float:\n pass","def save_scores(emb_scores, emb_filename, base_scores, base_filename, output_dir):\n\n emb_file = open(Path(output_dir) / emb_filename, 'w')\n base_file = open(Path(output_dir) / base_filename, 'w')\n for word, score in emb_scores.items():\n if word in base_scores:\n print(float(score), file=emb_file)\n print(float(base_scores[word]), file=base_file)","def generateScore(self):\n totalFreq = self.lazySum(key=None)\n for file in self._candidate_files:\n filename = os.path.basename(file)\n score_file = os.path.join(self._score_dir, filename)\n with open(score_file, 'w') as ofd:\n with open(file) as ifd:\n for line in ifd:\n words = line.strip().split('\\t')\n if len(words) < 2 or any(map(lambda word:len(word)<2, words)):\n continue\n\n XFreq = self.lazySum(words[0])\n YFreq = self.lazySum(words[1])\n XYFreq = self.lazySum(line.strip())\n # frequences filter\n #if XYFreq < 2 or XYFreq > 24:\n # continue\n if YFreq == 0 or XFreq == 0 or XYFreq == 0:\n # because when generating grams, we last last words' frequency\n continue\n PX = XFreq * 1.0 / totalFreq\n PY = YFreq * 1.0 / totalFreq\n PXY = XYFreq * 1.0 / totalFreq\n score = math.log(PXY/PX/PY, 2) * XYFreq\n #print \"Freq:\", XFreq, YFreq, XYFreq\n result = \"{0}\\t{1:.2f}\\n\".format(line.strip(), score)\n ofd.write(result)","def score_bag(self, bag):\n # pop_scores = bag.retrieve_external_scores(self.file_name, self.col_name)\n # true_scores = pop_scores.ix[bag.true_pop]\n # scores_col = true_scores.columns[0]\n # res = np.array([true_scores.index.values, true_scores[scores_col].values]).transpose()\n # return res\n\n pop_scores = bag.retrieve_external_scores(self.file_name, self.col_name)\n candidate_ids_numeric = frozenset(bag.universe) - frozenset(bag.whites)\n # candidate_id_strings = (str(int(idnum)) for idnum in candidate_ids_numeric)\n candidate_scores = pop_scores.ix[candidate_ids_numeric].dropna()\n scores_col = candidate_scores.columns[0]\n res = np.array([\n candidate_scores.index.values.astype(int),\n candidate_scores[scores_col].values\n ]).transpose()\n return res","def get_bad_score(self):\n logger.debug('Function get_bad_score start')\n\n cur = self.conn.cursor()\n\n # Selecting worst score\n cur.execute(\n \"SELECT id, img_name, img_score FROM sensor_data ORDER BY img_score ASC LIMIT 1\")\n\n # Getting worst score\n row = cur.fetchone()\n\n logger.debug('Function get_bad_score end')\n return row","def test_scores(self) -> np.ndarray:\n return np.asarray(self.test_metric_dict[self.metric_name])","def score(self, test_data):\n\n\t\tpass","def _read_scores(self,path):\n scores = dict()\n fin = open(path,'r')\n for line in fin:\n k, v = line.split()\n scores[k.strip()] = float(v.strip())\n return scores","def power2db(power):\r\n db = 10 * np.log10(power)\r\n return db","def _get_scores_list(self):\n self.scores = dict()\n for subdir in os.listdir(self.path):\n if subdir.startswith('.'):\n continue\n try:\n score = open('{0}/{1}/{2}/extract_all.sort.uniq.txt'.format(self.path, subdir, DOCKING_RUN_FILES),'r').read().split()[-1]\n self.scores[subdir] = float(score.strip())\n except:\n pass","def score(self):\n raise NotImplementedError()","def test_genomic(self):\n self.c.execute(\"\"\"select expIds,expScores from genomic_test\"\"\")\n rows = self.c.fetchall()\n self.assertEqual(len(rows), 1) # one probe\n self.assertEqual(rows[0][0], '0,1,2,3,4') # ordered by sample id\n values = map(lambda x: float(x), rows[0][1].split(',')) # scores are in correct order\n self.assertTrue(values[0] - 0.479005065149792 < self.tolerance)\n self.assertTrue(values[1] - 25.1 < self.tolerance)\n self.assertTrue(values[2] - 5.3 < self.tolerance)\n self.assertTrue(values[3] - 3.1 < self.tolerance)\n self.assertTrue(values[4] - -1.23 < self.tolerance)","def print_scores(self):\n print(\"scores: \", self.get_scores())","def scores_(self):\n return self.predictor.scores_","def get_scores(self):\n return pd.DataFrame(self._scores)","def calc_score(score):\n if not score:\n return 0\n dbot_score = 1\n if score >= 95:\n dbot_score = 3\n elif score >= 75:\n dbot_score = 2\n return dbot_score","def list_of_scores(self):\n with open(Constant.this_score, \"r\") as f:\n final= f.read()\n final = int(final)\n with open(Constant.list_scores, \"r\") as li:\n points_file = json.load(li)\n points = points_file['HighScores']\n\n with open(Constant.list_scores,\"w\") as files:\n\n if len(points) == 5:\n if points[0] < final:\n for i in range(len(points)-1,0,-1):\n points[i] = points[i-1]\n points[0] = final\n json.dump(points_file,files)\n elif points[0] > final:\n if points[1] < final:\n for i in range(len(points)-1,1,-1):\n points[i] = points[i-1]\n points[1] = final\n json.dump(points_file,files)\n elif points[1] > final:\n if points[2] < final:\n for i in range(len(points)-1,2,-1):\n points[i] = points[i-1]\n points[2] = final\n json.dump(points_file,files)\n elif points[2] > final:\n if points[3] < final:\n for i in range(len(points)-1,3,-1):\n points[i] = points[i-1]\n points[3] = final\n json.dump(points_file,files)\n elif points[3] > final:\n if points[4] < final:\n points[4] = final\n json.dump(points_file,files)\n else:\n json.dump(points_file,files)\n else:\n json.dump(points_file,files)\n else:\n json.dump(points_file,files)\n else:\n json.dump(points_file,files)\n else:\n json.dump(points_file,files)","def readScore(self):\n return self.zmwMetric(\"ReadScore\")","def all_scores(self):\r\n if not self.child_history:\r\n return None\r\n return [self.score_for_attempt(index) for index in xrange(0, len(self.child_history))]","def test_get_advanced_scores(self):\n\n this_score_table_xarray = learning_curves.get_advanced_scores(\n BASIC_SCORE_TABLE_XARRAY\n )\n\n self.assertTrue(_compare_advanced_score_tables(\n this_score_table_xarray, ADVANCED_SCORE_TABLE_XARRAY\n ))","def dBtoLinear(db):\r\n return 10**(db/20)","def test_query_score(self):\n id = get_rand_string()\n\n # Same data and user_id\n user_id = data = get_rand_string()\n\n self.conn.add(id=id, user_id=user_id, data=data)\n self.conn.commit()\n\n results = self.conn.query(\"id:\" + id).results\n self.assertEquals(len(results), 1,\n \"No documents fetched, expected id:%s\" % (id))\n\n doc = results[0]\n\n self.assertTrue(\"score\" in doc, \"No score returned, doc:%s\" % repr(doc))\n self.assertTrue(isinstance(doc[\"score\"], float),\n \"Score should be a float instance, doc:%s\" % repr(doc))","def PrintFieldLinearity(self):\n sql = (\"SELECT Frequency__GHz, Field__V_per_m, FieldResult__V_per_m\"\n \" FROM 场强线性度 ORDER BY Frequency__GHz, Field__V_per_m\")\n testdata = list(self.db.cursor.execute(sql).fetchall())\n testdata = [list(obj) for obj in testdata]\n rownum = testdata.__len__()\n sql = (\"SELECT TableNum FROM %s WHERE \"\n \"TestItems='场强线性度'\" % self.position)\n tablenum = self.basicinfo.cursor.execute(sql).fetchone()[0]\n self.doc.TableAddRow(tablenum=tablenum, row=rownum)\n for obj in testdata:\n obj[2] = \"%0.2f\" % obj[2]\n obj += (\"%0.2f\" % (float(obj[1]) / float(obj[2])), )\n sql = (\"SELECT Uncertainty FROM Uncertainty WHERE %s < FreqUpper \"\n \"AND %s >= FreqLower\" % (obj[0], obj[0]))\n obj += (\"%0.1f\"\n % (self.basicinfo.cursor.execute(sql).fetchone()[0]), )\n # 打印不确定度k值\n if not self.report:\n self.doc.TableContent(\n tablenum=tablenum - 1, cellrow=1, cellcolum=5,\n insertcontent=\"测量不确定度(k=2) (dB)\")\n for i in range(rownum):\n if i % 2 == 1:\n self.doc.CellDownMerge(\n tablenum=tablenum, row=i + 1, col=1, mergenum=1)\n self.doc.CellDownMerge(\n tablenum=tablenum, row=i + 1, col=5, mergenum=1)\n if i % 2 == 0:\n assert testdata[i][0] == testdata[i + 1][0]\n assert testdata[i][1] != testdata[i + 1][1]\n self.doc.TableContent(\n tablenum=tablenum - 1, cellrow=i + 2, cellcolum=1,\n insertcontent=testdata[i][0])\n self.doc.TableContent(\n tablenum=tablenum - 1, cellrow=i + 2, cellcolum=5,\n insertcontent=testdata[i][4])\n for j in range(1, 4):\n self.doc.TableContent(\n tablenum=tablenum - 1, cellrow=i + 2, cellcolum=j + 1,\n insertcontent=testdata[i][j])","def recognition(database, testSong, plot=True, info=True, latextable=False):\n #Score for every song in database with respect to testsong\n \n if latextable == True:\n print(testSong[\"name\"])\n print(\"\"\"\\\\begin{table}[H]\n \\centering\n \\\\begin{tabular}{|l|l|}\n \\hline\n Artist and Title & Score value\\\\\\\\\n \\hline\n \"\"\")\n for i in range(len(database)):\n scores = []\n for j in range(testSong[\"spectrogram\"].shape[1]): #every frame of STFT\n #Score is calculated\n u = np.transpose(database[i][\"eigen\"]) @ (testSong[\"spectrogram\"][:, j] - database[i][\"mean\"])\n scores.append(LA.norm(u - database[i][\"weights\"][:, j]))\n #mean value of the scores\n database[i][\"score\"] = sum(scores)/len(scores)\n if latextable == True:\n print(f'{database[i][\"name\"]} & {database[i][\"score\"]:0.7} \\\\\\\\')\n if info == True and latextable != True:\n print(f\"{database[i]['name']} has a score of {database[i]['score']}\")\n if plot == True:\n plt.plot(scores)\n plt.title(f\"{database[i]['name'].split('.w')[0]}\\n testsong: {testSong['name'].split('/')[1].split('(o')[0]}\")\n plt.show()\n\n #The lowest score is found and is the most alike to testSong\n lowsScore = database[0]\n for song in database[1:]:\n if lowsScore[\"score\"] > song[\"score\"]:\n lowsScore = song\n if info == True and latextable != True: \n print(\" \")\n print(f\"The song with the lowest score is {lowsScore['name']} with a score of {lowsScore['score']}\")\n \n if latextable == True:\n print(\"\"\"\\hline\n\\end{tabular}\n\\caption{Recognition of a recorded version of \"\"\" + testSong[\"name\"] + \"\"\" and the coherent score value.}\n\\label{tab: }\n\\end{table}\"\"\")\n print(\" \")\n return lowsScore['name'], lowsScore['score']","def test_figure2(self):\n\n encoder = TctColBertQueryEncoder('castorini/tct_colbert-msmarco')\n searcher = SimpleDenseSearcher.from_prebuilt_index('msmarco-passage-tct_colbert-hnsw', encoder)\n hits = searcher.search('what is a lobster roll')\n\n self.assertAlmostEqual(hits[0].score, 70.53741, delta=0.0001)\n self.assertEqual(hits[0].docid, '7157710')\n\n self.assertAlmostEqual(hits[9].score, 69.01737, delta=0.0001)\n self.assertEqual(hits[9].docid, '2920399')\n\n self.assertEqual(len(hits), 10)","def BEDreader(fname):\n\n bed_score = dict() \n bfh = open(fname)\n for line in bfh:\n line = line.strip('\\n\\r').split('\\t')\n assert len(line) == 5, '\\t'.join(line)\n bed_score[float(line[3])] = 1\n bfh.close()\n return bed_score.keys()","def super_complexs_score(df):\r\n return df['volume'] * 2","def get_score(self):\n return self.score","def evaluate(self):\n scores = []\n scores.append(self.word_analogy())\n print(\"Word Analogy (acc): \", scores[0])\n scores.append(self.word_similarity())\n print(\"Word Similarity (MSE): \", scores[1])\n scores.append(self.concept_categorization())\n print(\"Concept Categorization (purity): \", scores[2])\n scores.append(self.sentiment_analysis())\n print(\"Sentiment Analysis (acc): \", scores[3])\n return scores","def sample(\n self,\n scores: torch.Tensor\n ):\n perturbed = torch.log(scores) + self.gumbel_noise.sample((len(scores),))\n return torch.argsort(-perturbed.detach())","def db_statistics(per_frame_values):\n\n # strip off nan values\n with warnings.catch_warnings():\n warnings.simplefilter(\"ignore\", category=RuntimeWarning)\n M = np.nanmean(per_frame_values)\n O = np.nanmean(per_frame_values > 0.5)\n\n N_bins = 4\n ids = np.round(np.linspace(1, len(per_frame_values), N_bins + 1) + 1e-10) - 1\n ids = ids.astype(np.uint8)\n\n D_bins = [per_frame_values[ids[i]:ids[i + 1] + 1] for i in range(0, 4)]\n\n with warnings.catch_warnings():\n warnings.simplefilter(\"ignore\", category=RuntimeWarning)\n D = np.nanmean(D_bins[0]) - np.nanmean(D_bins[3])\n\n return M, O, D","def getSenSpeValuesByScores(datafram_scores):\n sen_spe_values_vec = []\n for index, row in datafram_scores.iterrows():\n sensitivity = libraries.measures_calculation.calculateSensitivity(row['tn'], row['fp'], row['fn'], row['tp'])\n specificity = libraries.measures_calculation.calculateSpecificity(row['tn'], row['fp'], row['fn'], row['tp'])\n model_file_name = row['file_name']\n sen_spe_values_vec.append([index, sensitivity, specificity, model_file_name])\n \n df = pd.DataFrame(sen_spe_values_vec, columns=['index', 'Sensitivity', 'Specificity', 'file_name'])\n return df","def test_accuracy():\n hits = 0\n total = 0\n # create_database()\n # gen_random_samples()\n song_to_id, id_to_song, hash_dict = load_database()\n for filename in os.listdir(\"Songs\"):\n sample_dict = hash_random_sample(filename)\n offset_dict, song_id = find_song(\n hash_dict, sample_dict, id_to_song)\n print(id_to_song[song_id])\n print(filename)\n if id_to_song[song_id] == filename:\n print(\"Success\")\n hits += 1\n else:\n print(\"Fail\")\n total += 1\n print((hits / total) * 100, \" %\")","def getScores():\r\n results = \"\"\r\n with sqlite3.connect(database_file) as conn:\r\n cursor = conn.cursor()\r\n team_scores = cursor.execute(\"\"\" SELECT * FROM scores;\"\"\")\r\n\r\n for row in team_scores.fetchall():\r\n teamname, auto, rc, spirit, video = row\r\n results += result_string.format(teamname, auto, rc, spirit, video) + \"\\n\"\r\n return results","def get_highscores(self):\n return self.database.get_high_scores(self.difficulty)","def get_score(self):\r\n return None","def measure(self, X2, X3, start_index): \n \n power = self.power\n X2 = np.array(X2)\n X3 = np.array(X3)\n if len(X2) == 0:\n score = 0\n else:\n X2 = np.fft.fft(X2);\n X3 = np.fft.fft(X3)\n score = np.linalg.norm(X2 - X3, ord = power)/len(X3)\n self.decision_scores_.append((start_index, score))\n return score","def compare_dictionaries(d1, d2):\r\n score = 0\r\n gef = 0\r\n for z in d1:\r\n gef += d1[z]\r\n total = gef\r\n \r\n for x in d2:\r\n if x in d1:\r\n score += math.log(d1[x] / total) * d2[x] \r\n else:\r\n score += math.log(0.5/total) * d2[x]\r\n return score","def f1_score(self):","def score( self ):\r\n result = 0.0\r\n for rr in self.ee.getRsrcs( ):\r\n value = self.scoreRsrc( rr )\r\n result += value\r\n print( \"INFO: Value for the schedule is %s \" % ( rr, result ) )\r\n return( result )","def HighScore_r():\r\n fichier=open(\"save/HGSC/HIGHSCORE.txt\",\"r\")\r\n l=1\r\n for ligne in fichier:\r\n if l==1:\r\n highscore=int(ligne)\r\n l=0\r\n fichier.close()\r\n return highscore","def view_scores(jenni, input):\n scores.view_scores(jenni, input)","def number_matches():\n ALL_FIXED_q = \"all_fixed_queries\" + str(17)\n ALL_FIXED_dbs = \"all_fixed_dbs\" + str(17)\n try:\n with open(\"matches\", \"rb\") as file_h:\n db_length = pickle.load(file_h)\n except:\n print \"reading files..\"\n t = time.time()\n \"\"\"with open(ALL_FILE+str(max_db_size), \"rb\") as file_h:\n all_queries = pickle.load(file_h)\n with open(DB_FILE+str(max_db_size), \"rb\") as file_h:\n db = pickle.load(file_h)\"\"\"\n with open(ALL_FIXED_q, \"rb\") as file_h:\n all_queries = pickle.load(file_h)\n with open(ALL_FIXED_dbs, \"rb\") as file_h:\n db = pickle.load(file_h)\n print \"done reading\", time.time() - t\n print [len(e.keys()) for e in db]\n print len(all_queries.keys())\n db_length = {}\n curr_num = 0\n for query in all_queries.keys():\n curr_num += 1\n\n if curr_num % 1000 == 0:\n print \"MATCHES: I'M Alive!\", curr_num, query\n\n matches = get_matches(query, all_queries[query])\n db_length[query] = len(matches)\n print \"save data\"\n with open(\"matches\", \"wb\") as file_h:\n pickle.dump(db_length, file_h)\n print \"saved!\"\n #pprint(db_length)\n flag = False\n sorted_d = OrderedDict(sorted(db_length.items(), key=lambda x: x[1]))\n for x in sorted_d:\n if not flag:\n print \"FIRST\", sorted_d[x]\n flag = True\n print \"LAST\", sorted_d[x]\n print \"AVG:\", float(sum(db_length.values()))/len(db_length.keys())","def save_scores(self):\n\n with open('.scores.pickle', 'wb') as f:\n pickle.dump(self.scores, f)","def read_statistics(self):\n self.psdata=[]\n self.powerspectra=[]\n self.ds=[]\n self.dsigmasq=[]\n self.dsigma=[]\n self.bsdata=[]\n self.eqbispectra=[]\n self.fNLeq=[]\n\n for sub in range(self.Nsubs):\n self.psdata.append(np.load(self.datadir+self.filebase+\"_\"+str(sub)+\".npy\"))\n self.powerspectra.append(np.trim_zeros(self.psdata[-1][0][1:]))\n self.bsdata.append(np.load(self.datadir+self.fbbispec+\"_\"+str(sub)+\".npy\"))\n self.eqbispectra.append(self.bsdata[-1][0][1:len(self.powerspectra[-1])])\n\n self.ds.append(np.load(self.datadir+\"stat_\"+str(sub)+\".npy\")[0])\n self.dsigmasq.append(np.load(self.datadir+\"stat_\"+str(sub)+\".npy\")[1])\n self.dsigma = np.array([np.sqrt(dsq) for dsq in self.dsigmasq])\n\n self.klist=np.arange(1, len(self.powerspectra[-1]))*(2.*np.pi/self.Lsub)\n # subtract the mean ds\n self.ds = self.ds - np.mean(self.ds)\n self.fNLeq=np.mean(self.eqbispectra, axis=0)\n self.fNLeqsubs=np.mean(self.eqbispectra, axis=1)\n self.fNLeqds=[]\n for i in range(len(self.eqbispectra)):\n self.fNLeqds.append(np.array([self.ds[i]*self.eqbispectra[i][j] for j in range(45)]))","def record_f1_score(record_examples: List[RecordNestedExample]):\n if not record_examples:\n return 0.\n f1_scores = []\n for example in record_examples:\n example_f1s = []\n for answer in example.answers:\n example_f1s.append(exact_match_score(example.prediction, answer))\n if example_f1s:\n f1_scores.append(max(example_f1s))\n return np.mean(f1_scores)","def score(self, sentence):\n\n\n # TODO your code here\n score = 0.0 \n prevWord = \"\"\n prevPrevWord = \"\"\n newSentence = []\n for word in sentence:\n newSentence += word.split()\n for currentWord in sentence:\n currentWord = currentWord.strip(STRIP_CHARS)\n currentWord = currentWord.lower()\n if prevWord != \"\":\n if prevPrevWord != \"\":\n trigram = (prevPrevWord, prevWord, currentWord)\n trigramCount = self.trigramCounts[trigram]\n if trigramCount > 0:\n score += math.log(max(self.trigramCounts[trigram] - DISCOUNT, 0)*len(self.trigramCounts) + DISCOUNT*self.followingCounts[(prevPrevWord, prevWord)]*self.continuationCounts[currentWord])\n # Subtraction by 1 removes the add one count from the laplace\n # smoothing\n score -= math.log((self.bigramCounts[(prevPrevWord, prevWord)]) * len(self.trigramCounts))\n elif self.bigramCounts[(prevWord, currentWord)] > 0:\n score += math.log(self.bigramCounts[(prevWord, currentWord)]*BI_BACKOFF_COEFFICIENT)\n score -= math.log(self.totalBigramCounts)\n else:\n count = self.unigramCounts[currentWord]\n score += math.log(count * UNI_BACKOFF_COEFFICIENT)\n score -= math.log(self.total)\n else:\n prevPrevWord = prevWord\n prevWord = currentWord\n else:\n prevWord = currentWord\n return -score","def score(self, sentence):\n s = 0;\n\n #for every word\n for i in xrange(len(sentence)):\n score = self.getBackOff(tuple(sentence[:i+1]));\n if(score != 0):\n s += math.log(score);\n\n return s","def __init__(self, treshold: float = 0.5):\n self.threshold: float = treshold\n self.dice_scores: list = []\n self.iou_scores: list = []\n self.sens_scores: list = []\n self.spec_scores: list = []\n self.accu_scores: list = []","def bls_peakfinder(results):\n maxima = find_peaks(results.power, distance=100)[0]\n\n top_power_inds = maxima[np.argsort(results.power[maxima])[::-1]]\n\n highest_peak = results.power[top_power_inds[0]]\n next_highest_peak = results.power[top_power_inds[1]]\n\n significance = highest_peak / next_highest_peak\n\n return top_power_inds, significance","def __save_aggregate_scores(self):\n results = pd.DataFrame(\n columns=self.results[list(self.results.keys())[0]],\n index=self.results.keys(),\n )\n for row_name, time_bins in self.results.items():\n for column_name, value in time_bins.items():\n results.loc[row_name, column_name] = value\n results.reindex(sorted(results.columns, reverse=True), axis=1).to_csv(\n self.df_save_path\n )","def get_score_history(self):\n return self._score_history","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 score_samples(self, x):\n raise NotImplementedError","def _scan_scores(self,handle, consumer):\n read_and_call(handle, consumer.scores, start=\"Smith-Waterman\")","def test_get_game_boxscore(self):\n pass"],"string":"[\n \"def db2score(self):\\n print(\\\"db2score\\\")\\n self.score.array_frame_start = self.arrayFrameStart\\n self.score.array_frame_end = self.arrayFrameEnd\\n self.score.arraySet = self.arraySet\\n self.score.arrayGame = self.arrayGame\\n self.score.arrayScore = self.arrayScore\\n self.score.arrayScoreResult = self.arrayScoreResult\\n self.score.arrayFirstSecond = self.arrayFirstSecond\\n self.score.arrayServer = self.arrayServer\\n\\n self.score.arrayPointWinner = self.arrayPointWinner\\n self.score.pointWin = self.pointWin\\n self.score.arrayPointPattern = self.arrayPointPattern\\n self.score.arrayForeBack = self.arrayForeBack\\n\\n self.score.arrayContactServe = self.arrayContactServe\\n self.score.arrayCourt = self.arrayCourt\\n\\n self.score.playerA = self.playerA\\n self.score.playerB = self.playerB\\n self.score.number = self.number\\n self.score.totalGame = self.totalGame\\n self.score.faultFlug = self.faultFlug\\n self.score.arrayFault = self.arrayFault\\n\\n # size = len(self.score.array_frame_start)\\n\\n self.score.shot_frame = self.shot_frame\\n self.score.array_ball_position_shot_x = self.array_ball_position_shot_x\\n self.score.array_ball_position_shot_y = self.array_ball_position_shot_y\\n self.score.arrayPlayerAPosition_x = self.arrayPlayerAPosition_x\\n print(self.score.arrayPlayerAPosition_x)\\n self.score.arrayPlayerAPosition_y = self.arrayPlayerAPosition_y\\n self.score.arrayPlayerBPosition_x = self.arrayPlayerBPosition_x\\n self.score.arrayPlayerBPosition_y = self.arrayPlayerBPosition_y\\n self.score.arrayHitPlayer = self.arrayHitPlayer\\n self.score.arrayBounceHit = self.arrayBounceHit\\n self.score.arrayForeBack = self.arrayForeBack\\n self.score.arrayDirection = self.arrayDirection\\n\\n self.score.array_x1 = self.array_x1\\n self.score.array_y1 = self.array_y1\\n self.score.array_x2 = self.array_x2\\n self.score.array_y2 = self.array_y2\\n self.score.array_x3 = self.array_x3\\n self.score.array_y3 = self.array_y3\\n self.score.array_x4 = self.array_x4\\n self.score.array_y4 = self.array_y4\\n\\n self.score.array_frame_start = self.arrayFrameStart\\n self.score.shot_index = self.score.create_index_shot(\\n self.score.array_frame_start, self.score.shot_frame\\n )\\n\\n # for i in len(self.score.array_frame_start):\\n # self.score.shot_index = [0 for i in range(len(self.array_ball_position_shot))]#あとで変更の必要あり\\n\\n # self.score.array_ball_position_shot = self.check_size_return_array(\\n # self.array_ball_position_shot, size\\n # )\\n # self.score.arrayPlayerAPosition = self.check_size_return_array(\\n # self.arrayPlayerAPosition, size\\n # )\\n # self.score.arrayPlayerBPosition = self.check_size_return_array(\\n # self.arrayPlayerBPosition, size\\n # )\\n # self.score.arrayHitPlayer = self.check_size_return_array(\\n # self.arrayHitPlayer, size\\n # )\\n # self.score.arrayBounceHit = self.check_size_return_array(\\n # self.arrayBounceHit, size\\n # )\\n # self.score.arrayForeBack = self.check_size_return_array(\\n # self.arrayForeBack, size\\n # )\\n # self.score.arrayDirection = self.check_size_return_array(\\n # self.arrayDirection, size\\n # )\\n\\n # self.score.array_x1 = self.check_size_return_array(self.array_x1, size)\\n # self.score.array_y1 = self.check_size_return_array(self.array_y1, size)\\n # self.score.array_x2 = self.check_size_return_array(self.array_x2, size)\\n # self.score.array_y2 = self.check_size_return_array(self.array_y2, size)\\n # self.score.array_x3 = self.check_size_return_array(self.array_x3, size)\\n # self.score.array_y3 = self.check_size_return_array(self.array_y3, size)\\n # self.score.array_x4 = self.check_size_return_array(self.array_x4, size)\\n # self.score.array_y4 = self.check_size_return_array(self.array_y4, size)\\n\\n return self.score\",\n \"def find_all(self):\\n cursor = self._connection.cursor()\\n cursor.execute('SELECT * FROM scores ORDER BY level')\\n all_scores = cursor.fetchall()\\n return all_scores\",\n \"def score(self):\",\n \"def find_scores(self):\\n p1_score = self.p1_store()\\n p2_score = self.p2_store()\\n return p1_score, p2_score\",\n \"def scoring(self):\\n pass\",\n \"def get_scores(self):\\n return self.score\",\n \"def get_rms_db(data):\\n # square root => multiply by 10 instead of 20 for dBs\\n mean_square = np.mean(data ** 2)\\n return 10 * np.log10(mean_square)\",\n \"def get_score(self):\\n files_flare = self.generate_flare_set()\\n files_non_flare = self.generate_non_flare_set()\\n timeseries = []\\n y = []\\n scores = {}\\n column_mapping = self.__get_column_mapping()\\n for col in tqdm(range(1, 25)):\\n for file in tqdm(files_flare):\\n s = Sample(\\\"FL\\\", file).get_data().iloc[:, col].values\\n y.append(self.mapping[file[0]])\\n timeseries.append(s)\\n\\n for file in tqdm(files_non_flare):\\n s = Sample(\\\"NF\\\", file).get_data().iloc[:, col].values\\n y.append(self.mapping[file[0]])\\n timeseries.append(s)\\n embed = self.get_embed_vector(timeseries)\\n\\n embed_y = KMeans(n_clusters=5).fit_predict(embed)\\n y = np.array(y).flatten()\\n scores[column_mapping[col]] = self.relevance_score(embed_y, y)\\n timeseries = []\\n y = []\\n scores_data = pd.DataFrame.from_dict(scores, orient='index', columns=['Relevance Score']).sort_values(\\n by='Relevance Score', ascending=False)\\n return scores_data\",\n \"def get_scores(tmp_dir, profile_file_directory, stderr_log_output):\\r\\n\\t\\r\\n\\tranges = pickle.load(open(os.path.join(tmp_dir, \\\"ranges.pkl\\\")))\\r\\n\\t\\r\\n\\t(database, locusList,list_of_all_allele_numbers_tuple) = try_and_except(stderr_log_output,\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tget_profiles,\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tprofile_file_directory)\\r\\n\\t\\r\\n\\tresults = try_and_except(stderr_log_output,\\r\\n\\t\\t\\t\\t\\t\\t\\t score,\\r\\n\\t\\t\\t\\t\\t\\t\\t tmp_dir,\\r\\n\\t\\t\\t\\t\\t\\t\\t locusList,\\r\\n\\t\\t\\t\\t\\t\\t\\t ranges)\\r\\n\\t\\r\\n\\treturn results, database, locusList, list_of_all_allele_numbers_tuple\",\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 getScores(self,query):\\n pass\",\n \"def rms_db(self):\\n mean_square = np.mean(self._samples ** 2, axis=0)\\n return 10 * np.log10(mean_square)\",\n \"def getScore(data):\\n return score\",\n \"def score_samples(self, X):\\n ...\",\n \"def getScore(self, seq, start):\\n sum = 0.0\\n seqdata = seq.getSequence()[start : start+self.cols]\\n for pos in range(len(seqdata)):\\n q = self.counts[pos].getFreq(seqdata[pos])\\n if q == 0:\\n q = 0.0001 # to avoid log(0) == -Infinity\\n logodds = math.log(q / self.background.getFreq(seqdata[pos]))\\n sum += logodds\\n return sum\",\n \"def get_scores(self, query):\\n self.init()\\n tokens = self.tokenizer.tokenize(query)\\n return self.bm25_instance.get_scores(query=tokens)\",\n \"def score(self, data):\\n return np.mean( np.log( mvn.getSamplePointDensity(self.dataFrame_, self.H_, pd.DataFrame(data)) ) )\",\n \"def resultat_match(self, binomes):\\n for binome in binomes:\\n while True:\\n score_un = self.vue.entree_resultats(binome[0])\\n score_deux = self.vue.entree_resultats(binome[1])\\n if score_un + score_deux != 1:\\n self.vue.erreur_score()\\n continue\\n else:\\n binome[0].ajout_score(score_un)\\n binome[1].ajout_score(score_deux)\\n table_players.update({\\\"score\\\": binome[0].points},\\n doc_ids=[binome[0].id])\\n table_players.update({\\\"score\\\": binome[1].points},\\n doc_ids=[binome[1].id])\\n break\\n self.vue.afficher_resultats(binomes)\",\n \"def disp_score():\",\n \"def get_score_matrix(self) -> int:\",\n \"def find_all_by_level(self, level):\\n cursor = self._connection.cursor()\\n command = 'SELECT * FROM scores WHERE level=? ORDER BY score'\\n cursor.execute(command, [level])\\n all_scores_by_level = cursor.fetchall()\\n return all_scores_by_level\",\n \"def all_scores(self):\\r\\n if not self.child_history:\\r\\n return None\\r\\n return [self.child_history[i].get('score') for i in xrange(0, len(self.child_history))]\",\n \"def test_get_score(self):\\r\\n\\r\\n score_dict = self.get_score(True, 3, 3)\\r\\n\\r\\n # Score should be 1.0.\\r\\n self.assertEqual(score_dict[\\\"score\\\"], 1.0)\\r\\n\\r\\n # Testing score after data is stored in student_data_for_location in xmodule.\\r\\n _score_dict = self.peer_grading.get_score()\\r\\n\\r\\n # Score should be 1.0.\\r\\n self.assertEqual(_score_dict[\\\"score\\\"], 1.0)\",\n \"def getThreshDB():\\n calDB = db.TinyDB(\\\"%s/calDB-v2.json\\\" % dsi.latSWDir)\\n pars = db.Query()\\n bkg = dsi.BkgInfo()\\n\\n # loop over datasets\\n # for ds in [0,1,2,3,4,5,6]:\\n for ds in [6]:\\n dsNum = ds if isinstance(ds, int) else 5\\n goodChans = det.getGoodChanList(dsNum)\\n\\n for bkgIdx in bkg.getRanges(ds):\\n\\n # ==== loop over sub-ranges (when TF was run) ====\\n rFirst, rLast = bkg.getRanges(ds)[bkgIdx][0], bkg.getRanges(ds)[bkgIdx][-1]\\n\\n subRanges = bkg.GetSubRanges(ds,bkgIdx)\\n if len(subRanges) == 0: subRanges.append((rFirst, rLast))\\n\\n for subIdx, (runLo, runHi) in enumerate(subRanges):\\n\\n key = \\\"thresh_ds%d_bkg%d_sub%d\\\" % (dsNum, bkgIdx, subIdx)\\n\\n thD = dsi.getDBRecord(key, False, calDB, pars)\\n print(key)\\n for ch in thD:\\n print(ch,\\\":\\\",thD[ch])\\n print(\\\"\\\")\",\n \"def getDocScore(self, qryid, docid):\\n retrievedDocs = self.getDocs(qryid)\\n for pos in range(len(retrievedDocs)):\\n if docid == retrievedDocs[pos]:\\n return self.qryScores[qryid][pos]\\n return 0\",\n \"def score(PDBfile):\\n from pro_angle import find_residue\\n from Bio.PDB.PDBParser import PDBParser\\n from pro_length import length\\n \\n (aas, gly, pro) = load_scores() ##define global tables\\n score = 0 #initialize \\n pars = PDBParser(PERMISSIVE = 1)\\n struct = pars.get_structure(PDBfile.rstrip('.pdb'), PDBfile)\\n model = struct.child_list[0]\\n chain = model.child_list[0]\\n pro_list = find_residue(chain, 'PRO')\\n gly_list = find_residue(chain, 'GLY')\\n aas_list = range(chain.child_list[1].id[1],\\n chain.child_list[len(chain)-1].id[1])\\n #need to remove pro/gly indices in first/last position\\n if pro_list.count(1) > 0: \\n pro_list.remove(1)\\n if pro_list.count(len(chain)-1) > 0:\\n pro_list.remove(len(chain)-1)\\n if gly_list.count(1) > 0:\\n gly_list.remove(1)\\n if gly_list.count(len(chain)-1) > 0:\\n gly_list.remove(len(chain)-1) \\n try:\\n for index in pro_list: \\n aas_list.remove(index) #remove pros from aas_list\\n for index in gly_list:\\n aas_list.remove(index) #remove glys from aas_list\\n except ValueError:\\n print 'incosistency in PDB file - will return score = 0' \\n return 0\\n else:\\n proscore = score_help(chain, pro_list, pro)\\n glyscore = score_help(chain, gly_list, gly)\\n aasscore = score_help(chain, aas_list, aas)\\n score = proscore+glyscore+aasscore\\n size=length(chain)\\n try:\\n score = (score/size)*1000 #normalize score\\n return score\\n except ZeroDivisionError:\\n print \\\"calculated protein length 0 -> returning score 0\\\"\\n score = 0\\n return score\",\n \"def score(self, x, y=None):\\n _, logp = self.score_samples(x)\\n return logp\",\n \"def test_get_score(self):\\r\\n score_dict = self.combinedoe.get_score()\\r\\n self.assertEqual(score_dict['score'], 15.0)\\r\\n self.assertEqual(score_dict['total'], 5.0)\",\n \"def find_fbs(self,q=99.9):\\n fbs=np.zeros((self.dataset['pred_avg'].shape[0],1))\\n for itrc in np.arange(0,self.dataset['pred_avg'].shape[0]):\\n trc=self.dataset['pred_avg'][itrc]\\n nonzero=np.where(trc!=0)[0]\\n perc=np.nanpercentile(trc[list(nonzero)],q)\\n potential_fbs=np.where(trc[:]>=perc)[0]\\n if len(potential_fbs)!=0:\\n fbs[itrc]=np.int(potential_fbs[0])\\n else:\\n print('FB was not found for trace id:\\\\t{}'.format(itrc))\\n print('Completed')\\n return fbs\",\n \"def count_score(self, docsInfo, avgdl, k1, b):\\n\\t\\tdocScore = []\\n\\t\\tfor doc in docsInfo:\\n\\t\\t\\tcurDocScore = 0\\n\\t\\t\\tfor queryWord in range(len(doc['meetCnt'])):\\n\\t\\t\\t\\tTF = float(doc['meetCnt'][queryWord])\\n\\t\\t\\t\\tfreaq = sum(1 for x in docsInfo if x['meetCnt'][queryWord])\\n\\t\\t\\t\\tcurDocScore += self.count_IDF(len(docsInfo), freaq) * self.count_main_fraction(TF, k1, b, avgdl, doc['len'])\\n\\t\\t\\tdocScore.append(curDocScore)\\n\\t\\treturn docScore\",\n \"def get_score(self, a, b):\\n ### FILL IN ###\",\n \"def __get_score(self):\\n for pair in zip(self.nu[self.nu_idx:], self.sw[self.sw_idx:]):\\n if pair[0] == pair[1]:\\n self.score += 1\\n else:\\n break\",\n \"def get_score(self, student_answers):\\r\\n pass\",\n \"def score(self):\\n return self.client.call('GET', self.name + 'score')\",\n \"def print_scores(self):\\n ### FILL IN ###\",\n \"def test_get_score(self):\\r\\n score_dict = self.combinedoe.get_score()\\r\\n self.assertEqual(score_dict['score'], 0)\\r\\n self.assertEqual(score_dict['total'], 1)\",\n \"def scores_statistics(self, scores):\\r\\n aux_scores = np.array(scores)\\r\\n return np.mean(aux_scores), np.median(aux_scores), np.std(aux_scores)\",\n \"def get_metrics(self, phase):\\n dice = {}\\n l = len(self.base_dice_scores[phase])\\n for i, d in enumerate(self.base_dice_scores[phase]):\\n for k in d:\\n if k not in dice:\\n dice[k] = 0\\n dice[k] += d[k] / l\\n \\n dice_neg = np.mean(self.dice_neg_scores[phase])\\n dice_pos = np.mean(self.dice_pos_scores[phase])\\n dices = [dice, dice_neg, dice_pos]\\n iou = np.nanmean(self.iou_scores[phase])\\n return dices, iou\",\n \"def get_scores(self):\\n hist = self.confusion_matrix\\n # hist = [TN,FP;FN,TP]\\n acc = np.diag(hist).sum() / hist.sum()\\n acc_cls = np.diag(hist) / hist.sum(axis=1)\\n acc_cls = np.nanmean(acc_cls)\\n iu = np.diag(hist) / (hist.sum(axis=1) + hist.sum(axis=0) - np.diag(hist))\\n # iou = iu.sum() / self.n_classes\\n mean_iou = np.nanmean(iu) # if classes = 2: iou = miou\\n freq = hist.sum(axis=1) / hist.sum()\\n fwavacc = (freq[freq > 0] * iu[freq > 0]).sum()\\n cls_iou = dict(zip(range(self.n_classes), iu))\\n\\n ##############################################\\n tn = hist[0, 0]\\n tp = np.diag(hist).sum() - tn\\n fp = np.triu(hist, 1).sum()\\n fn = np.tril(hist, -1).sum()\\n precision = tp / (tp + fp)\\n recall = tp / (tp + fn)\\n f1 = 2 * precision * recall / (precision + recall)\\n\\n # for medical img, img_seg \\\\in [0,1]\\n dice = 2 * tp / (tp + tp + fn + fp)\\n # dice = f1-score\\n dsc = 2 * tp / (tp + fn + fp)\\n # dsc = jaccard\\n # voe = 2 * abs(fp + fn) / (tp + tp + fn + fp)\\n # voe = 1 - dsc\\n\\n k2 = {\\n # \\\"Overall Acc: \\\\t\\\": acc,\\n 'Mean Acc': float(judge_nan(acc_cls)),\\n # \\\"FreqW Acc : \\\\t\\\": fwavacc,\\n 'Mean IoU': float(judge_nan(mean_iou)),\\n 'F1-score': float(judge_nan(f1)),\\n 'DSC': float(judge_nan(dsc)),\\n 'Precision': float(judge_nan(precision)),\\n 'Recall': float(judge_nan(recall)),\\n }\\n\\n return k2\",\n \"def test_get_score():\\n\\n assert sequence_threshold.get_score([]) == 0\\n assert sequence_threshold.get_score(SortedSet()) == 0\\n assert sequence_threshold.get_score(list(range(3, 36))) == 3\\n assert sequence_threshold.get_score([10, 11, 12, 14, 16, 17]) == 10 + 14 + 16\",\n \"def get_score(snack_data, percentage_data, snack, snack_query, protein_query, carb_query, fat_query):\\n\\tstart_time = time.time()\\n\\n\\t#Load necessary data\\n\\t\\\"\\\"\\\"\\twith open ('../../../Data/percentagesDict.pickle', 'rb') as f:\\n\\t\\tpercentage_data = pickle.load(f)\\n\\n\\twith open ('../../../Data/FINAL_snacks_data.pickle', 'rb') as f:\\n\\t\\tsnack_data = pickle.load(f)\\\"\\\"\\\"\\n\\n\\t#Set constants\\n\\tLOW_FAT = .3\\n\\tHIGH_FAT = .6\\n\\tLOW_CARB = .1\\n\\tHIGH_CARB = .2\\n\\tLOW_PRO = .2\\n\\tHIGH_PRO = .4\\n\\n\\t#Convert macro percentages to 'high', 'med', 'low' categories\\n\\tfat = percentage_data[snack]['fat']\\n\\tprotein = percentage_data[snack]['protein']\\n\\tcarb = percentage_data[snack]['carb']\\n\\n\\tif fat > HIGH_FAT:\\n\\t\\tfat_content = 'high'\\n\\telif fat < LOW_FAT:\\n\\t\\tfat_content = 'low'\\n\\telse:\\n\\t\\tfat_content = 'med'\\n\\n\\tif protein > HIGH_PRO:\\n\\t\\tprotein_content = 'high'\\n\\telif protein < LOW_PRO:\\n\\t\\tprotein_content = 'low'\\n\\telse:\\n\\t\\tprotein_content = 'med'\\n\\n\\tif carb > HIGH_CARB:\\n\\t\\tcarb_content = 'high'\\n\\telif carb < LOW_CARB:\\n\\t\\tcarb_content = 'low'\\n\\telse:\\n\\t\\tcarb_content = 'med'\\n\\n\\t#Set x values\\n\\tx1 = fat_query == fat_content\\n\\tx2 = carb_query == carb_content\\n\\tx3 = protein_query == protein_content\\n\\tx4 = cooccur(snack_data, snack, snack_query) \\n\\tx5 = snack_data[snack]['rating']\\n\\n\\tw1 = 1\\n\\tw2 = 1\\n\\tw3 = 1\\n\\tw4 = 1\\n\\tw5 = 1\\n\\t\\n\\t#print('x1: {}, x2: {}, x3: {}, x4: {}, x5: {}'.format(x1, x2, x3, x4, x5))\\n\\t#print(\\\"get_score() time: --- %s seconds ---\\\" % (time.time() - start_time))\\n\\n\\n\\treturn w1*x1 + w2*x2 + w3*x3 + w4*x4 + w5*x5\",\n \"def get_data(self):\\n\\n self.cur.execute('SELECT year, sex, education, score from vocabulary_scores;')\\n scores = dict()\\n education = dict()\\n count = dict()\\n\\n for row in self.cur :\\n if row[0] in scores:\\n if row[1] in scores[row[0]]:\\n scores[row[0]][row[1]] += int(row[3])\\n education[row[0]][row[1]] += int(row[2])\\n count[row[0]][row[1]] += 1\\n else:\\n scores[row[0]][row[1]] = int(row[3])\\n education[row[0]][row[1]] = int(row[2])\\n count[row[0]][row[1]] = 1\\n else:\\n # scores[year] = {gender: score}\\n scores[row[0]] = {row[1]: int(row[3])}\\n education[row[0]] = {row[1]: int(row[2])}\\n count[row[0]] = {row[1]: 1}\\n\\n scores, education = self.average_scores(scores, education, count)\\n\\n return scores, education\",\n \"def score(self):\\n return None\",\n \"def profile_mongodb(chembl_ids):\\n db = pymongo.MongoClient('localhost',27020).chembldb\\n size=len(chembl_ids)\\n for threshold in [0.65,0.75,0.85,0.95]:\\n report = []\\n times = []\\n counts = []\\n\\n # Perform the queries\\n for chembl_id in chembl_ids:\\n print chembl_id\\n qmol = db.molecules.find_one({'chembl_id': chembl_id})\\n report.append('Query: {}'.format(qmol['chembl_id']))\\n start = time.time()\\n results = similarity(qmol['mfp2']['bits'],db,threshold)\\n end = time.time()\\n counts.append(len(results))\\n report.append('Results ({})'.format(len(results)))\\n print report[-1]\\n for r in results:\\n report.append('{}: {}'.format(r['tanimoto'], r['chembl_id']))\\n print report[-1]\\n times.append(end - start)\\n\\n # Produce a report of the results\\n report.append('Counts: {}'.format(' '.join(str(c) for c in counts)))\\n report.append('Mean: {}'.format(np.mean(counts)))\\n report.append('Median: {}'.format(np.median(counts)))\\n report.append('Times: {}'.format(' '.join(str(t) for t in times)))\\n report.append('Mean: {}'.format(np.mean(times)))\\n report.append('Median: {}'.format(np.median(times)))\\n report.append('95th Percentile: {}'.format(np.percentile(times, 95)))\\n report = '\\\\n'.join(report)\\n #print report\\n with open('results-{}-{}--rdmaccs.txt'.format(threshold,size), 'w') as f:\\n f.write(report)\",\n \"def davies_bouldin_score(self):\\r\\n print(colored(\\\"The davies bouldin score of the clustering is %0.002f\\\\n\\\" %(davies_bouldin_score(self.X, self.labels)),color = 'red', attrs=['bold']))\\r\\n print()\\r\\n print(colored(\\\"The points in each cluster are : \\\",color = 'yellow', attrs=['bold']))\\r\\n print(collections.Counter(self.labels))\",\n \"def get_score(self, solution: np.array) -> float:\\n pass\",\n \"def save_scores(emb_scores, emb_filename, base_scores, base_filename, output_dir):\\n\\n emb_file = open(Path(output_dir) / emb_filename, 'w')\\n base_file = open(Path(output_dir) / base_filename, 'w')\\n for word, score in emb_scores.items():\\n if word in base_scores:\\n print(float(score), file=emb_file)\\n print(float(base_scores[word]), file=base_file)\",\n \"def generateScore(self):\\n totalFreq = self.lazySum(key=None)\\n for file in self._candidate_files:\\n filename = os.path.basename(file)\\n score_file = os.path.join(self._score_dir, filename)\\n with open(score_file, 'w') as ofd:\\n with open(file) as ifd:\\n for line in ifd:\\n words = line.strip().split('\\\\t')\\n if len(words) < 2 or any(map(lambda word:len(word)<2, words)):\\n continue\\n\\n XFreq = self.lazySum(words[0])\\n YFreq = self.lazySum(words[1])\\n XYFreq = self.lazySum(line.strip())\\n # frequences filter\\n #if XYFreq < 2 or XYFreq > 24:\\n # continue\\n if YFreq == 0 or XFreq == 0 or XYFreq == 0:\\n # because when generating grams, we last last words' frequency\\n continue\\n PX = XFreq * 1.0 / totalFreq\\n PY = YFreq * 1.0 / totalFreq\\n PXY = XYFreq * 1.0 / totalFreq\\n score = math.log(PXY/PX/PY, 2) * XYFreq\\n #print \\\"Freq:\\\", XFreq, YFreq, XYFreq\\n result = \\\"{0}\\\\t{1:.2f}\\\\n\\\".format(line.strip(), score)\\n ofd.write(result)\",\n \"def score_bag(self, bag):\\n # pop_scores = bag.retrieve_external_scores(self.file_name, self.col_name)\\n # true_scores = pop_scores.ix[bag.true_pop]\\n # scores_col = true_scores.columns[0]\\n # res = np.array([true_scores.index.values, true_scores[scores_col].values]).transpose()\\n # return res\\n\\n pop_scores = bag.retrieve_external_scores(self.file_name, self.col_name)\\n candidate_ids_numeric = frozenset(bag.universe) - frozenset(bag.whites)\\n # candidate_id_strings = (str(int(idnum)) for idnum in candidate_ids_numeric)\\n candidate_scores = pop_scores.ix[candidate_ids_numeric].dropna()\\n scores_col = candidate_scores.columns[0]\\n res = np.array([\\n candidate_scores.index.values.astype(int),\\n candidate_scores[scores_col].values\\n ]).transpose()\\n return res\",\n \"def get_bad_score(self):\\n logger.debug('Function get_bad_score start')\\n\\n cur = self.conn.cursor()\\n\\n # Selecting worst score\\n cur.execute(\\n \\\"SELECT id, img_name, img_score FROM sensor_data ORDER BY img_score ASC LIMIT 1\\\")\\n\\n # Getting worst score\\n row = cur.fetchone()\\n\\n logger.debug('Function get_bad_score end')\\n return row\",\n \"def test_scores(self) -> np.ndarray:\\n return np.asarray(self.test_metric_dict[self.metric_name])\",\n \"def score(self, test_data):\\n\\n\\t\\tpass\",\n \"def _read_scores(self,path):\\n scores = dict()\\n fin = open(path,'r')\\n for line in fin:\\n k, v = line.split()\\n scores[k.strip()] = float(v.strip())\\n return scores\",\n \"def power2db(power):\\r\\n db = 10 * np.log10(power)\\r\\n return db\",\n \"def _get_scores_list(self):\\n self.scores = dict()\\n for subdir in os.listdir(self.path):\\n if subdir.startswith('.'):\\n continue\\n try:\\n score = open('{0}/{1}/{2}/extract_all.sort.uniq.txt'.format(self.path, subdir, DOCKING_RUN_FILES),'r').read().split()[-1]\\n self.scores[subdir] = float(score.strip())\\n except:\\n pass\",\n \"def score(self):\\n raise NotImplementedError()\",\n \"def test_genomic(self):\\n self.c.execute(\\\"\\\"\\\"select expIds,expScores from genomic_test\\\"\\\"\\\")\\n rows = self.c.fetchall()\\n self.assertEqual(len(rows), 1) # one probe\\n self.assertEqual(rows[0][0], '0,1,2,3,4') # ordered by sample id\\n values = map(lambda x: float(x), rows[0][1].split(',')) # scores are in correct order\\n self.assertTrue(values[0] - 0.479005065149792 < self.tolerance)\\n self.assertTrue(values[1] - 25.1 < self.tolerance)\\n self.assertTrue(values[2] - 5.3 < self.tolerance)\\n self.assertTrue(values[3] - 3.1 < self.tolerance)\\n self.assertTrue(values[4] - -1.23 < self.tolerance)\",\n \"def print_scores(self):\\n print(\\\"scores: \\\", self.get_scores())\",\n \"def scores_(self):\\n return self.predictor.scores_\",\n \"def get_scores(self):\\n return pd.DataFrame(self._scores)\",\n \"def calc_score(score):\\n if not score:\\n return 0\\n dbot_score = 1\\n if score >= 95:\\n dbot_score = 3\\n elif score >= 75:\\n dbot_score = 2\\n return dbot_score\",\n \"def list_of_scores(self):\\n with open(Constant.this_score, \\\"r\\\") as f:\\n final= f.read()\\n final = int(final)\\n with open(Constant.list_scores, \\\"r\\\") as li:\\n points_file = json.load(li)\\n points = points_file['HighScores']\\n\\n with open(Constant.list_scores,\\\"w\\\") as files:\\n\\n if len(points) == 5:\\n if points[0] < final:\\n for i in range(len(points)-1,0,-1):\\n points[i] = points[i-1]\\n points[0] = final\\n json.dump(points_file,files)\\n elif points[0] > final:\\n if points[1] < final:\\n for i in range(len(points)-1,1,-1):\\n points[i] = points[i-1]\\n points[1] = final\\n json.dump(points_file,files)\\n elif points[1] > final:\\n if points[2] < final:\\n for i in range(len(points)-1,2,-1):\\n points[i] = points[i-1]\\n points[2] = final\\n json.dump(points_file,files)\\n elif points[2] > final:\\n if points[3] < final:\\n for i in range(len(points)-1,3,-1):\\n points[i] = points[i-1]\\n points[3] = final\\n json.dump(points_file,files)\\n elif points[3] > final:\\n if points[4] < final:\\n points[4] = final\\n json.dump(points_file,files)\\n else:\\n json.dump(points_file,files)\\n else:\\n json.dump(points_file,files)\\n else:\\n json.dump(points_file,files)\\n else:\\n json.dump(points_file,files)\\n else:\\n json.dump(points_file,files)\",\n \"def readScore(self):\\n return self.zmwMetric(\\\"ReadScore\\\")\",\n \"def all_scores(self):\\r\\n if not self.child_history:\\r\\n return None\\r\\n return [self.score_for_attempt(index) for index in xrange(0, len(self.child_history))]\",\n \"def test_get_advanced_scores(self):\\n\\n this_score_table_xarray = learning_curves.get_advanced_scores(\\n BASIC_SCORE_TABLE_XARRAY\\n )\\n\\n self.assertTrue(_compare_advanced_score_tables(\\n this_score_table_xarray, ADVANCED_SCORE_TABLE_XARRAY\\n ))\",\n \"def dBtoLinear(db):\\r\\n return 10**(db/20)\",\n \"def test_query_score(self):\\n id = get_rand_string()\\n\\n # Same data and user_id\\n user_id = data = get_rand_string()\\n\\n self.conn.add(id=id, user_id=user_id, data=data)\\n self.conn.commit()\\n\\n results = self.conn.query(\\\"id:\\\" + id).results\\n self.assertEquals(len(results), 1,\\n \\\"No documents fetched, expected id:%s\\\" % (id))\\n\\n doc = results[0]\\n\\n self.assertTrue(\\\"score\\\" in doc, \\\"No score returned, doc:%s\\\" % repr(doc))\\n self.assertTrue(isinstance(doc[\\\"score\\\"], float),\\n \\\"Score should be a float instance, doc:%s\\\" % repr(doc))\",\n \"def PrintFieldLinearity(self):\\n sql = (\\\"SELECT Frequency__GHz, Field__V_per_m, FieldResult__V_per_m\\\"\\n \\\" FROM 场强线性度 ORDER BY Frequency__GHz, Field__V_per_m\\\")\\n testdata = list(self.db.cursor.execute(sql).fetchall())\\n testdata = [list(obj) for obj in testdata]\\n rownum = testdata.__len__()\\n sql = (\\\"SELECT TableNum FROM %s WHERE \\\"\\n \\\"TestItems='场强线性度'\\\" % self.position)\\n tablenum = self.basicinfo.cursor.execute(sql).fetchone()[0]\\n self.doc.TableAddRow(tablenum=tablenum, row=rownum)\\n for obj in testdata:\\n obj[2] = \\\"%0.2f\\\" % obj[2]\\n obj += (\\\"%0.2f\\\" % (float(obj[1]) / float(obj[2])), )\\n sql = (\\\"SELECT Uncertainty FROM Uncertainty WHERE %s < FreqUpper \\\"\\n \\\"AND %s >= FreqLower\\\" % (obj[0], obj[0]))\\n obj += (\\\"%0.1f\\\"\\n % (self.basicinfo.cursor.execute(sql).fetchone()[0]), )\\n # 打印不确定度k值\\n if not self.report:\\n self.doc.TableContent(\\n tablenum=tablenum - 1, cellrow=1, cellcolum=5,\\n insertcontent=\\\"测量不确定度(k=2) (dB)\\\")\\n for i in range(rownum):\\n if i % 2 == 1:\\n self.doc.CellDownMerge(\\n tablenum=tablenum, row=i + 1, col=1, mergenum=1)\\n self.doc.CellDownMerge(\\n tablenum=tablenum, row=i + 1, col=5, mergenum=1)\\n if i % 2 == 0:\\n assert testdata[i][0] == testdata[i + 1][0]\\n assert testdata[i][1] != testdata[i + 1][1]\\n self.doc.TableContent(\\n tablenum=tablenum - 1, cellrow=i + 2, cellcolum=1,\\n insertcontent=testdata[i][0])\\n self.doc.TableContent(\\n tablenum=tablenum - 1, cellrow=i + 2, cellcolum=5,\\n insertcontent=testdata[i][4])\\n for j in range(1, 4):\\n self.doc.TableContent(\\n tablenum=tablenum - 1, cellrow=i + 2, cellcolum=j + 1,\\n insertcontent=testdata[i][j])\",\n \"def recognition(database, testSong, plot=True, info=True, latextable=False):\\n #Score for every song in database with respect to testsong\\n \\n if latextable == True:\\n print(testSong[\\\"name\\\"])\\n print(\\\"\\\"\\\"\\\\\\\\begin{table}[H]\\n \\\\centering\\n \\\\\\\\begin{tabular}{|l|l|}\\n \\\\hline\\n Artist and Title & Score value\\\\\\\\\\\\\\\\\\n \\\\hline\\n \\\"\\\"\\\")\\n for i in range(len(database)):\\n scores = []\\n for j in range(testSong[\\\"spectrogram\\\"].shape[1]): #every frame of STFT\\n #Score is calculated\\n u = np.transpose(database[i][\\\"eigen\\\"]) @ (testSong[\\\"spectrogram\\\"][:, j] - database[i][\\\"mean\\\"])\\n scores.append(LA.norm(u - database[i][\\\"weights\\\"][:, j]))\\n #mean value of the scores\\n database[i][\\\"score\\\"] = sum(scores)/len(scores)\\n if latextable == True:\\n print(f'{database[i][\\\"name\\\"]} & {database[i][\\\"score\\\"]:0.7} \\\\\\\\\\\\\\\\')\\n if info == True and latextable != True:\\n print(f\\\"{database[i]['name']} has a score of {database[i]['score']}\\\")\\n if plot == True:\\n plt.plot(scores)\\n plt.title(f\\\"{database[i]['name'].split('.w')[0]}\\\\n testsong: {testSong['name'].split('/')[1].split('(o')[0]}\\\")\\n plt.show()\\n\\n #The lowest score is found and is the most alike to testSong\\n lowsScore = database[0]\\n for song in database[1:]:\\n if lowsScore[\\\"score\\\"] > song[\\\"score\\\"]:\\n lowsScore = song\\n if info == True and latextable != True: \\n print(\\\" \\\")\\n print(f\\\"The song with the lowest score is {lowsScore['name']} with a score of {lowsScore['score']}\\\")\\n \\n if latextable == True:\\n print(\\\"\\\"\\\"\\\\hline\\n\\\\end{tabular}\\n\\\\caption{Recognition of a recorded version of \\\"\\\"\\\" + testSong[\\\"name\\\"] + \\\"\\\"\\\" and the coherent score value.}\\n\\\\label{tab: }\\n\\\\end{table}\\\"\\\"\\\")\\n print(\\\" \\\")\\n return lowsScore['name'], lowsScore['score']\",\n \"def test_figure2(self):\\n\\n encoder = TctColBertQueryEncoder('castorini/tct_colbert-msmarco')\\n searcher = SimpleDenseSearcher.from_prebuilt_index('msmarco-passage-tct_colbert-hnsw', encoder)\\n hits = searcher.search('what is a lobster roll')\\n\\n self.assertAlmostEqual(hits[0].score, 70.53741, delta=0.0001)\\n self.assertEqual(hits[0].docid, '7157710')\\n\\n self.assertAlmostEqual(hits[9].score, 69.01737, delta=0.0001)\\n self.assertEqual(hits[9].docid, '2920399')\\n\\n self.assertEqual(len(hits), 10)\",\n \"def BEDreader(fname):\\n\\n bed_score = dict() \\n bfh = open(fname)\\n for line in bfh:\\n line = line.strip('\\\\n\\\\r').split('\\\\t')\\n assert len(line) == 5, '\\\\t'.join(line)\\n bed_score[float(line[3])] = 1\\n bfh.close()\\n return bed_score.keys()\",\n \"def super_complexs_score(df):\\r\\n return df['volume'] * 2\",\n \"def get_score(self):\\n return self.score\",\n \"def evaluate(self):\\n scores = []\\n scores.append(self.word_analogy())\\n print(\\\"Word Analogy (acc): \\\", scores[0])\\n scores.append(self.word_similarity())\\n print(\\\"Word Similarity (MSE): \\\", scores[1])\\n scores.append(self.concept_categorization())\\n print(\\\"Concept Categorization (purity): \\\", scores[2])\\n scores.append(self.sentiment_analysis())\\n print(\\\"Sentiment Analysis (acc): \\\", scores[3])\\n return scores\",\n \"def sample(\\n self,\\n scores: torch.Tensor\\n ):\\n perturbed = torch.log(scores) + self.gumbel_noise.sample((len(scores),))\\n return torch.argsort(-perturbed.detach())\",\n \"def db_statistics(per_frame_values):\\n\\n # strip off nan values\\n with warnings.catch_warnings():\\n warnings.simplefilter(\\\"ignore\\\", category=RuntimeWarning)\\n M = np.nanmean(per_frame_values)\\n O = np.nanmean(per_frame_values > 0.5)\\n\\n N_bins = 4\\n ids = np.round(np.linspace(1, len(per_frame_values), N_bins + 1) + 1e-10) - 1\\n ids = ids.astype(np.uint8)\\n\\n D_bins = [per_frame_values[ids[i]:ids[i + 1] + 1] for i in range(0, 4)]\\n\\n with warnings.catch_warnings():\\n warnings.simplefilter(\\\"ignore\\\", category=RuntimeWarning)\\n D = np.nanmean(D_bins[0]) - np.nanmean(D_bins[3])\\n\\n return M, O, D\",\n \"def getSenSpeValuesByScores(datafram_scores):\\n sen_spe_values_vec = []\\n for index, row in datafram_scores.iterrows():\\n sensitivity = libraries.measures_calculation.calculateSensitivity(row['tn'], row['fp'], row['fn'], row['tp'])\\n specificity = libraries.measures_calculation.calculateSpecificity(row['tn'], row['fp'], row['fn'], row['tp'])\\n model_file_name = row['file_name']\\n sen_spe_values_vec.append([index, sensitivity, specificity, model_file_name])\\n \\n df = pd.DataFrame(sen_spe_values_vec, columns=['index', 'Sensitivity', 'Specificity', 'file_name'])\\n return df\",\n \"def test_accuracy():\\n hits = 0\\n total = 0\\n # create_database()\\n # gen_random_samples()\\n song_to_id, id_to_song, hash_dict = load_database()\\n for filename in os.listdir(\\\"Songs\\\"):\\n sample_dict = hash_random_sample(filename)\\n offset_dict, song_id = find_song(\\n hash_dict, sample_dict, id_to_song)\\n print(id_to_song[song_id])\\n print(filename)\\n if id_to_song[song_id] == filename:\\n print(\\\"Success\\\")\\n hits += 1\\n else:\\n print(\\\"Fail\\\")\\n total += 1\\n print((hits / total) * 100, \\\" %\\\")\",\n \"def getScores():\\r\\n results = \\\"\\\"\\r\\n with sqlite3.connect(database_file) as conn:\\r\\n cursor = conn.cursor()\\r\\n team_scores = cursor.execute(\\\"\\\"\\\" SELECT * FROM scores;\\\"\\\"\\\")\\r\\n\\r\\n for row in team_scores.fetchall():\\r\\n teamname, auto, rc, spirit, video = row\\r\\n results += result_string.format(teamname, auto, rc, spirit, video) + \\\"\\\\n\\\"\\r\\n return results\",\n \"def get_highscores(self):\\n return self.database.get_high_scores(self.difficulty)\",\n \"def get_score(self):\\r\\n return None\",\n \"def measure(self, X2, X3, start_index): \\n \\n power = self.power\\n X2 = np.array(X2)\\n X3 = np.array(X3)\\n if len(X2) == 0:\\n score = 0\\n else:\\n X2 = np.fft.fft(X2);\\n X3 = np.fft.fft(X3)\\n score = np.linalg.norm(X2 - X3, ord = power)/len(X3)\\n self.decision_scores_.append((start_index, score))\\n return score\",\n \"def compare_dictionaries(d1, d2):\\r\\n score = 0\\r\\n gef = 0\\r\\n for z in d1:\\r\\n gef += d1[z]\\r\\n total = gef\\r\\n \\r\\n for x in d2:\\r\\n if x in d1:\\r\\n score += math.log(d1[x] / total) * d2[x] \\r\\n else:\\r\\n score += math.log(0.5/total) * d2[x]\\r\\n return score\",\n \"def f1_score(self):\",\n \"def score( self ):\\r\\n result = 0.0\\r\\n for rr in self.ee.getRsrcs( ):\\r\\n value = self.scoreRsrc( rr )\\r\\n result += value\\r\\n print( \\\"INFO: Value for the schedule is %s \\\" % ( rr, result ) )\\r\\n return( result )\",\n \"def HighScore_r():\\r\\n fichier=open(\\\"save/HGSC/HIGHSCORE.txt\\\",\\\"r\\\")\\r\\n l=1\\r\\n for ligne in fichier:\\r\\n if l==1:\\r\\n highscore=int(ligne)\\r\\n l=0\\r\\n fichier.close()\\r\\n return highscore\",\n \"def view_scores(jenni, input):\\n scores.view_scores(jenni, input)\",\n \"def number_matches():\\n ALL_FIXED_q = \\\"all_fixed_queries\\\" + str(17)\\n ALL_FIXED_dbs = \\\"all_fixed_dbs\\\" + str(17)\\n try:\\n with open(\\\"matches\\\", \\\"rb\\\") as file_h:\\n db_length = pickle.load(file_h)\\n except:\\n print \\\"reading files..\\\"\\n t = time.time()\\n \\\"\\\"\\\"with open(ALL_FILE+str(max_db_size), \\\"rb\\\") as file_h:\\n all_queries = pickle.load(file_h)\\n with open(DB_FILE+str(max_db_size), \\\"rb\\\") as file_h:\\n db = pickle.load(file_h)\\\"\\\"\\\"\\n with open(ALL_FIXED_q, \\\"rb\\\") as file_h:\\n all_queries = pickle.load(file_h)\\n with open(ALL_FIXED_dbs, \\\"rb\\\") as file_h:\\n db = pickle.load(file_h)\\n print \\\"done reading\\\", time.time() - t\\n print [len(e.keys()) for e in db]\\n print len(all_queries.keys())\\n db_length = {}\\n curr_num = 0\\n for query in all_queries.keys():\\n curr_num += 1\\n\\n if curr_num % 1000 == 0:\\n print \\\"MATCHES: I'M Alive!\\\", curr_num, query\\n\\n matches = get_matches(query, all_queries[query])\\n db_length[query] = len(matches)\\n print \\\"save data\\\"\\n with open(\\\"matches\\\", \\\"wb\\\") as file_h:\\n pickle.dump(db_length, file_h)\\n print \\\"saved!\\\"\\n #pprint(db_length)\\n flag = False\\n sorted_d = OrderedDict(sorted(db_length.items(), key=lambda x: x[1]))\\n for x in sorted_d:\\n if not flag:\\n print \\\"FIRST\\\", sorted_d[x]\\n flag = True\\n print \\\"LAST\\\", sorted_d[x]\\n print \\\"AVG:\\\", float(sum(db_length.values()))/len(db_length.keys())\",\n \"def save_scores(self):\\n\\n with open('.scores.pickle', 'wb') as f:\\n pickle.dump(self.scores, f)\",\n \"def read_statistics(self):\\n self.psdata=[]\\n self.powerspectra=[]\\n self.ds=[]\\n self.dsigmasq=[]\\n self.dsigma=[]\\n self.bsdata=[]\\n self.eqbispectra=[]\\n self.fNLeq=[]\\n\\n for sub in range(self.Nsubs):\\n self.psdata.append(np.load(self.datadir+self.filebase+\\\"_\\\"+str(sub)+\\\".npy\\\"))\\n self.powerspectra.append(np.trim_zeros(self.psdata[-1][0][1:]))\\n self.bsdata.append(np.load(self.datadir+self.fbbispec+\\\"_\\\"+str(sub)+\\\".npy\\\"))\\n self.eqbispectra.append(self.bsdata[-1][0][1:len(self.powerspectra[-1])])\\n\\n self.ds.append(np.load(self.datadir+\\\"stat_\\\"+str(sub)+\\\".npy\\\")[0])\\n self.dsigmasq.append(np.load(self.datadir+\\\"stat_\\\"+str(sub)+\\\".npy\\\")[1])\\n self.dsigma = np.array([np.sqrt(dsq) for dsq in self.dsigmasq])\\n\\n self.klist=np.arange(1, len(self.powerspectra[-1]))*(2.*np.pi/self.Lsub)\\n # subtract the mean ds\\n self.ds = self.ds - np.mean(self.ds)\\n self.fNLeq=np.mean(self.eqbispectra, axis=0)\\n self.fNLeqsubs=np.mean(self.eqbispectra, axis=1)\\n self.fNLeqds=[]\\n for i in range(len(self.eqbispectra)):\\n self.fNLeqds.append(np.array([self.ds[i]*self.eqbispectra[i][j] for j in range(45)]))\",\n \"def record_f1_score(record_examples: List[RecordNestedExample]):\\n if not record_examples:\\n return 0.\\n f1_scores = []\\n for example in record_examples:\\n example_f1s = []\\n for answer in example.answers:\\n example_f1s.append(exact_match_score(example.prediction, answer))\\n if example_f1s:\\n f1_scores.append(max(example_f1s))\\n return np.mean(f1_scores)\",\n \"def score(self, sentence):\\n\\n\\n # TODO your code here\\n score = 0.0 \\n prevWord = \\\"\\\"\\n prevPrevWord = \\\"\\\"\\n newSentence = []\\n for word in sentence:\\n newSentence += word.split()\\n for currentWord in sentence:\\n currentWord = currentWord.strip(STRIP_CHARS)\\n currentWord = currentWord.lower()\\n if prevWord != \\\"\\\":\\n if prevPrevWord != \\\"\\\":\\n trigram = (prevPrevWord, prevWord, currentWord)\\n trigramCount = self.trigramCounts[trigram]\\n if trigramCount > 0:\\n score += math.log(max(self.trigramCounts[trigram] - DISCOUNT, 0)*len(self.trigramCounts) + DISCOUNT*self.followingCounts[(prevPrevWord, prevWord)]*self.continuationCounts[currentWord])\\n # Subtraction by 1 removes the add one count from the laplace\\n # smoothing\\n score -= math.log((self.bigramCounts[(prevPrevWord, prevWord)]) * len(self.trigramCounts))\\n elif self.bigramCounts[(prevWord, currentWord)] > 0:\\n score += math.log(self.bigramCounts[(prevWord, currentWord)]*BI_BACKOFF_COEFFICIENT)\\n score -= math.log(self.totalBigramCounts)\\n else:\\n count = self.unigramCounts[currentWord]\\n score += math.log(count * UNI_BACKOFF_COEFFICIENT)\\n score -= math.log(self.total)\\n else:\\n prevPrevWord = prevWord\\n prevWord = currentWord\\n else:\\n prevWord = currentWord\\n return -score\",\n \"def score(self, sentence):\\n s = 0;\\n\\n #for every word\\n for i in xrange(len(sentence)):\\n score = self.getBackOff(tuple(sentence[:i+1]));\\n if(score != 0):\\n s += math.log(score);\\n\\n return s\",\n \"def __init__(self, treshold: float = 0.5):\\n self.threshold: float = treshold\\n self.dice_scores: list = []\\n self.iou_scores: list = []\\n self.sens_scores: list = []\\n self.spec_scores: list = []\\n self.accu_scores: list = []\",\n \"def bls_peakfinder(results):\\n maxima = find_peaks(results.power, distance=100)[0]\\n\\n top_power_inds = maxima[np.argsort(results.power[maxima])[::-1]]\\n\\n highest_peak = results.power[top_power_inds[0]]\\n next_highest_peak = results.power[top_power_inds[1]]\\n\\n significance = highest_peak / next_highest_peak\\n\\n return top_power_inds, significance\",\n \"def __save_aggregate_scores(self):\\n results = pd.DataFrame(\\n columns=self.results[list(self.results.keys())[0]],\\n index=self.results.keys(),\\n )\\n for row_name, time_bins in self.results.items():\\n for column_name, value in time_bins.items():\\n results.loc[row_name, column_name] = value\\n results.reindex(sorted(results.columns, reverse=True), axis=1).to_csv(\\n self.df_save_path\\n )\",\n \"def get_score_history(self):\\n return self._score_history\",\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 score_samples(self, x):\\n raise NotImplementedError\",\n \"def _scan_scores(self,handle, consumer):\\n read_and_call(handle, consumer.scores, start=\\\"Smith-Waterman\\\")\",\n \"def test_get_game_boxscore(self):\\n pass\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6539071","0.6111469","0.5994292","0.59466934","0.5894456","0.58336383","0.58269984","0.574957","0.57047516","0.570122","0.5692787","0.5640297","0.5611841","0.55991805","0.55441713","0.5531436","0.5517619","0.5495602","0.547449","0.5464151","0.5445944","0.54443276","0.5444046","0.54190844","0.5418649","0.5413145","0.54104924","0.5409345","0.5404168","0.5388343","0.5380313","0.5368873","0.5368623","0.53392816","0.53371","0.5336029","0.5332145","0.5321648","0.5309899","0.5305274","0.5299466","0.5287433","0.52547026","0.5251298","0.5245252","0.5242978","0.5236487","0.52140707","0.5210075","0.5206344","0.52017885","0.5199732","0.5198898","0.5196102","0.5192718","0.5189682","0.51793015","0.51765954","0.5176571","0.51735955","0.51732105","0.51664335","0.5165158","0.51631016","0.51522267","0.51414686","0.5140767","0.5127559","0.51267827","0.5126706","0.5119464","0.51140755","0.511347","0.5112026","0.5109666","0.51078075","0.50954664","0.50938034","0.5086633","0.50865424","0.50773704","0.50704473","0.5068807","0.5065027","0.50636876","0.50603306","0.50596774","0.5055263","0.5054938","0.50496113","0.50460625","0.50336236","0.5025491","0.5020454","0.50158644","0.50123745","0.50092685","0.5007517","0.50066406","0.500605","0.5004383"],"string":"[\n \"0.6539071\",\n \"0.6111469\",\n \"0.5994292\",\n \"0.59466934\",\n \"0.5894456\",\n \"0.58336383\",\n \"0.58269984\",\n \"0.574957\",\n \"0.57047516\",\n \"0.570122\",\n \"0.5692787\",\n \"0.5640297\",\n \"0.5611841\",\n \"0.55991805\",\n \"0.55441713\",\n \"0.5531436\",\n \"0.5517619\",\n \"0.5495602\",\n \"0.547449\",\n \"0.5464151\",\n \"0.5445944\",\n \"0.54443276\",\n \"0.5444046\",\n \"0.54190844\",\n \"0.5418649\",\n \"0.5413145\",\n \"0.54104924\",\n \"0.5409345\",\n \"0.5404168\",\n \"0.5388343\",\n \"0.5380313\",\n \"0.5368873\",\n \"0.5368623\",\n \"0.53392816\",\n \"0.53371\",\n \"0.5336029\",\n \"0.5332145\",\n \"0.5321648\",\n \"0.5309899\",\n \"0.5305274\",\n \"0.5299466\",\n \"0.5287433\",\n \"0.52547026\",\n \"0.5251298\",\n \"0.5245252\",\n \"0.5242978\",\n \"0.5236487\",\n \"0.52140707\",\n \"0.5210075\",\n \"0.5206344\",\n \"0.52017885\",\n \"0.5199732\",\n \"0.5198898\",\n \"0.5196102\",\n \"0.5192718\",\n \"0.5189682\",\n \"0.51793015\",\n \"0.51765954\",\n \"0.5176571\",\n \"0.51735955\",\n \"0.51732105\",\n \"0.51664335\",\n \"0.5165158\",\n \"0.51631016\",\n \"0.51522267\",\n \"0.51414686\",\n \"0.5140767\",\n \"0.5127559\",\n \"0.51267827\",\n \"0.5126706\",\n \"0.5119464\",\n \"0.51140755\",\n \"0.511347\",\n \"0.5112026\",\n \"0.5109666\",\n \"0.51078075\",\n \"0.50954664\",\n \"0.50938034\",\n \"0.5086633\",\n \"0.50865424\",\n \"0.50773704\",\n \"0.50704473\",\n \"0.5068807\",\n \"0.5065027\",\n \"0.50636876\",\n \"0.50603306\",\n \"0.50596774\",\n \"0.5055263\",\n \"0.5054938\",\n \"0.50496113\",\n \"0.50460625\",\n \"0.50336236\",\n \"0.5025491\",\n \"0.5020454\",\n \"0.50158644\",\n \"0.50123745\",\n \"0.50092685\",\n \"0.5007517\",\n \"0.50066406\",\n \"0.500605\",\n \"0.5004383\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":75582,"cells":{"query":{"kind":"string","value":"This method demapp entity objects to data frame. It will help programmer to analys using pandas library."},"document":{"kind":"string","value":"def __object_demapper(self, data: list) -> pd.DataFrame:\n data = pd.DataFrame.from_records([s.to_dict() for s in data])\n\n return data"},"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 pandas_convert(self):\n data = {}\n\n for names in self.data[0]:\n col_values = []\n\n if names in objects:\n for items in self.data[0][names]:\n col_values = []\n\n col_name = names + \"_\" + items\n\n for i in range(len(self.data)):\n col_values.append(self.data[i][names][items])\n\n data[col_name] = col_values\n else:\n for i in range(len(self.data)):\n col_values.append(self.data[i][names])\n \n data[names] = col_values\n\n self.pandas_df = pd.DataFrame(data=data)\n self.__clean_df()\n\n return self.pandas_df","def get_obj_df(self) -> pd.DataFrame:\n df = pd.DataFrame(self.obj, columns=[\"x\", \"y\", \"m\", \"dx\", \"dy\"])\n df['iter'] = self.current_iteration\n return df","def to_df(self) -> pd.DataFrame:\n\n return pd.DataFrame(self.to_dict()).drop(\n DUMMY_ENTITY_ID, axis=1, errors=\"ignore\"\n )","def test_dataframe(self):\n\n url=\"http://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data\"\n readerobject=requester.url_to_df(url)\n self.assertIsInstance(readerobject,pd.DataFrame)","def to_df(self) -> pd.DataFrame:\n data = []\n for action in self.actions:\n data.append(action.to_df())\n df = pd.read_json(json.dumps(data), orient=\"list\")\n return df[self.fields]","def convert_to_df(data):\r\n ans = pd.DataFrame(data)\r\n return ans","def to_df(self):\r\n return pd.DataFrame([dict(self)])","def get_data(self)->pd.DataFrame:\n pass","def class2df(cl):\n\n attrs = vars(cl)\n cl = {item[0]: item[1] for item in attrs.items()}\n\n return pd.Series(cl)","def to_dataframe(self):\n import pandas as pd\n\n d = OrderedDict({\"y\": self.y})\n for pred in self.predictors:\n d[pred.name] = pred.x\n \n df = pd.DataFrame(d)\n return df","def set_data(self):\n # take care of samples\n patients = self.samples.iloc[:,1].tolist()\n samples = self.samples.iloc[:,0].tolist()\n self.samples = pd.DataFrame(patients,index = samples,columns = ['patient']) # indexed by sample\n #\n # take care of expression data\n cols = self.expression.SYMBOL.tolist() # set new column names to transposed expression_data \n \n new_exp = self.expression.T.ix[1:,:] # transpose\n new_exp.columns = cols\n self.expression = new_exp # add columns\n self.data = pd.merge(self.expression,self.samples,left_index = True,right_index=True) # merged data sets\n #pd.merge(df1,df2,how = 'left',left_index=True,right_index=True) # do a left join","def createDataFrames(self):\n self._atmDF = pd.DataFrame.from_dict(self._atmDict, orient='index')\n \n self._clientDF = pd.DataFrame.from_dict(self._clientDict, orient='index')\n self._clientDF['longAccount'] = self._clientDF.client.map(str) +\\\n '_' + self._clientDF.account.map(str)\n \n self._transactionDF = pd.DataFrame.from_dict(self._transactionDict, orient='index')","def to_dataframe(self, include_metadata: bool = True) -> pd.DataFrame:\n # Get all our data first with async\n # Note that all our pandas work will tax CPU so we wouldn't expect any\n # performance gains from doing the data parsing as a callback\n records = self.to_dict()\n data = []\n for series in records:\n df = pd.DataFrame(series.pop(\"data\"), columns=[\"period\", \"value\"])\n if include_metadata:\n df = df.assign(**series)\n data.append(df)\n return pd.concat(data, ignore_index=True)","def extract(self) -> pd.core.frame.DataFrame:\n pass","def construct_data_frame(self) -> pd.DataFrame:\n data_frame = self.base_data_frame[\n [self.name_col, self.description_col]\n ].reset_index()\n data_frame.columns = [\"label_encoder\", \"name\", \"description\"]\n\n return data_frame.set_index(\"label_encoder\")","def to_df(self):\n return pd.DataFrame([dict(self)])","def to_df(self):\n return pd.DataFrame([dict(self)])","def to_df(self):\n return pd.DataFrame([dict(self)])","def to_df(self):\n return pd.DataFrame([dict(self)])","def to_df(self):\n return pd.DataFrame([dict(self)])","def create_pandas_dataframes():\n train, test = Email.load_emails_from_data()\n\n train_y = [int(t.is_spam) for t in train]\n test_y = [int(t.is_spam) for t in test]\n\n vocab = get_vocabulary_vector(train)\n print(\"[ INF ] Vocab Size:\", len(vocab))\n\n train = [t.vectorize_tokens(vocab) for t in train]\n test = [t.vectorize_tokens(vocab) for t in test]\n\n train = pd.DataFrame.from_records(train, columns=vocab)\n test = pd.DataFrame.from_records(test, columns=vocab)\n\n train['is_spam'] = train_y\n test['is_spam'] = test_y\n\n return train, test","def return_data_as_pandas_df(self):\n if not self.response:\n return None\n\n data = self.response['data'][self.data_type.value]\n\n # flatten data dictionary by joining property and subproperty names\n data_flat = {}\n for i, entry in enumerate(data):\n id = self.id[i]\n curr_dict = {}\n for key, values in entry.items():\n if isinstance(values, list):\n v = values[0]\n else:\n v = values\n if isinstance(v, str):\n new_key = f\"{key}\"\n curr_dict[new_key] = v\n else:\n for subprop, val in v.items():\n new_key = f\"{key}.{subprop}\"\n curr_dict[new_key] = val\n data_flat[id] = curr_dict\n\n return pd.DataFrame.from_dict(data_flat, orient='index')","def df(self) -> \"pandas.DataFrame\":\n titles = []\n comments = []\n alternative_codes = []\n for cat in self.values():\n titles.append(cat.title)\n comments.append(cat.comment)\n alternative_codes.append(cat.codes[1:])\n return pandas.DataFrame(\n index=list(self.keys()),\n data={\n \"title\": titles,\n \"comment\": comments,\n \"alternative_codes\": alternative_codes,\n },\n )","def transform(self, dataframe: DataFrame) -> DataFrame:","def from_pandas(cls, df, data_cls):\n pass","def model_to_df(self, transpose=True):\n X = np.vstack([self.sales(), self.unit_contribution(),\n self.net_revenue(), self.depreciation(),\n self.before_tax_profit(), self.after_tax_profit(), self.cash_flow()])\n\n if transpose:\n X = np.transpose(X)\n df = pd.DataFrame(X, columns=['sales', 'unit_contribution', 'net_revenue',\n 'depreciation', 'before_tax_profit', 'after_tax_profit',\n 'cash_flow'])\n else:\n df = pd.DataFrame(X, index=['sales', 'unit_contribution', 'net_revenue',\n 'depreciation', 'before_tax_profit', 'after_tax_profit',\n 'cash_flow'])\n\n return df","def to_pandas(self):\n self.meta = pd.DataFrame(self.meta)\n return","def test_from_object_df(self):\n df_test = make_simple_dataframe()\n df_read = BaseDataClass.from_object(df_test).df\n self.assertEqual(\n pd.testing.assert_frame_equal(df_test, df_read),\n None,\n )","def dataframe(self):\n\n if self._dataframe is None:\n try:\n import pandas as pd\n except ImportError:\n raise RuntimeError('To enable dataframe support, '\n 'run \\'pip install datadotworld[pandas]\\'')\n\n self._dataframe = pd.DataFrame.from_records(self._iter_rows(),\n coerce_float=True)\n\n return self._dataframe","def to_df(self):\n from ..df import DataFrame\n\n return DataFrame(self)","def pd(self, *args, **kwargs):\n return pd.DataFrame.from_records(self.aslist(), *args, **kwargs)","def create_NER(self, dataframe):\n\n dataframe['entities'] = dataframe['line']\n entity_dict = {}\n entity_type = {}\n\n for i, val in enumerate(dataframe['entities']):\n e1 = re.findall('<e1>(.*?)</e1>', val)\n e2 = re.findall('<e2>(.*?)</e2>', val)\n entity_dict[i+1] = (str(e1[0]), str(e2[0]))\n doc = nlp(e1[0])\n for ent in doc.ents:\n if ent.label_:\n entity_type[i] = ent.label_\n else:\n entity_type[i] = ('NOT RECOGNIZED')\n \n doc = nlp(e2[0])\n for ent in doc.ents:\n if ent.label_:\n entity_type[i] = entity_type[i] + ent.label_\n else:\n entity_type[i] = entity_type[i] + ('NOT RECOGNIZED')\n\n entity_dataframe = self.create_dataframe(entity_dict, ['e1', 'e2'])\n entity_type_df = self.create_dataframe(entity_type, ['e1', 'e2'])\n\n dataframe = dataframe.drop(columns=['entities'])\n dataframe['e1'] = entity_dataframe['e1']\n dataframe['e2'] = entity_dataframe['e2']\n dataframe['e1_type'] = entity_type_df['e1']\n dataframe['e2_type'] = entity_type_df['e2']\n\n return dataframe","def obj_df(df):\n mask = np.array(df.dtypes == 'object')\n df_obj = df.iloc[:, mask]\n return df_obj","def from_dataframe(cls, df, data_cls):\n pass","def __create_data_frame(self, soup):\n self.__data_frame = pd.read_html(str(soup))[0]\n timestamp = self.__navigate_rows(soup)\n # rename dataframe columns by columns name in sqlite\n self.__data_frame = self.__data_frame.rename(\n columns=self.__columns_name)\n self.__data_frame['time'] = pd.Series(timestamp)\n self.__data_frame['chg_perc'] = self.__data_frame['chg_perc'].\\\n str.replace('%', '')\n self.__data_frame['created_date'] = datetime.now()\n # save_file(self.__name_file, self.__data_frame.to_string())","def to_dataframe(self):\n return df_util.to_dataframe(requests.get(self.__url).json())","def to_pandas(self):\n pass","def to_pandas(self):\n pass","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","def fetchAndCleanDataframe(self):\n\n df = pd.read_csv('/Users/apple4u/Desktop/goksel tez/results_with_scenarios.csv')\n df.insider_label.fillna(0, inplace=True) # replaces null fields with 0\n df = df.drop(columns=['employee_name', 'scenario', 'role'])\n df = df.rename(columns={'insider_label':'label'})\n #df['label'] = df['insider_label'].astype('int64')\n #df.drop(columns='insider_label', inplace=True)\n df.set_index('user_id', inplace=True)\n X = df.iloc[:, :5].values #fetch all records first 5 columns\n y = df.label.values\n print(df.head())\n return X, y","def show(obj):\n if isinstance(obj, pd.Series):\n df = pd.DataFrame(obj)\n return df\n elif hasattr(obj, '__dict__'):\n return pd.DataFrame(pd.Series(obj.__dict__),\n columns=['value'])\n else:\n return obj","def make_dataset(self, df, **kwargs):\n\t\treturn df","def as_DF(self):\n\n hc_df = pd.DataFrame(self.s, index=self.s_names)\n hc_df.columns.name = 'type'\n hc_df.index.name = 's'\n\n return hc_df","def _populate_df(self, df, objs,):\n for obj in objs:\n for prop in df.columns:\n df.loc[obj.name, prop] = getattr(obj, prop)","def transform(self, df: DataFrame) -> DataFrame:\n return df","def to_data_frame(self, all_entries):\n\n c_attr = ['ref_id', 'title', 'authors', 'publication', 'volume', 'issue',\n 'series', 'date', 'pages', 'volume', 'scopus_link', 'doi', 'pii',\n 'pdf_link', 'scopus_cite_count']\n all_data = []\n for entry in all_entries:\n all_data.append([getattr(entry, x) for x in c_attr])\n\n return pd.DataFrame(all_data, columns=c_attr)","def to_frame(self) -> pd.DataFrame:\n df = pd.DataFrame(data={\n 'Name': [p.name for p in self],\n 'Description': [p.desc for p in self],\n 'Value': [p.value for p in self],\n 'Hyper-Space': [p.hyper_space for p in self]\n }, columns=['Name', 'Description', 'Value', 'Hyper-Space'])\n return df","def transform(self, data: pd.DataFrame) -> pd.DataFrame:\n data = data[['premise', 'hypothesis', 'label']] # type: ignore\n return data","def to_dataframe(self):\n \n #Try importing pandas\n try:\n import pandas as pd\n except ImportError as e:\n raise RuntimeError(\"Error: pandas is not available. Install pandas in order to use this function.\") from e\n \n #Get season info\n season_info = self.annual_summary()\n season_info_keys = season_info['id']\n \n #Set up empty dict for dataframe\n ds = {'id':[],'name':[],'vmax':[],'mslp':[],'category':[],'ace':[],'start_time':[],'end_time':[]}\n \n #Add every key containing a list into the dict\n keys = [k for k in self.dict.keys()]\n for key in keys:\n if key in season_info_keys:\n sidx = season_info_keys.index(key)\n ds['id'].append(key)\n ds['name'].append(self.dict[key]['name'])\n ds['vmax'].append(season_info['max_wspd'][sidx])\n ds['mslp'].append(season_info['min_mslp'][sidx])\n ds['category'].append(season_info['category'][sidx])\n ds['start_time'].append(self.dict[key]['date'][0])\n ds['end_time'].append(self.dict[key]['date'][-1])\n ds['ace'].append(np.round(season_info['ace'][sidx],1))\n \n #Convert entire dict to a DataFrame\n ds = pd.DataFrame(ds)\n\n #Return dataset\n return ds","def get_adv_classification_dataset(self) -> pd.DataFrame:\n pass","def as_dataframe(self) -> \"pd.DataFrame\":\n import pandas as pd\n\n df = pd.DataFrame([row.as_series() for row in self.rows])\n return df","def _inv_dict_list_to_dataframe(inv: list) -> pd.DataFrame:\n columns = ['item_id', 'color_id', 'name', 'itemtype', 'category_id', 'quantity', ]\n df = pd.DataFrame(inv, columns=columns)\n # Create the element_id column and set as index\n df['element_id'] = df[['item_id', 'color_id']].apply(tuple, axis=1)\n df.set_index('element_id', inplace=True)\n return df","def infer_objects(self):\n return DataFrameDefault.register(pandas.DataFrame.infer_objects)(self)","def to_df(self):\n from ..df import DataFrame\n\n return DataFrame(self.table).filter_parts(self)","def df(self) -> \"pandas.DataFrame\":\n titles = []\n comments = []\n alternative_codes = []\n children = []\n for cat in self.values():\n titles.append(cat.title)\n comments.append(cat.comment)\n alternative_codes.append(cat.codes[1:])\n children.append(\n tuple(tuple(sorted(c.codes[0] for c in cs)) for cs in cat.children)\n )\n return pandas.DataFrame(\n index=self.keys(),\n data={\n \"title\": titles,\n \"comment\": comments,\n \"alternative_codes\": alternative_codes,\n \"children\": children,\n },\n )","def preprocessing_data(df_entity_raw, df_names_raw):\n # Cleaning unused rows\n df_entity_filter = clean.df_cleaning(df_entity_raw)\n # Filter entity contracts: only contracts issued to a mun/dept.\n # Also gets list of names of mun/dept. with contracts with the entity\n df_entity, names_mun_list = clean.df_filter_entity(df_entity_filter)\n\n # Get list of departments and municipalities of Colombia\n df_names = clean.df_cleaning_names(df_names_raw)\n\n # First standardization: names of mun/dept. with contracts with the entity\n names_mun_list = [clean.strip_accents(item) for item in names_mun_list]\n names_mun_standard = []\n for item in names_mun_list:\n if 'MUNICIPIO' in item:\n names_mun_standard.append(clean.standarize_mun(item))\n else:\n names_mun_standard.append(clean.standarize_depto(item))\n\n # Second standardization: accent standardization without accents with official names\n names_mun_standard = clean.standardize_accents_mun(df_names, names_mun_standard)\n\n # Third standardization: format standardization to ensure a right joining\n names_mun_standard = clean.standardize_format_mun(df_names, names_mun_standard)\n\n # Assign new column to entity dataframe\n df_entity = df_entity.assign(nom_raz_soc_stand=names_mun_standard)\n names_mun_standard_list = list(set(names_mun_standard))\n\n return df_entity, names_mun_standard_list","def to_dataframe(self, attrs_as_columns=False):\n\n # Set up empty dict for dataframe\n ds = {}\n\n # Add every key containing a list into the dict\n keys = [k for k in self.dict.keys()]\n for key in keys:\n if isinstance(self.dict[key], list):\n ds[key] = self.dict[key]\n else:\n if attrs_as_columns:\n ds[key] = self.dict[key]\n\n # Convert entire dict to a DataFrame\n ds = pd.DataFrame(ds)\n\n # Return dataset\n return ds","def create_NER(dataframe):\n\n dataframe['entities'] = dataframe['line']\n entity_dict = {}\n for i, val in enumerate(dataframe['entities']):\n e1 = re.findall('<e1>(.*?)</e1>', val)\n e2 = re.findall('<e2>(.*?)</e2>', val)\n entity_dict[i+1] = (str(e1[0]), str(e2[0]))\n\n entity_dataframe = create_dataframe(entity_dict, ['e1', 'e2'])\n dataframe = dataframe.drop(columns=['entities'])\n dataframe['e1'] = entity_dataframe['e1']\n dataframe['e2'] = entity_dataframe['e2']\n\n return dataframe","def test_from_object_class(self):\n df_test = make_simple_dataframe()\n Base_object = BaseDataClass.from_object(df_test)\n df_read = BaseDataClass.from_object(Base_object).df\n self.assertEqual(\n pd.testing.assert_frame_equal(df_test, df_read),\n None,\n )","def get_pybossa_df(obj):\n progress = tqdm.tqdm(desc='Downloading', unit=obj)\n r = get_objects(obj)\n last_fetched = r.json()\n data = last_fetched\n progress.update(len(last_fetched))\n respect_rate_limits(r, progress)\n while _not_exhausted(last_fetched):\n r = get_objects(obj, len(data))\n last_fetched = r.json()\n data += last_fetched\n progress.update(len(last_fetched))\n respect_rate_limits(r, progress)\n progress.close()\n df = pandas.DataFrame(data)\n df.set_index('id', inplace=True, verify_integrity=True)\n return df","def to_df(self):\n # check read only\n if self.__read_only:\n raise IOError(\"Table is for read only.\")\n\n # convert data to dicts\n data = dict(record.to_id_dict()\n for record in self.__data.values())\n\n # make data frame\n df = pd.DataFrame(data).T\n df.index.name = \"_id\"\n return df","def sourceToDataframe(self):\n df = pd.read_excel(self.filename)\n df.columns = df.iloc[10]\n df = df.drop(df.index[:11])\n self.df = df #makes this df accessible to the whole class now\n self.insertODN()\n display(df.head())","def model_state(appname, schema, table):\n modkey = f\"{appname}.orm.{schema}.{table}\"\n importlib.import_module(modkey)\n mod = sys.modules[modkey]\n obj = vars(mod)[table.title()]\n names = [key for key in vars(obj).keys()\n if not key.startswith('_')]\n fields = [getattr(obj, fld) for fld in names]\n flds, typs = [], []\n for name, field in zip(names, fields):\n if not isinstance(field, property):\n flds.append(name)\n typs.append(field)\n typs = [_pg_type_map[fld.__class__] for fld in typs]\n return pd.DataFrame.from_dict({\n 'table_catalog': appname,\n 'table_schema': schema,\n 'table_name': table,\n 'column_name': flds,\n 'data_type': typs\n })","def exchange_df(self, df: pandas.DataFrame) -> \"Predictions\":\n return self.__class__(**collections.ChainMap(dict(df=df), dataclasses.asdict(self)))","def _get_data_as_df(self, data):\n if isinstance(data, pd.DataFrame):\n return data\n if isinstance(data, dict):\n data = [data]\n data, original_df_dtypes = data_utils.json_to_dataframe(\n json_lines=data,\n selected_columns=self.selected_keys,\n read_in_string=False\n )\n self._original_df_dtypes = original_df_dtypes\n return data","def transform(self, df):\n\t\tdf = self.__parse_json(df)\n\t\tdf = self.__fillnan(df)\n\t\tdf = self.__parse_dates(df)\n\t\tdf['budget'] = df['budget'].apply(lambda x: self.missing_budget_imputing if int(x) == 0 else x)\n\t\tdf['has_collections'] = df['belongs_to_collection'].isna().astype(int)\n\t\tdf['homepage'] = df['homepage'].isna().astype(int)\n\t\tdf['is_en'] = df['original_language'].apply(lambda x: 1 if x == 'en' else 0)\n\t\tdf = self.__encode_genre_transform(df)\n\t\tdf = self.__top_countries_and_companies_transform(df)\n\t\tdf = self.__bin_columns_transform(df)\n\t\tdf.drop(\n\t\t\t['release_date', 'original_language', 'production_countries', 'production_companies', 'id', 'backdrop_path',\n\t\t\t 'imdb_id', 'poster_path', 'video', 'belongs_to_collection', 'status', 'runtime',\n\t\t\t 'original_title', 'overview', 'tagline', 'title'], axis=1, inplace=True)\n\t\treturn df","def data_frame_creator(self):\n\n return pd.DataFrame()","def _from_pandas(obj, index=True):\n if hasattr(obj, 'to_frame') and hasattr(obj, 'index'):\n # Convert series to DataFrame.\n df = obj.to_frame()\n elif hasattr(obj, 'to_frame') and not hasattr(obj, 'index'):\n # Convert MultiIndex (pandas >=0.20.0) or Index (pandas >=0.21.0)\n # to DataFrame.\n index = False\n df = obj.to_frame()\n elif hasattr(obj, 'to_series'):\n # Convert Index (pandas >=0.20.0,<0.21.0) to DataFrame.\n index = False\n df = obj.to_series().to_frame()\n else:\n # Else, it's already a DataFrame.\n df = obj\n\n if index:\n yield list(df.index.names) + list(df.columns)\n else:\n yield list(df.columns)\n\n records = df.to_records(index=index)\n for record in records:\n yield list(record)","def _dataframe_from_feather(fn, **kwargs):\n\treturn pd.read_feather(fn, **kwargs)","def __init__(self, df):\n self.original_data = df\n self.cleaned_data = pd.DataFrame()","def df(objs, labels: Optional[List[str]] = None):\n import pandas as pd\n from .objects import DynamicObject\n if objs:\n objs = list(objs)\n obj = objs[0]\n if is_dataclass(obj):\n df = pd.DataFrame.from_records(dataclassAsTuple(o) for o in objs)\n df.columns = [field.name for field in fields(obj)]\n elif isinstance(obj, DynamicObject):\n df = pd.DataFrame.from_records(o.__dict__ for o in objs)\n else:\n df = pd.DataFrame.from_records(objs)\n if isinstance(obj, tuple):\n _fields = getattr(obj, '_fields', None)\n if _fields:\n # assume it's a namedtuple\n df.columns = _fields\n else:\n df = None\n if labels:\n exclude = [label for label in df if label not in labels]\n df = df.drop(exclude, axis=1)\n return df","def convert_to_dataframe(H, endpoints=None, evidenced_nodes=None):\n generalization_obj = generalization_stats(H, endpoints, evidenced_nodes)\n # we need to duplicate node_id for all other values, we identify the length\n # of other arrays only ones\n series_size = None\n\n # final dataframe\n final_df = None\n\n for node_id, stats_obj in generalization_obj.items():\n if series_size is None:\n series_size = len(stats_obj['confidences'])\n\n stats_obj['unevidenced_node'] = [H.node[node_id]['label']] * \\\n series_size\n\n # first time creating the dataframe\n if final_df is None:\n final_df = pd.DataFrame(stats_obj)\n else:\n df = pd.DataFrame(stats_obj)\n final_df = pd.concat([final_df, df])\n final_df.reset_index()\n\n return final_df","def glass_pandas(self):\n # pandas.set_option('display.width', 120)\n # TODO timeit (git_implementation) vs (my_implementation)\n # * df = pd.DataFrame(json.loads(r.text))\n # * df = df.set_index('t')\n # * df.index = pd.to_datetime(df.index, unit='s')\n # * df = df.sort_index()\n # * s = df.v\n # * s.name = '_'.join(url.split('/')[-2:])\n # * return s\n # for elem in self.loaded:\n # _metric, _data = elem[1]['_metrics'], elem[1]['_data']\n # try:\n # frame_keys = ['t'] + list(_data[0]['o'].keys())\n # framed = pandas.DataFrame(\n # data=[{k: (_data[iters]['t'] if k in 't' else _data[iters]['o'][k])\n # for k in frame_keys} for iters in range(len(_data))],\n # columns=frame_keys)\n # except KeyError:\n # framed = pandas.DataFrame(_data)\n # framed.set_index('t', inplace=True)\n # framed.index = pandas.to_datetime(\n # framed.index.to_flat_index(), unit='s', infer_datetime_format=True)\n # framed.sort_index(inplace=True)\n # framed.name = _metric\n # print(framed.name)\n # print(framed)","def dataframe():\n headers = get_headers()\n headers = {'headers': headers}\n headers = pd.DataFrame.from_dict(headers, orient='index')\n headers = headers.replace(r'\\n', ' ', regex=True)\n headers = headers.replace(r'\\r', ' ', regex=True)\n headers = headers.replace(r'\\t', ' ', regex=True)\n headers = headers.replace(r'\\\\t', ' ', regex=True)\n headers = headers.replace(r' ', ' ', regex=True)\n headers = headers.replace(r' ', ' ', regex=True)\n\n paragraphs = get_paragraphs()\n paragraphs = {'paragraphs': paragraphs}\n paragraphs = pd.DataFrame.from_dict(paragraphs, orient='index')\n paragraphs = paragraphs.replace(r'\\n', ' ', regex=True)\n paragraphs = paragraphs.replace(r'\\r', ' ', regex=True)\n paragraphs = paragraphs.replace(r'\\t', ' ', regex=True)\n paragraphs = paragraphs.replace(r'\\\\t', ' ', regex=True)\n paragraphs = paragraphs.replace(r' ', ' ', regex=True)\n paragraphs = paragraphs.replace(r' ', ' ', regex=True)\n\n return headers.to_csv('headers.csv', index=False), paragraphs.to_csv('paragraphs.csv', index=False)","def dataframe(self):\n return self.generator.dataframe","def entity_data(self, entity_name, limit=10):\n from sagas.ofbiz.entities import OfEntity as e, finder, record_list_df\n # limit = 10\n offset = 0\n result = finder.find_list(entity_name, limit, offset)\n result = record_list_df(entity_name, result, drop_null_cols=True, contains_internal=False)\n print(result)","def prepareDataframeForTable(self, result):\n df = result\n if isinstance(df, pd.Series):\n df = pd.DataFrame({\"values\": df})\n\n if self._isIndexedDataframe(df):\n if df.size == 0:\n df[\"values\"] = np.nan\n elif len(df.columns) > 1:\n if isinstance(df.columns, pd.MultiIndex):\n df.columns = df.columns.map(' | '.join)\n df = df.stack()\n if isinstance(df, pd.Series):\n df = pd.DataFrame({\"values\": df})\n current_columns_name = list(df.index.names)\n current_columns_name[len(current_columns_name)-1] = \"Measures\"\n df.index.names = current_columns_name\n\n return df","def taxa_data_frame(self):\n cols = list(self._taxa.keys())\n cols.remove(\"uid\")\n cols.remove(\"object\")\n df = DataFrame(self._taxa, columns=cols, index=self._taxa[\"uid\"])\n df.index.name = \"uid\"\n\n return df","def process_data(self):\n structure_data = self.parse_root(self.root)\n\n dict_data = {}\n for d in structure_data:\n dict_data = {**dict_data, **d}\n df = pd.DataFrame(data=list(dict_data.values()), index=dict_data.keys()).T\n\n return df","def data_from_dataframe(self, dataframe):\n self.dataframe = dataframe.drop_duplicates()\n #Convert numerical values into float type\n self.dataframe.apply(pandas.to_numeric, errors='ignore')\n #Convert timestamps into regular dates\n time_range = [datetime.datetime.fromtimestamp(time) for time in list(self.dataframe['time'])]\n beg = time_range[0]\n end = time_range[len(time_range)-1]\n #Attribute begining and ending dates\n self.beg = beg\n self.end = end","def save_to_dataframe(self):\n titles, years, months, days, authors = list(), list(), list(), list(), list()\n for doc in self.results[\"documents\"]:\n titles.append(doc['title'])\n years.append(doc['year'])\n months.append(doc['month'])\n days.append(doc['day'])\n authors.append(doc['authors'])\n return pd.DataFrame({\"title\": titles, \"years\": years, \"months\": months, \"days\": days, \"author\": authors})","def sa_pandas_init(self):\n\n lca = self.lca\n\n ind_activity = 0\n ind_product = 1\n ind_biosphere = 2\n\n cols = []\n rows = []\n inputs = []\n\n #All exchanges in inputs\n for input_ in self.inputs:\n\n if input_ == 'biosphere':\n continue\n\n for i in self.inputs_dict[input_]['tech_params']:\n act = lca.reverse_dict() [ind_activity] [i['col']]\n prod = lca.reverse_dict() [ind_product] [i['row']]\n cols += [ bw.get_activity(act) ['name'] ]\n rows += [ bw.get_activity(prod)['name'] ]\n inputs += [input_]\n for j in self.inputs_dict[input_]['bio_params']:\n act = lca.reverse_dict() [ind_activity] [j['col']]\n bio = lca.reverse_dict() [ind_biosphere] [j['row']]\n cols += [ bw.get_activity(act) ['name'] ]\n rows += [ bw.get_activity(prod)['name'] ]\n inputs += [input_]\n\n if self.parameters != None:\n # All parameters\n parameters_names_list = [name for name in self.parameters_array['name']]\n cols += parameters_names_list\n rows += parameters_names_list\n inputs += ['Parameters']*len(parameters_names_list)\n\n df = pd.DataFrame([inputs, rows, cols], index = ['Inputs', 'Products or flows', 'Activities'])\n df = df.transpose()\n\n self.sensitivity_indices_df = df","def get_df(self) -> pd.DataFrame:\n return pd.DataFrame(self.fetchall(), columns=self.headers())","def transform(self, data: pd.DataFrame) -> pd.DataFrame:\n data = data[data.language == 'English'] # type: ignore\n data = data[['premise', 'hypothesis', 'label']] # type: ignore\n return data","def build_dataframe(textline):\n column_names = []\n records = [line.split(u',') for line in textline]\n records = [pd.np.nan if token in (u'\\\\N', 'NULL') else token for token in records]\n # df_line = pd.read_csv(textline, header=None, names=column_names)\n df = pd.DataFrame(records, columns=column_names)\n df = df.convert_objects(convert_numeric=True)\n df.set_index('msisdn', inplace=True)\n print('-----', df.dtypes)\n return df","def to_pandas(self):\n # TODO Add type translation.\n # Skipping analyzing 'pandas': found module but no type hints or library stubs\n import pandas as pd # type: ignore\n\n map = {}\n for n, c in self._field_data.items():\n map[n] = c.to_pandas()\n return pd.DataFrame(map)","def data_structure():\n\n items = [1.0, 2.0, 3.0, 4.0, 5.0 ]\n s = pd.Series(items, index=['a', 'b', 'c', 'd', 'e'])\n # s = pd.Series(np.random.randn(5), index=['a', 'b', 'c', 'd', 'e'])\n print s\n s = pd.Series(items)\n print s\n\n d= {'one': [1.0, 2.0, 3.0, 4.0], 'two': [4.0, 3.0, 2.0, 1.0]}\n\n df = pd.DataFrame(d)\n print df\n df = pd.DataFrame(d, index=['a', 'b', 'c', 'd'])\n print df\n\n\n data2 = [{'a': 1, 'b': 2}, {'a': 5, 'b': 10, 'c': 20}]\n df = pd.DataFrame(data2)\n\n print df","def dataframe(self):\n dictionary = OrderedDict(zip(self.keys, [[value] for value in self.values]))\n dataframe = pd.DataFrame(dictionary)\n return dataframe","def create_dataframe_for_training(data):\n feature_column_name = 'X'\n #data_cp = data[['label']].copy()\n for i, row in tqdm(data.iterrows(), total=len(data)):\n all_features = f'{row.claimant} {row.claim} {row.article_content}'\n data.loc[i, feature_column_name] = all_features\n\n return data[feature_column_name]","def get_obj_df():\n obj_df = pd.read_csv('data/object.csv')\n obj_df = obj_df.drop_duplicates()[['course_id', 'module_id', 'category', 'start']] \n obj_df['start'] = pd.to_datetime(obj_df[obj_df['start'] != 'null']['start'])\n return obj_df","def _dataframe_preprocess(self):\n # 1. add baisc feature like date, time in day, ....\n if self.data_type != 'porto':\n self.df['TIMESTAMP'] = self.df.apply(lambda df: df['TIMESTAMPS'][0], axis=1)\n self.df['TIME'] = pd.to_datetime(self.df['TIMESTAMP'], unit='s', utc=True)\n \n self.df.TIME = self.df.TIME.dt.tz_convert(self.timezone)\n # 2. group df for specific driver analysis\n self.grouped_df = self.df.groupby('LABEL')\n if self.count_od_info:\n if 'SD' not in self.df.columns:\n self._add_OD_info()\n self.grouped_od = self.df.groupby('SD')","def pandaData():\r\n tweets = pd.DataFrame()\r\n return tweets","def dataframe(self):\n return self.get_target().dataframe()","def test_train_to_df(\n train_dataframe: pd.DataFrame, train_items: List[JSONDict]\n) -> None:\n\n validated = TrainCollection(items=train_items)\n assert train_dataframe.equals(validated.to_dataframe()[train_dataframe.columns])","def as_DataFrame (self):\n return DataFrame(self.table)","def transform(self, data: pd.DataFrame, columns: list, verbose: int=1) -> pd.DataFrame:","def history_to_df(self):\n records = [pe.to_dict() for pe in self.history]\n return pd.DataFrame.from_records(\n records, columns=[\n \"date\", \"type\", \"description\", \"debit\", \"credit\", \"balance\"\n ]\n ).set_index(keys=[\"date\"])","def _prepare_inference_data(self, df: pd.DataFrame) -> pd.DataFrame:\n # TODO Is the target encoding necessary?\n if len(set(self.target) - set(df.columns)) > 0:\n if self.config.task == \"classification\":\n df.loc[:, self.target] = np.array([self.label_encoder.classes_[0]] * len(df)).reshape(-1, 1)\n else:\n df.loc[:, self.target] = np.zeros((len(df), len(self.target)))\n df, _ = self.preprocess_data(df, stage=\"inference\")\n return df","def prof2frame(prof):\n\n import pandas as pd\n\n # create data frame from profile dicts\n frame = pd.DataFrame(prof)\n\n # --------------------------------------------------------------------------\n # add a flag to indicate entity type\n def _entity (row):\n if not row['uid']:\n return 'session'\n if 'unit' in row['uid']:\n return 'unit'\n if 'pilot' in row['uid']:\n return 'pilot'\n return 'session'\n frame['entity'] = frame.apply(lambda row: _entity (row), axis=1)\n\n # --------------------------------------------------------------------------\n # add a flag to indicate if a unit / pilot / ... is cloned\n def _cloned (row):\n if not row['uid']:\n return False\n else:\n return 'clone' in row['uid'].lower()\n frame['cloned'] = frame.apply(lambda row: _cloned (row), axis=1)\n\n return frame","def to_dataframe(self, data_dict):\n return pd.DataFrame.from_dict(data_dict, orient='index')"],"string":"[\n \"def pandas_convert(self):\\n data = {}\\n\\n for names in self.data[0]:\\n col_values = []\\n\\n if names in objects:\\n for items in self.data[0][names]:\\n col_values = []\\n\\n col_name = names + \\\"_\\\" + items\\n\\n for i in range(len(self.data)):\\n col_values.append(self.data[i][names][items])\\n\\n data[col_name] = col_values\\n else:\\n for i in range(len(self.data)):\\n col_values.append(self.data[i][names])\\n \\n data[names] = col_values\\n\\n self.pandas_df = pd.DataFrame(data=data)\\n self.__clean_df()\\n\\n return self.pandas_df\",\n \"def get_obj_df(self) -> pd.DataFrame:\\n df = pd.DataFrame(self.obj, columns=[\\\"x\\\", \\\"y\\\", \\\"m\\\", \\\"dx\\\", \\\"dy\\\"])\\n df['iter'] = self.current_iteration\\n return df\",\n \"def to_df(self) -> pd.DataFrame:\\n\\n return pd.DataFrame(self.to_dict()).drop(\\n DUMMY_ENTITY_ID, axis=1, errors=\\\"ignore\\\"\\n )\",\n \"def test_dataframe(self):\\n\\n url=\\\"http://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data\\\"\\n readerobject=requester.url_to_df(url)\\n self.assertIsInstance(readerobject,pd.DataFrame)\",\n \"def to_df(self) -> pd.DataFrame:\\n data = []\\n for action in self.actions:\\n data.append(action.to_df())\\n df = pd.read_json(json.dumps(data), orient=\\\"list\\\")\\n return df[self.fields]\",\n \"def convert_to_df(data):\\r\\n ans = pd.DataFrame(data)\\r\\n return ans\",\n \"def to_df(self):\\r\\n return pd.DataFrame([dict(self)])\",\n \"def get_data(self)->pd.DataFrame:\\n pass\",\n \"def class2df(cl):\\n\\n attrs = vars(cl)\\n cl = {item[0]: item[1] for item in attrs.items()}\\n\\n return pd.Series(cl)\",\n \"def to_dataframe(self):\\n import pandas as pd\\n\\n d = OrderedDict({\\\"y\\\": self.y})\\n for pred in self.predictors:\\n d[pred.name] = pred.x\\n \\n df = pd.DataFrame(d)\\n return df\",\n \"def set_data(self):\\n # take care of samples\\n patients = self.samples.iloc[:,1].tolist()\\n samples = self.samples.iloc[:,0].tolist()\\n self.samples = pd.DataFrame(patients,index = samples,columns = ['patient']) # indexed by sample\\n #\\n # take care of expression data\\n cols = self.expression.SYMBOL.tolist() # set new column names to transposed expression_data \\n \\n new_exp = self.expression.T.ix[1:,:] # transpose\\n new_exp.columns = cols\\n self.expression = new_exp # add columns\\n self.data = pd.merge(self.expression,self.samples,left_index = True,right_index=True) # merged data sets\\n #pd.merge(df1,df2,how = 'left',left_index=True,right_index=True) # do a left join\",\n \"def createDataFrames(self):\\n self._atmDF = pd.DataFrame.from_dict(self._atmDict, orient='index')\\n \\n self._clientDF = pd.DataFrame.from_dict(self._clientDict, orient='index')\\n self._clientDF['longAccount'] = self._clientDF.client.map(str) +\\\\\\n '_' + self._clientDF.account.map(str)\\n \\n self._transactionDF = pd.DataFrame.from_dict(self._transactionDict, orient='index')\",\n \"def to_dataframe(self, include_metadata: bool = True) -> pd.DataFrame:\\n # Get all our data first with async\\n # Note that all our pandas work will tax CPU so we wouldn't expect any\\n # performance gains from doing the data parsing as a callback\\n records = self.to_dict()\\n data = []\\n for series in records:\\n df = pd.DataFrame(series.pop(\\\"data\\\"), columns=[\\\"period\\\", \\\"value\\\"])\\n if include_metadata:\\n df = df.assign(**series)\\n data.append(df)\\n return pd.concat(data, ignore_index=True)\",\n \"def extract(self) -> pd.core.frame.DataFrame:\\n pass\",\n \"def construct_data_frame(self) -> pd.DataFrame:\\n data_frame = self.base_data_frame[\\n [self.name_col, self.description_col]\\n ].reset_index()\\n data_frame.columns = [\\\"label_encoder\\\", \\\"name\\\", \\\"description\\\"]\\n\\n return data_frame.set_index(\\\"label_encoder\\\")\",\n \"def to_df(self):\\n return pd.DataFrame([dict(self)])\",\n \"def to_df(self):\\n return pd.DataFrame([dict(self)])\",\n \"def to_df(self):\\n return pd.DataFrame([dict(self)])\",\n \"def to_df(self):\\n return pd.DataFrame([dict(self)])\",\n \"def to_df(self):\\n return pd.DataFrame([dict(self)])\",\n \"def create_pandas_dataframes():\\n train, test = Email.load_emails_from_data()\\n\\n train_y = [int(t.is_spam) for t in train]\\n test_y = [int(t.is_spam) for t in test]\\n\\n vocab = get_vocabulary_vector(train)\\n print(\\\"[ INF ] Vocab Size:\\\", len(vocab))\\n\\n train = [t.vectorize_tokens(vocab) for t in train]\\n test = [t.vectorize_tokens(vocab) for t in test]\\n\\n train = pd.DataFrame.from_records(train, columns=vocab)\\n test = pd.DataFrame.from_records(test, columns=vocab)\\n\\n train['is_spam'] = train_y\\n test['is_spam'] = test_y\\n\\n return train, test\",\n \"def return_data_as_pandas_df(self):\\n if not self.response:\\n return None\\n\\n data = self.response['data'][self.data_type.value]\\n\\n # flatten data dictionary by joining property and subproperty names\\n data_flat = {}\\n for i, entry in enumerate(data):\\n id = self.id[i]\\n curr_dict = {}\\n for key, values in entry.items():\\n if isinstance(values, list):\\n v = values[0]\\n else:\\n v = values\\n if isinstance(v, str):\\n new_key = f\\\"{key}\\\"\\n curr_dict[new_key] = v\\n else:\\n for subprop, val in v.items():\\n new_key = f\\\"{key}.{subprop}\\\"\\n curr_dict[new_key] = val\\n data_flat[id] = curr_dict\\n\\n return pd.DataFrame.from_dict(data_flat, orient='index')\",\n \"def df(self) -> \\\"pandas.DataFrame\\\":\\n titles = []\\n comments = []\\n alternative_codes = []\\n for cat in self.values():\\n titles.append(cat.title)\\n comments.append(cat.comment)\\n alternative_codes.append(cat.codes[1:])\\n return pandas.DataFrame(\\n index=list(self.keys()),\\n data={\\n \\\"title\\\": titles,\\n \\\"comment\\\": comments,\\n \\\"alternative_codes\\\": alternative_codes,\\n },\\n )\",\n \"def transform(self, dataframe: DataFrame) -> DataFrame:\",\n \"def from_pandas(cls, df, data_cls):\\n pass\",\n \"def model_to_df(self, transpose=True):\\n X = np.vstack([self.sales(), self.unit_contribution(),\\n self.net_revenue(), self.depreciation(),\\n self.before_tax_profit(), self.after_tax_profit(), self.cash_flow()])\\n\\n if transpose:\\n X = np.transpose(X)\\n df = pd.DataFrame(X, columns=['sales', 'unit_contribution', 'net_revenue',\\n 'depreciation', 'before_tax_profit', 'after_tax_profit',\\n 'cash_flow'])\\n else:\\n df = pd.DataFrame(X, index=['sales', 'unit_contribution', 'net_revenue',\\n 'depreciation', 'before_tax_profit', 'after_tax_profit',\\n 'cash_flow'])\\n\\n return df\",\n \"def to_pandas(self):\\n self.meta = pd.DataFrame(self.meta)\\n return\",\n \"def test_from_object_df(self):\\n df_test = make_simple_dataframe()\\n df_read = BaseDataClass.from_object(df_test).df\\n self.assertEqual(\\n pd.testing.assert_frame_equal(df_test, df_read),\\n None,\\n )\",\n \"def dataframe(self):\\n\\n if self._dataframe is None:\\n try:\\n import pandas as pd\\n except ImportError:\\n raise RuntimeError('To enable dataframe support, '\\n 'run \\\\'pip install datadotworld[pandas]\\\\'')\\n\\n self._dataframe = pd.DataFrame.from_records(self._iter_rows(),\\n coerce_float=True)\\n\\n return self._dataframe\",\n \"def to_df(self):\\n from ..df import DataFrame\\n\\n return DataFrame(self)\",\n \"def pd(self, *args, **kwargs):\\n return pd.DataFrame.from_records(self.aslist(), *args, **kwargs)\",\n \"def create_NER(self, dataframe):\\n\\n dataframe['entities'] = dataframe['line']\\n entity_dict = {}\\n entity_type = {}\\n\\n for i, val in enumerate(dataframe['entities']):\\n e1 = re.findall('<e1>(.*?)</e1>', val)\\n e2 = re.findall('<e2>(.*?)</e2>', val)\\n entity_dict[i+1] = (str(e1[0]), str(e2[0]))\\n doc = nlp(e1[0])\\n for ent in doc.ents:\\n if ent.label_:\\n entity_type[i] = ent.label_\\n else:\\n entity_type[i] = ('NOT RECOGNIZED')\\n \\n doc = nlp(e2[0])\\n for ent in doc.ents:\\n if ent.label_:\\n entity_type[i] = entity_type[i] + ent.label_\\n else:\\n entity_type[i] = entity_type[i] + ('NOT RECOGNIZED')\\n\\n entity_dataframe = self.create_dataframe(entity_dict, ['e1', 'e2'])\\n entity_type_df = self.create_dataframe(entity_type, ['e1', 'e2'])\\n\\n dataframe = dataframe.drop(columns=['entities'])\\n dataframe['e1'] = entity_dataframe['e1']\\n dataframe['e2'] = entity_dataframe['e2']\\n dataframe['e1_type'] = entity_type_df['e1']\\n dataframe['e2_type'] = entity_type_df['e2']\\n\\n return dataframe\",\n \"def obj_df(df):\\n mask = np.array(df.dtypes == 'object')\\n df_obj = df.iloc[:, mask]\\n return df_obj\",\n \"def from_dataframe(cls, df, data_cls):\\n pass\",\n \"def __create_data_frame(self, soup):\\n self.__data_frame = pd.read_html(str(soup))[0]\\n timestamp = self.__navigate_rows(soup)\\n # rename dataframe columns by columns name in sqlite\\n self.__data_frame = self.__data_frame.rename(\\n columns=self.__columns_name)\\n self.__data_frame['time'] = pd.Series(timestamp)\\n self.__data_frame['chg_perc'] = self.__data_frame['chg_perc'].\\\\\\n str.replace('%', '')\\n self.__data_frame['created_date'] = datetime.now()\\n # save_file(self.__name_file, self.__data_frame.to_string())\",\n \"def to_dataframe(self):\\n return df_util.to_dataframe(requests.get(self.__url).json())\",\n \"def to_pandas(self):\\n pass\",\n \"def to_pandas(self):\\n pass\",\n \"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\",\n \"def fetchAndCleanDataframe(self):\\n\\n df = pd.read_csv('/Users/apple4u/Desktop/goksel tez/results_with_scenarios.csv')\\n df.insider_label.fillna(0, inplace=True) # replaces null fields with 0\\n df = df.drop(columns=['employee_name', 'scenario', 'role'])\\n df = df.rename(columns={'insider_label':'label'})\\n #df['label'] = df['insider_label'].astype('int64')\\n #df.drop(columns='insider_label', inplace=True)\\n df.set_index('user_id', inplace=True)\\n X = df.iloc[:, :5].values #fetch all records first 5 columns\\n y = df.label.values\\n print(df.head())\\n return X, y\",\n \"def show(obj):\\n if isinstance(obj, pd.Series):\\n df = pd.DataFrame(obj)\\n return df\\n elif hasattr(obj, '__dict__'):\\n return pd.DataFrame(pd.Series(obj.__dict__),\\n columns=['value'])\\n else:\\n return obj\",\n \"def make_dataset(self, df, **kwargs):\\n\\t\\treturn df\",\n \"def as_DF(self):\\n\\n hc_df = pd.DataFrame(self.s, index=self.s_names)\\n hc_df.columns.name = 'type'\\n hc_df.index.name = 's'\\n\\n return hc_df\",\n \"def _populate_df(self, df, objs,):\\n for obj in objs:\\n for prop in df.columns:\\n df.loc[obj.name, prop] = getattr(obj, prop)\",\n \"def transform(self, df: DataFrame) -> DataFrame:\\n return df\",\n \"def to_data_frame(self, all_entries):\\n\\n c_attr = ['ref_id', 'title', 'authors', 'publication', 'volume', 'issue',\\n 'series', 'date', 'pages', 'volume', 'scopus_link', 'doi', 'pii',\\n 'pdf_link', 'scopus_cite_count']\\n all_data = []\\n for entry in all_entries:\\n all_data.append([getattr(entry, x) for x in c_attr])\\n\\n return pd.DataFrame(all_data, columns=c_attr)\",\n \"def to_frame(self) -> pd.DataFrame:\\n df = pd.DataFrame(data={\\n 'Name': [p.name for p in self],\\n 'Description': [p.desc for p in self],\\n 'Value': [p.value for p in self],\\n 'Hyper-Space': [p.hyper_space for p in self]\\n }, columns=['Name', 'Description', 'Value', 'Hyper-Space'])\\n return df\",\n \"def transform(self, data: pd.DataFrame) -> pd.DataFrame:\\n data = data[['premise', 'hypothesis', 'label']] # type: ignore\\n return data\",\n \"def to_dataframe(self):\\n \\n #Try importing pandas\\n try:\\n import pandas as pd\\n except ImportError as e:\\n raise RuntimeError(\\\"Error: pandas is not available. Install pandas in order to use this function.\\\") from e\\n \\n #Get season info\\n season_info = self.annual_summary()\\n season_info_keys = season_info['id']\\n \\n #Set up empty dict for dataframe\\n ds = {'id':[],'name':[],'vmax':[],'mslp':[],'category':[],'ace':[],'start_time':[],'end_time':[]}\\n \\n #Add every key containing a list into the dict\\n keys = [k for k in self.dict.keys()]\\n for key in keys:\\n if key in season_info_keys:\\n sidx = season_info_keys.index(key)\\n ds['id'].append(key)\\n ds['name'].append(self.dict[key]['name'])\\n ds['vmax'].append(season_info['max_wspd'][sidx])\\n ds['mslp'].append(season_info['min_mslp'][sidx])\\n ds['category'].append(season_info['category'][sidx])\\n ds['start_time'].append(self.dict[key]['date'][0])\\n ds['end_time'].append(self.dict[key]['date'][-1])\\n ds['ace'].append(np.round(season_info['ace'][sidx],1))\\n \\n #Convert entire dict to a DataFrame\\n ds = pd.DataFrame(ds)\\n\\n #Return dataset\\n return ds\",\n \"def get_adv_classification_dataset(self) -> pd.DataFrame:\\n pass\",\n \"def as_dataframe(self) -> \\\"pd.DataFrame\\\":\\n import pandas as pd\\n\\n df = pd.DataFrame([row.as_series() for row in self.rows])\\n return df\",\n \"def _inv_dict_list_to_dataframe(inv: list) -> pd.DataFrame:\\n columns = ['item_id', 'color_id', 'name', 'itemtype', 'category_id', 'quantity', ]\\n df = pd.DataFrame(inv, columns=columns)\\n # Create the element_id column and set as index\\n df['element_id'] = df[['item_id', 'color_id']].apply(tuple, axis=1)\\n df.set_index('element_id', inplace=True)\\n return df\",\n \"def infer_objects(self):\\n return DataFrameDefault.register(pandas.DataFrame.infer_objects)(self)\",\n \"def to_df(self):\\n from ..df import DataFrame\\n\\n return DataFrame(self.table).filter_parts(self)\",\n \"def df(self) -> \\\"pandas.DataFrame\\\":\\n titles = []\\n comments = []\\n alternative_codes = []\\n children = []\\n for cat in self.values():\\n titles.append(cat.title)\\n comments.append(cat.comment)\\n alternative_codes.append(cat.codes[1:])\\n children.append(\\n tuple(tuple(sorted(c.codes[0] for c in cs)) for cs in cat.children)\\n )\\n return pandas.DataFrame(\\n index=self.keys(),\\n data={\\n \\\"title\\\": titles,\\n \\\"comment\\\": comments,\\n \\\"alternative_codes\\\": alternative_codes,\\n \\\"children\\\": children,\\n },\\n )\",\n \"def preprocessing_data(df_entity_raw, df_names_raw):\\n # Cleaning unused rows\\n df_entity_filter = clean.df_cleaning(df_entity_raw)\\n # Filter entity contracts: only contracts issued to a mun/dept.\\n # Also gets list of names of mun/dept. with contracts with the entity\\n df_entity, names_mun_list = clean.df_filter_entity(df_entity_filter)\\n\\n # Get list of departments and municipalities of Colombia\\n df_names = clean.df_cleaning_names(df_names_raw)\\n\\n # First standardization: names of mun/dept. with contracts with the entity\\n names_mun_list = [clean.strip_accents(item) for item in names_mun_list]\\n names_mun_standard = []\\n for item in names_mun_list:\\n if 'MUNICIPIO' in item:\\n names_mun_standard.append(clean.standarize_mun(item))\\n else:\\n names_mun_standard.append(clean.standarize_depto(item))\\n\\n # Second standardization: accent standardization without accents with official names\\n names_mun_standard = clean.standardize_accents_mun(df_names, names_mun_standard)\\n\\n # Third standardization: format standardization to ensure a right joining\\n names_mun_standard = clean.standardize_format_mun(df_names, names_mun_standard)\\n\\n # Assign new column to entity dataframe\\n df_entity = df_entity.assign(nom_raz_soc_stand=names_mun_standard)\\n names_mun_standard_list = list(set(names_mun_standard))\\n\\n return df_entity, names_mun_standard_list\",\n \"def to_dataframe(self, attrs_as_columns=False):\\n\\n # Set up empty dict for dataframe\\n ds = {}\\n\\n # Add every key containing a list into the dict\\n keys = [k for k in self.dict.keys()]\\n for key in keys:\\n if isinstance(self.dict[key], list):\\n ds[key] = self.dict[key]\\n else:\\n if attrs_as_columns:\\n ds[key] = self.dict[key]\\n\\n # Convert entire dict to a DataFrame\\n ds = pd.DataFrame(ds)\\n\\n # Return dataset\\n return ds\",\n \"def create_NER(dataframe):\\n\\n dataframe['entities'] = dataframe['line']\\n entity_dict = {}\\n for i, val in enumerate(dataframe['entities']):\\n e1 = re.findall('<e1>(.*?)</e1>', val)\\n e2 = re.findall('<e2>(.*?)</e2>', val)\\n entity_dict[i+1] = (str(e1[0]), str(e2[0]))\\n\\n entity_dataframe = create_dataframe(entity_dict, ['e1', 'e2'])\\n dataframe = dataframe.drop(columns=['entities'])\\n dataframe['e1'] = entity_dataframe['e1']\\n dataframe['e2'] = entity_dataframe['e2']\\n\\n return dataframe\",\n \"def test_from_object_class(self):\\n df_test = make_simple_dataframe()\\n Base_object = BaseDataClass.from_object(df_test)\\n df_read = BaseDataClass.from_object(Base_object).df\\n self.assertEqual(\\n pd.testing.assert_frame_equal(df_test, df_read),\\n None,\\n )\",\n \"def get_pybossa_df(obj):\\n progress = tqdm.tqdm(desc='Downloading', unit=obj)\\n r = get_objects(obj)\\n last_fetched = r.json()\\n data = last_fetched\\n progress.update(len(last_fetched))\\n respect_rate_limits(r, progress)\\n while _not_exhausted(last_fetched):\\n r = get_objects(obj, len(data))\\n last_fetched = r.json()\\n data += last_fetched\\n progress.update(len(last_fetched))\\n respect_rate_limits(r, progress)\\n progress.close()\\n df = pandas.DataFrame(data)\\n df.set_index('id', inplace=True, verify_integrity=True)\\n return df\",\n \"def to_df(self):\\n # check read only\\n if self.__read_only:\\n raise IOError(\\\"Table is for read only.\\\")\\n\\n # convert data to dicts\\n data = dict(record.to_id_dict()\\n for record in self.__data.values())\\n\\n # make data frame\\n df = pd.DataFrame(data).T\\n df.index.name = \\\"_id\\\"\\n return df\",\n \"def sourceToDataframe(self):\\n df = pd.read_excel(self.filename)\\n df.columns = df.iloc[10]\\n df = df.drop(df.index[:11])\\n self.df = df #makes this df accessible to the whole class now\\n self.insertODN()\\n display(df.head())\",\n \"def model_state(appname, schema, table):\\n modkey = f\\\"{appname}.orm.{schema}.{table}\\\"\\n importlib.import_module(modkey)\\n mod = sys.modules[modkey]\\n obj = vars(mod)[table.title()]\\n names = [key for key in vars(obj).keys()\\n if not key.startswith('_')]\\n fields = [getattr(obj, fld) for fld in names]\\n flds, typs = [], []\\n for name, field in zip(names, fields):\\n if not isinstance(field, property):\\n flds.append(name)\\n typs.append(field)\\n typs = [_pg_type_map[fld.__class__] for fld in typs]\\n return pd.DataFrame.from_dict({\\n 'table_catalog': appname,\\n 'table_schema': schema,\\n 'table_name': table,\\n 'column_name': flds,\\n 'data_type': typs\\n })\",\n \"def exchange_df(self, df: pandas.DataFrame) -> \\\"Predictions\\\":\\n return self.__class__(**collections.ChainMap(dict(df=df), dataclasses.asdict(self)))\",\n \"def _get_data_as_df(self, data):\\n if isinstance(data, pd.DataFrame):\\n return data\\n if isinstance(data, dict):\\n data = [data]\\n data, original_df_dtypes = data_utils.json_to_dataframe(\\n json_lines=data,\\n selected_columns=self.selected_keys,\\n read_in_string=False\\n )\\n self._original_df_dtypes = original_df_dtypes\\n return data\",\n \"def transform(self, df):\\n\\t\\tdf = self.__parse_json(df)\\n\\t\\tdf = self.__fillnan(df)\\n\\t\\tdf = self.__parse_dates(df)\\n\\t\\tdf['budget'] = df['budget'].apply(lambda x: self.missing_budget_imputing if int(x) == 0 else x)\\n\\t\\tdf['has_collections'] = df['belongs_to_collection'].isna().astype(int)\\n\\t\\tdf['homepage'] = df['homepage'].isna().astype(int)\\n\\t\\tdf['is_en'] = df['original_language'].apply(lambda x: 1 if x == 'en' else 0)\\n\\t\\tdf = self.__encode_genre_transform(df)\\n\\t\\tdf = self.__top_countries_and_companies_transform(df)\\n\\t\\tdf = self.__bin_columns_transform(df)\\n\\t\\tdf.drop(\\n\\t\\t\\t['release_date', 'original_language', 'production_countries', 'production_companies', 'id', 'backdrop_path',\\n\\t\\t\\t 'imdb_id', 'poster_path', 'video', 'belongs_to_collection', 'status', 'runtime',\\n\\t\\t\\t 'original_title', 'overview', 'tagline', 'title'], axis=1, inplace=True)\\n\\t\\treturn df\",\n \"def data_frame_creator(self):\\n\\n return pd.DataFrame()\",\n \"def _from_pandas(obj, index=True):\\n if hasattr(obj, 'to_frame') and hasattr(obj, 'index'):\\n # Convert series to DataFrame.\\n df = obj.to_frame()\\n elif hasattr(obj, 'to_frame') and not hasattr(obj, 'index'):\\n # Convert MultiIndex (pandas >=0.20.0) or Index (pandas >=0.21.0)\\n # to DataFrame.\\n index = False\\n df = obj.to_frame()\\n elif hasattr(obj, 'to_series'):\\n # Convert Index (pandas >=0.20.0,<0.21.0) to DataFrame.\\n index = False\\n df = obj.to_series().to_frame()\\n else:\\n # Else, it's already a DataFrame.\\n df = obj\\n\\n if index:\\n yield list(df.index.names) + list(df.columns)\\n else:\\n yield list(df.columns)\\n\\n records = df.to_records(index=index)\\n for record in records:\\n yield list(record)\",\n \"def _dataframe_from_feather(fn, **kwargs):\\n\\treturn pd.read_feather(fn, **kwargs)\",\n \"def __init__(self, df):\\n self.original_data = df\\n self.cleaned_data = pd.DataFrame()\",\n \"def df(objs, labels: Optional[List[str]] = None):\\n import pandas as pd\\n from .objects import DynamicObject\\n if objs:\\n objs = list(objs)\\n obj = objs[0]\\n if is_dataclass(obj):\\n df = pd.DataFrame.from_records(dataclassAsTuple(o) for o in objs)\\n df.columns = [field.name for field in fields(obj)]\\n elif isinstance(obj, DynamicObject):\\n df = pd.DataFrame.from_records(o.__dict__ for o in objs)\\n else:\\n df = pd.DataFrame.from_records(objs)\\n if isinstance(obj, tuple):\\n _fields = getattr(obj, '_fields', None)\\n if _fields:\\n # assume it's a namedtuple\\n df.columns = _fields\\n else:\\n df = None\\n if labels:\\n exclude = [label for label in df if label not in labels]\\n df = df.drop(exclude, axis=1)\\n return df\",\n \"def convert_to_dataframe(H, endpoints=None, evidenced_nodes=None):\\n generalization_obj = generalization_stats(H, endpoints, evidenced_nodes)\\n # we need to duplicate node_id for all other values, we identify the length\\n # of other arrays only ones\\n series_size = None\\n\\n # final dataframe\\n final_df = None\\n\\n for node_id, stats_obj in generalization_obj.items():\\n if series_size is None:\\n series_size = len(stats_obj['confidences'])\\n\\n stats_obj['unevidenced_node'] = [H.node[node_id]['label']] * \\\\\\n series_size\\n\\n # first time creating the dataframe\\n if final_df is None:\\n final_df = pd.DataFrame(stats_obj)\\n else:\\n df = pd.DataFrame(stats_obj)\\n final_df = pd.concat([final_df, df])\\n final_df.reset_index()\\n\\n return final_df\",\n \"def glass_pandas(self):\\n # pandas.set_option('display.width', 120)\\n # TODO timeit (git_implementation) vs (my_implementation)\\n # * df = pd.DataFrame(json.loads(r.text))\\n # * df = df.set_index('t')\\n # * df.index = pd.to_datetime(df.index, unit='s')\\n # * df = df.sort_index()\\n # * s = df.v\\n # * s.name = '_'.join(url.split('/')[-2:])\\n # * return s\\n # for elem in self.loaded:\\n # _metric, _data = elem[1]['_metrics'], elem[1]['_data']\\n # try:\\n # frame_keys = ['t'] + list(_data[0]['o'].keys())\\n # framed = pandas.DataFrame(\\n # data=[{k: (_data[iters]['t'] if k in 't' else _data[iters]['o'][k])\\n # for k in frame_keys} for iters in range(len(_data))],\\n # columns=frame_keys)\\n # except KeyError:\\n # framed = pandas.DataFrame(_data)\\n # framed.set_index('t', inplace=True)\\n # framed.index = pandas.to_datetime(\\n # framed.index.to_flat_index(), unit='s', infer_datetime_format=True)\\n # framed.sort_index(inplace=True)\\n # framed.name = _metric\\n # print(framed.name)\\n # print(framed)\",\n \"def dataframe():\\n headers = get_headers()\\n headers = {'headers': headers}\\n headers = pd.DataFrame.from_dict(headers, orient='index')\\n headers = headers.replace(r'\\\\n', ' ', regex=True)\\n headers = headers.replace(r'\\\\r', ' ', regex=True)\\n headers = headers.replace(r'\\\\t', ' ', regex=True)\\n headers = headers.replace(r'\\\\\\\\t', ' ', regex=True)\\n headers = headers.replace(r' ', ' ', regex=True)\\n headers = headers.replace(r' ', ' ', regex=True)\\n\\n paragraphs = get_paragraphs()\\n paragraphs = {'paragraphs': paragraphs}\\n paragraphs = pd.DataFrame.from_dict(paragraphs, orient='index')\\n paragraphs = paragraphs.replace(r'\\\\n', ' ', regex=True)\\n paragraphs = paragraphs.replace(r'\\\\r', ' ', regex=True)\\n paragraphs = paragraphs.replace(r'\\\\t', ' ', regex=True)\\n paragraphs = paragraphs.replace(r'\\\\\\\\t', ' ', regex=True)\\n paragraphs = paragraphs.replace(r' ', ' ', regex=True)\\n paragraphs = paragraphs.replace(r' ', ' ', regex=True)\\n\\n return headers.to_csv('headers.csv', index=False), paragraphs.to_csv('paragraphs.csv', index=False)\",\n \"def dataframe(self):\\n return self.generator.dataframe\",\n \"def entity_data(self, entity_name, limit=10):\\n from sagas.ofbiz.entities import OfEntity as e, finder, record_list_df\\n # limit = 10\\n offset = 0\\n result = finder.find_list(entity_name, limit, offset)\\n result = record_list_df(entity_name, result, drop_null_cols=True, contains_internal=False)\\n print(result)\",\n \"def prepareDataframeForTable(self, result):\\n df = result\\n if isinstance(df, pd.Series):\\n df = pd.DataFrame({\\\"values\\\": df})\\n\\n if self._isIndexedDataframe(df):\\n if df.size == 0:\\n df[\\\"values\\\"] = np.nan\\n elif len(df.columns) > 1:\\n if isinstance(df.columns, pd.MultiIndex):\\n df.columns = df.columns.map(' | '.join)\\n df = df.stack()\\n if isinstance(df, pd.Series):\\n df = pd.DataFrame({\\\"values\\\": df})\\n current_columns_name = list(df.index.names)\\n current_columns_name[len(current_columns_name)-1] = \\\"Measures\\\"\\n df.index.names = current_columns_name\\n\\n return df\",\n \"def taxa_data_frame(self):\\n cols = list(self._taxa.keys())\\n cols.remove(\\\"uid\\\")\\n cols.remove(\\\"object\\\")\\n df = DataFrame(self._taxa, columns=cols, index=self._taxa[\\\"uid\\\"])\\n df.index.name = \\\"uid\\\"\\n\\n return df\",\n \"def process_data(self):\\n structure_data = self.parse_root(self.root)\\n\\n dict_data = {}\\n for d in structure_data:\\n dict_data = {**dict_data, **d}\\n df = pd.DataFrame(data=list(dict_data.values()), index=dict_data.keys()).T\\n\\n return df\",\n \"def data_from_dataframe(self, dataframe):\\n self.dataframe = dataframe.drop_duplicates()\\n #Convert numerical values into float type\\n self.dataframe.apply(pandas.to_numeric, errors='ignore')\\n #Convert timestamps into regular dates\\n time_range = [datetime.datetime.fromtimestamp(time) for time in list(self.dataframe['time'])]\\n beg = time_range[0]\\n end = time_range[len(time_range)-1]\\n #Attribute begining and ending dates\\n self.beg = beg\\n self.end = end\",\n \"def save_to_dataframe(self):\\n titles, years, months, days, authors = list(), list(), list(), list(), list()\\n for doc in self.results[\\\"documents\\\"]:\\n titles.append(doc['title'])\\n years.append(doc['year'])\\n months.append(doc['month'])\\n days.append(doc['day'])\\n authors.append(doc['authors'])\\n return pd.DataFrame({\\\"title\\\": titles, \\\"years\\\": years, \\\"months\\\": months, \\\"days\\\": days, \\\"author\\\": authors})\",\n \"def sa_pandas_init(self):\\n\\n lca = self.lca\\n\\n ind_activity = 0\\n ind_product = 1\\n ind_biosphere = 2\\n\\n cols = []\\n rows = []\\n inputs = []\\n\\n #All exchanges in inputs\\n for input_ in self.inputs:\\n\\n if input_ == 'biosphere':\\n continue\\n\\n for i in self.inputs_dict[input_]['tech_params']:\\n act = lca.reverse_dict() [ind_activity] [i['col']]\\n prod = lca.reverse_dict() [ind_product] [i['row']]\\n cols += [ bw.get_activity(act) ['name'] ]\\n rows += [ bw.get_activity(prod)['name'] ]\\n inputs += [input_]\\n for j in self.inputs_dict[input_]['bio_params']:\\n act = lca.reverse_dict() [ind_activity] [j['col']]\\n bio = lca.reverse_dict() [ind_biosphere] [j['row']]\\n cols += [ bw.get_activity(act) ['name'] ]\\n rows += [ bw.get_activity(prod)['name'] ]\\n inputs += [input_]\\n\\n if self.parameters != None:\\n # All parameters\\n parameters_names_list = [name for name in self.parameters_array['name']]\\n cols += parameters_names_list\\n rows += parameters_names_list\\n inputs += ['Parameters']*len(parameters_names_list)\\n\\n df = pd.DataFrame([inputs, rows, cols], index = ['Inputs', 'Products or flows', 'Activities'])\\n df = df.transpose()\\n\\n self.sensitivity_indices_df = df\",\n \"def get_df(self) -> pd.DataFrame:\\n return pd.DataFrame(self.fetchall(), columns=self.headers())\",\n \"def transform(self, data: pd.DataFrame) -> pd.DataFrame:\\n data = data[data.language == 'English'] # type: ignore\\n data = data[['premise', 'hypothesis', 'label']] # type: ignore\\n return data\",\n \"def build_dataframe(textline):\\n column_names = []\\n records = [line.split(u',') for line in textline]\\n records = [pd.np.nan if token in (u'\\\\\\\\N', 'NULL') else token for token in records]\\n # df_line = pd.read_csv(textline, header=None, names=column_names)\\n df = pd.DataFrame(records, columns=column_names)\\n df = df.convert_objects(convert_numeric=True)\\n df.set_index('msisdn', inplace=True)\\n print('-----', df.dtypes)\\n return df\",\n \"def to_pandas(self):\\n # TODO Add type translation.\\n # Skipping analyzing 'pandas': found module but no type hints or library stubs\\n import pandas as pd # type: ignore\\n\\n map = {}\\n for n, c in self._field_data.items():\\n map[n] = c.to_pandas()\\n return pd.DataFrame(map)\",\n \"def data_structure():\\n\\n items = [1.0, 2.0, 3.0, 4.0, 5.0 ]\\n s = pd.Series(items, index=['a', 'b', 'c', 'd', 'e'])\\n # s = pd.Series(np.random.randn(5), index=['a', 'b', 'c', 'd', 'e'])\\n print s\\n s = pd.Series(items)\\n print s\\n\\n d= {'one': [1.0, 2.0, 3.0, 4.0], 'two': [4.0, 3.0, 2.0, 1.0]}\\n\\n df = pd.DataFrame(d)\\n print df\\n df = pd.DataFrame(d, index=['a', 'b', 'c', 'd'])\\n print df\\n\\n\\n data2 = [{'a': 1, 'b': 2}, {'a': 5, 'b': 10, 'c': 20}]\\n df = pd.DataFrame(data2)\\n\\n print df\",\n \"def dataframe(self):\\n dictionary = OrderedDict(zip(self.keys, [[value] for value in self.values]))\\n dataframe = pd.DataFrame(dictionary)\\n return dataframe\",\n \"def create_dataframe_for_training(data):\\n feature_column_name = 'X'\\n #data_cp = data[['label']].copy()\\n for i, row in tqdm(data.iterrows(), total=len(data)):\\n all_features = f'{row.claimant} {row.claim} {row.article_content}'\\n data.loc[i, feature_column_name] = all_features\\n\\n return data[feature_column_name]\",\n \"def get_obj_df():\\n obj_df = pd.read_csv('data/object.csv')\\n obj_df = obj_df.drop_duplicates()[['course_id', 'module_id', 'category', 'start']] \\n obj_df['start'] = pd.to_datetime(obj_df[obj_df['start'] != 'null']['start'])\\n return obj_df\",\n \"def _dataframe_preprocess(self):\\n # 1. add baisc feature like date, time in day, ....\\n if self.data_type != 'porto':\\n self.df['TIMESTAMP'] = self.df.apply(lambda df: df['TIMESTAMPS'][0], axis=1)\\n self.df['TIME'] = pd.to_datetime(self.df['TIMESTAMP'], unit='s', utc=True)\\n \\n self.df.TIME = self.df.TIME.dt.tz_convert(self.timezone)\\n # 2. group df for specific driver analysis\\n self.grouped_df = self.df.groupby('LABEL')\\n if self.count_od_info:\\n if 'SD' not in self.df.columns:\\n self._add_OD_info()\\n self.grouped_od = self.df.groupby('SD')\",\n \"def pandaData():\\r\\n tweets = pd.DataFrame()\\r\\n return tweets\",\n \"def dataframe(self):\\n return self.get_target().dataframe()\",\n \"def test_train_to_df(\\n train_dataframe: pd.DataFrame, train_items: List[JSONDict]\\n) -> None:\\n\\n validated = TrainCollection(items=train_items)\\n assert train_dataframe.equals(validated.to_dataframe()[train_dataframe.columns])\",\n \"def as_DataFrame (self):\\n return DataFrame(self.table)\",\n \"def transform(self, data: pd.DataFrame, columns: list, verbose: int=1) -> pd.DataFrame:\",\n \"def history_to_df(self):\\n records = [pe.to_dict() for pe in self.history]\\n return pd.DataFrame.from_records(\\n records, columns=[\\n \\\"date\\\", \\\"type\\\", \\\"description\\\", \\\"debit\\\", \\\"credit\\\", \\\"balance\\\"\\n ]\\n ).set_index(keys=[\\\"date\\\"])\",\n \"def _prepare_inference_data(self, df: pd.DataFrame) -> pd.DataFrame:\\n # TODO Is the target encoding necessary?\\n if len(set(self.target) - set(df.columns)) > 0:\\n if self.config.task == \\\"classification\\\":\\n df.loc[:, self.target] = np.array([self.label_encoder.classes_[0]] * len(df)).reshape(-1, 1)\\n else:\\n df.loc[:, self.target] = np.zeros((len(df), len(self.target)))\\n df, _ = self.preprocess_data(df, stage=\\\"inference\\\")\\n return df\",\n \"def prof2frame(prof):\\n\\n import pandas as pd\\n\\n # create data frame from profile dicts\\n frame = pd.DataFrame(prof)\\n\\n # --------------------------------------------------------------------------\\n # add a flag to indicate entity type\\n def _entity (row):\\n if not row['uid']:\\n return 'session'\\n if 'unit' in row['uid']:\\n return 'unit'\\n if 'pilot' in row['uid']:\\n return 'pilot'\\n return 'session'\\n frame['entity'] = frame.apply(lambda row: _entity (row), axis=1)\\n\\n # --------------------------------------------------------------------------\\n # add a flag to indicate if a unit / pilot / ... is cloned\\n def _cloned (row):\\n if not row['uid']:\\n return False\\n else:\\n return 'clone' in row['uid'].lower()\\n frame['cloned'] = frame.apply(lambda row: _cloned (row), axis=1)\\n\\n return frame\",\n \"def to_dataframe(self, data_dict):\\n return pd.DataFrame.from_dict(data_dict, orient='index')\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6892027","0.6508207","0.6472042","0.6281258","0.6169941","0.61465824","0.613803","0.61098313","0.6088293","0.6079013","0.6077937","0.6062319","0.60613316","0.6043503","0.60430515","0.60380507","0.60380507","0.60380507","0.60380507","0.60380507","0.60256153","0.601799","0.60160303","0.6007067","0.5994463","0.5987599","0.597265","0.5958738","0.59340596","0.5928337","0.5927193","0.5923566","0.59115684","0.5904532","0.58960164","0.5895881","0.58834034","0.58834034","0.5880604","0.5879303","0.5876584","0.5868591","0.5861418","0.585454","0.58445525","0.58361995","0.58233315","0.58208716","0.58132523","0.58078134","0.5807158","0.58065677","0.5792743","0.57860684","0.57734734","0.575603","0.5752425","0.57504576","0.57476175","0.57418454","0.57358843","0.57298523","0.57062626","0.57057023","0.5702423","0.57022476","0.5679669","0.56790656","0.5677882","0.5672647","0.56683946","0.5666906","0.5662189","0.56606066","0.56601274","0.5657322","0.5650009","0.56437916","0.5643428","0.56401837","0.56387085","0.5634622","0.5631356","0.5629594","0.5628367","0.5621471","0.56193185","0.5614708","0.56142384","0.5610054","0.56087166","0.5601508","0.5594679","0.55945736","0.5592288","0.5588418","0.5562829","0.556115","0.55556947","0.5554197"],"string":"[\n \"0.6892027\",\n \"0.6508207\",\n \"0.6472042\",\n \"0.6281258\",\n \"0.6169941\",\n \"0.61465824\",\n \"0.613803\",\n \"0.61098313\",\n \"0.6088293\",\n \"0.6079013\",\n \"0.6077937\",\n \"0.6062319\",\n \"0.60613316\",\n \"0.6043503\",\n \"0.60430515\",\n \"0.60380507\",\n \"0.60380507\",\n \"0.60380507\",\n \"0.60380507\",\n \"0.60380507\",\n \"0.60256153\",\n \"0.601799\",\n \"0.60160303\",\n \"0.6007067\",\n \"0.5994463\",\n \"0.5987599\",\n \"0.597265\",\n \"0.5958738\",\n \"0.59340596\",\n \"0.5928337\",\n \"0.5927193\",\n \"0.5923566\",\n \"0.59115684\",\n \"0.5904532\",\n \"0.58960164\",\n \"0.5895881\",\n \"0.58834034\",\n \"0.58834034\",\n \"0.5880604\",\n \"0.5879303\",\n \"0.5876584\",\n \"0.5868591\",\n \"0.5861418\",\n \"0.585454\",\n \"0.58445525\",\n \"0.58361995\",\n \"0.58233315\",\n \"0.58208716\",\n \"0.58132523\",\n \"0.58078134\",\n \"0.5807158\",\n \"0.58065677\",\n \"0.5792743\",\n \"0.57860684\",\n \"0.57734734\",\n \"0.575603\",\n \"0.5752425\",\n \"0.57504576\",\n \"0.57476175\",\n \"0.57418454\",\n \"0.57358843\",\n \"0.57298523\",\n \"0.57062626\",\n \"0.57057023\",\n \"0.5702423\",\n \"0.57022476\",\n \"0.5679669\",\n \"0.56790656\",\n \"0.5677882\",\n \"0.5672647\",\n \"0.56683946\",\n \"0.5666906\",\n \"0.5662189\",\n \"0.56606066\",\n \"0.56601274\",\n \"0.5657322\",\n \"0.5650009\",\n \"0.56437916\",\n \"0.5643428\",\n \"0.56401837\",\n \"0.56387085\",\n \"0.5634622\",\n \"0.5631356\",\n \"0.5629594\",\n \"0.5628367\",\n \"0.5621471\",\n \"0.56193185\",\n \"0.5614708\",\n \"0.56142384\",\n \"0.5610054\",\n \"0.56087166\",\n \"0.5601508\",\n \"0.5594679\",\n \"0.55945736\",\n \"0.5592288\",\n \"0.5588418\",\n \"0.5562829\",\n \"0.556115\",\n \"0.55556947\",\n \"0.5554197\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6550998"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":75583,"cells":{"query":{"kind":"string","value":"the method is to calculate the first products."},"document":{"kind":"string","value":"def __set_bias_score(self, row, best_score):\n if row.productid == best_score:\n result = 1\n else:\n result = row.final_score\n\n return result"},"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 compute_price(self, date = None):\n\t\tif date is None:\n\t\t\tdate = datetime.now()\n\t\tself.price = 0\n\t\t# Getting list of product in cart\n\t\tcontent = self.cart.cart_content_set.all()\n\t\t# Dictionnary in order to compute minimum state of multi promotion\n\t\tstate = {\n\t\t\t'products':{},\n\t\t\t'promotions':{}\n\t\t}\n\t\trequirements = {}\n\n\t\tfor element in content:\n\t\t\tproduct = element.product\n\t\t\tquantity = element.quantity\n\n\t\t\t# First look for promotion\n\t\t\tsimple_promotions = product.promotion_set.filter(end__gte = date, type = 's').distinct('reference', 'end').order_by('-end', 'reference')\n\t\t\tmulti_promotions = product.promotion_set.filter(end__gte = date, type = 'm').distinct('reference', 'end').order_by('-end', 'reference')\n\t\t\tif len(simple_promotions)>0:\n\t\t\t\tpromotion = simple_promotions[0]\n\t\t\t\tself.price = self.price + quantity*promotion.after\n\t\t\t\n\t\t\telif len(multi_promotions)>0:\n\t\t\t\tfor promotion in multi_promotions:\n\t\t\t\t\tprice_before = promotion.before\n\t\t\t\t\tprice_after = promotion.after\n\t\t\t\t\tcontent = [ (p, 1) for p in promotion.content.all()]\n\t\t\t\t\tfound, requirement = self.get_promotion_requirement(content, price_before)\n\t\t\t\t\tif found and requirement is not None:\n\t\t\t\t\t\trequirements[promotion.id] = { p.id:q for p, q in requirement} # updating promotion multi requirements\n\n\t\t\t\t\t# Updating promotion multi state\n\t\t\t\t\tprod, price = self.get_simple_price([{'product':product, 'quantity':1}], date)[0]\n\t\t\t\t\t# print quantity\n\t\t\t\t\tstate['products'][product.id] = {'price': price, 'qte':quantity}\n\t\t\t\t\t# print state['products'][product.id]\n\t\t\t\t\tstate['promotions'][promotion.id] = {'price': price_after, 'qte':0}\n\t\t\telse:\n\t\t\t\thistory = product.history_set.filter(created__gte = date-timedelta(hours = 24)).order_by('-created')\n\t\t\t\tif len(history)>0:\n\t\t\t\t\tself.price = self.price + quantity*history[0].price\n\t\t\t\telse:\n\t\t\t\t\thistory = product.history_set.all().order_by('-created')\n\t\t\t\t\tif len(history)>0:\n\t\t\t\t\t\tself.price = self.price + quantity*history[0].price\n\n\t\t# Dealing with multi promotion:\n\t\tmin_state, min_price = self.get_min_state(state, requirements)\n\t\tself.price = self.price + min_price\n\n\t\tself.save()\n\n\t\treturn self.price","def product(self):\n return None","def product(self):\n return None","def product(self):\n raise NotImplementedError","def first_order(self):\n for i in range(1,self.N+1):\n self.d1[i] = self.C[i] / 2 / i;","def get_product_input(self):\n result = []\n result.append(self.one_hot_translate_product(self.curr_productfea, self.depthlist))\n result.append(tf.nn.embedding_lookup(self.product_embeddings, self.curr_productid))\n result.append(tf.nn.embedding_lookup(self.aisle_embeddings, self.curr_aisleid))\n result.append(tf.nn.embedding_lookup(self.department_embeddings, self.curr_departmentid))\n result.append(self.one_hot_translate(self.curr_productidx, self.max_productlen+1))\n product_input = tf.concat(result, axis=-1)\n return product_input","def findAShin(self):\n #return reduce(lambda x, y: x*y, [self.DoS[key].get_price() for key in self.DoS] )\n a = array([self.DoS[key].get_Price() for key in self.DoS])\n return a.prod()**(1.0/len(a))","def choice_product(self):\n self.first_number = 0\n self.leave_choice_product = 1\n while self.leave_choice_product:\n print(fr.FR[11])\n self.display_product(self.products)\n self.input_product = input(fr.FR[12])\n self.choice_product_input()","def get_all_products(self):\n\t\tpass","def get_prices(name,products,sales):\r\n return tuple((products[0],products[1]*((1-tuple(filter(lambda x: x[0]==name, sales))[0][1]))) for products in products)","def calculate(self):","def get_product_kpis(self):\n kpi_1 = self.products[['products', 'client']].reset_index().groupby([\"client\", \"products\"]).agg(\n {\"index\": \"count\"}).reset_index().groupby(\"products\").agg(\n {\"index\": \"mean\"}).reset_index().rename(columns={\"index\": \"average_product_sold_per_user_kpi\"})\n kpi_2 = self.products[['products', 'payment_amount']].groupby(\"products\").agg(\n {\"payment_amount\": \"sum\"}).reset_index().rename(columns={\"payment_amount\": \"total_product_revenue_kpi\"})\n try:\n kpi_3 = self.products[['products', 'discount_amount']].query(\"discount_amount == discount_amount\")\n kpi_3['discount_amount'] = kpi_3['discount_amount'].apply(lambda x: float(x))\n kpi_3 = kpi_3.groupby(\"products\").agg({\"discount_amount\": \"sum\"}).reset_index().rename(\n columns={\"discount_amount\": \"total_product_discount_kpi\"})\n except:\n kpi_3 = self.products[['products']]\n kpi_3['total_product_discount_kpi'] = 0\n\n kpi_4 = self.products[['products', 'client']].groupby(\"products\").agg(\n {\"client\": lambda x: len(np.unique(x))}).reset_index().rename(columns={\"client\": \"total_product_cust_kpi\"})\n\n self.product_kpis = pd.DataFrame(self.products.groupby('products').count().reset_index()[['products']]).merge(\n kpi_1, on='products', how='left').merge(\n kpi_2, on='products', how='left').merge(\n kpi_3, on='products', how='left').merge(kpi_4, on='products', how='left').fillna(0)\n\n return self.product_kpis","def buy_one_get_one(products):\n if 'p1' in products and products['p1'] >= 2:\n return -20\n else:\n return 0","def product(self):\n return self._product","def product(self):\n return self._product","def product(self):\n return self._product","def compute_first(self):\n compute_first_sets(self, self.rules)","def prodri(items: Iterator[float]) -> float:\n try:\n head = next(items)\n except StopIteration:\n return 1\n return head*prodri(items)","def insert_products(self):\n logic = ProductLogic()\n \n try:\n # We create the list of product objects\n products = self.objects_factory.create_product_object_list()\n products = set(products)\n\n for product in products:\n logic.insert(product)\n except:\n print('Il y a eu un problème lors de la récupération des données, veuillez rééssayer')","def product1(a, b, c) :\n return a * b * c","def get_fastest_moving_products(num_products):\n\treturn execute_sql(\"SELECT slopes.slope, slopes.asin, products.product_name,\\\n\t\tproducts.manufacturer_name, categories.category_name, \\\n\t\tproducts.product_url FROM slopes INNER JOIN products ON \\\n\t\tslopes.asin=products.asin INNER JOIN categories ON \\\n\t\tslopes.category_url=categories.category_url ORDER BY slope ASC \\\n\t\tLIMIT %s\" % num_products)","def execQ6():\n frame = pan.DataFrame(data, columns=['Product', 'Price', 'Period'])\n expensive = frame.sort_values(by='Price', ascending=False).head(1)\n return expensive","def prod(self):\n # skipna == True\n # only_numerical == True\n return self._lift(\"prod\")","def _initialize_products(self, products: List) -> Dict[str, int]:\n\n product_request = urllib.request.Request(url=URL_PRODUCTS, headers={'User-Agent': URL_USER_AGENT})\n product_response = urllib.request.urlopen(product_request)\n all_products = json.load(product_response)\n\n product_details = {}\n\n for product in products:\n for cbpro_product in all_products:\n if cbpro_product[\"id\"] == product:\n quote_increment = float(cbpro_product[\"quote_increment\"])\n num_decimal_places = int(math.log10(1 / quote_increment))\n product_details[product] = num_decimal_places\n logging.debug(\n \"Retrieved quote increment for {}: {} = {} decimal places\".\n format(product, quote_increment, num_decimal_places))\n\n return product_details","def firstorder(self):\n f = self.img\n x = self.x\n y = self.y\n self.x1 = sum(f*x)/sum(f)\n self.y1 = sum(f*y)/sum(f)","def click_first_product(self):\n self.functions.wait_for_element(self.SELECT_PRODUCT).click()\n product_selected = self.functions.wait_for_element(self.PRODUCT_SELECTED)\n assert product_selected.is_displayed(), \"didn't selected product!\"","def products(self):\r\n return self._products","def get_simple_price(self, content, date = None):\n\t\tprices = []\n\t\tfor element in content:\n\t\t\tproduct = element['product']\n\t\t\tquantity = element['quantity']\n\n\t\t\thistory = product.history_set.filter(created__gte = date-timedelta(hours = 24)).order_by('-created')\n\t\t\tif len(history)>0:\n\t\t\t\tprices.append((element, quantity*history[0].price))\n\t\t\telse:\n\t\t\t\thistory = product.history_set.all().order_by('-created')\n\t\t\t\tif len(history)>0:\n\t\t\t\t\tprices.append((element, quantity*history[0].price))\n\t\t\t\telse:\n\t\t\t\t\tprices.append((element, None))\n\t\treturn prices","def test_product(self):\n self.assertEqual(functions.product(2, 2), 4)\n self.assertEqual(functions.product(2, -2), -4)","def generate_products(self = random.sample, name = random.choice(result), price = random.randint(5, 100), weight = random.randint(5, 100), \nflammability= random.uniform(0, 2.5)):\n return sample","def _first(self) -> Tuple[np.ndarray, np.ndarray, ModelGeneratorBase]:\n pass","def _core_calc_prod(self,bp,Lp) :\n\t\tprod = np.dot(Lp,bp)\n\t\tprod = np.exp(prod)\n\t\treturn prod","def prod(self, args):\n assert len(args) > 0, \"Cannot compute an empty product in a semigroup\"\n return prod(args[1:], args[0])","def get_most_combined_products(self):\n self.product_pairs = self.products.groupby(\"id\").agg({\"products\": lambda x: list(x)}).reset_index()\n self.product_pairs['product_pairs'] = self.product_pairs['products'].apply(\n lambda x: np.array(list(product(x, x))).tolist())\n self.product_pairs = pd.DataFrame(np.concatenate(list(self.product_pairs['product_pairs'])).tolist())\n self.product_pairs['total_pairs'] = 1\n self.product_pairs = self.product_pairs[self.product_pairs[0] != self.product_pairs[1]]\n self.product_pairs = self.product_pairs.groupby([0, 1]).agg({\"total_pairs\": \"sum\"}).reset_index()\n self.product_pairs = self.product_pairs.sort_values('total_pairs', ascending=False)\n self.product_pairs = self.product_pairs.rename(columns={0: \"pair_1\", 1: \"pair_2\"})\n self.product_pairs['product_pair'] = self.product_pairs.apply(\n lambda row: \" - \".join(list(sorted([row['pair_1'], row['pair_2']]))), axis=1)\n self.product_pairs = self.product_pairs.groupby(\"product_pair\").agg({\"total_pairs\": \"first\"}).reset_index()\n return self.product_pairs","def handle(self, *args, **options):\n\n ProductPair.objects.all().delete()\n person = Person.objects.get(name='klapshov')\n meals = Meal.objects.filter(person=person)\n for m in meals:\n products = [i.product for i in m.intake_set.all()]\n print(products)\n pairs = []\n for n in range(0, len(products)-1):\n for nn in range(n+1, len(products)):\n if n != nn:\n pairs.append([products[n], products[nn]])\n print(pairs)\n print(len(products), len(pairs))\n\n # REWRITE!!!!!!!!\n for p in pairs:\n try:\n pp = ProductPair.objects.get(product1=p[0], product2=p[1])\n except ProductPair.DoesNotExist:\n pp = ProductPair()\n pp.product1 = p[0]\n pp.product2 = p[1]\n pp.count = 1\n p2p = ProductToPerson.objects.get(\n person=person, product=p[0]\n )\n pp.ratio = round(pp.count/p2p.intakes_count*100)\n pp.save()\n else:\n pp.count += 1\n p2p = ProductToPerson.objects.get(\n person=person,\n product=p[0]\n )\n pp.ratio = round(pp.count/p2p.intakes_count*100)\n pp.save()\n\n for p in pairs:\n p.reverse()\n try:\n pp = ProductPair.objects.get(product1=p[0], product2=p[1])\n except ProductPair.DoesNotExist:\n pp = ProductPair()\n pp.product1 = p[0]\n pp.product2 = p[1]\n pp.count = 1\n p2p = ProductToPerson.objects.get(\n person=person,\n product=p[0]\n )\n pp.ratio = round(pp.count/p2p.intakes_count*100)\n pp.save()\n else:\n pp.count += 1\n p2p = ProductToPerson.objects.get(\n person=person,\n product=p[0]\n )\n pp.ratio = round(pp.count/p2p.intakes_count*100)\n pp.save()","def calculate(self, order):\n pass","def precalculate():\n pass","def precalculate():\n pass","def recommend_next_product(self, prod_list):\n scores = defaultdict(float)\n for prod in prod_list:\n for item in self._purchased.find({PROD1: prod}):\n if not item[PROD2] in prod_list:\n scores[item[PROD2]] += math.log(item[TIMES])\n if len(scores) == 0:\n return None\n max_tuple = max(scores.items(), key = operator.itemgetter(1))\n return max_tuple[0]","def choice_product_input(self):\n try:\n if self.input_product == \"s\" and (\n self.first_number + config.NUMBER_PRODUCT_DISPLAY)\\\n < len(self.products):\n self.first_number += config.NUMBER_PRODUCT_DISPLAY\n elif self.input_product == \"p\" and self.first_number > 0:\n self.first_number -= config.NUMBER_PRODUCT_DISPLAY\n elif self.input_product == \"q\":\n self.leave_choice_product -= 1\n elif 1 <= int(self.input_product) <= len(self.products):\n self.product_selected = self.products[int(self.input_product)\n - 1][0]\n del self.products_sub[int(self.input_product)-1]\n self.choice_sub()\n self.leave_choice_product -= 1\n except ValueError:\n print(fr.FR[10])","def get_new_products(self, reactions):\n\t\tnew_products = []\n\t\tnew_reactions = []\n\n\t\tESTNF = { c:c for c in self._E + self._S + self._T + self._N + self._F }\n\t\tB = { c:c for c in self._B}\n\n\t\t\n\t\t# Loop over every reaction\n\t\tfor reaction in reactions:\n\t\t\t\n\t\t\t# This will be set to False if we bail out of the inner loop upon finding a complex that's too large\n\t\t\tcomplex_size_ok = True\n\t\t\t\n\t\t\t# Check every product of the reaction to see if it is new\n\t\t\tfor (i, product) in enumerate(reaction.products):\n\t\t\t\t\n\t\t\t\tif (len(product.strands) > self.MAX_COMPLEX_SIZE):\n\t\t\t\t\tlogging.warning(\"Complex %(name)s (%(strands)d strands) too large, ignoring!\" % {\"name\":product.name,\"strands\":len(product.strands)})\n\t\t\t\t\tcomplex_size_ok = False\n\t\t\t\t\tbreak\n\t\t\t\n\t\t\t\t# This will be set to True if we've already seen this complex\n\t\t\t\tenumerated = False\n\t\t\t\t\n\t\t\t\t# If the product is in any of these lists, we don't need to\n\t\t\t\t# deal with it, so just update the reaction to point correctly\n\t\t\t\t# TODO: This could benefit from a substantial speedup if _E, _S, \n\t\t\t\t#\t_T, _N, _F were implemented as sets. Other parts of the \n\t\t\t\t#\talgorithm benefit from their representation as queues though... \n\t\t\t\t\n\t\t\t\tif product in ESTNF: reaction.products[i] = ESTNF[product]; enumerated = True\n\t\t\t\t# for complex in self._E + self._S + self._T + self._N + self._F:\n\t\t\t\t# \tif (product == complex):\n\t\t\t\t# \t\tenumerated = True\n\t\t\t\t# \t\treaction.products[i] = complex\n\t\t\t\t# \t\tbreak\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\tif not enumerated:\n\t\t\t\t\t# If the product is in list B, then we need to remove it from\n\t\t\t\t\t# that list so that it can be enumerated for self-interactions\n\t\t\t\t\t# as part of this neighborhood\n\n\t\t\t\t\tif product in B: \n\t\t\t\t\t\treaction.products[i] = B[product]; \n\t\t\t\t\t\tself._B.remove(B[product])\n\t\t\t\t\t\tproduct = B[product]\n\t\t\t\t\t\tdel B[product]\n\n\t\t\t\t\t# for complex in self._B:\n\t\t\t\t\t# \tif (product == complex):\n\t\t\t\t\t# \t\treaction.products[i] = complex\n\t\t\t\t\t# \t\tself._B.remove(complex)\n\t\t\t\t\t# \t\tproduct = complex\n\t\t\t\t\t# \t\tbreak\n\t\t\t\t\t\t\t\n\t\t\t\t\t# If the product has already been seen in this loop, update\n\t\t\t\t\t# the pointer appropriately\n\t\t\t\t\tfor complex in new_products:\n\t\t\t\t\t\tif (product == complex):\n\t\t\t\t\t\t\tenumerated = True\n\t\t\t\t\t\t\treaction.products[i] = complex\n\t\t\t\t\t\t\tbreak\n\t\t\t\t\n\t\t\t\tif not enumerated:\n\t\t\t\t\tnew_products.append(product)\n\t\t\t\n\t\t\t# If this reaction contained a complex that was too big, ignore the whole reaction.\n\t\t\tif complex_size_ok:\n\t\t\t\tnew_reactions.append(reaction)\n\t\t\n\t\t# Clobber the old value of reactions with the filtered list\n\t\treactions[:] = new_reactions\n\t\t\t\n\t\treturn new_products","def pre_compute(self, e_list):\n\t\tpass","def get_first_item(self):\n params = urllib.parse.urlencode({'o':'1', 'q':self.query})\n url = 'https://www.leboncoin.fr/annonces/offres/ile_de_france/?{:s}'.format(params) # Cree l'url de recherche en get\n html = urllib.request.urlopen(url)\n if url != html.geturl():\n return None\n soup = BeautifulSoup.BeautifulSoup(html, 'html5lib')\n try:\n products = soup.section.find_all('a', 'list_item clearfix trackable')\n except Exception as e:\n print('Nothing found on leboncoin')\n return None\n for product in products: # recupere les differentes informations de chaque produit\n if str(product.section.h2).strip() == 'None':\n continue\n name = product.section.h2.contents[0].strip()\n price = self.__get_price(product)\n link = 'http:' + product['href']\n return (name, price, link)\n return None","def generate_products():\n # initialize list of noun and adj\n num_products = 30\n products = [0] * num_products\n prices = [0] * num_products\n weights = [0] * num_products\n flammabilities = [0] * num_products\n\n # initlize random word object\n random = RandomWords()\n\n adj = [random.get_random_word(includePartOfSpeech=\"adjective\")\n for product in products]\n noun = [random.get_random_word(includePartOfSpeech=\"noun\")\n for product in products]\n products = [noun + \" \" + adj for noun, adj in zip(adj, noun)]\n\n prices = [random.randint(5, 100) for price in prices]\n weights = [random.randint(5, 100) for weight in weights]\n flammabilities = [random.randint(0.0, 2.5)\n for flammability in flammabilities]\n\n return products, prices, weights, flammabilities","def _make_product(terms):\n if terms:\n product = terms[0]\n for term in terms[1:]:\n product = Mul((product, term))\n return product \n else:\n return Const(1)","def __iter__(self):\n return self._product_generator()","def prodrc( collection ):\n if len(collection) == 0: return 1\n return collection[0] * prodrc(collection[1:])","def find_substitute(self):\n\n products_list = None\n\n while not products_list:\n self.get_targeted_category()\n\n db.connect()\n db.execute(\"\"\"\n SELECT product_id, nutriscore_id\n FROM Product_per_category\n INNER JOIN Product\n ON Product.id = product_id\n WHERE category_id = %s AND nutriscore_id < %s\n ORDER BY nutriscore_id\n \"\"\", (self.category_id, self.nutriscore,))\n products_list = db.fetch(True)\n db.disconnect()\n self.category_concordance += 1\n\n return products_list[0][0]","def calculate(self):\r\n\r\n pass","def filter_products(self, products: list):\n # Get all products that has no parent\n independent = [product for product in products if product[\"parent_id\"] is None]\n # Get all products that has parent\n dependent = [\n product for product in products if product[\"parent_id\"] is not None\n ]\n # Sort dependent products by parent_id, so that a child will be always\n # inserted after the parent\n dependent = sorted(dependent, key=lambda item: item[\"parent_id\"])\n # Saves the total of objects\n self.total = len(independent) + len(dependent)\n return independent, dependent","def getFPSA1(ChargeSA):\n temp=0.0\n for i in ChargeSA:\n temp=temp+i[2]\n if temp == 0.0:\n return 0.0\n else:\n return getPPSA1(ChargeSA)/temp","def products_for_influencers(parameters, page=0, page_size=50):\n product_ids, _, total_hits = elastic_search_helpers.es_product_query_runner_v2(\n parameters, page, page_size)\n\n num_pages = int(math.ceil(float(total_hits) / page_size))\n\n return product_ids, total_hits, num_pages","def execQ5():\n frame = pan.DataFrame(data, columns=['Product', 'Price', 'Period'])\n cheapest = frame.sort_values(by='Price', ascending=True).head(1)\n return cheapest","def product_summary(self):\n # products = self.products.filter(type='S') # TODO: explain the usage of this commented line or remove it\n from .utils import process_products\n\n subscription_products = SubscriptionProduct.objects.filter(subscription=self)\n dict_all_products = {}\n for sp in subscription_products:\n dict_all_products[str(sp.product.id)] = str(sp.copies)\n return process_products(dict_all_products)","def show_proposal(self, req):\n z = 1\n y = z\n x = 1\n self.dico_product = {}\n for prod in req:\n if z <= 5:\n List_store = orm_imp.find_store(prod.id)\n print(\"Choix numéro\", z, \":\", prod.name, \"| score : \",\n prod.nutriscore, \"| Magasins : \", List_store,\n \"| Lien :\",\n prod.url, \"| \\n ==> description :\",\n prod.ingredient, \"\\n==================================\")\n self.dico_product.update({z: prod.id})\n x += 1\n z += 1\n return self.dico_product, y, x - 1","def calculate(self):\r\n pass","def calculate(self):\n pass","def prodrc(collection: Sequence[float]) -> float:\n if len(collection) == 0:\n return 1\n return collection[0] * prodrc(collection[1:])","def printone(self):\n print('*' * 50)\n product = self.iterator().__next__()\n pprint(product)\n print('*'*50)\n converted_product = self.converter(product)\n pprint(converted_product)\n print('*' * 50)\n validated_product = self.validator(converted_product)\n pprint(validated_product)\n print('*' * 50)","def _calc_pareto_front(self, *args, **kwargs):\n fname = f\"{self.fct.name}_PF.dat\"\n F = load_pareto_front_from_file(os.path.join(\"modact\", fname))\n if F is not None:\n return F*self.weights*-1","def _get_prix_tarif(self,cout,pricelist):\n cr = self._cr\n product=cout.name\n prix_tarif=0\n date=time.strftime('%Y-%m-%d') # Date du jour\n if pricelist:\n #Convertion du lot_mini de US vers UA\n min_quantity = self.env['product.uom']._compute_qty(cout.name.uom_id.id, cout.name.lot_mini, cout.name.uom_po_id.id)\n #TODO : Pour contourner un bug d'arrondi (le 31/01/2017)\n min_quantity=min_quantity+0.00000000001\n #TODO en utilisant la fonction repr à la place de str, cela ne tronque pas les décimales\n SQL=\"\"\"\n select ppi.price_surcharge\n from product_pricelist_version ppv inner join product_pricelist_item ppi on ppv.id=ppi.price_version_id\n where ppv.pricelist_id=\"\"\"+str(pricelist.id)+ \"\"\" \n and min_quantity<=\"\"\"+repr(min_quantity)+\"\"\"\n and (ppv.date_start <= '\"\"\"+date+\"\"\"' or ppv.date_start is null)\n and (ppv.date_end >= '\"\"\"+date+\"\"\"' or ppv.date_end is null)\n\n and ppi.product_id=\"\"\"+str(product.id)+ \"\"\" \n and (ppi.date_start <= '\"\"\"+date+\"\"\"' or ppi.date_start is null)\n and (ppi.date_end >= '\"\"\"+date+\"\"\"' or ppi.date_end is null)\n order by ppi.sequence\n limit 1\n \"\"\"\n cr.execute(SQL)\n result = cr.fetchall()\n for row in result:\n coef=1\n if min_quantity:\n coef=cout.name.lot_mini/min_quantity\n prix_tarif=row[0]/coef\n\n\n\n return prix_tarif","def execQ4():\n frame = pan.DataFrame(data, columns=['Product', 'Price', 'Period'])\n tuna = frame[dFrame.Series_title_1 == \"Tuna - canned (supermarket only), 185g\"]\n expensive = tuna.sort_values(by=\"Price\", ascending=False).head(1)\n return expensive","def product(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"product\")","def product_on_basis(self, t1, t2):\n return tensor( (module.monomial(x1)*module.monomial(x2) for (module, x1, x2) in zip(self._sets, t1, t2)) ) #.","def proposal_product(self, user_prod):\n # Give the bests product, selected by nutriscore value.\n prod0 = orm_imp.list_prod(user_prod)\n prod = self.show_proposal(prod0)\n user_choice = int(input(\n \"==============================\\n\"\n \"= Que souhaitez-vous faire ? =\\n\"\n \"==============================\\n\"\n \"- Sauvegarder un produit proposé : taper 1\\n\"\n \"- Faire une autre recherche produit : taper 2\\n\"\n \"- Retourner à l'écran principal : taper 3\\n\"))\n if user_choice == 1: # save product, go to main section\n self.save_product(prod[0], prod[1], prod[2], user_prod)\n self.controle_point = 1\n elif user_choice == 2: # goback to product research section\n self.controle_point = 2\n elif user_choice == 3: # go to main section\n self.controle_point = 1\n else:\n print(\"Veuillez entrer un chiffre compris entre 1 et 3.\")\n self.controle_point = 4\n return self.controle_point","def test_mueller_product(self, ):\n mdims = ('mueller_v', 'mueller_h')\n mm_1 = xr.DataArray(np.random.rand(4, 4, ), dims=mdims, )\n mm_2 = xr.DataArray(np.identity(4, ), dims=mdims, )\n sv_1 = xr.DataArray(np.random.rand(4, ), dims=('stokes', ), )\n\n assert_almost_equal(mm_1.values, mueller_product(mm_1, mm_2).values, )\n assert_almost_equal(mm_1.values, mueller_product(mm_2, mm_1).values, )\n assert_almost_equal(sv_1.values, mueller_product(mm_2, sv_1).data, )","def product(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"product\")","def generateProducts(self):\r\n\r\n # Creates items in each category\r\n for i in range(self.num_of_items):\r\n self.ID_DICT[i+self.num_of_items] = random.randint(1, 10)\r\n self.ID_DICT[i+self.num_of_items*2] = random.randint(1, 10)\r\n self.ID_DICT[i+self.num_of_items*3] = random.randint(1, 10)\r\n self.ID_DICT[i+self.num_of_items*4] = random.randint(1, 10)\r\n self.ID_DICT[i+self.num_of_items*5] = random.randint(1, 10)\r\n self.ID_DICT[i+self.num_of_items*6] = random.randint(1, 10)\r\n\r\n\r\n # Sort for easy selection\r\n sorted(self.ID_DICT)\r\n\r\n for product in self.ID_DICT.keys():\r\n temp_int = self.ID_DICT[product]\r\n self.c.execute(\"INSERT INTO Products (ProductID, Price) VALUES (?, ?)\", (product, self.ID_DICT[product]))\r\n self.conn.commit()\r\n\r\n if self.print_items:\r\n print(\"\\nAll items in store:\")\r\n print(self.ID_DICT)\r\n print()","def _listProduct(self,lol,anch):#a recursive list product taker\n\t\ttry:\n\t\t\tif str(type(lol[0])) != \"<type 'list'>\":\n\t\t\t\traise IndexError\n\t\t\tself._listProduct(utils.xproduct(lol.pop(0),lol.pop(0))+lol,anch)\n\t\texcept IndexError:\n\t\t\tanch.extend(lol)","def initial_price(self) -> float:\n return self.__initial_price","def individual(prod):\n try:\n return 4. * arctan(sqrt(prod))\n except FloatingPointError:\n return 0.","def get_products(self):\n\n lst = []\n for product in self.products.findall('product'):\n id = product.find('id').text\n name = product.find('name').text\n dispensary_id = product.find('dispensary_id').text\n dispensary_name = product.find('dispensary_name').text\n canabis_brand = product.find('canabis_brand').text\n canabis_strain = product.find('canabis_strain').text\n category = product.find('category').text\n subcategory = product.find('subcategory').text\n thc_level = product.find('thc_level').text\n cbd_level = product.find('cbd_level').text\n cbn_level = product.find('cbn_level').text\n thc_level_type = product.find('thc_level_type').text\n cbd_level_type = product.find('cbd_level_type').text\n cbn_level_type = product.find('cbn_level_type').text\n\n description = product.find('description').text\n created_at = product.find('created_at').text\n updated_at = product.find('updated_at').text\n\n prices = []\n urls = []\n images = []\n\n for child in product:\n if child.tag == 'prices':\n for cost in child.findall('cost'):\n prices.append(Price(cost.attrib['unit'], cost.text))\n\n if child.tag == 'urls':\n admin = child.find('admin').text\n public = child.find('public').text\n urls.append(UrlInfo(admin, public))\n\n if child.tag == 'images':\n for image in child.findall('image'):\n images.append(Image(image.attrib['main'], image.text,))\n\n lst.append(Product(id, name, dispensary_id, dispensary_name,\n canabis_brand, canabis_strain,\n category, subcategory, thc_level, cbd_level,\n cbn_level, thc_level_type, cbd_level_type,\n cbn_level_type, prices, urls, images,\n description, created_at, updated_at))\n\n return lst","def prod(self):\n return self._summarize(lambda c: c.prod)","def products(self):\r\n return products.Products(self)","def prod(self, values):\n return self.aggregate(values, \"prod\")","def first(self):","def display_product(self, list_products):\n for element in list_products[self.first_number:self.first_number +\n config.NUMBER_PRODUCT_DISPLAY]:\n print(str(list_products.index(element) + 1)\n + \" - \" + element[1] + \" - \" + element[4].upper()\n + \" - \" + element[2] + \" - \" + element[3])","def one_from_cartesian_product_of_one_basis(self):\n return self.sum_of_monomials( zip( self._sets_keys(), (set.one_basis() for set in self._sets)) )","def add_products(self, products):\n return [self.add_product(product) for product in set(products)]","def calc_q1(data: list) -> float:\n py = copy.copy(data)\n py.sort()\n return py[round(len(py) / 4)]","def sum_ones(part, prod):\n if part in sum_ones_memory:\n partList, prodList = sum_ones_memory[part]\n else:\n n1 = part.count(1)\n partList = [part]\n prodList = [prod]\n if n1 > 1:\n npart = tuple(filter(lambda x: x != 1, part))\n npart = (n1,) + npart\n partList.append(npart)\n prodList.append(prod * n1)\n\n sum_ones_memory[part] = partList, prodList\n return partList, prodList","def get_first_product_by_priority(self):\n products = self.products.filter(type=\"S\").order_by(\"billing_priority\")\n if products.exists():\n return products.first()\n else:\n return None","def product(x,y):\n try:\n z = []\n for i in range(len(x)):\n z.append(x[i]*y[i])\n return z\n except IndexError:\n return None","def run_solution1(self):\n return reduce(lambda a, b: a + self.calculate_fuel(b), self.data, 0)","def prod_value(self, lv, rv):","def __iter__(self):\n return self._products.