft/idaft/xdft.swf\\\">',\\n u'width': 500}],\\\"\\\"\\\"\\n # Extract video links from post dict\\n found_links = []\\n video_items = post_dict[\\\"player\\\"]\\n for video_item in video_items:\\n embed_code = video_item[\\\"embed_code\\\"]\\n #\\n if embed_code:\\n # Find links in the field\\n field_links = link_handlers.find_links(embed_code)\\n found_links += field_links\\n continue\\n logging.debug(\\\"handle_flash_embed() found_links: \\\"+repr(found_links))\\n\\n # Remove duplicate links\\n links = uniquify(found_links)\\n\\n media_id_list = []\\n # Choose which links to save\\n for link in links:\\n # If link ends in .swf\\n if \\\".swf\\\" in link[-4:]:\\n media_id_list += download_image_link(session,link)\\n continue\\n # If link ends in .flv\\n elif \\\".flv\\\" in link[-4:]:\\n media_id_list += download_image_link(session,link)\\n continue\\n continue\\n\\n return media_id_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4f74a44a5911a768227ce1c7872bd16\",\n \"score\": \"0.46719703\",\n \"text\": \"def main(*args):\\n if len(args) >= 1:\\n image_source_id = bson.objectid.ObjectId(args[0])\\n\\n config = global_conf.load_global_config('config.yml')\\n db_client = database.client.DatabaseClient(config=config)\\n\\n image_source = None\\n s_image_source = db_client.image_source_collection.find_one({'_id': image_source_id})\\n if s_image_source is not None:\\n image_source = db_client.deserialize_entity(s_image_source)\\n del s_image_source\\n\\n if image_source is not None:\\n image_source.begin()\\n while not image_source.is_complete():\\n image, _ = image_source.get_next_image()\\n debug_img = image.data[:, :, ::-1].copy()\\n for obj in image.metadata.labelled_objects:\\n x, y, w, h = obj.bounding_box\\n cv2.rectangle(debug_img, (x, y), (x + w, y + h), (0, 0, 255), 2)\\n\\n text_label = str(obj.class_names[0])\\n (retval, baseLine) = cv2.getTextSize(text_label, cv2.FONT_HERSHEY_COMPLEX, 1, 1)\\n text_org = (x, y - 0)\\n\\n cv2.rectangle(debug_img, (text_org[0] - 5, text_org[1] + baseLine - 5),\\n (text_org[0] + retval[0] + 5, text_org[1] - retval[1] - 5), (0, 0, 0), 2)\\n cv2.rectangle(debug_img, (text_org[0] - 5, text_org[1] + baseLine - 5),\\n (text_org[0] + retval[0] + 5, text_org[1] - retval[1] - 5), (255, 255, 255), -1)\\n cv2.putText(debug_img, text_label, text_org, cv2.FONT_HERSHEY_DUPLEX, 1, (0, 0, 0), 1)\\n\\n cv2.imshow('debug', debug_img)\\n cv2.waitKey(0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"560fa680efd3836987a15f68557e5a71\",\n \"score\": \"0.46693918\",\n \"text\": \"def batch_pad(data_dict,ids):\\n batch_info = {}\\n b_fp = torch.tensor(())\\n b_dfpdX = torch.tensor(())\\n b_e_mask = torch.tensor(())\\n b_e = torch.tensor(())\\n b_f = torch.tensor(())\\n\\n # Find the largest image in the batch\\n N_max = 0\\n all_atoms = torch.tensor(())\\n for ID in ids:\\n N_atoms, N_element = data_dict[ID]['e_mask'].shape\\n all_atoms = torch.cat((all_atoms,torch.tensor(N_atoms).float().view(1,1)))\\n if N_atoms > N_max:\\n N_max = N_atoms\\n N_sym = data_dict[ID]['fp'].shape[1]\\n\\n # Loop through the ids to batch the values\\n for ID in ids:\\n pad_fp = torch.zeros(N_max,N_sym)\\n pad_dfpdX = torch.zeros(N_max,N_sym,N_max,3)\\n pad_e_mask = torch.zeros(N_max,N_element)\\n pad_f = torch.zeros(N_max,3)\\n fp = data_dict[ID]['fp']\\n dfpdX = data_dict[ID]['dfpdX']\\n e_mask = data_dict[ID]['e_mask']\\n pad_fp[:fp.shape[0],:fp.shape[1]] = fp\\n pad_dfpdX[:dfpdX.shape[0],:dfpdX.shape[1],:dfpdX.shape[2],:] = dfpdX\\n pad_e_mask[:e_mask.shape[0],:e_mask.shape[1]] = e_mask\\n pad_f[:fp.shape[0],:] = data_dict[ID]['f'] \\n b_fp = torch.cat((b_fp,pad_fp))\\n b_dfpdX = torch.cat((b_dfpdX,pad_dfpdX))\\n b_e_mask = torch.cat((b_e_mask,pad_e_mask))\\n b_e = torch.cat((b_e,data_dict[ID]['e'].view(1,1)),dim=0)\\n b_f = torch.cat((b_f,pad_f))\\n\\n # Update the output dictionary\\n batch_info.update({'N_atoms':all_atoms})\\n batch_info.update({'b_fp':b_fp.view(len(ids),N_max,N_sym)})\\n batch_info.update({'b_dfpdX':b_dfpdX.view(len(ids),N_max,N_sym,N_max,3)})\\n batch_info.update({'b_e_mask':b_e_mask.view(len(ids),N_max,N_element)})\\n batch_info.update({'b_e':b_e})\\n batch_info.update({'b_f':b_f.view(len(ids),N_max,3)})\\n return batch_info\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e43b5925594eab6f5f3eff9c3796fe00\",\n \"score\": \"0.4665801\",\n \"text\": \"def get_true_captions(img_ids, coco):\\n batch_captions = []\\n for img_id in img_ids:\\n image_metadata = coco.imgToAnns[img_id]\\n # Here, we compute the image metadata\\n #print(\\\"Image metadata is {}\\\".format(image_metadata))\\n captions = []\\n for metadata in image_metadata:\\n captions.append(metadata['caption'].lower())\\n batch_captions.append(captions)\\n return batch_captions\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2947ebef2f28671ca4cab0f9856cebd0\",\n \"score\": \"0.46594942\",\n \"text\": \"def handle_dailymotion_videos(session,post_dict):\\n logging.debug(\\\"Processing dailymotion video\\\")\\n # Extract video links from post dict\\n video_urls = []\\n video_items = post_dict[\\\"player\\\"]\\n for video_item in video_items:\\n embed_code = video_item[\\\"embed_code\\\"]\\n # u'embed_code': u'<iframe src=\\\"https://www.dailymotion.com/embed/video/x2msryd\\\" width=\\\"250\\\" height=\\\"139\\\" frameborder=\\\"0\\\" allowfullscreen></iframe>'\\n # https://www.dailymotion.com/embed/video/x2msryd\\n if embed_code:\\n # Process links so YT-DL can understand them\\n logging.debug(\\\"embed_code: \\\"+repr(embed_code))\\n embed_url_regex =\\\"\\\"\\\"src=[\\\"']([^?\\\"'#]+)\\\"\\\"\\\"\\n embed_url_search = re.search(embed_url_regex, embed_code, re.IGNORECASE|re.DOTALL)\\n if embed_url_search:\\n embed_url = embed_url_search.group(1)\\n video_urls.append(embed_url)\\n continue\\n logging.debug(\\\"video_urls: \\\"+repr(video_urls))\\n\\n # Download videos if there are any\\n media_id_list = run_yt_dl_multiple(\\n session = session,\\n download_urls = video_urls,\\n extractor_used=\\\"video_handlers.handle_dailymotion_videos()\\\",\\n )\\n\\n logging.debug(\\\"Finished downloading dailymotion embeds\\\")\\n return media_id_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c227bbc1238334453a77e67cd7660e0e\",\n \"score\": \"0.4656213\",\n \"text\": \"def setHelpEmbedThumbnails():\\n for levelSection in botCommands.helpSectionEmbeds:\\n for helpSection in levelSection.values():\\n for embed in helpSection:\\n embed.set_thumbnail(url=botState.client.user.avatar_url_as(size=64))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33ce80f1bf81423b7379d77e5a97b831\",\n \"score\": \"0.4655339\",\n \"text\": \"def add_images_to_visual(self, visual_id, images=[]):\\n for image in images:\\n logger.info(\\\"Adding image %s to visual %d\\\" % (image, visual_id))\\n image_buffer = self._load_file(image)\\n return self._post(\\\"projects/visuals/%d/images/\\\" % visual_id,\\n files={'image': image_buffer})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5336d6eabcf2ad7d1e70261c7a6fe426\",\n \"score\": \"0.46519732\",\n \"text\": \"def handle_inline(inline_query):\\n if not nekowat.is_allowed(inline_query.from_user.id):\\n nekowat.answer_inline_query(inline_query.id, [])\\n return\\n\\n # Normalize expression\\n expression = inline_query.query.lower().strip()\\n\\n if not expression:\\n # Get all images\\n wats = nekowat.get_all_wats()\\n\\n else:\\n # Get by expression\\n wats = nekowat.get_wats_by_expression(expression)\\n\\n try:\\n responses = []\\n\\n for index, wat in enumerate(wats):\\n r = telebot.types.InlineQueryResultCachedPhoto(\\n str(index),\\n # Get smallest file for inline reply\\n wat['file_ids'][0],\\n parse_mode='' # Workaround for Telegram API error\\n )\\n\\n responses.append(r)\\n\\n nekowat.answer_inline_query(inline_query.id, responses)\\n\\n except Exception as e:\\n print(e)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2eb13699da3751bd8ed7ed1093635c96\",\n \"score\": \"0.46486753\",\n \"text\": \"def detection_wanted_img(path_wanted,path_image,portrait,l_reso,l_seuil):\\n print(\\\"calcul des embeddings à rechercher\\\")\\n res=convert_embd(path_wanted)\\n list_emb,list_name_wanted=res[0],res[1]\\n print(\\\"calcul terminé\\\")\\n list_emb=np.asarray(list_emb)\\n list_name_wanted=np.asarray(list_name_wanted)\\n detection_wanted(list_emb,list_name_wanted,path_image,portrait,l_reso,l_seuil)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"255c0a3c0fc2021ebc041ad4c8c10171\",\n \"score\": \"0.46456274\",\n \"text\": \"def get_example(id):\\n c = get_chunk(id // aug_single_chunk_size)\\n return c[0][id % aug_single_chunk_size].reshape((ImageChannels*aug_image_side**2)), c[1][id % aug_single_chunk_size]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6db4b7f19262e4107771040e81754350\",\n \"score\": \"0.4644313\",\n \"text\": \"def GetQAofImage(id=61512):\\n page = 1\\n next = '/api/v0/image/' + str(id) + '/qa?page=' + str(page)\\n qas = []\\n image_map = {}\\n while True:\\n data = utils.RetrieveData(next)\\n for d in data['results']:\\n if d['image'] not in image_map:\\n image_map[d['image']] = GetImageData(id=d['image'])\\n qas.extend(utils.ParseQA(data['results'], image_map))\\n if data['next'] is None:\\n break\\n page += 1\\n next = '/api/v0/image/' + str(id) + '/qa?page=' + str(page)\\n return qas\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e76ef9f4b53da57548cd606270e2b42f\",\n \"score\": \"0.4642653\",\n \"text\": \"def embedplays(params):\\n # set up default values\\n default_from, default_to, yesterday, _ = make_default_times()\\n\\n # get params\\n try:\\n series = \\\"onionstudios\\\"\\n from_date = params.get(\\\"from\\\", [default_from])[0]\\n to_date = params.get(\\\"to\\\", [default_to])[0]\\n group_by = params.get(\\\"group_by\\\", [DEFAULT_GROUP_BY])[0]\\n except Exception as e:\\n LOGGER.exception(e)\\n return json.dumps({\\\"error\\\": e.message}), \\\"500 Internal Error\\\"\\n\\n # check the cache\\n cache_key = \\\"{}:{}:{}:{}:{}:{}\\\".format(memcached_prefix, \\\"embedplays.json\\\", series, from_date, to_date, group_by)\\n try:\\n data = MEMCACHED_CLIENT.get(cache_key)\\n if data:\\n return data, \\\"200 OK\\\"\\n except Exception as e:\\n LOGGER.exception(e)\\n\\n # parse from date\\n from_date = parse_datetime(from_date)\\n if from_date is None:\\n LOGGER.error(\\\"could not parse 'from'\\\")\\n return json.dumps({\\\"error\\\": \\\"could not parse 'from'\\\"}), \\\"400 Bad Request\\\"\\n\\n # parse to date\\n to_date = parse_datetime(to_date)\\n if to_date is None:\\n LOGGER.error(\\\"could not parse 'to'\\\")\\n return json.dumps({\\\"error\\\": \\\"could not parse 'to'\\\"}), \\\"400 Bad Request\\\"\\n\\n # influx will only keep non-aggregated data for a day, so if the from param is beyond that point\\n # we need to update the series name to use the rolled up values\\n rollup_query = False\\n if from_date < yesterday:\\n series = update_series(series, \\\"embedplays\\\")\\n rollup_query = True\\n\\n # format times\\n from_date = format_datetime(from_date)\\n to_date = format_datetime(to_date)\\n\\n # build out the query\\n if not rollup_query:\\n query = \\\"SELECT sum(value) as value \\\" \\\\\\n \\\"FROM {series} \\\" \\\\\\n \\\"WHERE time > '{from_date}' \\\" \\\\\\n \\\"AND time < '{to_date}' \\\" \\\\\\n \\\"AND event =~ /^embedplay$/ \\\" \\\\\\n \\\"GROUP BY time({group_by}) \\\" \\\\\\n \\\"fill(0);\\\"\\n else:\\n query = \\\"SELECT sum(value) as value \\\" \\\\\\n \\\"FROM {series} \\\" \\\\\\n \\\"WHERE time > '{from_date}' \\\" \\\\\\n \\\"AND time < '{to_date}' \\\" \\\\\\n \\\"GROUP BY time({group_by}) \\\" \\\\\\n \\\"fill(0);\\\"\\n args = {\\\"series\\\": series, \\\"from_date\\\": from_date, \\\"to_date\\\": to_date, \\\"group_by\\\": group_by}\\n\\n # send the request\\n try:\\n res = INFLUXDB_CLIENT.query(query.format(**args))\\n\\n # capture errors and send them back along with the query (for inspection/debugging)\\n except Exception as e:\\n LOGGER.exception(e)\\n return json.dumps({\\\"error\\\": e.message, \\\"query\\\": query.format(**args)}), \\\"500 Internal Error\\\"\\n\\n # build the response object\\n response = flatten_response(res)\\n res = json.dumps(response)\\n\\n # cache the response\\n try:\\n MEMCACHED_CLIENT.set(cache_key, res, time=MEMCACHED_EXPIRATION)\\n except Exception as e:\\n LOGGER.exception(e)\\n\\n return res, \\\"200 OK\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f890c7a86c30ef8bdba91e46f3abe82\",\n \"score\": \"0.4639542\",\n \"text\": \"def getPictures(base_url, sizes):\\n pictures = []\\n widths = []\\n names = {}\\n for name in sizes:\\n width = sizes[name][0]\\n widths.append(width)\\n names[str(width)] = name\\n widths.sort()\\n noscript = \\\"\\\"\\n previous = None\\n for width in widths:\\n name = names[str(width)]\\n if width >= 128 and not noscript:\\n noscript = base_url + name\\n previous = width\\n continue\\n if not previous:\\n continue\\n media = \\\"(min-width: %spx)\\\" % (previous + 1)\\n# media = \\\"(min-width: %spx)\\\" % width\\n# media = \\\"(max-width: %spx)\\\" % width\\n image = {'src': base_url + name, 'media': media}\\n pictures.append(image)\\n previous = width\\n media = \\\"(min-width: %spx)\\\" % (widths[-1] + 1)\\n image = {'src': base_url[:-1], 'media': media}\\n pictures.append(image)\\n\\n #try to find a width > 128px for the noscript image\\n return pictures, noscript\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9591abb4f30cc5e8feb027fd8ad4f26\",\n \"score\": \"0.46377063\",\n \"text\": \"def __woo_upload_metadata(self):\\n for item_id in self.active_item_ids:\\n self.woo.try_command('upload_images', item_id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e7c69f82972dec8d2f18d44603b04fc\",\n \"score\": \"0.46374556\",\n \"text\": \"def get_multiple_image(curid):\\n\\tapi_url = 'https://en.wikipedia.org/w/api.php?action=query&prop=revisions&rvprop=content&format=json&pageids='+str(curid)+'&rvsection=0'\\n\\tresult = _rget(api_url)[u'query'][u'pages']\\n\\tr = result[unicode(curid)][u'revisions'][0][u'*']\\n\\twikicode = mwparserfromhell.parse(r)\\n\\ttemplates = wikicode.filter_templates()\\n\\tbox = {}\\n\\tfor template in templates:\\n\\t\\tname = template.name.lstrip().rstrip().lower()\\n\\t\\tif 'image' in name:\\n\\t\\t\\tbox_ = {}\\n\\t\\t\\tfor param in template.params:\\n\\t\\t\\t\\tkey = drop_comments(param.name).strip().lower().replace(' ','_')\\n\\t\\t\\t\\tvalue = drop_comments(param.value).strip()\\n\\t\\t\\t\\tbox_[key] = value\\n\\t\\t\\tbox['image'] = box_\\n\\t\\t\\tbreak #Grab only the first one\\n\\treturn box\",\n \"title\": \"\"\n },\n {\n \"docid\": \"98783ff50dde8dd0e83dc5b01dba0ddf\",\n \"score\": \"0.46356267\",\n \"text\": \"def handle_kaltura_videos(session,post_dict):#TODO FIXME\\n logging.debug(\\\"Processing kaltura video\\\")\\n # Extract video links from post dict\\n video_urls = []\\n video_items = post_dict[\\\"player\\\"]\\n for video_item in video_items:\\n embed_code = video_item[\\\"embed_code\\\"]\\n # ...data=\\\"http://www.kaltura.com/index.php/kwidget/wid/0_jzs5tgmh/uiconf_id/7342382\\\">...\\n # http://www.kaltura.com/index.php/kwidget/wid/0_jzs5tgmh/uiconf_id/7342382\\n if embed_code:\\n # Process links so YT-DL can understand them\\n logging.debug(\\\"handle_kaltura_videos() embed_code: \\\"+repr(embed_code))\\n embed_url_regex =\\\"\\\"\\\"(?:http://www.)?kaltura.com/index.php/kwidget/wid/[^\\\"'<>]+\\\"\\\"\\\"\\n embed_url_search = re.search(embed_url_regex, embed_code, re.IGNORECASE|re.DOTALL)\\n if embed_url_search:\\n video_url = embed_url_search.group(0)\\n video_urls.append(video_url)\\n continue\\n logging.debug(\\\"handle_kaltura_videos() video_urls: \\\"+repr(video_urls))\\n\\n # Download videos if there are any\\n media_id_list = run_yt_dl_multiple(\\n session = session,\\n download_urls = video_urls,\\n extractor_used=\\\"video_handlers.handle_kaltura_videos()\\\",\\n )\\n logging.debug(\\\"Finished downloading kaltura embeds\\\")\\n return media_id_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"527b9b6d0c7dc5bd490b55107abebcb2\",\n \"score\": \"0.46338552\",\n \"text\": \"def encode_img(self,\\n images: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:\\n device = images.device\\n x = self.vision_encoder(images)[0]\\n image_embeds = self.ln_vision(x).to(device)\\n image_atts = torch.ones(\\n image_embeds.size()[:-1], dtype=torch.long).to(device)\\n\\n query_tokens = self.query_tokens.expand(image_embeds.shape[0], -1, -1)\\n query_output = self.q_former.bert(\\n query_embeds=query_tokens,\\n encoder_hidden_states=image_embeds,\\n encoder_attention_mask=image_atts,\\n return_dict=True,\\n )\\n\\n inputs_llama = self.llama_proj(query_output.last_hidden_state)\\n atts_llama = torch.ones(\\n inputs_llama.size()[:-1], dtype=torch.long).to(images.device)\\n return inputs_llama, atts_llama\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f70668f69c033880fc324f7d7f924523\",\n \"score\": \"0.4633523\",\n \"text\": \"def plot_pruned_seqs(model_output, inputs, n_logged_samples=3, max_seq_len=None):\\n assert \\\"images\\\" in model_output.dense_rec # need pruned predicted images of SH-Pred model\\n if inputs is not None:\\n batch, n_gt_imgs, channels, res, _ = inputs.traj_seq.shape\\n else:\\n batch = len(model_output.dense_rec.images)\\n assert batch == 1 # con currently only handle batch size 1\\n n_gt_imgs, channels, res, _ = model_output.dense_rec.images[0].shape\\n MAX_SEQ_LEN = int(n_gt_imgs * 1.5) if not max_seq_len else max_seq_len\\n\\n im_height = 2 * res\\n im_width = (MAX_SEQ_LEN+1) * res\\n im = np.asarray(0.7 * np.ones((n_logged_samples, im_height, im_width, 3)), dtype=np.float32)\\n pred_imgs = list(map(imgtensor2np, model_output.dense_rec.images[:n_logged_samples]))\\n max_len = min(n_gt_imgs, MAX_SEQ_LEN)\\n for b in range(n_logged_samples):\\n if pred_imgs[b] is None: continue\\n seq_len = min(pred_imgs[b].shape[0], MAX_SEQ_LEN)\\n max_len = max(max_len, seq_len)\\n im[b, -res:, res:(seq_len+1)*res] = pred_imgs[b][:seq_len].transpose(2, 0, 3, 1).reshape(res, seq_len*res, channels)\\n if inputs is not None:\\n im[:, :res, :(n_gt_imgs*res)] = imgtensor2np(inputs.traj_seq, n_logged_samples).transpose(0, 3, 1, 4, 2)\\\\\\n .reshape(n_logged_samples, res, n_gt_imgs*res, channels)\\n if \\\"actions\\\" in model_output.dense_rec \\\\\\n and model_output.dense_rec.actions is not None \\\\\\n and (True in [a is not None for a in model_output.dense_rec.actions]) \\\\\\n and (inputs is None or inputs.actions.shape[-1] == 2):\\n ac_im = np.asarray(0.7 * np.ones((n_logged_samples, res, im_width, 3)), dtype=np.float32)\\n pred_ac = list(map(tensor2np, model_output.dense_rec.actions[:n_logged_samples]))\\n for b in range(n_logged_samples):\\n if pred_ac[b] is None: continue\\n seq_len = min(pred_ac[b].shape[0], MAX_SEQ_LEN)\\n ac_im[b, :, :seq_len*res] = batch_action2img(pred_ac[b][None, :seq_len], res, channels).transpose(0, 2, 1, 3, 4)\\\\\\n .reshape(res, seq_len*res, channels)\\n im = np.concatenate((im, ac_im), axis=1)\\n\\n # prepare GIF version\\n gif_imgs = np.swapaxes(im.reshape(n_logged_samples, im.shape[1], MAX_SEQ_LEN+1, res, channels), 0, 2)[:max_len+1] \\\\\\n .reshape(max_len+1, im.shape[1], res * n_logged_samples, channels)\\n\\n return im, gif_imgs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c54fed09598be2964aae56673c1e44e\",\n \"score\": \"0.46313572\",\n \"text\": \"def parse_fewshot_anno(self, query: dict, support_list: List) -> dict:\\n # prepare n shots examples\\n shots = random.sample(support_list, self.num_shots)\\n\\n # append image path for n shots\\n img_path = [shot['img_path'] for shot in shots]\\n img_path.append(query['img_path'])\\n query['img_path'] = img_path\\n\\n query['shots'] = [\\n dict(\\n question=item['question'],\\n answer=item['gt_answer'][0],\\n ) for item in shots\\n ]\\n return query\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e229cc38993d00b972a9bc0fd50b3c6f\",\n \"score\": \"0.46307558\",\n \"text\": \"async def search_images(self, **kwargs):\\n\\n params = {\\n 'breed_id': kwargs.pop(\\\"breed_id\\\", \\\"\\\"),\\n 'category_ids': kwargs.pop(\\\"category_ids\\\", \\\"\\\"),\\n 'format': kwargs.pop(\\\"format\\\", \\\"\\\"),\\n 'limit': kwargs.pop(\\\"limit\\\", 1),\\n 'mime_types': kwargs.pop(\\\"mime_types\\\", \\\"\\\"),\\n 'order': kwargs.pop(\\\"order\\\", \\\"RANDOM\\\"),\\n 'size': kwargs.pop(\\\"size\\\", \\\"med\\\"),\\n 'page': kwargs.pop(\\\"page\\\", 0),\\n }\\n\\n url = f'{BASE_URL}/images/search'\\n\\n images = await self.api_get_session(url, params)\\n images = [Image(**image) for image in images]\\n return images\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7293401296aec8aa177ef92054ed2c40\",\n \"score\": \"0.46290153\",\n \"text\": \"def beam_search_batch(self, src_id, max_decode_length):\\n\\t\\tpass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf26fb747e81d58dabe00acc80b4859e\",\n \"score\": \"0.4619074\",\n \"text\": \"def images(self, **query):\\n if 'limit' not in query.keys():\\n query[\\\"limit\\\"] = 1000\\n return list(self._list(_image.Image, **query))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"221e7db9aa6d90102f21e2581e0349fd\",\n \"score\": \"0.4618204\",\n \"text\": \"def embed(self, input, batchsize):\\n if self.prm[\\\"wo_tok\\\"]:\\n # emb: [seq_len*nbatch, char_hid]\\n emb = self.drop(self.char_encoder(input[\\\"char\\\"]))\\n # emb: [seq_len, nbatch, char_hid]\\n if batchsize is not None:\\n emb = emb.reshape(-1, batchsize, emb.shape[-1])\\n else:\\n emb = emb.reshape(input[\\\"word\\\"].shape[0], input[\\\"word\\\"].shape[1], -1)\\n elif self.prm[\\\"wo_char\\\"]:\\n # emb: [seq_len, nbatch, tok_emb]\\n emb = self.drop(self.word_encoder(input[\\\"word\\\"]))\\n elif self.prm[\\\"wo_tok\\\"] and self.prm[\\\"wo_char\\\"]:\\n # At least one embedding layer is required.\\n assert(False)\\n else:\\n # emb: [seq_len, nbatch, tok_emb]\\n emb_word = self.drop(self.word_encoder(input[\\\"word\\\"]))\\n # emb: [seq_len*nbatch, char_hid]\\n emb_char = self.drop(self.char_encoder(input[\\\"char\\\"]))\\n # emb: [seq_len, nbatch, char_hid]\\n emb_char = emb_char.reshape(input[\\\"word\\\"].shape[0], input[\\\"word\\\"].shape[1], -1)\\n # emb: [seq_len, nbatch, tok_emb + char_hid]\\n emb = torch.cat([emb_word, emb_char], dim=2)\\n return emb\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c9fcf47c55227001f30b26cf2f54e2e\",\n \"score\": \"0.46140903\",\n \"text\": \"def get_similar_imgs(img_name, img_map, indices, distances,k, form =\\\"df\\\",img_dir=None):\\n index = get_key(img_map, img_name)\\n distances = distances[index][1:k+1]\\n \\n if img_dir == None:\\n images = [x for x in indices[index]]\\n else: \\n images = [img_dir+x for x in indices[index]] \\n \\n images = images[1:k+1]\\n \\n if form == \\\"df\\\":\\n return pd.DataFrame({\\\"img\\\": images,\\\"dist\\\": distances})\\n elif form == \\\"list\\\": \\n return images\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e696a6114c4d79b6bf659778d3a9ba22\",\n \"score\": \"0.46125808\",\n \"text\": \"def attack(self, imgs, targets):\\n r = []\\n print('go up to', imgs.shape[0])\\n for i in range(0, imgs.shape[0], self.batch_size):\\n print('tick', i)\\n # print(\\\"imgs[i:i + self.batch_size]\\\", imgs[i:i + self.batch_size])\\n # print(\\\"targets\\\", targets)\\n r.extend(self.attack_batch(\\n imgs[i:i + self.batch_size], targets))\\n return np.array(r)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fecf5e13cd785cb5a966ebf9ea07e75b\",\n \"score\": \"0.4611163\",\n \"text\": \"def parse_fewshot_anno(self, query: dict, support_list: List) -> dict:\\n # prepare n shots examples\\n shots = random.sample(support_list, self.num_shots)\\n\\n # append image path for n shots\\n img_path = [shot['img_path'] for shot in shots]\\n img_path.append(query['img_path'])\\n query['img_path'] = img_path\\n\\n query['shots'] = [dict(caption=item['gt_caption']) for item in shots]\\n return query\",\n \"title\": \"\"\n },\n {\n \"docid\": \"580b4ef695c411f550b2c57524a29ae6\",\n \"score\": \"0.46107417\",\n \"text\": \"def DisplayImages(self) -> LayerFilterDisplayImages:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"731c3c12b58e2092c84150a0090c8265\",\n \"score\": \"0.4608333\",\n \"text\": \"def build_image_embedding(self):\\r\\n\\r\\n with tf.variable_scope(\\\"D_\\\"):\\r\\n with tf.variable_scope(\\\"image_embedding\\\") as scope:\\r\\n image_embeddings = tf.contrib.layers.fully_connected(\\r\\n inputs=self.inception_output,\\r\\n num_outputs=self.config.embedding_size,\\r\\n activation_fn=None,\\r\\n weights_initializer=self.initializer,\\r\\n biases_initializer=None,\\r\\n scope=scope)\\r\\n\\r\\n self.image_embeddings = image_embeddings\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":697,"cells":{"query_id":{"kind":"string","value":"cf090fe9542868e99484e51b47cd4719"},"query":{"kind":"string","value":"Find the first status matching the given id"},"positive_passages":{"kind":"list like","value":[{"docid":"273f0441220e69f71c097180394e0b39","score":"0.5996115","text":"def get_card_status(status_id):\n status = data_manager.execute_select(\n \"\"\"\n SELECT * FROM statuses s\n WHERE s.id = %(status_id)s;\n \"\"\", {\"status_id\": status_id})\n return status","title":""}],"string":"[\n {\n \"docid\": \"273f0441220e69f71c097180394e0b39\",\n \"score\": \"0.5996115\",\n \"text\": \"def get_card_status(status_id):\\n status = data_manager.execute_select(\\n \\\"\\\"\\\"\\n SELECT * FROM statuses s\\n WHERE s.id = %(status_id)s;\\n \\\"\\\"\\\", {\\\"status_id\\\": status_id})\\n return status\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"3f3256587c96740dc1d2b1c1aaffc75b","score":"0.7008374","text":"def get_status(self, status_id: StatusID):\n pass","title":""},{"docid":"7c7e39e58fe3e485e0cc9f321a375e01","score":"0.6748259","text":"def get_card_status(status_id):\n statuses = persistence.get_statuses()\n return next((status['title'] for status in statuses if status['id'] == str(status_id)), 'Unknown')","title":""},{"docid":"9caa158321a0245d46485edc6a5f119b","score":"0.67374897","text":"def get_card_status(status_id):\n statuses = persistence.get_statuses()\n return next((status['title'] for status in statuses if status['id'] == int(status_id)), 'Unknown')","title":""},{"docid":"6b16f614d576f2ded4c7709c27d3d6fa","score":"0.65737146","text":"def find_one_by_id(id) -> Job:\n\n with DB.connection_context():\n return Job.get_or_none(Job.id == id)","title":""},{"docid":"e0477878b783b292ab62b01773fb412b","score":"0.64558583","text":"def find(self, id):\n pass","title":""},{"docid":"2372504d4950b8e20a55ea95699000ea","score":"0.6330566","text":"def get_status(value):\n query = Status.query.filter(Status.value==value)\n return query.one()","title":""},{"docid":"283fc1d6dbb7af9a64da41147ae9fbf5","score":"0.6308374","text":"def tt_lookup(id):\n status = status_lookup([id])\n status = status[0]\n return status['text']","title":""},{"docid":"75572946d0ea26efa6c7e38fcc495da5","score":"0.6250195","text":"async def find_by_id(self, id):\r\n return await self.db.find_one({\"_id\": id})","title":""},{"docid":"63ea667cefc45e2f2f5f79aa0553fba6","score":"0.6240588","text":"def get_one(self, id):\n return self.coll.find_one({'_id': self._get_valid_id(id)})","title":""},{"docid":"9eb8d47312aabd60ef2004dabd4b5701","score":"0.6216652","text":"async def find(self, id):\r\n return await self.find_by_id(id)","title":""},{"docid":"cbbd846d0c0d9bc912535ff12b03d597","score":"0.62122244","text":"def find_status_name(cls, status_id):\n statuses = cls.STATUSES\n reversed_statuses = dict(zip(statuses.values(), statuses.keys()))\n return reversed_statuses.get(status_id)","title":""},{"docid":"9e2d4ff4e9b0769f508fb28bfadde235","score":"0.62011504","text":"def get(self, id):\n ctx = self.context.copy()\n ctx['active_test'] = False\n results = self.rpc_model.search_read(\n [('id', '=', id)],\n None, None, None, self.fields,\n context=ctx\n )\n return results and results[0] or None","title":""},{"docid":"1985e8e806f83c1f6f770e645121199a","score":"0.619701","text":"def findById(self, id):\n return self.model().get(id)","title":""},{"docid":"b99d1060df9ddba39517c685d38ccc25","score":"0.61848927","text":"def defaultstatus():\n #NOTE: Takes the first status set as default. If there are more of these, it takes whichever is first.