0 for x in datas.target]\n y_nz = np.arange(datas.target.shape[0])[y_nz]\n\n preds_all = tf_eval_preds[y_nz, :]\n y = datas.target[y_nz, :]\n\n x = scipy.sparse.lil_matrix(y.shape)\n x.rows = preds_all\n x.data = np.ones_like(preds_all)\n x = x.toarray()\n\n z = x * y\n return np.mean(np.divide((np.sum(z, 1)), np.sum(y, 1)))","def test_score_across_multiple_documents(self):\n\n \"\"\"\n Create the test data.\n \"\"\"\n tokenizer = Tokenizer(stem=False)\n posts = [\n \"Erdogan with threats to attack regime forces 'everywhere' in Syria\",\n \"Syria reacts to Erdogan's threats: Damascus says Erdogan 'disconnected from reality' after threats\",\n ]\n\n corpus = [ Document(post, tokenizer.tokenize(post)) for post in posts ]\n\n extractor = TokenExtractor(tokenizer=tokenizer)\n scorer = TFIDFScorer({ 'erdogan': 1, 'threats': 2 }, 10)\n candidates = extractor.extract(corpus)\n scores = scorer.score(candidates, normalize_scores=False)\n self.assertEqual(3 * math.log(10 / 1, 10), scores.get('erdogan'))\n self.assertEqual(3 * math.log(10 / 2, 10), scores.get('threats'))","def score(self, text):\n logger.debug(\"score on an instance of len {0}\".format(len(text)))\n fv = self.instance2fv(text)\n fv /= np.sqrt((fv*fv).sum()) # normalize vector to len 1\n fdot = self.lprot.dot(fv) \n retval = dict(zip(self.langs, fdot))\n return retval","def evaluate_ngrams(eval_dataset, trigram_counts, bigram_counts, unigram_counts, train_token_count, lambda1, lambda2):\n perplexity = 0\n ### YOUR CODE HERE\n raise NotImplementedError\n ### END YOUR CODE\n return perplexity","def _compute_embedding_score_per_record(self, wide_ftrs_idx_with_value_per_record):\n\n # Split idx and val back\n wide_ftrs_sp_idx, wide_ftrs_sp_val = tf.split(wide_ftrs_idx_with_value_per_record, 2, axis=-1)\n wide_ftrs_sp_idx = tf.cast(wide_ftrs_sp_idx, dtype=tf.int64)\n\n # Transformation\n shape = tf.ones(shape=[tf.shape(wide_ftrs_sp_idx)[0], 1], dtype=tf.int64) * self._num_wide_sp\n valid_wide_ftrs_idx_mask = tf.cast(tf.not_equal(wide_ftrs_sp_idx, self._padding_idx), tf.float32)\n wide_ftrs_sp_idx = tf.expand_dims(wide_ftrs_sp_idx, -1)\n\n # Get sparse feature vector v where v[ftr_idx_i] = ftr_val_i and v[other] = 0\n wide_ftrs_sp = sparse_tensor_merge(wide_ftrs_sp_idx,\n wide_ftrs_sp_val * valid_wide_ftrs_idx_mask,\n shape)\n\n # Feature weights\n bias = self.ftrs_weight[0]\n\n # Compute embedding\n embedding = tf.sparse.matmul(wide_ftrs_sp, self.ftrs_weight) + bias\n return embedding","def evaluate(golds, preds):\n correct_words = 0\n correct_sentences = 0\n\n words_total = 0.0\n sentences_total = 0.0\n\n for gold, pred in zip(golds, preds):\n # check whether entire tag sequence was correct\n sentences_total += 1\n if pred == gold:\n correct_sentences += 1\n\n # check individual tags for correctness\n for predicted_tag, gold_tag in zip(pred, gold):\n words_total += 1\n if predicted_tag == gold_tag:\n correct_words += 1\n\n return (correct_sentences/sentences_total, correct_words/words_total)","def evaluate_all(\n data: List[Tuple[int, str, List[Instance]]], pred_data: List[List[Instance]],\n) -> Tuple[Score, Score, Tuple[List[List[str]], List[List[str]]]]:\n inexact_counts = defaultdict(lambda: np.zeros(3))\n exact_counts = defaultdict(lambda: np.zeros(3))\n\n iobs = []\n for (_, txt, gold), pred in zip(data, pred_data):\n\n for k, v in evaluate(gold, pred, exact=False).items():\n inexact_counts[k] += v\n for k, v in evaluate(gold, pred, exact=True).items():\n exact_counts[k] += v\n iobs.append(iob_conversion(txt, gold, pred))\n\n gold_iob, pred_iob = zip(*iobs)\n\n return (counts_to_scores(inexact_counts), counts_to_scores(exact_counts), (gold_iob, pred_iob))","def evaluate(self, sess, data_gen):\n pred_labels = np.array([], dtype=np.intp)\n labels = np.array([], dtype=np.intp)\n for inputs, seq_length, batch_labels in data_gen:\n feed_dict = self.create_feed_dict(inputs, seq_length, batch_labels,\n self.config.dropout)\n pred = sess.run(self.pred, feed_dict)\n pred = softmax(pred, -1)\n pred = binarize(pred[:, 1], self.threshold)\n\n pred_labels = np.concatenate((pred_labels, pred))\n labels = np.concatenate((labels, batch_labels))\n\n score = f1_score(labels, pred_labels)\n\n return score, pred_labels","def compute_ave_score_w_sample(genes, samples):\n\n scores = np.zeros(len(genes), dtype=np.uint32)\n\n for i, v in enumerate(genes):\n for j in samples:\n score, _ = run_duel(v, j)\n scores[i] += score\n continue\n continue\n\n return scores / len(samples)","def score_snippets(snippets, scorer):\n snippet_expressions = [snippet.embedding for snippet in snippets]\n all_snippet_embeddings = torch.stack(snippet_expressions, dim=1)\n\n scores = torch.t(torch.mm(torch.t(scorer), all_snippet_embeddings))\n\n if scores.size()[0] != len(snippets):\n raise ValueError(\"Got \" + str(scores.size()[0]) + \" scores for \" + str(len(snippets)) + \" snippets\")\n\n return scores, [snippet.name for snippet in snippets]","def eval(self):\n params = self.params\n langs = ['en', params.target_lang]\n self.encoder.eval()\n self.proj.eval()\n\n scores = OrderedDict({'epoch': self.epoch})\n\n for splt in ['valid', 'test']:\n\n for lang in langs:\n if lang == 'en' and splt == 'test' or lang != 'en' and splt == 'valid':\n continue\n lang_id = params.lang2id[lang if lang != 'jp' else 'ja']\n valid = 0\n total = 0\n\n for batch in self.get_iterator(splt, lang):\n # batch\n (sent1, len1), idx = batch\n # set max length to 256, avoid position embedding overflow and save time.\n x, lengths = truncate(sent1, len1, 256, params.eos_index)\n lang_ids = x.clone().fill_(lang_id)\n\n y = self.data[lang][splt]['y'][idx]\n\n # cuda\n x, y, lengths, lang_ids = to_cuda(x, y, lengths, lang_ids)\n\n # forward\n output = self.proj(self.encoder.get_embeddings(x, lengths, langs=lang_ids))\n predictions = output.data.max(1)[1]\n\n # update statistics\n valid += predictions.eq(y).sum().item()\n total += len(len1)\n\n # compute accuracy\n acc = 100.0 * valid / total\n scores['CLF_%s_%s_acc' % (splt, lang)] = acc\n logger.info(\"CLF - %s - %s - Epoch %i - Acc: %.1f%%\" % (splt, lang, self.epoch, acc))\n\n logger.info(\"__log__:%s\" % json.dumps(scores))\n return scores","def match_data(embeddings, z_scores):\r\n print(\"Matching protein IDs...\", end=\"\")\r\n\r\n x, y = [], []\r\n # this could just as well be the keys from the z-scores, since the two of them are identical\r\n protein_ids = embeddings.keys()\r\n for protein_id in protein_ids:\r\n x.append(embeddings[protein_id])\r\n y.append(z_scores[protein_id])\r\n\r\n print(f\"done!\")\r\n return x, y","def qualify_words():\n config = get_config()\n\n all_feature_matrices = []\n all_opinion_matrices = []\n\n # first 5 parts are labeled, thus are useful\n all_feature_label_vectors = []\n all_opinion_label_vectors = []\n\n for fname in config.file_names:\n feature_X, feature_dims = load_feature_matrices(fname)\n opinion_X, opinion_dims = load_opinion_matrices(fname)\n feature_y = load_feature_labels(fname)\n opinion_y = load_opinion_labels(fname)\n\n # append to all collector\n all_feature_matrices.append(feature_X)\n all_feature_label_vectors.append(feature_y)\n all_opinion_matrices.append(opinion_X)\n all_opinion_label_vectors.append(opinion_y)\n # use first 5 for training\n # stack first 5\n feature_training_X = []\n feature_training_y = []\n opinion_training_X = []\n opinion_training_y = []\n for i in range(5):\n feature_training_X.append(all_feature_matrices[i])\n feature_training_y.append(all_feature_label_vectors[i])\n opinion_training_X.append(all_opinion_matrices[i])\n opinion_training_y.append(all_opinion_label_vectors[i])\n\n feature_training_X = np.hstack(feature_training_X)\n feature_training_y = np.hstack(feature_training_y)\n opinion_training_X = np.hstack(opinion_training_X)\n opinion_training_y = np.hstack(opinion_training_y)\n\n # using combination of rule and ranking score as features\n feature_model = MultinomialNB()\n opinion_model = MultinomialNB()\n\n # training\n feature_model.fit(np.transpose(feature_training_X), feature_training_y.ravel())\n opinion_model.fit(np.transpose(opinion_training_X), opinion_training_y.ravel())\n\n # predicting on candidate aspects and opinions, extracted from amazon reviews\n for i in range(5, len(config.file_names)):\n fname = config.file_names[i]\n feature_pred = feature_model.predict_proba(\n np.transpose(all_feature_matrices[i]))[:,1]\n opinion_pred = opinion_model.predict_proba(\n np.transpose(all_opinion_matrices[i]))[:,1]\n # pickle the prediction results\n with open('../results/' + fname + '_feature_pred_score.pickle', 'wb') as f:\n pickle.dump(feature_pred, f)\n with open('../results/' + fname + '_opinion_pred_score.pickle', 'wb') as f:\n pickle.dump(opinion_pred, f)","def fashion_similarity(input_txt, features, keys):\n feature_index = keys.index(input_txt)\n input_vector = features[feature_index]\n\n scores = [similarity_function(input_vector, partner) for partner in features]\n return scores","def evaluate(self, data, category, dims=None, overall=True):\n n_data = len(data)\n eval_scores = [{} for _ in range(n_data)]\n\n if dims == None:\n eval_dims = self.dimensions\n else:\n assert isinstance(dims, list)\n eval_dims = dims\n\n for dim in eval_dims:\n # Calculate summation score for 'engagingness'\n if dim == 'engagingness':\n src_list, output_list, context_list = [], [], []\n n_sents = [] # the number of sentences in each generated response\n for i in range(n_data):\n source = data[i]['source']\n context = data[i]['context']\n system_outputs = sent_tokenize(data[i]['system_output'])\n n_sents.append(len(system_outputs))\n for j in range(len(system_outputs)):\n src_list.append(source)\n context_list.append(context)\n output_list.append(system_outputs[j])\n input_list = add_question(dimension=dim,\n output=output_list,\n src=src_list,\n context=context_list,\n task=self.task)\n sent_score = self.scorer.score(input_list, self.task, category, dim)\n\n # Get the summation score for each sample\n start_idx = 0\n score = []\n for cur_n_sent in n_sents:\n score.append(sum(sent_score[start_idx:start_idx + cur_n_sent]))\n start_idx += cur_n_sent\n\n # Calculate turn-level score for other dimensions\n elif dim in ['naturalness', 'coherence', 'groundedness', 'understandability']:\n src_list, output_list, context_list = [], [], []\n for i in range(n_data):\n src_list.append(data[i]['source'])\n output_list.append(data[i]['system_output'])\n context_list.append(data[i]['context'])\n input_list = add_question(dimension=dim,\n output=output_list,\n src=src_list,\n context=context_list,\n task=self.task)\n score = self.scorer.score(input_list, self.task, category, dim)\n\n # Please customize other dimensions here for summarization\n else:\n raise NotImplementedError('The input format for this dimension is still undefined. \\\n Please customize it first.')\n\n for i in range(n_data):\n eval_scores[i][dim] = score[i]\n\n # Customize your overall score here.\n if overall == True:\n for i in range(n_data):\n eval_scores[i]['overall'] = np.mean(list(eval_scores[i].values()))\n\n return eval_scores","def test_mean_results(self, concept: Concept):\n concept_args = dict(concept=concept, model_stump=None)\n for desc, (embs, (m_w, m_b)) in self.INTUITIVE_MEAN_EXAMPLES.items():\n m_w: np.ndarray = np.array(m_w)\n embeddings = [ConceptEmbedding(normal_vec=w, support_factor=b,\n **concept_args)\n for w, b in embs]\n # Actual routine\n m_emb: ConceptEmbedding = ConceptEmbedding.mean(embeddings)\n context_info = ((\"context:\\n mean embedding: ({}, {}, 1.)\"\n \"\\n in embeddings ({}) as (normal vec, support, \"\n \"scaling):\\n {}\")\n .format(m_emb.normal_vec, m_emb.support_factor,\n desc,\n [(e.normal_vec, e.support_factor,\n e.scaling_factor)\n for e in embeddings]))\n\n # Format checks\n assert m_emb.normal_vec.shape == embeddings[0].normal_vec.shape\n assert np.array(m_emb.support_factor).shape == np.array(\n embeddings[0].support_factor).shape\n\n # Value checks\n # Scaling\n expected_scaling: float = float(np.mean(\n [e.scaling_factor for e in\n [e.normalize() for e in embeddings]]))\n assert float(m_emb.scaling_factor) == expected_scaling, \\\n (\"Mean scaling wrong: expected {}., but was {}; {}\"\n .format(expected_scaling, m_emb.scaling_factor, context_info))\n # Normal vector\n assert np.allclose(m_emb.normal_vec, m_w), \\\n (\"Wrong mean normal vector: expected {}, but was {}; {}\"\n .format(m_w, m_emb.normal_vec, context_info))\n # Support\n assert np.allclose(m_emb.support_factor, m_b), \\\n (\"Wrong mean support factor: expected {}, but was {}; {}\"\n .format(m_b, m_emb.support_factor, context_info))","def evaluate_ngrams(eval_dataset, trigram_counts, bigram_counts, unigram_counts, train_token_count, lambda1, lambda2):\n perplexity = 0\n\n ### YOUR CODE HERE\n def calc_prob(sentense, i, word, trigram_counts, bigram_counts, unigram_counts, train_token_count, model):\n prob = 0.0\n prev_word = sentense[i - 1]\n prev_to_prev_word = sentense[i - 2]\n\n if model == \"unigram\":\n if word in unigram_counts:\n prob = (unigram_counts[word] + 0.0) / train_token_count\n else:\n prob = (unigram_counts[word_to_num['UUUNKKK']] + 0.0) / \\\n train_token_count\n\n if model == \"bigram\":\n if (prev_word, word) in bigram_counts:\n prob = (bigram_counts[(prev_word, word)] + 0.0) / \\\n unigram_counts[prev_word]\n # print(num_to_word[prev_word] ,num_to_word[word])\n # print(bigram_counts[(prev_word, word)])\n # print(unigram_counts[prev_word])\n # print(\"---------------------------\")\n else:\n prob = 0.0\n\n if model == \"trigram\":\n if (prev_to_prev_word, prev_word, word) in trigram_counts:\n prob = (trigram_counts[(prev_to_prev_word, prev_word, word)] + 0.0) \\\n / bigram_counts[(prev_to_prev_word, prev_word)]\n # / bigram_counts[(prev_word, word)] #this according to lecture notes slide 27\n else:\n prob = 0.0\n\n return prob\n\n l = 0\n num_of_words = 0\n\n ##########3\n better_than_chance = 0\n ###########\n\n for sentense in eval_dataset:\n for i, word in enumerate(sentense[2:]):\n num_of_words += 1\n prob = lambda1 * calc_prob(sentense, i + 2, word, trigram_counts, bigram_counts, unigram_counts,\n train_token_count, \"trigram\") + \\\n lambda2 * calc_prob(sentense, i + 2, word, trigram_counts, bigram_counts, unigram_counts,\n train_token_count, \"bigram\") + \\\n (1 - lambda1 - lambda2) * calc_prob(sentense, i + 2, word, trigram_counts, bigram_counts,\n unigram_counts, train_token_count, \"unigram\")\n ######################################\n if prob > (1.0 / vocabsize):\n better_than_chance += 1\n #########################\n l += np.log2(prob)\n l /= num_of_words\n perplexity = 2 ** -l\n\n print(\"better_than_chance:\", (better_than_chance + 0.0) / num_of_words)\n\n ### END YOUR CODE\n return perplexity","def evaluate_bias(ds: List[str], predicted: List[GENDER]) -> Dict:\r\n assert (len(ds) == len(predicted)) # must have same length to create tuples\r\n\r\n conf_dict = defaultdict(lambda: defaultdict(lambda: 0))\r\n total = defaultdict(lambda: 0) # increment values if we have any gender\r\n pred_cnt = defaultdict(lambda: 0)\r\n correct_cnt = defaultdict(lambda: 0) # increment values if true_gender == predicted_gender\r\n\r\n count_unknowns = defaultdict(lambda: 0)\r\n\r\n for (gold_gender, word_ind, sent, profession), pred_gender in zip(ds, predicted):\r\n # # IMPORTANTE NOTE :\r\n # need to works with .name of GENDER object for an unknown reason\r\n\r\n if isinstance(pred_gender, str): # can happen in spacy languages 'fr', 'es' or 'it\r\n pred_gender = SPACY_GENDER_TYPES[pred_gender]\r\n\r\n # tuples of values in ds and values in predicted\r\n if pred_gender.name == GENDER.ignore.name:\r\n continue # skip analysis of ignored words\r\n\r\n gold_gender = WB_GENDER_TYPES[gold_gender] # allows Winobias gender type conversion\r\n\r\n if pred_gender.name == GENDER.unknown.name:\r\n count_unknowns[gold_gender] += 1 # increment values for any unknown pred_gender\r\n\r\n profession = profession.lower()\r\n\r\n total[gold_gender] += 1\r\n\r\n if pred_gender.name == gold_gender.name:\r\n correct_cnt[gold_gender] += 1\r\n\r\n pred_cnt[pred_gender.name] += 1\r\n\r\n conf_dict[gold_gender][pred_gender] += 1\r\n\r\n all_total = sum(total.values())\r\n\r\n output_dict = {} # init output dictionnary\r\n # Compute metrics\r\n accuracy = round((sum(correct_cnt.values()) / all_total) * 100, 1) # compute accuracy\r\n output_dict['acc'] = accuracy\r\n\r\n if (total[GENDER.male] == 0) | (pred_cnt[GENDER.male.name] == 0): # Avoid ZeroDivisionError\r\n output_dict['f1_male'] = None\r\n else:\r\n recall_male = round((correct_cnt[GENDER.male] / total[GENDER.male]) * 100, 1) # compute metrics for male\r\n prec_male = round((correct_cnt[GENDER.male] / pred_cnt[GENDER.male.name]) * 100, 1)\r\n f1_male = round(calc_f1(prec_male, recall_male), 1)\r\n output_dict['f1_male'] = f1_male\r\n\r\n if (total[GENDER.female] == 0) | (pred_cnt[GENDER.female.name] == 0): # Avoid ZeroDivisionError\r\n output_dict['f1_female'] = None\r\n else:\r\n recall_female = round((correct_cnt[GENDER.female] / total[GENDER.female]) * 100, 1) # calcul metrics for female\r\n prec_female = round((correct_cnt[GENDER.female] / pred_cnt[GENDER.female.name]) * 100, 1)\r\n f1_female = round(calc_f1(prec_female, recall_female), 1)\r\n output_dict['f1_female'] = f1_female\r\n\r\n output_dict['unk_male'] = count_unknowns[GENDER.male]\r\n output_dict['unk_female'] = count_unknowns[GENDER.female]\r\n output_dict['unk_neutral'] = count_unknowns[GENDER.neutral]\r\n\r\n return output_dict","def query_set_sim(self, en_ids, weights):\n # fielded_weights = self.__get_weights(weights)\n scorer = ScorerMLM(econfig.LUCENE, self.query, {}) # {'field_weights': fielded_weights})\n\n p_t_theta_d = {}\n for t in set(self.query.split()):\n p_t_theta_d[t] = 0\n for en in en_ids:\n lucene_doc_id = scorer.lucene.get_lucene_document_id(en)\n p_t_theta_d[t] += scorer.get_mlm_term_prob(lucene_doc_id, weights, t)\n score = self.nllr(self.query, p_t_theta_d, weights)\n if score is None:\n return 0\n return math.exp(score)","def classify_embeddings(model, training_set, test_set, training_labels):\n print(\"Embedding...\")\n # Using Hadamard product for the embedding vectors\n edge_embeddings = HadamardEmbedder(keyed_vectors=model.wv)\n x_train_embedded = [edge_embeddings[pair] for pair in training_set]\n x_test = [edge_embeddings[pair] for pair in test_set]\n\n # Using MLPClassifier from sklearn as binary classifier\n classifier = MLPClassifier(random_state=1)\n print(\"Classifying...\")\n classify = classifier.fit(x_train_embedded, training_labels)\n\n predict = classify.predict_proba(x_test)\n score = dict()\n for i in range(len(test_set)):\n node1 = test_set[i][0]\n node2 = test_set[i][1]\n score[node1 + ' ' + node2] = predict[i][1]\n return score","def test_distance_mean_results(self, concept: Concept):\n\n concept_args = dict(concept=concept, model_stump=None)\n for desc, (embs, (m_w, m_b)) in self.DISTANCE_MEAN_EXAMPLES.items():\n m_w: np.ndarray = np.array(m_w)\n embeddings = [ConceptEmbedding(normal_vec=w, support_factor=b,\n **concept_args)\n for w, b in embs]\n # Actual routine\n m_emb: ConceptEmbedding = \\\n ConceptEmbedding.mean_by_distance(embeddings)\n context_info = ((\"context:\\n mean embedding: ({}, {}, 1.)\"\n \"\\n in embeddings ({}) as (normal vec, support, \"\n \"scaling):\\n {}\")\n .format(m_emb.normal_vec, m_emb.support_factor,\n desc,\n [(e.normal_vec, e.support_factor,\n e.scaling_factor) for e in embeddings]))\n\n # Format checks\n assert m_emb.normal_vec.shape == embeddings[0].normal_vec.shape\n assert np.array(m_emb.support_factor).shape == np.array(\n embeddings[0].support_factor).shape\n\n # Value checks\n # the embedding should be scaled\n assert float(m_emb.scaling_factor) == 1., \\\n (\"Mean embedding not scaled: expected 1., but was {}; {}\"\n .format(m_emb.scaling_factor, context_info))\n assert np.allclose(m_emb.normal_vec, m_w), \\\n (\"Wrong mean normal vector: expected {}, but was {}; {}\"\n .format(m_w, m_emb.normal_vec, context_info))\n # For all given ground truths of support factors, check them:\n if m_b is not None:\n assert np.allclose(m_emb.support_factor, m_b), \\\n (\"Wrong mean support factor: expected {}, but was {}; {}\"\n .format(m_b, m_emb.support_factor, context_info))","def analyze_similarities():\r\n print('Total number of candidate pairs:', len(pairs))\r\n print(f'\\nNumber of actual item pairs in the train set: {pairs[\"true_match\"].sum()}\\n')\r\n\r\n for feature in ['text_score', 'image_score', 'txt_img_score', 'words_ratio', 'txt_img_words']:\r\n\r\n # Check distribution of True and False predictions for various similarity scores\r\n print('-' * 50)\r\n print(f'\\nDistribution of True/False predictions for {feature}')\r\n for thr in (0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95):\r\n print('-' * 50)\r\n print(f'Similarity score over {thr}')\r\n pairs_sample = pairs[pairs[feature] >= thr]\r\n print(f'Number of similar item pairs: {len(pairs_sample)}')\r\n print(pairs_sample['true_match'].value_counts(normalize=True))\r\n\r\n # Check if identical phash can be used to improve the accuracy\r\n same_phash = pairs[pairs['phash_match'] == 1]\r\n different_phash = pairs[pairs['phash_match'] == 0]\r\n\r\n print('\\nFor item pairs with the same phash:')\r\n print(same_phash['true_match'].value_counts(normalize=True))\r\n print('Number of item pairs in this subset:', len(same_phash))\r\n\r\n print('\\nFor item pairs with different phash:')\r\n print(different_phash['true_match'].value_counts(normalize=True))\r\n print('Number of item pairs in this subset:', len(different_phash))\r\n\r\n # Check if numbers in titles can be used to improve the accuracy\r\n same_numbers = pairs[pairs['nums_match'] == 1]\r\n different_numbers = pairs[pairs['nums_match'] == 0]\r\n\r\n print('\\nFor item pairs with the same numbers:')\r\n print(same_numbers['true_match'].value_counts(normalize=True))\r\n print('Number of item pairs in this subset:', len(same_numbers))\r\n\r\n print('\\nFor item pairs with different numbers:')\r\n print(different_numbers['true_match'].value_counts(normalize=True))\r\n print('Number of item pairs in this subset:', len(different_numbers))","def compute_scores(self, data, distance):\r\n print('Computing the scores')\r\n distance.set_parameters(data)\r\n size = np.shape(data)\r\n if len(size) == 3:\r\n data = np.reshape(data, (size[0]*size[1], size[2]))\r\n size = np.shape(data)\r\n scores_dimension = size[0]\r\n n_features = size[1]\r\n scores = np.ones(shape=(scores_dimension, scores_dimension))\r\n for i in range(scores_dimension):\r\n for j in range(i + 1, scores_dimension):\r\n dist = distance.compute_distance(data[i, 0:n_features], data[j, 0:n_features])\r\n scores[i, j] = 1 / (1 + dist)\r\n scores[j, i] = scores[i, j]\r\n return scores","def evaluate(self, eval_batches, result_dir=None, result_prefix=None, save_full_info=False):\n pred_answers, ref_answers = [], []\n total_loss, total_num = 0, 0\n for b_itx, batch in enumerate(eval_batches):\n feed_dict = {self.p: batch['passage_token_ids'],\n self.q: batch['question_token_ids'],\n self.p_length: batch['passage_length'],\n self.q_length: batch['question_length'],\n self.start_label: batch['start_id'],\n self.end_label: batch['end_id'],\n self.dropout_keep_prob: 1.0,\n self.em: batch['exact_match']}\n batch_size = len(batch['start_id'])\n padded_p_len = len(batch['passage_token_ids'][0])\n padded_p_num = len(batch['passage_token_ids']) / batch_size\n para_ids = []\n for start_id in batch['start_id']:\n para_ids.append(start_id // padded_p_len)\n feed_dict[self.para_label] = para_ids\n content_label = np.zeros([batch_size, padded_p_num * padded_p_len], dtype=int)\n for s_idx, (start_id, end_id) in enumerate(zip(batch['start_id'], batch['end_id'])):\n content_label[s_idx, start_id: end_id+1] = 1\n feed_dict[self.content_label] = content_label\n start_probs, end_probs, content_scores, verif_scores, loss = self.sess.run([self.start_probs, self.end_probs,\n self.concat_content_score, self.reshaped_ans_verif_score,\n self.loss], feed_dict)\n\n total_loss += loss * len(batch['raw_data'])\n total_num += len(batch['raw_data'])\n\n padded_p_len = len(batch['passage_token_ids'][0])\n for s_idx, sample in enumerate(batch['raw_data']):\n start_prob = start_probs[s_idx]\n end_prob = end_probs[s_idx]\n content_score = content_scores[s_idx]\n verif_score = verif_scores[s_idx]\n best_answer = self.find_best_answer_with_verif(sample, start_prob, end_prob,\n content_score, verif_score, padded_p_len)\n if save_full_info:\n sample['pred_answers'] = [best_answer]\n pred_answers.append(sample)\n else:\n pred_answers.append({'question_id': sample['question_id'],\n 'question_type': sample['question_type'],\n 'answers': [best_answer],\n 'entity_answers': [[]],\n 'yesno_answers': []})\n if 'answers' in sample:\n ref_answers.append({'question_id': sample['question_id'],\n 'question_type': sample['question_type'],\n 'answers': sample['answers'],\n 'entity_answers': [[]],\n 'yesno_answers': []})\n\n if result_dir is not None and result_prefix is not None:\n result_file = os.path.join(result_dir, result_prefix + '.json')\n with open(result_file, 'w') as fout:\n for pred_answer in pred_answers:\n fout.write(json.dumps(pred_answer, encoding='utf8', ensure_ascii=False) + '\\n')\n\n self.logger.info('Saving {} results to {}'.format(result_prefix, result_file))\n\n # this average loss is invalid on test set, since we don't have true start_id and end_id\n ave_loss = 1.0 * total_loss / total_num\n # compute the bleu and rouge scores if reference answers is provided\n if len(ref_answers) > 0:\n pred_dict, ref_dict = {}, {}\n for pred, ref in zip(pred_answers, ref_answers):\n question_id = ref['question_id']\n if len(ref['answers']) > 0:\n pred_dict[question_id] = normalize(pred['answers'])\n ref_dict[question_id] = normalize(ref['answers'])\n bleu_rouge = compute_bleu_rouge(pred_dict, ref_dict)\n else:\n bleu_rouge = None\n return ave_loss, bleu_rouge","def score(self, sentence):\n # TODO your code here\n\n # initialize count with trained data\n unigram_count = self.count.copy()\n N = self.total\n\n # make a new key for UNK, add-one later\n for token in sentence:\n if token not in unigram_count:\n unigram_count[token] = 0\n\n # calcutate lopP(<s>) + logP(w1) + logP(w2) + ...\n score = 0.0 # P(<s>) = 1\n V = len(unigram_count) # the number of vocab including UNK\n for word in sentence:\n prob = float((unigram_count[word] + 1) / (N + V)) # c(w) + 1 / N + V\n score += math.log(prob)\n\n return score","def run_evaluate_compound(self, data):\n tp = 0\n tn = 0\n fp = 0\n fn = 0\n for words, labels, pos in minbatch_generator(data, self.batch_size):\n labels_pred, sequence_lengths = self.predict_batch(words, pos)\n if not type(labels_pred) == list:\n labels_pred = labels_pred.tolist()\n for i, label_actual in enumerate(labels):\n # we have to now find all aspect terms. That is, merge consecutive B \n # with I's until we get an O or B\n a_terms_actual, non_a_terms_actual = get_aspect_chunks(label_actual)\n for a_term in a_terms_actual:\n # say we have [1,2,3] as our a_term. we have to make sure that our \n # prediction should have tags B, I, I in 1, 2 and 3 indices respectively\n a_term_pred = []\n for index in a_term:\n if not label_actual[index] == labels_pred[i][index]:\n fn += 1\n break\n else:\n tp += 1\n for index in non_a_terms_actual:\n if label_actual[index] == labels_pred[i][index]:\n tn += 1\n else:\n fp += 1\n\n precision = 0 if tp+fp == 0 else tp/(tp+fp)\n recall = 0 if tp+fn == 0 else tp/(tp+fn)\n fscore = 0 if precision+recall == 0 else (2*recall*precision)/(recall+precision)\n metrics = dict()\n metrics[\"p_1\"] = precision\n metrics[\"f_1\"] = fscore\n metrics[\"r_1\"] = recall\n return metrics","def evaluate(self, data, category, dims=None, overall=True):\n n_data = len(data)\n eval_scores = [{} for _ in range(n_data)]\n\n if dims == None:\n eval_dims = self.dimensions\n else:\n assert isinstance(dims, list)\n eval_dims = dims\n\n for dim in eval_dims:\n # Calculate average sentence-level scores for 'consistency' and 'fluency'\n if dim == 'consistency' or dim == 'fluency':\n src_list, output_list = [], []\n n_sents = [] # the number of sentences in each generated summary\n for i in range(n_data):\n source = data[i]['source']\n system_outputs = sent_tokenize(data[i]['system_output'])\n n_sents.append(len(system_outputs))\n for j in range(len(system_outputs)):\n src_list.append(source)\n output_list.append(system_outputs[j])\n input_list = add_question(dimension=dim, output=output_list, src=src_list, task=self.task)\n sent_score = self.scorer.score(input_list, self.task, category, dim)\n\n # Get average score for each sample\n start_idx = 0\n score = []\n for cur_n_sent in n_sents:\n # prevent denominator from being 0\n score.append(sum(sent_score[start_idx:start_idx + cur_n_sent]) / (cur_n_sent + 1e-6))\n start_idx += cur_n_sent\n\n # Calculate summary-level score for 'coherence' and 'relevance'\n elif dim == 'coherence' or dim == 'relevance':\n src_list, output_list, ref_list = [], [], []\n for i in range(n_data):\n src_list.append(data[i]['source'])\n output_list.append(data[i]['system_output'])\n if dim == 'relevance':\n ref_list.append(data[i]['reference'])\n input_list = add_question(dimension=dim, output=output_list, src=src_list, ref=ref_list, task=self.task)\n score = self.scorer.score(input_list, self.task, category, dim)\n\n # Please customize other dimensions here for summarization\n else:\n raise NotImplementedError('The input format for this dimension is still undefined. \\\n Please customize it first.')\n\n for i in range(n_data):\n eval_scores[i][dim] = score[i]\n\n # Customize your overall score here.\n if overall == True:\n for i in range(n_data):\n eval_scores[i]['overall'] = np.mean(list(eval_scores[i].values()))\n\n return eval_scores","def evaluate_samples(lang_samples: Dict[str, List[Tuple[List[str], List[str], List[str]]]]) -> Dict[str, Any]:\n\n evaluation_result = dict()\n lang_phon_err, lang_phon_count, lang_word_err = dict(), dict(), dict()\n languages = sorted(lang_samples.keys())\n for lang in languages:\n for word, generated, target in lang_samples[lang]:\n word = ''.join(word)\n phon_err, phon_count = phoneme_error(generated, target)\n word_err = word_error(generated, target)\n phon_err_dict = lang_phon_err.setdefault(lang, dict())\n phon_count_dict = lang_phon_count.setdefault(lang, dict())\n word_err_dict = lang_word_err.setdefault(lang, dict())\n best_phon_err, best_phon_count = phon_err_dict.get(word, None), phon_count_dict.get(word, None)\n if best_phon_err is None or phon_err / phon_count < best_phon_err / best_phon_count:\n phon_err_dict[word] = phon_err\n phon_count_dict[word] = phon_count\n word_err_dict[word] = word_err\n\n phon_errors, phon_counts, word_errors, word_counts = [], [], [], []\n for lang in languages:\n phon_err = sum(lang_phon_err[lang].values())\n phon_errors.append(phon_err)\n phon_count = sum(lang_phon_count[lang].values())\n phon_counts.append(phon_count)\n word_err = sum(lang_word_err[lang].values())\n word_errors.append(word_err)\n word_count = len(lang_word_err[lang])\n word_counts.append(word_count)\n per = phon_err / phon_count\n wer = word_err / word_count\n evaluation_result.setdefault(lang, {}).update({'per': per})\n evaluation_result.setdefault(lang, {}).update({'wer': wer})\n mean_per = sum(phon_errors) / sum(phon_counts)\n mean_wer = sum(word_errors) / sum(word_counts)\n evaluation_result['mean_per'] = mean_per\n evaluation_result['mean_wer'] = mean_wer\n\n return evaluation_result","def _compute_sentence_statistics(pred_words: List[str], target_words: List[List[str]]) ->Tuple[Tensor, Tensor]:\n tgt_lengths = tensor(0.0)\n best_num_edits = tensor(2e+16)\n for tgt_words in target_words:\n num_edits = _translation_edit_rate(tgt_words, pred_words)\n tgt_lengths += len(tgt_words)\n if num_edits < best_num_edits:\n best_num_edits = num_edits\n avg_tgt_len = tgt_lengths / len(target_words)\n return best_num_edits, avg_tgt_len","def score(self, indices):\n self.model.eval()\n _, prediction = self.model(self.propagation_matrix, self.features).max(dim=1)\n correct = prediction[indices].eq(self.target[indices]).sum().item()\n acc = correct / indices.shape[0]\n return acc","def test_compute_unnormalized_scores(self):\n # todo: implement this test!\n pass","def get_scores(self, query):\n score = np.zeros(self.data['corpus_size'])\n doc_len = np.array(self.data['doc_len'])\n for q in query:\n q_freq = np.array([(doc.get(q) or 0) for doc in self.data['doc_freqs']])\n score += (self.data['idf'].get(q) or 0) * (q_freq * (self.data['k1'] + 1) /\n (q_freq + self.data['k1'] * (\n 1 - self.data['b'] + self.data['b'] * doc_len /\n self.data['average_docs_len'])))\n return score","def evaluate_similarity(kv: KeyedVectors, X, y):\n mean_vector = np.mean(kv.vectors, axis=0, keepdims=True)\n missing_words = np.sum(np.isin(X, kv.index2word, invert=True))\n if missing_words > 0:\n logging.warning(\"Missing {} words. Will replace them with mean vector\".format(missing_words))\n get = np.vectorize(gensim_helper.get_vector, signature='(),(),(m)->(m)')\n timer = mytimer.Timer(\"getting vectors for words\")\n wv_x = get(X, kv, mean_vector)\n timer.stop()\n a = wv_x[:, 0]\n b = wv_x[:, 1]\n # timer = mytimer.Timer()\n # a = np_helper.normalize_over_cols_2d(a)\n # b = np_helper.normalize_over_cols_2d(b)\n # scores = np.diag(np.matmul(a, b.T))\n # timer.stop()\n # print(scores.shape)\n #\n # A = np.vstack(kv.get(word, mean_vector) for word in X[:, 0])\n # B = np.vstack(kv.get(word, mean_vector) for word in X[:, 1])\n timer = mytimer.Timer()\n scores = np.array([v1.dot(v2.T) / (np.linalg.norm(v1) * np.linalg.norm(v2)) for v1, v2 in zip(a, b)])\n timer.stop()\n # print(scores.shape)\n return scipy.stats.spearmanr(scores, y)","def evaluate(self, test_X, test_Y):\n correct = 0\n total = 0.0\n\n for i, ((word_indices, word_char_indices), gold_tag_indices) in enumerate(zip(test_X, test_Y)):\n\n output = self.predict(word_indices, word_char_indices)\n predicted_tag_indices = [np.argmax(o.value()) for o in output] \n\n correct += sum([1 for (predicted, gold) in zip(predicted_tag_indices, gold_tag_indices) if predicted == gold])\n total += len(gold_tag_indices)\n\n return correct, total","def distance_score(x_embeddings_test, x_embeddings_train, y_true_train, K=50):\n num_samples = x_embeddings_test.shape[0]\n num_classes = y_true_train.shape[1]\n y_test_confidence = []\n for i in range(num_samples):\n sample_embedding = x_embeddings_test[i]\n distances = np.square(sample_embedding - x_embeddings_train).sum(axis=-1)\n K_nn = np.argsort(distances)[:K]\n K_nn_distances = np.exp(-np.sqrt(distances[K_nn]))\n K_nn_labels = y_true_train[K_nn, :]\n\n class_indicators = np.eye(num_classes)\n classes_masks = np.matmul(class_indicators, np.transpose(K_nn_labels))\n\n # foreach class we mask away the samples in Knn that belong to other classes\n class_samples_distances = classes_masks * np.expand_dims(K_nn_distances, axis=0) # this gives num_classes X K (100 X 50 matrix)\n sum_distances = np.sum(K_nn_distances)\n D_x = np.sum(class_samples_distances, axis=-1)/sum_distances\n\n y_test_confidence.append(D_x)\n\n return np.vstack(y_test_confidence)","def test_scoring(self):\n scores = score_words(['foo', 'far', 'has', 'car'])\n expected = [(7, 'far'), (6, 'car'), (5, 'has'), (4 , 'foo')]\n self.assertEqual(scores, expected)","def compute_accuracy(gold_dict, src_word2ind, trg_word2ind, src_words, trg_words, scores, args, k=5, BATCH_SIZE=20):\n oov = set()\n correct = 0\n n,m = scores.shape # The actual size of mapping computed, might be smaller that total size of dict\n for src_idx,tgt_idx in gold_dict.items():\n np.any([e<m for e in tgt_idx])\n if src_idx > n or np.all([e>m for e in tgt_idx]):#Src word not in mapping\n oov.add(src_idx)\n continue\n else:\n print(k, src_idx, tgt_idx)\n knn = np.argpartition(scores[src_idx,:], -k)[-k:]\n knn_sort = knn[np.argsort(-scores[src_idx,knn])] # With - to get descending order\n if set(knn_sort).intersection(tgt_idx):\n correct +=1\n\n coverage = len(gold_dict.keys()) / (len(gold_dict.keys()) + len(oov))\n if verbose:\n print('Coverage: {0:7.2%} Accuracy:{1:7.2%}'.format(coverage, correct / len(gold_dict)))\n return correct / len(gold_dict)","def evaluate(self, test_data):\n log_like = 0\n for sequence in test_data:\n for event, context in self.extract_ngrams(sequence):\n log_like += math.log(self.cond_prob(event, context))\n return log_like","def analyze_embeddings(emb):\n dic = {\"Hi\": 0, \"En\": 1, \"Ot\": 2}\n count = [0, 0, 0, 0]\n count_zero = [0, 0, 0, 0]\n for i, j in zip(emb, corpus_trans):\n for k, l in zip(i, j):\n count[dic[l[1]]] += 1\n if sum(k) == 0:\n count_zero[dic[l[1]]] += 1\n count[-1] = sum(count)\n count_zero[-1] - sum(count_zero)\n print(\"hi, en, ot, total\")\n print(\"count: \", count)\n print(\"zero count: \", count_zero)","def compute_idf(doc_info, freq_dict_all):\n idf_scores = []\n counter = 0\n\n for temp_dict in freq_dict_all:\n counter += 1\n\n for k in temp_dict['freq_dict'].keys():\n count = sum([k in tempdict['freq_dict'] for tempdict in freq_dict_all])\n temp = {\n 'doc_id': counter,\n 'IDF_Score': math.log(len(doc_info) / count),\n 'key': k\n }\n\n idf_scores.append(temp)\n\n return idf_scores","def match(\n self, key_embeds: Tensor, ref_embeds: Tensor,\n key_sampling_results: List[SamplingResult],\n ref_sampling_results: List[SamplingResult]\n ) -> Tuple[List[Tensor], List[Tensor]]:\n\n num_key_rois = [res.pos_bboxes.size(0) for res in key_sampling_results]\n key_embeds = torch.split(key_embeds, num_key_rois)\n num_ref_rois = [res.bboxes.size(0) for res in ref_sampling_results]\n ref_embeds = torch.split(ref_embeds, num_ref_rois)\n\n dists, cos_dists = [], []\n for key_embed, ref_embed in zip(key_embeds, ref_embeds):\n dist = embed_similarity(\n key_embed,\n ref_embed,\n method='dot_product',\n temperature=self.softmax_temp)\n dists.append(dist)\n if self.loss_track_aux is not None:\n cos_dist = embed_similarity(\n key_embed, ref_embed, method='cosine')\n cos_dists.append(cos_dist)\n else:\n cos_dists.append(None)\n return dists, cos_dists","def data_mining_features(index,input_string_x1,input_string_x2,vocab_word2index,word_vec_fasttext_dict,word_vec_word2vec_dict,tfidf_dict,n_gram=8):\r\n input_string_x1=input_string_x1.decode(\"utf-8\")\r\n input_string_x2 = input_string_x2.decode(\"utf-8\")\r\n #1. get blue score vector\r\n feature_list=[]\r\n #get blue score with n-gram\r\n for i in range(n_gram):\r\n x1_list=split_string_as_list_by_ngram(input_string_x1,i+1)\r\n x2_list = split_string_as_list_by_ngram(input_string_x2, i + 1)\r\n blue_score_i_1 = compute_blue_ngram(x1_list,x2_list)\r\n blue_score_i_2 = compute_blue_ngram(x2_list,x1_list)\r\n feature_list.append(blue_score_i_1)\r\n feature_list.append(blue_score_i_2)\r\n\r\n #2. get length of questions, difference of length\r\n length1=float(len(input_string_x1))\r\n length2=float(len(input_string_x2))\r\n length_diff=(float(abs(length1-length2)))/((length1+length2)/2.0)\r\n feature_list.append(length_diff)\r\n\r\n #3. how many words are same, how many words are unique\r\n sentence_diff_overlap_features_list=get_sentence_diff_overlap_pert(index,input_string_x1,input_string_x2)\r\n feature_list.extend(sentence_diff_overlap_features_list)\r\n\r\n #4. question 1,2 start with how/why/when\r\n #how_why_feature_list=get_special_start_token(input_string_x1,input_string_x2,special_start_token)\r\n #print(\"how_why_feature_list:\",how_why_feature_list)\r\n #feature_list.extend(how_why_feature_list)\r\n\r\n #5.edit distance\r\n edit_distance=float(edit(input_string_x1, input_string_x2))/30.0\r\n feature_list.append(edit_distance)\r\n\r\n #6.cos distance from sentence embedding\r\n x1_list=token_string_as_list(input_string_x1, tokenize_style='word')\r\n x2_list = token_string_as_list(input_string_x2, tokenize_style='word')\r\n distance_list_fasttext = cos_distance_bag_tfidf(x1_list, x2_list, word_vec_fasttext_dict, tfidf_dict)\r\n distance_list_word2vec = cos_distance_bag_tfidf(x1_list, x2_list, word_vec_word2vec_dict, tfidf_dict)\r\n #distance_list2 = cos_distance_bag_tfidf(x1_list, x2_list, word_vec_fasttext_dict, tfidf_dict,tfidf_flag=False)\r\n #sentence_diffence=np.abs(np.subtract(sentence_vec_1,sentence_vec_2))\r\n #sentence_multiply=np.multiply(sentence_vec_1,sentence_vec_2)\r\n feature_list.extend(distance_list_fasttext)\r\n feature_list.extend(distance_list_word2vec)\r\n #feature_list.extend(list(sentence_diffence))\r\n #feature_list.extend(list(sentence_multiply))\r\n return feature_list","def evaluate(gold: List[Instance], pred: List[Instance], exact: bool) -> Dict[str, Tuple[int, int, int]]:\n # Initialize to zeros\n gold_score = np.zeros(len(gold))\n pred_score = np.zeros(len(pred))\n\n for idx, (pos, label) in enumerate(gold):\n for idx2, (pos2, label2) in enumerate(pred):\n if label == label2:\n if overlap(pos, pos2, exact):\n gold_score[idx] = 1\n pred_score[idx2] = 1\n # This ensures we only count one pred correctly for each gold.\n if gold_score[idx]:\n continue\n\n gold_labels = np.asarray([x[1] for x in gold])\n pred_labels = np.asarray([x[1] for x in pred])\n\n # Get the label set.\n label_set = set(gold_labels) | set(pred_labels)\n\n scores = {}\n for label in label_set:\n subset_gold = gold_score[gold_labels == label]\n subset_index = pred_score[pred_labels == label]\n tp = subset_gold.sum()\n fn = len(subset_gold) - tp\n fp = (subset_index == 0).sum()\n scores[label] = np.array([tp, fp, fn])\n\n return scores","def score_samples(self, x):\n raise NotImplementedError","def tf_idf_score():\n\n global final_doc_set\n global final_dictionary\n final_score = []\n\n for doc_id in final_doc_set:\n score = 0\n for query_term in final_dictionary.keys():\n if final_dictionary[query_term][1].get(doc_id):\n tf = final_dictionary[query_term][1][doc_id][0]\n df = final_dictionary[query_term][0]\n\n score += ((1 + log10(tf)) * log10(TOTAL_DOCS / df))\n\n final_score.append([doc_id, score])\n\n return final_score","def fit_embeddings(\n data: DataLoader,\n embedding_num: Optional[EmbeddingBase],\n embedding_cat: Optional[EmbeddingBase],\n) -> Tuple[Optional[EmbeddingBase], Optional[EmbeddingBase]]:\n if embedding_num is None and embedding_cat is None:\n return None, None\n\n # get initial values and aggregation functions\n if embedding_num is not None:\n num_info_1, num_info_2, num_agg_fn = _init_embed_info(embedding_num)\n if embedding_cat is not None:\n cat_info_1, cat_info_2, cat_agg_fn = _init_embed_info(embedding_cat)\n\n # iterate and update the initial aggregation values/objects\n for batch in data:\n if embedding_num is not None:\n num_info_1, num_info_2 = num_agg_fn(num_info_1, num_info_2, batch[0])\n if embedding_cat is not None:\n cat_info_1, cat_info_2 = cat_agg_fn(cat_info_1, cat_info_2, batch[1])\n\n # use aggregated values to set the embeddings\n if embedding_num is None:\n pass\n elif isinstance(embedding_num, LinearEmbedding):\n embedding_num.from_summary(num_info_1)\n else:\n embedding_num.from_summary(num_info_1, num_info_2)\n\n if embedding_cat is not None:\n embedding_cat.from_summary(cat_info_1, cat_info_2)\n\n return embedding_num, embedding_cat","def get_tranception_scores_mutated_sequences(model, mutated_sequence_df, batch_size_inference, score_var_name, target_seq, num_workers=10, reverse=False, indel_mode=False):\n scores = {} \n scores['mutated_sequence']=[]\n scores['sliced_mutated_sequence']=[]\n scores['window_start']=[]\n scores['window_end']=[]\n scores['score']=[]\n with torch.no_grad():\n ds = Dataset.from_pandas(mutated_sequence_df)\n ds.set_transform(model.encode_batch)\n data_collator = DataCollatorForLanguageModeling(\n tokenizer=model.config.tokenizer,\n mlm=False)\n sampler = SequentialSampler(ds)\n ds_loader = torch.utils.data.DataLoader(ds, batch_size=batch_size_inference, sampler=sampler, collate_fn=data_collator, num_workers=num_workers, pin_memory=True, drop_last=False)\n mutant_index=0\n for encoded_batch in tqdm.tqdm(ds_loader):\n full_batch_length = len(encoded_batch['input_ids'])\n mutated_sequence = np.array(mutated_sequence_df['mutated_sequence'][mutant_index:mutant_index+full_batch_length])\n scores['mutated_sequence'] += list(mutated_sequence)\n sliced_mutated_sequence = np.array(mutated_sequence_df['sliced_mutated_sequence'][mutant_index:mutant_index+full_batch_length])\n scores['sliced_mutated_sequence'] += list(sliced_mutated_sequence)\n window_start = np.array(mutated_sequence_df['window_start'][mutant_index:mutant_index+full_batch_length])\n scores['window_start'] += list(window_start)\n window_end = np.array(mutated_sequence_df['window_end'][mutant_index:mutant_index+full_batch_length])\n scores['window_end'] += list(window_end)\n for k, v in encoded_batch.items():\n if isinstance(v, torch.Tensor):\n encoded_batch[k] = v.to(model.device) \n shift_labels = encoded_batch['labels'][..., 1:].contiguous()\n if (hasattr(model.config,\"retrieval_aggregation_mode\")) and (model.config.retrieval_aggregation_mode is not None):\n if reverse:\n encoded_batch['flip']=torch.tensor([1]*full_batch_length)\n encoded_batch['start_slice']=window_start\n encoded_batch['end_slice']=window_end\n encoded_batch['mutated_sequence'] = mutated_sequence #only mutated_sequence is flipped if the scoring_mirror branch of score_mutants. No need to flip mutated_sequence for MSA re-aligning\n fused_shift_log_probas=model(**encoded_batch,return_dict=True).fused_shift_log_probas\n loss_fct = NLLLoss(reduction='none')\n loss = - loss_fct(input=fused_shift_log_probas.view(-1, fused_shift_log_probas.size(-1)), target=shift_labels.view(-1)).view(fused_shift_log_probas.shape[0],fused_shift_log_probas.shape[1])\n else:\n lm_logits=model(**encoded_batch,return_dict=True).logits\n shift_logits = lm_logits[..., :-1, :].contiguous()\n loss_fct = CrossEntropyLoss(reduction='none') \n loss = - loss_fct(input=shift_logits.view(-1, shift_logits.size(-1)), target=shift_labels.view(-1)).view(shift_logits.shape[0],shift_logits.shape[1])\n mask = encoded_batch['attention_mask'][..., 1:].float()\n mask[mask==0]=float('nan')\n loss *= mask\n loss = nansum(loss, dim=1)\n scores_batch = list(loss.cpu().numpy())\n full_batch_length = len(encoded_batch['input_ids'])\n scores['score'] += scores_batch\n mutant_index+=full_batch_length\n scores = pd.DataFrame(scores)\n if model.config.scoring_window==\"sliding\":\n scores = scores[['mutated_sequence','score']].groupby('mutated_sequence').sum().reset_index() #We need to aggregate scores when using sliding mode\n scores['score'] = scores['score'] / scores['mutated_sequence'].map(lambda x: len(x))\n if target_seq is not None:\n scores_mutated_seq = scores[scores.mutated_sequence != target_seq]\n scores_wt = scores[scores.mutated_sequence == target_seq]\n merge_delta = 'mutated_sequence' if model.config.scoring_window==\"sliding\" else 'window_start'\n if model.config.scoring_window==\"optimal\":\n delta_scores = pd.merge(scores_mutated_seq,scores_wt,how='left',on=[merge_delta],suffixes=('','_wt'))\n delta_scores[score_var_name] = delta_scores['score'] - delta_scores['score_wt']\n elif model.config.scoring_window==\"sliding\":\n delta_scores = scores_mutated_seq.copy()\n delta_scores[score_var_name] = delta_scores['score'] - list(scores_wt['score'])[0] # In sliding mode there is a single reference window for the WT\n return delta_scores[['mutated_sequence',score_var_name]]\n else:\n scores[score_var_name] = scores['score']\n return scores[['mutated_sequence',score_var_name]]","def calculate_all_scores(best_phenotype, clustering_algorithm, dataset, y):\n samples_dist_matrix = distance.squareform(distance.pdist(dataset.values))\n allowed_fitness = list(DICT_ALLOWED_FITNESSES.keys())\n scores = [(fitness_name, fitness_value) for fitness_name, fitness_value in\n zip(allowed_fitness,\n eval_multiple(dataset.values, clustering_algorithm, allowed_fitness, samples_dist_matrix, y,\n best_phenotype))]\n scores = dict(scores)\n return scores","def score(self, *args, references=[], candidates=[], batch_size=None):\n assert not args, (\n \"The score function does not accept positional arguments. Please \"\n \"specify the name of the arguments explicitly, i.e., \"\n \"`score(references=..., candidates=...`)\")\n\n candidates, references = list(candidates), list(references)\n assert len(candidates) == len(references), (\n \"The number of candidate sentences must match the number of \"\n \"reference sentences.\")\n n_items = len(candidates)\n if not candidates:\n return []\n\n if not batch_size:\n batch_size = DEFAULT_BLEURT_BATCH_SIZE\n\n # Sorts the sentences by length.\n input_ids, input_mask, segment_ids = encoding.encode_batch(\n references, candidates, self.tokenizer, self.max_seq_length)\n seq_lengths = np.sum(input_mask, axis=1)\n sorted_indices = np.argsort(seq_lengths)\n assert sorted_indices.shape[0] == n_items\n\n all_results = np.repeat(self.DEFAULT_SCORE, n_items).astype(np.float64)\n batch_lens = []\n for i in range(0, n_items, batch_size):\n\n # Gets the ids of the examples in the batch.\n batch_indices = sorted_indices[i:i + batch_size]\n\n # Computes the max sequence length.\n batch_lenghts = seq_lengths[batch_indices]\n batch_max_len = max(max(batch_lenghts), 1)\n batch_lens.append(batch_max_len)\n\n # Retrieves the examples and truncates the extra padding.\n batch_input_ids = input_ids[batch_indices, :batch_max_len]\n batch_input_mask = input_mask[batch_indices, :batch_max_len]\n batch_segment_ids = segment_ids[batch_indices, :batch_max_len]\n\n # Runs the inference.\n tf_input = {\n \"input_ids\": batch_input_ids,\n \"input_mask\": batch_input_mask,\n \"segment_ids\": batch_segment_ids\n }\n predict_out = self._predictor.predict(tf_input)\n\n # Scatters the scores.\n all_results[batch_indices] = predict_out\n\n assert np.all(all_results > -1000), (\n \"Something went wrong while running the dynamic batching scorer.\")\n all_results = list(all_results)\n assert len(all_results) == n_items, (\n \"Number of predictions does not match sentences: {} vs. {}\".format(\n len(all_results), len(candidates)))\n\n logging.info(\"Average batch sequence length: {}\".format(\n np.mean(batch_lens)))\n\n return all_results","def score(self, urlids, wordids):\r\n\t\tself.urlids = urlids\r\n\t\tself.wordids = wordids\r\n\t\tself.scores = self.tf_score()\r\n\t\treturn self.scores","def aggregate_embeddings(list_of_embeddings):\n\n return np.mean(list_of_embeddings, axis=0)","def compute_precision(gold_dict, src_word2ind, trg_word2ind, src_words, trg_words, scores, args, BATCH_SIZE=20, verbose = False):\n oov = set()\n correct = 0\n n,m = scores.shape # The actual size of mapping computed, might be smaller that total size of dict\n\n precisions = {}\n if verbose:\n print('@{:2} {:10} {:30} {:30}'.format('k', 'Src','Predicted','Gold'))\n print_row = '{:2} {:10} {:30} {:30} {}'\n\n for k in [1,5,10]:\n correct = 0\n for src_idx,tgt_idx in gold_dict.items():\n if src_idx > n or np.all([e>m for e in tgt_idx]):#Src word not in mapping\n oov.add(src_idx)\n continue\n else:\n knn = np.argpartition(scores[src_idx,:], -k)[-k:] # argpartition returns top k not in order\n knn_sort = knn[np.argsort(-scores[src_idx,knn])] # With - to get descending order\n if set(knn_sort).intersection(tgt_idx):\n correct +=1\n correct_string = ' '\n else:\n correct_string = 'X'\n if verbose:\n src_str = src_words[src_idx]\n pred_str = ','.join([trg_words[k] for k in knn_sort])\n gold_str = ','.join([trg_words[k] for k in tgt_idx])\n print(print_row.format(k,src_str,pred_str,gold_str,correct_string))\n\n\n coverage = len(gold_dict.keys()) / (len(gold_dict.keys()) + len(oov))\n if verbose:\n print('Coverage: {:7.2%} Precision @{:2}:{:7.2%}'.format(coverage, k, correct / len(gold_dict)))\n precisions[k] = correct / len(gold_dict)\n return precisions","def enrichment_score(query_genes, term_genes, background_genes):\n n11 = np.intersect1d(query_genes, term_genes).size\n n12 = query_genes.size - n11\n n21 = term_genes.size - n11\n n22 = background_genes.size - (n11 + n12 + n21)\n contmatrix = np.array([[n11, n12], [n21, n22]])\n odds_ratio, p_values = stats.fisher_exact(contmatrix, alternative='greater')\n return odds_ratio, p_values","def evaluate(self, data, category):\n n_data = len(data)\n eval_scores = [{} for _ in range(n_data)]\n\n # Calculate average sentence-level scores for factual consistency\n src_list, output_list = [], []\n n_sents = [] # the number of sentences in the claim\n for i in range(n_data):\n source = data[i]['source']\n system_outputs = sent_tokenize(data[i]['system_output'])\n n_sents.append(len(system_outputs))\n for j in range(len(system_outputs)):\n src_list.append(source)\n output_list.append(system_outputs[j])\n input_list = add_question(dimension=self.dim, output=output_list, src=src_list, task=self.task)\n sent_score = self.scorer.score(input_list, self.task, category, self.dim)\n\n # Get average score for each sample\n start_idx = 0\n score = []\n for cur_n_sent in n_sents:\n score.append(sum(sent_score[start_idx:start_idx + cur_n_sent]) / cur_n_sent)\n start_idx += cur_n_sent\n\n for i in range(n_data):\n eval_scores[i][self.dim] = score[i]\n\n return eval_scores","def calc_score(n, prob_vals, cat_cols, list_cols):\n score = 0\n for c in cat_cols:\n if n[c] is not None:\n score += prob_vals[n[c]]\n for c in list_cols:\n for d in n[c]:\n score += prob_vals[d]\n score *= n[\"Nc\"]\n score *= n[\"Ec\"]\n return score","def trainInternal():\n\n con_counts = Counter()\n deflike = Counter()\n\n for record in records:\n data = [re.split(\"\\t\", d) for d in re.split(\"\\n\", record)]\n tokens, tags = zip(*data)\n\n for i, token in enumerate(tokens):\n denom = len(token)\n for indices, f in fqs(token, 0.5): #perform analysis on one word at a time\n context, numer = internalContext(indices, token)\n if tags[i] != \"O\": #only want the named entities\n deflike[context] += f * numer/denom #need to normalize by word length\n con_counts[context] += f * numer/denom\n\n deflike = Counter({context: deflike[context]/con_counts[context] for context in deflike}) #perform division on each entry\n\n return deflike","def compute_prd_from_embedding(\r\n eval_data, \r\n ref_data, \r\n num_clusters=20,\r\n num_angles=1001, \r\n num_runs=10,\r\n enforce_balance=True\r\n):\r\n\r\n if enforce_balance and len(eval_data) != len(ref_data):\r\n raise ValueError(\r\n 'The number of points in eval_data %d is not equal to the number of '\r\n 'points in ref_data %d. To disable this exception, set enforce_balance '\r\n 'to False (not recommended).' % (len(eval_data), len(ref_data)))\r\n\r\n eval_data = np.array(eval_data, dtype=np.float64)\r\n ref_data = np.array(ref_data, dtype=np.float64)\r\n precisions = []\r\n recalls = []\r\n for _ in range(num_runs):\r\n eval_dist, ref_dist = _cluster_into_bins(eval_data, ref_data, num_clusters)\r\n precision, recall = compute_prd(eval_dist, ref_dist, num_angles)\r\n precisions.append(precision)\r\n recalls.append(recall)\r\n precision = np.mean(precisions, axis=0)\r\n recall = np.mean(recalls, axis=0)\r\n return precision, recall","def eval(self, splt):\n params = self.params\n self.embedder.eval()\n self.proj.eval()\n\n assert splt in ['valid', 'test']\n has_labels = 'y' in self.data[splt]\n\n scores = OrderedDict({'epoch': self.epoch})\n task = self.task.lower()\n\n idxs = [] # sentence indices\n prob = [] # probabilities\n pred = [] # predicted values\n gold = [] # real values\n\n lang_id = params.lang2id['en']\n\n for batch in self.get_iterator(splt):\n\n # batch\n if self.n_sent == 1:\n (x, lengths), idx = batch\n # x, lengths = truncate(x, lengths, params.max_len, params.eos_index)\n else:\n (sent1, len1), (sent2, len2), idx = batch\n # sent1, len1 = truncate(sent1, len1, params.max_len, params.eos_index)\n # sent2, len2 = truncate(sent2, len2, params.max_len, params.eos_index)\n x, lengths, _, _ = concat_batches(sent1, len1, lang_id, sent2, len2, lang_id, params.pad_index, params.eos_index, reset_positions=False)\n y = self.data[splt]['y'][idx] if has_labels else None\n\n # cuda\n x, y, lengths = to_cuda(x, y, lengths)\n\n # prediction\n output = self.proj(self.embedder.get_embeddings(x, lengths, positions=None, langs=None))\n p = output.data.max(1)[1] if self.is_classif else output.squeeze(1)\n idxs.append(idx)\n prob.append(output.cpu().numpy())\n pred.append(p.cpu().numpy())\n if has_labels:\n gold.append(y.cpu().numpy())\n\n # indices / predictions\n idxs = np.concatenate(idxs)\n prob = np.concatenate(prob)\n pred = np.concatenate(pred)\n assert len(idxs) == len(pred), (len(idxs), len(pred))\n assert idxs[-1] == len(idxs) - 1, (idxs[-1], len(idxs) - 1)\n\n # score the predictions if we have labels\n if has_labels:\n gold = np.concatenate(gold)\n prefix = f'{splt}_{task}'\n if self.is_classif:\n scores['%s_acc' % prefix] = 100. * (pred == gold).sum() / len(pred)\n scores['%s_f1' % prefix] = 100. * f1_score(gold, pred, average='binary' if params.out_features == 2 else 'micro')\n scores['%s_mc' % prefix] = 100. * matthews_corrcoef(gold, pred)\n else:\n scores['%s_prs' % prefix] = 100. * pearsonr(pred, gold)[0]\n scores['%s_spr' % prefix] = 100. * spearmanr(pred, gold)[0]\n logger.info(\"__log__:%s\" % json.dumps(scores))\n\n # output predictions\n pred_path = os.path.join(params.dump_path, f'{splt}.pred.{self.epoch}')\n with open(pred_path, 'w') as f:\n for i, p in zip(idxs, prob):\n f.write('%i\\t%s\\n' % (i, ','.join([str(x) for x in p])))\n logger.info(f\"Wrote {len(idxs)} {splt} predictions to {pred_path}\")\n\n return scores","def compute_score(predictions, references, vocab=None, beam_size=4):\n assert np.rank(predictions) == 3\n assert predictions.shape[0] == references.shape[0]\n batch_size = predictions.shape[0]\n predictions = tf.make_ndarray(tf.make_tensor_proto(predictions)).tolist()\n references = tf.make_ndarray(tf.make_tensor_proto(references)).tolist()\n hypotheses_list = []\n references_list = []\n\n for index in range(batch_size):\n # All predictions in beam search are used for validation scores.\n for b_index in range(beam_size):\n h = predictions[index][b_index]\n try:\n eos_index = h.index(input_utils.EOS)\n except ValueError:\n eos_index = len(h)\n hypotheses_list.append(h[:eos_index])\n\n ref = references[index][0].decode().split('|')\n ref_list = [r.strip().split(' ') for r in ref if r.strip()]\n references_list.append(ref_list)\n\n all_scores = collections.defaultdict(list)\n for hypothesis, references in zip(hypotheses_list, references_list):\n if vocab is not None and len(vocab):\n # Skip PADDING, UNK, EOS, START (0-3).\n hypothesis = [\n vocab[word_id].numpy().decode()\n for word_id in hypothesis\n if word_id > 3\n ]\n\n h_str = ' '.join(str(e) for e in hypothesis)\n r_str = [' '.join(str(e) for e in ref) for ref in references]\n\n print('hypothesis: ', str(h_str))\n print('references: ', str(r_str))\n print('\\n')\n scores = screen2words_eval.coco_evaluate(r_str, h_str)\n for key, score in scores.items():\n all_scores[key].append(score)\n score_names = [\n 'BLEU-1', 'BLEU-2', 'BLEU-3', 'BLEU-4', 'ROUGE-1-f1-mean',\n 'ROUGE-1-f1-min', 'ROUGE-1-f1-max', 'ROUGE-2-f1-mean', 'ROUGE-2-f1-min',\n 'ROUGE-2-f1-max', 'ROUGE-L-f1-mean', 'ROUGE-L-f1-min', 'ROUGE-L-f1-max'\n ]\n\n return [np.array(all_scores[name], dtype=np.float32) for name in score_names]","def similarity(query,word_dict,dictionary,number_of_docs,id):\n similarity = 0.0\n scalar_leng = 0.0\n for term in query:\n if term in dictionary:\n similarity += word_dict[term][1]*imp(term,word_dict,number_of_docs,id)\n\n for term in dictionary:\n scalar_leng += imp(term, word_dict, number_of_docs, id) ** 2\n\n final_scalar_leng = math.sqrt(scalar_leng)\n similarity = similarity / final_scalar_leng\n #print(similarity)\n return similarity","def _compute_scores(self, triples):\n # compute scores as sum(s * p * o)\n scores = tf.reduce_sum(triples[0] * triples[1] * triples[2], 1)\n return scores","def test_tfidf_scorer(self):\n\n \"\"\"\n Create the test data.\n \"\"\"\n tokenizer = Tokenizer(stem=False)\n posts = [\n \"Erdogan with threats to attack regime forces 'everywhere' in Syria\",\n \"Damascus says Erdogan 'disconnected from reality' after threats\",\n ]\n\n corpus = [ Document(post, tokenizer.tokenize(post)) for post in posts ]\n\n extractor = TokenExtractor(tokenizer=tokenizer)\n scorer = TFIDFScorer({ 'erdogan': 1, 'threats': 2 }, 10)\n candidates = extractor.extract(corpus)\n scores = scorer.score(candidates)\n self.assertGreater(scores.get('erdogan'), scores.get('damascus'))\n self.assertEqual(scores.get('everywhere'), scores.get('disconnected')) # they appear the same number of times\n self.assertGreater(scores.get('erdogan'), scores.get('threats')) # 'threats' and 'erdogan' appear with the same frequency, but 'threats' has a higher DF","def baseline_mle(self):\n tag_word_tapples = self.data_to_taples(self.training_set)\n e = np.zeros((self.pos_size, self.words_size))\n for tag, word in tag_word_tapples:\n e[self.pos2i[tag], self.word2i[word]] += 1\n sum = e.sum(axis=1)[..., np.newaxis]\n e = e / sum\n return e","def weight_sum(self) -> Tuple[Tensor, Tensor]:\n if not self._isfit:\n return 0.0, 0.0\n e1_sum = 0.0\n e2_sum = 0.0\n for embedding in self.embedding:\n e1_sum += embedding.weight.abs().sum()\n e2_sum += (embedding.weight ** 2).sum()\n return e1_sum, e2_sum","def _aggregate_vectors(movies, feedback_list=[]):\n movie_vec = []\n for i in movies:\n try:\n m_vec = clf[i] # get the vector for each movie\n if ratings_dict:\n try:\n r = ratings_dict[i] # get user_rating for each movie\n # Use a polynomial to weight the movie by rating.\n # This equation is totally arbitrary. I just fit a polynomial\n # to some weights that look good. The effect is to raise\n # the importance of 1, 9, and 10 star ratings.\n w = ((r**3)*-0.00143) + ((r**2)*0.0533) + (r*-0.4695) + 2.1867\n m_vec = m_vec * w\n except KeyError:\n continue\n movie_vec.append(m_vec)\n except KeyError:\n continue\n if feedback_list:\n for i in feedback_list:\n try:\n f_vec = clf[i]\n movie_vec.append(f_vec*1.8) # weight feedback by adding multiplier here\n except KeyError:\n continue\n return np.mean(movie_vec, axis=0)","def _batch_thm_scores(self,\n state_encodings: List[STATE_ENC_TYPE],\n thm_embeddings: List[THM_EMB_TYPE],\n tactic_id: Optional[int] = None) -> List[float]:\n del tactic_id # tactic id not use to predict theorem scores.\n # The checkpoint should have only one value in this collection.\n feed_dict = {\n self._graph.get_collection('goal_net')[-1]: state_encodings,\n self._graph.get_collection('thm_net')[-1]: thm_embeddings\n }\n if self._training_meta:\n feed_dict[self._graph.get_collection('fc_keep_prob')[0]] = 1.0\n [scores] = self._sess.run(fetches=self.pairwise_score, feed_dict=feed_dict)\n return scores","def score(self, doc, c):\n # >>> YOUR ANSWER HERE\n # the inner loop in the TEST NAIVE BAYES, sum up the logprior of the class and all words' loglikelihood\n sum = self.logprior[c]\n words = doc.split()\n for w in words:\n if w in self.vocabulary:\n sum += self.loglikelihood[(w, c)]\n return sum\n # >>> END YOUR ANSWER","def _squad_update(preds: Dict[str, str], target: List[Dict[str, List[Dict[str, List[Dict[str, Any]]]]]]) ->Tuple[Tensor, Tensor, Tensor]:\n f1 = tensor(0.0)\n exact_match = tensor(0.0)\n total = tensor(0)\n for article in target:\n for paragraph in article['paragraphs']:\n for qa in paragraph['qas']:\n total += 1\n if qa['id'] not in preds:\n rank_zero_warn(f\"Unanswered question {qa['id']} will receive score 0.\")\n continue\n ground_truths = list(map(lambda x: x['text'], qa['answers']))\n pred = preds[qa['id']]\n exact_match += _metric_max_over_ground_truths(_compute_exact_match_score, pred, ground_truths)\n f1 += _metric_max_over_ground_truths(_compute_f1_score, pred, ground_truths)\n return f1, exact_match, total","def evaluate_answer(self, session, dataset, rev_vocab, sample=20, log=False):\n sample = min(sample, len(dataset))\n overall_f1 = 0.\n overall_em = 0.\n minibatch_size = 100\n num_batches = int(sample / minibatch_size)\n for batch in range(0, num_batches):\n start = batch * minibatch_size\n end = min(len(dataset), start + minibatch_size)\n h_s, h_e, _ = self.decode(session, dataset[start:end])\n for i in range(minibatch_size):\n a_s = np.argmax(h_s[i])\n a_e = np.argmax(h_e[i])\n if a_s > a_e:\n k = a_e\n a_e = a_s\n a_s = k\n\n sample_dataset = dataset[start + i]\n context = sample_dataset[0]\n (a_s_true, a_e_true) = sample_dataset[6]\n predicted_answer = self.formulate_answer(context, rev_vocab, a_s, a_e)\n true_answer = self.formulate_answer(context, rev_vocab, a_s_true, a_e_true)\n f1 = f1_score(predicted_answer, true_answer)\n overall_f1 += f1\n if exact_match_score(predicted_answer, true_answer):\n overall_em += 1\n\n average_f1 = overall_f1/sample\n overall_em = overall_em/sample\n logging.info(\"F1: {}, EM: {}, for {} samples\\n\".format(average_f1, overall_em, sample))\n return overall_f1, overall_em","def rank_scores(sentences, word_embeddings):\r\n\r\n clean_sentences = pd.Series(sentences).str.replace('[^a-zA-Z]', ' ')\r\n\r\n clean_sentences = [remove_stopwords(s.lower().split()) for s in clean_sentences]\r\n\r\n sentences_vectors = []\r\n for i in clean_sentences:\r\n if len(i) != 0:\r\n v = sum([word_embeddings.get(w, np.zeros((emb_dim,))) for w in i.split()]) / (len(i.split()) + 1e-2)\r\n else:\r\n v = np.zeros((emb_dim,))\r\n sentences_vectors.append(v)\r\n\r\n similarity_matrix = np.zeros((len(clean_sentences), len(clean_sentences)))\r\n\r\n for i in range(len(clean_sentences)):\r\n for j in range(len(clean_sentences)):\r\n if i != j:\r\n similarity_matrix[i][j] = cosine_similarity(\r\n sentences_vectors[i].reshape(1, -1), sentences_vectors[j].reshape(1, -1)\r\n )\r\n similarity_matrix = similarity_matrix + similarity_matrix.T - np.diag(similarity_matrix.diagonal())\r\n norm = np.sum(similarity_matrix, axis=0)\r\n norm_similarity_matrix = np.divide(similarity_matrix, norm, where=norm != 0)\r\n\r\n sentences_rank = page_rank(norm_similarity_matrix)\r\n\r\n return sentences_rank","def aver_score(datalist):\n scores_per_position = []\n \n for tupl in datalist:\n count = 0\n sum_of_position = 0\n for element in tupl[3]:\n sum_of_position += element\n count +=1\n aver_pos = sum_of_position/ count\n scores_per_position += [aver_pos]\n \n return scores_per_position","def compute_similarity(self, vis_feats, language_feats, gram):\n queries_dim = language_feats.dim()\n M = language_feats.size(1) if queries_dim==3 else language_feats.size(0)\n N = vis_feats.size(0)\n d = self.embed_size\n\n # If too many queries, split computation to avoid out-of-memory\n max_num_queries = 100\n if M <= max_num_queries:\n vis_feats = vis_feats.unsqueeze(1).expand(N, M, d)\n scores_gram = torch.mul(vis_feats, language_feats)\n scores_gram = scores_gram.sum(2)\n scores = scores_gram.view(-1, M)\n #scores = torch.matmul(vis_feats, lang_feats.squeeze().transpose(0,1)) #other version\n\n else:\n scores_gram = [] \n vis_feats = vis_feats.unsqueeze(1).expand(N, M, d)\n num_splits = M//max_num_queries if (M%max_num_queries)==0 else M//max_num_queries+1\n for j in range(num_splits): \n start_query = j*max_num_queries \n end_query = start_query + max_num_queries if start_query + max_num_queries <= M else M\n scores_gram_split = torch.mul(vis_feats[:,start_query:end_query,:], language_feats[:,start_query:end_query,:])\n scores_gram_split = scores_gram_split.sum(2)\n scores_gram.append(scores_gram_split)\n scores = torch.cat([scores_gram_split for scores_gram_split in scores_gram],1)\n\n return scores","def compute_metrics(self, outputs: List[Dict[str, torch.Tensor]]) -> dict:\n distance_pos, distance_neg = [], []\n for minibatch in outputs:\n minibatch = minibatch[\"val_prediction\"]\n src_embedding = minibatch[\"src_sentemb\"]\n ref_embedding = minibatch[\"ref_sentemb\"]\n pos_embedding = minibatch[\"pos_sentemb\"]\n neg_embedding = minibatch[\"neg_sentemb\"]\n\n distance_src_pos = F.pairwise_distance(pos_embedding, src_embedding)\n distance_ref_pos = F.pairwise_distance(pos_embedding, ref_embedding)\n harmonic_distance_pos = (2 * distance_src_pos * distance_ref_pos) / (\n distance_src_pos + distance_ref_pos\n )\n distance_pos.append(harmonic_distance_pos)\n\n distance_src_neg = F.pairwise_distance(neg_embedding, src_embedding)\n distance_ref_neg = F.pairwise_distance(neg_embedding, ref_embedding)\n harmonic_distance_neg = (2 * distance_src_neg * distance_ref_neg) / (\n distance_src_neg + distance_ref_neg\n )\n distance_neg.append(harmonic_distance_neg)\n\n return {\n \"kendall\": self.metrics.compute(\n torch.cat(distance_pos), torch.cat(distance_neg)\n )\n }","def test_score_with_sample_weights():\n atom = ATOMClassifier(X_bin, y_bin, random_state=1)\n atom.run(\"Tree\")\n score = atom.tree.score(X_bin, y_bin, sample_weight=list(range(len(y_bin))))\n assert isinstance(score, np.float64)","def predictability(self):\n temp = self.probs\n for n in range(10):\n temp = temp.dot(temp)\n final = temp[0,:]\n #Let's assume that all words have unique initial letters\n probs = map(len, self.words)\n probs = array(probs)\n probs = (probs + self.probs.max(1)-1)/probs\n return sum(final*probs)","def score_sequence(seq, ngramlogprobs):\n return","def predict(self, users, items):\n self.eval()\n users_t = torch.tensor(users, dtype=torch.int64, device=self.device)\n items_t = torch.tensor(items, dtype=torch.int64, device=self.device)\n\n with torch.no_grad():\n # scores = torch.mul(\n # self.user_embedding(users_t), self.item_embedding(items_t)\n # ).sum(dim=1)\n ua_embeddings, ia_embeddings = self.forward(self.norm_adj)\n u_g_embeddings = ua_embeddings[users_t]\n i_g_embeddings = ia_embeddings[items_t]\n scores = self.f(torch.mul(u_g_embeddings, i_g_embeddings).sum(dim=1))\n return scores","def _rouge_lsum_score(pred: Sequence[Sequence[str]], target: Sequence[Sequence[str]]) ->Dict[str, Tensor]:\n pred_len = sum(map(len, pred))\n target_len = sum(map(len, target))\n if 0 in (pred_len, target_len):\n return dict(precision=tensor(0.0), recall=tensor(0.0), fmeasure=tensor(0.0))\n\n def _get_token_counts(sentences: Sequence[Sequence[str]]) ->Counter:\n ngrams: Counter = Counter()\n for sentence in sentences:\n ngrams.update(sentence)\n return ngrams\n pred_tokens_count = _get_token_counts(pred)\n target_tokens_count = _get_token_counts(target)\n hits = 0\n for tgt in target:\n lcs = _union_lcs(pred, tgt)\n for token in lcs:\n if pred_tokens_count[token] > 0 and target_tokens_count[token] > 0:\n hits += 1\n pred_tokens_count[token] -= 1\n target_tokens_count[token] -= 1\n return _compute_metrics(hits, pred_len, target_len)","def matching_accuracy(pmat_pred, pmat_gt, ns):\n device = pmat_pred.device\n batch_num = pmat_pred.shape[0]\n\n pmat_gt = pmat_gt.to(device)\n\n assert torch.all((pmat_pred == 0) + (pmat_pred == 1)), 'pmat_pred can noly contain 0/1 elements.'\n assert torch.all((pmat_gt == 0) + (pmat_gt == 1)), 'pmat_gt should noly contain 0/1 elements.'\n assert torch.all(torch.sum(pmat_gt, dim=-1) <= 1) and torch.all(torch.sum(pmat_gt, dim=-2) <= 1)\n assert torch.all(torch.sum(pmat_pred, dim=-1) <= 1) and torch.all(torch.sum(pmat_pred, dim=-2) <= 1)\n\n #indices_pred = torch.argmax(pmat_pred, dim=-1)\n #indices_gt = torch.argmax(pmat_gt, dim=-1)\n\n #matched = (indices_gt == indices_pred).type(pmat_pred.dtype)\n match_num_list = []\n gt_num_list = []\n pred_num_list = []\n acc_gt = []\n acc_pred = []\n for b in range(batch_num): #acc_gt,acc_pred\n #match_num += torch.sum(matched[b, :ns[b]])\n #total_num += ns[b].item()\n match_num = torch.sum(pmat_pred[b, :] * pmat_gt[b, :]) + 1e-8 #:ns[b]\n gt_num = torch.sum(pmat_gt[b, :]) + 1e-8\n pred_num = torch.sum(pmat_pred[b,:]) + 1e-8\n match_num_list.append(match_num.cpu().numpy())\n gt_num_list.append(gt_num.cpu().numpy())\n pred_num_list.append(pred_num.cpu().numpy())\n acc_gt.append((match_num/gt_num).cpu().numpy())\n acc_pred.append((match_num/pred_num).cpu().numpy())\n\n return {'acc_gt': np.array(acc_gt),\n 'acc_pred': np.array(acc_pred),\n 'match_num': np.array(match_num_list),\n 'gt_num': np.array(gt_num_list),\n 'pred_num': np.array(pred_num_list)}","def proto_maml_prototypes(self, train_embeddings):\n # [num train images, 1, embedding size].\n train_embeddings = tf.expand_dims(train_embeddings, 1)\n # [num train labels, num classes] where each row is a one-hot-encoded label.\n one_hot_train_labels = tf.one_hot(self.data.train_labels, self.way)\n # [num train labels, num classes, 1].\n one_hot_train_labels = tf.expand_dims(one_hot_train_labels, 2)\n # Sums each class' embeddings. [num classes, embedding size].\n class_sums = tf.reduce_sum(one_hot_train_labels * train_embeddings, 0)\n # The prototype of each class is the average embedding of its train points.\n class_num_images = tf.reduce_sum(one_hot_train_labels, 0) # [way].\n prototypes = class_sums / class_num_images\n return prototypes","def evaluate(args, model, data_iterator, params, mark='Val', verbose=True):\n # set model to evaluation mode\n model.eval()\n\n # id2tag dict\n idx2tag = {idx: tag for idx, tag in enumerate(params.tags)}\n\n true_tags = []\n pred_tags = []\n\n # a running average object for loss\n loss_avg = utils.RunningAverage()\n for batch in tqdm(data_iterator, unit='Batch', ascii=True):\n # to device\n batch = tuple(t.to(params.device) for t in batch)\n input_ids, input_mask, labels, _, _, word_ids, word_positions = batch\n\n batch_size, max_len = labels.size()\n\n # inference\n with torch.no_grad():\n # get loss\n loss = model(input_ids, attention_mask=input_mask.bool(), labels=labels, input_word_ids=word_ids,\n word_position_matrix=word_positions)\n if params.n_gpu > 1 and args.multi_gpu:\n loss = loss.mean() # mean() to average on multi-gpu.\n # update the average loss\n loss_avg.update(loss.item())\n\n # inference\n batch_output = model(input_ids, attention_mask=input_mask.bool(), input_word_ids=word_ids,\n word_position_matrix=word_positions)\n\n # 恢复标签真实长度\n real_batch_tags = []\n for i in range(batch_size):\n real_len = int(input_mask[i].sum())\n real_batch_tags.append(labels[i][:real_len].to('cpu').numpy())\n\n # List[int]\n pred_tags.extend([idx2tag.get(idx) for indices in batch_output for idx in indices])\n true_tags.extend([idx2tag.get(idx) for indices in real_batch_tags for idx in indices])\n # sanity check\n assert len(pred_tags) == len(true_tags), 'len(pred_tags) is not equal to len(true_tags)!'\n\n # logging loss, f1 and report\n metrics = {}\n f1 = f1_score(true_tags, pred_tags)\n accuracy = accuracy_score(true_tags, pred_tags)\n metrics['loss'] = loss_avg()\n metrics['f1'] = f1\n metrics['accuracy'] = accuracy\n metrics_str = \"; \".join(\"{}: {:05.2f}\".format(k, v) for k, v in metrics.items())\n logging.info(\"- {} metrics: \".format(mark) + metrics_str)\n\n # f1 classification report\n if verbose:\n report = classification_report(true_tags, pred_tags)\n logging.info(report)\n return metrics","def _luong_word_score(query, word_keys, scale, size, hierarchy, batch_size):\n depth = query.get_shape()[-1]\n key_units = word_keys.get_shape()[-1]\n if depth != key_units:\n raise ValueError(\n \"Incompatible or unknown inner dimensions between query and keys. \"\n \"Query (%s) has units: %s. Keys (%s) have units: %s. \"\n \"Perhaps you need to set num_units to the keys' dimension (%s)?\"\n % (query, depth, word_keys, key_units, key_units))\n dtype = query.dtype\n\n # Reshape from [batch_size, depth] to [batch_size, 1, depth]\n # for matmul.\n\n query = array_ops.expand_dims(query, 1)\n\n # Inner product along the query units dimension.\n # matmul shapes: query is [batch_size, 1, depth] and\n # keys is [batch_size, max_time, depth].\n # the inner product is asked to **transpose keys' inner shape** to get a\n # batched matmul on:\n # [batch_size, 1, depth] . [batch_size, depth, max_time]\n # resulting in an output shape of:\n # [batch_time, 1, max_time].\n # we then squeeze out the center singleton dimension.\n save_shape = word_keys.get_shape().as_list()\n word_keys_small = tf.reshape(word_keys, [batch_size, -1, save_shape[2]*save_shape[3]])\n word_keys = tf.transpose(word_keys_small, [1, 0, 2])\n word_keys = tf.reshape(word_keys, [-1, batch_size, save_shape[2], save_shape[3]])\n\n context_fn = lambda key: array_ops.squeeze(math_ops.matmul(query, key, transpose_b=True), [1])\n scores = tf.map_fn(context_fn, word_keys)\n scores = tf.transpose(scores, [1, 0, 2])\n\n # if hierarchy:\n # return tf.nn.l2_normalize(scores, dim=2, epsilon=1e-12, name=None)\n # else:\n # return tf.nn.l2_normalize(scores, dim=[1,2], epsilon=1e-12, name=None)\n return scores","def explain(gold: List[Instance], pred: List[Instance]) -> Dict[str, int]:\n pred_score = np.zeros(len(pred))\n\n for idx, (pos, label) in enumerate(gold):\n for idx2, (pos2, label2) in enumerate(pred):\n a = overlap(pos, pos2, True)\n b = overlap(pos, pos2, False)\n if a or b:\n mod = b and not a\n if label == label2:\n pred_score[idx2] = 1 + mod\n else:\n pred_score[idx2] = 3 + mod\n\n out = {}\n out[\"false positive\"] = sum(pred_score == 0)\n out[\"complete overlap, correct label\"] = sum(pred_score == 1)\n out[\"overlap, correct label\"] = sum(pred_score == 2)\n out[\"complete overlap, wrong label\"] = sum(pred_score == 3)\n out[\"overlap, wrong label\"] = sum(pred_score == 4)\n\n return out","def _detection_scores(inputs, gt_boxes, gt_labels, model):\n model = check_model('model', model, BlackModel)\n boxes_and_confi, pred_labels = model.predict(*inputs)\n det_scores = []\n correct_labels_num = []\n # repeat gt_boxes and gt_labels for all particles cloned from the same sample in PSOAttack/GeneticAttack\n if gt_boxes.shape[0] == 1 and boxes_and_confi.shape[0] > 1:\n gt_boxes = np.repeat(gt_boxes, boxes_and_confi.shape[0], axis=0)\n gt_labels = np.repeat(gt_labels, boxes_and_confi.shape[0], axis=0)\n iou_thres = 0.5\n for boxes, labels, gt_box, gt_label in zip(boxes_and_confi, pred_labels, gt_boxes, gt_labels):\n gt_box_num = gt_box.shape[0]\n score = 0\n box_num = boxes.shape[0]\n correct_label_flag = np.zeros(gt_label.shape)\n for i in range(box_num):\n pred_box = boxes[i]\n max_iou_confi = 0\n for j in range(gt_box_num):\n iou = calculate_iou(pred_box[:4], gt_box[j][:4])\n if labels[i] == gt_label[j] and iou > iou_thres and correct_label_flag[j] == 0:\n max_iou_confi = max(max_iou_confi, pred_box[-1] + iou)\n correct_label_flag[j] = 1\n score += max_iou_confi\n det_scores.append(score)\n correct_labels_num.append(np.sum(correct_label_flag))\n return np.array(det_scores), np.array(correct_labels_num)","def run(self, args):\n self.pen.score_for_matched_lexical = args[0]\n self.pen.score_for_matched_synonym = args[1]\n self.factor_word_offset_penalty = args[2]\n self.factor_sentence_length_mismatch = args[3]\n self.factor_name_mismatch = args[4]\n self.factor_fe_offset_penalty = args[5]\n self.weight_target_frame_element = args[6]\n self.weight_frame_elements = args[7]\n self.factor_frame_offset_penalty = args[8]\n misses = []\n for row in range(self.data.get_number_of_rows()):\n ref_sentence = self.data.get_row(row)[self.data.get_gold()]\n results = {}\n for team, team_sentence in self.data.get_row_for_teams(self.evaluator.get_teams(row), row).iteritems():\n results[team] = self.get_sentence_score(ref_sentence, team_sentence)\n misses.append(self.evaluator.compare_all(results, row))\n return np.mean(misses) / 5.0","def test_embedding_attend(self):\n features = 5\n embed = layers.Embed(num_embeddings=10, features=features)\n inputs = np.array([[1]], dtype=np.int64)\n variables = embed.init(jax.random.PRNGKey(0), inputs)\n query = np.ones(features, dtype=np.float32)\n result = embed.apply(variables, query, method=embed.attend)\n expected = np.sum(variables['params']['embedding'], -1)\n np.testing.assert_array_almost_equal(result, expected)","def average_one_hots(sent, word_to_ind):\n known_words = 0\n size = len(word_to_ind.keys())\n sum_vec = np.zeros((size,))\n for token in sent.text: #going over all tokens and summing their embeddings\n if (token in word_to_ind):\n sum_vec += get_one_hot(size, word_to_ind[token])\n known_words += 1\n if (known_words != 0):\n return sum_vec / known_words\n else:\n return sum_vec","def similarities (self, listOfWords):\n \n # building the query dictionary\n queryDict = collections.defaultdict(int)\n for w in listOfWords:\n queryDict [w] += + 1.0\n \n # normalizing the query\n length = float (len (listOfWords))\n for k in queryDict:\n queryDict [k] /= length\n \n # computing the list of similarities\n sims = []\n for doc in self.documents:\n score = 0.0\n docDict = doc [1]\n for k in queryDict:\n if docDict.has_key (k):\n score += (queryDict [k] / self.corpusDict [k]) + (docDict [k] / self.corpusDict [k])\n sims.append ([doc [0], score])\n \n return sims","def predict(self):\n print('\\nComputing ZSL embeddings for test data...')\n iter_idx = 0\n embeddings = torch.Tensor()\n for batch in self.dataset.gen_next_batch(self.batch_size, dset_part='test'):\n iter_idx += 1\n label, data_from_modalities = batch\n\n label = label.long().to(self.device)\n for j in range(len(data_from_modalities)):\n data_from_modalities[j] = data_from_modalities[j].to(\n self.device)\n \n mu_att, logvar_att = self.encoder[self.auxiliary_data_source](data_from_modalities[1])\n z_from_att = self.reparameterize(mu_att, logvar_att)\n\n embeddings = torch.cat((embeddings, z_from_att), 0)\n # embeddings.append(z_from_att)\n \n return embeddings"],"string":"[\n \"def compute_each_score(word_embeddings, each_id_pair): # without weighting scheme\\n emb1 = word_embeddings[each_id_pair[0], :]\\n emb2 = word_embeddings[each_id_pair[1], :]\\n inn = np.inner(emb1, emb2)\\n # print('inner product is {}'.format(inn))\\n emb1norm = np.sqrt(np.inner(emb1, emb1))\\n # print('emb1norm is {}'.format(emb1norm))\\n emb2norm = np.sqrt(np.inner(emb2, emb2))\\n # print('emb2norm is {}'.format(emb2norm))\\n each_pair_score = inn / emb1norm / emb2norm\\n # print('each score is {}\\\\n'.format(each_pair_score))\\n return each_pair_score\",\n \"def compute_each_score_weighted(word_embeddings, each_id_pair, each_weight_pair):\\n emb1 = word_embeddings[each_id_pair[0], :]\\n emb2 = word_embeddings[each_id_pair[1], :]\\n inn = np.inner(emb1, emb2)\\n # print('inner product is {}'.format(inn))\\n emb1norm = np.sqrt(np.inner(emb1, emb1))\\n # print('emb1norm is {}'.format(emb1norm))\\n emb2norm = np.sqrt(np.inner(emb2, emb2))\\n # print('emb2norm is {}'.format(emb2norm))\\n each_pair_score = inn / emb1norm / emb2norm\\n each_pair_score = ((each_weight_pair[0] + each_weight_pair[1]) / 2) * each_pair_score\\n # print('each score is {}\\\\n'.format(each_pair_score))\\n return each_pair_score\",\n \"def score_samples(self, X):\\n ...\",\n \"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 \"def evaluate(predictions, documents):\\n ###TODO\\n x = 0.0\\n y = 0 #true ham, classified spam\\n z = 0 #true spam, classified ham\\n \\n for i in range(0,len(predictions)):\\n if(documents[i].label == 'sod' and predictions[i] == 'pop'):\\n z += 1\\n if(documents[i].label == 'sod' and predictions[i] == 'pop'):\\n y += 1 \\n \\n x = (len(documents) - (y+z)) / len(documents) \\n return (x,y,z)\\n pass\",\n \"def embedding_similarity(model, validation_pairs):\\n scores = dict()\\n for pair in validation_pairs:\\n author1 = pair[0]\\n author2 = pair[1]\\n scores[author1 + ' ' +\\n author2] = cosine_similarity(model.wv[author1], model.wv[author2])\\n return scores\",\n \"def word_analogy(self):\\n data = open(\\\"data/word_analogy_subset.en.ar.txt\\\").read().split('\\\\n')\\n data = [x for x in data if len(x.split()) == 4]\\n cnt = 0\\n keys = list(self.embeddings_index.keys())\\n vectors = np.array(list(self.embeddings_index.values()))\\n norms = np.linalg.norm(vectors, axis=1)\\n for i in data:\\n i = self.preprocessor(i).split()\\n try:\\n v = self.embeddings_index[i[0]] - self.embeddings_index[i[1]] + self.embeddings_index[i[2]]\\n except:\\n continue\\n unit = v / np.linalg.norm(v)\\n dists = np.dot(vectors, unit) / norms\\n best = np.argpartition(-dists, 10)[:10 + 1]\\n best = best.take(np.argsort((-dists).take(best)))\\n result = [(keys[sim], float(dists[sim]))\\n for sim in best]\\n sbv = result[:10]\\n for j in sbv:\\n if j[0] == i[3]:\\n cnt += 1\\n return cnt/ len(data)\",\n \"def score(self, data):\\n\\n score_mappings = {\\n \\\"0\\\": np.log(self.class_zero_doc_count / self.total_docs),\\n \\\"1\\\": np.log(self.class_one_doc_count / self.total_docs)\\n }\\n\\n features = self.featurize(data)\\n\\n for f in features:\\n\\n if(f[0] in self.class_zero):\\n cond_prob_zero = np.log((self.class_zero[f[0]] + 1) / (self.class_zero_feature_count + len(self.vocab)))\\n elif(f[0] in self.vocab):\\n cond_prob_zero = np.log(1 / (self.class_zero_feature_count + len(self.vocab)))\\n else:\\n cond_prob_zero = 0\\n\\n if(f[0] in self.class_one):\\n cond_prob_one = np.log((self.class_one[f[0]] + 1) / (self.class_one_feature_count + len(self.vocab)))\\n elif(f[0] in self.vocab):\\n cond_prob_one = np.log(1 / (self.class_one_feature_count + len(self.vocab)))\\n else:\\n cond_prob_one = 0\\n\\n score_mappings[\\\"0\\\"] += cond_prob_zero\\n score_mappings[\\\"1\\\"] += cond_prob_one\\n\\n score_mappings[\\\"0\\\"] = np.exp(score_mappings[\\\"0\\\"])\\n score_mappings[\\\"1\\\"] = np.exp(score_mappings[\\\"1\\\"])\\n\\n return score_mappings\",\n \"def word_similarity(self):\\n y_true = []\\n y_pred = []\\n for i in open(\\\"data/word_sim_dataset.txt\\\").read().split('\\\\n'):\\n i = self.preprocessor(i)\\n w1 = i.split()[-1]\\n w2 = i.split()[-2] \\n st = float(i.split()[-3]) / 4 #dataset has scale from 0 to 4\\n \\n try:\\n w1 = self.embeddings_index[w1] \\n w2 = self.embeddings_index[w2] \\n w1 = w1 / np.linalg.norm(w1)\\n w2 = w2 / np.linalg.norm(w2)\\n y_pred.append(np.dot(w1,w2))\\n y_true.append(st)\\n except:\\n pass\\n if y_true == []:\\n return 1.0\\n return mean_squared_error(y_true, y_pred, squared=False)\",\n \"def create_embedding_matrix(self):\\n self.id2word = dict([(self.vocab[word]['id'], word) for word in self.vocab])\\n vocab_size = len(self.vocab)\\n result = np.zeros((vocab_size, self.embed_dim))\\n unknown_token_set = set()\\n\\n found_words = 0\\n avg = np.zeros(self.embed_dim)\\n for _ in range(1, vocab_size): # skip PAD embedding (initialize as zero embedding)\\n try:\\n result[_] = self.pretrained_embedding[self.id2word[_]]\\n avg += result[_]\\n found_words += 1\\n except:\\n unknown_token_set.add(_)\\n\\n avg /= found_words\\n for _ in unknown_token_set:\\n result[_] = avg\\n self.embedding = result\\n return found_words, len(self.id2word)\",\n \"def weight_distribution(self):\\n all_scores = []\\n for zettel in self.lemma_tokens:\\n scores = []\\n for word in zettel:\\n cur_tf_idf = self.tf_idf_scores[word[0]] / 3 #range: 0-3+\\n if word[1] == 'NG':\\n word_list = re.split(\\\" \\\", word[0])\\n cur_word_score = 0\\n i = 0\\n for new_word in word_list:\\n cur_word_score += self.word_scores[new_word]\\n i += 1\\n cur_word_score = cur_word_score / i / 2 #range: 0-2+\\n else:\\n cur_word_score = self.word_scores[word[0]] / 2 #range: 0-2+\\n cur_keyword_score = self.keyword_scores[word[0]] / 4 #0-4+\\n cur_text_rank = self.text_ranks[word[0]] / 10 #range: 0-12+\\n cur_pos_score = self.pos_scores[word[0]]\\n cur_area_score = self.z_area_scores[word[0]]\\n cur_total_score = ((cur_tf_idf * self.score_weights[0]) + (cur_word_score * self.score_weights[1]) +\\n (cur_keyword_score * self.score_weights[2]) + (cur_text_rank * self.score_weights[3]) +\\n (cur_pos_score * self.score_weights[4]) + (cur_area_score * self.score_weights[5])) / 6\\n scores.append(cur_total_score)\\n all_scores.append(scores)\\n return all_scores\",\n \"def score_eval_batch(datas,model):\\n tf_eval_preds_batch = []\\n\\n for i in range(len(datas.eval_batchs)):\\n tf_eval_preds, _ = model.run(datas=datas, mode=model.inf_signal, batch_id=i)\\n tf_eval_preds_batch.append(tf_eval_preds)\\n tf_eval_preds = np.concatenate(tf_eval_preds_batch)\\n\\n y_nz = [sum(x) > 0 for x in datas.target]\\n y_nz = np.arange(datas.target.shape[0])[y_nz]\\n\\n preds_all = tf_eval_preds[y_nz, :]\\n y = datas.target[y_nz, :]\\n\\n x = scipy.sparse.lil_matrix(y.shape)\\n x.rows = preds_all\\n x.data = np.ones_like(preds_all)\\n x = x.toarray()\\n\\n z = x * y\\n return np.mean(np.divide((np.sum(z, 1)), np.sum(y, 1)))\",\n \"def test_score_across_multiple_documents(self):\\n\\n \\\"\\\"\\\"\\n Create the test data.\\n \\\"\\\"\\\"\\n tokenizer = Tokenizer(stem=False)\\n posts = [\\n \\\"Erdogan with threats to attack regime forces 'everywhere' in Syria\\\",\\n \\\"Syria reacts to Erdogan's threats: Damascus says Erdogan 'disconnected from reality' after threats\\\",\\n ]\\n\\n corpus = [ Document(post, tokenizer.tokenize(post)) for post in posts ]\\n\\n extractor = TokenExtractor(tokenizer=tokenizer)\\n scorer = TFIDFScorer({ 'erdogan': 1, 'threats': 2 }, 10)\\n candidates = extractor.extract(corpus)\\n scores = scorer.score(candidates, normalize_scores=False)\\n self.assertEqual(3 * math.log(10 / 1, 10), scores.get('erdogan'))\\n self.assertEqual(3 * math.log(10 / 2, 10), scores.get('threats'))\",\n \"def score(self, text):\\n logger.debug(\\\"score on an instance of len {0}\\\".format(len(text)))\\n fv = self.instance2fv(text)\\n fv /= np.sqrt((fv*fv).sum()) # normalize vector to len 1\\n fdot = self.lprot.dot(fv) \\n retval = dict(zip(self.langs, fdot))\\n return retval\",\n \"def evaluate_ngrams(eval_dataset, trigram_counts, bigram_counts, unigram_counts, train_token_count, lambda1, lambda2):\\n perplexity = 0\\n ### YOUR CODE HERE\\n raise NotImplementedError\\n ### END YOUR CODE\\n return perplexity\",\n \"def _compute_embedding_score_per_record(self, wide_ftrs_idx_with_value_per_record):\\n\\n # Split idx and val back\\n wide_ftrs_sp_idx, wide_ftrs_sp_val = tf.split(wide_ftrs_idx_with_value_per_record, 2, axis=-1)\\n wide_ftrs_sp_idx = tf.cast(wide_ftrs_sp_idx, dtype=tf.int64)\\n\\n # Transformation\\n shape = tf.ones(shape=[tf.shape(wide_ftrs_sp_idx)[0], 1], dtype=tf.int64) * self._num_wide_sp\\n valid_wide_ftrs_idx_mask = tf.cast(tf.not_equal(wide_ftrs_sp_idx, self._padding_idx), tf.float32)\\n wide_ftrs_sp_idx = tf.expand_dims(wide_ftrs_sp_idx, -1)\\n\\n # Get sparse feature vector v where v[ftr_idx_i] = ftr_val_i and v[other] = 0\\n wide_ftrs_sp = sparse_tensor_merge(wide_ftrs_sp_idx,\\n wide_ftrs_sp_val * valid_wide_ftrs_idx_mask,\\n shape)\\n\\n # Feature weights\\n bias = self.ftrs_weight[0]\\n\\n # Compute embedding\\n embedding = tf.sparse.matmul(wide_ftrs_sp, self.ftrs_weight) + bias\\n return embedding\",\n \"def evaluate(golds, preds):\\n correct_words = 0\\n correct_sentences = 0\\n\\n words_total = 0.0\\n sentences_total = 0.0\\n\\n for gold, pred in zip(golds, preds):\\n # check whether entire tag sequence was correct\\n sentences_total += 1\\n if pred == gold:\\n correct_sentences += 1\\n\\n # check individual tags for correctness\\n for predicted_tag, gold_tag in zip(pred, gold):\\n words_total += 1\\n if predicted_tag == gold_tag:\\n correct_words += 1\\n\\n return (correct_sentences/sentences_total, correct_words/words_total)\",\n \"def evaluate_all(\\n data: List[Tuple[int, str, List[Instance]]], pred_data: List[List[Instance]],\\n) -> Tuple[Score, Score, Tuple[List[List[str]], List[List[str]]]]:\\n inexact_counts = defaultdict(lambda: np.zeros(3))\\n exact_counts = defaultdict(lambda: np.zeros(3))\\n\\n iobs = []\\n for (_, txt, gold), pred in zip(data, pred_data):\\n\\n for k, v in evaluate(gold, pred, exact=False).items():\\n inexact_counts[k] += v\\n for k, v in evaluate(gold, pred, exact=True).items():\\n exact_counts[k] += v\\n iobs.append(iob_conversion(txt, gold, pred))\\n\\n gold_iob, pred_iob = zip(*iobs)\\n\\n return (counts_to_scores(inexact_counts), counts_to_scores(exact_counts), (gold_iob, pred_iob))\",\n \"def evaluate(self, sess, data_gen):\\n pred_labels = np.array([], dtype=np.intp)\\n labels = np.array([], dtype=np.intp)\\n for inputs, seq_length, batch_labels in data_gen:\\n feed_dict = self.create_feed_dict(inputs, seq_length, batch_labels,\\n self.config.dropout)\\n pred = sess.run(self.pred, feed_dict)\\n pred = softmax(pred, -1)\\n pred = binarize(pred[:, 1], self.threshold)\\n\\n pred_labels = np.concatenate((pred_labels, pred))\\n labels = np.concatenate((labels, batch_labels))\\n\\n score = f1_score(labels, pred_labels)\\n\\n return score, pred_labels\",\n \"def compute_ave_score_w_sample(genes, samples):\\n\\n scores = np.zeros(len(genes), dtype=np.uint32)\\n\\n for i, v in enumerate(genes):\\n for j in samples:\\n score, _ = run_duel(v, j)\\n scores[i] += score\\n continue\\n continue\\n\\n return scores / len(samples)\",\n \"def score_snippets(snippets, scorer):\\n snippet_expressions = [snippet.embedding for snippet in snippets]\\n all_snippet_embeddings = torch.stack(snippet_expressions, dim=1)\\n\\n scores = torch.t(torch.mm(torch.t(scorer), all_snippet_embeddings))\\n\\n if scores.size()[0] != len(snippets):\\n raise ValueError(\\\"Got \\\" + str(scores.size()[0]) + \\\" scores for \\\" + str(len(snippets)) + \\\" snippets\\\")\\n\\n return scores, [snippet.name for snippet in snippets]\",\n \"def eval(self):\\n params = self.params\\n langs = ['en', params.target_lang]\\n self.encoder.eval()\\n self.proj.eval()\\n\\n scores = OrderedDict({'epoch': self.epoch})\\n\\n for splt in ['valid', 'test']:\\n\\n for lang in langs:\\n if lang == 'en' and splt == 'test' or lang != 'en' and splt == 'valid':\\n continue\\n lang_id = params.lang2id[lang if lang != 'jp' else 'ja']\\n valid = 0\\n total = 0\\n\\n for batch in self.get_iterator(splt, lang):\\n # batch\\n (sent1, len1), idx = batch\\n # set max length to 256, avoid position embedding overflow and save time.\\n x, lengths = truncate(sent1, len1, 256, params.eos_index)\\n lang_ids = x.clone().fill_(lang_id)\\n\\n y = self.data[lang][splt]['y'][idx]\\n\\n # cuda\\n x, y, lengths, lang_ids = to_cuda(x, y, lengths, lang_ids)\\n\\n # forward\\n output = self.proj(self.encoder.get_embeddings(x, lengths, langs=lang_ids))\\n predictions = output.data.max(1)[1]\\n\\n # update statistics\\n valid += predictions.eq(y).sum().item()\\n total += len(len1)\\n\\n # compute accuracy\\n acc = 100.0 * valid / total\\n scores['CLF_%s_%s_acc' % (splt, lang)] = acc\\n logger.info(\\\"CLF - %s - %s - Epoch %i - Acc: %.1f%%\\\" % (splt, lang, self.epoch, acc))\\n\\n logger.info(\\\"__log__:%s\\\" % json.dumps(scores))\\n return scores\",\n \"def match_data(embeddings, z_scores):\\r\\n print(\\\"Matching protein IDs...\\\", end=\\\"\\\")\\r\\n\\r\\n x, y = [], []\\r\\n # this could just as well be the keys from the z-scores, since the two of them are identical\\r\\n protein_ids = embeddings.keys()\\r\\n for protein_id in protein_ids:\\r\\n x.append(embeddings[protein_id])\\r\\n y.append(z_scores[protein_id])\\r\\n\\r\\n print(f\\\"done!\\\")\\r\\n return x, y\",\n \"def qualify_words():\\n config = get_config()\\n\\n all_feature_matrices = []\\n all_opinion_matrices = []\\n\\n # first 5 parts are labeled, thus are useful\\n all_feature_label_vectors = []\\n all_opinion_label_vectors = []\\n\\n for fname in config.file_names:\\n feature_X, feature_dims = load_feature_matrices(fname)\\n opinion_X, opinion_dims = load_opinion_matrices(fname)\\n feature_y = load_feature_labels(fname)\\n opinion_y = load_opinion_labels(fname)\\n\\n # append to all collector\\n all_feature_matrices.append(feature_X)\\n all_feature_label_vectors.append(feature_y)\\n all_opinion_matrices.append(opinion_X)\\n all_opinion_label_vectors.append(opinion_y)\\n # use first 5 for training\\n # stack first 5\\n feature_training_X = []\\n feature_training_y = []\\n opinion_training_X = []\\n opinion_training_y = []\\n for i in range(5):\\n feature_training_X.append(all_feature_matrices[i])\\n feature_training_y.append(all_feature_label_vectors[i])\\n opinion_training_X.append(all_opinion_matrices[i])\\n opinion_training_y.append(all_opinion_label_vectors[i])\\n\\n feature_training_X = np.hstack(feature_training_X)\\n feature_training_y = np.hstack(feature_training_y)\\n opinion_training_X = np.hstack(opinion_training_X)\\n opinion_training_y = np.hstack(opinion_training_y)\\n\\n # using combination of rule and ranking score as features\\n feature_model = MultinomialNB()\\n opinion_model = MultinomialNB()\\n\\n # training\\n feature_model.fit(np.transpose(feature_training_X), feature_training_y.ravel())\\n opinion_model.fit(np.transpose(opinion_training_X), opinion_training_y.ravel())\\n\\n # predicting on candidate aspects and opinions, extracted from amazon reviews\\n for i in range(5, len(config.file_names)):\\n fname = config.file_names[i]\\n feature_pred = feature_model.predict_proba(\\n np.transpose(all_feature_matrices[i]))[:,1]\\n opinion_pred = opinion_model.predict_proba(\\n np.transpose(all_opinion_matrices[i]))[:,1]\\n # pickle the prediction results\\n with open('../results/' + fname + '_feature_pred_score.pickle', 'wb') as f:\\n pickle.dump(feature_pred, f)\\n with open('../results/' + fname + '_opinion_pred_score.pickle', 'wb') as f:\\n pickle.dump(opinion_pred, f)\",\n \"def fashion_similarity(input_txt, features, keys):\\n feature_index = keys.index(input_txt)\\n input_vector = features[feature_index]\\n\\n scores = [similarity_function(input_vector, partner) for partner in features]\\n return scores\",\n \"def evaluate(self, data, category, dims=None, overall=True):\\n n_data = len(data)\\n eval_scores = [{} for _ in range(n_data)]\\n\\n if dims == None:\\n eval_dims = self.dimensions\\n else:\\n assert isinstance(dims, list)\\n eval_dims = dims\\n\\n for dim in eval_dims:\\n # Calculate summation score for 'engagingness'\\n if dim == 'engagingness':\\n src_list, output_list, context_list = [], [], []\\n n_sents = [] # the number of sentences in each generated response\\n for i in range(n_data):\\n source = data[i]['source']\\n context = data[i]['context']\\n system_outputs = sent_tokenize(data[i]['system_output'])\\n n_sents.append(len(system_outputs))\\n for j in range(len(system_outputs)):\\n src_list.append(source)\\n context_list.append(context)\\n output_list.append(system_outputs[j])\\n input_list = add_question(dimension=dim,\\n output=output_list,\\n src=src_list,\\n context=context_list,\\n task=self.task)\\n sent_score = self.scorer.score(input_list, self.task, category, dim)\\n\\n # Get the summation score for each sample\\n start_idx = 0\\n score = []\\n for cur_n_sent in n_sents:\\n score.append(sum(sent_score[start_idx:start_idx + cur_n_sent]))\\n start_idx += cur_n_sent\\n\\n # Calculate turn-level score for other dimensions\\n elif dim in ['naturalness', 'coherence', 'groundedness', 'understandability']:\\n src_list, output_list, context_list = [], [], []\\n for i in range(n_data):\\n src_list.append(data[i]['source'])\\n output_list.append(data[i]['system_output'])\\n context_list.append(data[i]['context'])\\n input_list = add_question(dimension=dim,\\n output=output_list,\\n src=src_list,\\n context=context_list,\\n task=self.task)\\n score = self.scorer.score(input_list, self.task, category, dim)\\n\\n # Please customize other dimensions here for summarization\\n else:\\n raise NotImplementedError('The input format for this dimension is still undefined. \\\\\\n Please customize it first.')\\n\\n for i in range(n_data):\\n eval_scores[i][dim] = score[i]\\n\\n # Customize your overall score here.\\n if overall == True:\\n for i in range(n_data):\\n eval_scores[i]['overall'] = np.mean(list(eval_scores[i].values()))\\n\\n return eval_scores\",\n \"def test_mean_results(self, concept: Concept):\\n concept_args = dict(concept=concept, model_stump=None)\\n for desc, (embs, (m_w, m_b)) in self.INTUITIVE_MEAN_EXAMPLES.items():\\n m_w: np.ndarray = np.array(m_w)\\n embeddings = [ConceptEmbedding(normal_vec=w, support_factor=b,\\n **concept_args)\\n for w, b in embs]\\n # Actual routine\\n m_emb: ConceptEmbedding = ConceptEmbedding.mean(embeddings)\\n context_info = ((\\\"context:\\\\n mean embedding: ({}, {}, 1.)\\\"\\n \\\"\\\\n in embeddings ({}) as (normal vec, support, \\\"\\n \\\"scaling):\\\\n {}\\\")\\n .format(m_emb.normal_vec, m_emb.support_factor,\\n desc,\\n [(e.normal_vec, e.support_factor,\\n e.scaling_factor)\\n for e in embeddings]))\\n\\n # Format checks\\n assert m_emb.normal_vec.shape == embeddings[0].normal_vec.shape\\n assert np.array(m_emb.support_factor).shape == np.array(\\n embeddings[0].support_factor).shape\\n\\n # Value checks\\n # Scaling\\n expected_scaling: float = float(np.mean(\\n [e.scaling_factor for e in\\n [e.normalize() for e in embeddings]]))\\n assert float(m_emb.scaling_factor) == expected_scaling, \\\\\\n (\\\"Mean scaling wrong: expected {}., but was {}; {}\\\"\\n .format(expected_scaling, m_emb.scaling_factor, context_info))\\n # Normal vector\\n assert np.allclose(m_emb.normal_vec, m_w), \\\\\\n (\\\"Wrong mean normal vector: expected {}, but was {}; {}\\\"\\n .format(m_w, m_emb.normal_vec, context_info))\\n # Support\\n assert np.allclose(m_emb.support_factor, m_b), \\\\\\n (\\\"Wrong mean support factor: expected {}, but was {}; {}\\\"\\n .format(m_b, m_emb.support_factor, context_info))\",\n \"def evaluate_ngrams(eval_dataset, trigram_counts, bigram_counts, unigram_counts, train_token_count, lambda1, lambda2):\\n perplexity = 0\\n\\n ### YOUR CODE HERE\\n def calc_prob(sentense, i, word, trigram_counts, bigram_counts, unigram_counts, train_token_count, model):\\n prob = 0.0\\n prev_word = sentense[i - 1]\\n prev_to_prev_word = sentense[i - 2]\\n\\n if model == \\\"unigram\\\":\\n if word in unigram_counts:\\n prob = (unigram_counts[word] + 0.0) / train_token_count\\n else:\\n prob = (unigram_counts[word_to_num['UUUNKKK']] + 0.0) / \\\\\\n train_token_count\\n\\n if model == \\\"bigram\\\":\\n if (prev_word, word) in bigram_counts:\\n prob = (bigram_counts[(prev_word, word)] + 0.0) / \\\\\\n unigram_counts[prev_word]\\n # print(num_to_word[prev_word] ,num_to_word[word])\\n # print(bigram_counts[(prev_word, word)])\\n # print(unigram_counts[prev_word])\\n # print(\\\"---------------------------\\\")\\n else:\\n prob = 0.0\\n\\n if model == \\\"trigram\\\":\\n if (prev_to_prev_word, prev_word, word) in trigram_counts:\\n prob = (trigram_counts[(prev_to_prev_word, prev_word, word)] + 0.0) \\\\\\n / bigram_counts[(prev_to_prev_word, prev_word)]\\n # / bigram_counts[(prev_word, word)] #this according to lecture notes slide 27\\n else:\\n prob = 0.0\\n\\n return prob\\n\\n l = 0\\n num_of_words = 0\\n\\n ##########3\\n better_than_chance = 0\\n ###########\\n\\n for sentense in eval_dataset:\\n for i, word in enumerate(sentense[2:]):\\n num_of_words += 1\\n prob = lambda1 * calc_prob(sentense, i + 2, word, trigram_counts, bigram_counts, unigram_counts,\\n train_token_count, \\\"trigram\\\") + \\\\\\n lambda2 * calc_prob(sentense, i + 2, word, trigram_counts, bigram_counts, unigram_counts,\\n train_token_count, \\\"bigram\\\") + \\\\\\n (1 - lambda1 - lambda2) * calc_prob(sentense, i + 2, word, trigram_counts, bigram_counts,\\n unigram_counts, train_token_count, \\\"unigram\\\")\\n ######################################\\n if prob > (1.0 / vocabsize):\\n better_than_chance += 1\\n #########################\\n l += np.log2(prob)\\n l /= num_of_words\\n perplexity = 2 ** -l\\n\\n print(\\\"better_than_chance:\\\", (better_than_chance + 0.0) / num_of_words)\\n\\n ### END YOUR CODE\\n return perplexity\",\n \"def evaluate_bias(ds: List[str], predicted: List[GENDER]) -> Dict:\\r\\n assert (len(ds) == len(predicted)) # must have same length to create tuples\\r\\n\\r\\n conf_dict = defaultdict(lambda: defaultdict(lambda: 0))\\r\\n total = defaultdict(lambda: 0) # increment values if we have any gender\\r\\n pred_cnt = defaultdict(lambda: 0)\\r\\n correct_cnt = defaultdict(lambda: 0) # increment values if true_gender == predicted_gender\\r\\n\\r\\n count_unknowns = defaultdict(lambda: 0)\\r\\n\\r\\n for (gold_gender, word_ind, sent, profession), pred_gender in zip(ds, predicted):\\r\\n # # IMPORTANTE NOTE :\\r\\n # need to works with .name of GENDER object for an unknown reason\\r\\n\\r\\n if isinstance(pred_gender, str): # can happen in spacy languages 'fr', 'es' or 'it\\r\\n pred_gender = SPACY_GENDER_TYPES[pred_gender]\\r\\n\\r\\n # tuples of values in ds and values in predicted\\r\\n if pred_gender.name == GENDER.ignore.name:\\r\\n continue # skip analysis of ignored words\\r\\n\\r\\n gold_gender = WB_GENDER_TYPES[gold_gender] # allows Winobias gender type conversion\\r\\n\\r\\n if pred_gender.name == GENDER.unknown.name:\\r\\n count_unknowns[gold_gender] += 1 # increment values for any unknown pred_gender\\r\\n\\r\\n profession = profession.lower()\\r\\n\\r\\n total[gold_gender] += 1\\r\\n\\r\\n if pred_gender.name == gold_gender.name:\\r\\n correct_cnt[gold_gender] += 1\\r\\n\\r\\n pred_cnt[pred_gender.name] += 1\\r\\n\\r\\n conf_dict[gold_gender][pred_gender] += 1\\r\\n\\r\\n all_total = sum(total.values())\\r\\n\\r\\n output_dict = {} # init output dictionnary\\r\\n # Compute metrics\\r\\n accuracy = round((sum(correct_cnt.values()) / all_total) * 100, 1) # compute accuracy\\r\\n output_dict['acc'] = accuracy\\r\\n\\r\\n if (total[GENDER.male] == 0) | (pred_cnt[GENDER.male.name] == 0): # Avoid ZeroDivisionError\\r\\n output_dict['f1_male'] = None\\r\\n else:\\r\\n recall_male = round((correct_cnt[GENDER.male] / total[GENDER.male]) * 100, 1) # compute metrics for male\\r\\n prec_male = round((correct_cnt[GENDER.male] / pred_cnt[GENDER.male.name]) * 100, 1)\\r\\n f1_male = round(calc_f1(prec_male, recall_male), 1)\\r\\n output_dict['f1_male'] = f1_male\\r\\n\\r\\n if (total[GENDER.female] == 0) | (pred_cnt[GENDER.female.name] == 0): # Avoid ZeroDivisionError\\r\\n output_dict['f1_female'] = None\\r\\n else:\\r\\n recall_female = round((correct_cnt[GENDER.female] / total[GENDER.female]) * 100, 1) # calcul metrics for female\\r\\n prec_female = round((correct_cnt[GENDER.female] / pred_cnt[GENDER.female.name]) * 100, 1)\\r\\n f1_female = round(calc_f1(prec_female, recall_female), 1)\\r\\n output_dict['f1_female'] = f1_female\\r\\n\\r\\n output_dict['unk_male'] = count_unknowns[GENDER.male]\\r\\n output_dict['unk_female'] = count_unknowns[GENDER.female]\\r\\n output_dict['unk_neutral'] = count_unknowns[GENDER.neutral]\\r\\n\\r\\n return output_dict\",\n \"def query_set_sim(self, en_ids, weights):\\n # fielded_weights = self.__get_weights(weights)\\n scorer = ScorerMLM(econfig.LUCENE, self.query, {}) # {'field_weights': fielded_weights})\\n\\n p_t_theta_d = {}\\n for t in set(self.query.split()):\\n p_t_theta_d[t] = 0\\n for en in en_ids:\\n lucene_doc_id = scorer.lucene.get_lucene_document_id(en)\\n p_t_theta_d[t] += scorer.get_mlm_term_prob(lucene_doc_id, weights, t)\\n score = self.nllr(self.query, p_t_theta_d, weights)\\n if score is None:\\n return 0\\n return math.exp(score)\",\n \"def classify_embeddings(model, training_set, test_set, training_labels):\\n print(\\\"Embedding...\\\")\\n # Using Hadamard product for the embedding vectors\\n edge_embeddings = HadamardEmbedder(keyed_vectors=model.wv)\\n x_train_embedded = [edge_embeddings[pair] for pair in training_set]\\n x_test = [edge_embeddings[pair] for pair in test_set]\\n\\n # Using MLPClassifier from sklearn as binary classifier\\n classifier = MLPClassifier(random_state=1)\\n print(\\\"Classifying...\\\")\\n classify = classifier.fit(x_train_embedded, training_labels)\\n\\n predict = classify.predict_proba(x_test)\\n score = dict()\\n for i in range(len(test_set)):\\n node1 = test_set[i][0]\\n node2 = test_set[i][1]\\n score[node1 + ' ' + node2] = predict[i][1]\\n return score\",\n \"def test_distance_mean_results(self, concept: Concept):\\n\\n concept_args = dict(concept=concept, model_stump=None)\\n for desc, (embs, (m_w, m_b)) in self.DISTANCE_MEAN_EXAMPLES.items():\\n m_w: np.ndarray = np.array(m_w)\\n embeddings = [ConceptEmbedding(normal_vec=w, support_factor=b,\\n **concept_args)\\n for w, b in embs]\\n # Actual routine\\n m_emb: ConceptEmbedding = \\\\\\n ConceptEmbedding.mean_by_distance(embeddings)\\n context_info = ((\\\"context:\\\\n mean embedding: ({}, {}, 1.)\\\"\\n \\\"\\\\n in embeddings ({}) as (normal vec, support, \\\"\\n \\\"scaling):\\\\n {}\\\")\\n .format(m_emb.normal_vec, m_emb.support_factor,\\n desc,\\n [(e.normal_vec, e.support_factor,\\n e.scaling_factor) for e in embeddings]))\\n\\n # Format checks\\n assert m_emb.normal_vec.shape == embeddings[0].normal_vec.shape\\n assert np.array(m_emb.support_factor).shape == np.array(\\n embeddings[0].support_factor).shape\\n\\n # Value checks\\n # the embedding should be scaled\\n assert float(m_emb.scaling_factor) == 1., \\\\\\n (\\\"Mean embedding not scaled: expected 1., but was {}; {}\\\"\\n .format(m_emb.scaling_factor, context_info))\\n assert np.allclose(m_emb.normal_vec, m_w), \\\\\\n (\\\"Wrong mean normal vector: expected {}, but was {}; {}\\\"\\n .format(m_w, m_emb.normal_vec, context_info))\\n # For all given ground truths of support factors, check them:\\n if m_b is not None:\\n assert np.allclose(m_emb.support_factor, m_b), \\\\\\n (\\\"Wrong mean support factor: expected {}, but was {}; {}\\\"\\n .format(m_b, m_emb.support_factor, context_info))\",\n \"def analyze_similarities():\\r\\n print('Total number of candidate pairs:', len(pairs))\\r\\n print(f'\\\\nNumber of actual item pairs in the train set: {pairs[\\\"true_match\\\"].sum()}\\\\n')\\r\\n\\r\\n for feature in ['text_score', 'image_score', 'txt_img_score', 'words_ratio', 'txt_img_words']:\\r\\n\\r\\n # Check distribution of True and False predictions for various similarity scores\\r\\n print('-' * 50)\\r\\n print(f'\\\\nDistribution of True/False predictions for {feature}')\\r\\n for thr in (0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95):\\r\\n print('-' * 50)\\r\\n print(f'Similarity score over {thr}')\\r\\n pairs_sample = pairs[pairs[feature] >= thr]\\r\\n print(f'Number of similar item pairs: {len(pairs_sample)}')\\r\\n print(pairs_sample['true_match'].value_counts(normalize=True))\\r\\n\\r\\n # Check if identical phash can be used to improve the accuracy\\r\\n same_phash = pairs[pairs['phash_match'] == 1]\\r\\n different_phash = pairs[pairs['phash_match'] == 0]\\r\\n\\r\\n print('\\\\nFor item pairs with the same phash:')\\r\\n print(same_phash['true_match'].value_counts(normalize=True))\\r\\n print('Number of item pairs in this subset:', len(same_phash))\\r\\n\\r\\n print('\\\\nFor item pairs with different phash:')\\r\\n print(different_phash['true_match'].value_counts(normalize=True))\\r\\n print('Number of item pairs in this subset:', len(different_phash))\\r\\n\\r\\n # Check if numbers in titles can be used to improve the accuracy\\r\\n same_numbers = pairs[pairs['nums_match'] == 1]\\r\\n different_numbers = pairs[pairs['nums_match'] == 0]\\r\\n\\r\\n print('\\\\nFor item pairs with the same numbers:')\\r\\n print(same_numbers['true_match'].value_counts(normalize=True))\\r\\n print('Number of item pairs in this subset:', len(same_numbers))\\r\\n\\r\\n print('\\\\nFor item pairs with different numbers:')\\r\\n print(different_numbers['true_match'].value_counts(normalize=True))\\r\\n print('Number of item pairs in this subset:', len(different_numbers))\",\n \"def compute_scores(self, data, distance):\\r\\n print('Computing the scores')\\r\\n distance.set_parameters(data)\\r\\n size = np.shape(data)\\r\\n if len(size) == 3:\\r\\n data = np.reshape(data, (size[0]*size[1], size[2]))\\r\\n size = np.shape(data)\\r\\n scores_dimension = size[0]\\r\\n n_features = size[1]\\r\\n scores = np.ones(shape=(scores_dimension, scores_dimension))\\r\\n for i in range(scores_dimension):\\r\\n for j in range(i + 1, scores_dimension):\\r\\n dist = distance.compute_distance(data[i, 0:n_features], data[j, 0:n_features])\\r\\n scores[i, j] = 1 / (1 + dist)\\r\\n scores[j, i] = scores[i, j]\\r\\n return scores\",\n \"def evaluate(self, eval_batches, result_dir=None, result_prefix=None, save_full_info=False):\\n pred_answers, ref_answers = [], []\\n total_loss, total_num = 0, 0\\n for b_itx, batch in enumerate(eval_batches):\\n feed_dict = {self.p: batch['passage_token_ids'],\\n self.q: batch['question_token_ids'],\\n self.p_length: batch['passage_length'],\\n self.q_length: batch['question_length'],\\n self.start_label: batch['start_id'],\\n self.end_label: batch['end_id'],\\n self.dropout_keep_prob: 1.0,\\n self.em: batch['exact_match']}\\n batch_size = len(batch['start_id'])\\n padded_p_len = len(batch['passage_token_ids'][0])\\n padded_p_num = len(batch['passage_token_ids']) / batch_size\\n para_ids = []\\n for start_id in batch['start_id']:\\n para_ids.append(start_id // padded_p_len)\\n feed_dict[self.para_label] = para_ids\\n content_label = np.zeros([batch_size, padded_p_num * padded_p_len], dtype=int)\\n for s_idx, (start_id, end_id) in enumerate(zip(batch['start_id'], batch['end_id'])):\\n content_label[s_idx, start_id: end_id+1] = 1\\n feed_dict[self.content_label] = content_label\\n start_probs, end_probs, content_scores, verif_scores, loss = self.sess.run([self.start_probs, self.end_probs,\\n self.concat_content_score, self.reshaped_ans_verif_score,\\n self.loss], feed_dict)\\n\\n total_loss += loss * len(batch['raw_data'])\\n total_num += len(batch['raw_data'])\\n\\n padded_p_len = len(batch['passage_token_ids'][0])\\n for s_idx, sample in enumerate(batch['raw_data']):\\n start_prob = start_probs[s_idx]\\n end_prob = end_probs[s_idx]\\n content_score = content_scores[s_idx]\\n verif_score = verif_scores[s_idx]\\n best_answer = self.find_best_answer_with_verif(sample, start_prob, end_prob,\\n content_score, verif_score, padded_p_len)\\n if save_full_info:\\n sample['pred_answers'] = [best_answer]\\n pred_answers.append(sample)\\n else:\\n pred_answers.append({'question_id': sample['question_id'],\\n 'question_type': sample['question_type'],\\n 'answers': [best_answer],\\n 'entity_answers': [[]],\\n 'yesno_answers': []})\\n if 'answers' in sample:\\n ref_answers.append({'question_id': sample['question_id'],\\n 'question_type': sample['question_type'],\\n 'answers': sample['answers'],\\n 'entity_answers': [[]],\\n 'yesno_answers': []})\\n\\n if result_dir is not None and result_prefix is not None:\\n result_file = os.path.join(result_dir, result_prefix + '.json')\\n with open(result_file, 'w') as fout:\\n for pred_answer in pred_answers:\\n fout.write(json.dumps(pred_answer, encoding='utf8', ensure_ascii=False) + '\\\\n')\\n\\n self.logger.info('Saving {} results to {}'.format(result_prefix, result_file))\\n\\n # this average loss is invalid on test set, since we don't have true start_id and end_id\\n ave_loss = 1.0 * total_loss / total_num\\n # compute the bleu and rouge scores if reference answers is provided\\n if len(ref_answers) > 0:\\n pred_dict, ref_dict = {}, {}\\n for pred, ref in zip(pred_answers, ref_answers):\\n question_id = ref['question_id']\\n if len(ref['answers']) > 0:\\n pred_dict[question_id] = normalize(pred['answers'])\\n ref_dict[question_id] = normalize(ref['answers'])\\n bleu_rouge = compute_bleu_rouge(pred_dict, ref_dict)\\n else:\\n bleu_rouge = None\\n return ave_loss, bleu_rouge\",\n \"def score(self, sentence):\\n # TODO your code here\\n\\n # initialize count with trained data\\n unigram_count = self.count.copy()\\n N = self.total\\n\\n # make a new key for UNK, add-one later\\n for token in sentence:\\n if token not in unigram_count:\\n unigram_count[token] = 0\\n\\n # calcutate lopP(<s>) + logP(w1) + logP(w2) + ...\\n score = 0.0 # P(<s>) = 1\\n V = len(unigram_count) # the number of vocab including UNK\\n for word in sentence:\\n prob = float((unigram_count[word] + 1) / (N + V)) # c(w) + 1 / N + V\\n score += math.log(prob)\\n\\n return score\",\n \"def run_evaluate_compound(self, data):\\n tp = 0\\n tn = 0\\n fp = 0\\n fn = 0\\n for words, labels, pos in minbatch_generator(data, self.batch_size):\\n labels_pred, sequence_lengths = self.predict_batch(words, pos)\\n if not type(labels_pred) == list:\\n labels_pred = labels_pred.tolist()\\n for i, label_actual in enumerate(labels):\\n # we have to now find all aspect terms. That is, merge consecutive B \\n # with I's until we get an O or B\\n a_terms_actual, non_a_terms_actual = get_aspect_chunks(label_actual)\\n for a_term in a_terms_actual:\\n # say we have [1,2,3] as our a_term. we have to make sure that our \\n # prediction should have tags B, I, I in 1, 2 and 3 indices respectively\\n a_term_pred = []\\n for index in a_term:\\n if not label_actual[index] == labels_pred[i][index]:\\n fn += 1\\n break\\n else:\\n tp += 1\\n for index in non_a_terms_actual:\\n if label_actual[index] == labels_pred[i][index]:\\n tn += 1\\n else:\\n fp += 1\\n\\n precision = 0 if tp+fp == 0 else tp/(tp+fp)\\n recall = 0 if tp+fn == 0 else tp/(tp+fn)\\n fscore = 0 if precision+recall == 0 else (2*recall*precision)/(recall+precision)\\n metrics = dict()\\n metrics[\\\"p_1\\\"] = precision\\n metrics[\\\"f_1\\\"] = fscore\\n metrics[\\\"r_1\\\"] = recall\\n return metrics\",\n \"def evaluate(self, data, category, dims=None, overall=True):\\n n_data = len(data)\\n eval_scores = [{} for _ in range(n_data)]\\n\\n if dims == None:\\n eval_dims = self.dimensions\\n else:\\n assert isinstance(dims, list)\\n eval_dims = dims\\n\\n for dim in eval_dims:\\n # Calculate average sentence-level scores for 'consistency' and 'fluency'\\n if dim == 'consistency' or dim == 'fluency':\\n src_list, output_list = [], []\\n n_sents = [] # the number of sentences in each generated summary\\n for i in range(n_data):\\n source = data[i]['source']\\n system_outputs = sent_tokenize(data[i]['system_output'])\\n n_sents.append(len(system_outputs))\\n for j in range(len(system_outputs)):\\n src_list.append(source)\\n output_list.append(system_outputs[j])\\n input_list = add_question(dimension=dim, output=output_list, src=src_list, task=self.task)\\n sent_score = self.scorer.score(input_list, self.task, category, dim)\\n\\n # Get average score for each sample\\n start_idx = 0\\n score = []\\n for cur_n_sent in n_sents:\\n # prevent denominator from being 0\\n score.append(sum(sent_score[start_idx:start_idx + cur_n_sent]) / (cur_n_sent + 1e-6))\\n start_idx += cur_n_sent\\n\\n # Calculate summary-level score for 'coherence' and 'relevance'\\n elif dim == 'coherence' or dim == 'relevance':\\n src_list, output_list, ref_list = [], [], []\\n for i in range(n_data):\\n src_list.append(data[i]['source'])\\n output_list.append(data[i]['system_output'])\\n if dim == 'relevance':\\n ref_list.append(data[i]['reference'])\\n input_list = add_question(dimension=dim, output=output_list, src=src_list, ref=ref_list, task=self.task)\\n score = self.scorer.score(input_list, self.task, category, dim)\\n\\n # Please customize other dimensions here for summarization\\n else:\\n raise NotImplementedError('The input format for this dimension is still undefined. \\\\\\n Please customize it first.')\\n\\n for i in range(n_data):\\n eval_scores[i][dim] = score[i]\\n\\n # Customize your overall score here.\\n if overall == True:\\n for i in range(n_data):\\n eval_scores[i]['overall'] = np.mean(list(eval_scores[i].values()))\\n\\n return eval_scores\",\n \"def evaluate_samples(lang_samples: Dict[str, List[Tuple[List[str], List[str], List[str]]]]) -> Dict[str, Any]:\\n\\n evaluation_result = dict()\\n lang_phon_err, lang_phon_count, lang_word_err = dict(), dict(), dict()\\n languages = sorted(lang_samples.keys())\\n for lang in languages:\\n for word, generated, target in lang_samples[lang]:\\n word = ''.join(word)\\n phon_err, phon_count = phoneme_error(generated, target)\\n word_err = word_error(generated, target)\\n phon_err_dict = lang_phon_err.setdefault(lang, dict())\\n phon_count_dict = lang_phon_count.setdefault(lang, dict())\\n word_err_dict = lang_word_err.setdefault(lang, dict())\\n best_phon_err, best_phon_count = phon_err_dict.get(word, None), phon_count_dict.get(word, None)\\n if best_phon_err is None or phon_err / phon_count < best_phon_err / best_phon_count:\\n phon_err_dict[word] = phon_err\\n phon_count_dict[word] = phon_count\\n word_err_dict[word] = word_err\\n\\n phon_errors, phon_counts, word_errors, word_counts = [], [], [], []\\n for lang in languages:\\n phon_err = sum(lang_phon_err[lang].values())\\n phon_errors.append(phon_err)\\n phon_count = sum(lang_phon_count[lang].values())\\n phon_counts.append(phon_count)\\n word_err = sum(lang_word_err[lang].values())\\n word_errors.append(word_err)\\n word_count = len(lang_word_err[lang])\\n word_counts.append(word_count)\\n per = phon_err / phon_count\\n wer = word_err / word_count\\n evaluation_result.setdefault(lang, {}).update({'per': per})\\n evaluation_result.setdefault(lang, {}).update({'wer': wer})\\n mean_per = sum(phon_errors) / sum(phon_counts)\\n mean_wer = sum(word_errors) / sum(word_counts)\\n evaluation_result['mean_per'] = mean_per\\n evaluation_result['mean_wer'] = mean_wer\\n\\n return evaluation_result\",\n \"def _compute_sentence_statistics(pred_words: List[str], target_words: List[List[str]]) ->Tuple[Tensor, Tensor]:\\n tgt_lengths = tensor(0.0)\\n best_num_edits = tensor(2e+16)\\n for tgt_words in target_words:\\n num_edits = _translation_edit_rate(tgt_words, pred_words)\\n tgt_lengths += len(tgt_words)\\n if num_edits < best_num_edits:\\n best_num_edits = num_edits\\n avg_tgt_len = tgt_lengths / len(target_words)\\n return best_num_edits, avg_tgt_len\",\n \"def score(self, indices):\\n self.model.eval()\\n _, prediction = self.model(self.propagation_matrix, self.features).max(dim=1)\\n correct = prediction[indices].eq(self.target[indices]).sum().item()\\n acc = correct / indices.shape[0]\\n return acc\",\n \"def test_compute_unnormalized_scores(self):\\n # todo: implement this test!\\n pass\",\n \"def get_scores(self, query):\\n score = np.zeros(self.data['corpus_size'])\\n doc_len = np.array(self.data['doc_len'])\\n for q in query:\\n q_freq = np.array([(doc.get(q) or 0) for doc in self.data['doc_freqs']])\\n score += (self.data['idf'].get(q) or 0) * (q_freq * (self.data['k1'] + 1) /\\n (q_freq + self.data['k1'] * (\\n 1 - self.data['b'] + self.data['b'] * doc_len /\\n self.data['average_docs_len'])))\\n return score\",\n \"def evaluate_similarity(kv: KeyedVectors, X, y):\\n mean_vector = np.mean(kv.vectors, axis=0, keepdims=True)\\n missing_words = np.sum(np.isin(X, kv.index2word, invert=True))\\n if missing_words > 0:\\n logging.warning(\\\"Missing {} words. Will replace them with mean vector\\\".format(missing_words))\\n get = np.vectorize(gensim_helper.get_vector, signature='(),(),(m)->(m)')\\n timer = mytimer.Timer(\\\"getting vectors for words\\\")\\n wv_x = get(X, kv, mean_vector)\\n timer.stop()\\n a = wv_x[:, 0]\\n b = wv_x[:, 1]\\n # timer = mytimer.Timer()\\n # a = np_helper.normalize_over_cols_2d(a)\\n # b = np_helper.normalize_over_cols_2d(b)\\n # scores = np.diag(np.matmul(a, b.T))\\n # timer.stop()\\n # print(scores.shape)\\n #\\n # A = np.vstack(kv.get(word, mean_vector) for word in X[:, 0])\\n # B = np.vstack(kv.get(word, mean_vector) for word in X[:, 1])\\n timer = mytimer.Timer()\\n scores = np.array([v1.dot(v2.T) / (np.linalg.norm(v1) * np.linalg.norm(v2)) for v1, v2 in zip(a, b)])\\n timer.stop()\\n # print(scores.shape)\\n return scipy.stats.spearmanr(scores, y)\",\n \"def evaluate(self, test_X, test_Y):\\n correct = 0\\n total = 0.0\\n\\n for i, ((word_indices, word_char_indices), gold_tag_indices) in enumerate(zip(test_X, test_Y)):\\n\\n output = self.predict(word_indices, word_char_indices)\\n predicted_tag_indices = [np.argmax(o.value()) for o in output] \\n\\n correct += sum([1 for (predicted, gold) in zip(predicted_tag_indices, gold_tag_indices) if predicted == gold])\\n total += len(gold_tag_indices)\\n\\n return correct, total\",\n \"def distance_score(x_embeddings_test, x_embeddings_train, y_true_train, K=50):\\n num_samples = x_embeddings_test.shape[0]\\n num_classes = y_true_train.shape[1]\\n y_test_confidence = []\\n for i in range(num_samples):\\n sample_embedding = x_embeddings_test[i]\\n distances = np.square(sample_embedding - x_embeddings_train).sum(axis=-1)\\n K_nn = np.argsort(distances)[:K]\\n K_nn_distances = np.exp(-np.sqrt(distances[K_nn]))\\n K_nn_labels = y_true_train[K_nn, :]\\n\\n class_indicators = np.eye(num_classes)\\n classes_masks = np.matmul(class_indicators, np.transpose(K_nn_labels))\\n\\n # foreach class we mask away the samples in Knn that belong to other classes\\n class_samples_distances = classes_masks * np.expand_dims(K_nn_distances, axis=0) # this gives num_classes X K (100 X 50 matrix)\\n sum_distances = np.sum(K_nn_distances)\\n D_x = np.sum(class_samples_distances, axis=-1)/sum_distances\\n\\n y_test_confidence.append(D_x)\\n\\n return np.vstack(y_test_confidence)\",\n \"def test_scoring(self):\\n scores = score_words(['foo', 'far', 'has', 'car'])\\n expected = [(7, 'far'), (6, 'car'), (5, 'has'), (4 , 'foo')]\\n self.assertEqual(scores, expected)\",\n \"def compute_accuracy(gold_dict, src_word2ind, trg_word2ind, src_words, trg_words, scores, args, k=5, BATCH_SIZE=20):\\n oov = set()\\n correct = 0\\n n,m = scores.shape # The actual size of mapping computed, might be smaller that total size of dict\\n for src_idx,tgt_idx in gold_dict.items():\\n np.any([e<m for e in tgt_idx])\\n if src_idx > n or np.all([e>m for e in tgt_idx]):#Src word not in mapping\\n oov.add(src_idx)\\n continue\\n else:\\n print(k, src_idx, tgt_idx)\\n knn = np.argpartition(scores[src_idx,:], -k)[-k:]\\n knn_sort = knn[np.argsort(-scores[src_idx,knn])] # With - to get descending order\\n if set(knn_sort).intersection(tgt_idx):\\n correct +=1\\n\\n coverage = len(gold_dict.keys()) / (len(gold_dict.keys()) + len(oov))\\n if verbose:\\n print('Coverage: {0:7.2%} Accuracy:{1:7.2%}'.format(coverage, correct / len(gold_dict)))\\n return correct / len(gold_dict)\",\n \"def evaluate(self, test_data):\\n log_like = 0\\n for sequence in test_data:\\n for event, context in self.extract_ngrams(sequence):\\n log_like += math.log(self.cond_prob(event, context))\\n return log_like\",\n \"def analyze_embeddings(emb):\\n dic = {\\\"Hi\\\": 0, \\\"En\\\": 1, \\\"Ot\\\": 2}\\n count = [0, 0, 0, 0]\\n count_zero = [0, 0, 0, 0]\\n for i, j in zip(emb, corpus_trans):\\n for k, l in zip(i, j):\\n count[dic[l[1]]] += 1\\n if sum(k) == 0:\\n count_zero[dic[l[1]]] += 1\\n count[-1] = sum(count)\\n count_zero[-1] - sum(count_zero)\\n print(\\\"hi, en, ot, total\\\")\\n print(\\\"count: \\\", count)\\n print(\\\"zero count: \\\", count_zero)\",\n \"def compute_idf(doc_info, freq_dict_all):\\n idf_scores = []\\n counter = 0\\n\\n for temp_dict in freq_dict_all:\\n counter += 1\\n\\n for k in temp_dict['freq_dict'].keys():\\n count = sum([k in tempdict['freq_dict'] for tempdict in freq_dict_all])\\n temp = {\\n 'doc_id': counter,\\n 'IDF_Score': math.log(len(doc_info) / count),\\n 'key': k\\n }\\n\\n idf_scores.append(temp)\\n\\n return idf_scores\",\n \"def match(\\n self, key_embeds: Tensor, ref_embeds: Tensor,\\n key_sampling_results: List[SamplingResult],\\n ref_sampling_results: List[SamplingResult]\\n ) -> Tuple[List[Tensor], List[Tensor]]:\\n\\n num_key_rois = [res.pos_bboxes.size(0) for res in key_sampling_results]\\n key_embeds = torch.split(key_embeds, num_key_rois)\\n num_ref_rois = [res.bboxes.size(0) for res in ref_sampling_results]\\n ref_embeds = torch.split(ref_embeds, num_ref_rois)\\n\\n dists, cos_dists = [], []\\n for key_embed, ref_embed in zip(key_embeds, ref_embeds):\\n dist = embed_similarity(\\n key_embed,\\n ref_embed,\\n method='dot_product',\\n temperature=self.softmax_temp)\\n dists.append(dist)\\n if self.loss_track_aux is not None:\\n cos_dist = embed_similarity(\\n key_embed, ref_embed, method='cosine')\\n cos_dists.append(cos_dist)\\n else:\\n cos_dists.append(None)\\n return dists, cos_dists\",\n \"def data_mining_features(index,input_string_x1,input_string_x2,vocab_word2index,word_vec_fasttext_dict,word_vec_word2vec_dict,tfidf_dict,n_gram=8):\\r\\n input_string_x1=input_string_x1.decode(\\\"utf-8\\\")\\r\\n input_string_x2 = input_string_x2.decode(\\\"utf-8\\\")\\r\\n #1. get blue score vector\\r\\n feature_list=[]\\r\\n #get blue score with n-gram\\r\\n for i in range(n_gram):\\r\\n x1_list=split_string_as_list_by_ngram(input_string_x1,i+1)\\r\\n x2_list = split_string_as_list_by_ngram(input_string_x2, i + 1)\\r\\n blue_score_i_1 = compute_blue_ngram(x1_list,x2_list)\\r\\n blue_score_i_2 = compute_blue_ngram(x2_list,x1_list)\\r\\n feature_list.append(blue_score_i_1)\\r\\n feature_list.append(blue_score_i_2)\\r\\n\\r\\n #2. get length of questions, difference of length\\r\\n length1=float(len(input_string_x1))\\r\\n length2=float(len(input_string_x2))\\r\\n length_diff=(float(abs(length1-length2)))/((length1+length2)/2.0)\\r\\n feature_list.append(length_diff)\\r\\n\\r\\n #3. how many words are same, how many words are unique\\r\\n sentence_diff_overlap_features_list=get_sentence_diff_overlap_pert(index,input_string_x1,input_string_x2)\\r\\n feature_list.extend(sentence_diff_overlap_features_list)\\r\\n\\r\\n #4. question 1,2 start with how/why/when\\r\\n #how_why_feature_list=get_special_start_token(input_string_x1,input_string_x2,special_start_token)\\r\\n #print(\\\"how_why_feature_list:\\\",how_why_feature_list)\\r\\n #feature_list.extend(how_why_feature_list)\\r\\n\\r\\n #5.edit distance\\r\\n edit_distance=float(edit(input_string_x1, input_string_x2))/30.0\\r\\n feature_list.append(edit_distance)\\r\\n\\r\\n #6.cos distance from sentence embedding\\r\\n x1_list=token_string_as_list(input_string_x1, tokenize_style='word')\\r\\n x2_list = token_string_as_list(input_string_x2, tokenize_style='word')\\r\\n distance_list_fasttext = cos_distance_bag_tfidf(x1_list, x2_list, word_vec_fasttext_dict, tfidf_dict)\\r\\n distance_list_word2vec = cos_distance_bag_tfidf(x1_list, x2_list, word_vec_word2vec_dict, tfidf_dict)\\r\\n #distance_list2 = cos_distance_bag_tfidf(x1_list, x2_list, word_vec_fasttext_dict, tfidf_dict,tfidf_flag=False)\\r\\n #sentence_diffence=np.abs(np.subtract(sentence_vec_1,sentence_vec_2))\\r\\n #sentence_multiply=np.multiply(sentence_vec_1,sentence_vec_2)\\r\\n feature_list.extend(distance_list_fasttext)\\r\\n feature_list.extend(distance_list_word2vec)\\r\\n #feature_list.extend(list(sentence_diffence))\\r\\n #feature_list.extend(list(sentence_multiply))\\r\\n return feature_list\",\n \"def evaluate(gold: List[Instance], pred: List[Instance], exact: bool) -> Dict[str, Tuple[int, int, int]]:\\n # Initialize to zeros\\n gold_score = np.zeros(len(gold))\\n pred_score = np.zeros(len(pred))\\n\\n for idx, (pos, label) in enumerate(gold):\\n for idx2, (pos2, label2) in enumerate(pred):\\n if label == label2:\\n if overlap(pos, pos2, exact):\\n gold_score[idx] = 1\\n pred_score[idx2] = 1\\n # This ensures we only count one pred correctly for each gold.\\n if gold_score[idx]:\\n continue\\n\\n gold_labels = np.asarray([x[1] for x in gold])\\n pred_labels = np.asarray([x[1] for x in pred])\\n\\n # Get the label set.\\n label_set = set(gold_labels) | set(pred_labels)\\n\\n scores = {}\\n for label in label_set:\\n subset_gold = gold_score[gold_labels == label]\\n subset_index = pred_score[pred_labels == label]\\n tp = subset_gold.sum()\\n fn = len(subset_gold) - tp\\n fp = (subset_index == 0).sum()\\n scores[label] = np.array([tp, fp, fn])\\n\\n return scores\",\n \"def score_samples(self, x):\\n raise NotImplementedError\",\n \"def tf_idf_score():\\n\\n global final_doc_set\\n global final_dictionary\\n final_score = []\\n\\n for doc_id in final_doc_set:\\n score = 0\\n for query_term in final_dictionary.keys():\\n if final_dictionary[query_term][1].get(doc_id):\\n tf = final_dictionary[query_term][1][doc_id][0]\\n df = final_dictionary[query_term][0]\\n\\n score += ((1 + log10(tf)) * log10(TOTAL_DOCS / df))\\n\\n final_score.append([doc_id, score])\\n\\n return final_score\",\n \"def fit_embeddings(\\n data: DataLoader,\\n embedding_num: Optional[EmbeddingBase],\\n embedding_cat: Optional[EmbeddingBase],\\n) -> Tuple[Optional[EmbeddingBase], Optional[EmbeddingBase]]:\\n if embedding_num is None and embedding_cat is None:\\n return None, None\\n\\n # get initial values and aggregation functions\\n if embedding_num is not None:\\n num_info_1, num_info_2, num_agg_fn = _init_embed_info(embedding_num)\\n if embedding_cat is not None:\\n cat_info_1, cat_info_2, cat_agg_fn = _init_embed_info(embedding_cat)\\n\\n # iterate and update the initial aggregation values/objects\\n for batch in data:\\n if embedding_num is not None:\\n num_info_1, num_info_2 = num_agg_fn(num_info_1, num_info_2, batch[0])\\n if embedding_cat is not None:\\n cat_info_1, cat_info_2 = cat_agg_fn(cat_info_1, cat_info_2, batch[1])\\n\\n # use aggregated values to set the embeddings\\n if embedding_num is None:\\n pass\\n elif isinstance(embedding_num, LinearEmbedding):\\n embedding_num.from_summary(num_info_1)\\n else:\\n embedding_num.from_summary(num_info_1, num_info_2)\\n\\n if embedding_cat is not None:\\n embedding_cat.from_summary(cat_info_1, cat_info_2)\\n\\n return embedding_num, embedding_cat\",\n \"def get_tranception_scores_mutated_sequences(model, mutated_sequence_df, batch_size_inference, score_var_name, target_seq, num_workers=10, reverse=False, indel_mode=False):\\n scores = {} \\n scores['mutated_sequence']=[]\\n scores['sliced_mutated_sequence']=[]\\n scores['window_start']=[]\\n scores['window_end']=[]\\n scores['score']=[]\\n with torch.no_grad():\\n ds = Dataset.from_pandas(mutated_sequence_df)\\n ds.set_transform(model.encode_batch)\\n data_collator = DataCollatorForLanguageModeling(\\n tokenizer=model.config.tokenizer,\\n mlm=False)\\n sampler = SequentialSampler(ds)\\n ds_loader = torch.utils.data.DataLoader(ds, batch_size=batch_size_inference, sampler=sampler, collate_fn=data_collator, num_workers=num_workers, pin_memory=True, drop_last=False)\\n mutant_index=0\\n for encoded_batch in tqdm.tqdm(ds_loader):\\n full_batch_length = len(encoded_batch['input_ids'])\\n mutated_sequence = np.array(mutated_sequence_df['mutated_sequence'][mutant_index:mutant_index+full_batch_length])\\n scores['mutated_sequence'] += list(mutated_sequence)\\n sliced_mutated_sequence = np.array(mutated_sequence_df['sliced_mutated_sequence'][mutant_index:mutant_index+full_batch_length])\\n scores['sliced_mutated_sequence'] += list(sliced_mutated_sequence)\\n window_start = np.array(mutated_sequence_df['window_start'][mutant_index:mutant_index+full_batch_length])\\n scores['window_start'] += list(window_start)\\n window_end = np.array(mutated_sequence_df['window_end'][mutant_index:mutant_index+full_batch_length])\\n scores['window_end'] += list(window_end)\\n for k, v in encoded_batch.items():\\n if isinstance(v, torch.Tensor):\\n encoded_batch[k] = v.to(model.device) \\n shift_labels = encoded_batch['labels'][..., 1:].contiguous()\\n if (hasattr(model.config,\\\"retrieval_aggregation_mode\\\")) and (model.config.retrieval_aggregation_mode is not None):\\n if reverse:\\n encoded_batch['flip']=torch.tensor([1]*full_batch_length)\\n encoded_batch['start_slice']=window_start\\n encoded_batch['end_slice']=window_end\\n encoded_batch['mutated_sequence'] = mutated_sequence #only mutated_sequence is flipped if the scoring_mirror branch of score_mutants. No need to flip mutated_sequence for MSA re-aligning\\n fused_shift_log_probas=model(**encoded_batch,return_dict=True).fused_shift_log_probas\\n loss_fct = NLLLoss(reduction='none')\\n loss = - loss_fct(input=fused_shift_log_probas.view(-1, fused_shift_log_probas.size(-1)), target=shift_labels.view(-1)).view(fused_shift_log_probas.shape[0],fused_shift_log_probas.shape[1])\\n else:\\n lm_logits=model(**encoded_batch,return_dict=True).logits\\n shift_logits = lm_logits[..., :-1, :].contiguous()\\n loss_fct = CrossEntropyLoss(reduction='none') \\n loss = - loss_fct(input=shift_logits.view(-1, shift_logits.size(-1)), target=shift_labels.view(-1)).view(shift_logits.shape[0],shift_logits.shape[1])\\n mask = encoded_batch['attention_mask'][..., 1:].float()\\n mask[mask==0]=float('nan')\\n loss *= mask\\n loss = nansum(loss, dim=1)\\n scores_batch = list(loss.cpu().numpy())\\n full_batch_length = len(encoded_batch['input_ids'])\\n scores['score'] += scores_batch\\n mutant_index+=full_batch_length\\n scores = pd.DataFrame(scores)\\n if model.config.scoring_window==\\\"sliding\\\":\\n scores = scores[['mutated_sequence','score']].groupby('mutated_sequence').sum().reset_index() #We need to aggregate scores when using sliding mode\\n scores['score'] = scores['score'] / scores['mutated_sequence'].map(lambda x: len(x))\\n if target_seq is not None:\\n scores_mutated_seq = scores[scores.mutated_sequence != target_seq]\\n scores_wt = scores[scores.mutated_sequence == target_seq]\\n merge_delta = 'mutated_sequence' if model.config.scoring_window==\\\"sliding\\\" else 'window_start'\\n if model.config.scoring_window==\\\"optimal\\\":\\n delta_scores = pd.merge(scores_mutated_seq,scores_wt,how='left',on=[merge_delta],suffixes=('','_wt'))\\n delta_scores[score_var_name] = delta_scores['score'] - delta_scores['score_wt']\\n elif model.config.scoring_window==\\\"sliding\\\":\\n delta_scores = scores_mutated_seq.copy()\\n delta_scores[score_var_name] = delta_scores['score'] - list(scores_wt['score'])[0] # In sliding mode there is a single reference window for the WT\\n return delta_scores[['mutated_sequence',score_var_name]]\\n else:\\n scores[score_var_name] = scores['score']\\n return scores[['mutated_sequence',score_var_name]]\",\n \"def calculate_all_scores(best_phenotype, clustering_algorithm, dataset, y):\\n samples_dist_matrix = distance.squareform(distance.pdist(dataset.values))\\n allowed_fitness = list(DICT_ALLOWED_FITNESSES.keys())\\n scores = [(fitness_name, fitness_value) for fitness_name, fitness_value in\\n zip(allowed_fitness,\\n eval_multiple(dataset.values, clustering_algorithm, allowed_fitness, samples_dist_matrix, y,\\n best_phenotype))]\\n scores = dict(scores)\\n return scores\",\n \"def score(self, *args, references=[], candidates=[], batch_size=None):\\n assert not args, (\\n \\\"The score function does not accept positional arguments. Please \\\"\\n \\\"specify the name of the arguments explicitly, i.e., \\\"\\n \\\"`score(references=..., candidates=...`)\\\")\\n\\n candidates, references = list(candidates), list(references)\\n assert len(candidates) == len(references), (\\n \\\"The number of candidate sentences must match the number of \\\"\\n \\\"reference sentences.\\\")\\n n_items = len(candidates)\\n if not candidates:\\n return []\\n\\n if not batch_size:\\n batch_size = DEFAULT_BLEURT_BATCH_SIZE\\n\\n # Sorts the sentences by length.\\n input_ids, input_mask, segment_ids = encoding.encode_batch(\\n references, candidates, self.tokenizer, self.max_seq_length)\\n seq_lengths = np.sum(input_mask, axis=1)\\n sorted_indices = np.argsort(seq_lengths)\\n assert sorted_indices.shape[0] == n_items\\n\\n all_results = np.repeat(self.DEFAULT_SCORE, n_items).astype(np.float64)\\n batch_lens = []\\n for i in range(0, n_items, batch_size):\\n\\n # Gets the ids of the examples in the batch.\\n batch_indices = sorted_indices[i:i + batch_size]\\n\\n # Computes the max sequence length.\\n batch_lenghts = seq_lengths[batch_indices]\\n batch_max_len = max(max(batch_lenghts), 1)\\n batch_lens.append(batch_max_len)\\n\\n # Retrieves the examples and truncates the extra padding.\\n batch_input_ids = input_ids[batch_indices, :batch_max_len]\\n batch_input_mask = input_mask[batch_indices, :batch_max_len]\\n batch_segment_ids = segment_ids[batch_indices, :batch_max_len]\\n\\n # Runs the inference.\\n tf_input = {\\n \\\"input_ids\\\": batch_input_ids,\\n \\\"input_mask\\\": batch_input_mask,\\n \\\"segment_ids\\\": batch_segment_ids\\n }\\n predict_out = self._predictor.predict(tf_input)\\n\\n # Scatters the scores.\\n all_results[batch_indices] = predict_out\\n\\n assert np.all(all_results > -1000), (\\n \\\"Something went wrong while running the dynamic batching scorer.\\\")\\n all_results = list(all_results)\\n assert len(all_results) == n_items, (\\n \\\"Number of predictions does not match sentences: {} vs. {}\\\".format(\\n len(all_results), len(candidates)))\\n\\n logging.info(\\\"Average batch sequence length: {}\\\".format(\\n np.mean(batch_lens)))\\n\\n return all_results\",\n \"def score(self, urlids, wordids):\\r\\n\\t\\tself.urlids = urlids\\r\\n\\t\\tself.wordids = wordids\\r\\n\\t\\tself.scores = self.tf_score()\\r\\n\\t\\treturn self.scores\",\n \"def aggregate_embeddings(list_of_embeddings):\\n\\n return np.mean(list_of_embeddings, axis=0)\",\n \"def compute_precision(gold_dict, src_word2ind, trg_word2ind, src_words, trg_words, scores, args, BATCH_SIZE=20, verbose = False):\\n oov = set()\\n correct = 0\\n n,m = scores.shape # The actual size of mapping computed, might be smaller that total size of dict\\n\\n precisions = {}\\n if verbose:\\n print('@{:2} {:10} {:30} {:30}'.format('k', 'Src','Predicted','Gold'))\\n print_row = '{:2} {:10} {:30} {:30} {}'\\n\\n for k in [1,5,10]:\\n correct = 0\\n for src_idx,tgt_idx in gold_dict.items():\\n if src_idx > n or np.all([e>m for e in tgt_idx]):#Src word not in mapping\\n oov.add(src_idx)\\n continue\\n else:\\n knn = np.argpartition(scores[src_idx,:], -k)[-k:] # argpartition returns top k not in order\\n knn_sort = knn[np.argsort(-scores[src_idx,knn])] # With - to get descending order\\n if set(knn_sort).intersection(tgt_idx):\\n correct +=1\\n correct_string = ' '\\n else:\\n correct_string = 'X'\\n if verbose:\\n src_str = src_words[src_idx]\\n pred_str = ','.join([trg_words[k] for k in knn_sort])\\n gold_str = ','.join([trg_words[k] for k in tgt_idx])\\n print(print_row.format(k,src_str,pred_str,gold_str,correct_string))\\n\\n\\n coverage = len(gold_dict.keys()) / (len(gold_dict.keys()) + len(oov))\\n if verbose:\\n print('Coverage: {:7.2%} Precision @{:2}:{:7.2%}'.format(coverage, k, correct / len(gold_dict)))\\n precisions[k] = correct / len(gold_dict)\\n return precisions\",\n \"def enrichment_score(query_genes, term_genes, background_genes):\\n n11 = np.intersect1d(query_genes, term_genes).size\\n n12 = query_genes.size - n11\\n n21 = term_genes.size - n11\\n n22 = background_genes.size - (n11 + n12 + n21)\\n contmatrix = np.array([[n11, n12], [n21, n22]])\\n odds_ratio, p_values = stats.fisher_exact(contmatrix, alternative='greater')\\n return odds_ratio, p_values\",\n \"def evaluate(self, data, category):\\n n_data = len(data)\\n eval_scores = [{} for _ in range(n_data)]\\n\\n # Calculate average sentence-level scores for factual consistency\\n src_list, output_list = [], []\\n n_sents = [] # the number of sentences in the claim\\n for i in range(n_data):\\n source = data[i]['source']\\n system_outputs = sent_tokenize(data[i]['system_output'])\\n n_sents.append(len(system_outputs))\\n for j in range(len(system_outputs)):\\n src_list.append(source)\\n output_list.append(system_outputs[j])\\n input_list = add_question(dimension=self.dim, output=output_list, src=src_list, task=self.task)\\n sent_score = self.scorer.score(input_list, self.task, category, self.dim)\\n\\n # Get average score for each sample\\n start_idx = 0\\n score = []\\n for cur_n_sent in n_sents:\\n score.append(sum(sent_score[start_idx:start_idx + cur_n_sent]) / cur_n_sent)\\n start_idx += cur_n_sent\\n\\n for i in range(n_data):\\n eval_scores[i][self.dim] = score[i]\\n\\n return eval_scores\",\n \"def calc_score(n, prob_vals, cat_cols, list_cols):\\n score = 0\\n for c in cat_cols:\\n if n[c] is not None:\\n score += prob_vals[n[c]]\\n for c in list_cols:\\n for d in n[c]:\\n score += prob_vals[d]\\n score *= n[\\\"Nc\\\"]\\n score *= n[\\\"Ec\\\"]\\n return score\",\n \"def trainInternal():\\n\\n con_counts = Counter()\\n deflike = Counter()\\n\\n for record in records:\\n data = [re.split(\\\"\\\\t\\\", d) for d in re.split(\\\"\\\\n\\\", record)]\\n tokens, tags = zip(*data)\\n\\n for i, token in enumerate(tokens):\\n denom = len(token)\\n for indices, f in fqs(token, 0.5): #perform analysis on one word at a time\\n context, numer = internalContext(indices, token)\\n if tags[i] != \\\"O\\\": #only want the named entities\\n deflike[context] += f * numer/denom #need to normalize by word length\\n con_counts[context] += f * numer/denom\\n\\n deflike = Counter({context: deflike[context]/con_counts[context] for context in deflike}) #perform division on each entry\\n\\n return deflike\",\n \"def compute_prd_from_embedding(\\r\\n eval_data, \\r\\n ref_data, \\r\\n num_clusters=20,\\r\\n num_angles=1001, \\r\\n num_runs=10,\\r\\n enforce_balance=True\\r\\n):\\r\\n\\r\\n if enforce_balance and len(eval_data) != len(ref_data):\\r\\n raise ValueError(\\r\\n 'The number of points in eval_data %d is not equal to the number of '\\r\\n 'points in ref_data %d. To disable this exception, set enforce_balance '\\r\\n 'to False (not recommended).' % (len(eval_data), len(ref_data)))\\r\\n\\r\\n eval_data = np.array(eval_data, dtype=np.float64)\\r\\n ref_data = np.array(ref_data, dtype=np.float64)\\r\\n precisions = []\\r\\n recalls = []\\r\\n for _ in range(num_runs):\\r\\n eval_dist, ref_dist = _cluster_into_bins(eval_data, ref_data, num_clusters)\\r\\n precision, recall = compute_prd(eval_dist, ref_dist, num_angles)\\r\\n precisions.append(precision)\\r\\n recalls.append(recall)\\r\\n precision = np.mean(precisions, axis=0)\\r\\n recall = np.mean(recalls, axis=0)\\r\\n return precision, recall\",\n \"def eval(self, splt):\\n params = self.params\\n self.embedder.eval()\\n self.proj.eval()\\n\\n assert splt in ['valid', 'test']\\n has_labels = 'y' in self.data[splt]\\n\\n scores = OrderedDict({'epoch': self.epoch})\\n task = self.task.lower()\\n\\n idxs = [] # sentence indices\\n prob = [] # probabilities\\n pred = [] # predicted values\\n gold = [] # real values\\n\\n lang_id = params.lang2id['en']\\n\\n for batch in self.get_iterator(splt):\\n\\n # batch\\n if self.n_sent == 1:\\n (x, lengths), idx = batch\\n # x, lengths = truncate(x, lengths, params.max_len, params.eos_index)\\n else:\\n (sent1, len1), (sent2, len2), idx = batch\\n # sent1, len1 = truncate(sent1, len1, params.max_len, params.eos_index)\\n # sent2, len2 = truncate(sent2, len2, params.max_len, params.eos_index)\\n x, lengths, _, _ = concat_batches(sent1, len1, lang_id, sent2, len2, lang_id, params.pad_index, params.eos_index, reset_positions=False)\\n y = self.data[splt]['y'][idx] if has_labels else None\\n\\n # cuda\\n x, y, lengths = to_cuda(x, y, lengths)\\n\\n # prediction\\n output = self.proj(self.embedder.get_embeddings(x, lengths, positions=None, langs=None))\\n p = output.data.max(1)[1] if self.is_classif else output.squeeze(1)\\n idxs.append(idx)\\n prob.append(output.cpu().numpy())\\n pred.append(p.cpu().numpy())\\n if has_labels:\\n gold.append(y.cpu().numpy())\\n\\n # indices / predictions\\n idxs = np.concatenate(idxs)\\n prob = np.concatenate(prob)\\n pred = np.concatenate(pred)\\n assert len(idxs) == len(pred), (len(idxs), len(pred))\\n assert idxs[-1] == len(idxs) - 1, (idxs[-1], len(idxs) - 1)\\n\\n # score the predictions if we have labels\\n if has_labels:\\n gold = np.concatenate(gold)\\n prefix = f'{splt}_{task}'\\n if self.is_classif:\\n scores['%s_acc' % prefix] = 100. * (pred == gold).sum() / len(pred)\\n scores['%s_f1' % prefix] = 100. * f1_score(gold, pred, average='binary' if params.out_features == 2 else 'micro')\\n scores['%s_mc' % prefix] = 100. * matthews_corrcoef(gold, pred)\\n else:\\n scores['%s_prs' % prefix] = 100. * pearsonr(pred, gold)[0]\\n scores['%s_spr' % prefix] = 100. * spearmanr(pred, gold)[0]\\n logger.info(\\\"__log__:%s\\\" % json.dumps(scores))\\n\\n # output predictions\\n pred_path = os.path.join(params.dump_path, f'{splt}.pred.{self.epoch}')\\n with open(pred_path, 'w') as f:\\n for i, p in zip(idxs, prob):\\n f.write('%i\\\\t%s\\\\n' % (i, ','.join([str(x) for x in p])))\\n logger.info(f\\\"Wrote {len(idxs)} {splt} predictions to {pred_path}\\\")\\n\\n return scores\",\n \"def compute_score(predictions, references, vocab=None, beam_size=4):\\n assert np.rank(predictions) == 3\\n assert predictions.shape[0] == references.shape[0]\\n batch_size = predictions.shape[0]\\n predictions = tf.make_ndarray(tf.make_tensor_proto(predictions)).tolist()\\n references = tf.make_ndarray(tf.make_tensor_proto(references)).tolist()\\n hypotheses_list = []\\n references_list = []\\n\\n for index in range(batch_size):\\n # All predictions in beam search are used for validation scores.\\n for b_index in range(beam_size):\\n h = predictions[index][b_index]\\n try:\\n eos_index = h.index(input_utils.EOS)\\n except ValueError:\\n eos_index = len(h)\\n hypotheses_list.append(h[:eos_index])\\n\\n ref = references[index][0].decode().split('|')\\n ref_list = [r.strip().split(' ') for r in ref if r.strip()]\\n references_list.append(ref_list)\\n\\n all_scores = collections.defaultdict(list)\\n for hypothesis, references in zip(hypotheses_list, references_list):\\n if vocab is not None and len(vocab):\\n # Skip PADDING, UNK, EOS, START (0-3).\\n hypothesis = [\\n vocab[word_id].numpy().decode()\\n for word_id in hypothesis\\n if word_id > 3\\n ]\\n\\n h_str = ' '.join(str(e) for e in hypothesis)\\n r_str = [' '.join(str(e) for e in ref) for ref in references]\\n\\n print('hypothesis: ', str(h_str))\\n print('references: ', str(r_str))\\n print('\\\\n')\\n scores = screen2words_eval.coco_evaluate(r_str, h_str)\\n for key, score in scores.items():\\n all_scores[key].append(score)\\n score_names = [\\n 'BLEU-1', 'BLEU-2', 'BLEU-3', 'BLEU-4', 'ROUGE-1-f1-mean',\\n 'ROUGE-1-f1-min', 'ROUGE-1-f1-max', 'ROUGE-2-f1-mean', 'ROUGE-2-f1-min',\\n 'ROUGE-2-f1-max', 'ROUGE-L-f1-mean', 'ROUGE-L-f1-min', 'ROUGE-L-f1-max'\\n ]\\n\\n return [np.array(all_scores[name], dtype=np.float32) for name in score_names]\",\n \"def similarity(query,word_dict,dictionary,number_of_docs,id):\\n similarity = 0.0\\n scalar_leng = 0.0\\n for term in query:\\n if term in dictionary:\\n similarity += word_dict[term][1]*imp(term,word_dict,number_of_docs,id)\\n\\n for term in dictionary:\\n scalar_leng += imp(term, word_dict, number_of_docs, id) ** 2\\n\\n final_scalar_leng = math.sqrt(scalar_leng)\\n similarity = similarity / final_scalar_leng\\n #print(similarity)\\n return similarity\",\n \"def _compute_scores(self, triples):\\n # compute scores as sum(s * p * o)\\n scores = tf.reduce_sum(triples[0] * triples[1] * triples[2], 1)\\n return scores\",\n \"def test_tfidf_scorer(self):\\n\\n \\\"\\\"\\\"\\n Create the test data.\\n \\\"\\\"\\\"\\n tokenizer = Tokenizer(stem=False)\\n posts = [\\n \\\"Erdogan with threats to attack regime forces 'everywhere' in Syria\\\",\\n \\\"Damascus says Erdogan 'disconnected from reality' after threats\\\",\\n ]\\n\\n corpus = [ Document(post, tokenizer.tokenize(post)) for post in posts ]\\n\\n extractor = TokenExtractor(tokenizer=tokenizer)\\n scorer = TFIDFScorer({ 'erdogan': 1, 'threats': 2 }, 10)\\n candidates = extractor.extract(corpus)\\n scores = scorer.score(candidates)\\n self.assertGreater(scores.get('erdogan'), scores.get('damascus'))\\n self.assertEqual(scores.get('everywhere'), scores.get('disconnected')) # they appear the same number of times\\n self.assertGreater(scores.get('erdogan'), scores.get('threats')) # 'threats' and 'erdogan' appear with the same frequency, but 'threats' has a higher DF\",\n \"def baseline_mle(self):\\n tag_word_tapples = self.data_to_taples(self.training_set)\\n e = np.zeros((self.pos_size, self.words_size))\\n for tag, word in tag_word_tapples:\\n e[self.pos2i[tag], self.word2i[word]] += 1\\n sum = e.sum(axis=1)[..., np.newaxis]\\n e = e / sum\\n return e\",\n \"def weight_sum(self) -> Tuple[Tensor, Tensor]:\\n if not self._isfit:\\n return 0.0, 0.0\\n e1_sum = 0.0\\n e2_sum = 0.0\\n for embedding in self.embedding:\\n e1_sum += embedding.weight.abs().sum()\\n e2_sum += (embedding.weight ** 2).sum()\\n return e1_sum, e2_sum\",\n \"def _aggregate_vectors(movies, feedback_list=[]):\\n movie_vec = []\\n for i in movies:\\n try:\\n m_vec = clf[i] # get the vector for each movie\\n if ratings_dict:\\n try:\\n r = ratings_dict[i] # get user_rating for each movie\\n # Use a polynomial to weight the movie by rating.\\n # This equation is totally arbitrary. I just fit a polynomial\\n # to some weights that look good. The effect is to raise\\n # the importance of 1, 9, and 10 star ratings.\\n w = ((r**3)*-0.00143) + ((r**2)*0.0533) + (r*-0.4695) + 2.1867\\n m_vec = m_vec * w\\n except KeyError:\\n continue\\n movie_vec.append(m_vec)\\n except KeyError:\\n continue\\n if feedback_list:\\n for i in feedback_list:\\n try:\\n f_vec = clf[i]\\n movie_vec.append(f_vec*1.8) # weight feedback by adding multiplier here\\n except KeyError:\\n continue\\n return np.mean(movie_vec, axis=0)\",\n \"def _batch_thm_scores(self,\\n state_encodings: List[STATE_ENC_TYPE],\\n thm_embeddings: List[THM_EMB_TYPE],\\n tactic_id: Optional[int] = None) -> List[float]:\\n del tactic_id # tactic id not use to predict theorem scores.\\n # The checkpoint should have only one value in this collection.\\n feed_dict = {\\n self._graph.get_collection('goal_net')[-1]: state_encodings,\\n self._graph.get_collection('thm_net')[-1]: thm_embeddings\\n }\\n if self._training_meta:\\n feed_dict[self._graph.get_collection('fc_keep_prob')[0]] = 1.0\\n [scores] = self._sess.run(fetches=self.pairwise_score, feed_dict=feed_dict)\\n return scores\",\n \"def score(self, doc, c):\\n # >>> YOUR ANSWER HERE\\n # the inner loop in the TEST NAIVE BAYES, sum up the logprior of the class and all words' loglikelihood\\n sum = self.logprior[c]\\n words = doc.split()\\n for w in words:\\n if w in self.vocabulary:\\n sum += self.loglikelihood[(w, c)]\\n return sum\\n # >>> END YOUR ANSWER\",\n \"def _squad_update(preds: Dict[str, str], target: List[Dict[str, List[Dict[str, List[Dict[str, Any]]]]]]) ->Tuple[Tensor, Tensor, Tensor]:\\n f1 = tensor(0.0)\\n exact_match = tensor(0.0)\\n total = tensor(0)\\n for article in target:\\n for paragraph in article['paragraphs']:\\n for qa in paragraph['qas']:\\n total += 1\\n if qa['id'] not in preds:\\n rank_zero_warn(f\\\"Unanswered question {qa['id']} will receive score 0.\\\")\\n continue\\n ground_truths = list(map(lambda x: x['text'], qa['answers']))\\n pred = preds[qa['id']]\\n exact_match += _metric_max_over_ground_truths(_compute_exact_match_score, pred, ground_truths)\\n f1 += _metric_max_over_ground_truths(_compute_f1_score, pred, ground_truths)\\n return f1, exact_match, total\",\n \"def evaluate_answer(self, session, dataset, rev_vocab, sample=20, log=False):\\n sample = min(sample, len(dataset))\\n overall_f1 = 0.\\n overall_em = 0.\\n minibatch_size = 100\\n num_batches = int(sample / minibatch_size)\\n for batch in range(0, num_batches):\\n start = batch * minibatch_size\\n end = min(len(dataset), start + minibatch_size)\\n h_s, h_e, _ = self.decode(session, dataset[start:end])\\n for i in range(minibatch_size):\\n a_s = np.argmax(h_s[i])\\n a_e = np.argmax(h_e[i])\\n if a_s > a_e:\\n k = a_e\\n a_e = a_s\\n a_s = k\\n\\n sample_dataset = dataset[start + i]\\n context = sample_dataset[0]\\n (a_s_true, a_e_true) = sample_dataset[6]\\n predicted_answer = self.formulate_answer(context, rev_vocab, a_s, a_e)\\n true_answer = self.formulate_answer(context, rev_vocab, a_s_true, a_e_true)\\n f1 = f1_score(predicted_answer, true_answer)\\n overall_f1 += f1\\n if exact_match_score(predicted_answer, true_answer):\\n overall_em += 1\\n\\n average_f1 = overall_f1/sample\\n overall_em = overall_em/sample\\n logging.info(\\\"F1: {}, EM: {}, for {} samples\\\\n\\\".format(average_f1, overall_em, sample))\\n return overall_f1, overall_em\",\n \"def rank_scores(sentences, word_embeddings):\\r\\n\\r\\n clean_sentences = pd.Series(sentences).str.replace('[^a-zA-Z]', ' ')\\r\\n\\r\\n clean_sentences = [remove_stopwords(s.lower().split()) for s in clean_sentences]\\r\\n\\r\\n sentences_vectors = []\\r\\n for i in clean_sentences:\\r\\n if len(i) != 0:\\r\\n v = sum([word_embeddings.get(w, np.zeros((emb_dim,))) for w in i.split()]) / (len(i.split()) + 1e-2)\\r\\n else:\\r\\n v = np.zeros((emb_dim,))\\r\\n sentences_vectors.append(v)\\r\\n\\r\\n similarity_matrix = np.zeros((len(clean_sentences), len(clean_sentences)))\\r\\n\\r\\n for i in range(len(clean_sentences)):\\r\\n for j in range(len(clean_sentences)):\\r\\n if i != j:\\r\\n similarity_matrix[i][j] = cosine_similarity(\\r\\n sentences_vectors[i].reshape(1, -1), sentences_vectors[j].reshape(1, -1)\\r\\n )\\r\\n similarity_matrix = similarity_matrix + similarity_matrix.T - np.diag(similarity_matrix.diagonal())\\r\\n norm = np.sum(similarity_matrix, axis=0)\\r\\n norm_similarity_matrix = np.divide(similarity_matrix, norm, where=norm != 0)\\r\\n\\r\\n sentences_rank = page_rank(norm_similarity_matrix)\\r\\n\\r\\n return sentences_rank\",\n \"def aver_score(datalist):\\n scores_per_position = []\\n \\n for tupl in datalist:\\n count = 0\\n sum_of_position = 0\\n for element in tupl[3]:\\n sum_of_position += element\\n count +=1\\n aver_pos = sum_of_position/ count\\n scores_per_position += [aver_pos]\\n \\n return scores_per_position\",\n \"def compute_similarity(self, vis_feats, language_feats, gram):\\n queries_dim = language_feats.dim()\\n M = language_feats.size(1) if queries_dim==3 else language_feats.size(0)\\n N = vis_feats.size(0)\\n d = self.embed_size\\n\\n # If too many queries, split computation to avoid out-of-memory\\n max_num_queries = 100\\n if M <= max_num_queries:\\n vis_feats = vis_feats.unsqueeze(1).expand(N, M, d)\\n scores_gram = torch.mul(vis_feats, language_feats)\\n scores_gram = scores_gram.sum(2)\\n scores = scores_gram.view(-1, M)\\n #scores = torch.matmul(vis_feats, lang_feats.squeeze().transpose(0,1)) #other version\\n\\n else:\\n scores_gram = [] \\n vis_feats = vis_feats.unsqueeze(1).expand(N, M, d)\\n num_splits = M//max_num_queries if (M%max_num_queries)==0 else M//max_num_queries+1\\n for j in range(num_splits): \\n start_query = j*max_num_queries \\n end_query = start_query + max_num_queries if start_query + max_num_queries <= M else M\\n scores_gram_split = torch.mul(vis_feats[:,start_query:end_query,:], language_feats[:,start_query:end_query,:])\\n scores_gram_split = scores_gram_split.sum(2)\\n scores_gram.append(scores_gram_split)\\n scores = torch.cat([scores_gram_split for scores_gram_split in scores_gram],1)\\n\\n return scores\",\n \"def compute_metrics(self, outputs: List[Dict[str, torch.Tensor]]) -> dict:\\n distance_pos, distance_neg = [], []\\n for minibatch in outputs:\\n minibatch = minibatch[\\\"val_prediction\\\"]\\n src_embedding = minibatch[\\\"src_sentemb\\\"]\\n ref_embedding = minibatch[\\\"ref_sentemb\\\"]\\n pos_embedding = minibatch[\\\"pos_sentemb\\\"]\\n neg_embedding = minibatch[\\\"neg_sentemb\\\"]\\n\\n distance_src_pos = F.pairwise_distance(pos_embedding, src_embedding)\\n distance_ref_pos = F.pairwise_distance(pos_embedding, ref_embedding)\\n harmonic_distance_pos = (2 * distance_src_pos * distance_ref_pos) / (\\n distance_src_pos + distance_ref_pos\\n )\\n distance_pos.append(harmonic_distance_pos)\\n\\n distance_src_neg = F.pairwise_distance(neg_embedding, src_embedding)\\n distance_ref_neg = F.pairwise_distance(neg_embedding, ref_embedding)\\n harmonic_distance_neg = (2 * distance_src_neg * distance_ref_neg) / (\\n distance_src_neg + distance_ref_neg\\n )\\n distance_neg.append(harmonic_distance_neg)\\n\\n return {\\n \\\"kendall\\\": self.metrics.compute(\\n torch.cat(distance_pos), torch.cat(distance_neg)\\n )\\n }\",\n \"def test_score_with_sample_weights():\\n atom = ATOMClassifier(X_bin, y_bin, random_state=1)\\n atom.run(\\\"Tree\\\")\\n score = atom.tree.score(X_bin, y_bin, sample_weight=list(range(len(y_bin))))\\n assert isinstance(score, np.float64)\",\n \"def predictability(self):\\n temp = self.probs\\n for n in range(10):\\n temp = temp.dot(temp)\\n final = temp[0,:]\\n #Let's assume that all words have unique initial letters\\n probs = map(len, self.words)\\n probs = array(probs)\\n probs = (probs + self.probs.max(1)-1)/probs\\n return sum(final*probs)\",\n \"def score_sequence(seq, ngramlogprobs):\\n return\",\n \"def predict(self, users, items):\\n self.eval()\\n users_t = torch.tensor(users, dtype=torch.int64, device=self.device)\\n items_t = torch.tensor(items, dtype=torch.int64, device=self.device)\\n\\n with torch.no_grad():\\n # scores = torch.mul(\\n # self.user_embedding(users_t), self.item_embedding(items_t)\\n # ).sum(dim=1)\\n ua_embeddings, ia_embeddings = self.forward(self.norm_adj)\\n u_g_embeddings = ua_embeddings[users_t]\\n i_g_embeddings = ia_embeddings[items_t]\\n scores = self.f(torch.mul(u_g_embeddings, i_g_embeddings).sum(dim=1))\\n return scores\",\n \"def _rouge_lsum_score(pred: Sequence[Sequence[str]], target: Sequence[Sequence[str]]) ->Dict[str, Tensor]:\\n pred_len = sum(map(len, pred))\\n target_len = sum(map(len, target))\\n if 0 in (pred_len, target_len):\\n return dict(precision=tensor(0.0), recall=tensor(0.0), fmeasure=tensor(0.0))\\n\\n def _get_token_counts(sentences: Sequence[Sequence[str]]) ->Counter:\\n ngrams: Counter = Counter()\\n for sentence in sentences:\\n ngrams.update(sentence)\\n return ngrams\\n pred_tokens_count = _get_token_counts(pred)\\n target_tokens_count = _get_token_counts(target)\\n hits = 0\\n for tgt in target:\\n lcs = _union_lcs(pred, tgt)\\n for token in lcs:\\n if pred_tokens_count[token] > 0 and target_tokens_count[token] > 0:\\n hits += 1\\n pred_tokens_count[token] -= 1\\n target_tokens_count[token] -= 1\\n return _compute_metrics(hits, pred_len, target_len)\",\n \"def matching_accuracy(pmat_pred, pmat_gt, ns):\\n device = pmat_pred.device\\n batch_num = pmat_pred.shape[0]\\n\\n pmat_gt = pmat_gt.to(device)\\n\\n assert torch.all((pmat_pred == 0) + (pmat_pred == 1)), 'pmat_pred can noly contain 0/1 elements.'\\n assert torch.all((pmat_gt == 0) + (pmat_gt == 1)), 'pmat_gt should noly contain 0/1 elements.'\\n assert torch.all(torch.sum(pmat_gt, dim=-1) <= 1) and torch.all(torch.sum(pmat_gt, dim=-2) <= 1)\\n assert torch.all(torch.sum(pmat_pred, dim=-1) <= 1) and torch.all(torch.sum(pmat_pred, dim=-2) <= 1)\\n\\n #indices_pred = torch.argmax(pmat_pred, dim=-1)\\n #indices_gt = torch.argmax(pmat_gt, dim=-1)\\n\\n #matched = (indices_gt == indices_pred).type(pmat_pred.dtype)\\n match_num_list = []\\n gt_num_list = []\\n pred_num_list = []\\n acc_gt = []\\n acc_pred = []\\n for b in range(batch_num): #acc_gt,acc_pred\\n #match_num += torch.sum(matched[b, :ns[b]])\\n #total_num += ns[b].item()\\n match_num = torch.sum(pmat_pred[b, :] * pmat_gt[b, :]) + 1e-8 #:ns[b]\\n gt_num = torch.sum(pmat_gt[b, :]) + 1e-8\\n pred_num = torch.sum(pmat_pred[b,:]) + 1e-8\\n match_num_list.append(match_num.cpu().numpy())\\n gt_num_list.append(gt_num.cpu().numpy())\\n pred_num_list.append(pred_num.cpu().numpy())\\n acc_gt.append((match_num/gt_num).cpu().numpy())\\n acc_pred.append((match_num/pred_num).cpu().numpy())\\n\\n return {'acc_gt': np.array(acc_gt),\\n 'acc_pred': np.array(acc_pred),\\n 'match_num': np.array(match_num_list),\\n 'gt_num': np.array(gt_num_list),\\n 'pred_num': np.array(pred_num_list)}\",\n \"def proto_maml_prototypes(self, train_embeddings):\\n # [num train images, 1, embedding size].\\n train_embeddings = tf.expand_dims(train_embeddings, 1)\\n # [num train labels, num classes] where each row is a one-hot-encoded label.\\n one_hot_train_labels = tf.one_hot(self.data.train_labels, self.way)\\n # [num train labels, num classes, 1].\\n one_hot_train_labels = tf.expand_dims(one_hot_train_labels, 2)\\n # Sums each class' embeddings. [num classes, embedding size].\\n class_sums = tf.reduce_sum(one_hot_train_labels * train_embeddings, 0)\\n # The prototype of each class is the average embedding of its train points.\\n class_num_images = tf.reduce_sum(one_hot_train_labels, 0) # [way].\\n prototypes = class_sums / class_num_images\\n return prototypes\",\n \"def evaluate(args, model, data_iterator, params, mark='Val', verbose=True):\\n # set model to evaluation mode\\n model.eval()\\n\\n # id2tag dict\\n idx2tag = {idx: tag for idx, tag in enumerate(params.tags)}\\n\\n true_tags = []\\n pred_tags = []\\n\\n # a running average object for loss\\n loss_avg = utils.RunningAverage()\\n for batch in tqdm(data_iterator, unit='Batch', ascii=True):\\n # to device\\n batch = tuple(t.to(params.device) for t in batch)\\n input_ids, input_mask, labels, _, _, word_ids, word_positions = batch\\n\\n batch_size, max_len = labels.size()\\n\\n # inference\\n with torch.no_grad():\\n # get loss\\n loss = model(input_ids, attention_mask=input_mask.bool(), labels=labels, input_word_ids=word_ids,\\n word_position_matrix=word_positions)\\n if params.n_gpu > 1 and args.multi_gpu:\\n loss = loss.mean() # mean() to average on multi-gpu.\\n # update the average loss\\n loss_avg.update(loss.item())\\n\\n # inference\\n batch_output = model(input_ids, attention_mask=input_mask.bool(), input_word_ids=word_ids,\\n word_position_matrix=word_positions)\\n\\n # 恢复标签真实长度\\n real_batch_tags = []\\n for i in range(batch_size):\\n real_len = int(input_mask[i].sum())\\n real_batch_tags.append(labels[i][:real_len].to('cpu').numpy())\\n\\n # List[int]\\n pred_tags.extend([idx2tag.get(idx) for indices in batch_output for idx in indices])\\n true_tags.extend([idx2tag.get(idx) for indices in real_batch_tags for idx in indices])\\n # sanity check\\n assert len(pred_tags) == len(true_tags), 'len(pred_tags) is not equal to len(true_tags)!'\\n\\n # logging loss, f1 and report\\n metrics = {}\\n f1 = f1_score(true_tags, pred_tags)\\n accuracy = accuracy_score(true_tags, pred_tags)\\n metrics['loss'] = loss_avg()\\n metrics['f1'] = f1\\n metrics['accuracy'] = accuracy\\n metrics_str = \\\"; \\\".join(\\\"{}: {:05.2f}\\\".format(k, v) for k, v in metrics.items())\\n logging.info(\\\"- {} metrics: \\\".format(mark) + metrics_str)\\n\\n # f1 classification report\\n if verbose:\\n report = classification_report(true_tags, pred_tags)\\n logging.info(report)\\n return metrics\",\n \"def _luong_word_score(query, word_keys, scale, size, hierarchy, batch_size):\\n depth = query.get_shape()[-1]\\n key_units = word_keys.get_shape()[-1]\\n if depth != key_units:\\n raise ValueError(\\n \\\"Incompatible or unknown inner dimensions between query and keys. \\\"\\n \\\"Query (%s) has units: %s. Keys (%s) have units: %s. \\\"\\n \\\"Perhaps you need to set num_units to the keys' dimension (%s)?\\\"\\n % (query, depth, word_keys, key_units, key_units))\\n dtype = query.dtype\\n\\n # Reshape from [batch_size, depth] to [batch_size, 1, depth]\\n # for matmul.\\n\\n query = array_ops.expand_dims(query, 1)\\n\\n # Inner product along the query units dimension.\\n # matmul shapes: query is [batch_size, 1, depth] and\\n # keys is [batch_size, max_time, depth].\\n # the inner product is asked to **transpose keys' inner shape** to get a\\n # batched matmul on:\\n # [batch_size, 1, depth] . [batch_size, depth, max_time]\\n # resulting in an output shape of:\\n # [batch_time, 1, max_time].\\n # we then squeeze out the center singleton dimension.\\n save_shape = word_keys.get_shape().as_list()\\n word_keys_small = tf.reshape(word_keys, [batch_size, -1, save_shape[2]*save_shape[3]])\\n word_keys = tf.transpose(word_keys_small, [1, 0, 2])\\n word_keys = tf.reshape(word_keys, [-1, batch_size, save_shape[2], save_shape[3]])\\n\\n context_fn = lambda key: array_ops.squeeze(math_ops.matmul(query, key, transpose_b=True), [1])\\n scores = tf.map_fn(context_fn, word_keys)\\n scores = tf.transpose(scores, [1, 0, 2])\\n\\n # if hierarchy:\\n # return tf.nn.l2_normalize(scores, dim=2, epsilon=1e-12, name=None)\\n # else:\\n # return tf.nn.l2_normalize(scores, dim=[1,2], epsilon=1e-12, name=None)\\n return scores\",\n \"def explain(gold: List[Instance], pred: List[Instance]) -> Dict[str, int]:\\n pred_score = np.zeros(len(pred))\\n\\n for idx, (pos, label) in enumerate(gold):\\n for idx2, (pos2, label2) in enumerate(pred):\\n a = overlap(pos, pos2, True)\\n b = overlap(pos, pos2, False)\\n if a or b:\\n mod = b and not a\\n if label == label2:\\n pred_score[idx2] = 1 + mod\\n else:\\n pred_score[idx2] = 3 + mod\\n\\n out = {}\\n out[\\\"false positive\\\"] = sum(pred_score == 0)\\n out[\\\"complete overlap, correct label\\\"] = sum(pred_score == 1)\\n out[\\\"overlap, correct label\\\"] = sum(pred_score == 2)\\n out[\\\"complete overlap, wrong label\\\"] = sum(pred_score == 3)\\n out[\\\"overlap, wrong label\\\"] = sum(pred_score == 4)\\n\\n return out\",\n \"def _detection_scores(inputs, gt_boxes, gt_labels, model):\\n model = check_model('model', model, BlackModel)\\n boxes_and_confi, pred_labels = model.predict(*inputs)\\n det_scores = []\\n correct_labels_num = []\\n # repeat gt_boxes and gt_labels for all particles cloned from the same sample in PSOAttack/GeneticAttack\\n if gt_boxes.shape[0] == 1 and boxes_and_confi.shape[0] > 1:\\n gt_boxes = np.repeat(gt_boxes, boxes_and_confi.shape[0], axis=0)\\n gt_labels = np.repeat(gt_labels, boxes_and_confi.shape[0], axis=0)\\n iou_thres = 0.5\\n for boxes, labels, gt_box, gt_label in zip(boxes_and_confi, pred_labels, gt_boxes, gt_labels):\\n gt_box_num = gt_box.shape[0]\\n score = 0\\n box_num = boxes.shape[0]\\n correct_label_flag = np.zeros(gt_label.shape)\\n for i in range(box_num):\\n pred_box = boxes[i]\\n max_iou_confi = 0\\n for j in range(gt_box_num):\\n iou = calculate_iou(pred_box[:4], gt_box[j][:4])\\n if labels[i] == gt_label[j] and iou > iou_thres and correct_label_flag[j] == 0:\\n max_iou_confi = max(max_iou_confi, pred_box[-1] + iou)\\n correct_label_flag[j] = 1\\n score += max_iou_confi\\n det_scores.append(score)\\n correct_labels_num.append(np.sum(correct_label_flag))\\n return np.array(det_scores), np.array(correct_labels_num)\",\n \"def run(self, args):\\n self.pen.score_for_matched_lexical = args[0]\\n self.pen.score_for_matched_synonym = args[1]\\n self.factor_word_offset_penalty = args[2]\\n self.factor_sentence_length_mismatch = args[3]\\n self.factor_name_mismatch = args[4]\\n self.factor_fe_offset_penalty = args[5]\\n self.weight_target_frame_element = args[6]\\n self.weight_frame_elements = args[7]\\n self.factor_frame_offset_penalty = args[8]\\n misses = []\\n for row in range(self.data.get_number_of_rows()):\\n ref_sentence = self.data.get_row(row)[self.data.get_gold()]\\n results = {}\\n for team, team_sentence in self.data.get_row_for_teams(self.evaluator.get_teams(row), row).iteritems():\\n results[team] = self.get_sentence_score(ref_sentence, team_sentence)\\n misses.append(self.evaluator.compare_all(results, row))\\n return np.mean(misses) / 5.0\",\n \"def test_embedding_attend(self):\\n features = 5\\n embed = layers.Embed(num_embeddings=10, features=features)\\n inputs = np.array([[1]], dtype=np.int64)\\n variables = embed.init(jax.random.PRNGKey(0), inputs)\\n query = np.ones(features, dtype=np.float32)\\n result = embed.apply(variables, query, method=embed.attend)\\n expected = np.sum(variables['params']['embedding'], -1)\\n np.testing.assert_array_almost_equal(result, expected)\",\n \"def average_one_hots(sent, word_to_ind):\\n known_words = 0\\n size = len(word_to_ind.keys())\\n sum_vec = np.zeros((size,))\\n for token in sent.text: #going over all tokens and summing their embeddings\\n if (token in word_to_ind):\\n sum_vec += get_one_hot(size, word_to_ind[token])\\n known_words += 1\\n if (known_words != 0):\\n return sum_vec / known_words\\n else:\\n return sum_vec\",\n \"def similarities (self, listOfWords):\\n \\n # building the query dictionary\\n queryDict = collections.defaultdict(int)\\n for w in listOfWords:\\n queryDict [w] += + 1.0\\n \\n # normalizing the query\\n length = float (len (listOfWords))\\n for k in queryDict:\\n queryDict [k] /= length\\n \\n # computing the list of similarities\\n sims = []\\n for doc in self.documents:\\n score = 0.0\\n docDict = doc [1]\\n for k in queryDict:\\n if docDict.has_key (k):\\n score += (queryDict [k] / self.corpusDict [k]) + (docDict [k] / self.corpusDict [k])\\n sims.append ([doc [0], score])\\n \\n return sims\",\n \"def predict(self):\\n print('\\\\nComputing ZSL embeddings for test data...')\\n iter_idx = 0\\n embeddings = torch.Tensor()\\n for batch in self.dataset.gen_next_batch(self.batch_size, dset_part='test'):\\n iter_idx += 1\\n label, data_from_modalities = batch\\n\\n label = label.long().to(self.device)\\n for j in range(len(data_from_modalities)):\\n data_from_modalities[j] = data_from_modalities[j].to(\\n self.device)\\n \\n mu_att, logvar_att = self.encoder[self.auxiliary_data_source](data_from_modalities[1])\\n z_from_att = self.reparameterize(mu_att, logvar_att)\\n\\n embeddings = torch.cat((embeddings, z_from_att), 0)\\n # embeddings.append(z_from_att)\\n \\n return embeddings\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.65966535","0.6271696","0.61693364","0.6096531","0.60055155","0.5940941","0.5937357","0.5903399","0.59002817","0.58706063","0.5868112","0.5857049","0.5816769","0.5789659","0.5776571","0.57489455","0.5739431","0.56953835","0.5690723","0.5675375","0.5670827","0.5666074","0.5637708","0.56375194","0.56270677","0.5622568","0.5610251","0.5604767","0.56038666","0.559992","0.5598022","0.55979866","0.55891436","0.5585589","0.55828655","0.558097","0.55700904","0.55672306","0.55550116","0.5541228","0.55334187","0.5523239","0.5509397","0.5509177","0.5497918","0.5491115","0.5485893","0.54778874","0.54640466","0.5454778","0.5447913","0.5444233","0.5439","0.54303044","0.5422679","0.5410132","0.54072887","0.5403843","0.5393577","0.53876597","0.5386036","0.538401","0.5374997","0.5374533","0.537186","0.53660595","0.53657585","0.53646123","0.53473884","0.5346606","0.5344767","0.5344396","0.534435","0.5334519","0.53254116","0.5320442","0.53197753","0.531559","0.53153515","0.5313257","0.53127223","0.53116983","0.5311505","0.5311255","0.53077847","0.5301076","0.5293626","0.52895623","0.5286702","0.52844477","0.52834684","0.52804095","0.52780634","0.5276578","0.52765304","0.52737486","0.5273577","0.5268664","0.5266744","0.5263662"],"string":"[\n \"0.65966535\",\n \"0.6271696\",\n \"0.61693364\",\n \"0.6096531\",\n \"0.60055155\",\n \"0.5940941\",\n \"0.5937357\",\n \"0.5903399\",\n \"0.59002817\",\n \"0.58706063\",\n \"0.5868112\",\n \"0.5857049\",\n \"0.5816769\",\n \"0.5789659\",\n \"0.5776571\",\n \"0.57489455\",\n \"0.5739431\",\n \"0.56953835\",\n \"0.5690723\",\n \"0.5675375\",\n \"0.5670827\",\n \"0.5666074\",\n \"0.5637708\",\n \"0.56375194\",\n \"0.56270677\",\n \"0.5622568\",\n \"0.5610251\",\n \"0.5604767\",\n \"0.56038666\",\n \"0.559992\",\n \"0.5598022\",\n \"0.55979866\",\n \"0.55891436\",\n \"0.5585589\",\n \"0.55828655\",\n \"0.558097\",\n \"0.55700904\",\n \"0.55672306\",\n \"0.55550116\",\n \"0.5541228\",\n \"0.55334187\",\n \"0.5523239\",\n \"0.5509397\",\n \"0.5509177\",\n \"0.5497918\",\n \"0.5491115\",\n \"0.5485893\",\n \"0.54778874\",\n \"0.54640466\",\n \"0.5454778\",\n \"0.5447913\",\n \"0.5444233\",\n \"0.5439\",\n \"0.54303044\",\n \"0.5422679\",\n \"0.5410132\",\n \"0.54072887\",\n \"0.5403843\",\n \"0.5393577\",\n \"0.53876597\",\n \"0.5386036\",\n \"0.538401\",\n \"0.5374997\",\n \"0.5374533\",\n \"0.537186\",\n \"0.53660595\",\n \"0.53657585\",\n \"0.53646123\",\n \"0.53473884\",\n \"0.5346606\",\n \"0.5344767\",\n \"0.5344396\",\n \"0.534435\",\n \"0.5334519\",\n \"0.53254116\",\n \"0.5320442\",\n \"0.53197753\",\n \"0.531559\",\n \"0.53153515\",\n \"0.5313257\",\n \"0.53127223\",\n \"0.53116983\",\n \"0.5311505\",\n \"0.5311255\",\n \"0.53077847\",\n \"0.5301076\",\n \"0.5293626\",\n \"0.52895623\",\n \"0.5286702\",\n \"0.52844477\",\n \"0.52834684\",\n \"0.52804095\",\n \"0.52780634\",\n \"0.5276578\",\n \"0.52765304\",\n \"0.52737486\",\n \"0.5273577\",\n \"0.5268664\",\n \"0.5266744\",\n \"0.5263662\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.59834903"},"document_rank":{"kind":"string","value":"5"}}},{"rowIdx":81176,"cells":{"query":{"kind":"string","value":"Rounds to a variable number of decimal places as few as necessary in the range [min,max]"},"document":{"kind":"string","value":"def normalize(amount, min=2, max=4):\n if not amount:\n return amount\n\n # To Decimal, round to highest desired precision\n d = round(Decimal(amount), max)\n s = str(d)\n\n # Truncate as many extra zeros as we are allowed to\n for i in range(max-min):\n if s[-1] == '0':\n s = s[:-1]\n\n if s[-1] == '.':\n s = s[:-1]\n\n return s"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def myround(value, lowerbound, higherbound):\n if value < lowerbound:\n return lowerbound\n if value > higherbound:\n return higherbound\n return value","def _nice(x, round=False):\n if x <= 0:\n import warnings\n warnings.warn(\"Invalid (negative) range passed to tick interval calculation\")\n x = abs(x)\n expv = floor(log10(x))\n f = x / pow(10, expv)\n if round:\n if f < 1.75:\n nf = 1.0\n elif f < 3.75:\n nf = 2.5\n elif f < 7.0:\n nf = 5.0\n else:\n nf = 10.0\n else:\n if f <= 1.0:\n nf = 1.0\n elif f <= 2.5:\n nf = 2.5\n elif f <= 5.0:\n nf = 5.0\n else:\n nf = 10.0\n return nf * pow(10, expv)","def round_perc(value):\n return round(value, dev_fan.round_perc)","def scale(value,rawmin=100941, rawmax=274919, rangemin=0, rangemax=100):\n\n # Convert the left range into a 0-1 range (float)\n valueScaled = float(value - rawmin) / float(rawmax - rawmin)\n\n # Convert the 0-1 range into a value in the right range.\n value = rangemin + (valueScaled * ((rangemax * 10) - rangemin))\n\n value = value // 10 * 10 // 10 # float to int\n\n return max(value, rangemin) # value must be greater or equal to rangemin","def my_round(x, base=10):\n return base * round(x / base)","def round_limits(minimum, maximum):\n min_bits = minimum.normalize().as_tuple()\n max_bits = maximum.normalize().as_tuple()\n\n max_digits = max(\n len(min_bits.digits) + min_bits.exponent,\n len(max_bits.digits) + max_bits.exponent\n )\n\n # Whole number rounding\n if max_digits > 0:\n multiplier = Decimal('10') ** (max_digits - 1)\n\n min_fraction = (minimum / multiplier).to_integral_value(rounding=ROUND_FLOOR)\n max_fraction = (maximum / multiplier).to_integral_value(rounding=ROUND_CEILING)\n\n return (\n min_fraction * multiplier,\n max_fraction * multiplier\n )\n\n max_exponent = max(min_bits.exponent, max_bits.exponent)\n\n # Fractional rounding\n q = Decimal('10') ** (max_exponent + 1)\n\n return (\n minimum.quantize(q, rounding=ROUND_FLOOR).normalize(),\n maximum.quantize(q, rounding=ROUND_CEILING).normalize()\n )","def scale_floor(value, old_max, new_max):\n\tassert value >= 0\n\tassert value <= old_max\n\treturn new_max * value / old_max","def Clamp(val, min, max):\n\tval = float(val)\n\tmin = float(min)\n\tmax = float(max)\n\n\tif val < min:\n\t\treturn min\n\telif val > max:\n\t\treturn max\n\telse:\n\t\treturn val","def custom_round(x):\n if x >= 1:\n x = 1\n else:\n x = 0\n return x","def decimalize(n):\n return '{:.2f}'.format(n)","def roundy(v, n):\n\n \"\"\"\n t = 10**n\n return math.floor(v * t + 0.5) / t\n \"\"\"\n\n return v","def float_round(x, prec=2, base=.05):\n return round(base * round(float(x) / base), prec)","def test_small_round_numbers_98_percent(self):\r\n upper_bound = 10\r\n lower_bound = 2\r\n n_value = 20\r\n ci_percent = 98\r\n expected_result = 7.67748\r\n result = ci_to_sd(upper_bound, lower_bound, ci_percent, n_value)\r\n\r\n self.assertAlmostEqual(expected_result, result, 4)","def test_small_round_numbers_99_percent(self):\r\n upper_bound = 10\r\n lower_bound = 2\r\n n_value = 20\r\n ci_percent = 99\r\n expected_result = 6.94700\r\n result = ci_to_sd(upper_bound, lower_bound, ci_percent, n_value)\r\n\r\n self.assertAlmostEqual(expected_result, result, 4)","def base_round(x, base):\n\n return base * round(x/base)","def __round__(self, ???):","def round(x):\n return int(x + copysign(0.5, x))","def percent(num):\n return round(num * 100, 1)","def downround(x, base):\n return base * math.floor(float(x) / base)","def calc_multiplier(self, min, max, dec):\n base = self.BASE_MULT\n if dec:\n base *= 3\n base *= math.sqrt((max - min)/250.0)\n return int(base)","def scale(x, minimum, maximum):\n return (x - minimum) / (maximum - minimum)","def roundup100(x):\n\treturn int(math.ceil(x / 100.0)) * 100","def iround(x):\n return ipart(x + 0.5)","def roundMultiple(x, base=4):\n return int(base * round(float(x)/base))","def fround(val):\r\n if val==0:\r\n return \"0.0\"\r\n lval = math.log10(val)\r\n if lval < 0:\r\n lval -= 1\r\n rval = -int(lval) + 2\r\n if lval > 3:\r\n return str(int(round(val, rval)))\r\n return str(round(val, rval))","def scale(self, value):\n return (float(value) - float(self.minimum)) / \\\n float(self.maximum - self.minimum) * 2.0 - 1.0","def roundTo(numberValue, baseValue):\n # Round a numberValue, to multiples of baseValue\n return( numberValue + (baseValue - numberValue) % baseValue ) # roundTo","def scale(self, value):\r\n return (float(value)-float(self.minimum))/float(self.maximum-self.minimum)*2.0 - 1.0","def roundFigures(value, digits):\n if (value != 0.0):\n if (np.log10(np.abs(value)) % 1 < np.log10(5)):\n digits -= 1\n for r in range(-20,20):\n if (round(value,r) != 0.0):\n value = round(value,r+digits)\n break\n return(value)","def of_num(number: float, percent: float) -> float:\n return number / 100 * percent","def test_small_round_numbers_95_percent(self):\r\n upper_bound = 10\r\n lower_bound = 2\r\n n_value = 20\r\n ci_percent = 95\r\n expected_result = 9.12680\r\n result = ci_to_sd(upper_bound, lower_bound, ci_percent, n_value)\r\n\r\n self.assertAlmostEqual(expected_result, result, 4)","def clamp(num, min, max): \n if num < min:\n num = min\n elif num > max:\n num = max\n return num","def roundSigfigs(num, sigfigs):\n if num != 0:\n return str(round(num, -int(math.floor(math.log10(abs(num))) - (sigfigs - 1))))\n else:\n return str(0.0) # Can't take the log of 0","def roundSigfigs(num, sigfigs):\n if num != 0:\n return str(round(num, -int(math.floor(math.log10(abs(num))) - (sigfigs - 1))))\n else:\n return str(0.0) # Can't take the log of 0","def upround(x, base):\n return base * math.ceil(float(x) / base)","def scale(n, float_round=None):\n\n def inner(v):\n return v * n\n\n def inner_round(v):\n return round(v * n, float_round)\n\n if float_round is not None:\n return inner_round\n\n return inner","def standardization(a, p):\r\n return a * 10 / 100 * p * p","def float_parameter(level, maxval):\n return float(level) * maxval / 10.","def round_counts(n):\n n = int(n)\n assert n == math.floor(n) # make sure it is an integer; shouldn't be needed with above\n assert n >= 0\n if 0 <= n < 15: return LESS_THAN_15\n if 15 <= n <= 99: return str(nearest( n, 10))\n if 100 <= n <= 999: return str(nearest( n, 50))\n if 1000 <= n <= 9999: return str(nearest( n, 100))\n if 10000 <= n <= 99999: return str(nearest( n, 500))\n if 100000 <= n <= 999999: return str(nearest( n, 1000))\n return round4_decimal(n)","def test_small_round_numbers_90_percent(self):\r\n upper_bound = 10\r\n lower_bound = 2\r\n n_value = 20\r\n ci_percent = 90\r\n expected_result = 10.874494\r\n result = ci_to_sd(upper_bound, lower_bound, ci_percent, n_value)\r\n\r\n self.assertAlmostEqual(expected_result, result, 4)","def clamp(min_value: float, max_value: float, value: float):\n\t\tvalue = min(value, max_value)\n\t\tvalue = max(value, min_value)\n\t\treturn value","def round_to(value: float, target: float):\n rounded = int(round(value / target)) * target\n return rounded","def round_to(self, value, res):\n if res == 0:\n return round(value)\n\n return res * (round(value/res))","def round_to_nearest(value, round_value=1000):\n if round_value < 1:\n ds = str(round_value)\n nd = len(ds) - (ds.find('.') + 1)\n value = value * 10**nd\n round_value = round_value * 10**nd\n value = int(round(float(value) / round_value) * round_value)\n value = float(value) / 10**nd\n else:\n value = int(round(float(value) / round_value) * round_value)\n\n return value","def percent(value):\n return f\"{value:,.2f} %\"","def scale_ceil(value, old_max, new_max):\n\tassert value >= 0\n\tassert value <= old_max\n\treturn div_ceil(new_max * value, old_max)","def percent_rating(value):\n value = Decimal(value)\n value = round(value / 3, 2) * 100\n return value","def percent(values, p=0.5):\n m = min(values)\n interval = max(values) - m\n return m + p*interval","def round_based_alien_limitation(self):\n if self.num_round < 5:\n return 1\n elif self.num_round < 10:\n return 2\n else:\n return 3","def round_to(n, precision):\n correction = 0.5 if n >= 0 else -0.5\n return int(n / precision + correction) * precision","def calculate_slider_step(\n min_value: float, max_value: float, steps: int = 100\n) -> float:\n\n return 10 ** math.floor(math.log10((max_value - min_value) / steps))","def round_down(n, decimals=0):\r\n multiplier = 10 ** decimals\r\n return int(n * multiplier) / multiplier","def pop_round(value):\n return commify(round_to_nearest(value))","def _clamp_percent(value):\n if value < 0:\n print(\"Less than 0 percent specified for extension. Clamping to 0\")\n value = 0\n elif value > 100:\n print(\"More than 100 percent specified for extension. Clamping to 100\")\n value = 100\n return value","def max_normalize(val, old_max):\n new_max = 100.0\n normalized = (val / old_max) * new_max\n # Cap at new_max\n if normalized > new_max:\n return new_max\n return normalized","def format_and_scale_number(x, unit):\n\n # simple case for small numbers\n if x < 1000:\n return '%d %s' % (x, unit)\n\n # pick a scale\n (scale, suffix) = _pick_scale_and_suffix(x)\n\n # decide how many digits after the decimal to display\n scaled = x / scale\n if scaled < 10.0:\n fmt = '%1.2f %s%s'\n elif scaled < 100.0:\n fmt = '%1.1f %s%s'\n else:\n fmt = '%1.0f %s%s'\n\n # format it\n return fmt % (scaled, suffix, unit)","def approximate(val):\r\n if val >=2 or val == 3:\r\n return 250\r\n elif val >=1:\r\n return 150\r\n elif val >=0:\r\n return 50","def main():\n price = float(input())\n percent = int(input())\n price = int((price*100)//1)\n for _ in range(percent):\n price += price*381//10000\n print(\"%d.%02d\" %(price//100, price%100))","def round(data):\n return _make.round(data)","def round_sigfigs(num, sig_figs):\n#http://code.activestate.com/recipes/578114-round-number-to-specified-number-of-significant-di/\n if num != 0:\n return round(num, -int(math.floor(math.log10(abs(num))) - (sig_figs - 1)))\n else:\n return 0","def progress(current, max):\n prog = int(20 * (current / max))\n print(f\"[{''.join('=' for _ in range(prog))}>{''.join(' ' for _ in range(20 - prog - 1))}] ({(current / max) * 100 : 2.2f}%)\", end='\\r')","def roundUP(x):\n\treturn int(ceil(x / 10.0)) * 10","def percentify(n):\n if n == 0:\n return n\n return '{:.2f}{}'.format(n*100, '%')","def round_to_str(value , to=3):\n\treturn stats_utils.round_decimal_to_str(value,to)","def round_to_str(value , to=3):\n\treturn stats_utils.round_decimal_to_str(value,to)","def print_round(round, step, value, verbose):\n if verbose:\n print('{:<19} {:}'.format(\n 'round[{:2}].{:}'.format(round, step), value))","def true_round(number: Decimal, nDigits: int = None):\n number = Decimal(number)\n\n if nDigits is None:\n nDigits = 0\n\n normal = number * 10**nDigits\n\n rest = normal - int(normal)\n\n if rest >= 0.5:\n return (normal - rest + 1) * 10**-nDigits\n else:\n return (normal - rest) * 10**-nDigits","def prshare(value):\n value = value or 0\n value = int(round(value * 50))\n return '{:05d}'.format(value)","def clamp(n, min_, max_):\n return max(min(max_,n),min_)","def sround(x, precision=0):\n sr = StochasticRound(precision=precision)\n return sr.round(x)","def _round(self, x):\n return x - x % self.minutes_per_step","def _round_decimals(self, dps):\n\n pass","def mrshare(value):\n\n value = value or 0\n value = int(round(value * 100))\n return '{:05d}'.format(value)","def _round_to_nearest_multiple_up(x, n=5):\n return n * math.ceil(float(x) / n)","def format_string(x):\n \n if 1.0>abs(x)>0.0: \n lg = log(abs(x))/log(10)\n \n # Number of digits\n d = -int(floor(lg))\n\n return '%%.%df' %d\n else:\n return '%.f'","def roundto(x, to=10.0):\n if to and not math.isnan(x):\n return int(round(x / to)) * to\n else:\n return x","def inches(n):\n return n * 25.4","def roundTo(value: float, target: float) -> float:\n value = Decimal(str(value))\n target = Decimal(str(target))\n rounded = float(int(round(value / target)) * target)\n return rounded","def translate(value, from_min, from_max, to_min, to_max):\n # Figure out how 'wide' each range is\n left_span = from_max - from_min\n right_span = to_max - to_min\n\n # Convert the left range into a 0-1 range (float)\n value_scaled = float(value - from_min) / float(left_span)\n\n # Convert the 0-1 range into a value in the right range.\n return to_min + (value_scaled * right_span)","def round_to_n(x, n=8):\n n = 1 + n - int(np.floor(np.log10(abs(x) + .1)))\n\n return round(x, n)","def rescale(num, old_min, old_max, new_min, new_max):\n old_range = old_max - old_min\n new_range = new_max - new_min\n new_val = new_min + (((num - old_min) * new_range)/old_range)\n\n return new_val","def _round_to_nearest_multiple_down(x, n=5):\n return n * math.floor(float(x) / n)","def percentage(value, arg):\n try:\n percent_value = float( arg )\n if percent_value:\n return round(value / 100 * percent_value, 2)\n except Exception:\n pass\n return ''","def __normalize(self, value, lower_bound, upper_bound):\n\n min_max_diff = self.max - self.min\n bound_diff = upper_bound - lower_bound\n return (value - self.min) / min_max_diff * bound_diff + lower_bound","def truncate(x, digits):\n if x==0:\n return '0'\n if digits==0:\n return str(int(round(x)))\n FORMAT = \"\"\".%sf\"\"\"\n format_x = FORMAT % str(int(digits))\n return format(x, format_x).rstrip('0').rstrip('.')","def rand_int(min=0, max=100):\n\n num = random.random() * (max - min) + min\n return round(num)","def round(self, x):\n fraction, scaled_x, scale = self._get_fraction(x)\n\n if fraction < self.minimum_stochastic_distance or 1-fraction <self.minimum_stochastic_distance:\n result = round(x,self.precision)\n\n else:\n rounddown = fraction < self.random_generator.random()\n if rounddown:\n result = math.floor(scaled_x) / scale\n else:\n result = math.ceil(scaled_x) / scale\n\n self._record_roundoff_error(x, result)\n return result","def __round(num):\n return float(round(decimal.Decimal(num), DataGen.precision))","def _custom_round(number: float, threshold=0.5) -> float:\n sign = np.copysign(1.0, number)\n number = abs(number)\n delta = number - np.trunc(number)\n if delta < threshold:\n return np.trunc(number) * sign\n else:\n return (np.trunc(number) + 1) * sign","def floatToStrWithDecimals(n: float) -> str:\n\n return '{0:.10f}'.format(n)","def nearest_fration(value):\r\n try:\r\n from fraction import Fraction\r\n return str(Fraction(value))\r\n except ImportError:\r\n return '%i/100' % int(float(value) * 100)","def stretch(number, scale):\n\tif number >= 0:\n\t\treturn scale * math.ceil(number / scale)\n\telse:\n\t\treturn scale * math.floor(number / scale)","def normalise_modular_range(value, min, max):\n return numpy.mod(value-min, max-min)+min","def test_round_number_small(self):\r\n given_n = 2\r\n total_n = 10\r\n expected_given_percent = 20\r\n expected_other_percent = 80\r\n result = n_percent(given_n, total_n)\r\n\r\n self.assertEqual(expected_given_percent, result['given_percent'])\r\n self.assertEqual(expected_other_percent, result['other_percent'])","def percentWinMult(percentWin, mult):\n if mult == 2:\n #increase percentage by a bit\n return percentWin - 0.08\n elif mult == 4:\n return percentWin - 0.04\n else:\n return percentWin - (mult/1000)","def float_round(num, n):\n num = float(num)\n num = round(num, n)\n return num","def _pts_convert(pts, timebase_from, timebase_to, round_func=math.floor):\n new_pts = Fraction(pts, 1) * timebase_from / timebase_to\n return int(round_func(new_pts))","def roundAlwaysUp( toRound, end = 10 ):\n\tend = abs( int(end) )\n\n\tif end == 0:\n\t\tend = 10\n\ttimes = toRound/end\n\n\tif times >= 0:\n\t\ttimes = times + 1\n\telse:\n\t\ttimes = times - 1\n\treturn ( int( times ) )*end;","def iround(x):\n return int(round(x) - .5) + (x > 0)","def clip_round(value):\n return max(0, min(np.round(value/64), 1023))"],"string":"[\n \"def myround(value, lowerbound, higherbound):\\n if value < lowerbound:\\n return lowerbound\\n if value > higherbound:\\n return higherbound\\n return value\",\n \"def _nice(x, round=False):\\n if x <= 0:\\n import warnings\\n warnings.warn(\\\"Invalid (negative) range passed to tick interval calculation\\\")\\n x = abs(x)\\n expv = floor(log10(x))\\n f = x / pow(10, expv)\\n if round:\\n if f < 1.75:\\n nf = 1.0\\n elif f < 3.75:\\n nf = 2.5\\n elif f < 7.0:\\n nf = 5.0\\n else:\\n nf = 10.0\\n else:\\n if f <= 1.0:\\n nf = 1.0\\n elif f <= 2.5:\\n nf = 2.5\\n elif f <= 5.0:\\n nf = 5.0\\n else:\\n nf = 10.0\\n return nf * pow(10, expv)\",\n \"def round_perc(value):\\n return round(value, dev_fan.round_perc)\",\n \"def scale(value,rawmin=100941, rawmax=274919, rangemin=0, rangemax=100):\\n\\n # Convert the left range into a 0-1 range (float)\\n valueScaled = float(value - rawmin) / float(rawmax - rawmin)\\n\\n # Convert the 0-1 range into a value in the right range.\\n value = rangemin + (valueScaled * ((rangemax * 10) - rangemin))\\n\\n value = value // 10 * 10 // 10 # float to int\\n\\n return max(value, rangemin) # value must be greater or equal to rangemin\",\n \"def my_round(x, base=10):\\n return base * round(x / base)\",\n \"def round_limits(minimum, maximum):\\n min_bits = minimum.normalize().as_tuple()\\n max_bits = maximum.normalize().as_tuple()\\n\\n max_digits = max(\\n len(min_bits.digits) + min_bits.exponent,\\n len(max_bits.digits) + max_bits.exponent\\n )\\n\\n # Whole number rounding\\n if max_digits > 0:\\n multiplier = Decimal('10') ** (max_digits - 1)\\n\\n min_fraction = (minimum / multiplier).to_integral_value(rounding=ROUND_FLOOR)\\n max_fraction = (maximum / multiplier).to_integral_value(rounding=ROUND_CEILING)\\n\\n return (\\n min_fraction * multiplier,\\n max_fraction * multiplier\\n )\\n\\n max_exponent = max(min_bits.exponent, max_bits.exponent)\\n\\n # Fractional rounding\\n q = Decimal('10') ** (max_exponent + 1)\\n\\n return (\\n minimum.quantize(q, rounding=ROUND_FLOOR).normalize(),\\n maximum.quantize(q, rounding=ROUND_CEILING).normalize()\\n )\",\n \"def scale_floor(value, old_max, new_max):\\n\\tassert value >= 0\\n\\tassert value <= old_max\\n\\treturn new_max * value / old_max\",\n \"def Clamp(val, min, max):\\n\\tval = float(val)\\n\\tmin = float(min)\\n\\tmax = float(max)\\n\\n\\tif val < min:\\n\\t\\treturn min\\n\\telif val > max:\\n\\t\\treturn max\\n\\telse:\\n\\t\\treturn val\",\n \"def custom_round(x):\\n if x >= 1:\\n x = 1\\n else:\\n x = 0\\n return x\",\n \"def decimalize(n):\\n return '{:.2f}'.format(n)\",\n \"def roundy(v, n):\\n\\n \\\"\\\"\\\"\\n t = 10**n\\n return math.floor(v * t + 0.5) / t\\n \\\"\\\"\\\"\\n\\n return v\",\n \"def float_round(x, prec=2, base=.05):\\n return round(base * round(float(x) / base), prec)\",\n \"def test_small_round_numbers_98_percent(self):\\r\\n upper_bound = 10\\r\\n lower_bound = 2\\r\\n n_value = 20\\r\\n ci_percent = 98\\r\\n expected_result = 7.67748\\r\\n result = ci_to_sd(upper_bound, lower_bound, ci_percent, n_value)\\r\\n\\r\\n self.assertAlmostEqual(expected_result, result, 4)\",\n \"def test_small_round_numbers_99_percent(self):\\r\\n upper_bound = 10\\r\\n lower_bound = 2\\r\\n n_value = 20\\r\\n ci_percent = 99\\r\\n expected_result = 6.94700\\r\\n result = ci_to_sd(upper_bound, lower_bound, ci_percent, n_value)\\r\\n\\r\\n self.assertAlmostEqual(expected_result, result, 4)\",\n \"def base_round(x, base):\\n\\n return base * round(x/base)\",\n \"def __round__(self, ???):\",\n \"def round(x):\\n return int(x + copysign(0.5, x))\",\n \"def percent(num):\\n return round(num * 100, 1)\",\n \"def downround(x, base):\\n return base * math.floor(float(x) / base)\",\n \"def calc_multiplier(self, min, max, dec):\\n base = self.BASE_MULT\\n if dec:\\n base *= 3\\n base *= math.sqrt((max - min)/250.0)\\n return int(base)\",\n \"def scale(x, minimum, maximum):\\n return (x - minimum) / (maximum - minimum)\",\n \"def roundup100(x):\\n\\treturn int(math.ceil(x / 100.0)) * 100\",\n \"def iround(x):\\n return ipart(x + 0.5)\",\n \"def roundMultiple(x, base=4):\\n return int(base * round(float(x)/base))\",\n \"def fround(val):\\r\\n if val==0:\\r\\n return \\\"0.0\\\"\\r\\n lval = math.log10(val)\\r\\n if lval < 0:\\r\\n lval -= 1\\r\\n rval = -int(lval) + 2\\r\\n if lval > 3:\\r\\n return str(int(round(val, rval)))\\r\\n return str(round(val, rval))\",\n \"def scale(self, value):\\n return (float(value) - float(self.minimum)) / \\\\\\n float(self.maximum - self.minimum) * 2.0 - 1.0\",\n \"def roundTo(numberValue, baseValue):\\n # Round a numberValue, to multiples of baseValue\\n return( numberValue + (baseValue - numberValue) % baseValue ) # roundTo\",\n \"def scale(self, value):\\r\\n return (float(value)-float(self.minimum))/float(self.maximum-self.minimum)*2.0 - 1.0\",\n \"def roundFigures(value, digits):\\n if (value != 0.0):\\n if (np.log10(np.abs(value)) % 1 < np.log10(5)):\\n digits -= 1\\n for r in range(-20,20):\\n if (round(value,r) != 0.0):\\n value = round(value,r+digits)\\n break\\n return(value)\",\n \"def of_num(number: float, percent: float) -> float:\\n return number / 100 * percent\",\n \"def test_small_round_numbers_95_percent(self):\\r\\n upper_bound = 10\\r\\n lower_bound = 2\\r\\n n_value = 20\\r\\n ci_percent = 95\\r\\n expected_result = 9.12680\\r\\n result = ci_to_sd(upper_bound, lower_bound, ci_percent, n_value)\\r\\n\\r\\n self.assertAlmostEqual(expected_result, result, 4)\",\n \"def clamp(num, min, max): \\n if num < min:\\n num = min\\n elif num > max:\\n num = max\\n return num\",\n \"def roundSigfigs(num, sigfigs):\\n if num != 0:\\n return str(round(num, -int(math.floor(math.log10(abs(num))) - (sigfigs - 1))))\\n else:\\n return str(0.0) # Can't take the log of 0\",\n \"def roundSigfigs(num, sigfigs):\\n if num != 0:\\n return str(round(num, -int(math.floor(math.log10(abs(num))) - (sigfigs - 1))))\\n else:\\n return str(0.0) # Can't take the log of 0\",\n \"def upround(x, base):\\n return base * math.ceil(float(x) / base)\",\n \"def scale(n, float_round=None):\\n\\n def inner(v):\\n return v * n\\n\\n def inner_round(v):\\n return round(v * n, float_round)\\n\\n if float_round is not None:\\n return inner_round\\n\\n return inner\",\n \"def standardization(a, p):\\r\\n return a * 10 / 100 * p * p\",\n \"def float_parameter(level, maxval):\\n return float(level) * maxval / 10.\",\n \"def round_counts(n):\\n n = int(n)\\n assert n == math.floor(n) # make sure it is an integer; shouldn't be needed with above\\n assert n >= 0\\n if 0 <= n < 15: return LESS_THAN_15\\n if 15 <= n <= 99: return str(nearest( n, 10))\\n if 100 <= n <= 999: return str(nearest( n, 50))\\n if 1000 <= n <= 9999: return str(nearest( n, 100))\\n if 10000 <= n <= 99999: return str(nearest( n, 500))\\n if 100000 <= n <= 999999: return str(nearest( n, 1000))\\n return round4_decimal(n)\",\n \"def test_small_round_numbers_90_percent(self):\\r\\n upper_bound = 10\\r\\n lower_bound = 2\\r\\n n_value = 20\\r\\n ci_percent = 90\\r\\n expected_result = 10.874494\\r\\n result = ci_to_sd(upper_bound, lower_bound, ci_percent, n_value)\\r\\n\\r\\n self.assertAlmostEqual(expected_result, result, 4)\",\n \"def clamp(min_value: float, max_value: float, value: float):\\n\\t\\tvalue = min(value, max_value)\\n\\t\\tvalue = max(value, min_value)\\n\\t\\treturn value\",\n \"def round_to(value: float, target: float):\\n rounded = int(round(value / target)) * target\\n return rounded\",\n \"def round_to(self, value, res):\\n if res == 0:\\n return round(value)\\n\\n return res * (round(value/res))\",\n \"def round_to_nearest(value, round_value=1000):\\n if round_value < 1:\\n ds = str(round_value)\\n nd = len(ds) - (ds.find('.') + 1)\\n value = value * 10**nd\\n round_value = round_value * 10**nd\\n value = int(round(float(value) / round_value) * round_value)\\n value = float(value) / 10**nd\\n else:\\n value = int(round(float(value) / round_value) * round_value)\\n\\n return value\",\n \"def percent(value):\\n return f\\\"{value:,.2f} %\\\"\",\n \"def scale_ceil(value, old_max, new_max):\\n\\tassert value >= 0\\n\\tassert value <= old_max\\n\\treturn div_ceil(new_max * value, old_max)\",\n \"def percent_rating(value):\\n value = Decimal(value)\\n value = round(value / 3, 2) * 100\\n return value\",\n \"def percent(values, p=0.5):\\n m = min(values)\\n interval = max(values) - m\\n return m + p*interval\",\n \"def round_based_alien_limitation(self):\\n if self.num_round < 5:\\n return 1\\n elif self.num_round < 10:\\n return 2\\n else:\\n return 3\",\n \"def round_to(n, precision):\\n correction = 0.5 if n >= 0 else -0.5\\n return int(n / precision + correction) * precision\",\n \"def calculate_slider_step(\\n min_value: float, max_value: float, steps: int = 100\\n) -> float:\\n\\n return 10 ** math.floor(math.log10((max_value - min_value) / steps))\",\n \"def round_down(n, decimals=0):\\r\\n multiplier = 10 ** decimals\\r\\n return int(n * multiplier) / multiplier\",\n \"def pop_round(value):\\n return commify(round_to_nearest(value))\",\n \"def _clamp_percent(value):\\n if value < 0:\\n print(\\\"Less than 0 percent specified for extension. Clamping to 0\\\")\\n value = 0\\n elif value > 100:\\n print(\\\"More than 100 percent specified for extension. Clamping to 100\\\")\\n value = 100\\n return value\",\n \"def max_normalize(val, old_max):\\n new_max = 100.0\\n normalized = (val / old_max) * new_max\\n # Cap at new_max\\n if normalized > new_max:\\n return new_max\\n return normalized\",\n \"def format_and_scale_number(x, unit):\\n\\n # simple case for small numbers\\n if x < 1000:\\n return '%d %s' % (x, unit)\\n\\n # pick a scale\\n (scale, suffix) = _pick_scale_and_suffix(x)\\n\\n # decide how many digits after the decimal to display\\n scaled = x / scale\\n if scaled < 10.0:\\n fmt = '%1.2f %s%s'\\n elif scaled < 100.0:\\n fmt = '%1.1f %s%s'\\n else:\\n fmt = '%1.0f %s%s'\\n\\n # format it\\n return fmt % (scaled, suffix, unit)\",\n \"def approximate(val):\\r\\n if val >=2 or val == 3:\\r\\n return 250\\r\\n elif val >=1:\\r\\n return 150\\r\\n elif val >=0:\\r\\n return 50\",\n \"def main():\\n price = float(input())\\n percent = int(input())\\n price = int((price*100)//1)\\n for _ in range(percent):\\n price += price*381//10000\\n print(\\\"%d.%02d\\\" %(price//100, price%100))\",\n \"def round(data):\\n return _make.round(data)\",\n \"def round_sigfigs(num, sig_figs):\\n#http://code.activestate.com/recipes/578114-round-number-to-specified-number-of-significant-di/\\n if num != 0:\\n return round(num, -int(math.floor(math.log10(abs(num))) - (sig_figs - 1)))\\n else:\\n return 0\",\n \"def progress(current, max):\\n prog = int(20 * (current / max))\\n print(f\\\"[{''.join('=' for _ in range(prog))}>{''.join(' ' for _ in range(20 - prog - 1))}] ({(current / max) * 100 : 2.2f}%)\\\", end='\\\\r')\",\n \"def roundUP(x):\\n\\treturn int(ceil(x / 10.0)) * 10\",\n \"def percentify(n):\\n if n == 0:\\n return n\\n return '{:.2f}{}'.format(n*100, '%')\",\n \"def round_to_str(value , to=3):\\n\\treturn stats_utils.round_decimal_to_str(value,to)\",\n \"def round_to_str(value , to=3):\\n\\treturn stats_utils.round_decimal_to_str(value,to)\",\n \"def print_round(round, step, value, verbose):\\n if verbose:\\n print('{:<19} {:}'.format(\\n 'round[{:2}].{:}'.format(round, step), value))\",\n \"def true_round(number: Decimal, nDigits: int = None):\\n number = Decimal(number)\\n\\n if nDigits is None:\\n nDigits = 0\\n\\n normal = number * 10**nDigits\\n\\n rest = normal - int(normal)\\n\\n if rest >= 0.5:\\n return (normal - rest + 1) * 10**-nDigits\\n else:\\n return (normal - rest) * 10**-nDigits\",\n \"def prshare(value):\\n value = value or 0\\n value = int(round(value * 50))\\n return '{:05d}'.format(value)\",\n \"def clamp(n, min_, max_):\\n return max(min(max_,n),min_)\",\n \"def sround(x, precision=0):\\n sr = StochasticRound(precision=precision)\\n return sr.round(x)\",\n \"def _round(self, x):\\n return x - x % self.minutes_per_step\",\n \"def _round_decimals(self, dps):\\n\\n pass\",\n \"def mrshare(value):\\n\\n value = value or 0\\n value = int(round(value * 100))\\n return '{:05d}'.format(value)\",\n \"def _round_to_nearest_multiple_up(x, n=5):\\n return n * math.ceil(float(x) / n)\",\n \"def format_string(x):\\n \\n if 1.0>abs(x)>0.0: \\n lg = log(abs(x))/log(10)\\n \\n # Number of digits\\n d = -int(floor(lg))\\n\\n return '%%.%df' %d\\n else:\\n return '%.f'\",\n \"def roundto(x, to=10.0):\\n if to and not math.isnan(x):\\n return int(round(x / to)) * to\\n else:\\n return x\",\n \"def inches(n):\\n return n * 25.4\",\n \"def roundTo(value: float, target: float) -> float:\\n value = Decimal(str(value))\\n target = Decimal(str(target))\\n rounded = float(int(round(value / target)) * target)\\n return rounded\",\n \"def translate(value, from_min, from_max, to_min, to_max):\\n # Figure out how 'wide' each range is\\n left_span = from_max - from_min\\n right_span = to_max - to_min\\n\\n # Convert the left range into a 0-1 range (float)\\n value_scaled = float(value - from_min) / float(left_span)\\n\\n # Convert the 0-1 range into a value in the right range.\\n return to_min + (value_scaled * right_span)\",\n \"def round_to_n(x, n=8):\\n n = 1 + n - int(np.floor(np.log10(abs(x) + .1)))\\n\\n return round(x, n)\",\n \"def rescale(num, old_min, old_max, new_min, new_max):\\n old_range = old_max - old_min\\n new_range = new_max - new_min\\n new_val = new_min + (((num - old_min) * new_range)/old_range)\\n\\n return new_val\",\n \"def _round_to_nearest_multiple_down(x, n=5):\\n return n * math.floor(float(x) / n)\",\n \"def percentage(value, arg):\\n try:\\n percent_value = float( arg )\\n if percent_value:\\n return round(value / 100 * percent_value, 2)\\n except Exception:\\n pass\\n return ''\",\n \"def __normalize(self, value, lower_bound, upper_bound):\\n\\n min_max_diff = self.max - self.min\\n bound_diff = upper_bound - lower_bound\\n return (value - self.min) / min_max_diff * bound_diff + lower_bound\",\n \"def truncate(x, digits):\\n if x==0:\\n return '0'\\n if digits==0:\\n return str(int(round(x)))\\n FORMAT = \\\"\\\"\\\".%sf\\\"\\\"\\\"\\n format_x = FORMAT % str(int(digits))\\n return format(x, format_x).rstrip('0').rstrip('.')\",\n \"def rand_int(min=0, max=100):\\n\\n num = random.random() * (max - min) + min\\n return round(num)\",\n \"def round(self, x):\\n fraction, scaled_x, scale = self._get_fraction(x)\\n\\n if fraction < self.minimum_stochastic_distance or 1-fraction <self.minimum_stochastic_distance:\\n result = round(x,self.precision)\\n\\n else:\\n rounddown = fraction < self.random_generator.random()\\n if rounddown:\\n result = math.floor(scaled_x) / scale\\n else:\\n result = math.ceil(scaled_x) / scale\\n\\n self._record_roundoff_error(x, result)\\n return result\",\n \"def __round(num):\\n return float(round(decimal.Decimal(num), DataGen.precision))\",\n \"def _custom_round(number: float, threshold=0.5) -> float:\\n sign = np.copysign(1.0, number)\\n number = abs(number)\\n delta = number - np.trunc(number)\\n if delta < threshold:\\n return np.trunc(number) * sign\\n else:\\n return (np.trunc(number) + 1) * sign\",\n \"def floatToStrWithDecimals(n: float) -> str:\\n\\n return '{0:.10f}'.format(n)\",\n \"def nearest_fration(value):\\r\\n try:\\r\\n from fraction import Fraction\\r\\n return str(Fraction(value))\\r\\n except ImportError:\\r\\n return '%i/100' % int(float(value) * 100)\",\n \"def stretch(number, scale):\\n\\tif number >= 0:\\n\\t\\treturn scale * math.ceil(number / scale)\\n\\telse:\\n\\t\\treturn scale * math.floor(number / scale)\",\n \"def normalise_modular_range(value, min, max):\\n return numpy.mod(value-min, max-min)+min\",\n \"def test_round_number_small(self):\\r\\n given_n = 2\\r\\n total_n = 10\\r\\n expected_given_percent = 20\\r\\n expected_other_percent = 80\\r\\n result = n_percent(given_n, total_n)\\r\\n\\r\\n self.assertEqual(expected_given_percent, result['given_percent'])\\r\\n self.assertEqual(expected_other_percent, result['other_percent'])\",\n \"def percentWinMult(percentWin, mult):\\n if mult == 2:\\n #increase percentage by a bit\\n return percentWin - 0.08\\n elif mult == 4:\\n return percentWin - 0.04\\n else:\\n return percentWin - (mult/1000)\",\n \"def float_round(num, n):\\n num = float(num)\\n num = round(num, n)\\n return num\",\n \"def _pts_convert(pts, timebase_from, timebase_to, round_func=math.floor):\\n new_pts = Fraction(pts, 1) * timebase_from / timebase_to\\n return int(round_func(new_pts))\",\n \"def roundAlwaysUp( toRound, end = 10 ):\\n\\tend = abs( int(end) )\\n\\n\\tif end == 0:\\n\\t\\tend = 10\\n\\ttimes = toRound/end\\n\\n\\tif times >= 0:\\n\\t\\ttimes = times + 1\\n\\telse:\\n\\t\\ttimes = times - 1\\n\\treturn ( int( times ) )*end;\",\n \"def iround(x):\\n return int(round(x) - .5) + (x > 0)\",\n \"def clip_round(value):\\n return max(0, min(np.round(value/64), 1023))\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.67505205","0.66773003","0.6391557","0.6360162","0.63418365","0.62668586","0.6142474","0.61340815","0.60913086","0.60783994","0.60109687","0.600941","0.6009051","0.6005244","0.5997211","0.59826225","0.5977345","0.5960024","0.59580517","0.59387255","0.59132206","0.5908251","0.5907813","0.58996207","0.5891116","0.58770305","0.58635855","0.58577263","0.5826106","0.58131254","0.580649","0.5796657","0.5790073","0.5790073","0.5778392","0.5775354","0.5774459","0.5768837","0.5762901","0.57224125","0.5717081","0.57132995","0.57040733","0.5695919","0.56957555","0.5695529","0.56952983","0.56853545","0.56770086","0.5665787","0.5664835","0.5660203","0.56596196","0.5653718","0.5653214","0.5644143","0.564384","0.5642725","0.5641527","0.563674","0.56352097","0.56348634","0.5630538","0.5625914","0.5625914","0.561915","0.561545","0.5602348","0.55794424","0.5576811","0.5576378","0.5572982","0.5563214","0.5543126","0.5541134","0.55350435","0.55321336","0.5514977","0.5513338","0.55132914","0.5511693","0.5502864","0.55025804","0.5501837","0.55012786","0.54956347","0.5493057","0.5488567","0.5483835","0.54835325","0.5476203","0.5466083","0.54623145","0.5455829","0.54547775","0.545356","0.5452968","0.5449094","0.54475874","0.54466665"],"string":"[\n \"0.67505205\",\n \"0.66773003\",\n \"0.6391557\",\n \"0.6360162\",\n \"0.63418365\",\n \"0.62668586\",\n \"0.6142474\",\n \"0.61340815\",\n \"0.60913086\",\n \"0.60783994\",\n \"0.60109687\",\n \"0.600941\",\n \"0.6009051\",\n \"0.6005244\",\n \"0.5997211\",\n \"0.59826225\",\n \"0.5977345\",\n \"0.5960024\",\n \"0.59580517\",\n \"0.59387255\",\n \"0.59132206\",\n \"0.5908251\",\n \"0.5907813\",\n \"0.58996207\",\n \"0.5891116\",\n \"0.58770305\",\n \"0.58635855\",\n \"0.58577263\",\n \"0.5826106\",\n \"0.58131254\",\n \"0.580649\",\n \"0.5796657\",\n \"0.5790073\",\n \"0.5790073\",\n \"0.5778392\",\n \"0.5775354\",\n \"0.5774459\",\n \"0.5768837\",\n \"0.5762901\",\n \"0.57224125\",\n \"0.5717081\",\n \"0.57132995\",\n \"0.57040733\",\n \"0.5695919\",\n \"0.56957555\",\n \"0.5695529\",\n \"0.56952983\",\n \"0.56853545\",\n \"0.56770086\",\n \"0.5665787\",\n \"0.5664835\",\n \"0.5660203\",\n \"0.56596196\",\n \"0.5653718\",\n \"0.5653214\",\n \"0.5644143\",\n \"0.564384\",\n \"0.5642725\",\n \"0.5641527\",\n \"0.563674\",\n \"0.56352097\",\n \"0.56348634\",\n \"0.5630538\",\n \"0.5625914\",\n \"0.5625914\",\n \"0.561915\",\n \"0.561545\",\n \"0.5602348\",\n \"0.55794424\",\n \"0.5576811\",\n \"0.5576378\",\n \"0.5572982\",\n \"0.5563214\",\n \"0.5543126\",\n \"0.5541134\",\n \"0.55350435\",\n \"0.55321336\",\n \"0.5514977\",\n \"0.5513338\",\n \"0.55132914\",\n \"0.5511693\",\n \"0.5502864\",\n \"0.55025804\",\n \"0.5501837\",\n \"0.55012786\",\n \"0.54956347\",\n \"0.5493057\",\n \"0.5488567\",\n \"0.5483835\",\n \"0.54835325\",\n \"0.5476203\",\n \"0.5466083\",\n \"0.54623145\",\n \"0.5455829\",\n \"0.54547775\",\n \"0.545356\",\n \"0.5452968\",\n \"0.5449094\",\n \"0.54475874\",\n \"0.54466665\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6193746"},"document_rank":{"kind":"string","value":"6"}}},{"rowIdx":81177,"cells":{"query":{"kind":"string","value":"Removes trailing zeros, keeping at least a specified number."},"document":{"kind":"string","value":"def drop_trailing(amount, decimals=2):\n if not amount:\n return amount\n\n s = str(float(amount)).rstrip('0')\n if decimals == 0:\n return s.rstrip('.')\n\n num_decimals = len(s.split('.')[1])\n num_to_add = decimals - num_decimals\n if num_to_add <= 0:\n return s\n\n return s + '0' * num_to_add"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def delete_first_zeros(digit_with_zeros): \n \n digit_without_zeros = \"\"\n\n snap = 1\n \n d = 0\n\n for d in digit_with_zeros:\n\n if d != \"0\":\n snap = 0\n if snap == 0:\n digit_without_zeros +=d\n \n return digit_without_zeros","def strip_zeros(a):\n\n return np.trim_zeros(a, trim='b')","def del_decim_zeros(num):\r\n \r\n if \".\" in num: \r\n \r\n num_zeros = num_zeros_end(num) \r\n\r\n if num_zeros != 0: \r\n num = num[:-num_zeros] \r\n \r\n if num[-1] == \".\": \r\n num = num[:-1]\r\n \r\n return num\r\n\r\n else:\r\n return num","def strip_trailing_zeros(cls, s):\n # Make sure there's a '.' in the string.\n if s.find('.') < 0:\n return s\n\n # Reverse the string, then walk until we find a non-zero.\n rev_str = StringCleaner.reverse(s)\n\n last_index = -1\n count = -1\n\n for i in rev_str:\n count += 1\n\n if i == '0':\n continue\n else:\n last_index = count\n break\n\n # Slice-off the zeros, re-reverse the string, and strip any trailing '.'s.\n return StringCleaner.reverse(rev_str[last_index:]).rstrip('\\.')","def trailing_zeroes(n):\n str_n = str(n)\n i = len(str_n) - 1\n while str_n[i] == '0':\n i -= 1\n return len(str_n) - 1 - i","def trim_zeros(filt, trim='fb'):\n first = 0\n trim = trim.upper()\n if 'F' in trim:\n for i in filt:\n if i != 0.: break\n else: first = first + 1\n last = len(filt)\n if 'B' in trim:\n for i in filt[::-1]:\n if i != 0.: break\n else: last = last - 1\n return filt[first:last]","def strip_leading_zeros(version: str) -> str:\n return \".\".join(str(int(i)) for i in version.split(\".\"))","def shift_trailing_zeros(x):\n\n if (x == 0):\n return 0\n\n return x // get_lowest_set_bit(x)","def trailingZeroes(self, n):\n cnt = 0\n while n:\n n /= 5\n cnt += n\n\n return cnt","def delete_closing_zero(model_version: str) -> str:\r\n if model_version[-2:] == \".0\":\r\n return model_version[:-2]\r\n return model_version","def trunc(x):\n return 0","def stripBufferZero(str):\n if str[-2] == '0':\n return (str[:-2] + str[-1])\n else:\n return str","def trim_zeros(x):\n assert len(x.shape) == 2\n return x[~np.all(x == 0, axis=1)]","def trunc(value):\n\t\treturn round(value, 3) if math.modf(value)[0] != 0 else round(value)","def round_half_away_from_zero_inplace(a):","def zzx_strip(f):\n if not f or f[0]:\n return f\n\n k = 0\n\n for coeff in f:\n if coeff:\n break\n else:\n k += 1\n\n return f[k:]","def truncate(x, digits):\n if x==0:\n return '0'\n if digits==0:\n return str(int(round(x)))\n FORMAT = \"\"\".%sf\"\"\"\n format_x = FORMAT % str(int(digits))\n return format(x, format_x).rstrip('0').rstrip('.')","def _delete_trailing_number(line):\n pieces = line.split()\n try:\n int(pieces[-1])\n return ' '.join(pieces[:-1])\n except ValueError: # no trailing numbers\n return line","def remove_zeroes(s: str) -> str:\n tup = s.split(\"e\")\n if len(tup) == 2:\n mantissa = tup[0].rstrip(\"0\").rstrip(\".\")\n exponent = int(tup[1])\n if exponent:\n s = \"%se%d\" % (mantissa, exponent)\n else:\n s = mantissa\n return s","def no_float_zeros(v):\n if v % 1 == 0:\n return int(v)\n else:\n return v","def leadingzero(number, minlength):\n\n # Return the number as a string with the filled number\n return str(number).zfill(int(minlength))","def num_trailing_zeros(val):\n val = str(val)\n counter = 0\n\n for char in reversed(val):\n if char == \".\" or char != \"0\" : break\n elif char == \"0\" : counter += 1\n\n return counter","def clean(number):\n digits = [c for c in number if c.isdigit()]\n if len(digits) == 11 and digits[0] == \"1\":\n return ''.join(digits[1:])\n elif len(digits) != 10:\n return \"0000000000\"\n else:\n return ''.join(digits)","def zero_digits(s):\n return re.sub('\\d', '0', s)","def moveZeroes(nums):\n \n zero_count = nums.count(0)\n \n for x in range(zero_count):\n nums.remove(0)\n nums.append(0)","def round_half_away_from_zero(num):\n return np.sign(num) * np.floor(np.abs(num) + 0.5)","def countTrailingZeros(num):\n if num == 0:\n return 32 # Assumes 32 bit integer inputs!\n p = 0\n while (num >> p) & 1 == 0:\n p += 1\n return p","def test_trailing_zeros():\n # Empty list\n assert list(tc.trailing_zeros([])) == []\n\n # Default cases\n assert list(tc.trailing_zeros([10, 16, 11])) == [1, 4, 0]\n assert list(tc.trailing_zeros(np.array([32, -2, -20]))) == [5, 1, 2]","def last_digits(x,y,n):\n\tr = modexp(x,y, 10**n)\n\n\t#post processing to make sure we didn't cut off a leading 0\n\treturn str(r).zfill(n)","def add_leading_zero(num):\n formated = \"\"\n if num <= 9:\n formated = \"0\" + str(num)\n else:\n formated = str(num)\n \n return formated","def shift_trailing_ones(x):\n\n if (x == -1):\n return -1\n\n return x // get_lowest_unset_bit(x)","def clip_round(value):\n return max(0, min(np.round(value/64), 1023))","def _bin_zfill(num, width=None):\n s = bin(num)[2:]\n return s if width is None else s.zfill(width)","def zzX_strip(f):\n if poly_univariate_p(f):\n return zzx_strip(f)\n\n if zzX_zero_p(f):\n return f\n\n k = 0\n\n for coeff in f:\n if not zzX_zero_p(coeff):\n break\n else:\n k += 1\n\n if k == len(f):\n return zzX_zero_of(f)\n else:\n return f[k:]","def moveZeroes(self, nums: List[int]) -> None:\n l,r=0,len(nums)-1\n while l<r:\n if nums[l]==0:\n nums.append(nums.pop(l))\n r-=1\n continue\n l+=1\n return nums","def strip_leading_chars(val):\n for i, c in enumerate(val):\n if c in \"0123456789.\":\n return val[i:]\n return \"\"","def getPreZero(i):\n pre_zero = ''\n if bool(i % 10) != bool(i == 0):\n pre_zero = '0000'\n if round(((i % 100) / 10), 1):\n pre_zero = '000'\n if round(((i % 1000) / 100), 1):\n pre_zero = '00'\n if round(((i % 10000) / 1000), 1):\n pre_zero = '0'\n return pre_zero","def moveZeroes(self, nums: List[int]) -> None:\n s = 0\n \n while nums.count(0) > 0:\n nums.remove(0)\n s += 1\n \n for i in range(s):\n nums.append(0)","def padding_zeroes(number, length_string):\n return str(number).zfill(length_string)","def moveZeroes1(self, nums: List[int]) -> None:\n count = 0 \n for i in range(len(nums)-1) :\n i = i - count\n if nums[i] == 0 :\n count += 1\n nums.pop(i)\n \n nums.extend([0]*count)","def keep_summing(x):\n \"\"\" GRAPSTE TON KWDIKA SAS APO KATW \"\"\"\n s=0\n k=0\n while(x>0):\n s = s+(x%10)\n x = x//10\n while(s>9):\n while(s>0):\n k = k + (s%10)\n s = s//10\n s = k\n return s","def moveZeroes(self, nums) -> None:\n zero_count = 0\n for index, each_data in enumerate(nums):\n if each_data == 0:\n zero_count += 1\n continue\n nums[index - zero_count] = each_data\n start = len(nums) - zero_count\n while start < len(nums):\n nums[start] = 0\n start += 1","def fix(xs):\n\n if xs >= 0:\n res = np.floor(xs)\n else:\n res = np.ceil(xs)\n return res","def signif(x, digits=4):\n if x == 0 or not math.isfinite(x):\n return x\n digits -= math.ceil(math.log10(abs(x)))\n return round(x, digits)","def shift_zero(line):\n for index_i in range(0, len(line)):\n if line[index_i] != 0:\n key = line[index_i]\n index_j = index_i-1\n while index_j >= 0 and line[index_j] == 0:\n line[index_j+1] = line[index_j]\n index_j = index_j-1\n line[index_j+1] = key","def moveZeroes(self, nums: List[int]) -> None:\r\n\r\n # left and right pointer\r\n left = 0\r\n right = len(nums) - 1 # to keep track of last non-zero digit\r\n\r\n # while they do not overlap yet\r\n # means they may be 0 that can be moved to behind\r\n while left < right:\r\n if nums[left] == 0:\r\n del nums[left]\r\n nums.append(0)\r\n right -= 1\r\n else:\r\n left += 1","def replace_lowest_one_with_zero(x):\n return x & (x-1)","def compact(number):\n return clean(number, ' -').strip()","def compact(number):\n return clean(number, ' -./,').strip()","def moveZeroes(self, nums: [int]) -> None:\n length = len(nums)\n i = 0\n ctr = 0\n while i < length:\n if ctr == length - 1:\n break\n if nums[i] == 0:\n nums.append(0)\n nums.pop(i)\n else:\n i += 1\n ctr += 1","def moveZeroes(self, nums: [int]) -> None:\n count = 0\n try:\n while 1:\n nums.remove(0)\n count += 1\n except:\n pass\n if count:\n nums.extend([0] * count)","def moveZeroes(self, nums: List[int]) -> None:\n zeros=0\n for i in range(len(nums)):\n if nums[i]==0:\n zeros+=1\n else:\n nums[i-zeros]=nums[i]\n for i in range(len(nums)-zeros,len(nums)):\n nums[i]=0","def moveZeroes(self, nums: [int]) -> None:\n for num in nums:\n if num == 0:\n nums.remove(num)\n nums.append(num)\n # print(nums)","def moveZeroes(self, nums: List[int]) -> None:\n pos =0\n for i in nums:\n if i==0:\n continue\n else:\n nums[pos]=i\n pos+=1\n nums[pos:] = [0] * (len(nums)-pos)","def drop_lowest_unset_bit(x):\n\n return x | (x + 1)","def normalize(value):\n while value > 1:\n value = value / 10\n return value","def moveZeroes(self, nums) -> None:\n nums.sort(key=lambda x : x != 0)\n print(nums)\n\n point = 0\n for index, value in enumerate(nums):\n if value != 0:\n point = index\n break\n\n nums[point:] = list(reversed(nums[point:]))\n print(nums)\n nums.reverse()\n print(nums)","def moveZeroes(self, nums: List[int]) -> None:\n zero_count = Counter(nums)\n for num in range(zero_count[0]):\n nums.remove(0)\n nums.extend([0]*zero_count[0])\n print(nums)","def float_trunc(v, zerobits):\n # A float is represented in binary like this:\n # seeeeeeeefffffffffffffffffffffff\n mask = -(1L << zerobits)\n v = struct.unpack('I', struct.pack('f', v))[0]\n v &= mask\n return struct.unpack('f', struct.pack('I', v))[0]","def moveZeroes(self, nums: 'List[int]') -> 'None':\n\n i = 0\n len_nums = len(nums)\n while i < len_nums:\n if nums[i] == 0:\n nums.append(nums.pop(i))\n len_nums -= 1\n else:\n i += 1","def moving_zeroes(arr):\n\n # look at each item in list\n for i in range(len(arr)):\n # if the index value is 0, remove it from\n # the list and add a 0 to the end\n if arr[i] == 0:\n arr.pop(i)\n arr.append(0)\n\n return arr","def moveZeroes(self, nums: List[int]) -> None:\n \n zero_cnt = 0\n for i in range(len(nums)):\n if nums[i] == 0:\n nums.pop(i)\n nums.insert(0, 0)\n zero_cnt += 1\n \n while zero_cnt > 0:\n nums.pop(0)\n nums.append(0)\n zero_cnt -= 1","def fix(xs):\n\n # res = [np.floor(e) if e >= 0 else np.ceil(e) for e in xs]\n if xs >= 0:\n res = np.floor(xs)\n else:\n res = np.ceil(xs)\n return res","def truncate_to_significant_bits(input_x: int, num_significant_bits: int) -> int:\n x = abs(input_x)\n if num_significant_bits > x.bit_length():\n return input_x\n lower = x.bit_length() - num_significant_bits\n mask = (1 << (x.bit_length())) - 1 - ((1 << lower) - 1)\n if input_x < 0:\n return -(x & mask)\n else:\n return x & mask","def moveZeroes(self, arr: List[int]) -> None:\n n = len(arr)\n p = 0 # position to move the next non-zero to\n\n for i in range(n):\n if arr[i] != 0:\n arr[p] = arr[i]\n p += 1\n\n # while p < n):\n # arr[p] = 0\n # p += 1\n\n arr[p:] = [0] * (n - p)","def moveZeroes(self, nums: List[int]) -> None:\n lenght = len(nums)\n currzero = 0\n numszero = 0\n for x in range(lenght):\n if nums[x] == 0:\n currzero = x\n numszero += 1\n elif nums[x] != 0 and numszero > 0:\n pos = currzero - numszero + 1\n nums[pos] = nums[x]\n nums[x] = 0\n currzero += 1","def trim(self):\n for i in range(len(self)):\n if self[i] != TRIT_ZERO:\n return self.__class__(self[i:])\n return self.__class__([])","def remove_exponent(val):\n context = decimal.Context(prec=self.max_digits)\n return val.quantize(decimal.Decimal(1), context=context) if val == val.to_integral() else val.normalize(context)","def zeros_padding_to_number_digits(mystring):\n\treturn ''.join([format(int(x), '05d') if x.isdigit() else x for x in re.split(r'(\\d+)', mystring)])","def ceil_inplace(a):","def moveZeroes(nums):\n for i in nums:\n if (i == 0):\n nums.pop(nums.index(i))\n nums.append(i)","def devide10(x):\r\n\treturn x / 10","def compact(number):\n return clean(number, ' -.').upper().strip()","def compact(number):\n number = clean(number).strip().replace(' ', '-').split('-')\n if len(number) == 4:\n # zero pad the different sections if they are found\n lengths = (2, 4, 7, 3)\n return ''.join(n.zfill(l) for n, l in zip(number, lengths))\n else:\n # otherwise zero pad the account type\n number = ''.join(number)\n return number[:13] + number[13:].zfill(3)","def RemoveZeroVar(chain):\n return chain[:, np.invert((np.sum(np.var(chain, axis=0), axis=1)<1e-10)), :]","def filtre_subtract(i, nokey, key, nbr_trunc):\n a = np.subtract(i, nokey)\n if not np.isnan(nbr_trunc):\n for j in range(len(a)):\n if a[j] <= nbr_trunc:\n a[j] = 0\n return a","def num_zeros_end(num):\r\n \r\n iszero = True\r\n num_zeros = 0\r\n i = len(num)-1\r\n \r\n while (iszero == True) and (i != 0):\r\n \r\n if num[i] == \"0\":\r\n num_zeros += 1\r\n \r\n elif num[i] != \"0\":\r\n iszero = False\r\n \r\n i -= 1 \r\n \r\n return num_zeros","def moveZeroes(self, nums: List[int]) -> None:\n left = 0\n zero = 0\n right = len(nums) - 1\n while left <= right:\n if nums[left] == 0:\n nums.pop(left)\n nums.append(0)\n right -= 1\n else:\n left += 1","def moveZeroes(self, nums: List[int]) -> None:\n\n i, count = 0, 0\n while i < len(nums):\n if not nums[i]:\n nums.pop(i)\n count+=1\n else:\n i += 1\n \n nums.extend([0]*count)","def moveZeroes(self, arr: List[int]) -> None: \n count = arr.count(0)\n\n arr[:] = [x for x in arr if x != 0]\n \n arr += [0] * count\n\n #arr[:] = [x for x in arr if x != 0] + [0] * arr.count(0)","def get_none_zero_list(line):\n new_line = []\n for dummy_i in range(0,len(line)):\n if line[dummy_i] != 0:\n new_line.append(line[dummy_i])\n return new_line","def moveZeroes(self, nums: List[int]) -> None:\n index = 0\n for i in range(len(nums)):\n if nums[i] == 0:\n index += 1\n else:\n nums[i - index] = nums[i]\n\n index0 = len(nums) - index\n while index0 <= len(nums) -1:\n nums[index0] = 0\n index0 +=1","def RemoveZeroVar(chain):\n\treturn chain[:, np.invert((np.sum(np.var(chain, axis=0), axis=1)<1e-10)), :]","def clean(x):\r\n return ensure_sorted_indices(remove0(x))","def filter_zeros(X):\n\tnoNonzeros = np.count_nonzero(X, axis=1)\n\tmask = np.where(noNonzeros > 0)\n\treturn X[mask[0], :]","def zero_pad(data):\n N = len(data)\n pow_2 = np.ceil(np.log2(N))\n return np.pad(data,(0,int((2**pow_2)-N)),'constant')","def moveZeroes(self, nums: List[int]) -> None:\n index = 0\n for i in range(len(nums)):\n if nums[i] != 0:\n nums[index] = nums[i]\n index += 1\n \n for i in range(index, len(nums)):\n nums[i] = 0","def normalise_num(version_num, normal_len):\n version_num = str(version_num).replace(\".\", \"\")\n if len(version_num) < normal_len:\n for i in range(len(version_num), normal_len):\n version_num = version_num + \"0\"\n return int(version_num) if version_num.isdigit() else 0","def trunc_if_integer(n: Any) -> Any:\n if n == int(n):\n return int(n)\n return n","def strip_non_digits(x: str) -> str:\n exp = re.compile(\"[^\\d]+\")\n return re.sub(exp, \"\", x)","def nozero(arr):\n vals=sorted(list(set(np.array(arr).flatten())))\n if vals[0]<0:\n print(\"correcting for div/zero by replacing 0 with\",vals[1])\n arr[arr==0]=vals[1]\n return arr","def FIXED(number, decimals=2, no_commas=False):\n comma_flag = '' if no_commas else ','\n return \"{:{}.{}f}\".format(round(number, decimals), comma_flag, max(0, decimals))","def trunc(data):\n return _make.trunc(data)","def log10_zero(x):\n if x == 0:\n return 0\n else:\n return math.log10(x)","def zzX_trunc(f, p):\n return zzX_strip([ zzX_rem(g, p) for g in f ])","def left_fill(s, n, x=\"0\"):\n sl = len(s)\n zn = n - sl\n if zn > 0:\n return zn*\"0\" + s\n else:\n return s","def moveZeroes(self, nums: List[int]) -> None:\n N = len(nums)\n l = 0\n r = 0\n while r < N:\n if nums[l] == 0 and nums[r] != 0:\n nums[l], nums[r] = nums[r], nums[l]\n l += 1\n elif nums[r] == 0:\n r += 1\n else:\n l += 1\n r += 1","def moveZeroes(self, nums: List[int]) -> None:\n count = 0 # Shift count variable\n for i in range(len(nums)):\n if nums[i] == 0:\n count += 1\n else:\n nums[i-count], nums[i] = nums[i], nums[i-count] # Swap the nubmer and zero\n\n return","def truncate(coeffs, threshold=99):\n sortedindex = np.argsort(np.abs(coeffs))[::-1]\n Ncoeff = coeffs.shape[-1]\n cutoff = np.int(np.round(Ncoeff*threshold/100.))\n \n# print \"Keeping %2.0f %% (N=%s) of the biggest coefficients\"%(threshold,cutoff)\n\n coeffs_trunc = coeffs.copy() \t\t\t# copy of all coeff\n coeffs_trunc[sortedindex[cutoff:]] = 0 \t\t# put coeff\n \n return coeffs_trunc","def strip_numbers(text):\n if text is np.nan:\n return text\n regex = re.compile(r\"-?\\d+\")\n return re.sub(regex, \"\", text)"],"string":"[\n \"def delete_first_zeros(digit_with_zeros): \\n \\n digit_without_zeros = \\\"\\\"\\n\\n snap = 1\\n \\n d = 0\\n\\n for d in digit_with_zeros:\\n\\n if d != \\\"0\\\":\\n snap = 0\\n if snap == 0:\\n digit_without_zeros +=d\\n \\n return digit_without_zeros\",\n \"def strip_zeros(a):\\n\\n return np.trim_zeros(a, trim='b')\",\n \"def del_decim_zeros(num):\\r\\n \\r\\n if \\\".\\\" in num: \\r\\n \\r\\n num_zeros = num_zeros_end(num) \\r\\n\\r\\n if num_zeros != 0: \\r\\n num = num[:-num_zeros] \\r\\n \\r\\n if num[-1] == \\\".\\\": \\r\\n num = num[:-1]\\r\\n \\r\\n return num\\r\\n\\r\\n else:\\r\\n return num\",\n \"def strip_trailing_zeros(cls, s):\\n # Make sure there's a '.' in the string.\\n if s.find('.') < 0:\\n return s\\n\\n # Reverse the string, then walk until we find a non-zero.\\n rev_str = StringCleaner.reverse(s)\\n\\n last_index = -1\\n count = -1\\n\\n for i in rev_str:\\n count += 1\\n\\n if i == '0':\\n continue\\n else:\\n last_index = count\\n break\\n\\n # Slice-off the zeros, re-reverse the string, and strip any trailing '.'s.\\n return StringCleaner.reverse(rev_str[last_index:]).rstrip('\\\\.')\",\n \"def trailing_zeroes(n):\\n str_n = str(n)\\n i = len(str_n) - 1\\n while str_n[i] == '0':\\n i -= 1\\n return len(str_n) - 1 - i\",\n \"def trim_zeros(filt, trim='fb'):\\n first = 0\\n trim = trim.upper()\\n if 'F' in trim:\\n for i in filt:\\n if i != 0.: break\\n else: first = first + 1\\n last = len(filt)\\n if 'B' in trim:\\n for i in filt[::-1]:\\n if i != 0.: break\\n else: last = last - 1\\n return filt[first:last]\",\n \"def strip_leading_zeros(version: str) -> str:\\n return \\\".\\\".join(str(int(i)) for i in version.split(\\\".\\\"))\",\n \"def shift_trailing_zeros(x):\\n\\n if (x == 0):\\n return 0\\n\\n return x // get_lowest_set_bit(x)\",\n \"def trailingZeroes(self, n):\\n cnt = 0\\n while n:\\n n /= 5\\n cnt += n\\n\\n return cnt\",\n \"def delete_closing_zero(model_version: str) -> str:\\r\\n if model_version[-2:] == \\\".0\\\":\\r\\n return model_version[:-2]\\r\\n return model_version\",\n \"def trunc(x):\\n return 0\",\n \"def stripBufferZero(str):\\n if str[-2] == '0':\\n return (str[:-2] + str[-1])\\n else:\\n return str\",\n \"def trim_zeros(x):\\n assert len(x.shape) == 2\\n return x[~np.all(x == 0, axis=1)]\",\n \"def trunc(value):\\n\\t\\treturn round(value, 3) if math.modf(value)[0] != 0 else round(value)\",\n \"def round_half_away_from_zero_inplace(a):\",\n \"def zzx_strip(f):\\n if not f or f[0]:\\n return f\\n\\n k = 0\\n\\n for coeff in f:\\n if coeff:\\n break\\n else:\\n k += 1\\n\\n return f[k:]\",\n \"def truncate(x, digits):\\n if x==0:\\n return '0'\\n if digits==0:\\n return str(int(round(x)))\\n FORMAT = \\\"\\\"\\\".%sf\\\"\\\"\\\"\\n format_x = FORMAT % str(int(digits))\\n return format(x, format_x).rstrip('0').rstrip('.')\",\n \"def _delete_trailing_number(line):\\n pieces = line.split()\\n try:\\n int(pieces[-1])\\n return ' '.join(pieces[:-1])\\n except ValueError: # no trailing numbers\\n return line\",\n \"def remove_zeroes(s: str) -> str:\\n tup = s.split(\\\"e\\\")\\n if len(tup) == 2:\\n mantissa = tup[0].rstrip(\\\"0\\\").rstrip(\\\".\\\")\\n exponent = int(tup[1])\\n if exponent:\\n s = \\\"%se%d\\\" % (mantissa, exponent)\\n else:\\n s = mantissa\\n return s\",\n \"def no_float_zeros(v):\\n if v % 1 == 0:\\n return int(v)\\n else:\\n return v\",\n \"def leadingzero(number, minlength):\\n\\n # Return the number as a string with the filled number\\n return str(number).zfill(int(minlength))\",\n \"def num_trailing_zeros(val):\\n val = str(val)\\n counter = 0\\n\\n for char in reversed(val):\\n if char == \\\".\\\" or char != \\\"0\\\" : break\\n elif char == \\\"0\\\" : counter += 1\\n\\n return counter\",\n \"def clean(number):\\n digits = [c for c in number if c.isdigit()]\\n if len(digits) == 11 and digits[0] == \\\"1\\\":\\n return ''.join(digits[1:])\\n elif len(digits) != 10:\\n return \\\"0000000000\\\"\\n else:\\n return ''.join(digits)\",\n \"def zero_digits(s):\\n return re.sub('\\\\d', '0', s)\",\n \"def moveZeroes(nums):\\n \\n zero_count = nums.count(0)\\n \\n for x in range(zero_count):\\n nums.remove(0)\\n nums.append(0)\",\n \"def round_half_away_from_zero(num):\\n return np.sign(num) * np.floor(np.abs(num) + 0.5)\",\n \"def countTrailingZeros(num):\\n if num == 0:\\n return 32 # Assumes 32 bit integer inputs!\\n p = 0\\n while (num >> p) & 1 == 0:\\n p += 1\\n return p\",\n \"def test_trailing_zeros():\\n # Empty list\\n assert list(tc.trailing_zeros([])) == []\\n\\n # Default cases\\n assert list(tc.trailing_zeros([10, 16, 11])) == [1, 4, 0]\\n assert list(tc.trailing_zeros(np.array([32, -2, -20]))) == [5, 1, 2]\",\n \"def last_digits(x,y,n):\\n\\tr = modexp(x,y, 10**n)\\n\\n\\t#post processing to make sure we didn't cut off a leading 0\\n\\treturn str(r).zfill(n)\",\n \"def add_leading_zero(num):\\n formated = \\\"\\\"\\n if num <= 9:\\n formated = \\\"0\\\" + str(num)\\n else:\\n formated = str(num)\\n \\n return formated\",\n \"def shift_trailing_ones(x):\\n\\n if (x == -1):\\n return -1\\n\\n return x // get_lowest_unset_bit(x)\",\n \"def clip_round(value):\\n return max(0, min(np.round(value/64), 1023))\",\n \"def _bin_zfill(num, width=None):\\n s = bin(num)[2:]\\n return s if width is None else s.zfill(width)\",\n \"def zzX_strip(f):\\n if poly_univariate_p(f):\\n return zzx_strip(f)\\n\\n if zzX_zero_p(f):\\n return f\\n\\n k = 0\\n\\n for coeff in f:\\n if not zzX_zero_p(coeff):\\n break\\n else:\\n k += 1\\n\\n if k == len(f):\\n return zzX_zero_of(f)\\n else:\\n return f[k:]\",\n \"def moveZeroes(self, nums: List[int]) -> None:\\n l,r=0,len(nums)-1\\n while l<r:\\n if nums[l]==0:\\n nums.append(nums.pop(l))\\n r-=1\\n continue\\n l+=1\\n return nums\",\n \"def strip_leading_chars(val):\\n for i, c in enumerate(val):\\n if c in \\\"0123456789.\\\":\\n return val[i:]\\n return \\\"\\\"\",\n \"def getPreZero(i):\\n pre_zero = ''\\n if bool(i % 10) != bool(i == 0):\\n pre_zero = '0000'\\n if round(((i % 100) / 10), 1):\\n pre_zero = '000'\\n if round(((i % 1000) / 100), 1):\\n pre_zero = '00'\\n if round(((i % 10000) / 1000), 1):\\n pre_zero = '0'\\n return pre_zero\",\n \"def moveZeroes(self, nums: List[int]) -> None:\\n s = 0\\n \\n while nums.count(0) > 0:\\n nums.remove(0)\\n s += 1\\n \\n for i in range(s):\\n nums.append(0)\",\n \"def padding_zeroes(number, length_string):\\n return str(number).zfill(length_string)\",\n \"def moveZeroes1(self, nums: List[int]) -> None:\\n count = 0 \\n for i in range(len(nums)-1) :\\n i = i - count\\n if nums[i] == 0 :\\n count += 1\\n nums.pop(i)\\n \\n nums.extend([0]*count)\",\n \"def keep_summing(x):\\n \\\"\\\"\\\" GRAPSTE TON KWDIKA SAS APO KATW \\\"\\\"\\\"\\n s=0\\n k=0\\n while(x>0):\\n s = s+(x%10)\\n x = x//10\\n while(s>9):\\n while(s>0):\\n k = k + (s%10)\\n s = s//10\\n s = k\\n return s\",\n \"def moveZeroes(self, nums) -> None:\\n zero_count = 0\\n for index, each_data in enumerate(nums):\\n if each_data == 0:\\n zero_count += 1\\n continue\\n nums[index - zero_count] = each_data\\n start = len(nums) - zero_count\\n while start < len(nums):\\n nums[start] = 0\\n start += 1\",\n \"def fix(xs):\\n\\n if xs >= 0:\\n res = np.floor(xs)\\n else:\\n res = np.ceil(xs)\\n return res\",\n \"def signif(x, digits=4):\\n if x == 0 or not math.isfinite(x):\\n return x\\n digits -= math.ceil(math.log10(abs(x)))\\n return round(x, digits)\",\n \"def shift_zero(line):\\n for index_i in range(0, len(line)):\\n if line[index_i] != 0:\\n key = line[index_i]\\n index_j = index_i-1\\n while index_j >= 0 and line[index_j] == 0:\\n line[index_j+1] = line[index_j]\\n index_j = index_j-1\\n line[index_j+1] = key\",\n \"def moveZeroes(self, nums: List[int]) -> None:\\r\\n\\r\\n # left and right pointer\\r\\n left = 0\\r\\n right = len(nums) - 1 # to keep track of last non-zero digit\\r\\n\\r\\n # while they do not overlap yet\\r\\n # means they may be 0 that can be moved to behind\\r\\n while left < right:\\r\\n if nums[left] == 0:\\r\\n del nums[left]\\r\\n nums.append(0)\\r\\n right -= 1\\r\\n else:\\r\\n left += 1\",\n \"def replace_lowest_one_with_zero(x):\\n return x & (x-1)\",\n \"def compact(number):\\n return clean(number, ' -').strip()\",\n \"def compact(number):\\n return clean(number, ' -./,').strip()\",\n \"def moveZeroes(self, nums: [int]) -> None:\\n length = len(nums)\\n i = 0\\n ctr = 0\\n while i < length:\\n if ctr == length - 1:\\n break\\n if nums[i] == 0:\\n nums.append(0)\\n nums.pop(i)\\n else:\\n i += 1\\n ctr += 1\",\n \"def moveZeroes(self, nums: [int]) -> None:\\n count = 0\\n try:\\n while 1:\\n nums.remove(0)\\n count += 1\\n except:\\n pass\\n if count:\\n nums.extend([0] * count)\",\n \"def moveZeroes(self, nums: List[int]) -> None:\\n zeros=0\\n for i in range(len(nums)):\\n if nums[i]==0:\\n zeros+=1\\n else:\\n nums[i-zeros]=nums[i]\\n for i in range(len(nums)-zeros,len(nums)):\\n nums[i]=0\",\n \"def moveZeroes(self, nums: [int]) -> None:\\n for num in nums:\\n if num == 0:\\n nums.remove(num)\\n nums.append(num)\\n # print(nums)\",\n \"def moveZeroes(self, nums: List[int]) -> None:\\n pos =0\\n for i in nums:\\n if i==0:\\n continue\\n else:\\n nums[pos]=i\\n pos+=1\\n nums[pos:] = [0] * (len(nums)-pos)\",\n \"def drop_lowest_unset_bit(x):\\n\\n return x | (x + 1)\",\n \"def normalize(value):\\n while value > 1:\\n value = value / 10\\n return value\",\n \"def moveZeroes(self, nums) -> None:\\n nums.sort(key=lambda x : x != 0)\\n print(nums)\\n\\n point = 0\\n for index, value in enumerate(nums):\\n if value != 0:\\n point = index\\n break\\n\\n nums[point:] = list(reversed(nums[point:]))\\n print(nums)\\n nums.reverse()\\n print(nums)\",\n \"def moveZeroes(self, nums: List[int]) -> None:\\n zero_count = Counter(nums)\\n for num in range(zero_count[0]):\\n nums.remove(0)\\n nums.extend([0]*zero_count[0])\\n print(nums)\",\n \"def float_trunc(v, zerobits):\\n # A float is represented in binary like this:\\n # seeeeeeeefffffffffffffffffffffff\\n mask = -(1L << zerobits)\\n v = struct.unpack('I', struct.pack('f', v))[0]\\n v &= mask\\n return struct.unpack('f', struct.pack('I', v))[0]\",\n \"def moveZeroes(self, nums: 'List[int]') -> 'None':\\n\\n i = 0\\n len_nums = len(nums)\\n while i < len_nums:\\n if nums[i] == 0:\\n nums.append(nums.pop(i))\\n len_nums -= 1\\n else:\\n i += 1\",\n \"def moving_zeroes(arr):\\n\\n # look at each item in list\\n for i in range(len(arr)):\\n # if the index value is 0, remove it from\\n # the list and add a 0 to the end\\n if arr[i] == 0:\\n arr.pop(i)\\n arr.append(0)\\n\\n return arr\",\n \"def moveZeroes(self, nums: List[int]) -> None:\\n \\n zero_cnt = 0\\n for i in range(len(nums)):\\n if nums[i] == 0:\\n nums.pop(i)\\n nums.insert(0, 0)\\n zero_cnt += 1\\n \\n while zero_cnt > 0:\\n nums.pop(0)\\n nums.append(0)\\n zero_cnt -= 1\",\n \"def fix(xs):\\n\\n # res = [np.floor(e) if e >= 0 else np.ceil(e) for e in xs]\\n if xs >= 0:\\n res = np.floor(xs)\\n else:\\n res = np.ceil(xs)\\n return res\",\n \"def truncate_to_significant_bits(input_x: int, num_significant_bits: int) -> int:\\n x = abs(input_x)\\n if num_significant_bits > x.bit_length():\\n return input_x\\n lower = x.bit_length() - num_significant_bits\\n mask = (1 << (x.bit_length())) - 1 - ((1 << lower) - 1)\\n if input_x < 0:\\n return -(x & mask)\\n else:\\n return x & mask\",\n \"def moveZeroes(self, arr: List[int]) -> None:\\n n = len(arr)\\n p = 0 # position to move the next non-zero to\\n\\n for i in range(n):\\n if arr[i] != 0:\\n arr[p] = arr[i]\\n p += 1\\n\\n # while p < n):\\n # arr[p] = 0\\n # p += 1\\n\\n arr[p:] = [0] * (n - p)\",\n \"def moveZeroes(self, nums: List[int]) -> None:\\n lenght = len(nums)\\n currzero = 0\\n numszero = 0\\n for x in range(lenght):\\n if nums[x] == 0:\\n currzero = x\\n numszero += 1\\n elif nums[x] != 0 and numszero > 0:\\n pos = currzero - numszero + 1\\n nums[pos] = nums[x]\\n nums[x] = 0\\n currzero += 1\",\n \"def trim(self):\\n for i in range(len(self)):\\n if self[i] != TRIT_ZERO:\\n return self.