__iter__()","def get_hourly_products_of_sales(self):\n self.products['hours'] = self.products['session_start_date'].apply(lambda x: convert_str_to_hour(x))\n self.hourly_products = self.products.groupby([\"hours\", \"products\"]).agg(\n {\"id\": lambda x: len(np.unique(x))}).reset_index().rename(columns={\"id\": \"order_count\"})\n self.hourly_products['product_order_count_rank'] = self.hourly_products.sort_values(\n by=[\"hours\", \"order_count\"], ascending=False).groupby(\"hours\").cumcount() + 1\n self.hourly_products = pd.merge(self.hourly_products,\n self.products.groupby(\"hours\").agg(\n {\"id\": lambda x: len(np.unique(x))}).reset_index().rename(\n columns={\"id\": \"hourly_total_orders\"}), on='hours', how='left')\n self.hourly_products['hourly_order_ratio'] = self.hourly_products['order_count'] / self.hourly_products[\n 'hourly_total_orders']\n return self.hourly_products","def getPPSA2(ChargeSA):\n\n temp1=temp2=0.0\n for i in ChargeSA:\n if float(i[1])>0:\n temp1=temp1+float(i[1])\n temp2=temp2+i[2]\n res=temp1*temp2\n\n return res","def prod_parser(self, response):\n products = []\n pk = Product.objects.last()\n if pk:\n i = pk.id\n else:\n i = 0\n for product in response:\n # crawling product for name, desc, API_url, image_url, nutriscore, nutient_100g\n if 'ingredients_text_fr' not in product:\n desc = \"\"\n else:\n desc = product['ingredients_text_fr']\n nutrigrade = \"\".join(product[\"nutrition_grades_tags\"])\n if nutrigrade in (\"a\", \"b\", \"c\", \"d\", \"e\") \\\n and 'fat_100g' in product['nutriments'] \\\n and 'image_url' in product \\\n and 'product_name_fr' in product:\n i += 1\n product[i] = {\n \"id\": i,\n \"name\": product['product_name_fr'],\n \"desc\": desc,\n \"categories\": product[\"categories\"].split(\", \"),\n \"API_link\": product['url'],\n \"photo\": product['image_url'],\n \"nutriscore\": nutrigrade,\n \"nutrient_100g\":\n \"saturated_fat_100g:{}:{}, \".format(\n product['nutriments']['saturated-fat_100g'],\n product['nutrient_levels']['saturated-fat']) +\n \"fat_100g:{}:{}, \".format(\n product['nutriments']['fat_100g'], product['nutrient_levels']['fat']) +\n \"salt_100g:{}:{}, \".format(\n product['nutriments']['salt_100g'], product['nutrient_levels']['salt']) +\n \"sugars_100g:{}:{} \".format(\n product['nutriments']['sugars_100g'], product['nutrient_levels']['sugars'])\n }\n products.append(product[i])\n else:\n pass\n return products","def create_products():\n try:\n # Instantiate the class and separate objects into two lists\n challenge = Challenge()\n # Get all products\n product_base = challenge.get_products(\"product_groups.json\")\n # Divide the products into independent (no parent) and dependent (with parents)\n independent, dependent = challenge.filter_products(product_base)\n if not challenge.save_independent_products(independent):\n Exception(\"Function save_independent_products() couldn't complete\")\n\n if not challenge.save_dependent_products(\n dependent, product_base, len(independent)\n ):\n raise Exception(\"Function save_dependent_products() couldn't complete\")\n\n except Exception as err:\n logging.error(f\"[ERROR] While processing the objects. Traceback: {err}\")\n return False\n else:\n return True","def calculate_inner_products(self, s_gen, xr, yr):\n self.Ips, self.FP = self.tc.RFS(s_gen, xr, yr)","def obtener_productos():\n\n # Se crea la lista de objetos Producto()\n productos = [\n Producto(\"Caja chica\", 5, 100.0),\n Producto(\"Caja mediana\", 3, 185.0),\n Producto(\"Caja grande\", 1, 299.0)\n ]\n\n return productos","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 get_product_flow():\n return sum([i.get_total_flow('ton/day') for i in LAOs.products]) * LAOs.LAOs_tea.operating_days","def products(self):\n return self._products","def calculate_profit(self):","def get_uncomplete_products(self):\n\t\t# Getting products first, defined by : all informations fields are null and was created less than a month ago\n\t\tproducts = Product.objects.filter(exists = True, created__gte=datetime.today() - timedelta(days = 30)) # filter by date\n\t\t# products = products.filter(url__isnull = False, origine__isnull=True, informations__isnull=True, ingredients__isnull=True,conservation__isnull=True,avertissements__isnull=True, composition__isnull=True,conseils__isnull=True)\n\t\tproducts = products.filter(url__isnull = False, exists = True, html__isnull=True)\n\t\tproducts = [{'url' :p.url} for p in products]\n\n\t\t# Getting promotions : defined by start and end date of promotion are null\n\t\tpromotions_db = Promotion.objects.filter(Q(start__isnull = True)|Q(end__isnull = True), Q(availability = True))\n\t\tpromotions = [{'url' :p.url, 'location' : p.shipping_area.postal_code} for p in promotions_db if p.shipping_area is not None]\n\t\tpromotions = promotions+[{'url' :p.url} for p in promotions_db if p.shipping_area is None]\n\n\t\treturn promotions + products","def sexp(p,n,res_field=RR):\n assert n>=2, \"Carleman matrix must at least be of size 2 to retrieve the coefficients. But given was \" + repr(n)\n CM = p.carleman_matrix(n)\n ev = [ r[0] for r in CM.charpoly().roots(QQbar) ]\n assert len(ev) == n, \"Carleman matrix must have exactly \" + repr(n) + \"eigenvalues, but has \" + repr(len(ev))\n\n #We want to compute:\n #sum over k: evk^t*(CM-ev1*I)*(CM-ev2*I)*. omit k * (CM-evn*I)/(evk-ev1)*.. omit k *(evk-evn)\n Char = [0]*n\n for k in range(n):\n #here is possibility for improvement of precision\n #to separate the fractional from the root parts\n #expanding the product\n Char[k] = CM - ev[k]*identity_matrix(n)\n\n #we want to have the first row of the product of the matrices\n #thatswhy we mulitply in front with:\n prod = vector(QQbar,[0,1]+[0]*(n-2))\n prodwo = [0]*n\n for k in range(n):\n prodwo[k]=prod #these are the first terms until k-1\n\n #no need to continue\n if k == n-1:\n break\n\n #and we add the terms starting with k+1\n for i in range(k+1,n):\n prodwo[k] = prodwo[k] * Char[i]\n\n prod = prod * Char[k]\n\n sprodwo = [0]*n\n for k in range(n):\n if k==0:\n sprodwo[k] = ev[k] - ev[1]\n start = 2\n else:\n sprodwo[k] = ev[k] - ev[0]\n start = 1\n\n for i in range(start,n):\n if i != k:\n sprodwo[k] = sprodwo[k] * (ev[k] - ev[i])\n\n for k in range(n):\n print ev[k]\n print prodwo[k][0]/sprodwo[k]\n print res_field\n #return lambda t: sum(res_field(ev[k])**t*res_field(prodwo[k][0]/sprodwo[k]) for k in range(n))\n return [ev,[prodwo[k][0]/sprodwo[k] for k in range(n)]]","def __iter__(self):\n #gets product data keys e.g price, quantity\n product_ids = self.cart.keys()\n\n #checks if the product exist in the database by filtering by product_ids\n products = Product.objects.filter(id__in=product_ids)\n cart = self.cart.copy()\n\n #loop through the products 1 by 1 and re-assigns them to the product.id in the cart\n for product in products:\n cart[str(product.id)][\"product\"] = product\n\n # get price and quatity of items and mutiplies price by quantity to get total price of items\n for item in cart.values():\n item[\"price\"] = Decimal(item[\"price\"])\n item[\"total_price\"] = item[\"price\"] * item[\"qty\"]\n yield item","def get_products(self):\n con = dbcon()\n cur = con.cursor()\n cur.execute(\"SELECT * FROM products;\")\n res = cur.fetchall()\n if res:\n prdcts=[]\n for prodct_item in res:\n picked_prdct = {\n 'product_id':prodct_item[0],\n 'product_name':prodct_item[1],\n 'price':prodct_item[2],\n 'quantity':prodct_item[3]\n }\n prdcts.append(picked_prdct)\n return jsonify({\"Products\": prdcts}), 200\n return jsonify({\"message\":\"No products in store\"})","def generate_products(num_products=30):\n products = []\n for item in range(0, num_products):\n gen_name = str(random.choice(ADJECTIVES) + \" \" + random.choice(NOUNS))\n price = random.uniform(5, 100)\n weight = random.uniform(5, 100)\n flammability = random.uniform(0.0, 2.5)\n products.append(Product(name=gen_name,\n price=price, weight=weight,\n flammability=flammability))\n return products"],"string":"[\n \"def compute_price(self, date = None):\\n\\t\\tif date is None:\\n\\t\\t\\tdate = datetime.now()\\n\\t\\tself.price = 0\\n\\t\\t# Getting list of product in cart\\n\\t\\tcontent = self.cart.cart_content_set.all()\\n\\t\\t# Dictionnary in order to compute minimum state of multi promotion\\n\\t\\tstate = {\\n\\t\\t\\t'products':{},\\n\\t\\t\\t'promotions':{}\\n\\t\\t}\\n\\t\\trequirements = {}\\n\\n\\t\\tfor element in content:\\n\\t\\t\\tproduct = element.product\\n\\t\\t\\tquantity = element.quantity\\n\\n\\t\\t\\t# First look for promotion\\n\\t\\t\\tsimple_promotions = product.promotion_set.filter(end__gte = date, type = 's').distinct('reference', 'end').order_by('-end', 'reference')\\n\\t\\t\\tmulti_promotions = product.promotion_set.filter(end__gte = date, type = 'm').distinct('reference', 'end').order_by('-end', 'reference')\\n\\t\\t\\tif len(simple_promotions)>0:\\n\\t\\t\\t\\tpromotion = simple_promotions[0]\\n\\t\\t\\t\\tself.price = self.price + quantity*promotion.after\\n\\t\\t\\t\\n\\t\\t\\telif len(multi_promotions)>0:\\n\\t\\t\\t\\tfor promotion in multi_promotions:\\n\\t\\t\\t\\t\\tprice_before = promotion.before\\n\\t\\t\\t\\t\\tprice_after = promotion.after\\n\\t\\t\\t\\t\\tcontent = [ (p, 1) for p in promotion.content.all()]\\n\\t\\t\\t\\t\\tfound, requirement = self.get_promotion_requirement(content, price_before)\\n\\t\\t\\t\\t\\tif found and requirement is not None:\\n\\t\\t\\t\\t\\t\\trequirements[promotion.id] = { p.id:q for p, q in requirement} # updating promotion multi requirements\\n\\n\\t\\t\\t\\t\\t# Updating promotion multi state\\n\\t\\t\\t\\t\\tprod, price = self.get_simple_price([{'product':product, 'quantity':1}], date)[0]\\n\\t\\t\\t\\t\\t# print quantity\\n\\t\\t\\t\\t\\tstate['products'][product.id] = {'price': price, 'qte':quantity}\\n\\t\\t\\t\\t\\t# print state['products'][product.id]\\n\\t\\t\\t\\t\\tstate['promotions'][promotion.id] = {'price': price_after, 'qte':0}\\n\\t\\t\\telse:\\n\\t\\t\\t\\thistory = product.history_set.filter(created__gte = date-timedelta(hours = 24)).order_by('-created')\\n\\t\\t\\t\\tif len(history)>0:\\n\\t\\t\\t\\t\\tself.price = self.price + quantity*history[0].price\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\thistory = product.history_set.all().order_by('-created')\\n\\t\\t\\t\\t\\tif len(history)>0:\\n\\t\\t\\t\\t\\t\\tself.price = self.price + quantity*history[0].price\\n\\n\\t\\t# Dealing with multi promotion:\\n\\t\\tmin_state, min_price = self.get_min_state(state, requirements)\\n\\t\\tself.price = self.price + min_price\\n\\n\\t\\tself.save()\\n\\n\\t\\treturn self.price\",\n \"def product(self):\\n return None\",\n \"def product(self):\\n return None\",\n \"def product(self):\\n raise NotImplementedError\",\n \"def first_order(self):\\n for i in range(1,self.N+1):\\n self.d1[i] = self.C[i] / 2 / i;\",\n \"def get_product_input(self):\\n result = []\\n result.append(self.one_hot_translate_product(self.curr_productfea, self.depthlist))\\n result.append(tf.nn.embedding_lookup(self.product_embeddings, self.curr_productid))\\n result.append(tf.nn.embedding_lookup(self.aisle_embeddings, self.curr_aisleid))\\n result.append(tf.nn.embedding_lookup(self.department_embeddings, self.curr_departmentid))\\n result.append(self.one_hot_translate(self.curr_productidx, self.max_productlen+1))\\n product_input = tf.concat(result, axis=-1)\\n return product_input\",\n \"def findAShin(self):\\n #return reduce(lambda x, y: x*y, [self.DoS[key].get_price() for key in self.DoS] )\\n a = array([self.DoS[key].get_Price() for key in self.DoS])\\n return a.prod()**(1.0/len(a))\",\n \"def choice_product(self):\\n self.first_number = 0\\n self.leave_choice_product = 1\\n while self.leave_choice_product:\\n print(fr.FR[11])\\n self.display_product(self.products)\\n self.input_product = input(fr.FR[12])\\n self.choice_product_input()\",\n \"def get_all_products(self):\\n\\t\\tpass\",\n \"def get_prices(name,products,sales):\\r\\n return tuple((products[0],products[1]*((1-tuple(filter(lambda x: x[0]==name, sales))[0][1]))) for products in products)\",\n \"def calculate(self):\",\n \"def get_product_kpis(self):\\n kpi_1 = self.products[['products', 'client']].reset_index().groupby([\\\"client\\\", \\\"products\\\"]).agg(\\n {\\\"index\\\": \\\"count\\\"}).reset_index().groupby(\\\"products\\\").agg(\\n {\\\"index\\\": \\\"mean\\\"}).reset_index().rename(columns={\\\"index\\\": \\\"average_product_sold_per_user_kpi\\\"})\\n kpi_2 = self.products[['products', 'payment_amount']].groupby(\\\"products\\\").agg(\\n {\\\"payment_amount\\\": \\\"sum\\\"}).reset_index().rename(columns={\\\"payment_amount\\\": \\\"total_product_revenue_kpi\\\"})\\n try:\\n kpi_3 = self.products[['products', 'discount_amount']].query(\\\"discount_amount == discount_amount\\\")\\n kpi_3['discount_amount'] = kpi_3['discount_amount'].apply(lambda x: float(x))\\n kpi_3 = kpi_3.groupby(\\\"products\\\").agg({\\\"discount_amount\\\": \\\"sum\\\"}).reset_index().rename(\\n columns={\\\"discount_amount\\\": \\\"total_product_discount_kpi\\\"})\\n except:\\n kpi_3 = self.products[['products']]\\n kpi_3['total_product_discount_kpi'] = 0\\n\\n kpi_4 = self.products[['products', 'client']].groupby(\\\"products\\\").agg(\\n {\\\"client\\\": lambda x: len(np.unique(x))}).reset_index().rename(columns={\\\"client\\\": \\\"total_product_cust_kpi\\\"})\\n\\n self.product_kpis = pd.DataFrame(self.products.groupby('products').count().reset_index()[['products']]).merge(\\n kpi_1, on='products', how='left').merge(\\n kpi_2, on='products', how='left').merge(\\n kpi_3, on='products', how='left').merge(kpi_4, on='products', how='left').fillna(0)\\n\\n return self.product_kpis\",\n \"def buy_one_get_one(products):\\n if 'p1' in products and products['p1'] >= 2:\\n return -20\\n else:\\n return 0\",\n \"def product(self):\\n return self._product\",\n \"def product(self):\\n return self._product\",\n \"def product(self):\\n return self._product\",\n \"def compute_first(self):\\n compute_first_sets(self, self.rules)\",\n \"def prodri(items: Iterator[float]) -> float:\\n try:\\n head = next(items)\\n except StopIteration:\\n return 1\\n return head*prodri(items)\",\n \"def insert_products(self):\\n logic = ProductLogic()\\n \\n try:\\n # We create the list of product objects\\n products = self.objects_factory.create_product_object_list()\\n products = set(products)\\n\\n for product in products:\\n logic.insert(product)\\n except:\\n print('Il y a eu un problème lors de la récupération des données, veuillez rééssayer')\",\n \"def product1(a, b, c) :\\n return a * b * c\",\n \"def get_fastest_moving_products(num_products):\\n\\treturn execute_sql(\\\"SELECT slopes.slope, slopes.asin, products.product_name,\\\\\\n\\t\\tproducts.manufacturer_name, categories.category_name, \\\\\\n\\t\\tproducts.product_url FROM slopes INNER JOIN products ON \\\\\\n\\t\\tslopes.asin=products.asin INNER JOIN categories ON \\\\\\n\\t\\tslopes.category_url=categories.category_url ORDER BY slope ASC \\\\\\n\\t\\tLIMIT %s\\\" % num_products)\",\n \"def execQ6():\\n frame = pan.DataFrame(data, columns=['Product', 'Price', 'Period'])\\n expensive = frame.sort_values(by='Price', ascending=False).head(1)\\n return expensive\",\n \"def prod(self):\\n # skipna == True\\n # only_numerical == True\\n return self._lift(\\\"prod\\\")\",\n \"def _initialize_products(self, products: List) -> Dict[str, int]:\\n\\n product_request = urllib.request.Request(url=URL_PRODUCTS, headers={'User-Agent': URL_USER_AGENT})\\n product_response = urllib.request.urlopen(product_request)\\n all_products = json.load(product_response)\\n\\n product_details = {}\\n\\n for product in products:\\n for cbpro_product in all_products:\\n if cbpro_product[\\\"id\\\"] == product:\\n quote_increment = float(cbpro_product[\\\"quote_increment\\\"])\\n num_decimal_places = int(math.log10(1 / quote_increment))\\n product_details[product] = num_decimal_places\\n logging.debug(\\n \\\"Retrieved quote increment for {}: {} = {} decimal places\\\".\\n format(product, quote_increment, num_decimal_places))\\n\\n return product_details\",\n \"def firstorder(self):\\n f = self.img\\n x = self.x\\n y = self.y\\n self.x1 = sum(f*x)/sum(f)\\n self.y1 = sum(f*y)/sum(f)\",\n \"def click_first_product(self):\\n self.functions.wait_for_element(self.SELECT_PRODUCT).click()\\n product_selected = self.functions.wait_for_element(self.PRODUCT_SELECTED)\\n assert product_selected.is_displayed(), \\\"didn't selected product!\\\"\",\n \"def products(self):\\r\\n return self._products\",\n \"def get_simple_price(self, content, date = None):\\n\\t\\tprices = []\\n\\t\\tfor element in content:\\n\\t\\t\\tproduct = element['product']\\n\\t\\t\\tquantity = element['quantity']\\n\\n\\t\\t\\thistory = product.history_set.filter(created__gte = date-timedelta(hours = 24)).order_by('-created')\\n\\t\\t\\tif len(history)>0:\\n\\t\\t\\t\\tprices.append((element, quantity*history[0].price))\\n\\t\\t\\telse:\\n\\t\\t\\t\\thistory = product.history_set.all().order_by('-created')\\n\\t\\t\\t\\tif len(history)>0:\\n\\t\\t\\t\\t\\tprices.append((element, quantity*history[0].price))\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tprices.append((element, None))\\n\\t\\treturn prices\",\n \"def test_product(self):\\n self.assertEqual(functions.product(2, 2), 4)\\n self.assertEqual(functions.product(2, -2), -4)\",\n \"def generate_products(self = random.sample, name = random.choice(result), price = random.randint(5, 100), weight = random.randint(5, 100), \\nflammability= random.uniform(0, 2.5)):\\n return sample\",\n \"def _first(self) -> Tuple[np.ndarray, np.ndarray, ModelGeneratorBase]:\\n pass\",\n \"def _core_calc_prod(self,bp,Lp) :\\n\\t\\tprod = np.dot(Lp,bp)\\n\\t\\tprod = np.exp(prod)\\n\\t\\treturn prod\",\n \"def prod(self, args):\\n assert len(args) > 0, \\\"Cannot compute an empty product in a semigroup\\\"\\n return prod(args[1:], args[0])\",\n \"def get_most_combined_products(self):\\n self.product_pairs = self.products.groupby(\\\"id\\\").agg({\\\"products\\\": lambda x: list(x)}).reset_index()\\n self.product_pairs['product_pairs'] = self.product_pairs['products'].apply(\\n lambda x: np.array(list(product(x, x))).tolist())\\n self.product_pairs = pd.DataFrame(np.concatenate(list(self.product_pairs['product_pairs'])).tolist())\\n self.product_pairs['total_pairs'] = 1\\n self.product_pairs = self.product_pairs[self.product_pairs[0] != self.product_pairs[1]]\\n self.product_pairs = self.product_pairs.groupby([0, 1]).agg({\\\"total_pairs\\\": \\\"sum\\\"}).reset_index()\\n self.product_pairs = self.product_pairs.sort_values('total_pairs', ascending=False)\\n self.product_pairs = self.product_pairs.rename(columns={0: \\\"pair_1\\\", 1: \\\"pair_2\\\"})\\n self.product_pairs['product_pair'] = self.product_pairs.apply(\\n lambda row: \\\" - \\\".join(list(sorted([row['pair_1'], row['pair_2']]))), axis=1)\\n self.product_pairs = self.product_pairs.groupby(\\\"product_pair\\\").agg({\\\"total_pairs\\\": \\\"first\\\"}).reset_index()\\n return self.product_pairs\",\n \"def handle(self, *args, **options):\\n\\n ProductPair.objects.all().delete()\\n person = Person.objects.get(name='klapshov')\\n meals = Meal.objects.filter(person=person)\\n for m in meals:\\n products = [i.product for i in m.intake_set.all()]\\n print(products)\\n pairs = []\\n for n in range(0, len(products)-1):\\n for nn in range(n+1, len(products)):\\n if n != nn:\\n pairs.append([products[n], products[nn]])\\n print(pairs)\\n print(len(products), len(pairs))\\n\\n # REWRITE!!!!!!!!\\n for p in pairs:\\n try:\\n pp = ProductPair.objects.get(product1=p[0], product2=p[1])\\n except ProductPair.DoesNotExist:\\n pp = ProductPair()\\n pp.product1 = p[0]\\n pp.product2 = p[1]\\n pp.count = 1\\n p2p = ProductToPerson.objects.get(\\n person=person, product=p[0]\\n )\\n pp.ratio = round(pp.count/p2p.intakes_count*100)\\n pp.save()\\n else:\\n pp.count += 1\\n p2p = ProductToPerson.objects.get(\\n person=person,\\n product=p[0]\\n )\\n pp.ratio = round(pp.count/p2p.intakes_count*100)\\n pp.save()\\n\\n for p in pairs:\\n p.reverse()\\n try:\\n pp = ProductPair.objects.get(product1=p[0], product2=p[1])\\n except ProductPair.DoesNotExist:\\n pp = ProductPair()\\n pp.product1 = p[0]\\n pp.product2 = p[1]\\n pp.count = 1\\n p2p = ProductToPerson.objects.get(\\n person=person,\\n product=p[0]\\n )\\n pp.ratio = round(pp.count/p2p.intakes_count*100)\\n pp.save()\\n else:\\n pp.count += 1\\n p2p = ProductToPerson.objects.get(\\n person=person,\\n product=p[0]\\n )\\n pp.ratio = round(pp.count/p2p.intakes_count*100)\\n pp.save()\",\n \"def calculate(self, order):\\n pass\",\n \"def precalculate():\\n pass\",\n \"def precalculate():\\n pass\",\n \"def recommend_next_product(self, prod_list):\\n scores = defaultdict(float)\\n for prod in prod_list:\\n for item in self._purchased.find({PROD1: prod}):\\n if not item[PROD2] in prod_list:\\n scores[item[PROD2]] += math.log(item[TIMES])\\n if len(scores) == 0:\\n return None\\n max_tuple = max(scores.items(), key = operator.itemgetter(1))\\n return max_tuple[0]\",\n \"def choice_product_input(self):\\n try:\\n if self.input_product == \\\"s\\\" and (\\n self.first_number + config.NUMBER_PRODUCT_DISPLAY)\\\\\\n < len(self.products):\\n self.first_number += config.NUMBER_PRODUCT_DISPLAY\\n elif self.input_product == \\\"p\\\" and self.first_number > 0:\\n self.first_number -= config.NUMBER_PRODUCT_DISPLAY\\n elif self.input_product == \\\"q\\\":\\n self.leave_choice_product -= 1\\n elif 1 <= int(self.input_product) <= len(self.products):\\n self.product_selected = self.products[int(self.input_product)\\n - 1][0]\\n del self.products_sub[int(self.input_product)-1]\\n self.choice_sub()\\n self.leave_choice_product -= 1\\n except ValueError:\\n print(fr.FR[10])\",\n \"def get_new_products(self, reactions):\\n\\t\\tnew_products = []\\n\\t\\tnew_reactions = []\\n\\n\\t\\tESTNF = { c:c for c in self._E + self._S + self._T + self._N + self._F }\\n\\t\\tB = { c:c for c in self._B}\\n\\n\\t\\t\\n\\t\\t# Loop over every reaction\\n\\t\\tfor reaction in reactions:\\n\\t\\t\\t\\n\\t\\t\\t# This will be set to False if we bail out of the inner loop upon finding a complex that's too large\\n\\t\\t\\tcomplex_size_ok = True\\n\\t\\t\\t\\n\\t\\t\\t# Check every product of the reaction to see if it is new\\n\\t\\t\\tfor (i, product) in enumerate(reaction.products):\\n\\t\\t\\t\\t\\n\\t\\t\\t\\tif (len(product.strands) > self.MAX_COMPLEX_SIZE):\\n\\t\\t\\t\\t\\tlogging.warning(\\\"Complex %(name)s (%(strands)d strands) too large, ignoring!\\\" % {\\\"name\\\":product.name,\\\"strands\\\":len(product.strands)})\\n\\t\\t\\t\\t\\tcomplex_size_ok = False\\n\\t\\t\\t\\t\\tbreak\\n\\t\\t\\t\\n\\t\\t\\t\\t# This will be set to True if we've already seen this complex\\n\\t\\t\\t\\tenumerated = False\\n\\t\\t\\t\\t\\n\\t\\t\\t\\t# If the product is in any of these lists, we don't need to\\n\\t\\t\\t\\t# deal with it, so just update the reaction to point correctly\\n\\t\\t\\t\\t# TODO: This could benefit from a substantial speedup if _E, _S, \\n\\t\\t\\t\\t#\\t_T, _N, _F were implemented as sets. Other parts of the \\n\\t\\t\\t\\t#\\talgorithm benefit from their representation as queues though... \\n\\t\\t\\t\\t\\n\\t\\t\\t\\tif product in ESTNF: reaction.products[i] = ESTNF[product]; enumerated = True\\n\\t\\t\\t\\t# for complex in self._E + self._S + self._T + self._N + self._F:\\n\\t\\t\\t\\t# \\tif (product == complex):\\n\\t\\t\\t\\t# \\t\\tenumerated = True\\n\\t\\t\\t\\t# \\t\\treaction.products[i] = complex\\n\\t\\t\\t\\t# \\t\\tbreak\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\tif not enumerated:\\n\\t\\t\\t\\t\\t# If the product is in list B, then we need to remove it from\\n\\t\\t\\t\\t\\t# that list so that it can be enumerated for self-interactions\\n\\t\\t\\t\\t\\t# as part of this neighborhood\\n\\n\\t\\t\\t\\t\\tif product in B: \\n\\t\\t\\t\\t\\t\\treaction.products[i] = B[product]; \\n\\t\\t\\t\\t\\t\\tself._B.remove(B[product])\\n\\t\\t\\t\\t\\t\\tproduct = B[product]\\n\\t\\t\\t\\t\\t\\tdel B[product]\\n\\n\\t\\t\\t\\t\\t# for complex in self._B:\\n\\t\\t\\t\\t\\t# \\tif (product == complex):\\n\\t\\t\\t\\t\\t# \\t\\treaction.products[i] = complex\\n\\t\\t\\t\\t\\t# \\t\\tself._B.remove(complex)\\n\\t\\t\\t\\t\\t# \\t\\tproduct = complex\\n\\t\\t\\t\\t\\t# \\t\\tbreak\\n\\t\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t# If the product has already been seen in this loop, update\\n\\t\\t\\t\\t\\t# the pointer appropriately\\n\\t\\t\\t\\t\\tfor complex in new_products:\\n\\t\\t\\t\\t\\t\\tif (product == complex):\\n\\t\\t\\t\\t\\t\\t\\tenumerated = True\\n\\t\\t\\t\\t\\t\\t\\treaction.products[i] = complex\\n\\t\\t\\t\\t\\t\\t\\tbreak\\n\\t\\t\\t\\t\\n\\t\\t\\t\\tif not enumerated:\\n\\t\\t\\t\\t\\tnew_products.append(product)\\n\\t\\t\\t\\n\\t\\t\\t# If this reaction contained a complex that was too big, ignore the whole reaction.\\n\\t\\t\\tif complex_size_ok:\\n\\t\\t\\t\\tnew_reactions.append(reaction)\\n\\t\\t\\n\\t\\t# Clobber the old value of reactions with the filtered list\\n\\t\\treactions[:] = new_reactions\\n\\t\\t\\t\\n\\t\\treturn new_products\",\n \"def pre_compute(self, e_list):\\n\\t\\tpass\",\n \"def get_first_item(self):\\n params = urllib.parse.urlencode({'o':'1', 'q':self.query})\\n url = 'https://www.leboncoin.fr/annonces/offres/ile_de_france/?{:s}'.format(params) # Cree l'url de recherche en get\\n html = urllib.request.urlopen(url)\\n if url != html.geturl():\\n return None\\n soup = BeautifulSoup.BeautifulSoup(html, 'html5lib')\\n try:\\n products = soup.section.find_all('a', 'list_item clearfix trackable')\\n except Exception as e:\\n print('Nothing found on leboncoin')\\n return None\\n for product in products: # recupere les differentes informations de chaque produit\\n if str(product.section.h2).strip() == 'None':\\n continue\\n name = product.section.h2.contents[0].strip()\\n price = self.__get_price(product)\\n link = 'http:' + product['href']\\n return (name, price, link)\\n return None\",\n \"def generate_products():\\n # initialize list of noun and adj\\n num_products = 30\\n products = [0] * num_products\\n prices = [0] * num_products\\n weights = [0] * num_products\\n flammabilities = [0] * num_products\\n\\n # initlize random word object\\n random = RandomWords()\\n\\n adj = [random.get_random_word(includePartOfSpeech=\\\"adjective\\\")\\n for product in products]\\n noun = [random.get_random_word(includePartOfSpeech=\\\"noun\\\")\\n for product in products]\\n products = [noun + \\\" \\\" + adj for noun, adj in zip(adj, noun)]\\n\\n prices = [random.randint(5, 100) for price in prices]\\n weights = [random.randint(5, 100) for weight in weights]\\n flammabilities = [random.randint(0.0, 2.5)\\n for flammability in flammabilities]\\n\\n return products, prices, weights, flammabilities\",\n \"def _make_product(terms):\\n if terms:\\n product = terms[0]\\n for term in terms[1:]:\\n product = Mul((product, term))\\n return product \\n else:\\n return Const(1)\",\n \"def __iter__(self):\\n return self._product_generator()\",\n \"def prodrc( collection ):\\n if len(collection) == 0: return 1\\n return collection[0] * prodrc(collection[1:])\",\n \"def find_substitute(self):\\n\\n products_list = None\\n\\n while not products_list:\\n self.get_targeted_category()\\n\\n db.connect()\\n db.execute(\\\"\\\"\\\"\\n SELECT product_id, nutriscore_id\\n FROM Product_per_category\\n INNER JOIN Product\\n ON Product.id = product_id\\n WHERE category_id = %s AND nutriscore_id < %s\\n ORDER BY nutriscore_id\\n \\\"\\\"\\\", (self.category_id, self.nutriscore,))\\n products_list = db.fetch(True)\\n db.disconnect()\\n self.category_concordance += 1\\n\\n return products_list[0][0]\",\n \"def calculate(self):\\r\\n\\r\\n pass\",\n \"def filter_products(self, products: list):\\n # Get all products that has no parent\\n independent = [product for product in products if product[\\\"parent_id\\\"] is None]\\n # Get all products that has parent\\n dependent = [\\n product for product in products if product[\\\"parent_id\\\"] is not None\\n ]\\n # Sort dependent products by parent_id, so that a child will be always\\n # inserted after the parent\\n dependent = sorted(dependent, key=lambda item: item[\\\"parent_id\\\"])\\n # Saves the total of objects\\n self.total = len(independent) + len(dependent)\\n return independent, dependent\",\n \"def getFPSA1(ChargeSA):\\n temp=0.0\\n for i in ChargeSA:\\n temp=temp+i[2]\\n if temp == 0.0:\\n return 0.0\\n else:\\n return getPPSA1(ChargeSA)/temp\",\n \"def products_for_influencers(parameters, page=0, page_size=50):\\n product_ids, _, total_hits = elastic_search_helpers.es_product_query_runner_v2(\\n parameters, page, page_size)\\n\\n num_pages = int(math.ceil(float(total_hits) / page_size))\\n\\n return product_ids, total_hits, num_pages\",\n \"def execQ5():\\n frame = pan.DataFrame(data, columns=['Product', 'Price', 'Period'])\\n cheapest = frame.sort_values(by='Price', ascending=True).head(1)\\n return cheapest\",\n \"def product_summary(self):\\n # products = self.products.filter(type='S') # TODO: explain the usage of this commented line or remove it\\n from .utils import process_products\\n\\n subscription_products = SubscriptionProduct.objects.filter(subscription=self)\\n dict_all_products = {}\\n for sp in subscription_products:\\n dict_all_products[str(sp.product.id)] = str(sp.copies)\\n return process_products(dict_all_products)\",\n \"def show_proposal(self, req):\\n z = 1\\n y = z\\n x = 1\\n self.dico_product = {}\\n for prod in req:\\n if z <= 5:\\n List_store = orm_imp.find_store(prod.id)\\n print(\\\"Choix numéro\\\", z, \\\":\\\", prod.name, \\\"| score : \\\",\\n prod.nutriscore, \\\"| Magasins : \\\", List_store,\\n \\\"| Lien :\\\",\\n prod.url, \\\"| \\\\n ==> description :\\\",\\n prod.ingredient, \\\"\\\\n==================================\\\")\\n self.dico_product.update({z: prod.id})\\n x += 1\\n z += 1\\n return self.dico_product, y, x - 1\",\n \"def calculate(self):\\r\\n pass\",\n \"def calculate(self):\\n pass\",\n \"def prodrc(collection: Sequence[float]) -> float:\\n if len(collection) == 0:\\n return 1\\n return collection[0] * prodrc(collection[1:])\",\n \"def printone(self):\\n print('*' * 50)\\n product = self.iterator().__next__()\\n pprint(product)\\n print('*'*50)\\n converted_product = self.converter(product)\\n pprint(converted_product)\\n print('*' * 50)\\n validated_product = self.validator(converted_product)\\n pprint(validated_product)\\n print('*' * 50)\",\n \"def _calc_pareto_front(self, *args, **kwargs):\\n fname = f\\\"{self.fct.name}_PF.dat\\\"\\n F = load_pareto_front_from_file(os.path.join(\\\"modact\\\", fname))\\n if F is not None:\\n return F*self.weights*-1\",\n \"def _get_prix_tarif(self,cout,pricelist):\\n cr = self._cr\\n product=cout.name\\n prix_tarif=0\\n date=time.strftime('%Y-%m-%d') # Date du jour\\n if pricelist:\\n #Convertion du lot_mini de US vers UA\\n min_quantity = self.env['product.uom']._compute_qty(cout.name.uom_id.id, cout.name.lot_mini, cout.name.uom_po_id.id)\\n #TODO : Pour contourner un bug d'arrondi (le 31/01/2017)\\n min_quantity=min_quantity+0.00000000001\\n #TODO en utilisant la fonction repr à la place de str, cela ne tronque pas les décimales\\n SQL=\\\"\\\"\\\"\\n select ppi.price_surcharge\\n from product_pricelist_version ppv inner join product_pricelist_item ppi on ppv.id=ppi.price_version_id\\n where ppv.pricelist_id=\\\"\\\"\\\"+str(pricelist.id)+ \\\"\\\"\\\" \\n and min_quantity<=\\\"\\\"\\\"+repr(min_quantity)+\\\"\\\"\\\"\\n and (ppv.date_start <= '\\\"\\\"\\\"+date+\\\"\\\"\\\"' or ppv.date_start is null)\\n and (ppv.date_end >= '\\\"\\\"\\\"+date+\\\"\\\"\\\"' or ppv.date_end is null)\\n\\n and ppi.product_id=\\\"\\\"\\\"+str(product.id)+ \\\"\\\"\\\" \\n and (ppi.date_start <= '\\\"\\\"\\\"+date+\\\"\\\"\\\"' or ppi.date_start is null)\\n and (ppi.date_end >= '\\\"\\\"\\\"+date+\\\"\\\"\\\"' or ppi.date_end is null)\\n order by ppi.sequence\\n limit 1\\n \\\"\\\"\\\"\\n cr.execute(SQL)\\n result = cr.fetchall()\\n for row in result:\\n coef=1\\n if min_quantity:\\n coef=cout.name.lot_mini/min_quantity\\n prix_tarif=row[0]/coef\\n\\n\\n\\n return prix_tarif\",\n \"def execQ4():\\n frame = pan.DataFrame(data, columns=['Product', 'Price', 'Period'])\\n tuna = frame[dFrame.Series_title_1 == \\\"Tuna - canned (supermarket only), 185g\\\"]\\n expensive = tuna.sort_values(by=\\\"Price\\\", ascending=False).head(1)\\n return expensive\",\n \"def product(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"product\\\")\",\n \"def product_on_basis(self, t1, t2):\\n return tensor( (module.monomial(x1)*module.monomial(x2) for (module, x1, x2) in zip(self._sets, t1, t2)) ) #.\",\n \"def proposal_product(self, user_prod):\\n # Give the bests product, selected by nutriscore value.\\n prod0 = orm_imp.list_prod(user_prod)\\n prod = self.show_proposal(prod0)\\n user_choice = int(input(\\n \\\"==============================\\\\n\\\"\\n \\\"= Que souhaitez-vous faire ? =\\\\n\\\"\\n \\\"==============================\\\\n\\\"\\n \\\"- Sauvegarder un produit proposé : taper 1\\\\n\\\"\\n \\\"- Faire une autre recherche produit : taper 2\\\\n\\\"\\n \\\"- Retourner à l'écran principal : taper 3\\\\n\\\"))\\n if user_choice == 1: # save product, go to main section\\n self.save_product(prod[0], prod[1], prod[2], user_prod)\\n self.controle_point = 1\\n elif user_choice == 2: # goback to product research section\\n self.controle_point = 2\\n elif user_choice == 3: # go to main section\\n self.controle_point = 1\\n else:\\n print(\\\"Veuillez entrer un chiffre compris entre 1 et 3.\\\")\\n self.controle_point = 4\\n return self.controle_point\",\n \"def test_mueller_product(self, ):\\n mdims = ('mueller_v', 'mueller_h')\\n mm_1 = xr.DataArray(np.random.rand(4, 4, ), dims=mdims, )\\n mm_2 = xr.DataArray(np.identity(4, ), dims=mdims, )\\n sv_1 = xr.DataArray(np.random.rand(4, ), dims=('stokes', ), )\\n\\n assert_almost_equal(mm_1.values, mueller_product(mm_1, mm_2).values, )\\n assert_almost_equal(mm_1.values, mueller_product(mm_2, mm_1).values, )\\n assert_almost_equal(sv_1.values, mueller_product(mm_2, sv_1).data, )\",\n \"def product(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"product\\\")\",\n \"def generateProducts(self):\\r\\n\\r\\n # Creates items in each category\\r\\n for i in range(self.num_of_items):\\r\\n self.ID_DICT[i+self.num_of_items] = random.randint(1, 10)\\r\\n self.ID_DICT[i+self.num_of_items*2] = random.randint(1, 10)\\r\\n self.ID_DICT[i+self.num_of_items*3] = random.randint(1, 10)\\r\\n self.ID_DICT[i+self.num_of_items*4] = random.randint(1, 10)\\r\\n self.ID_DICT[i+self.num_of_items*5] = random.randint(1, 10)\\r\\n self.ID_DICT[i+self.num_of_items*6] = random.randint(1, 10)\\r\\n\\r\\n\\r\\n # Sort for easy selection\\r\\n sorted(self.ID_DICT)\\r\\n\\r\\n for product in self.ID_DICT.keys():\\r\\n temp_int = self.ID_DICT[product]\\r\\n self.c.execute(\\\"INSERT INTO Products (ProductID, Price) VALUES (?, ?)\\\", (product, self.ID_DICT[product]))\\r\\n self.conn.commit()\\r\\n\\r\\n if self.print_items:\\r\\n print(\\\"\\\\nAll items in store:\\\")\\r\\n print(self.ID_DICT)\\r\\n print()\",\n \"def _listProduct(self,lol,anch):#a recursive list product taker\\n\\t\\ttry:\\n\\t\\t\\tif str(type(lol[0])) != \\\"<type 'list'>\\\":\\n\\t\\t\\t\\traise IndexError\\n\\t\\t\\tself._listProduct(utils.xproduct(lol.pop(0),lol.pop(0))+lol,anch)\\n\\t\\texcept IndexError:\\n\\t\\t\\tanch.extend(lol)\",\n \"def initial_price(self) -> float:\\n return self.__initial_price\",\n \"def individual(prod):\\n try:\\n return 4. * arctan(sqrt(prod))\\n except FloatingPointError:\\n return 0.\",\n \"def get_products(self):\\n\\n lst = []\\n for product in self.products.findall('product'):\\n id = product.find('id').text\\n name = product.find('name').text\\n dispensary_id = product.find('dispensary_id').text\\n dispensary_name = product.find('dispensary_name').text\\n canabis_brand = product.find('canabis_brand').text\\n canabis_strain = product.find('canabis_strain').text\\n category = product.find('category').text\\n subcategory = product.find('subcategory').text\\n thc_level = product.find('thc_level').text\\n cbd_level = product.find('cbd_level').text\\n cbn_level = product.find('cbn_level').text\\n thc_level_type = product.find('thc_level_type').text\\n cbd_level_type = product.find('cbd_level_type').text\\n cbn_level_type = product.find('cbn_level_type').text\\n\\n description = product.find('description').text\\n created_at = product.find('created_at').text\\n updated_at = product.find('updated_at').text\\n\\n prices = []\\n urls = []\\n images = []\\n\\n for child in product:\\n if child.tag == 'prices':\\n for cost in child.findall('cost'):\\n prices.append(Price(cost.attrib['unit'], cost.text))\\n\\n if child.tag == 'urls':\\n admin = child.find('admin').text\\n public = child.find('public').text\\n urls.append(UrlInfo(admin, public))\\n\\n if child.tag == 'images':\\n for image in child.findall('image'):\\n images.append(Image(image.attrib['main'], image.text,))\\n\\n lst.append(Product(id, name, dispensary_id, dispensary_name,\\n canabis_brand, canabis_strain,\\n category, subcategory, thc_level, cbd_level,\\n cbn_level, thc_level_type, cbd_level_type,\\n cbn_level_type, prices, urls, images,\\n description, created_at, updated_at))\\n\\n return lst\",\n \"def prod(self):\\n return self._summarize(lambda c: c.prod)\",\n \"def products(self):\\r\\n return products.Products(self)\",\n \"def prod(self, values):\\n return self.aggregate(values, \\\"prod\\\")\",\n \"def first(self):\",\n \"def display_product(self, list_products):\\n for element in list_products[self.first_number:self.first_number +\\n config.NUMBER_PRODUCT_DISPLAY]:\\n print(str(list_products.index(element) + 1)\\n + \\\" - \\\" + element[1] + \\\" - \\\" + element[4].upper()\\n + \\\" - \\\" + element[2] + \\\" - \\\" + element[3])\",\n \"def one_from_cartesian_product_of_one_basis(self):\\n return self.sum_of_monomials( zip( self._sets_keys(), (set.one_basis() for set in self._sets)) )\",\n \"def add_products(self, products):\\n return [self.add_product(product) for product in set(products)]\",\n \"def calc_q1(data: list) -> float:\\n py = copy.copy(data)\\n py.sort()\\n return py[round(len(py) / 4)]\",\n \"def sum_ones(part, prod):\\n if part in sum_ones_memory:\\n partList, prodList = sum_ones_memory[part]\\n else:\\n n1 = part.count(1)\\n partList = [part]\\n prodList = [prod]\\n if n1 > 1:\\n npart = tuple(filter(lambda x: x != 1, part))\\n npart = (n1,) + npart\\n partList.append(npart)\\n prodList.append(prod * n1)\\n\\n sum_ones_memory[part] = partList, prodList\\n return partList, prodList\",\n \"def get_first_product_by_priority(self):\\n products = self.products.filter(type=\\\"S\\\").order_by(\\\"billing_priority\\\")\\n if products.exists():\\n return products.first()\\n else:\\n return None\",\n \"def product(x,y):\\n try:\\n z = []\\n for i in range(len(x)):\\n z.append(x[i]*y[i])\\n return z\\n except IndexError:\\n return None\",\n \"def run_solution1(self):\\n return reduce(lambda a, b: a + self.calculate_fuel(b), self.data, 0)\",\n \"def prod_value(self, lv, rv):\",\n \"def __iter__(self):\\n return self._products.__iter__()\",\n \"def get_hourly_products_of_sales(self):\\n self.products['hours'] = self.products['session_start_date'].apply(lambda x: convert_str_to_hour(x))\\n self.hourly_products = self.products.groupby([\\\"hours\\\", \\\"products\\\"]).agg(\\n {\\\"id\\\": lambda x: len(np.unique(x))}).reset_index().rename(columns={\\\"id\\\": \\\"order_count\\\"})\\n self.hourly_products['product_order_count_rank'] = self.hourly_products.sort_values(\\n by=[\\\"hours\\\", \\\"order_count\\\"], ascending=False).groupby(\\\"hours\\\").cumcount() + 1\\n self.hourly_products = pd.merge(self.hourly_products,\\n self.products.groupby(\\\"hours\\\").agg(\\n {\\\"id\\\": lambda x: len(np.unique(x))}).reset_index().rename(\\n columns={\\\"id\\\": \\\"hourly_total_orders\\\"}), on='hours', how='left')\\n self.hourly_products['hourly_order_ratio'] = self.hourly_products['order_count'] / self.hourly_products[\\n 'hourly_total_orders']\\n return self.hourly_products\",\n \"def getPPSA2(ChargeSA):\\n\\n temp1=temp2=0.0\\n for i in ChargeSA:\\n if float(i[1])>0:\\n temp1=temp1+float(i[1])\\n temp2=temp2+i[2]\\n res=temp1*temp2\\n\\n return res\",\n \"def prod_parser(self, response):\\n products = []\\n pk = Product.objects.last()\\n if pk:\\n i = pk.id\\n else:\\n i = 0\\n for product in response:\\n # crawling product for name, desc, API_url, image_url, nutriscore, nutient_100g\\n if 'ingredients_text_fr' not in product:\\n desc = \\\"\\\"\\n else:\\n desc = product['ingredients_text_fr']\\n nutrigrade = \\\"\\\".join(product[\\\"nutrition_grades_tags\\\"])\\n if nutrigrade in (\\\"a\\\", \\\"b\\\", \\\"c\\\", \\\"d\\\", \\\"e\\\") \\\\\\n and 'fat_100g' in product['nutriments'] \\\\\\n and 'image_url' in product \\\\\\n and 'product_name_fr' in product:\\n i += 1\\n product[i] = {\\n \\\"id\\\": i,\\n \\\"name\\\": product['product_name_fr'],\\n \\\"desc\\\": desc,\\n \\\"categories\\\": product[\\\"categories\\\"].split(\\\", \\\"),\\n \\\"API_link\\\": product['url'],\\n \\\"photo\\\": product['image_url'],\\n \\\"nutriscore\\\": nutrigrade,\\n \\\"nutrient_100g\\\":\\n \\\"saturated_fat_100g:{}:{}, \\\".format(\\n product['nutriments']['saturated-fat_100g'],\\n product['nutrient_levels']['saturated-fat']) +\\n \\\"fat_100g:{}:{}, \\\".format(\\n product['nutriments']['fat_100g'], product['nutrient_levels']['fat']) +\\n \\\"salt_100g:{}:{}, \\\".format(\\n product['nutriments']['salt_100g'], product['nutrient_levels']['salt']) +\\n \\\"sugars_100g:{}:{} \\\".format(\\n product['nutriments']['sugars_100g'], product['nutrient_levels']['sugars'])\\n }\\n products.append(product[i])\\n else:\\n pass\\n return products\",\n \"def create_products():\\n try:\\n # Instantiate the class and separate objects into two lists\\n challenge = Challenge()\\n # Get all products\\n product_base = challenge.get_products(\\\"product_groups.json\\\")\\n # Divide the products into independent (no parent) and dependent (with parents)\\n independent, dependent = challenge.filter_products(product_base)\\n if not challenge.save_independent_products(independent):\\n Exception(\\\"Function save_independent_products() couldn't complete\\\")\\n\\n if not challenge.save_dependent_products(\\n dependent, product_base, len(independent)\\n ):\\n raise Exception(\\\"Function save_dependent_products() couldn't complete\\\")\\n\\n except Exception as err:\\n logging.error(f\\\"[ERROR] While processing the objects. Traceback: {err}\\\")\\n return False\\n else:\\n return True\",\n \"def calculate_inner_products(self, s_gen, xr, yr):\\n self.Ips, self.FP = self.tc.RFS(s_gen, xr, yr)\",\n \"def obtener_productos():\\n\\n # Se crea la lista de objetos Producto()\\n productos = [\\n Producto(\\\"Caja chica\\\", 5, 100.0),\\n Producto(\\\"Caja mediana\\\", 3, 185.0),\\n Producto(\\\"Caja grande\\\", 1, 299.0)\\n ]\\n\\n return productos\",\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 get_product_flow():\\n return sum([i.get_total_flow('ton/day') for i in LAOs.products]) * LAOs.LAOs_tea.operating_days\",\n \"def products(self):\\n return self._products\",\n \"def calculate_profit(self):\",\n \"def get_uncomplete_products(self):\\n\\t\\t# Getting products first, defined by : all informations fields are null and was created less than a month ago\\n\\t\\tproducts = Product.objects.filter(exists = True, created__gte=datetime.today() - timedelta(days = 30)) # filter by date\\n\\t\\t# products = products.filter(url__isnull = False, origine__isnull=True, informations__isnull=True, ingredients__isnull=True,conservation__isnull=True,avertissements__isnull=True, composition__isnull=True,conseils__isnull=True)\\n\\t\\tproducts = products.filter(url__isnull = False, exists = True, html__isnull=True)\\n\\t\\tproducts = [{'url' :p.url} for p in products]\\n\\n\\t\\t# Getting promotions : defined by start and end date of promotion are null\\n\\t\\tpromotions_db = Promotion.objects.filter(Q(start__isnull = True)|Q(end__isnull = True), Q(availability = True))\\n\\t\\tpromotions = [{'url' :p.url, 'location' : p.shipping_area.postal_code} for p in promotions_db if p.shipping_area is not None]\\n\\t\\tpromotions = promotions+[{'url' :p.url} for p in promotions_db if p.shipping_area is None]\\n\\n\\t\\treturn promotions + products\",\n \"def sexp(p,n,res_field=RR):\\n assert n>=2, \\\"Carleman matrix must at least be of size 2 to retrieve the coefficients. But given was \\\" + repr(n)\\n CM = p.carleman_matrix(n)\\n ev = [ r[0] for r in CM.charpoly().roots(QQbar) ]\\n assert len(ev) == n, \\\"Carleman matrix must have exactly \\\" + repr(n) + \\\"eigenvalues, but has \\\" + repr(len(ev))\\n\\n #We want to compute:\\n #sum over k: evk^t*(CM-ev1*I)*(CM-ev2*I)*. omit k * (CM-evn*I)/(evk-ev1)*.. omit k *(evk-evn)\\n Char = [0]*n\\n for k in range(n):\\n #here is possibility for improvement of precision\\n #to separate the fractional from the root parts\\n #expanding the product\\n Char[k] = CM - ev[k]*identity_matrix(n)\\n\\n #we want to have the first row of the product of the matrices\\n #thatswhy we mulitply in front with:\\n prod = vector(QQbar,[0,1]+[0]*(n-2))\\n prodwo = [0]*n\\n for k in range(n):\\n prodwo[k]=prod #these are the first terms until k-1\\n\\n #no need to continue\\n if k == n-1:\\n break\\n\\n #and we add the terms starting with k+1\\n for i in range(k+1,n):\\n prodwo[k] = prodwo[k] * Char[i]\\n\\n prod = prod * Char[k]\\n\\n sprodwo = [0]*n\\n for k in range(n):\\n if k==0:\\n sprodwo[k] = ev[k] - ev[1]\\n start = 2\\n else:\\n sprodwo[k] = ev[k] - ev[0]\\n start = 1\\n\\n for i in range(start,n):\\n if i != k:\\n sprodwo[k] = sprodwo[k] * (ev[k] - ev[i])\\n\\n for k in range(n):\\n print ev[k]\\n print prodwo[k][0]/sprodwo[k]\\n print res_field\\n #return lambda t: sum(res_field(ev[k])**t*res_field(prodwo[k][0]/sprodwo[k]) for k in range(n))\\n return [ev,[prodwo[k][0]/sprodwo[k] for k in range(n)]]\",\n \"def __iter__(self):\\n #gets product data keys e.g price, quantity\\n product_ids = self.cart.keys()\\n\\n #checks if the product exist in the database by filtering by product_ids\\n products = Product.objects.filter(id__in=product_ids)\\n cart = self.cart.copy()\\n\\n #loop through the products 1 by 1 and re-assigns them to the product.id in the cart\\n for product in products:\\n cart[str(product.id)][\\\"product\\\"] = product\\n\\n # get price and quatity of items and mutiplies price by quantity to get total price of items\\n for item in cart.values():\\n item[\\\"price\\\"] = Decimal(item[\\\"price\\\"])\\n item[\\\"total_price\\\"] = item[\\\"price\\\"] * item[\\\"qty\\\"]\\n yield item\",\n \"def get_products(self):\\n con = dbcon()\\n cur = con.cursor()\\n cur.execute(\\\"SELECT * FROM products;\\\")\\n res = cur.fetchall()\\n if res:\\n prdcts=[]\\n for prodct_item in res:\\n picked_prdct = {\\n 'product_id':prodct_item[0],\\n 'product_name':prodct_item[1],\\n 'price':prodct_item[2],\\n 'quantity':prodct_item[3]\\n }\\n prdcts.append(picked_prdct)\\n return jsonify({\\\"Products\\\": prdcts}), 200\\n return jsonify({\\\"message\\\":\\\"No products in store\\\"})\",\n \"def generate_products(num_products=30):\\n products = []\\n for item in range(0, num_products):\\n gen_name = str(random.choice(ADJECTIVES) + \\\" \\\" + random.choice(NOUNS))\\n price = random.uniform(5, 100)\\n weight = random.uniform(5, 100)\\n flammability = random.uniform(0.0, 2.5)\\n products.append(Product(name=gen_name,\\n price=price, weight=weight,\\n flammability=flammability))\\n return products\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.64534867","0.644534","0.644534","0.6229142","0.58825815","0.5860691","0.5853895","0.58197695","0.56922","0.56867915","0.5632201","0.56160545","0.5575754","0.55730057","0.55730057","0.55730057","0.5558104","0.55553544","0.554885","0.5529817","0.5513311","0.55115616","0.54710263","0.54675525","0.54575485","0.54480267","0.5441147","0.5422784","0.5420402","0.5395654","0.5392286","0.53905594","0.5389104","0.5384254","0.538094","0.5379528","0.5374319","0.5374319","0.53661656","0.5342694","0.5332238","0.53186506","0.53153235","0.53130823","0.529245","0.5280328","0.52757597","0.52699685","0.5264008","0.5251562","0.5243955","0.5240793","0.52325743","0.5228333","0.5222441","0.52038705","0.52027416","0.5199958","0.5199797","0.5187963","0.5184794","0.5183589","0.51697433","0.51657414","0.51636595","0.51600987","0.5154535","0.51474273","0.514447","0.51370364","0.51356876","0.5130734","0.51276183","0.51254773","0.5123542","0.5122797","0.51224023","0.51189363","0.5118806","0.51164925","0.5107422","0.5098056","0.509792","0.50957835","0.5087438","0.5080643","0.50794274","0.5076991","0.50737095","0.50721914","0.5069168","0.5068183","0.5063092","0.5056988","0.5054912","0.5049764","0.50420153","0.5041923","0.5041296","0.5037948","0.5036581"],"string":"[\n \"0.64534867\",\n \"0.644534\",\n \"0.644534\",\n \"0.6229142\",\n \"0.58825815\",\n \"0.5860691\",\n \"0.5853895\",\n \"0.58197695\",\n \"0.56922\",\n \"0.56867915\",\n \"0.5632201\",\n \"0.56160545\",\n \"0.5575754\",\n \"0.55730057\",\n \"0.55730057\",\n \"0.55730057\",\n \"0.5558104\",\n \"0.55553544\",\n \"0.554885\",\n \"0.5529817\",\n \"0.5513311\",\n \"0.55115616\",\n \"0.54710263\",\n \"0.54675525\",\n \"0.54575485\",\n \"0.54480267\",\n \"0.5441147\",\n \"0.5422784\",\n \"0.5420402\",\n \"0.5395654\",\n \"0.5392286\",\n \"0.53905594\",\n \"0.5389104\",\n \"0.5384254\",\n \"0.538094\",\n \"0.5379528\",\n \"0.5374319\",\n \"0.5374319\",\n \"0.53661656\",\n \"0.5342694\",\n \"0.5332238\",\n \"0.53186506\",\n \"0.53153235\",\n \"0.53130823\",\n \"0.529245\",\n \"0.5280328\",\n \"0.52757597\",\n \"0.52699685\",\n \"0.5264008\",\n \"0.5251562\",\n \"0.5243955\",\n \"0.5240793\",\n \"0.52325743\",\n \"0.5228333\",\n \"0.5222441\",\n \"0.52038705\",\n \"0.52027416\",\n \"0.5199958\",\n \"0.5199797\",\n \"0.5187963\",\n \"0.5184794\",\n \"0.5183589\",\n \"0.51697433\",\n \"0.51657414\",\n \"0.51636595\",\n \"0.51600987\",\n \"0.5154535\",\n \"0.51474273\",\n \"0.514447\",\n \"0.51370364\",\n \"0.51356876\",\n \"0.5130734\",\n \"0.51276183\",\n \"0.51254773\",\n \"0.5123542\",\n \"0.5122797\",\n \"0.51224023\",\n \"0.51189363\",\n \"0.5118806\",\n \"0.51164925\",\n \"0.5107422\",\n \"0.5098056\",\n \"0.509792\",\n \"0.50957835\",\n \"0.5087438\",\n \"0.5080643\",\n \"0.50794274\",\n \"0.5076991\",\n \"0.50737095\",\n \"0.50721914\",\n \"0.5069168\",\n \"0.5068183\",\n \"0.5063092\",\n \"0.5056988\",\n \"0.5054912\",\n \"0.5049764\",\n \"0.50420153\",\n \"0.5041923\",\n \"0.5041296\",\n \"0.5037948\",\n \"0.5036581\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":75584,"cells":{"query":{"kind":"string","value":"get the top products."},"document":{"kind":"string","value":"def __get_top(self, result, top=10):\n result = result.sort_values(by=\"bias_score\", ascending=False).drop_duplicates(subset='productid', keep=\"first\")\n print(result)\n result = result[:top].sort_values(by=\"final_score\", ascending=False).productid\n\n return list(result)"},"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 top_products():\n products = product.Product.query. \\\n order_by(desc(product.Product.likes)).all()[:6]\n context = {\n 'products': products\n }\n return render_template('stores/topstores.html', **context)","def top(self, **kwargs):\n return self.client.api.top(self.id, **kwargs)","def do_top(cs, args):\n resp, data = cs.repositories.get_top(args.count)\n utils.print_list(data, ['name', 'count'], sortby='count')","def top(self, **kwargs) -> Dict[str, Any]:","def get_most_ordered_products(self):\n return self.products.groupby(\"products\").agg(\n {\"id\": lambda x: len(np.unique(x))}).reset_index().rename(\n columns={\"id\": \"order_count\"}).sort_values(by='order_count', ascending=False)","def query_top_buys(cls,N=20):\n # NOTE: this is not used currently\n gds = from_cache('VG_TOP_%d'%N)\n if not gds:\n gds = [str(g.id()) for g in SuiGoods.all(keys_only=True).order('-likes').fetch(N)]\n to_cache('VG_TOP_%d'%N,gds,3600*24)\n return SuiGoods.load_by_ids(gds)","def __get_top_with_detail(self, result, top=10):\n result = result.sort_values(by=\"bias_score\", ascending=False).drop_duplicates(subset='productId', keep=\"first\")[\n :top]\n\n return result","def get_all_top(self):\n return self.execute(TABELLE['top']['select']['all'])","def top(self):","def top(self):\r\n return self.topele","def topArticles():\n c = db.cursor()\n c.execute(\"select titles.title, tophits.hits\\\n from tophits, titles\\\n where tophits.path = titles.slug\\\n order by hits desc limit 3;\")\n results = c.fetchall()\n c.close()\n return results","async def get_most_popular_components(\n number_of_products: int = 10, db_path: Path = DB_PATH\n) -> dict:\n components = await get_query_results(\"SELECT * FROM components\", db_path=db_path)\n\n # Sorting by both counter and name from high to low\n components.sort(key=lambda x: (x[1], x[0]), reverse=True)\n\n popular_components = [\n {name: count} for name, count in components[0:number_of_products]\n ]\n\n return {\"most_popular_components\": popular_components}","def print_top_articles():\n\n output = get_query_results(\n '''SELECT articles.title, COUNT(path) AS views\n FROM articles\n JOIN log\n ON log.path=CONCAT('/article/', articles.slug)\n GROUP BY title\n ORDER BY views DESC\n LIMIT 3;'''\n )\n print(\"\\nMost Popular Articles: \\n\")\n for title, views in output:\n print(\"\\\"{}\\\" -- {} views\".format(title, views))","def top_products(data_frame):\n data_frame.loc[:, 'total_payment'] = (data_frame['unit_rental_price']\n * data_frame['quantity_rented']\n * data_frame['rental_period_months'])\n data_set = data_frame.groupby(['product_name']).agg({'total_payment': 'sum'})\n data_set = data_set.nlargest(10, 'total_payment')\n return data_set","def get_top_stories(self):\n stories = requests.get(f\"{HN_API_URL}/topstories.json\")\n stories_ids = json.loads(stories.text)\n urls = [f\"{HN_API_URL}/item/{story_id}.json\" for story_id in stories_ids]\n fetched_stories = asyncio.run(self.fetch_all(urls))\n sorted_stories = sorted(\n fetched_stories, key=lambda k: k[\"score\"], reverse=True)\n return sorted_stories[:STORIES_NUMBER]","def get_most_popular_articles():\n\tdb = psycopg2.connect(database=DBNAME)\n\tc = db.cursor()\n\tc.execute(\"select t2.title, count(*) as total from log as t1,articles as t2 where t1.path=concat('/article/',t2.slug) group by t2.title order by total desc limit 3 ;\")\n\tdata = c.fetchall()\n\tdb.close()\n\treturn data","def get_products(self):\n page = 1\n out = []\n while True:\n resp = self.get_session().Product.find(limit=10,page=page)\n if not len(resp):\n return\n yield resp\n page += 1","def get_top_news_and_the_rest(self):\n queryset = self.news.order_by('-marked', '-publication_date')\n return queryset.first(), queryset[1:]","def get_all_products(self):\n\t\tpass","def top(self):\n return self.List_store[len(self.List_store)]","def get_top_featured_entries(number=5):\n return list(Entry.published.filter(featured=True)[:number])","def select_most_sold_products_from_personas_vrouwen():\n return sql_select(\"\"\"SELECT prodid, name,\n COUNT(*)\n FROM personas_vrouwen\n\n GROUP BY prodid ,name \n ORDER BY COUNT(*) DESC ; \"\"\")","def get_topograms(self):\n return self.make_request(\"GET\", \"topograms\", {})","def select_most_sold_products_from_personas_mannen():\n return sql_select(\"\"\"SELECT prodid, name,\n COUNT(*)\n FROM personas_mannen\n \n GROUP BY prodid ,name \n ORDER BY COUNT(*) DESC ; \"\"\")","def test_top_songs(self):\n \n rss = AppleRSS()\n objs = rss.get_top_songs(limit=10)\n \n self.__test_artists('top_songs', objs)","def products(self):\r\n return self._products","def top(self):\n return self.q1.return_top()","def top_tiles(self):\n sorted_tiles = self.tiles_by_score()\n top_tiles = sorted_tiles[:NUM_TOP_TILES]\n return top_tiles","def getTopStories(self):\n source = self.getSource(\"http://news.ycombinator.com\")\n stories = self.getStories(source)\n return stories","def get_top_tags(tags):\n # ~3x faster\n return list(sorted(tags.items(), key=itemgetter(1), reverse=True))[:TOP_NUMBER]","def select_most_sold_products_from_personas_kinderen():\n return sql_select(\"\"\"SELECT prodid, name,\n COUNT(*)\n FROM v\n\n GROUP BY prodid ,name \n ORDER BY COUNT(*) DESC ; \"\"\")","def get_popular(self, max_items=None):\n data = [(x[1], x[0]) for x in self.iteritems()]\n data.sort(key=lambda x: (sys.maxint - x[0], x[1]))\n if max_items:\n return data[:max_items]\n else:\n return data","def get(self):\n return Products().get_all_products()","def topTags(db, topN=1000):\n c=db.cursor()\n c.execute(\"\"\"\n SELECT\n tag\n FROM tags\n GROUP BY tag\n ORDER BY COUNT(*) DESC\n LIMIT %d\n \"\"\" % topN)\n tops = [tag0[0] for tag0 in c.fetchall()]\n c.close()\n return tops","async def get_top(self, limit, iterator):\n if iterator == 'ITERATOR_LE':\n iterator = 'DESC'\n if iterator == 'ITERATOR_GE':\n iterator = 'ASC'\n sql = (\n \"select data from stickers order by (data->>1)::int {} limit {}\"\n ).format(iterator, limit)\n result = await self.db.fetch(sql)\n ret = []\n for i in result:\n ret.append(json.loads(i[0]))\n return ret","def popular_items(self):\n if self._popular_items is None:\n self._popular_items = self._get_popular_items(100)\n return self._popular_items","def get_popular_article():\n query_command = \"SELECT * from popular_posts LIMIT 3\"\n query_data = run_query(query_command)\n return query_data","def top(self) -> Optional[FloatObject]:\n return self.get(\"/Top\", None)","def get_top(self):\n\n sql = \"SELECT username FROM Users ORDER BY score DESC LIMIT 10\"\n self.cursor.execute(sql)\n return self.cursor.fetchall()","def products(self, start=None, limit=None):\r\n params = base.get_params(None, locals())\r\n url = '{0}/products'.format(self.get_url())\r\n return http.Request('GET', url, params), parsers.parse_json","def test_get_top_respects_limit(self):\r\n\r\n ranked_3_app = self.create_app_with_contributors(8, 0, name='three')\r\n ranked_2_app = self.create_app_with_contributors(9, 0, name='two')\r\n ranked_1_app = self.create_app_with_contributors(10, 0, name='one')\r\n ranked_4_app = self.create_app_with_contributors(7, 0, name='four')\r\n\r\n top_apps = cached_apps.get_top(n=2)\r\n\r\n assert len(top_apps) is 2, len(top_apps)","def get_top():\n print(\"This processes are using the cpu the most:\")\n print(os.system(\"ps axo %cpu,pid,euser,cmd | sort -nr | head -n 5\"))","def get_most_popular(self):\n\t\tpopular_rated = self.data_final[self.data_final['Rating'] == 10]\n\t\tpopular_jokes = popular_rated.groupby('JokeID').count().reset_index()\n\t\tpopular_jokes = popular_jokes[['JokeID','Rating']]\n\t\tpopular_jokes.columns = ['JokeID','Number_rated10']\n\t\ttop_joke = popular_jokes.sort_values(by=['Number_rated10'], ascending=False).head(1)\n\t\ttop_joke_val = top_joke['JokeID'].values[0]\n\t\tjokes_list = sorted(set(self.data_final['JokeID']))\n\t\tjoke_num = jokes_list.index(top_joke_val)\n\t\ttop_joke_desc = self.data_jokes[self.data_jokes['JokeID'] == top_joke_val].values[0][1]\n\n\t\treturn top_joke_desc, joke_num","def getProducts():\n return \"http://api.tcgplayer.com/catalog/products\"","def top(self, event, limit=None):\r\n params = base.get_params(('event', 'limit'), locals(), serialize_param)\r\n\r\n request = http.Request('GET', 'events/properties/top/', params)\r\n\r\n return request, parsers.parse_json","def top_ten(subreddit):\n\n if subreddit is None or not isinstance(subreddit, str):\n print(\"None\")\n\n user_agent = {'User-agent': 'Google Chrome Version 81.0.4044.129'}\n params = {'limit': 10}\n url = 'https://www.reddit.com/r/{}/hot/.json'.format(subreddit)\n\n response = get(url, headers=user_agent, params=params)\n all_data = response.json()\n\n try:\n raw1 = all_data.get('data').get('children')\n\n for i in raw1:\n print(i.get('data').get('title'))\n\n except:\n print(\"None\")","def top_playlists():\n page = request.args.get('page', 1, type=int)\n playlists = PlaylistModel.query.filter(PlaylistModel.import_count > 0).order_by(PlaylistModel.import_count.desc()).paginate(\n page=page, per_page=8, error_out=False\n )\n\n return render_template(\n 'top.html',\n playlists=playlists.items,\n pagination=playlists, # this is a pagination object,\n active='most-imported',\n )","def top_ten(subreddit):\n try:\n info = requests.get('https://www.reddit.com/r/{}/hot.json?limit=10'\n .format(subreddit), allow_redirects=False,\n headers={'User-Agent': 'Custom'}).json().get(\n 'data').get('children')\n for child in info:\n print(child.get('data').get('title'))\n except:\n print('None')","def topic_get_hot(request, limit):\n if request.method == 'GET':\n all_topics = Topic.objects.all()\n\n if int(limit) == 0:\n hot_topics = sorted(all_topics, key=lambda t: -t.hotness)\n else:\n hot_topics = sorted(all_topics, key=lambda t: -t.hotness)[:int(limit)]\n #hot_topics = Topic.objects.order_by('hotness')[:5]\n TopicNestedSerializer.Meta.depth = 2\n RelationSerializer.Meta.depth = 1\n serializer = TopicNestedSerializer(hot_topics, many=True)\n return Response(serializer.data)","def get_all_products():\n data = order_obj.get_all_products()\n return data","def top_stories(self, limit=None):\n return self._get_page('topstories').json()[:limit]","def top_ten(subreddit):\n h = requests.utils.default_headers()\n h.update({'User-Agent': 'My User Agent 1.0'})\n url = \"https://www.reddit.com/r/{}/hot.json?limit=10\".format(subreddit)\n r = requests.get(url, headers=h).json()\n result = r.get('data', {}).get('children', [])\n if not result:\n print(None)\n for i in result:\n print(i.get('data').get('title'))","def get_top_articles(update=False):\n # use caching to avoid running unnecessary DB queries at each page load\n key = 'top_ten'\n articles = memcache.get(key)\n\n logging.warn('MEMCACHE | Wiki articles %s' % str(articles))\n\n if (articles is None) or (len(articles) == 0) or update:\n # necessary artificial delay when a new article has just been persisted to the datastore\n if update:\n time.sleep(2)\n\n articles = db.GqlQuery('SELECT * FROM Article ORDER BY updated DESC LIMIT 10')\n articles = list(articles)\n memcache.set(key, articles)\n\n logging.warn('DATASTORE | Wiki articles count %s' % str(len(articles)))\n return articles","def query_top_recommended(cls,N=10):\n brkey = 'BooksMostRecommended'\n bks = from_cache(brkey)\n if not bks:\n bks = map(lambda e:str(e.id()), SuiBook.all(keys_only=True).order('-recommends').fetch(N))\n to_cache(brkey,bks)\n return bks","def get_most_popular_posts():\n popular_posts_ids = [post.id for post in Post.objects.popular()]\n return Post.objects.filter(id__in=popular_posts_ids). \\\n add_comments_count(). \\\n prefetch_related('author'). \\\n prefetch_with_tags_and_likes(). \\\n order_by('likes_count')","def getTopNTweets(retrievedTweets, numberOfTweets):\n if sortBy=='newest':\n retrievedTweets = sorted(retrievedTweets, key=lambda k: k['id'], reverse=True)\n elif sortBy=='oldest':\n retrievedTweets = sorted(retrievedTweets, key=lambda k: k['id'],reverse=False)\n elif sortBy=='favorite_count':\n retrievedTweets = sorted(retrievedTweets, key=lambda k: k['favorite_count'],reverse=True)\n elif sortBy=='retweet_count':\n retrievedTweets = sorted(retrievedTweets, key=lambda k: k['retweet_count'],reverse=True)\n else:\n retrievedTweets = random.sample(retrievedTweets, numberOfTweets)\n return retrievedTweets[:numberOfTweets]","def test_top(self):\n ds_uri = odata.ODataURI(\"Orders?$orderby=OrderDate%20desc&$top=10\",\n '/x.svc')\n top = ds_uri.sys_query_options[odata.SystemQueryOption.top]\n self.assertTrue(isinstance(top, int), \"top type\")\n self.assertTrue(top == 10, \"top 10\")\n ds_uri = odata.ODataURI(\"Customers('ALFKI')/Orders?$top=10\", '/x.svc')\n top = ds_uri.sys_query_options[odata.SystemQueryOption.top]\n self.assertTrue(top == 10, \"top 10\")\n ds_uri = odata.ODataURI(\"Orders?$orderby=OrderDate%20desc&$top=0\",\n '/x.svc')\n top = ds_uri.sys_query_options[odata.SystemQueryOption.top]\n self.assertTrue(top == 0, \"top 0\")\n try:\n ds_uri = odata.ODataURI(\n \"Orders?$orderby=OrderDate%20desc&$top=-1\", '/x.svc')\n self.fail(\"top=-1\")\n except odata.InvalidSystemQueryOption:\n pass","def get_popular_stocks(self):\n response = requests.get('https://brokerage-static.s3.amazonaws.com/popular_stocks/data.json')\n response_json = response.json()\n assert len(response_json) == 1\n return response_json[0]","def get_all_products(access_keeper):\n logger.debug('getting products...')\n headers = get_authorization_headers(access_keeper)\n\n response = requests.get('https://api.moltin.com/v2/products', headers=headers)\n raise_response_errors(response)\n\n products = response.json()['data']\n logger.debug(f'{len(products)} products was got')\n\n return products","def get(self, request, *args, **kwargs):\n return super(PopularSongsView, self).get(request, query=app.MOST_POPULAR_SONGS, *args, **kwargs)","def top_ten(subreddit):\n BASE_URL = 'http://www.reddit.com/r/{}/hot.json'.format(subreddit)\n head = {'User-Agent': 'Mozilla/5.0'}\n par = {'limit': 10}\n\n r = requests.get(BASE_URL, params=par, headers=head).json()\n\n res = r.get('data', {}).get('children', None)\n\n if res:\n for post in res:\n print(post.get('data').get('title'))\n else:\n print(None)","def get_popular_articles():\n db = psycopg2.connect(database=DBNAME)\n c = db.cursor()\n query_popular_articles = \"\"\"\n SELECT art.title, COUNT(lg.id) as views\n FROM articles as art\n JOIN log as lg\n ON art.slug = substring(lg.path,10)\n AND lg.status = '200 OK'\n GROUP BY art.title\n ORDER BY views desc\n LIMIT 3; \"\"\"\n c.execute(query_popular_articles)\n articles = from_db_cursor(c)\n db.close()\n return articles","def top_ten(subreddit):\n\n limit = \"10\"\n\n url = \"https://www.reddit.com/r/{}/hot.json?limit={}\".format(subreddit,\n limit)\n\n user_agent = {\"User-Agent\": \"Python\"}\n response = requests.get(url, headers=user_agent, allow_redirects=False)\n if response.status_code >= 300:\n print(\"None\")\n else:\n for elem in response.json().get(\"data\").get(\"children\"):\n print(elem.get(\"data\").get(\"title\"))","def get_top(self):\n elements = self.S.get_maximal_elements()\n data = {}\n alot = Nat().get_top()\n for e in elements:\n data[e] = alot\n return Multiset(data, self.S)","def get_top_recipes(df, sort_params=None, count=10):\n if not sort_params:\n logging.warning(\"Column names to soty by are not defined.\")\n return df\n\n return df.sort_values(sort_params[\"names\"],\n ascending=sort_params[\"order\"]).head(count)","def predict(self, products: list):\n try:\n self.log.info(\"Prediction is started.\")\n\n contentScores: list = self.content_score_repository.get_by_products(products)\n\n df = self.__fillScores(contentScores)\n\n result = self.__get_top(df)\n self.log.info(\"Top values are calculated.\")\n\n self.log.info(\"Prediction is finished.\")\n\n return result\n\n except Exception as ex:\n msg: str = \"Undefined error is occured in Hybrid recommender service. \"\n self.log.error(msg + \"err: {}\".format(ex))\n raise HybridRecommenderServiceException(msg)","def top_ten(subreddit):\n header = {\"User-Agent\": \"Holberton\"}\n url = \"https://www.reddit.com/r/{}/hot.json?limit=10\".format(subreddit)\n response = requests.get(url, headers=header, allow_redirects=False)\n if response.status_code == 200:\n\n for item in response.json().get(\"data\", None).get(\"children\", None):\n print(item.get(\"data\", None).get(\"title\", None))\n else:\n print(None)\n return","def top_ten(subreddit):\n url = \"https://www.reddit.com/r/\" + subreddit + \"/top/.json\"\n r = requests.get(url,\n headers={'User-agent': 'norman'},\n params={\"limit\": 10},\n allow_redirects=False)\n if r.status_code == 200:\n for dic in r.json().get('data').get('children'):\n print(dic.get('data').get('title'))\n else:\n print(\"None\")","def top_ten(request):\n if request.method == 'GET':\n movies = Movie.objects.filter(date_of_release__lte=datetime.date.today())\n movies = movies.order_by('-rating')[:10]\n serializer = MovieSerializer(movies, many=True)\n return Response(serializer.data)","def get_artists_most_played(session_):\n # artists = session_.query(Artist).order_by(Artist.name.asc()).paginate()\n artists = session_.query(Artist).order_by(Artist.plays.desc()).all()\n return artists","def list_products(self):\n return self._make_get_request(self._urls['products'])","def get_top_articles(\n limit: int = 5,\n date: int = int(datetime.now().strftime(\"%Y%m%d\"))\n):\n\n res = articles_db.get_top_articles_mongo(\n articles,\n limit,\n date\n )\n\n return res","def get_fastest_moving_products(num_products):\n\treturn execute_sql(\"SELECT slopes.slope, slopes.asin, products.product_name,\\\n\t\tproducts.manufacturer_name, categories.category_name, \\\n\t\tproducts.product_url FROM slopes INNER JOIN products ON \\\n\t\tslopes.asin=products.asin INNER JOIN categories ON \\\n\t\tslopes.category_url=categories.category_url ORDER BY slope ASC \\\n\t\tLIMIT %s\" % num_products)","def top_ten(subreddit):\n req = get(\n \"https://www.reddit.com/r/{}/hot.json\".format(subreddit),\n headers={\n \"User-Agent\": \"alx_app\"},\n params={\n \"limit\": 10},\n allow_redirects=False)\n if req.status_code != 200:\n print(None)\n else:\n posts = req.json().get(\"data\").get(\"children\")\n for post in posts:\n print(post.get(\"data\").get(\"title\"))","def top_5_news():\n top_five = []\n\n news = (db.news.find({}).sort([\n ('shares_count', pymongo.DESCENDING),\n ('comments_count', pymongo.DESCENDING),\n ('title', pymongo.ASCENDING)\n ]).limit(5))\n\n for new in news:\n top_five.append((new['title'], new['url']))\n\n return top_five","def top_news():\n data = get_top_news()\n return jsonify(data)","def get_most_and_least_expensive_high_review_product(df):\n try:\n df3 = merge_metadata(df)\n product_filter = df3['overall'] >= 4.0\n high_reviewed_products = df3[product_filter]\n # print high_reviewed_products[:10]\n # The data contained NaN so we use the nanmax/min funtions to get max/min\n most_exp = round(np.nanmax(high_reviewed_products['price'])[0], 2)\n least_exp = round(np.nanmin(high_reviewed_products['price'])[0], 2)\n\n most_exp_prod = df3.loc[df3['price'] == most_exp, 'asin'].iloc[0]\n least_exp_prod = df3.loc[df3['price'] == least_exp, 'asin'].iloc[0]\n write_text_tofile(\"Most Expensive Product: \" + str(most_exp_prod) + \", Price: \" + str(most_exp))\n write_text_tofile(\"Least Expensive Product: \" + str(least_exp_prod) + \", Price: \" + str(least_exp))\n return {most_exp_prod: most_exp, least_exp_prod: least_exp}\n except Exception as e:\n print \"Error getting most and least expensive high review product\"\n print str(e)\n pass","def topCountries(top=10):\r\n #top 10 deadly countries\r\n countries = agg('country')[:top].index\r\n #grab aggregated data for these countries\r\n dataOfTop10 = agg(['year','country']).query(\"country in @countries\")### interesting...\r\n #unstack data\r\n dataOfTop10 = dataOfTop10.unstack(1)\r\n #remove multiindexes\r\n dataOfTop10 = dataOfTop10.transpose().reset_index(level=0, drop=True).transpose()\r\n #sort by year\r\n dataOfTop10.sort_index(inplace=True)\r\n return dataOfTop10","def printTopThreeArticles():\n query = \"\"\"\n SELECT author_article_popularity_view.article,\n author_article_popularity_view.views\n FROM author_article_popularity_view\n LIMIT 3;\n \"\"\"\n cursor = connection.cursor()\n cursor.execute(query)\n results = cursor.fetchall()\n print(\"\\nTop 3 articles of all time: \")\n for i, result in enumerate(results):\n print(\"{}. \\\"{}\\\" - {:,} views\".format(i + 1, result[0], result[1]))","def products(self):\n return self._products","def top_by_ratings(self, n, metric=average):\n return top_movies","def top(self) -> int:\n return self.q[0]","def top(self) -> int:\n return self.q[0]","def top(self) -> int:\n return self.q[0]","def top_tracks(genre):\n\tartist = random.choice(genre_artist[genre])\n\ttop_tracks = search_for_artist_top_tracks(artist)\n\titems = []\n\tif top_tracks:\n\t\tfor track in top_tracks:\n\t\t\titems.append({\"artist\": track[\"artists\"][0][\"name\"], \"popularity\": track[\"popularity\"], \"track\": track[\"name\"],\n\t\t\t\t \"preview_url\": track[\"preview_url\"], \"album_image_url\": track[\"album\"][\"images\"][2][\"url\"]})\n\t\titems = sorted(items, key=lambda x: x['popularity'], reverse=True)\n\t\tfor item in items:\n\t\t\tdel item['popularity']\n\t\treturn items\n\telse:\n\t\treturn None","def top_ten(subreddit):\n headers = {\"User-Agent\": \"Holberton\"}\n url = \"https://www.reddit.com/r/{}/hot.json?limit=10\".format(\n subreddit)\n req = requests.get(url, headers=headers)\n\n if req.status_code != 200:\n print(None)\n return\n redit = req.json().get(\"data\").get(\"children\")\n for chil in redit:\n print(chil.get(\"data\").get(\"title\"))","def top_by_num_of_ratings(self, n):\n return top_movies","def top(self):\n return self[0]","async def top_specs(self):\r\n players = await self.get_players()\r\n specs = []\r\n for player in players:\r\n specs.append(player['specId'])\r\n await self.bot.send_message('Top 3v3 Composition:')\r\n for key in self.specs:\r\n if specs.count(int(key)) > 0:\r\n await self.bot.send_message('{:s}: {:d} ({:.2f}%)'.format(\r\n self.specs[key],\r\n specs.count(int(key)),\r\n float(specs.count(int(key))/965.0)*100)\r\n )","def top(self):\n try:\n if self.size() > 0:\n return self.items[len(self.items) - 1]\n else:\n raise IndexError('Cannot get top item, stack is empty.')\n except IndexError as err:\n print(err)\n raise","def get_most_popular_authors():\n\tdb = psycopg2.connect(database=DBNAME)\n\tc = db.cursor()\n\tc.execute(\" select t1.name,count(*) as total from authors as t1, articles as t2,log as t3 where t3.path=concat('/article/',t2.slug) and t1.id=t2.author group by t1.name order by total desc limit 3;\")\n\tdata = c.fetchall()\n\tdb.close()\n\n\treturn data","def get_top(n=4):\r\n sql = text('''SELECT app.id, app.name, app.short_name, app.description, app.info,\r\n COUNT(app_id) AS total FROM task_run, app\r\n WHERE app_id IS NOT NULL AND app.id=app_id AND app.hidden=0\r\n GROUP BY app.id ORDER BY total DESC LIMIT :limit;''')\r\n results = db.engine.execute(sql, limit=n)\r\n top_apps = []\r\n for row in results:\r\n app = dict(id=row.id, name=row.name, short_name=row.short_name,\r\n description=row.description,\r\n info=json.loads(row.info),\r\n n_volunteers=n_volunteers(row.id),\r\n n_completed_tasks=n_completed_tasks(row.id))\r\n top_apps.append(app)\r\n return top_apps","def top_ten(subreddit):\n url = \"https://www.reddit.com/r/\" + subreddit + \"/hot.json?limit=10\"\n identify = {\"User-Agent\": \"Requests library from Python\",\n \"From\": \"774@holbertonschool.com\"}\n to_print = []\n hot = requests.get(url, headers=identify, allow_redirects=False)\n if hot.status_code == 404:\n print(\"None\")\n return 0\n if hot.status_code == 200:\n hot = hot.json()\n hot = hot[\"data\"]\n hot = hot[\"children\"]\n for items in hot:\n del items[\"kind\"]\n for data in hot:\n to_print.append(data[\"data\"])\n hot = to_print\n to_print = []\n for dictio in hot:\n to_print.append(dictio[\"title\"])\n for itera in to_print:\n print(itera)","def get_most_combined_products(self):\n self.product_pairs = self.products.groupby(\"id\").agg({\"products\": lambda x: list(x)}).reset_index()\n self.product_pairs['product_pairs'] = self.product_pairs['products'].apply(\n lambda x: np.array(list(product(x, x))).tolist())\n self.product_pairs = pd.DataFrame(np.concatenate(list(self.product_pairs['product_pairs'])).tolist())\n self.product_pairs['total_pairs'] = 1\n self.product_pairs = self.product_pairs[self.product_pairs[0] != self.product_pairs[1]]\n self.product_pairs = self.product_pairs.groupby([0, 1]).agg({\"total_pairs\": \"sum\"}).reset_index()\n self.product_pairs = self.product_pairs.sort_values('total_pairs', ascending=False)\n self.product_pairs = self.product_pairs.rename(columns={0: \"pair_1\", 1: \"pair_2\"})\n self.product_pairs['product_pair'] = self.product_pairs.apply(\n lambda row: \" - \".join(list(sorted([row['pair_1'], row['pair_2']]))), axis=1)\n self.product_pairs = self.product_pairs.groupby(\"product_pair\").agg({\"total_pairs\": \"first\"}).reset_index()\n return self.product_pairs","def getTopDevices(self):\n logger.debug('Getting the list of Top Devices...')\n elements = get_elements_by_css(\"a[data-query-prepend='device eq']\")\n devices = []\n for element in elements:\n devices.append(get_text(element))\n return devices","def list_all_products():\n all_products = []\n max_pages = 1000\n page_num = 1\n while page_num < max_pages:\n\n param = {\n 'per_page': 100,\n 'page': page_num,\n }\n rs_api = woo_request_helper().get_details(wc_endpoint='products', params=param)\n\n if rs_api:\n page_num += 1\n all_products.extend(rs_api)\n else:\n break\n\n return all_products","def get_product(self, page_size=10, pages_number=1):\n products = []\n params = self.params.copy()\n params[\"page_size\"] = page_size\n\n try:\n response = requests.get(self.url, params=params, timeout=3)\n response.json()\n except requests.ConnectionError:\n print(\"Error when fetching the API\")\n for i in range(pages_number):\n params[\"page\"] = i + 1\n response = requests.get(self.url, params=params)\n if response.status_code == 200:\n products.extend(response.json()[\"products\"])\n return products","def get_top_cheese(self, index):\n if len(self._stools[index]) == 0:\n return None\n else:\n cheese = self._stools[index][-1]\n return cheese","def top_ten(subreddit):\n\n user_agent = {'User-agent': 'Mozilla/5.0 (Macintosh; \\\nIntel Mac OS X 10_10_1) AppleWebKit/537.36 (KHTML, like Gecko) \\\nChrome/39.0.2171.95 Safari/537.36'}\n\n res = requests.get('https://www.reddit.com/r/{}/hot.json?limit=10'.format(\n subreddit), headers=user_agent)\n\n if res.status_code == 404:\n print(None)\n\n else:\n for sub in res.json().get(\"data\").get(\"children\"):\n print(sub.get(\"data\").get(\"title\"))","def get_top_repos(feature, count):\n assert feature in [\"stars\", \"forks\"], \"Feature must be 'forks' or 'stars'.\"\n response = requests.get(f\"https://api.github.com/search/repositories?sort={feature}&order=desc&q={feature}:>=100+fork:false\")\n data = response.json()\n top_repos = data[\"items\"][:count]\n return top_repos"],"string":"[\n \"def top_products():\\n products = product.Product.query. \\\\\\n order_by(desc(product.Product.likes)).all()[:6]\\n context = {\\n 'products': products\\n }\\n return render_template('stores/topstores.html', **context)\",\n \"def top(self, **kwargs):\\n return self.client.api.top(self.id, **kwargs)\",\n \"def do_top(cs, args):\\n resp, data = cs.repositories.get_top(args.count)\\n utils.print_list(data, ['name', 'count'], sortby='count')\",\n \"def top(self, **kwargs) -> Dict[str, Any]:\",\n \"def get_most_ordered_products(self):\\n return self.products.groupby(\\\"products\\\").agg(\\n {\\\"id\\\": lambda x: len(np.unique(x))}).reset_index().rename(\\n columns={\\\"id\\\": \\\"order_count\\\"}).sort_values(by='order_count', ascending=False)\",\n \"def query_top_buys(cls,N=20):\\n # NOTE: this is not used currently\\n gds = from_cache('VG_TOP_%d'%N)\\n if not gds:\\n gds = [str(g.id()) for g in SuiGoods.all(keys_only=True).order('-likes').fetch(N)]\\n to_cache('VG_TOP_%d'%N,gds,3600*24)\\n return SuiGoods.load_by_ids(gds)\",\n \"def __get_top_with_detail(self, result, top=10):\\n result = result.sort_values(by=\\\"bias_score\\\", ascending=False).drop_duplicates(subset='productId', keep=\\\"first\\\")[\\n :top]\\n\\n return result\",\n \"def get_all_top(self):\\n return self.execute(TABELLE['top']['select']['all'])\",\n \"def top(self):\",\n \"def top(self):\\r\\n return self.topele\",\n \"def topArticles():\\n c = db.cursor()\\n c.execute(\\\"select titles.title, tophits.hits\\\\\\n from tophits, titles\\\\\\n where tophits.path = titles.slug\\\\\\n order by hits desc limit 3;\\\")\\n results = c.fetchall()\\n c.close()\\n return results\",\n \"async def get_most_popular_components(\\n number_of_products: int = 10, db_path: Path = DB_PATH\\n) -> dict:\\n components = await get_query_results(\\\"SELECT * FROM components\\\", db_path=db_path)\\n\\n # Sorting by both counter and name from high to low\\n components.sort(key=lambda x: (x[1], x[0]), reverse=True)\\n\\n popular_components = [\\n {name: count} for name, count in components[0:number_of_products]\\n ]\\n\\n return {\\\"most_popular_components\\\": popular_components}\",\n \"def print_top_articles():\\n\\n output = get_query_results(\\n '''SELECT articles.title, COUNT(path) AS views\\n FROM articles\\n JOIN log\\n ON log.path=CONCAT('/article/', articles.slug)\\n GROUP BY title\\n ORDER BY views DESC\\n LIMIT 3;'''\\n )\\n print(\\\"\\\\nMost Popular Articles: \\\\n\\\")\\n for title, views in output:\\n print(\\\"\\\\\\\"{}\\\\\\\" -- {} views\\\".format(title, views))\",\n \"def top_products(data_frame):\\n data_frame.loc[:, 'total_payment'] = (data_frame['unit_rental_price']\\n * data_frame['quantity_rented']\\n * data_frame['rental_period_months'])\\n data_set = data_frame.groupby(['product_name']).agg({'total_payment': 'sum'})\\n data_set = data_set.nlargest(10, 'total_payment')\\n return data_set\",\n \"def get_top_stories(self):\\n stories = requests.get(f\\\"{HN_API_URL}/topstories.json\\\")\\n stories_ids = json.loads(stories.text)\\n urls = [f\\\"{HN_API_URL}/item/{story_id}.json\\\" for story_id in stories_ids]\\n fetched_stories = asyncio.run(self.fetch_all(urls))\\n sorted_stories = sorted(\\n fetched_stories, key=lambda k: k[\\\"score\\\"], reverse=True)\\n return sorted_stories[:STORIES_NUMBER]\",\n \"def get_most_popular_articles():\\n\\tdb = psycopg2.connect(database=DBNAME)\\n\\tc = db.cursor()\\n\\tc.execute(\\\"select t2.title, count(*) as total from log as t1,articles as t2 where t1.path=concat('/article/',t2.slug) group by t2.title order by total desc limit 3 ;\\\")\\n\\tdata = c.fetchall()\\n\\tdb.close()\\n\\treturn data\",\n \"def get_products(self):\\n page = 1\\n out = []\\n while True:\\n resp = self.get_session().Product.find(limit=10,page=page)\\n if not len(resp):\\n return\\n yield resp\\n page += 1\",\n \"def get_top_news_and_the_rest(self):\\n queryset = self.news.order_by('-marked', '-publication_date')\\n return queryset.first(), queryset[1:]\",\n \"def get_all_products(self):\\n\\t\\tpass\",\n \"def top(self):\\n return self.List_store[len(self.List_store)]\",\n \"def get_top_featured_entries(number=5):\\n return list(Entry.published.filter(featured=True)[:number])\",\n \"def select_most_sold_products_from_personas_vrouwen():\\n return sql_select(\\\"\\\"\\\"SELECT prodid, name,\\n COUNT(*)\\n FROM personas_vrouwen\\n\\n GROUP BY prodid ,name \\n ORDER BY COUNT(*) DESC ; \\\"\\\"\\\")\",\n \"def get_topograms(self):\\n return self.make_request(\\\"GET\\\", \\\"topograms\\\", {})\",\n \"def select_most_sold_products_from_personas_mannen():\\n return sql_select(\\\"\\\"\\\"SELECT prodid, name,\\n COUNT(*)\\n FROM personas_mannen\\n \\n GROUP BY prodid ,name \\n ORDER BY COUNT(*) DESC ; \\\"\\\"\\\")\",\n \"def test_top_songs(self):\\n \\n rss = AppleRSS()\\n objs = rss.get_top_songs(limit=10)\\n \\n self.__test_artists('top_songs', objs)\",\n \"def products(self):\\r\\n return self._products\",\n \"def top(self):\\n return self.q1.return_top()\",\n \"def top_tiles(self):\\n sorted_tiles = self.tiles_by_score()\\n top_tiles = sorted_tiles[:NUM_TOP_TILES]\\n return top_tiles\",\n \"def getTopStories(self):\\n source = self.getSource(\\\"http://news.ycombinator.com\\\")\\n stories = self.getStories(source)\\n return stories\",\n \"def get_top_tags(tags):\\n # ~3x faster\\n return list(sorted(tags.items(), key=itemgetter(1), reverse=True))[:TOP_NUMBER]\",\n \"def select_most_sold_products_from_personas_kinderen():\\n return sql_select(\\\"\\\"\\\"SELECT prodid, name,\\n COUNT(*)\\n FROM v\\n\\n GROUP BY prodid ,name \\n ORDER BY COUNT(*) DESC ; \\\"\\\"\\\")\",\n \"def get_popular(self, max_items=None):\\n data = [(x[1], x[0]) for x in self.iteritems()]\\n data.sort(key=lambda x: (sys.maxint - x[0], x[1]))\\n if max_items:\\n return data[:max_items]\\n else:\\n return data\",\n \"def get(self):\\n return Products().get_all_products()\",\n \"def topTags(db, topN=1000):\\n c=db.cursor()\\n c.execute(\\\"\\\"\\\"\\n SELECT\\n tag\\n FROM tags\\n GROUP BY tag\\n ORDER BY COUNT(*) DESC\\n LIMIT %d\\n \\\"\\\"\\\" % topN)\\n tops = [tag0[0] for tag0 in c.fetchall()]\\n c.close()\\n return tops\",\n \"async def get_top(self, limit, iterator):\\n if iterator == 'ITERATOR_LE':\\n iterator = 'DESC'\\n if iterator == 'ITERATOR_GE':\\n iterator = 'ASC'\\n sql = (\\n \\\"select data from stickers order by (data->>1)::int {} limit {}\\\"\\n ).format(iterator, limit)\\n result = await self.db.fetch(sql)\\n ret = []\\n for i in result:\\n ret.append(json.loads(i[0]))\\n return ret\",\n \"def popular_items(self):\\n if self._popular_items is None:\\n self._popular_items = self._get_popular_items(100)\\n return self._popular_items\",\n \"def get_popular_article():\\n query_command = \\\"SELECT * from popular_posts LIMIT 3\\\"\\n query_data = run_query(query_command)\\n return query_data\",\n \"def top(self) -> Optional[FloatObject]:\\n return self.get(\\\"/Top\\\", None)\",\n \"def get_top(self):\\n\\n sql = \\\"SELECT username FROM Users ORDER BY score DESC LIMIT 10\\\"\\n self.cursor.execute(sql)\\n return self.cursor.fetchall()\",\n \"def products(self, start=None, limit=None):\\r\\n params = base.get_params(None, locals())\\r\\n url = '{0}/products'.format(self.get_url())\\r\\n return http.Request('GET', url, params), parsers.parse_json\",\n \"def test_get_top_respects_limit(self):\\r\\n\\r\\n ranked_3_app = self.create_app_with_contributors(8, 0, name='three')\\r\\n ranked_2_app = self.create_app_with_contributors(9, 0, name='two')\\r\\n ranked_1_app = self.create_app_with_contributors(10, 0, name='one')\\r\\n ranked_4_app = self.create_app_with_contributors(7, 0, name='four')\\r\\n\\r\\n top_apps = cached_apps.get_top(n=2)\\r\\n\\r\\n assert len(top_apps) is 2, len(top_apps)\",\n \"def get_top():\\n print(\\\"This processes are using the cpu the most:\\\")\\n print(os.system(\\\"ps axo %cpu,pid,euser,cmd | sort -nr | head -n 5\\\"))\",\n \"def get_most_popular(self):\\n\\t\\tpopular_rated = self.data_final[self.data_final['Rating'] == 10]\\n\\t\\tpopular_jokes = popular_rated.groupby('JokeID').count().reset_index()\\n\\t\\tpopular_jokes = popular_jokes[['JokeID','Rating']]\\n\\t\\tpopular_jokes.columns = ['JokeID','Number_rated10']\\n\\t\\ttop_joke = popular_jokes.sort_values(by=['Number_rated10'], ascending=False).head(1)\\n\\t\\ttop_joke_val = top_joke['JokeID'].values[0]\\n\\t\\tjokes_list = sorted(set(self.data_final['JokeID']))\\n\\t\\tjoke_num = jokes_list.index(top_joke_val)\\n\\t\\ttop_joke_desc = self.data_jokes[self.data_jokes['JokeID'] == top_joke_val].values[0][1]\\n\\n\\t\\treturn top_joke_desc, joke_num\",\n \"def getProducts():\\n return \\\"http://api.tcgplayer.com/catalog/products\\\"\",\n \"def top(self, event, limit=None):\\r\\n params = base.get_params(('event', 'limit'), locals(), serialize_param)\\r\\n\\r\\n request = http.Request('GET', 'events/properties/top/', params)\\r\\n\\r\\n return request, parsers.parse_json\",\n \"def top_ten(subreddit):\\n\\n if subreddit is None or not isinstance(subreddit, str):\\n print(\\\"None\\\")\\n\\n user_agent = {'User-agent': 'Google Chrome Version 81.0.4044.129'}\\n params = {'limit': 10}\\n url = 'https://www.reddit.com/r/{}/hot/.json'.format(subreddit)\\n\\n response = get(url, headers=user_agent, params=params)\\n all_data = response.json()\\n\\n try:\\n raw1 = all_data.get('data').get('children')\\n\\n for i in raw1:\\n print(i.get('data').get('title'))\\n\\n except:\\n print(\\\"None\\\")\",\n \"def top_playlists():\\n page = request.args.get('page', 1, type=int)\\n playlists = PlaylistModel.query.filter(PlaylistModel.import_count > 0).order_by(PlaylistModel.import_count.desc()).paginate(\\n page=page, per_page=8, error_out=False\\n )\\n\\n return render_template(\\n 'top.html',\\n playlists=playlists.items,\\n pagination=playlists, # this is a pagination object,\\n active='most-imported',\\n )\",\n \"def top_ten(subreddit):\\n try:\\n info = requests.get('https://www.reddit.com/r/{}/hot.json?limit=10'\\n .format(subreddit), allow_redirects=False,\\n headers={'User-Agent': 'Custom'}).json().get(\\n 'data').get('children')\\n for child in info:\\n print(child.get('data').get('title'))\\n except:\\n print('None')\",\n \"def topic_get_hot(request, limit):\\n if request.method == 'GET':\\n all_topics = Topic.objects.all()\\n\\n if int(limit) == 0:\\n hot_topics = sorted(all_topics, key=lambda t: -t.hotness)\\n else:\\n hot_topics = sorted(all_topics, key=lambda t: -t.hotness)[:int(limit)]\\n #hot_topics = Topic.objects.order_by('hotness')[:5]\\n TopicNestedSerializer.Meta.depth = 2\\n RelationSerializer.Meta.depth = 1\\n serializer = TopicNestedSerializer(hot_topics, many=True)\\n return Response(serializer.data)\",\n \"def get_all_products():\\n data = order_obj.get_all_products()\\n return data\",\n \"def top_stories(self, limit=None):\\n return self._get_page('topstories').json()[:limit]\",\n \"def top_ten(subreddit):\\n h = requests.utils.default_headers()\\n h.update({'User-Agent': 'My User Agent 1.0'})\\n url = \\\"https://www.reddit.com/r/{}/hot.json?limit=10\\\".format(subreddit)\\n r = requests.get(url, headers=h).json()\\n result = r.get('data', {}).get('children', [])\\n if not result:\\n print(None)\\n for i in result:\\n print(i.get('data').get('title'))\",\n \"def get_top_articles(update=False):\\n # use caching to avoid running unnecessary DB queries at each page load\\n key = 'top_ten'\\n articles = memcache.get(key)\\n\\n logging.warn('MEMCACHE | Wiki articles %s' % str(articles))\\n\\n if (articles is None) or (len(articles) == 0) or update:\\n # necessary artificial delay when a new article has just been persisted to the datastore\\n if update:\\n time.sleep(2)\\n\\n articles = db.GqlQuery('SELECT * FROM Article ORDER BY updated DESC LIMIT 10')\\n articles = list(articles)\\n memcache.set(key, articles)\\n\\n logging.warn('DATASTORE | Wiki articles count %s' % str(len(articles)))\\n return articles\",\n \"def query_top_recommended(cls,N=10):\\n brkey = 'BooksMostRecommended'\\n bks = from_cache(brkey)\\n if not bks:\\n bks = map(lambda e:str(e.id()), SuiBook.all(keys_only=True).order('-recommends').fetch(N))\\n to_cache(brkey,bks)\\n return bks\",\n \"def get_most_popular_posts():\\n popular_posts_ids = [post.id for post in Post.objects.popular()]\\n return Post.objects.filter(id__in=popular_posts_ids). \\\\\\n add_comments_count(). \\\\\\n prefetch_related('author'). \\\\\\n prefetch_with_tags_and_likes(). \\\\\\n order_by('likes_count')\",\n \"def getTopNTweets(retrievedTweets, numberOfTweets):\\n if sortBy=='newest':\\n retrievedTweets = sorted(retrievedTweets, key=lambda k: k['id'], reverse=True)\\n elif sortBy=='oldest':\\n retrievedTweets = sorted(retrievedTweets, key=lambda k: k['id'],reverse=False)\\n elif sortBy=='favorite_count':\\n retrievedTweets = sorted(retrievedTweets, key=lambda k: k['favorite_count'],reverse=True)\\n elif sortBy=='retweet_count':\\n retrievedTweets = sorted(retrievedTweets, key=lambda k: k['retweet_count'],reverse=True)\\n else:\\n retrievedTweets = random.sample(retrievedTweets, numberOfTweets)\\n return retrievedTweets[:numberOfTweets]\",\n \"def test_top(self):\\n ds_uri = odata.ODataURI(\\\"Orders?$orderby=OrderDate%20desc&$top=10\\\",\\n '/x.svc')\\n top = ds_uri.sys_query_options[odata.SystemQueryOption.top]\\n self.assertTrue(isinstance(top, int), \\\"top type\\\")\\n self.assertTrue(top == 10, \\\"top 10\\\")\\n ds_uri = odata.ODataURI(\\\"Customers('ALFKI')/Orders?$top=10\\\", '/x.svc')\\n top = ds_uri.sys_query_options[odata.SystemQueryOption.top]\\n self.assertTrue(top == 10, \\\"top 10\\\")\\n ds_uri = odata.ODataURI(\\\"Orders?$orderby=OrderDate%20desc&$top=0\\\",\\n '/x.svc')\\n top = ds_uri.sys_query_options[odata.SystemQueryOption.top]\\n self.assertTrue(top == 0, \\\"top 0\\\")\\n try:\\n ds_uri = odata.ODataURI(\\n \\\"Orders?$orderby=OrderDate%20desc&$top=-1\\\", '/x.svc')\\n self.fail(\\\"top=-1\\\")\\n except odata.InvalidSystemQueryOption:\\n pass\",\n \"def get_popular_stocks(self):\\n response = requests.get('https://brokerage-static.s3.amazonaws.com/popular_stocks/data.json')\\n response_json = response.json()\\n assert len(response_json) == 1\\n return response_json[0]\",\n \"def get_all_products(access_keeper):\\n logger.debug('getting products...')\\n headers = get_authorization_headers(access_keeper)\\n\\n response = requests.get('https://api.moltin.com/v2/products', headers=headers)\\n raise_response_errors(response)\\n\\n products = response.json()['data']\\n logger.debug(f'{len(products)} products was got')\\n\\n return products\",\n \"def get(self, request, *args, **kwargs):\\n return super(PopularSongsView, self).get(request, query=app.MOST_POPULAR_SONGS, *args, **kwargs)\",\n \"def top_ten(subreddit):\\n BASE_URL = 'http://www.reddit.com/r/{}/hot.json'.format(subreddit)\\n head = {'User-Agent': 'Mozilla/5.0'}\\n par = {'limit': 10}\\n\\n r = requests.get(BASE_URL, params=par, headers=head).json()\\n\\n res = r.get('data', {}).get('children', None)\\n\\n if res:\\n for post in res:\\n print(post.get('data').get('title'))\\n else:\\n print(None)\",\n \"def get_popular_articles():\\n db = psycopg2.connect(database=DBNAME)\\n c = db.cursor()\\n query_popular_articles = \\\"\\\"\\\"\\n SELECT art.title, COUNT(lg.id) as views\\n FROM articles as art\\n JOIN log as lg\\n ON art.slug = substring(lg.path,10)\\n AND lg.status = '200 OK'\\n GROUP BY art.title\\n ORDER BY views desc\\n LIMIT 3; \\\"\\\"\\\"\\n c.execute(query_popular_articles)\\n articles = from_db_cursor(c)\\n db.close()\\n return articles\",\n \"def top_ten(subreddit):\\n\\n limit = \\\"10\\\"\\n\\n url = \\\"https://www.reddit.com/r/{}/hot.json?limit={}\\\".format(subreddit,\\n limit)\\n\\n user_agent = {\\\"User-Agent\\\": \\\"Python\\\"}\\n response = requests.get(url, headers=user_agent, allow_redirects=False)\\n if response.status_code >= 300:\\n print(\\\"None\\\")\\n else:\\n for elem in response.json().get(\\\"data\\\").get(\\\"children\\\"):\\n print(elem.get(\\\"data\\\").get(\\\"title\\\"))\",\n \"def get_top(self):\\n elements = self.S.get_maximal_elements()\\n data = {}\\n alot = Nat().get_top()\\n for e in elements:\\n data[e] = alot\\n return Multiset(data, self.S)\",\n \"def get_top_recipes(df, sort_params=None, count=10):\\n if not sort_params:\\n logging.warning(\\\"Column names to soty by are not defined.\\\")\\n return df\\n\\n return df.sort_values(sort_params[\\\"names\\\"],\\n ascending=sort_params[\\\"order\\\"]).head(count)\",\n \"def predict(self, products: list):\\n try:\\n self.log.info(\\\"Prediction is started.\\\")\\n\\n contentScores: list = self.content_score_repository.get_by_products(products)\\n\\n df = self.__fillScores(contentScores)\\n\\n result = self.__get_top(df)\\n self.log.info(\\\"Top values are calculated.\\\")\\n\\n self.log.info(\\\"Prediction is finished.\\\")\\n\\n return result\\n\\n except Exception as ex:\\n msg: str = \\\"Undefined error is occured in Hybrid recommender service. \\\"\\n self.log.error(msg + \\\"err: {}\\\".format(ex))\\n raise HybridRecommenderServiceException(msg)\",\n \"def top_ten(subreddit):\\n header = {\\\"User-Agent\\\": \\\"Holberton\\\"}\\n url = \\\"https://www.reddit.com/r/{}/hot.json?limit=10\\\".format(subreddit)\\n response = requests.get(url, headers=header, allow_redirects=False)\\n if response.status_code == 200:\\n\\n for item in response.json().get(\\\"data\\\", None).get(\\\"children\\\", None):\\n print(item.get(\\\"data\\\", None).get(\\\"title\\\", None))\\n else:\\n print(None)\\n return\",\n \"def top_ten(subreddit):\\n url = \\\"https://www.reddit.com/r/\\\" + subreddit + \\\"/top/.json\\\"\\n r = requests.get(url,\\n headers={'User-agent': 'norman'},\\n params={\\\"limit\\\": 10},\\n allow_redirects=False)\\n if r.status_code == 200:\\n for dic in r.json().get('data').get('children'):\\n print(dic.get('data').get('title'))\\n else:\\n print(\\\"None\\\")\",\n \"def top_ten(request):\\n if request.method == 'GET':\\n movies = Movie.objects.filter(date_of_release__lte=datetime.date.today())\\n movies = movies.order_by('-rating')[:10]\\n serializer = MovieSerializer(movies, many=True)\\n return Response(serializer.data)\",\n \"def get_artists_most_played(session_):\\n # artists = session_.query(Artist).order_by(Artist.name.asc()).paginate()\\n artists = session_.query(Artist).order_by(Artist.plays.desc()).all()\\n return artists\",\n \"def list_products(self):\\n return self._make_get_request(self._urls['products'])\",\n \"def get_top_articles(\\n limit: int = 5,\\n date: int = int(datetime.now().strftime(\\\"%Y%m%d\\\"))\\n):\\n\\n res = articles_db.get_top_articles_mongo(\\n articles,\\n limit,\\n date\\n )\\n\\n return res\",\n \"def get_fastest_moving_products(num_products):\\n\\treturn execute_sql(\\\"SELECT slopes.slope, slopes.asin, products.product_name,\\\\\\n\\t\\tproducts.manufacturer_name, categories.category_name, \\\\\\n\\t\\tproducts.product_url FROM slopes INNER JOIN products ON \\\\\\n\\t\\tslopes.asin=products.asin INNER JOIN categories ON \\\\\\n\\t\\tslopes.category_url=categories.category_url ORDER BY slope ASC \\\\\\n\\t\\tLIMIT %s\\\" % num_products)\",\n \"def top_ten(subreddit):\\n req = get(\\n \\\"https://www.reddit.com/r/{}/hot.json\\\".format(subreddit),\\n headers={\\n \\\"User-Agent\\\": \\\"alx_app\\\"},\\n params={\\n \\\"limit\\\": 10},\\n allow_redirects=False)\\n if req.status_code != 200:\\n print(None)\\n else:\\n posts = req.json().get(\\\"data\\\").get(\\\"children\\\")\\n for post in posts:\\n print(post.get(\\\"data\\\").get(\\\"title\\\"))\",\n \"def top_5_news():\\n top_five = []\\n\\n news = (db.news.find({}).sort([\\n ('shares_count', pymongo.DESCENDING),\\n ('comments_count', pymongo.DESCENDING),\\n ('title', pymongo.ASCENDING)\\n ]).limit(5))\\n\\n for new in news:\\n top_five.append((new['title'], new['url']))\\n\\n return top_five\",\n \"def top_news():\\n data = get_top_news()\\n return jsonify(data)\",\n \"def get_most_and_least_expensive_high_review_product(df):\\n try:\\n df3 = merge_metadata(df)\\n product_filter = df3['overall'] >= 4.0\\n high_reviewed_products = df3[product_filter]\\n # print high_reviewed_products[:10]\\n # The data contained NaN so we use the nanmax/min funtions to get max/min\\n most_exp = round(np.nanmax(high_reviewed_products['price'])[0], 2)\\n least_exp = round(np.nanmin(high_reviewed_products['price'])[0], 2)\\n\\n most_exp_prod = df3.loc[df3['price'] == most_exp, 'asin'].iloc[0]\\n least_exp_prod = df3.loc[df3['price'] == least_exp, 'asin'].iloc[0]\\n write_text_tofile(\\\"Most Expensive Product: \\\" + str(most_exp_prod) + \\\", Price: \\\" + str(most_exp))\\n write_text_tofile(\\\"Least Expensive Product: \\\" + str(least_exp_prod) + \\\", Price: \\\" + str(least_exp))\\n return {most_exp_prod: most_exp, least_exp_prod: least_exp}\\n except Exception as e:\\n print \\\"Error getting most and least expensive high review product\\\"\\n print str(e)\\n pass\",\n \"def topCountries(top=10):\\r\\n #top 10 deadly countries\\r\\n countries = agg('country')[:top].index\\r\\n #grab aggregated data for these countries\\r\\n dataOfTop10 = agg(['year','country']).query(\\\"country in @countries\\\")### interesting...\\r\\n #unstack data\\r\\n dataOfTop10 = dataOfTop10.unstack(1)\\r\\n #remove multiindexes\\r\\n dataOfTop10 = dataOfTop10.transpose().reset_index(level=0, drop=True).transpose()\\r\\n #sort by year\\r\\n dataOfTop10.sort_index(inplace=True)\\r\\n return dataOfTop10\",\n \"def printTopThreeArticles():\\n query = \\\"\\\"\\\"\\n SELECT author_article_popularity_view.article,\\n author_article_popularity_view.views\\n FROM author_article_popularity_view\\n LIMIT 3;\\n \\\"\\\"\\\"\\n cursor = connection.cursor()\\n cursor.execute(query)\\n results = cursor.fetchall()\\n print(\\\"\\\\nTop 3 articles of all time: \\\")\\n for i, result in enumerate(results):\\n print(\\\"{}. \\\\\\\"{}\\\\\\\" - {:,} views\\\".format(i + 1, result[0], result[1]))\",\n \"def products(self):\\n return self._products\",\n \"def top_by_ratings(self, n, metric=average):\\n return top_movies\",\n \"def top(self) -> int:\\n return self.q[0]\",\n \"def top(self) -> int:\\n return self.q[0]\",\n \"def top(self) -> int:\\n return self.q[0]\",\n \"def top_tracks(genre):\\n\\tartist = random.choice(genre_artist[genre])\\n\\ttop_tracks = search_for_artist_top_tracks(artist)\\n\\titems = []\\n\\tif top_tracks:\\n\\t\\tfor track in top_tracks:\\n\\t\\t\\titems.append({\\\"artist\\\": track[\\\"artists\\\"][0][\\\"name\\\"], \\\"popularity\\\": track[\\\"popularity\\\"], \\\"track\\\": track[\\\"name\\\"],\\n\\t\\t\\t\\t \\\"preview_url\\\": track[\\\"preview_url\\\"], \\\"album_image_url\\\": track[\\\"album\\\"][\\\"images\\\"][2][\\\"url\\\"]})\\n\\t\\titems = sorted(items, key=lambda x: x['popularity'], reverse=True)\\n\\t\\tfor item in items:\\n\\t\\t\\tdel item['popularity']\\n\\t\\treturn items\\n\\telse:\\n\\t\\treturn None\",\n \"def top_ten(subreddit):\\n headers = {\\\"User-Agent\\\": \\\"Holberton\\\"}\\n url = \\\"https://www.reddit.com/r/{}/hot.json?limit=10\\\".format(\\n subreddit)\\n req = requests.get(url, headers=headers)\\n\\n if req.status_code != 200:\\n print(None)\\n return\\n redit = req.json().get(\\\"data\\\").get(\\\"children\\\")\\n for chil in redit:\\n print(chil.get(\\\"data\\\").get(\\\"title\\\"))\",\n \"def top_by_num_of_ratings(self, n):\\n return top_movies\",\n \"def top(self):\\n return self[0]\",\n \"async def top_specs(self):\\r\\n players = await self.get_players()\\r\\n specs = []\\r\\n for player in players:\\r\\n specs.append(player['specId'])\\r\\n await self.bot.send_message('Top 3v3 Composition:')\\r\\n for key in self.specs:\\r\\n if specs.count(int(key)) > 0:\\r\\n await self.bot.send_message('{:s}: {:d} ({:.2f}%)'.format(\\r\\n self.specs[key],\\r\\n specs.count(int(key)),\\r\\n float(specs.count(int(key))/965.0)*100)\\r\\n )\",\n \"def top(self):\\n try:\\n if self.size() > 0:\\n return self.items[len(self.items) - 1]\\n else:\\n raise IndexError('Cannot get top item, stack is empty.')\\n except IndexError as err:\\n print(err)\\n raise\",\n \"def get_most_popular_authors():\\n\\tdb = psycopg2.connect(database=DBNAME)\\n\\tc = db.cursor()\\n\\tc.execute(\\\" select t1.name,count(*) as total from authors as t1, articles as t2,log as t3 where t3.path=concat('/article/',t2.slug) and t1.id=t2.author group by t1.name order by total desc limit 3;\\\")\\n\\tdata = c.fetchall()\\n\\tdb.close()\\n\\n\\treturn data\",\n \"def get_top(n=4):\\r\\n sql = text('''SELECT app.id, app.name, app.short_name, app.description, app.info,\\r\\n COUNT(app_id) AS total FROM task_run, app\\r\\n WHERE app_id IS NOT NULL AND app.id=app_id AND app.hidden=0\\r\\n GROUP BY app.id ORDER BY total DESC LIMIT :limit;''')\\r\\n results = db.engine.execute(sql, limit=n)\\r\\n top_apps = []\\r\\n for row in results:\\r\\n app = dict(id=row.id, name=row.name, short_name=row.short_name,\\r\\n description=row.description,\\r\\n info=json.loads(row.info),\\r\\n n_volunteers=n_volunteers(row.id),\\r\\n n_completed_tasks=n_completed_tasks(row.id))\\r\\n top_apps.append(app)\\r\\n return top_apps\",\n \"def top_ten(subreddit):\\n url = \\\"https://www.reddit.com/r/\\\" + subreddit + \\\"/hot.json?limit=10\\\"\\n identify = {\\\"User-Agent\\\": \\\"Requests library from Python\\\",\\n \\\"From\\\": \\\"774@holbertonschool.com\\\"}\\n to_print = []\\n hot = requests.get(url, headers=identify, allow_redirects=False)\\n if hot.status_code == 404:\\n print(\\\"None\\\")\\n return 0\\n if hot.status_code == 200:\\n hot = hot.json()\\n hot = hot[\\\"data\\\"]\\n hot = hot[\\\"children\\\"]\\n for items in hot:\\n del items[\\\"kind\\\"]\\n for data in hot:\\n to_print.append(data[\\\"data\\\"])\\n hot = to_print\\n to_print = []\\n for dictio in hot:\\n to_print.append(dictio[\\\"title\\\"])\\n for itera in to_print:\\n print(itera)\",\n \"def get_most_combined_products(self):\\n self.product_pairs = self.products.groupby(\\\"id\\\").agg({\\\"products\\\": lambda x: list(x)}).reset_index()\\n self.product_pairs['product_pairs'] = self.product_pairs['products'].apply(\\n lambda x: np.array(list(product(x, x))).tolist())\\n self.product_pairs = pd.DataFrame(np.concatenate(list(self.product_pairs['product_pairs'])).tolist())\\n self.product_pairs['total_pairs'] = 1\\n self.product_pairs = self.product_pairs[self.product_pairs[0] != self.product_pairs[1]]\\n self.product_pairs = self.product_pairs.groupby([0, 1]).agg({\\\"total_pairs\\\": \\\"sum\\\"}).reset_index()\\n self.product_pairs = self.product_pairs.sort_values('total_pairs', ascending=False)\\n self.product_pairs = self.product_pairs.rename(columns={0: \\\"pair_1\\\", 1: \\\"pair_2\\\"})\\n self.product_pairs['product_pair'] = self.product_pairs.apply(\\n lambda row: \\\" - \\\".join(list(sorted([row['pair_1'], row['pair_2']]))), axis=1)\\n self.product_pairs = self.product_pairs.groupby(\\\"product_pair\\\").agg({\\\"total_pairs\\\": \\\"first\\\"}).reset_index()\\n return self.product_pairs\",\n \"def getTopDevices(self):\\n logger.debug('Getting the list of Top Devices...')\\n elements = get_elements_by_css(\\\"a[data-query-prepend='device eq']\\\")\\n devices = []\\n for element in elements:\\n devices.append(get_text(element))\\n return devices\",\n \"def list_all_products():\\n all_products = []\\n max_pages = 1000\\n page_num = 1\\n while page_num < max_pages:\\n\\n param = {\\n 'per_page': 100,\\n 'page': page_num,\\n }\\n rs_api = woo_request_helper().get_details(wc_endpoint='products', params=param)\\n\\n if rs_api:\\n page_num += 1\\n all_products.extend(rs_api)\\n else:\\n break\\n\\n return all_products\",\n \"def get_product(self, page_size=10, pages_number=1):\\n products = []\\n params = self.params.copy()\\n params[\\\"page_size\\\"] = page_size\\n\\n try:\\n response = requests.get(self.url, params=params, timeout=3)\\n response.json()\\n except requests.ConnectionError:\\n print(\\\"Error when fetching the API\\\")\\n for i in range(pages_number):\\n params[\\\"page\\\"] = i + 1\\n response = requests.get(self.url, params=params)\\n if response.status_code == 200:\\n products.extend(response.json()[\\\"products\\\"])\\n return products\",\n \"def get_top_cheese(self, index):\\n if len(self._stools[index]) == 0:\\n return None\\n else:\\n cheese = self._stools[index][-1]\\n return cheese\",\n \"def top_ten(subreddit):\\n\\n user_agent = {'User-agent': 'Mozilla/5.0 (Macintosh; \\\\\\nIntel Mac OS X 10_10_1) AppleWebKit/537.36 (KHTML, like Gecko) \\\\\\nChrome/39.0.2171.95 Safari/537.36'}\\n\\n res = requests.get('https://www.reddit.com/r/{}/hot.json?limit=10'.format(\\n subreddit), headers=user_agent)\\n\\n if res.status_code == 404:\\n print(None)\\n\\n else:\\n for sub in res.json().get(\\\"data\\\").get(\\\"children\\\"):\\n print(sub.get(\\\"data\\\").get(\\\"title\\\"))\",\n \"def get_top_repos(feature, count):\\n assert feature in [\\\"stars\\\", \\\"forks\\\"], \\\"Feature must be 'forks' or 'stars'.\\\"\\n response = requests.get(f\\\"https://api.github.com/search/repositories?sort={feature}&order=desc&q={feature}:>=100+fork:false\\\")\\n data = response.json()\\n top_repos = data[\\\"items\\\"][:count]\\n return top_repos\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.82457525","0.68613315","0.66651356","0.6496626","0.6484569","0.6435276","0.63997036","0.6326196","0.6323446","0.6315343","0.6285002","0.6270289","0.62692916","0.6223334","0.62199473","0.6199094","0.6195963","0.616619","0.6165667","0.6154092","0.61534995","0.6118348","0.6092541","0.607627","0.6048085","0.60315","0.6004572","0.59952366","0.5986597","0.596607","0.59487444","0.59380263","0.59321964","0.592584","0.5889942","0.5889484","0.58870685","0.5886804","0.5883757","0.58784807","0.58586377","0.58520454","0.5847859","0.5842015","0.5818679","0.5813121","0.5801399","0.5800208","0.5799681","0.5797809","0.57964647","0.5791052","0.5786833","0.5783364","0.57780206","0.5777809","0.5773422","0.57729775","0.5765895","0.57622117","0.5751313","0.57505244","0.57431394","0.5738173","0.57306325","0.5725074","0.5722376","0.5721203","0.57207185","0.57129824","0.57128656","0.5712154","0.5706617","0.5698635","0.56984437","0.5697964","0.5686652","0.56854945","0.5684296","0.56838965","0.56779826","0.5676337","0.5676337","0.5676337","0.567561","0.56742805","0.5667431","0.5664995","0.5663375","0.5650074","0.5644947","0.56441164","0.5640336","0.563391","0.5632891","0.5630132","0.5628603","0.5626662","0.56224936","0.5621873"],"string":"[\n \"0.82457525\",\n \"0.68613315\",\n \"0.66651356\",\n \"0.6496626\",\n \"0.6484569\",\n \"0.6435276\",\n \"0.63997036\",\n \"0.6326196\",\n \"0.6323446\",\n \"0.6315343\",\n \"0.6285002\",\n \"0.6270289\",\n \"0.62692916\",\n \"0.6223334\",\n \"0.62199473\",\n \"0.6199094\",\n \"0.6195963\",\n \"0.616619\",\n \"0.6165667\",\n \"0.6154092\",\n \"0.61534995\",\n \"0.6118348\",\n \"0.6092541\",\n \"0.607627\",\n \"0.6048085\",\n \"0.60315\",\n \"0.6004572\",\n \"0.59952366\",\n \"0.5986597\",\n \"0.596607\",\n \"0.59487444\",\n \"0.59380263\",\n \"0.59321964\",\n \"0.592584\",\n \"0.5889942\",\n \"0.5889484\",\n \"0.58870685\",\n \"0.5886804\",\n \"0.5883757\",\n \"0.58784807\",\n \"0.58586377\",\n \"0.58520454\",\n \"0.5847859\",\n \"0.5842015\",\n \"0.5818679\",\n \"0.5813121\",\n \"0.5801399\",\n \"0.5800208\",\n \"0.5799681\",\n \"0.5797809\",\n \"0.57964647\",\n \"0.5791052\",\n \"0.5786833\",\n \"0.5783364\",\n \"0.57780206\",\n \"0.5777809\",\n \"0.5773422\",\n \"0.57729775\",\n \"0.5765895\",\n \"0.57622117\",\n \"0.5751313\",\n \"0.57505244\",\n \"0.57431394\",\n \"0.5738173\",\n \"0.57306325\",\n \"0.5725074\",\n \"0.5722376\",\n \"0.5721203\",\n \"0.57207185\",\n \"0.57129824\",\n \"0.57128656\",\n \"0.5712154\",\n \"0.5706617\",\n \"0.5698635\",\n \"0.56984437\",\n \"0.5697964\",\n \"0.5686652\",\n \"0.56854945\",\n \"0.5684296\",\n \"0.56838965\",\n \"0.56779826\",\n \"0.5676337\",\n \"0.5676337\",\n \"0.5676337\",\n \"0.567561\",\n \"0.56742805\",\n \"0.5667431\",\n \"0.5664995\",\n \"0.5663375\",\n \"0.5650074\",\n \"0.5644947\",\n \"0.56441164\",\n \"0.5640336\",\n \"0.563391\",\n \"0.5632891\",\n \"0.5630132\",\n \"0.5628603\",\n \"0.5626662\",\n \"0.56224936\",\n \"0.5621873\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6796019"},"document_rank":{"kind":"string","value":"2"}}},{"rowIdx":75585,"cells":{"query":{"kind":"string","value":"get the top products with more detail."},"document":{"kind":"string","value":"def __get_top_with_detail(self, result, top=10):\n result = result.sort_values(by=\"bias_score\", ascending=False).drop_duplicates(subset='productId', keep=\"first\")[\n :top]\n\n return result"},"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 top_products():\n products = product.Product.query. \\\n order_by(desc(product.Product.likes)).all()[:6]\n context = {\n 'products': products\n }\n return render_template('stores/topstores.html', **context)","def top(self, **kwargs):\n return self.client.api.top(self.id, **kwargs)","def topic_get_hot(request, limit):\n if request.method == 'GET':\n all_topics = Topic.objects.all()\n\n if int(limit) == 0:\n hot_topics = sorted(all_topics, key=lambda t: -t.hotness)\n else:\n hot_topics = sorted(all_topics, key=lambda t: -t.hotness)[:int(limit)]\n #hot_topics = Topic.objects.order_by('hotness')[:5]\n TopicNestedSerializer.Meta.depth = 2\n RelationSerializer.Meta.depth = 1\n serializer = TopicNestedSerializer(hot_topics, many=True)\n return Response(serializer.data)","def do_top(cs, args):\n resp, data = cs.repositories.get_top(args.count)\n utils.print_list(data, ['name', 'count'], sortby='count')","def top(self, **kwargs) -> Dict[str, Any]:","def __get_top(self, result, top=10):\n result = result.sort_values(by=\"bias_score\", ascending=False).drop_duplicates(subset='productid', keep=\"first\")\n print(result)\n result = result[:top].sort_values(by=\"final_score\", ascending=False).productid\n\n return list(result)","def topArticles():\n c = db.cursor()\n c.execute(\"select titles.title, tophits.hits\\\n from tophits, titles\\\n where tophits.path = titles.slug\\\n order by hits desc limit 3;\")\n results = c.fetchall()\n c.close()\n return results","def print_top_articles():\n\n output = get_query_results(\n '''SELECT articles.title, COUNT(path) AS views\n FROM articles\n JOIN log\n ON log.path=CONCAT('/article/', articles.slug)\n GROUP BY title\n ORDER BY views DESC\n LIMIT 3;'''\n )\n print(\"\\nMost Popular Articles: \\n\")\n for title, views in output:\n print(\"\\\"{}\\\" -- {} views\".format(title, views))","def top_ten(request):\n if request.method == 'GET':\n movies = Movie.objects.filter(date_of_release__lte=datetime.date.today())\n movies = movies.order_by('-rating')[:10]\n serializer = MovieSerializer(movies, many=True)\n return Response(serializer.data)","def get_top_news_and_the_rest(self):\n queryset = self.news.order_by('-marked', '-publication_date')\n return queryset.first(), queryset[1:]","def query_top_buys(cls,N=20):\n # NOTE: this is not used currently\n gds = from_cache('VG_TOP_%d'%N)\n if not gds:\n gds = [str(g.id()) for g in SuiGoods.all(keys_only=True).order('-likes').fetch(N)]\n to_cache('VG_TOP_%d'%N,gds,3600*24)\n return SuiGoods.load_by_ids(gds)","def get_products(self):\n page = 1\n out = []\n while True:\n resp = self.get_session().Product.find(limit=10,page=page)\n if not len(resp):\n return\n yield resp\n page += 1","def top_products(data_frame):\n data_frame.loc[:, 'total_payment'] = (data_frame['unit_rental_price']\n * data_frame['quantity_rented']\n * data_frame['rental_period_months'])\n data_set = data_frame.groupby(['product_name']).agg({'total_payment': 'sum'})\n data_set = data_set.nlargest(10, 'total_payment')\n return data_set","def get_most_popular_articles():\n\tdb = psycopg2.connect(database=DBNAME)\n\tc = db.cursor()\n\tc.execute(\"select t2.title, count(*) as total from log as t1,articles as t2 where t1.path=concat('/article/',t2.slug) group by t2.title order by total desc limit 3 ;\")\n\tdata = c.fetchall()\n\tdb.close()\n\treturn data","def top_news():\n data = get_top_news()\n return jsonify(data)","def top(self, event, limit=None):\r\n params = base.get_params(('event', 'limit'), locals(), serialize_param)\r\n\r\n request = http.Request('GET', 'events/properties/top/', params)\r\n\r\n return request, parsers.parse_json","def top_ten(subreddit):\n url = \"https://www.reddit.com/r/\" + subreddit + \"/top/.json\"\n r = requests.get(url,\n headers={'User-agent': 'norman'},\n params={\"limit\": 10},\n allow_redirects=False)\n if r.status_code == 200:\n for dic in r.json().get('data').get('children'):\n print(dic.get('data').get('title'))\n else:\n print(\"None\")","def getProducts():\n return \"http://api.tcgplayer.com/catalog/products\"","def top_ten(subreddit):\n\n if subreddit is None or not isinstance(subreddit, str):\n print(\"None\")\n\n user_agent = {'User-agent': 'Google Chrome Version 81.0.4044.129'}\n params = {'limit': 10}\n url = 'https://www.reddit.com/r/{}/hot/.json'.format(subreddit)\n\n response = get(url, headers=user_agent, params=params)\n all_data = response.json()\n\n try:\n raw1 = all_data.get('data').get('children')\n\n for i in raw1:\n print(i.get('data').get('title'))\n\n except:\n print(\"None\")","def top_ten(subreddit):\n try:\n info = requests.get('https://www.reddit.com/r/{}/hot.json?limit=10'\n .format(subreddit), allow_redirects=False,\n headers={'User-Agent': 'Custom'}).json().get(\n 'data').get('children')\n for child in info:\n print(child.get('data').get('title'))\n except:\n print('None')","def top(self):","def top_ten(subreddit):\n\n limit = \"10\"\n\n url = \"https://www.reddit.com/r/{}/hot.json?limit={}\".format(subreddit,\n limit)\n\n user_agent = {\"User-Agent\": \"Python\"}\n response = requests.get(url, headers=user_agent, allow_redirects=False)\n if response.status_code >= 300:\n print(\"None\")\n else:\n for elem in response.json().get(\"data\").get(\"children\"):\n print(elem.get(\"data\").get(\"title\"))","def top_ten(subreddit):\n BASE_URL = 'http://www.reddit.com/r/{}/hot.json'.format(subreddit)\n head = {'User-Agent': 'Mozilla/5.0'}\n par = {'limit': 10}\n\n r = requests.get(BASE_URL, params=par, headers=head).json()\n\n res = r.get('data', {}).get('children', None)\n\n if res:\n for post in res:\n print(post.get('data').get('title'))\n else:\n print(None)","def products(self, start=None, limit=None):\r\n params = base.get_params(None, locals())\r\n url = '{0}/products'.format(self.get_url())\r\n return http.Request('GET', url, params), parsers.parse_json","def get(self, request, *args, **kwargs):\n return super(PopularSongsView, self).get(request, query=app.MOST_POPULAR_SONGS, *args, **kwargs)","def get_all_products(self):\n\t\tpass","def top_ten(subreddit):\n h = requests.utils.default_headers()\n h.update({'User-Agent': 'My User Agent 1.0'})\n url = \"https://www.reddit.com/r/{}/hot.json?limit=10\".format(subreddit)\n r = requests.get(url, headers=h).json()\n result = r.get('data', {}).get('children', [])\n if not result:\n print(None)\n for i in result:\n print(i.get('data').get('title'))","def get_top_featured_entries(number=5):\n return list(Entry.published.filter(featured=True)[:number])","def top_ten(subreddit):\n header = {\"User-Agent\": \"Holberton\"}\n url = \"https://www.reddit.com/r/{}/hot.json?limit=10\".format(subreddit)\n response = requests.get(url, headers=header, allow_redirects=False)\n if response.status_code == 200:\n\n for item in response.json().get(\"data\", None).get(\"children\", None):\n print(item.get(\"data\", None).get(\"title\", None))\n else:\n print(None)\n return","async def get_most_popular_components(\n number_of_products: int = 10, db_path: Path = DB_PATH\n) -> dict:\n components = await get_query_results(\"SELECT * FROM components\", db_path=db_path)\n\n # Sorting by both counter and name from high to low\n components.sort(key=lambda x: (x[1], x[0]), reverse=True)\n\n popular_components = [\n {name: count} for name, count in components[0:number_of_products]\n ]\n\n return {\"most_popular_components\": popular_components}","def shop_products(request):\n\n shop = Shop.objects.get(user=request.user)\n products = Products.objects.filter(shop_rel=shop)\n paginator = pagination.PageNumberPagination()\n paginator.page_size = 7\n result_page = paginator.paginate_queryset(products, request=request)\n serializer = ProductSerializer(result_page, many=True)\n return paginator.get_paginated_response(serializer.data)","def select_most_sold_products_from_personas_vrouwen():\n return sql_select(\"\"\"SELECT prodid, name,\n COUNT(*)\n FROM personas_vrouwen\n\n GROUP BY prodid ,name \n ORDER BY COUNT(*) DESC ; \"\"\")","def get_all_top(self):\n return self.execute(TABELLE['top']['select']['all'])","def get_popular_articles():\n db = psycopg2.connect(database=DBNAME)\n c = db.cursor()\n query_popular_articles = \"\"\"\n SELECT art.title, COUNT(lg.id) as views\n FROM articles as art\n JOIN log as lg\n ON art.slug = substring(lg.path,10)\n AND lg.status = '200 OK'\n GROUP BY art.title\n ORDER BY views desc\n LIMIT 3; \"\"\"\n c.execute(query_popular_articles)\n articles = from_db_cursor(c)\n db.close()\n return articles","def top_ten(subreddit):\n req = get(\n \"https://www.reddit.com/r/{}/hot.json\".format(subreddit),\n headers={\n \"User-Agent\": \"alx_app\"},\n params={\n \"limit\": 10},\n allow_redirects=False)\n if req.status_code != 200:\n print(None)\n else:\n posts = req.json().get(\"data\").get(\"children\")\n for post in posts:\n print(post.get(\"data\").get(\"title\"))","def top(self):\r\n return self.topele","def select_most_sold_products_from_personas_mannen():\n return sql_select(\"\"\"SELECT prodid, name,\n COUNT(*)\n FROM personas_mannen\n \n GROUP BY prodid ,name \n ORDER BY COUNT(*) DESC ; \"\"\")","def top_box(request):\n if request.method == 'GET':\n movies = Movie.objects.all()\n movies = movies.order_by('-box_office')[:10]\n serializer = MovieSerializer(movies, many=True)\n return_obj = serializer.data\n return Response(return_obj)","def top_ten(subreddit):\n headers = {\"User-Agent\": \"Holberton\"}\n url = \"https://www.reddit.com/r/{}/hot.json?limit=10\".format(\n subreddit)\n req = requests.get(url, headers=headers)\n\n if req.status_code != 200:\n print(None)\n return\n redit = req.json().get(\"data\").get(\"children\")\n for chil in redit:\n print(chil.get(\"data\").get(\"title\"))","def top_ten(subreddit):\n url = 'https://www.reddit.com/r/{}/hot.json'.format(subreddit)\n response = get(url, headers=headers, params=query, allow_redirects=False)\n if response.status_code is not 200:\n return print('None')\n\n r = response.json()\n data = r.get('data')\n children = data['children']\n for item in children:\n print(item.get('data')['title'])","def get_popular_article():\n query_command = \"SELECT * from popular_posts LIMIT 3\"\n query_data = run_query(query_command)\n return query_data","def top_ten(subreddit):\n\n user_agent = {'User-agent': 'Mozilla/5.0 (Macintosh; \\\nIntel Mac OS X 10_10_1) AppleWebKit/537.36 (KHTML, like Gecko) \\\nChrome/39.0.2171.95 Safari/537.36'}\n\n res = requests.get('https://www.reddit.com/r/{}/hot.json?limit=10'.format(\n subreddit), headers=user_agent)\n\n if res.status_code == 404:\n print(None)\n\n else:\n for sub in res.json().get(\"data\").get(\"children\"):\n print(sub.get(\"data\").get(\"title\"))","def top_ten(subreddit):\n url = \"https://api.reddit.com/r/{}/hot?limit=10\".format(subreddit)\n response = requests.get(url, headers={\"User-Agent\": \"Python3\"})\n if str(response) != \"<Response [200]>\": # response.status_code != 200\n print(None)\n return\n response = response.json()\n child = response[\"data\"][\"children\"]\n for tittle in child:\n print(tittle[\"data\"][\"title\"])","def top_ten(subreddit):\n url = \"https://www.reddit.com/r/\" + subreddit + \"/hot.json?limit=10\"\n identify = {\"User-Agent\": \"Requests library from Python\",\n \"From\": \"774@holbertonschool.com\"}\n to_print = []\n hot = requests.get(url, headers=identify, allow_redirects=False)\n if hot.status_code == 404:\n print(\"None\")\n return 0\n if hot.status_code == 200:\n hot = hot.json()\n hot = hot[\"data\"]\n hot = hot[\"children\"]\n for items in hot:\n del items[\"kind\"]\n for data in hot:\n to_print.append(data[\"data\"])\n hot = to_print\n to_print = []\n for dictio in hot:\n to_print.append(dictio[\"title\"])\n for itera in to_print:\n print(itera)","def products():\n username = session['username']\n api_key = session['api_key']\n url = 'https://consumernotebook.com/api/v1/products/?username={0}&apikey={1}'.format(username, api_key)\n r = requests.get(url)\n products = []\n if r.status_code != 200:\n error = \"{0} error. Are you sure you entered a valid API key?\".format(r.status_code)\n return render_template('products.html', error=error)\n else:\n products_json = json.loads(r.content)\n for product in products_json[u'objects']:\n products.append(product[u'title'])\n return render_template('products.html', products=products)","def get_top_recipes(df, sort_params=None, count=10):\n if not sort_params:\n logging.warning(\"Column names to soty by are not defined.\")\n return df\n\n return df.sort_values(sort_params[\"names\"],\n ascending=sort_params[\"order\"]).head(count)","def top_ten(subreddit):\n\n url = \"https://www.reddit.com/r/{}/hot.json?limit=10\".format(subreddit)\n headers = {\"user-agent\": \"Mozilla/5.0\"}\n\n res = requests.get(url, headers=headers, allow_redirects=False)\n\n if res.status_code == 200:\n for post in res.json()['data']['children']:\n print(post['data']['title'])\n else:\n print(None)","def all_products_view(request):\n products = Product.objects.all()\n paginator = Paginator(products, 6)\n page_number = request.GET.get('page', 1)\n page = paginator.page(page_number)\n\n \"\"\"render a products html page and within that page we will have access to products, so all_products\"\"\"\n\n if page.has_next():\n\n next_url = f'?page={page.next_page_number()}'\n\n else:\n\n next_url = ''\n\n if page.has_previous():\n\n prev_url = f'?page={page.previous_page_number()}'\n\n else:\n\n prev_url = ''\n\n \n return render(request, 'products.html', {'page': page, 'next_page_url': next_url, 'prev_page_url': prev_url})","def test_top(self):\n ds_uri = odata.ODataURI(\"Orders?$orderby=OrderDate%20desc&$top=10\",\n '/x.svc')\n top = ds_uri.sys_query_options[odata.SystemQueryOption.top]\n self.assertTrue(isinstance(top, int), \"top type\")\n self.assertTrue(top == 10, \"top 10\")\n ds_uri = odata.ODataURI(\"Customers('ALFKI')/Orders?$top=10\", '/x.svc')\n top = ds_uri.sys_query_options[odata.SystemQueryOption.top]\n self.assertTrue(top == 10, \"top 10\")\n ds_uri = odata.ODataURI(\"Orders?$orderby=OrderDate%20desc&$top=0\",\n '/x.svc')\n top = ds_uri.sys_query_options[odata.SystemQueryOption.top]\n self.assertTrue(top == 0, \"top 0\")\n try:\n ds_uri = odata.ODataURI(\n \"Orders?$orderby=OrderDate%20desc&$top=-1\", '/x.svc')\n self.fail(\"top=-1\")\n except odata.InvalidSystemQueryOption:\n pass","def top_stories(self, limit=None):\n return self._get_page('topstories').json()[:limit]","def extract(self, filter_by='relevance', all_pages=False, limit=None):\r\n\r\n page = 1\r\n total_of_pages = 1\r\n products_list = []\r\n\r\n while page <= total_of_pages:\r\n\r\n products_code = None\r\n while products_code is None:\r\n soup = self.__olx_requests(filter_by, page)\r\n products_code = soup.find('div', {'class': \"sc-1fcmfeb-0 WQhDk\"})\r\n\r\n if page == 1 and all_pages is True and limit is None:\r\n max_pages = self.__number_of_pages(soup)\r\n if max_pages is not None:\r\n total_of_pages = max_pages\r\n\r\n elif page == 1 and all_pages is False and isinstance(limit, int):\r\n max_pages = self.__number_of_pages(soup)\r\n if max_pages is not None and limit <= max_pages:\r\n total_of_pages = limit\r\n else:\r\n total_of_pages = 1\r\n\r\n # Individual product - TAG\r\n for tags_products in [\"sc-1fcmfeb-2 ggOGTJ\", \"sc-1fcmfeb-2 hFOgZc\"]:\r\n for each_product in products_code.findAll('li', {'class': tags_products}):\r\n\r\n # Each product dictionary\r\n product_dict = {}\r\n\r\n # Name\r\n PRODUCT_NAME_TAG = 'fnmrjs-8 kRlFBv'\r\n if each_product.find('div', {'class': PRODUCT_NAME_TAG}) is not None:\r\n product_name = each_product.find('div', {'class': PRODUCT_NAME_TAG}).text\r\n if 'Anunciante online' in product_name:\r\n product_name = product_name.replace('Anunciante online', '')\r\n product_dict['Name'] = product_name\r\n else:\r\n continue\r\n\r\n # ID\r\n product_id = each_product.find('a', {'data-lurker-detail': 'list_id'})['data-lurker_list_id']\r\n product_dict['ID'] = product_id\r\n\r\n # Image\r\n PRODUCT_IMAGE_TAG = 'fnmrjs-5 jksoiN'\r\n product_img = each_product.find('div', {'class': PRODUCT_IMAGE_TAG}).find('img')['src']\r\n product_dict['Image'] = product_img\r\n\r\n # Price\r\n PRODUCT_PRICE_TAG = 'fnmrjs-15 clbSMi'\r\n if each_product.find('div', {'class': PRODUCT_PRICE_TAG}).text:\r\n product_price = each_product.find('div', {'class': PRODUCT_PRICE_TAG}).text\r\n product_dict['Price'] = re.findall('R\\$ (\\d*,?\\.?\\d*)|$', product_price)[0].replace('.', '')\r\n else:\r\n product_dict['Price'] = '-'\r\n\r\n # Date\r\n PRODUCT_DATE_TAG = 'fnmrjs-18 gMKELN'\r\n product_date = each_product.find('div', {'class': PRODUCT_DATE_TAG}).text\r\n if 'Hoje' in product_date:\r\n product_date = product_date.replace('Hoje', datetime.date.today().strftime(\"%d/%m \"))\r\n product_dict['Date'] = product_date\r\n\r\n elif 'Ontem' in product_date:\r\n product_date = product_date.replace('Ontem', (datetime.date.today() - datetime.timedelta(days=1)).strftime(\"%d/%m \"))\r\n product_dict['Date'] = product_date\r\n\r\n else:\r\n product_date = product_date.replace(re.findall(' ([a-z]*)\\d*', product_date)[0], months[re.findall(' ([a-z]*)\\d*', product_date)[0]]).replace(' ', r'/', 1)\r\n product_dict['Date'] = product_date\r\n\r\n # Location\r\n PRODUCT_LOCATION_PARENT_TAG = 'fnmrjs-21 bktOWr'\r\n PRODUCT_LOCATION_CHILD_TAG = 'fnmrjs-13 hdwqVC'\r\n product_location = each_product.find('div', {'class': PRODUCT_LOCATION_PARENT_TAG}).find('p', {'class': PRODUCT_LOCATION_CHILD_TAG}).text\r\n product_dict['City'] = re.findall('(.*\\w*),|$', product_location)[0]\r\n product_dict['Neighborhood'] = re.findall(r',(.*\\w*) - |$', product_location)[0].strip()\r\n product_dict['State'] = states_ddd[re.findall(r'DDD (\\d*)|$', product_location)[0]]\r\n\r\n # Link\r\n product_link = each_product.find('a', {'data-lurker-detail': 'list_id'})['href']\r\n product_dict['Link'] = product_link\r\n\r\n # List of Products\r\n products_list.append(product_dict)\r\n\r\n # Next page\r\n page += 1\r\n\r\n return products_list","def printTopThreeArticles():\n query = \"\"\"\n SELECT author_article_popularity_view.article,\n author_article_popularity_view.views\n FROM author_article_popularity_view\n LIMIT 3;\n \"\"\"\n cursor = connection.cursor()\n cursor.execute(query)\n results = cursor.fetchall()\n print(\"\\nTop 3 articles of all time: \")\n for i, result in enumerate(results):\n print(\"{}. \\\"{}\\\" - {:,} views\".format(i + 1, result[0], result[1]))","def get_most_popular(self):\n\t\tpopular_rated = self.data_final[self.data_final['Rating'] == 10]\n\t\tpopular_jokes = popular_rated.groupby('JokeID').count().reset_index()\n\t\tpopular_jokes = popular_jokes[['JokeID','Rating']]\n\t\tpopular_jokes.columns = ['JokeID','Number_rated10']\n\t\ttop_joke = popular_jokes.sort_values(by=['Number_rated10'], ascending=False).head(1)\n\t\ttop_joke_val = top_joke['JokeID'].values[0]\n\t\tjokes_list = sorted(set(self.data_final['JokeID']))\n\t\tjoke_num = jokes_list.index(top_joke_val)\n\t\ttop_joke_desc = self.data_jokes[self.data_jokes['JokeID'] == top_joke_val].values[0][1]\n\n\t\treturn top_joke_desc, joke_num","def get_most_and_least_expensive_high_review_product(df):\n try:\n df3 = merge_metadata(df)\n product_filter = df3['overall'] >= 4.0\n high_reviewed_products = df3[product_filter]\n # print high_reviewed_products[:10]\n # The data contained NaN so we use the nanmax/min funtions to get max/min\n most_exp = round(np.nanmax(high_reviewed_products['price'])[0], 2)\n least_exp = round(np.nanmin(high_reviewed_products['price'])[0], 2)\n\n most_exp_prod = df3.loc[df3['price'] == most_exp, 'asin'].iloc[0]\n least_exp_prod = df3.loc[df3['price'] == least_exp, 'asin'].iloc[0]\n write_text_tofile(\"Most Expensive Product: \" + str(most_exp_prod) + \", Price: \" + str(most_exp))\n write_text_tofile(\"Least Expensive Product: \" + str(least_exp_prod) + \", Price: \" + str(least_exp))\n return {most_exp_prod: most_exp, least_exp_prod: least_exp}\n except Exception as e:\n print \"Error getting most and least expensive high review product\"\n print str(e)\n pass","def dvds_top_rentals(self, **kwargs):\n path = self._get_path('dvds_top_rentals')\n\n response = self._GET(path, kwargs)\n self._set_attrs_to_values(response)\n return response","def top_ten(subreddit):\n\n settings = {'allow_redirects': False, 'headers': {'User-agent': ''}}\n url = \"https://www.reddit.com/r/{}/hot.json\".format(subreddit)\n\n try:\n responses = get(url, **settings).json().get('data').get('children')\n for post in responses[:10]:\n print(post['data']['title'])\n except:\n print(\"None\")","def get_most_ordered_products(self):\n return self.products.groupby(\"products\").agg(\n {\"id\": lambda x: len(np.unique(x))}).reset_index().rename(\n columns={\"id\": \"order_count\"}).sort_values(by='order_count', ascending=False)","def get(self):\n return Products().get_all_products()","def test_top_songs(self):\n \n rss = AppleRSS()\n objs = rss.get_top_songs(limit=10)\n \n self.__test_artists('top_songs', objs)","def top_ten(subreddit):\n url = 'https://api.reddit.com/r/{}/hot.json'.format(subreddit)\n header = {'User-agent': 'your bot 0.1'}\n subred = requests.get(url, headers=header, allow_redirects=False)\n\n if subred.status_code != 200:\n print(None)\n return None\n\n try:\n theme = subred.json()\n except:\n print(\"Not a valid JSON\")\n return 0\n\n try:\n maindata = theme.get(\"data\")\n children = maindata.get(\"children\")\n for child in children[:10]:\n data = child.get(\"data\")\n print(data.get(\"title\"))\n except:\n return None","def popular_items(self):\n if self._popular_items is None:\n self._popular_items = self._get_popular_items(100)\n return self._popular_items","def products(self):\r\n return self._products","def get_top_stories(self):\n stories = requests.get(f\"{HN_API_URL}/topstories.json\")\n stories_ids = json.loads(stories.text)\n urls = [f\"{HN_API_URL}/item/{story_id}.json\" for story_id in stories_ids]\n fetched_stories = asyncio.run(self.fetch_all(urls))\n sorted_stories = sorted(\n fetched_stories, key=lambda k: k[\"score\"], reverse=True)\n return sorted_stories[:STORIES_NUMBER]","def top_artists_from_API(api_results):\r\n df = pd.DataFrame(api_results[\"items\"])\r\n cols = [\"name\",\"id\",\"genres\",\"popularity\",\"uri\"]\r\n return df[cols]","def list_all_products():\n all_products = []\n max_pages = 1000\n page_num = 1\n while page_num < max_pages:\n\n param = {\n 'per_page': 100,\n 'page': page_num,\n }\n rs_api = woo_request_helper().get_details(wc_endpoint='products', params=param)\n\n if rs_api:\n page_num += 1\n all_products.extend(rs_api)\n else:\n break\n\n return all_products","def select_most_sold_products_from_personas_kinderen():\n return sql_select(\"\"\"SELECT prodid, name,\n COUNT(*)\n FROM v\n\n GROUP BY prodid ,name \n ORDER BY COUNT(*) DESC ; \"\"\")","def top_by_num_of_ratings(self, n):\n return top_movies","def get(self, request, page_no=None):\n queryset = Productlisting.objects.using('scraped').all()\n if page_no is None:\n page_no = 1\n if page_no <= 0:\n return Response(\"Page Number must be >= 1\", status=status.HTTP_400_BAD_REQUEST)\n \n ITEMS_PER_PAGE = 10\n queryset = queryset[(page_no - 1) * ITEMS_PER_PAGE : (page_no) * ITEMS_PER_PAGE]\n \n serializer = ProductListingSerializer(queryset, many=True)\n return Response(serializer.data, status=status.HTTP_200_OK)","def list_products(self):\n return self._make_get_request(self._urls['products'])","def product_list(request):\n error = {\n 'status': False,\n 'name': None,\n 'text': None,\n 'level': None,\n 'debug': None\n }\n\n limit, error = get_limit(request, error)\n\n serializer = FreshSerializer()\n queryset = Product.objects.all()[:limit]\n\n if not queryset:\n error = {\n \"status\": True,\n \"name\": \"No Products\",\n \"text\": \"No Products found\",\n \"level\": \"Information\",\n \"debug\": \"\"\n }\n\n data = {\n \"products\": json.loads(serializer.serialize(queryset)),\n \"error\": error\n }\n\n return HttpResponse(json.dumps(data), content_type=\"application/json\")","def get_top_articles(\n limit: int = 5,\n date: int = int(datetime.now().strftime(\"%Y%m%d\"))\n):\n\n res = articles_db.get_top_articles_mongo(\n articles,\n limit,\n date\n )\n\n return res","def top_by_ratings(self, n, metric=average):\n return top_movies","def test_get_movie_top_all(self):\n\n r = self.client.get(reverse('movieapi:top'))\n\n qs = Movie.objects \\\n .annotate(total_comments=Count('comment__comment'),\n rank=Window(\n expression=DenseRank(),\n order_by=F('total_comments').desc(),\n )\n ).values('id', 'total_comments', 'rank')\n\n serializer = TopSerializer(qs, many=True)\n\n self.assertJSONEqual(\n r.content,\n serializer.data\n )\n self.assertEqual(r.status_code, 200)","def get_all_products(access_keeper):\n logger.debug('getting products...')\n headers = get_authorization_headers(access_keeper)\n\n response = requests.get('https://api.moltin.com/v2/products', headers=headers)\n raise_response_errors(response)\n\n products = response.json()['data']\n logger.debug(f'{len(products)} products was got')\n\n return products","def getRecommendedTopics(request, limit):\n if request.method == 'GET':\n user = request.user;\n scores = {};\n for topic in Topic.objects.all():\n\n neighbor_visits = Visit.objects.filter(user=user, topic__relates_to__topic_to=topic)\n\n neighbor_visits_count = len(neighbor_visits);\n if neighbor_visits_count > 0:\n last_neighbor_visit = neighbor_visits.order_by('-visit_date')[0].visit_date;\n else:\n last_neighbor_visit = topic.created_at\n\n relevance_score = 5*neighbor_visits_count - (timezone.now()-last_neighbor_visit).total_seconds()/3600\n recommendation = relevance_score + topic.hotness\n\n scores[topic] = recommendation;\n\n sorted_scores = sorted(scores.items(), key=operator.itemgetter(1), reverse=True)[:int(limit)]\n recommended_topics = [key for key, value in sorted_scores]\n #print(recommended_topics)\n serializer = TopicNestedSerializer(recommended_topics, many=True);\n return Response(serializer.data)","def get_product(self, page_size=10, pages_number=1):\n products = []\n params = self.params.copy()\n params[\"page_size\"] = page_size\n\n try:\n response = requests.get(self.url, params=params, timeout=3)\n response.json()\n except requests.ConnectionError:\n print(\"Error when fetching the API\")\n for i in range(pages_number):\n params[\"page\"] = i + 1\n response = requests.get(self.url, params=params)\n if response.status_code == 200:\n products.extend(response.json()[\"products\"])\n return products","def get_topograms(self):\n return self.make_request(\"GET\", \"topograms\", {})","def product_management():\n sort_by = request.args.get(\"sort\")\n\n \"\"\"\n Sort method is from https://docs.mongodb.com/manual/reference/\n method/cursor.sort/index.html\n \"\"\"\n if sort_by:\n products = list(mongo.db.products.find().sort(sort_items(sort_by)))\n\n else:\n products = list(mongo.db.products.find().sort('name', 1))\n\n \"\"\"\n Pagination code is from https://gist.github.com/mozillazg/\n 69fb40067ae6d80386e10e105e6803c9\n \"\"\"\n page, per_page, offset = get_page_args(\n page_parameter='page', per_page_parameter='per_page', per_page=10)\n pagination_products = paginate_items(products, offset, per_page)\n pagination = paginate(products, page, per_page)\n\n return render_template(\n \"product_management.html\",\n page_title=\"Product Management\",\n products=pagination_products,\n pagination=pagination)","def GetProductDirect(self, model):\n items = []\n\n def xml_parser(body_xml_etree):\n for product in body_xml_etree.find('Products').findall('Product'):\n sku = product.find('Skus').find('Sku')\n attrs = product.find('Attributes')\n\n images = []\n for img in sku.find('Images').findall('Image'):\n if img.text:\n imgurl = string.replace(img.text, 'catalog.jpg', 'zoom.jpg')\n images.append(imgurl)\n\n p = {\n 'name': attrs.find('name').text,\n 'description': attrs.find('short_description').text,\n 'model': sku.find('SellerSku').text,\n 'stocks': int(sku.find('Available').text) or int(sku.find('quantity').text),\n 'price': float(sku.find('price').text),\n 'images': images,\n 'weight': float(sku.find('package_weight').text) or 0.9,\n # 'category': 'PENDING',\n # 'logistics': 'PENDING', # Not in lazada\n }\n items.append(p)\n\n result = self._Request(_LIST_PRODUCTS_ACTION,\n search=model, body_xml_parser=xml_parser)\n if result.error_code:\n raise CommunicationError(\n 'Error communicating: %s' % result.error_description)\n\n items = [x for x in items if x['model'] == model]\n if len(items) == 0:\n raise NotFoundError('No results for %s' % model)\n elif len(items) > 1:\n raise MultipleResultsError('Multiple results for %s' % model)\n\n return items[0]","def top_rated(self, **kwargs):\n\n path = self._get_movie_id_path('top_rated')\n resp = self._get_method(path, kwargs)\n return resp","def get_artists_most_played(session_):\n # artists = session_.query(Artist).order_by(Artist.name.asc()).paginate()\n artists = session_.query(Artist).order_by(Artist.plays.desc()).all()\n return artists","def most_popular(n=5):\n cars = Car.objects.annotate(review_number=models.Count('reviews'))\n sorted_cars = cars.order_by('review_number')\n return sorted_cars[:n]","def top_5_news():\n top_five = []\n\n news = (db.news.find({}).sort([\n ('shares_count', pymongo.DESCENDING),\n ('comments_count', pymongo.DESCENDING),\n ('title', pymongo.ASCENDING)\n ]).limit(5))\n\n for new in news:\n top_five.append((new['title'], new['url']))\n\n return top_five","def products(self):\n response = requests.get(self._url(self._PRODUCTS_PATH), headers=self._headers)\n return response.json()","def test_get_list_most_expensive(self):\n\n expensive_goods_test = self.info_list.get_list_most_expensive()\n most_expensive_test = self.form_expensive_list_goods()\n\n self.assertEqual(expensive_goods_test, most_expensive_test)","def print_top_articles():\n\n create_view_top_articles = (\n \"CREATE VIEW top_articles AS \" +\n \"SELECT COUNT(path) AS num, path \" +\n \"FROM log GROUP BY path ORDER BY num DESC;\")\n get_popular_articles_names = (\n \"SELECT title, num \" +\n \"FROM top_articles, articles \" +\n \"WHERE top_articles.path = '/article/' || articles.slug limit 3;\")\n\n print(\"\\nRunning Task: \" + print_top_articles.__doc__ + \"\\n\")\n\n conn, cur = connect()\n cur.execute(create_view_top_articles)\n cur.execute(get_popular_articles_names)\n results = cur.fetchall()\n\n for title, views in results:\n print('\\t{} - {} views'.format(title, views))\n\n disconnect(conn, cur)","def print_top_articles(popular_articles):\n\n print('\\nThe three top most articles viewed are:\\n')\n for article in popular_articles:\n print(article[0] + '\\t-\\t' + str(article[1]) + ' views \\n')\n print('-------------------------------------------------------\\n')","def getTopStories(self):\n source = self.getSource(\"http://news.ycombinator.com\")\n stories = self.getStories(source)\n return stories","def query_top_recommended(cls,N=10):\n brkey = 'BooksMostRecommended'\n bks = from_cache(brkey)\n if not bks:\n bks = map(lambda e:str(e.id()), SuiBook.all(keys_only=True).order('-recommends').fetch(N))\n to_cache(brkey,bks)\n return bks","def post_get_recent(requst, limit):\n if requst.method == 'GET':\n recent_posts = Post.objects.order_by('-created_at')[:int(limit)]\n TopicNestedSerializer.Meta.depth = 1\n PostNestedSerializer.Meta.depth = 1\n serializer = PostNestedSerializer(recent_posts, many=True)\n return Response(serializer.data)","def get_all_products():\n data = order_obj.get_all_products()\n return data","def get_all_from_top_ten(title,users,max = 3):\n \"\"\" ten prolific users \"\"\"\n \"\"\" max : number of user with related followers \"\"\"\n getAllUsers(users,all_users,users_set,proceeded_users,max,user_cpt,title)\n for data in users_set:\n print(data.id)","def get_queryset(self, *args, **kwargs):\n return Product.objects.featured()","def projects_top_json():\n limit = request.args.get('limit') or 10\n #sort_by_score = request.args.get('score') or False\n #sort_by_update = request.args.get('update') or False\n pp = Project.query \\\n .filter_by(is_hidden=False) \\\n .order_by(Project.progress.desc()) \\\n .limit(limit).all()\n projects = expand_project_urls(\n [p.data for p in pp],\n request.host_url\n )\n return jsonify(projects=projects)","def top_task_element(request):\n if request.method == \"GET\":\n task = Task.objects.filter(status=\"Open\").order_by(\"-timestamp\").first()\n serializer = TaskSerializer(task)\n return Response(serializer.data)","def getPopularArticles():\n db = psycopg2.connect(\"dbname=news\")\n c = db.cursor()\n c.execute(\" select count (*) as views, title from articles \"\n + \"left join \"\n + \"log on concat('/article/', articles.slug) = log.path \"\n + \"group by title order by views desc limit 3\")\n views = c.fetchall()\n db.close()\n return views","def get_popular(self, max_items=None):\n data = [(x[1], x[0]) for x in self.iteritems()]\n data.sort(key=lambda x: (sys.maxint - x[0], x[1]))\n if max_items:\n return data[:max_items]\n else:\n return data","def list_products(self):\n url = self.base_url\n # TODO add filtering support when holvi api supports it\n obdata = self.connection.make_get(url)\n return ProductList(obdata, self)","def get_popular(min_rating=3):\n # gets back a list of movie ids, in descending order of rating\n movie_results = Rating.objects.values('movie__m_id').annotate(rating=Avg('rating'))\\\n .order_by('-rating').filter(rating__gte=min_rating)\n movie_ids = [m['movie__m_id'] for m in movie_results]\n # trickery to make sure they stay in the right order, but get back the entire movie objects.\n # http://stackoverflow.com/questions/4916851/django-get-a-queryset-from-array-of-ids-in-specific-order\n movies = Movie.objects.filter(m_id__in=movie_ids)\n movies_dict = dict([(obj.m_id, obj) for obj in movies])\n sorted_movies = [movies_dict[m_id] for m_id in movie_ids]\n logger.info(sorted_movies)\n logger.info('Found %s popular items based on Videe-o ratings.' % len(sorted_movies))\n return {\n 'items': sorted_movies,\n 'total_items': len(sorted_movies),\n 'total_pages': 1,\n 'current_page': 1\n }","def test_get_top_respects_limit(self):\r\n\r\n ranked_3_app = self.create_app_with_contributors(8, 0, name='three')\r\n ranked_2_app = self.create_app_with_contributors(9, 0, name='two')\r\n ranked_1_app = self.create_app_with_contributors(10, 0, name='one')\r\n ranked_4_app = self.create_app_with_contributors(7, 0, name='four')\r\n\r\n top_apps = cached_apps.get_top(n=2)\r\n\r\n assert len(top_apps) is 2, len(top_apps)"],"string":"[\n \"def top_products():\\n products = product.Product.query. \\\\\\n order_by(desc(product.Product.likes)).all()[:6]\\n context = {\\n 'products': products\\n }\\n return render_template('stores/topstores.html', **context)\",\n \"def top(self, **kwargs):\\n return self.client.api.top(self.id, **kwargs)\",\n \"def topic_get_hot(request, limit):\\n if request.method == 'GET':\\n all_topics = Topic.objects.all()\\n\\n if int(limit) == 0:\\n hot_topics = sorted(all_topics, key=lambda t: -t.hotness)\\n else:\\n hot_topics = sorted(all_topics, key=lambda t: -t.hotness)[:int(limit)]\\n #hot_topics = Topic.objects.order_by('hotness')[:5]\\n TopicNestedSerializer.Meta.depth = 2\\n RelationSerializer.Meta.depth = 1\\n serializer = TopicNestedSerializer(hot_topics, many=True)\\n return Response(serializer.data)\",\n \"def do_top(cs, args):\\n resp, data = cs.repositories.get_top(args.count)\\n utils.print_list(data, ['name', 'count'], sortby='count')\",\n \"def top(self, **kwargs) -> Dict[str, Any]:\",\n \"def __get_top(self, result, top=10):\\n result = result.sort_values(by=\\\"bias_score\\\", ascending=False).drop_duplicates(subset='productid', keep=\\\"first\\\")\\n print(result)\\n result = result[:top].sort_values(by=\\\"final_score\\\", ascending=False).productid\\n\\n return list(result)\",\n \"def topArticles():\\n c = db.cursor()\\n c.execute(\\\"select titles.title, tophits.hits\\\\\\n from tophits, titles\\\\\\n where tophits.path = titles.slug\\\\\\n order by hits desc limit 3;\\\")\\n results = c.fetchall()\\n c.close()\\n return results\",\n \"def print_top_articles():\\n\\n output = get_query_results(\\n '''SELECT articles.title, COUNT(path) AS views\\n FROM articles\\n JOIN log\\n ON log.path=CONCAT('/article/', articles.slug)\\n GROUP BY title\\n ORDER BY views DESC\\n LIMIT 3;'''\\n )\\n print(\\\"\\\\nMost Popular Articles: \\\\n\\\")\\n for title, views in output:\\n print(\\\"\\\\\\\"{}\\\\\\\" -- {} views\\\".format(title, views))\",\n \"def top_ten(request):\\n if request.method == 'GET':\\n movies = Movie.objects.filter(date_of_release__lte=datetime.date.today())\\n movies = movies.order_by('-rating')[:10]\\n serializer = MovieSerializer(movies, many=True)\\n return Response(serializer.data)\",\n \"def get_top_news_and_the_rest(self):\\n queryset = self.news.order_by('-marked', '-publication_date')\\n return queryset.first(), queryset[1:]\",\n \"def query_top_buys(cls,N=20):\\n # NOTE: this is not used currently\\n gds = from_cache('VG_TOP_%d'%N)\\n if not gds:\\n gds = [str(g.id()) for g in SuiGoods.all(keys_only=True).order('-likes').fetch(N)]\\n to_cache('VG_TOP_%d'%N,gds,3600*24)\\n return SuiGoods.load_by_ids(gds)\",\n \"def get_products(self):\\n page = 1\\n out = []\\n while True:\\n resp = self.get_session().Product.find(limit=10,page=page)\\n if not len(resp):\\n return\\n yield resp\\n page += 1\",\n \"def top_products(data_frame):\\n data_frame.loc[:, 'total_payment'] = (data_frame['unit_rental_price']\\n * data_frame['quantity_rented']\\n * data_frame['rental_period_months'])\\n data_set = data_frame.groupby(['product_name']).agg({'total_payment': 'sum'})\\n data_set = data_set.nlargest(10, 'total_payment')\\n return data_set\",\n \"def get_most_popular_articles():\\n\\tdb = psycopg2.connect(database=DBNAME)\\n\\tc = db.cursor()\\n\\tc.execute(\\\"select t2.title, count(*) as total from log as t1,articles as t2 where t1.path=concat('/article/',t2.slug) group by t2.title order by total desc limit 3 ;\\\")\\n\\tdata = c.fetchall()\\n\\tdb.close()\\n\\treturn data\",\n \"def top_news():\\n data = get_top_news()\\n return jsonify(data)\",\n \"def top(self, event, limit=None):\\r\\n params = base.get_params(('event', 'limit'), locals(), serialize_param)\\r\\n\\r\\n request = http.Request('GET', 'events/properties/top/', params)\\r\\n\\r\\n return request, parsers.parse_json\",\n \"def top_ten(subreddit):\\n url = \\\"https://www.reddit.com/r/\\\" + subreddit + \\\"/top/.json\\\"\\n r = requests.get(url,\\n headers={'User-agent': 'norman'},\\n params={\\\"limit\\\": 10},\\n allow_redirects=False)\\n if r.status_code == 200:\\n for dic in r.json().get('data').get('children'):\\n print(dic.get('data').get('title'))\\n else:\\n print(\\\"None\\\")\",\n \"def getProducts():\\n return \\\"http://api.tcgplayer.com/catalog/products\\\"\",\n \"def top_ten(subreddit):\\n\\n if subreddit is None or not isinstance(subreddit, str):\\n print(\\\"None\\\")\\n\\n user_agent = {'User-agent': 'Google Chrome Version 81.0.4044.129'}\\n params = {'limit': 10}\\n url = 'https://www.reddit.com/r/{}/hot/.json'.format(subreddit)\\n\\n response = get(url, headers=user_agent, params=params)\\n all_data = response.json()\\n\\n try:\\n raw1 = all_data.get('data').get('children')\\n\\n for i in raw1:\\n print(i.get('data').get('title'))\\n\\n except:\\n print(\\\"None\\\")\",\n \"def top_ten(subreddit):\\n try:\\n info = requests.get('https://www.reddit.com/r/{}/hot.json?limit=10'\\n .format(subreddit), allow_redirects=False,\\n headers={'User-Agent': 'Custom'}).json().get(\\n 'data').get('children')\\n for child in info:\\n print(child.get('data').get('title'))\\n except:\\n print('None')\",\n \"def top(self):\",\n \"def top_ten(subreddit):\\n\\n limit = \\\"10\\\"\\n\\n url = \\\"https://www.reddit.com/r/{}/hot.json?limit={}\\\".format(subreddit,\\n limit)\\n\\n user_agent = {\\\"User-Agent\\\": \\\"Python\\\"}\\n response = requests.get(url, headers=user_agent, allow_redirects=False)\\n if response.status_code >= 300:\\n print(\\\"None\\\")\\n else:\\n for elem in response.json().get(\\\"data\\\").get(\\\"children\\\"):\\n print(elem.get(\\\"data\\\").get(\\\"title\\\"))\",\n \"def top_ten(subreddit):\\n BASE_URL = 'http://www.reddit.com/r/{}/hot.json'.format(subreddit)\\n head = {'User-Agent': 'Mozilla/5.0'}\\n par = {'limit': 10}\\n\\n r = requests.get(BASE_URL, params=par, headers=head).json()\\n\\n res = r.get('data', {}).get('children', None)\\n\\n if res:\\n for post in res:\\n print(post.get('data').get('title'))\\n else:\\n print(None)\",\n \"def products(self, start=None, limit=None):\\r\\n params = base.get_params(None, locals())\\r\\n url = '{0}/products'.format(self.get_url())\\r\\n return http.Request('GET', url, params), parsers.parse_json\",\n \"def get(self, request, *args, **kwargs):\\n return super(PopularSongsView, self).get(request, query=app.MOST_POPULAR_SONGS, *args, **kwargs)\",\n \"def get_all_products(self):\\n\\t\\tpass\",\n \"def top_ten(subreddit):\\n h = requests.utils.default_headers()\\n h.update({'User-Agent': 'My User Agent 1.0'})\\n url = \\\"https://www.reddit.com/r/{}/hot.json?limit=10\\\".format(subreddit)\\n r = requests.get(url, headers=h).json()\\n result = r.get('data', {}).get('children', [])\\n if not result:\\n print(None)\\n for i in result:\\n print(i.get('data').get('title'))\",\n \"def get_top_featured_entries(number=5):\\n return list(Entry.published.filter(featured=True)[:number])\",\n \"def top_ten(subreddit):\\n header = {\\\"User-Agent\\\": \\\"Holberton\\\"}\\n url = \\\"https://www.reddit.com/r/{}/hot.json?limit=10\\\".format(subreddit)\\n response = requests.get(url, headers=header, allow_redirects=False)\\n if response.status_code == 200:\\n\\n for item in response.json().get(\\\"data\\\", None).get(\\\"children\\\", None):\\n print(item.get(\\\"data\\\", None).get(\\\"title\\\", None))\\n else:\\n print(None)\\n return\",\n \"async def get_most_popular_components(\\n number_of_products: int = 10, db_path: Path = DB_PATH\\n) -> dict:\\n components = await get_query_results(\\\"SELECT * FROM components\\\", db_path=db_path)\\n\\n # Sorting by both counter and name from high to low\\n components.sort(key=lambda x: (x[1], x[0]), reverse=True)\\n\\n popular_components = [\\n {name: count} for name, count in components[0:number_of_products]\\n ]\\n\\n return {\\\"most_popular_components\\\": popular_components}\",\n \"def shop_products(request):\\n\\n shop = Shop.objects.get(user=request.user)\\n products = Products.objects.filter(shop_rel=shop)\\n paginator = pagination.PageNumberPagination()\\n paginator.page_size = 7\\n result_page = paginator.paginate_queryset(products, request=request)\\n serializer = ProductSerializer(result_page, many=True)\\n return paginator.get_paginated_response(serializer.data)\",\n \"def select_most_sold_products_from_personas_vrouwen():\\n return sql_select(\\\"\\\"\\\"SELECT prodid, name,\\n COUNT(*)\\n FROM personas_vrouwen\\n\\n GROUP BY prodid ,name \\n ORDER BY COUNT(*) DESC ; \\\"\\\"\\\")\",\n \"def get_all_top(self):\\n return self.execute(TABELLE['top']['select']['all'])\",\n \"def get_popular_articles():\\n db = psycopg2.connect(database=DBNAME)\\n c = db.cursor()\\n query_popular_articles = \\\"\\\"\\\"\\n SELECT art.title, COUNT(lg.id) as views\\n FROM articles as art\\n JOIN log as lg\\n ON art.slug = substring(lg.path,10)\\n AND lg.status = '200 OK'\\n GROUP BY art.title\\n ORDER BY views desc\\n LIMIT 3; \\\"\\\"\\\"\\n c.execute(query_popular_articles)\\n articles = from_db_cursor(c)\\n db.close()\\n return articles\",\n \"def top_ten(subreddit):\\n req = get(\\n \\\"https://www.reddit.com/r/{}/hot.json\\\".format(subreddit),\\n headers={\\n \\\"User-Agent\\\": \\\"alx_app\\\"},\\n params={\\n \\\"limit\\\": 10},\\n allow_redirects=False)\\n if req.status_code != 200:\\n print(None)\\n else:\\n posts = req.json().get(\\\"data\\\").get(\\\"children\\\")\\n for post in posts:\\n print(post.get(\\\"data\\\").get(\\\"title\\\"))\",\n \"def top(self):\\r\\n return self.topele\",\n \"def select_most_sold_products_from_personas_mannen():\\n return sql_select(\\\"\\\"\\\"SELECT prodid, name,\\n COUNT(*)\\n FROM personas_mannen\\n \\n GROUP BY prodid ,name \\n ORDER BY COUNT(*) DESC ; \\\"\\\"\\\")\",\n \"def top_box(request):\\n if request.method == 'GET':\\n movies = Movie.objects.all()\\n movies = movies.order_by('-box_office')[:10]\\n serializer = MovieSerializer(movies, many=True)\\n return_obj = serializer.data\\n return Response(return_obj)\",\n \"def top_ten(subreddit):\\n headers = {\\\"User-Agent\\\": \\\"Holberton\\\"}\\n url = \\\"https://www.reddit.com/r/{}/hot.json?limit=10\\\".format(\\n subreddit)\\n req = requests.get(url, headers=headers)\\n\\n if req.status_code != 200:\\n print(None)\\n return\\n redit = req.json().get(\\\"data\\\").get(\\\"children\\\")\\n for chil in redit:\\n print(chil.get(\\\"data\\\").get(\\\"title\\\"))\",\n \"def top_ten(subreddit):\\n url = 'https://www.reddit.com/r/{}/hot.json'.format(subreddit)\\n response = get(url, headers=headers, params=query, allow_redirects=False)\\n if response.status_code is not 200:\\n return print('None')\\n\\n r = response.json()\\n data = r.get('data')\\n children = data['children']\\n for item in children:\\n print(item.get('data')['title'])\",\n \"def get_popular_article():\\n query_command = \\\"SELECT * from popular_posts LIMIT 3\\\"\\n query_data = run_query(query_command)\\n return query_data\",\n \"def top_ten(subreddit):\\n\\n user_agent = {'User-agent': 'Mozilla/5.0 (Macintosh; \\\\\\nIntel Mac OS X 10_10_1) AppleWebKit/537.36 (KHTML, like Gecko) \\\\\\nChrome/39.0.2171.95 Safari/537.36'}\\n\\n res = requests.get('https://www.reddit.com/r/{}/hot.json?limit=10'.format(\\n subreddit), headers=user_agent)\\n\\n if res.status_code == 404:\\n print(None)\\n\\n else:\\n for sub in res.json().get(\\\"data\\\").get(\\\"children\\\"):\\n print(sub.get(\\\"data\\\").get(\\\"title\\\"))\",\n \"def top_ten(subreddit):\\n url = \\\"https://api.reddit.com/r/{}/hot?limit=10\\\".format(subreddit)\\n response = requests.get(url, headers={\\\"User-Agent\\\": \\\"Python3\\\"})\\n if str(response) != \\\"<Response [200]>\\\": # response.status_code != 200\\n print(None)\\n return\\n response = response.json()\\n child = response[\\\"data\\\"][\\\"children\\\"]\\n for tittle in child:\\n print(tittle[\\\"data\\\"][\\\"title\\\"])\",\n \"def top_ten(subreddit):\\n url = \\\"https://www.reddit.com/r/\\\" + subreddit + \\\"/hot.json?limit=10\\\"\\n identify = {\\\"User-Agent\\\": \\\"Requests library from Python\\\",\\n \\\"From\\\": \\\"774@holbertonschool.com\\\"}\\n to_print = []\\n hot = requests.get(url, headers=identify, allow_redirects=False)\\n if hot.status_code == 404:\\n print(\\\"None\\\")\\n return 0\\n if hot.status_code == 200:\\n hot = hot.json()\\n hot = hot[\\\"data\\\"]\\n hot = hot[\\\"children\\\"]\\n for items in hot:\\n del items[\\\"kind\\\"]\\n for data in hot:\\n to_print.append(data[\\\"data\\\"])\\n hot = to_print\\n to_print = []\\n for dictio in hot:\\n to_print.append(dictio[\\\"title\\\"])\\n for itera in to_print:\\n print(itera)\",\n \"def products():\\n username = session['username']\\n api_key = session['api_key']\\n url = 'https://consumernotebook.com/api/v1/products/?username={0}&apikey={1}'.format(username, api_key)\\n r = requests.get(url)\\n products = []\\n if r.status_code != 200:\\n error = \\\"{0} error. Are you sure you entered a valid API key?\\\".format(r.status_code)\\n return render_template('products.html', error=error)\\n else:\\n products_json = json.loads(r.content)\\n for product in products_json[u'objects']:\\n products.append(product[u'title'])\\n return render_template('products.html', products=products)\",\n \"def get_top_recipes(df, sort_params=None, count=10):\\n if not sort_params:\\n logging.warning(\\\"Column names to soty by are not defined.\\\")\\n return df\\n\\n return df.sort_values(sort_params[\\\"names\\\"],\\n ascending=sort_params[\\\"order\\\"]).head(count)\",\n \"def top_ten(subreddit):\\n\\n url = \\\"https://www.reddit.com/r/{}/hot.json?limit=10\\\".format(subreddit)\\n headers = {\\\"user-agent\\\": \\\"Mozilla/5.0\\\"}\\n\\n res = requests.get(url, headers=headers, allow_redirects=False)\\n\\n if res.status_code == 200:\\n for post in res.json()['data']['children']:\\n print(post['data']['title'])\\n else:\\n print(None)\",\n \"def all_products_view(request):\\n products = Product.objects.all()\\n paginator = Paginator(products, 6)\\n page_number = request.GET.get('page', 1)\\n page = paginator.page(page_number)\\n\\n \\\"\\\"\\\"render a products html page and within that page we will have access to products, so all_products\\\"\\\"\\\"\\n\\n if page.has_next():\\n\\n next_url = f'?page={page.next_page_number()}'\\n\\n else:\\n\\n next_url = ''\\n\\n if page.has_previous():\\n\\n prev_url = f'?page={page.previous_page_number()}'\\n\\n else:\\n\\n prev_url = ''\\n\\n \\n return render(request, 'products.html', {'page': page, 'next_page_url': next_url, 'prev_page_url': prev_url})\",\n \"def test_top(self):\\n ds_uri = odata.ODataURI(\\\"Orders?$orderby=OrderDate%20desc&$top=10\\\",\\n '/x.svc')\\n top = ds_uri.sys_query_options[odata.SystemQueryOption.top]\\n self.assertTrue(isinstance(top, int), \\\"top type\\\")\\n self.assertTrue(top == 10, \\\"top 10\\\")\\n ds_uri = odata.ODataURI(\\\"Customers('ALFKI')/Orders?$top=10\\\", '/x.svc')\\n top = ds_uri.sys_query_options[odata.SystemQueryOption.top]\\n self.assertTrue(top == 10, \\\"top 10\\\")\\n ds_uri = odata.ODataURI(\\\"Orders?