\n s = IssueStatus.objects.filter(default=True)\n if len(s) > 0:\n return s[0].id","title":""},{"docid":"e0145e1be3582622b43f181fc83b04ee","score":"0.61794394","text":"def find(self,id):\r\n for i in self.__lista:\r\n if i.getId()==id:\r\n return i\r\n raise error_find","title":""},{"docid":"e0145e1be3582622b43f181fc83b04ee","score":"0.61794394","text":"def find(self,id):\r\n for i in self.__lista:\r\n if i.getId()==id:\r\n return i\r\n raise error_find","title":""},{"docid":"673c83cc8b21f2b9e01758d4587499ed","score":"0.6114049","text":"def get_by_id(self, id):\n return self.collection.find_one({\n 'data.general.externalIds.statistic': int(id)\n })","title":""},{"docid":"30f8871af75eeb9441ebfdf8217727af","score":"0.6078235","text":"def status(id):\n return Server.status(id)","title":""},{"docid":"97cf7a8589647e1aa551f22f7084844c","score":"0.6026908","text":"def by_id(self, id):\n status, data = self.client.get(self.endpoint + '/%s' % id)\n return self.clazz(self.client, data)","title":""},{"docid":"e6be725825b8d876429d31f0df73fbc7","score":"0.602375","text":"def find(self,id):\r\n return RepoClient.find(self, id)","title":""},{"docid":"04c99510386e91074826462ebc87e5b7","score":"0.60051286","text":"def _single_version(self, id_):\n s = self.client.search()\n s.params(id=id_)\n result = s.find_versions(limit=1)\n assert result\n return result[0]","title":""},{"docid":"afa870776e24275158966faca3c7f30f","score":"0.59777516","text":"def findbyid(self, id):\n l = list(filter(lambda x: x.id == id, self._widgets))\n return None if l == [] else l[0]","title":""},{"docid":"393fb947d3e374a122bb8d2aaaa46708","score":"0.5957893","text":"def get(self,id):\r\n item = get_one(id=id)\r\n if not item:\r\n api.abort(404)\r\n else:\r\n return item","title":""},{"docid":"a0a25669d534456c9cf2243d4e7db722","score":"0.5884681","text":"def identify(cls, id):\n return cls.query.get(id)","title":""},{"docid":"35ad8700e2980d70d0b958f3419264c5","score":"0.5871743","text":"def find_by_id(id):\n with get_db_cursor(commit=True) as cur:\n cur.execute(\n '''SELECT uuid, invoice_id_from, invoice_id_to, created_at, updated_at, amount, status \n FROM public.transaction WHERE id=%s::INT LIMIT 1''',\n (id,))\n result = cur.fetchone()\n if result:\n uid, inv_id_from, inv_id_to, created_at, updated_at, amount, status = result\n return Transaction(inv_id_from, inv_id_to, amount,\n uid=uid, created_at=created_at, updated_at=updated_at, id=id, status=status)\n return None","title":""},{"docid":"25a8e2117f7d37f40d24e53c67350506","score":"0.5827104","text":"def by_id(cls, _id):\n return cls.query.filter_by(id=_id).first()","title":""},{"docid":"2469e1ac6fee416acdcbc0ebf871bda7","score":"0.581723","text":"def find_by_id(self, id):\n self.cursor.execute(\"SELECT * FROM Accounts WHERE account_id=?\", [id])\n return self.__fetchone_account()","title":""},{"docid":"8903b78b93ed7017abed9012fad01172","score":"0.5792517","text":"async def get_by_id(self, id):\r\n return await self.find_by_id(id)","title":""},{"docid":"94cfad0d5805637784ea9ec736b5a471","score":"0.5790875","text":"def status_id(self, name):\n statuses = self.statuses()\n name_lower = name.lower()\n for s in statuses:\n if name_lower == s[\"name\"].lower():\n return s[\"id\"]\n else:\n raise JiraShellError(\"unknown status name: %r\" % name)","title":""},{"docid":"a61052520a9aba9fba4414e81e545891","score":"0.57817227","text":"def get_task_status(job_id, entity_id):\n sql = \"select \" + \", \".join(TaskStatus.list_properties()) + \" from taskstatus where job_id = %s and entity_id = %s\"\n rows = atp_env.mydb.select(sql, (job_id, entity_id))\n if rows and len(rows) > 0:\n return TaskStatus(rows[0])\n else:\n return None","title":""},{"docid":"e64a4e893d27c1c14be23f2832c3e01f","score":"0.57264507","text":"def find_by_id(self, book_id):\n raise NotImplementedError(\"find_one not implemented yet\")","title":""},{"docid":"f8a97ef938f6864fc59c8374da72f35b","score":"0.5718186","text":"def get_run_status(self, id):\n res = self.api_client.GetRunStatus(id)\n return response_handler(res)","title":""},{"docid":"8cb125c0e44d3974a6a10f1df2fc52cb","score":"0.5716425","text":"def by_id(self, id):\n return self._index.get(id, None)","title":""},{"docid":"bbe3a06feba802ea4241e76e06891a8f","score":"0.57131","text":"def check_status(self, inspection_id: str) -> Status:","title":""},{"docid":"7fc63cfeb64d2f96e408079dd937e5f8","score":"0.5692336","text":"def find_by_id(self, id):\n matches = [device for device in self.get_device_details() if device.id == id]\n\n if len(matches) > 1:\n raise RuntimeError(f\"Found more than one device for id '{id}'\")\n\n if len(matches) < 1:\n raise RuntimeError(f\"Could not find a device for id '{id}'\")\n\n return matches[0]","title":""},{"docid":"c7418996d0c8364e3daa28d4176a80d2","score":"0.56896317","text":"def _find_request_by_id(self, request_id):\n response = self.client.get_request(request_id)\n\n if response.ok:\n return self.parser.parse_request(response.json())\n else:\n self._handle_response_failure(response, default_exc=FetchError,\n raise_404=False)","title":""},{"docid":"0d8136dfa0ae4022d4a520fa4b59fa18","score":"0.56861335","text":"def call_get_status(board_id):\n global library\n return Status(library.getStatus(board_id))","title":""},{"docid":"c3df2508c2259fb4bca351b120f7be7f","score":"0.5680991","text":"def getOrderState(id_):\r\n for i in range(len(myOrders)):\r\n if myOrders[i][\"id\"] == id_:\r\n return myOrders[i][\"state\"]\r\n return None","title":""},{"docid":"9eaed028703edf386b0f6e386ac8577f","score":"0.5678187","text":"def findObjectById(self, id, current=None):\n raise NotImplementedError(\"servant method 'findObjectById' not implemented\")","title":""},{"docid":"819e625e8bdb97bc682993a965a27728","score":"0.56654525","text":"def find_by_id(self, id: int):\n if self.id == id:\n return self\n for child in self._children:\n ret = child.find_by_id(id)\n if ret is not None:\n return ret\n return None","title":""},{"docid":"710a25a9f8a5381bdd54926c48e9f5d6","score":"0.5662839","text":"def get_one(self, name_id):\r\n\r\n try:\r\n o = self.get_one_by_name(unicode(name_id))\r\n\r\n if o:\r\n return o\r\n except:\r\n pass\r\n\r\n try:\r\n o = self.get_one_by_id(int(name_id))\r\n\r\n if o:\r\n return o\r\n except:\r\n pass\r\n\r\n return None","title":""},{"docid":"a780c0da727fea2ced326b9689360891","score":"0.5659603","text":"def get_index_status(json2i, index):\n for d in json2i[\"status\"]:\n if d[\"name\"] == index:\n return d[\"status\"]\n return None","title":""},{"docid":"a780c0da727fea2ced326b9689360891","score":"0.5659603","text":"def get_index_status(json2i, index):\n for d in json2i[\"status\"]:\n if d[\"name\"] == index:\n return d[\"status\"]\n return None","title":""},{"docid":"eea387c2272abe8845da429e2915d553","score":"0.56534714","text":"def find_by_id(foo_id):\n return Foo.query.filter_by(id=foo_id).first()","title":""},{"docid":"b44712c0011f56aa706093bf3df58af5","score":"0.56517404","text":"def find_label_by_id(self, _id):\n search = True\n i = 0\n while search:\n if i == len(self.labels):\n break;\n\n if self.labels[i].id == _id:\n return self.labels[i]\n search = False\n #print self.labels[i].id\n i += 1\n if search:\n return None","title":""},{"docid":"8ee66c58c8e87e53f58e51e6cb12619c","score":"0.56329674","text":"def find_successor(self, id):\n np = self.find_predecessor(id)\n return self.call_rpc(np, 'successor')","title":""},{"docid":"0e526e63605d311488a12b965a5c3b72","score":"0.5621515","text":"def find_one(self, _topic, _id):\n return self.find_all(_topic).get(_id)","title":""},{"docid":"cc6a63fca731260585164e83ebe81957","score":"0.56211346","text":"def get_by_id(id):\n return Vaga.query.filter_by(\n id=id\n ).one_or_none()","title":""},{"docid":"b6c2e7a910446242ad055460fe8d16bf","score":"0.5619565","text":"def find_by_status(status):\n return Inventory.__find_by('status', status)\n # \"\"\" Returns all of the Inventories in a status\n #\n # Args:\n # status (string): the status of the Inventories you want to match\n # \"\"\"\n # return [inventory for inventory in Inventory.data if inventory.status == status]","title":""},{"docid":"ebcfe52c5037cc507ba4bc9b498142cf","score":"0.5612619","text":"def resolve_status(cls, status_name):\n status_results = ServerStatusOp.get(name=status_name)\n\n if len(status_results) is not 1:\n raise ServerStatusNotFoundError(f'Not found status name: \"{status_name}\".')\n\n status_id = status_results[0].id\n return status_id","title":""},{"docid":"08bb8cdf9a411df6c77f9adaf9366043","score":"0.5610765","text":"def get_state_by_id(cls, context, id):","title":""},{"docid":"3248ea51964120c0d5cedc14abb69d0b","score":"0.56092995","text":"def get(model, _id):\n try:\n return model.query.filter_by(id=_id).one()\n except:\n return None","title":""},{"docid":"59562cffb685594757502206e4dec159","score":"0.56038266","text":"def find(self, Id):\n if not Id in self.__items.keys():\n raise RepositoryError(\"an item with the given id does not exist\")\n return self.__items[Id]","title":""},{"docid":"ab09995732f145e3c896539eca56702b","score":"0.55944663","text":"def getbyId(id, lista):\n for avion in lista:\n if getId(avion) == id:\n return avion\n return None","title":""},{"docid":"137aa8fce3d68058f7a049bafb9a4a7e","score":"0.5592402","text":"def get_node(self, id:int):\n for node in self.nodes:\n if node.id == id:\n return node","title":""},{"docid":"031096adf7228a01ece449e3f24aefcb","score":"0.55861413","text":"def find(self, id):\n clients = self.__loadFromFile()\n for i in clients:\n if i.getId() == id:\n return i\n return None","title":""},{"docid":"8e65d1e8b0ed3167f521639a8d542864","score":"0.5570195","text":"def byId(self, Id):\n\t\tfor test in self._list:\n\t\t\tif test._Id == int(Id):\n\t\t\t\treturn test\n\t\treturn None","title":""},{"docid":"b02dcaa162f590d59aaa4ee57116cac3","score":"0.55676115","text":"def find_by_id(cls, id):\n for user in base.session.query(User).filter(User.id == id):\n return user","title":""},{"docid":"e3120b7835f6dc0449d79dfdb2f18450","score":"0.55611265","text":"def find_task_by_id(self, id):\n if id == 0:\n return None\n for r in self.root_nodes:\n task_with_id = r.find_subtask_by_id(id)\n if task_with_id:\n return task_with_id\n return None","title":""},{"docid":"cb31154eef30e5d678e7afe2d01bb324","score":"0.5550253","text":"def api_by_id(self, _id):\n apis = self.db.apis.find({\"id\": _id})\n result = [api for api in apis]\n if result:\n return result[0]","title":""},{"docid":"3b1e903302b9655d60e5473a54274061","score":"0.5541705","text":"def get_node_by_id(self, id):\n nodes = self.get_nodes_by_property(\"id\", id)\n if len(nodes) == 0:\n return None\n else:\n return nodes[0]","title":""},{"docid":"9758c4104e0324c184e6c2f8ffbe8074","score":"0.553512","text":"def find_successor(self, id):\n #print('find_successor called by node_id = {} for key: {} at timestamp = {}'.format(self.n_id, str(id), self.pr_now()))\n\n if (in_range(id, self.get_id(), self.successor().get_id()) and (self.n_id != self.successor().get_id()) and (id != self.n_id)):\n return self.successor()\n else:\n remote = self.closest_preceding_node(id)\n if self.my_address.get_hash() != remote.my_address.get_hash():\n return remote.find_successor(id)\n else:\n #print('returning self')\n return self","title":""},{"docid":"d42dbeaf20b1900510d8520a3ddb74e0","score":"0.5524069","text":"def get_id_by_status(cls, request, status_str):\n return request.dbsession.query(cls)\\\n .filter_by(status=status_str).one().id","title":""},{"docid":"8a174c993de5313bff38008d2b21965b","score":"0.5523496","text":"def find_by_id(self, _id):\n entity = new_instance_with_primary_key(_id)\n req, params = build_select_req(entity)\n return self.dao.do_select(req, params)","title":""},{"docid":"86e24e980af27207fb55034dfea85c03","score":"0.5522255","text":"def _get_current_status():\n return Status.query.get(1)","title":""},{"docid":"9984aafd84ebbcaa4e5963d6e67f9c38","score":"0.5510831","text":"def status_lookup(ids):\n ids = list(set(ids))\n len_ids = len(ids) # @DEBUG\n ids = ','.join(ids)\n tw = Twython(CREDENTIALS['APP_KEY'],\n CREDENTIALS['APP_SECRET'],\n CREDENTIALS['ACCESS_KEY'],\n CREDENTIALS['ACCESS_SECRET'])\n res = tw.lookup_status(id=ids)\n\n return res","title":""},{"docid":"97fb5c5de56ff9be377522f314a8b3b4","score":"0.5509235","text":"def find_first(self, label):\n results = self.find(label)\n\n if results:\n return results[0]\n\n return None","title":""},{"docid":"c6025b852ed3cafb91c0903cfb68a643","score":"0.5505244","text":"def get(id_: str) -> dict:\n return __col.find_one({ID: id_})","title":""},{"docid":"278ea3aadc66386e21e3a3f848dd7cd7","score":"0.5503628","text":"def get_by_id(self, id):\n\n return self.ent_dict[id]","title":""},{"docid":"e96af5cc6e720aa83c9d4821766b3f35","score":"0.55035645","text":"def __getStateById(self, stateId):\n for state in self.__listState:\n if state.getId() == stateId:\n return state\n return None","title":""},{"docid":"c1983281689db8dbd10a6466289f330e","score":"0.5500628","text":"def process_with_id(self, id):\n # ...\n for process in self.runningStack:\n if process.id == id:\n return process\n for process in self.waitingList:\n if not process == None:\n if process.id == id:\n return process\n return None","title":""},{"docid":"93ca593ff87abba1fd460809570bdf60","score":"0.5497836","text":"def getOne(self, _id, **kwargs):\n kwargs['id'] = self.sanitize_id(_id)\n kwargs['url'] = self.SINGLE_URL\n return self.transport.set_method('GET').request(**kwargs)","title":""},{"docid":"8658c8283a3974327caf9f8902c3209c","score":"0.5490123","text":"def get_object(self, id=None):\n # try:\n # obj = UpdateModel.objects.get(id=id)\n # except UpdateModel.DoesNotExist:\n # obj = None\n\n if id is None:\n return None\n\n qs= self.get_queryset().filter(id=id)\n if qs.count() == 1:\n return qs.first()\n return None","title":""},{"docid":"7d2cc1e203f90ca4d0c269d8453b97de","score":"0.5486001","text":"def find_job(context, job_id, state=None):\n jsondata = context.response.json()\n jobs = jsondata['jobs']\n job_ids = [job[\"job_id\"] for job in jobs]\n assert job_id in job_ids\n if state is not None:\n job = get_job_by_id(jobs, job_id)\n assert job is not None\n assert job[\"state\"] is not None\n assert job[\"state\"] == state","title":""},{"docid":"c46811d19a5a1e8b2179d91cd8621f5a","score":"0.54794425","text":"def fetch_record_or_abort(id):\n t = Todo.query.get(id)\n if not t:\n abort(404, message=\"To-do %s doesn't exist\" % id)\n return t","title":""},{"docid":"88827ae23be3510f7ad3ceeb72e578fc","score":"0.546806","text":"def get_one_book(id):\n one_book = BookModel.query.get(id)\n if not one_book:\n return None\n return one_book","title":""},{"docid":"cd170f236416826f6f51cfbe89ce787e","score":"0.5467214","text":"def getOne(self, _id, **kwargs):\n kwargs['url'] = self.SINGLE_URL\n kwargs['id'] = self.sanitize_id(_id)\n return self.transport.set_method('GET').request(**kwargs)","title":""},{"docid":"e88996f5f5636f98775da1c096a77a74","score":"0.5462106","text":"def get_by_id(id, lista):\n for vanzare in lista:\n if get_id(vanzare) == id:\n return vanzare\n return None","title":""},{"docid":"a11e7749007f2dff1cb1cee54a7bf898","score":"0.5455481","text":"def get_earliest_status(self):\n earliest = None\n if self.data:\n sorted_statuses = sorted(self.data.values(), key=lambda status: status.created_at)\n earliest = sorted_statuses[0]\n return earliest","title":""},{"docid":"5da32f847a364029abdc1f497b887639","score":"0.54494977","text":"def single_user_by_id(id):\n all_users = allusers()\n for user in all_users:\n if str(user.key.id()) == str(id):\n return user\n return None","title":""},{"docid":"66116039de789d26f77a719b0f7a59da","score":"0.54458266","text":"def get_object(self, id=None):\n # try:\n # obj = UpdateModel.objects.get(id=id)\n # except UpdateModel.DoesNotExist:\n # obj = None\n qs= UpdateModel.objects.filter(id=id)\n if qs.count() == 1:\n return qs.first()\n return None","title":""},{"docid":"da2226493c1e57c1f59ca9ee6480b9d4","score":"0.5440847","text":"def _get(self, id):\n return self.client.get(\n id=id\n )","title":""},{"docid":"550aeffc0a6a46d2c7844e8959420294","score":"0.54386216","text":"def get(self, id):\n return self.model_class.query.get(id)","title":""},{"docid":"019bd99e4ffbea60d156b40876daf442","score":"0.542419","text":"def get_draw_or_404(cls, id_):\n obj = cls.query.get(id_)\n if not obj: # Not found, try to search via private_id\n obj = cls.query.filter_by(private_id=id_).first_or_404()\n return obj","title":""},{"docid":"b2cc29e67ea5a043f5e36d7f7820f3b6","score":"0.54189336","text":"def get(self, id):\n return self._get(id)","title":""},{"docid":"fee45f7b759fd0025df3ec1f77fbd778","score":"0.5413169","text":"def returnClassFromID(dictionary, id):\n if id == \"\":\n print(\"Complaint ID field is empty.\")\n return None\n else:\n id = int(id)\n for key in dictionary:\n if key == id:\n return dictionary[key]\n print(str(id) + \" is not in dataset.\")","title":""},{"docid":"21618ee5fa2cbc37d7000714dc85e9f9","score":"0.5408651","text":"def get(self,id):\r\n person = get_one(id=id)\r\n if not person:\r\n api.abort(404)\r\n else:\r\n return person","title":""},{"docid":"43706cc72239748e530c0225cc4232a3","score":"0.54076946","text":"def findit(self,idnum):\r\n \r\n for student in self.students:\r\n if student.get_id() == idnum:\r\n return student\r\n return \"Student not found\"","title":""},{"docid":"421fe2e199eab214a569c850d4ae2301","score":"0.5399659","text":"def get_order_status_from_id(self, order_id):\r\n url = self.address + '/order/status/?id=%s' % order_id\r\n with self.opener.open(url) as result:\r\n response = result.read()\r\n if result.getcode() != 200:\r\n raise Exception(response)\r\n data = json.loads(response)\r\n closed = data['closed']\r\n if closed is not None:\r\n closed = closed.split('.', 1)[0]\r\n closed = datetime.strptime(closed, \"%Y-%m-%d %H:%M:%S\")\r\n return data['status'], data['progress'], closed","title":""},{"docid":"365b17cf6694bb6f88ecbdcb78bfdef3","score":"0.5398555","text":"def get(self, id):\n return self.model.get(id)","title":""},{"docid":"f1c020e26a7aab65df4e46a8decb5f9d","score":"0.53943","text":"def _get_single_instance(self, id):\n r = self._conn.get_all_instances(id)\n return r[0].instances[0] if r and r[0].instances else None","title":""},{"docid":"1511a2b15680f62ad215d7fad5149374","score":"0.5387435","text":"def get(id):\n try:\n obj = _store.objects[id]\n if (obj is not None):\n return obj\n else:\n raise Exception('Object not found')\n except KeyError:\n time.sleep(0.0001)\n logging.debug(\"No matching object found, trying again...\")\n return get(id)","title":""},{"docid":"79f55a4854504f7479a0def5112721f8","score":"0.5371744","text":"def _find_item(self, item_id):\n for item in self.library_items[int(item_id) - 1]:\n if item.id == item_id:\n return item\n return None","title":""},{"docid":"c34848c9fc527e7aa65d870b18c12289","score":"0.537127","text":"def find_one(self):\n self.query_params[\"limit\"] = 1\n return self.__execute_network_call()","title":""},{"docid":"35fb629615a0d589ad2e32cbe19ef6ed","score":"0.53695875","text":"def retrieve(self, request, pk=None):\n\n try:\n status = Status.objects.get(pk=pk)\n serializer = StatusSerializer(status, context={'request': request})\n return Response(serializer.data)\n\n except Exception as ex:\n return HttpResponseServerError(ex)","title":""},{"docid":"bab35dc3772a86dbd2a8dce385b28ccd","score":"0.5361243","text":"def get(self, id):\n return Team.query.filter(Team.id == id).one()","title":""},{"docid":"a56c138e971efdac002e67888ccffad0","score":"0.536081","text":"def at(cls, _id):\n return cls.where(cls.primarykey == _id)","title":""},{"docid":"24783f95d383b6d5aec6c7c0e4e850c4","score":"0.53573316","text":"def find_student(self, id_s):\n if id_s in self.__students:\n return self.__students[id_s]\n else:\n raise RepositoryFind","title":""},{"docid":"9701e24dc07f76665fee58be1a192473","score":"0.53505886","text":"async def find_object(self, cls, _id):\n raise NotImplementedError","title":""},{"docid":"6a9ee22ea5b7f46895bf0a1dc2fdd8d1","score":"0.5342875","text":"def get_entity(self, id):\n return self.entities.get(id, (None, None, None))[2]","title":""}],"string":"[\n {\n \"docid\": \"3f3256587c96740dc1d2b1c1aaffc75b\",\n \"score\": \"0.7008374\",\n \"text\": \"def get_status(self, status_id: StatusID):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c7e39e58fe3e485e0cc9f321a375e01\",\n \"score\": \"0.6748259\",\n \"text\": \"def get_card_status(status_id):\\n statuses = persistence.get_statuses()\\n return next((status['title'] for status in statuses if status['id'] == str(status_id)), 'Unknown')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9caa158321a0245d46485edc6a5f119b\",\n \"score\": \"0.67374897\",\n \"text\": \"def get_card_status(status_id):\\n statuses = persistence.get_statuses()\\n return next((status['title'] for status in statuses if status['id'] == int(status_id)), 'Unknown')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b16f614d576f2ded4c7709c27d3d6fa\",\n \"score\": \"0.65737146\",\n \"text\": \"def find_one_by_id(id) -> Job:\\n\\n with DB.connection_context():\\n return Job.get_or_none(Job.id == id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0477878b783b292ab62b01773fb412b\",\n \"score\": \"0.64558583\",\n \"text\": \"def find(self, id):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2372504d4950b8e20a55ea95699000ea\",\n \"score\": \"0.6330566\",\n \"text\": \"def get_status(value):\\n query = Status.query.filter(Status.value==value)\\n return query.one()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"283fc1d6dbb7af9a64da41147ae9fbf5\",\n \"score\": \"0.6308374\",\n \"text\": \"def tt_lookup(id):\\n status = status_lookup([id])\\n status = status[0]\\n return status['text']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75572946d0ea26efa6c7e38fcc495da5\",\n \"score\": \"0.6250195\",\n \"text\": \"async def find_by_id(self, id):\\r\\n return await self.db.find_one({\\\"_id\\\": id})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63ea667cefc45e2f2f5f79aa0553fba6\",\n \"score\": \"0.6240588\",\n \"text\": \"def get_one(self, id):\\n return self.coll.find_one({'_id': self._get_valid_id(id)})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9eb8d47312aabd60ef2004dabd4b5701\",\n \"score\": \"0.6216652\",\n \"text\": \"async def find(self, id):\\r\\n return await self.find_by_id(id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cbbd846d0c0d9bc912535ff12b03d597\",\n \"score\": \"0.62122244\",\n \"text\": \"def find_status_name(cls, status_id):\\n statuses = cls.STATUSES\\n reversed_statuses = dict(zip(statuses.values(), statuses.keys()))\\n return reversed_statuses.get(status_id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e2d4ff4e9b0769f508fb28bfadde235\",\n \"score\": \"0.62011504\",\n \"text\": \"def get(self, id):\\n ctx = self.context.copy()\\n ctx['active_test'] = False\\n results = self.rpc_model.search_read(\\n [('id', '=', id)],\\n None, None, None, self.fields,\\n context=ctx\\n )\\n return results and results[0] or None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1985e8e806f83c1f6f770e645121199a\",\n \"score\": \"0.619701\",\n \"text\": \"def findById(self, id):\\n return self.model().get(id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b99d1060df9ddba39517c685d38ccc25\",\n \"score\": \"0.61848927\",\n \"text\": \"def defaultstatus():\\n #NOTE: Takes the first status set as default. If there are more of these, it takes whichever is first.\\n s = IssueStatus.objects.filter(default=True)\\n if len(s) > 0:\\n return s[0].id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0145e1be3582622b43f181fc83b04ee\",\n \"score\": \"0.61794394\",\n \"text\": \"def find(self,id):\\r\\n for i in self.__lista:\\r\\n if i.getId()==id:\\r\\n return i\\r\\n raise error_find\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0145e1be3582622b43f181fc83b04ee\",\n \"score\": \"0.61794394\",\n \"text\": \"def find(self,id):\\r\\n for i in self.__lista:\\r\\n if i.getId()==id:\\r\\n return i\\r\\n raise error_find\",\n \"title\": \"\"\n },\n {\n \"docid\": \"673c83cc8b21f2b9e01758d4587499ed\",\n \"score\": \"0.6114049\",\n \"text\": \"def get_by_id(self, id):\\n return self.collection.find_one({\\n 'data.general.externalIds.statistic': int(id)\\n })\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30f8871af75eeb9441ebfdf8217727af\",\n \"score\": \"0.6078235\",\n \"text\": \"def status(id):\\n return Server.status(id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97cf7a8589647e1aa551f22f7084844c\",\n \"score\": \"0.6026908\",\n \"text\": \"def by_id(self, id):\\n status, data = self.client.get(self.endpoint + '/%s' % id)\\n return self.clazz(self.client, data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e6be725825b8d876429d31f0df73fbc7\",\n \"score\": \"0.602375\",\n \"text\": \"def find(self,id):\\r\\n return RepoClient.find(self, id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04c99510386e91074826462ebc87e5b7\",\n \"score\": \"0.60051286\",\n \"text\": \"def _single_version(self, id_):\\n s = self.client.search()\\n s.params(id=id_)\\n result = s.find_versions(limit=1)\\n assert result\\n return result[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"afa870776e24275158966faca3c7f30f\",\n \"score\": \"0.59777516\",\n \"text\": \"def findbyid(self, id):\\n l = list(filter(lambda x: x.id == id, self._widgets))\\n return None if l == [] else l[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"393fb947d3e374a122bb8d2aaaa46708\",\n \"score\": \"0.5957893\",\n \"text\": \"def get(self,id):\\r\\n item = get_one(id=id)\\r\\n if not item:\\r\\n api.abort(404)\\r\\n else:\\r\\n return item\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0a25669d534456c9cf2243d4e7db722\",\n \"score\": \"0.5884681\",\n \"text\": \"def identify(cls, id):\\n return cls.query.get(id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35ad8700e2980d70d0b958f3419264c5\",\n \"score\": \"0.5871743\",\n \"text\": \"def find_by_id(id):\\n with get_db_cursor(commit=True) as cur:\\n cur.execute(\\n '''SELECT uuid, invoice_id_from, invoice_id_to, created_at, updated_at, amount, status \\n FROM public.transaction WHERE id=%s::INT LIMIT 1''',\\n (id,))\\n result = cur.fetchone()\\n if result:\\n uid, inv_id_from, inv_id_to, created_at, updated_at, amount, status = result\\n return Transaction(inv_id_from, inv_id_to, amount,\\n uid=uid, created_at=created_at, updated_at=updated_at, id=id, status=status)\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25a8e2117f7d37f40d24e53c67350506\",\n \"score\": \"0.5827104\",\n \"text\": \"def by_id(cls, _id):\\n return cls.query.filter_by(id=_id).first()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2469e1ac6fee416acdcbc0ebf871bda7\",\n \"score\": \"0.581723\",\n \"text\": \"def find_by_id(self, id):\\n self.cursor.execute(\\\"SELECT * FROM Accounts WHERE account_id=?