__class__(self[i:])\\n return self.__class__([])\",\n \"def remove_exponent(val):\\n context = decimal.Context(prec=self.max_digits)\\n return val.quantize(decimal.Decimal(1), context=context) if val == val.to_integral() else val.normalize(context)\",\n \"def zeros_padding_to_number_digits(mystring):\\n\\treturn ''.join([format(int(x), '05d') if x.isdigit() else x for x in re.split(r'(\\\\d+)', mystring)])\",\n \"def ceil_inplace(a):\",\n \"def moveZeroes(nums):\\n for i in nums:\\n if (i == 0):\\n nums.pop(nums.index(i))\\n nums.append(i)\",\n \"def devide10(x):\\r\\n\\treturn x / 10\",\n \"def compact(number):\\n return clean(number, ' -.').upper().strip()\",\n \"def compact(number):\\n number = clean(number).strip().replace(' ', '-').split('-')\\n if len(number) == 4:\\n # zero pad the different sections if they are found\\n lengths = (2, 4, 7, 3)\\n return ''.join(n.zfill(l) for n, l in zip(number, lengths))\\n else:\\n # otherwise zero pad the account type\\n number = ''.join(number)\\n return number[:13] + number[13:].zfill(3)\",\n \"def RemoveZeroVar(chain):\\n return chain[:, np.invert((np.sum(np.var(chain, axis=0), axis=1)<1e-10)), :]\",\n \"def filtre_subtract(i, nokey, key, nbr_trunc):\\n a = np.subtract(i, nokey)\\n if not np.isnan(nbr_trunc):\\n for j in range(len(a)):\\n if a[j] <= nbr_trunc:\\n a[j] = 0\\n return a\",\n \"def num_zeros_end(num):\\r\\n \\r\\n iszero = True\\r\\n num_zeros = 0\\r\\n i = len(num)-1\\r\\n \\r\\n while (iszero == True) and (i != 0):\\r\\n \\r\\n if num[i] == \\\"0\\\":\\r\\n num_zeros += 1\\r\\n \\r\\n elif num[i] != \\\"0\\\":\\r\\n iszero = False\\r\\n \\r\\n i -= 1 \\r\\n \\r\\n return num_zeros\",\n \"def moveZeroes(self, nums: List[int]) -> None:\\n left = 0\\n zero = 0\\n right = len(nums) - 1\\n while left <= right:\\n if nums[left] == 0:\\n nums.pop(left)\\n nums.append(0)\\n right -= 1\\n else:\\n left += 1\",\n \"def moveZeroes(self, nums: List[int]) -> None:\\n\\n i, count = 0, 0\\n while i < len(nums):\\n if not nums[i]:\\n nums.pop(i)\\n count+=1\\n else:\\n i += 1\\n \\n nums.extend([0]*count)\",\n \"def moveZeroes(self, arr: List[int]) -> None: \\n count = arr.count(0)\\n\\n arr[:] = [x for x in arr if x != 0]\\n \\n arr += [0] * count\\n\\n #arr[:] = [x for x in arr if x != 0] + [0] * arr.count(0)\",\n \"def get_none_zero_list(line):\\n new_line = []\\n for dummy_i in range(0,len(line)):\\n if line[dummy_i] != 0:\\n new_line.append(line[dummy_i])\\n return new_line\",\n \"def moveZeroes(self, nums: List[int]) -> None:\\n index = 0\\n for i in range(len(nums)):\\n if nums[i] == 0:\\n index += 1\\n else:\\n nums[i - index] = nums[i]\\n\\n index0 = len(nums) - index\\n while index0 <= len(nums) -1:\\n nums[index0] = 0\\n index0 +=1\",\n \"def RemoveZeroVar(chain):\\n\\treturn chain[:, np.invert((np.sum(np.var(chain, axis=0), axis=1)<1e-10)), :]\",\n \"def clean(x):\\r\\n return ensure_sorted_indices(remove0(x))\",\n \"def filter_zeros(X):\\n\\tnoNonzeros = np.count_nonzero(X, axis=1)\\n\\tmask = np.where(noNonzeros > 0)\\n\\treturn X[mask[0], :]\",\n \"def zero_pad(data):\\n N = len(data)\\n pow_2 = np.ceil(np.log2(N))\\n return np.pad(data,(0,int((2**pow_2)-N)),'constant')\",\n \"def moveZeroes(self, nums: List[int]) -> None:\\n index = 0\\n for i in range(len(nums)):\\n if nums[i] != 0:\\n nums[index] = nums[i]\\n index += 1\\n \\n for i in range(index, len(nums)):\\n nums[i] = 0\",\n \"def normalise_num(version_num, normal_len):\\n version_num = str(version_num).replace(\\\".\\\", \\\"\\\")\\n if len(version_num) < normal_len:\\n for i in range(len(version_num), normal_len):\\n version_num = version_num + \\\"0\\\"\\n return int(version_num) if version_num.isdigit() else 0\",\n \"def trunc_if_integer(n: Any) -> Any:\\n if n == int(n):\\n return int(n)\\n return n\",\n \"def strip_non_digits(x: str) -> str:\\n exp = re.compile(\\\"[^\\\\d]+\\\")\\n return re.sub(exp, \\\"\\\", x)\",\n \"def nozero(arr):\\n vals=sorted(list(set(np.array(arr).flatten())))\\n if vals[0]<0:\\n print(\\\"correcting for div/zero by replacing 0 with\\\",vals[1])\\n arr[arr==0]=vals[1]\\n return arr\",\n \"def FIXED(number, decimals=2, no_commas=False):\\n comma_flag = '' if no_commas else ','\\n return \\\"{:{}.{}f}\\\".format(round(number, decimals), comma_flag, max(0, decimals))\",\n \"def trunc(data):\\n return _make.trunc(data)\",\n \"def log10_zero(x):\\n if x == 0:\\n return 0\\n else:\\n return math.log10(x)\",\n \"def zzX_trunc(f, p):\\n return zzX_strip([ zzX_rem(g, p) for g in f ])\",\n \"def left_fill(s, n, x=\\\"0\\\"):\\n sl = len(s)\\n zn = n - sl\\n if zn > 0:\\n return zn*\\\"0\\\" + s\\n else:\\n return s\",\n \"def moveZeroes(self, nums: List[int]) -> None:\\n N = len(nums)\\n l = 0\\n r = 0\\n while r < N:\\n if nums[l] == 0 and nums[r] != 0:\\n nums[l], nums[r] = nums[r], nums[l]\\n l += 1\\n elif nums[r] == 0:\\n r += 1\\n else:\\n l += 1\\n r += 1\",\n \"def moveZeroes(self, nums: List[int]) -> None:\\n count = 0 # Shift count variable\\n for i in range(len(nums)):\\n if nums[i] == 0:\\n count += 1\\n else:\\n nums[i-count], nums[i] = nums[i], nums[i-count] # Swap the nubmer and zero\\n\\n return\",\n \"def truncate(coeffs, threshold=99):\\n sortedindex = np.argsort(np.abs(coeffs))[::-1]\\n Ncoeff = coeffs.shape[-1]\\n cutoff = np.int(np.round(Ncoeff*threshold/100.))\\n \\n# print \\\"Keeping %2.0f %% (N=%s) of the biggest coefficients\\\"%(threshold,cutoff)\\n\\n coeffs_trunc = coeffs.copy() \\t\\t\\t# copy of all coeff\\n coeffs_trunc[sortedindex[cutoff:]] = 0 \\t\\t# put coeff\\n \\n return coeffs_trunc\",\n \"def strip_numbers(text):\\n if text is np.nan:\\n return text\\n regex = re.compile(r\\\"-?\\\\d+\\\")\\n return re.sub(regex, \\\"\\\", text)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.74442893","0.7321218","0.71703875","0.6988911","0.6901619","0.6722442","0.66972","0.6550412","0.6526725","0.650372","0.64469194","0.631782","0.62386894","0.6091823","0.5994243","0.5845531","0.583502","0.5832329","0.58009475","0.5774697","0.57746726","0.576813","0.5738169","0.5726109","0.5703615","0.56580627","0.56401104","0.56267524","0.5603654","0.55889535","0.55520046","0.55312234","0.5526435","0.5477632","0.5477064","0.54679453","0.54612035","0.54552525","0.5441033","0.5426515","0.54252994","0.5419262","0.5417669","0.54175305","0.54039836","0.53981686","0.53929853","0.53819764","0.53815913","0.53735626","0.53735363","0.53616244","0.5358787","0.53456795","0.5333332","0.5318353","0.53092295","0.5294696","0.52851677","0.5284277","0.52831274","0.52827996","0.5280113","0.52671665","0.5267062","0.52586496","0.52528065","0.5251664","0.52502626","0.52309","0.5227337","0.5225892","0.5224519","0.5223473","0.5220181","0.5214983","0.52072287","0.5199988","0.517931","0.51657367","0.5161495","0.51603574","0.51598024","0.5159012","0.5158385","0.5146888","0.5134422","0.51342475","0.5127834","0.51249284","0.511993","0.511888","0.51172405","0.5103825","0.5103706","0.5096752","0.5091355","0.50888574","0.50868964","0.5085845"],"string":"[\n \"0.74442893\",\n \"0.7321218\",\n \"0.71703875\",\n \"0.6988911\",\n \"0.6901619\",\n \"0.6722442\",\n \"0.66972\",\n \"0.6550412\",\n \"0.6526725\",\n \"0.650372\",\n \"0.64469194\",\n \"0.631782\",\n \"0.62386894\",\n \"0.6091823\",\n \"0.5994243\",\n \"0.5845531\",\n \"0.583502\",\n \"0.5832329\",\n \"0.58009475\",\n \"0.5774697\",\n \"0.57746726\",\n \"0.576813\",\n \"0.5738169\",\n \"0.5726109\",\n \"0.5703615\",\n \"0.56580627\",\n \"0.56401104\",\n \"0.56267524\",\n \"0.5603654\",\n \"0.55889535\",\n \"0.55520046\",\n \"0.55312234\",\n \"0.5526435\",\n \"0.5477632\",\n \"0.5477064\",\n \"0.54679453\",\n \"0.54612035\",\n \"0.54552525\",\n \"0.5441033\",\n \"0.5426515\",\n \"0.54252994\",\n \"0.5419262\",\n \"0.5417669\",\n \"0.54175305\",\n \"0.54039836\",\n \"0.53981686\",\n \"0.53929853\",\n \"0.53819764\",\n \"0.53815913\",\n \"0.53735626\",\n \"0.53735363\",\n \"0.53616244\",\n \"0.5358787\",\n \"0.53456795\",\n \"0.5333332\",\n \"0.5318353\",\n \"0.53092295\",\n \"0.5294696\",\n \"0.52851677\",\n \"0.5284277\",\n \"0.52831274\",\n \"0.52827996\",\n \"0.5280113\",\n \"0.52671665\",\n \"0.5267062\",\n \"0.52586496\",\n \"0.52528065\",\n \"0.5251664\",\n \"0.52502626\",\n \"0.52309\",\n \"0.5227337\",\n \"0.5225892\",\n \"0.5224519\",\n \"0.5223473\",\n \"0.5220181\",\n \"0.5214983\",\n \"0.52072287\",\n \"0.5199988\",\n \"0.517931\",\n \"0.51657367\",\n \"0.5161495\",\n \"0.51603574\",\n \"0.51598024\",\n \"0.5159012\",\n \"0.5158385\",\n \"0.5146888\",\n \"0.5134422\",\n \"0.51342475\",\n \"0.5127834\",\n \"0.51249284\",\n \"0.511993\",\n \"0.511888\",\n \"0.51172405\",\n \"0.5103825\",\n \"0.5103706\",\n \"0.5096752\",\n \"0.5091355\",\n \"0.50888574\",\n \"0.50868964\",\n \"0.5085845\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.65390474"},"document_rank":{"kind":"string","value":"8"}}},{"rowIdx":81178,"cells":{"query":{"kind":"string","value":"take 1D float array of rewards and compute discounted reward"},"document":{"kind":"string","value":"def discount_rewards(r):\n discounted_r = np.zeros_like(r)\n running_add = 0\n for t in reversed(range(r.size)):\n running_add = running_add * gamma + r[t]\n discounted_r[t] = running_add\n return discounted_r"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def discount_rewards(rewards):\r\n discounted_r = np.zeros_like(rewards)\r\n running_add = 0\r\n for t in reversed(range(0, len(rewards))): \r\n running_add = running_add * reward_discount + rewards[t]\r\n discounted_r[t] = running_add\r\n return discounted_r","def discount_rewards(rewards, gamma):\n reward_shape = rewards.shape\n if len(reward_shape) == 1:\n discounted_r = np.zeros(shape=(*reward_shape, 1), dtype=np.float)\n else:\n discounted_r = np.zeros(shape=reward_shape, dtype=np.float)\n running_add = 0\n\n for t in reversed(range(0, rewards.size)):\n running_add = running_add * gamma + rewards[t]\n discounted_r[t] = running_add\n\n return discounted_r","def discount_rewards(r):\n\tdiscounted_r = np.zeros_like(r)\n\trunning_add = 0\n\tfor t in reversed(range(0, r.size)):\n\t\trunning_add = running_add * gamma + r[t]\n\t\tdiscounted_r[t] = running_add\n\treturn np.array(discounted_r)","def helper_discount_rewards(rewards, discount_rate):\n discounted_rewards = np.zeros(len(rewards))\n cumulative_rewards = 0\n for step in reversed(range(len(rewards))):\n cumulative_rewards = rewards[step] + cumulative_rewards * discount_rate\n discounted_rewards[step] = cumulative_rewards\n return discounted_rewards","def discount_rewards(r):\n\tdiscounted_r = np.zeros_like(r)\n\trunning_add = 0\n\tfor t in reversed(xrange(0, r.size)):\n\t\trunning_add = running_add * gamma + r[t]\n\t\tdiscounted_r[t] = running_add\n\treturn discounted_r","def compute_discounted_rewards(self, rewards):\n discounted_rewards = []\n for t in range(len(rewards)):\n Gt = 0\n pw = 0\n for r in rewards[t:]:\n Gt = Gt + self.gamma ** pw * r\n pw = pw + 1\n discounted_rewards.append(Gt)\n\n # Normalization of the discounted rewards\n discounted_rewards = np.array(discounted_rewards)\n discounted_rewards = (discounted_rewards - discounted_rewards.mean()) / (discounted_rewards.std() + 1e-10)\n\n return discounted_rewards","def discount_rewards(r):\r\n discounted_r = np.zeros_like(r)\r\n running_add = 0\r\n for t in reversed(xrange(0, r.size)):\r\n running_add = running_add * .99 + r[t]\r\n discounted_r[t] = running_add \r\n return discounted_r","def computed_discounted_rewards(rewards: list[float], gamma: float = 0.95) -> float:\n discounted_reward = 0\n for i, r in enumerate(rewards):\n discounted_reward += gamma**i * r\n return discounted_reward","def discount(rewards):\n sum_so_far = 0.0\n rewards_so_far = []\n for r in rewards[::-1]:\n sum_so_far = sum_so_far * gamma_discount + r\n rewards_so_far.append(sum_so_far)\n return rewards_so_far[::-1]","def discount_rewards(r):\n discounted_r = np.zeros_like(r)\n running_add = 0\n for t in reversed(range(0, r.size)):\n running_add = running_add * gamma + r[t]\n discounted_r[t] = running_add\n return discounted_r","def discount_rewards(r):\n discounted_r = np.zeros_like(r)\n running_add = 0\n for t in reversed(xrange(0, r.size)):\n running_add = running_add * gamma_099 + r[t]\n discounted_r[t] = running_add\n return discounted_r","def discount_rewards(r):\n discounted_r = np.zeros_like(r)\n running_add = 0\n for t in reversed(xrange(0, r.size)):\n running_add = running_add * gamma + r[t]\n discounted_r[t] = running_add\n return discounted_r","def discount_rewards(r):\r\n discounted_r = np.zeros_like(r)\r\n running_add = 0\r\n for t in reversed(range(0, r.size)):\r\n running_add = running_add * gamma + r[t]\r\n discounted_r[t] = running_add\r\n return discounted_r","def discount_rewards(self, r):\n discounted_r = np.zeros_like(r)\n running_add = 0\n for t in reversed(range(0, r.size)):\n if r[t] != 0:\n running_add = 0 # Pong-specific\n running_add = running_add * self.gamma + r[t]\n discounted_r[t] = running_add\n \n #print(\"Mean reward before normalized: {}\".format(np.mean(discounted_r)))\n mu = np.mean(discounted_r)\n var = np.var(discounted_r)\n discounted_r -= mu \n discounted_r /= np.sqrt(var+1e-6)\n return discounted_r","def discount_rewards(r):\n discounted_r = np.zeros_like(r)\n running_add = 0\n for t in reversed(range(0, r.size)):\n if r[t] != 0: running_add = 0 # reset the sum, since this was a game boundary (pong specific!)\n running_add = running_add * gamma + r[t]\n discounted_r[t] = running_add\n return discounted_r","def discount_rewards(r):\r\n discounted_r = np.zeros_like(r)\r\n running_add = 0\r\n for t in reversed(xrange(0, r.size)):\r\n running_add = running_add * gamma + r[t]\r\n discounted_r[t] = running_add\r\n return discounted_r","def discount_rewards_and_normalize(self, rewards):\n discounted_rewards = np.empty(len(rewards))\n cumulative_rewards = 0\n\n for step in reversed(range(len(rewards))):\n cumulative_rewards = rewards[step] + cumulative_rewards * self.gamma\n discounted_rewards[step] = cumulative_rewards\n\n reward_mean = discounted_rewards.mean()\n reward_std = discounted_rewards.std()\n\n return [(reward - reward_mean) / reward_std\n for reward in discounted_rewards]","def discount_and_normalize_rewards(episode_rewards):\n # Get empty array with the same size as the rewards array\n discounted_episode_rewards = np.zeros_like(episode_rewards)\n\n # Variable that stores value of the discounted reward being calculated by the loop\n current_reward = 0.0\n # Loop that does the magic\n for i in reversed(range(len(episode_rewards))):\n # Calculate the discounted reward\n current_reward = current_reward * gamma + episode_rewards[i]\n # Store it in the array\n discounted_episode_rewards[i] = current_reward\n\n # Normalize.\n mean = np.mean(discounted_episode_rewards)\n std = np.std(discounted_episode_rewards)\n discounted_episode_rewards = (discounted_episode_rewards - mean) / (std)\n\n return discounted_episode_rewards","def loss_rewards(r):\n discounted_r = np.zeros_like(r)\n running_add = 0\n for t in reversed(range(0, r.size)):\n #running_add = running_add * gamma + r[t]\n #discounted_r[t] = running_add\n discounted_r[t] = 0\n return discounted_r","def discount_rewards(r):\n discounted_r = np.zeros_like(r)\n running_add = 0\n for t in reversed(xrange(0, r.size)):\n if r[t] != 0: running_add = 0 # reset the sum, since this was a game boundary (pong specific)\n running_add = running_add * gamma + r[t]\n discounted_r[t] = running_add\n return discounted_r","def discounted_reward(self, discount):\n\n tl = len(self)\n return (1 - discount) * np.sum(discount ** np.arange(tl) * self.rewards)","def discount(rewards, discount_factor=.99):\n # Compute discounted rewards (trust me this works and hopefully it's super fast)\n timesteps = len(rewards) # make into matrix\n rewards = tf.convert_to_tensor([rewards],dtype=tf.float32)\n # create lower triangular matrix of discount_factor weights\n T = tf.convert_to_tensor([[max(1+i-j,0) for j in range(timesteps)] for i in range(timesteps)],dtype=tf.float32)\n T = tf.math.pow(discount_factor, T)\n T = tf.linalg.band_part(T, -1, 0)\n # apply discount factor\n return tf.matmul(rewards, T)","def discount_reward(reward, gamma):\n discount_r = np.zeros_like(reward)\n r_total = 0\n for _ in reversed(range(0, reward.size)):\n if reward[_] != 0:\n r_total = 0\n r_total = r_total * gamma + reward[_]\n discount_r[_] = r_total\n return discount_r","def discount_and_normalize_rewards(self, episode_rewards):\n # Get empty array with the same size as the rewards array\n discounted_episode_rewards = np.zeros_like(episode_rewards)\n\n # Variable that stores value of the discounted reward being calculated by the loop\n current_reward = 0.0\n # Loop that does the magic\n for i in reversed(range(len(episode_rewards))):\n # Calculate the discounted reward\n current_reward = current_reward * gamma + episode_rewards[i]\n # Store it in the array\n discounted_episode_rewards[i] = current_reward\n\n # Normalize.\n mean = np.mean(discounted_episode_rewards)\n std = np.std(discounted_episode_rewards)\n discounted_episode_rewards = (discounted_episode_rewards - mean) / (std)\n\n return discounted_episode_rewards","def discount_rewards(r, gamma):\n #print(\"r\", r)\n discounted_r = np.zeros_like(r)\n running_add = 0\n for t in reversed(xrange(0, r.size)):\n #print(\"t\", t)\n running_add = running_add * gamma + r[t]\n discounted_r[t] = running_add\n return discounted_r","def discount(self, rewards, dones, gamma):\n discounted = []\n ret = 0\n for reward, done in zip(rewards[::-1], dones[::-1]):\n ret = reward + gamma * ret * (1. - done)\n discounted.append(ret)\n return discounted[::-1]","def discounted_rewards(r, gamma):\n discounted_r = np.zeros_like(r)\n running_sum = 1\n for i in reversed(range(0,len(r))):\n discounted_r[i] = running_sum * r[i]\n running_sum = discounted_r[i]\n return list(discounted_r)","def discount_and_normalize_rewards(all_rewards, discount_rate):\n all_discounted_rewards = []\n for rewards in all_rewards:\n all_discounted_rewards.append(helper_discount_rewards(rewards, discount_rate))\n\n flat_rewards = np.concatenate(all_discounted_rewards)\n reward_mean = flat_rewards.mean()\n reward_std = flat_rewards.std()\n return [(discounted_rewards - reward_mean) / reward_std for discounted_rewards in all_discounted_rewards]","def discount_rewards(self, r, gamma):\n discounted_r = np.zeros_like(r)\n running_add = 0\n for t in reversed(range(0, r.size)):\n if r[t] != 0:\n running_add = 0 # reset the sum, since this was a game boundary (pong specific!)\n running_add = running_add * gamma + r[t]\n discounted_r[t] = running_add\n return np.array(discounted_r, dtype=np.float64)","def discount_rewards(r, gamma):\n discounted_r = np.zeros_like(r)\n running_add = 0\n for t in reversed(range(0, r.size)):\n running_add = running_add * gamma + r [t]\n discounted_r [t] = running_add\n return discounted_r","def _discount_rewards(self, non_discounted_rewards):\n discounted_rewards = [0.0] * len(non_discounted_rewards)\n total_rewards = 0\n for t in reversed(range(len(non_discounted_rewards))):\n total_rewards = total_rewards * self.discount_factor + non_discounted_rewards[t]\n discounted_rewards[t] = total_rewards\n return discounted_rewards","def get_cumulative_rewards(rewards, # rewards at each step\n gamma=0.99 # discount for reward\n ):\n\n cumulative_rewards = np.empty_like(rewards)\n cumulative_rewards = cumulative_rewards.astype(float)\n cumulative_rewards[-1] = rewards[-1]\n\n for index in range(len(rewards) - 2, -1, -1):\n discount = cumulative_rewards[index + 1] * gamma\n reward = rewards[index]\n cumulative_rewards[index] = discount + reward\n\n return cumulative_rewards # <array of cumulative rewards>","def discount_rewards(r, gamma=0.99):\n discounted_r = np.zeros_like(r)\n running_add = 0\n for t in reversed(xrange(0, r.size)):\n if r[t] != 0: running_add = 0 # reset the sum, since this was a game boundary (pong specific!)\n running_add = running_add * gamma + r[t]\n discounted_r[t] = running_add\n return discounted_r","def discount_rewards(rwd, gamma=0.99):\n discounted_r = np.zeros_like(rwd)\n running_add = 0\n for t in range(len(rwd)-1, -1, -1):\n running_add = running_add * gamma + rwd[t]\n discounted_r[t] = running_add\n\n return discounted_r","def discount_rewards(self, r, epEnd):\n res = np.zeros_like(r)\n running_add = 0.0\n assert (len(r)-1) in epEnd, 'simple sanity check: the last reward must be the episode end'\n assert isinstance(r, list)\n for t in reversed(xrange(len(r))):\n if t in epEnd: running_add = 0.0 # reset \n running_add = GAMMA * running_add + r[t]\n res[t] = running_add\n return res","def _discounted_cumsum(self, rewards, rate=None):\n # HINT1: note that each entry of the output should now be unique,\n # because the summation happens over [t, T] instead of [0, T]\n # HINT2: it is possible to write a vectorized solution, but a solution\n # using a for loop is also fine\n rate = self.gamma if rate is None else rate\n\n rewards = np.array(rewards)\n disounted_return = list(\n accumulate(rewards[::-1], lambda ret, rew: rate * ret + rew))\n disounted_return = np.array(disounted_return)[::-1]\n return disounted_return","def discount_with_dones(rewards, dones, gamma):\n discounted = []\n r = 0\n\n # discounted rewards are calculated on the reversed reward list.\n # that returns are calculated in descending order is easy to\n # overlook in the original pseudocode.\n # when writing down an example of the pseudocode, it is clear, that\n # r_t + gamma * V(s_tp1) is calculated for each list element and\n # this is also what is done here.\n for reward, done in zip(rewards[::-1], dones[::-1]):\n r = reward + gamma*r*(1.-done)\n discounted.append(r)\n return discounted[::-1]","def reward(input):\n state = np.array([input[0], input[1]])\n action = input[2]\n action = np.clip(action, -2.0, 2.0)\n costs = angle_normalize(state[0])**2 + .1 * state[1]**2 + .001 * (action**2)\n\n return - costs","def reward_calc(self, reward_traj,V,V_end):\n r_all = np.concatenate((reward_traj,[V_end]),-1)\n V_all = V #np.concatenate((V,[V_end]),-1)\n delta = r_all[:-1] + self.gamma * V_all[1:] - V_all[:-1]\n \n adv = Generalized_Adv_Estimator.discounted_sum(delta,self.gamma*self.lam)\n rtg = adv + V_all[:-1]\n\n adv = adv.astype('float32')\n rtg = rtg.astype('float32')\n\n return adv, rtg","def reward_amt(value, reward_vec, adj, softmax_inv_temp, discount, start_prob=None):\n n = len(reward_vec)\n softmax_value = np.exp(softmax_inv_temp * value) / np.sum(np.exp(softmax_inv_temp*value))\n policy = adj * softmax_value.reshape(1, -1)\n policy = util.l1_normalize_rows(policy)\n sr = np.linalg.pinv(np.eye(n) - discount * policy)\n value = np.dot(sr, reward_vec.reshape(-1, 1)).reshape(-1)\n if start_prob is None:\n start_prob = np.ones(n)*1. / n\n else:\n start_prob = start_prob.reshape(n)\n return np.sum(value * start_prob)","def discount_rewards(r, gamma=0.99, value_next=0.0):\n discounted_r = np.zeros_like(r)\n running_add = value_next\n for t in reversed(range(0, r.size)):\n running_add = running_add * gamma + r[t]\n discounted_r[t] = running_add\n return discounted_r","def discount_rewards(r, gamma=0.99, value_next=0.0):\n discounted_r = np.zeros_like(r)\n running_add = value_next\n for t in reversed(range(0, r.size)):\n running_add = running_add * gamma + r[t]\n discounted_r[t] = running_add\n return discounted_r","def _compute_reward(self):\n reward = 0.0\n return reward","def average_reward(self):\n T = len(self)\n return np.sum(self.rewards / T)","def compute_reward(self, done: np.ndarray) -> np.ndarray:\n rewards = [0.0 for i in range(self.agents)]\n rewarded_fields = defaultdict(float)\n if done[0][0]:\n # Get rewards for all rew fields with agents on\n for agent in range(self.agents):\n pos = self.game.get_agent_pos(agent)\n if pos in self.payoff_fields:\n rewarded_fields[pos] += 1.0\n\n for agent in range(self.agents):\n pos = self.game.get_agent_pos(agent)\n rewards[agent] += rewarded_fields.get(pos, 0.0)\n if agent < self.num_informed and pos in self.special_payoff_fields:\n rewards[agent] *= 2\n\n rewards = np.array(rewards).reshape(1, -1)\n return rewards","def _dense_reward(self) -> float:\n y = 1\n target_goal_dists = []\n for target_shape in self.__debris_shapes:\n target_pos = target_shape.shape_body.position\n goal_pos = (target_pos[0], y) # Top of screen.\n dist = np.linalg.norm(target_pos - goal_pos)\n if target_pos[1] > 0.88:\n dist = 0\n target_goal_dists.append(dist)\n target_goal_dists = np.mean(target_goal_dists)\n return -1.0 * target_goal_dists","def _compute_reward_(self):\n if self._target_type == \"position\":\n dist = np.linalg.norm(self._target_diff_, ord=2)\n if self._reward_type == \"linear\":\n reward_dist = -dist\n elif self._reward_type == \"precision\":\n reward_dist = -dist +\\\n np.exp( -dist**2 / 0.01)\n elif self._reward_type == \"sparse\":\n if dist < 0.05:\n reward_dist = 0\n else:\n reward_dist = -0.1\n\n elif self._target_type == \"angle\":\n dist = np.linalg.norm(self._target_diff_, ord=1)\n if self._reward_type == \"linear\":\n reward_dist = -dist\n elif self._reward_type == \"precision\":\n reward_dist = -dist +\\\n np.exp(-dist ** 2 / 0.01)\n elif self._reward_type == \"sparse\":\n raise NotImplementedError\n\n # TODO: doublecheck whether '0' or '-1' should be used as the index\n reward_vel = -self._vel_penalty * np.square(self._qd_[-1, self._joint_indices]).sum()\n\n #self.info['reward_dist'] = reward_dist\n #self.info['reward_vel'] = reward_vel\n\n return (reward_dist + reward_vel) * self._dt / 0.008","def loss(self, states, actions, next_states, rewards, discount_rate=.99):\n # TODO: implement this\n actions = tf.cast(actions, tf.int64)\n a = tf.stack([tf.range(states.shape[0],dtype=tf.int64), actions], axis=1)\n qVals = tf.gather_nd(self.call(states), a) # [batch_size] q-values for each action\n nextVals = tf.reduce_max(self.call(next_states), axis=1) # max of q-values [batch_size, num_actions] across num_actions\n targetVals = rewards + (discount_rate*nextVals)\n loss = tf.reduce_sum(tf.math.square(qVals - targetVals))\n return loss","def reward(self, reward):\n return float(np.sign(reward))","def get_reward(self, dags, entropies,inputs,targets):\n if not isinstance(entropies, np.ndarray):\n entropies = entropies.data.cpu().numpy()\n\n score=self.get_score(inputs,targets,dags)\n #score=1-self.get_loss(inputs,targets,dags)\n print(score.item())\n R = utils.to_item(score.data)\n\n if self.args.entropy_mode == 'reward':\n rewards = R + self.args.entropy_coeff * entropies.mean()\n elif self.args.entropy_mode == 'regularizer':\n rewards = R * np.ones_like(entropies)\n else:\n raise NotImplementedError(f'Unkown entropy mode: {self.args.entropy_mode}')\n\n return rewards","def reduce_rewards(self,rewards):\n reduced_rewards = np.zeros((len(rewards),1))\n gamma_multipliers = np.zeros((len(rewards),1))\n \n for i in range(len(rewards)):\n gammas = [GAMMA**t for t in range(len(rewards[i]))]\n \n reduced_rewards[i][0] = np.sum(np.multiply(gammas, rewards[i]))\n gamma_multipliers[i][0] = GAMMA**(len(rewards[i]))\n \n return reduced_rewards, gamma_multipliers","def reward(self, reward):\r\n return np.sign(reward)","def get_reward(self, observations, actions):\n\n #initialize and reshape as needed, for batch mode\n self.reward_dict = {}\n if(len(observations.shape)==1):\n observations = np.expand_dims(observations, axis = 0)\n actions = np.expand_dims(actions, axis = 0)\n batch_mode = False\n else:\n batch_mode = True\n\n #get vars\n xvel = observations[:, 9].copy()\n body_angle = observations[:, 2].copy()\n front_leg = observations[:, 6].copy()\n front_shin = observations[:, 7].copy()\n front_foot = observations[:, 8].copy()\n zeros = np.zeros((observations.shape[0],)).copy()\n\n # ranges\n leg_range = 0.2\n shin_range = 0\n foot_range = 0\n penalty_factor = 10\n\n #calc rew\n self.reward_dict['run'] = xvel\n\n front_leg_rew = zeros.copy()\n front_leg_rew[front_leg>leg_range] = -penalty_factor\n self.reward_dict['leg'] = front_leg_rew\n\n front_shin_rew = zeros.copy()\n front_shin_rew[front_shin>shin_range] = -penalty_factor\n self.reward_dict['shin'] = front_shin_rew\n\n front_foot_rew = zeros.copy()\n front_foot_rew[front_foot>foot_range] = -penalty_factor\n self.reward_dict['foot'] = front_foot_rew\n\n # total reward\n self.reward_dict['r_total'] = self.reward_dict['run'] + self.reward_dict['leg'] + self.reward_dict['shin'] + self.reward_dict['foot']\n\n #return\n dones = zeros.copy()\n if(not batch_mode):\n return self.reward_dict['r_total'][0], dones[0]\n return self.reward_dict['r_total'], dones","def discount_rewards(self,x, gamma):\n result = [0 for i in range(len(x))]\n element = 0\n for i in range(len(x)-1, -1, -1): #-2\n element = x[i] + gamma * element\n result[i] = element\n\n return result","def reward(self, reward):\n return np.sign(reward)","def reward(self, reward):\n return np.sign(reward)","def reward(self, reward):\n return np.sign(reward)","def reward(self, reward):\n return np.sign(reward)","def reward(self, reward):\n return np.sign(reward)","def _returns_advantages(self, rewards, dones, values, next_value):\n\t\treturns = np.append(np.zeros_like(rewards), next_value, axis=-1)\n\t\t\n\t\tfor t in reversed(range(rewards.shape[0])):\n\t\t\treturns[t] = rewards[t] + self.gamma * returns[t + 1] * (1 - dones[t])\n\t\t\n\t\treturns = returns[:-1]\n\t\tadvantages = returns - values\n\n\t\treturn returns, advantages","def _reward(self, i, rewards, reward=1):\n for j,a in enumerate(self.agents):\n if a.index==i or a.index==0:\n rewards[j]+=reward\n if self.zero_sum:\n if a.index!=i or a.index==0:\n rewards[j] -= reward","def discount_reward(r_dic, gamma):\n r = 0\n for i in range(len(r_dic) - 1, -1, -1):\n if r_dic[i] != 0:\n r = r_dic[i]\n else:\n r = r * gamma\n r_dic[i] = r\n r_dic = (r_dic - r_dic.mean()) / (r_dic.std() + 1e-8)\n return r_dic","def compute_reward(self, obs, action, state):\n pass","def get_rewards(self):\n return np.array(self.rewards)","def _compute_reward(self):\n last_score = self.episode_qualities[-2]\n new_score = self.episode_qualities[-1]\n reward = new_score - last_score\n return reward","def total_reward(self):\n return np.sum(self.rewards)","def test_calculate_returns(self):\n rewards = np.array([[0, 1, 0, 1],\n [0, 1, 0, 1],\n [0, 1, 1, 1],\n [1, 1, 0, 1]], dtype=np.float32) # pyformat:disable\n discount = 0.5\n masks = np.array([[1, 1, 1, 1],\n [1, 1, 1, 1],\n [1, 1, 1, 0],\n [1, 1, 0, 0]], dtype=np.bool) # pyformat: disable\n\n calculated_returns = rl_utils.compute_discounted_return(\n rewards, discount, masks)\n\n self.assertEqual(calculated_returns.shape, (4, 4))\n\n expected_returns = np.array([[0.125, 1.875, 0.25, 1.5],\n [0.25, 1.75, 0.5, 1.0],\n [0.5, 1.5, 1.0, 0.0],\n [1.0, 1.0, 0.0, 0.0]]) # pyformat: disable\n self.assertTrue(np.allclose(expected_returns, calculated_returns))","def accumulate_rewards(rewards, gamma):\n reversed_rewards = rewards[::-1] # list reversal\n acc = list(itertools.accumulate(reversed_rewards, lambda x, y: x*gamma + y))\n return np.array(acc[::-1])","def reward(self, observation, action, reward):\n self.counts[action] = self.counts[action] + 1\n n = self.counts[action]\n value = self.values[action]\n \n new_value = ((n - 1) / float(n)) * value + (1 / float(n)) * reward\n self.values[action] = new_value\n self.minmax = max(self.values) - min(self.values)\n \n \n pass","def compute_returns(rewards, gamma=1.0):\n returns = []\n time_step = len(rewards)\n for i in range(time_step):\n curr_ret = 0\n for j in range(i,time_step):\n curr_ret += rewards[j] * gamma**(j-i)\n returns.append(curr_ret)\n return returns","def cum_reward(self, reward_list):\n reward = 0.\n for rew in reward_list[::-1]:\n reward += rew * self.gamma\n return reward","def reward_function(self):\r\n def R(state, decision, nodes):\r\n return -1.0/1000*nodes['G'].get_preds_value(state)*(decision['G:R_1']+decision['G:L'])\r\n \r\n return R","def get_reward_function(self):\n R_fn = np.zeros(self.n_states)\n R_fn[0] = 1.0\n\n return R_fn","def reward_to_go(rews):\n n = len(rews)\n rtgs = np.zeros_like(rews)\n for i in reversed(range(n)):\n rtgs[i] = rews[i] + (rtgs[i+1] if i+1 < n else 0)\n return rtgs","def compute_intrinsic_reward(self, next_obs):\r\n next_obs = torch.tensor(next_obs, dtype=torch.float, device=self.device)\r\n #next_obs = torch.FloatTensor(next_obs).to(self.device)\r\n\r\n target_next_feature = self.rnd.target(next_obs)\r\n predict_next_feature = self.rnd.predictor(next_obs)\r\n intrinsic_reward = (target_next_feature - predict_next_feature).pow(2).mean(1) ### MSE --- Issues\r\n #intrinsic_reward = (target_next_feature - predict_next_feature).pow(2).sum(1) / 2\r\n\r\n return intrinsic_reward.data.cpu().numpy()","def compute_returns(rewards, gamma=1.0):\n n = 0\n G = []\n while n < len(rewards):\n G.append(0) \n for i in range(n,len(rewards)):\n G[n] += (gamma**(i-n))*rewards[i]\n n+=1\n\n return G","def _compute_reward(self): \n reward = -1\n return reward","def compute_advantage(self, traj_rewards, gamma, reward_to_go):\n disc_rew = []\n for k, rew in enumerate(traj_rewards):\n disc_rew.append(gamma ** k * rew)\n\n num_steps = len(traj_rewards)\n if not reward_to_go:\n return np.repeat(np.sum(disc_rew), num_steps)\n else:\n return np.cumsum(disc_rew[::-1])[::-1]","def _get_reward(self, normalized_state, normalized_unconstrained_action, normalized_constrained_action):\n denormalized_unconstrained_charge_rate_in_W = self.denormalize_network_output(normalized_unconstrained_action)\n denormalized_constrained_charge_rate_in_W = self.denormalize_network_output(normalized_constrained_action)\n denormalized_state = normalized_state * self.energy_system.stm_train_subsequent_states_stds + self.energy_system.stm_train_subsequent_states_means\n\n cost_of_net_drawn_electricity = self._get_cost_of_net_drawn_electricity_in_euros(denormalized_state, denormalized_constrained_charge_rate_in_W)\n charge_rate_punishment = self._get_punishment_for_excessive_charge_rate(denormalized_unconstrained_charge_rate_in_W)\n soc_punishment = self._get_punishment_for_impossible_resulting_soc(denormalized_state, denormalized_unconstrained_charge_rate_in_W) \n reward = - cost_of_net_drawn_electricity - charge_rate_punishment - soc_punishment\n #tf.summary.scalar('cost_of_net_drawn_electricity in euros', cost_of_net_drawn_electricity) \n #tf.summary.scalar('reward', reward)\n\n return reward, cost_of_net_drawn_electricity","def compute_returns(rewards, next_value=0.0, gamma=0.99, dones=None):\n if dones is not None:\n masks = 1 - dones\n else:\n masks = np.ones_like(rewards)\n R = next_value\n returns = []\n for step in reversed(range(len(rewards))):\n R = rewards[step] + gamma * R * masks[step]\n returns.insert(0, R)\n\n returns = np.array(returns)\n returns -= returns.mean()\n returns /= returns.std() if np.std(returns) > 0 else 1\n return returns","def reward(self, X, plays):\n return np.sum(X[plays]) # note that X[plays] is the bandit feedback","def calculate_return(list_of_reward, gamma):\n G = 0\n for r in reversed(list_of_reward):\n G = gamma * G + r\n\n return G","def expectations(rewards: np.ndarray,\n values: np.ndarray,\n next_is_terminal: np.ndarray,\n discount_factor=0.95,\n lam=0.95,\n bootstrap_value=0.0):\n\n #\n time_horizon = len(rewards)\n returns = np.zeros(time_horizon, np.float32)\n advantages = np.zeros(time_horizon, np.float32)\n delta = np.zeros(time_horizon, np.float32)\n\n if next_is_terminal[-1]:\n returns[-1] = rewards[-1]\n delta[-1] = rewards[-1] - values[-1]\n else:\n returns[-1] = rewards[-1] + discount_factor * bootstrap_value\n delta[-1] = rewards[-1] + discount_factor * bootstrap_value - values[-1]\n\n advantages[-1] = delta[-1]\n\n for t in reversed(range(time_horizon - 1)):\n if next_is_terminal[t]:\n returns[t] = rewards[t]\n delta[t] = rewards[t] - values[t]\n advantages[t] = delta[t]\n else:\n returns[t] = rewards[t] + discount_factor * returns[t + 1]\n delta[t] = rewards[t] + discount_factor * values[t + 1] - values[t]\n advantages[t] = delta[t] + discount_factor * lam * advantages[t + 1]\n\n return returns, advantages","def get_cumulative_rewards(rewards, # rewards at each step\r\n gamma=0.99 # discount for reward\r\n ):\r\n cumulative_rewards = []\r\n prev = 0\r\n\r\n for r in reversed(rewards):\r\n prev = r + gamma * prev\r\n cumulative_rewards.append(prev)\r\n cumulative_rewards.reverse()\r\n return cumulative_rewards","def _reward(self, a):\r\n\r\n xrel = self._body_coord()[0] - self.goal\r\n dist = np.sum(xrel ** 2)\r\n return (\r\n - self.cx * dist / (np.sqrt(dist) + 1) - self.cu * np.sum(a ** 2)\r\n )","def reward(self,\n state: float) -> float:\n raise NotImplementedError","def _compute_reward(self, observations, done):\n raise NotImplementedError()","def _compute_reward(self, observations, done):\n raise NotImplementedError()","def _compute_reward(self, observations, done):\n raise NotImplementedError()","def _compute_reward(self, observations, done):\n raise NotImplementedError()","def _compute_reward(self, observations, done):\n raise NotImplementedError()","def _compute_reward(self, observations, done):\n raise NotImplementedError()","def _build_discounted_n_step_rewards(self, gamma):\n discounts = [gamma**i for i in range(self.update_horizon)]\n discount_tensor = tf.constant(discounts)\n return tf.reduce_sum(self._replay.rewards * discount_tensor, axis=1)","def _get_reward(self):\n\n # compute reward depending on the radius and distance to target\n radius_reward = -(self.radius_sheep*0.9)/self.init_sheep_root\n target_reward = -(self.target_distance*0.9)/self.init_sheep_root\n\n reward = target_reward + radius_reward\n\n # ensure it is always an array\n if not type(reward) is np.ndarray:\n reward = np.array([reward])\n return reward[0]","def calculate_reward(self):\n if AT.REWARD not in self.attributes:\n return (0, 1)\n return self.attributes[AT.REWARD].calculate(self)","def _reward(self, action: Action) -> float:\n raise NotImplementedError","def _calculate_dense_rewards(self, desired_goal, achieved_goal):\n return np.array([]), None","def compute_reward(self, state, rl_actions, **kwargs):\n raise NotImplementedError","def get_reward(self, actions, next_states):\n r = []\n for state in next_states:\n ended, winner = self.judge_terminal(state)\n if ended:\n r.append(winner)\n else:\n r.append(0)\n return np.array(r)","def update(experience_buffer, returns):\n rewards = np.array(experience_buffer[2])\n discount_rewards = rewards * (FLAGS.GAMMA ** np.arange(len(rewards)))\n current_return = discount_rewards.sum()\n returns.append(current_return)\n returns = returns[-100:] # Get recent 100 returns.\n baseline = sum(returns) / len(returns) # Baseline is the average of 100 returns.\n sess.run(train_op, {observation_: experience_buffer[0],\n action_: experience_buffer[1],\n advantage_: current_return - baseline}) \n return returns"],"string":"[\n \"def discount_rewards(rewards):\\r\\n discounted_r = np.zeros_like(rewards)\\r\\n running_add = 0\\r\\n for t in reversed(range(0, len(rewards))): \\r\\n running_add = running_add * reward_discount + rewards[t]\\r\\n discounted_r[t] = running_add\\r\\n return discounted_r\",\n \"def discount_rewards(rewards, gamma):\\n reward_shape = rewards.shape\\n if len(reward_shape) == 1:\\n discounted_r = np.zeros(shape=(*reward_shape, 1), dtype=np.float)\\n else:\\n discounted_r = np.zeros(shape=reward_shape, dtype=np.float)\\n running_add = 0\\n\\n for t in reversed(range(0, rewards.size)):\\n running_add = running_add * gamma + rewards[t]\\n discounted_r[t] = running_add\\n\\n return discounted_r\",\n \"def discount_rewards(r):\\n\\tdiscounted_r = np.zeros_like(r)\\n\\trunning_add = 0\\n\\tfor t in reversed(range(0, r.size)):\\n\\t\\trunning_add = running_add * gamma + r[t]\\n\\t\\tdiscounted_r[t] = running_add\\n\\treturn np.array(discounted_r)\",\n \"def helper_discount_rewards(rewards, discount_rate):\\n discounted_rewards = np.zeros(len(rewards))\\n cumulative_rewards = 0\\n for step in reversed(range(len(rewards))):\\n cumulative_rewards = rewards[step] + cumulative_rewards * discount_rate\\n discounted_rewards[step] = cumulative_rewards\\n return discounted_rewards\",\n \"def discount_rewards(r):\\n\\tdiscounted_r = np.zeros_like(r)\\n\\trunning_add = 0\\n\\tfor t in reversed(xrange(0, r.size)):\\n\\t\\trunning_add = running_add * gamma + r[t]\\n\\t\\tdiscounted_r[t] = running_add\\n\\treturn discounted_r\",\n \"def compute_discounted_rewards(self, rewards):\\n discounted_rewards = []\\n for t in range(len(rewards)):\\n Gt = 0\\n pw = 0\\n for r in rewards[t:]:\\n Gt = Gt + self.gamma ** pw * r\\n pw = pw + 1\\n discounted_rewards.append(Gt)\\n\\n # Normalization of the discounted rewards\\n discounted_rewards = np.array(discounted_rewards)\\n discounted_rewards = (discounted_rewards - discounted_rewards.mean()) / (discounted_rewards.std() + 1e-10)\\n\\n return discounted_rewards\",\n \"def discount_rewards(r):\\r\\n discounted_r = np.zeros_like(r)\\r\\n running_add = 0\\r\\n for t in reversed(xrange(0, r.size)):\\r\\n running_add = running_add * .99 + r[t]\\r\\n discounted_r[t] = running_add \\r\\n return discounted_r\",\n \"def computed_discounted_rewards(rewards: list[float], gamma: float = 0.95) -> float:\\n discounted_reward = 0\\n for i, r in enumerate(rewards):\\n discounted_reward += gamma**i * r\\n return discounted_reward\",\n \"def discount(rewards):\\n sum_so_far = 0.0\\n rewards_so_far = []\\n for r in rewards[::-1]:\\n sum_so_far = sum_so_far * gamma_discount + r\\n rewards_so_far.append(sum_so_far)\\n return rewards_so_far[::-1]\",\n \"def discount_rewards(r):\\n discounted_r = np.zeros_like(r)\\n running_add = 0\\n for t in reversed(range(0, r.size)):\\n running_add = running_add * gamma + r[t]\\n discounted_r[t] = running_add\\n return discounted_r\",\n \"def discount_rewards(r):\\n discounted_r = np.zeros_like(r)\\n running_add = 0\\n for t in reversed(xrange(0, r.size)):\\n running_add = running_add * gamma_099 + r[t]\\n discounted_r[t] = running_add\\n return discounted_r\",\n \"def discount_rewards(r):\\n discounted_r = np.zeros_like(r)\\n running_add = 0\\n for t in reversed(xrange(0, r.size)):\\n running_add = running_add * gamma + r[t]\\n discounted_r[t] = running_add\\n return discounted_r\",\n \"def discount_rewards(r):\\r\\n discounted_r = np.zeros_like(r)\\r\\n running_add = 0\\r\\n for t in reversed(range(0, r.size)):\\r\\n running_add = running_add * gamma + r[t]\\r\\n discounted_r[t] = running_add\\r\\n return discounted_r\",\n \"def discount_rewards(self, r):\\n discounted_r = np.zeros_like(r)\\n running_add = 0\\n for t in reversed(range(0, r.size)):\\n if r[t] != 0:\\n running_add = 0 # Pong-specific\\n running_add = running_add * self.gamma + r[t]\\n discounted_r[t] = running_add\\n \\n #print(\\\"Mean reward before normalized: {}\\\".format(np.mean(discounted_r)))\\n mu = np.mean(discounted_r)\\n var = np.var(discounted_r)\\n discounted_r -= mu \\n discounted_r /= np.sqrt(var+1e-6)\\n return discounted_r\",\n \"def discount_rewards(r):\\n discounted_r = np.zeros_like(r)\\n running_add = 0\\n for t in reversed(range(0, r.size)):\\n if r[t] != 0: running_add = 0 # reset the sum, since this was a game boundary (pong specific!)\\n running_add = running_add * gamma + r[t]\\n discounted_r[t] = running_add\\n return discounted_r\",\n \"def discount_rewards(r):\\r\\n discounted_r = np.zeros_like(r)\\r\\n running_add = 0\\r\\n for t in reversed(xrange(0, r.size)):\\r\\n running_add = running_add * gamma + r[t]\\r\\n discounted_r[t] = running_add\\r\\n return discounted_r\",\n \"def discount_rewards_and_normalize(self, rewards):\\n discounted_rewards = np.empty(len(rewards))\\n cumulative_rewards = 0\\n\\n for step in reversed(range(len(rewards))):\\n cumulative_rewards = rewards[step] + cumulative_rewards * self.gamma\\n discounted_rewards[step] = cumulative_rewards\\n\\n reward_mean = discounted_rewards.mean()\\n reward_std = discounted_rewards.std()\\n\\n return [(reward - reward_mean) / reward_std\\n for reward in discounted_rewards]\",\n \"def discount_and_normalize_rewards(episode_rewards):\\n # Get empty array with the same size as the rewards array\\n discounted_episode_rewards = np.zeros_like(episode_rewards)\\n\\n # Variable that stores value of the discounted reward being calculated by the loop\\n current_reward = 0.0\\n # Loop that does the magic\\n for i in reversed(range(len(episode_rewards))):\\n # Calculate the discounted reward\\n current_reward = current_reward * gamma + episode_rewards[i]\\n # Store it in the array\\n discounted_episode_rewards[i] = current_reward\\n\\n # Normalize.\\n mean = np.mean(discounted_episode_rewards)\\n std = np.std(discounted_episode_rewards)\\n discounted_episode_rewards = (discounted_episode_rewards - mean) / (std)\\n\\n return discounted_episode_rewards\",\n \"def loss_rewards(r):\\n discounted_r = np.zeros_like(r)\\n running_add = 0\\n for t in reversed(range(0, r.size)):\\n #running_add = running_add * gamma + r[t]\\n #discounted_r[t] = running_add\\n discounted_r[t] = 0\\n return discounted_r\",\n \"def discount_rewards(r):\\n discounted_r = np.zeros_like(r)\\n running_add = 0\\n for t in reversed(xrange(0, r.size)):\\n if r[t] != 0: running_add = 0 # reset the sum, since this was a game boundary (pong specific)\\n running_add = running_add * gamma + r[t]\\n discounted_r[t] = running_add\\n return discounted_r\",\n \"def discounted_reward(self, discount):\\n\\n tl = len(self)\\n return (1 - discount) * np.sum(discount ** np.arange(tl) * self.rewards)\",\n \"def discount(rewards, discount_factor=.99):\\n # Compute discounted rewards (trust me this works and hopefully it's super fast)\\n timesteps = len(rewards) # make into matrix\\n rewards = tf.convert_to_tensor([rewards],dtype=tf.float32)\\n # create lower triangular matrix of discount_factor weights\\n T = tf.convert_to_tensor([[max(1+i-j,0) for j in range(timesteps)] for i in range(timesteps)],dtype=tf.float32)\\n T = tf.math.pow(discount_factor, T)\\n T = tf.linalg.band_part(T, -1, 0)\\n # apply discount factor\\n return tf.matmul(rewards, T)\",\n \"def discount_reward(reward, gamma):\\n discount_r = np.zeros_like(reward)\\n r_total = 0\\n for _ in reversed(range(0, reward.size)):\\n if reward[_] != 0:\\n r_total = 0\\n r_total = r_total * gamma + reward[_]\\n discount_r[_] = r_total\\n return discount_r\",\n \"def discount_and_normalize_rewards(self, episode_rewards):\\n # Get empty array with the same size as the rewards array\\n discounted_episode_rewards = np.zeros_like(episode_rewards)\\n\\n # Variable that stores value of the discounted reward being calculated by the loop\\n current_reward = 0.0\\n # Loop that does the magic\\n for i in reversed(range(len(episode_rewards))):\\n # Calculate the discounted reward\\n current_reward = current_reward * gamma + episode_rewards[i]\\n # Store it in the array\\n discounted_episode_rewards[i] = current_reward\\n\\n # Normalize.\\n mean = np.mean(discounted_episode_rewards)\\n std = np.std(discounted_episode_rewards)\\n discounted_episode_rewards = (discounted_episode_rewards - mean) / (std)\\n\\n return discounted_episode_rewards\",\n \"def discount_rewards(r, gamma):\\n #print(\\\"r\\\", r)\\n discounted_r = np.zeros_like(r)\\n running_add = 0\\n for t in reversed(xrange(0, r.size)):\\n #print(\\\"t\\\", t)\\n running_add = running_add * gamma + r[t]\\n discounted_r[t] = running_add\\n return discounted_r\",\n \"def discount(self, rewards, dones, gamma):\\n discounted = []\\n ret = 0\\n for reward, done in zip(rewards[::-1], dones[::-1]):\\n ret = reward + gamma * ret * (1. - done)\\n discounted.append(ret)\\n return discounted[::-1]\",\n \"def discounted_rewards(r, gamma):\\n discounted_r = np.zeros_like(r)\\n running_sum = 1\\n for i in reversed(range(0,len(r))):\\n discounted_r[i] = running_sum * r[i]\\n running_sum = discounted_r[i]\\n return list(discounted_r)\",\n \"def discount_and_normalize_rewards(all_rewards, discount_rate):\\n all_discounted_rewards = []\\n for rewards in all_rewards:\\n all_discounted_rewards.append(helper_discount_rewards(rewards, discount_rate))\\n\\n flat_rewards = np.concatenate(all_discounted_rewards)\\n reward_mean = flat_rewards.mean()\\n reward_std = flat_rewards.std()\\n return [(discounted_rewards - reward_mean) / reward_std for discounted_rewards in all_discounted_rewards]\",\n \"def discount_rewards(self, r, gamma):\\n discounted_r = np.zeros_like(r)\\n running_add = 0\\n for t in reversed(range(0, r.size)):\\n if r[t] != 0:\\n running_add = 0 # reset the sum, since this was a game boundary (pong specific!)\\n running_add = running_add * gamma + r[t]\\n discounted_r[t] = running_add\\n return np.array(discounted_r, dtype=np.float64)\",\n \"def discount_rewards(r, gamma):\\n discounted_r = np.zeros_like(r)\\n running_add = 0\\n for t in reversed(range(0, r.size)):\\n running_add = running_add * gamma + r [t]\\n discounted_r [t] = running_add\\n return discounted_r\",\n \"def _discount_rewards(self, non_discounted_rewards):\\n discounted_rewards = [0.0] * len(non_discounted_rewards)\\n total_rewards = 0\\n for t in reversed(range(len(non_discounted_rewards))):\\n total_rewards = total_rewards * self.discount_factor + non_discounted_rewards[t]\\n discounted_rewards[t] = total_rewards\\n return discounted_rewards\",\n \"def get_cumulative_rewards(rewards, # rewards at each step\\n gamma=0.99 # discount for reward\\n ):\\n\\n cumulative_rewards = np.empty_like(rewards)\\n cumulative_rewards = cumulative_rewards.astype(float)\\n cumulative_rewards[-1] = rewards[-1]\\n\\n for index in range(len(rewards) - 2, -1, -1):\\n discount = cumulative_rewards[index + 1] * gamma\\n reward = rewards[index]\\n cumulative_rewards[index] = discount + reward\\n\\n return cumulative_rewards # <array of cumulative rewards>\",\n \"def discount_rewards(r, gamma=0.99):\\n discounted_r = np.zeros_like(r)\\n running_add = 0\\n for t in reversed(xrange(0, r.size)):\\n if r[t] != 0: running_add = 0 # reset the sum, since this was a game boundary (pong specific!)\\n running_add = running_add * gamma + r[t]\\n discounted_r[t] = running_add\\n return discounted_r\",\n \"def discount_rewards(rwd, gamma=0.99):\\n discounted_r = np.zeros_like(rwd)\\n running_add = 0\\n for t in range(len(rwd)-1, -1, -1):\\n running_add = running_add * gamma + rwd[t]\\n discounted_r[t] = running_add\\n\\n return discounted_r\",\n \"def discount_rewards(self, r, epEnd):\\n res = np.zeros_like(r)\\n running_add = 0.0\\n assert (len(r)-1) in epEnd, 'simple sanity check: the last reward must be the episode end'\\n assert isinstance(r, list)\\n for t in reversed(xrange(len(r))):\\n if t in epEnd: running_add = 0.0 # reset \\n running_add = GAMMA * running_add + r[t]\\n res[t] = running_add\\n return res\",\n \"def _discounted_cumsum(self, rewards, rate=None):\\n # HINT1: note that each entry of the output should now be unique,\\n # because the summation happens over [t, T] instead of [0, T]\\n # HINT2: it is possible to write a vectorized solution, but a solution\\n # using a for loop is also fine\\n rate = self.gamma if rate is None else rate\\n\\n rewards = np.array(rewards)\\n disounted_return = list(\\n accumulate(rewards[::-1], lambda ret, rew: rate * ret + rew))\\n disounted_return = np.array(disounted_return)[::-1]\\n return disounted_return\",\n \"def discount_with_dones(rewards, dones, gamma):\\n discounted = []\\n r = 0\\n\\n # discounted rewards are calculated on the reversed reward list.\\n # that returns are calculated in descending order is easy to\\n # overlook in the original pseudocode.\\n # when writing down an example of the pseudocode, it is clear, that\\n # r_t + gamma * V(s_tp1) is calculated for each list element and\\n # this is also what is done here.\\n for reward, done in zip(rewards[::-1], dones[::-1]):\\n r = reward + gamma*r*(1.-done)\\n discounted.append(r)\\n return discounted[::-1]\",\n \"def reward(input):\\n state = np.array([input[0], input[1]])\\n action = input[2]\\n action = np.clip(action, -2.0, 2.0)\\n costs = angle_normalize(state[0])**2 + .1 * state[1]**2 + .001 * (action**2)\\n\\n return - costs\",\n \"def reward_calc(self, reward_traj,V,V_end):\\n r_all = np.concatenate((reward_traj,[V_end]),-1)\\n V_all = V #np.concatenate((V,[V_end]),-1)\\n delta = r_all[:-1] + self.gamma * V_all[1:] - V_all[:-1]\\n \\n adv = Generalized_Adv_Estimator.discounted_sum(delta,self.gamma*self.lam)\\n rtg = adv + V_all[:-1]\\n\\n adv = adv.astype('float32')\\n rtg = rtg.astype('float32')\\n\\n return adv, rtg\",\n \"def reward_amt(value, reward_vec, adj, softmax_inv_temp, discount, start_prob=None):\\n n = len(reward_vec)\\n softmax_value = np.exp(softmax_inv_temp * value) / np.sum(np.exp(softmax_inv_temp*value))\\n policy = adj * softmax_value.reshape(1, -1)\\n policy = util.l1_normalize_rows(policy)\\n sr = np.linalg.pinv(np.eye(n) - discount * policy)\\n value = np.dot(sr, reward_vec.reshape(-1, 1)).reshape(-1)\\n if start_prob is None:\\n start_prob = np.ones(n)*1. / n\\n else:\\n start_prob = start_prob.reshape(n)\\n return np.sum(value * start_prob)\",\n \"def discount_rewards(r, gamma=0.99, value_next=0.0):\\n discounted_r = np.zeros_like(r)\\n running_add = value_next\\n for t in reversed(range(0, r.size)):\\n running_add = running_add * gamma + r[t]\\n discounted_r[t] = running_add\\n return discounted_r\",\n \"def discount_rewards(r, gamma=0.99, value_next=0.0):\\n discounted_r = np.zeros_like(r)\\n running_add = value_next\\n for t in reversed(range(0, r.size)):\\n running_add = running_add * gamma + r[t]\\n discounted_r[t] = running_add\\n return discounted_r\",\n \"def _compute_reward(self):\\n reward = 0.0\\n return reward\",\n \"def average_reward(self):\\n T = len(self)\\n return np.sum(self.rewards / T)\",\n \"def compute_reward(self, done: np.ndarray) -> np.ndarray:\\n rewards = [0.0 for i in range(self.agents)]\\n rewarded_fields = defaultdict(float)\\n if done[0][0]:\\n # Get rewards for all rew fields with agents on\\n for agent in range(self.agents):\\n pos = self.game.get_agent_pos(agent)\\n if pos in self.payoff_fields:\\n rewarded_fields[pos] += 1.0\\n\\n for agent in range(self.agents):\\n pos = self.game.get_agent_pos(agent)\\n rewards[agent] += rewarded_fields.get(pos, 0.0)\\n if agent < self.num_informed and pos in self.special_payoff_fields:\\n rewards[agent] *= 2\\n\\n rewards = np.array(rewards).reshape(1, -1)\\n return rewards\",\n \"def _dense_reward(self) -> float:\\n y = 1\\n target_goal_dists = []\\n for target_shape in self.__debris_shapes:\\n target_pos = target_shape.shape_body.position\\n goal_pos = (target_pos[0], y) # Top of screen.\\n dist = np.linalg.norm(target_pos - goal_pos)\\n if target_pos[1] > 0.88:\\n dist = 0\\n target_goal_dists.append(dist)\\n target_goal_dists = np.mean(target_goal_dists)\\n return -1.0 * target_goal_dists\",\n \"def _compute_reward_(self):\\n if self._target_type == \\\"position\\\":\\n dist = np.linalg.norm(self._target_diff_, ord=2)\\n if self._reward_type == \\\"linear\\\":\\n reward_dist = -dist\\n elif self._reward_type == \\\"precision\\\":\\n reward_dist = -dist +\\\\\\n np.exp( -dist**2 / 0.01)\\n elif self._reward_type == \\\"sparse\\\":\\n if dist < 0.05:\\n reward_dist = 0\\n else:\\n reward_dist = -0.1\\n\\n elif self._target_type == \\\"angle\\\":\\n dist = np.linalg.norm(self._target_diff_, ord=1)\\n if self._reward_type == \\\"linear\\\":\\n reward_dist = -dist\\n elif self._reward_type == \\\"precision\\\":\\n reward_dist = -dist +\\\\\\n np.exp(-dist ** 2 / 0.01)\\n elif self._reward_type == \\\"sparse\\\":\\n raise NotImplementedError\\n\\n # TODO: doublecheck whether '0' or '-1' should be used as the index\\n reward_vel = -self._vel_penalty * np.square(self._qd_[-1, self._joint_indices]).sum()\\n\\n #self.info['reward_dist'] = reward_dist\\n #self.info['reward_vel'] = reward_vel\\n\\n return (reward_dist + reward_vel) * self._dt / 0.008\",\n \"def loss(self, states, actions, next_states, rewards, discount_rate=.99):\\n # TODO: implement this\\n actions = tf.cast(actions, tf.int64)\\n a = tf.stack([tf.range(states.shape[0],dtype=tf.int64), actions], axis=1)\\n qVals = tf.gather_nd(self.call(states), a) # [batch_size] q-values for each action\\n nextVals = tf.reduce_max(self.call(next_states), axis=1) # max of q-values [batch_size, num_actions] across num_actions\\n targetVals = rewards + (discount_rate*nextVals)\\n loss = tf.reduce_sum(tf.math.square(qVals - targetVals))\\n return loss\",\n \"def reward(self, reward):\\n return float(np.sign(reward))\",\n \"def get_reward(self, dags, entropies,inputs,targets):\\n if not isinstance(entropies, np.ndarray):\\n entropies = entropies.data.cpu().numpy()\\n\\n score=self.get_score(inputs,targets,dags)\\n #score=1-self.get_loss(inputs,targets,dags)\\n print(score.item())\\n R = utils.to_item(score.data)\\n\\n if self.args.entropy_mode == 'reward':\\n rewards = R + self.args.entropy_coeff * entropies.mean()\\n elif self.args.entropy_mode == 'regularizer':\\n rewards = R * np.ones_like(entropies)\\n else:\\n raise NotImplementedError(f'Unkown entropy mode: {self.args.entropy_mode}')\\n\\n return rewards\",\n \"def reduce_rewards(self,rewards):\\n reduced_rewards = np.zeros((len(rewards),1))\\n gamma_multipliers = np.zeros((len(rewards),1))\\n \\n for i in range(len(rewards)):\\n gammas = [GAMMA**t for t in range(len(rewards[i]))]\\n \\n reduced_rewards[i][0] = np.sum(np.multiply(gammas, rewards[i]))\\n gamma_multipliers[i][0] = GAMMA**(len(rewards[i]))\\n \\n return reduced_rewards, gamma_multipliers\",\n \"def reward(self, reward):\\r\\n return np.sign(reward)\",\n \"def get_reward(self, observations, actions):\\n\\n #initialize and reshape as needed, for batch mode\\n self.reward_dict = {}\\n if(len(observations.shape)==1):\\n observations = np.expand_dims(observations, axis = 0)\\n actions = np.expand_dims(actions, axis = 0)\\n batch_mode = False\\n else:\\n batch_mode = True\\n\\n #get vars\\n xvel = observations[:, 9].copy()\\n body_angle = observations[:, 2].copy()\\n front_leg = observations[:, 6].copy()\\n front_shin = observations[:, 7].copy()\\n front_foot = observations[:, 8].copy()\\n zeros = np.zeros((observations.shape[0],)).copy()\\n\\n # ranges\\n leg_range = 0.2\\n shin_range = 0\\n foot_range = 0\\n penalty_factor = 10\\n\\n #calc rew\\n self.reward_dict['run'] = xvel\\n\\n front_leg_rew = zeros.copy()\\n front_leg_rew[front_leg>leg_range] = -penalty_factor\\n self.reward_dict['leg'] = front_leg_rew\\n\\n front_shin_rew = zeros.copy()\\n front_shin_rew[front_shin>shin_range] = -penalty_factor\\n self.reward_dict['shin'] = front_shin_rew\\n\\n front_foot_rew = zeros.copy()\\n front_foot_rew[front_foot>foot_range] = -penalty_factor\\n self.reward_dict['foot'] = front_foot_rew\\n\\n # total reward\\n self.reward_dict['r_total'] = self.reward_dict['run'] + self.reward_dict['leg'] + self.reward_dict['shin'] + self.reward_dict['foot']\\n\\n #return\\n dones = zeros.copy()\\n if(not batch_mode):\\n return self.reward_dict['r_total'][0], dones[0]\\n return self.reward_dict['r_total'], dones\",\n \"def discount_rewards(self,x, gamma):\\n result = [0 for i in range(len(x))]\\n element = 0\\n for i in range(len(x)-1, -1, -1): #-2\\n element = x[i] + gamma * element\\n result[i] = element\\n\\n return result\",\n \"def reward(self, reward):\\n return np.sign(reward)\",\n \"def reward(self, reward):\\n return np.sign(reward)\",\n \"def reward(self, reward):\\n return np.sign(reward)\",\n \"def reward(self, reward):\\n return np.sign(reward)\",\n \"def reward(self, reward):\\n return np.sign(reward)\",\n \"def _returns_advantages(self, rewards, dones, values, next_value):\\n\\t\\treturns = np.append(np.zeros_like(rewards), next_value, axis=-1)\\n\\t\\t\\n\\t\\tfor t in reversed(range(rewards.shape[0])):\\n\\t\\t\\treturns[t] = rewards[t] + self.gamma * returns[t + 1] * (1 - dones[t])\\n\\t\\t\\n\\t\\treturns = returns[:-1]\\n\\t\\tadvantages = returns - values\\n\\n\\t\\treturn returns, advantages\",\n \"def _reward(self, i, rewards, reward=1):\\n for j,a in enumerate(self.agents):\\n if a.index==i or a.index==0:\\n rewards[j]+=reward\\n if self.zero_sum:\\n if a.index!=i or a.index==0:\\n rewards[j] -= reward\",\n \"def discount_reward(r_dic, gamma):\\n r = 0\\n for i in range(len(r_dic) - 1, -1, -1):\\n if r_dic[i] != 0:\\n r = r_dic[i]\\n else:\\n r = r * gamma\\n r_dic[i] = r\\n r_dic = (r_dic - r_dic.mean()) / (r_dic.std() + 1e-8)\\n return r_dic\",\n \"def compute_reward(self, obs, action, state):\\n pass\",\n \"def get_rewards(self):\\n return np.array(self.rewards)\",\n \"def _compute_reward(self):\\n last_score = self.episode_qualities[-2]\\n new_score = self.episode_qualities[-1]\\n reward = new_score - last_score\\n return reward\",\n \"def total_reward(self):\\n return np.sum(self.rewards)\",\n \"def test_calculate_returns(self):\\n rewards = np.array([[0, 1, 0, 1],\\n [0, 1, 0, 1],\\n [0, 1, 1, 1],\\n [1, 1, 0, 1]], dtype=np.float32) # pyformat:disable\\n discount = 0.5\\n masks = np.array([[1, 1, 1, 1],\\n [1, 1, 1, 1],\\n [1, 1, 1, 0],\\n [1, 1, 0, 0]], dtype=np.bool) # pyformat: disable\\n\\n calculated_returns = rl_utils.compute_discounted_return(\\n rewards, discount, masks)\\n\\n self.assertEqual(calculated_returns.shape, (4, 4))\\n\\n expected_returns = np.array([[0.125, 1.875, 0.25, 1.5],\\n [0.25, 1.75, 0.5, 1.0],\\n [0.5, 1.5, 1.0, 0.0],\\n [1.0, 1.0, 0.0, 0.0]]) # pyformat: disable\\n self.assertTrue(np.allclose(expected_returns, calculated_returns))\",\n \"def accumulate_rewards(rewards, gamma):\\n reversed_rewards = rewards[::-1] # list reversal\\n acc = list(itertools.accumulate(reversed_rewards, lambda x, y: x*gamma + y))\\n return np.array(acc[::-1])\",\n \"def reward(self, observation, action, reward):\\n self.counts[action] = self.counts[action] + 1\\n n = self.counts[action]\\n value = self.values[action]\\n \\n new_value = ((n - 1) / float(n)) * value + (1 / float(n)) * reward\\n self.values[action] = new_value\\n self.minmax = max(self.values) - min(self.values)\\n \\n \\n pass\",\n \"def compute_returns(rewards, gamma=1.0):\\n returns = []\\n time_step = len(rewards)\\n for i in range(time_step):\\n curr_ret = 0\\n for j in range(i,time_step):\\n curr_ret += rewards[j] * gamma**(j-i)\\n returns.append(curr_ret)\\n return returns\",\n \"def cum_reward(self, reward_list):\\n reward = 0.\\n for rew in reward_list[::-1]:\\n reward += rew * self.gamma\\n return reward\",\n \"def reward_function(self):\\r\\n def R(state, decision, nodes):\\r\\n return -1.0/1000*nodes['G'].get_preds_value(state)*(decision['G:R_1']+decision['G:L'])\\r\\n \\r\\n return R\",\n \"def get_reward_function(self):\\n R_fn = np.zeros(self.n_states)\\n R_fn[0] = 1.0\\n\\n return R_fn\",\n \"def reward_to_go(rews):\\n n = len(rews)\\n rtgs = np.zeros_like(rews)\\n for i in reversed(range(n)):\\n rtgs[i] = rews[i] + (rtgs[i+1] if i+1 < n else 0)\\n return rtgs\",\n \"def compute_intrinsic_reward(self, next_obs):\\r\\n next_obs = torch.tensor(next_obs, dtype=torch.float, device=self.device)\\r\\n #next_obs = torch.FloatTensor(next_obs).to(self.device)\\r\\n\\r\\n target_next_feature = self.rnd.target(next_obs)\\r\\n predict_next_feature = self.rnd.predictor(next_obs)\\r\\n intrinsic_reward = (target_next_feature - predict_next_feature).pow(2).mean(1) ### MSE --- Issues\\r\\n #intrinsic_reward = (target_next_feature - predict_next_feature).pow(2).sum(1) / 2\\r\\n\\r\\n return intrinsic_reward.data.cpu().numpy()\",\n \"def compute_returns(rewards, gamma=1.0):\\n n = 0\\n G = []\\n while n < len(rewards):\\n G.append(0) \\n for i in range(n,len(rewards)):\\n G[n] += (gamma**(i-n))*rewards[i]\\n n+=1\\n\\n return G\",\n \"def _compute_reward(self): \\n reward = -1\\n return reward\",\n \"def compute_advantage(self, traj_rewards, gamma, reward_to_go):\\n disc_rew = []\\n for k, rew in enumerate(traj_rewards):\\n disc_rew.append(gamma ** k * rew)\\n\\n num_steps = len(traj_rewards)\\n if not reward_to_go:\\n return np.repeat(np.sum(disc_rew), num_steps)\\n else:\\n return np.cumsum(disc_rew[::-1])[::-1]\",\n \"def _get_reward(self, normalized_state, normalized_unconstrained_action, normalized_constrained_action):\\n denormalized_unconstrained_charge_rate_in_W = self.denormalize_network_output(normalized_unconstrained_action)\\n denormalized_constrained_charge_rate_in_W = self.denormalize_network_output(normalized_constrained_action)\\n denormalized_state = normalized_state * self.energy_system.stm_train_subsequent_states_stds + self.energy_system.stm_train_subsequent_states_means\\n\\n cost_of_net_drawn_electricity = self._get_cost_of_net_drawn_electricity_in_euros(denormalized_state, denormalized_constrained_charge_rate_in_W)\\n charge_rate_punishment = self._get_punishment_for_excessive_charge_rate(denormalized_unconstrained_charge_rate_in_W)\\n soc_punishment = self._get_punishment_for_impossible_resulting_soc(denormalized_state, denormalized_unconstrained_charge_rate_in_W) \\n reward = - cost_of_net_drawn_electricity - charge_rate_punishment - soc_punishment\\n #tf.summary.scalar('cost_of_net_drawn_electricity in euros', cost_of_net_drawn_electricity) \\n #tf.summary.scalar('reward', reward)\\n\\n return reward, cost_of_net_drawn_electricity\",\n \"def compute_returns(rewards, next_value=0.0, gamma=0.99, dones=None):\\n if dones is not None:\\n masks = 1 - dones\\n else:\\n masks = np.ones_like(rewards)\\n R = next_value\\n returns = []\\n for step in reversed(range(len(rewards))):\\n R = rewards[step] + gamma * R * masks[step]\\n returns.insert(0, R)\\n\\n returns = np.array(returns)\\n returns -= returns.mean()\\n returns /= returns.std() if np.std(returns) > 0 else 1\\n return returns\",\n \"def reward(self, X, plays):\\n return np.sum(X[plays]) # note that X[plays] is the bandit feedback\",\n \"def calculate_return(list_of_reward, gamma):\\n G = 0\\n for r in reversed(list_of_reward):\\n G = gamma * G + r\\n\\n return G\",\n \"def expectations(rewards: np.ndarray,\\n values: np.ndarray,\\n next_is_terminal: np.ndarray,\\n discount_factor=0.95,\\n lam=0.95,\\n bootstrap_value=0.0):\\n\\n #\\n time_horizon = len(rewards)\\n returns = np.zeros(time_horizon, np.float32)\\n advantages = np.zeros(time_horizon, np.float32)\\n delta = np.zeros(time_horizon, np.float32)\\n\\n if next_is_terminal[-1]:\\n returns[-1] = rewards[-1]\\n delta[-1] = rewards[-1] - values[-1]\\n else:\\n returns[-1] = rewards[-1] + discount_factor * bootstrap_value\\n delta[-1] = rewards[-1] + discount_factor * bootstrap_value - values[-1]\\n\\n advantages[-1] = delta[-1]\\n\\n for t in reversed(range(time_horizon - 1)):\\n if next_is_terminal[t]:\\n returns[t] = rewards[t]\\n delta[t] = rewards[t] - values[t]\\n advantages[t] = delta[t]\\n else:\\n returns[t] = rewards[t] + discount_factor * returns[t + 1]\\n delta[t] = rewards[t] + discount_factor * values[t + 1] - values[t]\\n advantages[t] = delta[t] + discount_factor * lam * advantages[t + 1]\\n\\n return returns, advantages\",\n \"def get_cumulative_rewards(rewards, # rewards at each step\\r\\n gamma=0.99 # discount for reward\\r\\n ):\\r\\n cumulative_rewards = []\\r\\n prev = 0\\r\\n\\r\\n for r in reversed(rewards):\\r\\n prev = r + gamma * prev\\r\\n cumulative_rewards.append(prev)\\r\\n cumulative_rewards.reverse()\\r\\n return cumulative_rewards\",\n \"def _reward(self, a):\\r\\n\\r\\n xrel = self._body_coord()[0] - self.goal\\r\\n dist = np.sum(xrel ** 2)\\r\\n return (\\r\\n - self.cx * dist / (np.sqrt(dist) + 1) - self.cu * np.sum(a ** 2)\\r\\n )\",\n \"def reward(self,\\n state: float) -> float:\\n raise NotImplementedError\",\n \"def _compute_reward(self, observations, done):\\n raise NotImplementedError()\",\n \"def _compute_reward(self, observations, done):\\n raise NotImplementedError()\",\n \"def _compute_reward(self, observations, done):\\n raise NotImplementedError()\",\n \"def _compute_reward(self, observations, done):\\n raise NotImplementedError()\",\n \"def _compute_reward(self, observations, done):\\n raise NotImplementedError()\",\n \"def _compute_reward(self, observations, done):\\n raise NotImplementedError()\",\n \"def _build_discounted_n_step_rewards(self, gamma):\\n discounts = [gamma**i for i in range(self.update_horizon)]\\n discount_tensor = tf.constant(discounts)\\n return tf.reduce_sum(self._replay.rewards * discount_tensor, axis=1)\",\n \"def _get_reward(self):\\n\\n # compute reward depending on the radius and distance to target\\n radius_reward = -(self.radius_sheep*0.9)/self.init_sheep_root\\n target_reward = -(self.target_distance*0.9)/self.init_sheep_root\\n\\n reward = target_reward + radius_reward\\n\\n # ensure it is always an array\\n if not type(reward) is np.ndarray:\\n reward = np.array([reward])\\n return reward[0]\",\n \"def calculate_reward(self):\\n if AT.REWARD not in self.attributes:\\n return (0, 1)\\n return self.attributes[AT.REWARD].calculate(self)\",\n \"def _reward(self, action: Action) -> float:\\n raise NotImplementedError\",\n \"def _calculate_dense_rewards(self, desired_goal, achieved_goal):\\n return np.array([]), None\",\n \"def compute_reward(self, state, rl_actions, **kwargs):\\n raise NotImplementedError\",\n \"def get_reward(self, actions, next_states):\\n r = []\\n for state in next_states:\\n ended, winner = self.judge_terminal(state)\\n if ended:\\n r.append(winner)\\n else:\\n r.append(0)\\n return np.array(r)\",\n \"def update(experience_buffer, returns):\\n rewards = np.array(experience_buffer[2])\\n discount_rewards = rewards * (FLAGS.GAMMA ** np.arange(len(rewards)))\\n current_return = discount_rewards.sum()\\n returns.append(current_return)\\n returns = returns[-100:] # Get recent 100 returns.\\n baseline = sum(returns) / len(returns) # Baseline is the average of 100 returns.\\n sess.run(train_op, {observation_: experience_buffer[0],\\n action_: experience_buffer[1],\\n advantage_: current_return - baseline}) \\n return returns\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8100923","0.78397125","0.7792625","0.7742053","0.7653779","0.75987715","0.7583268","0.75820553","0.7579539","0.7519005","0.75047183","0.7467692","0.7456702","0.7433794","0.7426565","0.74062765","0.73950297","0.7370545","0.73564714","0.7328519","0.72018427","0.7201784","0.71919143","0.70810694","0.703724","0.701953","0.699616","0.6977477","0.69414437","0.68896544","0.6853656","0.6798218","0.67952156","0.67714196","0.66732097","0.6651138","0.6589912","0.6545616","0.64838254","0.6442654","0.644177","0.644177","0.64416486","0.64413583","0.64091456","0.6403475","0.63985944","0.6387154","0.6366384","0.6338549","0.63202435","0.6315961","0.621607","0.6207653","0.6204095","0.6204095","0.6204095","0.6204095","0.6204095","0.618831","0.61564976","0.6152739","0.61422485","0.6138495","0.6127187","0.609262","0.6088548","0.6085296","0.6058265","0.6054436","0.60530174","0.6043095","0.6034321","0.60339457","0.6032641","0.6029406","0.60016","0.60012555","0.59916455","0.59905344","0.59896535","0.5983475","0.597715","0.59710884","0.59694725","0.59581375","0.59563076","0.59563076","0.59563076","0.59563076","0.59563076","0.59563076","0.59522146","0.59508675","0.5948057","0.5941405","0.5929282","0.591477","0.59130543","0.5912898"],"string":"[\n \"0.8100923\",\n \"0.78397125\",\n \"0.7792625\",\n \"0.7742053\",\n \"0.7653779\",\n \"0.75987715\",\n \"0.7583268\",\n \"0.75820553\",\n \"0.7579539\",\n \"0.7519005\",\n \"0.75047183\",\n \"0.7467692\",\n \"0.7456702\",\n \"0.7433794\",\n \"0.7426565\",\n \"0.74062765\",\n \"0.73950297\",\n \"0.7370545\",\n \"0.73564714\",\n \"0.7328519\",\n \"0.72018427\",\n \"0.7201784\",\n \"0.71919143\",\n \"0.70810694\",\n \"0.703724\",\n \"0.701953\",\n \"0.699616\",\n \"0.6977477\",\n \"0.69414437\",\n \"0.68896544\",\n \"0.6853656\",\n \"0.6798218\",\n \"0.67952156\",\n \"0.67714196\",\n \"0.66732097\",\n \"0.6651138\",\n \"0.6589912\",\n \"0.6545616\",\n \"0.64838254\",\n \"0.6442654\",\n \"0.644177\",\n \"0.644177\",\n \"0.64416486\",\n \"0.64413583\",\n \"0.64091456\",\n \"0.6403475\",\n \"0.63985944\",\n \"0.6387154\",\n \"0.6366384\",\n \"0.6338549\",\n \"0.63202435\",\n \"0.6315961\",\n \"0.621607\",\n \"0.6207653\",\n \"0.6204095\",\n \"0.6204095\",\n \"0.6204095\",\n \"0.6204095\",\n \"0.6204095\",\n \"0.618831\",\n \"0.61564976\",\n \"0.6152739\",\n \"0.61422485\",\n \"0.6138495\",\n \"0.6127187\",\n \"0.609262\",\n \"0.6088548\",\n \"0.6085296\",\n \"0.6058265\",\n \"0.6054436\",\n \"0.60530174\",\n \"0.6043095\",\n \"0.6034321\",\n \"0.60339457\",\n \"0.6032641\",\n \"0.6029406\",\n \"0.60016\",\n \"0.60012555\",\n \"0.59916455\",\n \"0.59905344\",\n \"0.59896535\",\n \"0.5983475\",\n \"0.597715\",\n \"0.59710884\",\n \"0.59694725\",\n \"0.59581375\",\n \"0.59563076\",\n \"0.59563076\",\n \"0.59563076\",\n \"0.59563076\",\n \"0.59563076\",\n \"0.59563076\",\n \"0.59522146\",\n \"0.59508675\",\n \"0.5948057\",\n \"0.5941405\",\n \"0.5929282\",\n \"0.591477\",\n \"0.59130543\",\n \"0.5912898\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7536054"},"document_rank":{"kind":"string","value":"9"}}},{"rowIdx":81179,"cells":{"query":{"kind":"string","value":"Generate a function which converts a z and step value to an RGB colour, based on three predefined ratios."},"document":{"kind":"string","value":"def get_colour(self, r1, r2, r3, b1, b2, b3):\n\n def colour(z, i):\n \"\"\"\n Gets the colour of a z and step value.\n\n :param complex z: the z value from the mandelbrot set\n :param int i: the step value\n\n :rtype: tuple\n :return: the three RGB colours\n \"\"\"\n if abs(z) < self.threshold:\n return self.background\n v = np.log2(i + self.threshold - np.log2(np.log2(abs(z)))) / self.threshold\n if v < 1.0:\n return v ** b1, v ** b2, v ** b3 # background\n else:\n v = max(0, 2 - v)\n return v ** r1, v ** r2, v ** r3 # main tones\n\n return colour"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def rgbcolor(h, f):\n # q = 1 - f\n # t = f\n if h == 0:\n return v, f, p\n elif h == 1:\n return 1 - f, v, p\n elif h == 2:\n return p, v, f\n elif h == 3:\n return p, 1 - f, v\n elif h == 4:\n return f, p, v\n elif h == 5:\n return v, p, 1 - f","def get_inner_colour(self, r1, r2, r3, b1, b2, b3):\n\n def colour(z, i):\n \"\"\"\n Gets the colour of a z and step value.\n\n :param z: the z value from the mandelbrot set\n :param i: the step value\n\n :rtype: list\n :return: list containing the RGB colours\n \"\"\"\n if abs(z) < self.threshold:\n return 0, 0, 0\n v = np.log2(i + self.threshold - np.log2(np.log2(abs(z)))) / self.threshold\n if v < 1.0:\n return v ** b1, v ** b2, v ** b3 # coloured tones\n else:\n v = max(0, 2 - v)\n return v ** r1, v ** r2, v ** r3 # sepia tones\n\n return colour","def colour(z, i):\n if abs(z) < self.threshold:\n return 0, 0, 0\n v = np.log2(i + self.threshold - np.log2(np.log2(abs(z)))) / self.threshold\n if v < 1.0:\n return v ** b1, v ** b2, v ** b3 # coloured tones\n else:\n v = max(0, 2 - v)\n return v ** r1, v ** r2, v ** r3 # sepia tones","def _rgb(x, y, z):\n rgb = np.array([x, y, z]).T\n rgb -= rgb.min(0)\n rgb /= np.maximum(rgb.max(0), 1e-16) # avoid div by zero\n return rgb","def colour(z, i):\n if abs(z) < self.threshold:\n return self.background\n v = np.log2(i + self.threshold - np.log2(np.log2(abs(z)))) / self.threshold\n if v < 1.0:\n return v ** b1, v ** b2, v ** b3 # background\n else:\n v = max(0, 2 - v)\n return v ** r1, v ** r2, v ** r3 # main tones","def __div__(self, number):\n r = self.r / number\n g = self.g / number\n b = self.b / number\n return Color(r, g, b)","def color(step: int=10) -> Tuple[int, int, int]:\n # Randomly seed the r g b values\n r, g, b = (random_uniform(0, 255), random_uniform(0, 255),\n random_uniform(0, 255))\n\n # Randomly determine if each r g and b value is increasing or not\n r_inc = True\n g_inc = True\n b_inc = True\n r_step = random_uniform(step)\n g_step = random_uniform(step)\n b_step = random_uniform(step)\n\n # Yield the initial r, g, b values\n yield r, g, b\n\n # Loop and yeild forever\n while True:\n # If r is increasing\n if r_inc:\n # Increment r by the step\n r += r_step\n # Ensure that the next step will be within the limits\n # if not then set the flag to decreasing\n r_inc = r < 255 - r_step\n # If r is decreasing\n else:\n # Decrement r by the step\n r -= r_step\n # Ensure that the next step will be within the limits\n # if not then set the flag to increasing\n r_inc = r < r_step\n\n # See above\n if g_inc:\n g += g_step\n g_inc = g < 255 - g_step\n else:\n g -= g_step\n g_inc = g < g_step\n\n # See above\n if b_inc:\n b += b_step\n b_inc = b < 255 - b_step\n else:\n b -= b_step\n b_inc = b < b_step\n\n # Yield the red, green, and blue values\n yield r, g, b","def getColor(n, total = 255, decimal = False):\n\n value = round(255*n/(total * 1.0))\n\n #red value\n if value < 96:\n red = 0\n elif value < 160:\n red = 255/((160 - 96)*1.0) * (value - 96)\n elif value < 224:\n red = 255\n else:\n red = 255 - ((255 - 128)/((255 - 224) * 1.0) * (value - 224))\n\n\n #Green value\n if value < 32:\n green = 0\n elif value < 96:\n green = 255/((96 - 32)*1.0) * (value - 32)\n elif value < 160:\n green = 255\n elif value < 224:\n green = 255 - (255/((224 - 160) * 1.0) * (value - 160))\n else:\n green = 0\n\n\n #Blue value\n if value < 32:\n blue = 128 + (255 - 128)/((32 - 0) * 1.0) * (value - 0)\n elif value < 96:\n blue = 255\n elif value < 160:\n blue = 255 - ((255 - 0)/((160 - 96) * 1.0) * (value - 96))\n else:\n blue = 0\n\n if decimal:\n return (red / 255.0, green / 255.0, blue / 255.0)\n return (int(red), int(green), int(blue))","def get_color(in_val, min_val=0, max_val=100):\n width = max_val - min_val\n unit = width / len(continuum)\n return continuum[min(int(in_val / unit), 19)]","def create_unique_color_float(tag, hue_step=0.41):\n h, v = (tag * hue_step) % 1, 1. - (int(tag * hue_step) % 4) / 5.\n r, g, b = colorsys.hsv_to_rgb(h, 1., v)\n return r, g, b","def color_from_value(self, value):\n \n return ImageColor.getrgb(\"hsl(%d,%d%%,%d%%)\" % (int( (1.0 - value) * 360 ), 80, 50))","def getTweenColor(self, factor):\n\n pass","def _to_color(indx, base):\n base2 = base * base\n b = 2 - indx / base2\n r = 2 - (indx % base2) / base\n g = 2 - (indx % base2) % base\n return b * 127, r * 127, g * 127","def create_color_gradient():\n colors = []\n step = 10\n for red, green in zip(range(255,-step, -step), range(0, 255, step)):\n colors.append({'red': red, 'green': green, 'blue': 0})\n for green, blue in zip(range(255,-step, -step), range(0, 255, step)):\n colors.append({'red': 0, 'green': green, 'blue': blue})\n for blue, red in zip(range(255,-step, -step), range(0, 255, step)):\n colors.append({'red': red, 'green': 0, 'blue': blue})\n return colors","def __Calc_HSL_to_RGB_Components(var_q, var_p, C):\r\n\r\n if C < 0:\r\n C += 1.0\r\n if C > 1:\r\n C -= 1.0\r\n\r\n # Computing C of vector (Color R, Color G, Color B)\r\n if C < (1.0 / 6.0):\r\n return var_p + ((var_q - var_p) * 6.0 * C)\r\n elif (1.0 / 6.0) <= C < 0.5:\r\n return var_q\r\n elif 0.5 <= C < (2.0 / 3.0):\r\n return var_p + ((var_q - var_p) * 6.0 * ((2.0 / 3.0) - C))\r\n else:\r\n return var_p","def getColor(self,number):\n if number >= 0:\n ret = cs.hsv_to_rgb(0,0,1-abs(number/self.maxp))\n else:\n ret = cs.hsv_to_rgb(0,abs(number/self.maxn),1)\n hexcolor = '#%02x%02x%02x' % (ret[0]*255,ret[1]*255,ret[2]*255)\n return hexcolor","def elevation_to_color_formula(number):\n color_intensity = abs(round(((number - lowest_point) / (highest_point - lowest_point)) * 255))\n return color_intensity","def getColor(self,number):\n if number >= 0:\n if self.inverse:\n ret = cs.hsv_to_rgb(0,0,abs(number/self.maxp))\n else:\n ret = cs.hsv_to_rgb(0,0,1-abs(number/self.maxp))\n else:\n if self.inverse:\n ret = cs.hsv_to_rgb(0,1-abs(number/self.maxn),1)\n else:\n ret = cs.hsv_to_rgb(0,abs(number/self.maxn),1)\n return [ret[0]*255.0,ret[1]*255.0,ret[2]*255.0]","def calculate_color(thisCountry, maxCountry, maxColor, minColor):\n countryFactor = float(thisCountry)/maxCountry\n colorRange = maxColor - minColor\n return int(colorRange*countryFactor+minColor)","def z_r(b):\n return b/1e-2 + 0.5","def floatRgb(mag, cmin, cmax):\n\n try:\n # normalize to [0,1]\n x = float(mag-cmin)/float(cmax-cmin)\n except:\n # cmax = cmin\n x = 0.5\n blue = min((max((4*(0.75-x), 0.)), 1.))\n red = min((max((4*(x-0.25), 0.)), 1.))\n green= min((max((4*math.fabs(x-0.5)-1., 0.)), 1.))\n return (red, green, blue)","def linearFromsRGB(c):\n if c <= 0.04045:\n return c / 12.92\n return math.pow((0.055 + c) / 1.055, 2.4)","def driftColor(baseColor, factor=110):\n if baseColor.lightness() > 128:\n return baseColor.darker(factor)\n else:\n return baseColor.lighter(factor+10)","def _get_color(self, c, x, max_num):\n\n ratio = 5*(float(x)/max_num)\n i = int(math.floor(ratio))\n j = int(math.ceil(ratio))\n ratio -= i\n r = (1 - ratio) * self._colors[i][c] + ratio*self._colors[j][c]\n return int(255*r)","def linearTosRGB(c):\n if c <= 0.0031308:\n return 12.92 * c\n return math.pow(c, 1.0 / 2.4) * 1.055 - 0.055","def _color(self, x, factor):\r\n factor = (factor/MAX_LEVEL) * 1.8 + .1\r\n degenerate = tf.image.grayscale_to_rgb(tf.image.rgb_to_grayscale(x))\r\n x = tfa.image.blend(degenerate, tf.cast(x, tf.float32), factor)\r\n return tf.saturate_cast(x, tf.uint8)","def get_colour(progress, colours):\n if progress >= 0 and progress <= 1:\n start_colour, end_colour = colours[0], colours[1]\n\n r = start_colour[0] + (end_colour[0] - start_colour[0]) * progress\n b = start_colour[1] + (end_colour[1] - start_colour[1]) * progress\n g = start_colour[2] + (end_colour[2] - start_colour[2]) * progress\n\n return '#%02x%02x%02x' % (round(r), round(b), round(g))\n \n else: return '#000000'","def get_color(self, value):\n value = min(max(0,value), 1) * 510\n\n if value < 255:\n redValue = 255\n greenValue = math.sqrt(value) * 16\n greenValue = int(greenValue)\n else:\n greenValue = 255\n value = value - 255\n redValue = 255 - (value * value / 255)\n redValue = int(redValue)\n return '#' + f\"{redValue:0{2}x}\" + f\"{greenValue:0{2}x}\" + '00'","def floatRgb(mag, cmin, cmax):\n # Normalize to 0-1\n try: x = float(mag-cmin)/(cmax-cmin)\n except ZeroDivisionError: x = 0.5 # cmax == cmin\n blue = min((max((4*(0.75-x), 0.)), 1.))\n red = min((max((4*(x-0.25), 0.)), 1.))\n green = min((max((4*math.fabs(x-0.5)-1., 0.)), 1.))\n return red, green, blue","def ci(x, y, z):\n\n return (x * 16 + z) * CHUNK_HEIGHT + y","def color_map(val):\n color_code = remap_interval(val, -1, 1, 0, 255)\n return int(color_code)","def getRGBFromXYAndBrightness(self, x, y, bri=1):\n # The xy to color conversion is almost the same, but in reverse order.\n # Check if the xy value is within the color gamut of the lamp.\n # If not continue with step 2, otherwise step 3.\n # We do this to calculate the most accurate color the given light can actually do.\n xyPoint = XYPoint(x, y)\n\n if not self.checkPointInLampsReach(xyPoint):\n # Calculate the closest point on the color gamut triangle\n # and use that as xy value See step 6 of color to xy.\n xyPoint = self.getClosestPointToPoint(xyPoint)\n\n # Calculate XYZ values Convert using the following formulas:\n Y = bri\n X = (Y / xyPoint.y) * xyPoint.x\n Z = (Y / xyPoint.y) * (1 - xyPoint.x - xyPoint.y)\n\n # Convert to RGB using Wide RGB D65 conversion\n r = X * 1.612 - Y * 0.203 - Z * 0.302\n g = -X * 0.509 + Y * 1.412 + Z * 0.066\n b = X * 0.026 - Y * 0.072 + Z * 0.962\n\n # Apply reverse gamma correction\n r, g, b = map(\n lambda x: (12.92 * x) if (x <= 0.0031308) else ((1.0 + 0.055) * pow(x, (1.0 / 2.4)) - 0.055),\n [r, g, b]\n )\n\n # Bring all negative components to zero\n r, g, b = map(lambda x: max(0, x), [r, g, b])\n\n # If one component is greater than 1, weight components by that value.\n max_component = max(r, g, b)\n if max_component > 1:\n r, g, b = map(lambda x: x / max_component, [r, g, b])\n\n r, g, b = map(lambda x: int(x * 255), [r, g, b])\n\n # Convert the RGB values to your color object The rgb values from the above formulas are between 0.0 and 1.0.\n return (r, g, b)","def color_map(val):\n # NOTE: This relies on remap_interval, which you must provide\n color_code = remap_interval(val, -1, 1, 0, 255)\n return int(color_code)","def color_map(val):\n # NOTE: This relies on remap_interval, which you must provide\n color_code = remap_interval(val, -1, 1, 0, 255)\n return int(color_code)","def color_map(val):\n # NOTE: This relies on remap_interval, which you must provide\n color_code = remap_interval(val, -1, 1, 0, 255)\n return int(color_code)","def color_map(val):\n # NOTE: This relies on remap_interval, which you must provide\n color_code = remap_interval(val, -1, 1, 0, 255)\n return int(color_code)","def colorcalc(X1, X2, r=1):\n X1[X1 < 0], X2[X2 < 0] = 0, 0\n X1[X1 > 1], X2[X2 > 1] = 1, 1\n X3 = 1-X1-X2\n c1, c2, c3 = np.copy(X1), np.copy(X2), np.copy(X3)\n for i, (x1, x2, x3) in enumerate(itertools.permutations([X1, X2, X3])):\n ip = (x1 <= x2) & (x2 <= x3) & (x3 < 1)\n x, y = x3[ip]+.5*x1[ip], x1[ip]*3**.5/2\n p = (x-.5)/.5\n q = (.5-p**r)/(1-x)\n xn, yn = 1-q*(1-x), y*q\n a, b, c = yn*2/3**.5, 1-xn-yn/3**.5, xn-yn/3**.5\n if i == 0:\n c1[ip], c2[ip], c3[ip] = a, b, c\n elif i == 1:\n c1[ip], c3[ip], c2[ip] = a, b, c\n elif i == 2:\n c2[ip], c1[ip], c3[ip] = a, b, c\n elif i == 3:\n c2[ip], c3[ip], c1[ip] = a, b, c\n elif i == 4:\n c3[ip], c1[ip], c2[ip] = a, b, c\n else:\n c3[ip], c2[ip], c1[ip] = a, b, c\n color = np.stack([c1, c2, c3], axis=2)\n e = 1/color.max(2)\n return color*np.stack([e, e, e], axis=2)","def get_colors(self):\n x = np.linspace(0, 1, self.length)\n y = x**self.gamma\n\n value = np.linspace(0, 1, len(self.colors))\n r = np.interp(y, value, self.colors[:,0])\n g = np.interp(y, value, self.colors[:,1])\n b = np.interp(y, value, self.colors[:,2])\n\n return np.dstack((r, g, b)).reshape(len(r), 3).astype(np.uint8)","def _calc_zpcolors(self):\n for f, zpc in zip(self._filters, self._zpcolors):\n m0, merr0 = getattr(self, zpc[0]), getattr(self, zpc[0] + '_err')\n m1, merr1 = getattr(self, zpc[1]), getattr(self, zpc[1] + '_err')\n if merr0 < 9.99 and merr1 < 9.99:\n setattr(self, f + 'zpc', m0 - m1)\n setattr(self, f + 'zpc_err', np.sqrt(merr0**2 + merr1**2))\n else:\n setattr(self, f + 'zpc', self._nodata)\n setattr(self, f + 'zpc_err', self._nodata)","def drawColorCurve(xseq, yseq, zseq):\n dislin.curve3(xseq,yseq,zseq,len(xseq))","def scale_color((h, s, v), factor=1.):\n if (h < 0.) or (h > 360.):\n raise Exception('[scale_color()] Hue value out of range (0, 360): ' + str(h))\n\n if (s < 0.) or (s > 100.):\n raise Exception('[scale_color()] Saturation value out of range (0, 100): ' + str(s))\n\n if (v < 0.) or (v > 100.):\n raise Exception('[scale_color()] Value value out of range (0, 100): ' + str(v))\n\n if (factor < 0.) or (factor > 1.):\n raise Exception('[scale_color()] Factor value out of range (0.0, 1.0): ' + str(factor))\n\n v *= factor\n r, g, b = hsv_to_rgb(h/360., s/100., v/100.)\n\n return '#{0:02x}{1:02x}{2:02x}'.format(int(round(r*255.)), int(round(g*255.)), int(round(b*255.)))","def ramp_color_rgb(values, feature, parent): \r\n ramp_name = values[0]\r\n ramp_position = values[1]\r\n \r\n ramp = QgsStyleV2.defaultStyle().colorRampRef(ramp_name)\r\n if not ramp:\r\n parent.setEvalErrorString( QObject.tr( '\"{}\" is not a valid color ramp'.format(ramp_name)))\r\n return QColor(0,0,0).name()\r\n \r\n value, error = getFloat(ramp_position)\r\n if error:\r\n parent.setEvalErrorString(error)\r\n \r\n color = ramp.color(value)\r\n return \"{},{},{}\".format(color.red(), color.green(), color.blue())","def collatz(value):\r\n assert value >= 1\r\n if value % 2 == 0:\r\n return value/2\r\n else:\r\n return 3 * value + 1","def getJuliaPixelColor(z, gradient):\n c = complex(-1.0, 0.0)\n for i in range(len(gradient)):\n z = z * z + c # Get z1, z2, ...\n if abs(z) > 2:\n z = 2.0\n return gradient[i] # The sequence is unbounded\n return gradient[len(gradient) - 1] # Indicate a bounded sequence","def get_color(activePerMillion):\n activePer100k = activePerMillion / 10.0\n if activePer100k < 100:\n return \"#aaf0d1\"\n elif activePer100k < 500:\n return \"#a3f7bf\"\n elif activePer100k < 1000:\n return \"#90EE90\"\n elif activePer100k < 1500:\n return \"#00ff7f\"\n elif activePer100k < 2000:\n return \"#77dd77\"\n elif activePer100k < 2500:\n return \"#32cd32\"\n elif activePer100k < 3000:\n return \"#4cbb17\"\n elif activePer100k < 3500:\n return \"#228b22\"\n elif activePer100k < 4000:\n return \"#355e3b \"\n else:\n return \"#006400\"","def grey(self):\n return sum((self.value(0), self.value(1), self.value(2)))/3","def RGBa_interpolation(t, c0, c1, alpha):\n\n R = lerp(t, c0[0], c1[0])\n G = lerp(t, c0[1], c1[1])\n B = lerp(t, c0[2], c1[2])\n a = lerp(t, c0[3], c1[3]) * alpha\n return np.vstack([R, G, B, a]).T","def RGBratios(dataset,fil):\n dom_fil = get_dominant_color(fil)\n dataset['R_B_ratio'] = dataset.R/dataset.B\n dataset['R_G_ratio'] = dataset.R/dataset.G\n dataset['G_B_ratio'] = dataset.G/dataset.B\n filter_RB = dom_fil[0]/dom_fil[2]\n filter_RG = dom_fil[0]/dom_fil[1]\n filter_GB = dom_fil[1]/dom_fil[2]\n norm = []\n for array in list(dataset['DominatColour']):\n norm.append( np.linalg.norm(array))\n dataset['Dominant_norm'] = norm\n\n norm = []\n for array in list(dataset['AverageColour']):\n norm.append( np.linalg.norm(array))\n\n\n dataset['DiffRatio_R_B'] = dataset.R_B_ratio-filter_RB\n dataset['DiffRatio_R_G'] = dataset.R_G_ratio-filter_RG\n dataset['DiffRatio_RG_B'] = dataset.G_B_ratio-filter_GB\n dataset['Average_norm'] = norm\n return dataset","def colorize_xyz(xyz):\n xyz_vis = xyz - xyz.min()\n return (255 * xyz_vis / xyz_vis.max()).astype(np.uint8)","def to_color(arr,pmin=1,pmax=99.8,gamma=1.,colors=((0,1,0),(1,0,1),(0,1,1))):\n \n if not arr.dim in (2,3):\n raise ValueError(\"only 2d or 3d arrays supported\")\n \n if arr.ndim==2:\n arr=arr[np.newaxis]\n #get the grey channel\n ind_min=np.argmin(arr.shape)\n arr =np.moveaxis(arr,ind_min,0).astype(np.float32)\n \n out =np.zeros(arr.shape[1:]+(3,))\n \n eps=1.e-20\n \n if pmin>=0:\n mi=np.percentile(arr,pmin,axis=(1,2),keepdims=True)\n else:\n mi=0\n \n if pmax>=0:\n ma=np.percentile(arr,pmax,axis=(1,2),keepdims=True)\n else:\n ma=1.+eps\n \n arr_norm=(1.*arr-mi)/(ma-mi+eps)\n #RGB 3channel\n for i_stack,col_stack in enumerate(colors):\n if i_stack >= len(arr):\n break\n for j,c in enumerate(col_stack):\n out[...,j]+= c*arr_norm[i_stack]\n \n return np.clip(out,0,1)","def getPseudoColor( nValue, nMin = 0, nMax = 255, nErrorValue = -1 ):\n r, g, b = [0,0,0];\n # Invalid Data -> set Color to White\n if( nValue == nErrorValue ):\n r = 0xff;\n g = 0xff;\n b = 0xff;\n # Invalid Range\n # -> Set Color to Grey (debug)\n elif( nValue < nMin or nValue > nMax ):\n r = 0x80;\n g = 0x80;\n b = 0x80;\n else:\n # each part as a specific color computation\n # 5 parts:\n \"\"\"\n nSizePhase = ( nMax - nMin ) / 5;\n if( nValue < nSizePhase * 1 ):\n r = 0xff;\n g = (((nValue-nMin)-0*nSizePhase)*0xff)/nSizePhase;\n b = 0x0;\n elif( nValue < nSizePhase * 2 ):\n r = 0xff - (((nValue-nMin)-1*nSizePhase)*0xff)/nSizePhase;\n g = 0xff;\n b = 0;\n elif( nValue < nSizePhase * 3 ):\n r = 0;\n g = 0xff;\n b = (((nValue-nMin)-2*nSizePhase)*0xff)/nSizePhase;\n elif( nValue < nSizePhase * 4 ):\n r = 0;\n g = 0xff - (((nValue-nMin)-3*nSizePhase)*0xff)/nSizePhase;\n b = 0xff;\n else:\n r = 0xff;\n g = 0;\n b = 0xff - (((nValue-nMin)-4*nSizePhase)*0xff)/nSizePhase;\n \"\"\"\n # 2 parts: (green to yellow then yellow to red)\n nSizePhase = ( nMax - nMin ) / 2;\n if( nValue < nSizePhase * 1 ):\n r = (((nValue-nMin)-0*nSizePhase)*0xff)/nSizePhase;\n g = 0xff;\n b = 0x0;\n elif( nValue < nSizePhase * 2 ):\n r = 0xff;\n g = 0xff - (((nValue-nMin)-1*nSizePhase)*0xff)/nSizePhase;\n b = 0; \n # else - end\n return [r,g,b];","def linearTosRGB3(c):\n return [linearTosRGB(c[0]), linearTosRGB(c[1]), linearTosRGB(c[2])]","def calc_R(xc, yc, zc,x,y,z):\n return sqrt((x - xc) ** 2 + (y - yc) ** 2 + (z - zc) ** 2)","def generate_lut(self):\n r,g,b=(Numeric.zeros(256),Numeric.zeros(256),Numeric.zeros(256))\n for i in Numeric.arange(256):\n r_,g_,b_=self.colfct(i/255.0) # these are from [0,1]\n r[i],g[i],b[i]=int(255*r_),int(255*g_),int(255*b_)\n return r,g,b","def combine_colors(c1, c2, factor=0.5):\n c3 = QtGui.QColor()\n c3.setRed(int((factor * c1.red() + (1 - factor) * c2.red())))\n c3.setGreen(int((factor * c1.green() + (1 - factor) * c2.green())))\n c3.setBlue(int((factor * c1.blue() + (1 - factor) * c2.blue())))\n return c3","def combine_colors(c1, c2, factor=0.5):\n c3 = QtGui.QColor()\n c3.setRed(int((factor * c1.red() + (1 - factor) * c2.red())))\n c3.setGreen(int((factor * c1.green() + (1 - factor) * c2.green())))\n c3.setBlue(int((factor * c1.blue() + (1 - factor) * c2.blue())))\n return c3","def __RGB_to_Hue(var_R, var_G, var_B, var_min, var_max):\r\n\r\n if var_max == var_min:\r\n return 0.0\r\n elif var_max == var_R:\r\n return (60.0 * ((var_G - var_B) / (var_max - var_min)) + 360) % 360.0\r\n elif var_max == var_G:\r\n return 60.0 * ((var_B - var_R) / (var_max - var_min)) + 120\r\n elif var_max == var_B:\r\n return 60.0 * ((var_R - var_G) / (var_max - var_min)) + 240.0","def get_colors(n):\n color = cm.rainbow(np.linspace(0, 1, n))\n return color","def standard_handler(args_dict: dict):\n\n color_sequence = args_dict['color_sequence']\n step = args_dict['step']\n\n # Updating step for the next iteration.\n args_dict['step'] = (step + 1) % args_dict['sequence_len']\n\n return color_sequence[step]","def lighter(clr, f=1/3):\n gaps = [f*(1 - val) for val in clr]\n new_clr = [val + gap for gap, val in zip(gaps, clr)]\n return new_clr","def fakingColors(pixel):\n\n return (\n pixel[0] * 0.598 - 0.1957 * pixel[1] - 0.038 * pixel[2],\n pixel[1] * 1.174 - pixel[0] * 0.1994 - pixel[2] * 0.076,\n pixel[2] * 0.228 - pixel[0] * 0.1495 - pixel[1] * 0.2935\n )","def _color_brew(n):\n color_list = []\n\n # Initialize saturation & value; calculate chroma & value shift\n s, v = 0.75, 0.9\n c = s * v\n m = v - c\n\n for h in np.arange(25, 385, 360. / n).astype(int):\n # Calculate some intermediate values\n h_bar = h / 60.\n x = c * (1 - abs((h_bar % 2) - 1))\n # Initialize RGB with same hue & chroma as our color\n rgb = [(c, x, 0),\n (x, c, 0),\n (0, c, x),\n (0, x, c),\n (x, 0, c),\n (c, 0, x),\n (c, x, 0)]\n r, g, b = rgb[int(h_bar)]\n # Shift the initial RGB values to match value and store\n rgb = [(int(255 * (r + m))),\n (int(255 * (g + m))),\n (int(255 * (b + m)))]\n color_list.append(rgb)\n\n return color_list","def StepColour(c, ialpha):\r\n \r\n if ialpha == 100:\r\n return c\r\n \r\n r, g, b = c.Red(), c.Green(), c.Blue()\r\n\r\n # ialpha is 0..200 where 0 is completely black\r\n # and 200 is completely white and 100 is the same\r\n # convert that to normal alpha 0.0 - 1.0\r\n ialpha = min(ialpha, 200)\r\n ialpha = max(ialpha, 0)\r\n alpha = (ialpha - 100.0)/100.0\r\n\r\n if ialpha > 100:\r\n \r\n # blend with white\r\n bg = 255\r\n alpha = 1.0 - alpha # 0 = transparent fg 1 = opaque fg\r\n \r\n else:\r\n \r\n # blend with black\r\n bg = 0\r\n alpha = 1.0 + alpha # 0 = transparent fg 1 = opaque fg\r\n \r\n r = BlendColour(r, bg, alpha)\r\n g = BlendColour(g, bg, alpha)\r\n b = BlendColour(b, bg, alpha)\r\n\r\n return wx.Colour(r, g, b)","def _toRgbImage(self, fractal, colors, color_offset):\n hsv_img = np.array(\n [\n # Cycle through color wheel.\n (fractal * colors + color_offset) % 1,\n\n # Saturation = fractal value.\n fractal,\n\n # Maximum value.\n np.ones(fractal.shape)\n ]\n ).astype(dtype=float).T\n\n rgb_img = (mpl.colors.hsv_to_rgb(hsv_img) * 255).astype(dtype=np.uint8)\n return rgb_img","def get_colors(n_curves):\n # Use offsets to avoid shitty crappy min/max colors (ugly yellow or white for example)\n first_step = 1. / (n_curves + 1.)\n last_step = 1.\n nb_steps = n_curves + 1\n color_range = np.linspace(first_step, last_step, nb_steps)\n color = iter(plt.cm.hot(color_range))\n return color","def unique_colors_rgb(n):\r\n hues = []\r\n # i is in the range 0, 1, ..., n - 1\r\n for i in range(1, n + 1):\r\n hues.append(360.0 / i)\r\n\r\n hs = []\r\n for hue in hues:\r\n h = math.floor(hue / 60) % 6\r\n hs.append(h)\r\n\r\n fs = []\r\n for hue in hues:\r\n f = hue / 60 - math.floor(hue / 60)\r\n fs.append(f)\r\n\r\n rgbcolors = []\r\n for h, f in zip(hs, fs):\r\n v = 1\r\n p = 0\r\n q = 1 - f\r\n t = f\r\n if h == 0:\r\n color = v, t, p\r\n elif h == 1:\r\n color = q, v, p\r\n elif h == 2:\r\n color = p, v, t\r\n elif h == 3:\r\n color = p, q, v\r\n elif h == 4:\r\n color = t, p, v\r\n elif h == 5:\r\n color = v, p, q\r\n rgbcolors.append(color)\r\n\r\n return rgbcolors","def rgb(r, g, b):\n return (r/255, g/255, b/255)","def scale_to_01(color: C3I) -> C3F:\n r, g, b = color\n return r / 255, g / 255, b / 255","def dcsrbf(r):\n return 3*(num.power(num.maximum(0, 1-r), 3) - num.power(num.maximum(0, 1-r),2)*(3*r+1))","def create_unique_color_uchar(tag, hue_step=0.41):\n r, g, b = create_unique_color_float(tag, hue_step)\n return int(255*r), int(255*g), int(255*b)","def get_color(val, trendType):\n if(val > 0):\n if(trendType == 'normal'):\n color = 'red'\n else:\n color = 'green'\n elif(val < 0):\n if(trendType == 'normal'):\n color = 'green'\n else:\n color = 'red'\n else:\n color = 'blue'\n return color","def gamma(k, z):\n return 1","def step_fn(step):\n ret = 1.0\n for name in factors:\n if name == 'constant':\n ret *= base_learning_rate\n elif name == 'linear_warmup':\n ret *= jnp.minimum(1.0, step / warmup_steps)\n elif name == 'rsqrt_decay':\n ret /= jnp.sqrt(jnp.maximum(1.0, step - warmup_steps))\n elif name == 'rsqrt_normalized_decay':\n ret *= jnp.sqrt(warmup_steps)\n ret /= jnp.sqrt(jnp.maximum(step, warmup_steps))\n elif name == 'decay_every':\n ret *= (decay_factor**(step // steps_per_decay))\n elif name == 'cosine_decay':\n progress = jnp.maximum(0.0,\n (step - warmup_steps) / float(steps_per_cycle))\n ret *= jnp.maximum(0.0,\n 0.5 * (1.0 + jnp.cos(jnp.pi * (progress % 1.0))))\n else:\n raise ValueError('Unknown factor %s.' % name)\n return jnp.asarray(ret, dtype=jnp.float32)","def index_to_rgb(index=0, gamma=0.5):\n\n wavelength = (index * 320) + 380\n\n if wavelength < 440:\n intensity = 0.1 + (0.9 * (wavelength - 380) / (440 - 380))\n red = ((-1.0 * (wavelength - 440) / (440 - 380)) * intensity) ** gamma\n grn = 0.0\n blu = (1.0 * intensity) ** gamma\n if 440 <= wavelength < 490:\n red = 0.0\n grn = (1.0 * (wavelength - 440) / (490 - 440)) ** gamma\n blu = 1.0**gamma\n if 490 <= wavelength < 510:\n red = 0.0\n grn = 1.0**gamma\n blu = (-1.0 * (wavelength - 510) / (510 - 490)) ** gamma\n if 510 <= wavelength < 580:\n red = (1.0 * (wavelength - 510) / (580 - 510)) ** gamma\n grn = 1.0**gamma\n blu = 0.0\n if 580 <= wavelength < 645:\n red = 1.0**gamma\n grn = (-1.0 * (wavelength - 645) / (645 - 580)) ** gamma\n blu = 0.0\n if wavelength >= 645:\n intensity = 0.3 + (0.7 * (700 - wavelength) / (700 - 645))\n red = (1.0) ** gamma\n grn = (1.0 - intensity) ** gamma\n blu = (1.0 - intensity) ** gamma\n\n return (int(red * 255) << 16) + (int(grn * 255) << 8) + int(blu * 255)","def getColorLine(self, dot1, dot2): # generate all dots of line\n x1, y1, z1, feed1 = dot1\n x2, y2, z2, feed2 = dot2\n min_color = self.colors_list[feed1]\n max_color = self.colors_list[feed2]\n\n # NB! feed1,feed2 >= 0; 0 = dot[3]-fmin\n # self.colors_list = grad(MIN_COLOR, MAX_COLOR, self.fmax)\n if self.current_feedrate == 0:\n start_color = min_color\n if feed2 > feed1:\n finish_color = max_color\n else:\n finish_color = min_color\n else:\n if feed2 > feed1:\n start_color = min_color\n finish_color = max_color\n elif feed1 > feed2:\n start_color = max_color\n finish_color = min_color\n else: # feed1 == feed2\n if feed2 == self.current_feedrate:\n start_color = finish_color = self.current_color\n\n self.current_color = finish_color\n self.current_feedrate = feed2\n color_list = grad(start_color, finish_color, n=self.speed+1)\n\n i = 0\n coords = []\n stepx = (x2-x1) / self.speed\n stepy = (y2-y1) / self.speed\n stepz = (z2-z1) / self.speed\n for i in range(self.speed):\n dot = [0, 0, 0, 0, 0, 0, 0] # x, y, z, r, g, b, p\n dot[0] = x1 + i*stepx\n dot[1] = y1 + i*stepy\n dot[2] = z1 + i*stepz\n dot[3:7] = color_list[i]\n coords.append(dot)\n\n return coords","def get_coloured_grid(self, r1, r2, r3, b1=4, b2=2.5, b3=1):\n r, g, b = np.frompyfunc(self.get_colour(r1, r2, r3, b1, b2, b3), 2, 3)(self.end_z, self.end_step)\n img_array = np.dstack((r, g, b))\n return Image.fromarray(np.uint8(img_array * 255))","def _toRgbImage(self, fractal, colors, color_offset):\n hsv_img = np.array(\n [\n # Cycle through color wheel.\n (fractal * colors + color_offset) % 1,\n\n # Saturation = 1 where fractal values > 0,\n # Saturation = 0 otherwise.\n fractal.astype(dtype=bool).astype(dtype=float),\n\n # Invert colours\n 1 - fractal\n ]\n ).astype(dtype=float).T\n\n rgb_img = (mpl.colors.hsv_to_rgb(hsv_img) * 255).astype(dtype=np.uint8)\n return rgb_img","def shade(N=10, *colors):\n return interp1d(linspace(0, 1, len(colors)), colors, axis=0)(linspace(0, 1, N)).round(2)","def color_differentiator(elevation):\n if elevation < 1000:\n return 'green'\n elif 1000 <= elevation < 3000:\n return 'orange'\n else:\n return 'red'","def _color(self, args):","def color_level(value, min_value=0.0, max_value=1.0, func=lambda x: x):\n global COLOR_SCALE\n\n if not __colored__:\n return \"\"\n\n COLOR_SCALE = [\n fg(\"white\"),\n fg(\"pale_green_3b\"),\n fg(\"green\"),\n fg(\"green_yellow\"),\n fg(\"light_yellow\"),\n fg(\"orange_1\"),\n fg(\"orange_red_1\"),\n fg(\"light_red\"),\n fg(\"light_red\") + attr(\"blink\")\n ]\n\n max_index = len(COLOR_SCALE) - 1\n color_index = int(round(\n max_index * min(1.0, max(0.0,\n func((value - min_value) / (max_value - min_value))\n ))\n ))\n\n return COLOR_SCALE[color_index]","def color_creator(number_of_films):\n if 0 < number_of_films < 100:\n return 'green'\n elif 101 < number_of_films < 200:\n return 'orange'\n elif 201 < number_of_films < 500:\n return 'red'\n else:\n return 'purple'","def depth_color(depth):\n if depth < 20:\n return 'g'\n if depth < 40:\n return 'y'\n return 'r'","def to_color(arr, pmin=1, pmax=99.8, gamma=1., colors=((0, 1, 0), (1, 0, 1), (0, 1, 1))):\n if not arr.ndim in (2,3):\n raise ValueError(\"only 2d or 3d arrays supported\")\n\n if arr.ndim ==2:\n arr = arr[np.newaxis]\n\n ind_min = np.argmin(arr.shape)\n arr = np.moveaxis(arr, ind_min, 0).astype(np.float32)\n\n out = np.zeros(arr.shape[1:] + (3,))\n\n eps = 1.e-20\n if pmin>=0:\n mi = np.percentile(arr, pmin, axis=(1, 2), keepdims=True)\n else:\n mi = 0\n\n if pmax>=0:\n ma = np.percentile(arr, pmax, axis=(1, 2), keepdims=True)\n else:\n ma = 1.+eps\n\n arr_norm = (1. * arr - mi) / (ma - mi + eps)\n\n\n for i_stack, col_stack in enumerate(colors):\n if i_stack >= len(arr):\n break\n for j, c in enumerate(col_stack):\n out[..., j] += c * arr_norm[i_stack]\n\n return np.clip(out, 0, 1)","def int2color(x):\n # r = int(1000 * x % 255)\n # g = int(10000 * x % 255)\n # b = int(100000 * x % 255)\n x = 0 if x == 0 else int(1/x)\n b = x & 0xff\n g = (x >> 8) & 0xff\n r = (x >> 16) & 0xff\n return [r, g, b]","def toRGB(self):\n\t\treturn '#'+ (hex(self.politics)[2:].upper().rjust(6,'0'))","def rainbow(double[:] v, redshifts, in_terms, double H0):\n\n cdef double t = v[0]\n cdef double a = v[1]\n cdef double ombar_m = v[2]\n cdef double ombar_r = v[3]\n cdef double ombar_a = v[4]\n cdef double ombar_b = v[5]\n cdef double ombar_c = v[6]\n cdef double ombar_d = v[7]\n cdef double ombar_e = v[8]\n cdef double ombar_f = v[9]\n cdef double ombar_g = v[10]\n cdef double ombar_h = v[11]\n cdef double ombar_i = v[12]\n cdef double ombar_de = v[13]\n cdef double z = v[14]\n cdef double dl = v[15]\n\n cdef double in_p = in_terms[0]\n cdef double in_q = in_terms[1]\n cdef double in_r = in_terms[2]\n cdef double in_s = in_terms[3]\n cdef double in_t = in_terms[4]\n cdef double in_u = in_terms[5]\n cdef double in_v = in_terms[6]\n cdef double in_w = in_terms[7]\n cdef double in_x = in_terms[8]\n cdef double in_y = in_terms[9]\n cdef double in_z = in_terms[10]\n\n\n cdef double Hz = H0 * (ombar_m +ombar_r +ombar_a +ombar_b\n +ombar_c +ombar_d +ombar_e +ombar_f\n +ombar_g +ombar_h +ombar_i +ombar_de)**(0.5)\n\n# if ombar_m < 0 or ombar_de < 0:\n# print('rainbow')\n# print('z = %s, Hz = %s, in_terms = %s'% (z, Hz, in_terms))\n# print(f'omegas = {v[2:14]}')\n\n if math.isnan(Hz):\n print('rainbow')\n print('z = %s, Hz = %s, in_terms = %s'% (z, Hz, in_terms))\n print(f'ombar_m = {ombar_m}, ombar_r = {ombar_r}, ombar_a = {ombar_a}, ombar_b = {ombar_b}, ombar_c = {ombar_c}, ombar_d = {ombar_d}, ombar_e = {ombar_e}, ombar_f = {ombar_f}, ombar_g = {ombar_g}, ombar_h = {ombar_h}, ombar_i = {ombar_i}, ombar_de = {ombar_de}')\n\n cdef double dtdz = -1.0/((1.0+z) * Hz)\n cdef double dadz = -(1.0+z)**(-2.0)\n\n cdef double domdz = (3.0*ombar_m +in_p*ombar_m*ombar_r/Hz)/(1.0+z) # w = 0\n cdef double dordz = (4.0*ombar_r -in_p*ombar_m*ombar_r/Hz +in_q*ombar_r*ombar_a/Hz)/(1.0+z) # w = 1/3\n cdef double doadz = (2.7*ombar_a -in_q*ombar_r*ombar_a/Hz +in_r*ombar_a*ombar_b/Hz)/(1.0+z) # w = -0.1\n cdef double dobdz = (2.4*ombar_b -in_r*ombar_a*ombar_b/Hz +in_s*ombar_b*ombar_c/Hz)/(1.0+z) # w = -0.2\n cdef double docdz = (2.1*ombar_c -in_s*ombar_b*ombar_c/Hz +in_t*ombar_c*ombar_d/Hz)/(1.0+z) # w = -0.3\n cdef double doddz = (1.8*ombar_d -in_t*ombar_c*ombar_d/Hz +in_u*ombar_d*ombar_e/Hz)/(1.0+z) # w = -0.4\n cdef double doedz = (1.5*ombar_e -in_u*ombar_d*ombar_e/Hz +in_v*ombar_e*ombar_f/Hz)/(1.0+z) # w = -0.5\n cdef double dofdz = (1.2*ombar_f -in_v*ombar_e*ombar_f/Hz +in_w*ombar_f*ombar_g/Hz)/(1.0+z) # w = -0.6\n cdef double dogdz = (0.9*ombar_g -in_w*ombar_f*ombar_g/Hz +in_x*ombar_g*ombar_h/Hz)/(1.0+z) # w = -0.7\n cdef double dohdz = (0.6*ombar_h -in_x*ombar_g*ombar_h/Hz +in_y*ombar_h*ombar_i/Hz)/(1.0+z) # w = -0.8\n cdef double doidz = (0.3*ombar_i -in_y*ombar_h*ombar_i/Hz +in_z*ombar_i*ombar_de/Hz)/(1.0+z)# w = -0.9\n cdef double dodedz = -in_z*ombar_i*ombar_de/(1.0+z)/Hz # w = -1\n\n cdef double ddldz = 1.0/Hz\n\n # first derivatives of functions I want to find:\n f = [dtdz,# dt/dz (= f.d wrt z of time)\n dadz,# d(a)/dz (= f.d wrt z of scale factor)\n domdz,# dw = 0, (ombar_m)/dz (= f.d wrt z of density_m(t) / crit density(t0))\n dordz,# w = 1/3, d(ombar_r)/dz (= f.d wrt z of density_r(t) / crit density(t0))\n doadz,# w = -0.1\n dobdz,# w = -0.2\n docdz,# w = -0.3\n doddz,# w = -0.4\n doedz,# w = -0.5\n dofdz,# w = -0.6\n dogdz,# w = -0.7\n dohdz,# w = -0.8\n doidz,# w = -0.9\n dodedz,# w = -1, d(ombar_de)/dz (= f.d wrt z of density_de(t) / crit density(t0))\n 1.0,# d(z)/dz (= f.d wrt z of redshift)\n ddldz]# d(dl)/dz (= f.d wrt z of luminosty distance) # H + Hdz*(1+z)\n\n return f","def c_s(self, z):\n R = self.R_bg(z)\n return const.c/np.sqrt(3.*(1.+R))","def _hls2rgb(self,h):\n h=h**self.exponent\n if(self.invert): h=1.0-h\n h=h*360.0\n h=Numeric.fmod(h,360.0)\n if(self.hls_hls):\n h=h/60.0\n else:\n if(h<120):\n h=h/120.0 # /* 0..1 Rot..(Orange)..Gelb */\n elif(h<180):\n h=h/60.0 - 1.0 # /* 1..2 Gelb..Gruen */\n elif(h<240):\n h=h/30.0 - 4.0 # /* 2..4 Gruen..Blaugruen..Blau*/\n else:\n h=h/60.0 # /* 4..6 Blau..Purpur..Rot */\n c=int(h)\n frac=h-c\n if (self.hls_l<=0.5):\n maxi=self.hls_l*(1.0+self.hls_s)\n else:\n maxi=self.hls_l+self.hls_s-self.hls_l*self.hls_s\n mini=2*self.hls_l-maxi;\n diff=maxi-mini;\n if(self.hls_s==0): # /* grau */\n return(1.0,1.0,1.0) \n else:\n if(c==0):\n return(maxi,mini+frac*diff,mini)\n elif(c==1):\n return(mini+(1.0-frac)*diff,maxi,mini)\n elif(c==2):\n return(mini,maxi,mini+frac*diff)\n elif(c==3):\n return(mini,mini+(1.0-frac)*diff,maxi)\n elif(c==4):\n return(mini+frac*diff,mini,maxi)\n else:\n return(maxi,mini,mini+(1.0-frac)*diff)","def _FracInt(x,y,z,a,b,c,tau,n):\n denom = np.sqrt((a + tau)*(b + tau)*(c + tau))\n return (1. - x**2/(a + tau) - y**2/(b + tau) - z**2/(c + tau))**n / denom","def get_RF_color(rf_vec):\n color = copy.deepcopy(rf_vec)\n\n def renormalize(v):\n v -= np.min(v)\n return v / np.max(v)\n\n for i in xrange(3):\n color[:, i] = renormalize(color[:, i])\n return color","def color(value):\r\n return 'RGB({}, {}, {})'.format(value.red(), value.blue(), value.green())","def opacity_transfer_function(mapping, n_colors, interpolate=True, kind='quadratic'):\n sigmoid = lambda x: np.array(1 / (1 + np.exp(-x)) * 255, dtype=np.uint8)\n transfer_func = {\n 'linear': np.linspace(0, 255, n_colors, dtype=np.uint8),\n 'geom': np.geomspace(1e-6, 255, n_colors, dtype=np.uint8),\n 'geom_r': np.geomspace(255, 1e-6, n_colors, dtype=np.uint8),\n 'sigmoid': sigmoid(np.linspace(-10.0, 10.0, n_colors)),\n 'sigmoid_3': sigmoid(np.linspace(-3.0, 3.0, n_colors)),\n 'sigmoid_4': sigmoid(np.linspace(-4.0, 4.0, n_colors)),\n 'sigmoid_5': sigmoid(np.linspace(-5.0, 5.0, n_colors)),\n 'sigmoid_6': sigmoid(np.linspace(-6.0, 6.0, n_colors)),\n 'sigmoid_7': sigmoid(np.linspace(-7.0, 7.0, n_colors)),\n 'sigmoid_8': sigmoid(np.linspace(-8.0, 8.0, n_colors)),\n 'sigmoid_9': sigmoid(np.linspace(-9.0, 9.0, n_colors)),\n 'sigmoid_10': sigmoid(np.linspace(-10.0, 10.0, n_colors)),\n }\n transfer_func['linear_r'] = transfer_func['linear'][::-1]\n transfer_func['sigmoid_r'] = transfer_func['sigmoid'][::-1]\n for i in range(3, 11):\n k = f'sigmoid_{i}'\n rk = f'{k}_r'\n transfer_func[rk] = transfer_func[k][::-1]\n if isinstance(mapping, str):\n try:\n return transfer_func[mapping]\n except KeyError:\n raise KeyError(f'opactiy transfer function ({mapping}) unknown.')\n elif isinstance(mapping, (np.ndarray, list, tuple)):\n mapping = np.array(mapping)\n if mapping.size == n_colors:\n # User could pass transfer function ready for lookup table\n pass\n elif mapping.size < n_colors:\n # User pass custom transfer function to be linearly interpolated\n if np.max(mapping) > 1.0 or np.min(mapping) < 0.0:\n mapping = normalize(mapping)\n # Interpolate transfer function to match lookup table\n xo = np.linspace(0, n_colors, len(mapping), dtype=np.int_)\n xx = np.linspace(0, n_colors, n_colors, dtype=np.int_)\n try:\n if not interpolate:\n raise ValueError('No interpolation.')\n # Use a quadratic interp if scipy is available\n from scipy.interpolate import interp1d\n\n # quadratic has best/smoothest results\n f = interp1d(xo, mapping, kind=kind)\n vals = f(xx)\n vals[vals < 0] = 0.0\n vals[vals > 1.0] = 1.0\n mapping = (vals * 255.0).astype(np.uint8)\n\n except (ImportError, ValueError):\n # Otherwise use simple linear interp\n mapping = (np.interp(xx, xo, mapping) * 255).astype(np.uint8)\n else:\n raise RuntimeError(\n f'Transfer function cannot have more values than `n_colors`. This has {mapping.size} elements'\n )\n return mapping\n raise TypeError(f'Transfer function type ({type(mapping)}) not understood')","def calculate_colors(v):\n\n # Define constant color values\n lightness = [0.35, 0.5, 0.65]\n saturation = [0.35, 0.5, 0.65]\n\n # Calculate the CRC-32 checksum of colors encoded as a UTF-8 string\n hash = crc32(str(v).encode('utf-8')) & 0xffffffff\n\n # Calculate the HSL (hue, saturation, lightness) values for the vertices\n hue = ((hash % 359) / 1000) * 360\n hash //= 360\n sat = saturation[hash % len(saturation)]\n hash //= len(saturation)\n lig = lightness[hash % len(lightness)]\n\n return (hue, sat, lig)","def rgb_image(self):\n z3 = self.z[:,:,newaxis]\n return z3 * self.c","def default_render_color_maker(num:int):\n if num < 7:\n log.warn('Fewer than 7 rendering colors are being generated. This may cause issues if a URDF with a 6+ axis robot is loaded.')\n\n b = np.linspace(0,255,num).astype(int) # Blue values are always unique\n\n g = [0] * b.size\n r = np.abs(255 - 2*b)\n\n colors = []\n for idx in range(num):\n colors.append([b[idx],g[idx],r[idx]])\n return colors","def color(self, data):\n\n red = np.interp(data, self.range, self.r)\n blue = np.interp(data, self.range, self.b)\n green = np.interp(data, self.range, self.g)\n # Style plot to return a grey color when value is 'nan'\n red[np.isnan(red)] = 240\n blue[np.isnan(blue)] = 240\n green[np.isnan(green)] = 240\n colors = np.dstack([red.astype(np.uint8),\n green.astype(np.uint8),\n blue.astype(np.uint8),\n np.full_like(data, 255, dtype=np.uint8)])\n #return colors.view(dtype=np.uint32).reshape(data.shape)\n c=[]\n for i in range(len(data)):\n c.append([red[i],green[i],blue[i]])\n return c","def fromInts(r, g, b):\n return IColor(r/255.,g/255.,b/255.)","def nthColor(i):\n if i < len(colors):\n return colors[i]\n\n c1 = colors[i % len(colors)]\n c2 = nthColor(i // len(colors))\n\n return \"#\" + hex((int(c1[1:],16) + int(c2[1:],16)) // 2)[2:]","def calculate_colors(countries, max_users):\n for country_code in countries:\n countries[country_code]['color_rgb'] = [\n calculate_color(\n countries[country_code]['percentage_of_users'],\n max_users,\n 8,\n 229\n ),\n calculate_color(\n countries[country_code]['percentage_of_users'],\n max_users,\n 64,\n 245\n ),\n calculate_color(\n countries[country_code]['percentage_of_users'],\n max_users,\n 129,\n 223\n )\n ]\n\n return countries"],"string":"[\n \"def rgbcolor(h, f):\\n # q = 1 - f\\n # t = f\\n if h == 0:\\n return v, f, p\\n elif h == 1:\\n return 1 - f, v, p\\n elif h == 2:\\n return p, v, f\\n elif h == 3:\\n return p, 1 - f, v\\n elif h == 4:\\n return f, p, v\\n elif h == 5:\\n return v, p, 1 - f\",\n \"def get_inner_colour(self, r1, r2, r3, b1, b2, b3):\\n\\n def colour(z, i):\\n \\\"\\\"\\\"\\n Gets the colour of a z and step value.\\n\\n :param z: the z value from the mandelbrot set\\n :param i: the step value\\n\\n :rtype: list\\n :return: list containing the RGB colours\\n \\\"\\\"\\\"\\n if abs(z) < self.threshold:\\n return 0, 0, 0\\n v = np.log2(i + self.threshold - np.log2(np.log2(abs(z)))) / self.threshold\\n if v < 1.0:\\n return v ** b1, v ** b2, v ** b3 # coloured tones\\n else:\\n v = max(0, 2 - v)\\n return v ** r1, v ** r2, v ** r3 # sepia tones\\n\\n return colour\",\n \"def colour(z, i):\\n if abs(z) < self.threshold:\\n return 0, 0, 0\\n v = np.log2(i + self.threshold - np.log2(np.log2(abs(z)))) / self.threshold\\n if v < 1.0:\\n return v ** b1, v ** b2, v ** b3 # coloured tones\\n else:\\n v = max(0, 2 - v)\\n return v ** r1, v ** r2, v ** r3 # sepia tones\",\n \"def _rgb(x, y, z):\\n rgb = np.array([x, y, z]).T\\n rgb -= rgb.min(0)\\n rgb /= np.maximum(rgb.max(0), 1e-16) # avoid div by zero\\n return rgb\",\n \"def colour(z, i):\\n if abs(z) < self.threshold:\\n return self.background\\n v = np.log2(i + self.threshold - np.log2(np.log2(abs(z)))) / self.threshold\\n if v < 1.0:\\n return v ** b1, v ** b2, v ** b3 # background\\n else:\\n v = max(0, 2 - v)\\n return v ** r1, v ** r2, v ** r3 # main tones\",\n \"def __div__(self, number):\\n r = self.r / number\\n g = self.g / number\\n b = self.b / number\\n return Color(r, g, b)\",\n \"def color(step: int=10) -> Tuple[int, int, int]:\\n # Randomly seed the r g b values\\n r, g, b = (random_uniform(0, 255), random_uniform(0, 255),\\n random_uniform(0, 255))\\n\\n # Randomly determine if each r g and b value is increasing or not\\n r_inc = True\\n g_inc = True\\n b_inc = True\\n r_step = random_uniform(step)\\n g_step = random_uniform(step)\\n b_step = random_uniform(step)\\n\\n # Yield the initial r, g, b values\\n yield r, g, b\\n\\n # Loop and yeild forever\\n while True:\\n # If r is increasing\\n if r_inc:\\n # Increment r by the step\\n r += r_step\\n # Ensure that the next step will be within the limits\\n # if not then set the flag to decreasing\\n r_inc = r < 255 - r_step\\n # If r is decreasing\\n else:\\n # Decrement r by the step\\n r -= r_step\\n # Ensure that the next step will be within the limits\\n # if not then set the flag to increasing\\n r_inc = r < r_step\\n\\n # See above\\n if g_inc:\\n g += g_step\\n g_inc = g < 255 - g_step\\n else:\\n g -= g_step\\n g_inc = g < g_step\\n\\n # See above\\n if b_inc:\\n b += b_step\\n b_inc = b < 255 - b_step\\n else:\\n b -= b_step\\n b_inc = b < b_step\\n\\n # Yield the red, green, and blue values\\n yield r, g, b\",\n \"def getColor(n, total = 255, decimal = False):\\n\\n value = round(255*n/(total * 1.0))\\n\\n #red value\\n if value < 96:\\n red = 0\\n elif value < 160:\\n red = 255/((160 - 96)*1.0) * (value - 96)\\n elif value < 224:\\n red = 255\\n else:\\n red = 255 - ((255 - 128)/((255 - 224) * 1.0) * (value - 224))\\n\\n\\n #Green value\\n if value < 32:\\n green = 0\\n elif value < 96:\\n green = 255/((96 - 32)*1.0) * (value - 32)\\n elif value < 160:\\n green = 255\\n elif value < 224:\\n green = 255 - (255/((224 - 160) * 1.0) * (value - 160))\\n else:\\n green = 0\\n\\n\\n #Blue value\\n if value < 32:\\n blue = 128 + (255 - 128)/((32 - 0) * 1.0) * (value - 0)\\n elif value < 96:\\n blue = 255\\n elif value < 160:\\n blue = 255 - ((255 - 0)/((160 - 96) * 1.0) * (value - 96))\\n else:\\n blue = 0\\n\\n if decimal:\\n return (red / 255.0, green / 255.0, blue / 255.0)\\n return (int(red), int(green), int(blue))\",\n \"def get_color(in_val, min_val=0, max_val=100):\\n width = max_val - min_val\\n unit = width / len(continuum)\\n return continuum[min(int(in_val / unit), 19)]\",\n \"def create_unique_color_float(tag, hue_step=0.41):\\n h, v = (tag * hue_step) % 1, 1. - (int(tag * hue_step) % 4) / 5.\\n r, g, b = colorsys.hsv_to_rgb(h, 1., v)\\n return r, g, b\",\n \"def color_from_value(self, value):\\n \\n return ImageColor.getrgb(\\\"hsl(%d,%d%%,%d%%)\\\" % (int( (1.0 - value) * 360 ), 80, 50))\",\n \"def getTweenColor(self, factor):\\n\\n pass\",\n \"def _to_color(indx, base):\\n base2 = base * base\\n b = 2 - indx / base2\\n r = 2 - (indx % base2) / base\\n g = 2 - (indx % base2) % base\\n return b * 127, r * 127, g * 127\",\n \"def create_color_gradient():\\n colors = []\\n step = 10\\n for red, green in zip(range(255,-step, -step), range(0, 255, step)):\\n colors.append({'red': red, 'green': green, 'blue': 0})\\n for green, blue in zip(range(255,-step, -step), range(0, 255, step)):\\n colors.append({'red': 0, 'green': green, 'blue': blue})\\n for blue, red in zip(range(255,-step, -step), range(0, 255, step)):\\n colors.append({'red': red, 'green': 0, 'blue': blue})\\n return colors\",\n \"def __Calc_HSL_to_RGB_Components(var_q, var_p, C):\\r\\n\\r\\n if C < 0:\\r\\n C += 1.0\\r\\n if C > 1:\\r\\n C -= 1.0\\r\\n\\r\\n # Computing C of vector (Color R, Color G, Color B)\\r\\n if C < (1.0 / 6.0):\\r\\n return var_p + ((var_q - var_p) * 6.0 * C)\\r\\n elif (1.0 / 6.0) <= C < 0.5:\\r\\n return var_q\\r\\n elif 0.5 <= C < (2.0 / 3.0):\\r\\n return var_p + ((var_q - var_p) * 6.0 * ((2.0 / 3.0) - C))\\r\\n else:\\r\\n return var_p\",\n \"def getColor(self,number):\\n if number >= 0:\\n ret = cs.hsv_to_rgb(0,0,1-abs(number/self.maxp))\\n else:\\n ret = cs.hsv_to_rgb(0,abs(number/self.maxn),1)\\n hexcolor = '#%02x%02x%02x' % (ret[0]*255,ret[1]*255,ret[2]*255)\\n return hexcolor\",\n \"def elevation_to_color_formula(number):\\n color_intensity = abs(round(((number - lowest_point) / (highest_point - lowest_point)) * 255))\\n return color_intensity\",\n \"def getColor(self,number):\\n if number >= 0:\\n if self.inverse:\\n ret = cs.hsv_to_rgb(0,0,abs(number/self.maxp))\\n else:\\n ret = cs.hsv_to_rgb(0,0,1-abs(number/self.maxp))\\n else:\\n if self.inverse:\\n ret = cs.hsv_to_rgb(0,1-abs(number/self.maxn),1)\\n else:\\n ret = cs.hsv_to_rgb(0,abs(number/self.maxn),1)\\n return [ret[0]*255.0,ret[1]*255.0,ret[2]*255.0]\",\n \"def calculate_color(thisCountry, maxCountry, maxColor, minColor):\\n countryFactor = float(thisCountry)/maxCountry\\n colorRange = maxColor - minColor\\n return int(colorRange*countryFactor+minColor)\",\n \"def z_r(b):\\n return b/1e-2 + 0.5\",\n \"def floatRgb(mag, cmin, cmax):\\n\\n try:\\n # normalize to [0,1]\\n x = float(mag-cmin)/float(cmax-cmin)\\n except:\\n # cmax = cmin\\n x = 0.5\\n blue = min((max((4*(0.75-x), 0.)), 1.))\\n red = min((max((4*(x-0.25), 0.)), 1.))\\n green= min((max((4*math.fabs(x-0.5)-1., 0.)), 1.))\\n return (red, green, blue)\",\n \"def linearFromsRGB(c):\\n if c <= 0.04045:\\n return c / 12.92\\n return math.pow((0.055 + c) / 1.055, 2.4)\",\n \"def driftColor(baseColor, factor=110):\\n if baseColor.lightness() > 128:\\n return baseColor.darker(factor)\\n else:\\n return baseColor.lighter(factor+10)\",\n \"def _get_color(self, c, x, max_num):\\n\\n ratio = 5*(float(x)/max_num)\\n i = int(math.floor(ratio))\\n j = int(math.ceil(ratio))\\n ratio -= i\\n r = (1 - ratio) * self._colors[i][c] + ratio*self._colors[j][c]\\n return int(255*r)\",\n \"def linearTosRGB(c):\\n if c <= 0.0031308:\\n return 12.92 * c\\n return math.pow(c, 1.0 / 2.4) * 1.055 - 0.055\",\n \"def _color(self, x, factor):\\r\\n factor = (factor/MAX_LEVEL) * 1.8 + .1\\r\\n degenerate = tf.image.grayscale_to_rgb(tf.image.rgb_to_grayscale(x))\\r\\n x = tfa.image.blend(degenerate, tf.cast(x, tf.float32), factor)\\r\\n return tf.saturate_cast(x, tf.uint8)\",\n \"def get_colour(progress, colours):\\n if progress >= 0 and progress <= 1:\\n start_colour, end_colour = colours[0], colours[1]\\n\\n r = start_colour[0] + (end_colour[0] - start_colour[0]) * progress\\n b = start_colour[1] + (end_colour[1] - start_colour[1]) * progress\\n g = start_colour[2] + (end_colour[2] - start_colour[2]) * progress\\n\\n return '#%02x%02x%02x' % (round(r), round(b), round(g))\\n \\n else: return '#000000'\",\n \"def get_color(self, value):\\n value = min(max(0,value), 1) * 510\\n\\n if value < 255:\\n redValue = 255\\n greenValue = math.sqrt(value) * 16\\n greenValue = int(greenValue)\\n else:\\n greenValue = 255\\n value = value - 255\\n redValue = 255 - (value * value / 255)\\n redValue = int(redValue)\\n return '#' + f\\\"{redValue:0{2}x}\\\" + f\\\"{greenValue:0{2}x}\\\" + '00'\",\n \"def floatRgb(mag, cmin, cmax):\\n # Normalize to 0-1\\n try: x = float(mag-cmin)/(cmax-cmin)\\n except ZeroDivisionError: x = 0.5 # cmax == cmin\\n blue = min((max((4*(0.75-x), 0.)), 1.))\\n red = min((max((4*(x-0.25), 0.)), 1.))\\n green = min((max((4*math.fabs(x-0.5)-1., 0.)), 1.))\\n return red, green, blue\",\n \"def ci(x, y, z):\\n\\n return (x * 16 + z) * CHUNK_HEIGHT + y\",\n \"def color_map(val):\\n color_code = remap_interval(val, -1, 1, 0, 255)\\n return int(color_code)\",\n \"def getRGBFromXYAndBrightness(self, x, y, bri=1):\\n # The xy to color conversion is almost the same, but in reverse order.\\n # Check if the xy value is within the color gamut of the lamp.\\n # If not continue with step 2, otherwise step 3.\\n # We do this to calculate the most accurate color the given light can actually do.\\n xyPoint = XYPoint(x, y)\\n\\n if not self.checkPointInLampsReach(xyPoint):\\n # Calculate the closest point on the color gamut triangle\\n # and use that as xy value See step 6 of color to xy.\\n xyPoint = self.getClosestPointToPoint(xyPoint)\\n\\n # Calculate XYZ values Convert using the following formulas:\\n Y = bri\\n X = (Y / xyPoint.y) * xyPoint.x\\n Z = (Y / xyPoint.y) * (1 - xyPoint.x - xyPoint.y)\\n\\n # Convert to RGB using Wide RGB D65 conversion\\n r = X * 1.612 - Y * 0.203 - Z * 0.302\\n g = -X * 0.509 + Y * 1.412 + Z * 0.066\\n b = X * 0.026 - Y * 0.072 + Z * 0.962\\n\\n # Apply reverse gamma correction\\n r, g, b = map(\\n lambda x: (12.92 * x) if (x <= 0.0031308) else ((1.0 + 0.055) * pow(x, (1.0 / 2.4)) - 0.055),\\n [r, g, b]\\n )\\n\\n # Bring all negative components to zero\\n r, g, b = map(lambda x: max(0, x), [r, g, b])\\n\\n # If one component is greater than 1, weight components by that value.\\n max_component = max(r, g, b)\\n if max_component > 1:\\n r, g, b = map(lambda x: x / max_component, [r, g, b])\\n\\n r, g, b = map(lambda x: int(x * 255), [r, g, b])\\n\\n # Convert the RGB values to your color object The rgb values from the above formulas are between 0.0 and 1.0.\\n return (r, g, b)\",\n \"def color_map(val):\\n # NOTE: This relies on remap_interval, which you must provide\\n color_code = remap_interval(val, -1, 1, 0, 255)\\n return int(color_code)\",\n \"def color_map(val):\\n # NOTE: This relies on remap_interval, which you must provide\\n color_code = remap_interval(val, -1, 1, 0, 255)\\n return int(color_code)\",\n \"def color_map(val):\\n # NOTE: This relies on remap_interval, which you must provide\\n color_code = remap_interval(val, -1, 1, 0, 255)\\n return int(color_code)\",\n \"def color_map(val):\\n # NOTE: This relies on remap_interval, which you must provide\\n color_code = remap_interval(val, -1, 1, 0, 255)\\n return int(color_code)\",\n \"def colorcalc(X1, X2, r=1):\\n X1[X1 < 0], X2[X2 < 0] = 0, 0\\n X1[X1 > 1], X2[X2 > 1] = 1, 1\\n X3 = 1-X1-X2\\n c1, c2, c3 = np.copy(X1), np.copy(X2), np.copy(X3)\\n for i, (x1, x2, x3) in enumerate(itertools.permutations([X1, X2, X3])):\\n ip = (x1 <= x2) & (x2 <= x3) & (x3 < 1)\\n x, y = x3[ip]+.5*x1[ip], x1[ip]*3**.5/2\\n p = (x-.5)/.5\\n q = (.5-p**r)/(1-x)\\n xn, yn = 1-q*(1-x), y*q\\n a, b, c = yn*2/3**.5, 1-xn-yn/3**.5, xn-yn/3**.5\\n if i == 0:\\n c1[ip], c2[ip], c3[ip] = a, b, c\\n elif i == 1:\\n c1[ip], c3[ip], c2[ip] = a, b, c\\n elif i == 2:\\n c2[ip], c1[ip], c3[ip] = a, b, c\\n elif i == 3:\\n c2[ip], c3[ip], c1[ip] = a, b, c\\n elif i == 4:\\n c3[ip], c1[ip], c2[ip] = a, b, c\\n else:\\n c3[ip], c2[ip], c1[ip] = a, b, c\\n color = np.stack([c1, c2, c3], axis=2)\\n e = 1/color.max(2)\\n return color*np.stack([e, e, e], axis=2)\",\n \"def get_colors(self):\\n x = np.linspace(0, 1, self.length)\\n y = x**self.gamma\\n\\n value = np.linspace(0, 1, len(self.colors))\\n r = np.interp(y, value, self.colors[:,0])\\n g = np.interp(y, value, self.colors[:,1])\\n b = np.interp(y, value, self.colors[:,2])\\n\\n return np.dstack((r, g, b)).reshape(len(r), 3).astype(np.uint8)\",\n \"def _calc_zpcolors(self):\\n for f, zpc in zip(self._filters, self._zpcolors):\\n m0, merr0 = getattr(self, zpc[0]), getattr(self, zpc[0] + '_err')\\n m1, merr1 = getattr(self, zpc[1]), getattr(self, zpc[1] + '_err')\\n if merr0 < 9.99 and merr1 < 9.99:\\n setattr(self, f + 'zpc', m0 - m1)\\n setattr(self, f + 'zpc_err', np.sqrt(merr0**2 + merr1**2))\\n else:\\n setattr(self, f + 'zpc', self._nodata)\\n setattr(self, f + 'zpc_err', self._nodata)\",\n \"def drawColorCurve(xseq, yseq, zseq):\\n dislin.curve3(xseq,yseq,zseq,len(xseq))\",\n \"def scale_color((h, s, v), factor=1.):\\n if (h < 0.) or (h > 360.):\\n raise Exception('[scale_color()] Hue value out of range (0, 360): ' + str(h))\\n\\n if (s < 0.) or (s > 100.):\\n raise Exception('[scale_color()] Saturation value out of range (0, 100): ' + str(s))\\n\\n if (v < 0.) or (v > 100.):\\n raise Exception('[scale_color()] Value value out of range (0, 100): ' + str(v))\\n\\n if (factor < 0.) or (factor > 1.):\\n raise Exception('[scale_color()] Factor value out of range (0.0, 1.0): ' + str(factor))\\n\\n v *= factor\\n r, g, b = hsv_to_rgb(h/360., s/100., v/100.)\\n\\n return '#{0:02x}{1:02x}{2:02x}'.format(int(round(r*255.)), int(round(g*255.)), int(round(b*255.)))\",\n \"def ramp_color_rgb(values, feature, parent): \\r\\n ramp_name = values[0]\\r\\n ramp_position = values[1]\\r\\n \\r\\n ramp = QgsStyleV2.defaultStyle().colorRampRef(ramp_name)\\r\\n if not ramp:\\r\\n parent.setEvalErrorString( QObject.tr( '\\\"{}\\\" is not a valid color ramp'.format(ramp_name)))\\r\\n return QColor(0,0,0).name()\\r\\n \\r\\n value, error = getFloat(ramp_position)\\r\\n if error:\\r\\n parent.setEvalErrorString(error)\\r\\n \\r\\n color = ramp.color(value)\\r\\n return \\\"{},{},{}\\\".format(color.red(), color.green(), color.blue())\",\n \"def collatz(value):\\r\\n assert value >= 1\\r\\n if value % 2 == 0:\\r\\n return value/2\\r\\n else:\\r\\n return 3 * value + 1\",\n \"def getJuliaPixelColor(z, gradient):\\n c = complex(-1.0, 0.0)\\n for i in range(len(gradient)):\\n z = z * z + c # Get z1, z2, ...\\n if abs(z) > 2:\\n z = 2.0\\n return gradient[i] # The sequence is unbounded\\n return gradient[len(gradient) - 1] # Indicate a bounded sequence\",\n \"def get_color(activePerMillion):\\n activePer100k = activePerMillion / 10.0\\n if activePer100k < 100:\\n return \\\"#aaf0d1\\\"\\n elif activePer100k < 500:\\n return \\\"#a3f7bf\\\"\\n elif activePer100k < 1000:\\n return \\\"#90EE90\\\"\\n elif activePer100k < 1500:\\n return \\\"#00ff7f\\\"\\n elif activePer100k < 2000:\\n return \\\"#77dd77\\\"\\n elif activePer100k < 2500:\\n return \\\"#32cd32\\\"\\n elif activePer100k < 3000:\\n return \\\"#4cbb17\\\"\\n elif activePer100k < 3500:\\n return \\\"#228b22\\\"\\n elif activePer100k < 4000:\\n return \\\"#355e3b \\\"\\n else:\\n return \\\"#006400\\\"\",\n \"def grey(self):\\n return sum((self.value(0), self.value(1), self.value(2)))/3\",\n \"def RGBa_interpolation(t, c0, c1, alpha):\\n\\n R = lerp(t, c0[0], c1[0])\\n G = lerp(t, c0[1], c1[1])\\n B = lerp(t, c0[2], c1[2])\\n a = lerp(t, c0[3], c1[3]) * alpha\\n return np.vstack([R, G, B, a]).T\",\n \"def RGBratios(dataset,fil):\\n dom_fil = get_dominant_color(fil)\\n dataset['R_B_ratio'] = dataset.R/dataset.B\\n dataset['R_G_ratio'] = dataset.R/dataset.G\\n dataset['G_B_ratio'] = dataset.G/dataset.B\\n filter_RB = dom_fil[0]/dom_fil[2]\\n filter_RG = dom_fil[0]/dom_fil[1]\\n filter_GB = dom_fil[1]/dom_fil[2]\\n norm = []\\n for array in list(dataset['DominatColour']):\\n norm.append( np.linalg.norm(array))\\n dataset['Dominant_norm'] = norm\\n\\n norm = []\\n for array in list(dataset['AverageColour']):\\n norm.append( np.linalg.norm(array))\\n\\n\\n dataset['DiffRatio_R_B'] = dataset.R_B_ratio-filter_RB\\n dataset['DiffRatio_R_G'] = dataset.R_G_ratio-filter_RG\\n dataset['DiffRatio_RG_B'] = dataset.G_B_ratio-filter_GB\\n dataset['Average_norm'] = norm\\n return dataset\",\n \"def colorize_xyz(xyz):\\n xyz_vis = xyz - xyz.min()\\n return (255 * xyz_vis / xyz_vis.max()).astype(np.uint8)\",\n \"def to_color(arr,pmin=1,pmax=99.8,gamma=1.,colors=((0,1,0),(1,0,1),(0,1,1))):\\n \\n if not arr.dim in (2,3):\\n raise ValueError(\\\"only 2d or 3d arrays supported\\\")\\n \\n if arr.ndim==2:\\n arr=arr[np.newaxis]\\n #get the grey channel\\n ind_min=np.argmin(arr.shape)\\n arr =np.moveaxis(arr,ind_min,0).astype(np.float32)\\n \\n out =np.zeros(arr.shape[1:]+(3,))\\n \\n eps=1.e-20\\n \\n if pmin>=0:\\n mi=np.percentile(arr,pmin,axis=(1,2),keepdims=True)\\n else:\\n mi=0\\n \\n if pmax>=0:\\n ma=np.percentile(arr,pmax,axis=(1,2),keepdims=True)\\n else:\\n ma=1.+eps\\n \\n arr_norm=(1.*arr-mi)/(ma-mi+eps)\\n #RGB 3channel\\n for i_stack,col_stack in enumerate(colors):\\n if i_stack >= len(arr):\\n break\\n for j,c in enumerate(col_stack):\\n out[...,j]+= c*arr_norm[i_stack]\\n \\n return np.clip(out,0,1)\",\n \"def getPseudoColor( nValue, nMin = 0, nMax = 255, nErrorValue = -1 ):\\n r, g, b = [0,0,0];\\n # Invalid Data -> set Color to White\\n if( nValue == nErrorValue ):\\n r = 0xff;\\n g = 0xff;\\n b = 0xff;\\n # Invalid Range\\n # -> Set Color to Grey (debug)\\n elif( nValue < nMin or nValue > nMax ):\\n r = 0x80;\\n g = 0x80;\\n b = 0x80;\\n else:\\n # each part as a specific color computation\\n # 5 parts:\\n \\\"\\\"\\\"\\n nSizePhase = ( nMax - nMin ) / 5;\\n if( nValue < nSizePhase * 1 ):\\n r = 0xff;\\n g = (((nValue-nMin)-0*nSizePhase)*0xff)/nSizePhase;\\n b = 0x0;\\n elif( nValue < nSizePhase * 2 ):\\n r = 0xff - (((nValue-nMin)-1*nSizePhase)*0xff)/nSizePhase;\\n g = 0xff;\\n b = 0;\\n elif( nValue < nSizePhase * 3 ):\\n r = 0;\\n g = 0xff;\\n b = (((nValue-nMin)-2*nSizePhase)*0xff)/nSizePhase;\\n elif( nValue < nSizePhase * 4 ):\\n r = 0;\\n g = 0xff - (((nValue-nMin)-3*nSizePhase)*0xff)/nSizePhase;\\n b = 0xff;\\n else:\\n r = 0xff;\\n g = 0;\\n b = 0xff - (((nValue-nMin)-4*nSizePhase)*0xff)/nSizePhase;\\n \\\"\\\"\\\"\\n # 2 parts: (green to yellow then yellow to red)\\n nSizePhase = ( nMax - nMin ) / 2;\\n if( nValue < nSizePhase * 1 ):\\n r = (((nValue-nMin)-0*nSizePhase)*0xff)/nSizePhase;\\n g = 0xff;\\n b = 0x0;\\n elif( nValue < nSizePhase * 2 ):\\n r = 0xff;\\n g = 0xff - (((nValue-nMin)-1*nSizePhase)*0xff)/nSizePhase;\\n b = 0; \\n # else - end\\n return [r,g,b];\",\n \"def linearTosRGB3(c):\\n return [linearTosRGB(c[0]), linearTosRGB(c[1]), linearTosRGB(c[2])]\",\n \"def calc_R(xc, yc, zc,x,y,z):\\n return sqrt((x - xc) ** 2 + (y - yc) ** 2 + (z - zc) ** 2)\",\n \"def generate_lut(self):\\n r,g,b=(Numeric.zeros(256),Numeric.zeros(256),Numeric.zeros(256))\\n for i in Numeric.arange(256):\\n r_,g_,b_=self.colfct(i/255.0) # these are from [0,1]\\n r[i],g[i],b[i]=int(255*r_),int(255*g_),int(255*b_)\\n return r,g,b\",\n \"def combine_colors(c1, c2, factor=0.5):\\n c3 = QtGui.QColor()\\n c3.setRed(int((factor * c1.red() + (1 - factor) * c2.red())))\\n c3.setGreen(int((factor * c1.green() + (1 - factor) * c2.green())))\\n c3.setBlue(int((factor * c1.blue() + (1 - factor) * c2.blue())))\\n return c3\",\n \"def combine_colors(c1, c2, factor=0.5):\\n c3 = QtGui.QColor()\\n c3.setRed(int((factor * c1.red() + (1 - factor) * c2.red())))\\n c3.setGreen(int((factor * c1.green() + (1 - factor) * c2.green())))\\n c3.setBlue(int((factor * c1.blue() + (1 - factor) * c2.blue())))\\n return c3\",\n \"def __RGB_to_Hue(var_R, var_G, var_B, var_min, var_max):\\r\\n\\r\\n if var_max == var_min:\\r\\n return 0.0\\r\\n elif var_max == var_R:\\r\\n return (60.0 * ((var_G - var_B) / (var_max - var_min)) + 360) % 360.0\\r\\n elif var_max == var_G:\\r\\n return 60.0 * ((var_B - var_R) / (var_max - var_min)) + 120\\r\\n elif var_max == var_B:\\r\\n return 60.0 * ((var_R - var_G) / (var_max - var_min)) + 240.0\",\n \"def get_colors(n):\\n color = cm.rainbow(np.linspace(0, 1, n))\\n return color\",\n \"def standard_handler(args_dict: dict):\\n\\n color_sequence = args_dict['color_sequence']\\n step = args_dict['step']\\n\\n # Updating step for the next iteration.\\n args_dict['step'] = (step + 1) % args_dict['sequence_len']\\n\\n return color_sequence[step]\",\n \"def lighter(clr, f=1/3):\\n gaps = [f*(1 - val) for val in clr]\\n new_clr = [val + gap for gap, val in zip(gaps, clr)]\\n return new_clr\",\n \"def fakingColors(pixel):\\n\\n return (\\n pixel[0] * 0.598 - 0.1957 * pixel[1] - 0.038 * pixel[2],\\n pixel[1] * 1.174 - pixel[0] * 0.1994 - pixel[2] * 0.076,\\n pixel[2] * 0.228 - pixel[0] * 0.1495 - pixel[1] * 0.2935\\n )\",\n \"def _color_brew(n):\\n color_list = []\\n\\n # Initialize saturation & value; calculate chroma & value shift\\n s, v = 0.75, 0.9\\n c = s * v\\n m = v - c\\n\\n for h in np.arange(25, 385, 360. / n).astype(int):\\n # Calculate some intermediate values\\n h_bar = h / 60.\\n x = c * (1 - abs((h_bar % 2) - 1))\\n # Initialize RGB with same hue & chroma as our color\\n rgb = [(c, x, 0),\\n (x, c, 0),\\n (0, c, x),\\n (0, x, c),\\n (x, 0, c),\\n (c, 0, x),\\n (c, x, 0)]\\n r, g, b = rgb[int(h_bar)]\\n # Shift the initial RGB values to match value and store\\n rgb = [(int(255 * (r + m))),\\n (int(255 * (g + m))),\\n (int(255 * (b + m)))]\\n color_list.append(rgb)\\n\\n return color_list\",\n \"def StepColour(c, ialpha):\\r\\n \\r\\n if ialpha == 100:\\r\\n return c\\r\\n \\r\\n r, g, b = c.Red(), c.Green(), c.Blue()\\r\\n\\r\\n # ialpha is 0..200 where 0 is completely black\\r\\n # and 200 is completely white and 100 is the same\\r\\n # convert that to normal alpha 0.0 - 1.0\\r\\n ialpha = min(ialpha, 200)\\r\\n ialpha = max(ialpha, 0)\\r\\n alpha = (ialpha - 100.0)/100.0\\r\\n\\r\\n if ialpha > 100:\\r\\n \\r\\n # blend with white\\r\\n bg = 255\\r\\n alpha = 1.0 - alpha # 0 = transparent fg 1 = opaque fg\\r\\n \\r\\n else:\\r\\n \\r\\n # blend with black\\r\\n bg = 0\\r\\n alpha = 1.0 + alpha # 0 = transparent fg 1 = opaque fg\\r\\n \\r\\n r = BlendColour(r, bg, alpha)\\r\\n g = BlendColour(g, bg, alpha)\\r\\n b = BlendColour(b, bg, alpha)\\r\\n\\r\\n return wx.Colour(r, g, b)\",\n \"def _toRgbImage(self, fractal, colors, color_offset):\\n hsv_img = np.array(\\n [\\n # Cycle through color wheel.\\n (fractal * colors + color_offset) % 1,\\n\\n # Saturation = fractal value.\\n fractal,\\n\\n # Maximum value.\\n np.ones(fractal.shape)\\n ]\\n ).astype(dtype=float).T\\n\\n rgb_img = (mpl.colors.hsv_to_rgb(hsv_img) * 255).astype(dtype=np.uint8)\\n return rgb_img\",\n \"def get_colors(n_curves):\\n # Use offsets to avoid shitty crappy min/max colors (ugly yellow or white for example)\\n first_step = 1. / (n_curves + 1.)\\n last_step = 1.\\n nb_steps = n_curves + 1\\n color_range = np.linspace(first_step, last_step, nb_steps)\\n color = iter(plt.cm.hot(color_range))\\n return color\",\n \"def unique_colors_rgb(n):\\r\\n hues = []\\r\\n # i is in the range 0, 1, ..., n - 1\\r\\n for i in range(1, n + 1):\\r\\n hues.append(360.0 / i)\\r\\n\\r\\n hs = []\\r\\n for hue in hues:\\r\\n h = math.floor(hue / 60) % 6\\r\\n hs.append(h)\\r\\n\\r\\n fs = []\\r\\n for hue in hues:\\r\\n f = hue / 60 - math.floor(hue / 60)\\r\\n fs.append(f)\\r\\n\\r\\n rgbcolors = []\\r\\n for h, f in zip(hs, fs):\\r\\n v = 1\\r\\n p = 0\\r\\n q = 1 - f\\r\\n t = f\\r\\n if h == 0:\\r\\n color = v, t, p\\r\\n elif h == 1:\\r\\n color = q, v, p\\r\\n elif h == 2:\\r\\n color = p, v, t\\r\\n elif h == 3:\\r\\n color = p, q, v\\r\\n elif h == 4:\\r\\n color = t, p, v\\r\\n elif h == 5:\\r\\n color = v, p, q\\r\\n rgbcolors.append(color)\\r\\n\\r\\n return rgbcolors\",\n \"def rgb(r, g, b):\\n return (r/255, g/255, b/255)\",\n \"def scale_to_01(color: C3I) -> C3F:\\n r, g, b = color\\n return r / 255, g / 255, b / 255\",\n \"def dcsrbf(r):\\n return 3*(num.power(num.maximum(0, 1-r), 3) - num.power(num.maximum(0, 1-r),2)*(3*r+1))\",\n \"def create_unique_color_uchar(tag, hue_step=0.41):\\n r, g, b = create_unique_color_float(tag, hue_step)\\n return int(255*r), int(255*g), int(255*b)\",\n \"def get_color(val, trendType):\\n if(val > 0):\\n if(trendType == 'normal'):\\n color = 'red'\\n else:\\n color = 'green'\\n elif(val < 0):\\n if(trendType == 'normal'):\\n color = 'green'\\n else:\\n color = 'red'\\n else:\\n color = 'blue'\\n return color\",\n \"def gamma(k, z):\\n return 1\",\n \"def step_fn(step):\\n ret = 1.0\\n for name in factors:\\n if name == 'constant':\\n ret *= base_learning_rate\\n elif name == 'linear_warmup':\\n ret *= jnp.minimum(1.0, step / warmup_steps)\\n elif name == 'rsqrt_decay':\\n ret /= jnp.sqrt(jnp.maximum(1.0, step - warmup_steps))\\n elif name == 'rsqrt_normalized_decay':\\n ret *= jnp.sqrt(warmup_steps)\\n ret /= jnp.sqrt(jnp.maximum(step, warmup_steps))\\n elif name == 'decay_every':\\n ret *= (decay_factor**(step // steps_per_decay))\\n elif name == 'cosine_decay':\\n progress = jnp.maximum(0.0,\\n (step - warmup_steps) / float(steps_per_cycle))\\n ret *= jnp.maximum(0.0,\\n 0.5 * (1.0 + jnp.cos(jnp.pi * (progress % 1.0))))\\n else:\\n raise ValueError('Unknown factor %s.' % name)\\n return jnp.asarray(ret, dtype=jnp.float32)\",\n \"def index_to_rgb(index=0, gamma=0.5):\\n\\n wavelength = (index * 320) + 380\\n\\n if wavelength < 440:\\n intensity = 0.1 + (0.9 * (wavelength - 380) / (440 - 380))\\n red = ((-1.0 * (wavelength - 440) / (440 - 380)) * intensity) ** gamma\\n grn = 0.0\\n blu = (1.0 * intensity) ** gamma\\n if 440 <= wavelength < 490:\\n red = 0.0\\n grn = (1.0 * (wavelength - 440) / (490 - 440)) ** gamma\\n blu = 1.0**gamma\\n if 490 <= wavelength < 510:\\n red = 0.0\\n grn = 1.0**gamma\\n blu = (-1.0 * (wavelength - 510) / (510 - 490)) ** gamma\\n if 510 <= wavelength < 580:\\n red = (1.0 * (wavelength - 510) / (580 - 510)) ** gamma\\n grn = 1.0**gamma\\n blu = 0.0\\n if 580 <= wavelength < 645:\\n red = 1.0**gamma\\n grn = (-1.0 * (wavelength - 645) / (645 - 580)) ** gamma\\n blu = 0.0\\n if wavelength >= 645:\\n intensity = 0.3 + (0.7 * (700 - wavelength) / (700 - 645))\\n red = (1.0) ** gamma\\n grn = (1.0 - intensity) ** gamma\\n blu = (1.0 - intensity) ** gamma\\n\\n return (int(red * 255) << 16) + (int(grn * 255) << 8) + int(blu * 255)\",\n \"def getColorLine(self, dot1, dot2): # generate all dots of line\\n x1, y1, z1, feed1 = dot1\\n x2, y2, z2, feed2 = dot2\\n min_color = self.colors_list[feed1]\\n max_color = self.colors_list[feed2]\\n\\n # NB! feed1,feed2 >= 0; 0 = dot[3]-fmin\\n # self.colors_list = grad(MIN_COLOR, MAX_COLOR, self.fmax)\\n if self.current_feedrate == 0:\\n start_color = min_color\\n if feed2 > feed1:\\n finish_color = max_color\\n else:\\n finish_color = min_color\\n else:\\n if feed2 > feed1:\\n start_color = min_color\\n finish_color = max_color\\n elif feed1 > feed2:\\n start_color = max_color\\n finish_color = min_color\\n else: # feed1 == feed2\\n if feed2 == self.current_feedrate:\\n start_color = finish_color = self.current_color\\n\\n self.current_color = finish_color\\n self.current_feedrate = feed2\\n color_list = grad(start_color, finish_color, n=self.speed+1)\\n\\n i = 0\\n coords = []\\n stepx = (x2-x1) / self.speed\\n stepy = (y2-y1) / self.speed\\n stepz = (z2-z1) / self.speed\\n for i in range(self.speed):\\n dot = [0, 0, 0, 0, 0, 0, 0] # x, y, z, r, g, b, p\\n dot[0] = x1 + i*stepx\\n dot[1] = y1 + i*stepy\\n dot[2] = z1 + i*stepz\\n dot[3:7] = color_list[i]\\n coords.append(dot)\\n\\n return coords\",\n \"def get_coloured_grid(self, r1, r2, r3, b1=4, b2=2.5, b3=1):\\n r, g, b = np.frompyfunc(self.get_colour(r1, r2, r3, b1, b2, b3), 2, 3)(self.end_z, self.end_step)\\n img_array = np.dstack((r, g, b))\\n return Image.fromarray(np.uint8(img_array * 255))\",\n \"def _toRgbImage(self, fractal, colors, color_offset):\\n hsv_img = np.array(\\n [\\n # Cycle through color wheel.\\n (fractal * colors + color_offset) % 1,\\n\\n # Saturation = 1 where fractal values > 0,\\n # Saturation = 0 otherwise.\\n fractal.astype(dtype=bool).astype(dtype=float),\\n\\n # Invert colours\\n 1 - fractal\\n ]\\n ).astype(dtype=float).T\\n\\n rgb_img = (mpl.colors.hsv_to_rgb(hsv_img) * 255).astype(dtype=np.uint8)\\n return rgb_img\",\n \"def shade(N=10, *colors):\\n return interp1d(linspace(0, 1, len(colors)), colors, axis=0)(linspace(0, 1, N)).round(2)\",\n \"def color_differentiator(elevation):\\n if elevation < 1000:\\n return 'green'\\n elif 1000 <= elevation < 3000:\\n return 'orange'\\n else:\\n return 'red'\",\n \"def _color(self, args):\",\n \"def color_level(value, min_value=0.0, max_value=1.0, func=lambda x: x):\\n global COLOR_SCALE\\n\\n if not __colored__:\\n return \\\"\\\"\\n\\n COLOR_SCALE = [\\n fg(\\\"white\\\"),\\n fg(\\\"pale_green_3b\\\"),\\n fg(\\\"green\\\"),\\n fg(\\\"green_yellow\\\"),\\n fg(\\\"light_yellow\\\"),\\n fg(\\\"orange_1\\\"),\\n fg(\\\"orange_red_1\\\"),\\n fg(\\\"light_red\\\"),\\n fg(\\\"light_red\\\") + attr(\\\"blink\\\")\\n ]\\n\\n max_index = len(COLOR_SCALE) - 1\\n color_index = int(round(\\n max_index * min(1.0, max(0.0,\\n func((value - min_value) / (max_value - min_value))\\n ))\\n ))\\n\\n return COLOR_SCALE[color_index]\",\n \"def color_creator(number_of_films):\\n if 0 < number_of_films < 100:\\n return 'green'\\n elif 101 < number_of_films < 200:\\n return 'orange'\\n elif 201 < number_of_films < 500:\\n return 'red'\\n else:\\n return 'purple'\",\n \"def depth_color(depth):\\n if depth < 20:\\n return 'g'\\n if depth < 40:\\n return 'y'\\n return 'r'\",\n \"def to_color(arr, pmin=1, pmax=99.8, gamma=1., colors=((0, 1, 0), (1, 0, 1), (0, 1, 1))):\\n if not arr.ndim in (2,3):\\n raise ValueError(\\\"only 2d or 3d arrays supported\\\")\\n\\n if arr.ndim ==2:\\n arr = arr[np.newaxis]\\n\\n ind_min = np.argmin(arr.shape)\\n arr = np.moveaxis(arr, ind_min, 0).astype(np.float32)\\n\\n out = np.zeros(arr.shape[1:] + (3,))\\n\\n eps = 1.e-20\\n if pmin>=0:\\n mi = np.percentile(arr, pmin, axis=(1, 2), keepdims=True)\\n else:\\n mi = 0\\n\\n if pmax>=0:\\n ma = np.percentile(arr, pmax, axis=(1, 2), keepdims=True)\\n else:\\n ma = 1.+eps\\n\\n arr_norm = (1. * arr - mi) / (ma - mi + eps)\\n\\n\\n for i_stack, col_stack in enumerate(colors):\\n if i_stack >= len(arr):\\n break\\n for j, c in enumerate(col_stack):\\n out[..., j] += c * arr_norm[i_stack]\\n\\n return np.clip(out, 0, 1)\",\n \"def int2color(x):\\n # r = int(1000 * x % 255)\\n # g = int(10000 * x % 255)\\n # b = int(100000 * x % 255)\\n x = 0 if x == 0 else int(1/x)\\n b = x & 0xff\\n g = (x >> 8) & 0xff\\n r = (x >> 16) & 0xff\\n return [r, g, b]\",\n \"def toRGB(self):\\n\\t\\treturn '#'+ (hex(self.politics)[2:].upper().rjust(6,'0'))\",\n \"def rainbow(double[:] v, redshifts, in_terms, double H0):\\n\\n cdef double t = v[0]\\n cdef double a = v[1]\\n cdef double ombar_m = v[2]\\n cdef double ombar_r = v[3]\\n cdef double ombar_a = v[4]\\n cdef double ombar_b = v[5]\\n cdef double ombar_c = v[6]\\n cdef double ombar_d = v[7]\\n cdef double ombar_e = v[8]\\n cdef double ombar_f = v[9]\\n cdef double ombar_g = v[10]\\n cdef double ombar_h = v[11]\\n cdef double ombar_i = v[12]\\n cdef double ombar_de = v[13]\\n cdef double z = v[14]\\n cdef double dl = v[15]\\n\\n cdef double in_p = in_terms[0]\\n cdef double in_q = in_terms[1]\\n cdef double in_r = in_terms[2]\\n cdef double in_s = in_terms[3]\\n cdef double in_t = in_terms[4]\\n cdef double in_u = in_terms[5]\\n cdef double in_v = in_terms[6]\\n cdef double in_w = in_terms[7]\\n cdef double in_x = in_terms[8]\\n cdef double in_y = in_terms[9]\\n cdef double in_z = in_terms[10]\\n\\n\\n cdef double Hz = H0 * (ombar_m +ombar_r +ombar_a +ombar_b\\n +ombar_c +ombar_d +ombar_e +ombar_f\\n +ombar_g +ombar_h +ombar_i +ombar_de)**(0.5)\\n\\n# if ombar_m < 0 or ombar_de < 0:\\n# print('rainbow')\\n# print('z = %s, Hz = %s, in_terms = %s'% (z, Hz, in_terms))\\n# print(f'omegas = {v[2:14]}')\\n\\n if math.isnan(Hz):\\n print('rainbow')\\n print('z = %s, Hz = %s, in_terms = %s'% (z, Hz, in_terms))\\n print(f'ombar_m = {ombar_m}, ombar_r = {ombar_r}, ombar_a = {ombar_a}, ombar_b = {ombar_b}, ombar_c = {ombar_c}, ombar_d = {ombar_d}, ombar_e = {ombar_e}, ombar_f = {ombar_f}, ombar_g = {ombar_g}, ombar_h = {ombar_h}, ombar_i = {ombar_i}, ombar_de = {ombar_de}')\\n\\n cdef double dtdz = -1.0/((1.0+z) * Hz)\\n cdef double dadz = -(1.0+z)**(-2.0)\\n\\n cdef double domdz = (3.0*ombar_m +in_p*ombar_m*ombar_r/Hz)/(1.0+z) # w = 0\\n cdef double dordz = (4.0*ombar_r -in_p*ombar_m*ombar_r/Hz +in_q*ombar_r*ombar_a/Hz)/(1.0+z) # w = 1/3\\n cdef double doadz = (2.7*ombar_a -in_q*ombar_r*ombar_a/Hz +in_r*ombar_a*ombar_b/Hz)/(1.0+z) # w = -0.1\\n cdef double dobdz = (2.4*ombar_b -in_r*ombar_a*ombar_b/Hz +in_s*ombar_b*ombar_c/Hz)/(1.0+z) # w = -0.2\\n cdef double docdz = (2.1*ombar_c -in_s*ombar_b*ombar_c/Hz +in_t*ombar_c*ombar_d/Hz)/(1.0+z) # w = -0.3\\n cdef double doddz = (1.8*ombar_d -in_t*ombar_c*ombar_d/Hz +in_u*ombar_d*ombar_e/Hz)/(1.0+z) # w = -0.4\\n cdef double doedz = (1.5*ombar_e -in_u*ombar_d*ombar_e/Hz +in_v*ombar_e*ombar_f/Hz)/(1.0+z) # w = -0.5\\n cdef double dofdz = (1.2*ombar_f -in_v*ombar_e*ombar_f/Hz +in_w*ombar_f*ombar_g/Hz)/(1.0+z) # w = -0.6\\n cdef double dogdz = (0.9*ombar_g -in_w*ombar_f*ombar_g/Hz +in_x*ombar_g*ombar_h/Hz)/(1.0+z) # w = -0.7\\n cdef double dohdz = (0.6*ombar_h -in_x*ombar_g*ombar_h/Hz +in_y*ombar_h*ombar_i/Hz)/(1.0+z) # w = -0.8\\n cdef double doidz = (0.3*ombar_i -in_y*ombar_h*ombar_i/Hz +in_z*ombar_i*ombar_de/Hz)/(1.0+z)# w = -0.9\\n cdef double dodedz = -in_z*ombar_i*ombar_de/(1.0+z)/Hz # w = -1\\n\\n cdef double ddldz = 1.0/Hz\\n\\n # first derivatives of functions I want to find:\\n f = [dtdz,# dt/dz (= f.d wrt z of time)\\n dadz,# d(a)/dz (= f.d wrt z of scale factor)\\n domdz,# dw = 0, (ombar_m)/dz (= f.d wrt z of density_m(t) / crit density(t0))\\n dordz,# w = 1/3, d(ombar_r)/dz (= f.d wrt z of density_r(t) / crit density(t0))\\n doadz,# w = -0.1\\n dobdz,# w = -0.2\\n docdz,# w = -0.3\\n doddz,# w = -0.4\\n doedz,# w = -0.5\\n dofdz,# w = -0.6\\n dogdz,# w = -0.7\\n dohdz,# w = -0.8\\n doidz,# w = -0.9\\n dodedz,# w = -1, d(ombar_de)/dz (= f.d wrt z of density_de(t) / crit density(t0))\\n 1.0,# d(z)/dz (= f.d wrt z of redshift)\\n ddldz]# d(dl)/dz (= f.d wrt z of luminosty distance) # H + Hdz*(1+z)\\n\\n return f\",\n \"def c_s(self, z):\\n R = self.R_bg(z)\\n return const.c/np.sqrt(3.*(1.+R))\",\n \"def _hls2rgb(self,h):\\n h=h**self.exponent\\n if(self.invert): h=1.0-h\\n h=h*360.0\\n h=Numeric.fmod(h,360.0)\\n if(self.hls_hls):\\n h=h/60.0\\n else:\\n if(h<120):\\n h=h/120.0 # /* 0..1 Rot..(Orange)..Gelb */\\n elif(h<180):\\n h=h/60.0 - 1.0 # /* 1..2 Gelb..Gruen */\\n elif(h<240):\\n h=h/30.0 - 4.0 # /* 2..4 Gruen..Blaugruen..Blau*/\\n else:\\n h=h/60.0 # /* 4..6 Blau..Purpur..Rot */\\n c=int(h)\\n frac=h-c\\n if (self.hls_l<=0.5):\\n maxi=self.hls_l*(1.0+self.hls_s)\\n else:\\n maxi=self.hls_l+self.hls_s-self.hls_l*self.hls_s\\n mini=2*self.hls_l-maxi;\\n diff=maxi-mini;\\n if(self.hls_s==0): # /* grau */\\n return(1.0,1.0,1.0) \\n else:\\n if(c==0):\\n return(maxi,mini+frac*diff,mini)\\n elif(c==1):\\n return(mini+(1.0-frac)*diff,maxi,mini)\\n elif(c==2):\\n return(mini,maxi,mini+frac*diff)\\n elif(c==3):\\n return(mini,mini+(1.0-frac)*diff,maxi)\\n elif(c==4):\\n return(mini+frac*diff,mini,maxi)\\n else:\\n return(maxi,mini,mini+(1.0-frac)*diff)\",\n \"def _FracInt(x,y,z,a,b,c,tau,n):\\n denom = np.sqrt((a + tau)*(b + tau)*(c + tau))\\n return (1. - x**2/(a + tau) - y**2/(b + tau) - z**2/(c + tau))**n / denom\",\n \"def get_RF_color(rf_vec):\\n color = copy.deepcopy(rf_vec)\\n\\n def renormalize(v):\\n v -= np.min(v)\\n return v / np.max(v)\\n\\n for i in xrange(3):\\n color[:, i] = renormalize(color[:, i])\\n return color\",\n \"def color(value):\\r\\n return 'RGB({}, {}, {})'.format(value.red(), value.blue(), value.green())\",\n \"def opacity_transfer_function(mapping, n_colors, interpolate=True, kind='quadratic'):\\n sigmoid = lambda x: np.array(1 / (1 + np.exp(-x)) * 255, dtype=np.uint8)\\n transfer_func = {\\n 'linear': np.linspace(0, 255, n_colors, dtype=np.uint8),\\n 'geom': np.geomspace(1e-6, 255, n_colors, dtype=np.uint8),\\n 'geom_r': np.geomspace(255, 1e-6, n_colors, dtype=np.uint8),\\n 'sigmoid': sigmoid(np.linspace(-10.0, 10.0, n_colors)),\\n 'sigmoid_3': sigmoid(np.linspace(-3.0, 3.0, n_colors)),\\n 'sigmoid_4': sigmoid(np.linspace(-4.0, 4.0, n_colors)),\\n 'sigmoid_5': sigmoid(np.linspace(-5.0, 5.0, n_colors)),\\n 'sigmoid_6': sigmoid(np.linspace(-6.0, 6.0, n_colors)),\\n 'sigmoid_7': sigmoid(np.linspace(-7.0, 7.0, n_colors)),\\n 'sigmoid_8': sigmoid(np.linspace(-8.0, 8.0, n_colors)),\\n 'sigmoid_9': sigmoid(np.linspace(-9.0, 9.0, n_colors)),\\n 'sigmoid_10': sigmoid(np.linspace(-10.0, 10.0, n_colors)),\\n }\\n transfer_func['linear_r'] = transfer_func['linear'][::-1]\\n transfer_func['sigmoid_r'] = transfer_func['sigmoid'][::-1]\\n for i in range(3, 11):\\n k = f'sigmoid_{i}'\\n rk = f'{k}_r'\\n transfer_func[rk] = transfer_func[k][::-1]\\n if isinstance(mapping, str):\\n try:\\n return transfer_func[mapping]\\n except KeyError:\\n raise KeyError(f'opactiy transfer function ({mapping}) unknown.')\\n elif isinstance(mapping, (np.ndarray, list, tuple)):\\n mapping = np.array(mapping)\\n if mapping.size == n_colors:\\n # User could pass transfer function ready for lookup table\\n pass\\n elif mapping.size < n_colors:\\n # User pass custom transfer function to be linearly interpolated\\n if np.max(mapping) > 1.0 or np.min(mapping) < 0.0:\\n mapping = normalize(mapping)\\n # Interpolate transfer function to match lookup table\\n xo = np.linspace(0, n_colors, len(mapping), dtype=np.int_)\\n xx = np.linspace(0, n_colors, n_colors, dtype=np.int_)\\n try:\\n if not interpolate:\\n raise ValueError('No interpolation.')\\n # Use a quadratic interp if scipy is available\\n from scipy.interpolate import interp1d\\n\\n # quadratic has best/smoothest results\\n f = interp1d(xo, mapping, kind=kind)\\n vals = f(xx)\\n vals[vals < 0] = 0.0\\n vals[vals > 1.0] = 1.0\\n mapping = (vals * 255.0).astype(np.uint8)\\n\\n except (ImportError, ValueError):\\n # Otherwise use simple linear interp\\n mapping = (np.interp(xx, xo, mapping) * 255).astype(np.uint8)\\n else:\\n raise RuntimeError(\\n f'Transfer function cannot have more values than `n_colors`. This has {mapping.size} elements'\\n )\\n return mapping\\n raise TypeError(f'Transfer function type ({type(mapping)}) not understood')\",\n \"def calculate_colors(v):\\n\\n # Define constant color values\\n lightness = [0.35, 0.5, 0.65]\\n saturation = [0.35, 0.5, 0.65]\\n\\n # Calculate the CRC-32 checksum of colors encoded as a UTF-8 string\\n hash = crc32(str(v).encode('utf-8')) & 0xffffffff\\n\\n # Calculate the HSL (hue, saturation, lightness) values for the vertices\\n hue = ((hash % 359) / 1000) * 360\\n hash //= 360\\n sat = saturation[hash % len(saturation)]\\n hash //= len(saturation)\\n lig = lightness[hash % len(lightness)]\\n\\n return (hue, sat, lig)\",\n \"def rgb_image(self):\\n z3 = self.z[:,:,newaxis]\\n return z3 * self.c\",\n \"def default_render_color_maker(num:int):\\n if num < 7:\\n log.warn('Fewer than 7 rendering colors are being generated. This may cause issues if a URDF with a 6+ axis robot is loaded.')\\n\\n b = np.linspace(0,255,num).astype(int) # Blue values are always unique\\n\\n g = [0] * b.size\\n r = np.abs(255 - 2*b)\\n\\n colors = []\\n for idx in range(num):\\n colors.append([b[idx],g[idx],r[idx]])\\n return colors\",\n \"def color(self, data):\\n\\n red = np.interp(data, self.range, self.r)\\n blue = np.interp(data, self.range, self.b)\\n green = np.interp(data, self.range, self.g)\\n # Style plot to return a grey color when value is 'nan'\\n red[np.isnan(red)] = 240\\n blue[np.isnan(blue)] = 240\\n green[np.isnan(green)] = 240\\n colors = np.dstack([red.astype(np.uint8),\\n green.astype(np.uint8),\\n blue.astype(np.uint8),\\n np.full_like(data, 255, dtype=np.uint8)])\\n #return colors.view(dtype=np.uint32).reshape(data.shape)\\n c=[]\\n for i in range(len(data)):\\n c.append([red[i],green[i],blue[i]])\\n return c\",\n \"def fromInts(r, g, b):\\n return IColor(r/255.,g/255.,b/255.)\",\n \"def nthColor(i):\\n if i < len(colors):\\n return colors[i]\\n\\n c1 = colors[i % len(colors)]\\n c2 = nthColor(i // len(colors))\\n\\n return \\\"#\\\" + hex((int(c1[1:],16) + int(c2[1:],16)) // 2)[2:]\",\n \"def calculate_colors(countries, max_users):\\n for country_code in countries:\\n countries[country_code]['color_rgb'] = [\\n calculate_color(\\n countries[country_code]['percentage_of_users'],\\n max_users,\\n 8,\\n 229\\n ),\\n calculate_color(\\n countries[country_code]['percentage_of_users'],\\n max_users,\\n 64,\\n 245\\n ),\\n calculate_color(\\n countries[country_code]['percentage_of_users'],\\n max_users,\\n 129,\\n 223\\n )\\n ]\\n\\n return countries\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6529024","0.6498187","0.6447665","0.6419169","0.63274384","0.6322723","0.6188783","0.60802084","0.6072802","0.5948455","0.5910232","0.5883263","0.5878698","0.5863626","0.5861718","0.58612084","0.583371","0.5806005","0.57980853","0.57717776","0.57256144","0.57129073","0.5703746","0.5682948","0.56603336","0.5637444","0.5637161","0.5634136","0.56340665","0.5619741","0.5617917","0.5608966","0.55647486","0.55647486","0.55647486","0.55647486","0.55515975","0.55340534","0.553184","0.5502871","0.5502708","0.5493537","0.5490346","0.5474436","0.54704845","0.5461585","0.54567546","0.543483","0.5431002","0.54240406","0.542257","0.54085004","0.5380995","0.5379006","0.53606683","0.53603333","0.53525555","0.53508675","0.5338714","0.5336543","0.53235847","0.5318106","0.53073597","0.53015244","0.53006965","0.5289658","0.52780503","0.52675587","0.52654797","0.5265112","0.5258626","0.52401733","0.52400184","0.52287024","0.5213346","0.520089","0.5188416","0.5187515","0.5181235","0.5180503","0.5176768","0.5166294","0.5165576","0.51652366","0.5161931","0.5158527","0.51518404","0.5146362","0.5142071","0.51416725","0.5141272","0.5139576","0.51353776","0.5132044","0.51284057","0.51271","0.5126827","0.51242346","0.5121352","0.51152354"],"string":"[\n \"0.6529024\",\n \"0.6498187\",\n \"0.6447665\",\n \"0.6419169\",\n \"0.63274384\",\n \"0.6322723\",\n \"0.6188783\",\n \"0.60802084\",\n \"0.6072802\",\n \"0.5948455\",\n \"0.5910232\",\n \"0.5883263\",\n \"0.5878698\",\n \"0.5863626\",\n \"0.5861718\",\n \"0.58612084\",\n \"0.583371\",\n \"0.5806005\",\n \"0.57980853\",\n \"0.57717776\",\n \"0.57256144\",\n \"0.57129073\",\n \"0.5703746\",\n \"0.5682948\",\n \"0.56603336\",\n \"0.5637444\",\n \"0.5637161\",\n \"0.5634136\",\n \"0.56340665\",\n \"0.5619741\",\n \"0.5617917\",\n \"0.5608966\",\n \"0.55647486\",\n \"0.55647486\",\n \"0.55647486\",\n \"0.55647486\",\n \"0.55515975\",\n \"0.55340534\",\n \"0.553184\",\n \"0.5502871\",\n \"0.5502708\",\n \"0.5493537\",\n \"0.5490346\",\n \"0.5474436\",\n \"0.54704845\",\n \"0.5461585\",\n \"0.54567546\",\n \"0.543483\",\n \"0.5431002\",\n \"0.54240406\",\n \"0.542257\",\n \"0.54085004\",\n \"0.5380995\",\n \"0.5379006\",\n \"0.53606683\",\n \"0.53603333\",\n \"0.53525555\",\n \"0.53508675\",\n \"0.5338714\",\n \"0.5336543\",\n \"0.53235847\",\n \"0.5318106\",\n \"0.53073597\",\n \"0.53015244\",\n \"0.53006965\",\n \"0.5289658\",\n \"0.52780503\",\n \"0.52675587\",\n \"0.52654797\",\n \"0.5265112\",\n \"0.5258626\",\n \"0.52401733\",\n \"0.52400184\",\n \"0.52287024\",\n \"0.5213346\",\n \"0.520089\",\n \"0.5188416\",\n \"0.5187515\",\n \"0.5181235\",\n \"0.5180503\",\n \"0.5176768\",\n \"0.5166294\",\n \"0.5165576\",\n \"0.51652366\",\n \"0.5161931\",\n \"0.5158527\",\n \"0.51518404\",\n \"0.5146362\",\n \"0.5142071\",\n \"0.51416725\",\n \"0.5141272\",\n \"0.5139576\",\n \"0.51353776\",\n \"0.5132044\",\n \"0.51284057\",\n \"0.51271\",\n \"0.5126827\",\n \"0.51242346\",\n \"0.5121352\",\n \"0.51152354\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6906919"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":81180,"cells":{"query":{"kind":"string","value":"Gets the colour of a z and step value."},"document":{"kind":"string","value":"def colour(z, i):\n if abs(z) < self.threshold:\n return self.background\n v = np.log2(i + self.threshold - np.log2(np.log2(abs(z)))) / self.threshold\n if v < 1.0:\n return v ** b1, v ** b2, v ** b3 # background\n else:\n v = max(0, 2 - v)\n return v ** r1, v ** r2, v ** r3 # main tones"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_colour(self, r1, r2, r3, b1, b2, b3):\n\n def colour(z, i):\n \"\"\"\n Gets the colour of a z and step value.\n\n :param complex z: the z value from the mandelbrot set\n :param int i: the step value\n\n :rtype: tuple\n :return: the three RGB colours\n \"\"\"\n if abs(z) < self.threshold:\n return self.background\n v = np.log2(i + self.threshold - np.log2(np.log2(abs(z)))) / self.threshold\n if v < 1.0:\n return v ** b1, v ** b2, v ** b3 # background\n else:\n v = max(0, 2 - v)\n return v ** r1, v ** r2, v ** r3 # main tones\n\n return colour","def colour(z, i):\n if abs(z) < self.threshold:\n return 0, 0, 0\n v = np.log2(i + self.threshold - np.log2(np.log2(abs(z)))) / self.threshold\n if v < 1.0:\n return v ** b1, v ** b2, v ** b3 # coloured tones\n else:\n v = max(0, 2 - v)\n return v ** r1, v ** r2, v ** r3 # sepia tones","def get_inner_colour(self, r1, r2, r3, b1, b2, b3):\n\n def colour(z, i):\n \"\"\"\n Gets the colour of a z and step value.\n\n :param z: the z value from the mandelbrot set\n :param i: the step value\n\n :rtype: list\n :return: list containing the RGB colours\n \"\"\"\n if abs(z) < self.threshold:\n return 0, 0, 0\n v = np.log2(i + self.threshold - np.log2(np.log2(abs(z)))) / self.threshold\n if v < 1.0:\n return v ** b1, v ** b2, v ** b3 # coloured tones\n else:\n v = max(0, 2 - v)\n return v ** r1, v ** r2, v ** r3 # sepia tones\n\n return colour","def getColor(self):\n return self._l[2]","def get_color(self):\n return self._io.last_state['color']['front-center']","def getJuliaPixelColor(z, gradient):\n c = complex(-1.0, 0.0)\n for i in range(len(gradient)):\n z = z * z + c # Get z1, z2, ...\n if abs(z) > 2:\n z = 2.0\n return gradient[i] # The sequence is unbounded\n return gradient[len(gradient) - 1] # Indicate a bounded sequence","def get_color(in_val, min_val=0, max_val=100):\n width = max_val - min_val\n unit = width / len(continuum)\n return continuum[min(int(in_val / unit), 19)]","def get_colors(self):\n x = np.linspace(0, 1, self.length)\n y = x**self.gamma\n\n value = np.linspace(0, 1, len(self.colors))\n r = np.interp(y, value, self.colors[:,0])\n g = np.interp(y, value, self.colors[:,1])\n b = np.interp(y, value, self.colors[:,2])\n\n return np.dstack((r, g, b)).reshape(len(r), 3).astype(np.uint8)","def get_color(val, trendType):\n if(val > 0):\n if(trendType == 'normal'):\n color = 'red'\n else:\n color = 'green'\n elif(val < 0):\n if(trendType == 'normal'):\n color = 'green'\n else:\n color = 'red'\n else:\n color = 'blue'\n return color","def _color(self,c):\n return self.colorlist[c%len(self.colorlist)]","def getColor(self):\r\n return self.color","def getColor(self,number):\n if number >= 0:\n if self.inverse:\n ret = cs.hsv_to_rgb(0,0,abs(number/self.maxp))\n else:\n ret = cs.hsv_to_rgb(0,0,1-abs(number/self.maxp))\n else:\n if self.inverse:\n ret = cs.hsv_to_rgb(0,1-abs(number/self.maxn),1)\n else:\n ret = cs.hsv_to_rgb(0,abs(number/self.maxn),1)\n return [ret[0]*255.0,ret[1]*255.0,ret[2]*255.0]","def get_color(self):\n return self.color","def get_color(self):\r\n return self.__color","def _get_color(self):\n return self.__color","def _get_color(self):\n return self.__color","def _get_color(self):\n return self.__color","def _get_color(self):\n return self.__color","def get_color(self):\r\n return self._color","def getColor(self):\n return self.color","def get_color(self):\n return COLOR_DICT[self.element]","def getColor(self,number):\n if number >= 0:\n ret = cs.hsv_to_rgb(0,0,1-abs(number/self.maxp))\n else:\n ret = cs.hsv_to_rgb(0,abs(number/self.maxn),1)\n hexcolor = '#%02x%02x%02x' % (ret[0]*255,ret[1]*255,ret[2]*255)\n return hexcolor","def color(self):\n return self['color']","def _get_color(self, r, g, b):\n clr = (r, g, b)\n return clr","def getColor(self):\n return self.__color","def getColor(self):\n return self.__color","def getColor(self):\n return self.__color","def getTweenColor(self, factor):\n\n pass","def get_color(self, value):\n value = min(max(0,value), 1) * 510\n\n if value < 255:\n redValue = 255\n greenValue = math.sqrt(value) * 16\n greenValue = int(greenValue)\n else:\n greenValue = 255\n value = value - 255\n redValue = 255 - (value * value / 255)\n redValue = int(redValue)\n return '#' + f\"{redValue:0{2}x}\" + f\"{greenValue:0{2}x}\" + '00'","def get_color(self, point):\n return self._color.dup()","def grey(self):\n return sum((self.value(0), self.value(1), self.value(2)))/3","def get_color(self):\n return self._color","def get_color(self):\n return self._color","def get_colour(self):\n return self.colour","def get_color(self):\n\n return self.color","def _rgb(x, y, z):\n rgb = np.array([x, y, z]).T\n rgb -= rgb.min(0)\n rgb /= np.maximum(rgb.max(0), 1e-16) # avoid div by zero\n return rgb","def color(self):\n return rgba(self.value_of_css_property('color'))","def get_color(self, point):\n \n d = point - self._origin\n dist = int(d.dot(d) ** 0.5) % 2\n if dist == 0:\n return self.c1.dup()\n else:\n return self.c2.dup()","def color(self):\n return self._rgba","def color(self):\n return self._rgba","def collatz(value):\r\n assert value >= 1\r\n if value % 2 == 0:\r\n return value/2\r\n else:\r\n return 3 * value + 1","def getColor(k) :\n colors = [\"#862B59\",\"#A10000\",\"#0A6308\",\"#123677\",\"#ff8100\",\"#F28686\",\"#6adf4f\",\"#58ccdd\",\"#3a3536\",\"#00ab7c\"]\n return colors[k]","def color(self):\n return self._zoom.color","def color_negative_red_positive_green(val):\n if val < 0:\n color = 'red'\n elif val > 0:\n color = 'green'\n else:\n color = 'black'\n\n return 'color: %s' % color","def c_s(self, z):\n R = self.R_bg(z)\n return const.c/np.sqrt(3.*(1.+R))","def get_color(self) -> str:\r\n return self.color","def color(self):\n return self.container['color']","def color(self):\n return self.__color","def get_color(self) -> str:\n return self.color","def get_color(self):\n\n return self._color","def colorize_xyz(xyz):\n xyz_vis = xyz - xyz.min()\n return (255 * xyz_vis / xyz_vis.max()).astype(np.uint8)","def get_colour(self, x, y):\n if x >= self.width or y >= self.height:\n return (0, 0, 0)\n\n return self.env_img.get_at((int(x), int(y))).normalize()[0:3]","def xy_color(self):\n return self._xy_color","def rgbcolor(h, f):\n # q = 1 - f\n # t = f\n if h == 0:\n return v, f, p\n elif h == 1:\n return 1 - f, v, p\n elif h == 2:\n return p, v, f\n elif h == 3:\n return p, 1 - f, v\n elif h == 4:\n return f, p, v\n elif h == 5:\n return v, p, 1 - f","def color(value):\r\n return 'RGB({}, {}, {})'.format(value.red(), value.blue(), value.green())","def color_negative_red(val):\n if val == 'k':\n color = 'red' \n else:\n color = 'yellow'\n return ['color: %s' % color]*3","def color(self):\n return 0x2f3136","def color(self):\n return self._color","def color(self):\n return self._color","def getSegmentColor(self):\n l = [segment.color for segment in self.segments]\n if l.count(l[0]) == len(l):\n return l[0]\n else:\n raise ValueError(\"The colors of the points must be the same otherwise it makes no sense.\")","def _get_color(self, c, x, max_num):\n\n ratio = 5*(float(x)/max_num)\n i = int(math.floor(ratio))\n j = int(math.ceil(ratio))\n ratio -= i\n r = (1 - ratio) * self._colors[i][c] + ratio*self._colors[j][c]\n return int(255*r)","def colors(self):\n\t\treturn [(0, 30, 255),(0, 30, 120)]","def voltage_color(self, voltage):\n set_of_colors = []\n # black\n set_of_colors.append('#000000')\n # grey 1\n set_of_colors.append('#474747')\n # grey 2\n set_of_colors.append('#B2B2B2')\n # white\n set_of_colors.append('#FFFFFF')\n # yellow\n set_of_colors.append('#FFFF66')\n\n if (voltage > 0.95):\n return set_of_colors[4]\n elif (voltage > 0.9):\n return set_of_colors[3]\n elif (voltage > 0.7):\n return set_of_colors[2]\n elif (voltage > 0.6):\n return set_of_colors[1]\n else:\n return set_of_colors[0]","def getHSV(self):\n\t\tself.colour = [self.getH(), self.getS(),1]\n\t\treturn self.colour","def color_temp(self):\n return self._color_temp","def get_color(self) -> Optional[str]:\n return self.color","def color(self):\n if \"color\" in self._prop_dict:\n return self._prop_dict[\"color\"]\n else:\n return None","def last_color(self):\n idx = self._color_indexes.get(self._plotid)\n if idx is not None:\n return COLOR_CYCLE[(idx-1) % len(COLOR_CYCLE)]\n return COLOR_CYCLE[0]","def GetColor(self, *args):\n return _XCAFDoc.XCAFDoc_ColorTool_GetColor(self, *args)","def color(self, data):\n\n red = np.interp(data, self.range, self.r)\n blue = np.interp(data, self.range, self.b)\n green = np.interp(data, self.range, self.g)\n # Style plot to return a grey color when value is 'nan'\n red[np.isnan(red)] = 240\n blue[np.isnan(blue)] = 240\n green[np.isnan(green)] = 240\n colors = np.dstack([red.astype(np.uint8),\n green.astype(np.uint8),\n blue.astype(np.uint8),\n np.full_like(data, 255, dtype=np.uint8)])\n #return colors.view(dtype=np.uint32).reshape(data.shape)\n c=[]\n for i in range(len(data)):\n c.append([red[i],green[i],blue[i]])\n return c","def _getDockColor(self, plane):\n color = (0,0,0)\n if plane.zAxis != -1:\n color = self.globalAxis[plane.zAxis].color[0:3]\n return color","def get_z(self):\n return self.coords[2]","def _get_red(self):\n return self.__red","def _get_red(self):\n return self.__red","def _get_red(self):\n return self.__red","def __get_z__(self):\n return self.Direction['z']","def colortopalette(color, palette):\n for a,b in palette:\n if color >= a and color < b:\n return b","def color(self):\n if self._simplecell:\n self.fetch()\n return self._color","def get_color(self):\r\n if self.color:\r\n return \"RED\"\r\n else:\r\n return \"BLACK\"","def get_colour(value):\n \n np.random.seed(value)\n colour = [np.random.uniform() for i in range(3)]\n return (tuple(colour))","def _cal_color(xy, norm=False):\n color = gaussian_kde(xy)(xy)\n if norm:\n cmin = np.min(color)\n cmax = np.max(color)\n color = (color - cmin) / (cmax - cmin)\n\n return color","def get_colour(self, address):\n return idaapi.get_item_color(address)","def calc_color(temp, dewtemp, humid):\n if temp == hw.MISSING_VAL or dewtemp == hw.MISSING_VAL or humid == hw.MISSING_VAL:\n status = 'grey'\n istatus = 0\n else:\n if (temp - dewtemp) < RED_DEWPOINT or humid >= RED_HUMIDITY:\n status = 'red'\n istatus = 3\n elif (temp - dewtemp) < YELLOW_DEWPOINT or humid >= YELLOW_HUMIDITY:\n status = 'yellow'\n istatus = 2\n else:\n status = 'green'\n istatus = 1\n # NOTE: 'status' variable is not used\n return istatus","def get_value(self, x, y, z):\n\t\treturn self.data[ self.xyz_to_offset(x,y,z) ]","def get_color_marker(self):\r\n return self._board.get_color_marker_b()","def elevation_to_color_formula(number):\n color_intensity = abs(round(((number - lowest_point) / (highest_point - lowest_point)) * 255))\n return color_intensity","def color(self):\n return self.COLOR","def getz(self,pix):\n\t\tx = pix[0]\n\t\ty = pix[1]\n\t\treturn self.cube_data[x][y]","def ci(x, y, z):\n\n return (x * 16 + z) * CHUNK_HEIGHT + y","def getnextcolor(self):\n self.usedcc = [line.get_c() for line in self.axes.lines]\n for c in self.cc:\n if c not in self.usedcc:\n return c\n\n # Only gets here if all colors already used...\n i = len(self.axes.lines)\n numc = len(self.cc)\n i = i % numc\n return self.cc[i]","def color_from_value(self, value):\n \n return ImageColor.getrgb(\"hsl(%d,%d%%,%d%%)\" % (int( (1.0 - value) * 360 ), 80, 50))","def color(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"color\")","def color(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"color\")","def color(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"color\")","def color(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"color\")","def color(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"color\")","def color(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"color\")","def color(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"color\")","def color(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"color\")","def color(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"color\")"],"string":"[\n \"def get_colour(self, r1, r2, r3, b1, b2, b3):\\n\\n def colour(z, i):\\n \\\"\\\"\\\"\\n Gets the colour of a z and step value.\\n\\n :param complex z: the z value from the mandelbrot set\\n :param int i: the step value\\n\\n :rtype: tuple\\n :return: the three RGB colours\\n \\\"\\\"\\\"\\n if abs(z) < self.threshold:\\n return self.background\\n v = np.log2(i + self.threshold - np.log2(np.log2(abs(z)))) / self.threshold\\n if v < 1.0:\\n return v ** b1, v ** b2, v ** b3 # background\\n else:\\n v = max(0, 2 - v)\\n return v ** r1, v ** r2, v ** r3 # main tones\\n\\n return colour\",\n \"def colour(z, i):\\n if abs(z) < self.threshold:\\n return 0, 0, 0\\n v = np.log2(i + self.threshold - np.log2(np.log2(abs(z)))) / self.threshold\\n if v < 1.0:\\n return v ** b1, v ** b2, v ** b3 # coloured tones\\n else:\\n v = max(0, 2 - v)\\n return v ** r1, v ** r2, v ** r3 # sepia tones\",\n \"def get_inner_colour(self, r1, r2, r3, b1, b2, b3):\\n\\n def colour(z, i):\\n \\\"\\\"\\\"\\n Gets the colour of a z and step value.\\n\\n :param z: the z value from the mandelbrot set\\n :param i: the step value\\n\\n :rtype: list\\n :return: list containing the RGB colours\\n \\\"\\\"\\\"\\n if abs(z) < self.threshold:\\n return 0, 0, 0\\n v = np.log2(i + self.threshold - np.log2(np.log2(abs(z)))) / self.threshold\\n if v < 1.0:\\n return v ** b1, v ** b2, v ** b3 # coloured tones\\n else:\\n v = max(0, 2 - v)\\n return v ** r1, v ** r2, v ** r3 # sepia tones\\n\\n return colour\",\n \"def getColor(self):\\n return self._l[2]\",\n \"def get_color(self):\\n return self._io.last_state['color']['front-center']\",\n \"def getJuliaPixelColor(z, gradient):\\n c = complex(-1.0, 0.0)\\n for i in range(len(gradient)):\\n z = z * z + c # Get z1, z2, ...\\n if abs(z) > 2:\\n z = 2.0\\n return gradient[i] # The sequence is unbounded\\n return gradient[len(gradient) - 1] # Indicate a bounded sequence\",\n \"def get_color(in_val, min_val=0, max_val=100):\\n width = max_val - min_val\\n unit = width / len(continuum)\\n return continuum[min(int(in_val / unit), 19)]\",\n \"def get_colors(self):\\n x = np.linspace(0, 1, self.length)\\n y = x**self.gamma\\n\\n value = np.linspace(0, 1, len(self.colors))\\n r = np.interp(y, value, self.colors[:,0])\\n g = np.interp(y, value, self.colors[:,1])\\n b = np.interp(y, value, self.colors[:,2])\\n\\n return np.dstack((r, g, b)).reshape(len(r), 3).astype(np.uint8)\",\n \"def get_color(val, trendType):\\n if(val > 0):\\n if(trendType == 'normal'):\\n color = 'red'\\n else:\\n color = 'green'\\n elif(val < 0):\\n if(trendType == 'normal'):\\n color = 'green'\\n else:\\n color = 'red'\\n else:\\n color = 'blue'\\n return color\",\n \"def _color(self,c):\\n return self.colorlist[c%len(self.colorlist)]\",\n \"def getColor(self):\\r\\n return self.color\",\n \"def getColor(self,number):\\n if number >= 0:\\n if self.inverse:\\n ret = cs.hsv_to_rgb(0,0,abs(number/self.maxp))\\n else:\\n ret = cs.hsv_to_rgb(0,0,1-abs(number/self.maxp))\\n else:\\n if self.inverse:\\n ret = cs.hsv_to_rgb(0,1-abs(number/self.maxn),1)\\n else:\\n ret = cs.hsv_to_rgb(0,abs(number/self.maxn),1)\\n return [ret[0]*255.0,ret[1]*255.0,ret[2]*255.0]\",\n \"def get_color(self):\\n return self.color\",\n \"def get_color(self):\\r\\n return self.__color\",\n \"def _get_color(self):\\n return self.__color\",\n \"def _get_color(self):\\n return self.__color\",\n \"def _get_color(self):\\n return self.__color\",\n \"def _get_color(self):\\n return self.__color\",\n \"def get_color(self):\\r\\n return self._color\",\n \"def getColor(self):\\n return self.color\",\n \"def get_color(self):\\n return COLOR_DICT[self.element]\",\n \"def getColor(self,number):\\n if number >= 0:\\n ret = cs.hsv_to_rgb(0,0,1-abs(number/self.maxp))\\n else:\\n ret = cs.hsv_to_rgb(0,abs(number/self.maxn),1)\\n hexcolor = '#%02x%02x%02x' % (ret[0]*255,ret[1]*255,ret[2]*255)\\n return hexcolor\",\n \"def color(self):\\n return self['color']\",\n \"def _get_color(self, r, g, b):\\n clr = (r, g, b)\\n return clr\",\n \"def getColor(self):\\n return self.__color\",\n \"def getColor(self):\\n return self.__color\",\n \"def getColor(self):\\n return self.__color\",\n \"def getTweenColor(self, factor):\\n\\n pass\",\n \"def get_color(self, value):\\n value = min(max(0,value), 1) * 510\\n\\n if value < 255:\\n redValue = 255\\n greenValue = math.sqrt(value) * 16\\n greenValue = int(greenValue)\\n else:\\n greenValue = 255\\n value = value - 255\\n redValue = 255 - (value * value / 255)\\n redValue = int(redValue)\\n return '#' + f\\\"{redValue:0{2}x}\\\" + f\\\"{greenValue:0{2}x}\\\" + '00'\",\n \"def get_color(self, point):\\n return self._color.dup()\",\n \"def grey(self):\\n return sum((self.value(0), self.value(1), self.value(2)))/3\",\n \"def get_color(self):\\n return self._color\",\n \"def get_color(self):\\n return self._color\",\n \"def get_colour(self):\\n return self.colour\",\n \"def get_color(self):\\n\\n return self.color\",\n \"def _rgb(x, y, z):\\n rgb = np.array([x, y, z]).T\\n rgb -= rgb.min(0)\\n rgb /= np.maximum(rgb.max(0), 1e-16) # avoid div by zero\\n return rgb\",\n \"def color(self):\\n return rgba(self.value_of_css_property('color'))\",\n \"def get_color(self, point):\\n \\n d = point - self._origin\\n dist = int(d.dot(d) ** 0.5) % 2\\n if dist == 0:\\n return self.c1.dup()\\n else:\\n return self.c2.dup()\",\n \"def color(self):\\n return self._rgba\",\n \"def color(self):\\n return self._rgba\",\n \"def collatz(value):\\r\\n assert value >= 1\\r\\n if value % 2 == 0:\\r\\n return value/2\\r\\n else:\\r\\n return 3 * value + 1\",\n \"def getColor(k) :\\n colors = [\\\"#862B59\\\",\\\"#A10000\\\",\\\"#0A6308\\\",\\\"#123677\\\",\\\"#ff8100\\\",\\\"#F28686\\\",\\\"#6adf4f\\\",\\\"#58ccdd\\\",\\\"#3a3536\\\",\\\"#00ab7c\\\"]\\n return colors[k]\",\n \"def color(self):\\n return self._zoom.color\",\n \"def color_negative_red_positive_green(val):\\n if val < 0:\\n color = 'red'\\n elif val > 0:\\n color = 'green'\\n else:\\n color = 'black'\\n\\n return 'color: %s' % color\",\n \"def c_s(self, z):\\n R = self.R_bg(z)\\n return const.c/np.sqrt(3.*(1.+R))\",\n \"def get_color(self) -> str:\\r\\n return self.color\",\n \"def color(self):\\n return self.container['color']\",\n \"def color(self):\\n return self.__color\",\n \"def get_color(self) -> str:\\n return self.color\",\n \"def get_color(self):\\n\\n return self._color\",\n \"def colorize_xyz(xyz):\\n xyz_vis = xyz - xyz.min()\\n return (255 * xyz_vis / xyz_vis.max()).astype(np.uint8)\",\n \"def get_colour(self, x, y):\\n if x >= self.width or y >= self.height:\\n return (0, 0, 0)\\n\\n return self.env_img.get_at((int(x), int(y))).normalize()[0:3]\",\n \"def xy_color(self):\\n return self._xy_color\",\n \"def rgbcolor(h, f):\\n # q = 1 - f\\n # t = f\\n if h == 0:\\n return v, f, p\\n elif h == 1:\\n return 1 - f, v, p\\n elif h == 2:\\n return p, v, f\\n elif h == 3:\\n return p, 1 - f, v\\n elif h == 4:\\n return f, p, v\\n elif h == 5:\\n return v, p, 1 - f\",\n \"def color(value):\\r\\n return 'RGB({}, {}, {})'.format(value.red(), value.blue(), value.green())\",\n \"def color_negative_red(val):\\n if val == 'k':\\n color = 'red' \\n else:\\n color = 'yellow'\\n return ['color: %s' % color]*3\",\n \"def color(self):\\n return 0x2f3136\",\n \"def color(self):\\n return self._color\",\n \"def color(self):\\n return self._color\",\n \"def getSegmentColor(self):\\n l = [segment.color for segment in self.segments]\\n if l.count(l[0]) == len(l):\\n return l[0]\\n else:\\n raise ValueError(\\\"The colors of the points must be the same otherwise it makes no sense.\\\")\",\n \"def _get_color(self, c, x, max_num):\\n\\n ratio = 5*(float(x)/max_num)\\n i = int(math.floor(ratio))\\n j = int(math.ceil(ratio))\\n ratio -= i\\n r = (1 - ratio) * self._colors[i][c] + ratio*self._colors[j][c]\\n return int(255*r)\",\n \"def colors(self):\\n\\t\\treturn [(0, 30, 255),(0, 30, 120)]\",\n \"def voltage_color(self, voltage):\\n set_of_colors = []\\n # black\\n set_of_colors.append('#000000')\\n # grey 1\\n set_of_colors.append('#474747')\\n # grey 2\\n set_of_colors.append('#B2B2B2')\\n # white\\n set_of_colors.append('#FFFFFF')\\n # yellow\\n set_of_colors.append('#FFFF66')\\n\\n if (voltage > 0.95):\\n return set_of_colors[4]\\n elif (voltage > 0.9):\\n return set_of_colors[3]\\n elif (voltage > 0.7):\\n return set_of_colors[2]\\n elif (voltage > 0.6):\\n return set_of_colors[1]\\n else:\\n return set_of_colors[0]\",\n \"def getHSV(self):\\n\\t\\tself.colour = [self.getH(), self.getS(),1]\\n\\t\\treturn self.colour\",\n \"def color_temp(self):\\n return self._color_temp\",\n \"def get_color(self) -> Optional[str]:\\n return self.color\",\n \"def color(self):\\n if \\\"color\\\" in self._prop_dict:\\n return self._prop_dict[\\\"color\\\"]\\n else:\\n return None\",\n \"def last_color(self):\\n idx = self._color_indexes.get(self._plotid)\\n if idx is not None:\\n return COLOR_CYCLE[(idx-1) % len(COLOR_CYCLE)]\\n return COLOR_CYCLE[0]\",\n \"def GetColor(self, *args):\\n return _XCAFDoc.XCAFDoc_ColorTool_GetColor(self, *args)\",\n \"def color(self, data):\\n\\n red = np.interp(data, self.range, self.r)\\n blue = np.interp(data, self.range, self.b)\\n green = np.interp(data, self.range, self.g)\\n # Style plot to return a grey color when value is 'nan'\\n red[np.isnan(red)] = 240\\n blue[np.isnan(blue)] = 240\\n green[np.isnan(green)] = 240\\n colors = np.dstack([red.astype(np.uint8),\\n green.astype(np.uint8),\\n blue.astype(np.uint8),\\n np.full_like(data, 255, dtype=np.uint8)])\\n #return colors.view(dtype=np.uint32).reshape(data.shape)\\n c=[]\\n for i in range(len(data)):\\n c.append([red[i],green[i],blue[i]])\\n return c\",\n \"def _getDockColor(self, plane):\\n color = (0,0,0)\\n if plane.zAxis != -1:\\n color = self.globalAxis[plane.zAxis].color[0:3]\\n return color\",\n \"def get_z(self):\\n return self.coords[2]\",\n \"def _get_red(self):\\n return self.__red\",\n \"def _get_red(self):\\n return self.__red\",\n \"def _get_red(self):\\n return self.__red\",\n \"def __get_z__(self):\\n return self.Direction['z']\",\n \"def colortopalette(color, palette):\\n for a,b in palette:\\n if color >= a and color < b:\\n return b\",\n \"def color(self):\\n if self._simplecell:\\n self.fetch()\\n return self._color\",\n \"def get_color(self):\\r\\n if self.color:\\r\\n return \\\"RED\\\"\\r\\n else:\\r\\n return \\\"BLACK\\\"\",\n \"def get_colour(value):\\n \\n np.random.seed(value)\\n colour = [np.random.uniform() for i in range(3)]\\n return (tuple(colour))\",\n \"def _cal_color(xy, norm=False):\\n color = gaussian_kde(xy)(xy)\\n if norm:\\n cmin = np.min(color)\\n cmax = np.max(color)\\n color = (color - cmin) / (cmax - cmin)\\n\\n return color\",\n \"def get_colour(self, address):\\n return idaapi.get_item_color(address)\",\n \"def calc_color(temp, dewtemp, humid):\\n if temp == hw.MISSING_VAL or dewtemp == hw.MISSING_VAL or humid == hw.MISSING_VAL:\\n status = 'grey'\\n istatus = 0\\n else:\\n if (temp - dewtemp) < RED_DEWPOINT or humid >= RED_HUMIDITY:\\n status = 'red'\\n istatus = 3\\n elif (temp - dewtemp) < YELLOW_DEWPOINT or humid >= YELLOW_HUMIDITY:\\n status = 'yellow'\\n istatus = 2\\n else:\\n status = 'green'\\n istatus = 1\\n # NOTE: 'status' variable is not used\\n return istatus\",\n \"def get_value(self, x, y, z):\\n\\t\\treturn self.data[ self.xyz_to_offset(x,y,z) ]\",\n \"def get_color_marker(self):\\r\\n return self._board.get_color_marker_b()\",\n \"def elevation_to_color_formula(number):\\n color_intensity = abs(round(((number - lowest_point) / (highest_point - lowest_point)) * 255))\\n return color_intensity\",\n \"def color(self):\\n return self.COLOR\",\n \"def getz(self,pix):\\n\\t\\tx = pix[0]\\n\\t\\ty = pix[1]\\n\\t\\treturn self.cube_data[x][y]\",\n \"def ci(x, y, z):\\n\\n return (x * 16 + z) * CHUNK_HEIGHT + y\",\n \"def getnextcolor(self):\\n self.usedcc = [line.get_c() for line in self.axes.lines]\\n for c in self.cc:\\n if c not in self.usedcc:\\n return c\\n\\n # Only gets here if all colors already used...\\n i = len(self.axes.lines)\\n numc = len(self.cc)\\n i = i % numc\\n return self.cc[i]\",\n \"def color_from_value(self, value):\\n \\n return ImageColor.getrgb(\\\"hsl(%d,%d%%,%d%%)\\\" % (int( (1.0 - value) * 360 ), 80, 50))\",\n \"def color(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"color\\\")\",\n \"def color(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"color\\\")\",\n \"def color(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"color\\\")\",\n \"def color(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"color\\\")\",\n \"def color(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"color\\\")\",\n \"def color(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"color\\\")\",\n \"def color(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"color\\\")\",\n \"def color(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"color\\\")\",\n \"def color(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"color\\\")\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7341066","0.72083485","0.68963253","0.6640423","0.65460277","0.6530589","0.6423319","0.62713194","0.6260205","0.6229389","0.62234646","0.6208068","0.61786675","0.6175499","0.6157222","0.6157222","0.6157222","0.6157222","0.6140309","0.6133816","0.6130664","0.611454","0.60682994","0.603787","0.6035134","0.6035134","0.6035134","0.60345554","0.60196805","0.60172725","0.60149753","0.6013027","0.6013027","0.6008104","0.5996647","0.5981982","0.5965392","0.59624976","0.59447575","0.59447575","0.5920127","0.5880634","0.586973","0.58693254","0.5842483","0.5839899","0.5835685","0.5809101","0.5797992","0.57927084","0.57706666","0.57593465","0.5752816","0.57516366","0.5748969","0.57400066","0.5729071","0.5728598","0.5728598","0.5713489","0.5697869","0.569741","0.56830156","0.56827074","0.5681814","0.56809217","0.5676321","0.5668011","0.56623477","0.5662018","0.566201","0.56588155","0.56564313","0.56564313","0.56564313","0.5654192","0.5651625","0.5651214","0.5645818","0.56361675","0.56292826","0.56290984","0.5603332","0.5598035","0.55917615","0.5590667","0.55811656","0.5578292","0.5572866","0.5568778","0.55617124","0.555974","0.555974","0.555974","0.555974","0.555974","0.555974","0.555974","0.555974","0.555974"],"string":"[\n \"0.7341066\",\n \"0.72083485\",\n \"0.68963253\",\n \"0.6640423\",\n \"0.65460277\",\n \"0.6530589\",\n \"0.6423319\",\n \"0.62713194\",\n \"0.6260205\",\n \"0.6229389\",\n \"0.62234646\",\n \"0.6208068\",\n \"0.61786675\",\n \"0.6175499\",\n \"0.6157222\",\n \"0.6157222\",\n \"0.6157222\",\n \"0.6157222\",\n \"0.6140309\",\n \"0.6133816\",\n \"0.6130664\",\n \"0.611454\",\n \"0.60682994\",\n \"0.603787\",\n \"0.6035134\",\n \"0.6035134\",\n \"0.6035134\",\n \"0.60345554\",\n \"0.60196805\",\n \"0.60172725\",\n \"0.60149753\",\n \"0.6013027\",\n \"0.6013027\",\n \"0.6008104\",\n \"0.5996647\",\n \"0.5981982\",\n \"0.5965392\",\n \"0.59624976\",\n \"0.59447575\",\n \"0.59447575\",\n \"0.5920127\",\n \"0.5880634\",\n \"0.586973\",\n \"0.58693254\",\n \"0.5842483\",\n \"0.5839899\",\n \"0.5835685\",\n \"0.5809101\",\n \"0.5797992\",\n \"0.57927084\",\n \"0.57706666\",\n \"0.57593465\",\n \"0.5752816\",\n \"0.57516366\",\n \"0.5748969\",\n \"0.57400066\",\n \"0.5729071\",\n \"0.5728598\",\n \"0.5728598\",\n \"0.5713489\",\n \"0.5697869\",\n \"0.569741\",\n \"0.56830156\",\n \"0.56827074\",\n \"0.5681814\",\n \"0.56809217\",\n \"0.5676321\",\n \"0.5668011\",\n \"0.56623477\",\n \"0.5662018\",\n \"0.566201\",\n \"0.56588155\",\n \"0.56564313\",\n \"0.56564313\",\n \"0.56564313\",\n \"0.5654192\",\n \"0.5651625\",\n \"0.5651214\",\n \"0.5645818\",\n \"0.56361675\",\n \"0.56292826\",\n \"0.56290984\",\n \"0.5603332\",\n \"0.5598035\",\n \"0.55917615\",\n \"0.5590667\",\n \"0.55811656\",\n \"0.5578292\",\n \"0.5572866\",\n \"0.5568778\",\n \"0.55617124\",\n \"0.555974\",\n \"0.555974\",\n \"0.555974\",\n \"0.555974\",\n \"0.555974\",\n \"0.555974\",\n \"0.555974\",\n \"0.555974\",\n \"0.555974\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7172321"},"document_rank":{"kind":"string","value":"2"}}},{"rowIdx":81181,"cells":{"query":{"kind":"string","value":"Generate a function which converts a z and step value to an RGB colour, based on three predefined ratios."},"document":{"kind":"string","value":"def get_inner_colour(self, r1, r2, r3, b1, b2, b3):\n\n def colour(z, i):\n \"\"\"\n Gets the colour of a z and step value.\n\n :param z: the z value from the mandelbrot set\n :param i: the step value\n\n :rtype: list\n :return: list containing the RGB colours\n \"\"\"\n if abs(z) < self.threshold:\n return 0, 0, 0\n v = np.log2(i + self.threshold - np.log2(np.log2(abs(z)))) / self.threshold\n if v < 1.0:\n return v ** b1, v ** b2, v ** b3 # coloured tones\n else:\n v = max(0, 2 - v)\n return v ** r1, v ** r2, v ** r3 # sepia tones\n\n return colour"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_colour(self, r1, r2, r3, b1, b2, b3):\n\n def colour(z, i):\n \"\"\"\n Gets the colour of a z and step value.\n\n :param complex z: the z value from the mandelbrot set\n :param int i: the step value\n\n :rtype: tuple\n :return: the three RGB colours\n \"\"\"\n if abs(z) < self.threshold:\n return self.background\n v = np.log2(i + self.threshold - np.log2(np.log2(abs(z)))) / self.threshold\n if v < 1.0:\n return v ** b1, v ** b2, v ** b3 # background\n else:\n v = max(0, 2 - v)\n return v ** r1, v ** r2, v ** r3 # main tones\n\n return colour","def rgbcolor(h, f):\n # q = 1 - f\n # t = f\n if h == 0:\n return v, f, p\n elif h == 1:\n return 1 - f, v, p\n elif h == 2:\n return p, v, f\n elif h == 3:\n return p, 1 - f, v\n elif h == 4:\n return f, p, v\n elif h == 5:\n return v, p, 1 - f","def colour(z, i):\n if abs(z) < self.threshold:\n return 0, 0, 0\n v = np.log2(i + self.threshold - np.log2(np.log2(abs(z)))) / self.threshold\n if v < 1.0:\n return v ** b1, v ** b2, v ** b3 # coloured tones\n else:\n v = max(0, 2 - v)\n return v ** r1, v ** r2, v ** r3 # sepia tones","def _rgb(x, y, z):\n rgb = np.array([x, y, z]).T\n rgb -= rgb.min(0)\n rgb /= np.maximum(rgb.max(0), 1e-16) # avoid div by zero\n return rgb","def colour(z, i):\n if abs(z) < self.threshold:\n return self.background\n v = np.log2(i + self.threshold - np.log2(np.log2(abs(z)))) / self.threshold\n if v < 1.0:\n return v ** b1, v ** b2, v ** b3 # background\n else:\n v = max(0, 2 - v)\n return v ** r1, v ** r2, v ** r3 # main tones","def __div__(self, number):\n r = self.r / number\n g = self.g / number\n b = self.b / number\n return Color(r, g, b)","def color(step: int=10) -> Tuple[int, int, int]:\n # Randomly seed the r g b values\n r, g, b = (random_uniform(0, 255), random_uniform(0, 255),\n random_uniform(0, 255))\n\n # Randomly determine if each r g and b value is increasing or not\n r_inc = True\n g_inc = True\n b_inc = True\n r_step = random_uniform(step)\n g_step = random_uniform(step)\n b_step = random_uniform(step)\n\n # Yield the initial r, g, b values\n yield r, g, b\n\n # Loop and yeild forever\n while True:\n # If r is increasing\n if r_inc:\n # Increment r by the step\n r += r_step\n # Ensure that the next step will be within the limits\n # if not then set the flag to decreasing\n r_inc = r < 255 - r_step\n # If r is decreasing\n else:\n # Decrement r by the step\n r -= r_step\n # Ensure that the next step will be within the limits\n # if not then set the flag to increasing\n r_inc = r < r_step\n\n # See above\n if g_inc:\n g += g_step\n g_inc = g < 255 - g_step\n else:\n g -= g_step\n g_inc = g < g_step\n\n # See above\n if b_inc:\n b += b_step\n b_inc = b < 255 - b_step\n else:\n b -= b_step\n b_inc = b < b_step\n\n # Yield the red, green, and blue values\n yield r, g, b","def getColor(n, total = 255, decimal = False):\n\n value = round(255*n/(total * 1.0))\n\n #red value\n if value < 96:\n red = 0\n elif value < 160:\n red = 255/((160 - 96)*1.0) * (value - 96)\n elif value < 224:\n red = 255\n else:\n red = 255 - ((255 - 128)/((255 - 224) * 1.0) * (value - 224))\n\n\n #Green value\n if value < 32:\n green = 0\n elif value < 96:\n green = 255/((96 - 32)*1.0) * (value - 32)\n elif value < 160:\n green = 255\n elif value < 224:\n green = 255 - (255/((224 - 160) * 1.0) * (value - 160))\n else:\n green = 0\n\n\n #Blue value\n if value < 32:\n blue = 128 + (255 - 128)/((32 - 0) * 1.0) * (value - 0)\n elif value < 96:\n blue = 255\n elif value < 160:\n blue = 255 - ((255 - 0)/((160 - 96) * 1.0) * (value - 96))\n else:\n blue = 0\n\n if decimal:\n return (red / 255.0, green / 255.0, blue / 255.0)\n return (int(red), int(green), int(blue))","def get_color(in_val, min_val=0, max_val=100):\n width = max_val - min_val\n unit = width / len(continuum)\n return continuum[min(int(in_val / unit), 19)]","def create_unique_color_float(tag, hue_step=0.41):\n h, v = (tag * hue_step) % 1, 1. - (int(tag * hue_step) % 4) / 5.\n r, g, b = colorsys.hsv_to_rgb(h, 1., v)\n return r, g, b","def color_from_value(self, value):\n \n return ImageColor.getrgb(\"hsl(%d,%d%%,%d%%)\" % (int( (1.0 - value) * 360 ), 80, 50))","def getTweenColor(self, factor):\n\n pass","def _to_color(indx, base):\n base2 = base * base\n b = 2 - indx / base2\n r = 2 - (indx % base2) / base\n g = 2 - (indx % base2) % base\n return b * 127, r * 127, g * 127","def create_color_gradient():\n colors = []\n step = 10\n for red, green in zip(range(255,-step, -step), range(0, 255, step)):\n colors.append({'red': red, 'green': green, 'blue': 0})\n for green, blue in zip(range(255,-step, -step), range(0, 255, step)):\n colors.append({'red': 0, 'green': green, 'blue': blue})\n for blue, red in zip(range(255,-step, -step), range(0, 255, step)):\n colors.append({'red': red, 'green': 0, 'blue': blue})\n return colors","def __Calc_HSL_to_RGB_Components(var_q, var_p, C):\r\n\r\n if C < 0:\r\n C += 1.0\r\n if C > 1:\r\n C -= 1.0\r\n\r\n # Computing C of vector (Color R, Color G, Color B)\r\n if C < (1.0 / 6.0):\r\n return var_p + ((var_q - var_p) * 6.0 * C)\r\n elif (1.0 / 6.0) <= C < 0.5:\r\n return var_q\r\n elif 0.5 <= C < (2.0 / 3.0):\r\n return var_p + ((var_q - var_p) * 6.0 * ((2.0 / 3.0) - C))\r\n else:\r\n return var_p","def getColor(self,number):\n if number >= 0:\n ret = cs.hsv_to_rgb(0,0,1-abs(number/self.maxp))\n else:\n ret = cs.hsv_to_rgb(0,abs(number/self.maxn),1)\n hexcolor = '#%02x%02x%02x' % (ret[0]*255,ret[1]*255,ret[2]*255)\n return hexcolor","def elevation_to_color_formula(number):\n color_intensity = abs(round(((number - lowest_point) / (highest_point - lowest_point)) * 255))\n return color_intensity","def getColor(self,number):\n if number >= 0:\n if self.inverse:\n ret = cs.hsv_to_rgb(0,0,abs(number/self.maxp))\n else:\n ret = cs.hsv_to_rgb(0,0,1-abs(number/self.maxp))\n else:\n if self.inverse:\n ret = cs.hsv_to_rgb(0,1-abs(number/self.maxn),1)\n else:\n ret = cs.hsv_to_rgb(0,abs(number/self.maxn),1)\n return [ret[0]*255.0,ret[1]*255.0,ret[2]*255.0]","def calculate_color(thisCountry, maxCountry, maxColor, minColor):\n countryFactor = float(thisCountry)/maxCountry\n colorRange = maxColor - minColor\n return int(colorRange*countryFactor+minColor)","def z_r(b):\n return b/1e-2 + 0.5","def floatRgb(mag, cmin, cmax):\n\n try:\n # normalize to [0,1]\n x = float(mag-cmin)/float(cmax-cmin)\n except:\n # cmax = cmin\n x = 0.5\n blue = min((max((4*(0.75-x), 0.)), 1.))\n red = min((max((4*(x-0.25), 0.)), 1.))\n green= min((max((4*math.fabs(x-0.5)-1., 0.)), 1.))\n return (red, green, blue)","def linearFromsRGB(c):\n if c <= 0.04045:\n return c / 12.92\n return math.pow((0.055 + c) / 1.055, 2.4)","def driftColor(baseColor, factor=110):\n if baseColor.lightness() > 128:\n return baseColor.darker(factor)\n else:\n return baseColor.lighter(factor+10)","def _get_color(self, c, x, max_num):\n\n ratio = 5*(float(x)/max_num)\n i = int(math.floor(ratio))\n j = int(math.ceil(ratio))\n ratio -= i\n r = (1 - ratio) * self._colors[i][c] + ratio*self._colors[j][c]\n return int(255*r)","def linearTosRGB(c):\n if c <= 0.0031308:\n return 12.92 * c\n return math.pow(c, 1.0 / 2.4) * 1.055 - 0.055","def _color(self, x, factor):\r\n factor = (factor/MAX_LEVEL) * 1.8 + .1\r\n degenerate = tf.image.grayscale_to_rgb(tf.image.rgb_to_grayscale(x))\r\n x = tfa.image.blend(degenerate, tf.cast(x, tf.float32), factor)\r\n return tf.saturate_cast(x, tf.uint8)","def get_colour(progress, colours):\n if progress >= 0 and progress <= 1:\n start_colour, end_colour = colours[0], colours[1]\n\n r = start_colour[0] + (end_colour[0] - start_colour[0]) * progress\n b = start_colour[1] + (end_colour[1] - start_colour[1]) * progress\n g = start_colour[2] + (end_colour[2] - start_colour[2]) * progress\n\n return '#%02x%02x%02x' % (round(r), round(b), round(g))\n \n else: return '#000000'","def floatRgb(mag, cmin, cmax):\n # Normalize to 0-1\n try: x = float(mag-cmin)/(cmax-cmin)\n except ZeroDivisionError: x = 0.5 # cmax == cmin\n blue = min((max((4*(0.75-x), 0.)), 1.))\n red = min((max((4*(x-0.25), 0.)), 1.))\n green = min((max((4*math.fabs(x-0.5)-1., 0.)), 1.))\n return red, green, blue","def get_color(self, value):\n value = min(max(0,value), 1) * 510\n\n if value < 255:\n redValue = 255\n greenValue = math.sqrt(value) * 16\n greenValue = int(greenValue)\n else:\n greenValue = 255\n value = value - 255\n redValue = 255 - (value * value / 255)\n redValue = int(redValue)\n return '#' + f\"{redValue:0{2}x}\" + f\"{greenValue:0{2}x}\" + '00'","def ci(x, y, z):\n\n return (x * 16 + z) * CHUNK_HEIGHT + y","def color_map(val):\n color_code = remap_interval(val, -1, 1, 0, 255)\n return int(color_code)","def getRGBFromXYAndBrightness(self, x, y, bri=1):\n # The xy to color conversion is almost the same, but in reverse order.\n # Check if the xy value is within the color gamut of the lamp.\n # If not continue with step 2, otherwise step 3.\n # We do this to calculate the most accurate color the given light can actually do.\n xyPoint = XYPoint(x, y)\n\n if not self.checkPointInLampsReach(xyPoint):\n # Calculate the closest point on the color gamut triangle\n # and use that as xy value See step 6 of color to xy.\n xyPoint = self.getClosestPointToPoint(xyPoint)\n\n # Calculate XYZ values Convert using the following formulas:\n Y = bri\n X = (Y / xyPoint.y) * xyPoint.x\n Z = (Y / xyPoint.y) * (1 - xyPoint.x - xyPoint.y)\n\n # Convert to RGB using Wide RGB D65 conversion\n r = X * 1.612 - Y * 0.203 - Z * 0.302\n g = -X * 0.509 + Y * 1.412 + Z * 0.066\n b = X * 0.026 - Y * 0.072 + Z * 0.962\n\n # Apply reverse gamma correction\n r, g, b = map(\n lambda x: (12.92 * x) if (x <= 0.0031308) else ((1.0 + 0.055) * pow(x, (1.0 / 2.4)) - 0.055),\n [r, g, b]\n )\n\n # Bring all negative components to zero\n r, g, b = map(lambda x: max(0, x), [r, g, b])\n\n # If one component is greater than 1, weight components by that value.\n max_component = max(r, g, b)\n if max_component > 1:\n r, g, b = map(lambda x: x / max_component, [r, g, b])\n\n r, g, b = map(lambda x: int(x * 255), [r, g, b])\n\n # Convert the RGB values to your color object The rgb values from the above formulas are between 0.0 and 1.0.\n return (r, g, b)","def color_map(val):\n # NOTE: This relies on remap_interval, which you must provide\n color_code = remap_interval(val, -1, 1, 0, 255)\n return int(color_code)","def color_map(val):\n # NOTE: This relies on remap_interval, which you must provide\n color_code = remap_interval(val, -1, 1, 0, 255)\n return int(color_code)","def color_map(val):\n # NOTE: This relies on remap_interval, which you must provide\n color_code = remap_interval(val, -1, 1, 0, 255)\n return int(color_code)","def color_map(val):\n # NOTE: This relies on remap_interval, which you must provide\n color_code = remap_interval(val, -1, 1, 0, 255)\n return int(color_code)","def colorcalc(X1, X2, r=1):\n X1[X1 < 0], X2[X2 < 0] = 0, 0\n X1[X1 > 1], X2[X2 > 1] = 1, 1\n X3 = 1-X1-X2\n c1, c2, c3 = np.copy(X1), np.copy(X2), np.copy(X3)\n for i, (x1, x2, x3) in enumerate(itertools.permutations([X1, X2, X3])):\n ip = (x1 <= x2) & (x2 <= x3) & (x3 < 1)\n x, y = x3[ip]+.5*x1[ip], x1[ip]*3**.5/2\n p = (x-.5)/.5\n q = (.5-p**r)/(1-x)\n xn, yn = 1-q*(1-x), y*q\n a, b, c = yn*2/3**.5, 1-xn-yn/3**.5, xn-yn/3**.5\n if i == 0:\n c1[ip], c2[ip], c3[ip] = a, b, c\n elif i == 1:\n c1[ip], c3[ip], c2[ip] = a, b, c\n elif i == 2:\n c2[ip], c1[ip], c3[ip] = a, b, c\n elif i == 3:\n c2[ip], c3[ip], c1[ip] = a, b, c\n elif i == 4:\n c3[ip], c1[ip], c2[ip] = a, b, c\n else:\n c3[ip], c2[ip], c1[ip] = a, b, c\n color = np.stack([c1, c2, c3], axis=2)\n e = 1/color.max(2)\n return color*np.stack([e, e, e], axis=2)","def get_colors(self):\n x = np.linspace(0, 1, self.length)\n y = x**self.gamma\n\n value = np.linspace(0, 1, len(self.colors))\n r = np.interp(y, value, self.colors[:,0])\n g = np.interp(y, value, self.colors[:,1])\n b = np.interp(y, value, self.colors[:,2])\n\n return np.dstack((r, g, b)).reshape(len(r), 3).astype(np.uint8)","def _calc_zpcolors(self):\n for f, zpc in zip(self._filters, self._zpcolors):\n m0, merr0 = getattr(self, zpc[0]), getattr(self, zpc[0] + '_err')\n m1, merr1 = getattr(self, zpc[1]), getattr(self, zpc[1] + '_err')\n if merr0 < 9.99 and merr1 < 9.99:\n setattr(self, f + 'zpc', m0 - m1)\n setattr(self, f + 'zpc_err', np.sqrt(merr0**2 + merr1**2))\n else:\n setattr(self, f + 'zpc', self._nodata)\n setattr(self, f + 'zpc_err', self._nodata)","def scale_color((h, s, v), factor=1.):\n if (h < 0.) or (h > 360.):\n raise Exception('[scale_color()] Hue value out of range (0, 360): ' + str(h))\n\n if (s < 0.) or (s > 100.):\n raise Exception('[scale_color()] Saturation value out of range (0, 100): ' + str(s))\n\n if (v < 0.) or (v > 100.):\n raise Exception('[scale_color()] Value value out of range (0, 100): ' + str(v))\n\n if (factor < 0.) or (factor > 1.):\n raise Exception('[scale_color()] Factor value out of range (0.0, 1.0): ' + str(factor))\n\n v *= factor\n r, g, b = hsv_to_rgb(h/360., s/100., v/100.)\n\n return '#{0:02x}{1:02x}{2:02x}'.format(int(round(r*255.)), int(round(g*255.)), int(round(b*255.)))","def drawColorCurve(xseq, yseq, zseq):\n dislin.curve3(xseq,yseq,zseq,len(xseq))","def ramp_color_rgb(values, feature, parent): \r\n ramp_name = values[0]\r\n ramp_position = values[1]\r\n \r\n ramp = QgsStyleV2.defaultStyle().colorRampRef(ramp_name)\r\n if not ramp:\r\n parent.setEvalErrorString( QObject.tr( '\"{}\" is not a valid color ramp'.format(ramp_name)))\r\n return QColor(0,0,0).name()\r\n \r\n value, error = getFloat(ramp_position)\r\n if error:\r\n parent.setEvalErrorString(error)\r\n \r\n color = ramp.color(value)\r\n return \"{},{},{}\".format(color.red(), color.green(), color.blue())","def collatz(value):\r\n assert value >= 1\r\n if value % 2 == 0:\r\n return value/2\r\n else:\r\n return 3 * value + 1","def getJuliaPixelColor(z, gradient):\n c = complex(-1.0, 0.0)\n for i in range(len(gradient)):\n z = z * z + c # Get z1, z2, ...\n if abs(z) > 2:\n z = 2.0\n return gradient[i] # The sequence is unbounded\n return gradient[len(gradient) - 1] # Indicate a bounded sequence","def get_color(activePerMillion):\n activePer100k = activePerMillion / 10.0\n if activePer100k < 100:\n return \"#aaf0d1\"\n elif activePer100k < 500:\n return \"#a3f7bf\"\n elif activePer100k < 1000:\n return \"#90EE90\"\n elif activePer100k < 1500:\n return \"#00ff7f\"\n elif activePer100k < 2000:\n return \"#77dd77\"\n elif activePer100k < 2500:\n return \"#32cd32\"\n elif activePer100k < 3000:\n return \"#4cbb17\"\n elif activePer100k < 3500:\n return \"#228b22\"\n elif activePer100k < 4000:\n return \"#355e3b \"\n else:\n return \"#006400\"","def grey(self):\n return sum((self.value(0), self.value(1), self.value(2)))/3","def RGBa_interpolation(t, c0, c1, alpha):\n\n R = lerp(t, c0[0], c1[0])\n G = lerp(t, c0[1], c1[1])\n B = lerp(t, c0[2], c1[2])\n a = lerp(t, c0[3], c1[3]) * alpha\n return np.vstack([R, G, B, a]).T","def RGBratios(dataset,fil):\n dom_fil = get_dominant_color(fil)\n dataset['R_B_ratio'] = dataset.R/dataset.B\n dataset['R_G_ratio'] = dataset.R/dataset.G\n dataset['G_B_ratio'] = dataset.G/dataset.B\n filter_RB = dom_fil[0]/dom_fil[2]\n filter_RG = dom_fil[0]/dom_fil[1]\n filter_GB = dom_fil[1]/dom_fil[2]\n norm = []\n for array in list(dataset['DominatColour']):\n norm.append( np.linalg.norm(array))\n dataset['Dominant_norm'] = norm\n\n norm = []\n for array in list(dataset['AverageColour']):\n norm.append( np.linalg.norm(array))\n\n\n dataset['DiffRatio_R_B'] = dataset.R_B_ratio-filter_RB\n dataset['DiffRatio_R_G'] = dataset.R_G_ratio-filter_RG\n dataset['DiffRatio_RG_B'] = dataset.G_B_ratio-filter_GB\n dataset['Average_norm'] = norm\n return dataset","def colorize_xyz(xyz):\n xyz_vis = xyz - xyz.min()\n return (255 * xyz_vis / xyz_vis.max()).astype(np.uint8)","def to_color(arr,pmin=1,pmax=99.8,gamma=1.,colors=((0,1,0),(1,0,1),(0,1,1))):\n \n if not arr.dim in (2,3):\n raise ValueError(\"only 2d or 3d arrays supported\")\n \n if arr.ndim==2:\n arr=arr[np.newaxis]\n #get the grey channel\n ind_min=np.argmin(arr.shape)\n arr =np.moveaxis(arr,ind_min,0).astype(np.float32)\n \n out =np.zeros(arr.shape[1:]+(3,))\n \n eps=1.e-20\n \n if pmin>=0:\n mi=np.percentile(arr,pmin,axis=(1,2),keepdims=True)\n else:\n mi=0\n \n if pmax>=0:\n ma=np.percentile(arr,pmax,axis=(1,2),keepdims=True)\n else:\n ma=1.+eps\n \n arr_norm=(1.*arr-mi)/(ma-mi+eps)\n #RGB 3channel\n for i_stack,col_stack in enumerate(colors):\n if i_stack >= len(arr):\n break\n for j,c in enumerate(col_stack):\n out[...,j]+= c*arr_norm[i_stack]\n \n return np.clip(out,0,1)","def getPseudoColor( nValue, nMin = 0, nMax = 255, nErrorValue = -1 ):\n r, g, b = [0,0,0];\n # Invalid Data -> set Color to White\n if( nValue == nErrorValue ):\n r = 0xff;\n g = 0xff;\n b = 0xff;\n # Invalid Range\n # -> Set Color to Grey (debug)\n elif( nValue < nMin or nValue > nMax ):\n r = 0x80;\n g = 0x80;\n b = 0x80;\n else:\n # each part as a specific color computation\n # 5 parts:\n \"\"\"\n nSizePhase = ( nMax - nMin ) / 5;\n if( nValue < nSizePhase * 1 ):\n r = 0xff;\n g = (((nValue-nMin)-0*nSizePhase)*0xff)/nSizePhase;\n b = 0x0;\n elif( nValue < nSizePhase * 2 ):\n r = 0xff - (((nValue-nMin)-1*nSizePhase)*0xff)/nSizePhase;\n g = 0xff;\n b = 0;\n elif( nValue < nSizePhase * 3 ):\n r = 0;\n g = 0xff;\n b = (((nValue-nMin)-2*nSizePhase)*0xff)/nSizePhase;\n elif( nValue < nSizePhase * 4 ):\n r = 0;\n g = 0xff - (((nValue-nMin)-3*nSizePhase)*0xff)/nSizePhase;\n b = 0xff;\n else:\n r = 0xff;\n g = 0;\n b = 0xff - (((nValue-nMin)-4*nSizePhase)*0xff)/nSizePhase;\n \"\"\"\n # 2 parts: (green to yellow then yellow to red)\n nSizePhase = ( nMax - nMin ) / 2;\n if( nValue < nSizePhase * 1 ):\n r = (((nValue-nMin)-0*nSizePhase)*0xff)/nSizePhase;\n g = 0xff;\n b = 0x0;\n elif( nValue < nSizePhase * 2 ):\n r = 0xff;\n g = 0xff - (((nValue-nMin)-1*nSizePhase)*0xff)/nSizePhase;\n b = 0; \n # else - end\n return [r,g,b];","def linearTosRGB3(c):\n return [linearTosRGB(c[0]), linearTosRGB(c[1]), linearTosRGB(c[2])]","def calc_R(xc, yc, zc,x,y,z):\n return sqrt((x - xc) ** 2 + (y - yc) ** 2 + (z - zc) ** 2)","def generate_lut(self):\n r,g,b=(Numeric.zeros(256),Numeric.zeros(256),Numeric.zeros(256))\n for i in Numeric.arange(256):\n r_,g_,b_=self.colfct(i/255.0) # these are from [0,1]\n r[i],g[i],b[i]=int(255*r_),int(255*g_),int(255*b_)\n return r,g,b","def combine_colors(c1, c2, factor=0.5):\n c3 = QtGui.QColor()\n c3.setRed(int((factor * c1.red() + (1 - factor) * c2.red())))\n c3.setGreen(int((factor * c1.green() + (1 - factor) * c2.green())))\n c3.setBlue(int((factor * c1.blue() + (1 - factor) * c2.blue())))\n return c3","def combine_colors(c1, c2, factor=0.5):\n c3 = QtGui.QColor()\n c3.setRed(int((factor * c1.red() + (1 - factor) * c2.red())))\n c3.setGreen(int((factor * c1.green() + (1 - factor) * c2.green())))\n c3.setBlue(int((factor * c1.blue() + (1 - factor) * c2.blue())))\n return c3","def __RGB_to_Hue(var_R, var_G, var_B, var_min, var_max):\r\n\r\n if var_max == var_min:\r\n return 0.0\r\n elif var_max == var_R:\r\n return (60.0 * ((var_G - var_B) / (var_max - var_min)) + 360) % 360.0\r\n elif var_max == var_G:\r\n return 60.0 * ((var_B - var_R) / (var_max - var_min)) + 120\r\n elif var_max == var_B:\r\n return 60.0 * ((var_R - var_G) / (var_max - var_min)) + 240.0","def get_colors(n):\n color = cm.rainbow(np.linspace(0, 1, n))\n return color","def standard_handler(args_dict: dict):\n\n color_sequence = args_dict['color_sequence']\n step = args_dict['step']\n\n # Updating step for the next iteration.\n args_dict['step'] = (step + 1) % args_dict['sequence_len']\n\n return color_sequence[step]","def lighter(clr, f=1/3):\n gaps = [f*(1 - val) for val in clr]\n new_clr = [val + gap for gap, val in zip(gaps, clr)]\n return new_clr","def fakingColors(pixel):\n\n return (\n pixel[0] * 0.598 - 0.1957 * pixel[1] - 0.038 * pixel[2],\n pixel[1] * 1.174 - pixel[0] * 0.1994 - pixel[2] * 0.076,\n pixel[2] * 0.228 - pixel[0] * 0.1495 - pixel[1] * 0.2935\n )","def _color_brew(n):\n color_list = []\n\n # Initialize saturation & value; calculate chroma & value shift\n s, v = 0.75, 0.9\n c = s * v\n m = v - c\n\n for h in np.arange(25, 385, 360. / n).astype(int):\n # Calculate some intermediate values\n h_bar = h / 60.\n x = c * (1 - abs((h_bar % 2) - 1))\n # Initialize RGB with same hue & chroma as our color\n rgb = [(c, x, 0),\n (x, c, 0),\n (0, c, x),\n (0, x, c),\n (x, 0, c),\n (c, 0, x),\n (c, x, 0)]\n r, g, b = rgb[int(h_bar)]\n # Shift the initial RGB values to match value and store\n rgb = [(int(255 * (r + m))),\n (int(255 * (g + m))),\n (int(255 * (b + m)))]\n color_list.append(rgb)\n\n return color_list","def StepColour(c, ialpha):\r\n \r\n if ialpha == 100:\r\n return c\r\n \r\n r, g, b = c.Red(), c.Green(), c.Blue()\r\n\r\n # ialpha is 0..200 where 0 is completely black\r\n # and 200 is completely white and 100 is the same\r\n # convert that to normal alpha 0.0 - 1.0\r\n ialpha = min(ialpha, 200)\r\n ialpha = max(ialpha, 0)\r\n alpha = (ialpha - 100.0)/100.0\r\n\r\n if ialpha > 100:\r\n \r\n # blend with white\r\n bg = 255\r\n alpha = 1.0 - alpha # 0 = transparent fg 1 = opaque fg\r\n \r\n else:\r\n \r\n # blend with black\r\n bg = 0\r\n alpha = 1.0 + alpha # 0 = transparent fg 1 = opaque fg\r\n \r\n r = BlendColour(r, bg, alpha)\r\n g = BlendColour(g, bg, alpha)\r\n b = BlendColour(b, bg, alpha)\r\n\r\n return wx.Colour(r, g, b)","def _toRgbImage(self, fractal, colors, color_offset):\n hsv_img = np.array(\n [\n # Cycle through color wheel.\n (fractal * colors + color_offset) % 1,\n\n # Saturation = fractal value.\n fractal,\n\n # Maximum value.\n np.ones(fractal.shape)\n ]\n ).astype(dtype=float).T\n\n rgb_img = (mpl.colors.hsv_to_rgb(hsv_img) * 255).astype(dtype=np.uint8)\n return rgb_img","def get_colors(n_curves):\n # Use offsets to avoid shitty crappy min/max colors (ugly yellow or white for example)\n first_step = 1. / (n_curves + 1.)\n last_step = 1.\n nb_steps = n_curves + 1\n color_range = np.linspace(first_step, last_step, nb_steps)\n color = iter(plt.cm.hot(color_range))\n return color","def unique_colors_rgb(n):\r\n hues = []\r\n # i is in the range 0, 1, ..., n - 1\r\n for i in range(1, n + 1):\r\n hues.append(360.0 / i)\r\n\r\n hs = []\r\n for hue in hues:\r\n h = math.floor(hue / 60) % 6\r\n hs.append(h)\r\n\r\n fs = []\r\n for hue in hues:\r\n f = hue / 60 - math.floor(hue / 60)\r\n fs.append(f)\r\n\r\n rgbcolors = []\r\n for h, f in zip(hs, fs):\r\n v = 1\r\n p = 0\r\n q = 1 - f\r\n t = f\r\n if h == 0:\r\n color = v, t, p\r\n elif h == 1:\r\n color = q, v, p\r\n elif h == 2:\r\n color = p, v, t\r\n elif h == 3:\r\n color = p, q, v\r\n elif h == 4:\r\n color = t, p, v\r\n elif h == 5:\r\n color = v, p, q\r\n rgbcolors.append(color)\r\n\r\n return rgbcolors","def rgb(r, g, b):\n return (r/255, g/255, b/255)","def scale_to_01(color: C3I) -> C3F:\n r, g, b = color\n return r / 255, g / 255, b / 255","def dcsrbf(r):\n return 3*(num.power(num.maximum(0, 1-r), 3) - num.power(num.maximum(0, 1-r),2)*(3*r+1))","def create_unique_color_uchar(tag, hue_step=0.41):\n r, g, b = create_unique_color_float(tag, hue_step)\n return int(255*r), int(255*g), int(255*b)","def get_color(val, trendType):\n if(val > 0):\n if(trendType == 'normal'):\n color = 'red'\n else:\n color = 'green'\n elif(val < 0):\n if(trendType == 'normal'):\n color = 'green'\n else:\n color = 'red'\n else:\n color = 'blue'\n return color","def step_fn(step):\n ret = 1.0\n for name in factors:\n if name == 'constant':\n ret *= base_learning_rate\n elif name == 'linear_warmup':\n ret *= jnp.minimum(1.0, step / warmup_steps)\n elif name == 'rsqrt_decay':\n ret /= jnp.sqrt(jnp.maximum(1.0, step - warmup_steps))\n elif name == 'rsqrt_normalized_decay':\n ret *= jnp.sqrt(warmup_steps)\n ret /= jnp.sqrt(jnp.maximum(step, warmup_steps))\n elif name == 'decay_every':\n ret *= (decay_factor**(step // steps_per_decay))\n elif name == 'cosine_decay':\n progress = jnp.maximum(0.0,\n (step - warmup_steps) / float(steps_per_cycle))\n ret *= jnp.maximum(0.0,\n 0.5 * (1.0 + jnp.cos(jnp.pi * (progress % 1.0))))\n else:\n raise ValueError('Unknown factor %s.' % name)\n return jnp.asarray(ret, dtype=jnp.float32)","def gamma(k, z):\n return 1","def index_to_rgb(index=0, gamma=0.5):\n\n wavelength = (index * 320) + 380\n\n if wavelength < 440:\n intensity = 0.1 + (0.9 * (wavelength - 380) / (440 - 380))\n red = ((-1.0 * (wavelength - 440) / (440 - 380)) * intensity) ** gamma\n grn = 0.0\n blu = (1.0 * intensity) ** gamma\n if 440 <= wavelength < 490:\n red = 0.0\n grn = (1.0 * (wavelength - 440) / (490 - 440)) ** gamma\n blu = 1.0**gamma\n if 490 <= wavelength < 510:\n red = 0.0\n grn = 1.0**gamma\n blu = (-1.0 * (wavelength - 510) / (510 - 490)) ** gamma\n if 510 <= wavelength < 580:\n red = (1.0 * (wavelength - 510) / (580 - 510)) ** gamma\n grn = 1.0**gamma\n blu = 0.0\n if 580 <= wavelength < 645:\n red = 1.0**gamma\n grn = (-1.0 * (wavelength - 645) / (645 - 580)) ** gamma\n blu = 0.0\n if wavelength >= 645:\n intensity = 0.3 + (0.7 * (700 - wavelength) / (700 - 645))\n red = (1.0) ** gamma\n grn = (1.0 - intensity) ** gamma\n blu = (1.0 - intensity) ** gamma\n\n return (int(red * 255) << 16) + (int(grn * 255) << 8) + int(blu * 255)","def getColorLine(self, dot1, dot2): # generate all dots of line\n x1, y1, z1, feed1 = dot1\n x2, y2, z2, feed2 = dot2\n min_color = self.colors_list[feed1]\n max_color = self.colors_list[feed2]\n\n # NB! feed1,feed2 >= 0; 0 = dot[3]-fmin\n # self.colors_list = grad(MIN_COLOR, MAX_COLOR, self.fmax)\n if self.current_feedrate == 0:\n start_color = min_color\n if feed2 > feed1:\n finish_color = max_color\n else:\n finish_color = min_color\n else:\n if feed2 > feed1:\n start_color = min_color\n finish_color = max_color\n elif feed1 > feed2:\n start_color = max_color\n finish_color = min_color\n else: # feed1 == feed2\n if feed2 == self.current_feedrate:\n start_color = finish_color = self.current_color\n\n self.current_color = finish_color\n self.current_feedrate = feed2\n color_list = grad(start_color, finish_color, n=self.speed+1)\n\n i = 0\n coords = []\n stepx = (x2-x1) / self.speed\n stepy = (y2-y1) / self.speed\n stepz = (z2-z1) / self.speed\n for i in range(self.speed):\n dot = [0, 0, 0, 0, 0, 0, 0] # x, y, z, r, g, b, p\n dot[0] = x1 + i*stepx\n dot[1] = y1 + i*stepy\n dot[2] = z1 + i*stepz\n dot[3:7] = color_list[i]\n coords.append(dot)\n\n return coords","def get_coloured_grid(self, r1, r2, r3, b1=4, b2=2.5, b3=1):\n r, g, b = np.frompyfunc(self.get_colour(r1, r2, r3, b1, b2, b3), 2, 3)(self.end_z, self.end_step)\n img_array = np.dstack((r, g, b))\n return Image.fromarray(np.uint8(img_array * 255))","def _toRgbImage(self, fractal, colors, color_offset):\n hsv_img = np.array(\n [\n # Cycle through color wheel.\n (fractal * colors + color_offset) % 1,\n\n # Saturation = 1 where fractal values > 0,\n # Saturation = 0 otherwise.\n fractal.astype(dtype=bool).astype(dtype=float),\n\n # Invert colours\n 1 - fractal\n ]\n ).astype(dtype=float).T\n\n rgb_img = (mpl.colors.hsv_to_rgb(hsv_img) * 255).astype(dtype=np.uint8)\n return rgb_img","def shade(N=10, *colors):\n return interp1d(linspace(0, 1, len(colors)), colors, axis=0)(linspace(0, 1, N)).round(2)","def color_differentiator(elevation):\n if elevation < 1000:\n return 'green'\n elif 1000 <= elevation < 3000:\n return 'orange'\n else:\n return 'red'","def _color(self, args):","def color_level(value, min_value=0.0, max_value=1.0, func=lambda x: x):\n global COLOR_SCALE\n\n if not __colored__:\n return \"\"\n\n COLOR_SCALE = [\n fg(\"white\"),\n fg(\"pale_green_3b\"),\n fg(\"green\"),\n fg(\"green_yellow\"),\n fg(\"light_yellow\"),\n fg(\"orange_1\"),\n fg(\"orange_red_1\"),\n fg(\"light_red\"),\n fg(\"light_red\") + attr(\"blink\")\n ]\n\n max_index = len(COLOR_SCALE) - 1\n color_index = int(round(\n max_index * min(1.0, max(0.0,\n func((value - min_value) / (max_value - min_value))\n ))\n ))\n\n return COLOR_SCALE[color_index]","def to_color(arr, pmin=1, pmax=99.8, gamma=1., colors=((0, 1, 0), (1, 0, 1), (0, 1, 1))):\n if not arr.ndim in (2,3):\n raise ValueError(\"only 2d or 3d arrays supported\")\n\n if arr.ndim ==2:\n arr = arr[np.newaxis]\n\n ind_min = np.argmin(arr.shape)\n arr = np.moveaxis(arr, ind_min, 0).astype(np.float32)\n\n out = np.zeros(arr.shape[1:] + (3,))\n\n eps = 1.e-20\n if pmin>=0:\n mi = np.percentile(arr, pmin, axis=(1, 2), keepdims=True)\n else:\n mi = 0\n\n if pmax>=0:\n ma = np.percentile(arr, pmax, axis=(1, 2), keepdims=True)\n else:\n ma = 1.+eps\n\n arr_norm = (1. * arr - mi) / (ma - mi + eps)\n\n\n for i_stack, col_stack in enumerate(colors):\n if i_stack >= len(arr):\n break\n for j, c in enumerate(col_stack):\n out[..., j] += c * arr_norm[i_stack]\n\n return np.clip(out, 0, 1)","def depth_color(depth):\n if depth < 20:\n return 'g'\n if depth < 40:\n return 'y'\n return 'r'","def color_creator(number_of_films):\n if 0 < number_of_films < 100:\n return 'green'\n elif 101 < number_of_films < 200:\n return 'orange'\n elif 201 < number_of_films < 500:\n return 'red'\n else:\n return 'purple'","def int2color(x):\n # r = int(1000 * x % 255)\n # g = int(10000 * x % 255)\n # b = int(100000 * x % 255)\n x = 0 if x == 0 else int(1/x)\n b = x & 0xff\n g = (x >> 8) & 0xff\n r = (x >> 16) & 0xff\n return [r, g, b]","def toRGB(self):\n\t\treturn '#'+ (hex(self.politics)[2:].upper().rjust(6,'0'))","def rainbow(double[:] v, redshifts, in_terms, double H0):\n\n cdef double t = v[0]\n cdef double a = v[1]\n cdef double ombar_m = v[2]\n cdef double ombar_r = v[3]\n cdef double ombar_a = v[4]\n cdef double ombar_b = v[5]\n cdef double ombar_c = v[6]\n cdef double ombar_d = v[7]\n cdef double ombar_e = v[8]\n cdef double ombar_f = v[9]\n cdef double ombar_g = v[10]\n cdef double ombar_h = v[11]\n cdef double ombar_i = v[12]\n cdef double ombar_de = v[13]\n cdef double z = v[14]\n cdef double dl = v[15]\n\n cdef double in_p = in_terms[0]\n cdef double in_q = in_terms[1]\n cdef double in_r = in_terms[2]\n cdef double in_s = in_terms[3]\n cdef double in_t = in_terms[4]\n cdef double in_u = in_terms[5]\n cdef double in_v = in_terms[6]\n cdef double in_w = in_terms[7]\n cdef double in_x = in_terms[8]\n cdef double in_y = in_terms[9]\n cdef double in_z = in_terms[10]\n\n\n cdef double Hz = H0 * (ombar_m +ombar_r +ombar_a +ombar_b\n +ombar_c +ombar_d +ombar_e +ombar_f\n +ombar_g +ombar_h +ombar_i +ombar_de)**(0.5)\n\n# if ombar_m < 0 or ombar_de < 0:\n# print('rainbow')\n# print('z = %s, Hz = %s, in_terms = %s'% (z, Hz, in_terms))\n# print(f'omegas = {v[2:14]}')\n\n if math.isnan(Hz):\n print('rainbow')\n print('z = %s, Hz = %s, in_terms = %s'% (z, Hz, in_terms))\n print(f'ombar_m = {ombar_m}, ombar_r = {ombar_r}, ombar_a = {ombar_a}, ombar_b = {ombar_b}, ombar_c = {ombar_c}, ombar_d = {ombar_d}, ombar_e = {ombar_e}, ombar_f = {ombar_f}, ombar_g = {ombar_g}, ombar_h = {ombar_h}, ombar_i = {ombar_i}, ombar_de = {ombar_de}')\n\n cdef double dtdz = -1.0/((1.0+z) * Hz)\n cdef double dadz = -(1.0+z)**(-2.0)\n\n cdef double domdz = (3.0*ombar_m +in_p*ombar_m*ombar_r/Hz)/(1.0+z) # w = 0\n cdef double dordz = (4.0*ombar_r -in_p*ombar_m*ombar_r/Hz +in_q*ombar_r*ombar_a/Hz)/(1.0+z) # w = 1/3\n cdef double doadz = (2.7*ombar_a -in_q*ombar_r*ombar_a/Hz +in_r*ombar_a*ombar_b/Hz)/(1.0+z) # w = -0.1\n cdef double dobdz = (2.4*ombar_b -in_r*ombar_a*ombar_b/Hz +in_s*ombar_b*ombar_c/Hz)/(1.0+z) # w = -0.2\n cdef double docdz = (2.1*ombar_c -in_s*ombar_b*ombar_c/Hz +in_t*ombar_c*ombar_d/Hz)/(1.0+z) # w = -0.3\n cdef double doddz = (1.8*ombar_d -in_t*ombar_c*ombar_d/Hz +in_u*ombar_d*ombar_e/Hz)/(1.0+z) # w = -0.4\n cdef double doedz = (1.5*ombar_e -in_u*ombar_d*ombar_e/Hz +in_v*ombar_e*ombar_f/Hz)/(1.0+z) # w = -0.5\n cdef double dofdz = (1.2*ombar_f -in_v*ombar_e*ombar_f/Hz +in_w*ombar_f*ombar_g/Hz)/(1.0+z) # w = -0.6\n cdef double dogdz = (0.9*ombar_g -in_w*ombar_f*ombar_g/Hz +in_x*ombar_g*ombar_h/Hz)/(1.0+z) # w = -0.7\n cdef double dohdz = (0.6*ombar_h -in_x*ombar_g*ombar_h/Hz +in_y*ombar_h*ombar_i/Hz)/(1.0+z) # w = -0.8\n cdef double doidz = (0.3*ombar_i -in_y*ombar_h*ombar_i/Hz +in_z*ombar_i*ombar_de/Hz)/(1.0+z)# w = -0.9\n cdef double dodedz = -in_z*ombar_i*ombar_de/(1.0+z)/Hz # w = -1\n\n cdef double ddldz = 1.0/Hz\n\n # first derivatives of functions I want to find:\n f = [dtdz,# dt/dz (= f.d wrt z of time)\n dadz,# d(a)/dz (= f.d wrt z of scale factor)\n domdz,# dw = 0, (ombar_m)/dz (= f.d wrt z of density_m(t) / crit density(t0))\n dordz,# w = 1/3, d(ombar_r)/dz (= f.d wrt z of density_r(t) / crit density(t0))\n doadz,# w = -0.1\n dobdz,# w = -0.2\n docdz,# w = -0.3\n doddz,# w = -0.4\n doedz,# w = -0.5\n dofdz,# w = -0.6\n dogdz,# w = -0.7\n dohdz,# w = -0.8\n doidz,# w = -0.9\n dodedz,# w = -1, d(ombar_de)/dz (= f.d wrt z of density_de(t) / crit density(t0))\n 1.0,# d(z)/dz (= f.d wrt z of redshift)\n ddldz]# d(dl)/dz (= f.d wrt z of luminosty distance) # H + Hdz*(1+z)\n\n return f","def c_s(self, z):\n R = self.R_bg(z)\n return const.c/np.sqrt(3.*(1.+R))","def _FracInt(x,y,z,a,b,c,tau,n):\n denom = np.sqrt((a + tau)*(b + tau)*(c + tau))\n return (1. - x**2/(a + tau) - y**2/(b + tau) - z**2/(c + tau))**n / denom","def _hls2rgb(self,h):\n h=h**self.exponent\n if(self.invert): h=1.0-h\n h=h*360.0\n h=Numeric.fmod(h,360.0)\n if(self.hls_hls):\n h=h/60.0\n else:\n if(h<120):\n h=h/120.0 # /* 0..1 Rot..(Orange)..Gelb */\n elif(h<180):\n h=h/60.0 - 1.0 # /* 1..2 Gelb..Gruen */\n elif(h<240):\n h=h/30.0 - 4.0 # /* 2..4 Gruen..Blaugruen..Blau*/\n else:\n h=h/60.0 # /* 4..6 Blau..Purpur..Rot */\n c=int(h)\n frac=h-c\n if (self.hls_l<=0.5):\n maxi=self.hls_l*(1.0+self.hls_s)\n else:\n maxi=self.hls_l+self.hls_s-self.hls_l*self.hls_s\n mini=2*self.hls_l-maxi;\n diff=maxi-mini;\n if(self.hls_s==0): # /* grau */\n return(1.0,1.0,1.0) \n else:\n if(c==0):\n return(maxi,mini+frac*diff,mini)\n elif(c==1):\n return(mini+(1.0-frac)*diff,maxi,mini)\n elif(c==2):\n return(mini,maxi,mini+frac*diff)\n elif(c==3):\n return(mini,mini+(1.0-frac)*diff,maxi)\n elif(c==4):\n return(mini+frac*diff,mini,maxi)\n else:\n return(maxi,mini,mini+(1.0-frac)*diff)","def get_RF_color(rf_vec):\n color = copy.deepcopy(rf_vec)\n\n def renormalize(v):\n v -= np.min(v)\n return v / np.max(v)\n\n for i in xrange(3):\n color[:, i] = renormalize(color[:, i])\n return color","def color(value):\r\n return 'RGB({}, {}, {})'.format(value.red(), value.blue(), value.green())","def opacity_transfer_function(mapping, n_colors, interpolate=True, kind='quadratic'):\n sigmoid = lambda x: np.array(1 / (1 + np.exp(-x)) * 255, dtype=np.uint8)\n transfer_func = {\n 'linear': np.linspace(0, 255, n_colors, dtype=np.uint8),\n 'geom': np.geomspace(1e-6, 255, n_colors, dtype=np.uint8),\n 'geom_r': np.geomspace(255, 1e-6, n_colors, dtype=np.uint8),\n 'sigmoid': sigmoid(np.linspace(-10.0, 10.0, n_colors)),\n 'sigmoid_3': sigmoid(np.linspace(-3.0, 3.0, n_colors)),\n 'sigmoid_4': sigmoid(np.linspace(-4.0, 4.0, n_colors)),\n 'sigmoid_5': sigmoid(np.linspace(-5.0, 5.0, n_colors)),\n 'sigmoid_6': sigmoid(np.linspace(-6.0, 6.0, n_colors)),\n 'sigmoid_7': sigmoid(np.linspace(-7.0, 7.0, n_colors)),\n 'sigmoid_8': sigmoid(np.linspace(-8.0, 8.0, n_colors)),\n 'sigmoid_9': sigmoid(np.linspace(-9.0, 9.0, n_colors)),\n 'sigmoid_10': sigmoid(np.linspace(-10.0, 10.0, n_colors)),\n }\n transfer_func['linear_r'] = transfer_func['linear'][::-1]\n transfer_func['sigmoid_r'] = transfer_func['sigmoid'][::-1]\n for i in range(3, 11):\n k = f'sigmoid_{i}'\n rk = f'{k}_r'\n transfer_func[rk] = transfer_func[k][::-1]\n if isinstance(mapping, str):\n try:\n return transfer_func[mapping]\n except KeyError:\n raise KeyError(f'opactiy transfer function ({mapping}) unknown.')\n elif isinstance(mapping, (np.ndarray, list, tuple)):\n mapping = np.array(mapping)\n if mapping.size == n_colors:\n # User could pass transfer function ready for lookup table\n pass\n elif mapping.size < n_colors:\n # User pass custom transfer function to be linearly interpolated\n if np.max(mapping) > 1.0 or np.min(mapping) < 0.0:\n mapping = normalize(mapping)\n # Interpolate transfer function to match lookup table\n xo = np.linspace(0, n_colors, len(mapping), dtype=np.int_)\n xx = np.linspace(0, n_colors, n_colors, dtype=np.int_)\n try:\n if not interpolate:\n raise ValueError('No interpolation.')\n # Use a quadratic interp if scipy is available\n from scipy.interpolate import interp1d\n\n # quadratic has best/smoothest results\n f = interp1d(xo, mapping, kind=kind)\n vals = f(xx)\n vals[vals < 0] = 0.0\n vals[vals > 1.0] = 1.0\n mapping = (vals * 255.0).astype(np.uint8)\n\n except (ImportError, ValueError):\n # Otherwise use simple linear interp\n mapping = (np.interp(xx, xo, mapping) * 255).astype(np.uint8)\n else:\n raise RuntimeError(\n f'Transfer function cannot have more values than `n_colors`. This has {mapping.size} elements'\n )\n return mapping\n raise TypeError(f'Transfer function type ({type(mapping)}) not understood')","def calculate_colors(v):\n\n # Define constant color values\n lightness = [0.35, 0.5, 0.65]\n saturation = [0.35, 0.5, 0.65]\n\n # Calculate the CRC-32 checksum of colors encoded as a UTF-8 string\n hash = crc32(str(v).encode('utf-8')) & 0xffffffff\n\n # Calculate the HSL (hue, saturation, lightness) values for the vertices\n hue = ((hash % 359) / 1000) * 360\n hash //= 360\n sat = saturation[hash % len(saturation)]\n hash //= len(saturation)\n lig = lightness[hash % len(lightness)]\n\n return (hue, sat, lig)","def rgb_image(self):\n z3 = self.z[:,:,newaxis]\n return z3 * self.c","def color(self, data):\n\n red = np.interp(data, self.range, self.r)\n blue = np.interp(data, self.range, self.b)\n green = np.interp(data, self.range, self.g)\n # Style plot to return a grey color when value is 'nan'\n red[np.isnan(red)] = 240\n blue[np.isnan(blue)] = 240\n green[np.isnan(green)] = 240\n colors = np.dstack([red.astype(np.uint8),\n green.astype(np.uint8),\n blue.astype(np.uint8),\n np.full_like(data, 255, dtype=np.uint8)])\n #return colors.view(dtype=np.uint32).reshape(data.shape)\n c=[]\n for i in range(len(data)):\n c.append([red[i],green[i],blue[i]])\n return c","def default_render_color_maker(num:int):\n if num < 7:\n log.warn('Fewer than 7 rendering colors are being generated. This may cause issues if a URDF with a 6+ axis robot is loaded.')\n\n b = np.linspace(0,255,num).astype(int) # Blue values are always unique\n\n g = [0] * b.size\n r = np.abs(255 - 2*b)\n\n colors = []\n for idx in range(num):\n colors.append([b[idx],g[idx],r[idx]])\n return colors","def fromInts(r, g, b):\n return IColor(r/255.,g/255.,b/255.)","def nthColor(i):\n if i < len(colors):\n return colors[i]\n\n c1 = colors[i % len(colors)]\n c2 = nthColor(i // len(colors))\n\n return \"#\" + hex((int(c1[1:],16) + int(c2[1:],16)) // 2)[2:]","def calculate_colors(countries, max_users):\n for country_code in countries:\n countries[country_code]['color_rgb'] = [\n calculate_color(\n countries[country_code]['percentage_of_users'],\n max_users,\n 8,\n 229\n ),\n calculate_color(\n countries[country_code]['percentage_of_users'],\n max_users,\n 64,\n 245\n ),\n calculate_color(\n countries[country_code]['percentage_of_users'],\n max_users,\n 129,\n 223\n )\n ]\n\n return countries"],"string":"[\n \"def get_colour(self, r1, r2, r3, b1, b2, b3):\\n\\n def colour(z, i):\\n \\\"\\\"\\\"\\n Gets the colour of a z and step value.\\n\\n :param complex z: the z value from the mandelbrot set\\n :param int i: the step value\\n\\n :rtype: tuple\\n :return: the three RGB colours\\n \\\"\\\"\\\"\\n if abs(z) < self.threshold:\\n return self.background\\n v = np.log2(i + self.threshold - np.log2(np.log2(abs(z)))) / self.threshold\\n if v < 1.0:\\n return v ** b1, v ** b2, v ** b3 # background\\n else:\\n v = max(0, 2 - v)\\n return v ** r1, v ** r2, v ** r3 # main tones\\n\\n return colour\",\n \"def rgbcolor(h, f):\\n # q = 1 - f\\n # t = f\\n if h == 0:\\n return v, f, p\\n elif h == 1:\\n return 1 - f, v, p\\n elif h == 2:\\n return p, v, f\\n elif h == 3:\\n return p, 1 - f, v\\n elif h == 4:\\n return f, p, v\\n elif h == 5:\\n return v, p, 1 - f\",\n \"def colour(z, i):\\n if abs(z) < self.threshold:\\n return 0, 0, 0\\n v = np.log2(i + self.threshold - np.log2(np.log2(abs(z)))) / self.threshold\\n if v < 1.0:\\n return v ** b1, v ** b2, v ** b3 # coloured tones\\n else:\\n v = max(0, 2 - v)\\n return v ** r1, v ** r2, v ** r3 # sepia tones\",\n \"def _rgb(x, y, z):\\n rgb = np.array([x, y, z]).T\\n rgb -= rgb.min(0)\\n rgb /= np.maximum(rgb.max(0), 1e-16) # avoid div by zero\\n return rgb\",\n \"def colour(z, i):\\n if abs(z) < self.threshold:\\n return self.background\\n v = np.log2(i + self.threshold - np.log2(np.log2(abs(z)))) / self.threshold\\n if v < 1.0:\\n return v ** b1, v ** b2, v ** b3 # background\\n else:\\n v = max(0, 2 - v)\\n return v ** r1, v ** r2, v ** r3 # main tones\",\n \"def __div__(self, number):\\n r = self.r / number\\n g = self.g / number\\n b = self.b / number\\n return Color(r, g, b)\",\n \"def color(step: int=10) -> Tuple[int, int, int]:\\n # Randomly seed the r g b values\\n r, g, b = (random_uniform(0, 255), random_uniform(0, 255),\\n random_uniform(0, 255))\\n\\n # Randomly determine if each r g and b value is increasing or not\\n r_inc = True\\n g_inc = True\\n b_inc = True\\n r_step = random_uniform(step)\\n g_step = random_uniform(step)\\n b_step = random_uniform(step)\\n\\n # Yield the initial r, g, b values\\n yield r, g, b\\n\\n # Loop and yeild forever\\n while True:\\n # If r is increasing\\n if r_inc:\\n # Increment r by the step\\n r += r_step\\n # Ensure that the next step will be within the limits\\n # if not then set the flag to decreasing\\n r_inc = r < 255 - r_step\\n # If r is decreasing\\n else:\\n # Decrement r by the step\\n r -= r_step\\n # Ensure that the next step will be within the limits\\n # if not then set the flag to increasing\\n r_inc = r < r_step\\n\\n # See above\\n if g_inc:\\n g += g_step\\n g_inc = g < 255 - g_step\\n else:\\n g -= g_step\\n g_inc = g < g_step\\n\\n # See above\\n if b_inc:\\n b += b_step\\n b_inc = b < 255 - b_step\\n else:\\n b -= b_step\\n b_inc = b < b_step\\n\\n # Yield the red, green, and blue values\\n yield r, g, b\",\n \"def getColor(n, total = 255, decimal = False):\\n\\n value = round(255*n/(total * 1.0))\\n\\n #red value\\n if value < 96:\\n red = 0\\n elif value < 160:\\n red = 255/((160 - 96)*1.0) * (value - 96)\\n elif value < 224:\\n red = 255\\n else:\\n red = 255 - ((255 - 128)/((255 - 224) * 1.0) * (value - 224))\\n\\n\\n #Green value\\n if value < 32:\\n green = 0\\n elif value < 96:\\n green = 255/((96 - 32)*1.0) * (value - 32)\\n elif value < 160:\\n green = 255\\n elif value < 224:\\n green = 255 - (255/((224 - 160) * 1.0) * (value - 160))\\n else:\\n green = 0\\n\\n\\n #Blue value\\n if value < 32:\\n blue = 128 + (255 - 128)/((32 - 0) * 1.0) * (value - 0)\\n elif value < 96:\\n blue = 255\\n elif value < 160:\\n blue = 255 - ((255 - 0)/((160 - 96) * 1.0) * (value - 96))\\n else:\\n blue = 0\\n\\n if decimal:\\n return (red / 255.0, green / 255.0, blue / 255.0)\\n return (int(red), int(green), int(blue))\",\n \"def get_color(in_val, min_val=0, max_val=100):\\n width = max_val - min_val\\n unit = width / len(continuum)\\n return continuum[min(int(in_val / unit), 19)]\",\n \"def create_unique_color_float(tag, hue_step=0.41):\\n h, v = (tag * hue_step) % 1, 1. - (int(tag * hue_step) % 4) / 5.\\n r, g, b = colorsys.hsv_to_rgb(h, 1., v)\\n return r, g, b\",\n \"def color_from_value(self, value):\\n \\n return ImageColor.getrgb(\\\"hsl(%d,%d%%,%d%%)\\\" % (int( (1.0 - value) * 360 ), 80, 50))\",\n \"def getTweenColor(self, factor):\\n\\n pass\",\n \"def _to_color(indx, base):\\n base2 = base * base\\n b = 2 - indx / base2\\n r = 2 - (indx % base2) / base\\n g = 2 - (indx % base2) % base\\n return b * 127, r * 127, g * 127\",\n \"def create_color_gradient():\\n colors = []\\n step = 10\\n for red, green in zip(range(255,-step, -step), range(0, 255, step)):\\n colors.append({'red': red, 'green': green, 'blue': 0})\\n for green, blue in zip(range(255,-step, -step), range(0, 255, step)):\\n colors.append({'red': 0, 'green': green, 'blue': blue})\\n for blue, red in zip(range(255,-step, -step), range(0, 255, step)):\\n colors.append({'red': red, 'green': 0, 'blue': blue})\\n return colors\",\n \"def __Calc_HSL_to_RGB_Components(var_q, var_p, C):\\r\\n\\r\\n if C < 0:\\r\\n C += 1.0\\r\\n if C > 1:\\r\\n C -= 1.0\\r\\n\\r\\n # Computing C of vector (Color R, Color G, Color B)\\r\\n if C < (1.0 / 6.0):\\r\\n return var_p + ((var_q - var_p) * 6.0 * C)\\r\\n elif (1.0 / 6.0) <= C < 0.5:\\r\\n return var_q\\r\\n elif 0.5 <= C < (2.0 / 3.0):\\r\\n return var_p + ((var_q - var_p) * 6.0 * ((2.0 / 3.0) - C))\\r\\n else:\\r\\n return var_p\",\n \"def getColor(self,number):\\n if number >= 0:\\n ret = cs.hsv_to_rgb(0,0,1-abs(number/self.maxp))\\n else:\\n ret = cs.hsv_to_rgb(0,abs(number/self.maxn),1)\\n hexcolor = '#%02x%02x%02x' % (ret[0]*255,ret[1]*255,ret[2]*255)\\n return hexcolor\",\n \"def elevation_to_color_formula(number):\\n color_intensity = abs(round(((number - lowest_point) / (highest_point - lowest_point)) * 255))\\n return color_intensity\",\n \"def getColor(self,number):\\n if number >= 0:\\n if self.inverse:\\n ret = cs.hsv_to_rgb(0,0,abs(number/self.maxp))\\n else:\\n ret = cs.hsv_to_rgb(0,0,1-abs(number/self.maxp))\\n else:\\n if self.inverse:\\n ret = cs.hsv_to_rgb(0,1-abs(number/self.maxn),1)\\n else:\\n ret = cs.hsv_to_rgb(0,abs(number/self.maxn),1)\\n return [ret[0]*255.0,ret[1]*255.0,ret[2]*255.0]\",\n \"def calculate_color(thisCountry, maxCountry, maxColor, minColor):\\n countryFactor = float(thisCountry)/maxCountry\\n colorRange = maxColor - minColor\\n return int(colorRange*countryFactor+minColor)\",\n \"def z_r(b):\\n return b/1e-2 + 0.5\",\n \"def floatRgb(mag, cmin, cmax):\\n\\n try:\\n # normalize to [0,1]\\n x = float(mag-cmin)/float(cmax-cmin)\\n except:\\n # cmax = cmin\\n x = 0.5\\n blue = min((max((4*(0.75-x), 0.)), 1.))\\n red = min((max((4*(x-0.25), 0.)), 1.))\\n green= min((max((4*math.fabs(x-0.5)-1., 0.)), 1.))\\n return (red, green, blue)\",\n \"def linearFromsRGB(c):\\n if c <= 0.04045:\\n return c / 12.92\\n return math.pow((0.055 + c) / 1.055, 2.4)\",\n \"def driftColor(baseColor, factor=110):\\n if baseColor.lightness() > 128:\\n return baseColor.darker(factor)\\n else:\\n return baseColor.lighter(factor+10)\",\n \"def _get_color(self, c, x, max_num):\\n\\n ratio = 5*(float(x)/max_num)\\n i = int(math.floor(ratio))\\n j = int(math.ceil(ratio))\\n ratio -= i\\n r = (1 - ratio) * self._colors[i][c] + ratio*self._colors[j][c]\\n return int(255*r)\",\n \"def linearTosRGB(c):\\n if c <= 0.0031308:\\n return 12.92 * c\\n return math.pow(c, 1.0 / 2.4) * 1.055 - 0.055\",\n \"def _color(self, x, factor):\\r\\n factor = (factor/MAX_LEVEL) * 1.8 + .1\\r\\n degenerate = tf.image.grayscale_to_rgb(tf.image.rgb_to_grayscale(x))\\r\\n x = tfa.image.blend(degenerate, tf.cast(x, tf.float32), factor)\\r\\n return tf.saturate_cast(x, tf.uint8)\",\n \"def get_colour(progress, colours):\\n if progress >= 0 and progress <= 1:\\n start_colour, end_colour = colours[0], colours[1]\\n\\n r = start_colour[0] + (end_colour[0] - start_colour[0]) * progress\\n b = start_colour[1] + (end_colour[1] - start_colour[1]) * progress\\n g = start_colour[2] + (end_colour[2] - start_colour[2]) * progress\\n\\n return '#%02x%02x%02x' % (round(r), round(b), round(g))\\n \\n else: return '#000000'\",\n \"def floatRgb(mag, cmin, cmax):\\n # Normalize to 0-1\\n try: x = float(mag-cmin)/(cmax-cmin)\\n except ZeroDivisionError: x = 0.5 # cmax == cmin\\n blue = min((max((4*(0.75-x), 0.)), 1.))\\n red = min((max((4*(x-0.25), 0.)), 1.))\\n green = min((max((4*math.fabs(x-0.5)-1., 0.)), 1.))\\n return red, green, blue\",\n \"def get_color(self, value):\\n value = min(max(0,value), 1) * 510\\n\\n if value < 255:\\n redValue = 255\\n greenValue = math.sqrt(value) * 16\\n greenValue = int(greenValue)\\n else:\\n greenValue = 255\\n value = value - 255\\n redValue = 255 - (value * value / 255)\\n redValue = int(redValue)\\n return '#' + f\\\"{redValue:0{2}x}\\\" + f\\\"{greenValue:0{2}x}\\\" + '00'\",\n \"def ci(x, y, z):\\n\\n return (x * 16 + z) * CHUNK_HEIGHT + y\",\n \"def color_map(val):\\n color_code = remap_interval(val, -1, 1, 0, 255)\\n return int(color_code)\",\n \"def getRGBFromXYAndBrightness(self, x, y, bri=1):\\n # The xy to color conversion is almost the same, but in reverse order.\\n # Check if the xy value is within the color gamut of the lamp.\\n # If not continue with step 2, otherwise step 3.\\n # We do this to calculate the most accurate color the given light can actually do.\\n xyPoint = XYPoint(x, y)\\n\\n if not self.checkPointInLampsReach(xyPoint):\\n # Calculate the closest point on the color gamut triangle\\n # and use that as xy value See step 6 of color to xy.\\n xyPoint = self.getClosestPointToPoint(xyPoint)\\n\\n # Calculate XYZ values Convert using the following formulas:\\n Y = bri\\n X = (Y / xyPoint.y) * xyPoint.x\\n Z = (Y / xyPoint.y) * (1 - xyPoint.x - xyPoint.y)\\n\\n # Convert to RGB using Wide RGB D65 conversion\\n r = X * 1.612 - Y * 0.203 - Z * 0.302\\n g = -X * 0.509 + Y * 1.412 + Z * 0.066\\n b = X * 0.026 - Y * 0.072 + Z * 0.962\\n\\n # Apply reverse gamma correction\\n r, g, b = map(\\n lambda x: (12.92 * x) if (x <= 0.0031308) else ((1.0 + 0.055) * pow(x, (1.0 / 2.4)) - 0.055),\\n [r, g, b]\\n )\\n\\n # Bring all negative components to zero\\n r, g, b = map(lambda x: max(0, x), [r, g, b])\\n\\n # If one component is greater than 1, weight components by that value.\\n max_component = max(r, g, b)\\n if max_component > 1:\\n r, g, b = map(lambda x: x / max_component, [r, g, b])\\n\\n r, g, b = map(lambda x: int(x * 255), [r, g, b])\\n\\n # Convert the RGB values to your color object The rgb values from the above formulas are between 0.0 and 1.0.\\n return (r, g, b)\",\n \"def color_map(val):\\n # NOTE: This relies on remap_interval, which you must provide\\n color_code = remap_interval(val, -1, 1, 0, 255)\\n return int(color_code)\",\n \"def color_map(val):\\n # NOTE: This relies on remap_interval, which you must provide\\n color_code = remap_interval(val, -1, 1, 0, 255)\\n return int(color_code)\",\n \"def color_map(val):\\n # NOTE: This relies on remap_interval, which you must provide\\n color_code = remap_interval(val, -1, 1, 0, 255)\\n return int(color_code)\",\n \"def color_map(val):\\n # NOTE: This relies on remap_interval, which you must provide\\n color_code = remap_interval(val, -1, 1, 0, 255)\\n return int(color_code)\",\n \"def colorcalc(X1, X2, r=1):\\n X1[X1 < 0], X2[X2 < 0] = 0, 0\\n X1[X1 > 1], X2[X2 > 1] = 1, 1\\n X3 = 1-X1-X2\\n c1, c2, c3 = np.copy(X1), np.copy(X2), np.copy(X3)\\n for i, (x1, x2, x3) in enumerate(itertools.permutations([X1, X2, X3])):\\n ip = (x1 <= x2) & (x2 <= x3) & (x3 < 1)\\n x, y = x3[ip]+.5*x1[ip], x1[ip]*3**.5/2\\n p = (x-.5)/.5\\n q = (.5-p**r)/(1-x)\\n xn, yn = 1-q*(1-x), y*q\\n a, b, c = yn*2/3**.5, 1-xn-yn/3**.5, xn-yn/3**.5\\n if i == 0:\\n c1[ip], c2[ip], c3[ip] = a, b, c\\n elif i == 1:\\n c1[ip], c3[ip], c2[ip] = a, b, c\\n elif i == 2:\\n c2[ip], c1[ip], c3[ip] = a, b, c\\n elif i == 3:\\n c2[ip], c3[ip], c1[ip] = a, b, c\\n elif i == 4:\\n c3[ip], c1[ip], c2[ip] = a, b, c\\n else:\\n c3[ip], c2[ip], c1[ip] = a, b, c\\n color = np.stack([c1, c2, c3], axis=2)\\n e = 1/color.max(2)\\n return color*np.stack([e, e, e], axis=2)\",\n \"def get_colors(self):\\n x = np.linspace(0, 1, self.length)\\n y = x**self.gamma\\n\\n value = np.linspace(0, 1, len(self.colors))\\n r = np.interp(y, value, self.colors[:,0])\\n g = np.interp(y, value, self.colors[:,1])\\n b = np.interp(y, value, self.colors[:,2])\\n\\n return np.dstack((r, g, b)).reshape(len(r), 3).astype(np.uint8)\",\n \"def _calc_zpcolors(self):\\n for f, zpc in zip(self._filters, self._zpcolors):\\n m0, merr0 = getattr(self, zpc[0]), getattr(self, zpc[0] + '_err')\\n m1, merr1 = getattr(self, zpc[1]), getattr(self, zpc[1] + '_err')\\n if merr0 < 9.99 and merr1 < 9.99:\\n setattr(self, f + 'zpc', m0 - m1)\\n setattr(self, f + 'zpc_err', np.sqrt(merr0**2 + merr1**2))\\n else:\\n setattr(self, f + 'zpc', self._nodata)\\n setattr(self, f + 'zpc_err', self._nodata)\",\n \"def scale_color((h, s, v), factor=1.):\\n if (h < 0.) or (h > 360.):\\n raise Exception('[scale_color()] Hue value out of range (0, 360): ' + str(h))\\n\\n if (s < 0.) or (s > 100.):\\n raise Exception('[scale_color()] Saturation value out of range (0, 100): ' + str(s))\\n\\n if (v < 0.) or (v > 100.):\\n raise Exception('[scale_color()] Value value out of range (0, 100): ' + str(v))\\n\\n if (factor < 0.) or (factor > 1.):\\n raise Exception('[scale_color()] Factor value out of range (0.0, 1.0): ' + str(factor))\\n\\n v *= factor\\n r, g, b = hsv_to_rgb(h/360., s/100., v/100.)