$orderby=OrderDate%20desc&$top=0\\\",\\n '/x.svc')\\n top = ds_uri.sys_query_options[odata.SystemQueryOption.top]\\n self.assertTrue(top == 0, \\\"top 0\\\")\\n try:\\n ds_uri = odata.ODataURI(\\n \\\"Orders?$orderby=OrderDate%20desc&$top=-1\\\", '/x.svc')\\n self.fail(\\\"top=-1\\\")\\n except odata.InvalidSystemQueryOption:\\n pass\",\n \"def top_stories(self, limit=None):\\n return self._get_page('topstories').json()[:limit]\",\n \"def extract(self, filter_by='relevance', all_pages=False, limit=None):\\r\\n\\r\\n page = 1\\r\\n total_of_pages = 1\\r\\n products_list = []\\r\\n\\r\\n while page <= total_of_pages:\\r\\n\\r\\n products_code = None\\r\\n while products_code is None:\\r\\n soup = self.__olx_requests(filter_by, page)\\r\\n products_code = soup.find('div', {'class': \\\"sc-1fcmfeb-0 WQhDk\\\"})\\r\\n\\r\\n if page == 1 and all_pages is True and limit is None:\\r\\n max_pages = self.__number_of_pages(soup)\\r\\n if max_pages is not None:\\r\\n total_of_pages = max_pages\\r\\n\\r\\n elif page == 1 and all_pages is False and isinstance(limit, int):\\r\\n max_pages = self.__number_of_pages(soup)\\r\\n if max_pages is not None and limit <= max_pages:\\r\\n total_of_pages = limit\\r\\n else:\\r\\n total_of_pages = 1\\r\\n\\r\\n # Individual product - TAG\\r\\n for tags_products in [\\\"sc-1fcmfeb-2 ggOGTJ\\\", \\\"sc-1fcmfeb-2 hFOgZc\\\"]:\\r\\n for each_product in products_code.findAll('li', {'class': tags_products}):\\r\\n\\r\\n # Each product dictionary\\r\\n product_dict = {}\\r\\n\\r\\n # Name\\r\\n PRODUCT_NAME_TAG = 'fnmrjs-8 kRlFBv'\\r\\n if each_product.find('div', {'class': PRODUCT_NAME_TAG}) is not None:\\r\\n product_name = each_product.find('div', {'class': PRODUCT_NAME_TAG}).text\\r\\n if 'Anunciante online' in product_name:\\r\\n product_name = product_name.replace('Anunciante online', '')\\r\\n product_dict['Name'] = product_name\\r\\n else:\\r\\n continue\\r\\n\\r\\n # ID\\r\\n product_id = each_product.find('a', {'data-lurker-detail': 'list_id'})['data-lurker_list_id']\\r\\n product_dict['ID'] = product_id\\r\\n\\r\\n # Image\\r\\n PRODUCT_IMAGE_TAG = 'fnmrjs-5 jksoiN'\\r\\n product_img = each_product.find('div', {'class': PRODUCT_IMAGE_TAG}).find('img')['src']\\r\\n product_dict['Image'] = product_img\\r\\n\\r\\n # Price\\r\\n PRODUCT_PRICE_TAG = 'fnmrjs-15 clbSMi'\\r\\n if each_product.find('div', {'class': PRODUCT_PRICE_TAG}).text:\\r\\n product_price = each_product.find('div', {'class': PRODUCT_PRICE_TAG}).text\\r\\n product_dict['Price'] = re.findall('R\\\\$ (\\\\d*,?\\\\.?\\\\d*)|$', product_price)[0].replace('.', '')\\r\\n else:\\r\\n product_dict['Price'] = '-'\\r\\n\\r\\n # Date\\r\\n PRODUCT_DATE_TAG = 'fnmrjs-18 gMKELN'\\r\\n product_date = each_product.find('div', {'class': PRODUCT_DATE_TAG}).text\\r\\n if 'Hoje' in product_date:\\r\\n product_date = product_date.replace('Hoje', datetime.date.today().strftime(\\\"%d/%m \\\"))\\r\\n product_dict['Date'] = product_date\\r\\n\\r\\n elif 'Ontem' in product_date:\\r\\n product_date = product_date.replace('Ontem', (datetime.date.today() - datetime.timedelta(days=1)).strftime(\\\"%d/%m \\\"))\\r\\n product_dict['Date'] = product_date\\r\\n\\r\\n else:\\r\\n product_date = product_date.replace(re.findall(' ([a-z]*)\\\\d*', product_date)[0], months[re.findall(' ([a-z]*)\\\\d*', product_date)[0]]).replace(' ', r'/', 1)\\r\\n product_dict['Date'] = product_date\\r\\n\\r\\n # Location\\r\\n PRODUCT_LOCATION_PARENT_TAG = 'fnmrjs-21 bktOWr'\\r\\n PRODUCT_LOCATION_CHILD_TAG = 'fnmrjs-13 hdwqVC'\\r\\n product_location = each_product.find('div', {'class': PRODUCT_LOCATION_PARENT_TAG}).find('p', {'class': PRODUCT_LOCATION_CHILD_TAG}).text\\r\\n product_dict['City'] = re.findall('(.*\\\\w*),|$', product_location)[0]\\r\\n product_dict['Neighborhood'] = re.findall(r',(.*\\\\w*) - |$', product_location)[0].strip()\\r\\n product_dict['State'] = states_ddd[re.findall(r'DDD (\\\\d*)|$', product_location)[0]]\\r\\n\\r\\n # Link\\r\\n product_link = each_product.find('a', {'data-lurker-detail': 'list_id'})['href']\\r\\n product_dict['Link'] = product_link\\r\\n\\r\\n # List of Products\\r\\n products_list.append(product_dict)\\r\\n\\r\\n # Next page\\r\\n page += 1\\r\\n\\r\\n return products_list\",\n \"def printTopThreeArticles():\\n query = \\\"\\\"\\\"\\n SELECT author_article_popularity_view.article,\\n author_article_popularity_view.views\\n FROM author_article_popularity_view\\n LIMIT 3;\\n \\\"\\\"\\\"\\n cursor = connection.cursor()\\n cursor.execute(query)\\n results = cursor.fetchall()\\n print(\\\"\\\\nTop 3 articles of all time: \\\")\\n for i, result in enumerate(results):\\n print(\\\"{}. \\\\\\\"{}\\\\\\\" - {:,} views\\\".format(i + 1, result[0], result[1]))\",\n \"def get_most_popular(self):\\n\\t\\tpopular_rated = self.data_final[self.data_final['Rating'] == 10]\\n\\t\\tpopular_jokes = popular_rated.groupby('JokeID').count().reset_index()\\n\\t\\tpopular_jokes = popular_jokes[['JokeID','Rating']]\\n\\t\\tpopular_jokes.columns = ['JokeID','Number_rated10']\\n\\t\\ttop_joke = popular_jokes.sort_values(by=['Number_rated10'], ascending=False).head(1)\\n\\t\\ttop_joke_val = top_joke['JokeID'].values[0]\\n\\t\\tjokes_list = sorted(set(self.data_final['JokeID']))\\n\\t\\tjoke_num = jokes_list.index(top_joke_val)\\n\\t\\ttop_joke_desc = self.data_jokes[self.data_jokes['JokeID'] == top_joke_val].values[0][1]\\n\\n\\t\\treturn top_joke_desc, joke_num\",\n \"def get_most_and_least_expensive_high_review_product(df):\\n try:\\n df3 = merge_metadata(df)\\n product_filter = df3['overall'] >= 4.0\\n high_reviewed_products = df3[product_filter]\\n # print high_reviewed_products[:10]\\n # The data contained NaN so we use the nanmax/min funtions to get max/min\\n most_exp = round(np.nanmax(high_reviewed_products['price'])[0], 2)\\n least_exp = round(np.nanmin(high_reviewed_products['price'])[0], 2)\\n\\n most_exp_prod = df3.loc[df3['price'] == most_exp, 'asin'].iloc[0]\\n least_exp_prod = df3.loc[df3['price'] == least_exp, 'asin'].iloc[0]\\n write_text_tofile(\\\"Most Expensive Product: \\\" + str(most_exp_prod) + \\\", Price: \\\" + str(most_exp))\\n write_text_tofile(\\\"Least Expensive Product: \\\" + str(least_exp_prod) + \\\", Price: \\\" + str(least_exp))\\n return {most_exp_prod: most_exp, least_exp_prod: least_exp}\\n except Exception as e:\\n print \\\"Error getting most and least expensive high review product\\\"\\n print str(e)\\n pass\",\n \"def dvds_top_rentals(self, **kwargs):\\n path = self._get_path('dvds_top_rentals')\\n\\n response = self._GET(path, kwargs)\\n self._set_attrs_to_values(response)\\n return response\",\n \"def top_ten(subreddit):\\n\\n settings = {'allow_redirects': False, 'headers': {'User-agent': ''}}\\n url = \\\"https://www.reddit.com/r/{}/hot.json\\\".format(subreddit)\\n\\n try:\\n responses = get(url, **settings).json().get('data').get('children')\\n for post in responses[:10]:\\n print(post['data']['title'])\\n except:\\n print(\\\"None\\\")\",\n \"def get_most_ordered_products(self):\\n return self.products.groupby(\\\"products\\\").agg(\\n {\\\"id\\\": lambda x: len(np.unique(x))}).reset_index().rename(\\n columns={\\\"id\\\": \\\"order_count\\\"}).sort_values(by='order_count', ascending=False)\",\n \"def get(self):\\n return Products().get_all_products()\",\n \"def test_top_songs(self):\\n \\n rss = AppleRSS()\\n objs = rss.get_top_songs(limit=10)\\n \\n self.__test_artists('top_songs', objs)\",\n \"def top_ten(subreddit):\\n url = 'https://api.reddit.com/r/{}/hot.json'.format(subreddit)\\n header = {'User-agent': 'your bot 0.1'}\\n subred = requests.get(url, headers=header, allow_redirects=False)\\n\\n if subred.status_code != 200:\\n print(None)\\n return None\\n\\n try:\\n theme = subred.json()\\n except:\\n print(\\\"Not a valid JSON\\\")\\n return 0\\n\\n try:\\n maindata = theme.get(\\\"data\\\")\\n children = maindata.get(\\\"children\\\")\\n for child in children[:10]:\\n data = child.get(\\\"data\\\")\\n print(data.get(\\\"title\\\"))\\n except:\\n return None\",\n \"def popular_items(self):\\n if self._popular_items is None:\\n self._popular_items = self._get_popular_items(100)\\n return self._popular_items\",\n \"def products(self):\\r\\n return self._products\",\n \"def get_top_stories(self):\\n stories = requests.get(f\\\"{HN_API_URL}/topstories.json\\\")\\n stories_ids = json.loads(stories.text)\\n urls = [f\\\"{HN_API_URL}/item/{story_id}.json\\\" for story_id in stories_ids]\\n fetched_stories = asyncio.run(self.fetch_all(urls))\\n sorted_stories = sorted(\\n fetched_stories, key=lambda k: k[\\\"score\\\"], reverse=True)\\n return sorted_stories[:STORIES_NUMBER]\",\n \"def top_artists_from_API(api_results):\\r\\n df = pd.DataFrame(api_results[\\\"items\\\"])\\r\\n cols = [\\\"name\\\",\\\"id\\\",\\\"genres\\\",\\\"popularity\\\",\\\"uri\\\"]\\r\\n return df[cols]\",\n \"def list_all_products():\\n all_products = []\\n max_pages = 1000\\n page_num = 1\\n while page_num < max_pages:\\n\\n param = {\\n 'per_page': 100,\\n 'page': page_num,\\n }\\n rs_api = woo_request_helper().get_details(wc_endpoint='products', params=param)\\n\\n if rs_api:\\n page_num += 1\\n all_products.extend(rs_api)\\n else:\\n break\\n\\n return all_products\",\n \"def select_most_sold_products_from_personas_kinderen():\\n return sql_select(\\\"\\\"\\\"SELECT prodid, name,\\n COUNT(*)\\n FROM v\\n\\n GROUP BY prodid ,name \\n ORDER BY COUNT(*) DESC ; \\\"\\\"\\\")\",\n \"def top_by_num_of_ratings(self, n):\\n return top_movies\",\n \"def get(self, request, page_no=None):\\n queryset = Productlisting.objects.using('scraped').all()\\n if page_no is None:\\n page_no = 1\\n if page_no <= 0:\\n return Response(\\\"Page Number must be >= 1\\\", status=status.HTTP_400_BAD_REQUEST)\\n \\n ITEMS_PER_PAGE = 10\\n queryset = queryset[(page_no - 1) * ITEMS_PER_PAGE : (page_no) * ITEMS_PER_PAGE]\\n \\n serializer = ProductListingSerializer(queryset, many=True)\\n return Response(serializer.data, status=status.HTTP_200_OK)\",\n \"def list_products(self):\\n return self._make_get_request(self._urls['products'])\",\n \"def product_list(request):\\n error = {\\n 'status': False,\\n 'name': None,\\n 'text': None,\\n 'level': None,\\n 'debug': None\\n }\\n\\n limit, error = get_limit(request, error)\\n\\n serializer = FreshSerializer()\\n queryset = Product.objects.all()[:limit]\\n\\n if not queryset:\\n error = {\\n \\\"status\\\": True,\\n \\\"name\\\": \\\"No Products\\\",\\n \\\"text\\\": \\\"No Products found\\\",\\n \\\"level\\\": \\\"Information\\\",\\n \\\"debug\\\": \\\"\\\"\\n }\\n\\n data = {\\n \\\"products\\\": json.loads(serializer.serialize(queryset)),\\n \\\"error\\\": error\\n }\\n\\n return HttpResponse(json.dumps(data), content_type=\\\"application/json\\\")\",\n \"def get_top_articles(\\n limit: int = 5,\\n date: int = int(datetime.now().strftime(\\\"%Y%m%d\\\"))\\n):\\n\\n res = articles_db.get_top_articles_mongo(\\n articles,\\n limit,\\n date\\n )\\n\\n return res\",\n \"def top_by_ratings(self, n, metric=average):\\n return top_movies\",\n \"def test_get_movie_top_all(self):\\n\\n r = self.client.get(reverse('movieapi:top'))\\n\\n qs = Movie.objects \\\\\\n .annotate(total_comments=Count('comment__comment'),\\n rank=Window(\\n expression=DenseRank(),\\n order_by=F('total_comments').desc(),\\n )\\n ).values('id', 'total_comments', 'rank')\\n\\n serializer = TopSerializer(qs, many=True)\\n\\n self.assertJSONEqual(\\n r.content,\\n serializer.data\\n )\\n self.assertEqual(r.status_code, 200)\",\n \"def get_all_products(access_keeper):\\n logger.debug('getting products...')\\n headers = get_authorization_headers(access_keeper)\\n\\n response = requests.get('https://api.moltin.com/v2/products', headers=headers)\\n raise_response_errors(response)\\n\\n products = response.json()['data']\\n logger.debug(f'{len(products)} products was got')\\n\\n return products\",\n \"def getRecommendedTopics(request, limit):\\n if request.method == 'GET':\\n user = request.user;\\n scores = {};\\n for topic in Topic.objects.all():\\n\\n neighbor_visits = Visit.objects.filter(user=user, topic__relates_to__topic_to=topic)\\n\\n neighbor_visits_count = len(neighbor_visits);\\n if neighbor_visits_count > 0:\\n last_neighbor_visit = neighbor_visits.order_by('-visit_date')[0].visit_date;\\n else:\\n last_neighbor_visit = topic.created_at\\n\\n relevance_score = 5*neighbor_visits_count - (timezone.now()-last_neighbor_visit).total_seconds()/3600\\n recommendation = relevance_score + topic.hotness\\n\\n scores[topic] = recommendation;\\n\\n sorted_scores = sorted(scores.items(), key=operator.itemgetter(1), reverse=True)[:int(limit)]\\n recommended_topics = [key for key, value in sorted_scores]\\n #print(recommended_topics)\\n serializer = TopicNestedSerializer(recommended_topics, many=True);\\n return Response(serializer.data)\",\n \"def get_product(self, page_size=10, pages_number=1):\\n products = []\\n params = self.params.copy()\\n params[\\\"page_size\\\"] = page_size\\n\\n try:\\n response = requests.get(self.url, params=params, timeout=3)\\n response.json()\\n except requests.ConnectionError:\\n print(\\\"Error when fetching the API\\\")\\n for i in range(pages_number):\\n params[\\\"page\\\"] = i + 1\\n response = requests.get(self.url, params=params)\\n if response.status_code == 200:\\n products.extend(response.json()[\\\"products\\\"])\\n return products\",\n \"def get_topograms(self):\\n return self.make_request(\\\"GET\\\", \\\"topograms\\\", {})\",\n \"def product_management():\\n sort_by = request.args.get(\\\"sort\\\")\\n\\n \\\"\\\"\\\"\\n Sort method is from https://docs.mongodb.com/manual/reference/\\n method/cursor.sort/index.html\\n \\\"\\\"\\\"\\n if sort_by:\\n products = list(mongo.db.products.find().sort(sort_items(sort_by)))\\n\\n else:\\n products = list(mongo.db.products.find().sort('name', 1))\\n\\n \\\"\\\"\\\"\\n Pagination code is from https://gist.github.com/mozillazg/\\n 69fb40067ae6d80386e10e105e6803c9\\n \\\"\\\"\\\"\\n page, per_page, offset = get_page_args(\\n page_parameter='page', per_page_parameter='per_page', per_page=10)\\n pagination_products = paginate_items(products, offset, per_page)\\n pagination = paginate(products, page, per_page)\\n\\n return render_template(\\n \\\"product_management.html\\\",\\n page_title=\\\"Product Management\\\",\\n products=pagination_products,\\n pagination=pagination)\",\n \"def GetProductDirect(self, model):\\n items = []\\n\\n def xml_parser(body_xml_etree):\\n for product in body_xml_etree.find('Products').findall('Product'):\\n sku = product.find('Skus').find('Sku')\\n attrs = product.find('Attributes')\\n\\n images = []\\n for img in sku.find('Images').findall('Image'):\\n if img.text:\\n imgurl = string.replace(img.text, 'catalog.jpg', 'zoom.jpg')\\n images.append(imgurl)\\n\\n p = {\\n 'name': attrs.find('name').text,\\n 'description': attrs.find('short_description').text,\\n 'model': sku.find('SellerSku').text,\\n 'stocks': int(sku.find('Available').text) or int(sku.find('quantity').text),\\n 'price': float(sku.find('price').text),\\n 'images': images,\\n 'weight': float(sku.find('package_weight').text) or 0.9,\\n # 'category': 'PENDING',\\n # 'logistics': 'PENDING', # Not in lazada\\n }\\n items.append(p)\\n\\n result = self._Request(_LIST_PRODUCTS_ACTION,\\n search=model, body_xml_parser=xml_parser)\\n if result.error_code:\\n raise CommunicationError(\\n 'Error communicating: %s' % result.error_description)\\n\\n items = [x for x in items if x['model'] == model]\\n if len(items) == 0:\\n raise NotFoundError('No results for %s' % model)\\n elif len(items) > 1:\\n raise MultipleResultsError('Multiple results for %s' % model)\\n\\n return items[0]\",\n \"def top_rated(self, **kwargs):\\n\\n path = self._get_movie_id_path('top_rated')\\n resp = self._get_method(path, kwargs)\\n return resp\",\n \"def get_artists_most_played(session_):\\n # artists = session_.query(Artist).order_by(Artist.name.asc()).paginate()\\n artists = session_.query(Artist).order_by(Artist.plays.desc()).all()\\n return artists\",\n \"def most_popular(n=5):\\n cars = Car.objects.annotate(review_number=models.Count('reviews'))\\n sorted_cars = cars.order_by('review_number')\\n return sorted_cars[:n]\",\n \"def top_5_news():\\n top_five = []\\n\\n news = (db.news.find({}).sort([\\n ('shares_count', pymongo.DESCENDING),\\n ('comments_count', pymongo.DESCENDING),\\n ('title', pymongo.ASCENDING)\\n ]).limit(5))\\n\\n for new in news:\\n top_five.append((new['title'], new['url']))\\n\\n return top_five\",\n \"def products(self):\\n response = requests.get(self._url(self._PRODUCTS_PATH), headers=self._headers)\\n return response.json()\",\n \"def test_get_list_most_expensive(self):\\n\\n expensive_goods_test = self.info_list.get_list_most_expensive()\\n most_expensive_test = self.form_expensive_list_goods()\\n\\n self.assertEqual(expensive_goods_test, most_expensive_test)\",\n \"def print_top_articles():\\n\\n create_view_top_articles = (\\n \\\"CREATE VIEW top_articles AS \\\" +\\n \\\"SELECT COUNT(path) AS num, path \\\" +\\n \\\"FROM log GROUP BY path ORDER BY num DESC;\\\")\\n get_popular_articles_names = (\\n \\\"SELECT title, num \\\" +\\n \\\"FROM top_articles, articles \\\" +\\n \\\"WHERE top_articles.path = '/article/' || articles.slug limit 3;\\\")\\n\\n print(\\\"\\\\nRunning Task: \\\" + print_top_articles.__doc__ + \\\"\\\\n\\\")\\n\\n conn, cur = connect()\\n cur.execute(create_view_top_articles)\\n cur.execute(get_popular_articles_names)\\n results = cur.fetchall()\\n\\n for title, views in results:\\n print('\\\\t{} - {} views'.format(title, views))\\n\\n disconnect(conn, cur)\",\n \"def print_top_articles(popular_articles):\\n\\n print('\\\\nThe three top most articles viewed are:\\\\n')\\n for article in popular_articles:\\n print(article[0] + '\\\\t-\\\\t' + str(article[1]) + ' views \\\\n')\\n print('-------------------------------------------------------\\\\n')\",\n \"def getTopStories(self):\\n source = self.getSource(\\\"http://news.ycombinator.com\\\")\\n stories = self.getStories(source)\\n return stories\",\n \"def query_top_recommended(cls,N=10):\\n brkey = 'BooksMostRecommended'\\n bks = from_cache(brkey)\\n if not bks:\\n bks = map(lambda e:str(e.id()), SuiBook.all(keys_only=True).order('-recommends').fetch(N))\\n to_cache(brkey,bks)\\n return bks\",\n \"def post_get_recent(requst, limit):\\n if requst.method == 'GET':\\n recent_posts = Post.objects.order_by('-created_at')[:int(limit)]\\n TopicNestedSerializer.Meta.depth = 1\\n PostNestedSerializer.Meta.depth = 1\\n serializer = PostNestedSerializer(recent_posts, many=True)\\n return Response(serializer.data)\",\n \"def get_all_products():\\n data = order_obj.get_all_products()\\n return data\",\n \"def get_all_from_top_ten(title,users,max = 3):\\n \\\"\\\"\\\" ten prolific users \\\"\\\"\\\"\\n \\\"\\\"\\\" max : number of user with related followers \\\"\\\"\\\"\\n getAllUsers(users,all_users,users_set,proceeded_users,max,user_cpt,title)\\n for data in users_set:\\n print(data.id)\",\n \"def get_queryset(self, *args, **kwargs):\\n return Product.objects.featured()\",\n \"def projects_top_json():\\n limit = request.args.get('limit') or 10\\n #sort_by_score = request.args.get('score') or False\\n #sort_by_update = request.args.get('update') or False\\n pp = Project.query \\\\\\n .filter_by(is_hidden=False) \\\\\\n .order_by(Project.progress.desc()) \\\\\\n .limit(limit).all()\\n projects = expand_project_urls(\\n [p.data for p in pp],\\n request.host_url\\n )\\n return jsonify(projects=projects)\",\n \"def top_task_element(request):\\n if request.method == \\\"GET\\\":\\n task = Task.objects.filter(status=\\\"Open\\\").order_by(\\\"-timestamp\\\").first()\\n serializer = TaskSerializer(task)\\n return Response(serializer.data)\",\n \"def getPopularArticles():\\n db = psycopg2.connect(\\\"dbname=news\\\")\\n c = db.cursor()\\n c.execute(\\\" select count (*) as views, title from articles \\\"\\n + \\\"left join \\\"\\n + \\\"log on concat('/article/', articles.slug) = log.path \\\"\\n + \\\"group by title order by views desc limit 3\\\")\\n views = c.fetchall()\\n db.close()\\n return views\",\n \"def get_popular(self, max_items=None):\\n data = [(x[1], x[0]) for x in self.iteritems()]\\n data.sort(key=lambda x: (sys.maxint - x[0], x[1]))\\n if max_items:\\n return data[:max_items]\\n else:\\n return data\",\n \"def list_products(self):\\n url = self.base_url\\n # TODO add filtering support when holvi api supports it\\n obdata = self.connection.make_get(url)\\n return ProductList(obdata, self)\",\n \"def get_popular(min_rating=3):\\n # gets back a list of movie ids, in descending order of rating\\n movie_results = Rating.objects.values('movie__m_id').annotate(rating=Avg('rating'))\\\\\\n .order_by('-rating').filter(rating__gte=min_rating)\\n movie_ids = [m['movie__m_id'] for m in movie_results]\\n # trickery to make sure they stay in the right order, but get back the entire movie objects.\\n # http://stackoverflow.com/questions/4916851/django-get-a-queryset-from-array-of-ids-in-specific-order\\n movies = Movie.objects.filter(m_id__in=movie_ids)\\n movies_dict = dict([(obj.m_id, obj) for obj in movies])\\n sorted_movies = [movies_dict[m_id] for m_id in movie_ids]\\n logger.info(sorted_movies)\\n logger.info('Found %s popular items based on Videe-o ratings.' % len(sorted_movies))\\n return {\\n 'items': sorted_movies,\\n 'total_items': len(sorted_movies),\\n 'total_pages': 1,\\n 'current_page': 1\\n }\",\n \"def test_get_top_respects_limit(self):\\r\\n\\r\\n ranked_3_app = self.create_app_with_contributors(8, 0, name='three')\\r\\n ranked_2_app = self.create_app_with_contributors(9, 0, name='two')\\r\\n ranked_1_app = self.create_app_with_contributors(10, 0, name='one')\\r\\n ranked_4_app = self.create_app_with_contributors(7, 0, name='four')\\r\\n\\r\\n top_apps = cached_apps.get_top(n=2)\\r\\n\\r\\n assert len(top_apps) is 2, len(top_apps)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7941322","0.68568563","0.6373423","0.63633126","0.6357755","0.63397264","0.6330925","0.62385094","0.62217975","0.6208066","0.61090535","0.6041962","0.6031527","0.6023199","0.60011786","0.59878904","0.5984639","0.59839493","0.59718734","0.59687245","0.59662586","0.59646875","0.59508514","0.5925802","0.59254956","0.5921282","0.5916909","0.59142107","0.59126365","0.589961","0.5881503","0.58794093","0.5870695","0.5864333","0.58586586","0.5858052","0.5850254","0.58312696","0.5831195","0.58290887","0.5826529","0.58202696","0.5801205","0.580097","0.57956237","0.5766108","0.5754258","0.571169","0.57093","0.5700915","0.5697379","0.5692087","0.56880873","0.568724","0.56862175","0.5684806","0.5678937","0.5667061","0.56630296","0.56603193","0.5659301","0.56545013","0.56489843","0.5648667","0.5639113","0.563806","0.563775","0.5636046","0.5632509","0.56254023","0.5624369","0.5620258","0.56125367","0.56105345","0.56037897","0.55905354","0.558881","0.55849206","0.55777436","0.55737597","0.5565117","0.5561046","0.5555038","0.5554256","0.5548788","0.554668","0.55435795","0.5527365","0.55258316","0.55249375","0.5524234","0.55230737","0.5507274","0.5504133","0.5503758","0.5503613","0.54898167","0.5487101","0.5484287","0.54773396"],"string":"[\n \"0.7941322\",\n \"0.68568563\",\n \"0.6373423\",\n \"0.63633126\",\n \"0.6357755\",\n \"0.63397264\",\n \"0.6330925\",\n \"0.62385094\",\n \"0.62217975\",\n \"0.6208066\",\n \"0.61090535\",\n \"0.6041962\",\n \"0.6031527\",\n \"0.6023199\",\n \"0.60011786\",\n \"0.59878904\",\n \"0.5984639\",\n \"0.59839493\",\n \"0.59718734\",\n \"0.59687245\",\n \"0.59662586\",\n \"0.59646875\",\n \"0.59508514\",\n \"0.5925802\",\n \"0.59254956\",\n \"0.5921282\",\n \"0.5916909\",\n \"0.59142107\",\n \"0.59126365\",\n \"0.589961\",\n \"0.5881503\",\n \"0.58794093\",\n \"0.5870695\",\n \"0.5864333\",\n \"0.58586586\",\n \"0.5858052\",\n \"0.5850254\",\n \"0.58312696\",\n \"0.5831195\",\n \"0.58290887\",\n \"0.5826529\",\n \"0.58202696\",\n \"0.5801205\",\n \"0.580097\",\n \"0.57956237\",\n \"0.5766108\",\n \"0.5754258\",\n \"0.571169\",\n \"0.57093\",\n \"0.5700915\",\n \"0.5697379\",\n \"0.5692087\",\n \"0.56880873\",\n \"0.568724\",\n \"0.56862175\",\n \"0.5684806\",\n \"0.5678937\",\n \"0.5667061\",\n \"0.56630296\",\n \"0.56603193\",\n \"0.5659301\",\n \"0.56545013\",\n \"0.56489843\",\n \"0.5648667\",\n \"0.5639113\",\n \"0.563806\",\n \"0.563775\",\n \"0.5636046\",\n \"0.5632509\",\n \"0.56254023\",\n \"0.5624369\",\n \"0.5620258\",\n \"0.56125367\",\n \"0.56105345\",\n \"0.56037897\",\n \"0.55905354\",\n \"0.558881\",\n \"0.55849206\",\n \"0.55777436\",\n \"0.55737597\",\n \"0.5565117\",\n \"0.5561046\",\n \"0.5555038\",\n \"0.5554256\",\n \"0.5548788\",\n \"0.554668\",\n \"0.55435795\",\n \"0.5527365\",\n \"0.55258316\",\n \"0.55249375\",\n \"0.5524234\",\n \"0.55230737\",\n \"0.5507274\",\n \"0.5504133\",\n \"0.5503758\",\n \"0.5503613\",\n \"0.54898167\",\n \"0.5487101\",\n \"0.5484287\",\n \"0.54773396\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.68398494"},"document_rank":{"kind":"string","value":"2"}}},{"rowIdx":75586,"cells":{"query":{"kind":"string","value":"This is prediction method."},"document":{"kind":"string","value":"def predict(self, products: list):\n try:\n self.log.info(\"Prediction is started.\")\n\n contentScores: list = self.content_score_repository.get_by_products(products)\n\n df = self.__fillScores(contentScores)\n\n result = self.__get_top(df)\n self.log.info(\"Top values are calculated.\")\n\n self.log.info(\"Prediction is finished.\")\n\n return result\n\n except Exception as ex:\n msg: str = \"Undefined error is occured in Hybrid recommender service. \"\n self.log.error(msg + \"err: {}\".format(ex))\n raise HybridRecommenderServiceException(msg)"},"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_prediction(self):\n raise NotImplementedError","def predict(self):\n raise NotImplementedError","def predict_proba(self):\n ...","def _predict(self, x):\n pass","def _predict(self, testX):\n pass","def predict(self, predPoints=None):","def predict_only(self):","def predict(self, X):\n ...","def predict(self, X):\n ...","def predict(self, X):\n ...","def prediction(self, v, imu_meas):\n # YOUR CODE HERE\n pass","def predict(self, instances):\r\n raise NotImplementedError","def predict(self, X):","def predict(self, X):","def _predict(self, X):\n raise NotImplementedError","def predict(self, data):\n\t\traise NotImplementedError","def run_prediction(self):\r\n self.get_prediction_indices()\r\n self.walk_forward_prediction()","def predict(self, src): # real signature unknown; restored from __doc__\n pass","def predict():\n import trace\n trace.predict()","def make_predictions(self):\n if is_classification(self.model):\n if self.ct == None:\n prediction = self.model.predict(self.input_data.to_numpy())\n probabilities = self.model.predict_proba(self.input_data.to_numpy())\n return prediction, probabilities\n elif self.ct != None: \n prediction = self.model.predict(self.data_into_model())\n probabilities = self.model.predict_proba(self.data_into_model())\n return prediction, probabilities\n else:\n raise Exception((\"{} not supported. Please create an issue on Github\").format(self.model))\n \n else:\n if self.ct == None:\n prediction = self.model.predict(self.input_data)\n return prediction\n elif self.ct != None: \n prediction = self.model.predict(self.data_into_model())\n return prediction\n else:\n raise Exception((\"{} not supported. Please create an issue on Github\").format(self.self.model))","def predict(self):\n for track in self.tracks:\n track.predict(self.kf)\n #track.du_doan(self.kf_test)","def predict(self, x):\n \n\n return predictions","def predict(self, xs, **kwargs):","def predict(self): \n return self.model.predict(self.test_x)","def predict(self, x):\n raise NotImplementedError(\"Please Implement this method\")","def predict(self, obs):\n pass","def predict(self, **kwargs):\n raise NotImplementedError","def predict(self, first_preprocessed_inputs,second_preprocessed_inputs,third_preprocessed_inputs):\r\n pass","def predict(self, X):\n pass","def predict(self, X):\n pass","def predict(self, X):\n pass","def predict(self, X):\n pred = np.zeros(X.shape[0])\n ### YOUR CODE HERE 1-3 lines\n probabilities = np.array([model.probability(X) for model in self.models])\n pred=np.argmax(probabilities, axis=0)\n ### END CODE\n assert pred.shape == (X.shape[0],)\n return pred","def predict(self, X, a, b):\n pass","def predict(self, X):\n # ! You could simply call the hypothesis here\n predictions= self._hypothesis(X)\n #empty_predictions = np.zeros((1,X.shape[1]))\n return predictions","def prediction_a(self):\n return self._prediction_a","def get_predictions():\n\n print(\"OK1\");\n print(\"OK2\");\n return;","def evaluate(self, prediction_fn):\n pass","def predict(self, data):\n return self.result.predict(data)","def predict(self, review):\n raise NotImplementedError","def get_prediction(self, x):\n \"*** YOUR CODE HERE ***\"\n dotProduct = nn.as_scalar(self.run(x))\n if dotProduct < 0:\n return -1\n else:\n return 1","def predict(self,X): \n return self._predict(X)","def prediction():\r\n\r\n\r\n\tpredictVal = []\r\n\taccuracy = 0.0\r\n\r\n\t# Calculate accuracy for each class in testData\r\n\tfor item in testData:\r\n\t\tclass0Prediction = posProb / 100\r\n\t\tclass1Prediction = negProb / 100\r\n\t\t\r\n\t\t# Multiply the prior probablities for negative and positive reviews by their feature likelihoods \r\n\t\tfor word in item[2]:\r\n\t\t\tclass0Prediction *= class0Dict[word]\r\n\t\t\tclass1Prediction *= class1Dict[word]\r\n\r\n\t\t# Give every item in testData a predicted value\r\n\t\tif(class0Prediction > class1Prediction):\r\n\t\t\tpredictVal.append('0')\r\n\t\telse:\r\n\t\t\tpredictVal.append('1')\r\n\r\n\tfor i in range(len(testData)):\r\n\t\tif(testData[i][1] == predictVal[i]):\r\n\t\t\taccuracy += 1\r\n\r\n\t\t\t\r\n\taccuracy = 100 * (accuracy / len(testData))\r\n\treturn(predictVal, accuracy)","def predict(self, data_in):\n pass","def predict(self):\n if ((self.kf.x[6] + self.kf.x[2]) <= 0):\n self.kf.x[6] *= 0.0\n self.kf.predict()\n self.age += 1\n if (self.time_since_update > 0):\n self.hit_streak = 0\n self.time_since_update += 1\n self.history.append(convert_x_to_bbox(self.kf.x))\n return self.history[-1]","def prediction_b(self):\r\n return self._prediction_b","def predict(self):\n self.kf.predict()\n self.nb_kf_pred += 1\n if self.time_since_update > 0:\n self.hit_streak = 0\n self.time_since_update += 1\n self.history.append(self.kf.x[:2].reshape(-1))\n return self.history[-1]","def getPredictions(self):\n\t\tself.bestLabel = self.testingProbs.apply(lambda x: x.argmax(),1)","def predict(self, params, exog=None, *args, **kwargs):\n raise NotImplementedError # pragma: no cover","def get_prediction(self, x):\n \"*** YOUR CODE HERE question 1 ***\"\n dProduct = nn.as_scalar(self.run(x))\n\n if dProduct < 0:\n return -1\n else:\n return 1","def predict(self, testData=[]):\n result = []\n for classValue in self._classAttrs:\n #print(f'Computing Label: {classValue}, {self._classLabelMap[classValue]}')\n result.append(self._computeCondProb(testData, classValue))\n return self._classLabelMap[result.index(max(result))]","def predict(self, X):\r\n return 1 if self.predict_prob(X) > 0.5 else 0","def predict(self):\n for track in self.tracks:\n track.predict(self.kf)","def predict(self):\n for track in self.tracks:\n track.predict(self.kf)","def predict(self):\n for track in self.tracks:\n track.predict(self.kf)","def predict(self):\n\n self.eval()\n return self.forward(self.data)","def predict(self, seq):\n raise Exception(\"You cannot predict with a base predictor.\")","def predict(probs):\n # Your code here.\n return np.argmax(probs, axis=1)","def calculatePrediction(self, a, X_train,x, t_train):\n pass","def get_prediction(self):\n return self.prediction","def doPredict(self, data: StockData) -> float:\r\n pass","def predict(self, X):\n raise NotImplementedError","def predict(self):\n return self.test_fn(self.gX, self.hX, self.sym_g, self.sym_h)","def get_prediction(self, x):\n \"*** YOUR CODE HERE ***\"\n if (nn.as_scalar(self.run(x)) >= 0.0):\n return 1\n return -1","def postprocess(self, prediction_dict, **params):\r\n pass","def predictions(self):\n return self._pred","def test_predict(self):\n\n docs = self.docs\n for m in self.models:\n preds = m.predict(docs)\n self.assertTrue(isinstance(preds, turicreate.SArray))\n self.assertEqual(len(preds), len(docs))\n self.assertEqual(preds.dtype, int)\n\n preds = m.predict(docs, output_type=\"probability\")\n self.assertTrue(isinstance(preds, turicreate.SArray))\n self.assertTrue(len(preds) == len(docs))\n s = preds.apply(lambda x: sum(x))\n self.assertTrue((s.apply(lambda x: abs(x - 1)) < 0.000001).all())\n\n # Test predictions when docs have new words\n new_docs = turicreate.SArray([{\"-1,-1\": 3.0, \"0,4\": 5.0, \"0,3\": 2.0}])\n preds = m.predict(new_docs)\n self.assertEqual(len(preds), len(new_docs))\n\n # Test additional burnin. Ideally we could show that things\n # converge as you increase burnin.\n preds_no_burnin = m.predict(docs, output_type=\"probability\", num_burnin=0)\n self.assertEqual(len(preds_no_burnin), len(docs))","def predict(self,X):\n return self.classifier.predict(X)","def predict(self,X):\n return self.classifier.predict(X)","def predict(self,X):\n return self.classifier.predict(X)","def predict(self,X):\n return self.classifier.predict(X)","def predict(self, model, x_test):\n pass","def predict(self, data: List):","def predict(self, X):\n raise NotImplementedError('Abstract method \"predict\" must be '\n 'specialised!')","def predict(self, model, context, data):\n pass","def predict_step(self, x):\n\n input_x = self.session.graph.get_operation_by_name(\"input_x\").outputs[0]\n predictions_op = self.session.graph.get_operation_by_name(\"output/predictions\").outputs[0] \n\n d_ = {\n input_x: x\n }\n\n self.init_dataset(d_)\n\n return self.session.run([predictions_op])","def _predict(self,\n X=None):\n\n if X is None:\n ypred = self.clf_pipeline.predict(self.Xtrain)\n else:\n ypred = self.clf_pipeline.predict(X)\n\n return ypred","def predict_and_update(self, z):","def prediction(self, x):\n t = self.model.predict(x)\n return t","def predict(self):\n prediction = np.multiply(self.alpha_vec, self.label[:,np.newaxis]).T\n pred = np.dot(prediction, np.inner(self.train_data, self.train_data)) + self.intercept\n self.prediction = np.sign(pred)\n return(self.prediction)","def predict(self, datafile):","def __do_predict(self, request, features):\n dmp_predictor.DmpPredictor().predict(request, features)\n\n return defines.ReturnCode.SUCC","def predict ( self, X: np.ndarray ):\n \n return self.predict_probability ( X = X )\n # End predict()","def predict(self, point):\n # TODO\n temp = 0\n for j in range(len(self.data.labels)):\n temp = temp + self.alpha[j]*self.data.labels[j]*self.kf(point, self.data.features[j])\n pred_temp = temp\n if pred_temp <= 0:\n pred = -1\n else:\n pred = 1\n # print(pred_temp)\n return pred","def fit_predict(self):\n raise AttributeError","def image_new_decision_function_predict(self, img_conv4_result, img_label):\r\n \r\n print(\"***Start new decision function prediction***\")\r\n y_hat_submodel = np.argmax(self.sub_model.predict(img_conv4_result), axis=1)\r\n \r\n poison_index = np.where(y_hat_submodel != img_label)\r\n y_hat = y_hat_submodel\r\n y_hat[poison_index] = self.num_class\r\n print(\"***Predict finish***\")\r\n \r\n return y_hat","def test_predict(self):\n self.regression_single.predict(self.X_test)\n self.assertTrue(len(self.regression_single.y_pred))\n self.regression_boston.predict(self.boston_x_test)\n self.assertTrue(len(self.regression_boston.y_pred))","def test_predict(self):\n \n\n model ,vec, x_testing=setup_log_reg_classifier(self.training_data, self.training_y, self.testing_data,\"text\", method=\"count\")\n \n model2 ,vec_tfidf, x_testing2=setup_log_reg_classifier(self.training_data, self.training_y, self.testing_data,\"text\", method=\"tfidf\")\n \n \n \"\"\" Test correct data types and corrrect range of predicted values (1,0) for predict with countVectorizer\"\"\" \n \n self.assertIsInstance(predict(model,x_testing),\n np.ndarray)\n \n self.assertTrue(([0,1] ==np.unique(predict(model2,x_testing2))).all())\n\n \n \"\"\" Test correct data types and corrrect range of predicted values (1,0) for predict with tfidfVectorizer\"\"\" \n \n self.assertIsInstance(predict(model,x_testing),\n np.ndarray)\n \n self.assertTrue(([0,1] ==np.unique(predict(model2,x_testing2))).all())","def predict(self, data):\n\t\tres = self.neuralNetworks.inference(self.dataCenter.process_inference_data(data))\n\t\tprint(res)","def get_prediction(self, datapoints, employee, location, device, next_risk):\n raise NotImplementedError","def prediction_prob(self):\n return self.a[np.arange(self.a.shape[0]), self.prediction]","def predict(self,x):\n return self._model.predict(x)","def _predict(self):\n \n # Initialization\n x_k = np.random.rand(4,0)\n w_k = np.ones((1,0))\n \n self.reset(phd_only=True)\n \n # Prediction of the targets' positions\n for k in range(1, self.n_time_steps+1):\n\n if k in self.observed_data.keys():\n\n # perform phd filter\n\n x_k1, w_k1, estimated_x_k1, n_targ_pred = self.PHDfilter(x_k, w_k, self.observed_data[k], k)\n\n # save predicted positions and update parameters\n self.phd_filter['n_targets_predicted'][k] = n_targ_pred\n self.phd_filter['particles_positions'][k] = x_k1\n if estimated_x_k1 is not None:\n self.phd_filter['estimated_positions'][k] = estimated_x_k1\n x_k, w_k = np.copy(x_k1), np.copy(w_k1)\n\n else:\n self.phd_filter['n_targets_predicted'][k] = 0","def _predict(self, dataset):\n binary_predictions = ProxyClassifier._predict(self, dataset)\n self.ca.estimates = binary_predictions\n predictions = [ {-1: self.__predictneg,\n +1: self.__predictpos}[x] for x in binary_predictions]\n self.ca.predictions = predictions\n return predictions","def prediction_fn(self, yhat):\n raise NotImplementedError","def predict(self, x):\n\n pred = self.decision_function(x)\n return pred","def predict(self, input_data):\n if not self.predict_as_probability_:\n return self.ensemble_model_.predict(input_data)\n else:\n return self.ensemble_model_.predict_proba(input_data)","def get_predictions (self, x): \n # Here is where you write a code to evaluate the data and produce predictions.\n w_i_1, w_i_2, w_i_3 = self.params\n\n l1_1 = sigmoid(np.dot(self.x, w_i_1))\n l1_2 = sigmoid(np.dot(self.x, w_i_2))\n x_new = np.concatenate(l1_1,l1_2)\n output = sigmoid(np.dot(x_new, w_i_3))\n print output\n print \"hi\"\n return output","def predict(self, X):\n return self.decision_function(X)","def predict(self, test_data):\r\n return self.gs.predict(test_data)","def predict(self, testing_set):\r\n # Run prediction by multiply inputs with the weight and map it\r\n # Through the activation function\r\n final_prob = 0\r\n probability = self.activation(self.weighted_sum(testing_set))\r\n prediction = self.threshold(probability)\r\n if prediction == 1:\r\n final_prob = probability\r\n else:\r\n final_prob = 1 - probability\r\n return [prediction, final_prob]","def do_predict(self):\n answer = []\n response = []\n\n for it_predictions in json.loads(request.data.decode('UTF-8')):\n prediction = it_predictions['score']\n for ite_clf in g_list_of_classifier:\n answer.append(ite_clf.predict(prediction))\n if answer.count(True) > answer.count(False):\n response.append({'answer' : True})\n else:\n response.append({'answer' : False})\n return json.dumps(response, indent=4)"],"string":"[\n \"def _get_prediction(self):\\n raise NotImplementedError\",\n \"def predict(self):\\n raise NotImplementedError\",\n \"def predict_proba(self):\\n ...\",\n \"def _predict(self, x):\\n pass\",\n \"def _predict(self, testX):\\n pass\",\n \"def predict(self, predPoints=None):\",\n \"def predict_only(self):\",\n \"def predict(self, X):\\n ...\",\n \"def predict(self, X):\\n ...\",\n \"def predict(self, X):\\n ...\",\n \"def prediction(self, v, imu_meas):\\n # YOUR CODE HERE\\n pass\",\n \"def predict(self, instances):\\r\\n raise NotImplementedError\",\n \"def predict(self, X):\",\n \"def predict(self, X):\",\n \"def _predict(self, X):\\n raise NotImplementedError\",\n \"def predict(self, data):\\n\\t\\traise NotImplementedError\",\n \"def run_prediction(self):\\r\\n self.get_prediction_indices()\\r\\n self.walk_forward_prediction()\",\n \"def predict(self, src): # real signature unknown; restored from __doc__\\n pass\",\n \"def predict():\\n import trace\\n trace.predict()\",\n \"def make_predictions(self):\\n if is_classification(self.model):\\n if self.ct == None:\\n prediction = self.model.predict(self.input_data.to_numpy())\\n probabilities = self.model.predict_proba(self.input_data.to_numpy())\\n return prediction, probabilities\\n elif self.ct != None: \\n prediction = self.model.predict(self.data_into_model())\\n probabilities = self.model.predict_proba(self.data_into_model())\\n return prediction, probabilities\\n else:\\n raise Exception((\\\"{} not supported. Please create an issue on Github\\\").format(self.model))\\n \\n else:\\n if self.ct == None:\\n prediction = self.model.predict(self.input_data)\\n return prediction\\n elif self.ct != None: \\n prediction = self.model.predict(self.data_into_model())\\n return prediction\\n else:\\n raise Exception((\\\"{} not supported. Please create an issue on Github\\\").format(self.self.model))\",\n \"def predict(self):\\n for track in self.tracks:\\n track.predict(self.kf)\\n #track.du_doan(self.kf_test)\",\n \"def predict(self, x):\\n \\n\\n return predictions\",\n \"def predict(self, xs, **kwargs):\",\n \"def predict(self): \\n return self.model.predict(self.test_x)\",\n \"def predict(self, x):\\n raise NotImplementedError(\\\"Please Implement this method\\\")\",\n \"def predict(self, obs):\\n pass\",\n \"def predict(self, **kwargs):\\n raise NotImplementedError\",\n \"def predict(self, first_preprocessed_inputs,second_preprocessed_inputs,third_preprocessed_inputs):\\r\\n pass\",\n \"def predict(self, X):\\n pass\",\n \"def predict(self, X):\\n pass\",\n \"def predict(self, X):\\n pass\",\n \"def predict(self, X):\\n pred = np.zeros(X.shape[0])\\n ### YOUR CODE HERE 1-3 lines\\n probabilities = np.array([model.probability(X) for model in self.models])\\n pred=np.argmax(probabilities, axis=0)\\n ### END CODE\\n assert pred.shape == (X.shape[0],)\\n return pred\",\n \"def predict(self, X, a, b):\\n pass\",\n \"def predict(self, X):\\n # ! You could simply call the hypothesis here\\n predictions= self._hypothesis(X)\\n #empty_predictions = np.zeros((1,X.shape[1]))\\n return predictions\",\n \"def prediction_a(self):\\n return self._prediction_a\",\n \"def get_predictions():\\n\\n print(\\\"OK1\\\");\\n print(\\\"OK2\\\");\\n return;\",\n \"def evaluate(self, prediction_fn):\\n pass\",\n \"def predict(self, data):\\n return self.result.predict(data)\",\n \"def predict(self, review):\\n raise NotImplementedError\",\n \"def get_prediction(self, x):\\n \\\"*** YOUR CODE HERE ***\\\"\\n dotProduct = nn.as_scalar(self.run(x))\\n if dotProduct < 0:\\n return -1\\n else:\\n return 1\",\n \"def predict(self,X): \\n return self._predict(X)\",\n \"def prediction():\\r\\n\\r\\n\\r\\n\\tpredictVal = []\\r\\n\\taccuracy = 0.0\\r\\n\\r\\n\\t# Calculate accuracy for each class in testData\\r\\n\\tfor item in testData:\\r\\n\\t\\tclass0Prediction = posProb / 100\\r\\n\\t\\tclass1Prediction = negProb / 100\\r\\n\\t\\t\\r\\n\\t\\t# Multiply the prior probablities for negative and positive reviews by their feature likelihoods \\r\\n\\t\\tfor word in item[2]:\\r\\n\\t\\t\\tclass0Prediction *= class0Dict[word]\\r\\n\\t\\t\\tclass1Prediction *= class1Dict[word]\\r\\n\\r\\n\\t\\t# Give every item in testData a predicted value\\r\\n\\t\\tif(class0Prediction > class1Prediction):\\r\\n\\t\\t\\tpredictVal.append('0')\\r\\n\\t\\telse:\\r\\n\\t\\t\\tpredictVal.append('1')\\r\\n\\r\\n\\tfor i in range(len(testData)):\\r\\n\\t\\tif(testData[i][1] == predictVal[i]):\\r\\n\\t\\t\\taccuracy += 1\\r\\n\\r\\n\\t\\t\\t\\r\\n\\taccuracy = 100 * (accuracy / len(testData))\\r\\n\\treturn(predictVal, accuracy)\",\n \"def predict(self, data_in):\\n pass\",\n \"def predict(self):\\n if ((self.kf.x[6] + self.kf.x[2]) <= 0):\\n self.kf.x[6] *= 0.0\\n self.kf.predict()\\n self.age += 1\\n if (self.time_since_update > 0):\\n self.hit_streak = 0\\n self.time_since_update += 1\\n self.history.append(convert_x_to_bbox(self.kf.x))\\n return self.history[-1]\",\n \"def prediction_b(self):\\r\\n return self._prediction_b\",\n \"def predict(self):\\n self.kf.predict()\\n self.nb_kf_pred += 1\\n if self.time_since_update > 0:\\n self.hit_streak = 0\\n self.time_since_update += 1\\n self.history.append(self.kf.x[:2].reshape(-1))\\n return self.history[-1]\",\n \"def getPredictions(self):\\n\\t\\tself.bestLabel = self.testingProbs.apply(lambda x: x.argmax(),1)\",\n \"def predict(self, params, exog=None, *args, **kwargs):\\n raise NotImplementedError # pragma: no cover\",\n \"def get_prediction(self, x):\\n \\\"*** YOUR CODE HERE question 1 ***\\\"\\n dProduct = nn.as_scalar(self.run(x))\\n\\n if dProduct < 0:\\n return -1\\n else:\\n return 1\",\n \"def predict(self, testData=[]):\\n result = []\\n for classValue in self._classAttrs:\\n #print(f'Computing Label: {classValue}, {self._classLabelMap[classValue]}')\\n result.append(self._computeCondProb(testData, classValue))\\n return self._classLabelMap[result.index(max(result))]\",\n \"def predict(self, X):\\r\\n return 1 if self.predict_prob(X) > 0.5 else 0\",\n \"def predict(self):\\n for track in self.tracks:\\n track.predict(self.kf)\",\n \"def predict(self):\\n for track in self.tracks:\\n track.predict(self.kf)\",\n \"def predict(self):\\n for track in self.tracks:\\n track.predict(self.kf)\",\n \"def predict(self):\\n\\n self.eval()\\n return self.forward(self.data)\",\n \"def predict(self, seq):\\n raise Exception(\\\"You cannot predict with a base predictor.\\\")\",\n \"def predict(probs):\\n # Your code here.\\n return np.argmax(probs, axis=1)\",\n \"def calculatePrediction(self, a, X_train,x, t_train):\\n pass\",\n \"def get_prediction(self):\\n return self.prediction\",\n \"def doPredict(self, data: StockData) -> float:\\r\\n pass\",\n \"def predict(self, X):\\n raise NotImplementedError\",\n \"def predict(self):\\n return self.test_fn(self.gX, self.hX, self.sym_g, self.sym_h)\",\n \"def get_prediction(self, x):\\n \\\"*** YOUR CODE HERE ***\\\"\\n if (nn.as_scalar(self.run(x)) >= 0.0):\\n return 1\\n return -1\",\n \"def postprocess(self, prediction_dict, **params):\\r\\n pass\",\n \"def predictions(self):\\n return self._pred\",\n \"def test_predict(self):\\n\\n docs = self.docs\\n for m in self.models:\\n preds = m.predict(docs)\\n self.assertTrue(isinstance(preds, turicreate.SArray))\\n self.assertEqual(len(preds), len(docs))\\n self.assertEqual(preds.dtype, int)\\n\\n preds = m.predict(docs, output_type=\\\"probability\\\")\\n self.assertTrue(isinstance(preds, turicreate.SArray))\\n self.assertTrue(len(preds) == len(docs))\\n s = preds.apply(lambda x: sum(x))\\n self.assertTrue((s.apply(lambda x: abs(x - 1)) < 0.000001).all())\\n\\n # Test predictions when docs have new words\\n new_docs = turicreate.SArray([{\\\"-1,-1\\\": 3.0, \\\"0,4\\\": 5.0, \\\"0,3\\\": 2.0}])\\n preds = m.predict(new_docs)\\n self.assertEqual(len(preds), len(new_docs))\\n\\n # Test additional burnin. Ideally we could show that things\\n # converge as you increase burnin.\\n preds_no_burnin = m.predict(docs, output_type=\\\"probability\\\", num_burnin=0)\\n self.assertEqual(len(preds_no_burnin), len(docs))\",\n \"def predict(self,X):\\n return self.classifier.predict(X)\",\n \"def predict(self,X):\\n return self.classifier.predict(X)\",\n \"def predict(self,X):\\n return self.classifier.predict(X)\",\n \"def predict(self,X):\\n return self.classifier.predict(X)\",\n \"def predict(self, model, x_test):\\n pass\",\n \"def predict(self, data: List):\",\n \"def predict(self, X):\\n raise NotImplementedError('Abstract method \\\"predict\\\" must be '\\n 'specialised!')\",\n \"def predict(self, model, context, data):\\n pass\",\n \"def predict_step(self, x):\\n\\n input_x = self.session.graph.get_operation_by_name(\\\"input_x\\\").outputs[0]\\n predictions_op = self.session.graph.get_operation_by_name(\\\"output/predictions\\\").outputs[0] \\n\\n d_ = {\\n input_x: x\\n }\\n\\n self.init_dataset(d_)\\n\\n return self.session.run([predictions_op])\",\n \"def _predict(self,\\n X=None):\\n\\n if X is None:\\n ypred = self.clf_pipeline.predict(self.Xtrain)\\n else:\\n ypred = self.clf_pipeline.predict(X)\\n\\n return ypred\",\n \"def predict_and_update(self, z):\",\n \"def prediction(self, x):\\n t = self.model.predict(x)\\n return t\",\n \"def predict(self):\\n prediction = np.multiply(self.alpha_vec, self.label[:,np.newaxis]).T\\n pred = np.dot(prediction, np.inner(self.train_data, self.train_data)) + self.intercept\\n self.prediction = np.sign(pred)\\n return(self.prediction)\",\n \"def predict(self, datafile):\",\n \"def __do_predict(self, request, features):\\n dmp_predictor.DmpPredictor().predict(request, features)\\n\\n return defines.ReturnCode.SUCC\",\n \"def predict ( self, X: np.ndarray ):\\n \\n return self.predict_probability ( X = X )\\n # End predict()\",\n \"def predict(self, point):\\n # TODO\\n temp = 0\\n for j in range(len(self.data.labels)):\\n temp = temp + self.alpha[j]*self.data.labels[j]*self.kf(point, self.data.features[j])\\n pred_temp = temp\\n if pred_temp <= 0:\\n pred = -1\\n else:\\n pred = 1\\n # print(pred_temp)\\n return pred\",\n \"def fit_predict(self):\\n raise AttributeError\",\n \"def image_new_decision_function_predict(self, img_conv4_result, img_label):\\r\\n \\r\\n print(\\\"***Start new decision function prediction***\\\")\\r\\n y_hat_submodel = np.argmax(self.sub_model.predict(img_conv4_result), axis=1)\\r\\n \\r\\n poison_index = np.where(y_hat_submodel != img_label)\\r\\n y_hat = y_hat_submodel\\r\\n y_hat[poison_index] = self.num_class\\r\\n print(\\\"***Predict finish***\\\")\\r\\n \\r\\n return y_hat\",\n \"def test_predict(self):\\n self.regression_single.predict(self.X_test)\\n self.assertTrue(len(self.regression_single.y_pred))\\n self.regression_boston.predict(self.boston_x_test)\\n self.assertTrue(len(self.regression_boston.y_pred))\",\n \"def test_predict(self):\\n \\n\\n model ,vec, x_testing=setup_log_reg_classifier(self.training_data, self.training_y, self.testing_data,\\\"text\\\", method=\\\"count\\\")\\n \\n model2 ,vec_tfidf, x_testing2=setup_log_reg_classifier(self.training_data, self.training_y, self.testing_data,\\\"text\\\", method=\\\"tfidf\\\")\\n \\n \\n \\\"\\\"\\\" Test correct data types and corrrect range of predicted values (1,0) for predict with countVectorizer\\\"\\\"\\\" \\n \\n self.assertIsInstance(predict(model,x_testing),\\n np.ndarray)\\n \\n self.assertTrue(([0,1] ==np.unique(predict(model2,x_testing2))).all())\\n\\n \\n \\\"\\\"\\\" Test correct data types and corrrect range of predicted values (1,0) for predict with tfidfVectorizer\\\"\\\"\\\" \\n \\n self.assertIsInstance(predict(model,x_testing),\\n np.ndarray)\\n \\n self.assertTrue(([0,1] ==np.unique(predict(model2,x_testing2))).all())\",\n \"def predict(self, data):\\n\\t\\tres = self.neuralNetworks.inference(self.dataCenter.process_inference_data(data))\\n\\t\\tprint(res)\",\n \"def get_prediction(self, datapoints, employee, location, device, next_risk):\\n raise NotImplementedError\",\n \"def prediction_prob(self):\\n return self.a[np.arange(self.a.shape[0]), self.prediction]\",\n \"def predict(self,x):\\n return self._model.predict(x)\",\n \"def _predict(self):\\n \\n # Initialization\\n x_k = np.random.rand(4,0)\\n w_k = np.ones((1,0))\\n \\n self.reset(phd_only=True)\\n \\n # Prediction of the targets' positions\\n for k in range(1, self.n_time_steps+1):\\n\\n if k in self.observed_data.keys():\\n\\n # perform phd filter\\n\\n x_k1, w_k1, estimated_x_k1, n_targ_pred = self.PHDfilter(x_k, w_k, self.observed_data[k], k)\\n\\n # save predicted positions and update parameters\\n self.phd_filter['n_targets_predicted'][k] = n_targ_pred\\n self.phd_filter['particles_positions'][k] = x_k1\\n if estimated_x_k1 is not None:\\n self.phd_filter['estimated_positions'][k] = estimated_x_k1\\n x_k, w_k = np.copy(x_k1), np.copy(w_k1)\\n\\n else:\\n self.phd_filter['n_targets_predicted'][k] = 0\",\n \"def _predict(self, dataset):\\n binary_predictions = ProxyClassifier._predict(self, dataset)\\n self.ca.estimates = binary_predictions\\n predictions = [ {-1: self.__predictneg,\\n +1: self.__predictpos}[x] for x in binary_predictions]\\n self.ca.predictions = predictions\\n return predictions\",\n \"def prediction_fn(self, yhat):\\n raise NotImplementedError\",\n \"def predict(self, x):\\n\\n pred = self.decision_function(x)\\n return pred\",\n \"def predict(self, input_data):\\n if not self.predict_as_probability_:\\n return self.ensemble_model_.predict(input_data)\\n else:\\n return self.ensemble_model_.predict_proba(input_data)\",\n \"def get_predictions (self, x): \\n # Here is where you write a code to evaluate the data and produce predictions.\\n w_i_1, w_i_2, w_i_3 = self.params\\n\\n l1_1 = sigmoid(np.dot(self.x, w_i_1))\\n l1_2 = sigmoid(np.dot(self.x, w_i_2))\\n x_new = np.concatenate(l1_1,l1_2)\\n output = sigmoid(np.dot(x_new, w_i_3))\\n print output\\n print \\\"hi\\\"\\n return output\",\n \"def predict(self, X):\\n return self.decision_function(X)\",\n \"def predict(self, test_data):\\r\\n return self.gs.predict(test_data)\",\n \"def predict(self, testing_set):\\r\\n # Run prediction by multiply inputs with the weight and map it\\r\\n # Through the activation function\\r\\n final_prob = 0\\r\\n probability = self.activation(self.weighted_sum(testing_set))\\r\\n prediction = self.threshold(probability)\\r\\n if prediction == 1:\\r\\n final_prob = probability\\r\\n else:\\r\\n final_prob = 1 - probability\\r\\n return [prediction, final_prob]\",\n \"def do_predict(self):\\n answer = []\\n response = []\\n\\n for it_predictions in json.loads(request.data.decode('UTF-8')):\\n prediction = it_predictions['score']\\n for ite_clf in g_list_of_classifier:\\n answer.append(ite_clf.predict(prediction))\\n if answer.count(True) > answer.count(False):\\n response.append({'answer' : True})\\n else:\\n response.append({'answer' : False})\\n return json.dumps(response, indent=4)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8059006","0.78803074","0.7860856","0.774276","0.76850456","0.7635276","0.760205","0.75458163","0.75458163","0.75458163","0.7529297","0.7527876","0.7512121","0.7512121","0.74871856","0.7483808","0.7418165","0.7404916","0.7338522","0.7328177","0.7263635","0.72458243","0.72356266","0.7227444","0.7209645","0.7201983","0.7201816","0.71850395","0.71707374","0.71707374","0.71707374","0.71664566","0.71602553","0.7152334","0.7140268","0.71394247","0.7139358","0.71271974","0.71208954","0.71206975","0.7094517","0.709044","0.7040328","0.70358765","0.7021875","0.7020558","0.7020187","0.70198375","0.70135105","0.7012168","0.69985384","0.6994094","0.6994094","0.6994094","0.6990189","0.69890285","0.6983007","0.69779444","0.6977076","0.6975937","0.6975748","0.69711775","0.69680125","0.69510293","0.69394964","0.69384974","0.69366413","0.69366413","0.69366413","0.69366413","0.69338614","0.6922354","0.69220257","0.691751","0.689661","0.68922836","0.68873036","0.687226","0.686207","0.6858238","0.68563205","0.68560624","0.6854456","0.6850506","0.68498284","0.6839281","0.68289375","0.6827667","0.68276364","0.68263924","0.68261665","0.68259805","0.68201995","0.68167937","0.68112916","0.6810667","0.680842","0.68039644","0.6801815","0.67801416","0.6776415"],"string":"[\n \"0.8059006\",\n \"0.78803074\",\n \"0.7860856\",\n \"0.774276\",\n \"0.76850456\",\n \"0.7635276\",\n \"0.760205\",\n \"0.75458163\",\n \"0.75458163\",\n \"0.75458163\",\n \"0.7529297\",\n \"0.7527876\",\n \"0.7512121\",\n \"0.7512121\",\n \"0.74871856\",\n \"0.7483808\",\n \"0.7418165\",\n \"0.7404916\",\n \"0.7338522\",\n \"0.7328177\",\n \"0.7263635\",\n \"0.72458243\",\n \"0.72356266\",\n \"0.7227444\",\n \"0.7209645\",\n \"0.7201983\",\n \"0.7201816\",\n \"0.71850395\",\n \"0.71707374\",\n \"0.71707374\",\n \"0.71707374\",\n \"0.71664566\",\n \"0.71602553\",\n \"0.7152334\",\n \"0.7140268\",\n \"0.71394247\",\n \"0.7139358\",\n \"0.71271974\",\n \"0.71208954\",\n \"0.71206975\",\n \"0.7094517\",\n \"0.709044\",\n \"0.7040328\",\n \"0.70358765\",\n \"0.7021875\",\n \"0.7020558\",\n \"0.7020187\",\n \"0.70198375\",\n \"0.70135105\",\n \"0.7012168\",\n \"0.69985384\",\n \"0.6994094\",\n \"0.6994094\",\n \"0.6994094\",\n \"0.6990189\",\n \"0.69890285\",\n \"0.6983007\",\n \"0.69779444\",\n \"0.6977076\",\n \"0.6975937\",\n \"0.6975748\",\n \"0.69711775\",\n \"0.69680125\",\n \"0.69510293\",\n \"0.69394964\",\n \"0.69384974\",\n \"0.69366413\",\n \"0.69366413\",\n \"0.69366413\",\n \"0.69366413\",\n \"0.69338614\",\n \"0.6922354\",\n \"0.69220257\",\n \"0.691751\",\n \"0.689661\",\n \"0.68922836\",\n \"0.68873036\",\n \"0.687226\",\n \"0.686207\",\n \"0.6858238\",\n \"0.68563205\",\n \"0.68560624\",\n \"0.6854456\",\n \"0.6850506\",\n \"0.68498284\",\n \"0.6839281\",\n \"0.68289375\",\n \"0.6827667\",\n \"0.68276364\",\n \"0.68263924\",\n \"0.68261665\",\n \"0.68259805\",\n \"0.68201995\",\n \"0.68167937\",\n \"0.68112916\",\n \"0.6810667\",\n \"0.680842\",\n \"0.68039644\",\n \"0.6801815\",\n \"0.67801416\",\n \"0.6776415\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":75587,"cells":{"query":{"kind":"string","value":"Gets the variables in the template from the logs limited to the ones represented by asterisks ('') only. These are the only ones being considered in the triples. As the pointers advance on both the log and the template the variables are only collected when is encountered on the template"},"document":{"kind":"string","value":"def __get_vars_list(self, template_idx, log):\n template = self.templates[template_idx].split()\n log = log.split()\n variables = []\n pt = pl = 0\n while pt < len(template) and pl < len(log):\n if template[pt] == log[pl]:\n pt += 1\n pl += 1\n continue\n elif template[pt] == '*':\n # found a variable\n while pt < len(template) and template[pt] == '*':\n # in case there are many variables together\n pt += 1\n if pt >= len(template):\n # it's the end of the template\n variables.append(' '.join(log[pl:]))\n break\n else:\n variable_tokens = []\n while pl < len(log) and log[pl] != template[pt]:\n variable_tokens.append(log[pl])\n pl += 1\n # it duplicates when many variables together for a correct output\n variables.append(' '.join(variable_tokens))\n else:\n # it is a variable not covered by the template asterisks\n # we move on on the log but stay on the template token\n pl += 1\n return variables"},"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_placeholders(template):\n return [p[1] for p in string.Formatter().parse(template)\n if p[1] is not None and len(p[1]) > 0]","def find_template_variables(code):\n return re.findall(re_template_var, code)","def get_variables(self):\n return [self.g_t, self.m_t]","def context(template):\n\n return {\n v.key: v.read()\n for v in [Variable(name) for name in extract_variables(template)]\n }","def _getTempsDelexpression(listedMatrix):\n\n \"\"\"\n each template is implemented as a boolean expression of symbols\n where each symbol represents each one of the 26 neighbors\n\n \"\"\"\n str1 = ''.join(str(e) for e in listedMatrix)\n a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q, r, s, t, u, v, w, x, y, z = map(exprvar, str1)\n\n direction1 = (~(a) & ~(b) & ~(c) & ~(j) & ~(k) & ~(l) & ~(r) & ~(s) & ~(t) & p & (d | e | f | m | n | u | v | w | g | h | i | o | q | x | y | z)) | \\\n (~(a) & ~(b) & ~(c) & ~(d) & ~(e) & ~(f) & ~(g) & ~(h) & ~(i) & v & (d | e | f | m | n | u | v | w | g | h | i | o | q | x | y | z)) | \\\n (~(a) & ~(b) & ~(c) & ~(j) & ~(k) & ~(l) & ~(r) & ~(s) & ~(t) & ~(d) & ~(e) & ~(f) & ~(g) & ~(g) & ~(h) & ~(i) & y & (m | n | u | w | o | q | x | z)) | \\\n (~(a) & ~(b) & ~(c) & ~(k) & ~(e) & ~(d & j) & ~ (l & f) & p & v) | \\\n (~(a) & ~(b) & ~(k) & ~(e) & c & v & p & ~(j & d) & ((l & ~(f)) | (~(l) & f))) | \\\n (a & v & p & ~(b) & ~(c) & ~(k) & ~(e) & ~(l & f) & ((j & ~(d)) | (~(j) & d))) | \\\n (~(a) & ~(b) & ~(k) & ~(e) & n & v & p & ~(j & d)) | \\\n (~(b) & ~(c) & ~(k) & ~(e) & m & v & p & ~(l & f)) | \\\n (~(b) & ~(k) & ~(e) & a & n & v & p & ((j & ~(d)) | (~(j) & d))) | \\\n (~(b) & ~(k) & ~(e) & c & m & v & p & ((l & ~(f)) | (~(l) & f))) | \\\n (~(a) & ~(b) & ~(c) & ~(j) & ~(k) & ~(l) & ~(r) & ~(s) & ~(t) & ~(d) & ~(e) & ~(g) & ~(h) & q & y) | \\\n (~(a) & ~(b) & ~(c) & ~(j) & ~(k) & ~(l) & ~(r) & ~(s) & ~(t) & ~(e) & ~(f) & ~(h) & ~(i) & o & y) | \\\n (~(a) & ~(b) & ~(c) & ~(j) & ~(k) & ~(r) & ~(s) & ~(d) & ~(e) & ~(f) & ~(g) & ~(h) & ~(i) & w & y) | \\\n (~(a) & ~(b) & ~(c) & ~(d) & ~(e) & ~(f) & ~(g) & ~(h) & ~(i) & ~(k) & ~(l) & ~(s) & ~(t) & u & y)\n return expr(direction1, simplify=True)","def calculate_diagnostic_vars(self):\n pass","def annotated_vars(prs):\n def trt(acc):\n vs,ann = acc\n if len(ann)==0:\n return vs\n assert(len(ann)==1)\n return [c.copy_token(v,ann[0]) for v in vs]\n return c.paren(var().plus() + colon_annotation_or_meta()).treat(trt)","def getVariables(self):\n statVars = [self[vn] for vn in self.statVars]\n timeVars = [self[vn] for vn in self.timeVars]\n return statVars + timeVars","def _build_pop_temp(self):\n value = self.value\n return dedent(\n f\"\"\"\n @{value}\n D=A // D = i\n @5\n D=D+A // addr = 5 + i\n @SP\n M=M-1\n A=M\n D=D+M // addr = addr + RAM[SP]\n A=D-M // A = addr - RAM[SP] \n M=D-A // RAM[A] = addr - A\n \"\"\"\n )","def variables_referenced(text):\n return set(substitution_pattern.findall(text))","def construct_out_logfields(*tups):\n fields = []\n\n for tupStruct in tups:\n for k in tupStruct._fields:\n if k.startswith(\"Diagnostic\"):\n k_str = k.replace('_', '/', 1)\n fields.append(k_str)\n else:\n for st in stat_list:\n fields.append(f'{k}/{st}')\n\n return fields","def variables_used (self) :\r\n\t\treturn [i[0] for i in self.parameters]","def wats_postprocessing(variables):\n return variables","def _build_push_temp(self):\n value = self.value\n return dedent(\n f\"\"\"\n // i = offset\n @{value}\n D=A\n // addr = i + 5\n @5\n D=D+A\n // *SP = *addr\n A=D\n D=M\n @SP\n A=M\n M=D\n // SP++\n @SP\n M=M+1\n \"\"\"\n )","def scanvars(reader, frame, locals):\n import tokenize\n import keyword\n vars, lasttoken, parent, prefix, value = [], None, None, '', __UNDEF__\n for ttype, token, start, end, line in tokenize.generate_tokens(reader):\n if ttype == tokenize.NEWLINE:\n break\n if ttype == tokenize.NAME and token not in keyword.kwlist:\n if lasttoken == '.':\n if parent is not __UNDEF__:\n value = getattr(parent, token, __UNDEF__)\n vars.append((prefix + token, prefix, value))\n else:\n where, value = lookup(token, frame, locals)\n vars.append((token, where, value))\n elif token == '.':\n prefix += lasttoken + '.'\n parent = value\n else:\n parent, prefix = None, ''\n lasttoken = token\n return vars","def isolate_relevant_information(self):\n\n def get_formula(oncat_formula):\n \"\"\"will need to go from something like\n \"${value/10e11}`\"\n to something more pythonic\n \"{value/10e11}\"\"\"\n regular_expression = r'\\$(?P<formula>.+)\\`'\n m = re.search(regular_expression, oncat_formula)\n if m:\n return m.group('formula')\n else:\n return \"\"\n\n template_information = {}\n for _index, _element in enumerate(self._oncat_default_template):\n _title = _element[\"name\"]\n _path = _element[\"path\"]\n if \"units\" in _element:\n _units = _element[\"units\"]\n else:\n _units = \"\"\n if \"transform\" in _element:\n _formula = get_formula(_element[\"transform\"])\n else:\n _formula = \"\"\n template_information[_index] = {'title': _title,\n 'path': _path,\n 'units': _units,\n 'formula': _formula}\n self.template_information = template_information","def variables_used (self) :\r\n\t\treturn []","def get_variable_names(text):\n names = []\n if '@@' in text:\n matches = _property_pattern.findall(text)\n for token, key in matches:\n names.append(key)\n\n return names","def variables(self):\r\n return self.get_field('variable')","def retrieve_temp(self):\n\n i = CodeGenerator.RESERVED_REGISTER_LOCATION_1\n j = CodeGenerator.RESERVED_REGISTER_LOCATION_2\n t = CodeGenerator.RESERVED_REGISTER_LOCATION_3\n fp = CodeGenerator.RESERVED_REGISTER_LOCATION_4\n self.add_pc(1)\n self.pb[self.pc - 1] = \"ASSIGN\", _m(self.top_sp, \"@\"), _m(fp)\n self.add_pc(7)\n self.pb[self.pc - 7] = \"ADD\", _m(self.top_sp), _m(4, \"#\"), _m(j)\n self.pb[self.pc - 6] = \"ASSIGN\", _m(CodeGenerator.INIT_MEMORY_VALUE + 4, \"#\"), _m(i)\n self.pb[self.pc - 5] = \"ADD\", _m(i), _m(CodeGenerator.INIT_MEMORY_VALUE + 4, \"#\"), _m(i)\n self.pb[self.pc - 4] = \"ASSIGN\", _m(i), _m(self.top_sp, \"@\")\n self.pb[self.pc - 3] = \"ADD\", _m(self.top_sp), _m(4, \"#\"), _m(self.top_sp)\n self.pb[self.pc - 2] = \"LT\", _m(CodeGenerator.INIT_MEMORY_VALUE + CodeGenerator.REGISTER_SIZE, \"#\"), _m(i), _m(\n t)\n self.pb[self.pc - 1] = \"JPF\", _m(t), _m(self.pc - 5)\n self.add_pc(1)\n self.pb[self.pc - 1] = \"ASSIGN\", _m(fp), _m(self.top_sp, \"@\")","def getAllPlaceholders(self, template_list):\n placeholders = []\n for line in template_list:\n found = re.findall(self.pattern, line)\n placeholders.extend(found)\n placeholders.reverse()\n #place the cursor placeholder at the begin\n placeholders = self._placeCursors(placeholders)\n return placeholders","def __extract_pattern_nodes(graph):\n tp_nodes = graph.subjects(RDF.type, AGORA.TriplePattern)\n for tpn in tp_nodes:\n subject = list(graph.objects(tpn, AGORA.subject)).pop()\n predicate = list(graph.objects(tpn, AGORA.predicate)).pop()\n obj = list(graph.objects(tpn, AGORA.object)).pop()\n subject_str = list(graph.objects(subject, RDFS.label)).pop().toPython()\n predicate_str = graph.qname(predicate)\n if (obj, RDF.type, AGORA.Variable) in graph:\n object_str = list(graph.objects(obj, RDFS.label)).pop().toPython()\n else:\n object_str = list(graph.objects(obj, AGORA.value)).pop().toPython()\n __plan_patterns[tpn] = '{} {} {}'.format(subject_str, predicate_str, object_str)","def get_loop_vars(rule):\n traverse_list = [rule]\n loop_vars = []\n while(traverse_list):\n one_rule = traverse_list.pop()\n operator = one_rule[0]\n operate_nums = one_rule[1:]\n # print(\"operate_nums: %s\" % operate_nums)\n for operate_num in operate_nums:\n if isinstance(operate_num, str):\n loop_vars.append(operate_num)\n elif isinstance(operate_num, list):\n traverse_list.append(operate_num)\n else:\n continue\n # remove redundant vars, and keep the order\n ans_vars = list(set(loop_vars))\n ans_vars.sort(key=loop_vars.index)\n return ans_vars","def GetVariableAttributes(template_src, env=None):\n env = env or jinja2.Environment()\n abstract_syntax_tree = env.parse(template_src)\n node_visitor = _GetattrNodeVisitor()\n node_visitor.visit(abstract_syntax_tree)\n\n output = set()\n undeclared_variables = meta.find_undeclared_variables(abstract_syntax_tree)\n used_variables = set()\n for node in node_visitor.getattr_nodes:\n attr_list = _GetAttributeList(node)\n if attr_list[0] in undeclared_variables:\n used_variables.add(attr_list[0])\n output.add('.'.join(attr_list))\n return output | (undeclared_variables - used_variables)","def find_star_step(tree: Tree, template: Tree) -> list:\n if type(template) == str and template == \"@\":\n return tree.leaves()\n\n if type(tree) == str:\n return []\n res = find_step(tree, template)\n\n if len(res) != 0:\n return res\n\n for node in tree:\n res_ = find_star_step(node, template)\n if res_ != []:\n return res_\n\n return []","def __extract_patterns_and_spaces(self):\n\n def __decorate_nodes(nodes, space):\n \"\"\"\n Performs a backward search from a list of pattern nodes and assigns a set of search spaces\n to all encountered nodes.\n :param nodes: List of pattern nodes that belongs to a search space\n :param space: List of search space id\n :return:\n \"\"\"\n for n in nodes:\n if n not in self.__node_spaces:\n self.__node_spaces[n] = set([])\n self.__node_spaces[n].add(space)\n pred_nodes = self.__plan_graph.subjects(AGORA.next, n)\n __decorate_nodes(pred_nodes, space)\n\n # Extract all search spaces in the plan and build a dictionary of subjects-to-ignore per each of them.\n # Ignored subjects are those that won't be dereferenced due to a explicit graph pattern (object) filter,\n # e.g. ?s doap:name \"jenkins\" -> All ?s that don't match the filter will be ignored.\n self.__spaces = set(self.__plan_graph.subjects(RDF.type, AGORA.SearchSpace))\n self.__subjects_to_ignore = dict([(sp, set([])) for sp in self.__spaces])\n\n patterns = list(self.__plan_graph.subjects(RDF.type, AGORA.TriplePattern))\n for tp in patterns:\n # A triple pattern belongs to a UNIQUE search space\n space = list(self.__plan_graph.subjects(AGORA.definedBy, tp)).pop()\n self.__patterns[tp] = {'space': space}\n\n # Depending on the format of each triple pattern (either '?s a Concept' or '?s prop O'),\n # it is required to extract different properties.\n tp_pred = list(self.__plan_graph.objects(tp, predicate=AGORA.predicate)).pop()\n\n if tp_pred == RDF.type: # ?s a Concept\n self.__patterns[tp]['type'] = list(self.__plan_graph.objects(tp, predicate=AGORA.object)).pop()\n try:\n check_type = list(self.__plan_graph.objects(tp, predicate=AGORA.checkType)).pop().toPython()\n except IndexError:\n check_type = True\n self.__patterns[tp]['check'] = check_type\n else: # ?s prop O\n self.__patterns[tp]['property'] = tp_pred\n tp_obj = list(self.__plan_graph.objects(tp, predicate=AGORA.object)).pop()\n if (tp_obj, RDF.type, AGORA.Literal) in self.__plan_graph: # In case O is a Literal\n self.__patterns[tp]['filter_object'] = list(self.__plan_graph.objects(tp_obj, AGORA.value)).pop()\n elif isinstance(tp_obj, URIRef):\n self.__patterns[tp]['filter_object'] = tp_obj\n\n tp_sub = list(self.__plan_graph.objects(tp, predicate=AGORA.subject)).pop()\n if isinstance(tp_sub, URIRef):\n self.__patterns[tp]['filter_subject'] = tp_sub\n\n # Get all pattern nodes (those that have a byPattern properties) of the search plan and search backwards\n # in order to set the scope of each search space.\n nodes = list(self.__plan_graph.subjects(AGORA.byPattern, tp))\n for n in nodes:\n if n not in self.__node_patterns:\n self.__node_patterns[n] = set([])\n self.__node_patterns[n].add(tp)\n __decorate_nodes(nodes, space)","def get_placeholders(self):\n\n # Define regex\n regex = f'\\{self.marker_string}([^$]+)\\{self.marker_string}'\n\n found_placeholders = [match.group(1) for match in re.finditer(regex, self.raw_sequence)]\n\n # Check for duplicates\n for found_placeholder in found_placeholders:\n if found_placeholders.count(found_placeholder) != 1:\n\n error_msg = f\"Placeholder {found_placeholder} found multiple times in sequence.\"\n self.hd.log.info(error_msg)\n\n # Remove duplicates from list\n found_placeholders = list(set(found_placeholders))\n\n return found_placeholders","def get_variables(self):\n\t\treturn self.variables","def variable_dicts(self):\n \n def get_variable_text(rtf_file):\n \"Returns a list of variable_texts for each variable\"\n st='Pos. = '\n return rtf_file.split(st)[1:]\n \n def get_variable_name(variable_text):\n st='Variable = '\n b=variable_text.split(st)[1]\n return b[b.find(' ')+1:b.find('\\t')]\n \n def find_pos(rtf):\n a=rtf\n b=a\n return b[b.find(' ')+1:b.find('\\t')]\n \n def find_variable_label(rtf):\n try:\n a=rtf\n b=a.split('Variable label = ')[1]\n return b[b.find(' ')+1:b.find('\\\\par')]\n except IndexError:\n return None\n \n def find_variable_type(rtf):\n if not 'This variable is ' in rtf: return ''\n a=rtf\n b=a.split('This variable is ')[1]\n i1=b.find(' ')+1\n i2=i1+b[i1:].find('}')\n return b[i1:i2]\n \n def find_SPSS_measurement_level(rtf):\n if not 'the SPSS measurement level is ' in rtf: return ''\n a=rtf\n b=a.split('the SPSS measurement level is ')[1]\n i1=b.find(' ')+1\n i2=i1+b[i1:].find('\\\\par')\n return b[i1:i2]\n \n def find_SPSS_user_missing_values(rtf):\n if not 'SPSS user missing values = ' in rtf: return dict()\n a=rtf\n d=a.split('SPSS user missing values = ')\n if len(d)<2: return None\n e=d[1]\n i1=e.find(' ')+1\n i2=i1+e[i1:].find('\\\\par')\n f=e[i1:i2]\n g=f.split(' ')\n i=' '.join([g[0],g[2],g[4]])\n return i\n \n def find_value_labels(rtf):\n if not 'Value = ' in rtf: return dict()\n a=rtf\n d=a.split('Value = ')[1:]\n z={}\n for e in d:\n value=e[e.find(' ')+1:e.find('\\t')]\n value=float(value)\n f=e.split('Label = ')[1]\n label=f[f.find(' ')+1:f.find('\\\\par')]\n z[value]=label\n #print(z)\n return z\n \n variable_texts=get_variable_text(self.rtf)\n #pprint(variable_texts[0:2])\n \n result=[]\n for variable_text in variable_texts:\n d={'pos':find_pos(variable_text),\n 'variable':get_variable_name(variable_text),\n 'variable_label':find_variable_label(variable_text),\n 'variable_type':find_variable_type(variable_text),\n 'SPSS_measurement_level':find_SPSS_measurement_level(variable_text),\n 'SPSS_user_missing_values':find_SPSS_user_missing_values(variable_text),\n 'value_labels':find_value_labels(variable_text) \n }\n result.append(d)\n \n return result","def get_step_tmp_vals_at_index(self, index):\n return self.routine_template.get_step_tmp_vals_at_index(index)","def reportCopyMap(self):\n copy_map = defaultdict(list)\n for para in self.block_map:\n offset = self.offset_map[para]\n for i in xrange(len(self.block_map[para]) - 1):\n start, var, block = self.block_map[para][i]\n span = self.block_map[para][i + 1][0] - start\n if var is not None:\n copy_map[para].append([start + offset, span, pulp.value(var)])\n prevVar = pulp.value(var)\n else:\n copy_map[para].append([start + offset, span, prevVar])\n finalStart, finalVar, finalBlock = self.block_map[para][-1]\n finalSpan = self.G.sizes[para] - finalStart\n if finalVar is not None:\n copy_map[para].append([finalStart + offset, finalSpan, pulp.value(var)])\n else:\n copy_map[para].append([finalStart + offset, finalSpan, prevVar])\n return copy_map","def retrieve_variables(content):\n variables = []\n in_var_section = False\n for line in content.splitlines():\n #print line\n if in_var_section:\n var_def = re.split(' +', line)\n if len(var_def) > 1:\n #print var_def[0], ':', var_def[1]\n var_name = var_def[0]\n def_value = var_def[1]\n if not def_value.startswith('%'): #not environment variable which would be directly passed to robot\n variables.append([var_name.strip('${').strip('}'), def_value])\n if '*** Variables ***' in line:\n in_var_section = True\n elif in_var_section and '*** ' in line:\n #end of Variables section\n break\n return variables","def _variable_pattern(self):\n variable_pattern = r'#([A-Za-z]+)' # match literal (#App,#Trigger) at beginning of String\n variable_pattern += r':([\\d]+)' # app id (:7979)\n variable_pattern += r':([A-Za-z0-9_\\.\\-\\[\\]]+)' # variable name (:variable_name)\n variable_pattern += r'!(StringArray|BinaryArray|KeyValueArray' # variable type (array)\n variable_pattern += r'|TCEntityArray|TCEnhancedEntityArray' # variable type (array)\n variable_pattern += r'|String|Binary|KeyValue|TCEntity|TCEnhancedEntity' # variable type\n variable_pattern += r'|(?:(?!String)(?!Binary)(?!KeyValue)' # non matching for custom\n variable_pattern += r'(?!TCEntity)(?!TCEnhancedEntity)' # non matching for custom\n variable_pattern += r'[A-Za-z0-9_-]+))' # variable type (custom)\n return variable_pattern","def vars(cls):\n for key in dir(cls):\n if key.startswith('var_'):\n yield key[4:]","def getRRDVariables(self, upToPoint=None):\n cmds = self.getFakeGraphCmds(upToPoint=upToPoint)\n names = [line[line.find(':')+1:line.find('=')]\n for line in cmds.split('\\n')\n if line[:line.find(':')] in ('DEF', 'CDEF', 'VDEF')]\n nameSet = set(names)\n result = []\n for name in names:\n #only allow -raw variables if a corresponding -rpn variable is present\n if name.endswith('-raw') and name.replace('-raw', '-rpn') in nameSet or not name.endswith('-raw'):\n result.append(name)\n return result","def get_all_variables(self):\n return self.start.get_all_variables() + self.end.get_all_variables()","def get_vars_to_record(self):\n\n vars_to_record = []\n\n if any({'spiketrains', 'spikerates', 'correlation', 'spikecounts',\n 'hist_spikerates_activations'} & self._plot_keys) \\\n or 'spiketrains_n_b_l_t' in self._log_keys:\n vars_to_record.append(str('spikes'))\n\n if 'mem_n_b_l_t' in self._log_keys or 'v_mem' in self._plot_keys:\n vars_to_record.append(str('v'))\n\n return vars_to_record","def parse_template(self):\n for line in self.raw_template.split(\"\\n\"):\n line = line.strip()\n if line.startswith('#m3'):\n key, val = line[3:].strip().split('=', 1)\n key = key.strip()\n val = val.strip()\n self.variables[key] = val\n\n for fitem in self.finditem.finditer(self.raw_template):\n fgrp = fitem.groups()\n categ = fgrp[0]\n name = fgrp[1]\n rest_str = fgrp[2]\n rest = {} # type: dict\n for item in rest_str.split('|'):\n item = item.strip()\n if item:\n key, val = item.split('=')\n rest[key] = val\n\n self.data[name] = (categ, rest)","def _some_variables(use_posInd=False):\n\n parent = (\n np.array(\n [\n 0,\n 1,\n 2,\n 3,\n 4,\n 5,\n 1,\n 7,\n 8,\n 9,\n 10,\n 1,\n 12,\n 13,\n 14,\n 15,\n 13,\n 17,\n 18,\n 19,\n 20,\n 21,\n 20,\n 23,\n 13,\n 25,\n 26,\n 27,\n 28,\n 29,\n 28,\n 31,\n ]\n )\n - 1\n )\n\n offset = np.array(\n [\n 0.000000,\n 0.000000,\n 0.000000,\n -132.948591,\n 0.000000,\n 0.000000,\n 0.000000,\n -442.894612,\n 0.000000,\n 0.000000,\n -454.206447,\n 0.000000,\n 0.000000,\n 0.000000,\n 162.767078,\n 0.000000,\n 0.000000,\n 74.999437,\n 132.948826,\n 0.000000,\n 0.000000,\n 0.000000,\n -442.894413,\n 0.000000,\n 0.000000,\n -454.206590,\n 0.000000,\n 0.000000,\n 0.000000,\n 162.767426,\n 0.000000,\n 0.000000,\n 74.999948,\n 0.000000,\n 0.100000,\n 0.000000,\n 0.000000,\n 233.383263,\n 0.000000,\n 0.000000,\n 257.077681,\n 0.000000,\n 0.000000,\n 121.134938,\n 0.000000,\n 0.000000,\n 115.002227,\n 0.000000,\n 0.000000,\n 257.077681,\n 0.000000,\n 0.000000,\n 151.034226,\n 0.000000,\n 0.000000,\n 278.882773,\n 0.000000,\n 0.000000,\n 251.733451,\n 0.000000,\n 0.000000,\n 0.000000,\n 0.000000,\n 0.000000,\n 0.000000,\n 99.999627,\n 0.000000,\n 100.000188,\n 0.000000,\n 0.000000,\n 0.000000,\n 0.000000,\n 0.000000,\n 257.077681,\n 0.000000,\n 0.000000,\n 151.031437,\n 0.000000,\n 0.000000,\n 278.892924,\n 0.000000,\n 0.000000,\n 251.728680,\n 0.000000,\n 0.000000,\n 0.000000,\n 0.000000,\n 0.000000,\n 0.000000,\n 99.999888,\n 0.000000,\n 137.499922,\n 0.000000,\n 0.000000,\n 0.000000,\n 0.000000,\n ]\n )\n\n offset = offset.reshape(-1, 3)\n\n rotInd = [\n [5, 6, 4],\n [8, 9, 7],\n [11, 12, 10],\n [14, 15, 13],\n [17, 18, 16],\n [],\n [20, 21, 19],\n [23, 24, 22],\n [26, 27, 25],\n [29, 30, 28],\n [],\n [32, 33, 31],\n [35, 36, 34],\n [38, 39, 37],\n [41, 42, 40],\n [],\n [44, 45, 43],\n [47, 48, 46],\n [50, 51, 49],\n [53, 54, 52],\n [56, 57, 55],\n [],\n [59, 60, 58],\n [],\n [62, 63, 61],\n [65, 66, 64],\n [68, 69, 67],\n [71, 72, 70],\n [74, 75, 73],\n [],\n [77, 78, 76],\n [],\n ]\n\n # definitions are originating from matlab file --> bring them to zero based indexing\n rotInd = [[e - 1 for e in s if len(s) > 0] for s in rotInd]\n posInd = [0, 1, 2] if use_posInd else None\n\n expmapInd = np.split(np.arange(4, 100) - 1, 32)\n\n return parent, offset, rotInd, expmapInd, posInd","def log_diff(template_index, exists_in_source, source_row, exists_in_dest,\n dest_row):\n\n template = nori.core.cfg['templates'][template_index]\n\n if nori.core.cfg['report_order'] == 'template':\n if template_index not in diff_dict:\n diff_dict[template_index] = []\n diff_dict[template_index].append((exists_in_source, source_row,\n exists_in_dest, dest_row, None))\n diff_k = template_index\n diff_i = len(diff_dict[template_index]) - 1\n elif nori.core.cfg['report_order'] == 'keys':\n keys_str = ()\n if source_row is not None:\n num_keys = source_row[0]\n source_data = source_row[1]\n keys_tuple = source_data[0:num_keys]\n elif dest_row is not None:\n num_keys = dest_row[0]\n dest_data = dest_row[1]\n keys_tuple = dest_data[0:num_keys]\n if keys_tuple not in diff_dict:\n diff_dict[keys_tuple] = []\n diff_dict[keys_tuple].append((template_index, exists_in_source,\n source_row, exists_in_dest, dest_row,\n None))\n diff_k = keys_tuple\n diff_i = len(diff_dict[keys_tuple]) - 1\n\n if exists_in_source:\n source_str = nori.pps(source_row[1])\n elif exists_in_source is None:\n source_str = '[no value match in source database]'\n else:\n source_str = '[no key match in source database]'\n if exists_in_dest:\n dest_str = nori.pps(dest_row[1])\n elif exists_in_dest is None:\n dest_str = '[no value match in destination database]'\n else:\n dest_str = '[no key match in destination database]'\n\n nori.core.status_logger.info(\n 'Diff found for template {0} ({1}):\\nS: {2}\\nD: {3}' .\n format(template_index, nori.pps(template[T_NAME_KEY]),\n source_str, dest_str)\n )\n return (diff_k, diff_i)","def _prepare_print_template(template):\n return tuple(map(lambda t: (t[0], len(t[0]), t[1]), template))","def variables(self):\n return {u for u in self if u.type == 'var'}","def variables(self):\n return ()","def array_templates(templates, max_R=5000):\n from grizli.utils_c.interp import interp_conserve_c\n \n wave = np.unique(np.hstack([templates[t].wave for t in templates]))\n clipsum, iter = 1, 0\n while (clipsum > 0) & (iter < 10):\n clip = np.gradient(wave)/wave < 1/max_R\n idx = np.arange(len(wave))[clip]\n wave[idx[::2]] = np.nan\n wave = wave[np.isfinite(wave)]\n iter += 1\n clipsum = clip.sum()\n #print(iter, clipsum)\n \n NTEMP = len(templates)\n flux_arr = np.zeros((NTEMP, len(wave)))\n \n for i, t in enumerate(templates):\n flux_arr[i,:] = interp_conserve_c(wave, templates[t].wave,\n templates[t].flux)\n \n is_line = np.array([t.startswith('line ') for t in templates])\n \n return wave, flux_arr, is_line","def get_vars(triple):\n return set([v for k, v in triple.items() if v.startswith('?')])","def get_all_variables(self):\n return []","def _extract_template_events(self):\n\t\ttry:\n\t\t\ttable = self.hdf5file[fastq_paths[self.version]['template'] % self.group]\n\t\t\tself.template_events = [Event(x) for x in table['Events'][()]]\n\t\texcept Exception, e:\n\t\t\tself.template_events = []","def get_all_variables(self):\n return [self.item]","def getVariables(self)->Dict[str,str]:\n pass","def collectVariableAccesses(self, emit_read, emit_write):","def get_comment(view, pt):\n\n shell_vars = view.meta_info(\"shellVariables\", pt)\n if not shell_vars:\n return ([], [])\n\n # transform the list of dicts into a single dict\n all_vars = {}\n for v in shell_vars:\n if 'name' in v and 'value' in v:\n all_vars[v['name']] = v['value']\n\n line_comments = []\n block_comments = []\n\n # transform the dict into a single array of valid comments\n suffixes = [\"\"] + [\"_\" + str(i) for i in range(1, 10)]\n for suffix in suffixes:\n start = all_vars.setdefault(\"TM_COMMENT_START\" + suffix)\n end = all_vars.setdefault(\"TM_COMMENT_END\" + suffix)\n\n if start and end is None:\n line_comments.append((start,))\n elif start and end:\n block_comments.append((start, end))\n\n return (line_comments, block_comments)","def variables(self, context):\n pn = None #Node()\n first = True\n if (context == Token.CONST):\n pn = self.nf.createVariables(Token.CONST, self.ts.getLineno())\n self.decompiler.addToken(Token.CONST)\n else:\n pn = self.nf.createVariables(Token.VAR, self.ts.getLineno())\n self.decompiler.addToken(Token.VAR)\n while True:\n name = None#Node()\n init = None#Node()\n self.mustMatchToken(Token.NAME, \"msg.bad.var\")\n s = self.ts.getString()\n if not first:\n self.decompiler.addToken(Token.COMMA)\n first = False\n self.decompiler.addName(s)\n if (context == Token.CONST):\n if not self.currentScriptOrFn.addConst(s):\n if (self.currentScriptOrFn.addVar(s) != ScriptOrFnNode.DUPLICATE_CONST):\n self.addError(\"msg.var.redecl\", s)\n else:\n self.addError(\"msg.const.redecl\", s)\n else:\n dupState = self.currentScriptOrFn.addVar(s)\n if (dupState == ScriptOrFnNode.DUPLICATE_CONST):\n self.addError(\"msg.const.redecl\", s)\n else:\n if (dupState == ScriptOrFnNode.DUPLICATE_PARAMETER):\n self.addStrictWarning(\"msg.var.hides.arg\", s)\n else:\n if (dupState == ScriptOrFnNode.DUPLICATE_VAR):\n self.addStrictWarning(\"msg.var.redecl\", s)\n name = self.nf.createName(s)\n if self.matchToken(Token.ASSIGN):\n self.decompiler.addToken(Token.ASSIGN)\n init = self.assignExpr((context == Token.FOR))\n self.nf.addChildToBack(name, init)\n self.nf.addChildToBack(pn, name)\n if not self.matchToken(Token.COMMA):\n break\n return pn","def T(request):\n\treturn all_templates[request.param]","def buffer_filtering(buffer):\n\n\tlocal = weechat.buffer_get_string(buffer, \"localvar_%s\" % SCRIPT_LOCALVAR)\n\treturn {\"\": None, \"0\": False, \"1\": True}[local]","def get_vars(self):\n return [self.mu, self.var]","def placeholders(self):\n x = [i.placeholder for i in self._input_desc]\n return x[0] if len(x) == 1 else x","def test_touchstone_get_comment_variables(self):\n\n given = {'p1': ('.03', ''), 'p2': ('0.03', ''), 'p3': ('100', ''), 'p4': ('2.5', 'um')}\n actual = Touchstone(self.ntwk_comments_file).get_comment_variables()\n self.assertEqual(given, actual)","def getVariables(self):\n return [x for x in self.variables.values() if x is not None]","def get_temp_var() -> str:\n if len(DEALLOCATED_TEMP_VARS) > 1:\n var = DEALLOCATED_TEMP_VARS.pop()\n ALLOCATED_TEMP_VARS.append(var)\n\n return var\n\n # Create a t<0~> variable name to be used\n else:\n i = 0\n while True:\n var = f't{i}'\n if var not in ALLOCATED_TEMP_VARS:\n ALLOCATED_TEMP_VARS.append(var)\n return var\n\n i += 1","def logExtractVar(nomFic,variable):\n for ligne in open(nomFic,'r'):\n if (ligne.startswith('FMT') & (variable in ligne)):\n listLigne = ligne.rstrip().replace(' ','').split(',')[5:]\n nbVal = len(listLigne)\n data=dict.fromkeys(listLigne)\n for k in data.keys():\n data[k] = np.array([])\n if ligne.startswith(variable):\n ligneSansVariable = ligne.replace(variable+',','')\n listVal = np.float_(ligneSansVariable.rstrip().split(','))\n for i in range(nbVal):\n data[listLigne[i]] = np.append(data[listLigne[i]], listVal[i])\n\n return data","def test_instance_vars_present(question, question_text):\n instance = question[\"instance\"]\n for v in instance.get(\"variables\", {}):\n v_pattern = \"${\" + v + \"}\"\n assert v_pattern in question_text","def extract_words(tree: Tree, template: Tree) -> str:\n list_words = find_step(tree, template)\n return \" \".join(list_words)","def find_step(tree: Tree, template: Tree) -> list:\n if template == \"@\":\n if type(tree) == str:\n return [tree]\n return tree.leaves()\n\n if template == '*':\n return []\n\n if type(template) != str and template.label() == '*':\n res_star = []\n for temp_node in template:\n res_star.extend(find_star_step(tree, temp_node))\n return res_star\n\n if type(tree) == str or type(template) == str:\n if tree == template:\n return []\n return []\n\n if tree.label() != template.label():\n return []\n else:\n\n res = []\n for t_node in template:\n for node in get_node_by_label(tree, t_node):\n res.extend(find_step(node, t_node))\n return res","def query_and_print_variables(md):\n\n # save x variable as dictionary with keys (s, v, t)\n x_searchers = {}\n # save beta variable as dictionary with keys (v, t)\n b_target = {}\n\n for var in md.getVars():\n my_var_name = var.varName\n my_var_value = var.x\n print('%s %g' % (my_var_name, my_var_value))\n\n if 'x' in my_var_name:\n s = int(my_var_name[2])\n v = int(my_var_name[4])\n t = int(my_var_name[6])\n\n if my_var_value >= 0.5:\n x_searchers[(s, v, t)] = 1\n else:\n x_searchers[(s, v, t)] = 0\n\n elif 'beta' in my_var_name:\n # print('%s %g' % (my_var_name, my_var_value))\n # remember b[0] is probability of capture\n v = int(my_var_name[5])\n t = int(my_var_name[7])\n b_target[v, t] = my_var_value\n\n obj = md.getObjective()\n print(obj.getValue())\n\n return x_searchers, b_target","def get_placeholders(self, format_string):\n placeholders = set()\n # Tokenize the format string and process them\n for token in self.tokens(format_string):\n if token.group(\"placeholder\"):\n placeholders.add(token.group(\"key\"))\n elif token.group(\"command\"):\n # get any placeholders used in commands\n commands = dict(parse_qsl(token.group(\"command\")))\n # placeholders only used in `if`\n if_ = commands.get(\"if\")\n if if_:\n placeholders.add(Condition(if_).variable)\n return placeholders","def parse_template(template):\n field_name = None\n field_value = []\n\n for line in template.strip().split('\\n') + ['end:']:\n if line.startswith('#'):\n continue\n match = RE_TEMPLATE_FIELD_LINE.match(line)\n if match:\n if field_name is not None:\n yield (field_name, '\\n'.join(field_value).strip())\n elif len(field_value) > 0:\n logging.warning('Ignoring lines: %r', field_value)\n\n field_name = match.group(1)\n field_value = [match.group(2)]\n else:\n field_value.append(line)","def extract_templates(im, interactive = False):\n\n im = np.flipud(im)\n# tmp = cv2.medianBlur(im, 5)\n# tmp = cv2.threshold(tmp, 255*0.65, 255, cv2.THRESH_BINARY)[1]\n\n im_filtered = filter_specgram(im, interactive)\n _, contours, _ = cv2.findContours(\n im_filtered,\n cv2.RETR_LIST,\n cv2.CHAIN_APPROX_SIMPLE\n )\n\n templates = []\n\n im_dbg_template_rejected = None\n im_dbg_template_overlay = None\n if interactive:\n im_dbg_template_rejected = im.copy()\n im_dbg_template_overlay = im.copy()\n\n #im_dbg_template_overlay *= 255/im_dbg_template_overlay.max()\n\n\n # apply trunc threshold\n # apply gaussian blur\n # apply binary threshold\n # remove small blobs\n # remove huge blobs\n # for each blob, check surrounding blobs within given radius and add \n # (how to choose which to add? what radius?\n smallest = -1\n average_val = np.average(im)\n print 'average: {}'.format(average_val)\n\n for i in xrange(len(contours)):\n r = cv2.boundingRect(contours[i])\n\n left = max(0, r[0] - 10)\n top = max(0, r[1] - 10)\n right = min(len(im[0]), r[0] + r[2] + 10)\n bottom = min(len(im), r[1] + r[3] + 10)\n\n area = r[2] * r[3]\n\n #TODO: use average values from sgram?\n if area < 50 or area > 10000: # : continue\n #if area > 10000:\n if not interactive: continue\n# cv2.putText(im_dbg_template_rejected, '{}'.format(area),\n# (left, top), cv2.FONT_HERSHEY_PLAIN, 1.0,\n# int(np.max(im_dbg_template_rejected)))\n cv2.rectangle(im_dbg_template_rejected, (left,top), (right,bottom), int(np.max(im_dbg_template_rejected)), 1)\n continue\n\n if smallest == -1 or area < smallest: smallest = area\n\n x = im[top:bottom, left:right]\n #x = im[r[1]:r[1]+r[3], r[0]:r[0]+r[2]]\n if np.min(x) >= average_val:\n if not interactive: continue\n cv2.putText(im_dbg_template_rejected, 'v:{}'.format(np.average(x)), (left, top), cv2.FONT_HERSHEY_PLAIN, 1.0, int(np.max(im_dbg_template_rejected)))\n cv2.rectangle(im_dbg_template_rejected, (left,top), (right,bottom), int(np.max(im_dbg_template_rejected)), 1)\n continue\n x = cv2.GaussianBlur(x, (0,0), 1.5)\n templates.append(x)\n\n if interactive:\n cv2.rectangle(im_dbg_template_overlay, (left, top), (right, bottom), int(np.max(im_dbg_template_overlay)), 1)\n #cv2.rectangle(im_dbg_template_overlay, (r[0]-10, r[1]-10), (r[0]+r[2]+10, r[1]+r[3]+10), (255,0,0), 1)\n if interactive:\n plotMultiple([im_dbg_template_overlay, im_dbg_template_rejected],\n #plotMultiple([im_filtered, im_dbg_template_rejected],\n None,\n ['templates', 'rejected'])\n\n\n# cv2.namedWindow('orig')\n# cv2.imshow('orig', im_dbg_template_overlay)\n# cv2.namedWindow('rejected')\n# cv2.imshow('rejected', im_dbg_template_rejected)\n # plt.imshow(im_dbg_template_overlay, aspect='auto')\n # plt.show()\n print 'smallest: {}'.format(smallest)\n plt_(im_dbg_template_rejected,'reject')\n plt_(im_dbg_template_overlay,'accept')\n# while cv2.waitKey(0) != ord('n'):\n# pass\n\n return templates","def parse_special(special):\n special_name = \"\"\n specials = {}\n for line in special:\n if check_template_start(line):\n special_name = line.split(\":\")[1]\n specials[special_name] = []\n elif check_template_end(line):\n special_name = \"\"\n elif special_name != \"\":\n specials[special_name].append(line)\n\n return specials","def record_vars(context, data):\n pass","def _node_templates(self, node, log=None):\n # noinspection PyProtectedMember\n templates = set(django_settings._MON_ZABBIX_TEMPLATES_NODE)\n templates.update(self.settings.MON_ZABBIX_TEMPLATES_NODE)\n templates.update(node.monitoring_templates)\n\n return self._get_templates(self._node_kwargs(node), templates, log=log)","def extract_locations(spoiler_logs):\n locations = []\n \n for log in spoiler_logs: # loop to check the spoiler log with try-except\n try:\n locations.append(log['locations'])\n except KeyError:\n print(\"Invalid spoiler log ...\")\n return locations","def vars(self):\n return self.v","def get_variable_matches(text):\n return _property_pattern.findall(text)","def variables(self):\n return self._.vars","def pattern_to_notes(pattern):\n pattern_notes = []\n for (i, m) in zip(range(0, 6), FRET_POSITION.findall(pattern)):\n pos = m[0].strip()\n if pos != 'x':\n pattern_notes.append(FRET_NOTES[i][int(pos) % 12])\n return pattern_notes","def format_variable(self, variablesReference, filter=None, start=None, count=None, format=None):\n\n # format is ignored, TODO?\n\n vs = None if start is None or start == 0 else start\n es = None if count is None or count == 0 else count\n\n var, name, tt, parent = self.scope_assign[variablesReference]\n\n # print(str(var) + \", \" + str(name) + \", \" + str(tt))\n\n is_slotted = False\n\n if not isinstance(var, dict) and not isinstance(var, list):\n if hasattr(var, \"__dict__\"):\n var = var.__dict__\n else:\n is_slotted = True\n\n # print (str(var))\n\n if not is_slotted and isinstance(var, dict):\n if filter is not None and filter == \"indexed\":\n return []\n keys = sorted(var.keys())\n elif not is_slotted:\n if filter is not None and filter == \"named\":\n return []\n keys = range(len(var))\n elif is_slotted:\n keys = dir(var)\n\n if \"self\" in keys:\n keys.remove(\"self\")\n keys = [\"self\"] + keys\n\n # print (str(keys))\n\n it = 0\n total = 0\n variables = []\n for vkey in keys:\n if vs is None or it >= vs:\n var_ref = self.scope_var_id\n if is_slotted:\n value = getattr(var, vkey)\n else:\n value = var[vkey]\n\n vardesc = {}\n variables.append(vardesc)\n\n vardesc[\"name\"] = vkey\n vardesc[\"value\"] = str(value)\n vardesc[\"type\"] = str(type(value))\n # vardesc[\"presentationHint\"] # TODO!!!\n vardesc[\"evaluateName\"] = vkey\n vardesc[\"variablesReference\"] = var_ref\n\n vv_inner = value\n vv_slotted = False\n if not isinstance(vv_inner, dict) and not isinstance(vv_inner, list):\n if hasattr(vv_inner, \"__dict__\"):\n vv_inner = vv_inner.__dict__\n else:\n vv_slotted = True\n\n if not vv_slotted and isinstance(vv_inner, dict):\n vardesc[\"namedVariables\"] = len(vv_inner.keys())\n elif not vv_slotted:\n vardesc[\"indexedVariables\"] = len(vv_inner)\n else:\n vardesc[\"namedVariables\"] = len(dir(vv_inner))\n\n self.scope_assign[var_ref] = (value, vkey, str(type(value)), var)\n\n self.scope_var_id += 1\n total += 1\n it += 1\n if es is not None and total >= es:\n break\n\n return variables","def get_step_tmp_vals_at_index(self, index):\n try:\n return self[index][3]\n except IndexError:\n return []","def variables(self):\n return tuple(flatten([a.variables for a in self.args]))","def get_log_parts_pattern(self,params=['2017-6-9','eVar85','pid=1']):\n result =[]\n query_params = {'parameter_name':params[1],'et_log_date':params[0],'parameter_value':''}\n logging.debug(\"Query params are: key {} and date {}\".format(params[1],params[0]))\n query = \"\"\"select b.key,b.parameter_name,b.parameter_value,a.uuid,b.source\n from wt_logs a,wt_log_parts b\n where a.key = b.key\n and a.et_log_date = b.et_log_date\n and a.et_log_date = :et_log_date\n and b.parameter_name = :parameter_name\n and b.parameter_value = :parameter_value\"\"\".replace('\\n',' ')\n logging.debug(\"The log parts part is {}\".format(query))\n with vertica_python.connect(**conn_info) as connection:\n #print(\"Connected to {} on host{} \".format(conn_info['database'],conn_info['host']))\n cur = connection.cursor()\n cur.execute(query,query_params)\n for row in cur.iterate():\n result.append(row)\n return(result)","def get_temp():\n\n\tall_temp = scipy.io.loadmat('../files/ALL.templates1')\n\ttemp = all_temp['templates'].astype(np.float64)\n\tt = temp.copy()\n\treturn (t)","def getOptimizableVariables(self, TiltAlignmentParameters_):\n ntilt = self._ntilt\n nmark = len(self._Markers)\n\n nopti = (nmark - 1) * 3\n\n # translation\n if self.optimizeMarkerPositions:\n nopti += (ntilt) * 2\n\n # variable magnifications for projections\n if TiltAlignmentParameters_.dmag:\n nopti = nopti + ntilt - 1\n\n #check that irefmark and ireftilt are set properly\n if not (TiltAlignmentParameters_.irefmark in range(nmark)):\n TiltAlignmentParameters_.irefmark = 0\n print(\"Warning: irefmark must be 1<= irefmark <=nmark\")\n print(\"New irefmark: \" + str(TiltAlignmentParameters_.irefmark))\n\n if not (TiltAlignmentParameters_.ireftilt in self._projIndices.astype(int)):\n TiltAlignmentParameters_.ireftilt = abs(self._tiltAngles).argmin()\n print(\"Warning: ireftilt must be in range of projection indices\")\n print(\"New ireftilt: \" + str(TiltAlignmentParameters_.ireftilt))\n\n #variable rotation for projections\n if TiltAlignmentParameters_.drot:\n nopti = nopti + ntilt\n else:\n nopti = nopti + 1\n\n # beam tilt\n if TiltAlignmentParameters_.dbeam:\n nopti = nopti + 1\n\n ## gradient on image rotation and magnification in projections\n #if TiltAlignmentParameters_.dGradRotMag:\n # nopti = nopti + 2\n\n\n # nopti += ntilt\n\n optimizableVariables = numpy.zeros((nopti), dtype='float')\n\n # marker 3D coords\n\n ivar = 0\n for (imark, Marker) in enumerate(self._Markers):\n # reference marker irefmark is fixed to standard value\n if ((imark ) != TiltAlignmentParameters_.irefmark):\n r = Marker.get_r()\n optimizableVariables[ivar] = r[0]\n optimizableVariables[ivar + 1] = r[1]\n optimizableVariables[ivar + 2] = r[2]\n ivar = ivar + 3\n\n # translations\n if self.optimizeMarkerPositions:\n for itilt in range(0, ntilt):\n # translation in reference projection is zero\n #if self._projIndices[itilt] != TiltAlignmentParameters_.ireftilt:\n optimizableVariables[ivar] = self._alignmentTransX[itilt]\n optimizableVariables[ivar + 1] = self._alignmentTransY[itilt]\n ivar = ivar + 2\n\n # magnification changes\n if TiltAlignmentParameters_.dmag:\n for itilt in range(0, ntilt):\n # magnification of reference projection is 1.\n if int(self._projIndices[itilt]) != TiltAlignmentParameters_.ireftilt:\n optimizableVariables[ivar] = self._alignmentMagnifications[itilt]\n ivar = ivar + 1\n\n # image rotations\n if TiltAlignmentParameters_.drot:\n for itilt in range(0, ntilt):\n optimizableVariables[ivar] = self._alignmentRotations[itilt]\n ivar = ivar + 1\n\n # all rotations are the same - take the first one\n else:\n optimizableVariables[ivar] = self._alignmentRotations[0]\n ivar = ivar + 1\n\n # beam inclination\n if TiltAlignmentParameters_.dbeam:\n optimizableVariables[ivar] = self._alignmentBeamTilt\n ivar = ivar + 1\n\n # focus gradient (TODO)\n #if TiltAlignmentParameters_.dGradRotMag:\n # optimizableVariables[ivar] = self._alignmentMagnFoc\n # optimizableVariables[ivar+1] = self._alignmentRotFoc\n\n # for i in range(ntilt):\n # optimizableVariables[ivar] = -1\n # ivar += 1\n\n return optimizableVariables","def extractLocals(trcback):\n\n\toutput = []\n\tstack = extractStack(getInnerMostFrame(trcback))\n\tfor frame, fileName, lineNumber, name, context, index in stack:\n\t\targsNames, nameless, keyword = extractArguments(frame)\n\t\targuments, namelessArgs, keywordArgs, locals = OrderedDict(), [], {}, {}\n\t\tfor key, data in frame.f_locals.iteritems():\n\t\t\tif key == nameless:\n\t\t\t\tnamelessArgs = map(repr, frame.f_locals.get(nameless, ()))\n\t\t\telif key == keyword:\n\t\t\t\tkeywordArgs = dict((arg, repr(value)) for arg, value in frame.f_locals.get(keyword, {}).iteritems())\n\t\t\telif key in argsNames:\n\t\t\t\targuments[key] = repr(data)\n\t\t\telse:\n\t\t\t\tlocals[key] = repr(data)\n\t\toutput.append(((name, fileName, lineNumber), (arguments, namelessArgs, keywordArgs, locals)))\n\treturn output","def _extract_inserts(self, query) :\n\t\tsparql = self.n.sparql\n\t\t\n\t\t# because the loop below alter's the contents of each insert\n\t\tquery = copy.copy(query)\n\t\t\n\t\t# grab the insert list\n\t\tinserts = query[sparql.insert]\n\t\t\n\t\tnew_inserts = []\n\t\tfor insert in inserts :\n\t\t\tif sparql.create in insert :\n\t\t\t\tvar = insert[sparql.subject]\n\t\t\t\tpredicate = insert[sparql.predicate]\n\t\t\t\t\n\t\t\t\tdel insert[sparql.subject]\n\t\t\t\tdel insert[sparql.predicate]\n\t\t\t\t\n\t\t\t\tif predicate is None :\n\t\t\t\t\tnew_inserts.append(insert)\n\t\t\t\telse :\n\t\t\t\t\tnew_inserts.append({\n\t\t\t\t\t\tsparql.var : var,\n\t\t\t\t\t\tpredicate : insert,\n\t\t\t\t\t})\n\t\treturn new_inserts","def variables(self):\n return [term.variable for term in self.terms]","def find_raw_variable(variable_name):\n raw_variable = variable_name\n variable_transform = None\n\n for transform, template in TRANSFORM_VARIABLE_TEMPLATES.items():\n if transform:\n pattern = '^' + re.escape(template).replace('x', '(.+?)') + '$'\n m = re.search(pattern, variable_name)\n if m:\n raw_variable = m.group(1)\n variable_transform = transform\n break\n\n return variable_transform, raw_variable","def get_list_slice_vars(list_):\n\n # Dump unwanted portions\n array, list_ = list_.split(\"[\")\n list_ = list_.split(\"]\", 1)[0]\n\n # Split at ':'\n variables = list_.split(\":\")\n var_count = len(variables)\n\n step = \"\"\n\n # If step provided\n if var_count == 3:\n\n # If provided, store provided values\n start, stop, step = variables\n else:\n\n # Else store start, stop with default step\n start, stop = variables\n\n # If values are not provided by user, fall back to defaults\n\n # Set start default to 0\n if not start:\n start = \"0\"\n\n # Set stop default to array length\n if not stop:\n stop = \"Array.length\"\n\n # Set step default to 1\n if not step:\n step = \"1\"\n\n # Return stripped array with extracted values\n return array, start, stop, step","def get_all_variables(self):\n return self.item.get_all_variables()","def random_var_triplets(global_vars1, global_vars2, templates):\n m1, m2 = type_var_map(global_vars1), type_var_map(global_vars2)\n mapping = []\n for t in templates:\n if t not in m1:\n continue\n if t not in m2:\n continue\n random_tuples = random_tuple_list(m1[t], m2[t])\n for tup in random_tuples:\n mapping.append((tup[0], tup[1], random.choice(templates[t]), t))\n return mapping","def _parse_opt_out(\n tn,\n constant_tags,\n to_constant,\n):\n tn_ag = tn.copy()\n variables = []\n\n for t in tn_ag:\n\n if t.tags & constant_tags:\n t.modify(apply=to_constant)\n continue\n\n # append the raw data but mark the corresponding tensor\n # for reinsertion\n data = t.get_params()\n variables.append(data)\n t.add_tag(_VARIABLE_TAG.format(len(variables) - 1))\n\n return tn_ag, variables","def get_entries(self):\n prefixes = self.spot_mappings\n with open(self.path, 'r') as f:\n prefix_key = self.seek_through_comments(f).rsplit(\"/\", 1)[-1]\n prefix = prefixes[prefix_key]\n\n for ln in self.split_log_lines(f, \"|\", prefix):\n yield LogItem(*ln).get_properties()","def dump_var_map(self):\n vars = []\n vars.append(\"--------- VARIABLES ---------\")\n for var in self.var_map:\n vars.append(var)\n\n return vars","def get_log_parts_pattern_limit(self,params=['2017-6-9','eVar85','pid=1']):\n result =[]\n query_params = {'parameter_name':params[1],'et_log_date':params[0],'parameter_value':'%' + params[2] + '%'}\n logging.debug(\"Query params are: key {} and date {}\".format(params[1],params[0]))\n query = \"\"\"select b.key,b.parameter_name,b.parameter_value,a.uuid,b.source\n from wt_logs a,wt_log_parts b\n where a.key = b.key\n and a.et_log_date = b.et_log_date\n and a.et_log_date = :et_log_date\n and b.parameter_name = :parameter_name\n and b.parameter_value LIKE :parameter_value\n order by a.et_log_date limit 500\"\"\".replace('\\n',' ')\n logging.debug(\"The log parts part is {}\".format(query))\n with vertica_python.connect(**conn_info) as connection:\n #print(\"Connected to {} on host{} \".format(conn_info['database'],conn_info['host']))\n cur = connection.cursor()\n cur.execute(query,query_params)\n for row in cur.iterate():\n result.append(row)\n return(result)","def variables_used (self) :\r\n\t\t## These names possibly contain dimension specification!\r\n\t\treturn self.variable_names","def log_pr(self, vertex):\n s_vtype = self.vp_type[vertex]\n if s_vtype == 'variable':\n e = vertex.out_edges().next()\n log_pr = self.ep_sp_log_msg_vf[e] + self.ep_sp_log_msg_fv[e]\n elif s_vtype == 'factor':\n msgs = { self.vp_name[e.target()]: self.ep_sp_log_msg_vf[e] for e in vertex.out_edges() }\n msgs = [ msgs[n] for n in self.vp_table_inputs[vertex] ]\n log_pr = self.vp_log_table[vertex] + reduce(np.add, np.ix_(*msgs))\n else:\n raise Exception('variable type error: {}'.format(s_vtype))\n\n return log_pr","def query_variables(md):\n\n # save as dictionaries with searchers as keys\n x_searchers = {}\n b_target = {}\n\n t_max = 0\n\n for var in md.getVars():\n my_var_name = var.varName\n my_var_value = var.x\n # print('%s %g' % (my_var_name, my_var_value))\n\n if 'x' in my_var_name:\n s = int(my_var_name[2:my_var_name.find(\",\")])\n v = int(my_var_name[my_var_name.find(\",\") + 1:my_var_name.rfind(\",\")])\n t = int(my_var_name[my_var_name.rfind(\",\") + 1:-1])\n\n # print('%s = %f ' % (my_var_name, my_var_value))\n x_searchers[(s, v, t)] = my_var_value\n\n if t > t_max:\n t_max = t\n\n elif 'beta' in my_var_name and '_s' not in my_var_name:\n # print('%s %g' % (my_var_name, my_var_value))\n # remember: b[0] is probability of capture\n v = int(my_var_name[5:my_var_name.find(\",\")])\n t = int(my_var_name[my_var_name.find(\",\") + 1:my_var_name.rfind(\"]\")])\n b_target[(v, t)] = my_var_value\n\n # make sure x is binary\n x_searchers = enforce_binary(x_searchers, t_max)\n b_target = enforce_sum_1(b_target, t_max)\n\n # x_searchers[(s, v, t)] and b_target[(v, t)]\n return x_searchers, b_target","def get_variables(self):\n return self.variables","def get_swp_values(self, var: str) -> List[Any]:\n return self._sweep_params[var]","def _any_depth_parse(match):\n markers = [match.p1, match.p2, match.p3, match.p4, match.p5, match.p6]\n for idx in (4, 5):\n if markers[idx]:\n markers[idx] = mtypes.emphasize(markers[idx])\n return [m for m in markers if m]","def get_variables(self, pretty=False):\n \n output = []\n #Get the model XML tree\n model = self.model.find(xmlns + 'Model')\n #Get list of metabolites\n metabolites = model.find(xmlns + 'ListOfMetabolites')\n \n for metabolite in metabolites:\n name = metabolite.attrib['name']\n simulationType = metabolite.attrib['simulationType']\n compartment_key = metabolite.attrib['compartment']\n \n if simulationType != 'fixed':\n if pretty:\n output.append(name + ' (Particle Number)')\n else:\n #Format the metabolite string as: CN=Root,Model=modelname,Vector=Compartments[compartment],Vector=Metabolites[a],Reference=ParticleNumber\n compartment_name = self._get_compartment_name(compartment_key)\n model_name = self.get_name()\n \n output_template = Template('CN=Root,Model=${model_name},Vector=Compartments[${compartment_name}],Vector=Metabolites[${name}],Reference=ParticleNumber')\n \n output_string = output_template.substitute(model_name=model_name, compartment_name=compartment_name, name=name)\n output.append(output_string)\n #Next, get list of non-fixed compartments:\n compartments = model.find(xmlns + 'ListOfCompartments')\n for compartment in compartments:\n name = compartment.attrib['name']\n simulationType = compartment.attrib['simulationType']\n \n if simulationType != 'fixed':\n if pretty:\n output.append(name + ' (' + model.attrib['volumeUnit'] + ')')\n else:\n #format the compartment string as: \"CN=Root,Model=Kummer calcium model,Vector=Compartments[compartment_2],Reference=Volume\"\n model_name = self.get_name()\n output_template = Template('CN=Root,Model=${model_name},Vector=Compartments[${name}],Reference=Volume')\n output_string = output_template.substitute(model_name=model_name, name=name)\n output.append(output_string)\n \n #Finally, get non-fixed global quantities\n values = model.find(xmlns + 'ListOfModelValues')\n #Hack - If no values have been set in the model, use the empty list to avoid a NoneType error\n if values == None:\n values = []\n for value in values:\n name = value.attrib['name']\n simulationType = value.attrib['simulationType']\n \n if simulationType != 'fixed':\n if pretty:\n output.append(name + ' (Value)')\n else:\n #format as: CN=Root,Model=Kummer calcium model,Vector=Values[quantity_1],Reference=Value\"\n model_name = self.get_name()\n output_template = Template('CN=Root,Model=${model_name},Vector=Values[${name}],Reference=Value')\n output_string = output_template.substitute(model_name=model_name, name=name)\n output.append(output_string)\n \n return output","def log_parts():\n return get_log_parts('derivaweb_start_time',\n 'derivaweb_request_guid',\n 'derivaweb_request_content_range',\n 'derivaweb_content_type')"],"string":"[\n \"def _get_placeholders(template):\\n return [p[1] for p in string.Formatter().parse(template)\\n if p[1] is not None and len(p[1]) > 0]\",\n \"def find_template_variables(code):\\n return re.findall(re_template_var, code)\",\n \"def get_variables(self):\\n return [self.g_t, self.m_t]\",\n \"def context(template):\\n\\n return {\\n v.key: v.read()\\n for v in [Variable(name) for name in extract_variables(template)]\\n }\",\n \"def _getTempsDelexpression(listedMatrix):\\n\\n \\\"\\\"\\\"\\n each template is implemented as a boolean expression of symbols\\n where each symbol represents each one of the 26 neighbors\\n\\n \\\"\\\"\\\"\\n str1 = ''.join(str(e) for e in listedMatrix)\\n a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q, r, s, t, u, v, w, x, y, z = map(exprvar, str1)\\n\\n direction1 = (~(a) & ~(b) & ~(c) & ~(j) & ~(k) & ~(l) & ~(r) & ~(s) & ~(t) & p & (d | e | f | m | n | u | v | w | g | h | i | o | q | x | y | z)) | \\\\\\n (~(a) & ~(b) & ~(c) & ~(d) & ~(e) & ~(f) & ~(g) & ~(h) & ~(i) & v & (d | e | f | m | n | u | v | w | g | h | i | o | q | x | y | z)) | \\\\\\n (~(a) & ~(b) & ~(c) & ~(j) & ~(k) & ~(l) & ~(r) & ~(s) & ~(t) & ~(d) & ~(e) & ~(f) & ~(g) & ~(g) & ~(h) & ~(i) & y & (m | n | u | w | o | q | x | z)) | \\\\\\n (~(a) & ~(b) & ~(c) & ~(k) & ~(e) & ~(d & j) & ~ (l & f) & p & v) | \\\\\\n (~(a) & ~(b) & ~(k) & ~(e) & c & v & p & ~(j & d) & ((l & ~(f)) | (~(l) & f))) | \\\\\\n (a & v & p & ~(b) & ~(c) & ~(k) & ~(e) & ~(l & f) & ((j & ~(d)) | (~(j) & d))) | \\\\\\n (~(a) & ~(b) & ~(k) & ~(e) & n & v & p & ~(j & d)) | \\\\\\n (~(b) & ~(c) & ~(k) & ~(e) & m & v & p & ~(l & f)) | \\\\\\n (~(b) & ~(k) & ~(e) & a & n & v & p & ((j & ~(d)) | (~(j) & d))) | \\\\\\n (~(b) & ~(k) & ~(e) & c & m & v & p & ((l & ~(f)) | (~(l) & f))) | \\\\\\n (~(a) & ~(b) & ~(c) & ~(j) & ~(k) & ~(l) & ~(r) & ~(s) & ~(t) & ~(d) & ~(e) & ~(g) & ~(h) & q & y) | \\\\\\n (~(a) & ~(b) & ~(c) & ~(j) & ~(k) & ~(l) & ~(r) & ~(s) & ~(t) & ~(e) & ~(f) & ~(h) & ~(i) & o & y) | \\\\\\n (~(a) & ~(b) & ~(c) & ~(j) & ~(k) & ~(r) & ~(s) & ~(d) & ~(e) & ~(f) & ~(g) & ~(h) & ~(i) & w & y) | \\\\\\n (~(a) & ~(b) & ~(c) & ~(d) & ~(e) & ~(f) & ~(g) & ~(h) & ~(i) & ~(k) & ~(l) & ~(s) & ~(t) & u & y)\\n return expr(direction1, simplify=True)\",\n \"def calculate_diagnostic_vars(self):\\n pass\",\n \"def annotated_vars(prs):\\n def trt(acc):\\n vs,ann = acc\\n if len(ann)==0:\\n return vs\\n assert(len(ann)==1)\\n return [c.copy_token(v,ann[0]) for v in vs]\\n return c.paren(var().plus() + colon_annotation_or_meta()).treat(trt)\",\n \"def getVariables(self):\\n statVars = [self[vn] for vn in self.statVars]\\n timeVars = [self[vn] for vn in self.timeVars]\\n return statVars + timeVars\",\n \"def _build_pop_temp(self):\\n value = self.value\\n return dedent(\\n f\\\"\\\"\\\"\\n @{value}\\n D=A // D = i\\n @5\\n D=D+A // addr = 5 + i\\n @SP\\n M=M-1\\n A=M\\n D=D+M // addr = addr + RAM[SP]\\n A=D-M // A = addr - RAM[SP] \\n M=D-A // RAM[A] = addr - A\\n \\\"\\\"\\\"\\n )\",\n \"def variables_referenced(text):\\n return set(substitution_pattern.findall(text))\",\n \"def construct_out_logfields(*tups):\\n fields = []\\n\\n for tupStruct in tups:\\n for k in tupStruct._fields:\\n if k.startswith(\\\"Diagnostic\\\"):\\n k_str = k.replace('_', '/', 1)\\n fields.append(k_str)\\n else:\\n for st in stat_list:\\n fields.append(f'{k}/{st}')\\n\\n return fields\",\n \"def variables_used (self) :\\r\\n\\t\\treturn [i[0] for i in self.parameters]\",\n \"def wats_postprocessing(variables):\\n return variables\",\n \"def _build_push_temp(self):\\n value = self.value\\n return dedent(\\n f\\\"\\\"\\\"\\n // i = offset\\n @{value}\\n D=A\\n // addr = i + 5\\n @5\\n D=D+A\\n // *SP = *addr\\n A=D\\n D=M\\n @SP\\n A=M\\n M=D\\n // SP++\\n @SP\\n M=M+1\\n \\\"\\\"\\\"\\n )\",\n \"def scanvars(reader, frame, locals):\\n import tokenize\\n import keyword\\n vars, lasttoken, parent, prefix, value = [], None, None, '', __UNDEF__\\n for ttype, token, start, end, line in tokenize.generate_tokens(reader):\\n if ttype == tokenize.NEWLINE:\\n break\\n if ttype == tokenize.NAME and token not in keyword.kwlist:\\n if lasttoken == '.':\\n if parent is not __UNDEF__:\\n value = getattr(parent, token, __UNDEF__)\\n vars.append((prefix + token, prefix, value))\\n else:\\n where, value = lookup(token, frame, locals)\\n vars.append((token, where, value))\\n elif token == '.':\\n prefix += lasttoken + '.'\\n parent = value\\n else:\\n parent, prefix = None, ''\\n lasttoken = token\\n return vars\",\n \"def isolate_relevant_information(self):\\n\\n def get_formula(oncat_formula):\\n \\\"\\\"\\\"will need to go from something like\\n \\\"${value/10e11}`\\\"\\n to something more pythonic\\n \\\"{value/10e11}\\\"\\\"\\\"\\n regular_expression = r'\\\\$(?P<formula>.+)\\\\`'\\n m = re.search(regular_expression, oncat_formula)\\n if m:\\n return m.group('formula')\\n else:\\n return \\\"\\\"\\n\\n template_information = {}\\n for _index, _element in enumerate(self._oncat_default_template):\\n _title = _element[\\\"name\\\"]\\n _path = _element[\\\"path\\\"]\\n if \\\"units\\\" in _element:\\n _units = _element[\\\"units\\\"]\\n else:\\n _units = \\\"\\\"\\n if \\\"transform\\\" in _element:\\n _formula = get_formula(_element[\\\"transform\\\"])\\n else:\\n _formula = \\\"\\\"\\n template_information[_index] = {'title': _title,\\n 'path': _path,\\n 'units': _units,\\n 'formula': _formula}\\n self.template_information = template_information\",\n \"def variables_used (self) :\\r\\n\\t\\treturn []\",\n \"def get_variable_names(text):\\n names = []\\n if '@@' in text:\\n matches = _property_pattern.findall(text)\\n for token, key in matches:\\n names.append(key)\\n\\n return names\",\n \"def variables(self):\\r\\n return self.get_field('variable')\",\n \"def retrieve_temp(self):\\n\\n i = CodeGenerator.RESERVED_REGISTER_LOCATION_1\\n j = CodeGenerator.RESERVED_REGISTER_LOCATION_2\\n t = CodeGenerator.RESERVED_REGISTER_LOCATION_3\\n fp = CodeGenerator.RESERVED_REGISTER_LOCATION_4\\n self.add_pc(1)\\n self.pb[self.pc - 1] = \\\"ASSIGN\\\", _m(self.top_sp, \\\"@\\\"), _m(fp)\\n self.add_pc(7)\\n self.pb[self.pc - 7] = \\\"ADD\\\", _m(self.top_sp), _m(4, \\\"#\\\"), _m(j)\\n self.pb[self.pc - 6] = \\\"ASSIGN\\\", _m(CodeGenerator.INIT_MEMORY_VALUE + 4, \\\"#\\\"), _m(i)\\n self.pb[self.pc - 5] = \\\"ADD\\\", _m(i), _m(CodeGenerator.INIT_MEMORY_VALUE + 4, \\\"#\\\"), _m(i)\\n self.pb[self.pc - 4] = \\\"ASSIGN\\\", _m(i), _m(self.top_sp, \\\"@\\\")\\n self.pb[self.pc - 3] = \\\"ADD\\\", _m(self.top_sp), _m(4, \\\"#\\\"), _m(self.top_sp)\\n self.pb[self.pc - 2] = \\\"LT\\\", _m(CodeGenerator.INIT_MEMORY_VALUE + CodeGenerator.REGISTER_SIZE, \\\"#\\\"), _m(i), _m(\\n t)\\n self.pb[self.pc - 1] = \\\"JPF\\\", _m(t), _m(self.pc - 5)\\n self.add_pc(1)\\n self.pb[self.pc - 1] = \\\"ASSIGN\\\", _m(fp), _m(self.top_sp, \\\"@\\\")\",\n \"def getAllPlaceholders(self, template_list):\\n placeholders = []\\n for line in template_list:\\n found = re.findall(self.pattern, line)\\n placeholders.extend(found)\\n placeholders.reverse()\\n #place the cursor placeholder at the begin\\n placeholders = self._placeCursors(placeholders)\\n return placeholders\",\n \"def __extract_pattern_nodes(graph):\\n tp_nodes = graph.subjects(RDF.type, AGORA.TriplePattern)\\n for tpn in tp_nodes:\\n subject = list(graph.objects(tpn, AGORA.subject)).pop()\\n predicate = list(graph.objects(tpn, AGORA.predicate)).pop()\\n obj = list(graph.objects(tpn, AGORA.object)).pop()\\n subject_str = list(graph.objects(subject, RDFS.label)).pop().toPython()\\n predicate_str = graph.qname(predicate)\\n if (obj, RDF.type, AGORA.Variable) in graph:\\n object_str = list(graph.objects(obj, RDFS.label)).pop().toPython()\\n else:\\n object_str = list(graph.objects(obj, AGORA.value)).pop().toPython()\\n __plan_patterns[tpn] = '{} {} {}'.format(subject_str, predicate_str, object_str)\",\n \"def get_loop_vars(rule):\\n traverse_list = [rule]\\n loop_vars = []\\n while(traverse_list):\\n one_rule = traverse_list.pop()\\n operator = one_rule[0]\\n operate_nums = one_rule[1:]\\n # print(\\\"operate_nums: %s\\\" % operate_nums)\\n for operate_num in operate_nums:\\n if isinstance(operate_num, str):\\n loop_vars.append(operate_num)\\n elif isinstance(operate_num, list):\\n traverse_list.append(operate_num)\\n else:\\n continue\\n # remove redundant vars, and keep the order\\n ans_vars = list(set(loop_vars))\\n ans_vars.sort(key=loop_vars.index)\\n return ans_vars\",\n \"def GetVariableAttributes(template_src, env=None):\\n env = env or jinja2.Environment()\\n abstract_syntax_tree = env.parse(template_src)\\n node_visitor = _GetattrNodeVisitor()\\n node_visitor.visit(abstract_syntax_tree)\\n\\n output = set()\\n undeclared_variables = meta.find_undeclared_variables(abstract_syntax_tree)\\n used_variables = set()\\n for node in node_visitor.getattr_nodes:\\n attr_list = _GetAttributeList(node)\\n if attr_list[0] in undeclared_variables:\\n used_variables.add(attr_list[0])\\n output.add('.'.join(attr_list))\\n return output | (undeclared_variables - used_variables)\",\n \"def find_star_step(tree: Tree, template: Tree) -> list:\\n if type(template) == str and template == \\\"@\\\":\\n return tree.leaves()\\n\\n if type(tree) == str:\\n return []\\n res = find_step(tree, template)\\n\\n if len(res) != 0:\\n return res\\n\\n for node in tree:\\n res_ = find_star_step(node, template)\\n if res_ != []:\\n return res_\\n\\n return []\",\n \"def __extract_patterns_and_spaces(self):\\n\\n def __decorate_nodes(nodes, space):\\n \\\"\\\"\\\"\\n Performs a backward search from a list of pattern nodes and assigns a set of search spaces\\n to all encountered nodes.\\n :param nodes: List of pattern nodes that belongs to a search space\\n :param space: List of search space id\\n :return:\\n \\\"\\\"\\\"\\n for n in nodes:\\n if n not in self.__node_spaces:\\n self.__node_spaces[n] = set([])\\n self.__node_spaces[n].add(space)\\n pred_nodes = self.__plan_graph.subjects(AGORA.next, n)\\n __decorate_nodes(pred_nodes, space)\\n\\n # Extract all search spaces in the plan and build a dictionary of subjects-to-ignore per each of them.\\n # Ignored subjects are those that won't be dereferenced due to a explicit graph pattern (object) filter,\\n # e.g. ?s doap:name \\\"jenkins\\\" -> All ?s that don't match the filter will be ignored.\\n self.__spaces = set(self.__plan_graph.subjects(RDF.type, AGORA.SearchSpace))\\n self.__subjects_to_ignore = dict([(sp, set([])) for sp in self.__spaces])\\n\\n patterns = list(self.__plan_graph.subjects(RDF.type, AGORA.TriplePattern))\\n for tp in patterns:\\n # A triple pattern belongs to a UNIQUE search space\\n space = list(self.__plan_graph.subjects(AGORA.definedBy, tp)).pop()\\n self.__patterns[tp] = {'space': space}\\n\\n # Depending on the format of each triple pattern (either '?s a Concept' or '?s prop O'),\\n # it is required to extract different properties.\\n tp_pred = list(self.__plan_graph.objects(tp, predicate=AGORA.predicate)).pop()\\n\\n if tp_pred == RDF.type: # ?s a Concept\\n self.__patterns[tp]['type'] = list(self.__plan_graph.objects(tp, predicate=AGORA.object)).pop()\\n try:\\n check_type = list(self.__plan_graph.objects(tp, predicate=AGORA.checkType)).pop().toPython()\\n except IndexError:\\n check_type = True\\n self.__patterns[tp]['check'] = check_type\\n else: # ?s prop O\\n self.__patterns[tp]['property'] = tp_pred\\n tp_obj = list(self.__plan_graph.objects(tp, predicate=AGORA.object)).pop()\\n if (tp_obj, RDF.type, AGORA.Literal) in self.__plan_graph: # In case O is a Literal\\n self.__patterns[tp]['filter_object'] = list(self.__plan_graph.objects(tp_obj, AGORA.value)).pop()\\n elif isinstance(tp_obj, URIRef):\\n self.__patterns[tp]['filter_object'] = tp_obj\\n\\n tp_sub = list(self.__plan_graph.objects(tp, predicate=AGORA.subject)).pop()\\n if isinstance(tp_sub, URIRef):\\n self.__patterns[tp]['filter_subject'] = tp_sub\\n\\n # Get all pattern nodes (those that have a byPattern properties) of the search plan and search backwards\\n # in order to set the scope of each search space.\\n nodes = list(self.__plan_graph.subjects(AGORA.byPattern, tp))\\n for n in nodes:\\n if n not in self.__node_patterns:\\n self.__node_patterns[n] = set([])\\n self.__node_patterns[n].add(tp)\\n __decorate_nodes(nodes, space)\",\n \"def get_placeholders(self):\\n\\n # Define regex\\n regex = f'\\\\{self.marker_string}([^$]+)\\\\{self.marker_string}'\\n\\n found_placeholders = [match.group(1) for match in re.finditer(regex, self.raw_sequence)]\\n\\n # Check for duplicates\\n for found_placeholder in found_placeholders:\\n if found_placeholders.count(found_placeholder) != 1:\\n\\n error_msg = f\\\"Placeholder {found_placeholder} found multiple times in sequence.\\\"\\n self.hd.log.info(error_msg)\\n\\n # Remove duplicates from list\\n found_placeholders = list(set(found_placeholders))\\n\\n return found_placeholders\",\n \"def get_variables(self):\\n\\t\\treturn self.variables\",\n \"def variable_dicts(self):\\n \\n def get_variable_text(rtf_file):\\n \\\"Returns a list of variable_texts for each variable\\\"\\n st='Pos. = '\\n return rtf_file.split(st)[1:]\\n \\n def get_variable_name(variable_text):\\n st='Variable = '\\n b=variable_text.split(st)[1]\\n return b[b.find(' ')+1:b.find('\\\\t')]\\n \\n def find_pos(rtf):\\n a=rtf\\n b=a\\n return b[b.find(' ')+1:b.find('\\\\t')]\\n \\n def find_variable_label(rtf):\\n try:\\n a=rtf\\n b=a.split('Variable label = ')[1]\\n return b[b.find(' ')+1:b.find('\\\\\\\\par')]\\n except IndexError:\\n return None\\n \\n def find_variable_type(rtf):\\n if not 'This variable is ' in rtf: return ''\\n a=rtf\\n b=a.split('This variable is ')[1]\\n i1=b.find(' ')+1\\n i2=i1+b[i1:].find('}')\\n return b[i1:i2]\\n \\n def find_SPSS_measurement_level(rtf):\\n if not 'the SPSS measurement level is ' in rtf: return ''\\n a=rtf\\n b=a.split('the SPSS measurement level is ')[1]\\n i1=b.find(' ')+1\\n i2=i1+b[i1:].find('\\\\\\\\par')\\n return b[i1:i2]\\n \\n def find_SPSS_user_missing_values(rtf):\\n if not 'SPSS user missing values = ' in rtf: return dict()\\n a=rtf\\n d=a.split('SPSS user missing values = ')\\n if len(d)<2: return None\\n e=d[1]\\n i1=e.find(' ')+1\\n i2=i1+e[i1:].find('\\\\\\\\par')\\n f=e[i1:i2]\\n g=f.split(' ')\\n i=' '.join([g[0],g[2],g[4]])\\n return i\\n \\n def find_value_labels(rtf):\\n if not 'Value = ' in rtf: return dict()\\n a=rtf\\n d=a.split('Value = ')[1:]\\n z={}\\n for e in d:\\n value=e[e.find(' ')+1:e.find('\\\\t')]\\n value=float(value)\\n f=e.split('Label = ')[1]\\n label=f[f.find(' ')+1:f.find('\\\\\\\\par')]\\n z[value]=label\\n #print(z)\\n return z\\n \\n variable_texts=get_variable_text(self.rtf)\\n #pprint(variable_texts[0:2])\\n \\n result=[]\\n for variable_text in variable_texts:\\n d={'pos':find_pos(variable_text),\\n 'variable':get_variable_name(variable_text),\\n 'variable_label':find_variable_label(variable_text),\\n 'variable_type':find_variable_type(variable_text),\\n 'SPSS_measurement_level':find_SPSS_measurement_level(variable_text),\\n 'SPSS_user_missing_values':find_SPSS_user_missing_values(variable_text),\\n 'value_labels':find_value_labels(variable_text) \\n }\\n result.append(d)\\n \\n return result\",\n \"def get_step_tmp_vals_at_index(self, index):\\n return self.routine_template.get_step_tmp_vals_at_index(index)\",\n \"def reportCopyMap(self):\\n copy_map = defaultdict(list)\\n for para in self.block_map:\\n offset = self.offset_map[para]\\n for i in xrange(len(self.block_map[para]) - 1):\\n start, var, block = self.block_map[para][i]\\n span = self.block_map[para][i + 1][0] - start\\n if var is not None:\\n copy_map[para].append([start + offset, span, pulp.value(var)])\\n prevVar = pulp.value(var)\\n else:\\n copy_map[para].append([start + offset, span, prevVar])\\n finalStart, finalVar, finalBlock = self.block_map[para][-1]\\n finalSpan = self.G.sizes[para] - finalStart\\n if finalVar is not None:\\n copy_map[para].append([finalStart + offset, finalSpan, pulp.value(var)])\\n else:\\n copy_map[para].append([finalStart + offset, finalSpan, prevVar])\\n return copy_map\",\n \"def retrieve_variables(content):\\n variables = []\\n in_var_section = False\\n for line in content.splitlines():\\n #print line\\n if in_var_section:\\n var_def = re.split(' +', line)\\n if len(var_def) > 1:\\n #print var_def[0], ':', var_def[1]\\n var_name = var_def[0]\\n def_value = var_def[1]\\n if not def_value.startswith('%'): #not environment variable which would be directly passed to robot\\n variables.append([var_name.strip('${').strip('}'), def_value])\\n if '*** Variables ***' in line:\\n in_var_section = True\\n elif in_var_section and '*** ' in line:\\n #end of Variables section\\n break\\n return variables\",\n \"def _variable_pattern(self):\\n variable_pattern = r'#([A-Za-z]+)' # match literal (#App,#Trigger) at beginning of String\\n variable_pattern += r':([\\\\d]+)' # app id (:7979)\\n variable_pattern += r':([A-Za-z0-9_\\\\.\\\\-\\\\[\\\\]]+)' # variable name (:variable_name)\\n variable_pattern += r'!(StringArray|BinaryArray|KeyValueArray' # variable type (array)\\n variable_pattern += r'|TCEntityArray|TCEnhancedEntityArray' # variable type (array)\\n variable_pattern += r'|String|Binary|KeyValue|TCEntity|TCEnhancedEntity' # variable type\\n variable_pattern += r'|(?:(?!String)(?!Binary)(?!KeyValue)' # non matching for custom\\n variable_pattern += r'(?!TCEntity)(?!TCEnhancedEntity)' # non matching for custom\\n variable_pattern += r'[A-Za-z0-9_-]+))' # variable type (custom)\\n return variable_pattern\",\n \"def vars(cls):\\n for key in dir(cls):\\n if key.startswith('var_'):\\n yield key[4:]\",\n \"def getRRDVariables(self, upToPoint=None):\\n cmds = self.getFakeGraphCmds(upToPoint=upToPoint)\\n names = [line[line.find(':')+1:line.find('=')]\\n for line in cmds.split('\\\\n')\\n if line[:line.find(':')] in ('DEF', 'CDEF', 'VDEF')]\\n nameSet = set(names)\\n result = []\\n for name in names:\\n #only allow -raw variables if a corresponding -rpn variable is present\\n if name.endswith('-raw') and name.replace('-raw', '-rpn') in nameSet or not name.endswith('-raw'):\\n result.append(name)\\n return result\",\n \"def get_all_variables(self):\\n return self.start.get_all_variables() + self.end.get_all_variables()\",\n \"def get_vars_to_record(self):\\n\\n vars_to_record = []\\n\\n if any({'spiketrains', 'spikerates', 'correlation', 'spikecounts',\\n 'hist_spikerates_activations'} & self._plot_keys) \\\\\\n or 'spiketrains_n_b_l_t' in self._log_keys:\\n vars_to_record.append(str('spikes'))\\n\\n if 'mem_n_b_l_t' in self._log_keys or 'v_mem' in self._plot_keys:\\n vars_to_record.append(str('v'))\\n\\n return vars_to_record\",\n \"def parse_template(self):\\n for line in self.raw_template.split(\\\"\\\\n\\\"):\\n line = line.strip()\\n if line.startswith('#m3'):\\n key, val = line[3:].strip().split('=', 1)\\n key = key.strip()\\n val = val.strip()\\n self.variables[key] = val\\n\\n for fitem in self.finditem.finditer(self.raw_template):\\n fgrp = fitem.groups()\\n categ = fgrp[0]\\n name = fgrp[1]\\n rest_str = fgrp[2]\\n rest = {} # type: dict\\n for item in rest_str.split('|'):\\n item = item.strip()\\n if item:\\n key, val = item.split('=')\\n rest[key] = val\\n\\n self.data[name] = (categ, rest)\",\n \"def _some_variables(use_posInd=False):\\n\\n parent = (\\n np.array(\\n [\\n 0,\\n 1,\\n 2,\\n 3,\\n 4,\\n 5,\\n 1,\\n 7,\\n 8,\\n 9,\\n 10,\\n 1,\\n 12,\\n 13,\\n 14,\\n 15,\\n 13,\\n 17,\\n 18,\\n 19,\\n 20,\\n 21,\\n 20,\\n 23,\\n 13,\\n 25,\\n 26,\\n 27,\\n 28,\\n 29,\\n 28,\\n 31,\\n ]\\n )\\n - 1\\n )\\n\\n offset = np.array(\\n [\\n 0.000000,\\n 0.000000,\\n 0.000000,\\n -132.948591,\\n 0.000000,\\n 0.000000,\\n 0.000000,\\n -442.894612,\\n 0.000000,\\n 0.000000,\\n -454.206447,\\n 0.000000,\\n 0.000000,\\n 0.000000,\\n 162.767078,\\n 0.000000,\\n 0.000000,\\n 74.999437,\\n 132.948826,\\n 0.000000,\\n 0.000000,\\n 0.000000,\\n -442.894413,\\n 0.000000,\\n 0.000000,\\n -454.206590,\\n 0.000000,\\n 0.000000,\\n 0.000000,\\n 162.767426,\\n 0.000000,\\n 0.000000,\\n 74.999948,\\n 0.000000,\\n 0.100000,\\n 0.000000,\\n 0.000000,\\n 233.383263,\\n 0.000000,\\n 0.000000,\\n 257.077681,\\n 0.000000,\\n 0.000000,\\n 121.134938,\\n 0.000000,\\n 0.000000,\\n 115.002227,\\n 0.000000,\\n 0.000000,\\n 257.077681,\\n 0.000000,\\n 0.000000,\\n 151.034226,\\n 0.000000,\\n 0.000000,\\n 278.882773,\\n 0.000000,\\n 0.000000,\\n 251.733451,\\n 0.000000,\\n 0.000000,\\n 0.000000,\\n 0.000000,\\n 0.000000,\\n 0.000000,\\n 99.999627,\\n 0.000000,\\n 100.000188,\\n 0.000000,\\n 0.000000,\\n 0.000000,\\n 0.000000,\\n 0.000000,\\n 257.077681,\\n 0.000000,\\n 0.000000,\\n 151.031437,\\n 0.000000,\\n 0.000000,\\n 278.892924,\\n 0.000000,\\n 0.000000,\\n 251.728680,\\n 0.000000,\\n 0.000000,\\n 0.000000,\\n 0.000000,\\n 0.000000,\\n 0.000000,\\n 99.999888,\\n 0.000000,\\n 137.499922,\\n 0.000000,\\n 0.000000,\\n 0.000000,\\n 0.000000,\\n ]\\n )\\n\\n offset = offset.reshape(-1, 3)\\n\\n rotInd = [\\n [5, 6, 4],\\n [8, 9, 7],\\n [11, 12, 10],\\n [14, 15, 13],\\n [17, 18, 16],\\n [],\\n [20, 21, 19],\\n [23, 24, 22],\\n [26, 27, 25],\\n [29, 30, 28],\\n [],\\n [32, 33, 31],\\n [35, 36, 34],\\n [38, 39, 37],\\n [41, 42, 40],\\n [],\\n [44, 45, 43],\\n [47, 48, 46],\\n [50, 51, 49],\\n [53, 54, 52],\\n [56, 57, 55],\\n [],\\n [59, 60, 58],\\n [],\\n [62, 63, 61],\\n [65, 66, 64],\\n [68, 69, 67],\\n [71, 72, 70],\\n [74, 75, 73],\\n [],\\n [77, 78, 76],\\n [],\\n ]\\n\\n # definitions are originating from matlab file --> bring them to zero based indexing\\n rotInd = [[e - 1 for e in s if len(s) > 0] for s in rotInd]\\n posInd = [0, 1, 2] if use_posInd else None\\n\\n expmapInd = np.split(np.arange(4, 100) - 1, 32)\\n\\n return parent, offset, rotInd, expmapInd, posInd\",\n \"def log_diff(template_index, exists_in_source, source_row, exists_in_dest,\\n dest_row):\\n\\n template = nori.core.cfg['templates'][template_index]\\n\\n if nori.core.cfg['report_order'] == 'template':\\n if template_index not in diff_dict:\\n diff_dict[template_index] = []\\n diff_dict[template_index].append((exists_in_source, source_row,\\n exists_in_dest, dest_row, None))\\n diff_k = template_index\\n diff_i = len(diff_dict[template_index]) - 1\\n elif nori.core.cfg['report_order'] == 'keys':\\n keys_str = ()\\n if source_row is not None:\\n num_keys = source_row[0]\\n source_data = source_row[1]\\n keys_tuple = source_data[0:num_keys]\\n elif dest_row is not None:\\n num_keys = dest_row[0]\\n dest_data = dest_row[1]\\n keys_tuple = dest_data[0:num_keys]\\n if keys_tuple not in diff_dict:\\n diff_dict[keys_tuple] = []\\n diff_dict[keys_tuple].append((template_index, exists_in_source,\\n source_row, exists_in_dest, dest_row,\\n None))\\n diff_k = keys_tuple\\n diff_i = len(diff_dict[keys_tuple]) - 1\\n\\n if exists_in_source:\\n source_str = nori.pps(source_row[1])\\n elif exists_in_source is None:\\n source_str = '[no value match in source database]'\\n else:\\n source_str = '[no key match in source database]'\\n if exists_in_dest:\\n dest_str = nori.pps(dest_row[1])\\n elif exists_in_dest is None:\\n dest_str = '[no value match in destination database]'\\n else:\\n dest_str = '[no key match in destination database]'\\n\\n nori.core.status_logger.info(\\n 'Diff found for template {0} ({1}):\\\\nS: {2}\\\\nD: {3}' .\\n format(template_index, nori.pps(template[T_NAME_KEY]),\\n source_str, dest_str)\\n )\\n return (diff_k, diff_i)\",\n \"def _prepare_print_template(template):\\n return tuple(map(lambda t: (t[0], len(t[0]), t[1]), template))\",\n \"def variables(self):\\n return {u for u in self if u.type == 'var'}\",\n \"def variables(self):\\n return ()\",\n \"def array_templates(templates, max_R=5000):\\n from grizli.utils_c.interp import interp_conserve_c\\n \\n wave = np.unique(np.hstack([templates[t].wave for t in templates]))\\n clipsum, iter = 1, 0\\n while (clipsum > 0) & (iter < 10):\\n clip = np.gradient(wave)/wave < 1/max_R\\n idx = np.arange(len(wave))[clip]\\n wave[idx[::2]] = np.nan\\n wave = wave[np.isfinite(wave)]\\n iter += 1\\n clipsum = clip.sum()\\n #print(iter, clipsum)\\n \\n NTEMP = len(templates)\\n flux_arr = np.zeros((NTEMP, len(wave)))\\n \\n for i, t in enumerate(templates):\\n flux_arr[i,:] = interp_conserve_c(wave, templates[t].wave,\\n templates[t].flux)\\n \\n is_line = np.array([t.startswith('line ') for t in templates])\\n \\n return wave, flux_arr, is_line\",\n \"def get_vars(triple):\\n return set([v for k, v in triple.items() if v.startswith('?')])\",\n \"def get_all_variables(self):\\n return []\",\n \"def _extract_template_events(self):\\n\\t\\ttry:\\n\\t\\t\\ttable = self.hdf5file[fastq_paths[self.version]['template'] % self.group]\\n\\t\\t\\tself.template_events = [Event(x) for x in table['Events'][()]]\\n\\t\\texcept Exception, e:\\n\\t\\t\\tself.template_events = []\",\n \"def get_all_variables(self):\\n return [self.item]\",\n \"def getVariables(self)->Dict[str,str]:\\n pass\",\n \"def collectVariableAccesses(self, emit_read, emit_write):\",\n \"def get_comment(view, pt):\\n\\n shell_vars = view.meta_info(\\\"shellVariables\\\", pt)\\n if not shell_vars:\\n return ([], [])\\n\\n # transform the list of dicts into a single dict\\n all_vars = {}\\n for v in shell_vars:\\n if 'name' in v and 'value' in v:\\n all_vars[v['name']] = v['value']\\n\\n line_comments = []\\n block_comments = []\\n\\n # transform the dict into a single array of valid comments\\n suffixes = [\\\"\\\"] + [\\\"_\\\" + str(i) for i in range(1, 10)]\\n for suffix in suffixes:\\n start = all_vars.setdefault(\\\"TM_COMMENT_START\\\" + suffix)\\n end = all_vars.setdefault(\\\"TM_COMMENT_END\\\" + suffix)\\n\\n if start and end is None:\\n line_comments.append((start,))\\n elif start and end:\\n block_comments.append((start, end))\\n\\n return (line_comments, block_comments)\",\n \"def variables(self, context):\\n pn = None #Node()\\n first = True\\n if (context == Token.CONST):\\n pn = self.nf.createVariables(Token.CONST, self.ts.getLineno())\\n self.decompiler.addToken(Token.CONST)\\n else:\\n pn = self.nf.createVariables(Token.VAR, self.ts.getLineno())\\n self.decompiler.addToken(Token.VAR)\\n while True:\\n name = None#Node()\\n init = None#Node()\\n self.mustMatchToken(Token.NAME, \\\"msg.bad.var\\\")\\n s = self.ts.getString()\\n if not first:\\n self.decompiler.addToken(Token.COMMA)\\n first = False\\n self.decompiler.addName(s)\\n if (context == Token.CONST):\\n if not self.currentScriptOrFn.addConst(s):\\n if (self.currentScriptOrFn.addVar(s) != ScriptOrFnNode.DUPLICATE_CONST):\\n self.addError(\\\"msg.var.redecl\\\", s)\\n else:\\n self.addError(\\\"msg.const.redecl\\\", s)\\n else:\\n dupState = self.currentScriptOrFn.addVar(s)\\n if (dupState == ScriptOrFnNode.DUPLICATE_CONST):\\n self.addError(\\\"msg.const.redecl\\\", s)\\n else:\\n if (dupState == ScriptOrFnNode.DUPLICATE_PARAMETER):\\n self.addStrictWarning(\\\"msg.var.hides.arg\\\", s)\\n else:\\n if (dupState == ScriptOrFnNode.DUPLICATE_VAR):\\n self.addStrictWarning(\\\"msg.var.redecl\\\", s)\\n name = self.nf.createName(s)\\n if self.matchToken(Token.ASSIGN):\\n self.decompiler.addToken(Token.ASSIGN)\\n init = self.assignExpr((context == Token.FOR))\\n self.nf.addChildToBack(name, init)\\n self.nf.addChildToBack(pn, name)\\n if not self.matchToken(Token.COMMA):\\n break\\n return pn\",\n \"def T(request):\\n\\treturn all_templates[request.param]\",\n \"def buffer_filtering(buffer):\\n\\n\\tlocal = weechat.buffer_get_string(buffer, \\\"localvar_%s\\\" % SCRIPT_LOCALVAR)\\n\\treturn {\\\"\\\": None, \\\"0\\\": False, \\\"1\\\": True}[local]\",\n \"def get_vars(self):\\n return [self.mu, self.var]\",\n \"def placeholders(self):\\n x = [i.placeholder for i in self._input_desc]\\n return x[0] if len(x) == 1 else x\",\n \"def test_touchstone_get_comment_variables(self):\\n\\n given = {'p1': ('.03', ''), 'p2': ('0.03', ''), 'p3': ('100', ''), 'p4': ('2.5', 'um')}\\n actual = Touchstone(self.ntwk_comments_file).get_comment_variables()\\n self.assertEqual(given, actual)\",\n \"def getVariables(self):\\n return [x for x in self.variables.values() if x is not None]\",\n \"def get_temp_var() -> str:\\n if len(DEALLOCATED_TEMP_VARS) > 1:\\n var = DEALLOCATED_TEMP_VARS.pop()\\n ALLOCATED_TEMP_VARS.append(var)\\n\\n return var\\n\\n # Create a t<0~> variable name to be used\\n else:\\n i = 0\\n while True:\\n var = f't{i}'\\n if var not in ALLOCATED_TEMP_VARS:\\n ALLOCATED_TEMP_VARS.append(var)\\n return var\\n\\n i += 1\",\n \"def logExtractVar(nomFic,variable):\\n for ligne in open(nomFic,'r'):\\n if (ligne.startswith('FMT') & (variable in ligne)):\\n listLigne = ligne.rstrip().replace(' ','').split(',')[5:]\\n nbVal = len(listLigne)\\n data=dict.fromkeys(listLigne)\\n for k in data.keys():\\n data[k] = np.array([])\\n if ligne.startswith(variable):\\n ligneSansVariable = ligne.replace(variable+',','')\\n listVal = np.float_(ligneSansVariable.rstrip().split(','))\\n for i in range(nbVal):\\n data[listLigne[i]] = np.append(data[listLigne[i]], listVal[i])\\n\\n return data\",\n \"def test_instance_vars_present(question, question_text):\\n instance = question[\\\"instance\\\"]\\n for v in instance.get(\\\"variables\\\", {}):\\n v_pattern = \\\"${\\\" + v + \\\"}\\\"\\n assert v_pattern in question_text\",\n \"def extract_words(tree: Tree, template: Tree) -> str:\\n list_words = find_step(tree, template)\\n return \\\" \\\".join(list_words)\",\n \"def find_step(tree: Tree, template: Tree) -> list:\\n if template == \\\"@\\\":\\n if type(tree) == str:\\n return [tree]\\n return tree.leaves()\\n\\n if template == '*':\\n return []\\n\\n if type(template) != str and template.label() == '*':\\n res_star = []\\n for temp_node in template:\\n res_star.extend(find_star_step(tree, temp_node))\\n return res_star\\n\\n if type(tree) == str or type(template) == str:\\n if tree == template:\\n return []\\n return []\\n\\n if tree.label() != template.label():\\n return []\\n else:\\n\\n res = []\\n for t_node in template:\\n for node in get_node_by_label(tree, t_node):\\n res.extend(find_step(node, t_node))\\n return res\",\n \"def query_and_print_variables(md):\\n\\n # save x variable as dictionary with keys (s, v, t)\\n x_searchers = {}\\n # save beta variable as dictionary with keys (v, t)\\n b_target = {}\\n\\n for var in md.getVars():\\n my_var_name = var.varName\\n my_var_value = var.x\\n print('%s %g' % (my_var_name, my_var_value))\\n\\n if 'x' in my_var_name:\\n s = int(my_var_name[2])\\n v = int(my_var_name[4])\\n t = int(my_var_name[6])\\n\\n if my_var_value >= 0.5:\\n x_searchers[(s, v, t)] = 1\\n else:\\n x_searchers[(s, v, t)] = 0\\n\\n elif 'beta' in my_var_name:\\n # print('%s %g' % (my_var_name, my_var_value))\\n # remember b[0] is probability of capture\\n v = int(my_var_name[5])\\n t = int(my_var_name[7])\\n b_target[v, t] = my_var_value\\n\\n obj = md.getObjective()\\n print(obj.getValue())\\n\\n return x_searchers, b_target\",\n \"def get_placeholders(self, format_string):\\n placeholders = set()\\n # Tokenize the format string and process them\\n for token in self.tokens(format_string):\\n if token.group(\\\"placeholder\\\"):\\n placeholders.add(token.group(\\\"key\\\"))\\n elif token.group(\\\"command\\\"):\\n # get any placeholders used in commands\\n commands = dict(parse_qsl(token.group(\\\"command\\\")))\\n # placeholders only used in `if`\\n if_ = commands.get(\\\"if\\\")\\n if if_:\\n placeholders.add(Condition(if_).variable)\\n return placeholders\",\n \"def parse_template(template):\\n field_name = None\\n field_value = []\\n\\n for line in template.strip().split('\\\\n') + ['end:']:\\n if line.startswith('#'):\\n continue\\n match = RE_TEMPLATE_FIELD_LINE.match(line)\\n if match:\\n if field_name is not None:\\n yield (field_name, '\\\\n'.join(field_value).strip())\\n elif len(field_value) > 0:\\n logging.warning('Ignoring lines: %r', field_value)\\n\\n field_name = match.group(1)\\n field_value = [match.group(2)]\\n else:\\n field_value.append(line)\",\n \"def extract_templates(im, interactive = False):\\n\\n im = np.flipud(im)\\n# tmp = cv2.medianBlur(im, 5)\\n# tmp = cv2.threshold(tmp, 255*0.65, 255, cv2.THRESH_BINARY)[1]\\n\\n im_filtered = filter_specgram(im, interactive)\\n _, contours, _ = cv2.findContours(\\n im_filtered,\\n cv2.RETR_LIST,\\n cv2.CHAIN_APPROX_SIMPLE\\n )\\n\\n templates = []\\n\\n im_dbg_template_rejected = None\\n im_dbg_template_overlay = None\\n if interactive:\\n im_dbg_template_rejected = im.copy()\\n im_dbg_template_overlay = im.copy()\\n\\n #im_dbg_template_overlay *= 255/im_dbg_template_overlay.max()\\n\\n\\n # apply trunc threshold\\n # apply gaussian blur\\n # apply binary threshold\\n # remove small blobs\\n # remove huge blobs\\n # for each blob, check surrounding blobs within given radius and add \\n # (how to choose which to add? what radius?\\n smallest = -1\\n average_val = np.average(im)\\n print 'average: {}'.format(average_val)\\n\\n for i in xrange(len(contours)):\\n r = cv2.boundingRect(contours[i])\\n\\n left = max(0, r[0] - 10)\\n top = max(0, r[1] - 10)\\n right = min(len(im[0]), r[0] + r[2] + 10)\\n bottom = min(len(im), r[1] + r[3] + 10)\\n\\n area = r[2] * r[3]\\n\\n #TODO: use average values from sgram?\\n if area < 50 or area > 10000: # : continue\\n #if area > 10000:\\n if not interactive: continue\\n# cv2.putText(im_dbg_template_rejected, '{}'.format(area),\\n# (left, top), cv2.FONT_HERSHEY_PLAIN, 1.0,\\n# int(np.max(im_dbg_template_rejected)))\\n cv2.rectangle(im_dbg_template_rejected, (left,top), (right,bottom), int(np.max(im_dbg_template_rejected)), 1)\\n continue\\n\\n if smallest == -1 or area < smallest: smallest = area\\n\\n x = im[top:bottom, left:right]\\n #x = im[r[1]:r[1]+r[3], r[0]:r[0]+r[2]]\\n if np.min(x) >= average_val:\\n if not interactive: continue\\n cv2.putText(im_dbg_template_rejected, 'v:{}'.format(np.average(x)), (left, top), cv2.FONT_HERSHEY_PLAIN, 1.0, int(np.max(im_dbg_template_rejected)))\\n cv2.rectangle(im_dbg_template_rejected, (left,top), (right,bottom), int(np.max(im_dbg_template_rejected)), 1)\\n continue\\n x = cv2.GaussianBlur(x, (0,0), 1.5)\\n templates.append(x)\\n\\n if interactive:\\n cv2.rectangle(im_dbg_template_overlay, (left, top), (right, bottom), int(np.max(im_dbg_template_overlay)), 1)\\n #cv2.rectangle(im_dbg_template_overlay, (r[0]-10, r[1]-10), (r[0]+r[2]+10, r[1]+r[3]+10), (255,0,0), 1)\\n if interactive:\\n plotMultiple([im_dbg_template_overlay, im_dbg_template_rejected],\\n #plotMultiple([im_filtered, im_dbg_template_rejected],\\n None,\\n ['templates', 'rejected'])\\n\\n\\n# cv2.namedWindow('orig')\\n# cv2.imshow('orig', im_dbg_template_overlay)\\n# cv2.namedWindow('rejected')\\n# cv2.imshow('rejected', im_dbg_template_rejected)\\n # plt.imshow(im_dbg_template_overlay, aspect='auto')\\n # plt.show()\\n print 'smallest: {}'.format(smallest)\\n plt_(im_dbg_template_rejected,'reject')\\n plt_(im_dbg_template_overlay,'accept')\\n# while cv2.waitKey(0) != ord('n'):\\n# pass\\n\\n return templates\",\n \"def parse_special(special):\\n special_name = \\\"\\\"\\n specials = {}\\n for line in special:\\n if check_template_start(line):\\n special_name = line.split(\\\":\\\")[1]\\n specials[special_name] = []\\n elif check_template_end(line):\\n special_name = \\\"\\\"\\n elif special_name != \\\"\\\":\\n specials[special_name].append(line)\\n\\n return specials\",\n \"def record_vars(context, data):\\n pass\",\n \"def _node_templates(self, node, log=None):\\n # noinspection PyProtectedMember\\n templates = set(django_settings._MON_ZABBIX_TEMPLATES_NODE)\\n templates.update(self.settings.MON_ZABBIX_TEMPLATES_NODE)\\n templates.update(node.monitoring_templates)\\n\\n return self._get_templates(self._node_kwargs(node), templates, log=log)\",\n \"def extract_locations(spoiler_logs):\\n locations = []\\n \\n for log in spoiler_logs: # loop to check the spoiler log with try-except\\n try:\\n locations.append(log['locations'])\\n except KeyError:\\n print(\\\"Invalid spoiler log ...\\\")\\n return locations\",\n \"def vars(self):\\n return self.v\",\n \"def get_variable_matches(text):\\n return _property_pattern.findall(text)\",\n \"def variables(self):\\n return self._.vars\",\n \"def pattern_to_notes(pattern):\\n pattern_notes = []\\n for (i, m) in zip(range(0, 6), FRET_POSITION.findall(pattern)):\\n pos = m[0].strip()\\n if pos != 'x':\\n pattern_notes.append(FRET_NOTES[i][int(pos) % 12])\\n return pattern_notes\",\n \"def format_variable(self, variablesReference, filter=None, start=None, count=None, format=None):\\n\\n # format is ignored, TODO?\\n\\n vs = None if start is None or start == 0 else start\\n es = None if count is None or count == 0 else count\\n\\n var, name, tt, parent = self.scope_assign[variablesReference]\\n\\n # print(str(var) + \\\", \\\" + str(name) + \\\", \\\" + str(tt))\\n\\n is_slotted = False\\n\\n if not isinstance(var, dict) and not isinstance(var, list):\\n if hasattr(var, \\\"__dict__\\\"):\\n var = var.__dict__\\n else:\\n is_slotted = True\\n\\n # print (str(var))\\n\\n if not is_slotted and isinstance(var, dict):\\n if filter is not None and filter == \\\"indexed\\\":\\n return []\\n keys = sorted(var.keys())\\n elif not is_slotted:\\n if filter is not None and filter == \\\"named\\\":\\n return []\\n keys = range(len(var))\\n elif is_slotted:\\n keys = dir(var)\\n\\n if \\\"self\\\" in keys:\\n keys.remove(\\\"self\\\")\\n keys = [\\\"self\\\"] + keys\\n\\n # print (str(keys))\\n\\n it = 0\\n total = 0\\n variables = []\\n for vkey in keys:\\n if vs is None or it >= vs:\\n var_ref = self.scope_var_id\\n if is_slotted:\\n value = getattr(var, vkey)\\n else:\\n value = var[vkey]\\n\\n vardesc = {}\\n variables.append(vardesc)\\n\\n vardesc[\\\"name\\\"] = vkey\\n vardesc[\\\"value\\\"] = str(value)\\n vardesc[\\\"type\\\"] = str(type(value))\\n # vardesc[\\\"presentationHint\\\"] # TODO!!!\\n vardesc[\\\"evaluateName\\\"] = vkey\\n vardesc[\\\"variablesReference\\\"] = var_ref\\n\\n vv_inner = value\\n vv_slotted = False\\n if not isinstance(vv_inner, dict) and not isinstance(vv_inner, list):\\n if hasattr(vv_inner, \\\"__dict__\\\"):\\n vv_inner = vv_inner.__dict__\\n else:\\n vv_slotted = True\\n\\n if not vv_slotted and isinstance(vv_inner, dict):\\n vardesc[\\\"namedVariables\\\"] = len(vv_inner.keys())\\n elif not vv_slotted:\\n vardesc[\\\"indexedVariables\\\"] = len(vv_inner)\\n else:\\n vardesc[\\\"namedVariables\\\"] = len(dir(vv_inner))\\n\\n self.scope_assign[var_ref] = (value, vkey, str(type(value)), var)\\n\\n self.scope_var_id += 1\\n total += 1\\n it += 1\\n if es is not None and total >= es:\\n break\\n\\n return variables\",\n \"def get_step_tmp_vals_at_index(self, index):\\n try:\\n return self[index][3]\\n except IndexError:\\n return []\",\n \"def variables(self):\\n return tuple(flatten([a.variables for a in self.args]))\",\n \"def get_log_parts_pattern(self,params=['2017-6-9','eVar85','pid=1']):\\n result =[]\\n query_params = {'parameter_name':params[1],'et_log_date':params[0],'parameter_value':''}\\n logging.debug(\\\"Query params are: key {} and date {}\\\".format(params[1],params[0]))\\n query = \\\"\\\"\\\"select b.key,b.parameter_name,b.parameter_value,a.uuid,b.source\\n from wt_logs a,wt_log_parts b\\n where a.key = b.key\\n and a.et_log_date = b.et_log_date\\n and a.et_log_date = :et_log_date\\n and b.parameter_name = :parameter_name\\n and b.parameter_value = :parameter_value\\\"\\\"\\\".replace('\\\\n',' ')\\n logging.debug(\\\"The log parts part is {}\\\".format(query))\\n with vertica_python.connect(**conn_info) as connection:\\n #print(\\\"Connected to {} on host{} \\\".format(conn_info['database'],conn_info['host']))\\n cur = connection.cursor()\\n cur.execute(query,query_params)\\n for row in cur.iterate():\\n result.append(row)\\n return(result)\",\n \"def get_temp():\\n\\n\\tall_temp = scipy.io.loadmat('../files/ALL.templates1')\\n\\ttemp = all_temp['templates'].astype(np.float64)\\n\\tt = temp.copy()\\n\\treturn (t)\",\n \"def getOptimizableVariables(self, TiltAlignmentParameters_):\\n ntilt = self._ntilt\\n nmark = len(self._Markers)\\n\\n nopti = (nmark - 1) * 3\\n\\n # translation\\n if self.optimizeMarkerPositions:\\n nopti += (ntilt) * 2\\n\\n # variable magnifications for projections\\n if TiltAlignmentParameters_.dmag:\\n nopti = nopti + ntilt - 1\\n\\n #check that irefmark and ireftilt are set properly\\n if not (TiltAlignmentParameters_.irefmark in range(nmark)):\\n TiltAlignmentParameters_.irefmark = 0\\n print(\\\"Warning: irefmark must be 1<= irefmark <=nmark\\\")\\n print(\\\"New irefmark: \\\" + str(TiltAlignmentParameters_.irefmark))\\n\\n if not (TiltAlignmentParameters_.ireftilt in self._projIndices.astype(int)):\\n TiltAlignmentParameters_.ireftilt = abs(self._tiltAngles).argmin()\\n print(\\\"Warning: ireftilt must be in range of projection indices\\\")\\n print(\\\"New ireftilt: \\\" + str(TiltAlignmentParameters_.ireftilt))\\n\\n #variable rotation for projections\\n if TiltAlignmentParameters_.drot:\\n nopti = nopti + ntilt\\n else:\\n nopti = nopti + 1\\n\\n # beam tilt\\n if TiltAlignmentParameters_.dbeam:\\n nopti = nopti + 1\\n\\n ## gradient on image rotation and magnification in projections\\n #if TiltAlignmentParameters_.dGradRotMag:\\n # nopti = nopti + 2\\n\\n\\n # nopti += ntilt\\n\\n optimizableVariables = numpy.zeros((nopti), dtype='float')\\n\\n # marker 3D coords\\n\\n ivar = 0\\n for (imark, Marker) in enumerate(self._Markers):\\n # reference marker irefmark is fixed to standard value\\n if ((imark ) != TiltAlignmentParameters_.irefmark):\\n r = Marker.get_r()\\n optimizableVariables[ivar] = r[0]\\n optimizableVariables[ivar + 1] = r[1]\\n optimizableVariables[ivar + 2] = r[2]\\n ivar = ivar + 3\\n\\n # translations\\n if self.optimizeMarkerPositions:\\n for itilt in range(0, ntilt):\\n # translation in reference projection is zero\\n #if self._projIndices[itilt] != TiltAlignmentParameters_.ireftilt:\\n optimizableVariables[ivar] = self._alignmentTransX[itilt]\\n optimizableVariables[ivar + 1] = self._alignmentTransY[itilt]\\n ivar = ivar + 2\\n\\n # magnification changes\\n if TiltAlignmentParameters_.dmag:\\n for itilt in range(0, ntilt):\\n # magnification of reference projection is 1.\\n if int(self._projIndices[itilt]) != TiltAlignmentParameters_.ireftilt:\\n optimizableVariables[ivar] = self._alignmentMagnifications[itilt]\\n ivar = ivar + 1\\n\\n # image rotations\\n if TiltAlignmentParameters_.drot:\\n for itilt in range(0, ntilt):\\n optimizableVariables[ivar] = self._alignmentRotations[itilt]\\n ivar = ivar + 1\\n\\n # all rotations are the same - take the first one\\n else:\\n optimizableVariables[ivar] = self._alignmentRotations[0]\\n ivar = ivar + 1\\n\\n # beam inclination\\n if TiltAlignmentParameters_.dbeam:\\n optimizableVariables[ivar] = self._alignmentBeamTilt\\n ivar = ivar + 1\\n\\n # focus gradient (TODO)\\n #if TiltAlignmentParameters_.dGradRotMag:\\n # optimizableVariables[ivar] = self._alignmentMagnFoc\\n # optimizableVariables[ivar+1] = self._alignmentRotFoc\\n\\n # for i in range(ntilt):\\n # optimizableVariables[ivar] = -1\\n # ivar += 1\\n\\n return optimizableVariables\",\n \"def extractLocals(trcback):\\n\\n\\toutput = []\\n\\tstack = extractStack(getInnerMostFrame(trcback))\\n\\tfor frame, fileName, lineNumber, name, context, index in stack:\\n\\t\\targsNames, nameless, keyword = extractArguments(frame)\\n\\t\\targuments, namelessArgs, keywordArgs, locals = OrderedDict(), [], {}, {}\\n\\t\\tfor key, data in frame.f_locals.iteritems():\\n\\t\\t\\tif key == nameless:\\n\\t\\t\\t\\tnamelessArgs = map(repr, frame.f_locals.get(nameless, ()))\\n\\t\\t\\telif key == keyword:\\n\\t\\t\\t\\tkeywordArgs = dict((arg, repr(value)) for arg, value in frame.f_locals.get(keyword, {}).iteritems())\\n\\t\\t\\telif key in argsNames:\\n\\t\\t\\t\\targuments[key] = repr(data)\\n\\t\\t\\telse:\\n\\t\\t\\t\\tlocals[key] = repr(data)\\n\\t\\toutput.append(((name, fileName, lineNumber), (arguments, namelessArgs, keywordArgs, locals)))\\n\\treturn output\",\n \"def _extract_inserts(self, query) :\\n\\t\\tsparql = self.n.sparql\\n\\t\\t\\n\\t\\t# because the loop below alter's the contents of each insert\\n\\t\\tquery = copy.copy(query)\\n\\t\\t\\n\\t\\t# grab the insert list\\n\\t\\tinserts = query[sparql.insert]\\n\\t\\t\\n\\t\\tnew_inserts = []\\n\\t\\tfor insert in inserts :\\n\\t\\t\\tif sparql.create in insert :\\n\\t\\t\\t\\tvar = insert[sparql.subject]\\n\\t\\t\\t\\tpredicate = insert[sparql.predicate]\\n\\t\\t\\t\\t\\n\\t\\t\\t\\tdel insert[sparql.subject]\\n\\t\\t\\t\\tdel insert[sparql.predicate]\\n\\t\\t\\t\\t\\n\\t\\t\\t\\tif predicate is None :\\n\\t\\t\\t\\t\\tnew_inserts.append(insert)\\n\\t\\t\\t\\telse :\\n\\t\\t\\t\\t\\tnew_inserts.append({\\n\\t\\t\\t\\t\\t\\tsparql.var : var,\\n\\t\\t\\t\\t\\t\\tpredicate : insert,\\n\\t\\t\\t\\t\\t})\\n\\t\\treturn new_inserts\",\n \"def variables(self):\\n return [term.variable for term in self.terms]\",\n \"def find_raw_variable(variable_name):\\n raw_variable = variable_name\\n variable_transform = None\\n\\n for transform, template in TRANSFORM_VARIABLE_TEMPLATES.items():\\n if transform:\\n pattern = '^' + re.escape(template).replace('x', '(.+?)') + '$'\\n m = re.search(pattern, variable_name)\\n if m:\\n raw_variable = m.group(1)\\n variable_transform = transform\\n break\\n\\n return variable_transform, raw_variable\",\n \"def get_list_slice_vars(list_):\\n\\n # Dump unwanted portions\\n array, list_ = list_.split(\\\"[\\\")\\n list_ = list_.split(\\\"]\\\", 1)[0]\\n\\n # Split at ':'\\n variables = list_.split(\\\":\\\")\\n var_count = len(variables)\\n\\n step = \\\"\\\"\\n\\n # If step provided\\n if var_count == 3:\\n\\n # If provided, store provided values\\n start, stop, step = variables\\n else:\\n\\n # Else store start, stop with default step\\n start, stop = variables\\n\\n # If values are not provided by user, fall back to defaults\\n\\n # Set start default to 0\\n if not start:\\n start = \\\"0\\\"\\n\\n # Set stop default to array length\\n if not stop:\\n stop = \\\"Array.length\\\"\\n\\n # Set step default to 1\\n if not step:\\n step = \\\"1\\\"\\n\\n # Return stripped array with extracted values\\n return array, start, stop, step\",\n \"def get_all_variables(self):\\n return self.item.get_all_variables()\",\n \"def random_var_triplets(global_vars1, global_vars2, templates):\\n m1, m2 = type_var_map(global_vars1), type_var_map(global_vars2)\\n mapping = []\\n for t in templates:\\n if t not in m1:\\n continue\\n if t not in m2:\\n continue\\n random_tuples = random_tuple_list(m1[t], m2[t])\\n for tup in random_tuples:\\n mapping.append((tup[0], tup[1], random.choice(templates[t]), t))\\n return mapping\",\n \"def _parse_opt_out(\\n tn,\\n constant_tags,\\n to_constant,\\n):\\n tn_ag = tn.copy()\\n variables = []\\n\\n for t in tn_ag:\\n\\n if t.tags & constant_tags:\\n t.modify(apply=to_constant)\\n continue\\n\\n # append the raw data but mark the corresponding tensor\\n # for reinsertion\\n data = t.get_params()\\n variables.append(data)\\n t.add_tag(_VARIABLE_TAG.format(len(variables) - 1))\\n\\n return tn_ag, variables\",\n \"def get_entries(self):\\n prefixes = self.spot_mappings\\n with open(self.path, 'r') as f:\\n prefix_key = self.seek_through_comments(f).rsplit(\\\"/\\\", 1)[-1]\\n prefix = prefixes[prefix_key]\\n\\n for ln in self.split_log_lines(f, \\\"|\\\", prefix):\\n yield LogItem(*ln).get_properties()\",\n \"def dump_var_map(self):\\n vars = []\\n vars.append(\\\"--------- VARIABLES ---------\\\")\\n for var in self.var_map:\\n vars.append(var)\\n\\n return vars\",\n \"def get_log_parts_pattern_limit(self,params=['2017-6-9','eVar85','pid=1']):\\n result =[]\\n query_params = {'parameter_name':params[1],'et_log_date':params[0],'parameter_value':'%' + params[2] + '%'}\\n logging.debug(\\\"Query params are: key {} and date {}\\\".format(params[1],params[0]))\\n query = \\\"\\\"\\\"select b.key,b.parameter_name,b.parameter_value,a.uuid,b.source\\n from wt_logs a,wt_log_parts b\\n where a.key = b.key\\n and a.et_log_date = b.et_log_date\\n and a.et_log_date = :et_log_date\\n and b.parameter_name = :parameter_name\\n and b.parameter_value LIKE :parameter_value\\n order by a.et_log_date limit 500\\\"\\\"\\\".replace('\\\\n',' ')\\n logging.debug(\\\"The log parts part is {}\\\".format(query))\\n with vertica_python.connect(**conn_info) as connection:\\n #print(\\\"Connected to {} on host{} \\\".format(conn_info['database'],conn_info['host']))\\n cur = connection.cursor()\\n cur.execute(query,query_params)\\n for row in cur.iterate():\\n result.append(row)\\n return(result)\",\n \"def variables_used (self) :\\r\\n\\t\\t## These names possibly contain dimension specification!