\\\", [id])\\n return self.__fetchone_account()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8903b78b93ed7017abed9012fad01172\",\n \"score\": \"0.5792517\",\n \"text\": \"async def get_by_id(self, id):\\r\\n return await self.find_by_id(id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94cfad0d5805637784ea9ec736b5a471\",\n \"score\": \"0.5790875\",\n \"text\": \"def status_id(self, name):\\n statuses = self.statuses()\\n name_lower = name.lower()\\n for s in statuses:\\n if name_lower == s[\\\"name\\\"].lower():\\n return s[\\\"id\\\"]\\n else:\\n raise JiraShellError(\\\"unknown status name: %r\\\" % name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a61052520a9aba9fba4414e81e545891\",\n \"score\": \"0.57817227\",\n \"text\": \"def get_task_status(job_id, entity_id):\\n sql = \\\"select \\\" + \\\", \\\".join(TaskStatus.list_properties()) + \\\" from taskstatus where job_id = %s and entity_id = %s\\\"\\n rows = atp_env.mydb.select(sql, (job_id, entity_id))\\n if rows and len(rows) > 0:\\n return TaskStatus(rows[0])\\n else:\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e64a4e893d27c1c14be23f2832c3e01f\",\n \"score\": \"0.57264507\",\n \"text\": \"def find_by_id(self, book_id):\\n raise NotImplementedError(\\\"find_one not implemented yet\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8a97ef938f6864fc59c8374da72f35b\",\n \"score\": \"0.5718186\",\n \"text\": \"def get_run_status(self, id):\\n res = self.api_client.GetRunStatus(id)\\n return response_handler(res)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8cb125c0e44d3974a6a10f1df2fc52cb\",\n \"score\": \"0.5716425\",\n \"text\": \"def by_id(self, id):\\n return self._index.get(id, None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bbe3a06feba802ea4241e76e06891a8f\",\n \"score\": \"0.57131\",\n \"text\": \"def check_status(self, inspection_id: str) -> Status:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7fc63cfeb64d2f96e408079dd937e5f8\",\n \"score\": \"0.5692336\",\n \"text\": \"def find_by_id(self, id):\\n matches = [device for device in self.get_device_details() if device.id == id]\\n\\n if len(matches) > 1:\\n raise RuntimeError(f\\\"Found more than one device for id '{id}'\\\")\\n\\n if len(matches) < 1:\\n raise RuntimeError(f\\\"Could not find a device for id '{id}'\\\")\\n\\n return matches[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7418996d0c8364e3daa28d4176a80d2\",\n \"score\": \"0.56896317\",\n \"text\": \"def _find_request_by_id(self, request_id):\\n response = self.client.get_request(request_id)\\n\\n if response.ok:\\n return self.parser.parse_request(response.json())\\n else:\\n self._handle_response_failure(response, default_exc=FetchError,\\n raise_404=False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d8136dfa0ae4022d4a520fa4b59fa18\",\n \"score\": \"0.56861335\",\n \"text\": \"def call_get_status(board_id):\\n global library\\n return Status(library.getStatus(board_id))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3df2508c2259fb4bca351b120f7be7f\",\n \"score\": \"0.5680991\",\n \"text\": \"def getOrderState(id_):\\r\\n for i in range(len(myOrders)):\\r\\n if myOrders[i][\\\"id\\\"] == id_:\\r\\n return myOrders[i][\\\"state\\\"]\\r\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9eaed028703edf386b0f6e386ac8577f\",\n \"score\": \"0.5678187\",\n \"text\": \"def findObjectById(self, id, current=None):\\n raise NotImplementedError(\\\"servant method 'findObjectById' not implemented\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"819e625e8bdb97bc682993a965a27728\",\n \"score\": \"0.56654525\",\n \"text\": \"def find_by_id(self, id: int):\\n if self.id == id:\\n return self\\n for child in self._children:\\n ret = child.find_by_id(id)\\n if ret is not None:\\n return ret\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"710a25a9f8a5381bdd54926c48e9f5d6\",\n \"score\": \"0.5662839\",\n \"text\": \"def get_one(self, name_id):\\r\\n\\r\\n try:\\r\\n o = self.get_one_by_name(unicode(name_id))\\r\\n\\r\\n if o:\\r\\n return o\\r\\n except:\\r\\n pass\\r\\n\\r\\n try:\\r\\n o = self.get_one_by_id(int(name_id))\\r\\n\\r\\n if o:\\r\\n return o\\r\\n except:\\r\\n pass\\r\\n\\r\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a780c0da727fea2ced326b9689360891\",\n \"score\": \"0.5659603\",\n \"text\": \"def get_index_status(json2i, index):\\n for d in json2i[\\\"status\\\"]:\\n if d[\\\"name\\\"] == index:\\n return d[\\\"status\\\"]\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a780c0da727fea2ced326b9689360891\",\n \"score\": \"0.5659603\",\n \"text\": \"def get_index_status(json2i, index):\\n for d in json2i[\\\"status\\\"]:\\n if d[\\\"name\\\"] == index:\\n return d[\\\"status\\\"]\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eea387c2272abe8845da429e2915d553\",\n \"score\": \"0.56534714\",\n \"text\": \"def find_by_id(foo_id):\\n return Foo.query.filter_by(id=foo_id).first()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b44712c0011f56aa706093bf3df58af5\",\n \"score\": \"0.56517404\",\n \"text\": \"def find_label_by_id(self, _id):\\n search = True\\n i = 0\\n while search:\\n if i == len(self.labels):\\n break;\\n\\n if self.labels[i].id == _id:\\n return self.labels[i]\\n search = False\\n #print self.labels[i].id\\n i += 1\\n if search:\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ee66c58c8e87e53f58e51e6cb12619c\",\n \"score\": \"0.56329674\",\n \"text\": \"def find_successor(self, id):\\n np = self.find_predecessor(id)\\n return self.call_rpc(np, 'successor')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e526e63605d311488a12b965a5c3b72\",\n \"score\": \"0.5621515\",\n \"text\": \"def find_one(self, _topic, _id):\\n return self.find_all(_topic).get(_id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc6a63fca731260585164e83ebe81957\",\n \"score\": \"0.56211346\",\n \"text\": \"def get_by_id(id):\\n return Vaga.query.filter_by(\\n id=id\\n ).one_or_none()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6c2e7a910446242ad055460fe8d16bf\",\n \"score\": \"0.5619565\",\n \"text\": \"def find_by_status(status):\\n return Inventory.__find_by('status', status)\\n # \\\"\\\"\\\" Returns all of the Inventories in a status\\n #\\n # Args:\\n # status (string): the status of the Inventories you want to match\\n # \\\"\\\"\\\"\\n # return [inventory for inventory in Inventory.data if inventory.status == status]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ebcfe52c5037cc507ba4bc9b498142cf\",\n \"score\": \"0.5612619\",\n \"text\": \"def resolve_status(cls, status_name):\\n status_results = ServerStatusOp.get(name=status_name)\\n\\n if len(status_results) is not 1:\\n raise ServerStatusNotFoundError(f'Not found status name: \\\"{status_name}\\\".')\\n\\n status_id = status_results[0].id\\n return status_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08bb8cdf9a411df6c77f9adaf9366043\",\n \"score\": \"0.5610765\",\n \"text\": \"def get_state_by_id(cls, context, id):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3248ea51964120c0d5cedc14abb69d0b\",\n \"score\": \"0.56092995\",\n \"text\": \"def get(model, _id):\\n try:\\n return model.query.filter_by(id=_id).one()\\n except:\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59562cffb685594757502206e4dec159\",\n \"score\": \"0.56038266\",\n \"text\": \"def find(self, Id):\\n if not Id in self.__items.keys():\\n raise RepositoryError(\\\"an item with the given id does not exist\\\")\\n return self.__items[Id]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab09995732f145e3c896539eca56702b\",\n \"score\": \"0.55944663\",\n \"text\": \"def getbyId(id, lista):\\n for avion in lista:\\n if getId(avion) == id:\\n return avion\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"137aa8fce3d68058f7a049bafb9a4a7e\",\n \"score\": \"0.5592402\",\n \"text\": \"def get_node(self, id:int):\\n for node in self.nodes:\\n if node.id == id:\\n return node\",\n \"title\": \"\"\n },\n {\n \"docid\": \"031096adf7228a01ece449e3f24aefcb\",\n \"score\": \"0.55861413\",\n \"text\": \"def find(self, id):\\n clients = self.__loadFromFile()\\n for i in clients:\\n if i.getId() == id:\\n return i\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e65d1e8b0ed3167f521639a8d542864\",\n \"score\": \"0.5570195\",\n \"text\": \"def byId(self, Id):\\n\\t\\tfor test in self._list:\\n\\t\\t\\tif test._Id == int(Id):\\n\\t\\t\\t\\treturn test\\n\\t\\treturn None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b02dcaa162f590d59aaa4ee57116cac3\",\n \"score\": \"0.55676115\",\n \"text\": \"def find_by_id(cls, id):\\n for user in base.session.query(User).filter(User.id == id):\\n return user\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3120b7835f6dc0449d79dfdb2f18450\",\n \"score\": \"0.55611265\",\n \"text\": \"def find_task_by_id(self, id):\\n if id == 0:\\n return None\\n for r in self.root_nodes:\\n task_with_id = r.find_subtask_by_id(id)\\n if task_with_id:\\n return task_with_id\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb31154eef30e5d678e7afe2d01bb324\",\n \"score\": \"0.5550253\",\n \"text\": \"def api_by_id(self, _id):\\n apis = self.db.apis.find({\\\"id\\\": _id})\\n result = [api for api in apis]\\n if result:\\n return result[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b1e903302b9655d60e5473a54274061\",\n \"score\": \"0.5541705\",\n \"text\": \"def get_node_by_id(self, id):\\n nodes = self.get_nodes_by_property(\\\"id\\\", id)\\n if len(nodes) == 0:\\n return None\\n else:\\n return nodes[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9758c4104e0324c184e6c2f8ffbe8074\",\n \"score\": \"0.553512\",\n \"text\": \"def find_successor(self, id):\\n #print('find_successor called by node_id = {} for key: {} at timestamp = {}'.format(self.n_id, str(id), self.pr_now()))\\n\\n if (in_range(id, self.get_id(), self.successor().get_id()) and (self.n_id != self.successor().get_id()) and (id != self.n_id)):\\n return self.successor()\\n else:\\n remote = self.closest_preceding_node(id)\\n if self.my_address.get_hash() != remote.my_address.get_hash():\\n return remote.find_successor(id)\\n else:\\n #print('returning self')\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d42dbeaf20b1900510d8520a3ddb74e0\",\n \"score\": \"0.5524069\",\n \"text\": \"def get_id_by_status(cls, request, status_str):\\n return request.dbsession.query(cls)\\\\\\n .filter_by(status=status_str).one().id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a174c993de5313bff38008d2b21965b\",\n \"score\": \"0.5523496\",\n \"text\": \"def find_by_id(self, _id):\\n entity = new_instance_with_primary_key(_id)\\n req, params = build_select_req(entity)\\n return self.dao.do_select(req, params)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86e24e980af27207fb55034dfea85c03\",\n \"score\": \"0.5522255\",\n \"text\": \"def _get_current_status():\\n return Status.query.get(1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9984aafd84ebbcaa4e5963d6e67f9c38\",\n \"score\": \"0.5510831\",\n \"text\": \"def status_lookup(ids):\\n ids = list(set(ids))\\n len_ids = len(ids) # @DEBUG\\n ids = ','.join(ids)\\n tw = Twython(CREDENTIALS['APP_KEY'],\\n CREDENTIALS['APP_SECRET'],\\n CREDENTIALS['ACCESS_KEY'],\\n CREDENTIALS['ACCESS_SECRET'])\\n res = tw.lookup_status(id=ids)\\n\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97fb5c5de56ff9be377522f314a8b3b4\",\n \"score\": \"0.5509235\",\n \"text\": \"def find_first(self, label):\\n results = self.find(label)\\n\\n if results:\\n return results[0]\\n\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6025b852ed3cafb91c0903cfb68a643\",\n \"score\": \"0.5505244\",\n \"text\": \"def get(id_: str) -> dict:\\n return __col.find_one({ID: id_})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"278ea3aadc66386e21e3a3f848dd7cd7\",\n \"score\": \"0.5503628\",\n \"text\": \"def get_by_id(self, id):\\n\\n return self.ent_dict[id]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e96af5cc6e720aa83c9d4821766b3f35\",\n \"score\": \"0.55035645\",\n \"text\": \"def __getStateById(self, stateId):\\n for state in self.__listState:\\n if state.getId() == stateId:\\n return state\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1983281689db8dbd10a6466289f330e\",\n \"score\": \"0.5500628\",\n \"text\": \"def process_with_id(self, id):\\n # ...\\n for process in self.runningStack:\\n if process.id == id:\\n return process\\n for process in self.waitingList:\\n if not process == None:\\n if process.id == id:\\n return process\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93ca593ff87abba1fd460809570bdf60\",\n \"score\": \"0.5497836\",\n \"text\": \"def getOne(self, _id, **kwargs):\\n kwargs['id'] = self.sanitize_id(_id)\\n kwargs['url'] = self.SINGLE_URL\\n return self.transport.set_method('GET').request(**kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8658c8283a3974327caf9f8902c3209c\",\n \"score\": \"0.5490123\",\n \"text\": \"def get_object(self, id=None):\\n # try:\\n # obj = UpdateModel.objects.get(id=id)\\n # except UpdateModel.DoesNotExist:\\n # obj = None\\n\\n if id is None:\\n return None\\n\\n qs= self.get_queryset().filter(id=id)\\n if qs.count() == 1:\\n return qs.first()\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d2cc1e203f90ca4d0c269d8453b97de\",\n \"score\": \"0.5486001\",\n \"text\": \"def find_job(context, job_id, state=None):\\n jsondata = context.response.json()\\n jobs = jsondata['jobs']\\n job_ids = [job[\\\"job_id\\\"] for job in jobs]\\n assert job_id in job_ids\\n if state is not None:\\n job = get_job_by_id(jobs, job_id)\\n assert job is not None\\n assert job[\\\"state\\\"] is not None\\n assert job[\\\"state\\\"] == state\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c46811d19a5a1e8b2179d91cd8621f5a\",\n \"score\": \"0.54794425\",\n \"text\": \"def fetch_record_or_abort(id):\\n t = Todo.query.get(id)\\n if not t:\\n abort(404, message=\\\"To-do %s doesn't exist\\\" % id)\\n return t\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88827ae23be3510f7ad3ceeb72e578fc\",\n \"score\": \"0.546806\",\n \"text\": \"def get_one_book(id):\\n one_book = BookModel.query.get(id)\\n if not one_book:\\n return None\\n return one_book\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd170f236416826f6f51cfbe89ce787e\",\n \"score\": \"0.5467214\",\n \"text\": \"def getOne(self, _id, **kwargs):\\n kwargs['url'] = self.SINGLE_URL\\n kwargs['id'] = self.sanitize_id(_id)\\n return self.transport.set_method('GET').request(**kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e88996f5f5636f98775da1c096a77a74\",\n \"score\": \"0.5462106\",\n \"text\": \"def get_by_id(id, lista):\\n for vanzare in lista:\\n if get_id(vanzare) == id:\\n return vanzare\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a11e7749007f2dff1cb1cee54a7bf898\",\n \"score\": \"0.5455481\",\n \"text\": \"def get_earliest_status(self):\\n earliest = None\\n if self.data:\\n sorted_statuses = sorted(self.data.values(), key=lambda status: status.created_at)\\n earliest = sorted_statuses[0]\\n return earliest\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5da32f847a364029abdc1f497b887639\",\n \"score\": \"0.54494977\",\n \"text\": \"def single_user_by_id(id):\\n all_users = allusers()\\n for user in all_users:\\n if str(user.key.id()) == str(id):\\n return user\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66116039de789d26f77a719b0f7a59da\",\n \"score\": \"0.54458266\",\n \"text\": \"def get_object(self, id=None):\\n # try:\\n # obj = UpdateModel.objects.get(id=id)\\n # except UpdateModel.DoesNotExist:\\n # obj = None\\n qs= UpdateModel.objects.filter(id=id)\\n if qs.count() == 1:\\n return qs.first()\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da2226493c1e57c1f59ca9ee6480b9d4\",\n \"score\": \"0.5440847\",\n \"text\": \"def _get(self, id):\\n return self.client.get(\\n id=id\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"550aeffc0a6a46d2c7844e8959420294\",\n \"score\": \"0.54386216\",\n \"text\": \"def get(self, id):\\n return self.model_class.query.get(id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"019bd99e4ffbea60d156b40876daf442\",\n \"score\": \"0.542419\",\n \"text\": \"def get_draw_or_404(cls, id_):\\n obj = cls.query.get(id_)\\n if not obj: # Not found, try to search via private_id\\n obj = cls.query.filter_by(private_id=id_).first_or_404()\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2cc29e67ea5a043f5e36d7f7820f3b6\",\n \"score\": \"0.54189336\",\n \"text\": \"def get(self, id):\\n return self._get(id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fee45f7b759fd0025df3ec1f77fbd778\",\n \"score\": \"0.5413169\",\n \"text\": \"def returnClassFromID(dictionary, id):\\n if id == \\\"\\\":\\n print(\\\"Complaint ID field is empty.\\\")\\n return None\\n else:\\n id = int(id)\\n for key in dictionary:\\n if key == id:\\n return dictionary[key]\\n print(str(id) + \\\" is not in dataset.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21618ee5fa2cbc37d7000714dc85e9f9\",\n \"score\": \"0.5408651\",\n \"text\": \"def get(self,id):\\r\\n person = get_one(id=id)\\r\\n if not person:\\r\\n api.abort(404)\\r\\n else:\\r\\n return person\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43706cc72239748e530c0225cc4232a3\",\n \"score\": \"0.54076946\",\n \"text\": \"def findit(self,idnum):\\r\\n \\r\\n for student in self.students:\\r\\n if student.get_id() == idnum:\\r\\n return student\\r\\n return \\\"Student not found\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"421fe2e199eab214a569c850d4ae2301\",\n \"score\": \"0.5399659\",\n \"text\": \"def get_order_status_from_id(self, order_id):\\r\\n url = self.address + '/order/status/?id=%s' % order_id\\r\\n with self.opener.open(url) as result:\\r\\n response = result.read()\\r\\n if result.getcode() != 200:\\r\\n raise Exception(response)\\r\\n data = json.loads(response)\\r\\n closed = data['closed']\\r\\n if closed is not None:\\r\\n closed = closed.split('.', 1)[0]\\r\\n closed = datetime.strptime(closed, \\\"%Y-%m-%d %H:%M:%S\\\")\\r\\n return data['status'], data['progress'], closed\",\n \"title\": \"\"\n },\n {\n \"docid\": \"365b17cf6694bb6f88ecbdcb78bfdef3\",\n \"score\": \"0.5398555\",\n \"text\": \"def get(self, id):\\n return self.model.get(id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1c020e26a7aab65df4e46a8decb5f9d\",\n \"score\": \"0.53943\",\n \"text\": \"def _get_single_instance(self, id):\\n r = self._conn.get_all_instances(id)\\n return r[0].instances[0] if r and r[0].instances else None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1511a2b15680f62ad215d7fad5149374\",\n \"score\": \"0.5387435\",\n \"text\": \"def get(id):\\n try:\\n obj = _store.objects[id]\\n if (obj is not None):\\n return obj\\n else:\\n raise Exception('Object not found')\\n except KeyError:\\n time.sleep(0.0001)\\n logging.debug(\\\"No matching object found, trying again...\\\")\\n return get(id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79f55a4854504f7479a0def5112721f8\",\n \"score\": \"0.5371744\",\n \"text\": \"def _find_item(self, item_id):\\n for item in self.library_items[int(item_id) - 1]:\\n if item.id == item_id:\\n return item\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c34848c9fc527e7aa65d870b18c12289\",\n \"score\": \"0.537127\",\n \"text\": \"def find_one(self):\\n self.query_params[\\\"limit\\\"] = 1\\n return self.__execute_network_call()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35fb629615a0d589ad2e32cbe19ef6ed\",\n \"score\": \"0.53695875\",\n \"text\": \"def retrieve(self, request, pk=None):\\n\\n try:\\n status = Status.objects.get(pk=pk)\\n serializer = StatusSerializer(status, context={'request': request})\\n return Response(serializer.data)\\n\\n except Exception as ex:\\n return HttpResponseServerError(ex)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bab35dc3772a86dbd2a8dce385b28ccd\",\n \"score\": \"0.5361243\",\n \"text\": \"def get(self, id):\\n return Team.query.filter(Team.id == id).one()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a56c138e971efdac002e67888ccffad0\",\n \"score\": \"0.536081\",\n \"text\": \"def at(cls, _id):\\n return cls.where(cls.primarykey == _id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24783f95d383b6d5aec6c7c0e4e850c4\",\n \"score\": \"0.53573316\",\n \"text\": \"def find_student(self, id_s):\\n if id_s in self.__students:\\n return self.__students[id_s]\\n else:\\n raise RepositoryFind\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9701e24dc07f76665fee58be1a192473\",\n \"score\": \"0.53505886\",\n \"text\": \"async def find_object(self, cls, _id):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a9ee22ea5b7f46895bf0a1dc2fdd8d1\",\n \"score\": \"0.5342875\",\n \"text\": \"def get_entity(self, id):\\n return self.entities.get(id, (None, None, None))[2]\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":698,"cells":{"query_id":{"kind":"string","value":"94c53df768d04da17d323d893257f0e7"},"query":{"kind":"string","value":"Testing the building class function."},"positive_passages":{"kind":"list like","value":[{"docid":"654d6599c56fe9c783f45854ff6d073c","score":"0.6755711","text":"def test_building_class():\n # Testing the ruleset for classifying Hazus building class\n res = []\n ref_class = ['WSF', 'WMUH']\n ref = np.ones(2)\n for i in range(2):\n data_dir = os.path.join(cur_dir, base_input_path, 'BuildingClass_Data',\n 'building_class_test_' + str(i+1) + '.json')\n with open(data_dir) as f:\n data_input = json.load(f)\n tmp = parse_BIM(data_input['GI'])\n data_output = building_class(tmp)\n print(data_output)\n res.append(int(data_output == ref_class[i]))\n # Check\n assert_allclose(res, ref, atol=1e-5)","title":""}],"string":"[\n {\n \"docid\": \"654d6599c56fe9c783f45854ff6d073c\",\n \"score\": \"0.6755711\",\n \"text\": \"def test_building_class():\\n # Testing the ruleset for classifying Hazus building class\\n res = []\\n ref_class = ['WSF', 'WMUH']\\n ref = np.ones(2)\\n for i in range(2):\\n data_dir = os.path.join(cur_dir, base_input_path, 'BuildingClass_Data',\\n 'building_class_test_' + str(i+1) + '.json')\\n with open(data_dir) as f:\\n data_input = json.load(f)\\n tmp = parse_BIM(data_input['GI'])\\n data_output = building_class(tmp)\\n print(data_output)\\n res.append(int(data_output == ref_class[i]))\\n # Check\\n assert_allclose(res, ref, atol=1e-5)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"79785cbd6d603ba51a4083520d3df1f4","score":"0.7564869","text":"def test_01_BuildObjects(self):\n pass","title":""},{"docid":"3b0ee69e1abd41003d408ff9161a2153","score":"0.7322961","text":"def test_build(self): \n\n Builder(self.app)\n\n assert 'labels' in dir(self.app)\n assert 'containers' in dir(self.app)\n assert 'entrys' in dir(self.app)\n assert 'button' in dir(self.app)","title":""},{"docid":"472c03cf677f5e41336f6ca3a0f5ef5e","score":"0.71330905","text":"def test_create_build_type(self):\n pass","title":""},{"docid":"171440fb99c27f79f66746fa5e94a0a5","score":"0.7018632","text":"def test_builder():","title":""},{"docid":"1206e6098ea5779aa83f574c5fd68c6a","score":"0.69523484","text":"def build(self):\r\n pass","title":""},{"docid":"496821a7b2f39891834fd89d6a116e6b","score":"0.69168097","text":"def _build(cls):\n from draco2.model.check import CheckVisitor\n from draco2.model.build import BuildVisitor\n checker = CheckVisitor()\n checker.visit(cls)\n builder = BuildVisitor()\n builder.visit(cls)","title":""},{"docid":"3ec91b74f79c99c1f15cf8796c7e7439","score":"0.6856415","text":"def __build(self):\n pass","title":""},{"docid":"a0ac9565236a2e1b9e78346c2d88f34d","score":"0.6850931","text":"def test_constructor_fill_fields(self):\r\n builder = AbstractBuilder(\"url\")\r\n\r\n self.assertEqual(builder.url, \"url\")\r\n self.assertEqual(builder.data, {})","title":""},{"docid":"d6e74b6ce1bef395f1e232911019c953","score":"0.68274146","text":"def build(self):\n\n raise NotImplementedError","title":""},{"docid":"cfe7d12c13df9ea6847862016d1692a4","score":"0.67328924","text":"def test_init_creates_builder_object(self):\n\n self.assertEqual(self.director._builder, None)","title":""},{"docid":"5b8a906fcd75592fc8da26dd57b4246b","score":"0.6689936","text":"def test_constructor(self):\n self.exporter_class()","title":""},{"docid":"9fb5ddcc23fbe7e0b80120772bb6c804","score":"0.66652966","text":"def _build(self):\n raise NotImplementedError()","title":""},{"docid":"bdd25cd8c38bbf4bf35041dad52203eb","score":"0.6648575","text":"def build(self):\n raise NotImplementedError(\"Must be implemented by subclass.\")","title":""},{"docid":"00715bf309783b709593feac3fffeb5c","score":"0.66266096","text":"def build(self):\n pass","title":""},{"docid":"00715bf309783b709593feac3fffeb5c","score":"0.66266096","text":"def build(self):\n pass","title":""},{"docid":"00715bf309783b709593feac3fffeb5c","score":"0.66266096","text":"def build(self):\n pass","title":""},{"docid":"00715bf309783b709593feac3fffeb5c","score":"0.66266096","text":"def build(self):\n pass","title":""},{"docid":"00715bf309783b709593feac3fffeb5c","score":"0.66266096","text":"def build(self):\n pass","title":""},{"docid":"ce715e484866b52cd69afd03acf27b54","score":"0.6613739","text":"def build(self):","title":""},{"docid":"960babe19fdbec85b450fa6acfbb0c46","score":"0.65682083","text":"def test_constructor(self):\n self.build_preprocessor()","title":""},{"docid":"dc9a4a9936b3508c4161ba98e3b52d5d","score":"0.65465474","text":"def test_builderBender_2():","title":""},{"docid":"910b4f85243dee8227382619284a094c","score":"0.6523219","text":"def test_builds_hierarchy(self):\n class A(object):\n seed = 101\n\n class B(object):\n pass\n\n builders = {A: mock.Mock()}\n extra_builders = {B: mock.Mock()}\n\n a = A()\n b = B()\n\n network_a = mock.Mock()\n network_a.seed = None\n network_a.connections = []\n network_a.ensembles = [a]\n network_a.nodes = []\n network_a.networks = []\n network_a.probes = []\n network_a.config = mock.Mock(name=\"config\")\n\n network_b = mock.Mock()\n network_b.seed = None\n network_b.connections = []\n network_b.ensembles = []\n network_b.nodes = [b]\n network_b.networks = []\n network_b.probes = []\n network_b.config = mock.Mock(name=\"config\")\n\n network = mock.Mock()\n network.seed = None\n network.connections = []\n network.ensembles = []\n network.nodes = []\n network.networks = [network_a, network_b]\n network.probes = []\n network.config = mock.Mock(name=\"config\")\n\n # Patch the default builders\n with patch.object(Model, \"builders\", new=builders):\n # Create a model and build the mock network\n model = Model()\n model.build(network, extra_builders=extra_builders)\n\n # Assert that the config was stored in the model\n assert model.config is network.config\n\n # Assert that seeds were supplied\n assert model.seeds[a] == a.seed\n assert model.seeds[b] is not None\n\n # Assert the builders got called\n builders[A].assert_called_once_with(model, a)\n extra_builders[B].assert_called_once_with(model, b)","title":""},{"docid":"3e57475e01715c5eecb9121be152687f","score":"0.6487616","text":"def test_builderBender_1():","title":""},{"docid":"0539b1e0837358d04c16e0f3afe1fede","score":"0.6480443","text":"def test_construct_all(self):\n self.constructors.construct_all()\n self.check_all_constructions()","title":""},{"docid":"981676979f4eef8174b74263f424d5db","score":"0.64783055","text":"def test_class_creation(self, us):\n assert us.subject == \"Subject\"\n assert us.epic == \"Epic\"\n assert us.tags == [\"expedientes\"]","title":""},{"docid":"d0a58ae9df803358baba692f85001d7c","score":"0.6455311","text":"def test_constructor(self):\n zone_name = 'example.com'\n builder = ZoneBuilder(zone_name)\n self.assertTrue(hasattr(builder, 'zone'))\n self.assertEqual(builder.zone, zone_name)","title":""},{"docid":"e5c202ae8336013a9105ef787112c70a","score":"0.64339846","text":"def __init__(self, builder):\n self.builder = builder","title":""},{"docid":"e322e19025aa35a6340bae2365c763a8","score":"0.63910186","text":"def test_create_instance(self):\n pass","title":""},{"docid":"e322e19025aa35a6340bae2365c763a8","score":"0.63910186","text":"def test_create_instance(self):\n pass","title":""},{"docid":"cd4af922f0d6fb7d9b83f68ce3260a85","score":"0.6387344","text":"def test_creation(self):","title":""},{"docid":"e291569a352687c4950fc70f60b8f3c9","score":"0.63830835","text":"def build(self, *args, **kwargs):","title":""},{"docid":"bbb422bd58c676c8f406162c045b69f8","score":"0.6379992","text":"def test_object_creation(self, dcc):\n dcc","title":""},{"docid":"e6d2a1d0e878bf61f725d43c158ddc0a","score":"0.6374741","text":"def test_constructor_element_default(self):","title":""},{"docid":"d18986f77987b58f8c56e7703ef5915b","score":"0.63620514","text":"def test_instantiation(self):\n projectbuild = ProjectBuild.objects.create(\n project=self.project, requested_by=self.user)\n self.assertEqual(self.user, projectbuild.requested_by)\n self.assertIsNotNone(projectbuild.requested_at)\n self.assertIsNone(projectbuild.ended_at)\n self.assertEqual(\"UNKNOWN\", projectbuild.status)\n self.assertEqual(\"UNKNOWN\", projectbuild.phase)","title":""},{"docid":"42c606f46e9fce84fbe465235d38b252","score":"0.63536096","text":"def test__init__(self):\n # tests based on basic object creation from self.create_self():\n assert self.l2_service_builder.driver is self.driver, \\\n \"Driver provisioning test\"\n assert self.l2_service_builder.conf is self.driver.conf, \\\n \"Dirver conf provisioning test\"\n assert self.l2_service_builder.f5_global_routed_mode is \\\n self.f5_global_routed_mode, \\\n \"f5_global_routed_mode provisioning test\"\n self.system_helper.assert_called_once_with()\n self.network_helper.assert_called_once_with()\n self.service_model_adopter.assert_called_once_with(self.driver.conf)\n\n # further tests needed:\n # add tests for...