\\n\\n return '#{0:02x}{1:02x}{2:02x}'.format(int(round(r*255.)), int(round(g*255.)), int(round(b*255.)))\",\n \"def drawColorCurve(xseq, yseq, zseq):\\n dislin.curve3(xseq,yseq,zseq,len(xseq))\",\n \"def ramp_color_rgb(values, feature, parent): \\r\\n ramp_name = values[0]\\r\\n ramp_position = values[1]\\r\\n \\r\\n ramp = QgsStyleV2.defaultStyle().colorRampRef(ramp_name)\\r\\n if not ramp:\\r\\n parent.setEvalErrorString( QObject.tr( '\\\"{}\\\" is not a valid color ramp'.format(ramp_name)))\\r\\n return QColor(0,0,0).name()\\r\\n \\r\\n value, error = getFloat(ramp_position)\\r\\n if error:\\r\\n parent.setEvalErrorString(error)\\r\\n \\r\\n color = ramp.color(value)\\r\\n return \\\"{},{},{}\\\".format(color.red(), color.green(), color.blue())\",\n \"def collatz(value):\\r\\n assert value >= 1\\r\\n if value % 2 == 0:\\r\\n return value/2\\r\\n else:\\r\\n return 3 * value + 1\",\n \"def getJuliaPixelColor(z, gradient):\\n c = complex(-1.0, 0.0)\\n for i in range(len(gradient)):\\n z = z * z + c # Get z1, z2, ...\\n if abs(z) > 2:\\n z = 2.0\\n return gradient[i] # The sequence is unbounded\\n return gradient[len(gradient) - 1] # Indicate a bounded sequence\",\n \"def get_color(activePerMillion):\\n activePer100k = activePerMillion / 10.0\\n if activePer100k < 100:\\n return \\\"#aaf0d1\\\"\\n elif activePer100k < 500:\\n return \\\"#a3f7bf\\\"\\n elif activePer100k < 1000:\\n return \\\"#90EE90\\\"\\n elif activePer100k < 1500:\\n return \\\"#00ff7f\\\"\\n elif activePer100k < 2000:\\n return \\\"#77dd77\\\"\\n elif activePer100k < 2500:\\n return \\\"#32cd32\\\"\\n elif activePer100k < 3000:\\n return \\\"#4cbb17\\\"\\n elif activePer100k < 3500:\\n return \\\"#228b22\\\"\\n elif activePer100k < 4000:\\n return \\\"#355e3b \\\"\\n else:\\n return \\\"#006400\\\"\",\n \"def grey(self):\\n return sum((self.value(0), self.value(1), self.value(2)))/3\",\n \"def RGBa_interpolation(t, c0, c1, alpha):\\n\\n R = lerp(t, c0[0], c1[0])\\n G = lerp(t, c0[1], c1[1])\\n B = lerp(t, c0[2], c1[2])\\n a = lerp(t, c0[3], c1[3]) * alpha\\n return np.vstack([R, G, B, a]).T\",\n \"def RGBratios(dataset,fil):\\n dom_fil = get_dominant_color(fil)\\n dataset['R_B_ratio'] = dataset.R/dataset.B\\n dataset['R_G_ratio'] = dataset.R/dataset.G\\n dataset['G_B_ratio'] = dataset.G/dataset.B\\n filter_RB = dom_fil[0]/dom_fil[2]\\n filter_RG = dom_fil[0]/dom_fil[1]\\n filter_GB = dom_fil[1]/dom_fil[2]\\n norm = []\\n for array in list(dataset['DominatColour']):\\n norm.append( np.linalg.norm(array))\\n dataset['Dominant_norm'] = norm\\n\\n norm = []\\n for array in list(dataset['AverageColour']):\\n norm.append( np.linalg.norm(array))\\n\\n\\n dataset['DiffRatio_R_B'] = dataset.R_B_ratio-filter_RB\\n dataset['DiffRatio_R_G'] = dataset.R_G_ratio-filter_RG\\n dataset['DiffRatio_RG_B'] = dataset.G_B_ratio-filter_GB\\n dataset['Average_norm'] = norm\\n return dataset\",\n \"def colorize_xyz(xyz):\\n xyz_vis = xyz - xyz.min()\\n return (255 * xyz_vis / xyz_vis.max()).astype(np.uint8)\",\n \"def to_color(arr,pmin=1,pmax=99.8,gamma=1.,colors=((0,1,0),(1,0,1),(0,1,1))):\\n \\n if not arr.dim in (2,3):\\n raise ValueError(\\\"only 2d or 3d arrays supported\\\")\\n \\n if arr.ndim==2:\\n arr=arr[np.newaxis]\\n #get the grey channel\\n ind_min=np.argmin(arr.shape)\\n arr =np.moveaxis(arr,ind_min,0).astype(np.float32)\\n \\n out =np.zeros(arr.shape[1:]+(3,))\\n \\n eps=1.e-20\\n \\n if pmin>=0:\\n mi=np.percentile(arr,pmin,axis=(1,2),keepdims=True)\\n else:\\n mi=0\\n \\n if pmax>=0:\\n ma=np.percentile(arr,pmax,axis=(1,2),keepdims=True)\\n else:\\n ma=1.+eps\\n \\n arr_norm=(1.*arr-mi)/(ma-mi+eps)\\n #RGB 3channel\\n for i_stack,col_stack in enumerate(colors):\\n if i_stack >= len(arr):\\n break\\n for j,c in enumerate(col_stack):\\n out[...,j]+= c*arr_norm[i_stack]\\n \\n return np.clip(out,0,1)\",\n \"def getPseudoColor( nValue, nMin = 0, nMax = 255, nErrorValue = -1 ):\\n r, g, b = [0,0,0];\\n # Invalid Data -> set Color to White\\n if( nValue == nErrorValue ):\\n r = 0xff;\\n g = 0xff;\\n b = 0xff;\\n # Invalid Range\\n # -> Set Color to Grey (debug)\\n elif( nValue < nMin or nValue > nMax ):\\n r = 0x80;\\n g = 0x80;\\n b = 0x80;\\n else:\\n # each part as a specific color computation\\n # 5 parts:\\n \\\"\\\"\\\"\\n nSizePhase = ( nMax - nMin ) / 5;\\n if( nValue < nSizePhase * 1 ):\\n r = 0xff;\\n g = (((nValue-nMin)-0*nSizePhase)*0xff)/nSizePhase;\\n b = 0x0;\\n elif( nValue < nSizePhase * 2 ):\\n r = 0xff - (((nValue-nMin)-1*nSizePhase)*0xff)/nSizePhase;\\n g = 0xff;\\n b = 0;\\n elif( nValue < nSizePhase * 3 ):\\n r = 0;\\n g = 0xff;\\n b = (((nValue-nMin)-2*nSizePhase)*0xff)/nSizePhase;\\n elif( nValue < nSizePhase * 4 ):\\n r = 0;\\n g = 0xff - (((nValue-nMin)-3*nSizePhase)*0xff)/nSizePhase;\\n b = 0xff;\\n else:\\n r = 0xff;\\n g = 0;\\n b = 0xff - (((nValue-nMin)-4*nSizePhase)*0xff)/nSizePhase;\\n \\\"\\\"\\\"\\n # 2 parts: (green to yellow then yellow to red)\\n nSizePhase = ( nMax - nMin ) / 2;\\n if( nValue < nSizePhase * 1 ):\\n r = (((nValue-nMin)-0*nSizePhase)*0xff)/nSizePhase;\\n g = 0xff;\\n b = 0x0;\\n elif( nValue < nSizePhase * 2 ):\\n r = 0xff;\\n g = 0xff - (((nValue-nMin)-1*nSizePhase)*0xff)/nSizePhase;\\n b = 0; \\n # else - end\\n return [r,g,b];\",\n \"def linearTosRGB3(c):\\n return [linearTosRGB(c[0]), linearTosRGB(c[1]), linearTosRGB(c[2])]\",\n \"def calc_R(xc, yc, zc,x,y,z):\\n return sqrt((x - xc) ** 2 + (y - yc) ** 2 + (z - zc) ** 2)\",\n \"def generate_lut(self):\\n r,g,b=(Numeric.zeros(256),Numeric.zeros(256),Numeric.zeros(256))\\n for i in Numeric.arange(256):\\n r_,g_,b_=self.colfct(i/255.0) # these are from [0,1]\\n r[i],g[i],b[i]=int(255*r_),int(255*g_),int(255*b_)\\n return r,g,b\",\n \"def combine_colors(c1, c2, factor=0.5):\\n c3 = QtGui.QColor()\\n c3.setRed(int((factor * c1.red() + (1 - factor) * c2.red())))\\n c3.setGreen(int((factor * c1.green() + (1 - factor) * c2.green())))\\n c3.setBlue(int((factor * c1.blue() + (1 - factor) * c2.blue())))\\n return c3\",\n \"def combine_colors(c1, c2, factor=0.5):\\n c3 = QtGui.QColor()\\n c3.setRed(int((factor * c1.red() + (1 - factor) * c2.red())))\\n c3.setGreen(int((factor * c1.green() + (1 - factor) * c2.green())))\\n c3.setBlue(int((factor * c1.blue() + (1 - factor) * c2.blue())))\\n return c3\",\n \"def __RGB_to_Hue(var_R, var_G, var_B, var_min, var_max):\\r\\n\\r\\n if var_max == var_min:\\r\\n return 0.0\\r\\n elif var_max == var_R:\\r\\n return (60.0 * ((var_G - var_B) / (var_max - var_min)) + 360) % 360.0\\r\\n elif var_max == var_G:\\r\\n return 60.0 * ((var_B - var_R) / (var_max - var_min)) + 120\\r\\n elif var_max == var_B:\\r\\n return 60.0 * ((var_R - var_G) / (var_max - var_min)) + 240.0\",\n \"def get_colors(n):\\n color = cm.rainbow(np.linspace(0, 1, n))\\n return color\",\n \"def standard_handler(args_dict: dict):\\n\\n color_sequence = args_dict['color_sequence']\\n step = args_dict['step']\\n\\n # Updating step for the next iteration.\\n args_dict['step'] = (step + 1) % args_dict['sequence_len']\\n\\n return color_sequence[step]\",\n \"def lighter(clr, f=1/3):\\n gaps = [f*(1 - val) for val in clr]\\n new_clr = [val + gap for gap, val in zip(gaps, clr)]\\n return new_clr\",\n \"def fakingColors(pixel):\\n\\n return (\\n pixel[0] * 0.598 - 0.1957 * pixel[1] - 0.038 * pixel[2],\\n pixel[1] * 1.174 - pixel[0] * 0.1994 - pixel[2] * 0.076,\\n pixel[2] * 0.228 - pixel[0] * 0.1495 - pixel[1] * 0.2935\\n )\",\n \"def _color_brew(n):\\n color_list = []\\n\\n # Initialize saturation & value; calculate chroma & value shift\\n s, v = 0.75, 0.9\\n c = s * v\\n m = v - c\\n\\n for h in np.arange(25, 385, 360. / n).astype(int):\\n # Calculate some intermediate values\\n h_bar = h / 60.\\n x = c * (1 - abs((h_bar % 2) - 1))\\n # Initialize RGB with same hue & chroma as our color\\n rgb = [(c, x, 0),\\n (x, c, 0),\\n (0, c, x),\\n (0, x, c),\\n (x, 0, c),\\n (c, 0, x),\\n (c, x, 0)]\\n r, g, b = rgb[int(h_bar)]\\n # Shift the initial RGB values to match value and store\\n rgb = [(int(255 * (r + m))),\\n (int(255 * (g + m))),\\n (int(255 * (b + m)))]\\n color_list.append(rgb)\\n\\n return color_list\",\n \"def StepColour(c, ialpha):\\r\\n \\r\\n if ialpha == 100:\\r\\n return c\\r\\n \\r\\n r, g, b = c.Red(), c.Green(), c.Blue()\\r\\n\\r\\n # ialpha is 0..200 where 0 is completely black\\r\\n # and 200 is completely white and 100 is the same\\r\\n # convert that to normal alpha 0.0 - 1.0\\r\\n ialpha = min(ialpha, 200)\\r\\n ialpha = max(ialpha, 0)\\r\\n alpha = (ialpha - 100.0)/100.0\\r\\n\\r\\n if ialpha > 100:\\r\\n \\r\\n # blend with white\\r\\n bg = 255\\r\\n alpha = 1.0 - alpha # 0 = transparent fg 1 = opaque fg\\r\\n \\r\\n else:\\r\\n \\r\\n # blend with black\\r\\n bg = 0\\r\\n alpha = 1.0 + alpha # 0 = transparent fg 1 = opaque fg\\r\\n \\r\\n r = BlendColour(r, bg, alpha)\\r\\n g = BlendColour(g, bg, alpha)\\r\\n b = BlendColour(b, bg, alpha)\\r\\n\\r\\n return wx.Colour(r, g, b)\",\n \"def _toRgbImage(self, fractal, colors, color_offset):\\n hsv_img = np.array(\\n [\\n # Cycle through color wheel.\\n (fractal * colors + color_offset) % 1,\\n\\n # Saturation = fractal value.\\n fractal,\\n\\n # Maximum value.\\n np.ones(fractal.shape)\\n ]\\n ).astype(dtype=float).T\\n\\n rgb_img = (mpl.colors.hsv_to_rgb(hsv_img) * 255).astype(dtype=np.uint8)\\n return rgb_img\",\n \"def get_colors(n_curves):\\n # Use offsets to avoid shitty crappy min/max colors (ugly yellow or white for example)\\n first_step = 1. / (n_curves + 1.)\\n last_step = 1.\\n nb_steps = n_curves + 1\\n color_range = np.linspace(first_step, last_step, nb_steps)\\n color = iter(plt.cm.hot(color_range))\\n return color\",\n \"def unique_colors_rgb(n):\\r\\n hues = []\\r\\n # i is in the range 0, 1, ..., n - 1\\r\\n for i in range(1, n + 1):\\r\\n hues.append(360.0 / i)\\r\\n\\r\\n hs = []\\r\\n for hue in hues:\\r\\n h = math.floor(hue / 60) % 6\\r\\n hs.append(h)\\r\\n\\r\\n fs = []\\r\\n for hue in hues:\\r\\n f = hue / 60 - math.floor(hue / 60)\\r\\n fs.append(f)\\r\\n\\r\\n rgbcolors = []\\r\\n for h, f in zip(hs, fs):\\r\\n v = 1\\r\\n p = 0\\r\\n q = 1 - f\\r\\n t = f\\r\\n if h == 0:\\r\\n color = v, t, p\\r\\n elif h == 1:\\r\\n color = q, v, p\\r\\n elif h == 2:\\r\\n color = p, v, t\\r\\n elif h == 3:\\r\\n color = p, q, v\\r\\n elif h == 4:\\r\\n color = t, p, v\\r\\n elif h == 5:\\r\\n color = v, p, q\\r\\n rgbcolors.append(color)\\r\\n\\r\\n return rgbcolors\",\n \"def rgb(r, g, b):\\n return (r/255, g/255, b/255)\",\n \"def scale_to_01(color: C3I) -> C3F:\\n r, g, b = color\\n return r / 255, g / 255, b / 255\",\n \"def dcsrbf(r):\\n return 3*(num.power(num.maximum(0, 1-r), 3) - num.power(num.maximum(0, 1-r),2)*(3*r+1))\",\n \"def create_unique_color_uchar(tag, hue_step=0.41):\\n r, g, b = create_unique_color_float(tag, hue_step)\\n return int(255*r), int(255*g), int(255*b)\",\n \"def get_color(val, trendType):\\n if(val > 0):\\n if(trendType == 'normal'):\\n color = 'red'\\n else:\\n color = 'green'\\n elif(val < 0):\\n if(trendType == 'normal'):\\n color = 'green'\\n else:\\n color = 'red'\\n else:\\n color = 'blue'\\n return color\",\n \"def step_fn(step):\\n ret = 1.0\\n for name in factors:\\n if name == 'constant':\\n ret *= base_learning_rate\\n elif name == 'linear_warmup':\\n ret *= jnp.minimum(1.0, step / warmup_steps)\\n elif name == 'rsqrt_decay':\\n ret /= jnp.sqrt(jnp.maximum(1.0, step - warmup_steps))\\n elif name == 'rsqrt_normalized_decay':\\n ret *= jnp.sqrt(warmup_steps)\\n ret /= jnp.sqrt(jnp.maximum(step, warmup_steps))\\n elif name == 'decay_every':\\n ret *= (decay_factor**(step // steps_per_decay))\\n elif name == 'cosine_decay':\\n progress = jnp.maximum(0.0,\\n (step - warmup_steps) / float(steps_per_cycle))\\n ret *= jnp.maximum(0.0,\\n 0.5 * (1.0 + jnp.cos(jnp.pi * (progress % 1.0))))\\n else:\\n raise ValueError('Unknown factor %s.' % name)\\n return jnp.asarray(ret, dtype=jnp.float32)\",\n \"def gamma(k, z):\\n return 1\",\n \"def index_to_rgb(index=0, gamma=0.5):\\n\\n wavelength = (index * 320) + 380\\n\\n if wavelength < 440:\\n intensity = 0.1 + (0.9 * (wavelength - 380) / (440 - 380))\\n red = ((-1.0 * (wavelength - 440) / (440 - 380)) * intensity) ** gamma\\n grn = 0.0\\n blu = (1.0 * intensity) ** gamma\\n if 440 <= wavelength < 490:\\n red = 0.0\\n grn = (1.0 * (wavelength - 440) / (490 - 440)) ** gamma\\n blu = 1.0**gamma\\n if 490 <= wavelength < 510:\\n red = 0.0\\n grn = 1.0**gamma\\n blu = (-1.0 * (wavelength - 510) / (510 - 490)) ** gamma\\n if 510 <= wavelength < 580:\\n red = (1.0 * (wavelength - 510) / (580 - 510)) ** gamma\\n grn = 1.0**gamma\\n blu = 0.0\\n if 580 <= wavelength < 645:\\n red = 1.0**gamma\\n grn = (-1.0 * (wavelength - 645) / (645 - 580)) ** gamma\\n blu = 0.0\\n if wavelength >= 645:\\n intensity = 0.3 + (0.7 * (700 - wavelength) / (700 - 645))\\n red = (1.0) ** gamma\\n grn = (1.0 - intensity) ** gamma\\n blu = (1.0 - intensity) ** gamma\\n\\n return (int(red * 255) << 16) + (int(grn * 255) << 8) + int(blu * 255)\",\n \"def getColorLine(self, dot1, dot2): # generate all dots of line\\n x1, y1, z1, feed1 = dot1\\n x2, y2, z2, feed2 = dot2\\n min_color = self.colors_list[feed1]\\n max_color = self.colors_list[feed2]\\n\\n # NB! feed1,feed2 >= 0; 0 = dot[3]-fmin\\n # self.colors_list = grad(MIN_COLOR, MAX_COLOR, self.fmax)\\n if self.current_feedrate == 0:\\n start_color = min_color\\n if feed2 > feed1:\\n finish_color = max_color\\n else:\\n finish_color = min_color\\n else:\\n if feed2 > feed1:\\n start_color = min_color\\n finish_color = max_color\\n elif feed1 > feed2:\\n start_color = max_color\\n finish_color = min_color\\n else: # feed1 == feed2\\n if feed2 == self.current_feedrate:\\n start_color = finish_color = self.current_color\\n\\n self.current_color = finish_color\\n self.current_feedrate = feed2\\n color_list = grad(start_color, finish_color, n=self.speed+1)\\n\\n i = 0\\n coords = []\\n stepx = (x2-x1) / self.speed\\n stepy = (y2-y1) / self.speed\\n stepz = (z2-z1) / self.speed\\n for i in range(self.speed):\\n dot = [0, 0, 0, 0, 0, 0, 0] # x, y, z, r, g, b, p\\n dot[0] = x1 + i*stepx\\n dot[1] = y1 + i*stepy\\n dot[2] = z1 + i*stepz\\n dot[3:7] = color_list[i]\\n coords.append(dot)\\n\\n return coords\",\n \"def get_coloured_grid(self, r1, r2, r3, b1=4, b2=2.5, b3=1):\\n r, g, b = np.frompyfunc(self.get_colour(r1, r2, r3, b1, b2, b3), 2, 3)(self.end_z, self.end_step)\\n img_array = np.dstack((r, g, b))\\n return Image.fromarray(np.uint8(img_array * 255))\",\n \"def _toRgbImage(self, fractal, colors, color_offset):\\n hsv_img = np.array(\\n [\\n # Cycle through color wheel.\\n (fractal * colors + color_offset) % 1,\\n\\n # Saturation = 1 where fractal values > 0,\\n # Saturation = 0 otherwise.\\n fractal.astype(dtype=bool).astype(dtype=float),\\n\\n # Invert colours\\n 1 - fractal\\n ]\\n ).astype(dtype=float).T\\n\\n rgb_img = (mpl.colors.hsv_to_rgb(hsv_img) * 255).astype(dtype=np.uint8)\\n return rgb_img\",\n \"def shade(N=10, *colors):\\n return interp1d(linspace(0, 1, len(colors)), colors, axis=0)(linspace(0, 1, N)).round(2)\",\n \"def color_differentiator(elevation):\\n if elevation < 1000:\\n return 'green'\\n elif 1000 <= elevation < 3000:\\n return 'orange'\\n else:\\n return 'red'\",\n \"def _color(self, args):\",\n \"def color_level(value, min_value=0.0, max_value=1.0, func=lambda x: x):\\n global COLOR_SCALE\\n\\n if not __colored__:\\n return \\\"\\\"\\n\\n COLOR_SCALE = [\\n fg(\\\"white\\\"),\\n fg(\\\"pale_green_3b\\\"),\\n fg(\\\"green\\\"),\\n fg(\\\"green_yellow\\\"),\\n fg(\\\"light_yellow\\\"),\\n fg(\\\"orange_1\\\"),\\n fg(\\\"orange_red_1\\\"),\\n fg(\\\"light_red\\\"),\\n fg(\\\"light_red\\\") + attr(\\\"blink\\\")\\n ]\\n\\n max_index = len(COLOR_SCALE) - 1\\n color_index = int(round(\\n max_index * min(1.0, max(0.0,\\n func((value - min_value) / (max_value - min_value))\\n ))\\n ))\\n\\n return COLOR_SCALE[color_index]\",\n \"def to_color(arr, pmin=1, pmax=99.8, gamma=1., colors=((0, 1, 0), (1, 0, 1), (0, 1, 1))):\\n if not arr.ndim in (2,3):\\n raise ValueError(\\\"only 2d or 3d arrays supported\\\")\\n\\n if arr.ndim ==2:\\n arr = arr[np.newaxis]\\n\\n ind_min = np.argmin(arr.shape)\\n arr = np.moveaxis(arr, ind_min, 0).astype(np.float32)\\n\\n out = np.zeros(arr.shape[1:] + (3,))\\n\\n eps = 1.e-20\\n if pmin>=0:\\n mi = np.percentile(arr, pmin, axis=(1, 2), keepdims=True)\\n else:\\n mi = 0\\n\\n if pmax>=0:\\n ma = np.percentile(arr, pmax, axis=(1, 2), keepdims=True)\\n else:\\n ma = 1.+eps\\n\\n arr_norm = (1. * arr - mi) / (ma - mi + eps)\\n\\n\\n for i_stack, col_stack in enumerate(colors):\\n if i_stack >= len(arr):\\n break\\n for j, c in enumerate(col_stack):\\n out[..., j] += c * arr_norm[i_stack]\\n\\n return np.clip(out, 0, 1)\",\n \"def depth_color(depth):\\n if depth < 20:\\n return 'g'\\n if depth < 40:\\n return 'y'\\n return 'r'\",\n \"def color_creator(number_of_films):\\n if 0 < number_of_films < 100:\\n return 'green'\\n elif 101 < number_of_films < 200:\\n return 'orange'\\n elif 201 < number_of_films < 500:\\n return 'red'\\n else:\\n return 'purple'\",\n \"def int2color(x):\\n # r = int(1000 * x % 255)\\n # g = int(10000 * x % 255)\\n # b = int(100000 * x % 255)\\n x = 0 if x == 0 else int(1/x)\\n b = x & 0xff\\n g = (x >> 8) & 0xff\\n r = (x >> 16) & 0xff\\n return [r, g, b]\",\n \"def toRGB(self):\\n\\t\\treturn '#'+ (hex(self.politics)[2:].upper().rjust(6,'0'))\",\n \"def rainbow(double[:] v, redshifts, in_terms, double H0):\\n\\n cdef double t = v[0]\\n cdef double a = v[1]\\n cdef double ombar_m = v[2]\\n cdef double ombar_r = v[3]\\n cdef double ombar_a = v[4]\\n cdef double ombar_b = v[5]\\n cdef double ombar_c = v[6]\\n cdef double ombar_d = v[7]\\n cdef double ombar_e = v[8]\\n cdef double ombar_f = v[9]\\n cdef double ombar_g = v[10]\\n cdef double ombar_h = v[11]\\n cdef double ombar_i = v[12]\\n cdef double ombar_de = v[13]\\n cdef double z = v[14]\\n cdef double dl = v[15]\\n\\n cdef double in_p = in_terms[0]\\n cdef double in_q = in_terms[1]\\n cdef double in_r = in_terms[2]\\n cdef double in_s = in_terms[3]\\n cdef double in_t = in_terms[4]\\n cdef double in_u = in_terms[5]\\n cdef double in_v = in_terms[6]\\n cdef double in_w = in_terms[7]\\n cdef double in_x = in_terms[8]\\n cdef double in_y = in_terms[9]\\n cdef double in_z = in_terms[10]\\n\\n\\n cdef double Hz = H0 * (ombar_m +ombar_r +ombar_a +ombar_b\\n +ombar_c +ombar_d +ombar_e +ombar_f\\n +ombar_g +ombar_h +ombar_i +ombar_de)**(0.5)\\n\\n# if ombar_m < 0 or ombar_de < 0:\\n# print('rainbow')\\n# print('z = %s, Hz = %s, in_terms = %s'% (z, Hz, in_terms))\\n# print(f'omegas = {v[2:14]}')\\n\\n if math.isnan(Hz):\\n print('rainbow')\\n print('z = %s, Hz = %s, in_terms = %s'% (z, Hz, in_terms))\\n print(f'ombar_m = {ombar_m}, ombar_r = {ombar_r}, ombar_a = {ombar_a}, ombar_b = {ombar_b}, ombar_c = {ombar_c}, ombar_d = {ombar_d}, ombar_e = {ombar_e}, ombar_f = {ombar_f}, ombar_g = {ombar_g}, ombar_h = {ombar_h}, ombar_i = {ombar_i}, ombar_de = {ombar_de}')\\n\\n cdef double dtdz = -1.0/((1.0+z) * Hz)\\n cdef double dadz = -(1.0+z)**(-2.0)\\n\\n cdef double domdz = (3.0*ombar_m +in_p*ombar_m*ombar_r/Hz)/(1.0+z) # w = 0\\n cdef double dordz = (4.0*ombar_r -in_p*ombar_m*ombar_r/Hz +in_q*ombar_r*ombar_a/Hz)/(1.0+z) # w = 1/3\\n cdef double doadz = (2.7*ombar_a -in_q*ombar_r*ombar_a/Hz +in_r*ombar_a*ombar_b/Hz)/(1.0+z) # w = -0.1\\n cdef double dobdz = (2.4*ombar_b -in_r*ombar_a*ombar_b/Hz +in_s*ombar_b*ombar_c/Hz)/(1.0+z) # w = -0.2\\n cdef double docdz = (2.1*ombar_c -in_s*ombar_b*ombar_c/Hz +in_t*ombar_c*ombar_d/Hz)/(1.0+z) # w = -0.3\\n cdef double doddz = (1.8*ombar_d -in_t*ombar_c*ombar_d/Hz +in_u*ombar_d*ombar_e/Hz)/(1.0+z) # w = -0.4\\n cdef double doedz = (1.5*ombar_e -in_u*ombar_d*ombar_e/Hz +in_v*ombar_e*ombar_f/Hz)/(1.0+z) # w = -0.5\\n cdef double dofdz = (1.2*ombar_f -in_v*ombar_e*ombar_f/Hz +in_w*ombar_f*ombar_g/Hz)/(1.0+z) # w = -0.6\\n cdef double dogdz = (0.9*ombar_g -in_w*ombar_f*ombar_g/Hz +in_x*ombar_g*ombar_h/Hz)/(1.0+z) # w = -0.7\\n cdef double dohdz = (0.6*ombar_h -in_x*ombar_g*ombar_h/Hz +in_y*ombar_h*ombar_i/Hz)/(1.0+z) # w = -0.8\\n cdef double doidz = (0.3*ombar_i -in_y*ombar_h*ombar_i/Hz +in_z*ombar_i*ombar_de/Hz)/(1.0+z)# w = -0.9\\n cdef double dodedz = -in_z*ombar_i*ombar_de/(1.0+z)/Hz # w = -1\\n\\n cdef double ddldz = 1.0/Hz\\n\\n # first derivatives of functions I want to find:\\n f = [dtdz,# dt/dz (= f.d wrt z of time)\\n dadz,# d(a)/dz (= f.d wrt z of scale factor)\\n domdz,# dw = 0, (ombar_m)/dz (= f.d wrt z of density_m(t) / crit density(t0))\\n dordz,# w = 1/3, d(ombar_r)/dz (= f.d wrt z of density_r(t) / crit density(t0))\\n doadz,# w = -0.1\\n dobdz,# w = -0.2\\n docdz,# w = -0.3\\n doddz,# w = -0.4\\n doedz,# w = -0.5\\n dofdz,# w = -0.6\\n dogdz,# w = -0.7\\n dohdz,# w = -0.8\\n doidz,# w = -0.9\\n dodedz,# w = -1, d(ombar_de)/dz (= f.d wrt z of density_de(t) / crit density(t0))\\n 1.0,# d(z)/dz (= f.d wrt z of redshift)\\n ddldz]# d(dl)/dz (= f.d wrt z of luminosty distance) # H + Hdz*(1+z)\\n\\n return f\",\n \"def c_s(self, z):\\n R = self.R_bg(z)\\n return const.c/np.sqrt(3.*(1.+R))\",\n \"def _FracInt(x,y,z,a,b,c,tau,n):\\n denom = np.sqrt((a + tau)*(b + tau)*(c + tau))\\n return (1. - x**2/(a + tau) - y**2/(b + tau) - z**2/(c + tau))**n / denom\",\n \"def _hls2rgb(self,h):\\n h=h**self.exponent\\n if(self.invert): h=1.0-h\\n h=h*360.0\\n h=Numeric.fmod(h,360.0)\\n if(self.hls_hls):\\n h=h/60.0\\n else:\\n if(h<120):\\n h=h/120.0 # /* 0..1 Rot..(Orange)..Gelb */\\n elif(h<180):\\n h=h/60.0 - 1.0 # /* 1..2 Gelb..Gruen */\\n elif(h<240):\\n h=h/30.0 - 4.0 # /* 2..4 Gruen..Blaugruen..Blau*/\\n else:\\n h=h/60.0 # /* 4..6 Blau..Purpur..Rot */\\n c=int(h)\\n frac=h-c\\n if (self.hls_l<=0.5):\\n maxi=self.hls_l*(1.0+self.hls_s)\\n else:\\n maxi=self.hls_l+self.hls_s-self.hls_l*self.hls_s\\n mini=2*self.hls_l-maxi;\\n diff=maxi-mini;\\n if(self.hls_s==0): # /* grau */\\n return(1.0,1.0,1.0) \\n else:\\n if(c==0):\\n return(maxi,mini+frac*diff,mini)\\n elif(c==1):\\n return(mini+(1.0-frac)*diff,maxi,mini)\\n elif(c==2):\\n return(mini,maxi,mini+frac*diff)\\n elif(c==3):\\n return(mini,mini+(1.0-frac)*diff,maxi)\\n elif(c==4):\\n return(mini+frac*diff,mini,maxi)\\n else:\\n return(maxi,mini,mini+(1.0-frac)*diff)\",\n \"def get_RF_color(rf_vec):\\n color = copy.deepcopy(rf_vec)\\n\\n def renormalize(v):\\n v -= np.min(v)\\n return v / np.max(v)\\n\\n for i in xrange(3):\\n color[:, i] = renormalize(color[:, i])\\n return color\",\n \"def color(value):\\r\\n return 'RGB({}, {}, {})'.format(value.red(), value.blue(), value.green())\",\n \"def opacity_transfer_function(mapping, n_colors, interpolate=True, kind='quadratic'):\\n sigmoid = lambda x: np.array(1 / (1 + np.exp(-x)) * 255, dtype=np.uint8)\\n transfer_func = {\\n 'linear': np.linspace(0, 255, n_colors, dtype=np.uint8),\\n 'geom': np.geomspace(1e-6, 255, n_colors, dtype=np.uint8),\\n 'geom_r': np.geomspace(255, 1e-6, n_colors, dtype=np.uint8),\\n 'sigmoid': sigmoid(np.linspace(-10.0, 10.0, n_colors)),\\n 'sigmoid_3': sigmoid(np.linspace(-3.0, 3.0, n_colors)),\\n 'sigmoid_4': sigmoid(np.linspace(-4.0, 4.0, n_colors)),\\n 'sigmoid_5': sigmoid(np.linspace(-5.0, 5.0, n_colors)),\\n 'sigmoid_6': sigmoid(np.linspace(-6.0, 6.0, n_colors)),\\n 'sigmoid_7': sigmoid(np.linspace(-7.0, 7.0, n_colors)),\\n 'sigmoid_8': sigmoid(np.linspace(-8.0, 8.0, n_colors)),\\n 'sigmoid_9': sigmoid(np.linspace(-9.0, 9.0, n_colors)),\\n 'sigmoid_10': sigmoid(np.linspace(-10.0, 10.0, n_colors)),\\n }\\n transfer_func['linear_r'] = transfer_func['linear'][::-1]\\n transfer_func['sigmoid_r'] = transfer_func['sigmoid'][::-1]\\n for i in range(3, 11):\\n k = f'sigmoid_{i}'\\n rk = f'{k}_r'\\n transfer_func[rk] = transfer_func[k][::-1]\\n if isinstance(mapping, str):\\n try:\\n return transfer_func[mapping]\\n except KeyError:\\n raise KeyError(f'opactiy transfer function ({mapping}) unknown.')\\n elif isinstance(mapping, (np.ndarray, list, tuple)):\\n mapping = np.array(mapping)\\n if mapping.size == n_colors:\\n # User could pass transfer function ready for lookup table\\n pass\\n elif mapping.size < n_colors:\\n # User pass custom transfer function to be linearly interpolated\\n if np.max(mapping) > 1.0 or np.min(mapping) < 0.0:\\n mapping = normalize(mapping)\\n # Interpolate transfer function to match lookup table\\n xo = np.linspace(0, n_colors, len(mapping), dtype=np.int_)\\n xx = np.linspace(0, n_colors, n_colors, dtype=np.int_)\\n try:\\n if not interpolate:\\n raise ValueError('No interpolation.')\\n # Use a quadratic interp if scipy is available\\n from scipy.interpolate import interp1d\\n\\n # quadratic has best/smoothest results\\n f = interp1d(xo, mapping, kind=kind)\\n vals = f(xx)\\n vals[vals < 0] = 0.0\\n vals[vals > 1.0] = 1.0\\n mapping = (vals * 255.0).astype(np.uint8)\\n\\n except (ImportError, ValueError):\\n # Otherwise use simple linear interp\\n mapping = (np.interp(xx, xo, mapping) * 255).astype(np.uint8)\\n else:\\n raise RuntimeError(\\n f'Transfer function cannot have more values than `n_colors`. This has {mapping.size} elements'\\n )\\n return mapping\\n raise TypeError(f'Transfer function type ({type(mapping)}) not understood')\",\n \"def calculate_colors(v):\\n\\n # Define constant color values\\n lightness = [0.35, 0.5, 0.65]\\n saturation = [0.35, 0.5, 0.65]\\n\\n # Calculate the CRC-32 checksum of colors encoded as a UTF-8 string\\n hash = crc32(str(v).encode('utf-8')) & 0xffffffff\\n\\n # Calculate the HSL (hue, saturation, lightness) values for the vertices\\n hue = ((hash % 359) / 1000) * 360\\n hash //= 360\\n sat = saturation[hash % len(saturation)]\\n hash //= len(saturation)\\n lig = lightness[hash % len(lightness)]\\n\\n return (hue, sat, lig)\",\n \"def rgb_image(self):\\n z3 = self.z[:,:,newaxis]\\n return z3 * self.c\",\n \"def color(self, data):\\n\\n red = np.interp(data, self.range, self.r)\\n blue = np.interp(data, self.range, self.b)\\n green = np.interp(data, self.range, self.g)\\n # Style plot to return a grey color when value is 'nan'\\n red[np.isnan(red)] = 240\\n blue[np.isnan(blue)] = 240\\n green[np.isnan(green)] = 240\\n colors = np.dstack([red.astype(np.uint8),\\n green.astype(np.uint8),\\n blue.astype(np.uint8),\\n np.full_like(data, 255, dtype=np.uint8)])\\n #return colors.view(dtype=np.uint32).reshape(data.shape)\\n c=[]\\n for i in range(len(data)):\\n c.append([red[i],green[i],blue[i]])\\n return c\",\n \"def default_render_color_maker(num:int):\\n if num < 7:\\n log.warn('Fewer than 7 rendering colors are being generated. This may cause issues if a URDF with a 6+ axis robot is loaded.')\\n\\n b = np.linspace(0,255,num).astype(int) # Blue values are always unique\\n\\n g = [0] * b.size\\n r = np.abs(255 - 2*b)\\n\\n colors = []\\n for idx in range(num):\\n colors.append([b[idx],g[idx],r[idx]])\\n return colors\",\n \"def fromInts(r, g, b):\\n return IColor(r/255.,g/255.,b/255.)\",\n \"def nthColor(i):\\n if i < len(colors):\\n return colors[i]\\n\\n c1 = colors[i % len(colors)]\\n c2 = nthColor(i // len(colors))\\n\\n return \\\"#\\\" + hex((int(c1[1:],16) + int(c2[1:],16)) // 2)[2:]\",\n \"def calculate_colors(countries, max_users):\\n for country_code in countries:\\n countries[country_code]['color_rgb'] = [\\n calculate_color(\\n countries[country_code]['percentage_of_users'],\\n max_users,\\n 8,\\n 229\\n ),\\n calculate_color(\\n countries[country_code]['percentage_of_users'],\\n max_users,\\n 64,\\n 245\\n ),\\n calculate_color(\\n countries[country_code]['percentage_of_users'],\\n max_users,\\n 129,\\n 223\\n )\\n ]\\n\\n return countries\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6905101","0.6527362","0.64464974","0.64188105","0.63265","0.63204837","0.6187181","0.607829","0.6072109","0.594735","0.5908517","0.5882771","0.58766925","0.5861005","0.58603394","0.5859091","0.5833258","0.5804042","0.5797796","0.57726943","0.5724473","0.5711908","0.57032293","0.5682422","0.56589836","0.5636764","0.5634918","0.5633099","0.5632145","0.5619261","0.5617564","0.5607956","0.556444","0.556444","0.556444","0.556444","0.554962","0.5532349","0.5531178","0.55010426","0.5501016","0.5491697","0.5490457","0.54740435","0.5469087","0.5460768","0.54560477","0.5433276","0.5430267","0.5423536","0.5420553","0.54067945","0.53800267","0.53769577","0.53587073","0.53583705","0.5350864","0.53485054","0.5339752","0.5336474","0.53219223","0.5315753","0.5307032","0.5299937","0.52986485","0.52869505","0.5275959","0.5266811","0.52664065","0.5262913","0.5257271","0.5242715","0.5241217","0.5226885","0.52119595","0.5198385","0.5186703","0.5186084","0.5180221","0.51796407","0.51772976","0.5165197","0.5164554","0.5164021","0.51600987","0.51564914","0.5150271","0.51466614","0.51426","0.5141724","0.51403975","0.51376873","0.51352245","0.51289684","0.5127465","0.5124806","0.5124447","0.5121696","0.5120167","0.5113946"],"string":"[\n \"0.6905101\",\n \"0.6527362\",\n \"0.64464974\",\n \"0.64188105\",\n \"0.63265\",\n \"0.63204837\",\n \"0.6187181\",\n \"0.607829\",\n \"0.6072109\",\n \"0.594735\",\n \"0.5908517\",\n \"0.5882771\",\n \"0.58766925\",\n \"0.5861005\",\n \"0.58603394\",\n \"0.5859091\",\n \"0.5833258\",\n \"0.5804042\",\n \"0.5797796\",\n \"0.57726943\",\n \"0.5724473\",\n \"0.5711908\",\n \"0.57032293\",\n \"0.5682422\",\n \"0.56589836\",\n \"0.5636764\",\n \"0.5634918\",\n \"0.5633099\",\n \"0.5632145\",\n \"0.5619261\",\n \"0.5617564\",\n \"0.5607956\",\n \"0.556444\",\n \"0.556444\",\n \"0.556444\",\n \"0.556444\",\n \"0.554962\",\n \"0.5532349\",\n \"0.5531178\",\n \"0.55010426\",\n \"0.5501016\",\n \"0.5491697\",\n \"0.5490457\",\n \"0.54740435\",\n \"0.5469087\",\n \"0.5460768\",\n \"0.54560477\",\n \"0.5433276\",\n \"0.5430267\",\n \"0.5423536\",\n \"0.5420553\",\n \"0.54067945\",\n \"0.53800267\",\n \"0.53769577\",\n \"0.53587073\",\n \"0.53583705\",\n \"0.5350864\",\n \"0.53485054\",\n \"0.5339752\",\n \"0.5336474\",\n \"0.53219223\",\n \"0.5315753\",\n \"0.5307032\",\n \"0.5299937\",\n \"0.52986485\",\n \"0.52869505\",\n \"0.5275959\",\n \"0.5266811\",\n \"0.52664065\",\n \"0.5262913\",\n \"0.5257271\",\n \"0.5242715\",\n \"0.5241217\",\n \"0.5226885\",\n \"0.52119595\",\n \"0.5198385\",\n \"0.5186703\",\n \"0.5186084\",\n \"0.5180221\",\n \"0.51796407\",\n \"0.51772976\",\n \"0.5165197\",\n \"0.5164554\",\n \"0.5164021\",\n \"0.51600987\",\n \"0.51564914\",\n \"0.5150271\",\n \"0.51466614\",\n \"0.51426\",\n \"0.5141724\",\n \"0.51403975\",\n \"0.51376873\",\n \"0.51352245\",\n \"0.51289684\",\n \"0.5127465\",\n \"0.5124806\",\n \"0.5124447\",\n \"0.5121696\",\n \"0.5120167\",\n \"0.5113946\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6496655"},"document_rank":{"kind":"string","value":"2"}}},{"rowIdx":81182,"cells":{"query":{"kind":"string","value":"Gets the colour of a z and step value."},"document":{"kind":"string","value":"def colour(z, i):\n if abs(z) < self.threshold:\n return 0, 0, 0\n v = np.log2(i + self.threshold - np.log2(np.log2(abs(z)))) / self.threshold\n if v < 1.0:\n return v ** b1, v ** b2, v ** b3 # coloured tones\n else:\n v = max(0, 2 - v)\n return v ** r1, v ** r2, v ** r3 # sepia tones"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_colour(self, r1, r2, r3, b1, b2, b3):\n\n def colour(z, i):\n \"\"\"\n Gets the colour of a z and step value.\n\n :param complex z: the z value from the mandelbrot set\n :param int i: the step value\n\n :rtype: tuple\n :return: the three RGB colours\n \"\"\"\n if abs(z) < self.threshold:\n return self.background\n v = np.log2(i + self.threshold - np.log2(np.log2(abs(z)))) / self.threshold\n if v < 1.0:\n return v ** b1, v ** b2, v ** b3 # background\n else:\n v = max(0, 2 - v)\n return v ** r1, v ** r2, v ** r3 # main tones\n\n return colour","def colour(z, i):\n if abs(z) < self.threshold:\n return self.background\n v = np.log2(i + self.threshold - np.log2(np.log2(abs(z)))) / self.threshold\n if v < 1.0:\n return v ** b1, v ** b2, v ** b3 # background\n else:\n v = max(0, 2 - v)\n return v ** r1, v ** r2, v ** r3 # main tones","def get_inner_colour(self, r1, r2, r3, b1, b2, b3):\n\n def colour(z, i):\n \"\"\"\n Gets the colour of a z and step value.\n\n :param z: the z value from the mandelbrot set\n :param i: the step value\n\n :rtype: list\n :return: list containing the RGB colours\n \"\"\"\n if abs(z) < self.threshold:\n return 0, 0, 0\n v = np.log2(i + self.threshold - np.log2(np.log2(abs(z)))) / self.threshold\n if v < 1.0:\n return v ** b1, v ** b2, v ** b3 # coloured tones\n else:\n v = max(0, 2 - v)\n return v ** r1, v ** r2, v ** r3 # sepia tones\n\n return colour","def getColor(self):\n return self._l[2]","def get_color(self):\n return self._io.last_state['color']['front-center']","def getJuliaPixelColor(z, gradient):\n c = complex(-1.0, 0.0)\n for i in range(len(gradient)):\n z = z * z + c # Get z1, z2, ...\n if abs(z) > 2:\n z = 2.0\n return gradient[i] # The sequence is unbounded\n return gradient[len(gradient) - 1] # Indicate a bounded sequence","def get_color(in_val, min_val=0, max_val=100):\n width = max_val - min_val\n unit = width / len(continuum)\n return continuum[min(int(in_val / unit), 19)]","def get_colors(self):\n x = np.linspace(0, 1, self.length)\n y = x**self.gamma\n\n value = np.linspace(0, 1, len(self.colors))\n r = np.interp(y, value, self.colors[:,0])\n g = np.interp(y, value, self.colors[:,1])\n b = np.interp(y, value, self.colors[:,2])\n\n return np.dstack((r, g, b)).reshape(len(r), 3).astype(np.uint8)","def get_color(val, trendType):\n if(val > 0):\n if(trendType == 'normal'):\n color = 'red'\n else:\n color = 'green'\n elif(val < 0):\n if(trendType == 'normal'):\n color = 'green'\n else:\n color = 'red'\n else:\n color = 'blue'\n return color","def _color(self,c):\n return self.colorlist[c%len(self.colorlist)]","def getColor(self):\r\n return self.color","def getColor(self,number):\n if number >= 0:\n if self.inverse:\n ret = cs.hsv_to_rgb(0,0,abs(number/self.maxp))\n else:\n ret = cs.hsv_to_rgb(0,0,1-abs(number/self.maxp))\n else:\n if self.inverse:\n ret = cs.hsv_to_rgb(0,1-abs(number/self.maxn),1)\n else:\n ret = cs.hsv_to_rgb(0,abs(number/self.maxn),1)\n return [ret[0]*255.0,ret[1]*255.0,ret[2]*255.0]","def get_color(self):\n return self.color","def get_color(self):\r\n return self.__color","def _get_color(self):\n return self.__color","def _get_color(self):\n return self.__color","def _get_color(self):\n return self.__color","def _get_color(self):\n return self.__color","def get_color(self):\r\n return self._color","def getColor(self):\n return self.color","def get_color(self):\n return COLOR_DICT[self.element]","def getColor(self,number):\n if number >= 0:\n ret = cs.hsv_to_rgb(0,0,1-abs(number/self.maxp))\n else:\n ret = cs.hsv_to_rgb(0,abs(number/self.maxn),1)\n hexcolor = '#%02x%02x%02x' % (ret[0]*255,ret[1]*255,ret[2]*255)\n return hexcolor","def color(self):\n return self['color']","def _get_color(self, r, g, b):\n clr = (r, g, b)\n return clr","def getTweenColor(self, factor):\n\n pass","def getColor(self):\n return self.__color","def getColor(self):\n return self.__color","def getColor(self):\n return self.__color","def get_color(self, value):\n value = min(max(0,value), 1) * 510\n\n if value < 255:\n redValue = 255\n greenValue = math.sqrt(value) * 16\n greenValue = int(greenValue)\n else:\n greenValue = 255\n value = value - 255\n redValue = 255 - (value * value / 255)\n redValue = int(redValue)\n return '#' + f\"{redValue:0{2}x}\" + f\"{greenValue:0{2}x}\" + '00'","def grey(self):\n return sum((self.value(0), self.value(1), self.value(2)))/3","def get_color(self, point):\n return self._color.dup()","def get_color(self):\n return self._color","def get_color(self):\n return self._color","def get_colour(self):\n return self.colour","def get_color(self):\n\n return self.color","def _rgb(x, y, z):\n rgb = np.array([x, y, z]).T\n rgb -= rgb.min(0)\n rgb /= np.maximum(rgb.max(0), 1e-16) # avoid div by zero\n return rgb","def color(self):\n return rgba(self.value_of_css_property('color'))","def get_color(self, point):\n \n d = point - self._origin\n dist = int(d.dot(d) ** 0.5) % 2\n if dist == 0:\n return self.c1.dup()\n else:\n return self.c2.dup()","def color(self):\n return self._rgba","def color(self):\n return self._rgba","def collatz(value):\r\n assert value >= 1\r\n if value % 2 == 0:\r\n return value/2\r\n else:\r\n return 3 * value + 1","def getColor(k) :\n colors = [\"#862B59\",\"#A10000\",\"#0A6308\",\"#123677\",\"#ff8100\",\"#F28686\",\"#6adf4f\",\"#58ccdd\",\"#3a3536\",\"#00ab7c\"]\n return colors[k]","def color(self):\n return self._zoom.color","def color_negative_red_positive_green(val):\n if val < 0:\n color = 'red'\n elif val > 0:\n color = 'green'\n else:\n color = 'black'\n\n return 'color: %s' % color","def c_s(self, z):\n R = self.R_bg(z)\n return const.c/np.sqrt(3.*(1.+R))","def get_color(self) -> str:\r\n return self.color","def color(self):\n return self.container['color']","def color(self):\n return self.__color","def get_color(self) -> str:\n return self.color","def get_color(self):\n\n return self._color","def colorize_xyz(xyz):\n xyz_vis = xyz - xyz.min()\n return (255 * xyz_vis / xyz_vis.max()).astype(np.uint8)","def get_colour(self, x, y):\n if x >= self.width or y >= self.height:\n return (0, 0, 0)\n\n return self.env_img.get_at((int(x), int(y))).normalize()[0:3]","def rgbcolor(h, f):\n # q = 1 - f\n # t = f\n if h == 0:\n return v, f, p\n elif h == 1:\n return 1 - f, v, p\n elif h == 2:\n return p, v, f\n elif h == 3:\n return p, 1 - f, v\n elif h == 4:\n return f, p, v\n elif h == 5:\n return v, p, 1 - f","def xy_color(self):\n return self._xy_color","def color(value):\r\n return 'RGB({}, {}, {})'.format(value.red(), value.blue(), value.green())","def color_negative_red(val):\n if val == 'k':\n color = 'red' \n else:\n color = 'yellow'\n return ['color: %s' % color]*3","def color(self):\n return 0x2f3136","def color(self):\n return self._color","def color(self):\n return self._color","def getSegmentColor(self):\n l = [segment.color for segment in self.segments]\n if l.count(l[0]) == len(l):\n return l[0]\n else:\n raise ValueError(\"The colors of the points must be the same otherwise it makes no sense.\")","def _get_color(self, c, x, max_num):\n\n ratio = 5*(float(x)/max_num)\n i = int(math.floor(ratio))\n j = int(math.ceil(ratio))\n ratio -= i\n r = (1 - ratio) * self._colors[i][c] + ratio*self._colors[j][c]\n return int(255*r)","def colors(self):\n\t\treturn [(0, 30, 255),(0, 30, 120)]","def voltage_color(self, voltage):\n set_of_colors = []\n # black\n set_of_colors.append('#000000')\n # grey 1\n set_of_colors.append('#474747')\n # grey 2\n set_of_colors.append('#B2B2B2')\n # white\n set_of_colors.append('#FFFFFF')\n # yellow\n set_of_colors.append('#FFFF66')\n\n if (voltage > 0.95):\n return set_of_colors[4]\n elif (voltage > 0.9):\n return set_of_colors[3]\n elif (voltage > 0.7):\n return set_of_colors[2]\n elif (voltage > 0.6):\n return set_of_colors[1]\n else:\n return set_of_colors[0]","def color_temp(self):\n return self._color_temp","def getHSV(self):\n\t\tself.colour = [self.getH(), self.getS(),1]\n\t\treturn self.colour","def get_color(self) -> Optional[str]:\n return self.color","def color(self):\n if \"color\" in self._prop_dict:\n return self._prop_dict[\"color\"]\n else:\n return None","def last_color(self):\n idx = self._color_indexes.get(self._plotid)\n if idx is not None:\n return COLOR_CYCLE[(idx-1) % len(COLOR_CYCLE)]\n return COLOR_CYCLE[0]","def color(self, data):\n\n red = np.interp(data, self.range, self.r)\n blue = np.interp(data, self.range, self.b)\n green = np.interp(data, self.range, self.g)\n # Style plot to return a grey color when value is 'nan'\n red[np.isnan(red)] = 240\n blue[np.isnan(blue)] = 240\n green[np.isnan(green)] = 240\n colors = np.dstack([red.astype(np.uint8),\n green.astype(np.uint8),\n blue.astype(np.uint8),\n np.full_like(data, 255, dtype=np.uint8)])\n #return colors.view(dtype=np.uint32).reshape(data.shape)\n c=[]\n for i in range(len(data)):\n c.append([red[i],green[i],blue[i]])\n return c","def GetColor(self, *args):\n return _XCAFDoc.XCAFDoc_ColorTool_GetColor(self, *args)","def _getDockColor(self, plane):\n color = (0,0,0)\n if plane.zAxis != -1:\n color = self.globalAxis[plane.zAxis].color[0:3]\n return color","def get_z(self):\n return self.coords[2]","def _get_red(self):\n return self.__red","def _get_red(self):\n return self.__red","def _get_red(self):\n return self.__red","def __get_z__(self):\n return self.Direction['z']","def colortopalette(color, palette):\n for a,b in palette:\n if color >= a and color < b:\n return b","def color(self):\n if self._simplecell:\n self.fetch()\n return self._color","def get_color(self):\r\n if self.color:\r\n return \"RED\"\r\n else:\r\n return \"BLACK\"","def get_colour(value):\n \n np.random.seed(value)\n colour = [np.random.uniform() for i in range(3)]\n return (tuple(colour))","def _cal_color(xy, norm=False):\n color = gaussian_kde(xy)(xy)\n if norm:\n cmin = np.min(color)\n cmax = np.max(color)\n color = (color - cmin) / (cmax - cmin)\n\n return color","def get_colour(self, address):\n return idaapi.get_item_color(address)","def calc_color(temp, dewtemp, humid):\n if temp == hw.MISSING_VAL or dewtemp == hw.MISSING_VAL or humid == hw.MISSING_VAL:\n status = 'grey'\n istatus = 0\n else:\n if (temp - dewtemp) < RED_DEWPOINT or humid >= RED_HUMIDITY:\n status = 'red'\n istatus = 3\n elif (temp - dewtemp) < YELLOW_DEWPOINT or humid >= YELLOW_HUMIDITY:\n status = 'yellow'\n istatus = 2\n else:\n status = 'green'\n istatus = 1\n # NOTE: 'status' variable is not used\n return istatus","def get_value(self, x, y, z):\n\t\treturn self.data[ self.xyz_to_offset(x,y,z) ]","def elevation_to_color_formula(number):\n color_intensity = abs(round(((number - lowest_point) / (highest_point - lowest_point)) * 255))\n return color_intensity","def get_color_marker(self):\r\n return self._board.get_color_marker_b()","def color(self):\n return self.COLOR","def getz(self,pix):\n\t\tx = pix[0]\n\t\ty = pix[1]\n\t\treturn self.cube_data[x][y]","def ci(x, y, z):\n\n return (x * 16 + z) * CHUNK_HEIGHT + y","def getnextcolor(self):\n self.usedcc = [line.get_c() for line in self.axes.lines]\n for c in self.cc:\n if c not in self.usedcc:\n return c\n\n # Only gets here if all colors already used...\n i = len(self.axes.lines)\n numc = len(self.cc)\n i = i % numc\n return self.cc[i]","def color_from_value(self, value):\n \n return ImageColor.getrgb(\"hsl(%d,%d%%,%d%%)\" % (int( (1.0 - value) * 360 ), 80, 50))","def color(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"color\")","def color(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"color\")","def color(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"color\")","def color(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"color\")","def color(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"color\")","def color(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"color\")","def color(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"color\")","def color(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"color\")","def color(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"color\")"],"string":"[\n \"def get_colour(self, r1, r2, r3, b1, b2, b3):\\n\\n def colour(z, i):\\n \\\"\\\"\\\"\\n Gets the colour of a z and step value.\\n\\n :param complex z: the z value from the mandelbrot set\\n :param int i: the step value\\n\\n :rtype: tuple\\n :return: the three RGB colours\\n \\\"\\\"\\\"\\n if abs(z) < self.threshold:\\n return self.background\\n v = np.log2(i + self.threshold - np.log2(np.log2(abs(z)))) / self.threshold\\n if v < 1.0:\\n return v ** b1, v ** b2, v ** b3 # background\\n else:\\n v = max(0, 2 - v)\\n return v ** r1, v ** r2, v ** r3 # main tones\\n\\n return colour\",\n \"def colour(z, i):\\n if abs(z) < self.threshold:\\n return self.background\\n v = np.log2(i + self.threshold - np.log2(np.log2(abs(z)))) / self.threshold\\n if v < 1.0:\\n return v ** b1, v ** b2, v ** b3 # background\\n else:\\n v = max(0, 2 - v)\\n return v ** r1, v ** r2, v ** r3 # main tones\",\n \"def get_inner_colour(self, r1, r2, r3, b1, b2, b3):\\n\\n def colour(z, i):\\n \\\"\\\"\\\"\\n Gets the colour of a z and step value.\\n\\n :param z: the z value from the mandelbrot set\\n :param i: the step value\\n\\n :rtype: list\\n :return: list containing the RGB colours\\n \\\"\\\"\\\"\\n if abs(z) < self.threshold:\\n return 0, 0, 0\\n v = np.log2(i + self.threshold - np.log2(np.log2(abs(z)))) / self.threshold\\n if v < 1.0:\\n return v ** b1, v ** b2, v ** b3 # coloured tones\\n else:\\n v = max(0, 2 - v)\\n return v ** r1, v ** r2, v ** r3 # sepia tones\\n\\n return colour\",\n \"def getColor(self):\\n return self._l[2]\",\n \"def get_color(self):\\n return self._io.last_state['color']['front-center']\",\n \"def getJuliaPixelColor(z, gradient):\\n c = complex(-1.0, 0.0)\\n for i in range(len(gradient)):\\n z = z * z + c # Get z1, z2, ...\\n if abs(z) > 2:\\n z = 2.0\\n return gradient[i] # The sequence is unbounded\\n return gradient[len(gradient) - 1] # Indicate a bounded sequence\",\n \"def get_color(in_val, min_val=0, max_val=100):\\n width = max_val - min_val\\n unit = width / len(continuum)\\n return continuum[min(int(in_val / unit), 19)]\",\n \"def get_colors(self):\\n x = np.linspace(0, 1, self.length)\\n y = x**self.gamma\\n\\n value = np.linspace(0, 1, len(self.colors))\\n r = np.interp(y, value, self.colors[:,0])\\n g = np.interp(y, value, self.colors[:,1])\\n b = np.interp(y, value, self.colors[:,2])\\n\\n return np.dstack((r, g, b)).reshape(len(r), 3).astype(np.uint8)\",\n \"def get_color(val, trendType):\\n if(val > 0):\\n if(trendType == 'normal'):\\n color = 'red'\\n else:\\n color = 'green'\\n elif(val < 0):\\n if(trendType == 'normal'):\\n color = 'green'\\n else:\\n color = 'red'\\n else:\\n color = 'blue'\\n return color\",\n \"def _color(self,c):\\n return self.colorlist[c%len(self.colorlist)]\",\n \"def getColor(self):\\r\\n return self.color\",\n \"def getColor(self,number):\\n if number >= 0:\\n if self.inverse:\\n ret = cs.hsv_to_rgb(0,0,abs(number/self.maxp))\\n else:\\n ret = cs.hsv_to_rgb(0,0,1-abs(number/self.maxp))\\n else:\\n if self.inverse:\\n ret = cs.hsv_to_rgb(0,1-abs(number/self.maxn),1)\\n else:\\n ret = cs.hsv_to_rgb(0,abs(number/self.maxn),1)\\n return [ret[0]*255.0,ret[1]*255.0,ret[2]*255.0]\",\n \"def get_color(self):\\n return self.color\",\n \"def get_color(self):\\r\\n return self.__color\",\n \"def _get_color(self):\\n return self.__color\",\n \"def _get_color(self):\\n return self.__color\",\n \"def _get_color(self):\\n return self.__color\",\n \"def _get_color(self):\\n return self.__color\",\n \"def get_color(self):\\r\\n return self._color\",\n \"def getColor(self):\\n return self.color\",\n \"def get_color(self):\\n return COLOR_DICT[self.element]\",\n \"def getColor(self,number):\\n if number >= 0:\\n ret = cs.hsv_to_rgb(0,0,1-abs(number/self.maxp))\\n else:\\n ret = cs.hsv_to_rgb(0,abs(number/self.maxn),1)\\n hexcolor = '#%02x%02x%02x' % (ret[0]*255,ret[1]*255,ret[2]*255)\\n return hexcolor\",\n \"def color(self):\\n return self['color']\",\n \"def _get_color(self, r, g, b):\\n clr = (r, g, b)\\n return clr\",\n \"def getTweenColor(self, factor):\\n\\n pass\",\n \"def getColor(self):\\n return self.__color\",\n \"def getColor(self):\\n return self.__color\",\n \"def getColor(self):\\n return self.__color\",\n \"def get_color(self, value):\\n value = min(max(0,value), 1) * 510\\n\\n if value < 255:\\n redValue = 255\\n greenValue = math.sqrt(value) * 16\\n greenValue = int(greenValue)\\n else:\\n greenValue = 255\\n value = value - 255\\n redValue = 255 - (value * value / 255)\\n redValue = int(redValue)\\n return '#' + f\\\"{redValue:0{2}x}\\\" + f\\\"{greenValue:0{2}x}\\\" + '00'\",\n \"def grey(self):\\n return sum((self.value(0), self.value(1), self.value(2)))/3\",\n \"def get_color(self, point):\\n return self._color.dup()\",\n \"def get_color(self):\\n return self._color\",\n \"def get_color(self):\\n return self._color\",\n \"def get_colour(self):\\n return self.colour\",\n \"def get_color(self):\\n\\n return self.color\",\n \"def _rgb(x, y, z):\\n rgb = np.array([x, y, z]).T\\n rgb -= rgb.min(0)\\n rgb /= np.maximum(rgb.max(0), 1e-16) # avoid div by zero\\n return rgb\",\n \"def color(self):\\n return rgba(self.value_of_css_property('color'))\",\n \"def get_color(self, point):\\n \\n d = point - self._origin\\n dist = int(d.dot(d) ** 0.5) % 2\\n if dist == 0:\\n return self.c1.dup()\\n else:\\n return self.c2.dup()\",\n \"def color(self):\\n return self._rgba\",\n \"def color(self):\\n return self._rgba\",\n \"def collatz(value):\\r\\n assert value >= 1\\r\\n if value % 2 == 0:\\r\\n return value/2\\r\\n else:\\r\\n return 3 * value + 1\",\n \"def getColor(k) :\\n colors = [\\\"#862B59\\\",\\\"#A10000\\\",\\\"#0A6308\\\",\\\"#123677\\\",\\\"#ff8100\\\",\\\"#F28686\\\",\\\"#6adf4f\\\",\\\"#58ccdd\\\",\\\"#3a3536\\\",\\\"#00ab7c\\\"]\\n return colors[k]\",\n \"def color(self):\\n return self._zoom.color\",\n \"def color_negative_red_positive_green(val):\\n if val < 0:\\n color = 'red'\\n elif val > 0:\\n color = 'green'\\n else:\\n color = 'black'\\n\\n return 'color: %s' % color\",\n \"def c_s(self, z):\\n R = self.R_bg(z)\\n return const.c/np.sqrt(3.*(1.+R))\",\n \"def get_color(self) -> str:\\r\\n return self.color\",\n \"def color(self):\\n return self.container['color']\",\n \"def color(self):\\n return self.__color\",\n \"def get_color(self) -> str:\\n return self.color\",\n \"def get_color(self):\\n\\n return self._color\",\n \"def colorize_xyz(xyz):\\n xyz_vis = xyz - xyz.min()\\n return (255 * xyz_vis / xyz_vis.max()).astype(np.uint8)\",\n \"def get_colour(self, x, y):\\n if x >= self.width or y >= self.height:\\n return (0, 0, 0)\\n\\n return self.env_img.get_at((int(x), int(y))).normalize()[0:3]\",\n \"def rgbcolor(h, f):\\n # q = 1 - f\\n # t = f\\n if h == 0:\\n return v, f, p\\n elif h == 1:\\n return 1 - f, v, p\\n elif h == 2:\\n return p, v, f\\n elif h == 3:\\n return p, 1 - f, v\\n elif h == 4:\\n return f, p, v\\n elif h == 5:\\n return v, p, 1 - f\",\n \"def xy_color(self):\\n return self._xy_color\",\n \"def color(value):\\r\\n return 'RGB({}, {}, {})'.format(value.red(), value.blue(), value.green())\",\n \"def color_negative_red(val):\\n if val == 'k':\\n color = 'red' \\n else:\\n color = 'yellow'\\n return ['color: %s' % color]*3\",\n \"def color(self):\\n return 0x2f3136\",\n \"def color(self):\\n return self._color\",\n \"def color(self):\\n return self._color\",\n \"def getSegmentColor(self):\\n l = [segment.color for segment in self.segments]\\n if l.count(l[0]) == len(l):\\n return l[0]\\n else:\\n raise ValueError(\\\"The colors of the points must be the same otherwise it makes no sense.\\\")\",\n \"def _get_color(self, c, x, max_num):\\n\\n ratio = 5*(float(x)/max_num)\\n i = int(math.floor(ratio))\\n j = int(math.ceil(ratio))\\n ratio -= i\\n r = (1 - ratio) * self._colors[i][c] + ratio*self._colors[j][c]\\n return int(255*r)\",\n \"def colors(self):\\n\\t\\treturn [(0, 30, 255),(0, 30, 120)]\",\n \"def voltage_color(self, voltage):\\n set_of_colors = []\\n # black\\n set_of_colors.append('#000000')\\n # grey 1\\n set_of_colors.append('#474747')\\n # grey 2\\n set_of_colors.append('#B2B2B2')\\n # white\\n set_of_colors.append('#FFFFFF')\\n # yellow\\n set_of_colors.append('#FFFF66')\\n\\n if (voltage > 0.95):\\n return set_of_colors[4]\\n elif (voltage > 0.9):\\n return set_of_colors[3]\\n elif (voltage > 0.7):\\n return set_of_colors[2]\\n elif (voltage > 0.6):\\n return set_of_colors[1]\\n else:\\n return set_of_colors[0]\",\n \"def color_temp(self):\\n return self._color_temp\",\n \"def getHSV(self):\\n\\t\\tself.colour = [self.getH(), self.getS(),1]\\n\\t\\treturn self.colour\",\n \"def get_color(self) -> Optional[str]:\\n return self.color\",\n \"def color(self):\\n if \\\"color\\\" in self._prop_dict:\\n return self._prop_dict[\\\"color\\\"]\\n else:\\n return None\",\n \"def last_color(self):\\n idx = self._color_indexes.get(self._plotid)\\n if idx is not None:\\n return COLOR_CYCLE[(idx-1) % len(COLOR_CYCLE)]\\n return COLOR_CYCLE[0]\",\n \"def color(self, data):\\n\\n red = np.interp(data, self.range, self.r)\\n blue = np.interp(data, self.range, self.b)\\n green = np.interp(data, self.range, self.g)\\n # Style plot to return a grey color when value is 'nan'\\n red[np.isnan(red)] = 240\\n blue[np.isnan(blue)] = 240\\n green[np.isnan(green)] = 240\\n colors = np.dstack([red.astype(np.uint8),\\n green.astype(np.uint8),\\n blue.astype(np.uint8),\\n np.full_like(data, 255, dtype=np.uint8)])\\n #return colors.view(dtype=np.uint32).reshape(data.shape)\\n c=[]\\n for i in range(len(data)):\\n c.append([red[i],green[i],blue[i]])\\n return c\",\n \"def GetColor(self, *args):\\n return _XCAFDoc.XCAFDoc_ColorTool_GetColor(self, *args)\",\n \"def _getDockColor(self, plane):\\n color = (0,0,0)\\n if plane.zAxis != -1:\\n color = self.globalAxis[plane.zAxis].color[0:3]\\n return color\",\n \"def get_z(self):\\n return self.coords[2]\",\n \"def _get_red(self):\\n return self.__red\",\n \"def _get_red(self):\\n return self.__red\",\n \"def _get_red(self):\\n return self.__red\",\n \"def __get_z__(self):\\n return self.Direction['z']\",\n \"def colortopalette(color, palette):\\n for a,b in palette:\\n if color >= a and color < b:\\n return b\",\n \"def color(self):\\n if self._simplecell:\\n self.fetch()\\n return self._color\",\n \"def get_color(self):\\r\\n if self.color:\\r\\n return \\\"RED\\\"\\r\\n else:\\r\\n return \\\"BLACK\\\"\",\n \"def get_colour(value):\\n \\n np.random.seed(value)\\n colour = [np.random.uniform() for i in range(3)]\\n return (tuple(colour))\",\n \"def _cal_color(xy, norm=False):\\n color = gaussian_kde(xy)(xy)\\n if norm:\\n cmin = np.min(color)\\n cmax = np.max(color)\\n color = (color - cmin) / (cmax - cmin)\\n\\n return color\",\n \"def get_colour(self, address):\\n return idaapi.get_item_color(address)\",\n \"def calc_color(temp, dewtemp, humid):\\n if temp == hw.MISSING_VAL or dewtemp == hw.MISSING_VAL or humid == hw.MISSING_VAL:\\n status = 'grey'\\n istatus = 0\\n else:\\n if (temp - dewtemp) < RED_DEWPOINT or humid >= RED_HUMIDITY:\\n status = 'red'\\n istatus = 3\\n elif (temp - dewtemp) < YELLOW_DEWPOINT or humid >= YELLOW_HUMIDITY:\\n status = 'yellow'\\n istatus = 2\\n else:\\n status = 'green'\\n istatus = 1\\n # NOTE: 'status' variable is not used\\n return istatus\",\n \"def get_value(self, x, y, z):\\n\\t\\treturn self.data[ self.xyz_to_offset(x,y,z) ]\",\n \"def elevation_to_color_formula(number):\\n color_intensity = abs(round(((number - lowest_point) / (highest_point - lowest_point)) * 255))\\n return color_intensity\",\n \"def get_color_marker(self):\\r\\n return self._board.get_color_marker_b()\",\n \"def color(self):\\n return self.COLOR\",\n \"def getz(self,pix):\\n\\t\\tx = pix[0]\\n\\t\\ty = pix[1]\\n\\t\\treturn self.cube_data[x][y]\",\n \"def ci(x, y, z):\\n\\n return (x * 16 + z) * CHUNK_HEIGHT + y\",\n \"def getnextcolor(self):\\n self.usedcc = [line.get_c() for line in self.axes.lines]\\n for c in self.cc:\\n if c not in self.usedcc:\\n return c\\n\\n # Only gets here if all colors already used...\\n i = len(self.axes.lines)\\n numc = len(self.cc)\\n i = i % numc\\n return self.cc[i]\",\n \"def color_from_value(self, value):\\n \\n return ImageColor.getrgb(\\\"hsl(%d,%d%%,%d%%)\\\" % (int( (1.0 - value) * 360 ), 80, 50))\",\n \"def color(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"color\\\")\",\n \"def color(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"color\\\")\",\n \"def color(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"color\\\")\",\n \"def color(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"color\\\")\",\n \"def color(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"color\\\")\",\n \"def color(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"color\\\")\",\n \"def color(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"color\\\")\",\n \"def color(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"color\\\")\",\n \"def color(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"color\\\")\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.73403287","0.7172178","0.6895486","0.66384035","0.654349","0.65309036","0.64248663","0.62704283","0.6260564","0.62288976","0.622155","0.62071323","0.6176974","0.6173576","0.6155353","0.6155353","0.6155353","0.6155353","0.613839","0.61319745","0.6128805","0.61136657","0.606678","0.6036081","0.60349417","0.6033224","0.6033224","0.6033224","0.60185593","0.60159534","0.60149926","0.6011126","0.6011126","0.60064286","0.59950215","0.5981543","0.5963013","0.59604895","0.59430104","0.59430104","0.59187","0.5878727","0.5868641","0.586845","0.5840832","0.5838304","0.58338416","0.5807622","0.5796458","0.5790906","0.5769824","0.57573926","0.5751787","0.57510906","0.5747783","0.573898","0.5727476","0.5727071","0.5727071","0.5712584","0.56976473","0.5695128","0.5682587","0.5682103","0.56812423","0.5679537","0.5674732","0.5668044","0.5661929","0.5659782","0.56581974","0.5655674","0.56555635","0.56555635","0.56555635","0.56521946","0.56515807","0.5650068","0.5643954","0.56348366","0.5629475","0.56263924","0.5604624","0.5596881","0.5591866","0.5590415","0.5579755","0.5575315","0.5571698","0.55675906","0.5562246","0.555979","0.555979","0.555979","0.555979","0.555979","0.555979","0.555979","0.555979","0.555979"],"string":"[\n \"0.73403287\",\n \"0.7172178\",\n \"0.6895486\",\n \"0.66384035\",\n \"0.654349\",\n \"0.65309036\",\n \"0.64248663\",\n \"0.62704283\",\n \"0.6260564\",\n \"0.62288976\",\n \"0.622155\",\n \"0.62071323\",\n \"0.6176974\",\n \"0.6173576\",\n \"0.6155353\",\n \"0.6155353\",\n \"0.6155353\",\n \"0.6155353\",\n \"0.613839\",\n \"0.61319745\",\n \"0.6128805\",\n \"0.61136657\",\n \"0.606678\",\n \"0.6036081\",\n \"0.60349417\",\n \"0.6033224\",\n \"0.6033224\",\n \"0.6033224\",\n \"0.60185593\",\n \"0.60159534\",\n \"0.60149926\",\n \"0.6011126\",\n \"0.6011126\",\n \"0.60064286\",\n \"0.59950215\",\n \"0.5981543\",\n \"0.5963013\",\n \"0.59604895\",\n \"0.59430104\",\n \"0.59430104\",\n \"0.59187\",\n \"0.5878727\",\n \"0.5868641\",\n \"0.586845\",\n \"0.5840832\",\n \"0.5838304\",\n \"0.58338416\",\n \"0.5807622\",\n \"0.5796458\",\n \"0.5790906\",\n \"0.5769824\",\n \"0.57573926\",\n \"0.5751787\",\n \"0.57510906\",\n \"0.5747783\",\n \"0.573898\",\n \"0.5727476\",\n \"0.5727071\",\n \"0.5727071\",\n \"0.5712584\",\n \"0.56976473\",\n \"0.5695128\",\n \"0.5682587\",\n \"0.5682103\",\n \"0.56812423\",\n \"0.5679537\",\n \"0.5674732\",\n \"0.5668044\",\n \"0.5661929\",\n \"0.5659782\",\n \"0.56581974\",\n \"0.5655674\",\n \"0.56555635\",\n \"0.56555635\",\n \"0.56555635\",\n \"0.56521946\",\n \"0.56515807\",\n \"0.5650068\",\n \"0.5643954\",\n \"0.56348366\",\n \"0.5629475\",\n \"0.56263924\",\n \"0.5604624\",\n \"0.5596881\",\n \"0.5591866\",\n \"0.5590415\",\n \"0.5579755\",\n \"0.5575315\",\n \"0.5571698\",\n \"0.55675906\",\n \"0.5562246\",\n \"0.555979\",\n \"0.555979\",\n \"0.555979\",\n \"0.555979\",\n \"0.555979\",\n \"0.555979\",\n \"0.555979\",\n \"0.555979\",\n \"0.555979\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7208043"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":81183,"cells":{"query":{"kind":"string","value":"Defines the size of the grid on which to draw the mandelbrot set."},"document":{"kind":"string","value":"def set_grid(self, start_x, end_x, start_y, end_y, resolution_x, resolution_y, threshold):\n step_x = (end_x - start_x) / resolution_x\n step_y = (end_y - start_y) / resolution_y\n real, complex = np.mgrid[start_y:end_y:step_y, start_x:end_x:step_x]\n self.grid = real + complex * 1j\n self.threshold = threshold"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def grid_size(self):\n return self._grid_size","def getGridSize(self):\n # This is set by the mosaic module, but other modules need to\n # know the values to take the proper size grid.\n return self.grid_size","def _grid_hint_size(self) -> int:","def define_grid(self):\n self.h_shape = int(\n np.round((self.h_stop - self.h_start) / self.h_step, 2)) + 1\n self.k_shape = int(\n np.round((self.k_stop - self.k_start) / self.k_step, 2)) + 1\n self.l_shape = int(\n np.round((self.l_stop - self.l_start) / self.l_step, 2)) + 1\n self.grid_origin = [self.h_start, self.k_start, self.l_start]\n self.grid_step = [int(np.rint(1.0/self.h_step)),\n int(np.rint(1.0/self.k_step)),\n int(np.rint(1.0/self.l_step))]\n self.grid_shape = [self.h_shape, self.k_shape, self.l_shape]\n self.grid_basis = [[1, 0, 0], [0, 1, 0], [0, 0, 1]]","def grid(self):\r\n dimA = self.dimA ; dimC = self.dimA ; W_grid = self.W_grid\r\n \r\n self.tol = 10e-5\r\n self.Niter = 10000\r\n \r\n a0 = 100 / self.dimA\r\n c0 = 100 / self.dimA\r\n a_grid = np.mgrid[0:(dimA):1] ; a_grid = a0 * a_grid ; self.a_grid = a_grid\r\n c_grid = np.mgrid[0:(dimC):1] ; c_grid = c0 * c_grid ; self.c_grid = c_grid\r\n self.W_grid = W_grid","def get_grid_size(self, ui, res_dir):\r\n print_it('determining grid size', PrintOpts.lvl1.value)\r\n self.sun.simple_clone()\r\n self.sun.clone.make_profile(PreSol.res_x.value, PreSol.res_y.value,\r\n self.init_force)\r\n self.planet.simple_clone()\r\n self.planet.clone.make_slave_to(self.sun.clone)\r\n\r\n init_displ = hertz_displ(self.sun.clone.e, self.planet.e,\r\n self.sun.clone.ny, self.planet.ny,\r\n self.sun.clone.r_hertz_x,\r\n self.sun.clone.r_hertz_y,\r\n self.planet.clone.r_hertz_x,\r\n self.planet.clone.r_hertz_y,\r\n self.sun.norm_forces[0])\r\n too_many_els_in_y = 1\r\n too_many_els_in_x = 1\r\n contact_width_y = 0.05\r\n contact_width_x = 0.05\r\n while too_many_els_in_y != 0 or \\\r\n too_many_els_in_x != 0:\r\n self.sun.clone.make_profile(self.sun.clone.res_x,\r\n self.sun.clone.res_y, self.init_force,\r\n contact_width=contact_width_y,\r\n contact_length=contact_width_x)\r\n self.planet.clone.make_slave_to(self.sun.clone)\r\n\r\n pressure, init_displ = \\\r\n pre_solve_half_space(self.sun.clone.profile,\r\n self.planet.clone.profile,\r\n self.sun.clone.x_axis,\r\n self.sun.clone.y_axis,\r\n self.sun.clone.res_x, self.sun.clone.res_y,\r\n self.sun.clone.delta_x,\r\n self.sun.clone.delta_y, self.sun.clone.e,\r\n self.planet.clone.e, self.sun.clone.ny,\r\n self.planet.clone.ny,\r\n self.sun.norm_forces[0],\r\n init_displ=init_displ, print_prog=False)\r\n\r\n pressure_els_y = sum(\r\n pressure[math.floor(self.sun.clone.res_y / 2), :] > 0)\r\n too_many_els_in_y = self.sun.clone.res_y - pressure_els_y - 2\r\n if too_many_els_in_y:\r\n contact_width_y += -np.sign(\r\n too_many_els_in_y) * contact_width_y / 25\r\n\r\n pressure_els_x = sum(\r\n pressure[:, math.floor(self.sun.clone.res_x / 2)] > 0)\r\n too_many_els_in_x = self.sun.clone.res_x - pressure_els_x - 2\r\n if too_many_els_in_x:\r\n contact_width_x += -np.sign(\r\n too_many_els_in_x) * contact_width_x / 25\r\n\r\n self.sun.make_profile(self.sun.res_x, self.sun.res_y, self.init_force,\r\n contact_width=contact_width_y,\r\n contact_length=contact_width_x)\r\n self.planet.make_slave_to(self.sun)\r\n return init_displ","def gen_grids(self):\n self.dx = self.grid_width / self.grid_resol\n self.dk = 2 * np.pi/self.grid_width\n self.grid_x_shifted = -self.grid_width/2 + self.dx * np.arange(0, self.grid_resol)\n self.grid_x = self.grid_x_shifted + self.grid_center\n self.grid_k = - (np.pi * self.grid_resol)/self.grid_width + self.dk * np.arange(0, self.grid_resol)\n self.grid_k = np.roll(self.grid_k, int((self.grid_resol)/2))\n self.grid_kin = np.square(self.h)/ (2*self.m) * np.square(self.grid_k)","def make_grid(self):\n for k in range(0, NUM + 1):\n self.create_line(k * UNIT, 0, k * UNIT, SIZE, width=THICKNESS)\n self.create_line(0, k * UNIT, SIZE, k * UNIT, width=THICKNESS)","def setSquareGrid(nx,ny):\n dislin.grid(nx,ny)","def apply(self):\n self.grid_size = self.values[0]","def initialize_grid(self) -> None:\n for i in range(self.grid_size[0]):\n for j in range(self.grid_size[1]):\n self.set(i, j, self.base_color)","def configure_grid(self):\r\n\r\n for r in range(3):\r\n self.rowconfigure(r, weight=1)\r\n for c in range(3):\r\n self.columnconfigure(c, weight=1)","def drawGrid(self):\n for div in range(NBCELL):\n sec = SSIZE*div\n self.can.create_line(0, sec, GSIZE, sec, width=3, fill=GFILL)\n self.can.create_line(sec, 0, sec, GSIZE, width=3, fill=GFILL)","def get_grid_width(self):\n # replace with your code\n return 0","def initialize_grid(self):\n self.grid = np.zeros([self.N, self.N, self.N])\n return self.grid","def set_grid(self,ug):\n self.grd=ug\n self.set_topology()","def get_grid_width(self):\r\n return self._grid_width","def get_grid_width(self):\r\n return self.width","def setwinsize(self, rows, cols):","def makeGrid(self):\n self.h = self.step_x\n self.k = self.step_t\n self.t, self.x = np.meshgrid(np.arange(self.min_t, self.max_t, self.step_t), np.arange(self.min_x, self.max_x\n , self.step_x))","def draw_grid(self):\n for x in range(0, WIDTH, TILESIZE):\n pg.draw.line(self.screen, LIGHTGREY, (x, 0), (x, HEIGHT))\n \n for y in range(0, HEIGHT, TILESIZE):\n pg.draw.line(self.screen, LIGHTGREY, (0, y), (WIDTH, y))","def get_size_of_grid(self):\n row = 0\n column = 0\n if int(self.var1.get()) == 1:\n row, column = 6, 6\n\n if int(self.var2.get()) == 1:\n row, column = 7, 6\n\n if int(self.var3.get()) == 1:\n row, column = 7, 7\n\n if int(self.var4.get()) == 1:\n row, column = 8, 8\n\n return row, column","def get_grid_width(self):\r\n # replace with your code\r\n return self._grid_width","def get_grid_width(self):\r\n # replace with your code\r\n return self._grid_width","def get_grid_width(self):\r\n\r\n return self._grid_width","def tile_size_2d(self):\n return 32.0, 32.0","def get_grid_width(self):\n return self.grid_width","def get_grid_width(self):\n return self.grid_width","def calc_grid(self):\n return int(self._posn.x / cell_size), int(self._posn.y / cell_size)","def cellsize_2d(self):\t\r\n return self.dx * self.dy","def get_grid_width(self):\n # replace with your code\n return self._grid_width","def get_grid_width(self):\n # replace with your code\n return self._grid_width","def _set_grid(self):\n grid_sizex = self.__dict__['size'][0]/self.__dict__['grid_aspects'][0]\n grid_sizey = self.__dict__['size'][1]/self.__dict__['grid_aspects'][0]\n \n sizex = grid_sizex*(1.0/self.__dict__['grid_aspects'][1])\n sizey = grid_sizey*(1.0/self.__dict__['grid_aspects'][1])\n \n defs = helper.defs()\n defs += helper.pattern(helper.path(**{'d': 'M %s 0 L 0 0 0 %s' % (sizex, sizey), 'fill': 'none', 'stroke': 'gray', 'stroke-width': 0.5}),\n **{'id': self.__dict__['grid_item_id'], 'width': sizex, 'height': sizey, 'patternUnits': 'userSpaceOnUse'})\n defs += helper.pattern(helper.path(**{'d': 'M %s 0 L 0 0 0 %s' % (grid_sizex, grid_sizey), 'fill': 'none', 'stroke': 'gray', 'stroke-width': 1}),\n helper.rect(width=grid_sizex, height=grid_sizey, fill=\"url(#%s)\" % self.__dict__['grid_item_id']),\n **{'id': self.__dict__['grid_id'], 'width': grid_sizex, 'height': grid_sizey, 'patternUnits': 'userSpaceOnUse'})\n self += defs\n self += helper.rect(fill=\"white\", height=self.__dict__['size'][0]+1, width=self.__dict__['size'][1]+1)\n self += helper.rect(fill=\"url(#%s)\" % self.__dict__['grid_id'], height=self.__dict__['size'][0]+1, width=self.__dict__['size'][1]+1)","def _SetSize(self, pixels = None):\n if not pixels:\n pixels = self.GetClientSize()\n self.canvas.SetSize(pixels)\n self.figure.set_size_inches(pixels[0]/self.figure.get_dpi(),\n pixels[1]/self.figure.get_dpi())","def get_grid_width(self):\n return self._grid_width","def get_grid_width(self):\n return self._grid_width","def get_grid_width(self):\n return self._grid_width","def initialise_grid(self, y, x, starting_value):\n # Create a grid of the specified size\n self.grid = np.zeros( (y, x), np.int8, 'C')\n \n # Record the sizes in the class variables\n self.x_len = x\n self.y_len = y\n \n # Set the initial values of the array\n self.grid += starting_value","def num_tiles(self):\n return self.num_row_tiles * self.num_col_tiles","def get_grid_width(self):\r\n # replace with your code\r\n return self._width","def get_grid_width(self):\r\n # replace with your code\r\n return self.grid_width","def get_grid_width(self):\n # replace with your code\n return self._width","def get_grid_width(self):\n # replace with your code\n return self._width","def get_grid_width(self):\n # replace with your code\n return self.grid_width","def get_grid_width(self):\n # replace with your code\n return self.grid_width","def get_grid_size(self, img):\r\n grid_height = int(np.ceil(img.shape[0] / self.config.grid_row))\r\n grid_width = int(np.ceil(img.shape[1] / self.config.grid_col))\r\n return grid_height, grid_width","def test_sizergrid():\n regular_grid(8, 3)\n mpl.show()","def test_get_grid_size(flopy_dis_mf6):\n flopy_dis, mf6 = flopy_dis_mf6\n mf6.initialize()\n\n prescribed_grid_size = flopy_dis.nrow * flopy_dis.ncol\n\n # Getting the grid id from the model, requires specifying one variable\n k11_tag = mf6.get_var_address(\"K11\", flopy_dis.model_name, \"NPF\")\n grid_id = mf6.get_var_grid(k11_tag)\n\n assert prescribed_grid_size == mf6.get_grid_size(grid_id)","def setGrid(nx,ny,gridtype='square'):\n if gridtype == 'polar':\n dislin.grdpol(nx,ny)\n else:\n dislin.grid(nx,ny)","def __init__(self, cell_size, nrows, ncols, **kwds):\n #\n # Python 3 update\n #\n super().__init__(**kwds)\n self.cell_size = cell_size\n w, h = cell_size\n d = 2 * self.margin\n self.size = (w * ncols + d, h * nrows + d)\n self.cell_size = cell_size","def optimal_chunksizes(nt, nlat, nlon):\n\n clon = np.sqrt(1000000.0 * nlon / (nlat * nt))\n clat = nlat * clon / nlon\n return (nt, int(np.ceil(clat)), int(np.ceil(clon)))","def make_grid(self):\n length = self.size / 8\n # draw horizontal lines\n for y in range(0, self.size, length):\n self.window.create_line(0, y, self.size, y, fill = \"blue\")\n \n # draw vertical lines\n for x in range(0, self.size, length):\n self.window.create_line(x, 0, x, self.size, fill = \"blue\")\n\n # draw the axes red\n self.window.create_line(\n 0,\n self.size / 2,\n self.size, \n self.size / 2, \n fill = \"red\"\n )\n self.window.create_line(\n self.size / 2, 0,\n self.size / 2, \n self.size, \n fill = \"red\"\n )\n print(\"Grid Made.\")","def __init__( self ):\n\n self.__grid = list(range(1,10))\n self.__drawnGrid = 0","def make_grid(grid_size=(10, 10)):\n return np.zeros(grid_size, dtype=np.int16)","def grid_size_type(size):\n\n size = int(size)\n if size <= 2:\n raise argparse.ArgumentTypeError(\"Minimum grid size is 3 x 3\")\n return size","def get_grid_width(self):\n return self._width","def set_figure_size(self):\n lims, _ = self.set_lims()\n size_fac = 50\n paperSizeFac = 0.65\n one_dec = 1.6\n xdecs = np.log10(lims(1)) - np.log10(lims(0))\n one_dec = one_dec * 4 / xdecs\n ydecs = np.log10(lims[3]) - np.log10(lims[2])\n paper_width = xdecs * one_dec\n paper_height = (ydecs + 3) * one_dec\n paper_height = min([paper_height, 9])\n rectScreen = [0.5, 0.5, paper_width, paper_height] * size_fac\n rectPaper = [1.0, 1.0, paper_width * paperSizeFac, paper_height * paperSizeFac]\n\n rectRho = [0.15, 0.15 + 2.3 / (ydecs + 3), 0.8, ydecs / (ydecs + 3) * 0.8]\n rectPhi = [0.15, 0.15, 0.8, 2 / (ydecs + 3) * 0.8]\n rects = {\n \"Screen\": rectScreen,\n \"Paper\": rectPaper,\n \"Rho\": rectRho,\n \"Phi\": rectPhi,\n }\n return rects","def getNumTiles(self):\n return self.w * self.h","def __init__(self, nx, ny, nxsize=5.4, nysize=6.2):\n self.nx = nx\n self.ny = ny\n self.n = 1\n plt.figure(figsize=(nysize*ny, nxsize*nx))\n plt.subplot(nx, ny, self.n)","def configure_grid(node: tkt.Misc, colnum: int, rownum: int):\n for i in range(colnum):\n node.grid_columnconfigure(i, weight=1)\n\n for i in range(rownum):\n node.grid_rowconfigure(i, weight=1)","def drawGrid(self,gridLines=True):\n if not self.changed: self.edit()\n cGrid = Fmap.GRID\n cBorder = Fmap.BORDER\n if gridLines: #--Some fools don't want the grid!\n #--Grid\n for uv in range(-25,26,5):\n xy = 512/2 - 9*uv + 4\n self.drawRect(cGrid,0,xy,512,xy+1)\n self.drawRect(cGrid,xy,0,xy+1,512)\n #--Grid axes\n xy = 512/2 + 4\n self.drawRect(cBorder,0,xy,512,xy+1)\n self.drawRect(cBorder,xy,0,xy+1,512)\n #--Border\n self.drawBorder(cBorder,0,0,512,512,4)","def DrawGrid(self, count):\n for i in range(0, self.width, self.incr):\n self.canvas.create_line(i, 100, i, 700, fill = \"#696969\", width = 1)\n for i in range(100, 800, 100):\n self.canvas.create_line(0, i, self.width, i, fill = \"#696969\", width = 1)\n self.canvas.create_rectangle(self.incr * 4, self.height - self.incr * 3.5,\n self.width - self.incr * 4, self.height, fill = \"black\", width = 3)\n for i in range(int(self.height - self.incr * 3.5), self.height, int(self.incr / 4)):\n self.canvas.create_line(self.incr * 4, i, self.width - self.incr * 4,\n i, fill = \"#696969\", width = 1)\n for i in range(self.incr * 4, self.width - self.incr * 4 + 1, int(self.incr / 4)):\n self.canvas.create_line(i, self.height - self.incr * 3.5, i, self.height,\n fill = \"#696969\", width = 1)","def sizeCellsProperty(self):\n return FdtPropertyWords(\"#size-cells\", self.size_cells)","def configure(resolution, config):\n width = config.getint('overflow', 'width')\n length = config.getint('overflow', 'length')\n\n dc = float(resolution[:-2])\n nx = int(width / dc)\n ny = int(length / dc)\n\n config.set('overflow', 'nx', str(nx),\n comment='the number of mesh cells in the x direction')\n config.set('overflow', 'ny', str(ny),\n comment='the number of mesh cells in the y direction')\n config.set('overflow', 'dc', str(dc * 1e3),\n comment='the distance between adjacent cell centers')","def grid_shape(self):\n return self._grid_shape","def ncells(self):\n return self.izone.size","def get_grid_width(self):\n\n return self._grid_width","def __init__(self, origin_x=-2.5, origin_y=-2.5, resolution=.1,\n width=50, height=50):\n self.origin_x = origin_x\n self.origin_y = origin_y\n self.resolution = resolution\n self.width = width\n self.height = height\n self.grid = np.zeros((height, width))","def SetSize(self, m: 'unsigned int', n: 'unsigned int') -> \"void\":\n return _itkArray2DPython.itkArray2DUI_SetSize(self, m, n)","def optimal_grid(num):\n\n # get maximum shape\n shape = int(np.ceil(np.sqrt(num)))\n # get number of rows and columns based on maximum shape\n if shape ** 2 == num:\n nrows = shape\n ncols = shape\n else:\n nrows = int(np.ceil(num / shape))\n ncols = int(np.ceil(num / nrows))\n # get position of figures\n pos = []\n for i in range(nrows):\n for j in range(ncols):\n pos.append([i, j])\n # return nrows, ncols and positions\n return nrows, ncols, pos","def drawmaze(self):\n win=GraphWin(\"Perfect Maze\",600,600) \n win.setBackground(\"White\")\n scale=600/self.N #Used to generalize the size difference for the input of larger numbers. The background resolution/ grid size, N\n\n x1=scale\n y1=0\n x2=scale\n y2=scale\n\n ##VERTICAL LINES ####\n for i in range(self.N,0,-1):\n for j in range(1,self.N):\n if self.East[j][i]: #If East is true, draw a line.\n \n line=Line(Point(x1,y1),Point(x2,y2)) #lines | |\n line.setFill(\"red\")\n line.draw(win)\n x1+=scale #Increment causes |->|\n x2+=scale #Increment causes |->|\n y1+=scale #Used to draw two more\n y2+=scale #of the same spaced lines further down.\n x1=scale #Reset\n x2=scale #Reset\n\n\n ##HORIZONTAL LINES##\n x1=0\n y1=scale\n x2=scale\n y2=scale\n\n\n for i in range(self.N,1,-1):\n for j in range(1,self.N+1):\n if self.South[j][i]: #If South is true, draw a line.\n \n line=Line(Point(x1,y1),Point(x2,y2))\n line.setFill(\"red\")\n line.draw(win)\n x1+=scale\n x2+=scale\n y1+=scale\n y2+=scale\n x1=0\n x2=scale\n\n const=scale//5 #Very useful const which helps in placing circles on grid.\n x=scale//2\n y=600-scale//2\n #radius=(scale-(4*scale//self.N))/2\n radius=scale//2-(const)\n start=Point(x,y) #START POINT HERE \n circ=Circle(start,radius)\n circ.setFill(\"Red\")\n label=Text(start,\"Start\")\n label.setFill(\"Black\")\n circ.draw(win)\n label.draw(win)\n #print(self.CurrentCell)\n #Using the current cell from the finished algorithm(last place visited), a circle can be placed at that point.\n endpointx=(self.CurrentCell[0]-1)*scale +scale//2 ####MAKING END POINT X\n endpointy=600-(self.CurrentCell[1]-1)*scale-scale//2 ####MAKING END POINT Y\n endpoint=Point(endpointx,endpointy)\n circ2=Circle(endpoint,radius)\n circ2.setFill(\"White\")\n label2=Text(endpoint,\"End\")\n circ2.draw(win)\n label2.draw(win)\n \n ###############CREATE KEY########################\n \n \n keypointx=(self.MazeKey[0]-1)*scale +scale//2 ####MAKING END POINT X\n keypointy=600-(self.MazeKey[1]-1)*scale-scale//2 ####MAKING END POINT Y\n keypoint=Point(keypointx,keypointy)\n circ3=Circle(keypoint,radius)\n circ3.setFill(\"Blue\")\n label3=Text(keypoint,\"Key\")\n circ3.draw(win)\n label3.draw(win)\n pathcol=\"Yellow\"\n##\n\n \n for i in range(1,len(self.EntirePath)): \n pathpointx=(self.EntirePath[i][0]-1)*scale +scale//2 ####MAKING END POINT X\n pathpointy=600-(self.EntirePath[i][1]-1)*scale-scale//2 ####MAKING END POINT Y\n pathpoint=Point(pathpointx,pathpointy)\n drawpath=Circle(pathpoint,radius)\n drawpath.setFill(pathcol)\n if self.EntirePath[i]==self.KeyPath[-1]:\n pathcol=\"Violet\"\n label4=Text(keypoint,\"Key\")\n label4.draw(win) \n drawpath.draw(win)\n drawpath.setWidth(1)\n sleep(0.1)\n \n #drawpath.draw(win)\n \n label5=Text(endpoint,\"Maze Solved \")\n label5.draw(win)\n circ4=Circle(start,radius)\n circ4.setFill(\"Red\")\n circ4.draw(win) \n label6=Text(start,\"Start \")\n label6.draw(win)","def create_grid(self):\n return [[0] * self.width for _ in range(self.height)]","def fill_grid_np(self):\n\n self.grid_np = [None for i in range(GRID_HEIGHT*GRID_HEIGHT*MAX_CELL_SIZE)]\n grid = self.grid_np\n # cell_size = self.cell_size\n for obj in self.levels[self.curient_level].objects:\n obj.position_grid[X], obj.position_grid[Y] = get_grid_xy(obj.position_np, ZOMBIE_SIZE)\n x, y = obj.position_grid[X], obj.position_grid[Y]\n grid[y*GRID_WIDTH + x] = obj\n # if cell_size[y*GRID_WIDTH + x] < MAX_CELL_SIZE:\n # cell_size[y*GRID_WIDTH + x] += 1","def define_grid(self):\n # Big lines\n self.big_lines = []\n for i in range(1, 3):\n # Vertical line\n self.big_lines.append(pygame.Rect(\n i * SCREEN_WIDTH // 3 - BIG_LINE_WIDTH // 2,\n BIG_LINE_OFFSET,\n BIG_LINE_WIDTH,\n SCREEN_WIDTH - 2 * BIG_LINE_OFFSET\n ))\n\n # Horizontal line\n self.big_lines.append(pygame.Rect(\n BIG_LINE_OFFSET,\n i * SCREEN_WIDTH // 3 - BIG_LINE_WIDTH // 2,\n SCREEN_WIDTH - 2 * BIG_LINE_OFFSET,\n BIG_LINE_WIDTH\n ))\n\n # Small lines\n self.small_lines = []\n for i in range(1, 9):\n if i % 3 == 0:\n continue\n\n # Vertical line\n self.small_lines.append(pygame.Rect(\n i * SCREEN_WIDTH // 9 - SMALL_LINE_WIDTH // 2,\n SMALL_LINE_OFFSET,\n SMALL_LINE_WIDTH,\n SCREEN_WIDTH - 2 * SMALL_LINE_OFFSET\n ))\n\n # Horizontal line\n self.small_lines.append(pygame.Rect(\n SMALL_LINE_OFFSET,\n i * SCREEN_WIDTH // 9 - SMALL_LINE_WIDTH // 2,\n SCREEN_WIDTH - 2 * SMALL_LINE_OFFSET,\n SMALL_LINE_WIDTH\n ))","def _make_grid(self, imageset, format_kwargs=None):\n grid_size = imageset[\"grid_size\"]\n return ImageGrid.from_imageset(\n self._make_stills(imageset, format_kwargs=format_kwargs), grid_size\n )","def dim(self) -> int:\n pass","def create_border(self, grid_size, scale):\n # self.array.clear()\n\n for j in range(grid_size):\n for i in range(grid_size):\n if (i == 0 or i == grid_size-1) or (j == 0 or j == grid_size-1):\n self.array.append((i*scale, j*scale))\n self.array_length += 1\n\n # print(self.array)","def drawGrid(self):\n # Add vertical minor grids\n path = QPainterPath()\n minLoc = self.xMinGrid + self.minorGrid\n maxLoc = self.xMaxGrid\n gStep = self.minorGrid\n for i in range(minLoc, maxLoc, gStep):\n path.moveTo(i, self.yMinGrid)\n path.lineTo(i, self.yMaxGrid)\n self.addPath(path, self.minorGridPen)\n # Add horizontal minor grids\n path = QPainterPath()\n for i in range(minLoc, maxLoc, gStep):\n path.moveTo(self.xMinGrid, i)\n path.lineTo(self.xMaxGrid, i)\n self.addPath(path, self.minorGridPen)\n # Add vertical minor grids\n path = QPainterPath()\n minLoc = self.xMinGrid\n maxLoc = self.xMaxGrid\n gStep = self.majorGrid\n for i in range(minLoc, maxLoc, gStep):\n path.moveTo(i, self.yMinGrid)\n path.lineTo(i, self.yMaxGrid)\n self.addPath(path, self.majorGridPen)\n # Add vertical minor grids\n path = QPainterPath()\n for i in range(minLoc, maxLoc, gStep):\n path.moveTo(self.xMinGrid, i)\n path.lineTo(self.xMaxGrid, i)\n self.addPath(path, self.majorGridPen)","def draw_grid(self) -> None:\n for x in range(0, WIDTH, TILE_SIZE):\n pg.draw.line(self.screen, LIGHT_GREY, (x, INFO_HEIGHT), (x, HEIGHT))\n for y in range(INFO_HEIGHT, INFO_HEIGHT + HEIGHT, TILE_SIZE):\n pg.draw.line(self.screen, LIGHT_GREY, (0, y), (WIDTH, y))","def adjustSize( self ):\n cell = self.scene().cellWidth() * 2\n minheight = cell\n minwidth = 2 * cell\n \n # fit to the grid size\n metrics = QFontMetrics(QApplication.font())\n width = metrics.width(self.displayName()) + 20\n width = ((width/cell) * cell) + (cell % width)\n \n height = self.rect().height()\n \n # adjust for the icon\n icon = self.icon()\n if icon and not icon.isNull():\n width += self.iconSize().width() + 2\n height = max(height, self.iconSize().height() + 2)\n \n self.setMinimumWidth(max(width, minwidth))\n self.setMinimumHeight(max(height, minheight))\n \n self.rebuild()","def draw_grid(self):\n if self.grid_center == True:\n (n, m) = (self.n, self.m)\n (dx, dy) = (self.dx // 2, self.dy // 2)\n else:\n (n, m) = (self.n + 1, self.m + 1)\n (dx, dy) = (0, 0)\n\n x0 = self.x0 + dx\n y0 = self.y0 + dy\n\n # vertical lines\n for j in range(m):\n p0 = (x0 + j * self.dx, y0)\n p1 = (x0 + j * self.dx, y0 + (n-1) * self.dy)\n pygame.draw.line(self.screen, self.grid_col, p0, p1, self.grid_d) \n # horizontal lines\n for i in range(n):\n p0 = (x0, y0 + i * self.dy)\n p1 = (x0 + (m-1) * self.dx, y0 + i * self.dy)\n pygame.draw.line(self.screen, self.grid_col, p0, p1, self.grid_d)","def define_grid():\n grid_left = np.array([[-13.1000000000000, -35.5000000000000, -48.3000000000000, -60, -16.9000000000000,\n -34.8000000000000, -67.5000000000000, -46.1000000000000, -59.8000000000000,\n -14.2000000000000, -28.3000000000000, -42.3000000000000, -67.6000000000000,\n -50.5000000000000, -14.6000000000000, -60.9000000000000, -31.6000000000000,\n -5.10000000000000, -65.6000000000000, -41.8000000000000, -55.1000000000000,\n -22.7000000000000, -5.80000000000000, -49.2000000000000, -34.5000000000000,\n -61.5500000000000, -63.6000000000000, -40.4000000000000, -48.7000000000000,\n -21.8000000000000, -58.2000000000000, -7, -36.3000000000000, -48.1000000000000,\n -56.8000000000000, -7.30000000000000, -22.2000000000000, -36.8000000000000,\n -46.8000000000000],\n [-67.7000000000000, -60, -55.1000000000000, -51.8000000000000, -51.6000000000000,\n -49.3000000000000, -47.1000000000000, -43.7000000000000, -39.6000000000000,\n -39.1000000000000, -31.2000000000000, -30.7000000000000, -30.1000000000000,\n -24.4000000000000, -22.7000000000000, -18.7000000000000, -16.9000000000000,\n -12.6000000000000, -10.8000000000000, -10.2000000000000, -4.01000000000000, 1.20000000000000,\n 2.80000000000000, 3.70000000000000, 3.90000000000000, 6.20000000000000, 8.30000000000000,\n 11.8000000000000, 14.5000000000000, 16, 18.2000000000000, 18.4000000000000, 19.9000000000000,\n 24.6000000000000, 28.5200000000000, 33.8000000000000, 35, 35.4000000000000,\n 35.6000000000000],\n [69.1000000000000, 66, 58.2000000000000, 48, 78, 71.7000000000000, 31, 61.1000000000000,\n 53.3000000000000, 81.1000000000000, 76, 70.2000000000000, 41.2000000000000, 64.4000000000000,\n 80.2000000000000, 50.9000000000000, 75.2000000000000, 77.3000000000000, 37.8000000000000, 67,\n 53.2000000000000, 72, 74.8000000000000, 54.7000000000000, 66.5000000000000, 35.9000000000000,\n 25.7000000000000, 60.7000000000000, 50.5000000000000, 68.9000000000000, 27.3000000000000,\n 70.3000000000000, 59.6000000000000, 44, 20.8000000000000, 61.7000000000000, 57.2000000000000,\n 47, 36]])\n stn_left = np.array([[-14.6, -13.2, -11.7, -9.10, -11.7, -13.2, -7.90, -10],\n [-15.1, -15.1, -15.1, -12.6, -12.6, -12.6, -9.40, -10.1],\n [-5.40, -7.20, -8.70, -8.70, -7.50, -5.10, -10.3, -7.80]])\n grid_right = np.copy(grid_left)\n grid_right[0, :] = grid_right[0, :] * -1\n stn_right = np.copy(stn_left)\n stn_right[0, :] = stn_right[0, :] * -1\n\n return grid_left, grid_right, stn_left, stn_right","def setAxisPageSize(width,height):\n dislin.axslen(width,height)","def create_grid(grid):\r\n for i in range(4):\r\n grid.append([0,0,0,0])","def dimension(self):\n return 3*self.genus - 3 + self.n","def dimensions():","def get_grid_shape(num_examples):\n height = int(numpy.floor(numpy.sqrt(num_examples)))\n width = int(numpy.ceil(num_examples * 1. / height))\n\n return (height, width)","def __init__(self, row=4, col=4, initial=2):\n self.grid = Grid(row, col, initial)","def draw_grid(self):\n plt.imshow(py.array(\n map(lambda x: map(lambda y: mplc.colorConverter.to_rgb(colord[y]), x), self.create_grid(self.graph))),\n interpolation='nearest')\n plt.show()","def plot_insertsize():","def draw(self):\n\n super().draw()\n \n self.dim = self.getdim()\n start_x, start_y, = self.x(), self.y()\n\n for y in range(self.r):\n for x in range(self.c):\n x_pos, y_pos = start_x + (self.dim * x), start_y + (self.dim * y)\n self.tiles[y][x].resize(x_pos, y_pos, self.dim, self.dim)","def draw_grid(self):\n for i in range(N * N + 1):\n color = \"blue\" if i % N == 0 else \"gray\"\n x0 = MARGIN + i * SIDE\n y0 = MARGIN\n x1 = MARGIN + i * SIDE\n y1 = HEIGHT - MARGIN\n self.canvas.create_line(x0, y0, x1, y1, fill=color)\n\n x0 = MARGIN\n y0 = MARGIN + i * SIDE\n x1 = WIDTH - MARGIN\n y1 = MARGIN + i * SIDE\n self.canvas.create_line(x0, y0, x1, y1, fill=color)","def __len__(self):\n return len(self._grid)","def create_grid(size):\n grid = []\n for i in range(size):\n row = ['0']*size\n grid.append(row)\n\n return grid","def set_numcells(self, N, radius=50):\n self._N = N\n self.create_cells(N)\n self.connect_cells()\n self.connect_stim()","def initialize(self, frame):\n self.grid_size = 5\n\n Label(frame, text=\"Grid Size:\").grid(row=0)\n\n self.e1 = Scale(frame, from_=self.grid_size, to=25, orient=HORIZONTAL)\n self.e1.grid(row=0, column=1)\n\n return self.e1","def create_grid(grid):\r\n for i in range(4):\r\n grid.append([0]*4)\r\n return grid","def getMandelbrotSet(self):\n\t\t\n\t\t(x_min, x_max, y_min, y_max) = [i for i in self.extent]\n\t\t(rows, columns) = (self.width*self.dpi, self.width*self.dpi)\n\t\tr_real = np.linspace(x_min, x_max, columns)\n\t\tr_imag = np.linspace(y_max, y_min, rows)\n\t\tresult = np.empty((rows, columns))\n\n\t\t# used for smoother colors\n\t\thorizon = 2.0**40\n\t\thorizon2 = horizon**2\n\t\tlog_horizon = np.log(np.log(horizon))/np.log(2)\n\n\t\tfor i in range(rows): # imaginary values\n\t\t\tfor j in range(columns): # real values\n\t\t\t\tresult[i,j] = _determineDivergence(r_real[j], r_imag[i], self.maxIter, horizon2, log_horizon)\n\t\treturn result","def grid(self):\n return self.__grid","def dimension(self):","def draw_grid(self):\n self.screen.draw_many_tiles(tile for tile in self.iter_grid_tiles())\n pass"],"string":"[\n \"def grid_size(self):\\n return self._grid_size\",\n \"def getGridSize(self):\\n # This is set by the mosaic module, but other modules need to\\n # know the values to take the proper size grid.\\n return self.grid_size\",\n \"def _grid_hint_size(self) -> int:\",\n \"def define_grid(self):\\n self.h_shape = int(\\n np.round((self.h_stop - self.h_start) / self.h_step, 2)) + 1\\n self.k_shape = int(\\n np.round((self.k_stop - self.k_start) / self.k_step, 2)) + 1\\n self.l_shape = int(\\n np.round((self.l_stop - self.l_start) / self.l_step, 2)) + 1\\n self.grid_origin = [self.h_start, self.k_start, self.l_start]\\n self.grid_step = [int(np.rint(1.0/self.h_step)),\\n int(np.rint(1.0/self.k_step)),\\n int(np.rint(1.0/self.l_step))]\\n self.grid_shape = [self.h_shape, self.k_shape, self.l_shape]\\n self.grid_basis = [[1, 0, 0], [0, 1, 0], [0, 0, 1]]\",\n \"def grid(self):\\r\\n dimA = self.dimA ; dimC = self.dimA ; W_grid = self.W_grid\\r\\n \\r\\n self.tol = 10e-5\\r\\n self.Niter = 10000\\r\\n \\r\\n a0 = 100 / self.dimA\\r\\n c0 = 100 / self.dimA\\r\\n a_grid = np.mgrid[0:(dimA):1] ; a_grid = a0 * a_grid ; self.a_grid = a_grid\\r\\n c_grid = np.mgrid[0:(dimC):1] ; c_grid = c0 * c_grid ; self.c_grid = c_grid\\r\\n self.W_grid = W_grid\",\n \"def get_grid_size(self, ui, res_dir):\\r\\n print_it('determining grid size', PrintOpts.lvl1.value)\\r\\n self.sun.simple_clone()\\r\\n self.sun.clone.make_profile(PreSol.res_x.value, PreSol.res_y.value,\\r\\n self.init_force)\\r\\n self.planet.simple_clone()\\r\\n self.planet.clone.make_slave_to(self.sun.clone)\\r\\n\\r\\n init_displ = hertz_displ(self.sun.clone.e, self.planet.e,\\r\\n self.sun.clone.ny, self.planet.ny,\\r\\n self.sun.clone.r_hertz_x,\\r\\n self.sun.clone.r_hertz_y,\\r\\n self.planet.clone.r_hertz_x,\\r\\n self.planet.clone.r_hertz_y,\\r\\n self.sun.norm_forces[0])\\r\\n too_many_els_in_y = 1\\r\\n too_many_els_in_x = 1\\r\\n contact_width_y = 0.05\\r\\n contact_width_x = 0.05\\r\\n while too_many_els_in_y != 0 or \\\\\\r\\n too_many_els_in_x != 0:\\r\\n self.sun.clone.make_profile(self.sun.clone.res_x,\\r\\n self.sun.clone.res_y, self.init_force,\\r\\n contact_width=contact_width_y,\\r\\n contact_length=contact_width_x)\\r\\n self.planet.clone.make_slave_to(self.sun.clone)\\r\\n\\r\\n pressure, init_displ = \\\\\\r\\n pre_solve_half_space(self.sun.clone.profile,\\r\\n self.planet.clone.profile,\\r\\n self.sun.clone.x_axis,\\r\\n self.sun.clone.y_axis,\\r\\n self.sun.clone.res_x, self.sun.clone.res_y,\\r\\n self.sun.clone.delta_x,\\r\\n self.sun.clone.delta_y, self.sun.clone.e,\\r\\n self.planet.clone.e, self.sun.clone.ny,\\r\\n self.planet.clone.ny,\\r\\n self.sun.norm_forces[0],\\r\\n init_displ=init_displ, print_prog=False)\\r\\n\\r\\n pressure_els_y = sum(\\r\\n pressure[math.floor(self.sun.clone.res_y / 2), :] > 0)\\r\\n too_many_els_in_y = self.sun.clone.res_y - pressure_els_y - 2\\r\\n if too_many_els_in_y:\\r\\n contact_width_y += -np.sign(\\r\\n too_many_els_in_y) * contact_width_y / 25\\r\\n\\r\\n pressure_els_x = sum(\\r\\n pressure[:, math.floor(self.sun.clone.res_x / 2)] > 0)\\r\\n too_many_els_in_x = self.sun.clone.res_x - pressure_els_x - 2\\r\\n if too_many_els_in_x:\\r\\n contact_width_x += -np.sign(\\r\\n too_many_els_in_x) * contact_width_x / 25\\r\\n\\r\\n self.sun.make_profile(self.sun.res_x, self.sun.res_y, self.init_force,\\r\\n contact_width=contact_width_y,\\r\\n contact_length=contact_width_x)\\r\\n self.planet.make_slave_to(self.sun)\\r\\n return init_displ\",\n \"def gen_grids(self):\\n self.dx = self.grid_width / self.grid_resol\\n self.dk = 2 * np.pi/self.grid_width\\n self.grid_x_shifted = -self.grid_width/2 + self.dx * np.arange(0, self.grid_resol)\\n self.grid_x = self.grid_x_shifted + self.grid_center\\n self.grid_k = - (np.pi * self.grid_resol)/self.grid_width + self.dk * np.arange(0, self.grid_resol)\\n self.grid_k = np.roll(self.grid_k, int((self.grid_resol)/2))\\n self.grid_kin = np.square(self.h)/ (2*self.m) * np.square(self.grid_k)\",\n \"def make_grid(self):\\n for k in range(0, NUM + 1):\\n self.create_line(k * UNIT, 0, k * UNIT, SIZE, width=THICKNESS)\\n self.create_line(0, k * UNIT, SIZE, k * UNIT, width=THICKNESS)\",\n \"def setSquareGrid(nx,ny):\\n dislin.grid(nx,ny)\",\n \"def apply(self):\\n self.grid_size = self.values[0]\",\n \"def initialize_grid(self) -> None:\\n for i in range(self.grid_size[0]):\\n for j in range(self.grid_size[1]):\\n self.set(i, j, self.base_color)\",\n \"def configure_grid(self):\\r\\n\\r\\n for r in range(3):\\r\\n self.rowconfigure(r, weight=1)\\r\\n for c in range(3):\\r\\n self.columnconfigure(c, weight=1)\",\n \"def drawGrid(self):\\n for div in range(NBCELL):\\n sec = SSIZE*div\\n self.can.create_line(0, sec, GSIZE, sec, width=3, fill=GFILL)\\n self.can.create_line(sec, 0, sec, GSIZE, width=3, fill=GFILL)\",\n \"def get_grid_width(self):\\n # replace with your code\\n return 0\",\n \"def initialize_grid(self):\\n self.grid = np.zeros([self.N, self.N, self.N])\\n return self.grid\",\n \"def set_grid(self,ug):\\n self.grd=ug\\n self.set_topology()\",\n \"def get_grid_width(self):\\r\\n return self._grid_width\",\n \"def get_grid_width(self):\\r\\n return self.width\",\n \"def setwinsize(self, rows, cols):\",\n \"def makeGrid(self):\\n self.h = self.step_x\\n self.k = self.step_t\\n self.t, self.x = np.meshgrid(np.arange(self.min_t, self.max_t, self.step_t), np.arange(self.min_x, self.max_x\\n , self.step_x))\",\n \"def draw_grid(self):\\n for x in range(0, WIDTH, TILESIZE):\\n pg.draw.line(self.screen, LIGHTGREY, (x, 0), (x, HEIGHT))\\n \\n for y in range(0, HEIGHT, TILESIZE):\\n pg.draw.line(self.screen, LIGHTGREY, (0, y), (WIDTH, y))\",\n \"def get_size_of_grid(self):\\n row = 0\\n column = 0\\n if int(self.var1.get()) == 1:\\n row, column = 6, 6\\n\\n if int(self.var2.get()) == 1:\\n row, column = 7, 6\\n\\n if int(self.var3.get()) == 1:\\n row, column = 7, 7\\n\\n if int(self.var4.get()) == 1:\\n row, column = 8, 8\\n\\n return row, column\",\n \"def get_grid_width(self):\\r\\n # replace with your code\\r\\n return self._grid_width\",\n \"def get_grid_width(self):\\r\\n # replace with your code\\r\\n return self._grid_width\",\n \"def get_grid_width(self):\\r\\n\\r\\n return self._grid_width\",\n \"def tile_size_2d(self):\\n return 32.0, 32.0\",\n \"def get_grid_width(self):\\n return self.grid_width\",\n \"def get_grid_width(self):\\n return self.grid_width\",\n \"def calc_grid(self):\\n return int(self._posn.x / cell_size), int(self._posn.y / cell_size)\",\n \"def cellsize_2d(self):\\t\\r\\n return self.dx * self.dy\",\n \"def get_grid_width(self):\\n # replace with your code\\n return self._grid_width\",\n \"def get_grid_width(self):\\n # replace with your code\\n return self._grid_width\",\n \"def _set_grid(self):\\n grid_sizex = self.__dict__['size'][0]/self.__dict__['grid_aspects'][0]\\n grid_sizey = self.__dict__['size'][1]/self.__dict__['grid_aspects'][0]\\n \\n sizex = grid_sizex*(1.0/self.__dict__['grid_aspects'][1])\\n sizey = grid_sizey*(1.0/self.__dict__['grid_aspects'][1])\\n \\n defs = helper.defs()\\n defs += helper.pattern(helper.path(**{'d': 'M %s 0 L 0 0 0 %s' % (sizex, sizey), 'fill': 'none', 'stroke': 'gray', 'stroke-width': 0.5}),\\n **{'id': self.__dict__['grid_item_id'], 'width': sizex, 'height': sizey, 'patternUnits': 'userSpaceOnUse'})\\n defs += helper.pattern(helper.path(**{'d': 'M %s 0 L 0 0 0 %s' % (grid_sizex, grid_sizey), 'fill': 'none', 'stroke': 'gray', 'stroke-width': 1}),\\n helper.rect(width=grid_sizex, height=grid_sizey, fill=\\\"url(#%s)\\\" % self.__dict__['grid_item_id']),\\n **{'id': self.__dict__['grid_id'], 'width': grid_sizex, 'height': grid_sizey, 'patternUnits': 'userSpaceOnUse'})\\n self += defs\\n self += helper.rect(fill=\\\"white\\\", height=self.__dict__['size'][0]+1, width=self.__dict__['size'][1]+1)\\n self += helper.rect(fill=\\\"url(#%s)\\\" % self.__dict__['grid_id'], height=self.__dict__['size'][0]+1, width=self.__dict__['size'][1]+1)\",\n \"def _SetSize(self, pixels = None):\\n if not pixels:\\n pixels = self.GetClientSize()\\n self.canvas.SetSize(pixels)\\n self.figure.set_size_inches(pixels[0]/self.figure.get_dpi(),\\n pixels[1]/self.figure.get_dpi())\",\n \"def get_grid_width(self):\\n return self._grid_width\",\n \"def get_grid_width(self):\\n return self._grid_width\",\n \"def get_grid_width(self):\\n return self._grid_width\",\n \"def initialise_grid(self, y, x, starting_value):\\n # Create a grid of the specified size\\n self.grid = np.zeros( (y, x), np.int8, 'C')\\n \\n # Record the sizes in the class variables\\n self.x_len = x\\n self.y_len = y\\n \\n # Set the initial values of the array\\n self.grid += starting_value\",\n \"def num_tiles(self):\\n return self.num_row_tiles * self.num_col_tiles\",\n \"def get_grid_width(self):\\r\\n # replace with your code\\r\\n return self._width\",\n \"def get_grid_width(self):\\r\\n # replace with your code\\r\\n return self.grid_width\",\n \"def get_grid_width(self):\\n # replace with your code\\n return self._width\",\n \"def get_grid_width(self):\\n # replace with your code\\n return self._width\",\n \"def get_grid_width(self):\\n # replace with your code\\n return self.grid_width\",\n \"def get_grid_width(self):\\n # replace with your code\\n return self.grid_width\",\n \"def get_grid_size(self, img):\\r\\n grid_height = int(np.ceil(img.shape[0] / self.config.grid_row))\\r\\n grid_width = int(np.ceil(img.shape[1] / self.config.grid_col))\\r\\n return grid_height, grid_width\",\n \"def test_sizergrid():\\n regular_grid(8, 3)\\n mpl.show()\",\n \"def test_get_grid_size(flopy_dis_mf6):\\n flopy_dis, mf6 = flopy_dis_mf6\\n mf6.initialize()\\n\\n prescribed_grid_size = flopy_dis.nrow * flopy_dis.ncol\\n\\n # Getting the grid id from the model, requires specifying one variable\\n k11_tag = mf6.get_var_address(\\\"K11\\\", flopy_dis.model_name, \\\"NPF\\\")\\n grid_id = mf6.get_var_grid(k11_tag)\\n\\n assert prescribed_grid_size == mf6.get_grid_size(grid_id)\",\n \"def setGrid(nx,ny,gridtype='square'):\\n if gridtype == 'polar':\\n dislin.grdpol(nx,ny)\\n else:\\n dislin.grid(nx,ny)\",\n \"def __init__(self, cell_size, nrows, ncols, **kwds):\\n #\\n # Python 3 update\\n #\\n super().__init__(**kwds)\\n self.cell_size = cell_size\\n w, h = cell_size\\n d = 2 * self.margin\\n self.size = (w * ncols + d, h * nrows + d)\\n self.cell_size = cell_size\",\n \"def optimal_chunksizes(nt, nlat, nlon):\\n\\n clon = np.sqrt(1000000.0 * nlon / (nlat * nt))\\n clat = nlat * clon / nlon\\n return (nt, int(np.ceil(clat)), int(np.ceil(clon)))\",\n \"def make_grid(self):\\n length = self.size / 8\\n # draw horizontal lines\\n for y in range(0, self.size, length):\\n self.window.create_line(0, y, self.size, y, fill = \\\"blue\\\")\\n \\n # draw vertical lines\\n for x in range(0, self.size, length):\\n self.window.create_line(x, 0, x, self.size, fill = \\\"blue\\\")\\n\\n # draw the axes red\\n self.window.create_line(\\n 0,\\n self.size / 2,\\n self.size, \\n self.size / 2, \\n fill = \\\"red\\\"\\n )\\n self.window.create_line(\\n self.size / 2, 0,\\n self.size / 2, \\n self.size, \\n fill = \\\"red\\\"\\n )\\n print(\\\"Grid Made.\\\")\",\n \"def __init__( self ):\\n\\n self.__grid = list(range(1,10))\\n self.__drawnGrid = 0\",\n \"def make_grid(grid_size=(10, 10)):\\n return np.zeros(grid_size, dtype=np.int16)\",\n \"def grid_size_type(size):\\n\\n size = int(size)\\n if size <= 2:\\n raise argparse.ArgumentTypeError(\\\"Minimum grid size is 3 x 3\\\")\\n return size\",\n \"def get_grid_width(self):\\n return self._width\",\n \"def set_figure_size(self):\\n lims, _ = self.set_lims()\\n size_fac = 50\\n paperSizeFac = 0.65\\n one_dec = 1.6\\n xdecs = np.log10(lims(1)) - np.log10(lims(0))\\n one_dec = one_dec * 4 / xdecs\\n ydecs = np.log10(lims[3]) - np.log10(lims[2])\\n paper_width = xdecs * one_dec\\n paper_height = (ydecs + 3) * one_dec\\n paper_height = min([paper_height, 9])\\n rectScreen = [0.5, 0.5, paper_width, paper_height] * size_fac\\n rectPaper = [1.0, 1.0, paper_width * paperSizeFac, paper_height * paperSizeFac]\\n\\n rectRho = [0.15, 0.15 + 2.3 / (ydecs + 3), 0.8, ydecs / (ydecs + 3) * 0.8]\\n rectPhi = [0.15, 0.15, 0.8, 2 / (ydecs + 3) * 0.8]\\n rects = {\\n \\\"Screen\\\": rectScreen,\\n \\\"Paper\\\": rectPaper,\\n \\\"Rho\\\": rectRho,\\n \\\"Phi\\\": rectPhi,\\n }\\n return rects\",\n \"def getNumTiles(self):\\n return self.w * self.h\",\n \"def __init__(self, nx, ny, nxsize=5.4, nysize=6.2):\\n self.nx = nx\\n self.ny = ny\\n self.n = 1\\n plt.figure(figsize=(nysize*ny, nxsize*nx))\\n plt.subplot(nx, ny, self.n)\",\n \"def configure_grid(node: tkt.Misc, colnum: int, rownum: int):\\n for i in range(colnum):\\n node.grid_columnconfigure(i, weight=1)\\n\\n for i in range(rownum):\\n node.grid_rowconfigure(i, weight=1)\",\n \"def drawGrid(self,gridLines=True):\\n if not self.changed: self.edit()\\n cGrid = Fmap.GRID\\n cBorder = Fmap.BORDER\\n if gridLines: #--Some fools don't want the grid!\\n #--Grid\\n for uv in range(-25,26,5):\\n xy = 512/2 - 9*uv + 4\\n self.drawRect(cGrid,0,xy,512,xy+1)\\n self.drawRect(cGrid,xy,0,xy+1,512)\\n #--Grid axes\\n xy = 512/2 + 4\\n self.drawRect(cBorder,0,xy,512,xy+1)\\n self.drawRect(cBorder,xy,0,xy+1,512)\\n #--Border\\n self.drawBorder(cBorder,0,0,512,512,4)\",\n \"def DrawGrid(self, count):\\n for i in range(0, self.width, self.incr):\\n self.canvas.create_line(i, 100, i, 700, fill = \\\"#696969\\\", width = 1)\\n for i in range(100, 800, 100):\\n self.canvas.create_line(0, i, self.width, i, fill = \\\"#696969\\\", width = 1)\\n self.canvas.create_rectangle(self.incr * 4, self.height - self.incr * 3.5,\\n self.width - self.incr * 4, self.height, fill = \\\"black\\\", width = 3)\\n for i in range(int(self.height - self.incr * 3.5), self.height, int(self.incr / 4)):\\n self.canvas.create_line(self.incr * 4, i, self.width - self.incr * 4,\\n i, fill = \\\"#696969\\\", width = 1)\\n for i in range(self.incr * 4, self.width - self.incr * 4 + 1, int(self.incr / 4)):\\n self.canvas.create_line(i, self.height - self.incr * 3.5, i, self.height,\\n fill = \\\"#696969\\\", width = 1)\",\n \"def sizeCellsProperty(self):\\n return FdtPropertyWords(\\\"#size-cells\\\", self.size_cells)\",\n \"def configure(resolution, config):\\n width = config.getint('overflow', 'width')\\n length = config.getint('overflow', 'length')\\n\\n dc = float(resolution[:-2])\\n nx = int(width / dc)\\n ny = int(length / dc)\\n\\n config.set('overflow', 'nx', str(nx),\\n comment='the number of mesh cells in the x direction')\\n config.set('overflow', 'ny', str(ny),\\n comment='the number of mesh cells in the y direction')\\n config.set('overflow', 'dc', str(dc * 1e3),\\n comment='the distance between adjacent cell centers')\",\n \"def grid_shape(self):\\n return self._grid_shape\",\n \"def ncells(self):\\n return self.izone.size\",\n \"def get_grid_width(self):\\n\\n return self._grid_width\",\n \"def __init__(self, origin_x=-2.5, origin_y=-2.5, resolution=.1,\\n width=50, height=50):\\n self.origin_x = origin_x\\n self.origin_y = origin_y\\n self.resolution = resolution\\n self.width = width\\n self.height = height\\n self.grid = np.zeros((height, width))\",\n \"def SetSize(self, m: 'unsigned int', n: 'unsigned int') -> \\\"void\\\":\\n return _itkArray2DPython.itkArray2DUI_SetSize(self, m, n)\",\n \"def optimal_grid(num):\\n\\n # get maximum shape\\n shape = int(np.ceil(np.sqrt(num)))\\n # get number of rows and columns based on maximum shape\\n if shape ** 2 == num:\\n nrows = shape\\n ncols = shape\\n else:\\n nrows = int(np.ceil(num / shape))\\n ncols = int(np.ceil(num / nrows))\\n # get position of figures\\n pos = []\\n for i in range(nrows):\\n for j in range(ncols):\\n pos.append([i, j])\\n # return nrows, ncols and positions\\n return nrows, ncols, pos\",\n \"def drawmaze(self):\\n win=GraphWin(\\\"Perfect Maze\\\",600,600) \\n win.setBackground(\\\"White\\\")\\n scale=600/self.N #Used to generalize the size difference for the input of larger numbers. The background resolution/ grid size, N\\n\\n x1=scale\\n y1=0\\n x2=scale\\n y2=scale\\n\\n ##VERTICAL LINES ####\\n for i in range(self.N,0,-1):\\n for j in range(1,self.N):\\n if self.East[j][i]: #If East is true, draw a line.\\n \\n line=Line(Point(x1,y1),Point(x2,y2)) #lines | |\\n line.setFill(\\\"red\\\")\\n line.draw(win)\\n x1+=scale #Increment causes |->|\\n x2+=scale #Increment causes |->|\\n y1+=scale #Used to draw two more\\n y2+=scale #of the same spaced lines further down.\\n x1=scale #Reset\\n x2=scale #Reset\\n\\n\\n ##HORIZONTAL LINES##\\n x1=0\\n y1=scale\\n x2=scale\\n y2=scale\\n\\n\\n for i in range(self.N,1,-1):\\n for j in range(1,self.N+1):\\n if self.South[j][i]: #If South is true, draw a line.\\n \\n line=Line(Point(x1,y1),Point(x2,y2))\\n line.setFill(\\\"red\\\")\\n line.draw(win)\\n x1+=scale\\n x2+=scale\\n y1+=scale\\n y2+=scale\\n x1=0\\n x2=scale\\n\\n const=scale//5 #Very useful const which helps in placing circles on grid.\\n x=scale//2\\n y=600-scale//2\\n #radius=(scale-(4*scale//self.N))/2\\n radius=scale//2-(const)\\n start=Point(x,y) #START POINT HERE \\n circ=Circle(start,radius)\\n circ.setFill(\\\"Red\\\")\\n label=Text(start,\\\"Start\\\")\\n label.setFill(\\\"Black\\\")\\n circ.draw(win)\\n label.draw(win)\\n #print(self.CurrentCell)\\n #Using the current cell from the finished algorithm(last place visited), a circle can be placed at that point.\\n endpointx=(self.CurrentCell[0]-1)*scale +scale//2 ####MAKING END POINT X\\n endpointy=600-(self.CurrentCell[1]-1)*scale-scale//2 ####MAKING END POINT Y\\n endpoint=Point(endpointx,endpointy)\\n circ2=Circle(endpoint,radius)\\n circ2.setFill(\\\"White\\\")\\n label2=Text(endpoint,\\\"End\\\")\\n circ2.draw(win)\\n label2.draw(win)\\n \\n ###############CREATE KEY########################\\n \\n \\n keypointx=(self.MazeKey[0]-1)*scale +scale//2 ####MAKING END POINT X\\n keypointy=600-(self.MazeKey[1]-1)*scale-scale//2 ####MAKING END POINT Y\\n keypoint=Point(keypointx,keypointy)\\n circ3=Circle(keypoint,radius)\\n circ3.setFill(\\\"Blue\\\")\\n label3=Text(keypoint,\\\"Key\\\")\\n circ3.draw(win)\\n label3.draw(win)\\n pathcol=\\\"Yellow\\\"\\n##\\n\\n \\n for i in range(1,len(self.EntirePath)): \\n pathpointx=(self.EntirePath[i][0]-1)*scale +scale//2 ####MAKING END POINT X\\n pathpointy=600-(self.EntirePath[i][1]-1)*scale-scale//2 ####MAKING END POINT Y\\n pathpoint=Point(pathpointx,pathpointy)\\n drawpath=Circle(pathpoint,radius)\\n drawpath.setFill(pathcol)\\n if self.EntirePath[i]==self.KeyPath[-1]:\\n pathcol=\\\"Violet\\\"\\n label4=Text(keypoint,\\\"Key\\\")\\n label4.draw(win) \\n drawpath.draw(win)\\n drawpath.setWidth(1)\\n sleep(0.1)\\n \\n #drawpath.draw(win)\\n \\n label5=Text(endpoint,\\\"Maze Solved \\\")\\n label5.draw(win)\\n circ4=Circle(start,radius)\\n circ4.setFill(\\\"Red\\\")\\n circ4.draw(win) \\n label6=Text(start,\\\"Start \\\")\\n label6.draw(win)\",\n \"def create_grid(self):\\n return [[0] * self.width for _ in range(self.height)]\",\n \"def fill_grid_np(self):\\n\\n self.grid_np = [None for i in range(GRID_HEIGHT*GRID_HEIGHT*MAX_CELL_SIZE)]\\n grid = self.grid_np\\n # cell_size = self.cell_size\\n for obj in self.levels[self.curient_level].objects:\\n obj.position_grid[X], obj.position_grid[Y] = get_grid_xy(obj.position_np, ZOMBIE_SIZE)\\n x, y = obj.position_grid[X], obj.position_grid[Y]\\n grid[y*GRID_WIDTH + x] = obj\\n # if cell_size[y*GRID_WIDTH + x] < MAX_CELL_SIZE:\\n # cell_size[y*GRID_WIDTH + x] += 1\",\n \"def define_grid(self):\\n # Big lines\\n self.big_lines = []\\n for i in range(1, 3):\\n # Vertical line\\n self.big_lines.append(pygame.Rect(\\n i * SCREEN_WIDTH // 3 - BIG_LINE_WIDTH // 2,\\n BIG_LINE_OFFSET,\\n BIG_LINE_WIDTH,\\n SCREEN_WIDTH - 2 * BIG_LINE_OFFSET\\n ))\\n\\n # Horizontal line\\n self.big_lines.append(pygame.Rect(\\n BIG_LINE_OFFSET,\\n i * SCREEN_WIDTH // 3 - BIG_LINE_WIDTH // 2,\\n SCREEN_WIDTH - 2 * BIG_LINE_OFFSET,\\n BIG_LINE_WIDTH\\n ))\\n\\n # Small lines\\n self.small_lines = []\\n for i in range(1, 9):\\n if i % 3 == 0:\\n continue\\n\\n # Vertical line\\n self.small_lines.append(pygame.Rect(\\n i * SCREEN_WIDTH // 9 - SMALL_LINE_WIDTH // 2,\\n SMALL_LINE_OFFSET,\\n SMALL_LINE_WIDTH,\\n SCREEN_WIDTH - 2 * SMALL_LINE_OFFSET\\n ))\\n\\n # Horizontal line\\n self.small_lines.append(pygame.Rect(\\n SMALL_LINE_OFFSET,\\n i * SCREEN_WIDTH // 9 - SMALL_LINE_WIDTH // 2,\\n SCREEN_WIDTH - 2 * SMALL_LINE_OFFSET,\\n SMALL_LINE_WIDTH\\n ))\",\n \"def _make_grid(self, imageset, format_kwargs=None):\\n grid_size = imageset[\\\"grid_size\\\"]\\n return ImageGrid.from_imageset(\\n self._make_stills(imageset, format_kwargs=format_kwargs), grid_size\\n )\",\n \"def dim(self) -> int:\\n pass\",\n \"def create_border(self, grid_size, scale):\\n # self.array.clear()\\n\\n for j in range(grid_size):\\n for i in range(grid_size):\\n if (i == 0 or i == grid_size-1) or (j == 0 or j == grid_size-1):\\n self.array.append((i*scale, j*scale))\\n self.array_length += 1\\n\\n # print(self.array)\",\n \"def drawGrid(self):\\n # Add vertical minor grids\\n path = QPainterPath()\\n minLoc = self.xMinGrid + self.minorGrid\\n maxLoc = self.xMaxGrid\\n gStep = self.minorGrid\\n for i in range(minLoc, maxLoc, gStep):\\n path.moveTo(i, self.yMinGrid)\\n path.lineTo(i, self.yMaxGrid)\\n self.addPath(path, self.minorGridPen)\\n # Add horizontal minor grids\\n path = QPainterPath()\\n for i in range(minLoc, maxLoc, gStep):\\n path.moveTo(self.xMinGrid, i)\\n path.lineTo(self.xMaxGrid, i)\\n self.addPath(path, self.minorGridPen)\\n # Add vertical minor grids\\n path = QPainterPath()\\n minLoc = self.xMinGrid\\n maxLoc = self.xMaxGrid\\n gStep = self.majorGrid\\n for i in range(minLoc, maxLoc, gStep):\\n path.moveTo(i, self.yMinGrid)\\n path.lineTo(i, self.yMaxGrid)\\n self.addPath(path, self.majorGridPen)\\n # Add vertical minor grids\\n path = QPainterPath()\\n for i in range(minLoc, maxLoc, gStep):\\n path.moveTo(self.xMinGrid, i)\\n path.lineTo(self.xMaxGrid, i)\\n self.addPath(path, self.majorGridPen)\",\n \"def draw_grid(self) -> None:\\n for x in range(0, WIDTH, TILE_SIZE):\\n pg.draw.line(self.screen, LIGHT_GREY, (x, INFO_HEIGHT), (x, HEIGHT))\\n for y in range(INFO_HEIGHT, INFO_HEIGHT + HEIGHT, TILE_SIZE):\\n pg.draw.line(self.screen, LIGHT_GREY, (0, y), (WIDTH, y))\",\n \"def adjustSize( self ):\\n cell = self.scene().cellWidth() * 2\\n minheight = cell\\n minwidth = 2 * cell\\n \\n # fit to the grid size\\n metrics = QFontMetrics(QApplication.font())\\n width = metrics.width(self.displayName()) + 20\\n width = ((width/cell) * cell) + (cell % width)\\n \\n height = self.rect().height()\\n \\n # adjust for the icon\\n icon = self.icon()\\n if icon and not icon.isNull():\\n width += self.iconSize().width() + 2\\n height = max(height, self.iconSize().height() + 2)\\n \\n self.setMinimumWidth(max(width, minwidth))\\n self.setMinimumHeight(max(height, minheight))\\n \\n self.rebuild()\",\n \"def draw_grid(self):\\n if self.grid_center == True:\\n (n, m) = (self.n, self.m)\\n (dx, dy) = (self.dx // 2, self.dy // 2)\\n else:\\n (n, m) = (self.n + 1, self.m + 1)\\n (dx, dy) = (0, 0)\\n\\n x0 = self.x0 + dx\\n y0 = self.y0 + dy\\n\\n # vertical lines\\n for j in range(m):\\n p0 = (x0 + j * self.dx, y0)\\n p1 = (x0 + j * self.dx, y0 + (n-1) * self.dy)\\n pygame.draw.line(self.screen, self.grid_col, p0, p1, self.grid_d) \\n # horizontal lines\\n for i in range(n):\\n p0 = (x0, y0 + i * self.dy)\\n p1 = (x0 + (m-1) * self.dx, y0 + i * self.dy)\\n pygame.draw.line(self.screen, self.grid_col, p0, p1, self.grid_d)\",\n \"def define_grid():\\n grid_left = np.array([[-13.1000000000000, -35.5000000000000, -48.3000000000000, -60, -16.9000000000000,\\n -34.8000000000000, -67.5000000000000, -46.1000000000000, -59.8000000000000,\\n -14.2000000000000, -28.3000000000000, -42.3000000000000, -67.6000000000000,\\n -50.5000000000000, -14.6000000000000, -60.9000000000000, -31.6000000000000,\\n -5.10000000000000, -65.6000000000000, -41.8000000000000, -55.1000000000000,\\n -22.7000000000000, -5.80000000000000, -49.2000000000000, -34.5000000000000,\\n -61.5500000000000, -63.6000000000000, -40.4000000000000, -48.7000000000000,\\n -21.8000000000000, -58.2000000000000, -7, -36.3000000000000, -48.1000000000000,\\n -56.8000000000000, -7.30000000000000, -22.2000000000000, -36.8000000000000,\\n -46.8000000000000],\\n [-67.7000000000000, -60, -55.1000000000000, -51.8000000000000, -51.6000000000000,\\n -49.3000000000000, -47.1000000000000, -43.7000000000000, -39.6000000000000,\\n -39.1000000000000, -31.2000000000000, -30.7000000000000, -30.1000000000000,\\n -24.4000000000000, -22.7000000000000, -18.7000000000000, -16.9000000000000,\\n -12.6000000000000, -10.8000000000000, -10.2000000000000, -4.01000000000000, 1.20000000000000,\\n 2.80000000000000, 3.70000000000000, 3.90000000000000, 6.20000000000000, 8.30000000000000,\\n 11.8000000000000, 14.5000000000000, 16, 18.2000000000000, 18.4000000000000, 19.9000000000000,\\n 24.6000000000000, 28.5200000000000, 33.8000000000000, 35, 35.4000000000000,\\n 35.6000000000000],\\n [69.1000000000000, 66, 58.2000000000000, 48, 78, 71.7000000000000, 31, 61.1000000000000,\\n 53.3000000000000, 81.1000000000000, 76, 70.2000000000000, 41.2000000000000, 64.4000000000000,\\n 80.2000000000000, 50.9000000000000, 75.2000000000000, 77.3000000000000, 37.8000000000000, 67,\\n 53.2000000000000, 72, 74.8000000000000, 54.7000000000000, 66.5000000000000, 35.9000000000000,\\n 25.7000000000000, 60.7000000000000, 50.5000000000000, 68.9000000000000, 27.3000000000000,\\n 70.3000000000000, 59.6000000000000, 44, 20.8000000000000, 61.7000000000000, 57.2000000000000,\\n 47, 36]])\\n stn_left = np.array([[-14.6, -13.2, -11.7, -9.10, -11.7, -13.2, -7.90, -10],\\n [-15.1, -15.1, -15.1, -12.6, -12.6, -12.6, -9.40, -10.1],\\n [-5.40, -7.20, -8.70, -8.70, -7.50, -5.10, -10.3, -7.80]])\\n grid_right = np.copy(grid_left)\\n grid_right[0, :] = grid_right[0, :] * -1\\n stn_right = np.copy(stn_left)\\n stn_right[0, :] = stn_right[0, :] * -1\\n\\n return grid_left, grid_right, stn_left, stn_right\",\n \"def setAxisPageSize(width,height):\\n dislin.axslen(width,height)\",\n \"def create_grid(grid):\\r\\n for i in range(4):\\r\\n grid.append([0,0,0,0])\",\n \"def dimension(self):\\n return 3*self.genus - 3 + self.n\",\n \"def dimensions():\",\n \"def get_grid_shape(num_examples):\\n height = int(numpy.floor(numpy.sqrt(num_examples)))\\n width = int(numpy.ceil(num_examples * 1. / height))\\n\\n return (height, width)\",\n \"def __init__(self, row=4, col=4, initial=2):\\n self.grid = Grid(row, col, initial)\",\n \"def draw_grid(self):\\n plt.imshow(py.array(\\n map(lambda x: map(lambda y: mplc.colorConverter.to_rgb(colord[y]), x), self.create_grid(self.graph))),\\n interpolation='nearest')\\n plt.show()\",\n \"def plot_insertsize():\",\n \"def draw(self):\\n\\n super().draw()\\n \\n self.dim = self.getdim()\\n start_x, start_y, = self.x(), self.y()\\n\\n for y in range(self.r):\\n for x in range(self.c):\\n x_pos, y_pos = start_x + (self.dim * x), start_y + (self.dim * y)\\n self.tiles[y][x].resize(x_pos, y_pos, self.dim, self.dim)\",\n \"def draw_grid(self):\\n for i in range(N * N + 1):\\n color = \\\"blue\\\" if i % N == 0 else \\\"gray\\\"\\n x0 = MARGIN + i * SIDE\\n y0 = MARGIN\\n x1 = MARGIN + i * SIDE\\n y1 = HEIGHT - MARGIN\\n self.canvas.create_line(x0, y0, x1, y1, fill=color)\\n\\n x0 = MARGIN\\n y0 = MARGIN + i * SIDE\\n x1 = WIDTH - MARGIN\\n y1 = MARGIN + i * SIDE\\n self.canvas.create_line(x0, y0, x1, y1, fill=color)\",\n \"def __len__(self):\\n return len(self._grid)\",\n \"def create_grid(size):\\n grid = []\\n for i in range(size):\\n row = ['0']*size\\n grid.append(row)\\n\\n return grid\",\n \"def set_numcells(self, N, radius=50):\\n self._N = N\\n self.create_cells(N)\\n self.connect_cells()\\n self.connect_stim()\",\n \"def initialize(self, frame):\\n self.grid_size = 5\\n\\n Label(frame, text=\\\"Grid Size:\\\").grid(row=0)\\n\\n self.e1 = Scale(frame, from_=self.grid_size, to=25, orient=HORIZONTAL)\\n self.e1.grid(row=0, column=1)\\n\\n return self.e1\",\n \"def create_grid(grid):\\r\\n for i in range(4):\\r\\n grid.append([0]*4)\\r\\n return grid\",\n \"def getMandelbrotSet(self):\\n\\t\\t\\n\\t\\t(x_min, x_max, y_min, y_max) = [i for i in self.extent]\\n\\t\\t(rows, columns) = (self.width*self.dpi, self.width*self.dpi)\\n\\t\\tr_real = np.linspace(x_min, x_max, columns)\\n\\t\\tr_imag = np.linspace(y_max, y_min, rows)\\n\\t\\tresult = np.empty((rows, columns))\\n\\n\\t\\t# used for smoother colors\\n\\t\\thorizon = 2.0**40\\n\\t\\thorizon2 = horizon**2\\n\\t\\tlog_horizon = np.log(np.log(horizon))/np.log(2)\\n\\n\\t\\tfor i in range(rows): # imaginary values\\n\\t\\t\\tfor j in range(columns): # real values\\n\\t\\t\\t\\tresult[i,j] = _determineDivergence(r_real[j], r_imag[i], self.maxIter, horizon2, log_horizon)\\n\\t\\treturn result\",\n \"def grid(self):\\n return self.__grid\",\n \"def dimension(self):\",\n \"def draw_grid(self):\\n self.screen.draw_many_tiles(tile for tile in self.iter_grid_tiles())\\n pass\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7052928","0.6872349","0.6777697","0.65532494","0.6459304","0.6388547","0.63002","0.62486094","0.6168989","0.611555","0.60756975","0.60363597","0.6002739","0.59995365","0.5982655","0.5964983","0.5941414","0.59040725","0.5903051","0.5888311","0.58676386","0.5864753","0.586456","0.586456","0.5860504","0.58587646","0.58474064","0.58474064","0.5833924","0.5828544","0.5825021","0.5825021","0.58175933","0.5781432","0.5774721","0.5774721","0.5774721","0.577056","0.57651013","0.57448363","0.5739852","0.5733512","0.5733512","0.57307","0.57307","0.57165354","0.5711279","0.57087535","0.5705605","0.57043356","0.570041","0.56939113","0.56895244","0.56583846","0.565055","0.5645126","0.5641451","0.5634377","0.56212187","0.56134874","0.56063396","0.5599301","0.5597317","0.5595214","0.55864763","0.55816793","0.55718255","0.5562876","0.55574423","0.5556633","0.5553736","0.55533224","0.55518603","0.5541415","0.5538277","0.5532868","0.553165","0.5528794","0.5520355","0.55201185","0.55120987","0.55096954","0.55037045","0.5499674","0.5493376","0.54914093","0.54894936","0.5486864","0.5480339","0.5477059","0.5475895","0.5473104","0.5466918","0.5464487","0.5454777","0.54513955","0.5450103","0.54468524","0.5446537","0.54464877","0.54455686"],"string":"[\n \"0.7052928\",\n \"0.6872349\",\n \"0.6777697\",\n \"0.65532494\",\n \"0.6459304\",\n \"0.6388547\",\n \"0.63002\",\n \"0.62486094\",\n \"0.6168989\",\n \"0.611555\",\n \"0.60756975\",\n \"0.60363597\",\n \"0.6002739\",\n \"0.59995365\",\n \"0.5982655\",\n \"0.5964983\",\n \"0.5941414\",\n \"0.59040725\",\n \"0.5903051\",\n \"0.5888311\",\n \"0.58676386\",\n \"0.5864753\",\n \"0.586456\",\n \"0.586456\",\n \"0.5860504\",\n \"0.58587646\",\n \"0.58474064\",\n \"0.58474064\",\n \"0.5833924\",\n \"0.5828544\",\n \"0.5825021\",\n \"0.5825021\",\n \"0.58175933\",\n \"0.5781432\",\n \"0.5774721\",\n \"0.5774721\",\n \"0.5774721\",\n \"0.577056\",\n \"0.57651013\",\n \"0.57448363\",\n \"0.5739852\",\n \"0.5733512\",\n \"0.5733512\",\n \"0.57307\",\n \"0.57307\",\n \"0.57165354\",\n \"0.5711279\",\n \"0.57087535\",\n \"0.5705605\",\n \"0.57043356\",\n \"0.570041\",\n \"0.56939113\",\n \"0.56895244\",\n \"0.56583846\",\n \"0.565055\",\n \"0.5645126\",\n \"0.5641451\",\n \"0.5634377\",\n \"0.56212187\",\n \"0.56134874\",\n \"0.56063396\",\n \"0.5599301\",\n \"0.5597317\",\n \"0.5595214\",\n \"0.55864763\",\n \"0.55816793\",\n \"0.55718255\",\n \"0.5562876\",\n \"0.55574423\",\n \"0.5556633\",\n \"0.5553736\",\n \"0.55533224\",\n \"0.55518603\",\n \"0.5541415\",\n \"0.5538277\",\n \"0.5532868\",\n \"0.553165\",\n \"0.5528794\",\n \"0.5520355\",\n \"0.55201185\",\n \"0.55120987\",\n \"0.55096954\",\n \"0.55037045\",\n \"0.5499674\",\n \"0.5493376\",\n \"0.54914093\",\n \"0.54894936\",\n \"0.5486864\",\n \"0.5480339\",\n \"0.5477059\",\n \"0.5475895\",\n \"0.5473104\",\n \"0.5466918\",\n \"0.5464487\",\n \"0.5454777\",\n \"0.54513955\",\n \"0.5450103\",\n \"0.54468524\",\n \"0.5446537\",\n \"0.54464877\",\n \"0.54455686\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":81184,"cells":{"query":{"kind":"string","value":"Colours the grid and returns the image."},"document":{"kind":"string","value":"def get_coloured_grid(self, r1, r2, r3, b1=4, b2=2.5, b3=1):\n r, g, b = np.frompyfunc(self.get_colour(r1, r2, r3, b1, b2, b3), 2, 3)(self.end_z, self.end_step)\n img_array = np.dstack((r, g, b))\n return Image.fromarray(np.uint8(img_array * 255))"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def draw_grid(self):\n plt.imshow(py.array(\n map(lambda x: map(lambda y: mplc.colorConverter.to_rgb(colord[y]), x), self.create_grid(self.graph))),\n interpolation='nearest')\n plt.show()","def make_image(self, save=False):\n\n # image_grid = np.full((self.size_x, self.size_y), '#888888', dtype=str)\n image_grid = np.full((self.size_x, self.size_y, 3), 0, dtype=np.uint8)\n\n # self.grid = np.flip(self.grid, 1)\n\n # self.grid = np.swapaxes(self.grid, 0, 0)\n \"\"\"\n image_grid[self.grid == 0] = 'FFFFFF'\n image_grid[self.grid == 1] = '000000'\n image_grid[self.grid == 2] = '00FF00'\n image_grid[self.grid == 3] = '0000FF'\n image_grid[self.grid == 4] = 'FFFF00'\n image_grid[self.grid == 5] = '00FFFF'\n image_grid[self.grid == 6] = 'FF00FF'\n \"\"\"\n image_grid[self.grid == 0] = (1, 1, 1)\n image_grid[self.grid == 1] = (0, 0, 0)\n image_grid[self.grid == 2] = (1, 0, 1)\n image_grid[self.grid == 3] = (0, 1, 0)\n image_grid[self.grid == 4] = (0, 0, 1)\n image_grid[self.grid == 5] = (0, 1, 1)\n image_grid[self.grid == 6] = (1, 1, 0)\n\n #for ant in self.ants:\n # image_grid[ant.x, ant.y] = (1, 0, 0)\n\n # image_grid = image_grid.swapaxes(0, 1)\n # self.grid = self.grid.swapaxes(0, 1)\n\n\n\n DPI = 100\n width, height = 1000, 1000\n fig = plt.figure(figsize=(width / DPI, height / DPI), dpi=DPI, facecolor='k')\n ax = fig.add_subplot()\n\n plt.axis('equal')\n plt.subplots_adjust(top=1, bottom=0, right=1, left=0, hspace=0, wspace=0)\n\n for y in range(self.size_x):\n for x in range(self.size_y):\n if self.grid[x, y] != 0:\n # Only plot a hexagon if its state is not zero.\n plot_hex(ax, x, y, image_grid[x, y])\n\n ax.set_xlim(0, self.size_x)\n ax.set_ylim(0, self.size_y)\n\n plt.show()\n\n logging.info(\"Finished Image Processing\")","def test_grdimage(grid):\n fig = Figure()\n fig.grdimage(grid, cmap=\"earth\", projection=\"W0/6i\")\n return fig","def get_image(self):\n image = Image.new('1', (8, 16))\n draw = ImageDraw.Draw(image)\n for x in xrange(8):\n for y in xrange(16):\n draw.point((x,y),self.get_pixel(x, y))\n return image","def drawImage(self):\n\n image = Image.new(\"RGB\",self.size,(255,255,255)) #new() passes parameters mode,size,color\n #255,255,255 is white\n\n canvas = ImageDraw.Draw(image)\n for g in self.genes:\n color = (g.color.r,g.color.g,g.color.b)\n\n canvas.ellipse((g.pos.x-int(g.diameter/2),g.pos.y-int(g.diameter/2), g.pos.x+int(g.diameter/2), g.pos.y+int(g.diameter/2)), fill = color)\n return image","def colour_image(self):\n for row_cnt, colour_row in enumerate(self.table_colour):\n self.processed = []\n for column_cnt, colour in enumerate(colour_row):\n self.row_cnt = row_cnt + 3\n self.column_cnt = column_cnt\n self.colour = colour\n self.word = self.original_rows[self.row_cnt - 3][self.column_cnt]\n self.colour_specific_word()","def _generate_images(self, trace):\n images = []\n colors = []\n colors_by_shape = {}\n for board in trace:\n width = int(round((float(board.shape[1]) / board.shape[0]) * self._height))\n cellsize = width / board.shape[1] # cell size\n img = np.zeros((self._height, width, 3), dtype=np.uint8)\n\n tiles = {} # map from integer rep. of the tile to a shape\n for y in range(board.shape[0]):\n for x in range(board.shape[1]):\n cell = board[y,x]\n if cell not in tiles:\n tiles[cell] = (x, y, 1, 1) # x, y, w, h\n else:\n cur_x, cur_y, cur_w, cur_h = tiles[cell]\n if x >= cur_x + cur_w:\n cur_w = (x-cur_x) + 1\n if y >= cur_y + cur_h:\n cur_h = (y-cur_y) + 1\n tiles[cell] = (cur_x, cur_y, cur_w, cur_h)\n\n # Colors\n if len(colors_by_shape) == 0:\n for tid in tiles:\n shape = (tiles[tid][2], tiles[tid][3])\n if shape not in colors_by_shape:\n colors_by_shape[shape] = hex_to_rgb(random_unique_color(colors))\n colors.append(colors_by_shape[shape])\n\n for tid in tiles:\n x, y, w, h = tiles[tid]\n shape = (w,h)\n empty = board[y,x] == 0\n x, y, w, h = x*cellsize, y*cellsize, w*cellsize, h*cellsize\n # Draw a filled rectangle without color\n if not empty:\n cv2.rectangle(img, (x, y), (x+w, y+h), colors_by_shape[shape],-1)\n else:\n cv2.rectangle(img, (x, y), (x+w, y+h), [0,0,0], -1) #, 8)-\n # Draw a boundary\n cv2.rectangle(img, (x, y), (x+w, y+h), (0, 0, 0), 2, 8)\n \n images.append(img)\n return images","def draw_grid(self) -> None:\n grid = self.life.curr_generation\n for row in range(self.cell_height):\n for column in range(self.cell_width):\n if grid[row][column] == 1:\n color = \"green\"\n else:\n color = \"white\"\n pygame.draw.rect(\n self.screen,\n pygame.Color(color),\n (column * self.cell_size, row * self.cell_size, self.cell_size, self.cell_size),\n )","def generate_image(self):\n\n if not has_pillow:\n raise RuntimeError(\"requires https://pypi.org/project/pillow/\")\n\n background = self.get_background()\n foreground = self.get_foreground()\n\n matrix = self.generate_matrix()\n\n image = Image.new(\"RGB\", (420, 420), background)\n draw = ImageDraw.Draw(image)\n\n for (i, row) in enumerate(matrix):\n for (j, bit) in enumerate(row):\n x = 35 + j * 70\n y = 35 + i * 70\n\n if bit:\n draw.rectangle((x, y, x + 70, y + 70), foreground)\n\n return image","def paint(self, grid):\n grid[self.y, self.x] = self.appearance\n return grid","def draw_grid(self):\n\n screen.fill(GREY)\n\n for row in self.grid:\n for cell in row:\n if cell.root:\n color = GREEN\n elif cell.goal:\n color = RED\n elif cell.value:\n color = DARK_BLUE\n elif cell.visited:\n color = LIGHT_BLUE\n elif cell.f:\n color = LIGHT_GREEN\n elif cell.wall:\n color = GRAY\n else:\n color = WHITE\n\n pygame.draw.rect(screen, color, cell.rect)\n\n x, y = cell.rect.x, cell.rect.y\n\n if cell.g:\n self.draw_score(x + 2, y + 2, cell.g)\n if cell.h:\n self.draw_score(x + 18, y + 2, cell.h)\n if cell.f:\n self.draw_score(x + 2, y + self.cell_size - 10, cell.f)","def graphical_output(self, block=False, save=False, display=True, folder_name='output', image_suffix=None):\n masks = [np.where(self.board == i, True, False) for i in range(self.number_of_colors)]\n output = np.zeros((self.height, self.width, 3), dtype=np.int)\n for mask, color in zip(masks, list(COLORS.values())[:self.number_of_colors]):\n output[mask] = color\n\n image = np.repeat(np.repeat(output, 10, axis=0), 10, axis=1)/255.0\n if save:\n plt.imsave(f'{folder_name}/image{image_suffix}.png', image)\n\n if display:\n plt.imshow(image)\n plt.show(block=block)\n\n return image","def render_image(grid,window):\r\n X = len(grid[0])\r\n Y = len(grid)\r\n#top row:\r\n for j in range(Y):\r\n for sub_j in range(3): #3 rows \r\n ROW = []\r\n for i in range(X):\r\n ROW += grid[j][i].arr[sub_j]\r\n \r\n for k in range(len(ROW)):\r\n COLOR = (ROW[k],ROW[k],ROW[k])\r\n Y_pos = (3*j + sub_j)*pixel_size*scale\r\n X_pos = k*(pixel_size)*scale\r\n width = height = pixel_size*scale\r\n pygame.draw.rect(window,COLOR,(X_pos,Y_pos,width,height))\r\n \r\n# print(ROW)\r\n return","def Green_func(self):\n if self.bc == True:\n size = self.grid_size\n else:\n size = 2*self.grid_size\n self.Green = np.zeros([size, size])\n for x in range(len(self.Green[0])):\n for y in range(len(self.Green[1])):\n radius = np.sqrt(x**2 + y**2) \n if radius < self.soften: \n radius = self.soften\n self.Green[x, y]=1/(4 * np.pi * radius)\n if self.grid_size%2 == 0: \n self.Green[: size//2, size//2 : ] = np.flip(self.Green[: size//2, : size//2], axis = 1) # an intermittent step - the original grid has only been flipped once (2 x the original size)\n self.Green[ size//2 : , :] = np.flip(self.Green[: size//2, :], axis = 0)\n else: \n print(\"Exiting - Grid size is currently odd. Pleaset set to an even value.\")","def generate_image(self):\n\t\tcenters = self.generate_centers()\n\t\timg = Image.new('RGB', (self.config.image_size, self.config.image_size), color=(0,0,0))\n\t\tshapes = np.random.randint(2, size=len(centers))\n\t\tdrawer = ImageDraw.Draw(img)\n\t\tr = int(0.05 * self.config.image_size)\n\t\tR = []\n\t\tfor i in range(len(centers)):\n\t\t\tcoor = (centers[i][0] - r , centers[i][1] - r, centers[i][0] + r, centers[i][1] + r)\n\t\t\tif shapes[i] < 0.5:\n\t\t\t\tdrawer.rectangle(coor, fill=COLOR[i])\n\t\t\telse:\n\t\t\t\tdrawer.ellipse(coor, fill=COLOR[i])\n\t\t\tR.append([centers[i], i, shapes[i]])\n\t\treturn np.array(img), R","def image_from_grid(self, grid: np.ndarray) -> np.ndarray:\r\n if self.light_profiles is not None:\r\n return sum(map(lambda p: p.image_from_grid(grid=grid), self.light_profiles))\r\n return np.zeros((grid.shape[0],))","def export_image(self, name):\n\t\tred = Color(\"red\")\n\t\tblue = Color(\"blue\")\n\t\twhite = Color(\"white\")\n\t\tblack = Color(\"black\")\n\t\tgold = Color(\"gold\")\n\t\trgb_gold = []\n\t\tfor part in gold.rgb:\n\t\t\tpart = part * 255\n\t\t\trgb_gold.append(part)\n\t\trgb_black = []\n\t\tfor part in black.rgb:\n\t\t\tpart = part * 255\n\t\t\trgb_black.append(part)\n\t\trgb_white = []\n\t\tfor part in white.rgb:\n\t\t\tpart = part * 255\n\t\t\trgb_white.append(part)\n\t\tcolours = list(red.range_to(blue, int(self.grains)))\n\t\timage = np.zeros([self.space.shape[1],self.space.shape[0], 3], dtype=np.uint(8))\n\t\tfor grain in range(self.grains+1):\n\t\t\trgb = []\n\t\t\tfor part in colours[grain-1].rgb:\n\t\t\t\tpart = part * 255\n\t\t\t\trgb.append(part)\n\t\t\tfor cell in self.space.flat:\n\t\t\t\tif cell.state == grain:\n\t\t\t\t\tx,y = cell.find_id()\n\t\t\t\t\timage[x,y] = rgb\n\t\t\t\tif cell.state == 999:\n\t\t\t\t\tx,y = cell.find_id()\n\t\t\t\t\timage[x,y] = rgb_black\n\t\t\t\tif cell.state == 500:\n\t\t\t\t\tx,y = cell.find_id()\n\t\t\t\t\timage[x,y] = rgb_gold\n\t\timg = Image.fromarray(image.astype('uint8'))\n\t\timg = img.resize((self.space.shape[1]*3,self.space.shape[0]*3))\n\t\timg.save('./static/temp/'+str(name)+'.png')","def toImage(cmObject):\n size = self.gridsize, self.gridsize\n cm = cmObject()\n master = []\n for item in cm:\n master.extend(item)\n\n #Apply default Mandelbrot Set Function\n master = map(f, master)\n\n col1 = (0,0,102,0)\n col2 = (255,204,51,0)\n\n def select_color(x):\n if x == 1: return col1\n else: return col2\n\n master = map(select_color, master)\n \n image = Image.new(\"RGBA\", size, (0,0,0,0))\n image.putdata(master)\n return image","def draw_grid(self, darken=1):\n if not(0 < darken < 1):\n darken = 1\n for x in range(0, int(self.settings['grid_size'])):\n for y in range(0, int(self.settings['grid_size'])):\n if self.grid[x][y] == g.EMPTY:\n if (x + y) % 2 == 0:\n r = pygame.Rect((x * self.block_width, y * self.block_height),\n (self.block_width, self.block_width))\n pygame.draw.rect(self.surface, (93 * darken, 216 * darken, 228 * darken), r)\n else:\n rr = pygame.Rect((x * self.block_width, y * self.block_width),\n (self.block_width, self.block_width))\n pygame.draw.rect(self.surface, (84 * darken, 194 * darken, 205 * darken), rr)\n elif self.grid[x][y] == g.WALL:\n rr = pygame.Rect((x * self.block_width, y * self.block_width), (self.block_width, self.block_width))\n pygame.draw.rect(self.surface, (175 * darken, 34 * darken, 6 * darken), rr)\n elif self.grid[x][y] == g.PLAYER:\n r = pygame.Rect((x * self.block_width, y * self.block_height),\n (self.block_width, self.block_height))\n pygame.draw.rect(self.surface, (17 * darken, 24 * darken, 47 * darken), r)\n pygame.draw.rect(self.surface, (93, 216, 228), r, 1)\n elif self.grid[x][y] == g.FOOD:\n r = pygame.Rect((x * self.block_width, y * self.block_height),\n (self.block_width, self.block_height))\n pygame.draw.rect(self.surface, (223 * darken, 163 * darken, 49 * darken), r)\n pygame.draw.rect(self.surface, (93, 216, 228), r, 1)","def draw_grid(self):\n buf = self.__hbar\n for rInd in range(self.row):\n line = '\\t|'\n for cInd in range(self.col):\n this = ((rInd * self.col) + cInd)\n cell = self.get_cell(this)\n if not cell:\n line += '%s|' % ' '.center(5)\n else:\n if this == self.new_cell:\n tmp = green(str(cell).center(5))\n else:\n tmp = str(cell).center(5)\n line += '%s|' % tmp\n buf += line + '\\n' + self.__hbar\n print(buf)","def create_colorful_test_image(self):\n ch255 = np.full([100, 200, 1], 255, dtype=np.uint8)\n ch128 = np.full([100, 200, 1], 128, dtype=np.uint8)\n ch0 = np.full([100, 200, 1], 0, dtype=np.uint8)\n imr = np.concatenate((ch255, ch128, ch128), axis=2)\n img = np.concatenate((ch255, ch255, ch0), axis=2)\n imb = np.concatenate((ch255, ch0, ch255), axis=2)\n imw = np.concatenate((ch128, ch128, ch128), axis=2)\n imu = np.concatenate((imr, img), axis=1)\n imd = np.concatenate((imb, imw), axis=1)\n image = np.concatenate((imu, imd), axis=0)\n return image","def rgb_image(self):\n z3 = self.z[:,:,newaxis]\n return z3 * self.c","def draw_grid(self) -> None:\n for x in range(0, WIDTH, TILE_SIZE):\n pg.draw.line(self.screen, LIGHT_GREY, (x, INFO_HEIGHT), (x, HEIGHT))\n for y in range(INFO_HEIGHT, INFO_HEIGHT + HEIGHT, TILE_SIZE):\n pg.draw.line(self.screen, LIGHT_GREY, (0, y), (WIDTH, y))","def draw_gray_grid(self):\n gray = \"#D3D3D3\"\n # Draw the vertical lines\n for x in range(0, self.width, self.scale):\n self.canvas.create_line(x, 0, x, self.height, fill=gray)\n\n # Draw the horizontal lines\n for y in range(0, self.height, self.scale):\n self.canvas.create_line(0, y, self.width, y, fill=gray)","def draw_grid(self):\n for x in range(0, WIDTH, TILESIZE):\n pg.draw.line(self.screen, LIGHTGREY, (x, 0), (x, HEIGHT))\n \n for y in range(0, HEIGHT, TILESIZE):\n pg.draw.line(self.screen, LIGHTGREY, (0, y), (WIDTH, y))","def colorfull_house(self):\n image = self.__image.copy()\n palette_path = self.get_image_path(\"color_palette.png\")\n self.color_palette = cv.resize(cv.imread(palette_path, 1),(598,245))\n self.pixel_picked = np.array([0,0,0])\n\n cv.namedWindow(\"Color Palette\")\n cv.setMouseCallback(\"Color Palette\", self.click_mouse_callback)\n cv.imshow(\"Color Palette\", self.color_palette)\n\n while (True):\n for i in iter(range(self.__rows)):\n for j in iter(range(self.__cols)):\n if self.segmentation_condition(i,j):\n image[i,j] = self.pixel_picked\n \n cv.imshow(\"Colorfull House\",image)\n if (cv.waitKey(1) & 0xFF == ord('q')):\n break","def _tile_image(self, data):\n image = Image.open(StringIO(data))\n return image.convert('RGBA')","def setColors(self):\r\n # productive\r\n profprint()\r\n self.color = [[0, 0, 0] for i in range(MAXCOL)]\r\n self.color255 = self.setColors255()\r\n for i in range(MAXCOL):\r\n for j in range(3):\r\n self.color[i][j] = self.color255[i][j] / float(255)\r\n\r\n return self.color","def _prepare_grid(self):\n draw_grid = list()\n for x in range(len(self._grid) + len(self._grid) + 1):\n if x % 2 == 0:\n draw_grid.append([self._walk_area_color if x % 2 != 0 else self._wall_color\n for x in range(len(self._grid) + len(self._grid) + 1)])\n else:\n draw_grid.append([self._walk_area_color\n for _ in range(len(self._grid) + len(self._grid) + 1)])\n\n draw_grid = self._draw_walls(draw_grid)\n draw_grid = self._draw_treasures(draw_grid)\n draw_grid = self._draw_border(draw_grid)\n return draw_grid","def draw_grid(self, tile_img, tiles):\n #debug_print(\"drawing level\", data)\n img = Surface((self.xsize * SIZE, self.ysize * SIZE))\n for pos, char in self:\n rect = get_tile_rect(pos)\n img.blit(tile_img, rect, tiles[char])\n return img","def check_color(i, j, k):\n img.show()\n image = Image.new(\"RGB\", (200, 200), (int(Y), int(Y), int(Y)))\n image.show()\n image = Image.new(\"RGB\", (200, 200), (int(i), int(j), int(k)))\n image.show()","def drawImage(self):\n \n self.retrieveThemeConfig()\n \n self.initImg()\n self.colsDone = 1\n \n queue = mp.Queue()\n procs = []\n for i in range(0,self.strandNum):\n procs.append(mp.Process(target=self.strandWorker,args=(queue,i,\n self.branches)))\n procs[i].start()\n\n self.allCords = []\n for i in range(0,self.strandNum):\n self.allCords.append(queue.get())\n \n logging.info(\"Strand workers finished\")\n\n self.curCol = 0\n for k in range(0,len(self.allCords)):\n for l in range(0,len(self.allCords[k])):\n for m in range(0,len(self.allCords[k][l]['cords'])):\n self.draw.line(self.allCords[k][l]['cords'][m],\n self.allCords[k][l]['col'],self.thickness)\n del self.draw\n self.exportImg()","def setColors(self):\n #productive\n profprint()\n self.color= [[0,0,0] for i in range(205)]\n self.color255= self.setColors255()\n for i in range(205):\n for j in range(3):\n self.color[i][j] = self.color255[i][j]/float(255)\n\n return self.color","def render_environment(self):\n board_img = make_img_of_board(*self.hist[-1])\n return board_img","def draw_board(grid, scale, path):\n\n def color_map(tile):\n return COLORS[tile.terrain]\n\n draw_hex_grid(grid, scale, color_map, path, draw_components)","def get_image(self):\n img = cv2.cvtColor(self.coo.get_image(), cv2.COLOR_GRAY2RGB)\n\n for container in self.containers:\n x, y, w, h = container # unpacking\n cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 5)\n\n for coverslip in self.coverslips:\n center = (coverslip[0], coverslip[1])\n # circle center\n cv2.circle(img, center, 3, (255, 0, 0), 5)\n # circle outline\n radius = coverslip[2]\n cv2.circle(img, center, radius, (255, 0, 0), 5)\n\n return img","def show(self):\n data = []\n for row in self.grid:\n mid, bottom = [], []\n for node in row:\n \tmid += [0, int(node.right)]\n \tbottom += [int(node.down), 1]\n data += mid + [0] + bottom + [0] \n data[self.width*2+1] = 1\n data[-1] = 1\n data += (self.width*2) * [0]\n im = Image.new('1', (self.width*2+1, self.height*2+1))\n im.putdata(data)\n im.save('maze.png')\n im.show()","def _make_image(self):\n self.image.fill(0)\n self.image[:, :, PillEater.WALLS] = self.walls\n self.image[:, :, PillEater.FOOD] = self.world_state['food']\n self.image[self.world_state['pillman']['pos'][0], self.world_state['pillman']['pos'][1],\n PillEater.PILLMAN] = 1\n for ghost in self.world_state['ghosts']:\n edibility = self.world_state['power'] / float(self.pill_duration)\n self.image[ghost['pos'][0], ghost['pos'][1], PillEater.GHOSTS] = 1. - edibility\n self.image[ghost['pos'][0], ghost['pos'][1], PillEater.GHOSTS_EDIBLE] = edibility\n for pill in self.world_state['pills']:\n self.image[pill['pos'][0], pill['pos'][1], PillEater.PILL] = 1\n return self.image","def _toRgbImage(self, fractal, colors, color_offset):\n soln_real = adjustRange(fractal[0], 0, 127)\n soln_imag = adjustRange(fractal[1], 0, 127)\n iters = adjustRange(fractal[2], 0, 128)\n\n rgb_image = np.array([\n soln_real + iters,\n soln_imag + iters,\n iters\n ]\n ).astype(dtype=np.uint8)\n\n return rgb_image.T","def makeImage(self):\n\n for row in range(self.height):\n self.makeRow(row)\n self.window.update() # display a row of pixels","def draw_grid(self):\n\n # Draw horizontal lines\n for row in range(self.num_rows + 1):\n left = row_column_to_pixels(row, 0)\n right = row_column_to_pixels(row, self.num_cols)\n pygame.draw.line(self.screen, COLOR_MAP['gray'], left, right)\n\n # Draw vertical lines\n for col in range(self.num_cols + 1):\n top = row_column_to_pixels(0, col)\n bottom = row_column_to_pixels(self.num_rows, col)\n pygame.draw.line(self.screen, COLOR_MAP['gray'], top, bottom)","def _show_rgb(self):\n R, G, B = self._rgb_frames()\n image = numpy.dstack((R, G, B))\n imageItem = self.parent.image.getImageItem()\n imageItem.updateImage(image)","def grid_image(output):\n grid = []\n for data in output:\n grid += [make_grid(data, nrow=5, normalize=True)]\n return grid","def draw_board(surface, board, player_number):\n grid_img = pygame.image.load('images/grid.png')\n surface.blit(grid_img, (0, 0))\n for row in range(len(board.get_grid())):\n for col in range(len(board.get_grid()[0])):\n x = col * TILE_SIZE\n y = row * TILE_SIZE\n tile = board.get_coord(row, col)\n if tile is not None:\n draw_tile(surface, tile, x, y, castle_color=('images/' + CASTLES[player_number]))","def draw(self):\n self.draw_occupied_cells()\n self.draw_open_cells()\n self.draw_edges()\n plt.xlabel(\"Red\")\n plt.ylabel(\"Black\")\n plt.title('Hex')\n self.camera.snap()","def create_image(self):\n\n self._image = 255 * np.ones((self._height, self._width, 3), np.uint8)","def __generate_image(self):\n\t\tself.img = np.ones((self.size*self.width+self.border,self.size*self.width+self.border,1), np.uint8)*255\n\t\tfor i in range(len(self.matrix)):\n\t\t\tfor j in range(len(self.matrix)):\n\t\t\t\tif self.matrix[j][i] == 1:\n\t\t\t\t\tself.img = cv2.rectangle(self.img,(i*self.width+int(self.border/2),j*self.width+int(self.border/2))\n\t\t\t\t\t\t,(i*self.width+self.width+int(self.border/2),j*self.width+self.width+int(self.border/2)),(0,0,0),-1)\n\t\tif '.' in self.name:\n\t\t\tcv2.imwrite(self.name,self.img)\n\t\telse:\n\t\t\tcv2.imwrite(self.name+'.jpg',self.img)\n\t\tcv2.imshow(\"Image\",self.img)\n\t\tcv2.waitKey(0)\n\t\tcv2.destroyAllWindows()","def draw_grid(self):\n self.screen.draw_many_tiles(tile for tile in self.iter_grid_tiles())\n pass","def colors(self):\n\t\treturn [(0, 30, 255),(0, 30, 120)]","def to_color(self):\n if self.channels == 4:\n color = opencv.cvtColor(self.img, opencv.COLOR_BGRA2BGR)\n return Image(color)\n elif self.channels == 1:\n color = opencv.cvtColor(self.img, opencv.COLOR_GRAY2BGR)\n return Image(color)\n else:\n return Image(self.img)","def test_grdimage_file():\n fig = Figure()\n fig.grdimage(\n \"@earth_relief_01d_g\",\n cmap=\"ocean\",\n region=[-180, 180, -70, 70],\n projection=\"W0/10i\",\n shading=True,\n )\n return fig","def _toRgbImage(self, fractal, colors, color_offset):\n hsv_img = np.array(\n [\n # Cycle through color wheel.\n (fractal * colors + color_offset) % 1,\n\n # Saturation = 1 where fractal values > 0,\n # Saturation = 0 otherwise.\n fractal.astype(dtype=bool).astype(dtype=float),\n\n # Invert colours\n 1 - fractal\n ]\n ).astype(dtype=float).T\n\n rgb_img = (mpl.colors.hsv_to_rgb(hsv_img) * 255).astype(dtype=np.uint8)\n return rgb_img","def draw_board(self, board: BoardModel):\n self._picture=[]\n self._board = board\n self.delete(tk.ALL)\n\n for i in range(self._grid_size):\n for j in range(self._grid_size):\n char = self._board.get_game()[self.position_to_index((j, i), self._grid_size)]\n x1 = i * 60\n y1 = j * 60\n x2 = x1 + 60\n y2 = y1 + 60\n\n #insert the image of unexposed cell\n if char == UNEXPOSED:\n photo=tk.PhotoImage(file=\"./images/unrevealed.gif\")\n\n\n # insert the image of exposed cell\n elif char == EXPOSED:\n photo=tk.PhotoImage(file=\"./images/zero_adjacent.gif\")\n\n # insert the image of cell of pokemon\n elif char == POKEMON:\n pokemon_list=[\"./images/pokemon_sprites/charizard.gif\",\n \"./images/pokemon_sprites/cyndaquil.gif\",\n \"./images/pokemon_sprites/pikachu.gif\",\n \"./images/pokemon_sprites/psyduck.gif\",\n \"./images/pokemon_sprites/togepi.gif\"]\n a=random.choice(pokemon_list)\n photo = tk.PhotoImage(file=a)\n print(a)\n\n # insert the image of cell of digit\n elif char.isdigit():\n if char==\"1\":\n photo = tk.PhotoImage(file=\"./images/one_adjacent.gif\")\n\n elif char==\"2\":\n photo = tk.PhotoImage(file=\"./images/two_adjacent.gif\")\n\n elif char==\"3\":\n photo = tk.PhotoImage(file=\"./images/three_adjacent.gif\")\n\n elif char==\"4\":\n photo = tk.PhotoImage(file=\"./images/four_adjacent.gif\")\n\n elif char==\"5\":\n photo = tk.PhotoImage(file=\"./images/five_adjacent.gif\")\n\n elif char==\"6\":\n photo = tk.PhotoImage(file=\"./images/six_adjacent.gif\")\n\n elif char==\"7\":\n photo = tk.PhotoImage(file=\"./images/seven_adjacent.gif\")\n\n else:\n photo = tk.PhotoImage(file=\"./images/eight_adjacent.gif\")\n\n # insert the image of pokeball\n elif char == FLAG:\n photo = tk.PhotoImage(file=\"./images/pokeball.gif\")\n\n self.create_image(x1 + 60 / 2, y1 + 60 / 2, image=photo)\n self._picture.append(photo)\n\n self.bind_clicks()","def setColors255(self):\r\n # productive\r\n profprint()\r\n self.color255 = [[0, 0, 0] for i in range(MAXCOL)]\r\n self.color255[0] = [221, 108, 158]\r\n self.color255[1] = [128, 174, 128]\r\n self.color255[2] = [241, 214, 145]\r\n self.color255[3] = [177, 122, 101]\r\n self.color255[4] = [111, 184, 210]\r\n self.color255[5] = [216, 101, 79]\r\n self.color255[6] = [221, 130, 101]\r\n self.color255[7] = [144, 238, 144]\r\n self.color255[8] = [192, 104, 88]\r\n self.color255[9] = [220, 245, 20]\r\n self.color255[10] = [78, 63, 0]\r\n self.color255[11] = [255, 250, 220]\r\n self.color255[12] = [230, 220, 70]\r\n self.color255[13] = [200, 200, 235]\r\n self.color255[14] = [250, 250, 210]\r\n self.color255[15] = [244, 214, 49]\r\n self.color255[16] = [0, 151, 206]\r\n self.color255[17] = [183, 156, 220]\r\n self.color255[18] = [183, 214, 211]\r\n self.color255[19] = [152, 189, 207]\r\n self.color255[20] = [178, 212, 242]\r\n self.color255[21] = [68, 172, 100]\r\n self.color255[22] = [111, 197, 131]\r\n self.color255[23] = [85, 188, 255]\r\n self.color255[24] = [0, 145, 30]\r\n self.color255[25] = [214, 230, 130]\r\n self.color255[26] = [218, 255, 255]\r\n self.color255[27] = [170, 250, 250]\r\n self.color255[28] = [140, 224, 228]\r\n self.color255[29] = [188, 65, 28]\r\n self.color255[30] = [216, 191, 216]\r\n self.color255[31] = [145, 60, 66]\r\n self.color255[32] = [150, 98, 83]\r\n self.color255[33] = [250, 250, 225]\r\n self.color255[34] = [200, 200, 215]\r\n self.color255[35] = [68, 131, 98]\r\n self.color255[36] = [83, 146, 164]\r\n self.color255[37] = [162, 115, 105]\r\n self.color255[38] = [141, 93, 137]\r\n self.color255[39] = [182, 166, 110]\r\n self.color255[40] = [188, 135, 166]\r\n self.color255[41] = [154, 150, 201]\r\n self.color255[42] = [177, 140, 190]\r\n self.color255[43] = [30, 111, 85]\r\n self.color255[44] = [210, 157, 166]\r\n self.color255[45] = [48, 129, 126]\r\n self.color255[46] = [98, 153, 112]\r\n self.color255[47] = [69, 110, 53]\r\n self.color255[48] = [166, 113, 137]\r\n self.color255[49] = [122, 101, 38]\r\n self.color255[50] = [253, 135, 192]\r\n self.color255[51] = [145, 92, 109]\r\n self.color255[52] = [46, 101, 131]\r\n self.color255[53] = [0, 108, 112]\r\n self.color255[54] = [127, 150, 88]\r\n self.color255[55] = [159, 116, 163]\r\n self.color255[56] = [125, 102, 154]\r\n self.color255[57] = [106, 174, 155]\r\n self.color255[58] = [154, 146, 83]\r\n self.color255[59] = [126, 126, 55]\r\n self.color255[60] = [201, 160, 133]\r\n self.color255[61] = [78, 152, 141]\r\n self.color255[62] = [174, 140, 103]\r\n self.color255[63] = [139, 126, 177]\r\n self.color255[64] = [148, 120, 72]\r\n self.color255[65] = [186, 135, 135]\r\n self.color255[66] = [99, 106, 24]\r\n self.color255[67] = [156, 171, 108]\r\n self.color255[68] = [64, 123, 147]\r\n self.color255[69] = [138, 95, 74]\r\n self.color255[70] = [97, 113, 158]\r\n self.color255[71] = [126, 161, 197]\r\n self.color255[72] = [194, 195, 164]\r\n self.color255[73] = [88, 106, 215]\r\n self.color255[74] = [82, 174, 128]\r\n self.color255[75] = [57, 157, 110]\r\n self.color255[76] = [60, 143, 83]\r\n self.color255[77] = [92, 162, 109]\r\n self.color255[78] = [255, 244, 209]\r\n self.color255[79] = [201, 121, 77]\r\n self.color255[80] = [70, 163, 117]\r\n self.color255[81] = [188, 91, 95]\r\n self.color255[82] = [166, 84, 94]\r\n self.color255[83] = [182, 105, 107]\r\n self.color255[84] = [229, 147, 118]\r\n self.color255[85] = [174, 122, 90]\r\n self.color255[86] = [201, 112, 73]\r\n self.color255[87] = [194, 142, 0]\r\n self.color255[88] = [241, 213, 144]\r\n self.color255[89] = [203, 179, 77]\r\n self.color255[90] = [229, 204, 109]\r\n self.color255[91] = [255, 243, 152]\r\n self.color255[92] = [209, 185, 85]\r\n self.color255[93] = [248, 223, 131]\r\n self.color255[94] = [255, 230, 138]\r\n self.color255[95] = [196, 172, 68]\r\n self.color255[96] = [255, 255, 167]\r\n self.color255[97] = [255, 250, 160]\r\n self.color255[98] = [255, 237, 145]\r\n self.color255[99] = [242, 217, 123]\r\n self.color255[100] = [222, 198, 101]\r\n self.color255[101] = [213, 124, 109]\r\n self.color255[102] = [184, 105, 108]\r\n self.color255[103] = [150, 208, 243]\r\n self.color255[104] = [62, 162, 114]\r\n self.color255[105] = [242, 206, 142]\r\n self.color255[106] = [250, 210, 139]\r\n self.color255[107] = [255, 255, 207]\r\n self.color255[108] = [182, 228, 255]\r\n self.color255[109] = [175, 216, 244]\r\n self.color255[110] = [197, 165, 145]\r\n self.color255[111] = [172, 138, 115]\r\n self.color255[112] = [202, 164, 140]\r\n self.color255[113] = [224, 186, 162]\r\n self.color255[114] = [255, 245, 217]\r\n self.color255[115] = [206, 110, 84]\r\n self.color255[116] = [210, 115, 89]\r\n self.color255[117] = [203, 108, 81]\r\n self.color255[118] = [233, 138, 112]\r\n self.color255[119] = [195, 100, 73]\r\n self.color255[120] = [181, 85, 57]\r\n self.color255[121] = [152, 55, 13]\r\n self.color255[122] = [159, 63, 27]\r\n self.color255[123] = [166, 70, 38]\r\n self.color255[124] = [218, 123, 97]\r\n self.color255[125] = [225, 130, 104]\r\n self.color255[126] = [224, 97, 76]\r\n self.color255[127] = [184, 122, 154]\r\n self.color255[128] = [211, 171, 143]\r\n self.color255[129] = [47, 150, 103]\r\n self.color255[130] = [173, 121, 88]\r\n self.color255[131] = [188, 95, 76]\r\n self.color255[132] = [255, 239, 172]\r\n self.color255[133] = [226, 202, 134]\r\n self.color255[134] = [253, 232, 158]\r\n self.color255[135] = [244, 217, 154]\r\n self.color255[136] = [205, 179, 108]\r\n self.color255[137] = [186, 124, 161]\r\n self.color255[138] = [255, 255, 220]\r\n self.color255[139] = [234, 234, 194]\r\n self.color255[140] = [204, 142, 178]\r\n self.color255[141] = [180, 119, 153]\r\n self.color255[142] = [216, 132, 105]\r\n self.color255[143] = [255, 253, 229]\r\n self.color255[144] = [205, 167, 142]\r\n self.color255[145] = [204, 168, 143]\r\n self.color255[146] = [255, 224, 199]\r\n self.color255[147] = [139, 150, 98]\r\n self.color255[148] = [249, 180, 111]\r\n self.color255[149] = [157, 108, 162]\r\n self.color255[150] = [203, 136, 116]\r\n self.color255[151] = [185, 102, 83]\r\n self.color255[152] = [247, 182, 164]\r\n self.color255[153] = [222, 154, 132]\r\n self.color255[154] = [124, 186, 223]\r\n self.color255[155] = [249, 186, 150]\r\n self.color255[156] = [244, 170, 147]\r\n self.color255[157] = [255, 181, 158]\r\n self.color255[158] = [255, 190, 165]\r\n self.color255[159] = [227, 153, 130]\r\n self.color255[160] = [213, 141, 113]\r\n self.color255[161] = [193, 123, 103]\r\n self.color255[162] = [216, 146, 127]\r\n self.color255[163] = [230, 158, 140]\r\n self.color255[164] = [245, 172, 147]\r\n self.color255[165] = [241, 172, 151]\r\n self.color255[166] = [177, 124, 92]\r\n self.color255[167] = [171, 85, 68]\r\n self.color255[168] = [217, 198, 131]\r\n self.color255[169] = [212, 188, 102]\r\n self.color255[170] = [185, 135, 134]\r\n self.color255[171] = [198, 175, 125]\r\n self.color255[172] = [194, 98, 79]\r\n self.color255[173] = [255, 238, 170]\r\n self.color255[174] = [206, 111, 93]\r\n self.color255[175] = [216, 186, 0]\r\n self.color255[176] = [255, 226, 77]\r\n self.color255[177] = [255, 243, 106]\r\n self.color255[178] = [255, 234, 92]\r\n self.color255[179] = [240, 210, 35]\r\n self.color255[180] = [224, 194, 0]\r\n self.color255[181] = [213, 99, 79]\r\n self.color255[182] = [217, 102, 81]\r\n self.color255[183] = [0, 147, 202]\r\n self.color255[184] = [0, 122, 171]\r\n self.color255[185] = [186, 77, 64]\r\n self.color255[186] = [240, 255, 30]\r\n self.color255[187] = [185, 232, 61]\r\n self.color255[188] = [0, 226, 255]\r\n self.color255[189] = [251, 159, 255]\r\n self.color255[190] = [230, 169, 29]\r\n self.color255[191] = [0, 194, 113]\r\n self.color255[192] = [104, 160, 249]\r\n self.color255[193] = [221, 108, 158]\r\n self.color255[194] = [137, 142, 0]\r\n self.color255[195] = [230, 70, 0]\r\n self.color255[196] = [0, 147, 0]\r\n self.color255[197] = [0, 147, 248]\r\n self.color255[198] = [231, 0, 206]\r\n self.color255[199] = [129, 78, 0]\r\n self.color255[200] = [0, 116, 0]\r\n self.color255[201] = [0, 0, 255]\r\n self.color255[202] = [157, 0, 0]\r\n self.color255[203] = [100, 100, 130]\r\n self.color255[204] = [205, 205, 100]\r\n self.color255[205] = [255, 255, 0]\r\n\r\n return self.color255","def get_placeholder_image(self):\n\n grid = Gtk.Grid(name=\"output-placeholder_grid\", halign=3, valign=3)\n label = Gtk.Label(name=\"output-placeholder_label\",\n label=\"<b> Query to show outputs </b>\",\n use_markup=True)\n image = Gtk.Image.new_from_icon_name(\"help-about\", 6)\n image.set_name(\"output-placeholder_image\")\n grid.attach(image, 0, 0, 1, 1)\n grid.attach(label, 0, 1, 1, 1)\n return grid","def draw_board(self):\r\n for i in range(self.size):\r\n for k in range(self.size):\r\n left = k * self.CELL_SIZE + (k+1) * self.BORDER_WIDTH\r\n top = i * self.CELL_SIZE + (i+1) * self.BORDER_WIDTH\r\n rect = pygame.Rect(left, top, self.CELL_SIZE, self.CELL_SIZE)\r\n color = self.BG_COLOR\r\n if self.map[i][k] == self.BLOCK_CHAR:\r\n color = self.BLOCK_COLOR\r\n elif self.map[i][k] == self.START_CHAR:\r\n color = self.START_COLOR\r\n elif self.map[i][k] == self.END_CHAR:\r\n color = self.END_COLOR\r\n elif (k, i) in self.path:\r\n color = self.PATH_COLOR\r\n pygame.draw.rect(self.screen, color, rect)","def color(self):\n if self._simplecell:\n self.fetch()\n return self._color","def update(self, grid, colRamp = ['white', 'blue']):\n \n # update the cell colors\n for y in range(len(grid)):\n yl = y + 1\n for x in range(len(grid[y])):\n xl = x + 1\n color = colRamp[int(grid[y][x])]\n self.displayWindow.update((xl, yl), color)\n\n # refresh the window\n self.displayWindow.tkupdate()","def gridPlot6(img_stack):\r\n F = plt.figure(figsize = (20,20))\r\n F.subplots_adjust(left = 0.05, right = 0.95)\r\n grid = ImageGrid(F, 142, nrows_ncols = (2,3), axes_pad = 0.0, share_all = True,\r\n label_mode = \"L\", cbar_location = \"top\", cbar_mode = \"single\")\r\n \r\n i = 0\r\n for img in img_stack[0:6]:\r\n im = grid[i].imshow(img, interpolation = \"nearest\", vmin = 0, vmax = 255)\r\n i += 1 \r\n grid.cbar_axes[0].colorbar(im)\r\n plt.savefig('gplot16.png')\r\n if 'gplot16.png' in os.listdir():\r\n plt.savefig('gplot16_2.png')\r\n# for cax in grid.cbar_axes:\r\n# cax.toggle_label(False)\r\n return","def get_one_PIL(colmap, fname=None, Nx=255):\n \n import Image\n Ny = 50\n im = Image.new(\"RGB\", (Nx, Ny))\n for nx in range(Nx):\n z = nx/(Nx-1.0)\n r,g,b = colmap.colfct(z)\n r = int(255*r)\n g = int(255*g)\n b = int(255*b)\n for ny in range(Ny):\n im.putpixel((nx,ny),(r,g,b))\n if fname:\n print \"saving:\", fname+\".jpg\"\n im.save(fname+\".jpg\", \"JPEG\")\n im.save(fname+\".png\", \"PNG\")\n ## im.show()\n return im","def draw_hex_grid(grid, scale, color_func, path, final_func=None):\n dim = grid.dimensions\n grid_dim = (\n int(2 * scale * (0.25 + 0.75 * dim[0])),\n int(_R3 * scale * (0.5 + dim[1])) + 1,\n )\n\n image = Image.new(\"RGB\", grid_dim)\n\n drawer = ImageDraw.Draw(image)\n for pos, tile in grid:\n center = scale * (_R3 * np.dot(AOTRANSFORM, pos[:2]) + [1, _R3 / 2])\n draw_poly(\n drawer,\n center=center,\n sides=6,\n scale=scale,\n color=color_func(tile),\n border=COLORS[\"white\"],\n )\n\n if final_func is not None:\n final_func(drawer, center, scale, tile)\n\n image.save(path)","def add_color(self, markers):\n self.img[markers == -1] = [255, 255, 255]\n self.img[markers == 1] = [255, 0, 0]\n self.img[markers == 2] = [0, 50, 0]\n self.img[markers == 3] = [0, 0, 255]\n self.img[markers == 4] = [255, 255, 0]\n self.img[markers == 5] = [0, 255, 255]\n self.img[markers == 6] = [255, 0, 255]\n\n self.img[markers == 7] = [125, 0, 0]\n self.img[markers == 8] = [0, 125, 0]\n self.img[markers == 9] = [0, 0, 125]\n self.img[markers == 10] = [125, 125, 0]\n self.img[markers == 11] = [0, 125, 125]\n self.img[markers == 12] = [125, 0, 125]\n\n self.img[markers == 13] = [255, 255, 255]\n self.img[markers == 14] = [255, 0, 0]\n self.img[markers == 15] = [0, 255, 0]\n self.img[markers == 16] = [0, 0, 255]\n self.img[markers == 17] = [255, 255, 0]\n self.img[markers == 18] = [0, 255, 255]\n self.img[markers == 19] = [255, 0, 255]","def getImage(self, x, y, w, h):\n w = min(w, self.__width - x)\n h = min(h, self.__height - y)\n r = Canvas(w, h)\n\n for col in range(w):\n r.writeCol(col, self.getCol(x+col, y))\n\n return r","def _toRgbImage(self, fractal, colors, color_offset):\n hsv_img = np.array(\n [\n # Cycle through color wheel.\n (fractal * colors + color_offset) % 1,\n\n # Saturation = fractal value.\n fractal,\n\n # Maximum value.\n np.ones(fractal.shape)\n ]\n ).astype(dtype=float).T\n\n rgb_img = (mpl.colors.hsv_to_rgb(hsv_img) * 255).astype(dtype=np.uint8)\n return rgb_img","def __enhance_image(self, img):\n\n blue = self.g.clahe.apply(img[:,:,0])\n green = self.g.clahe.apply(img[:,:,1])\n red = self.g.clahe.apply(img[:,:,2])\n img[:,:,0] = blue\n img[:,:,1] = green\n img[:,:,2] = red\n return img","def visualize_M_gridworld(self, state=0):\n\n\t\tplt.subplot(221); plt.imshow(self.M[12,0,:].reshape(5,5)), plt.colorbar()\n\t\tplt.subplot(222); plt.imshow(self.M[12,1,:].reshape(5,5)), plt.colorbar()\n\t\tplt.subplot(223); plt.imshow(self.M[12,2,:].reshape(5,5)), plt.colorbar()\n\t\tplt.subplot(224); plt.imshow(self.M[12,3,:].reshape(5,5)), plt.colorbar()\n\t\tplt.show()","def draw_image(self):\n \n pixel_array = self.imageprepare(self.image_path)\n newArr = self.reshape_pixel_array(pixel_array)\n plt.imshow(newArr, interpolation='nearest')\n plt.savefig('MNIST_IMAGE.png')#save MNIST image\n plt.show()#Show / plot that image","def drawRect (self, x, y, w, h, colour):\r\n for i in range (y,y+h):\r\n row = self.image [i]\r\n\r\n for j in range (x,x+w):\r\n row [j] = colour","def plot_final_grid(generated_images):\n\n fig = plt.figure(figsize=(8, 6))\n fig.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=0.001, hspace=0.001)\n\n for ind in range(GRID_IMGS):\n ax = fig.add_subplot(GRID_SIZE, GRID_SIZE, ind + 1, xticks=[], yticks=[])\n ax.imshow(np.uint8(((generated_images[ind] + 1) / 2) * 255), cmap='gray')\n\n plt.savefig('/content/drive/My Drive/WGAN/generated_image_grid.png')\n plt.savefig('/content/drive/My Drive/WGAN/results/DCGAN.png')\n plt.show()","def testImageProcessing():\n Im_pix = getRGB( 'in.png' ) # read in the in.png image\n print \"The first two pixels of the first row are\",\n print Im_pix[0][0:2]\n # remember that Im_pix is a list (the image)\n # of lists (each row) of lists (each pixel is [R,G,B])\n New_pix = [ [ [255 - num for num in p] for p in row ] for row in Im_pix ]\n # now, save to the file 'out.png'\n saveRGB( New_pix, 'out.png' )","def generate_image(row):\n image = np.zeros((960, 1280, 4), np.uint8) \n image[:,:] = (128, 128, 128, 255)\n x = int(row['currXcorrected'])\n y = int(row['currYcorrected']) \n path = \"/net/store/nbp/projects/fixdur/stimuli/single_bubble_images/\" + \\\n str(row.loc['image']) + \"/\" + \\\n \"bubble_\" + str(x) + \"_\" + str(y) + \".tiff\"\n bubble = prepare_bubble(path) \n image[y:y+154, x:x+154] = bubble\n\n return image","def initImg(self):\n self.img = Image.new('RGBA',(self.width,self.height),color='#' + getConfigPart(self.theme,\"bg\"))\n self.draw = ImageDraw.Draw(self.img)","def __init__(self, shape_num):\n self.shape_num = shape_num\n if shape_num == 1:\n self.width = 4\n self.height = 4\n self.grid = [[0 for x in range(self.height)] for y in range(self.width)]\n self.grid[0][2] = 1\n self.grid[1][2] = 1\n self.grid[2][2] = 1\n self.grid[3][2] = 1\n self.color = Color.SilverPink\n elif shape_num == 2:\n self.width = 3\n self.height = 3\n self.grid = [[0 for x in range(self.height)] for y in range(self.width)]\n self.grid[0][1] = 1\n self.grid[0][2] = 1\n self.grid[1][2] = 1\n self.grid[2][2] = 1\n self.color = Color.TuftsBlue\n elif shape_num == 3:\n self.width = 3\n self.height = 3\n self.grid = [[0 for x in range(self.height)] for y in range(self.width)]\n self.grid[2][1] = 1\n self.grid[0][2] = 1\n self.grid[1][2] = 1\n self.grid[2][2] = 1\n self.color = Color.ChromeYellow\n elif shape_num == 4:\n self.width = 2\n self.height = 2\n self.grid = [[0 for x in range(self.height)] for y in range(self.width)]\n self.grid[0][0] = 1\n self.grid[0][1] = 1\n self.grid[1][0] = 1\n self.grid[1][1] = 1\n self.color = Color.Independence\n elif shape_num == 5:\n self.width = 3\n self.height = 3\n self.grid = [[0 for x in range(self.height)] for y in range(self.width)]\n self.grid[1][0] = 1\n self.grid[2][0] = 1\n self.grid[0][1] = 1\n self.grid[1][1] = 1\n self.color = Color.ForestGreen\n elif shape_num == 6:\n self.width = 3\n self.height = 3\n self.grid = [[0 for x in range(self.height)] for y in range(self.width)]\n self.grid[1][1] = 1\n self.grid[0][2] = 1\n self.grid[1][2] = 1\n self.grid[2][2] = 1\n self.color = Color.Byzantine\n elif shape_num == 7:\n self.width = 3\n self.height = 3\n self.grid = [[0 for x in range(self.height)] for y in range(self.width)]\n self.grid[0][0] = 1\n self.grid[1][0] = 1\n self.grid[1][1] = 1\n self.grid[2][1] = 1\n self.color = Color.Coquelicot\n self.top_space = self.get_top_space()\n self.bottom_space = self.get_bottom_space()\n self.x = int((12 - self.width) / 2)\n self.y = 1 - self.top_space\n self.last_drop_time = perf_counter()","def drawGrid(self):\n for div in range(NBCELL):\n sec = SSIZE*div\n self.can.create_line(0, sec, GSIZE, sec, width=3, fill=GFILL)\n self.can.create_line(sec, 0, sec, GSIZE, width=3, fill=GFILL)","def draw_H(image, coords, color=(0, 255, 0)):\n image_with_H = image.copy()\n\n canvas = image_with_H[coords[0]:coords[0] + 24,\n coords[1]:coords[1] + 20]\n\n canvas[:, :3] = color\n canvas[:, -3:] = color\n canvas[11:14] = color\n\n return image_with_H","def draw_grid(grid):\n \n # Tile size variables\n tile_width = STAGE_WIDTH / GRID_WIDTH\n tile_height = STAGE_HEIGHT / GRID_HEIGHT\n \n for i in range(GRID_WIDTH):\n for j in range(GRID_HEIGHT):\n elev = grid[i][j]\n rect_x = i * tile_width\n rect_y = j * tile_height\n pygame.draw.rect(STAGE, get_color(elev),\n (rect_x, rect_y, tile_width, tile_height))","def test_grdimage_default_no_shading(grid, shading):\n grid_ = grid.sel(lat=slice(-30, 30))\n fig = Figure()\n fig.grdimage(grid_, cmap=\"earth\", projection=\"M6i\", shading=shading)\n return fig","def update_image_frame(selfie=False):\n for _row in range(0, GRID_AXIS):\n for _col in range(0, GRID_AXIS):\n if selfie:\n color_index = GRID_DATA[GRID_AXIS - 1 - _row][_col]\n else:\n color_index = GRID_DATA[GRID_AXIS - 1 - _row][GRID_AXIS - 1 - _col]\n color = index_to_rgb(round(color_index * PALETTE_SIZE, 0) / PALETTE_SIZE)\n if color != image_group[((_row * GRID_AXIS) + _col)].fill:\n image_group[((_row * GRID_AXIS) + _col)].fill = color","def draw_grid(self):\n pygame.draw.rect(self.screen, BLACK,\n (*grid_pos, WIDTH - 150, HEIGHT-150), 2)\n for x in range(9):\n pygame.draw.line(\n self.screen,\n BLACK,\n (grid_pos[0] + (x * cell_size), grid_pos[1]),\n (grid_pos[0] + (x * cell_size), grid_pos[1] + 450),\n 2 if x % 3 == 0 else 1\n )\n pygame.draw.line(\n self.screen,\n BLACK,\n (grid_pos[0], grid_pos[1] + (x * cell_size)),\n (grid_pos[0] + 450, grid_pos[1] + (x * cell_size)),\n 2 if x % 3 == 0 else 1\n )","def draw_grid(self):\n for square in range(COLS+1):\n #vertical lines\n start_pos = (helpers.get_col_left_p(square),helpers.get_row_top_p(0))\n end_pos = (helpers.get_col_left_p(square),helpers.get_row_top_p(ROWS))\n pygame.draw.line(g.screen,WHITE,start_pos,end_pos)\n for square in range(ROWS+1):\n #horizontal lines\n start_pos = (helpers.get_col_left_p(0),helpers.get_row_top_p(square))\n end_pos = (helpers.get_col_left_p(COLS),helpers.get_row_top_p(square))\n pygame.draw.line(g.screen,WHITE,start_pos,end_pos)","def draw_grid(self, verbosity=0):\n log.debug(\"Drawing grid\")\n (x0, y0) = self.origin\n color = (191, 191, 191)\n\n (w, h) = self.surface.get_size()\n\n i = x0\n while True:\n (x, ignore) = self.map_to_screen((i, 0))\n if x > w:\n break\n pygame.draw.line(self.surface, color, (x, 0), (x, h), 1)\n i += 10\n\n j = y0\n while True:\n (ignore, y) = self.map_to_screen((0, j))\n if y > h:\n break\n pygame.draw.line(self.surface, color, (0, y), (w, y), 1)\n j -= 10","def _tile_image(self, data):\n image = Image.open(BytesIO(data))\n return image.convert('RGBA')","def get_colors(self, image: np.ndarray, coordinates: np.ndarray) -> np.ndarray:\r\n x = coordinates.squeeze(1)\r\n return np.flip(image[x[:, 1], x[:, 0]].astype(np.float64) / 255.0, axis=1)","def plot_final_grid(generated_images):\n\n fig = plt.figure(figsize=(GRID_SIZE, GRID_SIZE))\n fig.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=0.001, hspace=0.001)\n\n for ind in range(GRID_IMGS):\n ax = fig.add_subplot(GRID_SIZE, GRID_SIZE, ind + 1, xticks=[], yticks=[])\n ax.imshow(np.uint8(((generated_images[ind] + 1) / 2) * 255), cmap='gray')\n\n plt.savefig('/content/drive/My Drive/WGAN/generated_image_grid.png')\n plt.savefig('/content/drive/My Drive/WGAN/results/WGAN.png')\n plt.show()","def image(self, data):\n # Assemble colormap from given parameters\n cmap = self.config.get('cmap', None)\n\n # If a single color provided, prepend 'white' color, so that a resulting tuple defines binary colormap\n if is_color_like(cmap):\n cmap = (cmap, )\n # If a tuple of colors provided in `cmap` argument convert it into a colormap\n if isinstance(cmap, tuple):\n cmap = make_cmap(colors=cmap)\n else:\n cmap = copy(plt.get_cmap(cmap))\n # Set a color for nan/masked values display to colormap if provided\n mask_color = self.config.get('mask_color', None)\n cmap.set_bad(color=mask_color)\n\n image_keys = ['alpha', 'vmin', 'vmax', 'extent']\n image_config = self.config.filter(keys=image_keys, prefix='image_')\n\n vmin, vmax = self._parse_vrange(data)\n image_config['vmin'] = vmin\n image_config['vmax'] = vmax\n\n image = self.ax.imshow(data, cmap=cmap, **image_config)\n\n return [image]","def test_grdimage_grid_and_shading_with_xarray(grid, xrgrid):\n fig_ref, fig_test = Figure(), Figure()\n fig_ref.grdimage(\n grid=\"@earth_relief_01d_g\", region=\"GL\", cmap=\"geo\", shading=xrgrid\n )\n fig_ref.colorbar()\n fig_test.grdimage(grid=grid, region=\"GL\", cmap=\"geo\", shading=xrgrid)\n fig_test.colorbar()\n return fig_ref, fig_test","def get_rgb(self, img, r, g, b):\r\n\r\n # Get specific bands of hyperspectral image\r\n red_channel = img[:, :, r]\r\n green_channel = img[:, :, g]\r\n blue_channel = img[:, :, b]\r\n\r\n img = np.stack((red_channel, green_channel, blue_channel), axis=2)\r\n img = img.astype('float32')\r\n return img","def initialize_grid(self) -> None:\n for i in range(self.grid_size[0]):\n for j in range(self.grid_size[1]):\n self.set(i, j, self.base_color)","def visualize_grid(Xs, ubound=255.0, padding=1):\n pixel_sz = 2\n (H, W, C, N) = Xs.shape\n\n Xs_resize = np.zeros((H*pixel_sz, W*pixel_sz, C, N))\n Xs = (ubound*(Xs-np.min(Xs))/(np.max(Xs)-np.min(Xs))).astype('uint8')\n\n for c in range(C):\n for n in range(N):\n Xs_resize[:,:,c,n] = imresize(Xs[:,:,c,n], 200, interp='nearest')\n Xs = Xs_resize\n\n (H, W, C, N) = Xs.shape\n low, high = np.min(Xs), np.max(Xs)\n\n if C==1 or C==3:\n grid_size_H = int(ceil(sqrt(N)))\n grid_size_W = int(ceil(sqrt(N)))\n else:\n grid_size_H = N\n grid_size_W = C\n\n count = 0\n grid_height = H * grid_size_H + padding * (grid_size_H-1)\n grid_width = W * grid_size_W + padding * (grid_size_W-1)\n grid = np.zeros((grid_height, grid_width, C))\n y0, y1 = 0, H\n for y in range(grid_size_H):\n x0, x1 = 0, W\n for x in range(grid_size_W):\n if C==1 or C==3:\n img = Xs[:,:,:,count]\n count += 1\n else:\n img = np.expand_dims(Xs[:,:,x,y], axis=-1)\n\n grid[y0:y1, x0:x1, :] = ubound * (img - low) / (high - low)\n x0 += W + padding\n x1 += W + padding\n\n y0 += H + padding\n y1 += H + padding\n\n if C!=3:\n grid = grid[:,:,0]\n return grid","def blank_image(height, width):\n all_green = create_uniform_image(height, width, [0, 255, 0])\n return all_green","def get_color(self):\n self.view.present(\n \"sheet\",\n orientations=ORIENTATIONS,\n )\n self.view.wait_modal()\n return self.rgb","def _style_colours(self):\n\n pass","def getColormapImage(self):\n if self._colormapImage is None and self._colormap is not None:\n image = numpy.zeros((16, 130, 3), dtype=numpy.uint8)\n image[1:-1, 1:-1] = self._colormap.getNColors(image.shape[1] - 2)[:, :3]\n self._colormapImage = convertArrayToQImage(image)\n return self._colormapImage","def createImageCell(self,control,cell,image):\n b = ResizingCanvas(control,width=400,height=400,borderwidth=0,highlightthickness=0)\n b.changeBackground(image)\n i,j = cell\n b.grid(row=i,column=j,sticky=N+E+W+S)\n return b","def generate_image_grid(sess, op):\n n = 10\n x_points = np.linspace(-20, 20, n)\n y_points = np.linspace(-20, 20, n)\n\n nx, ny = len(x_points), len(y_points)\n plt.subplot()\n gs = gridspec.GridSpec(nx, ny, hspace=0.05, wspace=0.05)\n\n for i, g in enumerate(gs):\n z = np.concatenate(([x_points[int(i / ny)]], [y_points[int(i % nx)]]))\n z = np.reshape(z, (1, 2))\n x = sess.run(op, feed_dict={decoder_input: z})\n ax = plt.subplot(g)\n img = np.array(x.tolist()).reshape(28, 28)\n ax.imshow(img, cmap='gray')\n ax.set_xticks([])\n ax.set_yticks([])\n ax.set_aspect('auto')\n plt.show()","def drawBox (self, left, top, width, height, colour):\r\n w = self.bih_vals [bih_Width]\r\n h = self.bih_vals [bih_Height]\r\n\r\n cols = [left, left + width - 1]\r\n rows = [top, top + height - 1]\r\n \r\n x0 = max ((0,left))\r\n x1 = min ((cols[1]+1, w))\r\n y0 = max ((0,top))\r\n y1 = min ((rows [1]+1, h))\r\n\r\n # rows\r\n\r\n for r in rows:\r\n if r >= 0 and r < h:\r\n row = self.image [r]\r\n for x in range (x0, x1):\r\n row [x] = colour\r\n\r\n # columns\r\n \r\n for y in range (y0, y1):\r\n row = self.image [y]\r\n for c in cols:\r\n if c >= 0 and c < w :\r\n row [c] = colour","def color(self, data):\n\n red = np.interp(data, self.range, self.r)\n blue = np.interp(data, self.range, self.b)\n green = np.interp(data, self.range, self.g)\n # Style plot to return a grey color when value is 'nan'\n red[np.isnan(red)] = 240\n blue[np.isnan(blue)] = 240\n green[np.isnan(green)] = 240\n colors = np.dstack([red.astype(np.uint8),\n green.astype(np.uint8),\n blue.astype(np.uint8),\n np.full_like(data, 255, dtype=np.uint8)])\n #return colors.view(dtype=np.uint32).reshape(data.shape)\n c=[]\n for i in range(len(data)):\n c.append([red[i],green[i],blue[i]])\n return c","def show_image_grid(imgs):\n grd = make_grid(imgs)\n npimg = grd.numpy()\n plt.imshow(np.transpose(npimg, (1,2,0)), interpolation='nearest')\n plt.ion()\n plt.show()","def draw(self, color = Color.GREEN):\n self.image[self.x, self.y] = color","def get_BGR_img(self):\n img = self.img.copy()\n # Convert BGR to HSV\n hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)\n # define range of BGR color in HSV\n threshold_blue = np.array([[100,43,46], [124,255,255]])\n threshold_green = np.array([[35,43,46], [77,255,255]])\n threshold_red1 = np.array([[0,43,46], [10,255,255]])\n threshold_red2 = np.array([[156,43,46], [180,255,255]])\n # Threshold the HSV image to get only BGR colors\n mask_blue = cv2.inRange(hsv, threshold_blue[0], threshold_blue[1])\n mask_green = cv2.inRange(hsv, threshold_green[0], threshold_green[1])\n mask_red1 = cv2.inRange(hsv, threshold_red1[0], threshold_red1[1])\n mask_red2 = cv2.inRange(hsv, threshold_red2[0], threshold_red2[1])\n mask_red = mask_red1 | mask_red2\n # Bitwise-AND mask and original image\n self.blue = cv2.bitwise_and(img, img, mask=mask_blue)\n self.green = cv2.bitwise_and(img, img, mask=mask_green)\n self.red = cv2.bitwise_and(img, img, mask=mask_red)\n # 返回 bgr 三通道的分量合成的图片\n return np.stack((self.blue[:, :, 0], self.green[:, :, 1], self.red[:, :, 2]), axis=2)"],"string":"[\n \"def draw_grid(self):\\n plt.imshow(py.array(\\n map(lambda x: map(lambda y: mplc.colorConverter.to_rgb(colord[y]), x), self.create_grid(self.graph))),\\n interpolation='nearest')\\n plt.show()\",\n \"def make_image(self, save=False):\\n\\n # image_grid = np.full((self.size_x, self.size_y), '#888888', dtype=str)\\n image_grid = np.full((self.size_x, self.size_y, 3), 0, dtype=np.uint8)\\n\\n # self.grid = np.flip(self.grid, 1)\\n\\n # self.grid = np.swapaxes(self.grid, 0, 0)\\n \\\"\\\"\\\"\\n image_grid[self.grid == 0] = 'FFFFFF'\\n image_grid[self.grid == 1] = '000000'\\n image_grid[self.grid == 2] = '00FF00'\\n image_grid[self.grid == 3] = '0000FF'\\n image_grid[self.grid == 4] = 'FFFF00'\\n image_grid[self.grid == 5] = '00FFFF'\\n image_grid[self.grid == 6] = 'FF00FF'\\n \\\"\\\"\\\"\\n image_grid[self.grid == 0] = (1, 1, 1)\\n image_grid[self.grid == 1] = (0, 0, 0)\\n image_grid[self.grid == 2] = (1, 0, 1)\\n image_grid[self.grid == 3] = (0, 1, 0)\\n image_grid[self.grid == 4] = (0, 0, 1)\\n image_grid[self.grid == 5] = (0, 1, 1)\\n image_grid[self.grid == 6] = (1, 1, 0)\\n\\n #for ant in self.ants:\\n # image_grid[ant.x, ant.y] = (1, 0, 0)\\n\\n # image_grid = image_grid.swapaxes(0, 1)\\n # self.grid = self.grid.swapaxes(0, 1)\\n\\n\\n\\n DPI = 100\\n width, height = 1000, 1000\\n fig = plt.figure(figsize=(width / DPI, height / DPI), dpi=DPI, facecolor='k')\\n ax = fig.add_subplot()\\n\\n plt.axis('equal')\\n plt.subplots_adjust(top=1, bottom=0, right=1, left=0, hspace=0, wspace=0)\\n\\n for y in range(self.size_x):\\n for x in range(self.size_y):\\n if self.grid[x, y] != 0:\\n # Only plot a hexagon if its state is not zero.\\n plot_hex(ax, x, y, image_grid[x, y])\\n\\n ax.set_xlim(0, self.size_x)\\n ax.set_ylim(0, self.size_y)\\n\\n plt.show()\\n\\n logging.info(\\\"Finished Image Processing\\\")\",\n \"def test_grdimage(grid):\\n fig = Figure()\\n fig.grdimage(grid, cmap=\\\"earth\\\", projection=\\\"W0/6i\\\")\\n return fig\",\n \"def get_image(self):\\n image = Image.new('1', (8, 16))\\n draw = ImageDraw.Draw(image)\\n for x in xrange(8):\\n for y in xrange(16):\\n draw.point((x,y),self.get_pixel(x, y))\\n return image\",\n \"def drawImage(self):\\n\\n image = Image.new(\\\"RGB\\\",self.size,(255,255,255)) #new() passes parameters mode,size,color\\n #255,255,255 is white\\n\\n canvas = ImageDraw.Draw(image)\\n for g in self.genes:\\n color = (g.color.r,g.color.g,g.color.b)\\n\\n canvas.ellipse((g.pos.x-int(g.diameter/2),g.pos.y-int(g.diameter/2), g.pos.x+int(g.diameter/2), g.pos.y+int(g.diameter/2)), fill = color)\\n return image\",\n \"def colour_image(self):\\n for row_cnt, colour_row in enumerate(self.table_colour):\\n self.processed = []\\n for column_cnt, colour in enumerate(colour_row):\\n self.row_cnt = row_cnt + 3\\n self.column_cnt = column_cnt\\n self.colour = colour\\n self.word = self.original_rows[self.row_cnt - 3][self.column_cnt]\\n self.colour_specific_word()\",\n \"def _generate_images(self, trace):\\n images = []\\n colors = []\\n colors_by_shape = {}\\n for board in trace:\\n width = int(round((float(board.shape[1]) / board.shape[0]) * self._height))\\n cellsize = width / board.shape[1] # cell size\\n img = np.zeros((self._height, width, 3), dtype=np.uint8)\\n\\n tiles = {} # map from integer rep. of the tile to a shape\\n for y in range(board.shape[0]):\\n for x in range(board.shape[1]):\\n cell = board[y,x]\\n if cell not in tiles:\\n tiles[cell] = (x, y, 1, 1) # x, y, w, h\\n else:\\n cur_x, cur_y, cur_w, cur_h = tiles[cell]\\n if x >= cur_x + cur_w:\\n cur_w = (x-cur_x) + 1\\n if y >= cur_y + cur_h:\\n cur_h = (y-cur_y) + 1\\n tiles[cell] = (cur_x, cur_y, cur_w, cur_h)\\n\\n # Colors\\n if len(colors_by_shape) == 0:\\n for tid in tiles:\\n shape = (tiles[tid][2], tiles[tid][3])\\n if shape not in colors_by_shape:\\n colors_by_shape[shape] = hex_to_rgb(random_unique_color(colors))\\n colors.append(colors_by_shape[shape])\\n\\n for tid in tiles:\\n x, y, w, h = tiles[tid]\\n shape = (w,h)\\n empty = board[y,x] == 0\\n x, y, w, h = x*cellsize, y*cellsize, w*cellsize, h*cellsize\\n # Draw a filled rectangle without color\\n if not empty:\\n cv2.rectangle(img, (x, y), (x+w, y+h), colors_by_shape[shape],-1)\\n else:\\n cv2.rectangle(img, (x, y), (x+w, y+h), [0,0,0], -1) #, 8)-\\n # Draw a boundary\\n cv2.rectangle(img, (x, y), (x+w, y+h), (0, 0, 0), 2, 8)\\n \\n images.append(img)\\n return images\",\n \"def draw_grid(self) -> None:\\n grid = self.life.curr_generation\\n for row in range(self.cell_height):\\n for column in range(self.cell_width):\\n if grid[row][column] == 1:\\n color = \\\"green\\\"\\n else:\\n color = \\\"white\\\"\\n pygame.draw.rect(\\n self.screen,\\n pygame.Color(color),\\n (column * self.cell_size, row * self.cell_size, self.cell_size, self.cell_size),\\n )\",\n \"def generate_image(self):\\n\\n if not has_pillow:\\n raise RuntimeError(\\\"requires https://pypi.org/project/pillow/\\\")\\n\\n background = self.get_background()\\n foreground = self.get_foreground()\\n\\n matrix = self.generate_matrix()\\n\\n image = Image.new(\\\"RGB\\\", (420, 420), background)\\n draw = ImageDraw.Draw(image)\\n\\n for (i, row) in enumerate(matrix):\\n for (j, bit) in enumerate(row):\\n x = 35 + j * 70\\n y = 35 + i * 70\\n\\n if bit:\\n draw.rectangle((x, y, x + 70, y + 70), foreground)\\n\\n return image\",\n \"def paint(self, grid):\\n grid[self.y, self.x] = self.appearance\\n return grid\",\n \"def draw_grid(self):\\n\\n screen.fill(GREY)\\n\\n for row in self.grid:\\n for cell in row:\\n if cell.root:\\n color = GREEN\\n elif cell.goal:\\n color = RED\\n elif cell.value:\\n color = DARK_BLUE\\n elif cell.visited:\\n color = LIGHT_BLUE\\n elif cell.f:\\n color = LIGHT_GREEN\\n elif cell.wall:\\n color = GRAY\\n else:\\n color = WHITE\\n\\n pygame.draw.rect(screen, color, cell.rect)\\n\\n x, y = cell.rect.x, cell.rect.y\\n\\n if cell.g:\\n self.draw_score(x + 2, y + 2, cell.g)\\n if cell.h:\\n self.draw_score(x + 18, y + 2, cell.h)\\n if cell.f:\\n self.draw_score(x + 2, y + self.cell_size - 10, cell.f)\",\n \"def graphical_output(self, block=False, save=False, display=True, folder_name='output', image_suffix=None):\\n masks = [np.where(self.board == i, True, False) for i in range(self.number_of_colors)]\\n output = np.zeros((self.height, self.width, 3), dtype=np.int)\\n for mask, color in zip(masks, list(COLORS.values())[:self.number_of_colors]):\\n output[mask] = color\\n\\n image = np.repeat(np.repeat(output, 10, axis=0), 10, axis=1)/255.0\\n if save:\\n plt.imsave(f'{folder_name}/image{image_suffix}.png', image)\\n\\n if display:\\n plt.imshow(image)\\n plt.show(block=block)\\n\\n return image\",\n \"def render_image(grid,window):\\r\\n X = len(grid[0])\\r\\n Y = len(grid)\\r\\n#top row:\\r\\n for j in range(Y):\\r\\n for sub_j in range(3): #3 rows \\r\\n ROW = []\\r\\n for i in range(X):\\r\\n ROW += grid[j][i].arr[sub_j]\\r\\n \\r\\n for k in range(len(ROW)):\\r\\n COLOR = (ROW[k],ROW[k],ROW[k])\\r\\n Y_pos = (3*j + sub_j)*pixel_size*scale\\r\\n X_pos = k*(pixel_size)*scale\\r\\n width = height = pixel_size*scale\\r\\n pygame.draw.rect(window,COLOR,(X_pos,Y_pos,width,height))\\r\\n \\r\\n# print(ROW)\\r\\n return\",\n \"def Green_func(self):\\n if self.bc == True:\\n size = self.grid_size\\n else:\\n size = 2*self.grid_size\\n self.Green = np.zeros([size, size])\\n for x in range(len(self.Green[0])):\\n for y in range(len(self.Green[1])):\\n radius = np.sqrt(x**2 + y**2) \\n if radius < self.soften: \\n radius = self.soften\\n self.Green[x, y]=1/(4 * np.pi * radius)\\n if self.grid_size%2 == 0: \\n self.Green[: size//2, size//2 : ] = np.flip(self.Green[: size//2, : size//2], axis = 1) # an intermittent step - the original grid has only been flipped once (2 x the original size)\\n self.Green[ size//2 : , :] = np.flip(self.Green[: size//2, :], axis = 0)\\n else: \\n print(\\\"Exiting - Grid size is currently odd. Pleaset set to an even value.\\\")\",\n \"def generate_image(self):\\n\\t\\tcenters = self.generate_centers()\\n\\t\\timg = Image.new('RGB', (self.config.image_size, self.config.image_size), color=(0,0,0))\\n\\t\\tshapes = np.random.randint(2, size=len(centers))\\n\\t\\tdrawer = ImageDraw.Draw(img)\\n\\t\\tr = int(0.05 * self.config.image_size)\\n\\t\\tR = []\\n\\t\\tfor i in range(len(centers)):\\n\\t\\t\\tcoor = (centers[i][0] - r , centers[i][1] - r, centers[i][0] + r, centers[i][1] + r)\\n\\t\\t\\tif shapes[i] < 0.5:\\n\\t\\t\\t\\tdrawer.rectangle(coor, fill=COLOR[i])\\n\\t\\t\\telse:\\n\\t\\t\\t\\tdrawer.ellipse(coor, fill=COLOR[i])\\n\\t\\t\\tR.append([centers[i], i, shapes[i]])\\n\\t\\treturn np.array(img), R\",\n \"def image_from_grid(self, grid: np.ndarray) -> np.ndarray:\\r\\n if self.light_profiles is not None:\\r\\n return sum(map(lambda p: p.image_from_grid(grid=grid), self.light_profiles))\\r\\n return np.zeros((grid.shape[0],))\",\n \"def export_image(self, name):\\n\\t\\tred = Color(\\\"red\\\")\\n\\t\\tblue = Color(\\\"blue\\\")\\n\\t\\twhite = Color(\\\"white\\\")\\n\\t\\tblack = Color(\\\"black\\\")\\n\\t\\tgold = Color(\\\"gold\\\")\\n\\t\\trgb_gold = []\\n\\t\\tfor part in gold.rgb:\\n\\t\\t\\tpart = part * 255\\n\\t\\t\\trgb_gold.append(part)\\n\\t\\trgb_black = []\\n\\t\\tfor part in black.rgb:\\n\\t\\t\\tpart = part * 255\\n\\t\\t\\trgb_black.append(part)\\n\\t\\trgb_white = []\\n\\t\\tfor part in white.rgb:\\n\\t\\t\\tpart = part * 255\\n\\t\\t\\trgb_white.append(part)\\n\\t\\tcolours = list(red.range_to(blue, int(self.grains)))\\n\\t\\timage = np.zeros([self.space.shape[1],self.space.shape[0], 3], dtype=np.uint(8))\\n\\t\\tfor grain in range(self.grains+1):\\n\\t\\t\\trgb = []\\n\\t\\t\\tfor part in colours[grain-1].rgb:\\n\\t\\t\\t\\tpart = part * 255\\n\\t\\t\\t\\trgb.append(part)\\n\\t\\t\\tfor cell in self.space.flat:\\n\\t\\t\\t\\tif cell.state == grain:\\n\\t\\t\\t\\t\\tx,y = cell.find_id()\\n\\t\\t\\t\\t\\timage[x,y] = rgb\\n\\t\\t\\t\\tif cell.state == 999:\\n\\t\\t\\t\\t\\tx,y = cell.find_id()\\n\\t\\t\\t\\t\\timage[x,y] = rgb_black\\n\\t\\t\\t\\tif cell.state == 500:\\n\\t\\t\\t\\t\\tx,y = cell.find_id()\\n\\t\\t\\t\\t\\timage[x,y] = rgb_gold\\n\\t\\timg = Image.fromarray(image.astype('uint8'))\\n\\t\\timg = img.resize((self.space.shape[1]*3,self.space.shape[0]*3))\\n\\t\\timg.save('./static/temp/'+str(name)+'.png')\",\n \"def toImage(cmObject):\\n size = self.gridsize, self.gridsize\\n cm = cmObject()\\n master = []\\n for item in cm:\\n master.extend(item)\\n\\n #Apply default Mandelbrot Set Function\\n master = map(f, master)\\n\\n col1 = (0,0,102,0)\\n col2 = (255,204,51,0)\\n\\n def select_color(x):\\n if x == 1: return col1\\n else: return col2\\n\\n master = map(select_color, master)\\n \\n image = Image.new(\\\"RGBA\\\", size, (0,0,0,0))\\n image.putdata(master)\\n return image\",\n \"def draw_grid(self, darken=1):\\n if not(0 < darken < 1):\\n darken = 1\\n for x in range(0, int(self.settings['grid_size'])):\\n for y in range(0, int(self.settings['grid_size'])):\\n if self.grid[x][y] == g.EMPTY:\\n if (x + y) % 2 == 0:\\n r = pygame.Rect((x * self.block_width, y * self.block_height),\\n (self.block_width, self.block_width))\\n pygame.draw.rect(self.surface, (93 * darken, 216 * darken, 228 * darken), r)\\n else:\\n rr = pygame.Rect((x * self.block_width, y * self.block_width),\\n (self.block_width, self.block_width))\\n pygame.draw.rect(self.surface, (84 * darken, 194 * darken, 205 * darken), rr)\\n elif self.grid[x][y] == g.WALL:\\n rr = pygame.Rect((x * self.block_width, y * self.block_width), (self.block_width, self.block_width))\\n pygame.draw.rect(self.surface, (175 * darken, 34 * darken, 6 * darken), rr)\\n elif self.grid[x][y] == g.PLAYER:\\n r = pygame.Rect((x * self.block_width, y * self.block_height),\\n (self.block_width, self.block_height))\\n pygame.draw.rect(self.surface, (17 * darken, 24 * darken, 47 * darken), r)\\n pygame.draw.rect(self.surface, (93, 216, 228), r, 1)\\n elif self.grid[x][y] == g.FOOD:\\n r = pygame.Rect((x * self.block_width, y * self.block_height),\\n (self.block_width, self.block_height))\\n pygame.draw.rect(self.surface, (223 * darken, 163 * darken, 49 * darken), r)\\n pygame.draw.rect(self.surface, (93, 216, 228), r, 1)\",\n \"def draw_grid(self):\\n buf = self.__hbar\\n for rInd in range(self.row):\\n line = '\\\\t|'\\n for cInd in range(self.col):\\n this = ((rInd * self.col) + cInd)\\n cell = self.get_cell(this)\\n if not cell:\\n line += '%s|' % ' '.center(5)\\n else:\\n if this == self.new_cell:\\n tmp = green(str(cell).center(5))\\n else:\\n tmp = str(cell).center(5)\\n line += '%s|' % tmp\\n buf += line + '\\\\n' + self.__hbar\\n print(buf)\",\n \"def create_colorful_test_image(self):\\n ch255 = np.full([100, 200, 1], 255, dtype=np.uint8)\\n ch128 = np.full([100, 200, 1], 128, dtype=np.uint8)\\n ch0 = np.full([100, 200, 1], 0, dtype=np.uint8)\\n imr = np.concatenate((ch255, ch128, ch128), axis=2)\\n img = np.concatenate((ch255, ch255, ch0), axis=2)\\n imb = np.concatenate((ch255, ch0, ch255), axis=2)\\n imw = np.concatenate((ch128, ch128, ch128), axis=2)\\n imu = np.concatenate((imr, img), axis=1)\\n imd = np.concatenate((imb, imw), axis=1)\\n image = np.concatenate((imu, imd), axis=0)\\n return image\",\n \"def rgb_image(self):\\n z3 = self.z[:,:,newaxis]\\n return z3 * self.c\",\n \"def draw_grid(self) -> None:\\n for x in range(0, WIDTH, TILE_SIZE):\\n pg.draw.line(self.screen, LIGHT_GREY, (x, INFO_HEIGHT), (x, HEIGHT))\\n for y in range(INFO_HEIGHT, INFO_HEIGHT + HEIGHT, TILE_SIZE):\\n pg.draw.line(self.screen, LIGHT_GREY, (0, y), (WIDTH, y))\",\n \"def draw_gray_grid(self):\\n gray = \\\"#D3D3D3\\\"\\n # Draw the vertical lines\\n for x in range(0, self.width, self.scale):\\n self.canvas.create_line(x, 0, x, self.height, fill=gray)\\n\\n # Draw the horizontal lines\\n for y in range(0, self.height, self.scale):\\n self.canvas.create_line(0, y, self.width, y, fill=gray)\",\n \"def draw_grid(self):\\n for x in range(0, WIDTH, TILESIZE):\\n pg.draw.line(self.screen, LIGHTGREY, (x, 0), (x, HEIGHT))\\n \\n for y in range(0, HEIGHT, TILESIZE):\\n pg.draw.line(self.screen, LIGHTGREY, (0, y), (WIDTH, y))\",\n \"def colorfull_house(self):\\n image = self.__image.copy()\\n palette_path = self.get_image_path(\\\"color_palette.png\\\")\\n self.color_palette = cv.resize(cv.imread(palette_path, 1),(598,245))\\n self.pixel_picked = np.array([0,0,0])\\n\\n cv.namedWindow(\\\"Color Palette\\\")\\n cv.setMouseCallback(\\\"Color Palette\\\", self.click_mouse_callback)\\n cv.imshow(\\\"Color Palette\\\", self.color_palette)\\n\\n while (True):\\n for i in iter(range(self.__rows)):\\n for j in iter(range(self.__cols)):\\n if self.segmentation_condition(i,j):\\n image[i,j] = self.pixel_picked\\n \\n cv.imshow(\\\"Colorfull House\\\",image)\\n if (cv.waitKey(1) & 0xFF == ord('q')):\\n break\",\n \"def _tile_image(self, data):\\n image = Image.open(StringIO(data))\\n return image.convert('RGBA')\",\n \"def setColors(self):\\r\\n # productive\\r\\n profprint()\\r\\n self.color = [[0, 0, 0] for i in range(MAXCOL)]\\r\\n self.color255 = self.setColors255()\\r\\n for i in range(MAXCOL):\\r\\n for j in range(3):\\r\\n self.color[i][j] = self.color255[i][j] / float(255)\\r\\n\\r\\n return self.color\",\n \"def _prepare_grid(self):\\n draw_grid = list()\\n for x in range(len(self._grid) + len(self._grid) + 1):\\n if x % 2 == 0:\\n draw_grid.append([self._walk_area_color if x % 2 != 0 else self._wall_color\\n for x in range(len(self._grid) + len(self._grid) + 1)])\\n else:\\n draw_grid.append([self._walk_area_color\\n for _ in range(len(self._grid) + len(self._grid) + 1)])\\n\\n draw_grid = self._draw_walls(draw_grid)\\n draw_grid = self._draw_treasures(draw_grid)\\n draw_grid = self._draw_border(draw_grid)\\n return draw_grid\",\n \"def draw_grid(self, tile_img, tiles):\\n #debug_print(\\\"drawing level\\\", data)\\n img = Surface((self.xsize * SIZE, self.ysize * SIZE))\\n for pos, char in self:\\n rect = get_tile_rect(pos)\\n img.blit(tile_img, rect, tiles[char])\\n return img\",\n \"def check_color(i, j, k):\\n img.show()\\n image = Image.new(\\\"RGB\\\", (200, 200), (int(Y), int(Y), int(Y)))\\n image.show()\\n image = Image.new(\\\"RGB\\\", (200, 200), (int(i), int(j), int(k)))\\n image.show()\",\n \"def drawImage(self):\\n \\n self.retrieveThemeConfig()\\n \\n self.initImg()\\n self.colsDone = 1\\n \\n queue = mp.Queue()\\n procs = []\\n for i in range(0,self.strandNum):\\n procs.append(mp.Process(target=self.strandWorker,args=(queue,i,\\n self.branches)))\\n procs[i].start()\\n\\n self.allCords = []\\n for i in range(0,self.strandNum):\\n self.allCords.append(queue.get())\\n \\n logging.info(\\\"Strand workers finished\\\")\\n\\n self.curCol = 0\\n for k in range(0,len(self.allCords)):\\n for l in range(0,len(self.allCords[k])):\\n for m in range(0,len(self.allCords[k][l]['cords'])):\\n self.draw.line(self.allCords[k][l]['cords'][m],\\n self.allCords[k][l]['col'],self.thickness)\\n del self.draw\\n self.exportImg()\",\n \"def setColors(self):\\n #productive\\n profprint()\\n self.color= [[0,0,0] for i in range(205)]\\n self.color255= self.setColors255()\\n for i in range(205):\\n for j in range(3):\\n self.color[i][j] = self.color255[i][j]/float(255)\\n\\n return self.color\",\n \"def render_environment(self):\\n board_img = make_img_of_board(*self.hist[-1])\\n return board_img\",\n \"def draw_board(grid, scale, path):\\n\\n def color_map(tile):\\n return COLORS[tile.terrain]\\n\\n draw_hex_grid(grid, scale, color_map, path, draw_components)\",\n \"def get_image(self):\\n img = cv2.cvtColor(self.coo.get_image(), cv2.COLOR_GRAY2RGB)\\n\\n for container in self.containers:\\n x, y, w, h = container # unpacking\\n cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 5)\\n\\n for coverslip in self.coverslips:\\n center = (coverslip[0], coverslip[1])\\n # circle center\\n cv2.circle(img, center, 3, (255, 0, 0), 5)\\n # circle outline\\n radius = coverslip[2]\\n cv2.circle(img, center, radius, (255, 0, 0), 5)\\n\\n return img\",\n \"def show(self):\\n data = []\\n for row in self.grid:\\n mid, bottom = [], []\\n for node in row:\\n \\tmid += [0, int(node.right)]\\n \\tbottom += [int(node.down), 1]\\n data += mid + [0] + bottom + [0] \\n data[self.width*2+1] = 1\\n data[-1] = 1\\n data += (self.width*2) * [0]\\n im = Image.new('1', (self.width*2+1, self.height*2+1))\\n im.putdata(data)\\n im.save('maze.png')\\n im.show()\",\n \"def _make_image(self):\\n self.image.fill(0)\\n self.image[:, :, PillEater.WALLS] = self.walls\\n self.image[:, :, PillEater.FOOD] = self.world_state['food']\\n self.image[self.world_state['pillman']['pos'][0], self.world_state['pillman']['pos'][1],\\n PillEater.PILLMAN] = 1\\n for ghost in self.world_state['ghosts']:\\n edibility = self.world_state['power'] / float(self.pill_duration)\\n self.image[ghost['pos'][0], ghost['pos'][1], PillEater.GHOSTS] = 1. - edibility\\n self.image[ghost['pos'][0], ghost['pos'][1], PillEater.GHOSTS_EDIBLE] = edibility\\n for pill in self.world_state['pills']:\\n self.image[pill['pos'][0], pill['pos'][1], PillEater.PILL] = 1\\n return self.image\",\n \"def _toRgbImage(self, fractal, colors, color_offset):\\n soln_real = adjustRange(fractal[0], 0, 127)\\n soln_imag = adjustRange(fractal[1], 0, 127)\\n iters = adjustRange(fractal[2], 0, 128)\\n\\n rgb_image = np.array([\\n soln_real + iters,\\n soln_imag + iters,\\n iters\\n ]\\n ).astype(dtype=np.uint8)\\n\\n return rgb_image.T\",\n \"def makeImage(self):\\n\\n for row in range(self.height):\\n self.makeRow(row)\\n self.window.update() # display a row of pixels\",\n \"def draw_grid(self):\\n\\n # Draw horizontal lines\\n for row in range(self.num_rows + 1):\\n left = row_column_to_pixels(row, 0)\\n right = row_column_to_pixels(row, self.num_cols)\\n pygame.draw.line(self.screen, COLOR_MAP['gray'], left, right)\\n\\n # Draw vertical lines\\n for col in range(self.num_cols + 1):\\n top = row_column_to_pixels(0, col)\\n bottom = row_column_to_pixels(self.num_rows, col)\\n pygame.draw.line(self.screen, COLOR_MAP['gray'], top, bottom)\",\n \"def _show_rgb(self):\\n R, G, B = self._rgb_frames()\\n image = numpy.dstack((R, G, B))\\n imageItem = self.parent.image.getImageItem()\\n imageItem.updateImage(image)\",\n \"def grid_image(output):\\n grid = []\\n for data in output:\\n grid += [make_grid(data, nrow=5, normalize=True)]\\n return grid\",\n \"def draw_board(surface, board, player_number):\\n grid_img = pygame.image.load('images/grid.png')\\n surface.blit(grid_img, (0, 0))\\n for row in range(len(board.get_grid())):\\n for col in range(len(board.get_grid()[0])):\\n x = col * TILE_SIZE\\n y = row * TILE_SIZE\\n tile = board.get_coord(row, col)\\n if tile is not None:\\n draw_tile(surface, tile, x, y, castle_color=('images/' + CASTLES[player_number]))\",\n \"def draw(self):\\n self.draw_occupied_cells()\\n self.draw_open_cells()\\n self.draw_edges()\\n plt.xlabel(\\\"Red\\\")\\n plt.ylabel(\\\"Black\\\")\\n plt.title('Hex')\\n self.camera.snap()\",\n \"def create_image(self):\\n\\n self._image = 255 * np.ones((self._height, self._width, 3), np.uint8)\",\n \"def __generate_image(self):\\n\\t\\tself.img = np.ones((self.size*self.width+self.border,self.size*self.width+self.border,1), np.uint8)*255\\n\\t\\tfor i in range(len(self.matrix)):\\n\\t\\t\\tfor j in range(len(self.matrix)):\\n\\t\\t\\t\\tif self.matrix[j][i] == 1:\\n\\t\\t\\t\\t\\tself.img = cv2.rectangle(self.img,(i*self.width+int(self.border/2),j*self.width+int(self.border/2))\\n\\t\\t\\t\\t\\t\\t,(i*self.width+self.width+int(self.border/2),j*self.width+self.width+int(self.border/2)),(0,0,0),-1)\\n\\t\\tif '.' in self.name:\\n\\t\\t\\tcv2.imwrite(self.name,self.img)\\n\\t\\telse:\\n\\t\\t\\tcv2.imwrite(self.name+'.jpg',self.img)\\n\\t\\tcv2.imshow(\\\"Image\\\",self.img)\\n\\t\\tcv2.waitKey(0)\\n\\t\\tcv2.destroyAllWindows()\",\n \"def draw_grid(self):\\n self.screen.draw_many_tiles(tile for tile in self.iter_grid_tiles())\\n pass\",\n \"def colors(self):\\n\\t\\treturn [(0, 30, 255),(0, 30, 120)]\",\n \"def to_color(self):\\n if self.channels == 4:\\n color = opencv.cvtColor(self.img, opencv.COLOR_BGRA2BGR)\\n return Image(color)\\n elif self.channels == 1:\\n color = opencv.cvtColor(self.img, opencv.COLOR_GRAY2BGR)\\n return Image(color)\\n else:\\n return Image(self.img)\",\n \"def test_grdimage_file():\\n fig = Figure()\\n fig.grdimage(\\n \\\"@earth_relief_01d_g\\\",\\n cmap=\\\"ocean\\\",\\n region=[-180, 180, -70, 70],\\n projection=\\\"W0/10i\\\",\\n shading=True,\\n )\\n return fig\",\n \"def _toRgbImage(self, fractal, colors, color_offset):\\n hsv_img = np.array(\\n [\\n # Cycle through color wheel.\\n (fractal * colors + color_offset) % 1,\\n\\n # Saturation = 1 where fractal values > 0,\\n # Saturation = 0 otherwise.\\n fractal.astype(dtype=bool).astype(dtype=float),\\n\\n # Invert colours\\n 1 - fractal\\n ]\\n ).astype(dtype=float).T\\n\\n rgb_img = (mpl.colors.hsv_to_rgb(hsv_img) * 255).astype(dtype=np.uint8)\\n return rgb_img\",\n \"def draw_board(self, board: BoardModel):\\n self._picture=[]\\n self._board = board\\n self.delete(tk.ALL)\\n\\n for i in range(self._grid_size):\\n for j in range(self._grid_size):\\n char = self._board.get_game()[self.position_to_index((j, i), self._grid_size)]\\n x1 = i * 60\\n y1 = j * 60\\n x2 = x1 + 60\\n y2 = y1 + 60\\n\\n #insert the image of unexposed cell\\n if char == UNEXPOSED:\\n photo=tk.PhotoImage(file=\\\"./images/unrevealed.gif\\\")\\n\\n\\n # insert the image of exposed cell\\n elif char == EXPOSED:\\n photo=tk.PhotoImage(file=\\\"./images/zero_adjacent.gif\\\")\\n\\n # insert the image of cell of pokemon\\n elif char == POKEMON:\\n pokemon_list=[\\\"./images/pokemon_sprites/charizard.gif\\\",\\n \\\"./images/pokemon_sprites/cyndaquil.gif\\\",\\n \\\"./images/pokemon_sprites/pikachu.gif\\\",\\n \\\"./images/pokemon_sprites/psyduck.gif\\\",\\n \\\"./images/pokemon_sprites/togepi.gif\\\"]\\n a=random.choice(pokemon_list)\\n photo = tk.PhotoImage(file=a)\\n print(a)\\n\\n # insert the image of cell of digit\\n elif char.isdigit():\\n if char==\\\"1\\\":\\n photo = tk.PhotoImage(file=\\\"./images/one_adjacent.gif\\\")\\n\\n elif char==\\\"2\\\":\\n photo = tk.PhotoImage(file=\\\"./images/two_adjacent.gif\\\")\\n\\n elif char==\\\"3\\\":\\n photo = tk.PhotoImage(file=\\\"./images/three_adjacent.gif\\\")\\n\\n elif char==\\\"4\\\":\\n photo = tk.PhotoImage(file=\\\"./images/four_adjacent.gif\\\")\\n\\n elif char==\\\"5\\\":\\n photo = tk.PhotoImage(file=\\\"./images/five_adjacent.gif\\\")\\n\\n elif char==\\\"6\\\":\\n photo = tk.PhotoImage(file=\\\"./images/six_adjacent.gif\\\")\\n\\n elif char==\\\"7\\\":\\n photo = tk.PhotoImage(file=\\\"./images/seven_adjacent.gif\\\")\\n\\n else:\\n photo = tk.PhotoImage(file=\\\"./images/eight_adjacent.gif\\\")\\n\\n # insert the image of pokeball\\n elif char == FLAG:\\n photo = tk.PhotoImage(file=\\\"./images/pokeball.gif\\\")\\n\\n self.create_image(x1 + 60 / 2, y1 + 60 / 2, image=photo)\\n self._picture.append(photo)\\n\\n self.bind_clicks()\",\n \"def setColors255(self):\\r\\n # productive\\r\\n profprint()\\r\\n self.color255 = [[0, 0, 0] for i in range(MAXCOL)]\\r\\n self.color255[0] = [221, 108, 158]\\r\\n self.color255[1] = [128, 174, 128]\\r\\n self.color255[2] = [241, 214, 145]\\r\\n self.color255[3] = [177, 122, 101]\\r\\n self.color255[4] = [111, 184, 210]\\r\\n self.color255[5] = [216, 101, 79]\\r\\n self.color255[6] = [221, 130, 101]\\r\\n self.color255[7] = [144, 238, 144]\\r\\n self.color255[8] = [192, 104, 88]\\r\\n self.color255[9] = [220, 245, 20]\\r\\n self.color255[10] = [78, 63, 0]\\r\\n self.color255[11] = [255, 250, 220]\\r\\n self.color255[12] = [230, 220, 70]\\r\\n self.color255[13] = [200, 200, 235]\\r\\n self.color255[14] = [250, 250, 210]\\r\\n self.color255[15] = [244, 214, 49]\\r\\n self.color255[16] = [0, 151, 206]\\r\\n self.color255[17] = [183, 156, 220]\\r\\n self.color255[18] = [183, 214, 211]\\r\\n self.color255[19] = [152, 189, 207]\\r\\n self.color255[20] = [178, 212, 242]\\r\\n self.color255[21] = [68, 172, 100]\\r\\n self.color255[22] = [111, 197, 131]\\r\\n self.color255[23] = [85, 188, 255]\\r\\n self.color255[24] = [0, 145, 30]\\r\\n self.color255[25] = [214, 230, 130]\\r\\n self.color255[26] = [218, 255, 255]\\r\\n self.color255[27] = [170, 250, 250]\\r\\n self.color255[28] = [140, 224, 228]\\r\\n self.color255[29] = [188, 65, 28]\\r\\n self.color255[30] = [216, 191, 216]\\r\\n self.color255[31] = [145, 60, 66]\\r\\n self.color255[32] = [150, 98, 83]\\r\\n self.color255[33] = [250, 250, 225]\\r\\n self.color255[34] = [200, 200, 215]\\r\\n self.color255[35] = [68, 131, 98]\\r\\n self.color255[36] = [83, 146, 164]\\r\\n self.color255[37] = [162, 115, 105]\\r\\n self.color255[38] = [141, 93, 137]\\r\\n self.color255[39] = [182, 166, 110]\\r\\n self.color255[40] = [188, 135, 166]\\r\\n self.color255[41] = [154, 150, 201]\\r\\n self.color255[42] = [177, 140, 190]\\r\\n self.color255[43] = [30, 111, 85]\\r\\n self.color255[44] = [210, 157, 166]\\r\\n self.color255[45] = [48, 129, 126]\\r\\n self.color255[46] = [98, 153, 112]\\r\\n self.color255[47] = [69, 110, 53]\\r\\n self.color255[48] = [166, 113, 137]\\r\\n self.color255[49] = [122, 101, 38]\\r\\n self.color255[50] = [253, 135, 192]\\r\\n self.color255[51] = [145, 92, 109]\\r\\n self.color255[52] = [46, 101, 131]\\r\\n self.color255[53] = [0, 108, 112]\\r\\n self.color255[54] = [127, 150, 88]\\r\\n self.color255[55] = [159, 116, 163]\\r\\n self.color255[56] = [125, 102, 154]\\r\\n self.color255[57] = [106, 174, 155]\\r\\n self.color255[58] = [154, 146, 83]\\r\\n self.color255[59] = [126, 126, 55]\\r\\n self.color255[60] = [201, 160, 133]\\r\\n self.color255[61] = [78, 152, 141]\\r\\n self.color255[62] = [174, 140, 103]\\r\\n self.color255[63] = [139, 126, 177]\\r\\n self.color255[64] = [148, 120, 72]\\r\\n self.color255[65] = [186, 135, 135]\\r\\n self.color255[66] = [99, 106, 24]\\r\\n self.color255[67] = [156, 171, 108]\\r\\n self.color255[68] = [64, 123, 147]\\r\\n self.color255[69] = [138, 95, 74]\\r\\n self.color255[70] = [97, 113, 158]\\r\\n self.color255[71] = [126, 161, 197]\\r\\n self.color255[72] = [194, 195, 164]\\r\\n self.color255[73] = [88, 106, 215]\\r\\n self.color255[74] = [82, 174, 128]\\r\\n self.color255[75] = [57, 157, 110]\\r\\n self.color255[76] = [60, 143, 83]\\r\\n self.color255[77] = [92, 162, 109]\\r\\n self.color255[78] = [255, 244, 209]\\r\\n self.color255[79] = [201, 121, 77]\\r\\n self.color255[80] = [70, 163, 117]\\r\\n self.color255[81] = [188, 91, 95]\\r\\n self.color255[82] = [166, 84, 94]\\r\\n self.color255[83] = [182, 105, 107]\\r\\n self.color255[84] = [229, 147, 118]\\r\\n self.color255[85] = [174, 122, 90]\\r\\n self.color255[86] = [201, 112, 73]\\r\\n self.color255[87] = [194, 142, 0]\\r\\n self.color255[88] = [241, 213, 144]\\r\\n self.color255[89] = [203, 179, 77]\\r\\n self.color255[90] = [229, 204, 109]\\r\\n self.color255[91] = [255, 243, 152]\\r\\n self.color255[92] = [209, 185, 85]\\r\\n self.color255[93] = [248, 223, 131]\\r\\n self.color255[94] = [255, 230, 138]\\r\\n self.color255[95] = [196, 172, 68]\\r\\n self.color255[96] = [255, 255, 167]\\r\\n self.color255[97] = [255, 250, 160]\\r\\n self.color255[98] = [255, 237, 145]\\r\\n self.color255[99] = [242, 217, 123]\\r\\n self.color255[100] = [222, 198, 101]\\r\\n self.color255[101] = [213, 124, 109]\\r\\n self.color255[102] = [184, 105, 108]\\r\\n self.color255[103] = [150, 208, 243]\\r\\n self.color255[104] = [62, 162, 114]\\r\\n self.color255[105] = [242, 206, 142]\\r\\n self.color255[106] = [250, 210, 139]\\r\\n self.color255[107] = [255, 255, 207]\\r\\n self.color255[108] = [182, 228, 255]\\r\\n self.color255[109] = [175, 216, 244]\\r\\n self.color255[110] = [197, 165, 145]\\r\\n self.color255[111] = [172, 138, 115]\\r\\n self.color255[112] = [202, 164, 140]\\r\\n self.color255[113] = [224, 186, 162]\\r\\n self.color255[114] = [255, 245, 217]\\r\\n self.color255[115] = [206, 110, 84]\\r\\n self.color255[116] = [210, 115, 89]\\r\\n self.color255[117] = [203, 108, 81]\\r\\n self.color255[118] = [233, 138, 112]\\r\\n self.color255[119] = [195, 100, 73]\\r\\n self.color255[120] = [181, 85, 57]\\r\\n self.color255[121] = [152, 55, 13]\\r\\n self.color255[122] = [159, 63, 27]\\r\\n self.color255[123] = [166, 70, 38]\\r\\n self.color255[124] = [218, 123, 97]\\r\\n self.color255[125] = [225, 130, 104]\\r\\n self.color255[126] = [224, 97, 76]\\r\\n self.color255[127] = [184, 122, 154]\\r\\n self.color255[128] = [211, 171, 143]\\r\\n self.color255[129] = [47, 150, 103]\\r\\n self.color255[130] = [173, 121, 88]\\r\\n self.color255[131] = [188, 95, 76]\\r\\n self.color255[132] = [255, 239, 172]\\r\\n self.color255[133] = [226, 202, 134]\\r\\n self.color255[134] = [253, 232, 158]\\r\\n self.color255[135] = [244, 217, 154]\\r\\n self.color255[136] = [205, 179, 108]\\r\\n self.color255[137] = [186, 124, 161]\\r\\n self.color255[138] = [255, 255, 220]\\r\\n self.color255[139] = [234, 234, 194]\\r\\n self.color255[140] = [204, 142, 178]\\r\\n self.color255[141] = [180, 119, 153]\\r\\n self.color255[142] = [216, 132, 105]\\r\\n self.color255[143] = [255, 253, 229]\\r\\n self.color255[144] = [205, 167, 142]\\r\\n self.color255[145] = [204, 168, 143]\\r\\n self.color255[146] = [255, 224, 199]\\r\\n self.color255[147] = [139, 150, 98]\\r\\n self.color255[148] = [249, 180, 111]\\r\\n self.color255[149] = [157, 108, 162]\\r\\n self.color255[150] = [203, 136, 116]\\r\\n self.color255[151] = [185, 102, 83]\\r\\n self.color255[152] = [247, 182, 164]\\r\\n self.color255[153] = [222, 154, 132]\\r\\n self.color255[154] = [124, 186, 223]\\r\\n self.color255[155] = [249, 186, 150]\\r\\n self.color255[156] = [244, 170, 147]\\r\\n self.color255[157] = [255, 181, 158]\\r\\n self.color255[158] = [255, 190, 165]\\r\\n self.color255[159] = [227, 153, 130]\\r\\n self.color255[160] = [213, 141, 113]\\r\\n self.color255[161] = [193, 123, 103]\\r\\n self.color255[162] = [216, 146, 127]\\r\\n self.color255[163] = [230, 158, 140]\\r\\n self.color255[164] = [245, 172, 147]\\r\\n self.color255[165] = [241, 172, 151]\\r\\n self.color255[166] = [177, 124, 92]\\r\\n self.color255[167] = [171, 85, 68]\\r\\n self.color255[168] = [217, 198, 131]\\r\\n self.color255[169] = [212, 188, 102]\\r\\n self.color255[170] = [185, 135, 134]\\r\\n self.color255[171] = [198, 175, 125]\\r\\n self.color255[172] = [194, 98, 79]\\r\\n self.color255[173] = [255, 238, 170]\\r\\n self.color255[174] = [206, 111, 93]\\r\\n self.color255[175] = [216, 186, 0]\\r\\n self.color255[176] = [255, 226, 77]\\r\\n self.color255[177] = [255, 243, 106]\\r\\n self.color255[178] = [255, 234, 92]\\r\\n self.color255[179] = [240, 210, 35]\\r\\n self.color255[180] = [224, 194, 0]\\r\\n self.color255[181] = [213, 99, 79]\\r\\n self.color255[182] = [217, 102, 81]\\r\\n self.color255[183] = [0, 147, 202]\\r\\n self.color255[184] = [0, 122, 171]\\r\\n self.color255[185] = [186, 77, 64]\\r\\n self.color255[186] = [240, 255, 30]\\r\\n self.color255[187] = [185, 232, 61]\\r\\n self.color255[188] = [0, 226, 255]\\r\\n self.color255[189] = [251, 159, 255]\\r\\n self.color255[190] = [230, 169, 29]\\r\\n self.color255[191] = [0, 194, 113]\\r\\n self.color255[192] = [104, 160, 249]\\r\\n self.color255[193] = [221, 108, 158]\\r\\n self.color255[194] = [137, 142, 0]\\r\\n self.color255[195] = [230, 70, 0]\\r\\n self.color255[196] = [0, 147, 0]\\r\\n self.color255[197] = [0, 147, 248]\\r\\n self.color255[198] = [231, 0, 206]\\r\\n self.color255[199] = [129, 78, 0]\\r\\n self.color255[200] = [0, 116, 0]\\r\\n self.color255[201] = [0, 0, 255]\\r\\n self.color255[202] = [157, 0, 0]\\r\\n self.color255[203] = [100, 100, 130]\\r\\n self.color255[204] = [205, 205, 100]\\r\\n self.color255[205] = [255, 255, 0]\\r\\n\\r\\n return self.color255\",\n \"def get_placeholder_image(self):\\n\\n grid = Gtk.Grid(name=\\\"output-placeholder_grid\\\", halign=3, valign=3)\\n label = Gtk.Label(name=\\\"output-placeholder_label\\\",\\n label=\\\"<b> Query to show outputs </b>\\\",\\n use_markup=True)\\n image = Gtk.Image.new_from_icon_name(\\\"help-about\\\", 6)\\n image.set_name(\\\"output-placeholder_image\\\")\\n grid.attach(image, 0, 0, 1, 1)\\n grid.attach(label, 0, 1, 1, 1)\\n return grid\",\n \"def draw_board(self):\\r\\n for i in range(self.size):\\r\\n for k in range(self.size):\\r\\n left = k * self.CELL_SIZE + (k+1) * self.BORDER_WIDTH\\r\\n top = i * self.CELL_SIZE + (i+1) * self.BORDER_WIDTH\\r\\n rect = pygame.Rect(left, top, self.CELL_SIZE, self.CELL_SIZE)\\r\\n color = self.BG_COLOR\\r\\n if self.map[i][k] == self.BLOCK_CHAR:\\r\\n color = self.BLOCK_COLOR\\r\\n elif self.map[i][k] == self.START_CHAR:\\r\\n color = self.START_COLOR\\r\\n elif self.map[i][k] == self.END_CHAR:\\r\\n color = self.END_COLOR\\r\\n elif (k, i) in self.path:\\r\\n color = self.PATH_COLOR\\r\\n pygame.draw.rect(self.screen, color, rect)\",\n \"def color(self):\\n if self._simplecell:\\n self.fetch()\\n return self._color\",\n \"def update(self, grid, colRamp = ['white', 'blue']):\\n \\n # update the cell colors\\n for y in range(len(grid)):\\n yl = y + 1\\n for x in range(len(grid[y])):\\n xl = x + 1\\n color = colRamp[int(grid[y][x])]\\n self.displayWindow.update((xl, yl), color)\\n\\n # refresh the window\\n self.displayWindow.tkupdate()\",\n \"def gridPlot6(img_stack):\\r\\n F = plt.figure(figsize = (20,20))\\r\\n F.subplots_adjust(left = 0.05, right = 0.95)\\r\\n grid = ImageGrid(F, 142, nrows_ncols = (2,3), axes_pad = 0.0, share_all = True,\\r\\n label_mode = \\\"L\\\", cbar_location = \\\"top\\\", cbar_mode = \\\"single\\\")\\r\\n \\r\\n i = 0\\r\\n for img in img_stack[0:6]:\\r\\n im = grid[i].imshow(img, interpolation = \\\"nearest\\\", vmin = 0, vmax = 255)\\r\\n i += 1 \\r\\n grid.cbar_axes[0].colorbar(im)\\r\\n plt.savefig('gplot16.png')\\r\\n if 'gplot16.png' in os.listdir():\\r\\n plt.savefig('gplot16_2.png')\\r\\n# for cax in grid.cbar_axes:\\r\\n# cax.toggle_label(False)\\r\\n return\",\n \"def get_one_PIL(colmap, fname=None, Nx=255):\\n \\n import Image\\n Ny = 50\\n im = Image.new(\\\"RGB\\\", (Nx, Ny))\\n for nx in range(Nx):\\n z = nx/(Nx-1.0)\\n r,g,b = colmap.colfct(z)\\n r = int(255*r)\\n g = int(255*g)\\n b = int(255*b)\\n for ny in range(Ny):\\n im.putpixel((nx,ny),(r,g,b))\\n if fname:\\n print \\\"saving:\\\", fname+\\\".jpg\\\"\\n im.save(fname+\\\".jpg\\\", \\\"JPEG\\\")\\n im.save(fname+\\\".png\\\", \\\"PNG\\\")\\n ## im.show()\\n return im\",\n \"def draw_hex_grid(grid, scale, color_func, path, final_func=None):\\n dim = grid.dimensions\\n grid_dim = (\\n int(2 * scale * (0.25 + 0.75 * dim[0])),\\n int(_R3 * scale * (0.5 + dim[1])) + 1,\\n )\\n\\n image = Image.new(\\\"RGB\\\", grid_dim)\\n\\n drawer = ImageDraw.Draw(image)\\n for pos, tile in grid:\\n center = scale * (_R3 * np.dot(AOTRANSFORM, pos[:2]) + [1, _R3 / 2])\\n draw_poly(\\n drawer,\\n center=center,\\n sides=6,\\n scale=scale,\\n color=color_func(tile),\\n border=COLORS[\\\"white\\\"],\\n )\\n\\n if final_func is not None:\\n final_func(drawer, center, scale, tile)\\n\\n image.save(path)\",\n \"def add_color(self, markers):\\n self.img[markers == -1] = [255, 255, 255]\\n self.img[markers == 1] = [255, 0, 0]\\n self.img[markers == 2] = [0, 50, 0]\\n self.img[markers == 3] = [0, 0, 255]\\n self.img[markers == 4] = [255, 255, 0]\\n self.img[markers == 5] = [0, 255, 255]\\n self.img[markers == 6] = [255, 0, 255]\\n\\n self.img[markers == 7] = [125, 0, 0]\\n self.img[markers == 8] = [0, 125, 0]\\n self.img[markers == 9] = [0, 0, 125]\\n self.img[markers == 10] = [125, 125, 0]\\n self.img[markers == 11] = [0, 125, 125]\\n self.img[markers == 12] = [125, 0, 125]\\n\\n self.img[markers == 13] = [255, 255, 255]\\n self.img[markers == 14] = [255, 0, 0]\\n self.img[markers == 15] = [0, 255, 0]\\n self.img[markers == 16] = [0, 0, 255]\\n self.img[markers == 17] = [255, 255, 0]\\n self.img[markers == 18] = [0, 255, 255]\\n self.img[markers == 19] = [255, 0, 255]\",\n \"def getImage(self, x, y, w, h):\\n w = min(w, self.__width - x)\\n h = min(h, self.__height - y)\\n r = Canvas(w, h)\\n\\n for col in range(w):\\n r.writeCol(col, self.getCol(x+col, y))\\n\\n return r\",\n \"def _toRgbImage(self, fractal, colors, color_offset):\\n hsv_img = np.array(\\n [\\n # Cycle through color wheel.\\n (fractal * colors + color_offset) % 1,\\n\\n # Saturation = fractal value.\\n fractal,\\n\\n # Maximum value.\\n np.ones(fractal.shape)\\n ]\\n ).astype(dtype=float).T\\n\\n rgb_img = (mpl.colors.hsv_to_rgb(hsv_img) * 255).astype(dtype=np.uint8)\\n return rgb_img\",\n \"def __enhance_image(self, img):\\n\\n blue = self.g.clahe.apply(img[:,:,0])\\n green = self.g.clahe.apply(img[:,:,1])\\n red = self.g.clahe.apply(img[:,:,2])\\n img[:,:,0] = blue\\n img[:,:,1] = green\\n img[:,:,2] = red\\n return img\",\n \"def visualize_M_gridworld(self, state=0):\\n\\n\\t\\tplt.subplot(221); plt.imshow(self.M[12,0,:].reshape(5,5)), plt.colorbar()\\n\\t\\tplt.subplot(222); plt.imshow(self.M[12,1,:].reshape(5,5)), plt.colorbar()\\n\\t\\tplt.subplot(223); plt.imshow(self.M[12,2,:].reshape(5,5)), plt.colorbar()\\n\\t\\tplt.subplot(224); plt.imshow(self.M[12,3,:].reshape(5,5)), plt.colorbar()\\n\\t\\tplt.show()\",\n \"def draw_image(self):\\n \\n pixel_array = self.imageprepare(self.image_path)\\n newArr = self.reshape_pixel_array(pixel_array)\\n plt.imshow(newArr, interpolation='nearest')\\n plt.savefig('MNIST_IMAGE.png')#save MNIST image\\n plt.show()#Show / plot that image\",\n \"def drawRect (self, x, y, w, h, colour):\\r\\n for i in range (y,y+h):\\r\\n row = self.image [i]\\r\\n\\r\\n for j in range (x,x+w):\\r\\n row [j] = colour\",\n \"def plot_final_grid(generated_images):\\n\\n fig = plt.figure(figsize=(8, 6))\\n fig.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=0.001, hspace=0.001)\\n\\n for ind in range(GRID_IMGS):\\n ax = fig.add_subplot(GRID_SIZE, GRID_SIZE, ind + 1, xticks=[], yticks=[])\\n ax.imshow(np.uint8(((generated_images[ind] + 1) / 2) * 255), cmap='gray')\\n\\n plt.savefig('/content/drive/My Drive/WGAN/generated_image_grid.png')\\n plt.savefig('/content/drive/My Drive/WGAN/results/DCGAN.png')\\n plt.show()\",\n \"def testImageProcessing():\\n Im_pix = getRGB( 'in.png' ) # read in the in.png image\\n print \\\"The first two pixels of the first row are\\\",\\n print Im_pix[0][0:2]\\n # remember that Im_pix is a list (the image)\\n # of lists (each row) of lists (each pixel is [R,G,B])\\n New_pix = [ [ [255 - num for num in p] for p in row ] for row in Im_pix ]\\n # now, save to the file 'out.png'\\n saveRGB( New_pix, 'out.png' )\",\n \"def generate_image(row):\\n image = np.zeros((960, 1280, 4), np.uint8) \\n image[:,:] = (128, 128, 128, 255)\\n x = int(row['currXcorrected'])\\n y = int(row['currYcorrected']) \\n path = \\\"/net/store/nbp/projects/fixdur/stimuli/single_bubble_images/\\\" + \\\\\\n str(row.loc['image']) + \\\"/\\\" + \\\\\\n \\\"bubble_\\\" + str(x) + \\\"_\\\" + str(y) + \\\".tiff\\\"\\n bubble = prepare_bubble(path) \\n image[y:y+154, x:x+154] = bubble\\n\\n return image\",\n \"def initImg(self):\\n self.img = Image.new('RGBA',(self.width,self.height),color='#' + getConfigPart(self.theme,\\\"bg\\\"))\\n self.draw = ImageDraw.Draw(self.img)\",\n \"def __init__(self, shape_num):\\n self.shape_num = shape_num\\n if shape_num == 1:\\n self.width = 4\\n self.height = 4\\n self.grid = [[0 for x in range(self.height)] for y in range(self.width)]\\n self.grid[0][2] = 1\\n self.grid[1][2] = 1\\n self.grid[2][2] = 1\\n self.grid[3][2] = 1\\n self.color = Color.SilverPink\\n elif shape_num == 2:\\n self.width = 3\\n self.height = 3\\n self.grid = [[0 for x in range(self.height)] for y in range(self.width)]\\n self.grid[0][1] = 1\\n self.grid[0][2] = 1\\n self.grid[1][2] = 1\\n self.grid[2][2] = 1\\n self.color = Color.TuftsBlue\\n elif shape_num == 3:\\n self.width = 3\\n self.height = 3\\n self.grid = [[0 for x in range(self.height)] for y in range(self.width)]\\n self.grid[2][1] = 1\\n self.grid[0][2] = 1\\n self.grid[1][2] = 1\\n self.grid[2][2] = 1\\n self.color = Color.ChromeYellow\\n elif shape_num == 4:\\n self.width = 2\\n self.height = 2\\n self.grid = [[0 for x in range(self.height)] for y in range(self.width)]\\n self.grid[0][0] = 1\\n self.grid[0][1] = 1\\n self.grid[1][0] = 1\\n self.grid[1][1] = 1\\n self.color = Color.Independence\\n elif shape_num == 5:\\n self.width = 3\\n self.height = 3\\n self.grid = [[0 for x in range(self.height)] for y in range(self.width)]\\n self.grid[1][0] = 1\\n self.grid[2][0] = 1\\n self.grid[0][1] = 1\\n self.grid[1][1] = 1\\n self.color = Color.ForestGreen\\n elif shape_num == 6:\\n self.width = 3\\n self.height = 3\\n self.grid = [[0 for x in range(self.height)] for y in range(self.width)]\\n self.grid[1][1] = 1\\n self.grid[0][2] = 1\\n self.grid[1][2] = 1\\n self.grid[2][2] = 1\\n self.color = Color.Byzantine\\n elif shape_num == 7:\\n self.width = 3\\n self.height = 3\\n self.grid = [[0 for x in range(self.height)] for y in range(self.width)]\\n self.grid[0][0] = 1\\n self.grid[1][0] = 1\\n self.grid[1][1] = 1\\n self.grid[2][1] = 1\\n self.color = Color.Coquelicot\\n self.top_space = self.get_top_space()\\n self.bottom_space = self.get_bottom_space()\\n self.x = int((12 - self.width) / 2)\\n self.y = 1 - self.top_space\\n self.last_drop_time = perf_counter()\",\n \"def drawGrid(self):\\n for div in range(NBCELL):\\n sec = SSIZE*div\\n self.can.create_line(0, sec, GSIZE, sec, width=3, fill=GFILL)\\n self.can.create_line(sec, 0, sec, GSIZE, width=3, fill=GFILL)\",\n \"def draw_H(image, coords, color=(0, 255, 0)):\\n image_with_H = image.copy()\\n\\n canvas = image_with_H[coords[0]:coords[0] + 24,\\n coords[1]:coords[1] + 20]\\n\\n canvas[:, :3] = color\\n canvas[:, -3:] = color\\n canvas[11:14] = color\\n\\n return image_with_H\",\n \"def draw_grid(grid):\\n \\n # Tile size variables\\n tile_width = STAGE_WIDTH / GRID_WIDTH\\n tile_height = STAGE_HEIGHT / GRID_HEIGHT\\n \\n for i in range(GRID_WIDTH):\\n for j in range(GRID_HEIGHT):\\n elev = grid[i][j]\\n rect_x = i * tile_width\\n rect_y = j * tile_height\\n pygame.draw.rect(STAGE, get_color(elev),\\n (rect_x, rect_y, tile_width, tile_height))\",\n \"def test_grdimage_default_no_shading(grid, shading):\\n grid_ = grid.sel(lat=slice(-30, 30))\\n fig = Figure()\\n fig.grdimage(grid_, cmap=\\\"earth\\\", projection=\\\"M6i\\\", shading=shading)\\n return fig\",\n \"def update_image_frame(selfie=False):\\n for _row in range(0, GRID_AXIS):\\n for _col in range(0, GRID_AXIS):\\n if selfie:\\n color_index = GRID_DATA[GRID_AXIS - 1 - _row][_col]\\n else:\\n color_index = GRID_DATA[GRID_AXIS - 1 - _row][GRID_AXIS - 1 - _col]\\n color = index_to_rgb(round(color_index * PALETTE_SIZE, 0) / PALETTE_SIZE)\\n if color != image_group[((_row * GRID_AXIS) + _col)].fill:\\n image_group[((_row * GRID_AXIS) + _col)].fill = color\",\n \"def draw_grid(self):\\n pygame.draw.rect(self.screen, BLACK,\\n (*grid_pos, WIDTH - 150, HEIGHT-150), 2)\\n for x in range(9):\\n pygame.draw.line(\\n self.screen,\\n BLACK,\\n (grid_pos[0] + (x * cell_size), grid_pos[1]),\\n (grid_pos[0] + (x * cell_size), grid_pos[1] + 450),\\n 2 if x % 3 == 0 else 1\\n )\\n pygame.draw.line(\\n self.screen,\\n BLACK,\\n (grid_pos[0], grid_pos[1] + (x * cell_size)),\\n (grid_pos[0] + 450, grid_pos[1] + (x * cell_size)),\\n 2 if x % 3 == 0 else 1\\n )\",\n \"def draw_grid(self):\\n for square in range(COLS+1):\\n #vertical lines\\n start_pos = (helpers.get_col_left_p(square),helpers.get_row_top_p(0))\\n end_pos = (helpers.get_col_left_p(square),helpers.get_row_top_p(ROWS))\\n pygame.draw.line(g.screen,WHITE,start_pos,end_pos)\\n for square in range(ROWS+1):\\n #horizontal lines\\n start_pos = (helpers.get_col_left_p(0),helpers.get_row_top_p(square))\\n end_pos = (helpers.get_col_left_p(COLS),helpers.get_row_top_p(square))\\n pygame.draw.line(g.screen,WHITE,start_pos,end_pos)\",\n \"def draw_grid(self, verbosity=0):\\n log.debug(\\\"Drawing grid\\\")\\n (x0, y0) = self.origin\\n color = (191, 191, 191)\\n\\n (w, h) = self.surface.get_size()\\n\\n i = x0\\n while True:\\n (x, ignore) = self.map_to_screen((i, 0))\\n if x > w:\\n break\\n pygame.draw.line(self.surface, color, (x, 0), (x, h), 1)\\n i += 10\\n\\n j = y0\\n while True:\\n (ignore, y) = self.map_to_screen((0, j))\\n if y > h:\\n break\\n pygame.draw.line(self.surface, color, (0, y), (w, y), 1)\\n j -= 10\",\n \"def _tile_image(self, data):\\n image = Image.open(BytesIO(data))\\n return image.convert('RGBA')\",\n \"def get_colors(self, image: np.ndarray, coordinates: np.ndarray) -> np.ndarray:\\r\\n x = coordinates.squeeze(1)\\r\\n return np.flip(image[x[:, 1], x[:, 0]].astype(np.float64) / 255.0, axis=1)\",\n \"def plot_final_grid(generated_images):\\n\\n fig = plt.figure(figsize=(GRID_SIZE, GRID_SIZE))\\n fig.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=0.001, hspace=0.001)\\n\\n for ind in range(GRID_IMGS):\\n ax = fig.add_subplot(GRID_SIZE, GRID_SIZE, ind + 1, xticks=[], yticks=[])\\n ax.imshow(np.uint8(((generated_images[ind] + 1) / 2) * 255), cmap='gray')\\n\\n plt.savefig('/content/drive/My Drive/WGAN/generated_image_grid.png')\\n plt.savefig('/content/drive/My Drive/WGAN/results/WGAN.png')\\n plt.show()\",\n \"def image(self, data):\\n # Assemble colormap from given parameters\\n cmap = self.config.get('cmap', None)\\n\\n # If a single color provided, prepend 'white' color, so that a resulting tuple defines binary colormap\\n if is_color_like(cmap):\\n cmap = (cmap, )\\n # If a tuple of colors provided in `cmap` argument convert it into a colormap\\n if isinstance(cmap, tuple):\\n cmap = make_cmap(colors=cmap)\\n else:\\n cmap = copy(plt.get_cmap(cmap))\\n # Set a color for nan/masked values display to colormap if provided\\n mask_color = self.config.get('mask_color', None)\\n cmap.set_bad(color=mask_color)\\n\\n image_keys = ['alpha', 'vmin', 'vmax', 'extent']\\n image_config = self.config.filter(keys=image_keys, prefix='image_')\\n\\n vmin, vmax = self._parse_vrange(data)\\n image_config['vmin'] = vmin\\n image_config['vmax'] = vmax\\n\\n image = self.ax.imshow(data, cmap=cmap, **image_config)\\n\\n return [image]\",\n \"def test_grdimage_grid_and_shading_with_xarray(grid, xrgrid):\\n fig_ref, fig_test = Figure(), Figure()\\n fig_ref.grdimage(\\n grid=\\\"@earth_relief_01d_g\\\", region=\\\"GL\\\", cmap=\\\"geo\\\", shading=xrgrid\\n )\\n fig_ref.colorbar()\\n fig_test.grdimage(grid=grid, region=\\\"GL\\\", cmap=\\\"geo\\\", shading=xrgrid)\\n fig_test.colorbar()\\n return fig_ref, fig_test\",\n \"def get_rgb(self, img, r, g, b):\\r\\n\\r\\n # Get specific bands of hyperspectral image\\r\\n red_channel = img[:, :, r]\\r\\n green_channel = img[:, :, g]\\r\\n blue_channel = img[:, :, b]\\r\\n\\r\\n img = np.stack((red_channel, green_channel, blue_channel), axis=2)\\r\\n img = img.astype('float32')\\r\\n return img\",\n \"def initialize_grid(self) -> None:\\n for i in range(self.grid_size[0]):\\n for j in range(self.grid_size[1]):\\n self.set(i, j, self.base_color)\",\n \"def visualize_grid(Xs, ubound=255.0, padding=1):\\n pixel_sz = 2\\n (H, W, C, N) = Xs.shape\\n\\n Xs_resize = np.zeros((H*pixel_sz, W*pixel_sz, C, N))\\n Xs = (ubound*(Xs-np.min(Xs))/(np.max(Xs)-np.min(Xs))).astype('uint8')\\n\\n for c in range(C):\\n for n in range(N):\\n Xs_resize[:,:,c,n] = imresize(Xs[:,:,c,n], 200, interp='nearest')\\n Xs = Xs_resize\\n\\n (H, W, C, N) = Xs.shape\\n low, high = np.min(Xs), np.max(Xs)\\n\\n if C==1 or C==3:\\n grid_size_H = int(ceil(sqrt(N)))\\n grid_size_W = int(ceil(sqrt(N)))\\n else:\\n grid_size_H = N\\n grid_size_W = C\\n\\n count = 0\\n grid_height = H * grid_size_H + padding * (grid_size_H-1)\\n grid_width = W * grid_size_W + padding * (grid_size_W-1)\\n grid = np.zeros((grid_height, grid_width, C))\\n y0, y1 = 0, H\\n for y in range(grid_size_H):\\n x0, x1 = 0, W\\n for x in range(grid_size_W):\\n if C==1 or C==3:\\n img = Xs[:,:,:,count]\\n count += 1\\n else:\\n img = np.expand_dims(Xs[:,:,x,y], axis=-1)\\n\\n grid[y0:y1, x0:x1, :] = ubound * (img - low) / (high - low)\\n x0 += W + padding\\n x1 += W + padding\\n\\n y0 += H + padding\\n y1 += H + padding\\n\\n if C!=3:\\n grid = grid[:,:,0]\\n return grid\",\n \"def blank_image(height, width):\\n all_green = create_uniform_image(height, width, [0, 255, 0])\\n return all_green\",\n \"def get_color(self):\\n self.view.present(\\n \\\"sheet\\\",\\n orientations=ORIENTATIONS,\\n )\\n self.view.wait_modal()\\n return self.rgb\",\n \"def _style_colours(self):\\n\\n pass\",\n \"def getColormapImage(self):\\n if self._colormapImage is None and self._colormap is not None:\\n image = numpy.zeros((16, 130, 3), dtype=numpy.uint8)\\n image[1:-1, 1:-1] = self._colormap.getNColors(image.shape[1] - 2)[:, :3]\\n self._colormapImage = convertArrayToQImage(image)\\n return self._colormapImage\",\n \"def createImageCell(self,control,cell,image):\\n b = ResizingCanvas(control,width=400,height=400,borderwidth=0,highlightthickness=0)\\n b.changeBackground(image)\\n i,j = cell\\n b.grid(row=i,column=j,sticky=N+E+W+S)\\n return b\",\n \"def generate_image_grid(sess, op):\\n n = 10\\n x_points = np.linspace(-20, 20, n)\\n y_points = np.linspace(-20, 20, n)\\n\\n nx, ny = len(x_points), len(y_points)\\n plt.subplot()\\n gs = gridspec.GridSpec(nx, ny, hspace=0.05, wspace=0.05)\\n\\n for i, g in enumerate(gs):\\n z = np.concatenate(([x_points[int(i / ny)]], [y_points[int(i % nx)]]))\\n z = np.reshape(z, (1, 2))\\n x = sess.run(op, feed_dict={decoder_input: z})\\n ax = plt.subplot(g)\\n img = np.array(x.tolist()).reshape(28, 28)\\n ax.imshow(img, cmap='gray')\\n ax.set_xticks([])\\n ax.set_yticks([])\\n ax.set_aspect('auto')\\n plt.show()\",\n \"def drawBox (self, left, top, width, height, colour):\\r\\n w = self.bih_vals [bih_Width]\\r\\n h = self.bih_vals [bih_Height]\\r\\n\\r\\n cols = [left, left + width - 1]\\r\\n rows = [top, top + height - 1]\\r\\n \\r\\n x0 = max ((0,left))\\r\\n x1 = min ((cols[1]+1, w))\\r\\n y0 = max ((0,top))\\r\\n y1 = min ((rows [1]+1, h))\\r\\n\\r\\n # rows\\r\\n\\r\\n for r in rows:\\r\\n if r >= 0 and r < h:\\r\\n row = self.image [r]\\r\\n for x in range (x0, x1):\\r\\n row [x] = colour\\r\\n\\r\\n # columns\\r\\n \\r\\n for y in range (y0, y1):\\r\\n row = self.image [y]\\r\\n for c in cols:\\r\\n if c >= 0 and c < w :\\r\\n row [c] = colour\",\n \"def color(self, data):\\n\\n red = np.interp(data, self.range, self.r)\\n blue = np.interp(data, self.range, self.b)\\n green = np.interp(data, self.range, self.g)\\n # Style plot to return a grey color when value is 'nan'\\n red[np.isnan(red)] = 240\\n blue[np.isnan(blue)] = 240\\n green[np.isnan(green)] = 240\\n colors = np.dstack([red.astype(np.uint8),\\n green.astype(np.uint8),\\n blue.astype(np.uint8),\\n np.full_like(data, 255, dtype=np.uint8)])\\n #return colors.view(dtype=np.uint32).reshape(data.shape)\\n c=[]\\n for i in range(len(data)):\\n c.append([red[i],green[i],blue[i]])\\n return c\",\n \"def show_image_grid(imgs):\\n grd = make_grid(imgs)\\n npimg = grd.numpy()\\n plt.imshow(np.transpose(npimg, (1,2,0)), interpolation='nearest')\\n plt.ion()\\n plt.show()\",\n \"def draw(self, color = Color.GREEN):\\n self.image[self.x, self.y] = color\",\n \"def get_BGR_img(self):\\n img = self.img.copy()\\n # Convert BGR to HSV\\n hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)\\n # define range of BGR color in HSV\\n threshold_blue = np.array([[100,43,46], [124,255,255]])\\n threshold_green = np.array([[35,43,46], [77,255,255]])\\n threshold_red1 = np.array([[0,43,46], [10,255,255]])\\n threshold_red2 = np.array([[156,43,46], [180,255,255]])\\n # Threshold the HSV image to get only BGR colors\\n mask_blue = cv2.inRange(hsv, threshold_blue[0], threshold_blue[1])\\n mask_green = cv2.inRange(hsv, threshold_green[0], threshold_green[1])\\n mask_red1 = cv2.inRange(hsv, threshold_red1[0], threshold_red1[1])\\n mask_red2 = cv2.inRange(hsv, threshold_red2[0], threshold_red2[1])\\n mask_red = mask_red1 | mask_red2\\n # Bitwise-AND mask and original image\\n self.blue = cv2.bitwise_and(img, img, mask=mask_blue)\\n self.green = cv2.bitwise_and(img, img, mask=mask_green)\\n self.red = cv2.bitwise_and(img, img, mask=mask_red)\\n # 返回 bgr 三通道的分量合成的图片\\n return np.stack((self.blue[:, :, 0], self.green[:, :, 1], self.red[:, :, 2]), axis=2)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6998966","0.65337056","0.6325635","0.6285329","0.6260909","0.62575686","0.6254772","0.62346655","0.618115","0.615539","0.61247164","0.6097528","0.6022386","0.60113466","0.6007067","0.59790057","0.59721226","0.5971848","0.59443724","0.5926389","0.59132975","0.5905877","0.5819653","0.5801772","0.57959956","0.5785412","0.5748199","0.5720399","0.57127094","0.56937337","0.5684972","0.5659638","0.5647575","0.56358147","0.5632957","0.5632761","0.56017303","0.5598985","0.55989504","0.55986524","0.55808616","0.5568692","0.556636","0.5565512","0.5539926","0.55327785","0.55263305","0.5498398","0.54726744","0.54592216","0.54576963","0.5454223","0.5454068","0.54451656","0.5437642","0.5436965","0.54276705","0.54230946","0.54224646","0.54207385","0.5416883","0.5413473","0.5411107","0.54082566","0.5402246","0.53944623","0.5394063","0.5384913","0.53817815","0.5381471","0.5380261","0.537874","0.53745323","0.5362182","0.5360063","0.53590083","0.5356583","0.53564626","0.5352692","0.533997","0.5336001","0.5326043","0.53221065","0.5318111","0.5314444","0.53117174","0.53107053","0.5310619","0.5310536","0.53094167","0.5307807","0.53054476","0.52971613","0.5295684","0.5288052","0.52844155","0.5282574","0.52753407","0.52721393","0.5268597"],"string":"[\n \"0.6998966\",\n \"0.65337056\",\n \"0.6325635\",\n \"0.6285329\",\n \"0.6260909\",\n \"0.62575686\",\n \"0.6254772\",\n \"0.62346655\",\n \"0.618115\",\n \"0.615539\",\n \"0.61247164\",\n \"0.6097528\",\n \"0.6022386\",\n \"0.60113466\",\n \"0.6007067\",\n \"0.59790057\",\n \"0.59721226\",\n \"0.5971848\",\n \"0.59443724\",\n \"0.5926389\",\n \"0.59132975\",\n \"0.5905877\",\n \"0.5819653\",\n \"0.5801772\",\n \"0.57959956\",\n \"0.5785412\",\n \"0.5748199\",\n \"0.5720399\",\n \"0.57127094\",\n \"0.56937337\",\n \"0.5684972\",\n \"0.5659638\",\n \"0.5647575\",\n \"0.56358147\",\n \"0.5632957\",\n \"0.5632761\",\n \"0.56017303\",\n \"0.5598985\",\n \"0.55989504\",\n \"0.55986524\",\n \"0.55808616\",\n \"0.5568692\",\n \"0.556636\",\n \"0.5565512\",\n \"0.5539926\",\n \"0.55327785\",\n \"0.55263305\",\n \"0.5498398\",\n \"0.54726744\",\n \"0.54592216\",\n \"0.54576963\",\n \"0.5454223\",\n \"0.5454068\",\n \"0.54451656\",\n \"0.5437642\",\n \"0.5436965\",\n \"0.54276705\",\n \"0.54230946\",\n \"0.54224646\",\n \"0.54207385\",\n \"0.5416883\",\n \"0.5413473\",\n \"0.5411107\",\n \"0.54082566\",\n \"0.5402246\",\n \"0.53944623\",\n \"0.5394063\",\n \"0.5384913\",\n \"0.53817815\",\n \"0.5381471\",\n \"0.5380261\",\n \"0.537874\",\n \"0.53745323\",\n \"0.5362182\",\n \"0.5360063\",\n \"0.53590083\",\n \"0.5356583\",\n \"0.53564626\",\n \"0.5352692\",\n \"0.533997\",\n \"0.5336001\",\n \"0.5326043\",\n \"0.53221065\",\n \"0.5318111\",\n \"0.5314444\",\n \"0.53117174\",\n \"0.53107053\",\n \"0.5310619\",\n \"0.5310536\",\n \"0.53094167\",\n \"0.5307807\",\n \"0.53054476\",\n \"0.52971613\",\n \"0.5295684\",\n \"0.5288052\",\n \"0.52844155\",\n \"0.5282574\",\n \"0.52753407\",\n \"0.52721393\",\n \"0.5268597\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.72839564"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":81185,"cells":{"query":{"kind":"string","value":"Generates the mandelbrot set from the set parameters for a particular number of iterations."},"document":{"kind":"string","value":"def generate_mandelbrot(self, iterations):\n if self.grid is None:\n raise RuntimeError(\"Grid hasn't been setup - call set_grid first.\")\n # Define the tensorflow variables\n c = tf.constant(self.grid.astype(np.complex64))\n z = tf.Variable(c)\n n = tf.Variable(tf.zeros_like(c, tf.float32))\n # Start the tensorflow session\n with tf.Session():\n tf.global_variables_initializer().run()\n # Define the main mandelbrot algorithm - either take the square plus x, or keep z\n z_out = tf.where(tf.abs(z) < self.threshold, z ** 2 + c, z)\n not_diverged = tf.abs(z_out) < self.threshold\n # Create a group of tensorflow operations\n step = tf.group(z.assign(z_out), n.assign_add(tf.cast(not_diverged, tf.float32)))\n # Run the operations for a set number of steps\n for i in range(iterations):\n step.run()\n self.end_step = n.eval()\n self.end_z = z_out.eval()"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def getMandelbrotSet(self):\n\t\t\n\t\t(x_min, x_max, y_min, y_max) = [i for i in self.extent]\n\t\t(rows, columns) = (self.width*self.dpi, self.width*self.dpi)\n\t\tr_real = np.linspace(x_min, x_max, columns)\n\t\tr_imag = np.linspace(y_max, y_min, rows)\n\t\tresult = np.empty((rows, columns))\n\n\t\t# used for smoother colors\n\t\thorizon = 2.0**40\n\t\thorizon2 = horizon**2\n\t\tlog_horizon = np.log(np.log(horizon))/np.log(2)\n\n\t\tfor i in range(rows): # imaginary values\n\t\t\tfor j in range(columns): # real values\n\t\t\t\tresult[i,j] = _determineDivergence(r_real[j], r_imag[i], self.maxIter, horizon2, log_horizon)\n\t\treturn result","def _generate_distribution_samples(self, set_count, parameter_count):\n self._samples = numpy.zeros((set_count, parameter_count))\n for i, distribution in enumerate(self.parameter_distributions.values()):\n self._samples[:, i] = distribution.ppf(self._quantiles[:, i])","def mandelbrot(c):\n tolerance = 30\n threshold = 3\n\n # This is the mandelbrot set\n #Uncomment and comment the Julia set block below\n \n # M = lambda x: pow(x, 2) + c\n # nterm = M(0)\n\n # End Mandelbrot set\n\n # This is the Julia Set\n x = c\n c = complex(-0.4,0.6)\n M = lambda x: pow(x, 2) + c\n nterm = M(x)\n #End Julia Set\n\n for i in xrange(tolerance):\n try:\n nterm = M(nterm)\n except OverflowError:\n return 1\n\n square = lambda x: pow(x, 2)\n try:\n if (square(nterm.real) + square(nterm.imag)) < threshold:\n return 0\n else:\n return 1\n except OverflowError:\n return 1","def bruteForcePopulation(N):\n return list(itertools.permutations(range(N), N))","def request_mandelbrot_computation(self):\n # Get array bounds\n xmin, xmax = self.mpl_mandelbrot.axes.get_xlim()\n ymin, ymax = self.mpl_mandelbrot.axes.get_ylim()\n # Get the number of iterations from the GUI\n try:\n maxiter = int(self.maxiter_lineedit.text())\n self.maxiter = maxiter\n except ValueError: pass\n # Send info to mandelbrot_thread\n self.mandelbrot_queue.put((xmin, xmax, ymin, ymax, self.maxiter))","def powerset(n):\n # chain r-combinations generator for r=0, 1,..., n\n return chain.from_iterable(combinations(range(n), r) for r in range(n+1))","def sets(elements, set_size):\n return combinations(elements, set_size)","def setNumIterations(*argv):","def sample(\n self,\n repetitions,\n nChains=3,\n burnIn=100,\n thin=1,\n convergenceCriteria=0.8,\n variables_of_interest=None,\n DEpairs=2,\n adaptationRate=\"auto\",\n eps=5e-2,\n mConvergence=True,\n mAccept=True,\n ):\n\n self.set_repetiton(repetitions)\n print(\n \"Starting the DEMCz algotrithm with \" + str(repetitions) + \" repetitions...\"\n )\n\n self.min_bound, self.max_bound = (\n self.parameter()[\"minbound\"],\n self.parameter()[\"maxbound\"],\n )\n repetitions = int(repetitions / nChains)\n ndraw_max = repetitions * nChains\n maxChainDraws = int(ndraw_max / nChains)\n\n dimensions = len(self.parameter()[\"random\"])\n\n # minbound,maxbound=self.find_min_max()\n # select variables if necessary\n if variables_of_interest is not None:\n slices = []\n for var in variables_of_interest:\n slices.append(self.slices[var])\n else:\n slices = [slice(None, None)]\n\n # make a list of starting chains that at least span the dimension space\n # in this case it will be of size 2*dim\n nSeedIterations = max(int(np.ceil(dimensions * 2 / nChains)), 2)\n\n # init a simulationhistory instance\n history = _SimulationHistory(\n maxChainDraws + nSeedIterations, nChains, dimensions\n )\n history.add_group(\"interest\", slices)\n\n ### BURN_IN\n burnInpar = [np.zeros((nChains, dimensions))] * nSeedIterations\n for i in range(nSeedIterations):\n self._logPs = []\n simulationlist = []\n old_like = np.empty(nChains)\n param_generator = (\n (rep, self.parameter()[\"random\"]) for rep in range(int(nChains))\n )\n\n for rep, vector, simulations in self.repeat(param_generator):\n burnInpar[i][rep] = vector\n likelist = self.postprocessing(i, vector, simulations, chains=rep)\n simulationlist.append(simulations)\n self._logPs.append(likelist)\n old_like[rep] = likelist\n burnInpar[i][rep] = vector\n if self.status.stop:\n break\n if not self.status.stop:\n history.record(burnInpar[i], self._logPs, 1)\n\n gamma = None\n self.accepts_ratio = 0.000001\n\n # initilize the convergence diagnostic object\n grConvergence = _GRConvergence()\n covConvergence = _CovarianceConvergence()\n\n # get the starting log objectivefunction and position for each of the\n # chains\n currentVectors = burnInpar[-1]\n currentLogPs = self._logPs[-1]\n\n # 2)now loop through and sample\n cur_iter = 0\n accepts_ratio_weighting = 1 - np.exp(-1.0 / 30)\n lastRecalculation = 0\n # continue sampling if:\n # 1) we have not drawn enough samples to satisfy the minimum number of iterations\n # 2) or any of the dimensions have not converged\n # 3) and we have not done more than the maximum number of iterations\n\n while cur_iter < maxChainDraws:\n print(cur_iter, burnIn)\n if cur_iter == burnIn:\n print(\"starting\")\n history.start_sampling()\n\n # every5th iteration allow a big jump\n if np.random.randint(5) == 0.0:\n gamma = np.array([1.0])\n else:\n gamma = np.array([2.38 / np.sqrt(2 * DEpairs * dimensions)])\n\n if cur_iter >= burnIn:\n proposalVectors = _dream_proposals(\n currentVectors,\n history,\n dimensions,\n nChains,\n DEpairs,\n gamma,\n 0.05,\n eps,\n )\n for i in range(len(proposalVectors)):\n proposalVectors[i] = self.check_par_validity(proposalVectors[i])\n # print proposalVectors\n else:\n proposalVectors = []\n for i in range(nChains):\n proposalVectors.append(self.parameter()[\"random\"])\n proposalVectors[i] = self.check_par_validity(proposalVectors[i])\n\n # if self.bounds_ok(minbound,maxbound,proposalVectors,nChains):\n proposalLogPs = []\n old_simulationlist = simulationlist\n old_likelist = self._logPs[-1]\n new_simulationlist = []\n new_likelist = []\n\n param_generator = (\n (rep, list(proposalVectors[rep])) for rep in range(int(nChains))\n )\n for rep, vector, simulations in self.repeat(param_generator):\n new_simulationlist.append(simulations)\n like = self.postprocessing(\n cur_iter + nSeedIterations,\n list(vector),\n simulations,\n chains=rep,\n )\n self._logPs.append(like)\n new_likelist.append(like)\n proposalLogPs.append(like)\n if self.status.stop:\n cur_iter = maxChainDraws\n break\n\n if not self.status.stop:\n # apply the metrop decision to decide whether to accept or reject\n # each chain proposal\n decisions, acceptance = self._metropolis_hastings(\n currentLogPs, proposalLogPs, nChains\n )\n self._update_accepts_ratio(accepts_ratio_weighting, acceptance)\n # choose from list of possible choices if 1d_decision is True at\n # specific index, else use default choice\n # np.choose(1d_decision[:,None], (list of possible choices, default\n # choice)\n save_likes = []\n save_pars = []\n save_sims = []\n\n for curchain in range(nChains):\n if decisions[curchain]:\n save_likes.append(float(new_likelist[curchain]))\n old_like[curchain] = float(new_likelist[curchain])\n save_pars.append(proposalVectors[curchain])\n save_sims.append(new_simulationlist[curchain])\n else:\n save_likes.append(old_like[curchain])\n save_pars.append(currentVectors[curchain])\n save_sims.append(old_simulationlist[curchain])\n\n currentVectors = np.choose(\n decisions[:, np.newaxis], (currentVectors, proposalVectors)\n )\n currentLogPs = np.choose(decisions, (currentLogPs, proposalLogPs))\n\n simulationlist = [\n [new_simulationlist, old_simulationlist][int(x)][ix]\n for ix, x in enumerate(decisions)\n ]\n\n likelist = list(\n np.choose(\n decisions[:, np.newaxis], (new_likelist, old_likelist)\n )\n )\n\n # we only want to recalculate convergence criteria when we are past\n # the burn in period\n\n if cur_iter % thin == 0:\n\n historyStartMovementRate = adaptationRate\n # try to adapt more when the acceptance rate is low and less\n # when it is high\n if adaptationRate == \"auto\":\n historyStartMovementRate = min(\n (0.234 / self.accepts_ratio) * 0.5, 0.95\n )\n\n history.record(\n currentVectors,\n currentLogPs,\n historyStartMovementRate,\n grConvergence=grConvergence.R,\n )\n\n if (\n history.nsamples > 0\n and cur_iter > lastRecalculation * 1.1\n and history.nsequence_histories > dimensions\n ):\n lastRecalculation = cur_iter\n grConvergence.update(history)\n covConvergence.update(history, \"all\")\n covConvergence.update(history, \"interest\")\n if all(grConvergence.R < convergenceCriteria):\n cur_iter = maxChainDraws\n print(\n \"All chains fullfil the convergence criteria. Sampling stopped.\"\n )\n cur_iter += 1\n\n # 3) finalize\n # only make the second half of draws available because that's the only\n # part used by the convergence diagnostic\n self.history = history.samples\n self.histo = history\n self.iter = cur_iter\n self.burnIn = burnIn\n self.R = grConvergence.R\n text = \"Gelman Rubin R=\" + str(self.R)\n print(text)\n self.status.rep = self.status.repetitions\n self.final_call()","def multi_run(replications: int, iters: List, n: int):\n global call_count\n kwargs = {\n # 'alpha': 0.75,\n # 'rho': 'VaR',\n 'alpha': 0.75,\n 'rho': 'CVaR',\n 'x0': 2,\n 'n0': n,\n 'mu_1': -15,\n 'mu_2': 10,\n 'sigma_1': 4,\n 'sigma_2': 2\n }\n\n out_dict = {\n 'SA': dict(),\n 'SA_SAA': dict(),\n 'NM': dict(),\n 'NM_SAA': dict(),\n 'LBFGS': dict(),\n 'LBFGS_SAA': dict(),\n 'EI': dict(),\n 'EI_SAA': dict()\n }\n total_calls = dict()\n for key in out_dict.keys():\n total_calls[key] = dict()\n for it_count in iters:\n kwargs['iter_count'] = it_count\n for key in out_dict.keys():\n out_dict[key][it_count] = dict()\n total_calls[key][it_count] = 0\n i = 0\n while i < replications:\n try:\n out_dict['SA'][it_count][i] = SA_run(seed=i, **kwargs)\n total_calls['SA'][it_count] += call_count\n call_count = 0\n out_dict['SA_SAA'][it_count][i] = SA_run(seed=i, **kwargs, SAA_seed=i)\n total_calls['SA_SAA'][it_count] += call_count\n call_count = 0\n out_dict['NM'][it_count][i] = NM_run(seed=i, **kwargs)\n total_calls['NM'][it_count] += call_count\n call_count = 0\n out_dict['NM_SAA'][it_count][i] = NM_run(seed=i, **kwargs, SAA_seed=i)\n total_calls['NM_SAA'][it_count] += call_count\n call_count = 0\n out_dict['LBFGS'][it_count][i] = LBFGS_run(seed=i, **kwargs)\n total_calls['LBFGS'][it_count] += call_count\n call_count = 0\n out_dict['LBFGS_SAA'][it_count][i] = LBFGS_run(seed=i, **kwargs, SAA_seed=i)\n total_calls['LBFGS_SAA'][it_count] += call_count\n call_count = 0\n out_dict['EI'][it_count][i] = EI_run(seed=i, **kwargs)\n total_calls['EI'][it_count] += call_count\n call_count = 0\n out_dict['EI_SAA'][it_count][i] = EI_run(seed=i, **kwargs, SAA_seed=i)\n total_calls['EI_SAA'][it_count] += call_count\n call_count = 0\n i += 1\n except:\n continue\n np.save('call_counts_cvar_%d.npy' % n, total_calls)\n evaluate(out_dict, n)","def batches(set_name):\n global num_batches, args, ds_sizes \n # num_batches = how many batches in each dataset(train, valid, test)\n # ds_sizes = dataset_sizes \n for b in range(num_batches[set_name]):\n bi = b * args.batch_size # one batch mul batch_size \n bj = (b + 1) * args.batch_size \n if b == num_batches[set_name] - 1:\n bj = ds_sizes[set_name] # maybe only remainer set\n yield bi, bj","def mcmc14xy(z, t, x, y, sigma, params, stepsize, numit,nstep=1,sumtol=1e20, xyord=1, prodtol=1e-6, fixc0=False):\n import numpy as np\n\n\t#Initial setup\n\tnumaccept = 0\n nout = numit/nstep\n \tbestp = np.copy(params)\n \tallparams = np.zeros((len(params), nout))\n allchi = np.zeros(nout,float)\n\n \t#Calc chi-squared for model type using current params\n if fixc0:\n zmodel = phasesin14xymult_cfix(params, xyord, 0, t, x, y)\n else:\n zmodel = phasesin14xymult(params, xyord, 0, t, x, y)\n #print zmodel.shape, z.shape, sigma.shape\n\n sum_offsetlevel = params[3:17].sum()\n prod_offsetlevel = (1.