\\r\\n\\t\\treturn self.variable_names\",\n \"def log_pr(self, vertex):\\n s_vtype = self.vp_type[vertex]\\n if s_vtype == 'variable':\\n e = vertex.out_edges().next()\\n log_pr = self.ep_sp_log_msg_vf[e] + self.ep_sp_log_msg_fv[e]\\n elif s_vtype == 'factor':\\n msgs = { self.vp_name[e.target()]: self.ep_sp_log_msg_vf[e] for e in vertex.out_edges() }\\n msgs = [ msgs[n] for n in self.vp_table_inputs[vertex] ]\\n log_pr = self.vp_log_table[vertex] + reduce(np.add, np.ix_(*msgs))\\n else:\\n raise Exception('variable type error: {}'.format(s_vtype))\\n\\n return log_pr\",\n \"def query_variables(md):\\n\\n # save as dictionaries with searchers as keys\\n x_searchers = {}\\n b_target = {}\\n\\n t_max = 0\\n\\n for var in md.getVars():\\n my_var_name = var.varName\\n my_var_value = var.x\\n # print('%s %g' % (my_var_name, my_var_value))\\n\\n if 'x' in my_var_name:\\n s = int(my_var_name[2:my_var_name.find(\\\",\\\")])\\n v = int(my_var_name[my_var_name.find(\\\",\\\") + 1:my_var_name.rfind(\\\",\\\")])\\n t = int(my_var_name[my_var_name.rfind(\\\",\\\") + 1:-1])\\n\\n # print('%s = %f ' % (my_var_name, my_var_value))\\n x_searchers[(s, v, t)] = my_var_value\\n\\n if t > t_max:\\n t_max = t\\n\\n elif 'beta' in my_var_name and '_s' not in my_var_name:\\n # print('%s %g' % (my_var_name, my_var_value))\\n # remember: b[0] is probability of capture\\n v = int(my_var_name[5:my_var_name.find(\\\",\\\")])\\n t = int(my_var_name[my_var_name.find(\\\",\\\") + 1:my_var_name.rfind(\\\"]\\\")])\\n b_target[(v, t)] = my_var_value\\n\\n # make sure x is binary\\n x_searchers = enforce_binary(x_searchers, t_max)\\n b_target = enforce_sum_1(b_target, t_max)\\n\\n # x_searchers[(s, v, t)] and b_target[(v, t)]\\n return x_searchers, b_target\",\n \"def get_variables(self):\\n return self.variables\",\n \"def get_swp_values(self, var: str) -> List[Any]:\\n return self._sweep_params[var]\",\n \"def _any_depth_parse(match):\\n markers = [match.p1, match.p2, match.p3, match.p4, match.p5, match.p6]\\n for idx in (4, 5):\\n if markers[idx]:\\n markers[idx] = mtypes.emphasize(markers[idx])\\n return [m for m in markers if m]\",\n \"def get_variables(self, pretty=False):\\n \\n output = []\\n #Get the model XML tree\\n model = self.model.find(xmlns + 'Model')\\n #Get list of metabolites\\n metabolites = model.find(xmlns + 'ListOfMetabolites')\\n \\n for metabolite in metabolites:\\n name = metabolite.attrib['name']\\n simulationType = metabolite.attrib['simulationType']\\n compartment_key = metabolite.attrib['compartment']\\n \\n if simulationType != 'fixed':\\n if pretty:\\n output.append(name + ' (Particle Number)')\\n else:\\n #Format the metabolite string as: CN=Root,Model=modelname,Vector=Compartments[compartment],Vector=Metabolites[a],Reference=ParticleNumber\\n compartment_name = self._get_compartment_name(compartment_key)\\n model_name = self.get_name()\\n \\n output_template = Template('CN=Root,Model=${model_name},Vector=Compartments[${compartment_name}],Vector=Metabolites[${name}],Reference=ParticleNumber')\\n \\n output_string = output_template.substitute(model_name=model_name, compartment_name=compartment_name, name=name)\\n output.append(output_string)\\n #Next, get list of non-fixed compartments:\\n compartments = model.find(xmlns + 'ListOfCompartments')\\n for compartment in compartments:\\n name = compartment.attrib['name']\\n simulationType = compartment.attrib['simulationType']\\n \\n if simulationType != 'fixed':\\n if pretty:\\n output.append(name + ' (' + model.attrib['volumeUnit'] + ')')\\n else:\\n #format the compartment string as: \\\"CN=Root,Model=Kummer calcium model,Vector=Compartments[compartment_2],Reference=Volume\\\"\\n model_name = self.get_name()\\n output_template = Template('CN=Root,Model=${model_name},Vector=Compartments[${name}],Reference=Volume')\\n output_string = output_template.substitute(model_name=model_name, name=name)\\n output.append(output_string)\\n \\n #Finally, get non-fixed global quantities\\n values = model.find(xmlns + 'ListOfModelValues')\\n #Hack - If no values have been set in the model, use the empty list to avoid a NoneType error\\n if values == None:\\n values = []\\n for value in values:\\n name = value.attrib['name']\\n simulationType = value.attrib['simulationType']\\n \\n if simulationType != 'fixed':\\n if pretty:\\n output.append(name + ' (Value)')\\n else:\\n #format as: CN=Root,Model=Kummer calcium model,Vector=Values[quantity_1],Reference=Value\\\"\\n model_name = self.get_name()\\n output_template = Template('CN=Root,Model=${model_name},Vector=Values[${name}],Reference=Value')\\n output_string = output_template.substitute(model_name=model_name, name=name)\\n output.append(output_string)\\n \\n return output\",\n \"def log_parts():\\n return get_log_parts('derivaweb_start_time',\\n 'derivaweb_request_guid',\\n 'derivaweb_request_content_range',\\n 'derivaweb_content_type')\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.5529201","0.5246363","0.51169056","0.5084532","0.5074781","0.49252644","0.4919884","0.486573","0.48477352","0.48411506","0.48227668","0.48202914","0.4777373","0.476468","0.47486478","0.473558","0.47355154","0.47204447","0.47187525","0.47119597","0.47052956","0.4703536","0.4686262","0.46732712","0.46647996","0.46646315","0.46307716","0.4630208","0.4624708","0.46091974","0.45759144","0.45679387","0.45634273","0.45606738","0.456008","0.4539842","0.4534898","0.4533805","0.45227796","0.45201534","0.4517113","0.45153257","0.45147607","0.44847327","0.44846272","0.44757396","0.44714278","0.4465057","0.44642264","0.44541883","0.4449446","0.44464865","0.44378054","0.44367868","0.4431713","0.44241536","0.4421313","0.44209275","0.4399209","0.43805587","0.4377152","0.43765917","0.437493","0.43609044","0.43570533","0.4356997","0.43554163","0.43532375","0.43468603","0.43403137","0.43402123","0.43296394","0.43262997","0.43232566","0.4323255","0.4322488","0.43201232","0.43186584","0.43071058","0.42998508","0.42875344","0.42825824","0.42790762","0.42776307","0.42695993","0.42666322","0.42663836","0.4264824","0.42647284","0.42633218","0.42619875","0.42601505","0.42549947","0.4245559","0.42451543","0.42264807","0.42260993","0.4224786","0.42231473","0.4220464"],"string":"[\n \"0.5529201\",\n \"0.5246363\",\n \"0.51169056\",\n \"0.5084532\",\n \"0.5074781\",\n \"0.49252644\",\n \"0.4919884\",\n \"0.486573\",\n \"0.48477352\",\n \"0.48411506\",\n \"0.48227668\",\n \"0.48202914\",\n \"0.4777373\",\n \"0.476468\",\n \"0.47486478\",\n \"0.473558\",\n \"0.47355154\",\n \"0.47204447\",\n \"0.47187525\",\n \"0.47119597\",\n \"0.47052956\",\n \"0.4703536\",\n \"0.4686262\",\n \"0.46732712\",\n \"0.46647996\",\n \"0.46646315\",\n \"0.46307716\",\n \"0.4630208\",\n \"0.4624708\",\n \"0.46091974\",\n \"0.45759144\",\n \"0.45679387\",\n \"0.45634273\",\n \"0.45606738\",\n \"0.456008\",\n \"0.4539842\",\n \"0.4534898\",\n \"0.4533805\",\n \"0.45227796\",\n \"0.45201534\",\n \"0.4517113\",\n \"0.45153257\",\n \"0.45147607\",\n \"0.44847327\",\n \"0.44846272\",\n \"0.44757396\",\n \"0.44714278\",\n \"0.4465057\",\n \"0.44642264\",\n \"0.44541883\",\n \"0.4449446\",\n \"0.44464865\",\n \"0.44378054\",\n \"0.44367868\",\n \"0.4431713\",\n \"0.44241536\",\n \"0.4421313\",\n \"0.44209275\",\n \"0.4399209\",\n \"0.43805587\",\n \"0.4377152\",\n \"0.43765917\",\n \"0.437493\",\n \"0.43609044\",\n \"0.43570533\",\n \"0.4356997\",\n \"0.43554163\",\n \"0.43532375\",\n \"0.43468603\",\n \"0.43403137\",\n \"0.43402123\",\n \"0.43296394\",\n \"0.43262997\",\n \"0.43232566\",\n \"0.4323255\",\n \"0.4322488\",\n \"0.43201232\",\n \"0.43186584\",\n \"0.43071058\",\n \"0.42998508\",\n \"0.42875344\",\n \"0.42825824\",\n \"0.42790762\",\n \"0.42776307\",\n \"0.42695993\",\n \"0.42666322\",\n \"0.42663836\",\n \"0.4264824\",\n \"0.42647284\",\n \"0.42633218\",\n \"0.42619875\",\n \"0.42601505\",\n \"0.42549947\",\n \"0.4245559\",\n \"0.42451543\",\n \"0.42264807\",\n \"0.42260993\",\n \"0.4224786\",\n \"0.42231473\",\n \"0.4220464\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.687035"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":75588,"cells":{"query":{"kind":"string","value":"Wait for the call count on the mock to reach or exceed a threashold, or a timeout occurs. Useful for multithreaded testing."},"document":{"kind":"string","value":"def wait_for_calls(mock, count, timeout_seconds):\n timeout = datetime.datetime.now() + datetime.timedelta(seconds=timeout_seconds)\n while mock.call_count < count:\n time.sleep(0.01)\n if datetime.datetime.now() > timeout:\n return False\n return True"},"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 _wait_before_call(self):\n while (dt.datetime.now() - self._last_call_ts) <= dt.timedelta(\n seconds=self.api_timeout\n ):\n time.sleep(0.5)\n self._last_call_ts = dt.datetime.now()","def wait_fluently(condition: Callable, timeout: TimeoutType, err_msg: str):\n if timeout is None:\n timeout = 0\n start_time = time.time()\n while True:\n res = condition()\n if res:\n return res\n if time.time() - start_time >= timeout:\n raise TimeoutException(err_msg)\n time.sleep(0.3)","def wait(self, timeout):\n raise NotImplementedError(\n u\"%s: Method not implemented\", self.__class__.__name__)","def wait_for_completion(self, timeout=10):\n cur_status = self.runtime_status()\n while cur_status not in ['FAILED', 'KILLED', 'FINISHED']:\n time.sleep(0.2)\n timeout -= 0.2\n cur_status = self.runtime_status()\n if timeout < 0:\n break\n\n return timeout > 0","def assert_timeout(self) -> None:","def wait(self, ms=None):\r\n util.raiseNotDefined()","def test__API_with_wrong_answer(self):\n self.mock_connection.state = MockConnection.WRONG_NUM_OF_CONFIRMATIONS\n\n # timeout supposed to be here\n self.assertEqual(self.mutex.lock(), False) # acquire mutex","def test_wait_tx_settled_timeout(self, is_transaction_settled_mock):\n with pytest.raises(TimeoutError):\n wait_tx_settled(\"some\", \"some\", timeout=0.5)","def wait_until_got_number_of_callbacks(self, number):\n slept = 0.0\n time_step = 0.1\n while not len(self.futures) == number and slept < TIMEOUT:\n time.sleep(time_step)\n slept += time_step","def wait(self, timeout=None):\n if self.counter > 0:\n return self.counter\n\n self._wait(timeout) # return value irrelevant, whether we got it or got a timeout\n return self.counter","def _wait_on_condition(self, timeout):\n self.__condition.wait(timeout)","def test_timeout(self):\n start = time.time()\n dr = EventualResult(Deferred(), None)\n self.assertRaises(TimeoutError, dr.wait, timeout=0.03)\n # be a little lenient for slow computers:\n self.assertTrue(abs(time.time() - start) < 0.05)","def test_timeoutReset(self):\n for i in range(3):\n self.circuit_breaker.failure()\n self.time.advance(29.0)\n available29sec = self.circuit_breaker.available()\n self.time.advance(1.1)\n available30sec = self.circuit_breaker.available()\n self.assertEqual((available29sec, available30sec),\n (False, True))","def test_wait_for_predicate_timeout(self):\n predicate_mock = mock.MagicMock(side_effect=[True, True, True])\n with self.assertRaises(TimeoutError):\n train_utils.wait_for_predicate(predicate_mock, num_retries=3)","def answer_waiting_call(self) -> None:","def TODO_testTimeout(self):\n return \"\"\"TODO: Highly dependent on hardcoded downstream timeout val\"\"\"\n\n # Assuming proxy's downstream_max is 1,\n # and number of threads is 1.\n\n self.client_connect(0)\n\n self.client_send('get time0\\r\\n', 0)\n self.mock_recv('get time0\\r\\n', 0)\n\n # Mock server is 'busy' at this point, so\n # downstream timeout logic should kick in,\n # without our mock server having to send anything.\n\n self.wait(210)\n\n self.client_recv('END\\r\\n', 0)\n\n # TODO: The number of server sessions should be 0,\n # except the close might not have propagated.","async def test_wait_for(self) -> None:\n trigger = auraxium.Trigger(auraxium.event.Death)\n\n def do_nothing(_: auraxium.event.Event) -> None:\n pass\n\n trigger.action = do_nothing\n\n await self.client.wait_for(trigger, timeout=-1.0)\n\n with self.assertRaises(TimeoutError):\n await self.client.wait_for(trigger, timeout=0.00001)","def test_wait_tx_settled_ok(self, is_transaction_settled_mock):\n wait_tx_settled(\"some\", \"some\", timeout=4)","def test_wait_race(self):\n mock_handler = mock.Mock()\n async_result = self._makeOne(mock_handler)\n\n async_result.set(\"immediate\")\n\n cv = threading.Event()\n\n def wait_for_val():\n # NB: should not sleep\n async_result.wait(20)\n cv.set()\n th = threading.Thread(target=wait_for_val)\n th.daemon = True\n th.start()\n\n # if the wait() didn't sleep (correctly), cv will be set quickly\n # if it did sleep, the cv will not be set yet and this will timeout\n cv.wait(10)\n eq_(cv.is_set(), True)\n th.join()","def test_timeout_pending(self):\n deadline = Deadline(MS)\n timeout = deadline.timeout()\n self.assertGreater(timeout, 0)\n self.assertLess(timeout, MS)","def _wait_reply(self, comm_id, call_id, call_name, timeout, retry=True):\n def reply_received():\n \"\"\"The reply is there!\"\"\"\n return call_id in self._reply_inbox\n if not self.wait_until(reply_received):\n if retry:\n self._wait_reply(comm_id, call_id, call_name, timeout, False)\n return\n raise TimeoutError(\n \"Timeout while waiting for '{}' reply.\".format(\n call_name))","def wait_until_responsive(self, check, timeout, pause,\n clock=timeit.default_timer):\n\n ref = clock()\n now = ref\n while (now - ref) < timeout:\n if check():\n return\n time.sleep(pause)\n now = clock()\n\n raise Exception(\n 'Timeout reached while waiting on service!'\n )","def waitUntilSuccess():","def wait(self, timeout=None):\n assert False, \"Deriving class must implement\"","def in_waiting(self) -> int:\n pass","async def test_wait_synchronized(self):\n client.wait_synchronized = AsyncMock(side_effect=[TimeoutException('timeout'), TimeoutException('timeout'),\n MagicMock()])\n await api.wait_synchronized()","def wait(n=3):\n sleep(n)","def check_done(self):\n if len(self._calls) != 0:\n raise MockException(\"Still expecting more function calls\")","def _WaitForLinkerTestStatus(adb, timeout):","def test_timeoutRaises(self):\n\n @self.eventloop.wait_for(timeout=0.5)\n def times_out():\n return Deferred().addErrback(lambda f: f.trap(CancelledError))\n\n start = time.time()\n self.assertRaises(TimeoutError, times_out)\n self.assertTrue(abs(time.time() - start - 0.5) < 0.1)","def test__API_other_locks_later(self):\n self.mock_connection.state = MockConnection.CORRECT_NUM_OF_CONFIRMATIONS\n\n # simulate release after 1.5s\n delay = 1.5\n thread = Thread(target=self.mock_connection.simulate_release_from_other_process, args=(1, delay))\n thread.start()\n\n start = time()\n self.mutex.lock()\n\n # simulate request\n self.mock_connection.simulate_request_from_other_process(10) # later than our time\n\n # locked before delay passed=> OK\n self.assertLess(time(), start + delay)\n\n self.mutex.unlock()","def call_in_waiting_thread(\n __call: Union[Callable[[], T], Call[T]],\n thread: threading.Thread,\n timeout: Optional[float] = None,\n ) -> T:\n raise NotImplementedError()","def timeout(n):\n time.sleep(int(n))\n return 'ok', 200","def wait(self):\n self.drain_call_queue()","def wait_condition(cond, timeout=1, sleeptime=.01):\n # NOTE Increasing sleeptime can dramatically increase testsuite runtime\n # It also reduces CPU load significantly\n if timeout is None:\n timeout = 1\n\n if timeout < sleeptime:\n print(\"Warning, timeout cannot be smaller than\", sleeptime)\n timeout = sleeptime\n\n # Max number of attempts until giving up\n tries = int(timeout / sleeptime)\n\n for i in range(tries):\n val = cond()\n\n if val is not None:\n break\n\n sleep(sleeptime)\n\n return val","def wait_condition(cond, timeout=1, sleeptime=.01):\n # NOTE Increasing sleeptime can dramatically increase testsuite runtime\n # It also reduces CPU load significantly\n if timeout is None:\n timeout = 1\n\n if timeout < sleeptime:\n print(\"Warning, timeout cannot be smaller than\", sleeptime)\n timeout = sleeptime\n\n # Max number of attempts until giving up\n tries = int(timeout / sleeptime)\n\n for i in range(tries):\n val = cond()\n\n if val is not None:\n break\n\n sleep(sleeptime)\n\n return val","def wait_until(self, check, timeout=None):\n self._wait_in_process_loop(lambda: (check(),None),timeout=timeout)","def _wait_for(\n func: Callable,\n expected_result: Any = True,\n timeout: int = 10,\n print_error: bool = True,\n sleep_for: int = 1,\n **kwargs,\n) -> None:\n end = time() + timeout\n\n while time() <= end:\n try:\n retval = func(**kwargs)\n except Exception as err: # pylint: disable=broad-except\n if print_error:\n logger.error(err)\n else:\n if retval == expected_result:\n return None\n sleep(sleep_for)\n\n raise WaitForException(\n f\"func: {func}, didn't return {expected_result} within specified timeout: {timeout}\"\n )","def functionThatWillTimeOut():\n time.sleep(5)","def wait_on_function ( func, func_args, func_wait_value, sleep_time, max_wait ) :\n func_response = func( *func_args )\n while func_response != func_wait_value :\n if max_wait <= 0 :\n break\n time.sleep( sleep_time )\n max_wait -= sleep_time\n func_response = func( *func_args )\n\n return func_response == func_wait_value","def wait_until_done(self, timeout=10.0):\r\n cfunc = lib_importer.windll.DAQmxWaitUntilTaskDone\r\n if cfunc.argtypes is None:\r\n with cfunc.arglock:\r\n if cfunc.argtypes is None:\r\n cfunc.argtypes = [lib_importer.task_handle, ctypes.c_double]\r\n\r\n error_code = cfunc(self._handle, timeout)\r\n check_for_error(error_code)","def waitFor(func, maxWait=None, message=None):\n\twaited = 0\n\tmaxWait = maxWait or defaultDelay\n\n\tif message:\n\t\tlog(message)\n\n\twhile True:\n\n\t\tret = func()\n\n\t\tif ret:\n\t\t\treturn ret\n\n\t\twaited += tickTime\n\n\t\tif waited > maxWait:\n\t\t\traise Exception('Maximum wait time exceded.' if not message else 'Failed waiting for: %s' % message)\n\n\t\tif message:\n\t\t\tdebug(message)\n\n\t\tdebug('Waited: %d, MaxWait: %d.' % (waited, maxWait))\n\t\tsleep(tickTime)","def test_lock_timeout_loop():\n lock_unlock_timeout(0.2)","def test_timeout_exceeded():\n connection = FakeBaseConnection(session_timeout=10)\n start = time.time() - 11\n try:\n connection._timeout_exceeded(start)\n except NetmikoTimeoutException as exc:\n assert isinstance(exc, NetmikoTimeoutException)\n return\n\n assert False","def test_wait_for_db_command_(self, mock_time_sleep):\n with patch(\n 'django.db.utils.ConnectionHandler.__getitem__',\n side_effect=[OperationalError]*5 + [True]\n ) as gi:\n call_command('wait_for_db')\n self.assertEqual(gi.call_count, 6)","def _selenium_wait_for(fn):\n start_time = time.time()\n while True:\n try:\n return fn()\n except (AssertionError, WebDriverException) as e:\n if time.time() - start_time > 10:\n raise e\n time.sleep(0.5)","def wait(self, timeoout=None, state=\"C-completed\"):","def wait_until(condition, delay, max_attempts):\n attempt = 0\n while not condition() and attempt < max_attempts:\n attempt += 1\n time.sleep(delay)\n\n if attempt >= max_attempts:\n raise Exception(\"Condition is still False after {} attempts.\".format(max_attempts))","async def test_wait_for_activity_timeout(aiopg_connector):\n pg_app = app.App(connector=aiopg_connector)\n worker = worker_module.Worker(app=pg_app, timeout=2)\n worker.notify_event = asyncio.Event()\n task = asyncio.ensure_future(worker.single_worker(worker_id=0))\n try:\n await asyncio.sleep(0.2) # should be enough so that we're waiting\n\n worker.stop_requested = True\n\n with pytest.raises(asyncio.TimeoutError):\n await asyncio.wait_for(task, timeout=0.2)\n finally:\n worker.notify_event.set()","def test_wait_for_page_in_timeout(self):\n start_time = datetime.now()\n with self.assertRaises(SpdbError):\n csdb = CacheStateDB(self.config_data)\n ch = csdb.create_page_in_channel()\n\n csdb.wait_for_page_in([\"MY_TEST_KEY1\", \"MY_TEST_KEY2\"], ch, 1)\n\n assert (datetime.now() - start_time).seconds < 3","def wait(self) -> None:\n now = time.time()\n if now < self.lockTime:\n diff = self.lockTime - now\n logger.debug(\"Waiting %ss to avoid ratelimit\", diff)\n time.sleep(diff)","def test_timeout_loop(self):\n\n self.assertIsNone(self.state.becomeCandidateTimeout)\n # This should do nothing\n self.state.cancelBecomeCandidateTimeout()\n self.assertIsNone(self.state.becomeCandidateTimeout)\n\n results = self.state.begin()\n\n self.state.resetElectionTimeout()\n self.assertTrue(self.state.electionTimeout > 0.150)\n self.assertTrue(self.state.electionTimeout < 0.350)\n self.assertTrue(isinstance(self.state.becomeCandidateTimeout,\n base.DelayedCall))","def get_timeout(self) -> int:","def wait_for_doc_count(self, db_name, expected, timeoutInMin):\n\t\ttimeout = time.time() + timeoutInMin * 60\n\t\t\n\t\twhile (time.time() < timeout):\n\t\t\tdoc_count = self.get_doc_count(db_name)\n\t\t\tif doc_count < int(expected):\n\t\t\t\ttime.sleep(5)\n\t\t\telse:\n\t\t\t\tprint (\"{} doc count = {}\".format(db_name, doc_count))\n\t\t\t\treturn\n\t\t\t\t\n\t\traise RuntimeError(\"Timeout waiting for {} to reach count {} after {} min(s)\".format(\n\t\t\tdb_name, expected, timeoutInMin))","def test_condition_waiter(self):\n\n class Holder(object):\n def __init__(self, start_value, max_value):\n self.value = start_value\n self.max_value = max_value\n self.num_calls = 0\n\n def my_check(holder):\n holder.num_calls += 1\n if holder.value == holder.max_value:\n return True\n holder.value += 1\n\n holder = Holder(1, 10)\n event = Event()\n condition = ConditionWaiter(event, my_check, holder)\n condition.start()\n self.assertTrue(event.wait(2))\n condition.stop()\n self.assertEqual(holder.num_calls, 10 - 1 + 1)\n\n holder = Holder(4, 10)\n event = Event()\n condition = ConditionWaiter(event, my_check, holder)\n condition.start()\n self.assertTrue(event.wait(3))\n condition.stop()\n self.assertEqual(holder.num_calls, 10 - 4 + 1)\n\n holder = Holder(1, 10)\n event = Event()\n condition = ConditionWaiter(event, my_check, holder)\n condition.start()\n self.assertFalse(event.wait(0.0001))\n condition.stop()","def __wait(min_sec, max_sec):\n time.sleep(randint(min_sec, max_sec))","def onWaited( self, result ):\n\t\treturn self.agi.sayNumber( self.count, '*' ).addErrback(\n\t\t\tself.onNumberFailed,\n\t\t).addCallbacks(\n\t\t\tself.onFinished, self.onFinished,\n\t\t)","def _wait_for_completion(conn, promise, wait_timeout, msg):\n if not promise:\n return\n wait_timeout = time.time() + wait_timeout\n while wait_timeout > time.time():\n time.sleep(5)\n operation_result = conn.get_request(\n request_id=promise[\"requestId\"], status=True\n )\n\n if operation_result[\"metadata\"][\"status\"] == \"DONE\":\n return\n elif operation_result[\"metadata\"][\"status\"] == \"FAILED\":\n raise Exception(\n \"Request: {}, requestId: {} failed to complete:\\n{}\".format(\n msg,\n str(promise[\"requestId\"]),\n operation_result[\"metadata\"][\"message\"],\n )\n )\n\n raise Exception(\n 'Timed out waiting for asynchronous operation {} \"{}\" to complete.'.format(\n msg, str(promise[\"requestId\"])\n )\n )","def out_waiting(self) -> int:\n pass","def is_call_waiting(self) -> bool:","def wait_for_interrupts(self, wait_time = 1):\n raise AssertionError(\"wait_for_interrupts function i not implemented\")","def fake_poll_until(retriever, condition=lambda value: value,\n sleep_time=1, time_out=0):\n from trove.common import exception\n slept_time = 0\n while True:\n resource = retriever()\n if condition(resource):\n return resource\n fake_sleep(sleep_time)\n slept_time += sleep_time\n if time_out and slept_time >= time_out:\n raise exception.PollTimeOut()","def wait(wait_time=WAIT_TIME):\n # time.sleep(wait_time)\n pass","def answered(self, maxwait=0):\n\t\ttry:\n\t\t\treturn self.pingresult.get(maxwait)\n\t\texcept FutureTimedOut:\n\t\t\treturn False","def test_timeout_twice(self):\n dr = EventualResult(Deferred(), None)\n self.assertRaises(TimeoutError, dr.wait, timeout=0.01)\n self.assertRaises(TimeoutError, dr.wait, timeout=0.01)","def wait(self):\n\t\traise NotImplementedError(\"must be redeclared\")","def simulate_waiting(self, exit_when=None):\n self._time_condition.acquire()\n\n # Helper function to reduce code copy\n def wait_block():\n self._increment_waiting_count(1)\n self._time_condition.wait()\n self._increment_waiting_count(-1)\n\n try:\n if exit_when is None:\n wait_block()\n else:\n while not exit_when(self._time):\n wait_block()\n finally:\n self._time_condition.release()","def test_handle_multiple_responses_throw_timeouterror():\n # GIVEN\n bboxes = [8.67066,49.41423,8.68177,49.4204]\n time = \"2010-01-01/2011-01-01/P1Y\"\n keys = [\"building\"]\n values = [\"\"]\n\n # WHEN\n client = ohsome.OhsomeClientParallel()\n response = client.elements.count.post(bboxes=bboxes, time=time, keys=keys, values=values, timeout=2)\n del client","def wait(self, timeout=600):\n s = datetime.datetime.now()\n status = json.loads(self.get())\n while status['status'] != 'COMPLETE':\n status = self.get()\n e = datetime.datetime.now()\n if (e - s).seconds > timeout:\n raise RuntimeError('timeout')\n return status","def timeout_wait(self):\n if self._dtr_enabled:\n while (self.__micros() - self._resume_time) < 0:\n if False:\n break # TODO: Check for printer status here\n else:\n while (self.__micros() - self._resume_time) < 0:\n pass","def _wait_for_response(self, millis):\n loops = millis / 10\n while not self.__atresponse_received:\n time.sleep(0.01)\n loops -= 1\n if loops == 0:\n return False\n return True","def wait(self, timeout=None):\n with self.condition:\n if not self.ready:\n self.condition.wait(timeout)","def test_wait(self):\n skill = create_skill()\n\n expected_response = 'Yes I do, very much'\n\n converser = Thread(target=create_converse_responder(expected_response,\n skill))\n converser.start()\n validator = mock.Mock()\n validator.return_value = True\n is_cancel = mock.Mock()\n is_cancel.return_value = False\n on_fail = mock.Mock()\n\n response = skill._wait_response(is_cancel, validator, on_fail, 1)\n self.assertEqual(response, expected_response)\n converser.join()","def wait_for(self, query_delay=0):\n raise NotImplementedError(\"Implement in subclass!\")","def wait(item=TIME, ants=0, tmo=0, waiton=-2, precomment=None, postcomment=None,\n subarray=DEFAULT) :\n if tmo > carmaIni.CORBA_CLIENT_CALL_TIMEOUT_S:\n warn = \"wait: Timeout (%ds) is greater than max CORBA timeout (%ds).\"\n warn += \"\\n\\tThrottling to %ds.\"\n warn = warn%( tmo, \n carmaIni.CORBA_CLIENT_CALL_TIMEOUT_S, \n carmaIni.CORBA_CLIENT_CALL_TIMEOUT_S - 10 )\n printWarning( warn )\n tmo = carmaIni.CORBA_CLIENT_CALL_TIMEOUT_S - 10\n \n return runKeyboardInterruptable( _wait, item, ants, tmo, waiton, \n precomment, postcomment, subarray )","def test_lock_timeout():\n lock_unlock_timeout(0)","def _wait_and_check(self, sleep=5, exclude_services=[]):\n u.log.debug('Extended wait and check ...')\n time.sleep(sleep)\n self.d.sentry.wait(timeout=900)\n time.sleep(sleep)\n self._auto_wait_for_status(exclude_services=exclude_services)\n time.sleep(sleep)\n self.d.sentry.wait()\n u.log.debug('Wait and check completed.')","def timeout(time_limit):\n\n class TimeoutException(Exception):\n \"\"\" Subclass Exception to catch timer expiration during search \"\"\"\n pass\n\n def handler(*args, **kwargs):\n \"\"\" Generic handler to raise an exception when a timer expires \"\"\"\n raise TimeoutException(\"Test aborted due to timeout. Test was \" +\n \"expected to finish in less than {} second(s).\".format(time_limit))\n\n def wrapUnitTest(testcase):\n\n @wraps(testcase)\n def testWrapper(self, *args, **kwargs):\n\n signal.signal(signal.SIGALRM, handler)\n signal.alarm(time_limit)\n\n try:\n return testcase(self, *args, **kwargs)\n finally:\n signal.alarm(0)\n\n return testWrapper\n\n return wrapUnitTest","def test_pool_timeout_hw(self):\n self.test_pool_timeout()","def test_lock_wait(self):\n get_lock('test_wait', expires=4)\n start_lock = timezone.now()\n with LockMgr('test_wait', wait=5):\n end_lock = timezone.now()\n self.assertGreaterEqual(end_lock, start_lock + timedelta(seconds=4),\n msg=\"Assert lock released after at least 4 seconds\")\n self.assertLess(end_lock, start_lock + timedelta(seconds=15), msg=\"Assert lock released in <15 seconds\")","def assert_timeout(self) -> None:\n if self._cancelled:\n raise asyncio.TimeoutError from None","def wait_out_ratelimit(self, event=None):\n delay = self.remaining\n if delay > 0:\n if not (event or hasattr(event, 'wait')):\n logger.id(logger.debug, self,\n 'No \\'wait\\' method found for event=\\'{event}\\'!'\n ' Using time.sleep ...',\n event=event,\n )\n do_wait = time.sleep\n else:\n do_wait = event.wait\n\n logger.id(logger.info, self,\n 'Rate limited! Waiting {time} (expires @ {strftime}) ...',\n time=delay,\n strftime='%H:%M:%S',\n strf_time=self.value,\n )\n do_wait(delay)","async def test_blocking_timeout(self):\n with await self.redis as r:\n result = await r.blpop('missing', timeout=1)\n assert result is None","def wait(cls, quad):\n\t\twait_time = cls.get_address_value(quad.result)\n\t\ttime.sleep(wait_time/1000.0)","def _wait_till_done(self, connection_observer, timeout, check_timeout):\n timeout_add = 0.1\n term_timeout = 0 if connection_observer.life_status.terminating_timeout is None else \\\n connection_observer.life_status.terminating_timeout\n remain_time = timeout - (time.time() - connection_observer.life_status.start_time) + term_timeout + timeout_add\n while remain_time >= 0:\n if connection_observer.done():\n return True\n time.sleep(self._tick)\n if check_timeout:\n timeout = connection_observer.timeout\n term_timeout = 0 if connection_observer.life_status.terminating_timeout is None else \\\n connection_observer.life_status.terminating_timeout\n remain_time = timeout - (time.time() - connection_observer.life_status.start_time) + term_timeout + \\\n timeout_add\n return False","def test_run_max_retries():\n responses = [httpretty.Response(body=\"Internal Server Error\", status=500),\n httpretty.Response(body=\"Internal Server Error\", status=500),\n httpretty.Response(body=\"Internal Server Error\", status=500)]\n httpretty.register_uri(httpretty.GET, URL, responses=responses)\n with mock.patch('httsleep.main.sleep'):\n httsleep = HttSleep(URL, {'status_code': 200}, max_retries=2)\n with pytest.raises(StopIteration):\n httsleep.run()","def wait(self, timeout=0):\n if timeout:\n self._finished.wait(timeout=timeout)\n else:\n self._finished.wait()","def test_results_throttling(self, affiliate_items):\n call_count = 0\n\n def throttle_initial(*args, **kwargs):\n nonlocal call_count\n call_count += 1\n if call_count < 4:\n raise ThrottlingError()\n\n update_function = mock.Mock(side_effect=throttle_initial)\n batch_job = BatchJob(affiliate_items, update_function)\n\n with mock.patch('chiton.rack.affiliates.bulk.sleep') as delay_function:\n for result in batch_job.run():\n pass\n\n assert delay_function.call_count == 3\n assert update_function.call_count == 7","def waitQueue(threshold=0, wait=3):\n\t\n\tif settings._tn == None:\n\t\treturn\n\t\n\tflushBuffer()\n\t\n\tb = False\n\twTrigger = False\n\t\n\twhile b == False:\n\t\tres = sendData('\"API\" GET API.Manager queueCount', read=True)\n\t\tfor r in res:\n\t\t\ts = str(r).lower()\n\t\t\t\n\t\t\tif s == 'ok' or 'error' in s:\n\t\t\t\tcontinue\n\t\t\t\n\t\t\tq = int(s)\n\t\t\t\n\t\t\tif q <= threshold:\n\t\t\t\tb = True\n\t\t\t\tbreak\n\t\t\t\n\t\t\toutput('Waiting for queue to flush, {} pending...'.format(q))\n\t\t\twTrigger = True\n\t\t\n\t\tif b == False:\n\t\t\ttime.sleep(wait)\n\t\n\t# NOTE: When a wait has been triggered, we will assure that there's nothing\n\t# else pending, so we run the wait again until there's no more fluctuations\n\t# on the queue. This is a issue that will only be resolved when the API \n\t# async problem is solved.\n\tif wTrigger == True:\n\t\twaitQueue(threshold=threshold, wait=wait)","def test_sleep_request(self):\n date = datetime.now() - timedelta(minutes=14)\n RequestAPI.objects.create(total_request=450, date=date)\n start = time.time()\n ManagerRequestApiTwitter().handle_rate_limit()\n stop = time.time()\n total_time = stop - start\n self.assertGreater(total_time, 60)","def wait_for(test, timeout_seconds=DEFAULT_TIMEOUT):\n start = time.time()\n while True:\n if test():\n return True\n if time.time() - start > timeout_seconds:\n return False\n time.sleep(0.5)","def check_for_timeouts(self) -> None:\n\n if self.id != self._leader_id:\n return\n\n for instance in self._undecided_instances:\n if (time.time() - self._last_prepare_time[instance]) > self._round_timeouts[instance]:\n #print('\\033[93m' + \"Instance {0} round {1} exceeded timeout!\".format(instance, self._round_id[instance]) + \"\\033[0m\")\n self._round_timeouts[instance] *= Proposer.TIMEOUT_GROWTH_FACTOR\n self._enable_phase1_optimization = False\n self.prepare_phase_parallel(instance)\n # Handle a single time out per loop to improve responsiveness to input messages\n break\n\n for instance in self._decided_values.keys():\n if not self._acked_decided_values[instance]:\n if (time.time() - self._last_decide_time[instance]) > self._learners_decide_timeout[instance]:\n # Timed out waiting the learner ACK: send the decided value to the learners and increase the timeout\n self._learners_decide_timeout[instance] *= Proposer.TIMEOUT_GROWTH_FACTOR\n decide_message: Decide = Decide(sender=self,\n receiver_role=Role.LEARNER,\n payload=DecidePayload((self._decided_values[instance], instance))\n )\n self.send(decide_message)\n\n # Handle a single time out per loop to improve responsiveness to input messages\n break","def _wait_for(self, check_func, desc, result=False, timeout=200):\r\n if result:\r\n return Promise(check_func, desc, timeout=timeout).fulfill()\r\n else:\r\n return EmptyPromise(check_func, desc, timeout=timeout).fulfill()","def doTimeout(self):\n\n log.info('Executed {} calls until timeout'.format(self.calls_to_timeout))\n self.calls_to_timeout = 0\n\n self.resetSession()\n time.sleep(self.timeout)","def wait_for_call_in_new_thread(\n __call: Union[Callable[[], T], Call[T]],\n timeout: Optional[float] = None,\n done_callbacks: Optional[Iterable[Call]] = None,\n ) -> T:\n raise NotImplementedError()","def test__API_without_answer(self):\n self.mock_connection.state = MockConnection.NO_CONFIRMATIONS\n\n # timeout supposed to be here\n self.assertEqual(self.mutex.lock(), False) # acquire mutex","def _busy_wait(self, timeout=40.0):\n # If the busy_pin is *high* (pulled up by host)\n # then assume we're not getting a signal from inky\n # and wait the timeout period to be safe.\n if self._gpio.input(self.busy_pin):\n warnings.warn(\"Busy Wait: Held high. Waiting for {:0.2f}s\".format(timeout))\n time.sleep(timeout)\n return\n\n # If the busy_pin is *low* (pulled down by inky)\n # then wait for it to high.\n t_start = time.time()\n while not self._gpio.input(self.busy_pin):\n time.sleep(0.01)\n if time.time() - t_start >= timeout:\n warnings.warn(\"Busy Wait: Timed out after {:0.2f}s\".format(time.time() - t_start))\n return\n\n # print(\"Busy_waited\", time.time()-t_start, \"out of\", timeout, \"seconds\")","async def check_timeout(self) -> None:\n async with self.lock:\n # Looping through the keys because we are mutating the dict\n for message_id in copy.copy(list(self.ongoing_events.keys())):\n ongoing_event = self.ongoing_events[message_id]\n if ongoing_event.timed_out():\n await self.end_ongoing_event(message_id)","def timeout(time_limit):\n\n def wrapUnitTest(testcase):\n\n @wraps(testcase)\n def testWrapper(self):\n\n queue = Queue()\n\n try:\n p = Thread(target=handler, args=(self, testcase, queue))\n p.daemon = True\n p.start()\n err, res = queue.get(timeout=time_limit)\n p.join()\n if err:\n raise err[0](err[1]).with_traceback(err[2])\n return res\n except QueueEmptyError:\n raise TimeoutError(\"Test aborted due to timeout. Test was \" +\n \"expected to finish in less than {} second(s).\".format(time_limit))\n\n return testWrapper\n\n return wrapUnitTest","async def test_rpc_timeout(bus: lightbus.BusNode, dummy_api):\n\n async def co_call_rpc():\n await asyncio.sleep(0.1)\n return await bus.my.dummy.sudden_death.call_async(n=0)\n\n async def co_consume_rpcs():\n return await bus.bus_client.consume_rpcs(apis=[dummy_api])\n\n (call_task, ), (consume_task, ) = await asyncio.wait([co_call_rpc(), co_consume_rpcs()], return_when=asyncio.FIRST_COMPLETED)\n consume_task.cancel()\n\n with pytest.raises(LightbusTimeout):\n call_task.result()"],"string":"[\n \"def _wait_before_call(self):\\n while (dt.datetime.now() - self._last_call_ts) <= dt.timedelta(\\n seconds=self.api_timeout\\n ):\\n time.sleep(0.5)\\n self._last_call_ts = dt.datetime.now()\",\n \"def wait_fluently(condition: Callable, timeout: TimeoutType, err_msg: str):\\n if timeout is None:\\n timeout = 0\\n start_time = time.time()\\n while True:\\n res = condition()\\n if res:\\n return res\\n if time.time() - start_time >= timeout:\\n raise TimeoutException(err_msg)\\n time.sleep(0.3)\",\n \"def wait(self, timeout):\\n raise NotImplementedError(\\n u\\\"%s: Method not implemented\\\", self.__class__.__name__)\",\n \"def wait_for_completion(self, timeout=10):\\n cur_status = self.runtime_status()\\n while cur_status not in ['FAILED', 'KILLED', 'FINISHED']:\\n time.sleep(0.2)\\n timeout -= 0.2\\n cur_status = self.runtime_status()\\n if timeout < 0:\\n break\\n\\n return timeout > 0\",\n \"def assert_timeout(self) -> None:\",\n \"def wait(self, ms=None):\\r\\n util.raiseNotDefined()\",\n \"def test__API_with_wrong_answer(self):\\n self.mock_connection.state = MockConnection.WRONG_NUM_OF_CONFIRMATIONS\\n\\n # timeout supposed to be here\\n self.assertEqual(self.mutex.lock(), False) # acquire mutex\",\n \"def test_wait_tx_settled_timeout(self, is_transaction_settled_mock):\\n with pytest.raises(TimeoutError):\\n wait_tx_settled(\\\"some\\\", \\\"some\\\", timeout=0.5)\",\n \"def wait_until_got_number_of_callbacks(self, number):\\n slept = 0.0\\n time_step = 0.1\\n while not len(self.futures) == number and slept < TIMEOUT:\\n time.sleep(time_step)\\n slept += time_step\",\n \"def wait(self, timeout=None):\\n if self.counter > 0:\\n return self.counter\\n\\n self._wait(timeout) # return value irrelevant, whether we got it or got a timeout\\n return self.counter\",\n \"def _wait_on_condition(self, timeout):\\n self.__condition.wait(timeout)\",\n \"def test_timeout(self):\\n start = time.time()\\n dr = EventualResult(Deferred(), None)\\n self.assertRaises(TimeoutError, dr.wait, timeout=0.03)\\n # be a little lenient for slow computers:\\n self.assertTrue(abs(time.time() - start) < 0.05)\",\n \"def test_timeoutReset(self):\\n for i in range(3):\\n self.circuit_breaker.failure()\\n self.time.advance(29.0)\\n available29sec = self.circuit_breaker.available()\\n self.time.advance(1.1)\\n available30sec = self.circuit_breaker.available()\\n self.assertEqual((available29sec, available30sec),\\n (False, True))\",\n \"def test_wait_for_predicate_timeout(self):\\n predicate_mock = mock.MagicMock(side_effect=[True, True, True])\\n with self.assertRaises(TimeoutError):\\n train_utils.wait_for_predicate(predicate_mock, num_retries=3)\",\n \"def answer_waiting_call(self) -> None:\",\n \"def TODO_testTimeout(self):\\n return \\\"\\\"\\\"TODO: Highly dependent on hardcoded downstream timeout val\\\"\\\"\\\"\\n\\n # Assuming proxy's downstream_max is 1,\\n # and number of threads is 1.\\n\\n self.client_connect(0)\\n\\n self.client_send('get time0\\\\r\\\\n', 0)\\n self.mock_recv('get time0\\\\r\\\\n', 0)\\n\\n # Mock server is 'busy' at this point, so\\n # downstream timeout logic should kick in,\\n # without our mock server having to send anything.\\n\\n self.wait(210)\\n\\n self.client_recv('END\\\\r\\\\n', 0)\\n\\n # TODO: The number of server sessions should be 0,\\n # except the close might not have propagated.\",\n \"async def test_wait_for(self) -> None:\\n trigger = auraxium.Trigger(auraxium.event.Death)\\n\\n def do_nothing(_: auraxium.event.Event) -> None:\\n pass\\n\\n trigger.action = do_nothing\\n\\n await self.client.wait_for(trigger, timeout=-1.0)\\n\\n with self.assertRaises(TimeoutError):\\n await self.client.wait_for(trigger, timeout=0.00001)\",\n \"def test_wait_tx_settled_ok(self, is_transaction_settled_mock):\\n wait_tx_settled(\\\"some\\\", \\\"some\\\", timeout=4)\",\n \"def test_wait_race(self):\\n mock_handler = mock.Mock()\\n async_result = self._makeOne(mock_handler)\\n\\n async_result.set(\\\"immediate\\\")\\n\\n cv = threading.Event()\\n\\n def wait_for_val():\\n # NB: should not sleep\\n async_result.wait(20)\\n cv.set()\\n th = threading.Thread(target=wait_for_val)\\n th.daemon = True\\n th.start()\\n\\n # if the wait() didn't sleep (correctly), cv will be set quickly\\n # if it did sleep, the cv will not be set yet and this will timeout\\n cv.wait(10)\\n eq_(cv.is_set(), True)\\n th.join()\",\n \"def test_timeout_pending(self):\\n deadline = Deadline(MS)\\n timeout = deadline.timeout()\\n self.assertGreater(timeout, 0)\\n self.assertLess(timeout, MS)\",\n \"def _wait_reply(self, comm_id, call_id, call_name, timeout, retry=True):\\n def reply_received():\\n \\\"\\\"\\\"The reply is there!\\\"\\\"\\\"\\n return call_id in self._reply_inbox\\n if not self.wait_until(reply_received):\\n if retry:\\n self._wait_reply(comm_id, call_id, call_name, timeout, False)\\n return\\n raise TimeoutError(\\n \\\"Timeout while waiting for '{}' reply.\\\".format(\\n call_name))\",\n \"def wait_until_responsive(self, check, timeout, pause,\\n clock=timeit.default_timer):\\n\\n ref = clock()\\n now = ref\\n while (now - ref) < timeout:\\n if check():\\n return\\n time.sleep(pause)\\n now = clock()\\n\\n raise Exception(\\n 'Timeout reached while waiting on service!'\\n )\",\n \"def waitUntilSuccess():\",\n \"def wait(self, timeout=None):\\n assert False, \\\"Deriving class must implement\\\"\",\n \"def in_waiting(self) -> int:\\n pass\",\n \"async def test_wait_synchronized(self):\\n client.wait_synchronized = AsyncMock(side_effect=[TimeoutException('timeout'), TimeoutException('timeout'),\\n MagicMock()])\\n await api.wait_synchronized()\",\n \"def wait(n=3):\\n sleep(n)\",\n \"def check_done(self):\\n if len(self._calls) != 0:\\n raise MockException(\\\"Still expecting more function calls\\\")\",\n \"def _WaitForLinkerTestStatus(adb, timeout):\",\n \"def test_timeoutRaises(self):\\n\\n @self.eventloop.wait_for(timeout=0.5)\\n def times_out():\\n return Deferred().addErrback(lambda f: f.trap(CancelledError))\\n\\n start = time.time()\\n self.assertRaises(TimeoutError, times_out)\\n self.assertTrue(abs(time.time() - start - 0.5) < 0.1)\",\n \"def test__API_other_locks_later(self):\\n self.mock_connection.state = MockConnection.CORRECT_NUM_OF_CONFIRMATIONS\\n\\n # simulate release after 1.5s\\n delay = 1.5\\n thread = Thread(target=self.mock_connection.simulate_release_from_other_process, args=(1, delay))\\n thread.start()\\n\\n start = time()\\n self.mutex.lock()\\n\\n # simulate request\\n self.mock_connection.simulate_request_from_other_process(10) # later than our time\\n\\n # locked before delay passed=> OK\\n self.assertLess(time(), start + delay)\\n\\n self.mutex.unlock()\",\n \"def call_in_waiting_thread(\\n __call: Union[Callable[[], T], Call[T]],\\n thread: threading.Thread,\\n timeout: Optional[float] = None,\\n ) -> T:\\n raise NotImplementedError()\",\n \"def timeout(n):\\n time.sleep(int(n))\\n return 'ok', 200\",\n \"def wait(self):\\n self.drain_call_queue()\",\n \"def wait_condition(cond, timeout=1, sleeptime=.01):\\n # NOTE Increasing sleeptime can dramatically increase testsuite runtime\\n # It also reduces CPU load significantly\\n if timeout is None:\\n timeout = 1\\n\\n if timeout < sleeptime:\\n print(\\\"Warning, timeout cannot be smaller than\\\", sleeptime)\\n timeout = sleeptime\\n\\n # Max number of attempts until giving up\\n tries = int(timeout / sleeptime)\\n\\n for i in range(tries):\\n val = cond()\\n\\n if val is not None:\\n break\\n\\n sleep(sleeptime)\\n\\n return val\",\n \"def wait_condition(cond, timeout=1, sleeptime=.01):\\n # NOTE Increasing sleeptime can dramatically increase testsuite runtime\\n # It also reduces CPU load significantly\\n if timeout is None:\\n timeout = 1\\n\\n if timeout < sleeptime:\\n print(\\\"Warning, timeout cannot be smaller than\\\", sleeptime)\\n timeout = sleeptime\\n\\n # Max number of attempts until giving up\\n tries = int(timeout / sleeptime)\\n\\n for i in range(tries):\\n val = cond()\\n\\n if val is not None:\\n break\\n\\n sleep(sleeptime)\\n\\n return val\",\n \"def wait_until(self, check, timeout=None):\\n self._wait_in_process_loop(lambda: (check(),None),timeout=timeout)\",\n \"def _wait_for(\\n func: Callable,\\n expected_result: Any = True,\\n timeout: int = 10,\\n print_error: bool = True,\\n sleep_for: int = 1,\\n **kwargs,\\n) -> None:\\n end = time() + timeout\\n\\n while time() <= end:\\n try:\\n retval = func(**kwargs)\\n except Exception as err: # pylint: disable=broad-except\\n if print_error:\\n logger.error(err)\\n else:\\n if retval == expected_result:\\n return None\\n sleep(sleep_for)\\n\\n raise WaitForException(\\n f\\\"func: {func}, didn't return {expected_result} within specified timeout: {timeout}\\\"\\n )\",\n \"def functionThatWillTimeOut():\\n time.sleep(5)\",\n \"def wait_on_function ( func, func_args, func_wait_value, sleep_time, max_wait ) :\\n func_response = func( *func_args )\\n while func_response != func_wait_value :\\n if max_wait <= 0 :\\n break\\n time.sleep( sleep_time )\\n max_wait -= sleep_time\\n func_response = func( *func_args )\\n\\n return func_response == func_wait_value\",\n \"def wait_until_done(self, timeout=10.0):\\r\\n cfunc = lib_importer.windll.DAQmxWaitUntilTaskDone\\r\\n if cfunc.argtypes is None:\\r\\n with cfunc.arglock:\\r\\n if cfunc.argtypes is None:\\r\\n cfunc.argtypes = [lib_importer.task_handle, ctypes.c_double]\\r\\n\\r\\n error_code = cfunc(self._handle, timeout)\\r\\n check_for_error(error_code)\",\n \"def waitFor(func, maxWait=None, message=None):\\n\\twaited = 0\\n\\tmaxWait = maxWait or defaultDelay\\n\\n\\tif message:\\n\\t\\tlog(message)\\n\\n\\twhile True:\\n\\n\\t\\tret = func()\\n\\n\\t\\tif ret:\\n\\t\\t\\treturn ret\\n\\n\\t\\twaited += tickTime\\n\\n\\t\\tif waited > maxWait:\\n\\t\\t\\traise Exception('Maximum wait time exceded.' if not message else 'Failed waiting for: %s' % message)\\n\\n\\t\\tif message:\\n\\t\\t\\tdebug(message)\\n\\n\\t\\tdebug('Waited: %d, MaxWait: %d.' % (waited, maxWait))\\n\\t\\tsleep(tickTime)\",\n \"def test_lock_timeout_loop():\\n lock_unlock_timeout(0.2)\",\n \"def test_timeout_exceeded():\\n connection = FakeBaseConnection(session_timeout=10)\\n start = time.time() - 11\\n try:\\n connection._timeout_exceeded(start)\\n except NetmikoTimeoutException as exc:\\n assert isinstance(exc, NetmikoTimeoutException)\\n return\\n\\n assert False\",\n \"def test_wait_for_db_command_(self, mock_time_sleep):\\n with patch(\\n 'django.db.utils.ConnectionHandler.__getitem__',\\n side_effect=[OperationalError]*5 + [True]\\n ) as gi:\\n call_command('wait_for_db')\\n self.assertEqual(gi.call_count, 6)\",\n \"def _selenium_wait_for(fn):\\n start_time = time.time()\\n while True:\\n try:\\n return fn()\\n except (AssertionError, WebDriverException) as e:\\n if time.time() - start_time > 10:\\n raise e\\n time.sleep(0.5)\",\n \"def wait(self, timeoout=None, state=\\\"C-completed\\\"):\",\n \"def wait_until(condition, delay, max_attempts):\\n attempt = 0\\n while not condition() and attempt < max_attempts:\\n attempt += 1\\n time.sleep(delay)\\n\\n if attempt >= max_attempts:\\n raise Exception(\\\"Condition is still False after {} attempts.\\\".format(max_attempts))\",\n \"async def test_wait_for_activity_timeout(aiopg_connector):\\n pg_app = app.App(connector=aiopg_connector)\\n worker = worker_module.Worker(app=pg_app, timeout=2)\\n worker.notify_event = asyncio.Event()\\n task = asyncio.ensure_future(worker.single_worker(worker_id=0))\\n try:\\n await asyncio.sleep(0.2) # should be enough so that we're waiting\\n\\n worker.stop_requested = True\\n\\n with pytest.raises(asyncio.TimeoutError):\\n await asyncio.wait_for(task, timeout=0.2)\\n finally:\\n worker.notify_event.set()\",\n \"def test_wait_for_page_in_timeout(self):\\n start_time = datetime.now()\\n with self.assertRaises(SpdbError):\\n csdb = CacheStateDB(self.config_data)\\n ch = csdb.create_page_in_channel()\\n\\n csdb.wait_for_page_in([\\\"MY_TEST_KEY1\\\", \\\"MY_TEST_KEY2\\\"], ch, 1)\\n\\n assert (datetime.now() - start_time).seconds < 3\",\n \"def wait(self) -> None:\\n now = time.time()\\n if now < self.lockTime:\\n diff = self.lockTime - now\\n logger.debug(\\\"Waiting %ss to avoid ratelimit\\\", diff)\\n time.sleep(diff)\",\n \"def test_timeout_loop(self):\\n\\n self.assertIsNone(self.state.becomeCandidateTimeout)\\n # This should do nothing\\n self.state.cancelBecomeCandidateTimeout()\\n self.assertIsNone(self.state.becomeCandidateTimeout)\\n\\n results = self.state.begin()\\n\\n self.state.resetElectionTimeout()\\n self.assertTrue(self.state.electionTimeout > 0.150)\\n self.assertTrue(self.state.electionTimeout < 0.350)\\n self.assertTrue(isinstance(self.state.becomeCandidateTimeout,\\n base.DelayedCall))\",\n \"def get_timeout(self) -> int:\",\n \"def wait_for_doc_count(self, db_name, expected, timeoutInMin):\\n\\t\\ttimeout = time.time() + timeoutInMin * 60\\n\\t\\t\\n\\t\\twhile (time.time() < timeout):\\n\\t\\t\\tdoc_count = self.get_doc_count(db_name)\\n\\t\\t\\tif doc_count < int(expected):\\n\\t\\t\\t\\ttime.sleep(5)\\n\\t\\t\\telse:\\n\\t\\t\\t\\tprint (\\\"{} doc count = {}\\\".format(db_name, doc_count))\\n\\t\\t\\t\\treturn\\n\\t\\t\\t\\t\\n\\t\\traise RuntimeError(\\\"Timeout waiting for {} to reach count {} after {} min(s)\\\".format(\\n\\t\\t\\tdb_name, expected, timeoutInMin))\",\n \"def test_condition_waiter(self):\\n\\n class Holder(object):\\n def __init__(self, start_value, max_value):\\n self.value = start_value\\n self.max_value = max_value\\n self.num_calls = 0\\n\\n def my_check(holder):\\n holder.num_calls += 1\\n if holder.value == holder.max_value:\\n return True\\n holder.value += 1\\n\\n holder = Holder(1, 10)\\n event = Event()\\n condition = ConditionWaiter(event, my_check, holder)\\n condition.start()\\n self.assertTrue(event.wait(2))\\n condition.stop()\\n self.assertEqual(holder.num_calls, 10 - 1 + 1)\\n\\n holder = Holder(4, 10)\\n event = Event()\\n condition = ConditionWaiter(event, my_check, holder)\\n condition.start()\\n self.assertTrue(event.wait(3))\\n condition.stop()\\n self.assertEqual(holder.num_calls, 10 - 4 + 1)\\n\\n holder = Holder(1, 10)\\n event = Event()\\n condition = ConditionWaiter(event, my_check, holder)\\n condition.start()\\n self.assertFalse(event.wait(0.0001))\\n condition.stop()\",\n \"def __wait(min_sec, max_sec):\\n time.sleep(randint(min_sec, max_sec))\",\n \"def onWaited( self, result ):\\n\\t\\treturn self.agi.sayNumber( self.count, '*' ).addErrback(\\n\\t\\t\\tself.onNumberFailed,\\n\\t\\t).addCallbacks(\\n\\t\\t\\tself.onFinished, self.onFinished,\\n\\t\\t)\",\n \"def _wait_for_completion(conn, promise, wait_timeout, msg):\\n if not promise:\\n return\\n wait_timeout = time.time() + wait_timeout\\n while wait_timeout > time.time():\\n time.sleep(5)\\n operation_result = conn.get_request(\\n request_id=promise[\\\"requestId\\\"], status=True\\n )\\n\\n if operation_result[\\\"metadata\\\"][\\\"status\\\"] == \\\"DONE\\\":\\n return\\n elif operation_result[\\\"metadata\\\"][\\\"status\\\"] == \\\"FAILED\\\":\\n raise Exception(\\n \\\"Request: {}, requestId: {} failed to complete:\\\\n{}\\\".format(\\n msg,\\n str(promise[\\\"requestId\\\"]),\\n operation_result[\\\"metadata\\\"][\\\"message\\\"],\\n )\\n )\\n\\n raise Exception(\\n 'Timed out waiting for asynchronous operation {} \\\"{}\\\" to complete.'.format(\\n msg, str(promise[\\\"requestId\\\"])\\n )\\n )\",\n \"def out_waiting(self) -> int:\\n pass\",\n \"def is_call_waiting(self) -> bool:\",\n \"def wait_for_interrupts(self, wait_time = 1):\\n raise AssertionError(\\\"wait_for_interrupts function i not implemented\\\")\",\n \"def fake_poll_until(retriever, condition=lambda value: value,\\n sleep_time=1, time_out=0):\\n from trove.common import exception\\n slept_time = 0\\n while True:\\n resource = retriever()\\n if condition(resource):\\n return resource\\n fake_sleep(sleep_time)\\n slept_time += sleep_time\\n if time_out and slept_time >= time_out:\\n raise exception.PollTimeOut()\",\n \"def wait(wait_time=WAIT_TIME):\\n # time.sleep(wait_time)\\n pass\",\n \"def answered(self, maxwait=0):\\n\\t\\ttry:\\n\\t\\t\\treturn self.pingresult.get(maxwait)\\n\\t\\texcept FutureTimedOut:\\n\\t\\t\\treturn False\",\n \"def test_timeout_twice(self):\\n dr = EventualResult(Deferred(), None)\\n self.assertRaises(TimeoutError, dr.wait, timeout=0.01)\\n self.assertRaises(TimeoutError, dr.wait, timeout=0.01)\",\n \"def wait(self):\\n\\t\\traise NotImplementedError(\\\"must be redeclared\\\")\",\n \"def simulate_waiting(self, exit_when=None):\\n self._time_condition.acquire()\\n\\n # Helper function to reduce code copy\\n def wait_block():\\n self._increment_waiting_count(1)\\n self._time_condition.wait()\\n self._increment_waiting_count(-1)\\n\\n try:\\n if exit_when is None:\\n wait_block()\\n else:\\n while not exit_when(self._time):\\n wait_block()\\n finally:\\n self._time_condition.release()\",\n \"def test_handle_multiple_responses_throw_timeouterror():\\n # GIVEN\\n bboxes = [8.67066,49.41423,8.68177,49.4204]\\n time = \\\"2010-01-01/2011-01-01/P1Y\\\"\\n keys = [\\\"building\\\"]\\n values = [\\\"\\\"]\\n\\n # WHEN\\n client = ohsome.OhsomeClientParallel()\\n response = client.elements.count.post(bboxes=bboxes, time=time, keys=keys, values=values, timeout=2)\\n del client\",\n \"def wait(self, timeout=600):\\n s = datetime.datetime.now()\\n status = json.loads(self.get())\\n while status['status'] != 'COMPLETE':\\n status = self.get()\\n e = datetime.datetime.now()\\n if (e - s).seconds > timeout:\\n raise RuntimeError('timeout')\\n return status\",\n \"def timeout_wait(self):\\n if self._dtr_enabled:\\n while (self.__micros() - self._resume_time) < 0:\\n if False:\\n break # TODO: Check for printer status here\\n else:\\n while (self.__micros() - self._resume_time) < 0:\\n pass\",\n \"def _wait_for_response(self, millis):\\n loops = millis / 10\\n while not self.__atresponse_received:\\n time.sleep(0.01)\\n loops -= 1\\n if loops == 0:\\n return False\\n return True\",\n \"def wait(self, timeout=None):\\n with self.condition:\\n if not self.ready:\\n self.condition.wait(timeout)\",\n \"def test_wait(self):\\n skill = create_skill()\\n\\n expected_response = 'Yes I do, very much'\\n\\n converser = Thread(target=create_converse_responder(expected_response,\\n skill))\\n converser.start()\\n validator = mock.Mock()\\n validator.return_value = True\\n is_cancel = mock.Mock()\\n is_cancel.return_value = False\\n on_fail = mock.Mock()\\n\\n response = skill._wait_response(is_cancel, validator, on_fail, 1)\\n self.assertEqual(response, expected_response)\\n converser.join()\",\n \"def wait_for(self, query_delay=0):\\n raise NotImplementedError(\\\"Implement in subclass!\\\")\",\n \"def wait(item=TIME, ants=0, tmo=0, waiton=-2, precomment=None, postcomment=None,\\n subarray=DEFAULT) :\\n if tmo > carmaIni.CORBA_CLIENT_CALL_TIMEOUT_S:\\n warn = \\\"wait: Timeout (%ds) is greater than max CORBA timeout (%ds).\\\"\\n warn += \\\"\\\\n\\\\tThrottling to %ds.\\\"\\n warn = warn%( tmo, \\n carmaIni.CORBA_CLIENT_CALL_TIMEOUT_S, \\n carmaIni.CORBA_CLIENT_CALL_TIMEOUT_S - 10 )\\n printWarning( warn )\\n tmo = carmaIni.CORBA_CLIENT_CALL_TIMEOUT_S - 10\\n \\n return runKeyboardInterruptable( _wait, item, ants, tmo, waiton, \\n precomment, postcomment, subarray )\",\n \"def test_lock_timeout():\\n lock_unlock_timeout(0)\",\n \"def _wait_and_check(self, sleep=5, exclude_services=[]):\\n u.log.debug('Extended wait and check ...')\\n time.sleep(sleep)\\n self.d.sentry.wait(timeout=900)\\n time.sleep(sleep)\\n self._auto_wait_for_status(exclude_services=exclude_services)\\n time.sleep(sleep)\\n self.d.sentry.wait()\\n u.log.debug('Wait and check completed.')\",\n \"def timeout(time_limit):\\n\\n class TimeoutException(Exception):\\n \\\"\\\"\\\" Subclass Exception to catch timer expiration during search \\\"\\\"\\\"\\n pass\\n\\n def handler(*args, **kwargs):\\n \\\"\\\"\\\" Generic handler to raise an exception when a timer expires \\\"\\\"\\\"\\n raise TimeoutException(\\\"Test aborted due to timeout. Test was \\\" +\\n \\\"expected to finish in less than {} second(s).\\\".format(time_limit))\\n\\n def wrapUnitTest(testcase):\\n\\n @wraps(testcase)\\n def testWrapper(self, *args, **kwargs):\\n\\n signal.signal(signal.SIGALRM, handler)\\n signal.alarm(time_limit)\\n\\n try:\\n return testcase(self, *args, **kwargs)\\n finally:\\n signal.alarm(0)\\n\\n return testWrapper\\n\\n return wrapUnitTest\",\n \"def test_pool_timeout_hw(self):\\n self.test_pool_timeout()\",\n \"def test_lock_wait(self):\\n get_lock('test_wait', expires=4)\\n start_lock = timezone.now()\\n with LockMgr('test_wait', wait=5):\\n end_lock = timezone.now()\\n self.assertGreaterEqual(end_lock, start_lock + timedelta(seconds=4),\\n msg=\\\"Assert lock released after at least 4 seconds\\\")\\n self.assertLess(end_lock, start_lock + timedelta(seconds=15), msg=\\\"Assert lock released in <15 seconds\\\")\",\n \"def assert_timeout(self) -> None:\\n if self._cancelled:\\n raise asyncio.TimeoutError from None\",\n \"def wait_out_ratelimit(self, event=None):\\n delay = self.remaining\\n if delay > 0:\\n if not (event or hasattr(event, 'wait')):\\n logger.id(logger.debug, self,\\n 'No \\\\'wait\\\\' method found for event=\\\\'{event}\\\\'!'\\n ' Using time.sleep ...',\\n event=event,\\n )\\n do_wait = time.sleep\\n else:\\n do_wait = event.wait\\n\\n logger.id(logger.info, self,\\n 'Rate limited! Waiting {time} (expires @ {strftime}) ...',\\n time=delay,\\n strftime='%H:%M:%S',\\n strf_time=self.value,\\n )\\n do_wait(delay)\",\n \"async def test_blocking_timeout(self):\\n with await self.redis as r:\\n result = await r.blpop('missing', timeout=1)\\n assert result is None\",\n \"def wait(cls, quad):\\n\\t\\twait_time = cls.get_address_value(quad.result)\\n\\t\\ttime.sleep(wait_time/1000.0)\",\n \"def _wait_till_done(self, connection_observer, timeout, check_timeout):\\n timeout_add = 0.1\\n term_timeout = 0 if connection_observer.life_status.terminating_timeout is None else \\\\\\n connection_observer.life_status.terminating_timeout\\n remain_time = timeout - (time.time() - connection_observer.life_status.start_time) + term_timeout + timeout_add\\n while remain_time >= 0:\\n if connection_observer.done():\\n return True\\n time.sleep(self._tick)\\n if check_timeout:\\n timeout = connection_observer.timeout\\n term_timeout = 0 if connection_observer.life_status.terminating_timeout is None else \\\\\\n connection_observer.life_status.terminating_timeout\\n remain_time = timeout - (time.time() - connection_observer.life_status.start_time) + term_timeout + \\\\\\n timeout_add\\n return False\",\n \"def test_run_max_retries():\\n responses = [httpretty.Response(body=\\\"Internal Server Error\\\", status=500),\\n httpretty.Response(body=\\\"Internal Server Error\\\", status=500),\\n httpretty.Response(body=\\\"Internal Server Error\\\", status=500)]\\n httpretty.register_uri(httpretty.GET, URL, responses=responses)\\n with mock.patch('httsleep.main.sleep'):\\n httsleep = HttSleep(URL, {'status_code': 200}, max_retries=2)\\n with pytest.raises(StopIteration):\\n httsleep.run()\",\n \"def wait(self, timeout=0):\\n if timeout:\\n self._finished.wait(timeout=timeout)\\n else:\\n self._finished.wait()\",\n \"def test_results_throttling(self, affiliate_items):\\n call_count = 0\\n\\n def throttle_initial(*args, **kwargs):\\n nonlocal call_count\\n call_count += 1\\n if call_count < 4:\\n raise ThrottlingError()\\n\\n update_function = mock.Mock(side_effect=throttle_initial)\\n batch_job = BatchJob(affiliate_items, update_function)\\n\\n with mock.patch('chiton.rack.affiliates.bulk.sleep') as delay_function:\\n for result in batch_job.run():\\n pass\\n\\n assert delay_function.call_count == 3\\n assert update_function.call_count == 7\",\n \"def waitQueue(threshold=0, wait=3):\\n\\t\\n\\tif settings._tn == None:\\n\\t\\treturn\\n\\t\\n\\tflushBuffer()\\n\\t\\n\\tb = False\\n\\twTrigger = False\\n\\t\\n\\twhile b == False:\\n\\t\\tres = sendData('\\\"API\\\" GET API.Manager queueCount', read=True)\\n\\t\\tfor r in res:\\n\\t\\t\\ts = str(r).lower()\\n\\t\\t\\t\\n\\t\\t\\tif s == 'ok' or 'error' in s:\\n\\t\\t\\t\\tcontinue\\n\\t\\t\\t\\n\\t\\t\\tq = int(s)\\n\\t\\t\\t\\n\\t\\t\\tif q <= threshold:\\n\\t\\t\\t\\tb = True\\n\\t\\t\\t\\tbreak\\n\\t\\t\\t\\n\\t\\t\\toutput('Waiting for queue to flush, {} pending...'.format(q))\\n\\t\\t\\twTrigger = True\\n\\t\\t\\n\\t\\tif b == False:\\n\\t\\t\\ttime.sleep(wait)\\n\\t\\n\\t# NOTE: When a wait has been triggered, we will assure that there's nothing\\n\\t# else pending, so we run the wait again until there's no more fluctuations\\n\\t# on the queue. This is a issue that will only be resolved when the API \\n\\t# async problem is solved.\\n\\tif wTrigger == True:\\n\\t\\twaitQueue(threshold=threshold, wait=wait)\",\n \"def test_sleep_request(self):\\n date = datetime.now() - timedelta(minutes=14)\\n RequestAPI.objects.create(total_request=450, date=date)\\n start = time.time()\\n ManagerRequestApiTwitter().handle_rate_limit()\\n stop = time.time()\\n total_time = stop - start\\n self.assertGreater(total_time, 60)\",\n \"def wait_for(test, timeout_seconds=DEFAULT_TIMEOUT):\\n start = time.time()\\n while True:\\n if test():\\n return True\\n if time.time() - start > timeout_seconds:\\n return False\\n time.sleep(0.5)\",\n \"def check_for_timeouts(self) -> None:\\n\\n if self.id != self._leader_id:\\n return\\n\\n for instance in self._undecided_instances:\\n if (time.time() - self._last_prepare_time[instance]) > self._round_timeouts[instance]:\\n #print('\\\\033[93m' + \\\"Instance {0} round {1} exceeded timeout!\\\".format(instance, self._round_id[instance]) + \\\"\\\\033[0m\\\")\\n self._round_timeouts[instance] *= Proposer.TIMEOUT_GROWTH_FACTOR\\n self._enable_phase1_optimization = False\\n self.prepare_phase_parallel(instance)\\n # Handle a single time out per loop to improve responsiveness to input messages\\n break\\n\\n for instance in self._decided_values.keys():\\n if not self._acked_decided_values[instance]:\\n if (time.time() - self._last_decide_time[instance]) > self._learners_decide_timeout[instance]:\\n # Timed out waiting the learner ACK: send the decided value to the learners and increase the timeout\\n self._learners_decide_timeout[instance] *= Proposer.TIMEOUT_GROWTH_FACTOR\\n decide_message: Decide = Decide(sender=self,\\n receiver_role=Role.LEARNER,\\n payload=DecidePayload((self._decided_values[instance], instance))\\n )\\n self.send(decide_message)\\n\\n # Handle a single time out per loop to improve responsiveness to input messages\\n break\",\n \"def _wait_for(self, check_func, desc, result=False, timeout=200):\\r\\n if result:\\r\\n return Promise(check_func, desc, timeout=timeout).fulfill()\\r\\n else:\\r\\n return EmptyPromise(check_func, desc, timeout=timeout).fulfill()\",\n \"def doTimeout(self):\\n\\n log.info('Executed {} calls until timeout'.format(self.calls_to_timeout))\\n self.calls_to_timeout = 0\\n\\n self.resetSession()\\n time.sleep(self.timeout)\",\n \"def wait_for_call_in_new_thread(\\n __call: Union[Callable[[], T], Call[T]],\\n timeout: Optional[float] = None,\\n done_callbacks: Optional[Iterable[Call]] = None,\\n ) -> T:\\n raise NotImplementedError()\",\n \"def test__API_without_answer(self):\\n self.mock_connection.state = MockConnection.NO_CONFIRMATIONS\\n\\n # timeout supposed to be here\\n self.assertEqual(self.mutex.lock(), False) # acquire mutex\",\n \"def _busy_wait(self, timeout=40.0):\\n # If the busy_pin is *high* (pulled up by host)\\n # then assume we're not getting a signal from inky\\n # and wait the timeout period to be safe.\\n if self._gpio.input(self.busy_pin):\\n warnings.warn(\\\"Busy Wait: Held high. Waiting for {:0.2f}s\\\".format(timeout))\\n time.sleep(timeout)\\n return\\n\\n # If the busy_pin is *low* (pulled down by inky)\\n # then wait for it to high.\\n t_start = time.time()\\n while not self._gpio.input(self.busy_pin):\\n time.sleep(0.01)\\n if time.time() - t_start >= timeout:\\n warnings.warn(\\\"Busy Wait: Timed out after {:0.2f}s\\\".format(time.time() - t_start))\\n return\\n\\n # print(\\\"Busy_waited\\\", time.time()-t_start, \\\"out of\\\", timeout, \\\"seconds\\\")\",\n \"async def check_timeout(self) -> None:\\n async with self.lock:\\n # Looping through the keys because we are mutating the dict\\n for message_id in copy.copy(list(self.ongoing_events.keys())):\\n ongoing_event = self.ongoing_events[message_id]\\n if ongoing_event.timed_out():\\n await self.end_ongoing_event(message_id)\",\n \"def timeout(time_limit):\\n\\n def wrapUnitTest(testcase):\\n\\n @wraps(testcase)\\n def testWrapper(self):\\n\\n queue = Queue()\\n\\n try:\\n p = Thread(target=handler, args=(self, testcase, queue))\\n p.daemon = True\\n p.start()\\n err, res = queue.get(timeout=time_limit)\\n p.join()\\n if err:\\n raise err[0](err[1]).with_traceback(err[2])\\n return res\\n except QueueEmptyError:\\n raise TimeoutError(\\\"Test aborted due to timeout. Test was \\\" +\\n \\\"expected to finish in less than {} second(s).\\\".format(time_limit))\\n\\n return testWrapper\\n\\n return wrapUnitTest\",\n \"async def test_rpc_timeout(bus: lightbus.BusNode, dummy_api):\\n\\n async def co_call_rpc():\\n await asyncio.sleep(0.1)\\n return await bus.my.dummy.sudden_death.call_async(n=0)\\n\\n async def co_consume_rpcs():\\n return await bus.bus_client.consume_rpcs(apis=[dummy_api])\\n\\n (call_task, ), (consume_task, ) = await asyncio.wait([co_call_rpc(), co_consume_rpcs()], return_when=asyncio.FIRST_COMPLETED)\\n consume_task.cancel()\\n\\n with pytest.raises(LightbusTimeout):\\n call_task.result()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6498459","0.6334335","0.632674","0.6275306","0.62697285","0.6268087","0.62155145","0.616202","0.6141317","0.61329114","0.60898757","0.6051971","0.60427356","0.6019092","0.60161436","0.6015936","0.6009689","0.6005865","0.596967","0.5925834","0.58968174","0.58814126","0.5868605","0.58554155","0.5852431","0.58309966","0.58238393","0.5816484","0.5801558","0.5797832","0.5791333","0.57884413","0.577645","0.5765794","0.57625484","0.57625484","0.5761611","0.5758649","0.5756442","0.5754941","0.5733323","0.5727264","0.57109696","0.5710193","0.5706825","0.5701407","0.56972486","0.56962603","0.56849855","0.568071","0.56806976","0.56781656","0.5648136","0.56439054","0.5617291","0.5607346","0.56065464","0.5603375","0.55991495","0.55890596","0.5585076","0.55731964","0.5559689","0.5558574","0.55497587","0.554681","0.554599","0.5541317","0.5539535","0.5537389","0.55246884","0.5521021","0.5519085","0.55185246","0.5515755","0.5514978","0.55148643","0.55031615","0.5488638","0.5486437","0.5485633","0.54853296","0.5482525","0.5481689","0.5478208","0.54770863","0.54756504","0.54732704","0.54540014","0.5438718","0.54384506","0.54374546","0.54368997","0.54354215","0.54343706","0.5432362","0.54298663","0.54295224","0.5427774","0.5425743"],"string":"[\n \"0.6498459\",\n \"0.6334335\",\n \"0.632674\",\n \"0.6275306\",\n \"0.62697285\",\n \"0.6268087\",\n \"0.62155145\",\n \"0.616202\",\n \"0.6141317\",\n \"0.61329114\",\n \"0.60898757\",\n \"0.6051971\",\n \"0.60427356\",\n \"0.6019092\",\n \"0.60161436\",\n \"0.6015936\",\n \"0.6009689\",\n \"0.6005865\",\n \"0.596967\",\n \"0.5925834\",\n \"0.58968174\",\n \"0.58814126\",\n \"0.5868605\",\n \"0.58554155\",\n \"0.5852431\",\n \"0.58309966\",\n \"0.58238393\",\n \"0.5816484\",\n \"0.5801558\",\n \"0.5797832\",\n \"0.5791333\",\n \"0.57884413\",\n \"0.577645\",\n \"0.5765794\",\n \"0.57625484\",\n \"0.57625484\",\n \"0.5761611\",\n \"0.5758649\",\n \"0.5756442\",\n \"0.5754941\",\n \"0.5733323\",\n \"0.5727264\",\n \"0.57109696\",\n \"0.5710193\",\n \"0.5706825\",\n \"0.5701407\",\n \"0.56972486\",\n \"0.56962603\",\n \"0.56849855\",\n \"0.568071\",\n \"0.56806976\",\n \"0.56781656\",\n \"0.5648136\",\n \"0.56439054\",\n \"0.5617291\",\n \"0.5607346\",\n \"0.56065464\",\n \"0.5603375\",\n \"0.55991495\",\n \"0.55890596\",\n \"0.5585076\",\n \"0.55731964\",\n \"0.5559689\",\n \"0.5558574\",\n \"0.55497587\",\n \"0.554681\",\n \"0.554599\",\n \"0.5541317\",\n \"0.5539535\",\n \"0.5537389\",\n \"0.55246884\",\n \"0.5521021\",\n \"0.5519085\",\n \"0.55185246\",\n \"0.5515755\",\n \"0.5514978\",\n \"0.55148643\",\n \"0.55031615\",\n \"0.5488638\",\n \"0.5486437\",\n \"0.5485633\",\n \"0.54853296\",\n \"0.5482525\",\n \"0.5481689\",\n \"0.5478208\",\n \"0.54770863\",\n \"0.54756504\",\n \"0.54732704\",\n \"0.54540014\",\n \"0.5438718\",\n \"0.54384506\",\n \"0.54374546\",\n \"0.54368997\",\n \"0.54354215\",\n \"0.54343706\",\n \"0.5432362\",\n \"0.54298663\",\n \"0.54295224\",\n \"0.5427774\",\n \"0.5425743\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7268512"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":75589,"cells":{"query":{"kind":"string","value":"Return all states formated as a dictionary"},"document":{"kind":"string","value":"def state_format(states: list) -> list:\n return list(map(_format_n0, states))"},"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 states():\n states = storage.all(State).values()\n return jsonify([item.to_dict() for item in states])","def all_states():\n dict = storage.all(State)\n list = []\n for state in dict.values():\n list.append(state.to_dict())\n return jsonify(list)","def get_states():\n all_states = []\n states = storage.all(State).values()\n for state in states:\n all_states.append(state.to_dict())\n return jsonify(all_states)","def all_States():\n new_dict = []\n for state in storage.all('State').values():\n new_dict.append(state.to_dict())\n return jsonify(new_dict)","def states_to_dict(self):\n test_cases = {}\n i = 0\n for element in self.states:\n test_cases[\"st\" + str(i)] = {}\n test_cases[\"st\" + str(i)][\"model\"] = int(element[0])\n test_cases[\"st\" + str(i)][\"duration\"] = int(element[1])\n test_cases[\"st\" + str(i)][\"temp\"] = int(element[2])\n i += 1\n\n return test_cases","def states():\n states = storage.all(State).values()\n states_list = []\n for state in states:\n states_list.append(state.to_dict())\n return jsonify(states_list)","def states(self):\n from geoid.core import names\n from geoid.censusnames import geo_names, stusab\n\n states = {}\n\n for state_no, stusab in stusab.items():\n states[stusab] = {\n 'name': geo_names[(state_no,0)],\n 'stusab': stusab,\n 'number' : state_no\n }\n\n states['US'] = {\n 'name': 'United States',\n 'stusab': 'US',\n 'number' : 0\n }\n\n return states","def to_state_json(self) -> Dict[str, Any]:\n return self.state_to_json()","def all_states():\n states = storage.all(State)\n states_list = []\n for state in states.values():\n states_list.append(state.to_dict())\n return jsonify(states_list)","def getstate(self):\n return {}","def states(self):\n states = _StatesDict({})\n for k, v in self.coordinates.items():\n states.__setitem__(k, v, allow=True)\n for k, v in self.speeds.items():\n states.__setitem__(k, v, allow=True)\n return states","def get_states():\n all_states = storage.all('State')\n states_list = all_states.values()\n states_json = []\n for state in states_list:\n states_json.append(state.to_dict())\n return jsonify(states_json)","def get_states():\n states = []\n for state in storage.all(\"State\").values():\n states.append(state.to_dict())\n return jsonify(states)","def states():\n states = []\n for state in storage.all('State').values():\n states.append(state.to_dict())\n return jsonify(states)","def get_states(self):\n from geoffrey.data import datakey\n criteria = datakey(**request.query)\n response.content_type = 'application/json'\n return utils.jsonencoder.encode(\n [s.serializable()\n for s in self.server.hub.get_states(criteria)])","def all_states():\n states = []\n states_values = storage.all(\"State\").values()\n for obj in states_values:\n states.append(obj.to_dict())\n return jsonify(states)","def get_all_states():\n states = storage.all(State).values()\n return jsonify([state.to_dict() for state in states]), 200","def _get_state_dict(self) -> dict:\n return {\n 'optimizers': [o.state_dict() for o in self.optimizers],\n 'schedulers': [s.state_dict() for s in self.schedulers],\n }","def _get_state_dict(self) -> dict:\n return {\n 'optimizers': [o.state_dict() for o in self.optimizers],\n 'schedulers': [s.state_dict() for s in self.schedulers],\n }","def get_states(self):\n states = {}\n if hasattr(self, 'random_mask_state'):\n states['random_mask_state'] = self.random_mask_state.get_state()\n if hasattr(self, 'deformrandomstate'):\n states['deformrandomstate'] = self.deformrandomstate.get_state()\n states['randomstate'] = self.randomstate.get_state()\n return states","def get_state(self):\n state_dict = OrderedDict()\n for key, target_object in self._map.items():\n state = self._get_single_state(target_object)\n if state is not None:\n # pushbuttons for example are not defined in the get function\n state_dict[key] = state\n return state_dict","def state_dict(self):\n return self._prbm.state_dict","def states():\n # Query all abbreviations by state\n States = engine.execute(\"SELECT * FROM states\").fetchall()\n \n return jsonify({'States': [dict(row) for row in States]})","def create_state_dict(self):\n return {\n 'resting': self.resting,\n 'moving': self.moving,\n 'animated resting': self.animated_resting,\n 'autoresting': self.auto_resting,\n 'automoving': self.auto_moving,\n 'battle resting': self.battle_resting,\n 'attack': self.attack,\n 'enemy attack': self.enemy_attack,\n c.RUN_AWAY: self.run_away,\n c.VICTORY_DANCE: self.victory_dance,\n c.KNOCK_BACK: self.knock_back,\n c.FADE_DEATH: self.fade_death\n }","def __getstate__(self):\n return dict(self.items())","def __getstate__(self):\n return dict(self.items())","def state_to_json(self) -> Dict[str, Any]:\n return self.rstate.to_json()","def state_dict(self, *args, **kwargs):\n return self.module.state_dict(*args, **kwargs)","def get_states_dict():\n\n with open(\"../data/states_data.txt\", \"r\") as states:\n us_states = []\n for state in states:\n us_states.append(state.strip())\n\n with open(\"../data/states_abv.txt\", \"r\") as states_abv:\n us_states_abv = []\n for state in states_abv:\n us_states_abv.append(state.strip())\n state_dict = dict(zip(us_states, us_states_abv))\n\n return state_dict","def dense_state(self):\n return {name: self.group[name].state for name in self.group.keys()}","def _stateDict(self):\n\n data = {}\n # if self.currentState[4]:\n # data['action'] = 'BRAK'\n # else:\n data['action'] = 'MCTL'\n data['speed'] = float(self.speed)\n data['steerAngle'] = float(self.steering_angle)\n\n return data","def state(self):\n return {self._reverse_mapping[k]: v for k, v in enumerate(self._state)}","def make_state_dict(self):\r\n state_dict = {c.TRANSITION_IN: self.transition_in,\r\n c.TRANSITION_OUT: self.transition_out,\r\n c.NORMAL: self.normal_update}\r\n\r\n return state_dict","def make_state_dict(self):\n state_dict = {c.TRANSITION_IN: self.transition_in,\n c.TRANSITION_OUT: self.transition_out,\n c.NORMAL: self.normal_update}\n\n return state_dict","def get_state(self):\n\n # TODO: Assemble a dictionary containing the tracker state\n\n return {}","def state_dict(self):\n if ADVERSARIAL_FLAG:\n return {'Net': self.net.state_dict(),\n 'AdvNet': self.adv_net.state_dict(),\n 'Optimizer': self.optimizer.state_dict(),\n 'AdvOptimizer': self.adv_optimizer.state_dict(),\n 'History': self.history,\n 'Stats': self.stats}\n return {'Net': self.net.state_dict(),\n 'Optimizer': self.optimizer.state_dict(),\n 'History': self.history,\n 'Stats': self.stats}","def get_states(self):\n raise NotImplementedError()","def get_state_dict(self):\n raise NotImplemented()","def exposed_get_state(self):\n return json.dumps(dict(state=random.choice(self.states)), indent=2)","def state_dict(self):\n return {k: getattr(self, k) for k in self.VARS}","def state_dict(self) -> dict:\n _state_dict: dict[str, Any] = super().state_dict\n _state_dict[\"rng_state\"] = self.rng.get_state()\n _state_dict[\"seed\"] = self.seed\n _state_dict[\"strategy\"] = self.strategy.state_dict\n return _state_dict","def get_state(self):\n xml = self.env.sim.model.get_xml() # model xml file\n state = np.array(self.env.sim.get_state().flatten()) # simulator state\n return dict(model=xml, states=state)","def showStates():\n count_l = []\n for value in storage.all(\"State\").values():\n count_l.append(value.to_dict())\n return(jsonify(count_l))","def to_json(self) -> Dict[str, Any]:\n return json.loads(self.get_state().to_json())","def save_state_to_dict(self):\n return self.__dict__.copy()","def __getstate__(self):\n\n state = {}\n for key in self.__slots__:\n state[key] = getattr(self, key)\n\n return state","def get_all_states(self):\n return self._states","def get_classy_state(self) -> Dict[str, Any]:\n return self.state_dict()","def get_list_of_states(self):\n return self.states","def all_states(self):\n return self._states","def __getstate__(self):\n exclude_keys = ['_is_zero', '_is_positive', '_is_nonneg']\n state = {}\n for key,value in self.__dict__.items():\n if key in exclude_keys:\n continue\n state[key] = value\n return state","def states():\n states = storage.all(State).values()\n return render_template('9-states.html', states=states)","def get_state(self):\n return self.state_map","def get_state_walk(self, state_description, class_=State):\n states = {}\n names = state_description.keys()\n for dt in self.data.index:\n name_value_mapping = {name: self.get(name, dt) for name in names}\n states[dt] = state_description.to_state(class_,\n **name_value_mapping)\n\n return states","def state_dict(self):\n return {\n 'XY_net': self.XY_net.state_dict(),\n 'XY_optimizer_minee': self.XY_optimizer_minee.state_dict(),\n 'X_net': self.X_net.state_dict(),\n 'X_optimizer_minee': self.X_optimizer_minee.state_dict(),\n 'Y_net': self.Y_net.state_dict(),\n 'Y_optimizer_minee': self.Y_optimizer_minee.state_dict(),\n 'X': self.X,\n 'Y': self.Y,\n 'lr': self.lr,\n 'batch_size': self.batch_size,\n 'ref_batch_factor': self.ref_batch_factor\n }","def states(self) -> Type[Any]:\n return []","def __getstate__(self):\n return {}","def states(self):\n knownstates = set(self.keys())\n for possiblestates in self.values():\n for i in possiblestates:\n knownstates.add(i)\n return list(knownstates)","def get_state(self, short=False):\n state = {}\n for attr, cls in self._stateobject_attributes.iteritems():\n if short and attr in self._stateobject_long_attributes:\n continue\n val = getattr(self, attr)\n if hasattr(val, \"get_state\"):\n state[attr] = val.get_state(short)\n else:\n state[attr] = val\n return state","def GetState(self):\n self.update()\n d = {}\n for n in ['imgtype', 'object', 'path', 'filename', 'nextfile', 'lastfile', 'filectr', 'observer', 'filter', 'filterid',\n 'guider', 'mirror', 'lastact', 'initialized', 'errors', 'highcap', 'preamp', 'hsspeed', 'vsspeed', 'cycletime',\n 'readouttime', 'mode', 'cool', 'tset', 'settemp', 'temp', 'tempstatus', 'imaging', 'shuttermode', 'exptime',\n 'xmin', 'xmax', 'ymin', 'ymax', 'roi', 'xbin', 'ybin', 'isdark', 'MonitorActive', 'lastact']:\n d[n] = self.__dict__.get(n)\n return d","def __getstate__(self) -> Dict[str, Any]:\n return {\"name\": self.name}","def get_finished_states_dict():\n\tfinished_states_dict = {}\n\tget_red_rcp_primary_result_data(finished_states_dict)\n\tget_blue_rcp_primary_result_data(finished_states_dict)\n\treturn finished_states_dict","def __getstate__(self):\n state = {\n 'connector_keys' : self.connector_keys,\n 'metric_key' : self.metric_key,\n 'location_key' : self.location_key,\n 'parameters' : self.parameters,\n 'mrsm_instance' : self.instance_keys,\n }\n return state","def state_dict(self):\n return (\n {\n \"scale\": self.get_scale(),\n \"growth_factor\": self._growth_factor,\n \"backoff_factor\": self._backoff_factor,\n \"growth_interval\": self._growth_interval,\n \"_growth_tracker\": self._get_growth_tracker(),\n \"_hysteresis_tracker\": self._hysteresis_tracker,\n }\n if self._enabled\n else {}\n )","def S(self):\n return self._states","def getStateData(self):\n data = {}\n # Traffic lights\n lights = {}\n for light in self._lights:\n lights[light.id] = light.getStateData()\n data['trafficLights'] = lights\n\n # Cars\n cars = {}\n for car in self._cars:\n cars[car.id] = car.getStateData()\n cars[car.id].update(car.getDescriptionData())\n data['cars'] = cars\n\n # Enter queue\n enterQueue = []\n for car in self._enterQueue:\n enterQueue.append(car.getStateData())\n enterQueue[-1].update(car.getDescriptionData())\n data['enterQueue'] = enterQueue\n\n # Fields\n data['time'] = kts46.utils.timedelta2str(self.time)\n data['lastCarGenerationTime'] = kts46.utils.timedelta2str(self._lastCarGenerationTime)\n data['lastCarId'] = self._lastCarId\n\n # Result.\n return data","def to_json(self):\n return {\n \"state_type\": self.state_type,\n \"state_id\": self.state_id,\n \"public\": self.public,\n \"private\": self.private\n }","def state_dict(self):\r\n return {'ImageModel': self.image_model.state_dict(),\r\n 'QuestionModel': self.question_model.state_dict(),\r\n 'AttentionModel': self.attention.state_dict()\r\n }","def get_states():\n try:\n ''' Returns a list of states in list named result '''\n data = State.select()\n return ListStyle.list(data, request), 200\n except Exception as e:\n abort(500)","def get_state(game_ID: str) -> dict:\n if r.exists(\"state:\" + game_ID) == 0:\n raise exceptions.GameNotFoundError(message=\"Game not Found\")\n\n state = {\n \"playerState\": decode_dict(r.hgetall(\"state:\" + game_ID)),\n \"wordsState\": decode_dict(r.hgetall(\"words:\" + game_ID)),\n }\n\n print(state[\"wordsState\"])\n\n return state","def toState(attrs=ALL):","def _extract_state(self, state):\n extracted_state = {}\n\n legal_actions = [self.actions.index(a) for a in state[\"legal_actions\"]]\n extracted_state[\"legal_actions\"] = legal_actions\n\n public_card = state[\"public_card\"]\n hand = state[\"hand\"]\n obs = np.zeros(36)\n obs[self.card2index[hand]] = 1\n if public_card:\n obs[self.card2index[public_card] + 3] = 1\n obs[state[\"my_chips\"] + 6] = 1\n obs[state[\"all_chips\"][1] + 20] = 1\n extracted_state[\"obs\"] = obs\n\n if self.allow_raw_data:\n extracted_state[\"raw_obs\"] = state\n extracted_state[\"raw_legal_actions\"] = [a for a in state[\"legal_actions\"]]\n if self.record_action:\n extracted_state[\"action_record\"] = self.action_recorder\n\n return extracted_state","def get_all_states(self):\n return tuple(self._transition_probs.keys())","def get_state(self) -> Dict:\n state_dict = {}\n for param in self.optim_objs:\n param_name = pyro.get_param_store().param_name(param)\n state_dict[param_name] = _get_state_dict(self.optim_objs[param])\n return state_dict","def get_states(self):\n\n try:\n response = requests.get(self.ROOT_URL + self.ALL_STATES_ENDPOINT)\n response = response.json()\n except ValueError:\n raise OpenSkyApiException(self.PARSE_ERROR)\n except RequestException:\n raise OpenSkyApiException(self.REQUEST_ERROR)\n return self.parse_response(response)","def __getstate__ (self):\n state = self.__dict__.copy()\n restore = []\n for name in state.keys():\n if ((len( name ) > 1) and (name[-1] == '_') and\n (name[:-1] in state)):\n try:\n dumps( state[ name ] )\n except:\n del state[ name ]\n restore.append( name[:-1] )\n if len( restore ) > 0:\n state[ '__HasTraits_restore__' ] = restore\n\n return state","def __getstate__(self):\n result_dict = {}\n for pickle_attr in self.pickle_attrs:\n result_dict[pickle_attr] = self.__dict__[pickle_attr]\n return result_dict","def state_dict(self) -> dict:\n return self.lr_scheduler.state_dict()","def get_states(self):\n return product(*[phi.automaton().states for phi in self])","def get_state(self) -> Dict[str, Any]:\n return {\"aq_potential_num\": self.aq_potential_num, \"wq_potential_num\": self.wq_potential_num}","def state_attributes(self) -> Dict[str, Any]:\n return {**super(WarmupThermostat, self).state_attributes, **self.attributes}","def states_list():\n return render_template('7-states_list.html',\n states=storage.all(State).values())","def state_attributes(self):\n return {\"uuid\": self.uuidAction, \"room\": self.room,\n \"category\": self.cat,\n \"device_typ\": self.type, \"plattform\": \"loxone\"}","def laststate_to_dict(self):\n state_dict = {\n # Swarm structure info\n 'last_date': self.last_date,\n 'last_exposure': self.last_exposure,\n 'last_prev_exposure': self.last_prev_exposure,\n 'last_exoquote': self.last_exoquote,\n 'last_members_list': self.last_members_list,\n 'last_rebalance_date': self.last_rebalance_date,\n 'last_delta': self.last_delta,\n 'max_exposure': self.max_exposure,\n 'swarm_series': pickle.dumps(self._swarm_series),\n # General info\n 'swarm_name': self.name,\n 'exo_name': self.exo_name,\n 'alpha_name': self.strategy.name,\n 'direction': self.direction[0],\n 'instrument': self.instrument,\n 'exo_type': self.exo_type,\n 'calc_date': datetime.now(),\n # Context info\n 'context_info': self.context_to_jsondict(self.context)\n }\n return state_dict","def get_state(self):\n return {\n \"board\": self.board,\n \"player\": self.player,\n \"winner\": self.winner\n }","def states(self):\n from copy import copy\n return copy(self._states_)","def __get_complete_state(self):\n\n # create a state with all objects and agents\n state = {}\n for obj_id, obj in self.__environment_objects.items():\n state[obj.obj_id] = obj.properties\n for agent_id, agent in self.__registered_agents.items():\n state[agent.obj_id] = agent.properties\n\n # Append generic properties (e.g. number of ticks, size of grid, etc.}\n state[\"World\"] = {\n \"nr_ticks\": self.__current_nr_ticks,\n \"curr_tick_timestamp\": int(round(time.time() * 1000)),\n \"grid_shape\": self.__shape,\n \"tick_duration\": self.tick_duration,\n \"world_ID\": self.world_ID,\n \"vis_settings\": {\n \"vis_bg_clr\": self.__visualization_bg_clr,\n \"vis_bg_img\": self.__visualization_bg_img\n }\n }\n\n return state","def get_state(self) -> dict:\n return {\n \"name\": self.name,\n \"namespace\": self.namespace,\n \"args\": self.args,\n \"kwargs\": self.kwargs,\n \"class_name\": self.class_name,\n \"call_hash\": self.call_hash,\n \"key\": self.key,\n \"hash\": self.hash,\n }","def states():\n all_states = storage.all(State)\n return render_template('9-states.html', States=all_states, ID=None,\n Stateobj=None)","def get_state_dict(self) -> Dict[Text, Text]:\n # get the containerStatus.state dict\n container_state: Dict = self._container_status_dict.get(KubernetesResource.Keys.STATE)\n\n # create a dictionary to hold the state values\n state_dict: Dict = dict()\n\n # if there is no state dict, return the empty dict object\n if not container_state:\n return state_dict\n\n # handle the \"running\" state\n if \"running\" in container_state:\n running: Dict = container_state[\"running\"]\n state_dict[\"container-state\"] = \"running\"\n state_dict[\"container-started-at\"] = running[\"startedAt\"]\n\n # handle the terminated state\n elif \"terminated\" in container_state:\n terminated: Dict = container_state[\"terminated\"]\n state_dict[\"container-state\"] = \"terminated\"\n state_dict[\"container-state-reason\"] = terminated[\"reason\"]\n state_dict[\"container-started-at\"] = terminated[\"startedAt\"]\n state_dict[\"container-finished-at\"] = terminated[\"finishedAt\"]\n\n # handle the waiting state\n elif \"waiting\" in container_state:\n waiting: Dict = container_state[\"waiting\"]\n state_dict[\"container-state\"] = \"waiting\"\n state_dict[\"container-state-reason\"] = waiting[\"reason\"]\n\n return state_dict","def amenity_all():\n state_holder = []\n for state in models.storage.all(\"Amenity\").values():\n state_holder.append(state.to_dict())\n return_holder = jsonify(state_holder)\n return return_holder","def states_list(self, states):\n self.log('List of states: [{}]'.format(\n ' | '.join([(lambda x: x[1:])(s) for s in\n states.keys()])))\n return","def _parse_states(self, raw_states, logger):\n jobs = raw_states.splitlines()\n parsed = {}\n if jobs and (len(jobs) > 1 or jobs[0] != ''):\n for job in jobs:\n first, second = job.strip().split(',')\n parsed[first] = self._parse_exit_codes(second)\n\n return parsed","def state_dict(self):\n return {\n 'epoch': self.epoch,\n 'iterations_in_epoch': self.iterations_in_epoch,\n }","def extra_state_attributes(self):\n return {\n \"stage_coct\": self.coordinator.data.get(\"stage\"),\n \"stage_eskom\": self.coordinator.data.get(\"stage_eskom\"),\n \"coct_area\": self.coordinator.data.get(\"coct_area\"),\n \"load_shedding_active\": self.coordinator.data.get(\"load_shedding_active\"),\n \"next_load_shedding_slot\": self.coordinator.data.get(\"next_load_shedding_slot\"),\n \"next_stage\": self.coordinator.data.get(\"next_stage\"),\n \"next_stage_start_time\": self.coordinator.data.get(\"next_stage_start_time\"),\n \"last_updated\": self.coordinator.data.get(\"last_updated\"),\n \"today_slots\": self.coordinator.data.get(\"today_slots\"),\n \"tomorrow_slots\": self.coordinator.data.get(\"tomorrow_slots\"),\n }","def states(self) -> dict:\n if not self._states:\n raise NotConnected(\"Please call connect first.\")\n return self._states","def state(self): # type: () -> t.Dict[str, t.Any]\n return self.inspection['State']","def get_map_state(self):\n return self.serialize()","def state_attributes(self):\n\n data = {\n \"devices\": {\n \"AC1\": {\n \"participate\": True,\n \"reset\": False,\n \"zone_min\": 0,\n \"zone_max\": 1,\n \"flexibility\": \"high\"\n },\n \"AC2\": {\n \"participate\": True,\n \"reset\": False,\n \"zone_min\": 0,\n \"zone_max\": 1,\n \"flexibility\": \"low\"\n },\n \"WH1\": {\n \"participate\": True,\n \"reset\": False,\n \"zone_min\": 0,\n \"zone_max\": 1,\n \"flexibility\": \"low\"\n }\n }\n }\n\n return data","def _state_actions(self) -> dict:\n return {}","def device_state_attributes(self):\n if self._data is not None:\n return {\n \"阳历\": self._data.yangli,\n \"阴历\": self._data.yinli,\n \"五行\": self._data.wuxing,\n \"冲煞\": self._data.chongsha,\n \"百忌\": self._data.baiji,\n \"吉神\": self._data.jishen,\n \"宜\": self._data.yi,\n \"凶神\": self._data.xiongshen,\n \"忌\": self._data.ji,\n }"],"string":"[\n \"def states():\\n states = storage.all(State).values()\\n return jsonify([item.to_dict() for item in states])\",\n \"def all_states():\\n dict = storage.all(State)\\n list = []\\n for state in dict.values():\\n list.append(state.to_dict())\\n return jsonify(list)\",\n \"def get_states():\\n all_states = []\\n states = storage.all(State).values()\\n for state in states:\\n all_states.append(state.to_dict())\\n return jsonify(all_states)\",\n \"def all_States():\\n new_dict = []\\n for state in storage.all('State').values():\\n new_dict.append(state.to_dict())\\n return jsonify(new_dict)\",\n \"def states_to_dict(self):\\n test_cases = {}\\n i = 0\\n for element in self.states:\\n test_cases[\\\"st\\\" + str(i)] = {}\\n test_cases[\\\"st\\\" + str(i)][\\\"model\\\"] = int(element[0])\\n test_cases[\\\"st\\\" + str(i)][\\\"duration\\\"] = int(element[1])\\n test_cases[\\\"st\\\" + str(i)][\\\"temp\\\"] = int(element[2])\\n i += 1\\n\\n return test_cases\",\n \"def states():\\n states = storage.all(State).values()\\n states_list = []\\n for state in states:\\n states_list.append(state.to_dict())\\n return jsonify(states_list)\",\n \"def states(self):\\n from geoid.core import names\\n from geoid.censusnames import geo_names, stusab\\n\\n states = {}\\n\\n for state_no, stusab in stusab.items():\\n states[stusab] = {\\n 'name': geo_names[(state_no,0)],\\n 'stusab': stusab,\\n 'number' : state_no\\n }\\n\\n states['US'] = {\\n 'name': 'United States',\\n 'stusab': 'US',\\n 'number' : 0\\n }\\n\\n return states\",\n \"def to_state_json(self) -> Dict[str, Any]:\\n return self.state_to_json()\",\n \"def all_states():\\n states = storage.all(State)\\n states_list = []\\n for state in states.values():\\n states_list.append(state.to_dict())\\n return jsonify(states_list)\",\n \"def getstate(self):\\n return {}\",\n \"def states(self):\\n states = _StatesDict({})\\n for k, v in self.coordinates.items():\\n states.__setitem__(k, v, allow=True)\\n for k, v in self.speeds.items():\\n states.__setitem__(k, v, allow=True)\\n return states\",\n \"def get_states():\\n all_states = storage.all('State')\\n states_list = all_states.values()\\n states_json = []\\n for state in states_list:\\n states_json.append(state.to_dict())\\n return jsonify(states_json)\",\n \"def get_states():\\n states = []\\n for state in storage.all(\\\"State\\\").values():\\n states.append(state.to_dict())\\n return jsonify(states)\",\n \"def states():\\n states = []\\n for state in storage.all('State').values():\\n states.append(state.to_dict())\\n return jsonify(states)\",\n \"def get_states(self):\\n from geoffrey.data import datakey\\n criteria = datakey(**request.query)\\n response.content_type = 'application/json'\\n return utils.jsonencoder.encode(\\n [s.serializable()\\n for s in self.server.hub.get_states(criteria)])\",\n \"def all_states():\\n states = []\\n states_values = storage.all(\\\"State\\\").values()\\n for obj in states_values:\\n states.append(obj.to_dict())\\n return jsonify(states)\",\n \"def get_all_states():\\n states = storage.all(State).values()\\n return jsonify([state.to_dict() for state in states]), 200\",\n \"def _get_state_dict(self) -> dict:\\n return {\\n 'optimizers': [o.state_dict() for o in self.optimizers],\\n 'schedulers': [s.state_dict() for s in self.schedulers],\\n }\",\n \"def _get_state_dict(self) -> dict:\\n return {\\n 'optimizers': [o.state_dict() for o in self.optimizers],\\n 'schedulers': [s.state_dict() for s in self.schedulers],\\n }\",\n \"def get_states(self):\\n states = {}\\n if hasattr(self, 'random_mask_state'):\\n states['random_mask_state'] = self.random_mask_state.get_state()\\n if hasattr(self, 'deformrandomstate'):\\n states['deformrandomstate'] = self.deformrandomstate.get_state()\\n states['randomstate'] = self.randomstate.get_state()\\n return states\",\n \"def get_state(self):\\n state_dict = OrderedDict()\\n for key, target_object in self._map.items():\\n state = self._get_single_state(target_object)\\n if state is not None:\\n # pushbuttons for example are not defined in the get function\\n state_dict[key] = state\\n return state_dict\",\n \"def state_dict(self):\\n return self._prbm.state_dict\",\n \"def states():\\n # Query all abbreviations by state\\n States = engine.execute(\\\"SELECT * FROM states\\\").fetchall()\\n \\n return jsonify({'States': [dict(row) for row in States]})\",\n \"def create_state_dict(self):\\n return {\\n 'resting': self.resting,\\n 'moving': self.moving,\\n 'animated resting': self.animated_resting,\\n 'autoresting': self.auto_resting,\\n 'automoving': self.auto_moving,\\n 'battle resting': self.battle_resting,\\n 'attack': self.attack,\\n 'enemy attack': self.enemy_attack,\\n c.RUN_AWAY: self.run_away,\\n c.VICTORY_DANCE: self.victory_dance,\\n c.KNOCK_BACK: self.knock_back,\\n c.FADE_DEATH: self.fade_death\\n }\",\n \"def __getstate__(self):\\n return dict(self.items())\",\n \"def __getstate__(self):\\n return dict(self.items())\",\n \"def state_to_json(self) -> Dict[str, Any]:\\n return self.rstate.to_json()\",\n \"def state_dict(self, *args, **kwargs):\\n return self.module.state_dict(*args, **kwargs)\",\n \"def get_states_dict():\\n\\n with open(\\\"../data/states_data.txt\\\", \\\"r\\\") as states:\\n us_states = []\\n for state in states:\\n us_states.append(state.strip())\\n\\n with open(\\\"../data/states_abv.txt\\\", \\\"r\\\") as states_abv:\\n us_states_abv = []\\n for state in states_abv:\\n us_states_abv.append(state.strip())\\n state_dict = dict(zip(us_states, us_states_abv))\\n\\n return state_dict\",\n \"def dense_state(self):\\n return {name: self.group[name].state for name in self.group.keys()}\",\n \"def _stateDict(self):\\n\\n data = {}\\n # if self.currentState[4]:\\n # data['action'] = 'BRAK'\\n # else:\\n data['action'] = 'MCTL'\\n data['speed'] = float(self.speed)\\n data['steerAngle'] = float(self.steering_angle)\\n\\n return data\",\n \"def state(self):\\n return {self._reverse_mapping[k]: v for k, v in enumerate(self._state)}\",\n \"def make_state_dict(self):\\r\\n state_dict = {c.TRANSITION_IN: self.transition_in,\\r\\n c.TRANSITION_OUT: self.transition_out,\\r\\n c.NORMAL: self.normal_update}\\r\\n\\r\\n return state_dict\",\n \"def make_state_dict(self):\\n state_dict = {c.TRANSITION_IN: self.transition_in,\\n c.TRANSITION_OUT: self.transition_out,\\n c.NORMAL: self.normal_update}\\n\\n return state_dict\",\n \"def get_state(self):\\n\\n # TODO: Assemble a dictionary containing the tracker state\\n\\n return {}\",\n \"def state_dict(self):\\n if ADVERSARIAL_FLAG:\\n return {'Net': self.net.state_dict(),\\n 'AdvNet': self.adv_net.state_dict(),\\n 'Optimizer': self.optimizer.state_dict(),\\n 'AdvOptimizer': self.adv_optimizer.state_dict(),\\n 'History': self.history,\\n 'Stats': self.stats}\\n return {'Net': self.net.state_dict(),\\n 'Optimizer': self.optimizer.state_dict(),\\n 'History': self.history,\\n 'Stats': self.stats}\",\n \"def get_states(self):\\n raise NotImplementedError()\",\n \"def get_state_dict(self):\\n raise NotImplemented()\",\n \"def exposed_get_state(self):\\n return json.dumps(dict(state=random.choice(self.states)), indent=2)\",\n \"def state_dict(self):\\n return {k: getattr(self, k) for k in self.VARS}\",\n \"def state_dict(self) -> dict:\\n _state_dict: dict[str, Any] = super().state_dict\\n _state_dict[\\\"rng_state\\\"] = self.rng.get_state()\\n _state_dict[\\\"seed\\\"] = self.seed\\n _state_dict[\\\"strategy\\\"] = self.strategy.state_dict\\n return _state_dict\",\n \"def get_state(self):\\n xml = self.env.sim.model.get_xml() # model xml file\\n state = np.array(self.env.sim.get_state().flatten()) # simulator state\\n return dict(model=xml, states=state)\",\n \"def showStates():\\n count_l = []\\n for value in storage.all(\\\"State\\\").values():\\n count_l.append(value.to_dict())\\n return(jsonify(count_l))\",\n \"def to_json(self) -> Dict[str, Any]:\\n return json.loads(self.get_state().to_json())\",\n \"def save_state_to_dict(self):\\n return self.__dict__.copy()\",\n \"def __getstate__(self):\\n\\n state = {}\\n for key in self.__slots__:\\n state[key] = getattr(self, key)\\n\\n return state\",\n \"def get_all_states(self):\\n return self._states\",\n \"def get_classy_state(self) -> Dict[str, Any]:\\n return self.state_dict()\",\n \"def get_list_of_states(self):\\n return self.states\",\n \"def all_states(self):\\n return self._states\",\n \"def __getstate__(self):\\n exclude_keys = ['_is_zero', '_is_positive', '_is_nonneg']\\n state = {}\\n for key,value in self.__dict__.items():\\n if key in exclude_keys:\\n continue\\n state[key] = value\\n return state\",\n \"def states():\\n states = storage.all(State).values()\\n return render_template('9-states.html', states=states)\",\n \"def get_state(self):\\n return self.state_map\",\n \"def get_state_walk(self, state_description, class_=State):\\n states = {}\\n names = state_description.keys()\\n for dt in self.data.index:\\n name_value_mapping = {name: self.get(name, dt) for name in names}\\n states[dt] = state_description.to_state(class_,\\n **name_value_mapping)\\n\\n return states\",\n \"def state_dict(self):\\n return {\\n 'XY_net': self.XY_net.state_dict(),\\n 'XY_optimizer_minee': self.XY_optimizer_minee.state_dict(),\\n 'X_net': self.X_net.state_dict(),\\n 'X_optimizer_minee': self.X_optimizer_minee.state_dict(),\\n 'Y_net': self.Y_net.state_dict(),\\n 'Y_optimizer_minee': self.Y_optimizer_minee.state_dict(),\\n 'X': self.X,\\n 'Y': self.Y,\\n 'lr': self.lr,\\n 'batch_size': self.batch_size,\\n 'ref_batch_factor': self.ref_batch_factor\\n }\",\n \"def states(self) -> Type[Any]:\\n return []\",\n \"def __getstate__(self):\\n return {}\",\n \"def states(self):\\n knownstates = set(self.keys())\\n for possiblestates in self.values():\\n for i in possiblestates:\\n knownstates.add(i)\\n return list(knownstates)\",\n \"def get_state(self, short=False):\\n state = {}\\n for attr, cls in self._stateobject_attributes.iteritems():\\n if short and attr in self._stateobject_long_attributes:\\n continue\\n val = getattr(self, attr)\\n if hasattr(val, \\\"get_state\\\"):\\n state[attr] = val.get_state(short)\\n else:\\n state[attr] = val\\n return state\",\n \"def GetState(self):\\n self.update()\\n d = {}\\n for n in ['imgtype', 'object', 'path', 'filename', 'nextfile', 'lastfile', 'filectr', 'observer', 'filter', 'filterid',\\n 'guider', 'mirror', 'lastact', 'initialized', 'errors', 'highcap', 'preamp', 'hsspeed', 'vsspeed', 'cycletime',\\n 'readouttime', 'mode', 'cool', 'tset', 'settemp', 'temp', 'tempstatus', 'imaging', 'shuttermode', 'exptime',\\n 'xmin', 'xmax', 'ymin', 'ymax', 'roi', 'xbin', 'ybin', 'isdark', 'MonitorActive', 'lastact']:\\n d[n] = self.__dict__.get(n)\\n return d\",\n \"def __getstate__(self) -> Dict[str, Any]:\\n return {\\\"name\\\": self.name}\",\n \"def get_finished_states_dict():\\n\\tfinished_states_dict = {}\\n\\tget_red_rcp_primary_result_data(finished_states_dict)\\n\\tget_blue_rcp_primary_result_data(finished_states_dict)\\n\\treturn finished_states_dict\",\n \"def __getstate__(self):\\n state = {\\n 'connector_keys' : self.connector_keys,\\n 'metric_key' : self.metric_key,\\n 'location_key' : self.location_key,\\n 'parameters' : self.parameters,\\n 'mrsm_instance' : self.instance_keys,\\n }\\n return state\",\n \"def state_dict(self):\\n return (\\n {\\n \\\"scale\\\": self.get_scale(),\\n \\\"growth_factor\\\": self._growth_factor,\\n \\\"backoff_factor\\\": self._backoff_factor,\\n \\\"growth_interval\\\": self._growth_interval,\\n \\\"_growth_tracker\\\": self._get_growth_tracker(),\\n \\\"_hysteresis_tracker\\\": self._hysteresis_tracker,\\n }\\n if self._enabled\\n else {}\\n )\",\n \"def S(self):\\n return self._states\",\n \"def getStateData(self):\\n data = {}\\n # Traffic lights\\n lights = {}\\n for light in self._lights:\\n lights[light.id] = light.getStateData()\\n data['trafficLights'] = lights\\n\\n # Cars\\n cars = {}\\n for car in self._cars:\\n cars[car.id] = car.getStateData()\\n cars[car.id].update(car.getDescriptionData())\\n data['cars'] = cars\\n\\n # Enter queue\\n enterQueue = []\\n for car in self._enterQueue:\\n enterQueue.append(car.getStateData())\\n enterQueue[-1].update(car.getDescriptionData())\\n data['enterQueue'] = enterQueue\\n\\n # Fields\\n data['time'] = kts46.utils.timedelta2str(self.time)\\n data['lastCarGenerationTime'] = kts46.utils.timedelta2str(self._lastCarGenerationTime)\\n data['lastCarId'] = self._lastCarId\\n\\n # Result.\\n return data\",\n \"def to_json(self):\\n return {\\n \\\"state_type\\\": self.state_type,\\n \\\"state_id\\\": self.state_id,\\n \\\"public\\\": self.public,\\n \\\"private\\\": self.private\\n }\",\n \"def state_dict(self):\\r\\n return {'ImageModel': self.image_model.state_dict(),\\r\\n 'QuestionModel': self.question_model.state_dict(),\\r\\n 'AttentionModel': self.attention.state_dict()\\r\\n }\",\n \"def get_states():\\n try:\\n ''' Returns a list of states in list named result '''\\n data = State.select()\\n return ListStyle.list(data, request), 200\\n except Exception as e:\\n abort(500)\",\n \"def get_state(game_ID: str) -> dict:\\n if r.exists(\\\"state:\\\" + game_ID) == 0:\\n raise exceptions.GameNotFoundError(message=\\\"Game not Found\\\")\\n\\n state = {\\n \\\"playerState\\\": decode_dict(r.hgetall(\\\"state:\\\" + game_ID)),\\n \\\"wordsState\\\": decode_dict(r.hgetall(\\\"words:\\\" + game_ID)),\\n }\\n\\n print(state[\\\"wordsState\\\"])\\n\\n return state\",\n \"def toState(attrs=ALL):\",\n \"def _extract_state(self, state):\\n extracted_state = {}\\n\\n legal_actions = [self.actions.index(a) for a in state[\\\"legal_actions\\\"]]\\n extracted_state[\\\"legal_actions\\\"] = legal_actions\\n\\n public_card = state[\\\"public_card\\\"]\\n hand = state[\\\"hand\\\"]\\n obs = np.zeros(36)\\n obs[self.card2index[hand]] = 1\\n if public_card:\\n obs[self.card2index[public_card] + 3] = 1\\n obs[state[\\\"my_chips\\\"] + 6] = 1\\n obs[state[\\\"all_chips\\\"][1] + 20] = 1\\n extracted_state[\\\"obs\\\"] = obs\\n\\n if self.allow_raw_data:\\n extracted_state[\\\"raw_obs\\\"] = state\\n extracted_state[\\\"raw_legal_actions\\\"] = [a for a in state[\\\"legal_actions\\\"]]\\n if self.record_action:\\n extracted_state[\\\"action_record\\\"] = self.action_recorder\\n\\n return extracted_state\",\n \"def get_all_states(self):\\n return tuple(self._transition_probs.keys())\",\n \"def get_state(self) -> Dict:\\n state_dict = {}\\n for param in self.optim_objs:\\n param_name = pyro.get_param_store().param_name(param)\\n state_dict[param_name] = _get_state_dict(self.optim_objs[param])\\n return state_dict\",\n \"def get_states(self):\\n\\n try:\\n response = requests.get(self.ROOT_URL + self.ALL_STATES_ENDPOINT)\\n response = response.json()\\n except ValueError:\\n raise OpenSkyApiException(self.PARSE_ERROR)\\n except RequestException:\\n raise OpenSkyApiException(self.REQUEST_ERROR)\\n return self.parse_response(response)\",\n \"def __getstate__ (self):\\n state = self.__dict__.copy()\\n restore = []\\n for name in state.keys():\\n if ((len( name ) > 1) and (name[-1] == '_') and\\n (name[:-1] in state)):\\n try:\\n dumps( state[ name ] )\\n except:\\n del state[ name ]\\n restore.append( name[:-1] )\\n if len( restore ) > 0:\\n state[ '__HasTraits_restore__' ] = restore\\n\\n return state\",\n \"def __getstate__(self):\\n result_dict = {}\\n for pickle_attr in self.pickle_attrs:\\n result_dict[pickle_attr] = self.__dict__[pickle_attr]\\n return result_dict\",\n \"def state_dict(self) -> dict:\\n return self.lr_scheduler.state_dict()\",\n \"def get_states(self):\\n return product(*[phi.automaton().states for phi in self])\",\n \"def get_state(self) -> Dict[str, Any]:\\n return {\\\"aq_potential_num\\\": self.aq_potential_num, \\\"wq_potential_num\\\": self.wq_potential_num}\",\n \"def state_attributes(self) -> Dict[str, Any]:\\n return {**super(WarmupThermostat, self).state_attributes, **self.attributes}\",\n \"def states_list():\\n return render_template('7-states_list.html',\\n states=storage.all(State).values())\",\n \"def state_attributes(self):\\n return {\\\"uuid\\\": self.uuidAction, \\\"room\\\": self.room,\\n \\\"category\\\": self.cat,\\n \\\"device_typ\\\": self.type, \\\"plattform\\\": \\\"loxone\\\"}\",\n \"def laststate_to_dict(self):\\n state_dict = {\\n # Swarm structure info\\n 'last_date': self.last_date,\\n 'last_exposure': self.last_exposure,\\n 'last_prev_exposure': self.last_prev_exposure,\\n 'last_exoquote': self.last_exoquote,\\n 'last_members_list': self.last_members_list,\\n 'last_rebalance_date': self.last_rebalance_date,\\n 'last_delta': self.last_delta,\\n 'max_exposure': self.max_exposure,\\n 'swarm_series': pickle.dumps(self._swarm_series),\\n # General info\\n 'swarm_name': self.name,\\n 'exo_name': self.exo_name,\\n 'alpha_name': self.strategy.name,\\n 'direction': self.direction[0],\\n 'instrument': self.instrument,\\n 'exo_type': self.exo_type,\\n 'calc_date': datetime.now(),\\n # Context info\\n 'context_info': self.context_to_jsondict(self.context)\\n }\\n return state_dict\",\n \"def get_state(self):\\n return {\\n \\\"board\\\": self.board,\\n \\\"player\\\": self.player,\\n \\\"winner\\\": self.winner\\n }\",\n \"def states(self):\\n from copy import copy\\n return copy(self._states_)\",\n \"def __get_complete_state(self):\\n\\n # create a state with all objects and agents\\n state = {}\\n for obj_id, obj in self.__environment_objects.items():\\n state[obj.obj_id] = obj.properties\\n for agent_id, agent in self.__registered_agents.items():\\n state[agent.obj_id] = agent.properties\\n\\n # Append generic properties (e.g. number of ticks, size of grid, etc.}\\n state[\\\"World\\\"] = {\\n \\\"nr_ticks\\\": self.__current_nr_ticks,\\n \\\"curr_tick_timestamp\\\": int(round(time.time() * 1000)),\\n \\\"grid_shape\\\": self.__shape,\\n \\\"tick_duration\\\": self.tick_duration,\\n \\\"world_ID\\\": self.world_ID,\\n \\\"vis_settings\\\": {\\n \\\"vis_bg_clr\\\": self.__visualization_bg_clr,\\n \\\"vis_bg_img\\\": self.__visualization_bg_img\\n }\\n }\\n\\n return state\",\n \"def get_state(self) -> dict:\\n return {\\n \\\"name\\\": self.name,\\n \\\"namespace\\\": self.namespace,\\n \\\"args\\\": self.args,\\n \\\"kwargs\\\": self.kwargs,\\n \\\"class_name\\\": self.class_name,\\n \\\"call_hash\\\": self.call_hash,\\n \\\"key\\\": self.key,\\n \\\"hash\\\": self.hash,\\n }\",\n \"def states():\\n all_states = storage.all(State)\\n return render_template('9-states.html', States=all_states, ID=None,\\n Stateobj=None)\",\n \"def get_state_dict(self) -> Dict[Text, Text]:\\n # get the containerStatus.state dict\\n container_state: Dict = self._container_status_dict.get(KubernetesResource.Keys.STATE)\\n\\n # create a dictionary to hold the state values\\n state_dict: Dict = dict()\\n\\n # if there is no state dict, return the empty dict object\\n if not container_state:\\n return state_dict\\n\\n # handle the \\\"running\\\" state\\n if \\\"running\\\" in container_state:\\n running: Dict = container_state[\\\"running\\\"]\\n state_dict[\\\"container-state\\\"] = \\\"running\\\"\\n state_dict[\\\"container-started-at\\\"] = running[\\\"startedAt\\\"]\\n\\n # handle the terminated state\\n elif \\\"terminated\\\" in container_state:\\n terminated: Dict = container_state[\\\"terminated\\\"]\\n state_dict[\\\"container-state\\\"] = \\\"terminated\\\"\\n state_dict[\\\"container-state-reason\\\"] = terminated[\\\"reason\\\"]\\n state_dict[\\\"container-started-at\\\"] = terminated[\\\"startedAt\\\"]\\n state_dict[\\\"container-finished-at\\\"] = terminated[\\\"finishedAt\\\"]\\n\\n # handle the waiting state\\n elif \\\"waiting\\\" in container_state:\\n waiting: Dict = container_state[\\\"waiting\\\"]\\n state_dict[\\\"container-state\\\"] = \\\"waiting\\\"\\n state_dict[\\\"container-state-reason\\\"] = waiting[\\\"reason\\\"]\\n\\n return state_dict\",\n \"def amenity_all():\\n state_holder = []\\n for state in models.storage.all(\\\"Amenity\\\").values():\\n state_holder.append(state.to_dict())\\n return_holder = jsonify(state_holder)\\n return return_holder\",\n \"def states_list(self, states):\\n self.log('List of states: [{}]'.format(\\n ' | '.join([(lambda x: x[1:])(s) for s in\\n states.keys()])))\\n return\",\n \"def _parse_states(self, raw_states, logger):\\n jobs = raw_states.splitlines()\\n parsed = {}\\n if jobs and (len(jobs) > 1 or jobs[0] != ''):\\n for job in jobs:\\n first, second = job.strip().split(',')\\n parsed[first] = self._parse_exit_codes(second)\\n\\n return parsed\",\n \"def state_dict(self):\\n return {\\n 'epoch': self.epoch,\\n 'iterations_in_epoch': self.iterations_in_epoch,\\n }\",\n \"def extra_state_attributes(self):\\n return {\\n \\\"stage_coct\\\": self.coordinator.data.get(\\\"stage\\\"),\\n \\\"stage_eskom\\\": self.coordinator.data.get(\\\"stage_eskom\\\"),\\n \\\"coct_area\\\": self.coordinator.data.get(\\\"coct_area\\\"),\\n \\\"load_shedding_active\\\": self.coordinator.data.get(\\\"load_shedding_active\\\"),\\n \\\"next_load_shedding_slot\\\": self.coordinator.data.get(\\\"next_load_shedding_slot\\\"),\\n \\\"next_stage\\\": self.coordinator.data.get(\\\"next_stage\\\"),\\n \\\"next_stage_start_time\\\": self.coordinator.data.get(\\\"next_stage_start_time\\\"),\\n \\\"last_updated\\\": self.coordinator.data.get(\\\"last_updated\\\"),\\n \\\"today_slots\\\": self.coordinator.data.get(\\\"today_slots\\\"),\\n \\\"tomorrow_slots\\\": self.coordinator.data.get(\\\"tomorrow_slots\\\"),\\n }\",\n \"def states(self) -> dict:\\n if not self._states:\\n raise NotConnected(\\\"Please call connect first.\\\")\\n return self._states\",\n \"def state(self): # type: () -> t.Dict[str, t.Any]\\n return self.inspection['State']\",\n \"def get_map_state(self):\\n return self.serialize()\",\n \"def state_attributes(self):\\n\\n data = {\\n \\\"devices\\\": {\\n \\\"AC1\\\": {\\n \\\"participate\\\": True,\\n \\\"reset\\\": False,\\n \\\"zone_min\\\": 0,\\n \\\"zone_max\\\": 1,\\n \\\"flexibility\\\": \\\"high\\\"\\n },\\n \\\"AC2\\\": {\\n \\\"participate\\\": True,\\n \\\"reset\\\": False,\\n \\\"zone_min\\\": 0,\\n \\\"zone_max\\\": 1,\\n \\\"flexibility\\\": \\\"low\\\"\\n },\\n \\\"WH1\\\": {\\n \\\"participate\\\": True,\\n \\\"reset\\\": False,\\n \\\"zone_min\\\": 0,\\n \\\"zone_max\\\": 1,\\n \\\"flexibility\\\": \\\"low\\\"\\n }\\n }\\n }\\n\\n return data\",\n \"def _state_actions(self) -> dict:\\n return {}\",\n \"def device_state_attributes(self):\\n if self._data is not None:\\n return {\\n \\\"阳历\\\": self._data.yangli,\\n \\\"阴历\\\": self._data.yinli,\\n \\\"五行\\\": self._data.wuxing,\\n \\\"冲煞\\\": self._data.chongsha,\\n \\\"百忌\\\": self._data.baiji,\\n \\\"吉神\\\": self._data.jishen,\\n \\\"宜\\\": self._data.yi,\\n \\\"凶神\\\": self._data.xiongshen,\\n \\\"忌\\\": self._data.ji,\\n }\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.77571523","0.7694623","0.7661228","0.7641882","0.75851685","0.7568326","0.7566142","0.75567716","0.75191","0.7493844","0.7472908","0.74583113","0.7420259","0.740409","0.73985374","0.7392214","0.7369381","0.73459816","0.73459816","0.7301348","0.7300903","0.7299131","0.7276609","0.7262565","0.72366095","0.72366095","0.7217574","0.72021997","0.71743923","0.71562123","0.7135392","0.7126817","0.71141315","0.7103085","0.70511496","0.70302826","0.7029731","0.70174277","0.6969511","0.6932351","0.68602383","0.6853087","0.6848524","0.6832249","0.6827828","0.6827263","0.6827042","0.6798673","0.67623603","0.67176616","0.6688163","0.66876686","0.6674755","0.66655195","0.6657498","0.66567254","0.66478205","0.6616229","0.6603605","0.65895","0.65825427","0.65659636","0.6564191","0.65592813","0.6548036","0.65443474","0.65099084","0.6506879","0.6503885","0.64737016","0.6455463","0.6441478","0.64413655","0.64383715","0.6432753","0.6411361","0.64076495","0.63987803","0.6389912","0.6388093","0.6386548","0.6379411","0.63757455","0.6375593","0.6364346","0.6361271","0.6352598","0.6351217","0.6345361","0.6345313","0.6343858","0.6342277","0.6341403","0.6333128","0.63288355","0.632812","0.63248897","0.63191766","0.6302319","0.6289024","0.6287796"],"string":"[\n \"0.77571523\",\n \"0.7694623\",\n \"0.7661228\",\n \"0.7641882\",\n \"0.75851685\",\n \"0.7568326\",\n \"0.7566142\",\n \"0.75567716\",\n \"0.75191\",\n \"0.7493844\",\n \"0.7472908\",\n \"0.74583113\",\n \"0.7420259\",\n \"0.740409\",\n \"0.73985374\",\n \"0.7392214\",\n \"0.7369381\",\n \"0.73459816\",\n \"0.73459816\",\n \"0.7301348\",\n \"0.7300903\",\n \"0.7299131\",\n \"0.7276609\",\n \"0.7262565\",\n \"0.72366095\",\n \"0.72366095\",\n \"0.7217574\",\n \"0.72021997\",\n \"0.71743923\",\n \"0.71562123\",\n \"0.7135392\",\n \"0.7126817\",\n \"0.71141315\",\n \"0.7103085\",\n \"0.70511496\",\n \"0.70302826\",\n \"0.7029731\",\n \"0.70174277\",\n \"0.6969511\",\n \"0.6932351\",\n \"0.68602383\",\n \"0.6853087\",\n \"0.6848524\",\n \"0.6832249\",\n \"0.6827828\",\n \"0.6827263\",\n \"0.6827042\",\n \"0.6798673\",\n \"0.67623603\",\n \"0.67176616\",\n \"0.6688163\",\n \"0.66876686\",\n \"0.6674755\",\n \"0.66655195\",\n \"0.6657498\",\n \"0.66567254\",\n \"0.66478205\",\n \"0.6616229\",\n \"0.6603605\",\n \"0.65895\",\n \"0.65825427\",\n \"0.65659636\",\n \"0.6564191\",\n \"0.65592813\",\n \"0.6548036\",\n \"0.65443474\",\n \"0.65099084\",\n \"0.6506879\",\n \"0.6503885\",\n \"0.64737016\",\n \"0.6455463\",\n \"0.6441478\",\n \"0.64413655\",\n \"0.64383715\",\n \"0.6432753\",\n \"0.6411361\",\n \"0.64076495\",\n \"0.63987803\",\n \"0.6389912\",\n \"0.6388093\",\n \"0.6386548\",\n \"0.6379411\",\n \"0.63757455\",\n \"0.6375593\",\n \"0.6364346\",\n \"0.6361271\",\n \"0.6352598\",\n \"0.6351217\",\n \"0.6345361\",\n \"0.6345313\",\n \"0.6343858\",\n \"0.6342277\",\n \"0.6341403\",\n \"0.6333128\",\n \"0.63288355\",\n \"0.632812\",\n \"0.63248897\",\n \"0.63191766\",\n \"0.6302319\",\n \"0.6289024\",\n \"0.6287796\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":75590,"cells":{"query":{"kind":"string","value":"Return a human readable string."},"document":{"kind":"string","value":"def __str__(self):\n return f\"#{self.number}| {self.active}| {self.name}: {self.desc}\""},"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 __str__(self):\n return 'str-human.%s' % self.name","def __str__(self):\n return f\"{self._desc:16s}\"","def summary_string(self) -> str:","def get_human_readable(self):\n\n def yesno(key):\n if getattr(self, key) and getattr(self, key) > 0:\n return \"Y\"\n else:\n return \"N\"\n\n keys = (\n \"pvs1\",\n \"ps1\",\n \"ps2\",\n \"ps3\",\n \"ps4\",\n \"pm1\",\n \"pm2\",\n \"pm3\",\n \"pm4\",\n \"pm5\",\n \"pm6\",\n \"pp1\",\n \"pp2\",\n \"pp3\",\n \"pp4\",\n \"pp5\",\n \"ba1\",\n \"bs1\",\n \"bs2\",\n \"bs3\",\n \"bs4\",\n \"bp1\",\n \"bp2\",\n \"bp3\",\n \"bp4\",\n \"bp5\",\n \"bp6\",\n \"bp7\",\n )\n result = \", \".join([\"%s: %s\" % (key.upper(), yesno(key)) for key in keys])\n result += \", ACMG classification: %s\" % self.class_auto\n if self.class_override:\n result += \", ACMG class. override: %s\" % self.class_override\n return result","def to_short_string(self):\n return f'{self.name} - {self.resource_type}'","def __str__(self):\n return str(self.description)[:10]","def __str__(self):\n if __debug__:\n description = ('CM' in debug.active)\n else:\n description = False\n return self.asstring(short=False, header=True, summary=True,\n description=description)","def pretty_str(self) -> str:\n ...","def title(self):\n strng = \"\"\n if self.type:\n strng = self.type\n if self.server:\n if self.status:\n strng = \"%s\" % (strng)\n if not strng:\n strng = \"Error\"\n strng = \"%s on %s\" % (strng, self.server)\n elif self.status:\n strng = self.status\n if self.server:\n strng = \"%s on server %s\" % (strng, self.server)\n elif self.raw:\n strng = self.raw\n else:\n strng = self.error_timestamp.isoformat()\n if self.uid:\n strng = \"%s\" % (strng)\n return strng","def get_descriptive_name(self):\r\n long_name = str(self.year) + ' ' + self.make + ' ' + self.model\r\n #Mostrar_Grande = long_name.upper()\r\n #return long_name.upper()\r\n #return Mostrar_Grande #Funciona Com Return TAMBÉM, mas olhe na linha 39 como seria necessário usar.\r\n print(long_name.upper())","def toString(self):\n\t\ts = \"A %s titled '%s':\\n\\n\" % (self.getSpecString(), self.getName())\n\t\ts += \"It's summary reads: %s\\n\\n\" % (self.getDescription())\n\t\ts += \"~~\\n%s\\n~~\" % (self.getAllItemsStr())\n\t\treturn s","def get_descriptive_name(self):\n long_name = f\"{self.make} {self.model} {self.year}\"\n \n return long_name.title()","def get_descriptive_name(self):\n long_name = f\"{self.year} {self.make} {self.model}\"\n return long_name.title()","def get_descriptive_name(self):\r\n long_name = str(self.year) + ' ' + self.make + ' ' + self.model\r\n return long_name.title()","def __str__(self):\n return \"<%s: %s>\" % (self.__class__, self.describe())","def __str__(self):\n to_print = '{} : {}\\n'.format('Name'.ljust(34),self.name)\n to_print = to_print + '{} : {}\\n'.format('Name'.ljust(34),self.pathloss.name)\n to_print = to_print + '{} : {}\\n'.format('Number of samples'.ljust(34),self.nsamples)\n to_print = to_print + '{} : {}\\n'.format('Sensor model'.ljust(34),self.sensor_model.name)\n to_print = to_print + '{} : {}\\n'.format('Motion model'.ljust(34),self.motion_model.name)\n return to_print","def get_descriptive_name(self):\n return f\"{self.year} {self.make} {self.model}\".title()","def get_descriptive_name(self):\r\n long_name = str(self.year)+' '+self.make + ' '+self.model\r\n return long_name.title()","def get_descriptive_name(self):\n long_name = str(self.year) + ' ' + self.make + ' ' + self.model\n return long_name.title()","def get_descriptive_name(self):\n long_name = str(self.year) + ' ' + self.make + ' ' + self.model\n return long_name.title()","def get_descriptive_name(self):\n long_name = str(self.year) + ' ' + self.make + ' ' + self.model\n return long_name.title()","def get_descriptive_name(self):\n long_name = str(self.year) + ' ' + self.make + ' ' + self.model\n return long_name.title()","def get_descriptive_name(self):\n long_name = str(self.year) + ' ' + self.make + ' ' + self.model\n return long_name.title()","def get_descriptive_name(self):\n long_name = str(self.year) + ' ' + self.make + ' ' + self.model\n return long_name.title()","def get_descriptive_name(self):\n long_name = str(self.year) + ' ' + self.make + ' ' + self.model\n return long_name.title()","def get_descriptive_name(self):\n long_name = str(self.year) + ' ' + self.make + ' ' + self.model\n return long_name.title()","def __str__(self):\r\n to_print = (\"Name: \" + self.name + \", Age: \" +\r\n str(self.age) + \", Hobbys: \" + str(self.hobbys))\r\n return to_print","def get_descriptive_name(self):\r\n long_name=str(self.year)+' '+self.make+' '+self.model\r\n return long_name.title()","def __str__(self):\n return self.format()","def get_descriptive_name(self):\n description = (f\"{self.year} {self.manufacturer.title()} \"\n f\"{self.model.title()}\")\n\n return description","def __str__(self):\n \n string = \"{}: {} ({}) - {}\".format(self.code, self.title, self.credit, self._term_name())\n return string","def _getDiagnosticString():\n text = '\\n## Diagnostic output from minimalmodbus ## \\n\\n'\n text += 'Minimalmodbus version: ' + __version__ + '\\n'\n text += 'Minimalmodbus status: ' + __status__ + '\\n'\n text += 'Revision: ' + __revision__ + '\\n'\n text += 'Revision date: ' + __date__ + '\\n'\n text += 'File name (with relative path): ' + __file__ + '\\n'\n text += 'Full file path: ' + os.path.abspath(__file__) + '\\n\\n'\n text += 'pySerial version: ' + serial.VERSION + '\\n'\n text += 'pySerial full file path: ' + os.path.abspath(serial.__file__) + '\\n\\n'\n text += 'Platform: ' + sys.platform + '\\n'\n text += 'Filesystem encoding: ' + repr(sys.getfilesystemencoding()) + '\\n'\n text += 'Byteorder: ' + sys.byteorder + '\\n'\n text += 'Python version: ' + sys.version + '\\n'\n text += 'Python version info: ' + repr(sys.version_info) + '\\n'\n text += 'Python flags: ' + repr(sys.flags) + '\\n'\n text += 'Python argv: ' + repr(sys.argv) + '\\n'\n text += 'Python prefix: ' + repr(sys.prefix) + '\\n'\n text += 'Python exec prefix: ' + repr(sys.exec_prefix) + '\\n'\n text += 'Python executable: ' + repr(sys.executable) + '\\n'\n try:\n text += 'Long info: ' + repr(sys.long_info) + '\\n'\n except:\n text += 'Long info: (none)\\n' # For Python3 compatibility\n try:\n text += 'Float repr style: ' + repr(sys.float_repr_style) + '\\n\\n'\n except:\n text += 'Float repr style: (none) \\n\\n' # For Python 2.6 compatibility\n text += 'Variable __name__: ' + __name__ + '\\n'\n text += 'Current directory: ' + os.getcwd() + '\\n\\n'\n text += 'Python path: \\n'\n text += '\\n'.join(sys.path) + '\\n'\n text += '\\n## End of diagnostic output ## \\n'\n return text","def format(self) -> str:","def __str__(self) -> str:\n return self.name + \" has a population of \" + str(self.population) + \" and is \" + str(self.area) + \" square \" \\\n \"kilometres.\"","def get_descriptive_name(self): # 定义描述完整信息的方法\n long_name = str(self.year) + \" \" + self.make + \" \" + self.model # 拼接变量字符串并赋值变量\n return long_name.title() # 返回字符串并首字母大写","def __str__(self):\r\n if len(self.hand) < 1:\r\n return \"No cards in hand.\"\r\n else:\r\n output = \"%s's hand contains\" % (self.tag.capitalize())\r\n for card in self.hand:\r\n output += \" %s\" % (card)\r\n return output + \".\"","def summary(self):\n if not self: return u''\n s = u'Character\\n=====\\nName: %s\\n' % \\\n self.get('name', u'')\n bio = self.get('biography')\n if bio:\n s += u'Biography: %s\\n' % bio[0]\n filmo = self.get('filmography')\n if filmo:\n a_list = [x.get('long imdb canonical title', u'')\n for x in filmo[:5]]\n s += u'Last movies with this character: %s.\\n' % u'; '.join(a_list)\n return s","def __str__(self):\n\n return (self._display_name + \"\\n\\t\" + self._description + \"\\n\")","def human_readable(self, value):\n return value","def __str__(self):\n return self.summary()","def to_str(self) -> str:","def __str__(self):\n\n descr = (\"Your health is \" + str(self.health))\n return descr","def stringify_short(self):\n return self.stringify()","def __str__(self): # pragma: no cover\n return self.display()","def human_readable_info(self) -> str:\n next_session = unix_str(self._stat.next_session)\n last_session = unix_str(self._stat.last_session)\n return \"\"\"\n Next Session: {}\n Last Session: {}\n Repetitions: {}\n Health: {}\n ------------------------\n Past Quality (last 20):\n ------------------------\n {}\n \"\"\".format(\n next_session,\n last_session,\n self._stat.actual_repetitions,\n self._health(),\n self._past_quality_graph(),\n )","def __str__(self):\n from ansys.dpf.core.core import _description\n\n return _description(self._message, self._server)","def __str__(self):\n \n is_random_print = \"\"\n if self.is_random == True:\n is_random_print = \"randomly\"\n else:\n is_random_print = \"deterministically\"\n\n return \"Player for \" + self.side + \", ply = \" + str(self.ply) + \", breaks ties \" + is_random_print","def __str__(self):\n description = \"-------- %s --------\" % (self.name)\n description += \"\\nnmax = %i\" % (self.nmax)\n description += \"\\nnslots = %i\" % (self.nslots)\n description += \"\\nbonus_power = %i\" % (self.bonus_power)\n description += \"\\nbonus_initiative = %.1f\" % (self.bonus_initiative)\n description += \"\\nneeds_drive = %i\" % (self.needs_drive)\n description += \"\\nis_mobile = %i\" % (self.is_mobile)\n description += \"\\n----- Default Parts -----\"\n for i in range(len(self.default_parts)):\n description += \"\\n%i) %s\" % (i + 1, self.default_parts[i].name)\n return description","def str(self):\n if self.num_dice is not None and self.dice_type is not None:\n descr = \"{}D{}\".format(self.num_dice, self.dice_type)\n if self.plus > 0:\n descr += \"+{}\".format(self.plus)\n elif self.min_value is not None and self.max_value is not None:\n descr = \"{}-{}\".format(self.min_value, self.max_value)\n elif self.plus != 0:\n descr = str(self.plus)\n else:\n descr = \"\"\n\n return descr","def __str__(self):\n return \"Author: \" + self.author +\"\\nTitle: \" + self.title + \"\\nHaiku: \" + str(self.haiku) + \\\n \"\\nText: \" + self.text + \"\\n\"","def __str__(self) -> str:","def __str__(self) -> str:","def __str__(self) -> str:","def __str__(self) -> str:","def __str__(self):\n if self.f_has_range():\n lenstr = \"len:%d\" % self.f_get_range_length()\n else:\n lenstr = \"\"\n\n if self.v_comment:\n commentstr = \"`%s`\" % self.v_comment\n else:\n commentstr = \"\"\n\n if commentstr or lenstr:\n if commentstr and lenstr:\n combined_str = \"%s, %s\" % (lenstr, commentstr)\n elif commentstr:\n combined_str = commentstr\n elif lenstr:\n combined_str = lenstr\n else:\n raise RuntimeError(\"You shall not pass!\")\n\n infostr = \" (%s)\" % combined_str\n\n else:\n infostr = \"\"\n\n return_string = \"%s %s%s\" % (self.f_get_class_name(), self.v_full_name, infostr)\n\n if not self.f_is_empty():\n return_string += \": \" + self.f_val_to_str()\n\n return return_string","def __str__(self):\n return self.printable()","def __str__(self):\n\n descr = \"You are in the \" + self.name + \"\\n\"\n for key in self.exits:\n descr += \"You can go \" + key + \" to the \" + self.exits[key].name + \"\\n\"\n for item in self.inventory:\n descr += \"There is a \" + item.name + \" here.\\n\"\n for item in self.objects:\n descr += item.name + \" is here.\"\n return descr","def details(self) -> str:\n return f\"- **language**: [{self.language}]\\n\" \\\n f\"- **opengame**: [{self.opengame}]\\n\" \\\n f\"- **system**: [{self.system}]\\n\" \\\n f\"- **mode**: [{self.mode}]\\n\" \\\n f\"- **attributes**: [{self.attributes}]\\n \" \\\n f\"- **score_threshold**: [{self.score_threshold}]\\n \" \\\n f\"- **monsters**: [{self.monsters}]\\n\"","def __str__(self):\n if self.__description:\n return self.__description\n return repr(self)","def __str__(self):\n description = \"Object class Critter.\"\n description += \"\\nName: \" + self.name + \"\\nMood: \" + self.mood + \"\\nHunger: \" + str(self.hunger) + \"\\nBoredom: \" + str(self.boredom) + \"\\n\"\n return description","def __str__(self):\n if len(self.label) > 0:\n descr = [\"'%s', target='%s' [%s]\" % (self.label, self.target.name, self.target.body_type)]\n else:\n descr = [\"target='%s' [%s]\" % (self.target.name, self.target.body_type)]\n if self.baseline:\n descr[0] += ', initial baseline offset=%f' % (self.baseline.poly[-1],)\n if self.beam:\n descr[0] += ', beam height=%f' % (self.beam.height,)\n for scan_ind, scan in enumerate(self.scans):\n descr.append('%4d: %s' % (scan_ind, str(scan)))\n return '\\n'.join(descr)","def __str__(self):\n return stringify(\n Inspect(\n self,\n help=True,\n methods=True,\n private=True,\n dunder=False,\n sort=True,\n all=False,\n ),\n maxlen=-1,\n )","def toString(self) -> str:\n raise NotImplementedError","def get_describe_name(self):\n long_name = str(self.year)+ ' ' + self.make.title()+ ' ' +self.model.title()\n return long_name","def human_readable(self):\n if self.no_flags_set():\n return \"no flags set\"\n else:\n flag_desc = []\n for name in (\"bookmarked\", \"for_validation\", \"candidate\", \"final causative\"):\n if getattr(self, \"flag_%s\" % name.replace(\" \", \"_\")):\n flag_desc.append(name)\n for name in (\"visual\", \"validation\", \"molecular\", \"phenotype_match\", \"summary\"):\n field = getattr(self, \"flag_%s\" % name)\n if field and field != \"empty\":\n flag_desc.append(\"%s rating is %s\" % (name.split(\"_\")[0], field))\n return \", \".join(flag_desc)","def __str__(self) -> str:\n return self.description","def __str__( self ) :\n\n return( '%s with projectile \"%s\", target \"%s\", evaluation \"%s\", path \"%s\", standardTarget \"%s\" and standardEvaluation \"%s\".' % \n ( self.moniker, self.projectile, self.target, self.evaluation, self.path, self.standardTarget, self.standardEvaluation ) )","def __str__(self):\n return f\"{self.full_name} ({self.short_name})\"","def __str__(self):\n return f\"{self.full_name} ({self.short_name})\"","def __str__ ( self ):\n return self.get_label()","def __str__(self):\n return '\\n'+'\\n'.join([\"%-15s: %s\" % (qq(w), str(v)) for w, v in sorted(self.value.items())]) + '\\0'","def __str__( self ) :\n\n return( '%s with projectile \"%s\", target \"%s\", evaluation \"%s\", path \"%s\" and interaction \"%s\".' % \n ( self.moniker, self.projectile, self.target, self.evaluation, self.path, self.interaction ) )","def usagestr(self) -> str:\n # * makes sure it's not wrapped\n ret: str = self.name(\"long\") + \"*\"\n ret += self.typestr().upper() if self.type_frozen else self.typestr()\n return ret","def __str__(self):\n return self.get_string()","def __str__(self):\n return_string = self.name + \"\\n\" + str(self.traits)\n\n return return_string","def describe(self):\n return str(self)","def __str__(self):\n if self.flaky:\n fmt = 'flaky | '\n else:\n fmt = ''\n fmt += '{2}: {0}'\n if self.variant:\n fmt += ' {1}'\n return fmt.format(*self)","def __str__(self) -> str:\n string = fr\"{self.id}\\. `{self.content}`\"\n if self.description:\n string += f\" - {self.description}\"\n return string","def __str__(self):\n print_info = f\"\\nStudent ID: {self._id}, Name: {self._name}, \" \\\n f\"Year: {self._year} \\nPhone: {str(self._phone)}, \" \\\n f\"Address: {str(self._address)} \" \\\n f\"\\nClasses: {str(self._classes)}\" \\\n f\"\\nBirth Date: {self._date}\"\n return print_info","def __str__(self):\n return \"{0} : {1}\".format(self.name, self.description)","def __str__(self):\n return self.s","def __str__(self):\n return \"%-10s\" % self._id + \"%-20s\" % self._name","def __str__(self):\n return self._name+self._description","def __str__(self):\n summary = '{} object in the {} language, consisting of {} tokens.'\n return summary.format(\n type(self).__name__, self.language,\n len(self.hand_tagged)\n )","def __str__(self):\n return u\"{} - {}\".format(self.label, self.message)","def toString(self):\n st = \" \\n\"\n st += \"Title: \" +self.getTitle()+ \"\\n\"\n st += \"Path: \" +self.getPath()+ \"\\n\"\n st += \"Description: \" +self.getDescription()+ \"\\n\"\n return st","def __str__(self):\n return self._str_hsp_header() + \"\\n\" + self._str_aln()","def __str__(self) -> str:\n if len(self.saliva_data) > 0:\n return \"\"\"{}\n Saliva Type(s): {}\n Saliva Sample Times: {}\n Structure: {}\n \"\"\".format(\n self.name, self.saliva_types, self.sample_times, self.structure\n )\n return \"\"\"{}\n Structure: {}\"\"\".format(\n self.name, self.structure\n )","def describe(self) -> str:\n return (\n \"{name} {surname} è nata/o a {birth_municipality} ({birth_province_code}) il {birthdate}.\"\n \" Ora vive a {municipality} ({province_code}) in {address} {house_number}.\"\n ).format(**self._data)","def __str__(self):\n return f'Character name: {self.name}\\nhealth: {self.health}\\n' \\\n f'strength: {self.strength}\\nchance dodge: ' \\\n f'{round(self.chance_dodge, 2)}\\nchance critical:' \\\n f' {round(self.chance_critical, 2)} '","def __str__(self):\n reprStr = 'Help Mario build Iron Man suit!'+'\\n' +'To make the ' + self._name + ',you need:'+'\\n'\n for part in self._supplies:\n reprStr = reprStr + str(part.getCount()) + ' ' + part.getData() + '\\n'\n return reprStr","def __str__(self):\n\n desc = self.description\n if desc is not None:\n return str(desc)\n\n desc = self.debugDescription\n if desc is not None:\n return str(desc)\n\n return repr(self)","def __str__(self):\n bold = \"*\" if self.bold else ''\n italic = \"/\" if self.italic else ''\n underline = \"_\" if self.underline else ''\n return bold + italic + underline + self.character","def __str__(self):\n _str = indent(self.name, color = self.color)\n _str += indent(\"-\"*len(self.name))\n _str += indent(\"( \" + self.movemement + \" - \"+ self.symbole + \" )\")\n _str += indent(\"Awareness: \" + str(self.awareness))\n for _iel in self.abilities:\n _str += indent(_iel)\n if self.description is not None:\n _str += indent(\"-\"*45 + \"\\n\")\n _str += indent(self.description)\n _str += indent(\"-\"*45 + \"\\n\")\n _str += indent(BOLD_BLACK + \" \"*15 + \"Combat Stats\" + RESET)\n _str += indent(\"-\"*45 + \"\\n\")\n _str += indent(BLUE + \" Horror\" + RESET + \" Toughness\"\\\n + RED + \" Combat\" + RESET)\n _str += indent(BLUE + \"Rating Damage \"\\\n + RED + \"Rating Damage\")\n _str += indent(BLUE + \" \" + str(self.horror_rating)\\\n + \" \"*8 + str(self.horror_damage) + RESET\\\n + \" \"*8 + str(self.toughness) + RED\\\n + \" \"*8 + str(self.combat_rating)\\\n + \" \"*8 + str(self.combat_damage) + RESET)\n _str += indent(\"-\"*45 + \"\\n\")\n return _str","def usagestr(self) -> str:\n return self.name(\"long\")","def __str__(self):\n datastr = self.f_val_to_str()\n return_string = \"%s %s\" % (self.f_get_class_name(), self.v_full_name)\n if self.v_comment:\n return_string += \" (`%s`)\" % self.v_comment\n if datastr:\n return_string += \": \" + datastr\n\n return return_string","def __str__(self):\n\n return \"%s:\\n%s\" % (self.name, self.top_terms_tuples(25))","def __str__(self):\n # Power/toughness, seen only if it's a creature\n pt = \"\"\n if \"power\" in self:\n pt = \"{0}/{1}\".format(self.power,\n self.toughness).replace(\"*\", \"\\*\")\n # Append loyalty to the end of oracle text if the creature is a\n # planeswalker\n if \"loyalty\" in self:\n self.oracle_text = \"{0}\\nStarting Loyalty: {1}\".format(\n self.oracle_text, self.loyalty)\n\n flavor = \"*{0}*\".format(\n self.flavor_text) if \"flavor_text\" in self else \"\"\n\n return \"**{0}** {1}\\n{2} {3}\\n{4}\\n{5}\\n\\n\".format(self.name,\n self.mana_cost,\n self.type_line,\n pt,\n self.oracle_text,\n flavor)","def __str__(self):\n return self.name + ' - ' + str(self.date_of_contact) + ' - ' + str(self.specialty)","def __str__(self):\n\n description = f\"{self.name} is a member of the {self.team} and possesses the following powers:\\n{self.powers}\"\n return description","def short_displayname(self):\n return self.get_short_displayname()"],"string":"[\n \"def __str__(self):\\n return 'str-human.%s' % self.name\",\n \"def __str__(self):\\n return f\\\"{self._desc:16s}\\\"\",\n \"def summary_string(self) -> str:\",\n \"def get_human_readable(self):\\n\\n def yesno(key):\\n if getattr(self, key) and getattr(self, key) > 0:\\n return \\\"Y\\\"\\n else:\\n return \\\"N\\\"\\n\\n keys = (\\n \\\"pvs1\\\",\\n \\\"ps1\\\",\\n \\\"ps2\\\",\\n \\\"ps3\\\",\\n \\\"ps4\\\",\\n \\\"pm1\\\",\\n \\\"pm2\\\",\\n \\\"pm3\\\",\\n \\\"pm4\\\",\\n \\\"pm5\\\",\\n \\\"pm6\\\",\\n \\\"pp1\\\",\\n \\\"pp2\\\",\\n \\\"pp3\\\",\\n \\\"pp4\\\",\\n \\\"pp5\\\",\\n \\\"ba1\\\",\\n \\\"bs1\\\",\\n \\\"bs2\\\",\\n \\\"bs3\\\",\\n \\\"bs4\\\",\\n \\\"bp1\\\",\\n \\\"bp2\\\",\\n \\\"bp3\\\",\\n \\\"bp4\\\",\\n \\\"bp5\\\",\\n \\\"bp6\\\",\\n \\\"bp7\\\",\\n )\\n result = \\\", \\\".join([\\\"%s: %s\\\" % (key.upper(), yesno(key)) for key in keys])\\n result += \\\", ACMG classification: %s\\\" % self.class_auto\\n if self.class_override:\\n result += \\\", ACMG class. override: %s\\\" % self.class_override\\n return result\",\n \"def to_short_string(self):\\n return f'{self.name} - {self.resource_type}'\",\n \"def __str__(self):\\n return str(self.description)[:10]\",\n \"def __str__(self):\\n if __debug__:\\n description = ('CM' in debug.active)\\n else:\\n description = False\\n return self.asstring(short=False, header=True, summary=True,\\n description=description)\",\n \"def pretty_str(self) -> str:\\n ...\",\n \"def title(self):\\n strng = \\\"\\\"\\n if self.type:\\n strng = self.type\\n if self.server:\\n if self.status:\\n strng = \\\"%s\\\" % (strng)\\n if not strng:\\n strng = \\\"Error\\\"\\n strng = \\\"%s on %s\\\" % (strng, self.server)\\n elif self.status:\\n strng = self.status\\n if self.server:\\n strng = \\\"%s on server %s\\\" % (strng, self.server)\\n elif self.raw:\\n strng = self.raw\\n else:\\n strng = self.error_timestamp.isoformat()\\n if self.uid:\\n strng = \\\"%s\\\" % (strng)\\n return strng\",\n \"def get_descriptive_name(self):\\r\\n long_name = str(self.year) + ' ' + self.make + ' ' + self.model\\r\\n #Mostrar_Grande = long_name.upper()\\r\\n #return long_name.upper()\\r\\n #return Mostrar_Grande #Funciona Com Return TAMBÉM, mas olhe na linha 39 como seria necessário usar.\\r\\n print(long_name.upper())\",\n \"def toString(self):\\n\\t\\ts = \\\"A %s titled '%s':\\\\n\\\\n\\\" % (self.getSpecString(), self.getName())\\n\\t\\ts += \\\"It's summary reads: %s\\\\n\\\\n\\\" % (self.getDescription())\\n\\t\\ts += \\\"~~\\\\n%s\\\\n~~\\\" % (self.getAllItemsStr())\\n\\t\\treturn s\",\n \"def get_descriptive_name(self):\\n long_name = f\\\"{self.make} {self.model} {self.year}\\\"\\n \\n return long_name.title()\",\n \"def get_descriptive_name(self):\\n long_name = f\\\"{self.year} {self.make} {self.model}\\\"\\n return long_name.title()\",\n \"def get_descriptive_name(self):\\r\\n long_name = str(self.year) + ' ' + self.make + ' ' + self.model\\r\\n return long_name.title()\",\n \"def __str__(self):\\n return \\\"<%s: %s>\\\" % (self.__class__, self.describe())\",\n \"def __str__(self):\\n to_print = '{} : {}\\\\n'.format('Name'.ljust(34),self.name)\\n to_print = to_print + '{} : {}\\\\n'.format('Name'.ljust(34),self.pathloss.name)\\n to_print = to_print + '{} : {}\\\\n'.format('Number of samples'.ljust(34),self.nsamples)\\n to_print = to_print + '{} : {}\\\\n'.format('Sensor model'.ljust(34),self.sensor_model.name)\\n to_print = to_print + '{} : {}\\\\n'.format('Motion model'.ljust(34),self.motion_model.name)\\n return to_print\",\n \"def get_descriptive_name(self):\\n return f\\\"{self.year} {self.make} {self.model}\\\".title()\",\n \"def get_descriptive_name(self):\\r\\n long_name = str(self.year)+' '+self.make + ' '+self.model\\r\\n return long_name.title()\",\n \"def get_descriptive_name(self):\\n long_name = str(self.year) + ' ' + self.make + ' ' + self.model\\n return long_name.title()\",\n \"def get_descriptive_name(self):\\n long_name = str(self.year) + ' ' + self.make + ' ' + self.model\\n return long_name.title()\",\n \"def get_descriptive_name(self):\\n long_name = str(self.year) + ' ' + self.make + ' ' + self.model\\n return long_name.title()\",\n \"def get_descriptive_name(self):\\n long_name = str(self.year) + ' ' + self.make + ' ' + self.model\\n return long_name.title()\",\n \"def get_descriptive_name(self):\\n long_name = str(self.year) + ' ' + self.make + ' ' + self.model\\n return long_name.title()\",\n \"def get_descriptive_name(self):\\n long_name = str(self.year) + ' ' + self.make + ' ' + self.model\\n return long_name.title()\",\n \"def get_descriptive_name(self):\\n long_name = str(self.year) + ' ' + self.make + ' ' + self.model\\n return long_name.title()\",\n \"def get_descriptive_name(self):\\n long_name = str(self.year) + ' ' + self.make + ' ' + self.model\\n return long_name.title()\",\n \"def __str__(self):\\r\\n to_print = (\\\"Name: \\\" + self.name + \\\", Age: \\\" +\\r\\n str(self.age) + \\\", Hobbys: \\\" + str(self.hobbys))\\r\\n return to_print\",\n \"def get_descriptive_name(self):\\r\\n long_name=str(self.year)+' '+self.make+' '+self.model\\r\\n return long_name.title()\",\n \"def __str__(self):\\n return self.format()\",\n \"def get_descriptive_name(self):\\n description = (f\\\"{self.year} {self.manufacturer.title()} \\\"\\n f\\\"{self.model.title()}\\\")\\n\\n return description\",\n \"def __str__(self):\\n \\n string = \\\"{}: {} ({}) - {}\\\".format(self.code, self.title, self.credit, self._term_name())\\n return string\",\n \"def _getDiagnosticString():\\n text = '\\\\n## Diagnostic output from minimalmodbus ## \\\\n\\\\n'\\n text += 'Minimalmodbus version: ' + __version__ + '\\\\n'\\n text += 'Minimalmodbus status: ' + __status__ + '\\\\n'\\n text += 'Revision: ' + __revision__ + '\\\\n'\\n text += 'Revision date: ' + __date__ + '\\\\n'\\n text += 'File name (with relative path): ' + __file__ + '\\\\n'\\n text += 'Full file path: ' + os.path.abspath(__file__) + '\\\\n\\\\n'\\n text += 'pySerial version: ' + serial.VERSION + '\\\\n'\\n text += 'pySerial full file path: ' + os.path.abspath(serial.__file__) + '\\\\n\\\\n'\\n text += 'Platform: ' + sys.platform + '\\\\n'\\n text += 'Filesystem encoding: ' + repr(sys.getfilesystemencoding()) + '\\\\n'\\n text += 'Byteorder: ' + sys.byteorder + '\\\\n'\\n text += 'Python version: ' + sys.version + '\\\\n'\\n text += 'Python version info: ' + repr(sys.version_info) + '\\\\n'\\n text += 'Python flags: ' + repr(sys.flags) + '\\\\n'\\n text += 'Python argv: ' + repr(sys.argv) + '\\\\n'\\n text += 'Python prefix: ' + repr(sys.prefix) + '\\\\n'\\n text += 'Python exec prefix: ' + repr(sys.exec_prefix) + '\\\\n'\\n text += 'Python executable: ' + repr(sys.executable) + '\\\\n'\\n try:\\n text += 'Long info: ' + repr(sys.long_info) + '\\\\n'\\n except:\\n text += 'Long info: (none)\\\\n' # For Python3 compatibility\\n try:\\n text += 'Float repr style: ' + repr(sys.float_repr_style) + '\\\\n\\\\n'\\n except:\\n text += 'Float repr style: (none) \\\\n\\\\n' # For Python 2.6 compatibility\\n text += 'Variable __name__: ' + __name__ + '\\\\n'\\n text += 'Current directory: ' + os.getcwd() + '\\\\n\\\\n'\\n text += 'Python path: \\\\n'\\n text += '\\\\n'.join(sys.path) + '\\\\n'\\n text += '\\\\n## End of diagnostic output ## \\\\n'\\n return text\",\n \"def format(self) -> str:\",\n \"def __str__(self) -> str:\\n return self.name + \\\" has a population of \\\" + str(self.population) + \\\" and is \\\" + str(self.area) + \\\" square \\\" \\\\\\n \\\"kilometres.\\\"\",\n \"def get_descriptive_name(self): # 定义描述完整信息的方法\\n long_name = str(self.year) + \\\" \\\" + self.make + \\\" \\\" + self.model # 拼接变量字符串并赋值变量\\n return long_name.title() # 返回字符串并首字母大写\",\n \"def __str__(self):\\r\\n if len(self.hand) < 1:\\r\\n return \\\"No cards in hand.\\\"\\r\\n else:\\r\\n output = \\\"%s's hand contains\\\" % (self.tag.capitalize())\\r\\n for card in self.hand:\\r\\n output += \\\" %s\\\" % (card)\\r\\n return output + \\\".\\\"\",\n \"def summary(self):\\n if not self: return u''\\n s = u'Character\\\\n=====\\\\nName: %s\\\\n' % \\\\\\n self.get('name', u'')\\n bio = self.get('biography')\\n if bio:\\n s += u'Biography: %s\\\\n' % bio[0]\\n filmo = self.get('filmography')\\n if filmo:\\n a_list = [x.get('long imdb canonical title', u'')\\n for x in filmo[:5]]\\n s += u'Last movies with this character: %s.\\\\n' % u'; '.join(a_list)\\n return s\",\n \"def __str__(self):\\n\\n return (self._display_name + \\\"\\\\n\\\\t\\\" + self._description + \\\"\\\\n\\\")\",\n \"def human_readable(self, value):\\n return value\",\n \"def __str__(self):\\n return self.summary()\",\n \"def to_str(self) -> str:\",\n \"def __str__(self):\\n\\n descr = (\\\"Your health is \\\" + str(self.health))\\n return descr\",\n \"def stringify_short(self):\\n return self.stringify()\",\n \"def __str__(self): # pragma: no cover\\n return self.display()\",\n \"def human_readable_info(self) -> str:\\n next_session = unix_str(self._stat.next_session)\\n last_session = unix_str(self._stat.last_session)\\n return \\\"\\\"\\\"\\n Next Session: {}\\n Last Session: {}\\n Repetitions: {}\\n Health: {}\\n ------------------------\\n Past Quality (last 20):\\n ------------------------\\n {}\\n \\\"\\\"\\\".format(\\n next_session,\\n last_session,\\n self._stat.actual_repetitions,\\n self._health(),\\n self._past_quality_graph(),\\n )\",\n \"def __str__(self):\\n from ansys.dpf.core.core import _description\\n\\n return _description(self._message, self._server)\",\n \"def __str__(self):\\n \\n is_random_print = \\\"\\\"\\n if self.is_random == True:\\n is_random_print = \\\"randomly\\\"\\n else:\\n is_random_print = \\\"deterministically\\\"\\n\\n return \\\"Player for \\\" + self.side + \\\", ply = \\\" + str(self.ply) + \\\", breaks ties \\\" + is_random_print\",\n \"def __str__(self):\\n description = \\\"-------- %s --------\\\" % (self.name)\\n description += \\\"\\\\nnmax = %i\\\" % (self.nmax)\\n description += \\\"\\\\nnslots = %i\\\" % (self.nslots)\\n description += \\\"\\\\nbonus_power = %i\\\" % (self.bonus_power)\\n description += \\\"\\\\nbonus_initiative = %.1f\\\" % (self.bonus_initiative)\\n description += \\\"\\\\nneeds_drive = %i\\\" % (self.needs_drive)\\n description += \\\"\\\\nis_mobile = %i\\\" % (self.is_mobile)\\n description += \\\"\\\\n----- Default Parts -----\\\"\\n for i in range(len(self.default_parts)):\\n description += \\\"\\\\n%i) %s\\\" % (i + 1, self.default_parts[i].name)\\n return description\",\n \"def str(self):\\n if self.num_dice is not None and self.dice_type is not None:\\n descr = \\\"{}D{}\\\".format(self.num_dice, self.dice_type)\\n if self.plus > 0:\\n descr += \\\"+{}\\\".format(self.plus)\\n elif self.min_value is not None and self.max_value is not None:\\n descr = \\\"{}-{}\\\".format(self.min_value, self.max_value)\\n elif self.plus != 0:\\n descr = str(self.plus)\\n else:\\n descr = \\\"\\\"\\n\\n return descr\",\n \"def __str__(self):\\n return \\\"Author: \\\" + self.author +\\\"\\\\nTitle: \\\" + self.title + \\\"\\\\nHaiku: \\\" + str(self.haiku) + \\\\\\n \\\"\\\\nText: \\\" + self.text + \\\"\\\\n\\\"\",\n \"def __str__(self) -> str:\",\n \"def __str__(self) -> str:\",\n \"def __str__(self) -> str:\",\n \"def __str__(self) -> str:\",\n \"def __str__(self):\\n if self.f_has_range():\\n lenstr = \\\"len:%d\\\" % self.f_get_range_length()\\n else:\\n lenstr = \\\"\\\"\\n\\n if self.v_comment:\\n commentstr = \\\"`%s`\\\" % self.v_comment\\n else:\\n commentstr = \\\"\\\"\\n\\n if commentstr or lenstr:\\n if commentstr and lenstr:\\n combined_str = \\\"%s, %s\\\" % (lenstr, commentstr)\\n elif commentstr:\\n combined_str = commentstr\\n elif lenstr:\\n combined_str = lenstr\\n else:\\n raise RuntimeError(\\\"You shall not pass!\\\")\\n\\n infostr = \\\" (%s)\\\" % combined_str\\n\\n else:\\n infostr = \\\"\\\"\\n\\n return_string = \\\"%s %s%s\\\" % (self.f_get_class_name(), self.v_full_name, infostr)\\n\\n if not self.f_is_empty():\\n return_string += \\\": \\\" + self.f_val_to_str()\\n\\n return return_string\",\n \"def __str__(self):\\n return self.printable()\",\n \"def __str__(self):\\n\\n descr = \\\"You are in the \\\" + self.name + \\\"\\\\n\\\"\\n for key in self.exits:\\n descr += \\\"You can go \\\" + key + \\\" to the \\\" + self.exits[key].name + \\\"\\\\n\\\"\\n for item in self.inventory:\\n descr += \\\"There is a \\\" + item.name + \\\" here.\\\\n\\\"\\n for item in self.objects:\\n descr += item.name + \\\" is here.\\\"\\n return descr\",\n \"def details(self) -> str:\\n return f\\\"- **language**: [{self.language}]\\\\n\\\" \\\\\\n f\\\"- **opengame**: [{self.opengame}]\\\\n\\\" \\\\\\n f\\\"- **system**: [{self.system}]\\\\n\\\" \\\\\\n f\\\"- **mode**: [{self.mode}]\\\\n\\\" \\\\\\n f\\\"- **attributes**: [{self.attributes}]\\\\n \\\" \\\\\\n f\\\"- **score_threshold**: [{self.score_threshold}]\\\\n \\\" \\\\\\n f\\\"- **monsters**: [{self.monsters}]\\\\n\\\"\",\n \"def __str__(self):\\n if self.__description:\\n return self.__description\\n return repr(self)\",\n \"def __str__(self):\\n description = \\\"Object class Critter.\\\"\\n description += \\\"\\\\nName: \\\" + self.name + \\\"\\\\nMood: \\\" + self.mood + \\\"\\\\nHunger: \\\" + str(self.hunger) + \\\"\\\\nBoredom: \\\" + str(self.boredom) + \\\"\\\\n\\\"\\n return description\",\n \"def __str__(self):\\n if len(self.label) > 0:\\n descr = [\\\"'%s', target='%s' [%s]\\\" % (self.label, self.target.name, self.target.body_type)]\\n else:\\n descr = [\\\"target='%s' [%s]\\\" % (self.target.name, self.target.body_type)]\\n if self.baseline:\\n descr[0] += ', initial baseline offset=%f' % (self.baseline.poly[-1],)\\n if self.beam:\\n descr[0] += ', beam height=%f' % (self.beam.height,)\\n for scan_ind, scan in enumerate(self.scans):\\n descr.append('%4d: %s' % (scan_ind, str(scan)))\\n return '\\\\n'.join(descr)\",\n \"def __str__(self):\\n return stringify(\\n Inspect(\\n self,\\n help=True,\\n methods=True,\\n private=True,\\n dunder=False,\\n sort=True,\\n all=False,\\n ),\\n maxlen=-1,\\n )\",\n \"def toString(self) -> str:\\n raise NotImplementedError\",\n \"def get_describe_name(self):\\n long_name = str(self.year)+ ' ' + self.make.title()+ ' ' +self.model.title()\\n return long_name\",\n \"def human_readable(self):\\n if self.no_flags_set():\\n return \\\"no flags set\\\"\\n else:\\n flag_desc = []\\n for name in (\\\"bookmarked\\\", \\\"for_validation\\\", \\\"candidate\\\", \\\"final causative\\\"):\\n if getattr(self, \\\"flag_%s\\\" % name.replace(\\\" \\\", \\\"_\\\")):\\n flag_desc.append(name)\\n for name in (\\\"visual\\\", \\\"validation\\\", \\\"molecular\\\", \\\"phenotype_match\\\", \\\"summary\\\"):\\n field = getattr(self, \\\"flag_%s\\\" % name)\\n if field and field != \\\"empty\\\":\\n flag_desc.append(\\\"%s rating is %s\\\" % (name.split(\\\"_\\\")[0], field))\\n return \\\", \\\".join(flag_desc)\",\n \"def __str__(self) -> str:\\n return self.description\",\n \"def __str__( self ) :\\n\\n return( '%s with projectile \\\"%s\\\", target \\\"%s\\\", evaluation \\\"%s\\\", path \\\"%s\\\", standardTarget \\\"%s\\\" and standardEvaluation \\\"%s\\\".' % \\n ( self.moniker, self.projectile, self.target, self.evaluation, self.path, self.standardTarget, self.standardEvaluation ) )\",\n \"def __str__(self):\\n return f\\\"{self.full_name} ({self.short_name})\\\"\",\n \"def __str__(self):\\n return f\\\"{self.full_name} ({self.short_name})\\\"\",\n \"def __str__ ( self ):\\n return self.get_label()\",\n \"def __str__(self):\\n return '\\\\n'+'\\\\n'.join([\\\"%-15s: %s\\\" % (qq(w), str(v)) for w, v in sorted(self.value.items())]) + '\\\\0'\",\n \"def __str__( self ) :\\n\\n return( '%s with projectile \\\"%s\\\", target \\\"%s\\\", evaluation \\\"%s\\\", path \\\"%s\\\" and interaction \\\"%s\\\".' % \\n ( self.moniker, self.projectile, self.target, self.evaluation, self.path, self.interaction ) )\",\n \"def usagestr(self) -> str:\\n # * makes sure it's not wrapped\\n ret: str = self.name(\\\"long\\\") + \\\"*\\\"\\n ret += self.typestr().upper() if self.type_frozen else self.typestr()\\n return ret\",\n \"def __str__(self):\\n return self.get_string()\",\n \"def __str__(self):\\n return_string = self.name + \\\"\\\\n\\\" + str(self.traits)\\n\\n return return_string\",\n \"def describe(self):\\n return str(self)\",\n \"def __str__(self):\\n if self.flaky:\\n fmt = 'flaky | '\\n else:\\n fmt = ''\\n fmt += '{2}: {0}'\\n if self.variant:\\n fmt += ' {1}'\\n return fmt.format(*self)\",\n \"def __str__(self) -> str:\\n string = fr\\\"{self.id}\\\\. `{self.content}`\\\"\\n if self.description:\\n string += f\\\" - {self.description}\\\"\\n return string\",\n \"def __str__(self):\\n print_info = f\\\"\\\\nStudent ID: {self._id}, Name: {self._name}, \\\" \\\\\\n f\\\"Year: {self._year} \\\\nPhone: {str(self._phone)}, \\\" \\\\\\n f\\\"Address: {str(self._address)} \\\" \\\\\\n f\\\"\\\\nClasses: {str(self._classes)}\\\" \\\\\\n f\\\"\\\\nBirth Date: {self._date}\\\"\\n return print_info\",\n \"def __str__(self):\\n return \\\"{0} : {1}\\\".format(self.name, self.description)\",\n \"def __str__(self):\\n return self.s\",\n \"def __str__(self):\\n return \\\"%-10s\\\" % self._id + \\\"%-20s\\\" % self._name\",\n \"def __str__(self):\\n return self._name+self._description\",\n \"def __str__(self):\\n summary = '{} object in the {} language, consisting of {} tokens.'\\n return summary.format(\\n type(self).__name__, self.language,\\n len(self.hand_tagged)\\n )\",\n \"def __str__(self):\\n return u\\\"{} - {}\\\".format(self.label, self.message)\",\n \"def toString(self):\\n st = \\\" \\\\n\\\"\\n st += \\\"Title: \\\" +self.getTitle()+ \\\"\\\\n\\\"\\n st += \\\"Path: \\\" +self.getPath()+ \\\"\\\\n\\\"\\n st += \\\"Description: \\\" +self.getDescription()+ \\\"\\\\n\\\"\\n return st\",\n \"def __str__(self):\\n return self._str_hsp_header() + \\\"\\\\n\\\" + self._str_aln()\",\n \"def __str__(self) -> str:\\n if len(self.saliva_data) > 0:\\n return \\\"\\\"\\\"{}\\n Saliva Type(s): {}\\n Saliva Sample Times: {}\\n Structure: {}\\n \\\"\\\"\\\".format(\\n self.name, self.saliva_types, self.sample_times, self.structure\\n )\\n return \\\"\\\"\\\"{}\\n Structure: {}\\\"\\\"\\\".format(\\n self.name, self.structure\\n )\",\n \"def describe(self) -> str:\\n return (\\n \\\"{name} {surname} è nata/o a {birth_municipality} ({birth_province_code}) il {birthdate}.\\\"\\n \\\" Ora vive a {municipality} ({province_code}) in {address} {house_number}.\\\"\\n ).format(**self._data)\",\n \"def __str__(self):\\n return f'Character name: {self.name}\\\\nhealth: {self.health}\\\\n' \\\\\\n f'strength: {self.strength}\\\\nchance dodge: ' \\\\\\n f'{round(self.chance_dodge, 2)}\\\\nchance critical:' \\\\\\n f' {round(self.chance_critical, 2)} '\",\n \"def __str__(self):\\n reprStr = 'Help Mario build Iron Man suit!'+'\\\\n' +'To make the ' + self._name + ',you need:'+'\\\\n'\\n for part in self._supplies:\\n reprStr = reprStr + str(part.getCount()) + ' ' + part.getData() + '\\\\n'\\n return reprStr\",\n \"def __str__(self):\\n\\n desc = self.description\\n if desc is not None:\\n return str(desc)\\n\\n desc = self.debugDescription\\n if desc is not None:\\n return str(desc)\\n\\n return repr(self)\",\n \"def __str__(self):\\n bold = \\\"*\\\" if self.bold else ''\\n italic = \\\"/\\\" if self.italic else ''\\n underline = \\\"_\\\" if self.underline else ''\\n return bold + italic + underline + self.character\",\n \"def __str__(self):\\n _str = indent(self.name, color = self.color)\\n _str += indent(\\\"-\\\"*len(self.name))\\n _str += indent(\\\"( \\\" + self.movemement + \\\" - \\\"+ self.symbole + \\\" )\\\")\\n _str += indent(\\\"Awareness: \\\" + str(self.awareness))\\n for _iel in self.abilities:\\n _str += indent(_iel)\\n if self.description is not None:\\n _str += indent(\\\"-\\\"*45 + \\\"\\\\n\\\")\\n _str += indent(self.description)\\n _str += indent(\\\"-\\\"*45 + \\\"\\\\n\\\")\\n _str += indent(BOLD_BLACK + \\\" \\\"*15 + \\\"Combat Stats\\\" + RESET)\\n _str += indent(\\\"-\\\"*45 + \\\"\\\\n\\\")\\n _str += indent(BLUE + \\\" Horror\\\" + RESET + \\\" Toughness\\\"\\\\\\n + RED + \\\" Combat\\\" + RESET)\\n _str += indent(BLUE + \\\"Rating Damage \\\"\\\\\\n + RED + \\\"Rating Damage\\\")\\n _str += indent(BLUE + \\\" \\\" + str(self.horror_rating)\\\\\\n + \\\" \\\"*8 + str(self.horror_damage) + RESET\\\\\\n + \\\" \\\"*8 + str(self.toughness) + RED\\\\\\n + \\\" \\\"*8 + str(self.combat_rating)\\\\\\n + \\\" \\\"*8 + str(self.combat_damage) + RESET)\\n _str += indent(\\\"-\\\"*45 + \\\"\\\\n\\\")\\n return _str\",\n \"def usagestr(self) -> str:\\n return self.name(\\\"long\\\")\",\n \"def __str__(self):\\n datastr = self.f_val_to_str()\\n return_string = \\\"%s %s\\\" % (self.f_get_class_name(), self.v_full_name)\\n if self.v_comment:\\n return_string += \\\" (`%s`)\\\" % self.v_comment\\n if datastr:\\n return_string += \\\": \\\" + datastr\\n\\n return return_string\",\n \"def __str__(self):\\n\\n return \\\"%s:\\\\n%s\\\" % (self.name, self.top_terms_tuples(25))\",\n \"def __str__(self):\\n # Power/toughness, seen only if it's a creature\\n pt = \\\"\\\"\\n if \\\"power\\\" in self:\\n pt = \\\"{0}/{1}\\\".format(self.power,\\n self.toughness).replace(\\\"*\\\", \\\"\\\\*\\\")\\n # Append loyalty to the end of oracle text if the creature is a\\n # planeswalker\\n if \\\"loyalty\\\" in self:\\n self.oracle_text = \\\"{0}\\\\nStarting Loyalty: {1}\\\".format(\\n self.oracle_text, self.loyalty)\\n\\n flavor = \\\"*{0}*\\\".format(\\n self.flavor_text) if \\\"flavor_text\\\" in self else \\\"\\\"\\n\\n return \\\"**{0}** {1}\\\\n{2} {3}\\\\n{4}\\\\n{5}\\\\n\\\\n\\\".format(self.name,\\n self.mana_cost,\\n self.type_line,\\n pt,\\n self.oracle_text,\\n flavor)\",\n \"def __str__(self):\\n return self.name + ' - ' + str(self.date_of_contact) + ' - ' + str(self.specialty)\",\n \"def __str__(self):\\n\\n description = f\\\"{self.name} is a member of the {self.team} and possesses the following powers:\\\\n{self.powers}\\\"\\n return description\",\n \"def short_displayname(self):\\n return self.get_short_displayname()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7690259","0.7156136","0.71460164","0.7129925","0.6952552","0.6923994","0.689927","0.6893318","0.6868879","0.6835699","0.6826505","0.6824305","0.68146354","0.6811943","0.6803569","0.6789633","0.6782024","0.6780865","0.6779728","0.6779728","0.6779728","0.6779728","0.6779728","0.6779728","0.6779728","0.6779728","0.67746735","0.67641765","0.67295104","0.6728937","0.6728515","0.6728019","0.67162704","0.670591","0.67030543","0.6692269","0.66899514","0.6688586","0.66683614","0.6664452","0.66575795","0.6655977","0.66524595","0.66382647","0.6637403","0.6593822","0.65890455","0.65781695","0.6565646","0.6555448","0.6554036","0.6554036","0.6554036","0.6554036","0.65515524","0.65477735","0.65474546","0.65349674","0.6525338","0.6515117","0.65043044","0.6494097","0.6493456","0.64930296","0.64920753","0.6490175","0.64866245","0.64862907","0.64862907","0.6481819","0.6475739","0.64746946","0.6474263","0.647059","0.64673877","0.6460163","0.64584684","0.64582485","0.6454623","0.6446807","0.64429486","0.6440411","0.6437982","0.6437414","0.643725","0.64317846","0.6431274","0.6428896","0.6428587","0.6416477","0.6415513","0.6411077","0.63985234","0.6396948","0.6393363","0.63773835","0.63713855","0.6368697","0.6360215","0.6356285","0.63504964"],"string":"[\n \"0.7690259\",\n \"0.7156136\",\n \"0.71460164\",\n \"0.7129925\",\n \"0.6952552\",\n \"0.6923994\",\n \"0.689927\",\n \"0.6893318\",\n \"0.6868879\",\n \"0.6835699\",\n \"0.6826505\",\n \"0.6824305\",\n \"0.68146354\",\n \"0.6811943\",\n \"0.6803569\",\n \"0.6789633\",\n \"0.6782024\",\n \"0.6780865\",\n \"0.6779728\",\n \"0.6779728\",\n \"0.6779728\",\n \"0.6779728\",\n \"0.6779728\",\n \"0.6779728\",\n \"0.6779728\",\n \"0.6779728\",\n \"0.67746735\",\n \"0.67641765\",\n \"0.67295104\",\n \"0.6728937\",\n \"0.6728515\",\n \"0.6728019\",\n \"0.67162704\",\n \"0.670591\",\n \"0.67030543\",\n \"0.6692269\",\n \"0.66899514\",\n \"0.6688586\",\n \"0.66683614\",\n \"0.6664452\",\n \"0.66575795\",\n \"0.6655977\",\n \"0.66524595\",\n \"0.66382647\",\n \"0.6637403\",\n \"0.6593822\",\n \"0.65890455\",\n \"0.65781695\",\n \"0.6565646\",\n \"0.6555448\",\n \"0.6554036\",\n \"0.6554036\",\n \"0.6554036\",\n \"0.6554036\",\n \"0.65515524\",\n \"0.65477735\",\n \"0.65474546\",\n \"0.65349674\",\n \"0.6525338\",\n \"0.6515117\",\n \"0.65043044\",\n \"0.6494097\",\n \"0.6493456\",\n \"0.64930296\",\n \"0.64920753\",\n \"0.6490175\",\n \"0.64866245\",\n \"0.64862907\",\n \"0.64862907\",\n \"0.6481819\",\n \"0.6475739\",\n \"0.64746946\",\n \"0.6474263\",\n \"0.647059\",\n \"0.64673877\",\n \"0.6460163\",\n \"0.64584684\",\n \"0.64582485\",\n \"0.6454623\",\n \"0.6446807\",\n \"0.64429486\",\n \"0.6440411\",\n \"0.6437982\",\n \"0.6437414\",\n \"0.643725\",\n \"0.64317846\",\n \"0.6431274\",\n \"0.6428896\",\n \"0.6428587\",\n \"0.6416477\",\n \"0.6415513\",\n \"0.6411077\",\n \"0.63985234\",\n \"0.6396948\",\n \"0.6393363\",\n \"0.63773835\",\n \"0.63713855\",\n \"0.6368697\",\n \"0.6360215\",\n \"0.6356285\",\n \"0.63504964\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":75591,"cells":{"query":{"kind":"string","value":"Return a string to display in the visual mode."},"document":{"kind":"string","value":"def __repr__(self):\n return f\"{self.number} {self.name}: {self.desc}\""},"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 show(self) -> str:\n return f'[{self.font}]{self.text}[{self.font}]' if self.font else self.text","def __repr__(self):\n return self._format() if self.always_visible or not self.is_pointless() else ''","def display(self) -> str:\n lines, _, _, _ = self._display_aux()\n return '\\n'.join(lines)","def graphic_auto():\r\n print(\"\\nCe mode n'est pas supporté. Passez en affichage textuel pour le mode automatique\")","def __repr__(self):\n return self.display()","def the_display(self):\r\n return f\"\"\"\r\n {self.display[0]}\\n\r\n {self.display[1]}\\n\r\n {self.display[2]}\\n\r\n {self.display[3]}\\n\r\n {self.display[4]}\\n\r\n \"\"\"","def visualizeWithContents(self, paths):\n return ExpressString(\"A totally mystical rune.