\n # - f5_external_physical_mappings assignment test\n # - vlan_binding_driver assignment/importation test\n # Suggest a refactor for implementing the above in unit tests...","title":""},{"docid":"5c84bdcac04eb10a73112b0046a35461","score":"0.63439816","text":"def __init__(self, name, packages=None, tests=None):\n if not packages:\n packages = []\n if not tests:\n tests = []\n\n # Assert that the correct objects are passed to the constructor\n # TODO: Fix validation after optimization is done...\n validate_build_contents(packages, tests)\n super(Build, self).__init__(name, packages, tests)","title":""},{"docid":"0ced0a322722630d75b8330157c2f2f8","score":"0.63296765","text":"def build(self) -> None: # type: ignore[override]\n pass","title":""},{"docid":"0a7adf77b116c0ca5fb70df358241704","score":"0.6324664","text":"def _build(self, *args, **kwargs):\n raise NotImplementedError","title":""},{"docid":"0a7adf77b116c0ca5fb70df358241704","score":"0.6324664","text":"def _build(self, *args, **kwargs):\n raise NotImplementedError","title":""},{"docid":"326be520d503261de5270ed2d2f36650","score":"0.6323604","text":"def build(self, *args, **kwargs) -> None:\n pass","title":""},{"docid":"199f3464672f4568fafbe5884b2c1ba9","score":"0.6310089","text":"def build(self):\n raise NotImplementedError(\"This method must be overwritten\")","title":""},{"docid":"bd3a9e5b3e51173a2a3e3e19be8051a1","score":"0.6307875","text":"def test_e6_example_create_building(self):\n from teaser.examples import e6_generate_building as e6\n\n prj = e6.example_create_building()","title":""},{"docid":"232931a9c5de82e24aafba0e69cbe5d8","score":"0.6306217","text":"def build(self):\n raise NotImplementedError","title":""},{"docid":"892b93af5f554ca51c4d84d50ea85b70","score":"0.6286135","text":"def construct(self):\n\n if tools.toolbox.get_setting('control', 'do_construct')=='0':\n print 'skip constructing.'\n return\n\n def build(building):\n \"\"\"\n constructs building\n \"\"\"\n self.open('main')\n\n try:\n self.browser.follow_link(self.browser.find_link(url_regex=\n re.compile(\"upgrade_building.+id=%s\"%building)))\n print_cstring(\"Building: [%s]\"%building, 'turq')\n self.statistics['buildings_constructed']+=1\n except LinkNotFoundError:\n print_cstring('fuck that shit, not enough ressources to build [%s]'%building, 'red')\n\n self.open('main')\n html=self.browser.response().read()\n farm_building='\\t\\t\\tBauernhof<br />\\n\\t\\t\\tStufe' in html\n if farm_building:\n self.pop_critical=0\n\n storage_building='\\t\\t\\tSpeicher<br />\\n\\t\\t\\tStufe' in html\n\n if self.buildings['under_construction']:\n return\n\n if self.pop_critical:\n if 'farm' in self.buildable:\n build('farm')\n if self.storage_critical and 'storage' in self.buildable and not storage_building:\n build('storage')\n\n\n elif self.next_building in self.buildable and not self.pop_critical:\n build(self.next_building)","title":""},{"docid":"8f49279d1bb317c91979afb820dd7ea2","score":"0.62846243","text":"def test_basic_build(build_turbine):\n m = build_turbine","title":""},{"docid":"4ff29206f93e9f7110d7dd781f5b764d","score":"0.6265389","text":"def build(self):\n raise NotImplementedError()","title":""},{"docid":"4ff29206f93e9f7110d7dd781f5b764d","score":"0.6265389","text":"def build(self):\n raise NotImplementedError()","title":""},{"docid":"f1b4367da5be9f618c144e2cc54afbd9","score":"0.62642974","text":"def test_build_channel_class():\n channel_class = build_channel_class(channel_number=2, mcaroi_numbers=(1, 2, 3))\n\n assert hasattr(channel_class, \"channel_number\")\n assert channel_class.channel_number == 2\n\n assert hasattr(channel_class, \"sca\")\n assert hasattr(channel_class, \"mca\")\n assert hasattr(channel_class, \"mca_sum\")\n\n # there should be 3 MCAROI attributes: mcaroi01, mcaroi02, mcaroi3\n expected_mcaroi_attr_names = {\n f\"mcaroi{mcaroi_i:02d}\" for mcaroi_i in range(1, 3 + 1)\n }\n\n # there should be no other MCAROI attributes\n all_mcaroi_attr_names = {\n attr_name\n for attr_name in dir(channel_class)\n if re.match(r\"mcaroi\\d{2}\", attr_name)\n }\n\n assert expected_mcaroi_attr_names == all_mcaroi_attr_names","title":""},{"docid":"03009a8e9b7e52e272271c86bdc1036c","score":"0.6263392","text":"def __init__(self, build=None):\n\t\tself.build = build","title":""},{"docid":"24f5a49a23ee6dbf1fdd33694f266544","score":"0.62596506","text":"def test_01_BuildObjects(self):\n self.assertNotEqual(self.m_pyhouse_obj.House.Hvac, None)","title":""},{"docid":"5b0ed901d8258b242825903cab009e1f","score":"0.62589186","text":"def build(self):\n raise NotImplementedError(\"Subclasses should implement this!\")","title":""},{"docid":"0cc6efff5fc99c83ea7928f3ed3746e2","score":"0.625586","text":"def construct(self):\n pass","title":""},{"docid":"0cc6efff5fc99c83ea7928f3ed3746e2","score":"0.625586","text":"def construct(self):\n pass","title":""},{"docid":"d17bc4ec97602f1479241c306abe35df","score":"0.62191135","text":"def init_test_object(self, method):\n if self.func in self.use_seed:\n paddle.seed(self.seed)\n if method == \"BuildClass\" or method == \"BuildClassWithInputSpec\":\n # 仅实例化一次，防止多次实例化后，因为随机种子不固定导致多个结果值res不相等\n obj = BuildClass(self.in_params, self.func)\n obj.eval()\n elif method == \"BuildFunc\" or method == \"BuildFuncWithInputSpec\":\n obj = BuildFunc(self.in_params, self.func)\n obj.eval()\n elif method == \"naive_func\":\n obj = naive_func\n\n return obj","title":""},{"docid":"4019fed8632cdbd1b4bbd90ee5deed0e","score":"0.621247","text":"def test__build(self):\n x = numpy.array([[1, 2],\n [3, 4],\n [5, 6]])\n # start with default initialization\n subject = gandy.models.bnns.BNN((2,), (4,), train_size=len(x))\n self.assertTrue(isinstance(subject.model, tf.keras.Model))\n self.assertTrue(subject.model._compile_was_called)\n self.assertTrue(subject.model.built)\n self.assertEqual(tuple(subject.model.input.shape.as_list())[1:],\n subject.xshape)\n predict = subject.model.predict(x)\n self.assertTrue(predict.shape == (3, 4))\n out = subject.model(x)\n self.assertTrue(isinstance(out, tfp.distributions.Distribution))\n\n # test keyword assignment\n subject = gandy.models.bnns.BNN((2,), (4,),\n train_size=len(x),\n optimizer='RMSprop')\n self.assertTrue(isinstance(subject.model.optimizer,\n tf.keras.optimizers.RMSprop))\n subject = gandy.models.bnns.BNN((2,), (4,),\n train_size=len(x),\n optimizer=tf.keras.optimizers.RMSprop)\n self.assertTrue(isinstance(subject.model.optimizer,\n tf.keras.optimizers.RMSprop))\n opt = tf.keras.optimizers.RMSprop()\n subject = gandy.models.bnns.BNN(\n (2,), (4,),\n train_size=len(x),\n optimizer=opt\n )\n self.assertTrue(subject.model.optimizer is opt)\n return","title":""},{"docid":"75e5fa6e2b104b3873ddebfaecd1c525","score":"0.6196077","text":"def build(self) -> BaseHamilton:\r\n pass","title":""},{"docid":"954f0d5b9a294c6621047ec5fbe8cb09","score":"0.6177918","text":"def build(self):\n raise NotImplementedError('build not implemented')","title":""},{"docid":"a0243886ee1575ea81a670f223c79fb5","score":"0.61775076","text":"def build(cls, **kwargs):\n return cls(**kwargs)","title":""},{"docid":"320dd0725c028e81ddd15a2fa2d416bb","score":"0.6166137","text":"def setup_class(klass):","title":""},{"docid":"287e22c11c3146d1d4879fb7d20d2367","score":"0.61563617","text":"def test_creation(self):\n classifier = LookUpClassifier()\n self.assertTrue(classifier)","title":""},{"docid":"3304793c6d84b318dab1a82509cae13e","score":"0.6152292","text":"def setup_class(self):","title":""},{"docid":"3304793c6d84b318dab1a82509cae13e","score":"0.6152292","text":"def setup_class(self):","title":""},{"docid":"3304793c6d84b318dab1a82509cae13e","score":"0.6152292","text":"def setup_class(self):","title":""},{"docid":"c24da61c1c630167ea96036f654b6907","score":"0.6150095","text":"def setup_class(cls):\n # ns.assert_true(False, \"setup_class run\")\n print('setup_class\\n')","title":""},{"docid":"c24da61c1c630167ea96036f654b6907","score":"0.6150095","text":"def setup_class(cls):\n # ns.assert_true(False, \"setup_class run\")\n print('setup_class\\n')","title":""},{"docid":"a1f69d8e7f30dd22e97c8541192f8495","score":"0.6137967","text":"def test_build(self):\n self.request.add_attribute(SearchAttribute, 'Test', 123)\n built = self.request.build()\n expected = {\n \"TableName\": \"testtable\",\n \"Test\": 123,\n }\n assert built == expected","title":""},{"docid":"bf46fc898a1002dffb975da236e47ef8","score":"0.6135326","text":"def test_registerClass():","title":""},{"docid":"ff5f80ce9487551ad2145db63945af70","score":"0.61318994","text":"def test_constructor(self):\n Exporter()","title":""},{"docid":"7e47ac8a2242fcea108249ebb4eed07c","score":"0.6104577","text":"def setUpClass(cls):\n annofile = os.path.join(util.UTFILES_DIR, \"make-3.82-emake-5.3.0.xml\")\n cls.build = annolib.AnnotatedBuild(annofile)\n\n # Collect all the jobs (so that we have the metrics)\n cls.jobs = cls.build.getAllJobs()\n\n cls.build.close()","title":""},{"docid":"4be12dc2ece63416767cc9fd0a4b0420","score":"0.61017764","text":"def example_type_building():\n\n from teaser.project import Project\n\n \"\"\"We instantiate the Project class. The parameter load_data = True indicates\n that we load the XML data bases into our Project.\n This can take a few sec.\"\"\"\n\n prj = Project(load_data=True,used_data_country=\"Belgium\") #we maken een project aan,\n # true because data bindings for\n #type elements and use conditions should be loaded\n #dus instance of dataclass() wordt aangemaakt\n #de 3 input XML's worden geladen en de bijhorende scripts aangeroepen\n #om de nodige waarden te kunnen uitlezen\n prj.name = \"ArchetypeBuildings_Ref_ANNEX_bel\" #de naam van het project is ...\n\n \"\"\"The five functions starting with type_bldg giving us the opportunity to\n create the specific type building (e.g. type_bldg_residential). The function\n automatically calculates all the necessary parameter. If not specified different\n it uses vdi calculation method.\n Dus type_bldg_residential maakt een instance aan van building() en zal vervolgens alles berekenen\n neen!! geen instantie van building() wel van singlefamilydwelling() dit is een specifieker gebouwtype\n \"\"\"\n\n prj.type_bldg_residential(name=\"ResidentialBuilding\",\n year_of_construction=1970,\n number_of_floors=2,\n height_of_floors=3.5,\n net_leased_area=100,\n construction_type=\"heavy\")\n\n '''Or we use Annex60 method (e.g with four elements). Which exports one\n Model per zone'''\n\n prj.used_library_calc = 'Annex60'\n prj.number_of_elements_calc = 4\n prj.merge_windows_calc = False\n prj.calc_all_buildings(raise_errors=True)\n prj.export_annex()","title":""},{"docid":"e85909cd18b3ac27f53865a64953be41","score":"0.6098726","text":"def build_test(self):\n\n super(AggregateBase, self).build_test()\n self.set_common_expected_parms()\n self.set_common_optional_parms()","title":""},{"docid":"7b98de1fefbecf8e02cf5a6d245da365","score":"0.6096737","text":"def build(self, *args, **kw):\n raise NotImplementedError","title":""},{"docid":"2c7bbb4b8571925c0bace6890a5b0386","score":"0.6086688","text":"def test_creation(self):\n classifier = DecisionTreeClassifier()\n self.assertTrue(classifier)","title":""},{"docid":"5cffd8588be512521a72cc019450fd47","score":"0.60760254","text":"def test_node_object_generation(self):","title":""},{"docid":"6c093f4399cd54397c2b17257cc011b2","score":"0.60751545","text":"def test_class_creation(self, report):\n assert report.project == \"SIEEL\"","title":""},{"docid":"782bb3da51cc0ba09cd6f76a807748da","score":"0.6072306","text":"def __call_creator_constructor_test(self, sport=None, prize_structure=None, start=None,\n duration=None,\n draft_group=None):\n if sport is None:\n sport = self.sport\n if prize_structure is None:\n class PrizeStructureChild(PrizeStructure):\n def __init__(self):\n pass\n\n prize_structure = PrizeStructureChild\n if start is None:\n start = timezone.now()\n if duration is None:\n duration = int(300)\n\n # try to construct a ContestPool\n try:\n # print('sport:', str(sport))\n self.assertRaises(IncorrectVariableTypeException,\n lambda: ContestPoolCreator(sport, prize_structure, start, duration,\n draft_group))\n except:\n # there might be other exceptions from\n # inner objects, but those arent tested here!\n pass","title":""},{"docid":"a594555aedaf1c3e6691cfa6639be2cd","score":"0.6063685","text":"def test_creation(self):\n classifier = MajorityClassifier()\n self.assertTrue(classifier)","title":""},{"docid":"01c0508071cc479a368388b7522f7e3d","score":"0.6058457","text":"def test_valid_construction(constructor_args):\n boxmc = hoomd.hpmc.update.BoxMC(**constructor_args)\n\n # validate the params were set properly\n obj_attr_check(boxmc, constructor_args)","title":""},{"docid":"d32f8ab65ec268511bd8d5293ce941db","score":"0.60434175","text":"def test_constructor(self):\n assert isinstance(self.j, Jungle)","title":""},{"docid":"67342dca7cd52466a19fbe618139e89e","score":"0.60257757","text":"def __init__(self, name, params):\n Tester.__init__(self, name, params)","title":""},{"docid":"b9860cd8a7a7fa554d317b27f4bdd0e2","score":"0.60252756","text":"def test_class_created():\n check_class_exists(task_22_1, \"Topology\")","title":""},{"docid":"116551fe248c6d21c8c43401a439c5f7","score":"0.6021173","text":"def test_facade_builder(self):\n\n\t\tgenerated_builder = self.manual_facade.get_object_builder()\n\t\tgenerated_builder.load_from_config(\"small_red_cube\")\n\t\tsmall_red_cube = generated_builder.create(\"small_red_cube\", \"small_offset\")\n\t\tself.assertEqual(small_red_cube.get_descriptor(), \"cube\")","title":""},{"docid":"e717c99c03b2e63b07daac6e4a99c1d2","score":"0.6010992","text":"def test_setup(self):\n self.create_iinfo_class()\n self.create_info_class()","title":""},{"docid":"d4f8eb698b46a2eba74506350d064519","score":"0.6007934","text":"def setUpClass(cls):","title":""},{"docid":"d4f8eb698b46a2eba74506350d064519","score":"0.6007934","text":"def setUpClass(cls):","title":""},{"docid":"7a54e2d90d78a39cfaed900ade004d47","score":"0.60028803","text":"def test_new(self):","title":""},{"docid":"7a54e2d90d78a39cfaed900ade004d47","score":"0.60028803","text":"def test_new(self):","title":""},{"docid":"10fb8bfe004bc3b095ad573daca00143","score":"0.60009766","text":"def test_creation(self):\n classifier = AdaBoost()\n self.assertTrue(classifier)","title":""},{"docid":"0ba8de071134389fefb87625cdb93dfc","score":"0.59946126","text":"def build(self,\n environment: 'Environment',\n bug: bugzoo.Bug\n ) -> 'TestSuite':\n ...","title":""},{"docid":"1b85105730caa397bf087b8e775e6d4c","score":"0.599112","text":"def construct(self):\n\t\traise NotImplementedError","title":""},{"docid":"430fd8ae59bc9180c21fa0d9371678d9","score":"0.5982696","text":"def test___init__(self):\n pass","title":""},{"docid":"2f084f3f6df37de5f4e25574ad0a0fd6","score":"0.598147","text":"def test_create(self):\r\n\r\n self.assertTrue(isinstance(self.DUT, Model))\r\n\r\n # Verify Hardware class was properly initialized.\r\n self.assertEqual(self.DUT.revision_id, None)\r\n self.assertEqual(self.DUT.category_id, 0)\r\n\r\n # Verify Relay class was properly initialized.\r\n self.assertEqual(self.DUT.category, 6)\r\n self.assertEqual(self.DUT.quality, 0)\r\n self.assertEqual(self.DUT.construction, 0)\r\n self.assertEqual(self.DUT.q_override, 0.0)\r\n self.assertEqual(self.DUT.base_hr, 0.0)\r\n self.assertEqual(self.DUT.piQ, 0.0)\r\n self.assertEqual(self.DUT.piE, 0.0)","title":""},{"docid":"3c86562f72e8f338ee82475a2446a095","score":"0.5974097","text":"def test_inferior_asserting(self):\n self.build()\n self.inferior_asserting()","title":""},{"docid":"7dcfbcc391862e179da70f514c9dcbdc","score":"0.5963725","text":"def test__init__(self):\n pass","title":""},{"docid":"173d16d18e39af15b541eb1ddb227fb8","score":"0.5959179","text":"def test_create_build_type_template(self):\n pass","title":""},{"docid":"cf08cc42b0a0d405427e99f1d114c3b9","score":"0.595564","text":"def setup_class(cls):","title":""},{"docid":"cf08cc42b0a0d405427e99f1d114c3b9","score":"0.595564","text":"def setup_class(cls):","title":""},{"docid":"cf08cc42b0a0d405427e99f1d114c3b9","score":"0.595564","text":"def setup_class(cls):","title":""},{"docid":"64bd06278ad63546f01744fc65c2f6fa","score":"0.595478","text":"def compile_class(self):","title":""},{"docid":"d190d5ec12b2a30a6a63bc3ff6f1a7ae","score":"0.5954159","text":"def construct(self):\n pass # To be implemented in subclasses","title":""}],"string":"[\n {\n \"docid\": \"79785cbd6d603ba51a4083520d3df1f4\",\n \"score\": \"0.7564869\",\n \"text\": \"def test_01_BuildObjects(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b0ee69e1abd41003d408ff9161a2153\",\n \"score\": \"0.7322961\",\n \"text\": \"def test_build(self): \\n\\n Builder(self.app)\\n\\n assert 'labels' in dir(self.app)\\n assert 'containers' in dir(self.app)\\n assert 'entrys' in dir(self.app)\\n assert 'button' in dir(self.app)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"472c03cf677f5e41336f6ca3a0f5ef5e\",\n \"score\": \"0.71330905\",\n \"text\": \"def test_create_build_type(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"171440fb99c27f79f66746fa5e94a0a5\",\n \"score\": \"0.7018632\",\n \"text\": \"def test_builder():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1206e6098ea5779aa83f574c5fd68c6a\",\n \"score\": \"0.69523484\",\n \"text\": \"def build(self):\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"496821a7b2f39891834fd89d6a116e6b\",\n \"score\": \"0.69168097\",\n \"text\": \"def _build(cls):\\n from draco2.model.check import CheckVisitor\\n from draco2.model.build import BuildVisitor\\n checker = CheckVisitor()\\n checker.visit(cls)\\n builder = BuildVisitor()\\n builder.visit(cls)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ec91b74f79c99c1f15cf8796c7e7439\",\n \"score\": \"0.6856415\",\n \"text\": \"def __build(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0ac9565236a2e1b9e78346c2d88f34d\",\n \"score\": \"0.6850931\",\n \"text\": \"def test_constructor_fill_fields(self):\\r\\n builder = AbstractBuilder(\\\"url\\\")\\r\\n\\r\\n self.assertEqual(builder.url, \\\"url\\\")\\r\\n self.assertEqual(builder.data, {})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d6e74b6ce1bef395f1e232911019c953\",\n \"score\": \"0.68274146\",\n \"text\": \"def build(self):\\n\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cfe7d12c13df9ea6847862016d1692a4\",\n \"score\": \"0.67328924\",\n \"text\": \"def test_init_creates_builder_object(self):\\n\\n self.assertEqual(self.director._builder, None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b8a906fcd75592fc8da26dd57b4246b\",\n \"score\": \"0.6689936\",\n \"text\": \"def test_constructor(self):\\n self.exporter_class()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9fb5ddcc23fbe7e0b80120772bb6c804\",\n \"score\": \"0.66652966\",\n \"text\": \"def _build(self):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bdd25cd8c38bbf4bf35041dad52203eb\",\n \"score\": \"0.6648575\",\n \"text\": \"def build(self):\\n raise NotImplementedError(\\\"Must be implemented by subclass.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00715bf309783b709593feac3fffeb5c\",\n \"score\": \"0.66266096\",\n \"text\": \"def build(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00715bf309783b709593feac3fffeb5c\",\n \"score\": \"0.66266096\",\n \"text\": \"def build(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00715bf309783b709593feac3fffeb5c\",\n \"score\": \"0.66266096\",\n \"text\": \"def build(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00715bf309783b709593feac3fffeb5c\",\n \"score\": \"0.66266096\",\n \"text\": \"def build(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00715bf309783b709593feac3fffeb5c\",\n \"score\": \"0.66266096\",\n \"text\": \"def build(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce715e484866b52cd69afd03acf27b54\",\n \"score\": \"0.6613739\",\n \"text\": \"def build(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"960babe19fdbec85b450fa6acfbb0c46\",\n \"score\": \"0.65682083\",\n \"text\": \"def test_constructor(self):\\n self.build_preprocessor()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc9a4a9936b3508c4161ba98e3b52d5d\",\n \"score\": \"0.65465474\",\n \"text\": \"def test_builderBender_2():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"910b4f85243dee8227382619284a094c\",\n \"score\": \"0.6523219\",\n \"text\": \"def test_builds_hierarchy(self):\\n class A(object):\\n seed = 101\\n\\n class B(object):\\n pass\\n\\n builders = {A: mock.Mock()}\\n extra_builders = {B: mock.Mock()}\\n\\n a = A()\\n b = B()\\n\\n network_a = mock.Mock()\\n network_a.seed = None\\n network_a.connections = []\\n network_a.ensembles = [a]\\n network_a.nodes = []\\n network_a.networks = []\\n network_a.probes = []\\n network_a.config = mock.Mock(name=\\\"config\\\")\\n\\n network_b = mock.Mock()\\n network_b.seed = None\\n network_b.connections = []\\n network_b.ensembles = []\\n network_b.nodes = [b]\\n network_b.networks = []\\n network_b.probes = []\\n network_b.config = mock.Mock(name=\\\"config\\\")\\n\\n network = mock.Mock()\\n network.seed = None\\n network.connections = []\\n network.ensembles = []\\n network.nodes = []\\n network.networks = [network_a, network_b]\\n network.probes = []\\n network.config = mock.Mock(name=\\\"config\\\")\\n\\n # Patch the default builders\\n with patch.object(Model, \\\"builders\\\", new=builders):\\n # Create a model and build the mock network\\n model = Model()\\n model.build(network, extra_builders=extra_builders)\\n\\n # Assert that the config was stored in the model\\n assert model.config is network.config\\n\\n # Assert that seeds were supplied\\n assert model.seeds[a] == a.seed\\n assert model.seeds[b] is not None\\n\\n # Assert the builders got called\\n builders[A].assert_called_once_with(model, a)\\n extra_builders[B].assert_called_once_with(model, b)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e57475e01715c5eecb9121be152687f\",\n \"score\": \"0.6487616\",\n \"text\": \"def test_builderBender_1():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0539b1e0837358d04c16e0f3afe1fede\",\n \"score\": \"0.6480443\",\n \"text\": \"def test_construct_all(self):\\n self.constructors.construct_all()\\n self.check_all_constructions()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"981676979f4eef8174b74263f424d5db\",\n \"score\": \"0.64783055\",\n \"text\": \"def test_class_creation(self, us):\\n assert us.subject == \\\"Subject\\\"\\n assert us.epic == \\\"Epic\\\"\\n assert us.tags == [\\\"expedientes\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0a58ae9df803358baba692f85001d7c\",\n \"score\": \"0.6455311\",\n \"text\": \"def test_constructor(self):\\n zone_name = 'example.com'\\n builder = ZoneBuilder(zone_name)\\n self.assertTrue(hasattr(builder, 'zone'))\\n self.assertEqual(builder.zone, zone_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5c202ae8336013a9105ef787112c70a\",\n \"score\": \"0.64339846\",\n \"text\": \"def __init__(self, builder):\\n self.builder = builder\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e322e19025aa35a6340bae2365c763a8\",\n \"score\": \"0.63910186\",\n \"text\": \"def test_create_instance(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e322e19025aa35a6340bae2365c763a8\",\n \"score\": \"0.63910186\",\n \"text\": \"def test_create_instance(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd4af922f0d6fb7d9b83f68ce3260a85\",\n \"score\": \"0.6387344\",\n \"text\": \"def test_creation(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e291569a352687c4950fc70f60b8f3c9\",\n \"score\": \"0.63830835\",\n \"text\": \"def build(self, *args, **kwargs):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bbb422bd58c676c8f406162c045b69f8\",\n \"score\": \"0.6379992\",\n \"text\": \"def test_object_creation(self, dcc):\\n dcc\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e6d2a1d0e878bf61f725d43c158ddc0a\",\n \"score\": \"0.6374741\",\n \"text\": \"def test_constructor_element_default(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d18986f77987b58f8c56e7703ef5915b\",\n \"score\": \"0.63620514\",\n \"text\": \"def test_instantiation(self):\\n projectbuild = ProjectBuild.objects.create(\\n project=self.project, requested_by=self.user)\\n self.assertEqual(self.user, projectbuild.requested_by)\\n self.assertIsNotNone(projectbuild.requested_at)\\n self.assertIsNone(projectbuild.ended_at)\\n self.assertEqual(\\\"UNKNOWN\\\", projectbuild.status)\\n self.assertEqual(\\\"UNKNOWN\\\", projectbuild.phase)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42c606f46e9fce84fbe465235d38b252\",\n \"score\": \"0.63536096\",\n \"text\": \"def test__init__(self):\\n # tests based on basic object creation from self.create_self():\\n assert self.l2_service_builder.driver is self.driver, \\\\\\n \\\"Driver provisioning test\\\"\\n assert self.l2_service_builder.conf is self.driver.conf, \\\\\\n \\\"Dirver conf provisioning test\\\"\\n assert self.l2_service_builder.f5_global_routed_mode is \\\\\\n self.f5_global_routed_mode, \\\\\\n \\\"f5_global_routed_mode provisioning test\\\"\\n self.system_helper.assert_called_once_with()\\n self.network_helper.assert_called_once_with()\\n self.service_model_adopter.assert_called_once_with(self.driver.conf)\\n\\n # further tests needed:\\n # add tests for...