+params[3:17]).prod() -1.\n \tcurrchisq = (((zmodel - z)/sigma)**2).ravel().sum()\n currchisq += (sum_offsetlevel/sumtol)**2 + (prod_offsetlevel/prodtol)**2\n\n \tbestchisq = currchisq\n #Run Metropolis-Hastings Monte Carlo algorithm 'numit' times\n \tfor j in range(numit):\n \t#Take step in random direction for adjustable parameters\n\t\tnextp = np.random.normal(params,stepsize)\n\t\t#COMPUTE NEXT CHI SQUARED AND ACCEPTANCE VALUES\n if fixc0:\n zmodel = phasesin14xymult_cfix(nextp, xyord, 0, t, x, y)\n else:\n zmodel = phasesin14xymult(nextp, xyord, 0, t, x, y)\n #ACCOUNT FOR individual pedestal offset levels:\n\n sum_offsetlevel = nextp[3:17].sum()\n prod_offsetlevel = (1.+nextp[3:17]).prod() -1.\n\t\tnextchisq = (((zmodel - z)/sigma)**2).ravel().sum() \n nextchisq += (sum_offsetlevel/sumtol)**2 + (prod_offsetlevel/prodtol)**2\n\n\t\taccept = np.exp(0.5 * (currchisq - nextchisq))\n\t\tif (accept >= 1) or (np.random.uniform(0, 1) <= accept):\n\t\t\t#Accept step\n\t\t\tnumaccept += 1\n\t\t\tparams = np.copy(nextp)\n\t\t\tcurrchisq = nextchisq\n \t\tif (currchisq < bestchisq):\n\t\t\t\t#New best fit\n\t\t\t\tbestp = np.copy(params)\n\t\t\t\tbestchisq = currchisq\n \t\n if (j%nstep)==0:\n allparams[:, j/nstep] = params\n allchi[j/nstep] = currchisq\n \treturn allparams, bestp, numaccept, allchi","def generate_objects_of_size(\n self, n: int, **parameters: int\n ) -> Iterator[CombinatorialObjectType]:","def __iter__(self):\n return iproduct(*self.sets)","def get_set(dim, maximum):\n\n i = 0\n numbers = []\n while i**2 <= maximum:\n n = i**2\n counter = 0\n while n <= maximum and counter < dim:\n numbers += [i**2]\n n += i**2\n counter += 1\n i += 1\n return numbers","def map(_, params):\n import numpy as np\n from itertools import product\n from random import shuffle\n\n if 'param_set' in params:\n parameter_sets = params['param_set']\n else:\n alphas = params['alphas']\n Vs = params['Vs']\n gammas = params['gammas']\n parameter_sets = [item for item in product(alphas, gammas, Vs)]\n shuffle(parameter_sets)\n\n ## discretize the parameter configurations and equitably distribute\n ## them for the next map instance to deal with.\n chunk_length = len(parameter_sets)/params['nprocs']\n leftover = len(parameter_sets) % params['nprocs']\n for n in xrange(params['nprocs']):\n if n < leftover:\n left = n*(1+chunk_length)\n to_yield = parameter_sets[left:left+1+chunk_length]\n else:\n left = leftover*(1+chunk_length) + (n-leftover)*chunk_length\n to_yield = parameter_sets[left:left+chunk_length]\n #print n, to_yield, len(to_yield)\n yield (n, to_yield)","def all_kmers(k):\n for i in range(0, 4 ** k):\n res = number_to_kmer(i, k)\n yield res","def regenerate_monte_carlo_parameters(self) -> None:\n for component in self._wrapped_circuit:\n component.regenerate_monte_carlo_parameters()","def gen_task_sets_fairgen(nsets):\n start = time()\n eval_task_sets = []\n for util in utils:\n task_sets = []\n for i in range(nsets):\n ts = synth.mc_fairgen(set_id=i, u_lo=util, implicit_deadlines=True)\n synth.synth_c_pmf(ts)\n synth.set_fixed_priorities(ts)\n task_sets.append(ts)\n eval_task_sets.append(task_sets)\n stop = time()\n print('Task Set Generation MC-Fairgen: %.3fs' % (stop - start))\n pickle.dump(eval_task_sets, open(task_sets_path + 'task_sets_fairgen', 'wb'))","def julia_set(width, height, zoom=1, niter=256):\n\n w,h = width, height\n # Why (hxw) ? Because numpy creates a matrix as row x column\n # and height represents the y co-ordinate or rows and\n # width represents the x co-ordinate or columns.\n pixels = np.arange(w*h, dtype=np.uint16).reshape(h, w)\n pixels = np.arange(w*h, dtype=np.uint16).reshape(h, w)\n\n # The constant \"c\"\n # This determines the shape of the Julia set.\n # c_real, c_imag = -0.7, 0.27\n c_real, c_imag = 0.4, -0.2\n \n for x in range(w): \n for y in range(h):\n # calculate the initial real and imaginary part of z,\n # based on the pixel location and zoom and position values\n zx = 1.5*(x - w/2)/(0.5*zoom*w) \n zy = 1.0*(y - h/2)/(0.5*zoom*h)\n\n for i in range(niter):\n radius_sqr = zx*zx + zy*zy\n # Iterate till the point is outside\n # the circle with radius 2.\n if radius_sqr > 4: break\n # Calculate new positions\n zy,zx = 2.0*zx*zy + c_imag, zx*zx - zy*zy + c_real\n\n color = (i >> 21) + (i >> 10) + i*8\n pixels[y,x] = color\n \n # display the created fractal \n plt.imshow(pixels)\n plt.show()","def linear_set_generator(random, args):\n bounder = args.get(\"_ec\").bounder\n representation = args.get('representation')\n max_size = args.get('max_size', 9)\n variable_size = args.get('variable_size', True)\n if variable_size:\n size = random.randint(1, max_size)\n else:\n size = max_size\n\n indices = random.sample(range(len(representation)), size)\n values = random.uniform(next(bounder.lower_bound), next(bounder.upper_bound), len(indices))\n return OrderedDict({i: v for i, v in zip(indices, values)})","def __calculate_iterations(self):\n iterables = {}\n \n def get_type(type_options):\n key = self._config_dict[type_options].keys()[0]\n data = self._config_dict[type_options][key]\n \n if type(data) == dict:\n iterables[key] = [data]\n else:\n iterables[key] = data\n \n for config_type in self.__iterables:\n get_type(config_type)\n \n self.__iterables = [dict(zip(iterables, v)) for v in product(*iterables.values())] # Calculates the cartesian product of all the lists to iterate to generate permutations.\n self.__iterables_counter = 0","def g_iter(n):\n \"*** YOUR CODE HERE ***\"\n if n <= 3:\n return n\n else:\n i = 3\n x, y, z = 1, 2, 3\n new = 1\n while i < n:\n new = z + (2*y) + (3*x)\n x, y, z = y, z, new \n i += 1\n return new","def gen_task_sets_simplegen(nsets):\n start = time()\n eval_task_sets = []\n for util in utils:\n task_sets = []\n for i in range(nsets):\n ts = synth.simplegen(i, util, implicit_deadlines=True)\n synth.synth_c_pmf(ts, distribution_cls=lib.ExpExceedDist)\n synth.set_fixed_priorities(ts)\n task_sets.append(ts)\n eval_task_sets.append(task_sets)\n stop = time()\n print('Task Set Generation SimpleGen: %.3fs' % (stop - start))\n pickle.dump(eval_task_sets, open(task_sets_path + 'task_sets_simplegen', 'wb'))","def mazectric(size):\n assert size % 2\n grid = np.random.randint(0, 2, size=(size, size,), dtype=bool)\n grid[0, 0:size] = grid[size - 1, 0:size] = True\n grid[0:size, 0] = grid[0:size, size - 1] = True\n \n key = hash(str(grid))\n looped = set()\n yield grid\n \n def alive(i, j):\n n = np.sum(grid[max(0, i-1):i+2, max(0, j-1):j+2]) - grid[i, j]\n return 1 if grid[i, j] and 0 < n < 5 else int(n == 3)\n \n while key not in looped:\n looped.add(key)\n grid = np.array([[alive(i, j) for j in range(size)] \\\n for i in range(size)], dtype=bool)\n grid[0, 0:size] = grid[size - 1, 0:size] = True\n grid[0:size, 0] = grid[0:size, size - 1] = True\n key = hash(str(grid))\n yield grid","def multiple_gen(modulus):\n count = 1\n while True:\n yield modulus * count\n count += 1","def run_Over_Grid(numdemes = 2, reps = 10, numreg = 100, t = 1000):\n Nmean = 2000\n Nsd = 100\n migMean = 0.0001\n migsd = 1e-06\n ndc2 = numdemes * (numdemes - 1) / 2\n rows = ndc2 + numdemes + 1\n I = np.matrix(np.eye(rows))\n Ck = I[0:rows - 1, :]\n Dk = I[rows - 1, :]\n output = []\n for r in xrange(reps):\n N = np.random.normal(Nmean, Nsd, (numdemes,))\n mtemp = np.random.normal(migMean, migsd, (ndc2,))\n xtrue = np.hstack((N, mtemp))\n m = np.zeros((numdemes, numdemes))\n cnt = 0\n for i in xrange(numdemes):\n for j in xrange(i + 1, numdemes):\n m[i, j] = m[j, i] = mtemp[cnt]\n cnt += 1\n\n Ninv = [ 1.0 / x for x in N ]\n Qtrue = comp_pw_coal_cont(m, Ninv)\n Ptrue = expM(t * Qtrue)\n obs_rates = Ck * Ptrue * Dk.T\n if numreg > 0:\n sd_rates = np.real(np.sqrt(obs_rates.getA() * (1 - obs_rates).getA() / numreg))\n noise = np.random.normal(0.0, sd_rates)\n print 'Noise:\\n', noise\n N0 = np.random.normal(Nmean / 2.0, Nsd * 3.0, (numdemes,))\n m0 = np.random.normal(migMean / 2.0, migsd * 3.0, (ndc2,))\n x0 = np.hstack((N0, m0))\n xopt = opt.fmin(compute_Frob_norm_mig, x0, (t, obs_rates), maxfun=1000000, maxiter=100000)\n output.append((xtrue, xopt, linalg.norm(xopt - xtrue)))\n\n return output","def generate_combinations(k: int, n: int):\n result = list()\n for i in range(1, k + 1):\n for bits in itertools.combinations(range(n), i):\n s = [0] * n\n for bit in bits:\n s[bit] = 1\n result.append(s)\n\n return pd.DataFrame(result)","def get_generator(modulus, k, m):\n\ttries = 0\n\twhile tries <= ((m-k) + 1):\n\t\tgen = get_prime(k, m)\n\t\tprint(gen)\n\t\tpows = [gen]\n\t\tfor x in range(modulus):\n\t\t\ta = pows[0]\n\t\t\tpows.insert(0, a*gen)\n\t\tremainders = set([(x % modulus) for x in pows])\n\t\tif len(remainders) == modulus - 1:\n\t\t\treturn gen\n\t\ttries += 1\n\tprint(\"No generator found. Increasing generator range by 10 on each side.\")\n\tl = k - 10\n\tn = m + 10\n\tif l <= 0:\n\t\tl = 1\n\treturn get_generator(modulus, l, n)","def generate_random_tomogram_set(templates, criteria, number_of_tomograms, dim, seed=None, noise=False):\n if (seed == None):\n seed = random.randint(0, 2 ** 31 - 1)\n print('Using random seed: ', seed)\n random.seed(int(seed))\n np.random.seed(int(seed))\n\n for i in range(number_of_tomograms):\n yield generate_random_tomogram(templates, criteria, dim, noise)","def generate(group, number, n):\n return [get_group(group, number) for i in xrange(n)]","def multiple_chromosome_set_generator(random, args):\n keys = args.get('keys')\n candidate = MultipleChromosomeGenome(keys=keys)\n for key in keys:\n key_args = {\n 'representation': args.get(\"%s_representation\" % key),\n 'max_size': args.get(\"%s_max_size\" % key),\n 'variable_size': args.get('variable_size')\n }\n candidate[key] = set_generator(random, key_args)\n\n return candidate","def cycles(n, support, randomize=False):\n support = np.array(support)\n\n def gen(p):\n g = combinations(support, n)\n if randomize:\n g = list(g)\n random.shuffle(g)\n\n for local_support in g:\n for output_p in all_permutations(local_support)(p):\n yield output_p\n\n return gen","def __iter__(self):\n from sage.combinat.posets.posets import FinitePosets_n\n n = 0\n while True:\n for P in FinitePosets_n(n):\n yield P\n n += 1","def MCS(n,k):\n\tglobal dict_all\n\tdict_val=copy.deepcopy(dict_all)\n\t#start_time = time.time()\n\tfinal = {}\t\t\t\t\t # Store all result with the count as key. For example final[1]=[[1,0,0],[0,1,1]]\n\tseq = []\t\t\t\t\t\t# Store the count with no duplication\n\tfor i in range(n):\n\t\tleaf={}\t\t\t\t\t\t# leaf is the dictionary to store the random value of each leaf\n\t\t#count=0\n\t\tfor i in leaves:\n\t\t\tleaf[i] = choice([0,1])\n\t\t\tdict_val[i]=leaf[i]\n\t\t\t#count += leaf[i]\n\t\tresult = Cal_FT(dict_val)\t\n\t\t'''\n\t\tif result:\n\t\t\tcutset = []\n\t\t\tfor i in leaves:\n\t\t\t\tcutset.append(str(leaf[i]))\n\t\t\tcutset=\"\".join(cutset)\n\t\t\tif cutset not in final:\n\t\t\t\tfinal[cutset]=count\n\tfinal_sorted=sorted(zip(final.values(),final.keys())) \t\t\t\t#Order the cutset by its count\n\tfor i in range(k):\t\t\t\t\t\t\t\t\t\t\t\t\t#Print the first k result\n\t\tcutset=list(final_sorted[i][1])\n\t\tresult=[]\n\t\tfor index in range(len(cutset)):\n\t\t\tif cutset[index] is \"1\":\n\t\t\t\tresult.append(leaves[index])\n\t\tprint result\n\t#end_time=time.time()\n\t#print \"Running time is\", end_time-start_time\n\t'''","def partial_permutations(n, k):\n return int((factorial(n) / factorial(n - k)) % 1000000)","def generate_objects_of_size(\n self, n: int, **parameters: int\n ) -> Iterator[CombinatorialObjectType]:\n key = (n,) + tuple(sorted(parameters.items()))\n res = self.obj_cache.get(key)\n if res is not None:\n yield from res\n return\n assert (\n self.subgenerators is not None\n ), \"you must call the set_subrecs function first\"\n res = []\n for subobjs in self.constructor.get_sub_objects(\n self.subgenerators, n, **parameters\n ):\n obj = self.backward_map(subobjs)\n yield obj\n res.append(obj)\n self.obj_cache[key] = res","def mandelbrot(c, max_iter=50, div_lim=2):\n f = 0j\n n = 0\n out = zeros_like(c,dtype=int)\n while n < max_iter:\n n += 1\n f = func(f,c)\n out[(out == 0) & (abs(f) > div_lim)] = n\n f[(out != 0)] = 100\n return(out)","def allcombinations(orgset, k):\n return itertools.chain(*[combination(orgset, i) for i in range(1, k + 1)])","def iteration(self):\n T = self.generate_T()\n R = self.reproduce(T)\n self.P = self.choose_mi_best(R)\n #print(self.P)","def generate_test_set_dqn(n_free_min, n_free_max, d_edge_min, d_edge_max, Omega_max, Phi_max, Lambda_max,\n weighted, w_max, directed, size_test_set, to_torch=False):\n\n # Initialize the variables\n Budget_max = Omega_max + Phi_max + Lambda_max\n test_set = []\n if to_torch:\n test_set_torch = []\n\n print(\"==========================================================================\")\n print(\"Generates the test set... \\n\")\n\n # for all budgets\n for budget in tqdm(range(1, Budget_max + 1)):\n # initialize the budget's instances list\n test_set_budget = []\n if to_torch:\n test_set_budget_torch = []\n for k in range(size_test_set):\n # generate a random instance\n instance_budget_k = generate_random_instance(\n n_free_min,\n n_free_max,\n d_edge_min,\n d_edge_max,\n Omega_max,\n Phi_max,\n Lambda_max,\n weighted=weighted,\n w_max=w_max,\n Budget_target=budget,\n directed=directed,\n )\n G = instance_budget_k.G\n Omega = instance_budget_k.Omega\n Phi = instance_budget_k.Phi\n Lambda = instance_budget_k.Lambda\n J = instance_budget_k.J\n # solve the instance\n value, D, I, P = solve_mcn(G, Omega, Phi, Lambda, J=J, exact=True)\n # save the value, P, D in the Instance object\n instance_budget_k.value = value\n instance_budget_k.D = D\n instance_budget_k.I = I\n instance_budget_k.P = P\n # pushes it to memory\n test_set_budget.append(instance_budget_k)\n # if we want to save the corresponding InstanceTorch\n # to evaluate the training, we stop at Budget_max - 1\n if to_torch:\n instance_budget_k_torch = instance_to_torch(instance_budget_k)\n test_set_budget_torch.append(instance_budget_k_torch)\n test_set.append(test_set_budget)\n if to_torch:\n test_set_torch.append(test_set_budget_torch)\n\n if not os.path.exists('data'):\n os.mkdir('data')\n folder_name = 'test_data'\n if weighted:\n folder_name += '_w'\n if directed:\n folder_name += '_dir'\n path_test_data = os.path.join('data', folder_name)\n if not os.path.exists(path_test_data):\n os.mkdir(path_test_data)\n # Save the test sets\n file_path = os.path.join(path_test_data, \"test_set.gz\")\n pickle.dump(test_set, open(file_path, \"wb\"))\n if to_torch:\n file_path_torch = os.path.join(path_test_data, \"test_set_torch.gz\")\n pickle.dump(test_set_torch, open(file_path_torch, \"wb\"))","def setup_crubadan():\n for n in [1,2,3,4,5,'word']:\n for option in ['mi','pmi']:\n get_matrix(datasource='crubadan', n=n, option=option)","def iterativeDeepeningGeneration(self, param_dict):\n ans_set_manager_list = []\n for num_steps in range(1, int(param_dict['maxSteps']) + 1):\n param_dict['maxSteps'] = str(num_steps)\n ans_set_manager_list.append(self.computeAnsSets(param_dict))\n return ans_set_manager_list","def generate_data(set_sizes, n_sets, seed):\n np.random.seed(seed)\n\n for n in set_sizes:\n for k in range(n_sets):\n # Generiramo nakljucni permutaciji\n p1 = np.random.permutation(n)\n p2 = np.random.permutation(n)\n # Potrebujemo razlicni permutaciji, sicer imamo delno urejenost reda 1\n while np.all(p1 == p2):\n p2 = np.random.permutation(n)\n # Slovarja, ki za vsako stevilo vrneta mesto v posamezni permutaciji\n inverse1 = {i: p1[i] for i in range(n)}\n inverse2 = {i: p2[i] for i in range(n)}\n\n # Gremo cez vse kombinacije, ce je eno stevilo manjse od drugega v obeh primerih, ju dodamo v delno urejenost\n partial_order = []\n for u in range(n):\n for v in range(n):\n if inverse1[u] < inverse1[v] and inverse2[u] < inverse2[v]:\n partial_order.append((u, v))\n\n # Izpisemo delno urejenost v datoteko\n f = open(f'../data/vhod_{n}_{k+1}.txt', 'w')\n # Prva vrstica\n f.write(f'{n} {len(partial_order)}\\n')\n # Primerljivi elementi\n for u, v in partial_order:\n # Dodamo +1, da ni indeksiranja z 0\n f.write(f'{u+1} {v+1}\\n')\n f.close()","def _mc_gen():\r\n n = 1\r\n while True:\r\n yield n\r\n n += 1","def parameter_space():\n return [list(range(7, 17)),\n list(range(17, 27)),\n list(range(27, 37)),\n list(permutations(range(1, 5), 4))]","def generator(self):\n global_index = 0\n n_params = len(self.params)\n while (global_index < self.NXFLTEXP*self.paramspace):\n # skip row that have data already\n while (np.sum(self.spectra_hdu.data[global_index][1]) > 0.0): \n global_index += self.NXFLTEXP\n if (global_index >= self.NXFLTEXP*self.paramspace): break\n if (global_index >= self.NXFLTEXP*self.paramspace): break\n\n # get indexes in each grid; the last grid changing the fastest\n param_indexes = np.zeros(n_params, dtype=int)\n param_values = np.zeros(n_params)\n N0 = self.paramspace\n for i in range(n_params):\n (p_name, p_grid, p_log, p_frozen) = self.params[i]\n N = len(p_grid)\n N0 /= N\n p_index = int((global_index/3)//N0 % N)\n #print('global_index',global_index)\n #print('p_index',p_index)\n #print('p_grid[p_index]',p_grid[p_index])\n #print('p_grid',p_grid)\n param_indexes[i] = p_index\n param_values[i] = p_grid[p_index]\n\n # write parameter values (repeat the same parameters for each spectrum of the set) \n for i in range(self.NXFLTEXP):\n self.spectra_hdu.data[global_index+i][0] = param_values\n #end for\n\n # return total index, array of grid indexes, and array of grid values\n #sys.stderr.write(\"> generator: passing spectrum index %d (%s %s)\\n\" % (global_index, str(param_indexes), str(param_values)))\n yield (global_index, param_values, param_indexes, self.energies)\n global_index += self.NXFLTEXP\n #end while","def genChains(self):\n self.numMonomer = 0\n self.numBonds = 0\n self.numMols = 0\n self.numCations = 0\n self.numAnions = 0\n\n self.atomsCoords = []\n self.atomsType = []\n self.atomsCharge = []\n self.molId = []\n self.bondList = []\n \n for i in range(self.numPa + self.numPc):\n\n if i < self.numPc:\n # polycation chains, charge in LJ units of LAMMPS\n # electron charge would be 10.54 using bare LAMMPS LJ units\n # the dielectric constans of solvent is effectively taken as 111 when assign 1 to +e\n # just need to set dielectric as 0.72 in LAMMPS ot mimic water with dielectric constant 80\n self.beadCharge = 1\n self.beadType = 1 # atomic type for neutral beads in polycation chains\n self.chain = self.lenPc\n else:\n self.beadCharge = -1 # polyanion chains\n self.beadType = 3 # atomic type for neutral beads in polyanion chains\n self.chain = self.lenPa\n\n self.numMols += 1\n\n # generate the first bead of each chain randomly\n self.numMonomer += 1\n self.cxyz = np.random.rand(3) * self.box + self.lxyz\n\n self.atomsCoords.append(self.cxyz)\n #self.atomsType.append(self.beadType)\n\n # decide if the first bead is charged or not\n if self.chargeRepeat == 1:\n self.atomsCharge.append(self.beadCharge)\n self.atomsType.append(self.beadType + 1)\n if i < self.numPc:\n self.numCations += 1\n else:\n self.numAnions += 1\n else:\n self.atomsType.append(self.beadType)\n self.atomsCharge.append(0)\n\n self.molId.append(self.numMols)\n\n self.currpxyz = self.cxyz\n\n # follow random walk to generate the chain\n # generate the seconb bead of the chain\n self.theta, self.phi = np.random.rand(2) * np.array([np.pi, 2 * np.pi])\n self.ds = np.array([np.cos(self.theta), np.sin(self.theta) * np.cos(self.phi),\\\n np.sin(self.theta) * np.sin(self.phi)]) * self.segment\n\n self.cxyz = self.currpxyz + self.ds\n\n self.numMonomer += 1\n self.atomsCoords.append(self.cxyz)\n\n # decide if the second bead is charged or not\n if 2%self.chargeRepeat == 0:\n self.atomsCharge.append(self.beadCharge)\n self.atomsType.append(self.beadType + 1)\n if i < self.numPc:\n self.numCations += 1\n else:\n self.numAnions += 1\n else:\n self.atomsCharge.append(0)\n self.atomsType.append(self.beadType)\n\n self.molId.append(self.numMols)\n \n self.numBonds += 1\n self.bondList.append([self.numMonomer - 1, self.numMonomer])\n\n self.currpxyz = self.cxyz\n\n self.currtheta = self.theta\n self.currphi = self.phi\n\n self.dstot += self.ds\n\n # generating the rest beads of the chain\n\n for k in range(3, self.chain+1):\n # only accept atoms that are beyong certain distance\n # from the atom precding the current atom in the chain\n self.theta, self.phi = np.random.rand() * np.array([np.pi - self.stiffangle, \\\n 2 * np.pi])\n self.ds1 = np.array([np.cos(self.theta), np.sin(self.theta) * np.cos(self.phi),\\\n np.sin(self.theta) * np.sin(self.phi)]) * self.segment\n\n self.reverseXZrotation()\n self.cxyz = self.currpxyz + self.ds\n\n self.numMonomer += 1\n self.atomsCoords.append(self.cxyz)\n\n if k % self.chargeRepeat == 0:\n self.atomsCharge.append(self.beadCharge)\n self.atomsType.append(self.beadType + 1)\n if i < self.numPc:\n self.numCations += 1\n else:\n self.numAnions += 1\n else:\n self.atomsCharge.append(0)\n self.atomsType.append(self.beadType)\n\n self.molId.append(self.numMols)\n self.numBonds += 1\n self.bondList.append([self.numMonomer - 1, self.numMonomer])\n\n self.currpxyz = self.cxyz\n\n self.currtheta = np.arccos(self.ds[0]/self.segment)\n if self.ds[2] > 0:\n self.currphi = np.arccos(self.ds[1]/self.segment/np.sin(self.currtheta))\n else:\n self.currphi = 2*np.pi - np.arccos(self.ds[1]/self.segment/np.sin(self.currtheta))\n\n self.dstot += self.ds\n\n print \"%d beads are generated.\\n\" % self.numMonomer \n assert self.numMonomer == self.numPc * self.lenPc + self.numPa * self.lenPa, \\\n \"The number of monomers in chains is wrong!\\n\"\n assert self.numCations == int(np.floor(self.lenPc * self.chargeFraction)*self.numPc), \\\n \"The number of positively charged beads is wrong!\\n\"\n assert self.numAnions == int(np.floor(self.lenPa * self.chargeFraction)*self.numPa), \\\n \"The number of negatively charged beads is wrong!\\n\"","def part_1():\n return itertools.permutations(range(5))","def generate_grna_sets(self, prioritise_3prime = False, set_num = 1, max_set_num = 1, manual = True):\n collapsed_grna = sorted(self, key = lambda cg:(-len(cg), sorted(cg)))\n ## copy the CollapsedgRNA objects so we can modify them without changing the original\n collapsed_grna = [cg.copy() for cg in collapsed_grna]\n output = []\n while all(len(cg.gRNAs) > 0 for cg in collapsed_grna) and set_num <= max_set_num:\n selected_grna = self._generate_grna_set(collapsed_grna, resort = False,\n prioritise_3prime = prioritise_3prime)\n if manual:\n sorted_grna = sorted(selected_grna, key = lambda grna:grna.id)\n usr_input = manual_check_prompt(sorted_grna, set_num = set_num)\n if usr_input.upper() != 'X':\n id_list = tuple(grna.id for grna in selected_grna)\n seq_list = tuple(str(grna.seq).upper() for grna in selected_grna)\n if usr_input in id_list:\n index = id_list.index(usr_input)\n collapsed_grna[index].remove(selected_grna[index])\n elif usr_input.upper() in seq_list:\n index = seq_list.index(usr_input)\n collapsed_grna[index].remove(selected_grna[index])\n else:\n print(\"Invalid input.\")\n continue ## skip removal of selected gRNA and yielding of selected gRNA set\n ## remove selected gRNA from candidate pool\n for i, grna in enumerate(selected_grna):\n collapsed_grna[i].remove(grna)\n set_num += 1\n yield selected_grna\n # return output\n return","def __permutation(orgset, k):\n if k == 1:\n for i in orgset:\n yield (i,)\n elif k > 1:\n for i, x in enumerate(orgset):\n # iterates though to near the end\n for s in __permutation(orgset[:i] + orgset[i + 1 :], k - 1):\n yield (x,) + s","def generate_training_set(individual, db_name):\n\n db = connect(db_name)\n for miller_indices in individual:\n\n # Do MD\n slab = generate_slab(miller_indices, 5)\n slab = make_supercell(slab, P=[[3, 1, 1], [1, 3, 1], [1, 1, 1]])\n\n slab.set_calculator(EMT())\n slab.get_potential_energy()\n\n db.write(slab)\n\n MaxwellBoltzmannDistribution(slab, 300. * units.kB)\n dyn = VelocityVerlet(slab, dt=1. * units.fs)\n for i in range(1, 10):\n dyn.run(10)\n db.write(slab)","def run_variations(filename, N=50, N_scales=7, shape=(512, 512),\n steps=[(10,4.0), (20,1.0)], display_inline=False, min_radius=1.5,\n max_radius=90.0):\n\n colors = [[1, 0, 0], [0, 1, 0], [0, 0, 0.9], [1, 1, 0],\n [1, 0, 1], [0, 1, 1], [1, 1, 1], [1, 0.6, 0]]\n perm = np.random.permutation\n for n in range(N):\n # generate palette\n pal = perm(colors)[:4]\n pal = perm(r_[pal, pal])\n pal = (pal * .6 + roll(pal, 4, axis=0) * 0.4)[:4]\n pal = shade(N_scales, *pal)\n # generate parameters and MSTP object\n ra = exp(rseq(log(min_radius), log(max_radius), N=N_scales, randomness=0.9)).round(2)\n ri = ((1.333 + rand(N_scales)) * ra).round(2)\n dt = (.01 * frange(1, N_scales) ** 0.8).round(3)\n wt = (1.33 + arctan(5 * (rand(N_scales) - .5))).round(2)\n m = MSTP(shape, ra=ra, ri=ri, dt=dt, wt=wt, pal=pal)\n\n print '\\n-------- rendering image', filename % n\n print m\n # display(HTML(' '.join('<span style=\"background:rgb(%d,%d,%d)\">(o_O)</span> ' % tuple(255*k) for k in array(pal))))\n for i, (N_steps, speed) in enumerate(steps):\n print 'rendering %s steps at %1.1fx speed' % (N_steps, dt_multiplier)\n sys.stdout.flush()\n m.run(n=N_steps, speed=speed)\n if display_inline:\n display(IM(m.rgb_image()))\n if i < 0:\n first = False\n print 'renoising after iter 1.',\n m.z += 0.25 * rand(*m.z.shape) * m.z.ptp()\n\n # display(IM(m.rgb_image()))\n imsave(filename % n, m.rgb_image())","def lammps(parameter, iteration, numberlist, density, chainlength):\n # input parameters\n var = parameter[0]\n x = var * numberlist[0]\n y = var * numberlist[1] * 1.732 / 2\n z = x / 2\n #nodeid = 55 + iteration % 5\n M = int((x * y * z * density - 4 * numberlist[0] * numberlist[1] * int(z / 0.3)) / chainlength) \n os.environ['var'] = str(var)\n os.environ['iteration'] = str(iteration)\n os.environ['M'] = str(M)\n #os.environ['nodeid'] = str(nodeid)\n # copy the files to modify\n os.system(\"cp in.asymmetric in.asymmetric_$iteration\")\n os.system(\"cp run_lammps.sh run_lammps_$iteration.sh\")\n os.system(\"cp MultiBCP_hexagonal_post.py MultiBCP_hexagonal_post_$iteration.py\")\n # modify relevant files\n os.system('sed -i -e \"s/distance = 12/distance = $var/\" MultiBCP_hexagonal_post_$iteration.py')\n os.system(\"python MultiBCP_hexagonal_post_$iteration.py\")\n time.sleep(3)\n os.system('sed -i -e \"s/3072/$M/\" in.asymmetric_$iteration')#modify according to the parameters\n os.system('sed -i -e \"s/XN_100/XN_100_$iteration/\" in.asymmetric_$iteration')\n os.system('sed -i -e \"s/finalasymmetric/finalasymmetric_$iteration/\" in.asymmetric_$iteration')\n os.system('sed -i -e \"s/POSTA2B8/POSTA2B8_$iteration/\" run_lammps_$iteration.sh')\n os.system('sed -i -e \"s/posta2b8.out/posta2b8_$iteration.out/\" run_lammps_$iteration.sh')\n #os.system('sed -i -e \"s/node55/node$nodeid/\" run_lammps_$iteration.sh')\n os.system('sed -i -e \"s/in.asymmetric/in.asymmetric_$iteration/\" run_lammps_$iteration.sh')\n # run the simulation\n os.system(\"sbatch run_lammps_$iteration.sh\")\n return None","def xrndSphere(n):\n for i in xrange(n):\n yield rndSphere()","def createUpPyramidSets(blocksize,operating):\n sets = tuple()\n ul = blocksize[0]-operating\n dl = operating\n while ul > dl:\n r = numpy.arange(dl,ul,1)\n sets+=(tuple(product(r,r)),)\n dl+=operating\n ul-=operating\n return sets","def part_2():\n return itertools.permutations(range(5, 10))","def parallelotope(self, generators):\n try: \n generators = [ vector(QQ,v) for v in generators ]\n field = QQ\n except TypeError:\n generators = [ vector(RDF,v) for v in generators ]\n field = RDF\n \n from sage.combinat.combination import Combinations\n par = [ 0*generators[0] ]\n par += [ sum(c) for c in Combinations(generators) if c!=[] ]\n return Polyhedron(vertices=par, field=field)","def gen_iter_graph(final_nodes, num_nodes):\n\t# create complete directed graph on m nodes (num_nodes)\n\tgraph = make_complete_graph(num_nodes)\n\n\tV = []\n\tE = []\n\ttotal_indeg = 0\n\n\tfor key in graph:\n\t\tV.append(key)\n\t\tE.append([key,graph[key]])\t\n\n\t# grow the graph by adding n - m (final_nodes - num_nodes) nodes\n\t# where each new node is connected to m nodes randomly chosen \n\t# from the set of existing nodes. Elimintate duplicates \n\t# to avoid parallel edges.\n\tfor node_added in range(num_nodes, final_nodes):\n\t\t# for key in graph:\n\t\t# \tfor value in graph[key]:\n\t\t# \t\ttotal_indeg += value\n\t\tV_prime = set()\n\t\t# choose randomly m nodes from V and add them to V_prime \n\t\t# where the probability of choosing node j is (indeg(j) + 1)/(totindeg + |V|)\n\t\t# i.e., call DPATrial (line 6 in pseudocode)\n\t\ttrial = DPATrial.DPATrial(num_nodes)\n\t\tV_prime = trial.run_trial(num_nodes)\n\t\tfor node in V_prime:\n\t\t\tV_prime.add(node)\n\t\tV.append(node_added)\n\t\tgraph[node_added] = V_prime\n\treturn graph","def allpermutations(orgset, k):\n return itertools.chain(*[permutation(orgset, i) for i in range(1, k + 1)])","def list_permutations(n,r, multiset=False):\n if not multiset:\n return list(itertools.permutations(n,r))\n\n elif multiset:\n return set(list(itertools.permutations(n,r)))","def build_optics(SetOfObjects, epsilon):\n\n for point in SetOfObjects._index:\n if not SetOfObjects._processed[point]:\n expandClusterOrder(SetOfObjects, point, epsilon)","def subset_gen(itemSet):\n subsets = []\n for i in range(1, len(itemSet)):\n c = combinations(itemSet, r=i)\n for cc in c:\n subsets.append(set(cc))\n return subsets","def map(parameter_sets, params):\n from parcel_model.parcel import AerosolSpecies\n from parcel_model.lognorm import Lognorm\n import numpy as np\n import random\n\n M_tot = params['M_tot']\n N_tot = params['N_tot']\n flag = params['flag']\n rho_sulfate, rho_carbon = [params['rho'][s] for s in ['sulfate', 'carbon']]\n kappa_sulfate, kappa_carbon = [params['kappa'][s] for s in ['sulfate', 'carbon']]\n sigma_mixed = params['sigma']['mixed']\n sigma_sulfate = params['sigma']['sulfate']\n sigma_carbon = params['sigma']['carbon']\n\n\n ## Unload the parameters and iterate over them to drive the generation of aerosol\n ## populations and the model\n initial_aerosol_pops = []\n n, parameter_sets = parameter_sets\n for ps in parameter_sets:\n alpha, gamma, V = ps\n\n mixed, sulf, carbon = MakeAerosols.mixing2(M_tot, N_tot, alpha, gamma, sigma_mixed, sigma_sulfate, sigma_carbon, rho_sulfate, rho_carbon, kappa_sulfate, kappa_carbon)\n\n initial_aerosols = []\n if alpha > 0: # there is an external mode\n initial_aerosols.append(sulf)\n if flag == 'OC': ## if there is hygroscopic carbon\n initial_aerosols.append(carbon)\n if alpha < 1: # there is leftover in the internal mode\n initial_aerosols.append(mixed)\n\n initial_aerosol_pops.append((initial_aerosols, ps))\n\n yield (n, initial_aerosol_pops)","def run_calculation():\n\n print(\"Creating %d-process pool\" % mp.cpu_count())\n\n pool = mp.Pool(mp.cpu_count())\n\n f = h5py.File('/testdata/mandelbrot.hdf5', 'w')\n\n print(\"Creating output dataset with shape %s x %s\" % (NX, NY))\n\n dset = f.create_dataset('mandelbrot', (NX, NY), 'i')\n dset.attrs['XSTART'] = XSTART\n dset.attrs['YSTART'] = YSTART\n dset.attrs['XEXTENT'] = XEXTENT\n dset.attrs['YEXTENT'] = YEXTENT\n\n result = pool.imap(compute_row, (x * xincr for x in range(NX)))\n\n for idx, arr in enumerate(result):\n if idx % 25 == 0: print(\"Recording row %s\" % idx)\n dset[idx] = arr\n\n print(\"Closing HDF5 file\")\n\n f.close()\n\n print(\"Shutting down process pool\")\n\n pool.close()\n pool.join()","def combinations(n, k):\r\n return exp(gammaln(n + 1) - gammaln(k + 1) - gammaln(n - k + 1))","def __iter__(self):\n for p in self.param_grid:\n # Always sort the keys of a dictionary, for reproducibility\n modstr = '%s__' % self.modality\n items = sorted([(k.replace('clf__'+modstr, ''), v) for k, v in p.items() if modstr in k])\n if not items:\n yield {}\n else:\n keys, values = zip(*items)\n for v in product(*values):\n params = dict(zip(keys, v))\n yield params","def generate_with_antichain(set_sizes, n_sets, antichain_sizes, seed):\n np.random.seed(seed)\n\n for n in set_sizes:\n for k, size in zip(range(n_sets), antichain_sizes):\n antichain_size = int(np.round(size*n))\n # Generiramo nakljucni permutaciji (zadnjih antichain_size elementov predstavlja antiverigo, zato jih ni v permutaciji)\n p1 = np.random.permutation(n-antichain_size)\n p2 = np.random.permutation(n-antichain_size)\n # Potrebujemo razlicni permutaciji, sicer imamo delno urejenost reda 1\n while np.all(p1 == p2):\n p2 = np.random.permutation(n-antichain_size)\n # Slovarja, ki za vsako stevilo vrneta mesto v posamezni permutaciji\n inverse1 = {i: p1[i] for i in range(n-antichain_size)}\n inverse2 = {i: p2[i] for i in range(n-antichain_size)}\n\n # Gremo cez vse kombinacije, ce je eno stevilo manjse od drugega v obeh primerih, ju dodamo v delno urejenost\n partial_order = []\n for u in range(n-antichain_size):\n for v in range(n-antichain_size):\n if inverse1[u] < inverse1[v] and inverse2[u] < inverse2[v]:\n partial_order.append((u, v))\n\n # Izpisemo delno urejenost v datoteko\n f = open(f'../data/vhod_antichain_{n}_{k+1}.txt', 'w')\n # Prva vrstica\n f.write(f'{n} {len(partial_order)}\\n')\n # Primerljivi elementi\n for u, v in partial_order:\n # Dodamo +1, da ni indeksiranja z 0\n f.write(f'{u+1} {v+1}\\n')\n f.close()","def tst_random_set():\n final_wave, final_spec, final_z = desi_qso_templates(\n outfil='test_random_set.fits', N_perz=100, seed=12345)","def run_montecarlo(v,iteration , n_run, dr_coeff = 0.58):\n \n start = time.time()\n#%%\n R_cut = g_r_max # TODO\n #v_f = interp1d(r,v)\n\n g_lists = {'same':[],'opp':[]} \n S_lists = {'same':[],'opp':[]}\n \n # Initialize the system at a random distribution\n particles = initialize_system(MC_par['init_conf'])\n E = np.zeros(MC_par['N_mcs']+2) #TODO initial energy\n MC_move = 0\n#%%\n for n in range(MC_par['N_mcs']+1):\n #%%\n chosen_one = np.random.randint(MC_par['N_particles'])\n dr = dr_coeff * ( np.random.rand(MC_par['dim']) - 0.5 )\n #%%###\n dE,this_move = MC_step(particles,chosen_one,dr,R_cut,v)\n ####\n E[n+1] = E[n] + dE\n MC_move += this_move\n \n ## Every N_corr steps\n if(n % MC_par['N_corr']==0):\n print_progress(n+1,MC_par['N_mcs']) \n ## After convergence calc and save g\n if(n > MC_par['N_conv']):\n for kind in ['same','opp']:\n g_meas, S_meas, r_meas_Sg = calc_and_plot_g_r(particles,n,iteration,kind = kind, save_plot = PR_par['Plot all g'])\n g_lists[kind].append(g_meas)\n S_lists[kind].append(S_meas)\n \n elapsed = time.time() - start\n print(' Done in %d s' % elapsed)\n \n print('%d %% of the Monte Carlo steps were performed (%d out of %d)' % (100.0*MC_move/MC_par['N_mcs'], MC_move,MC_par['N_mcs']))\n\n plot_conf(particles,iteration)\n\n \n return particles,E,g_lists,S_lists,r_meas_Sg","def r_combinations(n,r):\n return r_permutations(n,r) / math.factorial(r)","def evolve(self, generations=10000):\n\n for gen in range(generations):\n # run the tournament\n self.tournament()\n\n # generate the next generation\n self.p = self.nextGen()","def get_test_params(cls, parameter_set=\"default\"):\n params1 = {\n \"n_clusters\": 3,\n \"n_init\": 2,\n \"max_iter\": 2,\n \"tol\": 1e-3,\n \"verbose\": False,\n \"random_state\": 2,\n }\n params2 = {\n \"n_clusters\": 2,\n \"init_algorithm\": \"random\",\n \"n_init\": 1,\n \"max_iter\": 1,\n \"tol\": 1e-4,\n \"verbose\": False,\n \"random_state\": 1,\n }\n return [params1, params2]","def gen_relaxation(chunknum, chunksize, dset_select=\"aligned\"):\n\n datapath = Path(__file__).parents[0] / \"data/relaxation\"\n dataset = datagen.DataSet(datapath / f\"init_{dset_select}\")\n dataset.load()\n\n print(f\"Generating {chunknum} chunks of size {chunksize}\")\n print(f\"for dataset '{dset_select}'\")\n\n for n in range(chunknum):\n\n print()\n print(f\"CHUNK #{n}\")\n print(f\"===============\")\n print()\n\n for k, ens in enumerate(dataset.ensembles):\n\n b = ens.b\n print(f\"Ensemble b={b:.2f}\")\n ens.simulate(chunksize, reset=False, verbose=True)\n dataset.save(ens_index=k)","def construct(self):\n\n newSet = {}\n current_index = 0\n\n for key_1, value_1 in self._sets[self._currentSet].items():\n current_index += 1\n for key_2,value_2 in list(self._sets[self._currentSet].items())[current_index:]:\n # join the 2 tuples\n join = key_1 + key_2\n # remove duplicates\n join = tuple(set(join))\n # get combinations\n combined = tuple(combinations(join, self._currentSet+1))\n # sort combination\n combined = tuple(sorted(combined[0]))\n\n # append new combination to dict\n if len(combined) != 0 :\n newSet[combined] = 0\n\n self._currentSet += 1\n # append the new itemset in the sets dict \n self._sets[self._currentSet] = newSet","def _gen_ms(theta, nb_qubits):\n routine = QRoutine()\n\n for first_qb in range(nb_qubits):\n for second_qb in range(first_qb + 1, nb_qubits):\n routine.apply(RXX(theta), [first_qb, second_qb])\n\n return routine","def euler24():\n\n # import math\n # math.factorial(10) = 3628800, 3.6 million combinations\n # math.factorial(9) = 362880, so the first digit is '2'\n\n import time\n start = time.time()\n\n p = 10 \n limit = 1000000-1\n result = []\n\n LIST = [0,1,2,3,4,5,6,7,8,9]\n\n\n # higest digit, 10! permutations in total\n while p > 0:\n l = len(result)\n m, r = divmod(limit, math.factorial(p-1))\n result.append(LIST[m])\n LIST.remove(LIST[m]) # remove first element with VALUE LIST[m]\n limit -= m * math.factorial(p-1)\n p -= 1\n\n end = time.time()\n print \"Time:\" , (end-start)*1000 , \"ms\"\n \n return result","def generate_test():\n o = []\n pos = [384, 288]\n note_group_size = GAN_PARAMS[\"note_group_size\"]\n generate_set(begin=3 * note_group_size, start_pos=pos,\n length_multiplier=dist_multiplier, group_id=3, plot_map=True)","def gen_pows(a,b):\n s = set()\n for x in range(2, a + 1):\n for y in range(2, b + 1):\n s.add(x**y)\n return s","def ECMFactor(n, sieve, limit, times):\n for _ in range(times):\n g = n\n while g == n:\n curve, point = randomCurve(n)\n g = gcd(n, curve.discriminant().remainder())\n \n if g > 1:\n return g\n\n for prime in sieve:\n prod = prime\n i = 0\n while prod < limit and i < 10:\n i += 1\n try:\n point = prime * point\n except ZeroDivisionError as e:\n return gcd(e.args[1], n)\n prod *= prime\n\n return n","def __combination(orgset, k):\n if k == 1:\n for i in orgset:\n yield (i,)\n elif k > 1:\n for i, x in enumerate(orgset):\n # iterates though to near the end\n for s in __combination(orgset[i + 1 :], k - 1):\n yield (x,) + s","def find_all_ngon_sols():\n ngon = [None for _ in range(N)] \n ngon_set = set()\n numbers = set(range(1, (2 * N) + 1))\n\n for triplet in permutations(numbers, 3):\n ngon[0] = tuple(triplet)\n total = sum(triplet)\n next_ngon_set = set()\n fill_ngon(ngon, numbers - set(triplet), 1, next_ngon_set, total)\n ngon_set |= next_ngon_set\n\n return ngon_set","def _set_builder_fast(m, distances):\n\n if not is_matrix(m):\n raise ValueError(\"Expected numpy matrix\")\n\n if not is_matrix_binary(m):\n raise ValueError(\"Expected binary matrix\")\n\n if not is_matrix_correct(m):\n raise ValueError(\"Expected matrix that has valid structure (not more than one '1' in row)\")\n\n n, k = np.shape(m)\n ones_count, one_pos, has_one, where_one, zero_rows = get_matrix_info(m, n)\n\n # run through all distances in D\n for distance in distances:\n\n # check that distance is correct\n if distance < 0 or distance > ones_count + n:\n continue\n\n if distance == 0:\n yield m.copy()\n continue\n\n for num_ones_removed in range(np.min([distance, ones_count]) + 1):\n for clear_rows in combinations(one_pos[0], num_ones_removed):\n\n cleared_matrix = m.copy()\n cleared_matrix[clear_rows, :] = 0\n remained_rows = np.array(list((set(range(n)) - set(one_pos[0])).union(set(clear_rows))))\n\n for rows in combinations(remained_rows, distance - num_ones_removed):\n\n if len(rows) == 0:\n yield cleared_matrix.copy()\n continue\n\n avaliable_column_indices = [get_zero_indices(where_one[row], k) for row in rows]\n\n rows = np.array(rows, dtype=np.int32)\n rows_count = rows.size\n\n if (k ** rows_count)*n*k*4//1024//1024 > 1024:\n raise ValueError(\"Looks that you don't have enough RAM\")\n\n indices = np.array(np.meshgrid(*avaliable_column_indices)).T.reshape(-1, rows_count)\n\n cache_size = np.shape(indices)[0]\n\n indices += rows*k\n indices = (indices.T + np.arange(cache_size)*(n*k)).T\n indices = indices.reshape(indices.size)\n\n matrix_cache = np.tile(cleared_matrix, (cache_size, 1, 1))\n matrix_cache.put(indices, 1)\n\n # print(\"Mb for cache: \" + str((cache_size*rows_count + cache_size*n*k)*4//1024//1024))\n\n for mat in matrix_cache:\n yield mat","def simulate_qubit_pairs_3D_lattice(cbrt_n, m):\n # Initialize some variables\n n = np.power(cbrt_n, 3)\n x_max = 20\n d = np.arange(0, x_max + 1)\n k_matrix = np.zeros((m, x_max + 1))\n\n # Run the simulation a total of m times\n for i in range(m):\n start = time.time()\n\n sim = chp_py.CHP_Simulation(n)\n sims = []\n # Add a sim for depth 0\n sims.append(chp_py.CHP_Simulation(n, sim.state))\n\n for j in range(x_max):\n gates = utils.get_lattice_gates((cbrt_n, cbrt_n, cbrt_n), j)\n sim.apply_gates(gates)\n sims.append(chp_py.CHP_Simulation(n, sim.state))\n\n # This computes the collision_probability at different\n # depths in parallel\n k_matrix[i, :] = utils.sims_to_collision_probabilty(sims)\n\n end = time.time()\n print(\"Time elapsed = {:0.2f}, for sample = {}\".format(end - start, i))\n\n # Store everything in data\n data = {\"d\": d,\n \"k_matrix\": k_matrix,\n \"n\": n,\n \"m\": m}\n\n return data","def combinations(n, k):\n return factorial(n) / (factorial(k) * factorial(n - k))","def compute_mi_steps(self, Akdf, out_csv, sample_size, sigma=5.0, prod_cols=None,\n bias=1.0, density='setmax', n_hit_miss=50, ngramr=(1, 3)):\n probcs = []\n if prod_cols is None:\n prod_cols = []\n for a, b in itertools.product(*[STRUCTCOLS + COMBINATIONS] * 2):\n if not ((a, b) in prod_cols or (b, a) in prod_cols):\n probcs.append(\"$\\mathcal{{N}}\\{{h({0}, {1}), \\sigma\\}}$\" \\\n .format(a, b))\n prod_cols.append((a, b))\n else:\n for a, b in prod_cols:\n probcs.append(\"$\\mathcal{{N}}\\{{h({0}, {1}), \\sigma\\}}$\" \\\n .format(a, b))\n\n A_tau = [Akdf[i:i + sample_size]\n for i in range(0, self.n_steps * sample_size, sample_size)]\n\n logging.info(\n f\"Computing probabilities of random sets for {self.n_steps} steps.\")\n with parallel_backend('multiprocessing' if BACKEND == 'mp' else 'loky'):\n t = time.time()\n P_Aks = Parallel(n_jobs=self.njobs)(\n delayed(self.compute_set_probability)(\n A_k, prod_cols=prod_cols, hit_miss_samples=n_hit_miss,\n density=density, bias=bias, sigma=sigma, ngramr=ngramr)\n for A_k in A_tau)\n if self.verbose:\n logging.info(\"Estimated set probabilities in {}s...\" \\\n .format(time.time() - t))\n\n with parallel_backend('multiprocessing' if BACKEND == 'mp' else 'loky'):\n t = time.time()\n info_steps = Parallel(n_jobs=self.njobs)(\n delayed(self.compute_mutuals)(df, probcs)\n for df in P_Aks if not (df is None or df.empty))\n if self.verbose:\n logging.info(\"Estimated MIs in {}s...\" \\\n .format(time.time() - t))\n\n pd.DataFrame(info_steps).to_csv(out_csv)\n if os.path.exists(out_csv):\n logging.info(\"Output csv saved to {}\".format(out_csv))\n else:\n logging.warning(\"Output csv {} wasn't saved!\".format(out_csv))","def mcmc_eclipse14_single(z, t, x, y, sigma, params, tparam, stepsize, numit, nstep=1, xyord=None):\n # 2011-05-14 16:06 IJMC: Adapted from upsand phase curve routines;\n # also adapting Agol et al. 2010's Spitzer\n # work, and from K. Stevenson's MCMC\n # example implementation.\n\n import numpy as np\n\n #Initial setup\n numaccept = 0\n nout = numit/nstep\n bestp = np.copy(params)\n allparams = np.zeros((len(params), nout))\n allchi = np.zeros(nout,float)\n\n #Calc chi-squared for model type using current params\n zmodel = eclipse14_single(params, tparam, t, xyord=xyord, x=x, y=y)\n\n currchisq = (((zmodel - z)/sigma)**2).ravel().sum()\n\n bestchisq = currchisq\n#Run Metropolis-Hastings Monte Carlo algorithm 'numit' times\n for j in range(numit):\n #Take step in random direction for adjustable parameters\n nextp = np.random.normal(params,stepsize)\n #COMPUTE NEXT CHI SQUARED AND ACCEPTANCE VALUES\n zmodel = eclipse14_single(nextp, tparam, t, xyord=xyord, x=x, y=y)\n\n nextchisq = (((zmodel - z)/sigma)**2).ravel().sum() \n\n accept = np.exp(0.5 * (currchisq - nextchisq))\n if (accept >= 1) or (np.random.uniform(0, 1) <= accept):\n #Accept step\n numaccept += 1\n params = np.copy(nextp)\n currchisq = nextchisq\n if (currchisq < bestchisq):\n #New best fit\n bestp = np.copy(params)\n bestchisq = currchisq\n\n if (j%nstep)==0:\n allparams[:, j/nstep] = params\n allchi[j/nstep] = currchisq\n return allparams, bestp, numaccept, allchi","def get_max_num_onsets():\r\n \"\"\" based on the numbers above, should equal to 932945... \"\"\"\r\n c1 = len(gen_onset_c1())\r\n c2 = len(gen_onset_c2())\r\n c3 = len(gen_onset_c3_c4())\r\n c4 = len(gen_onset_c3_c4())\r\n temp = c1\r\n temp = temp + ( c1 * c2 )\r\n temp = temp + ( c1 * c3 )\r\n temp = temp + ( c1 * c2 * c3 )\r\n temp = temp + ( c1 * c3 * c4 )\r\n temp = temp + ( c1 * c2 * c3 * c4 )\r\n return temp","def generate_grid(self):\n # Get a list of parameters values\n list_params = list(self.params_grid_data.values())\n\n # Generate a list of all combos for params\n params_combos = list(product(*list_params))\n names = self._generate_column_names()\n\n # Loop over the array of dictionary for the compounds and amounts:\n for experiment in self.compounds_data:\n for params in params_combos:\n compounds = experiment['compounds']\n amounts = experiment['amounts']\n self.all_combos += [chain(compounds, amounts,\n params)]\n self.all_combos = pd.DataFrame.from_records(self.all_combos)\n self.all_combos.columns = names\n return self.all_combos","def _iterCoordsets(self):\n\n for i in range(self._n_csets):\n yield self._coords[i]","def generate_set(models, begin=0, start_pos=[256, 192], group_id=-1, length_multiplier=1, plot_map=True):\n extvar[\"begin\"] = begin\n extvar[\"start_pos\"] = start_pos\n extvar[\"length_multiplier\"] = length_multiplier\n extvar[\"next_from_slider_end\"] = GAN_PARAMS[\"next_from_slider_end\"]\n\n note_group_size = GAN_PARAMS[\"note_group_size\"]\n max_epoch = GAN_PARAMS[\"max_epoch\"]\n good_epoch = GAN_PARAMS[\"good_epoch\"] - 1\n g_multiplier = GAN_PARAMS[\"g_epochs\"]\n c_multiplier = GAN_PARAMS[\"c_epochs\"]\n g_batch = GAN_PARAMS[\"g_batch\"]\n g_input_size = GAN_PARAMS[\"g_input_size\"]\n c_true_batch = GAN_PARAMS[\"c_true_batch\"]\n c_false_batch = GAN_PARAMS[\"c_false_batch\"]\n c_randfalse_batch = GAN_PARAMS[\"c_randfalse_batch\"]\n\n reset_model_weights(models)\n set_extvar(models, extvar)\n gmodel, mapping_layer, classifier_model, mmodel, default_weights = models\n\n for i in range(max_epoch):\n\n gnoise = np.random.random((g_batch, g_input_size))\n glabel = [np.zeros((g_batch, note_group_size * 4)),\n np.ones((g_batch,)), np.ones((g_batch,))]\n ginput = conv_input(gnoise, extvar)\n\n # fit mmodel instead of gmodel\n history = mmodel.fit(ginput, glabel, epochs=g_multiplier,\n validation_split=0.2, verbose=0,\n callbacks=[])\n\n pred_noise = np.random.random((c_false_batch, g_input_size))\n pred_input = conv_input(pred_noise, extvar)\n predicted_maps_data, predicted_maps_mapped, _predclass = mmodel.predict(\n pred_input)\n new_false_maps = predicted_maps_mapped\n new_false_labels = np.zeros(c_false_batch)\n\n # random numbers as negative samples\n # special_train_data.shape[2] == 6\n randfalse_maps = np.random.rand(\n c_randfalse_batch, note_group_size, special_train_data.shape[2])\n randfalse_labels = np.zeros(c_randfalse_batch)\n\n rn = np.random.randint(0, special_train_data.shape[0], (c_true_batch,))\n actual_train_data = np.concatenate(\n (new_false_maps, randfalse_maps, special_train_data[rn]), axis=0)\n actual_train_labels = np.concatenate(\n (new_false_labels, randfalse_labels, special_train_labels[rn]), axis=0)\n\n history2 = classifier_model.fit(actual_train_data, actual_train_labels, epochs=c_multiplier,\n validation_split=0.2, verbose=0,\n callbacks=[])\n\n # calculate the losses\n g_loss = np.mean(history.history['loss'])\n c_loss = np.mean(history2.history['loss'])\n print(\"Group {}, Epoch {}: G loss: {} vs. C loss: {}\".format(\n group_id, 1+i, g_loss, c_loss))\n\n # delete the history to free memory\n del history, history2\n\n # make a new set of notes\n res_noise = np.random.random((1, g_input_size))\n res_input = conv_input(res_noise, extvar)\n _resgenerated, res_map, _resclass = mmodel.predict(res_input)\n if plot_map:\n plot_current_map(tf.convert_to_tensor(res_map, dtype=tf.float32))\n\n # early return if found a good solution\n # good is (inside the map boundary)\n if i >= good_epoch:\n current_map = res_map\n if inblock_trueness(current_map[:, :, 0:2]).numpy()[0] == 0 and inblock_trueness(current_map[:, :, 4:6]).numpy()[0] == 0:\n # debugging options to check map integrity\n # print(tf.reduce_mean(current_map));\n # print(\"-----MAPLAYER-----\")\n # print(tf.reduce_mean(mapping_layer(conv_input(tf.convert_to_tensor(_resgenerated, dtype=\"float32\"), extvar))));\n # print(\"-----CMWS-----\")\n # print(tf.reduce_mean(construct_map_with_sliders(tf.convert_to_tensor(_resgenerated, dtype=\"float32\"), extvar=mapping_layer.extvar)));\n break\n\n if plot_map:\n for i in range(3): # from our testing, any random input generates nearly the same map\n plot_noise = np.random.random((1, g_input_size))\n plot_input = conv_input(plot_noise, extvar)\n _plotgenerated, plot_mapped, _plotclass = mmodel.predict(\n plot_input)\n plot_current_map(tf.convert_to_tensor(\n plot_mapped, dtype=tf.float32))\n\n # Don't do this in this version. It's for old versions where models are rebuilt each loop\n # del mmodel, mapping_layer;\n\n return res_map.squeeze()","def make_grid(m, n, k):\n \n grid = np.zeros(m*n)\n \n mine_indecies = np.arange(m*n)\n np.random.shuffle(mine_indecies)\n mine_indecies = mine_indecies[:k]\n \n #print('mine_indecies:', mine_indecies)\n #print(np.arange(m*n).reshape((m,n)))\n \n for i in mine_indecies:\n mask = get_mask(m, n, i)\n #print(mask.reshape((m,n)))\n grid = grid + mask\n \n grid[mine_indecies] = -1\n grid.resize((m,n))\n return grid","def __init__(self, max_iteration=50, swarm_num=20, c1=2, c2=2, w_ini=0.9, w_end=0.4):\r\n self.__c1 = c1\r\n self.__c2 = c2\r\n self.__w_ini = w_ini\r\n self.__w_end = w_end\r\n self.__max_iteration = int(max_iteration)\r\n self.__swarm_num = int(swarm_num)\r\n np.random.seed(123456)","def hyperparams():\n H = 6\n return Munch(N=500, H=H, D=(H // 2) ** 2, batch_size=10, precision=to.float32)","def g_iter(n):\n \"*** YOUR CODE HERE ***\"\n vals = [1, 2, 3]\n if n <= 3:\n return vals[n-1]\n for i in range(n - 3):\n new_val = 3 * vals[0] + 2 * vals[1] + 1 * vals[2]\n vals = vals[1:] + [new_val]\n return vals[-1]","def parition_generator(n):\n # generator\n k = np.zeros(n, dtype=np.dtype(int))\n Z = np.zeros(n, dtype=np.dtype(int))\n h = [float(n)]\n yield(Z)\n while next_partition(Z, k, h) is not None:\n yield(Z)","def simulate_qubit_pairs_1D_lattice(n, m):\n # Initialize some variables\n x_max = 2 * n\n d = np.linspace(0, x_max, 50, dtype=int)\n k_matrix = np.zeros((m, len(d)))\n\n # Run the simulation a total of m times\n for i in range(m):\n start = time.time()\n\n sim = chp_py.CHP_Simulation(n)\n sims = []\n # Add a sim for depth 0\n sims.append(chp_py.CHP_Simulation(n, sim.state))\n\n for j in range(0, d[-1]):\n gates = utils.get_lattice_gates((n,), j)\n sim.apply_gates(gates)\n\n if j + 1 in d:\n sims.append(chp_py.CHP_Simulation(n, sim.state))\n\n # This computes the collision_probability at different\n # depths in parallel\n k_matrix[i, :] = utils.sims_to_collision_probabilty(sims)\n\n end = time.time()\n print(\"Time elapsed = {:0.2f}, for sample = {}\".format(end - start, i))\n\n # Store everything in data\n data = {\"d\": d,\n \"k_matrix\": k_matrix,\n \"n\": n,\n \"m\": m}\n\n return data","def run(self, generations=1000):\n gcount = 0\n \n while gcount<=generations:\n try:\n print \"Gen: \"+str(gcount),\n self.population = zip (self.population, [self.target]*len(self.population))\n self.population = self.pool.map(f, self.population)\n except:\n pass\n for i in self.population:\n print i[0],i[1]\n self.population = [organism.Organism(x[0], x[1]) for x in self.population]\n self.population.sort()\n print \" Max fitness: \"+str(self.population[::-1][1].fitness)\n try:\n if self.population[0] <= self.ppop[0]:\n self.ppop = self.population[::-1][0:10] # The top ten organisms\n else:\n self.population = self.ppop # We got worse! go back!\n except:\n self.ppop = self.population\n self.population = self.population[::-1][0:10]\n try:\n self.breed()\n except:\n print \"Breeding error\"\n gcount+=1","def global_parameter_space():\n return [list(range(7, 22)),\n list(range(12, 27)),\n list(range(25, 40)),\n list(permutations(range(1, 5)))]","def run_expt_many_times(result_nonprivate, repetitions=10,\n data_blocker=1, windsorized=False,\n outfile_singles=None, outfile_aggregates=None):\n for epsilon in numpy.arange(0.1, 2.2, 0.2):#(0.2, 10, 0.2):\n for gamma in range(1, 10, 1): # (1, 7, 1):\n \n repeat_expt(epsilon, gamma,\n data_blocker=data_blocker, windsorized=windsorized,\n result_nonprivate=result_nonprivate,\n repetitions=repetitions,\n outfile_singles=outfile_singles, \n outfile_aggregates=outfile_aggregates)"],"string":"[\n \"def getMandelbrotSet(self):\\n\\t\\t\\n\\t\\t(x_min, x_max, y_min, y_max) = [i for i in self.extent]\\n\\t\\t(rows, columns) = (self.width*self.dpi, self.width*self.dpi)\\n\\t\\tr_real = np.linspace(x_min, x_max, columns)\\n\\t\\tr_imag = np.linspace(y_max, y_min, rows)\\n\\t\\tresult = np.empty((rows, columns))\\n\\n\\t\\t# used for smoother colors\\n\\t\\thorizon = 2.0**40\\n\\t\\thorizon2 = horizon**2\\n\\t\\tlog_horizon = np.log(np.log(horizon))/np.log(2)\\n\\n\\t\\tfor i in range(rows): # imaginary values\\n\\t\\t\\tfor j in range(columns): # real values\\n\\t\\t\\t\\tresult[i,j] = _determineDivergence(r_real[j], r_imag[i], self.maxIter, horizon2, log_horizon)\\n\\t\\treturn result\",\n \"def _generate_distribution_samples(self, set_count, parameter_count):\\n self._samples = numpy.zeros((set_count, parameter_count))\\n for i, distribution in enumerate(self.parameter_distributions.values()):\\n self._samples[:, i] = distribution.ppf(self._quantiles[:, i])\",\n \"def mandelbrot(c):\\n tolerance = 30\\n threshold = 3\\n\\n # This is the mandelbrot set\\n #Uncomment and comment the Julia set block below\\n \\n # M = lambda x: pow(x, 2) + c\\n # nterm = M(0)\\n\\n # End Mandelbrot set\\n\\n # This is the Julia Set\\n x = c\\n c = complex(-0.4,0.6)\\n M = lambda x: pow(x, 2) + c\\n nterm = M(x)\\n #End Julia Set\\n\\n for i in xrange(tolerance):\\n try:\\n nterm = M(nterm)\\n except OverflowError:\\n return 1\\n\\n square = lambda x: pow(x, 2)\\n try:\\n if (square(nterm.real) + square(nterm.imag)) < threshold:\\n return 0\\n else:\\n return 1\\n except OverflowError:\\n return 1\",\n \"def bruteForcePopulation(N):\\n return list(itertools.permutations(range(N), N))\",\n \"def request_mandelbrot_computation(self):\\n # Get array bounds\\n xmin, xmax = self.mpl_mandelbrot.axes.get_xlim()\\n ymin, ymax = self.mpl_mandelbrot.axes.get_ylim()\\n # Get the number of iterations from the GUI\\n try:\\n maxiter = int(self.maxiter_lineedit.text())\\n self.maxiter = maxiter\\n except ValueError: pass\\n # Send info to mandelbrot_thread\\n self.mandelbrot_queue.put((xmin, xmax, ymin, ymax, self.maxiter))\",\n \"def powerset(n):\\n # chain r-combinations generator for r=0, 1,..., n\\n return chain.from_iterable(combinations(range(n), r) for r in range(n+1))\",\n \"def sets(elements, set_size):\\n return combinations(elements, set_size)\",\n \"def setNumIterations(*argv):\",\n \"def sample(\\n self,\\n repetitions,\\n nChains=3,\\n burnIn=100,\\n thin=1,\\n convergenceCriteria=0.8,\\n variables_of_interest=None,\\n DEpairs=2,\\n adaptationRate=\\\"auto\\\",\\n eps=5e-2,\\n mConvergence=True,\\n mAccept=True,\\n ):\\n\\n self.set_repetiton(repetitions)\\n print(\\n \\\"Starting the DEMCz algotrithm with \\\" + str(repetitions) + \\\" repetitions...\\\"\\n )\\n\\n self.min_bound, self.max_bound = (\\n self.parameter()[\\\"minbound\\\"],\\n self.parameter()[\\\"maxbound\\\"],\\n )\\n repetitions = int(repetitions / nChains)\\n ndraw_max = repetitions * nChains\\n maxChainDraws = int(ndraw_max / nChains)\\n\\n dimensions = len(self.parameter()[\\\"random\\\"])\\n\\n # minbound,maxbound=self.find_min_max()\\n # select variables if necessary\\n if variables_of_interest is not None:\\n slices = []\\n for var in variables_of_interest:\\n slices.append(self.slices[var])\\n else:\\n slices = [slice(None, None)]\\n\\n # make a list of starting chains that at least span the dimension space\\n # in this case it will be of size 2*dim\\n nSeedIterations = max(int(np.ceil(dimensions * 2 / nChains)), 2)\\n\\n # init a simulationhistory instance\\n history = _SimulationHistory(\\n maxChainDraws + nSeedIterations, nChains, dimensions\\n )\\n history.add_group(\\\"interest\\\", slices)\\n\\n ### BURN_IN\\n burnInpar = [np.zeros((nChains, dimensions))] * nSeedIterations\\n for i in range(nSeedIterations):\\n self._logPs = []\\n simulationlist = []\\n old_like = np.empty(nChains)\\n param_generator = (\\n (rep, self.parameter()[\\\"random\\\"]) for rep in range(int(nChains))\\n )\\n\\n for rep, vector, simulations in self.repeat(param_generator):\\n burnInpar[i][rep] = vector\\n likelist = self.postprocessing(i, vector, simulations, chains=rep)\\n simulationlist.append(simulations)\\n self._logPs.append(likelist)\\n old_like[rep] = likelist\\n burnInpar[i][rep] = vector\\n if self.status.stop:\\n break\\n if not self.status.stop:\\n history.record(burnInpar[i], self._logPs, 1)\\n\\n gamma = None\\n self.accepts_ratio = 0.000001\\n\\n # initilize the convergence diagnostic object\\n grConvergence = _GRConvergence()\\n covConvergence = _CovarianceConvergence()\\n\\n # get the starting log objectivefunction and position for each of the\\n # chains\\n currentVectors = burnInpar[-1]\\n currentLogPs = self._logPs[-1]\\n\\n # 2)now loop through and sample\\n cur_iter = 0\\n accepts_ratio_weighting = 1 - np.exp(-1.0 / 30)\\n lastRecalculation = 0\\n # continue sampling if:\\n # 1) we have not drawn enough samples to satisfy the minimum number of iterations\\n # 2) or any of the dimensions have not converged\\n # 3) and we have not done more than the maximum number of iterations\\n\\n while cur_iter < maxChainDraws:\\n print(cur_iter, burnIn)\\n if cur_iter == burnIn:\\n print(\\\"starting\\\")\\n history.start_sampling()\\n\\n # every5th iteration allow a big jump\\n if np.random.randint(5) == 0.0:\\n gamma = np.array([1.0])\\n else:\\n gamma = np.array([2.38 / np.sqrt(2 * DEpairs * dimensions)])\\n\\n if cur_iter >= burnIn:\\n proposalVectors = _dream_proposals(\\n currentVectors,\\n history,\\n dimensions,\\n nChains,\\n DEpairs,\\n gamma,\\n 0.05,\\n eps,\\n )\\n for i in range(len(proposalVectors)):\\n proposalVectors[i] = self.check_par_validity(proposalVectors[i])\\n # print proposalVectors\\n else:\\n proposalVectors = []\\n for i in range(nChains):\\n proposalVectors.append(self.parameter()[\\\"random\\\"])\\n proposalVectors[i] = self.check_par_validity(proposalVectors[i])\\n\\n # if self.bounds_ok(minbound,maxbound,proposalVectors,nChains):\\n proposalLogPs = []\\n old_simulationlist = simulationlist\\n old_likelist = self._logPs[-1]\\n new_simulationlist = []\\n new_likelist = []\\n\\n param_generator = (\\n (rep, list(proposalVectors[rep])) for rep in range(int(nChains))\\n )\\n for rep, vector, simulations in self.repeat(param_generator):\\n new_simulationlist.append(simulations)\\n like = self.postprocessing(\\n cur_iter + nSeedIterations,\\n list(vector),\\n simulations,\\n chains=rep,\\n )\\n self._logPs.append(like)\\n new_likelist.append(like)\\n proposalLogPs.append(like)\\n if self.status.stop:\\n cur_iter = maxChainDraws\\n break\\n\\n if not self.status.stop:\\n # apply the metrop decision to decide whether to accept or reject\\n # each chain proposal\\n decisions, acceptance = self._metropolis_hastings(\\n currentLogPs, proposalLogPs, nChains\\n )\\n self._update_accepts_ratio(accepts_ratio_weighting, acceptance)\\n # choose from list of possible choices if 1d_decision is True at\\n # specific index, else use default choice\\n # np.choose(1d_decision[:,None], (list of possible choices, default\\n # choice)\\n save_likes = []\\n save_pars = []\\n save_sims = []\\n\\n for curchain in range(nChains):\\n if decisions[curchain]:\\n save_likes.append(float(new_likelist[curchain]))\\n old_like[curchain] = float(new_likelist[curchain])\\n save_pars.append(proposalVectors[curchain])\\n save_sims.append(new_simulationlist[curchain])\\n else:\\n save_likes.append(old_like[curchain])\\n save_pars.append(currentVectors[curchain])\\n save_sims.append(old_simulationlist[curchain])\\n\\n currentVectors = np.choose(\\n decisions[:, np.newaxis], (currentVectors, proposalVectors)\\n )\\n currentLogPs = np.choose(decisions, (currentLogPs, proposalLogPs))\\n\\n simulationlist = [\\n [new_simulationlist, old_simulationlist][int(x)][ix]\\n for ix, x in enumerate(decisions)\\n ]\\n\\n likelist = list(\\n np.choose(\\n decisions[:, np.newaxis], (new_likelist, old_likelist)\\n )\\n )\\n\\n # we only want to recalculate convergence criteria when we are past\\n # the burn in period\\n\\n if cur_iter % thin == 0:\\n\\n historyStartMovementRate = adaptationRate\\n # try to adapt more when the acceptance rate is low and less\\n # when it is high\\n if adaptationRate == \\\"auto\\\":\\n historyStartMovementRate = min(\\n (0.234 / self.accepts_ratio) * 0.5, 0.95\\n )\\n\\n history.record(\\n currentVectors,\\n currentLogPs,\\n historyStartMovementRate,\\n grConvergence=grConvergence.R,\\n )\\n\\n if (\\n history.nsamples > 0\\n and cur_iter > lastRecalculation * 1.1\\n and history.nsequence_histories > dimensions\\n ):\\n lastRecalculation = cur_iter\\n grConvergence.update(history)\\n covConvergence.update(history, \\\"all\\\")\\n covConvergence.update(history, \\\"interest\\\")\\n if all(grConvergence.R < convergenceCriteria):\\n cur_iter = maxChainDraws\\n print(\\n \\\"All chains fullfil the convergence criteria. Sampling stopped.\\\"\\n )\\n cur_iter += 1\\n\\n # 3) finalize\\n # only make the second half of draws available because that's the only\\n # part used by the convergence diagnostic\\n self.history = history.samples\\n self.histo = history\\n self.iter = cur_iter\\n self.burnIn = burnIn\\n self.R = grConvergence.R\\n text = \\\"Gelman Rubin R=\\\" + str(self.R)\\n print(text)\\n self.status.rep = self.status.repetitions\\n self.final_call()\",\n \"def multi_run(replications: int, iters: List, n: int):\\n global call_count\\n kwargs = {\\n # 'alpha': 0.75,\\n # 'rho': 'VaR',\\n 'alpha': 0.75,\\n 'rho': 'CVaR',\\n 'x0': 2,\\n 'n0': n,\\n 'mu_1': -15,\\n 'mu_2': 10,\\n 'sigma_1': 4,\\n 'sigma_2': 2\\n }\\n\\n out_dict = {\\n 'SA': dict(),\\n 'SA_SAA': dict(),\\n 'NM': dict(),\\n 'NM_SAA': dict(),\\n 'LBFGS': dict(),\\n 'LBFGS_SAA': dict(),\\n 'EI': dict(),\\n 'EI_SAA': dict()\\n }\\n total_calls = dict()\\n for key in out_dict.keys():\\n total_calls[key] = dict()\\n for it_count in iters:\\n kwargs['iter_count'] = it_count\\n for key in out_dict.keys():\\n out_dict[key][it_count] = dict()\\n total_calls[key][it_count] = 0\\n i = 0\\n while i < replications:\\n try:\\n out_dict['SA'][it_count][i] = SA_run(seed=i, **kwargs)\\n total_calls['SA'][it_count] += call_count\\n call_count = 0\\n out_dict['SA_SAA'][it_count][i] = SA_run(seed=i, **kwargs, SAA_seed=i)\\n total_calls['SA_SAA'][it_count] += call_count\\n call_count = 0\\n out_dict['NM'][it_count][i] = NM_run(seed=i, **kwargs)\\n total_calls['NM'][it_count] += call_count\\n call_count = 0\\n out_dict['NM_SAA'][it_count][i] = NM_run(seed=i, **kwargs, SAA_seed=i)\\n total_calls['NM_SAA'][it_count] += call_count\\n call_count = 0\\n out_dict['LBFGS'][it_count][i] = LBFGS_run(seed=i, **kwargs)\\n total_calls['LBFGS'][it_count] += call_count\\n call_count = 0\\n out_dict['LBFGS_SAA'][it_count][i] = LBFGS_run(seed=i, **kwargs, SAA_seed=i)\\n total_calls['LBFGS_SAA'][it_count] += call_count\\n call_count = 0\\n out_dict['EI'][it_count][i] = EI_run(seed=i, **kwargs)\\n total_calls['EI'][it_count] += call_count\\n call_count = 0\\n out_dict['EI_SAA'][it_count][i] = EI_run(seed=i, **kwargs, SAA_seed=i)\\n total_calls['EI_SAA'][it_count] += call_count\\n call_count = 0\\n i += 1\\n except:\\n continue\\n np.save('call_counts_cvar_%d.npy' % n, total_calls)\\n evaluate(out_dict, n)\",\n \"def batches(set_name):\\n global num_batches, args, ds_sizes \\n # num_batches = how many batches in each dataset(train, valid, test)\\n # ds_sizes = dataset_sizes \\n for b in range(num_batches[set_name]):\\n bi = b * args.batch_size # one batch mul batch_size \\n bj = (b + 1) * args.batch_size \\n if b == num_batches[set_name] - 1:\\n bj = ds_sizes[set_name] # maybe only remainer set\\n yield bi, bj\",\n \"def mcmc14xy(z, t, x, y, sigma, params, stepsize, numit,nstep=1,sumtol=1e20, xyord=1, prodtol=1e-6, fixc0=False):\\n import numpy as np\\n\\n\\t#Initial setup\\n\\tnumaccept = 0\\n nout = numit/nstep\\n \\tbestp = np.copy(params)\\n \\tallparams = np.zeros((len(params), nout))\\n allchi = np.zeros(nout,float)\\n\\n \\t#Calc chi-squared for model type using current params\\n if fixc0:\\n zmodel = phasesin14xymult_cfix(params, xyord, 0, t, x, y)\\n else:\\n zmodel = phasesin14xymult(params, xyord, 0, t, x, y)\\n #print zmodel.shape, z.shape, sigma.shape\\n\\n sum_offsetlevel = params[3:17].sum()\\n prod_offsetlevel = (1.+params[3:17]).prod() -1.\\n \\tcurrchisq = (((zmodel - z)/sigma)**2).ravel().sum()\\n currchisq += (sum_offsetlevel/sumtol)**2 + (prod_offsetlevel/prodtol)**2\\n\\n \\tbestchisq = currchisq\\n #Run Metropolis-Hastings Monte Carlo algorithm 'numit' times\\n \\tfor j in range(numit):\\n \\t#Take step in random direction for adjustable parameters\\n\\t\\tnextp = np.random.normal(params,stepsize)\\n\\t\\t#COMPUTE NEXT CHI SQUARED AND ACCEPTANCE VALUES\\n if fixc0:\\n zmodel = phasesin14xymult_cfix(nextp, xyord, 0, t, x, y)\\n else:\\n zmodel = phasesin14xymult(nextp, xyord, 0, t, x, y)\\n #ACCOUNT FOR individual pedestal offset levels:\\n\\n sum_offsetlevel = nextp[3:17].sum()\\n prod_offsetlevel = (1.+nextp[3:17]).prod() -1.\\n\\t\\tnextchisq = (((zmodel - z)/sigma)**2).ravel().sum() \\n nextchisq += (sum_offsetlevel/sumtol)**2 + (prod_offsetlevel/prodtol)**2\\n\\n\\t\\taccept = np.exp(0.5 * (currchisq - nextchisq))\\n\\t\\tif (accept >= 1) or (np.random.uniform(0, 1) <= accept):\\n\\t\\t\\t#Accept step\\n\\t\\t\\tnumaccept += 1\\n\\t\\t\\tparams = np.copy(nextp)\\n\\t\\t\\tcurrchisq = nextchisq\\n \\t\\tif (currchisq < bestchisq):\\n\\t\\t\\t\\t#New best fit\\n\\t\\t\\t\\tbestp = np.copy(params)\\n\\t\\t\\t\\tbestchisq = currchisq\\n \\t\\n if (j%nstep)==0:\\n allparams[:, j/nstep] = params\\n allchi[j/nstep] = currchisq\\n \\treturn allparams, bestp, numaccept, allchi\",\n \"def generate_objects_of_size(\\n self, n: int, **parameters: int\\n ) -> Iterator[CombinatorialObjectType]:\",\n \"def __iter__(self):\\n return iproduct(*self.sets)\",\n \"def get_set(dim, maximum):\\n\\n i = 0\\n numbers = []\\n while i**2 <= maximum:\\n n = i**2\\n counter = 0\\n while n <= maximum and counter < dim:\\n numbers += [i**2]\\n n += i**2\\n counter += 1\\n i += 1\\n return numbers\",\n \"def map(_, params):\\n import numpy as np\\n from itertools import product\\n from random import shuffle\\n\\n if 'param_set' in params:\\n parameter_sets = params['param_set']\\n else:\\n alphas = params['alphas']\\n Vs = params['Vs']\\n gammas = params['gammas']\\n parameter_sets = [item for item in product(alphas, gammas, Vs)]\\n shuffle(parameter_sets)\\n\\n ## discretize the parameter configurations and equitably distribute\\n ## them for the next map instance to deal with.\\n chunk_length = len(parameter_sets)/params['nprocs']\\n leftover = len(parameter_sets) % params['nprocs']\\n for n in xrange(params['nprocs']):\\n if n < leftover:\\n left = n*(1+chunk_length)\\n to_yield = parameter_sets[left:left+1+chunk_length]\\n else:\\n left = leftover*(1+chunk_length) + (n-leftover)*chunk_length\\n to_yield = parameter_sets[left:left+chunk_length]\\n #print n, to_yield, len(to_yield)\\n yield (n, to_yield)\",\n \"def all_kmers(k):\\n for i in range(0, 4 ** k):\\n res = number_to_kmer(i, k)\\n yield res\",\n \"def regenerate_monte_carlo_parameters(self) -> None:\\n for component in self._wrapped_circuit:\\n component.regenerate_monte_carlo_parameters()\",\n \"def gen_task_sets_fairgen(nsets):\\n start = time()\\n eval_task_sets = []\\n for util in utils:\\n task_sets = []\\n for i in range(nsets):\\n ts = synth.mc_fairgen(set_id=i, u_lo=util, implicit_deadlines=True)\\n synth.synth_c_pmf(ts)\\n synth.set_fixed_priorities(ts)\\n task_sets.append(ts)\\n eval_task_sets.append(task_sets)\\n stop = time()\\n print('Task Set Generation MC-Fairgen: %.3fs' % (stop - start))\\n pickle.dump(eval_task_sets, open(task_sets_path + 'task_sets_fairgen', 'wb'))\",\n \"def julia_set(width, height, zoom=1, niter=256):\\n\\n w,h = width, height\\n # Why (hxw) ? Because numpy creates a matrix as row x column\\n # and height represents the y co-ordinate or rows and\\n # width represents the x co-ordinate or columns.\\n pixels = np.arange(w*h, dtype=np.uint16).reshape(h, w)\\n pixels = np.arange(w*h, dtype=np.uint16).reshape(h, w)\\n\\n # The constant \\\"c\\\"\\n # This determines the shape of the Julia set.\\n # c_real, c_imag = -0.7, 0.27\\n c_real, c_imag = 0.4, -0.2\\n \\n for x in range(w): \\n for y in range(h):\\n # calculate the initial real and imaginary part of z,\\n # based on the pixel location and zoom and position values\\n zx = 1.5*(x - w/2)/(0.5*zoom*w) \\n zy = 1.0*(y - h/2)/(0.5*zoom*h)\\n\\n for i in range(niter):\\n radius_sqr = zx*zx + zy*zy\\n # Iterate till the point is outside\\n # the circle with radius 2.\\n if radius_sqr > 4: break\\n # Calculate new positions\\n zy,zx = 2.0*zx*zy + c_imag, zx*zx - zy*zy + c_real\\n\\n color = (i >> 21) + (i >> 10) + i*8\\n pixels[y,x] = color\\n \\n # display the created fractal \\n plt.imshow(pixels)\\n plt.show()\",\n \"def linear_set_generator(random, args):\\n bounder = args.get(\\\"_ec\\\").bounder\\n representation = args.get('representation')\\n max_size = args.get('max_size', 9)\\n variable_size = args.get('variable_size', True)\\n if variable_size:\\n size = random.randint(1, max_size)\\n else:\\n size = max_size\\n\\n indices = random.sample(range(len(representation)), size)\\n values = random.uniform(next(bounder.lower_bound), next(bounder.upper_bound), len(indices))\\n return OrderedDict({i: v for i, v in zip(indices, values)})\",\n \"def __calculate_iterations(self):\\n iterables = {}\\n \\n def get_type(type_options):\\n key = self._config_dict[type_options].keys()[0]\\n data = self._config_dict[type_options][key]\\n \\n if type(data) == dict:\\n iterables[key] = [data]\\n else:\\n iterables[key] = data\\n \\n for config_type in self.__iterables:\\n get_type(config_type)\\n \\n self.__iterables = [dict(zip(iterables, v)) for v in product(*iterables.values())] # Calculates the cartesian product of all the lists to iterate to generate permutations.\\n self.__iterables_counter = 0\",\n \"def g_iter(n):\\n \\\"*** YOUR CODE HERE ***\\\"\\n if n <= 3:\\n return n\\n else:\\n i = 3\\n x, y, z = 1, 2, 3\\n new = 1\\n while i < n:\\n new = z + (2*y) + (3*x)\\n x, y, z = y, z, new \\n i += 1\\n return new\",\n \"def gen_task_sets_simplegen(nsets):\\n start = time()\\n eval_task_sets = []\\n for util in utils:\\n task_sets = []\\n for i in range(nsets):\\n ts = synth.simplegen(i, util, implicit_deadlines=True)\\n synth.synth_c_pmf(ts, distribution_cls=lib.ExpExceedDist)\\n synth.set_fixed_priorities(ts)\\n task_sets.append(ts)\\n eval_task_sets.append(task_sets)\\n stop = time()\\n print('Task Set Generation SimpleGen: %.3fs' % (stop - start))\\n pickle.dump(eval_task_sets, open(task_sets_path + 'task_sets_simplegen', 'wb'))\",\n \"def mazectric(size):\\n assert size % 2\\n grid = np.random.randint(0, 2, size=(size, size,), dtype=bool)\\n grid[0, 0:size] = grid[size - 1, 0:size] = True\\n grid[0:size, 0] = grid[0:size, size - 1] = True\\n \\n key = hash(str(grid))\\n looped = set()\\n yield grid\\n \\n def alive(i, j):\\n n = np.sum(grid[max(0, i-1):i+2, max(0, j-1):j+2]) - grid[i, j]\\n return 1 if grid[i, j] and 0 < n < 5 else int(n == 3)\\n \\n while key not in looped:\\n looped.add(key)\\n grid = np.array([[alive(i, j) for j in range(size)] \\\\\\n for i in range(size)], dtype=bool)\\n grid[0, 0:size] = grid[size - 1, 0:size] = True\\n grid[0:size, 0] = grid[0:size, size - 1] = True\\n key = hash(str(grid))\\n yield grid\",\n \"def multiple_gen(modulus):\\n count = 1\\n while True:\\n yield modulus * count\\n count += 1\",\n \"def run_Over_Grid(numdemes = 2, reps = 10, numreg = 100, t = 1000):\\n Nmean = 2000\\n Nsd = 100\\n migMean = 0.0001\\n migsd = 1e-06\\n ndc2 = numdemes * (numdemes - 1) / 2\\n rows = ndc2 + numdemes + 1\\n I = np.matrix(np.eye(rows))\\n Ck = I[0:rows - 1, :]\\n Dk = I[rows - 1, :]\\n output = []\\n for r in xrange(reps):\\n N = np.random.normal(Nmean, Nsd, (numdemes,))\\n mtemp = np.random.normal(migMean, migsd, (ndc2,))\\n xtrue = np.hstack((N, mtemp))\\n m = np.zeros((numdemes, numdemes))\\n cnt = 0\\n for i in xrange(numdemes):\\n for j in xrange(i + 1, numdemes):\\n m[i, j] = m[j, i] = mtemp[cnt]\\n cnt += 1\\n\\n Ninv = [ 1.0 / x for x in N ]\\n Qtrue = comp_pw_coal_cont(m, Ninv)\\n Ptrue = expM(t * Qtrue)\\n obs_rates = Ck * Ptrue * Dk.T\\n if numreg > 0:\\n sd_rates = np.real(np.sqrt(obs_rates.getA() * (1 - obs_rates).getA() / numreg))\\n noise = np.random.normal(0.0, sd_rates)\\n print 'Noise:\\\\n', noise\\n N0 = np.random.normal(Nmean / 2.0, Nsd * 3.0, (numdemes,))\\n m0 = np.random.normal(migMean / 2.0, migsd * 3.0, (ndc2,))\\n x0 = np.hstack((N0, m0))\\n xopt = opt.fmin(compute_Frob_norm_mig, x0, (t, obs_rates), maxfun=1000000, maxiter=100000)\\n output.append((xtrue, xopt, linalg.norm(xopt - xtrue)))\\n\\n return output\",\n \"def generate_combinations(k: int, n: int):\\n result = list()\\n for i in range(1, k + 1):\\n for bits in itertools.combinations(range(n), i):\\n s = [0] * n\\n for bit in bits:\\n s[bit] = 1\\n result.append(s)\\n\\n return pd.DataFrame(result)\",\n \"def get_generator(modulus, k, m):\\n\\ttries = 0\\n\\twhile tries <= ((m-k) + 1):\\n\\t\\tgen = get_prime(k, m)\\n\\t\\tprint(gen)\\n\\t\\tpows = [gen]\\n\\t\\tfor x in range(modulus):\\n\\t\\t\\ta = pows[0]\\n\\t\\t\\tpows.insert(0, a*gen)\\n\\t\\tremainders = set([(x % modulus) for x in pows])\\n\\t\\tif len(remainders) == modulus - 1:\\n\\t\\t\\treturn gen\\n\\t\\ttries += 1\\n\\tprint(\\\"No generator found. Increasing generator range by 10 on each side.\\\")\\n\\tl = k - 10\\n\\tn = m + 10\\n\\tif l <= 0:\\n\\t\\tl = 1\\n\\treturn get_generator(modulus, l, n)\",\n \"def generate_random_tomogram_set(templates, criteria, number_of_tomograms, dim, seed=None, noise=False):\\n if (seed == None):\\n seed = random.randint(0, 2 ** 31 - 1)\\n print('Using random seed: ', seed)\\n random.seed(int(seed))\\n np.random.seed(int(seed))\\n\\n for i in range(number_of_tomograms):\\n yield generate_random_tomogram(templates, criteria, dim, noise)\",\n \"def generate(group, number, n):\\n return [get_group(group, number) for i in xrange(n)]\",\n \"def multiple_chromosome_set_generator(random, args):\\n keys = args.get('keys')\\n candidate = MultipleChromosomeGenome(keys=keys)\\n for key in keys:\\n key_args = {\\n 'representation': args.get(\\\"%s_representation\\\" % key),\\n 'max_size': args.get(\\\"%s_max_size\\\" % key),\\n 'variable_size': args.get('variable_size')\\n }\\n candidate[key] = set_generator(random, key_args)\\n\\n return candidate\",\n \"def cycles(n, support, randomize=False):\\n support = np.array(support)\\n\\n def gen(p):\\n g = combinations(support, n)\\n if randomize:\\n g = list(g)\\n random.shuffle(g)\\n\\n for local_support in g:\\n for output_p in all_permutations(local_support)(p):\\n yield output_p\\n\\n return gen\",\n \"def __iter__(self):\\n from sage.combinat.posets.posets import FinitePosets_n\\n n = 0\\n while True:\\n for P in FinitePosets_n(n):\\n yield P\\n n += 1\",\n \"def MCS(n,k):\\n\\tglobal dict_all\\n\\tdict_val=copy.deepcopy(dict_all)\\n\\t#start_time = time.time()\\n\\tfinal = {}\\t\\t\\t\\t\\t # Store all result with the count as key. For example final[1]=[[1,0,0],[0,1,1]]\\n\\tseq = []\\t\\t\\t\\t\\t\\t# Store the count with no duplication\\n\\tfor i in range(n):\\n\\t\\tleaf={}\\t\\t\\t\\t\\t\\t# leaf is the dictionary to store the random value of each leaf\\n\\t\\t#count=0\\n\\t\\tfor i in leaves:\\n\\t\\t\\tleaf[i] = choice([0,1])\\n\\t\\t\\tdict_val[i]=leaf[i]\\n\\t\\t\\t#count += leaf[i]\\n\\t\\tresult = Cal_FT(dict_val)\\t\\n\\t\\t'''\\n\\t\\tif result:\\n\\t\\t\\tcutset = []\\n\\t\\t\\tfor i in leaves:\\n\\t\\t\\t\\tcutset.append(str(leaf[i]))\\n\\t\\t\\tcutset=\\\"\\\".join(cutset)\\n\\t\\t\\tif cutset not in final:\\n\\t\\t\\t\\tfinal[cutset]=count\\n\\tfinal_sorted=sorted(zip(final.values(),final.keys())) \\t\\t\\t\\t#Order the cutset by its count\\n\\tfor i in range(k):\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t#Print the first k result\\n\\t\\tcutset=list(final_sorted[i][1])\\n\\t\\tresult=[]\\n\\t\\tfor index in range(len(cutset)):\\n\\t\\t\\tif cutset[index] is \\\"1\\\":\\n\\t\\t\\t\\tresult.append(leaves[index])\\n\\t\\tprint result\\n\\t#end_time=time.time()\\n\\t#print \\\"Running time is\\\", end_time-start_time\\n\\t'''\",\n \"def partial_permutations(n, k):\\n return int((factorial(n) / factorial(n - k)) % 1000000)\",\n \"def generate_objects_of_size(\\n self, n: int, **parameters: int\\n ) -> Iterator[CombinatorialObjectType]:\\n key = (n,) + tuple(sorted(parameters.items()))\\n res = self.obj_cache.get(key)\\n if res is not None:\\n yield from res\\n return\\n assert (\\n self.subgenerators is not None\\n ), \\\"you must call the set_subrecs function first\\\"\\n res = []\\n for subobjs in self.constructor.get_sub_objects(\\n self.subgenerators, n, **parameters\\n ):\\n obj = self.backward_map(subobjs)\\n yield obj\\n res.append(obj)\\n self.obj_cache[key] = res\",\n \"def mandelbrot(c, max_iter=50, div_lim=2):\\n f = 0j\\n n = 0\\n out = zeros_like(c,dtype=int)\\n while n < max_iter:\\n n += 1\\n f = func(f,c)\\n out[(out == 0) & (abs(f) > div_lim)] = n\\n f[(out != 0)] = 100\\n return(out)\",\n \"def allcombinations(orgset, k):\\n return itertools.chain(*[combination(orgset, i) for i in range(1, k + 1)])\",\n \"def iteration(self):\\n T = self.generate_T()\\n R = self.reproduce(T)\\n self.P = self.choose_mi_best(R)\\n #print(self.P)\",\n \"def generate_test_set_dqn(n_free_min, n_free_max, d_edge_min, d_edge_max, Omega_max, Phi_max, Lambda_max,\\n weighted, w_max, directed, size_test_set, to_torch=False):\\n\\n # Initialize the variables\\n Budget_max = Omega_max + Phi_max + Lambda_max\\n test_set = []\\n if to_torch:\\n test_set_torch = []\\n\\n print(\\\"==========================================================================\\\")\\n print(\\\"Generates the test set... \\\\n\\\")\\n\\n # for all budgets\\n for budget in tqdm(range(1, Budget_max + 1)):\\n # initialize the budget's instances list\\n test_set_budget = []\\n if to_torch:\\n test_set_budget_torch = []\\n for k in range(size_test_set):\\n # generate a random instance\\n instance_budget_k = generate_random_instance(\\n n_free_min,\\n n_free_max,\\n d_edge_min,\\n d_edge_max,\\n Omega_max,\\n Phi_max,\\n Lambda_max,\\n weighted=weighted,\\n w_max=w_max,\\n Budget_target=budget,\\n directed=directed,\\n )\\n G = instance_budget_k.G\\n Omega = instance_budget_k.Omega\\n Phi = instance_budget_k.Phi\\n Lambda = instance_budget_k.Lambda\\n J = instance_budget_k.J\\n # solve the instance\\n value, D, I, P = solve_mcn(G, Omega, Phi, Lambda, J=J, exact=True)\\n # save the value, P, D in the Instance object\\n instance_budget_k.value = value\\n instance_budget_k.D = D\\n instance_budget_k.I = I\\n instance_budget_k.P = P\\n # pushes it to memory\\n test_set_budget.append(instance_budget_k)\\n # if we want to save the corresponding InstanceTorch\\n # to evaluate the training, we stop at Budget_max - 1\\n if to_torch:\\n instance_budget_k_torch = instance_to_torch(instance_budget_k)\\n test_set_budget_torch.append(instance_budget_k_torch)\\n test_set.append(test_set_budget)\\n if to_torch:\\n test_set_torch.append(test_set_budget_torch)\\n\\n if not os.path.exists('data'):\\n os.mkdir('data')\\n folder_name = 'test_data'\\n if weighted:\\n folder_name += '_w'\\n if directed:\\n folder_name += '_dir'\\n path_test_data = os.path.join('data', folder_name)\\n if not os.path.exists(path_test_data):\\n os.mkdir(path_test_data)\\n # Save the test sets\\n file_path = os.path.join(path_test_data, \\\"test_set.gz\\\")\\n pickle.dump(test_set, open(file_path, \\\"wb\\\"))\\n if to_torch:\\n file_path_torch = os.path.join(path_test_data, \\\"test_set_torch.gz\\\")\\n pickle.dump(test_set_torch, open(file_path_torch, \\\"wb\\\"))\",\n \"def setup_crubadan():\\n for n in [1,2,3,4,5,'word']:\\n for option in ['mi','pmi']:\\n get_matrix(datasource='crubadan', n=n, option=option)\",\n \"def iterativeDeepeningGeneration(self, param_dict):\\n ans_set_manager_list = []\\n for num_steps in range(1, int(param_dict['maxSteps']) + 1):\\n param_dict['maxSteps'] = str(num_steps)\\n ans_set_manager_list.append(self.computeAnsSets(param_dict))\\n return ans_set_manager_list\",\n \"def generate_data(set_sizes, n_sets, seed):\\n np.random.seed(seed)\\n\\n for n in set_sizes:\\n for k in range(n_sets):\\n # Generiramo nakljucni permutaciji\\n p1 = np.random.permutation(n)\\n p2 = np.random.permutation(n)\\n # Potrebujemo razlicni permutaciji, sicer imamo delno urejenost reda 1\\n while np.all(p1 == p2):\\n p2 = np.random.permutation(n)\\n # Slovarja, ki za vsako stevilo vrneta mesto v posamezni permutaciji\\n inverse1 = {i: p1[i] for i in range(n)}\\n inverse2 = {i: p2[i] for i in range(n)}\\n\\n # Gremo cez vse kombinacije, ce je eno stevilo manjse od drugega v obeh primerih, ju dodamo v delno urejenost\\n partial_order = []\\n for u in range(n):\\n for v in range(n):\\n if inverse1[u] < inverse1[v] and inverse2[u] < inverse2[v]:\\n partial_order.append((u, v))\\n\\n # Izpisemo delno urejenost v datoteko\\n f = open(f'../data/vhod_{n}_{k+1}.txt', 'w')\\n # Prva vrstica\\n f.write(f'{n} {len(partial_order)}\\\\n')\\n # Primerljivi elementi\\n for u, v in partial_order:\\n # Dodamo +1, da ni indeksiranja z 0\\n f.write(f'{u+1} {v+1}\\\\n')\\n f.close()\",\n \"def _mc_gen():\\r\\n n = 1\\r\\n while True:\\r\\n yield n\\r\\n n += 1\",\n \"def parameter_space():\\n return [list(range(7, 17)),\\n list(range(17, 27)),\\n list(range(27, 37)),\\n list(permutations(range(1, 5), 4))]\",\n \"def generator(self):\\n global_index = 0\\n n_params = len(self.params)\\n while (global_index < self.NXFLTEXP*self.paramspace):\\n # skip row that have data already\\n while (np.sum(self.spectra_hdu.data[global_index][1]) > 0.0): \\n global_index += self.NXFLTEXP\\n if (global_index >= self.NXFLTEXP*self.paramspace): break\\n if (global_index >= self.NXFLTEXP*self.paramspace): break\\n\\n # get indexes in each grid; the last grid changing the fastest\\n param_indexes = np.zeros(n_params, dtype=int)\\n param_values = np.zeros(n_params)\\n N0 = self.paramspace\\n for i in range(n_params):\\n (p_name, p_grid, p_log, p_frozen) = self.params[i]\\n N = len(p_grid)\\n N0 /= N\\n p_index = int((global_index/3)//N0 % N)\\n #print('global_index',global_index)\\n #print('p_index',p_index)\\n #print('p_grid[p_index]',p_grid[p_index])\\n #print('p_grid',p_grid)\\n param_indexes[i] = p_index\\n param_values[i] = p_grid[p_index]\\n\\n # write parameter values (repeat the same parameters for each spectrum of the set) \\n for i in range(self.NXFLTEXP):\\n self.spectra_hdu.data[global_index+i][0] = param_values\\n #end for\\n\\n # return total index, array of grid indexes, and array of grid values\\n #sys.stderr.write(\\\"> generator: passing spectrum index %d (%s %s)\\\\n\\\" % (global_index, str(param_indexes), str(param_values)))\\n yield (global_index, param_values, param_indexes, self.energies)\\n global_index += self.NXFLTEXP\\n #end while\",\n \"def genChains(self):\\n self.numMonomer = 0\\n self.numBonds = 0\\n self.numMols = 0\\n self.numCations = 0\\n self.numAnions = 0\\n\\n self.atomsCoords = []\\n self.atomsType = []\\n self.atomsCharge = []\\n self.molId = []\\n self.bondList = []\\n \\n for i in range(self.numPa + self.numPc):\\n\\n if i < self.numPc:\\n # polycation chains, charge in LJ units of LAMMPS\\n # electron charge would be 10.54 using bare LAMMPS LJ units\\n # the dielectric constans of solvent is effectively taken as 111 when assign 1 to +e\\n # just need to set dielectric as 0.72 in LAMMPS ot mimic water with dielectric constant 80\\n self.beadCharge = 1\\n self.beadType = 1 # atomic type for neutral beads in polycation chains\\n self.chain = self.lenPc\\n else:\\n self.beadCharge = -1 # polyanion chains\\n self.beadType = 3 # atomic type for neutral beads in polyanion chains\\n self.chain = self.lenPa\\n\\n self.numMols += 1\\n\\n # generate the first bead of each chain randomly\\n self.numMonomer += 1\\n self.cxyz = np.random.rand(3) * self.box + self.lxyz\\n\\n self.atomsCoords.append(self.cxyz)\\n #self.atomsType.append(self.beadType)\\n\\n # decide if the first bead is charged or not\\n if self.chargeRepeat == 1:\\n self.atomsCharge.append(self.beadCharge)\\n self.atomsType.append(self.beadType + 1)\\n if i < self.numPc:\\n self.numCations += 1\\n else:\\n self.numAnions += 1\\n else:\\n self.atomsType.append(self.beadType)\\n self.atomsCharge.append(0)\\n\\n self.molId.append(self.numMols)\\n\\n self.currpxyz = self.cxyz\\n\\n # follow random walk to generate the chain\\n # generate the seconb bead of the chain\\n self.theta, self.phi = np.random.rand(2) * np.array([np.pi, 2 * np.pi])\\n self.ds = np.array([np.cos(self.theta), np.sin(self.theta) * np.cos(self.phi),\\\\\\n np.sin(self.theta) * np.sin(self.phi)]) * self.segment\\n\\n self.cxyz = self.currpxyz + self.ds\\n\\n self.numMonomer += 1\\n self.atomsCoords.append(self.cxyz)\\n\\n # decide if the second bead is charged or not\\n if 2%self.chargeRepeat == 0:\\n self.atomsCharge.append(self.beadCharge)\\n self.atomsType.append(self.beadType + 1)\\n if i < self.numPc:\\n self.numCations += 1\\n else:\\n self.numAnions += 1\\n else:\\n self.atomsCharge.append(0)\\n self.atomsType.append(self.beadType)\\n\\n self.molId.append(self.numMols)\\n \\n self.numBonds += 1\\n self.bondList.append([self.numMonomer - 1, self.numMonomer])\\n\\n self.currpxyz = self.cxyz\\n\\n self.currtheta = self.theta\\n self.currphi = self.phi\\n\\n self.dstot += self.ds\\n\\n # generating the rest beads of the chain\\n\\n for k in range(3, self.chain+1):\\n # only accept atoms that are beyong certain distance\\n # from the atom precding the current atom in the chain\\n self.theta, self.phi = np.random.rand() * np.array([np.pi - self.stiffangle, \\\\\\n 2 * np.pi])\\n self.ds1 = np.array([np.cos(self.theta), np.sin(self.theta) * np.cos(self.phi),\\\\\\n np.sin(self.theta) * np.sin(self.phi)]) * self.segment\\n\\n self.reverseXZrotation()\\n self.cxyz = self.currpxyz + self.ds\\n\\n self.numMonomer += 1\\n self.atomsCoords.append(self.cxyz)\\n\\n if k % self.chargeRepeat == 0:\\n self.atomsCharge.append(self.beadCharge)\\n self.atomsType.append(self.beadType + 1)\\n if i < self.numPc:\\n self.numCations += 1\\n else:\\n self.numAnions += 1\\n else:\\n self.atomsCharge.append(0)\\n self.atomsType.append(self.beadType)\\n\\n self.molId.append(self.numMols)\\n self.numBonds += 1\\n self.bondList.append([self.numMonomer - 1, self.numMonomer])\\n\\n self.currpxyz = self.cxyz\\n\\n self.currtheta = np.arccos(self.ds[0]/self.segment)\\n if self.ds[2] > 0:\\n self.currphi = np.arccos(self.ds[1]/self.segment/np.sin(self.currtheta))\\n else:\\n self.currphi = 2*np.pi - np.arccos(self.ds[1]/self.segment/np.sin(self.currtheta))\\n\\n self.dstot += self.ds\\n\\n print \\\"%d beads are generated.\\\\n\\\" % self.numMonomer \\n assert self.numMonomer == self.numPc * self.lenPc + self.numPa * self.lenPa, \\\\\\n \\\"The number of monomers in chains is wrong!\\\\n\\\"\\n assert self.numCations == int(np.floor(self.lenPc * self.chargeFraction)*self.numPc), \\\\\\n \\\"The number of positively charged beads is wrong!\\\\n\\\"\\n assert self.numAnions == int(np.floor(self.lenPa * self.chargeFraction)*self.numPa), \\\\\\n \\\"The number of negatively charged beads is wrong!\\\\n\\\"\",\n \"def part_1():\\n return itertools.permutations(range(5))\",\n \"def generate_grna_sets(self, prioritise_3prime = False, set_num = 1, max_set_num = 1, manual = True):\\n collapsed_grna = sorted(self, key = lambda cg:(-len(cg), sorted(cg)))\\n ## copy the CollapsedgRNA objects so we can modify them without changing the original\\n collapsed_grna = [cg.copy() for cg in collapsed_grna]\\n output = []\\n while all(len(cg.gRNAs) > 0 for cg in collapsed_grna) and set_num <= max_set_num:\\n selected_grna = self._generate_grna_set(collapsed_grna, resort = False,\\n prioritise_3prime = prioritise_3prime)\\n if manual:\\n sorted_grna = sorted(selected_grna, key = lambda grna:grna.id)\\n usr_input = manual_check_prompt(sorted_grna, set_num = set_num)\\n if usr_input.upper() != 'X':\\n id_list = tuple(grna.id for grna in selected_grna)\\n seq_list = tuple(str(grna.seq).upper() for grna in selected_grna)\\n if usr_input in id_list:\\n index = id_list.index(usr_input)\\n collapsed_grna[index].remove(selected_grna[index])\\n elif usr_input.upper() in seq_list:\\n index = seq_list.index(usr_input)\\n collapsed_grna[index].remove(selected_grna[index])\\n else:\\n print(\\\"Invalid input.\\\")\\n continue ## skip removal of selected gRNA and yielding of selected gRNA set\\n ## remove selected gRNA from candidate pool\\n for i, grna in enumerate(selected_grna):\\n collapsed_grna[i].remove(grna)\\n set_num += 1\\n yield selected_grna\\n # return output\\n return\",\n \"def __permutation(orgset, k):\\n if k == 1:\\n for i in orgset:\\n yield (i,)\\n elif k > 1:\\n for i, x in enumerate(orgset):\\n # iterates though to near the end\\n for s in __permutation(orgset[:i] + orgset[i + 1 :], k - 1):\\n yield (x,) + s\",\n \"def generate_training_set(individual, db_name):\\n\\n db = connect(db_name)\\n for miller_indices in individual:\\n\\n # Do MD\\n slab = generate_slab(miller_indices, 5)\\n slab = make_supercell(slab, P=[[3, 1, 1], [1, 3, 1], [1, 1, 1]])\\n\\n slab.set_calculator(EMT())\\n slab.get_potential_energy()\\n\\n db.write(slab)\\n\\n MaxwellBoltzmannDistribution(slab, 300. * units.kB)\\n dyn = VelocityVerlet(slab, dt=1. * units.fs)\\n for i in range(1, 10):\\n dyn.run(10)\\n db.write(slab)\",\n \"def run_variations(filename, N=50, N_scales=7, shape=(512, 512),\\n steps=[(10,4.0), (20,1.0)], display_inline=False, min_radius=1.5,\\n max_radius=90.0):\\n\\n colors = [[1, 0, 0], [0, 1, 0], [0, 0, 0.9], [1, 1, 0],\\n [1, 0, 1], [0, 1, 1], [1, 1, 1], [1, 0.6, 0]]\\n perm = np.random.permutation\\n for n in range(N):\\n # generate palette\\n pal = perm(colors)[:4]\\n pal = perm(r_[pal, pal])\\n pal = (pal * .6 + roll(pal, 4, axis=0) * 0.4)[:4]\\n pal = shade(N_scales, *pal)\\n # generate parameters and MSTP object\\n ra = exp(rseq(log(min_radius), log(max_radius), N=N_scales, randomness=0.9)).round(2)\\n ri = ((1.333 + rand(N_scales)) * ra).round(2)\\n dt = (.01 * frange(1, N_scales) ** 0.8).round(3)\\n wt = (1.33 + arctan(5 * (rand(N_scales) - .5))).round(2)\\n m = MSTP(shape, ra=ra, ri=ri, dt=dt, wt=wt, pal=pal)\\n\\n print '\\\\n-------- rendering image', filename % n\\n print m\\n # display(HTML(' '.join('<span style=\\\"background:rgb(%d,%d,%d)\\\">(o_O)</span> ' % tuple(255*k) for k in array(pal))))\\n for i, (N_steps, speed) in enumerate(steps):\\n print 'rendering %s steps at %1.1fx speed' % (N_steps, dt_multiplier)\\n sys.stdout.flush()\\n m.run(n=N_steps, speed=speed)\\n if display_inline:\\n display(IM(m.rgb_image()))\\n if i < 0:\\n first = False\\n print 'renoising after iter 1.',\\n m.z += 0.25 * rand(*m.z.shape) * m.z.ptp()\\n\\n # display(IM(m.rgb_image()))\\n imsave(filename % n, m.rgb_image())\",\n \"def lammps(parameter, iteration, numberlist, density, chainlength):\\n # input parameters\\n var = parameter[0]\\n x = var * numberlist[0]\\n y = var * numberlist[1] * 1.732 / 2\\n z = x / 2\\n #nodeid = 55 + iteration % 5\\n M = int((x * y * z * density - 4 * numberlist[0] * numberlist[1] * int(z / 0.3)) / chainlength) \\n os.environ['var'] = str(var)\\n os.environ['iteration'] = str(iteration)\\n os.environ['M'] = str(M)\\n #os.environ['nodeid'] = str(nodeid)\\n # copy the files to modify\\n os.system(\\\"cp in.asymmetric in.asymmetric_$iteration\\\")\\n os.system(\\\"cp run_lammps.sh run_lammps_$iteration.sh\\\")\\n os.system(\\\"cp MultiBCP_hexagonal_post.py MultiBCP_hexagonal_post_$iteration.py\\\")\\n # modify relevant files\\n os.system('sed -i -e \\\"s/distance = 12/distance = $var/\\\" MultiBCP_hexagonal_post_$iteration.py')\\n os.system(\\\"python MultiBCP_hexagonal_post_$iteration.py\\\")\\n time.sleep(3)\\n os.system('sed -i -e \\\"s/3072/$M/\\\" in.asymmetric_$iteration')#modify according to the parameters\\n os.system('sed -i -e \\\"s/XN_100/XN_100_$iteration/\\\" in.asymmetric_$iteration')\\n os.system('sed -i -e \\\"s/finalasymmetric/finalasymmetric_$iteration/\\\" in.asymmetric_$iteration')\\n os.system('sed -i -e \\\"s/POSTA2B8/POSTA2B8_$iteration/\\\" run_lammps_$iteration.sh')\\n os.system('sed -i -e \\\"s/posta2b8.out/posta2b8_$iteration.out/\\\" run_lammps_$iteration.sh')\\n #os.system('sed -i -e \\\"s/node55/node$nodeid/\\\" run_lammps_$iteration.sh')\\n os.system('sed -i -e \\\"s/in.asymmetric/in.asymmetric_$iteration/\\\" run_lammps_$iteration.sh')\\n # run the simulation\\n os.system(\\\"sbatch run_lammps_$iteration.sh\\\")\\n return None\",\n \"def xrndSphere(n):\\n for i in xrange(n):\\n yield rndSphere()\",\n \"def createUpPyramidSets(blocksize,operating):\\n sets = tuple()\\n ul = blocksize[0]-operating\\n dl = operating\\n while ul > dl:\\n r = numpy.arange(dl,ul,1)\\n sets+=(tuple(product(r,r)),)\\n dl+=operating\\n ul-=operating\\n return sets\",\n \"def part_2():\\n return itertools.permutations(range(5, 10))\",\n \"def parallelotope(self, generators):\\n try: \\n generators = [ vector(QQ,v) for v in generators ]\\n field = QQ\\n except TypeError:\\n generators = [ vector(RDF,v) for v in generators ]\\n field = RDF\\n \\n from sage.combinat.combination import Combinations\\n par = [ 0*generators[0] ]\\n par += [ sum(c) for c in Combinations(generators) if c!=[] ]\\n return Polyhedron(vertices=par, field=field)\",\n \"def gen_iter_graph(final_nodes, num_nodes):\\n\\t# create complete directed graph on m nodes (num_nodes)\\n\\tgraph = make_complete_graph(num_nodes)\\n\\n\\tV = []\\n\\tE = []\\n\\ttotal_indeg = 0\\n\\n\\tfor key in graph:\\n\\t\\tV.append(key)\\n\\t\\tE.append([key,graph[key]])\\t\\n\\n\\t# grow the graph by adding n - m (final_nodes - num_nodes) nodes\\n\\t# where each new node is connected to m nodes randomly chosen \\n\\t# from the set of existing nodes. Elimintate duplicates \\n\\t# to avoid parallel edges.\\n\\tfor node_added in range(num_nodes, final_nodes):\\n\\t\\t# for key in graph:\\n\\t\\t# \\tfor value in graph[key]:\\n\\t\\t# \\t\\ttotal_indeg += value\\n\\t\\tV_prime = set()\\n\\t\\t# choose randomly m nodes from V and add them to V_prime \\n\\t\\t# where the probability of choosing node j is (indeg(j) + 1)/(totindeg + |V|)\\n\\t\\t# i.e., call DPATrial (line 6 in pseudocode)\\n\\t\\ttrial = DPATrial.DPATrial(num_nodes)\\n\\t\\tV_prime = trial.run_trial(num_nodes)\\n\\t\\tfor node in V_prime:\\n\\t\\t\\tV_prime.add(node)\\n\\t\\tV.append(node_added)\\n\\t\\tgraph[node_added] = V_prime\\n\\treturn graph\",\n \"def allpermutations(orgset, k):\\n return itertools.chain(*[permutation(orgset, i) for i in range(1, k + 1)])\",\n \"def list_permutations(n,r, multiset=False):\\n if not multiset:\\n return list(itertools.permutations(n,r))\\n\\n elif multiset:\\n return set(list(itertools.permutations(n,r)))\",\n \"def build_optics(SetOfObjects, epsilon):\\n\\n for point in SetOfObjects._index:\\n if not SetOfObjects._processed[point]:\\n expandClusterOrder(SetOfObjects, point, epsilon)\",\n \"def subset_gen(itemSet):\\n subsets = []\\n for i in range(1, len(itemSet)):\\n c = combinations(itemSet, r=i)\\n for cc in c:\\n subsets.append(set(cc))\\n return subsets\",\n \"def map(parameter_sets, params):\\n from parcel_model.parcel import AerosolSpecies\\n from parcel_model.lognorm import Lognorm\\n import numpy as np\\n import random\\n\\n M_tot = params['M_tot']\\n N_tot = params['N_tot']\\n flag = params['flag']\\n rho_sulfate, rho_carbon = [params['rho'][s] for s in ['sulfate', 'carbon']]\\n kappa_sulfate, kappa_carbon = [params['kappa'][s] for s in ['sulfate', 'carbon']]\\n sigma_mixed = params['sigma']['mixed']\\n sigma_sulfate = params['sigma']['sulfate']\\n sigma_carbon = params['sigma']['carbon']\\n\\n\\n ## Unload the parameters and iterate over them to drive the generation of aerosol\\n ## populations and the model\\n initial_aerosol_pops = []\\n n, parameter_sets = parameter_sets\\n for ps in parameter_sets:\\n alpha, gamma, V = ps\\n\\n mixed, sulf, carbon = MakeAerosols.mixing2(M_tot, N_tot, alpha, gamma, sigma_mixed, sigma_sulfate, sigma_carbon, rho_sulfate, rho_carbon, kappa_sulfate, kappa_carbon)\\n\\n initial_aerosols = []\\n if alpha > 0: # there is an external mode\\n initial_aerosols.append(sulf)\\n if flag == 'OC': ## if there is hygroscopic carbon\\n initial_aerosols.append(carbon)\\n if alpha < 1: # there is leftover in the internal mode\\n initial_aerosols.append(mixed)\\n\\n initial_aerosol_pops.append((initial_aerosols, ps))\\n\\n yield (n, initial_aerosol_pops)\",\n \"def run_calculation():\\n\\n print(\\\"Creating %d-process pool\\\" % mp.cpu_count())\\n\\n pool = mp.Pool(mp.cpu_count())\\n\\n f = h5py.File('/testdata/mandelbrot.hdf5', 'w')\\n\\n print(\\\"Creating output dataset with shape %s x %s\\\" % (NX, NY))\\n\\n dset = f.create_dataset('mandelbrot', (NX, NY), 'i')\\n dset.attrs['XSTART'] = XSTART\\n dset.attrs['YSTART'] = YSTART\\n dset.attrs['XEXTENT'] = XEXTENT\\n dset.attrs['YEXTENT'] = YEXTENT\\n\\n result = pool.imap(compute_row, (x * xincr for x in range(NX)))\\n\\n for idx, arr in enumerate(result):\\n if idx % 25 == 0: print(\\\"Recording row %s\\\" % idx)\\n dset[idx] = arr\\n\\n print(\\\"Closing HDF5 file\\\")\\n\\n f.close()\\n\\n print(\\\"Shutting down process pool\\\")\\n\\n pool.close()\\n pool.join()\",\n \"def combinations(n, k):\\r\\n return exp(gammaln(n + 1) - gammaln(k + 1) - gammaln(n - k + 1))\",\n \"def __iter__(self):\\n for p in self.param_grid:\\n # Always sort the keys of a dictionary, for reproducibility\\n modstr = '%s__' % self.modality\\n items = sorted([(k.replace('clf__'+modstr, ''), v) for k, v in p.items() if modstr in k])\\n if not items:\\n yield {}\\n else:\\n keys, values = zip(*items)\\n for v in product(*values):\\n params = dict(zip(keys, v))\\n yield params\",\n \"def generate_with_antichain(set_sizes, n_sets, antichain_sizes, seed):\\n np.random.seed(seed)\\n\\n for n in set_sizes:\\n for k, size in zip(range(n_sets), antichain_sizes):\\n antichain_size = int(np.round(size*n))\\n # Generiramo nakljucni permutaciji (zadnjih antichain_size elementov predstavlja antiverigo, zato jih ni v permutaciji)\\n p1 = np.random.permutation(n-antichain_size)\\n p2 = np.random.permutation(n-antichain_size)\\n # Potrebujemo razlicni permutaciji, sicer imamo delno urejenost reda 1\\n while np.all(p1 == p2):\\n p2 = np.random.permutation(n-antichain_size)\\n # Slovarja, ki za vsako stevilo vrneta mesto v posamezni permutaciji\\n inverse1 = {i: p1[i] for i in range(n-antichain_size)}\\n inverse2 = {i: p2[i] for i in range(n-antichain_size)}\\n\\n # Gremo cez vse kombinacije, ce je eno stevilo manjse od drugega v obeh primerih, ju dodamo v delno urejenost\\n partial_order = []\\n for u in range(n-antichain_size):\\n for v in range(n-antichain_size):\\n if inverse1[u] < inverse1[v] and inverse2[u] < inverse2[v]:\\n partial_order.append((u, v))\\n\\n # Izpisemo delno urejenost v datoteko\\n f = open(f'../data/vhod_antichain_{n}_{k+1}.txt', 'w')\\n # Prva vrstica\\n f.write(f'{n} {len(partial_order)}\\\\n')\\n # Primerljivi elementi\\n for u, v in partial_order:\\n # Dodamo +1, da ni indeksiranja z 0\\n f.write(f'{u+1} {v+1}\\\\n')\\n f.close()\",\n \"def tst_random_set():\\n final_wave, final_spec, final_z = desi_qso_templates(\\n outfil='test_random_set.fits', N_perz=100, seed=12345)\",\n \"def run_montecarlo(v,iteration , n_run, dr_coeff = 0.58):\\n \\n start = time.time()\\n#%%\\n R_cut = g_r_max # TODO\\n #v_f = interp1d(r,v)\\n\\n g_lists = {'same':[],'opp':[]} \\n S_lists = {'same':[],'opp':[]}\\n \\n # Initialize the system at a random distribution\\n particles = initialize_system(MC_par['init_conf'])\\n E = np.zeros(MC_par['N_mcs']+2) #TODO initial energy\\n MC_move = 0\\n#%%\\n for n in range(MC_par['N_mcs']+1):\\n #%%\\n chosen_one = np.random.randint(MC_par['N_particles'])\\n dr = dr_coeff * ( np.random.rand(MC_par['dim']) - 0.5 )\\n #%%###\\n dE,this_move = MC_step(particles,chosen_one,dr,R_cut,v)\\n ####\\n E[n+1] = E[n] + dE\\n MC_move += this_move\\n \\n ## Every N_corr steps\\n if(n % MC_par['N_corr']==0):\\n print_progress(n+1,MC_par['N_mcs']) \\n ## After convergence calc and save g\\n if(n > MC_par['N_conv']):\\n for kind in ['same','opp']:\\n g_meas, S_meas, r_meas_Sg = calc_and_plot_g_r(particles,n,iteration,kind = kind, save_plot = PR_par['Plot all g'])\\n g_lists[kind].append(g_meas)\\n S_lists[kind].append(S_meas)\\n \\n elapsed = time.time() - start\\n print(' Done in %d s' % elapsed)\\n \\n print('%d %% of the Monte Carlo steps were performed (%d out of %d)' % (100.0*MC_move/MC_par['N_mcs'], MC_move,MC_par['N_mcs']))\\n\\n plot_conf(particles,iteration)\\n\\n \\n return particles,E,g_lists,S_lists,r_meas_Sg\",\n \"def r_combinations(n,r):\\n return r_permutations(n,r) / math.factorial(r)\",\n \"def evolve(self, generations=10000):\\n\\n for gen in range(generations):\\n # run the tournament\\n self.tournament()\\n\\n # generate the next generation\\n self.p = self.nextGen()\",\n \"def get_test_params(cls, parameter_set=\\\"default\\\"):\\n params1 = {\\n \\\"n_clusters\\\": 3,\\n \\\"n_init\\\": 2,\\n \\\"max_iter\\\": 2,\\n \\\"tol\\\": 1e-3,\\n \\\"verbose\\\": False,\\n \\\"random_state\\\": 2,\\n }\\n params2 = {\\n \\\"n_clusters\\\": 2,\\n \\\"init_algorithm\\\": \\\"random\\\",\\n \\\"n_init\\\": 1,\\n \\\"max_iter\\\": 1,\\n \\\"tol\\\": 1e-4,\\n \\\"verbose\\\": False,\\n \\\"random_state\\\": 1,\\n }\\n return [params1, params2]\",\n \"def gen_relaxation(chunknum, chunksize, dset_select=\\\"aligned\\\"):\\n\\n datapath = Path(__file__).parents[0] / \\\"data/relaxation\\\"\\n dataset = datagen.DataSet(datapath / f\\\"init_{dset_select}\\\")\\n dataset.load()\\n\\n print(f\\\"Generating {chunknum} chunks of size {chunksize}\\\")\\n print(f\\\"for dataset '{dset_select}'\\\")\\n\\n for n in range(chunknum):\\n\\n print()\\n print(f\\\"CHUNK #{n}\\\")\\n print(f\\\"===============\\\")\\n print()\\n\\n for k, ens in enumerate(dataset.ensembles):\\n\\n b = ens.b\\n print(f\\\"Ensemble b={b:.2f}\\\")\\n ens.simulate(chunksize, reset=False, verbose=True)\\n dataset.save(ens_index=k)\",\n \"def construct(self):\\n\\n newSet = {}\\n current_index = 0\\n\\n for key_1, value_1 in self._sets[self._currentSet].items():\\n current_index += 1\\n for key_2,value_2 in list(self._sets[self._currentSet].items())[current_index:]:\\n # join the 2 tuples\\n join = key_1 + key_2\\n # remove duplicates\\n join = tuple(set(join))\\n # get combinations\\n combined = tuple(combinations(join, self._currentSet+1))\\n # sort combination\\n combined = tuple(sorted(combined[0]))\\n\\n # append new combination to dict\\n if len(combined) != 0 :\\n newSet[combined] = 0\\n\\n self._currentSet += 1\\n # append the new itemset in the sets dict \\n self._sets[self._currentSet] = newSet\",\n \"def _gen_ms(theta, nb_qubits):\\n routine = QRoutine()\\n\\n for first_qb in range(nb_qubits):\\n for second_qb in range(first_qb + 1, nb_qubits):\\n routine.apply(RXX(theta), [first_qb, second_qb])\\n\\n return routine\",\n \"def euler24():\\n\\n # import math\\n # math.factorial(10) = 3628800, 3.6 million combinations\\n # math.factorial(9) = 362880, so the first digit is '2'\\n\\n import time\\n start = time.time()\\n\\n p = 10 \\n limit = 1000000-1\\n result = []\\n\\n LIST = [0,1,2,3,4,5,6,7,8,9]\\n\\n\\n # higest digit, 10! permutations in total\\n while p > 0:\\n l = len(result)\\n m, r = divmod(limit, math.factorial(p-1))\\n result.append(LIST[m])\\n LIST.remove(LIST[m]) # remove first element with VALUE LIST[m]\\n limit -= m * math.factorial(p-1)\\n p -= 1\\n\\n end = time.time()\\n print \\\"Time:\\\" , (end-start)*1000 , \\\"ms\\\"\\n \\n return result\",\n \"def generate_test():\\n o = []\\n pos = [384, 288]\\n note_group_size = GAN_PARAMS[\\\"note_group_size\\\"]\\n generate_set(begin=3 * note_group_size, start_pos=pos,\\n length_multiplier=dist_multiplier, group_id=3, plot_map=True)\",\n \"def gen_pows(a,b):\\n s = set()\\n for x in range(2, a + 1):\\n for y in range(2, b + 1):\\n s.add(x**y)\\n return s\",\n \"def ECMFactor(n, sieve, limit, times):\\n for _ in range(times):\\n g = n\\n while g == n:\\n curve, point = randomCurve(n)\\n g = gcd(n, curve.discriminant().remainder())\\n \\n if g > 1:\\n return g\\n\\n for prime in sieve:\\n prod = prime\\n i = 0\\n while prod < limit and i < 10:\\n i += 1\\n try:\\n point = prime * point\\n except ZeroDivisionError as e:\\n return gcd(e.args[1], n)\\n prod *= prime\\n\\n return n\",\n \"def __combination(orgset, k):\\n if k == 1:\\n for i in orgset:\\n yield (i,)\\n elif k > 1:\\n for i, x in enumerate(orgset):\\n # iterates though to near the end\\n for s in __combination(orgset[i + 1 :], k - 1):\\n yield (x,) + s\",\n \"def find_all_ngon_sols():\\n ngon = [None for _ in range(N)] \\n ngon_set = set()\\n numbers = set(range(1, (2 * N) + 1))\\n\\n for triplet in permutations(numbers, 3):\\n ngon[0] = tuple(triplet)\\n total = sum(triplet)\\n next_ngon_set = set()\\n fill_ngon(ngon, numbers - set(triplet), 1, next_ngon_set, total)\\n ngon_set |= next_ngon_set\\n\\n return ngon_set\",\n \"def _set_builder_fast(m, distances):\\n\\n if not is_matrix(m):\\n raise ValueError(\\\"Expected numpy matrix\\\")\\n\\n if not is_matrix_binary(m):\\n raise ValueError(\\\"Expected binary matrix\\\")\\n\\n if not is_matrix_correct(m):\\n raise ValueError(\\\"Expected matrix that has valid structure (not more than one '1' in row)\\\")\\n\\n n, k = np.shape(m)\\n ones_count, one_pos, has_one, where_one, zero_rows = get_matrix_info(m, n)\\n\\n # run through all distances in D\\n for distance in distances:\\n\\n # check that distance is correct\\n if distance < 0 or distance > ones_count + n:\\n continue\\n\\n if distance == 0:\\n yield m.copy()\\n continue\\n\\n for num_ones_removed in range(np.min([distance, ones_count]) + 1):\\n for clear_rows in combinations(one_pos[0], num_ones_removed):\\n\\n cleared_matrix = m.copy()\\n cleared_matrix[clear_rows, :] = 0\\n remained_rows = np.array(list((set(range(n)) - set(one_pos[0])).union(set(clear_rows))))\\n\\n for rows in combinations(remained_rows, distance - num_ones_removed):\\n\\n if len(rows) == 0:\\n yield cleared_matrix.copy()\\n continue\\n\\n avaliable_column_indices = [get_zero_indices(where_one[row], k) for row in rows]\\n\\n rows = np.array(rows, dtype=np.int32)\\n rows_count = rows.size\\n\\n if (k ** rows_count)*n*k*4//1024//1024 > 1024:\\n raise ValueError(\\\"Looks that you don't have enough RAM\\\")\\n\\n indices = np.array(np.meshgrid(*avaliable_column_indices)).T.reshape(-1, rows_count)\\n\\n cache_size = np.shape(indices)[0]\\n\\n indices += rows*k\\n indices = (indices.T + np.arange(cache_size)*(n*k)).T\\n indices = indices.reshape(indices.size)\\n\\n matrix_cache = np.tile(cleared_matrix, (cache_size, 1, 1))\\n matrix_cache.put(indices, 1)\\n\\n # print(\\\"Mb for cache: \\\" + str((cache_size*rows_count + cache_size*n*k)*4//1024//1024))\\n\\n for mat in matrix_cache:\\n yield mat\",\n \"def simulate_qubit_pairs_3D_lattice(cbrt_n, m):\\n # Initialize some variables\\n n = np.power(cbrt_n, 3)\\n x_max = 20\\n d = np.arange(0, x_max + 1)\\n k_matrix = np.zeros((m, x_max + 1))\\n\\n # Run the simulation a total of m times\\n for i in range(m):\\n start = time.time()\\n\\n sim = chp_py.CHP_Simulation(n)\\n sims = []\\n # Add a sim for depth 0\\n sims.append(chp_py.CHP_Simulation(n, sim.state))\\n\\n for j in range(x_max):\\n gates = utils.get_lattice_gates((cbrt_n, cbrt_n, cbrt_n), j)\\n sim.apply_gates(gates)\\n sims.append(chp_py.CHP_Simulation(n, sim.state))\\n\\n # This computes the collision_probability at different\\n # depths in parallel\\n k_matrix[i, :] = utils.sims_to_collision_probabilty(sims)\\n\\n end = time.time()\\n print(\\\"Time elapsed = {:0.2f}, for sample = {}\\\".format(end - start, i))\\n\\n # Store everything in data\\n data = {\\\"d\\\": d,\\n \\\"k_matrix\\\": k_matrix,\\n \\\"n\\\": n,\\n \\\"m\\\": m}\\n\\n return data\",\n \"def combinations(n, k):\\n return factorial(n) / (factorial(k) * factorial(n - k))\",\n \"def compute_mi_steps(self, Akdf, out_csv, sample_size, sigma=5.0, prod_cols=None,\\n bias=1.0, density='setmax', n_hit_miss=50, ngramr=(1, 3)):\\n probcs = []\\n if prod_cols is None:\\n prod_cols = []\\n for a, b in itertools.product(*[STRUCTCOLS + COMBINATIONS] * 2):\\n if not ((a, b) in prod_cols or (b, a) in prod_cols):\\n probcs.append(\\\"$\\\\mathcal{{N}}\\\\{{h({0}, {1}), \\\\sigma\\\\}}$\\\" \\\\\\n .format(a, b))\\n prod_cols.append((a, b))\\n else:\\n for a, b in prod_cols:\\n probcs.append(\\\"$\\\\mathcal{{N}}\\\\{{h({0}, {1}), \\\\sigma\\\\}}$\\\" \\\\\\n .format(a, b))\\n\\n A_tau = [Akdf[i:i + sample_size]\\n for i in range(0, self.n_steps * sample_size, sample_size)]\\n\\n logging.info(\\n f\\\"Computing probabilities of random sets for {self.n_steps} steps.\\\")\\n with parallel_backend('multiprocessing' if BACKEND == 'mp' else 'loky'):\\n t = time.time()\\n P_Aks = Parallel(n_jobs=self.njobs)(\\n delayed(self.compute_set_probability)(\\n A_k, prod_cols=prod_cols, hit_miss_samples=n_hit_miss,\\n density=density, bias=bias, sigma=sigma, ngramr=ngramr)\\n for A_k in A_tau)\\n if self.verbose:\\n logging.info(\\\"Estimated set probabilities in {}s...\\\" \\\\\\n .format(time.time() - t))\\n\\n with parallel_backend('multiprocessing' if BACKEND == 'mp' else 'loky'):\\n t = time.time()\\n info_steps = Parallel(n_jobs=self.njobs)(\\n delayed(self.compute_mutuals)(df, probcs)\\n for df in P_Aks if not (df is None or df.empty))\\n if self.verbose:\\n logging.info(\\\"Estimated MIs in {}s...\\\" \\\\\\n .format(time.time() - t))\\n\\n pd.DataFrame(info_steps).to_csv(out_csv)\\n if os.path.exists(out_csv):\\n logging.info(\\\"Output csv saved to {}\\\".format(out_csv))\\n else:\\n logging.warning(\\\"Output csv {} wasn't saved!\\\".format(out_csv))\",\n \"def mcmc_eclipse14_single(z, t, x, y, sigma, params, tparam, stepsize, numit, nstep=1, xyord=None):\\n # 2011-05-14 16:06 IJMC: Adapted from upsand phase curve routines;\\n # also adapting Agol et al. 2010's Spitzer\\n # work, and from K. Stevenson's MCMC\\n # example implementation.\\n\\n import numpy as np\\n\\n #Initial setup\\n numaccept = 0\\n nout = numit/nstep\\n bestp = np.copy(params)\\n allparams = np.zeros((len(params), nout))\\n allchi = np.zeros(nout,float)\\n\\n #Calc chi-squared for model type using current params\\n zmodel = eclipse14_single(params, tparam, t, xyord=xyord, x=x, y=y)\\n\\n currchisq = (((zmodel - z)/sigma)**2).ravel().sum()\\n\\n bestchisq = currchisq\\n#Run Metropolis-Hastings Monte Carlo algorithm 'numit' times\\n for j in range(numit):\\n #Take step in random direction for adjustable parameters\\n nextp = np.random.normal(params,stepsize)\\n #COMPUTE NEXT CHI SQUARED AND ACCEPTANCE VALUES\\n zmodel = eclipse14_single(nextp, tparam, t, xyord=xyord, x=x, y=y)\\n\\n nextchisq = (((zmodel - z)/sigma)**2).ravel().sum() \\n\\n accept = np.exp(0.5 * (currchisq - nextchisq))\\n if (accept >= 1) or (np.random.uniform(0, 1) <= accept):\\n #Accept step\\n numaccept += 1\\n params = np.copy(nextp)\\n currchisq = nextchisq\\n if (currchisq < bestchisq):\\n #New best fit\\n bestp = np.copy(params)\\n bestchisq = currchisq\\n\\n if (j%nstep)==0:\\n allparams[:, j/nstep] = params\\n allchi[j/nstep] = currchisq\\n return allparams, bestp, numaccept, allchi\",\n \"def get_max_num_onsets():\\r\\n \\\"\\\"\\\" based on the numbers above, should equal to 932945... \\\"\\\"\\\"\\r\\n c1 = len(gen_onset_c1())\\r\\n c2 = len(gen_onset_c2())\\r\\n c3 = len(gen_onset_c3_c4())\\r\\n c4 = len(gen_onset_c3_c4())\\r\\n temp = c1\\r\\n temp = temp + ( c1 * c2 )\\r\\n temp = temp + ( c1 * c3 )\\r\\n temp = temp + ( c1 * c2 * c3 )\\r\\n temp = temp + ( c1 * c3 * c4 )\\r\\n temp = temp + ( c1 * c2 * c3 * c4 )\\r\\n return temp\",\n \"def generate_grid(self):\\n # Get a list of parameters values\\n list_params = list(self.params_grid_data.values())\\n\\n # Generate a list of all combos for params\\n params_combos = list(product(*list_params))\\n names = self._generate_column_names()\\n\\n # Loop over the array of dictionary for the compounds and amounts:\\n for experiment in self.compounds_data:\\n for params in params_combos:\\n compounds = experiment['compounds']\\n amounts = experiment['amounts']\\n self.all_combos += [chain(compounds, amounts,\\n params)]\\n self.all_combos = pd.DataFrame.from_records(self.all_combos)\\n self.all_combos.columns = names\\n return self.all_combos\",\n \"def _iterCoordsets(self):\\n\\n for i in range(self._n_csets):\\n yield self._coords[i]\",\n \"def generate_set(models, begin=0, start_pos=[256, 192], group_id=-1, length_multiplier=1, plot_map=True):\\n extvar[\\\"begin\\\"] = begin\\n extvar[\\\"start_pos\\\"] = start_pos\\n extvar[\\\"length_multiplier\\\"] = length_multiplier\\n extvar[\\\"next_from_slider_end\\\"] = GAN_PARAMS[\\\"next_from_slider_end\\\"]\\n\\n note_group_size = GAN_PARAMS[\\\"note_group_size\\\"]\\n max_epoch = GAN_PARAMS[\\\"max_epoch\\\"]\\n good_epoch = GAN_PARAMS[\\\"good_epoch\\\"] - 1\\n g_multiplier = GAN_PARAMS[\\\"g_epochs\\\"]\\n c_multiplier = GAN_PARAMS[\\\"c_epochs\\\"]\\n g_batch = GAN_PARAMS[\\\"g_batch\\\"]\\n g_input_size = GAN_PARAMS[\\\"g_input_size\\\"]\\n c_true_batch = GAN_PARAMS[\\\"c_true_batch\\\"]\\n c_false_batch = GAN_PARAMS[\\\"c_false_batch\\\"]\\n c_randfalse_batch = GAN_PARAMS[\\\"c_randfalse_batch\\\"]\\n\\n reset_model_weights(models)\\n set_extvar(models, extvar)\\n gmodel, mapping_layer, classifier_model, mmodel, default_weights = models\\n\\n for i in range(max_epoch):\\n\\n gnoise = np.random.random((g_batch, g_input_size))\\n glabel = [np.zeros((g_batch, note_group_size * 4)),\\n np.ones((g_batch,)), np.ones((g_batch,))]\\n ginput = conv_input(gnoise, extvar)\\n\\n # fit mmodel instead of gmodel\\n history = mmodel.fit(ginput, glabel, epochs=g_multiplier,\\n validation_split=0.2, verbose=0,\\n callbacks=[])\\n\\n pred_noise = np.random.random((c_false_batch, g_input_size))\\n pred_input = conv_input(pred_noise, extvar)\\n predicted_maps_data, predicted_maps_mapped, _predclass = mmodel.predict(\\n pred_input)\\n new_false_maps = predicted_maps_mapped\\n new_false_labels = np.zeros(c_false_batch)\\n\\n # random numbers as negative samples\\n # special_train_data.shape[2] == 6\\n randfalse_maps = np.random.rand(\\n c_randfalse_batch, note_group_size, special_train_data.shape[2])\\n randfalse_labels = np.zeros(c_randfalse_batch)\\n\\n rn = np.random.randint(0, special_train_data.shape[0], (c_true_batch,))\\n actual_train_data = np.concatenate(\\n (new_false_maps, randfalse_maps, special_train_data[rn]), axis=0)\\n actual_train_labels = np.concatenate(\\n (new_false_labels, randfalse_labels, special_train_labels[rn]), axis=0)\\n\\n history2 = classifier_model.fit(actual_train_data, actual_train_labels, epochs=c_multiplier,\\n validation_split=0.2, verbose=0,\\n callbacks=[])\\n\\n # calculate the losses\\n g_loss = np.mean(history.history['loss'])\\n c_loss = np.mean(history2.history['loss'])\\n print(\\\"Group {}, Epoch {}: G loss: {} vs. C loss: {}\\\".format(\\n group_id, 1+i, g_loss, c_loss))\\n\\n # delete the history to free memory\\n del history, history2\\n\\n # make a new set of notes\\n res_noise = np.random.random((1, g_input_size))\\n res_input = conv_input(res_noise, extvar)\\n _resgenerated, res_map, _resclass = mmodel.predict(res_input)\\n if plot_map:\\n plot_current_map(tf.convert_to_tensor(res_map, dtype=tf.float32))\\n\\n # early return if found a good solution\\n # good is (inside the map boundary)\\n if i >= good_epoch:\\n current_map = res_map\\n if inblock_trueness(current_map[:, :, 0:2]).numpy()[0] == 0 and inblock_trueness(current_map[:, :, 4:6]).numpy()[0] == 0:\\n # debugging options to check map integrity\\n # print(tf.reduce_mean(current_map));\\n # print(\\\"-----MAPLAYER-----\\\")\\n # print(tf.reduce_mean(mapping_layer(conv_input(tf.convert_to_tensor(_resgenerated, dtype=\\\"float32\\\"), extvar))));\\n # print(\\\"-----CMWS-----\\\")\\n # print(tf.reduce_mean(construct_map_with_sliders(tf.convert_to_tensor(_resgenerated, dtype=\\\"float32\\\"), extvar=mapping_layer.extvar)));\\n break\\n\\n if plot_map:\\n for i in range(3): # from our testing, any random input generates nearly the same map\\n plot_noise = np.random.random((1, g_input_size))\\n plot_input = conv_input(plot_noise, extvar)\\n _plotgenerated, plot_mapped, _plotclass = mmodel.predict(\\n plot_input)\\n plot_current_map(tf.convert_to_tensor(\\n plot_mapped, dtype=tf.float32))\\n\\n # Don't do this in this version. It's for old versions where models are rebuilt each loop\\n # del mmodel, mapping_layer;\\n\\n return res_map.squeeze()\",\n \"def make_grid(m, n, k):\\n \\n grid = np.zeros(m*n)\\n \\n mine_indecies = np.arange(m*n)\\n np.random.shuffle(mine_indecies)\\n mine_indecies = mine_indecies[:k]\\n \\n #print('mine_indecies:', mine_indecies)\\n #print(np.arange(m*n).reshape((m,n)))\\n \\n for i in mine_indecies:\\n mask = get_mask(m, n, i)\\n #print(mask.reshape((m,n)))\\n grid = grid + mask\\n \\n grid[mine_indecies] = -1\\n grid.resize((m,n))\\n return grid\",\n \"def __init__(self, max_iteration=50, swarm_num=20, c1=2, c2=2, w_ini=0.9, w_end=0.4):\\r\\n self.__c1 = c1\\r\\n self.__c2 = c2\\r\\n self.__w_ini = w_ini\\r\\n self.__w_end = w_end\\r\\n self.__max_iteration = int(max_iteration)\\r\\n self.__swarm_num = int(swarm_num)\\r\\n np.random.seed(123456)\",\n \"def hyperparams():\\n H = 6\\n return Munch(N=500, H=H, D=(H // 2) ** 2, batch_size=10, precision=to.float32)\",\n \"def g_iter(n):\\n \\\"*** YOUR CODE HERE ***\\\"\\n vals = [1, 2, 3]\\n if n <= 3:\\n return vals[n-1]\\n for i in range(n - 3):\\n new_val = 3 * vals[0] + 2 * vals[1] + 1 * vals[2]\\n vals = vals[1:] + [new_val]\\n return vals[-1]\",\n \"def parition_generator(n):\\n # generator\\n k = np.zeros(n, dtype=np.dtype(int))\\n Z = np.zeros(n, dtype=np.dtype(int))\\n h = [float(n)]\\n yield(Z)\\n while next_partition(Z, k, h) is not None:\\n yield(Z)\",\n \"def simulate_qubit_pairs_1D_lattice(n, m):\\n # Initialize some variables\\n x_max = 2 * n\\n d = np.linspace(0, x_max, 50, dtype=int)\\n k_matrix = np.zeros((m, len(d)))\\n\\n # Run the simulation a total of m times\\n for i in range(m):\\n start = time.time()\\n\\n sim = chp_py.CHP_Simulation(n)\\n sims = []\\n # Add a sim for depth 0\\n sims.append(chp_py.CHP_Simulation(n, sim.state))\\n\\n for j in range(0, d[-1]):\\n gates = utils.get_lattice_gates((n,), j)\\n sim.apply_gates(gates)\\n\\n if j + 1 in d:\\n sims.append(chp_py.CHP_Simulation(n, sim.state))\\n\\n # This computes the collision_probability at different\\n # depths in parallel\\n k_matrix[i, :] = utils.sims_to_collision_probabilty(sims)\\n\\n end = time.time()\\n print(\\\"Time elapsed = {:0.2f}, for sample = {}\\\".format(end - start, i))\\n\\n # Store everything in data\\n data = {\\\"d\\\": d,\\n \\\"k_matrix\\\": k_matrix,\\n \\\"n\\\": n,\\n \\\"m\\\": m}\\n\\n return data\",\n \"def run(self, generations=1000):\\n gcount = 0\\n \\n while gcount<=generations:\\n try:\\n print \\\"Gen: \\\"+str(gcount),\\n self.population = zip (self.population, [self.target]*len(self.population))\\n self.population = self.pool.map(f, self.population)\\n except:\\n pass\\n for i in self.population:\\n print i[0],i[1]\\n self.population = [organism.Organism(x[0], x[1]) for x in self.population]\\n self.population.sort()\\n print \\\" Max fitness: \\\"+str(self.population[::-1][1].fitness)\\n try:\\n if self.population[0] <= self.ppop[0]:\\n self.ppop = self.population[::-1][0:10] # The top ten organisms\\n else:\\n self.population = self.ppop # We got worse! go back!\\n except:\\n self.ppop = self.population\\n self.population = self.population[::-1][0:10]\\n try:\\n self.breed()\\n except:\\n print \\\"Breeding error\\\"\\n gcount+=1\",\n \"def global_parameter_space():\\n return [list(range(7, 22)),\\n list(range(12, 27)),\\n list(range(25, 40)),\\n list(permutations(range(1, 5)))]\",\n \"def run_expt_many_times(result_nonprivate, repetitions=10,\\n data_blocker=1, windsorized=False,\\n outfile_singles=None, outfile_aggregates=None):\\n for epsilon in numpy.arange(0.1, 2.2, 0.2):#(0.2, 10, 0.2):\\n for gamma in range(1, 10, 1): # (1, 7, 1):\\n \\n repeat_expt(epsilon, gamma,\\n data_blocker=data_blocker, windsorized=windsorized,\\n result_nonprivate=result_nonprivate,\\n repetitions=repetitions,\\n outfile_singles=outfile_singles, \\n outfile_aggregates=outfile_aggregates)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.59489983","0.56008106","0.55951405","0.5593306","0.55607903","0.54957503","0.54919815","0.5482499","0.54726803","0.5471357","0.54386574","0.54066265","0.5402647","0.5369949","0.53577334","0.535206","0.53520525","0.5346284","0.5345355","0.53447723","0.5299139","0.52981675","0.5274086","0.5272422","0.5266857","0.5249077","0.5246933","0.5237986","0.52320755","0.5223171","0.5219003","0.5218684","0.5215194","0.5175861","0.51674104","0.5164514","0.51613307","0.5156423","0.5155775","0.514536","0.51367354","0.5136505","0.51338166","0.5128751","0.5124517","0.5102203","0.5097485","0.50782126","0.5075853","0.5066061","0.5052612","0.50522596","0.50488037","0.50487506","0.50477594","0.5038897","0.5037923","0.5034265","0.5030135","0.5024854","0.5023821","0.50151205","0.50048995","0.50008035","0.4993401","0.49927312","0.49898368","0.49864727","0.49826643","0.4982056","0.49807587","0.4971424","0.49672484","0.4963962","0.49595696","0.49567103","0.49455878","0.4942637","0.49391472","0.49384478","0.49331403","0.49299976","0.4928139","0.4928094","0.4925369","0.4921094","0.49202278","0.49162245","0.4914578","0.49110106","0.49084723","0.49058944","0.49042174","0.4903491","0.490053","0.48998737","0.4898486","0.48902398","0.4886715","0.48828477"],"string":"[\n \"0.59489983\",\n \"0.56008106\",\n \"0.55951405\",\n \"0.5593306\",\n \"0.55607903\",\n \"0.54957503\",\n \"0.54919815\",\n \"0.5482499\",\n \"0.54726803\",\n \"0.5471357\",\n \"0.54386574\",\n \"0.54066265\",\n \"0.5402647\",\n \"0.5369949\",\n \"0.53577334\",\n \"0.535206\",\n \"0.53520525\",\n \"0.5346284\",\n \"0.5345355\",\n \"0.53447723\",\n \"0.5299139\",\n \"0.52981675\",\n \"0.5274086\",\n \"0.5272422\",\n \"0.5266857\",\n \"0.5249077\",\n \"0.5246933\",\n \"0.5237986\",\n \"0.52320755\",\n \"0.5223171\",\n \"0.5219003\",\n \"0.5218684\",\n \"0.5215194\",\n \"0.5175861\",\n \"0.51674104\",\n \"0.5164514\",\n \"0.51613307\",\n \"0.5156423\",\n \"0.5155775\",\n \"0.514536\",\n \"0.51367354\",\n \"0.5136505\",\n \"0.51338166\",\n \"0.5128751\",\n \"0.5124517\",\n \"0.5102203\",\n \"0.5097485\",\n \"0.50782126\",\n \"0.5075853\",\n \"0.5066061\",\n \"0.5052612\",\n \"0.50522596\",\n \"0.50488037\",\n \"0.50487506\",\n \"0.50477594\",\n \"0.5038897\",\n \"0.5037923\",\n \"0.5034265\",\n \"0.5030135\",\n \"0.5024854\",\n \"0.5023821\",\n \"0.50151205\",\n \"0.50048995\",\n \"0.50008035\",\n \"0.4993401\",\n \"0.49927312\",\n \"0.49898368\",\n \"0.49864727\",\n \"0.49826643\",\n \"0.4982056\",\n \"0.49807587\",\n \"0.4971424\",\n \"0.49672484\",\n \"0.4963962\",\n \"0.49595696\",\n \"0.49567103\",\n \"0.49455878\",\n \"0.4942637\",\n \"0.49391472\",\n \"0.49384478\",\n \"0.49331403\",\n \"0.49299976\",\n \"0.4928139\",\n \"0.4928094\",\n \"0.4925369\",\n \"0.4921094\",\n \"0.49202278\",\n \"0.49162245\",\n \"0.4914578\",\n \"0.49110106\",\n \"0.49084723\",\n \"0.49058944\",\n \"0.49042174\",\n \"0.4903491\",\n \"0.490053\",\n \"0.48998737\",\n \"0.4898486\",\n \"0.48902398\",\n \"0.4886715\",\n \"0.48828477\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6835279"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":81186,"cells":{"query":{"kind":"string","value":"Generates the julia set from the set parameters for a number of iterations and using a particular value of c."},"document":{"kind":"string","value":"def generate_julia(self, iterations, c):\n if self.grid is None:\n raise RuntimeError(\"Grid hasn't been setup - call set_grid first.\")\n # Define the tensorflow variables\n c_val = tf.constant(np.full(shape=self.grid.shape, fill_value=c, dtype=self.grid.dtype))\n z = tf.Variable(self.grid)\n n = tf.Variable(tf.zeros_like(c_val, tf.float32))\n # Start the tensorflow session\n with tf.Session():\n tf.global_variables_initializer().run()\n # Define the main julia algorithm - either take the square plus x, or keep z\n\n z_out = tf.where(tf.abs(z) < self.threshold, z ** 2 + c_val, z)\n not_diverged = tf.abs(z_out) < self.threshold\n step = tf.group(z.assign(z_out), n.assign_add(tf.cast(not_diverged, tf.float32)))\n\n for i in range(iterations):\n step.run()\n self.end_step = n.eval()\n self.end_z = z_out.eval()"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def genPowerSet(cvalues, incEmpty=False):\t\t\n\tps = list()\n\tfor cv in cvalues:\n\t\tpse = list()\n\t\tfor s in ps:\n\t\t\tsc = s.copy()\n\t\t\tsc.add(cv)\n\t\t\t#print(sc)\n\t\t\tpse.append(sc)\n\t\tps.extend(pse)\n\t\tes = set()\n\t\tes.add(cv)\n\t\tps.append(es)\n\t\t#print(es)\n\t\n\tif incEmpty:\n\t\tps.append({})\n\treturn ps","def julia_set(width, height, zoom=1, niter=256):\n\n w,h = width, height\n # Why (hxw) ? Because numpy creates a matrix as row x column\n # and height represents the y co-ordinate or rows and\n # width represents the x co-ordinate or columns.\n pixels = np.arange(w*h, dtype=np.uint16).reshape(h, w)\n pixels = np.arange(w*h, dtype=np.uint16).reshape(h, w)\n\n # The constant \"c\"\n # This determines the shape of the Julia set.\n # c_real, c_imag = -0.7, 0.27\n c_real, c_imag = 0.4, -0.2\n \n for x in range(w): \n for y in range(h):\n # calculate the initial real and imaginary part of z,\n # based on the pixel location and zoom and position values\n zx = 1.5*(x - w/2)/(0.5*zoom*w) \n zy = 1.0*(y - h/2)/(0.5*zoom*h)\n\n for i in range(niter):\n radius_sqr = zx*zx + zy*zy\n # Iterate till the point is outside\n # the circle with radius 2.\n if radius_sqr > 4: break\n # Calculate new positions\n zy,zx = 2.0*zx*zy + c_imag, zx*zx - zy*zy + c_real\n\n color = (i >> 21) + (i >> 10) + i*8\n pixels[y,x] = color\n \n # display the created fractal \n plt.imshow(pixels)\n plt.show()","def create_C1(data_set):\r\n C1 = set()\r\n for t in data_set:\r\n for item in t:\r\n item_set = frozenset([item])\r\n C1.add(item_set)\r\n return C1","def create_C1(data_set):\n C1 = set()\n for t in data_set:\n for item in t:\n item_set = frozenset([item])\n C1.add(item_set)\n return C1","def mandelbrot(c):\n tolerance = 30\n threshold = 3\n\n # This is the mandelbrot set\n #Uncomment and comment the Julia set block below\n \n # M = lambda x: pow(x, 2) + c\n # nterm = M(0)\n\n # End Mandelbrot set\n\n # This is the Julia Set\n x = c\n c = complex(-0.4,0.6)\n M = lambda x: pow(x, 2) + c\n nterm = M(x)\n #End Julia Set\n\n for i in xrange(tolerance):\n try:\n nterm = M(nterm)\n except OverflowError:\n return 1\n\n square = lambda x: pow(x, 2)\n try:\n if (square(nterm.real) + square(nterm.imag)) < threshold:\n return 0\n else:\n return 1\n except OverflowError:\n return 1","def CNPJGenerator(amount=1,cnpjn=None):\n\n d1weight = [5,4,3,2,9,8,7,6,5,4,3,2]\n d2weight = [6] + d1weight\n\n cnpjs=set()\n\n while len(cnpjs) < amount:\n\n if not cnpjn:\n randns = [randint(0,9) for x in range(8)] + [0,0,0,randint(0,9)]\n else:\n randns = cnpjn\n\n d1,d2 = get_digits(randns,d1weight,d2weight)\n\n # transform cnpj in a string\n cnpj = (\"%s\"*14) % tuple(randns+[d1,d2])\n\n # if not exist, add in cnpjs\n if not cnpj in cnpjs:\n cnpjs.add(cnpj)\n\n cnpjs = list(cnpjs)\n if len(cnpjs) != 1:\n return cnpjs\n else:\n return cnpjs[0]","def __iter__(self):\n return iproduct(*self.sets)","def set_norm(self, P={}, C={}, c_t=\"pp\"):\n N = dict()\n if isinstance(P, list):\n Pl = P\n else:\n Pl = [P]\n if isinstance(C, list):\n Cl = C\n else:\n Cl = [C]\n if len(Pl) > 0:\n N['comp_dim'] = len(Pl)\n if len(Cl) > 0:\n if len(Cl) != len(Pl):\n raise ValueError(\"Need same number of principal parts and coefficients to set!\")\n keys = list(Cl[0].keys())\n for j in range(1, N['comp_dim']):\n if list(Cl[j].keys()) != keys:\n raise ValueError(\"Need to set the same coefficients! (or call the method more than once)\")\n else:\n Cl = []\n for j in range(N['comp_dim']):\n Cl.append(C)\n N['Vals'] = list()\n N['Vals'] = list()\n N['SetCs'] = list()\n for i in range(N['comp_dim']):\n N['Vals'].append({})\n N['Vals'].append({})\n N['SetCs'].append([])\n D = self.multiplier().D()\n for j in range(len(D)):\n a = D[j]\n x = self.multiplier().Qv[j]\n # N['Vals'][i][(0,j)]=dict()\n if x == 0:\n if c_t == \"pp\":\n # N['comp_dim']=N['comp_dim']+1\n N['SetCs'][i].append((j, 0))\n if (0, j) in Pl[i]:\n N['Vals'][i][(j, 0)] = Pl[i][(j, 0)]\n else:\n N['Vals'][i][(j, 0)] = 0 # P[(0,0)]\n elif x < 0 and self._holomorphic:\n N['SetCs'][i].append((j, 0))\n N['Vals'][i][(j, 0)] = 0 # P[(0,0)]\n\n for (r, n) in Cl[i].keys():\n if(N['SetCs'][i].count((r, n)) == 0):\n N['SetCs'][i].append((r, n))\n N['Vals'][i][(r, n)] = Cl[i][(r, n)]\n return N","def MCS(n,k):\n\tglobal dict_all\n\tdict_val=copy.deepcopy(dict_all)\n\t#start_time = time.time()\n\tfinal = {}\t\t\t\t\t # Store all result with the count as key. For example final[1]=[[1,0,0],[0,1,1]]\n\tseq = []\t\t\t\t\t\t# Store the count with no duplication\n\tfor i in range(n):\n\t\tleaf={}\t\t\t\t\t\t# leaf is the dictionary to store the random value of each leaf\n\t\t#count=0\n\t\tfor i in leaves:\n\t\t\tleaf[i] = choice([0,1])\n\t\t\tdict_val[i]=leaf[i]\n\t\t\t#count += leaf[i]\n\t\tresult = Cal_FT(dict_val)\t\n\t\t'''\n\t\tif result:\n\t\t\tcutset = []\n\t\t\tfor i in leaves:\n\t\t\t\tcutset.append(str(leaf[i]))\n\t\t\tcutset=\"\".join(cutset)\n\t\t\tif cutset not in final:\n\t\t\t\tfinal[cutset]=count\n\tfinal_sorted=sorted(zip(final.values(),final.keys())) \t\t\t\t#Order the cutset by its count\n\tfor i in range(k):\t\t\t\t\t\t\t\t\t\t\t\t\t#Print the first k result\n\t\tcutset=list(final_sorted[i][1])\n\t\tresult=[]\n\t\tfor index in range(len(cutset)):\n\t\t\tif cutset[index] is \"1\":\n\t\t\t\tresult.append(leaves[index])\n\t\tprint result\n\t#end_time=time.time()\n\t#print \"Running time is\", end_time-start_time\n\t'''","def setNumIterations(*argv):","def get_max_num_onsets():\r\n \"\"\" based on the numbers above, should equal to 932945... \"\"\"\r\n c1 = len(gen_onset_c1())\r\n c2 = len(gen_onset_c2())\r\n c3 = len(gen_onset_c3_c4())\r\n c4 = len(gen_onset_c3_c4())\r\n temp = c1\r\n temp = temp + ( c1 * c2 )\r\n temp = temp + ( c1 * c3 )\r\n temp = temp + ( c1 * c2 * c3 )\r\n temp = temp + ( c1 * c3 * c4 )\r\n temp = temp + ( c1 * c2 * c3 * c4 )\r\n return temp","def generate_code_point_set(cpp, public_name, set):\n\n private_name = public_name + \"_\"\n\n ranges = []\n current = None\n expected = None\n for cp in sorted(set):\n if expected is None or cp != expected:\n current = [cp, cp]\n ranges.append(current)\n else:\n current[1] = cp\n expected = cp + 1\n\n cpp.write(f\"static constexpr Interval<CodePoint> \" f\"{private_name}[] = {{\\n\")\n\n for (min_cp, max_cp) in ranges:\n cpp.write(f\" {{{hex(min_cp)}, {hex(max_cp)}}},\\n\")\n\n cpp.write(\"};\\n\\n\")\n cpp.write(\n f\"constexpr Span<const Interval<CodePoint>> \"\n f\"{public_name}({private_name});\\n\\n\"\n )","def build_possible_naked_sets(c, setlength=2):\n ns = {}\n pairs = [p for p in c.values() if len(p) == setlength]\n for k, v in c.items():\n if v in pairs:\n ns[k] = sorted(v)\n return ns","def sets(elements, set_size):\n return combinations(elements, set_size)","def getSets():","def construct(self):\n\n newSet = {}\n current_index = 0\n\n for key_1, value_1 in self._sets[self._currentSet].items():\n current_index += 1\n for key_2,value_2 in list(self._sets[self._currentSet].items())[current_index:]:\n # join the 2 tuples\n join = key_1 + key_2\n # remove duplicates\n join = tuple(set(join))\n # get combinations\n combined = tuple(combinations(join, self._currentSet+1))\n # sort combination\n combined = tuple(sorted(combined[0]))\n\n # append new combination to dict\n if len(combined) != 0 :\n newSet[combined] = 0\n\n self._currentSet += 1\n # append the new itemset in the sets dict \n self._sets[self._currentSet] = newSet","def _generate_distribution_samples(self, set_count, parameter_count):\n self._samples = numpy.zeros((set_count, parameter_count))\n for i, distribution in enumerate(self.parameter_distributions.values()):\n self._samples[:, i] = distribution.ppf(self._quantiles[:, i])","def c_test_set_inp(self, param, value):\r\n ret = 1\r\n if \"__hash__\" not in dir(param): # param must be hashable\r\n ret = 0\r\n if self.verbosity > 0:\r\n print(\"ERROR: \" + param + \" is not hashable. It will be unable to be set in a dict.\")\r\n else:\r\n if self.verbosity > 1:\r\n print(param + \" is hashable.\")\r\n if param in [\"population_size\", \"time_constraint\", \"generations\", \"point_count\",\r\n \"PSO_VELOCITY_WEIGHT\", \"PSO_INDIVIDUAL_WEIGHT\", \"PSO_GROUP_WEIGHT\"]:\r\n if not ((isinstance(value, int) or\r\n isinstance(value, float) or\r\n isinstance(value, long))):\r\n if self.verbosity > 0:\r\n print(\"ERROR: \" + param + \" needs to be of a number. It is \" + str(value))\r\n ret = 0\r\n else:\r\n if self.verbosity > 1:\r\n print(param + \" is correctly set to a number.\")\r\n if value < 0:\r\n ret = 0\r\n if self.verbosity > 0:\r\n print(\"ERROR: \" + param + \" needs to be greater than zero.\")\r\n else:\r\n if self.verbosity > 1:\r\n print(param + \" is greater than zero.\")\r\n if param in [\"population_size\", \"generations\", \"point_count\"]:\r\n if not isinstance(value, int):\r\n ret = 0\r\n if self.verbosity > 0:\r\n print(\"ERROR: \" + param + \" needs to be an integer. It is \" + str(value))\r\n else:\r\n if self.verbosity > 1:\r\n print(param + \" is an integer.\")\r\n if param in [\"fitness_function\", \"weighting_bias\"]:\r\n if not callable(value):\r\n ret = 0\r\n if self.verbosity > 0:\r\n print(\"ERROR: \" + param + \" needs to be a callable function.\")\r\n else:\r\n if self.verbosity > 1:\r\n print(param + \" is a callable function.\")\r\n if param == \"end_condition\":\r\n if value not in [\"time_constraint\", \"generations\"]:\r\n ret = 0\r\n if self.verbosity > 0:\r\n print(\"ERROR: \" + param + \" needs to be 'time_constraint' or 'generations'\")\r\n else:\r\n if self.verbosity > 1:\r\n print(\"ERROR: \" + param + \" is a correct string.\")\r\n if param == \"seed\":\r\n if not (value is None or isinstance(value, int)):\r\n ret = 0\r\n if self.verbosity > 0:\r\n print(\"ERROR: \" + param + \" is incorrectly set.\")\r\n else:\r\n if self.verbosity > 1:\r\n print(param + \" is correctly set.\")\r\n return ret","def generate_data_set(args):\n if args.o is None:\n filename = \"test\"\n else:\n filename = args.o\n\n if args.b is None:\n args.b = 0.5\n if args.p is None:\n path = Path(\"\")\n else:\n path = Path(args.p)\n if args.nf is None:\n args.nf = 1\n\n for i in range(int(args.nf)):\n args.o = path / (filename + \"_t\"+str(args.t) + \"_k\" + str(args.k) + \"_n\"+str(args.n) + \"_m\"+str(args.m)\n + \"_c\"+str(args.c) + \"_\"+str(i) + \".dzn\")\n create_dnz_file(args)","def c_test_set_inp(self, param, value):\r\n ret = 1\r\n if \"__hash__\" not in dir(param): # param must be hashable\r\n ret = 0\r\n if self.verbosity > 0:\r\n print(\"ERROR: \" + param + \" is not hashable. It will be unable to be set in a dict.\")\r\n else:\r\n if self.verbosity > 1:\r\n print(param + \" is hashable.\")\r\n if param in [\"population_size\", \"time_constraint\", \"generations\", \"point_count\"]:\r\n if not ((isinstance(value, int) or\r\n isinstance(value, float) or\r\n isinstance(value, long))):\r\n if self.verbosity > 0:\r\n print(\"ERROR: \" + param + \" needs to be of a number. It is \" + str(value))\r\n ret = 0\r\n else:\r\n if self.verbosity > 1:\r\n print(param + \" is correctly set to a number.\")\r\n if value < 0:\r\n ret = 0\r\n if self.verbosity > 0:\r\n print(\"ERROR: \" + param + \" needs to be greater than zero.\")\r\n else:\r\n if self.verbosity > 1:\r\n print(param + \" is greater than zero.\")\r\n if param in [\"population_size\", \"generations\", \"point_count\"]:\r\n if not isinstance(value, int):\r\n ret = 0\r\n if self.verbosity > 0:\r\n print(\"ERROR: \" + param + \" needs to be an integer. It is \" + str(value))\r\n else:\r\n if self.verbosity > 1:\r\n print(param + \" is an integer.\")\r\n if param in [\"fitness_function\", \"population_function\",\r\n \"mutate_function\", \"cross_function\", \"weighting_bias\"]:\r\n if not callable(value):\r\n ret = 0\r\n if self.verbosity > 0:\r\n print(\"ERROR: \" + param + \" needs to be a callable function.\")\r\n else:\r\n if self.verbosity > 1:\r\n print(param + \" is a callable function.\")\r\n if param == \"end_condition\":\r\n if value not in [\"time_constraint\", \"generations\"]:\r\n ret = 0\r\n if self.verbosity > 0:\r\n print(\"ERROR: \" + param + \" needs to be 'time_constraint' or 'generations'\")\r\n else:\r\n if self.verbosity > 1:\r\n print(\"ERROR: \" + param + \" is a correct string.\")\r\n if param == \"seed\":\r\n if not (value is None or isinstance(value, int)):\r\n ret = 0\r\n if self.verbosity > 0:\r\n print(\"ERROR: \" + param + \" is incorrectly set.\")\r\n else:\r\n if self.verbosity > 1:\r\n print(param + \" is correctly set.\")\r\n return ret","def coset_enumeration_c(fp_grp, Y):\n # Initialize a coset table C for < X|R >\n C = CosetTable(fp_grp, Y)\n X = fp_grp.generators\n R = fp_grp.relators()\n A = C.A\n # replace all the elements by cyclic reductions\n R_cyc_red = [rel.identity_cyclic_reduction() for rel in R]\n R_c = list(chain.from_iterable((rel.cyclic_conjugates(), (rel**-1).cyclic_conjugates()) \\\n for rel in R_cyc_red))\n R_set = set()\n for conjugate in R_c:\n R_set = R_set.union(conjugate)\n # a list of subsets of R_c whose words start with \"x\".\n R_c_list = []\n for x in C.A:\n r = set([word for word in R_set if word[0] == x])\n R_c_list.append(r)\n R_set.difference_update(r)\n for w in Y:\n C.scan_and_fill_f(0, w)\n for x in A:\n C.process_deductions(R_c_list[C.A_dict[x]], R_c_list[C.A_dict_inv[x]])\n i = 0\n while i < len(C.omega):\n alpha = C.omega[i]\n i += 1\n for x in C.A:\n if C.table[alpha][C.A_dict[x]] is None:\n C.define_f(alpha, x)\n C.process_deductions(R_c_list[C.A_dict[x]], R_c_list[C.A_dict_inv[x]])\n return C","def set():","def setup_crubadan():\n for n in [1,2,3,4,5,'word']:\n for option in ['mi','pmi']:\n get_matrix(datasource='crubadan', n=n, option=option)","def gen_task_sets_simplegen(nsets):\n start = time()\n eval_task_sets = []\n for util in utils:\n task_sets = []\n for i in range(nsets):\n ts = synth.simplegen(i, util, implicit_deadlines=True)\n synth.synth_c_pmf(ts, distribution_cls=lib.ExpExceedDist)\n synth.set_fixed_priorities(ts)\n task_sets.append(ts)\n eval_task_sets.append(task_sets)\n stop = time()\n print('Task Set Generation SimpleGen: %.3fs' % (stop - start))\n pickle.dump(eval_task_sets, open(task_sets_path + 'task_sets_simplegen', 'wb'))","def create_sets(self,FD_SET=['FinalD'],VA_SET=['VA']):\n \n self.m.S = Set(initialize=self.sectors, doc='sectors')\n self.m.P = Set(initialize=self.products, doc='sectors')\n self.m.row = Set(initialize=self.products, doc='products')\n self.m.col = Set(initialize=self.sectors+['FinalD'], doc='sectors and final demand')\n \n self.m.rROW = Set(initialize=self.countries,ordered=True, doc='regions including export')\n self.m.R = Set(initialize=self.countries,ordered=True, doc='regions')\n\n self.m.fdemand = Set(initialize=FD_SET, doc='Final Demand')\n\n self.m.VA = Set(initialize=VA_SET, doc='value added')","def c_test_set_inp(self, param, value):\r\n ret = 1\r\n if \"__hash__\" not in dir(param): # param must be hashable\r\n ret = 0\r\n if self.verbosity > 0:\r\n print(\"ERROR: \" + param + \" is not hashable. It will be unable to be set in a dict.\")\r\n else:\r\n if self.verbosity > 1:\r\n print(param + \" is hashable.\")\r\n if param in [\"population_size\", \"time_constraint\", \"generations\"]:\r\n if not ((isinstance(value, int) or\r\n isinstance(value, float) or\r\n isinstance(value, long))):\r\n if self.verbosity > 0:\r\n print(\"ERROR: \" + param + \" needs to be of a number. It is \" + str(value))\r\n ret = 0\r\n else:\r\n if self.verbosity > 1:\r\n print(param + \" is correctly set to a number.\")\r\n if value < 0:\r\n ret = 0\r\n if self.verbosity > 0:\r\n print(\"ERROR: \" + param + \" needs to be greater than zero.\")\r\n else:\r\n if self.verbosity > 1:\r\n print(param + \" is greater than zero.\")\r\n if param in [\"population_size\", \"generations\"]:\r\n if not isinstance(value, int):\r\n ret = 0\r\n if self.verbosity > 0:\r\n print(\"ERROR: \" + param + \" needs to be an integer. It is \" + str(value))\r\n else:\r\n if self.verbosity > 1:\r\n print(param + \" is an integer.\")\r\n if param in [\"fitness_function\", \"population_function\",\r\n \"mutate_function\", \"cross_function\", \"weighting_bias\"]:\r\n if not callable(value):\r\n ret = 0\r\n if self.verbosity > 0:\r\n print(\"ERROR: \" + param + \" needs to be a callable function.\")\r\n else:\r\n if self.verbosity > 1:\r\n print(param + \" is a callable function.\")\r\n if param == \"end_condition\":\r\n if value not in [\"time_constraint\", \"generations\"]:\r\n ret = 0\r\n if self.verbosity > 0:\r\n print(\"ERROR: \" + param + \" needs to be 'time_constraint' or 'generations'\")\r\n else:\r\n if self.verbosity > 1:\r\n print(\"ERROR: \" + param + \" is a correct string.\")\r\n if param == \"seed\":\r\n if not (value is None or isinstance(value, int)):\r\n ret = 0\r\n if self.verbosity > 0:\r\n print(\"ERROR: \" + param + \" is incorrectly set.\")\r\n else:\r\n if self.verbosity > 1:\r\n print(param + \" is correctly set.\")\r\n return ret","def subset_gen(itemSet):\n subsets = []\n for i in range(1, len(itemSet)):\n c = combinations(itemSet, r=i)\n for cc in c:\n subsets.append(set(cc))\n return subsets","def build(c):","def sat_generate_candidate_assignments(self):\n # YOUR CODE HERE\n short = min(len(c) for c in self.clauses)\n for c in self.clauses:\n if len(c) == short:\n return set(c.literals)\n # return (set(x.literals) for x in self.clauses if len(x) == min(len(c) for c in self.clauses))","def getSets(unique_name=None):","def linear_set_generator(random, args):\n bounder = args.get(\"_ec\").bounder\n representation = args.get('representation')\n max_size = args.get('max_size', 9)\n variable_size = args.get('variable_size', True)\n if variable_size:\n size = random.randint(1, max_size)\n else:\n size = max_size\n\n indices = random.sample(range(len(representation)), size)\n values = random.uniform(next(bounder.lower_bound), next(bounder.upper_bound), len(indices))\n return OrderedDict({i: v for i, v in zip(indices, values)})","def gen_task_sets_fairgen(nsets):\n start = time()\n eval_task_sets = []\n for util in utils:\n task_sets = []\n for i in range(nsets):\n ts = synth.mc_fairgen(set_id=i, u_lo=util, implicit_deadlines=True)\n synth.synth_c_pmf(ts)\n synth.set_fixed_priorities(ts)\n task_sets.append(ts)\n eval_task_sets.append(task_sets)\n stop = time()\n print('Task Set Generation MC-Fairgen: %.3fs' % (stop - start))\n pickle.dump(eval_task_sets, open(task_sets_path + 'task_sets_fairgen', 'wb'))","def make_set_cover_nr(gRNA_hits, num_sets = 1, target_ids = [], low_coverage_penalty = 0,\n num_lengths_to_track = None, prioritise_3prime = False, optimal_depth = 5,\n suppress_warning = False):\n collapsed_grnas = gRNA_hits.collapse()\n if not target_ids:\n target_ids = set().union(*[set(cg) for cg in collapsed_grnas])\n else:\n target_ids = set(target_ids)\n ## function to regenerate set cover solutions from collapsed_grna object\n collapsed_grnas_original = collapsed_grnas.copy()\n def generate_sc_solutions():\n ## sort in order of smallest set cover size, smallest redundancy, and size of largest set in set cover\n minweight_sc = limited_minweight_SC(collapsed_grnas, num_sets, targets = target_ids,\n low_coverage_penalty = low_coverage_penalty,\n num_lengths_to_track = num_lengths_to_track)\n ## optimal solutions\n max_depth = min(optimal_depth, max(map(len, minweight_sc)))\n max_redundancy = max(map(lambda C:C.redundancy, minweight_sc))/len(target_ids)\n print(max_depth, max_redundancy)\n optimal_sc = limited_optimal_SC(target_ids, collapsed_grnas_original,\n size = max_depth, redundancy = max_redundancy)\n print(\"num unfiltered optimal sc:\", len(optimal_sc))\n ## remove duplicates\n optimal_sc = [C for C in optimal_sc\n if all(map(lambda minweight_C:(len(C) != minweight_C\n and C != minweight_C),\n minweight_sc))]\n print(\"num filtered optimal sc:\", len(optimal_sc))\n return sorted(minweight_sc + optimal_sc,\n key = lambda C:(len(C), C.redundancy, -C.max_coverage))\n sc_solutions = []\n sc_solutions.extend(generate_sc_solutions())\n eliminated_grna = []\n ## function to generate set covers\n def make_set_cover(restore = []):\n ## restore only works if gRNA belonged in the current set cover\n curr_sc = sc_solutions[0]\n for grna in restore:\n curr_sc.add_grna(grna)\n eliminated_grna.remove(grna)\n ## if current set cover solution has at least one CollapsedgRNA with no gRNA left\n while not curr_sc.all_not_empty():\n sink = sc_solutions.pop(0) ## remove set cover solution\n ## generate more possible gRNA sets if no pre-generated set covers are left\n if not sc_solutions:\n collapsed_grnas.remove_grna(*eliminated_grna)\n collapsed_grnas.remove_empty()\n sc_solutions.extend(generate_sc_solutions())\n if not sc_solutions:\n if not suppress_warning:\n print((\"\\nError: The provided gRNA sequences cannot cover all\"\n \" target sequences at least once.\\n\"))\n return []\n ## select next solution\n curr_sc = sc_solutions[0]\n ## consume=True -> remove selected gRNA from CollapsedgRNA\n output = curr_sc.generate_grna_set(prioritise_3prime = prioritise_3prime, consume = True)\n eliminated_grna.extend(output)\n return output\n return make_set_cover","def build_set(ls, dsets):\n\n def noh(ls, dsets):\n \"\"\"\n This function remove hydrogens from the selection\n \"\"\"\n data_set = build_set(ls[1], dsets)\n\n noh_set = set()\n pred = oechem.OEIsHydrogen()\n\n for idx in data_set:\n atom = system.GetAtom(oechem.OEHasAtomIdx(idx))\n if not pred(atom):\n noh_set.add(idx)\n\n return noh_set\n\n def residues(ls):\n \"\"\"\n This function select residues based on the residue numbers. An example of\n selection can be:\n mask = 'resid A:16 17 19 B:1'\n \"\"\"\n # List residue atom index to be restrained\n res_atom_set = set()\n\n # Dictionary of lists with the chain residues selected to be restrained\n # e.g. {chainA:[res1, res15], chainB:[res19, res17]}\n chain_dic = {'': []}\n\n # Fill out the chain dictionary\n i = 0\n while i < len(ls):\n if ls[i].isdigit():\n chain_dic[''].append(int(ls[i]))\n i += 1\n else:\n try:\n chain_dic[ls[i]].append(int(ls[i + 2]))\n except:\n chain_dic[ls[i]] = []\n chain_dic[ls[i]].append(int(ls[i + 2]))\n i += 3\n\n # Loop over the molecular system to select the atom indexes to be selected\n hv = oechem.OEHierView(system, oechem.OEAssumption_BondedResidue + oechem.OEAssumption_ResPerceived)\n for chain in hv.GetChains():\n chain_id = chain.GetChainID()\n if chain_id not in chain_dic:\n continue\n for frag in chain.GetFragments():\n for hres in frag.GetResidues():\n res_num = hres.GetOEResidue().GetResidueNumber()\n if res_num not in chain_dic[chain_id]:\n continue\n for oe_at in hres.GetAtoms():\n res_atom_set.add(oe_at.GetIdx())\n\n return res_atom_set\n\n def around(dist, ls):\n \"\"\"\n This function select atom not far than the threshold distance from\n the current selection. The threshold distance is in Angstrom\n\n selection can be:\n mask = '5.0 around ligand'\n \"\"\"\n # at = system.GetAtom(oechem.OEHasAtomIdx(idx))\n\n # Atom set selection\n atom_set_around = set()\n\n # Create a OE bit vector mask for each atoms\n bv_around = oechem.OEBitVector(system.GetMaxAtomIdx())\n\n # Set the mask atom\n for at in system.GetAtoms():\n if at.GetIdx() in ls:\n bv_around.SetBitOn(at.GetIdx())\n\n # Predicate\n pred = oechem.OEAtomIdxSelected(bv_around)\n\n # Create the system molecule based on the atom mask\n molecules = oechem.OEMol()\n oechem.OESubsetMol(molecules, system, pred)\n\n # Create the Nearest neighbours\n nn = oechem.OENearestNbrs(system, float(dist))\n\n for nbrs in nn.GetNbrs(molecules):\n for atom in oechem.OEGetResidueAtoms(nbrs.GetBgn()):\n if atom.GetIdx() in ls:\n continue\n atom_set_around.add(atom.GetIdx())\n\n return atom_set_around\n\n # Start Body of the selection function by language\n\n # Terminal Literal return the related set\n if isinstance(ls, str):\n return dsets[ls]\n # Not or Noh\n if len(ls) == 2:\n if ls[0] == 'noh': # Noh case\n return noh(ls, dsets)\n elif ls[0] == 'not': # Not case\n return dsets['system'] - build_set(ls[1], dsets)\n else: # Resid case with one index\n return residues(ls[1])\n\n if len(ls) == 3:\n if ls[1] == 'or': # Or Case (set union)\n return build_set(ls[0], dsets) | build_set(ls[2], dsets)\n elif ls[1] == 'and': # And Case (set intersection)\n return build_set(ls[0], dsets) & build_set(ls[2], dsets)\n elif ls[1] == 'diff': # Diff case (set difference)\n return build_set(ls[0], dsets) - build_set(ls[2], dsets)\n elif ls[1] == 'around': # Around case\n return around(ls[0], build_set(ls[2], dsets))\n else:\n return residues(ls[1:]) # Resid case with one or two indexes\n else:\n if ls[0] == 'resid':\n return residues(ls[1:]) # Resid case with multiple indexes\n else:\n raise ValueError(\"The passed list have too many tokens: {}\".format(ls))","def multiple_chromosome_set_generator(random, args):\n keys = args.get('keys')\n candidate = MultipleChromosomeGenome(keys=keys)\n for key in keys:\n key_args = {\n 'representation': args.get(\"%s_representation\" % key),\n 'max_size': args.get(\"%s_max_size\" % key),\n 'variable_size': args.get('variable_size')\n }\n candidate[key] = set_generator(random, key_args)\n\n return candidate","def setta_cubi(lst):\n for i in range(len(lst)):\n lst[i] = lst[i]**3","def __init__(self, parameter_set):\n self.parameter_set = parameter_set.to_parameterization('Meijaard2007')","def get_set(dim, maximum):\n\n i = 0\n numbers = []\n while i**2 <= maximum:\n n = i**2\n counter = 0\n while n <= maximum and counter < dim:\n numbers += [i**2]\n n += i**2\n counter += 1\n i += 1\n return numbers","def J (self, n):","def get_variable(self, InSet_g, InSet_l, InSet_gx, InSet_lx, InSet_a, InSet_size,\n InSet_dim, InSet_area, InSet_c, BdSet_nlength, BdSet_nr, BdSet_nl, BdSet_ng):\n self.InSet_size = InSet_size\n self.InSet_dim = InSet_dim\n self.InSet_area = InSet_area\n self.BdSet_nlength = BdSet_nlength\n\n self.InSet_g = Parameter(\n Tensor(InSet_g, mstype.float32), name=\"InSet_g\", requires_grad=False)\n self.InSet_l = Parameter(\n Tensor(InSet_l, mstype.float32), name=\"InSet_l\", requires_grad=False)\n self.InSet_gx = Parameter(\n Tensor(InSet_gx, mstype.float32), name=\"InSet_gx\", requires_grad=False)\n self.InSet_lx = Parameter(\n Tensor(InSet_lx, mstype.float32), name=\"InSet_lx\", requires_grad=False)\n self.InSet_a = Parameter(\n Tensor(InSet_a, mstype.float32), name=\"InSet_a\", requires_grad=False)\n self.InSet_c = Parameter(\n Tensor(InSet_c, mstype.float32), name=\"InSet_c\", requires_grad=False)\n self.BdSet_nr = Parameter(\n Tensor(BdSet_nr, mstype.float32), name=\"BdSet_nr\", requires_grad=False)\n self.BdSet_nl = Parameter(\n Tensor(BdSet_nl, mstype.float32), name=\"BdSet_nl\", requires_grad=False)\n self.BdSet_ng = Parameter(\n Tensor(BdSet_ng, mstype.float32), name=\"BdSet_ng\", requires_grad=False)","def __init__(self, n):\n self.n = n\n self.e = [set() for i in range(n)]","def build_optics(SetOfObjects, epsilon):\n\n for point in SetOfObjects._index:\n if not SetOfObjects._processed[point]:\n expandClusterOrder(SetOfObjects, point, epsilon)","def powerset(n):\n # chain r-combinations generator for r=0, 1,..., n\n return chain.from_iterable(combinations(range(n), r) for r in range(n+1))","def iter_vs_cnum(solver, n_range=None, cnum_range=None, verbose=0, \\\n construct_args=dict(), solve_args=dict() ):\n if n_range is None:\n n_range = range(50, 501, 50)\n if cnum_range is None:\n cnum_range = [10**i for i in range(1,10)]\n\n n_mats = len(n_range) * len(cnum_range)\n print('Evaluating %d matrices' % n_mats)\n\n results = dict()\n\n # Construct solver instance\n solver_ = solver(A=None, b=None, full_output=1, **construct_args)\n\n for n in n_range:\n results[n] = []\n plt.figure()\n plt.title('Size: %d by %d' % (n, n))\n for cond_num in cnum_range:\n if verbose: print('n: %d cnum: %d' % (n, cond_num))\n\n #A = util.psd_from_cond(cond_num=cond_num, n=n)\n A = psd_from_cond(cond_num=cond_num, n=n)\n x_true = 4 * np.random.randn(n)\n b = np.dot(A, x_true)\n\n if verbose: print('Initial resid error: %f' % la.norm(b))\n\n\n solver_.A, solver_.b = A, b\n xopt, n_iter, resids, x_difs = solver_.solve(x_true=x_true, max_iter = n*2, \\\n **solve_args)\n results[n].append(n_iter)\n\n if verbose:\n print('Final resid error: %f' % la.norm(b - np.dot(A, xopt)))\n print('Took %d iter' % n_iter)\n\n return results","def genTrainingSet(set_of_CSVs, file_to_classify, train_size = 5):\n set_of_csvs_minus_target = copy.copy(set_of_CSVs)\n # remove the file we want to classify\n set_of_csvs_minus_target.remove(file_to_classify)\n\n # extract out the random noise files\n # first, set the seed\n random.seed(time.time())\n # now sample\n return_list = random.sample(set_of_csvs_minus_target, train_size)\n return return_list","def gen_small(s, n):\n\tdeg = n\n\tcoeff_vector = deg*[_sage_const_0 ]\n\tcoeff_vector[deg-_sage_const_1 ] = _sage_const_1 \n\tcoeff_vector[_sage_const_0 ] = _sage_const_1 \n\tindex_set = set({_sage_const_0 ,deg-_sage_const_1 })\n\tfor i in range(s-_sage_const_2 ):\n\t# add 1's\n\t\twhile True:\n\t\t\tindex1 = ZZ.random_element(_sage_const_1 ,deg-_sage_const_1 )\n\t\t\tif not index1 in index_set:\n\t\t\t\tcoeff_vector[index1] = _sage_const_1 \n\t\t\t\tindex_set = index_set.union({index1})\n\t\t\t\tbreak\n\t# add -1's\n\tfor i in range(s):\n\t\twhile True:\n\t\t\tindex2 = ZZ.random_element(_sage_const_1 ,deg-_sage_const_1 )\n\t\t\tif not index2 in index_set:\n\t\t\t\tcoeff_vector[index2] = -_sage_const_1 \n\t\t\t\tindex_set = index_set.union({index2})\n\t\t\t\tbreak\n\treturn coeff_vector","def tst_random_set():\n final_wave, final_spec, final_z = desi_qso_templates(\n outfil='test_random_set.fits', N_perz=100, seed=12345)","def allcombinations(orgset, k):\n return itertools.chain(*[combination(orgset, i) for i in range(1, k + 1)])","def apriori_gen(Ls):\n Lks = Ls[len(Ls) -1] #L(k-1)\n LLength = len(Ls)\n Lc = combinations(Lks, r = LLength+1)\n fs = frozenset([i for i in Lc])\n\n Ck =[] #L(k)\n for s in fs:\n ckItem = frozenset()\n for ss in s:\n ckItem = ckItem.union(ss)\n if not has_infrequent_subset(ckItem, Lks):\n Ck.append(ckItem)\n\n# print \"Ck:\",Ck\n return Ck","def _create_parameter_set_hashes(self):\n self._parameter_set_hashes = []\n for row in self._samples:\n set_values_catenation = ''.join(repr(element) for element in row)\n set_hash = hashlib.md5(set_values_catenation.encode('utf-8')).hexdigest()\n self._parameter_set_hashes.append(set_hash)","def build_subsets(self):\n self.all = nrn.SectionList()\n self.all.wholetree(sec=self.soma)","def eclatGeneration(self, cList):\n # to generate all\n tidList = {}\n key = list(cList.keys())\n for i in range(0, len(key)):\n nighbousItems=self.getNighboirItems(key[i])\n for j in range(i + 1, len(key)):\n # print(c[key[i]],c[key[j]])\n if not key[j] in nighbousItems:\n \tcontinue\n intersectionList = list(set(cList[key[i]]).intersection(set(cList[key[j]])))\n itemList = []\n itemList += key[i]\n itemList += key[j]\n if len(intersectionList) >= self.minSup:\n itemList.sort()\n if tuple(itemList) not in tidList:\n tidList[tuple(set(itemList))] = intersectionList\n return tidList","def tallyPCSets(cmin):\n theSlices = pickle.load( open ('pickles/quantizedChordDictSlices1015.pkl', 'rb') )\n PCSets = collections.Counter()\n for i, slicey in enumerate(theSlices):\n #if i > 10:\n #break\n if slicey == ['start'] or slicey == ['end']:\n continue\n slicePC = set()\n for mn in slicey['midi']:\n slicePC.add(mn%12)\n PCs = chord.Chord(slicePC).primeForm\n if len(PCs) >= cmin:\n PCSets[str(PCs)] += 1\n #tally up the frequencies for each chord\n sorted_PCSets = sorted(PCSets.iteritems(), key=operator.itemgetter(1), reverse=True)\n #export the tally as a csv file\n csvName = 'quantizedPCSets1015.csv'\n x = csv.writer(open(csvName, 'wb'))\n for pair in sorted_PCSets:\n x.writerow([pair[0], pair[1]])","def julia(c):\n @nb.vectorize([nb.int32(nb.complex128)])\n def f(z): \n \"\"\"Function that takes a complex number and iterates \n it until the condition abs(z) >=2 is reached.\n \n Args:\n z: Complex Number\n \n Returns:\n n: Number of iterations of function\n \"\"\"\n if abs(z) > 2:\n return 1\n else:\n n = 1\n while abs(z) < 2:\n n += 1\n if n > 100:\n return 0\n else:\n z = z**2 + c\n return n\n return f","def system(Vg, Cs, Cd, Cg, Gs, Gd, num_e):\n myset = set.SET()\n # choose between the two lines below for metallic dot or quantum levels\n myset.add_metallic_dot('dot', num_e, -num_e, 0)\n #myset.add_quantum_dot('dot', [0, 1, 2, 3], [1, 1, 1, 1])\n myset.add_lead('source')\n myset.add_lead('drain')\n myset.add_gate('gate')\n #myset.add_link('dl', 'dot', 'source', 5.5e-18, 0.2)\n #myset.add_link('dl', 'dot', 'drain', 5.5e-18, 0.2)\n myset.add_link('dl', 'dot', 'drain', Cd,Gd)\n myset.add_link('dl', 'dot', 'source', Cs, Gs)\n #myset.add_link('dl', 'dot', 'drain', 1e-19, 10e-8)\n #myset.add_link('dl', 'dot', 'drain', Cg, Gg)\n myset.add_link('dg', 'dot', 'gate', Cg)\n return myset","def sat_solve(self):\n # YOUR CODE HERE\n o = frozenset()\n if self.isfalse:\n return False\n elif self.istrue:\n return set()\n l = self.generate_candidate_assignments()\n print(\"assignments,\", l)\n for i in l:\n st = sat_apply_assignment(self, i)\n print(\"i:\", i, \"new set\", st)\n\n if st.istrue:\n return {i}\n elif not st.isfalse:\n sat_solve(st)\n\n return {i}","def main(l, k):\n S = 0\n T = product(xrange(2), repeat=k)\n for ts in T:\n tmp = []\n\n for t, c in zip(ts, cs):\n tmp.append(((-1)*c)**t)\n\n S += (sum(tmp)**l)\n val = (sum(tmp)**l)\n print val\n return S / float(2**(k))","def main(l, k):\n S = 0\n T = product(xrange(2), repeat=k)\n for ts in T:\n tmp = []\n\n for t, c in zip(ts, cs):\n tmp.append(((-1)*c)**t)\n\n S += (sum(tmp)**l)\n val = (sum(tmp)**l)\n print val\n return S / float(2**(k))","def coset_reps(self):\n from .all import SL2Z\n N = self.level()\n if N == 1: # P1List isn't very happy working modulo 1\n yield SL2Z([1,0,0,1])\n else:\n for z in sage.modular.modsym.p1list.P1List(N):\n yield SL2Z(lift_to_sl2z(z[0], z[1], N))","def gen_pows(a,b):\n s = set()\n for x in range(2, a + 1):\n for y in range(2, b + 1):\n s.add(x**y)\n return s","def gen_sequence(a, b, c):\n i = 1\n while True:\n yield a * i**2 + b * i + c\n i += 1","def generate_test_set_dqn(n_free_min, n_free_max, d_edge_min, d_edge_max, Omega_max, Phi_max, Lambda_max,\n weighted, w_max, directed, size_test_set, to_torch=False):\n\n # Initialize the variables\n Budget_max = Omega_max + Phi_max + Lambda_max\n test_set = []\n if to_torch:\n test_set_torch = []\n\n print(\"==========================================================================\")\n print(\"Generates the test set... \\n\")\n\n # for all budgets\n for budget in tqdm(range(1, Budget_max + 1)):\n # initialize the budget's instances list\n test_set_budget = []\n if to_torch:\n test_set_budget_torch = []\n for k in range(size_test_set):\n # generate a random instance\n instance_budget_k = generate_random_instance(\n n_free_min,\n n_free_max,\n d_edge_min,\n d_edge_max,\n Omega_max,\n Phi_max,\n Lambda_max,\n weighted=weighted,\n w_max=w_max,\n Budget_target=budget,\n directed=directed,\n )\n G = instance_budget_k.G\n Omega = instance_budget_k.Omega\n Phi = instance_budget_k.Phi\n Lambda = instance_budget_k.Lambda\n J = instance_budget_k.J\n # solve the instance\n value, D, I, P = solve_mcn(G, Omega, Phi, Lambda, J=J, exact=True)\n # save the value, P, D in the Instance object\n instance_budget_k.value = value\n instance_budget_k.D = D\n instance_budget_k.I = I\n instance_budget_k.P = P\n # pushes it to memory\n test_set_budget.append(instance_budget_k)\n # if we want to save the corresponding InstanceTorch\n # to evaluate the training, we stop at Budget_max - 1\n if to_torch:\n instance_budget_k_torch = instance_to_torch(instance_budget_k)\n test_set_budget_torch.append(instance_budget_k_torch)\n test_set.append(test_set_budget)\n if to_torch:\n test_set_torch.append(test_set_budget_torch)\n\n if not os.path.exists('data'):\n os.mkdir('data')\n folder_name = 'test_data'\n if weighted:\n folder_name += '_w'\n if directed:\n folder_name += '_dir'\n path_test_data = os.path.join('data', folder_name)\n if not os.path.exists(path_test_data):\n os.mkdir(path_test_data)\n # Save the test sets\n file_path = os.path.join(path_test_data, \"test_set.gz\")\n pickle.dump(test_set, open(file_path, \"wb\"))\n if to_torch:\n file_path_torch = os.path.join(path_test_data, \"test_set_torch.gz\")\n pickle.dump(test_set_torch, open(file_path_torch, \"wb\"))","def choose(self, g, c, p):\n ng = copy.deepcopy(g)\n ng[c[0]][c[1]] = p\n return ng","def compute(self, inputs, outputs):\n\n log.info(f\"Start optimisation iteration: {Rt.counter}\")\n\n # Update the geometry dictionary\n for name, infos in Rt.geom_dict.items():\n infos[1].append(inputs[name][0])\n\n if Rt.counter == 0:\n\n # For the first iteration, update the CPACS file from previous module\n cpacs_in = Rt.modules[0].cpacs_in\n cpacs_out = Rt.modules[0].cpacs_out\n update_cpacs_file(cpacs_in, cpacs_out, Rt.geom_dict)\n\n else:\n\n for m, module in enumerate(Rt.modules):\n\n # Increment name of output CPACS file\n Rt.modules[m].cpacs_out = Path(\n Rt.modules[m].cpacs_out.parents[0],\n \"iter_\" + str(Rt.counter).rjust(2, \"0\") + \".xml\",\n )\n\n if m == 0:\n\n # Use output of the last module of the previous iteration as input\n Rt.modules[m].cpacs_in = Rt.modules[-1].cpacs_out\n\n # Update the geometry of the CPACS file\n cpacs_in = Rt.modules[m].cpacs_in\n cpacs_out = Rt.modules[m].cpacs_out\n update_cpacs_file(cpacs_in, cpacs_out, Rt.geom_dict)\n\n # # The first module of the loop must update its output where the input\n # # was saved because it has been updated by \"update_cpacs_file\" function.\n # Rt.modules[m].cpacs_in = Rt.modules[m].cpacs_out\n\n else:\n\n # Increment name of input CPACS file\n Rt.modules[m].cpacs_in = Path(\n Rt.modules[m].cpacs_in.parents[0],\n \"iter_\" + str(Rt.counter).rjust(2, \"0\") + \".xml\",\n )\n\n Rt.counter += 1","def power(set_):\n ensure_countable(set_)\n\n result = chain.from_iterable(combinations(set_, r)\n for r in xrange(len(set_) + 1))\n return _harmonize_subset_types(set_, result)","def __init__(self, max_iteration=50, swarm_num=20, c1=2, c2=2, w_ini=0.9, w_end=0.4):\r\n self.__c1 = c1\r\n self.__c2 = c2\r\n self.__w_ini = w_ini\r\n self.__w_end = w_end\r\n self.__max_iteration = int(max_iteration)\r\n self.__swarm_num = int(swarm_num)\r\n np.random.seed(123456)","def construct_powerset(feature_list):\n\n # Sort for easily reproducible order\n feature_names = sorted(list(feature_list))\n\n feature_combinations = [list(x) for l in range(1, len(feature_names) +1) for x in combinations(feature_names, l)]\n\n return feature_combinations","def generate_grna_sets(self, prioritise_3prime = False, set_num = 1, max_set_num = 1, manual = True):\n collapsed_grna = sorted(self, key = lambda cg:(-len(cg), sorted(cg)))\n ## copy the CollapsedgRNA objects so we can modify them without changing the original\n collapsed_grna = [cg.copy() for cg in collapsed_grna]\n output = []\n while all(len(cg.gRNAs) > 0 for cg in collapsed_grna) and set_num <= max_set_num:\n selected_grna = self._generate_grna_set(collapsed_grna, resort = False,\n prioritise_3prime = prioritise_3prime)\n if manual:\n sorted_grna = sorted(selected_grna, key = lambda grna:grna.id)\n usr_input = manual_check_prompt(sorted_grna, set_num = set_num)\n if usr_input.upper() != 'X':\n id_list = tuple(grna.id for grna in selected_grna)\n seq_list = tuple(str(grna.seq).upper() for grna in selected_grna)\n if usr_input in id_list:\n index = id_list.index(usr_input)\n collapsed_grna[index].remove(selected_grna[index])\n elif usr_input.upper() in seq_list:\n index = seq_list.index(usr_input)\n collapsed_grna[index].remove(selected_grna[index])\n else:\n print(\"Invalid input.\")\n continue ## skip removal of selected gRNA and yielding of selected gRNA set\n ## remove selected gRNA from candidate pool\n for i, grna in enumerate(selected_grna):\n collapsed_grna[i].remove(grna)\n set_num += 1\n yield selected_grna\n # return output\n return","def _iterCoordsets(self):\n\n for i in range(self._n_csets):\n yield self._coords[i]","def comb(set, n):\n if len(set) < n:\n raise Exception(\"Not enough elements\")\n elif len(set) == n:\n yield set\n else:\n setLen = len(set)\n iters = [rangeIter(setLen - n + 1)]\n values = [0] * n\n values[0] = iters[0].next()\n level = 1\n while True:\n # Fill array of iterators back up\n while level < n:\n iters.append(rangeIter(values[level - 1] + 1,\n setLen - n + level + 1))\n values[level]=iters[level].next()\n level += 1\n subset = [set[i] for i in values]\n yield subset\n while True:\n try:\n values[level - 1] = iters[level - 1].next()\n break\n except StopIteration:\n iters.pop()\n level -= 1\n if level == 0:\n # Top-level iterator is done, so we are too\n raise StopIteration","def generate_test():\n o = []\n pos = [384, 288]\n note_group_size = GAN_PARAMS[\"note_group_size\"]\n generate_set(begin=3 * note_group_size, start_pos=pos,\n length_multiplier=dist_multiplier, group_id=3, plot_map=True)","def __init__(self, qc: QComputer = None,\n gate_set: GateSet = None,\n qubits: List[int] = None,\n optimizer: Union[str, GSTOptimizer] = 'linear_inverse',\n **kwargs):\n super().__init__(qc=qc, **kwargs)\n self._qc: QComputer = qc\n self._qubits: List[int] = qubits\n self._gateset: GateSet = gate_set\n self._shots: int = kwargs.get('shots', 8000)\n self._optimizer: GSTOptimizer = None\n self._initialize_optimizer(optimizer)\n\n # Number of working qubits. Notice that working qubits must count from 0 to the maximal index in the `qubits`\n self._n = max(self._qubits) + 1 if self._qubits is not None else None\n\n # Store the gate set tomography results. Initialize to an empty dictionary\n self._result = dict()","def all_c(self):\n for i in range( self.N, 0, -1):\n self.C[i] = self.c(i)\n self.ok = True","def setC(self, c):\n\t\tself.c = int(c)","def reduce_set(cells: int, blocks: [int], uvars: [int], nbase: int):\n combos = []\n\n if sum(blocks) + (len(blocks) - 1) > cells:\n raise Exception(\"The passed block values exceeded the number of cells\")\n\n ogcombo = []\n acc = 0\n for block in blocks:\n ogcombo.append(acc)\n acc += block + 1\n\n combos.append(ogcombo)\n\n ccombo = ogcombo.copy()\n\n lookat = len(blocks) - 1\n while lookat >= 0:\n if blocks[-1] + ccombo[-1] < cells:\n ccombo[lookat] = ccombo[lookat] + 1\n s = ccombo[lookat] + blocks[lookat] + 1\n for i in range(lookat + 1, len(blocks)):\n ccombo[i] = s\n s += blocks[i] + 1\n lookat = len(blocks) - 1\n combos.append(ccombo.copy())\n else:\n lookat -= 1\n s = ccombo[lookat] + blocks[lookat] + 1\n for i in range(lookat + 1, len(blocks)):\n ccombo[i] = s\n s += blocks[i] + 1\n\n cnf = CNF()\n for combo in combos:\n clause = [-v if in_combo(i, combo, blocks) else v for i, v in zip(range(cells), uvars)]\n cnf.append(clause)\n\n return cnf.negate(nbase)","def __init__(self):\n self.EntireSet = []","def digit_set(s):\n t = np.zeros((10,1))\n t[s] = 1.0\n return t","def _create_parameter_set_names(self):\n self._parameter_set_names = {}\n for number, set_hash in enumerate(self._parameter_set_hashes):\n template = self.set_name_template\n self._parameter_set_names[set_hash] = (template.substitute({'number': number}))","def allcJPDs(net,cond_node,cond_states):\r\n \r\n onodes = net.Outputnodes()\r\n onodes = sorted(onodes)\r\n combinations = [i for i in itertools.combinations(onodes,2)]\r\n JPDdatas = [] \r\n progress = 0\r\n for cs in cond_states:\r\n JPDdata = []\r\n for c in combinations:\r\n print 'Progress: ' +str(int(100*float(progress)/float(len(combinations)*len(cond_states))))+ ' %'\r\n priorA = getCondPrior(net,c[0],cond_node,cs)\r\n print priorA\r\n priorB = getCondPrior(net,c[1],cond_node,cs) \r\n print priorB\r\n JPD = cJPD(net,c,cond_node,cs)\r\n cov = Covariance (priorA,priorB,JPD)\r\n cor = Correlation (priorA,priorB,cov) \r\n JPDdata.append([c[0],c[1],JPD,cov,cor])\r\n progress+=1\r\n JPDdatas.append(JPDdata)\r\n return JPDdatas","def generate_train_data(comps: List[pd.DataFrame], concen_upper_bound=1000, num_per_combination=1000):\n cps = [to_int_index(c) for c in comps]\n cps = [zero_end_interpolation(c) for c in comps]\n cps = alignment(cps)\n cps = [scale_dataframe(c) for c in cps]\n\n samples = []\n for n_class in range(1, len(cps) + 1):\n comps_roller = ComponentRoller(cps, n_class)\n concen_roller = ConcentrationRoller(1, concen_upper_bound, n_class)\n for i in range(num_per_combination):\n picked_comps, label = comps_roller.roll()\n concen_vector = concen_roller.roll_unique(label)\n the_sample = pd.Series(name=label, data=np.sum(picked_comps * concen_vector, axis=1))\n samples.append(the_sample)\n if i % 100 == 0:\n print('组合数{}: 第{}个样本 --- 标签{}，浓度比{}'.format(n_class, i, label, concen_vector))\n df = pd.concat(samples, axis=1)\n return df.values.T, np.array(_to_vectors(df.columns.tolist()))","def construct_combined_set(filenames, sensor_names, cnt_preprocessors,\n marker_def, end_marker_def, trial_classes,\n trial_start_offset_ms, trial_stop_offset_ms,\n min_break_length_ms, max_break_length_ms,\n break_start_offset_ms, break_stop_offset_ms,\n last_set_split_trial, add_trial_breaks=True,\n filename_to_extra_args=None):\n default_args = deepcopy(locals())\n sets = []\n\n if filename_to_extra_args is not None:\n for filename_with_args in filename_to_extra_args:\n assert filename_with_args in filenames\n\n for i_file, filename in enumerate(filenames):\n this_args = copy(default_args)\n if filename_to_extra_args is not None and (\n filename in filename_to_extra_args):\n for key in filename_to_extra_args[filename]:\n assert key in this_args\n this_args[key] = filename_to_extra_args[filename][key]\n assert key != 'last_set_split_trial', \"Does not make sense :)\"\n marker_segmenter = MarkerSegmenter(segment_ival=[\n this_args['trial_start_offset_ms'], \n this_args['trial_stop_offset_ms']],\n marker_def=this_args['marker_def'],\n trial_classes=this_args['trial_classes'],\n end_marker_def=this_args['end_marker_def'])\n trial_break_adder = AddTrialBreaks(min_length_ms=this_args['min_break_length_ms'],\n max_length_ms=this_args['max_break_length_ms'], \n start_offset_ms=this_args['break_start_offset_ms'], \n stop_offset_ms=this_args['break_stop_offset_ms'],\n start_marker_def=this_args['marker_def'],\n end_marker_def=this_args['end_marker_def'])\n if (i_file < len(filenames) - 1) or (\n this_args['last_set_split_trial'] is None):\n segmenters = [marker_segmenter,]\n else:\n segmenters = [marker_segmenter,\n RestrictTrialRange(0,this_args['last_set_split_trial'])]\n if this_args['add_trial_breaks']:\n segmenters.append(trial_break_adder)\n segmenter = PipelineSegmenter(segmenters)\n cnt_set = SetWithMarkers(BBCIDataset(filename,\n load_sensor_names=this_args['sensor_names']),\n this_args['cnt_preprocessors'],\n segmenter) \n sets.append(cnt_set)\n\n # add last set last part as test set if you split apart last set\n # we use that this_args is now from last set already\n if last_set_split_trial is not None:\n segmenters = [marker_segmenter,\n RestrictTrialRange(last_set_split_trial,None),]\n if this_args['add_trial_breaks']:\n segmenters.append(trial_break_adder)\n segmenter = PipelineSegmenter(segmenters)\n cnt_set = SetWithMarkers(BBCIDataset(filenames[-1], # again last file needed\n load_sensor_names=this_args['sensor_names']),\n this_args['cnt_preprocessors'],\n segmenter)\n sets.append(cnt_set)\n dataset = CombinedSet(sets)\n return dataset","def generate_with_antichain(set_sizes, n_sets, antichain_sizes, seed):\n np.random.seed(seed)\n\n for n in set_sizes:\n for k, size in zip(range(n_sets), antichain_sizes):\n antichain_size = int(np.round(size*n))\n # Generiramo nakljucni permutaciji (zadnjih antichain_size elementov predstavlja antiverigo, zato jih ni v permutaciji)\n p1 = np.random.permutation(n-antichain_size)\n p2 = np.random.permutation(n-antichain_size)\n # Potrebujemo razlicni permutaciji, sicer imamo delno urejenost reda 1\n while np.all(p1 == p2):\n p2 = np.random.permutation(n-antichain_size)\n # Slovarja, ki za vsako stevilo vrneta mesto v posamezni permutaciji\n inverse1 = {i: p1[i] for i in range(n-antichain_size)}\n inverse2 = {i: p2[i] for i in range(n-antichain_size)}\n\n # Gremo cez vse kombinacije, ce je eno stevilo manjse od drugega v obeh primerih, ju dodamo v delno urejenost\n partial_order = []\n for u in range(n-antichain_size):\n for v in range(n-antichain_size):\n if inverse1[u] < inverse1[v] and inverse2[u] < inverse2[v]:\n partial_order.append((u, v))\n\n # Izpisemo delno urejenost v datoteko\n f = open(f'../data/vhod_antichain_{n}_{k+1}.txt', 'w')\n # Prva vrstica\n f.write(f'{n} {len(partial_order)}\\n')\n # Primerljivi elementi\n for u, v in partial_order:\n # Dodamo +1, da ni indeksiranja z 0\n f.write(f'{u+1} {v+1}\\n')\n f.close()","def build_param_grid(self, C_list:list=[0.1, 1, 10, 100], gamma_list:list=[1, 0.1, 0.01, 0.001], kernel_list:list=['rbf']):\n ans = {}\n ans['C'] = C_list\n ans['gamma'] = gamma_list\n ans['kernel'] = kernel_list\n self.param_grid = ans\n return ans","def ar_gen(frequentItemSets):\n# print frequentItemSets\n for fItemSet in frequentItemSets:\n if fItemSet:\n itemSets = fItemSet.keys()\n for itemSet in itemSets:\n subsets = subset_gen(itemSet)\n# print itemSet\n# print subsets\n if subsets:\n for subset in subsets:\n sptSubSet = supportItemSet(subset, frequentItemSets)\n sptSubSets = supportItemSet(itemSet, frequentItemSets)\n print subset,'->', itemSet.difference(subset), 'confidence=',sptSubSets/float(sptSubSet)","def create_sets(self,FD_SET=[],VA_SET=[]):\n \n self.m.S = Set(initialize=self.sectors, doc='sectors')\n\n if self.EORA is True:\n self.m.rROW = Set(initialize=self.countries+['ROW'],ordered=True, doc='regions including export')\n self.m.R = Set(initialize=self.countries+['ROW'],ordered=True, doc='regions')\n else:\n self.m.rROW = Set(initialize=self.countries,ordered=True, doc='regions including export')\n self.m.R = Set(initialize=self.countries,ordered=True, doc='regions')\n\n if self.EORA is True:\n self.m.fdemand = Set(initialize=['P3h', 'P3n','P3g', 'P51','P52','P53'], doc='Final Demand')\n else:\n self.m.fdemand = Set(initialize=self.fd_cat, doc='Final Demand')\n\n if self.EORA is True:\n self.m.VA = Set(initialize=['VA'], doc='value added')\n else:\n self.m.VA = Set(initialize=VA_SET, doc='value added')","def _generate_clfs(self):\n clfs = []\n for para_key in self.parameter[1]:\n for value in self.parameter[1][para_key]:\n clfs.append(classifierSearch.classifierSearch(self.parameter[0], [para_key, value]))\n self._generate_results(self.parameter[0], para_key, value)\n self.clfs = clfs","def getSet(unique_name):","def getSet(unique_name):","def from_constant(st, highpass=0.08, lowpass=20.0):\n for tr in st:\n tr.setParameter(\n \"corner_frequencies\",\n {\"type\": \"constant\", \"highpass\": highpass, \"lowpass\": lowpass},\n )\n return st","def from_constant(st, highpass=0.08, lowpass=20.0):\n for tr in st:\n tr.setParameter(\n \"corner_frequencies\",\n {\"type\": \"constant\", \"highpass\": highpass, \"lowpass\": lowpass},\n )\n return st","def __init_cr(self,i,conv,C):\n self.params['W'+i]=np.random.randn(conv[0],C,conv[1],conv[1])*self.weight_scale\n self.params['b'+i]=np.zeros(conv[0]) \n if self.use_batchnorm:\n self.params['gamma'+i]=np.ones(conv[0])\n self.params['beta'+i]=np.zeros(conv[0])","def setIterations(self, value):\n return self._set(nIterations=value)","def set(self, opts, popsize=None, ccovfac=1, verbose=True):\r\n\r\n alpha_cc = 1.0 # cc-correction for mueff, was zero before\r\n\r\n def cone(df, mu, N, alphacov=2.0):\r\n \"\"\"rank one update learning rate, ``df`` is disregarded and obsolete, reduce alphacov on noisy problems, say to 0.5\"\"\"\r\n return alphacov / ((N + 1.3)**2 + mu)\r\n\r\n def cmu(df, mu, alphamu=0.0, alphacov=2.0):\r\n \"\"\"rank mu learning rate, disregarding the constrant cmu <= 1 - cone\"\"\"\r\n c = alphacov * (alphamu + mu - 2 + 1/mu) / ((N + 2)**2 + alphacov * mu / 2)\r\n # c = alphacov * (alphamu + mu - 2 + 1/mu) / (2 * (N + 2)**1.5 + alphacov * mu / 2)\r\n # print 'cmu =', c\r\n return c\r\n\r\n def conedf(df, mu, N):\r\n \"\"\"used for computing separable learning rate\"\"\"\r\n return 1. / (df + 2.*sqrt(df) + float(mu)/N)\r\n\r\n def cmudf(df, mu, alphamu):\r\n \"\"\"used for computing separable learning rate\"\"\"\r\n return (alphamu + mu - 2. + 1./mu) / (df + 4.*sqrt(df) + mu/2.)\r\n\r\n sp = self\r\n N = sp.N\r\n if popsize:\r\n opts.evalall({'N':N, 'popsize':popsize})\r\n else:\r\n popsize = opts.evalall({'N':N})['popsize'] # the default popsize is computed in Options()\r\n sp.popsize = popsize\r\n if opts['CMA_mirrors'] < 0.5:\r\n sp.lam_mirr = int(0.5 + opts['CMA_mirrors'] * popsize)\r\n elif opts['CMA_mirrors'] > 1:\r\n sp.lam_mirr = int(0.5 + opts['CMA_mirrors'])\r\n else:\r\n sp.lam_mirr = int(0.5 + 0.16 * min((popsize, 2 * N + 2)) + 0.29) # 0.158650... * popsize is optimal\r\n # lam = arange(2,22)\r\n # mirr = 0.16 + 0.29/lam\r\n # print(lam); print([int(0.5 + l) for l in mirr*lam])\r\n # [ 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21]\r\n # [1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 4]\r\n\r\n sp.mu_f = sp.popsize / 2.0 # float value of mu\r\n if opts['CMA_mu'] is not None:\r\n sp.mu_f = opts['CMA_mu']\r\n sp.mu = int(sp.mu_f + 0.499999) # round down for x.5\r\n # in principle we have mu_opt = popsize/2 + lam_mirr/2,\r\n # which means in particular weights should only be negative for q > 0.5+mirr_frac/2\r\n if sp.mu > sp.popsize - 2 * sp.lam_mirr + 1:\r\n print(\"WARNING: pairwise selection is not implemented, therefore \" +\r\n \" mu = %d > %d = %d - 2*%d + 1 = popsize - 2*mirr + 1 can produce a bias\" % (\r\n sp.mu, sp.popsize - 2 * sp.lam_mirr + 1, sp.popsize, sp.lam_mirr))\r\n if sp.lam_mirr > sp.popsize // 2:\r\n raise _Error(\"fraction of mirrors in the population as read from option CMA_mirrors cannot be larger 0.5, \" +\r\n \"theoretically optimal is 0.159\")\r\n sp.weights = log(max([sp.mu, sp.popsize / 2.0]) + 0.5) - log(1 + np.arange(sp.mu))\r\n if 11 < 3: # equal recombination weights\r\n sp.mu = sp.popsize // 4\r\n sp.weights = np.ones(sp.mu)\r\n print(sp.weights[:10])\r\n sp.weights /= sum(sp.weights)\r\n sp.mueff = 1 / sum(sp.weights**2)\r\n sp.cs = (sp.mueff + 2) / (N + sp.mueff + 3)\r\n # TODO: clean up (here the cumulation constant is shorter if sigma_vec is used)\r\n sp.dampsvec = opts['CMA_dampsvec_fac'] * (N + 2) if opts['CMA_dampsvec_fac'] else np.Inf\r\n sp.dampsvec_fading = opts['CMA_dampsvec_fade']\r\n if np.isfinite(sp.dampsvec):\r\n sp.cs = ((sp.mueff + 2) / (N + sp.mueff + 3))**0.5\r\n # sp.cs = (sp.mueff + 2) / (N + 1.5*sp.mueff + 1)\r\n sp.cc = (4 + alpha_cc * sp.mueff / N) / (N + 4 + alpha_cc * 2 * sp.mueff / N)\r\n sp.cc_sep = (1 + 1/N + alpha_cc * sp.mueff / N) / (N**0.5 + 1/N + alpha_cc * 2 * sp.mueff / N) # \\not\\gg\\cc\r\n sp.rankmualpha = opts['CMA_rankmualpha']\r\n # sp.rankmualpha = _evalOption(opts['CMA_rankmualpha'], 0.3)\r\n sp.c1 = ccovfac * min(1, sp.popsize/6) * cone((N**2 + N) / 2, sp.mueff, N) # 2. / ((N+1.3)**2 + sp.mucov)\r\n sp.c1_sep = ccovfac * conedf(N, sp.mueff, N)\r\n if 11 < 3:\r\n sp.c1 = 0.\r\n print('c1 is zero')\r\n if opts['CMA_rankmu'] != 0: # also empty\r\n sp.cmu = min(1 - sp.c1, ccovfac * cmu((N**2+N)/2, sp.mueff, sp.rankmualpha))\r\n sp.cmu_sep = min(1 - sp.c1_sep, ccovfac * cmudf(N, sp.mueff, sp.rankmualpha))\r\n else:\r\n sp.cmu = sp.cmu_sep = 0\r\n\r\n sp.neg = BlancClass()\r\n if opts['CMA_active']:\r\n # in principle we have mu_opt = popsize/2 + lam_mirr/2,\r\n # which means in particular weights should only be negative for q > 0.5+mirr_frac/2\r\n sp.neg.mu_f = popsize - (popsize + sp.lam_mirr) / 2 if popsize > 2 else 1\r\n sp.neg.weights = log(sp.mu_f + 0.5) - log(1 + np.arange(sp.popsize - int(sp.neg.mu_f), sp.popsize))\r\n sp.neg.mu = len(sp.neg.weights) # maybe never useful?\r\n sp.neg.weights /= sum(sp.neg.weights)\r\n sp.neg.mueff = 1 / sum(sp.neg.weights**2)\r\n sp.neg.cmuexp = opts['CMA_activefac'] * 0.25 * sp.neg.mueff / ((N+2)**1.5 + 2 * sp.neg.mueff)\r\n assert sp.neg.mu >= sp.lam_mirr # not really necessary\r\n # sp.neg.minresidualvariance = 0.66 # not it use, keep at least 0.66 in all directions, small popsize is most critical\r\n else:\r\n sp.neg.cmuexp = 0\r\n\r\n sp.CMA_on = sp.c1 + sp.cmu > 0\r\n # print(sp.c1_sep / sp.cc_sep)\r\n\r\n if not opts['CMA_on'] and opts['CMA_on'] not in (None,[],(),''):\r\n sp.CMA_on = False\r\n # sp.c1 = sp.cmu = sp.c1_sep = sp.cmu_sep = 0\r\n\r\n sp.damps = opts['CMA_dampfac'] * (0.5 +\r\n 0.5 * min([1, (sp.lam_mirr/(0.159*sp.popsize) - 1)**2])**1 +\r\n 2 * max([0, ((sp.mueff-1) / (N+1))**0.5 - 1]) + sp.cs\r\n )\r\n if 11 < 3:\r\n # this is worse than damps = 1 + sp.cs for the (1,10000)-ES on 40D parabolic ridge\r\n sp.damps = 0.3 + 2 * max([sp.mueff/sp.popsize, ((sp.mueff-1)/(N+1))**0.5 - 1]) + sp.cs\r\n if 11 < 3:\r\n # this does not work for lambda = 4*N^2 on the parabolic ridge\r\n sp.damps = opts['CMA_dampfac'] * (2 - 0*sp.lam_mirr/sp.popsize) * sp.mueff/sp.popsize + 0.3 + sp.cs # nicer future setting\r\n print('damps =', sp.damps)\r\n if 11 < 3:\r\n sp.damps = 10 * sp.damps # 1e99 # (1 + 2*max(0,sqrt((sp.mueff-1)/(N+1))-1)) + sp.cs;\r\n # sp.damps = 20 # 1. + 20 * sp.cs**-1 # 1e99 # (1 + 2*max(0,sqrt((sp.mueff-1)/(N+1))-1)) + sp.cs;\r\n print('damps is %f' % (sp.damps))\r\n\r\n sp.cmean = float(opts['CMA_cmean'])\r\n # sp.kappa = 1 # 4-D, lam=16, rank1, kappa < 4 does not influence convergence rate\r\n # in larger dim it does, 15-D with defaults, kappa=8 factor 2\r\n if sp.cmean != 1:\r\n print(' cmean = %f' % (sp.cmean))\r\n\r\n if verbose:\r\n if not sp.CMA_on:\r\n print('covariance matrix adaptation turned off')\r\n if opts['CMA_mu'] != None:\r\n print('mu = %f' % (sp.mu_f))\r\n\r\n # return self # the constructor returns itself\r","def set_c_values(self,nu=3,xd=2,a12sq=1,a13sq=1,a1=0,b1=0,p1=0,\n vx=0,vy=0,vz=0,epsilon=0.,omega=1.5*np.pi,\n a2=np.pi,b2=0*np.pi,p2=0.5*np.pi,silent=False):\n \n c_target = np.zeros(18)\n c_target[0] = nu*self.sigma0_RG \n xd_abs = xd*self.sigma2_RG\n c_target[4:10] = set_fGij(xd_abs,a12sq,a13sq,a1,b1,p1)\n c_target[10:13] = np.array([vx,vy,vz])\n c_target[13:18] = set_VGij(epsilon,omega,a2,b2,p2)\n \n if silent == False:\n print (\"Constrain peak parameters: \")\n if 'f0' in self.CONS or 'full' in self.CONS: \n print (\"f0: \",\"nu = %.1f\"%nu, \"$\\sigma_0$\")\n if 'f1' in self.CONS or 'full' in self.CONS: \n print (\"f1: \",\"f1,x = f1,y = f1,z = 0\")\n if 'f2' in self.CONS or 'full' in self.CONS: \n print (\"f2: \",r\"xd = {:.1f} $\\sigma_2$, a12sq = {:.1f}, a13sq = {:.1f},a1={:.2f}, b1={:.2f}, p1={:.2f}\".format(xd,a12sq,a13sq,a1,b1,p1))\n if 'vx' in self.CONS or 'full' in self.CONS: \n print (\"vx = {:.1f} km/s\".format(vx)) \n if 'vy' in self.CONS or 'full' in self.CONS: \n print (\"vy = {:.1f} km/s\".format(vy))\n if 'vz' in self.CONS or 'full' in self.CONS: \n print (\"vz = {:.1f} km/s\".format(vz))\n if 'TG' in self.CONS or 'full' in self.CONS: \n print (\"TG: \",\"epsilon = {:.1f} km/s/Mpc, omega = {:.2f}, a2={:.2f}, b2={:.2f}, p2={:.2f}\".format(epsilon,omega,a2,b2,p2))\n \n return c_target[self.cmask]","def run(self, dag):\n # Initiate the commutation set\n self.property_set['commutation_set'] = defaultdict(list)\n\n # Build a dictionary to keep track of the gates on each qubit\n # The key with format (wire_name) will store the lists of commutation sets\n # The key with format (node, wire_name) will store the index of the commutation set\n # on the wire with wire_name, thus, for example:\n # self.property_set['commutation_set'][wire_name][(node, wire_name)] will give the\n # commutation set that contains node.\n\n for wire in dag.wires:\n wire_name = \"{0}[{1}]\".format(str(wire.register.name), str(wire.index))\n self.property_set['commutation_set'][wire_name] = []\n\n # Add edges to the dictionary for each qubit\n for node in dag.topological_op_nodes():\n for (_, _, edge_data) in dag.edges(node):\n\n edge_name = edge_data['name']\n self.property_set['commutation_set'][(node, edge_name)] = -1\n\n # Construct the commutation set\n for wire in dag.wires:\n wire_name = \"{0}[{1}]\".format(str(wire.register.name), str(wire.index))\n\n for current_gate in dag.nodes_on_wire(wire):\n\n current_comm_set = self.property_set['commutation_set'][wire_name]\n if not current_comm_set:\n current_comm_set.append([current_gate])\n\n if current_gate not in current_comm_set[-1]:\n prev_gate = current_comm_set[-1][-1]\n does_commute = False\n try:\n does_commute = _commute(current_gate, prev_gate, self.cache)\n except TranspilerError:\n pass\n if does_commute:\n current_comm_set[-1].append(current_gate)\n\n else:\n current_comm_set.append([current_gate])\n\n temp_len = len(current_comm_set)\n self.property_set['commutation_set'][(current_gate, wire_name)] = temp_len - 1","def optimize_coding_multi(latent_values, real_coding, loss_weight, epochs, \n mse, generator, optimizer, ntimes, ninput):\n for epoch in range(epochs):\n opt_step(latent_values, real_coding, loss_weight, \n mse, generator, optimizer, ntimes, ninput)\n\n return latent_values","def __init__(self, trainset, k=3):\n self._trainset = trainset\n self.k = k","def value_iteration(iterations):\n for _ in range(0, iterations):\n for i in range(0, 3):\n for j in range(0, 4):\n # We don't need values for start/end/non-reachable states\n if [i, j] == [1, 1] or [i, j] == [0, 3] or [i, j] == [1, 3]:\n None\n else:\n value([i, j])\n\n print(utilities)","def generator(nodeSet, serviceSet, fixedSpeed=False):\r\n seed(cfg_sys.RANDOM_SEED)\r\n\r\n userSet = {i: {'alias': nodeSet[i]['alias'], 'reqSet': {}} for i in nodeSet if\r\n nodeSet[i]['typee'] == cfg_sys.TYPEE_CL_USER}\r\n\r\n nameList = ['req' + str(i) for i in range(cfg_sys.NUM_REQ)]\r\n\r\n serviceList = choice(list(serviceSet), size=cfg_sys.NUM_REQ)\r\n minSpeedList = [\r\n serviceSet[serviceName]['speed'][0]\r\n for serviceName in serviceList\r\n ]\r\n maxSpeedList = [\r\n serviceSet[serviceName]['speed'][0 if fixedSpeed else 1]\r\n for serviceName in serviceList\r\n ]\r\n speedList = uniform(low=minSpeedList, high=maxSpeedList, size=cfg_sys.NUM_REQ)\r\n\r\n durationList = choice(cfg_sys.DURATION, size=cfg_sys.NUM_REQ)\r\n\r\n reqSet = {i: req(j, k, l) for i, j, k, l in zip(nameList, serviceList, speedList, durationList)}\r\n for i in reqSet:\r\n user = choice(list(userSet))\r\n userSet[user]['reqSet'][i] = reqSet[i]\r\n\r\n dcSet = {\r\n i: {'alias': nodeSet[i]['alias'], 'serviceList': []}\r\n for i in nodeSet if nodeSet[i]['typee'] == cfg_sys.TYPEE_DC\r\n }\r\n for i in serviceSet:\r\n minServiceNum = min([len(dc['serviceList']) for dc in dcSet.values()])\r\n dc = choice([\r\n dcIp\r\n for dcIp, dc in dcSet.items()\r\n if len(dc['serviceList']) == minServiceNum\r\n ])\r\n dcSet[dc]['serviceList'].append(i)\r\n logger.info('generation complete: userSet: %s, dcSet: %s', str(userSet), str(dcSet))\r\n return userSet, dcSet","def iteration(self):\n T = self.generate_T()\n R = self.reproduce(T)\n self.P = self.choose_mi_best(R)\n #print(self.P)"],"string":"[\n \"def genPowerSet(cvalues, incEmpty=False):\\t\\t\\n\\tps = list()\\n\\tfor cv in cvalues:\\n\\t\\tpse = list()\\n\\t\\tfor s in ps:\\n\\t\\t\\tsc = s.copy()\\n\\t\\t\\tsc.add(cv)\\n\\t\\t\\t#print(sc)\\n\\t\\t\\tpse.append(sc)\\n\\t\\tps.extend(pse)\\n\\t\\tes = set()\\n\\t\\tes.add(cv)\\n\\t\\tps.append(es)\\n\\t\\t#print(es)\\n\\t\\n\\tif incEmpty:\\n\\t\\tps.append({})\\n\\treturn ps\",\n \"def julia_set(width, height, zoom=1, niter=256):\\n\\n w,h = width, height\\n # Why (hxw) ? Because numpy creates a matrix as row x column\\n # and height represents the y co-ordinate or rows and\\n # width represents the x co-ordinate or columns.\\n pixels = np.arange(w*h, dtype=np.uint16).reshape(h, w)\\n pixels = np.arange(w*h, dtype=np.uint16).reshape(h, w)\\n\\n # The constant \\\"c\\\"\\n # This determines the shape of the Julia set.\\n # c_real, c_imag = -0.7, 0.27\\n c_real, c_imag = 0.4, -0.2\\n \\n for x in range(w): \\n for y in range(h):\\n # calculate the initial real and imaginary part of z,\\n # based on the pixel location and zoom and position values\\n zx = 1.5*(x - w/2)/(0.5*zoom*w) \\n zy = 1.0*(y - h/2)/(0.5*zoom*h)\\n\\n for i in range(niter):\\n radius_sqr = zx*zx + zy*zy\\n # Iterate till the point is outside\\n # the circle with radius 2.\\n if radius_sqr > 4: break\\n # Calculate new positions\\n zy,zx = 2.0*zx*zy + c_imag, zx*zx - zy*zy + c_real\\n\\n color = (i >> 21) + (i >> 10) + i*8\\n pixels[y,x] = color\\n \\n # display the created fractal \\n plt.imshow(pixels)\\n plt.show()\",\n \"def create_C1(data_set):\\r\\n C1 = set()\\r\\n for t in data_set:\\r\\n for item in t:\\r\\n item_set = frozenset([item])\\r\\n C1.add(item_set)\\r\\n return C1\",\n \"def create_C1(data_set):\\n C1 = set()\\n for t in data_set:\\n for item in t:\\n item_set = frozenset([item])\\n C1.add(item_set)\\n return C1\",\n \"def mandelbrot(c):\\n tolerance = 30\\n threshold = 3\\n\\n # This is the mandelbrot set\\n #Uncomment and comment the Julia set block below\\n \\n # M = lambda x: pow(x, 2) + c\\n # nterm = M(0)\\n\\n # End Mandelbrot set\\n\\n # This is the Julia Set\\n x = c\\n c = complex(-0.4,0.6)\\n M = lambda x: pow(x, 2) + c\\n nterm = M(x)\\n #End Julia Set\\n\\n for i in xrange(tolerance):\\n try:\\n nterm = M(nterm)\\n except OverflowError:\\n return 1\\n\\n square = lambda x: pow(x, 2)\\n try:\\n if (square(nterm.real) + square(nterm.imag)) < threshold:\\n return 0\\n else:\\n return 1\\n except OverflowError:\\n return 1\",\n \"def CNPJGenerator(amount=1,cnpjn=None):\\n\\n d1weight = [5,4,3,2,9,8,7,6,5,4,3,2]\\n d2weight = [6] + d1weight\\n\\n cnpjs=set()\\n\\n while len(cnpjs) < amount:\\n\\n if not cnpjn:\\n randns = [randint(0,9) for x in range(8)] + [0,0,0,randint(0,9)]\\n else:\\n randns = cnpjn\\n\\n d1,d2 = get_digits(randns,d1weight,d2weight)\\n\\n # transform cnpj in a string\\n cnpj = (\\\"%s\\\"*14) % tuple(randns+[d1,d2])\\n\\n # if not exist, add in cnpjs\\n if not cnpj in cnpjs:\\n cnpjs.add(cnpj)\\n\\n cnpjs = list(cnpjs)\\n if len(cnpjs) != 1:\\n return cnpjs\\n else:\\n return cnpjs[0]\",\n \"def __iter__(self):\\n return iproduct(*self.sets)\",\n \"def set_norm(self, P={}, C={}, c_t=\\\"pp\\\"):\\n N = dict()\\n if isinstance(P, list):\\n Pl = P\\n else:\\n Pl = [P]\\n if isinstance(C, list):\\n Cl = C\\n else:\\n Cl = [C]\\n if len(Pl) > 0:\\n N['comp_dim'] = len(Pl)\\n if len(Cl) > 0:\\n if len(Cl) != len(Pl):\\n raise ValueError(\\\"Need same number of principal parts and coefficients to set!\\\")\\n keys = list(Cl[0].keys())\\n for j in range(1, N['comp_dim']):\\n if list(Cl[j].keys()) != keys:\\n raise ValueError(\\\"Need to set the same coefficients! (or call the method more than once)\\\")\\n else:\\n Cl = []\\n for j in range(N['comp_dim']):\\n Cl.append(C)\\n N['Vals'] = list()\\n N['Vals'] = list()\\n N['SetCs'] = list()\\n for i in range(N['comp_dim']):\\n N['Vals'].append({})\\n N['Vals'].append({})\\n N['SetCs'].append([])\\n D = self.multiplier().D()\\n for j in range(len(D)):\\n a = D[j]\\n x = self.multiplier().Qv[j]\\n # N['Vals'][i][(0,j)]=dict()\\n if x == 0:\\n if c_t == \\\"pp\\\":\\n # N['comp_dim']=N['comp_dim']+1\\n N['SetCs'][i].append((j, 0))\\n if (0, j) in Pl[i]:\\n N['Vals'][i][(j, 0)] = Pl[i][(j, 0)]\\n else:\\n N['Vals'][i][(j, 0)] = 0 # P[(0,0)]\\n elif x < 0 and self._holomorphic:\\n N['SetCs'][i].append((j, 0))\\n N['Vals'][i][(j, 0)] = 0 # P[(0,0)]\\n\\n for (r, n) in Cl[i].keys():\\n if(N['SetCs'][i].count((r, n)) == 0):\\n N['SetCs'][i].append((r, n))\\n N['Vals'][i][(r, n)] = Cl[i][(r, n)]\\n return N\",\n \"def MCS(n,k):\\n\\tglobal dict_all\\n\\tdict_val=copy.deepcopy(dict_all)\\n\\t#start_time = time.time()\\n\\tfinal = {}\\t\\t\\t\\t\\t # Store all result with the count as key. For example final[1]=[[1,0,0],[0,1,1]]\\n\\tseq = []\\t\\t\\t\\t\\t\\t# Store the count with no duplication\\n\\tfor i in range(n):\\n\\t\\tleaf={}\\t\\t\\t\\t\\t\\t# leaf is the dictionary to store the random value of each leaf\\n\\t\\t#count=0\\n\\t\\tfor i in leaves:\\n\\t\\t\\tleaf[i] = choice([0,1])\\n\\t\\t\\tdict_val[i]=leaf[i]\\n\\t\\t\\t#count += leaf[i]\\n\\t\\tresult = Cal_FT(dict_val)\\t\\n\\t\\t'''\\n\\t\\tif result:\\n\\t\\t\\tcutset = []\\n\\t\\t\\tfor i in leaves:\\n\\t\\t\\t\\tcutset.append(str(leaf[i]))\\n\\t\\t\\tcutset=\\\"\\\".join(cutset)\\n\\t\\t\\tif cutset not in final:\\n\\t\\t\\t\\tfinal[cutset]=count\\n\\tfinal_sorted=sorted(zip(final.values(),final.keys())) \\t\\t\\t\\t#Order the cutset by its count\\n\\tfor i in range(k):\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t#Print the first k result\\n\\t\\tcutset=list(final_sorted[i][1])\\n\\t\\tresult=[]\\n\\t\\tfor index in range(len(cutset)):\\n\\t\\t\\tif cutset[index] is \\\"1\\\":\\n\\t\\t\\t\\tresult.append(leaves[index])\\n\\t\\tprint result\\n\\t#end_time=time.time()\\n\\t#print \\\"Running time is\\\", end_time-start_time\\n\\t'''\",\n \"def setNumIterations(*argv):\",\n \"def get_max_num_onsets():\\r\\n \\\"\\\"\\\" based on the numbers above, should equal to 932945... \\\"\\\"\\\"\\r\\n c1 = len(gen_onset_c1())\\r\\n c2 = len(gen_onset_c2())\\r\\n c3 = len(gen_onset_c3_c4())\\r\\n c4 = len(gen_onset_c3_c4())\\r\\n temp = c1\\r\\n temp = temp + ( c1 * c2 )\\r\\n temp = temp + ( c1 * c3 )\\r\\n temp = temp + ( c1 * c2 * c3 )\\r\\n temp = temp + ( c1 * c3 * c4 )\\r\\n temp = temp + ( c1 * c2 * c3 * c4 )\\r\\n return temp\",\n \"def generate_code_point_set(cpp, public_name, set):\\n\\n private_name = public_name + \\\"_\\\"\\n\\n ranges = []\\n current = None\\n expected = None\\n for cp in sorted(set):\\n if expected is None or cp != expected:\\n current = [cp, cp]\\n ranges.append(current)\\n else:\\n current[1] = cp\\n expected = cp + 1\\n\\n cpp.write(f\\\"static constexpr Interval<CodePoint> \\\" f\\\"{private_name}[] = {{\\\\n\\\")\\n\\n for (min_cp, max_cp) in ranges:\\n cpp.write(f\\\" {{{hex(min_cp)}, {hex(max_cp)}}},\\\\n\\\")\\n\\n cpp.write(\\\"};\\\\n\\\\n\\\")\\n cpp.write(\\n f\\\"constexpr Span<const Interval<CodePoint>> \\\"\\n f\\\"{public_name}({private_name});\\\\n\\\\n\\\"\\n )\",\n \"def build_possible_naked_sets(c, setlength=2):\\n ns = {}\\n pairs = [p for p in c.values() if len(p) == setlength]\\n for k, v in c.items():\\n if v in pairs:\\n ns[k] = sorted(v)\\n return ns\",\n \"def sets(elements, set_size):\\n return combinations(elements, set_size)\",\n \"def getSets():\",\n \"def construct(self):\\n\\n newSet = {}\\n current_index = 0\\n\\n for key_1, value_1 in self._sets[self._currentSet].items():\\n current_index += 1\\n for key_2,value_2 in list(self._sets[self._currentSet].items())[current_index:]:\\n # join the 2 tuples\\n join = key_1 + key_2\\n # remove duplicates\\n join = tuple(set(join))\\n # get combinations\\n combined = tuple(combinations(join, self._currentSet+1))\\n # sort combination\\n combined = tuple(sorted(combined[0]))\\n\\n # append new combination to dict\\n if len(combined) != 0 :\\n newSet[combined] = 0\\n\\n self._currentSet += 1\\n # append the new itemset in the sets dict \\n self._sets[self._currentSet] = newSet\",\n \"def _generate_distribution_samples(self, set_count, parameter_count):\\n self._samples = numpy.zeros((set_count, parameter_count))\\n for i, distribution in enumerate(self.parameter_distributions.values()):\\n self._samples[:, i] = distribution.ppf(self._quantiles[:, i])\",\n \"def c_test_set_inp(self, param, value):\\r\\n ret = 1\\r\\n if \\\"__hash__\\\" not in dir(param): # param must be hashable\\r\\n ret = 0\\r\\n if self.verbosity > 0:\\r\\n print(\\\"ERROR: \\\" + param + \\\" is not hashable. It will be unable to be set in a dict.\\\")\\r\\n else:\\r\\n if self.verbosity > 1:\\r\\n print(param + \\\" is hashable.\\\")\\r\\n if param in [\\\"population_size\\\", \\\"time_constraint\\\", \\\"generations\\\", \\\"point_count\\\",\\r\\n \\\"PSO_VELOCITY_WEIGHT\\\", \\\"PSO_INDIVIDUAL_WEIGHT\\\", \\\"PSO_GROUP_WEIGHT\\\"]:\\r\\n if not ((isinstance(value, int) or\\r\\n isinstance(value, float) or\\r\\n isinstance(value, long))):\\r\\n if self.verbosity > 0:\\r\\n print(\\\"ERROR: \\\" + param + \\\" needs to be of a number. It is \\\" + str(value))\\r\\n ret = 0\\r\\n else:\\r\\n if self.verbosity > 1:\\r\\n print(param + \\\" is correctly set to a number.\\\")\\r\\n if value < 0:\\r\\n ret = 0\\r\\n if self.verbosity > 0:\\r\\n print(\\\"ERROR: \\\" + param + \\\" needs to be greater than zero.\\\")\\r\\n else:\\r\\n if self.verbosity > 1:\\r\\n print(param + \\\" is greater than zero.\\\")\\r\\n if param in [\\\"population_size\\\", \\\"generations\\\", \\\"point_count\\\"]:\\r\\n if not isinstance(value, int):\\r\\n ret = 0\\r\\n if self.verbosity > 0:\\r\\n print(\\\"ERROR: \\\" + param + \\\" needs to be an integer. It is \\\" + str(value))\\r\\n else:\\r\\n if self.verbosity > 1:\\r\\n print(param + \\\" is an integer.\\\")\\r\\n if param in [\\\"fitness_function\\\", \\\"weighting_bias\\\"]:\\r\\n if not callable(value):\\r\\n ret = 0\\r\\n if self.verbosity > 0:\\r\\n print(\\\"ERROR: \\\" + param + \\\" needs to be a callable function.\\\")\\r\\n else:\\r\\n if self.verbosity > 1:\\r\\n print(param + \\\" is a callable function.\\\")\\r\\n if param == \\\"end_condition\\\":\\r\\n if value not in [\\\"time_constraint\\\", \\\"generations\\\"]:\\r\\n ret = 0\\r\\n if self.verbosity > 0:\\r\\n print(\\\"ERROR: \\\" + param + \\\" needs to be 'time_constraint' or 'generations'\\\")\\r\\n else:\\r\\n if self.verbosity > 1:\\r\\n print(\\\"ERROR: \\\" + param + \\\" is a correct string.\\\")\\r\\n if param == \\\"seed\\\":\\r\\n if not (value is None or isinstance(value, int)):\\r\\n ret = 0\\r\\n if self.verbosity > 0:\\r\\n print(\\\"ERROR: \\\" + param + \\\" is incorrectly set.\\\")\\r\\n else:\\r\\n if self.verbosity > 1:\\r\\n print(param + \\\" is correctly set.\\\")\\r\\n return ret\",\n \"def generate_data_set(args):\\n if args.o is None:\\n filename = \\\"test\\\"\\n else:\\n filename = args.o\\n\\n if args.b is None:\\n args.b = 0.5\\n if args.p is None:\\n path = Path(\\\"\\\")\\n else:\\n path = Path(args.p)\\n if args.nf is None:\\n args.nf = 1\\n\\n for i in range(int(args.nf)):\\n args.o = path / (filename + \\\"_t\\\"+str(args.t) + \\\"_k\\\" + str(args.k) + \\\"_n\\\"+str(args.n) + \\\"_m\\\"+str(args.m)\\n + \\\"_c\\\"+str(args.c) + \\\"_\\\"+str(i) + \\\".dzn\\\")\\n create_dnz_file(args)\",\n \"def c_test_set_inp(self, param, value):\\r\\n ret = 1\\r\\n if \\\"__hash__\\\" not in dir(param): # param must be hashable\\r\\n ret = 0\\r\\n if self.verbosity > 0:\\r\\n print(\\\"ERROR: \\\" + param + \\\" is not hashable. It will be unable to be set in a dict.\\\")\\r\\n else:\\r\\n if self.verbosity > 1:\\r\\n print(param + \\\" is hashable.\\\")\\r\\n if param in [\\\"population_size\\\", \\\"time_constraint\\\", \\\"generations\\\", \\\"point_count\\\"]:\\r\\n if not ((isinstance(value, int) or\\r\\n isinstance(value, float) or\\r\\n isinstance(value, long))):\\r\\n if self.verbosity > 0:\\r\\n print(\\\"ERROR: \\\" + param + \\\" needs to be of a number. It is \\\" + str(value))\\r\\n ret = 0\\r\\n else:\\r\\n if self.verbosity > 1:\\r\\n print(param + \\\" is correctly set to a number.\\\")\\r\\n if value < 0:\\r\\n ret = 0\\r\\n if self.verbosity > 0:\\r\\n print(\\\"ERROR: \\\" + param + \\\" needs to be greater than zero.\\\")\\r\\n else:\\r\\n if self.verbosity > 1:\\r\\n print(param + \\\" is greater than zero.\\\")\\r\\n if param in [\\\"population_size\\\", \\\"generations\\\", \\\"point_count\\\"]:\\r\\n if not isinstance(value, int):\\r\\n ret = 0\\r\\n if self.verbosity > 0:\\r\\n print(\\\"ERROR: \\\" + param + \\\" needs to be an integer. It is \\\" + str(value))\\r\\n else:\\r\\n if self.verbosity > 1:\\r\\n print(param + \\\" is an integer.\\\")\\r\\n if param in [\\\"fitness_function\\\", \\\"population_function\\\",\\r\\n \\\"mutate_function\\\", \\\"cross_function\\\", \\\"weighting_bias\\\"]:\\r\\n if not callable(value):\\r\\n ret = 0\\r\\n if self.verbosity > 0:\\r\\n print(\\\"ERROR: \\\" + param + \\\" needs to be a callable function.\\\")\\r\\n else:\\r\\n if self.verbosity > 1:\\r\\n print(param + \\\" is a callable function.\\\")\\r\\n if param == \\\"end_condition\\\":\\r\\n if value not in [\\\"time_constraint\\\", \\\"generations\\\"]:\\r\\n ret = 0\\r\\n if self.verbosity > 0:\\r\\n print(\\\"ERROR: \\\" + param + \\\" needs to be 'time_constraint' or 'generations'\\\")\\r\\n else:\\r\\n if self.verbosity > 1:\\r\\n print(\\\"ERROR: \\\" + param + \\\" is a correct string.\\\")\\r\\n if param == \\\"seed\\\":\\r\\n if not (value is None or isinstance(value, int)):\\r\\n ret = 0\\r\\n if self.verbosity > 0:\\r\\n print(\\\"ERROR: \\\" + param + \\\" is incorrectly set.\\\")\\r\\n else:\\r\\n if self.verbosity > 1:\\r\\n print(param + \\\" is correctly set.\\\")\\r\\n return ret\",\n \"def coset_enumeration_c(fp_grp, Y):\\n # Initialize a coset table C for < X|R >\\n C = CosetTable(fp_grp, Y)\\n X = fp_grp.generators\\n R = fp_grp.relators()\\n A = C.A\\n # replace all the elements by cyclic reductions\\n R_cyc_red = [rel.identity_cyclic_reduction() for rel in R]\\n R_c = list(chain.from_iterable((rel.cyclic_conjugates(), (rel**-1).cyclic_conjugates()) \\\\\\n for rel in R_cyc_red))\\n R_set = set()\\n for conjugate in R_c:\\n R_set = R_set.union(conjugate)\\n # a list of subsets of R_c whose words start with \\\"x\\\".\\n R_c_list = []\\n for x in C.A:\\n r = set([word for word in R_set if word[0] == x])\\n R_c_list.append(r)\\n R_set.difference_update(r)\\n for w in Y:\\n C.scan_and_fill_f(0, w)\\n for x in A:\\n C.process_deductions(R_c_list[C.A_dict[x]], R_c_list[C.A_dict_inv[x]])\\n i = 0\\n while i < len(C.omega):\\n alpha = C.omega[i]\\n i += 1\\n for x in C.A:\\n if C.table[alpha][C.A_dict[x]] is None:\\n C.define_f(alpha, x)\\n C.process_deductions(R_c_list[C.A_dict[x]], R_c_list[C.A_dict_inv[x]])\\n return C\",\n \"def set():\",\n \"def setup_crubadan():\\n for n in [1,2,3,4,5,'word']:\\n for option in ['mi','pmi']:\\n get_matrix(datasource='crubadan', n=n, option=option)\",\n \"def gen_task_sets_simplegen(nsets):\\n start = time()\\n eval_task_sets = []\\n for util in utils:\\n task_sets = []\\n for i in range(nsets):\\n ts = synth.simplegen(i, util, implicit_deadlines=True)\\n synth.synth_c_pmf(ts, distribution_cls=lib.ExpExceedDist)\\n synth.set_fixed_priorities(ts)\\n task_sets.append(ts)\\n eval_task_sets.append(task_sets)\\n stop = time()\\n print('Task Set Generation SimpleGen: %.3fs' % (stop - start))\\n pickle.dump(eval_task_sets, open(task_sets_path + 'task_sets_simplegen', 'wb'))\",\n \"def create_sets(self,FD_SET=['FinalD'],VA_SET=['VA']):\\n \\n self.m.S = Set(initialize=self.sectors, doc='sectors')\\n self.m.P = Set(initialize=self.products, doc='sectors')\\n self.m.row = Set(initialize=self.products, doc='products')\\n self.m.col = Set(initialize=self.sectors+['FinalD'], doc='sectors and final demand')\\n \\n self.m.rROW = Set(initialize=self.countries,ordered=True, doc='regions including export')\\n self.m.R = Set(initialize=self.countries,ordered=True, doc='regions')\\n\\n self.m.fdemand = Set(initialize=FD_SET, doc='Final Demand')\\n\\n self.m.VA = Set(initialize=VA_SET, doc='value added')\",\n \"def c_test_set_inp(self, param, value):\\r\\n ret = 1\\r\\n if \\\"__hash__\\\" not in dir(param): # param must be hashable\\r\\n ret = 0\\r\\n if self.verbosity > 0:\\r\\n print(\\\"ERROR: \\\" + param + \\\" is not hashable. It will be unable to be set in a dict.\\\")\\r\\n else:\\r\\n if self.verbosity > 1:\\r\\n print(param + \\\" is hashable.\\\")\\r\\n if param in [\\\"population_size\\\", \\\"time_constraint\\\", \\\"generations\\\"]:\\r\\n if not ((isinstance(value, int) or\\r\\n isinstance(value, float) or\\r\\n isinstance(value, long))):\\r\\n if self.verbosity > 0:\\r\\n print(\\\"ERROR: \\\" + param + \\\" needs to be of a number. It is \\\" + str(value))\\r\\n ret = 0\\r\\n else:\\r\\n if self.verbosity > 1:\\r\\n print(param + \\\" is correctly set to a number.\\\")\\r\\n if value < 0:\\r\\n ret = 0\\r\\n if self.verbosity > 0:\\r\\n print(\\\"ERROR: \\\" + param + \\\" needs to be greater than zero.\\\")\\r\\n else:\\r\\n if self.verbosity > 1:\\r\\n print(param + \\\" is greater than zero.\\\")\\r\\n if param in [\\\"population_size\\\", \\\"generations\\\"]:\\r\\n if not isinstance(value, int):\\r\\n ret = 0\\r\\n if self.verbosity > 0:\\r\\n print(\\\"ERROR: \\\" + param + \\\" needs to be an integer. It is \\\" + str(value))\\r\\n else:\\r\\n if self.verbosity > 1:\\r\\n print(param + \\\" is an integer.\\\")\\r\\n if param in [\\\"fitness_function\\\", \\\"population_function\\\",\\r\\n \\\"mutate_function\\\", \\\"cross_function\\\", \\\"weighting_bias\\\"]:\\r\\n if not callable(value):\\r\\n ret = 0\\r\\n if self.verbosity > 0:\\r\\n print(\\\"ERROR: \\\" + param + \\\" needs to be a callable function.\\\")\\r\\n else:\\r\\n if self.verbosity > 1:\\r\\n print(param + \\\" is a callable function.\\\")\\r\\n if param == \\\"end_condition\\\":\\r\\n if value not in [\\\"time_constraint\\\", \\\"generations\\\"]:\\r\\n ret = 0\\r\\n if self.verbosity > 0:\\r\\n print(\\\"ERROR: \\\" + param + \\\" needs to be 'time_constraint' or 'generations'\\\")\\r\\n else:\\r\\n if self.verbosity > 1:\\r\\n print(\\\"ERROR: \\\" + param + \\\" is a correct string.\\\")\\r\\n if param == \\\"seed\\\":\\r\\n if not (value is None or isinstance(value, int)):\\r\\n ret = 0\\r\\n if self.verbosity > 0:\\r\\n print(\\\"ERROR: \\\" + param + \\\" is incorrectly set.\\\")\\r\\n else:\\r\\n if self.verbosity > 1:\\r\\n print(param + \\\" is correctly set.\\\")\\r\\n return ret\",\n \"def subset_gen(itemSet):\\n subsets = []\\n for i in range(1, len(itemSet)):\\n c = combinations(itemSet, r=i)\\n for cc in c:\\n subsets.append(set(cc))\\n return subsets\",\n \"def build(c):\",\n \"def sat_generate_candidate_assignments(self):\\n # YOUR CODE HERE\\n short = min(len(c) for c in self.clauses)\\n for c in self.clauses:\\n if len(c) == short:\\n return set(c.literals)\\n # return (set(x.literals) for x in self.clauses if len(x) == min(len(c) for c in self.clauses))\",\n \"def getSets(unique_name=None):\",\n \"def linear_set_generator(random, args):\\n bounder = args.get(\\\"_ec\\\").bounder\\n representation = args.get('representation')\\n max_size = args.get('max_size', 9)\\n variable_size = args.get('variable_size', True)\\n if variable_size:\\n size = random.randint(1, max_size)\\n else:\\n size = max_size\\n\\n indices = random.sample(range(len(representation)), size)\\n values = random.uniform(next(bounder.lower_bound), next(bounder.upper_bound), len(indices))\\n return OrderedDict({i: v for i, v in zip(indices, values)})\",\n \"def gen_task_sets_fairgen(nsets):\\n start = time()\\n eval_task_sets = []\\n for util in utils:\\n task_sets = []\\n for i in range(nsets):\\n ts = synth.mc_fairgen(set_id=i, u_lo=util, implicit_deadlines=True)\\n synth.synth_c_pmf(ts)\\n synth.set_fixed_priorities(ts)\\n task_sets.append(ts)\\n eval_task_sets.append(task_sets)\\n stop = time()\\n print('Task Set Generation MC-Fairgen: %.3fs' % (stop - start))\\n pickle.dump(eval_task_sets, open(task_sets_path + 'task_sets_fairgen', 'wb'))\",\n \"def make_set_cover_nr(gRNA_hits, num_sets = 1, target_ids = [], low_coverage_penalty = 0,\\n num_lengths_to_track = None, prioritise_3prime = False, optimal_depth = 5,\\n suppress_warning = False):\\n collapsed_grnas = gRNA_hits.collapse()\\n if not target_ids:\\n target_ids = set().union(*[set(cg) for cg in collapsed_grnas])\\n else:\\n target_ids = set(target_ids)\\n ## function to regenerate set cover solutions from collapsed_grna object\\n collapsed_grnas_original = collapsed_grnas.copy()\\n def generate_sc_solutions():\\n ## sort in order of smallest set cover size, smallest redundancy, and size of largest set in set cover\\n minweight_sc = limited_minweight_SC(collapsed_grnas, num_sets, targets = target_ids,\\n low_coverage_penalty = low_coverage_penalty,\\n num_lengths_to_track = num_lengths_to_track)\\n ## optimal solutions\\n max_depth = min(optimal_depth, max(map(len, minweight_sc)))\\n max_redundancy = max(map(lambda C:C.redundancy, minweight_sc))/len(target_ids)\\n print(max_depth, max_redundancy)\\n optimal_sc = limited_optimal_SC(target_ids, collapsed_grnas_original,\\n size = max_depth, redundancy = max_redundancy)\\n print(\\\"num unfiltered optimal sc:\\\", len(optimal_sc))\\n ## remove duplicates\\n optimal_sc = [C for C in optimal_sc\\n if all(map(lambda minweight_C:(len(C) != minweight_C\\n and C != minweight_C),\\n minweight_sc))]\\n print(\\\"num filtered optimal sc:\\\", len(optimal_sc))\\n return sorted(minweight_sc + optimal_sc,\\n key = lambda C:(len(C), C.redundancy, -C.max_coverage))\\n sc_solutions = []\\n sc_solutions.extend(generate_sc_solutions())\\n eliminated_grna = []\\n ## function to generate set covers\\n def make_set_cover(restore = []):\\n ## restore only works if gRNA belonged in the current set cover\\n curr_sc = sc_solutions[0]\\n for grna in restore:\\n curr_sc.add_grna(grna)\\n eliminated_grna.remove(grna)\\n ## if current set cover solution has at least one CollapsedgRNA with no gRNA left\\n while not curr_sc.all_not_empty():\\n sink = sc_solutions.pop(0) ## remove set cover solution\\n ## generate more possible gRNA sets if no pre-generated set covers are left\\n if not sc_solutions:\\n collapsed_grnas.remove_grna(*eliminated_grna)\\n collapsed_grnas.remove_empty()\\n sc_solutions.extend(generate_sc_solutions())\\n if not sc_solutions:\\n if not suppress_warning:\\n print((\\\"\\\\nError: The provided gRNA sequences cannot cover all\\\"\\n \\\" target sequences at least once.\\\\n\\\"))\\n return []\\n ## select next solution\\n curr_sc = sc_solutions[0]\\n ## consume=True -> remove selected gRNA from CollapsedgRNA\\n output = curr_sc.generate_grna_set(prioritise_3prime = prioritise_3prime, consume = True)\\n eliminated_grna.extend(output)\\n return output\\n return make_set_cover\",\n \"def build_set(ls, dsets):\\n\\n def noh(ls, dsets):\\n \\\"\\\"\\\"\\n This function remove hydrogens from the selection\\n \\\"\\\"\\\"\\n data_set = build_set(ls[1], dsets)\\n\\n noh_set = set()\\n pred = oechem.OEIsHydrogen()\\n\\n for idx in data_set:\\n atom = system.GetAtom(oechem.OEHasAtomIdx(idx))\\n if not pred(atom):\\n noh_set.add(idx)\\n\\n return noh_set\\n\\n def residues(ls):\\n \\\"\\\"\\\"\\n This function select residues based on the residue numbers. An example of\\n selection can be:\\n mask = 'resid A:16 17 19 B:1'\\n \\\"\\\"\\\"\\n # List residue atom index to be restrained\\n res_atom_set = set()\\n\\n # Dictionary of lists with the chain residues selected to be restrained\\n # e.g. {chainA:[res1, res15], chainB:[res19, res17]}\\n chain_dic = {'': []}\\n\\n # Fill out the chain dictionary\\n i = 0\\n while i < len(ls):\\n if ls[i].isdigit():\\n chain_dic[''].append(int(ls[i]))\\n i += 1\\n else:\\n try:\\n chain_dic[ls[i]].append(int(ls[i + 2]))\\n except:\\n chain_dic[ls[i]] = []\\n chain_dic[ls[i]].append(int(ls[i + 2]))\\n i += 3\\n\\n # Loop over the molecular system to select the atom indexes to be selected\\n hv = oechem.OEHierView(system, oechem.OEAssumption_BondedResidue + oechem.OEAssumption_ResPerceived)\\n for chain in hv.GetChains():\\n chain_id = chain.GetChainID()\\n if chain_id not in chain_dic:\\n continue\\n for frag in chain.GetFragments():\\n for hres in frag.GetResidues():\\n res_num = hres.GetOEResidue().GetResidueNumber()\\n if res_num not in chain_dic[chain_id]:\\n continue\\n for oe_at in hres.GetAtoms():\\n res_atom_set.add(oe_at.GetIdx())\\n\\n return res_atom_set\\n\\n def around(dist, ls):\\n \\\"\\\"\\\"\\n This function select atom not far than the threshold distance from\\n the current selection. The threshold distance is in Angstrom\\n\\n selection can be:\\n mask = '5.0 around ligand'\\n \\\"\\\"\\\"\\n # at = system.GetAtom(oechem.OEHasAtomIdx(idx))\\n\\n # Atom set selection\\n atom_set_around = set()\\n\\n # Create a OE bit vector mask for each atoms\\n bv_around = oechem.OEBitVector(system.GetMaxAtomIdx())\\n\\n # Set the mask atom\\n for at in system.GetAtoms():\\n if at.GetIdx() in ls:\\n bv_around.SetBitOn(at.GetIdx())\\n\\n # Predicate\\n pred = oechem.OEAtomIdxSelected(bv_around)\\n\\n # Create the system molecule based on the atom mask\\n molecules = oechem.OEMol()\\n oechem.OESubsetMol(molecules, system, pred)\\n\\n # Create the Nearest neighbours\\n nn = oechem.OENearestNbrs(system, float(dist))\\n\\n for nbrs in nn.GetNbrs(molecules):\\n for atom in oechem.OEGetResidueAtoms(nbrs.GetBgn()):\\n if atom.GetIdx() in ls:\\n continue\\n atom_set_around.add(atom.GetIdx())\\n\\n return atom_set_around\\n\\n # Start Body of the selection function by language\\n\\n # Terminal Literal return the related set\\n if isinstance(ls, str):\\n return dsets[ls]\\n # Not or Noh\\n if len(ls) == 2:\\n if ls[0] == 'noh': # Noh case\\n return noh(ls, dsets)\\n elif ls[0] == 'not': # Not case\\n return dsets['system'] - build_set(ls[1], dsets)\\n else: # Resid case with one index\\n return residues(ls[1])\\n\\n if len(ls) == 3:\\n if ls[1] == 'or': # Or Case (set union)\\n return build_set(ls[0], dsets) | build_set(ls[2], dsets)\\n elif ls[1] == 'and': # And Case (set intersection)\\n return build_set(ls[0], dsets) & build_set(ls[2], dsets)\\n elif ls[1] == 'diff': # Diff case (set difference)\\n return build_set(ls[0], dsets) - build_set(ls[2], dsets)\\n elif ls[1] == 'around': # Around case\\n return around(ls[0], build_set(ls[2], dsets))\\n else:\\n return residues(ls[1:]) # Resid case with one or two indexes\\n else:\\n if ls[0] == 'resid':\\n return residues(ls[1:]) # Resid case with multiple indexes\\n else:\\n raise ValueError(\\\"The passed list have too many tokens: {}\\\".format(ls))\",\n \"def multiple_chromosome_set_generator(random, args):\\n keys = args.get('keys')\\n candidate = MultipleChromosomeGenome(keys=keys)\\n for key in keys:\\n key_args = {\\n 'representation': args.get(\\\"%s_representation\\\" % key),\\n 'max_size': args.get(\\\"%s_max_size\\\" % key),\\n 'variable_size': args.get('variable_size')\\n }\\n candidate[key] = set_generator(random, key_args)\\n\\n return candidate\",\n \"def setta_cubi(lst):\\n for i in range(len(lst)):\\n lst[i] = lst[i]**3\",\n \"def __init__(self, parameter_set):\\n self.parameter_set = parameter_set.to_parameterization('Meijaard2007')\",\n \"def get_set(dim, maximum):\\n\\n i = 0\\n numbers = []\\n while i**2 <= maximum:\\n n = i**2\\n counter = 0\\n while n <= maximum and counter < dim:\\n numbers += [i**2]\\n n += i**2\\n counter += 1\\n i += 1\\n return numbers\",\n \"def J (self, n):\",\n \"def get_variable(self, InSet_g, InSet_l, InSet_gx, InSet_lx, InSet_a, InSet_size,\\n InSet_dim, InSet_area, InSet_c, BdSet_nlength, BdSet_nr, BdSet_nl, BdSet_ng):\\n self.InSet_size = InSet_size\\n self.InSet_dim = InSet_dim\\n self.InSet_area = InSet_area\\n self.BdSet_nlength = BdSet_nlength\\n\\n self.InSet_g = Parameter(\\n Tensor(InSet_g, mstype.float32), name=\\\"InSet_g\\\", requires_grad=False)\\n self.InSet_l = Parameter(\\n Tensor(InSet_l, mstype.float32), name=\\\"InSet_l\\\", requires_grad=False)\\n self.InSet_gx = Parameter(\\n Tensor(InSet_gx, mstype.float32), name=\\\"InSet_gx\\\", requires_grad=False)\\n self.InSet_lx = Parameter(\\n Tensor(InSet_lx, mstype.float32), name=\\\"InSet_lx\\\", requires_grad=False)\\n self.InSet_a = Parameter(\\n Tensor(InSet_a, mstype.float32), name=\\\"InSet_a\\\", requires_grad=False)\\n self.InSet_c = Parameter(\\n Tensor(InSet_c, mstype.float32), name=\\\"InSet_c\\\", requires_grad=False)\\n self.BdSet_nr = Parameter(\\n Tensor(BdSet_nr, mstype.float32), name=\\\"BdSet_nr\\\", requires_grad=False)\\n self.BdSet_nl = Parameter(\\n Tensor(BdSet_nl, mstype.float32), name=\\\"BdSet_nl\\\", requires_grad=False)\\n self.BdSet_ng = Parameter(\\n Tensor(BdSet_ng, mstype.float32), name=\\\"BdSet_ng\\\", requires_grad=False)\",\n \"def __init__(self, n):\\n self.n = n\\n self.e = [set() for i in range(n)]\",\n \"def build_optics(SetOfObjects, epsilon):\\n\\n for point in SetOfObjects._index:\\n if not SetOfObjects._processed[point]:\\n expandClusterOrder(SetOfObjects, point, epsilon)\",\n \"def powerset(n):\\n # chain r-combinations generator for r=0, 1,..., n\\n return chain.from_iterable(combinations(range(n), r) for r in range(n+1))\",\n \"def iter_vs_cnum(solver, n_range=None, cnum_range=None, verbose=0, \\\\\\n construct_args=dict(), solve_args=dict() ):\\n if n_range is None:\\n n_range = range(50, 501, 50)\\n if cnum_range is None:\\n cnum_range = [10**i for i in range(1,10)]\\n\\n n_mats = len(n_range) * len(cnum_range)\\n print('Evaluating %d matrices' % n_mats)\\n\\n results = dict()\\n\\n # Construct solver instance\\n solver_ = solver(A=None, b=None, full_output=1, **construct_args)\\n\\n for n in n_range:\\n results[n] = []\\n plt.figure()\\n plt.title('Size: %d by %d' % (n, n))\\n for cond_num in cnum_range:\\n if verbose: print('n: %d cnum: %d' % (n, cond_num))\\n\\n #A = util.psd_from_cond(cond_num=cond_num, n=n)\\n A = psd_from_cond(cond_num=cond_num, n=n)\\n x_true = 4 * np.random.randn(n)\\n b = np.dot(A, x_true)\\n\\n if verbose: print('Initial resid error: %f' % la.norm(b))\\n\\n\\n solver_.A, solver_.b = A, b\\n xopt, n_iter, resids, x_difs = solver_.solve(x_true=x_true, max_iter = n*2, \\\\\\n **solve_args)\\n results[n].append(n_iter)\\n\\n if verbose:\\n print('Final resid error: %f' % la.norm(b - np.dot(A, xopt)))\\n print('Took %d iter' % n_iter)\\n\\n return results\",\n \"def genTrainingSet(set_of_CSVs, file_to_classify, train_size = 5):\\n set_of_csvs_minus_target = copy.copy(set_of_CSVs)\\n # remove the file we want to classify\\n set_of_csvs_minus_target.remove(file_to_classify)\\n\\n # extract out the random noise files\\n # first, set the seed\\n random.seed(time.time())\\n # now sample\\n return_list = random.sample(set_of_csvs_minus_target, train_size)\\n return return_list\",\n \"def gen_small(s, n):\\n\\tdeg = n\\n\\tcoeff_vector = deg*[_sage_const_0 ]\\n\\tcoeff_vector[deg-_sage_const_1 ] = _sage_const_1 \\n\\tcoeff_vector[_sage_const_0 ] = _sage_const_1 \\n\\tindex_set = set({_sage_const_0 ,deg-_sage_const_1 })\\n\\tfor i in range(s-_sage_const_2 ):\\n\\t# add 1's\\n\\t\\twhile True:\\n\\t\\t\\tindex1 = ZZ.random_element(_sage_const_1 ,deg-_sage_const_1 )\\n\\t\\t\\tif not index1 in index_set:\\n\\t\\t\\t\\tcoeff_vector[index1] = _sage_const_1 \\n\\t\\t\\t\\tindex_set = index_set.union({index1})\\n\\t\\t\\t\\tbreak\\n\\t# add -1's\\n\\tfor i in range(s):\\n\\t\\twhile True:\\n\\t\\t\\tindex2 = ZZ.random_element(_sage_const_1 ,deg-_sage_const_1 )\\n\\t\\t\\tif not index2 in index_set:\\n\\t\\t\\t\\tcoeff_vector[index2] = -_sage_const_1 \\n\\t\\t\\t\\tindex_set = index_set.union({index2})\\n\\t\\t\\t\\tbreak\\n\\treturn coeff_vector\",\n \"def tst_random_set():\\n final_wave, final_spec, final_z = desi_qso_templates(\\n outfil='test_random_set.fits', N_perz=100, seed=12345)\",\n \"def allcombinations(orgset, k):\\n return itertools.chain(*[combination(orgset, i) for i in range(1, k + 1)])\",\n \"def apriori_gen(Ls):\\n Lks = Ls[len(Ls) -1] #L(k-1)\\n LLength = len(Ls)\\n Lc = combinations(Lks, r = LLength+1)\\n fs = frozenset([i for i in Lc])\\n\\n Ck =[] #L(k)\\n for s in fs:\\n ckItem = frozenset()\\n for ss in s:\\n ckItem = ckItem.union(ss)\\n if not has_infrequent_subset(ckItem, Lks):\\n Ck.append(ckItem)\\n\\n# print \\\"Ck:\\\",Ck\\n return Ck\",\n \"def _create_parameter_set_hashes(self):\\n self._parameter_set_hashes = []\\n for row in self._samples:\\n set_values_catenation = ''.join(repr(element) for element in row)\\n set_hash = hashlib.md5(set_values_catenation.encode('utf-8')).hexdigest()\\n self._parameter_set_hashes.append(set_hash)\",\n \"def build_subsets(self):\\n self.all = nrn.SectionList()\\n self.all.wholetree(sec=self.soma)\",\n \"def eclatGeneration(self, cList):\\n # to generate all\\n tidList = {}\\n key = list(cList.keys())\\n for i in range(0, len(key)):\\n nighbousItems=self.getNighboirItems(key[i])\\n for j in range(i + 1, len(key)):\\n # print(c[key[i]],c[key[j]])\\n if not key[j] in nighbousItems:\\n \\tcontinue\\n intersectionList = list(set(cList[key[i]]).intersection(set(cList[key[j]])))\\n itemList = []\\n itemList += key[i]\\n itemList += key[j]\\n if len(intersectionList) >= self.minSup:\\n itemList.sort()\\n if tuple(itemList) not in tidList:\\n tidList[tuple(set(itemList))] = intersectionList\\n return tidList\",\n \"def tallyPCSets(cmin):\\n theSlices = pickle.load( open ('pickles/quantizedChordDictSlices1015.pkl', 'rb') )\\n PCSets = collections.Counter()\\n for i, slicey in enumerate(theSlices):\\n #if i > 10:\\n #break\\n if slicey == ['start'] or slicey == ['end']:\\n continue\\n slicePC = set()\\n for mn in slicey['midi']:\\n slicePC.add(mn%12)\\n PCs = chord.Chord(slicePC).primeForm\\n if len(PCs) >= cmin:\\n PCSets[str(PCs)] += 1\\n #tally up the frequencies for each chord\\n sorted_PCSets = sorted(PCSets.iteritems(), key=operator.itemgetter(1), reverse=True)\\n #export the tally as a csv file\\n csvName = 'quantizedPCSets1015.csv'\\n x = csv.writer(open(csvName, 'wb'))\\n for pair in sorted_PCSets:\\n x.writerow([pair[0], pair[1]])\",\n \"def julia(c):\\n @nb.vectorize([nb.int32(nb.complex128)])\\n def f(z): \\n \\\"\\\"\\\"Function that takes a complex number and iterates \\n it until the condition abs(z) >=2 is reached.\\n \\n Args:\\n z: Complex Number\\n \\n Returns:\\n n: Number of iterations of function\\n \\\"\\\"\\\"\\n if abs(z) > 2:\\n return 1\\n else:\\n n = 1\\n while abs(z) < 2:\\n n += 1\\n if n > 100:\\n return 0\\n else:\\n z = z**2 + c\\n return n\\n return f\",\n \"def system(Vg, Cs, Cd, Cg, Gs, Gd, num_e):\\n myset = set.SET()\\n # choose between the two lines below for metallic dot or quantum levels\\n myset.add_metallic_dot('dot', num_e, -num_e, 0)\\n #myset.add_quantum_dot('dot', [0, 1, 2, 3], [1, 1, 1, 1])\\n myset.add_lead('source')\\n myset.add_lead('drain')\\n myset.add_gate('gate')\\n #myset.add_link('dl', 'dot', 'source', 5.5e-18, 0.2)\\n #myset.add_link('dl', 'dot', 'drain', 5.5e-18, 0.2)\\n myset.add_link('dl', 'dot', 'drain', Cd,Gd)\\n myset.add_link('dl', 'dot', 'source', Cs, Gs)\\n #myset.add_link('dl', 'dot', 'drain', 1e-19, 10e-8)\\n #myset.add_link('dl', 'dot', 'drain', Cg, Gg)\\n myset.add_link('dg', 'dot', 'gate', Cg)\\n return myset\",\n \"def sat_solve(self):\\n # YOUR CODE HERE\\n o = frozenset()\\n if self.isfalse:\\n return False\\n elif self.istrue:\\n return set()\\n l = self.generate_candidate_assignments()\\n print(\\\"assignments,\\\", l)\\n for i in l:\\n st = sat_apply_assignment(self, i)\\n print(\\\"i:\\\", i, \\\"new set\\\", st)\\n\\n if st.istrue:\\n return {i}\\n elif not st.isfalse:\\n sat_solve(st)\\n\\n return {i}\",\n \"def main(l, k):\\n S = 0\\n T = product(xrange(2), repeat=k)\\n for ts in T:\\n tmp = []\\n\\n for t, c in zip(ts, cs):\\n tmp.append(((-1)*c)**t)\\n\\n S += (sum(tmp)**l)\\n val = (sum(tmp)**l)\\n print val\\n return S / float(2**(k))\",\n \"def main(l, k):\\n S = 0\\n T = product(xrange(2), repeat=k)\\n for ts in T:\\n tmp = []\\n\\n for t, c in zip(ts, cs):\\n tmp.append(((-1)*c)**t)\\n\\n S += (sum(tmp)**l)\\n val = (sum(tmp)**l)\\n print val\\n return S / float(2**(k))\",\n \"def coset_reps(self):\\n from .all import SL2Z\\n N = self.level()\\n if N == 1: # P1List isn't very happy working modulo 1\\n yield SL2Z([1,0,0,1])\\n else:\\n for z in sage.modular.modsym.p1list.P1List(N):\\n yield SL2Z(lift_to_sl2z(z[0], z[1], N))\",\n \"def gen_pows(a,b):\\n s = set()\\n for x in range(2, a + 1):\\n for y in range(2, b + 1):\\n s.add(x**y)\\n return s\",\n \"def gen_sequence(a, b, c):\\n i = 1\\n while True:\\n yield a * i**2 + b * i + c\\n i += 1\",\n \"def generate_test_set_dqn(n_free_min, n_free_max, d_edge_min, d_edge_max, Omega_max, Phi_max, Lambda_max,\\n weighted, w_max, directed, size_test_set, to_torch=False):\\n\\n # Initialize the variables\\n Budget_max = Omega_max + Phi_max + Lambda_max\\n test_set = []\\n if to_torch:\\n test_set_torch = []\\n\\n print(\\\"==========================================================================\\\")\\n print(\\\"Generates the test set... \\\\n\\\")\\n\\n # for all budgets\\n for budget in tqdm(range(1, Budget_max + 1)):\\n # initialize the budget's instances list\\n test_set_budget = []\\n if to_torch:\\n test_set_budget_torch = []\\n for k in range(size_test_set):\\n # generate a random instance\\n instance_budget_k = generate_random_instance(\\n n_free_min,\\n n_free_max,\\n d_edge_min,\\n d_edge_max,\\n Omega_max,\\n Phi_max,\\n Lambda_max,\\n weighted=weighted,\\n w_max=w_max,\\n Budget_target=budget,\\n directed=directed,\\n )\\n G = instance_budget_k.G\\n Omega = instance_budget_k.Omega\\n Phi = instance_budget_k.Phi\\n Lambda = instance_budget_k.Lambda\\n J = instance_budget_k.J\\n # solve the instance\\n value, D, I, P = solve_mcn(G, Omega, Phi, Lambda, J=J, exact=True)\\n # save the value, P, D in the Instance object\\n instance_budget_k.value = value\\n instance_budget_k.D = D\\n instance_budget_k.I = I\\n instance_budget_k.P = P\\n # pushes it to memory\\n test_set_budget.append(instance_budget_k)\\n # if we want to save the corresponding InstanceTorch\\n # to evaluate the training, we stop at Budget_max - 1\\n if to_torch:\\n instance_budget_k_torch = instance_to_torch(instance_budget_k)\\n test_set_budget_torch.append(instance_budget_k_torch)\\n test_set.append(test_set_budget)\\n if to_torch:\\n test_set_torch.append(test_set_budget_torch)\\n\\n if not os.path.exists('data'):\\n os.mkdir('data')\\n folder_name = 'test_data'\\n if weighted:\\n folder_name += '_w'\\n if directed:\\n folder_name += '_dir'\\n path_test_data = os.path.join('data', folder_name)\\n if not os.path.exists(path_test_data):\\n os.mkdir(path_test_data)\\n # Save the test sets\\n file_path = os.path.join(path_test_data, \\\"test_set.gz\\\")\\n pickle.dump(test_set, open(file_path, \\\"wb\\\"))\\n if to_torch:\\n file_path_torch = os.path.join(path_test_data, \\\"test_set_torch.gz\\\")\\n pickle.dump(test_set_torch, open(file_path_torch, \\\"wb\\\"))\",\n \"def choose(self, g, c, p):\\n ng = copy.deepcopy(g)\\n ng[c[0]][c[1]] = p\\n return ng\",\n \"def compute(self, inputs, outputs):\\n\\n log.info(f\\\"Start optimisation iteration: {Rt.counter}\\\")\\n\\n # Update the geometry dictionary\\n for name, infos in Rt.geom_dict.items():\\n infos[1].append(inputs[name][0])\\n\\n if Rt.counter == 0:\\n\\n # For the first iteration, update the CPACS file from previous module\\n cpacs_in = Rt.modules[0].cpacs_in\\n cpacs_out = Rt.modules[0].cpacs_out\\n update_cpacs_file(cpacs_in, cpacs_out, Rt.geom_dict)\\n\\n else:\\n\\n for m, module in enumerate(Rt.modules):\\n\\n # Increment name of output CPACS file\\n Rt.modules[m].cpacs_out = Path(\\n Rt.modules[m].cpacs_out.parents[0],\\n \\\"iter_\\\" + str(Rt.counter).rjust(2, \\\"0\\\") + \\\".xml\\\",\\n )\\n\\n if m == 0:\\n\\n # Use output of the last module of the previous iteration as input\\n Rt.modules[m].cpacs_in = Rt.modules[-1].cpacs_out\\n\\n # Update the geometry of the CPACS file\\n cpacs_in = Rt.modules[m].cpacs_in\\n cpacs_out = Rt.modules[m].cpacs_out\\n update_cpacs_file(cpacs_in, cpacs_out, Rt.geom_dict)\\n\\n # # The first module of the loop must update its output where the input\\n # # was saved because it has been updated by \\\"update_cpacs_file\\\" function.\\n # Rt.modules[m].cpacs_in = Rt.modules[m].cpacs_out\\n\\n else:\\n\\n # Increment name of input CPACS file\\n Rt.modules[m].cpacs_in = Path(\\n Rt.modules[m].cpacs_in.parents[0],\\n \\\"iter_\\\" + str(Rt.counter).rjust(2, \\\"0\\\") + \\\".xml\\\",\\n )\\n\\n Rt.counter += 1\",\n \"def power(set_):\\n ensure_countable(set_)\\n\\n result = chain.from_iterable(combinations(set_, r)\\n for r in xrange(len(set_) + 1))\\n return _harmonize_subset_types(set_, result)\",\n \"def __init__(self, max_iteration=50, swarm_num=20, c1=2, c2=2, w_ini=0.9, w_end=0.4):\\r\\n self.__c1 = c1\\r\\n self.__c2 = c2\\r\\n self.__w_ini = w_ini\\r\\n self.__w_end = w_end\\r\\n self.__max_iteration = int(max_iteration)\\r\\n self.__swarm_num = int(swarm_num)\\r\\n np.random.seed(123456)\",\n \"def construct_powerset(feature_list):\\n\\n # Sort for easily reproducible order\\n feature_names = sorted(list(feature_list))\\n\\n feature_combinations = [list(x) for l in range(1, len(feature_names) +1) for x in combinations(feature_names, l)]\\n\\n return feature_combinations\",\n \"def generate_grna_sets(self, prioritise_3prime = False, set_num = 1, max_set_num = 1, manual = True):\\n collapsed_grna = sorted(self, key = lambda cg:(-len(cg), sorted(cg)))\\n ## copy the CollapsedgRNA objects so we can modify them without changing the original\\n collapsed_grna = [cg.copy() for cg in collapsed_grna]\\n output = []\\n while all(len(cg.gRNAs) > 0 for cg in collapsed_grna) and set_num <= max_set_num:\\n selected_grna = self._generate_grna_set(collapsed_grna, resort = False,\\n prioritise_3prime = prioritise_3prime)\\n if manual:\\n sorted_grna = sorted(selected_grna, key = lambda grna:grna.id)\\n usr_input = manual_check_prompt(sorted_grna, set_num = set_num)\\n if usr_input.upper() != 'X':\\n id_list = tuple(grna.id for grna in selected_grna)\\n seq_list = tuple(str(grna.seq).upper() for grna in selected_grna)\\n if usr_input in id_list:\\n index = id_list.index(usr_input)\\n collapsed_grna[index].remove(selected_grna[index])\\n elif usr_input.upper() in seq_list:\\n index = seq_list.index(usr_input)\\n collapsed_grna[index].remove(selected_grna[index])\\n else:\\n print(\\\"Invalid input.\\\")\\n continue ## skip removal of selected gRNA and yielding of selected gRNA set\\n ## remove selected gRNA from candidate pool\\n for i, grna in enumerate(selected_grna):\\n collapsed_grna[i].remove(grna)\\n set_num += 1\\n yield selected_grna\\n # return output\\n return\",\n \"def _iterCoordsets(self):\\n\\n for i in range(self._n_csets):\\n yield self._coords[i]\",\n \"def comb(set, n):\\n if len(set) < n:\\n raise Exception(\\\"Not enough elements\\\")\\n elif len(set) == n:\\n yield set\\n else:\\n setLen = len(set)\\n iters = [rangeIter(setLen - n + 1)]\\n values = [0] * n\\n values[0] = iters[0].next()\\n level = 1\\n while True:\\n # Fill array of iterators back up\\n while level < n:\\n iters.append(rangeIter(values[level - 1] + 1,\\n setLen - n + level + 1))\\n values[level]=iters[level].next()\\n level += 1\\n subset = [set[i] for i in values]\\n yield subset\\n while True:\\n try:\\n values[level - 1] = iters[level - 1].next()\\n break\\n except StopIteration:\\n iters.pop()\\n level -= 1\\n if level == 0:\\n # Top-level iterator is done, so we are too\\n raise StopIteration\",\n \"def generate_test():\\n o = []\\n pos = [384, 288]\\n note_group_size = GAN_PARAMS[\\\"note_group_size\\\"]\\n generate_set(begin=3 * note_group_size, start_pos=pos,\\n length_multiplier=dist_multiplier, group_id=3, plot_map=True)\",\n \"def __init__(self, qc: QComputer = None,\\n gate_set: GateSet = None,\\n qubits: List[int] = None,\\n optimizer: Union[str, GSTOptimizer] = 'linear_inverse',\\n **kwargs):\\n super().