\")","def __str__(self): # pragma: no cover\n return self.display()","def render(self, mode='human'):\n return self._env.render(mode)","def draw(self):\n rendered_string = \"\"\n\n # extract the wire labels as strings and get their maximum length\n wire_names = []\n padding = 0\n for i in range(self.full_representation_grid.num_wires):\n wire_name = str(self.active_wires.labels[i])\n padding = max(padding, len(wire_name))\n wire_names.append(wire_name)\n\n for i in range(self.full_representation_grid.num_wires):\n # format wire name nicely\n wire = self.full_representation_grid.wire(i)\n s = \" {:>\" + str(padding) + \"}: {}\"\n\n rendered_string += s.format(wire_names[i], 2 * self.charset.WIRE)\n\n for s in wire:\n rendered_string += s\n\n rendered_string += \"\\n\"\n\n for symbol, cache in [\n (\"U\", self.representation_resolver.unitary_matrix_cache),\n (\"H\", self.representation_resolver.hermitian_matrix_cache),\n (\"M\", self.representation_resolver.matrix_cache),\n ]:\n for idx, matrix in enumerate(cache):\n rendered_string += \"{}{} =\\n{}\\n\".format(symbol, idx, matrix)\n\n return rendered_string","def __repr__(self):\r\n # Initialize the return string\r\n s = ''\r\n\r\n for row in range(self.height):\r\n # Print the index of the row\r\n s = s + str(row % 10) + ' |'\r\n\r\n for col in range(self.width):\r\n s += self.grid[row][col]\r\n s += '|'\r\n\r\n s += '\\n'\r\n s += '--' * (self.width + 1)\r\n s += '-'\r\n s += '\\n'\r\n \r\n s += ' '\r\n for i in range(self.width):\r\n s += ' ' + str(i % 10) \r\n \r\n return s","def show(self):\n import IPython.display\n disp = IPython.display.HTML(self.render())\n return IPython.display.display(disp, display_id=str(id(self)))","def render(self, mode='human'):","def show(self, stdscr):\n stdscr.addstr(*self.pos, self.asset)","def __str__(self):\n if self.getType() == FRAGMENT:\n t = \"FRAGMENT\"\n else:\n t = \"VERTEX\"\n if not self.isCompiled():\n s = \"not compiled\"\n else:\n s = \"compiled\"\n return \"file: {0}\\ntype: {1}\\nstatus: {2}\\n\".format(self.getPath(), t, s)","def __repr__(self):\n\n return self._repr__base(rich_output=False)","def __repr__(self):\n\n return self._repr__base(rich_output=False)","def __repr__(self):\n #if self.parent: self.print_siblings()\n self.env.render()\n #self._print_children()\n return ''","def __str__(self):\n return self.get_canvas_as_str()","def render_prompt(self) -> str:\n # pylint: disable=no-member\n return '{}render: '.format(self.prompt)","def __str__(self):\n if self.fshader is None:\n f = \"not defined\"\n else:\n f = self.fshader.getPath()\n if self.vshader is None:\n v = \"not defined\"\n else:\n v = self.vshader.getPath()\n if self.enabled:\n e = \"enabled\"\n else:\n e = \"disabled\"\n if self.isCompiled():\n c = \"compiled | {0}\".format(e)\n else:\n c = \"not compiled | {0}\".format(e)\n return \"shader: {3}\\nfragment shader: {0}\\nvertex shader: {1}\\nstatus: {2}\".format(f, v, c, self.getName())","def _repr_(self):\n s = 'The projection of a polyhedron into ' + repr(self.dimension) \n s += ' dimensions.'\n return s + \"\\n\"","def _repr_options(self) -> str:\n return \"\"","def description(self):\n return self.visual_desc","def __str__(self):\n # TODO check the terminal width and adjust the presentation\n # only use for 1D, fall back to repr for ND\n if self._hist.rank() == 1:\n s = str(self._hist)\n # get rid of first line and last character\n s = s[s.index(\"\\n\") + 1 : -1]\n else:\n s = repr(self)\n return s","def __repr__(self):\n return \"ANSIString(%s, decoded=True)\" % repr(self._raw_string)","def show_win_screen():\n print (\"\"\"\n /$$ /$$\n |__/ | $$\n /$$ /$$ /$$$$$$ /$$ /$$ /$$ /$$ /$$ /$$ /$$$$$$$ | $$\n| $$ | $$ /$$__ $$| $$ | $$ | $$ | $$ | $$| $$| $$__ $$| $$\n| $$ | $$| $$ \\ $$| $$ | $$ | $$ | $$ | $$| $$| $$ \\ $$|__/\n| $$ | $$| $$ | $$| $$ | $$ | $$ | $$ | $$| $$| $$ | $$ \n| $$$$$$$| $$$$$$/| $$$$$$/ | $$$$$/$$$$/| $$| $$ | $$ /$$\n \\____ $$ \\______/ \\______/ \\_____/\\___/ |__/|__/ |__/|__/\n /$$ | $$ \n| $$$$$$/ \n \\______/ \n\"\"\")","def __str__(self):\n inside_list = lambda _v, _h, a: any(x == _h and y == _v for y, x in a)\n resultant = ''\n for _v in range(1, self.size_v + 1):\n for _h in range(1, self.size_h + 1):\n if self.current_location[1] == _h and self.current_location[0] == _v:\n resultant = resultant + '@'\n elif inside_list(_v, _h, self.boxes):\n resultant = resultant + '$'\n elif inside_list(_v, _h, self.storage_locations):\n resultant = resultant + '.'\n elif inside_list(_v, _h, self.wall_squares):\n resultant = resultant + '#'\n else:\n resultant = resultant + ' '\n resultant = resultant + '\\n'\n\n return resultant","def __str__(self) -> str:\n lengthscale = self.covar_module.base_kernel.lengthscale.detach()\n outputscale = self.covar_module.outputscale.detach()\n return \"\\toutputscale: {}\\n \\tlengthscale: {}\".format(outputscale, lengthscale)","def __str__(self):\r\n grid_text = \"\\n-------------------\\n|\"\r\n for i in range(len(self.grid)):\r\n grid_text = grid_text + ' %s '%(self.grid[i][-1])\r\n\r\n if i%3 == 2:\r\n grid_text = grid_text + '|\\n-------------------\\n|'\r\n else:\r\n grid_text = grid_text + '|'\r\n return grid_text[0:len(grid_text)-1]","def __str__(self):\r\n out = \"##\"*(self.width+1)+\"\\n\"\r\n for i in range(self.height):\r\n out += \"#\"\r\n for j in range(self.width):\r\n if self.grid[i][j] == 0:\r\n out += \"##\"\r\n else:\r\n if not self.showSolution:\r\n out += \" \"\r\n elif (i,j) in self.solution:\r\n out += \"**\"\r\n else:\r\n out += \" \"\r\n out += \"#\\n\"\r\n return out + \"##\"*(self.width+1)","def _repr_(self):\n s = \"Constellation of length {} and degree {}\".format(self.length(),\n self.degree())\n for i in range(self.length()):\n s += \"\\ng{} {}\".format(i, self._g[i].cycle_string(True))\n return s","def show(self):\n import IPython\n if self._output is None:\n self.render()\n IPython.display.display(self._output, display_id=str(id(self)))","def display(self):\n disptxt = str(self)\n if self.width == 0 or self.has_output:\n print(disptxt)\n else:\n print(\"\\r\", end='')\n print(disptxt, end='')\n sys.stdout.flush()","def __str__(self):\n name = self.__class__.__name__\n id = self.id\n x = self.x\n y = self.y\n wid = self.width\n hei = self.height\n return \"[{}] ({}) {}/{} - {}/{}\".format(name, id, x, y, wid, hei)","def debug_string(self):\n\n raise NotImplementedError","def __str__(self, width=2):\n \n if self.visibility == 1:\n if self.is_mine:\n out = '!'\n else:\n out = '.' if self.mines == 0 else self.mines\n else:\n out = '@' if self.visibility == 2 else '#'\n return f'{out: ^{width}}'","def __str__(self):\n return '(%d,%d,%d,%d,%d,%d,%d,%d,%d,%d,%d)' % (\n self.dwSize.Y, self.dwSize.X\n , self.dwCursorPosition.Y, self.dwCursorPosition.X\n , self.wAttributes\n , self.srWindow.Top, self.srWindow.Left, self.srWindow.Bottom, self.srWindow.Right\n , self.dwMaximumWindowSize.Y, self.dwMaximumWindowSize.X\n )","def __str__(self):\n\n # can't use backslash in f-string\n newline = \"\\n\"\n\n options = self.options\n kwoptions = self.kwoptions\n\n # add background to options\n if self.background:\n options.append(\"show background rectangle\")\n kwoptions.update({\"background rectangle/.style\": f\"{{fill={self.background}}}\"})\n\n return rf\"\"\"\\begin{{tikzpicture}}{wrap(fmt_options(self.options, self.kwoptions))}\n{newline.join(define_colours(self._used_colours))}\n{newline.join(self.defines)}\n{newline.join(self._commands)}\n\\end{{tikzpicture}}\"\"\"","def display_cli(self) -> None:\n if len(self.living_cells.keys()) == 0:\n print('.')\n return\n min_x, min_y = math.inf, math.inf\n max_x, max_y = -math.inf, -math.inf\n for x, y in self.living_cells.keys():\n min_x = min(min_x, x)\n min_y = min(min_y, y)\n max_x = max(max_x, x)\n max_y = max(max_y, y)\n for y in range(min_y, max_y + 1):\n chars = \"\"\n for x in range(min_x, max_x + 1):\n chars += '*' if (x, y) in self.living_cells.keys() else '.'\n print(chars)\n print()","def formatted_str(self):\n # Verify correct vertex values\n self.verify_vertex_values()\n # Generate output string\n output_string = \"\"\n for line in range(9):\n for row in range(9):\n if self.grid[line][row] is None:\n output_string += \".\"\n else:\n output_string += str(self.grid[line][row])\n if row != 8:\n output_string += \" \"\n elif line != 8:\n output_string += \"\\n\"\n if row in [2, 5]:\n output_string += \"| \"\n if line in [2, 5]:\n output_string += \"------+-------+------\\n\"\n return output_string","def console(self, vm=None):\n raise NotImplementedError\n return \"\"","def __str__(self):\n ans = \"\"\n for row in range(self._height):\n ans += str(self._grid[row])\n ans += \"\\n\"\n return ans","def __str__(self):\n ans = \"\"\n for row in range(self._height):\n ans += str(self._grid[row])\n ans += \"\\n\"\n return ans","def __str__(self):\n ans = \"\"\n for row in range(self._height):\n ans += str(self._grid[row])\n ans += \"\\n\"\n return ans","def __str__(self):\n ans = \"\"\n for row in range(self._height):\n ans += str(self._grid[row])\n ans += \"\\n\"\n return ans","def render(self, mode='human'):\n pass # no use in this situation","def render(self, mode: str = 'human'):\n # Note, this may seem badly aligned but it is correct!\n # It becomes aligned after substituting the variables by their real value\n content = \"\"\"\n x1 = {x1:.2f}\n ------------> City\n / x5 = {x5:.2f}\n / x2 = {x2:.2f} ----------------> Farms\n Q1 = {Q1} --------------> Dam ---------------- /\n S = {S} | /\n |-------------------------> Lower tributary\n | x6 = {x6:.2f}\n Q2 = {Q2} ------------------------------------\n \\\\ x3 = {x3:.2f}\n \\\\\n -------------> Farms\n x4 = {x4:.2f}\n \"\"\"\n print(\n content.format(\n Q1=self.Q1,\n Q2=self.Q2,\n S=self.S,\n x1=self.x[0],\n x2=self.x[1],\n x3=self.x[2],\n x4=self.x[3],\n x5=self.x[4],\n x6=self.x[5]\n )\n )","def _repr_html_(self):\n return (\n f'<b>GalaxyCluster:</b> {self.unique_id} '\n f'(ra={self.ra}, dec={self.dec}) at z={self.z}'\n f'<br>> <b>with columns:</b> {self._str_colnames()}'\n f'<br>> {len(self.galcat)} source galaxies'\n f'<br>{self.galcat._html_table()}'\n )","def displayScene(self):\n sceneprint = \"\"\n sceneprint += \" \"*40 + Back.LIGHTRED_EX + Fore.LIGHTCYAN_EX + Style.BRIGHT + \"M A N D A L O R I A N\\n\" + RESET\n sceneprint += Fore.LIGHTBLUE_EX +\"SCORE : \" +\\\n str(self.__score) + \" \"*30 +\"TIME : \" + str(self.__remaining_time) + \" \"*30 +\\\n \"LIVES:\" + str(self.__lives)+\"\\n\"+ RESET\n if self.__start >= self.__fullwidth - self.__width:\n self.__start = self.__fullwidth - self.__width\n for i in range(0, self.__height):\n for j in range(self.__start, self.__start + self.__width):\n sceneprint += str(self.__matrix[i][j])\n sceneprint += '\\n'\n \n if self.__start + sc_span < sc_full - 5:\n self.__start = self.__start + 1\n if self.__score < 420420420:\n self.__score += 1\n pass\n\n return sceneprint","def __repr__(self):\n string = ''\n for key, val in self.setting().items():\n string += '{}({})\\n'.format(key, val)\n return string","def __str__(self) -> str:\n return (\n f\"GlacierFlowModel '{self.model_name}' \"\n f\"{'' if self.steady_state else 'not '}in steady state with:\"\n f\"\\n - m: {self.m:20.5f} [m/m]\"\n f\"\\n - ela: {self.ela:20.2f} [m MSL]\"\n f\"\\n - resolution: {self.res:20.2f} [m]\"\n f\"\\n - extent: min max\"\n f\"\\n {self.extent[0]:10.1f} \"\n f\"{self.extent[1]:10.1f} [x]\"\n f\"\\n {self.extent[2]:10.1f} \"\n f\"{self.extent[3]:10.1f} [y]\"\n )","def to_str(self):\n return self.template.format(textcolor=self.color.to_str())","def __repr__(self):\n output = \"\"\n output +=\"V:\\n\"\n for row in self.V:\n output += \"\\t\"\n for el in row:\n output += str(el) + \" \" \n output += \"\\n\" \n \n output += \"\\nW:\\n\"\n for row in self.W:\n output += \"\\t\"\n for el in row:\n output += str(el) + \" \" \n output += \"\\n\"\n return output","def __str__(self):\n return \"%s\\n\" % self.text + \" \" * self.col + \"^\"","def get_mode(self):\n if self.vimiv.library.treeview.is_focus():\n return \"<b>-- LIBRARY --</b>\"\n elif self.vimiv.manipulate.scrolled_win.is_visible():\n return \"<b>-- MANIPULATE --</b>\"\n elif self.vimiv.thumbnail.toggled:\n return \"<b>-- THUMBNAIL --</b>\"\n else:\n return \"<b>-- IMAGE --</b>\"","def __str__(self):\r\n ans = \"\"\r\n for row in range(self._height):\r\n ans += str(self._grid[row])\r\n ans += \"\\n\"\r\n return ans","def __str__(self):\r\n ans = \"\"\r\n for row in range(self._height):\r\n ans += str(self._grid[row])\r\n ans += \"\\n\"\r\n return ans","def __str__(self):\r\n ans = \"\"\r\n for row in range(self._height):\r\n ans += str(self._grid[row])\r\n ans += \"\\n\"\r\n return ans","def __str__(self):\n t = Template(\n \"\"\"\n <h4>$title</h4>\n $imgs\n $footnotes\n <hr/>\"\"\")\n # Return result.\n return t.substitute({\n \"title\": self.title,\n \"imgs\": self.render_images(),\n \"footnotes\": self.render_footnotes()\n })","def to_str(self):\n return self.template.format(\n text_color=self.color.to_str(),\n font_type=self.font_type.to_str(),\n font_weight=self.font_weight.to_str(),\n font_size=self.font_size.to_str()\n )","def display(self):\n mg_w = self.width\n mg_h = self.height\n str_to_prt = \"\\n\" * self.y + (\" \" * self.x + \"#\" * mg_w + '\\n') * mg_h\n print(str_to_prt[:-1])","def __str__(self):\n return \"Normal:\" + str(self.norm) + \"\\nColour:\" + self.colour","def __str__(self):\n bold = \"*\" if self.bold else ''\n italic = \"/\" if self.italic else ''\n underline = \"_\" if self.underline else ''\n return bold + italic + underline + self.character","def __str__(self):\n # define the prefix\n prefix = \"py_SEX2GOL: \"\n\n # compose the feedback\n big_str = \"{0:s} Setup:\\n\".format(prefix)\n big_str += \"{0:s} Input g/prism image: {0:s} \\n\".format(prefix, self.grisim)\n big_str += \"{0:s} Configuration file name: {0:s} \\n\".format(prefix, self.config)\n big_str += \"{0:s} Direct image: {0:s} \\n\".format(prefix, self.dirname)\n big_str += \"{0:s} G/Prism extension: {0:s} \\n\".format(prefix, self.grism_extinfo['axe_ext'])\n big_str += \"{0:s} Direct image extension: {0:s} \\n\".format(prefix, self.dirname_extinfo['axe_ext'])\n big_str += \"{0:s} Input catalog name: {0:s} \\n\".format(prefix, self.in_sex)\n big_str += \"{0:s} Output catalog name: {0:s} \".format(prefix, self.out_sex)\n\n # return the string\n return big_str","def __str__(self):\n if __debug__:\n description = ('CM' in debug.active)\n else:\n description = False\n return self.asstring(short=False, header=True, summary=True,\n description=description)","def __str__(self):\n return str(self.preview)","def helptext(self):\n return \"\"\"<b> Edit Regions </b> <br/>\\n\n <b>R</b> to start a new region or set the edge if editing<br/>\\n\n <b>E</b> to edit nearest region<br/>\\n\n <b>D</b> to delete/cancel the current/nearest region\n \"\"\"","def code_display(self):\r\n return u'%s-%s-%s' % (self.code[:3], self.code[3:6], self.code[6:])","def display_characters(self):\n return f'{self.character_set}'","def __str__(self) -> str:\n string = ''\n for row in self._grid_display:\n string += row.__str__() + '\\n'\n return string","def __str__(self):\n string = \"\"\n for row in self.layout:\n for tile in row:\n string+= str(tile) + \" \"\n string+= \"\\n\"\n return string","def render(self, mode='human'):\n\n pass","def __repr__(self):\n \n s = '#cpt palette generated by gmt_interface.py\\n'\n s += '#COLOR_MODEL = %s\\n' %self.color_model\n s += '#\\n'\n \n for seg in self.segments:\n\n #print x, seg\n xmin = seg.lower_bound\n xmax = seg.upper_bound\n\n rgb_min = seg.rgb_min\n rgb_max = rgb_min + seg.rgb_dif\n \n # Determine number of decimal points\n xmax-xmin\n \n fmin = format_string(xmin) \n fmax = format_string(xmax)\n\n s += fmin %xmin\n s += ' %d %d %d ' %(rgb_min[0], rgb_min[1], rgb_min[2]) \n s += fmax %xmax\n s += ' %d %d %d ' %(rgb_max[0], rgb_max[1], rgb_max[2])\n s += ' %s' %seg.color_segment_boundary\n s += '\\n'\n \n return s","def gui_preview(self): \r\n\r\n if self.Debug_checkbox.isChecked() == True :\r\n cmdString = 'pyuic5 -p -d ' + self.fileName\r\n else:\r\n cmdString = 'pyuic5 -p ' + self.fileName\r\n \r\n output = subprocess.getoutput(cmdString)\r\n self.plainTextEdit.insertPlainText( output )","def helptext(self):\n return \"\"\"\n <b>A</b> to start the aperture or set the value<br/>\n <b>S</b> to select an existing aperture<br/>\n <b>C</b> to clear the selection<br/>\n <b>F</b> to find a peak close to the cursor<br/>\n <b>[</b> to edit the left edge of selected or closest<br/>\n <b>]</b> to edit the right edge of selected or closest<br/>\n <b>L</b> to edit the location of selected or closest<br/>\n <b>D</b> to delete the selected or closest aperture\n \"\"\"","def draw(self):\n drawing = \"\"\n \n if self.get_gene().get_choice_at(1) is 'c':\n drawing += 'o'\n else:\n drawing += 'x'\n \n if self.is_tft():\n drawing += \"tft\"\n return drawing\n elif self.is_t2t():\n drawing += \"t2t\"\n return drawing\n elif self.is_ftf():\n drawing += \"ftf\"\n return drawing\n\n rule = self.get_gene().get_defect_fraction()\n fraction_display = 0.166\n\n if rule >= 1.0:\n drawing += \"ddd\"\n elif rule > (5*fraction_display):\n drawing += \"ddc\"\n elif rule > (4*fraction_display):\n drawing += \"dcd\"\n elif rule > (3*fraction_display):\n drawing += \"dcc\"\n elif rule > (2*fraction_display):\n drawing += \"cdd\"\n elif rule > (1*fraction_display):\n drawing += \"cdc\"\n elif rule > (0*fraction_display):\n drawing += \"ccd\"\n else:\n drawing += \"ccc\"\n\n return drawing","def __str__( self ):\n return \"raster loader with the following available lines:\\n\\n\" + self.get_lines().to_string()","def __repr__(self):\n s = \"\"\n for y in range(0,HEIGHT):\n temp=\"\"\n for x in range(0,WIDTH):\n temp = temp+ str(self.gameState[x,y])\n s += temp+\"\\n\"\n return s","def debug(self) -> str:\n\n return Widget.debug(self)","def get_console(self):\n\t\treturn base.TEXT_CONSOLE","def __str__(self):\n rep = \"\"\n for row in range(self._dim):\n for col in range(self._dim):\n rep += STRMAP[self._board[row][col]]\n if col == self._dim - 1:\n rep += \"\\n\"\n else:\n rep += \" | \"\n if row != self._dim - 1:\n rep += \"-\" * (4 * self._dim - 3)\n rep += \"\\n\"\n return rep","def __str__( self ) :\n\n return( '%s with projectile \"%s\", target \"%s\", evaluation \"%s\", path \"%s\" and interaction \"%s\".' % \n ( self.moniker, self.projectile, self.target, self.evaluation, self.path, self.interaction ) )","def as_string(self):\n return self.__repr__()","def intf_VIEWSHOW(E):\n out= \"View Properties\\n\"\n out+= \"---------------\\n\"\n out+= \"svgoutfile=%s\\n\" % OUT.outfile\n out+= \"camera=%s {camset}\\n\" % (','.join([str(x) for x in OUT.camera]))\n out+= \"target=%s {tarset}\\n\" % (','.join([str(x) for x in OUT.target]))\n out+= \"opacity=%s {hlr,hide}\\n\" % str(OUT.opacity)\n out+= \"facelines=%s {facelines}\\n\" % str(OUT.facelines)\n out+= \"vlinewidth=%0.2f {vlw,viewlinewidth}\\n\" % OUT.vlinewidth\n out+= \"vrefreshms=%d {refreshms,viewrefreshms}\\n\" % OUT.vrefreshms\n out+= \"vbox=(%d,%d) {viewbox[xy]}\\n\" % (OUT.vboxX,OUT.vboxY)\n out+= \"vtran=(%d,%d) {vtran[xy],viewtran[xy]}\\n\" % (OUT.vtranX,OUT.vtranY)\n out+= \"vscale=(%d,%d) {vscale[xy],viewscale[xy]}\\n\" % (OUT.vscaleX,OUT.vscaleY)\n print(out)","def __str__(self):\n if self.__style == 'default':\n if self.living:\n return Cell.liveChar\n else:\n return Cell.deadChar","def __str__(self):\n return self.printable()","def getDebugText(self):\n timeDifference = time.time() - self.time_created\n hours = math.floor(timeDifference / 3600)\n minutes = math.floor((timeDifference % 3600) / 60)\n seconds = math.floor(timeDifference % 3600 % 60)\n\n output = \"\\n\" * 50\n output += \"Time started: %s\\n\" % time.ctime(self.time_created)\n output += \"Time now: %s\\n\" % time.ctime()\n output += \"Time elapsed: %02d:%02d:%02d\\n\" % (hours, minutes, seconds)\n output += (\"=\" * 80) + \"\\n\"\n output += \"Health potions used: %d\\n\" % self.hp_pots_used\n output += \"Health potions per hour: %d\\n\" % (self.hp_pots_used / (\n timeDifference / 3600))\n output += \"Mana potions used: %d\\n\" % self.mana_pots_used\n output += \"Mana potions per hour: %d\\n\" % (self.mana_pots_used / (\n timeDifference / 3600))\n return output","def _repr_(self):\n ring = self.coordinate_ring()\n args = ring.arguments()\n repr_x=self.change_ring(SR)._repr_()\n if len(args) == 1:\n return \"%s |--> %s\" % (args[0], repr_x)\n else:\n args = \", \".join(map(str, args))\n return \"(%s) |--> %s\" % (args, repr_x)","def __str__(self) -> str:\n if self.decorator is None:\n decorator_str = \"\"\n elif self.decorator:\n decorator_str = \"+\"\n else:\n decorator_str = \"-\"\n return \" \".join([\"The nilpotent orbit corresponding\",\n f\"to partition {self.my_diagram}{decorator_str}\",\n f\"in type {self.my_type.letter()} {self.lie_rank}\"])","def __str__(self):\n return (\"UUID: \" + str(self.uuid) + \"\\n\"\n \"Data: \" + str(self.data) + \"\\n\" +\n \"Tex: \" + str(self.texOutput) + \"\\n\")","def get_tooltip_text(self): # real signature unknown; restored from __doc__\n return \"\"","def _repr_html_(self):\n\n return self._repr__base(rich_output=True)","def _repr_html_(self):\n\n return self._repr__base(rich_output=True)","def draw(self):\n res = ''\n # ANSI code to clear the screen\n #res += chr(27) + \"[2J\"\n for position, value in enumerate(self.board.tttboard):\n if value is None:\n res += str(position)\n #sys.stdout.write(str(position))\n else:\n res += str(value)\n #sys.stdout.write(str(value))\n\n if (position + 1) % 3 != 0:\n res += str('|')\n #sys.stdout.write('|')\n else:\n #print ''\n\n res += str('\\n')\n if position == 2 or position == 5:\n #print '-' * 5\n\n res += '-' * 5\n res += str('\\n')\n return res","def _repr_html_(self):\n return self.__repr__()","def _repr_html_(self):\n return self.__repr__()","def __repr__(self):\n\n template = \"\"\"\n - inline : {}\n \"\"\"\n\n return super().__repr__() + template.format(self.inline)","def __repr__(self):\n\n return f\"Ufd(\"\\\n f\"title=\\\"{self.title}\\\",\"\\\n f\" icon=\\\"{self.icon}\\\",\"\\\n f\" show_hidden={self.show_hidden},\"\\\n f\" include_files={self.include_files},\"\\\n f\" multiselect={self.multiselect},\"\\\n f\" select_dirs={self.select_dirs},\"\\\n f\" select_files={self.select_files},\"\\\n f\" unix_delimiter={self.unix_delimiter})\"\\\n f\" stdout={self.stdout})\"\\\n f\" @ {hex(id(self))}\"","def show_on_screen(self, string, location, font='Arial', font_size=20, colour=WHITE):\n msg = pygame.font.SysFont(font, font_size).render(str(string), True, colour)\n self.screen.blit(msg, location)","def __str__(self):\n s = \"Scene Object: \" + self.name + \"\\n\"\n s += \" Wavelength: \" + str(np.min(self.wave)) + \":\" + str(self.bin_width) + \":\" + str(np.max(self.wave))\n s += \" nm\\n\"\n s += \" [Row, Col]: \" + str(self.shape) + \"\\n\"\n s += \" [Width, Height]: [\" + \"%.2g\" % self.width + \", \" + \"%.2g\" % self.height + \"] m\\n\"\n s += \" Horizontal field of view: \" + \"%.2g\" % self.fov + \" deg\\n\"\n s += \" Sample size: \" + \"%.2g\" % self.sample_size + \" meters/sample\\n\"\n s += \" Mean luminance: \" + \"%.2g\" % self.mean_luminance + \" cd/m2\"\n return s"],"string":"[\n \"def show(self) -> str:\\n return f'[{self.font}]{self.text}[{self.font}]' if self.font else self.text\",\n \"def __repr__(self):\\n return self._format() if self.always_visible or not self.is_pointless() else ''\",\n \"def display(self) -> str:\\n lines, _, _, _ = self._display_aux()\\n return '\\\\n'.join(lines)\",\n \"def graphic_auto():\\r\\n print(\\\"\\\\nCe mode n'est pas supporté. Passez en affichage textuel pour le mode automatique\\\")\",\n \"def __repr__(self):\\n return self.display()\",\n \"def the_display(self):\\r\\n return f\\\"\\\"\\\"\\r\\n {self.display[0]}\\\\n\\r\\n {self.display[1]}\\\\n\\r\\n {self.display[2]}\\\\n\\r\\n {self.display[3]}\\\\n\\r\\n {self.display[4]}\\\\n\\r\\n \\\"\\\"\\\"\",\n \"def visualizeWithContents(self, paths):\\n return ExpressString(\\\"A totally mystical rune.\\\")\",\n \"def __str__(self): # pragma: no cover\\n return self.display()\",\n \"def render(self, mode='human'):\\n return self._env.render(mode)\",\n \"def draw(self):\\n rendered_string = \\\"\\\"\\n\\n # extract the wire labels as strings and get their maximum length\\n wire_names = []\\n padding = 0\\n for i in range(self.full_representation_grid.num_wires):\\n wire_name = str(self.active_wires.labels[i])\\n padding = max(padding, len(wire_name))\\n wire_names.append(wire_name)\\n\\n for i in range(self.full_representation_grid.num_wires):\\n # format wire name nicely\\n wire = self.full_representation_grid.wire(i)\\n s = \\\" {:>\\\" + str(padding) + \\\"}: {}\\\"\\n\\n rendered_string += s.format(wire_names[i], 2 * self.charset.WIRE)\\n\\n for s in wire:\\n rendered_string += s\\n\\n rendered_string += \\\"\\\\n\\\"\\n\\n for symbol, cache in [\\n (\\\"U\\\", self.representation_resolver.unitary_matrix_cache),\\n (\\\"H\\\", self.representation_resolver.hermitian_matrix_cache),\\n (\\\"M\\\", self.representation_resolver.matrix_cache),\\n ]:\\n for idx, matrix in enumerate(cache):\\n rendered_string += \\\"{}{} =\\\\n{}\\\\n\\\".format(symbol, idx, matrix)\\n\\n return rendered_string\",\n \"def __repr__(self):\\r\\n # Initialize the return string\\r\\n s = ''\\r\\n\\r\\n for row in range(self.height):\\r\\n # Print the index of the row\\r\\n s = s + str(row % 10) + ' |'\\r\\n\\r\\n for col in range(self.width):\\r\\n s += self.grid[row][col]\\r\\n s += '|'\\r\\n\\r\\n s += '\\\\n'\\r\\n s += '--' * (self.width + 1)\\r\\n s += '-'\\r\\n s += '\\\\n'\\r\\n \\r\\n s += ' '\\r\\n for i in range(self.width):\\r\\n s += ' ' + str(i % 10) \\r\\n \\r\\n return s\",\n \"def show(self):\\n import IPython.display\\n disp = IPython.display.HTML(self.render())\\n return IPython.display.display(disp, display_id=str(id(self)))\",\n \"def render(self, mode='human'):\",\n \"def show(self, stdscr):\\n stdscr.addstr(*self.pos, self.asset)\",\n \"def __str__(self):\\n if self.getType() == FRAGMENT:\\n t = \\\"FRAGMENT\\\"\\n else:\\n t = \\\"VERTEX\\\"\\n if not self.isCompiled():\\n s = \\\"not compiled\\\"\\n else:\\n s = \\\"compiled\\\"\\n return \\\"file: {0}\\\\ntype: {1}\\\\nstatus: {2}\\\\n\\\".format(self.getPath(), t, s)\",\n \"def __repr__(self):\\n\\n return self._repr__base(rich_output=False)\",\n \"def __repr__(self):\\n\\n return self._repr__base(rich_output=False)\",\n \"def __repr__(self):\\n #if self.parent: self.print_siblings()\\n self.env.render()\\n #self._print_children()\\n return ''\",\n \"def __str__(self):\\n return self.get_canvas_as_str()\",\n \"def render_prompt(self) -> str:\\n # pylint: disable=no-member\\n return '{}render: '.format(self.prompt)\",\n \"def __str__(self):\\n if self.fshader is None:\\n f = \\\"not defined\\\"\\n else:\\n f = self.fshader.getPath()\\n if self.vshader is None:\\n v = \\\"not defined\\\"\\n else:\\n v = self.vshader.getPath()\\n if self.enabled:\\n e = \\\"enabled\\\"\\n else:\\n e = \\\"disabled\\\"\\n if self.isCompiled():\\n c = \\\"compiled | {0}\\\".format(e)\\n else:\\n c = \\\"not compiled | {0}\\\".format(e)\\n return \\\"shader: {3}\\\\nfragment shader: {0}\\\\nvertex shader: {1}\\\\nstatus: {2}\\\".format(f, v, c, self.getName())\",\n \"def _repr_(self):\\n s = 'The projection of a polyhedron into ' + repr(self.dimension) \\n s += ' dimensions.'\\n return s + \\\"\\\\n\\\"\",\n \"def _repr_options(self) -> str:\\n return \\\"\\\"\",\n \"def description(self):\\n return self.visual_desc\",\n \"def __str__(self):\\n # TODO check the terminal width and adjust the presentation\\n # only use for 1D, fall back to repr for ND\\n if self._hist.rank() == 1:\\n s = str(self._hist)\\n # get rid of first line and last character\\n s = s[s.index(\\\"\\\\n\\\") + 1 : -1]\\n else:\\n s = repr(self)\\n return s\",\n \"def __repr__(self):\\n return \\\"ANSIString(%s, decoded=True)\\\" % repr(self._raw_string)\",\n \"def show_win_screen():\\n print (\\\"\\\"\\\"\\n /$$ /$$\\n |__/ | $$\\n /$$ /$$ /$$$$$$ /$$ /$$ /$$ /$$ /$$ /$$ /$$$$$$$ | $$\\n| $$ | $$ /$$__ $$| $$ | $$ | $$ | $$ | $$| $$| $$__ $$| $$\\n| $$ | $$| $$ \\\\ $$| $$ | $$ | $$ | $$ | $$| $$| $$ \\\\ $$|__/\\n| $$ | $$| $$ | $$| $$ | $$ | $$ | $$ | $$| $$| $$ | $$ \\n| $$$$$$$| $$$$$$/| $$$$$$/ | $$$$$/$$$$/| $$| $$ | $$ /$$\\n \\\\____ $$ \\\\______/ \\\\______/ \\\\_____/\\\\___/ |__/|__/ |__/|__/\\n /$$ | $$ \\n| $$$$$$/ \\n \\\\______/ \\n\\\"\\\"\\\")\",\n \"def __str__(self):\\n inside_list = lambda _v, _h, a: any(x == _h and y == _v for y, x in a)\\n resultant = ''\\n for _v in range(1, self.size_v + 1):\\n for _h in range(1, self.size_h + 1):\\n if self.current_location[1] == _h and self.current_location[0] == _v:\\n resultant = resultant + '@'\\n elif inside_list(_v, _h, self.boxes):\\n resultant = resultant + '$'\\n elif inside_list(_v, _h, self.storage_locations):\\n resultant = resultant + '.'\\n elif inside_list(_v, _h, self.wall_squares):\\n resultant = resultant + '#'\\n else:\\n resultant = resultant + ' '\\n resultant = resultant + '\\\\n'\\n\\n return resultant\",\n \"def __str__(self) -> str:\\n lengthscale = self.covar_module.base_kernel.lengthscale.detach()\\n outputscale = self.covar_module.outputscale.detach()\\n return \\\"\\\\toutputscale: {}\\\\n \\\\tlengthscale: {}\\\".format(outputscale, lengthscale)\",\n \"def __str__(self):\\r\\n grid_text = \\\"\\\\n-------------------\\\\n|\\\"\\r\\n for i in range(len(self.grid)):\\r\\n grid_text = grid_text + ' %s '%(self.grid[i][-1])\\r\\n\\r\\n if i%3 == 2:\\r\\n grid_text = grid_text + '|\\\\n-------------------\\\\n|'\\r\\n else:\\r\\n grid_text = grid_text + '|'\\r\\n return grid_text[0:len(grid_text)-1]\",\n \"def __str__(self):\\r\\n out = \\\"##\\\"*(self.width+1)+\\\"\\\\n\\\"\\r\\n for i in range(self.height):\\r\\n out += \\\"#\\\"\\r\\n for j in range(self.width):\\r\\n if self.grid[i][j] == 0:\\r\\n out += \\\"##\\\"\\r\\n else:\\r\\n if not self.showSolution:\\r\\n out += \\\" \\\"\\r\\n elif (i,j) in self.solution:\\r\\n out += \\\"**\\\"\\r\\n else:\\r\\n out += \\\" \\\"\\r\\n out += \\\"#\\\\n\\\"\\r\\n return out + \\\"##\\\"*(self.width+1)\",\n \"def _repr_(self):\\n s = \\\"Constellation of length {} and degree {}\\\".format(self.length(),\\n self.degree())\\n for i in range(self.length()):\\n s += \\\"\\\\ng{} {}\\\".format(i, self._g[i].cycle_string(True))\\n return s\",\n \"def show(self):\\n import IPython\\n if self._output is None:\\n self.render()\\n IPython.display.display(self._output, display_id=str(id(self)))\",\n \"def display(self):\\n disptxt = str(self)\\n if self.width == 0 or self.has_output:\\n print(disptxt)\\n else:\\n print(\\\"\\\\r\\\", end='')\\n print(disptxt, end='')\\n sys.stdout.flush()\",\n \"def __str__(self):\\n name = self.__class__.__name__\\n id = self.id\\n x = self.x\\n y = self.y\\n wid = self.width\\n hei = self.height\\n return \\\"[{}] ({}) {}/{} - {}/{}\\\".format(name, id, x, y, wid, hei)\",\n \"def debug_string(self):\\n\\n raise NotImplementedError\",\n \"def __str__(self, width=2):\\n \\n if self.visibility == 1:\\n if self.is_mine:\\n out = '!'\\n else:\\n out = '.' if self.mines == 0 else self.mines\\n else:\\n out = '@' if self.visibility == 2 else '#'\\n return f'{out: ^{width}}'\",\n \"def __str__(self):\\n return '(%d,%d,%d,%d,%d,%d,%d,%d,%d,%d,%d)' % (\\n self.dwSize.Y, self.dwSize.X\\n , self.dwCursorPosition.Y, self.dwCursorPosition.X\\n , self.wAttributes\\n , self.srWindow.Top, self.srWindow.Left, self.srWindow.Bottom, self.srWindow.Right\\n , self.dwMaximumWindowSize.Y, self.dwMaximumWindowSize.X\\n )\",\n \"def __str__(self):\\n\\n # can't use backslash in f-string\\n newline = \\\"\\\\n\\\"\\n\\n options = self.options\\n kwoptions = self.kwoptions\\n\\n # add background to options\\n if self.background:\\n options.append(\\\"show background rectangle\\\")\\n kwoptions.update({\\\"background rectangle/.style\\\": f\\\"{{fill={self.background}}}\\\"})\\n\\n return rf\\\"\\\"\\\"\\\\begin{{tikzpicture}}{wrap(fmt_options(self.options, self.kwoptions))}\\n{newline.join(define_colours(self._used_colours))}\\n{newline.join(self.defines)}\\n{newline.join(self._commands)}\\n\\\\end{{tikzpicture}}\\\"\\\"\\\"\",\n \"def display_cli(self) -> None:\\n if len(self.living_cells.keys()) == 0:\\n print('.')\\n return\\n min_x, min_y = math.inf, math.inf\\n max_x, max_y = -math.inf, -math.inf\\n for x, y in self.living_cells.keys():\\n min_x = min(min_x, x)\\n min_y = min(min_y, y)\\n max_x = max(max_x, x)\\n max_y = max(max_y, y)\\n for y in range(min_y, max_y + 1):\\n chars = \\\"\\\"\\n for x in range(min_x, max_x + 1):\\n chars += '*' if (x, y) in self.living_cells.keys() else '.'\\n print(chars)\\n print()\",\n \"def formatted_str(self):\\n # Verify correct vertex values\\n self.verify_vertex_values()\\n # Generate output string\\n output_string = \\\"\\\"\\n for line in range(9):\\n for row in range(9):\\n if self.grid[line][row] is None:\\n output_string += \\\".\\\"\\n else:\\n output_string += str(self.grid[line][row])\\n if row != 8:\\n output_string += \\\" \\\"\\n elif line != 8:\\n output_string += \\\"\\\\n\\\"\\n if row in [2, 5]:\\n output_string += \\\"| \\\"\\n if line in [2, 5]:\\n output_string += \\\"------+-------+------\\\\n\\\"\\n return output_string\",\n \"def console(self, vm=None):\\n raise NotImplementedError\\n return \\\"\\\"\",\n \"def __str__(self):\\n ans = \\\"\\\"\\n for row in range(self._height):\\n ans += str(self._grid[row])\\n ans += \\\"\\\\n\\\"\\n return ans\",\n \"def __str__(self):\\n ans = \\\"\\\"\\n for row in range(self._height):\\n ans += str(self._grid[row])\\n ans += \\\"\\\\n\\\"\\n return ans\",\n \"def __str__(self):\\n ans = \\\"\\\"\\n for row in range(self._height):\\n ans += str(self._grid[row])\\n ans += \\\"\\\\n\\\"\\n return ans\",\n \"def __str__(self):\\n ans = \\\"\\\"\\n for row in range(self._height):\\n ans += str(self._grid[row])\\n ans += \\\"\\\\n\\\"\\n return ans\",\n \"def render(self, mode='human'):\\n pass # no use in this situation\",\n \"def render(self, mode: str = 'human'):\\n # Note, this may seem badly aligned but it is correct!\\n # It becomes aligned after substituting the variables by their real value\\n content = \\\"\\\"\\\"\\n x1 = {x1:.2f}\\n ------------> City\\n / x5 = {x5:.2f}\\n / x2 = {x2:.2f} ----------------> Farms\\n Q1 = {Q1} --------------> Dam ---------------- /\\n S = {S} | /\\n |-------------------------> Lower tributary\\n | x6 = {x6:.2f}\\n Q2 = {Q2} ------------------------------------\\n \\\\\\\\ x3 = {x3:.2f}\\n \\\\\\\\\\n -------------> Farms\\n x4 = {x4:.2f}\\n \\\"\\\"\\\"\\n print(\\n content.format(\\n Q1=self.Q1,\\n Q2=self.Q2,\\n S=self.S,\\n x1=self.x[0],\\n x2=self.x[1],\\n x3=self.x[2],\\n x4=self.x[3],\\n x5=self.x[4],\\n x6=self.x[5]\\n )\\n )\",\n \"def _repr_html_(self):\\n return (\\n f'<b>GalaxyCluster:</b> {self.unique_id} '\\n f'(ra={self.ra}, dec={self.dec}) at z={self.z}'\\n f'<br>> <b>with columns:</b> {self._str_colnames()}'\\n f'<br>> {len(self.galcat)} source galaxies'\\n f'<br>{self.galcat._html_table()}'\\n )\",\n \"def displayScene(self):\\n sceneprint = \\\"\\\"\\n sceneprint += \\\" \\\"*40 + Back.LIGHTRED_EX + Fore.LIGHTCYAN_EX + Style.BRIGHT + \\\"M A N D A L O R I A N\\\\n\\\" + RESET\\n sceneprint += Fore.LIGHTBLUE_EX +\\\"SCORE : \\\" +\\\\\\n str(self.__score) + \\\" \\\"*30 +\\\"TIME : \\\" + str(self.__remaining_time) + \\\" \\\"*30 +\\\\\\n \\\"LIVES:\\\" + str(self.__lives)+\\\"\\\\n\\\"+ RESET\\n if self.__start >= self.__fullwidth - self.__width:\\n self.__start = self.__fullwidth - self.__width\\n for i in range(0, self.__height):\\n for j in range(self.__start, self.__start + self.__width):\\n sceneprint += str(self.__matrix[i][j])\\n sceneprint += '\\\\n'\\n \\n if self.__start + sc_span < sc_full - 5:\\n self.__start = self.__start + 1\\n if self.__score < 420420420:\\n self.__score += 1\\n pass\\n\\n return sceneprint\",\n \"def __repr__(self):\\n string = ''\\n for key, val in self.setting().items():\\n string += '{}({})\\\\n'.format(key, val)\\n return string\",\n \"def __str__(self) -> str:\\n return (\\n f\\\"GlacierFlowModel '{self.model_name}' \\\"\\n f\\\"{'' if self.steady_state else 'not '}in steady state with:\\\"\\n f\\\"\\\\n - m: {self.m:20.5f} [m/m]\\\"\\n f\\\"\\\\n - ela: {self.ela:20.2f} [m MSL]\\\"\\n f\\\"\\\\n - resolution: {self.res:20.2f} [m]\\\"\\n f\\\"\\\\n - extent: min max\\\"\\n f\\\"\\\\n {self.extent[0]:10.1f} \\\"\\n f\\\"{self.extent[1]:10.1f} [x]\\\"\\n f\\\"\\\\n {self.extent[2]:10.1f} \\\"\\n f\\\"{self.extent[3]:10.1f} [y]\\\"\\n )\",\n \"def to_str(self):\\n return self.template.format(textcolor=self.color.to_str())\",\n \"def __repr__(self):\\n output = \\\"\\\"\\n output +=\\\"V:\\\\n\\\"\\n for row in self.V:\\n output += \\\"\\\\t\\\"\\n for el in row:\\n output += str(el) + \\\" \\\" \\n output += \\\"\\\\n\\\" \\n \\n output += \\\"\\\\nW:\\\\n\\\"\\n for row in self.W:\\n output += \\\"\\\\t\\\"\\n for el in row:\\n output += str(el) + \\\" \\\" \\n output += \\\"\\\\n\\\"\\n return output\",\n \"def __str__(self):\\n return \\\"%s\\\\n\\\" % self.text + \\\" \\\" * self.col + \\\"^\\\"\",\n \"def get_mode(self):\\n if self.vimiv.library.treeview.is_focus():\\n return \\\"<b>-- LIBRARY --</b>\\\"\\n elif self.vimiv.manipulate.scrolled_win.is_visible():\\n return \\\"<b>-- MANIPULATE --</b>\\\"\\n elif self.vimiv.thumbnail.toggled:\\n return \\\"<b>-- THUMBNAIL --</b>\\\"\\n else:\\n return \\\"<b>-- IMAGE --</b>\\\"\",\n \"def __str__(self):\\r\\n ans = \\\"\\\"\\r\\n for row in range(self._height):\\r\\n ans += str(self._grid[row])\\r\\n ans += \\\"\\\\n\\\"\\r\\n return ans\",\n \"def __str__(self):\\r\\n ans = \\\"\\\"\\r\\n for row in range(self._height):\\r\\n ans += str(self._grid[row])\\r\\n ans += \\\"\\\\n\\\"\\r\\n return ans\",\n \"def __str__(self):\\r\\n ans = \\\"\\\"\\r\\n for row in range(self._height):\\r\\n ans += str(self._grid[row])\\r\\n ans += \\\"\\\\n\\\"\\r\\n return ans\",\n \"def __str__(self):\\n t = Template(\\n \\\"\\\"\\\"\\n <h4>$title</h4>\\n $imgs\\n $footnotes\\n <hr/>\\\"\\\"\\\")\\n # Return result.\\n return t.substitute({\\n \\\"title\\\": self.title,\\n \\\"imgs\\\": self.render_images(),\\n \\\"footnotes\\\": self.render_footnotes()\\n })\",\n \"def to_str(self):\\n return self.template.format(\\n text_color=self.color.to_str(),\\n font_type=self.font_type.to_str(),\\n font_weight=self.font_weight.to_str(),\\n font_size=self.font_size.to_str()\\n )\",\n \"def display(self):\\n mg_w = self.width\\n mg_h = self.height\\n str_to_prt = \\\"\\\\n\\\" * self.y + (\\\" \\\" * self.x + \\\"#\\\" * mg_w + '\\\\n') * mg_h\\n print(str_to_prt[:-1])\",\n \"def __str__(self):\\n return \\\"Normal:\\\" + str(self.norm) + \\\"\\\\nColour:\\\" + self.colour\",\n \"def __str__(self):\\n bold = \\\"*\\\" if self.bold else ''\\n italic = \\\"/\\\" if self.italic else ''\\n underline = \\\"_\\\" if self.underline else ''\\n return bold + italic + underline + self.character\",\n \"def __str__(self):\\n # define the prefix\\n prefix = \\\"py_SEX2GOL: \\\"\\n\\n # compose the feedback\\n big_str = \\\"{0:s} Setup:\\\\n\\\".format(prefix)\\n big_str += \\\"{0:s} Input g/prism image: {0:s} \\\\n\\\".format(prefix, self.grisim)\\n big_str += \\\"{0:s} Configuration file name: {0:s} \\\\n\\\".format(prefix, self.config)\\n big_str += \\\"{0:s} Direct image: {0:s} \\\\n\\\".format(prefix, self.dirname)\\n big_str += \\\"{0:s} G/Prism extension: {0:s} \\\\n\\\".format(prefix, self.grism_extinfo['axe_ext'])\\n big_str += \\\"{0:s} Direct image extension: {0:s} \\\\n\\\".format(prefix, self.dirname_extinfo['axe_ext'])\\n big_str += \\\"{0:s} Input catalog name: {0:s} \\\\n\\\".format(prefix, self.in_sex)\\n big_str += \\\"{0:s} Output catalog name: {0:s} \\\".format(prefix, self.out_sex)\\n\\n # return the string\\n return big_str\",\n \"def __str__(self):\\n if __debug__:\\n description = ('CM' in debug.active)\\n else:\\n description = False\\n return self.asstring(short=False, header=True, summary=True,\\n description=description)\",\n \"def __str__(self):\\n return str(self.preview)\",\n \"def helptext(self):\\n return \\\"\\\"\\\"<b> Edit Regions </b> <br/>\\\\n\\n <b>R</b> to start a new region or set the edge if editing<br/>\\\\n\\n <b>E</b> to edit nearest region<br/>\\\\n\\n <b>D</b> to delete/cancel the current/nearest region\\n \\\"\\\"\\\"\",\n \"def code_display(self):\\r\\n return u'%s-%s-%s' % (self.code[:3], self.code[3:6], self.code[6:])\",\n \"def display_characters(self):\\n return f'{self.character_set}'\",\n \"def __str__(self) -> str:\\n string = ''\\n for row in self._grid_display:\\n string += row.__str__() + '\\\\n'\\n return string\",\n \"def __str__(self):\\n string = \\\"\\\"\\n for row in self.layout:\\n for tile in row:\\n string+= str(tile) + \\\" \\\"\\n string+= \\\"\\\\n\\\"\\n return string\",\n \"def render(self, mode='human'):\\n\\n pass\",\n \"def __repr__(self):\\n \\n s = '#cpt palette generated by gmt_interface.py\\\\n'\\n s += '#COLOR_MODEL = %s\\\\n' %self.color_model\\n s += '#\\\\n'\\n \\n for seg in self.segments:\\n\\n #print x, seg\\n xmin = seg.lower_bound\\n xmax = seg.upper_bound\\n\\n rgb_min = seg.rgb_min\\n rgb_max = rgb_min + seg.rgb_dif\\n \\n # Determine number of decimal points\\n xmax-xmin\\n \\n fmin = format_string(xmin) \\n fmax = format_string(xmax)\\n\\n s += fmin %xmin\\n s += ' %d %d %d ' %(rgb_min[0], rgb_min[1], rgb_min[2]) \\n s += fmax %xmax\\n s += ' %d %d %d ' %(rgb_max[0], rgb_max[1], rgb_max[2])\\n s += ' %s' %seg.color_segment_boundary\\n s += '\\\\n'\\n \\n return s\",\n \"def gui_preview(self): \\r\\n\\r\\n if self.Debug_checkbox.isChecked() == True :\\r\\n cmdString = 'pyuic5 -p -d ' + self.fileName\\r\\n else:\\r\\n cmdString = 'pyuic5 -p ' + self.fileName\\r\\n \\r\\n output = subprocess.getoutput(cmdString)\\r\\n self.plainTextEdit.insertPlainText( output )\",\n \"def helptext(self):\\n return \\\"\\\"\\\"\\n <b>A</b> to start the aperture or set the value<br/>\\n <b>S</b> to select an existing aperture<br/>\\n <b>C</b> to clear the selection<br/>\\n <b>F</b> to find a peak close to the cursor<br/>\\n <b>[</b> to edit the left edge of selected or closest<br/>\\n <b>]</b> to edit the right edge of selected or closest<br/>\\n <b>L</b> to edit the location of selected or closest<br/>\\n <b>D</b> to delete the selected or closest aperture\\n \\\"\\\"\\\"\",\n \"def draw(self):\\n drawing = \\\"\\\"\\n \\n if self.get_gene().get_choice_at(1) is 'c':\\n drawing += 'o'\\n else:\\n drawing += 'x'\\n \\n if self.is_tft():\\n drawing += \\\"tft\\\"\\n return drawing\\n elif self.is_t2t():\\n drawing += \\\"t2t\\\"\\n return drawing\\n elif self.is_ftf():\\n drawing += \\\"ftf\\\"\\n return drawing\\n\\n rule = self.get_gene().get_defect_fraction()\\n fraction_display = 0.166\\n\\n if rule >= 1.0:\\n drawing += \\\"ddd\\\"\\n elif rule > (5*fraction_display):\\n drawing += \\\"ddc\\\"\\n elif rule > (4*fraction_display):\\n drawing += \\\"dcd\\\"\\n elif rule > (3*fraction_display):\\n drawing += \\\"dcc\\\"\\n elif rule > (2*fraction_display):\\n drawing += \\\"cdd\\\"\\n elif rule > (1*fraction_display):\\n drawing += \\\"cdc\\\"\\n elif rule > (0*fraction_display):\\n drawing += \\\"ccd\\\"\\n else:\\n drawing += \\\"ccc\\\"\\n\\n return drawing\",\n \"def __str__( self ):\\n return \\\"raster loader with the following available lines:\\\\n\\\\n\\\" + self.get_lines().to_string()\",\n \"def __repr__(self):\\n s = \\\"\\\"\\n for y in range(0,HEIGHT):\\n temp=\\\"\\\"\\n for x in range(0,WIDTH):\\n temp = temp+ str(self.gameState[x,y])\\n s += temp+\\\"\\\\n\\\"\\n return s\",\n \"def debug(self) -> str:\\n\\n return Widget.debug(self)\",\n \"def get_console(self):\\n\\t\\treturn base.TEXT_CONSOLE\",\n \"def __str__(self):\\n rep = \\\"\\\"\\n for row in range(self._dim):\\n for col in range(self._dim):\\n rep += STRMAP[self._board[row][col]]\\n if col == self._dim - 1:\\n rep += \\\"\\\\n\\\"\\n else:\\n rep += \\\" | \\\"\\n if row != self._dim - 1:\\n rep += \\\"-\\\" * (4 * self._dim - 3)\\n rep += \\\"\\\\n\\\"\\n return rep\",\n \"def __str__( self ) :\\n\\n return( '%s with projectile \\\"%s\\\", target \\\"%s\\\", evaluation \\\"%s\\\", path \\\"%s\\\" and interaction \\\"%s\\\".' % \\n ( self.moniker, self.projectile, self.target, self.evaluation, self.path, self.interaction ) )\",\n \"def as_string(self):\\n return self.__repr__()\",\n \"def intf_VIEWSHOW(E):\\n out= \\\"View Properties\\\\n\\\"\\n out+= \\\"---------------\\\\n\\\"\\n out+= \\\"svgoutfile=%s\\\\n\\\" % OUT.outfile\\n out+= \\\"camera=%s {camset}\\\\n\\\" % (','.join([str(x) for x in OUT.camera]))\\n out+= \\\"target=%s {tarset}\\\\n\\\" % (','.join([str(x) for x in OUT.target]))\\n out+= \\\"opacity=%s {hlr,hide}\\\\n\\\" % str(OUT.opacity)\\n out+= \\\"facelines=%s {facelines}\\\\n\\\" % str(OUT.facelines)\\n out+= \\\"vlinewidth=%0.2f {vlw,viewlinewidth}\\\\n\\\" % OUT.vlinewidth\\n out+= \\\"vrefreshms=%d {refreshms,viewrefreshms}\\\\n\\\" % OUT.vrefreshms\\n out+= \\\"vbox=(%d,%d) {viewbox[xy]}\\\\n\\\" % (OUT.vboxX,OUT.vboxY)\\n out+= \\\"vtran=(%d,%d) {vtran[xy],viewtran[xy]}\\\\n\\\" % (OUT.vtranX,OUT.vtranY)\\n out+= \\\"vscale=(%d,%d) {vscale[xy],viewscale[xy]}\\\\n\\\" % (OUT.vscaleX,OUT.vscaleY)\\n print(out)\",\n \"def __str__(self):\\n if self.__style == 'default':\\n if self.living:\\n return Cell.liveChar\\n else:\\n return Cell.deadChar\",\n \"def __str__(self):\\n return self.printable()\",\n \"def getDebugText(self):\\n timeDifference = time.time() - self.time_created\\n hours = math.floor(timeDifference / 3600)\\n minutes = math.floor((timeDifference % 3600) / 60)\\n seconds = math.floor(timeDifference % 3600 % 60)\\n\\n output = \\\"\\\\n\\\" * 50\\n output += \\\"Time started: %s\\\\n\\\" % time.ctime(self.time_created)\\n output += \\\"Time now: %s\\\\n\\\" % time.ctime()\\n output += \\\"Time elapsed: %02d:%02d:%02d\\\\n\\\" % (hours, minutes, seconds)\\n output += (\\\"=\\\" * 80) + \\\"\\\\n\\\"\\n output += \\\"Health potions used: %d\\\\n\\\" % self.hp_pots_used\\n output += \\\"Health potions per hour: %d\\\\n\\\" % (self.hp_pots_used / (\\n timeDifference / 3600))\\n output += \\\"Mana potions used: %d\\\\n\\\" % self.mana_pots_used\\n output += \\\"Mana potions per hour: %d\\\\n\\\" % (self.mana_pots_used / (\\n timeDifference / 3600))\\n return output\",\n \"def _repr_(self):\\n ring = self.coordinate_ring()\\n args = ring.arguments()\\n repr_x=self.change_ring(SR)._repr_()\\n if len(args) == 1:\\n return \\\"%s |--> %s\\\" % (args[0], repr_x)\\n else:\\n args = \\\", \\\".join(map(str, args))\\n return \\\"(%s) |--> %s\\\" % (args, repr_x)\",\n \"def __str__(self) -> str:\\n if self.decorator is None:\\n decorator_str = \\\"\\\"\\n elif self.decorator:\\n decorator_str = \\\"+\\\"\\n else:\\n decorator_str = \\\"-\\\"\\n return \\\" \\\".join([\\\"The nilpotent orbit corresponding\\\",\\n f\\\"to partition {self.my_diagram}{decorator_str}\\\",\\n f\\\"in type {self.my_type.letter()} {self.lie_rank}\\\"])\",\n \"def __str__(self):\\n return (\\\"UUID: \\\" + str(self.uuid) + \\\"\\\\n\\\"\\n \\\"Data: \\\" + str(self.data) + \\\"\\\\n\\\" +\\n \\\"Tex: \\\" + str(self.texOutput) + \\\"\\\\n\\\")\",\n \"def get_tooltip_text(self): # real signature unknown; restored from __doc__\\n return \\\"\\\"\",\n \"def _repr_html_(self):\\n\\n return self._repr__base(rich_output=True)\",\n \"def _repr_html_(self):\\n\\n return self._repr__base(rich_output=True)\",\n \"def draw(self):\\n res = ''\\n # ANSI code to clear the screen\\n #res += chr(27) + \\\"[2J\\\"\\n for position, value in enumerate(self.board.tttboard):\\n if value is None:\\n res += str(position)\\n #sys.stdout.write(str(position))\\n else:\\n res += str(value)\\n #sys.stdout.write(str(value))\\n\\n if (position + 1) % 3 != 0:\\n res += str('|')\\n #sys.stdout.write('|')\\n else:\\n #print ''\\n\\n res += str('\\\\n')\\n if position == 2 or position == 5:\\n #print '-' * 5\\n\\n res += '-' * 5\\n res += str('\\\\n')\\n return res\",\n \"def _repr_html_(self):\\n return self.__repr__()\",\n \"def _repr_html_(self):\\n return self.__repr__()\",\n \"def __repr__(self):\\n\\n template = \\\"\\\"\\\"\\n - inline : {}\\n \\\"\\\"\\\"\\n\\n return super().__repr__() + template.format(self.inline)\",\n \"def __repr__(self):\\n\\n return f\\\"Ufd(\\\"\\\\\\n f\\\"title=\\\\\\\"{self.title}\\\\\\\",\\\"\\\\\\n f\\\" icon=\\\\\\\"{self.icon}\\\\\\\",\\\"\\\\\\n f\\\" show_hidden={self.show_hidden},\\\"\\\\\\n f\\\" include_files={self.include_files},\\\"\\\\\\n f\\\" multiselect={self.multiselect},\\\"\\\\\\n f\\\" select_dirs={self.select_dirs},\\\"\\\\\\n f\\\" select_files={self.select_files},\\\"\\\\\\n f\\\" unix_delimiter={self.unix_delimiter})\\\"\\\\\\n f\\\" stdout={self.stdout})\\\"\\\\\\n f\\\" @ {hex(id(self))}\\\"\",\n \"def show_on_screen(self, string, location, font='Arial', font_size=20, colour=WHITE):\\n msg = pygame.font.SysFont(font, font_size).render(str(string), True, colour)\\n self.screen.blit(msg, location)\",\n \"def __str__(self):\\n s = \\\"Scene Object: \\\" + self.name + \\\"\\\\n\\\"\\n s += \\\" Wavelength: \\\" + str(np.min(self.wave)) + \\\":\\\" + str(self.bin_width) + \\\":\\\" + str(np.max(self.wave))\\n s += \\\" nm\\\\n\\\"\\n s += \\\" [Row, Col]: \\\" + str(self.shape) + \\\"\\\\n\\\"\\n s += \\\" [Width, Height]: [\\\" + \\\"%.2g\\\" % self.width + \\\", \\\" + \\\"%.2g\\\" % self.height + \\\"] m\\\\n\\\"\\n s += \\\" Horizontal field of view: \\\" + \\\"%.2g\\\" % self.fov + \\\" deg\\\\n\\\"\\n s += \\\" Sample size: \\\" + \\\"%.2g\\\" % self.sample_size + \\\" meters/sample\\\\n\\\"\\n s += \\\" Mean luminance: \\\" + \\\"%.2g\\\" % self.mean_luminance + \\\" cd/m2\\\"\\n return s\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6805551","0.67020464","0.6681074","0.6531612","0.6505154","0.64923066","0.6411581","0.6372688","0.62355006","0.6220548","0.6206158","0.6187882","0.61819524","0.61762536","0.6133741","0.6095917","0.6095917","0.6075232","0.60512745","0.6040153","0.6035276","0.60252273","0.6012481","0.60011774","0.5966761","0.59485734","0.59255815","0.5924669","0.5913172","0.59114844","0.59109867","0.59097713","0.59067386","0.59010035","0.58961034","0.58902127","0.5873796","0.5872327","0.5860077","0.58566415","0.585588","0.5851924","0.583402","0.583402","0.583402","0.583402","0.5829178","0.5824806","0.58193815","0.58156693","0.5813512","0.5813494","0.5811584","0.58113337","0.5803301","0.58000773","0.57945895","0.57945895","0.57945895","0.57828856","0.57790506","0.57774657","0.57727265","0.57651657","0.5764661","0.57645196","0.5760444","0.57527107","0.57518655","0.57496935","0.5734845","0.57324886","0.5732224","0.57300866","0.572648","0.5719901","0.57150066","0.5710397","0.5709789","0.57073426","0.57067597","0.5702703","0.5701589","0.5695857","0.56837106","0.56833684","0.56795985","0.5677582","0.5677316","0.56767815","0.5676464","0.5676276","0.5675919","0.5675919","0.56725055","0.5672461","0.5672461","0.56716406","0.56697595","0.5665962","0.56549174"],"string":"[\n \"0.6805551\",\n \"0.67020464\",\n \"0.6681074\",\n \"0.6531612\",\n \"0.6505154\",\n \"0.64923066\",\n \"0.6411581\",\n \"0.6372688\",\n \"0.62355006\",\n \"0.6220548\",\n \"0.6206158\",\n \"0.6187882\",\n \"0.61819524\",\n \"0.61762536\",\n \"0.6133741\",\n \"0.6095917\",\n \"0.6095917\",\n \"0.6075232\",\n \"0.60512745\",\n \"0.6040153\",\n \"0.6035276\",\n \"0.60252273\",\n \"0.6012481\",\n \"0.60011774\",\n \"0.5966761\",\n \"0.59485734\",\n \"0.59255815\",\n \"0.5924669\",\n \"0.5913172\",\n \"0.59114844\",\n \"0.59109867\",\n \"0.59097713\",\n \"0.59067386\",\n \"0.59010035\",\n \"0.58961034\",\n \"0.58902127\",\n \"0.5873796\",\n \"0.5872327\",\n \"0.5860077\",\n \"0.58566415\",\n \"0.585588\",\n \"0.5851924\",\n \"0.583402\",\n \"0.583402\",\n \"0.583402\",\n \"0.583402\",\n \"0.5829178\",\n \"0.5824806\",\n \"0.58193815\",\n \"0.58156693\",\n \"0.5813512\",\n \"0.5813494\",\n \"0.5811584\",\n \"0.58113337\",\n \"0.5803301\",\n \"0.58000773\",\n \"0.57945895\",\n \"0.57945895\",\n \"0.57945895\",\n \"0.57828856\",\n \"0.57790506\",\n \"0.57774657\",\n \"0.57727265\",\n \"0.57651657\",\n \"0.5764661\",\n \"0.57645196\",\n \"0.5760444\",\n \"0.57527107\",\n \"0.57518655\",\n \"0.57496935\",\n \"0.5734845\",\n \"0.57324886\",\n \"0.5732224\",\n \"0.57300866\",\n \"0.572648\",\n \"0.5719901\",\n \"0.57150066\",\n \"0.5710397\",\n \"0.5709789\",\n \"0.57073426\",\n \"0.57067597\",\n \"0.5702703\",\n \"0.5701589\",\n \"0.5695857\",\n \"0.56837106\",\n \"0.56833684\",\n \"0.56795985\",\n \"0.5677582\",\n \"0.5677316\",\n \"0.56767815\",\n \"0.5676464\",\n \"0.5676276\",\n \"0.5675919\",\n \"0.5675919\",\n \"0.56725055\",\n \"0.5672461\",\n \"0.5672461\",\n \"0.56716406\",\n \"0.56697595\",\n \"0.5665962\",\n \"0.56549174\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":75592,"cells":{"query":{"kind":"string","value":"Print the description of the measure for the player."},"document":{"kind":"string","value":"def menu(self):\n print(f\"{str(self)}\")"},"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 description(self) -> str:\r\n description = \"The player must aim to put the most possible units \" + \\\r\n \"of \" + colour_name(self.colour) + \" on the outer\" +\\\r\n \" perimeter.\"\r\n return description","def description(self) -> str:\r\n descrip = 'The player must aim to put the most possible units of a ' \\\r\n 'given colour c on the outer perimeter of ' \\\r\n 'the board. The ' \\\r\n 'player’s score is the total number of unit cells ' \\\r\n 'of colour ' \\\r\n 'c that are on the perimeter. There is a ' \\\r\n 'premium on corner ' \\\r\n 'cells: they count twice towards the score. '\r\n return descrip","def show_myhero(self):\n description = (self.name + ' Level is: ' + str(self.level) + ' Age is: ' + str(\n self.age) + ' Rank is: ' + self.rank + ' health is: ' + str(self.health)).title()\n print(description)","def show_myhero(self):\n description = (self.name + ' Level is: ' + str(self.level) + ' Age is: ' + str(\n self.age) + ' Rank is: ' + self.rank + ' health is: ' + str(self.health) + ' magic is: ' + str(self.__magic)).title()\n print(description)","def show_stats(self):\n print(\"\\nName: \" + self.name)\n print(\"Element Type: \" + self.element)\n print(\"Health: \" + str(self.current_health) + \" / \" + str(self.max_health))\n print(\"Speed: \" + str(self.speed))","def summary(self):\n name = 'name : ' + self.get_name()\n damage = 'damage : ' + str(self.get_damage())\n ammos = 'ammo : ' + str(self.get_ammos())\n owner = 'owner : ' + str(self.get_owner())\n return '\\n'.join([name, damage, ammos, owner])","def nice_output(self):\n return 'Pitch: {0} at {1}: {2}'.format(\n self.pitch_type, self.start_speed, self.des)","def GetDescription(cls):\n return textwrap.dedent('''\n This trace step includes a diagram of the Ego long. acceleration in the report.\n ''').strip()","def summary(self):\n name = 'name : ' + self.get_name()\n description = 'description : ' + self.get_description()\n agility = 'agility : ' + str(self.get_agility())\n strength = 'strength : ' + str(self.get_strength())\n health_points = 'health_points : ' + str(self.get_health_points())\n summary = '\\n'.join([name, description, agility, strength, health_points])\n if self.take_weapon():\n summary += self.take_weapon().summary()\n return summary","def display(self):\n print(\n f'\\t\\t {self.name.upper()} {self.potency[0]}{self.potency[1]}\\t\\t'\n f' {self.dose_qty[0]} {self.dose_qty[1]} {self.dose[0]} {self.dose[1].upper()}')","def describe(self):\n print(self.description)","def describe(self):\n print(self.description)","def get_description(self):\n print(\"This Iron door.\")","def print_spec(self, spec):\n if spec == 'summary':\n print \"{}\\n{}\\n{}\\n{}\\n{}\".format(self.avgtime, self.avgcpu, self.avgram, self.maxram, self.maxcpu)\n else:\n print \"{}\".format(getattr(self, spec))","def printPlayerStats(self):\n\t\tplayerStats = ['Name = ' + self.name, \n\t\t\t\t\t 'Agility = ' + str(self.agility), \n\t\t\t\t\t 'Personality = ' + str(self.personality), \n\t\t\t\t\t 'Sanity = ' + str(self.sanity), \n\t\t\t\t\t 'Strength = ' + str(self.strength), \n\t\t\t\t\t 'Progress = ' + str(self.progress)]\n\t\tprint playerStats","def print_metric(self):\r\n print(f'\\n\\n{self.sort} metric of size {self.n}')\r\n print(f'algorithm: {self.algo}')\r\n print(f'number of comparisons: {self.comps}')\r\n print(f'number of exchanges: {self.exs}')\r\n print(f'regression equation for comparisons: {self.comp_eq}')\r\n print(f'regression equation for exchanges: {self.ex_eq}')\r\n print(f'presorted data: {self.predata}')\r\n print(f'postsorted data: {self.postdata}')","def display(self):\n print(\"----Player----\")\n print(\"Player {} is using {} as their mark\".format(self.name, self.mark))","def description(self) -> str:\r\n descrip = 'The player must aim for the largest “blob” of a given ' \\\r\n 'colour c. A blob is a group of connected blocks with the ' \\\r\n 'same colour. Two blocks are connected if their sides ' \\\r\n 'touch; touching corners doesn’t count. The player’s score ' \\\r\n 'is the number of unit cells in the largest blob of colour ' \\\r\n 'c. '\r\n return descrip","def details(self) -> str:\n return f\"- **language**: [{self.language}]\\n\" \\\n f\"- **opengame**: [{self.opengame}]\\n\" \\\n f\"- **system**: [{self.system}]\\n\" \\\n f\"- **mode**: [{self.mode}]\\n\" \\\n f\"- **attributes**: [{self.attributes}]\\n \" \\\n f\"- **score_threshold**: [{self.score_threshold}]\\n \" \\\n f\"- **monsters**: [{self.monsters}]\\n\"","def __str__(self):\n\n descr = (\"Your health is \" + str(self.health))\n return descr","def __repr__(self) -> str:\n return f'{self.name}|{self.hp}|{self.mp}'","def __str__(self):\n return f\"This player has {self.hand} for a current total of {self.total} and {self.aceCount} Aces \" \\\n f\"valued at a soft 11. This player is a dealer: {self.dealer}.\"","def _show(self, indent = 0):\n print(\" \"*indent, \"Name:\", self.name)\n print(\" \"*indent, \"Description:\", self.description)","def getDetail(self):\n\t\t\n\t\treturn (super().setParameters(0,self.getDefense(),0))\n\t\t\n\t\t#return \"\\n#########################################################\\n\"+\"\\nItem of Defense, Name of item:\"+self.getName()+\"\\nCapacity of defense:\"+str(self.getDefense())+\"\\nCapacity of attack:0 \\n Capacity of heal:0 \\n\"+\"#########################################################\\n\"","def _disp(self, t_elapsed):\n disp_str = \"Epoch: %4d/%4d | Duration: %6.2f secs\" % \\\n (self.iteration, self.Nepochs, t_elapsed) \n disp_str += \" | Objective: %4e\" % self.of_list[-1]\n if self.disp_p:\n disp_str += \" | Parameters: %s\" % self.params\n print(disp_str)","def getDebugText(self):\n timeDifference = time.time() - self.time_created\n hours = math.floor(timeDifference / 3600)\n minutes = math.floor((timeDifference % 3600) / 60)\n seconds = math.floor(timeDifference % 3600 % 60)\n\n output = \"\\n\" * 50\n output += \"Time started: %s\\n\" % time.ctime(self.time_created)\n output += \"Time now: %s\\n\" % time.ctime()\n output += \"Time elapsed: %02d:%02d:%02d\\n\" % (hours, minutes, seconds)\n output += (\"=\" * 80) + \"\\n\"\n output += \"Health potions used: %d\\n\" % self.hp_pots_used\n output += \"Health potions per hour: %d\\n\" % (self.hp_pots_used / (\n timeDifference / 3600))\n output += \"Mana potions used: %d\\n\" % self.mana_pots_used\n output += \"Mana potions per hour: %d\\n\" % (self.mana_pots_used / (\n timeDifference / 3600))\n return output","def info(self):\n return (f\"Match id: {self._id}\\n\"\n f\"dire_score: {self.dire_score}\\n\"\n f\"dire_team: {self.dire_team}\\n\"\n f\"duration: {self.duration}\\n\"\n f\"game_mode: {self.game_mode}\\n\"\n f\"patch: {self.patch}\\n\"\n f\"radiant_score: {self.radiant_score}\\n\"\n f\"radiant_team: {self.radiant_team}\\n\"\n f\"radiant_win: {self.radiant_win}\\n\"\n f\"skill: {self.skill}\\n\"\n f\"start_time: {self.start_time}\\n\")","def __str__(self):\n description = \"Object class Critter.\"\n description += \"\\nName: \" + self.name + \"\\nMood: \" + self.mood + \"\\nHunger: \" + str(self.hunger) + \"\\nBoredom: \" + str(self.boredom) + \"\\n\"\n return description","def value_printer():#todo: add all wanted items\n print(\"Max ascent speed = \"+ max_ascent_speed() + \" m/s\")\n print(\"Max ascent acceleration = \" + ascent_acc() + \" m/s^2\")\n print(\"Max ascent acceleration = \" + descent_acc() + \" m/s^2\")\n print(\"Max acceleration = \" + acc() + \" m/s^2\")","def description(self):\n\n\t\treturn \"%d %s %s\" % (self.vintage, self.winery.name, self.name)","def description():","def show_prop(self):\n print(self.population_size)\n print(self.max_generation)\n print(self.mutate_rate)\n print(self.elite_rate)\n print(self.cross_rate)\n print(self.cross_type)\n print(self.verify_num)\n print(self.proof)","def print_report(self):\n print '=' * 20 + ' %s ' % self.label + '=' * 20\n print '%-20s%5s\\t%4s\\t%4s\\t%4s\\t%4s' % (\n 'Hand' + '=' * 16, '#', 'Frac', 'W', 'Tie', 'L')\n for hand, result_dict in self.counts.iteritems():\n total_for_hand = sum(result_dict.itervalues())\n if total_for_hand == 0:\n win_frac = 0.0\n tie_frac = 0.0\n loss_frac = 0.0\n else:\n win_frac = float(result_dict[WIN_RESULT])/total_for_hand\n tie_frac = float(result_dict[TIE_RESULT])/total_for_hand\n loss_frac = float(\n result_dict[LOSS_RESULT])/total_for_hand\n print '%-20s%5d\\t%0.3f\\t%0.3f\\t%0.3f\\t%0.3f' % (\n hand, total_for_hand, float(total_for_hand)/self.total_items,\n win_frac, tie_frac, loss_frac)","def print_performance_info(self):\n pass","def showHP(self):\n return \"{} HP: {}/{} AP:{}/100\".format(self.name, self.chp, self.hp, self.ap)","def describe(self):\r\n print( self.name + \" is here!\" )\r\n print( self.description )","def descString(self):\n return \"\".join ([self.Name, \" (\", str(self.RollCount), \"d\"\\\n , str(self.RollMax), \"; \", str(self.CritRollMin), \"-\"\\\n , str(self.CritRollMax), \"x\", str (self.CritRollMult)\\\n , \") - \", str(self.Value), \" gp\"])","def describe(self) -> str:","def print_player_info(player):\n print(\"\\nPLAYER INFO\")\n player.print_energy()\n print(\"Current location: {}\".format(player.location.name))\n print(\"Carrying: {0}/{1}\".format(player.get_items_total_weight(), player.get_capacity()))","def descString(self):\n return \"\".join ([self.Name, \" (AR \", str(self.AR), \", Max DEX \"\\\n , str(self.MaxDEXMod), \") - \", str(self.Value), \" gp\"])","def __repr__(self: object) -> str:\n measstring: str = \"Tatort - {:04d} - {} - {} - {} - {}\".format(self.episode_id, self.episode_name, self.episode_inspectors, self.episode_sequence, self.episode_broadcast)\n return measstring","def print_DMM(self):\n\n if debug:\n logger.debug(\"\tmode_desc: \" + self.mode)\n\n string_to_print = f\"Reading [{self.mode}]: {self.value} {self.suffix}\"\n\n # is hold on?\n if self.hold:\n string_to_print = string_to_print + \", HOLD\"\n if self.rel:\n string_to_print = string_to_print + \", REL\"\n\n return string_to_print","def describe(self) -> Text:\n return self.__repr__()","def unit(self) -> str:","def __repr__(self):\n return \"{} hp:{:.1f} stunned: {} potions: {}\".format(self.name, self.hitpoints,\n self.stunned, self.potions)","def nice(self):\n print(self.getName(), \":\", self.getLen())","def summary(self):\n if not self: return u''\n s = u'Character\\n=====\\nName: %s\\n' % \\\n self.get('name', u'')\n bio = self.get('biography')\n if bio:\n s += u'Biography: %s\\n' % bio[0]\n filmo = self.get('filmography')\n if filmo:\n a_list = [x.get('long imdb canonical title', u'')\n for x in filmo[:5]]\n s += u'Last movies with this character: %s.\\n' % u'; '.join(a_list)\n return s","def summary(self) -> None:\n if not self._custom_wave:\n print(\n f\"{self.name}\\n\"\n + f\"{self.start_wavelength} < lambda (A) < {self.end_wavelength}\\n\"\n + f\"R = {self.R_res}\\n\"\n + f\"Sampling = {self.R_samp} pix/FWHM\"\n )\n else:\n print(\n f\"{self.name}\\n\"\n + f\"Custom Wavelength w/\\n\"\n + f\"({self.wave[0]} < lambda (A) < {self.wave[-1]})\\n\"\n + f\"R = {self.R_res}\\n\"\n )","def description(self):","def __str__(self):\n return f'Character name: {self.name}\\nhealth: {self.health}\\n' \\\n f'strength: {self.strength}\\nchance dodge: ' \\\n f'{round(self.chance_dodge, 2)}\\nchance critical:' \\\n f' {round(self.chance_critical, 2)} '","def measure(self):\n pass","def describe(self):\n composition = str()\n for n, comp in enumerate(self.components):\n if self.molefractions[n] > 0.0:\n composition += comp.name\n composition += \" %.2f\" % self.molefractions[n]\n composition += \"; \"\n return composition","def __str__(self):\n description = \"-------- %s --------\" % (self.name)\n description += \"\\nnmax = %i\" % (self.nmax)\n description += \"\\nnslots = %i\" % (self.nslots)\n description += \"\\nbonus_power = %i\" % (self.bonus_power)\n description += \"\\nbonus_initiative = %.1f\" % (self.bonus_initiative)\n description += \"\\nneeds_drive = %i\" % (self.needs_drive)\n description += \"\\nis_mobile = %i\" % (self.is_mobile)\n description += \"\\n----- Default Parts -----\"\n for i in range(len(self.default_parts)):\n description += \"\\n%i) %s\" % (i + 1, self.default_parts[i].name)\n return description","def describe(self):\n return ''","def nice_output(self):\n return self.des","def nice_output(self):\n return self.des","def __repr__(self):\n return f\"Player({self.hand}, {self.total}, {self.aceCount}, {self.dealer})\"","def print(self):\n print(\"State : {} \\nDensity : {} \\nVelocity : {} \\nPressure : {} \\nTemperature : {} \\nSound speed : {}\\n\".format(self.name, self.rho, self.velocity, self.pressure, self.T, self.c))","def displayStatistics(self):\n return \"\"","def meter(illustrate, size, value):\n display = illustrate(int(value))\n display += ' '*(size - len(display)/3)\n return display","def print_intro(self):\n \n print('Did you know mammals tend to have the shortest migration routes because walking takes more energy than flying or swimming?')","def printReport(self): \n \n print('Distribution: ', self._distribution_type)\n print('Distribution Type: ', str(self._measure_type).replace('MeasureType.','')) \n print('Type Detection Match: ', str(self._measure_type_match))\n print('MLE: ', str(self._mle))\n print('Goodness of Fit: ', str(self._gof)) \n print('Goodness of Fit Pass: ', str(self._pass)) \n print('Overall Score: ', str(self._score)) \n print('-------------')","def display_player_points():\r\n pass","def info(self):\n self.update_info()\n print('Number of electrodes: ' + str(self.n_elecs))\n print('Recording time in seconds: ' + str(self.dur))\n print('Sample Rate in Hz: '+ str(self.sample_rate))\n print('Number of sessions: ' + str(self.n_sessions))\n print('Date created: ' + str(self.date_created))\n print('Meta data: ' + str(self.meta))","def _printable(self):\n toPrint = \"Measurement Outcome header. \"\n toPrint += \"measurement outcome: \" + str(self.outcome) + \" \"\n\n return toPrint","def __str__(self):\n return \"Car with the maximum speed of \" + str(self.max_speed) + \\\n \" \" + self.speed_unit","def print_statistics(self):\n print 'Ran %s iterations in %0.3f seconds\\n' % (\n self.iterations, self.elapsed_time)\n\n print 'Overall Equity'\n for index in range(len(self.holdem_ranges)):\n range_short_form = '%r' % self.holdem_ranges[index]\n print 'P%s) %-15s %0.3f' % (\n index,\n range_short_form,\n float(self.win_stats.get(index, 0))/self.iterations)\n print '\\n'\n print 'Hand distribution for each player'\n for stats in self.player_stats:\n stats.print_report()","def __str__(self):\n return f\"{self._desc:16s}\"","def report(self):\n print(f\"Water: {self.resources['water']}ml\")\n print(f\"Milk: {self.resources['milk']}ml\")\n print(f\"Coffee: {self.resources['coffee']}g\")","def unit_of_measurement(self):\n return 'shows'","def __str__(self):\n \n is_random_print = \"\"\n if self.is_random == True:\n is_random_print = \"randomly\"\n else:\n is_random_print = \"deterministically\"\n\n return \"Player for \" + self.side + \", ply = \" + str(self.ply) + \", breaks ties \" + is_random_print","def describe():","def __repr__(self):\n out = ''\n out += f'\\nPlayer {self.number}: {self.name}\\n'\n\n # checks for trail options before printing.\n if len(self.trail_options) > 0:\n out += f'\\nTrail Options:\\n'\n for item in self.trail_options:\n out += f' {item}'\n else:\n out += f'\\nSadly, {self.name} is out of trail options.\\n'\n\n # checks for supplies before printing.\n if len(self.supplies) > 0:\n out += f'\\nSupplies:\\n'\n for item in self.supplies:\n out += f' {item[0]}\\n'\n else:\n out += f'\\nSadly, {self.name} is out of supplies.\\n'\n\n return out","def summary_string(self) -> str:","def show_stat_desc(self):\n qtw.QMessageBox.information(\n self, \"Stat Descriptions\", '*This list is not comprehensive, and '\n 'will vary depending on race/(sub)class.*\\n\\nStrength\\n\\t-damage '\n 'and attack bonuses for most melee and thrown weapons\\n\\t-carry '\n 'capacity\\n\\t-Athletics checks\\nDexterity\\n\\t-damage and attack '\n 'bonuses for ranged/finesse weapons\\n\\t-Acrobatics, Stealth, '\n 'Sleight of Hand, and Initiative checks\\nConstitution\\n\\t-hit '\n 'points\\n\\t-resistance to poisons, etc.\\nIntelligence\\n\\t-spell '\n 'save DC/attack bonus for Wizards\\n\\t-Arcana, History, '\n 'Investigation, Nature, and Religion checks\\nWisdom\\n\\t-spell save'\n ' DC/attack bonus for Druid, Cleric, and Ranger\\n\\t-Animal '\n 'Handling, Insight, Medicine, Perception, and Survival checks\\n'\n 'Charisma\\n\\t-spell save DC/attack bonus for Bard, Paladin, '\n 'Warlock and Sorceror\\n\\t-Deception, Intimidation, Performance and'\n ' Persuasion checks'\n )","def get_description(self):\n text = \"is a student's t distribution; characterised by its degrees of freedom, which here is\"+str(self.dofs)+\".\"\n return text","def show_info(self):\n x = self.x0 + self.m * self.dx + 20\n y = self.y0\n self.draw_text2('t={:.2f}'.format(self.t - self.t0), x, y)\n self.draw_text2('frame={}'.format(self.frame))\n self.draw_text2('fps={:.1f}'.format(self.frame/(self.t - self.t0)))\n self.draw_text2('pos={}'.format(self.pos))\n self.draw_text2('dir={}'.format(self.dir))\n self.draw_text2('type={}'.format(self.event_type))\n self.draw_text2('name={}'.format(self.event_name))\n self.draw_text2('key={}'.format(self.event_key))\n self.draw_text2('mod={}'.format(self.event_mod))\n self.draw_text2('unicode={}'.format(self.event_unicode))","def print_attr(self):\n return \"name : {0}\\nprice : {1}\\ndescription : {2}\".format(\n self.name, self.price, self.description\n )","def Description(self) -> str:","def Description(self) -> str:","def _player_info(self):\n return \"%r %s seat:%s m:%r c:%s b:%s \" % (self.name, self.serial, self.seat, self.money, self._chips, self._bet)","def description(self):\n return self.visual_desc","def displayInfo(self):\n # clear stdout for a smoother display\n # os.system('cls' if os.name=='nt' else 'clear')\n\n #print(\"=========== Status ============\")\n # print(\n # \"speed: \" + str(self.speed) +\n # \"\\nangle: \" + str(self.steering_angle) +\n # \"\\nsign: \" + str(self.detected_sign) +\n # \"\\nlane lines: \" + str(self.lane_lines) +\n # \"\\nintersection line flag: \" + str(self.intersection_line) +\n # \"\\ncurrent state label: \" + str(self.currentStateLabel) +\n # \"\\ncurrent states: \" + str(self.currentState)\n #)","def __str__(self):\n\n description = f\"{self.name} is a member of the {self.team} and possesses the following powers:\\n{self.powers}\"\n return description","def display_player_and_student_health(player: Character, opponent: Student):\n print(player.name.title() + \": \" + str(player.hp) + \"HP\") # display player hp\n print(\"Student: \" + str(opponent.hp) + \"HP\\n\") # display student hp","def display_health(self) -> None:\n\n print(self.player.hp)","def display_short(self):\n print(f'{self.name.upper()} ({self.speciality.upper()})')","def display_profile(self):\n statement = f\"\"\"\n ------\n {self.name.upper()}\n ------\n Fee: {self.fee} -/Rs.\n Rating: {self.rating} STARS\n Qualification: {self.qualification}\n Speciality: {self.speciality}\n Language: {self.language}\n Working Hours: {self.working_hrs}\n Contact: {self.contact}\n Location: {self.location}\n \"\"\"\n print(statement)","def __str__(self):\n return str(self.elapsed) + ' sec'","def print_player_info(self):\n\t\tclear_screen()\n\n\t\tprint(\"# PLAYER INFO #\\n\")\n\t\tprint(\"Name{:.>17} \".format(self.info['Name']))\n\t\tprint(\"Race{:.>17} \".format(self.info['Race']))\n\t\tprint(\"Level{:.>16} \".format(self.stats['Level']))\n\t\tprint(\"Hit Points{:.>11} \".format(self.stats['HP']))\n\t\tprint(\"Gold Pieces{:.>10} \".format(self.stats['GOLD']))\n\t\n\t\tpress_enter()","def description(self) -> str:\n return \"put units of a given colour on the perimeter of the board.\"","def printLatestMeasurement(self): \n \n if (len(self.measurement) == 0):\n x = 0\n y = 0\n L = 0\n \n else:\n data = self.measurement[len(self.measurement)-1]\n x = data['chrominance'][0]\n y = data['chrominance'][1]\n L = data['luminance']\n \n print\"(x,y) = ({0:.4f}, {1:.4f}), L = {2:.4f} cd/m2 ({3:.4f} fL)\".format( x, y, L, 0.291863508*L)","def report(self):\n print(f\"Water: {self.resources['water']}ml\")\n print(f\"Milk: {self.resources['milk']}ml\")\n print(f\"Coffee: {self.resources['coffee']}g\")","def displayMelody(self):\r\n print(self.notes)","def _print(self):\n print('center :', self.center, ' widht : ', self.width, ' height : ', self.height, ' heat : ', self.heat,\n ' speed ', self.speed)","def plane_desc(self) -> str:\n return self.planes[0].join(' ') + self.planes[1].join(' ') + self.planes[2].join(' ')","def __str__(self):\n return \"{} : {}\".format(self._team_name, self._win_ratio)","def dump(self):\n fmt='%20s:%10.4fs%6.1f%%'\n print('\\n----------------TIME MANAGER PROFILE----------------\\n\\n')\n total_t=time.time()-self.tic0\n for rec in self.record:\n print(fmt % (rec[0],rec[1],100.0*rec[1]/total_t))\n print(fmt % ('TOTAL ELAPSED TIME', total_t, 100.0))\n print('\\n----------------TIME MANAGER PROFILE----------------\\n\\n')","def __str__(self):\n _str = indent(self.name, color = self.color)\n _str += indent(\"-\"*len(self.name))\n _str += indent(\"( \" + self.movemement + \" - \"+ self.symbole + \" )\")\n _str += indent(\"Awareness: \" + str(self.awareness))\n for _iel in self.abilities:\n _str += indent(_iel)\n if self.description is not None:\n _str += indent(\"-\"*45 + \"\\n\")\n _str += indent(self.description)\n _str += indent(\"-\"*45 + \"\\n\")\n _str += indent(BOLD_BLACK + \" \"*15 + \"Combat Stats\" + RESET)\n _str += indent(\"-\"*45 + \"\\n\")\n _str += indent(BLUE + \" Horror\" + RESET + \" Toughness\"\\\n + RED + \" Combat\" + RESET)\n _str += indent(BLUE + \"Rating Damage \"\\\n + RED + \"Rating Damage\")\n _str += indent(BLUE + \" \" + str(self.horror_rating)\\\n + \" \"*8 + str(self.horror_damage) + RESET\\\n + \" \"*8 + str(self.toughness) + RED\\\n + \" \"*8 + str(self.combat_rating)\\\n + \" \"*8 + str(self.combat_damage) + RESET)\n _str += indent(\"-\"*45 + \"\\n\")\n return _str","def full_description(self):\n des = describe_dut(self.dut) if self.dut else ''\n if self.build:\n des += ' with ' + self.build\n if self.result_id:\n des += ' BVT result ID ' + str(self.result_id)\n return (self.description if self.description \n else 'unknown test') + ' on ' + des","def human_readable_info(self) -> str:\n next_session = unix_str(self._stat.next_session)\n last_session = unix_str(self._stat.last_session)\n return \"\"\"\n Next Session: {}\n Last Session: {}\n Repetitions: {}\n Health: {}\n ------------------------\n Past Quality (last 20):\n ------------------------\n {}\n \"\"\".format(\n next_session,\n last_session,\n self._stat.actual_repetitions,\n self._health(),\n self._past_quality_graph(),\n )"],"string":"[\n \"def description(self) -> str:\\r\\n description = \\\"The player must aim to put the most possible units \\\" + \\\\\\r\\n \\\"of \\\" + colour_name(self.colour) + \\\" on the outer\\\" +\\\\\\r\\n \\\" perimeter.\\\"\\r\\n return description\",\n \"def description(self) -> str:\\r\\n descrip = 'The player must aim to put the most possible units of a ' \\\\\\r\\n 'given colour c on the outer perimeter of ' \\\\\\r\\n 'the board. The ' \\\\\\r\\n 'player’s score is the total number of unit cells ' \\\\\\r\\n 'of colour ' \\\\\\r\\n 'c that are on the perimeter. There is a ' \\\\\\r\\n 'premium on corner ' \\\\\\r\\n 'cells: they count twice towards the score. '\\r\\n return descrip\",\n \"def show_myhero(self):\\n description = (self.name + ' Level is: ' + str(self.level) + ' Age is: ' + str(\\n self.age) + ' Rank is: ' + self.rank + ' health is: ' + str(self.health)).title()\\n print(description)\",\n \"def show_myhero(self):\\n description = (self.name + ' Level is: ' + str(self.level) + ' Age is: ' + str(\\n self.age) + ' Rank is: ' + self.rank + ' health is: ' + str(self.health) + ' magic is: ' + str(self.__magic)).title()\\n print(description)\",\n \"def show_stats(self):\\n print(\\\"\\\\nName: \\\" + self.name)\\n print(\\\"Element Type: \\\" + self.element)\\n print(\\\"Health: \\\" + str(self.current_health) + \\\" / \\\" + str(self.max_health))\\n print(\\\"Speed: \\\" + str(self.speed))\",\n \"def summary(self):\\n name = 'name : ' + self.get_name()\\n damage = 'damage : ' + str(self.get_damage())\\n ammos = 'ammo : ' + str(self.get_ammos())\\n owner = 'owner : ' + str(self.get_owner())\\n return '\\\\n'.join([name, damage, ammos, owner])\",\n \"def nice_output(self):\\n return 'Pitch: {0} at {1}: {2}'.format(\\n self.pitch_type, self.start_speed, self.des)\",\n \"def GetDescription(cls):\\n return textwrap.dedent('''\\n This trace step includes a diagram of the Ego long. acceleration in the report.\\n ''').strip()\",\n \"def summary(self):\\n name = 'name : ' + self.get_name()\\n description = 'description : ' + self.get_description()\\n agility = 'agility : ' + str(self.get_agility())\\n strength = 'strength : ' + str(self.get_strength())\\n health_points = 'health_points : ' + str(self.get_health_points())\\n summary = '\\\\n'.join([name, description, agility, strength, health_points])\\n if self.take_weapon():\\n summary += self.take_weapon().summary()\\n return summary\",\n \"def display(self):\\n print(\\n f'\\\\t\\\\t {self.name.upper()} {self.potency[0]}{self.potency[1]}\\\\t\\\\t'\\n f' {self.dose_qty[0]} {self.dose_qty[1]} {self.dose[0]} {self.dose[1].upper()}')\",\n \"def describe(self):\\n print(self.description)\",\n \"def describe(self):\\n print(self.description)\",\n \"def get_description(self):\\n print(\\\"This Iron door.\\\")\",\n \"def print_spec(self, spec):\\n if spec == 'summary':\\n print \\\"{}\\\\n{}\\\\n{}\\\\n{}\\\\n{}\\\".format(self.avgtime, self.avgcpu, self.avgram, self.maxram, self.maxcpu)\\n else:\\n print \\\"{}\\\".format(getattr(self, spec))\",\n \"def printPlayerStats(self):\\n\\t\\tplayerStats = ['Name = ' + self.name, \\n\\t\\t\\t\\t\\t 'Agility = ' + str(self.agility), \\n\\t\\t\\t\\t\\t 'Personality = ' + str(self.personality), \\n\\t\\t\\t\\t\\t 'Sanity = ' + str(self.sanity), \\n\\t\\t\\t\\t\\t 'Strength = ' + str(self.strength), \\n\\t\\t\\t\\t\\t 'Progress = ' + str(self.progress)]\\n\\t\\tprint playerStats\",\n \"def print_metric(self):\\r\\n print(f'\\\\n\\\\n{self.sort} metric of size {self.n}')\\r\\n print(f'algorithm: {self.algo}')\\r\\n print(f'number of comparisons: {self.comps}')\\r\\n print(f'number of exchanges: {self.exs}')\\r\\n print(f'regression equation for comparisons: {self.comp_eq}')\\r\\n print(f'regression equation for exchanges: {self.ex_eq}')\\r\\n print(f'presorted data: {self.predata}')\\r\\n print(f'postsorted data: {self.postdata}')\",\n \"def display(self):\\n print(\\\"----Player----\\\")\\n print(\\\"Player {} is using {} as their mark\\\".format(self.name, self.mark))\",\n \"def description(self) -> str:\\r\\n descrip = 'The player must aim for the largest “blob” of a given ' \\\\\\r\\n 'colour c. A blob is a group of connected blocks with the ' \\\\\\r\\n 'same colour. Two blocks are connected if their sides ' \\\\\\r\\n 'touch; touching corners doesn’t count. The player’s score ' \\\\\\r\\n 'is the number of unit cells in the largest blob of colour ' \\\\\\r\\n 'c. '\\r\\n return descrip\",\n \"def details(self) -> str:\\n return f\\\"- **language**: [{self.language}]\\\\n\\\" \\\\\\n f\\\"- **opengame**: [{self.opengame}]\\\\n\\\" \\\\\\n f\\\"- **system**: [{self.system}]\\\\n\\\" \\\\\\n f\\\"- **mode**: [{self.mode}]\\\\n\\\" \\\\\\n f\\\"- **attributes**: [{self.attributes}]\\\\n \\\" \\\\\\n f\\\"- **score_threshold**: [{self.score_threshold}]\\\\n \\\" \\\\\\n f\\\"- **monsters**: [{self.monsters}]\\\\n\\\"\",\n \"def __str__(self):\\n\\n descr = (\\\"Your health is \\\" + str(self.health))\\n return descr\",\n \"def __repr__(self) -> str:\\n return f'{self.name}|{self.hp}|{self.mp}'\",\n \"def __str__(self):\\n return f\\\"This player has {self.hand} for a current total of {self.total} and {self.aceCount} Aces \\\" \\\\\\n f\\\"valued at a soft 11. This player is a dealer: {self.dealer}.\\\"\",\n \"def _show(self, indent = 0):\\n print(\\\" \\\"*indent, \\\"Name:\\\", self.name)\\n print(\\\" \\\"*indent, \\\"Description:\\\", self.description)\",\n \"def getDetail(self):\\n\\t\\t\\n\\t\\treturn (super().setParameters(0,self.getDefense(),0))\\n\\t\\t\\n\\t\\t#return \\\"\\\\n#########################################################\\\\n\\\"+\\\"\\\\nItem of Defense, Name of item:\\\"+self.getName()+\\\"\\\\nCapacity of defense:\\\"+str(self.getDefense())+\\\"\\\\nCapacity of attack:0 \\\\n Capacity of heal:0 \\\\n\\\"+\\\"#########################################################\\\\n\\\"\",\n \"def _disp(self, t_elapsed):\\n disp_str = \\\"Epoch: %4d/%4d | Duration: %6.2f secs\\\" % \\\\\\n (self.iteration, self.Nepochs, t_elapsed) \\n disp_str += \\\" | Objective: %4e\\\" % self.of_list[-1]\\n if self.disp_p:\\n disp_str += \\\" | Parameters: %s\\\" % self.params\\n print(disp_str)\",\n \"def getDebugText(self):\\n timeDifference = time.time() - self.time_created\\n hours = math.floor(timeDifference / 3600)\\n minutes = math.floor((timeDifference % 3600) / 60)\\n seconds = math.floor(timeDifference % 3600 % 60)\\n\\n output = \\\"\\\\n\\\" * 50\\n output += \\\"Time started: %s\\\\n\\\" % time.ctime(self.time_created)\\n output += \\\"Time now: %s\\\\n\\\" % time.ctime()\\n output += \\\"Time elapsed: %02d:%02d:%02d\\\\n\\\" % (hours, minutes, seconds)\\n output += (\\\"=\\\" * 80) + \\\"\\\\n\\\"\\n output += \\\"Health potions used: %d\\\\n\\\" % self.hp_pots_used\\n output += \\\"Health potions per hour: %d\\\\n\\\" % (self.hp_pots_used / (\\n timeDifference / 3600))\\n output += \\\"Mana potions used: %d\\\\n\\\" % self.mana_pots_used\\n output += \\\"Mana potions per hour: %d\\\\n\\\" % (self.mana_pots_used / (\\n timeDifference / 3600))\\n return output\",\n \"def info(self):\\n return (f\\\"Match id: {self._id}\\\\n\\\"\\n f\\\"dire_score: {self.dire_score}\\\\n\\\"\\n f\\\"dire_team: {self.dire_team}\\\\n\\\"\\n f\\\"duration: {self.duration}\\\\n\\\"\\n f\\\"game_mode: {self.game_mode}\\\\n\\\"\\n f\\\"patch: {self.patch}\\\\n\\\"\\n f\\\"radiant_score: {self.radiant_score}\\\\n\\\"\\n f\\\"radiant_team: {self.radiant_team}\\\\n\\\"\\n f\\\"radiant_win: {self.radiant_win}\\\\n\\\"\\n f\\\"skill: {self.skill}\\\\n\\\"\\n f\\\"start_time: {self.start_time}\\\\n\\\")\",\n \"def __str__(self):\\n description = \\\"Object class Critter.\\\"\\n description += \\\"\\\\nName: \\\" + self.name + \\\"\\\\nMood: \\\" + self.mood + \\\"\\\\nHunger: \\\" + str(self.hunger) + \\\"\\\\nBoredom: \\\" + str(self.boredom) + \\\"\\\\n\\\"\\n return description\",\n \"def value_printer():#todo: add all wanted items\\n print(\\\"Max ascent speed = \\\"+ max_ascent_speed() + \\\" m/s\\\")\\n print(\\\"Max ascent acceleration = \\\" + ascent_acc() + \\\" m/s^2\\\")\\n print(\\\"Max ascent acceleration = \\\" + descent_acc() + \\\" m/s^2\\\")\\n print(\\\"Max acceleration = \\\" + acc() + \\\" m/s^2\\\")\",\n \"def description(self):\\n\\n\\t\\treturn \\\"%d %s %s\\\" % (self.vintage, self.winery.name, self.name)\",\n \"def description():\",\n \"def show_prop(self):\\n print(self.population_size)\\n print(self.max_generation)\\n print(self.mutate_rate)\\n print(self.elite_rate)\\n print(self.cross_rate)\\n print(self.cross_type)\\n print(self.verify_num)\\n print(self.proof)\",\n \"def print_report(self):\\n print '=' * 20 + ' %s ' % self.label + '=' * 20\\n print '%-20s%5s\\\\t%4s\\\\t%4s\\\\t%4s\\\\t%4s' % (\\n 'Hand' + '=' * 16, '#', 'Frac', 'W', 'Tie', 'L')\\n for hand, result_dict in self.counts.iteritems():\\n total_for_hand = sum(result_dict.itervalues())\\n if total_for_hand == 0:\\n win_frac = 0.0\\n tie_frac = 0.0\\n loss_frac = 0.0\\n else:\\n win_frac = float(result_dict[WIN_RESULT])/total_for_hand\\n tie_frac = float(result_dict[TIE_RESULT])/total_for_hand\\n loss_frac = float(\\n result_dict[LOSS_RESULT])/total_for_hand\\n print '%-20s%5d\\\\t%0.3f\\\\t%0.3f\\\\t%0.3f\\\\t%0.3f' % (\\n hand, total_for_hand, float(total_for_hand)/self.total_items,\\n win_frac, tie_frac, loss_frac)\",\n \"def print_performance_info(self):\\n pass\",\n \"def showHP(self):\\n return \\\"{} HP: {}/{} AP:{}/100\\\".format(self.name, self.chp, self.hp, self.ap)\",\n \"def describe(self):\\r\\n print( self.name + \\\" is here!\\\" )\\r\\n print( self.description )\",\n \"def descString(self):\\n return \\\"\\\".join ([self.Name, \\\" (\\\", str(self.RollCount), \\\"d\\\"\\\\\\n , str(self.RollMax), \\\"; \\\", str(self.CritRollMin), \\\"-\\\"\\\\\\n , str(self.CritRollMax), \\\"x\\\", str (self.CritRollMult)\\\\\\n , \\\") - \\\", str(self.Value), \\\" gp\\\"])\",\n \"def describe(self) -> str:\",\n \"def print_player_info(player):\\n print(\\\"\\\\nPLAYER INFO\\\")\\n player.print_energy()\\n print(\\\"Current location: {}\\\".format(player.location.name))\\n print(\\\"Carrying: {0}/{1}\\\".format(player.get_items_total_weight(), player.get_capacity()))\",\n \"def descString(self):\\n return \\\"\\\".join ([self.Name, \\\" (AR \\\", str(self.AR), \\\", Max DEX \\\"\\\\\\n , str(self.MaxDEXMod), \\\") - \\\", str(self.Value), \\\" gp\\\"])\",\n \"def __repr__(self: object) -> str:\\n measstring: str = \\\"Tatort - {:04d} - {} - {} - {} - {}\\\".format(self.episode_id, self.episode_name, self.episode_inspectors, self.episode_sequence, self.episode_broadcast)\\n return measstring\",\n \"def print_DMM(self):\\n\\n if debug:\\n logger.debug(\\\"\\tmode_desc: \\\" + self.mode)\\n\\n string_to_print = f\\\"Reading [{self.mode}]: {self.value} {self.suffix}\\\"\\n\\n # is hold on?\\n if self.hold:\\n string_to_print = string_to_print + \\\", HOLD\\\"\\n if self.rel:\\n string_to_print = string_to_print + \\\", REL\\\"\\n\\n return string_to_print\",\n \"def describe(self) -> Text:\\n return self.__repr__()\",\n \"def unit(self) -> str:\",\n \"def __repr__(self):\\n return \\\"{} hp:{:.1f} stunned: {} potions: {}\\\".format(self.name, self.hitpoints,\\n self.stunned, self.potions)\",\n \"def nice(self):\\n print(self.getName(), \\\":\\\", self.getLen())\",\n \"def summary(self):\\n if not self: return u''\\n s = u'Character\\\\n=====\\\\nName: %s\\\\n' % \\\\\\n self.get('name', u'')\\n bio = self.get('biography')\\n if bio:\\n s += u'Biography: %s\\\\n' % bio[0]\\n filmo = self.get('filmography')\\n if filmo:\\n a_list = [x.get('long imdb canonical title', u'')\\n for x in filmo[:5]]\\n s += u'Last movies with this character: %s.\\\\n' % u'; '.join(a_list)\\n return s\",\n \"def summary(self) -> None:\\n if not self._custom_wave:\\n print(\\n f\\\"{self.name}\\\\n\\\"\\n + f\\\"{self.start_wavelength} < lambda (A) < {self.end_wavelength}\\\\n\\\"\\n + f\\\"R = {self.R_res}\\\\n\\\"\\n + f\\\"Sampling = {self.R_samp} pix/FWHM\\\"\\n )\\n else:\\n print(\\n f\\\"{self.name}\\\\n\\\"\\n + f\\\"Custom Wavelength w/\\\\n\\\"\\n + f\\\"({self.wave[0]} < lambda (A) < {self.wave[-1]})\\\\n\\\"\\n + f\\\"R = {self.R_res}\\\\n\\\"\\n )\",\n \"def description(self):\",\n \"def __str__(self):\\n return f'Character name: {self.name}\\\\nhealth: {self.health}\\\\n' \\\\\\n f'strength: {self.strength}\\\\nchance dodge: ' \\\\\\n f'{round(self.chance_dodge, 2)}\\\\nchance critical:' \\\\\\n f' {round(self.chance_critical, 2)} '\",\n \"def measure(self):\\n pass\",\n \"def describe(self):\\n composition = str()\\n for n, comp in enumerate(self.components):\\n if self.molefractions[n] > 0.0:\\n composition += comp.name\\n composition += \\\" %.2f\\\" % self.molefractions[n]\\n composition += \\\"; \\\"\\n return composition\",\n \"def __str__(self):\\n description = \\\"-------- %s --------\\\" % (self.name)\\n description += \\\"\\\\nnmax = %i\\\" % (self.nmax)\\n description += \\\"\\\\nnslots = %i\\\" % (self.nslots)\\n description += \\\"\\\\nbonus_power = %i\\\" % (self.bonus_power)\\n description += \\\"\\\\nbonus_initiative = %.1f\\\" % (self.bonus_initiative)\\n description += \\\"\\\\nneeds_drive = %i\\\" % (self.needs_drive)\\n description += \\\"\\\\nis_mobile = %i\\\" % (self.is_mobile)\\n description += \\\"\\\\n----- Default Parts -----\\\"\\n for i in range(len(self.default_parts)):\\n description += \\\"\\\\n%i) %s\\\" % (i + 1, self.default_parts[i].name)\\n return description\",\n \"def describe(self):\\n return ''\",\n \"def nice_output(self):\\n return self.des\",\n \"def nice_output(self):\\n return self.des\",\n \"def __repr__(self):\\n return f\\\"Player({self.hand}, {self.total}, {self.aceCount}, {self.dealer})\\\"\",\n \"def print(self):\\n print(\\\"State : {} \\\\nDensity : {} \\\\nVelocity : {} \\\\nPressure : {} \\\\nTemperature : {} \\\\nSound speed : {}\\\\n\\\".format(self.name, self.rho, self.velocity, self.pressure, self.T, self.c))\",\n \"def displayStatistics(self):\\n return \\\"\\\"\",\n \"def meter(illustrate, size, value):\\n display = illustrate(int(value))\\n display += ' '*(size - len(display)/3)\\n return display\",\n \"def print_intro(self):\\n \\n print('Did you know mammals tend to have the shortest migration routes because walking takes more energy than flying or swimming?')\",\n \"def printReport(self): \\n \\n print('Distribution: ', self._distribution_type)\\n print('Distribution Type: ', str(self._measure_type).replace('MeasureType.','')) \\n print('Type Detection Match: ', str(self._measure_type_match))\\n print('MLE: ', str(self._mle))\\n print('Goodness of Fit: ', str(self._gof)) \\n print('Goodness of Fit Pass: ', str(self._pass)) \\n print('Overall Score: ', str(self._score)) \\n print('-------------')\",\n \"def display_player_points():\\r\\n pass\",\n \"def info(self):\\n self.update_info()\\n print('Number of electrodes: ' + str(self.n_elecs))\\n print('Recording time in seconds: ' + str(self.dur))\\n print('Sample Rate in Hz: '+ str(self.sample_rate))\\n print('Number of sessions: ' + str(self.n_sessions))\\n print('Date created: ' + str(self.date_created))\\n print('Meta data: ' + str(self.meta))\",\n \"def _printable(self):\\n toPrint = \\\"Measurement Outcome header. \\\"\\n toPrint += \\\"measurement outcome: \\\" + str(self.outcome) + \\\" \\\"\\n\\n return toPrint\",\n \"def __str__(self):\\n return \\\"Car with the maximum speed of \\\" + str(self.max_speed) + \\\\\\n \\\" \\\" + self.speed_unit\",\n \"def print_statistics(self):\\n print 'Ran %s iterations in %0.3f seconds\\\\n' % (\\n self.iterations, self.elapsed_time)\\n\\n print 'Overall Equity'\\n for index in range(len(self.holdem_ranges)):\\n range_short_form = '%r' % self.holdem_ranges[index]\\n print 'P%s) %-15s %0.3f' % (\\n index,\\n range_short_form,\\n float(self.win_stats.get(index, 0))/self.iterations)\\n print '\\\\n'\\n print 'Hand distribution for each player'\\n for stats in self.player_stats:\\n stats.print_report()\",\n \"def __str__(self):\\n return f\\\"{self._desc:16s}\\\"\",\n \"def report(self):\\n print(f\\\"Water: {self.resources['water']}ml\\\")\\n print(f\\\"Milk: {self.resources['milk']}ml\\\")\\n print(f\\\"Coffee: {self.resources['coffee']}g\\\")\",\n \"def unit_of_measurement(self):\\n return 'shows'\",\n \"def __str__(self):\\n \\n is_random_print = \\\"\\\"\\n if self.is_random == True:\\n is_random_print = \\\"randomly\\\"\\n else:\\n is_random_print = \\\"deterministically\\\"\\n\\n return \\\"Player for \\\" + self.side + \\\", ply = \\\" + str(self.ply) + \\\", breaks ties \\\" + is_random_print\",\n \"def describe():\",\n \"def __repr__(self):\\n out = ''\\n out += f'\\\\nPlayer {self.number}: {self.name}\\\\n'\\n\\n # checks for trail options before printing.\\n if len(self.trail_options) > 0:\\n out += f'\\\\nTrail Options:\\\\n'\\n for item in self.trail_options:\\n out += f' {item}'\\n else:\\n out += f'\\\\nSadly, {self.name} is out of trail options.\\\\n'\\n\\n # checks for supplies before printing.\\n if len(self.supplies) > 0:\\n out += f'\\\\nSupplies:\\\\n'\\n for item in self.supplies:\\n out += f' {item[0]}\\\\n'\\n else:\\n out += f'\\\\nSadly, {self.name} is out of supplies.\\\\n'\\n\\n return out\",\n \"def summary_string(self) -> str:\",\n \"def show_stat_desc(self):\\n qtw.QMessageBox.information(\\n self, \\\"Stat Descriptions\\\", '*This list is not comprehensive, and '\\n 'will vary depending on race/(sub)class.*\\\\n\\\\nStrength\\\\n\\\\t-damage '\\n 'and attack bonuses for most melee and thrown weapons\\\\n\\\\t-carry '\\n 'capacity\\\\n\\\\t-Athletics checks\\\\nDexterity\\\\n\\\\t-damage and attack '\\n 'bonuses for ranged/finesse weapons\\\\n\\\\t-Acrobatics, Stealth, '\\n 'Sleight of Hand, and Initiative checks\\\\nConstitution\\\\n\\\\t-hit '\\n 'points\\\\n\\\\t-resistance to poisons, etc.\\\\nIntelligence\\\\n\\\\t-spell '\\n 'save DC/attack bonus for Wizards\\\\n\\\\t-Arcana, History, '\\n 'Investigation, Nature, and Religion checks\\\\nWisdom\\\\n\\\\t-spell save'\\n ' DC/attack bonus for Druid, Cleric, and Ranger\\\\n\\\\t-Animal '\\n 'Handling, Insight, Medicine, Perception, and Survival checks\\\\n'\\n 'Charisma\\\\n\\\\t-spell save DC/attack bonus for Bard, Paladin, '\\n 'Warlock and Sorceror\\\\n\\\\t-Deception, Intimidation, Performance and'\\n ' Persuasion checks'\\n )\",\n \"def get_description(self):\\n text = \\\"is a student's t distribution; characterised by its degrees of freedom, which here is\\\"+str(self.dofs)+\\\".\\\"\\n return text\",\n \"def show_info(self):\\n x = self.x0 + self.m * self.dx + 20\\n y = self.y0\\n self.draw_text2('t={:.2f}'.format(self.t - self.t0), x, y)\\n self.draw_text2('frame={}'.format(self.frame))\\n self.draw_text2('fps={:.1f}'.format(self.frame/(self.t - self.t0)))\\n self.draw_text2('pos={}'.format(self.pos))\\n self.draw_text2('dir={}'.format(self.dir))\\n self.draw_text2('type={}'.format(self.event_type))\\n self.draw_text2('name={}'.format(self.event_name))\\n self.draw_text2('key={}'.format(self.event_key))\\n self.draw_text2('mod={}'.format(self.event_mod))\\n self.draw_text2('unicode={}'.format(self.event_unicode))\",\n \"def print_attr(self):\\n return \\\"name : {0}\\\\nprice : {1}\\\\ndescription : {2}\\\".format(\\n self.name, self.price, self.description\\n )\",\n \"def Description(self) -> str:\",\n \"def Description(self) -> str:\",\n \"def _player_info(self):\\n return \\\"%r %s seat:%s m:%r c:%s b:%s \\\" % (self.name, self.serial, self.seat, self.money, self._chips, self._bet)\",\n \"def description(self):\\n return self.visual_desc\",\n \"def displayInfo(self):\\n # clear stdout for a smoother display\\n # os.system('cls' if os.name=='nt' else 'clear')\\n\\n #print(\\\"=========== Status ============\\\")\\n # print(\\n # \\\"speed: \\\" + str(self.speed) +\\n # \\\"\\\\nangle: \\\" + str(self.steering_angle) +\\n # \\\"\\\\nsign: \\\" + str(self.detected_sign) +\\n # \\\"\\\\nlane lines: \\\" + str(self.lane_lines) +\\n # \\\"\\\\nintersection line flag: \\\" + str(self.intersection_line) +\\n # \\\"\\\\ncurrent state label: \\\" + str(self.currentStateLabel) +\\n # \\\"\\\\ncurrent states: \\\" + str(self.currentState)\\n #)\",\n \"def __str__(self):\\n\\n description = f\\\"{self.name} is a member of the {self.team} and possesses the following powers:\\\\n{self.powers}\\\"\\n return description\",\n \"def display_player_and_student_health(player: Character, opponent: Student):\\n print(player.name.title() + \\\": \\\" + str(player.hp) + \\\"HP\\\") # display player hp\\n print(\\\"Student: \\\" + str(opponent.hp) + \\\"HP\\\\n\\\") # display student hp\",\n \"def display_health(self) -> None:\\n\\n print(self.player.hp)\",\n \"def display_short(self):\\n print(f'{self.name.upper()} ({self.speciality.upper()})')\",\n \"def display_profile(self):\\n statement = f\\\"\\\"\\\"\\n ------\\n {self.name.upper()}\\n ------\\n Fee: {self.fee} -/Rs.\\n Rating: {self.rating} STARS\\n Qualification: {self.qualification}\\n Speciality: {self.speciality}\\n Language: {self.language}\\n Working Hours: {self.working_hrs}\\n Contact: {self.contact}\\n Location: {self.location}\\n \\\"\\\"\\\"\\n print(statement)\",\n \"def __str__(self):\\n return str(self.elapsed) + ' sec'\",\n \"def print_player_info(self):\\n\\t\\tclear_screen()\\n\\n\\t\\tprint(\\\"# PLAYER INFO #\\\\n\\\")\\n\\t\\tprint(\\\"Name{:.>17} \\\".format(self.info['Name']))\\n\\t\\tprint(\\\"Race{:.>17} \\\".format(self.info['Race']))\\n\\t\\tprint(\\\"Level{:.>16} \\\".format(self.stats['Level']))\\n\\t\\tprint(\\\"Hit Points{:.>11} \\\".format(self.stats['HP']))\\n\\t\\tprint(\\\"Gold Pieces{:.>10} \\\".format(self.stats['GOLD']))\\n\\t\\n\\t\\tpress_enter()\",\n \"def description(self) -> str:\\n return \\\"put units of a given colour on the perimeter of the board.\\\"\",\n \"def printLatestMeasurement(self): \\n \\n if (len(self.measurement) == 0):\\n x = 0\\n y = 0\\n L = 0\\n \\n else:\\n data = self.measurement[len(self.measurement)-1]\\n x = data['chrominance'][0]\\n y = data['chrominance'][1]\\n L = data['luminance']\\n \\n print\\\"(x,y) = ({0:.4f}, {1:.4f}), L = {2:.4f} cd/m2 ({3:.4f} fL)\\\".format( x, y, L, 0.291863508*L)\",\n \"def report(self):\\n print(f\\\"Water: {self.resources['water']}ml\\\")\\n print(f\\\"Milk: {self.resources['milk']}ml\\\")\\n print(f\\\"Coffee: {self.resources['coffee']}g\\\")\",\n \"def displayMelody(self):\\r\\n print(self.notes)\",\n \"def _print(self):\\n print('center :', self.center, ' widht : ', self.width, ' height : ', self.height, ' heat : ', self.heat,\\n ' speed ', self.speed)\",\n \"def plane_desc(self) -> str:\\n return self.planes[0].join(' ') + self.planes[1].join(' ') + self.planes[2].join(' ')\",\n \"def __str__(self):\\n return \\\"{} : {}\\\".format(self._team_name, self._win_ratio)\",\n \"def dump(self):\\n fmt='%20s:%10.4fs%6.1f%%'\\n print('\\\\n----------------TIME MANAGER PROFILE----------------\\\\n\\\\n')\\n total_t=time.time()-self.tic0\\n for rec in self.record:\\n print(fmt % (rec[0],rec[1],100.0*rec[1]/total_t))\\n print(fmt % ('TOTAL ELAPSED TIME', total_t, 100.0))\\n print('\\\\n----------------TIME MANAGER PROFILE----------------\\\\n\\\\n')\",\n \"def __str__(self):\\n _str = indent(self.name, color = self.color)\\n _str += indent(\\\"-\\\"*len(self.name))\\n _str += indent(\\\"( \\\" + self.movemement + \\\" - \\\"+ self.symbole + \\\" )\\\")\\n _str += indent(\\\"Awareness: \\\" + str(self.awareness))\\n for _iel in self.abilities:\\n _str += indent(_iel)\\n if self.description is not None:\\n _str += indent(\\\"-\\\"*45 + \\\"\\\\n\\\")\\n _str += indent(self.description)\\n _str += indent(\\\"-\\\"*45 + \\\"\\\\n\\\")\\n _str += indent(BOLD_BLACK + \\\" \\\"*15 + \\\"Combat Stats\\\" + RESET)\\n _str += indent(\\\"-\\\"*45 + \\\"\\\\n\\\")\\n _str += indent(BLUE + \\\" Horror\\\" + RESET + \\\" Toughness\\\"\\\\\\n + RED + \\\" Combat\\\" + RESET)\\n _str += indent(BLUE + \\\"Rating Damage \\\"\\\\\\n + RED + \\\"Rating Damage\\\")\\n _str += indent(BLUE + \\\" \\\" + str(self.horror_rating)\\\\\\n + \\\" \\\"*8 + str(self.horror_damage) + RESET\\\\\\n + \\\" \\\"*8 + str(self.toughness) + RED\\\\\\n + \\\" \\\"*8 + str(self.combat_rating)\\\\\\n + \\\" \\\"*8 + str(self.combat_damage) + RESET)\\n _str += indent(\\\"-\\\"*45 + \\\"\\\\n\\\")\\n return _str\",\n \"def full_description(self):\\n des = describe_dut(self.dut) if self.dut else ''\\n if self.build:\\n des += ' with ' + self.build\\n if self.result_id:\\n des += ' BVT result ID ' + str(self.result_id)\\n return (self.description if self.description \\n else 'unknown test') + ' on ' + des\",\n \"def human_readable_info(self) -> str:\\n next_session = unix_str(self._stat.next_session)\\n last_session = unix_str(self._stat.last_session)\\n return \\\"\\\"\\\"\\n Next Session: {}\\n Last Session: {}\\n Repetitions: {}\\n Health: {}\\n ------------------------\\n Past Quality (last 20):\\n ------------------------\\n {}\\n \\\"\\\"\\\".format(\\n next_session,\\n last_session,\\n self._stat.actual_repetitions,\\n self._health(),\\n self._past_quality_graph(),\\n )\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6940499","0.69227624","0.6609904","0.65955704","0.6549176","0.6460658","0.6451413","0.64315027","0.64310527","0.63806295","0.63690436","0.63690436","0.63664997","0.63568395","0.6350418","0.6337432","0.6329141","0.6290839","0.6274496","0.6263658","0.62533987","0.6221972","0.62217045","0.62147987","0.6203282","0.6197069","0.6186187","0.61791515","0.6165952","0.61641985","0.6161678","0.61588144","0.613694","0.61288565","0.6122933","0.6121118","0.61119705","0.6084609","0.60844284","0.60775924","0.6067556","0.6065952","0.6050854","0.60378134","0.6035918","0.60230464","0.60201454","0.6019298","0.6002932","0.5996702","0.59766966","0.5974196","0.5970893","0.59651357","0.59628546","0.59628546","0.59599566","0.595622","0.59459525","0.5943004","0.59378135","0.5937343","0.59349287","0.5933928","0.5927504","0.59262335","0.59190667","0.59029955","0.590246","0.5892038","0.5891953","0.58804077","0.5877047","0.58761346","0.5871812","0.58682317","0.58662975","0.58619815","0.58558655","0.58558655","0.58492327","0.58469504","0.5844827","0.58447176","0.5834851","0.5830929","0.58280635","0.582645","0.58258206","0.5819243","0.5804783","0.5800055","0.5796798","0.5794046","0.5792646","0.57925016","0.57901114","0.57896245","0.57882273","0.5786697","0.57846016"],"string":"[\n \"0.6940499\",\n \"0.69227624\",\n \"0.6609904\",\n \"0.65955704\",\n \"0.6549176\",\n \"0.6460658\",\n \"0.6451413\",\n \"0.64315027\",\n \"0.64310527\",\n \"0.63806295\",\n \"0.63690436\",\n \"0.63690436\",\n \"0.63664997\",\n \"0.63568395\",\n \"0.6350418\",\n \"0.6337432\",\n \"0.6329141\",\n \"0.6290839\",\n \"0.6274496\",\n \"0.6263658\",\n \"0.62533987\",\n \"0.6221972\",\n \"0.62217045\",\n \"0.62147987\",\n \"0.6203282\",\n \"0.6197069\",\n \"0.6186187\",\n \"0.61791515\",\n \"0.6165952\",\n \"0.61641985\",\n \"0.6161678\",\n \"0.61588144\",\n \"0.613694\",\n \"0.61288565\",\n \"0.6122933\",\n \"0.6121118\",\n \"0.61119705\",\n \"0.6084609\",\n \"0.60844284\",\n \"0.60775924\",\n \"0.6067556\",\n \"0.6065952\",\n \"0.6050854\",\n \"0.60378134\",\n \"0.6035918\",\n \"0.60230464\",\n \"0.60201454\",\n \"0.6019298\",\n \"0.6002932\",\n \"0.5996702\",\n \"0.59766966\",\n \"0.5974196\",\n \"0.5970893\",\n \"0.59651357\",\n \"0.59628546\",\n \"0.59628546\",\n \"0.59599566\",\n \"0.595622\",\n \"0.59459525\",\n \"0.5943004\",\n \"0.59378135\",\n \"0.5937343\",\n \"0.59349287\",\n \"0.5933928\",\n \"0.5927504\",\n \"0.59262335\",\n \"0.59190667\",\n \"0.59029955\",\n \"0.590246\",\n \"0.5892038\",\n \"0.5891953\",\n \"0.58804077\",\n \"0.5877047\",\n \"0.58761346\",\n \"0.5871812\",\n \"0.58682317\",\n \"0.58662975\",\n \"0.58619815\",\n \"0.58558655\",\n \"0.58558655\",\n \"0.58492327\",\n \"0.58469504\",\n \"0.5844827\",\n \"0.58447176\",\n \"0.5834851\",\n \"0.5830929\",\n \"0.58280635\",\n \"0.582645\",\n \"0.58258206\",\n \"0.5819243\",\n \"0.5804783\",\n \"0.5800055\",\n \"0.5796798\",\n \"0.5794046\",\n \"0.5792646\",\n \"0.57925016\",\n \"0.57901114\",\n \"0.57896245\",\n \"0.57882273\",\n \"0.5786697\",\n \"0.57846016\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":75593,"cells":{"query":{"kind":"string","value":"Returns the active status T|F of the measure."},"document":{"kind":"string","value":"def is_active(self):\n return self.active"},"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_lsp_frr_operational_status_active(self):\n return self.__lsp_frr_operational_status_active","def _tstat_alpha(self):\n return _handle_ab(self._tstat_all, self.use_const)[0]","def get_status():\n return ('off', 'off')","def get_current_s(self):\n return 1 if self.ff_states[0] else 0","def _get_measuring_status(self):\n readValue = self._read_register_1ubyte(self.BME680_EAS_STATUS_0)\n\n return True if ((readValue & 0b00100000) == 0b00100000) else False","def temperature_status(self):\n temp = ct.c_float()\n ans = self.lib.GetTemperatureF(ct.pointer(temp))\n return _ERRORS[ans]","def state(self):\n return self._measure","def get_set_current(self):\n status = self.get_status_response()\n flags = status[1]\n if (flags & 0x2) == 0:\n return 0.0 #test isn't running\n current = status[3] + (status[4] * 0x100) + (status[5] * 0x10000) + (status[6] * 0x1000000)\n current = float(current)\n current /= (1000.0 * 1000.0)\n return current\n #end get_set_current()","def tstat_alpha(self):\n return self._tstat_alpha","def status(self):\n\n # --- get 0 padded string representation of status register\n response = self.send_lens_cmd(['90', 'B9', '00'], fast_mode=True)\n state_str = bin(int('0x' + response['MISO'][2], 16))\n state_str = state_str[2:]\n for p in range(8 - len(state_str)):\n state_str = '0' + state_str\n\n self._status = dict(AF_switch=bool(int(state_str[0])),\n F_move=bool(int(state_str[5])),\n F_acc=bool(int(state_str[2])),\n FD_endStop=bool(int(state_str[3])),\n status_byte=state_str)\n\n return self._status","def _do_get_status(self):\n logging.info(__name__ + ' : Get status of the device.')\n result = self._execute('X')\n usage = {\n 0: \"Channel not in use\",\n 1: \"Channel used for Nitrogen level\",\n 2: \"Channel used for Helium Level (Normal pulsed operation)\",\n 3: \"Channel used for Helium Level (Continuous measurement)\",\n 9: \"Error on channel (Usually means probe unplugged)\"\n }\n # current_flowing = {\n # 0 : \"Curent not flowing in Helium Probe Wire\",\n # 1 : \"Curent not flowing in Helium Probe Wire\"\n # }\n # auto_fill_status = {\n # 00 : \"End Fill (Level > FULL)\",\n # 01 : \"Not Filling (Level < FULL, Level > FILL)\",\n # 10 : \"Filling (Level < FULL, Level > FILL)\",\n # 11 : \"Start Filling (Level < FILL)\"\n # }\n return usage.get(int(result[1]), \"Unknown\")","def state(self) -> object:\n # pylint: disable=protected-access\n val = self._node.status._val\n raw_units = self._node.uom\n\n if raw_units in [TEMP_CELSIUS, TEMP_FAHRENHEIT]:\n return self.hass.config.units.temperature(val, raw_units)\n return val","def get_status(self):\n return self.read_register(259, 0, 3)","def get_measured_current(self):\n status = self.get_status_response()\n current = status[16] + (status[17] * 0x100) + (status[18] * 0x10000) + (status[19] * 0x1000000)\n current = float(current)\n current /= (1000.0 * 1000.0)\n return current\n #end get_measured_current","def GetStatus(self):\r\n return self.status","def _get_lsp_frr_operational_status(self):\n return self.__lsp_frr_operational_status","def format_all_active_meas():\n\n result = \"No active meas found\\r\\n\"\n first = True\n\n # loop through all the measurements, looking for active ones\n for m in range(1, 32 + 1):\n if \"On\" in setup_read(\"M{} Active\".format(m)):\n # found an active measurement\n r = measure(m, READING_LAST)\n if first: # first active measurement found\n result = \"\\r\\n\"\n first = False\n\n # format up the label and the value with the correct number of right digits\n result += \"{}: {:.{}f}\\r\\n\".format(r.label, r.value, r.right_digits)\n\n return result","def tower_status_radiant(self):\n return self._get(\"tower_status_radiant\")","def state(self):\n appliance = self._coordinator.data[\"appliances\"][self._appliance_id]\n smart_meter = appliance[\"smart_meter\"]\n echonetlite_properties = smart_meter[\"echonetlite_properties\"]\n measured_instantaneous = next(\n value[\"val\"] for value in echonetlite_properties if value[\"epc\"] == 231\n )\n _LOGGER.debug(\"Current state: %sW\", measured_instantaneous)\n return measured_instantaneous","def status(self):\n return self._call_txtrader_api('status', {})","def get_current_status(cls):\n from sauna.plugins.base import Plugin\n from sauna import check_results_lock, check_results\n\n def reduce_status(accumulated, update_value):\n if update_value.status > Plugin.STATUS_CRIT:\n return accumulated\n return accumulated if accumulated > update_value.status else \\\n update_value.status\n\n with check_results_lock:\n code = reduce(reduce_status, check_results.values(), 0)\n\n return Plugin.status_code_to_str(code), code","def read_totalizer_status(self): #command 240\r\n status, data = self.comm2('f000') # command = f0, byte count = 00\r\n totaliser_status = int(Brooks.get_bytes(0,data,1),16)\r\n totaliser_unit = int(Brooks.get_bytes(1,data,1),16)\r\n return totaliser_status, totaliser_unit","def statflag(self) -> str | None:\n return self.status.get(\"STATFLAG\")","def staff_status(self):\r\n return self.q(css='#staffstatus').text[0]","def getStatus():","def Status(self):\r\n\t\treturn self._get_attribute('status')","def get_status(self):\n if self.cancelled:\n return \"Cancelled\"\n elif self.is_active():\n s = \"Active\"\n if self.is_paid():\n return \"{0} - Paid\".format(s)\n else:\n return \"{0} - Unpaid\".format(s)\n elif self.is_complete():\n s = \"Complete\"\n if self.is_paid():\n return \"{0} - Paid\".format(s)\n else:\n return \"{0} - Unpaid\".format(s)\n elif self.is_confirmed():\n s = \"Confirmed\"\n if self.is_paid():\n return \"{0} - Paid\".format(s)\n else:\n return \"{0} - Unpaid\".format(s)\n else:\n return \"Unknown - contact staff\"","def state(self):\n\n if self._device_attribute == ATTR_INSIDE_TEMPERATURE:\n if self._api.roomTemperature == 126 or self._api.roomTemperature == None:\n return 'unavailable'\n else:\n return self._api.roomTemperature\n\n if self._device_attribute == ATTR_OUTSIDE_TEMPERATURE:\n if self._api.outdoorTemperature == 126 or self._api.outdoorTemperature == None:\n return 'unavailable'\n else:\n return self._api.outdoorTemperature\n return None","def _get_status(self):\n return self.__status","def f_pha(self):\n return self._f_pha","def status(self):\n return STATUS[self.fields['status']]","def _get_status(trial: dict) -> int:\n if trial['overall_status'] in {'Not yet recruiting', 'Active, not recruiting'}:\n return 0\n elif trial['overall_status'] in {'Enrolling by invitation', 'Recruiting', 'Available'}:\n return 1\n elif trial['overall_status'] in {'Approved for marketing'}:\n return 2\n else:\n return 3","def get_status_instructor():\n if self.status == self.CH_SUBMITTED:\n return \"Submitted\"\n \n elif self.status == self.CH_AUTOGRADED:\n if self.autograderresult.visible:\n return \"Autograded (released)\"\n else:\n return \"Autograded (hidden)\"\n \n elif self.status == self.CH_GRADED:\n return \"Graded\"\n \n elif self.status == self.CH_PREVIOUS:\n return \"Previous Sub.\"\n \n elif self.status == self.CH_TO_AUTOGRADE:\n return \"Pending\"","def getstatus(self):\n with self.lock:\n return (self.status, self.time_start)","def status(self):\n return status_dict[self._get_property_(self.STATUS).upper()]","def get_globalstatus_foamactive(self) -> bool:\n\n return self.send(self.cmd.GET_GLOBALSTATUS_FOAMACTIVE)","def fuota_task_status(self) -> Optional[str]:\n return pulumi.get(self, \"fuota_task_status\")","def status(self):\n # pdb.set_trace()\n print(\"\\u001b[4m\\t\\tTimer Status\\u001b[0m\")\n print(\"\\n\\t# active timers: {}\".format(len(self.timer_threads)))\n for n in range(0, len(self.timer_threads)):\n active = True\n print(\"\\n\\tID: {}\\n\\tName: {}\\n\\tInit: {} AlarmT: {}\\n\\tTest?: {}\\n\\tTarget: {}\"\n .format(n, self.tuplist[n][1]['name'],\n self.tuplist[n][1]['init_time'].strftime(\"(%m-%d-%y)%H:%M:%S\"),\n self.tuplist[n][1]['alarm_time'],\n self.tuplist[n][1]['testmode'],\n self.tuplist[n][1]['todo']))\n return True","def _get_active(self):\n return self.__active","def _get_active(self):\n return self.__active","def _get_active(self):\n return self.__active","def _get_active(self):\n return self.__active","def get_status_student():\n if (self.status == self.CH_GRADED or \n (self.status == self.CH_AUTOGRADED and self.autograderresult.visible)):\n return \"Graded\"\n \n elif self.status == self.CH_PREVIOUS:\n return \"Previous Sub.\"\n \n return \"Submitted\"","def _get_status_from_qry(self):\n active_deadline = self.get_active_deadline()\n return active_deadline.status","def get_status(self, state):\n raise NotImplementedError","def status(self):\r\n return STATUS.FINE","def get_status(self):\n return self.status","def get_status(self):\n return self.status","def get_status(self):\n return self.status","def read_status(ctl):\n\tr = ctl.bus_read_struct_coherent(tm.status_addr, 'BBBBI')\n\treturn r","def ConfirmedTradeStatus():\n return 'FO Confirmed'","def state(self):\n return self.coordinator.data[METER_DEVICE_TYPE][self.base_unique_id][METER_STATE]","def _get_status(self):\n held_msg=\"\"\n return u'%s%s' % (self.get_status_display(), held_msg)","def train_op_ft(self):\r\n return self._train_op_ft","def get_raw_status(self):\n self.__param_lock.acquire()\n status = self.__status\n self.__param_lock.release()\n return status","def last_fmeasure(self):\n return self.get_fvalue(self.last_position())","def test_status(self):\n self.assertEqual('perfect', self.__metric.status())","def state(self):\n return self._attributes['status']","def status(self):\n return self.readvar('\\x5F\\x95',0)","def status(a):\n return a","def getstatus(self):\n return self.__status","def is_measuring(self):\n # 5.4.4 of the datasheet indicates the relevant bit\n return bool(self.read_register(STATUS_REGISTER) & 0b1000)","def status(self):\n return self.m.status","def state(self):\n return self._device.temperature","def get_status(self):\n return self._status","def get_status(self):\n request_format = \"{oscillating:01d} {initialising:01d} {initialised:01d} {width:03d} \" \\\n \"{offset:+04d} {speed:02d} {acceleration:03d} {cycles:05d} {backlash:03d}\"\n status_string = request_format.format(\n oscillating=int(self.device.is_oscillating()),\n initialising=int(self.device.is_initialising()),\n initialised=int(self.device.has_been_initialised()),\n width=int(self.device.get_window_width()),\n offset=int(self.device.get_offset()),\n speed=int(self.device.get_speed()),\n acceleration=int(self.device.get_acceleration()),\n cycles=int(self.device.get_complete_cycles()),\n backlash=int(self.device.get_backlash())\n )\n return status_string","def state(self):\n return self.roller.battery","def t_measure_estimate(self):\n ho = self.humidity_oversampling\n to = self.temperature_oversampling\n po = self.pressure_oversampling\n typ = 1. + 2.*to + (2.*po + 0.5)*bool(po) + (2.*ho +0.5)*bool(ho)\n mx = 1.25 + 2.3*to + (2.3*po + 0.575)*bool(po) + (2.3*ho +0.575)*bool(ho)\n return typ, mx","def state(self) -> float:\n return self.value","def read_srq_status(ctl):\n\treturn ctl.bus_read_struct_coherent(tm.status_addr + 3, 'B')","def status(self):\n return self.get(self._names[\"status\"])","def state(self):\n return self.device.device_data[self.device_id]['temperature']","def state(self):\n state = 'Unknown'\n closest = 0.5\n for device, method_name in self._methods:\n if method_name.startswith('wm_'):\n state_name = method_name.replace('wm_', '', 1)\n wm_state = getattr(device, method_name)\n diff = wm_state()\n if diff < closest:\n state = state_name\n closest = diff\n return state","def full_status(self):\n return self.raw_check_complete()","def status(self):\n st = ct.c_int()\n self.lib.GetStatus(ct.pointer(st))\n if st.value == 20073:\n return 'Camera is idle, waiting for instructions.'\n elif st.value == 20074:\n return 'Camera is executing the temperature cycle.'\n elif st.value == 20072:\n return 'Acquisition in progress.'\n elif st.value == 20023:\n return 'Unable to meet accumulate cycle time.'\n elif st.value == 20022:\n return 'Unable to meet kinetic cycle time.'\n elif st.value == 20013:\n return 'Unable to communicate with card.'\n elif st.value == 20018:\n return ('Computer unable to read the data via the ISA slot at the '\n 'required rate.')\n elif st.value == 20026:\n return 'Overflow of the spool buffer.'","def ti_func(self):\n return self.ti.val - self.calc_ti()","def _get_status(self):\n return u'%s' % (self.get_status_display())","def get_status(self):\n return self.o.read_register(self.dev_id, STATUS)","def status(self):","def _get_gas_measuring_status(self):\n readValue = self._read_register_1ubyte(self.BME680_EAS_STATUS_0)\n\n return True if ((readValue & 0b01000000) == 0b01000000) else False","def Status(self):\n return self._get_attribute('status')","def state_class(self):\n if self._state_characteristic in (\n STAT_MIN_AGE,\n STAT_MAX_AGE,\n STAT_QUANTILES,\n ):\n return None\n return STATE_CLASS_MEASUREMENT","def report_status(scheduled_time, estimated_time):\n if scheduled_time == estimated_time:\n return 'on time'\n elif scheduled_time > estimated_time:\n return 'early'\n else:\n return 'delayed'","def state(self):\n return self.device.status(station=self.station_number)","def status(self):\n\t\treturn self._status","def returnStatus(value, option):\n # Updated 03/19/17\n # if else to account for open/closed device vs on/off device\n if option == 'open':\n on = \"<font color='green'>Open</font>\"\n off = \"<font color='red'>Closed</font>\"\n else:\n on = \"<font color='green'>On</font>\"\n off = \"<font color='red'>Off</font>\"\n \n if value: \n status = on\n else: \n status = off\n \n return status","def get_status(self):\n status = self._status.get_message()\n \n if status == \"N\":\n return \"offline\"\n \n elif status == \"Y\":\n return \"online\"\n \n elif status == \"A\":\n return \"away\"\n \n elif status == \"B\":\n return \"busy\"","def state(self):\n return STATE_ON if self._state == 1.0 else STATE_OFF","def status(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"status\")","def status(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"status\")","def get_status(self):\n\n return self._system","def _getCurrentComponentStatus(self):\n resOverall = self.sysAdminClient.getOverallStatus()\n if not resOverall['OK']:\n return resOverall\n currentStatus = {'Down': set(), 'Run': set(), 'All': set()}\n informationDict = resOverall['Value']\n for systemsDict in informationDict.values():\n for system, instancesDict in systemsDict.items():\n for instanceName, instanceInfoDict in instancesDict.items():\n identifier = '%s__%s' % (system, instanceName)\n runitStatus = instanceInfoDict.get('RunitStatus')\n if runitStatus in ('Run', 'Down'):\n currentStatus[runitStatus].add(identifier)\n\n currentStatus['All'] = currentStatus['Run'] | currentStatus['Down']\n return S_OK(currentStatus)","def getDetailedStatus (self):\n try:\n if ((self._activateData == None) or (self._activateData.blockDevice == None)):\n self._log(\"no-block-device-no-status\").debug2(\"no block device was found for file system '%s'\",self._logicalDiskName)\n return None,ReturnCodes.kOk\n\n else:\n terminateTimeOut = self._activeTimeoutsConfig.getStatus\n timer = common.Timer(terminateTimeOut)\n blockDevice = self._activateData.blockDevice\n statusDisctionary,rc = self._tune2fs(blockDevice,timer)\n \n if (rc != ReturnCodes.kOk):\n self._log(\"get-status-failed\").error(\"getDetailedStatus() for file system '%s' failed!\",self._logicalDiskName)\n return None,ReturnCodes.kGeneralError\n\n if (statusDisctionary == None):\n self._log(\"have-block-but-no-status\").debug2(\"block device '%s' for file system '%s' - could not fins file-system status\",blockDevice,self._logicalDiskName)\n else:\n self._log(\"status-found\").debug2(\"block device '%s' for file system '%s' - status found!\",blockDevice,self._logicalDiskName)\n\n return statusDisctionary,rc\n\n except Exception,e:\n self._log(\"get-file-system-status-exception\").error(\"getDetailedStatus(terminateTimeOut=%.2f) faild! exception = '%s'\",terminateTimeOut,e)\n return None,ReturnCodes.kGeneralError","def _status_apf():\n status = 0\n table = iptc.Table(iptc.Table.FILTER)\n for chain in table.chains:\n if \"sanity\" in chain.name.lower():\n status = 1\n return True if status else False","def get_status(self):\n # find status\n # search in summary file first\n self.status = \"running\"\n status = self.search_summary(\"status\")\n if status:\n self.status = status.split()[1]\n # define running time\n # search in summary file first\n self.running_time = \"00:00:00\"\n running_time = self.search_summary(\"running-time\")\n if running_time:\n self.running_time = running_time.split()[1]\n # calculate running time\n else:\n now = datetime.datetime.now()\n elapsed_time = (now - self.ctime).seconds\n hours, remainder = divmod(elapsed_time, 3600)\n minutes, seconds = divmod(remainder, 60)\n self.running_time = (\n f\"{int(hours):02}:{int(minutes):02}:{int(seconds):02}\"\n )","def status(self, station=1):\n return self.statuslist()[station][2]","def GetStatus(reset=\"\"):\n\n if reset == \"reset\":\n state_mgr.states={}\n print \"States successfully reset.\"\n\n elif not reset.strip():\n sorted_keys=state_mgr.states.keys()\n sorted_keys.sort(key=lambda x:int(x[1:]))\n print \"{:<7s}{:^8s}{:^20s}\".format(\"Unit:\", \"State:\", \"Last Update:\")\n\n for unit in sorted_keys:\n try:\n status = str(round(state_mgr.states[unit][0],1))+\"%\"\n except TypeError:\n status = state_mgr.states[unit][0]\n\n t = time.localtime(float(state_mgr.states[unit][1]))\n hr = t.tm_hour\n ampm=\"am\"\n\n if t.tm_hour>11:\n hr -= 12\n ampm=\"pm\"\n\n pretty_time = \"%s:%s:%s %s %s/%s/%s\"%(hr,str(t.tm_min).zfill(2),str(t.tm_sec).zfill(2),ampm,t.tm_mon,t.tm_mday,str(t.tm_year)[2:])\n \n print \"{:<8s}{:8s}{:>20s}\".format(unit, status, pretty_time)\n\n else:\n raise TypeError,\"Invalid argument!\"","def get_state(self):\n return self.fmu._get_continuous_states()","def test_status(self):\n measurement = self.measurement(self.metric())\n s_m = ScaleMeasurement(\n previous_scale_measurement=None,\n measurement=measurement,\n status=\"target_met\",\n )\n status = s_m.status()\n self.assertIs(type(status), str)\n self.assertEqual(status, \"target_met\")","def rtt_get_status(self):\n status = structs.JLinkRTTerminalStatus()\n res = self.rtt_control(enums.JLinkRTTCommand.GETSTAT, status)\n return status","def get_active(self):\n return self._active"],"string":"[\n \"def _get_lsp_frr_operational_status_active(self):\\n return self.__lsp_frr_operational_status_active\",\n \"def _tstat_alpha(self):\\n return _handle_ab(self._tstat_all, self.use_const)[0]\",\n \"def get_status():\\n return ('off', 'off')\",\n \"def get_current_s(self):\\n return 1 if self.ff_states[0] else 0\",\n \"def _get_measuring_status(self):\\n readValue = self._read_register_1ubyte(self.BME680_EAS_STATUS_0)\\n\\n return True if ((readValue & 0b00100000) == 0b00100000) else False\",\n \"def temperature_status(self):\\n temp = ct.c_float()\\n ans = self.lib.GetTemperatureF(ct.pointer(temp))\\n return _ERRORS[ans]\",\n \"def state(self):\\n return self._measure\",\n \"def get_set_current(self):\\n status = self.get_status_response()\\n flags = status[1]\\n if (flags & 0x2) == 0:\\n return 0.0 #test isn't running\\n current = status[3] + (status[4] * 0x100) + (status[5] * 0x10000) + (status[6] * 0x1000000)\\n current = float(current)\\n current /= (1000.0 * 1000.0)\\n return current\\n #end get_set_current()\",\n \"def tstat_alpha(self):\\n return self._tstat_alpha\",\n \"def status(self):\\n\\n # --- get 0 padded string representation of status register\\n response = self.send_lens_cmd(['90', 'B9', '00'], fast_mode=True)\\n state_str = bin(int('0x' + response['MISO'][2], 16))\\n state_str = state_str[2:]\\n for p in range(8 - len(state_str)):\\n state_str = '0' + state_str\\n\\n self._status = dict(AF_switch=bool(int(state_str[0])),\\n F_move=bool(int(state_str[5])),\\n F_acc=bool(int(state_str[2])),\\n FD_endStop=bool(int(state_str[3])),\\n status_byte=state_str)\\n\\n return self._status\",\n \"def _do_get_status(self):\\n logging.info(__name__ + ' : Get status of the device.')\\n result = self._execute('X')\\n usage = {\\n 0: \\\"Channel not in use\\\",\\n 1: \\\"Channel used for Nitrogen level\\\",\\n 2: \\\"Channel used for Helium Level (Normal pulsed operation)\\\",\\n 3: \\\"Channel used for Helium Level (Continuous measurement)\\\",\\n 9: \\\"Error on channel (Usually means probe unplugged)\\\"\\n }\\n # current_flowing = {\\n # 0 : \\\"Curent not flowing in Helium Probe Wire\\\",\\n # 1 : \\\"Curent not flowing in Helium Probe Wire\\\"\\n # }\\n # auto_fill_status = {\\n # 00 : \\\"End Fill (Level > FULL)\\\",\\n # 01 : \\\"Not Filling (Level < FULL, Level > FILL)\\\",\\n # 10 : \\\"Filling (Level < FULL, Level > FILL)\\\",\\n # 11 : \\\"Start Filling (Level < FILL)\\\"\\n # }\\n return usage.get(int(result[1]), \\\"Unknown\\\")\",\n \"def state(self) -> object:\\n # pylint: disable=protected-access\\n val = self._node.status._val\\n raw_units = self._node.uom\\n\\n if raw_units in [TEMP_CELSIUS, TEMP_FAHRENHEIT]:\\n return self.hass.config.units.temperature(val, raw_units)\\n return val\",\n \"def get_status(self):\\n return self.read_register(259, 0, 3)\",\n \"def get_measured_current(self):\\n status = self.get_status_response()\\n current = status[16] + (status[17] * 0x100) + (status[18] * 0x10000) + (status[19] * 0x1000000)\\n current = float(current)\\n current /= (1000.0 * 1000.0)\\n return current\\n #end get_measured_current\",\n \"def GetStatus(self):\\r\\n return self.status\",\n \"def _get_lsp_frr_operational_status(self):\\n return self.__lsp_frr_operational_status\",\n \"def format_all_active_meas():\\n\\n result = \\\"No active meas found\\\\r\\\\n\\\"\\n first = True\\n\\n # loop through all the measurements, looking for active ones\\n for m in range(1, 32 + 1):\\n if \\\"On\\\" in setup_read(\\\"M{} Active\\\".format(m)):\\n # found an active measurement\\n r = measure(m, READING_LAST)\\n if first: # first active measurement found\\n result = \\\"\\\\r\\\\n\\\"\\n first = False\\n\\n # format up the label and the value with the correct number of right digits\\n result += \\\"{}: {:.{}f}\\\\r\\\\n\\\".format(r.label, r.value, r.right_digits)\\n\\n return result\",\n \"def tower_status_radiant(self):\\n return self._get(\\\"tower_status_radiant\\\")\",\n \"def state(self):\\n appliance = self._coordinator.data[\\\"appliances\\\"][self._appliance_id]\\n smart_meter = appliance[\\\"smart_meter\\\"]\\n echonetlite_properties = smart_meter[\\\"echonetlite_properties\\\"]\\n measured_instantaneous = next(\\n value[\\\"val\\\"] for value in echonetlite_properties if value[\\\"epc\\\"] == 231\\n )\\n _LOGGER.debug(\\\"Current state: %sW\\\", measured_instantaneous)\\n return measured_instantaneous\",\n \"def status(self):\\n return self._call_txtrader_api('status', {})\",\n \"def get_current_status(cls):\\n from sauna.plugins.base import Plugin\\n from sauna import check_results_lock, check_results\\n\\n def reduce_status(accumulated, update_value):\\n if update_value.status > Plugin.STATUS_CRIT:\\n return accumulated\\n return accumulated if accumulated > update_value.status else \\\\\\n update_value.status\\n\\n with check_results_lock:\\n code = reduce(reduce_status, check_results.values(), 0)\\n\\n return Plugin.status_code_to_str(code), code\",\n \"def read_totalizer_status(self): #command 240\\r\\n status, data = self.comm2('f000') # command = f0, byte count = 00\\r\\n totaliser_status = int(Brooks.get_bytes(0,data,1),16)\\r\\n totaliser_unit = int(Brooks.get_bytes(1,data,1),16)\\r\\n return totaliser_status, totaliser_unit\",\n \"def statflag(self) -> str | None:\\n return self.status.get(\\\"STATFLAG\\\")\",\n \"def staff_status(self):\\r\\n return self.q(css='#staffstatus').text[0]\",\n \"def getStatus():\",\n \"def Status(self):\\r\\n\\t\\treturn self._get_attribute('status')\",\n \"def get_status(self):\\n if self.cancelled:\\n return \\\"Cancelled\\\"\\n elif self.is_active():\\n s = \\\"Active\\\"\\n if self.is_paid():\\n return \\\"{0} - Paid\\\".format(s)\\n else:\\n return \\\"{0} - Unpaid\\\".format(s)\\n elif self.is_complete():\\n s = \\\"Complete\\\"\\n if self.is_paid():\\n return \\\"{0} - Paid\\\".format(s)\\n else:\\n return \\\"{0} - Unpaid\\\".format(s)\\n elif self.is_confirmed():\\n s = \\\"Confirmed\\\"\\n if self.is_paid():\\n return \\\"{0} - Paid\\\".format(s)\\n else:\\n return \\\"{0} - Unpaid\\\".format(s)\\n else:\\n return \\\"Unknown - contact staff\\\"\",\n \"def state(self):\\n\\n if self._device_attribute == ATTR_INSIDE_TEMPERATURE:\\n if self._api.roomTemperature == 126 or self._api.roomTemperature == None:\\n return 'unavailable'\\n else:\\n return self._api.roomTemperature\\n\\n if self._device_attribute == ATTR_OUTSIDE_TEMPERATURE:\\n if self._api.outdoorTemperature == 126 or self._api.outdoorTemperature == None:\\n return 'unavailable'\\n else:\\n return self._api.outdoorTemperature\\n return None\",\n \"def _get_status(self):\\n return self.__status\",\n \"def f_pha(self):\\n return self._f_pha\",\n \"def status(self):\\n return STATUS[self.fields['status']]\",\n \"def _get_status(trial: dict) -> int:\\n if trial['overall_status'] in {'Not yet recruiting', 'Active, not recruiting'}:\\n return 0\\n elif trial['overall_status'] in {'Enrolling by invitation', 'Recruiting', 'Available'}:\\n return 1\\n elif trial['overall_status'] in {'Approved for marketing'}:\\n return 2\\n else:\\n return 3\",\n \"def get_status_instructor():\\n if self.status == self.CH_SUBMITTED:\\n return \\\"Submitted\\\"\\n \\n elif self.status == self.CH_AUTOGRADED:\\n if self.autograderresult.visible:\\n return \\\"Autograded (released)\\\"\\n else:\\n return \\\"Autograded (hidden)\\\"\\n \\n elif self.status == self.CH_GRADED:\\n return \\\"Graded\\\"\\n \\n elif self.status == self.CH_PREVIOUS:\\n return \\\"Previous Sub.\\\"\\n \\n elif self.status == self.CH_TO_AUTOGRADE:\\n return \\\"Pending\\\"\",\n \"def getstatus(self):\\n with self.lock:\\n return (self.status, self.time_start)\",\n \"def status(self):\\n return status_dict[self._get_property_(self.STATUS).upper()]\",\n \"def get_globalstatus_foamactive(self) -> bool:\\n\\n return self.send(self.cmd.GET_GLOBALSTATUS_FOAMACTIVE)\",\n \"def fuota_task_status(self) -> Optional[str]:\\n return pulumi.get(self, \\\"fuota_task_status\\\")\",\n \"def status(self):\\n # pdb.set_trace()\\n print(\\\"\\\\u001b[4m\\\\t\\\\tTimer Status\\\\u001b[0m\\\")\\n print(\\\"\\\\n\\\\t# active timers: {}\\\".format(len(self.timer_threads)))\\n for n in range(0, len(self.timer_threads)):\\n active = True\\n print(\\\"\\\\n\\\\tID: {}\\\\n\\\\tName: {}\\\\n\\\\tInit: {} AlarmT: {}\\\\n\\\\tTest?: {}\\\\n\\\\tTarget: {}\\\"\\n .format(n, self.tuplist[n][1]['name'],\\n self.tuplist[n][1]['init_time'].strftime(\\\"(%m-%d-%y)%H:%M:%S\\\"),\\n self.tuplist[n][1]['alarm_time'],\\n self.tuplist[n][1]['testmode'],\\n self.tuplist[n][1]['todo']))\\n return True\",\n \"def _get_active(self):\\n return self.__active\",\n \"def _get_active(self):\\n return self.__active\",\n \"def _get_active(self):\\n return self.__active\",\n \"def _get_active(self):\\n return self.__active\",\n \"def get_status_student():\\n if (self.status == self.CH_GRADED or \\n (self.status == self.CH_AUTOGRADED and self.autograderresult.visible)):\\n return \\\"Graded\\\"\\n \\n elif self.status == self.CH_PREVIOUS:\\n return \\\"Previous Sub.\\\"\\n \\n return \\\"Submitted\\\"\",\n \"def _get_status_from_qry(self):\\n active_deadline = self.get_active_deadline()\\n return active_deadline.status\",\n \"def get_status(self, state):\\n raise NotImplementedError\",\n \"def status(self):\\r\\n return STATUS.FINE\",\n \"def get_status(self):\\n return self.status\",\n \"def get_status(self):\\n return self.status\",\n \"def get_status(self):\\n return self.status\",\n \"def read_status(ctl):\\n\\tr = ctl.bus_read_struct_coherent(tm.status_addr, 'BBBBI')\\n\\treturn r\",\n \"def ConfirmedTradeStatus():\\n return 'FO Confirmed'\",\n \"def state(self):\\n return self.coordinator.data[METER_DEVICE_TYPE][self.base_unique_id][METER_STATE]\",\n \"def _get_status(self):\\n held_msg=\\\"\\\"\\n return u'%s%s' % (self.get_status_display(), held_msg)\",\n \"def train_op_ft(self):\\r\\n return self._train_op_ft\",\n \"def get_raw_status(self):\\n self.__param_lock.acquire()\\n status = self.__status\\n self.__param_lock.release()\\n return status\",\n \"def last_fmeasure(self):\\n return self.get_fvalue(self.last_position())\",\n \"def test_status(self):\\n self.assertEqual('perfect', self.__metric.status())\",\n \"def state(self):\\n return self._attributes['status']\",\n \"def status(self):\\n return self.readvar('\\\\x5F\\\\x95',0)\",\n \"def status(a):\\n return a\",\n \"def getstatus(self):\\n return self.__status\",\n \"def is_measuring(self):\\n # 5.4.4 of the datasheet indicates the relevant bit\\n return bool(self.read_register(STATUS_REGISTER) & 0b1000)\",\n \"def status(self):\\n return self.m.status\",\n \"def state(self):\\n return self._device.temperature\",\n \"def get_status(self):\\n return self._status\",\n \"def get_status(self):\\n request_format = \\\"{oscillating:01d} {initialising:01d} {initialised:01d} {width:03d} \\\" \\\\\\n \\\"{offset:+04d} {speed:02d} {acceleration:03d} {cycles:05d} {backlash:03d}\\\"\\n status_string = request_format.format(\\n oscillating=int(self.device.is_oscillating()),\\n initialising=int(self.device.is_initialising()),\\n initialised=int(self.device.has_been_initialised()),\\n width=int(self.device.get_window_width()),\\n offset=int(self.device.get_offset()),\\n speed=int(self.device.get_speed()),\\n acceleration=int(self.device.get_acceleration()),\\n cycles=int(self.device.get_complete_cycles()),\\n backlash=int(self.device.get_backlash())\\n )\\n return status_string\",\n \"def state(self):\\n return self.roller.battery\",\n \"def t_measure_estimate(self):\\n ho = self.humidity_oversampling\\n to = self.temperature_oversampling\\n po = self.pressure_oversampling\\n typ = 1. + 2.*to + (2.*po + 0.5)*bool(po) + (2.*ho +0.5)*bool(ho)\\n mx = 1.25 + 2.3*to + (2.3*po + 0.575)*bool(po) + (2.3*ho +0.575)*bool(ho)\\n return typ, mx\",\n \"def state(self) -> float:\\n return self.value\",\n \"def read_srq_status(ctl):\\n\\treturn ctl.bus_read_struct_coherent(tm.status_addr + 3, 'B')\",\n \"def status(self):\\n return self.get(self._names[\\\"status\\\"])\",\n \"def state(self):\\n return self.device.device_data[self.device_id]['temperature']\",\n \"def state(self):\\n state = 'Unknown'\\n closest = 0.5\\n for device, method_name in self._methods:\\n if method_name.startswith('wm_'):\\n state_name = method_name.replace('wm_', '', 1)\\n wm_state = getattr(device, method_name)\\n diff = wm_state()\\n if diff < closest:\\n state = state_name\\n closest = diff\\n return state\",\n \"def full_status(self):\\n return self.raw_check_complete()\",\n \"def status(self):\\n st = ct.c_int()\\n self.lib.GetStatus(ct.pointer(st))\\n if st.value == 20073:\\n return 'Camera is idle, waiting for instructions.'\\n elif st.value == 20074:\\n return 'Camera is executing the temperature cycle.'\\n elif st.value == 20072:\\n return 'Acquisition in progress.'\\n elif st.value == 20023:\\n return 'Unable to meet accumulate cycle time.'\\n elif st.value == 20022:\\n return 'Unable to meet kinetic cycle time.'\\n elif st.value == 20013:\\n return 'Unable to communicate with card.'\\n elif st.value == 20018:\\n return ('Computer unable to read the data via the ISA slot at the '\\n 'required rate.')\\n elif st.value == 20026:\\n return 'Overflow of the spool buffer.'\",\n \"def ti_func(self):\\n return self.ti.val - self.calc_ti()\",\n \"def _get_status(self):\\n return u'%s' % (self.get_status_display())\",\n \"def get_status(self):\\n return self.o.read_register(self.dev_id, STATUS)\",\n \"def status(self):\",\n \"def _get_gas_measuring_status(self):\\n readValue = self._read_register_1ubyte(self.BME680_EAS_STATUS_0)\\n\\n return True if ((readValue & 0b01000000) == 0b01000000) else False\",\n \"def Status(self):\\n return self._get_attribute('status')\",\n \"def state_class(self):\\n if self._state_characteristic in (\\n STAT_MIN_AGE,\\n STAT_MAX_AGE,\\n STAT_QUANTILES,\\n ):\\n return None\\n return STATE_CLASS_MEASUREMENT\",\n \"def report_status(scheduled_time, estimated_time):\\n if scheduled_time == estimated_time:\\n return 'on time'\\n elif scheduled_time > estimated_time:\\n return 'early'\\n else:\\n return 'delayed'\",\n \"def state(self):\\n return self.device.status(station=self.station_number)\",\n \"def status(self):\\n\\t\\treturn self._status\",\n \"def returnStatus(value, option):\\n # Updated 03/19/17\\n # if else to account for open/closed device vs on/off device\\n if option == 'open':\\n on = \\\"<font color='green'>Open</font>\\\"\\n off = \\\"<font color='red'>Closed</font>\\\"\\n else:\\n on = \\\"<font color='green'>On</font>\\\"\\n off = \\\"<font color='red'>Off</font>\\\"\\n \\n if value: \\n status = on\\n else: \\n status = off\\n \\n return status\",\n \"def get_status(self):\\n status = self._status.get_message()\\n \\n if status == \\\"N\\\":\\n return \\\"offline\\\"\\n \\n elif status == \\\"Y\\\":\\n return \\\"online\\\"\\n \\n elif status == \\\"A\\\":\\n return \\\"away\\\"\\n \\n elif status == \\\"B\\\":\\n return \\\"busy\\\"\",\n \"def state(self):\\n return STATE_ON if self._state == 1.0 else STATE_OFF\",\n \"def status(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"status\\\")\",\n \"def status(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"status\\\")\",\n \"def get_status(self):\\n\\n return self._system\",\n \"def _getCurrentComponentStatus(self):\\n resOverall = self.sysAdminClient.getOverallStatus()\\n if not resOverall['OK']:\\n return resOverall\\n currentStatus = {'Down': set(), 'Run': set(), 'All': set()}\\n informationDict = resOverall['Value']\\n for systemsDict in informationDict.values():\\n for system, instancesDict in systemsDict.items():\\n for instanceName, instanceInfoDict in instancesDict.items():\\n identifier = '%s__%s' % (system, instanceName)\\n runitStatus = instanceInfoDict.get('RunitStatus')\\n if runitStatus in ('Run', 'Down'):\\n currentStatus[runitStatus].add(identifier)\\n\\n currentStatus['All'] = currentStatus['Run'] | currentStatus['Down']\\n return S_OK(currentStatus)\",\n \"def getDetailedStatus (self):\\n try:\\n if ((self._activateData == None) or (self._activateData.blockDevice == None)):\\n self._log(\\\"no-block-device-no-status\\\").debug2(\\\"no block device was found for file system '%s'\\\",self._logicalDiskName)\\n return None,ReturnCodes.kOk\\n\\n else:\\n terminateTimeOut = self._activeTimeoutsConfig.getStatus\\n timer = common.Timer(terminateTimeOut)\\n blockDevice = self._activateData.blockDevice\\n statusDisctionary,rc = self._tune2fs(blockDevice,timer)\\n \\n if (rc != ReturnCodes.kOk):\\n self._log(\\\"get-status-failed\\\").error(\\\"getDetailedStatus() for file system '%s' failed!\\\",self._logicalDiskName)\\n return None,ReturnCodes.kGeneralError\\n\\n if (statusDisctionary == None):\\n self._log(\\\"have-block-but-no-status\\\").debug2(\\\"block device '%s' for file system '%s' - could not fins file-system status\\\",blockDevice,self._logicalDiskName)\\n else:\\n self._log(\\\"status-found\\\").debug2(\\\"block device '%s' for file system '%s' - status found!\\\",blockDevice,self._logicalDiskName)\\n\\n return statusDisctionary,rc\\n\\n except Exception,e:\\n self._log(\\\"get-file-system-status-exception\\\").error(\\\"getDetailedStatus(terminateTimeOut=%.2f) faild! exception = '%s'\\\",terminateTimeOut,e)\\n return None,ReturnCodes.kGeneralError\",\n \"def _status_apf():\\n status = 0\\n table = iptc.Table(iptc.Table.FILTER)\\n for chain in table.chains:\\n if \\\"sanity\\\" in chain.name.lower():\\n status = 1\\n return True if status else False\",\n \"def get_status(self):\\n # find status\\n # search in summary file first\\n self.status = \\\"running\\\"\\n status = self.search_summary(\\\"status\\\")\\n if status:\\n self.status = status.split()[1]\\n # define running time\\n # search in summary file first\\n self.running_time = \\\"00:00:00\\\"\\n running_time = self.search_summary(\\\"running-time\\\")\\n if running_time:\\n self.running_time = running_time.split()[1]\\n # calculate running time\\n else:\\n now = datetime.datetime.now()\\n elapsed_time = (now - self.ctime).seconds\\n hours, remainder = divmod(elapsed_time, 3600)\\n minutes, seconds = divmod(remainder, 60)\\n self.running_time = (\\n f\\\"{int(hours):02}:{int(minutes):02}:{int(seconds):02}\\\"\\n )\",\n \"def status(self, station=1):\\n return self.statuslist()[station][2]\",\n \"def GetStatus(reset=\\\"\\\"):\\n\\n if reset == \\\"reset\\\":\\n state_mgr.states={}\\n print \\\"States successfully reset.\\\"\\n\\n elif not reset.strip():\\n sorted_keys=state_mgr.states.keys()\\n sorted_keys.sort(key=lambda x:int(x[1:]))\\n print \\\"{:<7s}{:^8s}{:^20s}\\\".format(\\\"Unit:\\\", \\\"State:\\\", \\\"Last Update:\\\")\\n\\n for unit in sorted_keys:\\n try:\\n status = str(round(state_mgr.states[unit][0],1))+\\\"%\\\"\\n except TypeError:\\n status = state_mgr.states[unit][0]\\n\\n t = time.localtime(float(state_mgr.states[unit][1]))\\n hr = t.tm_hour\\n ampm=\\\"am\\\"\\n\\n if t.tm_hour>11:\\n hr -= 12\\n ampm=\\\"pm\\\"\\n\\n pretty_time = \\\"%s:%s:%s %s %s/%s/%s\\\"%(hr,str(t.tm_min).zfill(2),str(t.tm_sec).zfill(2),ampm,t.tm_mon,t.tm_mday,str(t.tm_year)[2:])\\n \\n print \\\"{:<8s}{:8s}{:>20s}\\\".format(unit, status, pretty_time)\\n\\n else:\\n raise TypeError,\\\"Invalid argument!\\\"\",\n \"def get_state(self):\\n return self.fmu._get_continuous_states()\",\n \"def test_status(self):\\n measurement = self.measurement(self.metric())\\n s_m = ScaleMeasurement(\\n previous_scale_measurement=None,\\n measurement=measurement,\\n status=\\\"target_met\\\",\\n )\\n status = s_m.status()\\n self.assertIs(type(status), str)\\n self.assertEqual(status, \\\"target_met\\\")\",\n \"def rtt_get_status(self):\\n status = structs.JLinkRTTerminalStatus()\\n res = self.rtt_control(enums.JLinkRTTCommand.GETSTAT, status)\\n return status\",\n \"def get_active(self):\\n return self._active\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.65024024","0.62104243","0.610275","0.6102556","0.6042355","0.59762746","0.59715307","0.59574205","0.58851373","0.58848566","0.586097","0.58262444","0.5805399","0.5769604","0.5743072","0.57369876","0.5720854","0.56900233","0.56890976","0.56871843","0.5685843","0.5681428","0.5658275","0.5617444","0.56156194","0.5614844","0.5611123","0.5601581","0.55994195","0.5592617","0.5591808","0.55811805","0.5568929","0.55597126","0.555569","0.554926","0.5536934","0.5525921","0.5512575","0.5512575","0.5512575","0.5512575","0.5508503","0.54994524","0.54971606","0.54906934","0.54731524","0.54731524","0.54731524","0.54715776","0.5457152","0.54510856","0.5440014","0.5439418","0.54389846","0.5433842","0.5432386","0.5431523","0.54287297","0.54216534","0.54010814","0.53884566","0.5387296","0.5376843","0.5355065","0.5353639","0.5349987","0.53497624","0.5349016","0.53422654","0.53398216","0.53364533","0.533418","0.533014","0.53206927","0.5314011","0.5312512","0.5310964","0.5308002","0.5303802","0.5302721","0.5300309","0.5293319","0.529036","0.5287069","0.52852976","0.5285199","0.5280913","0.52805084","0.52805084","0.5279306","0.52732396","0.52732044","0.5271985","0.5268221","0.52682185","0.52670914","0.52653635","0.52650845","0.5263587","0.5258566"],"string":"[\n \"0.65024024\",\n \"0.62104243\",\n \"0.610275\",\n \"0.6102556\",\n \"0.6042355\",\n \"0.59762746\",\n \"0.59715307\",\n \"0.59574205\",\n \"0.58851373\",\n \"0.58848566\",\n \"0.586097\",\n \"0.58262444\",\n \"0.5805399\",\n \"0.5769604\",\n \"0.5743072\",\n \"0.57369876\",\n \"0.5720854\",\n \"0.56900233\",\n \"0.56890976\",\n \"0.56871843\",\n \"0.5685843\",\n \"0.5681428\",\n \"0.5658275\",\n \"0.5617444\",\n \"0.56156194\",\n \"0.5614844\",\n \"0.5611123\",\n \"0.5601581\",\n \"0.55994195\",\n \"0.5592617\",\n \"0.5591808\",\n \"0.55811805\",\n \"0.5568929\",\n \"0.55597126\",\n \"0.555569\",\n \"0.554926\",\n \"0.5536934\",\n \"0.5525921\",\n \"0.5512575\",\n \"0.5512575\",\n \"0.5512575\",\n \"0.5512575\",\n \"0.5508503\",\n \"0.54994524\",\n \"0.54971606\",\n \"0.54906934\",\n \"0.54731524\",\n \"0.54731524\",\n \"0.54731524\",\n \"0.54715776\",\n \"0.5457152\",\n \"0.54510856\",\n \"0.5440014\",\n \"0.5439418\",\n \"0.54389846\",\n \"0.5433842\",\n \"0.5432386\",\n \"0.5431523\",\n \"0.54287297\",\n \"0.54216534\",\n \"0.54010814\",\n \"0.53884566\",\n \"0.5387296\",\n \"0.5376843\",\n \"0.5355065\",\n \"0.5353639\",\n \"0.5349987\",\n \"0.53497624\",\n \"0.5349016\",\n \"0.53422654\",\n \"0.53398216\",\n \"0.53364533\",\n \"0.533418\",\n \"0.533014\",\n \"0.53206927\",\n \"0.5314011\",\n \"0.5312512\",\n \"0.5310964\",\n \"0.5308002\",\n \"0.5303802\",\n \"0.5302721\",\n \"0.5300309\",\n \"0.5293319\",\n \"0.529036\",\n \"0.5287069\",\n \"0.52852976\",\n \"0.5285199\",\n \"0.5280913\",\n \"0.52805084\",\n \"0.52805084\",\n \"0.5279306\",\n \"0.52732396\",\n \"0.52732044\",\n \"0.5271985\",\n \"0.5268221\",\n \"0.52682185\",\n \"0.52670914\",\n \"0.52653635\",\n \"0.52650845\",\n \"0.5263587\",\n \"0.5258566\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":75594,"cells":{"query":{"kind":"string","value":"Switches the measure status and returns the correct effect factor."},"document":{"kind":"string","value":"def update_return_factor(self):\n if not self.active:\n effect = self._activate_return_factor()\n elif self.active:\n effect = self._deactivate_return_factor()\n return effect"},"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 _deactivate_return_factor(self):\n self.active = False\n effect = 1 / self.factor\n return effect","def _activate_return_factor(self):\n self.active = True\n effect = self.factor\n return effect","def effectiveness(self):\n self._effectiveness = 0.20 * self.ANA + 0.20 * self.DAM + 0.20 * self.MOA + 0.20 * self.MFA + 0.20 * self.NOP\n return round(self._effectiveness, 5)","def state(self):\n return self._measure","def effect(self) -> Optional[str]:\n return pulumi.get(self, \"effect\")","def effect(self) -> Optional[str]:\n return pulumi.get(self, \"effect\")","def effect(self) -> Optional[str]:\n return pulumi.get(self, \"effect\")","def effect(self) -> Optional[str]:\n return pulumi.get(self, \"effect\")","def effect(self) -> Optional[str]:\n return pulumi.get(self, \"effect\")","def effect(self) -> Optional[str]:\n return pulumi.get(self, \"effect\")","def effect(self) -> Optional[str]:\n return pulumi.get(self, \"effect\")","def effect(self) -> Optional[str]:\n return pulumi.get(self, \"effect\")","def effect(self) -> Optional[str]:\n return pulumi.get(self, \"effect\")","def effect(self) -> Optional[str]:\n return pulumi.get(self, \"effect\")","def effect(self) -> Optional[str]:\n return pulumi.get(self, \"effect\")","def effect(self) -> Optional[str]:\n return pulumi.get(self, \"effect\")","def effect(self) -> Optional[str]:\n return pulumi.get(self, \"effect\")","def effect(self) -> Optional[str]:\n return pulumi.get(self, \"effect\")","def effect(self) -> Optional[str]:\n return pulumi.get(self, \"effect\")","def effect(self) -> Optional[str]:\n return pulumi.get(self, \"effect\")","def effect(self) -> Optional[str]:\n return pulumi.get(self, \"effect\")","def effect(self) -> Optional[str]:\n return pulumi.get(self, \"effect\")","def effect(self) -> Optional[str]:\n return pulumi.get(self, \"effect\")","def effect(self) -> Optional[str]:\n return pulumi.get(self, \"effect\")","def measure(self):\n return self._measure","def effect(self) -> str:\n return self._id_data.get(\"effect\", \"\")","def measure(self):\n pass","def measureDispatch(self):\n\n if self.ui.measureDevice.currentText().startswith('Built-In'):\n self.app.measure.startMeasurement()\n self.app.message.emit('Measurement enabled', 0)\n self.deviceStat['measure'] = True\n self.ui.measureDevice.setStyleSheet(self.BACK_GREEN)\n else:\n self.app.measure.stopMeasurement()\n self.app.message.emit('Measurement disabled', 0)\n self.deviceStat['measure'] = None\n self.ui.measureDevice.setStyleSheet(self.BACK_NORM)\n\n return True","def contrast(self, val):\n self._contrast = max(0, min(val, 0x7F)) # Clamp to values 0-0x7f\n self.write_cmd(self.CMD_SET_VOLUME_FIRST)\n self.write_cmd(self.CMD_SET_VOLUME_SECOND | (self._contrast & 0x3F))","def _take_measure(self):\n if self.simulate_noise:\n noise = random() * self._max_noise\n else:\n noise = 0 \n self._measures[self.position] = \\\n self.scene.fvalues[self.position] + noise","def proc_status_effect(\n self,\n status_att=None,\n status_val=False,\n resist=None\n ):\n\n # If a resist attribute is passed, the player\n # will attempt to resist the status change\n\n if resist is not None:\n succ,bonus = RandomRoll(\n self,\n getattr(self,resist),\n 75\n )\n else:\n succ = False\n\n if succ:\n pass\n else:\n setattr(self,status_att,status_val)","def preformance(self, perf):\n self.perf = perf","def functionality(self):\n self._functionality = 0.12 * self.CAMC + 0.22 * self.NOP + 0.22 * self.CIS + 0.22 * self.DSC + 0.22 * self.NOH\n return round(self._functionality, 5)","def updateMeter(self, name1, name2, op):\r\n mini = 0\r\n maxi = 100\r\n pos = (self.var.get() - mini) / (maxi - mini)\r\n self.updateMeterLine(pos * 0.6 + 0.2)","def contrast(self, value: int, /) -> None:","def get_measured_current(self):\n status = self.get_status_response()\n current = status[16] + (status[17] * 0x100) + (status[18] * 0x10000) + (status[19] * 0x1000000)\n current = float(current)\n current /= (1000.0 * 1000.0)\n return current\n #end get_measured_current","def lightSpeed():\n return const.c.value","def test_change_brightness_back_to_10():","def multiplier(self):\n if self.isPaused:\n return 0\n else:\n return self._multiplier","def turn_effects(self):\n if self.side_effects[\"shield\"] > 0:\n self.side_effects[\"shield\"] -= 1","def get_set_current(self):\n status = self.get_status_response()\n flags = status[1]\n if (flags & 0x2) == 0:\n return 0.0 #test isn't running\n current = status[3] + (status[4] * 0x100) + (status[5] * 0x10000) + (status[6] * 0x1000000)\n current = float(current)\n current /= (1000.0 * 1000.0)\n return current\n #end get_set_current()","def current_fan_mode(self):\n fan_speed = self.data.get(\"windspeed\")\n if fan_speed is None:\n return None\n if fan_speed == \"1\":\n return \"low\"\n elif fan_speed == \"2\":\n return \"medium\"\n elif fan_speed == \"3\":\n return \"high\"\n return fan_speed","def _measure(self, quantity: str) -> str:\n if self.dmm.active_terminal.get() == 'REAR':\n self.write(f\"SENS:FUNC '{quantity}', (@{self.channel:d})\")\n self.write(f\"ROUT:CLOS (@{self.channel:d})\")\n return self.ask(\"READ?\")\n else:\n raise RuntimeError(\"Front terminal is active instead of rear terminal.\")","def test_change_brightness_of_the_devicetrue():","def set_measured_activity(self):\n\t\t\n\t\t# True receptor activity\n\t\tself.Yy = receptor_activity(self.Ss, self.Kk1, self.Kk2, self.eps)\n\t\t\n\t\t# Learned background activity only utilizes average background signal \n\t\tself.Yy0 = receptor_activity(self.Ss0, self.Kk1, self.Kk2, self.eps)\n\t\t\n\t\t# Measured response above background\n\t\tself.dYy = self.Yy - self.Yy0\n\t\n\t\t# Add effects of divisive normalization if called.\n\t\tif self.divisive_normalization == True:\n\t\t\tself.Yy0 = inhibitory_normalization(self.Yy0, self.inh_C, \n\t\t\t\t\t\tself.inh_D, self.inh_eta, self.inh_R)\n\t\t\tself.Yy = inhibitory_normalization(self.Yy, self.inh_C, \n\t\t\t\t\t\tself.inh_D, self.inh_eta, self.inh_R)\n\t\t\tself.dYy = self.Yy - self.Yy0","def test_changeIlluminationLevel(self):\n fade_to_black = \"Your environs fade to black due to Ineffable Spooky Magic.\"\n no_change = \"You do it. Swell.\"\n dark_to_light = \"Your environs are suddenly alight.\"\n brighten = \"Your environs seem slightly brighter.\"\n endarken = \"Your environs seem slightly dimmer.\"\n Manipulator.createFor(self.playerWrapper.actor)\n\n self._test(\n \"illuminate 0\",\n [fade_to_black],\n [fade_to_black])\n\n ll = self.store.findUnique(\n objects.LocationLighting,\n objects.LocationLighting.thing == self.location)\n self.assertEquals(ll.candelas, 0)\n\n self._test(\n \"illuminate 0\",\n [no_change])\n self.assertEquals(ll.candelas, 0)\n\n self._test(\n \"illuminate 100\",\n [dark_to_light],\n [dark_to_light])\n self.assertEquals(ll.candelas, 100)\n\n self._test(\n \"illuminate 110\",\n [brighten],\n [brighten])\n self.assertEquals(ll.candelas, 110)\n\n self._test(\n \"illuminate 100\",\n [endarken],\n [endarken])\n self.assertEquals(ll.candelas, 100)\n\n self._test(\n \"illuminate 0\",\n [fade_to_black],\n [fade_to_black])\n self.assertEquals(ll.candelas, 0)","def getScale(self):\n return self.factor**self.turnOn","def desition_based_on_us(self, action_result):\n if isinstance(action_result, int) or action_result is None:\n pos_to_measure = self.determine_measure_position()\n return self.measure(pos_to_measure)\n #If the agent's last action was to measure, then shooting\n #should be considered only if green or yellow were the\n #result.\n elif isinstance(action_result, str):\n self.update_probs(action_result, self.last_measured_pos)\n #It is unlikely for the enemy to stay on a position if\n #it is being targeted. Based on that assumption, the\n #agent supposes measuring at the same position will\n #result on obtaining yellow.\n if action_result == GREEN:\n self.update_probs(YELLOW, self.last_measured_pos)\n pos_to_shoot = self.__get_best_pos_to_shoot()\n return self.shoot(pos_to_shoot)\n elif action_result == YELLOW:\n pos_to_shoot = self.__get_best_pos_to_shoot()\n return self.shoot(pos_to_shoot)\n \n pos_to_measure = self.determine_measure_position()\n return self.measure(pos_to_measure)","def test_set_and_get_led_brightness_level(self):","def test_status(self):\n measurement = self.measurement(self.metric())\n s_m = ScaleMeasurement(\n previous_scale_measurement=None,\n measurement=measurement,\n status=\"target_met\",\n )\n status = s_m.status()\n self.assertIs(type(status), str)\n self.assertEqual(status, \"target_met\")","def volume_level(self):\n if 'mixer volume' in self._status:\n return int(self._status['mixer volume']) / 100.0","def test_set_state(self):\n self.sensor.measure = MagicMock(return_value=True)\n self.assertEqual(self.sensor.measure(), True)","def get_fare(self):\r\n return super().get_fare()*self.fanciness_rating+self.flagfall","def sm_measure_current(self,num_readings=1):\n self.sm.set_measurement_function(\"CURRENT\")\n self.sm.format_readings(\"CURRENT\")\n ret = average(self.sm.take_measurement(num_readings))\n self.sm_restore_display\n return ret","def test_change_brightness_of_the_device_false():","def sensitivity(self):\n return self.recall","def getMeasurementMode(self): \n return getI1ProMeasurementMode()","def setMyStatus(self):\n self.clearMyStatus()\n for id, myComponent in self.components.iteritems():\n self.currentComps += 1\n if myComponent.type != '':\n compData = self.componentdata[myComponent.type]\n if myComponent.currentHP == myComponent.myComponentData.maxHP:\n # regular component set quad Attributes\n if compData.typeAP != '':\n self.typeAP = compData.typeAP\n elif compData.assault > 0:\n self.maxAssault += compData.assault\n self.maxAP += compData.maxAP\n self.maxSP += compData.maxSP\n self.genSP += compData.genSP\n self.maxPower += compData.power\n self.maxBattery += compData.battery\n self.thrust += compData.engine\n self.rotation += compData.rotate\n self.radar += compData.radar\n self.jamming += compData.jamming\n self.repair += compData.repair\n self.target += compData.target\n self.mass += compData.mass\n \n # tell weapons in quad to recalc their status\n for id, myWeapon in self.weapons.iteritems():\n myWeapon.setMyStatus()","def basePotionEffect(self):\n return int(self._basePotionEffect)","def set_contrast(level):\n send_command(0x81)\n send_command(level)","def mixfactor(self, segment):\n mixfactor = 0\n a = (89.0/1.5) + self.template['mixpoint']\n b = (188.0/1.5) + self.template['mixpoint']\n loud = self.loudness(self.original.analysis.segments, segment)\n if not loud:\n loud = self.original.analysis.loudness\n if loud != -1 * b:\n mixfactor = float(float(loud + a)/float(loud + b))\n if mixfactor > 0.8:\n mixfactor = 0.8\n elif mixfactor < 0.3:\n mixfactor = 0.3\n return mixfactor","def set_wdiff(self):\n try:\n self.wdiff=self.mdiff*self.ws.coef[1]\n except:\n self.wdiff=self.mdiff","def measure(self, lastMeasure=None, m=None):\n if m is None:\n m = {}\n m['_time'] = time.time()\n if lastMeasure is not None:\n m['_stepDuration'] = time.time() - lastMeasure['_time']\n else:\n m['_stepDuration'] = time.time() - self._start_t\n self._msr(m)\n return m","def treat(item=None, num=0):\n\n if item is None or num < 0:\n print 'Invalid input, please check the input.'\n return\n\n if num == 0:\n value = 0\n return value\n\n if item.lim == 0: # bed\n threshold = 100\n if num <= threshold:\n value = item.effect[0] * num\n else:\n value = item.effect[0] * threshold + \\\n item.effect[1] * (num - threshold)\n else:\n value = sum([item.effect[i] for i in xrange(min(num, item.lim))])\n\n return value","def OnSetContrast(self, evt=None):\n\t\twith self.playerLock :\n\t\t\t#self.player.video_set_adjust_int( vlc.VideoAdjustOption.Enable, 1 )\n\t\t\tcontrast = self.contrastSlider.GetValue() * 2\n\t\t\tprint( 'new contrast: {}'.format( contrast ) )\n\t\t\tif self.player.video_set_adjust_float( vlc.VideoAdjustOption.Contrast, contrast/100.0 ) == -1:\n\t\t\t\tself.errorDialog(\"Failed to set contrast\")\n\t\t\telse:\n\t\t\t\tself.State.contrast = contrast/2\n\t\t\t\tself.SaveState()","def reenlightenment(self):\n return self._reenlightenment","def test_status(self):\n self.assertEqual('perfect', self.__metric.status())","def performance_measure(self, x):\n # \"calculate performance measure\" \n pref = x.evaluate()\n return pref","def normalize(self, factor):\n self._value /= factor\n self._uncertainty /= factor","def get_capacitor(self):\n cap = 0.5 * self.metric_.logdet()\n return cap","def get_effect(self, label: str) -> Effect:\r\n return self._get_resource(label, self._effects, \"effect\")","def measure(self, item):\n return self.parent.measure(item, 'CHAN{}'.format(self.ch))","def getMeasure(unique_name):","def getMeasure(unique_name):","def sense_and_act(self):\n pass","def measure_darks(det, shutter, quantity):\n yield from set_dark_frame()\n yield from bps.mv(shutter, \"close\")\n yield from _acquire_n_frames(det, quantity)","def translate_fear_level(fear_level):\n if fear_level == 1:\n scariness_threshold = 6\n elif fear_level == 2:\n scariness_threshold = 5\n else:\n scariness_threshold = 4\n return scariness_threshold","def getMeasure(self, measure, Filter=False):\n assert not measure == 'XYZ', 'Measure \\'XYZ\\' cannot be used with getMeasure()'\n\n self.line1 = '-f' + str(self.functionRef[measure]) + ',' + '0,0,10'\n\n self.line2 = '-s' + self.LMOutputFName\n\n self.writeLMIn(self.line1, self.line2, self.line3)\n\n self.runLM()\n\n self.outputFormat = 1\n self.readOutput()\n\n return self.LMOutput","def treat(self):\r\n if self.noiseS > 0:\r\n self.evaluations = min((self.evaluations * self.alphaevals, self.maxevals))\r\n return self.alphasigma\r\n else:\r\n self.evaluations = max((self.evaluations * self.alphaevalsdown, self.minevals))\r\n return 1.0","def crater_factor(current_weather):\n if current_weather == 'SUNNY':\n factor = 0.9\n elif current_weather == 'RAINY':\n factor = 1.2\n else:\n factor = 1\n return factor","def gain(self):\n return self[1]","def set_volume_effects(self, value):\n\t\tif self._setting.get(FIFE_MODULE, \"PlaySounds\"):\n\t\t\tself.emitter['effects'].setGain(value)\n\t\t\tself.emitter['speech'].setGain(value)\n\t\t\tfor e in self.emitter['ambient']:\n\t\t\t\te.setGain(value*2)","def sense(p, Z):\n # TODO: insert your code here\n\n if Z == 'green':\n p = compute_measure_green(p, Z)\n elif Z == 'red':\n p = compute_measure_red(p, Z)\n\n norm = [float(i)/sum(p) for i in p]\n p = norm\n\n return p","def sense(p, Z):\n # TODO: insert your code here\n\n if Z == 'green':\n p = compute_measure_green(p, Z)\n elif Z == 'red':\n p = compute_measure_red(p, Z)\n\n norm = [float(i)/sum(p) for i in p]\n p = norm\n\n return p","def calibrate_meter(self):\r\n print(\"meter calibrated\")","def controlDelta(self, prev_delta, acc_rate):\n if (acc_rate > .6):\n return prev_delta * 1.1\n elif (acc_rate < .4):\n return prev_delta / 1.1\n else:\n return prev_delta","def sense_and_act(self):\n values = self.sensob_camera.get_value()\n if values[0] > values[1] and values[0] > values[2]:\n self.update_mr(\"L\", 8)\n self.match_degree = values[0]\n elif values[1] >= values[0] and values[1] >= values[2]:\n self.update_mr(\"F\", 30)\n self.match_degree = values[1]\n elif values[2] >= values[0] and values[2] > values[1]:\n self.update_mr(\"R\", 8)\n self.match_degree = values[2]\n self.update_weight(self.priority, self.match_degree)","def _update_state_value(self, old_state, new_state):\n if not self._test_mode:\n if isinstance(new_state, int):\n self._state_values[old_state] += self._alpha * \\\n (new_state - self._state_values[old_state])\n else:\n self._state_values[old_state] += self._alpha * \\\n (self._state_values[new_state] - self._state_values[old_state])","def test_is_ramped_using_int(self):\n self.feature_test.set_percentage(100)\n self.assertTrue(self.feature_test._is_ramped(5))","def change_speed(self, action):\r\n if action == \"faster\":\r\n self.speed += 1\r\n else:\r\n if self.speed > 1:\r\n self.speed -= 1","def change_mode(self):\n return (self.mode + 1) % 2","def disoverMode(system_sum):\n # Updated as of 11/19/16\n ## NO FLOATING INPUTS ALLOWED ##\n if system_sum == 0x00:\n status = \"System in Standby\"\n elif system_sum == 0x06:\n status = \"System Initializing\"\n elif system_sum == 0x1C:\n status = \"Side 1 Running\"\n elif system_sum == 0x31:\n status = \"Side 2 Running\"\n else:\n status = \"NOTICE: Unknown Mode\"\n \n return status","def fan_mode(self):\n if self.ac.status is None:\n _LOGGER.debug(f\"fan_mode: status is None, returning None\")\n return None\n if self.ac.status.is_on:\n fan_speed = self.ac.status.fan_speed\n value = self.FAN_MODE_MAPPING[fan_speed]\n _LOGGER.debug(f\"fan_mode: returning {value} (derived from {fan_speed})\")\n return value\n else:\n _LOGGER.debug(f\"fan_mode: returning FAN_OFF - device is off\")\n return FAN_OFF","def _get_new_oxygen_level(self, action):\n decay_constant = 1\n\n if action == 0: #breathingpneumostome\n self.oxygen_level += 5\n elif action == 1: #breathingskin\n self.oxygen_level +=1\n else : #swimming actions\n self.oxygen_level = self.oxygen_level * math.exp(-decay_constant * self.t)\n self.oxygen_level = int(round(self.oxygen_level))\n print('oxygen_level: {}'.format(self.oxygen_level))\n return self.oxygen_level","def eff(self, speed):\n\n # variables\n starting = self.working * self.starting\n shutdown = self.working * self.shutdown\n k = self.max_eff / (self.working - starting)\n b = - k * starting\n\n # 1: Before starting\n if speed < starting:\n return 0\n\n # 2: Increasing\n if speed >= starting and speed < self.working:\n return k * speed + b\n\n # 3: Shutdown\n if speed >= self.working and speed < shutdown:\n return -k * (speed - self.working) + self.max_eff\n\n # 4: Off\n if speed >= shutdown:\n return 0","def set_reduced_mode(self, on):\r\n return self._arm.set_reduced_mode(on)","def set_value(self, name, val, **kwargs):\n try:\n assert self.