\\n # - f5_external_physical_mappings assignment test\\n # - vlan_binding_driver assignment/importation test\\n # Suggest a refactor for implementing the above in unit tests...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c84bdcac04eb10a73112b0046a35461\",\n \"score\": \"0.63439816\",\n \"text\": \"def __init__(self, name, packages=None, tests=None):\\n if not packages:\\n packages = []\\n if not tests:\\n tests = []\\n\\n # Assert that the correct objects are passed to the constructor\\n # TODO: Fix validation after optimization is done...\\n validate_build_contents(packages, tests)\\n super(Build, self).__init__(name, packages, tests)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ced0a322722630d75b8330157c2f2f8\",\n \"score\": \"0.63296765\",\n \"text\": \"def build(self) -> None: # type: ignore[override]\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a7adf77b116c0ca5fb70df358241704\",\n \"score\": \"0.6324664\",\n \"text\": \"def _build(self, *args, **kwargs):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a7adf77b116c0ca5fb70df358241704\",\n \"score\": \"0.6324664\",\n \"text\": \"def _build(self, *args, **kwargs):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"326be520d503261de5270ed2d2f36650\",\n \"score\": \"0.6323604\",\n \"text\": \"def build(self, *args, **kwargs) -> None:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"199f3464672f4568fafbe5884b2c1ba9\",\n \"score\": \"0.6310089\",\n \"text\": \"def build(self):\\n raise NotImplementedError(\\\"This method must be overwritten\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd3a9e5b3e51173a2a3e3e19be8051a1\",\n \"score\": \"0.6307875\",\n \"text\": \"def test_e6_example_create_building(self):\\n from teaser.examples import e6_generate_building as e6\\n\\n prj = e6.example_create_building()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"232931a9c5de82e24aafba0e69cbe5d8\",\n \"score\": \"0.6306217\",\n \"text\": \"def build(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"892b93af5f554ca51c4d84d50ea85b70\",\n \"score\": \"0.6286135\",\n \"text\": \"def construct(self):\\n\\n if tools.toolbox.get_setting('control', 'do_construct')=='0':\\n print 'skip constructing.'\\n return\\n\\n def build(building):\\n \\\"\\\"\\\"\\n constructs building\\n \\\"\\\"\\\"\\n self.open('main')\\n\\n try:\\n self.browser.follow_link(self.browser.find_link(url_regex=\\n re.compile(\\\"upgrade_building.+id=%s\\\"%building)))\\n print_cstring(\\\"Building: [%s]\\\"%building, 'turq')\\n self.statistics['buildings_constructed']+=1\\n except LinkNotFoundError:\\n print_cstring('fuck that shit, not enough ressources to build [%s]'%building, 'red')\\n\\n self.open('main')\\n html=self.browser.response().read()\\n farm_building='\\\\t\\\\t\\\\tBauernhof<br />\\\\n\\\\t\\\\t\\\\tStufe' in html\\n if farm_building:\\n self.pop_critical=0\\n\\n storage_building='\\\\t\\\\t\\\\tSpeicher<br />\\\\n\\\\t\\\\t\\\\tStufe' in html\\n\\n if self.buildings['under_construction']:\\n return\\n\\n if self.pop_critical:\\n if 'farm' in self.buildable:\\n build('farm')\\n if self.storage_critical and 'storage' in self.buildable and not storage_building:\\n build('storage')\\n\\n\\n elif self.next_building in self.buildable and not self.pop_critical:\\n build(self.next_building)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f49279d1bb317c91979afb820dd7ea2\",\n \"score\": \"0.62846243\",\n \"text\": \"def test_basic_build(build_turbine):\\n m = build_turbine\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ff29206f93e9f7110d7dd781f5b764d\",\n \"score\": \"0.6265389\",\n \"text\": \"def build(self):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ff29206f93e9f7110d7dd781f5b764d\",\n \"score\": \"0.6265389\",\n \"text\": \"def build(self):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1b4367da5be9f618c144e2cc54afbd9\",\n \"score\": \"0.62642974\",\n \"text\": \"def test_build_channel_class():\\n channel_class = build_channel_class(channel_number=2, mcaroi_numbers=(1, 2, 3))\\n\\n assert hasattr(channel_class, \\\"channel_number\\\")\\n assert channel_class.channel_number == 2\\n\\n assert hasattr(channel_class, \\\"sca\\\")\\n assert hasattr(channel_class, \\\"mca\\\")\\n assert hasattr(channel_class, \\\"mca_sum\\\")\\n\\n # there should be 3 MCAROI attributes: mcaroi01, mcaroi02, mcaroi3\\n expected_mcaroi_attr_names = {\\n f\\\"mcaroi{mcaroi_i:02d}\\\" for mcaroi_i in range(1, 3 + 1)\\n }\\n\\n # there should be no other MCAROI attributes\\n all_mcaroi_attr_names = {\\n attr_name\\n for attr_name in dir(channel_class)\\n if re.match(r\\\"mcaroi\\\\d{2}\\\", attr_name)\\n }\\n\\n assert expected_mcaroi_attr_names == all_mcaroi_attr_names\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03009a8e9b7e52e272271c86bdc1036c\",\n \"score\": \"0.6263392\",\n \"text\": \"def __init__(self, build=None):\\n\\t\\tself.build = build\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24f5a49a23ee6dbf1fdd33694f266544\",\n \"score\": \"0.62596506\",\n \"text\": \"def test_01_BuildObjects(self):\\n self.assertNotEqual(self.m_pyhouse_obj.House.Hvac, None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b0ed901d8258b242825903cab009e1f\",\n \"score\": \"0.62589186\",\n \"text\": \"def build(self):\\n raise NotImplementedError(\\\"Subclasses should implement this!\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0cc6efff5fc99c83ea7928f3ed3746e2\",\n \"score\": \"0.625586\",\n \"text\": \"def construct(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0cc6efff5fc99c83ea7928f3ed3746e2\",\n \"score\": \"0.625586\",\n \"text\": \"def construct(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d17bc4ec97602f1479241c306abe35df\",\n \"score\": \"0.62191135\",\n \"text\": \"def init_test_object(self, method):\\n if self.func in self.use_seed:\\n paddle.seed(self.seed)\\n if method == \\\"BuildClass\\\" or method == \\\"BuildClassWithInputSpec\\\":\\n # 仅实例化一次，防止多次实例化后，因为随机种子不固定导致多个结果值res不相等\\n obj = BuildClass(self.in_params, self.func)\\n obj.eval()\\n elif method == \\\"BuildFunc\\\" or method == \\\"BuildFuncWithInputSpec\\\":\\n obj = BuildFunc(self.in_params, self.func)\\n obj.eval()\\n elif method == \\\"naive_func\\\":\\n obj = naive_func\\n\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4019fed8632cdbd1b4bbd90ee5deed0e\",\n \"score\": \"0.621247\",\n \"text\": \"def test__build(self):\\n x = numpy.array([[1, 2],\\n [3, 4],\\n [5, 6]])\\n # start with default initialization\\n subject = gandy.models.bnns.BNN((2,), (4,), train_size=len(x))\\n self.assertTrue(isinstance(subject.model, tf.keras.Model))\\n self.assertTrue(subject.model._compile_was_called)\\n self.assertTrue(subject.model.built)\\n self.assertEqual(tuple(subject.model.input.shape.as_list())[1:],\\n subject.xshape)\\n predict = subject.model.predict(x)\\n self.assertTrue(predict.shape == (3, 4))\\n out = subject.model(x)\\n self.assertTrue(isinstance(out, tfp.distributions.Distribution))\\n\\n # test keyword assignment\\n subject = gandy.models.bnns.BNN((2,), (4,),\\n train_size=len(x),\\n optimizer='RMSprop')\\n self.assertTrue(isinstance(subject.model.optimizer,\\n tf.keras.optimizers.RMSprop))\\n subject = gandy.models.bnns.BNN((2,), (4,),\\n train_size=len(x),\\n optimizer=tf.keras.optimizers.RMSprop)\\n self.assertTrue(isinstance(subject.model.optimizer,\\n tf.keras.optimizers.RMSprop))\\n opt = tf.keras.optimizers.RMSprop()\\n subject = gandy.models.bnns.BNN(\\n (2,), (4,),\\n train_size=len(x),\\n optimizer=opt\\n )\\n self.assertTrue(subject.model.optimizer is opt)\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75e5fa6e2b104b3873ddebfaecd1c525\",\n \"score\": \"0.6196077\",\n \"text\": \"def build(self) -> BaseHamilton:\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"954f0d5b9a294c6621047ec5fbe8cb09\",\n \"score\": \"0.6177918\",\n \"text\": \"def build(self):\\n raise NotImplementedError('build not implemented')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0243886ee1575ea81a670f223c79fb5\",\n \"score\": \"0.61775076\",\n \"text\": \"def build(cls, **kwargs):\\n return cls(**kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"320dd0725c028e81ddd15a2fa2d416bb\",\n \"score\": \"0.6166137\",\n \"text\": \"def setup_class(klass):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"287e22c11c3146d1d4879fb7d20d2367\",\n \"score\": \"0.61563617\",\n \"text\": \"def test_creation(self):\\n classifier = LookUpClassifier()\\n self.assertTrue(classifier)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3304793c6d84b318dab1a82509cae13e\",\n \"score\": \"0.6152292\",\n \"text\": \"def setup_class(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3304793c6d84b318dab1a82509cae13e\",\n \"score\": \"0.6152292\",\n \"text\": \"def setup_class(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3304793c6d84b318dab1a82509cae13e\",\n \"score\": \"0.6152292\",\n \"text\": \"def setup_class(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c24da61c1c630167ea96036f654b6907\",\n \"score\": \"0.6150095\",\n \"text\": \"def setup_class(cls):\\n # ns.assert_true(False, \\\"setup_class run\\\")\\n print('setup_class\\\\n')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c24da61c1c630167ea96036f654b6907\",\n \"score\": \"0.6150095\",\n \"text\": \"def setup_class(cls):\\n # ns.assert_true(False, \\\"setup_class run\\\")\\n print('setup_class\\\\n')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1f69d8e7f30dd22e97c8541192f8495\",\n \"score\": \"0.6137967\",\n \"text\": \"def test_build(self):\\n self.request.add_attribute(SearchAttribute, 'Test', 123)\\n built = self.request.build()\\n expected = {\\n \\\"TableName\\\": \\\"testtable\\\",\\n \\\"Test\\\": 123,\\n }\\n assert built == expected\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf46fc898a1002dffb975da236e47ef8\",\n \"score\": \"0.6135326\",\n \"text\": \"def test_registerClass():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff5f80ce9487551ad2145db63945af70\",\n \"score\": \"0.61318994\",\n \"text\": \"def test_constructor(self):\\n Exporter()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e47ac8a2242fcea108249ebb4eed07c\",\n \"score\": \"0.6104577\",\n \"text\": \"def setUpClass(cls):\\n annofile = os.path.join(util.UTFILES_DIR, \\\"make-3.82-emake-5.3.0.xml\\\")\\n cls.build = annolib.AnnotatedBuild(annofile)\\n\\n # Collect all the jobs (so that we have the metrics)\\n cls.jobs = cls.build.getAllJobs()\\n\\n cls.build.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4be12dc2ece63416767cc9fd0a4b0420\",\n \"score\": \"0.61017764\",\n \"text\": \"def example_type_building():\\n\\n from teaser.project import Project\\n\\n \\\"\\\"\\\"We instantiate the Project class. The parameter load_data = True indicates\\n that we load the XML data bases into our Project.\\n This can take a few sec.\\\"\\\"\\\"\\n\\n prj = Project(load_data=True,used_data_country=\\\"Belgium\\\") #we maken een project aan,\\n # true because data bindings for\\n #type elements and use conditions should be loaded\\n #dus instance of dataclass() wordt aangemaakt\\n #de 3 input XML's worden geladen en de bijhorende scripts aangeroepen\\n #om de nodige waarden te kunnen uitlezen\\n prj.name = \\\"ArchetypeBuildings_Ref_ANNEX_bel\\\" #de naam van het project is ...\\n\\n \\\"\\\"\\\"The five functions starting with type_bldg giving us the opportunity to\\n create the specific type building (e.g. type_bldg_residential). The function\\n automatically calculates all the necessary parameter. If not specified different\\n it uses vdi calculation method.\\n Dus type_bldg_residential maakt een instance aan van building() en zal vervolgens alles berekenen\\n neen!! geen instantie van building() wel van singlefamilydwelling() dit is een specifieker gebouwtype\\n \\\"\\\"\\\"\\n\\n prj.type_bldg_residential(name=\\\"ResidentialBuilding\\\",\\n year_of_construction=1970,\\n number_of_floors=2,\\n height_of_floors=3.5,\\n net_leased_area=100,\\n construction_type=\\\"heavy\\\")\\n\\n '''Or we use Annex60 method (e.g with four elements). Which exports one\\n Model per zone'''\\n\\n prj.used_library_calc = 'Annex60'\\n prj.number_of_elements_calc = 4\\n prj.merge_windows_calc = False\\n prj.calc_all_buildings(raise_errors=True)\\n prj.export_annex()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e85909cd18b3ac27f53865a64953be41\",\n \"score\": \"0.6098726\",\n \"text\": \"def build_test(self):\\n\\n super(AggregateBase, self).build_test()\\n self.set_common_expected_parms()\\n self.set_common_optional_parms()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b98de1fefbecf8e02cf5a6d245da365\",\n \"score\": \"0.6096737\",\n \"text\": \"def build(self, *args, **kw):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c7bbb4b8571925c0bace6890a5b0386\",\n \"score\": \"0.6086688\",\n \"text\": \"def test_creation(self):\\n classifier = DecisionTreeClassifier()\\n self.assertTrue(classifier)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5cffd8588be512521a72cc019450fd47\",\n \"score\": \"0.60760254\",\n \"text\": \"def test_node_object_generation(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c093f4399cd54397c2b17257cc011b2\",\n \"score\": \"0.60751545\",\n \"text\": \"def test_class_creation(self, report):\\n assert report.project == \\\"SIEEL\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"782bb3da51cc0ba09cd6f76a807748da\",\n \"score\": \"0.6072306\",\n \"text\": \"def __call_creator_constructor_test(self, sport=None, prize_structure=None, start=None,\\n duration=None,\\n draft_group=None):\\n if sport is None:\\n sport = self.sport\\n if prize_structure is None:\\n class PrizeStructureChild(PrizeStructure):\\n def __init__(self):\\n pass\\n\\n prize_structure = PrizeStructureChild\\n if start is None:\\n start = timezone.now()\\n if duration is None:\\n duration = int(300)\\n\\n # try to construct a ContestPool\\n try:\\n # print('sport:', str(sport))\\n self.assertRaises(IncorrectVariableTypeException,\\n lambda: ContestPoolCreator(sport, prize_structure, start, duration,\\n draft_group))\\n except:\\n # there might be other exceptions from\\n # inner objects, but those arent tested here!\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a594555aedaf1c3e6691cfa6639be2cd\",\n \"score\": \"0.6063685\",\n \"text\": \"def test_creation(self):\\n classifier = MajorityClassifier()\\n self.assertTrue(classifier)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"01c0508071cc479a368388b7522f7e3d\",\n \"score\": \"0.6058457\",\n \"text\": \"def test_valid_construction(constructor_args):\\n boxmc = hoomd.hpmc.update.BoxMC(**constructor_args)\\n\\n # validate the params were set properly\\n obj_attr_check(boxmc, constructor_args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d32f8ab65ec268511bd8d5293ce941db\",\n \"score\": \"0.60434175\",\n \"text\": \"def test_constructor(self):\\n assert isinstance(self.j, Jungle)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67342dca7cd52466a19fbe618139e89e\",\n \"score\": \"0.60257757\",\n \"text\": \"def __init__(self, name, params):\\n Tester.__init__(self, name, params)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9860cd8a7a7fa554d317b27f4bdd0e2\",\n \"score\": \"0.60252756\",\n \"text\": \"def test_class_created():\\n check_class_exists(task_22_1, \\\"Topology\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"116551fe248c6d21c8c43401a439c5f7\",\n \"score\": \"0.6021173\",\n \"text\": \"def test_facade_builder(self):\\n\\n\\t\\tgenerated_builder = self.manual_facade.get_object_builder()\\n\\t\\tgenerated_builder.load_from_config(\\\"small_red_cube\\\")\\n\\t\\tsmall_red_cube = generated_builder.create(\\\"small_red_cube\\\", \\\"small_offset\\\")\\n\\t\\tself.assertEqual(small_red_cube.get_descriptor(), \\\"cube\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e717c99c03b2e63b07daac6e4a99c1d2\",\n \"score\": \"0.6010992\",\n \"text\": \"def test_setup(self):\\n self.create_iinfo_class()\\n self.create_info_class()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4f8eb698b46a2eba74506350d064519\",\n \"score\": \"0.6007934\",\n \"text\": \"def setUpClass(cls):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4f8eb698b46a2eba74506350d064519\",\n \"score\": \"0.6007934\",\n \"text\": \"def setUpClass(cls):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a54e2d90d78a39cfaed900ade004d47\",\n \"score\": \"0.60028803\",\n \"text\": \"def test_new(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a54e2d90d78a39cfaed900ade004d47\",\n \"score\": \"0.60028803\",\n \"text\": \"def test_new(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10fb8bfe004bc3b095ad573daca00143\",\n \"score\": \"0.60009766\",\n \"text\": \"def test_creation(self):\\n classifier = AdaBoost()\\n self.assertTrue(classifier)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ba8de071134389fefb87625cdb93dfc\",\n \"score\": \"0.59946126\",\n \"text\": \"def build(self,\\n environment: 'Environment',\\n bug: bugzoo.Bug\\n ) -> 'TestSuite':\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b85105730caa397bf087b8e775e6d4c\",\n \"score\": \"0.599112\",\n \"text\": \"def construct(self):\\n\\t\\traise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"430fd8ae59bc9180c21fa0d9371678d9\",\n \"score\": \"0.5982696\",\n \"text\": \"def test___init__(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f084f3f6df37de5f4e25574ad0a0fd6\",\n \"score\": \"0.598147\",\n \"text\": \"def test_create(self):\\r\\n\\r\\n self.assertTrue(isinstance(self.DUT, Model))\\r\\n\\r\\n # Verify Hardware class was properly initialized.\\r\\n self.assertEqual(self.DUT.revision_id, None)\\r\\n self.assertEqual(self.DUT.category_id, 0)\\r\\n\\r\\n # Verify Relay class was properly initialized.\\r\\n self.assertEqual(self.DUT.category, 6)\\r\\n self.assertEqual(self.DUT.quality, 0)\\r\\n self.assertEqual(self.DUT.construction, 0)\\r\\n self.assertEqual(self.DUT.q_override, 0.0)\\r\\n self.assertEqual(self.DUT.base_hr, 0.0)\\r\\n self.assertEqual(self.DUT.piQ, 0.0)\\r\\n self.assertEqual(self.DUT.piE, 0.0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c86562f72e8f338ee82475a2446a095\",\n \"score\": \"0.5974097\",\n \"text\": \"def test_inferior_asserting(self):\\n self.build()\\n self.inferior_asserting()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7dcfbcc391862e179da70f514c9dcbdc\",\n \"score\": \"0.5963725\",\n \"text\": \"def test__init__(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"173d16d18e39af15b541eb1ddb227fb8\",\n \"score\": \"0.5959179\",\n \"text\": \"def test_create_build_type_template(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf08cc42b0a0d405427e99f1d114c3b9\",\n \"score\": \"0.595564\",\n \"text\": \"def setup_class(cls):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf08cc42b0a0d405427e99f1d114c3b9\",\n \"score\": \"0.595564\",\n \"text\": \"def setup_class(cls):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf08cc42b0a0d405427e99f1d114c3b9\",\n \"score\": \"0.595564\",\n \"text\": \"def setup_class(cls):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64bd06278ad63546f01744fc65c2f6fa\",\n \"score\": \"0.595478\",\n \"text\": \"def compile_class(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d190d5ec12b2a30a6a63bc3ff6f1a7ae\",\n \"score\": \"0.5954159\",\n \"text\": \"def construct(self):\\n pass # To be implemented in subclasses\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":699,"cells":{"query_id":{"kind":"string","value":"b9dd9e8181293cb24fa0c3e0b144cff8"},"query":{"kind":"string","value":"Entry point of program"},"positive_passages":{"kind":"list like","value":[{"docid":"7b29257fa5d2d624779dbb9d8e13a809","score":"0.0","text":"def main():\n client.run(secrets.TOKEN)","title":""}],"string":"[\n {\n \"docid\": \"7b29257fa5d2d624779dbb9d8e13a809\",\n \"score\": \"0.0\",\n \"text\": \"def main():\\n client.run(secrets.TOKEN)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"1d4484b0529dbe0541b241834cb12a3e","score":"0.8570562","text":"def main():\n\t\tpass","title":""},{"docid":"ec021328057f10f8af523fff413bbe9a","score":"0.8506079","text":"def main ():","title":""},{"docid":"ec021328057f10f8af523fff413bbe9a","score":"0.8506079","text":"def main ():","title":""},{"docid":"82c54a749d7508e492693a3059faed30","score":"0.84507304","text":"def main():\n return","title":""},{"docid":"82c54a749d7508e492693a3059faed30","score":"0.84507304","text":"def main():\n return","title":""},{"docid":"07e76a2dea489b69c96fdd82461f8592","score":"0.8364074","text":"def main(self):\n pass","title":""},{"docid":"07e76a2dea489b69c96fdd82461f8592","score":"0.8364074","text":"def main(self):\n pass","title":""},{"docid":"6a72f0523c7f4b19a3ff495c1baaa5c5","score":"0.8349577","text":"def main() -> None:","title":""},{"docid":"6a72f0523c7f4b19a3ff495c1baaa5c5","score":"0.8349577","text":"def main() -> None:","title":""},{"docid":"08aa0c5cc07c5efe590ba89320b45dbc","score":"0.8306582","text":"def main():","title":""},{"docid":"08aa0c5cc07c5efe590ba89320b45dbc","score":"0.8306582","text":"def main():","title":""},{"docid":"08aa0c5cc07c5efe590ba89320b45dbc","score":"0.8306582","text":"def main():","title":""},{"docid":"08aa0c5cc07c5efe590ba89320b45dbc","score":"0.8306582","text":"def main():","title":""},{"docid":"08aa0c5cc07c5efe590ba89320b45dbc","score":"0.8306582","text":"def main():","title":""},{"docid":"08aa0c5cc07c5efe590ba89320b45dbc","score":"0.8306582","text":"def main():","title":""},{"docid":"08aa0c5cc07c5efe590ba89320b45dbc","score":"0.8306582","text":"def main():","title":""},{"docid":"08aa0c5cc07c5efe590ba89320b45dbc","score":"0.8306582","text":"def main():","title":""},{"docid":"08aa0c5cc07c5efe590ba89320b45dbc","score":"0.8306582","text":"def main():","title":""},{"docid":"08aa0c5cc07c5efe590ba89320b45dbc","score":"0.8306582","text":"def main():","title":""},{"docid":"08aa0c5cc07c5efe590ba89320b45dbc","score":"0.8306582","text":"def main():","title":""},{"docid":"08aa0c5cc07c5efe590ba89320b45dbc","score":"0.8306582","text":"def main():","title":""},{"docid":"08aa0c5cc07c5efe590ba89320b45dbc","score":"0.8306582","text":"def main():","title":""},{"docid":"08aa0c5cc07c5efe590ba89320b45dbc","score":"0.8306582","text":"def main():","title":""},{"docid":"08aa0c5cc07c5efe590ba89320b45dbc","score":"0.8306582","text":"def main():","title":""},{"docid":"08aa0c5cc07c5efe590ba89320b45dbc","score":"0.8306582","text":"def main():","title":""},{"docid":"08aa0c5cc07c5efe590ba89320b45dbc","score":"0.8306582","text":"def main():","title":""},{"docid":"08aa0c5cc07c5efe590ba89320b45dbc","score":"0.8306582","text":"def main():","title":""},{"docid":"08aa0c5cc07c5efe590ba89320b45dbc","score":"0.8306582","text":"def main():","title":""},{"docid":"08aa0c5cc07c5efe590ba89320b45dbc","score":"0.8306582","text":"def main():","title":""},{"docid":"08aa0c5cc07c5efe590ba89320b45dbc","score":"0.8306582","text":"def main():","title":""},{"docid":"08aa0c5cc07c5efe590ba89320b45dbc","score":"0.8306582","text":"def main():","title":""},{"docid":"08aa0c5cc07c5efe590ba89320b45dbc","score":"0.8306582","text":"def main():","title":""},{"docid":"08aa0c5cc07c5efe590ba89320b45dbc","score":"0.8306582","text":"def main():","title":""},{"docid":"4301ce0ec56b79f3f0e020a93f26bdee","score":"0.82826084","text":"def main():\n \n pass","title":""},{"docid":"d01bb886890ed36b0f4c3f9b6f39bbe6","score":"0.82752603","text":"def main():\r\n pass","title":""},{"docid":"d01bb886890ed36b0f4c3f9b6f39bbe6","score":"0.82752603","text":"def main():\r\n pass","title":""},{"docid":"c8e238f9ea21384e3be28d52a1ee7939","score":"0.82637316","text":"def main():\n\tpass","title":""},{"docid":"cfc083a503ff24be9d8e1d2ad2fd1393","score":"0.82436484","text":"def main(self):\n return","title":""},{"docid":"59a11114512ac46a2a2d976a77d1c6d6","score":"0.8186553","text":"def main(self, *args):\n pass","title":""},{"docid":"59a11114512ac46a2a2d976a77d1c6d6","score":"0.8186553","text":"def main(self, *args):\n pass","title":""},{"docid":"29ddf12471e98d1f15306c861cce2085","score":"0.8155023","text":"def main():\n print(\"Call your main application code here\")","title":""},{"docid":"29ddf12471e98d1f15306c861cce2085","score":"0.8155023","text":"def main():\n print(\"Call your main application code here\")","title":""},{"docid":"29ddf12471e98d1f15306c861cce2085","score":"0.8155023","text":"def main():\n print(\"Call your main application code here\")","title":""},{"docid":"566036b3a07ddd1af043005be3b82644","score":"0.80748236","text":"def main(args=None):\r\n pass","title":""},{"docid":"1e8b2ebbe42aca7bf8565023e7af8326","score":"0.8069544","text":"def main(args=None):","title":""},{"docid":"737edbcdda02609a89cb40f455a292fb","score":"0.8060648","text":"def main(args):\n pass","title":""},{"docid":"321784f4004341db82df14bd42654ecc","score":"0.80290717","text":"def main():\n _main(sys.argv[1:])","title":""},{"docid":"e302ae8a2be1a50834705897601e1c4f","score":"0.79930955","text":"def _main():\n\tpass","title":""},{"docid":"bc0d536b098278d704e13b8e318d6af5","score":"0.79857004","text":"def main():\n pass","title":""},{"docid":"bc0d536b098278d704e13b8e318d6af5","score":"0.79857004","text":"def main():\n pass","title":""},{"docid":"bc0d536b098278d704e13b8e318d6af5","score":"0.79857004","text":"def main():\n pass","title":""},{"docid":"bc0d536b098278d704e13b8e318d6af5","score":"0.79857004","text":"def main():\n pass","title":""},{"docid":"bc0d536b098278d704e13b8e318d6af5","score":"0.79857004","text":"def main():\n pass","title":""},{"docid":"bc0d536b098278d704e13b8e318d6af5","score":"0.79857004","text":"def main():\n pass","title":""},{"docid":"bc0d536b098278d704e13b8e318d6af5","score":"0.79857004","text":"def main():\n pass","title":""},{"docid":"bc0d536b098278d704e13b8e318d6af5","score":"0.79857004","text":"def main():\n pass","title":""},{"docid":"bc0d536b098278d704e13b8e318d6af5","score":"0.79857004","text":"def main():\n pass","title":""},{"docid":"bc0d536b098278d704e13b8e318d6af5","score":"0.79857004","text":"def main():\n pass","title":""},{"docid":"bc0d536b098278d704e13b8e318d6af5","score":"0.79857004","text":"def main():\n pass","title":""},{"docid":"bc0d536b098278d704e13b8e318d6af5","score":"0.79857004","text":"def main():\n pass","title":""},{"docid":"bc0d536b098278d704e13b8e318d6af5","score":"0.79857004","text":"def main():\n pass","title":""},{"docid":"bc0d536b098278d704e13b8e318d6af5","score":"0.79857004","text":"def main():\n pass","title":""},{"docid":"bc0d536b098278d704e13b8e318d6af5","score":"0.79857004","text":"def main():\n pass","title":""},{"docid":"bc0d536b098278d704e13b8e318d6af5","score":"0.79857004","text":"def main():\n pass","title":""},{"docid":"bc0d536b098278d704e13b8e318d6af5","score":"0.79857004","text":"def main():\n pass","title":""},{"docid":"bc0d536b098278d704e13b8e318d6af5","score":"0.79857004","text":"def main():\n pass","title":""},{"docid":"bc0d536b098278d704e13b8e318d6af5","score":"0.79857004","text":"def main():\n pass","title":""},{"docid":"bc0d536b098278d704e13b8e318d6af5","score":"0.79857004","text":"def main():\n pass","title":""},{"docid":"bc0d536b098278d704e13b8e318d6af5","score":"0.79857004","text":"def main():\n pass","title":""},{"docid":"bc0d536b098278d704e13b8e318d6af5","score":"0.79857004","text":"def main():\n pass","title":""},{"docid":"bc0d536b098278d704e13b8e318d6af5","score":"0.79857004","text":"def main():\n pass","title":""},{"docid":"bc0d536b098278d704e13b8e318d6af5","score":"0.79857004","text":"def main():\n pass","title":""},{"docid":"bc0d536b098278d704e13b8e318d6af5","score":"0.79857004","text":"def main():\n pass","title":""},{"docid":"bc0d536b098278d704e13b8e318d6af5","score":"0.79857004","text":"def main():\n pass","title":""},{"docid":"bc0d536b098278d704e13b8e318d6af5","score":"0.79857004","text":"def main():\n pass","title":""},{"docid":"bc0d536b098278d704e13b8e318d6af5","score":"0.79857004","text":"def main():\n pass","title":""},{"docid":"bc0d536b098278d704e13b8e318d6af5","score":"0.79857004","text":"def main():\n pass","title":""},{"docid":"8105ce55096699eab7dce0d326a51948","score":"0.79610205","text":"def main():\n print(\"start ...