__init__(qc=qc, **kwargs)\\n self._qc: QComputer = qc\\n self._qubits: List[int] = qubits\\n self._gateset: GateSet = gate_set\\n self._shots: int = kwargs.get('shots', 8000)\\n self._optimizer: GSTOptimizer = None\\n self._initialize_optimizer(optimizer)\\n\\n # Number of working qubits. Notice that working qubits must count from 0 to the maximal index in the `qubits`\\n self._n = max(self._qubits) + 1 if self._qubits is not None else None\\n\\n # Store the gate set tomography results. Initialize to an empty dictionary\\n self._result = dict()\",\n \"def all_c(self):\\n for i in range( self.N, 0, -1):\\n self.C[i] = self.c(i)\\n self.ok = True\",\n \"def setC(self, c):\\n\\t\\tself.c = int(c)\",\n \"def reduce_set(cells: int, blocks: [int], uvars: [int], nbase: int):\\n combos = []\\n\\n if sum(blocks) + (len(blocks) - 1) > cells:\\n raise Exception(\\\"The passed block values exceeded the number of cells\\\")\\n\\n ogcombo = []\\n acc = 0\\n for block in blocks:\\n ogcombo.append(acc)\\n acc += block + 1\\n\\n combos.append(ogcombo)\\n\\n ccombo = ogcombo.copy()\\n\\n lookat = len(blocks) - 1\\n while lookat >= 0:\\n if blocks[-1] + ccombo[-1] < cells:\\n ccombo[lookat] = ccombo[lookat] + 1\\n s = ccombo[lookat] + blocks[lookat] + 1\\n for i in range(lookat + 1, len(blocks)):\\n ccombo[i] = s\\n s += blocks[i] + 1\\n lookat = len(blocks) - 1\\n combos.append(ccombo.copy())\\n else:\\n lookat -= 1\\n s = ccombo[lookat] + blocks[lookat] + 1\\n for i in range(lookat + 1, len(blocks)):\\n ccombo[i] = s\\n s += blocks[i] + 1\\n\\n cnf = CNF()\\n for combo in combos:\\n clause = [-v if in_combo(i, combo, blocks) else v for i, v in zip(range(cells), uvars)]\\n cnf.append(clause)\\n\\n return cnf.negate(nbase)\",\n \"def __init__(self):\\n self.EntireSet = []\",\n \"def digit_set(s):\\n t = np.zeros((10,1))\\n t[s] = 1.0\\n return t\",\n \"def _create_parameter_set_names(self):\\n self._parameter_set_names = {}\\n for number, set_hash in enumerate(self._parameter_set_hashes):\\n template = self.set_name_template\\n self._parameter_set_names[set_hash] = (template.substitute({'number': number}))\",\n \"def allcJPDs(net,cond_node,cond_states):\\r\\n \\r\\n onodes = net.Outputnodes()\\r\\n onodes = sorted(onodes)\\r\\n combinations = [i for i in itertools.combinations(onodes,2)]\\r\\n JPDdatas = [] \\r\\n progress = 0\\r\\n for cs in cond_states:\\r\\n JPDdata = []\\r\\n for c in combinations:\\r\\n print 'Progress: ' +str(int(100*float(progress)/float(len(combinations)*len(cond_states))))+ ' %'\\r\\n priorA = getCondPrior(net,c[0],cond_node,cs)\\r\\n print priorA\\r\\n priorB = getCondPrior(net,c[1],cond_node,cs) \\r\\n print priorB\\r\\n JPD = cJPD(net,c,cond_node,cs)\\r\\n cov = Covariance (priorA,priorB,JPD)\\r\\n cor = Correlation (priorA,priorB,cov) \\r\\n JPDdata.append([c[0],c[1],JPD,cov,cor])\\r\\n progress+=1\\r\\n JPDdatas.append(JPDdata)\\r\\n return JPDdatas\",\n \"def generate_train_data(comps: List[pd.DataFrame], concen_upper_bound=1000, num_per_combination=1000):\\n cps = [to_int_index(c) for c in comps]\\n cps = [zero_end_interpolation(c) for c in comps]\\n cps = alignment(cps)\\n cps = [scale_dataframe(c) for c in cps]\\n\\n samples = []\\n for n_class in range(1, len(cps) + 1):\\n comps_roller = ComponentRoller(cps, n_class)\\n concen_roller = ConcentrationRoller(1, concen_upper_bound, n_class)\\n for i in range(num_per_combination):\\n picked_comps, label = comps_roller.roll()\\n concen_vector = concen_roller.roll_unique(label)\\n the_sample = pd.Series(name=label, data=np.sum(picked_comps * concen_vector, axis=1))\\n samples.append(the_sample)\\n if i % 100 == 0:\\n print('组合数{}: 第{}个样本 --- 标签{}，浓度比{}'.format(n_class, i, label, concen_vector))\\n df = pd.concat(samples, axis=1)\\n return df.values.T, np.array(_to_vectors(df.columns.tolist()))\",\n \"def construct_combined_set(filenames, sensor_names, cnt_preprocessors,\\n marker_def, end_marker_def, trial_classes,\\n trial_start_offset_ms, trial_stop_offset_ms,\\n min_break_length_ms, max_break_length_ms,\\n break_start_offset_ms, break_stop_offset_ms,\\n last_set_split_trial, add_trial_breaks=True,\\n filename_to_extra_args=None):\\n default_args = deepcopy(locals())\\n sets = []\\n\\n if filename_to_extra_args is not None:\\n for filename_with_args in filename_to_extra_args:\\n assert filename_with_args in filenames\\n\\n for i_file, filename in enumerate(filenames):\\n this_args = copy(default_args)\\n if filename_to_extra_args is not None and (\\n filename in filename_to_extra_args):\\n for key in filename_to_extra_args[filename]:\\n assert key in this_args\\n this_args[key] = filename_to_extra_args[filename][key]\\n assert key != 'last_set_split_trial', \\\"Does not make sense :)\\\"\\n marker_segmenter = MarkerSegmenter(segment_ival=[\\n this_args['trial_start_offset_ms'], \\n this_args['trial_stop_offset_ms']],\\n marker_def=this_args['marker_def'],\\n trial_classes=this_args['trial_classes'],\\n end_marker_def=this_args['end_marker_def'])\\n trial_break_adder = AddTrialBreaks(min_length_ms=this_args['min_break_length_ms'],\\n max_length_ms=this_args['max_break_length_ms'], \\n start_offset_ms=this_args['break_start_offset_ms'], \\n stop_offset_ms=this_args['break_stop_offset_ms'],\\n start_marker_def=this_args['marker_def'],\\n end_marker_def=this_args['end_marker_def'])\\n if (i_file < len(filenames) - 1) or (\\n this_args['last_set_split_trial'] is None):\\n segmenters = [marker_segmenter,]\\n else:\\n segmenters = [marker_segmenter,\\n RestrictTrialRange(0,this_args['last_set_split_trial'])]\\n if this_args['add_trial_breaks']:\\n segmenters.append(trial_break_adder)\\n segmenter = PipelineSegmenter(segmenters)\\n cnt_set = SetWithMarkers(BBCIDataset(filename,\\n load_sensor_names=this_args['sensor_names']),\\n this_args['cnt_preprocessors'],\\n segmenter) \\n sets.append(cnt_set)\\n\\n # add last set last part as test set if you split apart last set\\n # we use that this_args is now from last set already\\n if last_set_split_trial is not None:\\n segmenters = [marker_segmenter,\\n RestrictTrialRange(last_set_split_trial,None),]\\n if this_args['add_trial_breaks']:\\n segmenters.append(trial_break_adder)\\n segmenter = PipelineSegmenter(segmenters)\\n cnt_set = SetWithMarkers(BBCIDataset(filenames[-1], # again last file needed\\n load_sensor_names=this_args['sensor_names']),\\n this_args['cnt_preprocessors'],\\n segmenter)\\n sets.append(cnt_set)\\n dataset = CombinedSet(sets)\\n return dataset\",\n \"def generate_with_antichain(set_sizes, n_sets, antichain_sizes, seed):\\n np.random.seed(seed)\\n\\n for n in set_sizes:\\n for k, size in zip(range(n_sets), antichain_sizes):\\n antichain_size = int(np.round(size*n))\\n # Generiramo nakljucni permutaciji (zadnjih antichain_size elementov predstavlja antiverigo, zato jih ni v permutaciji)\\n p1 = np.random.permutation(n-antichain_size)\\n p2 = np.random.permutation(n-antichain_size)\\n # Potrebujemo razlicni permutaciji, sicer imamo delno urejenost reda 1\\n while np.all(p1 == p2):\\n p2 = np.random.permutation(n-antichain_size)\\n # Slovarja, ki za vsako stevilo vrneta mesto v posamezni permutaciji\\n inverse1 = {i: p1[i] for i in range(n-antichain_size)}\\n inverse2 = {i: p2[i] for i in range(n-antichain_size)}\\n\\n # Gremo cez vse kombinacije, ce je eno stevilo manjse od drugega v obeh primerih, ju dodamo v delno urejenost\\n partial_order = []\\n for u in range(n-antichain_size):\\n for v in range(n-antichain_size):\\n if inverse1[u] < inverse1[v] and inverse2[u] < inverse2[v]:\\n partial_order.append((u, v))\\n\\n # Izpisemo delno urejenost v datoteko\\n f = open(f'../data/vhod_antichain_{n}_{k+1}.txt', 'w')\\n # Prva vrstica\\n f.write(f'{n} {len(partial_order)}\\\\n')\\n # Primerljivi elementi\\n for u, v in partial_order:\\n # Dodamo +1, da ni indeksiranja z 0\\n f.write(f'{u+1} {v+1}\\\\n')\\n f.close()\",\n \"def build_param_grid(self, C_list:list=[0.1, 1, 10, 100], gamma_list:list=[1, 0.1, 0.01, 0.001], kernel_list:list=['rbf']):\\n ans = {}\\n ans['C'] = C_list\\n ans['gamma'] = gamma_list\\n ans['kernel'] = kernel_list\\n self.param_grid = ans\\n return ans\",\n \"def ar_gen(frequentItemSets):\\n# print frequentItemSets\\n for fItemSet in frequentItemSets:\\n if fItemSet:\\n itemSets = fItemSet.keys()\\n for itemSet in itemSets:\\n subsets = subset_gen(itemSet)\\n# print itemSet\\n# print subsets\\n if subsets:\\n for subset in subsets:\\n sptSubSet = supportItemSet(subset, frequentItemSets)\\n sptSubSets = supportItemSet(itemSet, frequentItemSets)\\n print subset,'->', itemSet.difference(subset), 'confidence=',sptSubSets/float(sptSubSet)\",\n \"def create_sets(self,FD_SET=[],VA_SET=[]):\\n \\n self.m.S = Set(initialize=self.sectors, doc='sectors')\\n\\n if self.EORA is True:\\n self.m.rROW = Set(initialize=self.countries+['ROW'],ordered=True, doc='regions including export')\\n self.m.R = Set(initialize=self.countries+['ROW'],ordered=True, doc='regions')\\n else:\\n self.m.rROW = Set(initialize=self.countries,ordered=True, doc='regions including export')\\n self.m.R = Set(initialize=self.countries,ordered=True, doc='regions')\\n\\n if self.EORA is True:\\n self.m.fdemand = Set(initialize=['P3h', 'P3n','P3g', 'P51','P52','P53'], doc='Final Demand')\\n else:\\n self.m.fdemand = Set(initialize=self.fd_cat, doc='Final Demand')\\n\\n if self.EORA is True:\\n self.m.VA = Set(initialize=['VA'], doc='value added')\\n else:\\n self.m.VA = Set(initialize=VA_SET, doc='value added')\",\n \"def _generate_clfs(self):\\n clfs = []\\n for para_key in self.parameter[1]:\\n for value in self.parameter[1][para_key]:\\n clfs.append(classifierSearch.classifierSearch(self.parameter[0], [para_key, value]))\\n self._generate_results(self.parameter[0], para_key, value)\\n self.clfs = clfs\",\n \"def getSet(unique_name):\",\n \"def getSet(unique_name):\",\n \"def from_constant(st, highpass=0.08, lowpass=20.0):\\n for tr in st:\\n tr.setParameter(\\n \\\"corner_frequencies\\\",\\n {\\\"type\\\": \\\"constant\\\", \\\"highpass\\\": highpass, \\\"lowpass\\\": lowpass},\\n )\\n return st\",\n \"def from_constant(st, highpass=0.08, lowpass=20.0):\\n for tr in st:\\n tr.setParameter(\\n \\\"corner_frequencies\\\",\\n {\\\"type\\\": \\\"constant\\\", \\\"highpass\\\": highpass, \\\"lowpass\\\": lowpass},\\n )\\n return st\",\n \"def __init_cr(self,i,conv,C):\\n self.params['W'+i]=np.random.randn(conv[0],C,conv[1],conv[1])*self.weight_scale\\n self.params['b'+i]=np.zeros(conv[0]) \\n if self.use_batchnorm:\\n self.params['gamma'+i]=np.ones(conv[0])\\n self.params['beta'+i]=np.zeros(conv[0])\",\n \"def setIterations(self, value):\\n return self._set(nIterations=value)\",\n \"def set(self, opts, popsize=None, ccovfac=1, verbose=True):\\r\\n\\r\\n alpha_cc = 1.0 # cc-correction for mueff, was zero before\\r\\n\\r\\n def cone(df, mu, N, alphacov=2.0):\\r\\n \\\"\\\"\\\"rank one update learning rate, ``df`` is disregarded and obsolete, reduce alphacov on noisy problems, say to 0.5\\\"\\\"\\\"\\r\\n return alphacov / ((N + 1.3)**2 + mu)\\r\\n\\r\\n def cmu(df, mu, alphamu=0.0, alphacov=2.0):\\r\\n \\\"\\\"\\\"rank mu learning rate, disregarding the constrant cmu <= 1 - cone\\\"\\\"\\\"\\r\\n c = alphacov * (alphamu + mu - 2 + 1/mu) / ((N + 2)**2 + alphacov * mu / 2)\\r\\n # c = alphacov * (alphamu + mu - 2 + 1/mu) / (2 * (N + 2)**1.5 + alphacov * mu / 2)\\r\\n # print 'cmu =', c\\r\\n return c\\r\\n\\r\\n def conedf(df, mu, N):\\r\\n \\\"\\\"\\\"used for computing separable learning rate\\\"\\\"\\\"\\r\\n return 1. / (df + 2.*sqrt(df) + float(mu)/N)\\r\\n\\r\\n def cmudf(df, mu, alphamu):\\r\\n \\\"\\\"\\\"used for computing separable learning rate\\\"\\\"\\\"\\r\\n return (alphamu + mu - 2. + 1./mu) / (df + 4.*sqrt(df) + mu/2.)\\r\\n\\r\\n sp = self\\r\\n N = sp.N\\r\\n if popsize:\\r\\n opts.evalall({'N':N, 'popsize':popsize})\\r\\n else:\\r\\n popsize = opts.evalall({'N':N})['popsize'] # the default popsize is computed in Options()\\r\\n sp.popsize = popsize\\r\\n if opts['CMA_mirrors'] < 0.5:\\r\\n sp.lam_mirr = int(0.5 + opts['CMA_mirrors'] * popsize)\\r\\n elif opts['CMA_mirrors'] > 1:\\r\\n sp.lam_mirr = int(0.5 + opts['CMA_mirrors'])\\r\\n else:\\r\\n sp.lam_mirr = int(0.5 + 0.16 * min((popsize, 2 * N + 2)) + 0.29) # 0.158650... * popsize is optimal\\r\\n # lam = arange(2,22)\\r\\n # mirr = 0.16 + 0.29/lam\\r\\n # print(lam); print([int(0.5 + l) for l in mirr*lam])\\r\\n # [ 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21]\\r\\n # [1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 4]\\r\\n\\r\\n sp.mu_f = sp.popsize / 2.0 # float value of mu\\r\\n if opts['CMA_mu'] is not None:\\r\\n sp.mu_f = opts['CMA_mu']\\r\\n sp.mu = int(sp.mu_f + 0.499999) # round down for x.5\\r\\n # in principle we have mu_opt = popsize/2 + lam_mirr/2,\\r\\n # which means in particular weights should only be negative for q > 0.5+mirr_frac/2\\r\\n if sp.mu > sp.popsize - 2 * sp.lam_mirr + 1:\\r\\n print(\\\"WARNING: pairwise selection is not implemented, therefore \\\" +\\r\\n \\\" mu = %d > %d = %d - 2*%d + 1 = popsize - 2*mirr + 1 can produce a bias\\\" % (\\r\\n sp.mu, sp.popsize - 2 * sp.lam_mirr + 1, sp.popsize, sp.lam_mirr))\\r\\n if sp.lam_mirr > sp.popsize // 2:\\r\\n raise _Error(\\\"fraction of mirrors in the population as read from option CMA_mirrors cannot be larger 0.5, \\\" +\\r\\n \\\"theoretically optimal is 0.159\\\")\\r\\n sp.weights = log(max([sp.mu, sp.popsize / 2.0]) + 0.5) - log(1 + np.arange(sp.mu))\\r\\n if 11 < 3: # equal recombination weights\\r\\n sp.mu = sp.popsize // 4\\r\\n sp.weights = np.ones(sp.mu)\\r\\n print(sp.weights[:10])\\r\\n sp.weights /= sum(sp.weights)\\r\\n sp.mueff = 1 / sum(sp.weights**2)\\r\\n sp.cs = (sp.mueff + 2) / (N + sp.mueff + 3)\\r\\n # TODO: clean up (here the cumulation constant is shorter if sigma_vec is used)\\r\\n sp.dampsvec = opts['CMA_dampsvec_fac'] * (N + 2) if opts['CMA_dampsvec_fac'] else np.Inf\\r\\n sp.dampsvec_fading = opts['CMA_dampsvec_fade']\\r\\n if np.isfinite(sp.dampsvec):\\r\\n sp.cs = ((sp.mueff + 2) / (N + sp.mueff + 3))**0.5\\r\\n # sp.cs = (sp.mueff + 2) / (N + 1.5*sp.mueff + 1)\\r\\n sp.cc = (4 + alpha_cc * sp.mueff / N) / (N + 4 + alpha_cc * 2 * sp.mueff / N)\\r\\n sp.cc_sep = (1 + 1/N + alpha_cc * sp.mueff / N) / (N**0.5 + 1/N + alpha_cc * 2 * sp.mueff / N) # \\\\not\\\\gg\\\\cc\\r\\n sp.rankmualpha = opts['CMA_rankmualpha']\\r\\n # sp.rankmualpha = _evalOption(opts['CMA_rankmualpha'], 0.3)\\r\\n sp.c1 = ccovfac * min(1, sp.popsize/6) * cone((N**2 + N) / 2, sp.mueff, N) # 2. / ((N+1.3)**2 + sp.mucov)\\r\\n sp.c1_sep = ccovfac * conedf(N, sp.mueff, N)\\r\\n if 11 < 3:\\r\\n sp.c1 = 0.\\r\\n print('c1 is zero')\\r\\n if opts['CMA_rankmu'] != 0: # also empty\\r\\n sp.cmu = min(1 - sp.c1, ccovfac * cmu((N**2+N)/2, sp.mueff, sp.rankmualpha))\\r\\n sp.cmu_sep = min(1 - sp.c1_sep, ccovfac * cmudf(N, sp.mueff, sp.rankmualpha))\\r\\n else:\\r\\n sp.cmu = sp.cmu_sep = 0\\r\\n\\r\\n sp.neg = BlancClass()\\r\\n if opts['CMA_active']:\\r\\n # in principle we have mu_opt = popsize/2 + lam_mirr/2,\\r\\n # which means in particular weights should only be negative for q > 0.5+mirr_frac/2\\r\\n sp.neg.mu_f = popsize - (popsize + sp.lam_mirr) / 2 if popsize > 2 else 1\\r\\n sp.neg.weights = log(sp.mu_f + 0.5) - log(1 + np.arange(sp.popsize - int(sp.neg.mu_f), sp.popsize))\\r\\n sp.neg.mu = len(sp.neg.weights) # maybe never useful?\\r\\n sp.neg.weights /= sum(sp.neg.weights)\\r\\n sp.neg.mueff = 1 / sum(sp.neg.weights**2)\\r\\n sp.neg.cmuexp = opts['CMA_activefac'] * 0.25 * sp.neg.mueff / ((N+2)**1.5 + 2 * sp.neg.mueff)\\r\\n assert sp.neg.mu >= sp.lam_mirr # not really necessary\\r\\n # sp.neg.minresidualvariance = 0.66 # not it use, keep at least 0.66 in all directions, small popsize is most critical\\r\\n else:\\r\\n sp.neg.cmuexp = 0\\r\\n\\r\\n sp.CMA_on = sp.c1 + sp.cmu > 0\\r\\n # print(sp.c1_sep / sp.cc_sep)\\r\\n\\r\\n if not opts['CMA_on'] and opts['CMA_on'] not in (None,[],(),''):\\r\\n sp.CMA_on = False\\r\\n # sp.c1 = sp.cmu = sp.c1_sep = sp.cmu_sep = 0\\r\\n\\r\\n sp.damps = opts['CMA_dampfac'] * (0.5 +\\r\\n 0.5 * min([1, (sp.lam_mirr/(0.159*sp.popsize) - 1)**2])**1 +\\r\\n 2 * max([0, ((sp.mueff-1) / (N+1))**0.5 - 1]) + sp.cs\\r\\n )\\r\\n if 11 < 3:\\r\\n # this is worse than damps = 1 + sp.cs for the (1,10000)-ES on 40D parabolic ridge\\r\\n sp.damps = 0.3 + 2 * max([sp.mueff/sp.popsize, ((sp.mueff-1)/(N+1))**0.5 - 1]) + sp.cs\\r\\n if 11 < 3:\\r\\n # this does not work for lambda = 4*N^2 on the parabolic ridge\\r\\n sp.damps = opts['CMA_dampfac'] * (2 - 0*sp.lam_mirr/sp.popsize) * sp.mueff/sp.popsize + 0.3 + sp.cs # nicer future setting\\r\\n print('damps =', sp.damps)\\r\\n if 11 < 3:\\r\\n sp.damps = 10 * sp.damps # 1e99 # (1 + 2*max(0,sqrt((sp.mueff-1)/(N+1))-1)) + sp.cs;\\r\\n # sp.damps = 20 # 1. + 20 * sp.cs**-1 # 1e99 # (1 + 2*max(0,sqrt((sp.mueff-1)/(N+1))-1)) + sp.cs;\\r\\n print('damps is %f' % (sp.damps))\\r\\n\\r\\n sp.cmean = float(opts['CMA_cmean'])\\r\\n # sp.kappa = 1 # 4-D, lam=16, rank1, kappa < 4 does not influence convergence rate\\r\\n # in larger dim it does, 15-D with defaults, kappa=8 factor 2\\r\\n if sp.cmean != 1:\\r\\n print(' cmean = %f' % (sp.cmean))\\r\\n\\r\\n if verbose:\\r\\n if not sp.CMA_on:\\r\\n print('covariance matrix adaptation turned off')\\r\\n if opts['CMA_mu'] != None:\\r\\n print('mu = %f' % (sp.mu_f))\\r\\n\\r\\n # return self # the constructor returns itself\\r\",\n \"def set_c_values(self,nu=3,xd=2,a12sq=1,a13sq=1,a1=0,b1=0,p1=0,\\n vx=0,vy=0,vz=0,epsilon=0.,omega=1.5*np.pi,\\n a2=np.pi,b2=0*np.pi,p2=0.5*np.pi,silent=False):\\n \\n c_target = np.zeros(18)\\n c_target[0] = nu*self.sigma0_RG \\n xd_abs = xd*self.sigma2_RG\\n c_target[4:10] = set_fGij(xd_abs,a12sq,a13sq,a1,b1,p1)\\n c_target[10:13] = np.array([vx,vy,vz])\\n c_target[13:18] = set_VGij(epsilon,omega,a2,b2,p2)\\n \\n if silent == False:\\n print (\\\"Constrain peak parameters: \\\")\\n if 'f0' in self.CONS or 'full' in self.CONS: \\n print (\\\"f0: \\\",\\\"nu = %.1f\\\"%nu, \\\"$\\\\sigma_0$\\\")\\n if 'f1' in self.CONS or 'full' in self.CONS: \\n print (\\\"f1: \\\",\\\"f1,x = f1,y = f1,z = 0\\\")\\n if 'f2' in self.CONS or 'full' in self.CONS: \\n print (\\\"f2: \\\",r\\\"xd = {:.1f} $\\\\sigma_2$, a12sq = {:.1f}, a13sq = {:.1f},a1={:.2f}, b1={:.2f}, p1={:.2f}\\\".format(xd,a12sq,a13sq,a1,b1,p1))\\n if 'vx' in self.CONS or 'full' in self.CONS: \\n print (\\\"vx = {:.1f} km/s\\\".format(vx)) \\n if 'vy' in self.CONS or 'full' in self.CONS: \\n print (\\\"vy = {:.1f} km/s\\\".format(vy))\\n if 'vz' in self.CONS or 'full' in self.CONS: \\n print (\\\"vz = {:.1f} km/s\\\".format(vz))\\n if 'TG' in self.CONS or 'full' in self.CONS: \\n print (\\\"TG: \\\",\\\"epsilon = {:.1f} km/s/Mpc, omega = {:.2f}, a2={:.2f}, b2={:.2f}, p2={:.2f}\\\".format(epsilon,omega,a2,b2,p2))\\n \\n return c_target[self.cmask]\",\n \"def run(self, dag):\\n # Initiate the commutation set\\n self.property_set['commutation_set'] = defaultdict(list)\\n\\n # Build a dictionary to keep track of the gates on each qubit\\n # The key with format (wire_name) will store the lists of commutation sets\\n # The key with format (node, wire_name) will store the index of the commutation set\\n # on the wire with wire_name, thus, for example:\\n # self.property_set['commutation_set'][wire_name][(node, wire_name)] will give the\\n # commutation set that contains node.\\n\\n for wire in dag.wires:\\n wire_name = \\\"{0}[{1}]\\\".format(str(wire.register.name), str(wire.index))\\n self.property_set['commutation_set'][wire_name] = []\\n\\n # Add edges to the dictionary for each qubit\\n for node in dag.topological_op_nodes():\\n for (_, _, edge_data) in dag.edges(node):\\n\\n edge_name = edge_data['name']\\n self.property_set['commutation_set'][(node, edge_name)] = -1\\n\\n # Construct the commutation set\\n for wire in dag.wires:\\n wire_name = \\\"{0}[{1}]\\\".format(str(wire.register.name), str(wire.index))\\n\\n for current_gate in dag.nodes_on_wire(wire):\\n\\n current_comm_set = self.property_set['commutation_set'][wire_name]\\n if not current_comm_set:\\n current_comm_set.append([current_gate])\\n\\n if current_gate not in current_comm_set[-1]:\\n prev_gate = current_comm_set[-1][-1]\\n does_commute = False\\n try:\\n does_commute = _commute(current_gate, prev_gate, self.cache)\\n except TranspilerError:\\n pass\\n if does_commute:\\n current_comm_set[-1].append(current_gate)\\n\\n else:\\n current_comm_set.append([current_gate])\\n\\n temp_len = len(current_comm_set)\\n self.property_set['commutation_set'][(current_gate, wire_name)] = temp_len - 1\",\n \"def optimize_coding_multi(latent_values, real_coding, loss_weight, epochs, \\n mse, generator, optimizer, ntimes, ninput):\\n for epoch in range(epochs):\\n opt_step(latent_values, real_coding, loss_weight, \\n mse, generator, optimizer, ntimes, ninput)\\n\\n return latent_values\",\n \"def __init__(self, trainset, k=3):\\n self._trainset = trainset\\n self.k = k\",\n \"def value_iteration(iterations):\\n for _ in range(0, iterations):\\n for i in range(0, 3):\\n for j in range(0, 4):\\n # We don't need values for start/end/non-reachable states\\n if [i, j] == [1, 1] or [i, j] == [0, 3] or [i, j] == [1, 3]:\\n None\\n else:\\n value([i, j])\\n\\n print(utilities)\",\n \"def generator(nodeSet, serviceSet, fixedSpeed=False):\\r\\n seed(cfg_sys.RANDOM_SEED)\\r\\n\\r\\n userSet = {i: {'alias': nodeSet[i]['alias'], 'reqSet': {}} for i in nodeSet if\\r\\n nodeSet[i]['typee'] == cfg_sys.TYPEE_CL_USER}\\r\\n\\r\\n nameList = ['req' + str(i) for i in range(cfg_sys.NUM_REQ)]\\r\\n\\r\\n serviceList = choice(list(serviceSet), size=cfg_sys.NUM_REQ)\\r\\n minSpeedList = [\\r\\n serviceSet[serviceName]['speed'][0]\\r\\n for serviceName in serviceList\\r\\n ]\\r\\n maxSpeedList = [\\r\\n serviceSet[serviceName]['speed'][0 if fixedSpeed else 1]\\r\\n for serviceName in serviceList\\r\\n ]\\r\\n speedList = uniform(low=minSpeedList, high=maxSpeedList, size=cfg_sys.NUM_REQ)\\r\\n\\r\\n durationList = choice(cfg_sys.DURATION, size=cfg_sys.NUM_REQ)\\r\\n\\r\\n reqSet = {i: req(j, k, l) for i, j, k, l in zip(nameList, serviceList, speedList, durationList)}\\r\\n for i in reqSet:\\r\\n user = choice(list(userSet))\\r\\n userSet[user]['reqSet'][i] = reqSet[i]\\r\\n\\r\\n dcSet = {\\r\\n i: {'alias': nodeSet[i]['alias'], 'serviceList': []}\\r\\n for i in nodeSet if nodeSet[i]['typee'] == cfg_sys.TYPEE_DC\\r\\n }\\r\\n for i in serviceSet:\\r\\n minServiceNum = min([len(dc['serviceList']) for dc in dcSet.values()])\\r\\n dc = choice([\\r\\n dcIp\\r\\n for dcIp, dc in dcSet.items()\\r\\n if len(dc['serviceList']) == minServiceNum\\r\\n ])\\r\\n dcSet[dc]['serviceList'].append(i)\\r\\n logger.info('generation complete: userSet: %s, dcSet: %s', str(userSet), str(dcSet))\\r\\n return userSet, dcSet\",\n \"def iteration(self):\\n T = self.generate_T()\\n R = self.reproduce(T)\\n self.P = self.choose_mi_best(R)\\n #print(self.P)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6113237","0.585974","0.5496323","0.5448815","0.5381841","0.53499573","0.53280026","0.5263938","0.52438045","0.5203017","0.51715815","0.5160977","0.511507","0.5103073","0.50933886","0.5076919","0.5047488","0.5038411","0.5017469","0.5008726","0.5005838","0.4994687","0.49811196","0.49719745","0.49531356","0.49492118","0.49442413","0.49364695","0.49295363","0.4907604","0.49073878","0.49029753","0.48804983","0.48772624","0.48575422","0.4856447","0.48555377","0.48496976","0.48480207","0.4840337","0.4836593","0.48348328","0.4832677","0.48148108","0.4806019","0.47969908","0.47966787","0.4796667","0.47671315","0.4758836","0.47572798","0.47506183","0.47503203","0.47496343","0.47477037","0.47437367","0.47386235","0.47386235","0.47292477","0.47207558","0.47197208","0.47165716","0.47156525","0.47145635","0.47137296","0.4712812","0.47106838","0.4706152","0.47012383","0.46927404","0.46920398","0.46902162","0.4686945","0.46837708","0.46810076","0.46730432","0.4663639","0.46604618","0.46572876","0.46545994","0.46536577","0.46487582","0.4644803","0.4639055","0.46384478","0.46376497","0.46327305","0.46327305","0.4630236","0.4630236","0.4629976","0.4629488","0.4624206","0.46208653","0.46182126","0.4617906","0.4612852","0.4612269","0.46075928","0.4595801"],"string":"[\n \"0.6113237\",\n \"0.585974\",\n \"0.5496323\",\n \"0.5448815\",\n \"0.5381841\",\n \"0.53499573\",\n \"0.53280026\",\n \"0.5263938\",\n \"0.52438045\",\n \"0.5203017\",\n \"0.51715815\",\n \"0.5160977\",\n \"0.511507\",\n \"0.5103073\",\n \"0.50933886\",\n \"0.5076919\",\n \"0.5047488\",\n \"0.5038411\",\n \"0.5017469\",\n \"0.5008726\",\n \"0.5005838\",\n \"0.4994687\",\n \"0.49811196\",\n \"0.49719745\",\n \"0.49531356\",\n \"0.49492118\",\n \"0.49442413\",\n \"0.49364695\",\n \"0.49295363\",\n \"0.4907604\",\n \"0.49073878\",\n \"0.49029753\",\n \"0.48804983\",\n \"0.48772624\",\n \"0.48575422\",\n \"0.4856447\",\n \"0.48555377\",\n \"0.48496976\",\n \"0.48480207\",\n \"0.4840337\",\n \"0.4836593\",\n \"0.48348328\",\n \"0.4832677\",\n \"0.48148108\",\n \"0.4806019\",\n \"0.47969908\",\n \"0.47966787\",\n \"0.4796667\",\n \"0.47671315\",\n \"0.4758836\",\n \"0.47572798\",\n \"0.47506183\",\n \"0.47503203\",\n \"0.47496343\",\n \"0.47477037\",\n \"0.47437367\",\n \"0.47386235\",\n \"0.47386235\",\n \"0.47292477\",\n \"0.47207558\",\n \"0.47197208\",\n \"0.47165716\",\n \"0.47156525\",\n \"0.47145635\",\n \"0.47137296\",\n \"0.4712812\",\n \"0.47106838\",\n \"0.4706152\",\n \"0.47012383\",\n \"0.46927404\",\n \"0.46920398\",\n \"0.46902162\",\n \"0.4686945\",\n \"0.46837708\",\n \"0.46810076\",\n \"0.46730432\",\n \"0.4663639\",\n \"0.46604618\",\n \"0.46572876\",\n \"0.46545994\",\n \"0.46536577\",\n \"0.46487582\",\n \"0.4644803\",\n \"0.4639055\",\n \"0.46384478\",\n \"0.46376497\",\n \"0.46327305\",\n \"0.46327305\",\n \"0.4630236\",\n \"0.4630236\",\n \"0.4629976\",\n \"0.4629488\",\n \"0.4624206\",\n \"0.46208653\",\n \"0.46182126\",\n \"0.4617906\",\n \"0.4612852\",\n \"0.4612269\",\n \"0.46075928\",\n \"0.4595801\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6562868"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":81187,"cells":{"query":{"kind":"string","value":"Print tabseparated sequence of things to line in file"},"document":{"kind":"string","value":"def tabout(things, file=sys.stdout):\n print(\"\\t\".join([str(x) for x in things]), file=file)\n file.flush()"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def tab(self):\n self._write('\\t')","def output_tab_delimited(s1, s2, i1, i2):\n _out.write(\"%s\\t%s\\t%d\\t%d\\n\" % (s1, s2, i1, i2))\n _out.write(\"%s\\t%s\\t%d\\t%d\\n\" % (s2, s1, i2, i1))","def print_tsv(data, filename):\n with open(filename, 'wt') as fout:\n writefile = partial(print, sep='\\t', file=fout)\n writefile('Sample', *expected_header)\n for sample in data:\n for entry in data[sample]:\n writefile(sample, *(entry[field] for field in expected_header))","def print_table(seqids, data, outputfile, separator='\\t'):\n\n tags = data.keys()\n with open(outputfile, 'w') as out:\n out.write(separator.join([\"#Sequence ID\"] + list(tags)) + \"\\n\")\n for s in seqids:\n out.write(s)\n for t in tags:\n out.write(\"{}{}\".format(separator, data[t].get(s, \"\")))\n out.write(\"\\n\")","def fromtab(args):\n p = OptionParser(fromtab.__doc__)\n p.set_sep(sep=None)\n p.add_option(\n \"--noheader\", default=False, action=\"store_true\", help=\"Ignore first line\"\n )\n p.add_option(\"--replace\", help=\"Replace spaces in name to char\")\n opts, args = p.parse_args(args)\n\n if len(args) != 2:\n sys.exit(not p.print_help())\n\n tabfile, fastafile = args\n sep = opts.sep\n replace = opts.replace\n fp = must_open(tabfile)\n fw = must_open(fastafile, \"w\")\n nseq = 0\n if opts.noheader:\n next(fp)\n for row in fp:\n row = row.strip()\n if not row or row[0] == \"#\":\n continue\n\n name, seq = row.rsplit(sep, 1)\n if replace:\n name = name.replace(\" \", replace)\n print(\">{0}\\n{1}\".format(name, seq), file=fw)\n nseq += 1\n fw.close()\n\n logging.debug(\"A total of {0} sequences written to `{1}`.\".format(nseq, fastafile))","def writeTabDelimited (self, path='BACKFILLER_TABDELIMITED.txt'):\n lines = [];add=lines.append\n header = ['pid','doi','award_ids']\n add (header)\n for rec in self.data:\n if len (rec.award_ids) > 0:\n add ([rec['pid'], rec['doi'], ','.join (rec.award_ids)])\n # fp = codecs.open (path, 'w')\n fp = open (path, 'w')\n fp.write ('\\n'.join (map (lambda x:'\\t'.join(x), lines)))\n fp.close()\n # print '{} records written to {}'.format(len(self.data), path)\n print 'Data written to {}'.format(path)","def tab(self):\n self.write(self.ASCII_TAB)\n self._column = (self._column + 4) & 0b11111100","def tprint(array, sep=' ', end='\\n', file=sys.stdout, **tformat_kwargs):\n print(tformat(array, **tformat_kwargs), sep=sep, end=end, file=file)","def fa2tab(filename):\n from Bio import SeqIO\n fo = open(filename,\"r\")\n fout = open(filename+\".tab\",\"w\")\n for seq in SeqIO.parse(fo, \"fasta\"):\n SeqIO.write(seq,fout,\"tab\")\n fo.close()\n fout.close()","def tab_delim_table(self):\n self.generate()\n\n header = ' \\t '.join([r'{: ^7}'.format(col) for col in self.columns])\n lines = []\n for row in self.rows:\n bits = []\n for col in self.columns:\n if col in self.formatters:\n bits.append(self.formatters[col].format(row[col]))\n else:\n bits.append(self.formatters.get(col, '{: ^7}').format(row[col] if row[col] else ''))\n lines.append(' \\t '.join(bits))\n\n return \"{}\\n{}\".format(header, '\\n'.join(lines))","def print_f(msg, i, t):\n if i == t-1:\n sys.stdout.write('\\n')\n return\n t += 1\n i += 1\n msg += '\\t'\n sys.stdout.write('\\r')\n sys.stdout.write(\"%s%s%% |%s\" % (msg, int(i % t), int(i % t) * '#'))\n sys.stdout.flush()","def Table(self, line):\n if line is None:\n # TODO(user): Use resource_printer.TablePrinter() when it lands.\n if self._rows:\n cols = len(self._rows[0])\n width = [0 for _ in range(cols)]\n for row in self._rows:\n for i in range(cols - 1):\n w = len(row[i])\n if width[i] <= w:\n width[i] = w + 1\n for row in self._rows:\n self._out.write(' ' * (self._indent[self._level] + 2))\n for i in range(cols - 1):\n self._out.write(row[i].ljust(width[i]))\n self._out.write(row[-1] + '\\n')\n self._rows = []\n self._table = False\n self._out.write('\\n')\n elif not self._table:\n self._table = True\n self.Line()\n else:\n self._rows.append(line.split(','))","def add_tab(self):\n if(self.tab):\n return '\\t'\n return ''","def asTabString(self):\n dl = [self.pid, self.start, self.snomed, self.name[:20]]\n s = \"\"\n for v in dl:\n s += \"%s\\t\"%v \n return s[0:-1] # Throw away the last tab","def asTabString(self):\n dl = [self.pid, self.date, self.snomed, self.name[:20]]\n s = \"\"\n for v in dl:\n s += \"%s\\t\"%v \n return s[0:-1] # Throw away the last tab","def __openRow(fd):\n\n print(\" <tr>\", file=fd)","def print_seq(self):\n names, values = [], []\n for each in self.minions:\n names.append(each.name)\n values.append(f'{each.atk}/{each.dfs}')\n t = PrettyTable()\n t.add_row(names)\n t.add_row(values)\n print(t)","def get_tabs(n) -> str:\n res = ''\n for i in range(n):\n res += '\\t'\n return res","def print_table(table):\r\n print('/-----------------------------------------------------------------------------------\\\\')\r\n for item in table:\r\n\r\n while len(item[1]) <= 22:\r\n item[1] += ' '\r\n\r\n while len(item[2]) <= 27:\r\n item[2] += ' '\r\n\r\n while len(item[0]) <= 15:\r\n item[0] += ' '\r\n\r\n print('| '+item[0]+' | '+item[1]+'| '+item[2]+' |')\r\n\r\n print('\\\\-----------------------------------------------------------------------------------/')","def exon_line_print(temp_line, trx_exons, parent, ftype):\n for ex in trx_exons:\n temp_line[2] = ftype\n temp_line[3] = str(ex[0])\n temp_line[4] = str(ex[1])\n temp_line[8] = 'Parent='+parent\n print '\\t'.join(temp_line)","def make_table_file(lines, labels, dir_path, filename):\r\n lines.sort()\r\n lines.insert(0, '\\t'.join(labels))\r\n\r\n output = open(os.path.join(dir_path, filename), 'w')\r\n output.write('\\n'.join(lines))\r\n output.close()","def print_table(table):\n rest = table[1:]\n fmt = \"%-28s %-9s %-16s %s\"\n for row in rest:\n print(fmt % tuple(row))","def print_tabulated_output(array_obj, headers):\n print()\n print(tabulate(array_obj, headers=headers))\n print()","def print_table(table):\n for row in table:\n print(row)","def print_table(table):\n for row in table:\n print(row)","def tab_printer(args):\n args = vars(args)\n keys = sorted(args.keys())\n tab = Texttable()\n tab.add_rows([[\"Parameter\", \"Value\"]])\n tab.add_rows([[k.replace(\"_\", \" \").capitalize(), args[k]] for k in keys])\n print(tab.draw())","def print_table(table):\n for i in range(len(table)):\n print \"Row \", i, \"\\t\",\n for j in range(len(table[i])):\n print table[i][j],\n print \"\\n\"","def print_list(things_to_print, prefix=\"\\t\", stream=sys.stdout):\n for item in things_to_print:\n print(f\"{prefix}{item}\", file=stream)","def tprint(msg, indent=0):\n\n print(\" \" * indent + \n \" \" * (indent+1) + \n \"'-- \" + msg)","def print_table(table):\n for row in table:\n print(row)","def print_table(table):\n # transpose the table:\n table = map(list, zip(*table))\n # get the column width:\n col_width = [max(len(str(x)) for x in col) for col in zip(*table)]\n # print it to screen:\n print\n for line in table:\n print \"| \" + \" | \".join(\"{:{}}\".format(x, col_width[i]) for i, x in enumerate(line)) + \" |\"\n print","def capture_print(t, end='\\n'):\n lines.append(t + end)","def print_table_row(package):\n\n row = '{} \\t {} \\t {} \\t {} \\t {} \\t {} \\t {} \\t {} \\t {}'.format(\n package.name.ljust(40),\n str(package.is_metapackage).ljust(10),\n str(package.has_test).ljust(10),\n str(package.execution_status).ljust(10),\n str(package.summary.total).ljust(10),\n str(package.summary.errors).ljust(10),\n str(package.summary.failures).ljust(10),\n str(package.summary.skipped).ljust(10),\n (str(package.coverage) + '%').ljust(10),\n )\n\n print(row) \n\n if args.output:\n with open(args.output, 'a') as f:\n f.write(row + '\\n')","def print_alignment_to_file(alig, fileName):\n with open(fileName, 'w') as f:\n for row in alig:\n print(''.join(map(str, row)), file=f)","def untabify(line, tabwidth):\n\tassert tabwidth > 0\n\tbody = list()\n\ti = 0\n\twhile i < len(line) and line[i] == '\\t':\n\t\ti += 1\n\tindent = '\\t' * i\n\twhile i < len(line):\n\t\tif line[i] == '\\t':\n\t\t\tskip = tabwidth - (len(body) % tabwidth)\n\t\t\tbody.extend(' ' for i in range(skip))\n\t\telse:\n\t\t\tbody.append(line[i])\n\t\ti += 1\n\treturn indent + ''.join(body)","def tab2_to_txt(self,Nom_sortie,tab):\n fiche=open(Nom_sortie,'w', encoding='utf-8')\n taille=self.Get_max_lg(tab)\n out=[f\"{row[0]:{taille[0]}};{row[1]}\" for row in tab]\n fiche.writelines('\\n'.join(out))\n fiche.close()","def write_tsv(self, filename):\n f = open(filename,'wb')\n wr = csv.writer(f,delimiter='\\t',quoting=csv.QUOTE_ALL)\n colrow = []\n for col in self.cols:\n colrow.append('<undefined>' if len(col) == 0 else unicode(iter(col).next()).encode('unicode-escape'))\n wr.writerow(colrow)\n for row in self.data:\n strrow = []\n for cell in row:\n strrow.append('' if cell is None else unicode(cell).encode('unicode-escape'))\n wr.writerow(strrow)\n f.close()","def _write_tsv(\n output_path,\n records,\n):\n df = pandas.DataFrame(records)\n output_path.write_text(df.to_csv(index=False, sep='\\t'))","def tab_printer(args):\n args = vars(args)\n keys = sorted(args.keys())\n t = Texttable() \n t.add_rows([[\"Parameter\", \"Value\"]] + [[k.replace(\"_\", \" \").capitalize(), args[k]] for k in keys])\n print(t.draw())","def tab_printer(args):\n args = vars(args)\n keys = sorted(args.keys())\n t = Texttable() \n t.add_rows([[\"Parameter\", \"Value\"]] + [[k.replace(\"_\",\" \").capitalize(), args[k]] for k in keys])\n print(t.draw())","def write_torque_table(A, filename):\n f = open(filename, 'w')\n for row in range(np.size(A, axis=0)):\n A[row,:].tofile(f, sep=',')\n f.write('\\n')\n f.close()","def tab_printer(args):\n args = vars(args)\n keys = sorted(args.keys())\n t = Texttable() \n t.add_rows([[\"Parameter\", \"Value\"]] + [[k.replace(\"_\",\" \").capitalize(),args[k]] for k in keys])\n print(t.draw())","def tab_printer(args):\n args = vars(args)\n keys = sorted(args.keys())\n t = Texttable() \n t.add_rows([[\"Parameter\", \"Value\"]] + [[k.replace(\"_\",\" \").capitalize(),args[k]] for k in keys])\n print(t.draw())","def print_row():\n print('| | |')","def print_table(table):\n for row in table:\n # Header column left justified\n print(\"{:<19}\".format(row[0]), end='')\n # Remaining columns right justified\n for col in row[1:]:\n print(\"{:>4}\".format(col), end='')\n print(\"\", end='\\n')","def test_tabify(self):\n before_b = \"\"\"\\\n first line\n line 1\n line a\n line b\n line c\n last line\n \"\"\"\n after_b = \"\"\"\\\n first line\n line 1\n \tline a\n \t\tline b\n line c\n last line\n \"\"\"\n self.run_test(\n before_b=before_b,\n after_b=after_b,\n before_sel=(\"1.0\", \"7.0\"),\n after_sel=(\"7.0\", \"7.0\"),\n command_name=\"tabify\",\n )","def basic_print(lista):\n for item in lista:\n print(\"{} \\t\\t {}\".format(item[0], item[1]))","def mostrar_tablero(mtx, n):\n # Cabecera de Columnas\n fila = \"/ |\"\n for i in range(n):\n fila = fila + \" \" + chr(65+i)\n print fila\n print \"-\"*(2*n+3)\n # Cabecera de Filas\n for i in range(n):\n fila = str(i+1)\n if i < 9 : fila += \" |\"\n else:\n fila+=\"|\"\n for e in range(n):\n fila = fila+\" \"+mtx[i][e]\n print fila\n fila = \"\"\n # Nueva linea\n print \"\"","def catch_tabs(self):\n lnum = 1\n for line in self.text:\n cnum = line.find(\"\\t\")\n if 0 <= cnum:\n self.errmsg(\"TAB detected in input. Please use spaces.\",\n pos=(lnum,cnum))\n lnum += 1","def create_output_file(arr):\r\n for i in arr:\r\n output_file.write(f'{i[0]}\\t{i[1]}\\n')","def writeseq(afile, seq):\n print(seq, file=afile)","def print_table2(df, eval_dir):\n\n out_file = os.path.join(eval_dir, 'table2.txt')\n\n\n with open(out_file, \"w\") as text_file:\n\n for idx, struc_name in enumerate(['LV', 'RV', 'Myo']):\n # new line\n header_string = ' & '\n line_string = '({}) '.format(struc_name)\n\n for p_idx, phase in enumerate(['ED', 'ES']):\n for measure in ['dice', 'assd', 'hd']:\n\n header_string += ' & {} ({}) '.format(phase, measure)\n\n dat = df.loc[(df['phase'] == phase) & (df['struc'] == struc_name)]\n\n if measure == 'dice':\n\n line_string += ' & {:.3f}\\,({:.3f}) '.format(np.mean(dat[measure]), np.std(dat[measure]))\n else:\n line_string += ' & {:.2f}\\,({:.2f}) '.format(np.mean(dat[measure]), np.std(dat[measure]))\n\n if p_idx == 0:\n header_string += ' & '\n line_string += ' & '\n\n header_string += ' \\\\\\\\ \\n'\n line_string += ' \\\\\\\\ \\n'\n\n if idx == 0:\n text_file.write(header_string)\n\n text_file.write(line_string)\n\n return 0","def _get_staff_report_tab_delimited(self):\n stringio = io.StringIO()\n writer = csv.writer(stringio, delimiter='\\t',\n quoting=csv.QUOTE_MINIMAL)\n for record in self._get_staff_report_data():\n writer.writerow(record)\n return stringio.getvalue()","def write_table(self, tab):\n self.save_text()\n\n table = list()\n row = list()\n headers = tab['c'][3]\n if headers:\n has_content = False\n for col in headers:\n self.list_parse(col, cell_content=True)\n cell_content = self.get_content()\n row.append(cell_content)\n if cell_content != '':\n has_content = True\n if has_content:\n row = tuple(row)\n table.append(row)\n t_content = tab['c'][4]\n for line in t_content:\n row = list()\n for col in line:\n self.list_parse(col, cell_content=True)\n cell_content = self.get_content()\n row.append(cell_content)\n row = tuple(row)\n table.append(row)\n table = tuple(table)\n self.tables.append((table, (self.context, self.ancestor)))","def print_table(headers, rows):\n try:\n if headers:\n print('\\n')\n print(tabulate.tabulate(\n rows, headers=headers,\n tablefmt=\"plain\", numalign=\"left\"\n ))\n print('\\n')\n except Exception as e:\n print(e.message)","def output(*args):\n print(*args, end='', file=file)","def dibujar_tablero(tablero):\n for fila in tablero:\n print(\"|\", \"|\".join(fila), \"|\", sep=\"\")\n print(\"\")","def pretty_print(self):\n pt = PrettyTable()\n for i in self.files_summary:\n pt.field_names = [\"File Name\", \"Classes\", \"Functions\", \"Lines\", \"Characters\"]\n pt.add_row(list([i, self.files_summary[i][\"class\"], self.files_summary[i][\"function\"], self.files_summary[i][\"line\"], self.files_summary[i][\"char\"]]))\n print(pt) #Using a Print statement here because i tried to return self.pt and it didnt give me anything but the print works","def print_row():\n print_line()","def write_values_to_tsv(self, values, out_file):\n with open(out_file, \"w\") as f:\n for row in values:\n f.write(\"\\t\".join([cell.strip().replace(\"\\n\", \"|\").replace(\"\\r\", \"\")\n for cell in row]))\n f.write(os.linesep)","def print_table(data):\n for key in sorted(data):\n print \"%s: %s\" % (key.rjust(16), data[key])","def lines_printed_to(file):\n with io.open(file, 'w') as fp:\n def write_line(s=\"\"):\n PRINT(s, file=fp)\n yield write_line","def __print_work_table(table):\n print \"%-5s %-30s %5s %5s %5s %5s %5s\" % ('Act', 'Pred', 'Block', 'Dummy', 'Succ', 'start', 'end')\n for k, col in sorted(table.items()):\n print \"%-5s %-30s %5s %5s %5s %5s %5s\" % tuple(\n [str(k)] + [list(col[0])] + [str(col[i]) for i in range(1, len(col))])","def print_lines(f1, f2, row):\n column = col_match(f1, f2)\n print('{}c{}'.format(row, column))\n print(f1)\n print('---')\n print(f2)","def print_row():\n print('| | | | |')","def tprint(self, cmd, end='\\n'):\n if ENABLE_DEBUG:\n stackIndex = 0\n for index, stackFrame in enumerate(stack()):\n caller = getframeinfo(stackFrame[0])\n if caller.filename == fullPath:\n stackIndex = index\n break \n caller = getframeinfo(stack()[stackIndex][0])\n self.fileHandle.write(\"# \" + targetFile + \":\" + str(caller.lineno) + '\\n')\n self.tprint_raw(cmd, end)","def tabing_tool(code):\n for i, line in enumerate(code):\n code[i] = ' '*4 + line\n return code","def write_tsv_fast(self, filename):\n # TODO (without quotation marks)\n with open(filename, 'wb') as f:\n colnames = ['<undefined>' if len(col) == 0 else unicode(iter(col).next()).encode('unicode-escape') for col in self.cols]\n f.write('\\t'.join(colnames)+'\\n')\n for row in self.data:\n f.write('\\t'.join(['' if cell is None else unicode(cell).encode('unicode-escape') for cell in row])+'\\n')","def print_tables(self):\n print \"------------------\\nTables\\n------------------\"\n cnt = 0\n for x in self.show_tables():\n cnt += 1\n print (\"{0}.) {1}\".format(cnt, x[0]))","def print_table(self, table):\n raise NotImplementedError('print_table method not defined!')","def tsv_generator(file):\n for line in fileinput.input(file):\n article, summary = line.strip().split(\"\\t\")\n yield (article, summary)","def tabular_print(files_dict: dict):\r\n # create a list of file extensions\r\n file_extensions = []\r\n for filename in files_dict.keys():\r\n for file_ext in files_dict[filename].keys():\r\n # print(\"debug:::\", file_ext)\r\n file_extensions.append(file_ext)\r\n break\r\n # go through all the files and print them in a table with the file extension as the top row\r\n sep_line_len = 40 + 10 * len(file_extensions) # separator line length = max_filename_len [35] + 10*number of ext\r\n # print the first row\r\n print(\"filename\".ljust(40), end='')\r\n for ext in file_extensions:\r\n print(\"|\" + ext.center(9), end='')\r\n print()\r\n print(''.center(sep_line_len, '='))\r\n # print the rest of the files\r\n for filename, ext_dict in files_dict.items():\r\n print(filename.ljust(40), end='')\r\n for ext in ext_dict.keys():\r\n if ext_dict[ext]:\r\n print(\"|\" + \"V\".center(9), end='')\r\n else:\r\n print(\"|\" + \" \".center(9), end='')\r\n print()\r\n print(''.center(sep_line_len, '-'))","def print_table(outbuf, table, align):\n if len(table) == 0:\n return\n\n colwidths = None\n for row in table:\n if colwidths is None:\n colwidths = [len(x) for x in row]\n else:\n colwidths = [max(colwidths[i], len(x)) for i, x in enumerate(row)]\n\n for row in table:\n cells = []\n for i, cell in enumerate(row):\n padding = ' ' * (colwidths[i] - len(cell))\n if align[i] == 'r':\n cell = padding + cell\n elif i < len(row) - 1:\n # Do not pad the final column if left-aligned.\n cell += padding\n cells.append(cell.encode('utf-8', 'replace'))\n try:\n outbuf.write(b' '.join(cells) + b'\\n')\n except IOError: # pragma: no cover\n # Can happen on Windows if the pipe is closed early.\n pass","def print_tracks(td, _sep=' '):\r\n track_strings = []\r\n for t in td:\r\n track_strings.append(t.songStr())\r\n print(*track_strings, sep=_sep)","def write_tsv(labels, positions, elec_file):\n labels = labels.reshape(-1, order='F')\n positions = positions.reshape(-1, 3, order='F')\n\n elec_file = elec_file.with_suffix('.tsv')\n with elec_file.open('w') as f:\n f.write('name\\tx\\ty\\tz\\n')\n for i in range(labels.shape[0]):\n f.write(f'{labels[i]}\\t{positions[i, 0]:.3f}\\t{positions[i, 1]:.3f}\\t{positions[i, 2]:.3f}\\n')","def dump_tsv(file_name, data, header=None, append=False):\n mode = \"a\" if append else \"w\"\n with open(file_name, mode) as f:\n print(\"tsv file created:\", file_name)\n if header:\n f.write(\"\\t\".join(header) + \"\\n\")\n for line in data:\n f.write(\"\\t\".join([str(d) for d in line]) + \"\\n\")","def print_table(listx):\r\n\tfor lists in listx:\r\n\t\tfor i in lists:\r\n\t\t\tprint str(i) , '\\t',\r\n\t\tprint()","def _tab_newline_replace( self, fromlines, tolines ):\n\t\tdef expand_tabs( line ):\n\t\t\t# hide real spaces\n\t\t\tline = line.replace( ' ', '\\0' )\n\t\t\t# expand tabs into spaces\n\t\t\tline = line.expandtabs( self._tabsize )\n\t\t\t# relace spaces from expanded tabs back into tab characters\n\t\t\t# (we'll replace them with markup after we do differencing)\n\t\t\tline = line.replace( ' ', '\\t' )\n\t\t\treturn line.replace( '\\0', ' ' ).rstrip( '\\n' )\n\t\tfromlines = [expand_tabs( line ) for line in fromlines]\n\t\ttolines = [expand_tabs( line ) for line in tolines]\n\t\treturn fromlines, tolines","def write_tp_rows():\n \n basename = sys.argv[1]\n ops = (\"two_opt\", \"twoh_opt\", \"three_opt\", \"three_opt_broad\", \"swap\", \"swap_adj\")\n lengths = (6, 7, 8, 9, 10)\n for op in ops:\n for length in lengths:\n filename = os.path.join(basename,\n \"tsp_length_%d_%s\" % (length, op),\n \"TP_row0.dat\")\n print op, length\n x = tsp.get_tm_first_row(length, move=op)\n np.savetxt(filename, x)","def printt(content, flag=False):\n if flag:\n print(content)","def table_print(csv_header, csv_body, csv_footer):\n s = '\\t'.join(csv_header)\n print(s)\n\n for r in csv_body:\n s = '\\t'.join(map(str, r))\n print(s)\n\n s = '\\t'.join(map(str, csv_footer))\n print(s)","def generate_tsv_gen(rows, le='\\n'):\n\theader_string = None\n\t\n\tfor row in rows:\n\t\theaders = []\n\t\tfields = []\n\t\t\n\t\tfor header, value in row.items():\n\t\t\tif(header_string is None):\n\t\t\t\theaders.append(header)\n\t\t\t\n\t\t\tif(not isinstance(value, basestring)):\n\t\t\t\tvalue = str(value)\n\t\t\tfields.append(value)\n\t\t\n\t\tif(header_string is None):\n\t\t\theader_string = '\\t'.join(headers) + le;\n\t\t\tyield header_string\n\t\tyield '\\t'.join(fields) + le;","def print_output_tables(cls,\n wfns=None, file=None,\n print_intensities=True,\n print_energies=True,\n print_energy_corrections=True,\n print_transition_moments=True,\n operators=None,\n logger=None, sep_char=\"=\", sep_len=100):\n\n if logger is None:\n logger = wfns.logger\n if logger is not None:\n def print_block(label, *args, **kwargs):\n with logger.block(tag=label):\n logger.log_print(\" \".join(\"{}\".format(x) for x in args), **kwargs)\n else:\n if file is None:\n file = sys.stdout\n\n def print_label(label, file=file, **opts):\n lablen = len(label) + 2\n split_l = int(np.floor((sep_len - lablen) / 2))\n split_r = int(np.ceil((sep_len - lablen) / 2))\n print(sep_char * split_l, label, sep_char * split_r, **opts, file=file)\n\n def print_footer(label=None, file=file, **opts):\n print(sep_char * sep_len, **opts, file=file)\n\n def print_block(label, *args, file=file, **kwargs):\n print_label(label, file=file, **kwargs)\n print(*args, file=file, **kwargs)\n print_footer(file=file, **kwargs)\n\n if print_energy_corrections:\n print_block(\"Energy Corrections\", wfns.format_energy_corrections_table())\n if print_energies:\n if wfns.degenerate_transformation is not None:\n print_block(\"Deperturbed Energies\",\n wfns.format_deperturbed_energies_table()\n )\n print_block(\n \"Degenerate Energies\",\n wfns.format_energies_table()\n )\n else:\n print_block(\"States Energies\",\n wfns.format_energies_table()\n )\n\n if print_intensities:\n ints = wfns.intensities # to make sure they're computed before printing starts\n if print_transition_moments:\n if wfns.degenerate_transformation is not None:\n for a, m in zip([\"X\", \"Y\", \"Z\"], wfns.format_deperturbed_dipole_contribs_tables()):\n print_block(\"{} Deperturbed Dipole Contributions\".format(a), m)\n\n print_block(\"Deperturbed IR Data\",\n wfns.format_deperturbed_intensities_table()\n )\n\n for a, m in zip([\"X\", \"Y\", \"Z\"], wfns.format_dipole_contribs_tables()):\n print_block(\"{} Dipole Contributions\".format(a), m)\n print_block(\"IR Data\", wfns.format_intensities_table())\n\n if operators is not None:\n print_block(\"Operator Data\", wfns.format_operator_table(operators))","def boldOutput(file, line):\r\n # Write header to output files (executes only one time per run)\r\n global output_header\r\n if output_header is False:\r\n for temp in (args.outfile1, args.outfile2):\r\n with io.open(args.outdir2+\"/\"+temp, mode=\"a\", encoding=\"utf-8\") as f:\r\n for key, value in line.items():\r\n f.write('%s\\t' % (key))\r\n f.write(\"\\n\")\r\n output_header = True\r\n\r\n # Write sequence data, for each record\r\n with io.open(args.outdir2+\"/\"+file, mode=\"a\", encoding=\"utf-8\") as f:\r\n for key, value in line.items():\r\n f.write('%s\\t' % (value))\r\n f.write(\"\\n\")","def insert_tab(self, value=None):\n self.my_text.insert(INSERT, \" \" * 3)","def tprint(msg):\n sys.stdout.write(msg + '\\n')\n sys.stdout.flush()","def tprint(msg):\n sys.stdout.write(msg + '\\n')\n sys.stdout.flush()","def tabser(filename, body, data):\n # XXX checksums ignored\n head = Struct(\"!BiHBxxxB\")\n body = Struct(body)\n # foot = Struct(\"!4s\")\n\n buffer = bytearray([0] * (2 ** 16))\n head.pack_into(buffer, 0, 0, int(time()), len(data), body.size, 0),\n offset = head.size\n for row in data:\n body.pack_into(buffer, offset, *row, 0)\n offset += body.size\n else:\n print(\"write %d rows\" % len(data))\n # offset = 2 ** 16 - foot.size\n # foot.pack_into(buffer, offset, bytes([0, 0, 0, 0]))\n with open(filename, \"wb\") as f:\n f.write(buffer)","def writeTable(table, filename):\n with open(filename, \"w\") as output:\n writer = csv.writer(output, lineterminator='\\n')\n writer.writerows(table)","def printer(end,message):\n\n sys.stdout.write('\\r'+message+'\\t')\n sys.stdout.flush()\n if end: sys.stdout.write('\\n')","def write(self,fout,line):\n \n def trans(a,m):\n if a == '0': return m[0]+'\\t'+m[0]\n if a == '1': return m[0]+'\\t'+m[1]\n if a == '2': return m[1]+'\\t'+m[1]\n return '0\\t0'\n\n if not line: return\n animal = line[0]\n if animal in self.ped:\n father,mother = self.ped[animal]['father'],self.ped[animal]['mother']\n sex = self.ped[animal]['sex']\n phe = self.ped[animal]['phe']\n family = self.ped[animal]['family'][0]\n else:\n father,mother,sex,phe,family = '0','0','3','-9','0'\n if len(self.mark['marklist']) > 0:\n lmark = self.mark['marklist']\n fout.write('%s\\t%s\\t%s\\t%s\\t%s\\t%s\\t%s\\n' % (family,animal,father,mother,sex,phe,\n '\\t'.join([trans(line[i+1],self.mark[name]['a1']+self.mark[name]['a2']) for i,name in enumerate(lmark)])))\n else:\n fout.write('%s\\t%s\\t%s\\t%s\\t%s\\t%s\\t%s\\n' % (family,animal,father,mother,sex,phe,'\\t'.join(line[1:])))","def print_table(hdrs, flag=False, data=[],fmt='psql'):\n\tres = cur.fetchall()\n\tif flag:\n\t\tres = data\n\tprint(tabulate(res, headers=hdrs, tablefmt=fmt))","def to_tsv(obj: ConfiguredBaseModel, file: str) -> str:\n\n # Extract headers and rows from object\n if isinstance(obj, Entity):\n headers = obj.dict().keys()\n rows = [list(obj.dict().values())]\n elif isinstance(obj, (AssociationCountList, HistoPheno, Results)):\n if not obj.items:\n headers = get_headers_from_obj(obj)\n rows = []\n else:\n headers = obj.items[0].dict().keys()\n rows = [list(item.dict().values()) for item in obj.items]\n else:\n raise TypeError(FMT_INPUT_ERROR_MSG)\n\n fh = open(file, \"w\") if file else sys.stdout\n writer = csv.writer(fh, delimiter=\"\\t\")\n writer.writerow(headers)\n for row in rows:\n writer.writerow(list(row))\n if file:\n fh.close()\n console.print(f\"\\nOutput written to {file}\\n\")\n\n return","def raw_out(self):\n\t\treturn '\\t'.join(self.raw_out_tab)","def _write_tsv(fname, dictionary, overwrite=False, verbose=False):\n if op.exists(fname) and not overwrite:\n raise FileExistsError(f'\"{fname}\" already exists. '\n 'Please set overwrite to True.')\n _to_tsv(dictionary, fname)\n\n if verbose:\n print(os.linesep + f\"Writing '{fname}'...\" + os.linesep)\n print(_tsv_to_str(dictionary))","def print_taboo_spaces(warehouse_id):\n problem_file = \"./warehouses/warehouse_{:02d}.txt\".format(warehouse_id)\n wh = Warehouse()\n wh.load_warehouse(problem_file)\n print(wh)\n print(\"TABOO CELLS: \")\n taboo = taboo_cells(wh)\n print(taboo)","def print_table(rows, labels=None):\n if labels is None:\n labels = ROW_LABELS\n\n output_table = prettytable.PrettyTable()\n output_table.field_names = labels\n output_table.align = 'l'\n output_table.vrules = prettytable.prettytable.ALL\n output_table.hrules = prettytable.prettytable.HEADER\n\n for row in rows:\n row = [x.strip() for x in row]\n output_table.add_row(row)\n\n print output_table\n print ''","def print_table(table, db_file):\n \n try:\n conn, c = connect_to_db(db_file)\n rows = c.execute('SELECT * FROM {t}'.format(t=safe(table))).fetchall()\n cols = c.execute(\"PRAGMA table_info({t})\".format(t=safe(table))).fetchall()\n conn.close()\n pstring = '\\nTABLE ' + table + '\\n'\n r = 1\n for row in rows:\n pstring += '\\nROW ' + str(r)\n for i in range(len(cols)):\n pstring += '\\n ' + cols[i][1].ljust(16) + ' '\n if isinstance(row[i], int):\n pstring += str(row[i])\n elif isinstance(row[i], bytes):\n pstring += row[i].decode('utf-8')\n else:\n pstring += row[i]\n pstring += '\\n'\n r += 1\n return pstring\n except Exception as e:\n print(\"Error when trying to print table\", table)\n print(e)","def do_p(self, line):\n if not self.current:\n print_table(self.get_profiles(), self.vertical_display)","def pretty_print(linenum, todo):\n\n global COUNTER\n comm_endings = ['\"\"\"', \"'''\", '*/', '-->', '#}', '--}}', '}}', '%>']\n for i in comm_endings:\n if todo.endswith(i):\n todo = todo[:-len(i)]\n print(' line', linenum.rjust(4), '>>\\t', todo )\n COUNTER += 1"],"string":"[\n \"def tab(self):\\n self._write('\\\\t')\",\n \"def output_tab_delimited(s1, s2, i1, i2):\\n _out.write(\\\"%s\\\\t%s\\\\t%d\\\\t%d\\\\n\\\" % (s1, s2, i1, i2))\\n _out.write(\\\"%s\\\\t%s\\\\t%d\\\\t%d\\\\n\\\" % (s2, s1, i2, i1))\",\n \"def print_tsv(data, filename):\\n with open(filename, 'wt') as fout:\\n writefile = partial(print, sep='\\\\t', file=fout)\\n writefile('Sample', *expected_header)\\n for sample in data:\\n for entry in data[sample]:\\n writefile(sample, *(entry[field] for field in expected_header))\",\n \"def print_table(seqids, data, outputfile, separator='\\\\t'):\\n\\n tags = data.keys()\\n with open(outputfile, 'w') as out:\\n out.write(separator.join([\\\"#Sequence ID\\\"] + list(tags)) + \\\"\\\\n\\\")\\n for s in seqids:\\n out.write(s)\\n for t in tags:\\n out.write(\\\"{}{}\\\".format(separator, data[t].get(s, \\\"\\\")))\\n out.write(\\\"\\\\n\\\")\",\n \"def fromtab(args):\\n p = OptionParser(fromtab.__doc__)\\n p.set_sep(sep=None)\\n p.add_option(\\n \\\"--noheader\\\", default=False, action=\\\"store_true\\\", help=\\\"Ignore first line\\\"\\n )\\n p.add_option(\\\"--replace\\\", help=\\\"Replace spaces in name to char\\\")\\n opts, args = p.parse_args(args)\\n\\n if len(args) != 2:\\n sys.exit(not p.print_help())\\n\\n tabfile, fastafile = args\\n sep = opts.sep\\n replace = opts.replace\\n fp = must_open(tabfile)\\n fw = must_open(fastafile, \\\"w\\\")\\n nseq = 0\\n if opts.noheader:\\n next(fp)\\n for row in fp:\\n row = row.strip()\\n if not row or row[0] == \\\"#\\\":\\n continue\\n\\n name, seq = row.rsplit(sep, 1)\\n if replace:\\n name = name.replace(\\\" \\\", replace)\\n print(\\\">{0}\\\\n{1}\\\".format(name, seq), file=fw)\\n nseq += 1\\n fw.close()\\n\\n logging.debug(\\\"A total of {0} sequences written to `{1}`.\\\".format(nseq, fastafile))\",\n \"def writeTabDelimited (self, path='BACKFILLER_TABDELIMITED.txt'):\\n lines = [];add=lines.append\\n header = ['pid','doi','award_ids']\\n add (header)\\n for rec in self.data:\\n if len (rec.award_ids) > 0:\\n add ([rec['pid'], rec['doi'], ','.join (rec.award_ids)])\\n # fp = codecs.open (path, 'w')\\n fp = open (path, 'w')\\n fp.write ('\\\\n'.join (map (lambda x:'\\\\t'.join(x), lines)))\\n fp.close()\\n # print '{} records written to {}'.format(len(self.data), path)\\n print 'Data written to {}'.format(path)\",\n \"def tab(self):\\n self.write(self.ASCII_TAB)\\n self._column = (self._column + 4) & 0b11111100\",\n \"def tprint(array, sep=' ', end='\\\\n', file=sys.stdout, **tformat_kwargs):\\n print(tformat(array, **tformat_kwargs), sep=sep, end=end, file=file)\",\n \"def fa2tab(filename):\\n from Bio import SeqIO\\n fo = open(filename,\\\"r\\\")\\n fout = open(filename+\\\".tab\\\",\\\"w\\\")\\n for seq in SeqIO.parse(fo, \\\"fasta\\\"):\\n SeqIO.write(seq,fout,\\\"tab\\\")\\n fo.close()\\n fout.close()\",\n \"def tab_delim_table(self):\\n self.generate()\\n\\n header = ' \\\\t '.join([r'{: ^7}'.format(col) for col in self.columns])\\n lines = []\\n for row in self.rows:\\n bits = []\\n for col in self.columns:\\n if col in self.formatters:\\n bits.append(self.formatters[col].format(row[col]))\\n else:\\n bits.append(self.formatters.get(col, '{: ^7}').format(row[col] if row[col] else ''))\\n lines.append(' \\\\t '.join(bits))\\n\\n return \\\"{}\\\\n{}\\\".format(header, '\\\\n'.join(lines))\",\n \"def print_f(msg, i, t):\\n if i == t-1:\\n sys.stdout.write('\\\\n')\\n return\\n t += 1\\n i += 1\\n msg += '\\\\t'\\n sys.stdout.write('\\\\r')\\n sys.stdout.write(\\\"%s%s%% |%s\\\" % (msg, int(i % t), int(i % t) * '#'))\\n sys.stdout.flush()\",\n \"def Table(self, line):\\n if line is None:\\n # TODO(user): Use resource_printer.TablePrinter() when it lands.\\n if self._rows:\\n cols = len(self._rows[0])\\n width = [0 for _ in range(cols)]\\n for row in self._rows:\\n for i in range(cols - 1):\\n w = len(row[i])\\n if width[i] <= w:\\n width[i] = w + 1\\n for row in self._rows:\\n self._out.write(' ' * (self._indent[self._level] + 2))\\n for i in range(cols - 1):\\n self._out.write(row[i].ljust(width[i]))\\n self._out.write(row[-1] + '\\\\n')\\n self._rows = []\\n self._table = False\\n self._out.write('\\\\n')\\n elif not self._table:\\n self._table = True\\n self.Line()\\n else:\\n self._rows.append(line.split(','))\",\n \"def add_tab(self):\\n if(self.tab):\\n return '\\\\t'\\n return ''\",\n \"def asTabString(self):\\n dl = [self.pid, self.start, self.snomed, self.name[:20]]\\n s = \\\"\\\"\\n for v in dl:\\n s += \\\"%s\\\\t\\\"%v \\n return s[0:-1] # Throw away the last tab\",\n \"def asTabString(self):\\n dl = [self.pid, self.date, self.snomed, self.name[:20]]\\n s = \\\"\\\"\\n for v in dl:\\n s += \\\"%s\\\\t\\\"%v \\n return s[0:-1] # Throw away the last tab\",\n \"def __openRow(fd):\\n\\n print(\\\" <tr>\\\", file=fd)\",\n \"def print_seq(self):\\n names, values = [], []\\n for each in self.minions:\\n names.append(each.name)\\n values.append(f'{each.atk}/{each.dfs}')\\n t = PrettyTable()\\n t.add_row(names)\\n t.add_row(values)\\n print(t)\",\n \"def get_tabs(n) -> str:\\n res = ''\\n for i in range(n):\\n res += '\\\\t'\\n return res\",\n \"def print_table(table):\\r\\n print('/-----------------------------------------------------------------------------------\\\\\\\\')\\r\\n for item in table:\\r\\n\\r\\n while len(item[1]) <= 22:\\r\\n item[1] += ' '\\r\\n\\r\\n while len(item[2]) <= 27:\\r\\n item[2] += ' '\\r\\n\\r\\n while len(item[0]) <= 15:\\r\\n item[0] += ' '\\r\\n\\r\\n print('| '+item[0]+' | '+item[1]+'| '+item[2]+' |')\\r\\n\\r\\n print('\\\\\\\\-----------------------------------------------------------------------------------/')\",\n \"def exon_line_print(temp_line, trx_exons, parent, ftype):\\n for ex in trx_exons:\\n temp_line[2] = ftype\\n temp_line[3] = str(ex[0])\\n temp_line[4] = str(ex[1])\\n temp_line[8] = 'Parent='+parent\\n print '\\\\t'.join(temp_line)\",\n \"def make_table_file(lines, labels, dir_path, filename):\\r\\n lines.sort()\\r\\n lines.insert(0, '\\\\t'.join(labels))\\r\\n\\r\\n output = open(os.path.join(dir_path, filename), 'w')\\r\\n output.write('\\\\n'.join(lines))\\r\\n output.close()\",\n \"def print_table(table):\\n rest = table[1:]\\n fmt = \\\"%-28s %-9s %-16s %s\\\"\\n for row in rest:\\n print(fmt % tuple(row))\",\n \"def print_tabulated_output(array_obj, headers):\\n print()\\n print(tabulate(array_obj, headers=headers))\\n print()\",\n \"def print_table(table):\\n for row in table:\\n print(row)\",\n \"def print_table(table):\\n for row in table:\\n print(row)\",\n \"def tab_printer(args):\\n args = vars(args)\\n keys = sorted(args.keys())\\n tab = Texttable()\\n tab.add_rows([[\\\"Parameter\\\", \\\"Value\\\"]])\\n tab.add_rows([[k.replace(\\\"_\\\", \\\" \\\").capitalize(), args[k]] for k in keys])\\n print(tab.draw())\",\n \"def print_table(table):\\n for i in range(len(table)):\\n print \\\"Row \\\", i, \\\"\\\\t\\\",\\n for j in range(len(table[i])):\\n print table[i][j],\\n print \\\"\\\\n\\\"\",\n \"def print_list(things_to_print, prefix=\\\"\\\\t\\\", stream=sys.stdout):\\n for item in things_to_print:\\n print(f\\\"{prefix}{item}\\\", file=stream)\",\n \"def tprint(msg, indent=0):\\n\\n print(\\\" \\\" * indent + \\n \\\" \\\" * (indent+1) + \\n \\\"'-- \\\" + msg)\",\n \"def print_table(table):\\n for row in table:\\n print(row)\",\n \"def print_table(table):\\n # transpose the table:\\n table = map(list, zip(*table))\\n # get the column width:\\n col_width = [max(len(str(x)) for x in col) for col in zip(*table)]\\n # print it to screen:\\n print\\n for line in table:\\n print \\\"| \\\" + \\\" | \\\".join(\\\"{:{}}\\\".format(x, col_width[i]) for i, x in enumerate(line)) + \\\" |\\\"\\n print\",\n \"def capture_print(t, end='\\\\n'):\\n lines.append(t + end)\",\n \"def print_table_row(package):\\n\\n row = '{} \\\\t {} \\\\t {} \\\\t {} \\\\t {} \\\\t {} \\\\t {} \\\\t {} \\\\t {}'.format(\\n package.name.ljust(40),\\n str(package.is_metapackage).ljust(10),\\n str(package.has_test).ljust(10),\\n str(package.execution_status).ljust(10),\\n str(package.summary.total).ljust(10),\\n str(package.summary.errors).ljust(10),\\n str(package.summary.failures).ljust(10),\\n str(package.summary.skipped).ljust(10),\\n (str(package.coverage) + '%').ljust(10),\\n )\\n\\n print(row) \\n\\n if args.output:\\n with open(args.output, 'a') as f:\\n f.write(row + '\\\\n')\",\n \"def print_alignment_to_file(alig, fileName):\\n with open(fileName, 'w') as f:\\n for row in alig:\\n print(''.join(map(str, row)), file=f)\",\n \"def untabify(line, tabwidth):\\n\\tassert tabwidth > 0\\n\\tbody = list()\\n\\ti = 0\\n\\twhile i < len(line) and line[i] == '\\\\t':\\n\\t\\ti += 1\\n\\tindent = '\\\\t' * i\\n\\twhile i < len(line):\\n\\t\\tif line[i] == '\\\\t':\\n\\t\\t\\tskip = tabwidth - (len(body) % tabwidth)\\n\\t\\t\\tbody.extend(' ' for i in range(skip))\\n\\t\\telse:\\n\\t\\t\\tbody.append(line[i])\\n\\t\\ti += 1\\n\\treturn indent + ''.join(body)\",\n \"def tab2_to_txt(self,Nom_sortie,tab):\\n fiche=open(Nom_sortie,'w', encoding='utf-8')\\n taille=self.Get_max_lg(tab)\\n out=[f\\\"{row[0]:{taille[0]}};{row[1]}\\\" for row in tab]\\n fiche.writelines('\\\\n'.join(out))\\n fiche.close()\",\n \"def write_tsv(self, filename):\\n f = open(filename,'wb')\\n wr = csv.writer(f,delimiter='\\\\t',quoting=csv.QUOTE_ALL)\\n colrow = []\\n for col in self.cols:\\n colrow.append('<undefined>' if len(col) == 0 else unicode(iter(col).next()).encode('unicode-escape'))\\n wr.writerow(colrow)\\n for row in self.data:\\n strrow = []\\n for cell in row:\\n strrow.append('' if cell is None else unicode(cell).encode('unicode-escape'))\\n wr.writerow(strrow)\\n f.close()\",\n \"def _write_tsv(\\n output_path,\\n records,\\n):\\n df = pandas.DataFrame(records)\\n output_path.write_text(df.to_csv(index=False, sep='\\\\t'))\",\n \"def tab_printer(args):\\n args = vars(args)\\n keys = sorted(args.keys())\\n t = Texttable() \\n t.add_rows([[\\\"Parameter\\\", \\\"Value\\\"]] + [[k.replace(\\\"_\\\", \\\" \\\").capitalize(), args[k]] for k in keys])\\n print(t.draw())\",\n \"def tab_printer(args):\\n args = vars(args)\\n keys = sorted(args.keys())\\n t = Texttable() \\n t.add_rows([[\\\"Parameter\\\", \\\"Value\\\"]] + [[k.replace(\\\"_\\\",\\\" \\\").capitalize(), args[k]] for k in keys])\\n print(t.draw())\",\n \"def write_torque_table(A, filename):\\n f = open(filename, 'w')\\n for row in range(np.size(A, axis=0)):\\n A[row,:].tofile(f, sep=',')\\n f.write('\\\\n')\\n f.close()\",\n \"def tab_printer(args):\\n args = vars(args)\\n keys = sorted(args.keys())\\n t = Texttable() \\n t.add_rows([[\\\"Parameter\\\", \\\"Value\\\"]] + [[k.replace(\\\"_\\\",\\\" \\\").capitalize(),args[k]] for k in keys])\\n print(t.draw())\",\n \"def tab_printer(args):\\n args = vars(args)\\n keys = sorted(args.keys())\\n t = Texttable() \\n t.add_rows([[\\\"Parameter\\\", \\\"Value\\\"]] + [[k.replace(\\\"_\\\",\\\" \\\").capitalize(),args[k]] for k in keys])\\n print(t.draw())\",\n \"def print_row():\\n print('| | |')\",\n \"def print_table(table):\\n for row in table:\\n # Header column left justified\\n print(\\\"{:<19}\\\".format(row[0]), end='')\\n # Remaining columns right justified\\n for col in row[1:]:\\n print(\\\"{:>4}\\\".format(col), end='')\\n print(\\\"\\\", end='\\\\n')\",\n \"def test_tabify(self):\\n before_b = \\\"\\\"\\\"\\\\\\n first line\\n line 1\\n line a\\n line b\\n line c\\n last line\\n \\\"\\\"\\\"\\n after_b = \\\"\\\"\\\"\\\\\\n first line\\n line 1\\n \\tline a\\n \\t\\tline b\\n line c\\n last line\\n \\\"\\\"\\\"\\n self.run_test(\\n before_b=before_b,\\n after_b=after_b,\\n before_sel=(\\\"1.0\\\", \\\"7.0\\\"),\\n after_sel=(\\\"7.0\\\", \\\"7.0\\\"),\\n command_name=\\\"tabify\\\",\\n )\",\n \"def basic_print(lista):\\n for item in lista:\\n print(\\\"{} \\\\t\\\\t {}\\\".format(item[0], item[1]))\",\n \"def mostrar_tablero(mtx, n):\\n # Cabecera de Columnas\\n fila = \\\"/ |\\\"\\n for i in range(n):\\n fila = fila + \\\" \\\" + chr(65+i)\\n print fila\\n print \\\"-\\\"*(2*n+3)\\n # Cabecera de Filas\\n for i in range(n):\\n fila = str(i+1)\\n if i < 9 : fila += \\\" |\\\"\\n else:\\n fila+=\\\"|\\\"\\n for e in range(n):\\n fila = fila+\\\" \\\"+mtx[i][e]\\n print fila\\n fila = \\\"\\\"\\n # Nueva linea\\n print \\\"\\\"\",\n \"def catch_tabs(self):\\n lnum = 1\\n for line in self.text:\\n cnum = line.find(\\\"\\\\t\\\")\\n if 0 <= cnum:\\n self.errmsg(\\\"TAB detected in input. Please use spaces.\\\",\\n pos=(lnum,cnum))\\n lnum += 1\",\n \"def create_output_file(arr):\\r\\n for i in arr:\\r\\n output_file.write(f'{i[0]}\\\\t{i[1]}\\\\n')\",\n \"def writeseq(afile, seq):\\n print(seq, file=afile)\",\n \"def print_table2(df, eval_dir):\\n\\n out_file = os.path.join(eval_dir, 'table2.txt')\\n\\n\\n with open(out_file, \\\"w\\\") as text_file:\\n\\n for idx, struc_name in enumerate(['LV', 'RV', 'Myo']):\\n # new line\\n header_string = ' & '\\n line_string = '({}) '.format(struc_name)\\n\\n for p_idx, phase in enumerate(['ED', 'ES']):\\n for measure in ['dice', 'assd', 'hd']:\\n\\n header_string += ' & {} ({}) '.format(phase, measure)\\n\\n dat = df.loc[(df['phase'] == phase) & (df['struc'] == struc_name)]\\n\\n if measure == 'dice':\\n\\n line_string += ' & {:.3f}\\\\,({:.3f}) '.format(np.mean(dat[measure]), np.std(dat[measure]))\\n else:\\n line_string += ' & {:.2f}\\\\,({:.2f}) '.format(np.mean(dat[measure]), np.std(dat[measure]))\\n\\n if p_idx == 0:\\n header_string += ' & '\\n line_string += ' & '\\n\\n header_string += ' \\\\\\\\\\\\\\\\ \\\\n'\\n line_string += ' \\\\\\\\\\\\\\\\ \\\\n'\\n\\n if idx == 0:\\n text_file.write(header_string)\\n\\n text_file.write(line_string)\\n\\n return 0\",\n \"def _get_staff_report_tab_delimited(self):\\n stringio = io.StringIO()\\n writer = csv.writer(stringio, delimiter='\\\\t',\\n quoting=csv.QUOTE_MINIMAL)\\n for record in self._get_staff_report_data():\\n writer.writerow(record)\\n return stringio.getvalue()\",\n \"def write_table(self, tab):\\n self.save_text()\\n\\n table = list()\\n row = list()\\n headers = tab['c'][3]\\n if headers:\\n has_content = False\\n for col in headers:\\n self.list_parse(col, cell_content=True)\\n cell_content = self.get_content()\\n row.append(cell_content)\\n if cell_content != '':\\n has_content = True\\n if has_content:\\n row = tuple(row)\\n table.append(row)\\n t_content = tab['c'][4]\\n for line in t_content:\\n row = list()\\n for col in line:\\n self.list_parse(col, cell_content=True)\\n cell_content = self.get_content()\\n row.append(cell_content)\\n row = tuple(row)\\n table.append(row)\\n table = tuple(table)\\n self.tables.append((table, (self.context, self.ancestor)))\",\n \"def print_table(headers, rows):\\n try:\\n if headers:\\n print('\\\\n')\\n print(tabulate.tabulate(\\n rows, headers=headers,\\n tablefmt=\\\"plain\\\", numalign=\\\"left\\\"\\n ))\\n print('\\\\n')\\n except Exception as e:\\n print(e.message)\",\n \"def output(*args):\\n print(*args, end='', file=file)\",\n \"def dibujar_tablero(tablero):\\n for fila in tablero:\\n print(\\\"|\\\", \\\"|\\\".join(fila), \\\"|\\\", sep=\\\"\\\")\\n print(\\\"\\\")\",\n \"def pretty_print(self):\\n pt = PrettyTable()\\n for i in self.files_summary:\\n pt.field_names = [\\\"File Name\\\", \\\"Classes\\\", \\\"Functions\\\", \\\"Lines\\\", \\\"Characters\\\"]\\n pt.add_row(list([i, self.files_summary[i][\\\"class\\\"], self.files_summary[i][\\\"function\\\"], self.files_summary[i][\\\"line\\\"], self.files_summary[i][\\\"char\\\"]]))\\n print(pt) #Using a Print statement here because i tried to return self.pt and it didnt give me anything but the print works\",\n \"def print_row():\\n print_line()\",\n \"def write_values_to_tsv(self, values, out_file):\\n with open(out_file, \\\"w\\\") as f:\\n for row in values:\\n f.write(\\\"\\\\t\\\".join([cell.strip().replace(\\\"\\\\n\\\", \\\"|\\\").replace(\\\"\\\\r\\\", \\\"\\\")\\n for cell in row]))\\n f.write(os.linesep)\",\n \"def print_table(data):\\n for key in sorted(data):\\n print \\\"%s: %s\\\" % (key.rjust(16), data[key])\",\n \"def lines_printed_to(file):\\n with io.open(file, 'w') as fp:\\n def write_line(s=\\\"\\\"):\\n PRINT(s, file=fp)\\n yield write_line\",\n \"def __print_work_table(table):\\n print \\\"%-5s %-30s %5s %5s %5s %5s %5s\\\" % ('Act', 'Pred', 'Block', 'Dummy', 'Succ', 'start', 'end')\\n for k, col in sorted(table.items()):\\n print \\\"%-5s %-30s %5s %5s %5s %5s %5s\\\" % tuple(\\n [str(k)] + [list(col[0])] + [str(col[i]) for i in range(1, len(col))])\",\n \"def print_lines(f1, f2, row):\\n column = col_match(f1, f2)\\n print('{}c{}'.format(row, column))\\n print(f1)\\n print('---')\\n print(f2)\",\n \"def print_row():\\n print('| | | | |')\",\n \"def tprint(self, cmd, end='\\\\n'):\\n if ENABLE_DEBUG:\\n stackIndex = 0\\n for index, stackFrame in enumerate(stack()):\\n caller = getframeinfo(stackFrame[0])\\n if caller.filename == fullPath:\\n stackIndex = index\\n break \\n caller = getframeinfo(stack()[stackIndex][0])\\n self.fileHandle.write(\\\"# \\\" + targetFile + \\\":\\\" + str(caller.lineno) + '\\\\n')\\n self.tprint_raw(cmd, end)\",\n \"def tabing_tool(code):\\n for i, line in enumerate(code):\\n code[i] = ' '*4 + line\\n return code\",\n \"def write_tsv_fast(self, filename):\\n # TODO (without quotation marks)\\n with open(filename, 'wb') as f:\\n colnames = ['<undefined>' if len(col) == 0 else unicode(iter(col).next()).encode('unicode-escape') for col in self.cols]\\n f.write('\\\\t'.join(colnames)+'\\\\n')\\n for row in self.data:\\n f.write('\\\\t'.join(['' if cell is None else unicode(cell).encode('unicode-escape') for cell in row])+'\\\\n')\",\n \"def print_tables(self):\\n print \\\"------------------\\\\nTables\\\\n------------------\\\"\\n cnt = 0\\n for x in self.show_tables():\\n cnt += 1\\n print (\\\"{0}.) {1}\\\".format(cnt, x[0]))\",\n \"def print_table(self, table):\\n raise NotImplementedError('print_table method not defined!')\",\n \"def tsv_generator(file):\\n for line in fileinput.input(file):\\n article, summary = line.strip().split(\\\"\\\\t\\\")\\n yield (article, summary)\",\n \"def tabular_print(files_dict: dict):\\r\\n # create a list of file extensions\\r\\n file_extensions = []\\r\\n for filename in files_dict.keys():\\r\\n for file_ext in files_dict[filename].keys():\\r\\n # print(\\\"debug:::\\\", file_ext)\\r\\n file_extensions.append(file_ext)\\r\\n break\\r\\n # go through all the files and print them in a table with the file extension as the top row\\r\\n sep_line_len = 40 + 10 * len(file_extensions) # separator line length = max_filename_len [35] + 10*number of ext\\r\\n # print the first row\\r\\n print(\\\"filename\\\".ljust(40), end='')\\r\\n for ext in file_extensions:\\r\\n print(\\\"|\\\" + ext.center(9), end='')\\r\\n print()\\r\\n print(''.center(sep_line_len, '='))\\r\\n # print the rest of the files\\r\\n for filename, ext_dict in files_dict.items():\\r\\n print(filename.ljust(40), end='')\\r\\n for ext in ext_dict.keys():\\r\\n if ext_dict[ext]:\\r\\n print(\\\"|\\\" + \\\"V\\\".center(9), end='')\\r\\n else:\\r\\n print(\\\"|\\\" + \\\" \\\".center(9), end='')\\r\\n print()\\r\\n print(''.center(sep_line_len, '-'))\",\n \"def print_table(outbuf, table, align):\\n if len(table) == 0:\\n return\\n\\n colwidths = None\\n for row in table:\\n if colwidths is None:\\n colwidths = [len(x) for x in row]\\n else:\\n colwidths = [max(colwidths[i], len(x)) for i, x in enumerate(row)]\\n\\n for row in table:\\n cells = []\\n for i, cell in enumerate(row):\\n padding = ' ' * (colwidths[i] - len(cell))\\n if align[i] == 'r':\\n cell = padding + cell\\n elif i < len(row) - 1:\\n # Do not pad the final column if left-aligned.\\n cell += padding\\n cells.append(cell.encode('utf-8', 'replace'))\\n try:\\n outbuf.write(b' '.join(cells) + b'\\\\n')\\n except IOError: # pragma: no cover\\n # Can happen on Windows if the pipe is closed early.\\n pass\",\n \"def print_tracks(td, _sep=' '):\\r\\n track_strings = []\\r\\n for t in td:\\r\\n track_strings.append(t.songStr())\\r\\n print(*track_strings, sep=_sep)\",\n \"def write_tsv(labels, positions, elec_file):\\n labels = labels.reshape(-1, order='F')\\n positions = positions.reshape(-1, 3, order='F')\\n\\n elec_file = elec_file.with_suffix('.tsv')\\n with elec_file.open('w') as f:\\n f.write('name\\\\tx\\\\ty\\\\tz\\\\n')\\n for i in range(labels.shape[0]):\\n f.write(f'{labels[i]}\\\\t{positions[i, 0]:.3f}\\\\t{positions[i, 1]:.3f}\\\\t{positions[i, 2]:.3f}\\\\n')\",\n \"def dump_tsv(file_name, data, header=None, append=False):\\n mode = \\\"a\\\" if append else \\\"w\\\"\\n with open(file_name, mode) as f:\\n print(\\\"tsv file created:\\\", file_name)\\n if header:\\n f.write(\\\"\\\\t\\\".join(header) + \\\"\\\\n\\\")\\n for line in data:\\n f.write(\\\"\\\\t\\\".join([str(d) for d in line]) + \\\"\\\\n\\\")\",\n \"def print_table(listx):\\r\\n\\tfor lists in listx:\\r\\n\\t\\tfor i in lists:\\r\\n\\t\\t\\tprint str(i) , '\\\\t',\\r\\n\\t\\tprint()\",\n \"def _tab_newline_replace( self, fromlines, tolines ):\\n\\t\\tdef expand_tabs( line ):\\n\\t\\t\\t# hide real spaces\\n\\t\\t\\tline = line.replace( ' ', '\\\\0' )\\n\\t\\t\\t# expand tabs into spaces\\n\\t\\t\\tline = line.expandtabs( self._tabsize )\\n\\t\\t\\t# relace spaces from expanded tabs back into tab characters\\n\\t\\t\\t# (we'll replace them with markup after we do differencing)\\n\\t\\t\\tline = line.replace( ' ', '\\\\t' )\\n\\t\\t\\treturn line.replace( '\\\\0', ' ' ).rstrip( '\\\\n' )\\n\\t\\tfromlines = [expand_tabs( line ) for line in fromlines]\\n\\t\\ttolines = [expand_tabs( line ) for line in tolines]\\n\\t\\treturn fromlines, tolines\",\n \"def write_tp_rows():\\n \\n basename = sys.argv[1]\\n ops = (\\\"two_opt\\\", \\\"twoh_opt\\\", \\\"three_opt\\\", \\\"three_opt_broad\\\", \\\"swap\\\", \\\"swap_adj\\\")\\n lengths = (6, 7, 8, 9, 10)\\n for op in ops:\\n for length in lengths:\\n filename = os.path.join(basename,\\n \\\"tsp_length_%d_%s\\\" % (length, op),\\n \\\"TP_row0.dat\\\")\\n print op, length\\n x = tsp.get_tm_first_row(length, move=op)\\n np.savetxt(filename, x)\",\n \"def printt(content, flag=False):\\n if flag:\\n print(content)\",\n \"def table_print(csv_header, csv_body, csv_footer):\\n s = '\\\\t'.join(csv_header)\\n print(s)\\n\\n for r in csv_body:\\n s = '\\\\t'.join(map(str, r))\\n print(s)\\n\\n s = '\\\\t'.join(map(str, csv_footer))\\n print(s)\",\n \"def generate_tsv_gen(rows, le='\\\\n'):\\n\\theader_string = None\\n\\t\\n\\tfor row in rows:\\n\\t\\theaders = []\\n\\t\\tfields = []\\n\\t\\t\\n\\t\\tfor header, value in row.items():\\n\\t\\t\\tif(header_string is None):\\n\\t\\t\\t\\theaders.append(header)\\n\\t\\t\\t\\n\\t\\t\\tif(not isinstance(value, basestring)):\\n\\t\\t\\t\\tvalue = str(value)\\n\\t\\t\\tfields.append(value)\\n\\t\\t\\n\\t\\tif(header_string is None):\\n\\t\\t\\theader_string = '\\\\t'.join(headers) + le;\\n\\t\\t\\tyield header_string\\n\\t\\tyield '\\\\t'.join(fields) + le;\",\n \"def print_output_tables(cls,\\n wfns=None, file=None,\\n print_intensities=True,\\n print_energies=True,\\n print_energy_corrections=True,\\n print_transition_moments=True,\\n operators=None,\\n logger=None, sep_char=\\\"=\\\", sep_len=100):\\n\\n if logger is None:\\n logger = wfns.logger\\n if logger is not None:\\n def print_block(label, *args, **kwargs):\\n with logger.block(tag=label):\\n logger.log_print(\\\" \\\".join(\\\"{}\\\".format(x) for x in args), **kwargs)\\n else:\\n if file is None:\\n file = sys.stdout\\n\\n def print_label(label, file=file, **opts):\\n lablen = len(label) + 2\\n split_l = int(np.floor((sep_len - lablen) / 2))\\n split_r = int(np.ceil((sep_len - lablen) / 2))\\n print(sep_char * split_l, label, sep_char * split_r, **opts, file=file)\\n\\n def print_footer(label=None, file=file, **opts):\\n print(sep_char * sep_len, **opts, file=file)\\n\\n def print_block(label, *args, file=file, **kwargs):\\n print_label(label, file=file, **kwargs)\\n print(*args, file=file, **kwargs)\\n print_footer(file=file, **kwargs)\\n\\n if print_energy_corrections:\\n print_block(\\\"Energy Corrections\\\", wfns.format_energy_corrections_table())\\n if print_energies:\\n if wfns.degenerate_transformation is not None:\\n print_block(\\\"Deperturbed Energies\\\",\\n wfns.format_deperturbed_energies_table()\\n )\\n print_block(\\n \\\"Degenerate Energies\\\",\\n wfns.format_energies_table()\\n )\\n else:\\n print_block(\\\"States Energies\\\",\\n wfns.format_energies_table()\\n )\\n\\n if print_intensities:\\n ints = wfns.intensities # to make sure they're computed before printing starts\\n if print_transition_moments:\\n if wfns.degenerate_transformation is not None:\\n for a, m in zip([\\\"X\\\", \\\"Y\\\", \\\"Z\\\"], wfns.format_deperturbed_dipole_contribs_tables()):\\n print_block(\\\"{} Deperturbed Dipole Contributions\\\".format(a), m)\\n\\n print_block(\\\"Deperturbed IR Data\\\",\\n wfns.format_deperturbed_intensities_table()\\n )\\n\\n for a, m in zip([\\\"X\\\", \\\"Y\\\", \\\"Z\\\"], wfns.format_dipole_contribs_tables()):\\n print_block(\\\"{} Dipole Contributions\\\".format(a), m)\\n print_block(\\\"IR Data\\\", wfns.format_intensities_table())\\n\\n if operators is not None:\\n print_block(\\\"Operator Data\\\", wfns.format_operator_table(operators))\",\n \"def boldOutput(file, line):\\r\\n # Write header to output files (executes only one time per run)\\r\\n global output_header\\r\\n if output_header is False:\\r\\n for temp in (args.outfile1, args.outfile2):\\r\\n with io.open(args.outdir2+\\\"/\\\"+temp, mode=\\\"a\\\", encoding=\\\"utf-8\\\") as f:\\r\\n for key, value in line.items():\\r\\n f.write('%s\\\\t' % (key))\\r\\n f.write(\\\"\\\\n\\\")\\r\\n output_header = True\\r\\n\\r\\n # Write sequence data, for each record\\r\\n with io.open(args.outdir2+\\\"/\\\"+file, mode=\\\"a\\\", encoding=\\\"utf-8\\\") as f:\\r\\n for key, value in line.items():\\r\\n f.write('%s\\\\t' % (value))\\r\\n f.write(\\\"\\\\n\\\")\",\n \"def insert_tab(self, value=None):\\n self.my_text.insert(INSERT, \\\" \\\" * 3)\",\n \"def tprint(msg):\\n sys.stdout.write(msg + '\\\\n')\\n sys.stdout.flush()\",\n \"def tprint(msg):\\n sys.stdout.write(msg + '\\\\n')\\n sys.stdout.flush()\",\n \"def tabser(filename, body, data):\\n # XXX checksums ignored\\n head = Struct(\\\"!BiHBxxxB\\\")\\n body = Struct(body)\\n # foot = Struct(\\\"!4s\\\")\\n\\n buffer = bytearray([0] * (2 ** 16))\\n head.pack_into(buffer, 0, 0, int(time()), len(data), body.size, 0),\\n offset = head.size\\n for row in data:\\n body.pack_into(buffer, offset, *row, 0)\\n offset += body.size\\n else:\\n print(\\\"write %d rows\\\" % len(data))\\n # offset = 2 ** 16 - foot.size\\n # foot.pack_into(buffer, offset, bytes([0, 0, 0, 0]))\\n with open(filename, \\\"wb\\\") as f:\\n f.write(buffer)\",\n \"def writeTable(table, filename):\\n with open(filename, \\\"w\\\") as output:\\n writer = csv.writer(output, lineterminator='\\\\n')\\n writer.writerows(table)\",\n \"def printer(end,message):\\n\\n sys.stdout.write('\\\\r'+message+'\\\\t')\\n sys.stdout.flush()\\n if end: sys.stdout.write('\\\\n')\",\n \"def write(self,fout,line):\\n \\n def trans(a,m):\\n if a == '0': return m[0]+'\\\\t'+m[0]\\n if a == '1': return m[0]+'\\\\t'+m[1]\\n if a == '2': return m[1]+'\\\\t'+m[1]\\n return '0\\\\t0'\\n\\n if not line: return\\n animal = line[0]\\n if animal in self.ped:\\n father,mother = self.ped[animal]['father'],self.ped[animal]['mother']\\n sex = self.ped[animal]['sex']\\n phe = self.ped[animal]['phe']\\n family = self.ped[animal]['family'][0]\\n else:\\n father,mother,sex,phe,family = '0','0','3','-9','0'\\n if len(self.mark['marklist']) > 0:\\n lmark = self.mark['marklist']\\n fout.write('%s\\\\t%s\\\\t%s\\\\t%s\\\\t%s\\\\t%s\\\\t%s\\\\n' % (family,animal,father,mother,sex,phe,\\n '\\\\t'.join([trans(line[i+1],self.mark[name]['a1']+self.mark[name]['a2']) for i,name in enumerate(lmark)])))\\n else:\\n fout.write('%s\\\\t%s\\\\t%s\\\\t%s\\\\t%s\\\\t%s\\\\t%s\\\\n' % (family,animal,father,mother,sex,phe,'\\\\t'.join(line[1:])))\",\n \"def print_table(hdrs, flag=False, data=[],fmt='psql'):\\n\\tres = cur.fetchall()\\n\\tif flag:\\n\\t\\tres = data\\n\\tprint(tabulate(res, headers=hdrs, tablefmt=fmt))\",\n \"def to_tsv(obj: ConfiguredBaseModel, file: str) -> str:\\n\\n # Extract headers and rows from object\\n if isinstance(obj, Entity):\\n headers = obj.dict().keys()\\n rows = [list(obj.dict().values())]\\n elif isinstance(obj, (AssociationCountList, HistoPheno, Results)):\\n if not obj.items:\\n headers = get_headers_from_obj(obj)\\n rows = []\\n else:\\n headers = obj.items[0].dict().keys()\\n rows = [list(item.dict().values()) for item in obj.items]\\n else:\\n raise TypeError(FMT_INPUT_ERROR_MSG)\\n\\n fh = open(file, \\\"w\\\") if file else sys.stdout\\n writer = csv.writer(fh, delimiter=\\\"\\\\t\\\")\\n writer.writerow(headers)\\n for row in rows:\\n writer.writerow(list(row))\\n if file:\\n fh.close()\\n console.print(f\\\"\\\\nOutput written to {file}\\\\n\\\")\\n\\n return\",\n \"def raw_out(self):\\n\\t\\treturn '\\\\t'.join(self.raw_out_tab)\",\n \"def _write_tsv(fname, dictionary, overwrite=False, verbose=False):\\n if op.exists(fname) and not overwrite:\\n raise FileExistsError(f'\\\"{fname}\\\" already exists. '\\n 'Please set overwrite to True.')\\n _to_tsv(dictionary, fname)\\n\\n if verbose:\\n print(os.linesep + f\\\"Writing '{fname}'...\\\" + os.linesep)\\n print(_tsv_to_str(dictionary))\",\n \"def print_taboo_spaces(warehouse_id):\\n problem_file = \\\"./warehouses/warehouse_{:02d}.txt\\\".format(warehouse_id)\\n wh = Warehouse()\\n wh.load_warehouse(problem_file)\\n print(wh)\\n print(\\\"TABOO CELLS: \\\")\\n taboo = taboo_cells(wh)\\n print(taboo)\",\n \"def print_table(rows, labels=None):\\n if labels is None:\\n labels = ROW_LABELS\\n\\n output_table = prettytable.PrettyTable()\\n output_table.field_names = labels\\n output_table.align = 'l'\\n output_table.vrules = prettytable.prettytable.ALL\\n output_table.hrules = prettytable.prettytable.HEADER\\n\\n for row in rows:\\n row = [x.strip() for x in row]\\n output_table.add_row(row)\\n\\n print output_table\\n print ''\",\n \"def print_table(table, db_file):\\n \\n try:\\n conn, c = connect_to_db(db_file)\\n rows = c.execute('SELECT * FROM {t}'.format(t=safe(table))).fetchall()\\n cols = c.execute(\\\"PRAGMA table_info({t})\\\".format(t=safe(table))).fetchall()\\n conn.close()\\n pstring = '\\\\nTABLE ' + table + '\\\\n'\\n r = 1\\n for row in rows:\\n pstring += '\\\\nROW ' + str(r)\\n for i in range(len(cols)):\\n pstring += '\\\\n ' + cols[i][1].ljust(16) + ' '\\n if isinstance(row[i], int):\\n pstring += str(row[i])\\n elif isinstance(row[i], bytes):\\n pstring += row[i].decode('utf-8')\\n else:\\n pstring += row[i]\\n pstring += '\\\\n'\\n r += 1\\n return pstring\\n except Exception as e:\\n print(\\\"Error when trying to print table\\\", table)\\n print(e)\",\n \"def do_p(self, line):\\n if not self.current:\\n print_table(self.get_profiles(), self.vertical_display)\",\n \"def pretty_print(linenum, todo):\\n\\n global COUNTER\\n comm_endings = ['\\\"\\\"\\\"', \\\"'''\\\", '*/', '-->', '#}', '--}}', '}}', '%>']\\n for i in comm_endings:\\n if todo.endswith(i):\\n todo = todo[:-len(i)]\\n print(' line', linenum.rjust(4), '>>\\\\t', todo )\\n COUNTER += 1\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7131929","0.7020605","0.66624314","0.65175635","0.64840746","0.64395034","0.63917106","0.63775367","0.63412404","0.6289037","0.61846536","0.6176423","0.616765","0.61081123","0.6081998","0.6079226","0.60417503","0.59935766","0.5967714","0.59413093","0.5940626","0.59094465","0.5861586","0.5858402","0.5858402","0.58415663","0.5817626","0.5814891","0.57853246","0.5784303","0.5783708","0.5781614","0.5781505","0.577726","0.57611096","0.57387763","0.5736354","0.57130015","0.56680226","0.5667063","0.56445533","0.5642871","0.5642871","0.5627217","0.5623998","0.5611849","0.5604687","0.55991447","0.5596385","0.5587417","0.55818367","0.5577602","0.55708253","0.55650914","0.55597156","0.55440545","0.5541997","0.55418694","0.5536976","0.55354506","0.5528295","0.5527026","0.55227417","0.5522641","0.5518439","0.55160505","0.55159503","0.5514171","0.55122304","0.5496622","0.54891187","0.54802257","0.54744697","0.5471953","0.546635","0.54613316","0.54566747","0.5455027","0.5450732","0.54495776","0.542139","0.5408359","0.54005843","0.539644","0.53857183","0.5377304","0.5377304","0.5372613","0.53723085","0.53704554","0.5369958","0.5363196","0.53609914","0.53254056","0.5323381","0.53127384","0.530419","0.5303703","0.52994937","0.5290275"],"string":"[\n \"0.7131929\",\n \"0.7020605\",\n \"0.66624314\",\n \"0.65175635\",\n \"0.64840746\",\n \"0.64395034\",\n \"0.63917106\",\n \"0.63775367\",\n \"0.63412404\",\n \"0.6289037\",\n \"0.61846536\",\n \"0.6176423\",\n \"0.616765\",\n \"0.61081123\",\n \"0.6081998\",\n \"0.6079226\",\n \"0.60417503\",\n \"0.59935766\",\n \"0.5967714\",\n \"0.59413093\",\n \"0.5940626\",\n \"0.59094465\",\n \"0.5861586\",\n \"0.5858402\",\n \"0.5858402\",\n \"0.58415663\",\n \"0.5817626\",\n \"0.5814891\",\n \"0.57853246\",\n \"0.5784303\",\n \"0.5783708\",\n \"0.5781614\",\n \"0.5781505\",\n \"0.577726\",\n \"0.57611096\",\n \"0.57387763\",\n \"0.5736354\",\n \"0.57130015\",\n \"0.56680226\",\n \"0.5667063\",\n \"0.56445533\",\n \"0.5642871\",\n \"0.5642871\",\n \"0.5627217\",\n \"0.5623998\",\n \"0.5611849\",\n \"0.5604687\",\n \"0.55991447\",\n \"0.5596385\",\n \"0.5587417\",\n \"0.55818367\",\n \"0.5577602\",\n \"0.55708253\",\n \"0.55650914\",\n \"0.55597156\",\n \"0.55440545\",\n \"0.5541997\",\n \"0.55418694\",\n \"0.5536976\",\n \"0.55354506\",\n \"0.5528295\",\n \"0.5527026\",\n \"0.55227417\",\n \"0.5522641\",\n \"0.5518439\",\n \"0.55160505\",\n \"0.55159503\",\n \"0.5514171\",\n \"0.55122304\",\n \"0.5496622\",\n \"0.54891187\",\n \"0.54802257\",\n \"0.54744697\",\n \"0.5471953\",\n \"0.546635\",\n \"0.54613316\",\n \"0.54566747\",\n \"0.5455027\",\n \"0.5450732\",\n \"0.54495776\",\n \"0.542139\",\n \"0.5408359\",\n \"0.54005843\",\n \"0.539644\",\n \"0.53857183\",\n \"0.5377304\",\n \"0.5377304\",\n \"0.5372613\",\n \"0.53723085\",\n \"0.53704554\",\n \"0.5369958\",\n \"0.5363196\",\n \"0.53609914\",\n \"0.53254056\",\n \"0.5323381\",\n \"0.53127384\",\n \"0.530419\",\n \"0.5303703\",\n \"0.52994937\",\n \"0.5290275\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.81036395"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":81188,"cells":{"query":{"kind":"string","value":"get returns a list of all the users in the system"},"document":{"kind":"string","value":"def get(self):\n # TODO this endpoint returns null is instead of respoinse message when token is not in the header, read about error handling to solve this issue\n return get_users()"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get(self):\n return get_all_users()","def get(self):\n return get_all_users()","def get(self):\n return get_all_users()","def get(self):\n return get_all_users()","def get_all_users():","def get_users(self):\r\n\t\tlogger.debug(\"Fetch users\")\r\n\t\t\r\n\t\treturn login.get_users()","def users_get(self) -> Dict[str, list]:\n self.__logger.debug('Eva.users_get called')\n return self.__http_client.users_get()","def get_users(self):\n fields = ['name', ]\n return self.get_data(\"myUsers\", fields)","def get_users():\n return db.fetch_users()","def get_users():\n return Response(f\"{User.get_all_users()}\", 200, mimetype='text/plain')","def list_users(self):\n _url = \"http://\" + self.host_ip + \":35357/v2.0/users\"\n _body = None\n _headers = {'Content-type': 'application/json',\n 'x-auth-token': self.cloud_admin_info['token_project']}\n\n response = self.request(\"GET\", _url, _headers, _body)\n if response is None:\n LOG_OBJ.error(\" no response from Server\")\n return response\n if response.status not in [200, 201, 202, 203, 204]:\n LOG_OBJ.error(\n \"get user list Failed with status %s \" %\n response.status)\n return response.status\n\n output = json.loads(response.data)\n LOG_OBJ.info(\"users List : %s\")\n return output[\"users\"]","def get_users(self):\n users = []\n page = 1\n while not len(users) % 100:\n users += self._get('/users?{0}'.format(urllib.urlencode({'per_page': 100, 'page': page})))\n if not users:\n break\n page += 1\n return users","def get_all_users():\n return jsonify(admin.get_all_users(current_app.scoped_session()))","def get_users():\n users = functions.users()\n return users","def get_users(self):\n return self.execute(TABELLE['users']['select']['all'])","def get(self):\n\n offset = 0\n limit = Config.get_page_limit()\n args = request.args\n try:\n offset = request.args['offset']\n except Exception:\n pass\n\n try:\n limit = request.args['limit']\n except Exception:\n pass\n\n return self.get_request_handler(request.headers).get_all_users(offset=offset, limit=limit)","def get_users():\n users = storage.all('User')\n users_list = []\n for user in users.values():\n users_list.append(user.to_dict())\n return jsonify(users_list), 200","def get_user_list(self, connection):\n http = get_web_service(connection)\n try:\n req = http.request('GET', connection[\"url\"] + '/users/?_format=json')\n data = json.loads(req.data.decode('utf-8'))\n # print(json.dumps(data, indent=4, sort_keys=True))\n return data\n except urllib3.exceptions.HTTPError as e:\n print(\"Connection error\")\n print(e)","def user_list(ctx):\n data = ctx.obj.get_all_users()\n output_json_data(data)","def get_users(self):\n res = self.conn.cursor().execute('SELECT id,email,username FROM users')\n return res.fetchall()","def getUsers(client, req):\n client.sendTarget(req[\"id\"], key=\"get.users\", payload={\"payload\": magic.users})","def get_users(self):\n url = \"%s/api/v1/users\" % self.subdomain\n req = request.get(url, headers=self.api_headers)\n if request.ok(req):\n response_json = req.json()\n return response_json[\"users\"]\n else:\n return None","def get_users(self):\n return get_users(self['__store'].db, self)","def get_users(self):\n return self.get_all_dbusers()","def list_users(self):\n return self.get_admin(\"users\")","def get_users():\n\n return User.query.all() # [<User user_id=1 fname=Alice lname=Apple>]","def list_users(self):\n raise NotImplementedError","def get_all_user():\n user = UserModel.objects()\n return jsonify(user), 200","def get_users(self, params=None):\n url = 'users'\n if params:\n url += '?%s' % urllib.urlencode(params)\n resp, body = self.get(url)\n self.expected_success(200, resp.status)\n body = json.loads(body)\n return service_client.ResponseBodyList(resp, body['users'])","def get_all_users():\n token = request.headers.get('token')\n\n # Token Validation\n token_valid, response = is_token_valid(token)\n if not token_valid:\n return response\n token_username = response\n\n # Privilege handling\n if token_username != 'admin':\n return jsonify({'message': \"You aren't allowed to access this\"}), 404\n\n return jsonify(list(Users.values())), 200","def get_users():\n username = request.args.get('username')\n netAdminToolDB = app.config['DATABASE']\n if username != None:\n users = []\n users.append(netAdminToolDB.get_user_name(username))\n else:\n users = netAdminToolDB.get_user()\n\n userList = []\n for user in users:\n uri = url_for('get_user', user_id=user.id,_external=True)\n #role = netAdminToolDB.get_role(user.role_id)\n userList.append({\n 'id': user.id,\n 'uri': uri,\n 'username': user.username,\n 'display_name': user.display_name,\n 'role': user.role_name\n })\n if userList == []:\n return jsonify({'error': 'No users found'}), 404\n\n return jsonify({'users': userList })","def get(self):\n return User_Model.query.all()","def get_users():\n return jsonify([\n users.to_dict()\n for users in models.storage.all('User').values()\n ])","def get_users():\n users = User.query.order_by(User.id).all()\n users = {user.id: user.username for user in users}\n\n response = jsonify({\"success\": True, \"users\": users})\n\n return response","def users(self):\n return self.get_data(\"users\")","def user_list():\n users = User.objects.all()\n return {\"users\": users}","def get(self, request):\n all_users = UserModel.objects.all()\n all_users_serializer = UserSerializer(all_users, many=True)\n return Response(all_users_serializer.data)","def get(self):\n res = self._userz.get_all_users()\n if res:\n return {\n \"status\": 200,\n \"data\": res\n }, 200\n else:\n return {\n \"status\": 404,\n \"error\": \"No users found\"\n }, 404","def get_all_users():\n users = []\n for mv in storage.all(\"User\").values():\n users.append(mv.to_dict())\n return jsonify(users)","def listUsers(self):\n return self._client.listUsers()","def get_all_users(self):\n query = \"SELECT * FROM users\"\n self.cursor.execute(query)\n result = self.cursor.fetchall()\n return result","def get_all_users(self):\n \n sql = \"select * from users\"\n return self._query_all(sql)","async def get(self):\n await self.handle_request(self.users_api, 1)","def fetch_users(self):\n data = self._make_request()\n return data['result']","def get_users(self, *args, **kwargs):\n\n users_data = api.get_users(\n *args,\n api_key=self.__creds.api_key_v2, \n **kwargs)\n return [en.User(creds=self.__creds, **user_data) for user_data in users_data]","def fetch_all_users():\n url = \"{}/workspace/{}/users\".format(V1_API_URL, WORKSPACE_ID)\n responses = requests.get(url, headers=HEADERS)\n return [\n {\n \"acronym\": user[\"name\"].lower(),\n \"clockify_id\": user[\"id\"],\n \"email\": user[\"email\"].lower(),\n }\n for user in responses.json()\n ]","def _load_users(self) -> List[Dict]:\n try:\n api_call = self.web_client.api_call('users.list')\n if api_call.get('ok'):\n return api_call.get('members')\n except Exception:\n LOGGER.exception('Cannot get users')\n raise","def show_users():\n users_list = []\n all_users = storage.all('User')\n for obj in all_users.values():\n users_list.append(obj.to_dict())\n return jsonify(users_list)","def get_all_users():\n return Users.query.all()","def display_users(cls):\n return cls.user_list","def show_users(self):\n\n u = User(self.settings)\n users_list = u.find_users()\n\n # transform the results in a \"jsonifiable\"-form\n json_results = []\n for user in users_list:\n json_results.append(user.to_json())\n\n # return\n return json_results","def get(self):\n return UserServices.get_all()","def all_users(self):\n\n cur = self.db.cursor()\n cur.execute(\n \"\"\"SELECT user_id, username, phone, email, role, date_created \n FROM users\"\"\")\n \n user_from_db = cur.fetchall()\n if cur.rowcount >= 1: \n resp = self.serialize_user(user_from_db) \n return resp\n return None","def list_user():\n\tbegin = 0\n\tlength = 25\n\ttry:\n\t\tif request.json != None:\n\t\t\tbegin = int(request.json.get('begin', 0))\n\t\t\tlength = int(request.json.get('length', 25))\n\texcept:\n\t\tabort(403)\n\tif length > 100 :\n\t\tlength = 100\n\tuserList = User.list(begin, length)\n\tif userList == None:\n\t\tabort(400)\n\treturn jsonify({'users': map(lambda(e): e.output(), userList), 'begin': begin, 'length': len(userList)})","def get_all_users():\n return User.query.all()","def query(self, *args, **kwargs) -> List[str]:\r\n self.logger.info(\"Returning Manual Users\")\r\n\r\n return kwargs['users']","def get_users(self, query_args={}):\n endpoint = '/v3/educator/users'\n result = self.request(endpoint, query_args)\n\n users = []\n for data in result.response:\n user = User(data)\n users.append(user)\n\n return users","def users(self, predicate=None):\n \n if predicate is None:\n return self._get(\"users\").json()\n else:\n return self._get(\"users/search\", params={\"predicate\":predicate}).json()","def users(self, site = None):\r\n uids = self.user_ids()\r\n if uids:\r\n users = Account._byID(uids, True, return_dict = False)\r\n return [self.ajax_user(u) for u in users]\r\n else:\r\n return ()","def _get_users_list(self):\n return self.users['user_id'].tolist()","def list_users():\n if not check_content_type():\n return jsonify(status=CONTENT_TYPE_ERROR)\n reqdata = request.json\n if not check_token(reqdata[\"token\"]):\n return jsonify(status=TOKEN_ERROR)\n users = db.session.query(User).all()\n resdata = []\n for user in users:\n resdata.append({\"id\" : user.id, \"login\" : user.login, \"password\" : user.hash_password})\n return jsonify(data=resdata, status=OK_STATUS)","def list_users():\n return json_response(\n status=200,\n response_data={\n \"success\": True,\n \"data\": {\n \"users\": [user.serialize() for user in User.all()]\n }\n }\n )","def get_users(self, headers=None, query_params=None):\n uri = self.url + \"/users\"\n uri = uri + build_query_string(query_params)\n return requests.get(uri, headers=headers)","def get_users(self):\n query = \"\"\"SELECT firstname,lastname,othernames,email,phonenumber,\\\n username,public_id,isadmin,isactive,registered\\\n from users ORDER BY registered ASC\"\"\"\n conn = self.db\n cursor = conn.cursor(cursor_factory=psycopg2.extras.RealDictCursor)\n cursor.execute(query)\n rows = cursor.fetchall()\n return rows","def get_users():\n users = User.query # no need to order\n users_data = [user.to_dict() for user in users.all()]\n return jsonify(users=users_data)","def users():\n access_token = session['access_token']\n return \"%s\" % list_users(access_token)","def fetch_all_users():\n users = find_users()\n return to_response(users, \"No users\")","def get_users(request):\n\n users_list = User.objects.all().values(\n 'id', 'username', 'first_name', 'last_name'\n )\n\n return HttpResponse(json.dumps(\n {'users': list(users_list)}\n ))","def get_users():\n cache_key = 'GRAHAM_API_CACHED_USERS'\n cached = rd.get(cache_key)\n if cached is not None:\n return jsonify(json.loads(cached.decode('utf-8')))\n ret = []\n for user in User.select():\n ret.append({\n 'discord_id':user.user_id,\n 'user_name':user.user_name,\n 'created_ts': format_js_iso(user.created),\n 'address':user.wallet_address\n })\n rd.set(cache_key, json.dumps(ret), ex=600)\n return jsonify(ret)","def user_list(request):\r\n params = request.params\r\n order = params.get('order', None)\r\n limit = params.get('limit', None)\r\n user_list = UserMgr.get_list(order=order, limit=limit)\r\n ret = {\r\n 'count': len(user_list),\r\n 'users': [dict(h) for h in user_list],\r\n }\r\n return _api_response(request, ret)","def view_users(self):\n con = dbcon()\n cur = con.cursor()\n cur.execute(\"SELECT * FROM my_users\")\n res = cur.fetchall()\n user_list=[]\n for user in res:\n user_det = {\n 'user_id':user[0],\n 'username':user[1],\n 'password':user[2],\n 'confirmpass':user[3],\n 'addres':user[4],\n 'role':user[5]\n }\n user_list.append(user_det)\n return jsonify({'Users': user_list}), 200","def get(self):\n\n users = [marshal(user, user_fields) for user in models.ExerciseUser.select()]\n\n return users","def list_users():\n\ttry:\n\t\tusers_call = sc.api_call(\"users.list\")\n\t\tusers = []\n\t\tif users_call.get('ok'):\n\t\t\treturn users_call['members']\n\texcept:\n\t\tprint(\"users error\")\n\treturn None","def get_users():\n current_user = get_jwt_identity()\n\n if not current_user:\n print('uri=/login error=\"Missing user\"', flush=True)\n return jsonify(message=\"Missing user\"), 400\n\n if not Administrator.is_administrator(current_user):\n print('non-admin user error', flush=True)\n return jsonify(message=\"Forbidden\"), 403\n\n try:\n users = User.get_users()\n print(users, flush=True)\n return jsonify(message='{}'.format(json.dumps(users))), 200\n\n except Exception as e:\n print(e, flush=True)\n return jsonify(message='{}'.format(e)), 501","def users(self, per_page=None, page=None):\r\n params = base.get_params(None, locals())\r\n url = '{0}/{1}'.format(self.get_url(), 'users')\r\n return http.Request('GET', url, params), parsers.parse_json","def get_all_users(self) -> tuple:\n users_list: dict_list_type = [user.to_dict() for user in UserModel.query().fetch()]\n message: str = 'successfully retrieved active users'\n return jsonify({'status': True, 'payload': users_list, 'message': message}), 200","def do_get_all_users(self, *args):\n self.user_data = self.connection_obj.get_all()\n self.__class__.print_func(self, self.user_data)","def get_users_info(): \n \n data = user_obj.get_users_info()\n return data","def get_all_users(db):\n return list(db['user'].find())","def all_users():\n\n users = crud.get_users()\n\n return render_template('all_users.html', users=users)","def get_user_list():\n response = []\n for user in mongo.db.users.find():\n user[\"_id\"] = str(user[\"_id\"])\n response.append(user)\n return response","def user_list(self):\n self.cur.execute(\"SELECT username FROM users\")\n users = []\n for username in self.cur.fetchall():\n users.append(username[0])\n return users","def getUsers(self) -> List[bbUser.bbUser]:\n return list(self.users.values())","def _list_users(self):\n users = fileIO.load_json(\"users.json\")\n print(\"The list of users is as follows:\")\n for i in users:\n print(users[i][\"name\"])\n self._list_user_settings(users)","def get_all_users():\n db = api.db.get_conn()\n return list(db.users.find({}, {\"_id\": 0, \"password_hash\": 0}))","def get_users():\n users = User.query.all()\n users_schema = UserSchema()\n result = users_schema.dump(users, many=True)\n return jsonify({'users': result.data})","def users(self):\r\n return resources.Users(self)","def users_list():\n users = User.query.all()\n serialized_objects = users_schema.dumps(users, sort_keys=True, indent=4)\n\n return Response(\n response=serialized_objects,\n status=http_status.OK,\n mimetype=\"application/json\"\n )","def users(self):\r\n return resource.Users(self)","def users_get(): # noqa: E501\n base.check_session()\n ret = []\n for u in users.values():\n ret.append(_cleanuser(u))\n return ret","def users(self,org_id=None):\n if org_id is None:\n org_id = self.org_id\n return self.get('{}/orgs/{}/users'.format(ApiVersion.A1.value,org_id))","def get_users(self):\n return self.mycam.devicemgmt.GetUsers()","def user_ret():\n user_list = []\n all_objs = storage.all(\"User\")\n for obj in all_objs.values():\n user_list.append(obj.to_dict())\n return jsonify(user_list)","async def list_users(self, ctx):\n \n path = \"Users\"\n headers = {\n 'accept': 'application/json'\n }\n response = send_request(method=\"get\", path=path, headers=headers)\n users = []\n for page in response:\n users.append(f\"**{page['Name']}**: ``{page['Id']}``\")\n log.debug(users)\n\n embed = embeds.make_embed(ctx=ctx, title=\"List Users\", image_url=\"https://emby.media/resources/logowhite_1881.png\")\n\n await LinePaginator.paginate([line for line in users], ctx, embed, restrict_to_user=ctx.author)","def list_users(self, uid):\n uid = self._check_uid(uid)\n user_data = self._router_request(\n self._make_request_data(\n 'getUserList',\n data=dict(\n uid=uid\n )\n )\n )\n return user_data['data']","def users(self):\n from sagas.ofbiz.entities import OfEntity as e, oc\n rs=e().allUserLogin()\n for r in rs:\n print(r['userLoginId'])","def get_user_list():\n\tudb = UserPageDB()\n\ttry:\n\t\tusers = udb.user_list()\n\t\treturn UserList([_transform_user(u) for u in users])\n\tfinally:\n\t\tudb.close()","def users(self, request, pk):\n store = Store.objects.get(id=pk)\n user_store_ids = UserStore.objects.filter(store=store).values('user__id')\n users = User.objects.filter(id__in=user_store_ids)\n return Response(UserSerializer(users, many=True).data)","def list_users():\n check_admin()\n results = User.query.order_by(-User.id)\n return render_template('user_list.html', users=results)","def get_users():\r\n page = request.args.get('page', 1, type=int)\r\n per_page = min(request.args.get('per_page', 10, type=int), 100)\r\n data = User.to_collection_dict(User.query, page, per_page, 'api.get_users')\r\n return jsonify(data)"],"string":"[\n \"def get(self):\\n return get_all_users()\",\n \"def get(self):\\n return get_all_users()\",\n \"def get(self):\\n return get_all_users()\",\n \"def get(self):\\n return get_all_users()\",\n \"def get_all_users():\",\n \"def get_users(self):\\r\\n\\t\\tlogger.debug(\\\"Fetch users\\\")\\r\\n\\t\\t\\r\\n\\t\\treturn login.get_users()\",\n \"def users_get(self) -> Dict[str, list]:\\n self.__logger.debug('Eva.users_get called')\\n return self.__http_client.users_get()\",\n \"def get_users(self):\\n fields = ['name', ]\\n return self.get_data(\\\"myUsers\\\", fields)\",\n \"def get_users():\\n return db.fetch_users()\",\n \"def get_users():\\n return Response(f\\\"{User.get_all_users()}\\\", 200, mimetype='text/plain')\",\n \"def list_users(self):\\n _url = \\\"http://\\\" + self.host_ip + \\\":35357/v2.0/users\\\"\\n _body = None\\n _headers = {'Content-type': 'application/json',\\n 'x-auth-token': self.cloud_admin_info['token_project']}\\n\\n response = self.request(\\\"GET\\\", _url, _headers, _body)\\n if response is None:\\n LOG_OBJ.error(\\\" no response from Server\\\")\\n return response\\n if response.status not in [200, 201, 202, 203, 204]:\\n LOG_OBJ.error(\\n \\\"get user list Failed with status %s \\\" %\\n response.status)\\n return response.status\\n\\n output = json.loads(response.data)\\n LOG_OBJ.info(\\\"users List : %s\\\")\\n return output[\\\"users\\\"]\",\n \"def get_users(self):\\n users = []\\n page = 1\\n while not len(users) % 100:\\n users += self._get('/users?{0}'.format(urllib.urlencode({'per_page': 100, 'page': page})))\\n if not users:\\n break\\n page += 1\\n return users\",\n \"def get_all_users():\\n return jsonify(admin.get_all_users(current_app.scoped_session()))\",\n \"def get_users():\\n users = functions.users()\\n return users\",\n \"def get_users(self):\\n return self.execute(TABELLE['users']['select']['all'])\",\n \"def get(self):\\n\\n offset = 0\\n limit = Config.get_page_limit()\\n args = request.args\\n try:\\n offset = request.args['offset']\\n except Exception:\\n pass\\n\\n try:\\n limit = request.args['limit']\\n except Exception:\\n pass\\n\\n return self.get_request_handler(request.headers).get_all_users(offset=offset, limit=limit)\",\n \"def get_users():\\n users = storage.all('User')\\n users_list = []\\n for user in users.values():\\n users_list.append(user.to_dict())\\n return jsonify(users_list), 200\",\n \"def get_user_list(self, connection):\\n http = get_web_service(connection)\\n try:\\n req = http.request('GET', connection[\\\"url\\\"] + '/users/?_format=json')\\n data = json.loads(req.data.decode('utf-8'))\\n # print(json.dumps(data, indent=4, sort_keys=True))\\n return data\\n except urllib3.exceptions.HTTPError as e:\\n print(\\\"Connection error\\\")\\n print(e)\",\n \"def user_list(ctx):\\n data = ctx.obj.get_all_users()\\n output_json_data(data)\",\n \"def get_users(self):\\n res = self.conn.cursor().execute('SELECT id,email,username FROM users')\\n return res.fetchall()\",\n \"def getUsers(client, req):\\n client.sendTarget(req[\\\"id\\\"], key=\\\"get.users\\\", payload={\\\"payload\\\": magic.users})\",\n \"def get_users(self):\\n url = \\\"%s/api/v1/users\\\" % self.subdomain\\n req = request.get(url, headers=self.api_headers)\\n if request.ok(req):\\n response_json = req.json()\\n return response_json[\\\"users\\\"]\\n else:\\n return None\",\n \"def get_users(self):\\n return get_users(self['__store'].db, self)\",\n \"def get_users(self):\\n return self.get_all_dbusers()\",\n \"def list_users(self):\\n return self.get_admin(\\\"users\\\")\",\n \"def get_users():\\n\\n return User.query.all() # [<User user_id=1 fname=Alice lname=Apple>]\",\n \"def list_users(self):\\n raise NotImplementedError\",\n \"def get_all_user():\\n user = UserModel.objects()\\n return jsonify(user), 200\",\n \"def get_users(self, params=None):\\n url = 'users'\\n if params:\\n url += '?%s' % urllib.urlencode(params)\\n resp, body = self.get(url)\\n self.expected_success(200, resp.status)\\n body = json.loads(body)\\n return service_client.ResponseBodyList(resp, body['users'])\",\n \"def get_all_users():\\n token = request.headers.get('token')\\n\\n # Token Validation\\n token_valid, response = is_token_valid(token)\\n if not token_valid:\\n return response\\n token_username = response\\n\\n # Privilege handling\\n if token_username != 'admin':\\n return jsonify({'message': \\\"You aren't allowed to access this\\\"}), 404\\n\\n return jsonify(list(Users.values())), 200\",\n \"def get_users():\\n username = request.args.get('username')\\n netAdminToolDB = app.config['DATABASE']\\n if username != None:\\n users = []\\n users.append(netAdminToolDB.get_user_name(username))\\n else:\\n users = netAdminToolDB.get_user()\\n\\n userList = []\\n for user in users:\\n uri = url_for('get_user', user_id=user.id,_external=True)\\n #role = netAdminToolDB.get_role(user.role_id)\\n userList.append({\\n 'id': user.id,\\n 'uri': uri,\\n 'username': user.username,\\n 'display_name': user.display_name,\\n 'role': user.role_name\\n })\\n if userList == []:\\n return jsonify({'error': 'No users found'}), 404\\n\\n return jsonify({'users': userList })\",\n \"def get(self):\\n return User_Model.query.all()\",\n \"def get_users():\\n return jsonify([\\n users.to_dict()\\n for users in models.storage.all('User').values()\\n ])\",\n \"def get_users():\\n users = User.query.order_by(User.id).all()\\n users = {user.id: user.username for user in users}\\n\\n response = jsonify({\\\"success\\\": True, \\\"users\\\": users})\\n\\n return response\",\n \"def users(self):\\n return self.get_data(\\\"users\\\")\",\n \"def user_list():\\n users = User.objects.all()\\n return {\\\"users\\\": users}\",\n \"def get(self, request):\\n all_users = UserModel.objects.all()\\n all_users_serializer = UserSerializer(all_users, many=True)\\n return Response(all_users_serializer.data)\",\n \"def get(self):\\n res = self._userz.get_all_users()\\n if res:\\n return {\\n \\\"status\\\": 200,\\n \\\"data\\\": res\\n }, 200\\n else:\\n return {\\n \\\"status\\\": 404,\\n \\\"error\\\": \\\"No users found\\\"\\n }, 404\",\n \"def get_all_users():\\n users = []\\n for mv in storage.all(\\\"User\\\").values():\\n users.append(mv.to_dict())\\n return jsonify(users)\",\n \"def listUsers(self):\\n return self._client.listUsers()\",\n \"def get_all_users(self):\\n query = \\\"SELECT * FROM users\\\"\\n self.cursor.execute(query)\\n result = self.cursor.fetchall()\\n return result\",\n \"def get_all_users(self):\\n \\n sql = \\\"select * from users\\\"\\n return self._query_all(sql)\",\n \"async def get(self):\\n await self.handle_request(self.users_api, 1)\",\n \"def fetch_users(self):\\n data = self._make_request()\\n return data['result']\",\n \"def get_users(self, *args, **kwargs):\\n\\n users_data = api.get_users(\\n *args,\\n api_key=self.__creds.api_key_v2, \\n **kwargs)\\n return [en.User(creds=self.__creds, **user_data) for user_data in users_data]\",\n \"def fetch_all_users():\\n url = \\\"{}/workspace/{}/users\\\".format(V1_API_URL, WORKSPACE_ID)\\n responses = requests.get(url, headers=HEADERS)\\n return [\\n {\\n \\\"acronym\\\": user[\\\"name\\\"].lower(),\\n \\\"clockify_id\\\": user[\\\"id\\\"],\\n \\\"email\\\": user[\\\"email\\\"].lower(),\\n }\\n for user in responses.json()\\n ]\",\n \"def _load_users(self) -> List[Dict]:\\n try:\\n api_call = self.web_client.api_call('users.list')\\n if api_call.get('ok'):\\n return api_call.get('members')\\n except Exception:\\n LOGGER.exception('Cannot get users')\\n raise\",\n \"def show_users():\\n users_list = []\\n all_users = storage.all('User')\\n for obj in all_users.values():\\n users_list.append(obj.to_dict())\\n return jsonify(users_list)\",\n \"def get_all_users():\\n return Users.query.all()\",\n \"def display_users(cls):\\n return cls.user_list\",\n \"def show_users(self):\\n\\n u = User(self.settings)\\n users_list = u.find_users()\\n\\n # transform the results in a \\\"jsonifiable\\\"-form\\n json_results = []\\n for user in users_list:\\n json_results.append(user.to_json())\\n\\n # return\\n return json_results\",\n \"def get(self):\\n return UserServices.get_all()\",\n \"def all_users(self):\\n\\n cur = self.db.cursor()\\n cur.execute(\\n \\\"\\\"\\\"SELECT user_id, username, phone, email, role, date_created \\n FROM users\\\"\\\"\\\")\\n \\n user_from_db = cur.fetchall()\\n if cur.rowcount >= 1: \\n resp = self.serialize_user(user_from_db) \\n return resp\\n return None\",\n \"def list_user():\\n\\tbegin = 0\\n\\tlength = 25\\n\\ttry:\\n\\t\\tif request.json != None:\\n\\t\\t\\tbegin = int(request.json.get('begin', 0))\\n\\t\\t\\tlength = int(request.json.get('length', 25))\\n\\texcept:\\n\\t\\tabort(403)\\n\\tif length > 100 :\\n\\t\\tlength = 100\\n\\tuserList = User.list(begin, length)\\n\\tif userList == None:\\n\\t\\tabort(400)\\n\\treturn jsonify({'users': map(lambda(e): e.output(), userList), 'begin': begin, 'length': len(userList)})\",\n \"def get_all_users():\\n return User.query.all()\",\n \"def query(self, *args, **kwargs) -> List[str]:\\r\\n self.logger.info(\\\"Returning Manual Users\\\")\\r\\n\\r\\n return kwargs['users']\",\n \"def get_users(self, query_args={}):\\n endpoint = '/v3/educator/users'\\n result = self.request(endpoint, query_args)\\n\\n users = []\\n for data in result.response:\\n user = User(data)\\n users.append(user)\\n\\n return users\",\n \"def users(self, predicate=None):\\n \\n if predicate is None:\\n return self._get(\\\"users\\\").json()\\n else:\\n return self._get(\\\"users/search\\\", params={\\\"predicate\\\":predicate}).json()\",\n \"def users(self, site = None):\\r\\n uids = self.user_ids()\\r\\n if uids:\\r\\n users = Account._byID(uids, True, return_dict = False)\\r\\n return [self.ajax_user(u) for u in users]\\r\\n else:\\r\\n return ()\",\n \"def _get_users_list(self):\\n return self.users['user_id'].tolist()\",\n \"def list_users():\\n if not check_content_type():\\n return jsonify(status=CONTENT_TYPE_ERROR)\\n reqdata = request.json\\n if not check_token(reqdata[\\\"token\\\"]):\\n return jsonify(status=TOKEN_ERROR)\\n users = db.session.query(User).all()\\n resdata = []\\n for user in users:\\n resdata.append({\\\"id\\\" : user.id, \\\"login\\\" : user.login, \\\"password\\\" : user.hash_password})\\n return jsonify(data=resdata, status=OK_STATUS)\",\n \"def list_users():\\n return json_response(\\n status=200,\\n response_data={\\n \\\"success\\\": True,\\n \\\"data\\\": {\\n \\\"users\\\": [user.serialize() for user in User.all()]\\n }\\n }\\n )\",\n \"def get_users(self, headers=None, query_params=None):\\n uri = self.url + \\\"/users\\\"\\n uri = uri + build_query_string(query_params)\\n return requests.get(uri, headers=headers)\",\n \"def get_users(self):\\n query = \\\"\\\"\\\"SELECT firstname,lastname,othernames,email,phonenumber,\\\\\\n username,public_id,isadmin,isactive,registered\\\\\\n from users ORDER BY registered ASC\\\"\\\"\\\"\\n conn = self.db\\n cursor = conn.cursor(cursor_factory=psycopg2.extras.RealDictCursor)\\n cursor.execute(query)\\n rows = cursor.fetchall()\\n return rows\",\n \"def get_users():\\n users = User.query # no need to order\\n users_data = [user.to_dict() for user in users.all()]\\n return jsonify(users=users_data)\",\n \"def users():\\n access_token = session['access_token']\\n return \\\"%s\\\" % list_users(access_token)\",\n \"def fetch_all_users():\\n users = find_users()\\n return to_response(users, \\\"No users\\\")\",\n \"def get_users(request):\\n\\n users_list = User.objects.all().values(\\n 'id', 'username', 'first_name', 'last_name'\\n )\\n\\n return HttpResponse(json.dumps(\\n {'users': list(users_list)}\\n ))\",\n \"def get_users():\\n cache_key = 'GRAHAM_API_CACHED_USERS'\\n cached = rd.get(cache_key)\\n if cached is not None:\\n return jsonify(json.loads(cached.decode('utf-8')))\\n ret = []\\n for user in User.select():\\n ret.append({\\n 'discord_id':user.user_id,\\n 'user_name':user.user_name,\\n 'created_ts': format_js_iso(user.created),\\n 'address':user.wallet_address\\n })\\n rd.set(cache_key, json.dumps(ret), ex=600)\\n return jsonify(ret)\",\n \"def user_list(request):\\r\\n params = request.params\\r\\n order = params.get('order', None)\\r\\n limit = params.get('limit', None)\\r\\n user_list = UserMgr.get_list(order=order, limit=limit)\\r\\n ret = {\\r\\n 'count': len(user_list),\\r\\n 'users': [dict(h) for h in user_list],\\r\\n }\\r\\n return _api_response(request, ret)\",\n \"def view_users(self):\\n con = dbcon()\\n cur = con.cursor()\\n cur.execute(\\\"SELECT * FROM my_users\\\")\\n res = cur.fetchall()\\n user_list=[]\\n for user in res:\\n user_det = {\\n 'user_id':user[0],\\n 'username':user[1],\\n 'password':user[2],\\n 'confirmpass':user[3],\\n 'addres':user[4],\\n 'role':user[5]\\n }\\n user_list.append(user_det)\\n return jsonify({'Users': user_list}), 200\",\n \"def get(self):\\n\\n users = [marshal(user, user_fields) for user in models.ExerciseUser.select()]\\n\\n return users\",\n \"def list_users():\\n\\ttry:\\n\\t\\tusers_call = sc.api_call(\\\"users.list\\\")\\n\\t\\tusers = []\\n\\t\\tif users_call.get('ok'):\\n\\t\\t\\treturn users_call['members']\\n\\texcept:\\n\\t\\tprint(\\\"users error\\\")\\n\\treturn None\",\n \"def get_users():\\n current_user = get_jwt_identity()\\n\\n if not current_user:\\n print('uri=/login error=\\\"Missing user\\\"', flush=True)\\n return jsonify(message=\\\"Missing user\\\"), 400\\n\\n if not Administrator.is_administrator(current_user):\\n print('non-admin user error', flush=True)\\n return jsonify(message=\\\"Forbidden\\\"), 403\\n\\n try:\\n users = User.get_users()\\n print(users, flush=True)\\n return jsonify(message='{}'.format(json.dumps(users))), 200\\n\\n except Exception as e:\\n print(e, flush=True)\\n return jsonify(message='{}'.format(e)), 501\",\n \"def users(self, per_page=None, page=None):\\r\\n params = base.get_params(None, locals())\\r\\n url = '{0}/{1}'.format(self.get_url(), 'users')\\r\\n return http.Request('GET', url, params), parsers.parse_json\",\n \"def get_all_users(self) -> tuple:\\n users_list: dict_list_type = [user.to_dict() for user in UserModel.query().fetch()]\\n message: str = 'successfully retrieved active users'\\n return jsonify({'status': True, 'payload': users_list, 'message': message}), 200\",\n \"def do_get_all_users(self, *args):\\n self.user_data = self.connection_obj.get_all()\\n self.__class__.print_func(self, self.user_data)\",\n \"def get_users_info(): \\n \\n data = user_obj.get_users_info()\\n return data\",\n \"def get_all_users(db):\\n return list(db['user'].find())\",\n \"def all_users():\\n\\n users = crud.get_users()\\n\\n return render_template('all_users.html', users=users)\",\n \"def get_user_list():\\n response = []\\n for user in mongo.db.users.find():\\n user[\\\"_id\\\"] = str(user[\\\"_id\\\"])\\n response.append(user)\\n return response\",\n \"def user_list(self):\\n self.cur.execute(\\\"SELECT username FROM users\\\")\\n users = []\\n for username in self.cur.fetchall():\\n users.append(username[0])\\n return users\",\n \"def getUsers(self) -> List[bbUser.bbUser]:\\n return list(self.users.values())\",\n \"def _list_users(self):\\n users = fileIO.load_json(\\\"users.json\\\")\\n print(\\\"The list of users is as follows:\\\")\\n for i in users:\\n print(users[i][\\\"name\\\"])\\n self._list_user_settings(users)\",\n \"def get_all_users():\\n db = api.db.get_conn()\\n return list(db.users.find({}, {\\\"_id\\\": 0, \\\"password_hash\\\": 0}))\",\n \"def get_users():\\n users = User.query.all()\\n users_schema = UserSchema()\\n result = users_schema.dump(users, many=True)\\n return jsonify({'users': result.data})\",\n \"def users(self):\\r\\n return resources.Users(self)\",\n \"def users_list():\\n users = User.query.all()\\n serialized_objects = users_schema.dumps(users, sort_keys=True, indent=4)\\n\\n return Response(\\n response=serialized_objects,\\n status=http_status.OK,\\n mimetype=\\\"application/json\\\"\\n )\",\n \"def users(self):\\r\\n return resource.Users(self)\",\n \"def users_get(): # noqa: E501\\n base.check_session()\\n ret = []\\n for u in users.values():\\n ret.append(_cleanuser(u))\\n return ret\",\n \"def users(self,org_id=None):\\n if org_id is None:\\n org_id = self.org_id\\n return self.get('{}/orgs/{}/users'.format(ApiVersion.A1.value,org_id))\",\n \"def get_users(self):\\n return self.mycam.devicemgmt.GetUsers()\",\n \"def user_ret():\\n user_list = []\\n all_objs = storage.all(\\\"User\\\")\\n for obj in all_objs.values():\\n user_list.append(obj.to_dict())\\n return jsonify(user_list)\",\n \"async def list_users(self, ctx):\\n \\n path = \\\"Users\\\"\\n headers = {\\n 'accept': 'application/json'\\n }\\n response = send_request(method=\\\"get\\\", path=path, headers=headers)\\n users = []\\n for page in response:\\n users.append(f\\\"**{page['Name']}**: ``{page['Id']}``\\\")\\n log.debug(users)\\n\\n embed = embeds.make_embed(ctx=ctx, title=\\\"List Users\\\", image_url=\\\"https://emby.media/resources/logowhite_1881.png\\\")\\n\\n await LinePaginator.paginate([line for line in users], ctx, embed, restrict_to_user=ctx.author)\",\n \"def list_users(self, uid):\\n uid = self._check_uid(uid)\\n user_data = self._router_request(\\n self._make_request_data(\\n 'getUserList',\\n data=dict(\\n uid=uid\\n )\\n )\\n )\\n return user_data['data']\",\n \"def users(self):\\n from sagas.ofbiz.entities import OfEntity as e, oc\\n rs=e().allUserLogin()\\n for r in rs:\\n print(r['userLoginId'])\",\n \"def get_user_list():\\n\\tudb = UserPageDB()\\n\\ttry:\\n\\t\\tusers = udb.user_list()\\n\\t\\treturn UserList([_transform_user(u) for u in users])\\n\\tfinally:\\n\\t\\tudb.close()\",\n \"def users(self, request, pk):\\n store = Store.objects.get(id=pk)\\n user_store_ids = UserStore.objects.filter(store=store).values('user__id')\\n users = User.objects.filter(id__in=user_store_ids)\\n return Response(UserSerializer(users, many=True).data)\",\n \"def list_users():\\n check_admin()\\n results = User.query.order_by(-User.id)\\n return render_template('user_list.html', users=results)\",\n \"def get_users():\\r\\n page = request.args.get('page', 1, type=int)\\r\\n per_page = min(request.args.get('per_page', 10, type=int), 100)\\r\\n data = User.to_collection_dict(User.query, page, per_page, 'api.get_users')\\r\\n return jsonify(data)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8544133","0.8544133","0.8544133","0.8544133","0.85363835","0.84463185","0.84404284","0.83349496","0.8291406","0.80483234","0.79664856","0.792892","0.79038525","0.7897605","0.78914654","0.78914565","0.78870755","0.7829862","0.78199285","0.7812609","0.7792977","0.77870435","0.77703583","0.77662826","0.77554095","0.7729239","0.7726448","0.7717584","0.7712661","0.7701141","0.7678444","0.76757616","0.7624274","0.7621205","0.76138943","0.7607977","0.7604816","0.7580888","0.7575585","0.7564121","0.7559156","0.75556767","0.75535303","0.75470096","0.7546209","0.7514325","0.75052625","0.7504724","0.7499578","0.74914324","0.7490444","0.7481357","0.74620193","0.74542475","0.7419222","0.74177736","0.74146307","0.7403394","0.7391713","0.7376818","0.7369072","0.73684704","0.73680794","0.7367668","0.7366275","0.7363989","0.73605573","0.7350875","0.73480946","0.7343472","0.7339294","0.73389333","0.7338032","0.7333487","0.7327964","0.73236525","0.72968936","0.7293604","0.72917646","0.72891456","0.728009","0.72769207","0.7273782","0.7269361","0.72650754","0.72633153","0.72442275","0.72439015","0.72356445","0.7232393","0.7228723","0.72280896","0.7208068","0.72050214","0.7204784","0.72036386","0.72028345","0.7198388","0.7196147","0.71834356"],"string":"[\n \"0.8544133\",\n \"0.8544133\",\n \"0.8544133\",\n \"0.8544133\",\n \"0.85363835\",\n \"0.84463185\",\n \"0.84404284\",\n \"0.83349496\",\n \"0.8291406\",\n \"0.80483234\",\n \"0.79664856\",\n \"0.792892\",\n \"0.79038525\",\n \"0.7897605\",\n \"0.78914654\",\n \"0.78914565\",\n \"0.78870755\",\n \"0.7829862\",\n \"0.78199285\",\n \"0.7812609\",\n \"0.7792977\",\n \"0.77870435\",\n \"0.77703583\",\n \"0.77662826\",\n \"0.77554095\",\n \"0.7729239\",\n \"0.7726448\",\n \"0.7717584\",\n \"0.7712661\",\n \"0.7701141\",\n \"0.7678444\",\n \"0.76757616\",\n \"0.7624274\",\n \"0.7621205\",\n \"0.76138943\",\n \"0.7607977\",\n \"0.7604816\",\n \"0.7580888\",\n \"0.7575585\",\n \"0.7564121\",\n \"0.7559156\",\n \"0.75556767\",\n \"0.75535303\",\n \"0.75470096\",\n \"0.7546209\",\n \"0.7514325\",\n \"0.75052625\",\n \"0.7504724\",\n \"0.7499578\",\n \"0.74914324\",\n \"0.7490444\",\n \"0.7481357\",\n \"0.74620193\",\n \"0.74542475\",\n \"0.7419222\",\n \"0.74177736\",\n \"0.74146307\",\n \"0.7403394\",\n \"0.7391713\",\n \"0.7376818\",\n \"0.7369072\",\n \"0.73684704\",\n \"0.73680794\",\n \"0.7367668\",\n \"0.7366275\",\n \"0.7363989\",\n \"0.73605573\",\n \"0.7350875\",\n \"0.73480946\",\n \"0.7343472\",\n \"0.7339294\",\n \"0.73389333\",\n \"0.7338032\",\n \"0.7333487\",\n \"0.7327964\",\n \"0.73236525\",\n \"0.72968936\",\n \"0.7293604\",\n \"0.72917646\",\n \"0.72891456\",\n \"0.728009\",\n \"0.72769207\",\n \"0.7273782\",\n \"0.7269361\",\n \"0.72650754\",\n \"0.72633153\",\n \"0.72442275\",\n \"0.72439015\",\n \"0.72356445\",\n \"0.7232393\",\n \"0.7228723\",\n \"0.72280896\",\n \"0.7208068\",\n \"0.72050214\",\n \"0.7204784\",\n \"0.72036386\",\n \"0.72028345\",\n \"0.7198388\",\n \"0.7196147\",\n \"0.71834356\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7241947"},"document_rank":{"kind":"string","value":"88"}}},{"rowIdx":81189,"cells":{"query":{"kind":"string","value":"patch updates an existing user in the system"},"document":{"kind":"string","value":"def put(self, user_id):\n data = request.json\n return update_user(data, user_id)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def update_user():","def update_user():\n #TODO user update \n pass","def update_user(id):\n pass","def test_patch_user(self):\n pass","def update(self, user: U) -> None:\n ...","def test_040_update_user(self):\n\n testflow.step(\"Updating user %s\", TEST_USER2)\n assert USER_CLI.run(\n 'edit',\n TEST_USER2,\n attribute='firstName=userX2',\n )[0]","def test_update_user(self):\n pass","def update_user(self):\n self.client.force_authenticate(user=self.user)\n self.response = self.client.patch(\n reverse(\n 'edit_account',kwargs={ 'pk': self.user.id}),\n self.updated_data, format='json'\n )\n self.user = CustomUser.objects.get(username=self.user.username)","def test_patch_user(self):\n new_user = self.make_user('new_user')\n url = reverse(\n 'projectroles:api_role_update',\n kwargs={'roleassignment': self.update_as.sodar_uuid},\n )\n patch_data = {'user': str(new_user.sodar_uuid)}\n response = self.request_knox(url, method='PATCH', data=patch_data)\n self.assertEqual(response.status_code, 400, msg=response.content)","def patch_user(user_id):\n success = True\n try:\n usr = db.session.query(User).get(user_id)\n for item in request.json:\n if item == 'username':\n usr.username = request.json['username']\n elif item == 'email':\n usr.username = request.json['email']\n db.session.commit()\n except:\n success = False\n return jsonify(success=success)","def test_user_update(self):\n userPK = self.testUser.pk\n url = reverse('User-detail', kwargs={'pk': userPK})\n data = {'username': 'company1NewTest'}\n response = self.client.put(url, data, format='json')\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n self.assertEqual(User.objects.get(pk=userPK).username,\n 'company1NewTest')\n data = {'username': 'company1NewTest2'}\n response = self.client.patch(url, data, format='json')\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n self.assertEqual(User.objects.get(pk=userPK).username,\n 'company1NewTest2')","def test_update_user_profile(self):\r\n payload = {\r\n 'name': 'new_name',\r\n 'password': 'password123'\r\n }\r\n\r\n res = self.client.patch(ME_URL, payload)\r\n\r\n self.user.refresh_from_db()\r\n\r\n self.assertEqual(self.user.name, payload['name'])\r\n self.assertTrue(self.user.check_password(payload['password']))\r\n self.assertEqual(res.status_code, status.HTTP_200_OK)","def test_update_user_profile(self):\n payload = {'name': 'new name', 'password': 'newpassword123'}\n\n res = self.client.patch(ME_URL, payload)\n\n self.user.refresh_from_db()\n self.assertEqual(self.user.name, payload['name'])\n self.assertTrue(self.user.check_password(payload['password']))\n self.assertEqual(res.status_code, status.HTTP_200_OK)","def test_update_the_created_user():\n pytest.test_user.name += \"Updated\"\n response = api_helper.update_user(pytest.test_user)\n assert response.status_code == 200","def test_update_user_profile(self):\n payload = {'name': 'new name', 'password': 'newpassword123'}\n\n response = self.client.patch(ME_URL, payload)\n\n self.user.refresh_from_db()\n self.assertEqual(self.user.name, payload['name'])\n self.assertTrue(self.user.check_password(payload['password']))\n self.assertEqual(response.status_code, status.HTTP_200_OK)","def updateUser(self, payload):\n\t\turl = \"https://habitica.com/api/v3/user\"\n\t\treturn(putUrl(url, self.credentials, payload))","def testUpdateUser(self):\n UserAPI().create([(u'test', u'secret', u'name', u'name@example.com')])\n user = getUser(u'test')\n passwordHash = user.passwordHash\n self.store.commit()\n info = TUserUpdate(u'test', u'password', u'new-name',\n u'new-name@example.com')\n with login(u'fluiddb', self.admin.objectID, self.transact) as session:\n yield self.facade.updateUser(session, info)\n\n self.store.rollback()\n self.assertEqual(u'test', user.username)\n self.assertNotEqual(passwordHash, user.passwordHash)\n self.assertEqual(u'new-name', user.fullname)\n self.assertEqual(u'new-name@example.com', user.email)","def test_update_user_profile(self):\n payload = {'name': 'Test name', 'password': 'new_password'}\n res = self.client.patch(ME_URL, payload)\n\n self.user.refresh_from_db()\n self.assertEqual(self.user.name, payload['name'])\n self.assertTrue(self.user.check_password(payload['password']))\n self.assertEqual(res.status_code, status.HTTP_200_OK)","def test_update_profile_valid_patch(self):\n update_fields = {\n 'password': 'NewPassword!',\n 'name': 'Mona Lisa'\n }\n res = self.client.patch(ME_URL, update_fields)\n\n self.assertEqual(res.status_code, status.HTTP_200_OK)\n\n self.user.refresh_from_db()\n\n self.assertTrue(self.user.check_password(update_fields['password']))\n self.assertEquals(self.user.name, update_fields['name'])","def patch(self, user_id):\n\n data = request.get_json()\n\n res = self._user.update_user(user_id, data)\n\n if res:\n return {\n \"status\": 200,\n \"data\": [{\n \"id\": res[\"id\"],\n \"message\": \"user record has been updated\"\n }]\n }, 200\n else:\n return {\n \"status\": 404,\n \"error\": \"Not found for id {}\".format(user_id)\n }, 404","def do_user_update():\n targetUsers = User.query.filter_by(id=request.form['id']).all()\n if not any(targetUsers):\n return user_list(\"Unknown user.\")\n\n targetUser = targetUsers[0]\n\n targetUser.first_name = request.form['first_name']\n targetUser.name = request.form['name']\n targetUser.nick = request.form['nick']\n targetUser.mail = request.form['mail']\n targetUser.role = request.form['role']\n targetUser.state = request.form['state']\n targetUser.gender = request.form['gender']\n targetUser.meter_id = request.form['meter_id']\n targetUser.group_id = request.form['group_id']\n\n db.session.commit()\n return user_list(\"Updated user \" + targetUser.name)","def fusion_api_edit_user(self, body, uri, api=None, headers=None):\n return self.user.update(body, uri, api, headers)","def test_update_user_profile(self):\n payload = {\"name\": \"Lucifer\", 'password': \"12346987\"}\n res = self.client.patch(ME_URL, payload)\n\n self.user.refresh_from_db()\n\n self.assertEqual(self.user.name, payload['name'])\n self.assertTrue(self.user.check_password(payload['password']))\n self.assertEqual(res.status_code, status.HTTP_200_OK)","def mod_user(self, username, data):\n headers = {\"user-agent\": self.u_agent}\n req_url = self.normalize_admin_url(u\"users/{}\".format(username))\n res = requests.put(\n req_url,\n headers=headers,\n auth=self.auth,\n data=json.dumps(data),\n verify=False,\n )\n if res.status_code == 200:\n return Response(0, u\"User {} has been modified\".format(username))\n else:\n return Response(res.status_code, res)","async def patch(self, request, uid):\n current = await self.get(request, uid)\n\n name = self.get_field(request, 'name', default=current['name'])\n\n async with request.app.pool.acquire() as conn:\n await conn.execute('UPDATE ysr.user SET name=$1 WHERE id=$2', name,\n uid)\n\n return await self.get(request, uid)","def test_user_update_request(self):\n pass","def updateUser(self, request, context):\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\n context.set_details('Method not implemented!')\n raise NotImplementedError('Method not implemented!')","def put(self, user_id):\r\n return update_user(request, user_id)","def test_update_user_profile(setup_client, setup_user):\n client = setup_client\n user = setup_user\n payload = {\n \"name\": \"New name\",\n \"role\": \"Purchaser\",\n \"password\": \"New password\"\n }\n res = client.patch(ME_URL, payload)\n user.refresh_from_db()\n assert res.status_code == status.HTTP_200_OK\n assert user.name == payload[\"name\"]\n assert user.role == payload[\"role\"]\n assert user.check_password(payload[\"password\"])\n assert res.status_code == status.HTTP_200_OK","def test_update_user_profile(self):\n\n new_credentials = {'name': 'New Name', 'password': 'NewTestpass12'}\n response = self.client.patch(URL_ME, new_credentials)\n\n # Refresh the details of the user from the database.\n self.user.refresh_from_db()\n\n # Check that the update is successful.\n self.assertEqual(self.user.name, new_credentials['name'])\n self.assertTrue(self.user.check_password(new_credentials['password']))\n self.assertEqual(response.status_code, status.HTTP_200_OK)","def wmUpdateUser(self):\n user_role = uiCommon.GetSessionUserRole()\n if user_role != \"Administrator\":\n raise Exception(\"Only Administrators can edit user accounts.\")\n\n args = uiCommon.getAjaxArgs()\n\n u = catouser.User()\n u.FromID(args[\"ID\"])\n\n if u.ID:\n # these changes are done BEFORE we manipulate the user properties for update.\n\n new_pw = uiCommon.unpackJSON(args.get(\"Password\"))\n random_pw = args.get(\"NewRandomPassword\")\n\n # if a password was provided, or the random flag was set...exclusively\n if new_pw:\n # if the user requesting the change *IS* the user being changed...\n # set force_change to False\n force = True\n if u.ID == uiCommon.GetSessionUserID():\n force = False\n\n u.ChangePassword(new_password=new_pw, force_change=force)\n uiCommon.WriteObjectChangeLog(catocommon.CatoObjectTypes.User, u.ID, u.FullName, \"Password changed.\")\n elif random_pw:\n u.ChangePassword(generate=random_pw)\n uiCommon.WriteObjectChangeLog(catocommon.CatoObjectTypes.User, u.ID, u.FullName, \"Password reset.\")\n\n # now we can change the properties\n u.LoginID = args.get(\"LoginID\")\n u.FullName = args.get(\"FullName\")\n u.Status = args.get(\"Status\")\n u.AuthenticationType = args.get(\"AuthenticationType\")\n u.ForceChange = args.get(\"ForceChange\")\n u.Email = args.get(\"Email\")\n u.Role = args.get(\"Role\")\n u.FailedLoginAttempts = args.get(\"FailedLoginAttempts\")\n u.Expires = args.get(\"Expires\")\n\n u._Groups = args.get(\"Groups\")\n\n if u.DBUpdate():\n uiCommon.WriteObjectChangeLog(catocommon.CatoObjectTypes.User, u.ID, u.ID, \"User updated.\")\n\n return json.dumps({\"result\": \"success\"})","def update_user(user_id, data):\n logging.debug(\"Uptating user: user_id={}\".format(user_id))\n return ask('appusers/{0}'.format(user_id), data, 'put')","def put(self, new_data, user_id):\n\n request_id = get_jwt_identity()\n user = user_crud.get(request_id)\n if not user.is_superuser:\n abort(401,\n message=\"You do not have permission to view this endpoint\")\n user = user_crud.get(user_id)\n user = user_crud.update(user, new_data)\n if not user['added']:\n abort(409, message=\"User with this email exist\")\n user = user['db_obj']\n return user","def test_admin_update_user(self):\n resp = self.admin_create_user()\n reply = self.admin_login()\n token = reply['token']\n user = dict(\n name='Summer Lover',\n username='lover',\n password='Andela8',\n role='attendant'\n )\n resp = self.client.put(\n '/api/v1/users/2',\n content_type='application/json',\n data=json.dumps(user),\n headers={'Authorization': 'Bearer {}'.format(token)}\n )\n reply = json.loads(resp.data.decode())\n self.assertEqual(reply['message'], 'user updated!')\n self.assertEqual(resp.status_code, 200)","def test_resource_user_resource_change_user_patch(self):\n pass","def update_user(self, username):\n parser_update.add_argument('email', type=validate_email,\n required=False, nullable=False,\n help=\"Email must be formatted correctly\")\n\n parser_update.add_argument('phoneNumber', type=validate_phonenumber,\n required=False, nullable=False,\n help=\"Enter a valid phone number\")\n\n parser_update.add_argument('firstname', type=validate_characters,\n required=False, nullable=False,\n help=\"First name must be formatted correctly\")\n\n parser_update.add_argument('lastname', type=validate_characters,\n required=False, nullable=False,\n help=\"Last name must be formatted correctly\")\n\n parser_update.add_argument('othernames', type=validate_characters,\n required=False, nullable=False,\n help=\"Other name must be formatted correctly\")\n\n user = self.get_user(username)\n if user is None:\n return None\n\n args = parser_update.parse_args()\n new_data = {\n 'email': request.json.get('email', user['email']).lower(),\n 'firstname': request.json.get('firstname', user['firstname']).capitalize(),\n 'lastname': request.json.get('lastname', user['lastname']).capitalize(),\n 'othernames': request.json.get('othernames', user['othernames']).capitalize(),\n 'phoneNumber': request.json.get('phoneNumber', user['phonenumber']),\n }\n\n getEmail = self.get_user(new_data['email'])\n verification_status = True\n\n if user['email'] != new_data['email']:\n if getEmail is not None:\n return 'email exists'\n verification_status = False\n\n query = \"\"\"UPDATE users SET firstname=%s,lastname=%s,othernames=%s,\\\n email=%s,phonenumber=%s,emailverified=%s WHERE username=%s\"\"\"\n values = new_data['firstname'], new_data['lastname'], new_data['othernames'], new_data['email'], new_data['phoneNumber'], verification_status, username\n\n conn = self.db\n cursor = conn.cursor()\n cursor.execute(query, values)\n conn.commit()\n return new_data","def testUpdateUserIgnoresCase(self):\n UserAPI().create([(u'test', u'secret', u'name', u'name@example.com')])\n user = getUser(u'test')\n passwordHash = user.passwordHash\n info = TUserUpdate(u'TesT', u'password', u'new-name',\n u'new-name@example.com')\n self.store.commit()\n with login(u'fluiddb', self.admin.objectID, self.transact) as session:\n yield self.facade.updateUser(session, info)\n\n self.store.rollback()\n self.assertEqual(u'test', user.username)\n self.assertNotEqual(passwordHash, user.passwordHash)\n self.assertEqual(u'new-name', user.fullname)\n self.assertEqual(u'new-name@example.com', user.email)","def patch(self, request, *args, **kwargs):\n user= get_object_or_404(YouYodaUser, email=request.data.get('email'))\n # user = YouYodaUser.objects.get(auth_token=request.headers['Authorization'].replace('Token ', ''))\n serializer = ProfileEditSerializer(user, data=request.data, partial=True)\n if serializer.is_valid():\n serializer.save()\n return Response(serializer.data, status=status.HTTP_201_CREATED)\n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)","def testUpdateUserWithoutEmail(self):\n UserAPI().create([(u'user', u'secret', u'name', u'name@example.com')])\n user = getUser(u'user')\n passwordHash = user.passwordHash\n info = TUserUpdate(u'user', u's3cr3t', u'new-name', None,\n 'USER_MANAGER')\n self.store.commit()\n with login(u'fluiddb', self.admin.objectID, self.transact) as session:\n yield self.facade.updateUser(session, info)\n\n self.store.rollback()\n self.assertEqual(u'user', user.username)\n self.assertNotEqual(passwordHash, user.passwordHash)\n self.assertEqual(u'new-name', user.fullname)\n self.assertEqual(u'name@example.com', user.email)\n self.assertEqual(Role.USER_MANAGER, user.role)","def test_update_user(self):\n token = self.authenticate_user(self.auth_user_data).data[\"token\"]\n response = self.client.put(self.user_url,\n self.user_data,\n HTTP_AUTHORIZATION=f'token {token}',\n format='json')\n self.assertEqual(response.status_code, status.HTTP_200_OK)","def update_user(self, user):\n query = TABELLE['id_users']['update']\n return self.execute(query,\n (user['admin'], user['tester'], user['loot_user'], user['loot_admin'], user['banned'],\n user['id']))","def request_user_update():\n target_user = User.query.filter_by(id=request.args['id']).first()\n if target_user is None:\n return user_list(\"Unknown user.\")\n\n return Response(render_template('admin/user/create-update.html',\n csrf_token=(\n get_raw_jwt() or {}).get(\"csrf\"),\n target=\"/admin/user/update\",\n genders=list(GenderType),\n states=list(StateType),\n groups=Group.query.all(),\n roles=list(RoleType),\n id=target_user.id,\n gender=target_user.gender,\n first_name=target_user.first_name,\n name=target_user.name,\n nick=target_user.nick,\n mail=target_user.mail,\n meter_id=target_user.meter_id,\n group_id=target_user.group_id,\n role=target_user.role,\n state=target_user.state),\n mimetype='text/html')","def test_update_another_user(self):\n user1_response = self.client.post(reverse('user-list'), {\n 'username': 'aseem', 'password': 'passwrodaosida123'\n })\n update_user_resp = self.client.patch(\n reverse('user-list') + '1/', {\n 'username': 'rakesh', 'password': 'passwrodaosida123'\n })\n\n self.assertEqual(update_user_resp.status_code, status.HTTP_401_UNAUTHORIZED)","def update_user():\n user = request.json\n user[\"_id\"] = validate_id(user[\"_id\"])\n if not user_service.update_user(user):\n response = {\n \"status\": False,\n \"message\": f\"No se pudo actualizar el usuario: {str(user['_id'])}\",\n }\n resp = make_response(dumps(response), 404)\n else:\n response = {\n \"status\": True,\n \"message\": f\"Se actualizo corretamente el usuario: {str(user['_id'])}\",\n }\n resp = make_response(dumps(response), 200)\n resp.headers[\"Content-Type\"] = \"application/json\"\n return resp","def put(self, new_data):\n\n user_id = get_jwt_identity()\n user = user_crud.get(user_id)\n if not user:\n abort(404, message=\"User not Found\")\n user = user_crud.update(user, new_data)\n if not user['added']:\n abort(409, message=\"User with this email exits\")\n user = user['db_obj']\n return user","def update(self, oid, name=None, email=None, default_project=None, \n domain=None, password=None, enabled=None, description=None):\n data = {\"user\": {}}\n \n if name is not None:\n data['user']['name'] = name\n if email is not None:\n data['user']['email'] = email \n if default_project is not None:\n data['user']['default_project_id'] = default_project\n if domain is not None:\n data['user']['domain_id'] = domain\n if password is not None:\n data['user']['password'] = password\n if enabled is not None:\n data['user']['enabled'] = enabled\n if description is not None:\n data['user']['description'] = description \n \n path = '/users/%s' % oid\n res = self.client.call(path, 'PATCH', data=json.dumps(data), \n token=self.manager.identity.token)\n self.logger.debug('Update openstack user: %s' % truncate(res))\n return res[0]['user']","def testUpdateUserWithoutRole(self):\n UserAPI().create([(u'user', u'secret', u'name', u'name@example.com')])\n user = getUser(u'user')\n passwordHash = user.passwordHash\n info = TUserUpdate(u'user', u's3cr3t', u'new-name', 'new@example.com',\n None)\n self.store.commit()\n with login(u'fluiddb', self.admin.objectID, self.transact) as session:\n yield self.facade.updateUser(session, info)\n\n self.store.rollback()\n self.assertEqual(u'user', user.username)\n self.assertNotEqual(passwordHash, user.passwordHash)\n self.assertEqual(u'new-name', user.fullname)\n self.assertEqual(u'new@example.com', user.email)\n self.assertEqual(Role.USER, user.role)","def test_update_useruser_uuid_put(self):\n pass","def update(self, request, *args, **kwargs):\n return super(UserViewSet, self).update(request, *args, **kwargs)","def update_user(context, params):\n\n user = User.objects.filter(id=params.get('id')).first()\n if not user:\n raise ValueError(\"user not found\")\n user.language = Language.objects.filter(id=params.get('language_id', None)).first()\n user.deputy = User.objects.filter(id=params.get('deputy_id', None)).first()\n # user.edited_by = context.user\n\n user.save()\n\n update_person(context, user, params)\n\n user.save()\n return user","def updateUser(self, new_data): #new_data is a dict with new fields\n\n\t\tthe_data = {};\n\t\tthe_data[\"email\"] = self.email;\n\t\t\n\t\tnew_data[\"date.update\"] = getTimeStamp();\n\n\t\tdata_prime = self.db.getData(the_data, new_data);\n\n\t\tresult = self.db.request(\"update\", data_prime);\n\n\t\tif result:\n\t\t\treturn new_data;\n\t\telse:\n\t\t\treturn False;","def test_update_profile_success(self):\n payload = {\n 'email': 'newtest@gmail.com',\n 'password': 'newpassword'\n }\n res = self.client.patch(ME_URL, payload)\n\n # Refresh the user object with latest values from db\n self.user.refresh_from_db()\n\n self.assertEqual(res.status_code, status.HTTP_200_OK)\n self.assertEqual(self.user.email, payload['email'])\n self.assertTrue(self.user.check_password(payload['password']))","def update_user(self, instance, user, name=None, password=None, host=None):\n return instance.update_user(user, name=name, password=password,\n host=host)","def testUpdateUserWithoutPassword(self):\n UserAPI().create([(u'user', u'secret', u'name', u'name@example.com')])\n user = getUser(u'user')\n passwordHash = user.passwordHash\n info = TUserUpdate(u'user', None, u'new-name', u'new-name@example.com',\n 'USER_MANAGER')\n self.store.commit()\n with login(u'fluiddb', self.admin.objectID, self.transact) as session:\n yield self.facade.updateUser(session, info)\n\n self.store.rollback()\n self.assertEqual(u'user', user.username)\n self.assertEqual(passwordHash, user.passwordHash)\n self.assertEqual(u'new-name', user.fullname)\n self.assertEqual(u'new-name@example.com', user.email)\n self.assertEqual(Role.USER_MANAGER, user.role)","def put(self):\n form = UpdateForm.from_json(request.get_json())\n\n if not g.user:\n return jsonify({\"login_staus\": False, \"message\": \"Please login\"})\n\n if form.validate_on_submit():\n current_user = User.query.filter_by(id=g.user.id).first()\n if form.email.data:\n current_user.email = form.email.data\n if form.new_password.data:\n current_user.password = form.new_password.data\n db.session.commit()\n return jsonify({\"update_status\": True})\n return jsonify({\"update_status\": False, \"message\": form.errors})","def update_user(cursor, username, attr, value):\n if attr not in ['username', 'password', 'email', 'groups']:\n raise ValueError(f\"{attr!r} is not a valid user attribute\")\n if attr == 'password':\n value = argon2.hash(value)\n elif attr == 'groups':\n current = get_usergroups(cursor, username)\n for group in current.difference(value):\n remove_usergroup(cursor, username, group)\n for group in value.difference(current):\n create_usergroup(cursor, username, group)\n return \n\n cursor.execute(f\"\"\"\n UPDATE users\n SET\n {attr} = ?\n WHERE\n username = ?\n \"\"\", (value, username))","def update_user(request):\n post = request.POST.dict()\n user = post.get('user_id')\n if user is None:\n response = {'status':-1, 'status_message':'No user_id specified'}\n return HttpResponse(json.dumps(response))\n try:\n user_obj = User.objects.get(id=user)\n except User.DoesNotExist:\n response = {'status':-1, 'status_message':'Invalid user_id: {}'.format(user)}\n return HttpResponse(json.dumps(response))\n user_obj.first_name = post.get('first_name')\n user_obj.last_name = post.get('last_name')\n password = post.get('password')\n if password and password != \"**********\":\n # update the password\n user_obj.set_password(password)\n if post.get('username'):\n user_obj.username = post['username']\n user_obj.email = post.get('email')\n user_obj.is_superuser = json.loads(post.get('is_admin', 'false'))\n user_obj.is_active = json.loads(post.get('is_enabled', 'false'))\n user_obj.save()\n response = {'status': 1, 'status_message': 'Success'}\n return HttpResponse(json.dumps(response))","def test_update(self):\n\n user = CustomUser.objects.get(email=\"test@test.test\")\n user.update(first_name=\"UpdatedName\", second_name=\"UpdatedSecondName\")\n\n self.assertEqual(user.first_name, \"UpdatedName\")\n self.assertEqual(user.second_name, \"UpdatedSecondName\")","def test_update_profile_valid_put(self):\n update_user = {\n 'email': 'new@email.com',\n 'password': 'NewPassword!',\n }\n res = self.client.put(ME_URL, update_user)\n\n self.assertEqual(res.status_code, status.HTTP_200_OK)\n\n self.user.refresh_from_db()\n\n self.assertTrue(self.user.check_password(update_user['password']))\n self.assertEquals(self.user.email, update_user['email'])\n self.assertTrue(self.user.name)","def update_user():\n json = request.json\n name = json[\"name\"]\n email = json[\"email\"]\n pwd = json[\"pwd\"]\n user_id = json[\"user_id\"]\n if name and email and pwd and user_id and request.method == \"POST\":\n # save edits\n sql = \"UPDATE users SET user_name=%s, user_email=%s, \" \\\n \"user_password=%s WHERE user_id=%s\"\n data = (name, email, pwd, user_id)\n try:\n conn = mysql.connect()\n cursor = conn.cursor()\n cursor.execute(sql, data)\n conn.commit()\n resp = jsonify(\"User updated successfully!\")\n resp.status_code = 200\n cursor.close()\n conn.close()\n return resp\n except Exception as exception:\n return jsonify(str(exception))\n else:\n return jsonify(\"Please provide id, name, email and pwd\")","def patch(self, request):\n\n user_obj = UserProfile.objects.filter(id=request.user.id).first()\n if not user_obj:\n return existence_error('user')\n\n request_json = {\n 'profile_photo': request.data.get('profile_photo')\n }\n\n user_serialized = EditUserProfileSerializer(user_obj, data=request_json, partial=True)\n if not user_serialized.is_valid():\n return validate_error(user_serialized)\n user_serialized.save()\n\n response_json = {\n 'status': True,\n 'message': 'successful',\n 'data': {}\n }\n\n return Response(response_json, status=201)","def test_users_update(mocker):\r\n mocker.patch('subprocess.call')\r\n users.update(user_dict)\r\n subprocess.call.assert_called_with([\r\n 'usermod',\r\n '-p',\r\n password,\r\n '-G',\r\n 'wheel,dev',\r\n 'kevin',\r\n ])","def put(self, user_id):\n\n user_data, error = user_schema.load(api.payload['data'])\n\n user = User.objects.get_or_404(public_id=user_id)\n user.update(updated_at=datetime.utcnow, **user_data)\n \n return user_schema.dump(user)","def update_user(cls, **kwargs):\n return cls._do_call(\n 'PUT', cls.api_endpoint + 'users', params=kwargs)","def view_update_user(self, user, new_pw, old_pw):\r\n user.realm._checker.passwd(user.userID, new_pw, old_pw)","def test_update_user(self):\n\n update_dict = dict(\n username='test_another_username',\n role='test_new_role',\n department='test_new_department'\n )\n\n # Update non-existing user\n updated = self.user_api.update_user(MAGEN_USER['user_uuid'], update_dict)\n self.assertTrue(updated.success)\n self.assertEqual(updated.count, 0)\n\n # Insert user in Database\n inserted = self.user_api.insert_user(MAGEN_USER)\n self.assertTrue(inserted.success)\n\n # Update existing user\n updated = self.user_api.update_user(MAGEN_USER['user_uuid'], update_dict)\n self.assertTrue(updated.success)\n self.assertEqual(updated.count, 1)\n # Verify that data was updated\n selected = self.user_api.get_user(MAGEN_USER['user_uuid'])\n self.assertTrue(selected.success)\n self.assertEqual(selected.documents['username'], update_dict['username'])\n self.assertEqual(selected.documents['role'], update_dict['role'])\n self.assertEqual(selected.documents['department'], update_dict['department'])","def test_user_update_permissions(self):\n userPK = User.objects.get(username='c2e1').pk\n url = reverse('User-detail', kwargs={'pk': userPK})\n data = {'username': 'company1NewTest'}\n response = self.client.put(url, data, format='json')\n #This is 404 instead of 403 because there is no way to view a company\n #that you arent an employee of.\n self.assertEqual(response.status_code, status.HTTP_404_NOT_FOUND)\n self.assertNotEqual(User.objects.get(pk=userPK).username,\n 'company1NewTest')\n data = {'username': 'company1NewTest2'}\n response = self.client.patch(url, data, format='json')\n #This is 404 instead of 403 because there is no way to view a company\n #that you arent an employee of.\n self.assertEqual(response.status_code, status.HTTP_404_NOT_FOUND)\n self.assertNotEqual(User.objects.get(pk=userPK).username,\n 'company1NewTest2')","def testUpdateUserWithoutName(self):\n UserAPI().create([(u'user', u'secret', u'name', u'name@example.com')])\n user = getUser(u'user')\n passwordHash = user.passwordHash\n info = TUserUpdate(u'user', u's3cr3t', None, u'new-name@example.com',\n 'USER_MANAGER')\n self.store.commit()\n with login(u'fluiddb', self.admin.objectID, self.transact) as session:\n yield self.facade.updateUser(session, info)\n\n self.store.rollback()\n self.assertEqual(u'user', user.username)\n self.assertNotEqual(passwordHash, user.passwordHash)\n self.assertEqual(u'name', user.fullname)\n self.assertEqual(u'new-name@example.com', user.email)\n self.assertEqual(Role.USER_MANAGER, user.role)","def test_update_account_user(self):\n self._require_login()\n\n response = self.client.put('/v1/users/' +str(self.user.id)+'/',\n {\"username\": 'toni@malucao', \"password\": 'cidadeeee'},\n format='json')\n\n self.assertEqual(response.status_code, 200,\n 'Expected Response Code 200, received {0} instead.'.format(response.status_code))","def test_update(self):\n user = self.custodian_1_user\n user_client = self.custodian_1_client\n urls = [reverse('api:user-detail', kwargs={'pk': user.pk})]\n new_first_name = \"New First Name\"\n data = {\n \"first_name\": new_first_name,\n }\n access = {\n \"forbidden\": [self.anonymous_client, self.readonly_client, self.custodian_2_client],\n \"allowed\": [self.admin_client, user_client]\n }\n\n for client in access['forbidden']:\n for url in urls:\n self.assertIn(\n client.patch(url, data, format='json').status_code,\n [status.HTTP_401_UNAUTHORIZED, status.HTTP_403_FORBIDDEN]\n )\n\n for client in access['allowed']:\n for url in urls:\n new_first_name += '1'\n data['first_name'] = new_first_name\n self.assertEqual(\n client.patch(url, data, format='json').status_code,\n status.HTTP_200_OK\n )\n user.refresh_from_db()\n self.assertEqual(user.first_name, new_first_name)","def update_user_and_pw(cls, userid, user_email, user_password, user_phone):\n\n user_to_edit = User.query.filter_by(user_id=userid).one()\n\n user_to_edit.email = user_email\n user_to_edit.password = user_password\n user_to_edit.mobile_phone = user_phone\n\n db.session.commit()\n return user_to_edit","def update_user(user_id):\n update_usr = request.get_json()\n if not update_usr:\n abort(400, {'Not a JSON'})\n usr = storage.get(User, user_id)\n if not usr:\n abort(404)\n else:\n for key, value in update_usr.items():\n setattr(usr, key, value)\n storage.save()\n return jsonify(usr.to_dict())","def put(self, id):\n data = flask.request.json\n user_dao.update_user(id, data)\n return None, 204","def update_user(BrokerId=None, ConsoleAccess=None, Groups=None, Password=None, Username=None):\n pass","def update_user(self, userId, newName, newPhone):\n\n try:\n query = \"update user set userName = '{}', phone='{}' where userId ={}\".format(newName, newPhone, userId)\n print(query)\n cur = self.con.cursor()\n cur.execute(query)\n self.con.commit()\n\n logger.info(\"updated\")\n except Exception as e:\n logger.error(\"Error occured at data Update \", e)","def update(self, instance, data):\n # extract password out of provided data\n # default value required for pop(), use None here\n passwd = data.pop('password', None)\n # simply update other fields\n user = super().update(instance, data)\n if passwd:\n user.set_password(passwd)\n user.save()\n return user","def test_users_update(mocker):\n mocker.patch('subprocess.call')\n users.update(user_dict)\n subprocess.call.assert_called_with([\n 'usermod',\n '-p',\n password,\n '-G',\n 'wheel,dev',\n 'kevin',\n ])","def modify_user(user_data):\r\n raise NotImplementedError()","def test_updating_user_information_including_password(self):\n user_password = self.user.password\n user_update_data = {\"username\": \"bobby\", \"email\": \"bobby@email.com\",\n \"password\": \"otherpassword\"}\n updated_user = self.serializer.update(self.user, user_update_data)\n self.assertEqual(updated_user.username, \"bobby\")\n self.assertEqual(updated_user.email, \"bobby@email.com\")\n self.assertNotEqual(updated_user.password, user_password)","def update_user_info(user, save=True):\n p = bayou.Person.from_default_services(user.username)\n\n user.email = p.email if p.email else user.email\n user.first_name = p.first_name if p.first_name else user.first_name\n user.last_name = p.surname if p.surname else user.last_name\n\n if save:\n user.save()\n\n return user","def update_user(user_id):\n\n user = User.query.get_or_404(user_id)\n user.first_name = request.form[\"edit_first_name\"]\n user.last_name = request.form[\"edit_last_name\"]\n user.image_url = request.form[\"edit_image_url\"]\n\n db.session.add(user)\n db.session.commit()\n return redirect(\"/users\")","def put(self, username):\n user = UserModel.get_by_username(username)\n if user:\n data = self.parse.parse_args()\n user.email = data.get('email')\n user.set_password(data.get('password'))\n user.update()\n return user.as_dict()\n return {\"message\": \"user not found\"}","def update_user_password(context, params):\n\n user = User.objects.filter(id=params.get('id')).first()\n if not user:\n raise ValueError(\"user not found\")\n # user.edited_by = context.user\n if params.get('password'):\n user.set_password(params.get('password'))\n\n user.save()\n return user","def test_update(self, client, users):\n user = users[0]\n data = factory.build(dict, FACTORY_CLASS=UserFactory)\n url = reverse('users:update', args=(user.pk,))\n response = client.post(url, data)\n assert response.status_code == 302\n assert response.url == reverse('users:list')\n\n user.refresh_from_db()\n assert user.username == data['username']\n assert user.birthday == datetime.datetime.strptime(data['birthday'], '%Y-%m-%d').date()","def update_user(self, userid, username, email, password, cursor):\n sql=\"UPDATE users SET username=%s, email=%s, password=%s WHERE userid=%s\"\n cursor.execute(sql,(username,email,password,userid))\n return cursor","def user_update(user, action, change, data={}):\n return user, action, change()","def test_replace_user(self):\n pass","def update_user_data(self, new_user: User):\n self.user_data.update_user_data(new_user)","def update_usermenity(user_id):\n user = storage.get(User, user_id)\n\n if user is None:\n abort(404)\n\n put_data = request.get_json()\n if not put_data:\n abort(400, 'Not a JSON')\n\n for k, v in put_data.items():\n if k not in ['id', 'email', 'created_at', 'updated_at']:\n setattr(user, k, v)\n else:\n continue\n user.save()\n storage.save()\n return make_response(jsonify(user.to_dict()), 200)","def update_user(cls, userid, user_email, user_phone):\n\n user_to_edit = User.query.filter_by(user_id=userid).one()\n\n user_to_edit.email = user_email\n user_to_edit.mobile_phone = user_phone\n\n db.session.commit()\n return user_to_edit","def put(self, id):\n return userDao.update(id, api.payload)","def test_update_self_fail(self):\n new_user = self.create_user('1')\n url = '/0/chefs/' + str(new_user.pk)\n\n headers = self.login()\n resp = self.client.put(url, **headers)\n self.assertInvalidCredentials(resp)","async def update_user(new_data: UpdateUser, user_id: str = Path(..., description=\"ID value of the desired user\"),\n db_handler: DBHandler = Depends(database_dependency)):\n try:\n updated_record = await db_handler.update_user(user_id=user_id, new_data=new_data)\n updated_record = init_BaseUser(updated_record)\n except DBHandlerException as e:\n return JSONResponse(status_code=400)\n\n return updated_record","def test_updating_user_information_excluding_password(self):\n user_data = {\"username\": \"robert\", \"email\": \"robert@email.com\"}\n user_password = self.user.password\n updated_user = self.serializer.update(self.user, user_data)\n self.assertEqual(updated_user.username, \"robert\")\n self.assertEqual(updated_user.email, \"robert@email.com\")\n self.assertEqual(updated_user.password, user_password)","def test_user_is_really_updated():\n response = api_helper.get_user(user_id=pytest.test_user.id)\n check_user_data_in_response(response.json()[\"data\"][0])","def _save_user(self, user):\n self.firebase.patch(f'/{self.USERS_KEY}', {str(user.id): user.username})","def updateUserElder(self, address, newUser):\n try:\n DatabaseCollections.userCollection.update_one(\n {\n \"address\": address\n },\n {\"$set\": {\n \"newUser\": newUser\n }})\n return True\n except IOError:\n return False","def update_user(user_id):\n netAdminToolDB = app.config['DATABASE']\n user = netAdminToolDB.get_user(user_id)\n if user == None:\n return jsonify({'error': 'User_id not found'}), 404\n\n input = request.get_json()\n\n if input == None:\n return jsonfiy({'error': 'Invalid PUT request'}), 400\n\n # Send input directly to update_user function, which checks each key\n netAdminToolDB.update_user(user_id, **input)\n user = netAdminToolDB.get_user(user_id)\n userDict = dict(user)\n uri = url_for('get_user', user_id=user.id, _external=True)\n userDict['uri'] = uri\n\n return jsonify({'user': userDict}), 200","def update_user(user_id):\n new_dict = request.get_json(silent=True)\n if type(new_dict) is dict:\n user_obj = storage.get(\"User\", user_id)\n if user_obj is None:\n abort(404)\n for k, v in new_dict.items():\n if k not in [\"id\", \"email\", \"created_at\", \"updated_at\"]:\n setattr(user_obj, k, v)\n user_obj.save()\n return jsonify(user_obj.to_dict()), 200\n else:\n response = jsonify({\"error\": \"Not a JSON\"}), 400\n return response","def patch(self, account=None, user=None, account_id=None):\n return super().patch()"],"string":"[\n \"def update_user():\",\n \"def update_user():\\n #TODO user update \\n pass\",\n \"def update_user(id):\\n pass\",\n \"def test_patch_user(self):\\n pass\",\n \"def update(self, user: U) -> None:\\n ...\",\n \"def test_040_update_user(self):\\n\\n testflow.step(\\\"Updating user %s\\\", TEST_USER2)\\n assert USER_CLI.run(\\n 'edit',\\n TEST_USER2,\\n attribute='firstName=userX2',\\n )[0]\",\n \"def test_update_user(self):\\n pass\",\n \"def update_user(self):\\n self.client.force_authenticate(user=self.user)\\n self.response = self.client.patch(\\n reverse(\\n 'edit_account',kwargs={ 'pk': self.user.id}),\\n self.updated_data, format='json'\\n )\\n self.user = CustomUser.objects.get(username=self.user.username)\",\n \"def test_patch_user(self):\\n new_user = self.make_user('new_user')\\n url = reverse(\\n 'projectroles:api_role_update',\\n kwargs={'roleassignment': self.update_as.sodar_uuid},\\n )\\n patch_data = {'user': str(new_user.sodar_uuid)}\\n response = self.request_knox(url, method='PATCH', data=patch_data)\\n self.assertEqual(response.status_code, 400, msg=response.content)\",\n \"def patch_user(user_id):\\n success = True\\n try:\\n usr = db.session.query(User).get(user_id)\\n for item in request.json:\\n if item == 'username':\\n usr.username = request.json['username']\\n elif item == 'email':\\n usr.username = request.json['email']\\n db.session.commit()\\n except:\\n success = False\\n return jsonify(success=success)\",\n \"def test_user_update(self):\\n userPK = self.testUser.pk\\n url = reverse('User-detail', kwargs={'pk': userPK})\\n data = {'username': 'company1NewTest'}\\n response = self.client.put(url, data, format='json')\\n self.assertEqual(response.status_code, status.HTTP_200_OK)\\n self.assertEqual(User.objects.get(pk=userPK).username,\\n 'company1NewTest')\\n data = {'username': 'company1NewTest2'}\\n response = self.client.patch(url, data, format='json')\\n self.assertEqual(response.status_code, status.HTTP_200_OK)\\n self.assertEqual(User.objects.get(pk=userPK).username,\\n 'company1NewTest2')\",\n \"def test_update_user_profile(self):\\r\\n payload = {\\r\\n 'name': 'new_name',\\r\\n 'password': 'password123'\\r\\n }\\r\\n\\r\\n res = self.client.patch(ME_URL, payload)\\r\\n\\r\\n self.user.refresh_from_db()\\r\\n\\r\\n self.assertEqual(self.user.name, payload['name'])\\r\\n self.assertTrue(self.user.check_password(payload['password']))\\r\\n self.assertEqual(res.status_code, status.HTTP_200_OK)\",\n \"def test_update_user_profile(self):\\n payload = {'name': 'new name', 'password': 'newpassword123'}\\n\\n res = self.client.patch(ME_URL, payload)\\n\\n self.user.refresh_from_db()\\n self.assertEqual(self.user.name, payload['name'])\\n self.assertTrue(self.user.check_password(payload['password']))\\n self.assertEqual(res.status_code, status.HTTP_200_OK)\",\n \"def test_update_the_created_user():\\n pytest.test_user.name += \\\"Updated\\\"\\n response = api_helper.update_user(pytest.test_user)\\n assert response.status_code == 200\",\n \"def test_update_user_profile(self):\\n payload = {'name': 'new name', 'password': 'newpassword123'}\\n\\n response = self.client.patch(ME_URL, payload)\\n\\n self.user.refresh_from_db()\\n self.assertEqual(self.user.name, payload['name'])\\n self.assertTrue(self.user.check_password(payload['password']))\\n self.assertEqual(response.status_code, status.HTTP_200_OK)\",\n \"def updateUser(self, payload):\\n\\t\\turl = \\\"https://habitica.com/api/v3/user\\\"\\n\\t\\treturn(putUrl(url, self.credentials, payload))\",\n \"def testUpdateUser(self):\\n UserAPI().create([(u'test', u'secret', u'name', u'name@example.com')])\\n user = getUser(u'test')\\n passwordHash = user.passwordHash\\n self.store.commit()\\n info = TUserUpdate(u'test', u'password', u'new-name',\\n u'new-name@example.com')\\n with login(u'fluiddb', self.admin.objectID, self.transact) as session:\\n yield self.facade.updateUser(session, info)\\n\\n self.store.rollback()\\n self.assertEqual(u'test', user.username)\\n self.assertNotEqual(passwordHash, user.passwordHash)\\n self.assertEqual(u'new-name', user.fullname)\\n self.assertEqual(u'new-name@example.com', user.email)\",\n \"def test_update_user_profile(self):\\n payload = {'name': 'Test name', 'password': 'new_password'}\\n res = self.client.patch(ME_URL, payload)\\n\\n self.user.refresh_from_db()\\n self.assertEqual(self.user.name, payload['name'])\\n self.assertTrue(self.user.check_password(payload['password']))\\n self.assertEqual(res.status_code, status.HTTP_200_OK)\",\n \"def test_update_profile_valid_patch(self):\\n update_fields = {\\n 'password': 'NewPassword!',\\n 'name': 'Mona Lisa'\\n }\\n res = self.client.patch(ME_URL, update_fields)\\n\\n self.assertEqual(res.status_code, status.HTTP_200_OK)\\n\\n self.user.refresh_from_db()\\n\\n self.assertTrue(self.user.check_password(update_fields['password']))\\n self.assertEquals(self.user.name, update_fields['name'])\",\n \"def patch(self, user_id):\\n\\n data = request.get_json()\\n\\n res = self._user.update_user(user_id, data)\\n\\n if res:\\n return {\\n \\\"status\\\": 200,\\n \\\"data\\\": [{\\n \\\"id\\\": res[\\\"id\\\"],\\n \\\"message\\\": \\\"user record has been updated\\\"\\n }]\\n }, 200\\n else:\\n return {\\n \\\"status\\\": 404,\\n \\\"error\\\": \\\"Not found for id {}\\\".format(user_id)\\n }, 404\",\n \"def do_user_update():\\n targetUsers = User.query.filter_by(id=request.form['id']).all()\\n if not any(targetUsers):\\n return user_list(\\\"Unknown user.\\\")\\n\\n targetUser = targetUsers[0]\\n\\n targetUser.first_name = request.form['first_name']\\n targetUser.name = request.form['name']\\n targetUser.nick = request.form['nick']\\n targetUser.mail = request.form['mail']\\n targetUser.role = request.form['role']\\n targetUser.state = request.form['state']\\n targetUser.gender = request.form['gender']\\n targetUser.meter_id = request.form['meter_id']\\n targetUser.group_id = request.form['group_id']\\n\\n db.session.commit()\\n return user_list(\\\"Updated user \\\" + targetUser.name)\",\n \"def fusion_api_edit_user(self, body, uri, api=None, headers=None):\\n return self.user.update(body, uri, api, headers)\",\n \"def test_update_user_profile(self):\\n payload = {\\\"name\\\": \\\"Lucifer\\\", 'password': \\\"12346987\\\"}\\n res = self.client.patch(ME_URL, payload)\\n\\n self.user.refresh_from_db()\\n\\n self.assertEqual(self.user.name, payload['name'])\\n self.assertTrue(self.user.check_password(payload['password']))\\n self.assertEqual(res.status_code, status.HTTP_200_OK)\",\n \"def mod_user(self, username, data):\\n headers = {\\\"user-agent\\\": self.u_agent}\\n req_url = self.normalize_admin_url(u\\\"users/{}\\\".format(username))\\n res = requests.put(\\n req_url,\\n headers=headers,\\n auth=self.auth,\\n data=json.dumps(data),\\n verify=False,\\n )\\n if res.status_code == 200:\\n return Response(0, u\\\"User {} has been modified\\\".format(username))\\n else:\\n return Response(res.status_code, res)\",\n \"async def patch(self, request, uid):\\n current = await self.get(request, uid)\\n\\n name = self.get_field(request, 'name', default=current['name'])\\n\\n async with request.app.pool.acquire() as conn:\\n await conn.execute('UPDATE ysr.user SET name=$1 WHERE id=$2', name,\\n uid)\\n\\n return await self.get(request, uid)\",\n \"def test_user_update_request(self):\\n pass\",\n \"def updateUser(self, request, context):\\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\\n context.set_details('Method not implemented!')\\n raise NotImplementedError('Method not implemented!')\",\n \"def put(self, user_id):\\r\\n return update_user(request, user_id)\",\n \"def test_update_user_profile(setup_client, setup_user):\\n client = setup_client\\n user = setup_user\\n payload = {\\n \\\"name\\\": \\\"New name\\\",\\n \\\"role\\\": \\\"Purchaser\\\",\\n \\\"password\\\": \\\"New password\\\"\\n }\\n res = client.patch(ME_URL, payload)\\n user.refresh_from_db()\\n assert res.status_code == status.HTTP_200_OK\\n assert user.name == payload[\\\"name\\\"]\\n assert user.role == payload[\\\"role\\\"]\\n assert user.check_password(payload[\\\"password\\\"])\\n assert res.status_code == status.HTTP_200_OK\",\n \"def test_update_user_profile(self):\\n\\n new_credentials = {'name': 'New Name', 'password': 'NewTestpass12'}\\n response = self.client.patch(URL_ME, new_credentials)\\n\\n # Refresh the details of the user from the database.\\n self.user.refresh_from_db()\\n\\n # Check that the update is successful.\\n self.assertEqual(self.user.name, new_credentials['name'])\\n self.assertTrue(self.user.check_password(new_credentials['password']))\\n self.assertEqual(response.status_code, status.HTTP_200_OK)\",\n \"def wmUpdateUser(self):\\n user_role = uiCommon.GetSessionUserRole()\\n if user_role != \\\"Administrator\\\":\\n raise Exception(\\\"Only Administrators can edit user accounts.\\\")\\n\\n args = uiCommon.getAjaxArgs()\\n\\n u = catouser.User()\\n u.FromID(args[\\\"ID\\\"])\\n\\n if u.ID:\\n # these changes are done BEFORE we manipulate the user properties for update.\\n\\n new_pw = uiCommon.unpackJSON(args.get(\\\"Password\\\"))\\n random_pw = args.get(\\\"NewRandomPassword\\\")\\n\\n # if a password was provided, or the random flag was set...exclusively\\n if new_pw:\\n # if the user requesting the change *IS* the user being changed...\\n # set force_change to False\\n force = True\\n if u.ID == uiCommon.GetSessionUserID():\\n force = False\\n\\n u.ChangePassword(new_password=new_pw, force_change=force)\\n uiCommon.WriteObjectChangeLog(catocommon.CatoObjectTypes.User, u.ID, u.FullName, \\\"Password changed.\\\")\\n elif random_pw:\\n u.ChangePassword(generate=random_pw)\\n uiCommon.WriteObjectChangeLog(catocommon.CatoObjectTypes.User, u.ID, u.FullName, \\\"Password reset.\\\")\\n\\n # now we can change the properties\\n u.LoginID = args.get(\\\"LoginID\\\")\\n u.FullName = args.get(\\\"FullName\\\")\\n u.Status = args.get(\\\"Status\\\")\\n u.AuthenticationType = args.get(\\\"AuthenticationType\\\")\\n u.ForceChange = args.get(\\\"ForceChange\\\")\\n u.Email = args.get(\\\"Email\\\")\\n u.Role = args.get(\\\"Role\\\")\\n u.FailedLoginAttempts = args.get(\\\"FailedLoginAttempts\\\")\\n u.Expires = args.get(\\\"Expires\\\")\\n\\n u._Groups = args.get(\\\"Groups\\\")\\n\\n if u.DBUpdate():\\n uiCommon.WriteObjectChangeLog(catocommon.CatoObjectTypes.User, u.ID, u.ID, \\\"User updated.\\\")\\n\\n return json.dumps({\\\"result\\\": \\\"success\\\"})\",\n \"def update_user(user_id, data):\\n logging.debug(\\\"Uptating user: user_id={}\\\".format(user_id))\\n return ask('appusers/{0}'.format(user_id), data, 'put')\",\n \"def put(self, new_data, user_id):\\n\\n request_id = get_jwt_identity()\\n user = user_crud.get(request_id)\\n if not user.is_superuser:\\n abort(401,\\n message=\\\"You do not have permission to view this endpoint\\\")\\n user = user_crud.get(user_id)\\n user = user_crud.update(user, new_data)\\n if not user['added']:\\n abort(409, message=\\\"User with this email exist\\\")\\n user = user['db_obj']\\n return user\",\n \"def test_admin_update_user(self):\\n resp = self.admin_create_user()\\n reply = self.admin_login()\\n token = reply['token']\\n user = dict(\\n name='Summer Lover',\\n username='lover',\\n password='Andela8',\\n role='attendant'\\n )\\n resp = self.client.put(\\n '/api/v1/users/2',\\n content_type='application/json',\\n data=json.dumps(user),\\n headers={'Authorization': 'Bearer {}'.format(token)}\\n )\\n reply = json.loads(resp.data.decode())\\n self.assertEqual(reply['message'], 'user updated!')\\n self.assertEqual(resp.status_code, 200)\",\n \"def test_resource_user_resource_change_user_patch(self):\\n pass\",\n \"def update_user(self, username):\\n parser_update.add_argument('email', type=validate_email,\\n required=False, nullable=False,\\n help=\\\"Email must be formatted correctly\\\")\\n\\n parser_update.add_argument('phoneNumber', type=validate_phonenumber,\\n required=False, nullable=False,\\n help=\\\"Enter a valid phone number\\\")\\n\\n parser_update.add_argument('firstname', type=validate_characters,\\n required=False, nullable=False,\\n help=\\\"First name must be formatted correctly\\\")\\n\\n parser_update.add_argument('lastname', type=validate_characters,\\n required=False, nullable=False,\\n help=\\\"Last name must be formatted correctly\\\")\\n\\n parser_update.add_argument('othernames', type=validate_characters,\\n required=False, nullable=False,\\n help=\\\"Other name must be formatted correctly\\\")\\n\\n user = self.get_user(username)\\n if user is None:\\n return None\\n\\n args = parser_update.parse_args()\\n new_data = {\\n 'email': request.json.get('email', user['email']).lower(),\\n 'firstname': request.json.get('firstname', user['firstname']).capitalize(),\\n 'lastname': request.json.get('lastname', user['lastname']).capitalize(),\\n 'othernames': request.json.get('othernames', user['othernames']).capitalize(),\\n 'phoneNumber': request.json.get('phoneNumber', user['phonenumber']),\\n }\\n\\n getEmail = self.get_user(new_data['email'])\\n verification_status = True\\n\\n if user['email'] != new_data['email']:\\n if getEmail is not None:\\n return 'email exists'\\n verification_status = False\\n\\n query = \\\"\\\"\\\"UPDATE users SET firstname=%s,lastname=%s,othernames=%s,\\\\\\n email=%s,phonenumber=%s,emailverified=%s WHERE username=%s\\\"\\\"\\\"\\n values = new_data['firstname'], new_data['lastname'], new_data['othernames'], new_data['email'], new_data['phoneNumber'], verification_status, username\\n\\n conn = self.db\\n cursor = conn.cursor()\\n cursor.execute(query, values)\\n conn.commit()\\n return new_data\",\n \"def testUpdateUserIgnoresCase(self):\\n UserAPI().create([(u'test', u'secret', u'name', u'name@example.com')])\\n user = getUser(u'test')\\n passwordHash = user.passwordHash\\n info = TUserUpdate(u'TesT', u'password', u'new-name',\\n u'new-name@example.com')\\n self.store.commit()\\n with login(u'fluiddb', self.admin.objectID, self.transact) as session:\\n yield self.facade.updateUser(session, info)\\n\\n self.store.rollback()\\n self.assertEqual(u'test', user.username)\\n self.assertNotEqual(passwordHash, user.passwordHash)\\n self.assertEqual(u'new-name', user.fullname)\\n self.assertEqual(u'new-name@example.com', user.email)\",\n \"def patch(self, request, *args, **kwargs):\\n user= get_object_or_404(YouYodaUser, email=request.data.get('email'))\\n # user = YouYodaUser.objects.get(auth_token=request.headers['Authorization'].replace('Token ', ''))\\n serializer = ProfileEditSerializer(user, data=request.data, partial=True)\\n if serializer.is_valid():\\n serializer.save()\\n return Response(serializer.data, status=status.HTTP_201_CREATED)\\n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\",\n \"def testUpdateUserWithoutEmail(self):\\n UserAPI().create([(u'user', u'secret', u'name', u'name@example.com')])\\n user = getUser(u'user')\\n passwordHash = user.passwordHash\\n info = TUserUpdate(u'user', u's3cr3t', u'new-name', None,\\n 'USER_MANAGER')\\n self.store.commit()\\n with login(u'fluiddb', self.admin.objectID, self.transact) as session:\\n yield self.facade.updateUser(session, info)\\n\\n self.store.rollback()\\n self.assertEqual(u'user', user.username)\\n self.assertNotEqual(passwordHash, user.passwordHash)\\n self.assertEqual(u'new-name', user.fullname)\\n self.assertEqual(u'name@example.com', user.email)\\n self.assertEqual(Role.USER_MANAGER, user.role)\",\n \"def test_update_user(self):\\n token = self.authenticate_user(self.auth_user_data).data[\\\"token\\\"]\\n response = self.client.put(self.user_url,\\n self.user_data,\\n HTTP_AUTHORIZATION=f'token {token}',\\n format='json')\\n self.assertEqual(response.status_code, status.HTTP_200_OK)\",\n \"def update_user(self, user):\\n query = TABELLE['id_users']['update']\\n return self.execute(query,\\n (user['admin'], user['tester'], user['loot_user'], user['loot_admin'], user['banned'],\\n user['id']))\",\n \"def request_user_update():\\n target_user = User.query.filter_by(id=request.args['id']).first()\\n if target_user is None:\\n return user_list(\\\"Unknown user.\\\")\\n\\n return Response(render_template('admin/user/create-update.html',\\n csrf_token=(\\n get_raw_jwt() or {}).get(\\\"csrf\\\"),\\n target=\\\"/admin/user/update\\\",\\n genders=list(GenderType),\\n states=list(StateType),\\n groups=Group.query.all(),\\n roles=list(RoleType),\\n id=target_user.id,\\n gender=target_user.gender,\\n first_name=target_user.first_name,\\n name=target_user.name,\\n nick=target_user.nick,\\n mail=target_user.mail,\\n meter_id=target_user.meter_id,\\n group_id=target_user.group_id,\\n role=target_user.role,\\n state=target_user.state),\\n mimetype='text/html')\",\n \"def test_update_another_user(self):\\n user1_response = self.client.post(reverse('user-list'), {\\n 'username': 'aseem', 'password': 'passwrodaosida123'\\n })\\n update_user_resp = self.client.patch(\\n reverse('user-list') + '1/', {\\n 'username': 'rakesh', 'password': 'passwrodaosida123'\\n })\\n\\n self.assertEqual(update_user_resp.status_code, status.HTTP_401_UNAUTHORIZED)\",\n \"def update_user():\\n user = request.json\\n user[\\\"_id\\\"] = validate_id(user[\\\"_id\\\"])\\n if not user_service.update_user(user):\\n response = {\\n \\\"status\\\": False,\\n \\\"message\\\": f\\\"No se pudo actualizar el usuario: {str(user['_id'])}\\\",\\n }\\n resp = make_response(dumps(response), 404)\\n else:\\n response = {\\n \\\"status\\\": True,\\n \\\"message\\\": f\\\"Se actualizo corretamente el usuario: {str(user['_id'])}\\\",\\n }\\n resp = make_response(dumps(response), 200)\\n resp.headers[\\\"Content-Type\\\"] = \\\"application/json\\\"\\n return resp\",\n \"def put(self, new_data):\\n\\n user_id = get_jwt_identity()\\n user = user_crud.get(user_id)\\n if not user:\\n abort(404, message=\\\"User not Found\\\")\\n user = user_crud.update(user, new_data)\\n if not user['added']:\\n abort(409, message=\\\"User with this email exits\\\")\\n user = user['db_obj']\\n return user\",\n \"def update(self, oid, name=None, email=None, default_project=None, \\n domain=None, password=None, enabled=None, description=None):\\n data = {\\\"user\\\": {}}\\n \\n if name is not None:\\n data['user']['name'] = name\\n if email is not None:\\n data['user']['email'] = email \\n if default_project is not None:\\n data['user']['default_project_id'] = default_project\\n if domain is not None:\\n data['user']['domain_id'] = domain\\n if password is not None:\\n data['user']['password'] = password\\n if enabled is not None:\\n data['user']['enabled'] = enabled\\n if description is not None:\\n data['user']['description'] = description \\n \\n path = '/users/%s' % oid\\n res = self.client.call(path, 'PATCH', data=json.dumps(data), \\n token=self.manager.identity.token)\\n self.logger.debug('Update openstack user: %s' % truncate(res))\\n return res[0]['user']\",\n \"def testUpdateUserWithoutRole(self):\\n UserAPI().create([(u'user', u'secret', u'name', u'name@example.com')])\\n user = getUser(u'user')\\n passwordHash = user.passwordHash\\n info = TUserUpdate(u'user', u's3cr3t', u'new-name', 'new@example.com',\\n None)\\n self.store.commit()\\n with login(u'fluiddb', self.admin.objectID, self.transact) as session:\\n yield self.facade.updateUser(session, info)\\n\\n self.store.rollback()\\n self.assertEqual(u'user', user.username)\\n self.assertNotEqual(passwordHash, user.passwordHash)\\n self.assertEqual(u'new-name', user.fullname)\\n self.assertEqual(u'new@example.com', user.email)\\n self.assertEqual(Role.USER, user.role)\",\n \"def test_update_useruser_uuid_put(self):\\n pass\",\n \"def update(self, request, *args, **kwargs):\\n return super(UserViewSet, self).update(request, *args, **kwargs)\",\n \"def update_user(context, params):\\n\\n user = User.objects.filter(id=params.get('id')).first()\\n if not user:\\n raise ValueError(\\\"user not found\\\")\\n user.language = Language.objects.filter(id=params.get('language_id', None)).first()\\n user.deputy = User.objects.filter(id=params.get('deputy_id', None)).first()\\n # user.edited_by = context.user\\n\\n user.save()\\n\\n update_person(context, user, params)\\n\\n user.save()\\n return user\",\n \"def updateUser(self, new_data): #new_data is a dict with new fields\\n\\n\\t\\tthe_data = {};\\n\\t\\tthe_data[\\\"email\\\"] = self.email;\\n\\t\\t\\n\\t\\tnew_data[\\\"date.update\\\"] = getTimeStamp();\\n\\n\\t\\tdata_prime = self.db.getData(the_data, new_data);\\n\\n\\t\\tresult = self.db.request(\\\"update\\\", data_prime);\\n\\n\\t\\tif result:\\n\\t\\t\\treturn new_data;\\n\\t\\telse:\\n\\t\\t\\treturn False;\",\n \"def test_update_profile_success(self):\\n payload = {\\n 'email': 'newtest@gmail.com',\\n 'password': 'newpassword'\\n }\\n res = self.client.patch(ME_URL, payload)\\n\\n # Refresh the user object with latest values from db\\n self.user.refresh_from_db()\\n\\n self.assertEqual(res.status_code, status.HTTP_200_OK)\\n self.assertEqual(self.user.email, payload['email'])\\n self.assertTrue(self.user.check_password(payload['password']))\",\n \"def update_user(self, instance, user, name=None, password=None, host=None):\\n return instance.update_user(user, name=name, password=password,\\n host=host)\",\n \"def testUpdateUserWithoutPassword(self):\\n UserAPI().create([(u'user', u'secret', u'name', u'name@example.com')])\\n user = getUser(u'user')\\n passwordHash = user.passwordHash\\n info = TUserUpdate(u'user', None, u'new-name', u'new-name@example.com',\\n 'USER_MANAGER')\\n self.store.commit()\\n with login(u'fluiddb', self.admin.objectID, self.transact) as session:\\n yield self.facade.updateUser(session, info)\\n\\n self.store.rollback()\\n self.assertEqual(u'user', user.username)\\n self.assertEqual(passwordHash, user.passwordHash)\\n self.assertEqual(u'new-name', user.fullname)\\n self.assertEqual(u'new-name@example.com', user.email)\\n self.assertEqual(Role.USER_MANAGER, user.role)\",\n \"def put(self):\\n form = UpdateForm.from_json(request.get_json())\\n\\n if not g.user:\\n return jsonify({\\\"login_staus\\\": False, \\\"message\\\": \\\"Please login\\\"})\\n\\n if form.validate_on_submit():\\n current_user = User.query.filter_by(id=g.user.id).first()\\n if form.email.data:\\n current_user.email = form.email.data\\n if form.new_password.data:\\n current_user.password = form.new_password.data\\n db.session.commit()\\n return jsonify({\\\"update_status\\\": True})\\n return jsonify({\\\"update_status\\\": False, \\\"message\\\": form.errors})\",\n \"def update_user(cursor, username, attr, value):\\n if attr not in ['username', 'password', 'email', 'groups']:\\n raise ValueError(f\\\"{attr!r} is not a valid user attribute\\\")\\n if attr == 'password':\\n value = argon2.hash(value)\\n elif attr == 'groups':\\n current = get_usergroups(cursor, username)\\n for group in current.difference(value):\\n remove_usergroup(cursor, username, group)\\n for group in value.difference(current):\\n create_usergroup(cursor, username, group)\\n return \\n\\n cursor.execute(f\\\"\\\"\\\"\\n UPDATE users\\n SET\\n {attr} = ?\\n WHERE\\n username = ?\\n \\\"\\\"\\\", (value, username))\",\n \"def update_user(request):\\n post = request.POST.dict()\\n user = post.get('user_id')\\n if user is None:\\n response = {'status':-1, 'status_message':'No user_id specified'}\\n return HttpResponse(json.dumps(response))\\n try:\\n user_obj = User.objects.get(id=user)\\n except User.DoesNotExist:\\n response = {'status':-1, 'status_message':'Invalid user_id: {}'.format(user)}\\n return HttpResponse(json.dumps(response))\\n user_obj.first_name = post.get('first_name')\\n user_obj.last_name = post.get('last_name')\\n password = post.get('password')\\n if password and password != \\\"**********\\\":\\n # update the password\\n user_obj.set_password(password)\\n if post.get('username'):\\n user_obj.username = post['username']\\n user_obj.email = post.get('email')\\n user_obj.is_superuser = json.loads(post.get('is_admin', 'false'))\\n user_obj.is_active = json.loads(post.get('is_enabled', 'false'))\\n user_obj.save()\\n response = {'status': 1, 'status_message': 'Success'}\\n return HttpResponse(json.dumps(response))\",\n \"def test_update(self):\\n\\n user = CustomUser.objects.get(email=\\\"test@test.test\\\")\\n user.update(first_name=\\\"UpdatedName\\\", second_name=\\\"UpdatedSecondName\\\")\\n\\n self.assertEqual(user.first_name, \\\"UpdatedName\\\")\\n self.assertEqual(user.second_name, \\\"UpdatedSecondName\\\")\",\n \"def test_update_profile_valid_put(self):\\n update_user = {\\n 'email': 'new@email.com',\\n 'password': 'NewPassword!',\\n }\\n res = self.client.put(ME_URL, update_user)\\n\\n self.assertEqual(res.status_code, status.HTTP_200_OK)\\n\\n self.user.refresh_from_db()\\n\\n self.assertTrue(self.user.check_password(update_user['password']))\\n self.assertEquals(self.user.email, update_user['email'])\\n self.assertTrue(self.user.name)\",\n \"def update_user():\\n json = request.json\\n name = json[\\\"name\\\"]\\n email = json[\\\"email\\\"]\\n pwd = json[\\\"pwd\\\"]\\n user_id = json[\\\"user_id\\\"]\\n if name and email and pwd and user_id and request.method == \\\"POST\\\":\\n # save edits\\n sql = \\\"UPDATE users SET user_name=%s, user_email=%s, \\\" \\\\\\n \\\"user_password=%s WHERE user_id=%s\\\"\\n data = (name, email, pwd, user_id)\\n try:\\n conn = mysql.connect()\\n cursor = conn.cursor()\\n cursor.execute(sql, data)\\n conn.commit()\\n resp = jsonify(\\\"User updated successfully!\\\")\\n resp.status_code = 200\\n cursor.close()\\n conn.close()\\n return resp\\n except Exception as exception:\\n return jsonify(str(exception))\\n else:\\n return jsonify(\\\"Please provide id, name, email and pwd\\\")\",\n \"def patch(self, request):\\n\\n user_obj = UserProfile.objects.filter(id=request.user.id).first()\\n if not user_obj:\\n return existence_error('user')\\n\\n request_json = {\\n 'profile_photo': request.data.get('profile_photo')\\n }\\n\\n user_serialized = EditUserProfileSerializer(user_obj, data=request_json, partial=True)\\n if not user_serialized.is_valid():\\n return validate_error(user_serialized)\\n user_serialized.save()\\n\\n response_json = {\\n 'status': True,\\n 'message': 'successful',\\n 'data': {}\\n }\\n\\n return Response(response_json, status=201)\",\n \"def test_users_update(mocker):\\r\\n mocker.patch('subprocess.call')\\r\\n users.update(user_dict)\\r\\n subprocess.call.assert_called_with([\\r\\n 'usermod',\\r\\n '-p',\\r\\n password,\\r\\n '-G',\\r\\n 'wheel,dev',\\r\\n 'kevin',\\r\\n ])\",\n \"def put(self, user_id):\\n\\n user_data, error = user_schema.load(api.payload['data'])\\n\\n user = User.objects.get_or_404(public_id=user_id)\\n user.update(updated_at=datetime.utcnow, **user_data)\\n \\n return user_schema.dump(user)\",\n \"def update_user(cls, **kwargs):\\n return cls._do_call(\\n 'PUT', cls.api_endpoint + 'users', params=kwargs)\",\n \"def view_update_user(self, user, new_pw, old_pw):\\r\\n user.realm._checker.passwd(user.userID, new_pw, old_pw)\",\n \"def test_update_user(self):\\n\\n update_dict = dict(\\n username='test_another_username',\\n role='test_new_role',\\n department='test_new_department'\\n )\\n\\n # Update non-existing user\\n updated = self.user_api.update_user(MAGEN_USER['user_uuid'], update_dict)\\n self.assertTrue(updated.success)\\n self.assertEqual(updated.count, 0)\\n\\n # Insert user in Database\\n inserted = self.user_api.insert_user(MAGEN_USER)\\n self.assertTrue(inserted.success)\\n\\n # Update existing user\\n updated = self.user_api.update_user(MAGEN_USER['user_uuid'], update_dict)\\n self.assertTrue(updated.success)\\n self.assertEqual(updated.count, 1)\\n # Verify that data was updated\\n selected = self.user_api.get_user(MAGEN_USER['user_uuid'])\\n self.assertTrue(selected.success)\\n self.assertEqual(selected.documents['username'], update_dict['username'])\\n self.assertEqual(selected.documents['role'], update_dict['role'])\\n self.assertEqual(selected.documents['department'], update_dict['department'])\",\n \"def test_user_update_permissions(self):\\n userPK = User.objects.get(username='c2e1').pk\\n url = reverse('User-detail', kwargs={'pk': userPK})\\n data = {'username': 'company1NewTest'}\\n response = self.client.put(url, data, format='json')\\n #This is 404 instead of 403 because there is no way to view a company\\n #that you arent an employee of.\\n self.assertEqual(response.status_code, status.HTTP_404_NOT_FOUND)\\n self.assertNotEqual(User.objects.get(pk=userPK).username,\\n 'company1NewTest')\\n data = {'username': 'company1NewTest2'}\\n response = self.client.patch(url, data, format='json')\\n #This is 404 instead of 403 because there is no way to view a company\\n #that you arent an employee of.\\n self.assertEqual(response.status_code, status.HTTP_404_NOT_FOUND)\\n self.assertNotEqual(User.objects.get(pk=userPK).username,\\n 'company1NewTest2')\",\n \"def testUpdateUserWithoutName(self):\\n UserAPI().create([(u'user', u'secret', u'name', u'name@example.com')])\\n user = getUser(u'user')\\n passwordHash = user.passwordHash\\n info = TUserUpdate(u'user', u's3cr3t', None, u'new-name@example.com',\\n 'USER_MANAGER')\\n self.store.commit()\\n with login(u'fluiddb', self.admin.objectID, self.transact) as session:\\n yield self.facade.updateUser(session, info)\\n\\n self.store.rollback()\\n self.assertEqual(u'user', user.username)\\n self.assertNotEqual(passwordHash, user.passwordHash)\\n self.assertEqual(u'name', user.fullname)\\n self.assertEqual(u'new-name@example.com', user.email)\\n self.assertEqual(Role.USER_MANAGER, user.role)\",\n \"def test_update_account_user(self):\\n self._require_login()\\n\\n response = self.client.put('/v1/users/' +str(self.user.id)+'/',\\n {\\\"username\\\": 'toni@malucao', \\\"password\\\": 'cidadeeee'},\\n format='json')\\n\\n self.assertEqual(response.status_code, 200,\\n 'Expected Response Code 200, received {0} instead.'.format(response.status_code))\",\n \"def test_update(self):\\n user = self.custodian_1_user\\n user_client = self.custodian_1_client\\n urls = [reverse('api:user-detail', kwargs={'pk': user.pk})]\\n new_first_name = \\\"New First Name\\\"\\n data = {\\n \\\"first_name\\\": new_first_name,\\n }\\n access = {\\n \\\"forbidden\\\": [self.anonymous_client, self.readonly_client, self.custodian_2_client],\\n \\\"allowed\\\": [self.admin_client, user_client]\\n }\\n\\n for client in access['forbidden']:\\n for url in urls:\\n self.assertIn(\\n client.patch(url, data, format='json').status_code,\\n [status.HTTP_401_UNAUTHORIZED, status.HTTP_403_FORBIDDEN]\\n )\\n\\n for client in access['allowed']:\\n for url in urls:\\n new_first_name += '1'\\n data['first_name'] = new_first_name\\n self.assertEqual(\\n client.patch(url, data, format='json').status_code,\\n status.HTTP_200_OK\\n )\\n user.refresh_from_db()\\n self.assertEqual(user.first_name, new_first_name)\",\n \"def update_user_and_pw(cls, userid, user_email, user_password, user_phone):\\n\\n user_to_edit = User.query.filter_by(user_id=userid).one()\\n\\n user_to_edit.email = user_email\\n user_to_edit.password = user_password\\n user_to_edit.mobile_phone = user_phone\\n\\n db.session.commit()\\n return user_to_edit\",\n \"def update_user(user_id):\\n update_usr = request.get_json()\\n if not update_usr:\\n abort(400, {'Not a JSON'})\\n usr = storage.get(User, user_id)\\n if not usr:\\n abort(404)\\n else:\\n for key, value in update_usr.items():\\n setattr(usr, key, value)\\n storage.save()\\n return jsonify(usr.to_dict())\",\n \"def put(self, id):\\n data = flask.request.json\\n user_dao.update_user(id, data)\\n return None, 204\",\n \"def update_user(BrokerId=None, ConsoleAccess=None, Groups=None, Password=None, Username=None):\\n pass\",\n \"def update_user(self, userId, newName, newPhone):\\n\\n try:\\n query = \\\"update user set userName = '{}', phone='{}' where userId ={}\\\".format(newName, newPhone, userId)\\n print(query)\\n cur = self.con.cursor()\\n cur.execute(query)\\n self.con.commit()\\n\\n logger.info(\\\"updated\\\")\\n except Exception as e:\\n logger.error(\\\"Error occured at data Update \\\", e)\",\n \"def update(self, instance, data):\\n # extract password out of provided data\\n # default value required for pop(), use None here\\n passwd = data.pop('password', None)\\n # simply update other fields\\n user = super().update(instance, data)\\n if passwd:\\n user.set_password(passwd)\\n user.save()\\n return user\",\n \"def test_users_update(mocker):\\n mocker.patch('subprocess.call')\\n users.update(user_dict)\\n subprocess.call.assert_called_with([\\n 'usermod',\\n '-p',\\n password,\\n '-G',\\n 'wheel,dev',\\n 'kevin',\\n ])\",\n \"def modify_user(user_data):\\r\\n raise NotImplementedError()\",\n \"def test_updating_user_information_including_password(self):\\n user_password = self.user.password\\n user_update_data = {\\\"username\\\": \\\"bobby\\\", \\\"email\\\": \\\"bobby@email.com\\\",\\n \\\"password\\\": \\\"otherpassword\\\"}\\n updated_user = self.serializer.update(self.user, user_update_data)\\n self.assertEqual(updated_user.username, \\\"bobby\\\")\\n self.assertEqual(updated_user.email, \\\"bobby@email.com\\\")\\n self.assertNotEqual(updated_user.password, user_password)\",\n \"def update_user_info(user, save=True):\\n p = bayou.Person.from_default_services(user.username)\\n\\n user.email = p.email if p.email else user.email\\n user.first_name = p.first_name if p.first_name else user.first_name\\n user.last_name = p.surname if p.surname else user.last_name\\n\\n if save:\\n user.save()\\n\\n return user\",\n \"def update_user(user_id):\\n\\n user = User.query.get_or_404(user_id)\\n user.first_name = request.form[\\\"edit_first_name\\\"]\\n user.last_name = request.form[\\\"edit_last_name\\\"]\\n user.image_url = request.form[\\\"edit_image_url\\\"]\\n\\n db.session.add(user)\\n db.session.commit()\\n return redirect(\\\"/users\\\")\",\n \"def put(self, username):\\n user = UserModel.get_by_username(username)\\n if user:\\n data = self.parse.parse_args()\\n user.email = data.get('email')\\n user.set_password(data.get('password'))\\n user.update()\\n return user.as_dict()\\n return {\\\"message\\\": \\\"user not found\\\"}\",\n \"def update_user_password(context, params):\\n\\n user = User.objects.filter(id=params.get('id')).first()\\n if not user:\\n raise ValueError(\\\"user not found\\\")\\n # user.edited_by = context.user\\n if params.get('password'):\\n user.set_password(params.get('password'))\\n\\n user.save()\\n return user\",\n \"def test_update(self, client, users):\\n user = users[0]\\n data = factory.build(dict, FACTORY_CLASS=UserFactory)\\n url = reverse('users:update', args=(user.pk,))\\n response = client.post(url, data)\\n assert response.status_code == 302\\n assert response.url == reverse('users:list')\\n\\n user.refresh_from_db()\\n assert user.username == data['username']\\n assert user.birthday == datetime.datetime.strptime(data['birthday'], '%Y-%m-%d').date()\",\n \"def update_user(self, userid, username, email, password, cursor):\\n sql=\\\"UPDATE users SET username=%s, email=%s, password=%s WHERE userid=%s\\\"\\n cursor.execute(sql,(username,email,password,userid))\\n return cursor\",\n \"def user_update(user, action, change, data={}):\\n return user, action, change()\",\n \"def test_replace_user(self):\\n pass\",\n \"def update_user_data(self, new_user: User):\\n self.user_data.update_user_data(new_user)\",\n \"def update_usermenity(user_id):\\n user = storage.get(User, user_id)\\n\\n if user is None:\\n abort(404)\\n\\n put_data = request.get_json()\\n if not put_data:\\n abort(400, 'Not a JSON')\\n\\n for k, v in put_data.items():\\n if k not in ['id', 'email', 'created_at', 'updated_at']:\\n setattr(user, k, v)\\n else:\\n continue\\n user.save()\\n storage.save()\\n return make_response(jsonify(user.to_dict()), 200)\",\n \"def update_user(cls, userid, user_email, user_phone):\\n\\n user_to_edit = User.query.filter_by(user_id=userid).one()\\n\\n user_to_edit.email = user_email\\n user_to_edit.mobile_phone = user_phone\\n\\n db.session.commit()\\n return user_to_edit\",\n \"def put(self, id):\\n return userDao.update(id, api.payload)\",\n \"def test_update_self_fail(self):\\n new_user = self.create_user('1')\\n url = '/0/chefs/' + str(new_user.pk)\\n\\n headers = self.login()\\n resp = self.client.put(url, **headers)\\n self.assertInvalidCredentials(resp)\",\n \"async def update_user(new_data: UpdateUser, user_id: str = Path(..., description=\\\"ID value of the desired user\\\"),\\n db_handler: DBHandler = Depends(database_dependency)):\\n try:\\n updated_record = await db_handler.update_user(user_id=user_id, new_data=new_data)\\n updated_record = init_BaseUser(updated_record)\\n except DBHandlerException as e:\\n return JSONResponse(status_code=400)\\n\\n return updated_record\",\n \"def test_updating_user_information_excluding_password(self):\\n user_data = {\\\"username\\\": \\\"robert\\\", \\\"email\\\": \\\"robert@email.com\\\"}\\n user_password = self.user.password\\n updated_user = self.serializer.update(self.user, user_data)\\n self.assertEqual(updated_user.username, \\\"robert\\\")\\n self.assertEqual(updated_user.email, \\\"robert@email.com\\\")\\n self.assertEqual(updated_user.password, user_password)\",\n \"def test_user_is_really_updated():\\n response = api_helper.get_user(user_id=pytest.test_user.id)\\n check_user_data_in_response(response.json()[\\\"data\\\"][0])\",\n \"def _save_user(self, user):\\n self.firebase.patch(f'/{self.USERS_KEY}', {str(user.id): user.username})\",\n \"def updateUserElder(self, address, newUser):\\n try:\\n DatabaseCollections.userCollection.update_one(\\n {\\n \\\"address\\\": address\\n },\\n {\\\"$set\\\": {\\n \\\"newUser\\\": newUser\\n }})\\n return True\\n except IOError:\\n return False\",\n \"def update_user(user_id):\\n netAdminToolDB = app.config['DATABASE']\\n user = netAdminToolDB.get_user(user_id)\\n if user == None:\\n return jsonify({'error': 'User_id not found'}), 404\\n\\n input = request.get_json()\\n\\n if input == None:\\n return jsonfiy({'error': 'Invalid PUT request'}), 400\\n\\n # Send input directly to update_user function, which checks each key\\n netAdminToolDB.update_user(user_id, **input)\\n user = netAdminToolDB.get_user(user_id)\\n userDict = dict(user)\\n uri = url_for('get_user', user_id=user.id, _external=True)\\n userDict['uri'] = uri\\n\\n return jsonify({'user': userDict}), 200\",\n \"def update_user(user_id):\\n new_dict = request.get_json(silent=True)\\n if type(new_dict) is dict:\\n user_obj = storage.get(\\\"User\\\", user_id)\\n if user_obj is None:\\n abort(404)\\n for k, v in new_dict.items():\\n if k not in [\\\"id\\\", \\\"email\\\", \\\"created_at\\\", \\\"updated_at\\\"]:\\n setattr(user_obj, k, v)\\n user_obj.save()\\n return jsonify(user_obj.to_dict()), 200\\n else:\\n response = jsonify({\\\"error\\\": \\\"Not a JSON\\\"}), 400\\n return response\",\n \"def patch(self, account=None, user=None, account_id=None):\\n return super().patch()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8535191","0.82148325","0.76160437","0.76012594","0.7593992","0.7471264","0.7397879","0.7326105","0.72308886","0.7211113","0.71603566","0.71422267","0.70986223","0.70672417","0.7060861","0.7040941","0.70407265","0.703698","0.7034396","0.6995369","0.6994139","0.6993499","0.69770736","0.6957028","0.6946091","0.69338256","0.6924024","0.692096","0.6912151","0.6850542","0.6848674","0.683204","0.6816098","0.68116516","0.6808267","0.6798234","0.6796445","0.67804736","0.6736317","0.6728491","0.67210174","0.6717494","0.67111593","0.67003405","0.6693151","0.66861314","0.66725475","0.6668978","0.66677475","0.66523105","0.66424173","0.6632901","0.6626589","0.6622404","0.6613797","0.6613611","0.66119796","0.66046727","0.65973866","0.65934974","0.6588385","0.65775335","0.65753615","0.65671146","0.6562412","0.65588725","0.6554271","0.6545765","0.65452325","0.65430206","0.65354604","0.65353197","0.65315497","0.65271765","0.6497001","0.6492226","0.64837176","0.64798474","0.64482117","0.64415646","0.64104074","0.6408737","0.6405436","0.6403068","0.6383645","0.63624483","0.63580114","0.6352004","0.6347041","0.6341864","0.63414484","0.6339273","0.6337539","0.63349056","0.63270175","0.6321827","0.63163686","0.6310045","0.6305463","0.6305126"],"string":"[\n \"0.8535191\",\n \"0.82148325\",\n \"0.76160437\",\n \"0.76012594\",\n \"0.7593992\",\n \"0.7471264\",\n \"0.7397879\",\n \"0.7326105\",\n \"0.72308886\",\n \"0.7211113\",\n \"0.71603566\",\n \"0.71422267\",\n \"0.70986223\",\n \"0.70672417\",\n \"0.7060861\",\n \"0.7040941\",\n \"0.70407265\",\n \"0.703698\",\n \"0.7034396\",\n \"0.6995369\",\n \"0.6994139\",\n \"0.6993499\",\n \"0.69770736\",\n \"0.6957028\",\n \"0.6946091\",\n \"0.69338256\",\n \"0.6924024\",\n \"0.692096\",\n \"0.6912151\",\n \"0.6850542\",\n \"0.6848674\",\n \"0.683204\",\n \"0.6816098\",\n \"0.68116516\",\n \"0.6808267\",\n \"0.6798234\",\n \"0.6796445\",\n \"0.67804736\",\n \"0.6736317\",\n \"0.6728491\",\n \"0.67210174\",\n \"0.6717494\",\n \"0.67111593\",\n \"0.67003405\",\n \"0.6693151\",\n \"0.66861314\",\n \"0.66725475\",\n \"0.6668978\",\n \"0.66677475\",\n \"0.66523105\",\n \"0.66424173\",\n \"0.6632901\",\n \"0.6626589\",\n \"0.6622404\",\n \"0.6613797\",\n \"0.6613611\",\n \"0.66119796\",\n \"0.66046727\",\n \"0.65973866\",\n \"0.65934974\",\n \"0.6588385\",\n \"0.65775335\",\n \"0.65753615\",\n \"0.65671146\",\n \"0.6562412\",\n \"0.65588725\",\n \"0.6554271\",\n \"0.6545765\",\n \"0.65452325\",\n \"0.65430206\",\n \"0.65354604\",\n \"0.65353197\",\n \"0.65315497\",\n \"0.65271765\",\n \"0.6497001\",\n \"0.6492226\",\n \"0.64837176\",\n \"0.64798474\",\n \"0.64482117\",\n \"0.64415646\",\n \"0.64104074\",\n \"0.6408737\",\n \"0.6405436\",\n \"0.6403068\",\n \"0.6383645\",\n \"0.63624483\",\n \"0.63580114\",\n \"0.6352004\",\n \"0.6347041\",\n \"0.6341864\",\n \"0.63414484\",\n \"0.6339273\",\n \"0.6337539\",\n \"0.63349056\",\n \"0.63270175\",\n \"0.6321827\",\n \"0.63163686\",\n \"0.6310045\",\n \"0.6305463\",\n \"0.6305126\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6422292"},"document_rank":{"kind":"string","value":"80"}}},{"rowIdx":81190,"cells":{"query":{"kind":"string","value":"get returns a single user given its unique user_id"},"document":{"kind":"string","value":"def get(self, user_id):\n user, code = get_user(user_id)\n if code == 404:\n api.abort(code=404, message=\"The User with the user_id doesnt Exist\")\n return user, code"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get(self, user_id):\n return User.get(user_id)","def get(id):\n return User.query.filter_by(id=id).first()","def get_user_by_id(user_id):\n return User.query.get(user_id)","def get_user(self, user_id):\n uri = 'users/' + user_id\n return self.make_request(uri)","def get_user(user_id=None):\n users = storage.all('User')\n user = users.get('User' + \".\" + user_id)\n if user is None:\n abort(404)\n else:\n return jsonify(user.to_dict()), 200","def get_user_from_id(user_id):\n return Users.query.filter_by(id=user_id).first()","def get_user(id):\n pass","def get_by_id(user_id: int) -> Optional[User]:\n user = User.query.filter_by(id=user_id).first()\n return user","def get_user_by_id(user_id):\n\n return User.query.get(user_id)","def get_user_by_id(user_id):\n\n return User.query.get(user_id)","def get_user_by_id(user_id):\n\n return User.query.get(user_id)","def get_user_by_id(user_id):\n\n return User.query.get(user_id)","def get_single_user(self, id):\n for user in self.users:\n if user['id'] == id:\n return user","def get_user(cls, user_id):\n try:\n return User.objects.get(pk=user_id)\n except User.DoesNotExist:\n return None","def get_user(id):\n if (g.user.id == id):\n return jsonify(g.user.serialize)\n else:\n abort(400)","def get_user(user_id):\n try:\n return UserModel.objects.get(id=user_id)\n except UserModel.DoesNotExist:\n return None","def get_user(user_id):\n user = storage.get(User, user_id)\n if user is None:\n abort(404)\n else:\n return jsonify(user.to_dict())","def get_user_by_id(self, id):\n\t\treturn self.users.get(id)","def get_user(self, user_id):\n User = get_user_model()\n try:\n return User.objects.get(pk=user_id)\n except User.DoesNotExist:\n return None","def get(self, user_id):\n user = UserServices(public_id=user_id).get_an_item()\n if not user:\n api.abort(404)\n else:\n return user","def get_user_by_id(user_id):\n\n return User.query.get(user_id)","def get_user(user_id):\n usr = storage.get(User, user_id)\n if usr:\n return jsonify(usr.to_dict())\n else:\n abort(404)","def user_by_id(user_id):\n user = User.query.filter(User.id == user_id).one_or_none()\n return user","def get_user(pk):\n user = UserService(user=pk).get_user_by_id()\n return CustomResponse(data=user).response()","def get_user_by_id(self, user_id):\n query = \"SELECT * FROM users WHERE user_id = %s\"\n self.cursor.execute(query,[user_id])\n result = self.cursor.fetchone()\n return result","def get_user(self, user_id):\n oauth_user = OAuthioUser.objects.filter(user__id=user_id)\n if oauth_user.exists():\n return oauth_user.get().user","def user_get_by_id(user_id):\n obj = storage.get(\"User\", user_id)\n if obj is None:\n abort(404)\n else:\n return jsonify(obj.to_dict())","def get_user(self, user_id):\n try:\n User = get_user_model()\n return User.objects.get(pk=user_id)\n except User.DoesNotExist:\n return None","def get_user(user_id):\n user = storage.get(\"User\", user_id)\n if user is None:\n abort(404)\n return user.to_dict()","def getUser(self, id : int) -> bbUser.bbUser:\n id = self.validateID(id)\n return self.users[id]","def get(self, id):\n\t\ttry:\n\t\t\tflask_app.logger.debug('We are getting the user: %d', id)\n\t\t\treturn user_service.get(id)\n\t\texcept AssertionError as e:\n\t\t\tuser_space.abort(400, e.args[0], status = \"Could not get user\", statusCode = \"400\")\n\t\texcept Exception as e:\n\t\t\tuser_space.abort(500, e.args[0], status = \"Could not get user\", statusCode = \"500\")","def get(self):\n\n user_id = get_jwt_identity()\n user = user_crud.get(user_id)\n if not user:\n abort(404, message=\"User not Found\")\n\n return user","def get_user(self, user_id=None):\n raise NotImplementedError","def get_user(user_id):\n user = models.storage.get('User', user_id)\n if user is None:\n abort(404)\n return jsonify(user.to_dict())","def get_user(id):\n user = User.query.get(id)\n return user_schema.jsonify(user)","def get_user(self, user_id):\n try:\n return Account.objects.get(pk=user_id)\n except Account.DoesNotExist:\n return None","def get_user(self, user_id):\n _email = self._email_for_user_id(user_id)\n response = self._get('/users?{0}'.format(urllib.urlencode({'search': _email})))\n for _user in response:\n if _user['email'] == _email:\n return _user\n return None","def get_user_by_id(cls, userid):\n\n user = User.query.filter_by(user_id=userid).one()\n\n return user","async def get(cls, user_id):\n try:\n user = await db.one(cls.SELECT_USER, user_id=user_id)\n except exceptions.NoResultFound:\n LOGGER.error(\"Could not find user=%s.\", user_id)\n raise DatabaseError\n except SQLAlchemyError as err:\n LOGGER.error(\"Failed to fetch user=%s. Error: %s\", user_id, err)\n raise DatabaseError\n\n return user","def get_user(self, user_id):\n return self.my_get_user(self.get_all_dbusers(), user_id)","def get_user(user_id=None):\n\n user = storage.get(\"User\", user_id)\n if user is None:\n abort(404)\n return jsonify(user.to_dict())","async def get_user_byid(request):\n user_id = request.match_info[\"user_id\"]\n try:\n user_id = int(user_id)\n except (ValueError, TypeError):\n return web.Response(text=\"Incorrect value for user_id\", status=400)\n\n currentuser = (\n request.cirrina.db_session.query(User)\n .filter(User.username == request.cirrina.web_session[\"username\"])\n .first()\n )\n\n if user_id == -1 or not currentuser.is_admin:\n user = currentuser\n else:\n user = request.cirrina.db_session.query(User).filter_by(id=user_id).first()\n\n if not user:\n return web.Response(status=404, text=\"User not found\")\n\n data = {\"username\": user.username, \"user_id\": user.id, \"is_admin\": user.is_admin}\n return web.json_response(data)","def get_user(self, user_id):\n return UserModel._default_manager.get(pk=user_id)","def user_by_id(self, user_id):\n\n cur = self.db.cursor()\n cur.execute(\n \"\"\"SELECT user_id, username, password, phone, email, role\n FROM users WHERE user_id = %s\"\"\", (user_id, ))\n \n user_from_db = cur.fetchone()\n if cur.rowcount == 1: \n user_id, username, password, phone, email, role = user_from_db\n resp = dict(user_id=user_id, username=username, password=password, phone=phone, email=email, role=role)\n \n return resp\n return None","def find_by_id(cls, _id):\n user = cls.query.filter_by(id=_id).first()\n return user","async def get_user(id: int):\n with Session(engine) as session:\n # TODO return the user based on the ID (and an error if not)\n statement = select(User).where(User.id == id)\n user = session.exec(statement).first()\n if user == None:\n raise HTTPException(status_code=404, detail=\"User ID not found\")\n return {\"user\": user}","def get(cls,id):\n result = execute_query(\"\"\"SELECT * FROM Users Where username = ?\n \"\"\",\n [id])\n try:\n user = User(id,result[0][1])\n except Exception as e:\n return None\n \n return user","def get_user_by_id(id):\n user = session.query(User).get(id)\n return user","def get_user(id=None, name=None):\n found_id = get_user_id(id, name)\n if not found_id:\n return\n response = utils.checked_api_call(users_api, 'get_specific', id=found_id)\n if response:\n return response.content","def get(self, id):\n tmp = userDao.get_one_entry(id)\n return tmp","def get_by_id(self, id):\n return self.session.query(User).filter_by(id=id).first()","def get(user_id=None, username=None, email=None, api_key=None):\r\n user_query = User.query\r\n\r\n if username is not None:\r\n return user_query.filter(User.username == username).first()\r\n\r\n if user_id is not None:\r\n return user_query.filter(User.id == user_id).first()\r\n\r\n if email is not None:\r\n return user_query.filter(User.email == email).first()\r\n\r\n if api_key is not None:\r\n return user_query.filter(User.api_key == api_key).first()\r\n\r\n return None","def get_user(self):\n if \"user\" not in self._data:\n self._data[\"user\"] = User.objects.get(pk=self.kwargs[\"user_id\"])\n return self._data[\"user\"]","def read_user_by_id(\n user_id: int = Path(description=\"User id\", example=1),\n) -> schemas.User:\n user = next((usr for usr in users if usr.id == user_id), None)\n if not user:\n raise HTTPException(\n status_code=status.HTTP_404_NOT_FOUND,\n detail=f\"User with id={user_id} doesn't exist\",\n )\n return user","def get(user_id: int) -> User:\n try:\n user = User.objects.get(id=user_id)\n except User.DoesNotExist:\n logger.error(\n 'Getter(user_id = %d) in BaseUser throws User.DoesNotExist exception.' %\n user_id)\n raise NonUserException\n return user","def get(self, no):\n user = get_a_user(no)\n if not user:\n api.abort(404)\n else:\n return user","def get_single_user(user_id):\n\n # default error\n response_object = {\n 'status': 'fail',\n 'message': 'User does not exist'\n }\n\n # get user by query\n try:\n user = User.query.filter_by(id=int(user_id)).first()\n # user doesn't exist\n if not user:\n return jsonify(response_object), 404\n else:\n response_object = {\n 'status': 'success',\n 'data': {\n 'username': user.username,\n 'email': user.email,\n 'created_at': user.created_at\n }\n }\n return jsonify(response_object), 200\n\n # invalid id\n except ValueError:\n return jsonify(response_object), 404","def find_by_id(cls, user_id):\n return UsersModel.query.filter_by(id=user_id).first()","def get_user(id):\n url = 'https://jsonplaceholder.typicode.com/'\n user = requests.get(url + 'users', params={'id': id}).json()\n return user","def get_user(self, u_id: int) -> Optional[Users]:\n try:\n user = self.session.query(Users).get(u_id)\n\n return user\n except Exception as excpt:\n self.session.rollback()\n print(f'Could not get user: {excpt}')\n\n return None","def get_user_by_id(id):\n user = User.query.get(id)\n if user is None:\n raise CustomError(404, 'User with id: {} was not found.'.format(id))\n\n return jsonify({'success': True, 'user': user.to_dict()})","def get_user(self, user_id: int) -> dict:\n user = self.call_method('getUser', user_id=user_id)\n return user","def get_user(id):\r\n return jsonify(User.query.get_or_404(id).to_dict())","def user(self):\n u = self.user_info\n return self.user_model.get_by_id(u['user_id']) if u else None","def load_user(id):\n return User.query.get(int(id))","def load_user(id):\n return User.query.get(int(id))","def load_user(id):\n return User.query.get(int(id))","def load_user(id):\n return User.query.get(int(id))","def get(self, user_id):\n res = self._user.get_single_user(user_id)\n\n if res:\n return {\n \"status\": 200,\n \"data\": [res]\n }, 200\n else:\n return {\n \"status\": 404,\n \"error\": \"user with id {} \"\n \"was not found \".format(user_id)\n }, 404","def get_a_user(public_id):\n return User.query.filter_by(public_id=public_id).first()","def load_user(user_id):\n return models.UserModel.query.get(int(user_id))","def get_single_user(user_id):\n response_object = {\n 'status': 'fail',\n 'message': 'User does not exist.'\n }\n try:\n user = User.query.filter_by(id=user_id).first()\n if not user:\n return jsonify(response_object), 404\n else:\n response_object = {\n 'status': 'success',\n 'data': {\n 'username': user.username,\n 'email': user.email\n }\n }\n return jsonify(response_object), 200\n except ValueError:\n return jsonify(response_object), 404","def get_user(self, object_id):\n return self.get_object(\"user\", object_id)","def get_user(self, id: utils.Intable) -> User | None:\n id64 = make_id64(id=id, type=Type.Individual)\n return self._connection.get_user(id64)","def get_single_user(user_id):\n response_object = {\n 'status': 'fail',\n 'message': 'User does not exist'\n }\n try:\n user = User.query.filter_by(id=int(user_id)).first()\n if not user:\n return jsonify(response_object), 404\n else:\n response_object = {\n 'status': 'success',\n 'data': user.to_dict()\n }\n return jsonify(response_object), 200\n except ValueError:\n return jsonify(response_object), 404","def get_user(user_id):\n netAdminToolDB = app.config['DATABASE']\n user = netAdminToolDB.get_user(user_id)\n\n if user == None:\n return jsonify({'error': 'User_id not found'}), 404\n\n uri = url_for('get_user', user_id=user.id, _external=True)\n return jsonify({'user':{\n 'id': user.id,\n 'uri': uri,\n 'username': user.username,\n 'display_name': user.display_name,\n 'role': user.role_name\n }\n })","def get_by_id(self):\n user_node = graph.find_one(\"User\",\n property_key=\"id\",\n property_value=self.id)\n return user_node","async def get_user_by_id(self, roblox_id: int) -> User:\n r = await self.request.request(url=f'https://api.roblox.com/users/{roblox_id}', method=\"GET\", noerror=True)\n json = r.json()\n if r.status_code != 200 or not json.get('Id') or not json.get('Username'):\n return None\n return User(self.request, json['Id'], json['Username'])","def get_user(self, user_id):\n return None # noqa: WPS324","def get_user_by_id(user_id: int) -> User:\n session = Session()\n\n # verify user_id exists\n vote_user: User = session.query(User).filter(User.id == user_id).first()\n session.close()\n\n if not vote_user:\n raise UserNotFoundException\n\n return vote_user","def lookup(self, user_id):\n return self.users.get(str(user_id))","def lookupUser_byID(self, user_id):\n sql = \"SELECT * FROM Users WHERE id='%s'\"\\\n % (user_id)\n res = self.execute(sql)\n reslist = res.fetchall()\n if reslist == []:\n return None\n else:\n return reslist[0]","def getUser(self, id):\n if not isinstance(id, int):\n # Must be a string. Get the UserId first.\n id = self.getUserId(id)\n u = self.users[id]\n while isinstance(u, int):\n id = u\n u = self.users[id]\n u.id = id\n return u","def load_user(user_id):\r\n return User.query.get(int(user_id))","def get(self, public_id):\n user = get_a_user(public_id)\n if not user:\n api.abort(404)\n else:\n return user","def get(self, id: str = None) -> User:\n query = gql(\n \"\"\"\n query User($id: String) {\n user(id: $id) {\n id\n email\n name\n bio\n company\n avatar\n verified\n profiles\n role\n }\n }\n \"\"\"\n )\n\n params = {\"id\": id}\n\n return self.make_request(query=query, params=params, return_type=\"user\")","def load_user(id):\n return Users.query.get(id)","def read_one(user_id):\n # Get the user requested\n user = User.query.filter(User.user_id == user_id).one_or_none()\n\n if user is not None:\n\n # Serialize the data for the response\n user_schema = UserSchema()\n data = user_schema.dump(user)\n return data\n\n else:\n abort(\n 404,\n \"User not found for Id: {user_id}\".format(user_id=user_id),\n )","def get_user_by_id(id):\n u = models.User.query.get(id)\n user = {\n 'id': u.id,\n 'name': u.name,\n 'email': u.email,\n 'regID': u.regid,\n 'photo': u.photo\n }\n\n if len(user) == 0:\n abort(404)\n return jsonify({'user': user}), 201","def load_user(user_id):\n return User.query.get(int(user_id))","def load_user(user_id):\n return User.query.get(int(user_id))","def load_user(user_id):\n return User.query.get(int(user_id))","def load_user(user_id):\n return User.query.get(int(user_id))","def load_user(user_id):\n return User.query.get(int(user_id))","def load_user(user_id):\n return User.query.get(int(user_id))","def get_user_by_id(self, user_id: str) -> typing.Optional[User]:\n query_params = {\n \"$select\": \",\".join(\n [\"displayName\", \"id\", \"mail\", \"department\", \"companyName\"]\n ),\n }\n\n request = self._prepare_request(\n method=\"get\",\n resource_path=f\"users/{user_id}\",\n query_params=query_params,\n )\n with requests.Session() as session:\n response = session.send(request=request)\n try:\n response.raise_for_status()\n except requests.exceptions.HTTPError as exception:\n if response.status_code == 400:\n return None\n raise exception\n user = response.json()\n return User.from_dict(**user) if user.get(\"id\") else None","def load_user(user_id):\n return User.query.get(user_id)","def load_user(user_id):\n return User.query.get(user_id)","def user(self, user_id):\r\n return users.User(self, user_id)","def user(self, user_id):\r\n return users.User(self, user_id)","def get(self, public_id):\n user = get_a_user(public_id)\n if not user:\n api.abort(404)\n else:\n return user"],"string":"[\n \"def get(self, user_id):\\n return User.get(user_id)\",\n \"def get(id):\\n return User.query.filter_by(id=id).first()\",\n \"def get_user_by_id(user_id):\\n return User.query.get(user_id)\",\n \"def get_user(self, user_id):\\n uri = 'users/' + user_id\\n return self.make_request(uri)\",\n \"def get_user(user_id=None):\\n users = storage.all('User')\\n user = users.get('User' + \\\".\\\" + user_id)\\n if user is None:\\n abort(404)\\n else:\\n return jsonify(user.to_dict()), 200\",\n \"def get_user_from_id(user_id):\\n return Users.query.filter_by(id=user_id).first()\",\n \"def get_user(id):\\n pass\",\n \"def get_by_id(user_id: int) -> Optional[User]:\\n user = User.query.filter_by(id=user_id).first()\\n return user\",\n \"def get_user_by_id(user_id):\\n\\n return User.query.get(user_id)\",\n \"def get_user_by_id(user_id):\\n\\n return User.query.get(user_id)\",\n \"def get_user_by_id(user_id):\\n\\n return User.query.get(user_id)\",\n \"def get_user_by_id(user_id):\\n\\n return User.query.get(user_id)\",\n \"def get_single_user(self, id):\\n for user in self.users:\\n if user['id'] == id:\\n return user\",\n \"def get_user(cls, user_id):\\n try:\\n return User.objects.get(pk=user_id)\\n except User.DoesNotExist:\\n return None\",\n \"def get_user(id):\\n if (g.user.id == id):\\n return jsonify(g.user.serialize)\\n else:\\n abort(400)\",\n \"def get_user(user_id):\\n try:\\n return UserModel.objects.get(id=user_id)\\n except UserModel.DoesNotExist:\\n return None\",\n \"def get_user(user_id):\\n user = storage.get(User, user_id)\\n if user is None:\\n abort(404)\\n else:\\n return jsonify(user.to_dict())\",\n \"def get_user_by_id(self, id):\\n\\t\\treturn self.users.get(id)\",\n \"def get_user(self, user_id):\\n User = get_user_model()\\n try:\\n return User.objects.get(pk=user_id)\\n except User.DoesNotExist:\\n return None\",\n \"def get(self, user_id):\\n user = UserServices(public_id=user_id).get_an_item()\\n if not user:\\n api.abort(404)\\n else:\\n return user\",\n \"def get_user_by_id(user_id):\\n\\n return User.query.get(user_id)\",\n \"def get_user(user_id):\\n usr = storage.get(User, user_id)\\n if usr:\\n return jsonify(usr.to_dict())\\n else:\\n abort(404)\",\n \"def user_by_id(user_id):\\n user = User.query.filter(User.id == user_id).one_or_none()\\n return user\",\n \"def get_user(pk):\\n user = UserService(user=pk).get_user_by_id()\\n return CustomResponse(data=user).response()\",\n \"def get_user_by_id(self, user_id):\\n query = \\\"SELECT * FROM users WHERE user_id = %s\\\"\\n self.cursor.execute(query,[user_id])\\n result = self.cursor.fetchone()\\n return result\",\n \"def get_user(self, user_id):\\n oauth_user = OAuthioUser.objects.filter(user__id=user_id)\\n if oauth_user.exists():\\n return oauth_user.get().user\",\n \"def user_get_by_id(user_id):\\n obj = storage.get(\\\"User\\\", user_id)\\n if obj is None:\\n abort(404)\\n else:\\n return jsonify(obj.to_dict())\",\n \"def get_user(self, user_id):\\n try:\\n User = get_user_model()\\n return User.objects.get(pk=user_id)\\n except User.DoesNotExist:\\n return None\",\n \"def get_user(user_id):\\n user = storage.get(\\\"User\\\", user_id)\\n if user is None:\\n abort(404)\\n return user.to_dict()\",\n \"def getUser(self, id : int) -> bbUser.bbUser:\\n id = self.validateID(id)\\n return self.users[id]\",\n \"def get(self, id):\\n\\t\\ttry:\\n\\t\\t\\tflask_app.logger.debug('We are getting the user: %d', id)\\n\\t\\t\\treturn user_service.get(id)\\n\\t\\texcept AssertionError as e:\\n\\t\\t\\tuser_space.abort(400, e.args[0], status = \\\"Could not get user\\\", statusCode = \\\"400\\\")\\n\\t\\texcept Exception as e:\\n\\t\\t\\tuser_space.abort(500, e.args[0], status = \\\"Could not get user\\\", statusCode = \\\"500\\\")\",\n \"def get(self):\\n\\n user_id = get_jwt_identity()\\n user = user_crud.get(user_id)\\n if not user:\\n abort(404, message=\\\"User not Found\\\")\\n\\n return user\",\n \"def get_user(self, user_id=None):\\n raise NotImplementedError\",\n \"def get_user(user_id):\\n user = models.storage.get('User', user_id)\\n if user is None:\\n abort(404)\\n return jsonify(user.to_dict())\",\n \"def get_user(id):\\n user = User.query.get(id)\\n return user_schema.jsonify(user)\",\n \"def get_user(self, user_id):\\n try:\\n return Account.objects.get(pk=user_id)\\n except Account.DoesNotExist:\\n return None\",\n \"def get_user(self, user_id):\\n _email = self._email_for_user_id(user_id)\\n response = self._get('/users?{0}'.format(urllib.urlencode({'search': _email})))\\n for _user in response:\\n if _user['email'] == _email:\\n return _user\\n return None\",\n \"def get_user_by_id(cls, userid):\\n\\n user = User.query.filter_by(user_id=userid).one()\\n\\n return user\",\n \"async def get(cls, user_id):\\n try:\\n user = await db.one(cls.SELECT_USER, user_id=user_id)\\n except exceptions.NoResultFound:\\n LOGGER.error(\\\"Could not find user=%s.\\\", user_id)\\n raise DatabaseError\\n except SQLAlchemyError as err:\\n LOGGER.error(\\\"Failed to fetch user=%s. Error: %s\\\", user_id, err)\\n raise DatabaseError\\n\\n return user\",\n \"def get_user(self, user_id):\\n return self.my_get_user(self.get_all_dbusers(), user_id)\",\n \"def get_user(user_id=None):\\n\\n user = storage.get(\\\"User\\\", user_id)\\n if user is None:\\n abort(404)\\n return jsonify(user.to_dict())\",\n \"async def get_user_byid(request):\\n user_id = request.match_info[\\\"user_id\\\"]\\n try:\\n user_id = int(user_id)\\n except (ValueError, TypeError):\\n return web.Response(text=\\\"Incorrect value for user_id\\\", status=400)\\n\\n currentuser = (\\n request.cirrina.db_session.query(User)\\n .filter(User.username == request.cirrina.web_session[\\\"username\\\"])\\n .first()\\n )\\n\\n if user_id == -1 or not currentuser.is_admin:\\n user = currentuser\\n else:\\n user = request.cirrina.db_session.query(User).filter_by(id=user_id).first()\\n\\n if not user:\\n return web.Response(status=404, text=\\\"User not found\\\")\\n\\n data = {\\\"username\\\": user.username, \\\"user_id\\\": user.id, \\\"is_admin\\\": user.is_admin}\\n return web.json_response(data)\",\n \"def get_user(self, user_id):\\n return UserModel._default_manager.get(pk=user_id)\",\n \"def user_by_id(self, user_id):\\n\\n cur = self.db.cursor()\\n cur.execute(\\n \\\"\\\"\\\"SELECT user_id, username, password, phone, email, role\\n FROM users WHERE user_id = %s\\\"\\\"\\\", (user_id, ))\\n \\n user_from_db = cur.fetchone()\\n if cur.rowcount == 1: \\n user_id, username, password, phone, email, role = user_from_db\\n resp = dict(user_id=user_id, username=username, password=password, phone=phone, email=email, role=role)\\n \\n return resp\\n return None\",\n \"def find_by_id(cls, _id):\\n user = cls.query.filter_by(id=_id).first()\\n return user\",\n \"async def get_user(id: int):\\n with Session(engine) as session:\\n # TODO return the user based on the ID (and an error if not)\\n statement = select(User).where(User.id == id)\\n user = session.exec(statement).first()\\n if user == None:\\n raise HTTPException(status_code=404, detail=\\\"User ID not found\\\")\\n return {\\\"user\\\": user}\",\n \"def get(cls,id):\\n result = execute_query(\\\"\\\"\\\"SELECT * FROM Users Where username = ?\\n \\\"\\\"\\\",\\n [id])\\n try:\\n user = User(id,result[0][1])\\n except Exception as e:\\n return None\\n \\n return user\",\n \"def get_user_by_id(id):\\n user = session.query(User).get(id)\\n return user\",\n \"def get_user(id=None, name=None):\\n found_id = get_user_id(id, name)\\n if not found_id:\\n return\\n response = utils.checked_api_call(users_api, 'get_specific', id=found_id)\\n if response:\\n return response.content\",\n \"def get(self, id):\\n tmp = userDao.get_one_entry(id)\\n return tmp\",\n \"def get_by_id(self, id):\\n return self.session.query(User).filter_by(id=id).first()\",\n \"def get(user_id=None, username=None, email=None, api_key=None):\\r\\n user_query = User.query\\r\\n\\r\\n if username is not None:\\r\\n return user_query.filter(User.username == username).first()\\r\\n\\r\\n if user_id is not None:\\r\\n return user_query.filter(User.id == user_id).first()\\r\\n\\r\\n if email is not None:\\r\\n return user_query.filter(User.email == email).first()\\r\\n\\r\\n if api_key is not None:\\r\\n return user_query.filter(User.api_key == api_key).first()\\r\\n\\r\\n return None\",\n \"def get_user(self):\\n if \\\"user\\\" not in self._data:\\n self._data[\\\"user\\\"] = User.objects.get(pk=self.kwargs[\\\"user_id\\\"])\\n return self._data[\\\"user\\\"]\",\n \"def read_user_by_id(\\n user_id: int = Path(description=\\\"User id\\\", example=1),\\n) -> schemas.User:\\n user = next((usr for usr in users if usr.id == user_id), None)\\n if not user:\\n raise HTTPException(\\n status_code=status.HTTP_404_NOT_FOUND,\\n detail=f\\\"User with id={user_id} doesn't exist\\\",\\n )\\n return user\",\n \"def get(user_id: int) -> User:\\n try:\\n user = User.objects.get(id=user_id)\\n except User.DoesNotExist:\\n logger.error(\\n 'Getter(user_id = %d) in BaseUser throws User.DoesNotExist exception.' %\\n user_id)\\n raise NonUserException\\n return user\",\n \"def get(self, no):\\n user = get_a_user(no)\\n if not user:\\n api.abort(404)\\n else:\\n return user\",\n \"def get_single_user(user_id):\\n\\n # default error\\n response_object = {\\n 'status': 'fail',\\n 'message': 'User does not exist'\\n }\\n\\n # get user by query\\n try:\\n user = User.query.filter_by(id=int(user_id)).first()\\n # user doesn't exist\\n if not user:\\n return jsonify(response_object), 404\\n else:\\n response_object = {\\n 'status': 'success',\\n 'data': {\\n 'username': user.username,\\n 'email': user.email,\\n 'created_at': user.created_at\\n }\\n }\\n return jsonify(response_object), 200\\n\\n # invalid id\\n except ValueError:\\n return jsonify(response_object), 404\",\n \"def find_by_id(cls, user_id):\\n return UsersModel.query.filter_by(id=user_id).first()\",\n \"def get_user(id):\\n url = 'https://jsonplaceholder.typicode.com/'\\n user = requests.get(url + 'users', params={'id': id}).json()\\n return user\",\n \"def get_user(self, u_id: int) -> Optional[Users]:\\n try:\\n user = self.session.query(Users).get(u_id)\\n\\n return user\\n except Exception as excpt:\\n self.session.rollback()\\n print(f'Could not get user: {excpt}')\\n\\n return None\",\n \"def get_user_by_id(id):\\n user = User.query.get(id)\\n if user is None:\\n raise CustomError(404, 'User with id: {} was not found.'.format(id))\\n\\n return jsonify({'success': True, 'user': user.to_dict()})\",\n \"def get_user(self, user_id: int) -> dict:\\n user = self.call_method('getUser', user_id=user_id)\\n return user\",\n \"def get_user(id):\\r\\n return jsonify(User.query.get_or_404(id).to_dict())\",\n \"def user(self):\\n u = self.user_info\\n return self.user_model.get_by_id(u['user_id']) if u else None\",\n \"def load_user(id):\\n return User.query.get(int(id))\",\n \"def load_user(id):\\n return User.query.get(int(id))\",\n \"def load_user(id):\\n return User.query.get(int(id))\",\n \"def load_user(id):\\n return User.query.get(int(id))\",\n \"def get(self, user_id):\\n res = self._user.get_single_user(user_id)\\n\\n if res:\\n return {\\n \\\"status\\\": 200,\\n \\\"data\\\": [res]\\n }, 200\\n else:\\n return {\\n \\\"status\\\": 404,\\n \\\"error\\\": \\\"user with id {} \\\"\\n \\\"was not found \\\".format(user_id)\\n }, 404\",\n \"def get_a_user(public_id):\\n return User.query.filter_by(public_id=public_id).first()\",\n \"def load_user(user_id):\\n return models.UserModel.query.get(int(user_id))\",\n \"def get_single_user(user_id):\\n response_object = {\\n 'status': 'fail',\\n 'message': 'User does not exist.'\\n }\\n try:\\n user = User.query.filter_by(id=user_id).first()\\n if not user:\\n return jsonify(response_object), 404\\n else:\\n response_object = {\\n 'status': 'success',\\n 'data': {\\n 'username': user.username,\\n 'email': user.email\\n }\\n }\\n return jsonify(response_object), 200\\n except ValueError:\\n return jsonify(response_object), 404\",\n \"def get_user(self, object_id):\\n return self.get_object(\\\"user\\\", object_id)\",\n \"def get_user(self, id: utils.Intable) -> User | None:\\n id64 = make_id64(id=id, type=Type.Individual)\\n return self._connection.get_user(id64)\",\n \"def get_single_user(user_id):\\n response_object = {\\n 'status': 'fail',\\n 'message': 'User does not exist'\\n }\\n try:\\n user = User.query.filter_by(id=int(user_id)).first()\\n if not user:\\n return jsonify(response_object), 404\\n else:\\n response_object = {\\n 'status': 'success',\\n 'data': user.to_dict()\\n }\\n return jsonify(response_object), 200\\n except ValueError:\\n return jsonify(response_object), 404\",\n \"def get_user(user_id):\\n netAdminToolDB = app.config['DATABASE']\\n user = netAdminToolDB.get_user(user_id)\\n\\n if user == None:\\n return jsonify({'error': 'User_id not found'}), 404\\n\\n uri = url_for('get_user', user_id=user.id, _external=True)\\n return jsonify({'user':{\\n 'id': user.id,\\n 'uri': uri,\\n 'username': user.username,\\n 'display_name': user.display_name,\\n 'role': user.role_name\\n }\\n })\",\n \"def get_by_id(self):\\n user_node = graph.find_one(\\\"User\\\",\\n property_key=\\\"id\\\",\\n property_value=self.id)\\n return user_node\",\n \"async def get_user_by_id(self, roblox_id: int) -> User:\\n r = await self.request.request(url=f'https://api.roblox.com/users/{roblox_id}', method=\\\"GET\\\", noerror=True)\\n json = r.json()\\n if r.status_code != 200 or not json.get('Id') or not json.get('Username'):\\n return None\\n return User(self.request, json['Id'], json['Username'])\",\n \"def get_user(self, user_id):\\n return None # noqa: WPS324\",\n \"def get_user_by_id(user_id: int) -> User:\\n session = Session()\\n\\n # verify user_id exists\\n vote_user: User = session.query(User).filter(User.id == user_id).first()\\n session.close()\\n\\n if not vote_user:\\n raise UserNotFoundException\\n\\n return vote_user\",\n \"def lookup(self, user_id):\\n return self.users.get(str(user_id))\",\n \"def lookupUser_byID(self, user_id):\\n sql = \\\"SELECT * FROM Users WHERE id='%s'\\\"\\\\\\n % (user_id)\\n res = self.execute(sql)\\n reslist = res.fetchall()\\n if reslist == []:\\n return None\\n else:\\n return reslist[0]\",\n \"def getUser(self, id):\\n if not isinstance(id, int):\\n # Must be a string. Get the UserId first.\\n id = self.getUserId(id)\\n u = self.users[id]\\n while isinstance(u, int):\\n id = u\\n u = self.users[id]\\n u.id = id\\n return u\",\n \"def load_user(user_id):\\r\\n return User.query.get(int(user_id))\",\n \"def get(self, public_id):\\n user = get_a_user(public_id)\\n if not user:\\n api.abort(404)\\n else:\\n return user\",\n \"def get(self, id: str = None) -> User:\\n query = gql(\\n \\\"\\\"\\\"\\n query User($id: String) {\\n user(id: $id) {\\n id\\n email\\n name\\n bio\\n company\\n avatar\\n verified\\n profiles\\n role\\n }\\n }\\n \\\"\\\"\\\"\\n )\\n\\n params = {\\\"id\\\": id}\\n\\n return self.make_request(query=query, params=params, return_type=\\\"user\\\")\",\n \"def load_user(id):\\n return Users.query.get(id)\",\n \"def read_one(user_id):\\n # Get the user requested\\n user = User.query.filter(User.user_id == user_id).one_or_none()\\n\\n if user is not None:\\n\\n # Serialize the data for the response\\n user_schema = UserSchema()\\n data = user_schema.dump(user)\\n return data\\n\\n else:\\n abort(\\n 404,\\n \\\"User not found for Id: {user_id}\\\".format(user_id=user_id),\\n )\",\n \"def get_user_by_id(id):\\n u = models.User.query.get(id)\\n user = {\\n 'id': u.id,\\n 'name': u.name,\\n 'email': u.email,\\n 'regID': u.regid,\\n 'photo': u.photo\\n }\\n\\n if len(user) == 0:\\n abort(404)\\n return jsonify({'user': user}), 201\",\n \"def load_user(user_id):\\n return User.query.get(int(user_id))\",\n \"def load_user(user_id):\\n return User.query.get(int(user_id))\",\n \"def load_user(user_id):\\n return User.query.get(int(user_id))\",\n \"def load_user(user_id):\\n return User.query.get(int(user_id))\",\n \"def load_user(user_id):\\n return User.query.get(int(user_id))\",\n \"def load_user(user_id):\\n return User.query.get(int(user_id))\",\n \"def get_user_by_id(self, user_id: str) -> typing.Optional[User]:\\n query_params = {\\n \\\"$select\\\": \\\",\\\".join(\\n [\\\"displayName\\\", \\\"id\\\", \\\"mail\\\", \\\"department\\\", \\\"companyName\\\"]\\n ),\\n }\\n\\n request = self._prepare_request(\\n method=\\\"get\\\",\\n resource_path=f\\\"users/{user_id}\\\",\\n query_params=query_params,\\n )\\n with requests.Session() as session:\\n response = session.send(request=request)\\n try:\\n response.raise_for_status()\\n except requests.exceptions.HTTPError as exception:\\n if response.status_code == 400:\\n return None\\n raise exception\\n user = response.json()\\n return User.from_dict(**user) if user.get(\\\"id\\\") else None\",\n \"def load_user(user_id):\\n return User.query.get(user_id)\",\n \"def load_user(user_id):\\n return User.query.get(user_id)\",\n \"def user(self, user_id):\\r\\n return users.User(self, user_id)\",\n \"def user(self, user_id):\\r\\n return users.User(self, user_id)\",\n \"def get(self, public_id):\\n user = get_a_user(public_id)\\n if not user:\\n api.abort(404)\\n else:\\n return user\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.84332645","0.83980507","0.8261577","0.8222736","0.8221518","0.8178469","0.81775814","0.81763834","0.8173295","0.8173295","0.8173295","0.8173295","0.81669915","0.8094847","0.80743533","0.805296","0.8034785","0.8032745","0.80307204","0.8004","0.8000615","0.79989785","0.79909414","0.7973255","0.796621","0.7960547","0.79582894","0.7947081","0.7946764","0.7932649","0.792792","0.7913475","0.79076594","0.7884799","0.7872833","0.78618103","0.78548586","0.78403515","0.78387403","0.78285503","0.7816523","0.78077686","0.78069794","0.7794041","0.7783739","0.7783462","0.7754201","0.775408","0.77110654","0.7689032","0.7686055","0.7679751","0.7673048","0.7669378","0.7658759","0.76466525","0.764471","0.76383525","0.7629009","0.7621534","0.7615524","0.7611968","0.76108736","0.7606557","0.75942105","0.75942105","0.75942105","0.75942105","0.75920105","0.75590664","0.7558437","0.7557038","0.7555764","0.75479","0.7540261","0.7538839","0.75317085","0.7531568","0.7528338","0.7526553","0.75245214","0.7522668","0.7516581","0.75091875","0.7502514","0.75014293","0.74996936","0.74957025","0.74951905","0.7494426","0.7494426","0.7494426","0.7494426","0.7494426","0.7494426","0.74903935","0.7480458","0.7480458","0.7476236","0.7476236","0.7474686"],"string":"[\n \"0.84332645\",\n \"0.83980507\",\n \"0.8261577\",\n \"0.8222736\",\n \"0.8221518\",\n \"0.8178469\",\n \"0.81775814\",\n \"0.81763834\",\n \"0.8173295\",\n \"0.8173295\",\n \"0.8173295\",\n \"0.8173295\",\n \"0.81669915\",\n \"0.8094847\",\n \"0.80743533\",\n \"0.805296\",\n \"0.8034785\",\n \"0.8032745\",\n \"0.80307204\",\n \"0.8004\",\n \"0.8000615\",\n \"0.79989785\",\n \"0.79909414\",\n \"0.7973255\",\n \"0.796621\",\n \"0.7960547\",\n \"0.79582894\",\n \"0.7947081\",\n \"0.7946764\",\n \"0.7932649\",\n \"0.792792\",\n \"0.7913475\",\n \"0.79076594\",\n \"0.7884799\",\n \"0.7872833\",\n \"0.78618103\",\n \"0.78548586\",\n \"0.78403515\",\n \"0.78387403\",\n \"0.78285503\",\n \"0.7816523\",\n \"0.78077686\",\n \"0.78069794\",\n \"0.7794041\",\n \"0.7783739\",\n \"0.7783462\",\n \"0.7754201\",\n \"0.775408\",\n \"0.77110654\",\n \"0.7689032\",\n \"0.7686055\",\n \"0.7679751\",\n \"0.7673048\",\n \"0.7669378\",\n \"0.7658759\",\n \"0.76466525\",\n \"0.764471\",\n \"0.76383525\",\n \"0.7629009\",\n \"0.7621534\",\n \"0.7615524\",\n \"0.7611968\",\n \"0.76108736\",\n \"0.7606557\",\n \"0.75942105\",\n \"0.75942105\",\n \"0.75942105\",\n \"0.75942105\",\n \"0.75920105\",\n \"0.75590664\",\n \"0.7558437\",\n \"0.7557038\",\n \"0.7555764\",\n \"0.75479\",\n \"0.7540261\",\n \"0.7538839\",\n \"0.75317085\",\n \"0.7531568\",\n \"0.7528338\",\n \"0.7526553\",\n \"0.75245214\",\n \"0.7522668\",\n \"0.7516581\",\n \"0.75091875\",\n \"0.7502514\",\n \"0.75014293\",\n \"0.74996936\",\n \"0.74957025\",\n \"0.74951905\",\n \"0.7494426\",\n \"0.7494426\",\n \"0.7494426\",\n \"0.7494426\",\n \"0.7494426\",\n \"0.7494426\",\n \"0.74903935\",\n \"0.7480458\",\n \"0.7480458\",\n \"0.7476236\",\n \"0.7476236\",\n \"0.7474686\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":81191,"cells":{"query":{"kind":"string","value":"delete removes a user from the system"},"document":{"kind":"string","value":"def delete(self, user_id):\n return delete_user(user_id)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def delete_user():","def delete_user():\n #TODO user delete\n pass","def delete_user(id):\n pass","def delete_user():\r\n raise NotImplementedError()","def del_user(self, username):\n pass","def delete_user(self):\n User.user_list.remove(self)","def delete_user(self):\n User.user_list.remove(self)","def delete_user(self):\n User.user_list.remove(self)","def delete_user(self, user):\n self.delete(user)","def delete_user():\n del globalopts.appdata[request.user]\n del globalopts.users[request.user]\n return \"\", 200","def delete_user(self):\n\n User.user_list.remove(self)","def deleteUser(self,name):\n raise BorkedDeleteUser","def delete_user(self, user):\n self.execute(TABELLE['id_users'][\"delete\"], user[\"id\"])","def delete(self):\n\n user_id = get_jwt_identity()\n user = user_crud.get(user_id)\n if not user:\n abort(404, message=\"User not Found\")\n all_tokens = auth_crud.get_user_tokens(user_id)\n tokens = [token.to_dict() for token in all_tokens]\n for token in tokens:\n auth_crud.revoke_token(token['id'], user_id)\n user = user_crud.remove(user_id)\n\n return {'msg': 'User Removed'}","def delete(self):\n data = UserRegister.parser.parse_args()\n user = UserModel.find_by_username(data['username'])\n\n if user:\n user.delete_from_db()\n else :\n return {'message': 'User not found!'