__started\n val = float(val)\n except AssertionError:\n self.unknown_error(\"Start method must be called first!\")\n except (TypeError, ValueError):\n self.unknown_error(\"Performance measures must be numeric!\")\n\n val_dict = {'val': val, 'min': None, 'max': None, 'scale': None,\n 'threshold': 1, 'state': 'ok'}\n\n if 'lowest' in kwargs:\n val_dict['min'] = float(kwargs.get('lowest'))\n\n if 'highest' in kwargs:\n val_dict['max'] = float(kwargs.get('highest'))\n\n if 'threshold' in kwargs:\n val_dict['threshold'] = kwargs['threshold']\n\n # Nagios actually understands most byte and time oriented scales.\n # The developer docs also list a counter scale, but we're not certain\n # if any plugin has ever used that. Only accept known scales.\n\n if 'scale' in kwargs:\n scale = kwargs.get('scale')\n\n if scale.upper() in ('B', 'KB', 'MB', 'GB', 'TB'):\n val_dict['scale'] = scale.upper()\n elif scale.lower() in ('s', 'ms', 'us', 'ns', '%'):\n val_dict['scale'] = scale.lower()\n else:\n raise ValueError(\"\"\"Scale must be one of: B, KB, MB, GB,\n TB, s, ms, us, ns, or %.\"\"\")\n\n self.__perf[name] = val_dict\n\n # We'll use the opportunity to check the status ranges right when the\n # variable is set so we don't have to loop through all of them later.\n\n if len(self.__warning) > 0:\n self.__verbose_print(\"checking warning\")\n self.__check_range('warning', name)\n\n if len(self.__critical) > 0:\n self.__verbose_print(\"checking critical\")\n self.__check_range('critical', name)\n\n return self.__perf[name]['state']","def change_contrast(self, b):\n self.fft_plot.set_clim(0, self.contrast_slider.value * self.v_range)\n clear_output()\n display(self.fig)","def ErrorMeasure(self, new_int, old_int):\n \n \n mean=np.fabs(new_int[0].val - old_int[0].val)\n var=np.fabs(new_int[1].val - old_int[1].val)\n if old_int[1].val == 0.0:\n return (1.0-self.wmix) * var + self.wmix * mean\n else:\n return (1.0-self.wmix) * var / np.fabs(old_int[1].val) + self.wmix * mean / np.fabs(old_int[0].val)","def basePotionEffect(self, value):\n self._basePotionEffect = value"],"string":"[\n \"def _deactivate_return_factor(self):\\n self.active = False\\n effect = 1 / self.factor\\n return effect\",\n \"def _activate_return_factor(self):\\n self.active = True\\n effect = self.factor\\n return effect\",\n \"def effectiveness(self):\\n self._effectiveness = 0.20 * self.ANA + 0.20 * self.DAM + 0.20 * self.MOA + 0.20 * self.MFA + 0.20 * self.NOP\\n return round(self._effectiveness, 5)\",\n \"def state(self):\\n return self._measure\",\n \"def effect(self) -> Optional[str]:\\n return pulumi.get(self, \\\"effect\\\")\",\n \"def effect(self) -> Optional[str]:\\n return pulumi.get(self, \\\"effect\\\")\",\n \"def effect(self) -> Optional[str]:\\n return pulumi.get(self, \\\"effect\\\")\",\n \"def effect(self) -> Optional[str]:\\n return pulumi.get(self, \\\"effect\\\")\",\n \"def effect(self) -> Optional[str]:\\n return pulumi.get(self, \\\"effect\\\")\",\n \"def effect(self) -> Optional[str]:\\n return pulumi.get(self, \\\"effect\\\")\",\n \"def effect(self) -> Optional[str]:\\n return pulumi.get(self, \\\"effect\\\")\",\n \"def effect(self) -> Optional[str]:\\n return pulumi.get(self, \\\"effect\\\")\",\n \"def effect(self) -> Optional[str]:\\n return pulumi.get(self, \\\"effect\\\")\",\n \"def effect(self) -> Optional[str]:\\n return pulumi.get(self, \\\"effect\\\")\",\n \"def effect(self) -> Optional[str]:\\n return pulumi.get(self, \\\"effect\\\")\",\n \"def effect(self) -> Optional[str]:\\n return pulumi.get(self, \\\"effect\\\")\",\n \"def effect(self) -> Optional[str]:\\n return pulumi.get(self, \\\"effect\\\")\",\n \"def effect(self) -> Optional[str]:\\n return pulumi.get(self, \\\"effect\\\")\",\n \"def effect(self) -> Optional[str]:\\n return pulumi.get(self, \\\"effect\\\")\",\n \"def effect(self) -> Optional[str]:\\n return pulumi.get(self, \\\"effect\\\")\",\n \"def effect(self) -> Optional[str]:\\n return pulumi.get(self, \\\"effect\\\")\",\n \"def effect(self) -> Optional[str]:\\n return pulumi.get(self, \\\"effect\\\")\",\n \"def effect(self) -> Optional[str]:\\n return pulumi.get(self, \\\"effect\\\")\",\n \"def effect(self) -> Optional[str]:\\n return pulumi.get(self, \\\"effect\\\")\",\n \"def measure(self):\\n return self._measure\",\n \"def effect(self) -> str:\\n return self._id_data.get(\\\"effect\\\", \\\"\\\")\",\n \"def measure(self):\\n pass\",\n \"def measureDispatch(self):\\n\\n if self.ui.measureDevice.currentText().startswith('Built-In'):\\n self.app.measure.startMeasurement()\\n self.app.message.emit('Measurement enabled', 0)\\n self.deviceStat['measure'] = True\\n self.ui.measureDevice.setStyleSheet(self.BACK_GREEN)\\n else:\\n self.app.measure.stopMeasurement()\\n self.app.message.emit('Measurement disabled', 0)\\n self.deviceStat['measure'] = None\\n self.ui.measureDevice.setStyleSheet(self.BACK_NORM)\\n\\n return True\",\n \"def contrast(self, val):\\n self._contrast = max(0, min(val, 0x7F)) # Clamp to values 0-0x7f\\n self.write_cmd(self.CMD_SET_VOLUME_FIRST)\\n self.write_cmd(self.CMD_SET_VOLUME_SECOND | (self._contrast & 0x3F))\",\n \"def _take_measure(self):\\n if self.simulate_noise:\\n noise = random() * self._max_noise\\n else:\\n noise = 0 \\n self._measures[self.position] = \\\\\\n self.scene.fvalues[self.position] + noise\",\n \"def proc_status_effect(\\n self,\\n status_att=None,\\n status_val=False,\\n resist=None\\n ):\\n\\n # If a resist attribute is passed, the player\\n # will attempt to resist the status change\\n\\n if resist is not None:\\n succ,bonus = RandomRoll(\\n self,\\n getattr(self,resist),\\n 75\\n )\\n else:\\n succ = False\\n\\n if succ:\\n pass\\n else:\\n setattr(self,status_att,status_val)\",\n \"def preformance(self, perf):\\n self.perf = perf\",\n \"def functionality(self):\\n self._functionality = 0.12 * self.CAMC + 0.22 * self.NOP + 0.22 * self.CIS + 0.22 * self.DSC + 0.22 * self.NOH\\n return round(self._functionality, 5)\",\n \"def updateMeter(self, name1, name2, op):\\r\\n mini = 0\\r\\n maxi = 100\\r\\n pos = (self.var.get() - mini) / (maxi - mini)\\r\\n self.updateMeterLine(pos * 0.6 + 0.2)\",\n \"def contrast(self, value: int, /) -> None:\",\n \"def get_measured_current(self):\\n status = self.get_status_response()\\n current = status[16] + (status[17] * 0x100) + (status[18] * 0x10000) + (status[19] * 0x1000000)\\n current = float(current)\\n current /= (1000.0 * 1000.0)\\n return current\\n #end get_measured_current\",\n \"def lightSpeed():\\n return const.c.value\",\n \"def test_change_brightness_back_to_10():\",\n \"def multiplier(self):\\n if self.isPaused:\\n return 0\\n else:\\n return self._multiplier\",\n \"def turn_effects(self):\\n if self.side_effects[\\\"shield\\\"] > 0:\\n self.side_effects[\\\"shield\\\"] -= 1\",\n \"def get_set_current(self):\\n status = self.get_status_response()\\n flags = status[1]\\n if (flags & 0x2) == 0:\\n return 0.0 #test isn't running\\n current = status[3] + (status[4] * 0x100) + (status[5] * 0x10000) + (status[6] * 0x1000000)\\n current = float(current)\\n current /= (1000.0 * 1000.0)\\n return current\\n #end get_set_current()\",\n \"def current_fan_mode(self):\\n fan_speed = self.data.get(\\\"windspeed\\\")\\n if fan_speed is None:\\n return None\\n if fan_speed == \\\"1\\\":\\n return \\\"low\\\"\\n elif fan_speed == \\\"2\\\":\\n return \\\"medium\\\"\\n elif fan_speed == \\\"3\\\":\\n return \\\"high\\\"\\n return fan_speed\",\n \"def _measure(self, quantity: str) -> str:\\n if self.dmm.active_terminal.get() == 'REAR':\\n self.write(f\\\"SENS:FUNC '{quantity}', (@{self.channel:d})\\\")\\n self.write(f\\\"ROUT:CLOS (@{self.channel:d})\\\")\\n return self.ask(\\\"READ?\\\")\\n else:\\n raise RuntimeError(\\\"Front terminal is active instead of rear terminal.\\\")\",\n \"def test_change_brightness_of_the_devicetrue():\",\n \"def set_measured_activity(self):\\n\\t\\t\\n\\t\\t# True receptor activity\\n\\t\\tself.Yy = receptor_activity(self.Ss, self.Kk1, self.Kk2, self.eps)\\n\\t\\t\\n\\t\\t# Learned background activity only utilizes average background signal \\n\\t\\tself.Yy0 = receptor_activity(self.Ss0, self.Kk1, self.Kk2, self.eps)\\n\\t\\t\\n\\t\\t# Measured response above background\\n\\t\\tself.dYy = self.Yy - self.Yy0\\n\\t\\n\\t\\t# Add effects of divisive normalization if called.\\n\\t\\tif self.divisive_normalization == True:\\n\\t\\t\\tself.Yy0 = inhibitory_normalization(self.Yy0, self.inh_C, \\n\\t\\t\\t\\t\\t\\tself.inh_D, self.inh_eta, self.inh_R)\\n\\t\\t\\tself.Yy = inhibitory_normalization(self.Yy, self.inh_C, \\n\\t\\t\\t\\t\\t\\tself.inh_D, self.inh_eta, self.inh_R)\\n\\t\\t\\tself.dYy = self.Yy - self.Yy0\",\n \"def test_changeIlluminationLevel(self):\\n fade_to_black = \\\"Your environs fade to black due to Ineffable Spooky Magic.\\\"\\n no_change = \\\"You do it. Swell.\\\"\\n dark_to_light = \\\"Your environs are suddenly alight.\\\"\\n brighten = \\\"Your environs seem slightly brighter.\\\"\\n endarken = \\\"Your environs seem slightly dimmer.\\\"\\n Manipulator.createFor(self.playerWrapper.actor)\\n\\n self._test(\\n \\\"illuminate 0\\\",\\n [fade_to_black],\\n [fade_to_black])\\n\\n ll = self.store.findUnique(\\n objects.LocationLighting,\\n objects.LocationLighting.thing == self.location)\\n self.assertEquals(ll.candelas, 0)\\n\\n self._test(\\n \\\"illuminate 0\\\",\\n [no_change])\\n self.assertEquals(ll.candelas, 0)\\n\\n self._test(\\n \\\"illuminate 100\\\",\\n [dark_to_light],\\n [dark_to_light])\\n self.assertEquals(ll.candelas, 100)\\n\\n self._test(\\n \\\"illuminate 110\\\",\\n [brighten],\\n [brighten])\\n self.assertEquals(ll.candelas, 110)\\n\\n self._test(\\n \\\"illuminate 100\\\",\\n [endarken],\\n [endarken])\\n self.assertEquals(ll.candelas, 100)\\n\\n self._test(\\n \\\"illuminate 0\\\",\\n [fade_to_black],\\n [fade_to_black])\\n self.assertEquals(ll.candelas, 0)\",\n \"def getScale(self):\\n return self.factor**self.turnOn\",\n \"def desition_based_on_us(self, action_result):\\n if isinstance(action_result, int) or action_result is None:\\n pos_to_measure = self.determine_measure_position()\\n return self.measure(pos_to_measure)\\n #If the agent's last action was to measure, then shooting\\n #should be considered only if green or yellow were the\\n #result.\\n elif isinstance(action_result, str):\\n self.update_probs(action_result, self.last_measured_pos)\\n #It is unlikely for the enemy to stay on a position if\\n #it is being targeted. Based on that assumption, the\\n #agent supposes measuring at the same position will\\n #result on obtaining yellow.\\n if action_result == GREEN:\\n self.update_probs(YELLOW, self.last_measured_pos)\\n pos_to_shoot = self.__get_best_pos_to_shoot()\\n return self.shoot(pos_to_shoot)\\n elif action_result == YELLOW:\\n pos_to_shoot = self.__get_best_pos_to_shoot()\\n return self.shoot(pos_to_shoot)\\n \\n pos_to_measure = self.determine_measure_position()\\n return self.measure(pos_to_measure)\",\n \"def test_set_and_get_led_brightness_level(self):\",\n \"def test_status(self):\\n measurement = self.measurement(self.metric())\\n s_m = ScaleMeasurement(\\n previous_scale_measurement=None,\\n measurement=measurement,\\n status=\\\"target_met\\\",\\n )\\n status = s_m.status()\\n self.assertIs(type(status), str)\\n self.assertEqual(status, \\\"target_met\\\")\",\n \"def volume_level(self):\\n if 'mixer volume' in self._status:\\n return int(self._status['mixer volume']) / 100.0\",\n \"def test_set_state(self):\\n self.sensor.measure = MagicMock(return_value=True)\\n self.assertEqual(self.sensor.measure(), True)\",\n \"def get_fare(self):\\r\\n return super().get_fare()*self.fanciness_rating+self.flagfall\",\n \"def sm_measure_current(self,num_readings=1):\\n self.sm.set_measurement_function(\\\"CURRENT\\\")\\n self.sm.format_readings(\\\"CURRENT\\\")\\n ret = average(self.sm.take_measurement(num_readings))\\n self.sm_restore_display\\n return ret\",\n \"def test_change_brightness_of_the_device_false():\",\n \"def sensitivity(self):\\n return self.recall\",\n \"def getMeasurementMode(self): \\n return getI1ProMeasurementMode()\",\n \"def setMyStatus(self):\\n self.clearMyStatus()\\n for id, myComponent in self.components.iteritems():\\n self.currentComps += 1\\n if myComponent.type != '':\\n compData = self.componentdata[myComponent.type]\\n if myComponent.currentHP == myComponent.myComponentData.maxHP:\\n # regular component set quad Attributes\\n if compData.typeAP != '':\\n self.typeAP = compData.typeAP\\n elif compData.assault > 0:\\n self.maxAssault += compData.assault\\n self.maxAP += compData.maxAP\\n self.maxSP += compData.maxSP\\n self.genSP += compData.genSP\\n self.maxPower += compData.power\\n self.maxBattery += compData.battery\\n self.thrust += compData.engine\\n self.rotation += compData.rotate\\n self.radar += compData.radar\\n self.jamming += compData.jamming\\n self.repair += compData.repair\\n self.target += compData.target\\n self.mass += compData.mass\\n \\n # tell weapons in quad to recalc their status\\n for id, myWeapon in self.weapons.iteritems():\\n myWeapon.setMyStatus()\",\n \"def basePotionEffect(self):\\n return int(self._basePotionEffect)\",\n \"def set_contrast(level):\\n send_command(0x81)\\n send_command(level)\",\n \"def mixfactor(self, segment):\\n mixfactor = 0\\n a = (89.0/1.5) + self.template['mixpoint']\\n b = (188.0/1.5) + self.template['mixpoint']\\n loud = self.loudness(self.original.analysis.segments, segment)\\n if not loud:\\n loud = self.original.analysis.loudness\\n if loud != -1 * b:\\n mixfactor = float(float(loud + a)/float(loud + b))\\n if mixfactor > 0.8:\\n mixfactor = 0.8\\n elif mixfactor < 0.3:\\n mixfactor = 0.3\\n return mixfactor\",\n \"def set_wdiff(self):\\n try:\\n self.wdiff=self.mdiff*self.ws.coef[1]\\n except:\\n self.wdiff=self.mdiff\",\n \"def measure(self, lastMeasure=None, m=None):\\n if m is None:\\n m = {}\\n m['_time'] = time.time()\\n if lastMeasure is not None:\\n m['_stepDuration'] = time.time() - lastMeasure['_time']\\n else:\\n m['_stepDuration'] = time.time() - self._start_t\\n self._msr(m)\\n return m\",\n \"def treat(item=None, num=0):\\n\\n if item is None or num < 0:\\n print 'Invalid input, please check the input.'\\n return\\n\\n if num == 0:\\n value = 0\\n return value\\n\\n if item.lim == 0: # bed\\n threshold = 100\\n if num <= threshold:\\n value = item.effect[0] * num\\n else:\\n value = item.effect[0] * threshold + \\\\\\n item.effect[1] * (num - threshold)\\n else:\\n value = sum([item.effect[i] for i in xrange(min(num, item.lim))])\\n\\n return value\",\n \"def OnSetContrast(self, evt=None):\\n\\t\\twith self.playerLock :\\n\\t\\t\\t#self.player.video_set_adjust_int( vlc.VideoAdjustOption.Enable, 1 )\\n\\t\\t\\tcontrast = self.contrastSlider.GetValue() * 2\\n\\t\\t\\tprint( 'new contrast: {}'.format( contrast ) )\\n\\t\\t\\tif self.player.video_set_adjust_float( vlc.VideoAdjustOption.Contrast, contrast/100.0 ) == -1:\\n\\t\\t\\t\\tself.errorDialog(\\\"Failed to set contrast\\\")\\n\\t\\t\\telse:\\n\\t\\t\\t\\tself.State.contrast = contrast/2\\n\\t\\t\\t\\tself.SaveState()\",\n \"def reenlightenment(self):\\n return self._reenlightenment\",\n \"def test_status(self):\\n self.assertEqual('perfect', self.__metric.status())\",\n \"def performance_measure(self, x):\\n # \\\"calculate performance measure\\\" \\n pref = x.evaluate()\\n return pref\",\n \"def normalize(self, factor):\\n self._value /= factor\\n self._uncertainty /= factor\",\n \"def get_capacitor(self):\\n cap = 0.5 * self.metric_.logdet()\\n return cap\",\n \"def get_effect(self, label: str) -> Effect:\\r\\n return self._get_resource(label, self._effects, \\\"effect\\\")\",\n \"def measure(self, item):\\n return self.parent.measure(item, 'CHAN{}'.format(self.ch))\",\n \"def getMeasure(unique_name):\",\n \"def getMeasure(unique_name):\",\n \"def sense_and_act(self):\\n pass\",\n \"def measure_darks(det, shutter, quantity):\\n yield from set_dark_frame()\\n yield from bps.mv(shutter, \\\"close\\\")\\n yield from _acquire_n_frames(det, quantity)\",\n \"def translate_fear_level(fear_level):\\n if fear_level == 1:\\n scariness_threshold = 6\\n elif fear_level == 2:\\n scariness_threshold = 5\\n else:\\n scariness_threshold = 4\\n return scariness_threshold\",\n \"def getMeasure(self, measure, Filter=False):\\n assert not measure == 'XYZ', 'Measure \\\\'XYZ\\\\' cannot be used with getMeasure()'\\n\\n self.line1 = '-f' + str(self.functionRef[measure]) + ',' + '0,0,10'\\n\\n self.line2 = '-s' + self.LMOutputFName\\n\\n self.writeLMIn(self.line1, self.line2, self.line3)\\n\\n self.runLM()\\n\\n self.outputFormat = 1\\n self.readOutput()\\n\\n return self.LMOutput\",\n \"def treat(self):\\r\\n if self.noiseS > 0:\\r\\n self.evaluations = min((self.evaluations * self.alphaevals, self.maxevals))\\r\\n return self.alphasigma\\r\\n else:\\r\\n self.evaluations = max((self.evaluations * self.alphaevalsdown, self.minevals))\\r\\n return 1.0\",\n \"def crater_factor(current_weather):\\n if current_weather == 'SUNNY':\\n factor = 0.9\\n elif current_weather == 'RAINY':\\n factor = 1.2\\n else:\\n factor = 1\\n return factor\",\n \"def gain(self):\\n return self[1]\",\n \"def set_volume_effects(self, value):\\n\\t\\tif self._setting.get(FIFE_MODULE, \\\"PlaySounds\\\"):\\n\\t\\t\\tself.emitter['effects'].setGain(value)\\n\\t\\t\\tself.emitter['speech'].setGain(value)\\n\\t\\t\\tfor e in self.emitter['ambient']:\\n\\t\\t\\t\\te.setGain(value*2)\",\n \"def sense(p, Z):\\n # TODO: insert your code here\\n\\n if Z == 'green':\\n p = compute_measure_green(p, Z)\\n elif Z == 'red':\\n p = compute_measure_red(p, Z)\\n\\n norm = [float(i)/sum(p) for i in p]\\n p = norm\\n\\n return p\",\n \"def sense(p, Z):\\n # TODO: insert your code here\\n\\n if Z == 'green':\\n p = compute_measure_green(p, Z)\\n elif Z == 'red':\\n p = compute_measure_red(p, Z)\\n\\n norm = [float(i)/sum(p) for i in p]\\n p = norm\\n\\n return p\",\n \"def calibrate_meter(self):\\r\\n print(\\\"meter calibrated\\\")\",\n \"def controlDelta(self, prev_delta, acc_rate):\\n if (acc_rate > .6):\\n return prev_delta * 1.1\\n elif (acc_rate < .4):\\n return prev_delta / 1.1\\n else:\\n return prev_delta\",\n \"def sense_and_act(self):\\n values = self.sensob_camera.get_value()\\n if values[0] > values[1] and values[0] > values[2]:\\n self.update_mr(\\\"L\\\", 8)\\n self.match_degree = values[0]\\n elif values[1] >= values[0] and values[1] >= values[2]:\\n self.update_mr(\\\"F\\\", 30)\\n self.match_degree = values[1]\\n elif values[2] >= values[0] and values[2] > values[1]:\\n self.update_mr(\\\"R\\\", 8)\\n self.match_degree = values[2]\\n self.update_weight(self.priority, self.match_degree)\",\n \"def _update_state_value(self, old_state, new_state):\\n if not self._test_mode:\\n if isinstance(new_state, int):\\n self._state_values[old_state] += self._alpha * \\\\\\n (new_state - self._state_values[old_state])\\n else:\\n self._state_values[old_state] += self._alpha * \\\\\\n (self._state_values[new_state] - self._state_values[old_state])\",\n \"def test_is_ramped_using_int(self):\\n self.feature_test.set_percentage(100)\\n self.assertTrue(self.feature_test._is_ramped(5))\",\n \"def change_speed(self, action):\\r\\n if action == \\\"faster\\\":\\r\\n self.speed += 1\\r\\n else:\\r\\n if self.speed > 1:\\r\\n self.speed -= 1\",\n \"def change_mode(self):\\n return (self.mode + 1) % 2\",\n \"def disoverMode(system_sum):\\n # Updated as of 11/19/16\\n ## NO FLOATING INPUTS ALLOWED ##\\n if system_sum == 0x00:\\n status = \\\"System in Standby\\\"\\n elif system_sum == 0x06:\\n status = \\\"System Initializing\\\"\\n elif system_sum == 0x1C:\\n status = \\\"Side 1 Running\\\"\\n elif system_sum == 0x31:\\n status = \\\"Side 2 Running\\\"\\n else:\\n status = \\\"NOTICE: Unknown Mode\\\"\\n \\n return status\",\n \"def fan_mode(self):\\n if self.ac.status is None:\\n _LOGGER.debug(f\\\"fan_mode: status is None, returning None\\\")\\n return None\\n if self.ac.status.is_on:\\n fan_speed = self.ac.status.fan_speed\\n value = self.FAN_MODE_MAPPING[fan_speed]\\n _LOGGER.debug(f\\\"fan_mode: returning {value} (derived from {fan_speed})\\\")\\n return value\\n else:\\n _LOGGER.debug(f\\\"fan_mode: returning FAN_OFF - device is off\\\")\\n return FAN_OFF\",\n \"def _get_new_oxygen_level(self, action):\\n decay_constant = 1\\n\\n if action == 0: #breathingpneumostome\\n self.oxygen_level += 5\\n elif action == 1: #breathingskin\\n self.oxygen_level +=1\\n else : #swimming actions\\n self.oxygen_level = self.oxygen_level * math.exp(-decay_constant * self.t)\\n self.oxygen_level = int(round(self.oxygen_level))\\n print('oxygen_level: {}'.format(self.oxygen_level))\\n return self.oxygen_level\",\n \"def eff(self, speed):\\n\\n # variables\\n starting = self.working * self.starting\\n shutdown = self.working * self.shutdown\\n k = self.max_eff / (self.working - starting)\\n b = - k * starting\\n\\n # 1: Before starting\\n if speed < starting:\\n return 0\\n\\n # 2: Increasing\\n if speed >= starting and speed < self.working:\\n return k * speed + b\\n\\n # 3: Shutdown\\n if speed >= self.working and speed < shutdown:\\n return -k * (speed - self.working) + self.max_eff\\n\\n # 4: Off\\n if speed >= shutdown:\\n return 0\",\n \"def set_reduced_mode(self, on):\\r\\n return self._arm.set_reduced_mode(on)\",\n \"def set_value(self, name, val, **kwargs):\\n try:\\n assert self.__started\\n val = float(val)\\n except AssertionError:\\n self.unknown_error(\\\"Start method must be called first!\\\")\\n except (TypeError, ValueError):\\n self.unknown_error(\\\"Performance measures must be numeric!\\\")\\n\\n val_dict = {'val': val, 'min': None, 'max': None, 'scale': None,\\n 'threshold': 1, 'state': 'ok'}\\n\\n if 'lowest' in kwargs:\\n val_dict['min'] = float(kwargs.get('lowest'))\\n\\n if 'highest' in kwargs:\\n val_dict['max'] = float(kwargs.get('highest'))\\n\\n if 'threshold' in kwargs:\\n val_dict['threshold'] = kwargs['threshold']\\n\\n # Nagios actually understands most byte and time oriented scales.\\n # The developer docs also list a counter scale, but we're not certain\\n # if any plugin has ever used that. Only accept known scales.\\n\\n if 'scale' in kwargs:\\n scale = kwargs.get('scale')\\n\\n if scale.upper() in ('B', 'KB', 'MB', 'GB', 'TB'):\\n val_dict['scale'] = scale.upper()\\n elif scale.lower() in ('s', 'ms', 'us', 'ns', '%'):\\n val_dict['scale'] = scale.lower()\\n else:\\n raise ValueError(\\\"\\\"\\\"Scale must be one of: B, KB, MB, GB,\\n TB, s, ms, us, ns, or %.\\\"\\\"\\\")\\n\\n self.__perf[name] = val_dict\\n\\n # We'll use the opportunity to check the status ranges right when the\\n # variable is set so we don't have to loop through all of them later.\\n\\n if len(self.__warning) > 0:\\n self.__verbose_print(\\\"checking warning\\\")\\n self.__check_range('warning', name)\\n\\n if len(self.__critical) > 0:\\n self.__verbose_print(\\\"checking critical\\\")\\n self.__check_range('critical', name)\\n\\n return self.__perf[name]['state']\",\n \"def change_contrast(self, b):\\n self.fft_plot.set_clim(0, self.contrast_slider.value * self.v_range)\\n clear_output()\\n display(self.fig)\",\n \"def ErrorMeasure(self, new_int, old_int):\\n \\n \\n mean=np.fabs(new_int[0].val - old_int[0].val)\\n var=np.fabs(new_int[1].val - old_int[1].val)\\n if old_int[1].val == 0.0:\\n return (1.0-self.wmix) * var + self.wmix * mean\\n else:\\n return (1.0-self.wmix) * var / np.fabs(old_int[1].val) + self.wmix * mean / np.fabs(old_int[0].val)\",\n \"def basePotionEffect(self, value):\\n self._basePotionEffect = value\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6460598","0.6209007","0.6098955","0.57197714","0.565701","0.565701","0.565701","0.565701","0.565701","0.565701","0.565701","0.565701","0.565701","0.565701","0.565701","0.565701","0.565701","0.565701","0.565701","0.565701","0.565701","0.565701","0.565701","0.565701","0.55020374","0.5421164","0.5388489","0.53611124","0.53556967","0.534508","0.5340769","0.5285548","0.52663636","0.52647257","0.52455366","0.52369475","0.51922","0.51681364","0.5164508","0.5161631","0.5155975","0.51431465","0.5140909","0.5132407","0.5128159","0.5125754","0.5115604","0.50958675","0.5091051","0.5081078","0.5068349","0.5031534","0.501741","0.5009828","0.5000872","0.49989757","0.4984239","0.49513015","0.4944715","0.4944258","0.49425197","0.4938952","0.49387518","0.49381968","0.49311826","0.49265882","0.4925901","0.49257478","0.49224883","0.4910386","0.49082989","0.4905697","0.49013886","0.49013886","0.48969665","0.48913783","0.4890975","0.4889823","0.48783895","0.48726538","0.4872237","0.48669735","0.4863761","0.4863761","0.4852989","0.48419785","0.48408335","0.4830942","0.48287725","0.48238897","0.48225686","0.48205245","0.48182467","0.48151127","0.48052862","0.4805201","0.4804312","0.47983927","0.47967184","0.47911382"],"string":"[\n \"0.6460598\",\n \"0.6209007\",\n \"0.6098955\",\n \"0.57197714\",\n \"0.565701\",\n \"0.565701\",\n \"0.565701\",\n \"0.565701\",\n \"0.565701\",\n \"0.565701\",\n \"0.565701\",\n \"0.565701\",\n \"0.565701\",\n \"0.565701\",\n \"0.565701\",\n \"0.565701\",\n \"0.565701\",\n \"0.565701\",\n \"0.565701\",\n \"0.565701\",\n \"0.565701\",\n \"0.565701\",\n \"0.565701\",\n \"0.565701\",\n \"0.55020374\",\n \"0.5421164\",\n \"0.5388489\",\n \"0.53611124\",\n \"0.53556967\",\n \"0.534508\",\n \"0.5340769\",\n \"0.5285548\",\n \"0.52663636\",\n \"0.52647257\",\n \"0.52455366\",\n \"0.52369475\",\n \"0.51922\",\n \"0.51681364\",\n \"0.5164508\",\n \"0.5161631\",\n \"0.5155975\",\n \"0.51431465\",\n \"0.5140909\",\n \"0.5132407\",\n \"0.5128159\",\n \"0.5125754\",\n \"0.5115604\",\n \"0.50958675\",\n \"0.5091051\",\n \"0.5081078\",\n \"0.5068349\",\n \"0.5031534\",\n \"0.501741\",\n \"0.5009828\",\n \"0.5000872\",\n \"0.49989757\",\n \"0.4984239\",\n \"0.49513015\",\n \"0.4944715\",\n \"0.4944258\",\n \"0.49425197\",\n \"0.4938952\",\n \"0.49387518\",\n \"0.49381968\",\n \"0.49311826\",\n \"0.49265882\",\n \"0.4925901\",\n \"0.49257478\",\n \"0.49224883\",\n \"0.4910386\",\n \"0.49082989\",\n \"0.4905697\",\n \"0.49013886\",\n \"0.49013886\",\n \"0.48969665\",\n \"0.48913783\",\n \"0.4890975\",\n \"0.4889823\",\n \"0.48783895\",\n \"0.48726538\",\n \"0.4872237\",\n \"0.48669735\",\n \"0.4863761\",\n \"0.4863761\",\n \"0.4852989\",\n \"0.48419785\",\n \"0.48408335\",\n \"0.4830942\",\n \"0.48287725\",\n \"0.48238897\",\n \"0.48225686\",\n \"0.48205245\",\n \"0.48182467\",\n \"0.48151127\",\n \"0.48052862\",\n \"0.4805201\",\n \"0.4804312\",\n \"0.47983927\",\n \"0.47967184\",\n \"0.47911382\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5943068"},"document_rank":{"kind":"string","value":"3"}}},{"rowIdx":75595,"cells":{"query":{"kind":"string","value":"Helper function for update_return_factor > activates."},"document":{"kind":"string","value":"def _activate_return_factor(self):\n self.active = True\n effect = self.factor\n return effect"},"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_return_factor(self):\n if not self.active:\n effect = self._activate_return_factor()\n elif self.active:\n effect = self._deactivate_return_factor()\n return effect","def _deactivate_return_factor(self):\n self.active = False\n effect = 1 / self.factor\n return effect","def activate_2(self, act_1):\n x = act_1\n e = math.e\n result = 1/(1 + e **-x)\n #print (result)\n return result","def act(self, state):\n\t\trand_val = np.random.rand()\n\t\tif not self.is_eval and rand_val <= self.epsilon: # Do a random action only in train phase\n\t\t\treturn random.randrange(self.action_size)\n\n\t\tif self.firstIter: # If this is the first iteration, just do a \"hold\" action\n\t\t\tself.firstIter = False\n\t\t\treturn 2 # 2 = \"Hold action\"\n\n\t\toptions = self.model.predict(state) # Do a prediction based on a specific observation\n\t\t#print(options)\n\n\t\ttot = np.sum(options[0])\n\t\toptions[0] = options[0] / tot\n\t\t#print(options)\n\n\t\trand = random.random()\n\n\t\t#print(\"randm:\" + str(rand))\n\t\tif rand <= options[0][0]:\n\t\t\t#print(\"max:\" + str(np.argmax(options[0])) + \"ma 0\")\n\t\t\treturn 0\n\n\t\telif options[0][0] < rand <= (options[0][0] + options[0][1]):\n\t\t\t#print(\"max:\" + str(np.argmax(options[0])) + \"ma 1\")\n\t\t\treturn 1\n\t\telif (options[0][0] + options[0][1]) < rand <= (options[0][0] + options[0][1] + options[0][2]):\n\t\t\t#print(\"max:\" + str(np.argmax(options[0])) + \"ma 2\")\n\t\t\treturn 2\n\t\telse:\n\t\t\t#print(\"max:\" + str(np.argmax(options[0])) + \"ma 3\")\n\t\t\treturn 3\n\n\t\t#return np.argmax(options[0])'''","def activate_der(self):\r\n\t\treturn self.value * (1 - self.value)","def update(self, state, action, nextState, reward):\n \"\"\"Description:\n Use second equation in slide 71 of MDP\n Adjest weight of active features depend on tranistion \n \"\"\"\n \"\"\" YOUR CODE HERE \"\"\"\n feat = self.featExtractor.getFeatures(state, action)\n\n # if weight is empty, then weight will need to initial to 1 for all features\n # According to which Extractor user choose, weight counter will have equal number of keys.\n if len(self.weight) == 0:\n feat = self.featExtractor.getFeatures(state, action)\n self.weight.incrementAll(feat.keys(), 1)\n \n maxQns = self.getValue(nextState)\n if maxQns == None:\n maxQns = 0\n Qsa = self.getQValue(state, action)\n difference = ( reward + self.discountRate * maxQns ) - Qsa\n \n for key in self.weight.keys():\n self.weight[key] += (self.alpha * difference * feat[key])\n \n \n \"\"\" END CODE \"\"\"","def process_apply(self,succ,crit):\n\n if succ and crit:\n bonus = 2\n elif succ:\n bonus = 1\n else:\n bonus = 0\n\n return bonus","def alpha_beta(returns, factor_returns):\n\n ret_index = returns.index\n beta, alpha = sp.sp.stats.linregress(factor_returns.loc[ret_index].values,\n returns.values)[:2]\n\n return alpha * APPROX_BDAYS_PER_YEAR, beta","def activate(self, inputs):\n\t\tstrength = np.dot(self.weights, inputs)\n\t\t# if strength <= self.threshold:\n\t\t# \tself.result = 0\n\t\t# else:\n\t\t# \tself.result = 1\n\t\t# return self.result\n\t\treturn int(strength > self.threshold)","def act(self, state, eps=0.):","def state_update(self, evidence: List, return_mode: str = 'letter'):\n sample = uniform(len(self.alp))\n pairs = list(zip(self.alp, sample.tolist()))\n priors = {return_mode: pairs}\n\n self.log.debug(\"Language Model Random probabilities:\")\n self.log.debug(priors)\n return priors","def feedback(self, feedback: _Feedback) -> float:\n self.params, self.opt_state, cur_loss = self.update(self.params,\n self.opt_state,\n feedback)\n return cur_loss","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 powAlpha( n ):\n return (1-betaval)*Fib(n) + Fib(n-1)\n #return Fib(n+1) - Fib(n) * betaval","def act(self, current_state, setpoint):\n s = self.last_s # Refers only to theta\n a = self.last_a # Refers only the last action taken\n s_p = self.map_state(\n current_state['theta'], current_state['theta_dot'])\n\n # Update the q-table based on the Bellman Equation\n r = self.reward_func(current_state)\n q_predict = self.q_table[s + (a,)]\n\n if not self.reset_env:\n q_target = r + self.params['gamma'] * np.max(self.q_table[s_p])\n else:\n q_target = r\n print(str(r) + ' Reset')\n self.reset_env = False\n # self.q_table[40,1]=90\n\n self.q_table[s+(a,)] += self.params['learn_rate'] * \\\n (q_target - q_predict)\n # print(self.params['epsilon'])\n # Take the next action\n if (np.random.uniform() < self.params['epsilon']) or (np.max(np.abs(self.q_table[s_p])) < 1e-3):\n ac = np.random.randint(self.num_actions)\n self.dist[ac] += 1\n else:\n ac = np.argmax(self.q_table[s_p])\n\n #ac = 0\n\n self.last_a = ac\n self.last_s = s_p\n if self.cont_decay == 1000:\n self.params['epsilon'] *= self.params['decay_rate']\n self.cont_decay = 0\n else:\n self.cont_decay += 1\n u = self.map_force(ac)\n f_u = self.F_cum + u\n print(self.params)\n print(self.q_table[30:51])\n return f_u","def update(self, state, action, nextState, reward):\n \"*** YOUR CODE HERE ***\"\n self.qValues[(state, action)] = ((1 - self.alpha) * self.getQValue(state, action)) + self.alpha \\\n * (reward + self.discount * self.computeValueFromQValues(nextState))","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","def calculate_expected_return(beta, alpha, risk_free_rate, market_returns):\n return risk_free_rate + beta * (market_returns - risk_free_rate)","def _reward(self):\n\n return 1-self.step_count/ self.max_steps","def statePosteriors(log_alpha, log_beta):","def reward_calc(state, action, setpoint):\r\n return max(-np.square(state - setpoint) - action, -150)","def eval_prior(self, state, action):\n\n return np.dot(state, self.a.T) + np.dot(action, self.b.T)","def fake_reward_fn(obs, acts, next_obs, steps):\n pos, vel = obs[:, 0], obs[:, 1]\n return vel * 100 + pos","def reward_func(self, state):\n if abs(state['theta']) < 1:\n return 2.0\n\n elif abs(state['theta']) < 3:\n return 0.0\n\n elif abs(state['theta']) > 30:\n return -100.0\n return -2.0","def __call__(self, results):\n if np.random.rand() > self.prob:\n return results\n self._adjust_brightness_img(results, self.factor)\n return results","def update_weights(weights, alpha, y_true, y_pred):\n def change_labels(arr):\n for i,a in enumerate(arr):\n if a == 0:\n arr[i] = -1\n return arr \n \n y_true, y_pred = change_labels(y_true), change_labels(y_pred)\n w_hat = weights * np.exp(-alpha * y_true * y_pred)\n return w_hat / sum(w_hat)","def acc_b_tr(self):\r\n return self._acc_b_tr","def update(self):\n #self.consider_deactivation() if self.active_flag else self.consider_activation()\n if self.active_flag:\n self.consider_deactivation()\n else:\n self.consider_activation()\n if self.active_flag:\n self.sense_and_act()\n self.weight = self.match_degree*self.priority","def update(self):\n #self.consider_deactivation() if self.active_flag else self.consider_activation()\n if self.active_flag:\n self.consider_deactivation()\n else:\n self.consider_activation()\n if self.active_flag:\n self.sense_and_act()\n self.weight = self.match_degree*self.priority","def update(self):\n #self.consider_deactivation() if self.active_flag else self.consider_activation()\n if self.active_flag:\n self.consider_deactivation()\n else:\n self.consider_activation()\n if self.active_flag:\n self.sense_and_act()\n self.weight = self.match_degree*self.priority","def update(self):\n #self.consider_deactivation() if self.active_flag else self.consider_activation()\n if self.active_flag:\n self.consider_deactivation()\n else:\n self.consider_activation()\n if self.active_flag:\n self.sense_and_act()\n self.weight = self.match_degree*self.priority","def getWeights(self, gameState, action):\n return {'successorScore': 1.0}","def getWeights(self, gameState, action):\n return {'successorScore': 1.0}","def getWeights(self, gameState, action):\n return {'successorScore': 1.0}","def getWeights(self, gameState, action):\n return {'successorScore': 1.0}","def getWeights(self, gameState, action):\n return {'successorScore': 1.0}","def getWeights(self, gameState, action):\n return {'successorScore': 1.0}","def getWeights(self, gameState, action):\n return {'successorScore': 1.0}","def __call__(self, num_update):\n if num_update > self.count:\n self.base_lr = self.origin_lr * pow((1 - 1.0*num_update/self.max_samples), self.factor)\n if self.base_lr < self.stop_factor_lr:\n self.base_lr = self.stop_factor_lr\n logging.info(\"Update[%d]: now learning rate arrived at %0.5e, will not \"\n \"change in the future\", num_update, self.base_lr)\n elif num_update % self.show_num == 0:\n logging.info(\"Update[%d]: Change learning rate to %0.8e\",\n num_update, self.base_lr)\n self.count = num_update\n return self.base_lr","def total_return_from_returns(returns):\n return (returns + 1).prod() - 1","def _return_activation(x, nl):\n if nl == 'HS':\n x = Activation(_hard_swish)(x)\n if nl == 'RE':\n x = Activation(_relu6)(x)\n return x","def bias_prior(self):","def forward_activate(self, a_prev, w, b, func_type):\n\n\t\tz = np.dot(w, a_prev) + b\n\t\tif 'sigmod' == func_type.lower(): \n\t\t\ta = 1 / (1 + np.exp(-z))\n\t\telif 'relu' == func_type.lower():\n\t\t\ta = np.where(z >= 0, z, 0)\n\t\telif 'leaky relu' == func_type.lower():\n\t\t\ta = np.where(z >= 0, z, 0.01 * z)\n\t\telif 'tanh' == func_type.lower():\n\t\t\ta = (np.exp(z) - np.exp(-z)) / (np.exp(z) + np.exp(-z))\n\n\t\tcache = (a_prev, w, b, z)\n\t\treturn a, cache","def _return_activation(x, nl):\n if nl == 'HS':\n x = KL.Activation(_hard_swish)(x)\n if nl == 'RE':\n x = KL.ReLU(6.)(x)\n\n return x","def get_learn_after_each_decision(self):\r\n return 0","def _problem_handle_prev_evals(self):\n self._update_reward_values()","def front_column_model_p_gain():","def predict_action_probabilities(self, tracker, domain):\n # type: (DialogueStateTracker, Domain) -> (float, Optional[int])\n x = self.featurize(tracker, domain)\n logger.debug('Current tracker state [\\n\\t{}]'.format(\n \"\\n\\t\".join([\"{}\".format(e) for e in self.featurizer.decode_features(x, domain.input_features)])))\n\n memorised = self.recall(x, domain)\n result = [0.0] * domain.num_actions\n if memorised is not None and self.is_enabled:\n logger.debug(\"Used memorised next action '{}'\".format(memorised))\n result[memorised] = 1.0\n return result","def activate(self,x):\n return 1 / (1 + np.exp(-x))","def statePosteriors(log_alpha, log_beta):\n return log_alpha + log_beta - logsumexp(log_alpha[-1,:])","def learn(self, state, action, reward, next_state):\r\n\r\n \"\"\"Please Fill Your Code Here.\r\n \"\"\"\r\n self.Q[state][action] = self.Q[state][action] + self.alpha * (reward + self.gamma * max(self.Q[next_state]) - self.Q[state][action])\r\n\r\n return 0","def get_action_value(mdp, state_values, state, action, gamma):\n\n Q = 0\n # YOUR CODE HERE\n return Q","def uf_activate(self, output_reg):\n if len(self.inputs) is 2:\n self.two_activation(output_reg)\n elif len(self.inputs) is 3:\n self.three_activation(output_reg)\n else:\n self.large_activation(output_reg)","def _reward(self, action: Action) -> float:\n raise NotImplementedError","def update(self, state, action, nextState, reward):\n \"*** YOUR CODE HERE ***\"\n diff = reward + self.discount * self.computeValueFromQValues(nextState) - self.getQValue(state, action)\n for feature_name, feature_value in self.featExtractor.getFeatures(state, action).iteritems():\n self.weights[feature_name] += self.alpha * diff * feature_value","def update(self, state, action, nextState, reward):\n \"*** YOUR CODE HERE ***\"\n diff = reward + self.discount * self.computeValueFromQValues(nextState) - self.getQValue(state, action)\n for feature_name, feature_value in self.featExtractor.getFeatures(state, action).iteritems():\n self.weights[feature_name] += self.alpha * diff * feature_value","def compute_reward(self, obs, action, state):\n pass","def _balance_update(self):\n return_rate = self.df.loc[self.currentStep, \"return_Close\"]\n self.buy_amount += return_rate * self.buy_amount\n self.sell_amount -= return_rate * self.sell_amount","def _call(self, x):\n if functional.prior is None:\n return np.exp(x)\n else:\n return functional.prior * np.exp(x)","def apply_raise(self):\n self.pay = int(self.pay * self.raise_amnt)","def agent_step(self, reward, state):\n prev_val= self.state[self.prevAction]\n self.state[self.prevAction]=prev_val+self.alpha*(reward-prev_val)\n val=max(self.state)\n index=self.state.index(val)\n self.prevAction=index\n i=random.uniform(0,1)\n if i < 1-self.prob:\n self.prevAction=index\n return index\n else:\n index=random.randint(0,self.num_bandits-1)\n self.prevAction=index","def update_policy_values(policy_lookup, board, state, player, action, next_state, reward):\n\t\n\t# compute total expected reward including future rewards\n\tif board.check_end():\n\t\texpected = reward\n\telse:\n\t\tif player == 1:\n\t\t\texpected = reward + discount * min_value(policy_lookup, next_state, 2)\n\t\telif player == 2:\n\t\t\texpected = reward + discount * max_value(policy_lookup, next_state, 1)\n\t# get current policy action value\n\tpolicy_value = get_policy_value(policy_lookup, state, player, action)\n\t# update policy action value\n\tpolicy_lookup[(state, player)][action] += learning_rate * (expected - policy_value)","def final_amt(p,r,n,t):\n\n a = p * (1 + r/n) ** (n*t)\n return a #This is what makes the function \"fruitful\"","def backward_activate(self, da, a, cache, func_type):\n\n\t\ta_prev, w, b = cache\n\t\tif 'sigmod' == func_type.lower():\n\t\t\tdz = np.multiply(np.multiply(a, (1 - a)), da)\n\t\telif 'relu' == func_type.lower():\n\t\t\tif a > 0:\n\t\t\t\tdz = da \n\t\t\telse:\n\t\t\t\tdz = np.multiply(0, da)\n\t\telif 'leaky relu' == func_type.lower():\n\t\t\tif a >= 0:\n\t\t\t\tdz = da \n\t\t\telse:\n\t\t\t\tdz = np.multiply(0.01, da)\n\t\telif 'tanh' == func_type.lower():\n\t\t\tdz = np.multiply(1 - np.multiply(a, a), da)\n\t\telse:\n\t\t\tprint(\"activate type error: %s\" %(func_type))\n\t\t\treturn\n\n\t\tdw = np.dot(dz, a_prev.T)\n\t\tdb = np.sum(dz, axis = 1, keepdims = True)\n\t\tda_prev = np.dot(w.T, dz)\n\n\t\treturn da_prev, dw, db","def _calculate_reward( self, old_state_vars, new_state_vars ):\n if 'total-wealth' == self.objective:\n x_old = old_state_vars.ptf_asset_vals.sum()\n x_new = new_state_vars.ptf_asset_vals.sum()\n elif 'relative-profit' == self.objective:\n x_old = old_state_vars.ptf_asset_vals.sum() - old_state_vars.bmk_asset_vals.sum()\n x_new = new_state_vars.ptf_asset_vals.sum() - new_state_vars.bmk_asset_vals.sum()\n else:\n raise ValueError( 'Unsupported objective: {}'.format(objective) )\n\n # Discount the objective before applying the utility function\n gamma = self.get_gamma()\n reward = self.utility_fun( gamma * x_new ) - self.utility_fun( x_old )\n return reward","def next_move(self, cur_state):\n\n alpha, final_state, min_level, action_took = self.alpha_beta(cur_state, 2, 0, -math.inf, math.inf, math.inf)\n #print(\"-----------------------------------------\")\n #print(\"value = \"+str(alpha)+\", min_level = \"+str(min_level))\n #print(\"previous: top=\"+str(cur_state.top)+\", bottom=\"+str(cur_state.bottom)+\", left=\"+str(cur_state.left)+\", right=\"+str(cur_state.right))\n #print(final_state.pre_state)\n return action_took","def count_change(amount):\n \"*** YOUR CODE HERE ***\"\n\n return helper(1, amount)","def update(self, state, action, nextState, reward):\n \"*** YOUR CODE HERE ***\"\n # print \"update\"\n oldValue = self.getQValue(state, action)\n sample = reward + self.discount*self.computeValueFromQValues(nextState)\n self.qValues[(state, action)] = (1-self.alpha)*oldValue + self.alpha*(sample)","def act(self, observation):\n for i in range(len(self.counts)):\n if self.counts[i] == 0:\n return i\n \n total_counts = np.sum(self.counts)\n for action1 in range(len(self.counts)):\n add = math.sqrt((self.minmax/2 * math.log(total_counts)) / float(self.counts[action1]))\n self.ucb_values[action1] = self.values[action1] + add\n return np.argmax(self.ucb_values)","def recall(self):\n return float(self.tp) / (self.tp + self.fn) if self.fn != 0 else 1","def call(self, inputs, states):\r\n (out_prev, Vm_prev) = states\r\n\r\n #Vm = Vm_prev * (1.0 - out_prev)\r\n #Lateral inhibition logic:\r\n Vm = Vm_prev * (1.0 - tf.reduce_max(out_prev))\r\n\r\n Vm = Vm * self.decay\r\n Vm = Vm + tf.matmul(inputs, self.kernel)\r\n if self.recurrent:\r\n Vm = Vm + tf.matmul(out_prev, self.recurrent_kernel)\r\n Vm = self.g(Vm)\r\n overVth = Vm - self.bias\r\n out = self.activation(overVth)\r\n return out, (out, Vm)","def _call(self, x):\n if functional.prior is None:\n return 1.0 / (1 - x)\n else:\n return functional.prior / (1 - x)","def gen_outcome(alpha, delta, beta, win_counters, attempt_counters, h_features_win, h_features_att):\n return logistic(alpha+delta+beta+np.sum(np.log(1+np.array(attempt_counters))*np.array(h_features_att))+\\\n np.sum(np.log(1+np.array(win_counters))*np.array(h_features_win)))","def recoding_func(self, input_var: Tensor, hidden: HiddenDict, out: Tensor, device: Device,\n **additional: Dict) -> RecurrentOutput:\n target_idx = additional.get(\"target_idx\", None)\n\n # Estimate uncertainty of those same predictions\n delta = self.get_surprisal(out, target_idx)\n\n # Calculate gradient of uncertainty w.r.t. hidden states and make step\n new_out_dist, new_hidden = self.recode_activations(hidden, out, delta, device, **additional)\n\n return new_out_dist, new_hidden","def update(self, state, action, nextState, reward):\n \"*** YOUR CODE HERE ***\"\n feature_dictionary = self.featExtractor.getFeatures(state, action)\n difference = (reward + self.discount * self.computeValueFromQValues(nextState)) - self.getQValue(state, action)\n\n for feature in feature_dictionary:\n self.weights[feature] += self.alpha * difference * feature_dictionary[feature]","def update(self, state, action, nextState, reward):\n # print \"Update\"\n difference = (reward + self.discount*self.compValFromState(nextState)) - self.getQValue(state, action)\n features = self.featExtractor.getFeatures(state, self.index)\n #print \"features\", features, \"difference\", difference, \"weights\", self.weights\n for key in self.weights:\n self.weights[key] = self.alpha * difference * features[key]","def calculate_controller_reward(self, controller1, controller2):","def _update_state_value(self, old_state, new_state):\n if not self._test_mode:\n if isinstance(new_state, int):\n self._state_values[old_state] += self._alpha * \\\n (new_state - self._state_values[old_state])\n else:\n self._state_values[old_state] += self._alpha * \\\n (self._state_values[new_state] - self._state_values[old_state])","def compute_act(self, x):\n self.p = self.weights @ x.T #+ self.bias\n self.z = self.__act_f(self.p)\n \n return self.z","def _call(self, x):\n if functional.prior is None:\n return (-1.0) / x + 1\n else:\n return (-functional.prior) / x + 1","def update_sample_reward(self, r):\n self.r_exp[-1] += r * self.gamma ** (len(self.u_seq[-1])-1)","def rev_opt_implied_returns(delta, sigma_prior: pd.DataFrame, wts_prior: pd.Series):\r\n pi = (delta * sigma_prior @ wts_prior).squeeze() # @ may be used instead of dot, but some issues arise if a df is not passed\r\n pi.name = 'Implied Returns'\r\n return pi","def update(self, state, action, nextState, reward):\n \"*** YOUR CODE HERE ***\"\n features = self.featExtractor.getFeatures(state,action)\n\n learning_rate = self.alpha #gives us the learning rate\n\n temporary_QValue = self.getQValue(state,action) #to get the Q value of the state,action pair\n\n nextState_QValue = self.getValue(nextState) #to get the Q value of the landing state when taken action a and state s\n\n discount_factor = self.discount #to get the gamma/ discount factor\n\n weight = self.weights\n\n Q_Value = 0\n\n difference = (reward + discount_factor * nextState_QValue ) - (temporary_QValue) #refer to README_Reinforcement.txt for the formula\n\n for each_feature in features:\n\n #refer to README_Reinforcement.txt for the formula at line 20\n weight[each_feature] = weight[each_feature] + learning_rate * difference * features[each_feature]\n\n #util.raiseNotDefined()","def get_penalty(state, action, winrate_predictor):\n if violate_rule(state, action):\n return -1 \n return 0","def update(self, action):\n self._update_noise()\n self._update_state(action)\n self._update_status()\n self.steps += 1\n # Return current state and error\n return self.observation, self.error # returns two unctions when called","def __act_f(self, p):\n # linear activation if no function is given\n if self.activation is None:\n return p\n # tanh\n if self.activation == \"tanh\":\n return np.tanh(p)\n # sigmoid\n if self.activation == \"sigmoid\":\n return 1 / (1 + np.exp(-p))\n # relu\n if self.activation == \"relu\":\n return p * (p > 0)","def act(self, observation):\n self.t += 1\n\n probabilities = self.probabilities(observation)\n\n probabilities *= self.action_mask(observation[0])\n\n if probabilities.sum() == 0.0:\n probabilities += 1.0\n\n return torch.multinomial(probabilities, num_samples=1)[0]","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 update(self, state, action, nextState, reward):\n \"\"\"Description:\n Use Q-Learning algoritm in slide 58 of MDP\n \"\"\"\n \"\"\" YOUR CODE HERE \"\"\"\n maxQns = self.getValue(nextState) # get max q-value of next state\n if maxQns == None:\n maxQns = 0\n Qsa = self.getQValue(state, action) #self.qValues[(state, action)]\n difference = reward + self.discountRate * maxQns - Qsa\n self.qValues[(state, action)] += self.alpha * difference\n \n self.vitCount[(state, action)] += 1\n \"\"\" END CODE \"\"\"","def result(self, state, action):\n\n\t\t#Adds new number to the state\n\t\treturn state + action","def update_reward(state, reward, max_reward, alpha=1, c=100, gamma=0.9):\n\t\t# update number of actions done so far to this state\n\t\tactions[state] = actions.get(state, 0.0) + 1.0\n\t\t# compute learning rate\n\t\talpha *= c / (c + actions[state])\n\t\trewards[state] = rewards.get(state, 0.0) + alpha*(reward+gamma*max_reward-rewards.get(state, 0.0))","def update(self, state, action, nextState, reward):\n \"*** YOUR CODE HERE ***\"\n feature_dictionary = self.featExtractor.getFeatures(state, action)\n difference = (reward + self.discount * self.computeValueFromQValues(nextState)) - self.getQValue(state, action)\n\n for feature in feature_dictionary:\n self.weights[feature] += self.alpha * difference * feature_dictionary[feature]\n\n #if self.epsilon > self.epsilon_min:\n # self.epsilon *= self.epsilon_decay","def acc_b(self):\n return self._acc_b","def act_func_part4_v2_t(action_raw, action_raw_idx, raw_state_limits, stptLmt, ob_this_raw, logger, is_show_debug):\n IAT_STPT_IDX = 4;\n OCP_IDX = 7;\n PMV_IDX = 6;\n ACTIONS = [[-2.0],\n [-1.0],\n [-0.5],\n [0.0],\n [0.5],\n [1.0],\n [2.0],];\n\n ocp = ob_this_raw[OCP_IDX];\n pmv = ob_this_raw[PMV_IDX];\n iat_stpt_ob = ob_this_raw[IAT_STPT_IDX]; \n act_num = len(ACTIONS);\n # Process based on PMV\n action_prcd = action_raw;\n action_prcd_idx = action_raw_idx;\n if ocp == 1:\n if pmv < -0.5 and action_raw[0] < 0:\n # If pmv < -0.5 and the action is to reduce setpoint\n # Choose one action to increase the setpoint\n action_prcd_idx = np.random.randint(3, act_num);\n action_prcd = ACTIONS[action_prcd_idx];\n elif pmv > 0.5 and action_raw[0] > 0:\n # If pmv > 0.5 and the action is to increase setpoint\n # Choose one action to reduce the setpoint\n action_prcd_idx = np.random.randint(0, 4);\n action_prcd = ACTIONS[action_prcd_idx];\n # Get final action\n iat_stpt_act_raw = iat_stpt_ob + action_prcd[0];\n action_ret = [max(min(iat_stpt_act_raw, stptLmt[0][1]), stptLmt[0][0])];\n action_ret_idx = action_prcd_idx;\n # Log\n if action_raw_idx != action_ret_idx:\n if is_show_debug:\n logger.debug('Action function: raw action %s has been changed to %s for '\n 'the PMV %s.'%(action_raw_idx, action_ret_idx, pmv));\n return (action_ret, action_ret_idx);","def updated_rating(old: int, expected: int, actual: int) -> int:\n return round(old + ELO_K_FACTOR * (actual - expected))","def vanilaScore(self,attended,state,W):","def _update_activation_state(self):\n activation_count = 0\n for task_id in self.task_dict:\n activation_count += (1 if self.task_dict[task_id].is_activated else 0)\n # Result\n self.num_activated_task = activation_count\n self.is_activated = (activation_count > 0)","def _eval_prior_casadi(self, state, action):\n\n return mtimes(self.a, state.T) + mtimes(self.b, action.T)","def fc_activation(c_range, num_act, k_a, k_i, ep_a, ep_ap, ep_ai=4.5,\n n_ns=4.6E6, n_sites=int(2)):\n # Compute the MWC probability.\n mwc_term = p_act(c_range, k_a, k_i, n_sites=n_sites, ep_ai=ep_ai)\n\n # Compute and return the fold-change\n numer = 1 + mwc_term * (num_act / n_ns) * np.exp(-(ep_a + ep_ap))\n denom = 1 + mwc_term * (num_act / n_ns) * np.exp(-ep_a)\n return numer / denom","def update_action(self):\n self.action = self.automata > self.states\n self.inv_action = self.inv_automata > self.states"],"string":"[\n \"def update_return_factor(self):\\n if not self.active:\\n effect = self._activate_return_factor()\\n elif self.active:\\n effect = self._deactivate_return_factor()\\n return effect\",\n \"def _deactivate_return_factor(self):\\n self.active = False\\n effect = 1 / self.factor\\n return effect\",\n \"def activate_2(self, act_1):\\n x = act_1\\n e = math.e\\n result = 1/(1 + e **-x)\\n #print (result)\\n return result\",\n \"def act(self, state):\\n\\t\\trand_val = np.random.rand()\\n\\t\\tif not self.is_eval and rand_val <= self.epsilon: # Do a random action only in train phase\\n\\t\\t\\treturn random.randrange(self.action_size)\\n\\n\\t\\tif self.firstIter: # If this is the first iteration, just do a \\\"hold\\\" action\\n\\t\\t\\tself.firstIter = False\\n\\t\\t\\treturn 2 # 2 = \\\"Hold action\\\"\\n\\n\\t\\toptions = self.model.predict(state) # Do a prediction based on a specific observation\\n\\t\\t#print(options)\\n\\n\\t\\ttot = np.sum(options[0])\\n\\t\\toptions[0] = options[0] / tot\\n\\t\\t#print(options)\\n\\n\\t\\trand = random.random()\\n\\n\\t\\t#print(\\\"randm:\\\" + str(rand))\\n\\t\\tif rand <= options[0][0]:\\n\\t\\t\\t#print(\\\"max:\\\" + str(np.argmax(options[0])) + \\\"ma 0\\\")\\n\\t\\t\\treturn 0\\n\\n\\t\\telif options[0][0] < rand <= (options[0][0] + options[0][1]):\\n\\t\\t\\t#print(\\\"max:\\\" + str(np.argmax(options[0])) + \\\"ma 1\\\")\\n\\t\\t\\treturn 1\\n\\t\\telif (options[0][0] + options[0][1]) < rand <= (options[0][0] + options[0][1] + options[0][2]):\\n\\t\\t\\t#print(\\\"max:\\\" + str(np.argmax(options[0])) + \\\"ma 2\\\")\\n\\t\\t\\treturn 2\\n\\t\\telse:\\n\\t\\t\\t#print(\\\"max:\\\" + str(np.argmax(options[0])) + \\\"ma 3\\\")\\n\\t\\t\\treturn 3\\n\\n\\t\\t#return np.argmax(options[0])'''\",\n \"def activate_der(self):\\r\\n\\t\\treturn self.value * (1 - self.value)\",\n \"def update(self, state, action, nextState, reward):\\n \\\"\\\"\\\"Description:\\n Use second equation in slide 71 of MDP\\n Adjest weight of active features depend on tranistion \\n \\\"\\\"\\\"\\n \\\"\\\"\\\" YOUR CODE HERE \\\"\\\"\\\"\\n feat = self.featExtractor.getFeatures(state, action)\\n\\n # if weight is empty, then weight will need to initial to 1 for all features\\n # According to which Extractor user choose, weight counter will have equal number of keys.\\n if len(self.weight) == 0:\\n feat = self.featExtractor.getFeatures(state, action)\\n self.weight.incrementAll(feat.keys(), 1)\\n \\n maxQns = self.getValue(nextState)\\n if maxQns == None:\\n maxQns = 0\\n Qsa = self.getQValue(state, action)\\n difference = ( reward + self.discountRate * maxQns ) - Qsa\\n \\n for key in self.weight.keys():\\n self.weight[key] += (self.alpha * difference * feat[key])\\n \\n \\n \\\"\\\"\\\" END CODE \\\"\\\"\\\"\",\n \"def process_apply(self,succ,crit):\\n\\n if succ and crit:\\n bonus = 2\\n elif succ:\\n bonus = 1\\n else:\\n bonus = 0\\n\\n return bonus\",\n \"def alpha_beta(returns, factor_returns):\\n\\n ret_index = returns.index\\n beta, alpha = sp.sp.stats.linregress(factor_returns.loc[ret_index].values,\\n returns.values)[:2]\\n\\n return alpha * APPROX_BDAYS_PER_YEAR, beta\",\n \"def activate(self, inputs):\\n\\t\\tstrength = np.dot(self.weights, inputs)\\n\\t\\t# if strength <= self.threshold:\\n\\t\\t# \\tself.result = 0\\n\\t\\t# else:\\n\\t\\t# \\tself.result = 1\\n\\t\\t# return self.result\\n\\t\\treturn int(strength > self.threshold)\",\n \"def act(self, state, eps=0.):\",\n \"def state_update(self, evidence: List, return_mode: str = 'letter'):\\n sample = uniform(len(self.alp))\\n pairs = list(zip(self.alp, sample.tolist()))\\n priors = {return_mode: pairs}\\n\\n self.log.debug(\\\"Language Model Random probabilities:\\\")\\n self.log.debug(priors)\\n return priors\",\n \"def feedback(self, feedback: _Feedback) -> float:\\n self.params, self.opt_state, cur_loss = self.update(self.params,\\n self.opt_state,\\n feedback)\\n return cur_loss\",\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 powAlpha( n ):\\n return (1-betaval)*Fib(n) + Fib(n-1)\\n #return Fib(n+1) - Fib(n) * betaval\",\n \"def act(self, current_state, setpoint):\\n s = self.last_s # Refers only to theta\\n a = self.last_a # Refers only the last action taken\\n s_p = self.map_state(\\n current_state['theta'], current_state['theta_dot'])\\n\\n # Update the q-table based on the Bellman Equation\\n r = self.reward_func(current_state)\\n q_predict = self.q_table[s + (a,)]\\n\\n if not self.reset_env:\\n q_target = r + self.params['gamma'] * np.max(self.q_table[s_p])\\n else:\\n q_target = r\\n print(str(r) + ' Reset')\\n self.reset_env = False\\n # self.q_table[40,1]=90\\n\\n self.q_table[s+(a,)] += self.params['learn_rate'] * \\\\\\n (q_target - q_predict)\\n # print(self.params['epsilon'])\\n # Take the next action\\n if (np.random.uniform() < self.params['epsilon']) or (np.max(np.abs(self.q_table[s_p])) < 1e-3):\\n ac = np.random.randint(self.num_actions)\\n self.dist[ac] += 1\\n else:\\n ac = np.argmax(self.q_table[s_p])\\n\\n #ac = 0\\n\\n self.last_a = ac\\n self.last_s = s_p\\n if self.cont_decay == 1000:\\n self.params['epsilon'] *= self.params['decay_rate']\\n self.cont_decay = 0\\n else:\\n self.cont_decay += 1\\n u = self.map_force(ac)\\n f_u = self.F_cum + u\\n print(self.params)\\n print(self.q_table[30:51])\\n return f_u\",\n \"def update(self, state, action, nextState, reward):\\n \\\"*** YOUR CODE HERE ***\\\"\\n self.qValues[(state, action)] = ((1 - self.alpha) * self.getQValue(state, action)) + self.alpha \\\\\\n * (reward + self.discount * self.computeValueFromQValues(nextState))\",\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 \"def calculate_expected_return(beta, alpha, risk_free_rate, market_returns):\\n return risk_free_rate + beta * (market_returns - risk_free_rate)\",\n \"def _reward(self):\\n\\n return 1-self.step_count/ self.max_steps\",\n \"def statePosteriors(log_alpha, log_beta):\",\n \"def reward_calc(state, action, setpoint):\\r\\n return max(-np.square(state - setpoint) - action, -150)\",\n \"def eval_prior(self, state, action):\\n\\n return np.dot(state, self.a.T) + np.dot(action, self.b.T)\",\n \"def fake_reward_fn(obs, acts, next_obs, steps):\\n pos, vel = obs[:, 0], obs[:, 1]\\n return vel * 100 + pos\",\n \"def reward_func(self, state):\\n if abs(state['theta']) < 1:\\n return 2.0\\n\\n elif abs(state['theta']) < 3:\\n return 0.0\\n\\n elif abs(state['theta']) > 30:\\n return -100.0\\n return -2.0\",\n \"def __call__(self, results):\\n if np.random.rand() > self.prob:\\n return results\\n self._adjust_brightness_img(results, self.factor)\\n return results\",\n \"def update_weights(weights, alpha, y_true, y_pred):\\n def change_labels(arr):\\n for i,a in enumerate(arr):\\n if a == 0:\\n arr[i] = -1\\n return arr \\n \\n y_true, y_pred = change_labels(y_true), change_labels(y_pred)\\n w_hat = weights * np.exp(-alpha * y_true * y_pred)\\n return w_hat / sum(w_hat)\",\n \"def acc_b_tr(self):\\r\\n return self._acc_b_tr\",\n \"def update(self):\\n #self.consider_deactivation() if self.active_flag else self.consider_activation()\\n if self.active_flag:\\n self.consider_deactivation()\\n else:\\n self.consider_activation()\\n if self.active_flag:\\n self.sense_and_act()\\n self.weight = self.match_degree*self.priority\",\n \"def update(self):\\n #self.consider_deactivation() if self.active_flag else self.consider_activation()\\n if self.active_flag:\\n self.consider_deactivation()\\n else:\\n self.consider_activation()\\n if self.active_flag:\\n self.sense_and_act()\\n self.weight = self.match_degree*self.priority\",\n \"def update(self):\\n #self.consider_deactivation() if self.active_flag else self.consider_activation()\\n if self.active_flag:\\n self.consider_deactivation()\\n else:\\n self.consider_activation()\\n if self.active_flag:\\n self.sense_and_act()\\n self.weight = self.match_degree*self.priority\",\n \"def update(self):\\n #self.consider_deactivation() if self.active_flag else self.consider_activation()\\n if self.active_flag:\\n self.consider_deactivation()\\n else:\\n self.consider_activation()\\n if self.active_flag:\\n self.sense_and_act()\\n self.weight = self.match_degree*self.priority\",\n \"def getWeights(self, gameState, action):\\n return {'successorScore': 1.0}\",\n \"def getWeights(self, gameState, action):\\n return {'successorScore': 1.0}\",\n \"def getWeights(self, gameState, action):\\n return {'successorScore': 1.0}\",\n \"def getWeights(self, gameState, action):\\n return {'successorScore': 1.0}\",\n \"def getWeights(self, gameState, action):\\n return {'successorScore': 1.0}\",\n \"def getWeights(self, gameState, action):\\n return {'successorScore': 1.0}\",\n \"def getWeights(self, gameState, action):\\n return {'successorScore': 1.0}\",\n \"def __call__(self, num_update):\\n if num_update > self.count:\\n self.base_lr = self.origin_lr * pow((1 - 1.0*num_update/self.max_samples), self.factor)\\n if self.base_lr < self.stop_factor_lr:\\n self.base_lr = self.stop_factor_lr\\n logging.info(\\\"Update[%d]: now learning rate arrived at %0.5e, will not \\\"\\n \\\"change in the future\\\", num_update, self.base_lr)\\n elif num_update % self.show_num == 0:\\n logging.info(\\\"Update[%d]: Change learning rate to %0.8e\\\",\\n num_update, self.base_lr)\\n self.count = num_update\\n return self.base_lr\",\n \"def total_return_from_returns(returns):\\n return (returns + 1).prod() - 1\",\n \"def _return_activation(x, nl):\\n if nl == 'HS':\\n x = Activation(_hard_swish)(x)\\n if nl == 'RE':\\n x = Activation(_relu6)(x)\\n return x\",\n \"def bias_prior(self):\",\n \"def forward_activate(self, a_prev, w, b, func_type):\\n\\n\\t\\tz = np.dot(w, a_prev) + b\\n\\t\\tif 'sigmod' == func_type.lower(): \\n\\t\\t\\ta = 1 / (1 + np.exp(-z))\\n\\t\\telif 'relu' == func_type.lower():\\n\\t\\t\\ta = np.where(z >= 0, z, 0)\\n\\t\\telif 'leaky relu' == func_type.lower():\\n\\t\\t\\ta = np.where(z >= 0, z, 0.01 * z)\\n\\t\\telif 'tanh' == func_type.lower():\\n\\t\\t\\ta = (np.exp(z) - np.exp(-z)) / (np.exp(z) + np.exp(-z))\\n\\n\\t\\tcache = (a_prev, w, b, z)\\n\\t\\treturn a, cache\",\n \"def _return_activation(x, nl):\\n if nl == 'HS':\\n x = KL.Activation(_hard_swish)(x)\\n if nl == 'RE':\\n x = KL.ReLU(6.)(x)\\n\\n return x\",\n \"def get_learn_after_each_decision(self):\\r\\n return 0\",\n \"def _problem_handle_prev_evals(self):\\n self._update_reward_values()\",\n \"def front_column_model_p_gain():\",\n \"def predict_action_probabilities(self, tracker, domain):\\n # type: (DialogueStateTracker, Domain) -> (float, Optional[int])\\n x = self.featurize(tracker, domain)\\n logger.debug('Current tracker state [\\\\n\\\\t{}]'.format(\\n \\\"\\\\n\\\\t\\\".join([\\\"{}\\\".format(e) for e in self.featurizer.decode_features(x, domain.input_features)])))\\n\\n memorised = self.recall(x, domain)\\n result = [0.0] * domain.num_actions\\n if memorised is not None and self.is_enabled:\\n logger.debug(\\\"Used memorised next action '{}'\\\".format(memorised))\\n result[memorised] = 1.0\\n return result\",\n \"def activate(self,x):\\n return 1 / (1 + np.exp(-x))\",\n \"def statePosteriors(log_alpha, log_beta):\\n return log_alpha + log_beta - logsumexp(log_alpha[-1,:])\",\n \"def learn(self, state, action, reward, next_state):\\r\\n\\r\\n \\\"\\\"\\\"Please Fill Your Code Here.\\r\\n \\\"\\\"\\\"\\r\\n self.Q[state][action] = self.Q[state][action] + self.alpha * (reward + self.gamma * max(self.Q[next_state]) - self.Q[state][action])\\r\\n\\r\\n return 0\",\n \"def get_action_value(mdp, state_values, state, action, gamma):\\n\\n Q = 0\\n # YOUR CODE HERE\\n return Q\",\n \"def uf_activate(self, output_reg):\\n if len(self.inputs) is 2:\\n self.two_activation(output_reg)\\n elif len(self.inputs) is 3:\\n self.three_activation(output_reg)\\n else:\\n self.large_activation(output_reg)\",\n \"def _reward(self, action: Action) -> float:\\n raise NotImplementedError\",\n \"def update(self, state, action, nextState, reward):\\n \\\"*** YOUR CODE HERE ***\\\"\\n diff = reward + self.discount * self.computeValueFromQValues(nextState) - self.getQValue(state, action)\\n for feature_name, feature_value in self.featExtractor.getFeatures(state, action).iteritems():\\n self.weights[feature_name] += self.alpha * diff * feature_value\",\n \"def update(self, state, action, nextState, reward):\\n \\\"*** YOUR CODE HERE ***\\\"\\n diff = reward + self.discount * self.computeValueFromQValues(nextState) - self.getQValue(state, action)\\n for feature_name, feature_value in self.featExtractor.getFeatures(state, action).iteritems():\\n self.weights[feature_name] += self.alpha * diff * feature_value\",\n \"def compute_reward(self, obs, action, state):\\n pass\",\n \"def _balance_update(self):\\n return_rate = self.df.loc[self.currentStep, \\\"return_Close\\\"]\\n self.buy_amount += return_rate * self.buy_amount\\n self.sell_amount -= return_rate * self.sell_amount\",\n \"def _call(self, x):\\n if functional.prior is None:\\n return np.exp(x)\\n else:\\n return functional.prior * np.exp(x)\",\n \"def apply_raise(self):\\n self.pay = int(self.pay * self.raise_amnt)\",\n \"def agent_step(self, reward, state):\\n prev_val= self.state[self.prevAction]\\n self.state[self.prevAction]=prev_val+self.alpha*(reward-prev_val)\\n val=max(self.state)\\n index=self.state.index(val)\\n self.prevAction=index\\n i=random.uniform(0,1)\\n if i < 1-self.prob:\\n self.prevAction=index\\n return index\\n else:\\n index=random.randint(0,self.num_bandits-1)\\n self.prevAction=index\",\n \"def update_policy_values(policy_lookup, board, state, player, action, next_state, reward):\\n\\t\\n\\t# compute total expected reward including future rewards\\n\\tif board.check_end():\\n\\t\\texpected = reward\\n\\telse:\\n\\t\\tif player == 1:\\n\\t\\t\\texpected = reward + discount * min_value(policy_lookup, next_state, 2)\\n\\t\\telif player == 2:\\n\\t\\t\\texpected = reward + discount * max_value(policy_lookup, next_state, 1)\\n\\t# get current policy action value\\n\\tpolicy_value = get_policy_value(policy_lookup, state, player, action)\\n\\t# update policy action value\\n\\tpolicy_lookup[(state, player)][action] += learning_rate * (expected - policy_value)\",\n \"def final_amt(p,r,n,t):\\n\\n a = p * (1 + r/n) ** (n*t)\\n return a #This is what makes the function \\\"fruitful\\\"\",\n \"def backward_activate(self, da, a, cache, func_type):\\n\\n\\t\\ta_prev, w, b = cache\\n\\t\\tif 'sigmod' == func_type.lower():\\n\\t\\t\\tdz = np.multiply(np.multiply(a, (1 - a)), da)\\n\\t\\telif 'relu' == func_type.lower():\\n\\t\\t\\tif a > 0:\\n\\t\\t\\t\\tdz = da \\n\\t\\t\\telse:\\n\\t\\t\\t\\tdz = np.multiply(0, da)\\n\\t\\telif 'leaky relu' == func_type.lower():\\n\\t\\t\\tif a >= 0:\\n\\t\\t\\t\\tdz = da \\n\\t\\t\\telse:\\n\\t\\t\\t\\tdz = np.multiply(0.01, da)\\n\\t\\telif 'tanh' == func_type.lower():\\n\\t\\t\\tdz = np.multiply(1 - np.multiply(a, a), da)\\n\\t\\telse:\\n\\t\\t\\tprint(\\\"activate type error: %s\\\" %(func_type))\\n\\t\\t\\treturn\\n\\n\\t\\tdw = np.dot(dz, a_prev.T)\\n\\t\\tdb = np.sum(dz, axis = 1, keepdims = True)\\n\\t\\tda_prev = np.dot(w.T, dz)\\n\\n\\t\\treturn da_prev, dw, db\",\n \"def _calculate_reward( self, old_state_vars, new_state_vars ):\\n if 'total-wealth' == self.objective:\\n x_old = old_state_vars.ptf_asset_vals.sum()\\n x_new = new_state_vars.ptf_asset_vals.sum()\\n elif 'relative-profit' == self.objective:\\n x_old = old_state_vars.ptf_asset_vals.sum() - old_state_vars.bmk_asset_vals.sum()\\n x_new = new_state_vars.ptf_asset_vals.sum() - new_state_vars.bmk_asset_vals.sum()\\n else:\\n raise ValueError( 'Unsupported objective: {}'.format(objective) )\\n\\n # Discount the objective before applying the utility function\\n gamma = self.get_gamma()\\n reward = self.utility_fun( gamma * x_new ) - self.utility_fun( x_old )\\n return reward\",\n \"def next_move(self, cur_state):\\n\\n alpha, final_state, min_level, action_took = self.alpha_beta(cur_state, 2, 0, -math.inf, math.inf, math.inf)\\n #print(\\\"-----------------------------------------\\\")\\n #print(\\\"value = \\\"+str(alpha)+\\\", min_level = \\\"+str(min_level))\\n #print(\\\"previous: top=\\\"+str(cur_state.top)+\\\", bottom=\\\"+str(cur_state.bottom)+\\\", left=\\\"+str(cur_state.left)+\\\", right=\\\"+str(cur_state.right))\\n #print(final_state.pre_state)\\n return action_took\",\n \"def count_change(amount):\\n \\\"*** YOUR CODE HERE ***\\\"\\n\\n return helper(1, amount)\",\n \"def update(self, state, action, nextState, reward):\\n \\\"*** YOUR CODE HERE ***\\\"\\n # print \\\"update\\\"\\n oldValue = self.getQValue(state, action)\\n sample = reward + self.discount*self.computeValueFromQValues(nextState)\\n self.qValues[(state, action)] = (1-self.alpha)*oldValue + self.alpha*(sample)\",\n \"def act(self, observation):\\n for i in range(len(self.counts)):\\n if self.counts[i] == 0:\\n return i\\n \\n total_counts = np.sum(self.counts)\\n for action1 in range(len(self.counts)):\\n add = math.sqrt((self.minmax/2 * math.log(total_counts)) / float(self.counts[action1]))\\n self.ucb_values[action1] = self.values[action1] + add\\n return np.argmax(self.ucb_values)\",\n \"def recall(self):\\n return float(self.tp) / (self.tp + self.fn) if self.fn != 0 else 1\",\n \"def call(self, inputs, states):\\r\\n (out_prev, Vm_prev) = states\\r\\n\\r\\n #Vm = Vm_prev * (1.0 - out_prev)\\r\\n #Lateral inhibition logic:\\r\\n Vm = Vm_prev * (1.0 - tf.reduce_max(out_prev))\\r\\n\\r\\n Vm = Vm * self.decay\\r\\n Vm = Vm + tf.matmul(inputs, self.kernel)\\r\\n if self.recurrent:\\r\\n Vm = Vm + tf.matmul(out_prev, self.recurrent_kernel)\\r\\n Vm = self.g(Vm)\\r\\n overVth = Vm - self.bias\\r\\n out = self.activation(overVth)\\r\\n return out, (out, Vm)\",\n \"def _call(self, x):\\n if functional.prior is None:\\n return 1.0 / (1 - x)\\n else:\\n return functional.prior / (1 - x)\",\n \"def gen_outcome(alpha, delta, beta, win_counters, attempt_counters, h_features_win, h_features_att):\\n return logistic(alpha+delta+beta+np.sum(np.log(1+np.array(attempt_counters))*np.array(h_features_att))+\\\\\\n np.sum(np.log(1+np.array(win_counters))*np.array(h_features_win)))\",\n \"def recoding_func(self, input_var: Tensor, hidden: HiddenDict, out: Tensor, device: Device,\\n **additional: Dict) -> RecurrentOutput:\\n target_idx = additional.get(\\\"target_idx\\\", None)\\n\\n # Estimate uncertainty of those same predictions\\n delta = self.get_surprisal(out, target_idx)\\n\\n # Calculate gradient of uncertainty w.r.t. hidden states and make step\\n new_out_dist, new_hidden = self.recode_activations(hidden, out, delta, device, **additional)\\n\\n return new_out_dist, new_hidden\",\n \"def update(self, state, action, nextState, reward):\\n \\\"*** YOUR CODE HERE ***\\\"\\n feature_dictionary = self.featExtractor.getFeatures(state, action)\\n difference = (reward + self.discount * self.computeValueFromQValues(nextState)) - self.getQValue(state, action)\\n\\n for feature in feature_dictionary:\\n self.weights[feature] += self.alpha * difference * feature_dictionary[feature]\",\n \"def update(self, state, action, nextState, reward):\\n # print \\\"Update\\\"\\n difference = (reward + self.discount*self.compValFromState(nextState)) - self.getQValue(state, action)\\n features = self.featExtractor.getFeatures(state, self.index)\\n #print \\\"features\\\", features, \\\"difference\\\", difference, \\\"weights\\\", self.weights\\n for key in self.weights:\\n self.weights[key] = self.alpha * difference * features[key]\",\n \"def calculate_controller_reward(self, controller1, controller2):\",\n \"def _update_state_value(self, old_state, new_state):\\n if not self._test_mode:\\n if isinstance(new_state, int):\\n self._state_values[old_state] += self._alpha * \\\\\\n (new_state - self._state_values[old_state])\\n else:\\n self._state_values[old_state] += self._alpha * \\\\\\n (self._state_values[new_state] - self._state_values[old_state])\",\n \"def compute_act(self, x):\\n self.p = self.weights @ x.T #+ self.bias\\n self.z = self.__act_f(self.p)\\n \\n return self.z\",\n \"def _call(self, x):\\n if functional.prior is None:\\n return (-1.0) / x + 1\\n else:\\n return (-functional.prior) / x + 1\",\n \"def update_sample_reward(self, r):\\n self.r_exp[-1] += r * self.gamma ** (len(self.u_seq[-1])-1)\",\n \"def rev_opt_implied_returns(delta, sigma_prior: pd.DataFrame, wts_prior: pd.Series):\\r\\n pi = (delta * sigma_prior @ wts_prior).squeeze() # @ may be used instead of dot, but some issues arise if a df is not passed\\r\\n pi.name = 'Implied Returns'\\r\\n return pi\",\n \"def update(self, state, action, nextState, reward):\\n \\\"*** YOUR CODE HERE ***\\\"\\n features = self.featExtractor.getFeatures(state,action)\\n\\n learning_rate = self.alpha #gives us the learning rate\\n\\n temporary_QValue = self.getQValue(state,action) #to get the Q value of the state,action pair\\n\\n nextState_QValue = self.getValue(nextState) #to get the Q value of the landing state when taken action a and state s\\n\\n discount_factor = self.discount #to get the gamma/ discount factor\\n\\n weight = self.weights\\n\\n Q_Value = 0\\n\\n difference = (reward + discount_factor * nextState_QValue ) - (temporary_QValue) #refer to README_Reinforcement.txt for the formula\\n\\n for each_feature in features:\\n\\n #refer to README_Reinforcement.txt for the formula at line 20\\n weight[each_feature] = weight[each_feature] + learning_rate * difference * features[each_feature]\\n\\n #util.raiseNotDefined()\",\n \"def get_penalty(state, action, winrate_predictor):\\n if violate_rule(state, action):\\n return -1 \\n return 0\",\n \"def update(self, action):\\n self._update_noise()\\n self._update_state(action)\\n self._update_status()\\n self.steps += 1\\n # Return current state and error\\n return self.observation, self.error # returns two unctions when called\",\n \"def __act_f(self, p):\\n # linear activation if no function is given\\n if self.activation is None:\\n return p\\n # tanh\\n if self.activation == \\\"tanh\\\":\\n return np.tanh(p)\\n # sigmoid\\n if self.activation == \\\"sigmoid\\\":\\n return 1 / (1 + np.exp(-p))\\n # relu\\n if self.activation == \\\"relu\\\":\\n return p * (p > 0)\",\n \"def act(self, observation):\\n self.t += 1\\n\\n probabilities = self.probabilities(observation)\\n\\n probabilities *= self.action_mask(observation[0])\\n\\n if probabilities.sum() == 0.0:\\n probabilities += 1.0\\n\\n return torch.multinomial(probabilities, num_samples=1)[0]\",\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 update(self, state, action, nextState, reward):\\n \\\"\\\"\\\"Description:\\n Use Q-Learning algoritm in slide 58 of MDP\\n \\\"\\\"\\\"\\n \\\"\\\"\\\" YOUR CODE HERE \\\"\\\"\\\"\\n maxQns = self.getValue(nextState) # get max q-value of next state\\n if maxQns == None:\\n maxQns = 0\\n Qsa = self.getQValue(state, action) #self.qValues[(state, action)]\\n difference = reward + self.discountRate * maxQns - Qsa\\n self.qValues[(state, action)] += self.alpha * difference\\n \\n self.vitCount[(state, action)] += 1\\n \\\"\\\"\\\" END CODE \\\"\\\"\\\"\",\n \"def result(self, state, action):\\n\\n\\t\\t#Adds new number to the state\\n\\t\\treturn state + action\",\n \"def update_reward(state, reward, max_reward, alpha=1, c=100, gamma=0.9):\\n\\t\\t# update number of actions done so far to this state\\n\\t\\tactions[state] = actions.get(state, 0.0) + 1.0\\n\\t\\t# compute learning rate\\n\\t\\talpha *= c / (c + actions[state])\\n\\t\\trewards[state] = rewards.get(state, 0.0) + alpha*(reward+gamma*max_reward-rewards.get(state, 0.0))\",\n \"def update(self, state, action, nextState, reward):\\n \\\"*** YOUR CODE HERE ***\\\"\\n feature_dictionary = self.featExtractor.getFeatures(state, action)\\n difference = (reward + self.discount * self.computeValueFromQValues(nextState)) - self.getQValue(state, action)\\n\\n for feature in feature_dictionary:\\n self.weights[feature] += self.alpha * difference * feature_dictionary[feature]\\n\\n #if self.epsilon > self.epsilon_min:\\n # self.epsilon *= self.epsilon_decay\",\n \"def acc_b(self):\\n return self._acc_b\",\n \"def act_func_part4_v2_t(action_raw, action_raw_idx, raw_state_limits, stptLmt, ob_this_raw, logger, is_show_debug):\\n IAT_STPT_IDX = 4;\\n OCP_IDX = 7;\\n PMV_IDX = 6;\\n ACTIONS = [[-2.0],\\n [-1.0],\\n [-0.5],\\n [0.0],\\n [0.5],\\n [1.0],\\n [2.0],];\\n\\n ocp = ob_this_raw[OCP_IDX];\\n pmv = ob_this_raw[PMV_IDX];\\n iat_stpt_ob = ob_this_raw[IAT_STPT_IDX]; \\n act_num = len(ACTIONS);\\n # Process based on PMV\\n action_prcd = action_raw;\\n action_prcd_idx = action_raw_idx;\\n if ocp == 1:\\n if pmv < -0.5 and action_raw[0] < 0:\\n # If pmv < -0.5 and the action is to reduce setpoint\\n # Choose one action to increase the setpoint\\n action_prcd_idx = np.random.randint(3, act_num);\\n action_prcd = ACTIONS[action_prcd_idx];\\n elif pmv > 0.5 and action_raw[0] > 0:\\n # If pmv > 0.5 and the action is to increase setpoint\\n # Choose one action to reduce the setpoint\\n action_prcd_idx = np.random.randint(0, 4);\\n action_prcd = ACTIONS[action_prcd_idx];\\n # Get final action\\n iat_stpt_act_raw = iat_stpt_ob + action_prcd[0];\\n action_ret = [max(min(iat_stpt_act_raw, stptLmt[0][1]), stptLmt[0][0])];\\n action_ret_idx = action_prcd_idx;\\n # Log\\n if action_raw_idx != action_ret_idx:\\n if is_show_debug:\\n logger.debug('Action function: raw action %s has been changed to %s for '\\n 'the PMV %s.'%(action_raw_idx, action_ret_idx, pmv));\\n return (action_ret, action_ret_idx);\",\n \"def updated_rating(old: int, expected: int, actual: int) -> int:\\n return round(old + ELO_K_FACTOR * (actual - expected))\",\n \"def vanilaScore(self,attended,state,W):\",\n \"def _update_activation_state(self):\\n activation_count = 0\\n for task_id in self.task_dict:\\n activation_count += (1 if self.task_dict[task_id].is_activated else 0)\\n # Result\\n self.num_activated_task = activation_count\\n self.is_activated = (activation_count > 0)\",\n \"def _eval_prior_casadi(self, state, action):\\n\\n return mtimes(self.a, state.T) + mtimes(self.b, action.T)\",\n \"def fc_activation(c_range, num_act, k_a, k_i, ep_a, ep_ap, ep_ai=4.5,\\n n_ns=4.6E6, n_sites=int(2)):\\n # Compute the MWC probability.\\n mwc_term = p_act(c_range, k_a, k_i, n_sites=n_sites, ep_ai=ep_ai)\\n\\n # Compute and return the fold-change\\n numer = 1 + mwc_term * (num_act / n_ns) * np.exp(-(ep_a + ep_ap))\\n denom = 1 + mwc_term * (num_act / n_ns) * np.exp(-ep_a)\\n return numer / denom\",\n \"def update_action(self):\\n self.action = self.automata > self.states\\n self.inv_action = self.inv_automata > self.states\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7619489","0.7287964","0.58440965","0.5589851","0.55477417","0.55023503","0.5478167","0.5450696","0.5412159","0.54077435","0.5368345","0.53519577","0.53423446","0.53316164","0.5300739","0.52776587","0.5277615","0.52698386","0.52678233","0.52527237","0.52347153","0.523336","0.52333575","0.5230347","0.5223168","0.520596","0.51995134","0.51988316","0.51988316","0.51988316","0.51988316","0.5181035","0.5181035","0.5181035","0.5181035","0.5181035","0.5181035","0.5181035","0.51759374","0.5174431","0.51737344","0.51724744","0.51689875","0.5158656","0.5149387","0.51446885","0.51328003","0.5131721","0.51212305","0.5114072","0.5112559","0.51038384","0.50964636","0.50906235","0.5089748","0.5089748","0.5087412","0.5074176","0.507133","0.5068828","0.5067629","0.50672656","0.50585634","0.5046105","0.5038159","0.50376856","0.5035682","0.50321835","0.5029723","0.50279313","0.5026496","0.5018987","0.5018969","0.5009786","0.50084496","0.50073165","0.5007038","0.50061405","0.5005992","0.5005219","0.50023156","0.50005066","0.4995967","0.49954158","0.49952173","0.49906573","0.49904302","0.49891415","0.49839032","0.4980519","0.49706542","0.49671674","0.49630257","0.49614248","0.49603024","0.49591225","0.49584773","0.49462554","0.49443248","0.49426386"],"string":"[\n \"0.7619489\",\n \"0.7287964\",\n \"0.58440965\",\n \"0.5589851\",\n \"0.55477417\",\n \"0.55023503\",\n \"0.5478167\",\n \"0.5450696\",\n \"0.5412159\",\n \"0.54077435\",\n \"0.5368345\",\n \"0.53519577\",\n \"0.53423446\",\n \"0.53316164\",\n \"0.5300739\",\n \"0.52776587\",\n \"0.5277615\",\n \"0.52698386\",\n \"0.52678233\",\n \"0.52527237\",\n \"0.52347153\",\n \"0.523336\",\n \"0.52333575\",\n \"0.5230347\",\n \"0.5223168\",\n \"0.520596\",\n \"0.51995134\",\n \"0.51988316\",\n \"0.51988316\",\n \"0.51988316\",\n \"0.51988316\",\n \"0.5181035\",\n \"0.5181035\",\n \"0.5181035\",\n \"0.5181035\",\n \"0.5181035\",\n \"0.5181035\",\n \"0.5181035\",\n \"0.51759374\",\n \"0.5174431\",\n \"0.51737344\",\n \"0.51724744\",\n \"0.51689875\",\n \"0.5158656\",\n \"0.5149387\",\n \"0.51446885\",\n \"0.51328003\",\n \"0.5131721\",\n \"0.51212305\",\n \"0.5114072\",\n \"0.5112559\",\n \"0.51038384\",\n \"0.50964636\",\n \"0.50906235\",\n \"0.5089748\",\n \"0.5089748\",\n \"0.5087412\",\n \"0.5074176\",\n \"0.507133\",\n \"0.5068828\",\n \"0.5067629\",\n \"0.50672656\",\n \"0.50585634\",\n \"0.5046105\",\n \"0.5038159\",\n \"0.50376856\",\n \"0.5035682\",\n \"0.50321835\",\n \"0.5029723\",\n \"0.50279313\",\n \"0.5026496\",\n \"0.5018987\",\n \"0.5018969\",\n \"0.5009786\",\n \"0.50084496\",\n \"0.50073165\",\n \"0.5007038\",\n \"0.50061405\",\n \"0.5005992\",\n \"0.5005219\",\n \"0.50023156\",\n \"0.50005066\",\n \"0.4995967\",\n \"0.49954158\",\n \"0.49952173\",\n \"0.49906573\",\n \"0.49904302\",\n \"0.49891415\",\n \"0.49839032\",\n \"0.4980519\",\n \"0.49706542\",\n \"0.49671674\",\n \"0.49630257\",\n \"0.49614248\",\n \"0.49603024\",\n \"0.49591225\",\n \"0.49584773\",\n \"0.49462554\",\n \"0.49443248\",\n \"0.49426386\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7389867"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":75596,"cells":{"query":{"kind":"string","value":"Helper function for update_return_factor > deactivates."},"document":{"kind":"string","value":"def _deactivate_return_factor(self):\n self.active = False\n effect = 1 / self.factor\n return effect"},"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_return_factor(self):\n if not self.active:\n effect = self._activate_return_factor()\n elif self.active:\n effect = self._deactivate_return_factor()\n return effect","def _activate_return_factor(self):\n self.active = True\n effect = self.factor\n return effect","def activate_der(self):\r\n\t\treturn self.value * (1 - self.value)","def backward_activate(self, da, a, cache, func_type):\n\n\t\ta_prev, w, b = cache\n\t\tif 'sigmod' == func_type.lower():\n\t\t\tdz = np.multiply(np.multiply(a, (1 - a)), da)\n\t\telif 'relu' == func_type.lower():\n\t\t\tif a > 0:\n\t\t\t\tdz = da \n\t\t\telse:\n\t\t\t\tdz = np.multiply(0, da)\n\t\telif 'leaky relu' == func_type.lower():\n\t\t\tif a >= 0:\n\t\t\t\tdz = da \n\t\t\telse:\n\t\t\t\tdz = np.multiply(0.01, da)\n\t\telif 'tanh' == func_type.lower():\n\t\t\tdz = np.multiply(1 - np.multiply(a, a), da)\n\t\telse:\n\t\t\tprint(\"activate type error: %s\" %(func_type))\n\t\t\treturn\n\n\t\tdw = np.dot(dz, a_prev.T)\n\t\tdb = np.sum(dz, axis = 1, keepdims = True)\n\t\tda_prev = np.dot(w.T, dz)\n\n\t\treturn da_prev, dw, db","def delcomperrbypassedrate(self) :\n\t\ttry :\n\t\t\treturn self._delcomperrbypassedrate\n\t\texcept Exception as e:\n\t\t\traise e","def consider_deactivation(self):\n pass","def deactivate(self):\n pass","def cb_minus(event):\n delta_alpha = pm_rate\n # Decrease Alpha \n sAlpha0.set_val( np.clip(sAlpha0.val - delta_alpha, alpha_min[0], alpha_max[0]) )\n sAlpha1.set_val( np.clip(sAlpha1.val - delta_alpha, alpha_min[1], alpha_max[1]) )\n sAlpha2.set_val( np.clip(sAlpha2.val - delta_alpha, alpha_min[2], alpha_max[2]) )\n print(\"---\")","def reset_fn(**kwargs):\n return kwargs[\"state\"]","def recoding_func(self, input_var: Tensor, hidden: HiddenDict, out: Tensor, device: Device,\n **additional: Dict) -> RecurrentOutput:\n target_idx = additional.get(\"target_idx\", None)\n\n # Estimate uncertainty of those same predictions\n delta = self.get_surprisal(out, target_idx)\n\n # Calculate gradient of uncertainty w.r.t. hidden states and make step\n new_out_dist, new_hidden = self.recode_activations(hidden, out, delta, device, **additional)\n\n return new_out_dist, new_hidden","def on_deactivate(self):","def pre_revert(self):","def test_active_off(self):\n\n self.feature_test.set_percentage(0)\n self.assertFalse(self.feature_test.is_active)","def activate_2(self, act_1):\n x = act_1\n e = math.e\n result = 1/(1 + e **-x)\n #print (result)\n return result","def cost_derivative(self, output_activations, y):\n\t\treturn (output_activations - y)","def cost_derivative(output_activations, y):\n return (output_activations - y)","def post_revert(self):","def cost_derivative_for_output(output_activations, y):\n return output_activations - y","def fActiveResets(self) -> Dict[int, Optional[float]]:\n return {qs.id: qs.fActiveReset for qs in self.qubits_specs}","def cost_derivative(self, output_activations, y):\r\n return (output_activations-y)","def revise():","def fermi_fct_inv(fermi, beta):\n return -logit(fermi)/beta","def future_deceiveddown(self, a):\n nfav, succfav = self.control[a.name] \n #f_n = n+1;\n # f_worldround = self.world.round+1\n f_successRate = float(a.success) / float(self.world.round+1)\n f_successRatefav = float(succfav) / float(nfav+1)\n if hardrule:\n return self.nsucc(a) > (f_successRatefav + epsilonD) and \\\n (nfav+1 > 5) and ((self.world.round - nfav) > 5) \n else:\n return nfav+1 > 5 and (f_successRate > f_successRatefav + epsilonD \\\n or f_successRate < epsilonD)","def test_deactivate_guider_decenter_fails(self):\n sopTester.updateModel('guider', TestHelper.guiderState['guiderOnDecenter'])\n self.cmd.failOn = 'guider decenter off'\n self._deactivate_guider_decenter(1, 9, 0, 2, didFail=True)","def dec(self, params):\n reg = params[0]\n if self.reg_dct[reg] == 0:\n self.reg_dct[reg] = (2 ** 32) - 1\n else:\n self.reg_dct[reg] -= 1","def on_deactivate(self) -> None:","def _revert(self):\n self.kwargs[\"collect\"].change_status(self.kwargs[\"collect\"].ENDED)","def d_act_f(self, p):\n if self.activation is None:\n return p\n if self.activation == \"tanh\":\n return 1 - np.tanh(p)**2\n if self.activation == \"sigmoid\":\n return (1 / (1 + np.exp(-p))) * (1 - (1 / (1 + np.exp(-p))))\n if self.activation == \"relu\":\n p[np.where(p < 0)] = 0\n return p\n\n return p","def _hidden_activation(self, inputs):\n if self.act_enc is None:\n act_enc = lambda x: x\n else:\n act_enc = self.act_enc\n return act_enc(self._mappings(inputs))","def auto_deactivate_fe(*args, **kwargs):\n creation_date = kwargs.get(\n 'date',\n datetime.datetime.today() - datetime.timedelta(FE_AUTO_EXPIRE_PERIOD))\n\n FE_TYPE = kwargs.get('fe_type', DISPATCH_FE_TYPES)\n # get all fe whose emp_id has not been set and are still active\n fe_users = User.objects.filter(\n is_active=True,\n date_joined__lte=creation_date,\n userprofile__user_type__in=FE_TYPE,\n userprofile__employee_id=None)\n\n # deactivate all these users on\n for fe in fe_users:\n fe.is_active = False\n fe.save()","def f_restore(landscape, site, current, future, restore):\n cpt = {\n # landscape, site, current, future) : restore\n (True, True, True, True): 0.8,\n (True, True, True, False): 0.0,\n (True, True, False, True): 1.0,\n (True, True, False, False): 0.1,\n (True, False, True, True): 0.3,\n (True, False, True, False): 0.0,\n (True, False, False, True): 0.5,\n (True, False, False, False): 0.1,\n (False, True, True, True): 0.0,\n (False, True, True, False): 0.0,\n (False, True, False, True): 0.0,\n (False, True, False, False): 0.0,\n (False, False, True, True): 0.0,\n (False, False, True, False): 0.0,\n (False, False, False, True): 0.0,\n (False, False, False, False): 0.0\n }\n p = cpt[(landscape, site, current, future)]\n if restore:\n return p\n else:\n return 1.0 - p","def statePosteriors(log_alpha, log_beta):","def decline(self):\n self.is_active = False\n self.save()","def decline(self):\n self.is_active = False\n self.save()","def rev_opt_implied_returns(delta, sigma_prior: pd.DataFrame, wts_prior: pd.Series):\r\n pi = (delta * sigma_prior @ wts_prior).squeeze() # @ may be used instead of dot, but some issues arise if a df is not passed\r\n pi.name = 'Implied Returns'\r\n return pi","def alpha_beta(returns, factor_returns):\n\n ret_index = returns.index\n beta, alpha = sp.sp.stats.linregress(factor_returns.loc[ret_index].values,\n returns.values)[:2]\n\n return alpha * APPROX_BDAYS_PER_YEAR, beta","def backward(self, delta):\n if self.activation_type == \"sigmoid\":\n grad = self.grad_sigmoid()\n\n elif self.activation_type == \"tanh\":\n grad = self.grad_tanh()\n\n elif self.activation_type == \"ReLU\":\n grad = self.grad_ReLU()\n\n return grad * delta","def __call__(self, epoch):\n exp = np.floor((1 + epoch) / self.dropEvery)\n alpha = initAlpha * (self.factor ** exp)\n \n # return alpha \n return float(alpha)","def _return_activation(x, nl):\n if nl == 'HS':\n x = KL.Activation(_hard_swish)(x)\n if nl == 'RE':\n x = KL.ReLU(6.)(x)\n\n return x","def consider_deactivation(self):\n if self.sensob_ultrasonic.get_value() >= 0.20:\n self.active_flag = False\n self.bbcon.deactivate_behaviour(self)","def cost_derivative(self, output_activations, y):\n return (output_activations-y)","def cost_derivative(self, output_activations, y):\n return (output_activations-y)","def cost_derivative(self, output_activations, y):\n return (output_activations-y)","def cost_derivative(self, output_activations, y):\n return (output_activations-y)","def dactivate(self, y):\n return self.activate(y) * (1-self.activate(y))","def productactivate():\n pass","def test_deactivate_account02(self, client):\n a = AccountFactory.get_account(type=Account.DEPOSITORY)\n AccountFactory.get_account(item=a.item, type=Account.DEPOSITORY)\n client = self.get_auth_client(a.item.user)\n\n url = '/v1/accounts/set_active/{}'.format(a.id)\n dic = {'is_active': False}\n data = json.dumps(dic)\n response = client.put(url, data, content_type='application/json')\n assert response.status_code == 400","def flip():\n return random.choice((True, False))","def record_outcome(self, outcome: int):\n self.__outcome = outcome if abs(self.__flipping_encryption_angle - 0) < eps else (outcome + 1) % 2","def cost_derivative(self, output_activations, y):\n return 2 * (output_activations - y)","def ComputerFinalStateOfCharge(self):\r\n pass","def recall(self):\n return float(self.tp) / (self.tp + self.fn) if self.fn != 0 else 1","def flag_dec(func):\n def wrapfunc_and_call(*args, **kwargs):\n if func.__name__ == 'prompt':\n if PROMPT_ENABLED:\n return func(*args, **kwargs)\n else:\n return '' # use empty string to indicate default\n else:\n return None\n return wrapfunc_and_call","def _credit_func(difficulty, discrimination, thetas, output):\n # This model is based on exponentials and normalized to\n # make sure the expected probablity is equal to one\n output *= 0.0 # clear any previous values\n output[1:, :] += thetas\n output[1:, :] -= difficulty[:, None]\n output *= discrimination\n np.cumsum(output, axis=0, out=output)\n np.exp(output, out=output)\n\n normalizing_term = 1.0 / np.sum(output, axis=0)\n output *= normalizing_term","def test_deactivate_account01(self, client):\n a1 = AccountFactory.get_account()\n a2 = AccountFactory.get_account(item=a1.item)\n client = self.get_auth_client(a1.item.user)\n\n url = '/v1/accounts/set_active/{}'.format(a2.id)\n dic = {'is_active': False}\n data = json.dumps(dic)\n response = client.put(url, data, content_type='application/json')\n assert response.status_code == 204\n\n url = '/v1/accounts/set_active/{}'.format(a1.id)\n data = json.dumps(dic)\n response = client.put(url, data, content_type='application/json')\n assert response.status_code == 400","def state_update(self, evidence: List, return_mode: str = 'letter'):\n sample = uniform(len(self.alp))\n pairs = list(zip(self.alp, sample.tolist()))\n priors = {return_mode: pairs}\n\n self.log.debug(\"Language Model Random probabilities:\")\n self.log.debug(priors)\n return priors","def _return_activation(x, nl):\n if nl == 'HS':\n x = Activation(_hard_swish)(x)\n if nl == 'RE':\n x = Activation(_relu6)(x)\n return x","def disable(func):\n return func","def activation_ReLU(self):\n self.value = max(0, self.value)","def statePosteriors(log_alpha, log_beta):\n return log_alpha + log_beta - logsumexp(log_alpha[-1,:])","def deactivate(self):\n super(Pfsense, self).deactivate()","def _localSetState(self,pdict):\n self.low = pdict.pop('low' )\n self.alpha = pdict.pop('alpha')\n self.beta = pdict.pop('beta' )","def test_wrong_pwmerchactive_update_10(self):\n user1.pwmerchactive(method='update', params={'m_lid': user1.merchant1.lid, 'payway': 'visamc', 'is_active': None})\n assert user1.resp_pwmerchactive == {'code': -32002, 'data': {'field': 'is_active', 'reason': 'Should be provided'}, 'message': 'EParamInvalid'}","def inv_acquisition(x):\n return -acquisition(x)","def action_reactivate(self):\n options=self.env['plm.config.settings'].GetOptions()\n status = 'released'\n action = 'reactivate'\n default={\n 'engineering_writable': False,\n 'state': status,\n }\n doc_default = {\n 'state': status,\n 'writable': False,\n }\n operationParams = {\n 'status': status,\n 'statusName': _('Released'),\n 'action': action,\n 'docaction': 'reactivate',\n 'excludeStatuses': ['draft', 'confirmed', 'transmitted', 'released'],\n 'includeStatuses': ['undermodify', 'obsoleted'],\n 'default': default,\n 'doc_default': doc_default,\n }\n if options.get('opt_showWFanalysis', False):\n return self.action_check_workflow(operationParams)\n else:\n return self._action_to_perform(self._ids, operationParams, default)","def revert(self, *args, **kwargs):","def future_deceivedup(self, a): \n nfav, succfav = self.control[a.name]\n #f_n = n+1;\n # f_worldround = self.world.round+1\n f_successRate = float(a.success +1) / float(self.world.round+1)\n if hardrule:\n return (nfav+1 > 5) and ((self.world.round - nfav) > 5) and \\\n float(a.success+1-succfav)/(self.world.round+1 - nfav) > \\\n (float(succfav)/nfav) + epsilonD\n else:\n return nfav > 5 and (f_successRate > (float(succfav)/nfav) + epsilonD \\\n or f_successRate < epsilonD)","def getFactor(currency):","def user_numforms_erase(*args):\n return _ida_hexrays.user_numforms_erase(*args)","def test_wrong_pwmerchactive_update_4(self):\n payway = [pw['name'] for pw in admin.get_model(model='payway', _filter='is_active', value=False)][0]\n user1.pwmerchactive(method='update', params={'m_lid': user1.merchant1.lid, 'payway': payway, 'is_active': True})\n assert user1.resp_pwmerchactive == \"\"","def get_action_value(mdp, state_values, state, action, gamma):\n\n Q = 0\n # YOUR CODE HERE\n return Q","def count_change(amount):\n \"*** YOUR CODE HERE ***\"\n\n return helper(1, amount)","def on_DecreaseDeposit_clicked(self):\n # TODO: not implemented yet\n raise NotImplementedError","def reward(self, env):\n del env\n return 1","def delcomprequestsrate(self) :\n\t\ttry :\n\t\t\treturn self._delcomprequestsrate\n\t\texcept Exception as e:\n\t\t\traise e","def post_process_stage1(self, pred):\n return (pred>0)*1","def set_subtract():\n subtract.next = not (self.en_i and self.funct3_i==f3.RV32_F3_ADD_SUB and not self.funct7_6_i)","def process_apply(self,succ,crit):\n\n if succ and crit:\n bonus = 2\n elif succ:\n bonus = 1\n else:\n bonus = 0\n\n return bonus","def debit(self):\n debit = 0 #variable to track the remaining debit\n debit = self.total_purchase() - self.total_clearance()\n return debit","def test_wrong_pwmerchactive_update_11(self, _authorization):\n user1.headers['x-token'] = user1.headers['x-token'] + '1'\n user1.pwmerchactive(method='update', params={'m_lid': user1.merchant1.lid, 'payway': 'visamc', 'is_active': True})\n assert user1.resp_pwmerchactive == {'code': -32034, 'data': {'field': 'token', 'reason': 'Expired or invalid',\n 'value': user1.headers['x-token']}, 'message': 'EStateUnauth'}","def __toggle(self,x):\n\t\tif x == 1:\n\t\t\treturn 0\n\t\telse:\n\t\t\treturn 1","def test_activation_deactivated(self):\n new_user = self.registration_profile.objects.create_inactive_user(\n site=Site.objects.get_current(), **self.user_info)\n profile = self.registration_profile.objects.get(user=new_user)\n self.registration_profile.objects.activate_user(\n profile.activation_key, Site.objects.get_current())\n\n # Deactivate the new user.\n new_user.is_active = False\n new_user.save()\n\n # Try to activate again and ensure False is returned.\n user, activated = self.registration_profile.objects.activate_user(\n profile.activation_key, Site.objects.get_current())\n self.assertFalse(activated)","def flip(self):","def on_deactivate(self, state) -> TransitionCallbackReturn:\n return TransitionCallbackReturn.SUCCESS","def predict_action_probabilities(self, tracker, domain):\n # type: (DialogueStateTracker, Domain) -> (float, Optional[int])\n x = self.featurize(tracker, domain)\n logger.debug('Current tracker state [\\n\\t{}]'.format(\n \"\\n\\t\".join([\"{}\".format(e) for e in self.featurizer.decode_features(x, domain.input_features)])))\n\n memorised = self.recall(x, domain)\n result = [0.0] * domain.num_actions\n if memorised is not None and self.is_enabled:\n logger.debug(\"Used memorised next action '{}'\".format(memorised))\n result[memorised] = 1.0\n return result","def act(self, state, eps=0.):","def test_wrong_pwmerchactive_update_9(self):\n user1.pwmerchactive(method='update', params={'m_lid': int(user1.merchant1.lid), 'payway': 'visamc', 'is_active': True})\n assert user1.resp_pwmerchactive == {'code': -32003, 'data': {'field': 'm_lid', 'reason': \"'m_lid' must not be of 'int' type\",\n 'value': int(user1.merchant1.lid)}, 'message': 'EParamType'}","def deactivate(self):\r\n self.activated = False","def reactivate(self, creator):\r\n # if we reactivate then reinit this\r\n self.activation = Activation(creator)\r\n self.activated = False","def __invert__(self):\r\n return 1 - self","def backward(alphaIn, phi_x, y):\n alphaPhi_X = robot.Distribution()\n for x, alphaX in alphaIn.items():\n yProb = phi_x[x]\n tmpProd = yProb * alphaX\n if tmpProd > 0:\n alphaPhi_X[x] = tmpProd\n\n # compute alpha out\n alphaOut = robot.Distribution()\n for x, alphaPhi in alphaPhi_X.items():\n x2Poss = rev_transition_model(x)\n # multiply and add x2Poss to o/p\n for x2Key, x2pVal in x2Poss.items():\n alphaOut[x2Key] += x2pVal*alphaPhi\n #print(alphaOut)\n return alphaOut","def turned_off_callback(self, entity, attribute, old, new, kwargs):\n\n entity_dict = kwargs['entity_dict']\n self.log('{} turned off, setting mode to Automatic and resetting last setpoint.'.format(\n entity_dict['friendly']))\n\n self.set_value(entity_dict['setpoint'], 0)\n self.select_option(entity_dict['mode'], 'Automatic')","def _get_activate(self):\n return self.__activate","def _update_activation_state(self):\n activation_count = 0\n for task_id in self.task_dict:\n activation_count += (1 if self.task_dict[task_id].is_activated else 0)\n # Result\n self.num_activated_task = activation_count\n self.is_activated = (activation_count > 0)","def unreturnbank(self):\n pass","def deactivated(self, util):\n self.deactivate(util)\n\n try:\n yield\n finally:\n self.activate(util)","def dec(reg, registers):\n registers[reg] -= 1\n return 1","def deactivate(self):\n pass","def deactivate(self):\n pass","def payoff_difference(dist,benefit_function,b,c,*params):\n payoffs = mean_payoffs(dist,benefit_function,b,c,*params)\n return (payoffs['A_pay']-payoffs['B_pay']).rename(\"pdiff\")"],"string":"[\n \"def update_return_factor(self):\\n if not self.active:\\n effect = self._activate_return_factor()\\n elif self.active:\\n effect = self._deactivate_return_factor()\\n return effect\",\n \"def _activate_return_factor(self):\\n self.active = True\\n effect = self.factor\\n return effect\",\n \"def activate_der(self):\\r\\n\\t\\treturn self.value * (1 - self.value)\",\n \"def backward_activate(self, da, a, cache, func_type):\\n\\n\\t\\ta_prev, w, b = cache\\n\\t\\tif 'sigmod' == func_type.lower():\\n\\t\\t\\tdz = np.multiply(np.multiply(a, (1 - a)), da)\\n\\t\\telif 'relu' == func_type.lower():\\n\\t\\t\\tif a > 0:\\n\\t\\t\\t\\tdz = da \\n\\t\\t\\telse:\\n\\t\\t\\t\\tdz = np.multiply(0, da)\\n\\t\\telif 'leaky relu' == func_type.lower():\\n\\t\\t\\tif a >= 0:\\n\\t\\t\\t\\tdz = da \\n\\t\\t\\telse:\\n\\t\\t\\t\\tdz = np.multiply(0.01, da)\\n\\t\\telif 'tanh' == func_type.lower():\\n\\t\\t\\tdz = np.multiply(1 - np.multiply(a, a), da)\\n\\t\\telse:\\n\\t\\t\\tprint(\\\"activate type error: %s\\\" %(func_type))\\n\\t\\t\\treturn\\n\\n\\t\\tdw = np.dot(dz, a_prev.T)\\n\\t\\tdb = np.sum(dz, axis = 1, keepdims = True)\\n\\t\\tda_prev = np.dot(w.T, dz)\\n\\n\\t\\treturn da_prev, dw, db\",\n \"def delcomperrbypassedrate(self) :\\n\\t\\ttry :\\n\\t\\t\\treturn self._delcomperrbypassedrate\\n\\t\\texcept Exception as e:\\n\\t\\t\\traise e\",\n \"def consider_deactivation(self):\\n pass\",\n \"def deactivate(self):\\n pass\",\n \"def cb_minus(event):\\n delta_alpha = pm_rate\\n # Decrease Alpha \\n sAlpha0.set_val( np.clip(sAlpha0.val - delta_alpha, alpha_min[0], alpha_max[0]) )\\n sAlpha1.set_val( np.clip(sAlpha1.val - delta_alpha, alpha_min[1], alpha_max[1]) )\\n sAlpha2.set_val( np.clip(sAlpha2.val - delta_alpha, alpha_min[2], alpha_max[2]) )\\n print(\\\"---\\\")\",\n \"def reset_fn(**kwargs):\\n return kwargs[\\\"state\\\"]\",\n \"def recoding_func(self, input_var: Tensor, hidden: HiddenDict, out: Tensor, device: Device,\\n **additional: Dict) -> RecurrentOutput:\\n target_idx = additional.get(\\\"target_idx\\\", None)\\n\\n # Estimate uncertainty of those same predictions\\n delta = self.get_surprisal(out, target_idx)\\n\\n # Calculate gradient of uncertainty w.r.t. hidden states and make step\\n new_out_dist, new_hidden = self.recode_activations(hidden, out, delta, device, **additional)\\n\\n return new_out_dist, new_hidden\",\n \"def on_deactivate(self):\",\n \"def pre_revert(self):\",\n \"def test_active_off(self):\\n\\n self.feature_test.set_percentage(0)\\n self.assertFalse(self.feature_test.is_active)\",\n \"def activate_2(self, act_1):\\n x = act_1\\n e = math.e\\n result = 1/(1 + e **-x)\\n #print (result)\\n return result\",\n \"def cost_derivative(self, output_activations, y):\\n\\t\\treturn (output_activations - y)\",\n \"def cost_derivative(output_activations, y):\\n return (output_activations - y)\",\n \"def post_revert(self):\",\n \"def cost_derivative_for_output(output_activations, y):\\n return output_activations - y\",\n \"def fActiveResets(self) -> Dict[int, Optional[float]]:\\n return {qs.id: qs.fActiveReset for qs in self.qubits_specs}\",\n \"def cost_derivative(self, output_activations, y):\\r\\n return (output_activations-y)\",\n \"def revise():\",\n \"def fermi_fct_inv(fermi, beta):\\n return -logit(fermi)/beta\",\n \"def future_deceiveddown(self, a):\\n nfav, succfav = self.control[a.name] \\n #f_n = n+1;\\n # f_worldround = self.world.round+1\\n f_successRate = float(a.success) / float(self.world.round+1)\\n f_successRatefav = float(succfav) / float(nfav+1)\\n if hardrule:\\n return self.nsucc(a) > (f_successRatefav + epsilonD) and \\\\\\n (nfav+1 > 5) and ((self.world.round - nfav) > 5) \\n else:\\n return nfav+1 > 5 and (f_successRate > f_successRatefav + epsilonD \\\\\\n or f_successRate < epsilonD)\",\n \"def test_deactivate_guider_decenter_fails(self):\\n sopTester.updateModel('guider', TestHelper.guiderState['guiderOnDecenter'])\\n self.cmd.failOn = 'guider decenter off'\\n self._deactivate_guider_decenter(1, 9, 0, 2, didFail=True)\",\n \"def dec(self, params):\\n reg = params[0]\\n if self.reg_dct[reg] == 0:\\n self.reg_dct[reg] = (2 ** 32) - 1\\n else:\\n self.reg_dct[reg] -= 1\",\n \"def on_deactivate(self) -> None:\",\n \"def _revert(self):\\n self.kwargs[\\\"collect\\\"].change_status(self.kwargs[\\\"collect\\\"].ENDED)\",\n \"def d_act_f(self, p):\\n if self.activation is None:\\n return p\\n if self.activation == \\\"tanh\\\":\\n return 1 - np.tanh(p)**2\\n if self.activation == \\\"sigmoid\\\":\\n return (1 / (1 + np.exp(-p))) * (1 - (1 / (1 + np.exp(-p))))\\n if self.activation == \\\"relu\\\":\\n p[np.where(p < 0)] = 0\\n return p\\n\\n return p\",\n \"def _hidden_activation(self, inputs):\\n if self.act_enc is None:\\n act_enc = lambda x: x\\n else:\\n act_enc = self.act_enc\\n return act_enc(self._mappings(inputs))\",\n \"def auto_deactivate_fe(*args, **kwargs):\\n creation_date = kwargs.get(\\n 'date',\\n datetime.datetime.today() - datetime.timedelta(FE_AUTO_EXPIRE_PERIOD))\\n\\n FE_TYPE = kwargs.get('fe_type', DISPATCH_FE_TYPES)\\n # get all fe whose emp_id has not been set and are still active\\n fe_users = User.objects.filter(\\n is_active=True,\\n date_joined__lte=creation_date,\\n userprofile__user_type__in=FE_TYPE,\\n userprofile__employee_id=None)\\n\\n # deactivate all these users on\\n for fe in fe_users:\\n fe.is_active = False\\n fe.save()\",\n \"def f_restore(landscape, site, current, future, restore):\\n cpt = {\\n # landscape, site, current, future) : restore\\n (True, True, True, True): 0.8,\\n (True, True, True, False): 0.0,\\n (True, True, False, True): 1.0,\\n (True, True, False, False): 0.1,\\n (True, False, True, True): 0.3,\\n (True, False, True, False): 0.0,\\n (True, False, False, True): 0.5,\\n (True, False, False, False): 0.1,\\n (False, True, True, True): 0.0,\\n (False, True, True, False): 0.0,\\n (False, True, False, True): 0.0,\\n (False, True, False, False): 0.0,\\n (False, False, True, True): 0.0,\\n (False, False, True, False): 0.0,\\n (False, False, False, True): 0.0,\\n (False, False, False, False): 0.0\\n }\\n p = cpt[(landscape, site, current, future)]\\n if restore:\\n return p\\n else:\\n return 1.0 - p\",\n \"def statePosteriors(log_alpha, log_beta):\",\n \"def decline(self):\\n self.is_active = False\\n self.save()\",\n \"def decline(self):\\n self.is_active = False\\n self.save()\",\n \"def rev_opt_implied_returns(delta, sigma_prior: pd.DataFrame, wts_prior: pd.Series):\\r\\n pi = (delta * sigma_prior @ wts_prior).squeeze() # @ may be used instead of dot, but some issues arise if a df is not passed\\r\\n pi.name = 'Implied Returns'\\r\\n return pi\",\n \"def alpha_beta(returns, factor_returns):\\n\\n ret_index = returns.index\\n beta, alpha = sp.sp.stats.linregress(factor_returns.loc[ret_index].values,\\n returns.values)[:2]\\n\\n return alpha * APPROX_BDAYS_PER_YEAR, beta\",\n \"def backward(self, delta):\\n if self.activation_type == \\\"sigmoid\\\":\\n grad = self.grad_sigmoid()\\n\\n elif self.activation_type == \\\"tanh\\\":\\n grad = self.grad_tanh()\\n\\n elif self.activation_type == \\\"ReLU\\\":\\n grad = self.grad_ReLU()\\n\\n return grad * delta\",\n \"def __call__(self, epoch):\\n exp = np.floor((1 + epoch) / self.dropEvery)\\n alpha = initAlpha * (self.factor ** exp)\\n \\n # return alpha \\n return float(alpha)\",\n \"def _return_activation(x, nl):\\n if nl == 'HS':\\n x = KL.Activation(_hard_swish)(x)\\n if nl == 'RE':\\n x = KL.ReLU(6.)(x)\\n\\n return x\",\n \"def consider_deactivation(self):\\n if self.sensob_ultrasonic.get_value() >= 0.20:\\n self.active_flag = False\\n self.bbcon.deactivate_behaviour(self)\",\n \"def cost_derivative(self, output_activations, y):\\n return (output_activations-y)\",\n \"def cost_derivative(self, output_activations, y):\\n return (output_activations-y)\",\n \"def cost_derivative(self, output_activations, y):\\n return (output_activations-y)\",\n \"def cost_derivative(self, output_activations, y):\\n return (output_activations-y)\",\n \"def dactivate(self, y):\\n return self.activate(y) * (1-self.activate(y))\",\n \"def productactivate():\\n pass\",\n \"def test_deactivate_account02(self, client):\\n a = AccountFactory.get_account(type=Account.DEPOSITORY)\\n AccountFactory.get_account(item=a.item, type=Account.DEPOSITORY)\\n client = self.get_auth_client(a.item.user)\\n\\n url = '/v1/accounts/set_active/{}'.format(a.id)\\n dic = {'is_active': False}\\n data = json.dumps(dic)\\n response = client.put(url, data, content_type='application/json')\\n assert response.status_code == 400\",\n \"def flip():\\n return random.choice((True, False))\",\n \"def record_outcome(self, outcome: int):\\n self.__outcome = outcome if abs(self.__flipping_encryption_angle - 0) < eps else (outcome + 1) % 2\",\n \"def cost_derivative(self, output_activations, y):\\n return 2 * (output_activations - y)\",\n \"def ComputerFinalStateOfCharge(self):\\r\\n pass\",\n \"def recall(self):\\n return float(self.tp) / (self.tp + self.fn) if self.fn != 0 else 1\",\n \"def flag_dec(func):\\n def wrapfunc_and_call(*args, **kwargs):\\n if func.__name__ == 'prompt':\\n if PROMPT_ENABLED:\\n return func(*args, **kwargs)\\n else:\\n return '' # use empty string to indicate default\\n else:\\n return None\\n return wrapfunc_and_call\",\n \"def _credit_func(difficulty, discrimination, thetas, output):\\n # This model is based on exponentials and normalized to\\n # make sure the expected probablity is equal to one\\n output *= 0.0 # clear any previous values\\n output[1:, :] += thetas\\n output[1:, :] -= difficulty[:, None]\\n output *= discrimination\\n np.cumsum(output, axis=0, out=output)\\n np.exp(output, out=output)\\n\\n normalizing_term = 1.0 / np.sum(output, axis=0)\\n output *= normalizing_term\",\n \"def test_deactivate_account01(self, client):\\n a1 = AccountFactory.get_account()\\n a2 = AccountFactory.get_account(item=a1.item)\\n client = self.get_auth_client(a1.item.user)\\n\\n url = '/v1/accounts/set_active/{}'.format(a2.id)\\n dic = {'is_active': False}\\n data = json.dumps(dic)\\n response = client.put(url, data, content_type='application/json')\\n assert response.status_code == 204\\n\\n url = '/v1/accounts/set_active/{}'.format(a1.id)\\n data = json.dumps(dic)\\n response = client.put(url, data, content_type='application/json')\\n assert response.status_code == 400\",\n \"def state_update(self, evidence: List, return_mode: str = 'letter'):\\n sample = uniform(len(self.alp))\\n pairs = list(zip(self.alp, sample.tolist()))\\n priors = {return_mode: pairs}\\n\\n self.log.debug(\\\"Language Model Random probabilities:\\\")\\n self.log.debug(priors)\\n return priors\",\n \"def _return_activation(x, nl):\\n if nl == 'HS':\\n x = Activation(_hard_swish)(x)\\n if nl == 'RE':\\n x = Activation(_relu6)(x)\\n return x\",\n \"def disable(func):\\n return func\",\n \"def activation_ReLU(self):\\n self.value = max(0, self.value)\",\n \"def statePosteriors(log_alpha, log_beta):\\n return log_alpha + log_beta - logsumexp(log_alpha[-1,:])\",\n \"def deactivate(self):\\n super(Pfsense, self).deactivate()\",\n \"def _localSetState(self,pdict):\\n self.low = pdict.pop('low' )\\n self.alpha = pdict.pop('alpha')\\n self.beta = pdict.pop('beta' )\",\n \"def test_wrong_pwmerchactive_update_10(self):\\n user1.pwmerchactive(method='update', params={'m_lid': user1.merchant1.lid, 'payway': 'visamc', 'is_active': None})\\n assert user1.resp_pwmerchactive == {'code': -32002, 'data': {'field': 'is_active', 'reason': 'Should be provided'}, 'message': 'EParamInvalid'}\",\n \"def inv_acquisition(x):\\n return -acquisition(x)\",\n \"def action_reactivate(self):\\n options=self.env['plm.config.settings'].GetOptions()\\n status = 'released'\\n action = 'reactivate'\\n default={\\n 'engineering_writable': False,\\n 'state': status,\\n }\\n doc_default = {\\n 'state': status,\\n 'writable': False,\\n }\\n operationParams = {\\n 'status': status,\\n 'statusName': _('Released'),\\n 'action': action,\\n 'docaction': 'reactivate',\\n 'excludeStatuses': ['draft', 'confirmed', 'transmitted', 'released'],\\n 'includeStatuses': ['undermodify', 'obsoleted'],\\n 'default': default,\\n 'doc_default': doc_default,\\n }\\n if options.get('opt_showWFanalysis', False):\\n return self.action_check_workflow(operationParams)\\n else:\\n return self._action_to_perform(self._ids, operationParams, default)\",\n \"def revert(self, *args, **kwargs):\",\n \"def future_deceivedup(self, a): \\n nfav, succfav = self.control[a.name]\\n #f_n = n+1;\\n # f_worldround = self.world.round+1\\n f_successRate = float(a.success +1) / float(self.world.round+1)\\n if hardrule:\\n return (nfav+1 > 5) and ((self.world.round - nfav) > 5) and \\\\\\n float(a.success+1-succfav)/(self.world.round+1 - nfav) > \\\\\\n (float(succfav)/nfav) + epsilonD\\n else:\\n return nfav > 5 and (f_successRate > (float(succfav)/nfav) + epsilonD \\\\\\n or f_successRate < epsilonD)\",\n \"def getFactor(currency):\",\n \"def user_numforms_erase(*args):\\n return _ida_hexrays.user_numforms_erase(*args)\",\n \"def test_wrong_pwmerchactive_update_4(self):\\n payway = [pw['name'] for pw in admin.get_model(model='payway', _filter='is_active', value=False)][0]\\n user1.pwmerchactive(method='update', params={'m_lid': user1.merchant1.lid, 'payway': payway, 'is_active': True})\\n assert user1.resp_pwmerchactive == \\\"\\\"\",\n \"def get_action_value(mdp, state_values, state, action, gamma):\\n\\n Q = 0\\n # YOUR CODE HERE\\n return Q\",\n \"def count_change(amount):\\n \\\"*** YOUR CODE HERE ***\\\"\\n\\n return helper(1, amount)\",\n \"def on_DecreaseDeposit_clicked(self):\\n # TODO: not implemented yet\\n raise NotImplementedError\",\n \"def reward(self, env):\\n del env\\n return 1\",\n \"def delcomprequestsrate(self) :\\n\\t\\ttry :\\n\\t\\t\\treturn self._delcomprequestsrate\\n\\t\\texcept Exception as e:\\n\\t\\t\\traise e\",\n \"def post_process_stage1(self, pred):\\n return (pred>0)*1\",\n \"def set_subtract():\\n subtract.next = not (self.en_i and self.funct3_i==f3.RV32_F3_ADD_SUB and not self.funct7_6_i)\",\n \"def process_apply(self,succ,crit):\\n\\n if succ and crit:\\n bonus = 2\\n elif succ:\\n bonus = 1\\n else:\\n bonus = 0\\n\\n return bonus\",\n \"def debit(self):\\n debit = 0 #variable to track the remaining debit\\n debit = self.total_purchase() - self.total_clearance()\\n return debit\",\n \"def test_wrong_pwmerchactive_update_11(self, _authorization):\\n user1.headers['x-token'] = user1.headers['x-token'] + '1'\\n user1.pwmerchactive(method='update', params={'m_lid': user1.merchant1.lid, 'payway': 'visamc', 'is_active': True})\\n assert user1.resp_pwmerchactive == {'code': -32034, 'data': {'field': 'token', 'reason': 'Expired or invalid',\\n 'value': user1.headers['x-token']}, 'message': 'EStateUnauth'}\",\n \"def __toggle(self,x):\\n\\t\\tif x == 1:\\n\\t\\t\\treturn 0\\n\\t\\telse:\\n\\t\\t\\treturn 1\",\n \"def test_activation_deactivated(self):\\n new_user = self.registration_profile.objects.create_inactive_user(\\n site=Site.objects.get_current(), **self.user_info)\\n profile = self.registration_profile.objects.get(user=new_user)\\n self.registration_profile.objects.activate_user(\\n profile.activation_key, Site.objects.get_current())\\n\\n # Deactivate the new user.\\n new_user.is_active = False\\n new_user.save()\\n\\n # Try to activate again and ensure False is returned.\\n user, activated = self.registration_profile.objects.activate_user(\\n profile.activation_key, Site.objects.get_current())\\n self.assertFalse(activated)\",\n \"def flip(self):\",\n \"def on_deactivate(self, state) -> TransitionCallbackReturn:\\n return TransitionCallbackReturn.SUCCESS\",\n \"def predict_action_probabilities(self, tracker, domain):\\n # type: (DialogueStateTracker, Domain) -> (float, Optional[int])\\n x = self.featurize(tracker, domain)\\n logger.debug('Current tracker state [\\\\n\\\\t{}]'.format(\\n \\\"\\\\n\\\\t\\\".join([\\\"{}\\\".format(e) for e in self.featurizer.decode_features(x, domain.input_features)])))\\n\\n memorised = self.recall(x, domain)\\n result = [0.0] * domain.num_actions\\n if memorised is not None and self.is_enabled:\\n logger.debug(\\\"Used memorised next action '{}'\\\".format(memorised))\\n result[memorised] = 1.0\\n return result\",\n \"def act(self, state, eps=0.):\",\n \"def test_wrong_pwmerchactive_update_9(self):\\n user1.pwmerchactive(method='update', params={'m_lid': int(user1.merchant1.lid), 'payway': 'visamc', 'is_active': True})\\n assert user1.resp_pwmerchactive == {'code': -32003, 'data': {'field': 'm_lid', 'reason': \\\"'m_lid' must not be of 'int' type\\\",\\n 'value': int(user1.merchant1.lid)}, 'message': 'EParamType'}\",\n \"def deactivate(self):\\r\\n self.activated = False\",\n \"def reactivate(self, creator):\\r\\n # if we reactivate then reinit this\\r\\n self.activation = Activation(creator)\\r\\n self.activated = False\",\n \"def __invert__(self):\\r\\n return 1 - self\",\n \"def backward(alphaIn, phi_x, y):\\n alphaPhi_X = robot.Distribution()\\n for x, alphaX in alphaIn.items():\\n yProb = phi_x[x]\\n tmpProd = yProb * alphaX\\n if tmpProd > 0:\\n alphaPhi_X[x] = tmpProd\\n\\n # compute alpha out\\n alphaOut = robot.Distribution()\\n for x, alphaPhi in alphaPhi_X.items():\\n x2Poss = rev_transition_model(x)\\n # multiply and add x2Poss to o/p\\n for x2Key, x2pVal in x2Poss.items():\\n alphaOut[x2Key] += x2pVal*alphaPhi\\n #print(alphaOut)\\n return alphaOut\",\n \"def turned_off_callback(self, entity, attribute, old, new, kwargs):\\n\\n entity_dict = kwargs['entity_dict']\\n self.log('{} turned off, setting mode to Automatic and resetting last setpoint.'.format(\\n entity_dict['friendly']))\\n\\n self.set_value(entity_dict['setpoint'], 0)\\n self.select_option(entity_dict['mode'], 'Automatic')\",\n \"def _get_activate(self):\\n return self.__activate\",\n \"def _update_activation_state(self):\\n activation_count = 0\\n for task_id in self.task_dict:\\n activation_count += (1 if self.task_dict[task_id].is_activated else 0)\\n # Result\\n self.num_activated_task = activation_count\\n self.is_activated = (activation_count > 0)\",\n \"def unreturnbank(self):\\n pass\",\n \"def deactivated(self, util):\\n self.deactivate(util)\\n\\n try:\\n yield\\n finally:\\n self.activate(util)\",\n \"def dec(reg, registers):\\n registers[reg] -= 1\\n return 1\",\n \"def deactivate(self):\\n pass\",\n \"def deactivate(self):\\n pass\",\n \"def payoff_difference(dist,benefit_function,b,c,*params):\\n payoffs = mean_payoffs(dist,benefit_function,b,c,*params)\\n return (payoffs['A_pay']-payoffs['B_pay']).rename(\\\"pdiff\\\")\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.72491986","0.7173555","0.565905","0.548633","0.52655476","0.52646697","0.5120895","0.5088434","0.50735825","0.5009577","0.49936694","0.4986948","0.49810562","0.49603486","0.49565622","0.4942595","0.49352244","0.4931155","0.4921053","0.49184957","0.49069643","0.4869041","0.48353487","0.4823416","0.48162374","0.48143604","0.4812864","0.48102862","0.48094505","0.47952375","0.47806925","0.47798973","0.4777468","0.4777468","0.47680014","0.4762133","0.47596616","0.47474322","0.47445774","0.47439063","0.47417682","0.47417682","0.47417682","0.47417682","0.4734703","0.47160795","0.47145474","0.4707299","0.47017908","0.46837974","0.4683348","0.4682759","0.4682672","0.46769422","0.46719584","0.46673346","0.46631148","0.46618584","0.46550488","0.46538457","0.46457905","0.46329698","0.4625513","0.46226954","0.46183148","0.46150607","0.46069154","0.46068513","0.46055704","0.45973572","0.4595189","0.4593779","0.45935372","0.4589541","0.45885798","0.45820734","0.45801827","0.45726058","0.45698032","0.4566976","0.45594355","0.45573795","0.45555183","0.45524836","0.4548673","0.4539147","0.45380828","0.45375958","0.4536357","0.45348185","0.45342138","0.45313886","0.45299637","0.45264646","0.45212814","0.45209104","0.45192537","0.45185393","0.45185393","0.45171496"],"string":"[\n \"0.72491986\",\n \"0.7173555\",\n \"0.565905\",\n \"0.548633\",\n \"0.52655476\",\n \"0.52646697\",\n \"0.5120895\",\n \"0.5088434\",\n \"0.50735825\",\n \"0.5009577\",\n \"0.49936694\",\n \"0.4986948\",\n \"0.49810562\",\n \"0.49603486\",\n \"0.49565622\",\n \"0.4942595\",\n \"0.49352244\",\n \"0.4931155\",\n \"0.4921053\",\n \"0.49184957\",\n \"0.49069643\",\n \"0.4869041\",\n \"0.48353487\",\n \"0.4823416\",\n \"0.48162374\",\n \"0.48143604\",\n \"0.4812864\",\n \"0.48102862\",\n \"0.48094505\",\n \"0.47952375\",\n \"0.47806925\",\n \"0.47798973\",\n \"0.4777468\",\n \"0.4777468\",\n \"0.47680014\",\n \"0.4762133\",\n \"0.47596616\",\n \"0.47474322\",\n \"0.47445774\",\n \"0.47439063\",\n \"0.47417682\",\n \"0.47417682\",\n \"0.47417682\",\n \"0.47417682\",\n \"0.4734703\",\n \"0.47160795\",\n \"0.47145474\",\n \"0.4707299\",\n \"0.47017908\",\n \"0.46837974\",\n \"0.4683348\",\n \"0.4682759\",\n \"0.4682672\",\n \"0.46769422\",\n \"0.46719584\",\n \"0.46673346\",\n \"0.46631148\",\n \"0.46618584\",\n \"0.46550488\",\n \"0.46538457\",\n \"0.46457905\",\n \"0.46329698\",\n \"0.4625513\",\n \"0.46226954\",\n \"0.46183148\",\n \"0.46150607\",\n \"0.46069154\",\n \"0.46068513\",\n \"0.46055704\",\n \"0.45973572\",\n \"0.4595189\",\n \"0.4593779\",\n \"0.45935372\",\n \"0.4589541\",\n \"0.45885798\",\n \"0.45820734\",\n \"0.45801827\",\n \"0.45726058\",\n \"0.45698032\",\n \"0.4566976\",\n \"0.45594355\",\n \"0.45573795\",\n \"0.45555183\",\n \"0.45524836\",\n \"0.4548673\",\n \"0.4539147\",\n \"0.45380828\",\n \"0.45375958\",\n \"0.4536357\",\n \"0.45348185\",\n \"0.45342138\",\n \"0.45313886\",\n \"0.45299637\",\n \"0.45264646\",\n \"0.45212814\",\n \"0.45209104\",\n \"0.45192537\",\n \"0.45185393\",\n \"0.45185393\",\n \"0.45171496\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.8133898"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":75597,"cells":{"query":{"kind":"string","value":"Generates JSON for all categories"},"document":{"kind":"string","value":"def allCategoriesJSON():\n categories = db_session.query(Category).all()\n return jsonify(categories=[c.serialize for c in categories])"},"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 all_categories_handler():\n categories = getAllCategories()\n return jsonify(categories=[i.serialize for i in categories])","def get_all_categories():\n return jsonify({\n \"success\": True,\n \"categories\": _read_all_categories()\n })","def categoriesJSON():\n categories = session.query(Category).all()\n return jsonify(Categories=[r.serialize for r in categories])","def get_categories():\n categories = app.preprocessed.uniq_categs\n result = {\n 'success': True,\n 'data': {\n 'categories': categories\n }\n }\n return jsonify(result)","def categories():\n\tcategories = [\n\t\t'News',\n\t\t'Technology',\n\t\t'Music',\n\t\t'Sports'\n\t]\n\tresponse = { 'response': categories }\n\treturn jsonify(response)","def catelogJSON():\n categories = session.query(Category).all()\n items = session.query(Item).all()\n catelog = []\n for i in categories:\n li = []\n for j in items:\n if j.category_id == i.id:\n temp = j.serialize\n li += [temp]\n temp = i.serialize\n if len(li) > 0:\n temp['Items'] = li\n catelog += [temp]\n\n return jsonify(Categories=catelog)","def getCategoriesJSON():\r\n categories = session.query(Category).all()\r\n return jsonify(Categories=[c.serialize for c in categories])","def get_categories():\n try:\n result = {\n \"success\": True,\n \"categories\": get_all_categories()\n }\n return jsonify(result)\n\n except Exception as exp:\n abort(exp.code)","def showCategoriesJSON():\r\n session = DBSession()\r\n categories = session.query(Category).all()\r\n return jsonify(Categories=[c.serialize for c in categories])","def get_categories():\n categories = Category.query.all()\n formatted_categories = {}\n for category in categories:\n formatted_categories[category.id] = category.type\n\n return jsonify({\n 'success': True,\n 'categories': formatted_categories,\n 'total_categories': len(categories)\n })","def catalog_json():\n all_categories = (session.query(Categories).all())\n all_items = (session.query(Items).all())\n return jsonify(categories=([all_categories.serialize\n for all_categories in all_categories]),\n items=([all_items.serialize\n for all_items in all_items]))","def get_categories(request):\n return JsonResponse(get_all_objects(Category, CategorySerializer), safe=False)","def get_categories():\n\n url = 'https://fr.openfoodfacts.org/categories.json'\n data = requests.get(url).json()\n with open('data/categories.json', 'w') as file:\n file.write(json.dumps(data, indent=4))","def catalog_json():\n catalog_json = []\n try:\n all_categories = session.query(Category).all()\n for category in all_categories:\n items_for_category = session.query(\n Item).filter_by(\n category_id=category.id).all()\n items = []\n for current_item in items_for_category:\n items.append(current_item.serialize)\n catalog_json.append({\n 'category_name': category.name,\n 'category_id': category.id,\n 'items': items\n })\n except Exception as e:\n error = {\n 'result': 'No catalog data: ' + str(e)\n }\n catalog_json.append(error)\n return jsonify(catalog_json)","def api_categories():\n categories = session.query(Category)\n return jsonify(json_list=[i.to_json() for i in categories.all()])","def easyCategory():\n return prepJSON(cs411_dash.easyCategory())","def category():\n kwargs = {k: parse(v) for k, v in request.args.to_dict().items()}\n return jsonify(objects=get_categories(**kwargs))","def categories_json():\n try:\n categories = session.query(Category).all()\n return jsonify(categories=[category.serialize for category in categories])\n except Exception as inst:\n print(type(inst))\n print(inst.args)\n print(inst)","def get_categories():\n categories = [c.serialize for c in session.query(Category).all()]\n for c in range(len(categories)):\n items = [\n i.serialize for i in session.query(Item).filter_by(\n category_id=categories[c][\"id\"]).all()]\n if items:\n categories[c][\"Item\"] = items\n return jsonify(Category=categories)","def get_categories():\n categories = Category.query.order_by(Category.id).all()\n categories = {category.id: category.name for category in categories}\n\n response = jsonify({\"success\": True, \"categories\": categories})\n\n return response","def categoriesJSON():\n\n try:\n categories = session.query(Category).all()\n return jsonify(categories=[r.serialize for r in categories])\n except Exception as e:\n return '''There was a problem with your query %s, please Verify,\n Redirecting to main page...\n <script> setTimeout(function() {\n window.location.href = \"/\";\n }, 2000);\n </script> ''' % (str(e))","def load_categories(request: HttpRequest) -> JsonResponse:\n result = {'categories': []}\n categories = Category.objects.all().order_by('name')\n\n for category in categories:\n sources = category.source_set.all().order_by('name')\n result['categories'].append({\n 'name': category.name,\n 'sources': []\n })\n for source in sources:\n unread_count = source.feed_set.filter(checked=False).count()\n result['categories'][-1]['sources'].append({\n 'name': source.name,\n 'unread_count': unread_count\n })\n\n result['status_code'] = 200\n return JsonResponse(result)","def show_category_json(category):\n category = (session.query(Categories)\n .filter_by(name=category.replace('-', ' '))\n .one())\n category_items = (session.query(Items)\n .filter_by(category_id=category.id)\n .order_by(Items.name)\n .all())\n return jsonify(category=[category.serialize],\n items=([category_items.serialize\n for category_items in category_items]))","def browse_categories(request):\n\n result = {}\n\n u = request.user\n\n top = Category.objects.get(name=\"Best Buy\")\n result['categories'] = [c.get_json() for c in top.children.all()]\n \n return JSONHttpResponse(result)","def to_json(self):\n return {\n \"category_name\": self.category_name,\n \"summary\": self.summary,\n \"image\": self.image,\n \"category_slug\": self.category_slug,\n }","def toughCategory():\n return prepJSON(cs411_dash.toughCategory())","def serialize_categories(md: Metadata) -> list:\n categories = []\n all_cat = md.categories.all()\n for cat in all_cat:\n category = OrderedDict()\n\n category[\"id\"] = cat.id\n category[\"type\"] = cat.type\n category[\"title_EN\"] = cat.title_EN\n category[\"description_EN\"] = cat.description_EN\n category[\"title_locale_1\"] = cat.title_locale_1\n category[\"description_locale_1\"] = cat.description_locale_1\n category[\"title_locale_2\"] = cat.title_locale_2\n category[\"description_locale_2\"] = cat.description_locale_2\n category[\"symbol\"] = cat.symbol\n category[\"online_link\"] = cat.online_link\n\n categories.append(category)\n\n return categories","def query_categories_handler(query):\n categories = getCategoriesByName(query)\n return jsonify(categories=[i.serialize for i in categories])","def categoryWithBooksJSON(category_id):\n books = db_session.query(Book).filter_by(category_id=category_id).all()\n return jsonify(Category=[b.serialize for b in books])","def add_categories_handler():\n rq = request.get_json()\n name = rq['name']\n picture = rq['picture']\n description = rq['description']\n category = addCategory(name, picture, description, g.user.id)\n return jsonify(category=category.serialize)","def all_categories(request, slug=None):\n c = {\"categories\": Node.objects.filter(kind=\"C\")}\n return render_to_response(\"categories.html\", c)","def rest_get_catalogue_handler():\n cats = category.get_all_categories()\n items = item.get_all_items()\n result = {}\n result['categories'] = [c.serialize for c in cats]\n result['items'] = [i.serialize for i in items]\n return jsonify(result)","def get_categories():\n categories_query = Category.query.order_by(Category.id).all()\n\n if len(categories_query) == 0:\n abort(404)\n\n categories = []\n\n for category in categories_query:\n categories.append(category.type)\n\n return jsonify({\n 'success': True,\n 'categories': categories,\n })","def categories_list_current_json():\n event = Event.query.filter_by(is_current=True).first()\n categories = [c.data for c in event.categories_for_event()]\n return jsonify(categories=categories, event=event.data)","def get_categories(request):\n try:\n categories = []\n for category in Category.objects.all():\n categories.append({\"title\": category.title, \"id\": category.pk})\n\n return format_ajax_response(True, \"Knowledgebase categories retrieved successfully.\", {\"categories\": categories})\n except Exception as ex:\n logger.error(\"Failed to get_categories: %s\" % ex)\n return format_ajax_response(False, \"There was an error retreiving the knowledgebase categories.\")","def buscar_categorias(id):\n categorias = buscar_categorias_produtos(id)\n categorias = [c.json() for c in categorias]\n\n return jsonify(categorias)","def to_json(self):\n res = {\n 'id': self.mid,\n 'label': self.label or self.name,\n 'name': self.name,\n 'categories': []\n }\n for c in self.categories:\n cat = self.categories[c]\n interm = {\n 'id': cat['id'],\n 'label': cat['label'] or cat['name'],\n 'preferences': []\n }\n\n res['categories'].append(interm)\n\n for p in cat['preferences']:\n pref = (cat['preferences'][p]).to_json().copy()\n pref.update({'mid': self.mid, 'cid': cat['id']})\n interm['preferences'].append(pref)\n\n return res","def category_list():\n categories = Category.objects.filter(active=True)\n return {'categories': categories}","def get_jsonified_catalogs():\n\n json = []\n catalogs = session.query(Catalog).all()\n\n for catalog in catalogs:\n catalog_json = {}\n catalog_json[\"id\"] = catalog.id\n catalog_json[\"name\"] = catalog.name\n catalog_json[\"date\"] = catalog.date.strftime(\"%d %b %Y %H: %M: %S\")\n catalog_json[\"items\"] = []\n for item in catalog.items:\n item_json = {}\n item_json[\"id\"] = item.id\n item_json[\"title\"] = item.title\n item_json[\"desc\"] = item.desc\n item_json[\"date\"] = item.date.strftime(\"%d %b %Y %H: %M: %S\")\n item_json[\"catalog_id\"] = item.catalog_id\n catalog_json[\"items\"].append(item_json)\n json.append(catalog_json)\n return jsonify(Catalogs=json)","def categoryItemJSON(category_id):\n category = db.getByCategory(category_id)\n items = db.getItemsByCategory(category_id)\n return jsonify(Items=[i.serialize for i in items])","def _get_all_categories() -> dict:\n categories = database.fetchall(\"Categories\", \"id\", \"name_ua\")\n return categories","def fetch_all_categories():\n query_set = Categories.objects.filter(is_delete=False)\n serializer = CategoriesSerializer(query_set, many=True)\n return serializer.data","def json_api():\n if 'category' in request.args:\n sqlsession = SQLSESSION()\n category = sqlsession.query(Category)\\\n .filter_by(name=request.args['category']).first()\n items = sqlsession.query(Item).filter_by(category_id=category.id)\\\n .all()\n return json.dumps({'category_id': category.id,\n 'category_name': category.name,\n 'items': [item.serialize() for item in items]})\n elif 'item' in request.args:\n sqlsession = SQLSESSION()\n items = sqlsession.query(Item).filter_by(name=request.args['item'])\\\n .all()\n return json.dumps([item.serialize() for item in items])\n sqlsession = SQLSESSION()\n categories = sqlsession.query(Category).all()\n items = sqlsession.query(Item).all()\n return json.dumps(\n {'categories': [cat.serialize() for cat in categories],\n 'items': [item.serialize() for item in items]})","def Categories():\n cat = {\n \t \"Featured\": 0,\n \t \"All\": 1,\n \t \"Collectibles\": 2,\n \t \"Clothing\": 3,\n \t \"BodyParts\": 4,\n \t \"Gear\": 5,\n \t \"Models\": 6,\n \t \"Plugins\": 7,\n\t \"Decals\": 8,\n \t \"Audio\": 9,\n \t \"Meshes\": 10,\n\t \"Accessories\": 11,\n\t \"AvatarAnimations\": 12,\n\t \"CommunityCreations\": 13,\n\t \"Video\": 14,\n\t \"Recommended\": 15\n }\n return cat","def showItemsJSON():\n items = db.getAllItems()\n return jsonify(CategoryItems=[i.serialize for i in items])","def read_all():\r\n categories = Category.query.all()\r\n # Serialize the data for the response\r\n category_schema = CategorySchema(many=True)\r\n print('***********************************************************')\r\n return category_schema.dump(categories)","def get_categories(self):\n\n self.search([]).unlink()\n token = self.env['odoo.moodle'].search([('create_uid', '=', self.env.user.id)]).token\n domain = \"http://localhost:8888\"\n webservice_url = \"/webservice/rest/server.php?\"\n parameters = {\n \"wstoken\":token,\n 'wsfunction': 'core_course_get_categories',\n 'moodlewsrestformat': 'json'\n }\n request = requests.get(url=domain+webservice_url, params=parameters)\n request = request.json()\n for req in request:\n try:\n self.create({\n 'category_id': req['id'],\n 'name': req['name'],\n 'description': req['description'],\n 'category_parent': req['parent'],\n })\n except Exception:\n print('Category not created')","def to_json(self, category):\r\n return \"{{\\\"id\\\": {0}, \" \\\r\n \"\\\"name\\\": \\\"{1}\\\", \" \\\r\n \"\\\"abbreviation\\\": \\\"{2}\\\", \" \\\r\n \"\\\"category\\\": {3}, \" \\\r\n \"\\\"rank\\\": {4}}}\".format(self.id, self.name, self.abbreviation, category.id, self.rank)","def test_get_categories_from_json():\n allocator = RecipeAllocator()\n allocator.load_data(\n orders_dir=\"tests/orders.json\", recipes_dir=\"tests/recipes.json\"\n )\n allocator.get_categories_from_json()\n assert list(allocator.portion_categories_dict.keys()) == [\n \"two_portions\",\n \"four_portions\",\n ] and list(allocator.recipe_categories_dict.keys()) == [\n \"two_recipes\",\n \"three_recipes\",\n \"four_recipes\",\n ]","def get_catergories(request):\n shop_obj = Shop.objects.get(user=request.user)\n shop_categories = shop_obj.categories\n value_added = list(\n map(\n lambda x: {\n \"name\": x[\"name\"],\n \"slug\": x[\"slug\"],\n \"public_slug\": x[\"public_slug\"],\n \"product_count\": Products.objects.filter(\n shop_rel=shop_obj, genre__slug=x[\"slug\"]\n ).count(),\n },\n shop_categories,\n )\n )\n resp_payload = {\"shop_categories\": value_added}\n return Response(data=resp_payload, status=status.HTTP_200_OK)","def categories(self):\n pass","def cat_results_react():\n\n cats = petfinder.search_data_map()\n cats = list(cats.values())\n\n return jsonify(cats)","def filter_json_by_category(self, new_json_path):\n # {'supercategory': 'person', 'id': 1, 'name': 'person'}\n ### Filter images:\n print(\"Filtering the annotations ... \")\n json_parent = os.path.split(new_json_path)[0]\n os.makedirs(json_parent, exist_ok=True)\n imgs_ids = [x['id'] for x in self.images] # get img_ids of imgs with the category\n new_imgs = [x for x in self.coco.dataset['images'] if x['id'] in imgs_ids]\n catIds = self.catIds\n ### Filter annotations\n new_annots = [x for x in self.coco.dataset['annotations'] if x['category_id'] in catIds]\n ### Reorganize the ids\n new_imgs, annotations = self.modify_ids(new_imgs, new_annots)\n ### Filter categories\n new_categories = [x for x in self.coco.dataset['categories'] if x['id'] in catIds]\n print(\"new_categories: \", new_categories)\n data = {\n \"info\": self.coco.dataset['info'],\n \"licenses\": self.coco.dataset['licenses'],\n \"images\": new_imgs, \n \"annotations\": new_annots,\n \"categories\": new_categories \n }\n print(\"saving json: \")\n with open(new_json_path, 'w') as f:\n json.dump(data, f)","def inject_categories():\n return {'categories': entities.Listing.CATEGORIES,\n 'categories_dict': entities.Listing.CATEGORIES_DICT}","def api_category_items(category_id):\n items = session.query(Item).filter(Item.category_id == category_id)\n return jsonify(json_list=[i.to_json() for i in items.all()])","def showCategoryItemsJSON(category_id):\r\n session = DBSession()\r\n items = session.query(Item).filter_by(category_id=category_id)\r\n return jsonify(Items=[i.serialize for i in items])","def categories(data):\n if data:\n for i in data:\n category = CategoriesModel(categories=i['categories'])\n category.save()","def browse_category(request):\n\n result = {'categories':[], 'products':[]}\n\n u = request.user\n\n page = request.POST.get('page', 1)\n\n cat = Category.objects.get(id=request.POST['cat_id'])\n if cat.children.count() > 0:\n result['categories'] = [c.get_json() for c in cat.children.all()]\n else:\n # display items\n result = Product.objects.filter_category(cat.category_id, page, u) \n\n return JSONHttpResponse(result)","def categoryItemJSON(category_id):\n category = session.query(Category).\\\n filter_by(id=category_id).one()\n items = session.query(Item).\\\n filter_by(category_id=category_id).all()\n return jsonify(Items=[i.serialize for i in items])","def get(self, request, format=None):\n categories = Category.objects.all()\n data_out = CategorySerializer(categories, many=True)\n return Response(data_out.data)","def categories():\n return render_template('categories.html', recipe_categories=USERS[session['username']].recipe_categories)","def fill_category(self):\n cursor = self.conn.cursor(cursor_factory=psycopg2.extras.DictCursor)\n categories = dict()\n result = requests.get('https://fr.openfoodfacts.org/categories.json').json()\n for element in result['tags']:\n try:\n cursor.execute(\"INSERT INTO category (tag, name, url) VALUES (%s, %s, %s) RETURNING id, tag\",\n (element[\"id\"], element[\"name\"], element[\"url\"]))\n query_result = cursor.fetchone()\n categories.__setitem__(query_result[1], query_result[0])\n except self.conn.OperationalError:\n print(\"operation Error\")\n except self.conn.DataError:\n print(\"Data Error\")\n self.conn.commit()\n cursor.close()\n return categories","def get_category_data_off(self):\n list_categories_name=[]\n cat = requests.get('https://fr.openfoodfacts.org/categories?json=true')\n cat_data = cat.json()\n tags_list = cat_data['tags']\n print (len(tags_list))\n list_of_random_tags_list = random.sample(tags_list, k=self.view.num_to_select)\n\n for category in list_of_random_tags_list:\n try :\n category_name = category['name']\n print(category_name)\n list_categories_name.append(category_name)\n print (list_categories_name)\n self.list_categories = list_categories_name # list_categories_name is passed in the instance property\n except KeyError:\n pass\n except UnicodeEncodeError:\n pass","def avail_categories(self):\n # retrieve categories\n categories = self.show_all_categories()\n # for each category, retrieve packages\n output = {}\n for category in categories:\n packages = self.show_category(category)\n output[category] = packages\n\n return output","def url_to_json(self, category_name):\n request = requests.get(f\"https://fr.openfoodfacts.org/categorie/{category_name}.json\")\n return request.json()","def category_json(category_id):\n try:\n category = session.query(Category).filter_by(id=category_id).one()\n return jsonify(category=category.serialize)\n except Exception as inst:\n print(type(inst))\n print(inst.args)\n print(inst)","def get_all_categorizations():\n try:\n query = run_select_query('SELECT id, category_name, category_item_type, name \\\n FROM categorization \\\n JOIN item ON (categorization.item_id = item.id)')\n return [{'id': obj[0], 'category_name': obj[1], 'category_item_type': obj[2].capitalize(), 'item_name': obj[3]} for obj in query]\n except (Exception, psycopg2.Error) as error:\n return {\"status\": \"error\", \"error\": error}","def categoryItemsJSON(category_id):\n\n try:\n category = session.query(Category).filter_by(id=category_id).one()\n items = session.query(ListItem).filter_by(\n category_id=category_id).all()\n return jsonify(ListItems=[i.serialize for i in items])\n except Exception as e:\n return '''There was a problem with your query %s, please Verify,\n Redirecting to main page...\n <script> setTimeout(function() {\n window.location.href = \"/\";\n }, 2000);\n </script> ''' % (str(e))","def getCategoryJSON(category_id):\r\n category = session.query(Category).filter_by(id=category_id).first()\r\n if category:\r\n return jsonify(Category=category.serialize)\r\n else:\r\n return jsonify(Category={})","def getcategory(self):\n\n response = requests.get(\"https://fr.openfoodfacts.org/categories.json\")\n\n data = response.json()\n\n self.rawcategorydata = data","def test_get_all_categories(self):\n response = self.client.get('/api/v1/categories',\n headers=self.attendant_headers)\n self.assertEqual(response.status_code, 200)","def csv_category_to_api(self):\n # [{'id': 49}, {'id': 60}, {'id': 146}, {'id': 1882}]\n for data in self._api_data:\n data[\"categories\"] = self.map_csv_category(data[\"categories\"])\n # data[\"categories\"] = [{'id': 49}, {'id': 60}, {'id': 146}, {'id': 1882}]","def _loadCategories(self):\n logger.debug(\"Func: _loadCategories\")\n\n if os.path.isfile(self._pathsDict[\"categoriesFile\"]):\n categoriesData = self._loadJson(self._pathsDict[\"categoriesFile\"])\n if categoriesData == -2:\n return -2\n else:\n categoriesData = self._sceneManagerDefaults[\"defaultCategories\"]\n # categoriesData = [\"Model\", \"Shading\", \"Rig\", \"Layout\", \"Animation\", \"Render\", \"Other\"]\n self._dumpJson(categoriesData, self._pathsDict[\"categoriesFile\"])\n return categoriesData","def generate_category_dict(category_file):\n L = json.load(open(category_file))\n d = {}\n for dict_item in L:\n item = item_to_dict(dict_item)\n if not item['Label'] is None:\n d[item['URN']] = item['Label']\n else:\n d[item['URN']] = ''\n return d","def load_data_to_json(root_path, extract=True, decode=\"utf-8\"):\n category_map = {}\n\n for file in os.listdir(root_path):\n # 行业分类文件夹\n path = os.path.join(root_path, file)\n if os.path.isdir(path):\n for xml_file in os.listdir(path):\n # 读取每个行业分类的文件\n xml_file_path = os.path.join(path, xml_file)\n try:\n if xml_file_path.endswith(\".xml\"):\n doc = read_main_info(xml_file_path, extract=extract, decode=decode)\n\n if file in category_map.keys():\n category_map[file].append(doc)\n else:\n category_map[file] = list()\n category_map[file].append(doc)\n\n except UnicodeDecodeError:\n print(\"UnicodeDecodeError:%s\" % xml_file_path)\n continue\n\n with open(JSON_FILE, \"w\") as f:\n json.dump(category_map, f)","def list_categories(self):\n raise NotImplementedError()","def get_categories(self):\n _url = urljoin(self.base_url, self.API_CATEGORIES)\n return requests.get(_url)","def getItemsJSON(category_id):\r\n items = session.query(Item).filter_by(category_id=category_id).all()\r\n return jsonify(Items=[i.serialize for i in items])","def all_categories(local):\n\n categories = []\n if local == True:\n categories = list(map((lambda c: c.name), Category.all()))\n else:\n categories = jokes_api.categories()\n\n click.echo(categories)","def create_category(self): # , conf_dir, title):\n category_file_path = self.event_dir / 'category.json'\n category_data = {\n 'title': self.title,\n }\n category_data_text = json.dumps(category_data, **\n JSON_FORMAT_KWARGS) + '\\n'\n save_file(category_file_path, category_data_text)\n logger.debug('File {} created', category_file_path)","def movies_categories(request):\n return {'MOVIES_CATEGORIES': Category.objects.all()}","def list(self):\n return list(sorted(self.manager.data[\"category\"].keys()))","def page_to_json(self, category_name, page):\n request = requests.get(f\"https://fr.openfoodfacts.org/categorie/{category_name}/{page}.json\")\n return request.json()","def getAllCategory():\n return session.query(Category).all()","def get_categories():\n item_type = \"categories\"\n info_dict = spotify.categories()\n items = info_dict[item_type][\"items\"]\n categories = []\n for i in range(len(items)):\n category_name = items[i][\"name\"]\n category_id = items[i][\"id\"]\n categories.append({\"Category Name\": category_name,\n \"Category ID\": category_id\n })\n return categories","def print_categories_list():\n\n categories = []\n for item in data:\n cat = item[\"category\"]\n\n if cat not in categories:\n categories.append(cat)\n\n print(categories) # print the list","def _unit_test_only_get_category_map_files(self):\n result = {}\n for key in self.__all_model_categories:\n category_file_name = '%s.json' % self._get_category_file_prefix(key)\n category_file_path = '%s%s' % (self.__category_modules_dir_name, category_file_name)\n result[key] = category_file_path\n return result","def get_catalog():\n return jsonify(getCatalog())","def categories(self):\n return self._data[\"categories\"]","def get_categories(self, project):\n serializer = CategorySerializer(\n project.categories.all().exclude(fields=None), many=True)\n return serializer.data","def serialize(self):\n return {\n 'category': self.category.name,\n 'id': self.id,\n 'name': self.name\n }","def test_get_categories(self):\n\n res = self.client().get('/categories')\n\n data = json.loads(res.data)\n\n self.assertEqual(res.status_code, 200)\n self.assertEqual(data['success'], True)\n self.assertEqual(len(data['categories']), 6)","def update_categories(self):\n categories = {}\n datasets = self.data['dataset']\n used_categories = self._get_list_categories_used(datasets)\n for category in used_categories:\n categories.update({\n category: self._get_datasets_tasks_by_category(datasets, category)\n })\n self.data[\"category\"] = categories","def add_category(self, category: str) -> None:\n for letter in self.data:\n if not self.data[letter].get(category):\n self.data[letter][category] = []\n print(f'Categoria: {category} adicionada ao dicionário.')\n self.save()\n self.beautify_json()","def get_categories_group(self):\n m = {}\n for post in self:\n for cat in post.Categories:\n if cat not in m:\n m[cat] = []\n m[cat].append(post)\n return m","def get_all_categories_from_collection():\n api_endpoint = URL\n response = requests.get(api_endpoint)\n return response","def create_category_pages(app):\n env = app.builder.env\n\n template = \"category.html\"\n\n categories = env.categories\n for name, category in categories.iteritems():\n context = {}\n context[\"title\"] = category.name\n context[\"subcategories\"] = category.subcategories\n context[\"pages\"] = category.pages\n\n yield (name, context, template)","def make_jsons(self):\n self._jsons = [tree.to_json() for tree in self.reaction_trees]\n self._update_route_dict(self._jsons, \"json\")","def encode(category_main : ):","def show_categories(self):\n cat_model = TreeModel(('Categories', ))\n self.categoriesView.setModel(cat_model)\n\n categories = self.orm.fetch_parents()\n for category in categories:\n item = TreeItem(category, cat_model.rootItem)\n cat_model.rootItem.appendChild(item)\n\n subs = self.orm.fetch_subcategories_for_parent(category)\n\n for sub in subs:\n sub_item = TreeItem(sub, item)\n item.appendChild(sub_item)\n\n self.categoriesView.expandAll()"],"string":"[\n \"def all_categories_handler():\\n categories = getAllCategories()\\n return jsonify(categories=[i.serialize for i in categories])\",\n \"def get_all_categories():\\n return jsonify({\\n \\\"success\\\": True,\\n \\\"categories\\\": _read_all_categories()\\n })\",\n \"def categoriesJSON():\\n categories = session.query(Category).all()\\n return jsonify(Categories=[r.serialize for r in categories])\",\n \"def get_categories():\\n categories = app.preprocessed.uniq_categs\\n result = {\\n 'success': True,\\n 'data': {\\n 'categories': categories\\n }\\n }\\n return jsonify(result)\",\n \"def categories():\\n\\tcategories = [\\n\\t\\t'News',\\n\\t\\t'Technology',\\n\\t\\t'Music',\\n\\t\\t'Sports'\\n\\t]\\n\\tresponse = { 'response': categories }\\n\\treturn jsonify(response)\",\n \"def catelogJSON():\\n categories = session.query(Category).all()\\n items = session.query(Item).all()\\n catelog = []\\n for i in categories:\\n li = []\\n for j in items:\\n if j.category_id == i.id:\\n temp = j.serialize\\n li += [temp]\\n temp = i.serialize\\n if len(li) > 0:\\n temp['Items'] = li\\n catelog += [temp]\\n\\n return jsonify(Categories=catelog)\",\n \"def getCategoriesJSON():\\r\\n categories = session.query(Category).all()\\r\\n return jsonify(Categories=[c.serialize for c in categories])\",\n \"def get_categories():\\n try:\\n result = {\\n \\\"success\\\": True,\\n \\\"categories\\\": get_all_categories()\\n }\\n return jsonify(result)\\n\\n except Exception as exp:\\n abort(exp.code)\",\n \"def showCategoriesJSON():\\r\\n session = DBSession()\\r\\n categories = session.query(Category).all()\\r\\n return jsonify(Categories=[c.serialize for c in categories])\",\n \"def get_categories():\\n categories = Category.query.all()\\n formatted_categories = {}\\n for category in categories:\\n formatted_categories[category.id] = category.type\\n\\n return jsonify({\\n 'success': True,\\n 'categories': formatted_categories,\\n 'total_categories': len(categories)\\n })\",\n \"def catalog_json():\\n all_categories = (session.query(Categories).all())\\n all_items = (session.query(Items).all())\\n return jsonify(categories=([all_categories.serialize\\n for all_categories in all_categories]),\\n items=([all_items.serialize\\n for all_items in all_items]))\",\n \"def get_categories(request):\\n return JsonResponse(get_all_objects(Category, CategorySerializer), safe=False)\",\n \"def get_categories():\\n\\n url = 'https://fr.openfoodfacts.org/categories.json'\\n data = requests.get(url).json()\\n with open('data/categories.json', 'w') as file:\\n file.write(json.dumps(data, indent=4))\",\n \"def catalog_json():\\n catalog_json = []\\n try:\\n all_categories = session.query(Category).all()\\n for category in all_categories:\\n items_for_category = session.query(\\n Item).filter_by(\\n category_id=category.id).all()\\n items = []\\n for current_item in items_for_category:\\n items.append(current_item.serialize)\\n catalog_json.append({\\n 'category_name': category.name,\\n 'category_id': category.id,\\n 'items': items\\n })\\n except Exception as e:\\n error = {\\n 'result': 'No catalog data: ' + str(e)\\n }\\n catalog_json.append(error)\\n return jsonify(catalog_json)\",\n \"def api_categories():\\n categories = session.query(Category)\\n return jsonify(json_list=[i.to_json() for i in categories.all()])\",\n \"def easyCategory():\\n return prepJSON(cs411_dash.easyCategory())\",\n \"def category():\\n kwargs = {k: parse(v) for k, v in request.args.to_dict().items()}\\n return jsonify(objects=get_categories(**kwargs))\",\n \"def categories_json():\\n try:\\n categories = session.query(Category).all()\\n return jsonify(categories=[category.serialize for category in categories])\\n except Exception as inst:\\n print(type(inst))\\n print(inst.args)\\n print(inst)\",\n \"def get_categories():\\n categories = [c.serialize for c in session.query(Category).all()]\\n for c in range(len(categories)):\\n items = [\\n i.serialize for i in session.query(Item).filter_by(\\n category_id=categories[c][\\\"id\\\"]).all()]\\n if items:\\n categories[c][\\\"Item\\\"] = items\\n return jsonify(Category=categories)\",\n \"def get_categories():\\n categories = Category.query.order_by(Category.id).all()\\n categories = {category.id: category.name for category in categories}\\n\\n response = jsonify({\\\"success\\\": True, \\\"categories\\\": categories})\\n\\n return response\",\n \"def categoriesJSON():\\n\\n try:\\n categories = session.query(Category).all()\\n return jsonify(categories=[r.serialize for r in categories])\\n except Exception as e:\\n return '''There was a problem with your query %s, please Verify,\\n Redirecting to main page...\\n <script> setTimeout(function() {\\n window.location.href = \\\"/\\\";\\n }, 2000);\\n </script> ''' % (str(e))\",\n \"def load_categories(request: HttpRequest) -> JsonResponse:\\n result = {'categories': []}\\n categories = Category.objects.all().order_by('name')\\n\\n for category in categories:\\n sources = category.source_set.all().order_by('name')\\n result['categories'].append({\\n 'name': category.name,\\n 'sources': []\\n })\\n for source in sources:\\n unread_count = source.feed_set.filter(checked=False).count()\\n result['categories'][-1]['sources'].append({\\n 'name': source.name,\\n 'unread_count': unread_count\\n })\\n\\n result['status_code'] = 200\\n return JsonResponse(result)\",\n \"def show_category_json(category):\\n category = (session.query(Categories)\\n .filter_by(name=category.replace('-', ' '))\\n .one())\\n category_items = (session.query(Items)\\n .filter_by(category_id=category.id)\\n .order_by(Items.name)\\n .all())\\n return jsonify(category=[category.serialize],\\n items=([category_items.serialize\\n for category_items in category_items]))\",\n \"def browse_categories(request):\\n\\n result = {}\\n\\n u = request.user\\n\\n top = Category.objects.get(name=\\\"Best Buy\\\")\\n result['categories'] = [c.get_json() for c in top.children.all()]\\n \\n return JSONHttpResponse(result)\",\n \"def to_json(self):\\n return {\\n \\\"category_name\\\": self.category_name,\\n \\\"summary\\\": self.summary,\\n \\\"image\\\": self.image,\\n \\\"category_slug\\\": self.category_slug,\\n }\",\n \"def toughCategory():\\n return prepJSON(cs411_dash.toughCategory())\",\n \"def serialize_categories(md: Metadata) -> list:\\n categories = []\\n all_cat = md.categories.all()\\n for cat in all_cat:\\n category = OrderedDict()\\n\\n category[\\\"id\\\"] = cat.id\\n category[\\\"type\\\"] = cat.type\\n category[\\\"title_EN\\\"] = cat.title_EN\\n category[\\\"description_EN\\\"] = cat.description_EN\\n category[\\\"title_locale_1\\\"] = cat.title_locale_1\\n category[\\\"description_locale_1\\\"] = cat.description_locale_1\\n category[\\\"title_locale_2\\\"] = cat.title_locale_2\\n category[\\\"description_locale_2\\\"] = cat.description_locale_2\\n category[\\\"symbol\\\"] = cat.symbol\\n category[\\\"online_link\\\"] = cat.online_link\\n\\n categories.append(category)\\n\\n return categories\",\n \"def query_categories_handler(query):\\n categories = getCategoriesByName(query)\\n return jsonify(categories=[i.serialize for i in categories])\",\n \"def categoryWithBooksJSON(category_id):\\n books = db_session.query(Book).filter_by(category_id=category_id).all()\\n return jsonify(Category=[b.serialize for b in books])\",\n \"def add_categories_handler():\\n rq = request.get_json()\\n name = rq['name']\\n picture = rq['picture']\\n description = rq['description']\\n category = addCategory(name, picture, description, g.user.id)\\n return jsonify(category=category.serialize)\",\n \"def all_categories(request, slug=None):\\n c = {\\\"categories\\\": Node.objects.filter(kind=\\\"C\\\")}\\n return render_to_response(\\\"categories.html\\\", c)\",\n \"def rest_get_catalogue_handler():\\n cats = category.get_all_categories()\\n items = item.get_all_items()\\n result = {}\\n result['categories'] = [c.serialize for c in cats]\\n result['items'] = [i.serialize for i in items]\\n return jsonify(result)\",\n \"def get_categories():\\n categories_query = Category.query.order_by(Category.id).all()\\n\\n if len(categories_query) == 0:\\n abort(404)\\n\\n categories = []\\n\\n for category in categories_query:\\n categories.append(category.type)\\n\\n return jsonify({\\n 'success': True,\\n 'categories': categories,\\n })\",\n \"def categories_list_current_json():\\n event = Event.query.filter_by(is_current=True).first()\\n categories = [c.data for c in event.categories_for_event()]\\n return jsonify(categories=categories, event=event.data)\",\n \"def get_categories(request):\\n try:\\n categories = []\\n for category in Category.objects.all():\\n categories.append({\\\"title\\\": category.title, \\\"id\\\": category.pk})\\n\\n return format_ajax_response(True, \\\"Knowledgebase categories retrieved successfully.\\\", {\\\"categories\\\": categories})\\n except Exception as ex:\\n logger.error(\\\"Failed to get_categories: %s\\\" % ex)\\n return format_ajax_response(False, \\\"There was an error retreiving the knowledgebase categories.\\\")\",\n \"def buscar_categorias(id):\\n categorias = buscar_categorias_produtos(id)\\n categorias = [c.json() for c in categorias]\\n\\n return jsonify(categorias)\",\n \"def to_json(self):\\n res = {\\n 'id': self.mid,\\n 'label': self.label or self.name,\\n 'name': self.name,\\n 'categories': []\\n }\\n for c in self.categories:\\n cat = self.categories[c]\\n interm = {\\n 'id': cat['id'],\\n 'label': cat['label'] or cat['name'],\\n 'preferences': []\\n }\\n\\n res['categories'].append(interm)\\n\\n for p in cat['preferences']:\\n pref = (cat['preferences'][p]).to_json().copy()\\n pref.update({'mid': self.mid, 'cid': cat['id']})\\n interm['preferences'].append(pref)\\n\\n return res\",\n \"def category_list():\\n categories = Category.objects.filter(active=True)\\n return {'categories': categories}\",\n \"def get_jsonified_catalogs():\\n\\n json = []\\n catalogs = session.query(Catalog).all()\\n\\n for catalog in catalogs:\\n catalog_json = {}\\n catalog_json[\\\"id\\\"] = catalog.id\\n catalog_json[\\\"name\\\"] = catalog.name\\n catalog_json[\\\"date\\\"] = catalog.date.strftime(\\\"%d %b %Y %H: %M: %S\\\")\\n catalog_json[\\\"items\\\"] = []\\n for item in catalog.items:\\n item_json = {}\\n item_json[\\\"id\\\"] = item.id\\n item_json[\\\"title\\\"] = item.title\\n item_json[\\\"desc\\\"] = item.desc\\n item_json[\\\"date\\\"] = item.date.strftime(\\\"%d %b %Y %H: %M: %S\\\")\\n item_json[\\\"catalog_id\\\"] = item.catalog_id\\n catalog_json[\\\"items\\\"].append(item_json)\\n json.append(catalog_json)\\n return jsonify(Catalogs=json)\",\n \"def categoryItemJSON(category_id):\\n category = db.getByCategory(category_id)\\n items = db.getItemsByCategory(category_id)\\n return jsonify(Items=[i.serialize for i in items])\",\n \"def _get_all_categories() -> dict:\\n categories = database.fetchall(\\\"Categories\\\", \\\"id\\\", \\\"name_ua\\\")\\n return categories\",\n \"def fetch_all_categories():\\n query_set = Categories.objects.filter(is_delete=False)\\n serializer = CategoriesSerializer(query_set, many=True)\\n return serializer.data\",\n \"def json_api():\\n if 'category' in request.args:\\n sqlsession = SQLSESSION()\\n category = sqlsession.query(Category)\\\\\\n .filter_by(name=request.args['category']).first()\\n items = sqlsession.query(Item).filter_by(category_id=category.id)\\\\\\n .all()\\n return json.dumps({'category_id': category.id,\\n 'category_name': category.name,\\n 'items': [item.serialize() for item in items]})\\n elif 'item' in request.args:\\n sqlsession = SQLSESSION()\\n items = sqlsession.query(Item).filter_by(name=request.args['item'])\\\\\\n .all()\\n return json.dumps([item.serialize() for item in items])\\n sqlsession = SQLSESSION()\\n categories = sqlsession.query(Category).all()\\n items = sqlsession.query(Item).all()\\n return json.dumps(\\n {'categories': [cat.serialize() for cat in categories],\\n 'items': [item.serialize() for item in items]})\",\n \"def Categories():\\n cat = {\\n \\t \\\"Featured\\\": 0,\\n \\t \\\"All\\\": 1,\\n \\t \\\"Collectibles\\\": 2,\\n \\t \\\"Clothing\\\": 3,\\n \\t \\\"BodyParts\\\": 4,\\n \\t \\\"Gear\\\": 5,\\n \\t \\\"Models\\\": 6,\\n \\t \\\"Plugins\\\": 7,\\n\\t \\\"Decals\\\": 8,\\n \\t \\\"Audio\\\": 9,\\n \\t \\\"Meshes\\\": 10,\\n\\t \\\"Accessories\\\": 11,\\n\\t \\\"AvatarAnimations\\\": 12,\\n\\t \\\"CommunityCreations\\\": 13,\\n\\t \\\"Video\\\": 14,\\n\\t \\\"Recommended\\\": 15\\n }\\n return cat\",\n \"def showItemsJSON():\\n items = db.getAllItems()\\n return jsonify(CategoryItems=[i.serialize for i in items])\",\n \"def read_all():\\r\\n categories = Category.query.all()\\r\\n # Serialize the data for the response\\r\\n category_schema = CategorySchema(many=True)\\r\\n print('***********************************************************')\\r\\n return category_schema.dump(categories)\",\n \"def get_categories(self):\\n\\n self.search([]).unlink()\\n token = self.env['odoo.moodle'].search([('create_uid', '=', self.env.user.id)]).token\\n domain = \\\"http://localhost:8888\\\"\\n webservice_url = \\\"/webservice/rest/server.php?