\")","title":""},{"docid":"8a52887b08fa7fd181a79c9d75450e83","score":"0.79189616","text":"def main():\n if __name__ == '__main__':\n pass","title":""},{"docid":"5e4cec8ea848b3d798bca3b0fced58b1","score":"0.78941566","text":"def main(args):\n Run()","title":""},{"docid":"a4873d5cb2b9c8e72f57155f6e9b8d43","score":"0.78750134","text":"def main(args):\n\treturn 0","title":""},{"docid":"05b99850a4683aae56ee599491955a1b","score":"0.7862574","text":"def _main() -> None:\n pass","title":""},{"docid":"64a1a4261cea121e609f61fd60d0b36f","score":"0.7859942","text":"def main():\n return None","title":""},{"docid":"b816c59d61979d6c3de629bfe657acc3","score":"0.78479403","text":"def main():\n CLI().run(argv[1:])","title":""},{"docid":"ff6b5f435e231cc978bcc5e9148a452d","score":"0.7813415","text":"def run():\n main(sys.argv[1:])","title":""},{"docid":"ff6b5f435e231cc978bcc5e9148a452d","score":"0.7813415","text":"def run():\n main(sys.argv[1:])","title":""},{"docid":"ff6b5f435e231cc978bcc5e9148a452d","score":"0.7813415","text":"def run():\n main(sys.argv[1:])","title":""},{"docid":"ff6b5f435e231cc978bcc5e9148a452d","score":"0.7813415","text":"def run():\n main(sys.argv[1:])","title":""},{"docid":"ff6b5f435e231cc978bcc5e9148a452d","score":"0.7813415","text":"def run():\n main(sys.argv[1:])","title":""},{"docid":"a8550ef16b860a6cc3bad917dfb04f42","score":"0.7802033","text":"def main():\n ...","title":""},{"docid":"8e20fe7656852b06a2bcefab39b3b901","score":"0.7796682","text":"def main():\n print(run())","title":""},{"docid":"97569561f99d1987cecc56802d6aef43","score":"0.7781605","text":"def main():\n flag = init()\n run( flag )","title":""},{"docid":"04688cd98d9fcf835947139440a6440f","score":"0.7780548","text":"def main():\n\n pass\n\n return None","title":""},{"docid":"f8826e58b73a5885ad66a1bd972804dc","score":"0.7761393","text":"def main():\n app = Application(argv)\n app.run()","title":""},{"docid":"93e2db98c51fbce39b84a61c48c918ca","score":"0.7745705","text":"def main(args):\n return 0","title":""},{"docid":"f64ecdad4bada7fe6ab976b65fd314c4","score":"0.7737328","text":"def main():\n cli()","title":""},{"docid":"535c6e7d32028af3cc3d681a0804815d","score":"0.7719438","text":"def main(args=None):\n return 0","title":""},{"docid":"b8d45552f96ceaa95dc134f6f5d74cd7","score":"0.76849943","text":"def main():\n\n print(\"Have a nice day!\")","title":""},{"docid":"a8577939272433d16335be4b6d98c8f1","score":"0.7656128","text":"def main(argv):\n\treturn run()","title":""},{"docid":"c3cab5de9a654ae1f5d2da3c963dbd93","score":"0.76134276","text":"def main():\n log.debug(\"Calling CLI instance...\")\n input()\n log.debug(\"Application exit.\")","title":""},{"docid":"a01e5ea8aee35b5cf5d09a93b93ad122","score":"0.758695","text":"def main():\n\n execute_cli()","title":""}],"string":"[\n {\n \"docid\": \"1d4484b0529dbe0541b241834cb12a3e\",\n \"score\": \"0.8570562\",\n \"text\": \"def main():\\n\\t\\tpass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec021328057f10f8af523fff413bbe9a\",\n \"score\": \"0.8506079\",\n \"text\": \"def main ():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec021328057f10f8af523fff413bbe9a\",\n \"score\": \"0.8506079\",\n \"text\": \"def main ():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82c54a749d7508e492693a3059faed30\",\n \"score\": \"0.84507304\",\n \"text\": \"def main():\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82c54a749d7508e492693a3059faed30\",\n \"score\": \"0.84507304\",\n \"text\": \"def main():\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07e76a2dea489b69c96fdd82461f8592\",\n \"score\": \"0.8364074\",\n \"text\": \"def main(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07e76a2dea489b69c96fdd82461f8592\",\n \"score\": \"0.8364074\",\n \"text\": \"def main(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a72f0523c7f4b19a3ff495c1baaa5c5\",\n \"score\": \"0.8349577\",\n \"text\": \"def main() -> None:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a72f0523c7f4b19a3ff495c1baaa5c5\",\n \"score\": \"0.8349577\",\n \"text\": \"def main() -> None:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08aa0c5cc07c5efe590ba89320b45dbc\",\n \"score\": \"0.8306582\",\n \"text\": \"def main():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08aa0c5cc07c5efe590ba89320b45dbc\",\n \"score\": \"0.8306582\",\n \"text\": \"def main():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08aa0c5cc07c5efe590ba89320b45dbc\",\n \"score\": \"0.8306582\",\n \"text\": \"def main():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08aa0c5cc07c5efe590ba89320b45dbc\",\n \"score\": \"0.8306582\",\n \"text\": \"def main():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08aa0c5cc07c5efe590ba89320b45dbc\",\n \"score\": \"0.8306582\",\n \"text\": \"def main():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08aa0c5cc07c5efe590ba89320b45dbc\",\n \"score\": \"0.8306582\",\n \"text\": \"def main():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08aa0c5cc07c5efe590ba89320b45dbc\",\n \"score\": \"0.8306582\",\n \"text\": \"def main():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08aa0c5cc07c5efe590ba89320b45dbc\",\n \"score\": \"0.8306582\",\n \"text\": \"def main():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08aa0c5cc07c5efe590ba89320b45dbc\",\n \"score\": \"0.8306582\",\n \"text\": \"def main():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08aa0c5cc07c5efe590ba89320b45dbc\",\n \"score\": \"0.8306582\",\n \"text\": \"def main():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08aa0c5cc07c5efe590ba89320b45dbc\",\n \"score\": \"0.8306582\",\n \"text\": \"def main():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08aa0c5cc07c5efe590ba89320b45dbc\",\n \"score\": \"0.8306582\",\n \"text\": \"def main():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08aa0c5cc07c5efe590ba89320b45dbc\",\n \"score\": \"0.8306582\",\n \"text\": \"def main():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08aa0c5cc07c5efe590ba89320b45dbc\",\n \"score\": \"0.8306582\",\n \"text\": \"def main():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08aa0c5cc07c5efe590ba89320b45dbc\",\n \"score\": \"0.8306582\",\n \"text\": \"def main():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08aa0c5cc07c5efe590ba89320b45dbc\",\n \"score\": \"0.8306582\",\n \"text\": \"def main():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08aa0c5cc07c5efe590ba89320b45dbc\",\n \"score\": \"0.8306582\",\n \"text\": \"def main():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08aa0c5cc07c5efe590ba89320b45dbc\",\n \"score\": \"0.8306582\",\n \"text\": \"def main():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08aa0c5cc07c5efe590ba89320b45dbc\",\n \"score\": \"0.8306582\",\n \"text\": \"def main():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08aa0c5cc07c5efe590ba89320b45dbc\",\n \"score\": \"0.8306582\",\n \"text\": \"def main():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08aa0c5cc07c5efe590ba89320b45dbc\",\n \"score\": \"0.8306582\",\n \"text\": \"def main():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08aa0c5cc07c5efe590ba89320b45dbc\",\n \"score\": \"0.8306582\",\n \"text\": \"def main():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08aa0c5cc07c5efe590ba89320b45dbc\",\n \"score\": \"0.8306582\",\n \"text\": \"def main():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08aa0c5cc07c5efe590ba89320b45dbc\",\n \"score\": \"0.8306582\",\n \"text\": \"def main():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4301ce0ec56b79f3f0e020a93f26bdee\",\n \"score\": \"0.82826084\",\n \"text\": \"def main():\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d01bb886890ed36b0f4c3f9b6f39bbe6\",\n \"score\": \"0.82752603\",\n \"text\": \"def main():\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d01bb886890ed36b0f4c3f9b6f39bbe6\",\n \"score\": \"0.82752603\",\n \"text\": \"def main():\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8e238f9ea21384e3be28d52a1ee7939\",\n \"score\": \"0.82637316\",\n \"text\": \"def main():\\n\\tpass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cfc083a503ff24be9d8e1d2ad2fd1393\",\n \"score\": \"0.82436484\",\n \"text\": \"def main(self):\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59a11114512ac46a2a2d976a77d1c6d6\",\n \"score\": \"0.8186553\",\n \"text\": \"def main(self, *args):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59a11114512ac46a2a2d976a77d1c6d6\",\n \"score\": \"0.8186553\",\n \"text\": \"def main(self, *args):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29ddf12471e98d1f15306c861cce2085\",\n \"score\": \"0.8155023\",\n \"text\": \"def main():\\n print(\\\"Call your main application code here\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29ddf12471e98d1f15306c861cce2085\",\n \"score\": \"0.8155023\",\n \"text\": \"def main():\\n print(\\\"Call your main application code here\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29ddf12471e98d1f15306c861cce2085\",\n \"score\": \"0.8155023\",\n \"text\": \"def main():\\n print(\\\"Call your main application code here\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"566036b3a07ddd1af043005be3b82644\",\n \"score\": \"0.80748236\",\n \"text\": \"def main(args=None):\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e8b2ebbe42aca7bf8565023e7af8326\",\n \"score\": \"0.8069544\",\n \"text\": \"def main(args=None):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"737edbcdda02609a89cb40f455a292fb\",\n \"score\": \"0.8060648\",\n \"text\": \"def main(args):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"321784f4004341db82df14bd42654ecc\",\n \"score\": \"0.80290717\",\n \"text\": \"def main():\\n _main(sys.argv[1:])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e302ae8a2be1a50834705897601e1c4f\",\n \"score\": \"0.79930955\",\n \"text\": \"def _main():\\n\\tpass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc0d536b098278d704e13b8e318d6af5\",\n \"score\": \"0.79857004\",\n \"text\": \"def main():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc0d536b098278d704e13b8e318d6af5\",\n \"score\": \"0.79857004\",\n \"text\": \"def main():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc0d536b098278d704e13b8e318d6af5\",\n \"score\": \"0.79857004\",\n \"text\": \"def main():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc0d536b098278d704e13b8e318d6af5\",\n \"score\": \"0.79857004\",\n \"text\": \"def main():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc0d536b098278d704e13b8e318d6af5\",\n \"score\": \"0.79857004\",\n \"text\": \"def main():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc0d536b098278d704e13b8e318d6af5\",\n \"score\": \"0.79857004\",\n \"text\": \"def main():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc0d536b098278d704e13b8e318d6af5\",\n \"score\": \"0.79857004\",\n \"text\": \"def main():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc0d536b098278d704e13b8e318d6af5\",\n \"score\": \"0.79857004\",\n \"text\": \"def main():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc0d536b098278d704e13b8e318d6af5\",\n \"score\": \"0.79857004\",\n \"text\": \"def main():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc0d536b098278d704e13b8e318d6af5\",\n \"score\": \"0.79857004\",\n \"text\": \"def main():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc0d536b098278d704e13b8e318d6af5\",\n \"score\": \"0.79857004\",\n \"text\": \"def main():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc0d536b098278d704e13b8e318d6af5\",\n \"score\": \"0.79857004\",\n \"text\": \"def main():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc0d536b098278d704e13b8e318d6af5\",\n \"score\": \"0.79857004\",\n \"text\": \"def main():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc0d536b098278d704e13b8e318d6af5\",\n \"score\": \"0.79857004\",\n \"text\": \"def main():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc0d536b098278d704e13b8e318d6af5\",\n \"score\": \"0.79857004\",\n \"text\": \"def main():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc0d536b098278d704e13b8e318d6af5\",\n \"score\": \"0.79857004\",\n \"text\": \"def main():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc0d536b098278d704e13b8e318d6af5\",\n \"score\": \"0.79857004\",\n \"text\": \"def main():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc0d536b098278d704e13b8e318d6af5\",\n \"score\": \"0.79857004\",\n \"text\": \"def main():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc0d536b098278d704e13b8e318d6af5\",\n \"score\": \"0.79857004\",\n \"text\": \"def main():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc0d536b098278d704e13b8e318d6af5\",\n \"score\": \"0.79857004\",\n \"text\": \"def main():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc0d536b098278d704e13b8e318d6af5\",\n \"score\": \"0.79857004\",\n \"text\": \"def main():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc0d536b098278d704e13b8e318d6af5\",\n \"score\": \"0.79857004\",\n \"text\": \"def main():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc0d536b098278d704e13b8e318d6af5\",\n \"score\": \"0.79857004\",\n \"text\": \"def main():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc0d536b098278d704e13b8e318d6af5\",\n \"score\": \"0.79857004\",\n \"text\": \"def main():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc0d536b098278d704e13b8e318d6af5\",\n \"score\": \"0.79857004\",\n \"text\": \"def main():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc0d536b098278d704e13b8e318d6af5\",\n \"score\": \"0.79857004\",\n \"text\": \"def main():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc0d536b098278d704e13b8e318d6af5\",\n \"score\": \"0.79857004\",\n \"text\": \"def main():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc0d536b098278d704e13b8e318d6af5\",\n \"score\": \"0.79857004\",\n \"text\": \"def main():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc0d536b098278d704e13b8e318d6af5\",\n \"score\": \"0.79857004\",\n \"text\": \"def main():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8105ce55096699eab7dce0d326a51948\",\n \"score\": \"0.79610205\",\n \"text\": \"def main():\\n print(\\\"start ...\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a52887b08fa7fd181a79c9d75450e83\",\n \"score\": \"0.79189616\",\n \"text\": \"def main():\\n if __name__ == '__main__':\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e4cec8ea848b3d798bca3b0fced58b1\",\n \"score\": \"0.78941566\",\n \"text\": \"def main(args):\\n Run()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4873d5cb2b9c8e72f57155f6e9b8d43\",\n \"score\": \"0.78750134\",\n \"text\": \"def main(args):\\n\\treturn 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"05b99850a4683aae56ee599491955a1b\",\n \"score\": \"0.7862574\",\n \"text\": \"def _main() -> None:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64a1a4261cea121e609f61fd60d0b36f\",\n \"score\": \"0.7859942\",\n \"text\": \"def main():\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b816c59d61979d6c3de629bfe657acc3\",\n \"score\": \"0.78479403\",\n \"text\": \"def main():\\n CLI().run(argv[1:])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff6b5f435e231cc978bcc5e9148a452d\",\n \"score\": \"0.7813415\",\n \"text\": \"def run():\\n main(sys.argv[1:])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff6b5f435e231cc978bcc5e9148a452d\",\n \"score\": \"0.7813415\",\n \"text\": \"def run():\\n main(sys.argv[1:])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff6b5f435e231cc978bcc5e9148a452d\",\n \"score\": \"0.7813415\",\n \"text\": \"def run():\\n main(sys.argv[1:])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff6b5f435e231cc978bcc5e9148a452d\",\n \"score\": \"0.7813415\",\n \"text\": \"def run():\\n main(sys.argv[1:])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff6b5f435e231cc978bcc5e9148a452d\",\n \"score\": \"0.7813415\",\n \"text\": \"def run():\\n main(sys.argv[1:])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8550ef16b860a6cc3bad917dfb04f42\",\n \"score\": \"0.7802033\",\n \"text\": \"def main():\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e20fe7656852b06a2bcefab39b3b901\",\n \"score\": \"0.7796682\",\n \"text\": \"def main():\\n print(run())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97569561f99d1987cecc56802d6aef43\",\n \"score\": \"0.7781605\",\n \"text\": \"def main():\\n flag = init()\\n run( flag )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04688cd98d9fcf835947139440a6440f\",\n \"score\": \"0.7780548\",\n \"text\": \"def main():\\n\\n pass\\n\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8826e58b73a5885ad66a1bd972804dc\",\n \"score\": \"0.7761393\",\n \"text\": \"def main():\\n app = Application(argv)\\n app.run()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93e2db98c51fbce39b84a61c48c918ca\",\n \"score\": \"0.7745705\",\n \"text\": \"def main(args):\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f64ecdad4bada7fe6ab976b65fd314c4\",\n \"score\": \"0.7737328\",\n \"text\": \"def main():\\n cli()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"535c6e7d32028af3cc3d681a0804815d\",\n \"score\": \"0.7719438\",\n \"text\": \"def main(args=None):\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8d45552f96ceaa95dc134f6f5d74cd7\",\n \"score\": \"0.76849943\",\n \"text\": \"def main():\\n\\n print(\\\"Have a nice day!\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8577939272433d16335be4b6d98c8f1\",\n \"score\": \"0.7656128\",\n \"text\": \"def main(argv):\\n\\treturn run()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3cab5de9a654ae1f5d2da3c963dbd93\",\n \"score\": \"0.76134276\",\n \"text\": \"def main():\\n log.debug(\\\"Calling CLI instance...\\\")\\n input()\\n log.debug(\\\"Application exit.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a01e5ea8aee35b5cf5d09a93b93ad122\",\n \"score\": \"0.758695\",\n \"text\": \"def main():\\n\\n execute_cli()\",\n \"title\": \"\"\n 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query_id stringlengths 32 32 | query stringlengths 9 4.01k | positive_passages listlengths 1 1 | negative_passages listlengths 88 101 |
|---|---|---|---|
0e9ed4e2ae7a13df9cb4e74d0ee1ec9e | Update's the table from database using query as input use " " at the beginning and end of the query | [
{
"docid": "96313f56d509bf954f7d64f086669e44",
"score": "0.7863273",
"text": "def update_table(self,query):\n query=query\n self._cursor.execute(query)\n self._connection.commit()",
"title": ""
}
] | [
{
"docid": "bb68f0d9403b049569ad037b78d4e87d",
"score": "0.7928796",
"text": "def update(self, sql):",
"title": ""
},
{
"docid": "f3fa507bf7a6aceb5b4cf35ddd2ba805",
"score": "0.7482706",
"text": "def make_update_query(self, query: str):\n\n self.create_connection() # sets con... |
e1dcd7079a55f1c787e707ed12f112c4 | 1. Generate morphed images using face morpher | [
{
"docid": "8a4a078ac64b1e5be88467de13ba285e",
"score": "0.68224627",
"text": "def morph_emotion_images(emotion, subjects):\n \n head_direction = 'Rafd090'\n eye_direction = 'frontal'\n \n for subject in subjects:\n \n print('[INFO] Processing', subject...)\n \n ... | [
{
"docid": "cc9e78a261e7f4def8e8801d07438f48",
"score": "0.6737589",
"text": "def create_mini_images():\n\n emotions = [\"neutral\", \"anger\", \"contempt\", \"disgust\", \"fear\", \"happy\", \"sadness\", \"surprise\"]\n for emotion in emotions:\n source_path = \"ck-sorted/\"+emotion\n ... |
a8670cc1596318191a7492675d7b0937 | Initializes a Chirp signal. | [
{
"docid": "68205559a42a7f7dd982bb85900d878d",
"score": "0.0",
"text": "def __init__(self, startf=200, stopf=400, t1=1, method='linear'):\n self.startf = startf\n self.stopf = stopf\n self.t1 = t1\n self.method = method",
"title": ""
}
] | [
{
"docid": "57b079b9520727f56128997987e8372b",
"score": "0.6390474",
"text": "def define_chirp(self):\n\n sec = 1\n k = 50\n w1 = 100\n w2 = self.chirp_high\n\n t = np.linspace(0, sec, int(self.fs*sec))\n\n chirp = np.sin(2*np.pi * w1 * sec * (np.exp(t *\n ... |
3b627bf502dda60b5ec37a7e49e885b8 | Sets the criteriaboxid of this MdsQuery. | [
{
"docid": "ddc5167f97b59c706aae83e78182ca9b",
"score": "0.738749",
"text": "def criteriaboxid(self, criteriaboxid):\n if criteriaboxid is None:\n raise ValueError(\"Invalid value for `criteriaboxid`, must not be `None`\") # noqa: E501\n\n self._criteriaboxid = criteriaboxid",
... | [
{
"docid": "70d3a99d8262de3fefdb7c4708a5b9c7",
"score": "0.5326126",
"text": "def __init__(self, criteriaboxid=None, handlerclass=None, join=None, label=None, layout=None, properties=None, statement=None, stylename=None, widget=None): # noqa: E501 # noqa: E501\n self._criteriaboxid = None\n ... |
967191b6464eefb9b6ddcabbbe49ead0 | Get All Detectors in Detector Table | [
{
"docid": "919673a91f70b9104b20d9b152079b8e",
"score": "0.0",
"text": "def getAllPartitions(self):\n connection = sqlite3.connect(self.dataBaseFile)\n c = connection.cursor()\n try:\n c.execute(\"SELECT * FROM Partition\")\n res = c.fetchall()\n ret... | [
{
"docid": "78ac6ae6c2097d86d3ce2e41feca4a72",
"score": "0.734863",
"text": "def getAllDetectors(self):\n connection = sqlite3.connect(self.dataBaseFile)\n c = connection.cursor()\n try:\n c.execute(\"SELECT * FROM Detector\")\n res = c.fetchall()\n ... |
a3724545d34835fb51fbdf6c28e8d045 | Takes in original obs and randomly cutout a strip to a 55x55 | [
{
"docid": "991be187fc1ab739f6239b8152d43f1e",
"score": "0.7064706",
"text": "def cutout(obs, shape=(32, 32)): ##obs.shape==320,64,64,3\n x = np.random.randint(46)\n # obs[:, :x, :,:] = 0\n obs[:, x:x+4,:,:] = 0\n return obs",
"title": ""
}
] | [
{
"docid": "4daf049d01540af5c66255816ca15792",
"score": "0.67266196",
"text": "def rand_crop(obs, shape=(32, 32)): ##obs.shape==320,64,64,3\n x, y = np.random.randint(15,size=(2,))\n # obs[:, :x, :,:] = 0\n obs[:, :, :y,:] = 0\n # obs[:,64-x:, :, :] = 0\n obs[:, :, 64-y:, :] = 0\n retu... |
09eeddc452f5f4cd65017c3764caf1a9 | Used to specify whether file notifications are sent to IoT Hub on upload. Defaults to `false`. | [
{
"docid": "de9efd70cdd7ac2d1e5c27927577137f",
"score": "0.5638011",
"text": "def notifications_enabled(self) -> Optional[pulumi.Input[bool]]:\n return pulumi.get(self, \"notifications_enabled\")",
"title": ""
}
] | [
{
"docid": "63969bc2dcab7f4ea14e134acdbd3880",
"score": "0.6151067",
"text": "def requires_file_upload(self):\r\n return self._requires_file_upload",
"title": ""
},
{
"docid": "9a316cf340b732d001f328addd90ea05",
"score": "0.60951287",
"text": "def may_attach_files(self, user):... |
fc11d77f35850396f2e9f33e38fe0f03 | Clean up dict after converting everything to dict | [
{
"docid": "a1475a6c04b4671008aa165a5c686a8f",
"score": "0.6964649",
"text": "def scrub_dict(d):\n if type(d) is dict:\n return dict((k, scrub_dict(v)) for k, v in d.iteritems() if v and scrub_dict(v))\n else:\n return d",
"title": ""
}
] | [
{
"docid": "031c08b6ca06896819c3fcdb44be9439",
"score": "0.72440386",
"text": "def clean_dictionary(d: dict) -> dict:\n for key in d:\n if hasattr(d[key], '__call__'):\n d.pop(key)\n return d",
"title": ""
},
{
"docid": "38802b0e6b0715a331a543b5f946e829",
"score":... |
09886154c61e5e66c4e93fe553650ee5 | Average a list of datapoints. A list with no nonNone items has an average of zero. | [
{
"docid": "c6931123be8acf7dc94fed2857ccc05c",
"score": "0.6805623",
"text": "def avg(dpList):\n if not dpList:\n return 0.0\n\n dpList = [x for x in dpList if x is not None]\n if not dpList:\n return 0.0\n\n return sum(dpList) / len(dpList)",
"title": ""
}
] | [
{
"docid": "98ec3fc8563d74ec906d33c57f0247cd",
"score": "0.76778173",
"text": "def mean(lst):\n lst = [elem for elem in lst if elem is not None]\n return np.mean(np.array(lst)) if len(lst) > 0 else 0",
"title": ""
},
{
"docid": "4fb533ab645d793c19574b8c589dd3a4",
"score": "0.754892... |
75c9178eeb21d213ee9acfa8e3d59fe1 | My customized model function | [
{
"docid": "64c8770a14f07d7dcfe1534d34cbc624",
"score": "0.0",
"text": "def my_model_backward(labels, fc_filters, reg_scale, conv1d_filters, filter_channel_list ):\n \n ##Record the variables before Backwardmodel is created\n BeforeBackCollectionName = \"BeforeBack_Collection\"\n for var i... | [
{
"docid": "1542ec9e58a9ab3a9bdbf240d0a86296",
"score": "0.7477061",
"text": "def model(self):",
"title": ""
},
{
"docid": "89397b8e83794d799dce2718e3b00cd0",
"score": "0.7177977",
"text": "def inModel(self):\n \n pass",
"title": ""
},
{
"docid": "539ad520dd5224... |
c26ed3725398a4fed9398fe7c1e7c913 | Print list of commands to run | [
{
"docid": "99898ba4171ce55afb904a8e93227608",
"score": "0.7087355",
"text": "def print_commands(commands,\n status_update_callback,\n logger):\n #logger.write(\"Printing commands only.\\n\\n\")\n #for c in commands:\n # for e in c:\n # status_up... | [
{
"docid": "b9b7d4eed90f1015a2643fe265875fb2",
"score": "0.81645465",
"text": "def print_commands(self):\r\n pass",
"title": ""
},
{
"docid": "2a13ba3352fb1ea2bf46156e1906f339",
"score": "0.7973929",
"text": "def show_commands(self):\n print('-h\\t--lists all commands')\n ... |
503fa88ca6c1c1f072a8e6d8dfb77199 | Open communications with EBAM Plus unit | [
{
"docid": "4e6ef82766b8cadba65d720055a694ad",
"score": "0.0",
"text": "def connect(self):\n if not self.cli.isOpen():\n self.cli.open()",
"title": ""
}
] | [
{
"docid": "892e4220778f1b71eee360d1382bda18",
"score": "0.64897",
"text": "def performOpen(self, options={}):\n\n # connect, either through name or by autodetecting\n if self.comCfg.address == '<autodetect>': #input from labber in \"Communication\" section of the driver\n self.... |
e30dc2df7026bc10e25a72089707317c | Edits a Databricks cluster Policy. The specification for the request json can be found at | [
{
"docid": "65a789f753aad72de61c8a890fe8fab4",
"score": "0.66613555",
"text": "def edit_cli(api_client, json_file, json):\n if not bool(json_file) ^ bool(json):\n raise RuntimeError('Either --json-file or --json should be provided')\n if json_file:\n with open(json_file, 'r') as f:\n... | [
{
"docid": "9d1a3637d76fd45b18894681b2229123",
"score": "0.67657363",
"text": "def cluster_update_policy(self, cluster, policy, **attrs):\n return self.service.update_cluster_policy(cluster, policy, **attrs)",
"title": ""
},
{
"docid": "9304fa1bf5d10e026f38e6220ec3cb3d",
"score": ... |
ed6f5b0306438010b938131d2a4e1338 | Determines the reaction in which the molecule acts as an acid | [
{
"docid": "6ce159e012bddb79bb5caebedafcd836",
"score": "0.77446043",
"text": "def acid_reaction(self):\r\n return (self + H*H*O > self.conjugate_base() + H*H*H*O*ep)",
"title": ""
}
] | [
{
"docid": "bbb1be8060a4d49a5feb66d2af56c6de",
"score": "0.82701194",
"text": "def acid_reaction(self):\r\n return Molecule(self.parts, self.count, self.charge).acid_reaction()",
"title": ""
},
{
"docid": "580168c967bde3459afb0f4c7df80b43",
"score": "0.79462945",
"text": "def ... |
9684abc1e5468def436d92ce25f527ae | Multiply every element of self by value. Done in place. | [
{
"docid": "9d86fa04561e94c82d8a1531e8cf94cb",
"score": "0.7325298",