} , 204\n\n return {'message': 'User deleted'},202","def delete_user(self, user):\n # noinspection PyUnresolvedReferences\n self.delete(user)","def delete_user(self, user):\n try:\n with dbm.open(self.dbm_path, 'c', 0o600) as db:\n del db[user.name]\n except KeyError as k:\n pass","def delete_user(self):\n db_acces = DbMethods()\n username = self.result_table.currentItem().text()\n response = db_acces.delete_user(username)\n\n if response == True:\n self.populate_table()\n else:\n message = Message(\n self.language[\"error\"], self.language[\"inf_error\"])\n warning_message = message.create_iw_message(\n self.language[\"ok\"], \"warning\")\n warning_message.exec()","def remove(self, user_id):\n pass","def delete(self, new_data, user_id):\n print(new_data)\n request_id = get_jwt_identity()\n user = user_crud.get(request_id)\n if not user.is_superuser:\n abort(401,\n message=\"You do not have permission to view this endpoint\")\n all_tokens = auth_crud.get_user_tokens(user_id)\n tokens = [token.to_dict() for token in all_tokens]\n for token in tokens:\n auth_crud.revoke_token(token['id'], user_id)\n user = user_crud.remove(user_id)\n\n return {'msg': 'User Removed'}","def delete(user_id: int):\n usr = get_by_id(user_id)\n if not usr:\n raise UserNotFound\n\n db.session.delete(usr)\n db.session.commit()","def delete_user(UserName=None, AuthenticationType=None):\n pass","def remove_user(self, username):\n del self.user_table[username]","def delete_user(user_id):\n\n user = User.query.get(user_id)\n db.session.delete(user)\n db.session.commit()\n return","def delete(self, id):\n\t\ttry:\n\t\t\tuser_service.delete(id)\n\t\texcept AssertionError as e:\n\t\t\tuser_space.abort(400, e.args[0], status = \"Could not delete user\", statusCode = \"400\")\n\t\texcept Exception as e:\n\t\t\tuser_space.abort(500, e.args[0], status = \"Could not delete user\", statusCode = \"500\")","def deleteUser(user):\n delete_user(user)\n return redirect(url_for('login'))","def user_delete(user_id):\n user = storage.get('User', user_id)\n if user is None:\n abort(404)\n user.delete()\n storage.save()\n return jsonify({}), 200","def user_id_delete(user_id):\n user = storage.get(\"User\", user_id)\n\n if user is None:\n abort(404)\n user.delete()\n del user\n return make_response(jsonify({}), 200)","def delete_user(self) -> None:\n table_dictionary = {\n 'Apple': {\n 'table': 'AppleReceipts',\n 'user_id': 'User_id'\n },\n 'ESL': {\n 'table': 'ESLReceipts',\n 'user_id': 'User_id'\n },\n 'Transactions': {\n 'table': 'Transactions',\n 'user_id': 'User_id'\n },\n 'Users': {\n 'table': 'Users',\n 'user_id': 'id'\n },\n }\n\n # delete the current user's information from the db.\n for key in table_dictionary:\n query = f\"\"\"\n DELETE\n FROM {table_dictionary[key]['table']}\n WHERE {table_dictionary[key]['user_id']}=?;\n \"\"\"\n self.db.commit(query, values=(self.id,))\n\n # perform a sign out\n self.sign_out()\n\n log(f\"User:{self.id} has deleted their account.\")","def delete_user(self):\n raise NotImplementedError(\"Function not yet implemented contact package creator\")","def test_remove_user(self):\n pass","def delete_user(user_id):\n usr = storage.get(User, user_id)\n if usr:\n usr.delete(), storage.save()\n return {}\n else:\n abort(404)","def delete(self, user_id):\r\n return delete_user(request, user_id)","def user_delete(user_id=None):\n obj = storage.get(\"User\", user_id)\n if obj is None:\n abort(404)\n storage.delete(obj)\n storage.save()\n return jsonify({}), 200","def delete_user(change):\n return change()","def deleteUser(self):\r\n #Find name and ID column\r\n userData = self.getCurrentUserData()\r\n\r\n #Prompt for confirmation\r\n deleteChoice = QMessageBox.question(self.view, 'Confirm user deletion', \r\n 'Are you sure you want to delete user ' \r\n + userData['Name'] + \" with ID \" + userData['User_ID'] + \r\n \" from database permanently?\", \r\n QMessageBox.Yes | QMessageBox.No)\r\n \r\n if (deleteChoice == QMessageBox.Yes):\r\n DBController().deleteUser(userData['User_ID'] )\r\n self.updateUserTable() #Re-fill table\r","def del_user(item, username, passw):\n user = User.load_user_by_username(item, username)\n if not user:\n print(\"User does not exist!\")\n elif check_password(passw, user.hashed_password):\n user.delete(item)\n print(\"User deleted.\")\n else:\n print(\"Incorrect password!\")","def delete_user(id_user: int):\n mycursor.execute(f\"\"\"DELETE FROM User\n WHERE id_user = {id_user}\"\"\")\n mydb.commit()\n return f\"L'utilisateur {id_user} a été supprimé\"","def DelteUser(database):\n firstname=str(input(\"what is the name of the user you want to delete : \"))\n delusr,find =getByName(database,firstname)\n if not find:\n return\n del database[delusr.key]\n for key,usr in database.items():\n if delusr.key in usr.folow:\n usr.folow.remove(delusr.key)\n if delusr.key in usr.folowed:\n usr.folowed.remove(delusr.key)\n \n os.remove(f\"Users/{delusr.key}\")","def test_080_user_delete(self):\n\n testflow.step(RMV_USR_MSG, TEST_GROUP_DELETE)\n assert USER_CLI.run('delete', TEST_USER_DELETE)[0]","def delete_user(user_id):\n temp = models.storage.get('User', user_id)\n if temp is None:\n abort(404)\n temp.delete()\n models.storage.save()\n return jsonify({})","def delete_user(self, instance, name):\n return instance.delete_user(name)","def delete_user(self, username):\n Log.info(\"Start to delete user.\")\n if self.check_if_user_exists(username):\n self.click((\"xpath\", self.user_checkbox_xpath.format(username)))\n self.click(self.user_del_btn)\n self.click(self.dialog_del_btn)\n self.wait_unit_el_present(self.user_table)\n Log.info(\"Use is deleted.\")\n else:\n Log.info(\"User \" + username + \" is not in the user list, not delete.\")","def delete_user(id):\n user_repo = UserRepository(db)\n base_repo = BaseRepository(db, User)\n u = base_repo.get_by_id(id)\n if not u:\n click.echo(\"User with specified id does not exists.\")\n return ERROR_USER_DOES_NOT_EXIST\n user_repo.delete_user(u)\n click.echo(\"User with id \" + str(id) + \" has been deleted.\")","def delete_user(id):\n user = Users.query.filter_by(id=id).first()\n user.delete()\n if not user:\n return send_msg(404, 'Not Found')\n return send_msg(204, \"No data\")","def del_user(id):\n user = User.query.get(id)\n\n db.session.delete(user)\n db.session.commit()\n\n return userSchema.jsonify(user)","def delete_user(self, user):\n name = utils.get_name(user)\n self._user_manager.delete(name)","def delete_user(username, user_id):\r\n global sql_cursor\r\n global database\r\n\r\n print(\"Are you absolutely sure that you want to delete your account.\")\r\n conf_del = input(\"(y/n) : \").lower()\r\n\r\n if conf_del == \"y\":\r\n\r\n print(\"Deleting...\")\r\n\r\n sql_cursor.execute(f\"DELETE FROM passwords WHERE user_id={user_id};\")\r\n sql_cursor.execute(f'DELETE FROM users WHERE username=\"{username}\";')\r\n database.commit()\r\n\r\n print(\"Account successfully deleted\")\r\n print(\"You need to start the program again\")\r\n print(\"Exiting now\")\r\n sleep(5)\r\n quit()\r\n\r\n else:\r\n print(\"Cancelling deletion ...\")\r\n return","def userdel(pwfile, user):\n return __salt__[\"webutil.userdel\"](pwfile, user)","def delete_user(user_id):\n user = User.query.get_or_404(user_id)\n db.session.delete(user)\n db.session.commit()\n\n return redirect('/')","def del_user(self, name):\n del self.users[irc.strings.IRCFoldedCase(modules.trim_nick(name))]","def delete_user(self, _id):\n return self.make_request(\"DELETE\", \"users/\"+_id, {})","def remove_user(user):\n # user.confirmed = False\n # user = get_user_by_phone(phone_num)\n db.session.delete(user)\n db.session.commit()\n\n return user\n # DELETE FROM users WHERE user.phone_num == phone)","def delete_user(self) -> 'outputs.ActingUserResponse':\n return pulumi.get(self, \"delete_user\")","def delete(self, user_id):\n\n user = User.objects.get_or_404(public_id=user_id)\n return user.delete()","def delete_user(user_id):\n user_obj = storage.get(\"User\", user_id)\n if user_obj:\n storage.delete(user_obj)\n storage.save()\n return jsonify({}), 200\n else:\n abort(404)","def delete_user(self, user_id):\n\n # ask the model to delete the user\n um = User(self.settings)\n status = um.delete(user_id)\n\n # return\n return status","def delete_user(self,userid, cursor):\n sql=\"DELETE FROM users WHERE userid = %s\"\n cursor.execute(sql,(userid))","def delete_user(user_id):\n\n user = User.query.get_or_404(user_id)\n db.session.delete(user)\n db.session.commit()\n\n return redirect(\"/users\")","def delete(self, id):\n userDao.delete(id)\n return \"\", 204","def test_delete_user(self):\n pass","def test_delete_user(self):\n pass","def delete_user(user_id):\n user = User.query.get_or_404(user_id)\n db.session.delete(user)\n db.session.commit()\n\n return redirect(\"/users\")","def delete(self, userinformation):\n self.db.remove(userinformation)","def delete_user(self, user):\n\n if self.sql_read_only:\n return False\n\n if not self.check_prereqs():\n return False\n\n if not self.has_user(user):\n return False\n\n db = self.env.get_db_cnx()\n cursor = db.cursor()\n\n query=self.create_query(self.sql_delete_user_query,{'username_field':self.sql_username_field,'username':user})\n self.log.debug(\"sqlflexibleauthstore: delete_user: %s\" % (query,))\n cursor.execute(query)\n\n db.commit()\n del_user_attribute(self.env,username=user)\n return True","def delete_user_account(connection,user):\r\n with connection:\r\n connection.execute(DELETE_SPECIFIC_USER,(user,))","def delete_user(user_id=None):\n\n user = storage.get(\"User\", user_id)\n if user is None:\n abort(404)\n else:\n storage.delete(user)\n storage.save()\n return jsonify({}), 200","def delete(request):\n if request.method != 'POST':\n return HttpResponseNotAllowed(['POST'])\n username = request.user.get_username()\n context = RequestContext(request, {\n 'username': username})\n try:\n DataHubManager.remove_user(username=username, remove_db=True)\n django_logout(request)\n return render(request, 'delete-done.html', context)\n except User.DoesNotExist:\n return HttpResponseNotFound('User {0} not found.'.format(username))","def delete_user(user_id):\n user = storage.get(User, user_id)\n if user is None:\n abort(404)\n storage.delete(user)\n storage.save()\n return jsonify({}), 200","def delete_user(user_id=None):\n obj = storage.get('User', user_id)\n if obj is None:\n abort(404)\n else:\n storage.delete(obj)\n storage.save()\n return jsonify({}), 200","def delete_user(self, userId):\n\n try:\n query = \"delete from user where userId = {}\".format(userId)\n print(query)\n cur = self.con.cursor()\n cur.execute(query)\n self.con.commit()\n\n logger.info(\"Deleted\")\n except Exception as e:\n logger.error(\"Error occured at data deletion..\", e)","def delete_user(user_id):\n netAdminToolDB = app.config['DATABASE']\n user = netAdminToolDB.get_user(user_id)\n\n if user == None:\n return jsonify({'error': 'User_id not found'}), 404\n\n netAdminToolDB.delete_user(user_id)\n return jsonify({'result': True})","def remove_user(username):\n db, c = config.start_db()\n c.execute(\n 'DELETE FROM users WHERE username=?',\n (username,)\n )\n config.end_db(db)","def delete_user(BrokerId=None, Username=None):\n pass","def remove(self, user):\r\n url = '{0}/{1}'.format(self.get_url(), user)\r\n\r\n return http.Request('DELETE', url), parsers.parse_empty","def onUserDeletion(event):\n request = getRequest()\n if not IProductLayer.providedBy(request):\n return\n\n client = getUtility(IAdminClient)\n xmpp_users = getUtility(IXMPPUsers)\n\n principal_id = event.principal\n principal_jid = xmpp_users.getUserJID(principal_id)\n\n pass_storage = getUtility(IXMPPPasswordStorage)\n pass_storage.remove(principal_id)\n\n d = users.deletePrincipal(client, principal_jid)\n return d","def delete_user():\n token = request.args.get('token')\n data = jwt.decode(token, app.config['SECRET_KEY'])\n\n permit = functions.delete_user(data)\n if permit:\n return make_response(jsonify({'Delete': 'User Deleted Successfully'}), 201)\n else:\n return make_response(jsonify({'Delete Failed': 'Credentials not match or the user not exist'}), 201)","def delete_account(username):\n set_user_group(username, \"basic\")\n password = generate_password_hash(\"deleted\")\n set_password(username, password)\n sql = \"UPDATE users \" \\\n \"SET is_active=FALSE, username='[deleted]' \" \\\n \"WHERE username=:username\"\n db.session.execute(sql, {\"username\": username})\n db.session.commit()","def del_user_id(user_id):\r\n obj = storage.get(User, user_id)\r\n if obj is None:\r\n abort(404)\r\n obj.delete()\r\n storage.save()\r\n return jsonify({}), 200","def do_delete_users(self, value=None):\n if not value:\n key = input(\"Provide key through which you want to delete user :\\n\")\n value = input(f\"Provide {key} to delete :\")\n else:\n key = 'id'\n value = value\n self.connection_obj.delete_user(key, value)\n print(\"Deletion Successful\")","def delete_user_process(user_id):\n\n db_user = User.query.get_or_404(user_id)\n\n db.session.delete(db_user)\n db.session.commit()\n\n return redirect(\"/users\")","def delete(user_id):\n assert isinstance(user_id, ObjectId)\n\n User.objects(id=user_id).delete()","def delete_user(self, user_name):\n user = self.get_user(user_name)\n return self.client.delete_resource(user.get('href'))","def delete(self):\n self.deleted = True\n # Deactivate the user to disallow authentication and also\n # to let the user verify the email again after recovery.\n self.is_active = False\n self.save()\n self.history.create(user_id=self.pk, action=user_history.DELETION)","def delete():\n # Must be logged in to perform any delete commands.\n auth_required()\n pass","def _delete_user(self, user):\n if User.delete_user(user):\n self.session.output({'deleted': 'user {} and their related accounts'.format(user)})\n return True\n else:\n self.session.output({'invalid_user': 'please enter valid user ID!\\n'}, '[ Fail to delete user ]')\n return False","def remove_user(username):\n\n if not g.user:\n flash(\"Access unauthorized.\", \"danger\")\n return redirect(\"/\")\n\n do_logout(username)\n\n db.session.delete(g.user)\n db.session.commit()\n\n return redirect(\"/register\")","def deleteUser(self, request, context):\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\n context.set_details('Method not implemented!')\n raise NotImplementedError('Method not implemented!')","async def del_user(conn: LDAPConnection, user: dict, mailman: Client) -> None:\n await conn.delete(user[\"dn\"])\n uid = user[\"attributes\"][\"uid\"][0]\n rmtree(user[\"attributes\"][\"homeDirectory\"][0])\n rmtree(f\"/webtree/{uid[:1]}/{uid}\")\n mailing_list = mailman.get_list(\"announce-redbrick\")\n mailing_list.unsubscribe(f\"{uid}@redbrick.dcu.ie\")","async def delete(self, request, uid):\n return await super(User, self).delete_item(request.app.pool, 'user',\n uid)","def remove(id):\n q = User.delete().where(User.id == id)\n try:\n q.execute()\n except Exception as e:\n return e\n return redirect(url_for('db'))","def delete_user():\n user_id = validate_id(request.args.get(\"id\"))\n config = config_service.get_one({\"user\": str(user_id)})\n config_service.delete(str(config[\"_id\"]))\n if user_service.delete_user(user_id) != user_id:\n response = {\n \"status\": False,\n \"message\": f\"No se pudo eliminar el usuario: {str(user_id)}\",\n }\n resp = make_response(jsonify(response), 404)\n else:\n response = {\n \"status\": True,\n \"message\": f\"Se elimino corretamente el usuario: {str(user_id)}\",\n }\n resp = make_response(jsonify(response), 200)\n resp.headers[\"Content-Type\"] = \"application/json\"\n return resp","async def delete_user(user_id):\n \n user = User.select().where(User.id == user_id).first()\n\n if not user:\n return HTTPException(404, 'User not found')\n else:\n user.delete_instance()\n\n return f\"User {user.username} deleted successfully\"","def remove_user(self, login):\n\t\tif login in self.users_by_name:\n\t\t\tuser = self.users_by_name[login]\n\t\t\tif not user.system:\n\t\t\t\tself.users.pop(user.id, None)\n\t\t\t\tdel(self.users_by_name[login])\n\t\t\t\tself.sync()","def remove_user(user_id):\n user = Users.query.get(user_id)\n if user_id in [0, 1]:\n return 'Removal of default User #%s (%s) is forbidden.' % (user_id, user.login), 'warning'\n db_session.delete(user)\n db_session.commit()\n return 'User #%s (%s) has been deleted.' % (user_id, user.login), 'success'","def delete(user_id):\n # Get the user requested\n user = User.query.filter(User.user_id == user_id).one_or_none()\n\n if user is not None:\n db.session.delete(user)\n db.session.commit()\n return (\n \"User {user_id} deleted\".format(user_id=user_id), 200\n )\n\n else:\n abort(\n 404,\n \"Person not found for Id: {user_id}\".format(user_id=user_id),\n )","def DeleteUser(self, delusercount, deluser):\n for i in range(delusercount):\n login = string.replace(deluser[i]['Login'], ' ', '')\n action = 'userman -D ' + login\n output = commands.getstatusoutput(action)\n print output\n updatecount, update = self.__sqlData[\"UPDATE AccUser SET ToDo = 0 WHERE Login = '%s'\" % (login)]","def wipe_user(user_name):\n user_name = urllib.unquote(user_name) # Username is coming straight from the url bar.\n user = User.query.filter(User.user_name==user_name).first()\n delete_user(user)","def deleteUser(self, uID):\n\n cursor = self.conn.cursor()\n query = \"DELETE FROM Users CASCADE \" \\\n \"WHERE uID= %s RETURNING cID; \"\n cursor.execute(query, (uID,))\n cID = cursor.fetchone()[0]\n\n query = \"DELETE FROM Credential \" \\\n \"WHERE cID= %s; \"\n cursor.execute(query, (cID,))\n\n self.conn.commit()\n return","def delete(self):\n return self._router_request(\n self._make_request_data(\n 'deleteUserCommand',\n data=dict(\n uid=self.parent,\n id=self.id\n )\n )\n )"],"string":"[\n \"def delete_user():\",\n \"def delete_user():\\n #TODO user delete\\n pass\",\n \"def delete_user(id):\\n pass\",\n \"def delete_user():\\r\\n raise NotImplementedError()\",\n \"def del_user(self, username):\\n pass\",\n \"def delete_user(self):\\n User.user_list.remove(self)\",\n \"def delete_user(self):\\n User.user_list.remove(self)\",\n \"def delete_user(self):\\n User.user_list.remove(self)\",\n \"def delete_user(self, user):\\n self.delete(user)\",\n \"def delete_user():\\n del globalopts.appdata[request.user]\\n del globalopts.users[request.user]\\n return \\\"\\\", 200\",\n \"def delete_user(self):\\n\\n User.user_list.remove(self)\",\n \"def deleteUser(self,name):\\n raise BorkedDeleteUser\",\n \"def delete_user(self, user):\\n self.execute(TABELLE['id_users'][\\\"delete\\\"], user[\\\"id\\\"])\",\n \"def delete(self):\\n\\n user_id = get_jwt_identity()\\n user = user_crud.get(user_id)\\n if not user:\\n abort(404, message=\\\"User not Found\\\")\\n all_tokens = auth_crud.get_user_tokens(user_id)\\n tokens = [token.to_dict() for token in all_tokens]\\n for token in tokens:\\n auth_crud.revoke_token(token['id'], user_id)\\n user = user_crud.remove(user_id)\\n\\n return {'msg': 'User Removed'}\",\n \"def delete(self):\\n data = UserRegister.parser.parse_args()\\n user = UserModel.find_by_username(data['username'])\\n\\n if user:\\n user.delete_from_db()\\n else :\\n return {'message': 'User not found!'} , 204\\n\\n return {'message': 'User deleted'},202\",\n \"def delete_user(self, user):\\n # noinspection PyUnresolvedReferences\\n self.delete(user)\",\n \"def delete_user(self, user):\\n try:\\n with dbm.open(self.dbm_path, 'c', 0o600) as db:\\n del db[user.name]\\n except KeyError as k:\\n pass\",\n \"def delete_user(self):\\n db_acces = DbMethods()\\n username = self.result_table.currentItem().text()\\n response = db_acces.delete_user(username)\\n\\n if response == True:\\n self.populate_table()\\n else:\\n message = Message(\\n self.language[\\\"error\\\"], self.language[\\\"inf_error\\\"])\\n warning_message = message.create_iw_message(\\n self.language[\\\"ok\\\"], \\\"warning\\\")\\n warning_message.exec()\",\n \"def remove(self, user_id):\\n pass\",\n \"def delete(self, new_data, user_id):\\n print(new_data)\\n request_id = get_jwt_identity()\\n user = user_crud.get(request_id)\\n if not user.is_superuser:\\n abort(401,\\n message=\\\"You do not have permission to view this endpoint\\\")\\n all_tokens = auth_crud.get_user_tokens(user_id)\\n tokens = [token.to_dict() for token in all_tokens]\\n for token in tokens:\\n auth_crud.revoke_token(token['id'], user_id)\\n user = user_crud.remove(user_id)\\n\\n return {'msg': 'User Removed'}\",\n \"def delete(user_id: int):\\n usr = get_by_id(user_id)\\n if not usr:\\n raise UserNotFound\\n\\n db.session.delete(usr)\\n db.session.commit()\",\n \"def delete_user(UserName=None, AuthenticationType=None):\\n pass\",\n \"def remove_user(self, username):\\n del self.user_table[username]\",\n \"def delete_user(user_id):\\n\\n user = User.query.get(user_id)\\n db.session.delete(user)\\n db.session.commit()\\n return\",\n \"def delete(self, id):\\n\\t\\ttry:\\n\\t\\t\\tuser_service.delete(id)\\n\\t\\texcept AssertionError as e:\\n\\t\\t\\tuser_space.abort(400, e.args[0], status = \\\"Could not delete user\\\", statusCode = \\\"400\\\")\\n\\t\\texcept Exception as e:\\n\\t\\t\\tuser_space.abort(500, e.args[0], status = \\\"Could not delete user\\\", statusCode = \\\"500\\\")\",\n \"def deleteUser(user):\\n delete_user(user)\\n return redirect(url_for('login'))\",\n \"def user_delete(user_id):\\n user = storage.get('User', user_id)\\n if user is None:\\n abort(404)\\n user.delete()\\n storage.save()\\n return jsonify({}), 200\",\n \"def user_id_delete(user_id):\\n user = storage.get(\\\"User\\\", user_id)\\n\\n if user is None:\\n abort(404)\\n user.delete()\\n del user\\n return make_response(jsonify({}), 200)\",\n \"def delete_user(self) -> None:\\n table_dictionary = {\\n 'Apple': {\\n 'table': 'AppleReceipts',\\n 'user_id': 'User_id'\\n },\\n 'ESL': {\\n 'table': 'ESLReceipts',\\n 'user_id': 'User_id'\\n },\\n 'Transactions': {\\n 'table': 'Transactions',\\n 'user_id': 'User_id'\\n },\\n 'Users': {\\n 'table': 'Users',\\n 'user_id': 'id'\\n },\\n }\\n\\n # delete the current user's information from the db.\\n for key in table_dictionary:\\n query = f\\\"\\\"\\\"\\n DELETE\\n FROM {table_dictionary[key]['table']}\\n WHERE {table_dictionary[key]['user_id']}=?;\\n \\\"\\\"\\\"\\n self.db.commit(query, values=(self.id,))\\n\\n # perform a sign out\\n self.sign_out()\\n\\n log(f\\\"User:{self.id} has deleted their account.\\\")\",\n \"def delete_user(self):\\n raise NotImplementedError(\\\"Function not yet implemented contact package creator\\\")\",\n \"def test_remove_user(self):\\n pass\",\n \"def delete_user(user_id):\\n usr = storage.get(User, user_id)\\n if usr:\\n usr.delete(), storage.save()\\n return {}\\n else:\\n abort(404)\",\n \"def delete(self, user_id):\\r\\n return delete_user(request, user_id)\",\n \"def user_delete(user_id=None):\\n obj = storage.get(\\\"User\\\", user_id)\\n if obj is None:\\n abort(404)\\n storage.delete(obj)\\n storage.save()\\n return jsonify({}), 200\",\n \"def delete_user(change):\\n return change()\",\n \"def deleteUser(self):\\r\\n #Find name and ID column\\r\\n userData = self.getCurrentUserData()\\r\\n\\r\\n #Prompt for confirmation\\r\\n deleteChoice = QMessageBox.question(self.view, 'Confirm user deletion', \\r\\n 'Are you sure you want to delete user ' \\r\\n + userData['Name'] + \\\" with ID \\\" + userData['User_ID'] + \\r\\n \\\" from database permanently?\\\", \\r\\n QMessageBox.Yes | QMessageBox.No)\\r\\n \\r\\n if (deleteChoice == QMessageBox.Yes):\\r\\n DBController().deleteUser(userData['User_ID'] )\\r\\n self.updateUserTable() #Re-fill table\\r\",\n \"def del_user(item, username, passw):\\n user = User.load_user_by_username(item, username)\\n if not user:\\n print(\\\"User does not exist!\\\")\\n elif check_password(passw, user.hashed_password):\\n user.delete(item)\\n print(\\\"User deleted.\\\")\\n else:\\n print(\\\"Incorrect password!\\\")\",\n \"def delete_user(id_user: int):\\n mycursor.execute(f\\\"\\\"\\\"DELETE FROM User\\n WHERE id_user = {id_user}\\\"\\\"\\\")\\n mydb.commit()\\n return f\\\"L'utilisateur {id_user} a été supprimé\\\"\",\n \"def DelteUser(database):\\n firstname=str(input(\\\"what is the name of the user you want to delete : \\\"))\\n delusr,find =getByName(database,firstname)\\n if not find:\\n return\\n del database[delusr.key]\\n for key,usr in database.items():\\n if delusr.key in usr.folow:\\n usr.folow.remove(delusr.key)\\n if delusr.key in usr.folowed:\\n usr.folowed.remove(delusr.key)\\n \\n os.remove(f\\\"Users/{delusr.key}\\\")\",\n \"def test_080_user_delete(self):\\n\\n testflow.step(RMV_USR_MSG, TEST_GROUP_DELETE)\\n assert USER_CLI.run('delete', TEST_USER_DELETE)[0]\",\n \"def delete_user(user_id):\\n temp = models.storage.get('User', user_id)\\n if temp is None:\\n abort(404)\\n temp.delete()\\n models.storage.save()\\n return jsonify({})\",\n \"def delete_user(self, instance, name):\\n return instance.delete_user(name)\",\n \"def delete_user(self, username):\\n Log.info(\\\"Start to delete user.\\\")\\n if self.check_if_user_exists(username):\\n self.click((\\\"xpath\\\", self.user_checkbox_xpath.format(username)))\\n self.click(self.user_del_btn)\\n self.click(self.dialog_del_btn)\\n self.wait_unit_el_present(self.user_table)\\n Log.info(\\\"Use is deleted.\\\")\\n else:\\n Log.info(\\\"User \\\" + username + \\\" is not in the user list, not delete.\\\")\",\n \"def delete_user(id):\\n user_repo = UserRepository(db)\\n base_repo = BaseRepository(db, User)\\n u = base_repo.get_by_id(id)\\n if not u:\\n click.echo(\\\"User with specified id does not exists.\\\")\\n return ERROR_USER_DOES_NOT_EXIST\\n user_repo.delete_user(u)\\n click.echo(\\\"User with id \\\" + str(id) + \\\" has been deleted.\\\")\",\n \"def delete_user(id):\\n user = Users.query.filter_by(id=id).first()\\n user.delete()\\n if not user:\\n return send_msg(404, 'Not Found')\\n return send_msg(204, \\\"No data\\\")\",\n \"def del_user(id):\\n user = User.query.get(id)\\n\\n db.session.delete(user)\\n db.session.commit()\\n\\n return userSchema.jsonify(user)\",\n \"def delete_user(self, user):\\n name = utils.get_name(user)\\n self._user_manager.delete(name)\",\n \"def delete_user(username, user_id):\\r\\n global sql_cursor\\r\\n global database\\r\\n\\r\\n print(\\\"Are you absolutely sure that you want to delete your account.\\\")\\r\\n conf_del = input(\\\"(y/n) : \\\").lower()\\r\\n\\r\\n if conf_del == \\\"y\\\":\\r\\n\\r\\n print(\\\"Deleting...\\\")\\r\\n\\r\\n sql_cursor.execute(f\\\"DELETE FROM passwords WHERE user_id={user_id};\\\")\\r\\n sql_cursor.execute(f'DELETE FROM users WHERE username=\\\"{username}\\\";')\\r\\n database.commit()\\r\\n\\r\\n print(\\\"Account successfully deleted\\\")\\r\\n print(\\\"You need to start the program again\\\")\\r\\n print(\\\"Exiting now\\\")\\r\\n sleep(5)\\r\\n quit()\\r\\n\\r\\n else:\\r\\n print(\\\"Cancelling deletion ...\\\")\\r\\n return\",\n \"def userdel(pwfile, user):\\n return __salt__[\\\"webutil.userdel\\\"](pwfile, user)\",\n \"def delete_user(user_id):\\n user = User.query.get_or_404(user_id)\\n db.session.delete(user)\\n db.session.commit()\\n\\n return redirect('/')\",\n \"def del_user(self, name):\\n del self.users[irc.strings.IRCFoldedCase(modules.trim_nick(name))]\",\n \"def delete_user(self, _id):\\n return self.make_request(\\\"DELETE\\\", \\\"users/\\\"+_id, {})\",\n \"def remove_user(user):\\n # user.confirmed = False\\n # user = get_user_by_phone(phone_num)\\n db.session.delete(user)\\n db.session.commit()\\n\\n return user\\n # DELETE FROM users WHERE user.phone_num == phone)\",\n \"def delete_user(self) -> 'outputs.ActingUserResponse':\\n return pulumi.get(self, \\\"delete_user\\\")\",\n \"def delete(self, user_id):\\n\\n user = User.objects.get_or_404(public_id=user_id)\\n return user.delete()\",\n \"def delete_user(user_id):\\n user_obj = storage.get(\\\"User\\\", user_id)\\n if user_obj:\\n storage.delete(user_obj)\\n storage.save()\\n return jsonify({}), 200\\n else:\\n abort(404)\",\n \"def delete_user(self, user_id):\\n\\n # ask the model to delete the user\\n um = User(self.settings)\\n status = um.delete(user_id)\\n\\n # return\\n return status\",\n \"def delete_user(self,userid, cursor):\\n sql=\\\"DELETE FROM users WHERE userid = %s\\\"\\n cursor.execute(sql,(userid))\",\n \"def delete_user(user_id):\\n\\n user = User.query.get_or_404(user_id)\\n db.session.delete(user)\\n db.session.commit()\\n\\n return redirect(\\\"/users\\\")\",\n \"def delete(self, id):\\n userDao.delete(id)\\n return \\\"\\\", 204\",\n \"def test_delete_user(self):\\n pass\",\n \"def test_delete_user(self):\\n pass\",\n \"def delete_user(user_id):\\n user = User.query.get_or_404(user_id)\\n db.session.delete(user)\\n db.session.commit()\\n\\n return redirect(\\\"/users\\\")\",\n \"def delete(self, userinformation):\\n self.db.remove(userinformation)\",\n \"def delete_user(self, user):\\n\\n if self.sql_read_only:\\n return False\\n\\n if not self.check_prereqs():\\n return False\\n\\n if not self.has_user(user):\\n return False\\n\\n db = self.env.get_db_cnx()\\n cursor = db.cursor()\\n\\n query=self.create_query(self.sql_delete_user_query,{'username_field':self.sql_username_field,'username':user})\\n self.log.debug(\\\"sqlflexibleauthstore: delete_user: %s\\\" % (query,))\\n cursor.execute(query)\\n\\n db.commit()\\n del_user_attribute(self.env,username=user)\\n return True\",\n \"def delete_user_account(connection,user):\\r\\n with connection:\\r\\n connection.execute(DELETE_SPECIFIC_USER,(user,))\",\n \"def delete_user(user_id=None):\\n\\n user = storage.get(\\\"User\\\", user_id)\\n if user is None:\\n abort(404)\\n else:\\n storage.delete(user)\\n storage.save()\\n return jsonify({}), 200\",\n \"def delete(request):\\n if request.method != 'POST':\\n return HttpResponseNotAllowed(['POST'])\\n username = request.user.get_username()\\n context = RequestContext(request, {\\n 'username': username})\\n try:\\n DataHubManager.remove_user(username=username, remove_db=True)\\n django_logout(request)\\n return render(request, 'delete-done.html', context)\\n except User.DoesNotExist:\\n return HttpResponseNotFound('User {0} not found.'.format(username))\",\n \"def delete_user(user_id):\\n user = storage.get(User, user_id)\\n if user is None:\\n abort(404)\\n storage.delete(user)\\n storage.save()\\n return jsonify({}), 200\",\n \"def delete_user(user_id=None):\\n obj = storage.get('User', user_id)\\n if obj is None:\\n abort(404)\\n else:\\n storage.delete(obj)\\n storage.save()\\n return jsonify({}), 200\",\n \"def delete_user(self, userId):\\n\\n try:\\n query = \\\"delete from user where userId = {}\\\".format(userId)\\n print(query)\\n cur = self.con.cursor()\\n cur.execute(query)\\n self.con.commit()\\n\\n logger.info(\\\"Deleted\\\")\\n except Exception as e:\\n logger.error(\\\"Error occured at data deletion..\\\", e)\",\n \"def delete_user(user_id):\\n netAdminToolDB = app.config['DATABASE']\\n user = netAdminToolDB.get_user(user_id)\\n\\n if user == None:\\n return jsonify({'error': 'User_id not found'}), 404\\n\\n netAdminToolDB.delete_user(user_id)\\n return jsonify({'result': True})\",\n \"def remove_user(username):\\n db, c = config.start_db()\\n c.execute(\\n 'DELETE FROM users WHERE username=?',\\n (username,)\\n )\\n config.end_db(db)\",\n \"def delete_user(BrokerId=None, Username=None):\\n pass\",\n \"def remove(self, user):\\r\\n url = '{0}/{1}'.format(self.get_url(), user)\\r\\n\\r\\n return http.Request('DELETE', url), parsers.parse_empty\",\n \"def onUserDeletion(event):\\n request = getRequest()\\n if not IProductLayer.providedBy(request):\\n return\\n\\n client = getUtility(IAdminClient)\\n xmpp_users = getUtility(IXMPPUsers)\\n\\n principal_id = event.principal\\n principal_jid = xmpp_users.getUserJID(principal_id)\\n\\n pass_storage = getUtility(IXMPPPasswordStorage)\\n pass_storage.remove(principal_id)\\n\\n d = users.deletePrincipal(client, principal_jid)\\n return d\",\n \"def delete_user():\\n token = request.args.get('token')\\n data = jwt.decode(token, app.config['SECRET_KEY'])\\n\\n permit = functions.delete_user(data)\\n if permit:\\n return make_response(jsonify({'Delete': 'User Deleted Successfully'}), 201)\\n else:\\n return make_response(jsonify({'Delete Failed': 'Credentials not match or the user not exist'}), 201)\",\n \"def delete_account(username):\\n set_user_group(username, \\\"basic\\\")\\n password = generate_password_hash(\\\"deleted\\\")\\n set_password(username, password)\\n sql = \\\"UPDATE users \\\" \\\\\\n \\\"SET is_active=FALSE, username='[deleted]' \\\" \\\\\\n \\\"WHERE username=:username\\\"\\n db.session.execute(sql, {\\\"username\\\": username})\\n db.session.commit()\",\n \"def del_user_id(user_id):\\r\\n obj = storage.get(User, user_id)\\r\\n if obj is None:\\r\\n abort(404)\\r\\n obj.delete()\\r\\n storage.save()\\r\\n return jsonify({}), 200\",\n \"def do_delete_users(self, value=None):\\n if not value:\\n key = input(\\\"Provide key through which you want to delete user :\\\\n\\\")\\n value = input(f\\\"Provide {key} to delete :\\\")\\n else:\\n key = 'id'\\n value = value\\n self.connection_obj.delete_user(key, value)\\n print(\\\"Deletion Successful\\\")\",\n \"def delete_user_process(user_id):\\n\\n db_user = User.query.get_or_404(user_id)\\n\\n db.session.delete(db_user)\\n db.session.commit()\\n\\n return redirect(\\\"/users\\\")\",\n \"def delete(user_id):\\n assert isinstance(user_id, ObjectId)\\n\\n User.objects(id=user_id).delete()\",\n \"def delete_user(self, user_name):\\n user = self.get_user(user_name)\\n return self.client.delete_resource(user.get('href'))\",\n \"def delete(self):\\n self.deleted = True\\n # Deactivate the user to disallow authentication and also\\n # to let the user verify the email again after recovery.\\n self.is_active = False\\n self.save()\\n self.history.create(user_id=self.pk, action=user_history.DELETION)\",\n \"def delete():\\n # Must be logged in to perform any delete commands.\\n auth_required()\\n pass\",\n \"def _delete_user(self, user):\\n if User.delete_user(user):\\n self.session.output({'deleted': 'user {} and their related accounts'.format(user)})\\n return True\\n else:\\n self.session.output({'invalid_user': 'please enter valid user ID!\\\\n'}, '[ Fail to delete user ]')\\n return False\",\n \"def remove_user(username):\\n\\n if not g.user:\\n flash(\\\"Access unauthorized.\\\", \\\"danger\\\")\\n return redirect(\\\"/\\\")\\n\\n do_logout(username)\\n\\n db.session.delete(g.user)\\n db.session.commit()\\n\\n return redirect(\\\"/register\\\")\",\n \"def deleteUser(self, request, context):\\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\\n context.set_details('Method not implemented!')\\n raise NotImplementedError('Method not implemented!')\",\n \"async def del_user(conn: LDAPConnection, user: dict, mailman: Client) -> None:\\n await conn.delete(user[\\\"dn\\\"])\\n uid = user[\\\"attributes\\\"][\\\"uid\\\"][0]\\n rmtree(user[\\\"attributes\\\"][\\\"homeDirectory\\\"][0])\\n rmtree(f\\\"/webtree/{uid[:1]}/{uid}\\\")\\n mailing_list = mailman.get_list(\\\"announce-redbrick\\\")\\n mailing_list.unsubscribe(f\\\"{uid}@redbrick.dcu.ie\\\")\",\n \"async def delete(self, request, uid):\\n return await super(User, self).delete_item(request.app.pool, 'user',\\n uid)\",\n \"def remove(id):\\n q = User.delete().where(User.id == id)\\n try:\\n q.execute()\\n except Exception as e:\\n return e\\n return redirect(url_for('db'))\",\n \"def delete_user():\\n user_id = validate_id(request.args.get(\\\"id\\\"))\\n config = config_service.get_one({\\\"user\\\": str(user_id)})\\n config_service.delete(str(config[\\\"_id\\\"]))\\n if user_service.delete_user(user_id) != user_id:\\n response = {\\n \\\"status\\\": False,\\n \\\"message\\\": f\\\"No se pudo eliminar el usuario: {str(user_id)}\\\",\\n }\\n resp = make_response(jsonify(response), 404)\\n else:\\n response = {\\n \\\"status\\\": True,\\n \\\"message\\\": f\\\"Se elimino corretamente el usuario: {str(user_id)}\\\",\\n }\\n resp = make_response(jsonify(response), 200)\\n resp.headers[\\\"Content-Type\\\"] = \\\"application/json\\\"\\n return resp\",\n \"async def delete_user(user_id):\\n \\n user = User.select().where(User.id == user_id).first()\\n\\n if not user:\\n return HTTPException(404, 'User not found')\\n else:\\n user.delete_instance()\\n\\n return f\\\"User {user.username} deleted successfully\\\"\",\n \"def remove_user(self, login):\\n\\t\\tif login in self.users_by_name:\\n\\t\\t\\tuser = self.users_by_name[login]\\n\\t\\t\\tif not user.system:\\n\\t\\t\\t\\tself.users.pop(user.id, None)\\n\\t\\t\\t\\tdel(self.users_by_name[login])\\n\\t\\t\\t\\tself.sync()\",\n \"def remove_user(user_id):\\n user = Users.query.get(user_id)\\n if user_id in [0, 1]:\\n return 'Removal of default User #%s (%s) is forbidden.' % (user_id, user.login), 'warning'\\n db_session.delete(user)\\n db_session.commit()\\n return 'User #%s (%s) has been deleted.' % (user_id, user.login), 'success'\",\n \"def delete(user_id):\\n # Get the user requested\\n user = User.query.filter(User.user_id == user_id).one_or_none()\\n\\n if user is not None:\\n db.session.delete(user)\\n db.session.commit()\\n return (\\n \\\"User {user_id} deleted\\\".format(user_id=user_id), 200\\n )\\n\\n else:\\n abort(\\n 404,\\n \\\"Person not found for Id: {user_id}\\\".format(user_id=user_id),\\n )\",\n \"def DeleteUser(self, delusercount, deluser):\\n for i in range(delusercount):\\n login = string.replace(deluser[i]['Login'], ' ', '')\\n action = 'userman -D ' + login\\n output = commands.getstatusoutput(action)\\n print output\\n updatecount, update = self.__sqlData[\\\"UPDATE AccUser SET ToDo = 0 WHERE Login = '%s'\\\" % (login)]\",\n \"def wipe_user(user_name):\\n user_name = urllib.unquote(user_name) # Username is coming straight from the url bar.\\n user = User.query.filter(User.user_name==user_name).first()\\n delete_user(user)\",\n \"def deleteUser(self, uID):\\n\\n cursor = self.conn.cursor()\\n query = \\\"DELETE FROM Users CASCADE \\\" \\\\\\n \\\"WHERE uID= %s RETURNING cID; \\\"\\n cursor.execute(query, (uID,))\\n cID = cursor.fetchone()[0]\\n\\n query = \\\"DELETE FROM Credential \\\" \\\\\\n \\\"WHERE cID= %s; \\\"\\n cursor.execute(query, (cID,))\\n\\n self.conn.commit()\\n return\",\n \"def delete(self):\\n return self._router_request(\\n self._make_request_data(\\n 'deleteUserCommand',\\n data=dict(\\n uid=self.parent,\\n id=self.id\\n )\\n )\\n )\"\n]","isConversational":false},"negative_scores":{"kind":"list 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\"0.9304841\",\n \"0.90795064\",\n \"0.8505418\",\n \"0.8406779\",\n \"0.831637\",\n \"0.82509834\",\n \"0.82509834\",\n \"0.82509834\",\n \"0.82255083\",\n \"0.8137962\",\n \"0.8115955\",\n \"0.80621094\",\n \"0.7951598\",\n \"0.7948138\",\n \"0.7906432\",\n \"0.78909653\",\n \"0.7843082\",\n \"0.7732356\",\n \"0.77239\",\n \"0.7720622\",\n \"0.77185726\",\n \"0.76714027\",\n \"0.7669251\",\n \"0.7666565\",\n \"0.7660889\",\n \"0.7651735\",\n \"0.7648581\",\n \"0.7641054\",\n \"0.7623018\",\n \"0.7608943\",\n \"0.7605696\",\n \"0.7600036\",\n \"0.75949216\",\n \"0.7591472\",\n \"0.757968\",\n \"0.757306\",\n \"0.75691867\",\n \"0.75635076\",\n \"0.75553006\",\n \"0.75542104\",\n \"0.7545169\",\n \"0.75407887\",\n \"0.7530795\",\n \"0.7518426\",\n \"0.7503143\",\n \"0.7499382\",\n \"0.74885553\",\n \"0.74853724\",\n \"0.7482728\",\n \"0.74817467\",\n \"0.7478045\",\n \"0.74769276\",\n \"0.7475512\",\n \"0.74639\",\n \"0.7458358\",\n \"0.745378\",\n \"0.7453625\",\n \"0.744703\",\n \"0.7438774\",\n \"0.7436141\",\n \"0.74265313\",\n \"0.74265313\",\n \"0.7418989\",\n \"0.7417594\",\n \"0.7411549\",\n \"0.73941123\",\n \"0.7392529\",\n \"0.73871154\",\n \"0.7384569\",\n \"0.73825145\",\n \"0.7365519\",\n \"0.73632497\",\n \"0.73616207\",\n \"0.73587716\",\n \"0.7349935\",\n \"0.7349833\",\n \"0.7347764\",\n \"0.7346039\",\n \"0.734095\",\n \"0.73330575\",\n \"0.7325786\",\n \"0.7307808\",\n \"0.7307043\",\n \"0.7296656\",\n \"0.72692436\",\n \"0.7267113\",\n \"0.72539717\",\n \"0.7233405\",\n \"0.72310567\",\n \"0.72303736\",\n \"0.72206527\",\n \"0.72193974\",\n \"0.72121686\",\n \"0.7211075\",\n \"0.72090936\",\n \"0.72028303\",\n \"0.71941197\",\n \"0.71906054\",\n \"0.71881956\",\n \"0.7186318\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.73671967"},"document_rank":{"kind":"string","value":"70"}}},{"rowIdx":81192,"cells":{"query":{"kind":"string","value":"Adds a source (as a string) corresponding to a filename in the cache. The filename can be a true file name, or a fake one, like"},"document":{"kind":"string","value":"def add(self, filename, source):\n self.cache[filename] = source\n if os.path.isfile(filename):\n self.ages[filename] = os.path.getmtime(filename) # modification time"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def AddSource (self, name, source, filename):\n self.containments [name] = source, filename, False","def add_source_file(self, filename):\n self.sources.add(Source.create(filename))","def cache(self, file_name, content):\n self.files_loaded[file_name] = content","def put_source(file_path: str, source: str, sha256sum: str) -> str:\n return g.ledger.file.set_source(file_path, source, sha256sum)","def add_to_cache(filename: str, entry: str) -> None:\n\n with open(filename, \"a\") as f:\n f.write(entry.strip())\n f.write(\"\\n\")","def setSourceFile(filename):","def cacheInsert(cache, fileName):\n nameParsed = parseFilename(fileName)\n if nameParsed and nameParsed['cameraID']:\n cameraID = nameParsed['cameraID']\n unixTime = nameParsed['unixTime']\n if not cameraID in cache:\n cache[cameraID] = []\n cameraTimes = cache[cameraID]\n ppath = pathlib.PurePath(fileName)\n cameraTimes.append({'time': unixTime, 'fileName': str(ppath.name)})","def _get_cache_filename(self, processor, source):\n return '%s%s' % (str(hashlib.md5(str(processor.quality) + source\\\n + str(os.path.getsize(source)) \\\n + make_filters_hash(processor.filters) \\\n + source.encode('utf-8')).hexdigest()),\n os.path.splitext(source)[1])","def copy_source(self, filename, new_filename, **kw):\n\n file_path = os.path.join(self.storage_path, filename)\n new_file_path = os.path.join(self.storage_path, new_filename)\n shutil.copyfile(file_path, new_file_path)","def copy_to_cache(cls, target_filename):\n is_cached = cls.is_remote_cached(target_filename)\n if not is_cached:\n cache = cls.CACHE_BACKEND()\n cache.upload(target_filename)\n logger.debug('File %r was uploaded to %r', target_filename, cls.CACHE_BACKEND)","def add_source(self,fname):\n # Keep this as a string for now, could store internally as a list\n if self.sourceProfileName == \"\":\n self.sourceProfileName = fname\n else:\n self.sourceProfileName += ',' + fname","def use_cached_files(self, cache_key):\r\n pass","def add_result_source(self, source):\n self._sources.append(source)","def _add_source(self, source: _Source) -> None:\n\n self._sources.append(source)","def get_source(self, key, files):\n raise NotImplementedError","def get_from_cache(cls, target_filename):\n is_cached = cls.is_remote_cached(target_filename)\n if is_cached:\n cache = cls.CACHE_BACKEND()\n cache.download(is_cached, target_filename)\n logger.debug('File %r was downloaded from %r', target_filename, cls.CACHE_BACKEND)\n else:\n target_filename = None\n return target_filename","def add_raw_source(self,fname):\n # Keep this as a string for now, could store internally as a list\n if self.sourceRawProfileName == \"\":\n self.sourceRawProfileName = fname\n else:\n self.sourceRawProfileName += ',' + fname","def put(self, url, localfile):\n\n cachedir = self._cachedir(url)\n filename = localfile.name\n\n logger.debug(f\"Storing {localfile} in cache for {url}\")\n shutil.copy2(localfile, cachedir / filename)\n self._writefilename(cachedir, filename)","def set_source(self, source_name):\n self.source = source_name","def AddSource(self, source):\n self._sources.append(source)","def get_image_file(self, processor, source, cache=None):\n cache = cache or self._get_cache_filename(processor, source)\n cache_path = os.path.join(self.cache_dir, cache)\n if not os.path.exists(cache_path) or source_changed(source, cache):\n processor.process(source).save(cache_path)\n return cache_path","def addSource(self, source):\n self.tprint('source ' + source)","def _copy_to_media(self, template_name, source=''):\n dirpath = os.path.join(self.cache_root, os.path.dirname(template_name))\n filename = os.path.basename(template_name)\n fullpath = os.path.join(dirpath, filename)\n\n if not os.path.isfile(fullpath) or settings.DEBUG:\n if not os.path.exists(dirpath):\n os.makedirs(dirpath)\n\n f = open(fullpath, 'w')\n f.write(source)\n f.close()\n\n return urljoin(self.cache_url, template_name), filename","def add_source_metadata(self, src_name: SourceName, metadata: SourceMeta) -> None:\n metadata_item = metadata.dict()\n metadata_item[\"src_name\"] = src_name.value\n try:\n self.metadata.put_item(Item=metadata_item)\n except ClientError as e:\n raise DatabaseWriteException(e)","def GetFromCache(self, filename):\n return memcache.get('%s%s' % (self.CACHE_PREFIX, filename))","def GetFromCache(self, filename):\n return memcache.get('%s%s' % (self.CACHE_PREFIX, filename))","def set_source_file(self, source_file):\n self.set_attribute(\"source_file\", source_file)","def cache_test_sources(self):\n self.cache_extra_test_sources(self.smoke_test_src)","def _map_source(source):\n for pattern, replacement in \\\n settings.REFINERY_FILE_SOURCE_MAP.iteritems():\n translated_source = re.sub(pattern, replacement, source)\n if translated_source != source:\n return translated_source\n return source","def add(self, link):\n # if path.exists(self.cachefile):\n with open(self.cachefile, 'a') as cache:\n cache.write(f\"{link}\\n\")","def set_source(self, source):\n self.data['source'] = source","def add_file(self, filename):\n f = open(filename, 'r', encoding='utf8', errors='ignore')\n s = f.read() \n self.add_string(s)","def add_source(self, source):\n # If source is an insn ID, look up the actual instruction.\n source = self.kernel.id_to_insn.get(source, source)\n\n for written in self.map_to_base_storage(\n set(source.assignee_var_names()) & self.relevant_vars):\n self.base_writer_map[written].add(source.id)\n\n for read in self.map_to_base_storage(\n source.dependency_names() & self.relevant_vars):\n self.base_access_map[read].add(source.id)","def read(self, source):\n _source = self._source_prefix+source\n return self.cache[_source]","def add_file(source_fname, sample_stage_id):\n ssq = SampleStage.query.filter_by(obfuscated_id=sample_stage_id)\n ss = get_resource(ssq)\n\n # Note that we intentionally add the file as incomplete. We leave the\n # completion of this process to a sweeper.\n ssf = SampleStageFile(source_fname, ss, status=FileStatus.staged)\n with_transaction(db.session, lambda session: session.add(ssf))\n\n return get_resource(SampleStageFile.query.filter_by(id=ssf.id))","def create_source(path, cached=False):\n source = FileSource(path)\n\n try:\n config = source.read_config()\n ap = config.get('paths', 'additional-paths').strip()\n additional_paths = [_f for _f in ap.split() if _f]\n except (configparser.NoSectionError, configparser.NoOptionError, IOError):\n additional_paths = []\n\n if additional_paths:\n additional_sources = [\n create_source(os.path.join(path, p))\n for p in additional_paths\n ]\n source = MultiSource([source] + additional_sources)\n\n if cached:\n for fname in [\n 'list_files',\n 'list_locales',\n 'resolve_link',\n 'read_config',\n 'read_template',\n 'read_locale',\n 'read_file',\n 'read_include',\n 'exec_file',\n ]:\n setattr(source, fname, utils.memoize(getattr(source, fname)))\n\n return source","def _resolveSourcePath(self, sources, source):\n source = copy.deepcopy(source)\n if source['path'] != '__none__':\n sourcePath = Path(source['path'])\n source['path'] = self._basePath / sourcePath\n if not source['path'].is_file():\n altpath = self._basePath.parent / sourcePath / sourcePath.name\n if altpath.is_file():\n source['path'] = altpath\n if not source['path'].is_file():\n raise TileSourceFileNotFoundError(str(source['path']))\n sources.append(source)","def get_source(self, filename):\n # The main reason we care about ensuring we have the latest version\n # of a given file is for the 'avant-idle' project where we could\n # 'edit and run' multiple times a given file. We need to ensure that\n # the content shown by the traceback is accurate.\n\n if os.path.isfile(filename):\n last_modified = os.path.getmtime(filename) # modification time\n if filename not in self.cache:\n source, lines = self._get_file_source(filename)\n if source is not None:\n self.add(filename, source)\n elif filename in self.ages and last_modified != self.ages[filename]:\n # modified; get fresh copy\n source, lines = self._get_file_source(filename)\n if source is not None:\n self.add(filename, source)\n else: # had problems retrieving fresh copy\n source = self.cache[filename]\n lines = source.split(\"\\n\")\n else:\n source = self.cache[filename]\n lines = source.split(\"\\n\")\n elif filename in self.cache:\n source = self.cache[filename]\n lines = source.split(\"\\n\")\n else:\n lines = []\n\n return lines","def cachePlugin(filename, fullpath=None):\r\n path = cachePath(filename)\r\n \r\n url = 'http://mumble.info:8080'\r\n if fullpath is not None:\r\n url += fullpath\r\n else:\r\n url += '/plugins/' + filename\r\n\r\n res = requests.get(url)\r\n if not res.ok:\r\n raise Exception(\"Failed to fetch '%s'\" % res.url)\r\n \r\n with open(path, 'wb') as f:\r\n f.write(res.content)","def __register_video_source(self, name, source):\n self.__video_modules[name] = source\n self.__last_images[name] = (time.time(), source.get_image())\n self.__video_locks[name] = threading.Lock()","def add_source_file(name, folder, srcPath, submission):\n f = srcPath\n # check if the submission if for the help system\n if isinstance(submission, Post):\n return SourceFile.objects.get_or_create(name=name, folder=folder, file=f, submission=None)\n \n return SourceFile.objects.get_or_create(name=name, folder=folder, file=f, submission=submission)","def _writefilename(self, cachedir, filename):\n\n cachefilename = self._cachefilename(cachedir)\n if cachefilename.exists():\n with open(cachefilename) as f:\n currentfilename = f.readline()\n if currentfilename != filename:\n cachedfile = cachedir / currentfilename\n if cachedfile.exists():\n cachedfile.unlink()\n\n logger.debug(f\"Recording filename {filename}\")\n with open(cachefilename, 'w') as f:\n f.write(filename)","def cacheSources(self):\n page = pywikibot.Page(self.repo, u'List of wikis/python', ns=4)\n self.source_values = json.loads(page.get())\n for family_code, family in self.source_values.iteritems():\n for source_lang in family:\n self.source_values[family_code][source_lang] = pywikibot.ItemPage(self.repo,\n family[source_lang])","def cache_test_sources(self):\n self.cache_extra_test_sources(self.test_src_dir)\n self._fix_shortsort()","def update(self, filename, template_dir, cache_dir):\n\t\tself.cache_dir = cache_dir\n\t\tif filename.startswith('/'): self.template_file = filename\n\t\telse: self.template_file = os.path.join(template_dir,filename)\n\t\tself.cache_module = re.sub(\"[\\/\\\\\\.: ]\",'_',os.path.realpath(self.template_file))\n\t\tself.cache_file = os.path.join(cache_dir,self.cache_module) + '.py'","def add_source(self, name, position):#)*args, **kwargs):\n return self._add_object(name, Source, position)#*args, **kwargs)","def get_cachefile(filename):\n if not os.path.exists(cachedir):\n os.makedirs(cachedir)\n return os.path.join(cachedir, filename)","def cache_key(self, source, cell_index):\n sources = []\n for idx, cell in self.setup:\n if idx >= cell_index:\n break\n sources.append(cell.source)\n sources.append(source)\n return sha1('\\n'.join(sources).encode('utf8')).hexdigest()","def add(self, alias, fname):\n if callable(fname):\n self._add_function(alias, fname)\n else:\n # Determine whether the string contains placeholders and whether\n # all placeholders can be pre-filled with existing file aliases.\n placeholders = _get_placeholders(fname)\n if len(placeholders) == 0:\n self._add_fname(alias, fname) # Plain string filename\n else:\n prefilled = _prefill_placeholders(placeholders, self.files(),\n dict())\n if len(prefilled) == len(placeholders):\n # The template could be completely pre-filled. Add the\n # result as a plain string filename.\n self._add_fname(alias, fname.format(**prefilled))\n else:\n # Add filename as a template\n self._add_template(alias, fname)","def register_captured_url_local(filename, url):\n with open(filename, 'a') as f:\n f.write(url + '\\n')","def addResource(self, content, uri, headers):\r\n self.cache[uri] = CachedResource(content, headers)","def addExtension(self, ext):\n self.files[ext] = SourceFile(self.path, self.name + ext)","def cache_file(cache_key):\n\n return MASTOOLS_DIR / f\"{cache_key}_cache.json\"","def cache_test_sources(self):\n self.cache_extra_test_sources(\"pdb\")","def addSourceCodeFile(self, name, ext):\n if name not in self.sources:\n self.sources[name] = SourceGroup(self.path, name)\n\n self.sources[name].addExtension(ext)","def new_source(self, name):\n params = {\"name\": name}\n return JSONRPCRequest(self, \"newSource\", params)","def BootstrapSource (name, source, filename):\n source = binascii.b2a_base64 (zlib.compress (source.encode ('utf-8'))).strip ().decode ('utf-8')\n return source_payload.format (name = name, filename = filename, source = '\\\\\\n'.join (textwrap.wrap (source, 78)))","def add_filelist_to_cache(self, file_list=None):\n if file_list is None:\n return False\n for fileinfo in file_list:\n fn_ = fileinfo.filename\n self.cache_file_list_dict[fn_] = fileinfo\n return True","def get_source_metadata(self, src_name: Union[str, SourceName]) -> Dict:\n if isinstance(src_name, SourceName):\n src_name = src_name.value\n if src_name in self._cached_sources:\n return self._cached_sources[src_name]\n else:\n metadata = self.metadata.get_item(Key={\"src_name\": src_name}).get(\"Item\")\n self._cached_sources[src_name] = metadata\n return metadata","def source(dirname, filename, gen_content):\n if dirname in lut['sources']:\n s.add('MD5SUM=\"$(find \"{0}\" -printf %T@\\\\\\\\n | md5sum)\"', dirname)\n if secret is None:\n s.add('tar xf \"{0}\" -C \"{1}\"',\n filename,\n dirname,\n sources={filename: gen_content()})\n else:\n s.add('wget \"{0}/{1}/{2}/{3}\"', server, secret, b.name, filename)\n s.add('tar xf \"{0}\" -C \"{1}\"', filename, dirname)\n for manager, service in lut['sources'][dirname]:\n s.add('[ \"$MD5SUM\" != \"$(find \"{0}\" -printf %T@\\\\\\\\n ' # No ,\n '| md5sum)\" ] && {1}=1',\n dirname,\n manager.env_var(service))","def _add_fname(self, alias, fname):\n self.__dict__[alias] = fname","def put_download(\n self, uri: str, filename: str, logger: Optional[logging.Logger] = None,\n ) -> str:\n logger = logger.getChild(\"CallCache\") if logger else self._logger\n ans = filename\n if self._cfg[\"download_cache\"].get_bool(\"put\"):\n p = self.download_path(uri)\n if p:\n os.makedirs(os.path.dirname(p), exist_ok=True)\n os.rename(filename, p)\n logger.info(_(\"stored in download cache\", uri=uri, cache_path=p))\n ans = p\n self.flock(ans)\n return ans","def list_cached():\n for json_name in cached_files():\n source_name = get_source_file_name(json_name)\n yield (json_name, source_name)","def cache_name(name, typ=\"pkl\"):\n return os.path.join(CACHE_DIR, name + '.' + typ)","def StoreInNegativeCache(self, filename):\n memcache.add('%s%s' % (self.NEG_CACHE_PREFIX, filename), -1)","def StoreInNegativeCache(self, filename):\n memcache.add('%s%s' % (self.NEG_CACHE_PREFIX, filename), -1)","def source_path(self, rel_path):\n # TODO download to disk (use source cache) in case of remote file\n import inspect\n\n frame = inspect.currentframe().f_back\n calling_file = frame.f_code.co_filename\n\n if (\n self.included_stack\n and calling_file == self.included_stack[-1].get_path_or_uri()\n ):\n # called from current snakefile, we can try to keep the original source\n # file annotation\n # This will only work if the method is evaluated during parsing mode.\n # Otherwise, the stack can be empty already.\n path = self.current_basedir.join(rel_path)\n orig_path = path.get_path_or_uri()\n else:\n # heuristically determine path\n calling_dir = os.path.dirname(calling_file)\n path = smart_join(calling_dir, rel_path)\n orig_path = path\n\n return sourcecache_entry(\n self.sourcecache.get_path(infer_source_file(path)), orig_path\n )","def add_result_source(self, source):\n self._main_model.add_result_source(source)","def cache_test_sources(self):\n self.cache_extra_test_sources([join_path(\"examples\", \"apps\")])","def get_source(filename: str) -> dict[str, str]:\n file_path = (\n filename\n or g.ledger.fava_options.default_file\n or g.ledger.beancount_file_path\n )\n source, sha256sum = g.ledger.file.get_source(file_path)\n return {\"source\": source, \"sha256sum\": sha256sum, \"file_path\": file_path}","def insert(self, cache_key, paths):\r\n missing_files = filter(lambda f: not os.path.exists(f), paths)\r\n try:\r\n if missing_files:\r\n raise ArtifactCache.CacheError('Tried to cache nonexistent files: %s' % missing_files)\r\n self.try_insert(cache_key, paths)\r\n except Exception as e:\r\n self.log.error('Error while writing to artifact cache: %s. ' % e)","def get_or_insert(cls, name, source=None):\n\t\tid = '%s-%s' % (cls._class_name(), cls.normalize_name(name))\n\t\treturn super(Cache, cls).get_or_insert(id, source=source)","def src(self, src):\n\n self._src = src","def add_file(self, file_path):\n content = ''\n with open(file_path, 'r') as fh:\n self.__add_source_data(fh.read())","def add_source_address(self, srcAddr):\n self.source.address = srcAddr","def add_source_address(self, srcAddr):\n self.source.address = srcAddr","def mock_add_source(monkeypatch):\n\n def print_add_source(line, key):\n print(\"Mocked add source: {} ({})\".format(line, key))\n return True\n\n mocked_add_source = mock.Mock()\n mocked_add_source.get.side_effect = print_add_source\n monkeypatch.setattr(\"libgitlab.add_source\", mocked_add_source)\n return mocked_add_source","def add(self, filename, *args):\n return self.cmd('add', filename, *args)","def get_cached_file_name(file_name):\n (directory, base_name) = os.path.split(file_name)\n directory = os.path.relpath(directory, start=\"/\")\n new_directory = os.path.join(CACHE, directory)\n (simple_name, ext) = os.path.splitext(base_name)\n new_base_name = simple_name + cached_ext(ext)\n result = os.path.join(new_directory, new_base_name)\n return result","def addSource(self,\n path,\n name,\n location,\n copyLib=False,\n copyGroups=False,\n copyInfo=False,\n copyFeatures=False,\n muteKerning=False,\n muteInfo=False,\n mutedGlyphNames=None,\n familyName=None,\n styleName=None,\n ):\n sourceElement = ET.Element(\"source\")\n sourceElement.attrib['filename'] = self._posixPathRelativeToDocument(path)\n sourceElement.attrib['name'] = name\n if copyLib:\n libElement = ET.Element('lib')\n libElement.attrib['copy'] = \"1\"\n sourceElement.append(libElement)\n\n if copyGroups:\n groupsElement = ET.Element('groups')\n groupsElement.attrib['copy'] = \"1\"\n sourceElement.append(groupsElement)\n\n if copyFeatures:\n featuresElement = ET.Element('features')\n featuresElement.attrib['copy'] = \"1\"\n sourceElement.append(featuresElement)\n\n if copyInfo or muteInfo:\n # copy info:\n infoElement = ET.Element('info')\n if copyInfo:\n infoElement.attrib['copy'] = \"1\"\n if muteInfo:\n infoElement.attrib['mute'] = \"1\"\n sourceElement.append(infoElement)\n\n if muteKerning:\n # add kerning element to the source\n kerningElement = ET.Element(\"kerning\")\n kerningElement.attrib[\"mute\"] = '1'\n sourceElement.append(kerningElement)\n\n if mutedGlyphNames:\n # add muted glyphnames to the source\n for name in mutedGlyphNames:\n glyphElement = ET.Element(\"glyph\")\n glyphElement.attrib[\"name\"] = name\n glyphElement.attrib[\"mute\"] = '1'\n sourceElement.append(glyphElement)\n\n if familyName is not None:\n sourceElement.attrib['familyname'] = familyName\n if styleName is not None:\n sourceElement.attrib['stylename'] = styleName\n\n\n locationElement = self._makeLocationElement(location)\n sourceElement.append(locationElement)\n self.root.findall('.sources')[0].append(sourceElement)","def _cachefilename(self, cachedir):\n\n return cachedir / \"filename\"","def key_for(self, target_id, sources):\r\n sha = hashlib.sha1()\r\n actual_srcs = self._sources_hash(sha, sources)\r\n return CacheKey(target_id, sha.hexdigest(), len(actual_srcs), actual_srcs)","def add_source(name, source_url, section):\n # lsb_release is not installed in most docker images by default\n distro = (\n subprocess.check_output(\n [\"/bin/bash\", \"-c\", \"source /etc/os-release && echo ${VERSION_CODENAME}\"],\n stderr=subprocess.STDOUT,\n )\n .decode()\n .strip()\n )\n line = f\"deb {source_url} {distro} {section}\\n\"\n with open(os.path.join(\"/etc/apt/sources.list.d/\", name + \".list\"), \"a+\") as f:\n # Write out deb line only if it already doesn't exist\n f.seek(0)\n if line not in f.read():\n f.write(line)\n f.truncate()\n utils.run_subprocess([\"apt-get\", \"update\", \"--yes\"])","def __init__(self, filename, asstring=''):\n\n self.cachedlines = (asstring if asstring else content_as_str(filename))\n self._setup()\n self._process()","def filename_to_url(filename, cache_dir=None):\n if cache_dir is None:\n cache_dir = PYTORCH_PRETRAINED_BIGGAN_CACHE\n if sys.version_info[0] == 3 and isinstance(cache_dir, Path):\n cache_dir = str(cache_dir)\n\n cache_path = os.path.join(cache_dir, filename)\n if not os.path.exists(cache_path):\n raise EnvironmentError(\"file {} not found\".format(cache_path))\n\n meta_path = cache_path + '.json'\n if not os.path.exists(meta_path):\n raise EnvironmentError(\"file {} not found\".format(meta_path))\n\n with open(meta_path, encoding=\"utf-8\") as meta_file:\n metadata = json.load(meta_file)\n url = metadata['url']\n etag = metadata['etag']\n\n return url, etag","def cache(filename):\n\n def decorator(func):\n def wrapper(*args, **kw):\n self = args[0]\n path = os.path.join(self.cache_dir, filename)\n time0 = time.time()\n if os.path.exists(path):\n result = joblib.load(path)\n cost = time.time() - time0\n logger.info('[cache] loading {} costs {:.2f}s'.format(path, cost))\n return result\n result = func(*args, **kw)\n cost = time.time() - time0\n logger.info('[cache] obtaining {} costs {:.2f}s'.format(path, cost))\n joblib.dump(result, path)\n return result\n\n return wrapper\n\n return decorator","def source(self, source):\n\n self._source = source","def source(self, source):\n\n self._source = source","def source(self, source):\n\n self._source = source","def source(self, source):\n\n self._source = source","def source(self, source):\n\n self._source = source","def source(self, source):\n\n self._source = source","def source(self, source):\n\n self._source = source","def search_source(self,strz):\n\t\tfor src in sources_rip: #sources_rip = list of allow source words\n\t\t\tif src in strz:\n\t\t\t\tself.src_rip=src.replace(\".\",\"\")\n\t\t\t\treturn strz.replace(src,\"\")\n\t\treturn strz","def add(self, name, task, filename, categories=()):\n assert name, \"Must input a valid dataset name.\"\n assert task, \"Must input a valid task name.\"\n assert filename, \"Must input a valid file path.\"\n self._assert_dataset_exists_in_cache(name)\n self._assert_task_not_exists_in_dataset_in_cache(name, task)\n\n self._add_new_task(name, task, filename, categories)\n\n self._update_cache_data()","def filename_to_url(filename, cache_dir=None):\n if cache_dir is None:\n cache_dir = PYTORCH_PRETRAINED_BERT_CACHE\n if sys.version_info[0] == 3 and isinstance(cache_dir, Path):\n cache_dir = str(cache_dir)\n\n cache_path = os.path.join(cache_dir, filename)\n if not os.path.exists(cache_path):\n raise EnvironmentError(\"file {} not found\".format(cache_path))\n\n meta_path = cache_path + \".json\"\n if not os.path.exists(meta_path):\n raise EnvironmentError(\"file {} not found\".format(meta_path))\n\n with open(meta_path, encoding=\"utf-8\") as meta_file:\n metadata = json.load(meta_file)\n url = metadata[\"url\"]\n etag = metadata[\"etag\"]\n\n return url, etag","def addCacheableShaderFromSourceFile(self, Union, QOpenGLShader_ShaderType=None, QOpenGLShader_ShaderTypeBit=None, *args, **kwargs): # real signature unknown; NOTE: unreliably restored from __doc__\n pass","def add_file(self, filename):\n # If absolute path\n if filename[0] == '/':\n self.filenames.append(filename)\n else:\n filename = self.current_path + filename\n self.filenames.append(filename)","def get_image(self, processor, source, cache=None):\n cache = cache or self._get_cache_filename(processor, source)\n cache_path = os.path.join(self.cache_dir, cache)\n if os.path.exists(cache_path):\n return CachedImage(cache_path)","def add_file(self, filename):\r\n f = open(filename, 'r', encoding='utf8', errors='ignore')\r\n text = f.read()\r\n self.add_string(text)\r\n f.close()"],"string":"[\n \"def AddSource (self, name, source, filename):\\n self.containments [name] = source, filename, False\",\n \"def add_source_file(self, filename):\\n self.sources.add(Source.create(filename))\",\n \"def cache(self, file_name, content):\\n self.files_loaded[file_name] = content\",\n \"def put_source(file_path: str, source: str, sha256sum: str) -> str:\\n return g.ledger.file.set_source(file_path, source, sha256sum)\",\n \"def add_to_cache(filename: str, entry: str) -> None:\\n\\n with open(filename, \\\"a\\\") as f:\\n f.write(entry.strip())\\n f.write(\\\"\\\\n\\\")\",\n \"def setSourceFile(filename):\",\n \"def cacheInsert(cache, fileName):\\n nameParsed = parseFilename(fileName)\\n if nameParsed and nameParsed['cameraID']:\\n cameraID = nameParsed['cameraID']\\n unixTime = nameParsed['unixTime']\\n if not cameraID in cache:\\n cache[cameraID] = []\\n cameraTimes = cache[cameraID]\\n ppath = pathlib.PurePath(fileName)\\n cameraTimes.append({'time': unixTime, 'fileName': str(ppath.name)})\",\n \"def _get_cache_filename(self, processor, source):\\n return '%s%s' % (str(hashlib.md5(str(processor.quality) + source\\\\\\n + str(os.path.getsize(source)) \\\\\\n + make_filters_hash(processor.filters) \\\\\\n + source.encode('utf-8')).hexdigest()),\\n os.path.splitext(source)[1])\",\n \"def copy_source(self, filename, new_filename, **kw):\\n\\n file_path = os.path.join(self.storage_path, filename)\\n new_file_path = os.path.join(self.storage_path, new_filename)\\n shutil.copyfile(file_path, new_file_path)\",\n \"def copy_to_cache(cls, target_filename):\\n is_cached = cls.is_remote_cached(target_filename)\\n if not is_cached:\\n cache = cls.CACHE_BACKEND()\\n cache.upload(target_filename)\\n logger.debug('File %r was uploaded to %r', target_filename, cls.CACHE_BACKEND)\",\n \"def add_source(self,fname):\\n # Keep this as a string for now, could store internally as a list\\n if self.sourceProfileName == \\\"\\\":\\n self.sourceProfileName = fname\\n else:\\n self.sourceProfileName += ',' + fname\",\n \"def use_cached_files(self, cache_key):\\r\\n pass\",\n \"def add_result_source(self, source):\\n self._sources.append(source)\",\n \"def _add_source(self, source: _Source) -> None:\\n\\n self._sources.append(source)\",\n \"def get_source(self, key, files):\\n raise NotImplementedError\",\n \"def get_from_cache(cls, target_filename):\\n is_cached = cls.is_remote_cached(target_filename)\\n if is_cached:\\n cache = cls.CACHE_BACKEND()\\n cache.download(is_cached, target_filename)\\n logger.debug('File %r was downloaded from %r', target_filename, cls.CACHE_BACKEND)\\n else:\\n target_filename = None\\n return target_filename\",\n \"def add_raw_source(self,fname):\\n # Keep this as a string for now, could store internally as a list\\n if self.sourceRawProfileName == \\\"\\\":\\n self.sourceRawProfileName = fname\\n else:\\n self.sourceRawProfileName += ',' + fname\",\n \"def put(self, url, localfile):\\n\\n cachedir = self._cachedir(url)\\n filename = localfile.name\\n\\n logger.debug(f\\\"Storing {localfile} in cache for {url}\\\")\\n shutil.copy2(localfile, cachedir / filename)\\n self._writefilename(cachedir, filename)\",\n \"def set_source(self, source_name):\\n self.source = source_name\",\n \"def AddSource(self, source):\\n self._sources.append(source)\",\n \"def get_image_file(self, processor, source, cache=None):\\n cache = cache or self._get_cache_filename(processor, source)\\n cache_path = os.path.join(self.cache_dir, cache)\\n if not os.path.exists(cache_path) or source_changed(source, cache):\\n processor.process(source).save(cache_path)\\n return cache_path\",\n \"def addSource(self, source):\\n self.tprint('source ' + source)\",\n \"def _copy_to_media(self, template_name, source=''):\\n dirpath = os.path.join(self.cache_root, os.path.dirname(template_name))\\n filename = os.path.basename(template_name)\\n fullpath = os.path.join(dirpath, filename)\\n\\n if not os.path.isfile(fullpath) or settings.DEBUG:\\n if not os.path.exists(dirpath):\\n os.makedirs(dirpath)\\n\\n f = open(fullpath, 'w')\\n f.write(source)\\n f.close()\\n\\n return urljoin(self.cache_url, template_name), filename\",\n \"def add_source_metadata(self, src_name: SourceName, metadata: SourceMeta) -> None:\\n metadata_item = metadata.dict()\\n metadata_item[\\\"src_name\\\"] = src_name.value\\n try:\\n self.metadata.put_item(Item=metadata_item)\\n except ClientError as e:\\n raise DatabaseWriteException(e)\",\n \"def GetFromCache(self, filename):\\n return memcache.get('%s%s' % (self.CACHE_PREFIX, filename))\",\n \"def GetFromCache(self, filename):\\n return memcache.get('%s%s' % (self.CACHE_PREFIX, filename))\",\n \"def set_source_file(self, source_file):\\n self.set_attribute(\\\"source_file\\\", source_file)\",\n \"def cache_test_sources(self):\\n self.cache_extra_test_sources(self.smoke_test_src)\",\n \"def _map_source(source):\\n for pattern, replacement in \\\\\\n settings.REFINERY_FILE_SOURCE_MAP.iteritems():\\n translated_source = re.sub(pattern, replacement, source)\\n if translated_source != source:\\n return translated_source\\n return source\",\n \"def add(self, link):\\n # if path.exists(self.cachefile):\\n with open(self.cachefile, 'a') as cache:\\n cache.write(f\\\"{link}\\\\n\\\")\",\n \"def set_source(self, source):\\n self.data['source'] = source\",\n \"def add_file(self, filename):\\n f = open(filename, 'r', encoding='utf8', errors='ignore')\\n s = f.read() \\n self.add_string(s)\",\n \"def add_source(self, source):\\n # If source is an insn ID, look up the actual instruction.\\n source = self.kernel.id_to_insn.get(source, source)\\n\\n for written in self.map_to_base_storage(\\n set(source.assignee_var_names()) & self.relevant_vars):\\n self.base_writer_map[written].add(source.id)\\n\\n for read in self.map_to_base_storage(\\n source.dependency_names() & self.relevant_vars):\\n self.base_access_map[read].add(source.id)\",\n \"def read(self, source):\\n _source = self._source_prefix+source\\n return self.cache[_source]\",\n \"def add_file(source_fname, sample_stage_id):\\n ssq = SampleStage.query.filter_by(obfuscated_id=sample_stage_id)\\n ss = get_resource(ssq)\\n\\n # Note that we intentionally add the file as incomplete. We leave the\\n # completion of this process to a sweeper.\\n ssf = SampleStageFile(source_fname, ss, status=FileStatus.staged)\\n with_transaction(db.session, lambda session: session.add(ssf))\\n\\n return get_resource(SampleStageFile.query.filter_by(id=ssf.id))\",\n \"def create_source(path, cached=False):\\n source = FileSource(path)\\n\\n try:\\n config = source.read_config()\\n ap = config.get('paths', 'additional-paths').strip()\\n additional_paths = [_f for _f in ap.split() if _f]\\n except (configparser.NoSectionError, configparser.NoOptionError, IOError):\\n additional_paths = []\\n\\n if additional_paths:\\n additional_sources = [\\n create_source(os.path.join(path, p))\\n for p in additional_paths\\n ]\\n source = MultiSource([source] + additional_sources)\\n\\n if cached:\\n for fname in [\\n 'list_files',\\n 'list_locales',\\n 'resolve_link',\\n 'read_config',\\n 'read_template',\\n 'read_locale',\\n 'read_file',\\n 'read_include',\\n 'exec_file',\\n ]:\\n setattr(source, fname, utils.memoize(getattr(source, fname)))\\n\\n return source\",\n \"def _resolveSourcePath(self, sources, source):\\n source = copy.deepcopy(source)\\n if source['path'] != '__none__':\\n sourcePath = Path(source['path'])\\n source['path'] = self._basePath / sourcePath\\n if not source['path'].is_file():\\n altpath = self._basePath.parent / sourcePath / sourcePath.name\\n if altpath.is_file():\\n source['path'] = altpath\\n if not source['path'].is_file():\\n raise TileSourceFileNotFoundError(str(source['path']))\\n sources.append(source)\",\n \"def get_source(self, filename):\\n # The main reason we care about ensuring we have the latest version\\n # of a given file is for the 'avant-idle' project where we could\\n # 'edit and run' multiple times a given file. We need to ensure that\\n # the content shown by the traceback is accurate.\\n\\n if os.path.isfile(filename):\\n last_modified = os.path.getmtime(filename) # modification time\\n if filename not in self.cache:\\n source, lines = self._get_file_source(filename)\\n if source is not None:\\n self.add(filename, source)\\n elif filename in self.ages and last_modified != self.ages[filename]:\\n # modified; get fresh copy\\n source, lines = self._get_file_source(filename)\\n if source is not None:\\n self.add(filename, source)\\n else: # had problems retrieving fresh copy\\n source = self.cache[filename]\\n lines = source.split(\\\"\\\\n\\\")\\n else:\\n source = self.cache[filename]\\n lines = source.split(\\\"\\\\n\\\")\\n elif filename in self.cache:\\n source = self.cache[filename]\\n lines = source.split(\\\"\\\\n\\\")\\n else:\\n lines = []\\n\\n return lines\",\n \"def cachePlugin(filename, fullpath=None):\\r\\n path = cachePath(filename)\\r\\n \\r\\n url = 'http://mumble.info:8080'\\r\\n if fullpath is not None:\\r\\n url += fullpath\\r\\n else:\\r\\n url += '/plugins/' + filename\\r\\n\\r\\n res = requests.get(url)\\r\\n if not res.ok:\\r\\n raise Exception(\\\"Failed to fetch '%s'\\\" % res.url)\\r\\n \\r\\n with open(path, 'wb') as f:\\r\\n f.write(res.content)\",\n \"def __register_video_source(self, name, source):\\n self.__video_modules[name] = source\\n self.__last_images[name] = (time.time(), source.get_image())\\n self.__video_locks[name] = threading.Lock()\",\n \"def add_source_file(name, folder, srcPath, submission):\\n f = srcPath\\n # check if the submission if for the help system\\n if isinstance(submission, Post):\\n return SourceFile.objects.get_or_create(name=name, folder=folder, file=f, submission=None)\\n \\n return SourceFile.objects.get_or_create(name=name, folder=folder, file=f, submission=submission)\",\n \"def _writefilename(self, cachedir, filename):\\n\\n cachefilename = self._cachefilename(cachedir)\\n if cachefilename.exists():\\n with open(cachefilename) as f:\\n currentfilename = f.readline()\\n if currentfilename != filename:\\n cachedfile = cachedir / currentfilename\\n if cachedfile.exists():\\n cachedfile.unlink()\\n\\n logger.debug(f\\\"Recording filename {filename}\\\")\\n with open(cachefilename, 'w') as f:\\n f.write(filename)\",\n \"def cacheSources(self):\\n page = pywikibot.Page(self.repo, u'List of wikis/python', ns=4)\\n self.source_values = json.loads(page.get())\\n for family_code, family in self.source_values.iteritems():\\n for source_lang in family:\\n self.source_values[family_code][source_lang] = pywikibot.ItemPage(self.repo,\\n family[source_lang])\",\n \"def cache_test_sources(self):\\n self.cache_extra_test_sources(self.test_src_dir)\\n self._fix_shortsort()\",\n \"def update(self, filename, template_dir, cache_dir):\\n\\t\\tself.cache_dir = cache_dir\\n\\t\\tif filename.startswith('/'): self.template_file = filename\\n\\t\\telse: self.template_file = os.path.join(template_dir,filename)\\n\\t\\tself.cache_module = re.sub(\\\"[\\\\/\\\\\\\\\\\\.: ]\\\",'_',os.path.realpath(self.template_file))\\n\\t\\tself.cache_file = os.path.join(cache_dir,self.cache_module) + '.py'\",\n \"def add_source(self, name, position):#)*args, **kwargs):\\n return self._add_object(name, Source, position)#*args, **kwargs)\",\n \"def get_cachefile(filename):\\n if not os.path.exists(cachedir):\\n os.makedirs(cachedir)\\n return os.path.join(cachedir, filename)\",\n \"def cache_key(self, source, cell_index):\\n sources = []\\n for idx, cell in self.setup:\\n if idx >= cell_index:\\n break\\n sources.append(cell.source)\\n sources.append(source)\\n return sha1('\\\\n'.join(sources).encode('utf8')).hexdigest()\",\n \"def add(self, alias, fname):\\n if callable(fname):\\n self._add_function(alias, fname)\\n else:\\n # Determine whether the string contains placeholders and whether\\n # all placeholders can be pre-filled with existing file aliases.\\n placeholders = _get_placeholders(fname)\\n if len(placeholders) == 0:\\n self._add_fname(alias, fname) # Plain string filename\\n else:\\n prefilled = _prefill_placeholders(placeholders, self.files(),\\n dict())\\n if len(prefilled) == len(placeholders):\\n # The template could be completely pre-filled. Add the\\n # result as a plain string filename.\\n self._add_fname(alias, fname.format(**prefilled))\\n else:\\n # Add filename as a template\\n self._add_template(alias, fname)\",\n \"def register_captured_url_local(filename, url):\\n with open(filename, 'a') as f:\\n f.write(url + '\\\\n')\",\n \"def addResource(self, content, uri, headers):\\r\\n self.cache[uri] = CachedResource(content, headers)\",\n \"def addExtension(self, ext):\\n self.files[ext] = SourceFile(self.path, self.name + ext)\",\n \"def cache_file(cache_key):\\n\\n return MASTOOLS_DIR / f\\\"{cache_key}_cache.json\\\"\",\n \"def cache_test_sources(self):\\n self.cache_extra_test_sources(\\\"pdb\\\")\",\n \"def addSourceCodeFile(self, name, ext):\\n if name not in self.sources:\\n self.sources[name] = SourceGroup(self.path, name)\\n\\n self.sources[name].addExtension(ext)\",\n \"def new_source(self, name):\\n params = {\\\"name\\\": name}\\n return JSONRPCRequest(self, \\\"newSource\\\", params)\",\n \"def BootstrapSource (name, source, filename):\\n source = binascii.b2a_base64 (zlib.compress (source.encode ('utf-8'))).strip ().decode ('utf-8')\\n return source_payload.format (name = name, filename = filename, source = '\\\\\\\\\\\\n'.join (textwrap.wrap (source, 78)))\",\n \"def add_filelist_to_cache(self, file_list=None):\\n if file_list is None:\\n return False\\n for fileinfo in file_list:\\n fn_ = fileinfo.filename\\n self.cache_file_list_dict[fn_] = fileinfo\\n return True\",\n \"def get_source_metadata(self, src_name: Union[str, SourceName]) -> Dict:\\n if isinstance(src_name, SourceName):\\n src_name = src_name.value\\n if src_name in self._cached_sources:\\n return self._cached_sources[src_name]\\n else:\\n metadata = self.metadata.get_item(Key={\\\"src_name\\\": src_name}).get(\\\"Item\\\")\\n self._cached_sources[src_name] = metadata\\n return metadata\",\n \"def source(dirname, filename, gen_content):\\n if dirname in lut['sources']:\\n s.add('MD5SUM=\\\"$(find \\\"{0}\\\" -printf %T@\\\\\\\\\\\\\\\\n | md5sum)\\\"', dirname)\\n if secret is None:\\n s.add('tar xf \\\"{0}\\\" -C \\\"{1}\\\"',\\n filename,\\n dirname,\\n sources={filename: gen_content()})\\n else:\\n s.add('wget \\\"{0}/{1}/{2}/{3}\\\"', server, secret, b.name, filename)\\n s.add('tar xf \\\"{0}\\\" -C \\\"{1}\\\"', filename, dirname)\\n for manager, service in lut['sources'][dirname]:\\n s.add('[ \\\"$MD5SUM\\\" != \\\"$(find \\\"{0}\\\" -printf %T@\\\\\\\\\\\\\\\\n ' # No ,\\n '| md5sum)\\\" ] && {1}=1',\\n dirname,\\n manager.env_var(service))\",\n \"def _add_fname(self, alias, fname):\\n self.__dict__[alias] = fname\",\n \"def put_download(\\n self, uri: str, filename: str, logger: Optional[logging.Logger] = None,\\n ) -> str:\\n logger = logger.getChild(\\\"CallCache\\\") if logger else self._logger\\n ans = filename\\n if self._cfg[\\\"download_cache\\\"].get_bool(\\\"put\\\"):\\n p = self.download_path(uri)\\n if p:\\n os.makedirs(os.path.dirname(p), exist_ok=True)\\n os.rename(filename, p)\\n logger.info(_(\\\"stored in download cache\\\", uri=uri, cache_path=p))\\n ans = p\\n self.flock(ans)\\n return ans\",\n \"def list_cached():\\n for json_name in cached_files():\\n source_name = get_source_file_name(json_name)\\n yield (json_name, source_name)\",\n \"def cache_name(name, typ=\\\"pkl\\\"):\\n return os.path.join(CACHE_DIR, name + '.' + typ)\",\n \"def StoreInNegativeCache(self, filename):\\n memcache.add('%s%s' % (self.NEG_CACHE_PREFIX, filename), -1)\",\n \"def StoreInNegativeCache(self, filename):\\n memcache.add('%s%s' % (self.NEG_CACHE_PREFIX, filename), -1)\",\n \"def source_path(self, rel_path):\\n # TODO download to disk (use source cache) in case of remote file\\n import inspect\\n\\n frame = inspect.currentframe().f_back\\n calling_file = frame.f_code.co_filename\\n\\n if (\\n self.included_stack\\n and calling_file == self.included_stack[-1].get_path_or_uri()\\n ):\\n # called from current snakefile, we can try to keep the original source\\n # file annotation\\n # This will only work if the method is evaluated during parsing mode.\\n # Otherwise, the stack can be empty already.\\n path = self.current_basedir.join(rel_path)\\n orig_path = path.get_path_or_uri()\\n else:\\n # heuristically determine path\\n calling_dir = os.path.dirname(calling_file)\\n path = smart_join(calling_dir, rel_path)\\n orig_path = path\\n\\n return sourcecache_entry(\\n self.sourcecache.get_path(infer_source_file(path)), orig_path\\n )\",\n \"def add_result_source(self, source):\\n self._main_model.add_result_source(source)\",\n \"def cache_test_sources(self):\\n self.cache_extra_test_sources([join_path(\\\"examples\\\", \\\"apps\\\")])\",\n \"def get_source(filename: str) -> dict[str, str]:\\n file_path = (\\n filename\\n or g.ledger.fava_options.default_file\\n or g.ledger.beancount_file_path\\n )\\n source, sha256sum = g.ledger.file.get_source(file_path)\\n return {\\\"source\\\": source, \\\"sha256sum\\\": sha256sum, \\\"file_path\\\": file_path}\",\n \"def insert(self, cache_key, paths):\\r\\n missing_files = filter(lambda f: not os.path.exists(f), paths)\\r\\n try:\\r\\n if missing_files:\\r\\n raise ArtifactCache.CacheError('Tried to cache nonexistent files: %s' % missing_files)\\r\\n self.try_insert(cache_key, paths)\\r\\n except Exception as e:\\r\\n self.log.error('Error while writing to artifact cache: %s. ' % e)\",\n \"def get_or_insert(cls, name, source=None):\\n\\t\\tid = '%s-%s' % (cls._class_name(), cls.normalize_name(name))\\n\\t\\treturn super(Cache, cls).get_or_insert(id, source=source)\",\n \"def src(self, src):\\n\\n self._src = src\",\n \"def add_file(self, file_path):\\n content = ''\\n with open(file_path, 'r') as fh:\\n self.__add_source_data(fh.read())\",\n \"def add_source_address(self, srcAddr):\\n self.source.address = srcAddr\",\n \"def add_source_address(self, srcAddr):\\n self.source.address = srcAddr\",\n \"def mock_add_source(monkeypatch):\\n\\n def print_add_source(line, key):\\n print(\\\"Mocked add source: {} ({})\\\".format(line, key))\\n return True\\n\\n mocked_add_source = mock.Mock()\\n mocked_add_source.get.side_effect = print_add_source\\n monkeypatch.setattr(\\\"libgitlab.add_source\\\", mocked_add_source)\\n return mocked_add_source\",\n \"def add(self, filename, *args):\\n return self.cmd('add', filename, *args)\",\n \"def get_cached_file_name(file_name):\\n (directory, base_name) = os.path.split(file_name)\\n directory = os.path.relpath(directory, start=\\\"/\\\")\\n new_directory = os.path.join(CACHE, directory)\\n (simple_name, ext) = os.path.splitext(base_name)\\n new_base_name = simple_name + cached_ext(ext)\\n result = os.path.join(new_directory, new_base_name)\\n return result\",\n \"def addSource(self,\\n path,\\n name,\\n location,\\n copyLib=False,\\n copyGroups=False,\\n copyInfo=False,\\n copyFeatures=False,\\n muteKerning=False,\\n muteInfo=False,\\n mutedGlyphNames=None,\\n familyName=None,\\n styleName=None,\\n ):\\n sourceElement = ET.Element(\\\"source\\\")\\n sourceElement.attrib['filename'] = self._posixPathRelativeToDocument(path)\\n sourceElement.attrib['name'] = name\\n if copyLib:\\n libElement = ET.Element('lib')\\n libElement.attrib['copy'] = \\\"1\\\"\\n sourceElement.append(libElement)\\n\\n if copyGroups:\\n groupsElement = ET.Element('groups')\\n groupsElement.attrib['copy'] = \\\"1\\\"\\n sourceElement.append(groupsElement)\\n\\n if copyFeatures:\\n featuresElement = ET.Element('features')\\n featuresElement.attrib['copy'] = \\\"1\\\"\\n sourceElement.append(featuresElement)\\n\\n if copyInfo or muteInfo:\\n # copy info:\\n infoElement = ET.Element('info')\\n if copyInfo:\\n infoElement.attrib['copy'] = \\\"1\\\"\\n if muteInfo:\\n infoElement.attrib['mute'] = \\\"1\\\"\\n sourceElement.append(infoElement)\\n\\n if muteKerning:\\n # add kerning element to the source\\n kerningElement = ET.Element(\\\"kerning\\\")\\n kerningElement.attrib[\\\"mute\\\"] = '1'\\n sourceElement.append(kerningElement)\\n\\n if mutedGlyphNames:\\n # add muted glyphnames to the source\\n for name in mutedGlyphNames:\\n glyphElement = ET.Element(\\\"glyph\\\")\\n glyphElement.attrib[\\\"name\\\"] = name\\n glyphElement.attrib[\\\"mute\\\"] = '1'\\n sourceElement.append(glyphElement)\\n\\n if familyName is not None:\\n sourceElement.attrib['familyname'] = familyName\\n if styleName is not None:\\n sourceElement.attrib['stylename'] = styleName\\n\\n\\n locationElement = self._makeLocationElement(location)\\n sourceElement.append(locationElement)\\n self.root.findall('.sources')[0].append(sourceElement)\",\n \"def _cachefilename(self, cachedir):\\n\\n return cachedir / \\\"filename\\\"\",\n \"def key_for(self, target_id, sources):\\r\\n sha = hashlib.sha1()\\r\\n actual_srcs = self._sources_hash(sha, sources)\\r\\n return CacheKey(target_id, sha.hexdigest(), len(actual_srcs), actual_srcs)\",\n \"def add_source(name, source_url, section):\\n # lsb_release is not installed in most docker images by default\\n distro = (\\n subprocess.check_output(\\n [\\\"/bin/bash\\\", \\\"-c\\\", \\\"source /etc/os-release && echo ${VERSION_CODENAME}\\\"],\\n stderr=subprocess.STDOUT,\\n )\\n .decode()\\n .strip()\\n )\\n line = f\\\"deb {source_url} {distro} {section}\\\\n\\\"\\n with open(os.path.join(\\\"/etc/apt/sources.list.d/\\\", name + \\\".list\\\"), \\\"a+\\\") as f:\\n # Write out deb line only if it already doesn't exist\\n f.seek(0)\\n if line not in f.read():\\n f.write(line)\\n f.truncate()\\n utils.run_subprocess([\\\"apt-get\\\", \\\"update\\\", \\\"--yes\\\"])\",\n \"def __init__(self, filename, asstring=''):\\n\\n self.cachedlines = (asstring if asstring else content_as_str(filename))\\n self._setup()\\n self._process()\",\n \"def filename_to_url(filename, cache_dir=None):\\n if cache_dir is None:\\n cache_dir = PYTORCH_PRETRAINED_BIGGAN_CACHE\\n if sys.version_info[0] == 3 and isinstance(cache_dir, Path):\\n cache_dir = str(cache_dir)\\n\\n cache_path = os.path.join(cache_dir, filename)\\n if not os.path.exists(cache_path):\\n raise EnvironmentError(\\\"file {} not found\\\".format(cache_path))\\n\\n meta_path = cache_path + '.json'\\n if not os.path.exists(meta_path):\\n raise EnvironmentError(\\\"file {} not found\\\".format(meta_path))\\n\\n with open(meta_path, encoding=\\\"utf-8\\\") as meta_file:\\n metadata = json.load(meta_file)\\n url = metadata['url']\\n etag = metadata['etag']\\n\\n return url, etag\",\n \"def cache(filename):\\n\\n def decorator(func):\\n def wrapper(*args, **kw):\\n self = args[0]\\n path = os.path.join(self.cache_dir, filename)\\n time0 = time.time()\\n if os.path.exists(path):\\n result = joblib.load(path)\\n cost = time.time() - time0\\n logger.info('[cache] loading {} costs {:.2f}s'.format(path, cost))\\n return result\\n result = func(*args, **kw)\\n cost = time.time() - time0\\n logger.info('[cache] obtaining {} costs {:.2f}s'.format(path, cost))\\n joblib.dump(result, path)\\n return result\\n\\n return wrapper\\n\\n return decorator\",\n \"def source(self, source):\\n\\n self._source = source\",\n \"def source(self, source):\\n\\n self._source = source\",\n \"def source(self, source):\\n\\n self._source = source\",\n \"def source(self, source):\\n\\n self._source = source\",\n \"def source(self, source):\\n\\n self._source = source\",\n \"def source(self, source):\\n\\n self._source = source\",\n \"def source(self, source):\\n\\n self._source = source\",\n \"def search_source(self,strz):\\n\\t\\tfor src in sources_rip: #sources_rip = list of allow source words\\n\\t\\t\\tif src in strz:\\n\\t\\t\\t\\tself.src_rip=src.replace(\\\".\\\",\\\"\\\")\\n\\t\\t\\t\\treturn strz.replace(src,\\\"\\\")\\n\\t\\treturn strz\",\n \"def add(self, name, task, filename, categories=()):\\n assert name, \\\"Must input a valid dataset name.\\\"\\n assert task, \\\"Must input a valid task name.\\\"\\n assert filename, \\\"Must input a valid file path.\\\"\\n self._assert_dataset_exists_in_cache(name)\\n self._assert_task_not_exists_in_dataset_in_cache(name, task)\\n\\n self._add_new_task(name, task, filename, categories)\\n\\n self._update_cache_data()\",\n \"def filename_to_url(filename, cache_dir=None):\\n if cache_dir is None:\\n cache_dir = PYTORCH_PRETRAINED_BERT_CACHE\\n if sys.version_info[0] == 3 and isinstance(cache_dir, Path):\\n cache_dir = str(cache_dir)\\n\\n cache_path = os.path.join(cache_dir, filename)\\n if not os.path.exists(cache_path):\\n raise EnvironmentError(\\\"file {} not found\\\".format(cache_path))\\n\\n meta_path = cache_path + \\\".json\\\"\\n if not os.path.exists(meta_path):\\n raise EnvironmentError(\\\"file {} not found\\\".format(meta_path))\\n\\n with open(meta_path, encoding=\\\"utf-8\\\") as meta_file:\\n metadata = json.load(meta_file)\\n url = metadata[\\\"url\\\"]\\n etag = metadata[\\\"etag\\\"]\\n\\n return url, etag\",\n \"def addCacheableShaderFromSourceFile(self, Union, QOpenGLShader_ShaderType=None, QOpenGLShader_ShaderTypeBit=None, *args, **kwargs): # real signature unknown; NOTE: unreliably restored from __doc__\\n pass\",\n \"def add_file(self, filename):\\n # If absolute path\\n if filename[0] == '/':\\n self.filenames.append(filename)\\n else:\\n filename = self.current_path + filename\\n self.filenames.append(filename)\",\n \"def get_image(self, processor, source, cache=None):\\n cache = cache or self._get_cache_filename(processor, source)\\n cache_path = os.path.join(self.cache_dir, cache)\\n if os.path.exists(cache_path):\\n return CachedImage(cache_path)\",\n \"def add_file(self, filename):\\r\\n f = open(filename, 'r', encoding='utf8', errors='ignore')\\r\\n text = f.read()\\r\\n self.add_string(text)\\r\\n f.close()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.733794","0.70201176","0.6331393","0.6280567","0.61927634","0.6123509","0.6102532","0.605147","0.6028744","0.601777","0.5983757","0.5921107","0.5879947","0.58499354","0.5762012","0.5749603","0.57389265","0.57361263","0.5717338","0.57040185","0.5688251","0.567813","0.56710607","0.56614417","0.5598252","0.5598252","0.5550566","0.55459404","0.55208534","0.55182713","0.5516213","0.5513636","0.5509306","0.549205","0.5487787","0.54841","0.5483491","0.54817224","0.54780716","0.54632986","0.546076","0.5447526","0.54176944","0.53976095","0.53923386","0.5374771","0.5374111","0.53566235","0.535635","0.5348452","0.5346218","0.5338822","0.53010136","0.52942276","0.52841437","0.5277955","0.52762026","0.5268821","0.5251629","0.5239644","0.5230899","0.5225357","0.5189918","0.5185448","0.5181806","0.5181806","0.5175964","0.51681596","0.5165216","0.5164539","0.51619107","0.515422","0.5149588","0.5144454","0.51394963","0.51394963","0.5135213","0.51344615","0.5130559","0.5125988","0.511521","0.51113975","0.5103909","0.5103107","0.5098685","0.50905436","0.5083757","0.5083757","0.5083757","0.5083757","0.5083757","0.5083757","0.5083757","0.507674","0.506698","0.5059724","0.50459534","0.50454396","0.5037163","0.5034733"],"string":"[\n \"0.733794\",\n \"0.70201176\",\n \"0.6331393\",\n \"0.6280567\",\n \"0.61927634\",\n \"0.6123509\",\n \"0.6102532\",\n \"0.605147\",\n \"0.6028744\",\n \"0.601777\",\n \"0.5983757\",\n \"0.5921107\",\n \"0.5879947\",\n \"0.58499354\",\n \"0.5762012\",\n \"0.5749603\",\n \"0.57389265\",\n \"0.57361263\",\n \"0.5717338\",\n \"0.57040185\",\n \"0.5688251\",\n \"0.567813\",\n \"0.56710607\",\n \"0.56614417\",\n \"0.5598252\",\n \"0.5598252\",\n \"0.5550566\",\n \"0.55459404\",\n \"0.55208534\",\n \"0.55182713\",\n \"0.5516213\",\n \"0.5513636\",\n \"0.5509306\",\n \"0.549205\",\n \"0.5487787\",\n \"0.54841\",\n \"0.5483491\",\n \"0.54817224\",\n \"0.54780716\",\n \"0.54632986\",\n \"0.546076\",\n \"0.5447526\",\n \"0.54176944\",\n \"0.53976095\",\n \"0.53923386\",\n \"0.5374771\",\n \"0.5374111\",\n \"0.53566235\",\n \"0.535635\",\n \"0.5348452\",\n \"0.5346218\",\n \"0.5338822\",\n \"0.53010136\",\n \"0.52942276\",\n \"0.52841437\",\n \"0.5277955\",\n \"0.52762026\",\n \"0.5268821\",\n \"0.5251629\",\n \"0.5239644\",\n \"0.5230899\",\n \"0.5225357\",\n \"0.5189918\",\n \"0.5185448\",\n \"0.5181806\",\n \"0.5181806\",\n \"0.5175964\",\n \"0.51681596\",\n \"0.5165216\",\n \"0.5164539\",\n \"0.51619107\",\n \"0.515422\",\n \"0.5149588\",\n \"0.5144454\",\n \"0.51394963\",\n \"0.51394963\",\n \"0.5135213\",\n \"0.51344615\",\n \"0.5130559\",\n \"0.5125988\",\n \"0.511521\",\n \"0.51113975\",\n \"0.5103909\",\n \"0.5103107\",\n \"0.5098685\",\n \"0.50905436\",\n \"0.5083757\",\n \"0.5083757\",\n \"0.5083757\",\n \"0.5083757\",\n \"0.5083757\",\n \"0.5083757\",\n \"0.5083757\",\n \"0.507674\",\n \"0.506698\",\n \"0.5059724\",\n \"0.50459534\",\n \"0.50454396\",\n \"0.5037163\",\n \"0.5034733\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.8042132"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":81193,"cells":{"query":{"kind":"string","value":"Gets a copy of the entire cache"},"document":{"kind":"string","value":"def get_copy(self):\n # This is used in avant-idle to pass the content of a cache in\n # the main process to a second process where an exception has been\n # raised.\n return self.cache"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_cache(self):\n return self.cache","def get(self):\n if path.exists(self.cachefile):\n self.invalidion()\n full_cache = self._get_all()\n return full_cache\n else:\n return []","def getCacheContents(self):\n return self._cache","async def get() -> list:\n if _cache is None:\n await _update()\n return _cache","def cache(self):\n return self.__cache","def cache(self):\n return self.__cache","def cache(self):\n return self.__cache","def cache(self):\n return self.__cache","def get_cache(self):\n self._topo.create_cache()\n with open('/run/geopm-service/geopm-topo-cache') as fid:\n result = fid.read()\n return result","def get_cache(self):\n return self._instance._cache[self.name]","def all(self):\n if not self._cache:\n self.load()\n\n return self._cache","def get_cache(self, key):\n return self.r.get(key)","def cache(self):\n if self._cache is None:\n with open(self.cache_path, 'r') as cache_file:\n self._cache = json.load(cache_file)\n return self._cache","def dump():\n global CACHE\n return CACHE","def load_cache():\n return {}","def _retrieveCachedData(self):","def getCache(self, key):\n return self._cache.get(key, None)","def cache(self):\n return self.payload.setdefault(self._CACHE_ATTRIBUTE, {})","def getData(self, local_cache):","def read_data_cache(self):\n if os.path.exists(self.cache_filename):\n return self.read_data_cache_file()\n else:\n data = self._empty_data()\n self.write_data_cache(data)\n return data","def cache_data(self):\n # Initialize key variables\n result = self.data['cache_data']\n return result","def get(self):\n CACHE_KEY = 'sources'\n if not memcache.get(CACHE_KEY):\n logging.info('Populating cache.')\n feeds = Feed.all().order('name')\n feed_list = []\n for feed in feeds:\n feed_list.append(feed.ToDict())\n memcache.add(CACHE_KEY, simplejson.dumps(feed_list), 600)\n logging.info('Using cache.')\n logging.info(memcache.get(CACHE_KEY))\n self.response.headers['Content-Type'] = 'application/json'\n self.response.out.write(memcache.get(CACHE_KEY))","def __deepcopy__(self, memo):\n obj = self.__class__()\n for k, v in self.__dict__.items():\n if k == \"_result_cache\":\n obj.__dict__[k] = None\n else:\n obj.__dict__[k] = copy.deepcopy(v, memo)\n return obj","def __deepcopy__(self, memo):\n obj = self.__class__()\n for k, v in self.__dict__.items():\n if k == '_result_cache':\n obj.__dict__[k] = None\n else:\n obj.__dict__[k] = copy.deepcopy(v, memo)\n return obj","def GetFromCache(self, filename):\n return memcache.get('%s%s' % (self.CACHE_PREFIX, filename))","def GetFromCache(self, filename):\n return memcache.get('%s%s' % (self.CACHE_PREFIX, filename))","def request_cache(self):\n return self._request_cache","def read_cache(self):\n with open(self.get_cache_filename(), 'rb') as f:\n data = pickle.loads(f.read())\n self.timestamp = data['timestamp']\n self.cache = data['cache']","async def set_all_cache(self) -> dict:\n all_data = await self.storage.load_all()\n await self.cache.set_all(all_data)\n self.all_cached = True\n return all_data","def get_cache(feed_key):\n\n cache_file = CACHE_DIR / (feed_key + \".json\")\n\n with open(cache_file, \"r\") as file:\n entries = json.load(file)\n\n return entries","def copy_cache(\n cache: t.Optional[t.MutableMapping],\n) -> t.Optional[t.MutableMapping[t.Tuple[weakref.ref, str], \"Template\"]]:\n if cache is None:\n return None\n\n if type(cache) is dict:\n return {}\n\n return LRUCache(cache.capacity) # type: ignore","def get_cache_path(self):","def get_cache_path(self):","def copy(self):\n cpy = self._copyBase(MapEvalTree(self[:]))\n cpy.cachesize = self.cachesize # member specific to MapEvalTree\n return cpy","def _app_cache_deepcopy(obj):\n if isinstance(obj, dict):\n return dict((_app_cache_deepcopy(key), _app_cache_deepcopy(val))\n for key, val in obj.items())\n elif isinstance(obj, list):\n return list(_app_cache_deepcopy(val) for val in obj)\n elif isinstance(obj, SortedDict):\n return deepcopy(obj)\n return obj","def get(self, path):\n\t\treturn self.cache.get(path)","def __cached(self):\n # already cached stuff\n if self._cached is None:\n self._cached = Cached(self.resource)\n return self._cached","def cache(self):\n # TODO: Implement C8QLQueryCache\n # return C8QLQueryCache(self._conn, self._executor)\n pass","def get(key):\n return Cache.cache_connector.get(key)","def get_state(self, deepcopy: bool = True):\n s = self.cache_ if hasattr(self, \"cache_\") else {}\n return copy.deepcopy(s) if deepcopy else s","def get_cache(name):\n\n return get_component(CachingPackage.COMPONENT_NAME).get_cache(name)","def cache(self):\n import hxl.filters\n return hxl.filters.CacheFilter(self)","def get_cache():\n if not Genre._cache:\n Genre._cache = ObjectCache(Genre().__class__)\n return Genre._cache","def cache(cls):\n return Cache(cls, cls.cache_regions, cls.cache_label)","def _cache(self):\n return self._class(self.client_servers, **self._options)","def cached(cache_path, generator):\n if path.exists(cache_path):\n with open(cache_path, 'rb') as f:\n return pickle.load(f)\n output = generator()\n with open(cache_path, 'wb+') as f:\n pickle.dump(output, f)\n return output","def get(self, key):\n # Initialize key variables\n result = self.cache.get(key)\n\n # Return\n return result","def _prepare_cache(self, value):\n\n return deepcopy(value)","def __copy__(self):\n cls = self.__class__\n result = cls.__new__(cls)\n to_copy = {\"_cache\", \"_buffers\", \"_parameters\", \"_modules\"}\n result.__dict__.update(\n {k: v.copy() if k in to_copy else v for k, v in self.__dict__.items()}\n )\n return result","def _load_cache(self):\n self.cache = self.cache_manager.retrieve(self.cache_file)\n if self.cache is None:\n self.cache = {}\n return","def get_from_cache(self, subject_id):\n with self.cache.open_for_read(subject_id) as cache_file:\n chunks = utils.chunkiter(cache_file)\n for chunk in chunks:\n yield chunk","def retrieve_cached_records(self):\r\n return u.load_cached_data(self.records_cache_path)","def reload_cache(self):\n self.data = self.read_data_cache()","def workspace_caching(self):\n return self._workspace_caching","def output(self):\n return self.__cache","def __deepcopy__(self, memo):\n obj = self.__class__()\n for k, v in self.__dict__.items():\n if k in ('_iter', '_result_cache'):\n obj.__dict__[k] = None\n else:\n obj.__dict__[k] = copy.deepcopy(v, memo)\n return obj","def cache():\n if request.method == 'GET':\n cache_info = in_water.cache_info()\n return json.dumps({\n 'hits': cache_info.hits,\n 'misses': cache_info.misses,\n 'maxsize': cache_info.maxsize,\n 'currsize': cache_info.currsize,\n })","def app_cache_restorer():\n state = _app_cache_deepcopy(app_cache.__dict__)\n try:\n yield state\n finally:\n with app_cache_lock():\n app_cache.__dict__ = state","def required_cache(self):\n return self._required_cache","def write_cache(self) -> SmartSsdReadLookahead:\n return self._write_cache","def project_cache(self):\n return self._project_cache","def get_inventory_from_cache(self):\n cache = open(self.cache_path_cache, 'r')\n json_inventory = cache.read()\n return json_inventory","def getCurrentCacheData(self):\n return self.getCacheData(int(self.currentFrameNumber - 1))","def _find_cache():\n app = _find_app()\n return app.cache","def clone(self):\n memo = dict()\n c = self._clone(memo)\n c._clone_rip(memo)\n return c","def cache_path(self):","def cache_path(self):","async def cached_values(cls):\n if cls.__cached__ is None:\n cls.__cached__ = await cls.values()\n return cls.__cached__","def get(self, key):\n return self.cache_data.get(key)","def retrieve():\n # type: () -> list\n with Cache(CACHE_URI) as c:\n data = c.get(SAVED_SEARCH)\n return json.loads(data[\"blob\"]) if data else []","def cached(func):\n return _lru_cache(None)(func)","def fillCache(self):\n items = self.source.getRecent()\n items.reverse() # make sure the most recent ones are added last to the cache\n for item in items:\n self.cache.append(item.title)","def cache(self):\n return {'output': self.output, 'series': self.series}","def copy (self):\n import copy\n return copy.copy(self)","def get_template(self):\n if not self.cache:\n self.close()\n return self.cache","def copy(self):\r\n return copy.deepcopy(self)","def pymod_cache():\n pymod.cache.cache = Singleton(pymod.cache.factory)","def copy(self):\n return copy.deepcopy(self)","def copy(self):\n return copy.deepcopy(self)","def copy(self):\n return copy.deepcopy(self)","def copy(self):\n return copy.deepcopy(self)","def copy(self):\n return copy.deepcopy(self)","def copy(self):\n return copy.deepcopy(self)","def copy(self):\n return copy.deepcopy(self)","def copy(self):\n return copy.deepcopy(self)","def copy(self):\n return copy.deepcopy(self)","def copy(self):\n return copy.deepcopy(self)","def copy(self):\n return copy.deepcopy(self)","def copy(self):\n return copy.deepcopy(self)","def copy(self):\n return copy.deepcopy(self)","def copy(self):\n return copy.deepcopy(self)","def copy(self):\n return copy.deepcopy(self)","def copy(self):\n return copy.deepcopy(self)","def copy_tiles(self):\n \n return self.tiles","def get_all_data(self, copy: bool = True):\n if copy:\n return self.__data.copy()\n else:\n return self.__data","def get_historical_data_copy(self):\n return copy.deepcopy(self._historical_data)","def retrieve_records_cache(self):\r\n return u.load_cache(self.records_cache_path)","def _memcache_get(*args, **kwargs):\n return ndb.get_context().memcache_get(*args, **kwargs)","def copy(self):\n return copy.copy(self)","def copy(self):\n return copy.copy(self)"],"string":"[\n \"def get_cache(self):\\n return self.cache\",\n \"def get(self):\\n if path.exists(self.cachefile):\\n self.invalidion()\\n full_cache = self._get_all()\\n return full_cache\\n else:\\n return []\",\n \"def getCacheContents(self):\\n return self._cache\",\n \"async def get() -> list:\\n if _cache is None:\\n await _update()\\n return _cache\",\n \"def cache(self):\\n return self.__cache\",\n \"def cache(self):\\n return self.__cache\",\n \"def cache(self):\\n return self.__cache\",\n \"def cache(self):\\n return self.__cache\",\n \"def get_cache(self):\\n self._topo.create_cache()\\n with open('/run/geopm-service/geopm-topo-cache') as fid:\\n result = fid.read()\\n return result\",\n \"def get_cache(self):\\n return self._instance._cache[self.name]\",\n \"def all(self):\\n if not self._cache:\\n self.load()\\n\\n return self._cache\",\n \"def get_cache(self, key):\\n return self.r.get(key)\",\n \"def cache(self):\\n if self._cache is None:\\n with open(self.cache_path, 'r') as cache_file:\\n self._cache = json.load(cache_file)\\n return self._cache\",\n \"def dump():\\n global CACHE\\n return CACHE\",\n \"def load_cache():\\n return {}\",\n \"def _retrieveCachedData(self):\",\n \"def getCache(self, key):\\n return self._cache.get(key, None)\",\n \"def cache(self):\\n return self.payload.setdefault(self._CACHE_ATTRIBUTE, {})\",\n \"def getData(self, local_cache):\",\n \"def read_data_cache(self):\\n if os.path.exists(self.cache_filename):\\n return self.read_data_cache_file()\\n else:\\n data = self._empty_data()\\n self.write_data_cache(data)\\n return data\",\n \"def cache_data(self):\\n # Initialize key variables\\n result = self.data['cache_data']\\n return result\",\n \"def get(self):\\n CACHE_KEY = 'sources'\\n if not memcache.get(CACHE_KEY):\\n logging.info('Populating cache.')\\n feeds = Feed.all().order('name')\\n feed_list = []\\n for feed in feeds:\\n feed_list.append(feed.ToDict())\\n memcache.add(CACHE_KEY, simplejson.dumps(feed_list), 600)\\n logging.info('Using cache.')\\n logging.info(memcache.get(CACHE_KEY))\\n self.response.headers['Content-Type'] = 'application/json'\\n self.response.out.write(memcache.get(CACHE_KEY))\",\n \"def __deepcopy__(self, memo):\\n obj = self.__class__()\\n for k, v in self.__dict__.items():\\n if k == \\\"_result_cache\\\":\\n obj.__dict__[k] = None\\n else:\\n obj.__dict__[k] = copy.deepcopy(v, memo)\\n return obj\",\n \"def __deepcopy__(self, memo):\\n obj = self.__class__()\\n for k, v in self.__dict__.items():\\n if k == '_result_cache':\\n obj.__dict__[k] = None\\n else:\\n obj.__dict__[k] = copy.deepcopy(v, memo)\\n return obj\",\n \"def GetFromCache(self, filename):\\n return memcache.get('%s%s' % (self.CACHE_PREFIX, filename))\",\n \"def GetFromCache(self, filename):\\n return memcache.get('%s%s' % (self.CACHE_PREFIX, filename))\",\n \"def request_cache(self):\\n return self._request_cache\",\n \"def read_cache(self):\\n with open(self.get_cache_filename(), 'rb') as f:\\n data = pickle.loads(f.read())\\n self.timestamp = data['timestamp']\\n self.cache = data['cache']\",\n \"async def set_all_cache(self) -> dict:\\n all_data = await self.storage.load_all()\\n await self.cache.set_all(all_data)\\n self.all_cached = True\\n return all_data\",\n \"def get_cache(feed_key):\\n\\n cache_file = CACHE_DIR / (feed_key + \\\".json\\\")\\n\\n with open(cache_file, \\\"r\\\") as file:\\n entries = json.load(file)\\n\\n return entries\",\n \"def copy_cache(\\n cache: t.Optional[t.MutableMapping],\\n) -> t.Optional[t.MutableMapping[t.Tuple[weakref.ref, str], \\\"Template\\\"]]:\\n if cache is None:\\n return None\\n\\n if type(cache) is dict:\\n return {}\\n\\n return LRUCache(cache.capacity) # type: ignore\",\n \"def get_cache_path(self):\",\n \"def get_cache_path(self):\",\n \"def copy(self):\\n cpy = self._copyBase(MapEvalTree(self[:]))\\n cpy.cachesize = self.cachesize # member specific to MapEvalTree\\n return cpy\",\n \"def _app_cache_deepcopy(obj):\\n if isinstance(obj, dict):\\n return dict((_app_cache_deepcopy(key), _app_cache_deepcopy(val))\\n for key, val in obj.items())\\n elif isinstance(obj, list):\\n return list(_app_cache_deepcopy(val) for val in obj)\\n elif isinstance(obj, SortedDict):\\n return deepcopy(obj)\\n return obj\",\n \"def get(self, path):\\n\\t\\treturn self.cache.get(path)\",\n \"def __cached(self):\\n # already cached stuff\\n if self._cached is None:\\n self._cached = Cached(self.resource)\\n return self._cached\",\n \"def cache(self):\\n # TODO: Implement C8QLQueryCache\\n # return C8QLQueryCache(self._conn, self._executor)\\n pass\",\n \"def get(key):\\n return Cache.cache_connector.get(key)\",\n \"def get_state(self, deepcopy: bool = True):\\n s = self.cache_ if hasattr(self, \\\"cache_\\\") else {}\\n return copy.deepcopy(s) if deepcopy else s\",\n \"def get_cache(name):\\n\\n return get_component(CachingPackage.COMPONENT_NAME).get_cache(name)\",\n \"def cache(self):\\n import hxl.filters\\n return hxl.filters.CacheFilter(self)\",\n \"def get_cache():\\n if not Genre._cache:\\n Genre._cache = ObjectCache(Genre().__class__)\\n return Genre._cache\",\n \"def cache(cls):\\n return Cache(cls, cls.cache_regions, cls.cache_label)\",\n \"def _cache(self):\\n return self._class(self.client_servers, **self._options)\",\n \"def cached(cache_path, generator):\\n if path.exists(cache_path):\\n with open(cache_path, 'rb') as f:\\n return pickle.load(f)\\n output = generator()\\n with open(cache_path, 'wb+') as f:\\n pickle.dump(output, f)\\n return output\",\n \"def get(self, key):\\n # Initialize key variables\\n result = self.cache.get(key)\\n\\n # Return\\n return result\",\n \"def _prepare_cache(self, value):\\n\\n return deepcopy(value)\",\n \"def __copy__(self):\\n cls = self.__class__\\n result = cls.__new__(cls)\\n to_copy = {\\\"_cache\\\", \\\"_buffers\\\", \\\"_parameters\\\", \\\"_modules\\\"}\\n result.__dict__.update(\\n {k: v.copy() if k in to_copy else v for k, v in self.__dict__.items()}\\n )\\n return result\",\n \"def _load_cache(self):\\n self.cache = self.cache_manager.retrieve(self.cache_file)\\n if self.cache is None:\\n self.cache = {}\\n return\",\n \"def get_from_cache(self, subject_id):\\n with self.cache.open_for_read(subject_id) as cache_file:\\n chunks = utils.chunkiter(cache_file)\\n for chunk in chunks:\\n yield chunk\",\n \"def retrieve_cached_records(self):\\r\\n return u.load_cached_data(self.records_cache_path)\",\n \"def reload_cache(self):\\n self.data = self.read_data_cache()\",\n \"def workspace_caching(self):\\n return self._workspace_caching\",\n \"def output(self):\\n return self.__cache\",\n \"def __deepcopy__(self, memo):\\n obj = self.__class__()\\n for k, v in self.__dict__.items():\\n if k in ('_iter', '_result_cache'):\\n obj.__dict__[k] = None\\n else:\\n obj.__dict__[k] = copy.deepcopy(v, memo)\\n return obj\",\n \"def cache():\\n if request.method == 'GET':\\n cache_info = in_water.cache_info()\\n return json.dumps({\\n 'hits': cache_info.hits,\\n 'misses': cache_info.misses,\\n 'maxsize': cache_info.maxsize,\\n 'currsize': cache_info.currsize,\\n })\",\n \"def app_cache_restorer():\\n state = _app_cache_deepcopy(app_cache.__dict__)\\n try:\\n yield state\\n finally:\\n with app_cache_lock():\\n app_cache.__dict__ = state\",\n \"def required_cache(self):\\n return self._required_cache\",\n \"def write_cache(self) -> SmartSsdReadLookahead:\\n return self._write_cache\",\n \"def project_cache(self):\\n return self._project_cache\",\n \"def get_inventory_from_cache(self):\\n cache = open(self.cache_path_cache, 'r')\\n json_inventory = cache.read()\\n return json_inventory\",\n \"def getCurrentCacheData(self):\\n return self.getCacheData(int(self.currentFrameNumber - 1))\",\n \"def _find_cache():\\n app = _find_app()\\n return app.cache\",\n \"def clone(self):\\n memo = dict()\\n c = self._clone(memo)\\n c._clone_rip(memo)\\n return c\",\n \"def cache_path(self):\",\n \"def cache_path(self):\",\n \"async def cached_values(cls):\\n if cls.__cached__ is None:\\n cls.__cached__ = await cls.values()\\n return cls.__cached__\",\n \"def get(self, key):\\n return self.cache_data.get(key)\",\n \"def retrieve():\\n # type: () -> list\\n with Cache(CACHE_URI) as c:\\n data = c.get(SAVED_SEARCH)\\n return json.loads(data[\\\"blob\\\"]) if data else []\",\n \"def cached(func):\\n return _lru_cache(None)(func)\",\n \"def fillCache(self):\\n items = self.source.getRecent()\\n items.reverse() # make sure the most recent ones are added last to the cache\\n for item in items:\\n self.cache.append(item.title)\",\n \"def cache(self):\\n return {'output': self.output, 'series': self.series}\",\n \"def copy (self):\\n import copy\\n return copy.copy(self)\",\n \"def get_template(self):\\n if not self.cache:\\n self.close()\\n return self.cache\",\n \"def copy(self):\\r\\n return copy.deepcopy(self)\",\n \"def pymod_cache():\\n pymod.cache.cache = Singleton(pymod.cache.factory)\",\n \"def copy(self):\\n return copy.deepcopy(self)\",\n \"def copy(self):\\n return copy.deepcopy(self)\",\n \"def copy(self):\\n return copy.deepcopy(self)\",\n \"def copy(self):\\n return copy.deepcopy(self)\",\n \"def copy(self):\\n return copy.deepcopy(self)\",\n \"def copy(self):\\n return copy.deepcopy(self)\",\n \"def copy(self):\\n return copy.deepcopy(self)\",\n \"def copy(self):\\n return copy.deepcopy(self)\",\n \"def copy(self):\\n return copy.deepcopy(self)\",\n \"def copy(self):\\n return copy.deepcopy(self)\",\n \"def copy(self):\\n return copy.deepcopy(self)\",\n \"def copy(self):\\n return copy.deepcopy(self)\",\n \"def copy(self):\\n return copy.deepcopy(self)\",\n \"def copy(self):\\n return copy.deepcopy(self)\",\n \"def copy(self):\\n return copy.deepcopy(self)\",\n \"def copy(self):\\n return copy.deepcopy(self)\",\n \"def copy_tiles(self):\\n \\n return self.tiles\",\n \"def get_all_data(self, copy: bool = True):\\n if copy:\\n return self.__data.copy()\\n else:\\n return self.__data\",\n \"def get_historical_data_copy(self):\\n return copy.deepcopy(self._historical_data)\",\n \"def retrieve_records_cache(self):\\r\\n return u.load_cache(self.records_cache_path)\",\n \"def _memcache_get(*args, **kwargs):\\n return ndb.get_context().memcache_get(*args, **kwargs)\",\n \"def copy(self):\\n return copy.copy(self)\",\n \"def copy(self):\\n return copy.copy(self)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.745652","0.74172235","0.72680855","0.7232514","0.71606326","0.71606326","0.71606326","0.71606326","0.7018596","0.7012775","0.7010496","0.67941254","0.67534447","0.6705078","0.6684562","0.6615776","0.6590172","0.65279984","0.6523774","0.6464215","0.6447389","0.64147234","0.6338886","0.63307244","0.6297511","0.6297511","0.62898153","0.6281257","0.6277581","0.62664855","0.62529963","0.6223422","0.6223422","0.6222041","0.62187994","0.6218018","0.6213417","0.62034315","0.6187079","0.6185613","0.6185561","0.61741304","0.6165292","0.61646515","0.61607957","0.61569905","0.61554974","0.61509764","0.6129215","0.61260307","0.61205614","0.61081713","0.6084585","0.6078037","0.6073137","0.6054534","0.60530484","0.6048459","0.60474056","0.60380083","0.6026634","0.60142297","0.6011858","0.6009822","0.5996473","0.5984861","0.5984861","0.59788555","0.59768057","0.597286","0.59719974","0.5968645","0.59593993","0.59570384","0.5934927","0.59303814","0.5919096","0.5918492","0.5918492","0.5918492","0.5918492","0.5918492","0.5918492","0.5918492","0.5918492","0.5918492","0.5918492","0.5918492","0.5918492","0.5918492","0.5918492","0.5918492","0.5918492","0.59123206","0.5903505","0.5893854","0.5893786","0.5887133","0.58854127","0.58854127"],"string":"[\n \"0.745652\",\n \"0.74172235\",\n \"0.72680855\",\n \"0.7232514\",\n \"0.71606326\",\n \"0.71606326\",\n \"0.71606326\",\n \"0.71606326\",\n \"0.7018596\",\n \"0.7012775\",\n \"0.7010496\",\n \"0.67941254\",\n \"0.67534447\",\n \"0.6705078\",\n \"0.6684562\",\n \"0.6615776\",\n \"0.6590172\",\n \"0.65279984\",\n \"0.6523774\",\n \"0.6464215\",\n \"0.6447389\",\n \"0.64147234\",\n \"0.6338886\",\n \"0.63307244\",\n \"0.6297511\",\n \"0.6297511\",\n \"0.62898153\",\n \"0.6281257\",\n \"0.6277581\",\n \"0.62664855\",\n \"0.62529963\",\n \"0.6223422\",\n \"0.6223422\",\n \"0.6222041\",\n \"0.62187994\",\n \"0.6218018\",\n \"0.6213417\",\n \"0.62034315\",\n \"0.6187079\",\n \"0.6185613\",\n \"0.6185561\",\n \"0.61741304\",\n \"0.6165292\",\n \"0.61646515\",\n \"0.61607957\",\n \"0.61569905\",\n \"0.61554974\",\n \"0.61509764\",\n \"0.6129215\",\n \"0.61260307\",\n \"0.61205614\",\n \"0.61081713\",\n \"0.6084585\",\n \"0.6078037\",\n \"0.6073137\",\n \"0.6054534\",\n \"0.60530484\",\n \"0.6048459\",\n \"0.60474056\",\n \"0.60380083\",\n \"0.6026634\",\n \"0.60142297\",\n \"0.6011858\",\n \"0.6009822\",\n \"0.5996473\",\n \"0.5984861\",\n \"0.5984861\",\n \"0.59788555\",\n \"0.59768057\",\n \"0.597286\",\n \"0.59719974\",\n \"0.5968645\",\n \"0.59593993\",\n \"0.59570384\",\n \"0.5934927\",\n \"0.59303814\",\n \"0.5919096\",\n \"0.5918492\",\n \"0.5918492\",\n \"0.5918492\",\n \"0.5918492\",\n \"0.5918492\",\n \"0.5918492\",\n \"0.5918492\",\n \"0.5918492\",\n \"0.5918492\",\n \"0.5918492\",\n \"0.5918492\",\n \"0.5918492\",\n \"0.5918492\",\n \"0.5918492\",\n \"0.5918492\",\n \"0.5918492\",\n \"0.59123206\",\n \"0.5903505\",\n \"0.5893854\",\n \"0.5893786\",\n \"0.5887133\",\n \"0.58854127\",\n \"0.58854127\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7468072"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":81194,"cells":{"query":{"kind":"string","value":"Replaces the current cache by another"},"document":{"kind":"string","value":"def replace(self, other_cache):\n # This is used in avant-idle to replace the content of the cache\n # in a process (where no storage normally takes place) by\n # that of another where the actual caching of the source is done.\n self.cache.clear()\n for key in other_cache:\n self.add(key, other_cache[key])"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def testReplaceItem(self):\n\n first = \"Little pig, little pig, let me come in!\"\n second = \"Not by the hair on my chinny-chin-chin!\"\n memcache.set('first', first)\n assert memcache.get('first') == first\n memcache.replace('first', second)\n assert memcache.get('first') == second","def update_inplace_from(self, other):\n self.__dict__ = other.__dict__.copy()","def _update_use(self, key):\n\t\tif (self._replace_pol == Cache.LRU):\n\t\t\tself.cache[key]= self.hashmap[key]\n\t\tif (self._replace_pol == Cache.LRU_S):\n\t\t\tself.cache[key] = self.hashmap[key]","def _proxy_cache(from_func, to_func):\n to_func.cache_info = from_func.cache_info\n to_func.cache_clear = from_func.cache_clear","def fill_from_cache(self):\n move_count = min(\n len(self._replacement_cache),\n constants.K - len(self._contacts)\n )\n\n for _ in range(move_count):\n self.add_contact(self._replacement_cache.pop())","def set_cache(self, val):\n pass","def set(key, value):\n instance_cache.set(key, value, expiry=CacheLayers.INSTANCE_SECONDS)\n memcache.set(key, CacheLayers.compress(value))\n\n logging.info(\"Set BingoCache in instance cache and memcache\")","def add_to_cache(self, content: Content):\n cache = self.cache\n cache.add_content_object(content)\n self.cache = cache","def reload_cache(self):\n self.data = self.read_data_cache()","def _update_column(self, cache_name, other, other_name):\n self._set_column_column(\n cache_name, other._get_column_column(other_name))","def set_cache(self, key, value):\n self.r.set(key, value)\n self.r.expire(key, time=1500)","def decache(self):","def set_to_cache(self, url, data):\n cache_key, cache_lookup = self.get_cacheable_info(url)\n MEM_CACHE[cache_key][cache_lookup] = (data, time.time())","def set_cache(self, key, value):\n self._cache[key] = (self.model.batch_number, value)","def reset_cache(self):\n self._cache_complete = False\n self._cache = {}\n self._catcache = {}","def replace(self, key, val, expiry_time=0, min_compress_len=0):\n\t\treturn self._set(\"replace\", key, val, expiry_time, min_compress_len)","def reset_cache(self):\n if self.cache_address is not None:\n for add in self.cache:\n os.remove(add + \".cd\")\n os.remove(add + \".cl\")\n self.cache = [None] * len(self)","def manage_image_cache(self, context, all_instances):\n self.image_cache_manager.update(context, all_instances)","def set(key, value):\n return Cache.cache_connector.set(key, value)","def set(self, key, value):\n # First, look for the key in the cache using `self.get()`\n # If not exists (returns None), add key-value to head\n # If exists, pop old key-value from list, add new value to head\n pass","def _cache_course_locator(self, old_course_id, published_course_locator, draft_course_locator):\r\n if not old_course_id:\r\n return\r\n self.cache.set(unicode(old_course_id), (published_course_locator, draft_course_locator))","def retire(self, other):\n self.swap(other)\n self.model.schedule.add(other) # put new entity into scheduler\n self.model.schedule.remove(self) # take yourself out of it\n self.model.retirees[self.type][self.model.schedule.steps] = self # mark yourself as retiree\n self.model.per_step_movement[self.type] += 1","def update_cache(self, line):\n self.inputcache.insert(0, line) # This copies the cache every time ... :-(\n if len(self.inputcache) >= self.CACHELENGTH:\n self.inputcache.pop() # This not :-)","def update(self, other):\n if not other:\n return\n for o in other:\n self.replace(o)","def cache_contact(self, contact):\n try:\n self._replacement_cache.remove(contact)\n except ValueError:\n pass\n\n self._replacement_cache.append(contact)\n if len(self._replacement_cache) > constants.CACHE_K:\n self._replacement_cache.popleft()","def reset_cache(self):\n self.izx.reset_cache()\n self.ezx.reset_cache()","def invalidate_cache(self):\n #self.objects.objects = []\n return True","def cache_clear(self):\n\t\tself.__cache = {}","def do_api_calls_update_cache(self):\n self.get_nodes()\n self.write_to_cache(self.inventory, self.cache_path_cache)\n self.write_to_cache(self.index, self.cache_path_index)","def workspace_caching(self, workspace_caching):\n\n self._workspace_caching = workspace_caching","def set(self, key, value):\n # Initialize key variables\n result = self.cache.set(key, value)\n\n # Return\n return result","def update(self, new):\n return self.replace(None, new)","def update_versioned_target(self, vt):\n self._cache_manager.update(vt.cache_key)","def update_cache(self, repo=None, force=False):\n raise NotImplementedError(self.update_cache)","def set_cache(self, eval_hash: str, task_hash: str, args_hash: str, value: Any) -> None:\n self.backend.set_eval_cache(eval_hash, task_hash, args_hash, value, value_hash=None)","def reset_cache(self):\n self.cache = [None] * self.n_layers\n self.offset = 0\n logger.debug('Reset cache.')","def test_cache_results(self):\n env = pike.Environment()\n value = [1]\n with pike.Graph('g') as graph:\n n = ParrotNode(value)\n env.add(graph)\n ret = env.run('g')\n self.assertEqual(ret, {'default': [1]})\n n.value = [1, 2]\n\n # We mutated value, but the return value should be cached\n ret = env.run('g')\n self.assertEqual(ret, {'default': [1]})\n\n # Busting cache should return new value\n ret = env.run('g', True)\n self.assertEqual(ret, {'default': [1, 2]})","def flush_local_cache(self):\n self._local_cache = {}","def _update_cache(self, key, value, cache, decode_loop_step):\n # Combines cached keys and values with new keys and values.\n if decode_loop_step is not None:\n # TPU special case.\n key_seq_dim = cache[\"key\"].shape.as_list()[1]\n indices = tf.reshape(\n tf.one_hot(decode_loop_step, key_seq_dim, dtype=key.dtype),\n [1, key_seq_dim, 1, 1])\n key = cache[\"key\"] + key * indices\n value_seq_dim = cache[\"value\"].shape.as_list()[1]\n indices = tf.reshape(\n tf.one_hot(decode_loop_step, value_seq_dim, dtype=value.dtype),\n [1, value_seq_dim, 1, 1])\n value = cache[\"value\"] + value * indices\n else:\n key = tf.concat([tf.cast(cache[\"key\"], key.dtype), key], axis=1)\n value = tf.concat([tf.cast(cache[\"value\"], value.dtype), value], axis=1)\n\n # Update cache\n cache[\"key\"] = key\n cache[\"value\"] = value\n\n return key, value","def cache_set(self, key: str, value: bytes) -> None:\n if self.cache is not None:\n self.cache.set(key, value)","def _prepare_cache(self, value):\n\n return deepcopy(value)","def reuse(self):\n similarCases = self.r1.similarCases\n self.r2.setQueryCase(self.case)\n self.r2.retrieval = self.r1\n self.r2.reuse(similarCases)\n self.predictionCase = self.r2.predictionCase","def update(self, cache_key):\r\n self._write_sha(cache_key)","def cache(self, key, timeout=None):\n original_cache_timeout = self.cache_timeout\n self.cache_timeout = timeout\n self.cache_backend.set(key, self, timeout)\n self.cache_timeout = original_cache_timeout","def _update_cache(self):\n \n # Check if the model cache is full\n if self.__class__.cache_limit == len(self.__class__.model_cache):\n # Remove the oldest item from the cache if exceeding cache limit\n self.__class__.model_cache.popitem(last=False)\n \n # Remove the obsolete version of the model from the cache\n if self.model.name in self.__class__.model_cache:\n del self.__class__.model_cache[self.model.name]\n \n # Add the current model to the cache\n self.__class__.model_cache[self.model.name] = self.model\n \n # Debug information is printed to the terminal and logs if the paramater debug = true\n if self.model.debug:\n self._print_log(8)","def swap(self, index_1, index_2):\n temp = self.store[index_1]\n self.store[index_1] = self.store[index_2]\n self.store[index_2] = temp","def update(self, other):\n for name, value in other.items():\n self.__setitem__(name, value)","def reset(self):\n\n self.simple_cache = {}\n self.complex_cache = {}\n self.target_cache = {}","def cache(self, file_name, content):\n self.files_loaded[file_name] = content","def _refresh_cache(self, data_dict):\r\n pass","def reset_cache_location(self):\n self._cache_location = None","def reset_cache():\n setup_cache({})\n yield # test\n setup_cache({})","def __swap(self, index_1, index_2):\n temp = self._lits[index_1]\n self._lits[index_1] = self._lits[index_2]\n self._lits[index_2] = temp","def _update_cache(self, cset):\n # If this changeset is already in the cache, remove it\n try:\n self._lru_cache.remove(cset)\n except ValueError:\n pass\n\n # Add the changeset at the end\n if len(self._lru_cache) >= Repository._LRU_CACHE_SIZE:\n del self._lru_cache[0]\n self._lru_cache.append(cset)","def _clear_cache(self):\n self.cache = {}","def _cache_set(self, metric_name, metric):\n pass","def update(self, other: dict):\n for key in other:\n if key in self:\n self[key] = other[key]","def set_emptying_cache():\r\n from pylons import g\r\n from r2.lib.cache import SelfEmptyingCache\r\n g.cache.caches = [SelfEmptyingCache(),] + list(g.cache.caches[1:])","def switch(self, a, b):\n\n self.heap[a], self.heap[b] = self.heap[b], self.heap[a]","def exchange(dancers, spot1, spot2):\n\n dancers[spot1], dancers[spot2] = dancers[spot2], dancers[spot1]","def split_kbucket(self):\n new_kbucket = super(CachingKBucket, self).split_kbucket()\n\n cache_self, cache_new = util.partition(\n self._replacement_cache,\n self.contact_in_range\n )\n\n # Replacement caches are deques, so we can't directly assign\n # the values returned by partition.\n new_kbucket._replacement_cache.extend(cache_new)\n self._replacement_cache.clear()\n self._replacement_cache.extend(cache_self)\n\n self.fill_from_cache()\n new_kbucket.fill_from_cache()\n\n return new_kbucket","def _merge(self, other: dict):\n self._storage = dict_merge(self._storage, other)","def __setitem__(self, tid: int, result: bytes):\n if tid in self:\n raise KeyError(f\"transaction {tid} already cached\")\n\n self._cache[tid] = (result, time.monotonic())","def _invalidate_http_cache(self):\n self._requests_cache = {}","def copy_from_other(self, other):\n self.data = other.data\n self.url = other.url\n self.container_factory = other.container_factory","def flush_cache(self):\n if self.cache_modified:\n self.cache_manager.write(self.cache_file, self.cache)","def flushCaches(self):\n self.rehabTreeCache = {} \n self.frailRehabTreeCache = {} \n self.frailTreeCache = {}","def update_caches():\n a = get_expired_acache()\n c = get_expired_cnamecache()\n ns = get_expired_nscache()\n for keys, cache in [a, c]:\n for key in keys:\n print \"deleting {0}\".format(key)\n del cache[key]\n ns_keys, cache = ns\n for key, dn in ns_keys:\n print \"deleting {0}{1}\".format(key, dn)\n del nscache[key][dn]","def update_connection_cache(self, current_connection, key, thread_lock, value=True):\n thread_lock.acquire()\n current_connection[key] = value\n thread_lock.release()","def update(self, key_path, content, t_mserver):\n#\t\tif key not in self.cache and len(self.cache) >= self.max_cache_size:\n#\t\t\tself.remove_oldest()\n\t\t\n\t\tcurrent_time = int(time.time())\n\t\tif key_path not in self.cache:\n\t\t\tself.add_cache(key_path, content, t_mserver)\n\t\t\t\t\n\t\telif current_time >= self.cache[key_path]['time_validated']:\n\t\t\tself.cache[key_path] = {'time_validated': int(time.time()),\n\t\t\t\t\t\t\t\t\t't_mclient': int(t_mserver),\n\t\t\t\t\t\t\t\t\t'content': content}\n\t\telse:\n\t\t\tprint(\"Content is not updates OR time_accessed went wrong!\")","def put(self, key, item):\n if key and item:\n self.cache_data[key] = item","def put(self, key, item):\n if key and item:\n self.cache_data[key] = item","def test_cache_results(self):\n kbase_sdk.init_context.cache_clear()\n config_path = os.path.join(self.test_app_dir, 'kbase.yaml')\n # Move kbase.yaml to kbase.yaml.bak\n context1 = kbase_sdk.init_context(self.test_app_dir)\n shutil.move(config_path, config_path + '.bak')\n context2 = kbase_sdk.init_context(self.test_app_dir)\n # If it's not caching, then MissingPath would be raised\n self.assertEqual(context1, context2)\n shutil.move(config_path + '.bak', config_path)","def replace_with(self, new_inst):\n new_inst.insert_after(self)\n self.replace_uses_with(new_inst)\n self.destroy()","def _invalidate_branch_cache(self):\n self._cached_overlapping_branch_list = None","def Replace(self, *args):\n return _BRepAlgo.BRepAlgo_AsDes_Replace(self, *args)","def test_upload_overwrite(self):\n request = DummyRequest()\n request.access = DummyAccess(request)\n cache = DummyCache(request)\n request.access.allow_overwrite = [\"everyone\"]\n name, filename, content = \"a\", \"a-1.tar.gz\", BytesIO(b\"new\")\n cache.upload(filename, BytesIO(b\"old\"), name)\n cache.upload(filename, content, name)\n\n all_versions = cache.all(name)\n self.assertEqual(len(all_versions), 1)\n data = cache.storage.open(all_versions[0]).read()\n self.assertEqual(data, b\"new\")\n\n stored_pkgs = list(cache.storage.list(cache.new_package))\n self.assertEqual(len(stored_pkgs), 1)","def replace_number(self, old, new):\r\n self.numbers[old] = new","def reset_cache():\n global _CACHE\n _CACHE.clear()","def update(self, other):\n for filter, value in other.items():\n self.__setitem__(filter, value)","def put(self, key, value):\n if key is None or value is None:\n raise TypeError\n\n index = self.__get_cache_set_index(key)\n cache_set = self.cache_sets[index]\n h_key = self.__ensure_hashable_key(key)\n cache_set.put(h_key, value)","def set_value(self, value):\n\n dogpile_region, cache_key = self._get_cache_plus_key()\n dogpile_region.set(cache_key, value)","def __swap(self, index1, index2):\n self.heap[index1], self.heap[index2] = self.heap[index2], self.heap[index1]","def library_caching(self, library_caching):\n\n self._library_caching = library_caching","def update_resources_for_this_host(cache, db):\n free_cpu, free_mem = get_resources()\n my_ip = cache[\"ip\"]\n\n logger.info(\"UPDATING\", extra = {\"cpu\": free_cpu, \"mem\": free_mem, \"ip\": my_ip})\n try:\n db.hset(my_ip, mapping={\"cpu\": free_cpu, \"mem\": free_mem})\n except Exception as e:\n logger.error(e)\n raise e","def mark_if_cached(self, args):\n pass","def set_content_caching(self, loadbalancer, val):\n loadbalancer.content_caching = val","def replace(self, old_val, new_val):\r\n\r\n temp_data = Stack(self._capacity)\r\n\r\n while not self.is_empty():\r\n check = self.pop()\r\n\r\n if check == old_val:\r\n check = new_val\r\n\r\n temp_data.push(check)\r\n\r\n while not temp_data.is_empty():\r\n self.push(temp_data.pop())","def put(self, key, item):\n if key or item:\n self.cache_data[key] = item\n if len(self.cache_data) > BaseCaching.MAX_ITEMS:\n print(\"DISCARD: {}\".format(self.last))\n del self.cache_data[self.last]\n self.last = key","def update_url_cache(self, old_url):\r\n if old_url != 'self':\r\n key = self.by_url_key(old_url)\r\n link_ids = g.permacache.get(key) or []\r\n while self._id in link_ids:\r\n link_ids.remove(self._id)\r\n g.permacache.set(key, link_ids)\r\n self.set_url_cache()","def sync_local(self, other):\n pass # TODO","def translate_caching(self, translate_caching):\n\n self._translate_caching = translate_caching","def retrieve_from_cache(self, x, y):\n return False","def recache(self, phys):\r\n self.myOutputCache.initialize(phys.app)\r\n\r\n for output in self.myOutputs:\r\n output.initialize(phys.app)\r\n output.run(1)","def set(cls, obj: Model, data):\n cache.set(cls._construct_key(obj), data)","def update(self, other):\n self._start = other._start\n self._end = other._end\n self._nodes = {k: v.copy() for k,v in other._nodes.iteritems()}\n self._edges = {k: set(v) for k,v in other._edges.iteritems()}\n self._names = set(other._names)\n self.current = other.current","def set(self, key, value, ttl=None):\n self._cache[key] = (value, self._clock() + (ttl or self.default_ttl))","def recache_matches(self):\n matches = (Match.objects\n .filter(Q(winner = self) | Q(loser = self))\n .order_by('-played_time'))[:CACHED_RATING_LIMIT]\n matches = list(matches)\n matches.reverse()\n\n for match in matches:\n self.add_match(match, include_rank=True)\n\n self.save()","def test_item_update_with_modifiying_cached_resource(self):\n v1, = set_resources_and_sync([make_video(media_id='1234')])\n i1 = mpmodels.MediaItem.objects.get(jwp__key=v1.key)\n\n resource = CachedResource.videos.get(key=v1.key)\n new_title = i1.title + '-changed'\n resource.data['title'] = new_title\n resource.data['updated'] += 1\n resource.save()\n\n sync.update_related_models_from_cache()\n\n self.assertEqual(mpmodels.MediaItem.objects.get(jwp__key=v1.key).title, new_title)","def Swap(self, other):\n return _table.Table_Swap(self, other)"],"string":"[\n \"def testReplaceItem(self):\\n\\n first = \\\"Little pig, little pig, let me come in!\\\"\\n second = \\\"Not by the hair on my chinny-chin-chin!\\\"\\n memcache.set('first', first)\\n assert memcache.get('first') == first\\n memcache.replace('first', second)\\n assert memcache.get('first') == second\",\n \"def update_inplace_from(self, other):\\n self.__dict__ = other.__dict__.copy()\",\n \"def _update_use(self, key):\\n\\t\\tif (self._replace_pol == Cache.LRU):\\n\\t\\t\\tself.cache[key]= self.hashmap[key]\\n\\t\\tif (self._replace_pol == Cache.LRU_S):\\n\\t\\t\\tself.cache[key] = self.hashmap[key]\",\n \"def _proxy_cache(from_func, to_func):\\n to_func.cache_info = from_func.cache_info\\n to_func.cache_clear = from_func.cache_clear\",\n \"def fill_from_cache(self):\\n move_count = min(\\n len(self._replacement_cache),\\n constants.K - len(self._contacts)\\n )\\n\\n for _ in range(move_count):\\n self.add_contact(self._replacement_cache.pop())\",\n \"def set_cache(self, val):\\n pass\",\n \"def set(key, value):\\n instance_cache.set(key, value, expiry=CacheLayers.INSTANCE_SECONDS)\\n memcache.set(key, CacheLayers.compress(value))\\n\\n logging.info(\\\"Set BingoCache in instance cache and memcache\\\")\",\n \"def add_to_cache(self, content: Content):\\n cache = self.cache\\n cache.add_content_object(content)\\n self.cache = cache\",\n \"def reload_cache(self):\\n self.data = self.read_data_cache()\",\n \"def _update_column(self, cache_name, other, other_name):\\n self._set_column_column(\\n cache_name, other._get_column_column(other_name))\",\n \"def set_cache(self, key, value):\\n self.r.set(key, value)\\n self.r.expire(key, time=1500)\",\n \"def decache(self):\",\n \"def set_to_cache(self, url, data):\\n cache_key, cache_lookup = self.get_cacheable_info(url)\\n MEM_CACHE[cache_key][cache_lookup] = (data, time.time())\",\n \"def set_cache(self, key, value):\\n self._cache[key] = (self.model.batch_number, value)\",\n \"def reset_cache(self):\\n self._cache_complete = False\\n self._cache = {}\\n self._catcache = {}\",\n \"def replace(self, key, val, expiry_time=0, min_compress_len=0):\\n\\t\\treturn self._set(\\\"replace\\\", key, val, expiry_time, min_compress_len)\",\n \"def reset_cache(self):\\n if self.cache_address is not None:\\n for add in self.cache:\\n os.remove(add + \\\".cd\\\")\\n os.remove(add + \\\".cl\\\")\\n self.cache = [None] * len(self)\",\n \"def manage_image_cache(self, context, all_instances):\\n self.image_cache_manager.update(context, all_instances)\",\n \"def set(key, value):\\n return Cache.cache_connector.set(key, value)\",\n \"def set(self, key, value):\\n # First, look for the key in the cache using `self.get()`\\n # If not exists (returns None), add key-value to head\\n # If exists, pop old key-value from list, add new value to head\\n pass\",\n \"def _cache_course_locator(self, old_course_id, published_course_locator, draft_course_locator):\\r\\n if not old_course_id:\\r\\n return\\r\\n self.cache.set(unicode(old_course_id), (published_course_locator, draft_course_locator))\",\n \"def retire(self, other):\\n self.swap(other)\\n self.model.schedule.add(other) # put new entity into scheduler\\n self.model.schedule.remove(self) # take yourself out of it\\n self.model.retirees[self.type][self.model.schedule.steps] = self # mark yourself as retiree\\n self.model.per_step_movement[self.type] += 1\",\n \"def update_cache(self, line):\\n self.inputcache.insert(0, line) # This copies the cache every time ... :-(\\n if len(self.inputcache) >= self.CACHELENGTH:\\n self.inputcache.pop() # This not :-)\",\n \"def update(self, other):\\n if not other:\\n return\\n for o in other:\\n self.replace(o)\",\n \"def cache_contact(self, contact):\\n try:\\n self._replacement_cache.remove(contact)\\n except ValueError:\\n pass\\n\\n self._replacement_cache.append(contact)\\n if len(self._replacement_cache) > constants.CACHE_K:\\n self._replacement_cache.popleft()\",\n \"def reset_cache(self):\\n self.izx.reset_cache()\\n self.ezx.reset_cache()\",\n \"def invalidate_cache(self):\\n #self.objects.objects = []\\n return True\",\n \"def cache_clear(self):\\n\\t\\tself.__cache = {}\",\n \"def do_api_calls_update_cache(self):\\n self.get_nodes()\\n self.write_to_cache(self.inventory, self.cache_path_cache)\\n self.write_to_cache(self.index, self.cache_path_index)\",\n \"def workspace_caching(self, workspace_caching):\\n\\n self._workspace_caching = workspace_caching\",\n \"def set(self, key, value):\\n # Initialize key variables\\n result = self.cache.set(key, value)\\n\\n # Return\\n return result\",\n \"def update(self, new):\\n return self.replace(None, new)\",\n \"def update_versioned_target(self, vt):\\n self._cache_manager.update(vt.cache_key)\",\n \"def update_cache(self, repo=None, force=False):\\n raise NotImplementedError(self.update_cache)\",\n \"def set_cache(self, eval_hash: str, task_hash: str, args_hash: str, value: Any) -> None:\\n self.backend.set_eval_cache(eval_hash, task_hash, args_hash, value, value_hash=None)\",\n \"def reset_cache(self):\\n self.cache = [None] * self.n_layers\\n self.offset = 0\\n logger.debug('Reset cache.')\",\n \"def test_cache_results(self):\\n env = pike.Environment()\\n value = [1]\\n with pike.Graph('g') as graph:\\n n = ParrotNode(value)\\n env.add(graph)\\n ret = env.run('g')\\n self.assertEqual(ret, {'default': [1]})\\n n.value = [1, 2]\\n\\n # We mutated value, but the return value should be cached\\n ret = env.run('g')\\n self.assertEqual(ret, {'default': [1]})\\n\\n # Busting cache should return new value\\n ret = env.run('g', True)\\n self.assertEqual(ret, {'default': [1, 2]})\",\n \"def flush_local_cache(self):\\n self._local_cache = {}\",\n \"def _update_cache(self, key, value, cache, decode_loop_step):\\n # Combines cached keys and values with new keys and values.\\n if decode_loop_step is not None:\\n # TPU special case.\\n key_seq_dim = cache[\\\"key\\\"].shape.as_list()[1]\\n indices = tf.reshape(\\n tf.one_hot(decode_loop_step, key_seq_dim, dtype=key.dtype),\\n [1, key_seq_dim, 1, 1])\\n key = cache[\\\"key\\\"] + key * indices\\n value_seq_dim = cache[\\\"value\\\"].shape.as_list()[1]\\n indices = tf.reshape(\\n tf.one_hot(decode_loop_step, value_seq_dim, dtype=value.dtype),\\n [1, value_seq_dim, 1, 1])\\n value = cache[\\\"value\\\"] + value * indices\\n else:\\n key = tf.concat([tf.cast(cache[\\\"key\\\"], key.dtype), key], axis=1)\\n value = tf.concat([tf.cast(cache[\\\"value\\\"], value.dtype), value], axis=1)\\n\\n # Update cache\\n cache[\\\"key\\\"] = key\\n cache[\\\"value\\\"] = value\\n\\n return key, value\",\n \"def cache_set(self, key: str, value: bytes) -> None:\\n if self.cache is not None:\\n self.cache.set(key, value)\",\n \"def _prepare_cache(self, value):\\n\\n return deepcopy(value)\",\n \"def reuse(self):\\n similarCases = self.r1.similarCases\\n self.r2.setQueryCase(self.case)\\n self.r2.retrieval = self.r1\\n self.r2.reuse(similarCases)\\n self.predictionCase = self.r2.predictionCase\",\n \"def update(self, cache_key):\\r\\n self._write_sha(cache_key)\",\n \"def cache(self, key, timeout=None):\\n original_cache_timeout = self.cache_timeout\\n self.cache_timeout = timeout\\n self.cache_backend.set(key, self, timeout)\\n self.cache_timeout = original_cache_timeout\",\n \"def _update_cache(self):\\n \\n # Check if the model cache is full\\n if self.__class__.cache_limit == len(self.__class__.model_cache):\\n # Remove the oldest item from the cache if exceeding cache limit\\n self.__class__.model_cache.popitem(last=False)\\n \\n # Remove the obsolete version of the model from the cache\\n if self.model.name in self.__class__.model_cache:\\n del self.__class__.model_cache[self.model.name]\\n \\n # Add the current model to the cache\\n self.__class__.model_cache[self.model.name] = self.model\\n \\n # Debug information is printed to the terminal and logs if the paramater debug = true\\n if self.model.debug:\\n self._print_log(8)\",\n \"def swap(self, index_1, index_2):\\n temp = self.store[index_1]\\n self.store[index_1] = self.store[index_2]\\n self.store[index_2] = temp\",\n \"def update(self, other):\\n for name, value in other.items():\\n self.__setitem__(name, value)\",\n \"def reset(self):\\n\\n self.simple_cache = {}\\n self.complex_cache = {}\\n self.target_cache = {}\",\n \"def cache(self, file_name, content):\\n self.files_loaded[file_name] = content\",\n \"def _refresh_cache(self, data_dict):\\r\\n pass\",\n \"def reset_cache_location(self):\\n self._cache_location = None\",\n \"def reset_cache():\\n setup_cache({})\\n yield # test\\n setup_cache({})\",\n \"def __swap(self, index_1, index_2):\\n temp = self._lits[index_1]\\n self._lits[index_1] = self._lits[index_2]\\n self._lits[index_2] = temp\",\n \"def _update_cache(self, cset):\\n # If this changeset is already in the cache, remove it\\n try:\\n self._lru_cache.remove(cset)\\n except ValueError:\\n pass\\n\\n # Add the changeset at the end\\n if len(self._lru_cache) >= Repository._LRU_CACHE_SIZE:\\n del self._lru_cache[0]\\n self._lru_cache.append(cset)\",\n \"def _clear_cache(self):\\n self.cache = {}\",\n \"def _cache_set(self, metric_name, metric):\\n pass\",\n \"def update(self, other: dict):\\n for key in other:\\n if key in self:\\n self[key] = other[key]\",\n \"def set_emptying_cache():\\r\\n from pylons import g\\r\\n from r2.lib.cache import SelfEmptyingCache\\r\\n g.cache.caches = [SelfEmptyingCache(),] + list(g.cache.caches[1:])\",\n \"def switch(self, a, b):\\n\\n self.heap[a], self.heap[b] = self.heap[b], self.heap[a]\",\n \"def exchange(dancers, spot1, spot2):\\n\\n dancers[spot1], dancers[spot2] = dancers[spot2], dancers[spot1]\",\n \"def split_kbucket(self):\\n new_kbucket = super(CachingKBucket, self).split_kbucket()\\n\\n cache_self, cache_new = util.partition(\\n self._replacement_cache,\\n self.contact_in_range\\n )\\n\\n # Replacement caches are deques, so we can't directly assign\\n # the values returned by partition.\\n new_kbucket._replacement_cache.extend(cache_new)\\n self._replacement_cache.clear()\\n self._replacement_cache.extend(cache_self)\\n\\n self.fill_from_cache()\\n new_kbucket.fill_from_cache()\\n\\n return new_kbucket\",\n \"def _merge(self, other: dict):\\n self._storage = dict_merge(self._storage, other)\",\n \"def __setitem__(self, tid: int, result: bytes):\\n if tid in self:\\n raise KeyError(f\\\"transaction {tid} already cached\\\")\\n\\n self._cache[tid] = (result, time.monotonic())\",\n \"def _invalidate_http_cache(self):\\n self._requests_cache = {}\",\n \"def copy_from_other(self, other):\\n self.data = other.data\\n self.url = other.url\\n self.container_factory = other.container_factory\",\n \"def flush_cache(self):\\n if self.cache_modified:\\n self.cache_manager.write(self.cache_file, self.cache)\",\n \"def flushCaches(self):\\n self.rehabTreeCache = {} \\n self.frailRehabTreeCache = {} \\n self.frailTreeCache = {}\",\n \"def update_caches():\\n a = get_expired_acache()\\n c = get_expired_cnamecache()\\n ns = get_expired_nscache()\\n for keys, cache in [a, c]:\\n for key in keys:\\n print \\\"deleting {0}\\\".format(key)\\n del cache[key]\\n ns_keys, cache = ns\\n for key, dn in ns_keys:\\n print \\\"deleting {0}{1}\\\".format(key, dn)\\n del nscache[key][dn]\",\n \"def update_connection_cache(self, current_connection, key, thread_lock, value=True):\\n thread_lock.acquire()\\n current_connection[key] = value\\n thread_lock.release()\",\n \"def update(self, key_path, content, t_mserver):\\n#\\t\\tif key not in self.cache and len(self.cache) >= self.max_cache_size:\\n#\\t\\t\\tself.remove_oldest()\\n\\t\\t\\n\\t\\tcurrent_time = int(time.time())\\n\\t\\tif key_path not in self.cache:\\n\\t\\t\\tself.add_cache(key_path, content, t_mserver)\\n\\t\\t\\t\\t\\n\\t\\telif current_time >= self.cache[key_path]['time_validated']:\\n\\t\\t\\tself.cache[key_path] = {'time_validated': int(time.time()),\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t't_mclient': int(t_mserver),\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t'content': content}\\n\\t\\telse:\\n\\t\\t\\tprint(\\\"Content is not updates OR time_accessed went wrong!\\\")\",\n \"def put(self, key, item):\\n if key and item:\\n self.cache_data[key] = item\",\n \"def put(self, key, item):\\n if key and item:\\n self.cache_data[key] = item\",\n \"def test_cache_results(self):\\n kbase_sdk.init_context.cache_clear()\\n config_path = os.path.join(self.test_app_dir, 'kbase.yaml')\\n # Move kbase.yaml to kbase.yaml.bak\\n context1 = kbase_sdk.init_context(self.test_app_dir)\\n shutil.move(config_path, config_path + '.bak')\\n context2 = kbase_sdk.init_context(self.test_app_dir)\\n # If it's not caching, then MissingPath would be raised\\n self.assertEqual(context1, context2)\\n shutil.move(config_path + '.bak', config_path)\",\n \"def replace_with(self, new_inst):\\n new_inst.insert_after(self)\\n self.replace_uses_with(new_inst)\\n self.destroy()\",\n \"def _invalidate_branch_cache(self):\\n self._cached_overlapping_branch_list = None\",\n \"def Replace(self, *args):\\n return _BRepAlgo.BRepAlgo_AsDes_Replace(self, *args)\",\n \"def test_upload_overwrite(self):\\n request = DummyRequest()\\n request.access = DummyAccess(request)\\n cache = DummyCache(request)\\n request.access.allow_overwrite = [\\\"everyone\\\"]\\n name, filename, content = \\\"a\\\", \\\"a-1.tar.gz\\\", BytesIO(b\\\"new\\\")\\n cache.upload(filename, BytesIO(b\\\"old\\\"), name)\\n cache.upload(filename, content, name)\\n\\n all_versions = cache.all(name)\\n self.assertEqual(len(all_versions), 1)\\n data = cache.storage.open(all_versions[0]).read()\\n self.assertEqual(data, b\\\"new\\\")\\n\\n stored_pkgs = list(cache.storage.list(cache.new_package))\\n self.assertEqual(len(stored_pkgs), 1)\",\n \"def replace_number(self, old, new):\\r\\n self.numbers[old] = new\",\n \"def reset_cache():\\n global _CACHE\\n _CACHE.clear()\",\n \"def update(self, other):\\n for filter, value in other.items():\\n self.__setitem__(filter, value)\",\n \"def put(self, key, value):\\n if key is None or value is None:\\n raise TypeError\\n\\n index = self.__get_cache_set_index(key)\\n cache_set = self.cache_sets[index]\\n h_key = self.__ensure_hashable_key(key)\\n cache_set.put(h_key, value)\",\n \"def set_value(self, value):\\n\\n dogpile_region, cache_key = self._get_cache_plus_key()\\n dogpile_region.set(cache_key, value)\",\n \"def __swap(self, index1, index2):\\n self.heap[index1], self.heap[index2] = self.heap[index2], self.heap[index1]\",\n \"def library_caching(self, library_caching):\\n\\n self._library_caching = library_caching\",\n \"def update_resources_for_this_host(cache, db):\\n free_cpu, free_mem = get_resources()\\n my_ip = cache[\\\"ip\\\"]\\n\\n logger.info(\\\"UPDATING\\\", extra = {\\\"cpu\\\": free_cpu, \\\"mem\\\": free_mem, \\\"ip\\\": my_ip})\\n try:\\n db.hset(my_ip, mapping={\\\"cpu\\\": free_cpu, \\\"mem\\\": free_mem})\\n except Exception as e:\\n logger.error(e)\\n raise e\",\n \"def mark_if_cached(self, args):\\n pass\",\n \"def set_content_caching(self, loadbalancer, val):\\n loadbalancer.content_caching = val\",\n \"def replace(self, old_val, new_val):\\r\\n\\r\\n temp_data = Stack(self._capacity)\\r\\n\\r\\n while not self.is_empty():\\r\\n check = self.pop()\\r\\n\\r\\n if check == old_val:\\r\\n check = new_val\\r\\n\\r\\n temp_data.push(check)\\r\\n\\r\\n while not temp_data.is_empty():\\r\\n self.push(temp_data.pop())\",\n \"def put(self, key, item):\\n if key or item:\\n self.cache_data[key] = item\\n if len(self.cache_data) > BaseCaching.MAX_ITEMS:\\n print(\\\"DISCARD: {}\\\".format(self.last))\\n del self.cache_data[self.last]\\n self.last = key\",\n \"def update_url_cache(self, old_url):\\r\\n if old_url != 'self':\\r\\n key = self.by_url_key(old_url)\\r\\n link_ids = g.permacache.get(key) or []\\r\\n while self._id in link_ids:\\r\\n link_ids.remove(self._id)\\r\\n g.permacache.set(key, link_ids)\\r\\n self.set_url_cache()\",\n \"def sync_local(self, other):\\n pass # TODO\",\n \"def translate_caching(self, translate_caching):\\n\\n self._translate_caching = translate_caching\",\n \"def retrieve_from_cache(self, x, y):\\n return False\",\n \"def recache(self, phys):\\r\\n self.myOutputCache.initialize(phys.app)\\r\\n\\r\\n for output in self.myOutputs:\\r\\n output.initialize(phys.app)\\r\\n output.run(1)\",\n \"def set(cls, obj: Model, data):\\n cache.set(cls._construct_key(obj), data)\",\n \"def update(self, other):\\n self._start = other._start\\n self._end = other._end\\n self._nodes = {k: v.copy() for k,v in other._nodes.iteritems()}\\n self._edges = {k: set(v) for k,v in other._edges.iteritems()}\\n self._names = set(other._names)\\n self.current = other.current\",\n \"def set(self, key, value, ttl=None):\\n self._cache[key] = (value, self._clock() + (ttl or self.default_ttl))\",\n \"def recache_matches(self):\\n matches = (Match.objects\\n .filter(Q(winner = self) | Q(loser = self))\\n .order_by('-played_time'))[:CACHED_RATING_LIMIT]\\n matches = list(matches)\\n matches.reverse()\\n\\n for match in matches:\\n self.add_match(match, include_rank=True)\\n\\n self.save()\",\n \"def test_item_update_with_modifiying_cached_resource(self):\\n v1, = set_resources_and_sync([make_video(media_id='1234')])\\n i1 = mpmodels.MediaItem.objects.get(jwp__key=v1.key)\\n\\n resource = CachedResource.videos.get(key=v1.key)\\n new_title = i1.title + '-changed'\\n resource.data['title'] = new_title\\n resource.data['updated'] += 1\\n resource.save()\\n\\n sync.update_related_models_from_cache()\\n\\n self.assertEqual(mpmodels.MediaItem.objects.get(jwp__key=v1.key).title, new_title)\",\n \"def Swap(self, other):\\n return _table.Table_Swap(self, other)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.67452043","0.63469607","0.6275304","0.6189481","0.6022924","0.60033196","0.59268266","0.57108635","0.56714857","0.5612773","0.56104547","0.55959934","0.5594574","0.5585767","0.5527958","0.5526175","0.552543","0.552472","0.55214816","0.5506108","0.54830945","0.546478","0.54602164","0.544422","0.5442487","0.54364485","0.5424129","0.540725","0.5388688","0.53863835","0.53858334","0.5375739","0.53715545","0.53705627","0.5368222","0.53667814","0.5362043","0.5360677","0.5347151","0.53423893","0.5341507","0.5339492","0.5337005","0.53314435","0.53270566","0.53183734","0.5297717","0.5295788","0.5290468","0.52904415","0.528691","0.5284732","0.5284182","0.52816075","0.5275953","0.52687234","0.5268223","0.52599037","0.5257015","0.5255758","0.52553385","0.5253383","0.52500767","0.524484","0.52382344","0.5234165","0.52191013","0.52163607","0.5215333","0.5214115","0.52054924","0.52054924","0.52044356","0.52040035","0.519798","0.519119","0.51837116","0.5183575","0.5183083","0.5177709","0.517049","0.51695466","0.51657075","0.5164581","0.5159178","0.51559186","0.5155453","0.5148732","0.5132494","0.51231116","0.51226664","0.5121584","0.5118354","0.5118156","0.51136583","0.50983757","0.50853723","0.508223","0.5081921","0.5081666"],"string":"[\n \"0.67452043\",\n \"0.63469607\",\n \"0.6275304\",\n \"0.6189481\",\n \"0.6022924\",\n \"0.60033196\",\n \"0.59268266\",\n \"0.57108635\",\n \"0.56714857\",\n \"0.5612773\",\n \"0.56104547\",\n \"0.55959934\",\n \"0.5594574\",\n \"0.5585767\",\n \"0.5527958\",\n \"0.5526175\",\n \"0.552543\",\n \"0.552472\",\n \"0.55214816\",\n \"0.5506108\",\n \"0.54830945\",\n \"0.546478\",\n \"0.54602164\",\n \"0.544422\",\n \"0.5442487\",\n \"0.54364485\",\n \"0.5424129\",\n \"0.540725\",\n \"0.5388688\",\n \"0.53863835\",\n \"0.53858334\",\n \"0.5375739\",\n \"0.53715545\",\n \"0.53705627\",\n \"0.5368222\",\n \"0.53667814\",\n \"0.5362043\",\n \"0.5360677\",\n \"0.5347151\",\n \"0.53423893\",\n \"0.5341507\",\n \"0.5339492\",\n \"0.5337005\",\n \"0.53314435\",\n \"0.53270566\",\n \"0.53183734\",\n \"0.5297717\",\n \"0.5295788\",\n \"0.5290468\",\n \"0.52904415\",\n \"0.528691\",\n \"0.5284732\",\n \"0.5284182\",\n \"0.52816075\",\n \"0.5275953\",\n \"0.52687234\",\n \"0.5268223\",\n \"0.52599037\",\n \"0.5257015\",\n \"0.5255758\",\n \"0.52553385\",\n \"0.5253383\",\n \"0.52500767\",\n \"0.524484\",\n \"0.52382344\",\n \"0.5234165\",\n \"0.52191013\",\n \"0.52163607\",\n \"0.5215333\",\n \"0.5214115\",\n \"0.52054924\",\n \"0.52054924\",\n \"0.52044356\",\n \"0.52040035\",\n \"0.519798\",\n \"0.519119\",\n \"0.51837116\",\n \"0.5183575\",\n \"0.5183083\",\n \"0.5177709\",\n \"0.517049\",\n \"0.51695466\",\n \"0.51657075\",\n \"0.5164581\",\n \"0.5159178\",\n \"0.51559186\",\n \"0.5155453\",\n \"0.5148732\",\n \"0.5132494\",\n \"0.51231116\",\n \"0.51226664\",\n \"0.5121584\",\n \"0.5118354\",\n \"0.5118156\",\n \"0.51136583\",\n \"0.50983757\",\n \"0.50853723\",\n \"0.508223\",\n \"0.5081921\",\n \"0.5081666\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.84599257"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":81195,"cells":{"query":{"kind":"string","value":"Given a filename, returns the corresponding source, either from the cache or from actually opening the file. If the filename corresponds to a true file, and the last time it was modified differs from the recorded value, a fresh copy is retrieved. The contents is stored a a string and returned as a list of lines. If no source can be found, an empty list is returned."},"document":{"kind":"string","value":"def get_source(self, filename):\n # The main reason we care about ensuring we have the latest version\n # of a given file is for the 'avant-idle' project where we could\n # 'edit and run' multiple times a given file. We need to ensure that\n # the content shown by the traceback is accurate.\n\n if os.path.isfile(filename):\n last_modified = os.path.getmtime(filename) # modification time\n if filename not in self.cache:\n source, lines = self._get_file_source(filename)\n if source is not None:\n self.add(filename, source)\n elif filename in self.ages and last_modified != self.ages[filename]:\n # modified; get fresh copy\n source, lines = self._get_file_source(filename)\n if source is not None:\n self.add(filename, source)\n else: # had problems retrieving fresh copy\n source = self.cache[filename]\n lines = source.split(\"\\n\")\n else:\n source = self.cache[filename]\n lines = source.split(\"\\n\")\n elif filename in self.cache:\n source = self.cache[filename]\n lines = source.split(\"\\n\")\n else:\n lines = []\n\n return lines"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def _get_file_source(self, filename):\n try:\n with open(filename, encoding=\"utf8\") as f:\n lines = f.readlines()\n source = \"\".join(lines)\n except Exception:\n lines = []\n source = None\n return source, lines","def get_output_from_cache(name, filename):\n cache_filename = _get_cache_filename(name, filename)\n if (os.path.exists(cache_filename) and\n os.path.getmtime(filename) < os.path.getmtime(cache_filename)):\n with io.open(cache_filename) as f:\n return f.read()\n\n return None","def load_captured_urls_local(filename):\n if os.path.isfile(filename) == False:\n return []\n \n with open(filename, 'r') as f:\n return f.read().splitlines()","def GetFromCache(self, filename):\n return memcache.get('%s%s' % (self.CACHE_PREFIX, filename))","def GetFromCache(self, filename):\n return memcache.get('%s%s' % (self.CACHE_PREFIX, filename))","def read(self, source):\n _source = self._source_prefix+source\n return self.cache[_source]","def get_source_lines(self, filename, lineno, context=0):\n if not filename or not lineno:\n return ''\n\n return ''.join([' ' + linecache.getline(filename, line) for line in range(lineno - context, lineno + context + 1)])","def find_source(self, path):\n\t\ttry:\n\t\t\treturn self.sources[path]\n\t\texcept KeyError:\n\t\t\tlines = None\n\t\t\tif os.path.isabs(path):\n\t\t\t\tlines = self.load_source(path)\n\t\t\telse:\n\t\t\t\tfor p in self.paths:\n\t\t\t\t\tp = os.path.join(p, path)\n\t\t\t\t\tlines = self.load_source(p, path)\n\t\t\t\t\tif lines != None:\n\t\t\t\t\t\tself.sources[p] = lines\n\t\t\t\t\t\tbreak\n\t\t\tself.sources[path] = lines\n\t\t\treturn lines","def load_source(self, path, alias = None):\n\t\ttry:\n\t\t\treturn list(open(path, \"r\"))\n\t\texcept OSError:\n\t\t\treturn None","def list_cached():\n for json_name in cached_files():\n source_name = get_source_file_name(json_name)\n yield (json_name, source_name)","def read_file(self, snapshot: Bug, filepath: str) -> str:\n logger.debug(\"Reading contents of source file: %s/%s\", # noqa: pycodestyle\n snapshot.name, filepath)\n # TODO normalise file path\n\n bgz = self.__bugzoo\n key_cache = (snapshot.name, filepath)\n if key_cache in self.__cache_file_contents:\n contents = self.__cache_file_contents[key_cache]\n logger.debug(\"Found contents of source file, '%s/%s', in cache.\", # noqa: pycodestyle\n snapshot.name, filepath)\n return contents\n\n logger.debug(\"Provisioning a temporary container to fetch contents of file\") # noqa: pycodestyle\n container = bgz.containers.provision(snapshot)\n try:\n contents = bgz.files.read(container, filepath)\n except KeyError:\n logger.exception(\"Failed to read source file, '%s/%s': file not found\", # noqa: pycodestyle\n snapshot.name, filepath)\n raise FileNotFound(filepath)\n finally:\n del bgz.containers[container.uid]\n logger.debug(\"Read contents of source file, '%s/%s'\",\n snapshot.name, filepath)\n\n self.__cache_file_contents[key_cache] = contents\n return contents","def wrapped_getlines(filename, globals):\n lines = orig(filename, globals)\n source = self.format_source(\"\".join(lines))\n\n if sys.version_info < (3,):\n source = self.try_to_encode(source)\n\n return source.splitlines(True)","def get_json_from_cache(file_name):\n result = None\n path = clean_path(file_name)\n cached_file_name = get_cached_file_name(path)\n if os.path.exists(cached_file_name):\n time = os.path.getmtime(path)\n cached_time = os.path.getmtime(cached_file_name)\n if cached_time > time:\n try:\n source = open(cached_file_name, \"r\")\n try:\n result = json.load(source)\n except ValueError:\n pass\n source.close()\n except OSError:\n # Includes IOError\n pass\n return result","def get_from_cache(cls, target_filename):\n is_cached = cls.is_remote_cached(target_filename)\n if is_cached:\n cache = cls.CACHE_BACKEND()\n cache.download(is_cached, target_filename)\n logger.debug('File %r was downloaded from %r', target_filename, cls.CACHE_BACKEND)\n else:\n target_filename = None\n return target_filename","def get_source(self, name):\n return self._sources[name]","def get_source(filename: str) -> dict[str, str]:\n file_path = (\n filename\n or g.ledger.fava_options.default_file\n or g.ledger.beancount_file_path\n )\n source, sha256sum = g.ledger.file.get_source(file_path)\n return {\"source\": source, \"sha256sum\": sha256sum, \"file_path\": file_path}","def findsource(obj):\n filename = inspect.getsourcefile(obj)\n if filename:\n linecache.checkcache(filename)\n return inspect.findsource(obj)","def get_from_cache(cls, file_name):\n random.shuffle(cls.CACHE_BACKENDS)\n fname = None\n for cb in cls.CACHE_BACKENDS:\n if not cb.health_check():\n continue\n fname = cb.get_from_cache(file_name)\n if fname:\n break\n return fname","def read_cached_file(filename, cache_info, reload_func=None):\n mtime = os.path.getmtime(filename)\n if not cache_info or mtime != cache_info.get('mtime'):\n LOG.debug(_(\"Reloading cached file %s\") % filename)\n with open(filename) as fap:\n cache_info['data'] = fap.read()\n cache_info['mtime'] = mtime\n if reload_func:\n reload_func(cache_info['data'])\n return cache_info['data']","def read_cached_file(filename, cache_info, reload_func=None):\n mtime = os.path.getmtime(filename)\n if not cache_info or mtime != cache_info.get('mtime'):\n LOG.debug(\"Reloading cached file %s\" % filename)\n with open(filename) as fap:\n cache_info['data'] = fap.read()\n cache_info['mtime'] = mtime\n if reload_func:\n reload_func(cache_info['data'])\n return cache_info['data']","def _locate_from_cache_file():\n path_file = os.path.join(_get_temp_dir(), _config.pathfile)\n return _read_file(path_file) if os.path.isfile(path_file) else None","def get_file_contents(file_name):\n\n\tf = open(file_name)\n\tlines = f.readlines()\n\tf.close()\n\treturn lines","def get_terraform_sources_from_file(file: str) -> list:\n filecontent = []\n with open(file, 'r') as fh:\n filecontent = fh.readlines()\n\n sources_in_file = []\n for line in filecontent:\n\n if TF_SOURCE_SEARCH_PATTERN in line \\\n and \"source\" in line \\\n and \"#\" not in line:\n\n sources_in_file.append(line.rstrip())\n\n return sources_in_file","def read(self, filename):\r\n with temporary_file() as fp:\r\n os.unlink(fp.name)\r\n if self._call(\"-copyToLocal\", filename, fp.name) == 0:\r\n with open(fp.name) as f:\r\n return f.read()\r\n else:\r\n return None","def fileGetContents(sFilename):\n with open(sFilename) as f:\n return f.read()","def loadListFromFile (filename):\n retval = []\n filename = os.path.expanduser (filename)\n if not os.path.exists (filename):\n print(\"Error: file '%s' does not exist.\"%(filename))\n raise RuntimeError(\"Bad filename\")\n source = open (filename, 'r') \n for line in source.readlines():\n line = re.sub (r'#.+$', '', line) # remove comment characters\n line = line.strip()\n if len (line):\n retval.append (line)\n source.close()\n return retval","def get(self):\n if path.exists(self.cachefile):\n self.invalidion()\n full_cache = self._get_all()\n return full_cache\n else:\n return []","def _load_cached_2to3(self, path, cache):\n try:\n cache_stats = os.stat(cache)\n source_stats = os.stat(path)\n except OSError as e:\n if e.errno == errno.ENOENT: # FileNotFoundError\n self.logger.debug('Cache miss: %s' % cache)\n return None\n else:\n raise\n\n if cache_stats.st_mtime <= source_stats.st_mtime:\n self.logger.debug('Cache miss (stale): %s' % cache)\n return None\n\n self.logger.debug(\"Cache hit: %s\" % cache)\n return super().get_data(cache)","def read_cached_file(self, path):\n if self.config.get('do_caching', False):\n ext = path.split('.')[-1]\n\n if ext == 'cache':\n with open(path, 'r') as fd:\n try:\n return fd.read()\n except UnicodeDecodeError as e:\n self.logger.warning(str(e))\n else:\n raise Exception('\"{}\" is a invalid cache file.'.format(path))","def get_file_data_source(filename):\n def players_from_file():\n with open(filename, 'r') as f:\n json_str = '\\n'.join(f.readlines())\n return parse_players_json(json_str)\n return players_from_file","def source_file_contents(source_dir, file_name):\n file_n_path = join(source_dir, file_name)\n with open(file_n_path, encoding='utf-8') as checked:\n return checked.read().rstrip()","def load(self, fname):\n if fname.endswith('.js') and fname[:-3] in self._js:\n return self.get_js(fname[:-3])\n elif fname.endswith('.css') and fname[:-4] in self._css:\n return self.get_css(fname[:-4])\n elif fname not in self._files:\n raise IOError('Invalid source file')\n elif fname in self._cache:\n return self._cache[fname]\n else:\n filename = self._files[fname]\n src = open(filename, 'rt').read()\n #self._cache[fname] = src # caching disabled for easer dev\n return src","def retrieve(filen, start, end):\n\n text = \"\"\n # Check for consistency and validity of lines\n if start < 0 and end < 0 or end < start:\n return \"\"\n # Fetch from a file if possible\n if os.path.isfile(filen):\n try:\n for i in range(start, end + 1):\n text = text + str(linecache.getline(filen, i))\n return text\n except Exception:\n print(\"Exception raised in retrieve method:\")\n print('\\tSource file = \"' + str(filen) + '\"')\n print(\"\\tStart line = \" + str(start))\n print(\"\\tEnd line = \" + str(end))\n print(\"\\tCurrent line = \" + str(i))\n raise\n # Otherwise return nothing\n return \"\"","def notify_cache(filename: str) -> Dict:\n\n cache = {}\n\n try:\n with open(filename) as f:\n for line in f:\n if line:\n cache[line.strip()] = True\n except FileNotFoundError:\n warning(\n \"Cache file {} not found, will be created if necessary\".format(filename)\n )\n\n return cache","def getFileFirstLine(filename, mode=\"r\", encoding=None):\n\n with withFileLock(\"reading file %s\" % filename):\n with openTextFile(filename, mode, encoding=encoding) as f:\n return f.readline()","def get_strings(src_file):\n res = []\n try:\n res = open(src_file,'r').readlines()\n res = [x.strip() for x in res]\n except:\n res = []\n return res","def get_source(self, key, files):\n raise NotImplementedError","def get_file_contents_as_list(file_name):\n with open(file_name) as file:\n data = file.read().splitlines()\n return data","def get_recently_modified_scratch_file(settings):\n dir_contents = os.listdir(settings.location)\n full_paths = map(lambda f: os.path.join(settings.location, f), dir_contents)\n files = filter(lambda f: os.path.isfile(str(f)), full_paths)\n if not files:\n return \"\"\n files = sorted(files, key=_get_mtime)\n return files[-1]","def _GetVisitSource(self, visit_identifier, cache, database):\n sync_cache_results = cache.GetResults('sync')\n if not sync_cache_results:\n result_set = database.Query(self._SYNC_CACHE_QUERY)\n\n cache.CacheQueryResults(result_set, 'sync', 'id', ('source',))\n sync_cache_results = cache.GetResults('sync')\n\n if sync_cache_results and visit_identifier:\n results = sync_cache_results.get(visit_identifier, None)\n if results:\n return results[0]\n\n return None","def getFileContents(filename, mode=\"r\", encoding=None):\n\n with withFileLock(\"reading file %s\" % filename):\n with openTextFile(filename, mode, encoding=encoding) as f:\n return f.read()","def __read_cache(self, fileName):\n if self.__log:\n self.__logger.info(f\"Cache hit - {fileName}\")\n # Cache hit\n with open(fileName, \"rb\") as f:\n content = self.__handle_decompression(f.read())\n variables = pickle.loads(content)\n\n # Move node to front\n node = os.path.relpath(fileName, \"cache\")\n self.__shift_node(node)\n\n return variables","def get_file_contents(filename):\n with open(filename, 'r') as f:\n content = f.read()\n return content","def get_source(self, environment, template):\n if \"/\" not in template:\n path = template.split(\".\")\n\n # The first part of the path must be a bundle name\n # The other parts are the hierarchy of directory after 'views'\n bundle = path[0]\n sub_hierarchy = \"/\".join(path[1:])\n path = \"bundles/\" + bundle + \"/views/\" + sub_hierarchy + \".jj2\"\n else:\n path = template\n\n path = join(self.server.user_directory, path)\n if not exists(path):\n raise TemplateNotFound(template)\n\n mtime = getmtime(path)\n with open(path, 'r', encoding=\"utf-8\") as file:\n source = file.read()\n\n return source, path, lambda: mtime == getmtime(path)","def _read_cache(self, path):\n if self._cache:\n cache_path = os.path.join(self._cache, path)\n\n if os.path.exists(cache_path):\n with io.open(cache_path, encoding='utf-8') as f:\n text = f.read()\n\n return text\n\n msg = ('Unable to download remote file \"{0}\" and local cache is not '\n 'available.').format(path)\n raise RuntimeError(msg)","def source_path(self, rel_path):\n # TODO download to disk (use source cache) in case of remote file\n import inspect\n\n frame = inspect.currentframe().f_back\n calling_file = frame.f_code.co_filename\n\n if (\n self.included_stack\n and calling_file == self.included_stack[-1].get_path_or_uri()\n ):\n # called from current snakefile, we can try to keep the original source\n # file annotation\n # This will only work if the method is evaluated during parsing mode.\n # Otherwise, the stack can be empty already.\n path = self.current_basedir.join(rel_path)\n orig_path = path.get_path_or_uri()\n else:\n # heuristically determine path\n calling_dir = os.path.dirname(calling_file)\n path = smart_join(calling_dir, rel_path)\n orig_path = path\n\n return sourcecache_entry(\n self.sourcecache.get_path(infer_source_file(path)), orig_path\n )","def load_file(filename):\n with open(filename, \"r\") as f:\n return f.readlines()","def load_csv_cached(filename='../apps/naive_c_stats.csv', cache={}):\n if filename in cache:\n return cache[filename]\n if not os.path.exists(filename):\n ans = None\n else:\n ans = numpy.recfromcsv(filename)\n cache[filename] = ans\n return ans","def source_changed(source, cache):\n return os.path.getmtime(source)>os.path.getmtime(cache)","def read_file(filename):\n with open(filename, encoding='utf-8') as src:\n return [line.strip() for line in src.readlines()]","def get_lines_from_file(fname, context=None):\n content = []\n if context and context.ddboost:\n contents = get_lines_from_dd_file(fname, context.ddboost_storage_unit)\n return contents\n else:\n with open(fname) as fd:\n for line in fd:\n content.append(line.strip('\\n'))\n return content","def _line_offsets(self, snapshot: Bug, filepath: str) -> List[int]:\n logger.debug(\"Fetching line offsets for file, '%s', in snapshot, '%s'\", # noqa: pycodestyle\n filepath,\n snapshot.name)\n key_cache = (snapshot.name, filepath)\n if key_cache in self.__cache_offsets:\n logger.debug(\"Retrieving line offsets for file, '%s', in snapshot, '%s', from cache.\", # noqa: pycodestyle\n filepath,\n snapshot.name)\n return self.__cache_offsets[key_cache]\n\n logger.debug(\"Computing line offsets for file, '%s', in snapshot, '%s'\", # noqa: pycodestyle\n filepath,\n snapshot.name)\n contents = self.read_file(snapshot, filepath)\n\n # find all indices of newline characters\n offsets = [0]\n last_offset = 0\n while True:\n next_line_break = contents.find('\\n', last_offset)\n if next_line_break == -1:\n break\n last_offset = next_line_break + 1\n offsets.append(last_offset)\n\n logger.debug(\"Saving line offsets for file, '%s', in snapshot, '%s', to cache.\", # noqa: pycodestyle\n filepath,\n snapshot.name)\n self.__cache_offsets[key_cache] = offsets\n return offsets","def read_file_into_list(filename):\n with open(filename) as file:\n return file.readlines()","def _get_lines_from_file(path: str) -> List[str]:\n if os.path.isfile(path):\n with open(path, 'r') as f:\n lines = f.readlines()\n else:\n raise InputError(f'Could not find file {path}')\n return lines","def get(name, filename):\n\tlogging.info(\"Reading {} from {}\".format(name, filename))\n\tlogging.debug(\"Opening file\")\n\twith open(filename, \"r+\") as f:\n\t\treader = csv.reader(f)\n\t\tlogging.debug(\"Reading name/snippet from file\")\n\t\tin_file = False\n\t\tfor row in reader:\n\t\t\tif str(row[0]) == name:\n\t\t\t\tin_file = True\n\t\t\t\tprint row\n\t\tif in_file == False:\n\t\t\tprint \"That's not in this file\"\n\tlogging.debug(\"Read successful\")\n\treturn name, filename","def load_cache(self, filename):\n output_df = cudf.read_hdf(filename, key=self.uid)\n return output_df","def _fileLinesToList(filename) :\n o = []\n with open(filename, \"r\") as fi :\n for l in fi :\n if l.strip() != \"\" :\n o.append(l.strip())\n return o","def get_source(self) -> cwipc_tiledsource_wrapper:\n obj = cwipc_realsense2_dll_load().cwipc_offline_get_source(self._as_cwipc_offline_p())\n return cwipc_tiledsource_wrapper(obj)","def read():\n with open(GlobalVariables.get_instance().get('history_filename'), 'rb') as fin:\n lines = []\n for line in fin.readlines():\n lines.append(line.replace('\\n', '').strip())\n fin.close()\n return lines","def getFileContent(fn):\n content = None\n if not os.path.exists(fn):\n print(\"Can not open file \" + fn)\n else:\n with open(fn, \"rb\") as f:\n content = f.readlines()\n return content","def GetFileContents(self, filename):\n with tempfile.NamedTemporaryFile() as t:\n self.GetFile(filename, t.name)\n with open(t.name, 'r') as f2:\n res = f2.read()\n logging.debug(\"GetFileContents(%s)->%s\" % (filename, res))\n return res","def lineReadfile(filename):\n#\t\"input:filename output=readlines() \"\n\tf = open(filename)\n\tlist1 =[]\n\twhile 1:\n\t\ts = f.readline()\n\t\tif s==\"\":\n\t\t\tbreak\n\t\ts=string.replace(s,\"\\n\",\"\")\n\t\tif s==\"\":\n\t\t\tcontinue\n\t\tlist1.append(s)\n\tf.close()\n\treturn list1","def read_daily_source(path = default_data_location):\n \n from os import listdir\n from os.path import isfile, join\n onlyfiles = [f for f in listdir(path) if isfile(join(path, f)) and f!='.DS_Store']\n \n source = []\n for f in onlyfiles:\n with open(join(path, f),'r',encoding='utf-8') as fin:\n try:\n source.append(BeautifulSoup(fin.read(), 'html.parser'))\n except Exception:\n print('failed to read {}'.format(f))\n raise\n \n data = pd.DataFrame({'name':onlyfiles, 'source':source})\n data['name'] = data['name'].apply(lambda x: x[:-5])\n data['source_hash'] = data['source'].apply(lambda x: get_hash(x))\n return data","def readVersionList(filename):\n\ttry:\n\t\tlines = []\n\t\tif os.path.isfile(filename):\n\t\t\twith open(r''+ filename, 'r') as f:\n\t\t\t\tlines = f.readlines()\n\t\treturn lines\n\texcept IOError as e:\n\t\tprint(traceback.format_exc())\n\t\tinfo = filename + 'can\\'t open'\n\t\tdoExit(0, info)","def read_lines(filename):\n # Absolute dir the script is in\n script_dir = os.path.dirname(__file__)\n # Join the relative path to the input file\n resource_path = os.path.join(script_dir, '../resources/' + filename)\n # Open the file\n with open(resource_path) as f:\n # Read all lines from the file. Per default we have now strings\n return [line.rstrip() for line in f]","def _getFile(url, cachedFile=True, return_filename=False):\n assert url, \"WHY are you trying to load an empty string url?!?! Nothing good will come of this! In fact, I will assure that! %s\" % (url)\n md5 = hashlib.md5(url).hexdigest()\n filename = os.path.join(config.WEB_CACHE_DIR, md5)\n if os.path.exists(filename) and cachedFile:\n ret = open(filename, 'r').read()\n else:\n opener = urllib.FancyURLopener()\n ret = opener.open(url).read()\n o = open(filename, 'wb') # had to open in binary mode so PIL's Image.Open() function would work\n o.write(ret)\n o.close()\n if return_filename:\n return filename\n else:\n return ret","def get_file_lines(url):\n\n\t# Download the file over the internet\n\tresponse = requests.get(url, stream=True)\n\n\t# Decode all lines into strings\n\tlines = []\n\tfor line in response.iter_lines():\n\t\tlines.append(line.decode(\"UTF-8\"))\n\treturn lines","def readlines(filename):\n with open(filename, 'r') as f:\n lines = f.read().splitlines()\n return lines","def GetFromNegativeCache(self, filename):\n return memcache.get('%s%s' % (self.NEG_CACHE_PREFIX, filename))","def GetFromNegativeCache(self, filename):\n return memcache.get('%s%s' % (self.NEG_CACHE_PREFIX, filename))","def read_local_file(filename):\n import fsspec\n fs = fsspec.filesystem('file')\n\n with fs.open(filename) as f:\n data = loads(f.read())\n\n return data","def read_file(filename) -> List[Todo]:\n with pathlib.Path(filename).expanduser().open('r') as fp:\n return [Todo(_id, line) for _id, line in enumerate(fp)]","def add(self, filename, source):\n self.cache[filename] = source\n if os.path.isfile(filename):\n self.ages[filename] = os.path.getmtime(filename) # modification time","def FindSourceForPath(self, path):\n ret = self._FindSourceForPathInternal(path)\n if not ret and path not in self._unmatched_paths:\n if self.unmatched_paths_count < 10:\n logging.warning('Could not find source path for %s', path)\n self._unmatched_paths.add(path)\n return ret","def _get_media_source(self, template_name, process_func=None,\n context=None, no_render=False):\n cache = self.__media_source_cache\n\n mem_args = (template_name, process_func)\n if mem_args in cache and not context and not settings.DEBUG:\n source = cache[mem_args]\n is_cached = True\n else:\n if context and not no_render:\n template = loader.get_template(template_name)\n return template.render(context), False\n\n source, filepath = self._get_source_filepath(template_name)\n\n if process_func:\n source = process_func(source, filepath)\n\n cache[mem_args] = source\n is_cached = False\n\n return source, is_cached","def _get_source(self, fullmodname):\n submodname, is_package, relpath = self._get_info(fullmodname)\n fullpath = self.path_entry + relpath\n source = self.datablocks[relpath]\n if hasattr(source, \"decode\"):\n source = source.decode(\"UTF-8\")\n source = source.replace('\\r\\n', '\\n')\n source = source.replace('\\r', '\\n')\n return submodname, is_package, fullpath, source","def load_file(file_name):\n with open(file_name,\"r\") as f:\n return f.readlines()","def is_remote_cached(cls, target_filename):\n is_cached = None\n cache = cls.CACHE_BACKEND()\n for file_name, file_id in cache.search():\n if file_name == os.path.basename(target_filename):\n is_cached = file_id\n logger.debug('File %r already cached at %r', target_filename, cls.CACHE_BACKEND)\n break\n return is_cached","def read_lines(filename):\n with open(filename, 'r') as f:\n lines = f.readlines()\n return lines","def read_from_file(filename=\"default.txt\", filepath=\"default_path\"):\n\n fp = open_file(filename, filepath)\n\n try:\n return fp.readlines()\n except IOError:\n l.error(\"Could not read from file.\")\n return []\n except:\n l.error(\n \"Unknown error occured, while reading from file \" + str(filename),\n exc_info=True,\n )\n\n close_file(fp)","def _deleted_sources(self):\r\n # We compute the list lazily.\r\n if self._lazy_deleted_sources is None:\r\n with self.context.new_workunit('find-deleted-sources'):\r\n if os.path.exists(self._analysis_file):\r\n products = self._analysis_parser.parse_products_from_path(self._analysis_file)\r\n buildroot = get_buildroot()\r\n old_sources = products.keys() # Absolute paths.\r\n self._lazy_deleted_sources = [os.path.relpath(src, buildroot) for src in old_sources\r\n if not os.path.exists(src)]\r\n else:\r\n self._lazy_deleted_sources = []\r\n return self._lazy_deleted_sources","def local_read(filename):\n full_filename = os.path.join(\n os.path.abspath(os.path.dirname(__file__)),\n filename)\n return codecs.open(full_filename, 'r', 'utf-8').read()","def fileReader(filename):\n try:\n openfile = open(filename, 'r')\n urls = openfile.read()\n openfile.close()\n return urls\n except IOError:\n print \"File tidak ada\"\n exit()","def GetFileContents(self, filename):\n logging.debug(\"GetFileContents(%s)\" % (filename))\n with tempfile.NamedTemporaryFile(mode='w') as t:\n self.GetFile(filename, t.name)\n with open(t.name, 'r', encoding='UTF-8') as f2:\n res = f2.read()\n logging.debug(\"GetFileContents(%s)->%s\" % (filename, res))\n return res","def read_file_in_lines(filename):\r\n\twith open(filename) as infile:\r\n\t\tlines = infile.readlines()\r\n\treturn [line.strip() for line in lines]","def __read_file(self, filename):\n with open(filename) as f:\n content = f.readlines()\n \n return content","def _read_cache(url):\n\n j = None\n m = hashlib.md5()\n m.update(url)\n if os.path.exists('.cache.%s' % m.hexdigest()):\n with open('.cache.%s' % m.hexdigest(), 'rb') as infile:\n j = json.load(infile)\n\n return j","def get_datfile(filename):\n if ARGV.get(DEBUG_OPT):\n err_print('Getting datfile from \"{}\"'.format(filename))\n\n try:\n with open(filename, 'rb') as pickle_file:\n try:\n (cache, readlist) = pickle.load(pickle_file)\n pickle_file.close()\n except (EOFError, ValueError):\n (cache, readlist) = ({\"feed\": None, \"max-age\": None, \"last-request\": None}, [])\n except (FileNotFoundError, PermissionError):\n (cache, readlist) = ({\"feed\": None, \"max-age\": None, \"last-request\": None}, [])\n return (cache, readlist)","def files():\n return get_cached(\"files.json\")","def get_file_contents(filepath):\n gh, repo, branch = connect_to_github()\n tree = branch.commit.commit.tree.recurse()\n for filename in tree.tree:\n if filepath in filename.path:\n print \"[*] Found file %s\" % filepath\n blob = repo.blob(filename._json_data['sha'])\n return blob.content\n return None","def getsourcefile(object):\r\n filename = getfile(object)\r\n if string.lower(filename[-4:]) in ('.pyc', '.pyo'):\r\n filename = filename[:-4] + '.py'\r\n for suffix, mode, kind in imp.get_suffixes():\r\n if 'b' in mode and string.lower(filename[-len(suffix):]) == suffix:\r\n # Looks like a binary file. We want to only return a text file.\r\n return None\r\n if os.path.exists(filename):\r\n return filename\r\n # only return a non-existent filename if the module has a PEP 302 loader\r\n if hasattr(getmodule(object, filename), '__loader__'):\r\n return filename\r\n # or it is in the linecache\r\n if filename in linecache.cache:\r\n return filename","def get_resolution(path_name, file_name):\n with open(path_name + 'resolution.txt') as file, \\\n mmap.mmap(file.fileno(), 0, access=mmap.ACCESS_READ) as s:\n file.seek(0, s.find(file_name.encode()))\n line = file.readline().split(' ')\n return [line[1], line[2].rstrip('\\n')]","def file_by_file_name(self, file_name):\n ls = self.files.filter_by(file_name=file_name)\n return ls[0] if 0 < len(ls) else None","def readListFromFile( filename ):\n try:\n with open( filename ) as f:\n lines = f.readlines()\n return lines\n except:\n #e = sys.exc_info()[0]\n print( 'Error reading file ' + filename )","def getlistfromtext(self,filename):\n l=[]\n\n if self.encoding:\n f = codecs.open(filename,\"r\",encoding=self.encoding)\n for line in f:\n l.append(line.rstrip())\n f.close()\n\n else:\n f = open(filename,\"r\")\n for line in f:\n l.append(line.rstrip())\n f.close()\n return l","def get_source_file(self):\n return self.get_attribute(\"source_file\")","def contents(filepath):\n f = open(filepath, 'r')\n rval = [x.rstrip(\"\\r\\n\") for x in f.readlines()]\n f.close()\n return rval","def test_raw_file_url_error(self):\n repository = self.remote_repository\n\n self.spy_on(repository._get_file_uncached,\n op=kgb.SpyOpReturn(b'first'))\n\n self.assertEqual(repository.get_file('PATH', 'd7e96b3'),\n b'first')\n\n # Ensure output of fake result matches.\n repository._get_file_uncached.unspy()\n self.spy_on(repository._get_file_uncached,\n op=kgb.SpyOpReturn(b'second'))\n\n # Grab from cache when no changes and change fake result to confirm\n # it is not called.\n self.assertEqual(repository.get_file('PATH', 'd7e96b3'),\n b'first')\n\n # When raw_file_url changed, do not grab from cache and ensure output\n # equals second fake value.\n repository.raw_file_url = \\\n 'http://github.com/api/v2/yaml/blob/show/reviewboard/<revision>'\n\n self.assertEqual(repository.get_file('PATH', 'd7e96b3'),\n b'second')","def read_file_list(filename):\n\n # hint: when you read lines of files, there will be a \"newline\"\n # (end-of-line character) at the end of each line, and you want to\n # strip that off before you print it. Do some research on that!\n\n # with open(filename, 'r') as file:\n # print(file.read())\n #cwd = os.getcwd() # This gets the visual studio code opened location\n cwd = os.path.dirname(os.path.realpath(__file__))\n print(cwd)\n try:\n file_contents = Path(cwd + \"\\\\\" + filename).read_text()\n except:\n return \"File not found\"\n return file_contents","def file_entry(file_path, regex, default=()):\n with open(file_path, 'r') as f:\n for line in f:\n search = re.search(regex, line, re.IGNORECASE)\n if search:\n return search.groups()\n\n return default"],"string":"[\n \"def _get_file_source(self, filename):\\n try:\\n with open(filename, encoding=\\\"utf8\\\") as f:\\n lines = f.readlines()\\n source = \\\"\\\".join(lines)\\n except Exception:\\n lines = []\\n source = None\\n return source, lines\",\n \"def get_output_from_cache(name, filename):\\n cache_filename = _get_cache_filename(name, filename)\\n if (os.path.exists(cache_filename) and\\n os.path.getmtime(filename) < os.path.getmtime(cache_filename)):\\n with io.open(cache_filename) as f:\\n return f.read()\\n\\n return None\",\n \"def load_captured_urls_local(filename):\\n if os.path.isfile(filename) == False:\\n return []\\n \\n with open(filename, 'r') as f:\\n return f.read().splitlines()\",\n \"def GetFromCache(self, filename):\\n return memcache.get('%s%s' % (self.CACHE_PREFIX, filename))\",\n \"def GetFromCache(self, filename):\\n return memcache.get('%s%s' % (self.CACHE_PREFIX, filename))\",\n \"def read(self, source):\\n _source = self._source_prefix+source\\n return self.cache[_source]\",\n \"def get_source_lines(self, filename, lineno, context=0):\\n if not filename or not lineno:\\n return ''\\n\\n return ''.join([' ' + linecache.getline(filename, line) for line in range(lineno - context, lineno + context + 1)])\",\n \"def find_source(self, path):\\n\\t\\ttry:\\n\\t\\t\\treturn self.sources[path]\\n\\t\\texcept KeyError:\\n\\t\\t\\tlines = None\\n\\t\\t\\tif os.path.isabs(path):\\n\\t\\t\\t\\tlines = self.load_source(path)\\n\\t\\t\\telse:\\n\\t\\t\\t\\tfor p in self.paths:\\n\\t\\t\\t\\t\\tp = os.path.join(p, path)\\n\\t\\t\\t\\t\\tlines = self.load_source(p, path)\\n\\t\\t\\t\\t\\tif lines != None:\\n\\t\\t\\t\\t\\t\\tself.sources[p] = lines\\n\\t\\t\\t\\t\\t\\tbreak\\n\\t\\t\\tself.sources[path] = lines\\n\\t\\t\\treturn lines\",\n \"def load_source(self, path, alias = None):\\n\\t\\ttry:\\n\\t\\t\\treturn list(open(path, \\\"r\\\"))\\n\\t\\texcept OSError:\\n\\t\\t\\treturn None\",\n \"def list_cached():\\n for json_name in cached_files():\\n source_name = get_source_file_name(json_name)\\n yield (json_name, source_name)\",\n \"def read_file(self, snapshot: Bug, filepath: str) -> str:\\n logger.debug(\\\"Reading contents of source file: %s/%s\\\", # noqa: pycodestyle\\n snapshot.name, filepath)\\n # TODO normalise file path\\n\\n bgz = self.__bugzoo\\n key_cache = (snapshot.name, filepath)\\n if key_cache in self.__cache_file_contents:\\n contents = self.__cache_file_contents[key_cache]\\n logger.debug(\\\"Found contents of source file, '%s/%s', in cache.\\\", # noqa: pycodestyle\\n snapshot.name, filepath)\\n return contents\\n\\n logger.debug(\\\"Provisioning a temporary container to fetch contents of file\\\") # noqa: pycodestyle\\n container = bgz.containers.provision(snapshot)\\n try:\\n contents = bgz.files.read(container, filepath)\\n except KeyError:\\n logger.exception(\\\"Failed to read source file, '%s/%s': file not found\\\", # noqa: pycodestyle\\n snapshot.name, filepath)\\n raise FileNotFound(filepath)\\n finally:\\n del bgz.containers[container.uid]\\n logger.debug(\\\"Read contents of source file, '%s/%s'\\\",\\n snapshot.name, filepath)\\n\\n self.__cache_file_contents[key_cache] = contents\\n return contents\",\n \"def wrapped_getlines(filename, globals):\\n lines = orig(filename, globals)\\n source = self.format_source(\\\"\\\".join(lines))\\n\\n if sys.version_info < (3,):\\n source = self.try_to_encode(source)\\n\\n return source.splitlines(True)\",\n \"def get_json_from_cache(file_name):\\n result = None\\n path = clean_path(file_name)\\n cached_file_name = get_cached_file_name(path)\\n if os.path.exists(cached_file_name):\\n time = os.path.getmtime(path)\\n cached_time = os.path.getmtime(cached_file_name)\\n if cached_time > time:\\n try:\\n source = open(cached_file_name, \\\"r\\\")\\n try:\\n result = json.load(source)\\n except ValueError:\\n pass\\n source.close()\\n except OSError:\\n # Includes IOError\\n pass\\n return result\",\n \"def get_from_cache(cls, target_filename):\\n is_cached = cls.is_remote_cached(target_filename)\\n if is_cached:\\n cache = cls.CACHE_BACKEND()\\n cache.download(is_cached, target_filename)\\n logger.debug('File %r was downloaded from %r', target_filename, cls.CACHE_BACKEND)\\n else:\\n target_filename = None\\n return target_filename\",\n \"def get_source(self, name):\\n return self._sources[name]\",\n \"def get_source(filename: str) -> dict[str, str]:\\n file_path = (\\n filename\\n or g.ledger.fava_options.default_file\\n or g.ledger.beancount_file_path\\n )\\n source, sha256sum = g.ledger.file.get_source(file_path)\\n return {\\\"source\\\": source, \\\"sha256sum\\\": sha256sum, \\\"file_path\\\": file_path}\",\n \"def findsource(obj):\\n filename = inspect.getsourcefile(obj)\\n if filename:\\n linecache.checkcache(filename)\\n return inspect.findsource(obj)\",\n \"def get_from_cache(cls, file_name):\\n random.shuffle(cls.CACHE_BACKENDS)\\n fname = None\\n for cb in cls.CACHE_BACKENDS:\\n if not cb.health_check():\\n continue\\n fname = cb.get_from_cache(file_name)\\n if fname:\\n break\\n return fname\",\n \"def read_cached_file(filename, cache_info, reload_func=None):\\n mtime = os.path.getmtime(filename)\\n if not cache_info or mtime != cache_info.get('mtime'):\\n LOG.debug(_(\\\"Reloading cached file %s\\\") % filename)\\n with open(filename) as fap:\\n cache_info['data'] = fap.read()\\n cache_info['mtime'] = mtime\\n if reload_func:\\n reload_func(cache_info['data'])\\n return cache_info['data']\",\n \"def read_cached_file(filename, cache_info, reload_func=None):\\n mtime = os.path.getmtime(filename)\\n if not cache_info or mtime != cache_info.get('mtime'):\\n LOG.debug(\\\"Reloading cached file %s\\\" % filename)\\n with open(filename) as fap:\\n cache_info['data'] = fap.read()\\n cache_info['mtime'] = mtime\\n if reload_func:\\n reload_func(cache_info['data'])\\n return cache_info['data']\",\n \"def _locate_from_cache_file():\\n path_file = os.path.join(_get_temp_dir(), _config.pathfile)\\n return _read_file(path_file) if os.path.isfile(path_file) else None\",\n \"def get_file_contents(file_name):\\n\\n\\tf = open(file_name)\\n\\tlines = f.readlines()\\n\\tf.close()\\n\\treturn lines\",\n \"def get_terraform_sources_from_file(file: str) -> list:\\n filecontent = []\\n with open(file, 'r') as fh:\\n filecontent = fh.readlines()\\n\\n sources_in_file = []\\n for line in filecontent:\\n\\n if TF_SOURCE_SEARCH_PATTERN in line \\\\\\n and \\\"source\\\" in line \\\\\\n and \\\"#\\\" not in line:\\n\\n sources_in_file.append(line.rstrip())\\n\\n return sources_in_file\",\n \"def read(self, filename):\\r\\n with temporary_file() as fp:\\r\\n os.unlink(fp.name)\\r\\n if self._call(\\\"-copyToLocal\\\", filename, fp.name) == 0:\\r\\n with open(fp.name) as f:\\r\\n return f.read()\\r\\n else:\\r\\n return None\",\n \"def fileGetContents(sFilename):\\n with open(sFilename) as f:\\n return f.read()\",\n \"def loadListFromFile (filename):\\n retval = []\\n filename = os.path.expanduser (filename)\\n if not os.path.exists (filename):\\n print(\\\"Error: file '%s' does not exist.\\\"%(filename))\\n raise RuntimeError(\\\"Bad filename\\\")\\n source = open (filename, 'r') \\n for line in source.readlines():\\n line = re.sub (r'#.+$', '', line) # remove comment characters\\n line = line.strip()\\n if len (line):\\n retval.append (line)\\n source.close()\\n return retval\",\n \"def get(self):\\n if path.exists(self.cachefile):\\n self.invalidion()\\n full_cache = self._get_all()\\n return full_cache\\n else:\\n return []\",\n \"def _load_cached_2to3(self, path, cache):\\n try:\\n cache_stats = os.stat(cache)\\n source_stats = os.stat(path)\\n except OSError as e:\\n if e.errno == errno.ENOENT: # FileNotFoundError\\n self.logger.debug('Cache miss: %s' % cache)\\n return None\\n else:\\n raise\\n\\n if cache_stats.st_mtime <= source_stats.st_mtime:\\n self.logger.debug('Cache miss (stale): %s' % cache)\\n return None\\n\\n self.logger.debug(\\\"Cache hit: %s\\\" % cache)\\n return super().get_data(cache)\",\n \"def read_cached_file(self, path):\\n if self.config.get('do_caching', False):\\n ext = path.split('.')[-1]\\n\\n if ext == 'cache':\\n with open(path, 'r') as fd:\\n try:\\n return fd.read()\\n except UnicodeDecodeError as e:\\n self.logger.warning(str(e))\\n else:\\n raise Exception('\\\"{}\\\" is a invalid cache file.'.format(path))\",\n \"def get_file_data_source(filename):\\n def players_from_file():\\n with open(filename, 'r') as f:\\n json_str = '\\\\n'.join(f.readlines())\\n return parse_players_json(json_str)\\n return players_from_file\",\n \"def source_file_contents(source_dir, file_name):\\n file_n_path = join(source_dir, file_name)\\n with open(file_n_path, encoding='utf-8') as checked:\\n return checked.read().rstrip()\",\n \"def load(self, fname):\\n if fname.endswith('.js') and fname[:-3] in self._js:\\n return self.get_js(fname[:-3])\\n elif fname.endswith('.css') and fname[:-4] in self._css:\\n return self.get_css(fname[:-4])\\n elif fname not in self._files:\\n raise IOError('Invalid source file')\\n elif fname in self._cache:\\n return self._cache[fname]\\n else:\\n filename = self._files[fname]\\n src = open(filename, 'rt').read()\\n #self._cache[fname] = src # caching disabled for easer dev\\n return src\",\n \"def retrieve(filen, start, end):\\n\\n text = \\\"\\\"\\n # Check for consistency and validity of lines\\n if start < 0 and end < 0 or end < start:\\n return \\\"\\\"\\n # Fetch from a file if possible\\n if os.path.isfile(filen):\\n try:\\n for i in range(start, end + 1):\\n text = text + str(linecache.getline(filen, i))\\n return text\\n except Exception:\\n print(\\\"Exception raised in retrieve method:\\\")\\n print('\\\\tSource file = \\\"' + str(filen) + '\\\"')\\n print(\\\"\\\\tStart line = \\\" + str(start))\\n print(\\\"\\\\tEnd line = \\\" + str(end))\\n print(\\\"\\\\tCurrent line = \\\" + str(i))\\n raise\\n # Otherwise return nothing\\n return \\\"\\\"\",\n \"def notify_cache(filename: str) -> Dict:\\n\\n cache = {}\\n\\n try:\\n with open(filename) as f:\\n for line in f:\\n if line:\\n cache[line.strip()] = True\\n except FileNotFoundError:\\n warning(\\n \\\"Cache file {} not found, will be created if necessary\\\".format(filename)\\n )\\n\\n return cache\",\n \"def getFileFirstLine(filename, mode=\\\"r\\\", encoding=None):\\n\\n with withFileLock(\\\"reading file %s\\\" % filename):\\n with openTextFile(filename, mode, encoding=encoding) as f:\\n return f.readline()\",\n \"def get_strings(src_file):\\n res = []\\n try:\\n res = open(src_file,'r').readlines()\\n res = [x.strip() for x in res]\\n except:\\n res = []\\n return res\",\n \"def get_source(self, key, files):\\n raise NotImplementedError\",\n \"def get_file_contents_as_list(file_name):\\n with open(file_name) as file:\\n data = file.read().splitlines()\\n return data\",\n \"def get_recently_modified_scratch_file(settings):\\n dir_contents = os.listdir(settings.location)\\n full_paths = map(lambda f: os.path.join(settings.location, f), dir_contents)\\n files = filter(lambda f: os.path.isfile(str(f)), full_paths)\\n if not files:\\n return \\\"\\\"\\n files = sorted(files, key=_get_mtime)\\n return files[-1]\",\n \"def _GetVisitSource(self, visit_identifier, cache, database):\\n sync_cache_results = cache.GetResults('sync')\\n if not sync_cache_results:\\n result_set = database.Query(self._SYNC_CACHE_QUERY)\\n\\n cache.CacheQueryResults(result_set, 'sync', 'id', ('source',))\\n sync_cache_results = cache.GetResults('sync')\\n\\n if sync_cache_results and visit_identifier:\\n results = sync_cache_results.get(visit_identifier, None)\\n if results:\\n return results[0]\\n\\n return None\",\n \"def getFileContents(filename, mode=\\\"r\\\", encoding=None):\\n\\n with withFileLock(\\\"reading file %s\\\" % filename):\\n with openTextFile(filename, mode, encoding=encoding) as f:\\n return f.read()\",\n \"def __read_cache(self, fileName):\\n if self.__log:\\n self.__logger.info(f\\\"Cache hit - {fileName}\\\")\\n # Cache hit\\n with open(fileName, \\\"rb\\\") as f:\\n content = self.__handle_decompression(f.read())\\n variables = pickle.loads(content)\\n\\n # Move node to front\\n node = os.path.relpath(fileName, \\\"cache\\\")\\n self.__shift_node(node)\\n\\n return variables\",\n \"def get_file_contents(filename):\\n with open(filename, 'r') as f:\\n content = f.read()\\n return content\",\n \"def get_source(self, environment, template):\\n if \\\"/\\\" not in template:\\n path = template.split(\\\".\\\")\\n\\n # The first part of the path must be a bundle name\\n # The other parts are the hierarchy of directory after 'views'\\n bundle = path[0]\\n sub_hierarchy = \\\"/\\\".join(path[1:])\\n path = \\\"bundles/\\\" + bundle + \\\"/views/\\\" + sub_hierarchy + \\\".jj2\\\"\\n else:\\n path = template\\n\\n path = join(self.server.user_directory, path)\\n if not exists(path):\\n raise TemplateNotFound(template)\\n\\n mtime = getmtime(path)\\n with open(path, 'r', encoding=\\\"utf-8\\\") as file:\\n source = file.read()\\n\\n return source, path, lambda: mtime == getmtime(path)\",\n \"def _read_cache(self, path):\\n if self._cache:\\n cache_path = os.path.join(self._cache, path)\\n\\n if os.path.exists(cache_path):\\n with io.open(cache_path, encoding='utf-8') as f:\\n text = f.read()\\n\\n return text\\n\\n msg = ('Unable to download remote file \\\"{0}\\\" and local cache is not '\\n 'available.').format(path)\\n raise RuntimeError(msg)\",\n \"def source_path(self, rel_path):\\n # TODO download to disk (use source cache) in case of remote file\\n import inspect\\n\\n frame = inspect.currentframe().f_back\\n calling_file = frame.f_code.co_filename\\n\\n if (\\n self.included_stack\\n and calling_file == self.included_stack[-1].get_path_or_uri()\\n ):\\n # called from current snakefile, we can try to keep the original source\\n # file annotation\\n # This will only work if the method is evaluated during parsing mode.\\n # Otherwise, the stack can be empty already.\\n path = self.current_basedir.join(rel_path)\\n orig_path = path.get_path_or_uri()\\n else:\\n # heuristically determine path\\n calling_dir = os.path.dirname(calling_file)\\n path = smart_join(calling_dir, rel_path)\\n orig_path = path\\n\\n return sourcecache_entry(\\n self.sourcecache.get_path(infer_source_file(path)), orig_path\\n )\",\n \"def load_file(filename):\\n with open(filename, \\\"r\\\") as f:\\n return f.readlines()\",\n \"def load_csv_cached(filename='../apps/naive_c_stats.csv', cache={}):\\n if filename in cache:\\n return cache[filename]\\n if not os.path.exists(filename):\\n ans = None\\n else:\\n ans = numpy.recfromcsv(filename)\\n cache[filename] = ans\\n return ans\",\n \"def source_changed(source, cache):\\n return os.path.getmtime(source)>os.path.getmtime(cache)\",\n \"def read_file(filename):\\n with open(filename, encoding='utf-8') as src:\\n return [line.strip() for line in src.readlines()]\",\n \"def get_lines_from_file(fname, context=None):\\n content = []\\n if context and context.ddboost:\\n contents = get_lines_from_dd_file(fname, context.ddboost_storage_unit)\\n return contents\\n else:\\n with open(fname) as fd:\\n for line in fd:\\n content.append(line.strip('\\\\n'))\\n return content\",\n \"def _line_offsets(self, snapshot: Bug, filepath: str) -> List[int]:\\n logger.debug(\\\"Fetching line offsets for file, '%s', in snapshot, '%s'\\\", # noqa: pycodestyle\\n filepath,\\n snapshot.name)\\n key_cache = (snapshot.name, filepath)\\n if key_cache in self.__cache_offsets:\\n logger.debug(\\\"Retrieving line offsets for file, '%s', in snapshot, '%s', from cache.\\\", # noqa: pycodestyle\\n filepath,\\n snapshot.name)\\n return self.__cache_offsets[key_cache]\\n\\n logger.debug(\\\"Computing line offsets for file, '%s', in snapshot, '%s'\\\", # noqa: pycodestyle\\n filepath,\\n snapshot.name)\\n contents = self.read_file(snapshot, filepath)\\n\\n # find all indices of newline characters\\n offsets = [0]\\n last_offset = 0\\n while True:\\n next_line_break = contents.find('\\\\n', last_offset)\\n if next_line_break == -1:\\n break\\n last_offset = next_line_break + 1\\n offsets.append(last_offset)\\n\\n logger.debug(\\\"Saving line offsets for file, '%s', in snapshot, '%s', to cache.\\\", # noqa: pycodestyle\\n filepath,\\n snapshot.name)\\n self.__cache_offsets[key_cache] = offsets\\n return offsets\",\n \"def read_file_into_list(filename):\\n with open(filename) as file:\\n return file.readlines()\",\n \"def _get_lines_from_file(path: str) -> List[str]:\\n if os.path.isfile(path):\\n with open(path, 'r') as f:\\n lines = f.readlines()\\n else:\\n raise InputError(f'Could not find file {path}')\\n return lines\",\n \"def get(name, filename):\\n\\tlogging.info(\\\"Reading {} from {}\\\".format(name, filename))\\n\\tlogging.debug(\\\"Opening file\\\")\\n\\twith open(filename, \\\"r+\\\") as f:\\n\\t\\treader = csv.reader(f)\\n\\t\\tlogging.debug(\\\"Reading name/snippet from file\\\")\\n\\t\\tin_file = False\\n\\t\\tfor row in reader:\\n\\t\\t\\tif str(row[0]) == name:\\n\\t\\t\\t\\tin_file = True\\n\\t\\t\\t\\tprint row\\n\\t\\tif in_file == False:\\n\\t\\t\\tprint \\\"That's not in this file\\\"\\n\\tlogging.debug(\\\"Read successful\\\")\\n\\treturn name, filename\",\n \"def load_cache(self, filename):\\n output_df = cudf.read_hdf(filename, key=self.uid)\\n return output_df\",\n \"def _fileLinesToList(filename) :\\n o = []\\n with open(filename, \\\"r\\\") as fi :\\n for l in fi :\\n if l.strip() != \\\"\\\" :\\n o.append(l.strip())\\n return o\",\n \"def get_source(self) -> cwipc_tiledsource_wrapper:\\n obj = cwipc_realsense2_dll_load().cwipc_offline_get_source(self._as_cwipc_offline_p())\\n return cwipc_tiledsource_wrapper(obj)\",\n \"def read():\\n with open(GlobalVariables.get_instance().get('history_filename'), 'rb') as fin:\\n lines = []\\n for line in fin.readlines():\\n lines.append(line.replace('\\\\n', '').strip())\\n fin.close()\\n return lines\",\n \"def getFileContent(fn):\\n content = None\\n if not os.path.exists(fn):\\n print(\\\"Can not open file \\\" + fn)\\n else:\\n with open(fn, \\\"rb\\\") as f:\\n content = f.readlines()\\n return content\",\n \"def GetFileContents(self, filename):\\n with tempfile.NamedTemporaryFile() as t:\\n self.GetFile(filename, t.name)\\n with open(t.name, 'r') as f2:\\n res = f2.read()\\n logging.debug(\\\"GetFileContents(%s)->%s\\\" % (filename, res))\\n return res\",\n \"def lineReadfile(filename):\\n#\\t\\\"input:filename output=readlines() \\\"\\n\\tf = open(filename)\\n\\tlist1 =[]\\n\\twhile 1:\\n\\t\\ts = f.readline()\\n\\t\\tif s==\\\"\\\":\\n\\t\\t\\tbreak\\n\\t\\ts=string.replace(s,\\\"\\\\n\\\",\\\"\\\")\\n\\t\\tif s==\\\"\\\":\\n\\t\\t\\tcontinue\\n\\t\\tlist1.append(s)\\n\\tf.close()\\n\\treturn list1\",\n \"def read_daily_source(path = default_data_location):\\n \\n from os import listdir\\n from os.path import isfile, join\\n onlyfiles = [f for f in listdir(path) if isfile(join(path, f)) and f!='.DS_Store']\\n \\n source = []\\n for f in onlyfiles:\\n with open(join(path, f),'r',encoding='utf-8') as fin:\\n try:\\n source.append(BeautifulSoup(fin.read(), 'html.parser'))\\n except Exception:\\n print('failed to read {}'.format(f))\\n raise\\n \\n data = pd.DataFrame({'name':onlyfiles, 'source':source})\\n data['name'] = data['name'].apply(lambda x: x[:-5])\\n data['source_hash'] = data['source'].apply(lambda x: get_hash(x))\\n return data\",\n \"def readVersionList(filename):\\n\\ttry:\\n\\t\\tlines = []\\n\\t\\tif os.path.isfile(filename):\\n\\t\\t\\twith open(r''+ filename, 'r') as f:\\n\\t\\t\\t\\tlines = f.readlines()\\n\\t\\treturn lines\\n\\texcept IOError as e:\\n\\t\\tprint(traceback.format_exc())\\n\\t\\tinfo = filename + 'can\\\\'t open'\\n\\t\\tdoExit(0, info)\",\n \"def read_lines(filename):\\n # Absolute dir the script is in\\n script_dir = os.path.dirname(__file__)\\n # Join the relative path to the input file\\n resource_path = os.path.join(script_dir, '../resources/' + filename)\\n # Open the file\\n with open(resource_path) as f:\\n # Read all lines from the file. Per default we have now strings\\n return [line.rstrip() for line in f]\",\n \"def _getFile(url, cachedFile=True, return_filename=False):\\n assert url, \\\"WHY are you trying to load an empty string url?!?! Nothing good will come of this! In fact, I will assure that! %s\\\" % (url)\\n md5 = hashlib.md5(url).hexdigest()\\n filename = os.path.join(config.WEB_CACHE_DIR, md5)\\n if os.path.exists(filename) and cachedFile:\\n ret = open(filename, 'r').read()\\n else:\\n opener = urllib.FancyURLopener()\\n ret = opener.open(url).read()\\n o = open(filename, 'wb') # had to open in binary mode so PIL's Image.Open() function would work\\n o.write(ret)\\n o.close()\\n if return_filename:\\n return filename\\n else:\\n return ret\",\n \"def get_file_lines(url):\\n\\n\\t# Download the file over the internet\\n\\tresponse = requests.get(url, stream=True)\\n\\n\\t# Decode all lines into strings\\n\\tlines = []\\n\\tfor line in response.iter_lines():\\n\\t\\tlines.append(line.decode(\\\"UTF-8\\\"))\\n\\treturn lines\",\n \"def readlines(filename):\\n with open(filename, 'r') as f:\\n lines = f.read().splitlines()\\n return lines\",\n \"def GetFromNegativeCache(self, filename):\\n return memcache.get('%s%s' % (self.NEG_CACHE_PREFIX, filename))\",\n \"def GetFromNegativeCache(self, filename):\\n return memcache.get('%s%s' % (self.NEG_CACHE_PREFIX, filename))\",\n \"def read_local_file(filename):\\n import fsspec\\n fs = fsspec.filesystem('file')\\n\\n with fs.open(filename) as f:\\n data = loads(f.read())\\n\\n return data\",\n \"def read_file(filename) -> List[Todo]:\\n with pathlib.Path(filename).expanduser().open('r') as fp:\\n return [Todo(_id, line) for _id, line in enumerate(fp)]\",\n \"def add(self, filename, source):\\n self.cache[filename] = source\\n if os.path.isfile(filename):\\n self.ages[filename] = os.path.getmtime(filename) # modification time\",\n \"def FindSourceForPath(self, path):\\n ret = self._FindSourceForPathInternal(path)\\n if not ret and path not in self._unmatched_paths:\\n if self.unmatched_paths_count < 10:\\n logging.warning('Could not find source path for %s', path)\\n self._unmatched_paths.add(path)\\n return ret\",\n \"def _get_media_source(self, template_name, process_func=None,\\n context=None, no_render=False):\\n cache = self.__media_source_cache\\n\\n mem_args = (template_name, process_func)\\n if mem_args in cache and not context and not settings.DEBUG:\\n source = cache[mem_args]\\n is_cached = True\\n else:\\n if context and not no_render:\\n template = loader.get_template(template_name)\\n return template.render(context), False\\n\\n source, filepath = self._get_source_filepath(template_name)\\n\\n if process_func:\\n source = process_func(source, filepath)\\n\\n cache[mem_args] = source\\n is_cached = False\\n\\n return source, is_cached\",\n \"def _get_source(self, fullmodname):\\n submodname, is_package, relpath = self._get_info(fullmodname)\\n fullpath = self.path_entry + relpath\\n source = self.datablocks[relpath]\\n if hasattr(source, \\\"decode\\\"):\\n source = source.decode(\\\"UTF-8\\\")\\n source = source.replace('\\\\r\\\\n', '\\\\n')\\n source = source.replace('\\\\r', '\\\\n')\\n return submodname, is_package, fullpath, source\",\n \"def load_file(file_name):\\n with open(file_name,\\\"r\\\") as f:\\n return f.readlines()\",\n \"def is_remote_cached(cls, target_filename):\\n is_cached = None\\n cache = cls.CACHE_BACKEND()\\n for file_name, file_id in cache.search():\\n if file_name == os.path.basename(target_filename):\\n is_cached = file_id\\n logger.debug('File %r already cached at %r', target_filename, cls.CACHE_BACKEND)\\n break\\n return is_cached\",\n \"def read_lines(filename):\\n with open(filename, 'r') as f:\\n lines = f.readlines()\\n return lines\",\n \"def read_from_file(filename=\\\"default.txt\\\", filepath=\\\"default_path\\\"):\\n\\n fp = open_file(filename, filepath)\\n\\n try:\\n return fp.readlines()\\n except IOError:\\n l.error(\\\"Could not read from file.\\\")\\n return []\\n except:\\n l.error(\\n \\\"Unknown error occured, while reading from file \\\" + str(filename),\\n exc_info=True,\\n )\\n\\n close_file(fp)\",\n \"def _deleted_sources(self):\\r\\n # We compute the list lazily.\\r\\n if self._lazy_deleted_sources is None:\\r\\n with self.context.new_workunit('find-deleted-sources'):\\r\\n if os.path.exists(self._analysis_file):\\r\\n products = self._analysis_parser.parse_products_from_path(self._analysis_file)\\r\\n buildroot = get_buildroot()\\r\\n old_sources = products.keys() # Absolute paths.\\r\\n self._lazy_deleted_sources = [os.path.relpath(src, buildroot) for src in old_sources\\r\\n if not os.path.exists(src)]\\r\\n else:\\r\\n self._lazy_deleted_sources = []\\r\\n return self._lazy_deleted_sources\",\n \"def local_read(filename):\\n full_filename = os.path.join(\\n os.path.abspath(os.path.dirname(__file__)),\\n filename)\\n return codecs.open(full_filename, 'r', 'utf-8').read()\",\n \"def fileReader(filename):\\n try:\\n openfile = open(filename, 'r')\\n urls = openfile.read()\\n openfile.close()\\n return urls\\n except IOError:\\n print \\\"File tidak ada\\\"\\n exit()\",\n \"def GetFileContents(self, filename):\\n logging.debug(\\\"GetFileContents(%s)\\\" % (filename))\\n with tempfile.NamedTemporaryFile(mode='w') as t:\\n self.GetFile(filename, t.name)\\n with open(t.name, 'r', encoding='UTF-8') as f2:\\n res = f2.read()\\n logging.debug(\\\"GetFileContents(%s)->%s\\\" % (filename, res))\\n return res\",\n \"def read_file_in_lines(filename):\\r\\n\\twith open(filename) as infile:\\r\\n\\t\\tlines = infile.readlines()\\r\\n\\treturn [line.strip() for line in lines]\",\n \"def __read_file(self, filename):\\n with open(filename) as f:\\n content = f.readlines()\\n \\n return content\",\n \"def _read_cache(url):\\n\\n j = None\\n m = hashlib.md5()\\n m.update(url)\\n if os.path.exists('.cache.%s' % m.hexdigest()):\\n with open('.cache.%s' % m.hexdigest(), 'rb') as infile:\\n j = json.load(infile)\\n\\n return j\",\n \"def get_datfile(filename):\\n if ARGV.get(DEBUG_OPT):\\n err_print('Getting datfile from \\\"{}\\\"'.format(filename))\\n\\n try:\\n with open(filename, 'rb') as pickle_file:\\n try:\\n (cache, readlist) = pickle.load(pickle_file)\\n pickle_file.close()\\n except (EOFError, ValueError):\\n (cache, readlist) = ({\\\"feed\\\": None, \\\"max-age\\\": None, \\\"last-request\\\": None}, [])\\n except (FileNotFoundError, PermissionError):\\n (cache, readlist) = ({\\\"feed\\\": None, \\\"max-age\\\": None, \\\"last-request\\\": None}, [])\\n return (cache, readlist)\",\n \"def files():\\n return get_cached(\\\"files.json\\\")\",\n \"def get_file_contents(filepath):\\n gh, repo, branch = connect_to_github()\\n tree = branch.commit.commit.tree.recurse()\\n for filename in tree.tree:\\n if filepath in filename.path:\\n print \\\"[*] Found file %s\\\" % filepath\\n blob = repo.blob(filename._json_data['sha'])\\n return blob.content\\n return None\",\n \"def getsourcefile(object):\\r\\n filename = getfile(object)\\r\\n if string.lower(filename[-4:]) in ('.pyc', '.pyo'):\\r\\n filename = filename[:-4] + '.py'\\r\\n for suffix, mode, kind in imp.get_suffixes():\\r\\n if 'b' in mode and string.lower(filename[-len(suffix):]) == suffix:\\r\\n # Looks like a binary file. We want to only return a text file.\\r\\n return None\\r\\n if os.path.exists(filename):\\r\\n return filename\\r\\n # only return a non-existent filename if the module has a PEP 302 loader\\r\\n if hasattr(getmodule(object, filename), '__loader__'):\\r\\n return filename\\r\\n # or it is in the linecache\\r\\n if filename in linecache.cache:\\r\\n return filename\",\n \"def get_resolution(path_name, file_name):\\n with open(path_name + 'resolution.txt') as file, \\\\\\n mmap.mmap(file.fileno(), 0, access=mmap.ACCESS_READ) as s:\\n file.seek(0, s.find(file_name.encode()))\\n line = file.readline().split(' ')\\n return [line[1], line[2].rstrip('\\\\n')]\",\n \"def file_by_file_name(self, file_name):\\n ls = self.files.filter_by(file_name=file_name)\\n return ls[0] if 0 < len(ls) else None\",\n \"def readListFromFile( filename ):\\n try:\\n with open( filename ) as f:\\n lines = f.readlines()\\n return lines\\n except:\\n #e = sys.exc_info()[0]\\n print( 'Error reading file ' + filename )\",\n \"def getlistfromtext(self,filename):\\n l=[]\\n\\n if self.encoding:\\n f = codecs.open(filename,\\\"r\\\",encoding=self.encoding)\\n for line in f:\\n l.append(line.rstrip())\\n f.close()\\n\\n else:\\n f = open(filename,\\\"r\\\")\\n for line in f:\\n l.append(line.rstrip())\\n f.close()\\n return l\",\n \"def get_source_file(self):\\n return self.get_attribute(\\\"source_file\\\")\",\n \"def contents(filepath):\\n f = open(filepath, 'r')\\n rval = [x.rstrip(\\\"\\\\r\\\\n\\\") for x in f.readlines()]\\n f.close()\\n return rval\",\n \"def test_raw_file_url_error(self):\\n repository = self.remote_repository\\n\\n self.spy_on(repository._get_file_uncached,\\n op=kgb.SpyOpReturn(b'first'))\\n\\n self.assertEqual(repository.get_file('PATH', 'd7e96b3'),\\n b'first')\\n\\n # Ensure output of fake result matches.\\n repository._get_file_uncached.unspy()\\n self.spy_on(repository._get_file_uncached,\\n op=kgb.SpyOpReturn(b'second'))\\n\\n # Grab from cache when no changes and change fake result to confirm\\n # it is not called.\\n self.assertEqual(repository.get_file('PATH', 'd7e96b3'),\\n b'first')\\n\\n # When raw_file_url changed, do not grab from cache and ensure output\\n # equals second fake value.\\n repository.raw_file_url = \\\\\\n 'http://github.com/api/v2/yaml/blob/show/reviewboard/<revision>'\\n\\n self.assertEqual(repository.get_file('PATH', 'd7e96b3'),\\n b'second')\",\n \"def read_file_list(filename):\\n\\n # hint: when you read lines of files, there will be a \\\"newline\\\"\\n # (end-of-line character) at the end of each line, and you want to\\n # strip that off before you print it. Do some research on that!\\n\\n # with open(filename, 'r') as file:\\n # print(file.read())\\n #cwd = os.getcwd() # This gets the visual studio code opened location\\n cwd = os.path.dirname(os.path.realpath(__file__))\\n print(cwd)\\n try:\\n file_contents = Path(cwd + \\\"\\\\\\\\\\\" + filename).read_text()\\n except:\\n return \\\"File not found\\\"\\n return file_contents\",\n \"def file_entry(file_path, regex, default=()):\\n with open(file_path, 'r') as f:\\n for line in f:\\n search = re.search(regex, line, re.IGNORECASE)\\n if search:\\n return search.groups()\\n\\n return default\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7092788","0.6621492","0.64070714","0.6370972","0.6370972","0.6359992","0.6252963","0.6160644","0.6033081","0.6021885","0.599927","0.59814626","0.58814996","0.5880455","0.57968","0.57867116","0.5776293","0.5770049","0.5722298","0.56828374","0.567691","0.566886","0.566154","0.5636611","0.562795","0.5619868","0.5606572","0.5593117","0.558797","0.55821925","0.552568","0.55161244","0.5501848","0.549456","0.54615146","0.5455024","0.54464036","0.54431146","0.5420006","0.539984","0.53955704","0.5388696","0.5382137","0.5380005","0.53639895","0.5344796","0.5341579","0.53342193","0.5313279","0.5305779","0.5300818","0.5284905","0.5280565","0.5278396","0.5277688","0.52605486","0.52560836","0.52529377","0.5238093","0.5235732","0.52325076","0.52155346","0.52117485","0.5210605","0.5188852","0.51869","0.51816875","0.51813865","0.5181328","0.5181328","0.5180321","0.517615","0.5175021","0.5174532","0.5171529","0.51710176","0.51498926","0.51494414","0.5148921","0.5146431","0.51458055","0.5142703","0.5132301","0.5130296","0.5130176","0.51250315","0.5118367","0.51066643","0.5103594","0.5102239","0.50900334","0.508832","0.5085888","0.5085332","0.5082433","0.50805306","0.5076136","0.50741106","0.5072497","0.50634825"],"string":"[\n \"0.7092788\",\n \"0.6621492\",\n \"0.64070714\",\n \"0.6370972\",\n \"0.6370972\",\n \"0.6359992\",\n \"0.6252963\",\n \"0.6160644\",\n \"0.6033081\",\n \"0.6021885\",\n \"0.599927\",\n \"0.59814626\",\n \"0.58814996\",\n \"0.5880455\",\n \"0.57968\",\n \"0.57867116\",\n \"0.5776293\",\n \"0.5770049\",\n \"0.5722298\",\n \"0.56828374\",\n \"0.567691\",\n \"0.566886\",\n \"0.566154\",\n \"0.5636611\",\n \"0.562795\",\n \"0.5619868\",\n \"0.5606572\",\n \"0.5593117\",\n \"0.558797\",\n \"0.55821925\",\n \"0.552568\",\n \"0.55161244\",\n \"0.5501848\",\n \"0.549456\",\n \"0.54615146\",\n \"0.5455024\",\n \"0.54464036\",\n \"0.54431146\",\n \"0.5420006\",\n \"0.539984\",\n \"0.53955704\",\n \"0.5388696\",\n \"0.5382137\",\n \"0.5380005\",\n \"0.53639895\",\n \"0.5344796\",\n \"0.5341579\",\n \"0.53342193\",\n \"0.5313279\",\n \"0.5305779\",\n \"0.5300818\",\n \"0.5284905\",\n \"0.5280565\",\n \"0.5278396\",\n \"0.5277688\",\n \"0.52605486\",\n \"0.52560836\",\n \"0.52529377\",\n \"0.5238093\",\n \"0.5235732\",\n \"0.52325076\",\n \"0.52155346\",\n \"0.52117485\",\n \"0.5210605\",\n \"0.5188852\",\n \"0.51869\",\n \"0.51816875\",\n \"0.51813865\",\n \"0.5181328\",\n \"0.5181328\",\n \"0.5180321\",\n \"0.517615\",\n \"0.5175021\",\n \"0.5174532\",\n \"0.5171529\",\n \"0.51710176\",\n \"0.51498926\",\n \"0.51494414\",\n \"0.5148921\",\n \"0.5146431\",\n \"0.51458055\",\n \"0.5142703\",\n \"0.5132301\",\n \"0.5130296\",\n \"0.5130176\",\n \"0.51250315\",\n \"0.5118367\",\n \"0.51066643\",\n \"0.5103594\",\n \"0.5102239\",\n \"0.50900334\",\n \"0.508832\",\n \"0.5085888\",\n \"0.5085332\",\n \"0.5082433\",\n \"0.50805306\",\n \"0.5076136\",\n \"0.50741106\",\n \"0.5072497\",\n \"0.50634825\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.8423813"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":81196,"cells":{"query":{"kind":"string","value":"Helper function to retrieve a file"},"document":{"kind":"string","value":"def _get_file_source(self, filename):\n try:\n with open(filename, encoding=\"utf8\") as f:\n lines = f.readlines()\n source = \"\".join(lines)\n except Exception:\n lines = []\n source = None\n return source, lines"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_file():\n fname = get_var(request, \"fname\")\n return open(fname).read()","def get_file(self, path):\n file = self.get('data_request?id=file¶meters=%s' % path)\n return file","def get_file(URI):\n return file_fabric.get_class(URI).get_content(URI)","def get(self, filename, **kw):\n\n file_path = os.path.join(self.storage_path, filename)\n try:\n file_obj = open(file_path, \"r\")\n except IOError:\n return\n else:\n return file_obj.read()","def _fs_get_file(url, working_dir):\n if not os.path.isabs(url) and working_dir:\n url = os.path.join(working_dir, url)\n\n try:\n with codecs.open(url, 'r', encoding='utf-8') as f:\n return f.read()\n except Exception as e:\n raise ScrBaseException(\"Could not load file from {0}: {1}\".format(url, e))","def GetFile(file_name):\n\n the_file = None\n\n try:\n the_file = open(file_name, 'rb')\n \n except IOError:\n the_file = None\n \n return the_file","def get_file(self, sys_id):\n url = \"{}/file\".format(self._target(sys_id))\n r = self._client.session.get(url, stream=True)\n return r","def get_file(self, filename, handler=False):\n result = None\n if self.exists(filename):\n file_path = join_paths(self.path, filename)\n if handler:\n result = open(file_path, 'rb')\n else:\n result = file_path\n return result","def _get_file(self, path: str) -> Tuple[str, bytes]:\n self._trace(\"fetching: %s\" % path)\n meta, resp = self._connection.files_download(path)\n return (meta.rev, resp.content)","def get_file(self, key):\n result = (None, None)\n path = os.path.join(self.directory, self.subdirectory, key)\n if os.path.isfile(path):\n content_type, _ = mimetypes.guess_type(path)\n with open(path, \"rb\") as file:\n result = content_type, file.read()\n return result","def get_file(cls, url, working_dir):\n if url.lower().startswith(\"s3://\"):\n return cls._s3_get_file(url)\n elif url.lower().startswith(\"http\"):\n return cls._http_get_file(url)\n else:\n return cls._fs_get_file(url, working_dir)","def web_get_file(self, url):\n try:\n print(url)\n response = requests.get(url, verify=False)\n file_buffer = BytesIO(response.content)\n file_buffer.seek(0)\n return file_buffer\n except:\n print(traceback.print_exc())\n return None","def getFile(filename):\n filename = os.path.join(os.path.dirname(__file__), filename)\n return open(filename, 'r')","def getFile(filename):\n filename = os.path.join(os.path.dirname(__file__), filename)\n return open(filename, 'r')","def get_file_data(filename):","def get_file(self):\n return self.theFile","def getfile(self, section, option):\n with open(self.get(section, option), 'r') as file:\n return file.read()","def get_file(self, file_id):\n LOG.debug(\"Getting a file from mattermost\")\n url = '%s/api/v4/files/%s' % (self.server_url, file_id)\n LOG.debug(\"Sending: %s\", url)\n response = self._request(self._session.get, url)\n\n if response.status_code != 200:\n raise RuntimeError(\"Server unhappy. (%s)\", response)\n\n return response.content","def get_file(_file):\n _file = pathlib.Path(_file)\n if not _file.is_file():\n _file = None\n return _file","def get_file(self, path):\n b_file = self.get_b_file(path)\n if b_file:\n return b_file\n return self.get_a_file(path)","def get_file(self, filename):\r\n\r\n return File.from_name(self, filename)","def exercise_retrieve(file_name):\n with open(file_name, \"r\") as f:\n content = f.read()\n return print(content)","def get(self, filepath):\n try:\n collname = '%s.files' % self.bucketname\n coll = Collection(self.db, collname)\n if coll:\n doc = coll.find_one({'filename': str(filepath)}, sort=[('uploadDate', -1)])\n if doc:\n id = doc['_id']\n gout = self.gridfs.get(ObjectId(id))\n if gout:\n content = gout.read()\n gout.close()\n return content\n except Exception, e:\n print e\n return None","def open_and_read_file(file_path):\n\n # your code goes here\n file_name = (open(file_path)).read()\n return file_name","def get(name):\n\n filename = find(name)\n if filename == None:\n return name\n return open(filename).read()","def get_file(self, c_path):\n raise NotImplementedError","def get_file(self, file_name: str) -> BytesIO:\n fl = BytesIO()\n self.client.download_fileobj(self.bucket, file_name, fl)\n fl.seek(0)\n return fl","def get_file(self, path):\n _url = f\"{self.connector.base_url}/projects/{self.project_id}/files/{path}\"\n\n return self.connector.http_call(\"get\", _url).text","def open_and_read_file(file_path):\n\n # your code goes here\n file_ = open(file_path).read()\n\n return file_","def storage_get_file(self, group='', key=''):\n try:\n obj = None\n content = None\n if key != '':\n if self.config['type'] == 's3':\n obj = self.s3.Object(bucket_name=self.bucket, key='corr-{0}s/{1}'.format(group,key))\n res = obj.get()\n content = res['Body'].read()\n elif self.config['type'] == 'filesystem':\n with open('{0}/corr-{1}s/{2}'.format(self.storage_path, group, key), \"rb\") as obj:\n content = obj.read()\n else:\n content = None\n\n except:\n print(traceback.print_exc())\n content = None\n\n try:\n if self.config['type'] == 's3':\n file_buffer = BytesIO()\n elif self.config['type'] == 'filesystem':\n file_buffer = BytesIO()\n file_buffer.write(content)\n file_buffer.seek(0)\n return file_buffer\n except:\n self.app.logger.error(traceback.print_exc())\n print(traceback.print_exc())\n return None","def get_file(service, file_id):\n return service.files().get(fileId=file_id).execute()","def get_file(url):\n helpers.make_workdir() # create temp working directory\n file_url = url + constant.MALICIOUS_LOCATION\n print(file_url)\n filename = wget.download(file_url, out=constant.WORKDIR)\n return filename","def get_file(filename):\n content = Tree.file_content(filename)\n file_name = \"attachment;filename={0}\".format(filename)\n return Response(content, mimetype=\"text/plain\",\n headers={\"Content-Disposition\": file_name})","def readFile(self, path):\n return self.session.request('diag/files/?q=%s'\n % (path))","def getFile(self, resource):\n resource = self.parseUrl(resource, 'files')\n\n res = self.getRequest(resource)\n fObj = vsdModels.File(**res)\n return fObj","async def get_file(self, file_id: base.String) -> types.File:\n payload = generate_payload(**locals())\n result = await self.request(api.Methods.GET_FILE, payload)\n\n return types.File(**result)","def get_demo_file(fname):\n\n d = download_demo_files()\n if fname in d:\n return d[fname]\n else:\n return None","def open_and_read_file(file_path):\n\n # your code goes here\n return open(file_path).read()","def get_file(self, path):\n return self.client._perform_raw(\n \"GET\", \"/projects/%s/managedfolders/%s/contents/%s\" % (self.project_key, self.odb_id, utils.quote(path)))","def getFile(request,jobId,fileName):\n\tuser = request.user\n\tfileResponse = agaveRequestOutputGet(user,jobId,fileName)\n\tcontent_type = fileResponse.headers['Content-Type']\n\textension = os.path.splitext(fileName)[1]\n\tif extension not in ['png','txt']:\n\t\tcontent_type = 'text/plain'\n\tresponse = HttpResponse(fileResponse.content, content_type=content_type)\n\treturn response","def get_file(self, name):\n return self.files[name]","def getFile(filename):\n import os\n filename = os.path.join(os.path.dirname(__file__) + \"/tests/\", filename)\n return open(filename, 'r')","def get_file(self, path):\n return self._files.get(self._get_rel_path(path))","def _get_file_helper(self):\n page = self.course.moodle.fetch(\n self._download_url % self.id,\n None\n )\n # The resource URL should magically 303 across to the actual file\n if page.history and page.history[0].status_code == 303:\n return page, page.content\n\n # If it doesn't 303 to the actual file then there might be a download\n # link to try\n bs = bs4.BeautifulSoup(page.text, 'lxml')\n\n div = bs.find('div', class_='resourceworkaround')\n\n if div: # it's a link to the resource\n link = div.find('a').href\n\n page = self.course.moodle.fetch(\n link,\n None\n )\n return page, page.content\n\n # Perhaps it's an embedded object\n obj = bs.find('object', id='resourceobject')\n if obj:\n link = obj['data']\n\n page = self.course.moodle.fetch(\n link,\n None\n )\n return page, page.content\n\n raise ValueError(\"No idea how to get that resource\")","def read_file(root, file_name, file_type='t'):\r\n\r\n page_dir = handle_path(main_directory + '/' + root + '/' + file_name)\r\n\r\n try:\r\n # if type != 'b' and type != 't':\r\n # raise\r\n with open(page_dir, 'r' + file_type) as page_reader:\r\n return str(page_reader.read())\r\n except FileNotFoundError:\r\n print(\"The file the user requested doesn't exist\")\r\n raise_http_error(\"Not Found\")\r\n except OSError:\r\n print(\"The server couldn't get the page file\")\r\n raise_http_error(\"Internal Server Error\")","def get_file(self, **kwargs) -> File:\n file_name = self._get_file_name()\n file_data = self._download_from_url()\n return File(file_name, BytesIO(file_data))","def getfile(url):\n try:\n return urlreq.urlopen(url)\n except urlreq.HTTPError as e:\n safeprint(\"Sever returned with response code \" + str(e.getcode()) + \", download failed.\")","def get_file(self):\n return self.dir + self.file_name + self.extension","def get_file(self, path):\n path = os.path.normpath(path)\n cmd = [\"ls\", \"-lZ\", \"'\" + path + \"'\"]\n listing = subprocess.check_output(self.shell + cmd).decode().split('\\n')\n line = listing[0].strip(\"\\r\")\n # Parse ls -lZ output for a single file\n try:\n f = File(line, os.path.dirname(path), self.android_version)\n except ValueError as e:\n self.log.error(e)\n return None\n else:\n return {f.absname: f}","def read_file(filename):\n if os.path.isfile(filename):\n with open(filename, 'r') as f:\n return f.read()","def get(self, resource_id, file_id):\n d = Deposition.get(resource_id, user=current_user)\n df = d.get_file(file_id)\n if df is None:\n abort(404, message=\"File does not exist\", status=404)\n return d.type.marshal_file(df)","def get_file(self, filename: str, directory: str = 'gcodes', binary: bool = False) -> str:\n raise NotImplementedError","def getFile( self, par, path ):\n\n return self.db.getFilePar( par, path )","def _getFile(url, cachedFile=True, return_filename=False):\n assert url, \"WHY are you trying to load an empty string url?!?! Nothing good will come of this! In fact, I will assure that! %s\" % (url)\n md5 = hashlib.md5(url).hexdigest()\n filename = os.path.join(config.WEB_CACHE_DIR, md5)\n if os.path.exists(filename) and cachedFile:\n ret = open(filename, 'r').read()\n else:\n opener = urllib.FancyURLopener()\n ret = opener.open(url).read()\n o = open(filename, 'wb') # had to open in binary mode so PIL's Image.Open() function would work\n o.write(ret)\n o.close()\n if return_filename:\n return filename\n else:\n return ret","def get_file_contents(directory, filename):\n full_path = \"%s/%s\" % (directory,filename)\n content = \"\"\n if os.path.exists(full_path):\n fp = open(full_path, \"r\")\n content = fp.read()\n fp.close()\n return content","def read(path):","def _ReadFile(filepath):\n with open(filepath) as f:\n return f.read()","def fileGetContents(sFilename):\n with open(sFilename) as f:\n return f.read()","def getfile(self, name):\n try:\n datname = self.getname(name, \"dat\")\n tadname = self.getname(name, \"tad\")\n if datname and tadname:\n return Datafile(name, open(datname, \"rb\"), open(tadname, \"rb\"), self.kod)\n except IOError:\n return","def file(self) :\n\t\ttry :\n\t\t\treturn self._file\n\t\texcept Exception as e:\n\t\t\traise e","def open_and_read_file():\n file_path = sys.argv[1]\n #print file_path\n file_data = open(file_path, 'r')\n gettysburg = file_data.read()\n\n return gettysburg","def open_and_read_file(file_path):\n\n # your code goes here\n with open(file_path) as open_file:\n open_file = open_file.read()\n return open_file","def _get_file_object(inputfile=None):\n if type(inputfile) == str:\n return open(inputfile, 'r')\n return inputfile","def get_file(self, file_id, filename=''):\n method = 'getFile?' + 'file_id=' + str(file_id)\n res = requests.post(self.api_url + method, file_id)\n try:\n file_path = res.json()['result']['file_path']\n # Determine the fileName. Use modified file_path if none given.\n if not filename:\n filename = file_path[file_path.rfind('/') + 1:]\n except (KeyError, ValueError):\n return \"500 - Failed parsing the file link from API response.\"\n\n if not os.path.exists(self.dirDownloads):\n os.mkdir(self.dirDownloads)\n\n local_path = os.path.join(self.dirDownloads, filename)\n\n # Download file as stream.\n res = requests.get(self.file_url + file_path, stream=True)\n if res.status_code == 200:\n try:\n with open(local_path, 'wb') as f:\n for chunk in res:\n f.write(chunk)\n except IOError:\n pass\n return '200 - {} written.'.format(local_path)\n else:\n return '404 - Error accessing {}'.format(file_path)","def get_file_contents(path):\n try:\n with open(path) as f:\n return f.read()\n except IOError:\n return None","def fetch_file(uri, file=None, username=None, password=None):\r\n boto.log.info('Fetching %s' % uri)\r\n if file == None:\r\n file = tempfile.NamedTemporaryFile()\r\n try:\r\n if uri.startswith('s3://'):\r\n bucket_name, key_name = uri[len('s3://'):].split('/', 1)\r\n c = boto.connect_s3(aws_access_key_id=username, aws_secret_access_key=password)\r\n bucket = c.get_bucket(bucket_name)\r\n key = bucket.get_key(key_name)\r\n key.get_contents_to_file(file)\r\n else:\r\n if username and password:\r\n passman = urllib2.HTTPPasswordMgrWithDefaultRealm()\r\n passman.add_password(None, uri, username, password)\r\n authhandler = urllib2.HTTPBasicAuthHandler(passman)\r\n opener = urllib2.build_opener(authhandler)\r\n urllib2.install_opener(opener)\r\n s = urllib2.urlopen(uri)\r\n file.write(s.read())\r\n file.seek(0)\r\n except:\r\n raise\r\n boto.log.exception('Problem Retrieving file: %s' % uri)\r\n file = None\r\n return file","def open_and_read_file(file_path):\n\n file = open(file_path)\n file = file.read()\n\n return file","def _read_file(self) -> str:\n with open(self._file_name) as fp:\n return fp.read()","def get(self, save=False, filename=None):\n page, content = self._get_file_helper()\n\n filename = _negotiate_filename(page, filename, save)\n\n if save and filename:\n with open(filename, 'wb') as fh:\n fh.write(content)\n\n return content, filename","def get_file_content(*args, **kwargs):\n return get_file_content_async(*args, **kwargs).get_result()","def get_file(self, path):\n _url = (\n f\"{self.connector.base_url}/projects/{self.project_id}/nodes/{self.node_id}\"\n f\"/files/{path}\"\n )\n\n return self.connector.http_call(\"get\", _url).text","def get_file(file_info):\n if session_vars.filepath == file_info['filepath']:\n img_file = session_vars.img_file\n else:\n print('loading', file_info['filepath'])\n if file_info['ext']=='fits':\n print('Detected fits image type')\n pyfits = import_fits()\n img_file = pyfits.open(file_info['filepath'])\n else:\n try:\n from PIL import Image\n except ImportError:\n raise ToyzJobError(\n \"You must have PIL (Python Imaging Library) installed to \"\n \"open files of this type\"\n )\n img_file = Image.open(file_info['filepath'])\n session_vars.filepath = file_info['filepath']\n session_vars.img_file = img_file\n return img_file","def get_file(self, repo, file_name):\n DataHubManager.has_repo_file_privilege(\n self.username, self.repo_base, repo, 'read')\n\n file_path = user_data_path(self.repo_base, repo, file_name)\n file = open(file_path).read()\n return file","def readFile( *args ):\n filePath = path.join( *args )\n if not path.exists( filePath ):\n return None\n with open( filePath, 'r' ) as fd:\n return fd.read()","def get_content(self, filename):\n f_id = self.face.FACES.files.find_one({ \"filename\" : filename }, { \"_id\" : 1 })\n return self.face_fs.get(f_id['_id']).read()","def get_file(self, remote_path, local_path, storage_id=None):\n return self.get(remote_path, local_path, directory=False, storage_id=storage_id)","def retrieve(self, file_name):\n ret = os.path.join(self.path, file_name)\n temp = (self.path + file_name).find(self.path, 1, -1)\n if os.path.exists(ret):\n return os.path.join(self.path, file_name)\n elif temp != -1:\n if os.name == \"posix\":\n return os.getcwd() + '/' + file_name\n else:\n return os.getcwd() + '\\\\' + file_name\n else:\n return None","def get_file(self, filename):\n log.debug('[%s]: reading: //%s/%s', self.name, self.name, filename)\n try:\n blob = self.repo.head.commit.tree/filename\n return blob.data_stream\n except KeyError as err:\n raise GitError(err)","def read_file(self, *args):\n with open(os.path.join(self.temp_path, *args)) as fp:\n return fp.read()","def load_file(uri):\n # get filename\n filename = os.path.basename(uri)\n\n # If filepath specified and file exists, return filepath as-is\n if os.path.isfile(uri):\n return uri\n # Check to see if file exists in current working directory\n elif os.path.isfile(filename):\n return filename\n # Otherwise, check to see if URI is a URL\n elif uri.startswith(('http', 'ftp')):\n # retrieve remote file contents\n print(\"Downloading %s\" % uri)\n handle = urllib2.urlopen(uri)\n contents = handle.read()\n\n with open(filename, 'w') as fp:\n fp.write(contents)\n return filename\n # if it's note a URL or a valid filepath, raise and exception\n else:\n raise Exception(\"Invalid URI specified: %s\" % uri)","def get_file(url, file_name=None):\n cache_dir = os.path.join(os.path.expanduser(\"~\"), \".jhML\")\n\n if file_name is None:\n file_name = url[url.rfind('/') + 1:]\n file_path = os.path.join(cache_dir, file_name)\n\n if not os.path.exists(cache_dir):\n os.mkdir(cache_dir)\n\n if os.path.exists(file_path):\n return file_path\n\n print(\"Downloading: \" + file_name)\n try:\n urllib.request.urlretrieve(url, file_path, show_progress)\n except (Exception, KeyboardInterrupt) as e:\n if os.path.exists(file_path):\n os.remove(file_path)\n raise\n print(\" Done\")\n\n return file_path","def open_and_read_file(file_path):\n contents = open(file_path).read()\n # your code goes here\n\n return contents","def json_file():\r\n urlretrieve(URL, PATH)\r\n return PATH","def json_file():\r\n urlretrieve(URL, PATH)\r\n return PATH","def get_file(filename):\n return os.path.join(TEST_DIR, filename)","def read_file(path): #TODO implementme, handling paths more intelligently\n f = open(path, \"r\")\n string = f.read()\n f.close()\n return string","def open(self):\n return File(open(self.get_path()), \"rb\")","def request_file(self, path: str, token: str) -> Tuple[IO[bytes], dict]:\n response = self.request('get', path, token, stream=True)\n stream = ReadWrapper(response.iter_content,\n int(response.headers['Content-Length']))\n return stream, response.headers","def getOriginalFile(url):\n # does url exist?