\\\"\\n parameters = {\\n \\\"wstoken\\\":token,\\n 'wsfunction': 'core_course_get_categories',\\n 'moodlewsrestformat': 'json'\\n }\\n request = requests.get(url=domain+webservice_url, params=parameters)\\n request = request.json()\\n for req in request:\\n try:\\n self.create({\\n 'category_id': req['id'],\\n 'name': req['name'],\\n 'description': req['description'],\\n 'category_parent': req['parent'],\\n })\\n except Exception:\\n print('Category not created')\",\n \"def to_json(self, category):\\r\\n return \\\"{{\\\\\\\"id\\\\\\\": {0}, \\\" \\\\\\r\\n \\\"\\\\\\\"name\\\\\\\": \\\\\\\"{1}\\\\\\\", \\\" \\\\\\r\\n \\\"\\\\\\\"abbreviation\\\\\\\": \\\\\\\"{2}\\\\\\\", \\\" \\\\\\r\\n \\\"\\\\\\\"category\\\\\\\": {3}, \\\" \\\\\\r\\n \\\"\\\\\\\"rank\\\\\\\": {4}}}\\\".format(self.id, self.name, self.abbreviation, category.id, self.rank)\",\n \"def test_get_categories_from_json():\\n allocator = RecipeAllocator()\\n allocator.load_data(\\n orders_dir=\\\"tests/orders.json\\\", recipes_dir=\\\"tests/recipes.json\\\"\\n )\\n allocator.get_categories_from_json()\\n assert list(allocator.portion_categories_dict.keys()) == [\\n \\\"two_portions\\\",\\n \\\"four_portions\\\",\\n ] and list(allocator.recipe_categories_dict.keys()) == [\\n \\\"two_recipes\\\",\\n \\\"three_recipes\\\",\\n \\\"four_recipes\\\",\\n ]\",\n \"def get_catergories(request):\\n shop_obj = Shop.objects.get(user=request.user)\\n shop_categories = shop_obj.categories\\n value_added = list(\\n map(\\n lambda x: {\\n \\\"name\\\": x[\\\"name\\\"],\\n \\\"slug\\\": x[\\\"slug\\\"],\\n \\\"public_slug\\\": x[\\\"public_slug\\\"],\\n \\\"product_count\\\": Products.objects.filter(\\n shop_rel=shop_obj, genre__slug=x[\\\"slug\\\"]\\n ).count(),\\n },\\n shop_categories,\\n )\\n )\\n resp_payload = {\\\"shop_categories\\\": value_added}\\n return Response(data=resp_payload, status=status.HTTP_200_OK)\",\n \"def categories(self):\\n pass\",\n \"def cat_results_react():\\n\\n cats = petfinder.search_data_map()\\n cats = list(cats.values())\\n\\n return jsonify(cats)\",\n \"def filter_json_by_category(self, new_json_path):\\n # {'supercategory': 'person', 'id': 1, 'name': 'person'}\\n ### Filter images:\\n print(\\\"Filtering the annotations ... \\\")\\n json_parent = os.path.split(new_json_path)[0]\\n os.makedirs(json_parent, exist_ok=True)\\n imgs_ids = [x['id'] for x in self.images] # get img_ids of imgs with the category\\n new_imgs = [x for x in self.coco.dataset['images'] if x['id'] in imgs_ids]\\n catIds = self.catIds\\n ### Filter annotations\\n new_annots = [x for x in self.coco.dataset['annotations'] if x['category_id'] in catIds]\\n ### Reorganize the ids\\n new_imgs, annotations = self.modify_ids(new_imgs, new_annots)\\n ### Filter categories\\n new_categories = [x for x in self.coco.dataset['categories'] if x['id'] in catIds]\\n print(\\\"new_categories: \\\", new_categories)\\n data = {\\n \\\"info\\\": self.coco.dataset['info'],\\n \\\"licenses\\\": self.coco.dataset['licenses'],\\n \\\"images\\\": new_imgs, \\n \\\"annotations\\\": new_annots,\\n \\\"categories\\\": new_categories \\n }\\n print(\\\"saving json: \\\")\\n with open(new_json_path, 'w') as f:\\n json.dump(data, f)\",\n \"def inject_categories():\\n return {'categories': entities.Listing.CATEGORIES,\\n 'categories_dict': entities.Listing.CATEGORIES_DICT}\",\n \"def api_category_items(category_id):\\n items = session.query(Item).filter(Item.category_id == category_id)\\n return jsonify(json_list=[i.to_json() for i in items.all()])\",\n \"def showCategoryItemsJSON(category_id):\\r\\n session = DBSession()\\r\\n items = session.query(Item).filter_by(category_id=category_id)\\r\\n return jsonify(Items=[i.serialize for i in items])\",\n \"def categories(data):\\n if data:\\n for i in data:\\n category = CategoriesModel(categories=i['categories'])\\n category.save()\",\n \"def browse_category(request):\\n\\n result = {'categories':[], 'products':[]}\\n\\n u = request.user\\n\\n page = request.POST.get('page', 1)\\n\\n cat = Category.objects.get(id=request.POST['cat_id'])\\n if cat.children.count() > 0:\\n result['categories'] = [c.get_json() for c in cat.children.all()]\\n else:\\n # display items\\n result = Product.objects.filter_category(cat.category_id, page, u) \\n\\n return JSONHttpResponse(result)\",\n \"def categoryItemJSON(category_id):\\n category = session.query(Category).\\\\\\n filter_by(id=category_id).one()\\n items = session.query(Item).\\\\\\n filter_by(category_id=category_id).all()\\n return jsonify(Items=[i.serialize for i in items])\",\n \"def get(self, request, format=None):\\n categories = Category.objects.all()\\n data_out = CategorySerializer(categories, many=True)\\n return Response(data_out.data)\",\n \"def categories():\\n return render_template('categories.html', recipe_categories=USERS[session['username']].recipe_categories)\",\n \"def fill_category(self):\\n cursor = self.conn.cursor(cursor_factory=psycopg2.extras.DictCursor)\\n categories = dict()\\n result = requests.get('https://fr.openfoodfacts.org/categories.json').json()\\n for element in result['tags']:\\n try:\\n cursor.execute(\\\"INSERT INTO category (tag, name, url) VALUES (%s, %s, %s) RETURNING id, tag\\\",\\n (element[\\\"id\\\"], element[\\\"name\\\"], element[\\\"url\\\"]))\\n query_result = cursor.fetchone()\\n categories.__setitem__(query_result[1], query_result[0])\\n except self.conn.OperationalError:\\n print(\\\"operation Error\\\")\\n except self.conn.DataError:\\n print(\\\"Data Error\\\")\\n self.conn.commit()\\n cursor.close()\\n return categories\",\n \"def get_category_data_off(self):\\n list_categories_name=[]\\n cat = requests.get('https://fr.openfoodfacts.org/categories?json=true')\\n cat_data = cat.json()\\n tags_list = cat_data['tags']\\n print (len(tags_list))\\n list_of_random_tags_list = random.sample(tags_list, k=self.view.num_to_select)\\n\\n for category in list_of_random_tags_list:\\n try :\\n category_name = category['name']\\n print(category_name)\\n list_categories_name.append(category_name)\\n print (list_categories_name)\\n self.list_categories = list_categories_name # list_categories_name is passed in the instance property\\n except KeyError:\\n pass\\n except UnicodeEncodeError:\\n pass\",\n \"def avail_categories(self):\\n # retrieve categories\\n categories = self.show_all_categories()\\n # for each category, retrieve packages\\n output = {}\\n for category in categories:\\n packages = self.show_category(category)\\n output[category] = packages\\n\\n return output\",\n \"def url_to_json(self, category_name):\\n request = requests.get(f\\\"https://fr.openfoodfacts.org/categorie/{category_name}.json\\\")\\n return request.json()\",\n \"def category_json(category_id):\\n try:\\n category = session.query(Category).filter_by(id=category_id).one()\\n return jsonify(category=category.serialize)\\n except Exception as inst:\\n print(type(inst))\\n print(inst.args)\\n print(inst)\",\n \"def get_all_categorizations():\\n try:\\n query = run_select_query('SELECT id, category_name, category_item_type, name \\\\\\n FROM categorization \\\\\\n JOIN item ON (categorization.item_id = item.id)')\\n return [{'id': obj[0], 'category_name': obj[1], 'category_item_type': obj[2].capitalize(), 'item_name': obj[3]} for obj in query]\\n except (Exception, psycopg2.Error) as error:\\n return {\\\"status\\\": \\\"error\\\", \\\"error\\\": error}\",\n \"def categoryItemsJSON(category_id):\\n\\n try:\\n category = session.query(Category).filter_by(id=category_id).one()\\n items = session.query(ListItem).filter_by(\\n category_id=category_id).all()\\n return jsonify(ListItems=[i.serialize for i in items])\\n except Exception as e:\\n return '''There was a problem with your query %s, please Verify,\\n Redirecting to main page...\\n <script> setTimeout(function() {\\n window.location.href = \\\"/\\\";\\n }, 2000);\\n </script> ''' % (str(e))\",\n \"def getCategoryJSON(category_id):\\r\\n category = session.query(Category).filter_by(id=category_id).first()\\r\\n if category:\\r\\n return jsonify(Category=category.serialize)\\r\\n else:\\r\\n return jsonify(Category={})\",\n \"def getcategory(self):\\n\\n response = requests.get(\\\"https://fr.openfoodfacts.org/categories.json\\\")\\n\\n data = response.json()\\n\\n self.rawcategorydata = data\",\n \"def test_get_all_categories(self):\\n response = self.client.get('/api/v1/categories',\\n headers=self.attendant_headers)\\n self.assertEqual(response.status_code, 200)\",\n \"def csv_category_to_api(self):\\n # [{'id': 49}, {'id': 60}, {'id': 146}, {'id': 1882}]\\n for data in self._api_data:\\n data[\\\"categories\\\"] = self.map_csv_category(data[\\\"categories\\\"])\\n # data[\\\"categories\\\"] = [{'id': 49}, {'id': 60}, {'id': 146}, {'id': 1882}]\",\n \"def _loadCategories(self):\\n logger.debug(\\\"Func: _loadCategories\\\")\\n\\n if os.path.isfile(self._pathsDict[\\\"categoriesFile\\\"]):\\n categoriesData = self._loadJson(self._pathsDict[\\\"categoriesFile\\\"])\\n if categoriesData == -2:\\n return -2\\n else:\\n categoriesData = self._sceneManagerDefaults[\\\"defaultCategories\\\"]\\n # categoriesData = [\\\"Model\\\", \\\"Shading\\\", \\\"Rig\\\", \\\"Layout\\\", \\\"Animation\\\", \\\"Render\\\", \\\"Other\\\"]\\n self._dumpJson(categoriesData, self._pathsDict[\\\"categoriesFile\\\"])\\n return categoriesData\",\n \"def generate_category_dict(category_file):\\n L = json.load(open(category_file))\\n d = {}\\n for dict_item in L:\\n item = item_to_dict(dict_item)\\n if not item['Label'] is None:\\n d[item['URN']] = item['Label']\\n else:\\n d[item['URN']] = ''\\n return d\",\n \"def load_data_to_json(root_path, extract=True, decode=\\\"utf-8\\\"):\\n category_map = {}\\n\\n for file in os.listdir(root_path):\\n # 行业分类文件夹\\n path = os.path.join(root_path, file)\\n if os.path.isdir(path):\\n for xml_file in os.listdir(path):\\n # 读取每个行业分类的文件\\n xml_file_path = os.path.join(path, xml_file)\\n try:\\n if xml_file_path.endswith(\\\".xml\\\"):\\n doc = read_main_info(xml_file_path, extract=extract, decode=decode)\\n\\n if file in category_map.keys():\\n category_map[file].append(doc)\\n else:\\n category_map[file] = list()\\n category_map[file].append(doc)\\n\\n except UnicodeDecodeError:\\n print(\\\"UnicodeDecodeError:%s\\\" % xml_file_path)\\n continue\\n\\n with open(JSON_FILE, \\\"w\\\") as f:\\n json.dump(category_map, f)\",\n \"def list_categories(self):\\n raise NotImplementedError()\",\n \"def get_categories(self):\\n _url = urljoin(self.base_url, self.API_CATEGORIES)\\n return requests.get(_url)\",\n \"def getItemsJSON(category_id):\\r\\n items = session.query(Item).filter_by(category_id=category_id).all()\\r\\n return jsonify(Items=[i.serialize for i in items])\",\n \"def all_categories(local):\\n\\n categories = []\\n if local == True:\\n categories = list(map((lambda c: c.name), Category.all()))\\n else:\\n categories = jokes_api.categories()\\n\\n click.echo(categories)\",\n \"def create_category(self): # , conf_dir, title):\\n category_file_path = self.event_dir / 'category.json'\\n category_data = {\\n 'title': self.title,\\n }\\n category_data_text = json.dumps(category_data, **\\n JSON_FORMAT_KWARGS) + '\\\\n'\\n save_file(category_file_path, category_data_text)\\n logger.debug('File {} created', category_file_path)\",\n \"def movies_categories(request):\\n return {'MOVIES_CATEGORIES': Category.objects.all()}\",\n \"def list(self):\\n return list(sorted(self.manager.data[\\\"category\\\"].keys()))\",\n \"def page_to_json(self, category_name, page):\\n request = requests.get(f\\\"https://fr.openfoodfacts.org/categorie/{category_name}/{page}.json\\\")\\n return request.json()\",\n \"def getAllCategory():\\n return session.query(Category).all()\",\n \"def get_categories():\\n item_type = \\\"categories\\\"\\n info_dict = spotify.categories()\\n items = info_dict[item_type][\\\"items\\\"]\\n categories = []\\n for i in range(len(items)):\\n category_name = items[i][\\\"name\\\"]\\n category_id = items[i][\\\"id\\\"]\\n categories.append({\\\"Category Name\\\": category_name,\\n \\\"Category ID\\\": category_id\\n })\\n return categories\",\n \"def print_categories_list():\\n\\n categories = []\\n for item in data:\\n cat = item[\\\"category\\\"]\\n\\n if cat not in categories:\\n categories.append(cat)\\n\\n print(categories) # print the list\",\n \"def _unit_test_only_get_category_map_files(self):\\n result = {}\\n for key in self.__all_model_categories:\\n category_file_name = '%s.json' % self._get_category_file_prefix(key)\\n category_file_path = '%s%s' % (self.__category_modules_dir_name, category_file_name)\\n result[key] = category_file_path\\n return result\",\n \"def get_catalog():\\n return jsonify(getCatalog())\",\n \"def categories(self):\\n return self._data[\\\"categories\\\"]\",\n \"def get_categories(self, project):\\n serializer = CategorySerializer(\\n project.categories.all().exclude(fields=None), many=True)\\n return serializer.data\",\n \"def serialize(self):\\n return {\\n 'category': self.category.name,\\n 'id': self.id,\\n 'name': self.name\\n }\",\n \"def test_get_categories(self):\\n\\n res = self.client().get('/categories')\\n\\n data = json.loads(res.data)\\n\\n self.assertEqual(res.status_code, 200)\\n self.assertEqual(data['success'], True)\\n self.assertEqual(len(data['categories']), 6)\",\n \"def update_categories(self):\\n categories = {}\\n datasets = self.data['dataset']\\n used_categories = self._get_list_categories_used(datasets)\\n for category in used_categories:\\n categories.update({\\n category: self._get_datasets_tasks_by_category(datasets, category)\\n })\\n self.data[\\\"category\\\"] = categories\",\n \"def add_category(self, category: str) -> None:\\n for letter in self.data:\\n if not self.data[letter].get(category):\\n self.data[letter][category] = []\\n print(f'Categoria: {category} adicionada ao dicionário.')\\n self.save()\\n self.beautify_json()\",\n \"def get_categories_group(self):\\n m = {}\\n for post in self:\\n for cat in post.Categories:\\n if cat not in m:\\n m[cat] = []\\n m[cat].append(post)\\n return m\",\n \"def get_all_categories_from_collection():\\n api_endpoint = URL\\n response = requests.get(api_endpoint)\\n return response\",\n \"def create_category_pages(app):\\n env = app.builder.env\\n\\n template = \\\"category.html\\\"\\n\\n categories = env.categories\\n for name, category in categories.iteritems():\\n context = {}\\n context[\\\"title\\\"] = category.name\\n context[\\\"subcategories\\\"] = category.subcategories\\n context[\\\"pages\\\"] = category.pages\\n\\n yield (name, context, template)\",\n \"def make_jsons(self):\\n self._jsons = [tree.to_json() for tree in self.reaction_trees]\\n self._update_route_dict(self._jsons, \\\"json\\\")\",\n \"def encode(category_main : ):\",\n \"def show_categories(self):\\n cat_model = TreeModel(('Categories', ))\\n self.categoriesView.setModel(cat_model)\\n\\n categories = self.orm.fetch_parents()\\n for category in categories:\\n item = TreeItem(category, cat_model.rootItem)\\n cat_model.rootItem.appendChild(item)\\n\\n subs = self.orm.fetch_subcategories_for_parent(category)\\n\\n for sub in subs:\\n sub_item = TreeItem(sub, item)\\n item.appendChild(sub_item)\\n\\n self.categoriesView.expandAll()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.80022","0.7764015","0.7749237","0.7702453","0.7634991","0.76159203","0.75909245","0.7532368","0.74889326","0.7449233","0.74030787","0.7385225","0.7377895","0.7366753","0.73639095","0.7266052","0.721529","0.714454","0.7087932","0.7027108","0.6994505","0.6856818","0.68351555","0.6825399","0.67950624","0.67887956","0.6752299","0.67095804","0.66448635","0.66206336","0.65965855","0.657076","0.6564334","0.6483177","0.6471253","0.6440917","0.643653","0.6424912","0.63306105","0.6318866","0.6310578","0.6305357","0.62857217","0.6276477","0.6261673","0.626024","0.6256754","0.6240216","0.6232716","0.6228448","0.62277806","0.61678827","0.6159394","0.61193806","0.61152256","0.6108899","0.60860443","0.6074099","0.60737115","0.6058178","0.6051144","0.60492086","0.6035242","0.60302967","0.6006092","0.5989608","0.5974278","0.5964505","0.5956564","0.5954254","0.59124494","0.5896138","0.5853033","0.5849001","0.5816043","0.5812562","0.57900596","0.5784338","0.57830584","0.5782868","0.5782327","0.5778732","0.5775384","0.5773164","0.57592016","0.57557863","0.57416165","0.5738541","0.57283914","0.5726428","0.5722348","0.57043076","0.57027936","0.5702129","0.56990355","0.56868774","0.5685791","0.56831735","0.5683119","0.56818706"],"string":"[\n \"0.80022\",\n \"0.7764015\",\n \"0.7749237\",\n \"0.7702453\",\n \"0.7634991\",\n \"0.76159203\",\n \"0.75909245\",\n \"0.7532368\",\n \"0.74889326\",\n \"0.7449233\",\n \"0.74030787\",\n \"0.7385225\",\n \"0.7377895\",\n \"0.7366753\",\n \"0.73639095\",\n \"0.7266052\",\n \"0.721529\",\n \"0.714454\",\n \"0.7087932\",\n \"0.7027108\",\n \"0.6994505\",\n \"0.6856818\",\n \"0.68351555\",\n \"0.6825399\",\n \"0.67950624\",\n \"0.67887956\",\n \"0.6752299\",\n \"0.67095804\",\n \"0.66448635\",\n \"0.66206336\",\n \"0.65965855\",\n \"0.657076\",\n \"0.6564334\",\n \"0.6483177\",\n \"0.6471253\",\n \"0.6440917\",\n \"0.643653\",\n \"0.6424912\",\n \"0.63306105\",\n \"0.6318866\",\n \"0.6310578\",\n \"0.6305357\",\n \"0.62857217\",\n \"0.6276477\",\n \"0.6261673\",\n \"0.626024\",\n \"0.6256754\",\n \"0.6240216\",\n \"0.6232716\",\n \"0.6228448\",\n \"0.62277806\",\n \"0.61678827\",\n \"0.6159394\",\n \"0.61193806\",\n \"0.61152256\",\n \"0.6108899\",\n \"0.60860443\",\n \"0.6074099\",\n \"0.60737115\",\n \"0.6058178\",\n \"0.6051144\",\n \"0.60492086\",\n \"0.6035242\",\n \"0.60302967\",\n \"0.6006092\",\n \"0.5989608\",\n \"0.5974278\",\n \"0.5964505\",\n \"0.5956564\",\n \"0.5954254\",\n \"0.59124494\",\n \"0.5896138\",\n \"0.5853033\",\n \"0.5849001\",\n \"0.5816043\",\n \"0.5812562\",\n \"0.57900596\",\n \"0.5784338\",\n \"0.57830584\",\n \"0.5782868\",\n \"0.5782327\",\n \"0.5778732\",\n \"0.5775384\",\n \"0.5773164\",\n \"0.57592016\",\n \"0.57557863\",\n \"0.57416165\",\n \"0.5738541\",\n \"0.57283914\",\n \"0.5726428\",\n \"0.5722348\",\n \"0.57043076\",\n \"0.57027936\",\n \"0.5702129\",\n \"0.56990355\",\n \"0.56868774\",\n \"0.5685791\",\n \"0.56831735\",\n \"0.5683119\",\n \"0.56818706\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.78281426"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":75598,"cells":{"query":{"kind":"string","value":"Generates JSON for a book specified by the book_id"},"document":{"kind":"string","value":"def bookJSON(book_id):\n book = db_session.query(Book).filter_by(id=book_id).one()\n return jsonify(book=book.serialize)"},"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 book_info(book_id):\n\n book = data_manager.get_book(book_id)\n if not book:\n return jsonify({\"error\": \"No book found\"})\n if book.has_records == False:\n return jsonify({'book_id': book_id,\n 'title': book.title,\n 'author': book.author,\n 'records': []})\n\n records = data_manager.get_stored_availability(book_id)\n if not records: # If availability is not in the database, otherwise it's []\n unchecked_records = data_manager.records_from_book(book)\n records = []\n for record in unchecked_records:\n data_manager.update_availability(record)\n\n records = data_manager.get_stored_availability(book_id)\n if not records:\n data_manager.mark_unfindable(data_manager.get_book(book_id))\n\n return jsonify({'book_id': book_id, \n 'title': book.title, \n 'author': book.author, \n 'records': records})","def book_api(isbn):\n\n # make sure book exists\n book = db.execute(\"SELECT * FROM books WHERE isbn= :isbn\",\n {\"isbn\": isbn}).fetchone()\n if book is None:\n return jsonify({\"error\": \"Invalid flight_id\"}), 422\n\n # get book details\n res = requests.get(\"https://www.goodreads.com/book/review_counts.json\",\n params={\"key\": \"zXR2kKmTJ0tV9D3LG8ekug\", \"isbns\": isbn})\n goodreads_rating = res.json()['books'][0]['average_rating']\n goodreads_num_of_ratings = res.json()['books'][0]['work_ratings_count']\n reviews_data = db.execute(\n \"SELECT * FROM reviews WHERE isbn= :isbn\", {\"isbn\": isbn}).fetchall()\n reviews=[]\n for review in reviews_data:\n reviews.append(review.review)\n\n return jsonify({\n \"ISBN\": book.isbn,\n \"Title\": book.title,\n \"Author\": book.author,\n \"Year\": book.year,\n \"Goodreads Rating\": goodreads_rating,\n \"Votes\": goodreads_num_of_ratings,\n \"Reviews\": reviews\n })","def book_list_manipulation():\n global book_list\n book_json = {}\n for book in book_list:\n book_container = {'title': book.title,\n 'author': book.author,\n 'read': str(book.read),\n 'rating': book.rating,\n 'review': book.review,\n 'date read': book.date_read,\n 'id': str(book.id)}\n book_json[str(book.id)] = book_container\n\n return book_json","def book_api(isbn):\n\n # Get book data.\n book = db.execute(\"SELECT * FROM books WHERE isbn = :isbn\", {\"isbn\": isbn}).fetchone()\n # Make sure book exists.\n if book is None:\n return jsonify({\"error\": \"Invalid isbn\"}), 404\n\n # Get average score\n # Count reviews for that book.\n review_count = db.execute(\"SELECT * FROM reviews WHERE book_id = :book_id\", {\"book_id\": book.id}).rowcount\n \n # Get all ratings for that book.\n ratings = db.execute(\"SELECT rating FROM reviews WHERE book_id = :book_id\", {\"book_id\": book.id}).fetchall()\n \n # Count the sum of ratings for that book.\n rating_sum = 0\n for rating in ratings:\n rating_sum += rating[0]\n\n average_score = 0\n\n # Make sure review_count is not a zero\n if review_count > 0:\n average_score = rating_sum/review_count\n\n return jsonify({\n \"title\": book.title,\n \"author\": book.author,\n \"year\": book.year,\n \"isbn\": book.isbn,\n \"review_count\": review_count,\n \"average_score\": average_score,\n \"rating_sum\": rating_sum\n })","def get_books():\n return jsonify({'books': Book.get_all_books()})","def get_books():\n return jsonify({'books': Book.get_all_books()})","def get_book_details(book_id, key):\n\n # call goodreads search method with book id here\n payload = {\"key\": key}\n\n query = requests.get(\"https://www.goodreads.com/book/show/{}.json\".format(book_id), params=payload)\n # parse response to get data needed to create a book object\n\n doc = untangle.parse(query.content)\n book_data = doc.GoodreadsResponse.book\n book = {}\n\n # create dictionary of book object data, subdictionary of edition data\n\n # book info\n #==========\n book[\"title\"] = book_data.title.cdata.encode(\"utf8\")\n book[\"author_name\"], book[\"author_gr_id\"] = get_author_data(book_data.authors)\n book['work_id'] = int(book_data.work.id.cdata.encode('utf8'))\n book[\"description\"] = book_data.description.cdata\n\n # edition info\n #=============\n book[\"edition\"] = {}\n book[\"edition\"][\"isbn\"] = valid_isbn(book_data.isbn.cdata.encode(\"utf8\"))\n book[\"edition\"][\"format_id\"] = get_format_id(book_data.format.cdata.encode(\"utf8\"))\n book[\"edition\"][\"pic_url\"] = book_data.image_url.cdata.encode(\"utf8\")\n book[\"edition\"][\"publisher\"] = book_data.publisher.cdata.encode(\"utf8\")\n book[\"edition\"][\"num_pages\"] = valid_page_count(book_data.num_pages.cdata.encode(\"utf8\"))\n year = date_is_valid(book_data.work.original_publication_year.cdata.encode(\"utf8\"))\n month = date_is_valid(book_data.work.original_publication_month.cdata.encode(\"utf8\"))\n day = date_is_valid(book_data.work.original_publication_day.cdata.encode(\"utf8\"))\n book[\"edition\"][\"date\"] = datetime.date(year, month, day)\n book[\"edition\"][\"gr_url\"] = book_data.url.cdata.encode(\"utf8\")\n book[\"edition\"][\"gr_id\"] = int(book_data.id.cdata.encode(\"utf8\"))\n\n return book","def categoryWithBooksJSON(category_id):\n books = db_session.query(Book).filter_by(category_id=category_id).all()\n return jsonify(Category=[b.serialize for b in books])","def book_api(isbn):\n\n db = get_db()\n book_info = db.execute(\"\"\"\n SELECT *\n FROM books\n WHERE isbn = :isbn;\n \"\"\", {\"isbn\": isbn}).fetchone()\n\n if book_info is None:\n return jsonify({\"error\": \"Invalid ISBN or ISBN is not available\"}), 404\n\n review_stats = db.execute(\n \"\"\"\n SELECT COUNT(*) as count,\n ROUND(AVG(rating), 2) AS avg_rating\n from reviews\n WHERE book_id = :id\n \"\"\",\n {\"id\": book_info['id']}\n ).fetchone()\n\n return jsonify({\n \"title\": book_info['title'],\n \"author\": book_info['author'],\n \"year\": book_info['year'],\n \"isbn\": isbn,\n \"review_count\": review_stats['count'],\n \"average_score\": float(review_stats['avg_rating'])\n })","def get_single_books(isbn):\n return_value = Book.get_book(isbn)\n return jsonify(return_value)","def show_books():\n result = {'books': query.get_book_list()}\n return json.dumps(result, ensure_ascii=False)","def book_serialize(self):\n return {\n 'book_id': self.id,\n 'author': self.author,\n 'publisher': self.publisher,\n 'edition': self.edition,\n 'category': self.category,\n 'copies': self.copies\n }","def api_book(isbn):\n isbn = Markup.escape(isbn)\n # check if book exist in database\n book_db = db.execute(\n \"SELECT * FROM books WHERE isbn LIKE :isbn\", {\"isbn\": isbn}\n ).fetchone()\n if book_db == None:\n return jsonify({\"error\": \"Invalid isbn or not in our database\"}), 404\n\n # Get detail from Goodreads\n res = requests.get(\n \"https://www.goodreads.com/book/review_counts.json\",\n params={\"key\": os.getenv(\"GOODREADS_API\"), \"isbns\": isbn},\n )\n\n if res.status_code != 200:\n raise Exception(\"ERROR: API request unsuccessful.\")\n data = res.json()\n book = data[\"books\"][0]\n\n # Print results\n return jsonify(\n {\n \"title\": book_db.title,\n \"author\": book_db.author,\n \"year\": book_db.year,\n \"isbn\": book_db.isbn,\n \"review_count\": book[\"work_ratings_count\"],\n \"average_score\": book[\"average_rating\"],\n }\n )","def book(book_id):\n book = db.execute(\"SELECT * FROM books WHERE id = :id\", {\"id\": book_id}).fetchone()\n # Get all reviews.\n reviews = db.execute(\"SELECT review, grade FROM reviews WHERE books_id = :book_id\", {\"book_id\": book_id}).fetchall()\n return render_template(\"book.html\", book=book, reviews=reviews)","def detail(request,book_id):\n\n #prodive all informations of book by GoogleBooksApi function\n if book_id is not None:\n book = Response.response_front(book_id)\n \n context = {\n \"title\":Response.build(book['title'][0]),\n \"desc\": Response.build(book['description'][0]),\n \"picture_detail\":book['picture_detail'][0],\n \"book_id\":book_id,\n \"book_cat\":book['categorie'][0],\n \"book_author\":(book['author'][0]),\n }\n return render(\n request,\n \"book.html\",context)","def serialize(self):\r\n return {\r\n \"book_id\": self.id,\r\n \"title\": self.title,\r\n \"author\": self.author,\r\n \"category\": self.category,\r\n }","def export_chapter_links_to_json(book_name, book_link, idd):\n chapters = {\"01\" : book_link}\n home = Ripper(book_link, save_path=save_path)\n \n if not os.path.exists(chapter_store):\n os.mkdir(chapter_store)\n \n for each in home.raw_links:\n str_each = str(each)\n excludes = [\"next\" in str_each, \"previous\" in str_each, \"chapter\" in str_each, \"statcounter\" in str_each,\n \"/x/\" in str_each, \"DRBO.ORG\" in str_each, \"theologica\" in str_each]\n \n if not any(excludes):\n chapters[each.text] = domain + \"chapter/\" + each.get(\"href\")\n\n book_chaps = {book_name : chapters}\n \n book_save_name = \"{}.json\".format(os.path.join(chapter_store, normalize_filename(book_name)))\n\n with open(book_save_name, \"w+\") as wh:\n json.dump(book_chaps, wh)\n return book_chaps","def within_book_search_json(request, book_id):\n query = request.GET.get('q')\n term = query # todo: meta options?\n book = Book.objects.get(pk=book_id)\n\n if not query or len(query) < 3:\n return\n\n # todo: method on objectmanager to search by keyword\n notes = book.notes.filter(\n Q(subject__icontains=term) |\n Q(quote__icontains=term) |\n Q(comment__icontains=term)\n )\n terms = book.terms.filter(\n Q(term__text__icontains=term) |\n Q(term__definition__icontains=term) |\n Q(quote__icontains=term) |\n Q(quote__icontains=term)\n )\n sections = book.sections.filter(\n Q(title__icontains=term) |\n Q(authors__name__icontains=term) |\n Q(subtitle__icontains=term) |\n Q(summary__icontains=term)\n )\n\n results = {'notes': [], 'terms': [], 'sections': []}\n for note in notes:\n results['notes'].append({\n 'title': highlighter.highlight(note.subject, query),\n 'description': highlighter.highlight(note.quote, query, 200),\n 'price': note.get_page_display(),\n 'url': note.get_absolute_url(),\n })\n\n for term in terms:\n results['terms'].append({\n 'title': highlighter.highlight(term.term.text, query),\n 'description': highlighter.highlight(term.quote, query, 200),\n 'price': term.get_page_display(),\n 'url': term.get_absolute_url(),\n })\n\n for section in sections:\n authors = ', '.join(a.name for a in section.authors.all())\n results['sections'].append({\n 'title': highlighter.highlight(section.title, query),\n 'description': highlighter.highlight(authors, query),\n 'price': section.get_page_display(),\n 'url': section.get_absolute_url(),\n })\n\n return JsonResponse({\n 'results': {\n 'books': {\n 'name': 'Notes',\n 'results': results['notes'],\n },\n 'authors': {\n 'name': 'Terms',\n 'results': results['terms'],\n },\n 'sections': {\n 'name': 'Sections',\n 'results': results['sections'],\n },\n }\n })","def book(book_isbn):\n\n if len(book_isbn) == 10 and book_isbn.isdigit():\n # Get book details\n book_res = db.execute(\"SELECT * FROM books WHERE isbn = :book_isbn\",\n {\"book_isbn\": book_isbn}).fetchone()\n session[\"book_res\"] = book_res\n session[\"book_id\"] = book_res.id\n session[\"book_isbn\"] = book_res.isbn\n\n res = requests.get(\"https://www.goodreads.com/book/review_counts.json\", params={\"key\": \"bgh2LQUdiQnnDznXzFMbg\", \"isbns\": book_isbn})\n resp = res.json()\n # return resp\n\n\n # Get reviews\n review_res = db.execute(\"SELECT * FROM reviews WHERE book_id = :book_id\",\n {\"book_id\": session[\"book_id\"]}).fetchall()\n session[\"review_res\"] = review_res\n\n return render_template(\"book.html\", book=book_res, reviews=review_res, count=resp[\"books\"][0],name=session[\"name\"])\n\n return render_template(\"book.html\", message=\"Oops, something went wrong.\",name=session[\"name\"])","def api(isbn):\n # Ensure valid isbn-10 format provided\n if len(isbn) != 10:\n response = make_response(\n jsonify(\"Please provide a valid ISBN-10\"), 404)\n response.headers['X-Error'] = \"Please provide a valid ISBN-10\"\n return response\n\n # Ensure requested book is in our database\n isInDB = db.execute(\n \"SELECT * from books \"\n \"WHERE isbn = :isbn \", {\n 'isbn': isbn\n }).fetchone()\n if isInDB is None:\n response = make_response(\n jsonify(\"Book does not exist in database\"), 404)\n response.headers['X-Error'] = \"Book does not exist in database\"\n return response\n\n # Query data for API response\n proxy = db.execute(\n \"SELECT books.title, books.author, books.year, books.isbn, \"\n \"COUNT(reviews.*) AS review_count, CAST(AVG(reviews.rating) AS float) AS average_score \"\n \"FROM books LEFT JOIN reviews ON reviews.book_id=books.id \"\n \"WHERE books.isbn=:isbn \"\n \"GROUP BY books.id\", {\n 'isbn': isbn\n }).fetchone()\n\n # Return json data\n book_data = {\n \"title\": proxy.title,\n \"author\": proxy.author,\n \"year\": int(proxy.year),\n \"isbn\": proxy.isbn,\n \"review_count\": proxy.review_count,\n \"average_score\": proxy.average_score\n }\n\n # Send requested data\n return make_response(jsonify(book_data), 200)","def book_info(book_id):\n \n #Get the info of the book\n book = db.execute(\"SELECT * FROM book WHERE id = :book_id\",{\"book_id\": book_id}).fetchone()\n if book is None:\n return render_template(\"error.html\", message = \"No such Book.\")\n\n #Get all the reviews joined with the name of each user\n stmt = \"SELECT user_library.*, reviewer.* FROM user_library INNER JOIN reviewer ON user_library.id=reviewer.id_user WHERE id_book = :book_id\"\n reviews = db.execute(stmt,{\"book_id\": book_id}).fetchall()\n\n #Get the user_review info\n user_review = db.execute(\"SELECT * FROM reviewer WHERE id_book = :book_id AND id_user = :user_id\",\n {\"book_id\": book_id, \"user_id\": session[\"user_id\"]}).fetchone()\n\n #If this info not exist we could add a comment, else we can not.\n is_commented = True\n if user_review is None:\n is_commented = False\n\n #Proccess for rating count of Goofreaders\n goodreader_info = requests.get(\"https://www.goodreads.com/book/review_counts.json\", params={\"key\": KEY, \"isbns\": book.isbn })\n goodreader_info = goodreader_info.json()\n goodreader_info = goodreader_info[\"books\"]\n\n average_rating = goodreader_info[0][\"average_rating\"]\n ratings_counts = goodreader_info[0][\"ratings_count\"]\n\n return render_template(\"book_info.html\",book=book, reviews = reviews, is_commented = is_commented\n , average_rating = average_rating, ratings_counts = ratings_counts )","def put(self, book_id):\n a_book = query_book_by_id(book_id)\n if a_book is None:\n return 'Book does not exit', 404\n body = request.get_json()\n a_book.parse_body(body)\n db.session.add(a_book)\n db.session.commit()\n return a_book.serialize(), 200","def get_book_by_id(self, book_id):\n query = \"SELECT * FROM library WHERE book_id=%s\"\n response = Database().fetch_one(query, book_id)\n return response","def get_audiobook(_id):\r\n return [Audiobook.audiobook_json(Audiobook.query.filter_by(id=_id).first())]\r\n # Audiobook.audiobook_json() coverts our output to the json format defined earlier\r\n # the filter_by method filters the query by the id\r\n # since our id is unique we will only get one result\r\n # the .first() method will get that first value returned\r","def test_get_book_with_id(self):\n\t\tadd_book = {\n\t\t\t'title': 'Hello Books',\n\t\t\t'isbn': '5698745124'\n\t\t}\n\t\tlogin_data = self.login_test_user()\n\t\ttoken = login_data['auth_token']\n\t\tres = self.client.post(\n\t\t\tf'{URL_BOOKS}',\n\t\t\theaders=dict(Authorization=f'Bearer {token}'),\n\t\t\tcontent_type='application/json',\n\t\t\tdata=json.dumps(add_book)\n\t\t)\n\n\t\t# get book id\n\t\tbook = self.client.get(\n\t\t\tf'{URL_BOOKS}/1',\n\t\t\theaders=dict(Authorization=f'Bearer {token}'),\n\t\t\tcontent_type='application/json'\n\t\t)\n\n\t\tbook_res = json.loads(book.data.decode())\n\t\tself.assertTrue(book_res['books']['title'] == 'hello books')","def detail(request, bid):\n result = {\n 'status': '', # 'success' or 'failure'\n 'msg': '', # msg of the book\n 'error_msg': '', # notes of failure\n }\n # the error method\n if not is_get(request, result):\n return HttpResponse(json.dumps(result))\n\n # filter the list of book\n # actually, the number of book is 0 or 1\n books = models.BookInfo.objects.filter(id=bid).first()\n\n # if the number of book is 0\n if books is None:\n result['status'] = 'failure'\n result['error_msg'] = 'invalid book id'\n return HttpResponse(json.dumps(result))\n\n book_dict = dict()\n # transfer db obj to dict\n book = process_book_obj(books)\n book_dict[str(books.id)] = json.dumps(book)\n\n result['status'] = \"success\"\n result['msg'] = json.dumps(book_dict)\n\n return HttpResponse(json.dumps(result))","def get_bookings(userid):\n # get all the bookings for userid\n bookings = Booking.query.filter(Booking.userid == userid)\n # dump in the Schema\n results = bookingsSchema.dump(bookings)\n\n return jsonify(results)","def book_json_manipulation(json_as_dict):\n global book_list\n\n for key, value in json_as_dict.items():\n book_container = json_as_dict[key]\n book = Book(book_container['title'],\n book_container['author'],\n book_container['read'] == 'True',\n int(book_container['id']))\n book.rating = book_container['rating']\n book_list.append(book)","def convert_library_to_json(library: list) -> list:\n books_to_save = []\n for book in library:\n book_dict = book.__dict__\n books_to_save.append(dict((key.title(), value) for key, value in book_dict.items()))\n return books_to_save","def save_book(request,book_id):\n \n if request.user.is_authenticated:\n user = get_object_or_404(\n UserModel,\n id=request.user.id\n )\n book = Response.response_front(book_id)\n\n #save book with all informations on table Book\n \n Book.objects.add_book(\n book_id,\n user,\n title=(book['title'][0]),\n book_cat=Response.build(book['categorie'][0]),\n picture=book['picture'][0],\n picture_detail=book['picture_detail'][0],\n description=book['description'][0],\n author=book['author'][0]\n )\n\n return redirect(\"home\")","def borrow_book(self, author, title, publisher, edition, email, book_id):\n for book in self.books_list:\n if book['book_id'] != str(book_id):\n return 'book does not exist'\n continue\n else: \n book = {\n 'author' : author,\n 'title' : title,\n 'publisher' : publisher,\n 'edition' : edition,\n 'email' : email\n }\n self.borrowed_books.append(book)\n return book","def create_books_data():\n pass","def get_books_from_api(request, url='https://www.googleapis.com/books/v1/volumes?q=Hobbit'):\n response = requests.get(url)\n data = response.json()\n items = data.get('items')\n if items is None:\n items = []\n for item in items:\n book = item.get('volumeInfo')\n title = book.get('title', '--')\n authors = book.get('authors', ['unknown'])\n publishedDate = book.get('publishedDate')\n isbns = book.get('industryIdentifiers', [])\n pages = book.get('pageCount')\n cover_url = book.get('imageLinks')\n if cover_url:\n cover_url = cover_url.get('thumbnail')\n language = book.get('language')\n authors_list = []\n for author in authors:\n auth = get_author_object(author)\n authors_list.append(auth)\n isbn_10 = None\n isbn_13 = None\n for isbn in isbns:\n if isbn['type'] == 'ISBN_10':\n isbn_10 = isbn['identifier']\n elif isbn['type'] == 'ISBN_13':\n isbn_13 = isbn['identifier']\n lang = get_language_object(language)\n try:\n published = datetime.strptime(publishedDate, '%Y-%m-%d')\n except ValueError:\n year = int(publishedDate[:4])\n month = None\n day = None\n except TypeError:\n year = None\n month = None\n day = None\n else:\n year = published.year\n month = published.month\n day = published.day\n try:\n book = get_object_or_404(Book, title=title, publishedYear=year, publishedMonth=month, publishedDay=day,\n language=lang, pages=pages, cover=cover_url, isbn_10=isbn_10, isbn_13=isbn_13)\n for name in book.authors.all():\n if name not in authors_list:\n raise Http404\n except Http404:\n book = Book.objects.create(title=title, publishedYear=year, publishedMonth=month, publishedDay=day,\n language=lang, pages=pages, cover=cover_url, isbn_10=isbn_10, isbn_13=isbn_13)\n book.authors.set(authors_list)\n return redirect('all-books')","def get_book(arg):\n book = requests.get(BASE_URL+'books/'+arg)\n print book.json()\n return book.status_code","def book(isbn):\n isbn = Markup.escape(isbn)\n # check if book exist in database\n book_db = db.execute(\n \"SELECT * FROM books WHERE isbn LIKE :isbn\", {\"isbn\": isbn}\n ).fetchone()\n if book_db == None:\n return render_template(\n \"error.html\", error=\"ISBN invalid or not in our Database.\"\n )\n\n # Get detail from Goodreads\n res = requests.get(\n \"https://www.goodreads.com/book/review_counts.json\",\n params={\"key\": os.getenv(\"GOODREADS_API\"), \"isbns\": isbn},\n )\n\n if res.status_code != 200:\n return render_template(\"error.html\", error=\"Not found on our API.\")\n data = res.json()\n book = data[\"books\"][0]\n\n # Get the reviews for the book.\n book_reviews = db.execute(\n \"SELECT review.*, users.nickname FROM review JOIN users ON review.user_id = users.id WHERE book_id = :book_id\",\n {\"book_id\": book_db.id},\n ).fetchall()\n\n # Get my own review\n user = session.get(\"user\")\n my_review = db.execute(\n \"SELECT * FROM review WHERE (book_id = :book_id) AND user_id = (SELECT id from users WHERE username LIKE :user)\",\n {\"book_id\": book_db.id, \"user\": user},\n ).fetchone()\n\n if my_review is not None:\n # Print results\n return render_template(\n \"book.html\",\n book=book,\n book_db=book_db,\n book_reviews=book_reviews,\n my_review=my_review,\n )\n else:\n return render_template(\n \"book.html\",\n book=book,\n book_db=book_db,\n book_reviews=book_reviews,\n my_review=None,\n )","def books_list(request):\n if request.method == 'GET':\n # books = Books.objects.all()\n # serializer = BooksSerializers(books, many=True)\n # print(serializer)\n # return Response(serializer.data)\n response = requests.get(\"https://www.anapioficeandfire.com/api/books\")\n data = response.json()\n print(response.status_code)\n urls = list(map(lambda x: (x['url']), data))\n ids = []\n for i in urls:\n i = re.sub(r'.*/', '', i)\n ids.append(i)\n names = list(map(lambda x: (x['name']), data))\n isbns = list(map(lambda x: (x['isbn']), data))\n authors = list(map(lambda x: (x['authors']), data))\n nopages = list(map(lambda x: (x['numberOfPages']), data))\n publishers = list(map(lambda x: (x['publisher']), data))\n country = list(map(lambda x: (x['country']), data))\n release_dates = list(map(lambda x: (x['released']), data))\n\n li = zip(ids, names, isbns, authors, nopages, publishers, country, release_dates)\n\n # for i in li:\n # Books.objects.get_or_create(url=i[0],name=i[1],isbn=i[2],authors=i[3],nopages=i[4],publisher=i[5],\n # country=i[6],released=i[7])\n d = {\"status_code\": response.status_code, \"status\": \"success\"}\n records = []\n for i in li:\n record = {\n \"id\": i[0],\n \"name\": i[1],\n \"isbn\": i[2],\n \"authors\": i[3],\n \"nopages\": i[4],\n \"publisher\": i[5],\n \"country\": i[6],\n \"release_date\": i[7]\n }\n\n records.append(record)\n d[\"data\"] = records\n pickup_records = json.dumps(d, indent=4)\n return HttpResponse(pickup_records, content_type=\"application/json\")\n\n\n elif request.method == 'POST':\n serializer = BooksSerializers(data=request.data)\n records = []\n if serializer.is_valid():\n serializer.save()\n d = {\"status_code\": 201, \"status\": \"success\"}\n\n d1 = {}\n d1[\"books\"] = serializer.data\n records.append(d1)\n d[\"data\"] = records\n return Response(d, status=status.HTTP_201_CREATED)\n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)","def get_book(self, book_id):\n # Implemented from template for\n # osid.resource.BinLookupSession.get_bin\n if self._catalog_session is not None:\n return self._catalog_session.get_catalog(catalog_id=book_id)\n collection = JSONClientValidated('commenting',\n collection='Book',\n runtime=self._runtime)\n # Need to consider how to best deal with the \"phantom root\" catalog issue\n if book_id.get_identifier() == PHANTOM_ROOT_IDENTIFIER:\n return self._get_phantom_root_catalog(cat_class=objects.Book, cat_name='Book')\n try:\n result = collection.find_one({'_id': ObjectId(self._get_id(book_id, 'commenting').get_identifier())})\n except errors.NotFound:\n # Try creating an orchestrated Book. Let it raise errors.NotFound()\n result = self._create_orchestrated_cat(book_id, 'commenting', 'Book')\n\n return objects.Book(osid_object_map=result, runtime=self._runtime, proxy=self._proxy)","def book_by_id(id_):\n data = {}\n for book in root.findall('Book'):\n for elem in book:\n book_id = book.attrib['id']\n if int(book_id) == id_:\n data['id'] = book.attrib['id']\n data[elem.tag] = elem.text\n return data","def book(response):\n\n response = response.json()\n\n if response.get(\"items\"):\n # response looks like {\"items\": [<items>], ...}\n # collapse to just [<items>]\n response = response[\"items\"]\n else:\n # just one item, so wrap in a list to match\n response = [response]\n\n books = []\n for book in response:\n info = OrderedDict()\n info[\"Title\"] = book[\"volumeInfo\"][\"title\"]\n info[\"ID\"] = book[\"id\"]\n try:\n info[\"Author\"] = book[\"volumeInfo\"][\"authors\"][0]\n except:\n info[\"Author\"] = \"<unknown>\"\n try:\n info[\"Year\"] = book[\"volumeInfo\"][\"publishedDate\"].split(\"-\")[0]\n except:\n info[\"Year\"] = 0\n\n books.append(info)\n\n return books","def get_data_from_book(book):\n src_img = book.find(\"img\").get(\"src\")\n src_img = src_img.replace(\"../\", \"\")\n image = \"http://books.toscrape.com/\" + src_img\n\n in_stock = False\n in_stock_or_not = book.find(\"p\", {\"class\", \"instock\"}).text\n if \"In stock\" in in_stock_or_not:\n in_stock = True\n\n name = book.find(\"h3\").find(\"a\").text\n\n price = book.find(\"p\", {\"class\", \"price_color\"}).text\n price = price.replace(\"Â\", \"\")\n\n rating = book.find(\"p\", {\"class\", \"star-rating\"}).get(\"class\")[1]\n rating = w2n.word_to_num(rating)\n\n return {\n \"image\": image,\n \"in_stock\": in_stock,\n \"name\": name,\n \"price\": price,\n \"rating\": rating,\n }","def add_book(book):\n\n global book_list\n book.id = generate_id()\n book_list.append(book)","def test_get_specific_book_method(self):\n # When book id is int\n book_id = 1\n result = self.book.get_book(book_id)\n self.assertEqual(result, [{\"Title\": \"Harry Potter and Chamber of Secrets\",\n \"Author\": \"J.K Rowling\",\n \"Copies\": 2}])","def get_all_bookings():\n # get all the bookings for userid\n bookings = Booking.query.all()\n # dump in the Schema\n results = bookingsSchema.dump(bookings)\n\n return jsonify(results)","def books_detail(request, pk):\n try:\n snippet = Books.objects.get(url=pk)\n except Books.DoesNotExist:\n return Response(status=status.HTTP_404_NOT_FOUND)\n\n if request.method == 'GET':\n serializer = BooksSerializers(snippet)\n return Response(serializer.data)\n\n elif request.method == 'PUT':\n serializer = BooksSerializers(snippet, data=request.data)\n if serializer.is_valid():\n serializer.save()\n return Response(serializer.data)\n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\n\n elif request.method == 'DELETE':\n snippet.delete()\n return Response(status=status.HTTP_204_NO_CONTENT)","def get_book_details(self):\n\n try:\n # gives response for the request from the API url\n response = requests.get(self.book_url)\n\n \n # using ElementTree to store the response content in a tree\n root = ET.fromstring(response.content)\n book = root.find('book')\n\n # getting the required details\n self.book_details[\"title\"] = book.find('title').text\n self.book_details[\"average_rating\"] = book.find('average_rating').text\n self.book_details[\"ratings_count\"] = book.find('ratings_count').text\n self.book_details[\"num_pages\"] = book.find('num_pages').text\n self.book_details[\"image_url\"] = book.find('image_url').text\n self.book_details[\"publication_year\"] = book.find('publication_year').text\n\n # getting list of all the authors\n authors = book.find('authors')\n if authors:\n author_names_list = []\n for author in authors.iter('author'):\n author_names_list.append(author.find('name').text)\n author_names_sentence = \", \".join(author_names_list)\n self.book_details[\"authors\"] = author_names_sentence\n except:\n raise Exception(\"invalid XML response\")","def item_json(item_id):\n item_details_json = {}\n try:\n item_in_db = session.query(Item).filter_by(id=item_id).one()\n item_details_json['item'] = item_in_db.serialize\n except Exception as e:\n item_details_json['result'] = 'No data for item ID ' \\\n + str(item_id) + ': ' + str(e)\n return jsonify(item_details_json)","def itemJSON(category_id, item_id):\n item = session.query(Item).filter_by(id=item_id).one()\n return jsonify(Item=item.serialize)","def recipes():\n if request.method == 'GET':\n return Response(\n json.dumps(recipebook.to_json_list()),\n mimetype=\"application/json\")\n elif request.method == 'POST':\n new_dict = request.get_json()\n recipebook.recipes.append(models.Recipe.from_json_dict(new_dict))\n write_out()\n return Response(status=200)","def get(self):\n books = db.session.query(models.Book)\n return [book.serialize() for book in books], 200","def post_book_data():\n data = None\n if request.get_json() is None:\n data = request.form.to_dict()\n print(data)\n else:\n data = request.get_json()\n\n if data is None or data == {} or all(value == '' for value in data.values()):\n return render_template('error.html', message='Please Enter Information'), 400\n data.get('book_url', None)\n data.get('title', None)\n data.get('book_id', None)\n data.get('ISBN', None)\n data.get('author', None)\n data.get('author_url', None)\n data.get('rating', None)\n data.get('rating_count', None)\n data.get('review_count', None)\n data.get('image_url', None)\n data.get('similar_books', None)\n if isinstance(data, list):\n mongo.db.Books.insert_many(data)\n else:\n mongo.db.Books.insert_one(data)\n print(data)\n return render_template('post_book.html', output=data), 200","def test_get_book(self):\n\n response = self.client.post(\n '/api/v1/books', data=json.dumps(add_book), content_type='application/json',\n headers=self.get_admin_token())\n response1 = self.client.get(\n '/api/v1/books/NJCF4001', content_type='application/json', headers=self.get_admin_token())\n result = json.loads(response1.data.decode())\n self.assertEqual(result['message'],\n 'Retrieved successfully')\n assert response1.status_code == 200","def make_books_dicts(xml, book_list):\n\n books_response = xml.GoodreadsResponse.reviews.review\n for book in books_response:\n a_book = {}\n a_book['title'] = book.book.title.cdata.encode('utf8')\n a_book['author_name'] = book.book.authors.author.name.cdata.encode('utf8')\n a_book['author_gr_id'] = int(book.book.authors.author.id.cdata.encode('utf8'))\n a_book['gr_work_id'] = int(book.book.work.id.cdata.encode('utf8'))\n a_book['description'] = book.book.description.cdata\n\n a_book['edition'] = {}\n a_book['edition']['isbn'] = valid_isbn(book.book.isbn.cdata.encode('utf8'))\n a_book['edition']['format_id'] = get_format_id(book.book.format.cdata.encode('utf8'))\n a_book['edition']['pic_url'] = book.book.image_url.cdata.encode('utf8')\n a_book['edition']['publisher'] = book.book.publisher.cdata.encode('utf8')\n a_book['edition']['gr_url'] = book.book.link.cdata.encode('utf8')\n a_book['edition']['gr_id'] = int(book.book.id.cdata.encode('utf8'))\n year = date_is_valid(book.book.publication_year.cdata.encode(\"utf8\"))\n month = date_is_valid(book.book.publication_month.cdata.encode(\"utf8\"))\n day = date_is_valid(book.book.publication_day.cdata.encode(\"utf8\"))\n a_book['edition']['date'] = datetime.date(year, month, day)\n a_book['edition']['num_pages'] = valid_page_count(book.book.num_pages.cdata.encode('utf8'))\n book_list.append(a_book)\n\n print \"*******THERE ARE \" + str(len(book_list)) + \" ON THIS SHELF*******\"\n\n return book_list","def show_book_details(book_id):\n\n book = get_book_details(book_id, GR_KEY)\n acct = get_current_account(session[\"acct\"])\n user = get_user_by_acct(acct)\n # query to get all friends of the current user that have read any edition of\n # the currently displayed book.\n friend_matches = db.session.query(Shelf, Edition, ShelfBook, Friendship) \\\n .filter(Friendship.user_id == user.user_id) \\\n .filter(Shelf.user_id == Friendship.friend_id) \\\n .filter(ShelfBook.shelf_id == Shelf.shelf_id) \\\n .filter(ShelfBook.ed_id == Edition.ed_id) \\\n .filter(Edition.book_id == Book.book_id) \\\n .filter(Book.gr_work_id == book['work_id'])\\\n .all()\n\n matches = set() # create set to prevent duplicate matches (caused by rereading)\n\n if friend_matches:\n for match in friend_matches:\n user = User.query.get(match.Shelf.user_id)\n matches.add((user.image_url, user.gr_name, match.Shelf.name, user.gr_url))\n\n matches = list(matches) # cast matches to a list for iteration in html\n PRODUCT_URL = get_lib_products(WCCLS, OVRDRV_KEY, OVRDRV_SECRET)\n lib_copies = search_lib_for_copies(PRODUCT_URL, book, OVRDRV_KEY, OVRDRV_SECRET)\n\n return render_template(\"book_detail.html\",\n book=book,\n acct=acct,\n user=user,\n matches=matches,\n lib_copies=lib_copies)","def serialize(self):\r\n return {\r\n \"bookshelf_id\": self.id,\r\n \"bookshelf_name\": self.name,\r\n \"bookshelf_image\": self.image_url,\r\n }","def generate_books(nr):\n for n in range(int(nr)):\n d6 = random.randint(1, 6)\n #A book cannot be generated with the same name in different genres due to the nature of the data.\n if d6 <= 4:\n book = generate_fantasy_title()\n book_genres[book] = \"FANTASY\"\n elif d6 == 5:\n book = generate_romance_title()\n book_genres[book] = \"ROMANCE\"\n elif d6 == 6:\n book = generate_horror_title()\n book_genres[book] = \"HORROR\"\n author = generate_author()\n if book in books:\n #a new author for a book cannot have the same name as one already saved for that book\n while author in books[book]:\n author = generate_author()\n books[book] += [author]\n else:\n books[book] = [author]\n counter = 1\n for book in books:\n book_ids[book] = f'Book/{counter}'\n counter += 1","def insert_book():\n \n\n if request.method == 'POST':\n new_author = mongo.db.authors.insert_one({\n 'author_name': request.form.to_dict()['author_name']\n })\n \n author_id = new_author.inserted_id\n \n # Create new book in mongo.db.books\n new_book = mongo.db.books.insert_one({\n 'title': request.form.to_dict()['title'],\n 'genre': request.form.to_dict()['genre'],\n 'pages': request.form.to_dict()['pages'],\n 'reviews': [],\n 'likes': [],\n 'dislikes': [],\n 'author_id': str(ObjectId(author_id)),\n 'isbn_num': request.form.to_dict()['isbn_num']\n })\n \n return redirect(url_for('library'))\n \n return render_template('insert_book.html', \n genres=[genre for genre in mongo.db.genres.find()])","def book(title):\n\n # Make sure user is logged in\n if \"username\" not in session:\n return render_template(\"index.html\", message=\"Please login to view that page!\")\n\n book = db.execute(\"SELECT * FROM books WHERE title = :title\", {\"title\": title}).fetchone()\n # Make sure it exists if someone types in a random route\n if book is None:\n return render_template(\"search.html\", search=title)\n\n res = requests.get(\"https://www.goodreads.com/book/review_counts.json\", params={\"key\": \"hYlepXnUC1zpQHUOHkxluQ\", \"isbns\": book.isbn})\n if res.status_code != 200:\n raise Exception(\"ERROR: API request unsuccessful.\")\n data = res.json()\n average_rating = data[\"books\"][0][\"average_rating\"]\n ratings_count = data[\"books\"][0][\"ratings_count\"]\n\n return render_template(\"book.html\", username=session[\"username\"], book=book, average_rating=average_rating, ratings_count=ratings_count)","def get_commentary_for_book_chapters(book_file_name):\n if not os.path.exists(challoner_store):\n os.mkdir(challoner_store)\n with open(book_file_name, \"r+\") as rh:\n book = json.load(rh)\n chapter_text = {}\n\n for name, chapters_dictionary in book.items():\n \n for chap, location in chapters_dictionary.items():\n norm = normalize_filename(\"{}_{}\".format(name, chap))\n \n outfile = \"{}/{}.json\".format(challoner_store, norm)\n \n if os.path.exists(outfile):\n continue\n else:\n chapter_text[name + \"__\" + chap] = get_commentary_for_chapter(location)\n with open(outfile, \"w+\") as wh:\n json.dump(chapter_text, wh)\n chapter_text = {}","def show_books():\n# need + or %20 for spaces in author (set encoding?)\n\n args = request.args\n column_names = get_column_names()\n\n sql_cmd = [\"SELECT title, author FROM books\"]\n if len(args) > 0:\n for j, arg in enumerate(args):\n if arg not in column_names: # return empty list\n sql_cmd = []\n break\n else:\n if not \" WHERE \" in sql_cmd:\n sql_cmd.append(\" WHERE \")\n sql_cmd.append(\"%s='%s'\" % (arg, args[arg]))\n if j+1 < len(args):\n sql_cmd.append(\" AND \")\n sql_cmd.append(\";\")\n sql_cmd = \"\".join(sql_cmd)\n# print('sql_cmd: ', sql_cmd)\n\n books = []\n if len(sql_cmd) > 1:\n cur = g.db.cursor()\n cur.execute(sql_cmd)\n if cur:\n books = [dict(title=row[0], author=row[1]) for row in cur.fetchall()]\n# return jsonify({'results': books})\n return json_dumps({'results': books}, indent=4)","def show_books_by_page(page):\n result = {'books': query.get_book_list()}\n return json.dumps(result, ensure_ascii=False)","def toJSON(self):\n \n return {\n 'id': self.id,\n 'bibcode': self.bibcode,\n 'bib_data': self.bib_data\n }","def json_export(json_file,gradebook):\n \n try:\n import json\n except ImportError:\n warnings.warn('Failed to import json module. Cannot execute json_export')\n return\n if not hasattr(json_file,'write'):\n if not isinstance(json_file,basestring) or not \\\n os.path.exists(os.path.dirname(os.path.abspath(json_file))):\n raise ValueError, 'Argument \\'json_file\\' is not readable, ' \\\n 'and could not be validated as a file path.'\n else:\n json_file = open(json_file,'w+')\n \n if not isinstance(gradebook,Gradebook):\n raise TypeError, 'gradebook argument must be a Gradebook objcet.'\n \n cat_dict = dict(gradebook._Gradebook__categories)\n all_items = []\n for cat_name in cat_dict:\n gr_leest = cat_dict[cat_name].grades.select(docopy=True,aslist=True)\n for i in gr_leest:\n x = {}\n if not isinstance(i,Grade):\n continue\n x['type']='Grade'\n x['name']=i.name\n if i.parent is not None:\n if i.parent.name == cat_name or i.parent.name in cat_dict:\n x['parent'] = i.parent.name\n else:\n x['parent'] = None\n attribs = {}\n attribs['score'] = i.score\n attribs['maximum'] = i.maximum\n attribs['weight'] = i.weight\n attribs['extra_credit'] = i.extra_credit\n if i.overrides:\n attribs['overrides'] = list(i.overrides)\n for ovrrd in i.overrides:\n if not isinstance(overrd,basestring):\n attribs['overrides'].remove(overrd)\n if i.timestamp and isinstance(i.timestamp,datetime.datetime):\n tmstmp = calendar.timegm(i.timestamp.utctimetuple())\n attribs['timestamp'] = tmstmp\n if i.identifiers:\n attribs['identifiers'] = dict(i.identifiers)\n \n x['attribs'] = attribs\n all_items.append(x)\n catt = {'type':'Category','name':cat_name}\n if cat_dict[cat_name].parent is not None:\n catt['parent'] = cat_dict[cat_name].parent.name\n else:\n catt['parent'] = None\n catt['attribs'] = dict(cat_dict[cat_name]._Category__attribs)\n all_items.append(catt)\n \n grbk = {'type':'Gradebook','name':gradebook.name,'user':gradebook.user}\n all_items.append(grbk)\n enc_me = {\"grading\": all_items}\n \n encoder = json.JSONEncoder(indent=4,separators=(', ',': '))\n enc = encoder.encode(enc_me)\n \n json_file.write(enc)\n json_file.close()","def get_autor_with_papers(author_id):\n print(\"getting information from dblp about author {}\".format(author_id))\n data = get(\"https://dblp.org/pid/\" + author_id + \".xml\")[\"dblpperson\"]\n author_id = author_id.replace(\"/\", \"_\")\n author = {\n \"name\": data[\"@name\"],\n \"papers\": [get_paper(paper_container) for paper_container in data[\"r\"]],\n }\n\n with open(local.file_name(\"authors\", author_id), \"w\") as out:\n json.dump(author, out, ensure_ascii=False, indent=4)","def textbooks_detail_handler(request, course_key_string, textbook_id):\r\n course_key = CourseKey.from_string(course_key_string)\r\n course_module = _get_course_module(course_key, request.user)\r\n store = get_modulestore(course_module.location)\r\n matching_id = [tb for tb in course_module.pdf_textbooks\r\n if unicode(tb.get(\"id\")) == unicode(textbook_id)]\r\n if matching_id:\r\n textbook = matching_id[0]\r\n else:\r\n textbook = None\r\n\r\n if request.method == 'GET':\r\n if not textbook:\r\n return JsonResponse(status=404)\r\n return JsonResponse(textbook)\r\n elif request.method in ('POST', 'PUT'): # can be either and sometimes\r\n # django is rewriting one to the other\r\n try:\r\n new_textbook = validate_textbook_json(request.body)\r\n except TextbookValidationError as err:\r\n return JsonResponse({\"error\": err.message}, status=400)\r\n new_textbook[\"id\"] = textbook_id\r\n if textbook:\r\n i = course_module.pdf_textbooks.index(textbook)\r\n new_textbooks = course_module.pdf_textbooks[0:i]\r\n new_textbooks.append(new_textbook)\r\n new_textbooks.extend(course_module.pdf_textbooks[i + 1:])\r\n course_module.pdf_textbooks = new_textbooks\r\n else:\r\n course_module.pdf_textbooks.append(new_textbook)\r\n store.update_item(course_module, request.user.id)\r\n return JsonResponse(new_textbook, status=201)\r\n elif request.method == 'DELETE':\r\n if not textbook:\r\n return JsonResponse(status=404)\r\n i = course_module.pdf_textbooks.index(textbook)\r\n remaining_textbooks = course_module.pdf_textbooks[0:i]\r\n remaining_textbooks.extend(course_module.pdf_textbooks[i + 1:])\r\n course_module.pdf_textbooks = remaining_textbooks\r\n store.update_item(course_module, request.user.id)\r\n return JsonResponse()","def getBookById(self, id):\n cur = self.__execute__(\n '''select ID, NAME, AUTHOR from books where ID = %d;''' % id)\n return BookIter(cur)","def download_a_book():\n try:\n book_name = request.args.get('book_name')\n record = search_a_book(replica_one, book_name)\n if 'success' in record:\n blob = replica_one.blob(book_name)\n size = sys.getsizeof(blob.download_as_string())\n response = Response(blob.download_as_string())\n response.headers.add('Content-Range'.format('bytes'), size)\n return response\n else:\n return json.dumps({'error': 'file not found'})\n except Exception as e:\n logging.basicConfig(level=logging.DEBUG,\n format='%(asctime)s %(levelname)-8s %(message)s',\n datefmt='%a, %d %b %Y %H:%M:%S',\n filename='myapp.log',\n filemode='w')\n with open('myapp.log') as log:\n line = log.readline()\n if line is not None:\n loggingdb.insert_one({'log': str(line)})\n return json.dumps({\"error\": \"exception found\"})","def _save_book_detail(self, book_dict, book_nmb):\n content = self._get_book_content(book_dict['book_page_href'])\n with open(f'{PATH_TO_DATA}/{book_nmb}.html', 'w') as file:\n file.write(content)","def book(isbn):\n\n if request.method == \"POST\":\n \n # Save current user info\n currentUser = session[\"user_id\"]\n \n # Fetch form data\n rating = request.form.get(\"rating\")\n comment = request.form.get(\"comment\")\n\n # Search book_id by ISBN\n row = db.execute(\"SELECT id FROM books WHERE isbn = :isbn\",\n {\"isbn\": isbn})\n\n # Save id into variable\n id_books = row.fetchone() # (id,)\n bookId = id_books[0]\n\n # Check for user submission (ONLY 1 review/user allowed per book)\n row2 = db.execute(\"SELECT * FROM reviews WHERE user_id = :user_id AND book_id = :book_id\",\n {\"user_id\": currentUser,\n \"book_id\": bookId})\n\n # A review already exists\n if row2.rowcount == 1:\n \n flash('You already submitted a review for this book', 'warning')\n return redirect(\"/book/\" + isbn)\n\n # Convert to save into DB\n rating = int(rating)\n\n db.execute(\"INSERT INTO reviews (user_id, book_id, comment, rating) VALUES \\\n (:user_id, :book_id, :comment, :rating)\",\n {\"user_id\": currentUser, \n \"book_id\": bookId, \n \"comment\": comment, \n \"rating\": rating})\n\n # Commit transactions to DB and close the connection\n db.commit()\n\n flash('Review submitted!', 'info')\n\n return redirect(\"/book/\" + isbn)\n \n # Take the book ISBN and redirect to his page (GET)\n else:\n row = db.execute(\"SELECT isbn, title, author, year FROM books WHERE \\\n isbn = :isbn\",\n {\"isbn\": isbn})\n\n # Hence this is the bookInfo[0] for using in reviews.html\n bookInfo = row.fetchall()\n\n \"\"\" GOODREADS reviews \"\"\"\n\n # Read API key from env variable\n key = os.getenv(\"GOODREADS_KEY\")\n \n # Query the api with key and ISBN as parameters\n query = requests.get(\"https://www.goodreads.com/book/review_counts.json\",\n params={\"key\": key, \"isbns\": isbn})\n \n # Convert the response to JSON\n json_response = query.json()\n\n # \"Clean\" the JSON before passing it to the bookInfo list\n response = json_response['books'][0]\n\n # Append it as the second element on the list to get bookInfo[1] for using in reviews.html\n bookInfo.append(response)\n\n \"\"\" Users reviews \"\"\"\n\n # Search book_id by ISBN\n row = db.execute(\"SELECT id FROM books WHERE isbn = :isbn\",\n {\"isbn\": isbn})\n\n # Save id into variable\n # 'id' of the user is found in both tables 'reviews' and 'users'\n # Hence this would be used for framing the JOIN query\n book = row.fetchone() # (id,)\n book = book[0]\n\n # Fetch book reviews\n # Date formatting (https://www.postgresql.org/docs/9.1/functions-formatting.html)\n results = db.execute(\"SELECT users.username, comment, rating, \\\n to_char(time, 'DD Mon YY - HH24:MI:SS') as time \\\n FROM users \\\n INNER JOIN reviews \\\n ON users.id = reviews.user_id \\\n WHERE book_id = :book \\\n ORDER BY time\",\n {\"book\": book})\n\n reviews = results.fetchall()\n\n return render_template(\"reviews.html\", bookInfo=bookInfo, reviews=reviews)","def __str__(self):\n return '{0} ({1})'.format(self.id, self.book)","def write():\n output_data = book_list_manipulation()\n\n # Create data directory\n try:\n os.mkdir(DATA_DIR)\n except FileExistsError:\n pass # Ignore - if directory exists, don't need to do anything.\n\n with open(BOOKS_FILE_NAME, 'w') as f:\n json.dump(output_data, f)\n\n with open(COUNTER_FILE_NAME, 'w') as f:\n f.write(str(counter))","def test_api_can_create_a_book(self):\n book = {\n 'bookid': '23',\n 'title': 'Neues Buch',\n 'description': 'Unsinn',\n 'seite50_sentence': 'Ein neuer Satz ohne Verb.',\n 'published_date': '1980-01-01',\n }\n res = self.client.post(\n reverse('create_book'), book, format=\"json\")\n self.assertEqual(res.status_code, status.HTTP_201_CREATED)","def book(isbn, message=None):\n # Make sure book exists.\n book = db.execute(\"SELECT * FROM books WHERE isbn = :isbn\", {\"isbn\": isbn}).fetchone()\n\n if book is None:\n return render_template(\"error.html\", message=\"No such book.\")\n\n\n \"\"\" Goodreads info \"\"\"\n res = requests.get(\"https://www.goodreads.com/book/review_counts.json\", params={\"key\": KEY_goodreads, \"isbns\":isbn.strip()}).json()\n\n\n\n\n \"\"\" Adding comment section \"\"\"\n comments = db.execute(\"SELECT reviews.date, reviews.review, reviews.rating, users.id FROM reviews \\\n INNER JOIN users ON reviews.comment_id=users.user_id WHERE books = :books\", {\"books\": book[0]}).fetchall()\n\n\n \"\"\" Adding review \"\"\"\n review = request.form.get(\"review\")\n rating_value = request.form.get(\"rating\")\n\n # Checking if user made a comment\n if review != None:\n # Checking if the user already made a comment\n if db.execute(\"SELECT * FROM reviews WHERE books = :books AND usr = :user\", {\"books\": book[0], \"user\":session[\"user_id\"][0]}).rowcount == 0:\n today = date.today() # adding date to the comment\n # Inserting the relevant info into the database\n db.execute(\"INSERT INTO reviews (books, usr, review, date, rating) VALUES (:books, :usr, :review, :date, :rating)\",\n {\"books\":int(book[0]), \"usr\": int(session[\"user_id\"][0]), \"review\": review, \"date\":today, \"rating\": rating_value})\n db.commit()\n\n # I have to redo this line of code, so the next time it renders the webpage the new comment shows up\n comments = db.execute(\"SELECT * FROM reviews WHERE books = :books\", {\"books\": book[0]}).fetchall()\n return render_template(\"book.html\", book=book, comments=comments, goodreads = res[\"books\"][0], message=\"Thanks for your comment!\")\n\n else:\n return render_template(\"book.html\", book=book, comments=comments, goodreads = res[\"books\"][0], message=\"You've already commented this book!\")\n\n\n\n return render_template(\"book.html\", book=book, review=review, comments=comments, goodreads = res[\"books\"][0])","def test_api_can_get_a_book(self):\n response = self.client.get(\n reverse('details_book', kwargs={'pk': book_pk}), format='json')\n self.assertEqual(response.status_code, status.HTTP_200_OK)","def get_book_info(self, book):\n request_url = \"%s?q=%s\" % (self.API_URL, book)\n json_data = self.make_request(request_url)\n if not json_data:\n return []\n books_info = []\n for book in json_data['docs']:\n info = {'title': book['title']}\n if 'publisher' in book:\n info.update({'publisher': book['publisher']})\n if 'publish_year' in book:\n info.update({'publish_year': book['publish_year']})\n if 'language' in book:\n info.update({'language': book['language']})\n books_info.append(info)\n return books_info","def test_get_a_book_by_id(self):\n create_admin()\n response = self.client().post('/api/v1/login', json=self.test_admin)\n json_data = json.loads(response.data)\n access_token = json_data.get('access_token')\n self.client().post('/api/v1/products',\n headers={\"Authorization\": \"Bearer \" + access_token}, json=self.test_book)\n response = self.client().get('/api/v1/products/1')\n json_data = json.loads(response.data)\n self.assertTrue(json_data.get('Book'))\n self.assertEqual(response.status_code, 200)","def return_book(user_id):\n user = User.query.filter_by(user_id=user_id).first_or_404()\n\n # make sure the user who will fill the form is the user\n # whose user_id is reponsitive to the URL\n if user.username == session.get('username') or \\\n user.username == session.get('adminname') :\n book_ids = request.args.get('book')\n # fill books list\n books = []\n for book_id in book_ids :\n if type(book_id) != type(0) or book_id < 1 :\n abort(404)\n book = Entries.query.filter_by(id=book_id).first_or_404()\n books.append(book)\n return render_template('return.html', books=books, user=user)\n else :\n abort(404)","def test_book_must_be_json(self):\n\n\t\twith self.client:\n\t\t\tadd_book = {\n\t\t\t\t'title': 'Hello Books',\n\t\t\t\t'isbn': \"8985652145\"\n\t\t\t}\n\t\t\tlogin_data = self.login_test_user()\n\t\t\ttoken = login_data['auth_token']\n\t\t\tres = self.client.post(\n\t\t\t\tf'{URL_BOOKS}',\n\t\t\t\theaders=dict(Authorization=f'Bearer {token}'),\n\t\t\t\tcontent_type='text',\n\t\t\t\tdata=json.dumps(add_book)\n\t\t\t)\n\t\t\tres2 = json.loads(res.data.decode())\n\t\t\tself.assertIn('Content-type must be json', str(res2))\n\t\t\tself.assertEqual(res.status_code, 202)","def getGRAuthorByID(id, book_callee=None, series_callee=None, printout=True): \n author_entry = session.query(author).get(id)\n if author_entry is None:\n request = requests.get('https://www.goodreads.com/author/show/'+str(id)+'.xml?key='+API_KEY['GOODREADS'])\n if request.status_code == 200:\n data = xmltodict.parse(request.text)['GoodreadsResponse']['author']\n \n auth = {}\n auth['id'] = int(data['id'])\n auth['author'] = data['name']\n auth['description'] = data['about']\n auth['hometown'] = data['hometown']\n auth['small_img'] = data['small_image_url']\n auth['large_img'] = data['image_url']\n \n author_entry = author(**auth) \n session.add(author_entry)\n session.commit() \n for key, book in data['books'].items():\n while type(book) is list:\n book = book[0]\n if type(book) is OrderedDict and (book_callee is None or book_callee != int(book['id']['#text'])):\n a_book = getGRBookByID(int(book['id']['#text']), id)\n if a_book is not None:\n session.query(author).get(id).books.append(a_book)\n session.commit()\n if(printout):\n print(author_entry)\n \n return author_entry","def get_books():\n\n parameters = {'access_token': ACCESS_TOKEN}\n r = requests.get('https://graph.facebook.com/741504455940261/books', params=parameters)\n result = json.loads(r.text)\n return result[\"data\"]","def get_json(self):\n json_item = {\"id: \": self.id,\n \"question: \": self.question,\n \"documents: \": self.documents,\n \"document_ids: \": self.document_ids,\n \"gold answers: \": self.gold}\n return json_item","def test_create_book(self):\n url = reverse('book-list')\n data = {'isbn':'96712116-1',\n 'title':'New Star',\n 'author_last_name':'Khaled',\n 'author_first_name':'Roshdy',\n 'page_count':250,\n 'description':'the book description'}\n response = self.client.post(url, data, format='json')\n self.assertEqual(response.status_code, status.HTTP_201_CREATED)","def init_books(author_file, json_file):\n with open(author_file) as f:\n authors = list(f)\n\n authors = [i.strip() for i in authors]\n\n books = []\n for author in authors:\n s = get_etexts('author', author)\n for i in s:\n try:\n if list(get_metadata('language', i))[0] == 'en':\n title, etext = list(get_metadata('title', i))[0], strip_headers(load_etext(i)).strip()\n b = Book(i, title, etext)\n books.append(b)\n except UnknownDownloadUriException:\n # this book does not have a load_etext corresponding to it.\n pass\n\n with open(json_file, 'wb') as f:\n pickle.dump(books, f)\n\n print (len(books))","def make_book_api_url(self):\n\n # format API_LINK = \"https://www.goodreads.com/book/show/<book id>.xml?key=<your dev key>\"\n\n self.book_url = f\"https://www.goodreads.com/book/show/{self.book_id}.xml?key={self.key}\"","def get_all_text_for_book(book_file_name):\n if not os.path.exists(verse_store):\n os.mkdir(verse_store)\n with open(book_file_name, \"r+\") as rh:\n book = json.load(rh)\n chapter_text = {}\n\n for name, chapters_dictionary in book.items():\n for chap, location in chapters_dictionary.items():\n outfile = \"{}/{}_{}.json\".format(verse_store, normalize_filename(name), chap)\n \n if os.path.exists(outfile):\n continue\n else:\n chapter_text[name + \"__\" + chap] = get_chapter_text(location)\n with open(outfile, \"w+\") as wh:\n json.dump(chapter_text, wh)\n chapter_text = {}","def generate():\n book = Book('inventarios.xls')\n pages = list(book.pages)\n pages = [pages[1], pages[0]] + pages[2:]\n entries = list(itertools.chain.from_iterable(p.entries for p in pages))\n keys = {}\n books = {}\n for entry in entries:\n if entry['book_id'] in books:\n continue\n\n entry['key'] = title_key(entry['title'], keys)\n print(entry['key'], '\\t', entry['title'])\n\n # mark entry\n keys[entry['key']] = True\n books[entry['book_id']] = True\n\n entries = (e for e in entries if 'key' in e)\n entries = sorted(entries, key=lambda e: e['book_id'])\n export_to_excel('listing.xlsx', entries)","def add_book(request, **kwargs):\n kwargs.setdefault('template_name', 'bookreader/editor/add_book.html')\n kwargs.setdefault('form_class', BookForm)\n kwargs.setdefault('identifier_generator', generate_random_identifier) \n \n if request.method == 'POST':\n form = kwargs['form_class'](request.POST)\n if form.is_valid():\n book = form.save(commit=False)\n book.identifier = kwargs['identifier_generator']()\n book.published = False\n book.save()\n \n messages.add_message(request, messages.INFO,\n 'Added a new book, %s, to the collection %s.' % \n (book.title, book.collection.name,))\n \n return HttpResponseRedirect(reverse('bookreader-book',\n args=[book.identifier]))\n else:\n form = kwargs['form_class']()\n \n return render_to_response(kwargs['template_name'], {'form': form},\n RequestContext(request))","def __str__(self):\n return f'{self.id} ({self.book.title})'","def add_book(name, author):\n BOOKS.append({'name': name, 'author': author, 'read': False})","def create_book(self, title, isbn):\n # new_book = Book(title, isbn)\n # return new_book\n return Book(title, isbn)","def get_books_data():\n entry = mongo.db.Books\n output = list()\n look_up_type = None\n if 'title' in request.args:\n look_up_type = 'title'\n if len(request.args['title']) <= 2:\n return render_template('error.html', message=\"Must enter characters\"), 400\n value = request.args['title'].strip('\"')\n title = entry.find({'title': {'$regex': value}})\n if title:\n for book in title:\n output.append({'title': book['title']})\n elif 'related_books' in request.args:\n look_up_type = 'similar_books'\n if len(request.args['related_books']) <= 2:\n return render_template('error.html', message=\"Must enter characters\"), 400\n value = request.args['related_books'].strip('\"')\n related_books = entry.find(\n {'similar_books': {'$regex': value}})\n if related_books:\n for related in related_books:\n for link in related['similar_books']:\n if value in link:\n output.append(({'similar_books': link}))\n elif 'author' in request.args:\n look_up_type = 'author'\n if len(request.args['author']) <= 2:\n return render_template('error.html', message=\"Must enter characters\"), 400\n value = request.args['author'].strip('\"')\n authors = entry.find({'author': {'$regex': value}})\n if authors:\n for name in authors:\n output.append({'author': name['author']})\n if len(output) == 0:\n return render_template('error.html', message=\"No Entries Found\"), 400\n return render_template('gottenBooks.html', output=output, look_up_type=look_up_type), 200","def create_book():\n Book.objects.create(book_id=\"test_id\",\n title=\"test_title\",\n authors=\"test_author\",\n published_date=\"2021\",\n categories=[\"test_category\"],\n average_rating=5,\n ratings_count=5,\n thumbnail=\"http://books.google.com/books/test\"\n )","def save_book_details(self, book_dict_with_href):\n for i, book_dict in enumerate(book_dict_with_href):\n print(f'Saving data for book {i + 1}')\n self._save_book_detail(book_dict, i)","def get_item(id):\n return jsonify(id=id, name='name', number=123)","def scrape_one_book(self, url):\n\n if url in self.url_to_explore:\n self.url_to_explore.remove(url)\n req = requests.get(url, headers = self.headers).content\n soup = BeautifulSoup(req, 'html5lib')\n soupbody = soup.body\n\n book_data = {}\n # get book url\n book_url = url\n book_data[\"url\"] = book_url\n\n # get book title\n book_title = soupbody.find('h1', attrs={'id':'bookTitle'}).text.strip()\n if book_title:\n book_data[\"title\"] = book_title\n\n # # get book id\n reg = 'https://www.goodreads.com/book/show/([0-9]+)'\n book_id = re.search(reg, url).group(1)\n book_data[\"id\"] = book_id\n\n # get book ISBN\n book_databox = soupbody.find('div', attrs={'id':'bookDataBox'})\n if book_databox:\n all_float_divs = book_databox.find_all('div',\n attrs = {'class' : 'clearFloats'})\n book_isbn = ''\n for div in all_float_divs:\n title = div.find('div',\n attrs = {'class':'infoBoxRowTitle'}).text.strip()\n if title == 'ISBN':\n book_isbn = div.find('div',\n attrs = {'class':'infoBoxRowItem'}).contents[0].strip()\n book_data[\"ISBN\"] = book_isbn\n\n # get book author url and author name\n author_name_container = soupbody.find('div',\n attrs = {'class':\"authorName__container\"})\n if author_name_container:\n all_authors = author_name_container.find_all('a',\n href = True, attrs = {'class':\"authorName\"})\n cur_author_url = []\n cur_author_name = []\n for author in all_authors:\n cur_author_url.append(author['href'])\n name = author.find('span', attrs = {'itemprop':'name'}).text.strip()\n cur_author_name.append(name)\n book_data[\"authorURLs\"] = cur_author_url\n book_data[\"author_names\"] = cur_author_name\n\n # get book rating and review\n book_meta = soupbody.find('div', attrs = {'id':'bookMeta'})\n if book_meta:\n rating = book_meta.find('span',\n attrs = {'itemprop':'ratingValue'}).text.strip()\n book_data[\"rating\"] = rating\n\n book_rating_count_container = book_meta.find('meta',\n attrs = {'itemprop':'ratingCount'})\n if book_rating_count_container:\n book_rating_count = book_rating_count_container['content']\n book_data[\"rating_count\"] = book_rating_count\n\n book_review_count_container = book_meta.find('meta',\n attrs = {'itemprop':'reviewCount'})\n if book_review_count_container:\n book_review_count = book_review_count_container['content']\n book_data[\"review_count\"] = book_review_count\n\n # get book image\n image_tag = soupbody.find('img', attrs = {'id':'coverImage'})\n if image_tag:\n image_src = image_tag['src']\n book_data[\"bookImage\"] = image_src\n # print(authorLink.span.text)\n\n # get related_books\n related_works_container = soupbody.find('div', id=re.compile('relatedWorks-'))\n if related_works_container:\n related_books_div = related_works_container.find('div', class_='bigBoxBody')\n if related_books_div:\n related_books_carousel = related_books_div.find('div', class_='bookCarousel')\n if related_books_carousel:\n carousel_row = related_books_carousel.find('div', class_='carouselRow')\n if carousel_row:\n related_books_list_li = carousel_row.find('ul').find_all('li')\n related_books = []\n for item in related_books_list_li:\n link = item.find('a', href = True)['href']\n self.url_to_explore.add(link)\n related_books.append(link)\n book_data[\"similar_books\"] = related_books\n\n self.data_collection.push_to_collection(book_data)\n print(\"Book successfully scraped: \" + book_title)","def showModelInfoJSON(style_id, model_id):\n model = session.query(Model).filter_by(id=model_id).one()\n return jsonify(model=model.serialize)","def result(request):\n\n books = list()\n\n try:\n query = request.POST.get('query')\n \n \n qs = Response.response_front(query)\n\n for book in qs:\n books.append(qs)\n\n result = {\n 'picture':Response.build(books[0]['picture']),\n 'title':books[0]['title']\n }\n \n return JsonResponse(result,safe=False)\n except:\n print('error')","def get_cars_by_id(carid):\n carlist = Car.query.all()\n results = carsSchema.dump(carlist)\n return jsonify(results)","def _get_book(self):\n print('%s get_book request made.' % self.sym)\n start_time = time()\n\n self.clear_book()\n path = ('https://api.pro.coinbase.com/products/%s/book' % self.sym)\n book = requests.get(path, params={'level': 3}).json()\n\n elapsed = time() - start_time\n print('%s get_book request completed in %f seconds.' % (self.sym, elapsed))\n return book","def __str__(self):\n return f'{self.id} ({self.book.title})'","def duplicated_code():\n author_ids = []\n updated_books = []\n updated_authors = []\n \n for author in mongo.db.authors.find():\n updated_authors.append(author)\n for book in mongo.db.books.find():\n \n # Create a new key/value pair in each book for author_name\n # by looking up the author_id and matching it to the author_name\n # of the selected author_id.\n \n book_title = book['title']\n author_id = book['author_id']\n \n for author in updated_authors:\n if author['_id'] == ObjectId(author_id):\n book['author_name'] = author['author_name']\n \n \n # Using the googlebooks API search for each book and retrieve\n # a thumbnail of the book.\n \n google_api_title = book_title.replace(' ', '+')\n book_isbn_num = book['isbn_num']\n with urllib.request.urlopen(BASE_API_LINK + google_api_title) as f:\n text = f.read()\n decoded_text = text.decode(\"utf-8\")\n obj = json.loads(decoded_text) \n google_book_obj = obj[\"items\"][0]\n book_href = google_book_obj['volumeInfo']\n if 'imageLinks' in book_href:\n book['href'] = book_href['imageLinks']['thumbnail']\n \n # Append book to new book dictionary.\n updated_books.append(book)\n \n return updated_books"],"string":"[\n \"def book_info(book_id):\\n\\n book = data_manager.get_book(book_id)\\n if not book:\\n return jsonify({\\\"error\\\": \\\"No book found\\\"})\\n if book.has_records == False:\\n return jsonify({'book_id': book_id,\\n 'title': book.title,\\n 'author': book.author,\\n 'records': []})\\n\\n records = data_manager.get_stored_availability(book_id)\\n if not records: # If availability is not in the database, otherwise it's []\\n unchecked_records = data_manager.records_from_book(book)\\n records = []\\n for record in unchecked_records:\\n data_manager.update_availability(record)\\n\\n records = data_manager.get_stored_availability(book_id)\\n if not records:\\n data_manager.mark_unfindable(data_manager.get_book(book_id))\\n\\n return jsonify({'book_id': book_id, \\n 'title': book.title, \\n 'author': book.author, \\n 'records': records})\",\n \"def book_api(isbn):\\n\\n # make sure book exists\\n book = db.execute(\\\"SELECT * FROM books WHERE isbn= :isbn\\\",\\n {\\\"isbn\\\": isbn}).fetchone()\\n if book is None:\\n return jsonify({\\\"error\\\": \\\"Invalid flight_id\\\"}), 422\\n\\n # get book details\\n res = requests.get(\\\"https://www.goodreads.com/book/review_counts.json\\\",\\n params={\\\"key\\\": \\\"zXR2kKmTJ0tV9D3LG8ekug\\\", \\\"isbns\\\": isbn})\\n goodreads_rating = res.json()['books'][0]['average_rating']\\n goodreads_num_of_ratings = res.json()['books'][0]['work_ratings_count']\\n reviews_data = db.execute(\\n \\\"SELECT * FROM reviews WHERE isbn= :isbn\\\", {\\\"isbn\\\": isbn}).fetchall()\\n reviews=[]\\n for review in reviews_data:\\n reviews.append(review.review)\\n\\n return jsonify({\\n \\\"ISBN\\\": book.isbn,\\n \\\"Title\\\": book.title,\\n \\\"Author\\\": book.author,\\n \\\"Year\\\": book.year,\\n \\\"Goodreads Rating\\\": goodreads_rating,\\n \\\"Votes\\\": goodreads_num_of_ratings,\\n \\\"Reviews\\\": reviews\\n })\",\n \"def book_list_manipulation():\\n global book_list\\n book_json = {}\\n for book in book_list:\\n book_container = {'title': book.title,\\n 'author': book.author,\\n 'read': str(book.read),\\n 'rating': book.rating,\\n 'review': book.review,\\n 'date read': book.date_read,\\n 'id': str(book.id)}\\n book_json[str(book.id)] = book_container\\n\\n return book_json\",\n \"def book_api(isbn):\\n\\n # Get book data.\\n book = db.execute(\\\"SELECT * FROM books WHERE isbn = :isbn\\\", {\\\"isbn\\\": isbn}).fetchone()\\n # Make sure book exists.\\n if book is None:\\n return jsonify({\\\"error\\\": \\\"Invalid isbn\\\"}), 404\\n\\n # Get average score\\n # Count reviews for that book.\\n review_count = db.execute(\\\"SELECT * FROM reviews WHERE book_id = :book_id\\\", {\\\"book_id\\\": book.id}).rowcount\\n \\n # Get all ratings for that book.\\n ratings = db.execute(\\\"SELECT rating FROM reviews WHERE book_id = :book_id\\\", {\\\"book_id\\\": book.id}).fetchall()\\n \\n # Count the sum of ratings for that book.\\n rating_sum = 0\\n for rating in ratings:\\n rating_sum += rating[0]\\n\\n average_score = 0\\n\\n # Make sure review_count is not a zero\\n if review_count > 0:\\n average_score = rating_sum/review_count\\n\\n return jsonify({\\n \\\"title\\\": book.title,\\n \\\"author\\\": book.author,\\n \\\"year\\\": book.year,\\n \\\"isbn\\\": book.isbn,\\n \\\"review_count\\\": review_count,\\n \\\"average_score\\\": average_score,\\n \\\"rating_sum\\\": rating_sum\\n })\",\n \"def get_books():\\n return jsonify({'books': Book.get_all_books()})\",\n \"def get_books():\\n return jsonify({'books': Book.get_all_books()})\",\n \"def get_book_details(book_id, key):\\n\\n # call goodreads search method with book id here\\n payload = {\\\"key\\\": key}\\n\\n query = requests.get(\\\"https://www.goodreads.com/book/show/{}.json\\\".format(book_id), params=payload)\\n # parse response to get data needed to create a book object\\n\\n doc = untangle.parse(query.content)\\n book_data = doc.GoodreadsResponse.book\\n book = {}\\n\\n # create dictionary of book object data, subdictionary of edition data\\n\\n # book info\\n #==========\\n book[\\\"title\\\"] = book_data.title.cdata.encode(\\\"utf8\\\")\\n book[\\\"author_name\\\"], book[\\\"author_gr_id\\\"] = get_author_data(book_data.authors)\\n book['work_id'] = int(book_data.work.id.cdata.encode('utf8'))\\n book[\\\"description\\\"] = book_data.description.cdata\\n\\n # edition info\\n #=============\\n book[\\\"edition\\\"] = {}\\n book[\\\"edition\\\"][\\\"isbn\\\"] = valid_isbn(book_data.isbn.cdata.encode(\\\"utf8\\\"))\\n book[\\\"edition\\\"][\\\"format_id\\\"] = get_format_id(book_data.format.cdata.encode(\\\"utf8\\\"))\\n book[\\\"edition\\\"][\\\"pic_url\\\"] = book_data.image_url.cdata.encode(\\\"utf8\\\")\\n book[\\\"edition\\\"][\\\"publisher\\\"] = book_data.publisher.cdata.encode(\\\"utf8\\\")\\n book[\\\"edition\\\"][\\\"num_pages\\\"] = valid_page_count(book_data.num_pages.cdata.encode(\\\"utf8\\\"))\\n year = date_is_valid(book_data.work.original_publication_year.cdata.encode(\\\"utf8\\\"))\\n month = date_is_valid(book_data.work.original_publication_month.cdata.encode(\\\"utf8\\\"))\\n day = date_is_valid(book_data.work.original_publication_day.cdata.encode(\\\"utf8\\\"))\\n book[\\\"edition\\\"][\\\"date\\\"] = datetime.date(year, month, day)\\n book[\\\"edition\\\"][\\\"gr_url\\\"] = book_data.url.cdata.encode(\\\"utf8\\\")\\n book[\\\"edition\\\"][\\\"gr_id\\\"] = int(book_data.id.cdata.encode(\\\"utf8\\\"))\\n\\n return book\",\n \"def categoryWithBooksJSON(category_id):\\n books = db_session.query(Book).filter_by(category_id=category_id).all()\\n return jsonify(Category=[b.serialize for b in books])\",\n \"def book_api(isbn):\\n\\n db = get_db()\\n book_info = db.execute(\\\"\\\"\\\"\\n SELECT *\\n FROM books\\n WHERE isbn = :isbn;\\n \\\"\\\"\\\", {\\\"isbn\\\": isbn}).fetchone()\\n\\n if book_info is None:\\n return jsonify({\\\"error\\\": \\\"Invalid ISBN or ISBN is not available\\\"}), 404\\n\\n review_stats = db.execute(\\n \\\"\\\"\\\"\\n SELECT COUNT(*) as count,\\n ROUND(AVG(rating), 2) AS avg_rating\\n from reviews\\n WHERE book_id = :id\\n \\\"\\\"\\\",\\n {\\\"id\\\": book_info['id']}\\n ).fetchone()\\n\\n return jsonify({\\n \\\"title\\\": book_info['title'],\\n \\\"author\\\": book_info['author'],\\n \\\"year\\\": book_info['year'],\\n \\\"isbn\\\": isbn,\\n \\\"review_count\\\": review_stats['count'],\\n \\\"average_score\\\": float(review_stats['avg_rating'])\\n })\",\n \"def get_single_books(isbn):\\n return_value = Book.get_book(isbn)\\n return jsonify(return_value)\",\n \"def show_books():\\n result = {'books': query.get_book_list()}\\n return json.dumps(result, ensure_ascii=False)\",\n \"def book_serialize(self):\\n return {\\n 'book_id': self.id,\\n 'author': self.author,\\n 'publisher': self.publisher,\\n 'edition': self.edition,\\n 'category': self.category,\\n 'copies': self.copies\\n }\",\n \"def api_book(isbn):\\n isbn = Markup.escape(isbn)\\n # check if book exist in database\\n book_db = db.execute(\\n \\\"SELECT * FROM books WHERE isbn LIKE :isbn\\\", {\\\"isbn\\\": isbn}\\n ).fetchone()\\n if book_db == None:\\n return jsonify({\\\"error\\\": \\\"Invalid isbn or not in our database\\\"}), 404\\n\\n # Get detail from Goodreads\\n res = requests.get(\\n \\\"https://www.goodreads.com/book/review_counts.json\\\",\\n params={\\\"key\\\": os.getenv(\\\"GOODREADS_API\\\"), \\\"isbns\\\": isbn},\\n )\\n\\n if res.status_code != 200:\\n raise Exception(\\\"ERROR: API request unsuccessful.\\\")\\n data = res.json()\\n book = data[\\\"books\\\"][0]\\n\\n # Print results\\n return jsonify(\\n {\\n \\\"title\\\": book_db.title,\\n \\\"author\\\": book_db.author,\\n \\\"year\\\": book_db.year,\\n \\\"isbn\\\": book_db.isbn,\\n \\\"review_count\\\": book[\\\"work_ratings_count\\\"],\\n \\\"average_score\\\": book[\\\"average_rating\\\"],\\n }\\n )\",\n \"def book(book_id):\\n book = db.execute(\\\"SELECT * FROM books WHERE id = :id\\\", {\\\"id\\\": book_id}).fetchone()\\n # Get all reviews.\\n reviews = db.execute(\\\"SELECT review, grade FROM reviews WHERE books_id = :book_id\\\", {\\\"book_id\\\": book_id}).fetchall()\\n return render_template(\\\"book.html\\\", book=book, reviews=reviews)\",\n \"def detail(request,book_id):\\n\\n #prodive all informations of book by GoogleBooksApi function\\n if book_id is not None:\\n book = Response.response_front(book_id)\\n \\n context = {\\n \\\"title\\\":Response.build(book['title'][0]),\\n \\\"desc\\\": Response.build(book['description'][0]),\\n \\\"picture_detail\\\":book['picture_detail'][0],\\n \\\"book_id\\\":book_id,\\n \\\"book_cat\\\":book['categorie'][0],\\n \\\"book_author\\\":(book['author'][0]),\\n }\\n return render(\\n request,\\n \\\"book.html\\\",context)\",\n \"def serialize(self):\\r\\n return {\\r\\n \\\"book_id\\\": self.id,\\r\\n \\\"title\\\": self.title,\\r\\n \\\"author\\\": self.author,\\r\\n \\\"category\\\": self.category,\\r\\n }\",\n \"def export_chapter_links_to_json(book_name, book_link, idd):\\n chapters = {\\\"01\\\" : book_link}\\n home = Ripper(book_link, save_path=save_path)\\n \\n if not os.path.exists(chapter_store):\\n os.mkdir(chapter_store)\\n \\n for each in home.raw_links:\\n str_each = str(each)\\n excludes = [\\\"next\\\" in str_each, \\\"previous\\\" in str_each, \\\"chapter\\\" in str_each, \\\"statcounter\\\" in str_each,\\n \\\"/x/\\\" in str_each, \\\"DRBO.ORG\\\" in str_each, \\\"theologica\\\" in str_each]\\n \\n if not any(excludes):\\n chapters[each.text] = domain + \\\"chapter/\\\" + each.get(\\\"href\\\")\\n\\n book_chaps = {book_name : chapters}\\n \\n book_save_name = \\\"{}.json\\\".format(os.path.join(chapter_store, normalize_filename(book_name)))\\n\\n with open(book_save_name, \\\"w+\\\") as wh:\\n json.dump(book_chaps, wh)\\n return book_chaps\",\n \"def within_book_search_json(request, book_id):\\n query = request.GET.get('q')\\n term = query # todo: meta options?\\n book = Book.objects.get(pk=book_id)\\n\\n if not query or len(query) < 3:\\n return\\n\\n # todo: method on objectmanager to search by keyword\\n notes = book.notes.filter(\\n Q(subject__icontains=term) |\\n Q(quote__icontains=term) |\\n Q(comment__icontains=term)\\n )\\n terms = book.terms.filter(\\n Q(term__text__icontains=term) |\\n Q(term__definition__icontains=term) |\\n Q(quote__icontains=term) |\\n Q(quote__icontains=term)\\n )\\n sections = book.sections.filter(\\n Q(title__icontains=term) |\\n Q(authors__name__icontains=term) |\\n Q(subtitle__icontains=term) |\\n Q(summary__icontains=term)\\n )\\n\\n results = {'notes': [], 'terms': [], 'sections': []}\\n for note in notes:\\n results['notes'].append({\\n 'title': highlighter.highlight(note.subject, query),\\n 'description': highlighter.highlight(note.quote, query, 200),\\n 'price': note.get_page_display(),\\n 'url': note.get_absolute_url(),\\n })\\n\\n for term in terms:\\n results['terms'].append({\\n 'title': highlighter.highlight(term.term.text, query),\\n 'description': highlighter.highlight(term.quote, query, 200),\\n 'price': term.get_page_display(),\\n 'url': term.get_absolute_url(),\\n })\\n\\n for section in sections:\\n authors = ', '.join(a.name for a in section.authors.all())\\n results['sections'].append({\\n 'title': highlighter.highlight(section.title, query),\\n 'description': highlighter.highlight(authors, query),\\n 'price': section.get_page_display(),\\n 'url': section.get_absolute_url(),\\n })\\n\\n return JsonResponse({\\n 'results': {\\n 'books': {\\n 'name': 'Notes',\\n 'results': results['notes'],\\n },\\n 'authors': {\\n 'name': 'Terms',\\n 'results': results['terms'],\\n },\\n 'sections': {\\n 'name': 'Sections',\\n 'results': results['sections'],\\n },\\n }\\n })\",\n \"def book(book_isbn):\\n\\n if len(book_isbn) == 10 and book_isbn.isdigit():\\n # Get book details\\n book_res = db.execute(\\\"SELECT * FROM books WHERE isbn = :book_isbn\\\",\\n {\\\"book_isbn\\\": book_isbn}).fetchone()\\n session[\\\"book_res\\\"] = book_res\\n session[\\\"book_id\\\"] = book_res.id\\n session[\\\"book_isbn\\\"] = book_res.isbn\\n\\n res = requests.get(\\\"https://www.goodreads.com/book/review_counts.json\\\", params={\\\"key\\\": \\\"bgh2LQUdiQnnDznXzFMbg\\\", \\\"isbns\\\": book_isbn})\\n resp = res.json()\\n # return resp\\n\\n\\n # Get reviews\\n review_res = db.execute(\\\"SELECT * FROM reviews WHERE book_id = :book_id\\\",\\n {\\\"book_id\\\": session[\\\"book_id\\\"]}).fetchall()\\n session[\\\"review_res\\\"] = review_res\\n\\n return render_template(\\\"book.html\\\", book=book_res, reviews=review_res, count=resp[\\\"books\\\"][0],name=session[\\\"name\\\"])\\n\\n return render_template(\\\"book.html\\\", message=\\\"Oops, something went wrong.\\\",name=session[\\\"name\\\"])\",\n \"def api(isbn):\\n # Ensure valid isbn-10 format provided\\n if len(isbn) != 10:\\n response = make_response(\\n jsonify(\\\"Please provide a valid ISBN-10\\\"), 404)\\n response.headers['X-Error'] = \\\"Please provide a valid ISBN-10\\\"\\n return response\\n\\n # Ensure requested book is in our database\\n isInDB = db.execute(\\n \\\"SELECT * from books \\\"\\n \\\"WHERE isbn = :isbn \\\", {\\n 'isbn': isbn\\n }).fetchone()\\n if isInDB is None:\\n response = make_response(\\n jsonify(\\\"Book does not exist in database\\\"), 404)\\n response.headers['X-Error'] = \\\"Book does not exist in database\\\"\\n return response\\n\\n # Query data for API response\\n proxy = db.execute(\\n \\\"SELECT books.title, books.author, books.year, books.isbn, \\\"\\n \\\"COUNT(reviews.*) AS review_count, CAST(AVG(reviews.rating) AS float) AS average_score \\\"\\n \\\"FROM books LEFT JOIN reviews ON reviews.book_id=books.id \\\"\\n \\\"WHERE books.isbn=:isbn \\\"\\n \\\"GROUP BY books.id\\\", {\\n 'isbn': isbn\\n }).fetchone()\\n\\n # Return json data\\n book_data = {\\n \\\"title\\\": proxy.title,\\n \\\"author\\\": proxy.author,\\n \\\"year\\\": int(proxy.year),\\n \\\"isbn\\\": proxy.isbn,\\n \\\"review_count\\\": proxy.review_count,\\n \\\"average_score\\\": proxy.average_score\\n }\\n\\n # Send requested data\\n return make_response(jsonify(book_data), 200)\",\n \"def book_info(book_id):\\n \\n #Get the info of the book\\n book = db.execute(\\\"SELECT * FROM book WHERE id = :book_id\\\",{\\\"book_id\\\": book_id}).fetchone()\\n if book is None:\\n return render_template(\\\"error.html\\\", message = \\\"No such Book.\\\")\\n\\n #Get all the reviews joined with the name of each user\\n stmt = \\\"SELECT user_library.*, reviewer.* FROM user_library INNER JOIN reviewer ON user_library.id=reviewer.id_user WHERE id_book = :book_id\\\"\\n reviews = db.execute(stmt,{\\\"book_id\\\": book_id}).fetchall()\\n\\n #Get the user_review info\\n user_review = db.execute(\\\"SELECT * FROM reviewer WHERE id_book = :book_id AND id_user = :user_id\\\",\\n {\\\"book_id\\\": book_id, \\\"user_id\\\": session[\\\"user_id\\\"]}).fetchone()\\n\\n #If this info not exist we could add a comment, else we can not.\\n is_commented = True\\n if user_review is None:\\n is_commented = False\\n\\n #Proccess for rating count of Goofreaders\\n goodreader_info = requests.get(\\\"https://www.goodreads.com/book/review_counts.json\\\", params={\\\"key\\\": KEY, \\\"isbns\\\": book.isbn })\\n goodreader_info = goodreader_info.json()\\n goodreader_info = goodreader_info[\\\"books\\\"]\\n\\n average_rating = goodreader_info[0][\\\"average_rating\\\"]\\n ratings_counts = goodreader_info[0][\\\"ratings_count\\\"]\\n\\n return render_template(\\\"book_info.html\\\",book=book, reviews = reviews, is_commented = is_commented\\n , average_rating = average_rating, ratings_counts = ratings_counts )\",\n \"def put(self, book_id):\\n a_book = query_book_by_id(book_id)\\n if a_book is None:\\n return 'Book does not exit', 404\\n body = request.get_json()\\n a_book.parse_body(body)\\n db.session.add(a_book)\\n db.session.commit()\\n return a_book.serialize(), 200\",\n \"def get_book_by_id(self, book_id):\\n query = \\\"SELECT * FROM library WHERE book_id=%s\\\"\\n response = Database().fetch_one(query, book_id)\\n return response\",\n \"def get_audiobook(_id):\\r\\n return [Audiobook.audiobook_json(Audiobook.query.filter_by(id=_id).first())]\\r\\n # Audiobook.audiobook_json() coverts our output to the json format defined earlier\\r\\n # the filter_by method filters the query by the id\\r\\n # since our id is unique we will only get one result\\r\\n # the .first() method will get that first value returned\\r\",\n \"def test_get_book_with_id(self):\\n\\t\\tadd_book = {\\n\\t\\t\\t'title': 'Hello Books',\\n\\t\\t\\t'isbn': '5698745124'\\n\\t\\t}\\n\\t\\tlogin_data = self.login_test_user()\\n\\t\\ttoken = login_data['auth_token']\\n\\t\\tres = self.client.post(\\n\\t\\t\\tf'{URL_BOOKS}',\\n\\t\\t\\theaders=dict(Authorization=f'Bearer {token}'),\\n\\t\\t\\tcontent_type='application/json',\\n\\t\\t\\tdata=json.dumps(add_book)\\n\\t\\t)\\n\\n\\t\\t# get book id\\n\\t\\tbook = self.client.get(\\n\\t\\t\\tf'{URL_BOOKS}/1',\\n\\t\\t\\theaders=dict(Authorization=f'Bearer {token}'),\\n\\t\\t\\tcontent_type='application/json'\\n\\t\\t)\\n\\n\\t\\tbook_res = json.loads(book.data.decode())\\n\\t\\tself.assertTrue(book_res['books']['title'] == 'hello books')\",\n \"def detail(request, bid):\\n result = {\\n 'status': '', # 'success' or 'failure'\\n 'msg': '', # msg of the book\\n 'error_msg': '', # notes of failure\\n }\\n # the error method\\n if not is_get(request, result):\\n return HttpResponse(json.dumps(result))\\n\\n # filter the list of book\\n # actually, the number of book is 0 or 1\\n books = models.BookInfo.objects.filter(id=bid).first()\\n\\n # if the number of book is 0\\n if books is None:\\n result['status'] = 'failure'\\n result['error_msg'] = 'invalid book id'\\n return HttpResponse(json.dumps(result))\\n\\n book_dict = dict()\\n # transfer db obj to dict\\n book = process_book_obj(books)\\n book_dict[str(books.id)] = json.dumps(book)\\n\\n result['status'] = \\\"success\\\"\\n result['msg'] = json.dumps(book_dict)\\n\\n return HttpResponse(json.dumps(result))\",\n \"def get_bookings(userid):\\n # get all the bookings for userid\\n bookings = Booking.query.filter(Booking.userid == userid)\\n # dump in the Schema\\n results = bookingsSchema.dump(bookings)\\n\\n return jsonify(results)\",\n \"def book_json_manipulation(json_as_dict):\\n global book_list\\n\\n for key, value in json_as_dict.items():\\n book_container = json_as_dict[key]\\n book = Book(book_container['title'],\\n book_container['author'],\\n book_container['read'] == 'True',\\n int(book_container['id']))\\n book.rating = book_container['rating']\\n book_list.append(book)\",\n \"def convert_library_to_json(library: list) -> list:\\n books_to_save = []\\n for book in library:\\n book_dict = book.__dict__\\n books_to_save.append(dict((key.title(), value) for key, value in book_dict.items()))\\n return books_to_save\",\n \"def save_book(request,book_id):\\n \\n if request.user.is_authenticated:\\n user = get_object_or_404(\\n UserModel,\\n id=request.user.id\\n )\\n book = Response.response_front(book_id)\\n\\n #save book with all informations on table Book\\n \\n Book.objects.add_book(\\n book_id,\\n user,\\n title=(book['title'][0]),\\n book_cat=Response.build(book['categorie'][0]),\\n picture=book['picture'][0],\\n picture_detail=book['picture_detail'][0],\\n description=book['description'][0],\\n author=book['author'][0]\\n )\\n\\n return redirect(\\\"home\\\")\",\n \"def borrow_book(self, author, title, publisher, edition, email, book_id):\\n for book in self.books_list:\\n if book['book_id'] != str(book_id):\\n return 'book does not exist'\\n continue\\n else: \\n book = {\\n 'author' : author,\\n 'title' : title,\\n 'publisher' : publisher,\\n 'edition' : edition,\\n 'email' : email\\n }\\n self.borrowed_books.append(book)\\n return book\",\n \"def create_books_data():\\n pass\",\n \"def get_books_from_api(request, url='https://www.googleapis.com/books/v1/volumes?q=Hobbit'):\\n response = requests.get(url)\\n data = response.json()\\n items = data.get('items')\\n if items is None:\\n items = []\\n for item in items:\\n book = item.get('volumeInfo')\\n title = book.get('title', '--')\\n authors = book.get('authors', ['unknown'])\\n publishedDate = book.get('publishedDate')\\n isbns = book.get('industryIdentifiers', [])\\n pages = book.get('pageCount')\\n cover_url = book.get('imageLinks')\\n if cover_url:\\n cover_url = cover_url.get('thumbnail')\\n language = book.get('language')\\n authors_list = []\\n for author in authors:\\n auth = get_author_object(author)\\n authors_list.append(auth)\\n isbn_10 = None\\n isbn_13 = None\\n for isbn in isbns:\\n if isbn['type'] == 'ISBN_10':\\n isbn_10 = isbn['identifier']\\n elif isbn['type'] == 'ISBN_13':\\n isbn_13 = isbn['identifier']\\n lang = get_language_object(language)\\n try:\\n published = datetime.strptime(publishedDate, '%Y-%m-%d')\\n except ValueError:\\n year = int(publishedDate[:4])\\n month = None\\n day = None\\n except TypeError:\\n year = None\\n month = None\\n day = None\\n else:\\n year = published.year\\n month = published.month\\n day = published.day\\n try:\\n book = get_object_or_404(Book, title=title, publishedYear=year, publishedMonth=month, publishedDay=day,\\n language=lang, pages=pages, cover=cover_url, isbn_10=isbn_10, isbn_13=isbn_13)\\n for name in book.authors.all():\\n if name not in authors_list:\\n raise Http404\\n except Http404:\\n book = Book.objects.create(title=title, publishedYear=year, publishedMonth=month, publishedDay=day,\\n language=lang, pages=pages, cover=cover_url, isbn_10=isbn_10, isbn_13=isbn_13)\\n book.authors.set(authors_list)\\n return redirect('all-books')\",\n \"def get_book(arg):\\n book = requests.get(BASE_URL+'books/'+arg)\\n print book.json()\\n return book.status_code\",\n \"def book(isbn):\\n isbn = Markup.escape(isbn)\\n # check if book exist in database\\n book_db = db.execute(\\n \\\"SELECT * FROM books WHERE isbn LIKE :isbn\\\", {\\\"isbn\\\": isbn}\\n ).fetchone()\\n if book_db == None:\\n return render_template(\\n \\\"error.html\\\", error=\\\"ISBN invalid or not in our Database.\\\"\\n )\\n\\n # Get detail from Goodreads\\n res = requests.get(\\n \\\"https://www.goodreads.com/book/review_counts.json\\\",\\n params={\\\"key\\\": os.getenv(\\\"GOODREADS_API\\\"), \\\"isbns\\\": isbn},\\n )\\n\\n if res.status_code != 200:\\n return render_template(\\\"error.html\\\", error=\\\"Not found on our API.\\\")\\n data = res.json()\\n book = data[\\\"books\\\"][0]\\n\\n # Get the reviews for the book.\\n book_reviews = db.execute(\\n \\\"SELECT review.*, users.nickname FROM review JOIN users ON review.user_id = users.id WHERE book_id = :book_id\\\",\\n {\\\"book_id\\\": book_db.id},\\n ).fetchall()\\n\\n # Get my own review\\n user = session.get(\\\"user\\\")\\n my_review = db.execute(\\n \\\"SELECT * FROM review WHERE (book_id = :book_id) AND user_id = (SELECT id from users WHERE username LIKE :user)\\\",\\n {\\\"book_id\\\": book_db.id, \\\"user\\\": user},\\n ).fetchone()\\n\\n if my_review is not None:\\n # Print results\\n return render_template(\\n \\\"book.html\\\",\\n book=book,\\n book_db=book_db,\\n book_reviews=book_reviews,\\n my_review=my_review,\\n )\\n else:\\n return render_template(\\n \\\"book.html\\\",\\n book=book,\\n book_db=book_db,\\n book_reviews=book_reviews,\\n my_review=None,\\n )\",\n \"def books_list(request):\\n if request.method == 'GET':\\n # books = Books.objects.all()\\n # serializer = BooksSerializers(books, many=True)\\n # print(serializer)\\n # return Response(serializer.data)\\n response = requests.get(\\\"https://www.anapioficeandfire.com/api/books\\\")\\n data = response.json()\\n print(response.status_code)\\n urls = list(map(lambda x: (x['url']), data))\\n ids = []\\n for i in urls:\\n i = re.sub(r'.*/', '', i)\\n ids.append(i)\\n names = list(map(lambda x: (x['name']), data))\\n isbns = list(map(lambda x: (x['isbn']), data))\\n authors = list(map(lambda x: (x['authors']), data))\\n nopages = list(map(lambda x: (x['numberOfPages']), data))\\n publishers = list(map(lambda x: (x['publisher']), data))\\n country = list(map(lambda x: (x['country']), data))\\n release_dates = list(map(lambda x: (x['released']), data))\\n\\n li = zip(ids, names, isbns, authors, nopages, publishers, country, release_dates)\\n\\n # for i in li:\\n # Books.objects.get_or_create(url=i[0],name=i[1],isbn=i[2],authors=i[3],nopages=i[4],publisher=i[5],\\n # country=i[6],released=i[7])\\n d = {\\\"status_code\\\": response.status_code, \\\"status\\\": \\\"success\\\"}\\n records = []\\n for i in li:\\n record = {\\n \\\"id\\\": i[0],\\n \\\"name\\\": i[1],\\n \\\"isbn\\\": i[2],\\n \\\"authors\\\": i[3],\\n \\\"nopages\\\": i[4],\\n \\\"publisher\\\": i[5],\\n \\\"country\\\": i[6],\\n \\\"release_date\\\": i[7]\\n }\\n\\n records.append(record)\\n d[\\\"data\\\"] = records\\n pickup_records = json.dumps(d, indent=4)\\n return HttpResponse(pickup_records, content_type=\\\"application/json\\\")\\n\\n\\n elif request.method == 'POST':\\n serializer = BooksSerializers(data=request.data)\\n records = []\\n if serializer.is_valid():\\n serializer.save()\\n d = {\\\"status_code\\\": 201, \\\"status\\\": \\\"success\\\"}\\n\\n d1 = {}\\n d1[\\\"books\\\"] = serializer.data\\n records.append(d1)\\n d[\\\"data\\\"] = records\\n return Response(d, status=status.HTTP_201_CREATED)\\n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\",\n \"def get_book(self, book_id):\\n # Implemented from template for\\n # osid.resource.BinLookupSession.get_bin\\n if self._catalog_session is not None:\\n return self._catalog_session.get_catalog(catalog_id=book_id)\\n collection = JSONClientValidated('commenting',\\n collection='Book',\\n runtime=self._runtime)\\n # Need to consider how to best deal with the \\\"phantom root\\\" catalog issue\\n if book_id.get_identifier() == PHANTOM_ROOT_IDENTIFIER:\\n return self._get_phantom_root_catalog(cat_class=objects.Book, cat_name='Book')\\n try:\\n result = collection.find_one({'_id': ObjectId(self._get_id(book_id, 'commenting').get_identifier())})\\n except errors.NotFound:\\n # Try creating an orchestrated Book. Let it raise errors.NotFound()\\n result = self._create_orchestrated_cat(book_id, 'commenting', 'Book')\\n\\n return objects.Book(osid_object_map=result, runtime=self._runtime, proxy=self._proxy)\",\n \"def book_by_id(id_):\\n data = {}\\n for book in root.findall('Book'):\\n for elem in book:\\n book_id = book.attrib['id']\\n if int(book_id) == id_:\\n data['id'] = book.attrib['id']\\n data[elem.tag] = elem.text\\n return data\",\n \"def book(response):\\n\\n response = response.json()\\n\\n if response.get(\\\"items\\\"):\\n # response looks like {\\\"items\\\": [<items>], ...}\\n # collapse to just [<items>]\\n response = response[\\\"items\\\"]\\n else:\\n # just one item, so wrap in a list to match\\n response = [response]\\n\\n books = []\\n for book in response:\\n info = OrderedDict()\\n info[\\\"Title\\\"] = book[\\\"volumeInfo\\\"][\\\"title\\\"]\\n info[\\\"ID\\\"] = book[\\\"id\\\"]\\n try:\\n info[\\\"Author\\\"] = book[\\\"volumeInfo\\\"][\\\"authors\\\"][0]\\n except:\\n info[\\\"Author\\\"] = \\\"<unknown>\\\"\\n try:\\n info[\\\"Year\\\"] = book[\\\"volumeInfo\\\"][\\\"publishedDate\\\"].split(\\\"-\\\")[0]\\n except:\\n info[\\\"Year\\\"] = 0\\n\\n books.append(info)\\n\\n return books\",\n \"def get_data_from_book(book):\\n src_img = book.find(\\\"img\\\").get(\\\"src\\\")\\n src_img = src_img.replace(\\\"../\\\", \\\"\\\")\\n image = \\\"http://books.toscrape.com/\\\" + src_img\\n\\n in_stock = False\\n in_stock_or_not = book.find(\\\"p\\\", {\\\"class\\\", \\\"instock\\\"}).text\\n if \\\"In stock\\\" in in_stock_or_not:\\n in_stock = True\\n\\n name = book.find(\\\"h3\\\").find(\\\"a\\\").text\\n\\n price = book.find(\\\"p\\\", {\\\"class\\\", \\\"price_color\\\"}).text\\n price = price.replace(\\\"Â\\\", \\\"\\\")\\n\\n rating = book.find(\\\"p\\\", {\\\"class\\\", \\\"star-rating\\\"}).get(\\\"class\\\")[1]\\n rating = w2n.word_to_num(rating)\\n\\n return {\\n \\\"image\\\": image,\\n \\\"in_stock\\\": in_stock,\\n \\\"name\\\": name,\\n \\\"price\\\": price,\\n \\\"rating\\\": rating,\\n }\",\n \"def add_book(book):\\n\\n global book_list\\n book.id = generate_id()\\n book_list.append(book)\",\n \"def test_get_specific_book_method(self):\\n # When book id is int\\n book_id = 1\\n result = self.book.get_book(book_id)\\n self.assertEqual(result, [{\\\"Title\\\": \\\"Harry Potter and Chamber of Secrets\\\",\\n \\\"Author\\\": \\\"J.K Rowling\\\",\\n \\\"Copies\\\": 2}])\",\n \"def get_all_bookings():\\n # get all the bookings for userid\\n bookings = Booking.query.all()\\n # dump in the Schema\\n results = bookingsSchema.dump(bookings)\\n\\n return jsonify(results)\",\n \"def books_detail(request, pk):\\n try:\\n snippet = Books.objects.get(url=pk)\\n except Books.DoesNotExist:\\n return Response(status=status.HTTP_404_NOT_FOUND)\\n\\n if request.method == 'GET':\\n serializer = BooksSerializers(snippet)\\n return Response(serializer.data)\\n\\n elif request.method == 'PUT':\\n serializer = BooksSerializers(snippet, data=request.data)\\n if serializer.is_valid():\\n serializer.save()\\n return Response(serializer.data)\\n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\\n\\n elif request.method == 'DELETE':\\n snippet.delete()\\n return Response(status=status.HTTP_204_NO_CONTENT)\",\n \"def get_book_details(self):\\n\\n try:\\n # gives response for the request from the API url\\n response = requests.get(self.book_url)\\n\\n \\n # using ElementTree to store the response content in a tree\\n root = ET.fromstring(response.content)\\n book = root.find('book')\\n\\n # getting the required details\\n self.book_details[\\\"title\\\"] = book.find('title').text\\n self.book_details[\\\"average_rating\\\"] = book.find('average_rating').text\\n self.book_details[\\\"ratings_count\\\"] = book.find('ratings_count').text\\n self.book_details[\\\"num_pages\\\"] = book.find('num_pages').text\\n self.book_details[\\\"image_url\\\"] = book.find('image_url').text\\n self.book_details[\\\"publication_year\\\"] = book.find('publication_year').text\\n\\n # getting list of all the authors\\n authors = book.find('authors')\\n if authors:\\n author_names_list = []\\n for author in authors.iter('author'):\\n author_names_list.append(author.find('name').text)\\n author_names_sentence = \\\", \\\".join(author_names_list)\\n self.book_details[\\\"authors\\\"] = author_names_sentence\\n except:\\n raise Exception(\\\"invalid XML response\\\")\",\n \"def item_json(item_id):\\n item_details_json = {}\\n try:\\n item_in_db = session.query(Item).filter_by(id=item_id).one()\\n item_details_json['item'] = item_in_db.serialize\\n except Exception as e:\\n item_details_json['result'] = 'No data for item ID ' \\\\\\n + str(item_id) + ': ' + str(e)\\n return jsonify(item_details_json)\",\n \"def itemJSON(category_id, item_id):\\n item = session.query(Item).filter_by(id=item_id).one()\\n return jsonify(Item=item.serialize)\",\n \"def recipes():\\n if request.method == 'GET':\\n return Response(\\n json.dumps(recipebook.to_json_list()),\\n mimetype=\\\"application/json\\\")\\n elif request.method == 'POST':\\n new_dict = request.get_json()\\n recipebook.recipes.append(models.Recipe.from_json_dict(new_dict))\\n write_out()\\n return Response(status=200)\",\n \"def get(self):\\n books = db.session.query(models.Book)\\n return [book.serialize() for book in books], 200\",\n \"def post_book_data():\\n data = None\\n if request.get_json() is None:\\n data = request.form.to_dict()\\n print(data)\\n else:\\n data = request.get_json()\\n\\n if data is None or data == {} or all(value == '' for value in data.values()):\\n return render_template('error.html', message='Please Enter Information'), 400\\n data.get('book_url', None)\\n data.get('title', None)\\n data.get('book_id', None)\\n data.get('ISBN', None)\\n data.get('author', None)\\n data.get('author_url', None)\\n data.get('rating', None)\\n data.get('rating_count', None)\\n data.get('review_count', None)\\n data.get('image_url', None)\\n data.get('similar_books', None)\\n if isinstance(data, list):\\n mongo.db.Books.insert_many(data)\\n else:\\n mongo.db.Books.insert_one(data)\\n print(data)\\n return render_template('post_book.html', output=data), 200\",\n \"def test_get_book(self):\\n\\n response = self.client.post(\\n '/api/v1/books', data=json.dumps(add_book), content_type='application/json',\\n headers=self.get_admin_token())\\n response1 = self.client.get(\\n '/api/v1/books/NJCF4001', content_type='application/json', headers=self.get_admin_token())\\n result = json.loads(response1.data.decode())\\n self.assertEqual(result['message'],\\n 'Retrieved successfully')\\n assert response1.status_code == 200\",\n \"def make_books_dicts(xml, book_list):\\n\\n books_response = xml.GoodreadsResponse.reviews.review\\n for book in books_response:\\n a_book = {}\\n a_book['title'] = book.book.title.cdata.encode('utf8')\\n a_book['author_name'] = book.book.authors.author.name.cdata.encode('utf8')\\n a_book['author_gr_id'] = int(book.book.authors.author.id.cdata.encode('utf8'))\\n a_book['gr_work_id'] = int(book.book.work.id.cdata.encode('utf8'))\\n a_book['description'] = book.book.description.cdata\\n\\n a_book['edition'] = {}\\n a_book['edition']['isbn'] = valid_isbn(book.book.isbn.cdata.encode('utf8'))\\n a_book['edition']['format_id'] = get_format_id(book.book.format.cdata.encode('utf8'))\\n a_book['edition']['pic_url'] = book.book.image_url.cdata.encode('utf8')\\n a_book['edition']['publisher'] = book.book.publisher.cdata.encode('utf8')\\n a_book['edition']['gr_url'] = book.book.link.cdata.encode('utf8')\\n a_book['edition']['gr_id'] = int(book.book.id.cdata.encode('utf8'))\\n year = date_is_valid(book.book.publication_year.cdata.encode(\\\"utf8\\\"))\\n month = date_is_valid(book.book.publication_month.cdata.encode(\\\"utf8\\\"))\\n day = date_is_valid(book.book.publication_day.cdata.encode(\\\"utf8\\\"))\\n a_book['edition']['date'] = datetime.date(year, month, day)\\n a_book['edition']['num_pages'] = valid_page_count(book.book.num_pages.cdata.encode('utf8'))\\n book_list.append(a_book)\\n\\n print \\\"*******THERE ARE \\\" + str(len(book_list)) + \\\" ON THIS SHELF*******\\\"\\n\\n return book_list\",\n \"def show_book_details(book_id):\\n\\n book = get_book_details(book_id, GR_KEY)\\n acct = get_current_account(session[\\\"acct\\\"])\\n user = get_user_by_acct(acct)\\n # query to get all friends of the current user that have read any edition of\\n # the currently displayed book.\\n friend_matches = db.session.query(Shelf, Edition, ShelfBook, Friendship) \\\\\\n .filter(Friendship.user_id == user.user_id) \\\\\\n .filter(Shelf.user_id == Friendship.friend_id) \\\\\\n .filter(ShelfBook.shelf_id == Shelf.shelf_id) \\\\\\n .filter(ShelfBook.ed_id == Edition.ed_id) \\\\\\n .filter(Edition.book_id == Book.book_id) \\\\\\n .filter(Book.gr_work_id == book['work_id'])\\\\\\n .all()\\n\\n matches = set() # create set to prevent duplicate matches (caused by rereading)\\n\\n if friend_matches:\\n for match in friend_matches:\\n user = User.query.get(match.Shelf.user_id)\\n matches.add((user.image_url, user.gr_name, match.Shelf.name, user.gr_url))\\n\\n matches = list(matches) # cast matches to a list for iteration in html\\n PRODUCT_URL = get_lib_products(WCCLS, OVRDRV_KEY, OVRDRV_SECRET)\\n lib_copies = search_lib_for_copies(PRODUCT_URL, book, OVRDRV_KEY, OVRDRV_SECRET)\\n\\n return render_template(\\\"book_detail.html\\\",\\n book=book,\\n acct=acct,\\n user=user,\\n matches=matches,\\n lib_copies=lib_copies)\",\n \"def serialize(self):\\r\\n return {\\r\\n \\\"bookshelf_id\\\": self.id,\\r\\n \\\"bookshelf_name\\\": self.name,\\r\\n \\\"bookshelf_image\\\": self.image_url,\\r\\n }\",\n \"def generate_books(nr):\\n for n in range(int(nr)):\\n d6 = random.randint(1, 6)\\n #A book cannot be generated with the same name in different genres due to the nature of the data.\\n if d6 <= 4:\\n book = generate_fantasy_title()\\n book_genres[book] = \\\"FANTASY\\\"\\n elif d6 == 5:\\n book = generate_romance_title()\\n book_genres[book] = \\\"ROMANCE\\\"\\n elif d6 == 6:\\n book = generate_horror_title()\\n book_genres[book] = \\\"HORROR\\\"\\n author = generate_author()\\n if book in books:\\n #a new author for a book cannot have the same name as one already saved for that book\\n while author in books[book]:\\n author = generate_author()\\n books[book] += [author]\\n else:\\n books[book] = [author]\\n counter = 1\\n for book in books:\\n book_ids[book] = f'Book/{counter}'\\n counter += 1\",\n \"def insert_book():\\n \\n\\n if request.method == 'POST':\\n new_author = mongo.db.authors.insert_one({\\n 'author_name': request.form.to_dict()['author_name']\\n })\\n \\n author_id = new_author.inserted_id\\n \\n # Create new book in mongo.db.books\\n new_book = mongo.db.books.insert_one({\\n 'title': request.form.to_dict()['title'],\\n 'genre': request.form.to_dict()['genre'],\\n 'pages': request.form.to_dict()['pages'],\\n 'reviews': [],\\n 'likes': [],\\n 'dislikes': [],\\n 'author_id': str(ObjectId(author_id)),\\n 'isbn_num': request.form.to_dict()['isbn_num']\\n })\\n \\n return redirect(url_for('library'))\\n \\n return render_template('insert_book.html', \\n genres=[genre for genre in mongo.db.genres.find()])\",\n \"def book(title):\\n\\n # Make sure user is logged in\\n if \\\"username\\\" not in session:\\n return render_template(\\\"index.html\\\", message=\\\"Please login to view that page!\\\")\\n\\n book = db.execute(\\\"SELECT * FROM books WHERE title = :title\\\", {\\\"title\\\": title}).fetchone()\\n # Make sure it exists if someone types in a random route\\n if book is None:\\n return render_template(\\\"search.html\\\", search=title)\\n\\n res = requests.get(\\\"https://www.goodreads.com/book/review_counts.json\\\", params={\\\"key\\\": \\\"hYlepXnUC1zpQHUOHkxluQ\\\", \\\"isbns\\\": book.isbn})\\n if res.status_code != 200:\\n raise Exception(\\\"ERROR: API request unsuccessful.\\\")\\n data = res.json()\\n average_rating = data[\\\"books\\\"][0][\\\"average_rating\\\"]\\n ratings_count = data[\\\"books\\\"][0][\\\"ratings_count\\\"]\\n\\n return render_template(\\\"book.html\\\", username=session[\\\"username\\\"], book=book, average_rating=average_rating, ratings_count=ratings_count)\",\n \"def get_commentary_for_book_chapters(book_file_name):\\n if not os.path.exists(challoner_store):\\n os.mkdir(challoner_store)\\n with open(book_file_name, \\\"r+\\\") as rh:\\n book = json.load(rh)\\n chapter_text = {}\\n\\n for name, chapters_dictionary in book.items():\\n \\n for chap, location in chapters_dictionary.items():\\n norm = normalize_filename(\\\"{}_{}\\\".format(name, chap))\\n \\n outfile = \\\"{}/{}.json\\\".format(challoner_store, norm)\\n \\n if os.path.exists(outfile):\\n continue\\n else:\\n chapter_text[name + \\\"__\\\" + chap] = get_commentary_for_chapter(location)\\n with open(outfile, \\\"w+\\\") as wh:\\n json.dump(chapter_text, wh)\\n chapter_text = {}\",\n \"def show_books():\\n# need + or %20 for spaces in author (set encoding?)\\n\\n args = request.args\\n column_names = get_column_names()\\n\\n sql_cmd = [\\\"SELECT title, author FROM books\\\"]\\n if len(args) > 0:\\n for j, arg in enumerate(args):\\n if arg not in column_names: # return empty list\\n sql_cmd = []\\n break\\n else:\\n if not \\\" WHERE \\\" in sql_cmd:\\n sql_cmd.append(\\\" WHERE \\\")\\n sql_cmd.append(\\\"%s='%s'\\\" % (arg, args[arg]))\\n if j+1 < len(args):\\n sql_cmd.append(\\\" AND \\\")\\n sql_cmd.append(\\\";\\\")\\n sql_cmd = \\\"\\\".join(sql_cmd)\\n# print('sql_cmd: ', sql_cmd)\\n\\n books = []\\n if len(sql_cmd) > 1:\\n cur = g.db.cursor()\\n cur.execute(sql_cmd)\\n if cur:\\n books = [dict(title=row[0], author=row[1]) for row in cur.fetchall()]\\n# return jsonify({'results': books})\\n return json_dumps({'results': books}, indent=4)\",\n \"def show_books_by_page(page):\\n result = {'books': query.get_book_list()}\\n return json.dumps(result, ensure_ascii=False)\",\n \"def toJSON(self):\\n \\n return {\\n 'id': self.id,\\n 'bibcode': self.bibcode,\\n 'bib_data': self.bib_data\\n }\",\n \"def json_export(json_file,gradebook):\\n \\n try:\\n import json\\n except ImportError:\\n warnings.warn('Failed to import json module. Cannot execute json_export')\\n return\\n if not hasattr(json_file,'write'):\\n if not isinstance(json_file,basestring) or not \\\\\\n os.path.exists(os.path.dirname(os.path.abspath(json_file))):\\n raise ValueError, 'Argument \\\\'json_file\\\\' is not readable, ' \\\\\\n 'and could not be validated as a file path.'\\n else:\\n json_file = open(json_file,'w+')\\n \\n if not isinstance(gradebook,Gradebook):\\n raise TypeError, 'gradebook argument must be a Gradebook objcet.'\\n \\n cat_dict = dict(gradebook._Gradebook__categories)\\n all_items = []\\n for cat_name in cat_dict:\\n gr_leest = cat_dict[cat_name].grades.select(docopy=True,aslist=True)\\n for i in gr_leest:\\n x = {}\\n if not isinstance(i,Grade):\\n continue\\n x['type']='Grade'\\n x['name']=i.name\\n if i.parent is not None:\\n if i.parent.name == cat_name or i.parent.name in cat_dict:\\n x['parent'] = i.parent.name\\n else:\\n x['parent'] = None\\n attribs = {}\\n attribs['score'] = i.score\\n attribs['maximum'] = i.maximum\\n attribs['weight'] = i.weight\\n attribs['extra_credit'] = i.extra_credit\\n if i.overrides:\\n attribs['overrides'] = list(i.overrides)\\n for ovrrd in i.overrides:\\n if not isinstance(overrd,basestring):\\n attribs['overrides'].remove(overrd)\\n if i.timestamp and isinstance(i.timestamp,datetime.datetime):\\n tmstmp = calendar.timegm(i.timestamp.utctimetuple())\\n attribs['timestamp'] = tmstmp\\n if i.identifiers:\\n attribs['identifiers'] = dict(i.identifiers)\\n \\n x['attribs'] = attribs\\n all_items.append(x)\\n catt = {'type':'Category','name':cat_name}\\n if cat_dict[cat_name].parent is not None:\\n catt['parent'] = cat_dict[cat_name].parent.name\\n else:\\n catt['parent'] = None\\n catt['attribs'] = dict(cat_dict[cat_name]._Category__attribs)\\n all_items.append(catt)\\n \\n grbk = {'type':'Gradebook','name':gradebook.name,'user':gradebook.user}\\n all_items.append(grbk)\\n enc_me = {\\\"grading\\\": all_items}\\n \\n encoder = json.JSONEncoder(indent=4,separators=(', ',': '))\\n enc = encoder.encode(enc_me)\\n \\n json_file.write(enc)\\n json_file.close()\",\n \"def get_autor_with_papers(author_id):\\n print(\\\"getting information from dblp about author {}\\\".format(author_id))\\n data = get(\\\"https://dblp.org/pid/\\\" + author_id + \\\".xml\\\")[\\\"dblpperson\\\"]\\n author_id = author_id.replace(\\\"/\\\", \\\"_\\\")\\n author = {\\n \\\"name\\\": data[\\\"@name\\\"],\\n \\\"papers\\\": [get_paper(paper_container) for paper_container in data[\\\"r\\\"]],\\n }\\n\\n with open(local.file_name(\\\"authors\\\", author_id), \\\"w\\\") as out:\\n json.dump(author, out, ensure_ascii=False, indent=4)\",\n \"def textbooks_detail_handler(request, course_key_string, textbook_id):\\r\\n course_key = CourseKey.from_string(course_key_string)\\r\\n course_module = _get_course_module(course_key, request.user)\\r\\n store = get_modulestore(course_module.location)\\r\\n matching_id = [tb for tb in course_module.pdf_textbooks\\r\\n if unicode(tb.get(\\\"id\\\")) == unicode(textbook_id)]\\r\\n if matching_id:\\r\\n textbook = matching_id[0]\\r\\n else:\\r\\n textbook = None\\r\\n\\r\\n if request.method == 'GET':\\r\\n if not textbook:\\r\\n return JsonResponse(status=404)\\r\\n return JsonResponse(textbook)\\r\\n elif request.method in ('POST', 'PUT'): # can be either and sometimes\\r\\n # django is rewriting one to the other\\r\\n try:\\r\\n new_textbook = validate_textbook_json(request.body)\\r\\n except TextbookValidationError as err:\\r\\n return JsonResponse({\\\"error\\\": err.message}, status=400)\\r\\n new_textbook[\\\"id\\\"] = textbook_id\\r\\n if textbook:\\r\\n i = course_module.pdf_textbooks.index(textbook)\\r\\n new_textbooks = course_module.pdf_textbooks[0:i]\\r\\n new_textbooks.append(new_textbook)\\r\\n new_textbooks.extend(course_module.pdf_textbooks[i + 1:])\\r\\n course_module.pdf_textbooks = new_textbooks\\r\\n else:\\r\\n course_module.pdf_textbooks.append(new_textbook)\\r\\n store.update_item(course_module, request.user.id)\\r\\n return JsonResponse(new_textbook, status=201)\\r\\n elif request.method == 'DELETE':\\r\\n if not textbook:\\r\\n return JsonResponse(status=404)\\r\\n i = course_module.pdf_textbooks.index(textbook)\\r\\n remaining_textbooks = course_module.pdf_textbooks[0:i]\\r\\n remaining_textbooks.extend(course_module.pdf_textbooks[i + 1:])\\r\\n course_module.pdf_textbooks = remaining_textbooks\\r\\n store.update_item(course_module, request.user.id)\\r\\n return JsonResponse()\",\n \"def getBookById(self, id):\\n cur = self.__execute__(\\n '''select ID, NAME, AUTHOR from books where ID = %d;''' % id)\\n return BookIter(cur)\",\n \"def download_a_book():\\n try:\\n book_name = request.args.get('book_name')\\n record = search_a_book(replica_one, book_name)\\n if 'success' in record:\\n blob = replica_one.blob(book_name)\\n size = sys.getsizeof(blob.download_as_string())\\n response = Response(blob.download_as_string())\\n response.headers.add('Content-Range'.format('bytes'), size)\\n return response\\n else:\\n return json.dumps({'error': 'file not found'})\\n except Exception as e:\\n logging.basicConfig(level=logging.DEBUG,\\n format='%(asctime)s %(levelname)-8s %(message)s',\\n datefmt='%a, %d %b %Y %H:%M:%S',\\n filename='myapp.log',\\n filemode='w')\\n with open('myapp.log') as log:\\n line = log.readline()\\n if line is not None:\\n loggingdb.insert_one({'log': str(line)})\\n return json.dumps({\\\"error\\\": \\\"exception found\\\"})\",\n \"def _save_book_detail(self, book_dict, book_nmb):\\n content = self._get_book_content(book_dict['book_page_href'])\\n with open(f'{PATH_TO_DATA}/{book_nmb}.html', 'w') as file:\\n file.write(content)\",\n \"def book(isbn):\\n\\n if request.method == \\\"POST\\\":\\n \\n # Save current user info\\n currentUser = session[\\\"user_id\\\"]\\n \\n # Fetch form data\\n rating = request.form.get(\\\"rating\\\")\\n comment = request.form.get(\\\"comment\\\")\\n\\n # Search book_id by ISBN\\n row = db.execute(\\\"SELECT id FROM books WHERE isbn = :isbn\\\",\\n {\\\"isbn\\\": isbn})\\n\\n # Save id into variable\\n id_books = row.fetchone() # (id,)\\n bookId = id_books[0]\\n\\n # Check for user submission (ONLY 1 review/user allowed per book)\\n row2 = db.execute(\\\"SELECT * FROM reviews WHERE user_id = :user_id AND book_id = :book_id\\\",\\n {\\\"user_id\\\": currentUser,\\n \\\"book_id\\\": bookId})\\n\\n # A review already exists\\n if row2.rowcount == 1:\\n \\n flash('You already submitted a review for this book', 'warning')\\n return redirect(\\\"/book/\\\" + isbn)\\n\\n # Convert to save into DB\\n rating = int(rating)\\n\\n db.execute(\\\"INSERT INTO reviews (user_id, book_id, comment, rating) VALUES \\\\\\n (:user_id, :book_id, :comment, :rating)\\\",\\n {\\\"user_id\\\": currentUser, \\n \\\"book_id\\\": bookId, \\n \\\"comment\\\": comment, \\n \\\"rating\\\": rating})\\n\\n # Commit transactions to DB and close the connection\\n db.commit()\\n\\n flash('Review submitted!', 'info')\\n\\n return redirect(\\\"/book/\\\" + isbn)\\n \\n # Take the book ISBN and redirect to his page (GET)\\n else:\\n row = db.execute(\\\"SELECT isbn, title, author, year FROM books WHERE \\\\\\n isbn = :isbn\\\",\\n {\\\"isbn\\\": isbn})\\n\\n # Hence this is the bookInfo[0] for using in reviews.html\\n bookInfo = row.fetchall()\\n\\n \\\"\\\"\\\" GOODREADS reviews \\\"\\\"\\\"\\n\\n # Read API key from env variable\\n key = os.getenv(\\\"GOODREADS_KEY\\\")\\n \\n # Query the api with key and ISBN as parameters\\n query = requests.get(\\\"https://www.goodreads.com/book/review_counts.json\\\",\\n params={\\\"key\\\": key, \\\"isbns\\\": isbn})\\n \\n # Convert the response to JSON\\n json_response = query.json()\\n\\n # \\\"Clean\\\" the JSON before passing it to the bookInfo list\\n response = json_response['books'][0]\\n\\n # Append it as the second element on the list to get bookInfo[1] for using in reviews.html\\n bookInfo.append(response)\\n\\n \\\"\\\"\\\" Users reviews \\\"\\\"\\\"\\n\\n # Search book_id by ISBN\\n row = db.execute(\\\"SELECT id FROM books WHERE isbn = :isbn\\\",\\n {\\\"isbn\\\": isbn})\\n\\n # Save id into variable\\n # 'id' of the user is found in both tables 'reviews' and 'users'\\n # Hence this would be used for framing the JOIN query\\n book = row.fetchone() # (id,)\\n book = book[0]\\n\\n # Fetch book reviews\\n # Date formatting (https://www.postgresql.org/docs/9.1/functions-formatting.html)\\n results = db.execute(\\\"SELECT users.username, comment, rating, \\\\\\n to_char(time, 'DD Mon YY - HH24:MI:SS') as time \\\\\\n FROM users \\\\\\n INNER JOIN reviews \\\\\\n ON users.id = reviews.user_id \\\\\\n WHERE book_id = :book \\\\\\n ORDER BY time\\\",\\n {\\\"book\\\": book})\\n\\n reviews = results.fetchall()\\n\\n return render_template(\\\"reviews.html\\\", bookInfo=bookInfo, reviews=reviews)\",\n \"def __str__(self):\\n return '{0} ({1})'.format(self.id, self.book)\",\n \"def write():\\n output_data = book_list_manipulation()\\n\\n # Create data directory\\n try:\\n os.mkdir(DATA_DIR)\\n except FileExistsError:\\n pass # Ignore - if directory exists, don't need to do anything.\\n\\n with open(BOOKS_FILE_NAME, 'w') as f:\\n json.dump(output_data, f)\\n\\n with open(COUNTER_FILE_NAME, 'w') as f:\\n f.write(str(counter))\",\n \"def test_api_can_create_a_book(self):\\n book = {\\n 'bookid': '23',\\n 'title': 'Neues Buch',\\n 'description': 'Unsinn',\\n 'seite50_sentence': 'Ein neuer Satz ohne Verb.',\\n 'published_date': '1980-01-01',\\n }\\n res = self.client.post(\\n reverse('create_book'), book, format=\\\"json\\\")\\n self.assertEqual(res.status_code, status.HTTP_201_CREATED)\",\n \"def book(isbn, message=None):\\n # Make sure book exists.\\n book = db.execute(\\\"SELECT * FROM books WHERE isbn = :isbn\\\", {\\\"isbn\\\": isbn}).fetchone()\\n\\n if book is None:\\n return render_template(\\\"error.html\\\", message=\\\"No such book.\\\")\\n\\n\\n \\\"\\\"\\\" Goodreads info \\\"\\\"\\\"\\n res = requests.get(\\\"https://www.goodreads.com/book/review_counts.json\\\", params={\\\"key\\\": KEY_goodreads, \\\"isbns\\\":isbn.strip()}).json()\\n\\n\\n\\n\\n \\\"\\\"\\\" Adding comment section \\\"\\\"\\\"\\n comments = db.execute(\\\"SELECT reviews.date, reviews.review, reviews.rating, users.id FROM reviews \\\\\\n INNER JOIN users ON reviews.comment_id=users.user_id WHERE books = :books\\\", {\\\"books\\\": book[0]}).fetchall()\\n\\n\\n \\\"\\\"\\\" Adding review \\\"\\\"\\\"\\n review = request.form.get(\\\"review\\\")\\n rating_value = request.form.get(\\\"rating\\\")\\n\\n # Checking if user made a comment\\n if review != None:\\n # Checking if the user already made a comment\\n if db.execute(\\\"SELECT * FROM reviews WHERE books = :books AND usr = :user\\\", {\\\"books\\\": book[0], \\\"user\\\":session[\\\"user_id\\\"][0]}).rowcount == 0:\\n today = date.today() # adding date to the comment\\n # Inserting the relevant info into the database\\n db.execute(\\\"INSERT INTO reviews (books, usr, review, date, rating) VALUES (:books, :usr, :review, :date, :rating)\\\",\\n {\\\"books\\\":int(book[0]), \\\"usr\\\": int(session[\\\"user_id\\\"][0]), \\\"review\\\": review, \\\"date\\\":today, \\\"rating\\\": rating_value})\\n db.commit()\\n\\n # I have to redo this line of code, so the next time it renders the webpage the new comment shows up\\n comments = db.execute(\\\"SELECT * FROM reviews WHERE books = :books\\\", {\\\"books\\\": book[0]}).fetchall()\\n return render_template(\\\"book.html\\\", book=book, comments=comments, goodreads = res[\\\"books\\\"][0], message=\\\"Thanks for your comment!\\\")\\n\\n else:\\n return render_template(\\\"book.html\\\", book=book, comments=comments, goodreads = res[\\\"books\\\"][0], message=\\\"You've already commented this book!\\\")\\n\\n\\n\\n return render_template(\\\"book.html\\\", book=book, review=review, comments=comments, goodreads = res[\\\"books\\\"][0])\",\n \"def test_api_can_get_a_book(self):\\n response = self.client.get(\\n reverse('details_book', kwargs={'pk': book_pk}), format='json')\\n self.assertEqual(response.status_code, status.HTTP_200_OK)\",\n \"def get_book_info(self, book):\\n request_url = \\\"%s?q=%s\\\" % (self.API_URL, book)\\n json_data = self.make_request(request_url)\\n if not json_data:\\n return []\\n books_info = []\\n for book in json_data['docs']:\\n info = {'title': book['title']}\\n if 'publisher' in book:\\n info.update({'publisher': book['publisher']})\\n if 'publish_year' in book:\\n info.update({'publish_year': book['publish_year']})\\n if 'language' in book:\\n info.update({'language': book['language']})\\n books_info.append(info)\\n return books_info\",\n \"def test_get_a_book_by_id(self):\\n create_admin()\\n response = self.client().post('/api/v1/login', json=self.test_admin)\\n json_data = json.loads(response.data)\\n access_token = json_data.get('access_token')\\n self.client().post('/api/v1/products',\\n headers={\\\"Authorization\\\": \\\"Bearer \\\" + access_token}, json=self.test_book)\\n response = self.client().get('/api/v1/products/1')\\n json_data = json.loads(response.data)\\n self.assertTrue(json_data.get('Book'))\\n self.assertEqual(response.status_code, 200)\",\n \"def return_book(user_id):\\n user = User.query.filter_by(user_id=user_id).first_or_404()\\n\\n # make sure the user who will fill the form is the user\\n # whose user_id is reponsitive to the URL\\n if user.username == session.get('username') or \\\\\\n user.username == session.get('adminname') :\\n book_ids = request.args.get('book')\\n # fill books list\\n books = []\\n for book_id in book_ids :\\n if type(book_id) != type(0) or book_id < 1 :\\n abort(404)\\n book = Entries.query.filter_by(id=book_id).first_or_404()\\n books.append(book)\\n return render_template('return.html', books=books, user=user)\\n else :\\n abort(404)\",\n \"def test_book_must_be_json(self):\\n\\n\\t\\twith self.client:\\n\\t\\t\\tadd_book = {\\n\\t\\t\\t\\t'title': 'Hello Books',\\n\\t\\t\\t\\t'isbn': \\\"8985652145\\\"\\n\\t\\t\\t}\\n\\t\\t\\tlogin_data = self.login_test_user()\\n\\t\\t\\ttoken = login_data['auth_token']\\n\\t\\t\\tres = self.client.post(\\n\\t\\t\\t\\tf'{URL_BOOKS}',\\n\\t\\t\\t\\theaders=dict(Authorization=f'Bearer {token}'),\\n\\t\\t\\t\\tcontent_type='text',\\n\\t\\t\\t\\tdata=json.dumps(add_book)\\n\\t\\t\\t)\\n\\t\\t\\tres2 = json.loads(res.data.decode())\\n\\t\\t\\tself.assertIn('Content-type must be json', str(res2))\\n\\t\\t\\tself.assertEqual(res.status_code, 202)\",\n \"def getGRAuthorByID(id, book_callee=None, series_callee=None, printout=True): \\n author_entry = session.query(author).get(id)\\n if author_entry is None:\\n request = requests.get('https://www.goodreads.com/author/show/'+str(id)+'.xml?key='+API_KEY['GOODREADS'])\\n if request.status_code == 200:\\n data = xmltodict.parse(request.text)['GoodreadsResponse']['author']\\n \\n auth = {}\\n auth['id'] = int(data['id'])\\n auth['author'] = data['name']\\n auth['description'] = data['about']\\n auth['hometown'] = data['hometown']\\n auth['small_img'] = data['small_image_url']\\n auth['large_img'] = data['image_url']\\n \\n author_entry = author(**auth) \\n session.add(author_entry)\\n session.commit() \\n for key, book in data['books'].items():\\n while type(book) is list:\\n book = book[0]\\n if type(book) is OrderedDict and (book_callee is None or book_callee != int(book['id']['#text'])):\\n a_book = getGRBookByID(int(book['id']['#text']), id)\\n if a_book is not None:\\n session.query(author).get(id).books.append(a_book)\\n session.commit()\\n if(printout):\\n print(author_entry)\\n \\n return author_entry\",\n \"def get_books():\\n\\n parameters = {'access_token': ACCESS_TOKEN}\\n r = requests.get('https://graph.facebook.com/741504455940261/books', params=parameters)\\n result = json.loads(r.text)\\n return result[\\\"data\\\"]\",\n \"def get_json(self):\\n json_item = {\\\"id: \\\": self.id,\\n \\\"question: \\\": self.question,\\n \\\"documents: \\\": self.documents,\\n \\\"document_ids: \\\": self.document_ids,\\n \\\"gold answers: \\\": self.gold}\\n return json_item\",\n \"def test_create_book(self):\\n url = reverse('book-list')\\n data = {'isbn':'96712116-1',\\n 'title':'New Star',\\n 'author_last_name':'Khaled',\\n 'author_first_name':'Roshdy',\\n 'page_count':250,\\n 'description':'the book description'}\\n response = self.client.post(url, data, format='json')\\n self.assertEqual(response.status_code, status.HTTP_201_CREATED)\",\n \"def init_books(author_file, json_file):\\n with open(author_file) as f:\\n authors = list(f)\\n\\n authors = [i.strip() for i in authors]\\n\\n books = []\\n for author in authors:\\n s = get_etexts('author', author)\\n for i in s:\\n try:\\n if list(get_metadata('language', i))[0] == 'en':\\n title, etext = list(get_metadata('title', i))[0], strip_headers(load_etext(i)).strip()\\n b = Book(i, title, etext)\\n books.append(b)\\n except UnknownDownloadUriException:\\n # this book does not have a load_etext corresponding to it.\\n pass\\n\\n with open(json_file, 'wb') as f:\\n pickle.dump(books, f)\\n\\n print (len(books))\",\n \"def make_book_api_url(self):\\n\\n # format API_LINK = \\\"https://www.goodreads.com/book/show/<book id>.xml?key=<your dev key>\\\"\\n\\n self.book_url = f\\\"https://www.goodreads.com/book/show/{self.book_id}.xml?key={self.key}\\\"\",\n \"def get_all_text_for_book(book_file_name):\\n if not os.path.exists(verse_store):\\n os.mkdir(verse_store)\\n with open(book_file_name, \\\"r+\\\") as rh:\\n book = json.load(rh)\\n chapter_text = {}\\n\\n for name, chapters_dictionary in book.items():\\n for chap, location in chapters_dictionary.items():\\n outfile = \\\"{}/{}_{}.json\\\".format(verse_store, normalize_filename(name), chap)\\n \\n if os.path.exists(outfile):\\n continue\\n else:\\n chapter_text[name + \\\"__\\\" + chap] = get_chapter_text(location)\\n with open(outfile, \\\"w+\\\") as wh:\\n json.dump(chapter_text, wh)\\n chapter_text = {}\",\n \"def generate():\\n book = Book('inventarios.xls')\\n pages = list(book.pages)\\n pages = [pages[1], pages[0]] + pages[2:]\\n entries = list(itertools.chain.from_iterable(p.entries for p in pages))\\n keys = {}\\n books = {}\\n for entry in entries:\\n if entry['book_id'] in books:\\n continue\\n\\n entry['key'] = title_key(entry['title'], keys)\\n print(entry['key'], '\\\\t', entry['title'])\\n\\n # mark entry\\n keys[entry['key']] = True\\n books[entry['book_id']] = True\\n\\n entries = (e for e in entries if 'key' in e)\\n entries = sorted(entries, key=lambda e: e['book_id'])\\n export_to_excel('listing.xlsx', entries)\",\n \"def add_book(request, **kwargs):\\n kwargs.setdefault('template_name', 'bookreader/editor/add_book.html')\\n kwargs.setdefault('form_class', BookForm)\\n kwargs.setdefault('identifier_generator', generate_random_identifier) \\n \\n if request.method == 'POST':\\n form = kwargs['form_class'](request.POST)\\n if form.is_valid():\\n book = form.save(commit=False)\\n book.identifier = kwargs['identifier_generator']()\\n book.published = False\\n book.save()\\n \\n messages.add_message(request, messages.INFO,\\n 'Added a new book, %s, to the collection %s.' % \\n (book.title, book.collection.name,))\\n \\n return HttpResponseRedirect(reverse('bookreader-book',\\n args=[book.identifier]))\\n else:\\n form = kwargs['form_class']()\\n \\n return render_to_response(kwargs['template_name'], {'form': form},\\n RequestContext(request))\",\n \"def __str__(self):\\n return f'{self.id} ({self.book.title})'\",\n \"def add_book(name, author):\\n BOOKS.append({'name': name, 'author': author, 'read': False})\",\n \"def create_book(self, title, isbn):\\n # new_book = Book(title, isbn)\\n # return new_book\\n return Book(title, isbn)\",\n \"def get_books_data():\\n entry = mongo.db.Books\\n output = list()\\n look_up_type = None\\n if 'title' in request.args:\\n look_up_type = 'title'\\n if len(request.args['title']) <= 2:\\n return render_template('error.html', message=\\\"Must enter characters\\\"), 400\\n value = request.args['title'].strip('\\\"')\\n title = entry.find({'title': {'$regex': value}})\\n if title:\\n for book in title:\\n output.append({'title': book['title']})\\n elif 'related_books' in request.args:\\n look_up_type = 'similar_books'\\n if len(request.args['related_books']) <= 2:\\n return render_template('error.html', message=\\\"Must enter characters\\\"), 400\\n value = request.args['related_books'].strip('\\\"')\\n related_books = entry.find(\\n {'similar_books': {'$regex': value}})\\n if related_books:\\n for related in related_books:\\n for link in related['similar_books']:\\n if value in link:\\n output.append(({'similar_books': link}))\\n elif 'author' in request.args:\\n look_up_type = 'author'\\n if len(request.args['author']) <= 2:\\n return render_template('error.html', message=\\\"Must enter characters\\\"), 400\\n value = request.args['author'].strip('\\\"')\\n authors = entry.find({'author': {'$regex': value}})\\n if authors:\\n for name in authors:\\n output.append({'author': name['author']})\\n if len(output) == 0:\\n return render_template('error.html', message=\\\"No Entries Found\\\"), 400\\n return render_template('gottenBooks.html', output=output, look_up_type=look_up_type), 200\",\n \"def create_book():\\n Book.objects.create(book_id=\\\"test_id\\\",\\n title=\\\"test_title\\\",\\n authors=\\\"test_author\\\",\\n published_date=\\\"2021\\\",\\n categories=[\\\"test_category\\\"],\\n average_rating=5,\\n ratings_count=5,\\n thumbnail=\\\"http://books.google.com/books/test\\\"\\n )\",\n \"def save_book_details(self, book_dict_with_href):\\n for i, book_dict in enumerate(book_dict_with_href):\\n print(f'Saving data for book {i + 1}')\\n self._save_book_detail(book_dict, i)\",\n \"def get_item(id):\\n return jsonify(id=id, name='name', number=123)\",\n \"def scrape_one_book(self, url):\\n\\n if url in self.url_to_explore:\\n self.url_to_explore.remove(url)\\n req = requests.get(url, headers = self.headers).content\\n soup = BeautifulSoup(req, 'html5lib')\\n soupbody = soup.body\\n\\n book_data = {}\\n # get book url\\n book_url = url\\n book_data[\\\"url\\\"] = book_url\\n\\n # get book title\\n book_title = soupbody.find('h1', attrs={'id':'bookTitle'}).text.strip()\\n if book_title:\\n book_data[\\\"title\\\"] = book_title\\n\\n # # get book id\\n reg = 'https://www.goodreads.com/book/show/([0-9]+)'\\n book_id = re.search(reg, url).group(1)\\n book_data[\\\"id\\\"] = book_id\\n\\n # get book ISBN\\n book_databox = soupbody.find('div', attrs={'id':'bookDataBox'})\\n if book_databox:\\n all_float_divs = book_databox.find_all('div',\\n attrs = {'class' : 'clearFloats'})\\n book_isbn = ''\\n for div in all_float_divs:\\n title = div.find('div',\\n attrs = {'class':'infoBoxRowTitle'}).text.strip()\\n if title == 'ISBN':\\n book_isbn = div.find('div',\\n attrs = {'class':'infoBoxRowItem'}).contents[0].strip()\\n book_data[\\\"ISBN\\\"] = book_isbn\\n\\n # get book author url and author name\\n author_name_container = soupbody.find('div',\\n attrs = {'class':\\\"authorName__container\\\"})\\n if author_name_container:\\n all_authors = author_name_container.find_all('a',\\n href = True, attrs = {'class':\\\"authorName\\\"})\\n cur_author_url = []\\n cur_author_name = []\\n for author in all_authors:\\n cur_author_url.append(author['href'])\\n name = author.find('span', attrs = {'itemprop':'name'}).text.strip()\\n cur_author_name.append(name)\\n book_data[\\\"authorURLs\\\"] = cur_author_url\\n book_data[\\\"author_names\\\"] = cur_author_name\\n\\n # get book rating and review\\n book_meta = soupbody.find('div', attrs = {'id':'bookMeta'})\\n if book_meta:\\n rating = book_meta.find('span',\\n attrs = {'itemprop':'ratingValue'}).text.strip()\\n book_data[\\\"rating\\\"] = rating\\n\\n book_rating_count_container = book_meta.find('meta',\\n attrs = {'itemprop':'ratingCount'})\\n if book_rating_count_container:\\n book_rating_count = book_rating_count_container['content']\\n book_data[\\\"rating_count\\\"] = book_rating_count\\n\\n book_review_count_container = book_meta.find('meta',\\n attrs = {'itemprop':'reviewCount'})\\n if book_review_count_container:\\n book_review_count = book_review_count_container['content']\\n book_data[\\\"review_count\\\"] = book_review_count\\n\\n # get book image\\n image_tag = soupbody.find('img', attrs = {'id':'coverImage'})\\n if image_tag:\\n image_src = image_tag['src']\\n book_data[\\\"bookImage\\\"] = image_src\\n # print(authorLink.span.text)\\n\\n # get related_books\\n related_works_container = soupbody.find('div', id=re.compile('relatedWorks-'))\\n if related_works_container:\\n related_books_div = related_works_container.find('div', class_='bigBoxBody')\\n if related_books_div:\\n related_books_carousel = related_books_div.find('div', class_='bookCarousel')\\n if related_books_carousel:\\n carousel_row = related_books_carousel.find('div', class_='carouselRow')\\n if carousel_row:\\n related_books_list_li = carousel_row.find('ul').find_all('li')\\n related_books = []\\n for item in related_books_list_li:\\n link = item.find('a', href = True)['href']\\n self.url_to_explore.add(link)\\n related_books.append(link)\\n book_data[\\\"similar_books\\\"] = related_books\\n\\n self.data_collection.push_to_collection(book_data)\\n print(\\\"Book successfully scraped: \\\" + book_title)\",\n \"def showModelInfoJSON(style_id, model_id):\\n model = session.query(Model).filter_by(id=model_id).one()\\n return jsonify(model=model.serialize)\",\n \"def result(request):\\n\\n books = list()\\n\\n try:\\n query = request.POST.get('query')\\n \\n \\n qs = Response.response_front(query)\\n\\n for book in qs:\\n books.append(qs)\\n\\n result = {\\n 'picture':Response.build(books[0]['picture']),\\n 'title':books[0]['title']\\n }\\n \\n return JsonResponse(result,safe=False)\\n except:\\n print('error')\",\n \"def get_cars_by_id(carid):\\n carlist = Car.query.all()\\n results = carsSchema.dump(carlist)\\n return jsonify(results)\",\n \"def _get_book(self):\\n print('%s get_book request made.' % self.sym)\\n start_time = time()\\n\\n self.clear_book()\\n path = ('https://api.pro.coinbase.com/products/%s/book' % self.sym)\\n book = requests.get(path, params={'level': 3}).json()\\n\\n elapsed = time() - start_time\\n print('%s get_book request completed in %f seconds.' % (self.sym, elapsed))\\n return book\",\n \"def __str__(self):\\n return f'{self.id} ({self.book.title})'\",\n \"def duplicated_code():\\n author_ids = []\\n updated_books = []\\n updated_authors = []\\n \\n for author in mongo.db.authors.find():\\n updated_authors.append(author)\\n for book in mongo.db.books.find():\\n \\n # Create a new key/value pair in each book for author_name\\n # by looking up the author_id and matching it to the author_name\\n # of the selected author_id.\\n \\n book_title = book['title']\\n author_id = book['author_id']\\n \\n for author in updated_authors:\\n if author['_id'] == ObjectId(author_id):\\n book['author_name'] = author['author_name']\\n \\n \\n # Using the googlebooks API search for each book and retrieve\\n # a thumbnail of the book.\\n \\n google_api_title = book_title.replace(' ', '+')\\n book_isbn_num = book['isbn_num']\\n with urllib.request.urlopen(BASE_API_LINK + google_api_title) as f:\\n text = f.read()\\n decoded_text = text.decode(\\\"utf-8\\\")\\n obj = json.loads(decoded_text) \\n google_book_obj = obj[\\\"items\\\"][0]\\n book_href = google_book_obj['volumeInfo']\\n if 'imageLinks' in book_href:\\n book['href'] = book_href['imageLinks']['thumbnail']\\n \\n # Append book to new book dictionary.\\n updated_books.append(book)\\n \\n return updated_books\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.68194824","0.66605145","0.6633134","0.6563902","0.65159816","0.65159816","0.6498835","0.6487234","0.64699584","0.6375881","0.6324021","0.63159806","0.63023865","0.6282721","0.62815195","0.6272364","0.60790217","0.59934425","0.5943473","0.5903797","0.58920157","0.58765775","0.5874491","0.58512235","0.58397883","0.5818711","0.5817598","0.5802955","0.57766634","0.57690454","0.56702906","0.5607772","0.5582426","0.5576417","0.5573827","0.554303","0.5538698","0.5516142","0.54824483","0.54663515","0.54614466","0.54548323","0.54527706","0.5440743","0.54394335","0.543127","0.53980094","0.5370942","0.5369911","0.53557664","0.53530985","0.5347655","0.53406125","0.53283906","0.5319382","0.5308367","0.53056836","0.53051245","0.5303992","0.52871037","0.5279078","0.5267178","0.523183","0.52090347","0.5207869","0.5197656","0.5187037","0.5178188","0.5166178","0.5165458","0.5151871","0.51489526","0.5143521","0.51371294","0.51313555","0.511686","0.510991","0.5105852","0.5102865","0.5101903","0.5094379","0.50923324","0.5091845","0.50853974","0.5078799","0.5078632","0.50768244","0.50718343","0.5070701","0.50679845","0.5064998","0.5059427","0.5058406","0.5055071","0.5050225","0.50493664","0.5047731","0.5022454","0.5022128","0.5014635"],"string":"[\n \"0.68194824\",\n \"0.66605145\",\n \"0.6633134\",\n \"0.6563902\",\n \"0.65159816\",\n \"0.65159816\",\n \"0.6498835\",\n \"0.6487234\",\n \"0.64699584\",\n \"0.6375881\",\n \"0.6324021\",\n \"0.63159806\",\n \"0.63023865\",\n \"0.6282721\",\n \"0.62815195\",\n \"0.6272364\",\n \"0.60790217\",\n \"0.59934425\",\n \"0.5943473\",\n \"0.5903797\",\n \"0.58920157\",\n \"0.58765775\",\n \"0.5874491\",\n \"0.58512235\",\n \"0.58397883\",\n \"0.5818711\",\n \"0.5817598\",\n \"0.5802955\",\n \"0.57766634\",\n \"0.57690454\",\n \"0.56702906\",\n \"0.5607772\",\n \"0.5582426\",\n \"0.5576417\",\n \"0.5573827\",\n \"0.554303\",\n \"0.5538698\",\n \"0.5516142\",\n \"0.54824483\",\n \"0.54663515\",\n \"0.54614466\",\n \"0.54548323\",\n \"0.54527706\",\n \"0.5440743\",\n \"0.54394335\",\n \"0.543127\",\n \"0.53980094\",\n \"0.5370942\",\n \"0.5369911\",\n \"0.53557664\",\n \"0.53530985\",\n \"0.5347655\",\n \"0.53406125\",\n \"0.53283906\",\n \"0.5319382\",\n \"0.5308367\",\n \"0.53056836\",\n \"0.53051245\",\n \"0.5303992\",\n \"0.52871037\",\n \"0.5279078\",\n \"0.5267178\",\n \"0.523183\",\n \"0.52090347\",\n \"0.5207869\",\n \"0.5197656\",\n \"0.5187037\",\n \"0.5178188\",\n \"0.5166178\",\n \"0.5165458\",\n \"0.5151871\",\n \"0.51489526\",\n \"0.5143521\",\n \"0.51371294\",\n \"0.51313555\",\n \"0.511686\",\n \"0.510991\",\n \"0.5105852\",\n \"0.5102865\",\n \"0.5101903\",\n \"0.5094379\",\n \"0.50923324\",\n \"0.5091845\",\n \"0.50853974\",\n \"0.5078799\",\n \"0.5078632\",\n \"0.50768244\",\n \"0.50718343\",\n \"0.5070701\",\n \"0.50679845\",\n \"0.5064998\",\n \"0.5059427\",\n \"0.5058406\",\n \"0.5055071\",\n \"0.5050225\",\n \"0.50493664\",\n \"0.5047731\",\n \"0.5022454\",\n \"0.5022128\",\n \"0.5014635\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.8344132"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":75599,"cells":{"query":{"kind":"string","value":"Generates JSON for a category with all books in the category Specified by the category_id"},"document":{"kind":"string","value":"def categoryWithBooksJSON(category_id):\n books = db_session.query(Book).filter_by(category_id=category_id).all()\n return jsonify(Category=[b.serialize for b in books])"},"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 api_category_items(category_id):\n items = session.query(Item).filter(Item.category_id == category_id)\n return jsonify(json_list=[i.to_json() for i in items.all()])","def categoryItemJSON(category_id):\n category = db.getByCategory(category_id)\n items = db.getItemsByCategory(category_id)\n return jsonify(Items=[i.serialize for i in items])","def show_category_json(category):\n category = (session.query(Categories)\n .filter_by(name=category.replace('-', ' '))\n .one())\n category_items = (session.query(Items)\n .filter_by(category_id=category.id)\n .order_by(Items.name)\n .all())\n return jsonify(category=[category.serialize],\n items=([category_items.serialize\n for category_items in category_items]))","def categoryItemJSON(category_id):\n category = session.query(Category).\\\n filter_by(id=category_id).one()\n items = session.query(Item).\\\n filter_by(category_id=category_id).all()\n return jsonify(Items=[i.serialize for i in items])","def showCategoryItemsJSON(category_id):\r\n session = DBSession()\r\n items = session.query(Item).filter_by(category_id=category_id)\r\n return jsonify(Items=[i.serialize for i in items])","def api_categories():\n categories = session.query(Category)\n return jsonify(json_list=[i.to_json() for i in categories.all()])","def get_categories():\n categories = [c.serialize for c in session.query(Category).all()]\n for c in range(len(categories)):\n items = [\n i.serialize for i in session.query(Item).filter_by(\n category_id=categories[c][\"id\"]).all()]\n if items:\n categories[c][\"Item\"] = items\n return jsonify(Category=categories)","def getCategoryJSON(category_id):\r\n category = session.query(Category).filter_by(id=category_id).first()\r\n if category:\r\n return jsonify(Category=category.serialize)\r\n else:\r\n return jsonify(Category={})","def catelogJSON():\n categories = session.query(Category).all()\n items = session.query(Item).all()\n catelog = []\n for i in categories:\n li = []\n for j in items:\n if j.category_id == i.id:\n temp = j.serialize\n li += [temp]\n temp = i.serialize\n if len(li) > 0:\n temp['Items'] = li\n catelog += [temp]\n\n return jsonify(Categories=catelog)","def categoriesJSON():\n categories = session.query(Category).all()\n return jsonify(Categories=[r.serialize for r in categories])","def getItemsJSON(category_id):\r\n items = session.query(Item).filter_by(category_id=category_id).all()\r\n return jsonify(Items=[i.serialize for i in items])","def allCategoriesJSON():\n categories = db_session.query(Category).all()\n return jsonify(categories=[c.serialize for c in categories])","def getCategoriesJSON():\r\n categories = session.query(Category).all()\r\n return jsonify(Categories=[c.serialize for c in categories])","def category_json(category_id):\n try:\n category = session.query(Category).filter_by(id=category_id).one()\n return jsonify(category=category.serialize)\n except Exception as inst:\n print(type(inst))\n print(inst.args)\n print(inst)","def category():\n kwargs = {k: parse(v) for k, v in request.args.to_dict().items()}\n return jsonify(objects=get_categories(**kwargs))","def categoryItemsJSON(category_id):\n\n try:\n category = session.query(Category).filter_by(id=category_id).one()\n items = session.query(ListItem).filter_by(\n category_id=category_id).all()\n return jsonify(ListItems=[i.serialize for i in items])\n except Exception as e:\n return '''There was a problem with your query %s, please Verify,\n Redirecting to main page...