"text": "def scale(self, value:Any):\r\n self._inert = (i*value for i in self)\r\n return self",
"title": ""
}
] | [
{
"docid": "f911b7d4237ef29b024e6616813f694d",
"score": "0.727317",
"text": "def __mul__(self, scalar):\n # BAD python 2.3 compat change\n return type(self)( [scalar*item for item in self] )",
"title": ""
},
{
"docid": "4c920e9ce16034ec191b54391418f4ff",
"score": "0.7248915... |
aef59dbb3900ae0a18478a838687c9a8 | Creates a new patient. | [
{
"docid": "9a6f2fd99b6165685488448bbb96c4b1",
"score": "0.7603765",
"text": "def new_patient(patient_data: dict) -> Patients:\n database = get_connection()\n patient = Patients(id=str(uuid.uuid4()), created_at=datetime.now(), clinical_information=patient_data)\n database.patients.insert(\n ... | [
{
"docid": "b89d660e229974b4ffde32f91bc4ad23",
"score": "0.76125675",
"text": "def createPatient(self):\n p = Prescription()\n p.patient_id = self.patients.data\n p.medication = self.medication.data\n p.frequency = self.frequency.data\n p.start_dt = self.start_dt.data\... |
4ea9e42b0e93fcfff85b39a7b86c1d6c | Cross rows and columns on an iteration to obtain the diagonal | [
{
"docid": "0d56d91b140566eb4cbc70485a88c8ef",
"score": "0.63815534",
"text": "def get_inv_diagonal(rows, columns):\n diagonal_boxes = []\n j = len(rows) - 1\n for i in range(len(rows)):\n diagonal_boxes.append(rows[i] + columns[j])\n j = j - 1\n return diagonal_boxes",
"ti... | [
{
"docid": "e64b4d30af1573095f8b90fb69ceed39",
"score": "0.676744",
"text": "def _diags(self):\n indices = jnp.arange(self.dim)[:,jnp.newaxis]\n bin_reps = (indices >> jnp.arange(self.N)[::-1]) & 1\n spins = 1 - 2 * bin_reps\n spins_prime = jnp.hstack( (spins[:,1:] , spins[:,... |
44dc5cc5a85b70e4d2e57f030e7e7e14 | Compute the Section, in the coordinate syteme given by the Location Law. To have the Normal to section equal to the Location Law Normal. If contact beetween and is forced. | [
{
"docid": "3abe8a46cf705b9ace13b235e31319b7",
"score": "0.52541476",
"text": "def ModifiedSection(self, *args):\n return _GeomFill.GeomFill_SectionPlacement_ModifiedSection(self, *args)",
"title": ""
}
] | [
{
"docid": "6f00126098607b4a21a1e7a81f945b25",
"score": "0.58446944",
"text": "def ConstantSection(self, *args):\n return _GeomFill.GeomFill_SectionLaw_ConstantSection(self, *args)",
"title": ""
},
{
"docid": "a4ed5004f5f44261468c0151db23d3eb",
"score": "0.5688747",
"text": "d... |
f81c994135f7b7437388af46633d6c0d | Promote im1, im2 to nearest appropriate floating point precision. | [
{
"docid": "2ce479e3318c24ed1654c0ae94b655c7",
"score": "0.6312333",
"text": "def _as_floats(im1, im2):\n float_type = np.result_type(im1.dtype, im2.dtype, np.float32)\n im1 = np.asarray(im1, dtype=float_type)\n im2 = np.asarray(im2, dtype=float_type)\n return im1, im2",
"title": ""
}
... | [
{
"docid": "a31b8f00695681215b37a0107c052974",
"score": "0.58750874",
"text": "def _normalize(op1, op2, shouldround = 0, prec = 0):\r\n # Yes, the exponent is a long, but the difference between exponents\r\n # must be an int-- otherwise you'd get a big memory problem.\r\n numdigits = int(op1.ex... |
05af6a2b3aebd38c9780fc80281b3661 | Creates one alignment by going through a traceback path. | [
{
"docid": "77995b1a764fbe875ed1c5f68ef3c08c",
"score": "0.6657382",
"text": "def _create_alignment(self, path):\r\n path.reverse()\r\n start = path[0]\r\n\r\n alignment_a = strings.EMPTY\r\n alignment_b = strings.EMPTY\r\n\r\n cur_char_align_1 = 0\r\n cur_char_... | [
{
"docid": "b68da0baa939d1d9b8ef083853ff5e18",
"score": "0.6136285",
"text": "def doAlignment(self):\n\n seq1len = len(self.seq1)\n seq2len = len(self.seq2)\n\n # 1st subscript = sequence 1,\n # 2nd subscript = sequence 2\n scores = [ [0 for i in range(seq2len+1)] for ... |
eb61ee2d5e6ae2243355dfd18f6e52c1 | Construct a multibox layer, return a class and localization predictions. | [
{
"docid": "3a35ef534a00913dc524ad506142ceac",
"score": "0.5202887",
"text": "def ssd_multibox_layer(inputs,\n num_classes,\n sizes,\n ratios=[1],\n normalization=-1,\n bn_normalization=False):\... | [
{
"docid": "a909d4e71632524361ad39fd32aef038",
"score": "0.6050399",
"text": "def build_classifier(self, **kwargs):\n # loc and conf layers\n in_channels = tuple(self.feature_layers[name].out_channels for name in self.classifier_source_names)\n\n _dbox_num_per_fpixel = [len(aspect_r... |
6a9bd6c9182d895d73b587ffbc60567d | Setup mock response 404 | [
{
"docid": "e7ddeeff820f007b87fef5066cd97f48",
"score": "0.0",
"text": "def __init__(self, data, status_code=404):\n self.data = data\n self.status_code = status_code",
"title": ""
}
] | [
{
"docid": "f54208934f6fd1b96047516946acd0a7",
"score": "0.76103675",
"text": "def test_return_404(self):\n buffer = {}\n\n def start_response(code, headers):\n buffer['code'] = code\n buffer['headers'] = headers\n\n result = self.instance.return_404(\n ... |
93ce6917d53ddbc7e14b937630567268 | shut down continuous recording. | [
{
"docid": "ba300671e375e518c21b746a7550fa37",
"score": "0.0",
"text": "def continuousEnd(self):\n print \"Die!\"\n self.threadsDieNow = True",
"title": ""
}
] | [
{
"docid": "1781197fec3dc9f16a56bb3ff133e825",
"score": "0.7465613",
"text": "def recording_stop(self):\n self.recording = False\n self.threads['recording'] = None",
"title": ""
},
{
"docid": "f5575fc4a17a3afdc8b91b3c6c9272fb",
"score": "0.7301088",
"text": "def stop_ca... |
ffd55afce1716d645c191e4472691883 | Add validation result objects to a list of results. | [
{
"docid": "490a20fdad84e9b78d1db58b47fd8327",
"score": "0.0",
"text": "async def get_valid_invalid_results(\n self, classification_tokens: List, transcripts: List,\n classification: Classification, results: List, gene_tokens: List,\n mane_data_found: Dict, is_identifier: bool,\n ... | [
{
"docid": "cb8ce75ee4fcce71f7ba9fb8bc9628ce",
"score": "0.693104",
"text": "def merge_results(self, final, result):\n final.errors += result.errors\n final.failures += result.failures\n final.skipped += result.skipped\n final.expectedFailures += result.expectedFailures\n ... |
87d1242011a4ea0509275c443be6bd65 | Fixture function which creates the model metadata json file without the sensor or neural_network information | [
{
"docid": "eec09f5a125600f51fb444f12b957841",
"score": "0.5558178",
"text": "def create_model_metadata_action_space():\n model_metadata_path = \"test_model_metadata_action_space.json\"\n model_metadata = {\n \"action_space\": [\n {\n \"steering_angle\": 45,\n ... | [
{
"docid": "b9b61a5120be8456706b5b11b043eaa7",
"score": "0.7641225",
"text": "def create_model_metadata():\n model_metadata_path = \"test_model_metadata.json\"\n model_metadata = {\n \"action_space\": [\n {\n \"steering_angle\": -30,\n \"speed\": 0.6... |
7eb379bc8c9ad909a2f3fdef4962d706 | Test the time step increment of a ToySquares object | [
{
"docid": "ba63fcb9b4c122394b641ca64959ac70",
"score": "0.8912831",
"text": "def test_increment_time_step(self):\n toy_squares = self.test_constructor()\n toy_squares.increment_time_step()",
"title": ""
}
] | [
{
"docid": "92261ed520f4713cf8c807793616e38a",
"score": "0.74221474",
"text": "def timeStep(self):",
"title": ""
},
{
"docid": "5bd38aef68c994295bfa238f3756f482",
"score": "0.6563983",
"text": "def increment_time_step(self):\n self.time_step += 1",
"title": ""
},
{
... |
f7246852e12b56d8f4f1b9b13c43776f | Add a client to the block list. | [
{
"docid": "54876cdd5ba23390cdbba72baed894dd",
"score": "0.0",
"text": "def block_client(self, mac):\n\n self._mac_cmd(mac, 'block-sta')",
"title": ""
}
] | [
{
"docid": "59f946378fcf9476230d213bfc604000",
"score": "0.77596116",
"text": "def add_client(self, client: socket.socket) -> None:\n self._clients.append(client)",
"title": ""
},
{
"docid": "e1384726375c262471690e2be48147fa",
"score": "0.7421137",
"text": "def register(self, ... |
2e9296b5bd5a7a3a9fb4203aac2f90f1 | Compute precision and recall of different score threshold | [
{
"docid": "69bd9b243c1d4b4825931a395827e329",
"score": "0.0",
"text": "def rp_various_th(gt_pds, ths, etol, metric=azimuth_distance):\n pr = []\n for t in ths:\n pdf = pds_to_pdf(gt_pds, t)\n _, r, p, _, _ = eval_recall_precision(pdf, [etol], metric=metric)\n pr.append((r[0],... | [
{
"docid": "7f9a8dc1f92914411102b0d0628a5bb2",
"score": "0.78416824",
"text": "def precision_at_recall(scores, labels, target_recall):\n positive_scores = scores[labels == 1.0]\n threshold = np.percentile(positive_scores, 100 - target_recall*100)\n predicted = scores >= threshold\n return pr... |
0f2f04ded56e31af9f78b9577d14f7e1 | Get a set of legal combinations of target (exposure time, sensitivity). Gets the target exposure value, which is a product of sensitivity (ISO) and exposure time, and returns equivalent tuples of (exposure time,sensitivity) that are all legal and that correspond to the four extrema in this 2D param space, as well as to... | [
{
"docid": "912be3127dd161a655a69a2043356cf1",
"score": "0.7153181",
"text": "def get_target_exposure_combos(its_session=None):\n if its_session is None:\n with its.device.ItsSession() as cam:\n exposure = get_target_exposure(cam)\n props = cam.get_camera_properties()\n ... | [
{
"docid": "8994c58b2d36c61ffa0f80920bee15bd",
"score": "0.60776895",
"text": "def get_target_exposure(its_session=None):\n cached_exposure = None\n for s in sys.argv[1:]:\n if s == \"target\":\n cached_exposure = __get_cached_target_exposure()\n if cached_exposure is not None... |
13a1f4a01c8bdb5bf9ed48373336cc8e | Clone(itkIsolatedConnectedImageFilterIF2IF2 self) > itkIsolatedConnectedImageFilterIF2IF2_Pointer | [
{
"docid": "8e4deb70da60c0e10db653b12fc735da",
"score": "0.9369704",
"text": "def Clone(self) -> \"itkIsolatedConnectedImageFilterIF2IF2_Pointer\":\n return _itkIsolatedConnectedImageFilterPython.itkIsolatedConnectedImageFilterIF2IF2_Clone(self)",
"title": ""
}
] | [
{
"docid": "8381db5192bb2f2dea69a19d8ddd520c",
"score": "0.88956755",
"text": "def Clone(self) -> \"itkIsolatedConnectedImageFilterIUC2IUC2_Pointer\":\n return _itkIsolatedConnectedImageFilterPython.itkIsolatedConnectedImageFilterIUC2IUC2_Clone(self)",
"title": ""
},
{
"docid": "99f90... |
e5e042c8fc342e1994f4fc2cb4e561e3 | Get item with the specified Id. | [
{
"docid": "5e24fdda50f697c9459795fa135b522c",
"score": "0.71838367",
"text": "def get(self, itemId):\n\n tableRow = self.__queryTableRow(itemId)\n return self.__getItemFromTableRow(tableRow)",
"title": ""
}
] | [
{
"docid": "b88dfccb495f954fb8fd0f791e6b5d7f",
"score": "0.86855495",
"text": "def getItem(self, id):\n path = 'item/' + id\n return self.sendRestRequest('GET', path)",
"title": ""
},
{
"docid": "15d5ee78bc3f44b0c0619e5b4e135c81",
"score": "0.7920818",
"text": "def get_... |
8110df8e5976ea4b2be197eebacd69f5 | Ensures correct output from is_v_wind_field. In this case the answer is yes. | [
{
"docid": "55dd6928d4b8a4b81d8bb41fc8db8adc",
"score": "0.8026145",
"text": "def test_is_v_wind_field_true(self):\n\n self.assertTrue(grib_io.is_v_wind_field(V_WIND_NAME))",
"title": ""
}
] | [
{
"docid": "c22039e2105441ccda68e2fe307fa85b",
"score": "0.7764373",
"text": "def test_is_u_wind_field_v_wind(self):\n\n self.assertFalse(grib_io.is_u_wind_field(V_WIND_NAME))",
"title": ""
},
{
"docid": "cab1e36c9939960c2ac1794dca943e7f",
"score": "0.7331233",
"text": "def te... |
c9ba8f4bfdaae302393c82e504eb69bf | Test that the string is correctly parsed. | [
{
"docid": "b967bf926cc3d869cb6e8c5e13feb784",
"score": "0.0",
"text": "def test_parse_string_key_val(key: str, value: str, enclosing: str):\n enclosed_value = enclosing.format(value)\n bibtex_str = f\"\"\"@string{{{key} = {enclosed_value}}}\"\"\"\n library: Library = Splitter(bibtex_str).split... | [
{
"docid": "68bcb075ee9023bc6504ab3b98c0912c",
"score": "0.8762374",
"text": "def __test_parse_string():",
"title": ""
},
{
"docid": "f21244c00c5bec4a58b7f54687975541",
"score": "0.74926984",
"text": "def test_parse_str(self):\n\n obj = self.parser.parse_str(STR)\n self... |
2ae863de650db4ee3a7ff292d859e71b | Ensure ``path`` outputs proper settings file path. | [
{
"docid": "7f2eb865576fa67708552492ef14276c",
"score": "0.6299038",
"text": "def test_settings_path(self):\n with self.cli_runner.isolated_filesystem():\n with open(\"settings.json\", \"w\") as handler:\n handler.write(PULP_SMASH_CONFIG)\n with mock.patch.obj... | [
{
"docid": "d49d3192faad7077206e313d0d2e8fa0",
"score": "0.62478626",
"text": "def test_get_settings_file_path_returns_the_settings_path_correctly(self):\n self.assertEqual(\n os.path.expanduser('~/.atrc/last_version'),\n ExternalDCC.get_settings_file_path()\n )",
... |
db56b62c837eeb813a767122f02d3d5c | generate all graident targets for CC Graph | [
{
"docid": "8def199e03ddb3e57dd452bf2c938c97",
"score": "0.53105754",
"text": "def GraphDef_Grad(graph_def, targets):\n all_pairs = set()\n for target in targets:\n for wrt in target.grad_wrts:\n all_pairs.add((target.name, wrt))\n\n for pair in all_pairs:\n g_target = ... | [
{
"docid": "60d287fb34ae7e3076cd7efcf7539469",
"score": "0.6744482",
"text": "def __generate_targets(self):\n targets = list()\n for i in range(len(self.images)):\n mat = sio.loadmat(self.masks[i], mat_dtype=True, squeeze_me=True, struct_as_record=False)\n categories ... |
da5f3f850684cd950c772a6c423981fc | Execute all commands issued after MULTI | [
{
"docid": "eeb79144b48cc547bd8eda52b1d4704d",
"score": "0.6023346",
"text": "def _exec(self):\r\n if not self._in_transaction:\r\n raise Error('Not in transaction')\r\n\r\n futures_and_postprocessors = self._transaction_response_queue\r\n self._transaction_response_queue... | [
{
"docid": "dda936b07c092ad782de1aba63e81567",
"score": "0.6528384",
"text": "def execute_commands(self):\n for command in self.commands:\n command.execute(self)\n\n self.commands.clear()\n self.controller.loading = False",
"title": ""
},
{
"docid": "cce11de11... |
3303d6927f44a64c4d0fdd3feca1faf9 | decode control choice strings >>> a = Parameter([]) >>> a._onOffParser(1) 'on' | [
{
"docid": "37aea494cada7c57b9ba3f650af682da",
"score": "0.6830201",
"text": "def _onOffParser(self, usrStr):\n ref = {\n 'on' : ['1'],\n 'off' : ['0'],\n }\n usrStr = drawer.selectionParse(usrStr, ref)\n if usrStr == None:\n selStr = ... | [
{
"docid": "621779a6535a480d22d4f8db6d1da406",
"score": "0.55207944",
"text": "def _decode_boolean(data: str) -> Tuple[int, base.Asn1Item]:\r\n if data[0] == '1':\r\n return 1, univ.Boolean(value=True)\r\n else:\r\n return 1, univ.Boolean(value=False)",
"title": ""
},
{
"... |
39ab20ecd199378318d85b3d7501b6ba | Cuts the selected text from the line edit. Copies the selected text to the clipboard then deletes the selected text from the line edit. | [
{
"docid": "668e040d2ce6d90fca8f89da6da9aef5",
"score": "0.7026711",
"text": "def cut(self):\n self.widget.Cut()\n self._update_shell_selection_and_cursor()",
"title": ""
}
] | [
{
"docid": "70e29e1fe2c3ae489f6f68a3d2a73b81",
"score": "0.77484757",
"text": "def editCut(self):\n widget = QtGui.QApplication.focusWidget()\n try:\n if widget.hasSelectedText():\n widget.cut()\n return\n except AttributeError:\n ... |
6272898a2a26e5319b121f4f311870f2 | This method configures Serial Port option in Bios Policy. | [
{
"docid": "5d1cc36694c82efaef1184495a6799c0",
"score": "0.6220808",
"text": "def bios_conf_serial_port_a(handle, name, parent_org_dn,\n vp_serial_port_a_enable=\"platform-default\"):\n\n from ucsmsdk.mometa.bios.BiosVfSerialPortAEnable import \\\n BiosVfSerialPortAE... | [
{
"docid": "c64bd2d416d7d52c6d562b64a9da0c3b",
"score": "0.67158395",
"text": "def _update_serial_port(self):",
"title": ""
},
{
"docid": "d27573773a5effb1fc3f30e4f1001f6e",
"score": "0.67053586",
"text": "def __init__(self, serialPort):\n self.__serialPort = serialPort",
... |
ab4561e60b8d17ad3e9706a5bdad0f5e | vraci posledni vygenerovany obrazek | [
{
"docid": "9862ae685f443540f28e99e363c75520",
"score": "0.0",
"text": "def getImage(self):\n return self.mergeImages()",
"title": ""
}
] | [
{
"docid": "67ea62c603f56ebebe165f8302d920e1",
"score": "0.63692826",
"text": "def graficar(self):",
"title": ""
},
{
"docid": "66dbd1ccb817c652c7178c06c7368c7d",
"score": "0.62632704",
"text": "def preparation(self):",
"title": ""
},
{
"docid": "a1799f1e0af250b75b587be6b... |
e90564c8c32db5ea742e1378643912c8 | Tokenization/string cleaning for all datasets except for SST. | [
{
"docid": "2c7da3f2b0c6c61cde56150c7bad40da",
"score": "0.0",
"text": "def clean_str(string):\n string = re.sub(r\"[^A-Za-z0-9(),\\+!?\\'\\`]\", \" \", string)\n string = re.sub(r\"\\'s\", \" \\'s\", string)\n string = re.sub(r\"\\'ve\", \" \\'ve\", string)\n string = re.sub(r\"n\\'t\", \" ... | [
{
"docid": "b83641c6804b9e83900942ae4337486e",
"score": "0.6654124",
"text": "def cleanup_tokens(self):\n raise NotImplementedError",
"title": ""
},
{
"docid": "5e9fe54b86bfae9f8b800afed3853334",
"score": "0.66179585",
"text": "def clean(str_input_dataset):\n\tstr_clean_datase... |
5dd02d0415547f3f2075d2b995d22465 | List all registered user names. | [
{
"docid": "de0509e2edc83fc9a2b96c68761c6277",
"score": "0.676247",
"text": "def get_users_view(request):\n user_name_list = ax.evaluate_call(lambda: [user.user_name for user in\n UserService.all(models.User, db_session=request.db)],\n ... | [
{
"docid": "e11550c2bc2053986e8a1101486dbabb",
"score": "0.79452044",
"text": "def list_users(self):\n pass",
"title": ""
},
{
"docid": "ec1ec8aa3e65420fb561c7e7d9a81399",
"score": "0.79144025",
"text": "def list(self, request, format=None):\n usernames = [user.username... |
02f8ec1dbb4565a65a735cd47153f869 | This Dataset returns sys.maxsize, but is effectively unlimited. | [
{
"docid": "d8c9aeeecff6b2c39021299813852f14",
"score": "0.7583838",
"text": "def __len__(self):\n return sys.maxsize",
"title": ""
}
] | [
{
"docid": "014ad5c757cf79d33f34e3caa8b16f23",
"score": "0.7852997",
"text": "def __len__(self):\n\t\treturn min(len(self.dataset), self.opt.max_dataset_size)",
"title": ""
},
{
"docid": "591f645eecd2c5835f57f0dfe90c7e6f",
"score": "0.77031475",
"text": "def maxsize(self):\n r... |
7b27d376791b46a38ba562f6fe6bd2d6 | Return dict of current parameters. | [
{
"docid": "d9ddcdb75d127dfd6736448eb7236734",
"score": "0.75191206",
"text": "def get_params(self):\n parameter_dict = {\n 'regression_model': self.regression_model,\n 'holiday': self.holiday,\n 'mean_rolling_periods': self.mean_rolling_periods,\n 'mac... | [
{
"docid": "3cf9e43ecc4bec239accccc8d23c8210",
"score": "0.8382747",
"text": "def param_dict(self):\n return self.params.param_dict()",
"title": ""
},
{
"docid": "9f9705309e0e045c816935e2ba229178",
"score": "0.8358229",
"text": "def get_params(self) -> dict:\n\t\treturn dict()... |
5fc41bac473de89bde8fd57a79bcc753 | Sets the next_quote_number of this InvoiceSettings. | [
{
"docid": "5b4899f73aacda3408acf6a4ab04ad3c",
"score": "0.83951414",
"text": "def next_quote_number(self, next_quote_number):\n\n self._next_quote_number = next_quote_number",
"title": ""
}
] | [
{
"docid": "04adb1996b68cc40f09a039527a77589",
"score": "0.63772535",
"text": "def next_invoice_number(self, next_invoice_number):\n\n self._next_invoice_number = next_invoice_number",
"title": ""
},
{
"docid": "8103b18f09d7d8b0c612bfe99c3997b0",
"score": "0.62827",
"text": "d... |
6e8b8262a19b1c75022347aaab7573e6 | This function takes in 2 lists of tuples, one for visited cities and their distance from the outbreak city, and another for the unvisited cities and it's distance from the outbreak city. it moves the city with the shortest distance, which is the distance from the visited city to its nearest neighbouring city, from the ... | [
{
"docid": "be55ec6d9760f8a38fe94ecd3f55a4bd",
"score": "0.7617503",
"text": "def visit_next(visited, unvisited, distance):\n\n # Takes in the visited cities as a list\n visited_list = visited\n\n # Takes in the unvisited cities as a list\n unvisited_list = unvisited\n\n # Gets the shorte... | [
{
"docid": "3ee2efd959a25fbdb8ae5f6d6baefb41",
"score": "0.61393",
"text": "def dijsktra(initial, destination):\n \n distances = {}\n\n for i in city_list:\n distances[i.state] = 10000000000000\n\n distances[initial.state] = 0\n\n q = PriorityQueue()\n\n q.put( (0, initial.state... |
c0f7a1b92bd168f6b5656caefb477e56 | Sets objective of model to minimization of enzymatic mass. | [
{
"docid": "5381cdaae655f133f3562b505f58e64d",
"score": "0.5934148",
"text": "def set_enzymatic_objective(cobra_model, coefficients_forward, coefficients_reverse):\n coefficients = dict()\n for (bigg_id, cf) in coefficients_forward.items():\n rxn = cobra_model.reactions.get_by_id(bigg_id)\n... | [
{
"docid": "57a0a352db57b18e123cc27b029c93a8",
"score": "0.73641044",
"text": "def Minimize(self, obj: ObjLinearExprT):\n self._SetObjective(obj, minimize=True)",
"title": ""
},
{
"docid": "ce71e8f03db5f70ea793377ffb73845d",
"score": "0.69110936",
"text": "def _SetObjective(se... |
53679d492b371e2133afbc443afe9005 | computes residuals based on distance from ellipsoid can be used with different lossfunctions on residual | [
{
"docid": "7c6ae09ece715a1bb31ffcedcd812579",
"score": "0.0",
"text": "def fitting_obj_stack(param, x, y, z, i):\n\n\n # centers\n cx = param[0]\n cy = param[1]\n\n #num_layers = len(set(z))\n #assert len(param) == num_layers+2\n\n radii = param[2:]\n\n num_layers = len(radii) / 2\... | [
{
"docid": "6bde0a984bdbc9de25e970990587fd95",
"score": "0.6588138",
"text": "def ellipsoid_fit_rmse(query_points, axes, center, rotmat, return_full=True): \n # Find starting angles for all query points\n ellipsoid_points, ell_theta, ell_phi = generate_ellipsoid_even(axes, center, rotmat, \n ... |
3a64a0b9859bfe2c14156c5350510d65 | If the download doesn't complete, the validator fails. | [
{
"docid": "f317b436fec237c212be7e1c65623b82",
"score": "0.0",
"text": "def test_download_fail(mock_tools, tmp_path):\n # Mock the environment as if there is not WiX variable\n mock_tools.os.environ.get.return_value = None\n\n # Mock the download failure\n mock_tools.download.file.side_effec... | [
{
"docid": "3b8e5b802d54159eac4931d814661471",
"score": "0.67761797",
"text": "def __download_and_verify(url):\n \n n = __download_url(url)\n if n==1:\n FAILED = True\n elif n==0:\n if __verify(url):\n VERIFYFAIL=True\n elif n==3:\n if __check_md5(url):\n ... |
1ccfae3bff6322e4b4bf1fc7c24361f4 | Make a query to the Telegram Bot API. | [
{
"docid": "4c183467346d812399c2676a2c673dc1",
"score": "0.0",
"text": "def make_request(method, message):\n url = f'{BASE_URL}{BOT_TOKEN}/{method}'\n post = requests.post(url, json=message)\n return post.json()",
"title": ""
}
] | [
{
"docid": "65b103293b5c140607ffef35562dd283",
"score": "0.6427141",
"text": "def make_query_request(self, api_query):\n\t\ttry:\n\t\t\treturn requests.post(self.query_url, json=api_query, headers=headers, timeout=self.request_timeout)\n\t\texcept Exception as e:\n\t\t\tlogging.warning(\"Exception in ca... |
69ade830278b509dc236a0d7b91ad23a | Gets the current option. | [
{
"docid": "51d4d91a6a0cfaeeb71fc180401d8b75",
"score": "0.8279675",
"text": "def get_opt(self): \n\t\treturn self.cur_opt",
"title": ""
}
] | [
{
"docid": "285f88057b249e864cd5e59d8158f198",
"score": "0.8269581",
"text": "def getOption(self):\n return self._option",
"title": ""
},
{
"docid": "0a465ad4e648c0b76efb2d228ac9ff35",
"score": "0.81982434",
"text": "def get_selected_option(self):\n return self._current... |
37006b8d55639e2f4b6cd00a7d4978ff | Prints a pretty banner for starting up script | [
{
"docid": "19e35a89a46841a8ba19db6f42ced08e",
"score": "0.67428637",
"text": "def print_start_banner(target : str, start : str, end : str) -> None:\n print(\"-\" * 60)\n print(\"Starting scan on ports {0} - {1} target: {2}\".format(start, end, target))\n print(\"Time started: {}\".format(datet... | [
{
"docid": "c96c0c7aedac0dc614e89712f21ebfb4",
"score": "0.7727753",
"text": "def print_banner():\n print_green(\"///////////////////\")\n print_green(\"// s l y t h e r //\")\n print_green(\"///////////////////\")",
"title": ""
},
{
"docid": "7250930078f091c89fd13e83115bc528",
... |
8d3afb91c26db22102b0a220ca2c055d | build the content for one option | [
{
"docid": "936c529a999bf1e3861633927b45549e",
"score": "0.7659812",
"text": "def _build_option_content(self):\n return Step(\n name=\"option_content\",\n tipe=\"content\",\n value=self._config,\n index=self.steps.current.index,\n )",
"title"... | [
{
"docid": "ad66492fca0c1c1d8d521f00ee802da2",
"score": "0.6674333",
"text": "def _create_content(self, options):\n sizer = wx.BoxSizer(wx.VERTICAL)\n self.SetSizer(sizer)\n\n for opt in options:\n\n if opt.get_type() == \"bool\":\n self.__add_bool(opt.get_... |
b3e7fb4c0bec180d6cc29cd90e15c3af | picks player to go first | [
{
"docid": "ba5e66c73444f077c9a555f95d974d94",
"score": "0.7084739",
"text": "def pick_player():\n \n number = random.randint(0, 1)\n\n if number == 0:\n return 'Player 1'\n else:\n return 'Player 2'",
"title": ""
}
] | [
{
"docid": "743fd69dc486d9851a5f7b2a45df898d",
"score": "0.8099728",
"text": "def choose_first_player(self):\n self.turn = random.choice([self.player1, self.cpu]) \n self.first_player = self.turn",
"title": ""
},
{
"docid": "76ef58f5bee4e05fd4c01a9dfb2e66a6",
"score": "0.80... |
7cf2e49eae0d6e3fa469ffd16542de71 | Test read 4 series in 6 pages. | [
{
"docid": "fda7fba2af675a3944dd133dc5af19ff",
"score": "0.5077106",
"text": "def test_read_generic_series():\n fname = public_file('tifffile/generic_series.tif')\n with TiffFile(fname) as tif:\n assert tif.byteorder == '<'\n assert len(tif.pages) == 6\n assert len(tif.series)... | [
{
"docid": "1dc788c32847d7e6de2a26a47e6ac0a4",
"score": "0.61458886",
"text": "def test_get_page_size_12_page17(self):\n response = self.client.get(self.url + \"?page_size=12&page=6\")\n self.assertEqual(len(response.data[\"results\"]), 3)",
"title": ""
},
{
"docid": "2780c1bda... |
29d68b084388e0271e4254f03d17719b | Called on game end. | [
{
"docid": "6c52813fb82e4da6560c338a205bca95",
"score": "0.9067296",
"text": "def on_game_end(self) -> None:",
"title": ""
}
] | [
{
"docid": "1d3c2859a1ecc59bc02ef9fdf50581c7",
"score": "0.84280807",
"text": "def hook_end_of_game(self, game, player):",
"title": ""
},
{
"docid": "0439697e16f72cf8fc8dbcc339bd1bfd",
"score": "0.8149644",
"text": "async def end_game(self):",
"title": ""
},
{
"docid": "8... |
406e848dba14958e11fbeac4d1148241 | Checks that `true_negative_rate` calculates the right quantity. | [
{
"docid": "9a1893e4df5308175962382670e3d5c8",
"score": "0.7326625",
"text": "def test_true_negative_rate(self):\n # For the penalty, the default loss is hinge.\n expected_penalty_numerator = np.sum(\n np.maximum(0.0, 1.0 - self._penalty_predictions) *\n (self._penalty_labels <= 0.0)... | [
{
"docid": "0dc92908a3c6787e6ea8d9b75225f569",
"score": "0.7055266",
"text": "def test_false_negative_rate(self):\n # For the penalty, the default loss is hinge.\n expected_penalty_numerator = np.sum(\n np.maximum(\n 0.0, 1.0 - self._penalty_predictions) * (self._penalty_labels >... |
c0d84acff8b3c57d9ac6f26fd8edc728 | Add an ECDF confidence interval to a plot. This method of computing a confidence interval can be thought of as computing confidence intervals of the inverse ECDF in the sense that we compute a confidence interval for the xvalues for each of the discrete values of the ECDF. This is equivalent to computing bootstrap conf... | [
{
"docid": "a218626aeed8ffd9717c12b0ff268c93",
"score": "0.6035395",
"text": "def _ecdf_conf_int(\n p,\n data,\n complementary=False,\n q_axis=\"x\",\n n_bs_reps=1000,\n ptiles=[2.5, 97.5],\n **kwargs,\n):\n data = utils._convert_data(data)\n\n bs_reps = np.array(\n [np... | [
{
"docid": "3d6ee8375a96da8e4774c9274ea54b55",
"score": "0.6659887",