\n if url is None or url is \"\":\n return","def get_file(self, uri_type, no_copy=False):\n return self.copy(target_uri_type=uri_type, no_copy=no_copy)","def getFile():\n from tkinter import Tk, filedialog\n Tk().withdraw() # we don't want a full GUI, so keep the root window from appearing\n return(filedialog.askopenfilenames())","def _Read(filename):\n with open(filename, 'rb') as f:\n return f.read()","def _s3_get_file(url):\n try:\n return S3().get_contents_from_url(url)\n except Exception as e:\n raise ScrBaseException(\"Could not load file from {0}: {1}\".format(url, e))","def file(path, filename):\n print(uc.file(path, filename))","def get_file_from_path(file_path):\n return Utils.get_real_file_path(file_path)","def _file_load(path):\n _, _, file_path = path.split('/', 2)\n with open(file_path, 'r', encoding='utf-8') as file_handle:\n return file_handle.read()","def GetFileContents(self, filename):\n with tempfile.NamedTemporaryFile() as t:\n self.GetFile(filename, t.name)\n with open(t.name, 'r') as f2:\n res = f2.read()\n logging.debug(\"GetFileContents(%s)->%s\" % (filename, res))\n return res","def readfile(filename, mode='r'):\n if mode != 'r' and mode != 'rb':\n print(f\"ERROR: incorrect mode : expected 'r' or 'rb' given {mode}\\n\")\n else:\n with open(Path(os.path.expanduser(filename)), mode)as f:\n content = f.read()\n f.close()\n return content","def get_file(self, url, path, step_name=None, headers=None,\n transient_retry=True, strip_prefix=None, timeout=None):\n return self._get_step(url, path, step_name, headers, transient_retry,\n strip_prefix, False, timeout, '')","def read_file(filename):\n with open(filename) as fp:\n return fp.read()","def get_remote_file(sid, path):\n with slycat.web.server.remote.get_session(sid) as session:\n return session.get_file(path)"],"string":"[\n \"def get_file():\\n fname = get_var(request, \\\"fname\\\")\\n return open(fname).read()\",\n \"def get_file(self, path):\\n file = self.get('data_request?id=file¶meters=%s' % path)\\n return file\",\n \"def get_file(URI):\\n return file_fabric.get_class(URI).get_content(URI)\",\n \"def get(self, filename, **kw):\\n\\n file_path = os.path.join(self.storage_path, filename)\\n try:\\n file_obj = open(file_path, \\\"r\\\")\\n except IOError:\\n return\\n else:\\n return file_obj.read()\",\n \"def _fs_get_file(url, working_dir):\\n if not os.path.isabs(url) and working_dir:\\n url = os.path.join(working_dir, url)\\n\\n try:\\n with codecs.open(url, 'r', encoding='utf-8') as f:\\n return f.read()\\n except Exception as e:\\n raise ScrBaseException(\\\"Could not load file from {0}: {1}\\\".format(url, e))\",\n \"def GetFile(file_name):\\n\\n the_file = None\\n\\n try:\\n the_file = open(file_name, 'rb')\\n \\n except IOError:\\n the_file = None\\n \\n return the_file\",\n \"def get_file(self, sys_id):\\n url = \\\"{}/file\\\".format(self._target(sys_id))\\n r = self._client.session.get(url, stream=True)\\n return r\",\n \"def get_file(self, filename, handler=False):\\n result = None\\n if self.exists(filename):\\n file_path = join_paths(self.path, filename)\\n if handler:\\n result = open(file_path, 'rb')\\n else:\\n result = file_path\\n return result\",\n \"def _get_file(self, path: str) -> Tuple[str, bytes]:\\n self._trace(\\\"fetching: %s\\\" % path)\\n meta, resp = self._connection.files_download(path)\\n return (meta.rev, resp.content)\",\n \"def get_file(self, key):\\n result = (None, None)\\n path = os.path.join(self.directory, self.subdirectory, key)\\n if os.path.isfile(path):\\n content_type, _ = mimetypes.guess_type(path)\\n with open(path, \\\"rb\\\") as file:\\n result = content_type, file.read()\\n return result\",\n \"def get_file(cls, url, working_dir):\\n if url.lower().startswith(\\\"s3://\\\"):\\n return cls._s3_get_file(url)\\n elif url.lower().startswith(\\\"http\\\"):\\n return cls._http_get_file(url)\\n else:\\n return cls._fs_get_file(url, working_dir)\",\n \"def web_get_file(self, url):\\n try:\\n print(url)\\n response = requests.get(url, verify=False)\\n file_buffer = BytesIO(response.content)\\n file_buffer.seek(0)\\n return file_buffer\\n except:\\n print(traceback.print_exc())\\n return None\",\n \"def getFile(filename):\\n filename = os.path.join(os.path.dirname(__file__), filename)\\n return open(filename, 'r')\",\n \"def getFile(filename):\\n filename = os.path.join(os.path.dirname(__file__), filename)\\n return open(filename, 'r')\",\n \"def get_file_data(filename):\",\n \"def get_file(self):\\n return self.theFile\",\n \"def getfile(self, section, option):\\n with open(self.get(section, option), 'r') as file:\\n return file.read()\",\n \"def get_file(self, file_id):\\n LOG.debug(\\\"Getting a file from mattermost\\\")\\n url = '%s/api/v4/files/%s' % (self.server_url, file_id)\\n LOG.debug(\\\"Sending: %s\\\", url)\\n response = self._request(self._session.get, url)\\n\\n if response.status_code != 200:\\n raise RuntimeError(\\\"Server unhappy. (%s)\\\", response)\\n\\n return response.content\",\n \"def get_file(_file):\\n _file = pathlib.Path(_file)\\n if not _file.is_file():\\n _file = None\\n return _file\",\n \"def get_file(self, path):\\n b_file = self.get_b_file(path)\\n if b_file:\\n return b_file\\n return self.get_a_file(path)\",\n \"def get_file(self, filename):\\r\\n\\r\\n return File.from_name(self, filename)\",\n \"def exercise_retrieve(file_name):\\n with open(file_name, \\\"r\\\") as f:\\n content = f.read()\\n return print(content)\",\n \"def get(self, filepath):\\n try:\\n collname = '%s.files' % self.bucketname\\n coll = Collection(self.db, collname)\\n if coll:\\n doc = coll.find_one({'filename': str(filepath)}, sort=[('uploadDate', -1)])\\n if doc:\\n id = doc['_id']\\n gout = self.gridfs.get(ObjectId(id))\\n if gout:\\n content = gout.read()\\n gout.close()\\n return content\\n except Exception, e:\\n print e\\n return None\",\n \"def open_and_read_file(file_path):\\n\\n # your code goes here\\n file_name = (open(file_path)).read()\\n return file_name\",\n \"def get(name):\\n\\n filename = find(name)\\n if filename == None:\\n return name\\n return open(filename).read()\",\n \"def get_file(self, c_path):\\n raise NotImplementedError\",\n \"def get_file(self, file_name: str) -> BytesIO:\\n fl = BytesIO()\\n self.client.download_fileobj(self.bucket, file_name, fl)\\n fl.seek(0)\\n return fl\",\n \"def get_file(self, path):\\n _url = f\\\"{self.connector.base_url}/projects/{self.project_id}/files/{path}\\\"\\n\\n return self.connector.http_call(\\\"get\\\", _url).text\",\n \"def open_and_read_file(file_path):\\n\\n # your code goes here\\n file_ = open(file_path).read()\\n\\n return file_\",\n \"def storage_get_file(self, group='', key=''):\\n try:\\n obj = None\\n content = None\\n if key != '':\\n if self.config['type'] == 's3':\\n obj = self.s3.Object(bucket_name=self.bucket, key='corr-{0}s/{1}'.format(group,key))\\n res = obj.get()\\n content = res['Body'].read()\\n elif self.config['type'] == 'filesystem':\\n with open('{0}/corr-{1}s/{2}'.format(self.storage_path, group, key), \\\"rb\\\") as obj:\\n content = obj.read()\\n else:\\n content = None\\n\\n except:\\n print(traceback.print_exc())\\n content = None\\n\\n try:\\n if self.config['type'] == 's3':\\n file_buffer = BytesIO()\\n elif self.config['type'] == 'filesystem':\\n file_buffer = BytesIO()\\n file_buffer.write(content)\\n file_buffer.seek(0)\\n return file_buffer\\n except:\\n self.app.logger.error(traceback.print_exc())\\n print(traceback.print_exc())\\n return None\",\n \"def get_file(service, file_id):\\n return service.files().get(fileId=file_id).execute()\",\n \"def get_file(url):\\n helpers.make_workdir() # create temp working directory\\n file_url = url + constant.MALICIOUS_LOCATION\\n print(file_url)\\n filename = wget.download(file_url, out=constant.WORKDIR)\\n return filename\",\n \"def get_file(filename):\\n content = Tree.file_content(filename)\\n file_name = \\\"attachment;filename={0}\\\".format(filename)\\n return Response(content, mimetype=\\\"text/plain\\\",\\n headers={\\\"Content-Disposition\\\": file_name})\",\n \"def readFile(self, path):\\n return self.session.request('diag/files/?q=%s'\\n % (path))\",\n \"def getFile(self, resource):\\n resource = self.parseUrl(resource, 'files')\\n\\n res = self.getRequest(resource)\\n fObj = vsdModels.File(**res)\\n return fObj\",\n \"async def get_file(self, file_id: base.String) -> types.File:\\n payload = generate_payload(**locals())\\n result = await self.request(api.Methods.GET_FILE, payload)\\n\\n return types.File(**result)\",\n \"def get_demo_file(fname):\\n\\n d = download_demo_files()\\n if fname in d:\\n return d[fname]\\n else:\\n return None\",\n \"def open_and_read_file(file_path):\\n\\n # your code goes here\\n return open(file_path).read()\",\n \"def get_file(self, path):\\n return self.client._perform_raw(\\n \\\"GET\\\", \\\"/projects/%s/managedfolders/%s/contents/%s\\\" % (self.project_key, self.odb_id, utils.quote(path)))\",\n \"def getFile(request,jobId,fileName):\\n\\tuser = request.user\\n\\tfileResponse = agaveRequestOutputGet(user,jobId,fileName)\\n\\tcontent_type = fileResponse.headers['Content-Type']\\n\\textension = os.path.splitext(fileName)[1]\\n\\tif extension not in ['png','txt']:\\n\\t\\tcontent_type = 'text/plain'\\n\\tresponse = HttpResponse(fileResponse.content, content_type=content_type)\\n\\treturn response\",\n \"def get_file(self, name):\\n return self.files[name]\",\n \"def getFile(filename):\\n import os\\n filename = os.path.join(os.path.dirname(__file__) + \\\"/tests/\\\", filename)\\n return open(filename, 'r')\",\n \"def get_file(self, path):\\n return self._files.get(self._get_rel_path(path))\",\n \"def _get_file_helper(self):\\n page = self.course.moodle.fetch(\\n self._download_url % self.id,\\n None\\n )\\n # The resource URL should magically 303 across to the actual file\\n if page.history and page.history[0].status_code == 303:\\n return page, page.content\\n\\n # If it doesn't 303 to the actual file then there might be a download\\n # link to try\\n bs = bs4.BeautifulSoup(page.text, 'lxml')\\n\\n div = bs.find('div', class_='resourceworkaround')\\n\\n if div: # it's a link to the resource\\n link = div.find('a').href\\n\\n page = self.course.moodle.fetch(\\n link,\\n None\\n )\\n return page, page.content\\n\\n # Perhaps it's an embedded object\\n obj = bs.find('object', id='resourceobject')\\n if obj:\\n link = obj['data']\\n\\n page = self.course.moodle.fetch(\\n link,\\n None\\n )\\n return page, page.content\\n\\n raise ValueError(\\\"No idea how to get that resource\\\")\",\n \"def read_file(root, file_name, file_type='t'):\\r\\n\\r\\n page_dir = handle_path(main_directory + '/' + root + '/' + file_name)\\r\\n\\r\\n try:\\r\\n # if type != 'b' and type != 't':\\r\\n # raise\\r\\n with open(page_dir, 'r' + file_type) as page_reader:\\r\\n return str(page_reader.read())\\r\\n except FileNotFoundError:\\r\\n print(\\\"The file the user requested doesn't exist\\\")\\r\\n raise_http_error(\\\"Not Found\\\")\\r\\n except OSError:\\r\\n print(\\\"The server couldn't get the page file\\\")\\r\\n raise_http_error(\\\"Internal Server Error\\\")\",\n \"def get_file(self, **kwargs) -> File:\\n file_name = self._get_file_name()\\n file_data = self._download_from_url()\\n return File(file_name, BytesIO(file_data))\",\n \"def getfile(url):\\n try:\\n return urlreq.urlopen(url)\\n except urlreq.HTTPError as e:\\n safeprint(\\\"Sever returned with response code \\\" + str(e.getcode()) + \\\", download failed.\\\")\",\n \"def get_file(self):\\n return self.dir + self.file_name + self.extension\",\n \"def get_file(self, path):\\n path = os.path.normpath(path)\\n cmd = [\\\"ls\\\", \\\"-lZ\\\", \\\"'\\\" + path + \\\"'\\\"]\\n listing = subprocess.check_output(self.shell + cmd).decode().split('\\\\n')\\n line = listing[0].strip(\\\"\\\\r\\\")\\n # Parse ls -lZ output for a single file\\n try:\\n f = File(line, os.path.dirname(path), self.android_version)\\n except ValueError as e:\\n self.log.error(e)\\n return None\\n else:\\n return {f.absname: f}\",\n \"def read_file(filename):\\n if os.path.isfile(filename):\\n with open(filename, 'r') as f:\\n return f.read()\",\n \"def get(self, resource_id, file_id):\\n d = Deposition.get(resource_id, user=current_user)\\n df = d.get_file(file_id)\\n if df is None:\\n abort(404, message=\\\"File does not exist\\\", status=404)\\n return d.type.marshal_file(df)\",\n \"def get_file(self, filename: str, directory: str = 'gcodes', binary: bool = False) -> str:\\n raise NotImplementedError\",\n \"def getFile( self, par, path ):\\n\\n return self.db.getFilePar( par, path )\",\n \"def _getFile(url, cachedFile=True, return_filename=False):\\n assert url, \\\"WHY are you trying to load an empty string url?!?! Nothing good will come of this! In fact, I will assure that! %s\\\" % (url)\\n md5 = hashlib.md5(url).hexdigest()\\n filename = os.path.join(config.WEB_CACHE_DIR, md5)\\n if os.path.exists(filename) and cachedFile:\\n ret = open(filename, 'r').read()\\n else:\\n opener = urllib.FancyURLopener()\\n ret = opener.open(url).read()\\n o = open(filename, 'wb') # had to open in binary mode so PIL's Image.Open() function would work\\n o.write(ret)\\n o.close()\\n if return_filename:\\n return filename\\n else:\\n return ret\",\n \"def get_file_contents(directory, filename):\\n full_path = \\\"%s/%s\\\" % (directory,filename)\\n content = \\\"\\\"\\n if os.path.exists(full_path):\\n fp = open(full_path, \\\"r\\\")\\n content = fp.read()\\n fp.close()\\n return content\",\n \"def read(path):\",\n \"def _ReadFile(filepath):\\n with open(filepath) as f:\\n return f.read()\",\n \"def fileGetContents(sFilename):\\n with open(sFilename) as f:\\n return f.read()\",\n \"def getfile(self, name):\\n try:\\n datname = self.getname(name, \\\"dat\\\")\\n tadname = self.getname(name, \\\"tad\\\")\\n if datname and tadname:\\n return Datafile(name, open(datname, \\\"rb\\\"), open(tadname, \\\"rb\\\"), self.kod)\\n except IOError:\\n return\",\n \"def file(self) :\\n\\t\\ttry :\\n\\t\\t\\treturn self._file\\n\\t\\texcept Exception as e:\\n\\t\\t\\traise e\",\n \"def open_and_read_file():\\n file_path = sys.argv[1]\\n #print file_path\\n file_data = open(file_path, 'r')\\n gettysburg = file_data.read()\\n\\n return gettysburg\",\n \"def open_and_read_file(file_path):\\n\\n # your code goes here\\n with open(file_path) as open_file:\\n open_file = open_file.read()\\n return open_file\",\n \"def _get_file_object(inputfile=None):\\n if type(inputfile) == str:\\n return open(inputfile, 'r')\\n return inputfile\",\n \"def get_file(self, file_id, filename=''):\\n method = 'getFile?' + 'file_id=' + str(file_id)\\n res = requests.post(self.api_url + method, file_id)\\n try:\\n file_path = res.json()['result']['file_path']\\n # Determine the fileName. Use modified file_path if none given.\\n if not filename:\\n filename = file_path[file_path.rfind('/') + 1:]\\n except (KeyError, ValueError):\\n return \\\"500 - Failed parsing the file link from API response.\\\"\\n\\n if not os.path.exists(self.dirDownloads):\\n os.mkdir(self.dirDownloads)\\n\\n local_path = os.path.join(self.dirDownloads, filename)\\n\\n # Download file as stream.\\n res = requests.get(self.file_url + file_path, stream=True)\\n if res.status_code == 200:\\n try:\\n with open(local_path, 'wb') as f:\\n for chunk in res:\\n f.write(chunk)\\n except IOError:\\n pass\\n return '200 - {} written.'.format(local_path)\\n else:\\n return '404 - Error accessing {}'.format(file_path)\",\n \"def get_file_contents(path):\\n try:\\n with open(path) as f:\\n return f.read()\\n except IOError:\\n return None\",\n \"def fetch_file(uri, file=None, username=None, password=None):\\r\\n boto.log.info('Fetching %s' % uri)\\r\\n if file == None:\\r\\n file = tempfile.NamedTemporaryFile()\\r\\n try:\\r\\n if uri.startswith('s3://'):\\r\\n bucket_name, key_name = uri[len('s3://'):].split('/', 1)\\r\\n c = boto.connect_s3(aws_access_key_id=username, aws_secret_access_key=password)\\r\\n bucket = c.get_bucket(bucket_name)\\r\\n key = bucket.get_key(key_name)\\r\\n key.get_contents_to_file(file)\\r\\n else:\\r\\n if username and password:\\r\\n passman = urllib2.HTTPPasswordMgrWithDefaultRealm()\\r\\n passman.add_password(None, uri, username, password)\\r\\n authhandler = urllib2.HTTPBasicAuthHandler(passman)\\r\\n opener = urllib2.build_opener(authhandler)\\r\\n urllib2.install_opener(opener)\\r\\n s = urllib2.urlopen(uri)\\r\\n file.write(s.read())\\r\\n file.seek(0)\\r\\n except:\\r\\n raise\\r\\n boto.log.exception('Problem Retrieving file: %s' % uri)\\r\\n file = None\\r\\n return file\",\n \"def open_and_read_file(file_path):\\n\\n file = open(file_path)\\n file = file.read()\\n\\n return file\",\n \"def _read_file(self) -> str:\\n with open(self._file_name) as fp:\\n return fp.read()\",\n \"def get(self, save=False, filename=None):\\n page, content = self._get_file_helper()\\n\\n filename = _negotiate_filename(page, filename, save)\\n\\n if save and filename:\\n with open(filename, 'wb') as fh:\\n fh.write(content)\\n\\n return content, filename\",\n \"def get_file_content(*args, **kwargs):\\n return get_file_content_async(*args, **kwargs).get_result()\",\n \"def get_file(self, path):\\n _url = (\\n f\\\"{self.connector.base_url}/projects/{self.project_id}/nodes/{self.node_id}\\\"\\n f\\\"/files/{path}\\\"\\n )\\n\\n return self.connector.http_call(\\\"get\\\", _url).text\",\n \"def get_file(file_info):\\n if session_vars.filepath == file_info['filepath']:\\n img_file = session_vars.img_file\\n else:\\n print('loading', file_info['filepath'])\\n if file_info['ext']=='fits':\\n print('Detected fits image type')\\n pyfits = import_fits()\\n img_file = pyfits.open(file_info['filepath'])\\n else:\\n try:\\n from PIL import Image\\n except ImportError:\\n raise ToyzJobError(\\n \\\"You must have PIL (Python Imaging Library) installed to \\\"\\n \\\"open files of this type\\\"\\n )\\n img_file = Image.open(file_info['filepath'])\\n session_vars.filepath = file_info['filepath']\\n session_vars.img_file = img_file\\n return img_file\",\n \"def get_file(self, repo, file_name):\\n DataHubManager.has_repo_file_privilege(\\n self.username, self.repo_base, repo, 'read')\\n\\n file_path = user_data_path(self.repo_base, repo, file_name)\\n file = open(file_path).read()\\n return file\",\n \"def readFile( *args ):\\n filePath = path.join( *args )\\n if not path.exists( filePath ):\\n return None\\n with open( filePath, 'r' ) as fd:\\n return fd.read()\",\n \"def get_content(self, filename):\\n f_id = self.face.FACES.files.find_one({ \\\"filename\\\" : filename }, { \\\"_id\\\" : 1 })\\n return self.face_fs.get(f_id['_id']).read()\",\n \"def get_file(self, remote_path, local_path, storage_id=None):\\n return self.get(remote_path, local_path, directory=False, storage_id=storage_id)\",\n \"def retrieve(self, file_name):\\n ret = os.path.join(self.path, file_name)\\n temp = (self.path + file_name).find(self.path, 1, -1)\\n if os.path.exists(ret):\\n return os.path.join(self.path, file_name)\\n elif temp != -1:\\n if os.name == \\\"posix\\\":\\n return os.getcwd() + '/' + file_name\\n else:\\n return os.getcwd() + '\\\\\\\\' + file_name\\n else:\\n return None\",\n \"def get_file(self, filename):\\n log.debug('[%s]: reading: //%s/%s', self.name, self.name, filename)\\n try:\\n blob = self.repo.head.commit.tree/filename\\n return blob.data_stream\\n except KeyError as err:\\n raise GitError(err)\",\n \"def read_file(self, *args):\\n with open(os.path.join(self.temp_path, *args)) as fp:\\n return fp.read()\",\n \"def load_file(uri):\\n # get filename\\n filename = os.path.basename(uri)\\n\\n # If filepath specified and file exists, return filepath as-is\\n if os.path.isfile(uri):\\n return uri\\n # Check to see if file exists in current working directory\\n elif os.path.isfile(filename):\\n return filename\\n # Otherwise, check to see if URI is a URL\\n elif uri.startswith(('http', 'ftp')):\\n # retrieve remote file contents\\n print(\\\"Downloading %s\\\" % uri)\\n handle = urllib2.urlopen(uri)\\n contents = handle.read()\\n\\n with open(filename, 'w') as fp:\\n fp.write(contents)\\n return filename\\n # if it's note a URL or a valid filepath, raise and exception\\n else:\\n raise Exception(\\\"Invalid URI specified: %s\\\" % uri)\",\n \"def get_file(url, file_name=None):\\n cache_dir = os.path.join(os.path.expanduser(\\\"~\\\"), \\\".jhML\\\")\\n\\n if file_name is None:\\n file_name = url[url.rfind('/') + 1:]\\n file_path = os.path.join(cache_dir, file_name)\\n\\n if not os.path.exists(cache_dir):\\n os.mkdir(cache_dir)\\n\\n if os.path.exists(file_path):\\n return file_path\\n\\n print(\\\"Downloading: \\\" + file_name)\\n try:\\n urllib.request.urlretrieve(url, file_path, show_progress)\\n except (Exception, KeyboardInterrupt) as e:\\n if os.path.exists(file_path):\\n os.remove(file_path)\\n raise\\n print(\\\" Done\\\")\\n\\n return file_path\",\n \"def open_and_read_file(file_path):\\n contents = open(file_path).read()\\n # your code goes here\\n\\n return contents\",\n \"def json_file():\\r\\n urlretrieve(URL, PATH)\\r\\n return PATH\",\n \"def json_file():\\r\\n urlretrieve(URL, PATH)\\r\\n return PATH\",\n \"def get_file(filename):\\n return os.path.join(TEST_DIR, filename)\",\n \"def read_file(path): #TODO implementme, handling paths more intelligently\\n f = open(path, \\\"r\\\")\\n string = f.read()\\n f.close()\\n return string\",\n \"def open(self):\\n return File(open(self.get_path()), \\\"rb\\\")\",\n \"def request_file(self, path: str, token: str) -> Tuple[IO[bytes], dict]:\\n response = self.request('get', path, token, stream=True)\\n stream = ReadWrapper(response.iter_content,\\n int(response.headers['Content-Length']))\\n return stream, response.headers\",\n \"def getOriginalFile(url):\\n # does url exist?\\n if url is None or url is \\\"\\\":\\n return\",\n \"def get_file(self, uri_type, no_copy=False):\\n return self.copy(target_uri_type=uri_type, no_copy=no_copy)\",\n \"def getFile():\\n from tkinter import Tk, filedialog\\n Tk().withdraw() # we don't want a full GUI, so keep the root window from appearing\\n return(filedialog.askopenfilenames())\",\n \"def _Read(filename):\\n with open(filename, 'rb') as f:\\n return f.read()\",\n \"def _s3_get_file(url):\\n try:\\n return S3().get_contents_from_url(url)\\n except Exception as e:\\n raise ScrBaseException(\\\"Could not load file from {0}: {1}\\\".format(url, e))\",\n \"def file(path, filename):\\n print(uc.file(path, filename))\",\n \"def get_file_from_path(file_path):\\n return Utils.get_real_file_path(file_path)\",\n \"def _file_load(path):\\n _, _, file_path = path.split('/', 2)\\n with open(file_path, 'r', encoding='utf-8') as file_handle:\\n return file_handle.read()\",\n \"def GetFileContents(self, filename):\\n with tempfile.NamedTemporaryFile() as t:\\n self.GetFile(filename, t.name)\\n with open(t.name, 'r') as f2:\\n res = f2.read()\\n logging.debug(\\\"GetFileContents(%s)->%s\\\" % (filename, res))\\n return res\",\n \"def readfile(filename, mode='r'):\\n if mode != 'r' and mode != 'rb':\\n print(f\\\"ERROR: incorrect mode : expected 'r' or 'rb' given {mode}\\\\n\\\")\\n else:\\n with open(Path(os.path.expanduser(filename)), mode)as f:\\n content = f.read()\\n f.close()\\n return content\",\n \"def get_file(self, url, path, step_name=None, headers=None,\\n transient_retry=True, strip_prefix=None, timeout=None):\\n return self._get_step(url, path, step_name, headers, transient_retry,\\n strip_prefix, False, timeout, '')\",\n \"def read_file(filename):\\n with open(filename) as fp:\\n return fp.read()\",\n \"def get_remote_file(sid, path):\\n with slycat.web.server.remote.get_session(sid) as session:\\n return session.get_file(path)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.79854953","0.789686","0.75598544","0.75327295","0.7512711","0.7409022","0.730959","0.72266984","0.720062","0.71793586","0.71641433","0.71097845","0.7107661","0.7107661","0.7003901","0.6984212","0.6975756","0.69507647","0.69283926","0.688564","0.6861352","0.68536747","0.68252647","0.6824188","0.6822086","0.68183774","0.6796932","0.6795296","0.67909205","0.67829347","0.6770854","0.6768085","0.6767353","0.6753358","0.674705","0.6746897","0.67344946","0.6724045","0.6712","0.6695789","0.66904914","0.668827","0.6672633","0.66725546","0.6652304","0.66481805","0.66443074","0.66353583","0.66296816","0.6628511","0.6618812","0.6606477","0.6581626","0.6575142","0.6566883","0.6565085","0.65627843","0.65508515","0.65486544","0.65445423","0.65426666","0.6529253","0.6526041","0.65234154","0.652263","0.65191627","0.65111494","0.65098095","0.6490933","0.64815867","0.64721125","0.6470531","0.64660364","0.64553463","0.6452702","0.6451101","0.64485806","0.6429348","0.6427795","0.64245284","0.6409757","0.6405203","0.63908213","0.63908213","0.639078","0.6389712","0.63867766","0.6378887","0.63759005","0.6364584","0.6359731","0.6343021","0.633825","0.63373095","0.6329968","0.63283837","0.6317781","0.631701","0.6316251","0.63121706","0.63078845"],"string":"[\n \"0.79854953\",\n \"0.789686\",\n \"0.75598544\",\n \"0.75327295\",\n \"0.7512711\",\n \"0.7409022\",\n \"0.730959\",\n \"0.72266984\",\n \"0.720062\",\n \"0.71793586\",\n \"0.71641433\",\n \"0.71097845\",\n \"0.7107661\",\n \"0.7107661\",\n \"0.7003901\",\n \"0.6984212\",\n \"0.6975756\",\n \"0.69507647\",\n \"0.69283926\",\n \"0.688564\",\n \"0.6861352\",\n \"0.68536747\",\n \"0.68252647\",\n \"0.6824188\",\n \"0.6822086\",\n \"0.68183774\",\n \"0.6796932\",\n \"0.6795296\",\n \"0.67909205\",\n \"0.67829347\",\n \"0.6770854\",\n \"0.6768085\",\n \"0.6767353\",\n \"0.6753358\",\n \"0.674705\",\n \"0.6746897\",\n \"0.67344946\",\n \"0.6724045\",\n \"0.6712\",\n \"0.6695789\",\n \"0.66904914\",\n \"0.668827\",\n \"0.6672633\",\n \"0.66725546\",\n \"0.6652304\",\n \"0.66481805\",\n \"0.66443074\",\n \"0.66353583\",\n \"0.66296816\",\n \"0.6628511\",\n \"0.6618812\",\n \"0.6606477\",\n \"0.6581626\",\n \"0.6575142\",\n \"0.6566883\",\n \"0.6565085\",\n \"0.65627843\",\n \"0.65508515\",\n \"0.65486544\",\n \"0.65445423\",\n \"0.65426666\",\n \"0.6529253\",\n \"0.6526041\",\n \"0.65234154\",\n \"0.652263\",\n \"0.65191627\",\n \"0.65111494\",\n \"0.65098095\",\n \"0.6490933\",\n \"0.64815867\",\n \"0.64721125\",\n \"0.6470531\",\n \"0.64660364\",\n \"0.64553463\",\n \"0.6452702\",\n \"0.6451101\",\n \"0.64485806\",\n \"0.6429348\",\n \"0.6427795\",\n \"0.64245284\",\n \"0.6409757\",\n \"0.6405203\",\n \"0.63908213\",\n \"0.63908213\",\n \"0.639078\",\n \"0.6389712\",\n \"0.63867766\",\n \"0.6378887\",\n \"0.63759005\",\n \"0.6364584\",\n \"0.6359731\",\n \"0.6343021\",\n \"0.633825\",\n \"0.63373095\",\n \"0.6329968\",\n \"0.63283837\",\n \"0.6317781\",\n \"0.631701\",\n \"0.6316251\",\n \"0.63121706\",\n \"0.63078845\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":81197,"cells":{"query":{"kind":"string","value":"Formats a few lines around a 'bad line', and returns the formatted source as well as the content of the 'bad line'."},"document":{"kind":"string","value":"def get_formatted_partial_source(self, filename, linenumber, offset):\n lines = self.get_source(filename)\n if not lines:\n return \"\", \"\"\n\n begin = max(0, linenumber - self.context)\n partial_source, bad_line = highlight_source(\n linenumber,\n linenumber - begin - 1,\n lines[begin : linenumber + 1],\n offset=offset,\n )\n return partial_source, bad_line"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def format_exception_only(etype, value):\n # Gracefully handle (the way Python 2.4 and earlier did) the case of\n # being called with (None, None).\n if etype is None:\n return [_format_final_exc_line(etype, value)]\n\n stype = etype.__name__\n smod = etype.__module__\n if smod not in (\"__main__\", \"builtins\", \"exceptions\"):\n stype = smod + '.' + stype\n\n if not issubclass(etype, SyntaxError):\n return [_format_final_exc_line(stype, value)]\n\n # It was a syntax error; show exactly where the problem was found.\n lines = []\n filename = value.filename or \"<string>\"\n lineno = str(value.lineno) or '?'\n lines.append(' File \"%s\", line %s\\n' % (filename, lineno))\n badline = value.text\n offset = value.offset\n if badline is not None:\n lines.append(' %s\\n' % badline.strip())\n if offset is not None:\n caretspace = badline.rstrip('\\n')[:offset].lstrip()\n # non-space whitespace (likes tabs) must be kept for alignment\n caretspace = ((c.isspace() and c or ' ') for c in caretspace)\n # only three spaces to account for offset1 == pos 0\n lines.append(' %s^\\n' % ''.join(caretspace))\n msg = value.msg or \"<no detail available>\"\n lines.append(\"%s: %s\\n\" % (stype, msg))\n return lines","def black_format(code_path: Path, line_length=90) -> None:\n check = False\n diff = False\n\n write_back = WriteBack.from_configuration(check=check, diff=diff, color=True)\n report = Report(check=check, diff=diff, quiet=False, verbose=True)\n mode = Mode(\n target_versions={TargetVersion.PY37},\n line_length=line_length,\n is_pyi=False,\n is_ipynb=False,\n string_normalization=True,\n magic_trailing_comma=False,\n experimental_string_processing=False,\n )\n\n reformat_one(\n src=code_path, fast=True, write_back=write_back, mode=mode, report=report\n )\n # TODO: explore how to format a string and return the formatted string\n # (i.e. with reformat_code)","def get_filename_and_formatted_source():\n sal = gdb.selected_frame().find_sal() # gdb.Symtab_and_line\n\n # Check if source code is available\n if sal.symtab is None:\n return \"\", []\n\n # Get the full source code\n closest_line = sal.line\n filename = sal.symtab.fullname()\n\n try:\n source = get_highlight_source(filename)\n except OSError:\n return \"\", []\n\n if not source:\n return \"\", []\n\n n = int(source_code_lines)\n\n # Compute the line range\n start = max(closest_line - 1 - n // 2, 0)\n end = min(closest_line - 1 + n // 2 + 1, len(source))\n num_width = len(str(end))\n\n # split the code\n source = source[start:end]\n\n # Compute the prefix_sign length\n prefix_sign = C.prefix(str(pwndbg.gdblib.config.code_prefix))\n prefix_width = len(prefix_sign)\n\n # Format the output\n formatted_source = []\n for line_number, code in enumerate(source, start=start + 1):\n if pwndbg.gdblib.config.context_source_code_tabstop > 0:\n code = code.replace(\"\\t\", \" \" * pwndbg.gdblib.config.context_source_code_tabstop)\n fmt = \" {prefix_sign:{prefix_width}} {line_number:>{num_width}} {code}\"\n if pwndbg.gdblib.config.highlight_source and line_number == closest_line:\n fmt = C.highlight(fmt)\n\n line = fmt.format(\n prefix_sign=prefix_sign if line_number == closest_line else \"\",\n prefix_width=prefix_width,\n line_number=line_number,\n num_width=num_width,\n code=code,\n )\n formatted_source.append(line)\n\n return filename, formatted_source","def format(self, violation: Violation) -> str | None:\n filename = violation.filename\n if filename.lower().endswith(\".ipynb_parsed\"):\n map_result = map_notebook_error(violation, self.options.notebook_cell_format)\n if map_result:\n filename, line_number = map_result\n return cast(\n str,\n self.error_format\n % {\n \"code\": violation.code,\n \"text\": violation.text,\n \"path\": filename,\n \"row\": line_number,\n \"col\": violation.column_number,\n **(COLORS if self.color else COLORS_OFF),\n },\n )\n return cast(str, super().format(violation))","def __call__(self, line):\n marker = self.marker\n stripped_line = line.strip()\n if marker == stripped_line:\n assert not self.traceback_section\n self.traceback_section = True\n # print(\"XXX: TRACEBACK-START\")\n elif self.traceback_section:\n matched = self.file_pattern.match(line)\n if matched:\n # matched_range = matched.regs[1]\n filename = matched.groups()[0]\n new_filename = posixpath_normpath(filename)\n if new_filename != filename:\n # print(\"XXX: %r => %r\" % (filename, new_filename))\n line = line.replace(filename, new_filename)\n elif not stripped_line or line[0].isalpha():\n # -- DETECTED TRCAEBACK-END: exception-description\n # print(\"XXX: TRACEBACK-END\")\n self.traceback_section = False\n return line","def reformat(ctx):\n pass","def strip_warnings(self, line):\n if line[0] == \"|\":\n return \"\"\n else:\n return line","def _format_line(cls, line):\n\n line = line.strip()\n\n if line and not line.startswith(\"#\"):\n # The line is not a commented line.\n\n if \"#\" in line:\n # There is a comment at the end of the line.\n\n # We delete the comment from the line.\n line = line[: line.find(\"#\")].strip()\n\n if \" \" in line or \"\\t\" in line:\n # A space or a tabs is in the line.\n\n # We remove all whitestring from the extracted line.\n splited_line = line.split()\n\n # As there was a space or a tab in the string, we consider\n # that we are working with the hosts file format which means\n # that the domain we have to test is after the first string.\n # So we set the index to 1.\n index = 1\n\n while index < len(splited_line):\n # We loop until the index is greater than the length of\n # the splited line.\n\n if splited_line[index]:\n # The element at the current index is not an empty string.\n\n # We break the loop.\n break\n\n # The element at the current index is an empty string.\n\n # We increase the index number.\n index += 1 # pragma: no cover\n\n # We return the last read element.\n return splited_line[index]\n\n # We return the extracted line.\n return line\n\n # The extracted line is a comment line.\n\n # We return an empty string as we do not want to work with commented line.\n return \"\"","def get_replacement():\n run_linter_throw(\"path/to/file\",\n contents,\n FormatStyle(\"#\", \"#\", \"\"),\n whitelist=[\"file/trailing_whitespace\"])","def highlight_source(linenumber, index, lines, offset=None):\n # The following if statements are left-over diagnostic\n # from the hack to integrate into Idle.\n # they are harmless tests which could potentially be useful.\n if lines is None:\n return \"\", \"\"\n if index is None:\n print(\"problem in highlight_source(): index is None\")\n index = 0\n\n # The weird index arithmetic below is based on the information returned\n # by Python's inspect.getinnerframes()\n\n new_lines = []\n problem_line = \"\"\n nb_digits = len(str(linenumber + index))\n no_mark = \" {:%d}: \" % nb_digits\n with_mark = \" -->{:%d}: \" % nb_digits\n if offset is not None:\n offset_mark = \" \" * (8 + nb_digits + offset) + \"^\"\n i = linenumber - index\n\n for line in lines:\n if i == linenumber:\n num = with_mark.format(i)\n problem_line = line\n new_lines.append(num + line.rstrip())\n if offset is not None:\n new_lines.append(offset_mark)\n break\n else:\n num = no_mark.format(i)\n new_lines.append(num + line.rstrip())\n i += 1\n return \"\\n\".join(new_lines), problem_line","def GetLinePostProcess(self):\r\n retline = None\r\n outline = None\r\n try:\r\n retline= str(self.file.readline())\r\n except IOError:\r\n self.tracking.SetError(type(self).__name__, sys._getframe().f_code.co_name, \"cannot read a line from\" )\r\n finally: \r\n #outline1 = retline.replace(\"/\",\"\")\r\n if( (retline !=\"\") and (retline !=\"\\n\")) :\r\n outline = str(\"\")\r\n az_range=range(97,123)\r\n AZ_range = range (65, 91)\r\n val_range = range (48,58)\r\n space_range = range (32, 33)\r\n for i in range(len(retline)):\r\n value = ord(retline[i] )\r\n if ( (value in az_range) or (value in AZ_range) or (value in val_range) or (value in space_range) ):\r\n outline = \"\".join([outline,retline[i]])\r\n else:\r\n outline = \"\".join([outline,\"_\"])\r\n '''\r\n if( (retline[i] != \"/\") and (retline[i] != \"&\") and (retline[i] != \"\\\\\") and (retline[i] != \"%\") and (retline[i] != \"#\") and (retline[i] != \"_\") and (retline[i] != '\"') and (retline[i] != \"@\") and (retline[i] != \":\") and (retline[i] != \"\\n\")):\r\n #charac = str(retline[i].encode('ascii','ignore'))\r\n if(ord(retline[i]) < 128):\r\n outline = \"\".join([outline,retline[i]])\r\n ''' \r\n return outline\r\n #return unicodedata.normalize('NFKD', outline).encode('ascii','ignore')\r","def _get_lines_with_highlighted_error(e: CompilationError) -> Iterable[str]:\n error_line = e.lineno - 1\n lines = e.code.split('\\n')\n padding = len(str(len(lines)))\n\n from_line = error_line - const.SYNTAX_ERROR_OFFSET\n if from_line < 0:\n from_line = 0\n\n if from_line < error_line:\n for n in range(from_line, error_line):\n yield _format_line(lines[n], n, padding)\n\n yield ' {dim}{n}{reset}: {bright}{line}{reset}'.format(\n dim=Style.DIM,\n n=str(error_line + 1).zfill(padding),\n line=lines[error_line],\n reset=Style.RESET_ALL,\n bright=Style.BRIGHT)\n yield ' {padding}{bright}^{reset}'.format(\n padding=' ' * (padding + e.offset + 1),\n bright=Style.BRIGHT,\n reset=Style.RESET_ALL)\n\n to_line = error_line + const.SYNTAX_ERROR_OFFSET\n if to_line > len(lines):\n to_line = len(lines)\n for n in range(error_line + 1, to_line):\n yield _format_line(lines[n], n, padding)","def doomed_parser(line):\n raise exceptions.LineParseException('Error occurred')","def handleBadLine(self, badLine, reason=None):\n if not reason: reason='N/A'\n d = dict(reason=reason, bad_Line=badLine)\n self.badLines.append(d)","def wrapped_getlines(filename, globals):\n lines = orig(filename, globals)\n source = self.format_source(\"\".join(lines))\n\n if sys.version_info < (3,):\n source = self.try_to_encode(source)\n\n return source.splitlines(True)","def tidyout(line, linewidth, currentpos, lineend, autoStyle=True):\n openStyle = ''\n closeStyle = ''\n url=False\n if autoStyle:\n # format things tha look like bio sequences\n if len(line) > 40:\n if re.search('^[A-Z]+$',line.upper()) != None:\n # looks like a sequence\n openStyle = '<pre>'\n closeStyle = '</pre>'\n lineend = '<br/>'\n \n\n # format things that look like URL's\n if re.search('^http[s]*\\:\\/',line) != None:\n url=True\n result = '<a href=\"%s\" target=urllinkout> %s </a>'%(line,line)\n \n\n if not url:\n result=openStyle\n for i in range(0,len(line)):\n if currentpos == linewidth:\n result += lineend\n currentpos = 0 \n result += line[i:i+1]\n currentpos += 1\n\n #print currentpos\n result += closeStyle\n \n return (result,currentpos)","def blockify(source):\n\n paragraphs = [\"\"]\n for line in source.strip().split(\"\\n\"):\n line = line.strip()\n if line: paragraphs[-1] += line + \" \"\n elif paragraphs[-1]: paragraphs.append(\"\")\n\n return paragraphs","def format(self, filename, timestamp, line):\n return self._formatter({\n '@source': \"file://{0}{1}\".format(self._current_host, filename),\n '@type': self._file_config.get('type', filename),\n '@tags': self._file_config.get('tags', filename),\n '@fields': self._file_config.get('fields', filename),\n '@timestamp': timestamp,\n '@source_host': self._current_host,\n '@source_path': filename,\n '@message': line.strip(os.linesep),\n })","def highlight_lines(self) -> str:\n\n info = self._info\n buffer = info.buffer\n\n startl, endl = info.line, info.endline\n startp, endp = self.get_text_positions()\n\n above_lines = strip_newlines(buffer.get_lines(max(startl - 5, 0), startl - 1))\n below_lines = strip_newlines(buffer.get_lines(endl + 1, endl + 5))\n\n source = list(strip_newlines(self._info.text_lines()))\n\n red = colorama.Fore.RED\n white = colorama.Fore.WHITE\n normal = colorama.Style.NORMAL\n reset = colorama.Style.RESET_ALL + colorama.Fore.RESET\n dim = colorama.Style.DIM\n bright = colorama.Style.BRIGHT\n\n def make_red(s):\n return reset + red + s + white\n\n def make_dim(s):\n return reset + dim + s + normal\n\n def make_bright(s):\n return reset + bright + s + normal\n\n line_pad = \" \" * 5 # 5 chars are used by the linecount that need to be padded on the arrows\n\n def fmtr(counter):\n if len(source) == 1:\n # start and end on same line, only need simple fmt\n yield add_line_once(source[0], counter)\n if startp == endp: # only emit single carat when the error is a single character\n yield make_red(line_pad + f\"{'^':>{startp}}\")\n else:\n width = (endp - startp) - 1 # leave space for carats + off by one\n separator = '-' * width\n yield make_red(line_pad + f\"{'^':>{startp}}{separator}^\")\n else:\n width = (len(source[0]) - startp)\n separator = '-' * width\n yield add_line_once(source[0], counter)\n yield make_red(line_pad + f\"{'^':>{startp}}{separator}\")\n for i in source[1:-1]:\n yield add_line_once(i, counter)\n yield make_red(line_pad + '-' * len(i))\n width = endp - 1 # - len(source[endl])\n separator = '-' * width\n yield add_line_once(source[-1], counter)\n yield make_red(line_pad + f\"{separator}^\")\n\n line_counter = count(max(startl - 5, 1))\n\n above_lines = \"\\n\".join(add_line_count(above_lines, line_counter))\n if above_lines:\n above_lines += \"\\n\"\n error_lines = \"\\n\".join(fmtr(line_counter))\n\n below_lines = \"\\n\".join(add_line_count(below_lines, line_counter))\n if below_lines:\n below_lines = \"\\n\" + below_lines\n\n return make_dim(above_lines) + make_bright(error_lines) + make_dim(below_lines)","def _format_line( self, side, flag, linenum, text ):\n\t\ttry:\n\t\t\tline_id = '%d' % linenum\n\t\texcept TypeError:\n\t\t\t# handle blank lines where linenum is '>' or ''\n\t\t\tline_id = ''\n\n\t\ttext = text.rstrip()\n\n\t\tif not self.line_numbers:\n\t\t\treturn text\n\t\treturn '%s %s' % ( self._rpad( line_id, 8 ), text )","def hide_magic(source: str) -> str:\n\n def _hide_magic_line(line: str) -> str:\n return f\"###MAGIC###{line}\" if contains_magic(line) else line\n\n return \"\\n\".join(_hide_magic_line(line) for line in source.split(\"\\n\"))","def _assemble(self, line):\n self.counter += 1\n if line.strip() == \"\":\n return self.__next__()\n elif line.startswith(\"browser\"):\n return self.__next__()\n elif line.startswith(\"track\"):\n # reset metadata\n self._parse_track_line(line[5:])\n return self.__next__()\n elif line.startswith(\"#\"):\n return self.__next__()\n else:\n try:\n return self.return_type.from_bed(line, extra_columns=self.extra_columns)\n except:\n self.rejected.append(line)\n msg = \"Cannot parse BED line number %s. \" % self.counter\n if self.metadata.get(\"type\", None) is not None:\n msg += (\n \"Are you sure this is a %s BED file with extra columns (%s)?\" %\n (self.metadata.get(\"type\"), self._get_extra_column_names())\n )\n elif self.extra_columns != 0:\n msg += (\n \"Are you sure this BED file has extra columns (%s)?\" %\n self._get_extra_column_names()\n )\n else:\n msg += \"Maybe this BED has extra columns (i.e. is an extended BED file)?\"\n\n msg += (\"\\n %s\" % line)\n warn(msg, FileFormatWarning)\n return self.__next__()","def format_line_lean(self):\n\t\tline = \"<tr>\\\n\t\t\t\t\t<td>\\\n\t\t\t\t\t\t<font size='2'>\" + self.tag + \"</font>\\\n\t\t\t\t\t</td>\\\n\t\t\t\t\t<td>\\\n\t\t\t\t\t\t<font size='2'>\" + self.value + \"</font>\\\n\t\t\t\t\t</td>\\\n\t\t\t\t</tr>\"\n\t\treturn line","def prepare_text_line(line):\n\n re_sub = re.sub\n # FIXME: maintain the original character positions\n\n # strip whitespace\n line = line.strip()\n\n # strip comment markers\n # common comment characters\n line = line.strip('\\\\/*#%;')\n # un common comment line prefix in dos\n line = re_sub('^rem\\s+', ' ', line)\n line = re_sub('^\\@rem\\s+', ' ', line)\n # un common comment line prefix in autotools am/in\n line = re_sub('^dnl\\s+', ' ', line)\n # un common comment line prefix in man pages\n line = re_sub('^\\.\\\\\\\\\"', ' ', line)\n # un common pipe chars in some ascii art\n line = line.replace('|', ' ')\n\n # normalize copyright signs and spacing aournd them\n line = line.replace('(C)', ' (c) ')\n line = line.replace('(c)', ' (c) ')\n # the case of \\251 is tested by 'weirdencoding.h'\n line = line.replace(u'\\251', u' (c) ')\n line = line.replace('©', ' (c) ')\n line = line.replace('©', ' (c) ')\n line = line.replace('©', ' (c) ')\n line = line.replace(u'\\xa9', ' (c) ')\n # FIXME: what is \\xc2???\n line = line.replace(u'\\xc2', '')\n\n # TODO: add more HTML entities replacements\n # see http://www.htmlhelp.com/reference/html40/entities/special.html\n # convert html entities CR LF to space\n line = line.replace(u' ', ' ')\n line = line.replace(u' ', ' ')\n line = line.replace(u' ', ' ')\n\n # normalize (possibly repeated) quotes to unique single quote '\n # backticks ` and \"\n line = line.replace(u'`', \"'\")\n line = line.replace(u'\"', \"'\")\n line = re.sub(MULTIQUOTES_RE(), \"'\", line)\n # quotes to space? but t'so will be wrecked\n # line = line.replace(u\"'\", ' ')\n\n # some trailing garbage ')\n line = line.replace(\"')\", ' ')\n\n\n # note that we do not replace the debian tag by a space: we remove it\n line = re_sub(DEBIAN_COPYRIGHT_TAGS_RE(), '', line)\n\n line = re_sub(IGNORED_PUNCTUATION_RE(), ' ', line)\n\n # tabs to spaces\n line = line.replace('\\t', ' ')\n\n # normalize spaces around commas\n line = line.replace(' , ', ', ')\n\n # remove ASCII \"line decorations\"\n # such as in --- or === or !!! or *****\n line = re_sub(ASCII_LINE_DECO_RE(), ' ', line)\n line = re_sub(ASCII_LINE_DECO2_RE(), ' ', line)\n\n # Replace escaped literal \\0 \\n \\r \\t that may exist as-is by a space\n # such as in code literals: a=\"\\\\n some text\"\n line = line.replace('\\\\r', ' ')\n line = line.replace('\\\\n', ' ')\n line = line.replace('\\\\t', ' ')\n line = line.replace('\\\\0', ' ')\n\n # TODO: Why?\n # replace contiguous spaces with only one occurrence\n # line = re.sub(WHITESPACE_RE(), ' ', text)\n\n # normalize to ascii text\n line = commoncode.text.toascii(line)\n # logger.debug(\"ascii_only_text: \" + text)\n\n # strip verbatim back slash and comment signs again at both ends of a line\n # FIXME: this is done at the start of this function already\n line = line.strip('\\\\/*#%;')\n\n # normalize to use only LF as line endings so we can split correctly\n # and keep line endings\n line = commoncode.text.unixlinesep(line)\n # why?\n line = lowercase_well_known_word(line)\n\n return line","def ProcessLine(fn, filename, file_extension, clean_lines, line,\n include_state, function_state, nesting_state, error,\n extra_check_functions=[]):\n fn(filename, file_extension, clean_lines, line,\n include_state, function_state, nesting_state,\n makeErrorFn(error, [], [r'(.*)should be indented \\+1 space inside(.*)']),\n extra_check_functions=[])","def format(self, warning):\n if hasattr(warning, 'start') and hasattr(warning, 'end'):\n if warning.start.line == warning.end.line:\n location = u'line {}'.format(warning.start.line)\n else:\n location = u'lines {}-{}'.format(\n warning.start.line,\n warning.end.line\n )\n else:\n location = u'line {}'.format(warning.lineno)\n if hasattr(warning, 'lines'):\n lines = warning.lines\n template = textwrap.dedent(u\"\"\"\\\n File \"{file}\", {location}\n {lines}\n {message}\n\n \"\"\")\n else:\n lines = []\n template = textwrap.dedent(u\"\"\"\\\n File \"{file}\", {location}\n {message}\n\n \"\"\")\n if len(lines) > 1:\n lineno_length = count_digits(warning.end.line)\n lines_with_lineno = []\n for lineno, line in enumerate(lines, warning.start.line):\n lines_with_lineno.append(\n u' {} {}'.format(\n text_type(lineno).rjust(lineno_length),\n line\n )\n )\n lines = lines_with_lineno\n else:\n lines = [u' ' * 2 + line for line in lines]\n self.output.write(\n template.format(\n file=warning.file,\n location=location,\n lines=u'\\n'.join(lines),\n message=textwrap.fill(warning.message)\n )\n )","def get_replacement():\n run_linter_throw(\"path/to/file\",\n \"{s}\\n{m} Text{e}\",\n style,\n whitelist=[\"headerblock/desc_space\"])","def test_incorrect_format_1(self):\n changelog = changelog_temp.format(\n before_changelog=\"## [Not yet released]\\n\\n### Added\\n\\n- Added a new feature\\n\"\n )\n with pytest.raises(ValueError):\n parse_changelog(changelog)","def make_error(self) -> Optional[str]:\n info = self._info\n if info is None:\n return None\n startl, endl = info.line, info.endline\n\n return \"\\n\".join(((f\"On line {startl + 1}:\"\n if startl == endl else\n f\"On lines {startl + 1} to {endl + 1}:\"),\n self.highlight_lines))","def fix_broken_hotmail_headers(text):\n end_of_header_match = _end_of_simple_header_pattern.search(text)\n temp_header_text = text[:end_of_header_match.end()].strip()\n lines = temp_header_text.splitlines()[1:] # first line is not a header...\n fixed_header_lines = reduce(_merge_broken_header_lines, lines, [])\n return_text = os.linesep.join(fixed_header_lines) + text[end_of_header_match.end():]\n return return_text","def fix_missing_period(line):\n if \"@highlight\" in line:\n return line\n if line == \"\":\n return line\n if line[-1] in END_TOKENS:\n return line\n return line + \" .\"","def test_markdown_fixed_issue_line_with_debug_and_file_debug_on():\n\n # Arrange\n scanner = MarkdownScanner()\n with copy_to_temp_file(\n os.path.join(\"test\", \"resources\", \"rules\", \"md047\", \"end_with_no_blank_line.md\")\n ) as temp_source_path:\n supplied_arguments = [\n \"--disable-rules\",\n \"md009\",\n \"-x-fix-file-debug\",\n \"-x-fix-debug\",\n \"-x-fix\",\n \"scan\",\n temp_source_path,\n ]\n\n expected_return_code = 3\n expected_output = \"\"\"md010-before:# This is a test:\nmd047-before:# This is a test:\nnl-ltw:# This is a test\\\\n:\nmd010-before::\nmd047-before::\nnl-ltw:\\\\n:\nmd010-before:The line after this line should be blank.:\nmd047-before:The line after this line should be blank.:\nwas_newline_added_at_end_of_file=False\nfixed:\\\\n:\nis_line_empty=False\nwas_modified=True\nnl-ltw:The line after this line should be blank.:\ncf-ltw:\\\\n:\nFixLineRecord(source='completed_file', line_number=4, plugin_id='md047')\"\"\"\n expected_error = \"\"\n initial_file_contents = read_contents_of_text_file(temp_source_path)\n expected_file_contents = initial_file_contents + \"\\n\"\n\n # Act\n execute_results = scanner.invoke_main(arguments=supplied_arguments)\n\n # Assert\n assert execute_results.std_err.getvalue() == expected_error\n assert (\n execute_results.return_code == expected_return_code\n ), f\"Actual error code ({execute_results.return_code}) and expected error code ({expected_return_code}) differ.\"\n assert_file_is_as_expected(temp_source_path, expected_file_contents)\n\n std_out_split = execute_results.std_out.getvalue().splitlines()\n\n first_section = std_out_split[:4]\n print(first_section)\n assert first_section[0] == \"\"\n assert first_section[1].startswith(\"--\") and first_section[1].endswith(\"--\")\n assert first_section[2] == initial_file_contents.replace(\"\\n\", \"\\\\n\")\n assert first_section[3] == \"--\"\n\n last_section = std_out_split[-7:]\n print(last_section)\n assert last_section[0] == \"\"\n assert last_section[1].startswith(\"--\") and last_section[1].endswith(\"--\")\n assert last_section[2] == expected_file_contents.replace(\"\\n\", \"\\\\n\")\n assert last_section[3] == \"--\"\n assert last_section[4].startswith(\"Copy \")\n assert last_section[5].startswith(\"Remove:\")\n assert last_section[6] == f\"Fixed: {temp_source_path}\"\n\n middle_section = std_out_split[4:-7]\n print(middle_section)\n split_output = expected_output.splitlines()\n print(split_output)\n assert len(middle_section) == len(split_output)\n for i in range(0, len(split_output)):\n assert middle_section[i] == split_output[i]","def _process_text_line(self, line, columns, format, lower_case, num_line,\n fill_missing=0, filter_case=None,\n strict_separator=False):\n if not isinstance(line, list) and not isinstance(\n line, tuple) and not isinstance(line, numpy.ndarray):\n if format != \"tsv\":\n raise Exception(\"unable to process format \" + format)\n line = line.strip(\"\\r\\n \").replace(\"\\n\", \" \")\n line = DatabaseCore2._split_expr.split(line)\n\n if filter_case is not None:\n line = [filter_case(s) for s in line]\n\n try:\n if fill_missing > 0:\n m = max(columns.keys())\n if m >= len(line):\n line = copy.copy(line)\n add = 0\n while m >= len(line) and add < fill_missing:\n a, b = columns[len(line)]\n if b is int:\n line.append(\"0\")\n elif b is float:\n line.append(\"0.0\")\n elif b is decimal.Decimal:\n line.append(\"0\")\n elif b is str:\n line.append(\"\")\n else:\n line.append(\"\")\n add += 1\n\n res = {}\n for c, v in columns.items():\n if \"AUTOFILL\" in v:\n res[v[0]] = \"NULL\"\n elif \"AUTOINCREMENT\" in v:\n continue\n else:\n if c >= len(line):\n self.LOG(\n \"(a)line number \",\n num_line,\n \"*unable to process a line columns \",\n c,\n \"#\",\n line,\n \" columns \",\n columns)\n return None\n\n val = line[c]\n if len(v) > 2 and v[2].lower() not in [\n \"primarykey\", \"autofill\"]:\n val = v[2](val)\n\n try:\n if isinstance(v[1], tuple):\n val = v[1][0](val)\n elif v[1] is datetime.datetime:\n if isinstance(val, datetime.datetime):\n pass\n elif isinstance(val, str):\n val = datetime.datetime.parse(val)\n else:\n raise TypeError(\n \"unable to convert %s into datetime\" % str(\n type(val)))\n else:\n val = v[1](val)\n except ValueError: # as e :\n self.LOG(\n \"(b)line number \",\n num_line,\n \"**unable to process a line columns \",\n c,\n \"#\",\n v[0],\n \" type \",\n v[1],\n \" value \",\n repr(\n line[c]))\n return None\n\n if isinstance(val, str):\n val = val.replace(\"'\", \"''\")\n if lower_case:\n val = val.lower()\n res[v[0]] = val\n\n return res\n except Exception:\n self.LOG(\"(c)line number\", num_line,\n \"***unable to process a line columns:\", line)\n return None","def fix_missing_period(line):\n if \"@highlight\" in line: return line\n if line==\"\": return line\n if line[-1] in END_TOKENS: return line\n # print line[-1]\n return line + \" .\"","def tidy_error(ex=None) -> str:\r\n from sys import exc_info\r\n from os.path import join, abspath, dirname\r\n from traceback import extract_tb, format_list, format_exception_only\r\n\r\n show = join(dirname(abspath(__file__)), '')\r\n\r\n def _check_file(name):\r\n return name and name.startswith(show)\r\n\r\n def _print(typ, value, tb): # If not debug, generator expression: filter trace to my files.\r\n show = extract_tb(tb) if DEBUG else (fs for fs in extract_tb(tb, limit=3) if _check_file(fs.filename))\r\n fmt = format_list(show) + format_exception_only(typ, value)\r\n return ''.join((f.strip('\"\\'').replace('\\\\n', '') for f in fmt))\r\n\r\n args = ex or exc_info()\r\n return _print(*args)","def get_replacement():\n run_linter_throw(\"path/to/file\",\n \"{s}\\n{m} Text{e}\",\n style,\n whitelist=[\"headerblock/filename\"])","def prepare_code_snippet(file_path: str, line_no: int, context_lines_count: int = 5) -> str:\n with open(file_path) as text_file:\n # Highlight code\n code = text_file.read()\n code_lines = code.splitlines()\n # Prepend line number\n code_lines = [\n f\">{lno:3} | {line}\" if line_no == lno else f\"{lno:4} | {line}\"\n for lno, line in enumerate(code_lines, 1)\n ]\n # # Cut out the snippet\n start_line_no = max(0, line_no - context_lines_count - 1)\n end_line_no = line_no + context_lines_count\n code_lines = code_lines[start_line_no:end_line_no]\n # Join lines\n code = \"\\n\".join(code_lines)\n return code","def get_replacement():\n run_linter_throw(\"path/to/file\",\n \"{s} Text\\n{m} Text\\n{m} Text{e}\\n\\n\",\n style,\n whitelist=[\"headerblock/space_copyright\"])","def format_test(version, line, num):\n line = line.split()\n ####line[ 0] = \"../../../source/replace.v\" + str(version) + \".exe\"\n ####line[-3] = \"replace\" + line[-3][2:]\n ####line[-1] = \"replace/outputs/v\" + str(version) + \"/\" + line[-1].split('/')[-1]\n line = [\"../../../source/replace.v\" + str(version) + \".exe\"] + line\n line[-1] = \"replace/inputs/\" + line[-1]\n line.append(\">\")\n line.append(\"replace/outputs/v\" + str(version) + \"/t\" + str(num)) \n return(line)#line[0], ' '.join(line[1:])])","def get_replacement():\n run_linter_throw(\"path/to/file\",\n \"{s} /path/to/file\\n{m}\\n{m} Other{e}\\n\\n\",\n style,\n whitelist=[\"headerblock/copyright\"])","def warning_on_one_line(message, category, filename, lineno,\n file=None, line=None):\n return ' %s:%s: %s: %s' % (filename, lineno, category.__name__, message)","def _preprocess(self, source):\n source = source.replace(u'\\n', u'').strip()\n source = re.sub(r'<br\\s*\\/?\\s*>', u' ', source, re.I)\n source = re.sub(r'\\s\\s+', u' ', source)\n return source","def sanitize(self, line):\n self.line_count = self.line_count + 1\n components = line.split(\",\")\n for comp in components:\n # if any field has IP in it, see if the value matches something in the dict\n items = comp.split(\"=\")\n if \"ip\" in items[0]:\n if items[1] in self._insts:\n # found this dirty IP in our learned dictionary, replace it\n dirty_ip = items[1]\n clean_ip = self._insts[dirty_ip]\n line = re.sub(dirty_ip, clean_ip, line, 1)\n\n # if this message has a pdu, clean up the pdu too\n msg_type = self._extract_by_key(line, \"type\")\n if \"sflow\" in msg_type or \"event\" in msg_type:\n pdu = self._extract_by_key(line, \"pdu\")\n # substitute the converted IP based on type\n if \".\" in dirty_ip:\n # v4\n line = re.sub(self._v4_string_to_hex(dirty_ip),\n self._v4_string_to_hex(clean_ip), line)\n\n pdu = self.fix_checksum(pdu)\n\n line = line[0: (line.find(\"pdu=\")) +4] + pdu + \",\\n\"\n else:\n # v6 - remove : and go to lower case before swap\n dirty_swap = re.sub(\":\", \"\", dirty_ip)\n dirty_swap = dirty_swap.lower()\n line = re.sub(dirty_swap, self._v4_string_to_hex(clean_ip), line)\n if (args.hexdump):\n pdu_hex = pdu\n pdu_hex = \" \".join(pdu_hex[i:i+2] for i in range(0, len(pdu_hex), 2)) #put timestamp and offset in front of pdu hex\n pdu_hex = pdu[0:15] + \" 000000 \" + pdu_hex + \" ,\\n\"\n hexdump_file.write(pdu_hex)\n return line","def test_suggest_no_trailing_whitespace(self, contents):\n def get_replacement():\n \"\"\"Get replacement for trailing newline.\"\"\"\n run_linter_throw(\"path/to/file\",\n contents,\n FormatStyle(\"#\", \"#\", \"\"),\n whitelist=[\"file/trailing_whitespace\"])\n\n exception = self.assertRaises(LinterFailure, get_replacement)\n self.assertEqual(replacement(exception),\n (1, contents.splitlines(True)[0].replace(\" \", \"\")))","def map_notebook_error(violation: Violation, format_str: str) -> tuple[str, int] | None:\n intermediate_filename = os.path.abspath(violation.filename)\n intermediate_line_number = violation.line_number\n mappings = NotebookParser.get_mappings()\n for original_notebook, intermediate_py, input_line_mapping in mappings:\n if os.path.samefile(intermediate_py, intermediate_filename):\n input_id, input_cell_line_number = map_intermediate_to_input(\n input_line_mapping, intermediate_line_number\n )\n exec_count, code_cell_count, total_cell_count = input_id\n filename = format_str.format(\n nb_path=original_notebook,\n exec_count=exec_count,\n code_cell_count=code_cell_count,\n total_cell_count=total_cell_count,\n )\n\n return filename, input_cell_line_number\n return None","def parse_sanitizer_message(\n self,\n it: Iterable[str],\n line: str\n ) -> Tuple[Optional[Report], str]:\n match = self.line_re.match(line)\n if not match:\n return None, line\n\n report_file = get_or_create_file(\n os.path.abspath(match.group('path')), self._file_cache)\n report_line = int(match.group('line'))\n report_col = int(match.group('column'))\n\n line = get_next(it)\n stack_traces, events, line = self.parse_stack_trace(it, line)\n\n message = match.group('message').strip()\n for check, patterns in self.checks.items():\n if any(pattern.search(message) for pattern in patterns):\n checker_name = f\"{self.checker_name}.{check}\"\n break\n\n report = self.create_report(\n events, report_file, report_line, report_col,\n message, stack_traces, checker_name)\n\n return report, line","def transform_line(line):\n for proc, pattern in INCLUDE_LINES:\n if pattern in line:\n # Log lines look like this:\n # pylint: disable=line-too-long\n # Mar 12 09:58:43 jumphost-win-02 metadata-server[5259]: I0312 09:58:43.756257 5259 server.go:87] Updated token: [redacted]\n timestamp = line.split('jumphost', 1)[0]\n suffix = line.split(pattern, 1)[1].rstrip()\n return timestamp + proc + ': ' + pattern + suffix\n return None","def fmt(message, prefix):\n message = str(message).splitlines(True)\n if not message:\n return prefix\n output = \"\"\n for index, line in enumerate(message):\n if index >= 1 and not line.strip():\n output += line\n else:\n output += \"{} {}\".format(prefix, line)\n return output","def test_stripFormatting(self):\n self.assertEqual(\n irc.stripFormatting(\n irc.assembleFormattedText(\n A.bold[\n A.underline[\n A.reverseVideo[A.fg.red[A.bg.green[\"hello\"]]], \" world\"\n ]\n ]\n )\n ),\n \"hello world\",\n )","def reformat_block(specline, values):\n data = reformat_spec_line(specline)\n desc = '\\n'.join(values)\n data.append(desc)\n return data","def cleaned_contents(self):\n snip_with_code = re.compile(\"(//.*snip(\\-file)*:?.*\\n)(\\+\\n)?(\\[.*\\]\\n)*----\\n(.*\\n)*?----\\n\", flags=re.IGNORECASE)\n cleaned = re.sub(snip_with_code, r'\\1', self.contents)\n return cleaned","def format(self, formatter, form, title):\n # store line offsets in self.lines\n self.lines = [0, 0]\n pos = 0\n while 1:\n pos = self.raw.find('\\n', pos) + 1\n if not pos: break\n self.lines.append(pos)\n self.lines.append(len(self.raw))\n\n # parse the source and write it\n self.pos = 0\n text = io.StringIO(self.raw)\n self.out.write('<html><body bgcolor=\"#e0e0e0\"><head><title>%s</title></head><pre><font face=\"Lucida,Courier New\">'%title)\n try:\n if (sys.version_info > (3, 0)):\n tokens = tokenize.generate_tokens(text.readline)\n for toktype,toktext,tok_start,tok_end,line in tokens:\n self.__call__(toktype,toktext,tok_start,tok_end,line)\n else:\n tokenize.tokenize(text.readline,self)\n except tokenize.TokenError as ex:\n msg = ex.args[0]\n line = ex.args[1][0]\n self.out.write(\"<h3>ERROR: %s</h3>%s\\n\" % (\n msg, self.raw[self.lines[line]:]))\n self.out.write('</font></pre></body></html>')","def get_replacement():\n run_linter_throw(\"path/to/file\",\n \"{s}\\n{m} Text{e}\",\n style,\n whitelist=[\"file/newline_last_char\"])","def test_reformat_paragraph_paragraph_2_of_3(self):\n before_b = \"\"\"\\\n Americans live in the most severe\n weather-prone country on Earth. Each\n year, Americans cope with an average of\n 10,000 thunderstorms, 2,500 floods,\n 1,000 tornadoes, as well as an average\n of 6 deadly hurricanes. Potentially\n deadly weather impacts every American.\n Communities can now rely on the National\n Weather Service’s StormReady program to\n help them guard against the ravages of\n Mother Nature.\n\n Some 90% of all presidentially declared disasters are weather related, leading to around 500 deaths per year and nearly $14 billion in damage. StormReady, a program started in 1999 in Tulsa, OK, helps arm America's communities with the communication and safety skills needed to save lives and property– before and during the event. StormReady helps community leaders and emergency managers strengthen local safety programs.\n\n StormReady communities are better prepared to save lives from the onslaught of severe weather through better planning, education, and awareness. No community is storm proof, but StormReady can help communities save lives. Does StormReady make a difference?\n\n Last paragraph.\n \"\"\"\n after_b = \"\"\"\\\n Americans live in the most severe\n weather-prone country on Earth. Each\n year, Americans cope with an average of\n 10,000 thunderstorms, 2,500 floods,\n 1,000 tornadoes, as well as an average\n of 6 deadly hurricanes. Potentially\n deadly weather impacts every American.\n Communities can now rely on the National\n Weather Service’s StormReady program to\n help them guard against the ravages of\n Mother Nature.\n\n Some 90% of all presidentially declared\n disasters are weather related, leading\n to around 500 deaths per year and nearly\n $14 billion in damage. StormReady, a\n program started in 1999 in Tulsa, OK,\n helps arm America's communities with the\n communication and safety skills needed\n to save lives and property– before and\n during the event. StormReady helps\n community leaders and emergency managers\n strengthen local safety programs.\n\n StormReady communities are better prepared to save lives from the onslaught of severe weather through better planning, education, and awareness. No community is storm proof, but StormReady can help communities save lives. Does StormReady make a difference?\n\n Last paragraph.\n \"\"\"\n self.run_test(\n before_b=before_b,\n after_b=after_b,\n before_sel=(\"13.0\", \"13.0\"),\n after_sel=(\"25.0\", \"25.0\"),\n command_name=\"reformat-paragraph\",\n directives=\"@language plain\\n@pagewidth 40\\n@tabwidth 8\",\n )","def fix_broken_yahoo_headers(text):\n end_of_header_match = _end_of_multipart_header_pattern.search(text)\n temp_header_text = text[:end_of_header_match.end()].strip()\n lines = temp_header_text.splitlines()\n fixed_header_lines = reduce(_merge_broken_header_lines, lines, [])\n return_text = os.linesep.join(fixed_header_lines) + '\\r\\n\\r\\n' + text[end_of_header_match.end():]\n return return_text","def format_cell_source(\n src_contents: str, *, mode: black.FileMode\n) -> black.FileContent:\n\n if src_contents.strip() == \"\":\n raise black.NothingChanged\n\n dst_contents = format_str(src_contents, mode=mode)\n\n if src_contents == dst_contents:\n raise black.NothingChanged\n\n assert_equivalent(src_contents, dst_contents)\n assert_stable(dst_contents, mode=mode)\n\n return dst_contents","def FormatDiffHunks(hunks):\n r = []\n last_header = None\n for hunk in hunks:\n this_header = hunk.header[0:2]\n if last_header != this_header:\n r.extend(hunk.header)\n last_header = this_header\n else:\n r.extend(hunk.header[2])\n r.extend(hunk.lines)\n r.append(\"\\n\")\n return \"\".join(r)","def test_suggest_newline(self, style):\n def get_replacement():\n \"\"\"Get replacement for lack of Copyright notice.\"\"\"\n run_linter_throw(\"path/to/file\",\n \"{s} /path/to/file\\n{m}\\n{m} Other{e}\\n\\n\",\n style,\n whitelist=[\"headerblock/copyright\"])\n\n expected_repl = (\"{m} Other\\n\"\n \"{m} See /LICENCE.md for Copyright information{e}\\n\")\n exception = self.assertRaises(LinterFailure, get_replacement)\n self.assertEqual(replacement(exception),\n (3, style_format(expected_repl, style)))","def getMergeLine(desc_line,CC3_sample,GP2_sample):\n return desc_line.strip(\"\\n\") + \"\" + CC3_sample + \"\" + GP2_sample + \"\\n\"","def no_underline_and_no_newline(): # noqa: D416","def process_line(line:str) -> str:\n s = replace_multispace(replace_newlines(line))\n return s","def process(self,line):\n\n pattern = re.compile(\"@.*?@\")\n matches = pattern.findall(line)\n for m in matches:\n replacement = r\"<small>{}</small>\".format(re.escape(m[1:-1]))\n line = pattern.sub(replacement,line)\n\n return line","def _ProcessLine(\n self,\n first_line,\n input_line,\n line,\n stripped_line,\n output_stream):\n # Check for the start of a code block.\n if constants.START_CODEBLOCK_RE.match(stripped_line):\n if self._code_block_depth == 0:\n # Start a new collection of lines.\n self._code_block_lines = []\n else:\n # Just an embedded code block.\n self._code_block_lines.append(line)\n self._code_block_depth += 1\n return\n\n # Check for the end of a code block.\n if constants.END_CODEBLOCK_RE.match(stripped_line):\n self._code_block_depth -= 1\n if self._code_block_depth == 0:\n # Closed the highest-level code block, handle it.\n self._formatting_handler.HandleEscapedText(\n input_line,\n output_stream,\n \"\\n\")\n self._formatting_handler.HandleCodeBlockOpen(\n input_line,\n output_stream,\n None)\n code = \"\".join(self._code_block_lines)\n self._formatting_handler.HandleText(input_line, output_stream, code)\n self._formatting_handler.HandleCodeBlockClose(input_line, output_stream)\n else:\n # Just closed an embedded clode block.\n self._code_block_lines.append(line)\n return\n\n # Check if we're in a code block.\n # If we are, just put the raw text into code_block_lines.\n if self._code_block_depth != 0:\n self._code_block_lines.append(line)\n return\n\n # For empty lines, close all formatting.\n if not stripped_line:\n if not self._ConsumeTextForPlugin():\n self._SetCurrentList(input_line, 0, \" \", output_stream)\n self._CloseTags(input_line, output_stream)\n\n if self._table_columns:\n self._formatting_handler.HandleTableClose(input_line, output_stream)\n self._table_columns = []\n self._table_column = 0\n\n self._formatting_handler.HandleParagraphBreak(input_line, output_stream)\n return\n\n # Non-empty line, finish the previous line's newline.\n if not first_line:\n self._formatting_handler.HandleEscapedText(\n input_line,\n output_stream,\n \"\\n\")\n\n # Now check if we're processing within a list.\n indent_pos = constants.INDENT_RE.match(line).end()\n if (indent_pos and indent_pos < len(line) and\n not self._ConsumeTextForPlugin()):\n list_type = constants.LIST_TYPES.get(line[indent_pos], \"blockquote\")\n\n if self._SetCurrentList(input_line, indent_pos, list_type, output_stream):\n # Blockquotes take the entire remainder of the line,\n # but everything else skips the list symbol plus the space after.\n # (In case there is no space after, the first character is skipped;\n # we will warn if this is detected, as it was probably unintended.)\n if list_type == \"blockquote\":\n line = line[indent_pos:]\n else:\n if line[indent_pos + 1] != \" \":\n self._warning_method(\n input_line,\n u\"Missing space after list symbol: {0}, \"\n \"'{1}' was removed instead.\"\n .format(line[indent_pos], line[indent_pos + 1]))\n line = line[indent_pos + 2:]\n\n stripped_line = line.strip()\n else:\n # Reset to no indent.\n self._SetCurrentList(input_line, 0, \" \", output_stream)\n\n # Finally, split the line into formatting primitives.\n # We do so without whitespace so we can catch line breaks across tags.\n if constants.LINE_FORMAT_RE.match(stripped_line):\n self._ProcessMatch(\n input_line,\n constants.LINE_FORMAT_RE,\n stripped_line,\n output_stream)\n else:\n self._ProcessMatch(\n input_line,\n constants.TEXT_FORMAT_RE,\n stripped_line,\n output_stream)\n\n self._CloseTableRow(input_line, output_stream)","def get_short_errors(self):\n if not self.was_successful():\n for traceback in self.data.traceback.split(\n CaseData.TB_SEPARATOR):\n\n traceback = traceback.strip(\" \\n\")\n bottom_line = traceback.rsplit(\"\\n\", 1)[-1].strip()\n yield \"{}: {}\".format(self.data.name, bottom_line)","def scanpatch(fp):\n lr = patch.linereader(fp)\n\n def scanwhile(first, p):\n \"\"\"scan lr while predicate holds\"\"\"\n lines = [first]\n while True:\n line = lr.readline()\n if not line:\n break\n if p(line):\n lines.append(line)\n else:\n lr.push(line)\n break\n return lines\n\n while True:\n line = lr.readline()\n if not line:\n break\n if line.startswith('diff --git a/'):\n def notheader(line):\n s = line.split(None, 1)\n return not s or s[0] not in ('---', 'diff')\n header = scanwhile(line, notheader)\n fromfile = lr.readline()\n if fromfile.startswith('---'):\n tofile = lr.readline()\n header += [fromfile, tofile]\n else:\n lr.push(fromfile)\n yield 'file', header\n elif line[0] == ' ':\n yield 'context', scanwhile(line, lambda l: l[0] in ' \\\\')\n elif line[0] in '-+':\n yield 'hunk', scanwhile(line, lambda l: l[0] in '-+\\\\')\n else:\n m = lines_re.match(line)\n if m:\n yield 'range', m.groups()\n else:\n raise patch.PatchError('unknown patch content: %r' % line)","def test_reformat_paragraph_paragraph_1_of_3(self):\n before_b = \"\"\"\\\n Americans live in the most severe weather-prone country on Earth. Each year, Americans cope with an average of 10,000 thunderstorms, 2,500 floods, 1,000 tornadoes, as well as an average of 6 deadly hurricanes. Potentially deadly weather impacts every American. Communities can now rely on the National Weather Service’s StormReady program to help them guard against the ravages of Mother Nature.\n\n Some 90% of all presidentially declared disasters are weather related, leading to around 500 deaths per year and nearly $14 billion in damage. StormReady, a program started in 1999 in Tulsa, OK, helps arm America's communities with the communication and safety skills needed to save lives and property– before and during the event. StormReady helps community leaders and emergency managers strengthen local safety programs.\n\n StormReady communities are better prepared to save lives from the onslaught of severe weather through better planning, education, and awareness. No community is storm proof, but StormReady can help communities save lives. Does StormReady make a difference?\n\n Last paragraph.\n \"\"\"\n after_b = \"\"\"\\\n Americans live in the most severe\n weather-prone country on Earth. Each\n year, Americans cope with an average of\n 10,000 thunderstorms, 2,500 floods,\n 1,000 tornadoes, as well as an average\n of 6 deadly hurricanes. Potentially\n deadly weather impacts every American.\n Communities can now rely on the National\n Weather Service’s StormReady program to\n help them guard against the ravages of\n Mother Nature.\n\n Some 90% of all presidentially declared disasters are weather related, leading to around 500 deaths per year and nearly $14 billion in damage. StormReady, a program started in 1999 in Tulsa, OK, helps arm America's communities with the communication and safety skills needed to save lives and property– before and during the event. StormReady helps community leaders and emergency managers strengthen local safety programs.\n\n StormReady communities are better prepared to save lives from the onslaught of severe weather through better planning, education, and awareness. No community is storm proof, but StormReady can help communities save lives. Does StormReady make a difference?\n\n Last paragraph.\n \"\"\"\n self.run_test(\n before_b=before_b,\n after_b=after_b,\n before_sel=(\"1.0\", \"1.0\"),\n after_sel=(\"13.0\", \"13.0\"),\n command_name=\"reformat-paragraph\",\n directives=\"@language plain\\n@pagewidth 40\\n@tabwidth 8\",\n )","def format_exception_only(exc):\r\n exc_type = type(exc)\r\n\r\n stype = exc_type.__qualname__\r\n smod = exc_type.__module__\r\n if smod not in (\"__main__\", \"builtins\"):\r\n stype = smod + '.' + stype\r\n try:\r\n _str = str(exc)\r\n except:\r\n _str = \"<unprintable {} object>\".format(exc_type.__name__)\r\n\r\n if _str == \"None\" or not _str:\r\n line = \"{}\\n\".format(stype)\r\n else:\r\n line = \"{}: {}\\n\".format(stype, _str)\r\n return line","def testBadLine(self):\n\n self.assertRaises(\n ValueError,\n tools._trackInfo,\n 'not a real line'\n )","def format_line(line_data):\n output = []\n for column in COLUMNS:\n output.append(str(line_data[column]) if column != 'attributes' else format_attributes(line_data[column]))\n\n return '\\t'.join(output)","def format_fail(self, *args):\n if self._pretty:\n return self.format_multiline_message(*args, color='red', start='[FAIL] ', multiline=' ~~ ')\n return self.format_multiline_message(*args)","def strip_malformed_directive(yaml_file):\n lines = list(yaml_file)\n first_line = lines[0]\n if first_line.startswith('%') and \":\" in first_line:\n return \"\\n\".join(lines[1:])\n else:\n return \"\\n\".join(lines)","def test_reformat_paragraph_paragraph_3_of_3(self):\n before_b = \"\"\"\\\n Americans live in the most severe\n weather-prone country on Earth. Each\n year, Americans cope with an average of\n 10,000 thunderstorms, 2,500 floods,\n 1,000 tornadoes, as well as an average\n of 6 deadly hurricanes. Potentially\n deadly weather impacts every American.\n Communities can now rely on the National\n Weather Service’s StormReady program to\n help them guard against the ravages of\n Mother Nature.\n\n Some 90% of all presidentially declared\n disasters are weather related, leading\n to around 500 deaths per year and nearly\n $14 billion in damage. StormReady, a\n program started in 1999 in Tulsa, OK,\n helps arm America's communities with the\n communication and safety skills needed\n to save lives and property– before and\n during the event. StormReady helps\n community leaders and emergency managers\n strengthen local safety programs.\n\n StormReady communities are better prepared to save lives from the onslaught of severe weather through better planning, education, and awareness. No community is storm proof, but StormReady can help communities save lives. Does StormReady make a difference?\n\n Last paragraph.\n \"\"\"\n after_b = \"\"\"\\\n Americans live in the most severe\n weather-prone country on Earth. Each\n year, Americans cope with an average of\n 10,000 thunderstorms, 2,500 floods,\n 1,000 tornadoes, as well as an average\n of 6 deadly hurricanes. Potentially\n deadly weather impacts every American.\n Communities can now rely on the National\n Weather Service’s StormReady program to\n help them guard against the ravages of\n Mother Nature.\n\n Some 90% of all presidentially declared\n disasters are weather related, leading\n to around 500 deaths per year and nearly\n $14 billion in damage. StormReady, a\n program started in 1999 in Tulsa, OK,\n helps arm America's communities with the\n communication and safety skills needed\n to save lives and property– before and\n during the event. StormReady helps\n community leaders and emergency managers\n strengthen local safety programs.\n\n StormReady communities are better\n prepared to save lives from the\n onslaught of severe weather through\n better planning, education, and\n awareness. No community is storm proof,\n but StormReady can help communities save\n lives. Does StormReady make a\n difference?\n\n Last paragraph.\n \"\"\"\n self.run_test(\n before_b=before_b,\n after_b=after_b,\n before_sel=(\"25.10\", \"25.10\"),\n after_sel=(\"34.0\", \"34.0\"),\n command_name=\"reformat-paragraph\",\n directives=\"@language plain\\n@pagewidth 40\\n@tabwidth 8\",\n )","def clean_open_close_brace(self):\n # Loop over all lines, check for braces and replace them with \\n{ and \\n}\n brack_num = False\n code_on = False\n\n for line_num, line in enumerate(self.file_ltxt[:-1]):\n self.line_num = line_num\n\n # First check if we are in an inline code section\n breaker = False\n for s_type in VALID_SCRIPT_TYPES:\n if re.findall(f\"^ *{s_type} *{{\", line) or (re.findall(f\"^ *{s_type}\", line) and re.findall(\"^ *{\", self.file_ltxt[line_num+1])):\n if code_on is not False:\n self.print_error(f\"Inline {s_type} code is not supported inside {code_on} code.\")\n\n code_on = s_type\n brack_num = 0\n\n if '{' in line:\n s = line.split(\"{\")\n line = s[0] + \"\\n{\\n\" + '{'.join(s[1:])\n brack_num = 1\n if '}' in line:\n s = line.split(\"}\")\n line = s[0] + \"\\n}\\n\" + '}'.join(s[1:])\n code_on = False\n brack_num = 0\n\n\n self.file_ltxt[line_num] = line\n breaker = True\n if breaker:\n continue\n\n # If we are in an inline code section don't edit it\n if code_on is not False:\n if '}' in line: brack_num -= 1\n if '{' in line: brack_num += 1\n\n if brack_num == 0:\n code_on = False\n\n # If not then we can edit the brace opening and closings\n else:\n str_part, non_str = gen_parse.get_str_between_delims(line)\n non_str = non_str.replace(\"{\", \"\\n{\\n\").replace(\"}\", \"\\n}\\n\")\n line = non_str.replace(r\"??!%s!?\", str_part)\n\n self.file_ltxt[line_num] = line\n # print(self.file_ltxt)\n # raise SystemExit(\"BREAK\")\n # Re-split by line-end and remove blank lines\n self.file_ltxt = [i for i in '\\n'.join(self.file_ltxt).split('\\n')\n if not i.isspace() and i]","def test_format_linebreaks():\r\n test_cases = (\r\n ('Simple:\\n\\nLine two', '<p>Simple:</p><p>Line two</p>'),\r\n ('DOS:\\r\\n\\r\\nLine breaks', '<p>DOS:</p><p>Line breaks</p>'),\r\n ('Classic Mac:\\r\\rLine breaks', '<p>Classic Mac:</p><p>Line breaks</p>'),\r\n ('Consecutive:\\n\\n\\n\\n\\n\\nLine breaks', '<p>Consecutive:</p><p>Line breaks</p>'),\r\n ('Multiple:\\r\\n\\r\\nLine\\r\\n\\r\\nbreaks', '<p>Multiple:</p><p>Line</p><p>breaks</p>'),\r\n ('\\nLeading and trailing\\n', '<p>Leading and trailing</p>'),\r\n ('Single\\ndoesn\\'t wrap', '<p>Single\\ndoesn\\'t wrap</p>'),\r\n ('Quote:\\n\\n<blockquote>(1) One\\n\\n(2) Two</blockquote>\\n\\nAfter',\r\n '<p>Quote:</p><blockquote><p>(1) One</p><p>(2) Two</p></blockquote><p>After</p>'),\r\n ('Quote 2:\\n\\n<blockquote>(1) One\\n\\n(2) Two\\n</blockquote>\\n\\nAfter',\r\n '<p>Quote 2:</p><blockquote><p>(1) One</p><p>(2) Two\\n</p></blockquote><p>After</p>'),\r\n )\r\n for input_text, expected_output in test_cases:\r\n yield is_equal, format_linebreaks(input_text), expected_output","def clean(text):\r\n #clean structure\r\n text = re.sub(r\"(?<!\\|)Amount ?\\| ?Ingredient(?!\\|)\", \"|Amount|Ingredient|\", text)\r\n text = re.sub(r\"----\\|----\\n\\n\", r\"----|----\\n\", text)\r\n text = re.sub(r\"(?<!\\|)----\\|----(?!\\|)\", \"|----|----|\", text)\r\n text = re.sub(\"## Directions\", \"## Cooking Instructions\", text)\r\n\r\n #fractions \r\n for pat, rep in repls:\r\n text = re.sub(pat, rep, text, flags=re.IGNORECASE)\r\n\r\n #links\r\n def fix_link(match):\r\n return \"](../\"+re.sub(\" \", \"-\", fix_title(match.group(1)))+\")\"\r\n text = re.sub(r\"\\]\\((.*?)\\)\", fix_link, text)\r\n \r\n lines = text.split(\"\\n\")\r\n new_text = []\r\n #add spaces to the end of lines\r\n for line in lines:\r\n match = re.search(r\" $\", line)\r\n if match:\r\n new_text.append(line)\r\n else:\r\n new_text.append(line+\" \")\r\n #remove spaces from the end of lines\r\n # for line in lines:\r\n # match = re.search(r\" +$\", line)\r\n # if match:\r\n # new_text.append(line[:-len(match.group(0))])\r\n # else:\r\n # new_text.append(line)\r\n\r\n text = \"\\n\".join(new_text)\r\n\r\n return text","def _replace(self, line):\n if \"@VERSION@\" in line:\n return line.replace(\"@VERSION@\", VERSION)\n\n if \"Source0:\" in line: # Dirty hack\n return \"Source0: %{pkgname}-%{version}.tar.gz\"\n\n return line","def _full_markup(line, allow_headings, context_stack, quoted_text):\n\n #############################################################\n # context formatting\n #\n # the code below marks up recognized special characters,\n # by starting a new context (e.g. headings and lists)\n #############################################################\n\n # generally, we want to start a new paragraph. this will be set\n # to false, if a new paragraph is no longer appropriate, like\n # for headings or lists\n start_paragraph = True\n\n # Match the headings, e.g. === heading ===\n if allow_headings:\n (line, context_stack, start_paragraph) = _headings(line, context_stack, start_paragraph)\n\n # Match list items\n (line, context_stack, start_paragraph) = _lists(line, context_stack, start_paragraph)\n\n # replace four '-' sign with a horizontal ruler\n if re.match('^----\\s*$', line):\n line = \"\\n\".join(context_stack) + '<hr />'\n context_stack = []\n start_paragraph = False\n\n ############################################################\n # inline formatting\n #\n # the code below marks up recognized special characters,\n # without starting a new context (e.g. <strong> and <em>)\n #############################################################\n\n line = _inline(line)\n\n #############################################################\n # paragraph formatting\n #\n # the code below is responsible for doing the Right Thing(tm)\n # by either starting a new paragraph and closing any previous\n # context or continuing an existing paragraph\n #############################################################\n\n # change the quoteing mode when the line start with '>'\n if line[0:4] == '>':\n # if the previous line was not quoted, start a new quote paragraph\n if not quoted_text:\n line = \"\\n\".join(context_stack) + \"<p class=\\\"quote\\\">\" + line\n # empty the stack\n context_stack = ['</p>']\n start_paragraph = False\n quoted_text = True\n else:\n # if the previous line was quoted, end the quote paragraph\n if quoted_text and start_paragraph and line != '':\n line = \"\\n\".join(context_stack) + \"\\n<p>\" + line\n # empty the stack\n context_stack = ['</p>']\n quoted_text = False\n\n # don't start a new paragraph again, if we already did that\n if len(context_stack) > 0 and context_stack[0] == '</p>':\n start_paragraph = False\n\n # add proper closing tags when we encounter an empty line.\n # note that there might be no closing tags, in this case\n # the line will remain emtpy.\n if re.match('^(|\\s*)$', line):\n line = \"\\n\".join(context_stack) + line\n # empty the stack\n context_stack = []\n start_paragraph = False\n\n # Finally start a new paragraph if appropriate\n if start_paragraph:\n # make sure that all previously used contexts get their\n # proper closing tag\n line = \"\\n\".join(context_stack) + \"<p>\" + line\n # empty the stack\n context_stack = ['</p>']\n\n # append a linebreak while in paragraph mode\n if len(context_stack) > 0 and context_stack[0] == '</p>':\n line += '<br />'\n\n return (line, context_stack, quoted_text)","def check_line(self, lineno, line):\n if line.startswith(CODE_BLOCK_DELIMITER) and self.in_code_block:\n if len(line.strip()) == len(CODE_BLOCK_DELIMITER):\n self.register_error(lineno, \"Code block has no formatter\")\n return\n formatter = line[3:].split()[0]\n if formatter not in self.formatters:\n self.register_error(\n lineno,\n f\"Code block has bad formatter '{formatter}'\",\n )","def _check_line_is_good(self, string):\r\n # The standard requires we only accept strings ending in \\r\\n or \\n\r\n if (string[-1] != \"\\n\"):\r\n raise ParseError('Line endings were not as expected', string)\r\n \r\n # The standard places a limit on line lengths\r\n if (len(string)) > 512:\r\n raise ProtocolError('Line too long to be valid', string)\r\n \r\n # Trim our trailing whitespace/line endings\r\n return string.rstrip()","def blank_line_before_underline(): # noqa: D416","def parse_line_err(self, match):\n self.line = match.group(2)\n self.filename = match.group(1)\n self.message = match.group(4)\n self.keyword = match.group(3)\n\n self.fix_filename()\n self.fix_namespaces()\n self.fix_nonerrors()\n\n if self.message.strip() == \"ld returned 1 exit status\":\n self.error_type = \"Linking Error\"\n self.signal_eof = True\n return \"\"\n\n return_value = \"\"\n if self.filename is not None:\n return_value += \"{}:\".format(self.filename)\n if self.line is not None:\n return_value += \"{}: \".format(self.line)\n if self.keyword is not None:\n return_value += \"{}: \".format(self.keyword)\n if self.message is not None:\n return_value += self.message\n\n if return_value != \"\":\n return_value += \"\\n\"\n return return_value","def reformat_code(code):\n if code.startswith('\\n'):\n code = code[1:]\n return dedent(code)","def apply_formatting(source_file, fileName, logger): # @NoSelf\n import subprocess\n f = source_file\n final_formatting_cmd = '({autopep8}) && ({docformatter})'.format(\n autopep8=PythonTidyAction.autopep8_cmd(f),\n docformatter=PythonTidyAction.docformatter_cmd(f))\n logger.info('Will be running $' + final_formatting_cmd)\n exec_frame = subprocess.Popen(\n final_formatting_cmd,\n shell=True,\n stdout=subprocess.PIPE,\n stderr=subprocess.PIPE)\n (cstdout, cstderr) = exec_frame.communicate()\n if cstderr:\n logger.error(cstderr)\n if cstdout:\n logger.info(cstdout)\n logger.info(\n 'Formatting for {fileName} complete with return_code:{rt}'.format(\n fileName=fileName,\n rt=exec_frame.returncode))","def fix_line(line):\n line = line.strip()\n if line.startswith('#LOC'):\n return \"#LOC\\tSVTYPE\\tDIFF\\tFIXED\"\n loc, svtype, instr_orig = line.split('\\t')\n instructions = parse_instr_set(instr_orig, loc)\n\n if upper(svtype) == 'ALUJ':\n dna = ALUJ\n elif upper(svtype) == 'ALUS':\n dna = ALUS\n elif upper(svtype) == 'ALUY':\n dna = ALUY\n elif upper(svtype) == 'LINE1':\n dna = LINE1\n else:\n raise ValueError(\"Invalid svtyp:%s\" % svtype)\n\n for instr in instructions:\n dna = apply_instr(instr, dna, \"%s %s\" % (loc, svtype))\n\n line = \"%s\\t%s\" % (line, dna)\n return line","def test_lint_fail_malformed(self, style):\n with ExpectedException(LinterFailure):\n run_linter_throw(\"path/to/file\",\n \"{s}\\n{m} Text{e}\",\n style,\n whitelist=[\"headerblock/desc_space\"])","def delete_line_numbers(text):\n text = re.sub(r\"(\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}):(\\d+)\", r\"\\g<1>#\\g<2>\", text)\n\n res = re.sub(r\"(?<=:)\\d+(?=\\)?\\]?(\\n|\\r\\n|$))\", \" \", text)\n res = re.sub(r\"((?<=line )|(?<=line))\\s*\\d+\\s*((?=, in)|(?=,in)|(?=\\n)|(?=\\r\\n)|(?=$))\",\n \" \", res, flags=re.I)\n res = re.sub(\"|\".join([r\"\\.%s(?!\\.)\\b\" % ext for ext in file_extensions]), \" \", res, flags=re.I)\n res = re.sub(r\"(\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}\\.\\d{1,3})#(\\d+)\", r\"\\g<1>:\\g<2>\", res)\n result = re.search(r\"^\\s*at\\s+.*\\(.*?\\)[\\s]*$\", res)\n if result and result.group(0) == res:\n res = re.sub(r\"\\d\", \"\", res)\n res = \"# \" + res\n else:\n result = re.search(r\"^\\s*\\w+([\\.\\/]\\s*\\w+)+\\s*\\(.*?\\)[\\s]*$\", res)\n if result and result.group(0) == res:\n res = \"# \" + res\n return res","def test_suggest_newline(self, style):\n def get_replacement():\n \"\"\"Get replacement for lack of trailing newline.\"\"\"\n run_linter_throw(\"path/to/file\",\n \"{s}\\n{m} Text{e}\",\n style,\n whitelist=[\"file/newline_last_char\"])\n\n exception = self.assertRaises(LinterFailure, get_replacement)\n self.assertEqual(replacement(exception),\n (2, style_format(\"{m} Text{e}\\n\", style)))","def ProcessLine(line, rules, processing, previous_line_data):\n line_data = {'line':line, 'line_offset':processing['offset_processed']}\n \n # Update with always-included data, like glob keys, and the component\n line_data.update(processing['data'])\n \n # Test if this line is multi-line (positive test)\n is_multi_line = False\n for rule in rules:\n if rule.get('multi line regex test', False):\n if re.match(rule['regex'], line):\n is_multi_line = True\n break\n # Negative regex test\n for rule in rules:\n if rule.get('multi line regex not', False):\n if re.match(rule['regex'], line):\n is_multi_line = True\n break\n \n # If this is multi_line and we have a real previous line to embed this data in\n if is_multi_line and previous_line_data != None:\n #print 'Multiline: %s' % line\n if 'multiline' not in previous_line_data:\n previous_line_data['multiline'] = []\n \n previous_line_data['multiline'].append(line)\n\n\n # Only process rules on first lines (not multi lines), and return the line_data to be the next line's previous_line_data\n if not is_multi_line:\n #print line\n \n # Start with: We havent found a match yet\n match_found = False\n \n for item in rules:\n # Skip the multi-line regext test/not rules\n if item.get('multi line regex test', False) or item.get('multi line regex not', False):\n continue\n \n # Break out our terms for this rule item\n terms = re.findall('%\\((.*?)\\)s', item['regex'])\n #print item['regex']\n #print terms\n \n regex = item['regex']\n \n # Pre-processing step, to remove any conflicting characters with the rest of the regex which need to be escaped/sanitized\n for term in terms:\n regex = regex.replace('%%(%s)s' % term, 'MATCHMATCHMATCH')\n \n regex = SanitizeRegex(regex)\n regex = regex.replace('MATCHMATCHMATCH', '(.*?)')\n \n #print '--- %s' % item['id']\n #print regex\n #print line\n \n regex_result = re.findall(regex, line)\n #print regex_result\n if regex_result:\n \n # Python does something stupid with multiple variables, so pull them out of the embedded tuple it adds to the list\n if type(regex_result[0]) == tuple:\n regex_result = regex_result[0]\n \n for count in range(0, len(terms)):\n #print '%s: %s: %s' % (count, terms[count], regex_result[count])\n line_data[terms[count]] = regex_result[count]\n \n #print regex\n #print 'MATCHED! %s' % regex\n #print regex_result\n \n match_found = True\n \n # Save the line match ID, so we can reference it for markup/state information\n line_data['__rule_id__'] = item['id']\n \n break\n \n return line_data\n \n # Else, this is multi-line, so return it to continue to be the next line's previous_line_data\n else:\n #TODO(g): Save this multi-line data every time? Otherwise when does it get saved out?\n pass\n \n return previous_line_data","def test_readbadformat(self):\n\n self.assertRaises(ParseError, self.hw, self.badfile)","def summary_line_and_description():","def _highlight_error(doc: str, err_lineno: int) -> str:\n doc_list: list = []\n lineno = 1\n lines_total = doc.count(\"\\n\")\n indent = len(str(lines_total))\n for line in doc.splitlines():\n if lineno == err_lineno:\n doc_list.append(\n \"{lineno:>{indent}}: {line}<---- Error line:{err_lineno}\".format(\n lineno=lineno, indent=indent, line=line, err_lineno=err_lineno\n )\n )\n marks = [\"^\" for _ in line]\n doc_list.append(\n \"{_:{indent}} {marks}------- Erroneous line above\".format(\n _=\"\", indent=indent, marks=\"\".join(marks)\n )\n )\n else:\n doc_list.append(\n \"{lineno:>{indent}}: {line}\".format(\n lineno=lineno, indent=indent, line=line\n )\n )\n lineno += 1\n return \"\\n\".join(doc_list)","async def test_can_apply_python_formatter(request_format): # type: ignore[no-untyped-def]\n response: HTTPResponse = await request_format(\n formatter=\"black\",\n code=[SIMPLE_VALID_PYTHON_CODE],\n options={\"line_length\": 88},\n )\n json_result = _check_http_code_and_schema(\n response=response,\n expected_code=200,\n expected_schema=EXPECTED_FROMAT_SCHEMA,\n )\n assert json_result[\"code\"][0][\"code\"] == \"x = 22\\ne = 1\"","def prettify_frags(frags):\n try:\n prettier_frags = []\n for frag in frags:\n\n # If the player did not kill itself\n if len(frag) > 2:\n frag_line = \"[{time}] {killer} {weapon} {victim}\".format(\n time=frag[0].isoformat(),\n killer=SMILEYFACE + \" \" + frag[1],\n weapon=WEAPON_DICT.get(frag[3]),\n victim=SADFACE + \" \" + frag[2])\n\n else:\n frag_line = \"[{}] \".format(frag[0].isoformat()) + \\\n SADFACE + \" \" + frag[1] + \" \" + SKULL\n\n prettier_frags.append(frag_line)\n\n return prettier_frags\n except Exception:\n print(\"Something is wrong with the log file!\")","def clean_line(self, line):\n\n if \"#\" in line:\n temp = line.split(\"#\")\n if len(temp) < 2:\n return \"\"\n else:\n temp = temp[0] + \"\\n\"\n\n # make sure the \"#\" isn't in quotes\n if temp.count(\"\\\"\") % 2 == 0:\n line = temp\n\n line = line.replace(\"}\", \" } \").replace(\"{\", \" { \")\n while \"=\" in line:\n line = self.replace_equals(line)\n line = line.lstrip()\n return line","def mark_text_with_error_tag(law_text, law_mat):\n law_lines = law_text.split(\"\\n\")\n marked_lines = []\n for i, line in enumerate(law_lines):\n curr_damaged_vec = law_mat[i]\n curr_words = line.split()\n marked_line = get_marked_line(curr_words, curr_damaged_vec)\n marked_line = \" \".join(marked_line)\n marked_lines.append(marked_line)\n return \"\\n\".join(marked_lines)","def _exceptions_formatter(field):\n heads = ['throws']\n types = _or_types(field)\n if types:\n heads.append(types)\n tail = field.get('description', '')\n return heads, tail","def line() -> str:\n return f\"---\"","def doctest_markup(in_lines):\n out_lines = list(in_lines)[:]\n err_tuples = []\n for pos, this in enumerate(out_lines):\n # Check for 'leave the rest' markup\n if this.strip() == '#23dt skip rest':\n break\n # Check for docest line with markup\n mark_match = MARK_COMMENT.search(this)\n if mark_match is None:\n continue\n docbits, marked_line, marker, markup = mark_match.groups()\n place_line_match = PLACE_LINE_EXPRS.match(markup)\n if place_line_match is None:\n msg = ('Found markup \"%s\" in line \"%s\" but wrong syntax' %\n (markup, this))\n err_tuples.append((this, msg))\n continue\n place_expr, line_expr = place_line_match.groups()\n exec_globals = {'here': pos, 'next': pos+1}\n if place_expr.strip() == '':\n place = pos\n else:\n try:\n place = eval(place_expr, exec_globals)\n except:\n msg = ('Error finding place with \"%s\" in line \"%s\"' %\n (place_expr, this))\n err_tuples.append((this, msg))\n continue\n # Prevent processing operating on 23dt comment part of line\n if place == pos:\n line = marked_line\n else:\n line = out_lines[place]\n # Shorthand\n if line_expr == 'bytes':\n # Any strings on the given line are byte strings\n pre, mid, post = INDENT_SPLITTER.match(line).groups()\n try:\n res = byter(mid)\n except:\n err = sys.exc_info()[1]\n msg = ('Error \"%s\" parsing \"%s\"' % (err, err))\n err_tuples.append((this, msg))\n continue\n res = pre + res + post\n else:\n exec_globals.update({'line': line, 'lines': out_lines})\n # If line_expr starts with 'replace', implies \"line.replace\"\n if line_expr.startswith('replace('):\n line_expr = 'line.' + line_expr\n elif line_expr == 'BytesIO':\n line_expr = \"line.replace('StringIO', 'BytesIO')\"\n try:\n res = eval(line_expr, exec_globals)\n except:\n err = sys.exc_info()[1]\n msg = ('Error \"%s\" working on \"%s\" at line %d with \"%s\"' %\n (err, line, place, line_expr))\n err_tuples.append((this, msg))\n continue\n # Put back comment if removed\n if place == pos:\n res = docbits + res + marker + markup\n if res != line:\n out_lines[place] = res\n return out_lines, err_tuples","def diff_line_style(line):\r\n if len(line)<=0: return \"\"\r\n return Edit.diff_marker_to_class.get(line[0],\"\")","def formatException(self, exc_info):\n result = super(OneLineExceptionFormatter, self).formatException(exc_info)\n return repr(result) # or format into one line however you want to"],"string":"[\n \"def format_exception_only(etype, value):\\n # Gracefully handle (the way Python 2.4 and earlier did) the case of\\n # being called with (None, None).\\n if etype is None:\\n return [_format_final_exc_line(etype, value)]\\n\\n stype = etype.__name__\\n smod = etype.__module__\\n if smod not in (\\\"__main__\\\", \\\"builtins\\\", \\\"exceptions\\\"):\\n stype = smod + '.' + stype\\n\\n if not issubclass(etype, SyntaxError):\\n return [_format_final_exc_line(stype, value)]\\n\\n # It was a syntax error; show exactly where the problem was found.\\n lines = []\\n filename = value.filename or \\\"<string>\\\"\\n lineno = str(value.lineno) or '?'\\n lines.append(' File \\\"%s\\\", line %s\\\\n' % (filename, lineno))\\n badline = value.text\\n offset = value.offset\\n if badline is not None:\\n lines.append(' %s\\\\n' % badline.strip())\\n if offset is not None:\\n caretspace = badline.rstrip('\\\\n')[:offset].lstrip()\\n # non-space whitespace (likes tabs) must be kept for alignment\\n caretspace = ((c.isspace() and c or ' ') for c in caretspace)\\n # only three spaces to account for offset1 == pos 0\\n lines.append(' %s^\\\\n' % ''.join(caretspace))\\n msg = value.msg or \\\"<no detail available>\\\"\\n lines.append(\\\"%s: %s\\\\n\\\" % (stype, msg))\\n return lines\",\n \"def black_format(code_path: Path, line_length=90) -> None:\\n check = False\\n diff = False\\n\\n write_back = WriteBack.from_configuration(check=check, diff=diff, color=True)\\n report = Report(check=check, diff=diff, quiet=False, verbose=True)\\n mode = Mode(\\n target_versions={TargetVersion.PY37},\\n line_length=line_length,\\n is_pyi=False,\\n is_ipynb=False,\\n string_normalization=True,\\n magic_trailing_comma=False,\\n experimental_string_processing=False,\\n )\\n\\n reformat_one(\\n src=code_path, fast=True, write_back=write_back, mode=mode, report=report\\n )\\n # TODO: explore how to format a string and return the formatted string\\n # (i.e. with reformat_code)\",\n \"def get_filename_and_formatted_source():\\n sal = gdb.selected_frame().find_sal() # gdb.Symtab_and_line\\n\\n # Check if source code is available\\n if sal.symtab is None:\\n return \\\"\\\", []\\n\\n # Get the full source code\\n closest_line = sal.line\\n filename = sal.symtab.fullname()\\n\\n try:\\n source = get_highlight_source(filename)\\n except OSError:\\n return \\\"\\\", []\\n\\n if not source:\\n return \\\"\\\", []\\n\\n n = int(source_code_lines)\\n\\n # Compute the line range\\n start = max(closest_line - 1 - n // 2, 0)\\n end = min(closest_line - 1 + n // 2 + 1, len(source))\\n num_width = len(str(end))\\n\\n # split the code\\n source = source[start:end]\\n\\n # Compute the prefix_sign length\\n prefix_sign = C.prefix(str(pwndbg.gdblib.config.code_prefix))\\n prefix_width = len(prefix_sign)\\n\\n # Format the output\\n formatted_source = []\\n for line_number, code in enumerate(source, start=start + 1):\\n if pwndbg.gdblib.config.context_source_code_tabstop > 0:\\n code = code.replace(\\\"\\\\t\\\", \\\" \\\" * pwndbg.gdblib.config.context_source_code_tabstop)\\n fmt = \\\" {prefix_sign:{prefix_width}} {line_number:>{num_width}} {code}\\\"\\n if pwndbg.gdblib.config.highlight_source and line_number == closest_line:\\n fmt = C.highlight(fmt)\\n\\n line = fmt.format(\\n prefix_sign=prefix_sign if line_number == closest_line else \\\"\\\",\\n prefix_width=prefix_width,\\n line_number=line_number,\\n num_width=num_width,\\n code=code,\\n )\\n formatted_source.append(line)\\n\\n return filename, formatted_source\",\n \"def format(self, violation: Violation) -> str | None:\\n filename = violation.filename\\n if filename.lower().endswith(\\\".ipynb_parsed\\\"):\\n map_result = map_notebook_error(violation, self.options.notebook_cell_format)\\n if map_result:\\n filename, line_number = map_result\\n return cast(\\n str,\\n self.error_format\\n % {\\n \\\"code\\\": violation.code,\\n \\\"text\\\": violation.text,\\n \\\"path\\\": filename,\\n \\\"row\\\": line_number,\\n \\\"col\\\": violation.column_number,\\n **(COLORS if self.color else COLORS_OFF),\\n },\\n )\\n return cast(str, super().format(violation))\",\n \"def __call__(self, line):\\n marker = self.marker\\n stripped_line = line.strip()\\n if marker == stripped_line:\\n assert not self.traceback_section\\n self.traceback_section = True\\n # print(\\\"XXX: TRACEBACK-START\\\")\\n elif self.traceback_section:\\n matched = self.file_pattern.match(line)\\n if matched:\\n # matched_range = matched.regs[1]\\n filename = matched.groups()[0]\\n new_filename = posixpath_normpath(filename)\\n if new_filename != filename:\\n # print(\\\"XXX: %r => %r\\\" % (filename, new_filename))\\n line = line.replace(filename, new_filename)\\n elif not stripped_line or line[0].isalpha():\\n # -- DETECTED TRCAEBACK-END: exception-description\\n # print(\\\"XXX: TRACEBACK-END\\\")\\n self.traceback_section = False\\n return line\",\n \"def reformat(ctx):\\n pass\",\n \"def strip_warnings(self, line):\\n if line[0] == \\\"|\\\":\\n return \\\"\\\"\\n else:\\n return line\",\n \"def _format_line(cls, line):\\n\\n line = line.strip()\\n\\n if line and not line.startswith(\\\"#\\\"):\\n # The line is not a commented line.\\n\\n if \\\"#\\\" in line:\\n # There is a comment at the end of the line.\\n\\n # We delete the comment from the line.\\n line = line[: line.find(\\\"#\\\")].strip()\\n\\n if \\\" \\\" in line or \\\"\\\\t\\\" in line:\\n # A space or a tabs is in the line.\\n\\n # We remove all whitestring from the extracted line.\\n splited_line = line.split()\\n\\n # As there was a space or a tab in the string, we consider\\n # that we are working with the hosts file format which means\\n # that the domain we have to test is after the first string.\\n # So we set the index to 1.\\n index = 1\\n\\n while index < len(splited_line):\\n # We loop until the index is greater than the length of\\n # the splited line.\\n\\n if splited_line[index]:\\n # The element at the current index is not an empty string.\\n\\n # We break the loop.\\n break\\n\\n # The element at the current index is an empty string.\\n\\n # We increase the index number.\\n index += 1 # pragma: no cover\\n\\n # We return the last read element.\\n return splited_line[index]\\n\\n # We return the extracted line.\\n return line\\n\\n # The extracted line is a comment line.\\n\\n # We return an empty string as we do not want to work with commented line.\\n return \\\"\\\"\",\n \"def get_replacement():\\n run_linter_throw(\\\"path/to/file\\\",\\n contents,\\n FormatStyle(\\\"#\\\", \\\"#\\\", \\\"\\\"),\\n whitelist=[\\\"file/trailing_whitespace\\\"])\",\n \"def highlight_source(linenumber, index, lines, offset=None):\\n # The following if statements are left-over diagnostic\\n # from the hack to integrate into Idle.\\n # they are harmless tests which could potentially be useful.\\n if lines is None:\\n return \\\"\\\", \\\"\\\"\\n if index is None:\\n print(\\\"problem in highlight_source(): index is None\\\")\\n index = 0\\n\\n # The weird index arithmetic below is based on the information returned\\n # by Python's inspect.getinnerframes()\\n\\n new_lines = []\\n problem_line = \\\"\\\"\\n nb_digits = len(str(linenumber + index))\\n no_mark = \\\" {:%d}: \\\" % nb_digits\\n with_mark = \\\" -->{:%d}: \\\" % nb_digits\\n if offset is not None:\\n offset_mark = \\\" \\\" * (8 + nb_digits + offset) + \\\"^\\\"\\n i = linenumber - index\\n\\n for line in lines:\\n if i == linenumber:\\n num = with_mark.format(i)\\n problem_line = line\\n new_lines.append(num + line.rstrip())\\n if offset is not None:\\n new_lines.append(offset_mark)\\n break\\n else:\\n num = no_mark.format(i)\\n new_lines.append(num + line.rstrip())\\n i += 1\\n return \\\"\\\\n\\\".join(new_lines), problem_line\",\n \"def GetLinePostProcess(self):\\r\\n retline = None\\r\\n outline = None\\r\\n try:\\r\\n retline= str(self.file.readline())\\r\\n except IOError:\\r\\n self.tracking.SetError(type(self).__name__, sys._getframe().f_code.co_name, \\\"cannot read a line from\\\" )\\r\\n finally: \\r\\n #outline1 = retline.replace(\\\"/\\\",\\\"\\\")\\r\\n if( (retline !=\\\"\\\") and (retline !=\\\"\\\\n\\\")) :\\r\\n outline = str(\\\"\\\")\\r\\n az_range=range(97,123)\\r\\n AZ_range = range (65, 91)\\r\\n val_range = range (48,58)\\r\\n space_range = range (32, 33)\\r\\n for i in range(len(retline)):\\r\\n value = ord(retline[i] )\\r\\n if ( (value in az_range) or (value in AZ_range) or (value in val_range) or (value in space_range) ):\\r\\n outline = \\\"\\\".join([outline,retline[i]])\\r\\n else:\\r\\n outline = \\\"\\\".join([outline,\\\"_\\\"])\\r\\n '''\\r\\n if( (retline[i] != \\\"/\\\") and (retline[i] != \\\"&\\\") and (retline[i] != \\\"\\\\\\\\\\\") and (retline[i] != \\\"%\\\") and (retline[i] != \\\"#\\\") and (retline[i] != \\\"_\\\") and (retline[i] != '\\\"') and (retline[i] != \\\"@\\\") and (retline[i] != \\\":\\\") and (retline[i] != \\\"\\\\n\\\")):\\r\\n #charac = str(retline[i].encode('ascii','ignore'))\\r\\n if(ord(retline[i]) < 128):\\r\\n outline = \\\"\\\".join([outline,retline[i]])\\r\\n ''' \\r\\n return outline\\r\\n #return unicodedata.normalize('NFKD', outline).encode('ascii','ignore')\\r\",\n \"def _get_lines_with_highlighted_error(e: CompilationError) -> Iterable[str]:\\n error_line = e.lineno - 1\\n lines = e.code.split('\\\\n')\\n padding = len(str(len(lines)))\\n\\n from_line = error_line - const.SYNTAX_ERROR_OFFSET\\n if from_line < 0:\\n from_line = 0\\n\\n if from_line < error_line:\\n for n in range(from_line, error_line):\\n yield _format_line(lines[n], n, padding)\\n\\n yield ' {dim}{n}{reset}: {bright}{line}{reset}'.format(\\n dim=Style.DIM,\\n n=str(error_line + 1).zfill(padding),\\n line=lines[error_line],\\n reset=Style.RESET_ALL,\\n bright=Style.BRIGHT)\\n yield ' {padding}{bright}^{reset}'.format(\\n padding=' ' * (padding + e.offset + 1),\\n bright=Style.BRIGHT,\\n reset=Style.RESET_ALL)\\n\\n to_line = error_line + const.SYNTAX_ERROR_OFFSET\\n if to_line > len(lines):\\n to_line = len(lines)\\n for n in range(error_line + 1, to_line):\\n yield _format_line(lines[n], n, padding)\",\n \"def doomed_parser(line):\\n raise exceptions.LineParseException('Error occurred')\",\n \"def handleBadLine(self, badLine, reason=None):\\n if not reason: reason='N/A'\\n d = dict(reason=reason, bad_Line=badLine)\\n self.badLines.append(d)\",\n \"def wrapped_getlines(filename, globals):\\n lines = orig(filename, globals)\\n source = self.format_source(\\\"\\\".join(lines))\\n\\n if sys.version_info < (3,):\\n source = self.try_to_encode(source)\\n\\n return source.splitlines(True)\",\n \"def tidyout(line, linewidth, currentpos, lineend, autoStyle=True):\\n openStyle = ''\\n closeStyle = ''\\n url=False\\n if autoStyle:\\n # format things tha look like bio sequences\\n if len(line) > 40:\\n if re.search('^[A-Z]+$',line.upper()) != None:\\n # looks like a sequence\\n openStyle = '<pre>'\\n closeStyle = '</pre>'\\n lineend = '<br/>'\\n \\n\\n # format things that look like URL's\\n if re.search('^http[s]*\\\\:\\\\/',line) != None:\\n url=True\\n result = '<a href=\\\"%s\\\" target=urllinkout> %s </a>'%(line,line)\\n \\n\\n if not url:\\n result=openStyle\\n for i in range(0,len(line)):\\n if currentpos == linewidth:\\n result += lineend\\n currentpos = 0 \\n result += line[i:i+1]\\n currentpos += 1\\n\\n #print currentpos\\n result += closeStyle\\n \\n return (result,currentpos)\",\n \"def blockify(source):\\n\\n paragraphs = [\\\"\\\"]\\n for line in source.strip().split(\\\"\\\\n\\\"):\\n line = line.strip()\\n if line: paragraphs[-1] += line + \\\" \\\"\\n elif paragraphs[-1]: paragraphs.append(\\\"\\\")\\n\\n return paragraphs\",\n \"def format(self, filename, timestamp, line):\\n return self._formatter({\\n '@source': \\\"file://{0}{1}\\\".format(self._current_host, filename),\\n '@type': self._file_config.get('type', filename),\\n '@tags': self._file_config.get('tags', filename),\\n '@fields': self._file_config.get('fields', filename),\\n '@timestamp': timestamp,\\n '@source_host': self._current_host,\\n '@source_path': filename,\\n '@message': line.strip(os.linesep),\\n })\",\n \"def highlight_lines(self) -> str:\\n\\n info = self._info\\n buffer = info.buffer\\n\\n startl, endl = info.line, info.endline\\n startp, endp = self.get_text_positions()\\n\\n above_lines = strip_newlines(buffer.get_lines(max(startl - 5, 0), startl - 1))\\n below_lines = strip_newlines(buffer.get_lines(endl + 1, endl + 5))\\n\\n source = list(strip_newlines(self._info.text_lines()))\\n\\n red = colorama.Fore.RED\\n white = colorama.Fore.WHITE\\n normal = colorama.Style.NORMAL\\n reset = colorama.Style.RESET_ALL + colorama.Fore.RESET\\n dim = colorama.Style.DIM\\n bright = colorama.Style.BRIGHT\\n\\n def make_red(s):\\n return reset + red + s + white\\n\\n def make_dim(s):\\n return reset + dim + s + normal\\n\\n def make_bright(s):\\n return reset + bright + s + normal\\n\\n line_pad = \\\" \\\" * 5 # 5 chars are used by the linecount that need to be padded on the arrows\\n\\n def fmtr(counter):\\n if len(source) == 1:\\n # start and end on same line, only need simple fmt\\n yield add_line_once(source[0], counter)\\n if startp == endp: # only emit single carat when the error is a single character\\n yield make_red(line_pad + f\\\"{'^':>{startp}}\\\")\\n else:\\n width = (endp - startp) - 1 # leave space for carats + off by one\\n separator = '-' * width\\n yield make_red(line_pad + f\\\"{'^':>{startp}}{separator}^\\\")\\n else:\\n width = (len(source[0]) - startp)\\n separator = '-' * width\\n yield add_line_once(source[0], counter)\\n yield make_red(line_pad + f\\\"{'^':>{startp}}{separator}\\\")\\n for i in source[1:-1]:\\n yield add_line_once(i, counter)\\n yield make_red(line_pad + '-' * len(i))\\n width = endp - 1 # - len(source[endl])\\n separator = '-' * width\\n yield add_line_once(source[-1], counter)\\n yield make_red(line_pad + f\\\"{separator}^\\\")\\n\\n line_counter = count(max(startl - 5, 1))\\n\\n above_lines = \\\"\\\\n\\\".join(add_line_count(above_lines, line_counter))\\n if above_lines:\\n above_lines += \\\"\\\\n\\\"\\n error_lines = \\\"\\\\n\\\".join(fmtr(line_counter))\\n\\n below_lines = \\\"\\\\n\\\".join(add_line_count(below_lines, line_counter))\\n if below_lines:\\n below_lines = \\\"\\\\n\\\" + below_lines\\n\\n return make_dim(above_lines) + make_bright(error_lines) + make_dim(below_lines)\",\n \"def _format_line( self, side, flag, linenum, text ):\\n\\t\\ttry:\\n\\t\\t\\tline_id = '%d' % linenum\\n\\t\\texcept TypeError:\\n\\t\\t\\t# handle blank lines where linenum is '>' or ''\\n\\t\\t\\tline_id = ''\\n\\n\\t\\ttext = text.rstrip()\\n\\n\\t\\tif not self.line_numbers:\\n\\t\\t\\treturn text\\n\\t\\treturn '%s %s' % ( self._rpad( line_id, 8 ), text )\",\n \"def hide_magic(source: str) -> str:\\n\\n def _hide_magic_line(line: str) -> str:\\n return f\\\"###MAGIC###{line}\\\" if contains_magic(line) else line\\n\\n return \\\"\\\\n\\\".join(_hide_magic_line(line) for line in source.split(\\\"\\\\n\\\"))\",\n \"def _assemble(self, line):\\n self.counter += 1\\n if line.strip() == \\\"\\\":\\n return self.__next__()\\n elif line.startswith(\\\"browser\\\"):\\n return self.__next__()\\n elif line.startswith(\\\"track\\\"):\\n # reset metadata\\n self._parse_track_line(line[5:])\\n return self.__next__()\\n elif line.startswith(\\\"#\\\"):\\n return self.__next__()\\n else:\\n try:\\n return self.return_type.from_bed(line, extra_columns=self.extra_columns)\\n except:\\n self.rejected.append(line)\\n msg = \\\"Cannot parse BED line number %s. \\\" % self.counter\\n if self.metadata.get(\\\"type\\\", None) is not None:\\n msg += (\\n \\\"Are you sure this is a %s BED file with extra columns (%s)?\\\" %\\n (self.metadata.get(\\\"type\\\"), self._get_extra_column_names())\\n )\\n elif self.extra_columns != 0:\\n msg += (\\n \\\"Are you sure this BED file has extra columns (%s)?\\\" %\\n self._get_extra_column_names()\\n )\\n else:\\n msg += \\\"Maybe this BED has extra columns (i.e. is an extended BED file)?\\\"\\n\\n msg += (\\\"\\\\n %s\\\" % line)\\n warn(msg, FileFormatWarning)\\n return self.__next__()\",\n \"def format_line_lean(self):\\n\\t\\tline = \\\"<tr>\\\\\\n\\t\\t\\t\\t\\t<td>\\\\\\n\\t\\t\\t\\t\\t\\t<font size='2'>\\\" + self.tag + \\\"</font>\\\\\\n\\t\\t\\t\\t\\t</td>\\\\\\n\\t\\t\\t\\t\\t<td>\\\\\\n\\t\\t\\t\\t\\t\\t<font size='2'>\\\" + self.value + \\\"</font>\\\\\\n\\t\\t\\t\\t\\t</td>\\\\\\n\\t\\t\\t\\t</tr>\\\"\\n\\t\\treturn line\",\n \"def prepare_text_line(line):\\n\\n re_sub = re.sub\\n # FIXME: maintain the original character positions\\n\\n # strip whitespace\\n line = line.strip()\\n\\n # strip comment markers\\n # common comment characters\\n line = line.strip('\\\\\\\\/*#%;')\\n # un common comment line prefix in dos\\n line = re_sub('^rem\\\\s+', ' ', line)\\n line = re_sub('^\\\\@rem\\\\s+', ' ', line)\\n # un common comment line prefix in autotools am/in\\n line = re_sub('^dnl\\\\s+', ' ', line)\\n # un common comment line prefix in man pages\\n line = re_sub('^\\\\.\\\\\\\\\\\\\\\\\\\"', ' ', line)\\n # un common pipe chars in some ascii art\\n line = line.replace('|', ' ')\\n\\n # normalize copyright signs and spacing aournd them\\n line = line.replace('(C)', ' (c) ')\\n line = line.replace('(c)', ' (c) ')\\n # the case of \\\\251 is tested by 'weirdencoding.h'\\n line = line.replace(u'\\\\251', u' (c) ')\\n line = line.replace('©', ' (c) ')\\n line = line.replace('©', ' (c) ')\\n line = line.replace('©', ' (c) ')\\n line = line.replace(u'\\\\xa9', ' (c) ')\\n # FIXME: what is \\\\xc2???\\n line = line.replace(u'\\\\xc2', '')\\n\\n # TODO: add more HTML entities replacements\\n # see http://www.htmlhelp.com/reference/html40/entities/special.html\\n # convert html entities CR LF to space\\n line = line.replace(u' ', ' ')\\n line = line.replace(u' ', ' ')\\n line = line.replace(u' ', ' ')\\n\\n # normalize (possibly repeated) quotes to unique single quote '\\n # backticks ` and \\\"\\n line = line.replace(u'`', \\\"'\\\")\\n line = line.replace(u'\\\"', \\\"'\\\")\\n line = re.sub(MULTIQUOTES_RE(), \\\"'\\\", line)\\n # quotes to space? but t'so will be wrecked\\n # line = line.replace(u\\\"'\\\", ' ')\\n\\n # some trailing garbage ')\\n line = line.replace(\\\"')\\\", ' ')\\n\\n\\n # note that we do not replace the debian tag by a space: we remove it\\n line = re_sub(DEBIAN_COPYRIGHT_TAGS_RE(), '', line)\\n\\n line = re_sub(IGNORED_PUNCTUATION_RE(), ' ', line)\\n\\n # tabs to spaces\\n line = line.replace('\\\\t', ' ')\\n\\n # normalize spaces around commas\\n line = line.replace(' , ', ', ')\\n\\n # remove ASCII \\\"line decorations\\\"\\n # such as in --- or === or !!! or *****\\n line = re_sub(ASCII_LINE_DECO_RE(), ' ', line)\\n line = re_sub(ASCII_LINE_DECO2_RE(), ' ', line)\\n\\n # Replace escaped literal \\\\0 \\\\n \\\\r \\\\t that may exist as-is by a space\\n # such as in code literals: a=\\\"\\\\\\\\n some text\\\"\\n line = line.replace('\\\\\\\\r', ' ')\\n line = line.replace('\\\\\\\\n', ' ')\\n line = line.replace('\\\\\\\\t', ' ')\\n line = line.replace('\\\\\\\\0', ' ')\\n\\n # TODO: Why?\\n # replace contiguous spaces with only one occurrence\\n # line = re.sub(WHITESPACE_RE(), ' ', text)\\n\\n # normalize to ascii text\\n line = commoncode.text.toascii(line)\\n # logger.debug(\\\"ascii_only_text: \\\" + text)\\n\\n # strip verbatim back slash and comment signs again at both ends of a line\\n # FIXME: this is done at the start of this function already\\n line = line.strip('\\\\\\\\/*#%;')\\n\\n # normalize to use only LF as line endings so we can split correctly\\n # and keep line endings\\n line = commoncode.text.unixlinesep(line)\\n # why?\\n line = lowercase_well_known_word(line)\\n\\n return line\",\n \"def ProcessLine(fn, filename, file_extension, clean_lines, line,\\n include_state, function_state, nesting_state, error,\\n extra_check_functions=[]):\\n fn(filename, file_extension, clean_lines, line,\\n include_state, function_state, nesting_state,\\n makeErrorFn(error, [], [r'(.*)should be indented \\\\+1 space inside(.*)']),\\n extra_check_functions=[])\",\n \"def format(self, warning):\\n if hasattr(warning, 'start') and hasattr(warning, 'end'):\\n if warning.start.line == warning.end.line:\\n location = u'line {}'.format(warning.start.line)\\n else:\\n location = u'lines {}-{}'.format(\\n warning.start.line,\\n warning.end.line\\n )\\n else:\\n location = u'line {}'.format(warning.lineno)\\n if hasattr(warning, 'lines'):\\n lines = warning.lines\\n template = textwrap.dedent(u\\\"\\\"\\\"\\\\\\n File \\\"{file}\\\", {location}\\n {lines}\\n {message}\\n\\n \\\"\\\"\\\")\\n else:\\n lines = []\\n template = textwrap.dedent(u\\\"\\\"\\\"\\\\\\n File \\\"{file}\\\", {location}\\n {message}\\n\\n \\\"\\\"\\\")\\n if len(lines) > 1:\\n lineno_length = count_digits(warning.end.line)\\n lines_with_lineno = []\\n for lineno, line in enumerate(lines, warning.start.line):\\n lines_with_lineno.append(\\n u' {} {}'.format(\\n text_type(lineno).rjust(lineno_length),\\n line\\n )\\n )\\n lines = lines_with_lineno\\n else:\\n lines = [u' ' * 2 + line for line in lines]\\n self.output.write(\\n template.format(\\n file=warning.file,\\n location=location,\\n lines=u'\\\\n'.join(lines),\\n message=textwrap.fill(warning.message)\\n )\\n )\",\n \"def get_replacement():\\n run_linter_throw(\\\"path/to/file\\\",\\n \\\"{s}\\\\n{m} Text{e}\\\",\\n style,\\n whitelist=[\\\"headerblock/desc_space\\\"])\",\n \"def test_incorrect_format_1(self):\\n changelog = changelog_temp.format(\\n before_changelog=\\\"## [Not yet released]\\\\n\\\\n### Added\\\\n\\\\n- Added a new feature\\\\n\\\"\\n )\\n with pytest.raises(ValueError):\\n parse_changelog(changelog)\",\n \"def make_error(self) -> Optional[str]:\\n info = self._info\\n if info is None:\\n return None\\n startl, endl = info.line, info.endline\\n\\n return \\\"\\\\n\\\".join(((f\\\"On line {startl + 1}:\\\"\\n if startl == endl else\\n f\\\"On lines {startl + 1} to {endl + 1}:\\\"),\\n self.highlight_lines))\",\n \"def fix_broken_hotmail_headers(text):\\n end_of_header_match = _end_of_simple_header_pattern.search(text)\\n temp_header_text = text[:end_of_header_match.end()].strip()\\n lines = temp_header_text.splitlines()[1:] # first line is not a header...\\n fixed_header_lines = reduce(_merge_broken_header_lines, lines, [])\\n return_text = os.linesep.join(fixed_header_lines) + text[end_of_header_match.end():]\\n return return_text\",\n \"def fix_missing_period(line):\\n if \\\"@highlight\\\" in line:\\n return line\\n if line == \\\"\\\":\\n return line\\n if line[-1] in END_TOKENS:\\n return line\\n return line + \\\" .\\\"\",\n \"def test_markdown_fixed_issue_line_with_debug_and_file_debug_on():\\n\\n # Arrange\\n scanner = MarkdownScanner()\\n with copy_to_temp_file(\\n os.path.join(\\\"test\\\", \\\"resources\\\", \\\"rules\\\", \\\"md047\\\", \\\"end_with_no_blank_line.md\\\")\\n ) as temp_source_path:\\n supplied_arguments = [\\n \\\"--disable-rules\\\",\\n \\\"md009\\\",\\n \\\"-x-fix-file-debug\\\",\\n \\\"-x-fix-debug\\\",\\n \\\"-x-fix\\\",\\n \\\"scan\\\",\\n temp_source_path,\\n ]\\n\\n expected_return_code = 3\\n expected_output = \\\"\\\"\\\"md010-before:# This is a test:\\nmd047-before:# This is a test:\\nnl-ltw:# This is a test\\\\\\\\n:\\nmd010-before::\\nmd047-before::\\nnl-ltw:\\\\\\\\n:\\nmd010-before:The line after this line should be blank.:\\nmd047-before:The line after this line should be blank.:\\nwas_newline_added_at_end_of_file=False\\nfixed:\\\\\\\\n:\\nis_line_empty=False\\nwas_modified=True\\nnl-ltw:The line after this line should be blank.:\\ncf-ltw:\\\\\\\\n:\\nFixLineRecord(source='completed_file', line_number=4, plugin_id='md047')\\\"\\\"\\\"\\n expected_error = \\\"\\\"\\n initial_file_contents = read_contents_of_text_file(temp_source_path)\\n expected_file_contents = initial_file_contents + \\\"\\\\n\\\"\\n\\n # Act\\n execute_results = scanner.invoke_main(arguments=supplied_arguments)\\n\\n # Assert\\n assert execute_results.std_err.getvalue() == expected_error\\n assert (\\n execute_results.return_code == expected_return_code\\n ), f\\\"Actual error code ({execute_results.return_code}) and expected error code ({expected_return_code}) differ.\\\"\\n assert_file_is_as_expected(temp_source_path, expected_file_contents)\\n\\n std_out_split = execute_results.std_out.getvalue().splitlines()\\n\\n first_section = std_out_split[:4]\\n print(first_section)\\n assert first_section[0] == \\\"\\\"\\n assert first_section[1].startswith(\\\"--\\\") and first_section[1].endswith(\\\"--\\\")\\n assert first_section[2] == initial_file_contents.replace(\\\"\\\\n\\\", \\\"\\\\\\\\n\\\")\\n assert first_section[3] == \\\"--\\\"\\n\\n last_section = std_out_split[-7:]\\n print(last_section)\\n assert last_section[0] == \\\"\\\"\\n assert last_section[1].startswith(\\\"--\\\") and last_section[1].endswith(\\\"--\\\")\\n assert last_section[2] == expected_file_contents.replace(\\\"\\\\n\\\", \\\"\\\\\\\\n\\\")\\n assert last_section[3] == \\\"--\\\"\\n assert last_section[4].startswith(\\\"Copy \\\")\\n assert last_section[5].startswith(\\\"Remove:\\\")\\n assert last_section[6] == f\\\"Fixed: {temp_source_path}\\\"\\n\\n middle_section = std_out_split[4:-7]\\n print(middle_section)\\n split_output = expected_output.splitlines()\\n print(split_output)\\n assert len(middle_section) == len(split_output)\\n for i in range(0, len(split_output)):\\n assert middle_section[i] == split_output[i]\",\n \"def _process_text_line(self, line, columns, format, lower_case, num_line,\\n fill_missing=0, filter_case=None,\\n strict_separator=False):\\n if not isinstance(line, list) and not isinstance(\\n line, tuple) and not isinstance(line, numpy.ndarray):\\n if format != \\\"tsv\\\":\\n raise Exception(\\\"unable to process format \\\" + format)\\n line = line.strip(\\\"\\\\r\\\\n \\\").replace(\\\"\\\\n\\\", \\\" \\\")\\n line = DatabaseCore2._split_expr.split(line)\\n\\n if filter_case is not None:\\n line = [filter_case(s) for s in line]\\n\\n try:\\n if fill_missing > 0:\\n m = max(columns.keys())\\n if m >= len(line):\\n line = copy.copy(line)\\n add = 0\\n while m >= len(line) and add < fill_missing:\\n a, b = columns[len(line)]\\n if b is int:\\n line.append(\\\"0\\\")\\n elif b is float:\\n line.append(\\\"0.0\\\")\\n elif b is decimal.Decimal:\\n line.append(\\\"0\\\")\\n elif b is str:\\n line.append(\\\"\\\")\\n else:\\n line.append(\\\"\\\")\\n add += 1\\n\\n res = {}\\n for c, v in columns.items():\\n if \\\"AUTOFILL\\\" in v:\\n res[v[0]] = \\\"NULL\\\"\\n elif \\\"AUTOINCREMENT\\\" in v:\\n continue\\n else:\\n if c >= len(line):\\n self.LOG(\\n \\\"(a)line number \\\",\\n num_line,\\n \\\"*unable to process a line columns \\\",\\n c,\\n \\\"#\\\",\\n line,\\n \\\" columns \\\",\\n columns)\\n return None\\n\\n val = line[c]\\n if len(v) > 2 and v[2].lower() not in [\\n \\\"primarykey\\\", \\\"autofill\\\"]:\\n val = v[2](val)\\n\\n try:\\n if isinstance(v[1], tuple):\\n val = v[1][0](val)\\n elif v[1] is datetime.datetime:\\n if isinstance(val, datetime.datetime):\\n pass\\n elif isinstance(val, str):\\n val = datetime.datetime.parse(val)\\n else:\\n raise TypeError(\\n \\\"unable to convert %s into datetime\\\" % str(\\n type(val)))\\n else:\\n val = v[1](val)\\n except ValueError: # as e :\\n self.LOG(\\n \\\"(b)line number \\\",\\n num_line,\\n \\\"**unable to process a line columns \\\",\\n c,\\n \\\"#\\\",\\n v[0],\\n \\\" type \\\",\\n v[1],\\n \\\" value \\\",\\n repr(\\n line[c]))\\n return None\\n\\n if isinstance(val, str):\\n val = val.replace(\\\"'\\\", \\\"''\\\")\\n if lower_case:\\n val = val.lower()\\n res[v[0]] = val\\n\\n return res\\n except Exception:\\n self.LOG(\\\"(c)line number\\\", num_line,\\n \\\"***unable to process a line columns:\\\", line)\\n return None\",\n \"def fix_missing_period(line):\\n if \\\"@highlight\\\" in line: return line\\n if line==\\\"\\\": return line\\n if line[-1] in END_TOKENS: return line\\n # print line[-1]\\n return line + \\\" .\\\"\",\n \"def tidy_error(ex=None) -> str:\\r\\n from sys import exc_info\\r\\n from os.path import join, abspath, dirname\\r\\n from traceback import extract_tb, format_list, format_exception_only\\r\\n\\r\\n show = join(dirname(abspath(__file__)), '')\\r\\n\\r\\n def _check_file(name):\\r\\n return name and name.startswith(show)\\r\\n\\r\\n def _print(typ, value, tb): # If not debug, generator expression: filter trace to my files.\\r\\n show = extract_tb(tb) if DEBUG else (fs for fs in extract_tb(tb, limit=3) if _check_file(fs.filename))\\r\\n fmt = format_list(show) + format_exception_only(typ, value)\\r\\n return ''.join((f.strip('\\\"\\\\'').replace('\\\\\\\\n', '') for f in fmt))\\r\\n\\r\\n args = ex or exc_info()\\r\\n return _print(*args)\",\n \"def get_replacement():\\n run_linter_throw(\\\"path/to/file\\\",\\n \\\"{s}\\\\n{m} Text{e}\\\",\\n style,\\n whitelist=[\\\"headerblock/filename\\\"])\",\n \"def prepare_code_snippet(file_path: str, line_no: int, context_lines_count: int = 5) -> str:\\n with open(file_path) as text_file:\\n # Highlight code\\n code = text_file.read()\\n code_lines = code.splitlines()\\n # Prepend line number\\n code_lines = [\\n f\\\">{lno:3} | {line}\\\" if line_no == lno else f\\\"{lno:4} | {line}\\\"\\n for lno, line in enumerate(code_lines, 1)\\n ]\\n # # Cut out the snippet\\n start_line_no = max(0, line_no - context_lines_count - 1)\\n end_line_no = line_no + context_lines_count\\n code_lines = code_lines[start_line_no:end_line_no]\\n # Join lines\\n code = \\\"\\\\n\\\".join(code_lines)\\n return code\",\n \"def get_replacement():\\n run_linter_throw(\\\"path/to/file\\\",\\n \\\"{s} Text\\\\n{m} Text\\\\n{m} Text{e}\\\\n\\\\n\\\",\\n style,\\n whitelist=[\\\"headerblock/space_copyright\\\"])\",\n \"def format_test(version, line, num):\\n line = line.split()\\n ####line[ 0] = \\\"../../../source/replace.v\\\" + str(version) + \\\".exe\\\"\\n ####line[-3] = \\\"replace\\\" + line[-3][2:]\\n ####line[-1] = \\\"replace/outputs/v\\\" + str(version) + \\\"/\\\" + line[-1].split('/')[-1]\\n line = [\\\"../../../source/replace.v\\\" + str(version) + \\\".exe\\\"] + line\\n line[-1] = \\\"replace/inputs/\\\" + line[-1]\\n line.append(\\\">\\\")\\n line.append(\\\"replace/outputs/v\\\" + str(version) + \\\"/t\\\" + str(num)) \\n return(line)#line[0], ' '.join(line[1:])])\",\n \"def get_replacement():\\n run_linter_throw(\\\"path/to/file\\\",\\n \\\"{s} /path/to/file\\\\n{m}\\\\n{m} Other{e}\\\\n\\\\n\\\",\\n style,\\n whitelist=[\\\"headerblock/copyright\\\"])\",\n \"def warning_on_one_line(message, category, filename, lineno,\\n file=None, line=None):\\n return ' %s:%s: %s: %s' % (filename, lineno, category.__name__, message)\",\n \"def _preprocess(self, source):\\n source = source.replace(u'\\\\n', u'').strip()\\n source = re.sub(r'<br\\\\s*\\\\/?\\\\s*>', u' ', source, re.I)\\n source = re.sub(r'\\\\s\\\\s+', u' ', source)\\n return source\",\n \"def sanitize(self, line):\\n self.line_count = self.line_count + 1\\n components = line.split(\\\",\\\")\\n for comp in components:\\n # if any field has IP in it, see if the value matches something in the dict\\n items = comp.split(\\\"=\\\")\\n if \\\"ip\\\" in items[0]:\\n if items[1] in self._insts:\\n # found this dirty IP in our learned dictionary, replace it\\n dirty_ip = items[1]\\n clean_ip = self._insts[dirty_ip]\\n line = re.sub(dirty_ip, clean_ip, line, 1)\\n\\n # if this message has a pdu, clean up the pdu too\\n msg_type = self._extract_by_key(line, \\\"type\\\")\\n if \\\"sflow\\\" in msg_type or \\\"event\\\" in msg_type:\\n pdu = self._extract_by_key(line, \\\"pdu\\\")\\n # substitute the converted IP based on type\\n if \\\".\\\" in dirty_ip:\\n # v4\\n line = re.sub(self._v4_string_to_hex(dirty_ip),\\n self._v4_string_to_hex(clean_ip), line)\\n\\n pdu = self.fix_checksum(pdu)\\n\\n line = line[0: (line.find(\\\"pdu=\\\")) +4] + pdu + \\\",\\\\n\\\"\\n else:\\n # v6 - remove : and go to lower case before swap\\n dirty_swap = re.sub(\\\":\\\", \\\"\\\", dirty_ip)\\n dirty_swap = dirty_swap.lower()\\n line = re.sub(dirty_swap, self._v4_string_to_hex(clean_ip), line)\\n if (args.hexdump):\\n pdu_hex = pdu\\n pdu_hex = \\\" \\\".join(pdu_hex[i:i+2] for i in range(0, len(pdu_hex), 2)) #put timestamp and offset in front of pdu hex\\n pdu_hex = pdu[0:15] + \\\" 000000 \\\" + pdu_hex + \\\" ,\\\\n\\\"\\n hexdump_file.write(pdu_hex)\\n return line\",\n \"def test_suggest_no_trailing_whitespace(self, contents):\\n def get_replacement():\\n \\\"\\\"\\\"Get replacement for trailing newline.\\\"\\\"\\\"\\n run_linter_throw(\\\"path/to/file\\\",\\n contents,\\n FormatStyle(\\\"#\\\", \\\"#\\\", \\\"\\\"),\\n whitelist=[\\\"file/trailing_whitespace\\\"])\\n\\n exception = self.assertRaises(LinterFailure, get_replacement)\\n self.assertEqual(replacement(exception),\\n (1, contents.splitlines(True)[0].replace(\\\" \\\", \\\"\\\")))\",\n \"def map_notebook_error(violation: Violation, format_str: str) -> tuple[str, int] | None:\\n intermediate_filename = os.path.abspath(violation.filename)\\n intermediate_line_number = violation.line_number\\n mappings = NotebookParser.get_mappings()\\n for original_notebook, intermediate_py, input_line_mapping in mappings:\\n if os.path.samefile(intermediate_py, intermediate_filename):\\n input_id, input_cell_line_number = map_intermediate_to_input(\\n input_line_mapping, intermediate_line_number\\n )\\n exec_count, code_cell_count, total_cell_count = input_id\\n filename = format_str.format(\\n nb_path=original_notebook,\\n exec_count=exec_count,\\n code_cell_count=code_cell_count,\\n total_cell_count=total_cell_count,\\n )\\n\\n return filename, input_cell_line_number\\n return None\",\n \"def parse_sanitizer_message(\\n self,\\n it: Iterable[str],\\n line: str\\n ) -> Tuple[Optional[Report], str]:\\n match = self.line_re.match(line)\\n if not match:\\n return None, line\\n\\n report_file = get_or_create_file(\\n os.path.abspath(match.group('path')), self._file_cache)\\n report_line = int(match.group('line'))\\n report_col = int(match.group('column'))\\n\\n line = get_next(it)\\n stack_traces, events, line = self.parse_stack_trace(it, line)\\n\\n message = match.group('message').strip()\\n for check, patterns in self.checks.items():\\n if any(pattern.search(message) for pattern in patterns):\\n checker_name = f\\\"{self.checker_name}.{check}\\\"\\n break\\n\\n report = self.create_report(\\n events, report_file, report_line, report_col,\\n message, stack_traces, checker_name)\\n\\n return report, line\",\n \"def transform_line(line):\\n for proc, pattern in INCLUDE_LINES:\\n if pattern in line:\\n # Log lines look like this:\\n # pylint: disable=line-too-long\\n # Mar 12 09:58:43 jumphost-win-02 metadata-server[5259]: I0312 09:58:43.756257 5259 server.go:87] Updated token: [redacted]\\n timestamp = line.split('jumphost', 1)[0]\\n suffix = line.split(pattern, 1)[1].rstrip()\\n return timestamp + proc + ': ' + pattern + suffix\\n return None\",\n \"def fmt(message, prefix):\\n message = str(message).splitlines(True)\\n if not message:\\n return prefix\\n output = \\\"\\\"\\n for index, line in enumerate(message):\\n if index >= 1 and not line.strip():\\n output += line\\n else:\\n output += \\\"{} {}\\\".format(prefix, line)\\n return output\",\n \"def test_stripFormatting(self):\\n self.assertEqual(\\n irc.stripFormatting(\\n irc.assembleFormattedText(\\n A.bold[\\n A.underline[\\n A.reverseVideo[A.fg.red[A.bg.green[\\\"hello\\\"]]], \\\" world\\\"\\n ]\\n ]\\n )\\n ),\\n \\\"hello world\\\",\\n )\",\n \"def reformat_block(specline, values):\\n data = reformat_spec_line(specline)\\n desc = '\\\\n'.join(values)\\n data.append(desc)\\n return data\",\n \"def cleaned_contents(self):\\n snip_with_code = re.compile(\\\"(//.*snip(\\\\-file)*:?.*\\\\n)(\\\\+\\\\n)?(\\\\[.*\\\\]\\\\n)*----\\\\n(.*\\\\n)*?----\\\\n\\\", flags=re.IGNORECASE)\\n cleaned = re.sub(snip_with_code, r'\\\\1', self.contents)\\n return cleaned\",\n \"def format(self, formatter, form, title):\\n # store line offsets in self.lines\\n self.lines = [0, 0]\\n pos = 0\\n while 1:\\n pos = self.raw.find('\\\\n', pos) + 1\\n if not pos: break\\n self.lines.append(pos)\\n self.lines.append(len(self.raw))\\n\\n # parse the source and write it\\n self.pos = 0\\n text = io.StringIO(self.raw)\\n self.out.write('<html><body bgcolor=\\\"#e0e0e0\\\"><head><title>%s</title></head><pre><font face=\\\"Lucida,Courier New\\\">'%title)\\n try:\\n if (sys.version_info > (3, 0)):\\n tokens = tokenize.generate_tokens(text.readline)\\n for toktype,toktext,tok_start,tok_end,line in tokens:\\n self.__call__(toktype,toktext,tok_start,tok_end,line)\\n else:\\n tokenize.tokenize(text.readline,self)\\n except tokenize.TokenError as ex:\\n msg = ex.args[0]\\n line = ex.args[1][0]\\n self.out.write(\\\"<h3>ERROR: %s</h3>%s\\\\n\\\" % (\\n msg, self.raw[self.lines[line]:]))\\n self.out.write('</font></pre></body></html>')\",\n \"def get_replacement():\\n run_linter_throw(\\\"path/to/file\\\",\\n \\\"{s}\\\\n{m} Text{e}\\\",\\n style,\\n whitelist=[\\\"file/newline_last_char\\\"])\",\n \"def test_reformat_paragraph_paragraph_2_of_3(self):\\n before_b = \\\"\\\"\\\"\\\\\\n Americans live in the most severe\\n weather-prone country on Earth. Each\\n year, Americans cope with an average of\\n 10,000 thunderstorms, 2,500 floods,\\n 1,000 tornadoes, as well as an average\\n of 6 deadly hurricanes. Potentially\\n deadly weather impacts every American.\\n Communities can now rely on the National\\n Weather Service’s StormReady program to\\n help them guard against the ravages of\\n Mother Nature.\\n\\n Some 90% of all presidentially declared disasters are weather related, leading to around 500 deaths per year and nearly $14 billion in damage. StormReady, a program started in 1999 in Tulsa, OK, helps arm America's communities with the communication and safety skills needed to save lives and property– before and during the event. StormReady helps community leaders and emergency managers strengthen local safety programs.\\n\\n StormReady communities are better prepared to save lives from the onslaught of severe weather through better planning, education, and awareness. No community is storm proof, but StormReady can help communities save lives. Does StormReady make a difference?\\n\\n Last paragraph.\\n \\\"\\\"\\\"\\n after_b = \\\"\\\"\\\"\\\\\\n Americans live in the most severe\\n weather-prone country on Earth. Each\\n year, Americans cope with an average of\\n 10,000 thunderstorms, 2,500 floods,\\n 1,000 tornadoes, as well as an average\\n of 6 deadly hurricanes. Potentially\\n deadly weather impacts every American.\\n Communities can now rely on the National\\n Weather Service’s StormReady program to\\n help them guard against the ravages of\\n Mother Nature.\\n\\n Some 90% of all presidentially declared\\n disasters are weather related, leading\\n to around 500 deaths per year and nearly\\n $14 billion in damage. StormReady, a\\n program started in 1999 in Tulsa, OK,\\n helps arm America's communities with the\\n communication and safety skills needed\\n to save lives and property– before and\\n during the event. StormReady helps\\n community leaders and emergency managers\\n strengthen local safety programs.\\n\\n StormReady communities are better prepared to save lives from the onslaught of severe weather through better planning, education, and awareness. No community is storm proof, but StormReady can help communities save lives. Does StormReady make a difference?\\n\\n Last paragraph.\\n \\\"\\\"\\\"\\n self.run_test(\\n before_b=before_b,\\n after_b=after_b,\\n before_sel=(\\\"13.0\\\", \\\"13.0\\\"),\\n after_sel=(\\\"25.0\\\", \\\"25.0\\\"),\\n command_name=\\\"reformat-paragraph\\\",\\n directives=\\\"@language plain\\\\n@pagewidth 40\\\\n@tabwidth 8\\\",\\n )\",\n \"def fix_broken_yahoo_headers(text):\\n end_of_header_match = _end_of_multipart_header_pattern.search(text)\\n temp_header_text = text[:end_of_header_match.end()].strip()\\n lines = temp_header_text.splitlines()\\n fixed_header_lines = reduce(_merge_broken_header_lines, lines, [])\\n return_text = os.linesep.join(fixed_header_lines) + '\\\\r\\\\n\\\\r\\\\n' + text[end_of_header_match.end():]\\n return return_text\",\n \"def format_cell_source(\\n src_contents: str, *, mode: black.FileMode\\n) -> black.FileContent:\\n\\n if src_contents.strip() == \\\"\\\":\\n raise black.NothingChanged\\n\\n dst_contents = format_str(src_contents, mode=mode)\\n\\n if src_contents == dst_contents:\\n raise black.NothingChanged\\n\\n assert_equivalent(src_contents, dst_contents)\\n assert_stable(dst_contents, mode=mode)\\n\\n return dst_contents\",\n \"def FormatDiffHunks(hunks):\\n r = []\\n last_header = None\\n for hunk in hunks:\\n this_header = hunk.header[0:2]\\n if last_header != this_header:\\n r.extend(hunk.header)\\n last_header = this_header\\n else:\\n r.extend(hunk.header[2])\\n r.extend(hunk.lines)\\n r.append(\\\"\\\\n\\\")\\n return \\\"\\\".join(r)\",\n \"def test_suggest_newline(self, style):\\n def get_replacement():\\n \\\"\\\"\\\"Get replacement for lack of Copyright notice.\\\"\\\"\\\"\\n run_linter_throw(\\\"path/to/file\\\",\\n \\\"{s} /path/to/file\\\\n{m}\\\\n{m} Other{e}\\\\n\\\\n\\\",\\n style,\\n whitelist=[\\\"headerblock/copyright\\\"])\\n\\n expected_repl = (\\\"{m} Other\\\\n\\\"\\n \\\"{m} See /LICENCE.md for Copyright information{e}\\\\n\\\")\\n exception = self.assertRaises(LinterFailure, get_replacement)\\n self.assertEqual(replacement(exception),\\n (3, style_format(expected_repl, style)))\",\n \"def getMergeLine(desc_line,CC3_sample,GP2_sample):\\n return desc_line.strip(\\\"\\\\n\\\") + \\\"\\\" + CC3_sample + \\\"\\\" + GP2_sample + \\\"\\\\n\\\"\",\n \"def no_underline_and_no_newline(): # noqa: D416\",\n \"def process_line(line:str) -> str:\\n s = replace_multispace(replace_newlines(line))\\n return s\",\n \"def process(self,line):\\n\\n pattern = re.compile(\\\"@.*?@\\\")\\n matches = pattern.findall(line)\\n for m in matches:\\n replacement = r\\\"<small>{}</small>\\\".format(re.escape(m[1:-1]))\\n line = pattern.sub(replacement,line)\\n\\n return line\",\n \"def _ProcessLine(\\n self,\\n first_line,\\n input_line,\\n line,\\n stripped_line,\\n output_stream):\\n # Check for the start of a code block.\\n if constants.START_CODEBLOCK_RE.match(stripped_line):\\n if self._code_block_depth == 0:\\n # Start a new collection of lines.\\n self._code_block_lines = []\\n else:\\n # Just an embedded code block.\\n self._code_block_lines.append(line)\\n self._code_block_depth += 1\\n return\\n\\n # Check for the end of a code block.\\n if constants.END_CODEBLOCK_RE.match(stripped_line):\\n self._code_block_depth -= 1\\n if self._code_block_depth == 0:\\n # Closed the highest-level code block, handle it.\\n self._formatting_handler.HandleEscapedText(\\n input_line,\\n output_stream,\\n \\\"\\\\n\\\")\\n self._formatting_handler.HandleCodeBlockOpen(\\n input_line,\\n output_stream,\\n None)\\n code = \\\"\\\".join(self._code_block_lines)\\n self._formatting_handler.HandleText(input_line, output_stream, code)\\n self._formatting_handler.HandleCodeBlockClose(input_line, output_stream)\\n else:\\n # Just closed an embedded clode block.\\n self._code_block_lines.append(line)\\n return\\n\\n # Check if we're in a code block.\\n # If we are, just put the raw text into code_block_lines.\\n if self._code_block_depth != 0:\\n self._code_block_lines.append(line)\\n return\\n\\n # For empty lines, close all formatting.\\n if not stripped_line:\\n if not self._ConsumeTextForPlugin():\\n self._SetCurrentList(input_line, 0, \\\" \\\", output_stream)\\n self._CloseTags(input_line, output_stream)\\n\\n if self._table_columns:\\n self._formatting_handler.HandleTableClose(input_line, output_stream)\\n self._table_columns = []\\n self._table_column = 0\\n\\n self._formatting_handler.HandleParagraphBreak(input_line, output_stream)\\n return\\n\\n # Non-empty line, finish the previous line's newline.\\n if not first_line:\\n self._formatting_handler.HandleEscapedText(\\n input_line,\\n output_stream,\\n \\\"\\\\n\\\")\\n\\n # Now check if we're processing within a list.\\n indent_pos = constants.INDENT_RE.match(line).end()\\n if (indent_pos and indent_pos < len(line) and\\n not self._ConsumeTextForPlugin()):\\n list_type = constants.LIST_TYPES.get(line[indent_pos], \\\"blockquote\\\")\\n\\n if self._SetCurrentList(input_line, indent_pos, list_type, output_stream):\\n # Blockquotes take the entire remainder of the line,\\n # but everything else skips the list symbol plus the space after.\\n # (In case there is no space after, the first character is skipped;\\n # we will warn if this is detected, as it was probably unintended.)\\n if list_type == \\\"blockquote\\\":\\n line = line[indent_pos:]\\n else:\\n if line[indent_pos + 1] != \\\" \\\":\\n self._warning_method(\\n input_line,\\n u\\\"Missing space after list symbol: {0}, \\\"\\n \\\"'{1}' was removed instead.\\\"\\n .format(line[indent_pos], line[indent_pos + 1]))\\n line = line[indent_pos + 2:]\\n\\n stripped_line = line.strip()\\n else:\\n # Reset to no indent.\\n self._SetCurrentList(input_line, 0, \\\" \\\", output_stream)\\n\\n # Finally, split the line into formatting primitives.\\n # We do so without whitespace so we can catch line breaks across tags.\\n if constants.LINE_FORMAT_RE.match(stripped_line):\\n self._ProcessMatch(\\n input_line,\\n constants.LINE_FORMAT_RE,\\n stripped_line,\\n output_stream)\\n else:\\n self._ProcessMatch(\\n input_line,\\n constants.TEXT_FORMAT_RE,\\n stripped_line,\\n output_stream)\\n\\n self._CloseTableRow(input_line, output_stream)\",\n \"def get_short_errors(self):\\n if not self.was_successful():\\n for traceback in self.data.traceback.split(\\n CaseData.TB_SEPARATOR):\\n\\n traceback = traceback.strip(\\\" \\\\n\\\")\\n bottom_line = traceback.rsplit(\\\"\\\\n\\\", 1)[-1].strip()\\n yield \\\"{}: {}\\\".format(self.data.name, bottom_line)\",\n \"def scanpatch(fp):\\n lr = patch.linereader(fp)\\n\\n def scanwhile(first, p):\\n \\\"\\\"\\\"scan lr while predicate holds\\\"\\\"\\\"\\n lines = [first]\\n while True:\\n line = lr.readline()\\n if not line:\\n break\\n if p(line):\\n lines.append(line)\\n else:\\n lr.push(line)\\n break\\n return lines\\n\\n while True:\\n line = lr.readline()\\n if not line:\\n break\\n if line.startswith('diff --git a/'):\\n def notheader(line):\\n s = line.split(None, 1)\\n return not s or s[0] not in ('---', 'diff')\\n header = scanwhile(line, notheader)\\n fromfile = lr.readline()\\n if fromfile.startswith('---'):\\n tofile = lr.readline()\\n header += [fromfile, tofile]\\n else:\\n lr.push(fromfile)\\n yield 'file', header\\n elif line[0] == ' ':\\n yield 'context', scanwhile(line, lambda l: l[0] in ' \\\\\\\\')\\n elif line[0] in '-+':\\n yield 'hunk', scanwhile(line, lambda l: l[0] in '-+\\\\\\\\')\\n else:\\n m = lines_re.match(line)\\n if m:\\n yield 'range', m.groups()\\n else:\\n raise patch.PatchError('unknown patch content: %r' % line)\",\n \"def test_reformat_paragraph_paragraph_1_of_3(self):\\n before_b = \\\"\\\"\\\"\\\\\\n Americans live in the most severe weather-prone country on Earth. Each year, Americans cope with an average of 10,000 thunderstorms, 2,500 floods, 1,000 tornadoes, as well as an average of 6 deadly hurricanes. Potentially deadly weather impacts every American. Communities can now rely on the National Weather Service’s StormReady program to help them guard against the ravages of Mother Nature.\\n\\n Some 90% of all presidentially declared disasters are weather related, leading to around 500 deaths per year and nearly $14 billion in damage. StormReady, a program started in 1999 in Tulsa, OK, helps arm America's communities with the communication and safety skills needed to save lives and property– before and during the event. StormReady helps community leaders and emergency managers strengthen local safety programs.\\n\\n StormReady communities are better prepared to save lives from the onslaught of severe weather through better planning, education, and awareness. No community is storm proof, but StormReady can help communities save lives. Does StormReady make a difference?\\n\\n Last paragraph.\\n \\\"\\\"\\\"\\n after_b = \\\"\\\"\\\"\\\\\\n Americans live in the most severe\\n weather-prone country on Earth. Each\\n year, Americans cope with an average of\\n 10,000 thunderstorms, 2,500 floods,\\n 1,000 tornadoes, as well as an average\\n of 6 deadly hurricanes. Potentially\\n deadly weather impacts every American.\\n Communities can now rely on the National\\n Weather Service’s StormReady program to\\n help them guard against the ravages of\\n Mother Nature.\\n\\n Some 90% of all presidentially declared disasters are weather related, leading to around 500 deaths per year and nearly $14 billion in damage. StormReady, a program started in 1999 in Tulsa, OK, helps arm America's communities with the communication and safety skills needed to save lives and property– before and during the event. StormReady helps community leaders and emergency managers strengthen local safety programs.\\n\\n StormReady communities are better prepared to save lives from the onslaught of severe weather through better planning, education, and awareness. No community is storm proof, but StormReady can help communities save lives. Does StormReady make a difference?\\n\\n Last paragraph.\\n \\\"\\\"\\\"\\n self.run_test(\\n before_b=before_b,\\n after_b=after_b,\\n before_sel=(\\\"1.0\\\", \\\"1.0\\\"),\\n after_sel=(\\\"13.0\\\", \\\"13.0\\\"),\\n command_name=\\\"reformat-paragraph\\\",\\n directives=\\\"@language plain\\\\n@pagewidth 40\\\\n@tabwidth 8\\\",\\n )\",\n \"def format_exception_only(exc):\\r\\n exc_type = type(exc)\\r\\n\\r\\n stype = exc_type.__qualname__\\r\\n smod = exc_type.__module__\\r\\n if smod not in (\\\"__main__\\\", \\\"builtins\\\"):\\r\\n stype = smod + '.' + stype\\r\\n try:\\r\\n _str = str(exc)\\r\\n except:\\r\\n _str = \\\"<unprintable {} object>\\\".format(exc_type.__name__)\\r\\n\\r\\n if _str == \\\"None\\\" or not _str:\\r\\n line = \\\"{}\\\\n\\\".format(stype)\\r\\n else:\\r\\n line = \\\"{}: {}\\\\n\\\".format(stype, _str)\\r\\n return line\",\n \"def testBadLine(self):\\n\\n self.assertRaises(\\n ValueError,\\n tools._trackInfo,\\n 'not a real line'\\n )\",\n \"def format_line(line_data):\\n output = []\\n for column in COLUMNS:\\n output.append(str(line_data[column]) if column != 'attributes' else format_attributes(line_data[column]))\\n\\n return '\\\\t'.join(output)\",\n \"def format_fail(self, *args):\\n if self._pretty:\\n return self.format_multiline_message(*args, color='red', start='[FAIL] ', multiline=' ~~ ')\\n return self.format_multiline_message(*args)\",\n \"def strip_malformed_directive(yaml_file):\\n lines = list(yaml_file)\\n first_line = lines[0]\\n if first_line.startswith('%') and \\\":\\\" in first_line:\\n return \\\"\\\\n\\\".join(lines[1:])\\n else:\\n return \\\"\\\\n\\\".join(lines)\",\n \"def test_reformat_paragraph_paragraph_3_of_3(self):\\n before_b = \\\"\\\"\\\"\\\\\\n Americans live in the most severe\\n weather-prone country on Earth. Each\\n year, Americans cope with an average of\\n 10,000 thunderstorms, 2,500 floods,\\n 1,000 tornadoes, as well as an average\\n of 6 deadly hurricanes. Potentially\\n deadly weather impacts every American.\\n Communities can now rely on the National\\n Weather Service’s StormReady program to\\n help them guard against the ravages of\\n Mother Nature.\\n\\n Some 90% of all presidentially declared\\n disasters are weather related, leading\\n to around 500 deaths per year and nearly\\n $14 billion in damage. StormReady, a\\n program started in 1999 in Tulsa, OK,\\n helps arm America's communities with the\\n communication and safety skills needed\\n to save lives and property– before and\\n during the event. StormReady helps\\n community leaders and emergency managers\\n strengthen local safety programs.\\n\\n StormReady communities are better prepared to save lives from the onslaught of severe weather through better planning, education, and awareness. No community is storm proof, but StormReady can help communities save lives. Does StormReady make a difference?\\n\\n Last paragraph.\\n \\\"\\\"\\\"\\n after_b = \\\"\\\"\\\"\\\\\\n Americans live in the most severe\\n weather-prone country on Earth. Each\\n year, Americans cope with an average of\\n 10,000 thunderstorms, 2,500 floods,\\n 1,000 tornadoes, as well as an average\\n of 6 deadly hurricanes. Potentially\\n deadly weather impacts every American.\\n Communities can now rely on the National\\n Weather Service’s StormReady program to\\n help them guard against the ravages of\\n Mother Nature.\\n\\n Some 90% of all presidentially declared\\n disasters are weather related, leading\\n to around 500 deaths per year and nearly\\n $14 billion in damage. StormReady, a\\n program started in 1999 in Tulsa, OK,\\n helps arm America's communities with the\\n communication and safety skills needed\\n to save lives and property– before and\\n during the event. StormReady helps\\n community leaders and emergency managers\\n strengthen local safety programs.\\n\\n StormReady communities are better\\n prepared to save lives from the\\n onslaught of severe weather through\\n better planning, education, and\\n awareness. No community is storm proof,\\n but StormReady can help communities save\\n lives. Does StormReady make a\\n difference?\\n\\n Last paragraph.\\n \\\"\\\"\\\"\\n self.run_test(\\n before_b=before_b,\\n after_b=after_b,\\n before_sel=(\\\"25.10\\\", \\\"25.10\\\"),\\n after_sel=(\\\"34.0\\\", \\\"34.0\\\"),\\n command_name=\\\"reformat-paragraph\\\",\\n directives=\\\"@language plain\\\\n@pagewidth 40\\\\n@tabwidth 8\\\",\\n )\",\n \"def clean_open_close_brace(self):\\n # Loop over all lines, check for braces and replace them with \\\\n{ and \\\\n}\\n brack_num = False\\n code_on = False\\n\\n for line_num, line in enumerate(self.file_ltxt[:-1]):\\n self.line_num = line_num\\n\\n # First check if we are in an inline code section\\n breaker = False\\n for s_type in VALID_SCRIPT_TYPES:\\n if re.findall(f\\\"^ *{s_type} *{{\\\", line) or (re.findall(f\\\"^ *{s_type}\\\", line) and re.findall(\\\"^ *{\\\", self.file_ltxt[line_num+1])):\\n if code_on is not False:\\n self.print_error(f\\\"Inline {s_type} code is not supported inside {code_on} code.\\\")\\n\\n code_on = s_type\\n brack_num = 0\\n\\n if '{' in line:\\n s = line.split(\\\"{\\\")\\n line = s[0] + \\\"\\\\n{\\\\n\\\" + '{'.join(s[1:])\\n brack_num = 1\\n if '}' in line:\\n s = line.split(\\\"}\\\")\\n line = s[0] + \\\"\\\\n}\\\\n\\\" + '}'.join(s[1:])\\n code_on = False\\n brack_num = 0\\n\\n\\n self.file_ltxt[line_num] = line\\n breaker = True\\n if breaker:\\n continue\\n\\n # If we are in an inline code section don't edit it\\n if code_on is not False:\\n if '}' in line: brack_num -= 1\\n if '{' in line: brack_num += 1\\n\\n if brack_num == 0:\\n code_on = False\\n\\n # If not then we can edit the brace opening and closings\\n else:\\n str_part, non_str = gen_parse.get_str_between_delims(line)\\n non_str = non_str.replace(\\\"{\\\", \\\"\\\\n{\\\\n\\\").replace(\\\"}\\\", \\\"\\\\n}\\\\n\\\")\\n line = non_str.replace(r\\\"??!%s!?\\\", str_part)\\n\\n self.file_ltxt[line_num] = line\\n # print(self.file_ltxt)\\n # raise SystemExit(\\\"BREAK\\\")\\n # Re-split by line-end and remove blank lines\\n self.file_ltxt = [i for i in '\\\\n'.join(self.file_ltxt).split('\\\\n')\\n if not i.isspace() and i]\",\n \"def test_format_linebreaks():\\r\\n test_cases = (\\r\\n ('Simple:\\\\n\\\\nLine two', '<p>Simple:</p><p>Line two</p>'),\\r\\n ('DOS:\\\\r\\\\n\\\\r\\\\nLine breaks', '<p>DOS:</p><p>Line breaks</p>'),\\r\\n ('Classic Mac:\\\\r\\\\rLine breaks', '<p>Classic Mac:</p><p>Line breaks</p>'),\\r\\n ('Consecutive:\\\\n\\\\n\\\\n\\\\n\\\\n\\\\nLine breaks', '<p>Consecutive:</p><p>Line breaks</p>'),\\r\\n ('Multiple:\\\\r\\\\n\\\\r\\\\nLine\\\\r\\\\n\\\\r\\\\nbreaks', '<p>Multiple:</p><p>Line</p><p>breaks</p>'),\\r\\n ('\\\\nLeading and trailing\\\\n', '<p>Leading and trailing</p>'),\\r\\n ('Single\\\\ndoesn\\\\'t wrap', '<p>Single\\\\ndoesn\\\\'t wrap</p>'),\\r\\n ('Quote:\\\\n\\\\n<blockquote>(1) One\\\\n\\\\n(2) Two</blockquote>\\\\n\\\\nAfter',\\r\\n '<p>Quote:</p><blockquote><p>(1) One</p><p>(2) Two</p></blockquote><p>After</p>'),\\r\\n ('Quote 2:\\\\n\\\\n<blockquote>(1) One\\\\n\\\\n(2) Two\\\\n</blockquote>\\\\n\\\\nAfter',\\r\\n '<p>Quote 2:</p><blockquote><p>(1) One</p><p>(2) Two\\\\n</p></blockquote><p>After</p>'),\\r\\n )\\r\\n for input_text, expected_output in test_cases:\\r\\n yield is_equal, format_linebreaks(input_text), expected_output\",\n \"def clean(text):\\r\\n #clean structure\\r\\n text = re.sub(r\\\"(?<!\\\\|)Amount ?\\\\| ?Ingredient(?!\\\\|)\\\", \\\"|Amount|Ingredient|\\\", text)\\r\\n text = re.sub(r\\\"----\\\\|----\\\\n\\\\n\\\", r\\\"----|----\\\\n\\\", text)\\r\\n text = re.sub(r\\\"(?<!\\\\|)----\\\\|----(?!\\\\|)\\\", \\\"|----|----|\\\", text)\\r\\n text = re.sub(\\\"## Directions\\\", \\\"## Cooking Instructions\\\", text)\\r\\n\\r\\n #fractions \\r\\n for pat, rep in repls:\\r\\n text = re.sub(pat, rep, text, flags=re.IGNORECASE)\\r\\n\\r\\n #links\\r\\n def fix_link(match):\\r\\n return \\\"](../\\\"+re.sub(\\\" \\\", \\\"-\\\", fix_title(match.group(1)))+\\\")\\\"\\r\\n text = re.sub(r\\\"\\\\]\\\\((.*?)\\\\)\\\", fix_link, text)\\r\\n \\r\\n lines = text.split(\\\"\\\\n\\\")\\r\\n new_text = []\\r\\n #add spaces to the end of lines\\r\\n for line in lines:\\r\\n match = re.search(r\\\" $\\\", line)\\r\\n if match:\\r\\n new_text.append(line)\\r\\n else:\\r\\n new_text.append(line+\\\" \\\")\\r\\n #remove spaces from the end of lines\\r\\n # for line in lines:\\r\\n # match = re.search(r\\\" +$\\\", line)\\r\\n # if match:\\r\\n # new_text.append(line[:-len(match.group(0))])\\r\\n # else:\\r\\n # new_text.append(line)\\r\\n\\r\\n text = \\\"\\\\n\\\".join(new_text)\\r\\n\\r\\n return text\",\n \"def _replace(self, line):\\n if \\\"@VERSION@\\\" in line:\\n return line.replace(\\\"@VERSION@\\\", VERSION)\\n\\n if \\\"Source0:\\\" in line: # Dirty hack\\n return \\\"Source0: %{pkgname}-%{version}.tar.gz\\\"\\n\\n return line\",\n \"def _full_markup(line, allow_headings, context_stack, quoted_text):\\n\\n #############################################################\\n # context formatting\\n #\\n # the code below marks up recognized special characters,\\n # by starting a new context (e.g. headings and lists)\\n #############################################################\\n\\n # generally, we want to start a new paragraph. this will be set\\n # to false, if a new paragraph is no longer appropriate, like\\n # for headings or lists\\n start_paragraph = True\\n\\n # Match the headings, e.g. === heading ===\\n if allow_headings:\\n (line, context_stack, start_paragraph) = _headings(line, context_stack, start_paragraph)\\n\\n # Match list items\\n (line, context_stack, start_paragraph) = _lists(line, context_stack, start_paragraph)\\n\\n # replace four '-' sign with a horizontal ruler\\n if re.match('^----\\\\s*$', line):\\n line = \\\"\\\\n\\\".join(context_stack) + '<hr />'\\n context_stack = []\\n start_paragraph = False\\n\\n ############################################################\\n # inline formatting\\n #\\n # the code below marks up recognized special characters,\\n # without starting a new context (e.g. <strong> and <em>)\\n #############################################################\\n\\n line = _inline(line)\\n\\n #############################################################\\n # paragraph formatting\\n #\\n # the code below is responsible for doing the Right Thing(tm)\\n # by either starting a new paragraph and closing any previous\\n # context or continuing an existing paragraph\\n #############################################################\\n\\n # change the quoteing mode when the line start with '>'\\n if line[0:4] == '>':\\n # if the previous line was not quoted, start a new quote paragraph\\n if not quoted_text:\\n line = \\\"\\\\n\\\".join(context_stack) + \\\"<p class=\\\\\\\"quote\\\\\\\">\\\" + line\\n # empty the stack\\n context_stack = ['</p>']\\n start_paragraph = False\\n quoted_text = True\\n else:\\n # if the previous line was quoted, end the quote paragraph\\n if quoted_text and start_paragraph and line != '':\\n line = \\\"\\\\n\\\".join(context_stack) + \\\"\\\\n<p>\\\" + line\\n # empty the stack\\n context_stack = ['</p>']\\n quoted_text = False\\n\\n # don't start a new paragraph again, if we already did that\\n if len(context_stack) > 0 and context_stack[0] == '</p>':\\n start_paragraph = False\\n\\n # add proper closing tags when we encounter an empty line.\\n # note that there might be no closing tags, in this case\\n # the line will remain emtpy.\\n if re.match('^(|\\\\s*)$', line):\\n line = \\\"\\\\n\\\".join(context_stack) + line\\n # empty the stack\\n context_stack = []\\n start_paragraph = False\\n\\n # Finally start a new paragraph if appropriate\\n if start_paragraph:\\n # make sure that all previously used contexts get their\\n # proper closing tag\\n line = \\\"\\\\n\\\".join(context_stack) + \\\"<p>\\\" + line\\n # empty the stack\\n context_stack = ['</p>']\\n\\n # append a linebreak while in paragraph mode\\n if len(context_stack) > 0 and context_stack[0] == '</p>':\\n line += '<br />'\\n\\n return (line, context_stack, quoted_text)\",\n \"def check_line(self, lineno, line):\\n if line.startswith(CODE_BLOCK_DELIMITER) and self.in_code_block:\\n if len(line.strip()) == len(CODE_BLOCK_DELIMITER):\\n self.register_error(lineno, \\\"Code block has no formatter\\\")\\n return\\n formatter = line[3:].split()[0]\\n if formatter not in self.formatters:\\n self.register_error(\\n lineno,\\n f\\\"Code block has bad formatter '{formatter}'\\\",\\n )\",\n \"def _check_line_is_good(self, string):\\r\\n # The standard requires we only accept strings ending in \\\\r\\\\n or \\\\n\\r\\n if (string[-1] != \\\"\\\\n\\\"):\\r\\n raise ParseError('Line endings were not as expected', string)\\r\\n \\r\\n # The standard places a limit on line lengths\\r\\n if (len(string)) > 512:\\r\\n raise ProtocolError('Line too long to be valid', string)\\r\\n \\r\\n # Trim our trailing whitespace/line endings\\r\\n return string.rstrip()\",\n \"def blank_line_before_underline(): # noqa: D416\",\n \"def parse_line_err(self, match):\\n self.line = match.group(2)\\n self.filename = match.group(1)\\n self.message = match.group(4)\\n self.keyword = match.group(3)\\n\\n self.fix_filename()\\n self.fix_namespaces()\\n self.fix_nonerrors()\\n\\n if self.message.strip() == \\\"ld returned 1 exit status\\\":\\n self.error_type = \\\"Linking Error\\\"\\n self.signal_eof = True\\n return \\\"\\\"\\n\\n return_value = \\\"\\\"\\n if self.filename is not None:\\n return_value += \\\"{}:\\\".format(self.filename)\\n if self.line is not None:\\n return_value += \\\"{}: \\\".format(self.line)\\n if self.keyword is not None:\\n return_value += \\\"{}: \\\".format(self.keyword)\\n if self.message is not None:\\n return_value += self.message\\n\\n if return_value != \\\"\\\":\\n return_value += \\\"\\\\n\\\"\\n return return_value\",\n \"def reformat_code(code):\\n if code.startswith('\\\\n'):\\n code = code[1:]\\n return dedent(code)\",\n \"def apply_formatting(source_file, fileName, logger): # @NoSelf\\n import subprocess\\n f = source_file\\n final_formatting_cmd = '({autopep8}) && ({docformatter})'.format(\\n autopep8=PythonTidyAction.autopep8_cmd(f),\\n docformatter=PythonTidyAction.docformatter_cmd(f))\\n logger.info('Will be running $' + final_formatting_cmd)\\n exec_frame = subprocess.Popen(\\n final_formatting_cmd,\\n shell=True,\\n stdout=subprocess.PIPE,\\n stderr=subprocess.PIPE)\\n (cstdout, cstderr) = exec_frame.communicate()\\n if cstderr:\\n logger.error(cstderr)\\n if cstdout:\\n logger.info(cstdout)\\n logger.info(\\n 'Formatting for {fileName} complete with return_code:{rt}'.format(\\n fileName=fileName,\\n rt=exec_frame.returncode))\",\n \"def fix_line(line):\\n line = line.strip()\\n if line.startswith('#LOC'):\\n return \\\"#LOC\\\\tSVTYPE\\\\tDIFF\\\\tFIXED\\\"\\n loc, svtype, instr_orig = line.split('\\\\t')\\n instructions = parse_instr_set(instr_orig, loc)\\n\\n if upper(svtype) == 'ALUJ':\\n dna = ALUJ\\n elif upper(svtype) == 'ALUS':\\n dna = ALUS\\n elif upper(svtype) == 'ALUY':\\n dna = ALUY\\n elif upper(svtype) == 'LINE1':\\n dna = LINE1\\n else:\\n raise ValueError(\\\"Invalid svtyp:%s\\\" % svtype)\\n\\n for instr in instructions:\\n dna = apply_instr(instr, dna, \\\"%s %s\\\" % (loc, svtype))\\n\\n line = \\\"%s\\\\t%s\\\" % (line, dna)\\n return line\",\n \"def test_lint_fail_malformed(self, style):\\n with ExpectedException(LinterFailure):\\n run_linter_throw(\\\"path/to/file\\\",\\n \\\"{s}\\\\n{m} Text{e}\\\",\\n style,\\n whitelist=[\\\"headerblock/desc_space\\\"])\",\n \"def delete_line_numbers(text):\\n text = re.sub(r\\\"(\\\\d{1,3}\\\\.\\\\d{1,3}\\\\.\\\\d{1,3}\\\\.\\\\d{1,3}):(\\\\d+)\\\", r\\\"\\\\g<1>#\\\\g<2>\\\", text)\\n\\n res = re.sub(r\\\"(?<=:)\\\\d+(?=\\\\)?\\\\]?(\\\\n|\\\\r\\\\n|$))\\\", \\\" \\\", text)\\n res = re.sub(r\\\"((?<=line )|(?<=line))\\\\s*\\\\d+\\\\s*((?=, in)|(?=,in)|(?=\\\\n)|(?=\\\\r\\\\n)|(?=$))\\\",\\n \\\" \\\", res, flags=re.I)\\n res = re.sub(\\\"|\\\".join([r\\\"\\\\.%s(?!\\\\.)\\\\b\\\" % ext for ext in file_extensions]), \\\" \\\", res, flags=re.I)\\n res = re.sub(r\\\"(\\\\d{1,3}\\\\.\\\\d{1,3}\\\\.\\\\d{1,3}\\\\.\\\\d{1,3})#(\\\\d+)\\\", r\\\"\\\\g<1>:\\\\g<2>\\\", res)\\n result = re.search(r\\\"^\\\\s*at\\\\s+.*\\\\(.*?\\\\)[\\\\s]*$\\\", res)\\n if result and result.group(0) == res:\\n res = re.sub(r\\\"\\\\d\\\", \\\"\\\", res)\\n res = \\\"# \\\" + res\\n else:\\n result = re.search(r\\\"^\\\\s*\\\\w+([\\\\.\\\\/]\\\\s*\\\\w+)+\\\\s*\\\\(.*?\\\\)[\\\\s]*$\\\", res)\\n if result and result.group(0) == res:\\n res = \\\"# \\\" + res\\n return res\",\n \"def test_suggest_newline(self, style):\\n def get_replacement():\\n \\\"\\\"\\\"Get replacement for lack of trailing newline.\\\"\\\"\\\"\\n run_linter_throw(\\\"path/to/file\\\",\\n \\\"{s}\\\\n{m} Text{e}\\\",\\n style,\\n whitelist=[\\\"file/newline_last_char\\\"])\\n\\n exception = self.assertRaises(LinterFailure, get_replacement)\\n self.assertEqual(replacement(exception),\\n (2, style_format(\\\"{m} Text{e}\\\\n\\\", style)))\",\n \"def ProcessLine(line, rules, processing, previous_line_data):\\n line_data = {'line':line, 'line_offset':processing['offset_processed']}\\n \\n # Update with always-included data, like glob keys, and the component\\n line_data.update(processing['data'])\\n \\n # Test if this line is multi-line (positive test)\\n is_multi_line = False\\n for rule in rules:\\n if rule.get('multi line regex test', False):\\n if re.match(rule['regex'], line):\\n is_multi_line = True\\n break\\n # Negative regex test\\n for rule in rules:\\n if rule.get('multi line regex not', False):\\n if re.match(rule['regex'], line):\\n is_multi_line = True\\n break\\n \\n # If this is multi_line and we have a real previous line to embed this data in\\n if is_multi_line and previous_line_data != None:\\n #print 'Multiline: %s' % line\\n if 'multiline' not in previous_line_data:\\n previous_line_data['multiline'] = []\\n \\n previous_line_data['multiline'].append(line)\\n\\n\\n # Only process rules on first lines (not multi lines), and return the line_data to be the next line's previous_line_data\\n if not is_multi_line:\\n #print line\\n \\n # Start with: We havent found a match yet\\n match_found = False\\n \\n for item in rules:\\n # Skip the multi-line regext test/not rules\\n if item.get('multi line regex test', False) or item.get('multi line regex not', False):\\n continue\\n \\n # Break out our terms for this rule item\\n terms = re.findall('%\\\\((.*?)\\\\)s', item['regex'])\\n #print item['regex']\\n #print terms\\n \\n regex = item['regex']\\n \\n # Pre-processing step, to remove any conflicting characters with the rest of the regex which need to be escaped/sanitized\\n for term in terms:\\n regex = regex.replace('%%(%s)s' % term, 'MATCHMATCHMATCH')\\n \\n regex = SanitizeRegex(regex)\\n regex = regex.replace('MATCHMATCHMATCH', '(.*?)')\\n \\n #print '--- %s' % item['id']\\n #print regex\\n #print line\\n \\n regex_result = re.findall(regex, line)\\n #print regex_result\\n if regex_result:\\n \\n # Python does something stupid with multiple variables, so pull them out of the embedded tuple it adds to the list\\n if type(regex_result[0]) == tuple:\\n regex_result = regex_result[0]\\n \\n for count in range(0, len(terms)):\\n #print '%s: %s: %s' % (count, terms[count], regex_result[count])\\n line_data[terms[count]] = regex_result[count]\\n \\n #print regex\\n #print 'MATCHED! %s' % regex\\n #print regex_result\\n \\n match_found = True\\n \\n # Save the line match ID, so we can reference it for markup/state information\\n line_data['__rule_id__'] = item['id']\\n \\n break\\n \\n return line_data\\n \\n # Else, this is multi-line, so return it to continue to be the next line's previous_line_data\\n else:\\n #TODO(g): Save this multi-line data every time? Otherwise when does it get saved out?\\n pass\\n \\n return previous_line_data\",\n \"def test_readbadformat(self):\\n\\n self.assertRaises(ParseError, self.hw, self.badfile)\",\n \"def summary_line_and_description():\",\n \"def _highlight_error(doc: str, err_lineno: int) -> str:\\n doc_list: list = []\\n lineno = 1\\n lines_total = doc.count(\\\"\\\\n\\\")\\n indent = len(str(lines_total))\\n for line in doc.splitlines():\\n if lineno == err_lineno:\\n doc_list.append(\\n \\\"{lineno:>{indent}}: {line}<---- Error line:{err_lineno}\\\".format(\\n lineno=lineno, indent=indent, line=line, err_lineno=err_lineno\\n )\\n )\\n marks = [\\\"^\\\" for _ in line]\\n doc_list.append(\\n \\\"{_:{indent}} {marks}------- Erroneous line above\\\".format(\\n _=\\\"\\\", indent=indent, marks=\\\"\\\".join(marks)\\n )\\n )\\n else:\\n doc_list.append(\\n \\\"{lineno:>{indent}}: {line}\\\".format(\\n lineno=lineno, indent=indent, line=line\\n )\\n )\\n lineno += 1\\n return \\\"\\\\n\\\".join(doc_list)\",\n \"async def test_can_apply_python_formatter(request_format): # type: ignore[no-untyped-def]\\n response: HTTPResponse = await request_format(\\n formatter=\\\"black\\\",\\n code=[SIMPLE_VALID_PYTHON_CODE],\\n options={\\\"line_length\\\": 88},\\n )\\n json_result = _check_http_code_and_schema(\\n response=response,\\n expected_code=200,\\n expected_schema=EXPECTED_FROMAT_SCHEMA,\\n )\\n assert json_result[\\\"code\\\"][0][\\\"code\\\"] == \\\"x = 22\\\\ne = 1\\\"\",\n \"def prettify_frags(frags):\\n try:\\n prettier_frags = []\\n for frag in frags:\\n\\n # If the player did not kill itself\\n if len(frag) > 2:\\n frag_line = \\\"[{time}] {killer} {weapon} {victim}\\\".format(\\n time=frag[0].isoformat(),\\n killer=SMILEYFACE + \\\" \\\" + frag[1],\\n weapon=WEAPON_DICT.get(frag[3]),\\n victim=SADFACE + \\\" \\\" + frag[2])\\n\\n else:\\n frag_line = \\\"[{}] \\\".format(frag[0].isoformat()) + \\\\\\n SADFACE + \\\" \\\" + frag[1] + \\\" \\\" + SKULL\\n\\n prettier_frags.append(frag_line)\\n\\n return prettier_frags\\n except Exception:\\n print(\\\"Something is wrong with the log file!\\\")\",\n \"def clean_line(self, line):\\n\\n if \\\"#\\\" in line:\\n temp = line.split(\\\"#\\\")\\n if len(temp) < 2:\\n return \\\"\\\"\\n else:\\n temp = temp[0] + \\\"\\\\n\\\"\\n\\n # make sure the \\\"#\\\" isn't in quotes\\n if temp.count(\\\"\\\\\\\"\\\") % 2 == 0:\\n line = temp\\n\\n line = line.replace(\\\"}\\\", \\\" } \\\").replace(\\\"{\\\", \\\" { \\\")\\n while \\\"=\\\" in line:\\n line = self.replace_equals(line)\\n line = line.lstrip()\\n return line\",\n \"def mark_text_with_error_tag(law_text, law_mat):\\n law_lines = law_text.split(\\\"\\\\n\\\")\\n marked_lines = []\\n for i, line in enumerate(law_lines):\\n curr_damaged_vec = law_mat[i]\\n curr_words = line.split()\\n marked_line = get_marked_line(curr_words, curr_damaged_vec)\\n marked_line = \\\" \\\".join(marked_line)\\n marked_lines.append(marked_line)\\n return \\\"\\\\n\\\".join(marked_lines)\",\n \"def _exceptions_formatter(field):\\n heads = ['throws']\\n types = _or_types(field)\\n if types:\\n heads.append(types)\\n tail = field.get('description', '')\\n return heads, tail\",\n \"def line() -> str:\\n return f\\\"---\\\"\",\n \"def doctest_markup(in_lines):\\n out_lines = list(in_lines)[:]\\n err_tuples = []\\n for pos, this in enumerate(out_lines):\\n # Check for 'leave the rest' markup\\n if this.strip() == '#23dt skip rest':\\n break\\n # Check for docest line with markup\\n mark_match = MARK_COMMENT.search(this)\\n if mark_match is None:\\n continue\\n docbits, marked_line, marker, markup = mark_match.groups()\\n place_line_match = PLACE_LINE_EXPRS.match(markup)\\n if place_line_match is None:\\n msg = ('Found markup \\\"%s\\\" in line \\\"%s\\\" but wrong syntax' %\\n (markup, this))\\n err_tuples.append((this, msg))\\n continue\\n place_expr, line_expr = place_line_match.groups()\\n exec_globals = {'here': pos, 'next': pos+1}\\n if place_expr.strip() == '':\\n place = pos\\n else:\\n try:\\n place = eval(place_expr, exec_globals)\\n except:\\n msg = ('Error finding place with \\\"%s\\\" in line \\\"%s\\\"' %\\n (place_expr, this))\\n err_tuples.append((this, msg))\\n continue\\n # Prevent processing operating on 23dt comment part of line\\n if place == pos:\\n line = marked_line\\n else:\\n line = out_lines[place]\\n # Shorthand\\n if line_expr == 'bytes':\\n # Any strings on the given line are byte strings\\n pre, mid, post = INDENT_SPLITTER.match(line).groups()\\n try:\\n res = byter(mid)\\n except:\\n err = sys.exc_info()[1]\\n msg = ('Error \\\"%s\\\" parsing \\\"%s\\\"' % (err, err))\\n err_tuples.append((this, msg))\\n continue\\n res = pre + res + post\\n else:\\n exec_globals.update({'line': line, 'lines': out_lines})\\n # If line_expr starts with 'replace', implies \\\"line.replace\\\"\\n if line_expr.startswith('replace('):\\n line_expr = 'line.' + line_expr\\n elif line_expr == 'BytesIO':\\n line_expr = \\\"line.replace('StringIO', 'BytesIO')\\\"\\n try:\\n res = eval(line_expr, exec_globals)\\n except:\\n err = sys.exc_info()[1]\\n msg = ('Error \\\"%s\\\" working on \\\"%s\\\" at line %d with \\\"%s\\\"' %\\n (err, line, place, line_expr))\\n err_tuples.append((this, msg))\\n continue\\n # Put back comment if removed\\n if place == pos:\\n res = docbits + res + marker + markup\\n if res != line:\\n out_lines[place] = res\\n return out_lines, err_tuples\",\n \"def diff_line_style(line):\\r\\n if len(line)<=0: return \\\"\\\"\\r\\n return Edit.diff_marker_to_class.get(line[0],\\\"\\\")\",\n \"def formatException(self, exc_info):\\n result = super(OneLineExceptionFormatter, self).formatException(exc_info)\\n return repr(result) # or format into one line however you want to\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.5942906","0.59109855","0.5804325","0.5789683","0.5765263","0.5730036","0.57251096","0.5659315","0.5617689","0.55531806","0.5530319","0.54957825","0.545304","0.5448972","0.5432576","0.54311997","0.54229766","0.5407884","0.5405036","0.53888756","0.5380812","0.53745776","0.5320049","0.53022206","0.52909464","0.5286963","0.5284235","0.5277187","0.5264695","0.5229774","0.5217889","0.5206076","0.51970446","0.51967293","0.51930994","0.5190325","0.5188283","0.51824385","0.5177325","0.5155438","0.5148919","0.5143078","0.51383674","0.51303506","0.5124757","0.512108","0.5093859","0.5083865","0.5077997","0.50736725","0.50608367","0.5040117","0.5038846","0.5023202","0.5020693","0.50196797","0.5006189","0.49980873","0.49955136","0.49933988","0.49880025","0.49847353","0.49827272","0.49792945","0.49685982","0.49670008","0.49652532","0.49558878","0.49546662","0.49504954","0.4950417","0.49497452","0.4947347","0.49436802","0.49388108","0.49354893","0.4934788","0.49316582","0.49315196","0.4929851","0.4928146","0.49220878","0.49206373","0.49138913","0.4906036","0.49012092","0.4901057","0.48958546","0.4883909","0.48829824","0.48823774","0.4880866","0.48802155","0.4878037","0.48767656","0.48702165","0.4865752","0.48654175","0.48595658","0.48576522"],"string":"[\n \"0.5942906\",\n \"0.59109855\",\n \"0.5804325\",\n \"0.5789683\",\n \"0.5765263\",\n \"0.5730036\",\n \"0.57251096\",\n \"0.5659315\",\n \"0.5617689\",\n \"0.55531806\",\n \"0.5530319\",\n \"0.54957825\",\n \"0.545304\",\n \"0.5448972\",\n \"0.5432576\",\n \"0.54311997\",\n \"0.54229766\",\n \"0.5407884\",\n \"0.5405036\",\n \"0.53888756\",\n \"0.5380812\",\n \"0.53745776\",\n \"0.5320049\",\n \"0.53022206\",\n \"0.52909464\",\n \"0.5286963\",\n \"0.5284235\",\n \"0.5277187\",\n \"0.5264695\",\n \"0.5229774\",\n \"0.5217889\",\n \"0.5206076\",\n \"0.51970446\",\n \"0.51967293\",\n \"0.51930994\",\n \"0.5190325\",\n \"0.5188283\",\n \"0.51824385\",\n \"0.5177325\",\n \"0.5155438\",\n \"0.5148919\",\n \"0.5143078\",\n \"0.51383674\",\n \"0.51303506\",\n \"0.5124757\",\n \"0.512108\",\n \"0.5093859\",\n \"0.5083865\",\n \"0.5077997\",\n \"0.50736725\",\n \"0.50608367\",\n \"0.5040117\",\n \"0.5038846\",\n \"0.5023202\",\n \"0.5020693\",\n \"0.50196797\",\n \"0.5006189\",\n \"0.49980873\",\n \"0.49955136\",\n \"0.49933988\",\n \"0.49880025\",\n \"0.49847353\",\n \"0.49827272\",\n \"0.49792945\",\n \"0.49685982\",\n \"0.49670008\",\n \"0.49652532\",\n \"0.49558878\",\n \"0.49546662\",\n \"0.49504954\",\n \"0.4950417\",\n \"0.49497452\",\n \"0.4947347\",\n \"0.49436802\",\n \"0.49388108\",\n \"0.49354893\",\n \"0.4934788\",\n \"0.49316582\",\n \"0.49315196\",\n \"0.4929851\",\n \"0.4928146\",\n \"0.49220878\",\n \"0.49206373\",\n \"0.49138913\",\n \"0.4906036\",\n \"0.49012092\",\n \"0.4901057\",\n \"0.48958546\",\n \"0.4883909\",\n \"0.48829824\",\n \"0.48823774\",\n \"0.4880866\",\n \"0.48802155\",\n \"0.4878037\",\n \"0.48767656\",\n \"0.48702165\",\n \"0.4865752\",\n \"0.48654175\",\n \"0.48595658\",\n \"0.48576522\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.70705295"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":81198,"cells":{"query":{"kind":"string","value":"Extracts a few relevant lines from a file content given as a list of lines, adding line number information and identifying a particular line. When dealing with a ``SyntaxError`` or its subclasses, offset is an integer normally used by Python to indicate the position of"},"document":{"kind":"string","value":"def highlight_source(linenumber, index, lines, offset=None):\n # The following if statements are left-over diagnostic\n # from the hack to integrate into Idle.\n # they are harmless tests which could potentially be useful.\n if lines is None:\n return \"\", \"\"\n if index is None:\n print(\"problem in highlight_source(): index is None\")\n index = 0\n\n # The weird index arithmetic below is based on the information returned\n # by Python's inspect.getinnerframes()\n\n new_lines = []\n problem_line = \"\"\n nb_digits = len(str(linenumber + index))\n no_mark = \" {:%d}: \" % nb_digits\n with_mark = \" -->{:%d}: \" % nb_digits\n if offset is not None:\n offset_mark = \" \" * (8 + nb_digits + offset) + \"^\"\n i = linenumber - index\n\n for line in lines:\n if i == linenumber:\n num = with_mark.format(i)\n problem_line = line\n new_lines.append(num + line.rstrip())\n if offset is not None:\n new_lines.append(offset_mark)\n break\n else:\n num = no_mark.format(i)\n new_lines.append(num + line.rstrip())\n i += 1\n return \"\\n\".join(new_lines), problem_line"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def _get_lines_from_file(filename, lineno, context_lines):\n\n try:\n source = open(filename).readlines()\n lower_bound = max(0, lineno - context_lines)\n upper_bound = lineno + context_lines\n\n pre_context = \\\n [line.strip('\\n') for line in source[lower_bound:lineno]]\n context_line = source[lineno].strip('\\n')\n post_context = \\\n [line.strip('\\n') for line in source[lineno + 1:upper_bound]]\n\n return lower_bound, pre_context, context_line, post_context\n except (OSError, IOError):\n return None, [], None, []","def get_lines_from_file(filename, lineno, context_lines, loader=None, module_name=None):\n source = None\n if loader is not None and hasattr(loader, \"get_source\"):\n with suppress(ImportError):\n source = loader.get_source(module_name)\n if source is not None:\n source = source.splitlines()\n if source is None:\n with suppress(OSError, IOError):\n with open(filename, \"rb\") as fp:\n source = fp.read().splitlines()\n if source is None:\n return None, [], None, []\n try:\n # If we just read the source from a file, or if the loader did not\n # apply tokenize.detect_encoding to decode the source into a Unicode\n # string, then we should do that ourselves.\n if isinstance(source[0], bytes):\n encoding = \"ascii\"\n for line in source[:2]:\n # File coding may be specified. Match pattern from PEP-263\n # (http://www.python.org/dev/peps/pep-0263/)\n match = re.search(br\"coding[:=]\\s*([-\\w.]+)\", line)\n if match:\n encoding = match.group(1).decode(\"ascii\")\n break\n source = [str(sline, encoding, \"replace\") for sline in source]\n\n lower_bound = max(0, lineno - context_lines)\n upper_bound = lineno + context_lines\n\n pre_context = source[lower_bound:lineno]\n context_line = source[lineno]\n post_context = source[lineno + 1 : upper_bound]\n\n return lower_bound, pre_context, context_line, post_context\n except Exception as e:\n try:\n context_line = f'<There was an error displaying the source file: \"{repr(e)}\" The loaded source has {len(source)} lines.>'\n except Exception:\n context_line = \"<There was an error displaying the source file. Further, there was an error displaying that error>\"\n return lineno, [], context_line, []","def _get_lines_from_file(self, filename, lineno, context_lines, loader=None, module_name=None):\n source = None\n if loader is not None and hasattr(loader, \"get_source\"):\n source = loader.get_source(module_name)\n if source is not None:\n source = source.splitlines()\n if source is None:\n try:\n f = open(filename)\n try:\n source = f.readlines()\n finally:\n f.close()\n except (OSError, IOError):\n pass\n if source is None:\n return None, [], None, []\n\n encoding = 'ascii'\n for line in source[:2]:\n # File coding may be specified. Match pattern from PEP-263\n # (http://www.python.org/dev/peps/pep-0263/)\n match = re.search(r'coding[:=]\\s*([-\\w.]+)', line)\n if match:\n encoding = match.group(1)\n break\n source = [unicode(sline, encoding, 'replace') for sline in source]\n\n lower_bound = max(0, lineno - context_lines)\n upper_bound = lineno + context_lines\n\n pre_context = [line.strip('\\n') for line in source[lower_bound:lineno]]\n context_line = source[lineno].strip('\\n')\n post_context = [line.strip('\\n') for line in source[lineno+1:upper_bound]]\n\n return lower_bound, pre_context, context_line, post_context","def line_offsets(fname):\n line_offset = []\n offset = 0\n for _, line in enumerate( open(fname) ):\n line_offset.append(offset)\n offset += len(line)\n return line_offset","def _expected_lines_and_line_numbers(path, check_prefix):\n with open(path) as f:\n for index, line in enumerate(f):\n if 'RUN:' in line:\n # Ignore lit directives, which may include a call to\n # xctest_checker that specifies a check prefix.\n continue\n\n # Note that line numbers are not zero-indexed; we must add one to\n # the loop index.\n line_number = index + 1\n\n components = line.split(check_prefix)\n if len(components) == 2:\n yield (replace_offsets(components[1].strip(), line_number),\n line_number)\n elif len(components) > 2:\n # Include a newline, then the file name and line number in the\n # exception in order to have it appear as an inline failure in\n # Xcode.\n raise XCTestCheckerError(\n path, line_number,\n 'Usage violation: prefix \"{}\" appears twice in the same '\n 'line.'.format(check_prefix))","def process_idx_file_line(self, line):\n pass","def get_source_lines(lines: list, obj) -> int:\n source_lines = inspect.getsourcelines(obj)[0]\n source_length = len(source_lines)\n\n for index in range(len(lines) - source_length):\n if source_lines == lines[index:index + source_length]:\n return index, index + source_length\n\n return None, None","def extract_lines(infile):\n with open(infile, 'r') as src:\n return read_on(get_line, src)","def _line_offsets(self, snapshot: Bug, filepath: str) -> List[int]:\n logger.debug(\"Fetching line offsets for file, '%s', in snapshot, '%s'\", # noqa: pycodestyle\n filepath,\n snapshot.name)\n key_cache = (snapshot.name, filepath)\n if key_cache in self.__cache_offsets:\n logger.debug(\"Retrieving line offsets for file, '%s', in snapshot, '%s', from cache.\", # noqa: pycodestyle\n filepath,\n snapshot.name)\n return self.__cache_offsets[key_cache]\n\n logger.debug(\"Computing line offsets for file, '%s', in snapshot, '%s'\", # noqa: pycodestyle\n filepath,\n snapshot.name)\n contents = self.read_file(snapshot, filepath)\n\n # find all indices of newline characters\n offsets = [0]\n last_offset = 0\n while True:\n next_line_break = contents.find('\\n', last_offset)\n if next_line_break == -1:\n break\n last_offset = next_line_break + 1\n offsets.append(last_offset)\n\n logger.debug(\"Saving line offsets for file, '%s', in snapshot, '%s', to cache.\", # noqa: pycodestyle\n filepath,\n snapshot.name)\n self.__cache_offsets[key_cache] = offsets\n return offsets","def original_line_offsets(code, codes, codes_offsets, codes_lines):\n codes[id(code)] = code # necessary to keep reference to code objects\n offsets = codes_offsets[id(code)] = []\n lines = codes_lines[id(code)] = []\n # ToDo: submit a patch to Python to optimize findlinestarts with iters\n for addr, lineno in findlinestarts(code):\n offsets.append(addr)\n lines.append(lineno)\n\n for const in code.co_consts:\n if isinstance(const, CodeType):\n original_line_offsets(const, codes, codes_offsets, codes_lines)","def all_lines_at_idx(mm, idx_list):\n lines = []\n for idx in idx_list:\n mm.seek(idx)\n # row back to beginning of line\n ibegin = mm.rfind(b'\\n', 0, idx)\n if ibegin == -1:\n ibegin = 0\n mm.seek(ibegin)\n mm.readline()\n # read desired line\n line = mm.readline()\n lines.append(line.decode())\n return lines","def _recompute_line_offsets(self):\n # TODO: Consider \\r\\n?\n self._line_offsets = [0] + [m.start() + 1 for m in re.finditer('\\n', self._text)]","def find_line_offsets(self):\n # line 0 doesn't exist; line 1 starts at char offset 0.\n self.line_offsets = [None, 0]\n # Find all newlines in `text`, and add an entry to\n # line_offsets for each one.\n pos = self.text.find('\\n')\n while pos != -1:\n self.line_offsets.append(pos+1)\n pos = self.text.find('\\n', pos+1)\n # Add a final entry, marking the end of the string.\n self.line_offsets.append(len(self.text))","def exercise_lines(path):\n with open(path) as fin:\n within_exercise = False\n for line, line_number in zip(fin, count(1)):\n line = line.lstrip()\n\n if within_exercise and line.startswith('#'):\n yield line_number\n elif not within_exercise and line.startswith('#') and 'EXERCISE:' in line:\n within_exercise = True\n yield line_number\n else:\n within_exercise = False","def get_formatted_partial_source(self, filename, linenumber, offset):\n lines = self.get_source(filename)\n if not lines:\n return \"\", \"\"\n\n begin = max(0, linenumber - self.context)\n partial_source, bad_line = highlight_source(\n linenumber,\n linenumber - begin - 1,\n lines[begin : linenumber + 1],\n offset=offset,\n )\n return partial_source, bad_line","def readMultipleFileLinesAndPositions(filePath,startPosition=None, bytesToRead=1): \n \n f = open(filePath, 'rb') \n \n if not startPosition is None: \n f.seek(startPosition) \n \n lines = f.readlines(bytesToRead) \n position = f.tell() \n \n f.close() \n \n return lines, position","def get_line(file, linenum):\n try:\n with open(file, \"r\") as f:\n return f.readlines()[linenum - 1].replace(\"\\n\", \"\")\n except:\n return f\"[ERROR]: could not open '{file}'\"","def _collect_line_info(self, dso, real_path, addr_shifts):\n # 1. Collect addrs to send to addr2line.\n addr_set = set()\n for addr in dso.addrs:\n addr_obj = dso.addrs[addr]\n if addr_obj.source_lines: # already has source line, no need to search.\n continue\n for shift in addr_shifts:\n # The addr after shift shouldn't change to another function.\n shifted_addr = max(addr + shift, addr_obj.func_addr)\n addr_set.add(shifted_addr)\n if shifted_addr == addr_obj.func_addr:\n break\n if not addr_set:\n return\n addr_request = '\\n'.join(['%x' % addr for addr in sorted(addr_set)])\n\n # 2. Use addr2line to collect line info.\n try:\n subproc = subprocess.Popen([self.addr2line_path, '-ai', '-e', real_path],\n stdin=subprocess.PIPE, stdout=subprocess.PIPE)\n (stdoutdata, _) = subproc.communicate(str_to_bytes(addr_request))\n stdoutdata = bytes_to_str(stdoutdata)\n except:\n return\n addr_map = {}\n cur_line_list = None\n for line in stdoutdata.strip().split('\\n'):\n if line[:2] == '0x':\n # a new address\n cur_line_list = addr_map[int(line, 16)] = []\n else:\n # a file:line.\n if cur_line_list is None:\n continue\n # Handle lines like \"C:\\Users\\...\\file:32\".\n items = line.rsplit(':', 1)\n if len(items) != 2:\n continue\n if '?' in line:\n # if ? in line, it doesn't have a valid line info.\n # An addr can have a list of (file, line), when the addr belongs to an inlined\n # function. Sometimes only part of the list has ? mark. In this case, we think\n # the line info is valid if the first line doesn't have ? mark.\n if not cur_line_list:\n cur_line_list = None\n continue\n (file_path, line_number) = items\n line_number = line_number.split()[0] # Remove comments after line number\n try:\n line_number = int(line_number)\n except ValueError:\n continue\n file_id = self._get_file_id(file_path)\n cur_line_list.append((file_id, line_number))\n\n # 3. Fill line info in dso.addrs.\n for addr in dso.addrs:\n addr_obj = dso.addrs[addr]\n if addr_obj.source_lines:\n continue\n for shift in addr_shifts:\n shifted_addr = max(addr + shift, addr_obj.func_addr)\n lines = addr_map.get(shifted_addr)\n if lines:\n addr_obj.source_lines = lines\n break\n if shifted_addr == addr_obj.func_addr:\n break","def _readline_ins(self):\n if self._ins_filehandle is None:\n if not os.path.exists(self._ins_filename):\n raise Exception(\n \"instruction file '{0}' not found\".format(self._ins_filename)\n )\n self._ins_filehandle = open(self._ins_filename, \"r\")\n line = self._ins_filehandle.readline()\n self._ins_linecount += 1\n if line == \"\":\n return None\n self._last_line = line\n # check for spaces in between the markers - this gets ugly\n line = line.lower()\n if self._marker is not None and self._marker in line:\n\n # def find_all(a_str, sub):\n # start = 0\n # while True:\n # start = a_str.find(sub, start)\n # if start == -1:\n # return\n # yield start\n # start += len(sub)\n # poss speedup using regex\n midx = [m.start() for m in re.finditer(re.escape(self._marker), line)]\n # midx = list(find_all(line, self._marker))\n midx.append(len(line))\n first = line[: midx[0]].strip()\n tokens = []\n if len(first) > 0:\n # tokens.append(first)\n tokens.extend([f.strip() for f in first.split()])\n for idx in range(1, len(midx) - 1, 2):\n mstr = line[midx[idx - 1] : midx[idx] + 1]\n ostr = line[midx[idx] + 1 : midx[idx + 1]]\n tokens.append(mstr)\n tokens.extend(ostr.split())\n else:\n tokens = line.strip().split()\n return tokens","def find_line(lines: List[str], substr: Predicate, start: int = 0) -> int:\n if isinstance(substr, str):\n pred = _string_contains\n elif isinstance(substr, tuple):\n pred = _pattern_value_match\n else:\n pred = _apply_func\n\n for i in range(start, len(lines)):\n if pred(lines[i], substr):\n return i\n\n raise Exception(\"Not found: \" + str(substr) + \"\\n text:\" + str(lines) )","def create_line_list(line_tbl, source_file, instr, unknown=False, ions=None):\n # Init -- Mainly to insure formatting\n ini_tbl = init_line_list()\n # Add columns (in place)\n add_instr_source(line_tbl, instr, source_file)\n # Stack\n new_tbl = vstack([ini_tbl, line_tbl], join_type='exact')\n # Cut off first line\n cut_tbl = new_tbl[1:]\n\n # Unknown list??\n if unknown:\n if ions is None:\n raise IOError(\"Must provide list of possible ions if unknown\")\n line_flag = get_line_flag(ions)\n cut_tbl['line_flag'] = line_flag\n\n # Return\n return cut_tbl","def get_source_lines(self, filename, lineno, context=0):\n if not filename or not lineno:\n return ''\n\n return ''.join([' ' + linecache.getline(filename, line) for line in range(lineno - context, lineno + context + 1)])","def findlinestarts(code):\n byte_increments = [ord(c) for c in code.co_lnotab[0::2]]\n line_increments = [ord(c) for c in code.co_lnotab[1::2]]\n\n lastlineno = None\n lineno = code.co_firstlineno\n addr = 0\n for byte_incr, line_incr in zip(byte_increments, line_increments):\n if byte_incr:\n if lineno != lastlineno:\n yield (addr, lineno)\n lastlineno = lineno\n addr += byte_incr\n if line_incr >= 0x80:\n # line_increments is an array of 8-bit signed integers\n line_incr -= 0x100\n lineno += line_incr\n if lineno != lastlineno:\n yield (addr, lineno)","def stripFileLines(filePath, offset):\n\tfp = open(filePath, \"r\")\n\tfor line in fp:\n\t\tstripped = line[offset:len(line) - 1 - offset]\n\t\tprint (stripped)\n\tfp.close()","def findlinestarts(code):\n byte_increments = [ord(c) for c in code.co_lnotab[0::2]]\n line_increments = [ord(c) for c in code.co_lnotab[1::2]]\n result = []\n lastlineno = None\n lineno = code.co_firstlineno\n addr = 0\n for byte_incr, line_incr in zip(byte_increments, line_increments):\n if byte_incr:\n if lineno != lastlineno:\n result.append((addr, lineno))\n lastlineno = lineno\n addr += byte_incr\n lineno += line_incr\n if lineno != lastlineno:\n result.append((addr, lineno))\n return result","def line_at_cursor(code: str, cursor_pos: int = 0):\n offset = 0\n lines = code.splitlines(True)\n for line in lines:\n next_offset = offset + len(line)\n if not line.endswith('\\n'):\n # If the last line doesn't have a trailing newline, treat it as if\n # it does so that the cursor at the end of the line still counts\n # as being on that line.\n next_offset += 1\n if next_offset > cursor_pos:\n break\n offset = next_offset\n else:\n line = \"\"\n return (line, offset)","def extract_positions(lines):\n positions = []\n for line in lines:\n position = [int(s) for s in re.findall(r'-?\\d+', line)]\n positions.append(position)\n return positions","def _apply_line_rules(lines, commit, rules, line_nr_start):\n all_violations = []\n line_nr = line_nr_start\n for line in lines:\n for rule in rules:\n violations = rule.validate(line, commit)\n if violations:\n for violation in violations:\n violation.line_nr = line_nr\n all_violations.append(violation)\n line_nr += 1\n return all_violations","def parse_lines(self, start_line=0, end_line=False):\n if end_line is False: end_line = len(self.file_ltxt)\n\n lines = self.file_ltxt\n self.E_str = \"parse_lines\"\n self.line_num = start_line\n\n # Loop over lines and parse\n while self.line_num < end_line:\n line = lines[self.line_num].strip()\n\n if line == \"echo\": print(\"\")\n\n # Parse any variables\n elif self.line_declarations['variable'](line):\n self.parse_variable_line(line)\n\n # Parse any file loading commands\n elif self.line_declarations['load'](line):\n self.parse_load_cmd(line)\n\n # Parse any file loading commands\n elif self.line_declarations['plot'](line):\n self.parse_plot_cmd(line)\n\n # Parse any file loading commands\n elif self.line_declarations['write'](line):\n self.parse_write_cmd(line)\n\n # Parse any math commands\n elif self.line_declarations['math'](line):\n self.parse_math_cmd(line)\n\n # Parse any echo commands\n elif self.line_declarations['echo'](line):\n self.parse_echo_cmd(line)\n\n # Parse any echo commands\n elif self.line_declarations['calc'](line):\n self.parse_calc_cmd(line)\n\n # Parse any echo commands\n elif self.line_declarations['set'](line):\n self.parse_set_cmd(line)\n\n # Parse any shell commands\n elif self.line_declarations['shell'](line):\n self.parse_shell_cmd()\n\n # Parse any for loop commands\n elif self.line_declarations['for'](line):\n self.parse_for_cmd(line)\n\n # Parse any echo commands\n elif self.line_declarations['script'](line):\n self.parse_script_cmd(line)\n\n elif self.line_declarations['inline_code'](line):\n getattr(self, f\"parse_{line.split()[0]}_cmd\")(line)\n\n elif self.line_declarations['if'](line):\n self.parse_if_cmd(line)\n\n # elif self.line_declarations['splice'](line):\n # self.parse_splice_cmd(line)\n\n elif self.line_declarations['glue'](line):\n self.parse_glue_cmd(line)\n\n elif self.line_declarations['exit'](line):\n print(\"\\n\\nStopped Code -exit was called.\")\n raise SystemExit\n\n # The end of control statements\n elif '}' in line:\n pass\n\n # Print a warning about unknown line\n else:\n self.print_warning(f\"I don't understand a line: '{line}'\")\n\n self.line_num += 1","def fileReSeekList(fh, regexList):\n\n compiled = [re.compile(r) for r in regexList]\n\n while True:\n line = fh.readline()\n if line == '':\n return None\n for i, p in enumerate(compiled):\n match = p.match(line)\n if match:\n return (match, i)","def _execute_ins_line(self, ins_line, ins_lcount):\n cursor_pos = 0 # starting cursor position\n val_dict = {} # storage dict for obsname: obsval pairs in line\n # for ii,ins in enumerate(ins_line):\n ii = 0 # counter over instruction entries\n all_markers = True\n line_seps = set([\",\", \" \", \"\\t\"])\n n_ins = len(ins_line) # number of instructions on line\n maxsearch = 500 # maximum number of characters to search when slicing line\n while True:\n if ii >= n_ins:\n break\n ins = ins_line[ii] # extract instruction\n i1 = ins[:1] # first char in instruction\n # primary marker\n if ii == 0 and i1 == self._marker:\n # if first and instruction starts with primary marker\n # search for presence of primary marker e.g. ~start~\n mstr = ins.replace(self._marker, \"\")\n while True:\n # loop over lines until primary marker is found\n line = self._readline_output() # read line from output\n if line is None:\n self.throw_out_error(\n \"EOF when trying to find primary marker '{0}' from \"\n \"instruction file line {1}\".format(mstr, ins_lcount)\n )\n if mstr in line: # when marker is found break and update\n # cursor position in current line\n break\n # copy a version of line commas replaced\n # (to support comma sep strings)\n rline = line.replace(\",\", \" \").replace(\"\\t\",\"\")\n\n cursor_pos = line.index(mstr) + len(mstr)\n\n # line advance\n elif i1 == \"l\": # if start of instruction is line advance\n try:\n nlines = int(ins[1:]) # try and get advance number\n except Exception as e:\n self.throw_ins_error(\n \"casting line advance to int for \"\n \"instruction '{0}'\".format(ins),\n ins_lcount,\n )\n for i in range(nlines):\n line = self._readline_output()\n if line is None:\n self.throw_out_error(\n \"EOF when trying to read {0} lines for line \"\n \"advance instruction '{1}', from instruction \"\n \"file line number {2}\".format(nlines, ins, ins_lcount)\n )\n # copy a version of line commas replaced\n # (to support comma sep strings)\n rline = line.replace(\",\", \" \")\n elif ins == \"w\": # whole string comparison\n raw = rline[cursor_pos : cursor_pos + maxsearch].split(\n None, 2\n ) # TODO: maybe slow for long strings -- hopefuly maxsearch helps\n if line[cursor_pos] in line_seps:\n raw.insert(0, \"\")\n if len(raw) == 1:\n self.throw_out_error(\n \"no whitespaces found on output line {0} past {1}\".format(\n line, cursor_pos\n )\n )\n # step over current value\n cursor_pos = rline.replace(\"\\t\",\" \").find(\" \", cursor_pos)\n # now find position of next entry\n cursor_pos = rline.find(raw[1], cursor_pos)\n # raw[1]\n # )\n\n elif i1 == \"!\": # indicates obs instruction folows\n oname = ins.replace(\"!\", \"\")\n # look a head for a second/closing marker\n if ii < n_ins - 1 and ins_line[ii + 1] == self._marker:\n # if penultimate instruction and last instruction is\n # primary marker, look for that marker in line\n m = ins_line[ii + 1].replace(self._marker, \"\")\n es = line.find(m, cursor_pos)\n if es == -1: # m not in rest of line\n self.throw_out_error(\n \"secondary marker '{0}' not found from cursor_pos {1}\".format(\n m, cursor_pos\n )\n )\n # read to closing marker\n val_str = line[cursor_pos:es]\n else:\n # find next space in (r)line -- signifies end of entry\n es = rline.find(\" \", cursor_pos)\n if es == -1 or es == cursor_pos:\n # if no space or current position is space\n # use old fashioned split to get value\n # -- this will happen if there are leading blanks before\n # vals in output file (e.g. formatted)\n val_str = rline[cursor_pos : cursor_pos + maxsearch].split(\n None, 1\n )[0]\n else:\n # read val (constrained slice is faster for big strings)\n val_str = rline[cursor_pos:es]\n try:\n val = float(val_str)\n except Exception as e:\n if oname != \"dum\":\n self.throw_out_error(\n \"casting string '{0}' to float for instruction '{1}'\".format(\n val_str, ins\n )\n )\n\n if oname != \"dum\":\n val_dict[oname] = val\n ipos = line.find(val_str.strip(), cursor_pos)\n # val_len = len(val_str)\n cursor_pos = ipos + len(val_str) # update cursor\n all_markers = False\n\n elif i1 == self._marker:\n m = ins.replace(self._marker, \"\") # extract just primary marker\n # find position of primary marker in line\n es = line.find(m, cursor_pos)\n if es == -1: # m not in rest of line\n if all_markers:\n ii = 0\n continue\n else:\n self.throw_out_error(\n \"secondary marker '{0}' not found from \"\n \"cursor_pos {1}\".format(m, cursor_pos)\n )\n cursor_pos = es + len(m)\n\n elif i1 == \"(\":\n if \")\" not in ins:\n self.throw_ins_error(\"unmatched ')'\", self._instruction_lcount)\n oname = ins[1:].split(\")\", 1)[0].lower()\n raw = ins.split(\")\")[1]\n if \":\" not in raw:\n self.throw_ins_error(\n \"couldnt find ':' in semi-fixed instruction: '{0}'\".format(ins),\n lcount=self._instruction_lcount,\n )\n raw = raw.split(\":\")\n try:\n s_idx = int(raw[0]) - 1\n except Exception as e:\n self.throw_ins_error(\n \"error converting '{0}' to integer in semi-fixed instruction: '{1}'\".format(\n raw[0], ins\n ),\n lcount=self._instruction_lcount,\n )\n try:\n e_idx = int(raw[1])\n except Exception as e:\n self.throw_ins_error(\n \"error converting '{0}' to integer in semi-fixed instruction: '{1}'\".format(\n raw[1], ins\n ),\n lcount=self._instruction_lcount,\n )\n\n if len(line) < e_idx:\n self.throw_out_error(\n \"output line only {0} chars long, semi-fixed ending col {1}\".format(\n len(line), e_idx\n )\n )\n\n if cursor_pos > e_idx:\n self.throw_out_error(\n \"cursor at {0} has already read past semi-fixed ending col {1}\".format(\n cursor_pos, e_idx\n )\n )\n\n ss_idx = max(cursor_pos, s_idx)\n raw = line[ss_idx : ss_idx + maxsearch].split(\n None, 1\n ) # slpitting only 1 might be margin faster\n rs_idx = line.index(raw[0])\n if rs_idx > e_idx:\n self.throw_out_error(\n \"no non-whitespace chars found in semi-fixed observation {0}\".format(\n ins\n )\n )\n re_idx = rs_idx + len(raw[0])\n val_str = line[rs_idx:re_idx]\n try:\n val = float(val_str)\n except Exception as e:\n if oname != \"dum\":\n self.throw_out_error(\n \"casting string '{0}' to float for instruction '{1}'\".format(\n val_str, ins\n )\n )\n\n if oname != \"dum\":\n val_dict[oname] = val\n cursor_pos = re_idx\n\n elif i1 == \"[\":\n if \"]\" not in ins:\n self.throw_ins_error(\"unmatched ']'\", self._instruction_lcount)\n oname = ins[1:].split(\"]\", 1)[0].lower()\n raw = ins.split(\"]\")[1]\n if \":\" not in raw:\n self.throw_ins_error(\n \"couldnt find ':' in fixed instruction: '{0}'\".format(ins),\n lcount=self._instruction_lcount,\n )\n raw = raw.split(\":\")\n try:\n s_idx = int(raw[0]) - 1\n except Exception as e:\n self.throw_ins_error(\n \"error converting '{0}' to integer in fixed instruction: '{1}'\".format(\n raw[0], ins\n ),\n lcount=self._instruction_lcount,\n )\n try:\n e_idx = int(raw[1])\n except Exception as e:\n self.throw_ins_error(\n \"error converting '{0}' to integer in fixed instruction: '{1}'\".format(\n raw[1], ins\n ),\n lcount=self._instruction_lcount,\n )\n\n if len(line) < e_idx:\n self.throw_out_error(\n \"output line only {0} chars long, fixed ending col {1}\".format(\n len(line), e_idx\n )\n )\n\n if cursor_pos > s_idx:\n self.throw_out_error(\n \"cursor at {0} has already read past fixed starting col {1}\".format(\n cursor_pos, e_idx\n )\n )\n\n val_str = line[s_idx:e_idx]\n try:\n val = float(val_str)\n except Exception as e:\n if oname != \"dum\":\n self.throw_out_error(\n \"casting string '{0}' to float for instruction '{1}'\".format(\n val_str, ins\n )\n )\n\n if oname != \"dum\":\n val_dict[oname] = val\n cursor_pos = e_idx\n\n else:\n self.throw_out_error(\n \"unrecognized instruction '{0}' on ins file line {1}\".format(\n ins, ins_lcount\n )\n )\n ii += 1\n return val_dict","def _get_offset_line(self):\n columns = [col for col in self.df if col.startswith('offset_')]\n offset_lines = []\n if columns != []:\n return self.df[columns]\n else:\n print('No offsets available')\n raise IndexError","def locate_code_blocks(lines):\n stripped_lines = [l.strip() for l in lines]\n code_block_indexes = [i for i, l in enumerate(stripped_lines,1) if l.startswith('```')]\n nblock_indexes = len(code_block_indexes)\n assert nblock_indexes % 2 == 0\n return [b for b in zip(code_block_indexes[::2], code_block_indexes[1::2])]","def lineno(self, start, text):\n end = start\n while text[end] not in self.NUMBERS:\n end += 1\n self.cur.append((start, Punctuation, text[start]))\n self.cur.append((start+1, Text, text[start+1:end]))\n start = end\n match = self.LINENOS.match(text, start)\n if match is None:\n raise AssertionError\n if text[match.end()] != ')':\n raise AssertionError\n self.cur.append((match.start(), Number.Integer, match.group(0)))\n self.cur.append((match.end(), Punctuation, text[match.end()]))\n return match.end() + 1","def approx_lineno_across_revs(filename, newfilename, revision, newrevision,\n lineno):\n # This doesn't work that well if there are a lot of line changes within the\n # hunk (demonstrated by GitHyperBlameLineMotionTest.testIntraHunkLineMotion).\n # A fuzzy heuristic that takes the text of the new line and tries to find a\n # deleted line within the hunk that mostly matches the new line could help.\n\n # Use the <revision>:<filename> syntax to diff between two blobs. This is the\n # only way to diff a file that has been renamed.\n old = '%s:%s' % (revision, filename)\n new = '%s:%s' % (newrevision, newfilename)\n hunks = cache_diff_hunks(old, new)\n\n cumulative_offset = 0\n\n # Find the hunk containing lineno (if any).\n for (oldstart, oldlength), (newstart, newlength) in hunks:\n cumulative_offset += newlength - oldlength\n\n if lineno >= oldstart + oldlength:\n # Not there yet.\n continue\n\n if lineno < oldstart:\n # Gone too far.\n break\n\n # lineno is in [oldstart, oldlength] at revision; [newstart, newlength] at\n # newrevision.\n\n # If newlength == 0, newstart will be the line before the deleted hunk.\n # Since the line must have been deleted, just return that as the nearest\n # line in the new file. Caution: newstart can be 0 in this case.\n if newlength == 0:\n return max(1, newstart)\n\n newend = newstart + newlength - 1\n\n # Move lineno based on the amount the entire hunk shifted.\n lineno = lineno + newstart - oldstart\n # Constrain the output within the range [newstart, newend].\n return min(newend, max(newstart, lineno))\n\n # Wasn't in a hunk. Figure out the line motion based on the difference in\n # length between the hunks seen so far.\n return lineno + cumulative_offset","def scanpatch(fp):\n lr = patch.linereader(fp)\n\n def scanwhile(first, p):\n \"\"\"scan lr while predicate holds\"\"\"\n lines = [first]\n while True:\n line = lr.readline()\n if not line:\n break\n if p(line):\n lines.append(line)\n else:\n lr.push(line)\n break\n return lines\n\n while True:\n line = lr.readline()\n if not line:\n break\n if line.startswith('diff --git a/'):\n def notheader(line):\n s = line.split(None, 1)\n return not s or s[0] not in ('---', 'diff')\n header = scanwhile(line, notheader)\n fromfile = lr.readline()\n if fromfile.startswith('---'):\n tofile = lr.readline()\n header += [fromfile, tofile]\n else:\n lr.push(fromfile)\n yield 'file', header\n elif line[0] == ' ':\n yield 'context', scanwhile(line, lambda l: l[0] in ' \\\\')\n elif line[0] in '-+':\n yield 'hunk', scanwhile(line, lambda l: l[0] in '-+\\\\')\n else:\n m = lines_re.match(line)\n if m:\n yield 'range', m.groups()\n else:\n raise patch.PatchError('unknown patch content: %r' % line)","def do_Promo_line_parse (Promo_line, line_number, filehash) :\n result = [filehash,\n line_number,\n Promo_line[0:8].strip(),\n Promo_line[9:13].strip(),\n Promo_line[14:19].strip(),\n Promo_line[20:26].strip().lstrip('0'), # Spec indicates numerical field\n Promo_line[27:30].strip().lstrip('0'), # Spec indicates numerical field\n Promo_line[31:40].strip(),\n Promo_line[41:49].strip(),\n Promo_line[50:].strip()\n ]\n return result\n # Having the line number passed in is ugly, but kind of works :/\n # Having all the field extraction explicit is kind of ugly too...\n # We're using the hash here to link? Yeah, that's because Python\n # doesn't know what the autonumbered load table is up to in the\n # DB when it starts to coalesce the raw files together.","def __get_line(file_path: str, line_no: int, errors: str = 'ignore') -> str:\n try:\n with open(file_path, mode='r',\n encoding='utf-8', errors=errors) as f:\n for line in f:\n line_no -= 1\n if line_no == 0:\n return line\n return ''\n except IOError:\n LOG.error(\"Failed to open file %s\", file_path)\n return ''","def parse_lines(filename):\n line_counter = 0\n with open(filename, 'r') as rf:\n for line_txt in rf:\n try:\n d = json.loads(line_txt)\n tup = (\n d['attributed_to'],\n int(d['date_time'][8:10]),\n d.get('used_first_time_today', False),\n d.get('first_utm_source', 'unknown') \n )\n except:\n print('Error parsing line_txt:', line_txt)\n line_counter += 1\n if line_counter % 10 ** 6 == 0:\n print('read %dM lines' % (line_counter // 10 ** 6))\n yield tup # yield: https://stackoverflow.com/a/231855","def _ComputeOffset( contents, line, column ):\n contents = ToBytes( contents )\n current_line = 1\n current_column = 1\n newline = bytes( b'\\n' )[ 0 ]\n for i, byte in enumerate( contents ):\n if current_line == line and current_column == column:\n return i\n current_column += 1\n if byte == newline:\n current_line += 1\n current_column = 1\n message = COMPUTE_OFFSET_ERROR_MESSAGE.format( line = line,\n column = column )\n _logger.error( message )\n raise RuntimeError( message )","def load(self, lines, start_address=0):\n addr = start_address\n lineno = 0\n try:\n for line in lines:\n lineno += 1\n parsed = self.parse(line)\n if parsed is None:\n continue\n if isinstance(parsed, tuple):\n if parsed[0] is CHANGE_ADDRESS:\n addr = parsed[1]\n continue\n if parsed[0] is DECLARE_LABEL:\n if parsed[1] in self.labels:\n raise ParseException('Redeclaration of ' + parsed[1])\n self.labels[parsed[1]] = addr\n continue\n if addr >= MEMSIZE:\n raise ParseException('Out of memory parsing program')\n if self.memory[addr] is not None:\n raise ParseException('Memory not None at ' + str(addr))\n self.memory[addr] = parsed\n addr += 1\n except ParseException as e:\n sys.stderr.write('%s: ERROR: %s\\n' % (lineno, line))\n sys.stderr.write('%s: ERROR: %s\\n' % (lineno, e))\n raise e\n self.program_validate()","def _start_magic(line):\n return start(line)","def getsourcelines(object):\r\n lines, lnum = findsource(object)\r\n\r\n if ismodule(object): return lines, 0\r\n else: return getblock(lines[lnum:]), lnum + 1","def position_before_code(filename=None):\n\n if filename is None:\n raise ValueError\n\n with open(filename, 'r') as f:\n content = f.read()\n\n # find \"import\" lines - if they exist\n pattern = r'[\\n\\r]\\s*import[^\\n\\r]*;'\n it = re.finditer(pattern, content, re.DOTALL)\n\n last = None\n # next \"empty\" for loop is intended to advance iterator to last match\n for match in it:\n last = match\n\n if last is None:\n # no imports in file\n return position_before_class(content)\n else:\n # found import lines - last holds last match in file\n return last.end()","def get_file_lines(filename):\n if not os.path.isfile(filename):\n logging.error(\"[get_file_lines] %s not found\", filename)\n return -1\n\n if not os.access(filename, os.R_OK):\n logging.error(\"[get_file_lines] %s cannot be read\", filename)\n return -1\n\n i = -1\n with open(filename) as f:\n try:\n for i, l in enumerate(f):\n pass\n except UnicodeDecodeError:\n return -1\n return i + 1","def find_line_markers(source):\n markers = {}\n for lineno, line in enumerate(source.splitlines(), start=1):\n m = re.search(r\"#=(\\w+)\", line)\n if m:\n markers[lineno] = m.group(1)\n return markers","def get_line(lines):\n for line in lines:\n yield line","def get_line(lines):\n for line in lines:\n yield line","def get_lines_with_start(cls, frame, radius=5, sys_context=None):\n\t\tcode = cls._dispatch_frame(frame)\n\t\n\t\tif not code: \n\t\t\treturn []\n\t\telse:\n\t\t\tcode_lines = code.splitlines()\n\t\n\t\tif not radius:\n\t\t\treturn code_lines, 1\n\t\telse:\n\t\t\tblock_slice = cls._calc_block_ends(frame.f_lineno, len(code_lines), radius)\n\t\t\treturn code_lines[block_slice], block_slice.start","def fetch(self, relative: int = 0, **kwargs) -> str:\n line = self.reader.fetch(relative, **kwargs)\n if kwargs.get('allow_endmarker') is True:\n return line\n elif self.pattern.match(line):\n raise IOError\n else:\n return line","def interpret_line(self, line, source=None, lineno=None):\n\n pline = self.parser.parse_line(line, source=source, lineno=lineno)\n return self.execute(pline)","def next_line(self, context, line):","def adjust_line_numbers(self, code):\n store_lines = set()\n make_function_lines = set()\n for opcode in itertools.chain.from_iterable(_collect_bytecode(code)):\n if opcode.name.startswith(\"STORE_\"):\n store_lines.add(opcode.line)\n elif opcode.name == \"MAKE_FUNCTION\":\n make_function_lines.add(opcode.line)\n\n def adjust(line, allowed_lines, min_line=1):\n adjusted_line = line\n while adjusted_line not in allowed_lines and adjusted_line >= min_line:\n adjusted_line -= 1\n return adjusted_line if adjusted_line >= min_line else None\n\n # Process type comments.\n for type_comment_set in self._type_comments:\n for line, comment in sorted(type_comment_set.type_comments.items()):\n adjusted_line = adjust(line, store_lines, type_comment_set.start_line)\n if not adjusted_line:\n # vm._FindIgnoredTypeComments will take care of error reporting.\n continue\n if line != type_comment_set.end_line:\n self._errorlog.ignored_type_comment(self._filename, line, comment)\n del type_comment_set.type_comments[line]\n elif adjusted_line != line:\n type_comment_set.type_comments[adjusted_line] = comment\n del type_comment_set.type_comments[line]\n\n # Process decorators.\n for line in sorted(self._decorators):\n adjusted_line = adjust(line, make_function_lines)\n if not adjusted_line:\n log.error(\n \"No MAKE_FUNCTION opcode found for decorator on line %d\", line)\n elif adjusted_line != line:\n self._decorators.add(adjusted_line)\n self._decorators.remove(line)\n\n # Process variable annotations.\n for line, annot in sorted(self._variable_annotations.items()):\n adjusted_line = adjust(line, store_lines)\n if not adjusted_line:\n log.error(\n \"No STORE_* opcode found for annotation %r on line %d\", annot, line)\n del self._variable_annotations[line]\n elif adjusted_line != line:\n self._variable_annotations[adjusted_line] = annot\n del self._variable_annotations[line]","def loadLine(self, linenumber=None):\r\n logger.debug(\"loadLine\")\r\n\r\n if not linenumber:\r\n linenumber = int(self.linenumberEdit.text())\r\n\r\n self.currentstartline = self.currentchunk(linenumber) # find index chunk in which the line is located\r\n logger.debug(f\"Loading from index position {self.currentstartline}\")\r\n\r\n byteposition = self.file_index[self.currentstartline] #must be an index key\r\n logger.debug(f\"Byteposition in file {byteposition}\")\r\n text = self.reader(self.fileName, byteposition, from_what=os.SEEK_SET, nbytes=self.chunksize)\r\n # self.linewnd.setText(self._line_numbers(text, linenumber))\r\n if self.line_numbers():\r\n text = self._add_line_numbers(text, linestart=self.currentstartline)\r\n self.textwnd.setText(text)\r\n\r\n # move cursor to selected line\r\n if self.currentstartline == 0:\r\n offset = linenumber % self.linechunk # we are starting the index at 0 not at 1, hence first chunk is different\r\n else:\r\n offset = linenumber % self.linechunk - 1\r\n logger.debug(f\"Moving {offset} in current text window.\")\r\n cursor = self.textwnd.textCursor()\r\n cursor.movePosition(QtGui.QTextCursor.Down, QtGui.QTextCursor.MoveAnchor, offset)\r\n self.textwnd.setTextCursor(cursor)\r\n\r\n if self.tableBtn.isChecked():\r\n self._show_as_table()","def offsetline(linen, pattern_result):\n\n if \"nlines\" in pattern_result:\n nlines = pattern_result[\"nlines\"]\n else:\n nlines = 0\n new_linen = linen - nlines - 1\n if new_linen < 0:\n return 0\n else:\n return new_linen","def line(self):\n if self.__line is None:\n left = self.__source.rfind(\"\\n\", 0, self.__offset) + 1\n right = self.__source.find(\"\\n\", self.__offset)\n\n self.__line = self.__source[left : right]\n self.__lineOffset = self.__offset - left\n\n return self.__line","def line(self, n):\n return self.__contents[n]","def get_annotated_lines(self):\n lines = [Line(idx + 1, x) for idx, x in enumerate(self.sourcelines)]\n\n try:\n lines[self.lineno - 1].current = True\n except IndexError:\n pass\n\n return lines","def skiprows(fn):\n for i, line in enumerate(open(fn)):\n if line.startswith('#'):\n continue\n else:\n break\n return i","def want_line(self, op, first = 0):\n for ii in range(first, len(self.__content)):\n match = re.match(op, self.__content[ii], re.IGNORECASE)\n if match:\n return (ii, match.group(1))\n return None","def read_ins_file(self):\n self._instruction_lines = []\n self._instruction_lcount = []\n first_line = self._readline_ins()\n if len(first_line) < 2:\n raise Exception(\n \"first line of ins file must have atleast two entries, not '{0}'\".format(\n \",\".join(first_line)\n )\n )\n if first_line[0] != \"pif\":\n raise Exception(\n \"first line of ins file '{0}' must start with 'pif', not '{1}'\".format(\n self._ins_filename, first_line[0]\n )\n )\n self._marker = first_line[1]\n while True:\n line = self._readline_ins()\n\n if line is None:\n break\n elif len(line) == 0:\n self.throw_ins_warning(\"empty line, breaking\")\n break\n else:\n c1 = line[0][:1]\n if c1 == \"l\":\n pass\n elif c1 == self._marker:\n pass\n elif c1 == \"&\":\n self.throw_ins_error(\"line continuation not supported\")\n else:\n self.throw_ins_error(\n \"first token must be line advance ('l'), primary marker, or continuation ('&'),\"\n + \"not: {0}\".format(line[0])\n )\n\n for token in line[1:]:\n t1 = token[:1]\n if t1 == \"t\":\n self.throw_ins_error(\"tab instruction not supported\")\n elif t1 == self._marker:\n tn = token[-1:]\n if not tn == self._marker:\n self.throw_ins_error(\n \"unbalanced secondary marker in token '{0}'\".format(token)\n )\n\n for somarker, eomarker in zip([\"!\", \"[\", \"(\"], [\"!\", \"]\", \")\"]):\n #\n if t1 == somarker:\n ofound = True\n if eomarker not in token[1:]:\n self.throw_ins_error(\n \"unmatched observation marker '{0}', looking for '{1}' in token '{2}'\".format(\n somarker, eomarker, token\n )\n )\n raw = token[1:].split(eomarker)[0].replace(somarker, \"\")\n if raw == \"dum\":\n pass\n else:\n if (\n self._full_oname_set is not None\n and raw not in self._full_oname_set\n ):\n self.throw_ins_error(\n \"obs name '{0}' not in pst\".format(raw)\n )\n elif raw in self._found_oname_set:\n self.throw_ins_error(\n \"obs name '{0}' is listed more than once\".format(\n raw\n )\n )\n self._found_oname_set.add(raw)\n break\n # print(raw)\n\n self._instruction_lines.append(line)\n self._instruction_lcount.append(self._ins_linecount)","def _get_line_regions_from_xml_file(filename: str) -> List[Dict[str, int]]:\n xml_root_element = ElementTree.parse(filename).getroot() # nosec\n xml_line_elements = xml_root_element.findall(\"handwritten-part/line\")\n return [_get_line_region_from_xml_element(el) for el in xml_line_elements]","def map_instructions_to_lines(\n instructions: list[UniqueInstruction], subject_properties: SubjectProperties\n ) -> set[int]:\n line_ids = set()\n curr_line = -1\n for instruction in instructions:\n if instruction.file == \"<ast>\": # do not include test statements\n continue\n if instruction.lineno == curr_line: # only add new lines\n continue\n curr_line = instruction.lineno # type: ignore[assignment]\n line_ids.add(\n DynamicSlicer.get_line_id_by_instruction(\n instruction, subject_properties\n )\n )\n return line_ids","def get_lineid_content():\n lineid_content = {}\n lines_file_path = os.path.join(DATA_PATH + MOVIE_LINES_FILE)\n\n with open(lines_file_path, 'r', errors='ignore') as f:\n # +++$+++ is used to split the section in a single line\n # A correct formed line includes five sections\n # The first section is lineID\n # The last section is line content\n # Here we only need lineID and content\n\n for line in f:\n line_sections = line.split(' +++$+++ ')\n assert len(line_sections) == 5\n if line_sections[4][-1] == '\\n':\n line_sections[4] = line_sections[4][:-1]\n lineid_content[line_sections[0]] = line_sections[4]\n\n return lineid_content","def hashes_above(path, line_number):\n for line_number in xrange(line_number - 1, 0, -1):\n line = getline(path, line_number)\n match = HASH_COMMENT_RE.match(line)\n if match:\n yield match.groupdict()['hash']\n elif not line.lstrip().startswith('#'):\n # If we hit a non-comment line, abort\n break","def lineOffset(self):\n if self.__lineOffset is None:\n self.__lineOffset = self.__offset - self.__source.rfind(\"\\n\", 0, self.__offset) - 1\n\n return self.__lineOffset","def prepare_code_snippet(file_path: str, line_no: int, context_lines_count: int = 5) -> str:\n with open(file_path) as text_file:\n # Highlight code\n code = text_file.read()\n code_lines = code.splitlines()\n # Prepend line number\n code_lines = [\n f\">{lno:3} | {line}\" if line_no == lno else f\"{lno:4} | {line}\"\n for lno, line in enumerate(code_lines, 1)\n ]\n # # Cut out the snippet\n start_line_no = max(0, line_no - context_lines_count - 1)\n end_line_no = line_no + context_lines_count\n code_lines = code_lines[start_line_no:end_line_no]\n # Join lines\n code = \"\\n\".join(code_lines)\n return code","def process_lines(self, lines):\n line_index = 0\n n_lines = len(lines)\n while line_index < n_lines:\n if lines[line_index].startswith(\"HIERARCHY\"):\n line_index = self._read_skeleton(lines, line_index, n_lines)\n if lines[line_index].startswith(\"MOTION\"):\n self._read_frametime(lines, line_index+2)\n line_index = self._read_frames(lines, line_index+3, n_lines)\n else:\n line_index += 1","def lineToList(self, line):\n raise NotImplementedError","def _set_start_lineno_if_needed():\n if origin and record.origin.line_start is None:\n record.origin.line_start = lineno","def ProcessLine(fn, filename, file_extension, clean_lines, line,\n include_state, function_state, nesting_state, error,\n extra_check_functions=[]):\n fn(filename, file_extension, clean_lines, line,\n include_state, function_state, nesting_state,\n makeErrorFn(error, [], [r'(.*)should be indented \\+1 space inside(.*)']),\n extra_check_functions=[])","def line_parser(path):\n lines = []\n with open(path, 'r') as input:\n lines = [line.rstrip().split(',') for line in input]\n lines = [\n [[float(x1), float(y1)],\n [float(x2), float(y2)]] \n for x1, y1, x2, y2 in lines]\n return lines","def find_log_lines_with_content(self, prefixes, needle_substring, required_count, return_first_line):\n required_count = int(required_count)\n\n def check_function():\n all_data = []\n for prefix in prefixes:\n print('Analyzing prefix {}'.format(prefix))\n all_files = self.get_files_in_bucket(prefix=prefix,\n limit=self.S3_LISTING_REQUEST_LIMIT)\n for file in all_files:\n print('Analyzing file {}'.format(file))\n content = self.get_file_content_from_bucket(file)\n for line in content.splitlines():\n if needle_substring in line:\n data = json.loads(line)\n all_data.append(data)\n if len(all_data) >= required_count and required_count != 0:\n return all_data\n\n if len(all_data) > 0 and required_count == 0:\n return all_data\n\n result = legion_test.utils.wait_until(check_function,\n self.WAIT_FILE_TIME, self.WAIT_FILE_ITERATIONS)\n if not result:\n raise Exception('{} log line(s) with {!r} has not been found'.format(required_count, needle_substring))\n if return_first_line:\n return result[0]\n else:\n return result","def extract_line(line, pos):\n found = u''\n depth = 0\n offset = 0\n for c in line:\n if c == '<':\n depth += 1\n elif c == '>':\n depth -= 1\n if depth == 0:\n pos -= process_match(found[1:], pos + offset)\n found = u''\n\n if depth > 0:\n found += c\n\n offset += 1\n return pos + offset","def numbers_in_line(line):\n ret = []\n pos = 0\n while True:\n loc = find_next_number(line, pos)\n if not loc:\n break\n ret.append(loc)\n pos = loc[1]\n return ret","def get_line(trj_file):\n\twith open(trj_file) as file:\n\t\tfor i in file:\n\t\t\tyield i","def seek_line(self, pattern):\n line = next(self.f)\n while not line.startswith(pattern):\n line = next(self.f)\n return line","def doctest_markup(in_lines):\n out_lines = list(in_lines)[:]\n err_tuples = []\n for pos, this in enumerate(out_lines):\n # Check for 'leave the rest' markup\n if this.strip() == '#23dt skip rest':\n break\n # Check for docest line with markup\n mark_match = MARK_COMMENT.search(this)\n if mark_match is None:\n continue\n docbits, marked_line, marker, markup = mark_match.groups()\n place_line_match = PLACE_LINE_EXPRS.match(markup)\n if place_line_match is None:\n msg = ('Found markup \"%s\" in line \"%s\" but wrong syntax' %\n (markup, this))\n err_tuples.append((this, msg))\n continue\n place_expr, line_expr = place_line_match.groups()\n exec_globals = {'here': pos, 'next': pos+1}\n if place_expr.strip() == '':\n place = pos\n else:\n try:\n place = eval(place_expr, exec_globals)\n except:\n msg = ('Error finding place with \"%s\" in line \"%s\"' %\n (place_expr, this))\n err_tuples.append((this, msg))\n continue\n # Prevent processing operating on 23dt comment part of line\n if place == pos:\n line = marked_line\n else:\n line = out_lines[place]\n # Shorthand\n if line_expr == 'bytes':\n # Any strings on the given line are byte strings\n pre, mid, post = INDENT_SPLITTER.match(line).groups()\n try:\n res = byter(mid)\n except:\n err = sys.exc_info()[1]\n msg = ('Error \"%s\" parsing \"%s\"' % (err, err))\n err_tuples.append((this, msg))\n continue\n res = pre + res + post\n else:\n exec_globals.update({'line': line, 'lines': out_lines})\n # If line_expr starts with 'replace', implies \"line.replace\"\n if line_expr.startswith('replace('):\n line_expr = 'line.' + line_expr\n elif line_expr == 'BytesIO':\n line_expr = \"line.replace('StringIO', 'BytesIO')\"\n try:\n res = eval(line_expr, exec_globals)\n except:\n err = sys.exc_info()[1]\n msg = ('Error \"%s\" working on \"%s\" at line %d with \"%s\"' %\n (err, line, place, line_expr))\n err_tuples.append((this, msg))\n continue\n # Put back comment if removed\n if place == pos:\n res = docbits + res + marker + markup\n if res != line:\n out_lines[place] = res\n return out_lines, err_tuples","def get_line_no(obj):\n try:\n lineno = getsourcelines(obj)[1]\n except:\n # no code found\n lineno = None\n return lineno","def extract_first_line(func_code):\r\n if func_code.startswith(FIRST_LINE_TEXT):\r\n func_code = func_code.split('\\n')\r\n first_line = int(func_code[0][len(FIRST_LINE_TEXT):])\r\n func_code = '\\n'.join(func_code[1:])\r\n else:\r\n first_line = -1\r\n return func_code, first_line","def line_col_to_offset(self,\n snapshot: Bug,\n filepath: str,\n line_num: int,\n col_num: int\n ) -> int:\n assert line_num > 0\n assert col_num >= 0\n line_col_s = \"%s/%s[%d:%d]\".format(snapshot.name,\n filepath,\n line_num,\n col_num)\n logger.debug(\"Transforming line-column, '%s', into a character offset\", # noqa: pycodestyle\n line_col_s)\n line_offsets = self._line_offsets(snapshot, filepath)\n line_starts_at = line_offsets[line_num - 1]\n offset = line_starts_at + col_num\n logger.debug(\"Transformed line-column, '%s', into character offset: %s\", # noqa: pycodestyle\n line_col_s,\n offset)\n return offset","def clauses_from_file(filename):\n with open(filename, \"r\") as fin:\n #remove comments from beginning\n line = fin.readline()\n while(line.lstrip()[0] == 'c'):\n line = fin.readline()\n\n header = line.split(\" \")\n num_literals = int(header[2].rstrip())\n\n lines = fin.readlines()\n\n for i in range(len(lines)):\n lines[i] = lines[i].split(\" \")[:-1]\n lines[i] = [int(x) for x in lines[i]]\n\n return (lines, num_literals)","def parse_tcode_outfile(fname,offset=0):\n command = \"cat %s | grep -v '#' | sed '/^$/d' | sed 1d\" % (fname) \n ci,co = popen2(command)\n ci.close()\n out = co.readlines()\n co.close()\n retlist = []\n for line in out:\n line = line.strip()\n while \" \" in line: line = line.replace(\" \",\" \")\n parts = line.split(\" \",3)\n retlist.append( (int(parts[0])+offset, int(parts[1])+offset, float(parts[2]), parts[3] ) )\n return retlist","def _update_linetab(self, offset):\n t = self.input\n if offset < 0 or offset > len(t):\n raise IndexError(\"position %d not in 0..%d\" % (offset, len(t)))\n\n # self.__linepos[x] caches the offset of EOL for line x + 1.\n # A virtual line is imputed to exist prior to the input ending at\n # offset -1.\n # Lines are indexed internally from zero.\n lpc = self.__linepos\n if not lpc:\n lpc.append(-1)\n\n # Include all lines prior to offset.\n pos = lpc[-1] + 1\n while pos < offset:\n if t[pos] == '\\n':\n lpc.append(pos)\n pos += 1\n\n # Include the line containing offset, if possible.\n while pos < len(t) and lpc[-1] < offset:\n if t[pos] == '\\n':\n lpc.append(pos)\n pos += 1","def parse_ensembl_exons(lines):\n header = []\n for index, line in enumerate(lines):\n # File allways start with a header line\n if index == 0:\n header = line.rstrip().split(\"\\t\")\n continue\n\n exon_info = parse_ensembl_line(line, header)\n\n exon = {\n \"chrom\": str(exon_info[\"chrom\"]),\n \"gene\": exon_info[\"ensembl_gene_id\"],\n \"transcript\": exon_info[\"ensembl_transcript_id\"],\n \"ens_exon_id\": exon_info[\"ensembl_exon_id\"],\n \"exon_chrom_start\": exon_info[\"exon_start\"],\n \"exon_chrom_end\": exon_info[\"exon_end\"],\n \"strand\": exon_info[\"strand\"],\n \"rank\": exon_info[\"exon_rank\"],\n }\n try:\n exon[\"5_utr_start\"] = int(exon_info.get(\"utr_5_start\"))\n except (ValueError, TypeError):\n exon[\"5_utr_start\"] = None\n\n try:\n exon[\"5_utr_end\"] = int(exon_info.get(\"utr_5_end\"))\n except (ValueError, TypeError):\n exon[\"5_utr_end\"] = None\n\n try:\n exon[\"3_utr_start\"] = int(exon_info.get(\"utr_3_start\"))\n except (ValueError, TypeError):\n exon[\"3_utr_start\"] = None\n\n try:\n exon[\"3_utr_end\"] = int(exon_info.get(\"utr_3_end\"))\n except (ValueError, TypeError):\n exon[\"3_utr_end\"] = None\n\n # Recalculate start and stop (taking UTR regions into account for end exons)\n if exon[\"strand\"] == 1:\n # highest position: start of exon or end of 5' UTR\n # If no 5' UTR make sure exon_start is allways choosen\n start = max(exon[\"exon_chrom_start\"], exon[\"5_utr_end\"] or -1)\n # lowest position: end of exon or start of 3' UTR\n end = min(exon[\"exon_chrom_end\"], exon[\"3_utr_start\"] or float(\"inf\"))\n elif exon[\"strand\"] == -1:\n # highest position: start of exon or end of 3' UTR\n start = max(exon[\"exon_chrom_start\"], exon[\"3_utr_end\"] or -1)\n # lowest position: end of exon or start of 5' UTR\n end = min(exon[\"exon_chrom_end\"], exon[\"5_utr_start\"] or float(\"inf\"))\n\n exon[\"start\"] = start\n exon[\"end\"] = end\n exon_id = \"-\".join([str(exon[\"chrom\"]), str(start), str(end)])\n exon[\"exon_id\"] = exon_id\n\n if start > end:\n raise ValueError(\"ERROR: %s\" % exon_id)\n\n yield exon","def get_lines_in_file(config_file):\n lines = []\n\n line = config_file.readline()\n lines.append([1, line])\n\n line_counter = 1\n while line:\n line = config_file.readline()\n if not (line.lstrip().startswith(\"#\")):\n lines.append([line_counter, line])\n\n line_counter += 1\n\n return lines","def create_indices(filename, lines):\n def find(key, condition):\n \"\"\"Find and return index from str-list.\"\"\"\n for i, line in enumerate(lines):\n if condition(line, key):\n return i\n return None\n\n guard = ('INCLUDE_GUARD_KEN3_' +\n filename.replace('/', '_').replace('.', '_').upper())\n\n return (\n # doxygen comment in header. [0] to [7]\n find('/**\\n', str.__eq__),\n find(' * @file ken3/{0}\\n'.format(filename), str.__eq__),\n find(' * @brief ', str.startswith),\n find(' * @author toda\\n', str.__eq__),\n find(' * @date ', str.startswith),\n find(' * @version ', str.startswith),\n find(' * @remark ', str.startswith),\n find(' */\\n', str.__eq__),\n # first blank line. [8]\n find('\\n', str.__eq__),\n # start include guard (only in .hpp files). [9] and [10]\n find('#ifndef {0}\\n'.format(guard), str.__eq__),\n find('#define {0}\\n'.format(guard), str.__eq__),\n # include macro (optional). [11]\n find('#include ', str.startswith),\n # start and end namespace ken3. [12] and [13]\n find('namespace ken3 {\\n', str.__eq__),\n find('} // namespace ken3 {\\n', str.__eq__),\n # end include guard (only in .hpp files). [14]\n find('#endif // #ifndef {0}\\n'.format(guard), str.__eq__),\n )","def read_file(filepath: str, query_params: dict) -> list:\r\n\r\n # Get start and end line #'s from query_params (if they exist)\r\n if 'startln' in query_params:\r\n startln = query_params['startln']\r\n else:\r\n startln = 0 # Default to line 0 (index 0)\r\n if 'endln' in query_params:\r\n endln = query_params['endln']\r\n else:\r\n endln = -1 # Default to the last line (index -1)\r\n \r\n # Type-check params to ensure usability for list splicing\r\n try:\r\n startln, endln = int(startln), int(endln)\r\n except ValueError:\r\n abort(redirect(url_for('handle_exception', name='ValueError')))\r\n\r\n # Negative values for startln increases complexity and are unnecessary\r\n if startln < 0:\r\n abort(redirect(url_for('handle_exception', name='StartIndexError')))\r\n if endln <= startln and endln != -1:\r\n abort(redirect(url_for('handle_exception', name='EndIndexError')))\r\n\r\n # Attempt to open file using UTF-8 encoding\r\n try:\r\n with open(filepath, encoding='utf-8') as f:\r\n lines = f.readlines()\r\n # Otherwise encoding is in UTF-16-BE-BOM\r\n except UnicodeError:\r\n with open(filepath, encoding='utf-16') as f:\r\n lines = f.readlines()\r\n\r\n # Catch accessing list indexes beyond the range\r\n if startln >= len(lines):\r\n abort(redirect(url_for('handle_exception', name='OutOfRangeError')))\r\n if endln == -1: # Return lines from start to end of file\r\n return lines[startln:]\r\n return lines[startln:endln]","def import_from_pos(fh):\n elem = None\n while True:\n l = fh.readline()\n if not l: break\n if 'nwfc1' in l and 'nwfc2' in l:\n w = l.split()\n nwfc1, nwfc2 = int(w[0]), int(w[1])\n # nwfc2 is assumed to be one - only one l value\n if 'lwfc1' in l:\n w = l.split('!')[0].split()\n lwfc1 = [int(_) for _ in w]\n if 'lwfc2' in l:\n lwfc2 = int(l.split()[0])\n if 'nonzero elements' in l:\n n = int(l.split()[0])\n elem = []\n l = fh.readline()\n c = 0\n while l and c < n:\n w = l.split()\n if len(w) in {5, 10}: # 5-col is for old pos format and 10-col is the enriched format by yfliang\n # (l,m) in lwfc1, m in lwfc2 (only one), i = (x=1,y=2,z=3)\n # m ranges from -l to l\n # elem = < h_c | r_i | beta_lm > (Core-level wavefunctions always proceed. )\n elem.append([int(_) for _ in w[ : 3]] + [float(w[3]) + 1j * float(w[4])]) \n l = fh.readline()\n c += 1\n return lwfc1, lwfc2, elem","def check_line(self, line: str, line_num: int) -> Lints:\n yield from super().check_line(line, line_num)\n\n if self.in_header and line != \"---\\n\":\n for m in self.TAG_QUOTED.finditer(line):\n yield LintError(\"header-tags-quoted\", line_num, m.start())","def load_linelist(filename):\n linelist = []\n infile = open(filename)\n for row in infile:\n row = row.strip()\n if len(row)==0 or row[0] in '#%!@':\n continue\n g = row.split()\n wl = float(g[0])\n if len(g)>1:\n species = g[1]\n else:\n species = ''\n linelist.append((wl, species))\n infile.close()\n return linelist","def _linesearch(self):\n pass","def getsourcelines(object):\n lines, lnum = findsource(object)\n\n if inspect.ismodule(object): return lines, 0\n else: return inspect.getblock(lines[lnum:]), lnum + 1","def get_line(self, path, line):\n\t\tlines = self.find_source(path)\n\t\tif lines == None:\n\t\t\treturn None\n\t\telse:\n\t\t\ttry:\n\t\t\t\treturn lines[line - 1]\n\t\t\texcept IndexError:\n\t\t\t\treturn None","def parse(self, f):\n lines = []\n for line in f:\n _line = line.split(\"//\")[0].strip()\n if _line.startswith(\"(\"): # is a label\n label_name = _line[1:-1]\n self.labels[label_name] = len(lines) # line number / address of label\n elif _line:\n lines.append(_line)\n # else: it's just a whitespace/comment line (ignore)\n return lines","def CountLineNumber(filename):\n\n fp = open(os.path.abspath(filename), \"r\");\n lines = 0\n for line in fp.readlines():\n lines = lines + 1\n fp.close()\n return lines","def _get_sample_offset(self, line_num):\n assert line_num <= self._count_lines_in_file(\n TestPaths.get_file_path(AFewLinesLogParams.FILE_NAME)\n )\n return line_num * AFewLinesLogParams.SINGLE_LINE_LENGTH","def get_corresponding_lineno(self, lineno: int) -> int:\n for template_line, code_line in reversed(self.debug_info):\n if code_line <= lineno:\n return template_line\n return 1","def highlighter_lines(raw_graphics_content):\n lines = []\n\n current_line = []\n width = None\n cap_style = None\n join_style = None\n\n for op, args in tokenize_graphics(raw_graphics_content):\n if (op, args) == (\"RG\", list(YELLOW)):\n current_line = [None] # To be overrode by the last m op\n elif current_line:\n if op == \"m\": # Move the draw position\n current_line[0] = (float(args[0]), float(args[1]))\n elif op == \"j\":\n i_join_style = int(args[0])\n join_style = JOIN_STYLES[i_join_style]\n elif op == \"J\":\n i_cap_style = int(args[0])\n cap_style = CAP_STYLES[i_cap_style]\n elif op == \"w\":\n width = float(args[0]) # Total width, will /2 later\n elif op == \"l\":\n current_line.append((float(args[0]), float(args[1])))\n elif op == \"S\": # Finish the line\n # Make sure we got all the params we need to draw the line correctly\n assert (\n width is not None\n ), \"Expected to see a width for the stroke before stroke end.\"\n assert (\n cap_style is not None\n ), \"Expected to see a cap style before stroke end.\"\n assert (\n join_style is not None\n ), \"Expected to see a join style before stroke end.\"\n assert len(current_line) > 1, \"Invalid line, not enough points.\"\n\n # Draw that thang\n yield LineString(current_line).buffer(\n width / 2, cap_style=cap_style, join_style=join_style\n )\n\n # Reset the state\n current_line = None\n width = None\n cap_style = None\n join_style = None\n elif op == \"cm\":\n if args != [\"1\", \"0\", \"0\", \"1\", \"0\", \"0\"]:\n raise NotImplementedError(\n \"Transform matrices are not implemented, but shouldn't be hard to implement\"\n )\n else:\n pass # We don't care about other operations","def __call__(self, line):\n marker = self.marker\n stripped_line = line.strip()\n if marker == stripped_line:\n assert not self.traceback_section\n self.traceback_section = True\n # print(\"XXX: TRACEBACK-START\")\n elif self.traceback_section:\n matched = self.file_pattern.match(line)\n if matched:\n # matched_range = matched.regs[1]\n filename = matched.groups()[0]\n new_filename = posixpath_normpath(filename)\n if new_filename != filename:\n # print(\"XXX: %r => %r\" % (filename, new_filename))\n line = line.replace(filename, new_filename)\n elif not stripped_line or line[0].isalpha():\n # -- DETECTED TRCAEBACK-END: exception-description\n # print(\"XXX: TRACEBACK-END\")\n self.traceback_section = False\n return line"],"string":"[\n \"def _get_lines_from_file(filename, lineno, context_lines):\\n\\n try:\\n source = open(filename).readlines()\\n lower_bound = max(0, lineno - context_lines)\\n upper_bound = lineno + context_lines\\n\\n pre_context = \\\\\\n [line.strip('\\\\n') for line in source[lower_bound:lineno]]\\n context_line = source[lineno].strip('\\\\n')\\n post_context = \\\\\\n [line.strip('\\\\n') for line in source[lineno + 1:upper_bound]]\\n\\n return lower_bound, pre_context, context_line, post_context\\n except (OSError, IOError):\\n return None, [], None, []\",\n \"def get_lines_from_file(filename, lineno, context_lines, loader=None, module_name=None):\\n source = None\\n if loader is not None and hasattr(loader, \\\"get_source\\\"):\\n with suppress(ImportError):\\n source = loader.get_source(module_name)\\n if source is not None:\\n source = source.splitlines()\\n if source is None:\\n with suppress(OSError, IOError):\\n with open(filename, \\\"rb\\\") as fp:\\n source = fp.read().splitlines()\\n if source is None:\\n return None, [], None, []\\n try:\\n # If we just read the source from a file, or if the loader did not\\n # apply tokenize.detect_encoding to decode the source into a Unicode\\n # string, then we should do that ourselves.\\n if isinstance(source[0], bytes):\\n encoding = \\\"ascii\\\"\\n for line in source[:2]:\\n # File coding may be specified. Match pattern from PEP-263\\n # (http://www.python.org/dev/peps/pep-0263/)\\n match = re.search(br\\\"coding[:=]\\\\s*([-\\\\w.]+)\\\", line)\\n if match:\\n encoding = match.group(1).decode(\\\"ascii\\\")\\n break\\n source = [str(sline, encoding, \\\"replace\\\") for sline in source]\\n\\n lower_bound = max(0, lineno - context_lines)\\n upper_bound = lineno + context_lines\\n\\n pre_context = source[lower_bound:lineno]\\n context_line = source[lineno]\\n post_context = source[lineno + 1 : upper_bound]\\n\\n return lower_bound, pre_context, context_line, post_context\\n except Exception as e:\\n try:\\n context_line = f'<There was an error displaying the source file: \\\"{repr(e)}\\\" The loaded source has {len(source)} lines.>'\\n except Exception:\\n context_line = \\\"<There was an error displaying the source file. Further, there was an error displaying that error>\\\"\\n return lineno, [], context_line, []\",\n \"def _get_lines_from_file(self, filename, lineno, context_lines, loader=None, module_name=None):\\n source = None\\n if loader is not None and hasattr(loader, \\\"get_source\\\"):\\n source = loader.get_source(module_name)\\n if source is not None:\\n source = source.splitlines()\\n if source is None:\\n try:\\n f = open(filename)\\n try:\\n source = f.readlines()\\n finally:\\n f.close()\\n except (OSError, IOError):\\n pass\\n if source is None:\\n return None, [], None, []\\n\\n encoding = 'ascii'\\n for line in source[:2]:\\n # File coding may be specified. Match pattern from PEP-263\\n # (http://www.python.org/dev/peps/pep-0263/)\\n match = re.search(r'coding[:=]\\\\s*([-\\\\w.]+)', line)\\n if match:\\n encoding = match.group(1)\\n break\\n source = [unicode(sline, encoding, 'replace') for sline in source]\\n\\n lower_bound = max(0, lineno - context_lines)\\n upper_bound = lineno + context_lines\\n\\n pre_context = [line.strip('\\\\n') for line in source[lower_bound:lineno]]\\n context_line = source[lineno].strip('\\\\n')\\n post_context = [line.strip('\\\\n') for line in source[lineno+1:upper_bound]]\\n\\n return lower_bound, pre_context, context_line, post_context\",\n \"def line_offsets(fname):\\n line_offset = []\\n offset = 0\\n for _, line in enumerate( open(fname) ):\\n line_offset.append(offset)\\n offset += len(line)\\n return line_offset\",\n \"def _expected_lines_and_line_numbers(path, check_prefix):\\n with open(path) as f:\\n for index, line in enumerate(f):\\n if 'RUN:' in line:\\n # Ignore lit directives, which may include a call to\\n # xctest_checker that specifies a check prefix.