"text": "def display_confidence_interval_of_two_independant_eer(roc1, roc2, alpha):\n\n base, lower, upper, e = confidence_interval_of_two_independant_eer(roc1, roc2, alpha)\n plt.figure()\n plt.hist(e)\n n, bins, patches =... |
1878690f3a2e767f1c480802400d82f0 | Decorator for Flask API routes that configures a structlog logging context. This decorator also logs the entry and exit from the route. | [
{
"docid": "2d832e7e5483354d907c2b610640ffa1",
"score": "0.7450892",
"text": "def log_route(f):\n @wraps(f)\n def decorated_function(*args, **kwargs):\n # new() creates a new logging context\n logger = get_logger(\"ltddasher\").new()\n # bind information about request that app... | [
{
"docid": "95d41ca0a128e51527551a1736829200",
"score": "0.58519137",
"text": "def register_logging(app):\n\n class RequestFormatter(logging.Formatter):\n\n def format(self, record):\n record.url = request.url\n record.remote_addr = request.remote_addr\n return... |
8189ff5ceb326cc763f8574719002d01 | Iterate over the jobs. | [
{
"docid": "37989d955fd11cd99c46db115f2a7726",
"score": "0.8211515",
"text": "def __iter__(self):\n for j in self.jobs:\n yield j",
"title": ""
}
] | [
{
"docid": "f7e47df71a6a35e785ddba578270c9a4",
"score": "0.809505",
"text": "def jobs(self):\n for job in self._jobs:\n yield job",
"title": ""
},
{
"docid": "407993a0dcab8166e87568ddc55eb712",
"score": "0.739263",
"text": "def _all_jobs(self):\n for child in sel... |
d080f5aec8528d6068e0df21d0371990 | Get the shaker's target speed in RPM, if set. | [
{
"docid": "d925a35697aceb752043a937dbef162f",
"score": "0.73180777",
"text": "def get_target_speed(self) -> Optional[int]:\n return self._sync_module_hardware.target_speed # type: ignore[no-any-return]",
"title": ""
}
] | [
{
"docid": "70e56cf43a1248fb1d17dec37a3a5e8b",
"score": "0.68449944",
"text": "def get_speed(self):\n return self.speed",
"title": ""
},
{
"docid": "1ea5984678f409270b4204aef0be6c84",
"score": "0.67140687",
"text": "def get_speed(self):\r\n return self._speed",
"tit... |
b28f5773fdb4318bcc9e943c8a126dd8 | Returns dictonary that maps each word to its letter code words list of words return dictonary | [
{
"docid": "8d885fb8271e024d34facbf930cbf6d0",
"score": "0.8214427",
"text": "def map_of_codes(words):\n\tmap_of_codes = dict()\n\tcodes = []\n\tfor word in words:\n\t\tmap_of_codes[word] = letter_code(word)\n\treturn map_of_codes",
"title": ""
}
] | [
{
"docid": "5f97c1f59df0a248e91209194694929d",
"score": "0.7414668",
"text": "def make_word_dict(wordlist):\n alphabet = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'\n word_dict = dict()\n for word in wordlist:\n word_dict.setdefault(word[0].upper(), []).append(word.upper())\n return word_dict",
... |
bf6eda80eb6f20a7499caa7bc94fa3d0 | Parse and return input rangefile as dict | [
{
"docid": "6a631f5147d3d486373264ba1b05b358",
"score": "0.7883238",
"text": "def _parsefile(self, rngpath: str) -> dict:\n\n # TODO check it's a rng file (avoid utf-8 encoding errors)\n try:\n with open(rngpath, 'r') as file:\n r = [v.split() for v in file]\n ... | [
{
"docid": "d10c6e0f8acb3aa42547aa3663edd755",
"score": "0.6517698",
"text": "def _parse_input_file(input_file):\n input_dict = dict()\n logger = logging.getLogger(__name__)\n\n try:\n f = open(input_file, \"r\")\n for line in f:\n # Ignore comments in input file!\n ... |
b258891e1e264a29c11ff6d0fcfa079b | Create an HDFS paths to look at from provided main hdfs dir and provided time range list. | [
{
"docid": "9b30c587ff7b852baa427ddc269af5c9",
"score": "0.7487591",
"text": "def make_hdfs_path(hdir, trange):\n return ['%s/%s' % (hdir, d) for d in range_dates(trange)]",
"title": ""
}
] | [
{
"docid": "75d211320736d6cda3ae25d0d0e34509",
"score": "0.6589645",
"text": "def test_hdfs_paths ():\n \"\"\"\n hdfs = PyWebHdfsClient(host='localhost',port='50070', user_name='hdfs')\n hdfs.make_dir(\"inbound/bloomberg\") \n hdfs.make_dir(\"inbound/bloomberg/ohlcv\")\n hdfs.make_dir(... |
2fdd7fa013ed09db82187224e9f0dfd7 | Cleans the string from problematic chars | [
{
"docid": "70329abdd6d6f66c0d1ffdf75eb2ffab",
"score": "0.0",
"text": "def replace_char(text):\n\n for ch in ['/', '`', '*', '{', '}', '[', ']', '(', ')', '#', '+', '-', '.', '!', '\\$', ':', '|']:\n text = text.replace(ch, \"_\")\n return text",
"title": ""
}
] | [
{
"docid": "8c6de6119fd3932290e8db0f3495cf46",
"score": "0.7372472",
"text": "def Clean(s):\n for c in BAD_CHARACTERS:\n s = s.replace(c, '_')\n return s",
"title": ""
},
{
"docid": "a71c91b0a137d047cb307a2063bea342",
"score": "0.7360314",
"text": "def clean_string(s):\n c = ... |
2f40207c805b23dcf4c5f28e87c60024 | Retrun this object's __task_name | [
{
"docid": "c734aab3ea8671bfaac122fc4477f21f",
"score": "0.9185114",
"text": "def task_name(self) -> str:\n return self.__task_name",
"title": ""
}
] | [
{
"docid": "429f8ece8d00bee28363560ebd509627",
"score": "0.93508923",
"text": "def task_name(self):\n return self._task_name",
"title": ""
},
{
"docid": "633258a65ced3a28d4a6a4094dcccf55",
"score": "0.91086996",
"text": "def task_name(self):\n return function_or_class_name(... |
7f034481e85837eda6d8b7bd7bd789bb | r""" Resets the various data arrays on the object back to their original state. This is useful for repeating a simulation at different inlet conditions, or invasion points for instance. | [
{
"docid": "2193e11289f06efaf8f3a5957103b8c2",
"score": "0.6870243",
"text": "def reset(self):\n self[\"pore.invasion_pressure\"] = np.inf\n self[\"throat.invasion_pressure\"] = np.inf\n self[\"pore.invasion_sequence\"] = -1\n self[\"throat.invasion_sequence\"] = -1\n ... | [
{
"docid": "f0eddd2f77ea202d0c6aaaef3004d993",
"score": "0.7440329",
"text": "def reset(self):\n self.set_t(0.0)\n self.m = 0\n self.n = 0\n self.o = 0\n self.resize_arrays()\n self.clear_ijv()\n self.clear_ts()",
"title": ""
},
{
"docid": "63... |
1f541f95fbd8bd940dea6d66b7fb9ccc | Retrieve a list of all categories. | [
{
"docid": "e3e9bd171ad02482f1c9fb8c3af4bed6",
"score": "0.0",
"text": "def get_category() -> jsonify:\n\tcategories = []\n\tcategory_results = db.session.query(IncidentCategory).all()\n\tfor category in category_results:\n\t\tnew_category = {}\n\t\tnew_category['id'] = category.id\n\t\tnew_category['na... | [
{
"docid": "07b4db07f5ded899901b6122913b1154",
"score": "0.8321011",
"text": "def get_all_categories():\n categories_dict = get_categories_dict_db()\n return jsonify(categories_dict)",
"title": ""
},
{
"docid": "d1e978a0dd31186c3637d6d570a03874",
"score": "0.82590973",
"text": ... |
45ce4a07d98b6c6a05f38ced74b4eb0c | The number of successful requests the account has performed. | [
{
"docid": "6b0c1d7f270bf6b56f0725f0ccc3c186",
"score": "0.62084264",
"text": "def successful_api_calls(self):\n return self._successful_api_calls",
"title": ""
}
] | [
{
"docid": "496d049346c0727358a4441acc8cb687",
"score": "0.7959406",
"text": "def successfulCount(self) -> int:",
"title": ""
},
{
"docid": "2521faaba0023070a9f908979978721e",
"score": "0.69510853",
"text": "def nreturned(self):\n if not self._counters_calculated:\n ... |
8f03b306abc58c21ce7eb710ad1a723f | Define observation groups for a given table of bins. Define one group for each possible combination of the observation group axis bins, defined as rows in the input table. | [
{
"docid": "58d54a7800624b4b26151f929e05d855",
"score": "0.7689213",
"text": "def define_groups(self, table):\n if len(self.obs_groups_table.columns) is not 0:\n raise RuntimeError(\n \"Catched attempt to overwrite existing obs groups table.\")\n\n # define number... | [
{
"docid": "479a74bfcb57297f06609ec038a52285",
"score": "0.73450506",
"text": "def group_observation_table(self, obs_table):\n if 'GROUP_ID' in obs_table.colnames:\n raise KeyError(\n \"Catched attempt to overwrite existing grouping in the table.\")\n\n # read the... |
5200f887684da296076c157cf535b32d | create a new game play for the player | [
{
"docid": "468cfde9f3d6990ec2b206b070a01e69",
"score": "0.70035684",
"text": "def make_a_play(self, new_play):\n self.plays.append(new_play)",
"title": ""
}
] | [
{
"docid": "cee521afb114678b3670112c326212d2",
"score": "0.77359706",
"text": "def newGame():\r\n # click on Play\r\n pyautogui.click(PLAY_COORDS, duration=0.25)\r\n logging.debug('New game...')",
"title": ""
},
{
"docid": "73e3ac0ca67deb029ff26e2e9573bb94",
"score": "0.7456077"... |
ae80bc40a3c04877f91ff122bd1b76ea | Returns a dictionary of cluster groups, containg the users in each group. | [
{
"docid": "21e6d049f8a77f5287b79678d4323ba1",
"score": "0.6638795",
"text": "def get_cluster_groups(\n ocm_api: OCMBaseClient, cluster_id: str\n) -> dict[OCMClusterGroupId, OCMClusterGroup]:\n cluster_groups: dict[OCMClusterGroupId, OCMClusterGroup] = {}\n for group_dict in ocm_api.get_paginat... | [
{
"docid": "239f4d1489db15274ec8efcb1994e027",
"score": "0.7212028",
"text": "def list_groups(self):\n return self.session.query(schema.ClusterGroup).all()",
"title": ""
},
{
"docid": "c8d4adec92e4fc81ecacf195a047e5eb",
"score": "0.69516546",
"text": "def get_cluster_groups(gr... |
14ce7b744e56af6e0a63445334f0e67e | Internal method to get values of time series values in spring. Part of year aggregator function for gvg method. | [
{
"docid": "792bab097e456deb76234d4b9407e6d3",
"score": "0.0",
"text": "def _get_spring(series: Series, min_n_meas: int) -> float:\n inspring = _in_spring(series)\n if inspring.sum() < min_n_meas:\n return Series(nan)\n else:\n return series.loc[inspring]",
"title": ""
}
] | [
{
"docid": "78faf76c1525b9844ca51e96fbc8b9ac",
"score": "0.59957784",
"text": "def feb29(ts, dim='doy'):\n #return (ts.where(ts.doy.isin([59,60,61]),drop=True).mean(dim=dim).values)\n return (ts.where(ts.doy.isin([59,61]),drop=True).mean(dim=dim).values)",
"title": ""
},
{
"docid": "ea... |
597931493f291bce8f94e74758017c86 | Apply rankbased thresholding on given matrix. In RCut (also known as `kperdoc`), only `rank` best topics are assigned to each document. | [
{
"docid": "1f5e53d86b0f5f3524cee7273be71aeb",
"score": "0.5806677",
"text": "def r_cut(y, rank=3):\n y = np.array(y)\n y_pred = np.zeros(y.shape, dtype=bool)\n for i, row in enumerate(y):\n max_js = row.argsort()[-rank:][::-1]\n for j in max_js:\n y_pred[i, j] = True\n... | [
{
"docid": "e91e6f4e70eb1b78646730d67fa0b6ab",
"score": "0.61783934",
"text": "def rankify(mat, size=11):\n return generic_filter(mat, rankkern, size=(\n size, size), mode='constant', cval=-1)",
"title": ""
},
{
"docid": "0fad1c1f3d96675b4d2e7da640495382",
"score": "0.5966574",... |
52af6ff21b973aa2cd9c9089aed1f599 | Save a dataset in a way that it's readable by load_dataset. | [
{
"docid": "ae1e677c9c29db4870dce2133ad9eba1",
"score": "0.0",
"text": "def save_dataset(filename, theta_vector, amp_dataset, ph_dataset, x_grid):\n\tto_save = np.concatenate((theta_vector, amp_dataset, ph_dataset), axis = 1)\n\ttemp_x_grid = np.zeros((1,to_save.shape[1]))\n\tK = int((to_save.shape[1]-3... | [
{
"docid": "e208299ef2e05512fa69fd21543ef0cf",
"score": "0.7637095",
"text": "def _save_dataset(self):\n dataset = datastore.Resource.new(\n self.dataset_name,\n self._dataset_files,\n self._meta_data)\n repo = self.data_repo()\n repo.save(dataset, o... |
00c417c15b524e968fdfa3a8835f51cd | Updates the config with data from the cache | [
{
"docid": "513e60e2aad3bfe65b8444e2c411f1ae",
"score": "0.7814324",
"text": "async def update(self, data=None):\n if data is not None:\n self.cache.update(data)\n await self.api.update_config(self.cache)",
"title": ""
}
] | [
{
"docid": "a75a38ce910e27ffeae5c022e27e18c4",
"score": "0.786449",
"text": "def update_cache(self):\n pass",
"title": ""
},
{
"docid": "d29532092cd1e14fedd9af2eb790a4c7",
"score": "0.7683715",
"text": "async def update(self):\n await self.bot.api.update_config(self.fil... |
95d80d6dfef902c68f1348978cb4d026 | Read stock data (adjusted close) for given symbols from CSV files. | [
{
"docid": "85cde913e57d414b2527d791f792b1de",
"score": "0.6046349",
"text": "def get_data(symbols, dates):\r\n df = pd.DataFrame(index=dates)\r\n for symbol in symbols:\r\n df_temp = pd.read_csv(symbol_to_path(symbol), index_col='Date',\r\n parse_dates=True, usecols=['Date',... | [
{
"docid": "a8d8645fcc925ecee2412ed9a3db1768",
"score": "0.7471986",
"text": "def _open_csv(self):\n combined_index = None\n for symbol in self.symbol_list:\n path = self.csv_path + '/{}.csv'.format(symbol)\n self.symbol_data[symbol] = pd.read_csv(\n pa... |
a5717931205c99dbc7e5b99b793f36a7 | Ova funkcija je poopcenje funkcije operator_kanbaza. Ona daje matricni prikaz linearnog operatora f u paru baza (bazaD2,bazaK2) ako je on zadan u paru baza (bazaD1,bazaK1). Ako bazaK1 nije eksplicitno navedena, podrazumijeva se da je bazaK1=bazaD1. Ako bazaK2 nije eksplicitno navedena, podrazumijeva se da je bazaK2=baz... | [
{
"docid": "11a9ac321f1d945453fabe1b8393c2b6",
"score": "0.7151839",
"text": "def operator_baza(f,bazaD1,bazaD2,bazaK1=None,bazaK2=None,klasa='sympy'): \n if isfunction(f):\n #operator f je zadan formulom\n M = operator_kan(f,klasa)\n else:\n #operator f je zadan matricom ... | [
{
"docid": "af7549cc5d18b9cf9d56a081cd3520b8",
"score": "0.7329589",
"text": "def operator_kanbaza(f,baza1,baza2=None,klasa='sympy'): \n if isfunction(f):\n #operator f je zadan formulom\n M = operator_kan(f,klasa)\n else:\n #operator f je zadan matricom u paru kanonskih baza\... |
a2ba58efdc9e91c122fb0243afe6f507 | Extract the course descriptions from the document. Write them to the appropriate Course records in the database. | [
{
"docid": "ba84b03c2eca5d2d3c535e6f92301885",
"score": "0.75976646",
"text": "def ExtractCourseDescriptions(document: Document, courses: List[Course]) -> None:\n\n courseDescriptionOn = False\n allparagraphs = []\n pertinentParagraphs = []\n number = 0\n pHeader = \"\"\n hIsCourseTitl... | [
{
"docid": "3c0601b75ff7eab4168a97065371a8bf",
"score": "0.7158249",
"text": "def ExtractCourseAndDescription(firebase: firebase, document: Document, knowledgeAreas: List[KnowledgeArea], courses: List[Course], catalogId: str) -> None:\n\n global currentKnowledgeArea\n\n knowledgeAreaId = \"\"\n ... |
10be4055dc44747101cc3d0847c103b0 | This is the main method of the tool. | [
{
"docid": "5987afff833f5856b1bc716afc9b2e8a",
"score": "0.0",
"text": "def main(self):\n if not os.path.isfile(os.path.join(self.project_directory, \"make_project.json\")):\n print(f\"Generating CProject in {self.project_directory}...\")\n self.normami(\"ami-makeproject\", ... | [
{
"docid": "ec021328057f10f8af523fff413bbe9a",
"score": "0.8747809",
"text": "def main ():",
"title": ""
},
{
"docid": "ec021328057f10f8af523fff413bbe9a",
"score": "0.8747809",
"text": "def main ():",
"title": ""
},
{
"docid": "1d4484b0529dbe0541b241834cb12a3e",
"scor... |
c7cafb5f4313d1b5fa9cdfd206771bee | Binary must wrap a string type | [
{
"docid": "9ed9bd995a217fb464f9ddab65e1e4cd",
"score": "0.7420099",
"text": "def test_binary_force_string(self):\n with self.assertRaises(TypeError):\n Binary(2)",
"title": ""
}
] | [
{
"docid": "eea0267dc26b875303145a2447b0192b",
"score": "0.72017926",
"text": "def isbinary(s, params, ui, **kwargs):\n return s",
"title": ""
},
{
"docid": "5242fe89c082c090012e14c26675050c",
"score": "0.7061668",
"text": "def test_binary_converts_unicode(self):\n b = Bina... |
1b26a7e2470731048736735d41e820ca | centers the window to the screen | [
{
"docid": "6622fe436845f7b3986af42688cfc35c",
"score": "0.8327689",
"text": "def _center_window(self):\r\n screen = QtGui.QDesktopWidget().screenGeometry()\r\n size = self.geometry()\r\n self.move(\r\n (screen.width() - size.width()) * 0.5,\r\n (screen.height(... | [
{
"docid": "0470869de0fe4c2dc345e28a653fcd8f",
"score": "0.8163197",
"text": "def center(self,win):\n win.update_idletasks()\n width = win.winfo_width()\n frm_width = win.winfo_rootx() - win.winfo_x()\n win_width = width + 2 * frm_width\n height = win.winfo_height()\n ... |
495cf48c5e18e12b7bfaf87cc50f1428 | Wisdom AI engine. This extracts insights from documents and returns key points, abstracts, wordclouds and PDF viewer if possible. | [
{
"docid": "932e0ca123feee64a87a4865f30a383f",
"score": "0.5761071",
"text": "def wisdom(search_me, source, pdfurl, userid):\n ### source needs to be name of data source (\"arxiv\", \"google scholar\", \"doaj\")\n search_me = search_me.strip()\n # check if pdfurl has been found before\n pdf ... | [
{
"docid": "d04f78c317274e362e0222600c00b016",
"score": "0.5694965",
"text": "def main():\n logging.basicConfig(level=logging.DEBUG)\n custom_embedding = True\n\n # Download embeddings'\n if custom_embedding:\n embedding_path = '../data/custom_embedding.pkl'\n embedding_index_p... |
8e011d0b414fc36e0eedeb9448e99c0d | returns a dict with some joint info | [
{
"docid": "8972d83bad2dffd5c842b188e5eca22d",
"score": "0.7336723",
"text": "def _get_joint_info(self, body_id, joint_id):\n # todo: make joint_info a class so we don't have to memorise the keys\n info = self._p.getJointInfo(body_id, joint_id)\n joint_info = {\n 'id': in... | [
{
"docid": "d062645d5feee624eb9b8bbb5e39aa28",
"score": "0.7694279",
"text": "def joint_dict(self):\n self.check_joint_names()\n return dict(zip(self.joint_names, self.joint_values))",
"title": ""
},
{
"docid": "711b5a3870f4e152646742d8714a6112",
"score": "0.6767711",
"... |
5d53dc63cf45f2b3db50f8910018ee32 | Initialize with the column names (in a XiboEvent tuple) use in Xibo JSON responses. | [
{
"docid": "4b51f78cbba9b28efcdfefb61709a950",
"score": "0.58089435",
"text": "def __init__(self, column_names):\n self.column_names = column_names",
"title": ""
}
] | [
{
"docid": "b91406418d4f7587da0fc1f8a3998de4",
"score": "0.5988165",
"text": "def json_to_xibo_event(self, json_event):\n return XiboEvent(\n xibo_id=json_event[self.column_names.xibo_id],\n meetup_id=json_event[self.column_names.meetup_id],\n name=json_event[self... |
9567da02ab3490901b639507bf63db42 | Returns a list of locales found in the "locales" property of the manifest. This will convert locales found in the SHORTER_LANGUAGES setting to their full locale. It will also remove locales not found in AMO_LANGUAGES. | [
{
"docid": "b3e587f2da19952e54367eb789e2dd40",
"score": "0.6775279",
"text": "def get_supported_locales(manifest):\n return sorted(filter(None, map(find_language, set(\n manifest.get('locales', {}).keys()))))",
"title": ""
}
] | [
{
"docid": "c7eeb0b6810a336325fe917618b5dcf9",
"score": "0.74035686",
"text": "def get_locales(app: Sphinx) -> List[str]:\n # Manually configured list of locales\n sitemap_locales: Optional[List[str]] = app.builder.config.sitemap_locales\n if sitemap_locales:\n # special value to add not... |
0514c3e3f00ed75fc520a4e64741f14f | Renders a template from the given template source string with the given context. Template variables will be autoescaped. | [
{
"docid": "a770a3bef0b5686c817732c498e625f6",
"score": "0.8366585",
"text": "def render_template_string(source, **context):\n ctx = _app_ctx_stack.top\n ctx.app.update_template_context(context)\n return _render(ctx.app.jinja_env.from_string(source), context, ctx.app)",
"title": ""
}
] | [
{
"docid": "2591aad30c9c871ce95371d363072d1e",
"score": "0.8500779",
"text": "def render_template_string(source, **context):\n ctx = stack.top\n lookup = _lookup(ctx.app)\n template = Template(source, lookup=_lookup(ctx.app), **lookup.template_args)\n return _render(template, context, ctx.ap... |
6287c1dd2a1e7937b08a68ddf5349771 | T.__new__(S, ...) > a new object with type S, a subtype of T | [
{
"docid": "4cd774e88eb43b6786dfb6dbd212399c",
"score": "0.72122014",
"text": "def __new__(S, *more): # real signature unknown; restored from __doc__\r\n pass",
"title": ""
}
] | [
{
"docid": "4fb50b5679d0bc2205d77fa7704c8195",
"score": "0.74194497",
"text": "def __new__(self,S, ):\n pass",
"title": ""
},
{
"docid": "4fb50b5679d0bc2205d77fa7704c8195",
"score": "0.74194497",
"text": "def __new__(self,S, ):\n pass",
"title": ""
},
{
"doc... |
b5590739bfefe0c7424bd86d5dede73b | returns a connection to mechanical turk. | [
{
"docid": "3b946ba33bb556f7b8bda8e254aea043",
"score": "0.79187876",
"text": "def get_mt_conn(sandbox=settings.SANDBOX):\n if sandbox:\n host=\"mechanicalturk.sandbox.amazonaws.com\"\n else:\n host=\"mechanicalturk.amazonaws.com\"\n\n return connection.MTurkConnection(\n a... | [
{
"docid": "56a5a7204653cff73a19f5b0b1ac1780",
"score": "0.627893",
"text": "def connection():\n return get_connection()",
"title": ""
},
{
"docid": "c29a8ed3dbdcb9193cd9222e6b99893b",
"score": "0.6158203",
"text": "def _connect(self):\n return httplib.HTTPConnection(se... |
9b337da8305531e87506f09a76a8b1cf | Receives a message from the CCM using the configured adapter | [
{
"docid": "13b97c92b7ab12f1f249fbb976afc713",
"score": "0.73328966",
"text": "def receive_message_from_ccm(self) -> str:\n with hw_lock:\n return self.ccm_adapter.receive_message_with_stop_byte()",
"title": ""
}
] | [
{
"docid": "8c6cd8c20e92b52dfd75e49af8253aa7",
"score": "0.7003274",
"text": "def receive_message(self):\n pass",
"title": ""
},
{
"docid": "586a9036a9863930cd60d2c5f50d037a",
"score": "0.6642486",
"text": "def rcv_message(self, message):\n pass",
"title": ""
},
{
... |
94d97e50552b686245c4cf9ada41c177 | Permission Checking Function to be used as a Dependency for API endpoints. This is used as a helper. This will either return a User object to the calling method if the user meets the authentication requirements, or it will raise a CredentialException and prevent the method that depends on this from continuing. | [
{
"docid": "70d2872c3c88f205c82a40f0a7ad2ad2",
"score": "0.0",
"text": "def current_user_researcher(token: str = Depends(oauth2_scheme)):\n user = get_current_user(token)\n if not any(role in [Roles.admin.name, Roles.researcher.name] for role in user.roles):\n raise CredentialException()\n\... | [
{
"docid": "5e224920ce0bc70b58729d9fc529e91d",
"score": "0.7034291",
"text": "def check_auth(func):\n\n @wraps(func)\n def invoke(self, *args, **kwargs):\n try:\n self.user = self.state.get_user()\n if self.user is None:\n print(\... |
814d29215dd48fa3003c1a2a39a7b4ff | Looks up the UID of the resource with the given name. | [
{
"docid": "7ccbfb8989ccbd49b774db442cfa0174",
"score": "0.8512743",
"text": "def resourceUIDForName(self, name):\n uid = self._db_value_for_sql(\"select UID from RESOURCE where NAME = :1\", name)\n\n return uid",
"title": ""
}
] | [
{
"docid": "22c43a1d43961de77cb6605b3291cf35",
"score": "0.6511902",
"text": "def resource_uid(self) -> Optional[str]:\n return pulumi.get(self, \"resource_uid\")",
"title": ""
},
{
"docid": "bd16aa5bb834eff63cf6559219c3e5ea",
"score": "0.65088683",
"text": "def _get_uid(self,... |
7fedc36119e96a11cbfecfce4483d863 | Creates a view that allows ANY logged in user to submit a field trip request but checks to make sure the admin is accepting requests first. | [
{
"docid": "d808caee7bdabd7b0bddaebc979f5bd0",
"score": "0.66285205",
"text": "def create(request):\n admin_option = AdminOption.objects.get()\n if not admin_option.window_open:\n return HttpResponse(\"New field trip requests have been disabled.\")\n\n title = \"Submit a Field Trip Reque... | [
{
"docid": "34a30345c20e0a3d5e61f19441df8845",
"score": "0.6473721",
"text": "def home():\n\n form = TripForm()\n \n if form.validate_on_submit():\n \n if validate_dates(form.start_date_time.data, form.end_date_time.data, form):\n \n return render_template('creat... |
99d3bdd659d55dca74810a5a89293664 | Write a pd.DataFrame to a google sheets sheet | [
{
"docid": "eaa125b9edb84e9ad4fed7d55fc2e99d",
"score": "0.63241166",
"text": "def write_df(wks, df, row, col):\n\n # header row\n for j, col_name in enumerate(df.columns):\n if 'AOL' in col_name:\n wks.update_cell(row+1, col+j+1, 'AOL')\n wks.update_cell(row+2, col+j+... | [
{
"docid": "f2cbcdddc55b0d33b6dba497953238b6",
"score": "0.7028948",
"text": "def write_google_sheet(results_dict, row=2, name='results_layers', sheet_name='run1'):\n # Google API stuff\n scope = [\"https://www.googleapis.com/auth/drive\"]\n creds = ServiceAccountCredentials.from_json_keyfile_n... |
9410c012f0a34a67a772c66287a39474 | moves item identified by key to the head of the list | [
{
"docid": "82f0a5cf6411760d15775794141626bc",
"score": "0.814508",
"text": "def movehead(self, key):\n #remove from old position in list\n n = self.d[key]\n n.next.prev = n.prev\n n.prev.next = n.next\n #put in front position of list\n self.l.next.prev = n\n ... | [
{
"docid": "67072f43b5e8d065011b2c282e4bfc4b",
"score": "0.67528284",
"text": "def change_key(self, key, newkey):\n if key in self.list:\n self.list.remove(key)\n self.list.append(newkey)\n self.sort()\n else:\n raise KeyError('No such key')",
... |
fe32bdc2e08352e3b214f2ef83cc9a05 | Return vector of distances (from vector a to each vector in matrix B). | [
{
"docid": "f90a260f3c1a7dec66483c160d36697c",
"score": "0.74742806",
"text": "def distance(self, a, B):\n return np.linalg.norm(a-B, axis=1, ord=2)",
"title": ""
}
] | [
{
"docid": "b999fcf1356d308e2fae0525e6491457",
"score": "0.6998436",
"text": "def calculate_distance_list(rA, rB):\n squared_sum = 0\n for dim in range(len(rA)):\n squared_sum += (rA[dim] - rB[dim])**2\n \n distance = np.sqrt(squared_sum)\n return distance",
"title": ""
},
... |
e3681f8e80d992b6a48d659329f3e97f | @ param query_embedds = (n, d) @ param target_embedds = (n, d) @ param img_ids = (n,) | [
{
"docid": "5aac2fbdaeacbcc9c51d58942cf4ec1f",
"score": "0.6189335",
"text": "def ranking(query_embedds, target_embedds, img_ids):\n\n cos_sim = torch.mm(query_embedds,target_embedds.T)/ \\\n torch.mm(query_embedds.norm(2, dim=1, keepdim=True),\n target_embedds.n... | [
{
"docid": "2a0d8c3f6444f00756c4da15a4161f82",
"score": "0.6125822",
"text": "def get_images(k):\n\n keys = np.linspace(0, 99, 50)\n values = np.random.randn(50, 50)\n image_embeddings = dict(zip(keys, values))\n\n # with open(\"image_embeddings.pickle\", \"rb\") as f:\n # image_embedding... |
cf090fe9542868e99484e51b47cd4719 | Find the first status matching the given id | [
{
"docid": "273f0441220e69f71c097180394e0b39",
"score": "0.5996115",
"text": "def get_card_status(status_id):\n status = data_manager.execute_select(\n \"\"\"\n SELECT * FROM statuses s\n WHERE s.id = %(status_id)s;\n \"\"\", {\"status_id\": status_id})\n return status"... | [
{
"docid": "3f3256587c96740dc1d2b1c1aaffc75b",
"score": "0.7008374",
"text": "def get_status(self, status_id: StatusID):\n pass",
"title": ""
},
{
"docid": "7c7e39e58fe3e485e0cc9f321a375e01",
"score": "0.6748259",
"text": "def get_card_status(status_id):\n statuses = persis... |
94c53df768d04da17d323d893257f0e7 | Testing the building class function. | [
{
"docid": "654d6599c56fe9c783f45854ff6d073c",
"score": "0.6755711",
"text": "def test_building_class():\n # Testing the ruleset for classifying Hazus building class\n res = []\n ref_class = ['WSF', 'WMUH']\n ref = np.ones(2)\n for i in range(2):\n data_dir = os.path.join(cur_dir, ... | [
{
"docid": "79785cbd6d603ba51a4083520d3df1f4",
"score": "0.7564869",
"text": "def test_01_BuildObjects(self):\n pass",
"title": ""
},
{
"docid": "3b0ee69e1abd41003d408ff9161a2153",
"score": "0.7322961",
"text": "def test_build(self): \n\n Builder(self.app)\... |
b9dd9e8181293cb24fa0c3e0b144cff8 | Entry point of program | [
{
"docid": "7b29257fa5d2d624779dbb9d8e13a809",
"score": "0.0",
"text": "def main():\n client.run(secrets.TOKEN)",
"title": ""
}
] | [
{
"docid": "1d4484b0529dbe0541b241834cb12a3e",
"score": "0.8570562",
"text": "def main():\n\t\tpass",
"title": ""
},
{
"docid": "ec021328057f10f8af523fff413bbe9a",
"score": "0.8506079",
"text": "def main ():",
"title": ""
},
{
"docid": "ec021328057f10f8af523fff413bbe9a",
... |