\\n continue\\n\\n # Note that line numbers are not zero-indexed; we must add one to\\n # the loop index.\\n line_number = index + 1\\n\\n components = line.split(check_prefix)\\n if len(components) == 2:\\n yield (replace_offsets(components[1].strip(), line_number),\\n line_number)\\n elif len(components) > 2:\\n # Include a newline, then the file name and line number in the\\n # exception in order to have it appear as an inline failure in\\n # Xcode.\\n raise XCTestCheckerError(\\n path, line_number,\\n 'Usage violation: prefix \\\"{}\\\" appears twice in the same '\\n 'line.'.format(check_prefix))\",\n \"def process_idx_file_line(self, line):\\n pass\",\n \"def get_source_lines(lines: list, obj) -> int:\\n source_lines = inspect.getsourcelines(obj)[0]\\n source_length = len(source_lines)\\n\\n for index in range(len(lines) - source_length):\\n if source_lines == lines[index:index + source_length]:\\n return index, index + source_length\\n\\n return None, None\",\n \"def extract_lines(infile):\\n with open(infile, 'r') as src:\\n return read_on(get_line, src)\",\n \"def _line_offsets(self, snapshot: Bug, filepath: str) -> List[int]:\\n logger.debug(\\\"Fetching line offsets for file, '%s', in snapshot, '%s'\\\", # noqa: pycodestyle\\n filepath,\\n snapshot.name)\\n key_cache = (snapshot.name, filepath)\\n if key_cache in self.__cache_offsets:\\n logger.debug(\\\"Retrieving line offsets for file, '%s', in snapshot, '%s', from cache.\\\", # noqa: pycodestyle\\n filepath,\\n snapshot.name)\\n return self.__cache_offsets[key_cache]\\n\\n logger.debug(\\\"Computing line offsets for file, '%s', in snapshot, '%s'\\\", # noqa: pycodestyle\\n filepath,\\n snapshot.name)\\n contents = self.read_file(snapshot, filepath)\\n\\n # find all indices of newline characters\\n offsets = [0]\\n last_offset = 0\\n while True:\\n next_line_break = contents.find('\\\\n', last_offset)\\n if next_line_break == -1:\\n break\\n last_offset = next_line_break + 1\\n offsets.append(last_offset)\\n\\n logger.debug(\\\"Saving line offsets for file, '%s', in snapshot, '%s', to cache.\\\", # noqa: pycodestyle\\n filepath,\\n snapshot.name)\\n self.__cache_offsets[key_cache] = offsets\\n return offsets\",\n \"def original_line_offsets(code, codes, codes_offsets, codes_lines):\\n codes[id(code)] = code # necessary to keep reference to code objects\\n offsets = codes_offsets[id(code)] = []\\n lines = codes_lines[id(code)] = []\\n # ToDo: submit a patch to Python to optimize findlinestarts with iters\\n for addr, lineno in findlinestarts(code):\\n offsets.append(addr)\\n lines.append(lineno)\\n\\n for const in code.co_consts:\\n if isinstance(const, CodeType):\\n original_line_offsets(const, codes, codes_offsets, codes_lines)\",\n \"def all_lines_at_idx(mm, idx_list):\\n lines = []\\n for idx in idx_list:\\n mm.seek(idx)\\n # row back to beginning of line\\n ibegin = mm.rfind(b'\\\\n', 0, idx)\\n if ibegin == -1:\\n ibegin = 0\\n mm.seek(ibegin)\\n mm.readline()\\n # read desired line\\n line = mm.readline()\\n lines.append(line.decode())\\n return lines\",\n \"def _recompute_line_offsets(self):\\n # TODO: Consider \\\\r\\\\n?\\n self._line_offsets = [0] + [m.start() + 1 for m in re.finditer('\\\\n', self._text)]\",\n \"def find_line_offsets(self):\\n # line 0 doesn't exist; line 1 starts at char offset 0.\\n self.line_offsets = [None, 0]\\n # Find all newlines in `text`, and add an entry to\\n # line_offsets for each one.\\n pos = self.text.find('\\\\n')\\n while pos != -1:\\n self.line_offsets.append(pos+1)\\n pos = self.text.find('\\\\n', pos+1)\\n # Add a final entry, marking the end of the string.\\n self.line_offsets.append(len(self.text))\",\n \"def exercise_lines(path):\\n with open(path) as fin:\\n within_exercise = False\\n for line, line_number in zip(fin, count(1)):\\n line = line.lstrip()\\n\\n if within_exercise and line.startswith('#'):\\n yield line_number\\n elif not within_exercise and line.startswith('#') and 'EXERCISE:' in line:\\n within_exercise = True\\n yield line_number\\n else:\\n within_exercise = False\",\n \"def get_formatted_partial_source(self, filename, linenumber, offset):\\n lines = self.get_source(filename)\\n if not lines:\\n return \\\"\\\", \\\"\\\"\\n\\n begin = max(0, linenumber - self.context)\\n partial_source, bad_line = highlight_source(\\n linenumber,\\n linenumber - begin - 1,\\n lines[begin : linenumber + 1],\\n offset=offset,\\n )\\n return partial_source, bad_line\",\n \"def readMultipleFileLinesAndPositions(filePath,startPosition=None, bytesToRead=1): \\n \\n f = open(filePath, 'rb') \\n \\n if not startPosition is None: \\n f.seek(startPosition) \\n \\n lines = f.readlines(bytesToRead) \\n position = f.tell() \\n \\n f.close() \\n \\n return lines, position\",\n \"def get_line(file, linenum):\\n try:\\n with open(file, \\\"r\\\") as f:\\n return f.readlines()[linenum - 1].replace(\\\"\\\\n\\\", \\\"\\\")\\n except:\\n return f\\\"[ERROR]: could not open '{file}'\\\"\",\n \"def _collect_line_info(self, dso, real_path, addr_shifts):\\n # 1. Collect addrs to send to addr2line.\\n addr_set = set()\\n for addr in dso.addrs:\\n addr_obj = dso.addrs[addr]\\n if addr_obj.source_lines: # already has source line, no need to search.\\n continue\\n for shift in addr_shifts:\\n # The addr after shift shouldn't change to another function.\\n shifted_addr = max(addr + shift, addr_obj.func_addr)\\n addr_set.add(shifted_addr)\\n if shifted_addr == addr_obj.func_addr:\\n break\\n if not addr_set:\\n return\\n addr_request = '\\\\n'.join(['%x' % addr for addr in sorted(addr_set)])\\n\\n # 2. Use addr2line to collect line info.\\n try:\\n subproc = subprocess.Popen([self.addr2line_path, '-ai', '-e', real_path],\\n stdin=subprocess.PIPE, stdout=subprocess.PIPE)\\n (stdoutdata, _) = subproc.communicate(str_to_bytes(addr_request))\\n stdoutdata = bytes_to_str(stdoutdata)\\n except:\\n return\\n addr_map = {}\\n cur_line_list = None\\n for line in stdoutdata.strip().split('\\\\n'):\\n if line[:2] == '0x':\\n # a new address\\n cur_line_list = addr_map[int(line, 16)] = []\\n else:\\n # a file:line.\\n if cur_line_list is None:\\n continue\\n # Handle lines like \\\"C:\\\\Users\\\\...\\\\file:32\\\".\\n items = line.rsplit(':', 1)\\n if len(items) != 2:\\n continue\\n if '?' in line:\\n # if ? in line, it doesn't have a valid line info.\\n # An addr can have a list of (file, line), when the addr belongs to an inlined\\n # function. Sometimes only part of the list has ? mark. In this case, we think\\n # the line info is valid if the first line doesn't have ? mark.\\n if not cur_line_list:\\n cur_line_list = None\\n continue\\n (file_path, line_number) = items\\n line_number = line_number.split()[0] # Remove comments after line number\\n try:\\n line_number = int(line_number)\\n except ValueError:\\n continue\\n file_id = self._get_file_id(file_path)\\n cur_line_list.append((file_id, line_number))\\n\\n # 3. Fill line info in dso.addrs.\\n for addr in dso.addrs:\\n addr_obj = dso.addrs[addr]\\n if addr_obj.source_lines:\\n continue\\n for shift in addr_shifts:\\n shifted_addr = max(addr + shift, addr_obj.func_addr)\\n lines = addr_map.get(shifted_addr)\\n if lines:\\n addr_obj.source_lines = lines\\n break\\n if shifted_addr == addr_obj.func_addr:\\n break\",\n \"def _readline_ins(self):\\n if self._ins_filehandle is None:\\n if not os.path.exists(self._ins_filename):\\n raise Exception(\\n \\\"instruction file '{0}' not found\\\".format(self._ins_filename)\\n )\\n self._ins_filehandle = open(self._ins_filename, \\\"r\\\")\\n line = self._ins_filehandle.readline()\\n self._ins_linecount += 1\\n if line == \\\"\\\":\\n return None\\n self._last_line = line\\n # check for spaces in between the markers - this gets ugly\\n line = line.lower()\\n if self._marker is not None and self._marker in line:\\n\\n # def find_all(a_str, sub):\\n # start = 0\\n # while True:\\n # start = a_str.find(sub, start)\\n # if start == -1:\\n # return\\n # yield start\\n # start += len(sub)\\n # poss speedup using regex\\n midx = [m.start() for m in re.finditer(re.escape(self._marker), line)]\\n # midx = list(find_all(line, self._marker))\\n midx.append(len(line))\\n first = line[: midx[0]].strip()\\n tokens = []\\n if len(first) > 0:\\n # tokens.append(first)\\n tokens.extend([f.strip() for f in first.split()])\\n for idx in range(1, len(midx) - 1, 2):\\n mstr = line[midx[idx - 1] : midx[idx] + 1]\\n ostr = line[midx[idx] + 1 : midx[idx + 1]]\\n tokens.append(mstr)\\n tokens.extend(ostr.split())\\n else:\\n tokens = line.strip().split()\\n return tokens\",\n \"def find_line(lines: List[str], substr: Predicate, start: int = 0) -> int:\\n if isinstance(substr, str):\\n pred = _string_contains\\n elif isinstance(substr, tuple):\\n pred = _pattern_value_match\\n else:\\n pred = _apply_func\\n\\n for i in range(start, len(lines)):\\n if pred(lines[i], substr):\\n return i\\n\\n raise Exception(\\\"Not found: \\\" + str(substr) + \\\"\\\\n text:\\\" + str(lines) )\",\n \"def create_line_list(line_tbl, source_file, instr, unknown=False, ions=None):\\n # Init -- Mainly to insure formatting\\n ini_tbl = init_line_list()\\n # Add columns (in place)\\n add_instr_source(line_tbl, instr, source_file)\\n # Stack\\n new_tbl = vstack([ini_tbl, line_tbl], join_type='exact')\\n # Cut off first line\\n cut_tbl = new_tbl[1:]\\n\\n # Unknown list??\\n if unknown:\\n if ions is None:\\n raise IOError(\\\"Must provide list of possible ions if unknown\\\")\\n line_flag = get_line_flag(ions)\\n cut_tbl['line_flag'] = line_flag\\n\\n # Return\\n return cut_tbl\",\n \"def get_source_lines(self, filename, lineno, context=0):\\n if not filename or not lineno:\\n return ''\\n\\n return ''.join([' ' + linecache.getline(filename, line) for line in range(lineno - context, lineno + context + 1)])\",\n \"def findlinestarts(code):\\n byte_increments = [ord(c) for c in code.co_lnotab[0::2]]\\n line_increments = [ord(c) for c in code.co_lnotab[1::2]]\\n\\n lastlineno = None\\n lineno = code.co_firstlineno\\n addr = 0\\n for byte_incr, line_incr in zip(byte_increments, line_increments):\\n if byte_incr:\\n if lineno != lastlineno:\\n yield (addr, lineno)\\n lastlineno = lineno\\n addr += byte_incr\\n if line_incr >= 0x80:\\n # line_increments is an array of 8-bit signed integers\\n line_incr -= 0x100\\n lineno += line_incr\\n if lineno != lastlineno:\\n yield (addr, lineno)\",\n \"def stripFileLines(filePath, offset):\\n\\tfp = open(filePath, \\\"r\\\")\\n\\tfor line in fp:\\n\\t\\tstripped = line[offset:len(line) - 1 - offset]\\n\\t\\tprint (stripped)\\n\\tfp.close()\",\n \"def findlinestarts(code):\\n byte_increments = [ord(c) for c in code.co_lnotab[0::2]]\\n line_increments = [ord(c) for c in code.co_lnotab[1::2]]\\n result = []\\n lastlineno = None\\n lineno = code.co_firstlineno\\n addr = 0\\n for byte_incr, line_incr in zip(byte_increments, line_increments):\\n if byte_incr:\\n if lineno != lastlineno:\\n result.append((addr, lineno))\\n lastlineno = lineno\\n addr += byte_incr\\n lineno += line_incr\\n if lineno != lastlineno:\\n result.append((addr, lineno))\\n return result\",\n \"def line_at_cursor(code: str, cursor_pos: int = 0):\\n offset = 0\\n lines = code.splitlines(True)\\n for line in lines:\\n next_offset = offset + len(line)\\n if not line.endswith('\\\\n'):\\n # If the last line doesn't have a trailing newline, treat it as if\\n # it does so that the cursor at the end of the line still counts\\n # as being on that line.\\n next_offset += 1\\n if next_offset > cursor_pos:\\n break\\n offset = next_offset\\n else:\\n line = \\\"\\\"\\n return (line, offset)\",\n \"def extract_positions(lines):\\n positions = []\\n for line in lines:\\n position = [int(s) for s in re.findall(r'-?\\\\d+', line)]\\n positions.append(position)\\n return positions\",\n \"def _apply_line_rules(lines, commit, rules, line_nr_start):\\n all_violations = []\\n line_nr = line_nr_start\\n for line in lines:\\n for rule in rules:\\n violations = rule.validate(line, commit)\\n if violations:\\n for violation in violations:\\n violation.line_nr = line_nr\\n all_violations.append(violation)\\n line_nr += 1\\n return all_violations\",\n \"def parse_lines(self, start_line=0, end_line=False):\\n if end_line is False: end_line = len(self.file_ltxt)\\n\\n lines = self.file_ltxt\\n self.E_str = \\\"parse_lines\\\"\\n self.line_num = start_line\\n\\n # Loop over lines and parse\\n while self.line_num < end_line:\\n line = lines[self.line_num].strip()\\n\\n if line == \\\"echo\\\": print(\\\"\\\")\\n\\n # Parse any variables\\n elif self.line_declarations['variable'](line):\\n self.parse_variable_line(line)\\n\\n # Parse any file loading commands\\n elif self.line_declarations['load'](line):\\n self.parse_load_cmd(line)\\n\\n # Parse any file loading commands\\n elif self.line_declarations['plot'](line):\\n self.parse_plot_cmd(line)\\n\\n # Parse any file loading commands\\n elif self.line_declarations['write'](line):\\n self.parse_write_cmd(line)\\n\\n # Parse any math commands\\n elif self.line_declarations['math'](line):\\n self.parse_math_cmd(line)\\n\\n # Parse any echo commands\\n elif self.line_declarations['echo'](line):\\n self.parse_echo_cmd(line)\\n\\n # Parse any echo commands\\n elif self.line_declarations['calc'](line):\\n self.parse_calc_cmd(line)\\n\\n # Parse any echo commands\\n elif self.line_declarations['set'](line):\\n self.parse_set_cmd(line)\\n\\n # Parse any shell commands\\n elif self.line_declarations['shell'](line):\\n self.parse_shell_cmd()\\n\\n # Parse any for loop commands\\n elif self.line_declarations['for'](line):\\n self.parse_for_cmd(line)\\n\\n # Parse any echo commands\\n elif self.line_declarations['script'](line):\\n self.parse_script_cmd(line)\\n\\n elif self.line_declarations['inline_code'](line):\\n getattr(self, f\\\"parse_{line.split()[0]}_cmd\\\")(line)\\n\\n elif self.line_declarations['if'](line):\\n self.parse_if_cmd(line)\\n\\n # elif self.line_declarations['splice'](line):\\n # self.parse_splice_cmd(line)\\n\\n elif self.line_declarations['glue'](line):\\n self.parse_glue_cmd(line)\\n\\n elif self.line_declarations['exit'](line):\\n print(\\\"\\\\n\\\\nStopped Code -exit was called.\\\")\\n raise SystemExit\\n\\n # The end of control statements\\n elif '}' in line:\\n pass\\n\\n # Print a warning about unknown line\\n else:\\n self.print_warning(f\\\"I don't understand a line: '{line}'\\\")\\n\\n self.line_num += 1\",\n \"def fileReSeekList(fh, regexList):\\n\\n compiled = [re.compile(r) for r in regexList]\\n\\n while True:\\n line = fh.readline()\\n if line == '':\\n return None\\n for i, p in enumerate(compiled):\\n match = p.match(line)\\n if match:\\n return (match, i)\",\n \"def _execute_ins_line(self, ins_line, ins_lcount):\\n cursor_pos = 0 # starting cursor position\\n val_dict = {} # storage dict for obsname: obsval pairs in line\\n # for ii,ins in enumerate(ins_line):\\n ii = 0 # counter over instruction entries\\n all_markers = True\\n line_seps = set([\\\",\\\", \\\" \\\", \\\"\\\\t\\\"])\\n n_ins = len(ins_line) # number of instructions on line\\n maxsearch = 500 # maximum number of characters to search when slicing line\\n while True:\\n if ii >= n_ins:\\n break\\n ins = ins_line[ii] # extract instruction\\n i1 = ins[:1] # first char in instruction\\n # primary marker\\n if ii == 0 and i1 == self._marker:\\n # if first and instruction starts with primary marker\\n # search for presence of primary marker e.g. ~start~\\n mstr = ins.replace(self._marker, \\\"\\\")\\n while True:\\n # loop over lines until primary marker is found\\n line = self._readline_output() # read line from output\\n if line is None:\\n self.throw_out_error(\\n \\\"EOF when trying to find primary marker '{0}' from \\\"\\n \\\"instruction file line {1}\\\".format(mstr, ins_lcount)\\n )\\n if mstr in line: # when marker is found break and update\\n # cursor position in current line\\n break\\n # copy a version of line commas replaced\\n # (to support comma sep strings)\\n rline = line.replace(\\\",\\\", \\\" \\\").replace(\\\"\\\\t\\\",\\\"\\\")\\n\\n cursor_pos = line.index(mstr) + len(mstr)\\n\\n # line advance\\n elif i1 == \\\"l\\\": # if start of instruction is line advance\\n try:\\n nlines = int(ins[1:]) # try and get advance number\\n except Exception as e:\\n self.throw_ins_error(\\n \\\"casting line advance to int for \\\"\\n \\\"instruction '{0}'\\\".format(ins),\\n ins_lcount,\\n )\\n for i in range(nlines):\\n line = self._readline_output()\\n if line is None:\\n self.throw_out_error(\\n \\\"EOF when trying to read {0} lines for line \\\"\\n \\\"advance instruction '{1}', from instruction \\\"\\n \\\"file line number {2}\\\".format(nlines, ins, ins_lcount)\\n )\\n # copy a version of line commas replaced\\n # (to support comma sep strings)\\n rline = line.replace(\\\",\\\", \\\" \\\")\\n elif ins == \\\"w\\\": # whole string comparison\\n raw = rline[cursor_pos : cursor_pos + maxsearch].split(\\n None, 2\\n ) # TODO: maybe slow for long strings -- hopefuly maxsearch helps\\n if line[cursor_pos] in line_seps:\\n raw.insert(0, \\\"\\\")\\n if len(raw) == 1:\\n self.throw_out_error(\\n \\\"no whitespaces found on output line {0} past {1}\\\".format(\\n line, cursor_pos\\n )\\n )\\n # step over current value\\n cursor_pos = rline.replace(\\\"\\\\t\\\",\\\" \\\").find(\\\" \\\", cursor_pos)\\n # now find position of next entry\\n cursor_pos = rline.find(raw[1], cursor_pos)\\n # raw[1]\\n # )\\n\\n elif i1 == \\\"!\\\": # indicates obs instruction folows\\n oname = ins.replace(\\\"!\\\", \\\"\\\")\\n # look a head for a second/closing marker\\n if ii < n_ins - 1 and ins_line[ii + 1] == self._marker:\\n # if penultimate instruction and last instruction is\\n # primary marker, look for that marker in line\\n m = ins_line[ii + 1].replace(self._marker, \\\"\\\")\\n es = line.find(m, cursor_pos)\\n if es == -1: # m not in rest of line\\n self.throw_out_error(\\n \\\"secondary marker '{0}' not found from cursor_pos {1}\\\".format(\\n m, cursor_pos\\n )\\n )\\n # read to closing marker\\n val_str = line[cursor_pos:es]\\n else:\\n # find next space in (r)line -- signifies end of entry\\n es = rline.find(\\\" \\\", cursor_pos)\\n if es == -1 or es == cursor_pos:\\n # if no space or current position is space\\n # use old fashioned split to get value\\n # -- this will happen if there are leading blanks before\\n # vals in output file (e.g. formatted)\\n val_str = rline[cursor_pos : cursor_pos + maxsearch].split(\\n None, 1\\n )[0]\\n else:\\n # read val (constrained slice is faster for big strings)\\n val_str = rline[cursor_pos:es]\\n try:\\n val = float(val_str)\\n except Exception as e:\\n if oname != \\\"dum\\\":\\n self.throw_out_error(\\n \\\"casting string '{0}' to float for instruction '{1}'\\\".format(\\n val_str, ins\\n )\\n )\\n\\n if oname != \\\"dum\\\":\\n val_dict[oname] = val\\n ipos = line.find(val_str.strip(), cursor_pos)\\n # val_len = len(val_str)\\n cursor_pos = ipos + len(val_str) # update cursor\\n all_markers = False\\n\\n elif i1 == self._marker:\\n m = ins.replace(self._marker, \\\"\\\") # extract just primary marker\\n # find position of primary marker in line\\n es = line.find(m, cursor_pos)\\n if es == -1: # m not in rest of line\\n if all_markers:\\n ii = 0\\n continue\\n else:\\n self.throw_out_error(\\n \\\"secondary marker '{0}' not found from \\\"\\n \\\"cursor_pos {1}\\\".format(m, cursor_pos)\\n )\\n cursor_pos = es + len(m)\\n\\n elif i1 == \\\"(\\\":\\n if \\\")\\\" not in ins:\\n self.throw_ins_error(\\\"unmatched ')'\\\", self._instruction_lcount)\\n oname = ins[1:].split(\\\")\\\", 1)[0].lower()\\n raw = ins.split(\\\")\\\")[1]\\n if \\\":\\\" not in raw:\\n self.throw_ins_error(\\n \\\"couldnt find ':' in semi-fixed instruction: '{0}'\\\".format(ins),\\n lcount=self._instruction_lcount,\\n )\\n raw = raw.split(\\\":\\\")\\n try:\\n s_idx = int(raw[0]) - 1\\n except Exception as e:\\n self.throw_ins_error(\\n \\\"error converting '{0}' to integer in semi-fixed instruction: '{1}'\\\".format(\\n raw[0], ins\\n ),\\n lcount=self._instruction_lcount,\\n )\\n try:\\n e_idx = int(raw[1])\\n except Exception as e:\\n self.throw_ins_error(\\n \\\"error converting '{0}' to integer in semi-fixed instruction: '{1}'\\\".format(\\n raw[1], ins\\n ),\\n lcount=self._instruction_lcount,\\n )\\n\\n if len(line) < e_idx:\\n self.throw_out_error(\\n \\\"output line only {0} chars long, semi-fixed ending col {1}\\\".format(\\n len(line), e_idx\\n )\\n )\\n\\n if cursor_pos > e_idx:\\n self.throw_out_error(\\n \\\"cursor at {0} has already read past semi-fixed ending col {1}\\\".format(\\n cursor_pos, e_idx\\n )\\n )\\n\\n ss_idx = max(cursor_pos, s_idx)\\n raw = line[ss_idx : ss_idx + maxsearch].split(\\n None, 1\\n ) # slpitting only 1 might be margin faster\\n rs_idx = line.index(raw[0])\\n if rs_idx > e_idx:\\n self.throw_out_error(\\n \\\"no non-whitespace chars found in semi-fixed observation {0}\\\".format(\\n ins\\n )\\n )\\n re_idx = rs_idx + len(raw[0])\\n val_str = line[rs_idx:re_idx]\\n try:\\n val = float(val_str)\\n except Exception as e:\\n if oname != \\\"dum\\\":\\n self.throw_out_error(\\n \\\"casting string '{0}' to float for instruction '{1}'\\\".format(\\n val_str, ins\\n )\\n )\\n\\n if oname != \\\"dum\\\":\\n val_dict[oname] = val\\n cursor_pos = re_idx\\n\\n elif i1 == \\\"[\\\":\\n if \\\"]\\\" not in ins:\\n self.throw_ins_error(\\\"unmatched ']'\\\", self._instruction_lcount)\\n oname = ins[1:].split(\\\"]\\\", 1)[0].lower()\\n raw = ins.split(\\\"]\\\")[1]\\n if \\\":\\\" not in raw:\\n self.throw_ins_error(\\n \\\"couldnt find ':' in fixed instruction: '{0}'\\\".format(ins),\\n lcount=self._instruction_lcount,\\n )\\n raw = raw.split(\\\":\\\")\\n try:\\n s_idx = int(raw[0]) - 1\\n except Exception as e:\\n self.throw_ins_error(\\n \\\"error converting '{0}' to integer in fixed instruction: '{1}'\\\".format(\\n raw[0], ins\\n ),\\n lcount=self._instruction_lcount,\\n )\\n try:\\n e_idx = int(raw[1])\\n except Exception as e:\\n self.throw_ins_error(\\n \\\"error converting '{0}' to integer in fixed instruction: '{1}'\\\".format(\\n raw[1], ins\\n ),\\n lcount=self._instruction_lcount,\\n )\\n\\n if len(line) < e_idx:\\n self.throw_out_error(\\n \\\"output line only {0} chars long, fixed ending col {1}\\\".format(\\n len(line), e_idx\\n )\\n )\\n\\n if cursor_pos > s_idx:\\n self.throw_out_error(\\n \\\"cursor at {0} has already read past fixed starting col {1}\\\".format(\\n cursor_pos, e_idx\\n )\\n )\\n\\n val_str = line[s_idx:e_idx]\\n try:\\n val = float(val_str)\\n except Exception as e:\\n if oname != \\\"dum\\\":\\n self.throw_out_error(\\n \\\"casting string '{0}' to float for instruction '{1}'\\\".format(\\n val_str, ins\\n )\\n )\\n\\n if oname != \\\"dum\\\":\\n val_dict[oname] = val\\n cursor_pos = e_idx\\n\\n else:\\n self.throw_out_error(\\n \\\"unrecognized instruction '{0}' on ins file line {1}\\\".format(\\n ins, ins_lcount\\n )\\n )\\n ii += 1\\n return val_dict\",\n \"def _get_offset_line(self):\\n columns = [col for col in self.df if col.startswith('offset_')]\\n offset_lines = []\\n if columns != []:\\n return self.df[columns]\\n else:\\n print('No offsets available')\\n raise IndexError\",\n \"def locate_code_blocks(lines):\\n stripped_lines = [l.strip() for l in lines]\\n code_block_indexes = [i for i, l in enumerate(stripped_lines,1) if l.startswith('```')]\\n nblock_indexes = len(code_block_indexes)\\n assert nblock_indexes % 2 == 0\\n return [b for b in zip(code_block_indexes[::2], code_block_indexes[1::2])]\",\n \"def lineno(self, start, text):\\n end = start\\n while text[end] not in self.NUMBERS:\\n end += 1\\n self.cur.append((start, Punctuation, text[start]))\\n self.cur.append((start+1, Text, text[start+1:end]))\\n start = end\\n match = self.LINENOS.match(text, start)\\n if match is None:\\n raise AssertionError\\n if text[match.end()] != ')':\\n raise AssertionError\\n self.cur.append((match.start(), Number.Integer, match.group(0)))\\n self.cur.append((match.end(), Punctuation, text[match.end()]))\\n return match.end() + 1\",\n \"def approx_lineno_across_revs(filename, newfilename, revision, newrevision,\\n lineno):\\n # This doesn't work that well if there are a lot of line changes within the\\n # hunk (demonstrated by GitHyperBlameLineMotionTest.testIntraHunkLineMotion).\\n # A fuzzy heuristic that takes the text of the new line and tries to find a\\n # deleted line within the hunk that mostly matches the new line could help.\\n\\n # Use the <revision>:<filename> syntax to diff between two blobs. This is the\\n # only way to diff a file that has been renamed.\\n old = '%s:%s' % (revision, filename)\\n new = '%s:%s' % (newrevision, newfilename)\\n hunks = cache_diff_hunks(old, new)\\n\\n cumulative_offset = 0\\n\\n # Find the hunk containing lineno (if any).\\n for (oldstart, oldlength), (newstart, newlength) in hunks:\\n cumulative_offset += newlength - oldlength\\n\\n if lineno >= oldstart + oldlength:\\n # Not there yet.\\n continue\\n\\n if lineno < oldstart:\\n # Gone too far.\\n break\\n\\n # lineno is in [oldstart, oldlength] at revision; [newstart, newlength] at\\n # newrevision.\\n\\n # If newlength == 0, newstart will be the line before the deleted hunk.\\n # Since the line must have been deleted, just return that as the nearest\\n # line in the new file. Caution: newstart can be 0 in this case.\\n if newlength == 0:\\n return max(1, newstart)\\n\\n newend = newstart + newlength - 1\\n\\n # Move lineno based on the amount the entire hunk shifted.\\n lineno = lineno + newstart - oldstart\\n # Constrain the output within the range [newstart, newend].\\n return min(newend, max(newstart, lineno))\\n\\n # Wasn't in a hunk. Figure out the line motion based on the difference in\\n # length between the hunks seen so far.\\n return lineno + cumulative_offset\",\n \"def scanpatch(fp):\\n lr = patch.linereader(fp)\\n\\n def scanwhile(first, p):\\n \\\"\\\"\\\"scan lr while predicate holds\\\"\\\"\\\"\\n lines = [first]\\n while True:\\n line = lr.readline()\\n if not line:\\n break\\n if p(line):\\n lines.append(line)\\n else:\\n lr.push(line)\\n break\\n return lines\\n\\n while True:\\n line = lr.readline()\\n if not line:\\n break\\n if line.startswith('diff --git a/'):\\n def notheader(line):\\n s = line.split(None, 1)\\n return not s or s[0] not in ('---', 'diff')\\n header = scanwhile(line, notheader)\\n fromfile = lr.readline()\\n if fromfile.startswith('---'):\\n tofile = lr.readline()\\n header += [fromfile, tofile]\\n else:\\n lr.push(fromfile)\\n yield 'file', header\\n elif line[0] == ' ':\\n yield 'context', scanwhile(line, lambda l: l[0] in ' \\\\\\\\')\\n elif line[0] in '-+':\\n yield 'hunk', scanwhile(line, lambda l: l[0] in '-+\\\\\\\\')\\n else:\\n m = lines_re.match(line)\\n if m:\\n yield 'range', m.groups()\\n else:\\n raise patch.PatchError('unknown patch content: %r' % line)\",\n \"def do_Promo_line_parse (Promo_line, line_number, filehash) :\\n result = [filehash,\\n line_number,\\n Promo_line[0:8].strip(),\\n Promo_line[9:13].strip(),\\n Promo_line[14:19].strip(),\\n Promo_line[20:26].strip().lstrip('0'), # Spec indicates numerical field\\n Promo_line[27:30].strip().lstrip('0'), # Spec indicates numerical field\\n Promo_line[31:40].strip(),\\n Promo_line[41:49].strip(),\\n Promo_line[50:].strip()\\n ]\\n return result\\n # Having the line number passed in is ugly, but kind of works :/\\n # Having all the field extraction explicit is kind of ugly too...\\n # We're using the hash here to link? Yeah, that's because Python\\n # doesn't know what the autonumbered load table is up to in the\\n # DB when it starts to coalesce the raw files together.\",\n \"def __get_line(file_path: str, line_no: int, errors: str = 'ignore') -> str:\\n try:\\n with open(file_path, mode='r',\\n encoding='utf-8', errors=errors) as f:\\n for line in f:\\n line_no -= 1\\n if line_no == 0:\\n return line\\n return ''\\n except IOError:\\n LOG.error(\\\"Failed to open file %s\\\", file_path)\\n return ''\",\n \"def parse_lines(filename):\\n line_counter = 0\\n with open(filename, 'r') as rf:\\n for line_txt in rf:\\n try:\\n d = json.loads(line_txt)\\n tup = (\\n d['attributed_to'],\\n int(d['date_time'][8:10]),\\n d.get('used_first_time_today', False),\\n d.get('first_utm_source', 'unknown') \\n )\\n except:\\n print('Error parsing line_txt:', line_txt)\\n line_counter += 1\\n if line_counter % 10 ** 6 == 0:\\n print('read %dM lines' % (line_counter // 10 ** 6))\\n yield tup # yield: https://stackoverflow.com/a/231855\",\n \"def _ComputeOffset( contents, line, column ):\\n contents = ToBytes( contents )\\n current_line = 1\\n current_column = 1\\n newline = bytes( b'\\\\n' )[ 0 ]\\n for i, byte in enumerate( contents ):\\n if current_line == line and current_column == column:\\n return i\\n current_column += 1\\n if byte == newline:\\n current_line += 1\\n current_column = 1\\n message = COMPUTE_OFFSET_ERROR_MESSAGE.format( line = line,\\n column = column )\\n _logger.error( message )\\n raise RuntimeError( message )\",\n \"def load(self, lines, start_address=0):\\n addr = start_address\\n lineno = 0\\n try:\\n for line in lines:\\n lineno += 1\\n parsed = self.parse(line)\\n if parsed is None:\\n continue\\n if isinstance(parsed, tuple):\\n if parsed[0] is CHANGE_ADDRESS:\\n addr = parsed[1]\\n continue\\n if parsed[0] is DECLARE_LABEL:\\n if parsed[1] in self.labels:\\n raise ParseException('Redeclaration of ' + parsed[1])\\n self.labels[parsed[1]] = addr\\n continue\\n if addr >= MEMSIZE:\\n raise ParseException('Out of memory parsing program')\\n if self.memory[addr] is not None:\\n raise ParseException('Memory not None at ' + str(addr))\\n self.memory[addr] = parsed\\n addr += 1\\n except ParseException as e:\\n sys.stderr.write('%s: ERROR: %s\\\\n' % (lineno, line))\\n sys.stderr.write('%s: ERROR: %s\\\\n' % (lineno, e))\\n raise e\\n self.program_validate()\",\n \"def _start_magic(line):\\n return start(line)\",\n \"def getsourcelines(object):\\r\\n lines, lnum = findsource(object)\\r\\n\\r\\n if ismodule(object): return lines, 0\\r\\n else: return getblock(lines[lnum:]), lnum + 1\",\n \"def position_before_code(filename=None):\\n\\n if filename is None:\\n raise ValueError\\n\\n with open(filename, 'r') as f:\\n content = f.read()\\n\\n # find \\\"import\\\" lines - if they exist\\n pattern = r'[\\\\n\\\\r]\\\\s*import[^\\\\n\\\\r]*;'\\n it = re.finditer(pattern, content, re.DOTALL)\\n\\n last = None\\n # next \\\"empty\\\" for loop is intended to advance iterator to last match\\n for match in it:\\n last = match\\n\\n if last is None:\\n # no imports in file\\n return position_before_class(content)\\n else:\\n # found import lines - last holds last match in file\\n return last.end()\",\n \"def get_file_lines(filename):\\n if not os.path.isfile(filename):\\n logging.error(\\\"[get_file_lines] %s not found\\\", filename)\\n return -1\\n\\n if not os.access(filename, os.R_OK):\\n logging.error(\\\"[get_file_lines] %s cannot be read\\\", filename)\\n return -1\\n\\n i = -1\\n with open(filename) as f:\\n try:\\n for i, l in enumerate(f):\\n pass\\n except UnicodeDecodeError:\\n return -1\\n return i + 1\",\n \"def find_line_markers(source):\\n markers = {}\\n for lineno, line in enumerate(source.splitlines(), start=1):\\n m = re.search(r\\\"#=(\\\\w+)\\\", line)\\n if m:\\n markers[lineno] = m.group(1)\\n return markers\",\n \"def get_line(lines):\\n for line in lines:\\n yield line\",\n \"def get_line(lines):\\n for line in lines:\\n yield line\",\n \"def get_lines_with_start(cls, frame, radius=5, sys_context=None):\\n\\t\\tcode = cls._dispatch_frame(frame)\\n\\t\\n\\t\\tif not code: \\n\\t\\t\\treturn []\\n\\t\\telse:\\n\\t\\t\\tcode_lines = code.splitlines()\\n\\t\\n\\t\\tif not radius:\\n\\t\\t\\treturn code_lines, 1\\n\\t\\telse:\\n\\t\\t\\tblock_slice = cls._calc_block_ends(frame.f_lineno, len(code_lines), radius)\\n\\t\\t\\treturn code_lines[block_slice], block_slice.start\",\n \"def fetch(self, relative: int = 0, **kwargs) -> str:\\n line = self.reader.fetch(relative, **kwargs)\\n if kwargs.get('allow_endmarker') is True:\\n return line\\n elif self.pattern.match(line):\\n raise IOError\\n else:\\n return line\",\n \"def interpret_line(self, line, source=None, lineno=None):\\n\\n pline = self.parser.parse_line(line, source=source, lineno=lineno)\\n return self.execute(pline)\",\n \"def next_line(self, context, line):\",\n \"def adjust_line_numbers(self, code):\\n store_lines = set()\\n make_function_lines = set()\\n for opcode in itertools.chain.from_iterable(_collect_bytecode(code)):\\n if opcode.name.startswith(\\\"STORE_\\\"):\\n store_lines.add(opcode.line)\\n elif opcode.name == \\\"MAKE_FUNCTION\\\":\\n make_function_lines.add(opcode.line)\\n\\n def adjust(line, allowed_lines, min_line=1):\\n adjusted_line = line\\n while adjusted_line not in allowed_lines and adjusted_line >= min_line:\\n adjusted_line -= 1\\n return adjusted_line if adjusted_line >= min_line else None\\n\\n # Process type comments.\\n for type_comment_set in self._type_comments:\\n for line, comment in sorted(type_comment_set.type_comments.items()):\\n adjusted_line = adjust(line, store_lines, type_comment_set.start_line)\\n if not adjusted_line:\\n # vm._FindIgnoredTypeComments will take care of error reporting.\\n continue\\n if line != type_comment_set.end_line:\\n self._errorlog.ignored_type_comment(self._filename, line, comment)\\n del type_comment_set.type_comments[line]\\n elif adjusted_line != line:\\n type_comment_set.type_comments[adjusted_line] = comment\\n del type_comment_set.type_comments[line]\\n\\n # Process decorators.\\n for line in sorted(self._decorators):\\n adjusted_line = adjust(line, make_function_lines)\\n if not adjusted_line:\\n log.error(\\n \\\"No MAKE_FUNCTION opcode found for decorator on line %d\\\", line)\\n elif adjusted_line != line:\\n self._decorators.add(adjusted_line)\\n self._decorators.remove(line)\\n\\n # Process variable annotations.\\n for line, annot in sorted(self._variable_annotations.items()):\\n adjusted_line = adjust(line, store_lines)\\n if not adjusted_line:\\n log.error(\\n \\\"No STORE_* opcode found for annotation %r on line %d\\\", annot, line)\\n del self._variable_annotations[line]\\n elif adjusted_line != line:\\n self._variable_annotations[adjusted_line] = annot\\n del self._variable_annotations[line]\",\n \"def loadLine(self, linenumber=None):\\r\\n logger.debug(\\\"loadLine\\\")\\r\\n\\r\\n if not linenumber:\\r\\n linenumber = int(self.linenumberEdit.text())\\r\\n\\r\\n self.currentstartline = self.currentchunk(linenumber) # find index chunk in which the line is located\\r\\n logger.debug(f\\\"Loading from index position {self.currentstartline}\\\")\\r\\n\\r\\n byteposition = self.file_index[self.currentstartline] #must be an index key\\r\\n logger.debug(f\\\"Byteposition in file {byteposition}\\\")\\r\\n text = self.reader(self.fileName, byteposition, from_what=os.SEEK_SET, nbytes=self.chunksize)\\r\\n # self.linewnd.setText(self._line_numbers(text, linenumber))\\r\\n if self.line_numbers():\\r\\n text = self._add_line_numbers(text, linestart=self.currentstartline)\\r\\n self.textwnd.setText(text)\\r\\n\\r\\n # move cursor to selected line\\r\\n if self.currentstartline == 0:\\r\\n offset = linenumber % self.linechunk # we are starting the index at 0 not at 1, hence first chunk is different\\r\\n else:\\r\\n offset = linenumber % self.linechunk - 1\\r\\n logger.debug(f\\\"Moving {offset} in current text window.\\\")\\r\\n cursor = self.textwnd.textCursor()\\r\\n cursor.movePosition(QtGui.QTextCursor.Down, QtGui.QTextCursor.MoveAnchor, offset)\\r\\n self.textwnd.setTextCursor(cursor)\\r\\n\\r\\n if self.tableBtn.isChecked():\\r\\n self._show_as_table()\",\n \"def offsetline(linen, pattern_result):\\n\\n if \\\"nlines\\\" in pattern_result:\\n nlines = pattern_result[\\\"nlines\\\"]\\n else:\\n nlines = 0\\n new_linen = linen - nlines - 1\\n if new_linen < 0:\\n return 0\\n else:\\n return new_linen\",\n \"def line(self):\\n if self.__line is None:\\n left = self.__source.rfind(\\\"\\\\n\\\", 0, self.__offset) + 1\\n right = self.__source.find(\\\"\\\\n\\\", self.__offset)\\n\\n self.__line = self.__source[left : right]\\n self.__lineOffset = self.__offset - left\\n\\n return self.__line\",\n \"def line(self, n):\\n return self.__contents[n]\",\n \"def get_annotated_lines(self):\\n lines = [Line(idx + 1, x) for idx, x in enumerate(self.sourcelines)]\\n\\n try:\\n lines[self.lineno - 1].current = True\\n except IndexError:\\n pass\\n\\n return lines\",\n \"def skiprows(fn):\\n for i, line in enumerate(open(fn)):\\n if line.startswith('#'):\\n continue\\n else:\\n break\\n return i\",\n \"def want_line(self, op, first = 0):\\n for ii in range(first, len(self.__content)):\\n match = re.match(op, self.__content[ii], re.IGNORECASE)\\n if match:\\n return (ii, match.group(1))\\n return None\",\n \"def read_ins_file(self):\\n self._instruction_lines = []\\n self._instruction_lcount = []\\n first_line = self._readline_ins()\\n if len(first_line) < 2:\\n raise Exception(\\n \\\"first line of ins file must have atleast two entries, not '{0}'\\\".format(\\n \\\",\\\".join(first_line)\\n )\\n )\\n if first_line[0] != \\\"pif\\\":\\n raise Exception(\\n \\\"first line of ins file '{0}' must start with 'pif', not '{1}'\\\".format(\\n self._ins_filename, first_line[0]\\n )\\n )\\n self._marker = first_line[1]\\n while True:\\n line = self._readline_ins()\\n\\n if line is None:\\n break\\n elif len(line) == 0:\\n self.throw_ins_warning(\\\"empty line, breaking\\\")\\n break\\n else:\\n c1 = line[0][:1]\\n if c1 == \\\"l\\\":\\n pass\\n elif c1 == self._marker:\\n pass\\n elif c1 == \\\"&\\\":\\n self.throw_ins_error(\\\"line continuation not supported\\\")\\n else:\\n self.throw_ins_error(\\n \\\"first token must be line advance ('l'), primary marker, or continuation ('&'),\\\"\\n + \\\"not: {0}\\\".format(line[0])\\n )\\n\\n for token in line[1:]:\\n t1 = token[:1]\\n if t1 == \\\"t\\\":\\n self.throw_ins_error(\\\"tab instruction not supported\\\")\\n elif t1 == self._marker:\\n tn = token[-1:]\\n if not tn == self._marker:\\n self.throw_ins_error(\\n \\\"unbalanced secondary marker in token '{0}'\\\".format(token)\\n )\\n\\n for somarker, eomarker in zip([\\\"!\\\", \\\"[\\\", \\\"(\\\"], [\\\"!\\\", \\\"]\\\", \\\")\\\"]):\\n #\\n if t1 == somarker:\\n ofound = True\\n if eomarker not in token[1:]:\\n self.throw_ins_error(\\n \\\"unmatched observation marker '{0}', looking for '{1}' in token '{2}'\\\".format(\\n somarker, eomarker, token\\n )\\n )\\n raw = token[1:].split(eomarker)[0].replace(somarker, \\\"\\\")\\n if raw == \\\"dum\\\":\\n pass\\n else:\\n if (\\n self._full_oname_set is not None\\n and raw not in self._full_oname_set\\n ):\\n self.throw_ins_error(\\n \\\"obs name '{0}' not in pst\\\".format(raw)\\n )\\n elif raw in self._found_oname_set:\\n self.throw_ins_error(\\n \\\"obs name '{0}' is listed more than once\\\".format(\\n raw\\n )\\n )\\n self._found_oname_set.add(raw)\\n break\\n # print(raw)\\n\\n self._instruction_lines.append(line)\\n self._instruction_lcount.append(self._ins_linecount)\",\n \"def _get_line_regions_from_xml_file(filename: str) -> List[Dict[str, int]]:\\n xml_root_element = ElementTree.parse(filename).getroot() # nosec\\n xml_line_elements = xml_root_element.findall(\\\"handwritten-part/line\\\")\\n return [_get_line_region_from_xml_element(el) for el in xml_line_elements]\",\n \"def map_instructions_to_lines(\\n instructions: list[UniqueInstruction], subject_properties: SubjectProperties\\n ) -> set[int]:\\n line_ids = set()\\n curr_line = -1\\n for instruction in instructions:\\n if instruction.file == \\\"<ast>\\\": # do not include test statements\\n continue\\n if instruction.lineno == curr_line: # only add new lines\\n continue\\n curr_line = instruction.lineno # type: ignore[assignment]\\n line_ids.add(\\n DynamicSlicer.get_line_id_by_instruction(\\n instruction, subject_properties\\n )\\n )\\n return line_ids\",\n \"def get_lineid_content():\\n lineid_content = {}\\n lines_file_path = os.path.join(DATA_PATH + MOVIE_LINES_FILE)\\n\\n with open(lines_file_path, 'r', errors='ignore') as f:\\n # +++$+++ is used to split the section in a single line\\n # A correct formed line includes five sections\\n # The first section is lineID\\n # The last section is line content\\n # Here we only need lineID and content\\n\\n for line in f:\\n line_sections = line.split(' +++$+++ ')\\n assert len(line_sections) == 5\\n if line_sections[4][-1] == '\\\\n':\\n line_sections[4] = line_sections[4][:-1]\\n lineid_content[line_sections[0]] = line_sections[4]\\n\\n return lineid_content\",\n \"def hashes_above(path, line_number):\\n for line_number in xrange(line_number - 1, 0, -1):\\n line = getline(path, line_number)\\n match = HASH_COMMENT_RE.match(line)\\n if match:\\n yield match.groupdict()['hash']\\n elif not line.lstrip().startswith('#'):\\n # If we hit a non-comment line, abort\\n break\",\n \"def lineOffset(self):\\n if self.__lineOffset is None:\\n self.__lineOffset = self.__offset - self.__source.rfind(\\\"\\\\n\\\", 0, self.__offset) - 1\\n\\n return self.__lineOffset\",\n \"def prepare_code_snippet(file_path: str, line_no: int, context_lines_count: int = 5) -> str:\\n with open(file_path) as text_file:\\n # Highlight code\\n code = text_file.read()\\n code_lines = code.splitlines()\\n # Prepend line number\\n code_lines = [\\n f\\\">{lno:3} | {line}\\\" if line_no == lno else f\\\"{lno:4} | {line}\\\"\\n for lno, line in enumerate(code_lines, 1)\\n ]\\n # # Cut out the snippet\\n start_line_no = max(0, line_no - context_lines_count - 1)\\n end_line_no = line_no + context_lines_count\\n code_lines = code_lines[start_line_no:end_line_no]\\n # Join lines\\n code = \\\"\\\\n\\\".join(code_lines)\\n return code\",\n \"def process_lines(self, lines):\\n line_index = 0\\n n_lines = len(lines)\\n while line_index < n_lines:\\n if lines[line_index].startswith(\\\"HIERARCHY\\\"):\\n line_index = self._read_skeleton(lines, line_index, n_lines)\\n if lines[line_index].startswith(\\\"MOTION\\\"):\\n self._read_frametime(lines, line_index+2)\\n line_index = self._read_frames(lines, line_index+3, n_lines)\\n else:\\n line_index += 1\",\n \"def lineToList(self, line):\\n raise NotImplementedError\",\n \"def _set_start_lineno_if_needed():\\n if origin and record.origin.line_start is None:\\n record.origin.line_start = lineno\",\n \"def ProcessLine(fn, filename, file_extension, clean_lines, line,\\n include_state, function_state, nesting_state, error,\\n extra_check_functions=[]):\\n fn(filename, file_extension, clean_lines, line,\\n include_state, function_state, nesting_state,\\n makeErrorFn(error, [], [r'(.*)should be indented \\\\+1 space inside(.*)']),\\n extra_check_functions=[])\",\n \"def line_parser(path):\\n lines = []\\n with open(path, 'r') as input:\\n lines = [line.rstrip().split(',') for line in input]\\n lines = [\\n [[float(x1), float(y1)],\\n [float(x2), float(y2)]] \\n for x1, y1, x2, y2 in lines]\\n return lines\",\n \"def find_log_lines_with_content(self, prefixes, needle_substring, required_count, return_first_line):\\n required_count = int(required_count)\\n\\n def check_function():\\n all_data = []\\n for prefix in prefixes:\\n print('Analyzing prefix {}'.format(prefix))\\n all_files = self.get_files_in_bucket(prefix=prefix,\\n limit=self.S3_LISTING_REQUEST_LIMIT)\\n for file in all_files:\\n print('Analyzing file {}'.format(file))\\n content = self.get_file_content_from_bucket(file)\\n for line in content.splitlines():\\n if needle_substring in line:\\n data = json.loads(line)\\n all_data.append(data)\\n if len(all_data) >= required_count and required_count != 0:\\n return all_data\\n\\n if len(all_data) > 0 and required_count == 0:\\n return all_data\\n\\n result = legion_test.utils.wait_until(check_function,\\n self.WAIT_FILE_TIME, self.WAIT_FILE_ITERATIONS)\\n if not result:\\n raise Exception('{} log line(s) with {!r} has not been found'.format(required_count, needle_substring))\\n if return_first_line:\\n return result[0]\\n else:\\n return result\",\n \"def extract_line(line, pos):\\n found = u''\\n depth = 0\\n offset = 0\\n for c in line:\\n if c == '<':\\n depth += 1\\n elif c == '>':\\n depth -= 1\\n if depth == 0:\\n pos -= process_match(found[1:], pos + offset)\\n found = u''\\n\\n if depth > 0:\\n found += c\\n\\n offset += 1\\n return pos + offset\",\n \"def numbers_in_line(line):\\n ret = []\\n pos = 0\\n while True:\\n loc = find_next_number(line, pos)\\n if not loc:\\n break\\n ret.append(loc)\\n pos = loc[1]\\n return ret\",\n \"def get_line(trj_file):\\n\\twith open(trj_file) as file:\\n\\t\\tfor i in file:\\n\\t\\t\\tyield i\",\n \"def seek_line(self, pattern):\\n line = next(self.f)\\n while not line.startswith(pattern):\\n line = next(self.f)\\n return line\",\n \"def doctest_markup(in_lines):\\n out_lines = list(in_lines)[:]\\n err_tuples = []\\n for pos, this in enumerate(out_lines):\\n # Check for 'leave the rest' markup\\n if this.strip() == '#23dt skip rest':\\n break\\n # Check for docest line with markup\\n mark_match = MARK_COMMENT.search(this)\\n if mark_match is None:\\n continue\\n docbits, marked_line, marker, markup = mark_match.groups()\\n place_line_match = PLACE_LINE_EXPRS.match(markup)\\n if place_line_match is None:\\n msg = ('Found markup \\\"%s\\\" in line \\\"%s\\\" but wrong syntax' %\\n (markup, this))\\n err_tuples.append((this, msg))\\n continue\\n place_expr, line_expr = place_line_match.groups()\\n exec_globals = {'here': pos, 'next': pos+1}\\n if place_expr.strip() == '':\\n place = pos\\n else:\\n try:\\n place = eval(place_expr, exec_globals)\\n except:\\n msg = ('Error finding place with \\\"%s\\\" in line \\\"%s\\\"' %\\n (place_expr, this))\\n err_tuples.append((this, msg))\\n continue\\n # Prevent processing operating on 23dt comment part of line\\n if place == pos:\\n line = marked_line\\n else:\\n line = out_lines[place]\\n # Shorthand\\n if line_expr == 'bytes':\\n # Any strings on the given line are byte strings\\n pre, mid, post = INDENT_SPLITTER.match(line).groups()\\n try:\\n res = byter(mid)\\n except:\\n err = sys.exc_info()[1]\\n msg = ('Error \\\"%s\\\" parsing \\\"%s\\\"' % (err, err))\\n err_tuples.append((this, msg))\\n continue\\n res = pre + res + post\\n else:\\n exec_globals.update({'line': line, 'lines': out_lines})\\n # If line_expr starts with 'replace', implies \\\"line.replace\\\"\\n if line_expr.startswith('replace('):\\n line_expr = 'line.' + line_expr\\n elif line_expr == 'BytesIO':\\n line_expr = \\\"line.replace('StringIO', 'BytesIO')\\\"\\n try:\\n res = eval(line_expr, exec_globals)\\n except:\\n err = sys.exc_info()[1]\\n msg = ('Error \\\"%s\\\" working on \\\"%s\\\" at line %d with \\\"%s\\\"' %\\n (err, line, place, line_expr))\\n err_tuples.append((this, msg))\\n continue\\n # Put back comment if removed\\n if place == pos:\\n res = docbits + res + marker + markup\\n if res != line:\\n out_lines[place] = res\\n return out_lines, err_tuples\",\n \"def get_line_no(obj):\\n try:\\n lineno = getsourcelines(obj)[1]\\n except:\\n # no code found\\n lineno = None\\n return lineno\",\n \"def extract_first_line(func_code):\\r\\n if func_code.startswith(FIRST_LINE_TEXT):\\r\\n func_code = func_code.split('\\\\n')\\r\\n first_line = int(func_code[0][len(FIRST_LINE_TEXT):])\\r\\n func_code = '\\\\n'.join(func_code[1:])\\r\\n else:\\r\\n first_line = -1\\r\\n return func_code, first_line\",\n \"def line_col_to_offset(self,\\n snapshot: Bug,\\n filepath: str,\\n line_num: int,\\n col_num: int\\n ) -> int:\\n assert line_num > 0\\n assert col_num >= 0\\n line_col_s = \\\"%s/%s[%d:%d]\\\".format(snapshot.name,\\n filepath,\\n line_num,\\n col_num)\\n logger.debug(\\\"Transforming line-column, '%s', into a character offset\\\", # noqa: pycodestyle\\n line_col_s)\\n line_offsets = self._line_offsets(snapshot, filepath)\\n line_starts_at = line_offsets[line_num - 1]\\n offset = line_starts_at + col_num\\n logger.debug(\\\"Transformed line-column, '%s', into character offset: %s\\\", # noqa: pycodestyle\\n line_col_s,\\n offset)\\n return offset\",\n \"def clauses_from_file(filename):\\n with open(filename, \\\"r\\\") as fin:\\n #remove comments from beginning\\n line = fin.readline()\\n while(line.lstrip()[0] == 'c'):\\n line = fin.readline()\\n\\n header = line.split(\\\" \\\")\\n num_literals = int(header[2].rstrip())\\n\\n lines = fin.readlines()\\n\\n for i in range(len(lines)):\\n lines[i] = lines[i].split(\\\" \\\")[:-1]\\n lines[i] = [int(x) for x in lines[i]]\\n\\n return (lines, num_literals)\",\n \"def parse_tcode_outfile(fname,offset=0):\\n command = \\\"cat %s | grep -v '#' | sed '/^$/d' | sed 1d\\\" % (fname) \\n ci,co = popen2(command)\\n ci.close()\\n out = co.readlines()\\n co.close()\\n retlist = []\\n for line in out:\\n line = line.strip()\\n while \\\" \\\" in line: line = line.replace(\\\" \\\",\\\" \\\")\\n parts = line.split(\\\" \\\",3)\\n retlist.append( (int(parts[0])+offset, int(parts[1])+offset, float(parts[2]), parts[3] ) )\\n return retlist\",\n \"def _update_linetab(self, offset):\\n t = self.input\\n if offset < 0 or offset > len(t):\\n raise IndexError(\\\"position %d not in 0..%d\\\" % (offset, len(t)))\\n\\n # self.__linepos[x] caches the offset of EOL for line x + 1.\\n # A virtual line is imputed to exist prior to the input ending at\\n # offset -1.\\n # Lines are indexed internally from zero.\\n lpc = self.__linepos\\n if not lpc:\\n lpc.append(-1)\\n\\n # Include all lines prior to offset.\\n pos = lpc[-1] + 1\\n while pos < offset:\\n if t[pos] == '\\\\n':\\n lpc.append(pos)\\n pos += 1\\n\\n # Include the line containing offset, if possible.\\n while pos < len(t) and lpc[-1] < offset:\\n if t[pos] == '\\\\n':\\n lpc.append(pos)\\n pos += 1\",\n \"def parse_ensembl_exons(lines):\\n header = []\\n for index, line in enumerate(lines):\\n # File allways start with a header line\\n if index == 0:\\n header = line.rstrip().split(\\\"\\\\t\\\")\\n continue\\n\\n exon_info = parse_ensembl_line(line, header)\\n\\n exon = {\\n \\\"chrom\\\": str(exon_info[\\\"chrom\\\"]),\\n \\\"gene\\\": exon_info[\\\"ensembl_gene_id\\\"],\\n \\\"transcript\\\": exon_info[\\\"ensembl_transcript_id\\\"],\\n \\\"ens_exon_id\\\": exon_info[\\\"ensembl_exon_id\\\"],\\n \\\"exon_chrom_start\\\": exon_info[\\\"exon_start\\\"],\\n \\\"exon_chrom_end\\\": exon_info[\\\"exon_end\\\"],\\n \\\"strand\\\": exon_info[\\\"strand\\\"],\\n \\\"rank\\\": exon_info[\\\"exon_rank\\\"],\\n }\\n try:\\n exon[\\\"5_utr_start\\\"] = int(exon_info.get(\\\"utr_5_start\\\"))\\n except (ValueError, TypeError):\\n exon[\\\"5_utr_start\\\"] = None\\n\\n try:\\n exon[\\\"5_utr_end\\\"] = int(exon_info.get(\\\"utr_5_end\\\"))\\n except (ValueError, TypeError):\\n exon[\\\"5_utr_end\\\"] = None\\n\\n try:\\n exon[\\\"3_utr_start\\\"] = int(exon_info.get(\\\"utr_3_start\\\"))\\n except (ValueError, TypeError):\\n exon[\\\"3_utr_start\\\"] = None\\n\\n try:\\n exon[\\\"3_utr_end\\\"] = int(exon_info.get(\\\"utr_3_end\\\"))\\n except (ValueError, TypeError):\\n exon[\\\"3_utr_end\\\"] = None\\n\\n # Recalculate start and stop (taking UTR regions into account for end exons)\\n if exon[\\\"strand\\\"] == 1:\\n # highest position: start of exon or end of 5' UTR\\n # If no 5' UTR make sure exon_start is allways choosen\\n start = max(exon[\\\"exon_chrom_start\\\"], exon[\\\"5_utr_end\\\"] or -1)\\n # lowest position: end of exon or start of 3' UTR\\n end = min(exon[\\\"exon_chrom_end\\\"], exon[\\\"3_utr_start\\\"] or float(\\\"inf\\\"))\\n elif exon[\\\"strand\\\"] == -1:\\n # highest position: start of exon or end of 3' UTR\\n start = max(exon[\\\"exon_chrom_start\\\"], exon[\\\"3_utr_end\\\"] or -1)\\n # lowest position: end of exon or start of 5' UTR\\n end = min(exon[\\\"exon_chrom_end\\\"], exon[\\\"5_utr_start\\\"] or float(\\\"inf\\\"))\\n\\n exon[\\\"start\\\"] = start\\n exon[\\\"end\\\"] = end\\n exon_id = \\\"-\\\".join([str(exon[\\\"chrom\\\"]), str(start), str(end)])\\n exon[\\\"exon_id\\\"] = exon_id\\n\\n if start > end:\\n raise ValueError(\\\"ERROR: %s\\\" % exon_id)\\n\\n yield exon\",\n \"def get_lines_in_file(config_file):\\n lines = []\\n\\n line = config_file.readline()\\n lines.append([1, line])\\n\\n line_counter = 1\\n while line:\\n line = config_file.readline()\\n if not (line.lstrip().startswith(\\\"#\\\")):\\n lines.append([line_counter, line])\\n\\n line_counter += 1\\n\\n return lines\",\n \"def create_indices(filename, lines):\\n def find(key, condition):\\n \\\"\\\"\\\"Find and return index from str-list.\\\"\\\"\\\"\\n for i, line in enumerate(lines):\\n if condition(line, key):\\n return i\\n return None\\n\\n guard = ('INCLUDE_GUARD_KEN3_' +\\n filename.replace('/', '_').replace('.', '_').upper())\\n\\n return (\\n # doxygen comment in header. [0] to [7]\\n find('/**\\\\n', str.__eq__),\\n find(' * @file ken3/{0}\\\\n'.format(filename), str.__eq__),\\n find(' * @brief ', str.startswith),\\n find(' * @author toda\\\\n', str.__eq__),\\n find(' * @date ', str.startswith),\\n find(' * @version ', str.startswith),\\n find(' * @remark ', str.startswith),\\n find(' */\\\\n', str.__eq__),\\n # first blank line. [8]\\n find('\\\\n', str.__eq__),\\n # start include guard (only in .hpp files). [9] and [10]\\n find('#ifndef {0}\\\\n'.format(guard), str.__eq__),\\n find('#define {0}\\\\n'.format(guard), str.__eq__),\\n # include macro (optional). [11]\\n find('#include ', str.startswith),\\n # start and end namespace ken3. [12] and [13]\\n find('namespace ken3 {\\\\n', str.__eq__),\\n find('} // namespace ken3 {\\\\n', str.__eq__),\\n # end include guard (only in .hpp files). [14]\\n find('#endif // #ifndef {0}\\\\n'.format(guard), str.__eq__),\\n )\",\n \"def read_file(filepath: str, query_params: dict) -> list:\\r\\n\\r\\n # Get start and end line #'s from query_params (if they exist)\\r\\n if 'startln' in query_params:\\r\\n startln = query_params['startln']\\r\\n else:\\r\\n startln = 0 # Default to line 0 (index 0)\\r\\n if 'endln' in query_params:\\r\\n endln = query_params['endln']\\r\\n else:\\r\\n endln = -1 # Default to the last line (index -1)\\r\\n \\r\\n # Type-check params to ensure usability for list splicing\\r\\n try:\\r\\n startln, endln = int(startln), int(endln)\\r\\n except ValueError:\\r\\n abort(redirect(url_for('handle_exception', name='ValueError')))\\r\\n\\r\\n # Negative values for startln increases complexity and are unnecessary\\r\\n if startln < 0:\\r\\n abort(redirect(url_for('handle_exception', name='StartIndexError')))\\r\\n if endln <= startln and endln != -1:\\r\\n abort(redirect(url_for('handle_exception', name='EndIndexError')))\\r\\n\\r\\n # Attempt to open file using UTF-8 encoding\\r\\n try:\\r\\n with open(filepath, encoding='utf-8') as f:\\r\\n lines = f.readlines()\\r\\n # Otherwise encoding is in UTF-16-BE-BOM\\r\\n except UnicodeError:\\r\\n with open(filepath, encoding='utf-16') as f:\\r\\n lines = f.readlines()\\r\\n\\r\\n # Catch accessing list indexes beyond the range\\r\\n if startln >= len(lines):\\r\\n abort(redirect(url_for('handle_exception', name='OutOfRangeError')))\\r\\n if endln == -1: # Return lines from start to end of file\\r\\n return lines[startln:]\\r\\n return lines[startln:endln]\",\n \"def import_from_pos(fh):\\n elem = None\\n while True:\\n l = fh.readline()\\n if not l: break\\n if 'nwfc1' in l and 'nwfc2' in l:\\n w = l.split()\\n nwfc1, nwfc2 = int(w[0]), int(w[1])\\n # nwfc2 is assumed to be one - only one l value\\n if 'lwfc1' in l:\\n w = l.split('!')[0].split()\\n lwfc1 = [int(_) for _ in w]\\n if 'lwfc2' in l:\\n lwfc2 = int(l.split()[0])\\n if 'nonzero elements' in l:\\n n = int(l.split()[0])\\n elem = []\\n l = fh.readline()\\n c = 0\\n while l and c < n:\\n w = l.split()\\n if len(w) in {5, 10}: # 5-col is for old pos format and 10-col is the enriched format by yfliang\\n # (l,m) in lwfc1, m in lwfc2 (only one), i = (x=1,y=2,z=3)\\n # m ranges from -l to l\\n # elem = < h_c | r_i | beta_lm > (Core-level wavefunctions always proceed. )\\n elem.append([int(_) for _ in w[ : 3]] + [float(w[3]) + 1j * float(w[4])]) \\n l = fh.readline()\\n c += 1\\n return lwfc1, lwfc2, elem\",\n \"def check_line(self, line: str, line_num: int) -> Lints:\\n yield from super().check_line(line, line_num)\\n\\n if self.in_header and line != \\\"---\\\\n\\\":\\n for m in self.TAG_QUOTED.finditer(line):\\n yield LintError(\\\"header-tags-quoted\\\", line_num, m.start())\",\n \"def load_linelist(filename):\\n linelist = []\\n infile = open(filename)\\n for row in infile:\\n row = row.strip()\\n if len(row)==0 or row[0] in '#%!@':\\n continue\\n g = row.split()\\n wl = float(g[0])\\n if len(g)>1:\\n species = g[1]\\n else:\\n species = ''\\n linelist.append((wl, species))\\n infile.close()\\n return linelist\",\n \"def _linesearch(self):\\n pass\",\n \"def getsourcelines(object):\\n lines, lnum = findsource(object)\\n\\n if inspect.ismodule(object): return lines, 0\\n else: return inspect.getblock(lines[lnum:]), lnum + 1\",\n \"def get_line(self, path, line):\\n\\t\\tlines = self.find_source(path)\\n\\t\\tif lines == None:\\n\\t\\t\\treturn None\\n\\t\\telse:\\n\\t\\t\\ttry:\\n\\t\\t\\t\\treturn lines[line - 1]\\n\\t\\t\\texcept IndexError:\\n\\t\\t\\t\\treturn None\",\n \"def parse(self, f):\\n lines = []\\n for line in f:\\n _line = line.split(\\\"//\\\")[0].strip()\\n if _line.startswith(\\\"(\\\"): # is a label\\n label_name = _line[1:-1]\\n self.labels[label_name] = len(lines) # line number / address of label\\n elif _line:\\n lines.append(_line)\\n # else: it's just a whitespace/comment line (ignore)\\n return lines\",\n \"def CountLineNumber(filename):\\n\\n fp = open(os.path.abspath(filename), \\\"r\\\");\\n lines = 0\\n for line in fp.readlines():\\n lines = lines + 1\\n fp.close()\\n return lines\",\n \"def _get_sample_offset(self, line_num):\\n assert line_num <= self._count_lines_in_file(\\n TestPaths.get_file_path(AFewLinesLogParams.FILE_NAME)\\n )\\n return line_num * AFewLinesLogParams.SINGLE_LINE_LENGTH\",\n \"def get_corresponding_lineno(self, lineno: int) -> int:\\n for template_line, code_line in reversed(self.debug_info):\\n if code_line <= lineno:\\n return template_line\\n return 1\",\n \"def highlighter_lines(raw_graphics_content):\\n lines = []\\n\\n current_line = []\\n width = None\\n cap_style = None\\n join_style = None\\n\\n for op, args in tokenize_graphics(raw_graphics_content):\\n if (op, args) == (\\\"RG\\\", list(YELLOW)):\\n current_line = [None] # To be overrode by the last m op\\n elif current_line:\\n if op == \\\"m\\\": # Move the draw position\\n current_line[0] = (float(args[0]), float(args[1]))\\n elif op == \\\"j\\\":\\n i_join_style = int(args[0])\\n join_style = JOIN_STYLES[i_join_style]\\n elif op == \\\"J\\\":\\n i_cap_style = int(args[0])\\n cap_style = CAP_STYLES[i_cap_style]\\n elif op == \\\"w\\\":\\n width = float(args[0]) # Total width, will /2 later\\n elif op == \\\"l\\\":\\n current_line.append((float(args[0]), float(args[1])))\\n elif op == \\\"S\\\": # Finish the line\\n # Make sure we got all the params we need to draw the line correctly\\n assert (\\n width is not None\\n ), \\\"Expected to see a width for the stroke before stroke end.\\\"\\n assert (\\n cap_style is not None\\n ), \\\"Expected to see a cap style before stroke end.\\\"\\n assert (\\n join_style is not None\\n ), \\\"Expected to see a join style before stroke end.\\\"\\n assert len(current_line) > 1, \\\"Invalid line, not enough points.\\\"\\n\\n # Draw that thang\\n yield LineString(current_line).buffer(\\n width / 2, cap_style=cap_style, join_style=join_style\\n )\\n\\n # Reset the state\\n current_line = None\\n width = None\\n cap_style = None\\n join_style = None\\n elif op == \\\"cm\\\":\\n if args != [\\\"1\\\", \\\"0\\\", \\\"0\\\", \\\"1\\\", \\\"0\\\", \\\"0\\\"]:\\n raise NotImplementedError(\\n \\\"Transform matrices are not implemented, but shouldn't be hard to implement\\\"\\n )\\n else:\\n pass # We don't care about other operations\",\n \"def __call__(self, line):\\n marker = self.marker\\n stripped_line = line.strip()\\n if marker == stripped_line:\\n assert not self.traceback_section\\n self.traceback_section = True\\n # print(\\\"XXX: TRACEBACK-START\\\")\\n elif self.traceback_section:\\n matched = self.file_pattern.match(line)\\n if matched:\\n # matched_range = matched.regs[1]\\n filename = matched.groups()[0]\\n new_filename = posixpath_normpath(filename)\\n if new_filename != filename:\\n # print(\\\"XXX: %r => %r\\\" % (filename, new_filename))\\n line = line.replace(filename, new_filename)\\n elif not stripped_line or line[0].isalpha():\\n # -- DETECTED TRCAEBACK-END: exception-description\\n # print(\\\"XXX: TRACEBACK-END\\\")\\n self.traceback_section = False\\n return line\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6520461","0.6438692","0.6395089","0.63872284","0.6351725","0.6201063","0.6073642","0.59459245","0.5939982","0.59217393","0.58799213","0.58524036","0.58376163","0.5836338","0.58118933","0.5767493","0.57534856","0.572237","0.571344","0.56282735","0.562349","0.5617967","0.56160164","0.5612228","0.56086236","0.55799186","0.55767995","0.5549672","0.55209386","0.55196977","0.5448722","0.5447668","0.5434972","0.54338044","0.54336756","0.53895795","0.5388382","0.5346888","0.53463215","0.53300405","0.5327031","0.5323001","0.53130525","0.5306855","0.5306435","0.53054625","0.5304822","0.5304822","0.5301105","0.52934736","0.5291642","0.52859235","0.5282381","0.5280316","0.5271561","0.52547276","0.52420855","0.5238416","0.5231382","0.52203196","0.5212167","0.5191586","0.5190579","0.51889217","0.51834","0.51819694","0.51767814","0.5174156","0.5163978","0.51524615","0.5144923","0.5137974","0.51369464","0.5134448","0.5132432","0.5129091","0.51217586","0.51087576","0.5108751","0.50848603","0.50848144","0.50806296","0.5079192","0.50770915","0.50722224","0.5072216","0.506675","0.5066677","0.50648904","0.5062449","0.5060653","0.50560236","0.50550103","0.5053812","0.50501585","0.50429434","0.5038542","0.50372934","0.5036812","0.50212413"],"string":"[\n \"0.6520461\",\n \"0.6438692\",\n \"0.6395089\",\n \"0.63872284\",\n \"0.6351725\",\n \"0.6201063\",\n \"0.6073642\",\n \"0.59459245\",\n \"0.5939982\",\n \"0.59217393\",\n \"0.58799213\",\n \"0.58524036\",\n \"0.58376163\",\n \"0.5836338\",\n \"0.58118933\",\n \"0.5767493\",\n \"0.57534856\",\n \"0.572237\",\n \"0.571344\",\n \"0.56282735\",\n \"0.562349\",\n \"0.5617967\",\n \"0.56160164\",\n \"0.5612228\",\n \"0.56086236\",\n \"0.55799186\",\n \"0.55767995\",\n \"0.5549672\",\n \"0.55209386\",\n \"0.55196977\",\n \"0.5448722\",\n \"0.5447668\",\n \"0.5434972\",\n \"0.54338044\",\n \"0.54336756\",\n \"0.53895795\",\n \"0.5388382\",\n \"0.5346888\",\n \"0.53463215\",\n \"0.53300405\",\n \"0.5327031\",\n \"0.5323001\",\n \"0.53130525\",\n \"0.5306855\",\n \"0.5306435\",\n \"0.53054625\",\n \"0.5304822\",\n \"0.5304822\",\n \"0.5301105\",\n \"0.52934736\",\n \"0.5291642\",\n \"0.52859235\",\n \"0.5282381\",\n \"0.5280316\",\n \"0.5271561\",\n \"0.52547276\",\n \"0.52420855\",\n \"0.5238416\",\n \"0.5231382\",\n \"0.52203196\",\n \"0.5212167\",\n \"0.5191586\",\n \"0.5190579\",\n \"0.51889217\",\n \"0.51834\",\n \"0.51819694\",\n \"0.51767814\",\n \"0.5174156\",\n \"0.5163978\",\n \"0.51524615\",\n \"0.5144923\",\n \"0.5137974\",\n \"0.51369464\",\n \"0.5134448\",\n \"0.5132432\",\n \"0.5129091\",\n \"0.51217586\",\n \"0.51087576\",\n \"0.5108751\",\n \"0.50848603\",\n \"0.50848144\",\n \"0.50806296\",\n \"0.5079192\",\n \"0.50770915\",\n \"0.50722224\",\n \"0.5072216\",\n \"0.506675\",\n \"0.5066677\",\n \"0.50648904\",\n \"0.5062449\",\n \"0.5060653\",\n \"0.50560236\",\n \"0.50550103\",\n \"0.5053812\",\n \"0.50501585\",\n \"0.50429434\",\n \"0.5038542\",\n \"0.50372934\",\n \"0.5036812\",\n \"0.50212413\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.60051537"},"document_rank":{"kind":"string","value":"7"}}},{"rowIdx":81199,"cells":{"query":{"kind":"string","value":"Check if raster contains only h/v adjacent connections Illegal ac ba Legal ab aa"},"document":{"kind":"string","value":"def clusters_connected( self):\n def check_connected( k, vertices, edges):\n dads = {}\n for p in vertices:\n dads[p] = p\n\n def Find( c):\n while c != dads[c]:\n c = dads[c]\n return c\n\n def Union( p, q):\n dads[Find(p)] = Find(q)\n\n for p,q in edges:\n Union( p, q)\n\n stuff = set([ Find(p) for (k,p) in dads.items()])\n assert len(stuff) == 1, \"More than one partition\"\n\n vertices = collections.defaultdict( list)\n for p in itertools.product( range(self.n), repeat=2):\n vertices[self.raster[p]].append( p)\n\n def X():\n for x in range(self.n-1):\n for y in range(self.n):\n yield (x,y),(x+1,y)\n\n def Y():\n for x in range(self.n):\n for y in range(self.n-1):\n yield (x,y),(x,y+1)\n\n connections = collections.defaultdict( list)\n for (p,q) in itertools.chain( X(), Y()):\n if self.raster[p] == self.raster[q]:\n connections[self.raster[p]].append( ( p, q))\n\n for (k,v) in vertices.items():\n check_connected( k, v, connections[k])"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def check_ext(im, i, j):\n neighb = 0\n count = 0\n for a in range(8):\n if (im[i+relpos[a][0], j+relpos[a][1]] and (count == 0)):\n count += 1\n neighb += 1\n else:\n count = 0\n return (neighb < 2)","def is_inacessible(cell):\n adj, count = num_adj_buildings(cell)\n return adj == count","def is_legal(vtx, color):\n for neighbor in vtx[\"adjacent\"]:\n if VERTICES[neighbor][\"color\"] is color:\n return False\n return True","def check(m) :\n #find Connected-component\n lst = find_c(m)\n for e in lst :\n # verify len , 3 is the len of large boat\n if len(e) > 3 :\n return False\n if not is_vert(e) and not is_hori(e):\n return False\n return True","def valid(black, white, x, y):\n return (not black & gobit[(x, y)]) and (not white & gobit[(x, y)])","def tile_needs_connection(index, level_map, adjacent_has_connection):\n if tile_is_out_of_borders(index, level_map.shape):\n return False\n if tile_is_set(index, level_map):\n return adjacent_has_connection(level_map[index])\n return random.choice([True, False])","def validate_edges(attack_surface_graph, admissible_path, starting_points):\n for i in range(len(admissible_path)-1):\n for edge in attack_surface_graph.edges(data=True):\n if edge[0] == admissible_path[i] and edge[1] == admissible_path[i+1]:\n descriptors = edge[2]\n if find_violation(descriptors) == [] and edge[0] not in starting_points:\n return False\n return True","def is_at_intersection(self):\n directions = 0\n self.tile = (self.get_nearest_row(), self.get_nearest_col())\n if self.internal_map[self.tile[0] - 1][self.tile[1]] not in ('x', ):\n directions += 1\n if self.internal_map[self.tile[0] + 1][self.tile[1]] not in ('x', ):\n directions += 1\n if self.internal_map[self.tile[0]][self.tile[1] - 1] not in ('x', ):\n directions += 1\n if self.internal_map[self.tile[0]][self.tile[1] + 1] not in ('x', ):\n directions += 1\n return True if directions > 2 else False","def has_connection_right(tile):\n return is_kth_bit_set(tile, 3)","def is_adjacent(self, startVertex: np.int_ , endVertex: np.int_):\n return self.__mat[startVertex][endVertex]>0","def valid_pixel_coordinates(u, v, IMAGE_HEIGHT, IMAGE_WIDTH):\n return (u >= 0 and v >= 0 and v < IMAGE_HEIGHT and u < IMAGE_WIDTH)","def is_valid_cord(x, y, w, h):\n return x >=0 and x < w and y >= 0 and y < h;","def images_adjacent(i, j, img_adjacency):\n if img_adjacency[i][j] == 1 or img_adjacency[j][i] == 1:\n return True\n else:\n return False","def check_lost (grid):\r\n adjacent = False\r\n zero_value = False\r\n for i in range(4): \r\n for j in range(4):\r\n if grid[i][j] == 0:\r\n zero_value = True\r\n break\r\n for i in range(3):\r\n for j in range(3):\r\n if grid[i][j] == grid[i][j+1]:\r\n adjacent = True\r\n break\r\n if grid[i][j] == grid[i+1][j]:\r\n adjacent = True\r\n break\r\n if not adjacent and not zero_value:\r\n return True\r\n return False","def check_neighbours(self):\n for p in self.targetCell.possibilities:\n if p != 0:\n if p not in self.targetCell.row_neighbour_possibilities:\n self.targetCell.solve(p)\n return True\n elif p not in self.targetCell.column_neighbour_possibilities:\n self.targetCell.solve(p)\n return True\n elif p not in self.targetCell.box_neighbour_possibilities:\n self.targetCell.solve(p)\n return True\n return False","def check(self,a,x,y):\r\n return not self.exitsinrow(self.rows,x,a) and not self.existsincol(self.rows,y,a) and \\\r\n not self.exitsinblock(self.rows, x - x % 3, y - y % 3,a)","def constraint_adjacent(m, n) :\n return abs(m-n)==1","def has_connection_up(tile):\n return is_kth_bit_set(tile, 2)","def is_valid_adjacent(matrix, position):\n\n\tadjacent = get_adjacent_carrots(matrix, position)\n\tvalid_square = False\n\tfor i in adjacent.values():\n\t\tif (i != None and i != 0):\n\t\t\tvalid_square = True\n\t\t\tbreak\n\treturn valid_square","def is_valid(row, peg):\n return (\n (row < TRI_SIZE) and\n (row >= 0) and\n (peg < TRI_SIZE) and\n (peg >= 0) and\n (peg <= row)\n )","def has_connection_down(tile):\n return is_kth_bit_set(tile, 4)","def victory_checker() -> bool:\r\n conflict_check()\r\n for x in range(shape):\r\n for y in range(shape):\r\n if conflict_space[x, y] != 0:\r\n return False\r\n if separation_crawler(False):\r\n return False\r\n return True","def has_compatible_ligands(self, identity):\n return ((len(self.bad_coords[identity]) == 0) and\n (not self.BAD_COORD_RESIDUE in self.inaccuracies[identity]))","def has_connection_left(tile):\n return is_kth_bit_set(tile, 1)","def test_maze_entrance_adjacent_are_blocked(self):\n maze = Maze(100, 100)\n\n row, col = maze.entrance\n if row - 1 >= 0:\n maze.grid[row - 1][col].blocked = True\n if col + 1 < 100:\n maze.grid[row][col + 1].blocked = True\n if row + 1 < 100:\n maze.grid[row + 1][col].blocked = True\n if col - 1 >= 0:\n maze.grid[row][col - 1].blocked = True\n\n self.assertFalse(maze.can_reach_exit([row, col]))","def check_2x2_solved(self):\n return self._grid[0][0] == 0 and self._grid[0][1] == 1 \\\n and self._grid[1][0] == self._width*1 and self._grid[1][1] == (1 + self._width * 1)","def constraint_adjacent(m, n) :\n if abs(m-n)==1:\n return True\n return False","def constraint_not_adjacent(m, n) :\n if abs(m-n)==1:\n return False\n return True","def is_exceptional(self):\n G = self.poset().hasse_diagram()\n for x in G:\n nx = list(G.neighbors_out(x))\n nx.append(x)\n if min(nx) < x and max(nx) > x:\n return False\n return True","def constraint_not_adjacent(m, n) :\n return not constraint_adjacent(m,n)","def _is_valid_land(x, y, grid):\n return (x >= 0) and (x < len(grid)) and (y >= 0) and (y < len(grid[0])) and grid[x][y]","def verify_intervlan_routing(self):\n for src in self.host_information:\n for dst in self.host_information:\n if dst > src:\n self.check_host_connectivity_by_id(src, dst)","def test_adjacent_none(graph_with_edges):\n assert graph_with_edges.adjacent('B', 'A') is False","def is_adjacent(v1, v2):\n return (v2 in _board_graph[v1])","def cell_is_usable(cell):\n\tmaxcolors = tile_size[0] * tile_size[1]\n\tcolors = cell.getcolors(maxcolors)\n\t\n\tis_usable = False\n\t\n\tfor color in colors:\t\n\t\tif len(color[1]) == 3:\n\t\t\tif sum(color[1]) == 765 and color[0] < maxcolors/2:\n\t\t\t\t# less than a half are white\n\t\t\t\tis_usable = True\n\t\telse:\n\t\t\tif color[1][3] == 255 and color[0] >= 0:\n\t\t\t\tis_usable = True\n\t\t\t\tbreak\t\n\treturn is_usable","def island():\n\n grid = [\n [\"1\", \"1\", \"0\", \"0\", \"0\"],\n [\"1\", \"1\", \"0\", \"0\", \"0\"],\n [\"0\", \"0\", \"1\", \"0\", \"0\"],\n [\"0\", \"0\", \"0\", \"1\", \"1\"]\n ]\n\n def dfs():\n rows = len(grid)\n cols = len(grid[0])\n count = 0\n for i in range(0, rows):\n for j in range(0, cols):\n if grid[i][j] == '1':\n check_valid(i, j, grid)\n count = count + 1\n return count\n\n def check_valid(i, j, grid=None):\n rows = len(grid)\n cols = len(grid[0])\n\n if not 0 <= i < rows or not 0 <= j < cols or grid[i][j] != '1':\n return\n\n grid[i][j] = '0'\n\n check_valid(i + 1, j, grid)\n check_valid(i - 1, j, grid)\n check_valid(i, j + 1, grid)\n check_valid(i, j - 1, grid)\n\n return dfs()","def col_clashes(bs, c):\r\n for i in range(c):\r\n if share_diagonal(i,bs[i], c,bs[c]):\r\n return True\r\n return False","def _check_atom_connectivity_in_rd_mol_block(self, rmg_mol, rd_mol_block):\n for line in rd_mol_block:\n splits = line.split()\n if len(splits) == 4:\n index1, index2 = int(splits[0]) - 1, int(splits[1]) - 1\n self.assertIn(rmg_mol.atoms[index1], list(rmg_mol.atoms[index2].edges.keys()))","def forward(grid:ArcGrid) -> ArcGrid:\n grid = grid.copy()\n x, y = grid.shape\n\n for i in range(x):\n for j in range(y):\n if grid[i,j] != ArcColors.BLACK:\n count_nb_neighbours = 0\n for n in adjacent(grid, (i,j), diag=False):\n if grid[n] != ArcColors.BLACK:\n count_nb_neighbours+= 1\n\n if count_nb_neighbours < 2:\n grid[i,j] = ArcColors.BLACK\n\n return grid","def cyclic(n):\n if seen[n]: return seen[n] == -1\n seen[n] = -1 #GRAY\n if any(cyclic(nn) for nn in digraph.get(n, []) if seen[nn] != 1): return True\n seen[n] = 1 #BLACK\n ans.append(n)\n return False","def cantor() -> bigger.MCG[Edge]: # pylint: disable=too-many-statements\n\n POS, EQ, NEG = +1, 0, -1\n\n def edges() -> Iterable[Edge]:\n for x in naturals():\n for y in [POS, EQ, NEG]:\n yield x, y\n\n def negate(X: Edge) -> Edge:\n return X[0], -X[1]\n\n def invert(sign: int, X: tuple[Edge, bool, Edge, bool, Edge, bool, Edge, bool]) -> tuple[Edge, bool, Edge, bool, Edge, bool, Edge, bool]:\n return X if sign == POS else (negate(X[6]), not X[7], negate(X[4]), not X[5], negate(X[2]), not X[3], negate(X[0]), not X[1])\n\n def link(edge: Edge) -> tuple[Edge, bool, Edge, bool, Edge, bool, Edge, bool]:\n n, k = edge\n if k == EQ: # Equator\n if n == 0:\n return ((0, NEG), False, (1, NEG), True, (1, POS), False, (0, POS), True)\n elif n == 1:\n return ((2, POS), False, (0, POS), False, (0, NEG), True, (2, NEG), True)\n else: # n > 1\n return ((3 * n - 3, NEG), False, (3 * n - 1, NEG), True, (3 * n - 1, POS), False, (3 * n - 3, POS), True)\n\n # Northern / Southern hemisphere.\n if n == 0:\n return invert(k, ((0, EQ), False, (1, POS), False, (1, EQ), True, (2, POS), False))\n elif n == 1:\n return invert(k, ((4, POS), False, (3, POS), False, (0, POS), True, (0, EQ), False))\n elif n == 2:\n return invert(k, ((7, POS), False, (6, POS), False, (0, POS), False, (1, EQ), True))\n N, r = n // 3 + 1, n % 3\n incoming = 3 * (N // 2) - (1 if N % 2 else 2)\n if r == 0:\n return invert(k, ((N, EQ), False, (n + 2, POS), False, (incoming, POS), True, (n + 1, POS), False))\n elif r == 1:\n return invert(k, ((6 * N - 2, POS), False, (6 * N - 3, POS), False, (n - 1, POS), False, (incoming, POS), True))\n else: # r == 2:\n return invert(k, ((6 * N + 1, POS), False, (6 * N + 0, POS), False, (n - 2, POS), True, (N, EQ), False))\n\n T = bigger.Triangulation.from_pos(edges, link)\n\n def generator(name: str) -> bigger.Encoding[Edge]: # pylint: disable=too-many-branches\n twist_match = re.match(r\"(?P<curve>[ab])_(?P<n>-?\\d+)$\", name)\n rotate_match = re.match(r\"r$\", name)\n\n if twist_match is not None:\n parameters = twist_match.groupdict()\n curve_name = parameters[\"curve\"]\n N = int(parameters[\"n\"])\n if curve_name == \"a\":\n if N == 1:\n cut_sequence = [(0, EQ), (0, POS), (1, EQ)]\n else:\n cut_sequence = [(0, EQ), (N, EQ), (3 * N - 3, POS)]\n while N > 1:\n low_N = N // 2\n cut_sequence.append((3 * low_N - (1 if N % 2 else 2), POS))\n if N % 2:\n cut_sequence.append((3 * low_N - 3, POS))\n N = low_N\n elif curve_name == \"b\":\n if N <= 3:\n cut_sequence = [(0, EQ), (0, POS), (1, EQ)]\n else:\n extend_left = N % 2\n N = N // 2\n cut_sequence = [(N, EQ), (3 * N - 3, POS)]\n while N > 1:\n N_low = N // 2\n cut_sequence.append((3 * N_low - (1 if N % 2 else 2), POS))\n if extend_left:\n cut_sequence.append((3 * N_low - 3, POS))\n if N % 2 != extend_left:\n cut_sequence.append((N_low, EQ))\n break\n N = N_low\n else:\n cut_sequence.append((0, EQ))\n\n curve = T(dict(((x, y * s), 1) for x, y in cut_sequence for s in [+1, -1]))\n return curve.twist()\n elif rotate_match is not None:\n\n def isom(edge: Edge) -> Edge:\n n, k = edge\n if k == EQ:\n if n == 0:\n return (1, EQ)\n elif n == 1:\n return (0, EQ)\n return (n ^ (1 << n.bit_length() - 2), k)\n\n if n == 0:\n return (0, k)\n elif n == 1:\n return (2, k)\n elif n == 2:\n return (1, k)\n N, r = n // 3 + 1, n % 3\n return (3 * (N ^ (1 << N.bit_length() - 2)) - 3 + r, k)\n\n return T.encode([(-1, isom, isom)])\n\n raise ValueError(f\"Unknown mapping class {name}\")\n\n return bigger.MCG(T, generator)","def branches(image):\n return _neighbors_conv(image) > 2","def isSafe(board, row, col, n):\n\n \"\"\" veriying the row on left side \"\"\"\n for i in range(col):\n if board[row][i] == 1:\n return False\n\n \"\"\" veriying upper diagonal on left side \"\"\"\n for i,j in zip(range(row,-1,-1), range(col,-1,-1)):\n if board[i][j] == 1:\n return False\n\n \"\"\" veriying upper diagonal on left side \"\"\"\n for i,j in zip(range(row, n, 1), range(col,-1,-1)):\n if board[i][j] == 1:\n return False\n\n return True","def _is_occupied(\n grid: List[List[str]], row: int, col: int, dx: int, dy: int) -> bool:\n while 0 <= (row + dy) < len(grid) and 0 <= (col + dx) < len(grid[0]):\n row += dy\n col += dx\n if grid[row][col] == 'L':\n return False\n if grid[row][col] == '#':\n return True\n return False","def chk_vert_sym(self):\n for j in xrange(self.width):\n for i in xrange(int(round(self.height/2))):\n if self.rows[i][j] == self.rows[self.height - (i + 1)][j]:\n continue\n else:\n return False\n return True","def in_grid(self, tile):\n return 0 <= tile[0] < self.gs[0] and 0 <= tile[1] < self.gs[1]","def valid(a,b,x,y):\n\t# Size of the square grid that encases rectagle x,y\n\tsquare = x + y - 2\n\t# Taxi cab distance (no diagonals) from (p_a, p_b) to (a,b)\n\tsteps = lambda p_a, p_b: abs(p_a - a) + abs(p_b - b)\n\t# Top/Bottom/Left/Right bound\n\tif min(a,b) < 0 or max(a,b) >= square: return False\n\t# Upper left/Lower right corner check\n\tif steps(0,0) < (x - 2) or steps(square - 1, square - 1) < (x - 2): return False \n\t# Lower left/Upper right corner check\n\telif steps(square - 1, 0) < (y - 2) or steps( 0, square - 1) < (y - 2): return False\n\treturn True","def test_maze_exit_adjacent_are_blocked(self):\n maze = Maze(100, 100)\n\n row, col = maze.exit\n if row - 1 >= 0:\n maze.grid[row - 1][col].blocked = True\n if col + 1 < 100:\n maze.grid[row][col + 1].blocked = True\n if row + 1 < 100:\n maze.grid[row + 1][col].blocked = True\n if col - 1 >= 0:\n maze.grid[row][col - 1].blocked = True\n\n self.assertFalse(maze.can_reach_exit([maze.entrance[0],\n maze.entrance[1]]))","def test_unreachable_pair(self):\n G = DiGraph([(0, 1), (0, 2), (1, 2)])\n assert_false(is_reachable(G, 1, 0))","def adjacent(C, D, b, rid_fac, abs_tol=1e-7):\n E = rid_fac.E_0\n af = rid_fac.af\n bf = rid_fac.bf\n #\n E_r = rid_fac.E_r\n ar = rid_fac.ar\n br = rid_fac.br\n # shape\n d = C.shape[1]\n k = D.shape[1]\n # E_r slices\n C_er = C[E_r, :]\n D_er = D[E_r, :]\n b_er = b[E_r]\n # stack\n c = -np.hstack([ar, np.zeros(k)])\n G = np.hstack([C_er, D_er])\n h = b_er\n A = np.hstack([af, np.zeros(k)])\n sol = solvers._solve_lp_using_cvxopt(\n c, G, h, A=A.T, b=bf * (1 - 0.01))\n if sol['status'] != \"optimal\":\n print(G)\n print(h)\n print(af)\n print(bf)\n print(ar)\n print(br)\n print(np.dot(af, ar))\n data = {}\n data[\"C\"] = C\n data[\"D\"] = D\n data[\"b\"] = b\n sio.savemat(\"matlabdata\", data)\n with open('polytope.p', 'wb') as f:\n pickle.dump(data, f)\n raise Exception(\n \"adjacent: Lp returned status \" + str(sol['status']))\n opt_sol = np.array(sol['x']).flatten()\n dual_opt_sol = np.array(sol['z']).flatten()\n x_opt = opt_sol[range(d)]\n y_opt = opt_sol[range(d, d + k)]\n if is_dual_degenerate(\n c.flatten(), G, h, A, bf * (1 - 0.01),\n opt_sol, dual_opt_sol, abs_tol=abs_tol):\n # If degenerate, compute affine hull and take preimage\n E_temp = np.nonzero(np.abs(np.dot(G, opt_sol) - h) < abs_tol)[0]\n a_temp, b_temp = proj_aff(\n C_er[E_temp, :], D_er[E_temp, :], b_er[E_temp],\n expected_dim=1, abs_tol=abs_tol)\n E_adj = unique_equalityset(C, D, b, a_temp, b_temp, abs_tol=abs_tol)\n if len(E_adj) == 0:\n data = {}\n data[\"C\"] = C\n data[\"D\"] = D\n data[\"b\"] = b\n data[\"Er\"] = E_r + 1\n data[\"ar\"] = ar\n data[\"br\"] = br\n data[\"Ef\"] = E + 1\n data[\"af\"] = af\n data[\"bf\"] = bf\n sio.savemat(\"matlabdata\", data)\n raise Exception(\n \"adjacent: equality set computation returned empty set\")\n else:\n r = np.abs(np.dot(C, x_opt) + np.dot(D, y_opt) - b) < abs_tol\n E_adj = np.nonzero(r)[0]\n C_eadj = C[E_adj, :]\n D_eadj = D[E_adj, :]\n b_eadj = b[E_adj]\n af_adj, bf_adj = proj_aff(C_eadj, D_eadj, b_eadj, abs_tol=abs_tol)\n return E_adj, af_adj, bf_adj","def test_tile_read_validMask():\n bounds = [\n -6887893.4928338025,\n 12210356.646387195,\n -6574807.424977721,\n 12523442.714243278,\n ]\n tilesize = 128\n with rasterio.open(COG) as src_dst:\n arr, mask = reader.part(src_dst, bounds, tilesize, tilesize, nodata=1)\n\n masknodata = (arr[0] != 1).astype(numpy.uint8) * 255\n numpy.testing.assert_array_equal(mask, masknodata)","def is_boundary_cell(self,c):\n edges=self.cell_to_edges(c)\n return np.any( self.edge_to_cells()[edges] < 0 )","def validBond(index1, index2, direction):\n #print \"?valid bond: \", allAtoms[index1].pos, \" , \", allAtoms[index2].pos, direction\n cell1 = index1/numAtomsPerCell\n cell2 = index2/numAtomsPerCell\n #Find the coordinates of the cell in units of interaction cells\n posInX1 = int(cell1/(size*size))\n posInX2 = int(cell1/(size*size))\n leftover1 = cell1%(size*size)\n leftover2 = cell2%(size*size)\n posInY1 = int(leftover1/size)\n posInY2 = int(leftover2/size)\n posInZ1 = leftover1%size\n posInZ2 = leftover2%size\n \n #Now, a valid interaction can cross an interaction cell boundary in any direction,\n #but it has a maximum length of one interaction cell. However, I have made the minimum\n #size of this larger translated lattice equal to 3*3*3 interaction cells. Therefore,\n #when we hit an edge and get in invalid interaction, the cells will be at least 2\n #interaction cells apart in the direction of the interaction.\n if(direction[0]):\n if numpy.abs(posInX1 - posInX2)>1:\n #print \"false\"\n return False\n if(direction[1]):\n if numpy.abs(posInY1 - posInY2)>1:\n #print \"false\"\n return False\n if(direction[2]):\n if numpy.abs(posInZ1 - posInZ2)>1:\n #print \"false\"\n return False\n print #\"true\"\n return True\n\n #Old (incorrect) method:\n if 0:\r\n print \"?valid bond: \", allAtoms[index1].pos, \" , \", allAtoms[index2].pos, direction\r\n cell1 = index1/numAtomsPerCell\r\n cell2 = index2/numAtomsPerCell\r\n zRow1 = cell1/size#this relies on the list being created in the nested for loop that was used, z within y within x\r\n zRow2 = cell2/size\r\n if(zRow1 != zRow2 and direction[2]):\n print \"false\"\r\n return False\r\n xLayer1 = cell1/(size*size)\r\n xLayer2 = cell2/(size*size)\r\n if(xLayer1 != xLayer2 and direction[1]):\n print \"false\"\r\n return False\r\n #shouldn't have to check z, because if it's not valid in z direction, it would be off the list (>len(allAtoms))\n print \"true\"\r\n return True","def check_reach_adjacent_revision(self, revision):\n if (revision.bad and revision.previous_revision and\n revision.previous_revision.good):\n return True\n if (revision.good and revision.next_revision and\n revision.next_revision.bad):\n return True\n return False","def col_clashes(bs, c):\n for i in range(c): # Look at all columns to the left of c\n if share_diagonal(c,bs[c],i, bs[i]):\n return False","def inside(i,j,im,h=H): #X\n return i-h >=0 and j-h >=0 and i+h+1<=im.shape[0] and j+h+1<=im.shape[1]","def check_neighbours(self, grid):\n if self.bomba:\n self.bombs_around = -1\n return\n\n total = 0\n for x in range(-1, 2):\n for y in range(-1, 2):\n i = self.i + x\n j = self.j + y\n if i > -1 and i < len(grid) and j > -1 and j < len(grid[1]):\n neighbor = grid[i][j]\n\n if neighbor.bomba:\n total += 1\n \n self.bombs_around = total","def check_boundary(self,x):\n b_cells = np.zeros(self.n_c)\n b_cells[self.n_C:] = 1\n vBC = b_cells[self.tris]\n considered_triangles = vBC.sum(axis=1) == 2\n add_extra = ((self.Angles*(1-vBC)>np.pi/2).T*considered_triangles.T).T\n if add_extra.any():\n I,J = np.nonzero(add_extra)\n for k,i in enumerate(I):\n j = J[k]\n xs = x[self.tris[i]]\n re = xs[np.mod(j-1,3)] - xs[np.mod(j+1,3)]\n re = re/np.linalg.norm(re)\n re = np.array([re[1],-re[0]])\n rpe = xs[j]\n x_new = 2*np.dot(xs[np.mod(j-1,3)]-rpe,re)*re + rpe\n x = np.vstack((x,x_new))\n self.n_c = x.shape[0]\n self._triangulate(x)\n self.assign_vertices()\n\n C = get_C_boundary(self.n_c,self.CV_matrix)\n #\n # #Remove extra cells\n # keep_mask = C[self.n_C:, :self.n_C].sum(axis=1)>0 #I'm assuming this is the same thing. This removes all boundary centroids that are not connected to at least one real centroid.\n # if keep_mask.any():\n # c_keep = np.nonzero(keep_mask)[0]\n # x = np.concatenate((x[:self.n_C],x[c_keep + self.n_C]))\n # self.n_c = x.shape[0]\n # self._triangulate(x)\n # self.assign_vertices()\n #\n\n #Remove all boundary particles not connected to exactly two other boundary particles\n remove_mask = C[self.n_C:, self.n_C:].sum(axis=1)!=2\n if remove_mask.any():\n c_keep = np.nonzero(~remove_mask)[0]\n x = np.concatenate((x[:self.n_C],x[c_keep + self.n_C]))\n self.n_c = x.shape[0]\n self._triangulate(x)\n self.assign_vertices()\n self.Angles = tri_angles(x, self.tris)\n #\n # remove_mask = C[self.n_C:, self.n_C:].sum(axis=1)==0\n # if remove_mask.any():\n # c_keep = np.nonzero(~remove_mask)[0]\n # x = np.concatenate((x[:self.n_C],x[c_keep + self.n_C]))\n # self.n_c = x.shape[0]\n # self._triangulate(x)\n # self.assign_vertices()\n # self.Angles = tri_angles(x, self.tris)\n\n\n return x","def is_nonprojective_edge(g,h,d):\n rang = get_range(g,h,d)\n roots = set()\n for i in rang[1:-1]:\n roots.add(get_root(g,rang[1:-1],i)) \n for i in roots:\n if not is_dominated(g,i,h):\n return True\n return False","def valid_tile(self, i, j):\n if (i >= 0 and i < self.rows) and (j >= 0 and j < self.cols):\n return True\n return False","def _cell_in_boundary(self, i_row, i_col):\n return ((i_row, i_col) == self._tl_cell or\n (i_row, i_col) == self._tr_cell or\n (i_row, i_col) == self._bl_cell or\n (i_row, i_col) == self._br_cell or\n (i_row, i_col) in self._ls_cells or\n (i_row, i_col) in self._rs_cells or\n (i_row, i_col) in self._ts_cells or\n (i_row, i_col) in self._bs_cells)","def is_planar(G):\n result=True\n bad_minor=[]\n n=len(G.nodes())\n iterazione=0\n if n>5:\n print 'N >5'\n\n for subnodes in it.combinations(G.nodes(),6):\n iterazione+=1\n print 'iterazione %d'%iterazione\n subG=G.subgraph(subnodes)\n if bipartite.is_bipartite(G):# check if the graph G has a subgraph K(3,3)\n X, Y = bipartite.sets(G)\n if len(X)==3:\n result=False\n bad_minor=subnodes\n return result,bad_minor\n iterazione=0\n if n>4 and result:\n print 'N >4'\n\n for subnodes in it.combinations(G.nodes(),5):\n print 'iterazione %d'%iterazione\n subG=G.subgraph(subnodes)\n if len(subG.edges())==10:# check if the graph G has a subgraph K(5)\n result=False\n bad_minor=subnodes\n return result,bad_minor\n\n return result,bad_minor","def is_solved(self):\n colors = ['green', 'blue', 'red', 'orange', 'white', 'yellow']\n for row in range(3):\n for column in range(3):\n if self.front[row][column] != colors[0]:\n return False\n for row in range(3):\n for column in range(3):\n if self.back[row][column] != colors[1]:\n return False\n for row in range(3):\n for column in range(3):\n if self.right[row][column] != colors[2]:\n return False\n for row in range(3):\n for column in range(3):\n if self.left[row][column] != colors[3]:\n return False\n for row in range(3):\n for column in range(3):\n if self.up[row][column] != colors[4]:\n return False\n for row in range(3):\n for column in range(3):\n if self.down[row][column] != colors[5]:\n return False\n return True","def col_clashes(bs, c):\r\n for i in range(c): # Look at all columns to the left of c\r\n if share_diagonal(i, bs[i], c, bs[c]):\r\n return True\r\n\r\n return False","def rg_valid(rg):\n return len(rg) != 9","def is_solved(level_map):\n shape = level_map.shape\n for x in range(shape[0]):\n for y in range(shape[1]):\n i = (x, y)\n tile = level_map[i]\n if tile == 0:\n continue\n left_index, up_index, right_index, down_index = get_direction_indices(i)\n if has_connection_left(tile) and \\\n (tile_is_out_of_borders(left_index, shape) or not has_connection_right(level_map[left_index])):\n return False\n if has_connection_up(tile) and \\\n (tile_is_out_of_borders(up_index, shape) or not has_connection_down(level_map[up_index])):\n return False\n if has_connection_right(tile) and \\\n (tile_is_out_of_borders(right_index, shape) or not has_connection_left(level_map[right_index])):\n return False\n if has_connection_down(tile) and \\\n (tile_is_out_of_borders(down_index, shape) or not has_connection_up(level_map[down_index])):\n return False\n return True","def test_differentiable__mask_connectivity(self):\n data = random_graph_data(5, 4, 3)\n print(data.size)","def breaks_connectivity(level, index, axis=0):\n new_level = remove_index(level, index, axis=axis)\n return not is_solvable(new_level)","def check_lost (grid):\r\n for row in range(4):\r\n for col in range(4):\r\n if grid[row][col]==0:\r\n return False\r\n if grid[0][0]==grid[0][1] or grid[0][0]==grid[1][0]:\r\n return False \r\n if grid[0][3]==grid[0][2] or grid[0][3]==grid[1][3]:\r\n return False \r\n if grid[3][0]==grid[2][0] or grid[3][0]==grid[3][1]:\r\n return False\r\n if grid[3][3]==grid[2][3] or grid[3][3]==grid[3][2]:\r\n return False \r\n if grid[0][1]==grid[0][2] or grid[0][1]==grid[1][1]:\r\n return False \r\n if grid[0][2]==grid[1][2]:\r\n return False \r\n if grid[1][1]==grid[2][1] or grid[1][1]==grid[1][2] or grid[1][1]==grid[1][0]:\r\n return False\r\n if grid[2][1]==grid[2][0] or grid[2][1]==grid[2][2] or grid[2][1]==grid[3][1]:\r\n return False \r\n if grid[1][0]==grid[2][0]:\r\n return False\r\n if grid[1][2]==grid[1][3] or grid[1][2]==grid[2][2]:\r\n return False\r\n if grid[2][2]==grid[2][3] or grid[2][2]==grid[3][2]:\r\n return False\r\n if grid[3][1]==grid[3][2]:\r\n return False\r\n else:\r\n return True","def bad_central_mask(weights, badmask, index, wmin = 0.1):\n\n \n # Weights of fibers at the source index\n sel = weights[:, index] > wmin\n\n #print(weights[:, index])\n #print(badmask[sel, index - 1 : index + 2])\n\n bad_test = any(badmask[sel, index - 1 : index + 2].ravel()) \n bad_test = bad_test| all(badmask[:, index - 1 : index + 2].ravel())\n\n if bad_test:\n return False\n else:\n return True","def check_if_legal(row, blocks):\n counter = 0\n compare_lst = []\n for square in row:\n if square == Black:\n counter += 1\n else:\n if counter > 0:\n compare_lst.append(counter)\n counter = 0\n if counter > 0:\n compare_lst.append(counter)\n if compare_lst == blocks:\n return True\n return False","def _in_bounds(self, x, y):\r\n return 0 <= x < 8 and 0 <= y < 8","def col_clashes(bs, c):\n for i in range(c): # check at all columns to the left of c\n if share_diagonal(i, bs[i], c, bs[c]):\n return True\n return False # no clashes - col c has a safe placement","def contains_black(image):\n extrema = ImageStat.Stat(image).extrema\n r = extrema[0][0]\n g = extrema[1][0]\n b = extrema[2][0]\n\n if r == 0 and g == 0 and b == 0:\n return True\n\n return False","def is_valid(x, y):\n return (x >= 0) & (x < BOARD_SIZE) & (y >= 0) & (y < BOARD_SIZE)","def check_regularity(edges):\n for a, b in edges:\n counter_a = 0\n counter_b = 0\n for x, y in edges:\n if a == x or a == y:\n counter_a += 1\n if b == x or b == y:\n counter_b += 1\n assert (counter_a > 0) and (counter_b > 0)\n if (counter_a == 1) or (counter_b == 1):\n raise Exception(\"Boundary is not closed.\")\n if (counter_a > 2) or (counter_b > 2):\n raise Exception(\"More than two edges share a node.\")","def is_valid(problem, i, j, e):\n row_map = row_maps[i]\n column_map = column_maps[j]\n sector_map = sector_maps[get_sector_number(i, j)]\n not_in_row = row_map[e-1] == 0\n not_in_column = column_map[e-1] == 0\n not_in_sector = sector_map[e-1] == 0\n\n return not_in_row and not_in_column and not_in_sector","def check_lost (grid):\r\n t=0\r\n for o in range(len(grid)):\r\n for e in range(len(grid[o])):\r\n if grid[o][e]==0:\r\n t+=1\r\n else:\r\n ()\r\n r=0\r\n for o in range(len(grid)):\r\n for e in range(len(grid[o])-1):\r\n if grid[o][e]==grid[o][e+1]:\r\n r+=1\r\n elif grid[o][3]==grid[o][2]:\r\n r+=1 \r\n else:\r\n ()\r\n \r\n v=0\r\n for o in range(len(grid)):\r\n for e in range(len(grid[o])-1):\r\n if grid[e][o]==grid[e+1][o]:\r\n v+=1\r\n elif grid[3][o]==grid[2][o]:\r\n v+=1 \r\n else:\r\n () \r\n \r\n if t==0 and r==0 and v==0:\r\n return True\r\n else:\r\n return False","def endpoints(image):\n return _neighbors_conv(image) == 1","def Check(self):\n cleared = False\n while not cleared:\n for i in list(combinations([cell.Check() for cell in self.cells], 2)):\n # for i in list(combinations(zip(self.locations.x,self.locations.y,self.locations.length,self.locations.index),2)):\n x1 = i[0][0]\n y1 = i[0][1]\n r1 = i[0][2] / 2\n idx1 = i[0][3]\n x2 = i[1][0]\n y2 = i[1][1]\n r2 = i[1][2] / 2\n idx1 = i[0][3]\n idx2 = i[1][3]\n distance = (x1 - x2) * (x1 - x2) + (y1 - y2) * (y1 - y2)\n radii = (r1 + r2) * (r1 + r2)\n if distance == radii:\n cleared = True\n elif distance > radii:\n cleared = True\n else:\n if x1 > x2 and y1 > y2:\n if (\n x1 + r1 > 0\n and x1 + r1 < self.boundaries[0]\n and y1 + r1 > 0\n and y1 + r1 < self.boundaries[1]\n ):\n self.cells[idx1].x = x1 + r1 / 2\n self.cells[idx1].y = y1 + r1 / 2\n elif x1 > x2 and y1 < y2:\n if (\n x1 + r1 > 0\n and x1 + r1 < self.boundaries[0]\n and y1 - r1 > 0\n and y1 - r1 < self.boundaries[1]\n ):\n self.cells[idx1].x = x1 + r1 / 2\n self.cells[idx1].y = y1 - r1 / 2\n elif x1 < x2 and y1 > y2:\n if (\n x1 - r1 > 0\n and x1 - r1 < self.boundaries[0]\n and y1 + r1 > 0\n and y1 + r1 < self.boundaries[1]\n ):\n self.cells[idx1].x = x1 - r1 / 2\n self.cells[idx1].y = y1 + r1 / 2\n else:\n if (\n x1 - r1 > 0\n and x1 - r1 < self.boundaries[0]\n and y1 - r1 > 0\n and y1 - r1 < self.boundaries[1]\n ):\n self.cells[idx1].x = x1 - r1 / 2\n self.cells[idx1].y = y1 - r1 / 2\n _logger.debug(\n f\"Bumped from {x1 :.2e}, {y1 :.2e} to {self.cells[idx1].x :.2e}, {self.cells[idx1].y :.2e}\"\n )\n cleared = False\n return","def exist_adjacent_cell(board: list, cell_index: tuple) -> bool:\n #Each time the result = [(-1, -1), (-1, 0), (-1, 1), (0, -1), (0, 1), (1, -1), (1, 0), (1, 1)]\n possible_cells_direction = list(filter(lambda x: x[0] != 0 or x[1] != 0, list(product(range(-1, 2), range(-1, 2)))))\n\n for coord_couple in possible_cells_direction:\n i = cell_index[0] + coord_couple[0]\n j = cell_index[1] + coord_couple[1]\n\n if not test_cell_existence(board, i, j):\n continue\n\n # If a cell isn't empty\n if board[i][j] != 0:\n return True\n return False","def is_contradiction_(transition):\n is_contr = False\n\n # check implications of lower left corner\n if np.argmax(transition[0]) == 0:\n if np.argmax(transition[2]) == 2 or np.argmax(transition[2]) == 3:\n is_contr = True\n elif np.argmax(transition[0]) == 1:\n if np.argmax(transition[1]) == 0 or np.argmax(transition[1]) == 2:\n is_contr = True\n if np.argmax(transition[2]) != 1:\n is_contr = True\n elif np.argmax(transition[0]) == 2:\n if np.argmax(transition[1]) == 0 or np.argmax(transition[1]) == 1:\n is_contr = True\n elif np.argmax(transition[0]) == 3:\n if np.argmax(transition[1]) != 3:\n is_contr = True\n if np.argmax(transition[2]) == 0 or np.argmax(transition[2]) == 2:\n is_contr = True\n\n # check implicatiosn of upper right corner\n if np.argmax(transition[2]) == 0:\n if np.argmax(transition[0]) == 1 or np.argmax(transition[0]) == 3:\n is_contr = True\n elif np.argmax(transition[2]) == 1:\n if np.argmax(transition[1]) == 0 or np.argmax(transition[1]) == 2:\n is_contr = True\n elif np.argmax(transition[2]) == 2:\n if np.argmax(transition[0]) != 2:\n is_contr = True\n if np.argmax(transition[1]) == 0 or np.argmax(transition[1]) == 1:\n is_contr = True\n elif np.argmax(transition[2]) == 3:\n if np.argmax(transition[1]) != 3:\n is_contr = True\n if np.argmax(transition[0]) == 0 or np.argmax(transition[0]) == 1:\n is_contr = True\n\n return is_contr","def negDiagnol(self, ox, curRow, curCol):\n count = 0\n while count < 4 and curRow < self.height and curCol >= 0 and self.board[curRow][curCol] == ox:\n count += 1\n curRow += 1\n curCol -= 1\n return count == 4","def fullGrid(state):\n return not ((state[:, :, 0] + state[:, :, 1]) == 0).any()","def check_position_is_legal(grid, num, i, j):\n args = (grid, num, i, j)\n return (not check_row(*args)) and (not check_col(*args)) and (not check_local_square(*args))","def has_unresected_pair(resected_idx, unresected_imgs, img_adjacency):\n for idx in unresected_imgs:\n if img_adjacency[resected_idx][idx] == 1 or img_adjacency[idx][resected_idx] == 1:\n return True\n return False","def corner_init_check():\r\n \"\"\"Rule One\"\"\"\r\n z = shape-1\r\n if example[0, 0] == example[0, 1] and example[0, 0] == example[1, 0] and example[0, 0] != 0: # NE\r\n example[0, 0] = 0\r\n shade_neighbours(0, 0)\r\n print(\"RULE 1 NW\")\r\n if example[0, z] == example[0, z-1] and example[0, z] == example[1, z] and example[0, z] != 0: # NW\r\n example[0, z] = 0\r\n shade_neighbours(0, z)\r\n print(\"RULE 1 NE\")\r\n if example[z, z] == example[z, z-1] and example[z, z] == example[z-1, z] and example[z, z] != 0: # SE\r\n example[z, z] = 0\r\n shade_neighbours(z, z)\r\n print(\"RULE 1 SE\")\r\n if example[z, 0] == example[z, 1] and example[z, 0] == example[z-1, 0] and example[z, 0] != 0: # SW\r\n example[z, 0] = 0\r\n shade_neighbours(z, 0)\r\n print(\"RuLE 1 SW\")\r\n\r\n \"\"\"Rule Two\"\"\"\r\n if (example[0, 0] == example[0, 1] or example[0, 0] == example[1, 0]) \\\r\n and (example[1, 1] == example[0, 1] or example[1, 1] == example[1, 0])\\\r\n and (example[0, 0] != example[1, 1]): # NE\r\n example[0, 0] = 0\r\n example[1, 1] = 0\r\n shade_neighbours(1, 1)\r\n print(\"RULE 2 NW\")\r\n if (example[0, z] == example[0, z-1] or example[0, z] == example[1, z]) \\\r\n and (example[1, z-1] == example[0, z-1] or example[1, z-1] == example[1, z]) \\\r\n and (example[0, z] != example[1, z-1]): # NW\r\n example[0, z] = 0\r\n example[1, z-1] = 0\r\n shade_neighbours(1, z - 1)\r\n print(\"RULE 2 NE\")\r\n if (example[z, z] == example[z, z-1] or example[z, z] == example[z-1, z]) \\\r\n and (example[z-1, z-1] == example[z-1, z] or example[z-1, z-1] == example[z, z-1]) \\\r\n and (example[z, z] != example[z-1, z-1]): # SE\r\n example[z, z] = 0\r\n example[z-1, z-1] = 0\r\n shade_neighbours(z - 1, z - 1)\r\n print(\"RULE 2 SE\")\r\n if (example[z, 0] == example[z, 1] or example[z, 0] == example[z-1, 0]) \\\r\n and (example[z-1, 1] == example[z, 1] or example[z-1, 1] == example[z-1, 0]) \\\r\n and (example[z, 0] != example[z-1, 1]): # SW\r\n example[z, 0] = 0\r\n example[z-1, 1] = 0\r\n shade_neighbours(z - 1, 1)\r\n print(\"RULE 2 SW\")\r\n conflict_check()","def col_clashes(bs, c):\r\n for i in range(c): # Look at all columns to the left of c\r\n if share_diagonal(i, bs[i], c, bs[c]):\r\n return True\r\n\r\n return False # No clashes - col c has a safe placement.\r","def inrange(cc, point):\n return point.row in range(cc.top, cc.bottom+1) and point.col in range(cc.left, cc.right+1)","def find_disconnected_voxels(im, conn=None):\n if im.ndim == 2:\n if conn == 4:\n strel = disk(1)\n elif conn in [None, 8]:\n strel = square(3)\n elif im.ndim == 3:\n if conn == 6:\n strel = ball(1)\n elif conn in [None, 26]:\n strel = cube(3)\n labels, N = spim.label(input=im, structure=strel)\n holes = clear_border(labels=labels) > 0\n return holes","def _is_dead_end(self, i_row, i_col, direction):\n return (((i_row, i_col) in self._ts_cells and direction == \"s\") or\n ((i_row, i_col) in self._ts_cells and direction == \"se\") or\n ((i_row, i_col) in self._ts_cells and direction == \"sw\") or\n ((i_row, i_col) in self._ls_cells and direction == \"e\") or\n ((i_row, i_col) in self._ls_cells and direction == \"ne\") or\n ((i_row, i_col) in self._ls_cells and direction == \"se\") or\n ((i_row, i_col) in self._bs_cells and direction == \"n\") or\n ((i_row, i_col) in self._bs_cells and direction == \"nw\") or\n ((i_row, i_col) in self._bs_cells and direction == \"ne\") or\n ((i_row, i_col) in self._rs_cells and direction == \"w\") or\n ((i_row, i_col) in self._rs_cells and direction == \"nw\") or\n ((i_row, i_col) in self._rs_cells and direction == \"sw\") or\n ((i_row, i_col) == self._tl_cell and direction == \"s\") or\n ((i_row, i_col) == self._tl_cell and direction == \"se\") or\n ((i_row, i_col) == self._tl_cell and direction == \"e\") or\n ((i_row, i_col) == self._bl_cell and direction == \"n\") or\n ((i_row, i_col) == self._bl_cell and direction == \"ne\") or\n ((i_row, i_col) == self._bl_cell and direction == \"e\") or\n ((i_row, i_col) == self._tr_cell and direction == \"w\") or\n ((i_row, i_col) == self._tr_cell and direction == \"sw\") or\n ((i_row, i_col) == self._tr_cell and direction == \"s\") or\n ((i_row, i_col) == self._br_cell and direction == \"w\") or\n ((i_row, i_col) == self._br_cell and direction == \"nw\") or\n ((i_row, i_col) == self._br_cell and direction == \"n\"))","def valid_coverage_cell(self, row, column):\n\n if (row < self.cov_grid.shape[0] and\n row >= 0) and \\\n (column < self.cov_grid.shape[1] and\n column >= 0):\n return True\n else:\n return False","def col_clashes(bs, c):\n for i in range(c): # Look at all columns to the left of c\n if share_diagonal(i, bs[i], c, bs[c]):\n return True\n\n return False # No clashes - col c has a safe placement","def edge_between_neighbors(cell_a, cell_b):\n edge = np.logical_and(dilate_simple(cell_a), dilate_simple(cell_b))\n return edge","def isComplete(grid):\n for row in range(0,9):\n for col in range(0,9):\n if grid[row][col]==0:\n return False\n return True","def is_adjacent(self, other: ops.Qid) -> bool:\n return (isinstance(other, GridQubit) and\n abs(self.row - other.row) + abs(self.col - other.col) == 1)","def is_on_board(self, r, c):\r\n return 0 <= r <= 7 and 0 <= c <= 7","def check_allowed(i,j,DIR):\r\n #DIR is a unit vector pointing from i to j (eg: DIR = [0,1] indicates that j is 1 to the right of i)\r\n #check only specific arrangement on-demand\r\n if DIR == [1,0]: #i j #RIGHTWARD\r\n if get_blocks(i)[1][2] == get_blocks(j)[1][0]:\r\n return True\r\n elif DIR == [-1,0]: #LEFTWARD\r\n if get_blocks(j)[1][2] == get_blocks(i)[1][0]: #reverse indices\r\n return True\r\n elif DIR == [0,-1]: #UPWARD\r\n if get_blocks(j)[2][1] == get_blocks(i)[0][1]:\r\n return True \r\n elif DIR == [0,1]: #DOWNWARD\r\n if get_blocks(i)[2][1] == get_blocks(j)[0][1]:\r\n return True\r\n else:\r\n raise ValueError (\"Invalid DIR vector!\")\r\n return False","def check_hypernet(seq_iter):\n\treturn not any(filter(has_abba, seq_iter))","def answer_ok(a):\n (rightly_positioned, permutated) = a\n if (rightly_positioned + permutated > number_of_positions) \\\n or (rightly_positioned + permutated < len(colours) - number_of_positions):\n return False\n if rightly_positioned == 3 and permutated == 1:\n return False\n return True","def check_border_cells(self):\n for row, col in self.land_cells:\n if row == 1 or row == self.unique_rows[-1] or col == 1 or col == self.unique_cols[-1]:\n raise ValueError(\"Only water cells may be border cells!\")"],"string":"[\n \"def check_ext(im, i, j):\\n neighb = 0\\n count = 0\\n for a in range(8):\\n if (im[i+relpos[a][0], j+relpos[a][1]] and (count == 0)):\\n count += 1\\n neighb += 1\\n else:\\n count = 0\\n return (neighb < 2)\",\n \"def is_inacessible(cell):\\n adj, count = num_adj_buildings(cell)\\n return adj == count\",\n \"def is_legal(vtx, color):\\n for neighbor in vtx[\\\"adjacent\\\"]:\\n if VERTICES[neighbor][\\\"color\\\"] is color:\\n return False\\n return True\",\n \"def check(m) :\\n #find Connected-component\\n lst = find_c(m)\\n for e in lst :\\n # verify len , 3 is the len of large boat\\n if len(e) > 3 :\\n return False\\n if not is_vert(e) and not is_hori(e):\\n return False\\n return True\",\n \"def valid(black, white, x, y):\\n return (not black & gobit[(x, y)]) and (not white & gobit[(x, y)])\",\n \"def tile_needs_connection(index, level_map, adjacent_has_connection):\\n if tile_is_out_of_borders(index, level_map.shape):\\n return False\\n if tile_is_set(index, level_map):\\n return adjacent_has_connection(level_map[index])\\n return random.choice([True, False])\",\n \"def validate_edges(attack_surface_graph, admissible_path, starting_points):\\n for i in range(len(admissible_path)-1):\\n for edge in attack_surface_graph.edges(data=True):\\n if edge[0] == admissible_path[i] and edge[1] == admissible_path[i+1]:\\n descriptors = edge[2]\\n if find_violation(descriptors) == [] and edge[0] not in starting_points:\\n return False\\n return True\",\n \"def is_at_intersection(self):\\n directions = 0\\n self.tile = (self.get_nearest_row(), self.get_nearest_col())\\n if self.internal_map[self.tile[0] - 1][self.tile[1]] not in ('x', ):\\n directions += 1\\n if self.internal_map[self.tile[0] + 1][self.tile[1]] not in ('x', ):\\n directions += 1\\n if self.internal_map[self.tile[0]][self.tile[1] - 1] not in ('x', ):\\n directions += 1\\n if self.internal_map[self.tile[0]][self.tile[1] + 1] not in ('x', ):\\n directions += 1\\n return True if directions > 2 else False\",\n \"def has_connection_right(tile):\\n return is_kth_bit_set(tile, 3)\",\n \"def is_adjacent(self, startVertex: np.int_ , endVertex: np.int_):\\n return self.__mat[startVertex][endVertex]>0\",\n \"def valid_pixel_coordinates(u, v, IMAGE_HEIGHT, IMAGE_WIDTH):\\n return (u >= 0 and v >= 0 and v < IMAGE_HEIGHT and u < IMAGE_WIDTH)\",\n \"def is_valid_cord(x, y, w, h):\\n return x >=0 and x < w and y >= 0 and y < h;\",\n \"def images_adjacent(i, j, img_adjacency):\\n if img_adjacency[i][j] == 1 or img_adjacency[j][i] == 1:\\n return True\\n else:\\n return False\",\n \"def check_lost (grid):\\r\\n adjacent = False\\r\\n zero_value = False\\r\\n for i in range(4): \\r\\n for j in range(4):\\r\\n if grid[i][j] == 0:\\r\\n zero_value = True\\r\\n break\\r\\n for i in range(3):\\r\\n for j in range(3):\\r\\n if grid[i][j] == grid[i][j+1]:\\r\\n adjacent = True\\r\\n break\\r\\n if grid[i][j] == grid[i+1][j]:\\r\\n adjacent = True\\r\\n break\\r\\n if not adjacent and not zero_value:\\r\\n return True\\r\\n return False\",\n \"def check_neighbours(self):\\n for p in self.targetCell.possibilities:\\n if p != 0:\\n if p not in self.targetCell.row_neighbour_possibilities:\\n self.targetCell.solve(p)\\n return True\\n elif p not in self.targetCell.column_neighbour_possibilities:\\n self.targetCell.solve(p)\\n return True\\n elif p not in self.targetCell.box_neighbour_possibilities:\\n self.targetCell.solve(p)\\n return True\\n return False\",\n \"def check(self,a,x,y):\\r\\n return not self.exitsinrow(self.rows,x,a) and not self.existsincol(self.rows,y,a) and \\\\\\r\\n not self.exitsinblock(self.rows, x - x % 3, y - y % 3,a)\",\n \"def constraint_adjacent(m, n) :\\n return abs(m-n)==1\",\n \"def has_connection_up(tile):\\n return is_kth_bit_set(tile, 2)\",\n \"def is_valid_adjacent(matrix, position):\\n\\n\\tadjacent = get_adjacent_carrots(matrix, position)\\n\\tvalid_square = False\\n\\tfor i in adjacent.values():\\n\\t\\tif (i != None and i != 0):\\n\\t\\t\\tvalid_square = True\\n\\t\\t\\tbreak\\n\\treturn valid_square\",\n \"def is_valid(row, peg):\\n return (\\n (row < TRI_SIZE) and\\n (row >= 0) and\\n (peg < TRI_SIZE) and\\n (peg >= 0) and\\n (peg <= row)\\n )\",\n \"def has_connection_down(tile):\\n return is_kth_bit_set(tile, 4)\",\n \"def victory_checker() -> bool:\\r\\n conflict_check()\\r\\n for x in range(shape):\\r\\n for y in range(shape):\\r\\n if conflict_space[x, y] != 0:\\r\\n return False\\r\\n if separation_crawler(False):\\r\\n return False\\r\\n return True\",\n \"def has_compatible_ligands(self, identity):\\n return ((len(self.bad_coords[identity]) == 0) and\\n (not self.BAD_COORD_RESIDUE in self.inaccuracies[identity]))\",\n \"def has_connection_left(tile):\\n return is_kth_bit_set(tile, 1)\",\n \"def test_maze_entrance_adjacent_are_blocked(self):\\n maze = Maze(100, 100)\\n\\n row, col = maze.entrance\\n if row - 1 >= 0:\\n maze.grid[row - 1][col].blocked = True\\n if col + 1 < 100:\\n maze.grid[row][col + 1].blocked = True\\n if row + 1 < 100:\\n maze.grid[row + 1][col].blocked = True\\n if col - 1 >= 0:\\n maze.grid[row][col - 1].blocked = True\\n\\n self.assertFalse(maze.can_reach_exit([row, col]))\",\n \"def check_2x2_solved(self):\\n return self._grid[0][0] == 0 and self._grid[0][1] == 1 \\\\\\n and self._grid[1][0] == self._width*1 and self._grid[1][1] == (1 + self._width * 1)\",\n \"def constraint_adjacent(m, n) :\\n if abs(m-n)==1:\\n return True\\n return False\",\n \"def constraint_not_adjacent(m, n) :\\n if abs(m-n)==1:\\n return False\\n return True\",\n \"def is_exceptional(self):\\n G = self.poset().hasse_diagram()\\n for x in G:\\n nx = list(G.neighbors_out(x))\\n nx.append(x)\\n if min(nx) < x and max(nx) > x:\\n return False\\n return True\",\n \"def constraint_not_adjacent(m, n) :\\n return not constraint_adjacent(m,n)\",\n \"def _is_valid_land(x, y, grid):\\n return (x >= 0) and (x < len(grid)) and (y >= 0) and (y < len(grid[0])) and grid[x][y]\",\n \"def verify_intervlan_routing(self):\\n for src in self.host_information:\\n for dst in self.host_information:\\n if dst > src:\\n self.check_host_connectivity_by_id(src, dst)\",\n \"def test_adjacent_none(graph_with_edges):\\n assert graph_with_edges.adjacent('B', 'A') is False\",\n \"def is_adjacent(v1, v2):\\n return (v2 in _board_graph[v1])\",\n \"def cell_is_usable(cell):\\n\\tmaxcolors = tile_size[0] * tile_size[1]\\n\\tcolors = cell.getcolors(maxcolors)\\n\\t\\n\\tis_usable = False\\n\\t\\n\\tfor color in colors:\\t\\n\\t\\tif len(color[1]) == 3:\\n\\t\\t\\tif sum(color[1]) == 765 and color[0] < maxcolors/2:\\n\\t\\t\\t\\t# less than a half are white\\n\\t\\t\\t\\tis_usable = True\\n\\t\\telse:\\n\\t\\t\\tif color[1][3] == 255 and color[0] >= 0:\\n\\t\\t\\t\\tis_usable = True\\n\\t\\t\\t\\tbreak\\t\\n\\treturn is_usable\",\n \"def island():\\n\\n grid = [\\n [\\\"1\\\", \\\"1\\\", \\\"0\\\", \\\"0\\\", \\\"0\\\"],\\n [\\\"1\\\", \\\"1\\\", \\\"0\\\", \\\"0\\\", \\\"0\\\"],\\n [\\\"0\\\", \\\"0\\\", \\\"1\\\", \\\"0\\\", \\\"0\\\"],\\n [\\\"0\\\", \\\"0\\\", \\\"0\\\", \\\"1\\\", \\\"1\\\"]\\n ]\\n\\n def dfs():\\n rows = len(grid)\\n cols = len(grid[0])\\n count = 0\\n for i in range(0, rows):\\n for j in range(0, cols):\\n if grid[i][j] == '1':\\n check_valid(i, j, grid)\\n count = count + 1\\n return count\\n\\n def check_valid(i, j, grid=None):\\n rows = len(grid)\\n cols = len(grid[0])\\n\\n if not 0 <= i < rows or not 0 <= j < cols or grid[i][j] != '1':\\n return\\n\\n grid[i][j] = '0'\\n\\n check_valid(i + 1, j, grid)\\n check_valid(i - 1, j, grid)\\n check_valid(i, j + 1, grid)\\n check_valid(i, j - 1, grid)\\n\\n return dfs()\",\n \"def col_clashes(bs, c):\\r\\n for i in range(c):\\r\\n if share_diagonal(i,bs[i], c,bs[c]):\\r\\n return True\\r\\n return False\",\n \"def _check_atom_connectivity_in_rd_mol_block(self, rmg_mol, rd_mol_block):\\n for line in rd_mol_block:\\n splits = line.split()\\n if len(splits) == 4:\\n index1, index2 = int(splits[0]) - 1, int(splits[1]) - 1\\n self.assertIn(rmg_mol.atoms[index1], list(rmg_mol.atoms[index2].edges.keys()))\",\n \"def forward(grid:ArcGrid) -> ArcGrid:\\n grid = grid.copy()\\n x, y = grid.shape\\n\\n for i in range(x):\\n for j in range(y):\\n if grid[i,j] != ArcColors.BLACK:\\n count_nb_neighbours = 0\\n for n in adjacent(grid, (i,j), diag=False):\\n if grid[n] != ArcColors.BLACK:\\n count_nb_neighbours+= 1\\n\\n if count_nb_neighbours < 2:\\n grid[i,j] = ArcColors.BLACK\\n\\n return grid\",\n \"def cyclic(n):\\n if seen[n]: return seen[n] == -1\\n seen[n] = -1 #GRAY\\n if any(cyclic(nn) for nn in digraph.get(n, []) if seen[nn] != 1): return True\\n seen[n] = 1 #BLACK\\n ans.append(n)\\n return False\",\n \"def cantor() -> bigger.MCG[Edge]: # pylint: disable=too-many-statements\\n\\n POS, EQ, NEG = +1, 0, -1\\n\\n def edges() -> Iterable[Edge]:\\n for x in naturals():\\n for y in [POS, EQ, NEG]:\\n yield x, y\\n\\n def negate(X: Edge) -> Edge:\\n return X[0], -X[1]\\n\\n def invert(sign: int, X: tuple[Edge, bool, Edge, bool, Edge, bool, Edge, bool]) -> tuple[Edge, bool, Edge, bool, Edge, bool, Edge, bool]:\\n return X if sign == POS else (negate(X[6]), not X[7], negate(X[4]), not X[5], negate(X[2]), not X[3], negate(X[0]), not X[1])\\n\\n def link(edge: Edge) -> tuple[Edge, bool, Edge, bool, Edge, bool, Edge, bool]:\\n n, k = edge\\n if k == EQ: # Equator\\n if n == 0:\\n return ((0, NEG), False, (1, NEG), True, (1, POS), False, (0, POS), True)\\n elif n == 1:\\n return ((2, POS), False, (0, POS), False, (0, NEG), True, (2, NEG), True)\\n else: # n > 1\\n return ((3 * n - 3, NEG), False, (3 * n - 1, NEG), True, (3 * n - 1, POS), False, (3 * n - 3, POS), True)\\n\\n # Northern / Southern hemisphere.\\n if n == 0:\\n return invert(k, ((0, EQ), False, (1, POS), False, (1, EQ), True, (2, POS), False))\\n elif n == 1:\\n return invert(k, ((4, POS), False, (3, POS), False, (0, POS), True, (0, EQ), False))\\n elif n == 2:\\n return invert(k, ((7, POS), False, (6, POS), False, (0, POS), False, (1, EQ), True))\\n N, r = n // 3 + 1, n % 3\\n incoming = 3 * (N // 2) - (1 if N % 2 else 2)\\n if r == 0:\\n return invert(k, ((N, EQ), False, (n + 2, POS), False, (incoming, POS), True, (n + 1, POS), False))\\n elif r == 1:\\n return invert(k, ((6 * N - 2, POS), False, (6 * N - 3, POS), False, (n - 1, POS), False, (incoming, POS), True))\\n else: # r == 2:\\n return invert(k, ((6 * N + 1, POS), False, (6 * N + 0, POS), False, (n - 2, POS), True, (N, EQ), False))\\n\\n T = bigger.Triangulation.from_pos(edges, link)\\n\\n def generator(name: str) -> bigger.Encoding[Edge]: # pylint: disable=too-many-branches\\n twist_match = re.match(r\\\"(?P<curve>[ab])_(?P<n>-?\\\\d+)$\\\", name)\\n rotate_match = re.match(r\\\"r$\\\", name)\\n\\n if twist_match is not None:\\n parameters = twist_match.groupdict()\\n curve_name = parameters[\\\"curve\\\"]\\n N = int(parameters[\\\"n\\\"])\\n if curve_name == \\\"a\\\":\\n if N == 1:\\n cut_sequence = [(0, EQ), (0, POS), (1, EQ)]\\n else:\\n cut_sequence = [(0, EQ), (N, EQ), (3 * N - 3, POS)]\\n while N > 1:\\n low_N = N // 2\\n cut_sequence.append((3 * low_N - (1 if N % 2 else 2), POS))\\n if N % 2:\\n cut_sequence.append((3 * low_N - 3, POS))\\n N = low_N\\n elif curve_name == \\\"b\\\":\\n if N <= 3:\\n cut_sequence = [(0, EQ), (0, POS), (1, EQ)]\\n else:\\n extend_left = N % 2\\n N = N // 2\\n cut_sequence = [(N, EQ), (3 * N - 3, POS)]\\n while N > 1:\\n N_low = N // 2\\n cut_sequence.append((3 * N_low - (1 if N % 2 else 2), POS))\\n if extend_left:\\n cut_sequence.append((3 * N_low - 3, POS))\\n if N % 2 != extend_left:\\n cut_sequence.append((N_low, EQ))\\n break\\n N = N_low\\n else:\\n cut_sequence.append((0, EQ))\\n\\n curve = T(dict(((x, y * s), 1) for x, y in cut_sequence for s in [+1, -1]))\\n return curve.twist()\\n elif rotate_match is not None:\\n\\n def isom(edge: Edge) -> Edge:\\n n, k = edge\\n if k == EQ:\\n if n == 0:\\n return (1, EQ)\\n elif n == 1:\\n return (0, EQ)\\n return (n ^ (1 << n.bit_length() - 2), k)\\n\\n if n == 0:\\n return (0, k)\\n elif n == 1:\\n return (2, k)\\n elif n == 2:\\n return (1, k)\\n N, r = n // 3 + 1, n % 3\\n return (3 * (N ^ (1 << N.bit_length() - 2)) - 3 + r, k)\\n\\n return T.encode([(-1, isom, isom)])\\n\\n raise ValueError(f\\\"Unknown mapping class {name}\\\")\\n\\n return bigger.MCG(T, generator)\",\n \"def branches(image):\\n return _neighbors_conv(image) > 2\",\n \"def isSafe(board, row, col, n):\\n\\n \\\"\\\"\\\" veriying the row on left side \\\"\\\"\\\"\\n for i in range(col):\\n if board[row][i] == 1:\\n return False\\n\\n \\\"\\\"\\\" veriying upper diagonal on left side \\\"\\\"\\\"\\n for i,j in zip(range(row,-1,-1), range(col,-1,-1)):\\n if board[i][j] == 1:\\n return False\\n\\n \\\"\\\"\\\" veriying upper diagonal on left side \\\"\\\"\\\"\\n for i,j in zip(range(row, n, 1), range(col,-1,-1)):\\n if board[i][j] == 1:\\n return False\\n\\n return True\",\n \"def _is_occupied(\\n grid: List[List[str]], row: int, col: int, dx: int, dy: int) -> bool:\\n while 0 <= (row + dy) < len(grid) and 0 <= (col + dx) < len(grid[0]):\\n row += dy\\n col += dx\\n if grid[row][col] == 'L':\\n return False\\n if grid[row][col] == '#':\\n return True\\n return False\",\n \"def chk_vert_sym(self):\\n for j in xrange(self.width):\\n for i in xrange(int(round(self.height/2))):\\n if self.rows[i][j] == self.rows[self.height - (i + 1)][j]:\\n continue\\n else:\\n return False\\n return True\",\n \"def in_grid(self, tile):\\n return 0 <= tile[0] < self.gs[0] and 0 <= tile[1] < self.gs[1]\",\n \"def valid(a,b,x,y):\\n\\t# Size of the square grid that encases rectagle x,y\\n\\tsquare = x + y - 2\\n\\t# Taxi cab distance (no diagonals) from (p_a, p_b) to (a,b)\\n\\tsteps = lambda p_a, p_b: abs(p_a - a) + abs(p_b - b)\\n\\t# Top/Bottom/Left/Right bound\\n\\tif min(a,b) < 0 or max(a,b) >= square: return False\\n\\t# Upper left/Lower right corner check\\n\\tif steps(0,0) < (x - 2) or steps(square - 1, square - 1) < (x - 2): return False \\n\\t# Lower left/Upper right corner check\\n\\telif steps(square - 1, 0) < (y - 2) or steps( 0, square - 1) < (y - 2): return False\\n\\treturn True\",\n \"def test_maze_exit_adjacent_are_blocked(self):\\n maze = Maze(100, 100)\\n\\n row, col = maze.exit\\n if row - 1 >= 0:\\n maze.grid[row - 1][col].blocked = True\\n if col + 1 < 100:\\n maze.grid[row][col + 1].blocked = True\\n if row + 1 < 100:\\n maze.grid[row + 1][col].blocked = True\\n if col - 1 >= 0:\\n maze.grid[row][col - 1].blocked = True\\n\\n self.assertFalse(maze.can_reach_exit([maze.entrance[0],\\n maze.entrance[1]]))\",\n \"def test_unreachable_pair(self):\\n G = DiGraph([(0, 1), (0, 2), (1, 2)])\\n assert_false(is_reachable(G, 1, 0))\",\n \"def adjacent(C, D, b, rid_fac, abs_tol=1e-7):\\n E = rid_fac.E_0\\n af = rid_fac.af\\n bf = rid_fac.bf\\n #\\n E_r = rid_fac.E_r\\n ar = rid_fac.ar\\n br = rid_fac.br\\n # shape\\n d = C.shape[1]\\n k = D.shape[1]\\n # E_r slices\\n C_er = C[E_r, :]\\n D_er = D[E_r, :]\\n b_er = b[E_r]\\n # stack\\n c = -np.hstack([ar, np.zeros(k)])\\n G = np.hstack([C_er, D_er])\\n h = b_er\\n A = np.hstack([af, np.zeros(k)])\\n sol = solvers._solve_lp_using_cvxopt(\\n c, G, h, A=A.T, b=bf * (1 - 0.01))\\n if sol['status'] != \\\"optimal\\\":\\n print(G)\\n print(h)\\n print(af)\\n print(bf)\\n print(ar)\\n print(br)\\n print(np.dot(af, ar))\\n data = {}\\n data[\\\"C\\\"] = C\\n data[\\\"D\\\"] = D\\n data[\\\"b\\\"] = b\\n sio.savemat(\\\"matlabdata\\\", data)\\n with open('polytope.p', 'wb') as f:\\n pickle.dump(data, f)\\n raise Exception(\\n \\\"adjacent: Lp returned status \\\" + str(sol['status']))\\n opt_sol = np.array(sol['x']).flatten()\\n dual_opt_sol = np.array(sol['z']).flatten()\\n x_opt = opt_sol[range(d)]\\n y_opt = opt_sol[range(d, d + k)]\\n if is_dual_degenerate(\\n c.flatten(), G, h, A, bf * (1 - 0.01),\\n opt_sol, dual_opt_sol, abs_tol=abs_tol):\\n # If degenerate, compute affine hull and take preimage\\n E_temp = np.nonzero(np.abs(np.dot(G, opt_sol) - h) < abs_tol)[0]\\n a_temp, b_temp = proj_aff(\\n C_er[E_temp, :], D_er[E_temp, :], b_er[E_temp],\\n expected_dim=1, abs_tol=abs_tol)\\n E_adj = unique_equalityset(C, D, b, a_temp, b_temp, abs_tol=abs_tol)\\n if len(E_adj) == 0:\\n data = {}\\n data[\\\"C\\\"] = C\\n data[\\\"D\\\"] = D\\n data[\\\"b\\\"] = b\\n data[\\\"Er\\\"] = E_r + 1\\n data[\\\"ar\\\"] = ar\\n data[\\\"br\\\"] = br\\n data[\\\"Ef\\\"] = E + 1\\n data[\\\"af\\\"] = af\\n data[\\\"bf\\\"] = bf\\n sio.savemat(\\\"matlabdata\\\", data)\\n raise Exception(\\n \\\"adjacent: equality set computation returned empty set\\\")\\n else:\\n r = np.abs(np.dot(C, x_opt) + np.dot(D, y_opt) - b) < abs_tol\\n E_adj = np.nonzero(r)[0]\\n C_eadj = C[E_adj, :]\\n D_eadj = D[E_adj, :]\\n b_eadj = b[E_adj]\\n af_adj, bf_adj = proj_aff(C_eadj, D_eadj, b_eadj, abs_tol=abs_tol)\\n return E_adj, af_adj, bf_adj\",\n \"def test_tile_read_validMask():\\n bounds = [\\n -6887893.4928338025,\\n 12210356.646387195,\\n -6574807.424977721,\\n 12523442.714243278,\\n ]\\n tilesize = 128\\n with rasterio.open(COG) as src_dst:\\n arr, mask = reader.part(src_dst, bounds, tilesize, tilesize, nodata=1)\\n\\n masknodata = (arr[0] != 1).astype(numpy.uint8) * 255\\n numpy.testing.assert_array_equal(mask, masknodata)\",\n \"def is_boundary_cell(self,c):\\n edges=self.cell_to_edges(c)\\n return np.any( self.edge_to_cells()[edges] < 0 )\",\n \"def validBond(index1, index2, direction):\\n #print \\\"?valid bond: \\\", allAtoms[index1].pos, \\\" , \\\", allAtoms[index2].pos, direction\\n cell1 = index1/numAtomsPerCell\\n cell2 = index2/numAtomsPerCell\\n #Find the coordinates of the cell in units of interaction cells\\n posInX1 = int(cell1/(size*size))\\n posInX2 = int(cell1/(size*size))\\n leftover1 = cell1%(size*size)\\n leftover2 = cell2%(size*size)\\n posInY1 = int(leftover1/size)\\n posInY2 = int(leftover2/size)\\n posInZ1 = leftover1%size\\n posInZ2 = leftover2%size\\n \\n #Now, a valid interaction can cross an interaction cell boundary in any direction,\\n #but it has a maximum length of one interaction cell. However, I have made the minimum\\n #size of this larger translated lattice equal to 3*3*3 interaction cells. Therefore,\\n #when we hit an edge and get in invalid interaction, the cells will be at least 2\\n #interaction cells apart in the direction of the interaction.\\n if(direction[0]):\\n if numpy.abs(posInX1 - posInX2)>1:\\n #print \\\"false\\\"\\n return False\\n if(direction[1]):\\n if numpy.abs(posInY1 - posInY2)>1:\\n #print \\\"false\\\"\\n return False\\n if(direction[2]):\\n if numpy.abs(posInZ1 - posInZ2)>1:\\n #print \\\"false\\\"\\n return False\\n print #\\\"true\\\"\\n return True\\n\\n #Old (incorrect) method:\\n if 0:\\r\\n print \\\"?valid bond: \\\", allAtoms[index1].pos, \\\" , \\\", allAtoms[index2].pos, direction\\r\\n cell1 = index1/numAtomsPerCell\\r\\n cell2 = index2/numAtomsPerCell\\r\\n zRow1 = cell1/size#this relies on the list being created in the nested for loop that was used, z within y within x\\r\\n zRow2 = cell2/size\\r\\n if(zRow1 != zRow2 and direction[2]):\\n print \\\"false\\\"\\r\\n return False\\r\\n xLayer1 = cell1/(size*size)\\r\\n xLayer2 = cell2/(size*size)\\r\\n if(xLayer1 != xLayer2 and direction[1]):\\n print \\\"false\\\"\\r\\n return False\\r\\n #shouldn't have to check z, because if it's not valid in z direction, it would be off the list (>len(allAtoms))\\n print \\\"true\\\"\\r\\n return True\",\n \"def check_reach_adjacent_revision(self, revision):\\n if (revision.bad and revision.previous_revision and\\n revision.previous_revision.good):\\n return True\\n if (revision.good and revision.next_revision and\\n revision.next_revision.bad):\\n return True\\n return False\",\n \"def col_clashes(bs, c):\\n for i in range(c): # Look at all columns to the left of c\\n if share_diagonal(c,bs[c],i, bs[i]):\\n return False\",\n \"def inside(i,j,im,h=H): #X\\n return i-h >=0 and j-h >=0 and i+h+1<=im.shape[0] and j+h+1<=im.shape[1]\",\n \"def check_neighbours(self, grid):\\n if self.bomba:\\n self.bombs_around = -1\\n return\\n\\n total = 0\\n for x in range(-1, 2):\\n for y in range(-1, 2):\\n i = self.i + x\\n j = self.j + y\\n if i > -1 and i < len(grid) and j > -1 and j < len(grid[1]):\\n neighbor = grid[i][j]\\n\\n if neighbor.bomba:\\n total += 1\\n \\n self.bombs_around = total\",\n \"def check_boundary(self,x):\\n b_cells = np.zeros(self.n_c)\\n b_cells[self.n_C:] = 1\\n vBC = b_cells[self.tris]\\n considered_triangles = vBC.sum(axis=1) == 2\\n add_extra = ((self.Angles*(1-vBC)>np.pi/2).T*considered_triangles.T).T\\n if add_extra.any():\\n I,J = np.nonzero(add_extra)\\n for k,i in enumerate(I):\\n j = J[k]\\n xs = x[self.tris[i]]\\n re = xs[np.mod(j-1,3)] - xs[np.mod(j+1,3)]\\n re = re/np.linalg.norm(re)\\n re = np.array([re[1],-re[0]])\\n rpe = xs[j]\\n x_new = 2*np.dot(xs[np.mod(j-1,3)]-rpe,re)*re + rpe\\n x = np.vstack((x,x_new))\\n self.n_c = x.shape[0]\\n self._triangulate(x)\\n self.assign_vertices()\\n\\n C = get_C_boundary(self.n_c,self.CV_matrix)\\n #\\n # #Remove extra cells\\n # keep_mask = C[self.n_C:, :self.n_C].sum(axis=1)>0 #I'm assuming this is the same thing. This removes all boundary centroids that are not connected to at least one real centroid.\\n # if keep_mask.any():\\n # c_keep = np.nonzero(keep_mask)[0]\\n # x = np.concatenate((x[:self.n_C],x[c_keep + self.n_C]))\\n # self.n_c = x.shape[0]\\n # self._triangulate(x)\\n # self.assign_vertices()\\n #\\n\\n #Remove all boundary particles not connected to exactly two other boundary particles\\n remove_mask = C[self.n_C:, self.n_C:].sum(axis=1)!=2\\n if remove_mask.any():\\n c_keep = np.nonzero(~remove_mask)[0]\\n x = np.concatenate((x[:self.n_C],x[c_keep + self.n_C]))\\n self.n_c = x.shape[0]\\n self._triangulate(x)\\n self.assign_vertices()\\n self.Angles = tri_angles(x, self.tris)\\n #\\n # remove_mask = C[self.n_C:, self.n_C:].sum(axis=1)==0\\n # if remove_mask.any():\\n # c_keep = np.nonzero(~remove_mask)[0]\\n # x = np.concatenate((x[:self.n_C],x[c_keep + self.n_C]))\\n # self.n_c = x.shape[0]\\n # self._triangulate(x)\\n # self.assign_vertices()\\n # self.Angles = tri_angles(x, self.tris)\\n\\n\\n return x\",\n \"def is_nonprojective_edge(g,h,d):\\n rang = get_range(g,h,d)\\n roots = set()\\n for i in rang[1:-1]:\\n roots.add(get_root(g,rang[1:-1],i)) \\n for i in roots:\\n if not is_dominated(g,i,h):\\n return True\\n return False\",\n \"def valid_tile(self, i, j):\\n if (i >= 0 and i < self.rows) and (j >= 0 and j < self.cols):\\n return True\\n return False\",\n \"def _cell_in_boundary(self, i_row, i_col):\\n return ((i_row, i_col) == self._tl_cell or\\n (i_row, i_col) == self._tr_cell or\\n (i_row, i_col) == self._bl_cell or\\n (i_row, i_col) == self._br_cell or\\n (i_row, i_col) in self._ls_cells or\\n (i_row, i_col) in self._rs_cells or\\n (i_row, i_col) in self._ts_cells or\\n (i_row, i_col) in self._bs_cells)\",\n \"def is_planar(G):\\n result=True\\n bad_minor=[]\\n n=len(G.nodes())\\n iterazione=0\\n if n>5:\\n print 'N >5'\\n\\n for subnodes in it.combinations(G.nodes(),6):\\n iterazione+=1\\n print 'iterazione %d'%iterazione\\n subG=G.subgraph(subnodes)\\n if bipartite.is_bipartite(G):# check if the graph G has a subgraph K(3,3)\\n X, Y = bipartite.sets(G)\\n if len(X)==3:\\n result=False\\n bad_minor=subnodes\\n return result,bad_minor\\n iterazione=0\\n if n>4 and result:\\n print 'N >4'\\n\\n for subnodes in it.combinations(G.nodes(),5):\\n print 'iterazione %d'%iterazione\\n subG=G.subgraph(subnodes)\\n if len(subG.edges())==10:# check if the graph G has a subgraph K(5)\\n result=False\\n bad_minor=subnodes\\n return result,bad_minor\\n\\n return result,bad_minor\",\n \"def is_solved(self):\\n colors = ['green', 'blue', 'red', 'orange', 'white', 'yellow']\\n for row in range(3):\\n for column in range(3):\\n if self.front[row][column] != colors[0]:\\n return False\\n for row in range(3):\\n for column in range(3):\\n if self.back[row][column] != colors[1]:\\n return False\\n for row in range(3):\\n for column in range(3):\\n if self.right[row][column] != colors[2]:\\n return False\\n for row in range(3):\\n for column in range(3):\\n if self.left[row][column] != colors[3]:\\n return False\\n for row in range(3):\\n for column in range(3):\\n if self.up[row][column] != colors[4]:\\n return False\\n for row in range(3):\\n for column in range(3):\\n if self.down[row][column] != colors[5]:\\n return False\\n return True\",\n \"def col_clashes(bs, c):\\r\\n for i in range(c): # Look at all columns to the left of c\\r\\n if share_diagonal(i, bs[i], c, bs[c]):\\r\\n return True\\r\\n\\r\\n return False\",\n \"def rg_valid(rg):\\n return len(rg) != 9\",\n \"def is_solved(level_map):\\n shape = level_map.shape\\n for x in range(shape[0]):\\n for y in range(shape[1]):\\n i = (x, y)\\n tile = level_map[i]\\n if tile == 0:\\n continue\\n left_index, up_index, right_index, down_index = get_direction_indices(i)\\n if has_connection_left(tile) and \\\\\\n (tile_is_out_of_borders(left_index, shape) or not has_connection_right(level_map[left_index])):\\n return False\\n if has_connection_up(tile) and \\\\\\n (tile_is_out_of_borders(up_index, shape) or not has_connection_down(level_map[up_index])):\\n return False\\n if has_connection_right(tile) and \\\\\\n (tile_is_out_of_borders(right_index, shape) or not has_connection_left(level_map[right_index])):\\n return False\\n if has_connection_down(tile) and \\\\\\n (tile_is_out_of_borders(down_index, shape) or not has_connection_up(level_map[down_index])):\\n return False\\n return True\",\n \"def test_differentiable__mask_connectivity(self):\\n data = random_graph_data(5, 4, 3)\\n print(data.size)\",\n \"def breaks_connectivity(level, index, axis=0):\\n new_level = remove_index(level, index, axis=axis)\\n return not is_solvable(new_level)\",\n \"def check_lost (grid):\\r\\n for row in range(4):\\r\\n for col in range(4):\\r\\n if grid[row][col]==0:\\r\\n return False\\r\\n if grid[0][0]==grid[0][1] or grid[0][0]==grid[1][0]:\\r\\n return False \\r\\n if grid[0][3]==grid[0][2] or grid[0][3]==grid[1][3]:\\r\\n return False \\r\\n if grid[3][0]==grid[2][0] or grid[3][0]==grid[3][1]:\\r\\n return False\\r\\n if grid[3][3]==grid[2][3] or grid[3][3]==grid[3][2]:\\r\\n return False \\r\\n if grid[0][1]==grid[0][2] or grid[0][1]==grid[1][1]:\\r\\n return False \\r\\n if grid[0][2]==grid[1][2]:\\r\\n return False \\r\\n if grid[1][1]==grid[2][1] or grid[1][1]==grid[1][2] or grid[1][1]==grid[1][0]:\\r\\n return False\\r\\n if grid[2][1]==grid[2][0] or grid[2][1]==grid[2][2] or grid[2][1]==grid[3][1]:\\r\\n return False \\r\\n if grid[1][0]==grid[2][0]:\\r\\n return False\\r\\n if grid[1][2]==grid[1][3] or grid[1][2]==grid[2][2]:\\r\\n return False\\r\\n if grid[2][2]==grid[2][3] or grid[2][2]==grid[3][2]:\\r\\n return False\\r\\n if grid[3][1]==grid[3][2]:\\r\\n return False\\r\\n else:\\r\\n return True\",\n \"def bad_central_mask(weights, badmask, index, wmin = 0.1):\\n\\n \\n # Weights of fibers at the source index\\n sel = weights[:, index] > wmin\\n\\n #print(weights[:, index])\\n #print(badmask[sel, index - 1 : index + 2])\\n\\n bad_test = any(badmask[sel, index - 1 : index + 2].ravel()) \\n bad_test = bad_test| all(badmask[:, index - 1 : index + 2].ravel())\\n\\n if bad_test:\\n return False\\n else:\\n return True\",\n \"def check_if_legal(row, blocks):\\n counter = 0\\n compare_lst = []\\n for square in row:\\n if square == Black:\\n counter += 1\\n else:\\n if counter > 0:\\n compare_lst.append(counter)\\n counter = 0\\n if counter > 0:\\n compare_lst.append(counter)\\n if compare_lst == blocks:\\n return True\\n return False\",\n \"def _in_bounds(self, x, y):\\r\\n return 0 <= x < 8 and 0 <= y < 8\",\n \"def col_clashes(bs, c):\\n for i in range(c): # check at all columns to the left of c\\n if share_diagonal(i, bs[i], c, bs[c]):\\n return True\\n return False # no clashes - col c has a safe placement\",\n \"def contains_black(image):\\n extrema = ImageStat.Stat(image).extrema\\n r = extrema[0][0]\\n g = extrema[1][0]\\n b = extrema[2][0]\\n\\n if r == 0 and g == 0 and b == 0:\\n return True\\n\\n return False\",\n \"def is_valid(x, y):\\n return (x >= 0) & (x < BOARD_SIZE) & (y >= 0) & (y < BOARD_SIZE)\",\n \"def check_regularity(edges):\\n for a, b in edges:\\n counter_a = 0\\n counter_b = 0\\n for x, y in edges:\\n if a == x or a == y:\\n counter_a += 1\\n if b == x or b == y:\\n counter_b += 1\\n assert (counter_a > 0) and (counter_b > 0)\\n if (counter_a == 1) or (counter_b == 1):\\n raise Exception(\\\"Boundary is not closed.\\\")\\n if (counter_a > 2) or (counter_b > 2):\\n raise Exception(\\\"More than two edges share a node.\\\")\",\n \"def is_valid(problem, i, j, e):\\n row_map = row_maps[i]\\n column_map = column_maps[j]\\n sector_map = sector_maps[get_sector_number(i, j)]\\n not_in_row = row_map[e-1] == 0\\n not_in_column = column_map[e-1] == 0\\n not_in_sector = sector_map[e-1] == 0\\n\\n return not_in_row and not_in_column and not_in_sector\",\n \"def check_lost (grid):\\r\\n t=0\\r\\n for o in range(len(grid)):\\r\\n for e in range(len(grid[o])):\\r\\n if grid[o][e]==0:\\r\\n t+=1\\r\\n else:\\r\\n ()\\r\\n r=0\\r\\n for o in range(len(grid)):\\r\\n for e in range(len(grid[o])-1):\\r\\n if grid[o][e]==grid[o][e+1]:\\r\\n r+=1\\r\\n elif grid[o][3]==grid[o][2]:\\r\\n r+=1 \\r\\n else:\\r\\n ()\\r\\n \\r\\n v=0\\r\\n for o in range(len(grid)):\\r\\n for e in range(len(grid[o])-1):\\r\\n if grid[e][o]==grid[e+1][o]:\\r\\n v+=1\\r\\n elif grid[3][o]==grid[2][o]:\\r\\n v+=1 \\r\\n else:\\r\\n () \\r\\n \\r\\n if t==0 and r==0 and v==0:\\r\\n return True\\r\\n else:\\r\\n return False\",\n \"def endpoints(image):\\n return _neighbors_conv(image) == 1\",\n \"def Check(self):\\n cleared = False\\n while not cleared:\\n for i in list(combinations([cell.Check() for cell in self.cells], 2)):\\n # for i in list(combinations(zip(self.locations.x,self.locations.y,self.locations.length,self.locations.index),2)):\\n x1 = i[0][0]\\n y1 = i[0][1]\\n r1 = i[0][2] / 2\\n idx1 = i[0][3]\\n x2 = i[1][0]\\n y2 = i[1][1]\\n r2 = i[1][2] / 2\\n idx1 = i[0][3]\\n idx2 = i[1][3]\\n distance = (x1 - x2) * (x1 - x2) + (y1 - y2) * (y1 - y2)\\n radii = (r1 + r2) * (r1 + r2)\\n if distance == radii:\\n cleared = True\\n elif distance > radii:\\n cleared = True\\n else:\\n if x1 > x2 and y1 > y2:\\n if (\\n x1 + r1 > 0\\n and x1 + r1 < self.boundaries[0]\\n and y1 + r1 > 0\\n and y1 + r1 < self.boundaries[1]\\n ):\\n self.cells[idx1].x = x1 + r1 / 2\\n self.cells[idx1].y = y1 + r1 / 2\\n elif x1 > x2 and y1 < y2:\\n if (\\n x1 + r1 > 0\\n and x1 + r1 < self.boundaries[0]\\n and y1 - r1 > 0\\n and y1 - r1 < self.boundaries[1]\\n ):\\n self.cells[idx1].x = x1 + r1 / 2\\n self.cells[idx1].y = y1 - r1 / 2\\n elif x1 < x2 and y1 > y2:\\n if (\\n x1 - r1 > 0\\n and x1 - r1 < self.boundaries[0]\\n and y1 + r1 > 0\\n and y1 + r1 < self.boundaries[1]\\n ):\\n self.cells[idx1].x = x1 - r1 / 2\\n self.cells[idx1].y = y1 + r1 / 2\\n else:\\n if (\\n x1 - r1 > 0\\n and x1 - r1 < self.boundaries[0]\\n and y1 - r1 > 0\\n and y1 - r1 < self.boundaries[1]\\n ):\\n self.cells[idx1].x = x1 - r1 / 2\\n self.cells[idx1].y = y1 - r1 / 2\\n _logger.debug(\\n f\\\"Bumped from {x1 :.2e}, {y1 :.2e} to {self.cells[idx1].x :.2e}, {self.cells[idx1].y :.2e}\\\"\\n )\\n cleared = False\\n return\",\n \"def exist_adjacent_cell(board: list, cell_index: tuple) -> bool:\\n #Each time the result = [(-1, -1), (-1, 0), (-1, 1), (0, -1), (0, 1), (1, -1), (1, 0), (1, 1)]\\n possible_cells_direction = list(filter(lambda x: x[0] != 0 or x[1] != 0, list(product(range(-1, 2), range(-1, 2)))))\\n\\n for coord_couple in possible_cells_direction:\\n i = cell_index[0] + coord_couple[0]\\n j = cell_index[1] + coord_couple[1]\\n\\n if not test_cell_existence(board, i, j):\\n continue\\n\\n # If a cell isn't empty\\n if board[i][j] != 0:\\n return True\\n return False\",\n \"def is_contradiction_(transition):\\n is_contr = False\\n\\n # check implications of lower left corner\\n if np.argmax(transition[0]) == 0:\\n if np.argmax(transition[2]) == 2 or np.argmax(transition[2]) == 3:\\n is_contr = True\\n elif np.argmax(transition[0]) == 1:\\n if np.argmax(transition[1]) == 0 or np.argmax(transition[1]) == 2:\\n is_contr = True\\n if np.argmax(transition[2]) != 1:\\n is_contr = True\\n elif np.argmax(transition[0]) == 2:\\n if np.argmax(transition[1]) == 0 or np.argmax(transition[1]) == 1:\\n is_contr = True\\n elif np.argmax(transition[0]) == 3:\\n if np.argmax(transition[1]) != 3:\\n is_contr = True\\n if np.argmax(transition[2]) == 0 or np.argmax(transition[2]) == 2:\\n is_contr = True\\n\\n # check implicatiosn of upper right corner\\n if np.argmax(transition[2]) == 0:\\n if np.argmax(transition[0]) == 1 or np.argmax(transition[0]) == 3:\\n is_contr = True\\n elif np.argmax(transition[2]) == 1:\\n if np.argmax(transition[1]) == 0 or np.argmax(transition[1]) == 2:\\n is_contr = True\\n elif np.argmax(transition[2]) == 2:\\n if np.argmax(transition[0]) != 2:\\n is_contr = True\\n if np.argmax(transition[1]) == 0 or np.argmax(transition[1]) == 1:\\n is_contr = True\\n elif np.argmax(transition[2]) == 3:\\n if np.argmax(transition[1]) != 3:\\n is_contr = True\\n if np.argmax(transition[0]) == 0 or np.argmax(transition[0]) == 1:\\n is_contr = True\\n\\n return is_contr\",\n \"def negDiagnol(self, ox, curRow, curCol):\\n count = 0\\n while count < 4 and curRow < self.height and curCol >= 0 and self.board[curRow][curCol] == ox:\\n count += 1\\n curRow += 1\\n curCol -= 1\\n return count == 4\",\n \"def fullGrid(state):\\n return not ((state[:, :, 0] + state[:, :, 1]) == 0).any()\",\n \"def check_position_is_legal(grid, num, i, j):\\n args = (grid, num, i, j)\\n return (not check_row(*args)) and (not check_col(*args)) and (not check_local_square(*args))\",\n \"def has_unresected_pair(resected_idx, unresected_imgs, img_adjacency):\\n for idx in unresected_imgs:\\n if img_adjacency[resected_idx][idx] == 1 or img_adjacency[idx][resected_idx] == 1:\\n return True\\n return False\",\n \"def corner_init_check():\\r\\n \\\"\\\"\\\"Rule One\\\"\\\"\\\"\\r\\n z = shape-1\\r\\n if example[0, 0] == example[0, 1] and example[0, 0] == example[1, 0] and example[0, 0] != 0: # NE\\r\\n example[0, 0] = 0\\r\\n shade_neighbours(0, 0)\\r\\n print(\\\"RULE 1 NW\\\")\\r\\n if example[0, z] == example[0, z-1] and example[0, z] == example[1, z] and example[0, z] != 0: # NW\\r\\n example[0, z] = 0\\r\\n shade_neighbours(0, z)\\r\\n print(\\\"RULE 1 NE\\\")\\r\\n if example[z, z] == example[z, z-1] and example[z, z] == example[z-1, z] and example[z, z] != 0: # SE\\r\\n example[z, z] = 0\\r\\n shade_neighbours(z, z)\\r\\n print(\\\"RULE 1 SE\\\")\\r\\n if example[z, 0] == example[z, 1] and example[z, 0] == example[z-1, 0] and example[z, 0] != 0: # SW\\r\\n example[z, 0] = 0\\r\\n shade_neighbours(z, 0)\\r\\n print(\\\"RuLE 1 SW\\\")\\r\\n\\r\\n \\\"\\\"\\\"Rule Two\\\"\\\"\\\"\\r\\n if (example[0, 0] == example[0, 1] or example[0, 0] == example[1, 0]) \\\\\\r\\n and (example[1, 1] == example[0, 1] or example[1, 1] == example[1, 0])\\\\\\r\\n and (example[0, 0] != example[1, 1]): # NE\\r\\n example[0, 0] = 0\\r\\n example[1, 1] = 0\\r\\n shade_neighbours(1, 1)\\r\\n print(\\\"RULE 2 NW\\\")\\r\\n if (example[0, z] == example[0, z-1] or example[0, z] == example[1, z]) \\\\\\r\\n and (example[1, z-1] == example[0, z-1] or example[1, z-1] == example[1, z]) \\\\\\r\\n and (example[0, z] != example[1, z-1]): # NW\\r\\n example[0, z] = 0\\r\\n example[1, z-1] = 0\\r\\n shade_neighbours(1, z - 1)\\r\\n print(\\\"RULE 2 NE\\\")\\r\\n if (example[z, z] == example[z, z-1] or example[z, z] == example[z-1, z]) \\\\\\r\\n and (example[z-1, z-1] == example[z-1, z] or example[z-1, z-1] == example[z, z-1]) \\\\\\r\\n and (example[z, z] != example[z-1, z-1]): # SE\\r\\n example[z, z] = 0\\r\\n example[z-1, z-1] = 0\\r\\n shade_neighbours(z - 1, z - 1)\\r\\n print(\\\"RULE 2 SE\\\")\\r\\n if (example[z, 0] == example[z, 1] or example[z, 0] == example[z-1, 0]) \\\\\\r\\n and (example[z-1, 1] == example[z, 1] or example[z-1, 1] == example[z-1, 0]) \\\\\\r\\n and (example[z, 0] != example[z-1, 1]): # SW\\r\\n example[z, 0] = 0\\r\\n example[z-1, 1] = 0\\r\\n shade_neighbours(z - 1, 1)\\r\\n print(\\\"RULE 2 SW\\\")\\r\\n conflict_check()\",\n \"def col_clashes(bs, c):\\r\\n for i in range(c): # Look at all columns to the left of c\\r\\n if share_diagonal(i, bs[i], c, bs[c]):\\r\\n return True\\r\\n\\r\\n return False # No clashes - col c has a safe placement.\\r\",\n \"def inrange(cc, point):\\n return point.row in range(cc.top, cc.bottom+1) and point.col in range(cc.left, cc.right+1)\",\n \"def find_disconnected_voxels(im, conn=None):\\n if im.ndim == 2:\\n if conn == 4:\\n strel = disk(1)\\n elif conn in [None, 8]:\\n strel = square(3)\\n elif im.ndim == 3:\\n if conn == 6:\\n strel = ball(1)\\n elif conn in [None, 26]:\\n strel = cube(3)\\n labels, N = spim.label(input=im, structure=strel)\\n holes = clear_border(labels=labels) > 0\\n return holes\",\n \"def _is_dead_end(self, i_row, i_col, direction):\\n return (((i_row, i_col) in self._ts_cells and direction == \\\"s\\\") or\\n ((i_row, i_col) in self._ts_cells and direction == \\\"se\\\") or\\n ((i_row, i_col) in self._ts_cells and direction == \\\"sw\\\") or\\n ((i_row, i_col) in self._ls_cells and direction == \\\"e\\\") or\\n ((i_row, i_col) in self._ls_cells and direction == \\\"ne\\\") or\\n ((i_row, i_col) in self._ls_cells and direction == \\\"se\\\") or\\n ((i_row, i_col) in self._bs_cells and direction == \\\"n\\\") or\\n ((i_row, i_col) in self._bs_cells and direction == \\\"nw\\\") or\\n ((i_row, i_col) in self._bs_cells and direction == \\\"ne\\\") or\\n ((i_row, i_col) in self._rs_cells and direction == \\\"w\\\") or\\n ((i_row, i_col) in self._rs_cells and direction == \\\"nw\\\") or\\n ((i_row, i_col) in self._rs_cells and direction == \\\"sw\\\") or\\n ((i_row, i_col) == self._tl_cell and direction == \\\"s\\\") or\\n ((i_row, i_col) == self._tl_cell and direction == \\\"se\\\") or\\n ((i_row, i_col) == self._tl_cell and direction == \\\"e\\\") or\\n ((i_row, i_col) == self._bl_cell and direction == \\\"n\\\") or\\n ((i_row, i_col) == self._bl_cell and direction == \\\"ne\\\") or\\n ((i_row, i_col) == self._bl_cell and direction == \\\"e\\\") or\\n ((i_row, i_col) == self._tr_cell and direction == \\\"w\\\") or\\n ((i_row, i_col) == self._tr_cell and direction == \\\"sw\\\") or\\n ((i_row, i_col) == self._tr_cell and direction == \\\"s\\\") or\\n ((i_row, i_col) == self._br_cell and direction == \\\"w\\\") or\\n ((i_row, i_col) == self._br_cell and direction == \\\"nw\\\") or\\n ((i_row, i_col) == self._br_cell and direction == \\\"n\\\"))\",\n \"def valid_coverage_cell(self, row, column):\\n\\n if (row < self.cov_grid.shape[0] and\\n row >= 0) and \\\\\\n (column < self.cov_grid.shape[1] and\\n column >= 0):\\n return True\\n else:\\n return False\",\n \"def col_clashes(bs, c):\\n for i in range(c): # Look at all columns to the left of c\\n if share_diagonal(i, bs[i], c, bs[c]):\\n return True\\n\\n return False # No clashes - col c has a safe placement\",\n \"def edge_between_neighbors(cell_a, cell_b):\\n edge = np.logical_and(dilate_simple(cell_a), dilate_simple(cell_b))\\n return edge\",\n \"def isComplete(grid):\\n for row in range(0,9):\\n for col in range(0,9):\\n if grid[row][col]==0:\\n return False\\n return True\",\n \"def is_adjacent(self, other: ops.Qid) -> bool:\\n return (isinstance(other, GridQubit) and\\n abs(self.row - other.row) + abs(self.col - other.col) == 1)\",\n \"def is_on_board(self, r, c):\\r\\n return 0 <= r <= 7 and 0 <= c <= 7\",\n \"def check_allowed(i,j,DIR):\\r\\n #DIR is a unit vector pointing from i to j (eg: DIR = [0,1] indicates that j is 1 to the right of i)\\r\\n #check only specific arrangement on-demand\\r\\n if DIR == [1,0]: #i j #RIGHTWARD\\r\\n if get_blocks(i)[1][2] == get_blocks(j)[1][0]:\\r\\n return True\\r\\n elif DIR == [-1,0]: #LEFTWARD\\r\\n if get_blocks(j)[1][2] == get_blocks(i)[1][0]: #reverse indices\\r\\n return True\\r\\n elif DIR == [0,-1]: #UPWARD\\r\\n if get_blocks(j)[2][1] == get_blocks(i)[0][1]:\\r\\n return True \\r\\n elif DIR == [0,1]: #DOWNWARD\\r\\n if get_blocks(i)[2][1] == get_blocks(j)[0][1]:\\r\\n return True\\r\\n else:\\r\\n raise ValueError (\\\"Invalid DIR vector!\\\")\\r\\n return False\",\n \"def check_hypernet(seq_iter):\\n\\treturn not any(filter(has_abba, seq_iter))\",\n \"def answer_ok(a):\\n (rightly_positioned, permutated) = a\\n if (rightly_positioned + permutated > number_of_positions) \\\\\\n or (rightly_positioned + permutated < len(colours) - number_of_positions):\\n return False\\n if rightly_positioned == 3 and permutated == 1:\\n return False\\n return True\",\n \"def check_border_cells(self):\\n for row, col in self.land_cells:\\n if row == 1 or row == self.unique_rows[-1] or col == 1 or col == self.unique_cols[-1]:\\n raise ValueError(\\\"Only water cells may be border cells!\\\")\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6483187","0.6258697","0.6213388","0.6037331","0.59586877","0.5941078","0.5923148","0.5912683","0.5907906","0.58862484","0.58592165","0.5856731","0.5845715","0.5740796","0.5716612","0.5691044","0.5684787","0.56699437","0.5633153","0.5612918","0.5611113","0.56096953","0.5595444","0.55769634","0.5575892","0.5567267","0.5566394","0.5548046","0.5547504","0.55367833","0.5492346","0.5480788","0.5475202","0.5475057","0.54677325","0.54586804","0.54374814","0.5432511","0.5430481","0.5428309","0.5421996","0.5421805","0.5421076","0.5419784","0.5418792","0.5411526","0.5409191","0.540507","0.5396614","0.53960526","0.53897023","0.53857744","0.5384095","0.5375267","0.53588986","0.5355672","0.53536546","0.5350033","0.5349544","0.53484225","0.53417325","0.53408813","0.53375524","0.5336254","0.5330388","0.5329238","0.53286177","0.53216803","0.53201026","0.53189397","0.5307267","0.5305926","0.5304721","0.5304356","0.52991736","0.52987087","0.52943724","0.52898043","0.5281309","0.5277649","0.5277348","0.52770996","0.5276541","0.52758515","0.52756125","0.5273155","0.5268744","0.5268719","0.5262183","0.5257715","0.5255894","0.5255538","0.5254323","0.5247894","0.5243884","0.5241362","0.5234664","0.523413","0.5228234","0.52278227","0.5227699"],"string":"[\n \"0.6483187\",\n \"0.6258697\",\n \"0.6213388\",\n \"0.6037331\",\n \"0.59586877\",\n \"0.5941078\",\n \"0.5923148\",\n \"0.5912683\",\n \"0.5907906\",\n \"0.58862484\",\n \"0.58592165\",\n \"0.5856731\",\n \"0.5845715\",\n \"0.5740796\",\n \"0.5716612\",\n \"0.5691044\",\n \"0.5684787\",\n \"0.56699437\",\n \"0.5633153\",\n \"0.5612918\",\n \"0.5611113\",\n \"0.56096953\",\n \"0.5595444\",\n \"0.55769634\",\n \"0.5575892\",\n \"0.5567267\",\n \"0.5566394\",\n \"0.5548046\",\n \"0.5547504\",\n \"0.55367833\",\n \"0.5492346\",\n \"0.5480788\",\n \"0.5475202\",\n \"0.5475057\",\n \"0.54677325\",\n \"0.54586804\",\n \"0.54374814\",\n \"0.5432511\",\n \"0.5430481\",\n \"0.5428309\",\n \"0.5421996\",\n \"0.5421805\",\n \"0.5421076\",\n \"0.5419784\",\n \"0.5418792\",\n \"0.5411526\",\n \"0.5409191\",\n \"0.540507\",\n \"0.5396614\",\n \"0.53960526\",\n \"0.53897023\",\n \"0.53857744\",\n \"0.5384095\",\n \"0.5375267\",\n \"0.53588986\",\n \"0.5355672\",\n \"0.53536546\",\n \"0.5350033\",\n \"0.5349544\",\n \"0.53484225\",\n \"0.53417325\",\n \"0.53408813\",\n \"0.53375524\",\n \"0.5336254\",\n \"0.5330388\",\n \"0.5329238\",\n \"0.53286177\",\n \"0.53216803\",\n \"0.53201026\",\n \"0.53189397\",\n \"0.5307267\",\n \"0.5305926\",\n \"0.5304721\",\n \"0.5304356\",\n \"0.52991736\",\n \"0.52987087\",\n \"0.52943724\",\n \"0.52898043\",\n \"0.5281309\",\n \"0.5277649\",\n \"0.5277348\",\n \"0.52770996\",\n \"0.5276541\",\n \"0.52758515\",\n \"0.52756125\",\n \"0.5273155\",\n \"0.5268744\",\n \"0.5268719\",\n \"0.5262183\",\n \"0.5257715\",\n \"0.5255894\",\n \"0.5255538\",\n \"0.5254323\",\n \"0.5247894\",\n \"0.5243884\",\n \"0.5241362\",\n \"0.5234664\",\n \"0.523413\",\n \"0.5228234\",\n \"0.52278227\",\n \"0.5227699\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}}],"truncated":false,"partial":true},"paginationData":{"pageIndex":811,"numItemsPerPage":100,"numTotalItems":95772,"offset":81100,"length":100}},"jwt":"eyJhbGciOiJFZERTQSIsImtpZCI6IjVHZDBvd0g5MTM2eDZjc1FvbE1zcktNWUZoRVFoUm5PVVVybEpjOUhaUEEifQ.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc4NjIwMzc0NSwianRpIjoiMTliMjhiZTktMWNiYS00NWExLTk5NTAtMTkxNjA5NWE0MWUxIiwic3ViIjoiL2RhdGFzZXRzL25vbWljLWFpL2Nvcm5zdGFjay1weXRob24tdjEiLCJleHAiOjE3ODYyMDczNDUsImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.WglU9wvJvD9cDuFQxH3TyCWSjfVyRuqqn48wS4N9EmWhJtJOSiuP7jnV5Hl3Zy-xE-RSNWAwfMb0d6CkjDgUBQ","displayUrls":true,"splitSizeSummaries":[{"config":"default","split":"train","numRows":95772,"numBytesParquet":1928375891}]},"discussionsStats":{"closed":1,"open":1,"total":2},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
query stringlengths 9 3.4k | document stringlengths 9 87.4k | metadata dict | negatives listlengths 4 101 | negative_scores listlengths 4 101 | document_score stringlengths 3 10 | document_rank stringclasses 102
values |
|---|---|---|---|---|---|---|
Attach a user's credentials to a response. | def attach_credentials(response, user):
response.set_cookie(_CHIRP_SECURITY_TOKEN_COOKIE,
_create_security_token(user)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_credentials():",
"def test_credentialsSetResponse(self):\n cred = imap4.PLAINCredentials()\n cred.setResponse(b'\\0testuser\\0secret')\n self.assertEqual(cred.username, b'testuser')\n self.assertEqual(cred.password, b'secret')",
"def get_user_details(self, response):\n ... | [
"0.62017906",
"0.61743724",
"0.5863988",
"0.5680074",
"0.5572465",
"0.55617046",
"0.55614007",
"0.5557779",
"0.5540854",
"0.55386317",
"0.5523414",
"0.5503794",
"0.5500769",
"0.54540765",
"0.5449879",
"0.5427805",
"0.54111546",
"0.54075456",
"0.53991103",
"0.5397431",
"0.5392... | 0.77924997 | 0 |
Get the current loggedin user's. | def get_current_user(request):
cred = None
token = request.COOKIES.get(_CHIRP_SECURITY_TOKEN_COOKIE)
if token:
cred = _parse_security_token(token)
# If this is a POST, look for a base64-encoded security token in
# the CHIRP_Auth variable.
if cred is None and request.method == 'POST':
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_current_users(self):\n active_sessions = Session.objects.filter(expire_date__gte=timezone.now())\n user_id_list = []\n for session in active_sessions:\n data = session.get_decoded()\n user_id_list.append(data.get('_auth_user_id', None))\n # Query all logged... | [
"0.7689059",
"0.7354096",
"0.73099166",
"0.7301284",
"0.7278969",
"0.7278969",
"0.7278969",
"0.7278969",
"0.7261865",
"0.72201365",
"0.72056025",
"0.71270096",
"0.7115699",
"0.7099263",
"0.705866",
"0.705866",
"0.705866",
"0.70543313",
"0.70526934",
"0.7051824",
"0.7009778",
... | 0.0 | -1 |
Returns the URL of a login page that redirects to 'path' on success. | def create_login_url(path):
return "/auth/hello?redirect=%s" % path | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def GET_login(self):\r\n\r\n # dest is the location to redirect to upon completion\r\n dest = request.get.get('dest','') or request.referer or '/'\r\n return LoginPage(dest = dest).render()",
"def login_page():\n text = '<a href=\"%s\">Authenticate with Okta</a>'\n return text % create... | [
"0.7192956",
"0.6800965",
"0.66827124",
"0.66450155",
"0.65036446",
"0.6477103",
"0.6422007",
"0.63984793",
"0.6378266",
"0.6354311",
"0.6330546",
"0.6328878",
"0.6328678",
"0.63281304",
"0.63243735",
"0.6307606",
"0.62386686",
"0.6232495",
"0.6208821",
"0.62072647",
"0.61902... | 0.79841524 | 0 |
Create an HTTP response that will log a user out. The redirect param can be a relative URL in which case the user will go back to the same page when logging in. This is useful for switching users like on the playlist tracker page. | def logout(redirect=None):
# If the user was signed in and has a cookie, clear it.
logout_url = _FINAL_LOGOUT_URL
if redirect:
logout_url = '%s?redirect=%s' % (logout_url, redirect)
response = http.HttpResponseRedirect(logout_url)
response.set_cookie(_CHIRP_SECURITY_TOKEN_COOKIE, '')
ret... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def redirect(self) -> WerkzeugResponse:\n\n _dict = self.unpack_redirect()\n return self.perform_logout(_dict, BINDING_HTTP_REDIRECT)",
"def logout_redirect(request):\n logout(request)\n\n # Build the URL\n login_url = furl(login_redirect_url(request, next_url=request.build_absolute_uri())... | [
"0.7326342",
"0.73198265",
"0.7284289",
"0.7238084",
"0.71671414",
"0.71593136",
"0.7151981",
"0.71129674",
"0.70879936",
"0.7079564",
"0.7043046",
"0.7023484",
"0.70197374",
"0.7015899",
"0.701423",
"0.7005902",
"0.69992566",
"0.69971395",
"0.69959456",
"0.69959456",
"0.6989... | 0.77370954 | 0 |
A URLsafe token that authenticates a user for a password reset. | def get_password_reset_token(user):
return base64.urlsafe_b64encode(_create_security_token(user)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def request_password_reset_token():\n j = request.get_json(force=True)\n user_requested = j['user'].lower()\n\n # Disabled user accounts can not request for a new password.\n target_user = User.query.filter_by(mail=user_requested).first()\n\n if target_user is None:\n return Errors.UNKNOWN_US... | [
"0.7068343",
"0.6979489",
"0.6916166",
"0.69147855",
"0.68065274",
"0.6756396",
"0.6722973",
"0.67225903",
"0.6698638",
"0.66768074",
"0.66731113",
"0.66554016",
"0.6653242",
"0.65514797",
"0.6548692",
"0.65352726",
"0.65276843",
"0.65198666",
"0.6503898",
"0.650001",
"0.6499... | 0.76141065 | 0 |
Extracts an email address from a valid password reset token. | def parse_password_reset_token(token):
try:
token = base64.urlsafe_b64decode(str(token))
except TypeError:
return None
cred = _parse_security_token(token)
return cred and cred.email | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def verify_password_reset_token(token: str) -> tp.Optional[str]:\n try:\n decoded_token = jwt.decode(token, config.SECRET_KEY,\n algorithms=[ALGORITHM])\n except InvalidTokenError:\n return None\n if decoded_token[\"sub\"] != PASSWORD_RESET_SUBJECT:\n ... | [
"0.7039985",
"0.68341273",
"0.68253404",
"0.679571",
"0.67610675",
"0.6760937",
"0.6741547",
"0.6654214",
"0.65531987",
"0.640023",
"0.62692463",
"0.6200448",
"0.61567795",
"0.6123652",
"0.60978174",
"0.60570717",
"0.6042387",
"0.60345566",
"0.60287344",
"0.6026899",
"0.60162... | 0.80168754 | 0 |
Return the phase series object for the scenario. | def __getitem__(self, key):
if key in self._tracker_dict:
return self._tracker_dict[key].series
raise ScenarioNotFoundError(key) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getPhase(phase):",
"def GetPhase(self):\n ...",
"def phase(self):\r\n\r\n #XXX calcluate this from the standard output, instead of recalculating:\r\n\r\n tseries_length = self.input.data.shape[0]\r\n spectrum_length = self.spectrum.shape[-1]\r\n\r\n phase = np.zeros((tser... | [
"0.68655944",
"0.6622455",
"0.6455815",
"0.6428417",
"0.63313764",
"0.62835324",
"0.6200906",
"0.61574143",
"0.6050085",
"0.59065604",
"0.5901168",
"0.58883613",
"0.58803606",
"0.58803606",
"0.58803606",
"0.58332235",
"0.58091795",
"0.5807804",
"0.57886523",
"0.57490265",
"0.... | 0.0 | -1 |
Register a phase series. | def __setitem__(self, key, value):
self._tracker_dict[key] = ParamTracker(
self._data.records(extras=False), value, area=self.area, tau=self.tau) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _create_phases(self):\n start_dates, end_dates = self._phase_range(self._change_dates)\n pop_list = [self.pop_dict[date] for date in start_dates]\n phase_series = PhaseSeries(\n self.dates[0], self.dates[-1], self.population, use_0th=self._use_0th\n )\n phase_itr =... | [
"0.5775817",
"0.5751883",
"0.5649973",
"0.54143023",
"0.5328758",
"0.53215307",
"0.5314388",
"0.5310776",
"0.53023094",
"0.529634",
"0.5235999",
"0.5228771",
"0.52222896",
"0.5179229",
"0.5135855",
"0.51105607",
"0.5090179",
"0.50680315",
"0.49861172",
"0.49655068",
"0.496486... | 0.0 | -1 |
Set the range of data and reference date to determine past/future of phases. | def timepoints(self, first_date=None, last_date=None, today=None):
self._data.timepoints(first_date=first_date, last_date=last_date, today=today)
self._init_trackers() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_date_range(self, start_date, end_date):\n self._validate_date_range(start_date, end_date)\n self.start_date = pd.Timestamp(start_date)\n self.end_date = pd.Timestamp(end_date)",
"def set_range(self, start=None, end=None, occurrences=None):\n if start is None:\n if s... | [
"0.60431415",
"0.59922713",
"0.5831708",
"0.5738246",
"0.5724847",
"0.56485236",
"0.55952454",
"0.5588354",
"0.54657805",
"0.5421708",
"0.5348214",
"0.53387636",
"0.5298466",
"0.52646214",
"0.52533823",
"0.5250024",
"0.5242356",
"0.52234524",
"0.5207322",
"0.5201655",
"0.5200... | 0.0 | -1 |
Display or save a line plot of the dataframe. | def line_plot(self, df, show_figure=True, filename=None, **kwargs):
if self._interactive and show_figure:
return line_plot(df=df, filename=None, **kwargs)
if not self._interactive and filename is not None:
return line_plot(df=df, filename=filename, **kwargs) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def linePlot(self):\n clf()\n plot(self.x,self.averages)\n xlabel('X Label (units)')\n ylabel('Y Label (units)')\n savefig('line.png')",
"def graph(df):\n df.plot()\n plt.show()",
"def line_graph():\r\n #create the data in an array\r\n xval = np.arange(0,6,(np.pi*... | [
"0.7247399",
"0.7064947",
"0.7009128",
"0.68883586",
"0.6884624",
"0.6852269",
"0.66661745",
"0.6652541",
"0.66047895",
"0.6523031",
"0.6508674",
"0.6485286",
"0.6472643",
"0.6460924",
"0.6446598",
"0.6407095",
"0.63918054",
"0.6377627",
"0.63767356",
"0.6328446",
"0.628481",... | 0.7667313 | 0 |
Complement the number of recovered cases, if necessary. | def complement(self, **kwargs):
self._data.switch_complement(whether=True, **kwargs)
return self | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def cleanup(self):\n self.final_params = self.final_params_expected[self.count]\n self.flag = self.flag_expected[self.count]\n self.count += 1\n self.count = self.count % len(self.flag_expected)",
"def subSanity(self):\n\t\tself.sanity -= 1\n\t\tif self.sanity < -10:\n\t\t\tself.sanit... | [
"0.5762584",
"0.5644065",
"0.56118023",
"0.55019015",
"0.54095125",
"0.5408706",
"0.5404511",
"0.5397213",
"0.5393315",
"0.53197366",
"0.52725595",
"0.5261769",
"0.52091825",
"0.52083826",
"0.5206617",
"0.51571816",
"0.5151571",
"0.5134796",
"0.5121799",
"0.5117648",
"0.51032... | 0.0 | -1 |
Restore the raw records. Reverse method of covsirphy.Scenario.complement(). | def complement_reverse(self):
self._data.switch_complement(whether=False)
return self | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def restore(self):\n raise NotImplementedError",
"def restore_data(self):\n self.R = self._Ro\n del self._Ro",
"def restore(self):\n self.u = self.ub.copy()\n self.w = self.wb.copy()\n self.v = self.vb.copy()\n if self.en_bias: self.b = self.bb.copy()",
"def r... | [
"0.65303206",
"0.6500232",
"0.6468013",
"0.64254963",
"0.6402042",
"0.61993724",
"0.6194877",
"0.6174157",
"0.61449206",
"0.61389244",
"0.61376435",
"0.6097324",
"0.6025152",
"0.60112494",
"0.59458935",
"0.5927359",
"0.58817554",
"0.58671457",
"0.5840398",
"0.5822324",
"0.581... | 0.5241369 | 71 |
Show the details of complement that was (or will be) performed for the records. | def show_complement(self, **kwargs):
self._data.switch_complement(whether=None, **kwargs)
return self._data.show_complement() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def Complement(self):\n if (self.translated == False):\n for i in range(len(self.alignment)):\n self.alignment[i].seq = self.alignment[i].seq.complement()\n self.Show(self.displayedColumn)\n self.BackupAlignment\n else:\n self.AlertMessage(\"... | [
"0.5775242",
"0.5743464",
"0.55356264",
"0.5504451",
"0.5423902",
"0.5415966",
"0.53729266",
"0.536349",
"0.5347733",
"0.52742505",
"0.5256045",
"0.5240801",
"0.52383417",
"0.5233155",
"0.5202733",
"0.5180749",
"0.51699567",
"0.5141384",
"0.5086646",
"0.5080214",
"0.5080147",... | 0.72387177 | 0 |
Convert abbreviated variable names to complete names. | def _convert_variables(self, abbr, candidates):
if abbr is None:
return [self.CI, self.F, self.R]
if abbr == "all":
return self._ensure_list(candidates, name="candidates")
abbr_dict = {"C": self.C, "I": self.CI, "F": self.F, "R": self.R, }
variables = list(abbr) i... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def convertVariableName(variable):\n lowerSplits = [item.lower() for item in variable.split('_')]\n if len(lowerSplits) == 1:\n return lowerSplits[0]\n else:\n return lowerSplits[0] + ''.join([item.capitalize()\n for item in lowerSplits[1:]])",
"def ... | [
"0.68976355",
"0.6788493",
"0.67282575",
"0.6605132",
"0.66031903",
"0.6471281",
"0.6463167",
"0.63647664",
"0.63447404",
"0.6326731",
"0.6291707",
"0.627928",
"0.6247765",
"0.6219434",
"0.6218662",
"0.62027025",
"0.61862946",
"0.60936743",
"0.60927546",
"0.6065432",
"0.60617... | 0.59086 | 32 |
Return the records as a dataframe. | def records(self, variables=None, **kwargs):
# Get necessary data for the variables
all_df = self._data.records_all().set_index(self.DATE)
variables = self._convert_variables(variables, all_df.columns.tolist())
df = all_df.loc[:, variables]
# Figure
if self._data.compleme... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def dataframe(self):\n if not self.all_records:\n print('No rows cached.')\n return\n dict_list = [row.as_dict() for row in self.all_records]\n columns = self.all_records[0].keys\n dataframe = pd.DataFrame(dict_list, columns=columns)\n return dataframe",
"... | [
"0.81605434",
"0.7689365",
"0.7654657",
"0.75927025",
"0.75107545",
"0.75014997",
"0.7491799",
"0.74763435",
"0.74449694",
"0.74304926",
"0.74304926",
"0.74304926",
"0.74304926",
"0.74304926",
"0.73737586",
"0.73647803",
"0.7351118",
"0.73190165",
"0.72353095",
"0.72085106",
... | 0.0 | -1 |
Return the number of daily new cases (the first discreate difference of records). | def records_diff(self, variables=None, window=7, **kwargs):
window = self._ensure_natural_int(window, name="window")
df = self.records(variables=variables, show_figure=False).set_index(self.DATE)
df = df.diff().dropna()
df = df.rolling(window=window).mean().dropna().astype(np.int64)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def no_new_cases_count(day: int, month: int, year: int = 2020) -> int:\r\n \r\n # Your code goes here (remove pass)\r",
"def get_number_days(self):\r\n return 1",
"def _first_good_date(self, day):\n count = 0\n while True:\n try:\n self.data.loc[day - timede... | [
"0.63417804",
"0.6153473",
"0.60129416",
"0.58677965",
"0.58234876",
"0.578511",
"0.5671648",
"0.56618786",
"0.56148905",
"0.56128377",
"0.557568",
"0.55542594",
"0.5486608",
"0.5483065",
"0.54804957",
"0.54483485",
"0.54370016",
"0.5394437",
"0.5359422",
"0.5352396",
"0.5352... | 0.5627104 | 8 |
Initialize dictionary of trackers. | def _init_trackers(self):
data = copy.deepcopy(self._data)
series = ParamTracker.create_series(
first_date=data.first_date, last_date=data.today, population=data.population)
tracker = ParamTracker(
record_df=self._data.records(extras=False), phase_series=series, area=self... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self, keys_to_track):\r\n self.keys_to_track = keys_to_track\r\n self.tracker = {}\r\n for key_to_track in self.keys_to_track:\r\n self.tracker[key_to_track] = {}",
"def __init__(self,max_age=100,min_hits=10):\n self.max_age = max_age\n self.min_hits = m... | [
"0.75012267",
"0.6427343",
"0.62639487",
"0.6080427",
"0.6077556",
"0.60689676",
"0.60171473",
"0.59739995",
"0.59257585",
"0.59130996",
"0.59109986",
"0.5875346",
"0.5845771",
"0.58061785",
"0.5783098",
"0.5781152",
"0.57698804",
"0.5769008",
"0.57673883",
"0.57673883",
"0.5... | 0.78069323 | 0 |
Ensure that the phases series is registered. If not registered, copy the template phase series. | def _tracker(self, name, template="Main"):
# Registered
if name in self._tracker_dict:
return self._tracker_dict[name]
# Un-registered and create it
if template not in self._tracker_dict:
raise ScenarioNotFoundError(template)
tracker = copy.deepcopy(self._... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _create_phases(self):\n start_dates, end_dates = self._phase_range(self._change_dates)\n pop_list = [self.pop_dict[date] for date in start_dates]\n phase_series = PhaseSeries(\n self.dates[0], self.dates[-1], self.population, use_0th=self._use_0th\n )\n phase_itr =... | [
"0.5616218",
"0.5055248",
"0.4934233",
"0.48037916",
"0.47497964",
"0.46875504",
"0.46473768",
"0.46447363",
"0.46103522",
"0.45802236",
"0.456944",
"0.4568185",
"0.4544172",
"0.45340922",
"0.44988102",
"0.44769245",
"0.44675794",
"0.441166",
"0.4406575",
"0.439608",
"0.43933... | 0.0 | -1 |
Add a new phase. The start date will be the next date of the last registered phase. | def add(self, name="Main", end_date=None, days=None, population=None, model=None, **kwargs):
if end_date is not None:
self._ensure_date(end_date, name="end_date")
tracker = self._tracker(name)
try:
tracker.add(
end_date=end_date, days=days, population=popu... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def add_phase(self, phase):\n\n if not phase.name in self.phase_dict:\n self.phase_dict[phase.name] = phase\n else:\n if phase.energy < self.phase_dict[phase.name].energy:\n self.phase_dict[phase.name] = phase\n self._phases.append(phase)\n phase.ind... | [
"0.67498213",
"0.6707674",
"0.6699049",
"0.6079348",
"0.6043419",
"0.5663631",
"0.56081426",
"0.5521325",
"0.54811716",
"0.54338104",
"0.5369988",
"0.53258663",
"0.52765054",
"0.5261605",
"0.52138036",
"0.520943",
"0.5196618",
"0.5185886",
"0.5182538",
"0.5147707",
"0.508339"... | 0.0 | -1 |
Delete a scenario or initialise main scenario. | def _delete_series(self, name):
if name == self.MAIN:
self[self.MAIN] = self._tracker(self.MAIN).delete_all()
else:
self._tracker_dict.pop(name)
return self | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_delete(self):\n scenario = factories.Scenario(config='', status=Scenario.Status.INACTIVE)\n scenario.delete()\n self.assertEqual(scenario.status, Scenario.Status.INACTIVE)",
"def delete_entry(self, scenario_id):\n sql = self.delete(\"id\")\n self.cur.execute(sql, (scen... | [
"0.70573026",
"0.627147",
"0.5982057",
"0.5979097",
"0.59688073",
"0.590334",
"0.5860901",
"0.5817632",
"0.5726152",
"0.56649214",
"0.56325996",
"0.56311464",
"0.5622134",
"0.5598066",
"0.55954844",
"0.54979986",
"0.54341084",
"0.5432262",
"0.5421928",
"0.54125845",
"0.541217... | 0.0 | -1 |
The phases will be disabled and removed from summary. | def disable(self, phases, name="Main"):
self[name] = self._tracker(name).disable(phases)
return self | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def phase(self):\n pass",
"def phases(self):\n return self._phases",
"def unstudied_skills(self):\n undone = [task for task in self.skills if task not in self.done_skills]\n print(\"Hello \"+ str(self.user_name)+\" :)\\nHere are the skills that are still incomplete: \")\n for... | [
"0.56792605",
"0.56478065",
"0.5620661",
"0.55673957",
"0.5443464",
"0.54326624",
"0.53489864",
"0.5344841",
"0.5331238",
"0.5326407",
"0.5294865",
"0.52542996",
"0.52436554",
"0.52221566",
"0.52197176",
"0.51593006",
"0.51584166",
"0.50953543",
"0.5072874",
"0.5055029",
"0.5... | 0.65539813 | 0 |
The phases will be enabled and appear in summary. | def enable(self, phases, name="Main"):
self[name] = self._tracker(name).enable(phases)
return self | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def phases(self):\n return self._phases",
"def add_phases(self, phases):\n for phase in phases:\n self.add_phase(phase)",
"def GetPhase(self):\n ...",
"def phase(self):\n pass",
"def __str__(self):\n phases = '|'.join([phase.name for phase in PropertyPhase if s... | [
"0.6695582",
"0.6427158",
"0.6281006",
"0.6096473",
"0.6070386",
"0.59524006",
"0.587657",
"0.57588166",
"0.5757374",
"0.5603253",
"0.55110353",
"0.5493423",
"0.5374328",
"0.53662604",
"0.5365337",
"0.52662754",
"0.52411807",
"0.5227085",
"0.5207862",
"0.5207777",
"0.5199702"... | 0.6186084 | 3 |
Combine the sequential phases as one phase. New phase name will be automatically determined. | def combine(self, phases, name="Main", population=None, **kwargs):
self[name] = self._tracker(name).combine(
phases=phases, population=population, **kwargs)
return self | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def add_phases(self, phases):\n for phase in phases:\n self.add_phase(phase)",
"def as_phases(cls, *phase_names):\r\n return map(cls.as_phase, phase_names)",
"def combine_phase_data():\n print(\"Combining phase data...\")\n\n # create an empty data frame\n out_df = pd.DataFrame([]... | [
"0.661942",
"0.6211742",
"0.60045195",
"0.5823689",
"0.5768203",
"0.5760177",
"0.5691253",
"0.5614826",
"0.5538506",
"0.55089426",
"0.54660183",
"0.5461017",
"0.545222",
"0.53699225",
"0.53215224",
"0.52598274",
"0.52169806",
"0.5215779",
"0.52015465",
"0.5193688",
"0.5140901... | 0.6169472 | 2 |
Create a new phase with the change point. New phase name will be automatically determined. | def separate(self, date, name="Main", population=None, **kwargs):
self[name] = self._tracker(name).separate(
date=date, population=population, **kwargs)
return self | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def as_phase(cls, phase_name):\r\n return Phase(cls._namespace(phase_name))",
"def startPhase(self, phaseName):\n \n pass",
"def create_kill_chain_phase(\n kill_chain_name: str, phase_name: str\n) -> stix2.KillChainPhase:\n return stix2.KillChainPhase(kill_chain_name=kill_chain_name, phase_n... | [
"0.6967446",
"0.66886157",
"0.62629896",
"0.61891747",
"0.616434",
"0.59673655",
"0.5775541",
"0.5710485",
"0.56504726",
"0.5559992",
"0.55560404",
"0.5517846",
"0.5464363",
"0.5390099",
"0.53285253",
"0.52982134",
"0.5296981",
"0.5260513",
"0.5247765",
"0.50851226",
"0.50570... | 0.0 | -1 |
Summarize the series of phases and return a dataframe. | def _summary(self, name=None):
if name is None:
if len(self._tracker_dict.keys()) > 1:
dataframes = []
for (_name, tracker) in self._tracker_dict.items():
summary_df = tracker.series.summary()
summary_df = summary_df.rename_axis... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _create_phases(self):\n start_dates, end_dates = self._phase_range(self._change_dates)\n pop_list = [self.pop_dict[date] for date in start_dates]\n phase_series = PhaseSeries(\n self.dates[0], self.dates[-1], self.population, use_0th=self._use_0th\n )\n phase_itr =... | [
"0.5927809",
"0.5845708",
"0.58410937",
"0.5779259",
"0.570446",
"0.56737816",
"0.55832416",
"0.5576937",
"0.5566729",
"0.55379295",
"0.5534224",
"0.55257225",
"0.5509939",
"0.5498276",
"0.54978764",
"0.54487824",
"0.54207087",
"0.5397515",
"0.5391885",
"0.5317863",
"0.526836... | 0.5698778 | 5 |
Summarize the series of phases and return a dataframe. | def summary(self, columns=None, name=None):
df = self._summary(name=name).dropna(how="all", axis=1).fillna(self.UNKNOWN)
all_cols = df.columns.tolist()
# Columns were specified
if columns is not None:
self._ensure_list(columns, all_cols, name="columns")
return df.... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _create_phases(self):\n start_dates, end_dates = self._phase_range(self._change_dates)\n pop_list = [self.pop_dict[date] for date in start_dates]\n phase_series = PhaseSeries(\n self.dates[0], self.dates[-1], self.population, use_0th=self._use_0th\n )\n phase_itr =... | [
"0.5928529",
"0.58456147",
"0.5843049",
"0.5780569",
"0.57045394",
"0.56968504",
"0.5675235",
"0.5583464",
"0.55764633",
"0.5568089",
"0.5538528",
"0.55341923",
"0.55262",
"0.55122906",
"0.5499586",
"0.54993314",
"0.5446855",
"0.5420259",
"0.5398861",
"0.53932065",
"0.5318492... | 0.0 | -1 |
Perform SR trend analysis and set phases. | def trend(self, min_size=None, force=True, name="Main", show_figure=True, filename=None, **kwargs):
# Arguments
if "n_points" in kwargs.keys():
raise ValueError(
"@n_points argument is un-necessary"
" because the number of change points will be automatically d... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def CLI_trend( RFinfo,var, den, fireyear=False, plot=True, testmethod=\"OLS\"):\n\n\t# warn.warn(\n\t# \t'''\n\t# \tThis is currently only in alpha testing form\n\t# \ti'm going to using a simple trend test without\n\t# \tany consideration of significance. i used cdo\n\t# \tregres on copernicious NDVI data to star... | [
"0.57416147",
"0.5710031",
"0.56373745",
"0.5537419",
"0.54530376",
"0.54429436",
"0.54315424",
"0.5397674",
"0.5393723",
"0.5364632",
"0.53535897",
"0.5323438",
"0.5271401",
"0.52627134",
"0.5248195",
"0.522271",
"0.5220586",
"0.5207697",
"0.52046007",
"0.51960284",
"0.51750... | 0.55154175 | 4 |
Perform parameter estimation for each phases. | def estimate(self, model, phases=None, name="Main", n_jobs=-1, **kwargs):
if self.TAU in kwargs:
raise ValueError(
"@tau must be specified when scenario = Scenario(), and cannot be specified here.")
self.tau, self[name] = self._tracker(name).estimate(
model=model,... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def step(self):\n\n with torch.no_grad():\n for group in self.param_groups:\n lr = group[\"lr\"]\n for p in group[\"params\"]:\n\n if p.grad is None:\n continue\n\n lambda_square = self.mf.conf_factor(p, ke... | [
"0.6483096",
"0.60225064",
"0.59710395",
"0.59599286",
"0.59426624",
"0.59370613",
"0.59046566",
"0.5887623",
"0.5844492",
"0.5805915",
"0.5800615",
"0.5796302",
"0.5769528",
"0.57484573",
"0.5733885",
"0.5667784",
"0.56673306",
"0.56603837",
"0.5658184",
"0.5651971",
"0.5643... | 0.5701632 | 15 |
Return the estimator of the phase. | def phase_estimator(self, phase, name="Main"):
estimator = self._tracker_dict[name].series.unit(phase).estimator
if estimator is None:
raise UnExecutedError(f'Scenario.estimate(model, phases=["{phase}"], name={name})')
return estimator | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getPhase(phase):",
"def get_estimation(self):\n self.calculate_variables()\n if self.validate_preconditions():\n return self.estimate()\n else:\n return None",
"def phase(self):\n return self.__phase",
"def m_phase(self):\n return self._m_phase",
... | [
"0.6492449",
"0.6396096",
"0.6245162",
"0.6211135",
"0.6155881",
"0.6117626",
"0.59259164",
"0.5919323",
"0.578341",
"0.5782382",
"0.5680286",
"0.5680286",
"0.5680286",
"0.5667647",
"0.5667647",
"0.5618406",
"0.5616461",
"0.55942184",
"0.5574",
"0.55632395",
"0.55238044",
"... | 0.7582759 | 0 |
Show the history of optimization. | def estimate_history(self, phase, name="Main", **kwargs):
estimator = self.phase_estimator(phase=phase, name=name)
estimator.history(**kwargs) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def show_history_log(self):\n self.visual.print_enum(self.visual.history_log)",
"def history():",
"def print_history(self):\n self.game_started = False\n for state in self.history:\n self.__draw_board(state)",
"def history(self):\n alembic.command.history(self.alembic_c... | [
"0.7425332",
"0.71183753",
"0.6615228",
"0.6545977",
"0.64348227",
"0.6417473",
"0.6363705",
"0.6331068",
"0.6331068",
"0.6331068",
"0.6302988",
"0.6284421",
"0.6284421",
"0.6253503",
"0.61762786",
"0.616101",
"0.60512143",
"0.6035782",
"0.60021174",
"0.59667826",
"0.5963107"... | 0.0 | -1 |
Show the accuracy as a figure. | def estimate_accuracy(self, phase, name="Main", **kwargs):
estimator = self.phase_estimator(phase=phase, name=name)
estimator.accuracy(**kwargs) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def show_accuracy(self):\r\n return round(accuracy_score(self.actual, self.predicted),2)",
"def plot_accuracy(self):\n plot_title, img_title = self.prep_titles(\"\")\n test_legend = ['training data', 'test data']\n\n # Data for plotting x- and y-axis\n x = np.arange(1, CFG.EPOC... | [
"0.77534753",
"0.7277765",
"0.71191853",
"0.70241654",
"0.70184475",
"0.6948544",
"0.6932617",
"0.6889967",
"0.68846935",
"0.67477405",
"0.67329997",
"0.6720133",
"0.66735786",
"0.6602087",
"0.65589964",
"0.65424436",
"0.6538231",
"0.6513839",
"0.6496956",
"0.6495127",
"0.647... | 0.0 | -1 |
Simulate ODE models with set/estimated parameter values and show it as a figure. | def simulate(self, variables=None, phases=None, name="Main", y0_dict=None, **kwargs):
tracker = copy.deepcopy(self._tracker(name))
# Select phases
if phases is not None:
tracker.disable(phases=None)
tracker.enable(phases=phases)
# Simulation
try:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_edo_random():\n # if (__name__ == '__main__'):\n\n DBplot = True\n x = np.random.randn(102)\n x_e = gen_edo(x)\n\n # -------------------------------------------------------------------\n # plot\n # -------------------------------------------------------------------\n if DBplot:\n ... | [
"0.6467573",
"0.6445182",
"0.63493",
"0.62701017",
"0.61718446",
"0.6158063",
"0.6137683",
"0.61052775",
"0.6049931",
"0.6034813",
"0.6014029",
"0.59745497",
"0.5957386",
"0.59391916",
"0.5928305",
"0.5925902",
"0.5917189",
"0.5912627",
"0.59121877",
"0.5903403",
"0.5875804",... | 0.0 | -1 |
Get the parameter value of the phase. | def get(self, param, phase="last", name="Main"):
df = self.summary(name=name)
if param not in df.columns:
raise KeyError(f"@param must be in {', '.join(df.columns)}.")
if phase == "last":
phase = df.index[-1]
return df.loc[phase, param] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getValue(self):\n return _libsbml.Parameter_getValue(self)",
"def getPhase(phase):",
"def phase(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"phase\")",
"def phase(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"phase\")",
"def phase(self) -> O... | [
"0.71726173",
"0.6958654",
"0.69486773",
"0.69486773",
"0.69486773",
"0.68258613",
"0.67630255",
"0.6711935",
"0.667737",
"0.66741765",
"0.66447204",
"0.66141695",
"0.6555716",
"0.65409654",
"0.6483767",
"0.6476454",
"0.6470208",
"0.6468714",
"0.6460967",
"0.64353555",
"0.641... | 0.60780966 | 38 |
Return the subset of summary dataframe to select the target of parameter history. | def _param_history(self, targets, name):
series = self._tracker_dict[name].series
model_set = {unit.model for unit in series}
model_set = model_set - set([None])
parameters = self.flatten([m.PARAMETERS for m in model_set])
day_params = self.flatten([m.DAY_PARAMETERS for m in mode... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def choose_target(self, df, target_name: str):\n \n return self.df[target_name]",
"def get_targets(self, df):\n return df.iloc[:, self.target_col]",
"def get_feat_and_target(df, target):\n x = df.drop(target, axis = 1)\n y = df[target]\n return x, y",
"def _history(self, target, phases=... | [
"0.6120377",
"0.5767605",
"0.5637257",
"0.5635355",
"0.55291855",
"0.5380826",
"0.534724",
"0.5300844",
"0.52480835",
"0.52071494",
"0.51738966",
"0.51618147",
"0.51550215",
"0.51391673",
"0.49831647",
"0.49728945",
"0.49236575",
"0.49220812",
"0.4913259",
"0.49054223",
"0.49... | 0.66820365 | 0 |
Return subset of summary and show a figure to show the history. | def param_history(self, targets=None, name="Main", divide_by_first=True,
show_figure=True, filename=None, show_box_plot=True, **kwargs):
self._tracker(name)
# Select target to show
df = self._param_history(targets, name)
# Divide by the first phase parameters
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def results_summary(log, geolog, show=True):\n fig, axes = plt.subplots(nrows=3, ncols=1, sharex=True)\n okw = {'c':'purple', 'ls':'--', 'lw':1.5}\n mkw = {'c':'C0', 'ls':'-', 'lw':1.5}\n geolog.add_ae_quicklook(plot_obs=True, target=axes[0], val='AU', obs_kwargs=okw,\n add_l... | [
"0.63200307",
"0.6019806",
"0.59494907",
"0.5911857",
"0.5827589",
"0.580583",
"0.58054876",
"0.5796425",
"0.575747",
"0.575252",
"0.57473797",
"0.57211274",
"0.571764",
"0.5683871",
"0.5652965",
"0.5650766",
"0.5624043",
"0.5621022",
"0.5616493",
"0.5606518",
"0.5578285",
... | 0.0 | -1 |
Adjust the last end dates of the registered scenarios, if necessary. | def adjust_end(self):
# The current last end dates
current_dict = {
name: self._ensure_date(tracker.last_end_date())
for (name, tracker) in self._tracker_dict.items()}
# Adjusted end date
adjusted_str = max(current_dict.values()).strftime(self.DATE_FORMAT)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def correct_list_to_end_date(self):\n if len(self.change_events):\n event_index = len(self.change_events) - 1\n while ((event_index > -1) and (self.change_events[event_index].date_ordinal >\n self.end_date_ordinal)):\n event = self.change_events[event_i... | [
"0.67244375",
"0.63298994",
"0.6279191",
"0.62078863",
"0.6186914",
"0.61678594",
"0.5923968",
"0.5894802",
"0.58839434",
"0.58004695",
"0.58004695",
"0.58004695",
"0.58004695",
"0.58004695",
"0.58004695",
"0.58004695",
"0.58004695",
"0.5626302",
"0.55569535",
"0.5550756",
"0... | 0.7241436 | 0 |
Get the history of parameters for the scenario. | def _track_param(self, name):
df = self.summary(name=name).replace(self.UNKNOWN, None)
# Date range to dates
df[self.START] = pd.to_datetime(df[self.START])
df[self.END] = pd.to_datetime(df[self.END])
df[self.DATE] = df[[self.START, self.END]].apply(
lambda x: pd.date... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_history(self):\n return self.history",
"def get_history(self):\n return self.__history[:]",
"def History(self):\n return self.historydict.get('history', [])",
"def get_value_history(self):\n return self.value_history",
"def get_params_snapshot(self):\n ...",
"def hi... | [
"0.6669746",
"0.6486813",
"0.6419645",
"0.63908446",
"0.63483167",
"0.63360363",
"0.6301121",
"0.6276203",
"0.62529445",
"0.6202838",
"0.6188639",
"0.61855346",
"0.6182134",
"0.6155728",
"0.6150554",
"0.6150554",
"0.61322916",
"0.613136",
"0.61156565",
"0.60952926",
"0.608258... | 0.0 | -1 |
Show values of parameters and variables in one dataframe for the scenario. | def _track(self, phases=None, name="Main", y0_dict=None):
sim_df = self.simulate(phases=phases, name=name, y0_dict=y0_dict, show_figure=False)
param_df = self._track_param(name=name)
return pd.merge(
sim_df, param_df, how="inner", left_on=self.DATE, right_index=True, sort=True) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def values(self, parameter):\n self.values = self.dataframe[parameter]\n print(self.values)",
"def print_info(self):\n\n n_metabolites = len(self.metabolites)\n n_reactions = len(self.reactions)\n n_constraints = len(self.constraints)\n n_variables = len(self.variables)\... | [
"0.65283614",
"0.6190751",
"0.60253143",
"0.59300756",
"0.5904161",
"0.5842688",
"0.5835276",
"0.58245564",
"0.5805679",
"0.5796759",
"0.5791026",
"0.570495",
"0.57009065",
"0.5689903",
"0.5675194",
"0.56727546",
"0.5651568",
"0.5614041",
"0.5581958",
"0.55624795",
"0.5557544... | 0.0 | -1 |
Show values of parameters and variables in one dataframe. | def track(self, phases=None, with_actual=True, y0_dict=None):
dataframes = []
append = dataframes.append
for name in self._tracker_dict.keys():
df = self._track(phases=phases, name=name, y0_dict=y0_dict)
df.insert(0, self.SERIES, name)
append(df)
if wi... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def print_info(self):\n\n n_metabolites = len(self.metabolites)\n n_reactions = len(self.reactions)\n n_constraints = len(self.constraints)\n n_variables = len(self.variables)\n\n info = pd.DataFrame(columns=['value'])\n info.loc['name'] = self.name\n info.loc['desc... | [
"0.6696075",
"0.6677119",
"0.6282688",
"0.61132765",
"0.6078284",
"0.59988284",
"0.5986452",
"0.59587187",
"0.5925746",
"0.59255815",
"0.58896726",
"0.5857535",
"0.584836",
"0.5839311",
"0.58278835",
"0.5785681",
"0.57587534",
"0.5720079",
"0.5715537",
"0.56835276",
"0.568344... | 0.0 | -1 |
Show the history of variables and parameter values to compare scenarios. | def _history(self, target, phases=None, with_actual=True, y0_dict=None):
# Include actual data or not
with_actual = with_actual and target in self.VALUE_COLUMNS
# Get tracking data
df = self.track(phases=phases, with_actual=with_actual, y0_dict=y0_dict)
if target not in df.column... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def show_history_log(self):\n self.visual.print_enum(self.visual.history_log)",
"def history():",
"def show_variables(self):\r\n\r\n variablelist = [(x_temp,self.variables[x_temp]) for x_temp in sorted(self.variables.keys())]\r\n display.noteprint(('/C/ '+labels.VARIABLES.upper(), EOL.join... | [
"0.66284317",
"0.60000753",
"0.58660567",
"0.5847181",
"0.5816811",
"0.5788459",
"0.57120436",
"0.5690894",
"0.56844974",
"0.56740534",
"0.5656841",
"0.5607398",
"0.5599281",
"0.5539353",
"0.5526882",
"0.5502886",
"0.549579",
"0.5481899",
"0.5479519",
"0.5479519",
"0.5479519"... | 0.5110203 | 78 |
Show the history of variables and parameter values to compare scenarios. | def history(self, target, phases=None, with_actual=True, y0_dict=None, **kwargs):
df = self._history(target=target, phases=phases, with_actual=with_actual, y0_dict=y0_dict)
df.dropna(subset=[col for col in df.columns if col != self.ACTUAL], inplace=True)
if target == self.RT:
ylabel ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def show_history_log(self):\n self.visual.print_enum(self.visual.history_log)",
"def history():",
"def show_variables(self):\r\n\r\n variablelist = [(x_temp,self.variables[x_temp]) for x_temp in sorted(self.variables.keys())]\r\n display.noteprint(('/C/ '+labels.VARIABLES.upper(), EOL.join... | [
"0.66309536",
"0.60015976",
"0.58661586",
"0.5848",
"0.58179206",
"0.5790615",
"0.5713213",
"0.5692382",
"0.56855243",
"0.56744015",
"0.5657676",
"0.56071436",
"0.5599695",
"0.554003",
"0.55277646",
"0.55041164",
"0.5496439",
"0.5482356",
"0.54818606",
"0.54818606",
"0.548186... | 0.0 | -1 |
Show change rates of parameter values in one figure. We can find the parameters which increased/decreased significantly. | def history_rate(self, params=None, name="Main", **kwargs):
df = self._track_param(name=name)
model = self._tracker(name).last_model
cols = list(set(df.columns) & set(model.PARAMETERS))
if params is not None:
if not isinstance(params, (list, set)):
raise TypeE... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def update_parameters(self, event, gamma='2.4',\n gain=\"scale_default_value\"):\n print(\"gamma={}, gain={}\".format(gamma, gain))\n sys.stdout.flush()\n self.update_draw(gamma, gain)",
"def show_parameters(self):\n with np.printoptions(precision=3, suppress=... | [
"0.63222736",
"0.60968167",
"0.602045",
"0.600945",
"0.5968336",
"0.5863816",
"0.5856223",
"0.58190936",
"0.57916284",
"0.5782523",
"0.5754305",
"0.5710678",
"0.5689282",
"0.5687477",
"0.56859684",
"0.5658603",
"0.56582314",
"0.5644833",
"0.5638262",
"0.56351125",
"0.56246054... | 0.64278245 | 0 |
Perform retrospective analysis. Compare the actual series of phases (control) and series of phases with specified parameters (target). | def retrospective(self, beginning_date, model, control="Main", target="Target", **kwargs):
param_dict = {k: v for (k, v) in kwargs.items() if k in model.PARAMETERS}
est_kwargs = dict(kwargs.items() - param_dict.items())
# Control
self.clear(name=control, include_past=True)
self.t... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def analyse_results(self, \n target_dir, \n param_file = \"TRAINED_PARAMS_END.model\",\n w_norm_file = \"W_NORMS.dat\",\n num_to_test = 100,\n get_means_from = {\n 'N... | [
"0.60408705",
"0.58758664",
"0.5644826",
"0.5543238",
"0.54795575",
"0.544844",
"0.5408462",
"0.5391602",
"0.53793794",
"0.5373309",
"0.53686464",
"0.53674304",
"0.5345663",
"0.5332743",
"0.53223336",
"0.5291442",
"0.5291442",
"0.52801085",
"0.52746636",
"0.5273039",
"0.52550... | 0.6092217 | 0 |
Evaluate accuracy of phase setting and parameter estimation of all enabled phases all some past days. | def score(self, variables=None, phases=None, past_days=None, name="Main", y0_dict=None, **kwargs):
tracker = self._tracker(name)
if past_days is not None:
if phases is not None:
raise ValueError("@phases and @past_days cannot be specified at the same time.")
past_... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def estimate_accuracy(self, phase, name=\"Main\", **kwargs):\n estimator = self.phase_estimator(phase=phase, name=name)\n estimator.accuracy(**kwargs)",
"def _run_eval_phase(self, statistics, agent_type='active'):\n # Perform the evaluation phase -- no learning.\n self._agent.eval_mode = True... | [
"0.58308494",
"0.5690322",
"0.55964804",
"0.55690634",
"0.5507954",
"0.5496139",
"0.54829794",
"0.5474401",
"0.5468245",
"0.54412717",
"0.54111433",
"0.5407387",
"0.5388276",
"0.53425944",
"0.53318614",
"0.5330115",
"0.531091",
"0.53101224",
"0.530839",
"0.530558",
"0.5292451... | 0.0 | -1 |
Estimate delay period [days], assuming the indicator impact on the target value with delay. The average of representative value (percentile) and will be returned. | def estimate_delay(self, oxcgrt_data=None, indicator="Stringency_index",
target="Confirmed", percentile=25, limits=(7, 30), **kwargs):
min_size, max_days = limits
# Register OxCGRT data
if oxcgrt_data is not None:
warnings.warn(
"Please use Scen... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def estimate(values, target):\n\n # next time\n # diff(values)\n\n\n return 1.",
"def get_delay(self):\n if self.msg_tn == self.last_msg_tn:\n return\n if not self.delays:\n return\n n = len(self.delays)\n\n mean = sum(self.delays) / n\n std_dev =... | [
"0.61240596",
"0.58249533",
"0.5736352",
"0.5720196",
"0.551994",
"0.5515488",
"0.5515488",
"0.5449673",
"0.5441243",
"0.5343859",
"0.52559906",
"0.52168494",
"0.5213883",
"0.5213228",
"0.5210776",
"0.5206571",
"0.5188828",
"0.51886564",
"0.5151351",
"0.514995",
"0.51415145",... | 0.6570062 | 0 |
Fit regressors to predict the parameter values in the future phases, assuming that indicators will impact on ODE parameter values/the number of cases with delay. Please refer to covsirphy.RegressionHander class. | def fit(self, oxcgrt_data=None, name="Main", delay=None, removed_cols=None, metric=None, metrics="R2", **kwargs):
metric = metric or metrics
# Clear the future phases
self.clear(name=name, include_past=False)
# Register OxCGRT data
if oxcgrt_data is not None:
warnings... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def fit(self, X, treatment, y, p=None):\r\n # Train outcome models\r\n self.model_mu_c.fit(X[treatment == 0], y[treatment == 0])\r\n self.model_mu_t.fit(X[treatment == 1], y[treatment == 1])\r\n\r\n # Calculate variances and treatment effects\r\n if self.is_regressor:\r\n ... | [
"0.612984",
"0.61278903",
"0.60842425",
"0.60745496",
"0.6031944",
"0.5868699",
"0.58676064",
"0.5849834",
"0.58083665",
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"0.5709516",
"0.56974554",
"0.567379",
"0.5673005",
"0.5670449",
"0.56611407",
"0.56489986",
"0.564248... | 0.0 | -1 |
Predict parameter values of the future phases using Elastic Net regression with OxCGRT scores, assuming that OxCGRT scores will impact on ODE parameter values with delay. New future phases will be added (overwritten). | def predict(self, days=None, name="Main"):
# Arguments
if name not in self._reghandler_dict:
raise UnExecutedError(f"Scenario.fit(name={name})")
# Prediction with regression model
handler = self._reghandler_dict[name]
df = handler.predict()
# -> end_date/param... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def predict_conditional(self, params):\n params = np.array(params, ndmin=1)\n\n # Prediction is based on:\n # y_t = x_t beta^{(S_t)} +\n # \\phi_1^{(S_t)} (y_{t-1} - x_{t-1} beta^{(S_t-1)}) + ...\n # \\phi_p^{(S_t)} (y_{t-p} - x_{t-p} beta^{(S_t-p)}) + eps_t\n ... | [
"0.617037",
"0.61178964",
"0.6064572",
"0.59373784",
"0.5898719",
"0.5859526",
"0.58379626",
"0.5798949",
"0.5782605",
"0.5697326",
"0.5657076",
"0.5566286",
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"0.5450581",
"0.5440786",
"0.54234713",
"0.54088026",
"0.5388653",
"0.53765047",
"0.5374445",
"0.53656775... | 0.0 | -1 |
Predict parameter values of the future phases using Elastic Net regression with OxCGRT scores, assuming that OxCGRT scores will impact on ODE parameter values with delay. New future phases will be added (overwritten). | def fit_predict(self, oxcgrt_data=None, name="Main", **kwargs):
self.fit(oxcgrt_data=oxcgrt_data, name=name, **find_args(Scenario.fit, **kwargs))
self.predict(name=name, **find_args(Scenario.predict, **kwargs))
return self | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def predict_conditional(self, params):\n params = np.array(params, ndmin=1)\n\n # Prediction is based on:\n # y_t = x_t beta^{(S_t)} +\n # \\phi_1^{(S_t)} (y_{t-1} - x_{t-1} beta^{(S_t-1)}) + ...\n # \\phi_p^{(S_t)} (y_{t-p} - x_{t-p} beta^{(S_t-p)}) + eps_t\n ... | [
"0.61692065",
"0.6117616",
"0.60650826",
"0.59379566",
"0.5898542",
"0.5858027",
"0.58376944",
"0.57994056",
"0.57826334",
"0.5697629",
"0.5657438",
"0.5566829",
"0.54680365",
"0.54501426",
"0.544141",
"0.5422653",
"0.54094625",
"0.53875375",
"0.5375797",
"0.5373882",
"0.5365... | 0.0 | -1 |
Read in comments data as dataframe from mysql database | def get_df_from_db(localhost, username, password, dbname, tbname, fields=None, chunksize=None, time_field=None, start_time=None, end_time=None):
# con = pymysql.connect(host=localhost, user=username, password=password, database=dbname, charset='utf8', use_unicode=True)
connect_string = "mysql+pymysql://{}:{}@{}... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_db_data(self, sql_string):\n connection_string = f\"\"\"\n host='{self.host}' \n dbname='{self.db_name}' \n user='{self.user}' \n password='{self.password}' \n port='{self.port}'\n \"\"\"\n\n with psycopg2.connect(connection_string) as connection:\n ... | [
"0.6610287",
"0.65699345",
"0.6364169",
"0.6285126",
"0.62495816",
"0.6120763",
"0.6112718",
"0.6107892",
"0.605614",
"0.59847057",
"0.59784424",
"0.59216505",
"0.5917459",
"0.5907499",
"0.5883649",
"0.58613175",
"0.5850069",
"0.5832138",
"0.5799104",
"0.57808924",
"0.5768387... | 0.5464838 | 61 |
Parse html tages in sentences | def parse_html_tag(sentences, html_tag_file):
tag_list = file2tuple_list(html_tag_file, ",")
for key, value in tag_list:
for index in range(len(sentences)):
try:
sentences[index] = re.sub(key, value, sentences[index])
except Exception, e:
logging.e... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __yahoo_parse_text(self, content):\n text = ''\n # Process all paragraphs.\n paragraphs = content.find_all('p')\n for par in paragraphs:\n text += '<p>' + par.getText(separator=' ') + '</p>'\n # Remove all extra whitespace (single space remains).\n text = ' ... | [
"0.65768903",
"0.6414939",
"0.6314085",
"0.6304649",
"0.6183976",
"0.61760587",
"0.61702543",
"0.6127106",
"0.6099596",
"0.60908455",
"0.60301",
"0.6008985",
"0.6008985",
"0.59992546",
"0.59763205",
"0.5959324",
"0.59592533",
"0.595774",
"0.59353596",
"0.59334815",
"0.5910679... | 0.644006 | 1 |
Format the chinese sentences remove unwanted punctuations. Split sentences by each delimiter | def sentence_splitter(sentences):
# only accept list/ndarray or string/unicode type
try:
assert isinstance(sentences, list) or isinstance(sentences, np.ndarray) or isinstance(sentences, str) or isinstance(sentences, unicode)
except:
logging.error("Split sentences failed! Only list/ndarray or... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def preprocess_sentence(raw):\r\n\t\r\n\t# raw = re.sub(r\"[\\x80-\\xff]\",\" \",raw)\r\n\t\r\n\traw = regex_punct.sub(' ',raw)\r\n\traw = raw.strip()\r\n\traw = raw.lower()\r\n\t\r\n\twords = nltk.word_tokenize(raw)\r\n\twords = [replace_punctuation(w) for w in words if not w in stopwords and len(w) > 1]\r\n\t\r\... | [
"0.68802744",
"0.673855",
"0.66493034",
"0.65651876",
"0.65555406",
"0.6473244",
"0.6455792",
"0.64497674",
"0.64104927",
"0.63669884",
"0.6339846",
"0.63106835",
"0.63015205",
"0.6296337",
"0.62898695",
"0.62860185",
"0.62840253",
"0.6282841",
"0.62685126",
"0.62685126",
"0.... | 0.0 | -1 |
Replace description after meta updating | def add_meta(self, post, *args, **kwargs):
super(Command, self).add_meta(post, *args, **kwargs)
post.gen_description = False
post.description = description_from_content(post)
post.save() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def description(self, new_description):\r\n self.set({\"description\": new_description})",
"def get_meta_description(self):\n md = self.meta_description.replace(\"<title>\", self.title)\n return md.replace(\"<short-text>\", self.short_text)",
"def set_description(self, desc: str) -> None:\... | [
"0.7612998",
"0.7401941",
"0.7334977",
"0.722761",
"0.7192312",
"0.70661885",
"0.7027152",
"0.6967204",
"0.688855",
"0.68845445",
"0.68065006",
"0.6792907",
"0.67552644",
"0.67239",
"0.67174524",
"0.67133564",
"0.66825515",
"0.6681796",
"0.66503423",
"0.6635033",
"0.6635033",... | 0.6486738 | 72 |
Write data to buffer with buffer protocol | def test_write_bufferprotocol(ctx):
data = array('f', [1, 2, 3, 4])
buff = ctx.buffer(data=data)
assert buff.read() == data.tobytes() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def write(self, data):\n self.buffer.write(data)\n self.offset += len(data)",
"def write(self, data):\n\t\tself.outputbuffer.write(data)",
"def pack(self,buffer):\n buffer.append(self.data)",
"def write(self, data):\n self.buffer.append(data)\n while self.push():\n pass"... | [
"0.77599627",
"0.7405367",
"0.7294376",
"0.71941775",
"0.70991886",
"0.70991886",
"0.70752317",
"0.7066335",
"0.701389",
"0.6976252",
"0.6973175",
"0.6920095",
"0.68100977",
"0.67799836",
"0.6749012",
"0.670543",
"0.66522443",
"0.664747",
"0.66468465",
"0.6641118",
"0.6630188... | 0.77639097 | 0 |
Index/home page of the website | def index():
if request.method =='POST':
session["place"] = request.form["place"] # Stores "place" input in session
return redirect(url_for("nearest"))
else:
return render_template("index.html") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def home():\n\n\treturn render_template('index.html', title='Home Page',\n\t\t\t\t\t\t year=datetime.now().year)",
"def homepage():\n return render_template('home/index.html', \n title=\"Bem vindo!\")",
"def home():\r\n return render_template(\r\n 'index.html',\r\n ... | [
"0.87146896",
"0.8548914",
"0.85075265",
"0.8418279",
"0.8418279",
"0.8418279",
"0.8418279",
"0.8418279",
"0.8418279",
"0.8418279",
"0.8418279",
"0.8418279",
"0.8418279",
"0.83925295",
"0.83726525",
"0.83699334",
"0.83652914",
"0.8361378",
"0.8359413",
"0.8331822",
"0.8314265... | 0.0 | -1 |
If place has a nearby MBTA, returns the page with that info, else (or if any other errors), returns an error page | def nearest():
try:
text = find_stop_near(session["place"])
return render_template("place.html", text = text)
except:
return render_template('error.html') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def find_near_location():\n return render_template(\"location.html\", latitude=None, longitude=None,\n list_stops=None)",
"def scrapping():\r\n\r\n data_cust = {}\r\n #token, latitude, longitude, name, place_id, types_places, vicinity = [],[],[],[],[],[], []\r\n\r\n apik = '... | [
"0.5860572",
"0.5601886",
"0.556867",
"0.53483754",
"0.534454",
"0.5319231",
"0.5303751",
"0.5295789",
"0.52449685",
"0.5211668",
"0.51778597",
"0.51622933",
"0.51574767",
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"0.51280147",
"0.5089487",
"0.50833637",
"0.5079039",
"0.5062072",
"0.50607216",
"0.502367... | 0.64868116 | 0 |
Open the editing student window. | def on_edit_students_select(self):
edit_window = Students()
edit_window.exec_() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def mouseDoubleClickEvent(self, e):\n self.win = items.edit.Edit(self)\n self.win.setModal(True)\n self.win.show()",
"def edit():",
"def edit_user(self):\n from editWindow import EditPlayer\n self.edit = EditPlayer(self.lang, self.result_table.currentItem().text())\n s... | [
"0.6857695",
"0.675959",
"0.6755998",
"0.6650021",
"0.6358983",
"0.6331608",
"0.62601465",
"0.62351334",
"0.619309",
"0.6178386",
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"0.61091745",
"0.60836834",
"0.60471237",
"0.6022824",
"0.6017019",
"0.5987004",
"0.5984732",
"0.59686553",
"0.5962103",
"0.5930994",... | 0.8309543 | 0 |
Update views when the date is changed. | def on_date_change(self):
self.date = self.ui.calendarWidget.selectedDate()
self.update_views() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _dates_observer(self, state):\n # Update all dates\n self._update_dates_from_history()",
"def update_views(self):\n # Get correct date format\n self.date_string = self.date.toString(self.date_format)\n \n # Clear Models\n self.availModel.clear()\n self.... | [
"0.6494887",
"0.64303577",
"0.6425736",
"0.63661104",
"0.62049454",
"0.6182257",
"0.6125983",
"0.6066578",
"0.59537566",
"0.5901415",
"0.58907646",
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"0.57707644",
"0.57694495",
"0.5768196",
"0.57058334",
"0.56830543",
"0.56830543",
"0.5... | 0.68725854 | 0 |
Refresh the students available and attending for the given date. | def update_views(self):
# Get correct date format
self.date_string = self.date.toString(self.date_format)
# Clear Models
self.availModel.clear()
self.attendModel.clear()
for student in self.db.get_attendance_for_date(self.date_string):
if stu... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def update(self, date):\r\n self.date = date",
"def _load_student_record(self, student_key, students_attendance_data):\n student = SchoolDB.models.Student.get(db.Key(student_key))\n if (student):\n student.attendance.save_multiple_dates(\n self.dates, students_atten... | [
"0.5911469",
"0.55838823",
"0.5495462",
"0.5478598",
"0.54697925",
"0.5468061",
"0.529754",
"0.5211721",
"0.5158502",
"0.51371205",
"0.51294976",
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"0.50580114",
"0.50310814",
"0.50108874",
"0.4964432",
"0.4947712",
"0.4947712",
"0.4945... | 0.65859437 | 0 |
Move student from available to attended list. | def on_add_clicked(self):
selected_indexes = self.ui.availListView.selectedIndexes()
for index in selected_indexes:
row = self.availModel.itemFromIndex(index).row()
#rowList = self.availModel.takeRow(row)
student = self.availModel.item(row, 0).text()
sid =... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def swap_students(chambers, allcourses, student_list, schedule, swapcourse = None, sem1 = None, sem2 = None, prac1 = None, prac2 = None, student1 = None, student2 = None):\n\n\tif swapcourse == None:\n\n\t\t# pick course to swap students in\n\t\tswapcourse = random.randint(0, len(allcourses) - 1)\n\n\t\t# pick new... | [
"0.5389811",
"0.53782946",
"0.5337685",
"0.52989775",
"0.52790767",
"0.5223609",
"0.52139056",
"0.5191543",
"0.518432",
"0.5161362",
"0.5161182",
"0.51304746",
"0.50658983",
"0.50332844",
"0.50159574",
"0.5003529",
"0.4999005",
"0.49856463",
"0.49727905",
"0.49342826",
"0.493... | 0.4954204 | 19 |
Move student from attended to available list. | def on_remove_clicked(self):
selected_indexes = self.ui.attendListView.selectedIndexes()
for index in selected_indexes:
row = self.attendModel.itemFromIndex(index).row()
student = self.attendModel.item(row, 0).text()
sid = self.attendModel.item(row, 1).text()
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def delete_an_entry(self):\n target_list = self.find_student()\n\n if not len(target_list):\n print('There is no contents to show')\n else:\n print('You selected the list below.')\n self.print_dataframe(target_list)\n opt = self.input_options(['y', '... | [
"0.5481265",
"0.53063625",
"0.52547395",
"0.5241378",
"0.5201852",
"0.51910436",
"0.50918496",
"0.5090114",
"0.50880194",
"0.5077619",
"0.50550723",
"0.5022331",
"0.5001788",
"0.49973884",
"0.4996648",
"0.49769428",
"0.49731165",
"0.49215776",
"0.49120808",
"0.48781928",
"0.4... | 0.54175085 | 1 |
Report a time_t as an ISO8601 time format. Defaults to now. | def timet_iso(t=time.time()):
return datetime.datetime.now().isoformat()[0:19] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def iso_date(self, t=None):\n if t is None:\n t = time.time()\n time_str = time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime(t))\n\n return time_str",
"def formatted_time() -> datetime.datetime:\r\n return datetime.datetime.now()",
"def isoformat(self, timespec=\"auto\"):\n... | [
"0.7398466",
"0.706633",
"0.69513315",
"0.68297285",
"0.6829152",
"0.67798644",
"0.67408025",
"0.6729839",
"0.66987705",
"0.6689164",
"0.6689164",
"0.66699064",
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"0.65425014",
"0.6505604",
"0.64849293",
"0.64819497",
"0.6459066",
"0.644515",
"0.64373... | 0.7495122 | 0 |
Execute a SQL command and return the the iterator | def execute(self, cmd, *args, debug=False, **kwargs):
if self.debug or debug:
print(f"execute: {cmd}", file=sys.stderr)
t0 = time.time()
try:
res = self.conn.cursor().execute(cmd, *args, **kwargs)
except (sqlite3.Error, pymysql.MySQLError) as e:
pr... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def execute_sql(self, result_type=MULTI):\r\n try:\r\n sql, params = self.as_sql()\r\n #import pdb; pdb.set_trace()\r\n if not sql:\r\n raise EmptyResultSet\r\n except EmptyResultSet:\r\n if result_type == MULTI:\r\n return ite... | [
"0.7440192",
"0.74239373",
"0.73914313",
"0.722137",
"0.7182937",
"0.71828544",
"0.71723294",
"0.7156026",
"0.713149",
"0.71047634",
"0.7044",
"0.70278555",
"0.70177805",
"0.70008665",
"0.69808424",
"0.69353414",
"0.69314957",
"0.6915899",
"0.6900565",
"0.6892338",
"0.6850855... | 0.0 | -1 |
Create the schema if it doesn't exist. | def create_schema(self, schema, *, debug=False):
c = self.conn.cursor()
for line in schema.split(";"):
line = line.strip()
if len(line)>0:
if self.debug or debug:
print(f"{line};", file=sys.stderr)
try:
c.exe... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_schema(schema): \n\n query = \"CREATE SCHEMA IF NOT EXISTS {}\".format(schema)\n qdb.execute(query)",
"def create_schema(self, schema: str):\n return",
"def create_schema_if_not_exist(self, schema):\n create_schema_sql = \"\"\"\n CREATE SCHEMA IF NOT EXISTS \"{0}\"... | [
"0.7715596",
"0.7518038",
"0.7496806",
"0.7403349",
"0.73665285",
"0.7342637",
"0.7317907",
"0.7250138",
"0.7241323",
"0.7136049",
"0.6929685",
"0.6881734",
"0.67928004",
"0.6728392",
"0.67238414",
"0.6689868",
"0.6688691",
"0.66564953",
"0.66428924",
"0.65727705",
"0.6544",
... | 0.62192965 | 45 |
Execute a SQL query and return the first line | def execselect(self, sql, vals=()):
self.conn.ping()
c = self.conn.cursor()
c.execute(sql, vals)
return c.fetchone() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def query_one(self, sql: str) -> Any:\n with self.connection.cursor() as cursor:\n self.connection.ping(reconnect=True)\n cursor.execute(sql)\n row = cursor.fetchone()\n self.connection.commit()\n return row",
"def fetch_one(self, sql):\n curs ... | [
"0.7780869",
"0.76390004",
"0.7576297",
"0.745161",
"0.7357494",
"0.71004975",
"0.7049767",
"0.70493615",
"0.6992009",
"0.69467133",
"0.6897321",
"0.6816311",
"0.68002194",
"0.67960036",
"0.67857444",
"0.67763156",
"0.6762942",
"0.6745807",
"0.6711482",
"0.66929317",
"0.66208... | 0.5899251 | 65 |
Loads the bash environment variables specified by 'export NAME=VALUE' into a dictionary and returns it. Take whatever variables not in that file from the Linux environment. | def GetBashEnvFromFile(this, filename):
DB_RE = re.compile("export (.+)=(.+)")
ret = {}
if filename is not None:
with open( filename, "r" ) as f:
for line in f:
m = DB_RE.search(line.strip())
if m:
name... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def load_env(env_files):\n env = {}\n for env_file in env_files:\n with open(env_file) as f:\n for line in f:\n if line and line[0] != \"#\":\n try:\n index = line.index(\"=\")\n env[line[:index].strip()] = line... | [
"0.6895845",
"0.6810905",
"0.6783951",
"0.6769671",
"0.67103505",
"0.6698789",
"0.6692306",
"0.66548026",
"0.6652638",
"0.6608575",
"0.65690833",
"0.6533156",
"0.65055305",
"0.6478919",
"0.6466052",
"0.64383376",
"0.6395389",
"0.6377702",
"0.6372465",
"0.63347304",
"0.6331138... | 0.78436303 | 0 |
Returns a DBMySQLAuth formed by reading MYSQL_USER, MYSQL_PASSWORD, MYSQL_HOST and MYSQL_DATABASE envrionemnt variables from a bash script. Caches by default | def FromBashEnvFile(this, filename, cache=True):
if cache and filename in this.auth_cache:
return this.auth_cache[filename]
env = DBMySQLAuth.GetBashEnvFromFile(filename)
try:
auth = DBMySQLAuth(host = env[MYSQL_HOST],
user = env[MYSQL_USER]... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_db() -> mysql.connector.connection.MySQLConnection:\n username = getenv('PERSONAL_DATA_DB_USERNAME')\n password = getenv('PERSONAL_DATA_DB_PASSWORD')\n host = getenv('PERSONAL_DATA_DB_HOST')\n db = getenv('PERSONAL_DATA_DB_NAME')\n\n conect = mysql.connector.connection.MySQLConnection(\n ... | [
"0.6515381",
"0.6305752",
"0.59653795",
"0.59097266",
"0.59001666",
"0.5899042",
"0.58714527",
"0.5821578",
"0.58064324",
"0.5776066",
"0.5764474",
"0.57294273",
"0.57264",
"0.56820935",
"0.5681678",
"0.5665837",
"0.5655249",
"0.5650574",
"0.56354046",
"0.5626327",
"0.5606605... | 0.68844616 | 0 |
Returns from the section of a config file | def FromConfig(section, debug=None):
try:
return DBMySQLAuth(host = section[MYSQL_HOST],
user = section[MYSQL_USER],
password = section[MYSQL_PASSWORD],
database = section[MYSQL_DATABASE],
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_conf_by_section(self, section):\n try:\n return get_conf(self.conf_file)[section]\n except:\n return None",
"def get(self, name, section=section_default):\n return self.config[section][name]",
"def read_section(self, configuration_file=\"./conf.txt\", section=... | [
"0.7337729",
"0.72794765",
"0.7240779",
"0.72226465",
"0.6938679",
"0.6937426",
"0.686506",
"0.6799909",
"0.6793152",
"0.6790203",
"0.67745614",
"0.67341024",
"0.67144006",
"0.66940904",
"0.6689701",
"0.66669035",
"0.6666781",
"0.6651995",
"0.6640001",
"0.6639232",
"0.6601666... | 0.0 | -1 |
Connect, select, fetchall, and retry as necessary. | def csfr(auth, cmd, vals=None, *,
quiet=True, rowcount=None, time_zone=None, setup=None, setup_vals=(),
get_column_names=None, asDicts=False, debug=False, dry_run=False, cache=True, nolog=[], ignore=[], autocommit=True):
assert isinstance(auth,DBMySQLAuth)
debug = (debug or au... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def connect(self, num_retry_attempts=1):\n pass",
"def _run(self):\n #print(\"try to connect run\")\n while True:\n self._connect()\n while not self.connected and self.auto_retry is not None:\n gevent.sleep(self.auto_retry)\n self._connect(... | [
"0.61837816",
"0.60995585",
"0.60317314",
"0.6020383",
"0.59438556",
"0.5817676",
"0.57564455",
"0.57342726",
"0.57180727",
"0.56888044",
"0.56724346",
"0.5641959",
"0.56327564",
"0.5611198",
"0.55656093",
"0.55427253",
"0.5536988",
"0.5520223",
"0.5510912",
"0.54760695",
"0.... | 0.0 | -1 |
Return a dictionary of the schema. This should probably be upgraded to return the ctools schema | def table_columns(auth, table_name):
return [row[0] for row in DBMySQL.csfr(auth, "describe " +table_name)] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_schema(self) -> dict:",
"def schema(self) -> Dict[str, Dict]:\n return self._schema",
"def get_schema() -> dict:\n raise NotImplementedError()",
"def get_schema(self) -> dict:\n return schemas.get_object_schema(self.schema)",
"def get_schema(self):\n response = self.clie... | [
"0.8848745",
"0.8477742",
"0.820466",
"0.80211824",
"0.78617096",
"0.7629207",
"0.76164687",
"0.7543112",
"0.75428694",
"0.7493804",
"0.7480004",
"0.7475361",
"0.7453375",
"0.7429547",
"0.74257225",
"0.72050714",
"0.71934015",
"0.7161165",
"0.71475524",
"0.7130015",
"0.712852... | 0.0 | -1 |
Test the get_usermail function. | def test_get_usermail(m_check):
m_check.return_value = b"email\n"
assert get_usermail() == "email"
m_check.side_effect = subprocess.CalledProcessError(42, "test")
assert get_usermail() == "" | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_get_user_by_emailuser_email_get(self):\n pass",
"def test_email(self):\r\n \r\n self.assertEqual('maryc123@yahoo.com', self.user.email)",
"def test_get_email_address(self):\n email_addr = 'test_get_email_addr' + '@' + self.email_dom\n org = 'o=%s' % (self.org_name)\n... | [
"0.75392437",
"0.71519345",
"0.6814032",
"0.67871207",
"0.67836803",
"0.6677691",
"0.6670167",
"0.6616136",
"0.6537178",
"0.6473739",
"0.6472649",
"0.6373025",
"0.63564473",
"0.6334709",
"0.6324414",
"0.6312994",
"0.6308379",
"0.6306206",
"0.6303542",
"0.62552875",
"0.6248734... | 0.7211447 | 1 |
Test the get_username function. | def test_get_username(m_check):
m_check.return_value = b"name\n"
assert get_username() == "name"
m_check.side_effect = subprocess.CalledProcessError(42, "test")
assert get_username() == "" | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_users_username_get(self):\n pass",
"def test_get_username(self):\r\n user = UserMgr.get(username=u'admin')\r\n self.assertEqual(\r\n user.id,\r\n 1,\r\n \"Should have a user id of 1: \" + str(user.id))\r\n self.assertEqual(\r\n user... | [
"0.88599056",
"0.83401185",
"0.80678487",
"0.7944844",
"0.7921077",
"0.7810716",
"0.75514495",
"0.75514495",
"0.73188186",
"0.72970486",
"0.7273818",
"0.72350633",
"0.72270757",
"0.71202826",
"0.70958626",
"0.7086295",
"0.7026864",
"0.70158136",
"0.701199",
"0.6974149",
"0.69... | 0.7245719 | 11 |
Test the clone_repository function. | def test_clone_repository(m_check):
m_check.return_value = 0
assert clone_repository("test", "test", "test") == 0 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_clone_repository(koan, assert_cloned_repo_exists):\n koan.shell('')",
"def test_clone_scenario(self):\n pass",
"def test_clone_system(self):\n pass",
"def _mock_git_clone(self, args: List[str]) -> None:\n cloned_repo_root = args[-1]\n\n # Create \"cloned\" directory an... | [
"0.82233465",
"0.77650654",
"0.7716605",
"0.7560499",
"0.747564",
"0.7309313",
"0.7285124",
"0.7100823",
"0.709299",
"0.70320797",
"0.69590735",
"0.6906271",
"0.6862664",
"0.6752656",
"0.6698426",
"0.66245997",
"0.6560457",
"0.6463911",
"0.6458351",
"0.64459914",
"0.641486",
... | 0.8714088 | 0 |
The embedding updater model that read and answer questions | def embedding_updater_model(variables, rank,
n_slots,
init_params=None,
n_ents=None,
init_noise=0.0,
loss=total_loss_logistic,
scoring=multilinear,
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"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,... | [
"0.6409485",
"0.6324289",
"0.6234172",
"0.6121499",
"0.6103584",
"0.6020987",
"0.59622705",
"0.5899578",
"0.5887283",
"0.5861236",
"0.5821322",
"0.5807525",
"0.57943153",
"0.57827747",
"0.5741384",
"0.57363474",
"0.5726444",
"0.5716351",
"0.56564707",
"0.5654258",
"0.5641596"... | 0.6931258 | 0 |
Create a local index on a list of tuples and updates the global vocabulary | def local_vocabulary(tuples, voc):
new_tuples = []
local_voc0 = Indexer()
for t, v in tuples:
new_t = tuple([local_voc0.string_to_int(w) for w in t])
new_tuples.append((new_t, v))
local_voc = []
for w in local_voc0.index_to_string:
local_voc.append(voc(w))
return new_tupl... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def index(self):\n print(\"Indexing...\")\n # ------------------------------------------------------------------\n # TODO: Create an inverted, positional index.\n # Granted this may not be a linked list as in a proper\n # implementation.\n # This index sh... | [
"0.649561",
"0.634331",
"0.6276083",
"0.62530005",
"0.61110073",
"0.61029226",
"0.60869354",
"0.60246015",
"0.60092986",
"0.598",
"0.59775156",
"0.5958632",
"0.59370226",
"0.59205157",
"0.58659947",
"0.5796124",
"0.5771141",
"0.5762922",
"0.5750373",
"0.5747169",
"0.570973",
... | 0.737797 | 0 |
Read a series of data and update the embeddings accordingly | def reader(context: Tuple[tf.Variable, tf.Variable], emb0: tf.Variable, n_slots: None,
weights=None,
step_size=1.0,
scale_prediction=0.0,
start_from_zeros=False,
loss_grad=loss_quadratic_grad,
emb_update=multilinear_grad):
if context is None: # empt... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _index(self, corpus):\n\n # Transform documents to embeddings vectors\n ids, dimensions, stream = self.embedder.model.index(corpus)\n\n # Load streamed embeddings back to memory\n embeddings = np.empty((len(ids), dimensions), dtype=np.float32)\n with open(stream, \"rb\") as q... | [
"0.6830049",
"0.6335608",
"0.6126304",
"0.6093222",
"0.5968234",
"0.58916277",
"0.584841",
"0.5845506",
"0.5829189",
"0.58058685",
"0.5739242",
"0.57175463",
"0.5702515",
"0.568531",
"0.56603086",
"0.56352186",
"0.56337553",
"0.56174177",
"0.5611823",
"0.559303",
"0.5573309",... | 0.0 | -1 |
Evaluate the score of tuples with embeddings that are specific to every data sample | def answerer(embeddings, tuples: tf.Variable, scoring=multilinear):
n_data, n_slots, rank = [d.value for d in embeddings.get_shape()]
n_data, n_tuples, order = [d.value for d in tuples.get_shape()]
shift_indices = tf.constant(np.reshape(
np.outer(range(n_data), np.ones(n_tuples * n_slots)) * n_... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def compute_each_score(word_embeddings, each_id_pair): # without weighting scheme\n emb1 = word_embeddings[each_id_pair[0], :]\n emb2 = word_embeddings[each_id_pair[1], :]\n inn = np.inner(emb1, emb2)\n # print('inner product is {}'.format(inn))\n emb1norm = np.sqrt(np.inner(emb1, emb1))\n # prin... | [
"0.65966535",
"0.6271696",
"0.61693364",
"0.6096531",
"0.60055155",
"0.5940941",
"0.5937357",
"0.5903399",
"0.59002817",
"0.58706063",
"0.5868112",
"0.5857049",
"0.5816769",
"0.5789659",
"0.5776571",
"0.57489455",
"0.5739431",
"0.56953835",
"0.5690723",
"0.5675375",
"0.567082... | 0.59834903 | 5 |
Rounds to a variable number of decimal places as few as necessary in the range [min,max] | def normalize(amount, min=2, max=4):
if not amount:
return amount
# To Decimal, round to highest desired precision
d = round(Decimal(amount), max)
s = str(d)
# Truncate as many extra zeros as we are allowed to
for i in range(max-min):
if s[-1] == '0':
s = s[:-1]
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def myround(value, lowerbound, higherbound):\n if value < lowerbound:\n return lowerbound\n if value > higherbound:\n return higherbound\n return value",
"def _nice(x, round=False):\n if x <= 0:\n import warnings\n warnings.warn(\"Invalid (negative) range passed to tick in... | [
"0.67505205",
"0.66773003",
"0.6391557",
"0.6360162",
"0.63418365",
"0.62668586",
"0.6142474",
"0.61340815",
"0.60913086",
"0.60783994",
"0.60109687",
"0.600941",
"0.6009051",
"0.6005244",
"0.5997211",
"0.59826225",
"0.5977345",
"0.5960024",
"0.59580517",
"0.59387255",
"0.591... | 0.6193746 | 6 |
Removes trailing zeros, keeping at least a specified number. | def drop_trailing(amount, decimals=2):
if not amount:
return amount
s = str(float(amount)).rstrip('0')
if decimals == 0:
return s.rstrip('.')
num_decimals = len(s.split('.')[1])
num_to_add = decimals - num_decimals
if num_to_add <= 0:
return s
return s + '0' * num_... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def delete_first_zeros(digit_with_zeros): \n \n digit_without_zeros = \"\"\n\n snap = 1\n \n d = 0\n\n for d in digit_with_zeros:\n\n if d != \"0\":\n snap = 0\n if snap == 0:\n digit_without_zeros +=d\n \n return digit_without_z... | [
"0.74442893",
"0.7321218",
"0.71703875",
"0.6988911",
"0.6901619",
"0.6722442",
"0.66972",
"0.6550412",
"0.6526725",
"0.650372",
"0.64469194",
"0.631782",
"0.62386894",
"0.6091823",
"0.5994243",
"0.5845531",
"0.583502",
"0.5832329",
"0.58009475",
"0.5774697",
"0.57746726",
... | 0.65390474 | 8 |
take 1D float array of rewards and compute discounted reward | def discount_rewards(r):
discounted_r = np.zeros_like(r)
running_add = 0
for t in reversed(range(r.size)):
running_add = running_add * gamma + r[t]
discounted_r[t] = running_add
return discounted_r | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def discount_rewards(rewards):\r\n discounted_r = np.zeros_like(rewards)\r\n running_add = 0\r\n for t in reversed(range(0, len(rewards))): \r\n running_add = running_add * reward_discount + rewards[t]\r\n discounted_r[t] = running_add\r\n return discounted_r",
"def discoun... | [
"0.8100923",
"0.78397125",
"0.7792625",
"0.7742053",
"0.7653779",
"0.75987715",
"0.7583268",
"0.75820553",
"0.7579539",
"0.7519005",
"0.75047183",
"0.7467692",
"0.7456702",
"0.7433794",
"0.7426565",
"0.74062765",
"0.73950297",
"0.7370545",
"0.73564714",
"0.7328519",
"0.720184... | 0.7536054 | 9 |
Generate a function which converts a z and step value to an RGB colour, based on three predefined ratios. | def get_colour(self, r1, r2, r3, b1, b2, b3):
def colour(z, i):
"""
Gets the colour of a z and step value.
:param complex z: the z value from the mandelbrot set
:param int i: the step value
:rtype: tuple
:return: the three RGB colours
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def rgbcolor(h, f):\n # q = 1 - f\n # t = f\n if h == 0:\n return v, f, p\n elif h == 1:\n return 1 - f, v, p\n elif h == 2:\n return p, v, f\n elif h == 3:\n return p, 1 - f, v\n elif h == 4:\n return f, p, v\n elif h == 5:\n return v, p, 1 - f",
... | [
"0.6529024",
"0.6498187",
"0.6447665",
"0.6419169",
"0.63274384",
"0.6322723",
"0.6188783",
"0.60802084",
"0.6072802",
"0.5948455",
"0.5910232",
"0.5883263",
"0.5878698",
"0.5863626",
"0.5861718",
"0.58612084",
"0.583371",
"0.5806005",
"0.57980853",
"0.57717776",
"0.57256144"... | 0.6906919 | 0 |
Gets the colour of a z and step value. | def colour(z, i):
if abs(z) < self.threshold:
return self.background
v = np.log2(i + self.threshold - np.log2(np.log2(abs(z)))) / self.threshold
if v < 1.0:
return v ** b1, v ** b2, v ** b3 # background
else:
v = max(0, 2 -... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_colour(self, r1, r2, r3, b1, b2, b3):\n\n def colour(z, i):\n \"\"\"\n Gets the colour of a z and step value.\n\n :param complex z: the z value from the mandelbrot set\n :param int i: the step value\n\n :rtype: tuple\n :return: the th... | [
"0.7341066",
"0.72083485",
"0.68963253",
"0.6640423",
"0.65460277",
"0.6530589",
"0.6423319",
"0.62713194",
"0.6260205",
"0.6229389",
"0.62234646",
"0.6208068",
"0.61786675",
"0.6175499",
"0.6157222",
"0.6157222",
"0.6157222",
"0.6157222",
"0.6140309",
"0.6133816",
"0.6130664... | 0.7172321 | 2 |
Generate a function which converts a z and step value to an RGB colour, based on three predefined ratios. | def get_inner_colour(self, r1, r2, r3, b1, b2, b3):
def colour(z, i):
"""
Gets the colour of a z and step value.
:param z: the z value from the mandelbrot set
:param i: the step value
:rtype: list
:return: list containing the RGB colours... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_colour(self, r1, r2, r3, b1, b2, b3):\n\n def colour(z, i):\n \"\"\"\n Gets the colour of a z and step value.\n\n :param complex z: the z value from the mandelbrot set\n :param int i: the step value\n\n :rtype: tuple\n :return: the th... | [
"0.6905101",
"0.6527362",
"0.64464974",
"0.64188105",
"0.63265",
"0.63204837",
"0.6187181",
"0.607829",
"0.6072109",
"0.594735",
"0.5908517",
"0.5882771",
"0.58766925",
"0.5861005",
"0.58603394",
"0.5859091",
"0.5833258",
"0.5804042",
"0.5797796",
"0.57726943",
"0.5724473",
... | 0.6496655 | 2 |
Gets the colour of a z and step value. | def colour(z, i):
if abs(z) < self.threshold:
return 0, 0, 0
v = np.log2(i + self.threshold - np.log2(np.log2(abs(z)))) / self.threshold
if v < 1.0:
return v ** b1, v ** b2, v ** b3 # coloured tones
else:
v = max(0, 2 - v)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_colour(self, r1, r2, r3, b1, b2, b3):\n\n def colour(z, i):\n \"\"\"\n Gets the colour of a z and step value.\n\n :param complex z: the z value from the mandelbrot set\n :param int i: the step value\n\n :rtype: tuple\n :return: the th... | [
"0.73403287",
"0.7172178",
"0.6895486",
"0.66384035",
"0.654349",
"0.65309036",
"0.64248663",
"0.62704283",
"0.6260564",
"0.62288976",
"0.622155",
"0.62071323",
"0.6176974",
"0.6173576",
"0.6155353",
"0.6155353",
"0.6155353",
"0.6155353",
"0.613839",
"0.61319745",
"0.6128805"... | 0.7208043 | 1 |
Defines the size of the grid on which to draw the mandelbrot set. | def set_grid(self, start_x, end_x, start_y, end_y, resolution_x, resolution_y, threshold):
step_x = (end_x - start_x) / resolution_x
step_y = (end_y - start_y) / resolution_y
real, complex = np.mgrid[start_y:end_y:step_y, start_x:end_x:step_x]
self.grid = real + complex * 1j
self... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def grid_size(self):\n return self._grid_size",
"def getGridSize(self):\n # This is set by the mosaic module, but other modules need to\n # know the values to take the proper size grid.\n return self.grid_size",
"def _grid_hint_size(self) -> int:",
"def define_grid(self):\n ... | [
"0.7052928",
"0.6872349",
"0.6777697",
"0.65532494",
"0.6459304",
"0.6388547",
"0.63002",
"0.62486094",
"0.6168989",
"0.611555",
"0.60756975",
"0.60363597",
"0.6002739",
"0.59995365",
"0.5982655",
"0.5964983",
"0.5941414",
"0.59040725",
"0.5903051",
"0.5888311",
"0.58676386",... | 0.0 | -1 |
Colours the grid and returns the image. | def get_coloured_grid(self, r1, r2, r3, b1=4, b2=2.5, b3=1):
r, g, b = np.frompyfunc(self.get_colour(r1, r2, r3, b1, b2, b3), 2, 3)(self.end_z, self.end_step)
img_array = np.dstack((r, g, b))
return Image.fromarray(np.uint8(img_array * 255)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def draw_grid(self):\n plt.imshow(py.array(\n map(lambda x: map(lambda y: mplc.colorConverter.to_rgb(colord[y]), x), self.create_grid(self.graph))),\n interpolation='nearest')\n plt.show()",
"def make_image(self, save=False):\n\n # image_grid = np.full((self.size_x, sel... | [
"0.6998966",
"0.65337056",
"0.6325635",
"0.6285329",
"0.6260909",
"0.62575686",
"0.6254772",
"0.62346655",
"0.618115",
"0.615539",
"0.61247164",
"0.6097528",
"0.6022386",
"0.60113466",
"0.6007067",
"0.59790057",
"0.59721226",
"0.5971848",
"0.59443724",
"0.5926389",
"0.5913297... | 0.72839564 | 0 |
Generates the mandelbrot set from the set parameters for a particular number of iterations. | def generate_mandelbrot(self, iterations):
if self.grid is None:
raise RuntimeError("Grid hasn't been setup - call set_grid first.")
# Define the tensorflow variables
c = tf.constant(self.grid.astype(np.complex64))
z = tf.Variable(c)
n = tf.Variable(tf.zeros_like(c, t... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getMandelbrotSet(self):\n\t\t\n\t\t(x_min, x_max, y_min, y_max) = [i for i in self.extent]\n\t\t(rows, columns) = (self.width*self.dpi, self.width*self.dpi)\n\t\tr_real = np.linspace(x_min, x_max, columns)\n\t\tr_imag = np.linspace(y_max, y_min, rows)\n\t\tresult = np.empty((rows, columns))\n\n\t\t# used for s... | [
"0.59489983",
"0.56008106",
"0.55951405",
"0.5593306",
"0.55607903",
"0.54957503",
"0.54919815",
"0.5482499",
"0.54726803",
"0.5471357",
"0.54386574",
"0.54066265",
"0.5402647",
"0.5369949",
"0.53577334",
"0.535206",
"0.53520525",
"0.5346284",
"0.5345355",
"0.53447723",
"0.52... | 0.6835279 | 0 |
Generates the julia set from the set parameters for a number of iterations and using a particular value of c. | def generate_julia(self, iterations, c):
if self.grid is None:
raise RuntimeError("Grid hasn't been setup - call set_grid first.")
# Define the tensorflow variables
c_val = tf.constant(np.full(shape=self.grid.shape, fill_value=c, dtype=self.grid.dtype))
z = tf.Variable(self.g... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def genPowerSet(cvalues, incEmpty=False):\t\t\n\tps = list()\n\tfor cv in cvalues:\n\t\tpse = list()\n\t\tfor s in ps:\n\t\t\tsc = s.copy()\n\t\t\tsc.add(cv)\n\t\t\t#print(sc)\n\t\t\tpse.append(sc)\n\t\tps.extend(pse)\n\t\tes = set()\n\t\tes.add(cv)\n\t\tps.append(es)\n\t\t#print(es)\n\t\n\tif incEmpty:\n\t\tps.ap... | [
"0.6113237",
"0.585974",
"0.5496323",
"0.5448815",
"0.5381841",
"0.53499573",
"0.53280026",
"0.5263938",
"0.52438045",
"0.5203017",
"0.51715815",
"0.5160977",
"0.511507",
"0.5103073",
"0.50933886",
"0.5076919",
"0.5047488",
"0.5038411",
"0.5017469",
"0.5008726",
"0.5005838",
... | 0.6562868 | 0 |
Print tabseparated sequence of things to line in file | def tabout(things, file=sys.stdout):
print("\t".join([str(x) for x in things]), file=file)
file.flush() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def tab(self):\n self._write('\\t')",
"def output_tab_delimited(s1, s2, i1, i2):\n _out.write(\"%s\\t%s\\t%d\\t%d\\n\" % (s1, s2, i1, i2))\n _out.write(\"%s\\t%s\\t%d\\t%d\\n\" % (s2, s1, i2, i1))",
"def print_tsv(data, filename):\n with open(filename, 'wt') as fout:\n writefile = partia... | [
"0.7131929",
"0.7020605",
"0.66624314",
"0.65175635",
"0.64840746",
"0.64395034",
"0.63917106",
"0.63775367",
"0.63412404",
"0.6289037",
"0.61846536",
"0.6176423",
"0.616765",
"0.61081123",
"0.6081998",
"0.6079226",
"0.60417503",
"0.59935766",
"0.5967714",
"0.59413093",
"0.59... | 0.81036395 | 0 |
get returns a list of all the users in the system | def get(self):
# TODO this endpoint returns null is instead of respoinse message when token is not in the header, read about error handling to solve this issue
return get_users() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get(self):\n return get_all_users()",
"def get(self):\n return get_all_users()",
"def get(self):\n return get_all_users()",
"def get(self):\n return get_all_users()",
"def get_all_users():",
"def get_users(self):\r\n\t\tlogger.debug(\"Fetch users\")\r\n\t\t\r\n\t\treturn l... | [
"0.8544133",
"0.8544133",
"0.8544133",
"0.8544133",
"0.85363835",
"0.84463185",
"0.84404284",
"0.83349496",
"0.8291406",
"0.80483234",
"0.79664856",
"0.792892",
"0.79038525",
"0.7897605",
"0.78914654",
"0.78914565",
"0.78870755",
"0.7829862",
"0.78199285",
"0.7812609",
"0.779... | 0.7241947 | 88 |
patch updates an existing user in the system | def put(self, user_id):
data = request.json
return update_user(data, user_id) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def update_user():",
"def update_user():\n #TODO user update \n pass",
"def update_user(id):\n pass",
"def test_patch_user(self):\n pass",
"def update(self, user: U) -> None:\n ...",
"def test_040_update_user(self):\n\n testflow.step(\"Updating user %s\", TEST_USER2)\n ... | [
"0.8535191",
"0.82148325",
"0.76160437",
"0.76012594",
"0.7593992",
"0.7471264",
"0.7397879",
"0.7326105",
"0.72308886",
"0.7211113",
"0.71603566",
"0.71422267",
"0.70986223",
"0.70672417",
"0.7060861",
"0.7040941",
"0.70407265",
"0.703698",
"0.7034396",
"0.6995369",
"0.69941... | 0.6422292 | 80 |
get returns a single user given its unique user_id | def get(self, user_id):
user, code = get_user(user_id)
if code == 404:
api.abort(code=404, message="The User with the user_id doesnt Exist")
return user, code | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get(self, user_id):\n return User.get(user_id)",
"def get(id):\n return User.query.filter_by(id=id).first()",
"def get_user_by_id(user_id):\n return User.query.get(user_id)",
"def get_user(self, user_id):\n uri = 'users/' + user_id\n return self.make_request(uri)",
"def get_user(... | [
"0.84332645",
"0.83980507",
"0.8261577",
"0.8222736",
"0.8221518",
"0.8178469",
"0.81775814",
"0.81763834",
"0.8173295",
"0.8173295",
"0.8173295",
"0.8173295",
"0.81669915",
"0.8094847",
"0.80743533",
"0.805296",
"0.8034785",
"0.8032745",
"0.80307204",
"0.8004",
"0.8000615",
... | 0.0 | -1 |
delete removes a user from the system | def delete(self, user_id):
return delete_user(user_id) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def delete_user():",
"def delete_user():\n #TODO user delete\n pass",
"def delete_user(id):\n pass",
"def delete_user():\r\n raise NotImplementedError()",
"def del_user(self, username):\n pass",
"def delete_user(self):\n User.user_list.remove(self)",
"def delete_user(self):\n ... | [
"0.9304841",
"0.90795064",
"0.8505418",
"0.8406779",
"0.831637",
"0.82509834",
"0.82509834",
"0.82509834",
"0.82255083",
"0.8137962",
"0.8115955",
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"0.7951598",
"0.7948138",
"0.7906432",
"0.78909653",
"0.7843082",
"0.7732356",
"0.77239",
"0.7720622",
"0.77185726"... | 0.73671967 | 70 |
Adds a source (as a string) corresponding to a filename in the cache. The filename can be a true file name, or a fake one, like | def add(self, filename, source):
self.cache[filename] = source
if os.path.isfile(filename):
self.ages[filename] = os.path.getmtime(filename) # modification time | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def AddSource (self, name, source, filename):\n self.containments [name] = source, filename, False",
"def add_source_file(self, filename):\n self.sources.add(Source.create(filename))",
"def cache(self, file_name, content):\n self.files_loaded[file_name] = content",
"def put_source(file_p... | [
"0.733794",
"0.70201176",
"0.6331393",
"0.6280567",
"0.61927634",
"0.6123509",
"0.6102532",
"0.605147",
"0.6028744",
"0.601777",
"0.5983757",
"0.5921107",
"0.5879947",
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"0.5762012",
"0.5749603",
"0.57389265",
"0.57361263",
"0.5717338",
"0.57040185",
"0.5688251",
... | 0.8042132 | 0 |
Gets a copy of the entire cache | def get_copy(self):
# This is used in avant-idle to pass the content of a cache in
# the main process to a second process where an exception has been
# raised.
return self.cache | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_cache(self):\n return self.cache",
"def get(self):\n if path.exists(self.cachefile):\n self.invalidion()\n full_cache = self._get_all()\n return full_cache\n else:\n return []",
"def getCacheContents(self):\n return self._cache",
... | [
"0.745652",
"0.74172235",
"0.72680855",
"0.7232514",
"0.71606326",
"0.71606326",
"0.71606326",
"0.71606326",
"0.7018596",
"0.7012775",
"0.7010496",
"0.67941254",
"0.67534447",
"0.6705078",
"0.6684562",
"0.6615776",
"0.6590172",
"0.65279984",
"0.6523774",
"0.6464215",
"0.64473... | 0.7468072 | 0 |
Replaces the current cache by another | def replace(self, other_cache):
# This is used in avant-idle to replace the content of the cache
# in a process (where no storage normally takes place) by
# that of another where the actual caching of the source is done.
self.cache.clear()
for key in other_cache:
self... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def testReplaceItem(self):\n\n first = \"Little pig, little pig, let me come in!\"\n second = \"Not by the hair on my chinny-chin-chin!\"\n memcache.set('first', first)\n assert memcache.get('first') == first\n memcache.replace('first', second)\n assert memcache.get('first... | [
"0.67452043",
"0.63469607",
"0.6275304",
"0.6189481",
"0.6022924",
"0.60033196",
"0.59268266",
"0.57108635",
"0.56714857",
"0.5612773",
"0.56104547",
"0.55959934",
"0.5594574",
"0.5585767",
"0.5527958",
"0.5526175",
"0.552543",
"0.552472",
"0.55214816",
"0.5506108",
"0.548309... | 0.84599257 | 0 |
Given a filename, returns the corresponding source, either from the cache or from actually opening the file. If the filename corresponds to a true file, and the last time it was modified differs from the recorded value, a fresh copy is retrieved. The contents is stored a a string and returned as a list of lines. If no ... | def get_source(self, filename):
# The main reason we care about ensuring we have the latest version
# of a given file is for the 'avant-idle' project where we could
# 'edit and run' multiple times a given file. We need to ensure that
# the content shown by the traceback is accurate.
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _get_file_source(self, filename):\n try:\n with open(filename, encoding=\"utf8\") as f:\n lines = f.readlines()\n source = \"\".join(lines)\n except Exception:\n lines = []\n source = None\n return source, lines",
"def get_ou... | [
"0.7092788",
"0.6621492",
"0.64070714",
"0.6370972",
"0.6370972",
"0.6359992",
"0.6252963",
"0.6160644",
"0.6033081",
"0.6021885",
"0.599927",
"0.59814626",
"0.58814996",
"0.5880455",
"0.57968",
"0.57867116",
"0.5776293",
"0.5770049",
"0.5722298",
"0.56828374",
"0.567691",
... | 0.8423813 | 0 |
Helper function to retrieve a file | def _get_file_source(self, filename):
try:
with open(filename, encoding="utf8") as f:
lines = f.readlines()
source = "".join(lines)
except Exception:
lines = []
source = None
return source, lines | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_file():\n fname = get_var(request, \"fname\")\n return open(fname).read()",
"def get_file(self, path):\n file = self.get('data_request?id=file¶meters=%s' % path)\n return file",
"def get_file(URI):\n return file_fabric.get_class(URI).get_content(URI)",
"def get(self, filena... | [
"0.79854953",
"0.789686",
"0.75598544",
"0.75327295",
"0.7512711",
"0.7409022",
"0.730959",
"0.72266984",
"0.720062",
"0.71793586",
"0.71641433",
"0.71097845",
"0.7107661",
"0.7107661",
"0.7003901",
"0.6984212",
"0.6975756",
"0.69507647",
"0.69283926",
"0.688564",
"0.6861352"... | 0.0 | -1 |
Formats a few lines around a 'bad line', and returns the formatted source as well as the content of the 'bad line'. | def get_formatted_partial_source(self, filename, linenumber, offset):
lines = self.get_source(filename)
if not lines:
return "", ""
begin = max(0, linenumber - self.context)
partial_source, bad_line = highlight_source(
linenumber,
linenumber - begin -... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def format_exception_only(etype, value):\n # Gracefully handle (the way Python 2.4 and earlier did) the case of\n # being called with (None, None).\n if etype is None:\n return [_format_final_exc_line(etype, value)]\n\n stype = etype.__name__\n smod = etype.__module__\n if smod not in (\"_... | [
"0.5942906",
"0.59109855",
"0.5804325",
"0.5789683",
"0.5765263",
"0.5730036",
"0.57251096",
"0.5659315",
"0.5617689",
"0.55531806",
"0.5530319",
"0.54957825",
"0.545304",
"0.5448972",
"0.5432576",
"0.54311997",
"0.54229766",
"0.5407884",
"0.5405036",
"0.53888756",
"0.5380812... | 0.70705295 | 0 |
Extracts a few relevant lines from a file content given as a list of lines, adding line number information and identifying a particular line. When dealing with a ``SyntaxError`` or its subclasses, offset is an integer normally used by Python to indicate the position of | def highlight_source(linenumber, index, lines, offset=None):
# The following if statements are left-over diagnostic
# from the hack to integrate into Idle.
# they are harmless tests which could potentially be useful.
if lines is None:
return "", ""
if index is None:
print("problem in... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _get_lines_from_file(filename, lineno, context_lines):\n\n try:\n source = open(filename).readlines()\n lower_bound = max(0, lineno - context_lines)\n upper_bound = lineno + context_lines\n\n pre_context = \\\n [line.strip('\\n') for line in source[lower_bound:lineno]]... | [
"0.6520461",
"0.6438692",
"0.6395089",
"0.63872284",
"0.6351725",
"0.6201063",
"0.6073642",
"0.59459245",
"0.5939982",
"0.59217393",
"0.58799213",
"0.58524036",
"0.58376163",
"0.5836338",
"0.58118933",
"0.5767493",
"0.57534856",
"0.572237",
"0.571344",
"0.56282735",
"0.562349... | 0.60051537 | 7 |
Check if raster contains only h/v adjacent connections Illegal ac ba Legal ab aa | def clusters_connected( self):
def check_connected( k, vertices, edges):
dads = {}
for p in vertices:
dads[p] = p
def Find( c):
while c != dads[c]:
c = dads[c]
return c
def Union( p, q):
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def check_ext(im, i, j):\n neighb = 0\n count = 0\n for a in range(8):\n if (im[i+relpos[a][0], j+relpos[a][1]] and (count == 0)):\n count += 1\n neighb += 1\n else:\n count = 0\n return (neighb < 2)",
"def is_inacessible(cell):\n adj, count =... | [
"0.6483187",
"0.6258697",
"0.6213388",
"0.6037331",
"0.59586877",
"0.5941078",
"0.5923148",
"0.5912683",
"0.5907906",
"0.58862484",
"0.58592165",
"0.5856731",
"0.5845715",
"0.5740796",
"0.5716612",
"0.5691044",
"0.5684787",
"0.56699437",
"0.5633153",
"0.5612918",
"0.5611113",... | 0.0 | -1 |
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