\n <script> setTimeout(function() {\n window.location.href = \"/\";\n }, 2000);\n </script> ''' % (str(e))","def showCategoriesJSON():\r\n session = DBSession()\r\n categories = session.query(Category).all()\r\n return jsonify(Categories=[c.serialize for c in categories])","def get_category(category_name):\n category = session.query(Category).filter_by(\n name=category_name).one_or_none()\n if category:\n category = category.serialize\n items = [\n i.serialize for i in session.query(Item).filter_by(\n category_id=category[\"id\"]).all()]\n if items:\n category[\"Item\"] = items\n return jsonify(Category=category)","def buscar_categorias(id):\n categorias = buscar_categorias_produtos(id)\n categorias = [c.json() for c in categorias]\n\n return jsonify(categorias)","def get_categories():\n categories = Category.query.all()\n formatted_categories = {}\n for category in categories:\n formatted_categories[category.id] = category.type\n\n return jsonify({\n 'success': True,\n 'categories': formatted_categories,\n 'total_categories': len(categories)\n })","def all_categories_handler():\n categories = getAllCategories()\n return jsonify(categories=[i.serialize for i in categories])","def get_categories():\n categories = Category.query.order_by(Category.id).all()\n categories = {category.id: category.name for category in categories}\n\n response = jsonify({\"success\": True, \"categories\": categories})\n\n return response","def catalog_json():\n catalog_json = []\n try:\n all_categories = session.query(Category).all()\n for category in all_categories:\n items_for_category = session.query(\n Item).filter_by(\n category_id=category.id).all()\n items = []\n for current_item in items_for_category:\n items.append(current_item.serialize)\n catalog_json.append({\n 'category_name': category.name,\n 'category_id': category.id,\n 'items': items\n })\n except Exception as e:\n error = {\n 'result': 'No catalog data: ' + str(e)\n }\n catalog_json.append(error)\n return jsonify(catalog_json)","def browse_category(request):\n\n result = {'categories':[], 'products':[]}\n\n u = request.user\n\n page = request.POST.get('page', 1)\n\n cat = Category.objects.get(id=request.POST['cat_id'])\n if cat.children.count() > 0:\n result['categories'] = [c.get_json() for c in cat.children.all()]\n else:\n # display items\n result = Product.objects.filter_category(cat.category_id, page, u) \n\n return JSONHttpResponse(result)","def get_categories(request):\n return JsonResponse(get_all_objects(Category, CategorySerializer), safe=False)","def to_json(self, category):\r\n return \"{{\\\"id\\\": {0}, \" \\\r\n \"\\\"name\\\": \\\"{1}\\\", \" \\\r\n \"\\\"abbreviation\\\": \\\"{2}\\\", \" \\\r\n \"\\\"category\\\": {3}, \" \\\r\n \"\\\"rank\\\": {4}}}\".format(self.id, self.name, self.abbreviation, category.id, self.rank)","def category_retrieve(id):\n category = getCategory(id)\n if category is None:\n return jsonify({}), 204\n else:\n return jsonify(category=category.serialize)","def get_categories():\n\n url = 'https://fr.openfoodfacts.org/categories.json'\n data = requests.get(url).json()\n with open('data/categories.json', 'w') as file:\n file.write(json.dumps(data, indent=4))","def questions_per_category(category_id):\n questions = Question.query.filter(Question.category == category_id\n ).all()\n results = [question.format() for question in questions]\n return jsonify({'success': True, 'questions': results}), 200","def catalog_json():\n all_categories = (session.query(Categories).all())\n all_items = (session.query(Items).all())\n return jsonify(categories=([all_categories.serialize\n for all_categories in all_categories]),\n items=([all_items.serialize\n for all_items in all_items]))","def rest_get_catalogue_handler():\n cats = category.get_all_categories()\n items = item.get_all_items()\n result = {}\n result['categories'] = [c.serialize for c in cats]\n result['items'] = [i.serialize for i in items]\n return jsonify(result)","def get_categories():\n try:\n result = {\n \"success\": True,\n \"categories\": get_all_categories()\n }\n return jsonify(result)\n\n except Exception as exp:\n abort(exp.code)","def get_categories():\n categories = app.preprocessed.uniq_categs\n result = {\n 'success': True,\n 'data': {\n 'categories': categories\n }\n }\n return jsonify(result)","def get_books():\n return jsonify({'books': Book.get_all_books()})","def get_books():\n return jsonify({'books': Book.get_all_books()})","def get_all_categories():\n return jsonify({\n \"success\": True,\n \"categories\": _read_all_categories()\n })","def fill_category(self):\n cursor = self.conn.cursor(cursor_factory=psycopg2.extras.DictCursor)\n categories = dict()\n result = requests.get('https://fr.openfoodfacts.org/categories.json').json()\n for element in result['tags']:\n try:\n cursor.execute(\"INSERT INTO category (tag, name, url) VALUES (%s, %s, %s) RETURNING id, tag\",\n (element[\"id\"], element[\"name\"], element[\"url\"]))\n query_result = cursor.fetchone()\n categories.__setitem__(query_result[1], query_result[0])\n except self.conn.OperationalError:\n print(\"operation Error\")\n except self.conn.DataError:\n print(\"Data Error\")\n self.conn.commit()\n cursor.close()\n return categories","def get(self, id):\n category = Categories.query.get(id)\n\n return marshal(category, Categories.response_fields), 200, {'Content_Type': 'application/json'}","def scrapeCategories():\n page = requests.get(\"http://books.toscrape.com/index.html\")\n soup = BeautifulSoup(page.content, 'html.parser')\n content = soup.find(\"div\", {\"class\": \"side_categories\"}).findChild().find(\"ul\")\n categories = content.find_all(\"a\")\n for category in categories:\n Categories.objects.create(name=category.text.strip())\n return HttpResponse(status=204)","def browse_categories(request):\n\n result = {}\n\n u = request.user\n\n top = Category.objects.get(name=\"Best Buy\")\n result['categories'] = [c.get_json() for c in top.children.all()]\n \n return JSONHttpResponse(result)","def json_api():\n if 'category' in request.args:\n sqlsession = SQLSESSION()\n category = sqlsession.query(Category)\\\n .filter_by(name=request.args['category']).first()\n items = sqlsession.query(Item).filter_by(category_id=category.id)\\\n .all()\n return json.dumps({'category_id': category.id,\n 'category_name': category.name,\n 'items': [item.serialize() for item in items]})\n elif 'item' in request.args:\n sqlsession = SQLSESSION()\n items = sqlsession.query(Item).filter_by(name=request.args['item'])\\\n .all()\n return json.dumps([item.serialize() for item in items])\n sqlsession = SQLSESSION()\n categories = sqlsession.query(Category).all()\n items = sqlsession.query(Item).all()\n return json.dumps(\n {'categories': [cat.serialize() for cat in categories],\n 'items': [item.serialize() for item in items]})","def get_category_questions(category_id):\n questions = (\n Question.query.filter(Question.category_id == category_id)\n .order_by(Question.id)\n .all()\n )\n page = request.args.get(\"page\", 1, type=int)\n current_questions = paginate_questions(questions, page)\n\n if len(current_questions) == 0:\n abort(404)\n\n categories = Category.query.order_by(Category.id).all()\n categories = {category.id: category.name for category in categories}\n\n response = jsonify(\n {\n \"success\": True,\n \"questions\": current_questions,\n \"total_questions\": len(questions),\n \"current_category_id\": category_id,\n \"categories\": categories,\n }\n )\n\n return response","def categories_json():\n try:\n categories = session.query(Category).all()\n return jsonify(categories=[category.serialize for category in categories])\n except Exception as inst:\n print(type(inst))\n print(inst.args)\n print(inst)","def url_to_json(self, category_name):\n request = requests.get(f\"https://fr.openfoodfacts.org/categorie/{category_name}.json\")\n return request.json()","def categories():\n\tcategories = [\n\t\t'News',\n\t\t'Technology',\n\t\t'Music',\n\t\t'Sports'\n\t]\n\tresponse = { 'response': categories }\n\treturn jsonify(response)","def query_categories_handler(query):\n categories = getCategoriesByName(query)\n return jsonify(categories=[i.serialize for i in categories])","def retrieve_questions_by_category(category_id):\n try:\n selection = Question.query.filter(\n Question.category == category_id).order_by(Question.id).all()\n\n if len(selection) == 0:\n abort(404)\n\n questions = []\n\n for question in selection:\n questions.append(question.format())\n\n return jsonify({\n 'success': True,\n 'questions': questions,\n 'total_questions': len(questions),\n 'current_category': Category.query.filter(Category.id == category_id).first().type,\n })\n\n except:\n abort(422)","def read_all():\r\n categories = Category.query.all()\r\n # Serialize the data for the response\r\n category_schema = CategorySchema(many=True)\r\n print('***********************************************************')\r\n return category_schema.dump(categories)","def get_questions_per_category(category_id):\n questions = Question.query.filter(Question.category == category_id).all()\n\n if len(questions) == 0:\n abort(404)\n\n formatted_questions = [question.format() for question in questions]\n return jsonify({\n 'success': True,\n 'questions': formatted_questions,\n 'total_questions': len(formatted_questions),\n 'current_category': category_id\n })","def itemJSON(category_id, item_id):\n item = session.query(Item).filter_by(id=item_id).one()\n return jsonify(Item=item.serialize)","def csv_category_to_api(self):\n # [{'id': 49}, {'id': 60}, {'id': 146}, {'id': 1882}]\n for data in self._api_data:\n data[\"categories\"] = self.map_csv_category(data[\"categories\"])\n # data[\"categories\"] = [{'id': 49}, {'id': 60}, {'id': 146}, {'id': 1882}]","def filter_json_by_category(self, new_json_path):\n # {'supercategory': 'person', 'id': 1, 'name': 'person'}\n ### Filter images:\n print(\"Filtering the annotations ... \")\n json_parent = os.path.split(new_json_path)[0]\n os.makedirs(json_parent, exist_ok=True)\n imgs_ids = [x['id'] for x in self.images] # get img_ids of imgs with the category\n new_imgs = [x for x in self.coco.dataset['images'] if x['id'] in imgs_ids]\n catIds = self.catIds\n ### Filter annotations\n new_annots = [x for x in self.coco.dataset['annotations'] if x['category_id'] in catIds]\n ### Reorganize the ids\n new_imgs, annotations = self.modify_ids(new_imgs, new_annots)\n ### Filter categories\n new_categories = [x for x in self.coco.dataset['categories'] if x['id'] in catIds]\n print(\"new_categories: \", new_categories)\n data = {\n \"info\": self.coco.dataset['info'],\n \"licenses\": self.coco.dataset['licenses'],\n \"images\": new_imgs, \n \"annotations\": new_annots,\n \"categories\": new_categories \n }\n print(\"saving json: \")\n with open(new_json_path, 'w') as f:\n json.dump(data, f)","def categories(request, pk):\n if Category.objects.filter(item_type_id=pk).exists(): # Checks if product_category exists with given id.\n all_categories = Category.objects.filter(item_type_id=pk)\n else:\n return Response(messages.CATEGORY_ITEM_DOES_NOT_EXIST, status=status.HTTP_404_NOT_FOUND)\n if request.method == 'GET':\n category_serializer = CategorySerializer(all_categories, many=True)\n\n return Response(category_serializer.data[::-1], status=status.HTTP_200_OK)","def easyCategory():\n return prepJSON(cs411_dash.easyCategory())","def categoriesJSON():\n\n try:\n categories = session.query(Category).all()\n return jsonify(categories=[r.serialize for r in categories])\n except Exception as e:\n return '''There was a problem with your query %s, please Verify,\n Redirecting to main page...\n <script> setTimeout(function() {\n window.location.href = \"/\";\n }, 2000);\n </script> ''' % (str(e))","def load_categories(request: HttpRequest) -> JsonResponse:\n result = {'categories': []}\n categories = Category.objects.all().order_by('name')\n\n for category in categories:\n sources = category.source_set.all().order_by('name')\n result['categories'].append({\n 'name': category.name,\n 'sources': []\n })\n for source in sources:\n unread_count = source.feed_set.filter(checked=False).count()\n result['categories'][-1]['sources'].append({\n 'name': source.name,\n 'unread_count': unread_count\n })\n\n result['status_code'] = 200\n return JsonResponse(result)","def bookJSON(book_id):\n book = db_session.query(Book).filter_by(id=book_id).one()\n return jsonify(book=book.serialize)","def show_item_json(category, item):\n category = (session.query(Categories)\n .filter_by(name=category.replace('-', ' '))\n .one())\n item = (session.query(Items)\n .filter_by(name=item.replace('-', ' '))\n .one())\n return jsonify(item=[item.serialize])","def toughCategory():\n return prepJSON(cs411_dash.toughCategory())","def get_questions_by_category(category_id: int):\n category = Category.query.get(category_id)\n if category is None:\n abort(400)\n questions_by_category = Question.query.filter(\n Question.category == str(category_id)).all()\n return jsonify({\n \"success\": True,\n \"questions\": list(\n map(lambda x: _map_question(x), questions_by_category)),\n \"total_questions\": len(questions_by_category),\n \"current_category\": _read_category(category_id),\n })","def add_categories_handler():\n rq = request.get_json()\n name = rq['name']\n picture = rq['picture']\n description = rq['description']\n category = addCategory(name, picture, description, g.user.id)\n return jsonify(category=category.serialize)","def write_category(name, books):\n print(f\"Ecriture de la categorie {name}...\")\n # Save in one csv file\n csv_path = os.path.abspath(os.path.dirname(__file__)) + r'\\Results\\\\' + name + '.csv'\n with open(csv_path, 'w', newline='', encoding='utf-8') as csvfile:\n # Headers name\n fieldnames = ['product_page_url',\n 'universal_ product_code (upc)',\n 'title', 'price_including_tax',\n 'price_excluding_tax',\n 'number_available',\n 'product_description',\n 'category',\n 'review_rating',\n 'image_url']\n writer = csv.DictWriter(csvfile, fieldnames=fieldnames, delimiter=';')\n writer.writeheader()\n for book in books:\n # Writing the book's infos\n writer.writerow(book)","def category_list():\n categories = Category.objects.filter(active=True)\n return {'categories': categories}","def add_category(self, category: str) -> None:\n for letter in self.data:\n if not self.data[letter].get(category):\n self.data[letter][category] = []\n print(f'Categoria: {category} adicionada ao dicionário.')\n self.save()\n self.beautify_json()","def items_for_category(category_id):\n items_under_category = session.query(\n Item).filter_by(category_id=category_id).all()\n all_categories = session.query(Category).all()\n category = get_category_in_list(category_id, all_categories)\n return render_template(\n 'views/items.html',\n items=items_under_category,\n current_category=category,\n categories=all_categories)","def book_list_manipulation():\n global book_list\n book_json = {}\n for book in book_list:\n book_container = {'title': book.title,\n 'author': book.author,\n 'read': str(book.read),\n 'rating': book.rating,\n 'review': book.review,\n 'date read': book.date_read,\n 'id': str(book.id)}\n book_json[str(book.id)] = book_container\n\n return book_json","def download_blob():\n r1 = requests.get(url + '/dog.json')\n dicta = r1.json()\n dictb = {}\n for i in range(len(dicta)):\n try:\n dictb[dicta[i]['category']].append(dicta[i]['name'])\n except KeyError:\n dictb[dicta[i]['category']] = []\n jdata = json.dumps(dictb)\n response = requests.put(url + '/category.json', jdata)","def page_to_json(self, category_name, page):\n request = requests.get(f\"https://fr.openfoodfacts.org/categorie/{category_name}/{page}.json\")\n return request.json()","def get(self, catid=None, catname=None):\n print('CAT GET')\n data = []\n\n if catid:\n print(catid)\n catid_info = mongo.db.cats.find_one(\n {\"catid\": catid}, {\"_id\": 0})\n if catid_info:\n return jsonify(\n {\n \"status\": \"200\",\n \"data\": catid_info,\n }\n )\n else:\n return {\"response\": \"no cat found for {}\".format(catid)}\n\n elif catname:\n print(catname)\n cursor = mongo.db.cats.find({\"catname\": catname}, {\"_id\": 0}).limit(\n LIMIT_QUERY\n )\n for cat in cursor:\n cat[\"url\"] = API_URL + \\\n url_for(\"cats\") + \"/\" + cat.get(\"catid\")\n data.append(cat)\n\n return jsonify({\"catname\": catname, \"response\": data})\n\n else:\n cursor = mongo.db.cats.find({}, {\"_id\": 0, \"update_time\": 0}).limit(\n LIMIT_QUERY\n )\n\n for cat in cursor:\n print(cat, API_URL, url_for(\"cats\"), \"/\", cat.get(\"catid\"))\n cat[\"url\"] = API_URL + url_for(\"cats\") + \"/\" + cat.get(\"catid\")\n data.append(cat)\n\n return jsonify({\"response\": data})","def get_categories():\n categories_query = Category.query.order_by(Category.id).all()\n\n if len(categories_query) == 0:\n abort(404)\n\n categories = []\n\n for category in categories_query:\n categories.append(category.type)\n\n return jsonify({\n 'success': True,\n 'categories': categories,\n })","def show_books():\n result = {'books': query.get_book_list()}\n return json.dumps(result, ensure_ascii=False)","def get_all_categorizations():\n try:\n query = run_select_query('SELECT id, category_name, category_item_type, name \\\n FROM categorization \\\n JOIN item ON (categorization.item_id = item.id)')\n return [{'id': obj[0], 'category_name': obj[1], 'category_item_type': obj[2].capitalize(), 'item_name': obj[3]} for obj in query]\n except (Exception, psycopg2.Error) as error:\n return {\"status\": \"error\", \"error\": error}","def getcategory(self):\n\n response = requests.get(\"https://fr.openfoodfacts.org/categories.json\")\n\n data = response.json()\n\n self.rawcategorydata = data","def serialize_categories(md: Metadata) -> list:\n categories = []\n all_cat = md.categories.all()\n for cat in all_cat:\n category = OrderedDict()\n\n category[\"id\"] = cat.id\n category[\"type\"] = cat.type\n category[\"title_EN\"] = cat.title_EN\n category[\"description_EN\"] = cat.description_EN\n category[\"title_locale_1\"] = cat.title_locale_1\n category[\"description_locale_1\"] = cat.description_locale_1\n category[\"title_locale_2\"] = cat.title_locale_2\n category[\"description_locale_2\"] = cat.description_locale_2\n category[\"symbol\"] = cat.symbol\n category[\"online_link\"] = cat.online_link\n\n categories.append(category)\n\n return categories","def test_get_categories_from_json():\n allocator = RecipeAllocator()\n allocator.load_data(\n orders_dir=\"tests/orders.json\", recipes_dir=\"tests/recipes.json\"\n )\n allocator.get_categories_from_json()\n assert list(allocator.portion_categories_dict.keys()) == [\n \"two_portions\",\n \"four_portions\",\n ] and list(allocator.recipe_categories_dict.keys()) == [\n \"two_recipes\",\n \"three_recipes\",\n \"four_recipes\",\n ]","def get_jsonified_catalogs():\n\n json = []\n catalogs = session.query(Catalog).all()\n\n for catalog in catalogs:\n catalog_json = {}\n catalog_json[\"id\"] = catalog.id\n catalog_json[\"name\"] = catalog.name\n catalog_json[\"date\"] = catalog.date.strftime(\"%d %b %Y %H: %M: %S\")\n catalog_json[\"items\"] = []\n for item in catalog.items:\n item_json = {}\n item_json[\"id\"] = item.id\n item_json[\"title\"] = item.title\n item_json[\"desc\"] = item.desc\n item_json[\"date\"] = item.date.strftime(\"%d %b %Y %H: %M: %S\")\n item_json[\"catalog_id\"] = item.catalog_id\n catalog_json[\"items\"].append(item_json)\n json.append(catalog_json)\n return jsonify(Catalogs=json)","def showItemJSON(category_id, item_id):\r\n session = DBSession()\r\n item = session.query(Item).filter_by(id=item_id).one()\r\n return jsonify(Item=item.serialize)","def get(current_user, self, category_id):\n\n if not current_user:\n return is_unauthorized()\n\n category = current_user.categories.filter_by(id=category_id).first()\n if category:\n recipes = category.recipes.all()\n\n if not recipes:\n response_payload = dict(\n message='No recipes added to this category yet!'\n )\n response_payload = jsonify(response_payload)\n return make_response(response_payload, 404)\n\n # search and/or paginate\n args = parser.parse(SEARCH_PAGE_ARGS, request)\n if 'q' in args:\n try:\n recipes = current_user.recipes.filter(\n Recipe.name.ilike(\"%\" + args['q'] + \"%\"),\n Recipe.category_id == category.id\n ).paginate(page=args['page'], per_page=args['per_page'], error_out=False)\n except KeyError:\n recipes = current_user.recipes.filter(\n Recipe.name.ilike(\"%\" + args['q'] + \"%\"),\n Recipe.category_id == category.id\n ).paginate(page=1, per_page=5)\n else:\n recipes = category.recipes.paginate(per_page=2)\n base_url = request.base_url\n if 'q' in args:\n pagination_details = _pagination(recipes, base_url, q=args['q'])\n else:\n pagination_details = _pagination(recipes, base_url)\n user_recipes = []\n for current_recipe in recipes.items:\n this_recipe = make_payload(recipe=current_recipe)\n user_recipes.append(this_recipe)\n if user_recipes:\n response_payload = {\n \"recipes\": user_recipes,\n \"page_details\": pagination_details\n }\n return make_response(jsonify(response_payload), 200)\n response_payload = {\n \"message\": \"Recipe does not exist.\"\n }\n return make_response(jsonify(response_payload), 400)\n response_payload = dict(\n message='Invalid category!'\n )\n response_payload = jsonify(response_payload)\n return make_response(response_payload, 400)","def get_categories(request):\n try:\n categories = []\n for category in Category.objects.all():\n categories.append({\"title\": category.title, \"id\": category.pk})\n\n return format_ajax_response(True, \"Knowledgebase categories retrieved successfully.\", {\"categories\": categories})\n except Exception as ex:\n logger.error(\"Failed to get_categories: %s\" % ex)\n return format_ajax_response(False, \"There was an error retreiving the knowledgebase categories.\")","def get_questions_by_category(category_id):\n try:\n category = get_category_by_id(category_id)\n\n if category is None:\n abort(STATUS_NOT_FOUND)\n\n questions = get_all_questions(category_id=category_id)\n return jsonify({\n \"success\": True,\n \"questions\": questions,\n \"total_questions\": len(questions),\n \"current_category\": category.format(),\n })\n\n except Exception as exp:\n abort(exp.code)","def export_chapter_links_to_json(book_name, book_link, idd):\n chapters = {\"01\" : book_link}\n home = Ripper(book_link, save_path=save_path)\n \n if not os.path.exists(chapter_store):\n os.mkdir(chapter_store)\n \n for each in home.raw_links:\n str_each = str(each)\n excludes = [\"next\" in str_each, \"previous\" in str_each, \"chapter\" in str_each, \"statcounter\" in str_each,\n \"/x/\" in str_each, \"DRBO.ORG\" in str_each, \"theologica\" in str_each]\n \n if not any(excludes):\n chapters[each.text] = domain + \"chapter/\" + each.get(\"href\")\n\n book_chaps = {book_name : chapters}\n \n book_save_name = \"{}.json\".format(os.path.join(chapter_store, normalize_filename(book_name)))\n\n with open(book_save_name, \"w+\") as wh:\n json.dump(book_chaps, wh)\n return book_chaps","def _traverse_categories():\n \n csv_output_dict = _get_csv_data()\n print csv_output_dict.keys()\n #~ if csv_output_dict:\n #~ return csv_output_dict\n #~ else:\n final_output_url_list = []\n traverse_cat_output = {}\n # brand_seed_page = 'https://www.broderbros.com/cgi-bin/online/webshr/browse-brand-all.w'\n seed_page = 'https://www.broderbros.com/cgi-bin/online/webshr/browse-category-all.w'\n \n browser = utils.create_browser(SLEEP_MIN, SLEEP_MAX)\n browser.open(seed_page)\n html_response = browser.response().read()\n \n soup = BeautifulSoup(html_response)\n \n all_category_names = soup.findAll('div', {'class': 'brwsBrndLogoClip'})\n for category_names in all_category_names:\n temp_category_url = str(category_names.a['href'])\n category_url_name = temp_category_url.split('&catname=')[-1]\n category_url = 'https://www.broderbros.com/cgi-bin/online/webshr/search-result.w?nResults=5000&mc=&RequestAction=advisor&RequestData=CA_CategoryExpand&bpath=c&CatPath=All Products////BRO-Categories////'+str(category_url_name)\n category_url = category_url.replace(' ', '%20')\n category_names = category_names.a.img['alt'].lower()\n returned_url_list = _get_product_urls(category_url, category_names)\n final_output_url_list.extend(returned_url_list)\n \n for productsurl in final_output_url_list:\n if productsurl['id'] in traverse_cat_output.keys() and productsurl['category_list'] != traverse_cat_output[productsurl['id']]['category_list']:\n traverse_cat_output[productsurl['id']]['category_list'].extend(productsurl['category_list'])\n else:\n traverse_cat_output[productsurl['id']] = productsurl\n \n brand_output_categories = _traverse_categories_brand()\n for brand_url in brand_output_categories:\n if brand_url['id'] in traverse_cat_output.keys():\n traverse_cat_output[brand_url['id']]['brand'] = brand_url['brand']\n else:\n traverse_cat_output['id'] = brand_url\n \n if TESTRUN:\n for k,v in traverse_cat_output.items():\n print v\n print '*'*78\n for item_id in traverse_cat_output.keys():\n print item_id\n csv_data = csv_output_dict.get(item_id)\n if csv_data:\n traverse_cat_output[item_id]['brand'] = csv_data['brand']\n if not traverse_cat_output[item_id].get('brand'):\n del traverse_cat_output[item_id]\n \n return traverse_cat_output","def get_all_by_category(self, category):\n products = self.db.query(f\"\"\"\n SELECT product.id, product.name, product.nutrition_grade,\n product.url from product\n JOIN product_category ON product_category.product_id = product.id\n JOIN category ON product_category.category_id = category.id\n WHERE category.id = :id\n \"\"\", id=category.id).all(as_dict=True)\n return [self.model(**product) for product in products]","def category_items(category_id):\n\n items = Item.query.filter(\n Item.category_id == category_id,\n Item.user_id == current_user.id\n ).all()\n categories = Category.query.filter(\n Category.user_id == current_user.id).all()\n if not categories:\n flash(\"Couldn't find this category\", category='warning')\n\n return render_template(\n 'index.html',\n categories=categories,\n items=items,\n current_category_id=category_id)","def get_all_by_category(self, category):\n products = self.db.query(f\"\"\"\n SELECT product.id, product.name, product.nutrition_grade\n from product\n JOIN product_category ON product_category.product_id = product.id\n JOIN category ON product_category.category_id = category.id\n WHERE category.id = :id\n \"\"\", id=category.id).all(as_dict=True)\n return [self.model(**product) for product in products]","def getAllCategory():\n return session.query(Category).all()","def json_export(json_file,gradebook):\n \n try:\n import json\n except ImportError:\n warnings.warn('Failed to import json module. Cannot execute json_export')\n return\n if not hasattr(json_file,'write'):\n if not isinstance(json_file,basestring) or not \\\n os.path.exists(os.path.dirname(os.path.abspath(json_file))):\n raise ValueError, 'Argument \\'json_file\\' is not readable, ' \\\n 'and could not be validated as a file path.'\n else:\n json_file = open(json_file,'w+')\n \n if not isinstance(gradebook,Gradebook):\n raise TypeError, 'gradebook argument must be a Gradebook objcet.'\n \n cat_dict = dict(gradebook._Gradebook__categories)\n all_items = []\n for cat_name in cat_dict:\n gr_leest = cat_dict[cat_name].grades.select(docopy=True,aslist=True)\n for i in gr_leest:\n x = {}\n if not isinstance(i,Grade):\n continue\n x['type']='Grade'\n x['name']=i.name\n if i.parent is not None:\n if i.parent.name == cat_name or i.parent.name in cat_dict:\n x['parent'] = i.parent.name\n else:\n x['parent'] = None\n attribs = {}\n attribs['score'] = i.score\n attribs['maximum'] = i.maximum\n attribs['weight'] = i.weight\n attribs['extra_credit'] = i.extra_credit\n if i.overrides:\n attribs['overrides'] = list(i.overrides)\n for ovrrd in i.overrides:\n if not isinstance(overrd,basestring):\n attribs['overrides'].remove(overrd)\n if i.timestamp and isinstance(i.timestamp,datetime.datetime):\n tmstmp = calendar.timegm(i.timestamp.utctimetuple())\n attribs['timestamp'] = tmstmp\n if i.identifiers:\n attribs['identifiers'] = dict(i.identifiers)\n \n x['attribs'] = attribs\n all_items.append(x)\n catt = {'type':'Category','name':cat_name}\n if cat_dict[cat_name].parent is not None:\n catt['parent'] = cat_dict[cat_name].parent.name\n else:\n catt['parent'] = None\n catt['attribs'] = dict(cat_dict[cat_name]._Category__attribs)\n all_items.append(catt)\n \n grbk = {'type':'Gradebook','name':gradebook.name,'user':gradebook.user}\n all_items.append(grbk)\n enc_me = {\"grading\": all_items}\n \n encoder = json.JSONEncoder(indent=4,separators=(', ',': '))\n enc = encoder.encode(enc_me)\n \n json_file.write(enc)\n json_file.close()","def get_recipies_from_category(category_id,\n usertype=1,\n page=0,\n count=0,\n session=None):\n if session is None:\n session = requests.Session()\n payload = {'categoryId': category_id,\n 'usertype': usertype,\n 'page': page,\n 'offset': count}\n response = session.get(\n 'https://cms.sortedfood.com/apiRecipe/getFeaturedByUsertype',\n params=payload)\n if response.status_code != 200:\n raise response.HTTPError(response)\n response.encoding = 'utf-8'\n return json.loads(response.text)","def fetch_all_categories():\n query_set = Categories.objects.filter(is_delete=False)\n serializer = CategoriesSerializer(query_set, many=True)\n return serializer.data","def pass_category(url, name):\n print(f\"Passage de la catégorie {name}...\")\n # Initialize list of books\n books = []\n while url != '':\n response = requests.get(url)\n if response.status_code == 200:\n soup = BeautifulSoup(response.content, 'html.parser')\n # Books are notify with article balise\n articles = soup.findAll('article', {'class': 'product_pod'})\n for article in tqdm(articles):\n # Get the book's link\n link = url + \"/../\" + article.find('h3').find('a')['href']\n # Run the function to get book's info\n dict_product = get_book_infos(link)\n books.append(dict_product)\n # Searching for a next page\n next_page = soup.find('li', {'class': 'next'})\n if not next_page:\n url = ''\n else:\n url = url + \"/../\" + soup.find('li', {'class': 'next'}).find('a')['href']\n write_category(name, books)","def categories(data):\n if data:\n for i in data:\n category = CategoriesModel(categories=i['categories'])\n category.save()","def recipes(category):\n # if statements to display the recipes base on category name\n if category == \"Pre Workout Meal\":\n recipe = mongo.db.recipes.find({\"category_name\": \"Pre Workout Meal\"})\n elif category == \"Post Workout Meal\":\n recipe = mongo.db.recipes.find({\"category_name\": \"Post Workout Meal\"})\n else:\n recipe = mongo.db.recipes.find()\n\n return render_template('pages/allrecipe.html', recipe=recipe, category_title=category, recipes=mongo.db.recipes.find(), isFooter=True)","def to_json(self):\n return {\n \"category_name\": self.category_name,\n \"summary\": self.summary,\n \"image\": self.image,\n \"category_slug\": self.category_slug,\n }","def recipes():\n if request.method == 'GET':\n return Response(\n json.dumps(recipebook.to_json_list()),\n mimetype=\"application/json\")\n elif request.method == 'POST':\n new_dict = request.get_json()\n recipebook.recipes.append(models.Recipe.from_json_dict(new_dict))\n write_out()\n return Response(status=200)","def show_category_items(category_id):\n all_categories = session.execute(\n 'SELECT category.name, category.id, count(item.id) AS item_count '\n 'FROM category LEFT JOIN item ON category.id = item.category_id '\n 'GROUP BY category.name, category.id')\n category = session.query(Category).filter(Category.id == category_id).first()\n items = session.query(Item).filter(Item.category_id == category_id)\n item_count = items.count()\n return render_template('category_items.html',\n all_categories=all_categories,\n category=category,\n items=items,\n item_count=item_count,\n login_session=login_session)","def get_products(categories_info):\n\n for name, urlnames in categories_info.items():\n for i in range(1, (NB_PAGES+1)):\n url = 'https://world.openfoodfacts.org/cgi/search.pl?\\\n search_tag=categories&search_terms={}&\\\n purchase_places=France&page_size={}&page={}&json=1'.format(\n urlnames, str(PRODUCTS_PER_PAGE), str(i))\n data = requests.get(url).json()\n name_file = 'data/Products_' + name + str(i) + '.json'\n with open(name_file, 'w') as file:\n file.write(json.dumps(data, indent=4))","def generate_category_dict(category_file):\n L = json.load(open(category_file))\n d = {}\n for dict_item in L:\n item = item_to_dict(dict_item)\n if not item['Label'] is None:\n d[item['URN']] = item['Label']\n else:\n d[item['URN']] = ''\n return d","def cat_results_react():\n\n cats = petfinder.search_data_map()\n cats = list(cats.values())\n\n return jsonify(cats)","def showItemsJSON():\n items = db.getAllItems()\n return jsonify(CategoryItems=[i.serialize for i in items])","def get(self, request, format=None):\n categories = Category.objects.all()\n data_out = CategorySerializer(categories, many=True)\n return Response(data_out.data)"],"string":"[\n \"def api_category_items(category_id):\\n items = session.query(Item).filter(Item.category_id == category_id)\\n return jsonify(json_list=[i.to_json() for i in items.all()])\",\n \"def categoryItemJSON(category_id):\\n category = db.getByCategory(category_id)\\n items = db.getItemsByCategory(category_id)\\n return jsonify(Items=[i.serialize for i in items])\",\n \"def show_category_json(category):\\n category = (session.query(Categories)\\n .filter_by(name=category.replace('-', ' '))\\n .one())\\n category_items = (session.query(Items)\\n .filter_by(category_id=category.id)\\n .order_by(Items.name)\\n .all())\\n return jsonify(category=[category.serialize],\\n items=([category_items.serialize\\n for category_items in category_items]))\",\n \"def categoryItemJSON(category_id):\\n category = session.query(Category).\\\\\\n filter_by(id=category_id).one()\\n items = session.query(Item).\\\\\\n filter_by(category_id=category_id).all()\\n return jsonify(Items=[i.serialize for i in items])\",\n \"def showCategoryItemsJSON(category_id):\\r\\n session = DBSession()\\r\\n items = session.query(Item).filter_by(category_id=category_id)\\r\\n return jsonify(Items=[i.serialize for i in items])\",\n \"def api_categories():\\n categories = session.query(Category)\\n return jsonify(json_list=[i.to_json() for i in categories.all()])\",\n \"def get_categories():\\n categories = [c.serialize for c in session.query(Category).all()]\\n for c in range(len(categories)):\\n items = [\\n i.serialize for i in session.query(Item).filter_by(\\n category_id=categories[c][\\\"id\\\"]).all()]\\n if items:\\n categories[c][\\\"Item\\\"] = items\\n return jsonify(Category=categories)\",\n \"def getCategoryJSON(category_id):\\r\\n category = session.query(Category).filter_by(id=category_id).first()\\r\\n if category:\\r\\n return jsonify(Category=category.serialize)\\r\\n else:\\r\\n return jsonify(Category={})\",\n \"def catelogJSON():\\n categories = session.query(Category).all()\\n items = session.query(Item).all()\\n catelog = []\\n for i in categories:\\n li = []\\n for j in items:\\n if j.category_id == i.id:\\n temp = j.serialize\\n li += [temp]\\n temp = i.serialize\\n if len(li) > 0:\\n temp['Items'] = li\\n catelog += [temp]\\n\\n return jsonify(Categories=catelog)\",\n \"def categoriesJSON():\\n categories = session.query(Category).all()\\n return jsonify(Categories=[r.serialize for r in categories])\",\n \"def getItemsJSON(category_id):\\r\\n items = session.query(Item).filter_by(category_id=category_id).all()\\r\\n return jsonify(Items=[i.serialize for i in items])\",\n \"def allCategoriesJSON():\\n categories = db_session.query(Category).all()\\n return jsonify(categories=[c.serialize for c in categories])\",\n \"def getCategoriesJSON():\\r\\n categories = session.query(Category).all()\\r\\n return jsonify(Categories=[c.serialize for c in categories])\",\n \"def category_json(category_id):\\n try:\\n category = session.query(Category).filter_by(id=category_id).one()\\n return jsonify(category=category.serialize)\\n except Exception as inst:\\n print(type(inst))\\n print(inst.args)\\n print(inst)\",\n \"def category():\\n kwargs = {k: parse(v) for k, v in request.args.to_dict().items()}\\n return jsonify(objects=get_categories(**kwargs))\",\n \"def categoryItemsJSON(category_id):\\n\\n try:\\n category = session.query(Category).filter_by(id=category_id).one()\\n items = session.query(ListItem).filter_by(\\n category_id=category_id).all()\\n return jsonify(ListItems=[i.serialize for i in items])\\n except Exception as e:\\n return '''There was a problem with your query %s, please Verify,\\n Redirecting to main page...\\n <script> setTimeout(function() {\\n window.location.href = \\\"/\\\";\\n }, 2000);\\n </script> ''' % (str(e))\",\n \"def showCategoriesJSON():\\r\\n session = DBSession()\\r\\n categories = session.query(Category).all()\\r\\n return jsonify(Categories=[c.serialize for c in categories])\",\n \"def get_category(category_name):\\n category = session.query(Category).filter_by(\\n name=category_name).one_or_none()\\n if category:\\n category = category.serialize\\n items = [\\n i.serialize for i in session.query(Item).filter_by(\\n category_id=category[\\\"id\\\"]).all()]\\n if items:\\n category[\\\"Item\\\"] = items\\n return jsonify(Category=category)\",\n \"def buscar_categorias(id):\\n categorias = buscar_categorias_produtos(id)\\n categorias = [c.json() for c in categorias]\\n\\n return jsonify(categorias)\",\n \"def get_categories():\\n categories = Category.query.all()\\n formatted_categories = {}\\n for category in categories:\\n formatted_categories[category.id] = category.type\\n\\n return jsonify({\\n 'success': True,\\n 'categories': formatted_categories,\\n 'total_categories': len(categories)\\n })\",\n \"def all_categories_handler():\\n categories = getAllCategories()\\n return jsonify(categories=[i.serialize for i in categories])\",\n \"def get_categories():\\n categories = Category.query.order_by(Category.id).all()\\n categories = {category.id: category.name for category in categories}\\n\\n response = jsonify({\\\"success\\\": True, \\\"categories\\\": categories})\\n\\n return response\",\n \"def catalog_json():\\n catalog_json = []\\n try:\\n all_categories = session.query(Category).all()\\n for category in all_categories:\\n items_for_category = session.query(\\n Item).filter_by(\\n category_id=category.id).all()\\n items = []\\n for current_item in items_for_category:\\n items.append(current_item.serialize)\\n catalog_json.append({\\n 'category_name': category.name,\\n 'category_id': category.id,\\n 'items': items\\n })\\n except Exception as e:\\n error = {\\n 'result': 'No catalog data: ' + str(e)\\n }\\n catalog_json.append(error)\\n return jsonify(catalog_json)\",\n \"def browse_category(request):\\n\\n result = {'categories':[], 'products':[]}\\n\\n u = request.user\\n\\n page = request.POST.get('page', 1)\\n\\n cat = Category.objects.get(id=request.POST['cat_id'])\\n if cat.children.count() > 0:\\n result['categories'] = [c.get_json() for c in cat.children.all()]\\n else:\\n # display items\\n result = Product.objects.filter_category(cat.category_id, page, u) \\n\\n return JSONHttpResponse(result)\",\n \"def get_categories(request):\\n return JsonResponse(get_all_objects(Category, CategorySerializer), safe=False)\",\n \"def to_json(self, category):\\r\\n return \\\"{{\\\\\\\"id\\\\\\\": {0}, \\\" \\\\\\r\\n \\\"\\\\\\\"name\\\\\\\": \\\\\\\"{1}\\\\\\\", \\\" \\\\\\r\\n \\\"\\\\\\\"abbreviation\\\\\\\": \\\\\\\"{2}\\\\\\\", \\\" \\\\\\r\\n \\\"\\\\\\\"category\\\\\\\": {3}, \\\" \\\\\\r\\n \\\"\\\\\\\"rank\\\\\\\": {4}}}\\\".format(self.id, self.name, self.abbreviation, category.id, self.rank)\",\n \"def category_retrieve(id):\\n category = getCategory(id)\\n if category is None:\\n return jsonify({}), 204\\n else:\\n return jsonify(category=category.serialize)\",\n \"def get_categories():\\n\\n url = 'https://fr.openfoodfacts.org/categories.json'\\n data = requests.get(url).json()\\n with open('data/categories.json', 'w') as file:\\n file.write(json.dumps(data, indent=4))\",\n \"def questions_per_category(category_id):\\n questions = Question.query.filter(Question.category == category_id\\n ).all()\\n results = [question.format() for question in questions]\\n return jsonify({'success': True, 'questions': results}), 200\",\n \"def catalog_json():\\n all_categories = (session.query(Categories).all())\\n all_items = (session.query(Items).all())\\n return jsonify(categories=([all_categories.serialize\\n for all_categories in all_categories]),\\n items=([all_items.serialize\\n for all_items in all_items]))\",\n \"def rest_get_catalogue_handler():\\n cats = category.get_all_categories()\\n items = item.get_all_items()\\n result = {}\\n result['categories'] = [c.serialize for c in cats]\\n result['items'] = [i.serialize for i in items]\\n return jsonify(result)\",\n \"def get_categories():\\n try:\\n result = {\\n \\\"success\\\": True,\\n \\\"categories\\\": get_all_categories()\\n }\\n return jsonify(result)\\n\\n except Exception as exp:\\n abort(exp.code)\",\n \"def get_categories():\\n categories = app.preprocessed.uniq_categs\\n result = {\\n 'success': True,\\n 'data': {\\n 'categories': categories\\n }\\n }\\n return jsonify(result)\",\n \"def get_books():\\n return jsonify({'books': Book.get_all_books()})\",\n \"def get_books():\\n return jsonify({'books': Book.get_all_books()})\",\n \"def get_all_categories():\\n return jsonify({\\n \\\"success\\\": True,\\n \\\"categories\\\": _read_all_categories()\\n })\",\n \"def fill_category(self):\\n cursor = self.conn.cursor(cursor_factory=psycopg2.extras.DictCursor)\\n categories = dict()\\n result = requests.get('https://fr.openfoodfacts.org/categories.json').json()\\n for element in result['tags']:\\n try:\\n cursor.execute(\\\"INSERT INTO category (tag, name, url) VALUES (%s, %s, %s) RETURNING id, tag\\\",\\n (element[\\\"id\\\"], element[\\\"name\\\"], element[\\\"url\\\"]))\\n query_result = cursor.fetchone()\\n categories.__setitem__(query_result[1], query_result[0])\\n except self.conn.OperationalError:\\n print(\\\"operation Error\\\")\\n except self.conn.DataError:\\n print(\\\"Data Error\\\")\\n self.conn.commit()\\n cursor.close()\\n return categories\",\n \"def get(self, id):\\n category = Categories.query.get(id)\\n\\n return marshal(category, Categories.response_fields), 200, {'Content_Type': 'application/json'}\",\n \"def scrapeCategories():\\n page = requests.get(\\\"http://books.toscrape.com/index.html\\\")\\n soup = BeautifulSoup(page.content, 'html.parser')\\n content = soup.find(\\\"div\\\", {\\\"class\\\": \\\"side_categories\\\"}).findChild().find(\\\"ul\\\")\\n categories = content.find_all(\\\"a\\\")\\n for category in categories:\\n Categories.objects.create(name=category.text.strip())\\n return HttpResponse(status=204)\",\n \"def browse_categories(request):\\n\\n result = {}\\n\\n u = request.user\\n\\n top = Category.objects.get(name=\\\"Best Buy\\\")\\n result['categories'] = [c.get_json() for c in top.children.all()]\\n \\n return JSONHttpResponse(result)\",\n \"def json_api():\\n if 'category' in request.args:\\n sqlsession = SQLSESSION()\\n category = sqlsession.query(Category)\\\\\\n .filter_by(name=request.args['category']).first()\\n items = sqlsession.query(Item).filter_by(category_id=category.id)\\\\\\n .all()\\n return json.dumps({'category_id': category.id,\\n 'category_name': category.name,\\n 'items': [item.serialize() for item in items]})\\n elif 'item' in request.args:\\n sqlsession = SQLSESSION()\\n items = sqlsession.query(Item).filter_by(name=request.args['item'])\\\\\\n .all()\\n return json.dumps([item.serialize() for item in items])\\n sqlsession = SQLSESSION()\\n categories = sqlsession.query(Category).all()\\n items = sqlsession.query(Item).all()\\n return json.dumps(\\n {'categories': [cat.serialize() for cat in categories],\\n 'items': [item.serialize() for item in items]})\",\n \"def get_category_questions(category_id):\\n questions = (\\n Question.query.filter(Question.category_id == category_id)\\n .order_by(Question.id)\\n .all()\\n )\\n page = request.args.get(\\\"page\\\", 1, type=int)\\n current_questions = paginate_questions(questions, page)\\n\\n if len(current_questions) == 0:\\n abort(404)\\n\\n categories = Category.query.order_by(Category.id).all()\\n categories = {category.id: category.name for category in categories}\\n\\n response = jsonify(\\n {\\n \\\"success\\\": True,\\n \\\"questions\\\": current_questions,\\n \\\"total_questions\\\": len(questions),\\n \\\"current_category_id\\\": category_id,\\n \\\"categories\\\": categories,\\n }\\n )\\n\\n return response\",\n \"def categories_json():\\n try:\\n categories = session.query(Category).all()\\n return jsonify(categories=[category.serialize for category in categories])\\n except Exception as inst:\\n print(type(inst))\\n print(inst.args)\\n print(inst)\",\n \"def url_to_json(self, category_name):\\n request = requests.get(f\\\"https://fr.openfoodfacts.org/categorie/{category_name}.json\\\")\\n return request.json()\",\n \"def categories():\\n\\tcategories = [\\n\\t\\t'News',\\n\\t\\t'Technology',\\n\\t\\t'Music',\\n\\t\\t'Sports'\\n\\t]\\n\\tresponse = { 'response': categories }\\n\\treturn jsonify(response)\",\n \"def query_categories_handler(query):\\n categories = getCategoriesByName(query)\\n return jsonify(categories=[i.serialize for i in categories])\",\n \"def retrieve_questions_by_category(category_id):\\n try:\\n selection = Question.query.filter(\\n Question.category == category_id).order_by(Question.id).all()\\n\\n if len(selection) == 0:\\n abort(404)\\n\\n questions = []\\n\\n for question in selection:\\n questions.append(question.format())\\n\\n return jsonify({\\n 'success': True,\\n 'questions': questions,\\n 'total_questions': len(questions),\\n 'current_category': Category.query.filter(Category.id == category_id).first().type,\\n })\\n\\n except:\\n abort(422)\",\n \"def read_all():\\r\\n categories = Category.query.all()\\r\\n # Serialize the data for the response\\r\\n category_schema = CategorySchema(many=True)\\r\\n print('***********************************************************')\\r\\n return category_schema.dump(categories)\",\n \"def get_questions_per_category(category_id):\\n questions = Question.query.filter(Question.category == category_id).all()\\n\\n if len(questions) == 0:\\n abort(404)\\n\\n formatted_questions = [question.format() for question in questions]\\n return jsonify({\\n 'success': True,\\n 'questions': formatted_questions,\\n 'total_questions': len(formatted_questions),\\n 'current_category': category_id\\n })\",\n \"def itemJSON(category_id, item_id):\\n item = session.query(Item).filter_by(id=item_id).one()\\n return jsonify(Item=item.serialize)\",\n \"def csv_category_to_api(self):\\n # [{'id': 49}, {'id': 60}, {'id': 146}, {'id': 1882}]\\n for data in self._api_data:\\n data[\\\"categories\\\"] = self.map_csv_category(data[\\\"categories\\\"])\\n # data[\\\"categories\\\"] = [{'id': 49}, {'id': 60}, {'id': 146}, {'id': 1882}]\",\n \"def filter_json_by_category(self, new_json_path):\\n # {'supercategory': 'person', 'id': 1, 'name': 'person'}\\n ### Filter images:\\n print(\\\"Filtering the annotations ... \\\")\\n json_parent = os.path.split(new_json_path)[0]\\n os.makedirs(json_parent, exist_ok=True)\\n imgs_ids = [x['id'] for x in self.images] # get img_ids of imgs with the category\\n new_imgs = [x for x in self.coco.dataset['images'] if x['id'] in imgs_ids]\\n catIds = self.catIds\\n ### Filter annotations\\n new_annots = [x for x in self.coco.dataset['annotations'] if x['category_id'] in catIds]\\n ### Reorganize the ids\\n new_imgs, annotations = self.modify_ids(new_imgs, new_annots)\\n ### Filter categories\\n new_categories = [x for x in self.coco.dataset['categories'] if x['id'] in catIds]\\n print(\\\"new_categories: \\\", new_categories)\\n data = {\\n \\\"info\\\": self.coco.dataset['info'],\\n \\\"licenses\\\": self.coco.dataset['licenses'],\\n \\\"images\\\": new_imgs, \\n \\\"annotations\\\": new_annots,\\n \\\"categories\\\": new_categories \\n }\\n print(\\\"saving json: \\\")\\n with open(new_json_path, 'w') as f:\\n json.dump(data, f)\",\n \"def categories(request, pk):\\n if Category.objects.filter(item_type_id=pk).exists(): # Checks if product_category exists with given id.\\n all_categories = Category.objects.filter(item_type_id=pk)\\n else:\\n return Response(messages.CATEGORY_ITEM_DOES_NOT_EXIST, status=status.HTTP_404_NOT_FOUND)\\n if request.method == 'GET':\\n category_serializer = CategorySerializer(all_categories, many=True)\\n\\n return Response(category_serializer.data[::-1], status=status.HTTP_200_OK)\",\n \"def easyCategory():\\n return prepJSON(cs411_dash.easyCategory())\",\n \"def categoriesJSON():\\n\\n try:\\n categories = session.query(Category).all()\\n return jsonify(categories=[r.serialize for r in categories])\\n except Exception as e:\\n return '''There was a problem with your query %s, please Verify,\\n Redirecting to main page...\\n <script> setTimeout(function() {\\n window.location.href = \\\"/\\\";\\n }, 2000);\\n </script> ''' % (str(e))\",\n \"def load_categories(request: HttpRequest) -> JsonResponse:\\n result = {'categories': []}\\n categories = Category.objects.all().order_by('name')\\n\\n for category in categories:\\n sources = category.source_set.all().order_by('name')\\n result['categories'].append({\\n 'name': category.name,\\n 'sources': []\\n })\\n for source in sources:\\n unread_count = source.feed_set.filter(checked=False).count()\\n result['categories'][-1]['sources'].append({\\n 'name': source.name,\\n 'unread_count': unread_count\\n })\\n\\n result['status_code'] = 200\\n return JsonResponse(result)\",\n \"def bookJSON(book_id):\\n book = db_session.query(Book).filter_by(id=book_id).one()\\n return jsonify(book=book.serialize)\",\n \"def show_item_json(category, item):\\n category = (session.query(Categories)\\n .filter_by(name=category.replace('-', ' '))\\n .one())\\n item = (session.query(Items)\\n .filter_by(name=item.replace('-', ' '))\\n .one())\\n return jsonify(item=[item.serialize])\",\n \"def toughCategory():\\n return prepJSON(cs411_dash.toughCategory())\",\n \"def get_questions_by_category(category_id: int):\\n category = Category.query.get(category_id)\\n if category is None:\\n abort(400)\\n questions_by_category = Question.query.filter(\\n Question.category == str(category_id)).all()\\n return jsonify({\\n \\\"success\\\": True,\\n \\\"questions\\\": list(\\n map(lambda x: _map_question(x), questions_by_category)),\\n \\\"total_questions\\\": len(questions_by_category),\\n \\\"current_category\\\": _read_category(category_id),\\n })\",\n \"def add_categories_handler():\\n rq = request.get_json()\\n name = rq['name']\\n picture = rq['picture']\\n description = rq['description']\\n category = addCategory(name, picture, description, g.user.id)\\n return jsonify(category=category.serialize)\",\n \"def write_category(name, books):\\n print(f\\\"Ecriture de la categorie {name}...\\\")\\n # Save in one csv file\\n csv_path = os.path.abspath(os.path.dirname(__file__)) + r'\\\\Results\\\\\\\\' + name + '.csv'\\n with open(csv_path, 'w', newline='', encoding='utf-8') as csvfile:\\n # Headers name\\n fieldnames = ['product_page_url',\\n 'universal_ product_code (upc)',\\n 'title', 'price_including_tax',\\n 'price_excluding_tax',\\n 'number_available',\\n 'product_description',\\n 'category',\\n 'review_rating',\\n 'image_url']\\n writer = csv.DictWriter(csvfile, fieldnames=fieldnames, delimiter=';')\\n writer.writeheader()\\n for book in books:\\n # Writing the book's infos\\n writer.writerow(book)\",\n \"def category_list():\\n categories = Category.objects.filter(active=True)\\n return {'categories': categories}\",\n \"def add_category(self, category: str) -> None:\\n for letter in self.data:\\n if not self.data[letter].get(category):\\n self.data[letter][category] = []\\n print(f'Categoria: {category} adicionada ao dicionário.')\\n self.save()\\n self.beautify_json()\",\n \"def items_for_category(category_id):\\n items_under_category = session.query(\\n Item).filter_by(category_id=category_id).all()\\n all_categories = session.query(Category).all()\\n category = get_category_in_list(category_id, all_categories)\\n return render_template(\\n 'views/items.html',\\n items=items_under_category,\\n current_category=category,\\n categories=all_categories)\",\n \"def book_list_manipulation():\\n global book_list\\n book_json = {}\\n for book in book_list:\\n book_container = {'title': book.title,\\n 'author': book.author,\\n 'read': str(book.read),\\n 'rating': book.rating,\\n 'review': book.review,\\n 'date read': book.date_read,\\n 'id': str(book.id)}\\n book_json[str(book.id)] = book_container\\n\\n return book_json\",\n \"def download_blob():\\n r1 = requests.get(url + '/dog.json')\\n dicta = r1.json()\\n dictb = {}\\n for i in range(len(dicta)):\\n try:\\n dictb[dicta[i]['category']].append(dicta[i]['name'])\\n except KeyError:\\n dictb[dicta[i]['category']] = []\\n jdata = json.dumps(dictb)\\n response = requests.put(url + '/category.json', jdata)\",\n \"def page_to_json(self, category_name, page):\\n request = requests.get(f\\\"https://fr.openfoodfacts.org/categorie/{category_name}/{page}.json\\\")\\n return request.json()\",\n \"def get(self, catid=None, catname=None):\\n print('CAT GET')\\n data = []\\n\\n if catid:\\n print(catid)\\n catid_info = mongo.db.cats.find_one(\\n {\\\"catid\\\": catid}, {\\\"_id\\\": 0})\\n if catid_info:\\n return jsonify(\\n {\\n \\\"status\\\": \\\"200\\\",\\n \\\"data\\\": catid_info,\\n }\\n )\\n else:\\n return {\\\"response\\\": \\\"no cat found for {}\\\".format(catid)}\\n\\n elif catname:\\n print(catname)\\n cursor = mongo.db.cats.find({\\\"catname\\\": catname}, {\\\"_id\\\": 0}).limit(\\n LIMIT_QUERY\\n )\\n for cat in cursor:\\n cat[\\\"url\\\"] = API_URL + \\\\\\n url_for(\\\"cats\\\") + \\\"/\\\" + cat.get(\\\"catid\\\")\\n data.append(cat)\\n\\n return jsonify({\\\"catname\\\": catname, \\\"response\\\": data})\\n\\n else:\\n cursor = mongo.db.cats.find({}, {\\\"_id\\\": 0, \\\"update_time\\\": 0}).limit(\\n LIMIT_QUERY\\n )\\n\\n for cat in cursor:\\n print(cat, API_URL, url_for(\\\"cats\\\"), \\\"/\\\", cat.get(\\\"catid\\\"))\\n cat[\\\"url\\\"] = API_URL + url_for(\\\"cats\\\") + \\\"/\\\" + cat.get(\\\"catid\\\")\\n data.append(cat)\\n\\n return jsonify({\\\"response\\\": data})\",\n \"def get_categories():\\n categories_query = Category.query.order_by(Category.id).all()\\n\\n if len(categories_query) == 0:\\n abort(404)\\n\\n categories = []\\n\\n for category in categories_query:\\n categories.append(category.type)\\n\\n return jsonify({\\n 'success': True,\\n 'categories': categories,\\n })\",\n \"def show_books():\\n result = {'books': query.get_book_list()}\\n return json.dumps(result, ensure_ascii=False)\",\n \"def get_all_categorizations():\\n try:\\n query = run_select_query('SELECT id, category_name, category_item_type, name \\\\\\n FROM categorization \\\\\\n JOIN item ON (categorization.item_id = item.id)')\\n return [{'id': obj[0], 'category_name': obj[1], 'category_item_type': obj[2].capitalize(), 'item_name': obj[3]} for obj in query]\\n except (Exception, psycopg2.Error) as error:\\n return {\\\"status\\\": \\\"error\\\", \\\"error\\\": error}\",\n \"def getcategory(self):\\n\\n response = requests.get(\\\"https://fr.openfoodfacts.org/categories.json\\\")\\n\\n data = response.json()\\n\\n self.rawcategorydata = data\",\n \"def serialize_categories(md: Metadata) -> list:\\n categories = []\\n all_cat = md.categories.all()\\n for cat in all_cat:\\n category = OrderedDict()\\n\\n category[\\\"id\\\"] = cat.id\\n category[\\\"type\\\"] = cat.type\\n category[\\\"title_EN\\\"] = cat.title_EN\\n category[\\\"description_EN\\\"] = cat.description_EN\\n category[\\\"title_locale_1\\\"] = cat.title_locale_1\\n category[\\\"description_locale_1\\\"] = cat.description_locale_1\\n category[\\\"title_locale_2\\\"] = cat.title_locale_2\\n category[\\\"description_locale_2\\\"] = cat.description_locale_2\\n category[\\\"symbol\\\"] = cat.symbol\\n category[\\\"online_link\\\"] = cat.online_link\\n\\n categories.append(category)\\n\\n return categories\",\n \"def test_get_categories_from_json():\\n allocator = RecipeAllocator()\\n allocator.load_data(\\n orders_dir=\\\"tests/orders.json\\\", recipes_dir=\\\"tests/recipes.json\\\"\\n )\\n allocator.get_categories_from_json()\\n assert list(allocator.portion_categories_dict.keys()) == [\\n \\\"two_portions\\\",\\n \\\"four_portions\\\",\\n ] and list(allocator.recipe_categories_dict.keys()) == [\\n \\\"two_recipes\\\",\\n \\\"three_recipes\\\",\\n \\\"four_recipes\\\",\\n ]\",\n \"def get_jsonified_catalogs():\\n\\n json = []\\n catalogs = session.query(Catalog).all()\\n\\n for catalog in catalogs:\\n catalog_json = {}\\n catalog_json[\\\"id\\\"] = catalog.id\\n catalog_json[\\\"name\\\"] = catalog.name\\n catalog_json[\\\"date\\\"] = catalog.date.strftime(\\\"%d %b %Y %H: %M: %S\\\")\\n catalog_json[\\\"items\\\"] = []\\n for item in catalog.items:\\n item_json = {}\\n item_json[\\\"id\\\"] = item.id\\n item_json[\\\"title\\\"] = item.title\\n item_json[\\\"desc\\\"] = item.desc\\n item_json[\\\"date\\\"] = item.date.strftime(\\\"%d %b %Y %H: %M: %S\\\")\\n item_json[\\\"catalog_id\\\"] = item.catalog_id\\n catalog_json[\\\"items\\\"].append(item_json)\\n json.append(catalog_json)\\n return jsonify(Catalogs=json)\",\n \"def showItemJSON(category_id, item_id):\\r\\n session = DBSession()\\r\\n item = session.query(Item).filter_by(id=item_id).one()\\r\\n return jsonify(Item=item.serialize)\",\n \"def get(current_user, self, category_id):\\n\\n if not current_user:\\n return is_unauthorized()\\n\\n category = current_user.categories.filter_by(id=category_id).first()\\n if category:\\n recipes = category.recipes.all()\\n\\n if not recipes:\\n response_payload = dict(\\n message='No recipes added to this category yet!'\\n )\\n response_payload = jsonify(response_payload)\\n return make_response(response_payload, 404)\\n\\n # search and/or paginate\\n args = parser.parse(SEARCH_PAGE_ARGS, request)\\n if 'q' in args:\\n try:\\n recipes = current_user.recipes.filter(\\n Recipe.name.ilike(\\\"%\\\" + args['q'] + \\\"%\\\"),\\n Recipe.category_id == category.id\\n ).paginate(page=args['page'], per_page=args['per_page'], error_out=False)\\n except KeyError:\\n recipes = current_user.recipes.filter(\\n Recipe.name.ilike(\\\"%\\\" + args['q'] + \\\"%\\\"),\\n Recipe.category_id == category.id\\n ).paginate(page=1, per_page=5)\\n else:\\n recipes = category.recipes.paginate(per_page=2)\\n base_url = request.base_url\\n if 'q' in args:\\n pagination_details = _pagination(recipes, base_url, q=args['q'])\\n else:\\n pagination_details = _pagination(recipes, base_url)\\n user_recipes = []\\n for current_recipe in recipes.items:\\n this_recipe = make_payload(recipe=current_recipe)\\n user_recipes.append(this_recipe)\\n if user_recipes:\\n response_payload = {\\n \\\"recipes\\\": user_recipes,\\n \\\"page_details\\\": pagination_details\\n }\\n return make_response(jsonify(response_payload), 200)\\n response_payload = {\\n \\\"message\\\": \\\"Recipe does not exist.\\\"\\n }\\n return make_response(jsonify(response_payload), 400)\\n response_payload = dict(\\n message='Invalid category!'\\n )\\n response_payload = jsonify(response_payload)\\n return make_response(response_payload, 400)\",\n \"def get_categories(request):\\n try:\\n categories = []\\n for category in Category.objects.all():\\n categories.append({\\\"title\\\": category.title, \\\"id\\\": category.pk})\\n\\n return format_ajax_response(True, \\\"Knowledgebase categories retrieved successfully.\\\", {\\\"categories\\\": categories})\\n except Exception as ex:\\n logger.error(\\\"Failed to get_categories: %s\\\" % ex)\\n return format_ajax_response(False, \\\"There was an error retreiving the knowledgebase categories.\\\")\",\n \"def get_questions_by_category(category_id):\\n try:\\n category = get_category_by_id(category_id)\\n\\n if category is None:\\n abort(STATUS_NOT_FOUND)\\n\\n questions = get_all_questions(category_id=category_id)\\n return jsonify({\\n \\\"success\\\": True,\\n \\\"questions\\\": questions,\\n \\\"total_questions\\\": len(questions),\\n \\\"current_category\\\": category.format(),\\n })\\n\\n except Exception as exp:\\n abort(exp.code)\",\n \"def export_chapter_links_to_json(book_name, book_link, idd):\\n chapters = {\\\"01\\\" : book_link}\\n home = Ripper(book_link, save_path=save_path)\\n \\n if not os.path.exists(chapter_store):\\n os.mkdir(chapter_store)\\n \\n for each in home.raw_links:\\n str_each = str(each)\\n excludes = [\\\"next\\\" in str_each, \\\"previous\\\" in str_each, \\\"chapter\\\" in str_each, \\\"statcounter\\\" in str_each,\\n \\\"/x/\\\" in str_each, \\\"DRBO.ORG\\\" in str_each, \\\"theologica\\\" in str_each]\\n \\n if not any(excludes):\\n chapters[each.text] = domain + \\\"chapter/\\\" + each.get(\\\"href\\\")\\n\\n book_chaps = {book_name : chapters}\\n \\n book_save_name = \\\"{}.json\\\".format(os.path.join(chapter_store, normalize_filename(book_name)))\\n\\n with open(book_save_name, \\\"w+\\\") as wh:\\n json.dump(book_chaps, wh)\\n return book_chaps\",\n \"def _traverse_categories():\\n \\n csv_output_dict = _get_csv_data()\\n print csv_output_dict.keys()\\n #~ if csv_output_dict:\\n #~ return csv_output_dict\\n #~ else:\\n final_output_url_list = []\\n traverse_cat_output = {}\\n # brand_seed_page = 'https://www.broderbros.com/cgi-bin/online/webshr/browse-brand-all.w'\\n seed_page = 'https://www.broderbros.com/cgi-bin/online/webshr/browse-category-all.w'\\n \\n browser = utils.create_browser(SLEEP_MIN, SLEEP_MAX)\\n browser.open(seed_page)\\n html_response = browser.response().read()\\n \\n soup = BeautifulSoup(html_response)\\n \\n all_category_names = soup.findAll('div', {'class': 'brwsBrndLogoClip'})\\n for category_names in all_category_names:\\n temp_category_url = str(category_names.a['href'])\\n category_url_name = temp_category_url.split('&catname=')[-1]\\n category_url = 'https://www.broderbros.com/cgi-bin/online/webshr/search-result.w?nResults=5000&mc=&RequestAction=advisor&RequestData=CA_CategoryExpand&bpath=c&CatPath=All Products////BRO-Categories////'+str(category_url_name)\\n category_url = category_url.replace(' ', '%20')\\n category_names = category_names.a.img['alt'].lower()\\n returned_url_list = _get_product_urls(category_url, category_names)\\n final_output_url_list.extend(returned_url_list)\\n \\n for productsurl in final_output_url_list:\\n if productsurl['id'] in traverse_cat_output.keys() and productsurl['category_list'] != traverse_cat_output[productsurl['id']]['category_list']:\\n traverse_cat_output[productsurl['id']]['category_list'].extend(productsurl['category_list'])\\n else:\\n traverse_cat_output[productsurl['id']] = productsurl\\n \\n brand_output_categories = _traverse_categories_brand()\\n for brand_url in brand_output_categories:\\n if brand_url['id'] in traverse_cat_output.keys():\\n traverse_cat_output[brand_url['id']]['brand'] = brand_url['brand']\\n else:\\n traverse_cat_output['id'] = brand_url\\n \\n if TESTRUN:\\n for k,v in traverse_cat_output.items():\\n print v\\n print '*'*78\\n for item_id in traverse_cat_output.keys():\\n print item_id\\n csv_data = csv_output_dict.get(item_id)\\n if csv_data:\\n traverse_cat_output[item_id]['brand'] = csv_data['brand']\\n if not traverse_cat_output[item_id].get('brand'):\\n del traverse_cat_output[item_id]\\n \\n return traverse_cat_output\",\n \"def get_all_by_category(self, category):\\n products = self.db.query(f\\\"\\\"\\\"\\n SELECT product.id, product.name, product.nutrition_grade,\\n product.url from product\\n JOIN product_category ON product_category.product_id = product.id\\n JOIN category ON product_category.category_id = category.id\\n WHERE category.id = :id\\n \\\"\\\"\\\", id=category.id).all(as_dict=True)\\n return [self.model(**product) for product in products]\",\n \"def category_items(category_id):\\n\\n items = Item.query.filter(\\n Item.category_id == category_id,\\n Item.user_id == current_user.id\\n ).all()\\n categories = Category.query.filter(\\n Category.user_id == current_user.id).all()\\n if not categories:\\n flash(\\\"Couldn't find this category\\\", category='warning')\\n\\n return render_template(\\n 'index.html',\\n categories=categories,\\n items=items,\\n current_category_id=category_id)\",\n \"def get_all_by_category(self, category):\\n products = self.db.query(f\\\"\\\"\\\"\\n SELECT product.id, product.name, product.nutrition_grade\\n from product\\n JOIN product_category ON product_category.product_id = product.id\\n JOIN category ON product_category.category_id = category.id\\n WHERE category.id = :id\\n \\\"\\\"\\\", id=category.id).all(as_dict=True)\\n return [self.model(**product) for product in products]\",\n \"def getAllCategory():\\n return session.query(Category).all()\",\n \"def json_export(json_file,gradebook):\\n \\n try:\\n import json\\n except ImportError:\\n warnings.warn('Failed to import json module. Cannot execute json_export')\\n return\\n if not hasattr(json_file,'write'):\\n if not isinstance(json_file,basestring) or not \\\\\\n os.path.exists(os.path.dirname(os.path.abspath(json_file))):\\n raise ValueError, 'Argument \\\\'json_file\\\\' is not readable, ' \\\\\\n 'and could not be validated as a file path.'\\n else:\\n json_file = open(json_file,'w+')\\n \\n if not isinstance(gradebook,Gradebook):\\n raise TypeError, 'gradebook argument must be a Gradebook objcet.'\\n \\n cat_dict = dict(gradebook._Gradebook__categories)\\n all_items = []\\n for cat_name in cat_dict:\\n gr_leest = cat_dict[cat_name].grades.select(docopy=True,aslist=True)\\n for i in gr_leest:\\n x = {}\\n if not isinstance(i,Grade):\\n continue\\n x['type']='Grade'\\n x['name']=i.name\\n if i.parent is not None:\\n if i.parent.name == cat_name or i.parent.name in cat_dict:\\n x['parent'] = i.parent.name\\n else:\\n x['parent'] = None\\n attribs = {}\\n attribs['score'] = i.score\\n attribs['maximum'] = i.maximum\\n attribs['weight'] = i.weight\\n attribs['extra_credit'] = i.extra_credit\\n if i.overrides:\\n attribs['overrides'] = list(i.overrides)\\n for ovrrd in i.overrides:\\n if not isinstance(overrd,basestring):\\n attribs['overrides'].remove(overrd)\\n if i.timestamp and isinstance(i.timestamp,datetime.datetime):\\n tmstmp = calendar.timegm(i.timestamp.utctimetuple())\\n attribs['timestamp'] = tmstmp\\n if i.identifiers:\\n attribs['identifiers'] = dict(i.identifiers)\\n \\n x['attribs'] = attribs\\n all_items.append(x)\\n catt = {'type':'Category','name':cat_name}\\n if cat_dict[cat_name].parent is not None:\\n catt['parent'] = cat_dict[cat_name].parent.name\\n else:\\n catt['parent'] = None\\n catt['attribs'] = dict(cat_dict[cat_name]._Category__attribs)\\n all_items.append(catt)\\n \\n grbk = {'type':'Gradebook','name':gradebook.name,'user':gradebook.user}\\n all_items.append(grbk)\\n enc_me = {\\\"grading\\\": all_items}\\n \\n encoder = json.JSONEncoder(indent=4,separators=(', ',': '))\\n enc = encoder.encode(enc_me)\\n \\n json_file.write(enc)\\n json_file.close()\",\n \"def get_recipies_from_category(category_id,\\n usertype=1,\\n page=0,\\n count=0,\\n session=None):\\n if session is None:\\n session = requests.Session()\\n payload = {'categoryId': category_id,\\n 'usertype': usertype,\\n 'page': page,\\n 'offset': count}\\n response = session.get(\\n 'https://cms.sortedfood.com/apiRecipe/getFeaturedByUsertype',\\n params=payload)\\n if response.status_code != 200:\\n raise response.HTTPError(response)\\n response.encoding = 'utf-8'\\n return json.loads(response.text)\",\n \"def fetch_all_categories():\\n query_set = Categories.objects.filter(is_delete=False)\\n serializer = CategoriesSerializer(query_set, many=True)\\n return serializer.data\",\n \"def pass_category(url, name):\\n print(f\\\"Passage de la catégorie {name}...\\\")\\n # Initialize list of books\\n books = []\\n while url != '':\\n response = requests.get(url)\\n if response.status_code == 200:\\n soup = BeautifulSoup(response.content, 'html.parser')\\n # Books are notify with article balise\\n articles = soup.findAll('article', {'class': 'product_pod'})\\n for article in tqdm(articles):\\n # Get the book's link\\n link = url + \\\"/../\\\" + article.find('h3').find('a')['href']\\n # Run the function to get book's info\\n dict_product = get_book_infos(link)\\n books.append(dict_product)\\n # Searching for a next page\\n next_page = soup.find('li', {'class': 'next'})\\n if not next_page:\\n url = ''\\n else:\\n url = url + \\\"/../\\\" + soup.find('li', {'class': 'next'}).find('a')['href']\\n write_category(name, books)\",\n \"def categories(data):\\n if data:\\n for i in data:\\n category = CategoriesModel(categories=i['categories'])\\n category.save()\",\n \"def recipes(category):\\n # if statements to display the recipes base on category name\\n if category == \\\"Pre Workout Meal\\\":\\n recipe = mongo.db.recipes.find({\\\"category_name\\\": \\\"Pre Workout Meal\\\"})\\n elif category == \\\"Post Workout Meal\\\":\\n recipe = mongo.db.recipes.find({\\\"category_name\\\": \\\"Post Workout Meal\\\"})\\n else:\\n recipe = mongo.db.recipes.find()\\n\\n return render_template('pages/allrecipe.html', recipe=recipe, category_title=category, recipes=mongo.db.recipes.find(), isFooter=True)\",\n \"def to_json(self):\\n return {\\n \\\"category_name\\\": self.category_name,\\n \\\"summary\\\": self.summary,\\n \\\"image\\\": self.image,\\n \\\"category_slug\\\": self.category_slug,\\n }\",\n \"def recipes():\\n if request.method == 'GET':\\n return Response(\\n json.dumps(recipebook.to_json_list()),\\n mimetype=\\\"application/json\\\")\\n elif request.method == 'POST':\\n new_dict = request.get_json()\\n recipebook.recipes.append(models.Recipe.from_json_dict(new_dict))\\n write_out()\\n return Response(status=200)\",\n \"def show_category_items(category_id):\\n all_categories = session.execute(\\n 'SELECT category.name, category.id, count(item.id) AS item_count '\\n 'FROM category LEFT JOIN item ON category.id = item.category_id '\\n 'GROUP BY category.name, category.id')\\n category = session.query(Category).filter(Category.id == category_id).first()\\n items = session.query(Item).filter(Item.category_id == category_id)\\n item_count = items.count()\\n return render_template('category_items.html',\\n all_categories=all_categories,\\n category=category,\\n items=items,\\n item_count=item_count,\\n login_session=login_session)\",\n \"def get_products(categories_info):\\n\\n for name, urlnames in categories_info.items():\\n for i in range(1, (NB_PAGES+1)):\\n url = 'https://world.openfoodfacts.org/cgi/search.pl?\\\\\\n search_tag=categories&search_terms={}&\\\\\\n purchase_places=France&page_size={}&page={}&json=1'.format(\\n urlnames, str(PRODUCTS_PER_PAGE), str(i))\\n data = requests.get(url).json()\\n name_file = 'data/Products_' + name + str(i) + '.json'\\n with open(name_file, 'w') as file:\\n file.write(json.dumps(data, indent=4))\",\n \"def generate_category_dict(category_file):\\n L = json.load(open(category_file))\\n d = {}\\n for dict_item in L:\\n item = item_to_dict(dict_item)\\n if not item['Label'] is None:\\n d[item['URN']] = item['Label']\\n else:\\n d[item['URN']] = ''\\n return d\",\n \"def cat_results_react():\\n\\n cats = petfinder.search_data_map()\\n cats = list(cats.values())\\n\\n return jsonify(cats)\",\n \"def showItemsJSON():\\n items = db.getAllItems()\\n return jsonify(CategoryItems=[i.serialize for i in items])\",\n \"def get(self, request, format=None):\\n categories = Category.objects.all()\\n data_out = CategorySerializer(categories, many=True)\\n return Response(data_out.data)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7138958","0.7041303","0.69258463","0.6853855","0.67250127","0.6645023","0.66027105","0.65864533","0.6539297","0.6498139","0.642202","0.6416721","0.63761836","0.6373455","0.6321239","0.62749213","0.62676096","0.6253013","0.62493944","0.6240726","0.622268","0.62040627","0.61613387","0.605303","0.60393393","0.60376203","0.6034248","0.60226893","0.6003965","0.5997589","0.59838074","0.5944783","0.5940745","0.5917295","0.5917295","0.5897778","0.58831245","0.5881196","0.58700526","0.5869629","0.58647025","0.58565366","0.5854906","0.5847012","0.5833817","0.5813998","0.5802672","0.5799083","0.5774403","0.5772975","0.5753679","0.5742198","0.5732036","0.5715935","0.57134753","0.5712716","0.5696447","0.5617276","0.56063175","0.559598","0.5586632","0.5584787","0.55753106","0.55730015","0.5571487","0.55664474","0.55577356","0.554377","0.5541501","0.5511635","0.5506292","0.5501072","0.54645187","0.54427725","0.5438762","0.54165256","0.5414814","0.54126304","0.5410835","0.5400333","0.5400229","0.53939056","0.53831655","0.53816223","0.5374886","0.5366738","0.5360629","0.5355518","0.53537667","0.5340364","0.5335722","0.532887","0.5307962","0.5301721","0.5294503","0.5265977","0.52599484","0.525818","0.52478325","0.52465004"],"string":"[\n \"0.7138958\",\n \"0.7041303\",\n \"0.69258463\",\n \"0.6853855\",\n \"0.67250127\",\n \"0.6645023\",\n \"0.66027105\",\n \"0.65864533\",\n \"0.6539297\",\n \"0.6498139\",\n \"0.642202\",\n \"0.6416721\",\n \"0.63761836\",\n \"0.6373455\",\n \"0.6321239\",\n \"0.62749213\",\n \"0.62676096\",\n \"0.6253013\",\n \"0.62493944\",\n \"0.6240726\",\n \"0.622268\",\n \"0.62040627\",\n \"0.61613387\",\n \"0.605303\",\n \"0.60393393\",\n \"0.60376203\",\n \"0.6034248\",\n \"0.60226893\",\n \"0.6003965\",\n \"0.5997589\",\n \"0.59838074\",\n \"0.5944783\",\n \"0.5940745\",\n \"0.5917295\",\n \"0.5917295\",\n \"0.5897778\",\n \"0.58831245\",\n \"0.5881196\",\n \"0.58700526\",\n \"0.5869629\",\n \"0.58647025\",\n \"0.58565366\",\n \"0.5854906\",\n \"0.5847012\",\n \"0.5833817\",\n \"0.5813998\",\n \"0.5802672\",\n \"0.5799083\",\n \"0.5774403\",\n \"0.5772975\",\n \"0.5753679\",\n \"0.5742198\",\n \"0.5732036\",\n \"0.5715935\",\n \"0.57134753\",\n \"0.5712716\",\n \"0.5696447\",\n \"0.5617276\",\n \"0.56063175\",\n \"0.559598\",\n \"0.5586632\",\n \"0.5584787\",\n \"0.55753106\",\n \"0.55730015\",\n \"0.5571487\",\n \"0.55664474\",\n \"0.55577356\",\n \"0.554377\",\n \"0.5541501\",\n \"0.5511635\",\n \"0.5506292\",\n \"0.5501072\",\n \"0.54645187\",\n \"0.54427725\",\n \"0.5438762\",\n \"0.54165256\",\n \"0.5414814\",\n \"0.54126304\",\n \"0.5410835\",\n \"0.5400333\",\n \"0.5400229\",\n \"0.53939056\",\n \"0.53831655\",\n \"0.53816223\",\n \"0.5374886\",\n \"0.5366738\",\n \"0.5360629\",\n \"0.5355518\",\n \"0.53537667\",\n \"0.5340364\",\n \"0.5335722\",\n \"0.532887\",\n \"0.5307962\",\n \"0.5301721\",\n \"0.5294503\",\n \"0.5265977\",\n \"0.52599484\",\n \"0.525818\",\n \"0.52478325\",\n \"0.52465004\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.8570861"},"document_rank":{"kind":"string","value":"0"}}}],"truncated":false,"partial":true},"paginationData":{"pageIndex":755,"numItemsPerPage":100,"numTotalItems":95772,"offset":75500,"length":100}},"jwt":"eyJhbGciOiJFZERTQSIsImtpZCI6IjVHZDBvd0g5MTM2eDZjc1FvbE1zcktNWUZoRVFoUm5PVVVybEpjOUhaUEEifQ.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc4NjYwMjk4MywianRpIjoiNjA3ZmUzZjItNzU1OC00MTVlLTlmZjgtMGVlNDNlOWYzY2E1Iiwic3ViIjoiL2RhdGFzZXRzL25vbWljLWFpL2Nvcm5zdGFjay1weXRob24tdjEiLCJleHAiOjE3ODY2MDY1ODMsImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.q7y0_ig_dZ6gIpqJAtkdCQA066APVvsosAjpKE9HgTEyJ5JQe3SHIDn4SLmpK0agjVke7EvlcmtfOeuHQ2gYBA","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 |
|---|---|---|---|---|---|---|
print out a set of column headings | def printHeadings(headings, format):
print("")
print(format % headings)
# how wide should a dash be
dashes = 0
for s in headings:
if len(s) > dashes:
dashes = len(s)
# create a line with that many dashes
s = ""
while dashes > 0:
s += '-'
dashes -= 1
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def columnTitles(self):\n \n pass",
"def columnTitles(self):\n \n pass",
"def print_column_names(self):\n counter = 1\n try:\n for col_names in self.cursor.description:\n # print(self.cursor.description[col_names][0])\n print(\"\"\"Attr... | [
"0.7428256",
"0.7428256",
"0.7366831",
"0.71996266",
"0.71262294",
"0.7073909",
"0.7065758",
"0.70184225",
"0.7011047",
"0.69628793",
"0.68577343",
"0.68374974",
"0.68102086",
"0.6808478",
"0.6774094",
"0.67638534",
"0.67233557",
"0.66981995",
"0.6683833",
"0.6661272",
"0.665... | 0.6680496 | 19 |
print out a size with the appropriate unit suffix | def printSize(sz, unit=1000):
fmt10 = ["%dB", "%dKiB", "%dMiB", "%dGiB", "%dTiB", "%dPiB"]
fmt2 = ["%dB", "%dKB", "%dMB", "%dGB", "%dTB", "%dPB"]
fmt = fmt10 if unit == 1000 else fmt2
i = 0
while i < len(fmt):
if sz < unit:
break
sz /= unit
i += 1
return fmt[... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def sizeof_fmt(size, suffix='B'):\r\n for unit in ['', 'K', 'M', 'G', 'T', 'P', 'E', 'Z']:\r\n if abs(size) < 1024.0:\r\n return f'{size:3.1f} {unit}{suffix}'\r\n size /= 1024.0\r\n return f'{size:3.1f} Y{suffix}'",
"def format_size(size):\n size = float(size)\n for unit in ['bit... | [
"0.7967207",
"0.77675575",
"0.7738173",
"0.7724627",
"0.75752074",
"0.752115",
"0.7514448",
"0.74665457",
"0.7454024",
"0.7452294",
"0.7434771",
"0.74308723",
"0.73925203",
"0.7369652",
"0.73430955",
"0.73430955",
"0.7312293",
"0.73056585",
"0.7280283",
"0.7270896",
"0.722881... | 0.8110585 | 0 |
print out a time in an appropriate unit | def printTime(t):
if t < 2 * MINUTE:
return "%d seconds" % (t / SECOND)
if t < 5 * HOUR:
return "%d minutes" % (t / MINUTE)
if t < 3 * DAY:
return "%d hours" % (t / HOUR)
if t < YEAR:
return "%d days" % (t / DAY)
if (t % YEAR) == 0:
return "%d years" % (t / YE... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def print_time(t):\n print(\"Time is %.2d:%.2d:%.2d\"%(t.hour,t.minute,t.second))",
"def print_time(self):\n print('%.2d:%.2d:%.2d' % (self.hour, self.minute, self.second))",
"def print_time(s, start_time):\n print(\"%s, time %ds, %s.\" % (s, (time.time() - start_time), time.ctime()))\n sys.std... | [
"0.7638004",
"0.7474871",
"0.70115083",
"0.6985422",
"0.6863995",
"0.6811769",
"0.6808201",
"0.6712119",
"0.66642284",
"0.65760577",
"0.65497994",
"0.65276873",
"0.6514214",
"0.64789295",
"0.64748365",
"0.6457335",
"0.6439744",
"0.6428694",
"0.64256114",
"0.64196414",
"0.6402... | 0.7288681 | 2 |
print out a durability in a reasonable format | def printDurability(d):
if d < .99999:
return "%6.3f%%" % (d * 100)
else:
nines = 0
while d > .9:
nines += 1
d -= .9
d *= 10
return "%d-nines" % (nines) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def nice_output(self):\n return 'Pitch: {0} at {1}: {2}'.format(\n self.pitch_type, self.start_speed, self.des)",
"def pretty_str(self, unit_time):\n return \"{}: {} left and {}% done\".format(self.name, get_time_str(self.time * unit_time), round((self.total_time - self.time) / self.tota... | [
"0.61984223",
"0.60872424",
"0.60736126",
"0.60736126",
"0.5913698",
"0.58824956",
"0.5882147",
"0.5882147",
"0.5821221",
"0.5758424",
"0.57571703",
"0.5739486",
"0.5727247",
"0.57142395",
"0.5679935",
"0.5608496",
"0.56013834",
"0.55811",
"0.55756927",
"0.5565094",
"0.556344... | 0.6071856 | 4 |
print out a probability in a reasonable format | def printProbability(p):
if p > .0000001:
return "%9.6f%%" % (p * 100)
else:
return "%9.3e" % (p) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __str__(self):\n prob = str(round(self.probability, 5))\n dprob = str(round(self.postdProbability, 5))\n output = \"dprob: \" + dprob + \" \\tprob: \" + prob + \"\\t: \"\n for key in self.attackDict.keys():\n output += key + \" \"\n return output",
"def format_probability(chance, n=4):\n... | [
"0.7008854",
"0.69079965",
"0.6850042",
"0.67920554",
"0.6706265",
"0.65928787",
"0.6556326",
"0.65150875",
"0.6484869",
"0.6395371",
"0.6380937",
"0.63522184",
"0.62861836",
"0.62230384",
"0.6202777",
"0.61732423",
"0.61580294",
"0.6151522",
"0.6142543",
"0.6141717",
"0.6109... | 0.80756766 | 0 |
run and report a set of specified simulations tests actual list of simulations to run (print a header line for each None test) period simulation period verbosity output options | def Run(tests, period=YEAR, verbosity="all"):
# figure out what output he wants
headings = True
parms = True
descr = True
if verbosity == "parameters":
descr = False
elif verbosity == "headings":
parms = False
descr = False
elif verbosity == "data only":
parm... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def main():\n\tresults = []\n\n\tconfig = configparser.ConfigParser()\n\tconfig.read(\"simulation.ini\")\n\tsettings = config['sim']\n\n\tcompleted_obj_hw = int(settings[\"ClientsPerCampaign\"]) * float(settings[\"CompletedPctgHW\"])\n\texceeded_obj_hw = float(settings[\"ExceededPctgHW\"])\n\tsignificance_level = ... | [
"0.66855633",
"0.6444111",
"0.6423009",
"0.63771325",
"0.63536066",
"0.6350432",
"0.6322105",
"0.6304921",
"0.62810755",
"0.6244929",
"0.6231836",
"0.6231481",
"0.6206141",
"0.6166658",
"0.61662865",
"0.61633986",
"0.61437416",
"0.60853714",
"0.60736054",
"0.606979",
"0.60636... | 0.70470625 | 0 |
Function for setting the color range of a plot. | def set_color_range(mic, N, indx, mat, quat, rod):
first = True
#print(indx)
for i in range(N):
if i in indx:
mat[i,:,:] = RotRep.EulerZXZ2Mat(mic.snp[i,6:9]/180.0*np.pi)
quat[i,:] = RotRep.quaternion_from_matrix(mat[i,:,:])
rod[i,:] = RotRep.rod_from_quate... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setColorBarRange(start=1,end=254):\n dislin.colran(start,end)",
"def set_colormap_range(self):\n cmin = self.settingsWidget.ui.colormap_min\n cmax = self.settingsWidget.ui.colormap_max\n region = self.plot.getHistogramWidget().region\n\n if(self.sender() == region):\n ... | [
"0.8215091",
"0.7468511",
"0.7109257",
"0.67486906",
"0.65688646",
"0.6499905",
"0.6471031",
"0.6469902",
"0.6400476",
"0.63015574",
"0.62633973",
"0.6258973",
"0.62575865",
"0.6116931",
"0.6020242",
"0.5986115",
"0.5984191",
"0.59652615",
"0.5948478",
"0.5913041",
"0.5897966... | 0.61357 | 13 |
Function that checks if a string x is that of a numerical value | def is_float(x):
try:
float(x)
except ValueError:
return False
return True | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def isnum(self, x):\n\n return x in '1234567890.-'",
"def is_valid_numeric(inString):\r\n return is_int(inString) or is_float(inString)",
"def is_some_number(mystring):\n # print(Bcolors.cyan + re.findall(r\".*\\\\(.*)\", inspect.stack()[0][1])[0] + \" --- \"\n # + inspect.stack()[0][3] + \"()\... | [
"0.7822679",
"0.7567585",
"0.7523138",
"0.75093275",
"0.7441029",
"0.74222386",
"0.73752314",
"0.7369164",
"0.7337491",
"0.73187983",
"0.73058134",
"0.7300748",
"0.7293591",
"0.7293591",
"0.7293591",
"0.7198686",
"0.716271",
"0.7161545",
"0.7160261",
"0.71473813",
"0.71310437... | 0.0 | -1 |
Function that runs the main loop Returns | def run():
square_s = input("Is your data file a square matrix file? [y/n]: ")
assert(square_s == "y" or square_s == "Y" or square_s == "n" or square_s == "N"), "Please enter in 'y' or 'n' format."
if square_s == "y" or square_s == "Y":
is_square = True
else:
is_square = False
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def run():\n main()",
"def main_loop(self):\n # main loop...don't ever exit\n while True:\n # collect data\n # get the time...the local clock is set with NTP regularly\n self._get_time()\n \n # get the latest metar data from the closest location\n self._get_metar()\n \... | [
"0.7868547",
"0.7794168",
"0.7726057",
"0.7713218",
"0.76401484",
"0.7506694",
"0.7290598",
"0.7290598",
"0.7161035",
"0.7127927",
"0.71084046",
"0.70540714",
"0.70376754",
"0.6989831",
"0.6983434",
"0.69737995",
"0.6970171",
"0.6953087",
"0.6942654",
"0.6942654",
"0.69379056... | 0.0 | -1 |
Using a combination of data [and suffix] pinch and twist an array of numbers. | def sparse_hash(data, rounds=1, suffix=None):
data = data if suffix is None else data + suffix
hash = [x for x in range(256)]
hash_length = len(hash)
pos = 0
skip = 0
for round in range(rounds):
for instruction in data:
# reverse the number of hash digits from the pos
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def onetwo_beep_gen(numbeep, interval, finalstim_tc, finalstim_nb):\n##### if one beep:#############################################################\n\n # 8ms + 50ms + 8ms = 195 + 1220 + 195 which is a total of 1610 samples\n interval = 0.050\n gap = np.zeros(24414. * interval, float)\n onebeep_tc = np... | [
"0.504756",
"0.49947897",
"0.49193612",
"0.48903674",
"0.48394477",
"0.48082486",
"0.47910482",
"0.47910482",
"0.4781527",
"0.47504583",
"0.4746146",
"0.47451112",
"0.4735967",
"0.470217",
"0.47013378",
"0.46999824",
"0.4698733",
"0.46851736",
"0.46697286",
"0.46351287",
"0.4... | 0.0 | -1 |
Takes in a sparse hash and xors all numbers in groups of 'block_size' a 256 length sparse hash will generate a 'block_size' numbered array. | def dense_hash(hash_, block_size=16):
results = list()
for index in range(0, len(hash_), block_size):
block = hash_[index:index + block_size]
total = 0
for i in block:
total ^= i
results.append(total)
return results | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def to_dense_hash(hash):\n dense_hash = []\n for i in range(0, len(hash), 16):\n block = reduce(lambda x, y: x ^ y, hash[i:i+16])\n dense_hash.append(block)\n return dense_hash",
"def k1xk2(data: typing.List[int], hash_size: int = 256) -> int:\n kk = sparse_knot_hash(data, hash_size, 1)... | [
"0.64490837",
"0.60304624",
"0.59660774",
"0.5825108",
"0.57991964",
"0.5717893",
"0.56716293",
"0.56199217",
"0.5568617",
"0.55538166",
"0.54708904",
"0.5447922",
"0.5439429",
"0.5383834",
"0.5338911",
"0.5331041",
"0.53225833",
"0.5314728",
"0.5307399",
"0.52971476",
"0.526... | 0.7294205 | 0 |
Can be used to describe anyone who is playing, but will describe me at the beginning of the game | def __init__(self, name, fave_football_team):
self.name = name
self.fave_football_team = fave_football_team | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def who_goes_first(self):\n if random.randint(0, 1) == 0:\n return 'computer'\n return 'player'",
"def show_myhero(self):\n description = (self.name + ' Level is: ' + str(self.level) + ' Age is: ' + str(\n self.age) + ' Rank is: ' + self.rank + ' health is: ' + str(self... | [
"0.6429199",
"0.61463565",
"0.60963666",
"0.60843235",
"0.6071096",
"0.60621256",
"0.60448706",
"0.59654844",
"0.5950625",
"0.5902745",
"0.5893237",
"0.5866605",
"0.5788793",
"0.57887214",
"0.57846844",
"0.5770601",
"0.5769687",
"0.57649755",
"0.57466066",
"0.5746066",
"0.573... | 0.0 | -1 |
Choosing your team and opponent | def choose_team():
blah = True
global team_choice
global opponent_choice
while blah:
team_choice = input("Which NFL team would you like to play with? \n").title()
NFL_teams = ["Jets", "Bills", "Dolphins", "Patriots", "Titans", "Texans", "Colts", "Jaguars", "Broncos", "Chiefs", "Chargers", "Rai... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def choose_opponent(self):\n possible_opponents = [\n self.data['personalities'].pop(),\n self.data['events'].pop()\n ]\n title = 'Recruit a member or gain experience:'\n options = []\n for possible_opponent in possible_opponents:\n option = card_... | [
"0.7527788",
"0.651584",
"0.6435174",
"0.640273",
"0.64010537",
"0.6302428",
"0.6267415",
"0.61891365",
"0.6188227",
"0.6178674",
"0.616317",
"0.61611134",
"0.6142924",
"0.61102605",
"0.6105685",
"0.6103712",
"0.60909766",
"0.60868555",
"0.60826194",
"0.6060774",
"0.605628",
... | 0.7136275 | 1 |
Player decides whether they want to return the kickoff, associated gain on kickoff depending on input | def kickoff():
kickoff_choice = input(f"The {opponent_choice} are kicking off to your end zone. Do you want to return the kickoff? \n").lower()
global kick_return
global location
if kickoff_choice == "yes":
if random.random() < 0.1:
if random.random() < 0.7:
kick_retu... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def run_func():\n global gain\n if random.random() < 0.8:\n if random.random() < .9:\n gain = random.randint(0, 5) #For example, you have an 80% chance of gaining between 1 and 9 yards on a run play, and you have about a 72% chance to gain between 1 and 6 yards\n else:\n gain = random... | [
"0.6319561",
"0.62356794",
"0.6226591",
"0.6195144",
"0.6065509",
"0.6050107",
"0.6003843",
"0.59457415",
"0.5942352",
"0.5925448",
"0.58969015",
"0.5894861",
"0.5847418",
"0.58423024",
"0.58215815",
"0.5810432",
"0.5764698",
"0.57618344",
"0.575565",
"0.57321423",
"0.5730262... | 0.6866473 | 0 |
Determines yard gain on running play | def run_func():
global gain
if random.random() < 0.8:
if random.random() < .9:
gain = random.randint(0, 5) #For example, you have an 80% chance of gaining between 1 and 9 yards on a run play, and you have about a 72% chance to gain between 1 and 6 yards
else:
gain = random.randint(6,... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def dcgain(sys):\n return sys.dcgain()",
"def _gain(self):\n return None",
"def gain(self):\n return self[1]",
"def gain(self) -> int:\n return self._gain",
"def update_playback_gain(self, val):\n self.playbackGain = 10**(5.0*(val - self.speedDial.maximum()/2)/self.speedDial.... | [
"0.6587091",
"0.62599635",
"0.6200276",
"0.6061966",
"0.5924991",
"0.58686435",
"0.58041745",
"0.56663793",
"0.56192064",
"0.56159306",
"0.55986327",
"0.5584499",
"0.5583384",
"0.5576937",
"0.5571284",
"0.554195",
"0.5529443",
"0.5524498",
"0.5494729",
"0.5489482",
"0.5483618... | 0.6012537 | 4 |
Determines yard gain on passing play | def pass_func():
global gain
if random.random() < 0.35:
gain = 0 #You don't gain yardage if the pass is incomplete
print("The pass is incomplete.\n")
elif random.random() > 0.35 and random.random() < 0.43:
if random.random() < .7:
gain = random.randint(1, 5)
else:
gai... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _gain(self):\n return None",
"def gain(self):\n return self[1]",
"def gain(self) -> int:\n return self._gain",
"def update_playback_gain(self, val):\n self.playbackGain = 10**(5.0*(val - self.speedDial.maximum()/2)/self.speedDial.maximum())",
"def dcgain(sys):\n return sy... | [
"0.64661837",
"0.6415016",
"0.6104374",
"0.59965986",
"0.5939903",
"0.5935967",
"0.58814234",
"0.5853398",
"0.581609",
"0.57570386",
"0.5736254",
"0.5732244",
"0.57280487",
"0.57192993",
"0.569461",
"0.5688064",
"0.5660062",
"0.5658602",
"0.56367904",
"0.5631949",
"0.56276673... | 0.6713752 | 0 |
Bottleneck block for ResNeXt. If style is "pytorch", the stridetwo layer is the 3x3 conv layer, if it is "caffe", the stridetwo layer is the first 1x1 conv layer. | def __init__(self,
inplanes,
planes,
groups=1,
base_width=4,
base_channels=64,
**kwargs):
super(Bottleneck, self).__init__(inplanes, planes, **kwargs)
if groups == 1:
width = self.planes
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _bottleneck(x: tf.Tensor, depth: int, depth_bottleneck: int, stride: int, rate: int = 1) -> tf.Tensor:\n with tf.variable_scope(None, 'bottleneck_v2', [x]):\n depth_in = slim.utils.last_dimension(x.get_shape(), min_rank=4)\n preact = slim.batch_norm(x, activation_fn=tf.nn.relu, sco... | [
"0.6889076",
"0.6795593",
"0.67665195",
"0.6523262",
"0.6499701",
"0.6218676",
"0.6191233",
"0.6187817",
"0.61875874",
"0.61188823",
"0.60808504",
"0.5914599",
"0.5914326",
"0.5914326",
"0.59128946",
"0.58922887",
"0.5889086",
"0.5873635",
"0.5873635",
"0.58704764",
"0.585307... | 0.0 | -1 |
Pack all blocks in a stage into a ``ResLayer`` | def make_res_layer(self, **kwargs):
return ResLayer(
groups=self.groups,
base_width=self.base_width,
base_channels=self.base_channels,
**kwargs) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def make_stage(num_blocks, input_channels, output_channels, stride, expand_ratio, norm, activation):\n blocks = []\n blocks.append(\n InvertedResBlock(input_channels, output_channels, stride=stride, expand_ratio=expand_ratio,\n norm=norm, activation=activation, use_shortcut=Fal... | [
"0.62111896",
"0.6077535",
"0.5642527",
"0.5591934",
"0.55416477",
"0.5539544",
"0.5538041",
"0.5529021",
"0.55200046",
"0.54654443",
"0.5445331",
"0.5445331",
"0.54439485",
"0.54404974",
"0.54329515",
"0.5399873",
"0.5393651",
"0.5337562",
"0.5324735",
"0.5324147",
"0.529566... | 0.0 | -1 |
ppiFile the name of input file to be parsed, which should be a file with one PPI at each line returns ppbConf, an adjacency matrix represented as a numpy array | def getConf(ppiFile):
validpattern = re.compile('^[\w _\-.,\t"\':;]+$')
splitpattern = re.compile('[\t ;,]+')
numericpattern = re.compile('^[0-9. \t,\-]+')
### collect node names ###
finfile = open(ppiFile, 'r')
names = {}
index = 0
for temp in finfile:
temp = temp.strip('\t \n\r')
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def readPfile(filename):\n\n with smart_open(filename, \"rb\") as f:\n # Read header\n # Assuming all data are consistent\n for line in f:\n tokens = line.decode().split()\n if tokens[0] == \"-pfile_header\":\n headerSize = int(tokens[4])\n el... | [
"0.6192037",
"0.58578134",
"0.5780167",
"0.57182866",
"0.5705861",
"0.5597269",
"0.55652106",
"0.5564362",
"0.55439883",
"0.5541717",
"0.5494945",
"0.54893816",
"0.548049",
"0.5466671",
"0.546124",
"0.544112",
"0.54269356",
"0.5412758",
"0.53786016",
"0.5374732",
"0.53634465"... | 0.76946753 | 0 |
Function that maps helper functions to option entered | def dispatch(ch):
if(ch == "1"):
b1()
elif(ch == "2"):
b2()
elif(ch == "3"):
b3()
else:
print("Error: Invalid Option") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __choose_options(self):\n\t\tswitcher = {\n\t\t\t0: self.__zero,\n\t\t\t1: self.__one,\n\t\t\t2: self.__two,\n\t\t\t3: self.__three,\n\t\t\t4: self.four,\n\t\t\t5: self.four,\n\t\t\t6: self.four,\n\t\t\t7: self.four,\n\t\t}\n\t\tfunc = switcher.get(self.__options(), lambda: \"Invalid option\")\n\t\treturn func... | [
"0.6700154",
"0.61381847",
"0.6029187",
"0.5890568",
"0.586972",
"0.58272517",
"0.57739186",
"0.57490337",
"0.5740765",
"0.56789273",
"0.56414676",
"0.56257343",
"0.5584021",
"0.557838",
"0.5540199",
"0.5539945",
"0.5539914",
"0.5533887",
"0.5532508",
"0.5516099",
"0.54937947... | 0.5469205 | 23 |
Trip is an independent table | def d113():
try:
row = {}
print("Enter Vehicle's details: ")
while True:
row["ChassisNo"] = input("Chassis Number (integer): ")
if row["ChassisNo"] != '':
try:
row["ChassisNo"] = int(row["ChassisNo"])
break
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_trips(self) -> Tuple[Trip]:\n ...",
"def create_simple_trips(tours, households, persons, trace_hh_id):\n\n logger.info(\"Running simple trips table creation with %d tours\" % len(tours.index))\n\n tours_df = tours.to_frame()\n\n # we now have a tour_id column\n tours_df.reset_index(inp... | [
"0.6550611",
"0.63067746",
"0.6140956",
"0.60403574",
"0.60078037",
"0.594432",
"0.58633554",
"0.574974",
"0.56700605",
"0.55899435",
"0.55866647",
"0.5530094",
"0.5522383",
"0.55037534",
"0.5468205",
"0.5413393",
"0.539458",
"0.53460616",
"0.53455234",
"0.5323009",
"0.531092... | 0.0 | -1 |
MATERIAL should be updated along with BELONGS_TO which contains `IDnumber`, `ChassisNo`, `Model`, `MatName`, `WingName` | def d114():
tmp = sp.call('clear', shell=True)
print ()
print ("Insert Material (Query)")
print ()
try:
row = {}
print("Enter Material details: ")
while True:
row["Name"] = input("Material Name: ")
if row["Name"] != '':
row["Name"] = ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def link_material(obj, mat):\n if not has_material(obj, mat.name):\n obj.data.materials.append(mat)",
"def set_material(self, material):\r\n for b in self.buf:\r\n b.set_material(material)",
"def create_blender_material(self, ogremat, mat, meshId, matIdx):\n logger.debug(\"create_blend... | [
"0.60823786",
"0.6042933",
"0.5889305",
"0.5757764",
"0.57329404",
"0.57257676",
"0.57234836",
"0.55613345",
"0.55553347",
"0.5536481",
"0.5505619",
"0.5462696",
"0.54444814",
"0.539518",
"0.5303347",
"0.5277466",
"0.52473974",
"0.5243334",
"0.5204655",
"0.5201424",
"0.516103... | 0.49658856 | 34 |
Only have to make sure that there is only one wing per personnel and vehicle | def h1(pid, cno, mdl, mat, wng):
try:
cur = con.cursor()
print()
chassisnos = get_values("BELONGS_TO","ChassisNo")
models = get_values("BELONGS_TO","Model")
if wng is None:
print("This is an invalid input")
return False
if pid is None and (c... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def won(self, vehicles):\n return vehicles[0].x == self.size - 2",
"def avoids(w, forbidden):\n\treturn set(w).isdisjoint(set(forbidden))",
"def continueCheck(building, lift):\n\n continue_lift = False\n # if passengers remain in any of the dictionaries, both same length.\n for i in range(0,len... | [
"0.5374783",
"0.53616667",
"0.52596354",
"0.5177859",
"0.50081074",
"0.50024396",
"0.49865574",
"0.49453175",
"0.49276868",
"0.49215102",
"0.4904473",
"0.48815972",
"0.48504478",
"0.48390892",
"0.48256508",
"0.48201686",
"0.48201415",
"0.48075408",
"0.4785895",
"0.4785568",
"... | 0.47742835 | 20 |
Change the bot's discord game/stream! | async def _set(Type=None,*,thing=None):
server = len(bot.servers)
if Type is None:
await bot.say('Usage: `.presence [game/stream] [message]`')
else:
if Type.lower() == 'stream':
await bot.change_presence(game=discord.Game(name=thing,type=1,url='https://www.twitch.tv/a'),statu... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"async def stream(self, ctx, streamer=None, *, stream_title=None):\n # [p]set stream <streamer> <stream_title>\n\n server = ctx.message.server\n\n current_status = server.me.status if server is not None else None\n\n if stream_title:\n stream_title = stream_title.strip()\n ... | [
"0.70109564",
"0.68822455",
"0.6671628",
"0.653264",
"0.64807236",
"0.63751477",
"0.63281924",
"0.6198349",
"0.61466295",
"0.612521",
"0.60996115",
"0.60762423",
"0.60760796",
"0.6037178",
"0.6022677",
"0.5978298",
"0.59709203",
"0.58689505",
"0.5831055",
"0.5820006",
"0.5809... | 0.6065055 | 13 |
Automatically removes code blocks from the code. | def cleanup_code( content):
# remove ```py\n```
if content.startswith('```') and content.endswith('```'):
return '\n'.join(content.split('\n')[1:-1])
# remove `foo`
return content.strip('` \n') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def pre_process_code_block(block):\n if 'indent' in block and block['indent']:\n indent = r'^' + block['indent']\n block['content'] = re.sub(indent, '', block['icontent'],\n flags=re.MULTILINE)",
"def clean_code(ls):\r\n ls = remove_white_space... | [
"0.69041",
"0.68703216",
"0.67584676",
"0.6666892",
"0.66578245",
"0.65801555",
"0.65633136",
"0.65602493",
"0.6497586",
"0.64269185",
"0.6405686",
"0.63828623",
"0.63365126",
"0.6305939",
"0.628164",
"0.62530655",
"0.620662",
"0.6188894",
"0.61441284",
"0.6132309",
"0.607823... | 0.6688275 | 3 |
Get stars from query text. According to format of the query text different methods are called. | def getStars(queries, lcs_fold, query_path=None, progb_txt="Querying stars: "):
ORDINARY_QUERY_KEY = "QUERY:"
stars = []
for query in tqdm(queries, desc=progb_txt):
query = query.strip()
if query.startswith(ORDINARY_QUERY_KEY):
stars += getStarsFromRemoteDb(
que... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getSimpleRating(self,ratingLine):\n\n ratingStepartist = re.search(\"^\\[([\\d]+\\.?[\\d]?)/10\\](.*)\\{(.*)\\}[\\s]*\\((.*)\\)$\", ratingLine)\n passRating = re.search(\"^\\[([\\d]*\\.?[\\d]?)(PASS).*\\]\", ratingLine)\n plus = re.search(\"^\\[([\\d]*\\.?[\\d]?)(\\+\\+).*\\]\", ratingLine... | [
"0.5948951",
"0.58353686",
"0.5699146",
"0.5515522",
"0.5428978",
"0.5378395",
"0.53607404",
"0.5349942",
"0.5316735",
"0.5293226",
"0.5238394",
"0.5216603",
"0.5174512",
"0.5134198",
"0.5081347",
"0.5047674",
"0.49897566",
"0.49563116",
"0.49219772",
"0.48989",
"0.48985943",... | 0.62427163 | 0 |
Get stars from folder/s. If path is iterable (case that more folders were given, light curves from that all folder will be loaded | def getStarsFromFolder(single_path, lcs_fold):
p, restr = _check_sample_name(single_path)
try:
st = StarsProvider().getProvider(
"FileManager", {"path": os.path.join(lcs_fold, p)}).getStars()
stars = _split_stars(st, restr)
except KeyError:
raise IOError("\n\nThere no fo... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_sar_paths(directory_path: str) -> list:\n dataset_path = Path(directory_path)\n\n path_generator = dataset_path.rglob('*.tif')\n paths = sorted([path for path in path_generator if path.is_file()])\n return [sar_set(*g) for k, g in groupby(paths, key=lambda path: re.match(TYPE_REGEX, path.name)[... | [
"0.6114552",
"0.60083556",
"0.5877471",
"0.58240753",
"0.5820839",
"0.57919365",
"0.57441986",
"0.5722332",
"0.5715297",
"0.5675058",
"0.56288785",
"0.5609739",
"0.55748695",
"0.55511475",
"0.5519499",
"0.54943717",
"0.5487072",
"0.5483709",
"0.5482678",
"0.54466915",
"0.5430... | 0.7668567 | 0 |
This method parsing the query text in order to return desired stars from remote database. | def getStarsFromRemoteDb(query, query_path):
try:
db_key, query_file = query.split(":")
except:
QueryInputError(
"Key for resolving stars source was not recognized:\n%s" % query)
queries = StatusResolver(
os.path.join(query_path, query_file)).getQueries()
stars = [... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def parse_for_query(query):\n index = query.find('@')\n if index == -1:\n return \"\"\n elif index == len(query)-1:\n # Make sure the final return doesn't index outside the list.\n return \"\"\n else:\n return query[index+1:]",
"def process_query(self, query_str):\n ... | [
"0.56206834",
"0.5586432",
"0.55854344",
"0.55172074",
"0.54510283",
"0.535934",
"0.52816796",
"0.5249843",
"0.5221207",
"0.5220947",
"0.5200863",
"0.51649463",
"0.5164076",
"0.51512545",
"0.51384133",
"0.5105419",
"0.51047957",
"0.5100868",
"0.5099509",
"0.5096608",
"0.50962... | 0.5791404 | 0 |
This takes a netcdf fill and pulls out the lat and lons array | def nc_getLatsandLons(fn):
from netCDF4 import Dataset
# load the netcdf file
ncf1 = Dataset(fn, mode='r')
# Pull out the lon and lat data
lats = ncf1.variables["lat"][:]
lons = ncf1.variables["lon"][:]
return lats, lons | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def load_2D_netCDF(filename, var_name, lat_name, lon_name):\n data = Dataset(filename, 'r')\n var = data[var_name][:]\n lats = data[lat_name][:]\n lons = data[lon_name][:]\n data.close()\n return var, lats, lons",
"def get_ecmwf_lat_lon(nc_file):\n from netCDF4 import Dataset\n \n fh =... | [
"0.6686081",
"0.65038174",
"0.64324605",
"0.63821316",
"0.63641685",
"0.6291383",
"0.6236775",
"0.611699",
"0.60048854",
"0.5986861",
"0.5980002",
"0.5972642",
"0.5938534",
"0.59312713",
"0.5917963",
"0.58900464",
"0.5883775",
"0.5870067",
"0.58416015",
"0.5827574",
"0.581921... | 0.6993417 | 0 |
setup and save a netcdf file | def write_netcdf(ncinfo):
# ========== Create new netcdf ==========
NAME=nc.netcdf_file(ncinfo.fname,'w')
# ========== Set up the Dimensions ==========
NAME.createDimension('time', None) #Question: Shouldn't time be unlimited?
# NAME.createDimension('lev',11)
NAME.createDimension('lat',ncinfo.lat)
NAME... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def to_netcdf(self, outfile):",
"def save_to_disk(self, filename='ens_state.nc'):\n self.to_netcdf(filename)",
"def save_to_netcdf(img, filename):\n filename = os.path.join(datadir, filename + '.nc')\n print('Saving: ' + filename)\n img.to_netcdf(filename)",
"def read_netcdf(self,filename):",... | [
"0.77461386",
"0.7399838",
"0.7267147",
"0.7080664",
"0.7080244",
"0.68906844",
"0.6874828",
"0.68231076",
"0.68165976",
"0.6804859",
"0.67870426",
"0.6741072",
"0.67256397",
"0.66731715",
"0.6647463",
"0.65752",
"0.6552807",
"0.6535576",
"0.647932",
"0.6437388",
"0.64214903"... | 0.7063954 | 5 |
(str) > str Replaces 3 or more consecutive newlines with 2 newlines. Removes multiple newlines at the end of the string. | def beautify(text):
text = re.sub('\n{3,}', '\n\n', text)
text = re.sub('\n+$', '\n', text)
return text | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def modify(s):\n\tl = s.splitlines(True)\n\ttry:\n\t\tif l[1] in ('\\r\\n', '\\n', '</br>'):\n\t\t\tl = everyother(l)\n\texcept IndexError:\n\t\tpass\n\ttry:\n\t\tif l[-1] not in ('\\r\\n', '\\n', '</br>'):\n\t\t\tl.append('\\r\\n')\n\texcept IndexError:\n\t\tpass\n\treturn ''.join(l)",
"def cleanup_newlines(str... | [
"0.710905",
"0.68628454",
"0.67626935",
"0.65454465",
"0.6492721",
"0.64468503",
"0.6399429",
"0.6366013",
"0.6259932",
"0.6204685",
"0.61964625",
"0.61156934",
"0.6046275",
"0.6034635",
"0.6033117",
"0.5945784",
"0.5924168",
"0.5903102",
"0.5898147",
"0.5855274",
"0.5854465"... | 0.7270905 | 0 |
(str) > str Generator function, yields all .rst files' names from the given path. | def list_files(folder_path):
try:
for name in os.listdir(folder_path):
base, ext = os.path.splitext(name)
if ext != '.rst':
continue
yield os.path.join(folder_path, name)
except OSError as ex:
log.error('Exception occured in list_files: {0}'.fo... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def scan_docs():\n\n\n def scan_file(fn):\n f = open(fn)\n\n for l in f:\n m = re.search(r\"\\.\\. (\\w+):: ([.\\w+]+)\", l)\n\n if not m:\n continue\n\n name_kind[m.group(2)] = m.group(1)\n\n for i in os.listdir(\"source\"):\n if i.endswit... | [
"0.6643023",
"0.6321115",
"0.6180466",
"0.6087502",
"0.6051104",
"0.59749836",
"0.5908224",
"0.5885887",
"0.5877877",
"0.5859597",
"0.5819454",
"0.58117485",
"0.5770539",
"0.5751046",
"0.5707234",
"0.5693174",
"0.56727237",
"0.5656135",
"0.56490225",
"0.5645481",
"0.5626505",... | 0.70348614 | 0 |
(str) > dict, str Reads file at given path, interprets its content, returning a metadata dictionary, that will be used at template render, and text content. | def read_file(file_path):
raw_metadata = ""
content = ""
try:
with open(file_path, 'rb') as f:
for line in f:
if line.strip() == '---':
break
raw_metadata += line
for line in f:
content += line
except IOE... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_content(self, file_path: str) -> Tuple[defaultdict, str]:\n if not os.path.exists(file_path): # If file doesn't exist\n raise FileNotFoundError(f\"{fg(1)} Could not find file: {file_path}{fg(15)}\\n\")\n html = self.__html__(file_path)\n metadata = self.__metadata__()\n ... | [
"0.7257744",
"0.6562548",
"0.65443784",
"0.6472406",
"0.6455771",
"0.6454604",
"0.6451337",
"0.6428394",
"0.63324225",
"0.6325068",
"0.6200013",
"0.612579",
"0.6113794",
"0.6112224",
"0.6104595",
"0.6103509",
"0.60870445",
"0.6027246",
"0.59874994",
"0.59812385",
"0.5966934",... | 0.6521285 | 3 |
(str, str, str) > None Writes output .html file using the path, filename and content received. Creates output directory if it does not exist. | def write_output(directory, name, html):
if not os.path.isdir(directory):
os.mkdir(directory)
with open(os.path.join(directory, '.'.join((name, 'html'))), 'w') as f:
f.write(beautify(html)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _write_file(self, slug, folderpath, html):\n # check directories\n if not os.path.isdir(folderpath):\n try:\n os.makedirs(folderpath)\n self.info(\"Creating directory \" + folderpath)\n except Exception as e:\n self.err(e)\n ... | [
"0.69191366",
"0.68946815",
"0.6861655",
"0.6787746",
"0.677637",
"0.669781",
"0.669775",
"0.65559417",
"0.64281124",
"0.6404245",
"0.6380128",
"0.6371389",
"0.631972",
"0.63082826",
"0.6286958",
"0.6281795",
"0.6272514",
"0.622664",
"0.62128615",
"0.6191709",
"0.6179286",
... | 0.67756796 | 5 |
(str, str) > None Initializes jinja environment, creates desired output from the files found in folder_path. | def generate_site(folder_path, output_path):
log.info("Generating site from {0}".format(folder_path))
jinja_env = jinja2.Environment(loader=jinja2.FileSystemLoader(
os.path.join(folder_path, 'layout')))
for file_path in list_files(folder_path):
metadata, content = read_file(file_path)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def jinja_files(self, val: Pattern):\n self[\"jinja_files\"] = str(val)",
"def new(root: str = \".\", name: str = \"piccolo_project\"):\n tree = os.walk(TEMPLATE_DIR)\n\n router = get_routing_framework()\n\n template_context = {\n \"router\": router,\n \"router_dependencies\": ROUTE... | [
"0.6169542",
"0.60679686",
"0.60545796",
"0.59503293",
"0.58086735",
"0.5763365",
"0.56672734",
"0.5580044",
"0.55667",
"0.5536887",
"0.54676056",
"0.5458258",
"0.54351616",
"0.54244095",
"0.5400392",
"0.5299988",
"0.52996176",
"0.529737",
"0.52925354",
"0.52380043",
"0.52066... | 0.57296884 | 6 |
() > None Configures argument parser, generates site files. | def main():
parser = argparse.ArgumentParser()
parser.add_argument("layout_path", help="relative path to the directory "
"containing .rst files with site content and jinja "
"templates that define the site structure")
parser.add_argument("output_path", help="r... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def parse_arguments(args):\n\n parent_parser = argparse.ArgumentParser(add_help=False)\n parent_parser.add_argument('--debug', '-d', action='count', default=0)\n parent_parser.add_argument('--version', '-v', action='version',\n version='%(prog)s {version}'.format(version=version.__ve... | [
"0.6615437",
"0.6609897",
"0.66001606",
"0.6552956",
"0.6536455",
"0.6516835",
"0.6481947",
"0.6475446",
"0.6441392",
"0.6417566",
"0.6365299",
"0.6311957",
"0.6306291",
"0.6297207",
"0.6176481",
"0.6133819",
"0.6131787",
"0.607976",
"0.6054829",
"0.60472035",
"0.6026954",
... | 0.7562603 | 0 |
Takes in a trained policy, runs the policy for a specified number of rollouts, and returns the results of the experiment. | def _run_policy(env, policy, num_rollouts):
start_states, final_states, goal_states, actions, paths = [], [], [], [], []
for i in range(num_rollouts):
path = rollout(
env,
policy,
max_path_length=100,
animated=False,
)
obs = path["observat... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def rollout(env, our_policy, expert_policy, num_rollouts, max_steps):\n returns = []\n observations = []\n actions = []\n for i in range(num_rollouts):\n # print('iter', i)\n obs = env.reset()\n done = False\n totalr = 0.\n steps = 0\n while not done:\n ... | [
"0.7071595",
"0.70170295",
"0.68903935",
"0.6639821",
"0.6559121",
"0.6324267",
"0.620789",
"0.6170936",
"0.6127437",
"0.60635906",
"0.60242164",
"0.6003384",
"0.5966104",
"0.5851238",
"0.58464557",
"0.5844492",
"0.5844492",
"0.58353084",
"0.5820814",
"0.5737581",
"0.5696761"... | 0.7488749 | 0 |
Parse a shot of the form "letterNUMBER" to array coordinates. | def shotparser(shot: str):
characters = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 't', 'u',
'v', 'w', 'x', 'y', 'z']
# Get index of letter
return int(shot[1:]) - 1, characters.index(shot[0]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def gx_coords1(s: str) -> list[float]:\n return numarray(s.split(\" \"))",
"def coords1(s: str) -> list[float]:\n return numarray(re.sub(SPACE, \"\", s).split(\",\"))",
"def mapToCoordinates(self, shot):\r\n toks = shot.split(\"-\")\r\n return Coordinates(ord(toks[0]) - ord(\"A\"), int(toks... | [
"0.5952631",
"0.5823283",
"0.57569534",
"0.5628835",
"0.55996495",
"0.5588718",
"0.5460892",
"0.5442877",
"0.5430098",
"0.53694767",
"0.5343003",
"0.5340652",
"0.53300834",
"0.53288764",
"0.5307314",
"0.52952194",
"0.52372366",
"0.5217806",
"0.51957154",
"0.51949483",
"0.5188... | 0.5715052 | 3 |
Attacks a random point on a board that has not been previously attacked. | def enemyattack(playerboard):
while True:
attempt = shotgenerator()
# Generates a valid shot
if playerboard[attempt[0]][attempt[1]] == " " or playerboard[attempt[0]][attempt[1]] == "O":
break
return attempt | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def choose_attack(self, board):\n board_size = board.get_board_size()\n \n coordinate = self._pick_potential_coordinate(board_size)\n \n if not coordinate:\n coordinate = self._pick_unconnected_coordinate(board_size)\n attack_result = board.set_attac... | [
"0.69671375",
"0.6635215",
"0.66189456",
"0.6607825",
"0.6607825",
"0.65785706",
"0.631702",
"0.6306051",
"0.630326",
"0.62780935",
"0.622881",
"0.62050444",
"0.6174146",
"0.6149704",
"0.6112133",
"0.60293716",
"0.59930074",
"0.59873855",
"0.5954399",
"0.5915794",
"0.5871713"... | 0.6238277 | 10 |
Prints a board with a title. board = [[1, 2, 3, 4], [1, 2, 3, 4], [1, 2, 3, 4], [1, 2, 3, 4], [1, 2, 3, 4], [1, 2, 3, 4], [1, 2, 3, 4], [1, 2, 3, 4], [1, 2, 3, 4], [1, 2, 3, 4], ] | def boardprinter(board: list, title: str):
padding = " "
header = padding + " |A|B|C|D|E|F|G|H|I|J|"
protoboard = ""
for i in range(10):
newrow = str(board[i])
newrow = newrow.replace("[", "#|").replace(
", ", "|").replace("'", "").replace("]", "|")
# Prep... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def print_board(self):\n num_rows = len(self.board)\n num_cols = len(self.board[0])\n \n for i in range(num_rows):\n if i % 3 == 0 and i != 0:\n print(\"- - - - - - - - - - - -\")\n \n for j in range(num_cols):\n if j % 3 == 0 and j != 0:\n print(\" | \", end=\"\")... | [
"0.7980977",
"0.79472417",
"0.7928891",
"0.7881975",
"0.7878489",
"0.78713536",
"0.7841725",
"0.7832346",
"0.78164923",
"0.7790345",
"0.77899647",
"0.77899647",
"0.77850956",
"0.77644897",
"0.77406794",
"0.7719308",
"0.7688303",
"0.76780593",
"0.76677835",
"0.76352805",
"0.76... | 0.8417716 | 0 |
Generates a valid shot | def shotgenerator():
return random.randint(0, 9), random.randint(0, 9) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def createNewShot(*args):\n createDir.createShot(pi.shotsFolder)",
"def createShot(shotFolder, *args):\n createShotUI(shotFolder)",
"def make_shot(self, target, force):\n intersections = self._get_edge_intersections(target)\n\n if target[0] > self.cue_coords[0]:\n start = max(int... | [
"0.65291405",
"0.61991775",
"0.61182004",
"0.60835326",
"0.60244423",
"0.5980719",
"0.59457535",
"0.5912019",
"0.5864049",
"0.5780368",
"0.572628",
"0.5722106",
"0.567072",
"0.5562419",
"0.55616623",
"0.5552547",
"0.55457574",
"0.55451393",
"0.551989",
"0.55038774",
"0.549977... | 0.70925194 | 0 |
Validates a ship placement. Assumes points are already valid. | def shipvalidator(point1: tuple, point2: tuple, board: list):
valid = True
# Is horizontal
if point1[0] == point2[0]:
# No collisions
for i in range(min(point1[1], point2[1]), max(point1[1], point2[1])):
if board[point1[0]][i] != " ":
valid = False
# Is vertic... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def validate_ship(cls, ship_type, star_square, orientation):\n try:\n cls.validate_square(star_square)\n cls.validate_type(ship_type)\n cls.check_if_fit_in_grid(ship_type, star_square, orientation)\n except ValueError as e:\n raise ValueError('%s for %s at ... | [
"0.7364583",
"0.7090558",
"0.6627818",
"0.63202465",
"0.6307072",
"0.6304159",
"0.614453",
"0.611397",
"0.6086512",
"0.60463244",
"0.6039142",
"0.603421",
"0.6026416",
"0.6020171",
"0.60170776",
"0.599188",
"0.5988496",
"0.59128296",
"0.5907643",
"0.58383584",
"0.5812146",
... | 0.6815466 | 2 |
Places a ship on an board. Assumes ship is valid. | def placeship(point1: tuple, point2: tuple, board: list):
# Is horizontal
if point1[0] == point2[0]:
for i in range(min(point1[1], point2[1]), max(point1[1], point2[1]) + 1):
board[point1[0]][i] = "O"
# Is vertical
elif point1[1] == point2[1]:
for i in range(min(point1[0], po... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def place_ship(self, row, col, ship, aligment):\n self._validate_place_params(row, col, ship, aligment)\n\n if aligment == ShipPosition.HORIZONTAL:\n # Place ship in horizontal position, populating in right direction\n for i in range(self.get_col_index(col), ship.LENGH + self.ge... | [
"0.8136066",
"0.77643454",
"0.7752637",
"0.740094",
"0.7397912",
"0.73814833",
"0.735342",
"0.71544975",
"0.7044392",
"0.70412576",
"0.69499886",
"0.69271797",
"0.68341196",
"0.68257225",
"0.675496",
"0.6746818",
"0.6714631",
"0.67056274",
"0.6677966",
"0.65760803",
"0.653700... | 0.65420336 | 20 |
Generate a ship of length N. | def generateship(length: int):
length = length - 1
vertical = bool(random.randint(0, 1))
if vertical:
col1 = random.randint(0, 9)
row1 = random.randint(0, 9 - length)
col2 = col1
row2 = row1 + length
return (col1, row1), (col2, row2)
if not vertical:
col1 ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_ship(length, *args):\n coord = (randint(0, 10 - length), randint(0, 10 - length))\n check = True\n while check:\n check = False\n if args:\n for arg in args:\n check = point_in_ship(arg, coord) if check == False else True\n while point_in_s... | [
"0.71276236",
"0.6535593",
"0.6233696",
"0.61020637",
"0.60862756",
"0.60614854",
"0.6044193",
"0.6028837",
"0.5943878",
"0.58799267",
"0.58623815",
"0.5815113",
"0.5723559",
"0.5715128",
"0.5660159",
"0.5649343",
"0.5647397",
"0.56422246",
"0.5617939",
"0.561721",
"0.5591131... | 0.658633 | 1 |
Return True for leap years, False for nonleap years | def is_leap(year):
return year % 4 == 0 and (year % 100 != 0 or year % 400 == 0) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_leap_year():",
"def is_leap_year(self):\n\n yr = self.year\n if not yr%4 == 0:\n return False\n elif not yr%100 == 0: #if divisible by 4 and not divisible by 100\n return True\n elif not yr%400 == 0: #if divisible by 4, divisible by 100 and not divisible 4... | [
"0.91418904",
"0.8760494",
"0.8699257",
"0.8699257",
"0.85824883",
"0.85733265",
"0.8568897",
"0.8495486",
"0.8475959",
"0.8471232",
"0.8421707",
"0.8419007",
"0.83912635",
"0.837288",
"0.83475894",
"0.8343614",
"0.8324799",
"0.8307901",
"0.8289238",
"0.8282688",
"0.82801807"... | 0.84470236 | 10 |
Returns no of days in that month in that year. | def days_in_month(year, month):
if not 1 <= month <= 12:
return 'Invalid Month'
if month == 2 and is_leap(year):
return 29
return month_days[month] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def days_in_month(year, month):\n num_days = monthrange(year, month)[1]\n return num_days",
"def numDays(month, year):\n\tif month in [9, 4, 6, 11]:\n\t\treturn 30\n\telif month == 2 and year % 4 == 0 and (year % 100 != 0 or year % 400 == 0):\n\t\treturn 29\n\telif month == 2:\n\t\treturn 28\n\telse:\n\t\t... | [
"0.81629515",
"0.8069864",
"0.79608065",
"0.784114",
"0.77834785",
"0.7639529",
"0.7614766",
"0.75591195",
"0.73463625",
"0.728101",
"0.7228919",
"0.717201",
"0.7168249",
"0.7129094",
"0.7107501",
"0.71060723",
"0.7065934",
"0.7065934",
"0.7065934",
"0.7061095",
"0.70126194",... | 0.7311546 | 9 |
Stores min, max and avg box sides (for height and width) | def boxes_stats(self):
all_boxes = []
nb_detections = []
convexities = []
all_ids = set()
for image_id in self.dataset_handler.image_ids:
masks, ids = self.dataset_handler.load_mask(image_id)
all_ids = all_ids.union(set(ids))
boxes = utils.extr... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_box(self, state, min_x, min_y, max_x, max_y):\n ret_val, min_x.value, min_y.value, max_x.value, max_y.value = self._get_box(state.encode(), min_x.value, min_y.value, max_x.value, max_y.value)\n return ret_val",
"def box_size(self) -> np.ndarray:\n return self.upper - self.lower + 1",... | [
"0.6205536",
"0.61921114",
"0.61921114",
"0.6136274",
"0.6073941",
"0.6073941",
"0.6055319",
"0.5978771",
"0.59486884",
"0.59391594",
"0.58966076",
"0.5886958",
"0.5836002",
"0.5829803",
"0.5827152",
"0.58222336",
"0.5815708",
"0.58145404",
"0.5793903",
"0.57711303",
"0.57473... | 0.0 | -1 |
Filter results according to stats. | def filter(self, result):
convexities = []
for mask_idx in range(result.masks.shape[2]):
mask = result.masks[:, :, mask_idx]
props = regionprops(mask.numpy().astype(np.int8))[0]
convexities.append(props.filled_area/props.convex_area)
convexities = np.array(co... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def filter(self, filters):",
"def filterRansac():\n pass",
"def filter_results(results, main_class=None):\n # Gets request parameters/arguments\n args = get_request_args()\n # Big block of ifs to filter\n if args['user_id']: results = results.filter(User.user_id==args['user_id'])\n if args['f... | [
"0.67229635",
"0.6499244",
"0.6495151",
"0.6472337",
"0.64192593",
"0.635806",
"0.63370085",
"0.6204995",
"0.61245126",
"0.60211843",
"0.60050136",
"0.598326",
"0.59564465",
"0.59132177",
"0.5856923",
"0.5845993",
"0.58319396",
"0.5794037",
"0.5786162",
"0.5761385",
"0.575883... | 0.0 | -1 |
Get now as microseconds since the UNIX epoch. | def now():
return int(datetime.datetime.now().strftime("%s")) * 1000 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def timestamp(self):\n # this only returns second precision, which is why we don't use it\n #now = calendar.timegm(datetime.datetime.utcnow().utctimetuple())\n\n # this returns microsecond precision\n # http://bugs.python.org/msg180110\n epoch = datetime.datetime(1970, 1, 1)\n ... | [
"0.7489993",
"0.74439853",
"0.72787327",
"0.7272514",
"0.72541904",
"0.7161329",
"0.7107288",
"0.70058936",
"0.69860166",
"0.69800115",
"0.69408554",
"0.69129497",
"0.6905701",
"0.6893554",
"0.68712544",
"0.6860828",
"0.6805546",
"0.6799936",
"0.67947143",
"0.6786573",
"0.676... | 0.6928512 | 11 |
Get now as microseconds since the UNIX epoch. | def datetime_to_epoch(datetime_obj):
return int(datetime_obj.strftime("%s")) * 1000 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def timestamp(self):\n # this only returns second precision, which is why we don't use it\n #now = calendar.timegm(datetime.datetime.utcnow().utctimetuple())\n\n # this returns microsecond precision\n # http://bugs.python.org/msg180110\n epoch = datetime.datetime(1970, 1, 1)\n ... | [
"0.74894196",
"0.7442043",
"0.7278196",
"0.7273182",
"0.7252672",
"0.71613514",
"0.71069956",
"0.70050436",
"0.6984224",
"0.69784194",
"0.69391435",
"0.69267714",
"0.6912264",
"0.6905124",
"0.689079",
"0.68695486",
"0.68592054",
"0.68064374",
"0.6800234",
"0.6795791",
"0.6785... | 0.0 | -1 |
environment init before test | def setUp(self):
# excel文件1.xlsx的路径
# 加载excel中的测试数据
excel_path = os.path.join(os.path.dirname(__file__), '1.xlsx')
excel_obj = ExcelUtil(excel_path, 'Sheet1')
print("start test")
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setUp(self):\n test_env_setup()",
"def initialize():\n environment = Environment()\n environment.setup()",
"def testInit(self):\n self.globalInit()\n self.test.start()",
"def SetupEnvironment(self):\n pass",
"def setUp(self) -> None:\n self.s3 = boto3.client('s3')\n... | [
"0.8477952",
"0.7773781",
"0.7656406",
"0.75087535",
"0.73838836",
"0.73441076",
"0.7292142",
"0.7292142",
"0.72866803",
"0.7260709",
"0.7255964",
"0.7255964",
"0.7222324",
"0.72035205",
"0.7152396",
"0.7147133",
"0.70943093",
"0.7089195",
"0.7052635",
"0.70239663",
"0.702188... | 0.0 | -1 |
environment clear after test | def tearDown(self):
print("end test")
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def tearDown(self):\n tests.utils.cleanup_environment()",
"def tearDown(self):\n tests.utils.cleanup_environment()",
"def teardown_test_env():\n if not keep_tmp_dirs:\n print('\\nCleaning up temporary directories...')\n shutil.rmtree(tmp_elm_dpath, ignore_errors=True)... | [
"0.80781776",
"0.80781776",
"0.8025331",
"0.795834",
"0.78031003",
"0.72159874",
"0.71568036",
"0.7120855",
"0.7064884",
"0.7049674",
"0.70482063",
"0.704096",
"0.704096",
"0.704096",
"0.704096",
"0.70360094",
"0.70360094",
"0.702821",
"0.69660354",
"0.6965543",
"0.6965543",
... | 0.0 | -1 |
Apply the image transformation specified by a matrix. | def apply_transform(x,
transform_matrix,
channel_axis=0,
fill_mode='constant',
cval=0.):
x = np.rollaxis(x, channel_axis, 0)
final_affine_matrix = transform_matrix[:2, :2]
final_offset = transform_matrix[:2, 2]
channel_images = [
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def apply_transform(img,\n transform_matrix):\n rows,cols = img.shape[:2]\n dst = cv2.warpAffine(img,transform_matrix,(cols,rows))\n\n\n return dst",
"def apply_transform_matrix(self, img: np.ndarray, transform_matrix):\n h, w = img.shape[0], img.shape[1]\n transform_matrix = tr... | [
"0.7891312",
"0.7722452",
"0.77214634",
"0.6940536",
"0.69030356",
"0.6731182",
"0.6722956",
"0.6610788",
"0.6610788",
"0.6405551",
"0.63846016",
"0.632801",
"0.6246744",
"0.6242733",
"0.6241952",
"0.6209053",
"0.6175359",
"0.61352855",
"0.6126648",
"0.6123695",
"0.6076114",
... | 0.6429553 | 10 |
Read png images from input directory in batches. | def load_images(input_dir, batch_shape, vgg_batch_shape):
ens_images = np.zeros(batch_shape)
inc_images = np.zeros(batch_shape)
tcd_images = np.zeros(batch_shape)
vgg_images = np.zeros(vgg_batch_shape)
filenames = []
idx = 0
batch_size = batch_shape[0]
for filepath in tf.gfile.Glob(os.path.join(input_di... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def load_images(input_dir, batch_shape):\n images = np.zeros(batch_shape)\n filenames = []\n idx = 0\n batch_size = batch_shape[0]\n for filepath in tf.gfile.Glob(os.path.join(input_dir, '*.png')):\n with tf.gfile.Open(filepath) as f:\n image = imread(f, mode='RGB').astype(np.float) / 255.0\n # Ima... | [
"0.7794853",
"0.77915066",
"0.77869236",
"0.77746576",
"0.77045417",
"0.75936383",
"0.7586308",
"0.7476024",
"0.7464507",
"0.7414358",
"0.70712256",
"0.70034915",
"0.69416773",
"0.68530375",
"0.68395364",
"0.68052876",
"0.6768019",
"0.67624897",
"0.67129254",
"0.66466576",
"0... | 0.68980294 | 13 |
Constructs model and return probabilities for given input. | def __call__(self, ens_x_input, vgg_x_input, inc_x_input, tcd_x_input):
reuse = True if self.built else None
logits = None
aux_logits = None
weights = [[0.7, 0.1], [0.2, 0.1]]
all_inputs = [[ens_x_input, tcd_x_input], [inc_x_input, tcd_x_input]]
scopes = [inception_resnet_v2.inception_resnet_v2_... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def predict(cls, input):\n clf = cls.get_model()\n print('input=')\n print(input)\n return clf.predict(input)",
"def predict_proba(self, inputs):\n return self.model.predict_proba(inputs)",
"def run(self, input):\n\n with torch.no_grad():\n input_tensor = se... | [
"0.63823074",
"0.6323266",
"0.62877744",
"0.6283942",
"0.62525046",
"0.61957985",
"0.6172311",
"0.6114245",
"0.61092055",
"0.6090099",
"0.6075938",
"0.60474825",
"0.60085744",
"0.6007718",
"0.5942609",
"0.59334004",
"0.5921581",
"0.5892854",
"0.5889096",
"0.5858429",
"0.58498... | 0.0 | -1 |
Send emails to recipients. | def send(self, smtp_server_instance: SMTPServer = None):
if not self.can_send_now():
return
with SendMailContext(self, smtp_server_instance) as ctx:
message = None
for recipient in self.recipients:
if recipient.is_mail_sent():
continue
message = ctx.build_message(recipient.recipient)
if ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def send_email_users():\n\n # Get users emails\n users_emails = User.objects.exclude(\n Q(email='') |\n Q(email=None)\n ).values_list(\n 'email',\n flat=True\n )\n\n # Send email to each user\n # for email_user in users_emails:\n\n title = 'Se han calculado nuevos H... | [
"0.7351532",
"0.7231405",
"0.72113985",
"0.7196959",
"0.7194989",
"0.7145822",
"0.70656997",
"0.7054774",
"0.7039921",
"0.7032839",
"0.696968",
"0.6959936",
"0.69466656",
"0.6911196",
"0.6857288",
"0.6817667",
"0.6813234",
"0.68113726",
"0.68011487",
"0.6783675",
"0.67317784"... | 0.6068696 | 97 |
Remove low priority older than 31 days in Outbox or configured in Log Settings. | def clear_old_logs(days=30):
days = days or 31
email_queue = frappe.qb.DocType("Email Queue")
email_recipient = frappe.qb.DocType("Email Queue Recipient")
# Delete queue table
(
frappe.qb.from_(email_queue)
.delete()
.where(email_queue.modified < (Now() - Interval(days=days)))
).run()
# delete ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def remove_priorities_from_all_not_due_today(self):\n\n today = datetime.now().date()\n\n for item in self.api_wrapper.get_all_items():\n\n try:\n item_due_date = datetime.strptime(item['due']['date'], \"%Y-%m-%d\").date()\n if item['priority'] != 1 and item_d... | [
"0.61522824",
"0.6127307",
"0.585736",
"0.5584973",
"0.5424045",
"0.53852594",
"0.5287806",
"0.5262454",
"0.52023005",
"0.5179946",
"0.51432645",
"0.5141446",
"0.51373243",
"0.5134785",
"0.5101212",
"0.50913",
"0.50795054",
"0.5073887",
"0.506944",
"0.50674236",
"0.5040266",
... | 0.49397492 | 30 |
This is equivalent to EmailQueue.send. This provides a way to make sending mail as a background job. | def send_mail(email_queue_name, smtp_server_instance: SMTPServer = None):
record = EmailQueue.find(email_queue_name)
record.send(smtp_server_instance=smtp_server_instance) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def send_mail(self, msg):\n mail_queue.put(msg)",
"def send_async_email(self, msg):\n with app.app_context():\n result = mail.send(msg)\n print result",
"def quick_email(self, send_to, subject, body, style=None):\n message = Message(body, style=style)\n\n self.... | [
"0.7172754",
"0.68388176",
"0.6546201",
"0.6405928",
"0.63104117",
"0.6251",
"0.6232571",
"0.6101235",
"0.607073",
"0.60418105",
"0.5989472",
"0.5973994",
"0.5913236",
"0.5909689",
"0.58735156",
"0.5871084",
"0.5864278",
"0.585043",
"0.5849195",
"0.5838661",
"0.5824061",
"0... | 0.66771394 | 2 |
Build message specific to the recipient. | def build_message(self, recipient_email):
message = self.queue_doc.message
if not message:
return ""
message = message.replace(
self.message_placeholder("tracker"), self.get_tracker_str(recipient_email)
)
message = message.replace(
self.message_placeholder("unsubscribe_url"), self.get_unsubscribe_st... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def construct_message(self):\n msg_type = self.msg_type\n if msg_type == \"PUBMSG\":\n msg_type = \"PRIVMSG\"\n ret = \"{} {}\".format(msg_type, self.target)\n if self.content:\n ret += \" :{}\".format(self.content)\n return ret + \"\\r\\n\"",
"def build_m... | [
"0.6769794",
"0.6737969",
"0.62899005",
"0.6173471",
"0.61571157",
"0.6078235",
"0.6017565",
"0.60136795",
"0.59706426",
"0.5963227",
"0.5956112",
"0.5827875",
"0.5805116",
"0.57555825",
"0.57555825",
"0.57119703",
"0.57029104",
"0.569709",
"0.5649452",
"0.5649261",
"0.563867... | 0.72548765 | 0 |
Add index in `tabCommunication` for `(reference_doctype, reference_name)` | def on_doctype_update():
frappe.db.add_index(
"Email Queue", ("status", "send_after", "priority", "creation"), "index_bulk_flush"
)
frappe.db.add_index("Email Queue", ["message_id(140)"]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_index():",
"def typesense_index_referral(ref, client=None):\n if not client:\n client = typesense_client()\n\n ref_document = {\n 'id': str(ref.pk),\n 'created': ref.created.timestamp(),\n 'type': ref.type.name,\n 'referring_org': ref.referring_org.name,\n ... | [
"0.5856325",
"0.57169074",
"0.5688665",
"0.5684541",
"0.5623131",
"0.5605301",
"0.55933005",
"0.55597967",
"0.5525972",
"0.52962345",
"0.52801585",
"0.52396655",
"0.523762",
"0.5230654",
"0.52270794",
"0.5187034",
"0.5185155",
"0.5145689",
"0.5129705",
"0.510814",
"0.5104973"... | 0.46001127 | 86 |
Add email to sending queue (Email Queue) | def __init__(
self,
recipients=None,
sender=None,
subject=None,
message=None,
text_content=None,
reference_doctype=None,
reference_name=None,
unsubscribe_method=None,
unsubscribe_params=None,
unsubscribe_message=None,
attachments=None,
reply_to=None,
cc=None,
bcc=None,
message_id=None,
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def send_mail(self, msg):\n mail_queue.put(msg)",
"def _send_mail(self, sender, subject, body, html=None):\n self.emails.append((sender, subject, body, html))",
"def _send_mail(self, sender, subject, body, html=None):\n self.emails.append((sender, subject, body, html))",
"def send_mail(email_que... | [
"0.7520886",
"0.7370168",
"0.7370168",
"0.714263",
"0.7081094",
"0.69674784",
"0.6915373",
"0.6751236",
"0.64361167",
"0.6408204",
"0.640441",
"0.6377486",
"0.6270715",
"0.62467325",
"0.61820465",
"0.6142096",
"0.6090388",
"0.60861367",
"0.6045202",
"0.60185254",
"0.6003094",... | 0.0 | -1 |
Build and return the email queues those are created. Sends email incase if it is requested to send now. | def process(self, send_now=False):
final_recipients = self.final_recipients()
queue_separately = (final_recipients and self.queue_separately) or len(final_recipients) > 20
if not (final_recipients + self.final_cc()):
return []
queue_data = self.as_dict(include_recipients=False)
if not queue_data:
retur... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def init_email_queue():\n g.setdefault('email_queue', [])",
"def send_queued_mail():\r\n now = datetime.datetime.now(g.tz)\r\n if not c.site:\r\n c.site = Default\r\n\r\n clear = False\r\n session = smtplib.SMTP(g.smtp_server)\r\n # convienence funciton for sending the mail to the singly... | [
"0.6772418",
"0.62025726",
"0.6147615",
"0.6054284",
"0.6054284",
"0.6054284",
"0.6054284",
"0.6054284",
"0.6054284",
"0.6054284",
"0.6054284",
"0.6054284",
"0.58631426",
"0.5812916",
"0.5798254",
"0.5771639",
"0.5767222",
"0.5765894",
"0.5718625",
"0.5701637",
"0.5682031",
... | 0.65649277 | 1 |
Creates a new instance of the appropriate class based on discriminator value | def create_from_discriminator_value(parse_node: Optional[ParseNode] = None) -> PrintTaskDefinition:
if not parse_node:
raise TypeError("parse_node cannot be null.")
return PrintTaskDefinition() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_from_discriminator_value(parse_node: Optional[ParseNode] = None) -> OnenoteEntityHierarchyModel:\n if not parse_node:\n raise TypeError(\"parse_node cannot be null.\")\n try:\n mapping_value = parse_node.get_child_node(\"@odata.type\").get_str_value()\n except ... | [
"0.65867877",
"0.65305114",
"0.6343514",
"0.6252549",
"0.60611624",
"0.60551363",
"0.6040931",
"0.60100836",
"0.59928304",
"0.59867793",
"0.59777546",
"0.59384596",
"0.59132653",
"0.5886811",
"0.58785224",
"0.5829891",
"0.5829891",
"0.5826961",
"0.5816853",
"0.58128166",
"0.5... | 0.51968336 | 69 |
The deserialization information for the current model | def get_field_deserializers(self,) -> Dict[str, Callable[[ParseNode], None]]:
from .app_identity import AppIdentity
from .entity import Entity
from .print_task import PrintTask
from .app_identity import AppIdentity
from .entity import Entity
from .print_task import Print... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def model_info(self):\n if not self._model_info:\n self._load_model_info()\n try:\n data = json.loads(self._model_info)\n except (TypeError, ValueError):\n data = {}\n return data",
"def _post_deserialize (self):\n pass",
"def _serialise(self)... | [
"0.7466499",
"0.707194",
"0.67322314",
"0.6634526",
"0.6614095",
"0.64530206",
"0.6389254",
"0.63054293",
"0.63052577",
"0.6239454",
"0.62091255",
"0.619561",
"0.6184919",
"0.6148526",
"0.60944825",
"0.6040633",
"0.6039713",
"0.6035496",
"0.60268706",
"0.602527",
"0.6023899",... | 0.0 | -1 |
Serializes information the current object | def serialize(self,writer: SerializationWriter) -> None:
if not writer:
raise TypeError("writer cannot be null.")
super().serialize(writer)
writer.write_object_value("createdBy", self.created_by)
writer.write_str_value("displayName", self.display_name)
writer.write_co... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def serialize(self):\n pass",
"def serialize(self):",
"def serialize(self, obj):\n pass",
"def _serialise(self):\n # TODO (M Foley)\n pass",
"def serialize(self):\n raise NotImplementedError(\"Abstract class, implemented in sub class\")",
"def serialize(self):\n\n\t\tre... | [
"0.8327273",
"0.8142914",
"0.7973026",
"0.78005224",
"0.77396905",
"0.7624675",
"0.76167446",
"0.7545493",
"0.743923",
"0.7393768",
"0.7392551",
"0.73903686",
"0.73865026",
"0.73563176",
"0.73563176",
"0.7347126",
"0.7302598",
"0.72678256",
"0.72639143",
"0.72588915",
"0.7251... | 0.0 | -1 |
check if a param is a python reserved word. if so append the PREPEND_STR and return. If not just return the param | def check_param(param):
return PREPEND_STR+param if keyword.iskeyword(param) else param | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def param_name(p):\n prefix = ['limit_', 'error_', 'fix_']\n for prf in prefix:\n if p.startswith(prf):\n return p[len(prf):]\n return p",
"def format_parameter(param, required):\n\n param_string = check_param(flatten_param(param))\n if not required:\n param_string += '=No... | [
"0.62164277",
"0.60627276",
"0.59327525",
"0.58690375",
"0.5847756",
"0.58377063",
"0.57170993",
"0.5695834",
"0.5646491",
"0.5615303",
"0.5615303",
"0.558056",
"0.5556343",
"0.5556343",
"0.5484624",
"0.5424852",
"0.5399515",
"0.53664017",
"0.53664017",
"0.5356545",
"0.530470... | 0.81484944 | 0 |
clean param looks for parameters with '' and removes them | def clean_param(param):
if '<' in param:
param = param.replace("<", "")
if '>' in param:
param = param.replace(">", "")
return param | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def clean_params(self, url):\n if isinstance(url, unicode):\n url = url.encode(\"utf-8\")\n parts = list(urlparse.urlsplit(url))\n if not parts[3]:\n return url\n query = urlparse.parse_qsl(parts[3])\n query = [q for q in query if self._is_param_allowed(*q)]... | [
"0.69229156",
"0.6752244",
"0.65578884",
"0.6452081",
"0.64409894",
"0.6366064",
"0.628932",
"0.6192528",
"0.6158099",
"0.6077513",
"0.6062495",
"0.6045371",
"0.60109264",
"0.6010546",
"0.5998438",
"0.59780425",
"0.59707963",
"0.59661484",
"0.5924474",
"0.59158826",
"0.591525... | 0.7192391 | 0 |
Turn a parameter that looks like this param[name_one][name_two][name_three] into this param_name_one_name_two_name_three | def flatten_param(param):
param = param.replace(']', '').replace('[', '_').replace('<','').replace('>','')
if param.startswith('_'):
param = param.replace('_', '', 1)
return param | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _parameterize_string(raw):\n\n parts = []\n s_index = 0\n\n for match in _PARAMETER_PATTERN.finditer(raw):\n parts.append(raw[s_index:match.start()])\n parts.append({u\"Ref\": match.group(1)})\n s_index = match.end()\n\n if not parts:\n return GenericHelperFn(raw)\n\n ... | [
"0.5780257",
"0.5667719",
"0.5615841",
"0.55319816",
"0.5456321",
"0.5438268",
"0.53272706",
"0.5323091",
"0.5322933",
"0.5312868",
"0.53086865",
"0.52487105",
"0.5246531",
"0.5241464",
"0.5229589",
"0.5199713",
"0.5155024",
"0.5140652",
"0.5140652",
"0.513402",
"0.5124002",
... | 0.679576 | 0 |
Determines if a parameter should be treated as an array | def is_array_param(param):
return param.get('tags') and param['tags']['type'] == 'array' | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_array(self):\n return False",
"def is_array(val):\n return (\n isinstance(val, tuple) or \\\n isinstance(val, dict) or \\\n isinstance(val, list)\n )",
"def IsArray(obj):\n return isinstance(obj, (list, tuple))",
"def is_array(self, arr):\n return isinstance(arr, np.ndarray... | [
"0.75525606",
"0.7493666",
"0.74254745",
"0.7411446",
"0.734913",
"0.7297551",
"0.7130163",
"0.7086796",
"0.7065588",
"0.70564675",
"0.6974437",
"0.6965529",
"0.68480897",
"0.68339354",
"0.67768043",
"0.67439604",
"0.6680122",
"0.664659",
"0.6624795",
"0.6542464",
"0.64139646... | 0.80148953 | 0 |
build_payload creates a list of parameters to be used in the payload of the api call | def build_payload(parameters):
payload = []
for param in parameters:
"""
Do not include path parameters in the payload
"""
if param['paramType'] != 'path':
field_name = clean_param(param['name'])
field = flatten_param(field_name)
if is_array_pa... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def build_payload(self, **kwargs):\n\n return None",
"def build_payload():\n payload = json.dumps({\"method\": \"ListAccounts\", \"params\": {}, \"id\": 1})\n return payload",
"def _build_payload(self, body: Dict) -> Dict[str, Any]:\n return {'jsonrpc': '2.0',\n 'id': self._i... | [
"0.73088354",
"0.71508425",
"0.6918581",
"0.69140506",
"0.6796427",
"0.67317855",
"0.66412216",
"0.6116565",
"0.6034691",
"0.6007825",
"0.5997449",
"0.5952233",
"0.59042174",
"0.5879261",
"0.5848162",
"0.5779257",
"0.5766688",
"0.5747198",
"0.5743052",
"0.57350177",
"0.572885... | 0.72281057 | 1 |
format_parameter build the a parameter to be used in the paramter list of the methods calls we are creating | def format_parameter(param, required):
param_string = check_param(flatten_param(param))
if not required:
param_string += '=None'
return param_string | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _params_formatter(field, description):\n heads = ['param']\n types = _or_types(field)\n if types:\n heads.append(types)\n heads.append(rst.escape(field['name']))\n tail = description\n return heads, tail",
"def render_param(self, format):\n\t\tdef renderer(ctx, data):\n\t\t\tparName ... | [
"0.6774295",
"0.6679267",
"0.6585214",
"0.6363438",
"0.6351442",
"0.633546",
"0.63323075",
"0.61973155",
"0.6140997",
"0.6104696",
"0.60986304",
"0.6089245",
"0.60883564",
"0.5919709",
"0.59121525",
"0.59110725",
"0.5835087",
"0.58008426",
"0.579243",
"0.5762046",
"0.57561606... | 0.6277223 | 7 |
get paramters creates the parameter list for the method call Places all required params at the begining of the param list | def get_parameters(parameters):
arg_list = []
opt_list = []
for param in parameters:
param_name = param['name']
param_required = param['required']
if param_required:
arg_list.append(format_parameter(param_name, param_required))
else:
opt_list.append(f... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_params(self):",
"def get_params(self):\n pass",
"def get_params(self):\n raise NotImplementedError",
"def params():\n raise NotImplementedError",
"def get_params(self):\n return []",
"def get_params(self, deep=...):\n ...",
"def parameters(self):",
"def para... | [
"0.77528787",
"0.7571475",
"0.7385595",
"0.7349339",
"0.72589153",
"0.7256588",
"0.7008006",
"0.69573146",
"0.68612456",
"0.6818249",
"0.67983365",
"0.6753536",
"0.6746479",
"0.6741851",
"0.67194295",
"0.67194295",
"0.67194295",
"0.67127293",
"0.66983944",
"0.66978526",
"0.66... | 0.6139878 | 71 |
get paramters creates the parameter list for the method calls in rst format | def get_parameter_descriptions(parameters):
lines = []
opt_lines = []
for param in parameters:
param_name = check_param(flatten_param(param['name']))
if param['required']:
required = 'required'
lines.append(':param {0}: ({1}) {2}'.format(param_name, required,
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_params(self):",
"def parameters(self):",
"def params(self):\n pass",
"def params():\n raise NotImplementedError",
"def _formal_params(self, doclet):\n name, paren, params = self.arguments[0].partition('(')\n return ('(%s' % params) if params else '(%s)' % ', '.join(d... | [
"0.7224196",
"0.68872964",
"0.6800735",
"0.66922843",
"0.6662566",
"0.6645085",
"0.6556769",
"0.65225315",
"0.6496837",
"0.64939827",
"0.6491988",
"0.6447467",
"0.643595",
"0.643595",
"0.6430054",
"0.6428785",
"0.64283556",
"0.6427815",
"0.63589954",
"0.6352107",
"0.63115436"... | 0.0 | -1 |
get paramters creates the parameter list for the method call | def get_path_parameters(parameters):
param_list = []
for param in parameters:
if param['paramType'] == 'path':
param_name = param['name']
param_list.append('{0}={1}'.format(param_name, param_name))
return param_list | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_params(self):",
"def get_params(self):\n pass",
"def get_params(self):\n raise NotImplementedError",
"def get_params(self):\n return []",
"def _get_parameters(self) -> list:\n return self.parameters",
"def params():\n raise NotImplementedError",
"def get_param... | [
"0.79626054",
"0.76968235",
"0.7587891",
"0.7461619",
"0.73960304",
"0.73912436",
"0.73753273",
"0.7243488",
"0.7119335",
"0.7075366",
"0.70457923",
"0.70423514",
"0.70313346",
"0.70313346",
"0.70131594",
"0.70106745",
"0.69905233",
"0.695566",
"0.6907039",
"0.69069386",
"0.6... | 0.0 | -1 |
Check for the existance of enums in the parameter list. If an enum exists, we need to build a tuple of the emum names as well as the code that will validate the the enum. We create two lists one that contains the enums as tuples and another that contains the valdiate code. The method returns a list that consistes of th... | def check_for_enums(parameters):
enum_line = ''
enum_lines = []
validate_enums = []
for param in parameters:
if 'enum' in param:
param_name = check_param(flatten_param(param['name']))
param_enum = param['enum']
enum_line = param_name + '_types = ('
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def list_available_enum(enum_type: Type[Enum]) -> List[str]:\n return [f\"{i}: {v}\" for (i, v) in enumerate(enum_type)] # type: ignore[var-annotated]",
"def handle_enum(enum_annotations: Any) -> list:\n result = []\n for attribute in list(enum_annotations):\n result.append(attribute... | [
"0.6401213",
"0.63181007",
"0.63141215",
"0.61072165",
"0.60310066",
"0.56923455",
"0.56852007",
"0.55856186",
"0.55427897",
"0.5502843",
"0.548926",
"0.5463231",
"0.542895",
"0.5422927",
"0.5402525",
"0.5377337",
"0.5361586",
"0.5300357",
"0.5296258",
"0.5267555",
"0.5186078... | 0.7524469 | 0 |
convert camelCase to camel_case | def convert(name):
s1 = re.sub('(.)([A-Z][a-z]+)', r'\1_\2', name)
return re.sub('([a-z0-9])([A-Z])', r'\1_\2', s1).lower() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def convert_camel_case(name):\n s1 = re.sub('(.)([A-Z][a-z]+)', r'\\1_\\2', name)\n return re.sub('([a-z0-9])([A-Z])', r'\\1_\\2', s1).lower()",
"def _to_camel_case(text: str) -> str:\n return \"\".join(word.title() for word in text.split(\"_\"))",
"def camel_case(value: str, **kwargs: Any) -> str:\n ... | [
"0.8216925",
"0.8098905",
"0.7977879",
"0.78845215",
"0.7877296",
"0.7851086",
"0.78430486",
"0.7833407",
"0.77893585",
"0.7788128",
"0.77770317",
"0.77436364",
"0.77385837",
"0.7737896",
"0.771665",
"0.771665",
"0.7715725",
"0.77044356",
"0.7662114",
"0.7646547",
"0.7639699"... | 0.6941522 | 76 |
check the parameter list for the pre_attachment[] parameter. This param is not correct, it's simply a placeholder for the | def check_for_pre_attachment_param(parameters):
for idx, param in enumerate(parameters):
if param['name'] == 'pre_attachment[*]':
del parameters[idx]
parameters.insert(idx, pre_attachment_content_type)
parameters.insert(idx + 1, pre_attachment_parent_folder_id)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def pre_provider_attachment_create(self, resource_dict):\n pass",
"def required_attachments(self, required_attachments):\n\n self._required_attachments = required_attachments",
"def pre_customer_attachment_create(self, resource_dict):\n pass",
"def setPreUp(self, pre):\n # type: (... | [
"0.5808577",
"0.5733911",
"0.55609274",
"0.530507",
"0.52761936",
"0.5258297",
"0.52529293",
"0.5246867",
"0.5238447",
"0.5232915",
"0.5209431",
"0.5200901",
"0.51895285",
"0.5150902",
"0.5100585",
"0.5098105",
"0.5080987",
"0.50742763",
"0.50629646",
"0.50629646",
"0.5062964... | 0.8184055 | 0 |
build method is used build the methods of the class we are processing. | def build_method(method_name, description, parameters, api_path, http_method, summary, return_type):
allow_per_page = False
parameters = check_for_pre_attachment_param(parameters)
arg_list = get_parameters(parameters)
param_descriptions = get_parameter_descriptions(parameters)
payload = build_payloa... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _build(self):",
"def _build(self):",
"def build(self):",
"def build(self):",
"def build(self):",
"def build(self) -> None:",
"def _build_impl(self):",
"def build(self):\n pass",
"def build(self):\n pass",
"def build (self):\n raise NotImplementedError",
"def build(self)... | [
"0.736621",
"0.736621",
"0.735565",
"0.735565",
"0.735565",
"0.7179113",
"0.7177833",
"0.7170467",
"0.7170467",
"0.70449257",
"0.6981165",
"0.69558483",
"0.69547623",
"0.69329727",
"0.6886207",
"0.6885989",
"0.68603706",
"0.67853916",
"0.6755418",
"0.65356845",
"0.6430983",
... | 0.0 | -1 |
build class reads in the api call for a class and contructs a class object to be written to a file. | def build_module(json_api_url):
resp = urllib.request.urlopen(json_api_url)
json_resp = json.load(resp)
apis = json_resp['apis']
content = line_format('from canvas_sdk import client, utils', NONE)
content += '\n\n'
"""
Extract the data needed to build the method from the json source
""... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self, api=None):\n self.file = open(OUTPUT_FILE, \"w\")",
"def createClassFile( p ):\n create_modules( p[\"package\"] )\n name = p[\"protocol\"][\"name\"]\n name.lower()\n path = os.path.join( *p[\"package\"].split( \".\" ) )\n with open( \"./%s/%s.py\" % ( path, name ), \"w\" ) as f:\n ... | [
"0.6720253",
"0.6239841",
"0.620899",
"0.6151118",
"0.6045171",
"0.5912636",
"0.58857656",
"0.58770084",
"0.58720964",
"0.5856519",
"0.57769555",
"0.575365",
"0.5580163",
"0.55573606",
"0.5530266",
"0.55120766",
"0.55050176",
"0.5504219",
"0.5500604",
"0.5449884",
"0.54467934... | 0.0 | -1 |
Create the canvas_sdk/methods directory if it doesn't already exist | def create_sdk_directories():
try:
os.makedirs(METHODS_DIR)
init_file = METHODS_DIR+'/__init__.py'
if not os.path.isfile(init_file):
new_init_file = open(init_file, 'w')
new_init_file.close()
except OSError as exception:
if exception.errno != errno.EEXIST... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def main(argv=None):\n if argv is None:\n argv = sys.argv\n\n parser = argparse.ArgumentParser(description='Build Canvas SDK methods')\n parser.add_argument('-u','--url', help='Base Canvas url, default is (https://canvas.instructure.com)')\n args = vars(parser.parse_args())\n\n \"\"\"\n De... | [
"0.6024213",
"0.5447391",
"0.54416734",
"0.5415922",
"0.5409212",
"0.5375797",
"0.52315885",
"0.52250993",
"0.51826185",
"0.5177353",
"0.51455146",
"0.5119641",
"0.5115451",
"0.50884795",
"0.50842744",
"0.50561905",
"0.5041633",
"0.5029327",
"0.50157297",
"0.49913386",
"0.497... | 0.7748334 | 0 |
the main method of the script calls the url provided by the user via command line arguments or a displays a usage message | def main(argv=None):
if argv is None:
argv = sys.argv
parser = argparse.ArgumentParser(description='Build Canvas SDK methods')
parser.add_argument('-u','--url', help='Base Canvas url, default is (https://canvas.instructure.com)')
args = vars(parser.parse_args())
"""
Default to instruct... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def main(args):\n parser = create_parser()\n\n if not args:\n parser.print_usage()\n sys.exit(1)\n\n parsed_args = parser.parse_args(args)\n scrape_url(parsed_args.url)",
"def main():\n\n # Title\n st.title(\"AB URL Helper\")\n st.subheader(\"Paste URL link below\")\n\n ####... | [
"0.7676096",
"0.72840065",
"0.7260419",
"0.7059537",
"0.70089793",
"0.7007226",
"0.6997864",
"0.69791156",
"0.69606084",
"0.69430315",
"0.6926483",
"0.6851956",
"0.6725049",
"0.67130995",
"0.67058045",
"0.66876036",
"0.66785455",
"0.6665331",
"0.66448766",
"0.66308403",
"0.66... | 0.0 | -1 |
This method is to find scores which are stored in dB. | def __fillScores(self, contentScores):
list_of_data_frames = list()
for cntScr in contentScores:
res = json.loads(cntScr.score)
df_pro = pd.DataFrame(res)
df_pro["productId_requested"] = cntScr.productId
df_pro["cart_score"] = self.cart_score_repository... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def db2score(self):\n print(\"db2score\")\n self.score.array_frame_start = self.arrayFrameStart\n self.score.array_frame_end = self.arrayFrameEnd\n self.score.arraySet = self.arraySet\n self.score.arrayGame = self.arrayGame\n self.score.arrayScore = self.arrayScore\n ... | [
"0.6539071",
"0.6111469",
"0.5994292",
"0.59466934",
"0.5894456",
"0.58336383",
"0.58269984",
"0.574957",
"0.57047516",
"0.570122",
"0.5692787",
"0.5640297",
"0.5611841",
"0.55991805",
"0.55441713",
"0.5531436",
"0.5517619",
"0.5495602",
"0.547449",
"0.5464151",
"0.5445944",
... | 0.0 | -1 |
This method demapp entity objects to data frame. It will help programmer to analys using pandas library. | def __object_demapper(self, data: list) -> pd.DataFrame:
data = pd.DataFrame.from_records([s.to_dict() for s in data])
return data | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def pandas_convert(self):\n data = {}\n\n for names in self.data[0]:\n col_values = []\n\n if names in objects:\n for items in self.data[0][names]:\n col_values = []\n\n col_name = names + \"_\" + items\n\n ... | [
"0.6892027",
"0.6508207",
"0.6472042",
"0.6281258",
"0.6169941",
"0.61465824",
"0.613803",
"0.61098313",
"0.6088293",
"0.6079013",
"0.6077937",
"0.6062319",
"0.60613316",
"0.6043503",
"0.60430515",
"0.60380507",
"0.60380507",
"0.60380507",
"0.60380507",
"0.60380507",
"0.60256... | 0.6550998 | 1 |
the method is to calculate the first products. | def __set_bias_score(self, row, best_score):
if row.productid == best_score:
result = 1
else:
result = row.final_score
return result | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def compute_price(self, date = None):\n\t\tif date is None:\n\t\t\tdate = datetime.now()\n\t\tself.price = 0\n\t\t# Getting list of product in cart\n\t\tcontent = self.cart.cart_content_set.all()\n\t\t# Dictionnary in order to compute minimum state of multi promotion\n\t\tstate = {\n\t\t\t'products':{},\n\t\t\t'pr... | [
"0.64534867",
"0.644534",
"0.644534",
"0.6229142",
"0.58825815",
"0.5860691",
"0.5853895",
"0.58197695",
"0.56922",
"0.56867915",
"0.5632201",
"0.56160545",
"0.5575754",
"0.55730057",
"0.55730057",
"0.55730057",
"0.5558104",
"0.55553544",
"0.554885",
"0.5529817",
"0.5513311",... | 0.0 | -1 |
get the top products. | def __get_top(self, result, top=10):
result = result.sort_values(by="bias_score", ascending=False).drop_duplicates(subset='productid', keep="first")
print(result)
result = result[:top].sort_values(by="final_score", ascending=False).productid
return list(result) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def top_products():\n products = product.Product.query. \\\n order_by(desc(product.Product.likes)).all()[:6]\n context = {\n 'products': products\n }\n return render_template('stores/topstores.html', **context)",
"def top(self, **kwargs):\n return self.client.api.top(self.id, **k... | [
"0.82457525",
"0.68613315",
"0.66651356",
"0.6496626",
"0.6484569",
"0.6435276",
"0.63997036",
"0.6326196",
"0.6323446",
"0.6315343",
"0.6285002",
"0.6270289",
"0.62692916",
"0.6223334",
"0.62199473",
"0.6199094",
"0.6195963",
"0.616619",
"0.6165667",
"0.6154092",
"0.61534995... | 0.6796019 | 2 |
get the top products with more detail. | def __get_top_with_detail(self, result, top=10):
result = result.sort_values(by="bias_score", ascending=False).drop_duplicates(subset='productId', keep="first")[
:top]
return result | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def top_products():\n products = product.Product.query. \\\n order_by(desc(product.Product.likes)).all()[:6]\n context = {\n 'products': products\n }\n return render_template('stores/topstores.html', **context)",
"def top(self, **kwargs):\n return self.client.api.top(self.id, **k... | [
"0.7941322",
"0.68568563",
"0.6373423",
"0.63633126",
"0.6357755",
"0.63397264",
"0.6330925",
"0.62385094",
"0.62217975",
"0.6208066",
"0.61090535",
"0.6041962",
"0.6031527",
"0.6023199",
"0.60011786",
"0.59878904",
"0.5984639",
"0.59839493",
"0.59718734",
"0.59687245",
"0.59... | 0.68398494 | 2 |
This is prediction method. | def predict(self, products: list):
try:
self.log.info("Prediction is started.")
contentScores: list = self.content_score_repository.get_by_products(products)
df = self.__fillScores(contentScores)
result = self.__get_top(df)
self.log.info("Top values... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _get_prediction(self):\n raise NotImplementedError",
"def predict(self):\n raise NotImplementedError",
"def predict_proba(self):\n ...",
"def _predict(self, x):\n pass",
"def _predict(self, testX):\n pass",
"def predict(self, predPoints=None):",
"def predict_only(... | [
"0.8059006",
"0.78803074",
"0.7860856",
"0.774276",
"0.76850456",
"0.7635276",
"0.760205",
"0.75458163",
"0.75458163",
"0.75458163",
"0.7529297",
"0.7527876",
"0.7512121",
"0.7512121",
"0.74871856",
"0.7483808",
"0.7418165",
"0.7404916",
"0.7338522",
"0.7328177",
"0.7263635",... | 0.0 | -1 |
Gets the variables in the template from the logs limited to the ones represented by asterisks ('') only. These are the only ones being considered in the triples. As the pointers advance on both the log and the template the variables are only collected when is encountered on the template | def __get_vars_list(self, template_idx, log):
template = self.templates[template_idx].split()
log = log.split()
variables = []
pt = pl = 0
while pt < len(template) and pl < len(log):
if template[pt] == log[pl]:
pt += 1
pl += 1
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _get_placeholders(template):\n return [p[1] for p in string.Formatter().parse(template)\n if p[1] is not None and len(p[1]) > 0]",
"def find_template_variables(code):\n return re.findall(re_template_var, code)",
"def get_variables(self):\n return [self.g_t, self.m_t]",
"def context(te... | [
"0.5529201",
"0.5246363",
"0.51169056",
"0.5084532",
"0.5074781",
"0.49252644",
"0.4919884",
"0.486573",
"0.48477352",
"0.48411506",
"0.48227668",
"0.48202914",
"0.4777373",
"0.476468",
"0.47486478",
"0.473558",
"0.47355154",
"0.47204447",
"0.47187525",
"0.47119597",
"0.47052... | 0.687035 | 0 |
Wait for the call count on the mock to reach or exceed a threashold, or a timeout occurs. Useful for multithreaded testing. | def wait_for_calls(mock, count, timeout_seconds):
timeout = datetime.datetime.now() + datetime.timedelta(seconds=timeout_seconds)
while mock.call_count < count:
time.sleep(0.01)
if datetime.datetime.now() > timeout:
return False
return True | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _wait_before_call(self):\n while (dt.datetime.now() - self._last_call_ts) <= dt.timedelta(\n seconds=self.api_timeout\n ):\n time.sleep(0.5)\n self._last_call_ts = dt.datetime.now()",
"def wait_fluently(condition: Callable, timeout: TimeoutType, err_msg: str):\n ... | [
"0.6498459",
"0.6334335",
"0.632674",
"0.6275306",
"0.62697285",
"0.6268087",
"0.62155145",
"0.616202",
"0.6141317",
"0.61329114",
"0.60898757",
"0.6051971",
"0.60427356",
"0.6019092",
"0.60161436",
"0.6015936",
"0.6009689",
"0.6005865",
"0.596967",
"0.5925834",
"0.58968174",... | 0.7268512 | 0 |
Return all states formated as a dictionary | def state_format(states: list) -> list:
return list(map(_format_n0, states)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def states():\n states = storage.all(State).values()\n return jsonify([item.to_dict() for item in states])",
"def all_states():\n dict = storage.all(State)\n list = []\n for state in dict.values():\n list.append(state.to_dict())\n return jsonify(list)",
"def get_states():\n all_stat... | [
"0.77571523",
"0.7694623",
"0.7661228",
"0.7641882",
"0.75851685",
"0.7568326",
"0.7566142",
"0.75567716",
"0.75191",
"0.7493844",
"0.7472908",
"0.74583113",
"0.7420259",
"0.740409",
"0.73985374",
"0.7392214",
"0.7369381",
"0.73459816",
"0.73459816",
"0.7301348",
"0.7300903",... | 0.0 | -1 |
Return a human readable string. | def __str__(self):
return f"#{self.number}| {self.active}| {self.name}: {self.desc}" | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __str__(self):\n return 'str-human.%s' % self.name",
"def __str__(self):\n return f\"{self._desc:16s}\"",
"def summary_string(self) -> str:",
"def get_human_readable(self):\n\n def yesno(key):\n if getattr(self, key) and getattr(self, key) > 0:\n return \"Y\... | [
"0.7690259",
"0.7156136",
"0.71460164",
"0.7129925",
"0.6952552",
"0.6923994",
"0.689927",
"0.6893318",
"0.6868879",
"0.6835699",
"0.6826505",
"0.6824305",
"0.68146354",
"0.6811943",
"0.6803569",
"0.6789633",
"0.6782024",
"0.6780865",
"0.6779728",
"0.6779728",
"0.6779728",
... | 0.0 | -1 |
Return a string to display in the visual mode. | def __repr__(self):
return f"{self.number} {self.name}: {self.desc}" | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def show(self) -> str:\n return f'[{self.font}]{self.text}[{self.font}]' if self.font else self.text",
"def __repr__(self):\n return self._format() if self.always_visible or not self.is_pointless() else ''",
"def display(self) -> str:\n lines, _, _, _ = self._display_aux()\n return ... | [
"0.6805551",
"0.67020464",
"0.6681074",
"0.6531612",
"0.6505154",
"0.64923066",
"0.6411581",
"0.6372688",
"0.62355006",
"0.6220548",
"0.6206158",
"0.6187882",
"0.61819524",
"0.61762536",
"0.6133741",
"0.6095917",
"0.6095917",
"0.6075232",
"0.60512745",
"0.6040153",
"0.6035276... | 0.0 | -1 |
Print the description of the measure for the player. | def menu(self):
print(f"{str(self)}") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def description(self) -> str:\r\n description = \"The player must aim to put the most possible units \" + \\\r\n \"of \" + colour_name(self.colour) + \" on the outer\" +\\\r\n \" perimeter.\"\r\n return description",
"def description(self) -> str:\r\n ... | [
"0.6940499",
"0.69227624",
"0.6609904",
"0.65955704",
"0.6549176",
"0.6460658",
"0.6451413",
"0.64315027",
"0.64310527",
"0.63806295",
"0.63690436",
"0.63690436",
"0.63664997",
"0.63568395",
"0.6350418",
"0.6337432",
"0.6329141",
"0.6290839",
"0.6274496",
"0.6263658",
"0.6253... | 0.0 | -1 |
Returns the active status T|F of the measure. | def is_active(self):
return self.active | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _get_lsp_frr_operational_status_active(self):\n return self.__lsp_frr_operational_status_active",
"def _tstat_alpha(self):\n return _handle_ab(self._tstat_all, self.use_const)[0]",
"def get_status():\n return ('off', 'off')",
"def get_current_s(self):\n return 1 if self.ff_states[0] e... | [
"0.65024024",
"0.62104243",
"0.610275",
"0.6102556",
"0.6042355",
"0.59762746",
"0.59715307",
"0.59574205",
"0.58851373",
"0.58848566",
"0.586097",
"0.58262444",
"0.5805399",
"0.5769604",
"0.5743072",
"0.57369876",
"0.5720854",
"0.56900233",
"0.56890976",
"0.56871843",
"0.568... | 0.0 | -1 |
Switches the measure status and returns the correct effect factor. | def update_return_factor(self):
if not self.active:
effect = self._activate_return_factor()
elif self.active:
effect = self._deactivate_return_factor()
return effect | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _deactivate_return_factor(self):\n self.active = False\n effect = 1 / self.factor\n return effect",
"def _activate_return_factor(self):\n self.active = True\n effect = self.factor\n return effect",
"def effectiveness(self):\n self._effectiveness = 0.20 * sel... | [
"0.6460598",
"0.6209007",
"0.6098955",
"0.57197714",
"0.565701",
"0.565701",
"0.565701",
"0.565701",
"0.565701",
"0.565701",
"0.565701",
"0.565701",
"0.565701",
"0.565701",
"0.565701",
"0.565701",
"0.565701",
"0.565701",
"0.565701",
"0.565701",
"0.565701",
"0.565701",
"0.... | 0.5943068 | 3 |
Helper function for update_return_factor > activates. | def _activate_return_factor(self):
self.active = True
effect = self.factor
return effect | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def update_return_factor(self):\n if not self.active:\n effect = self._activate_return_factor()\n elif self.active:\n effect = self._deactivate_return_factor()\n return effect",
"def _deactivate_return_factor(self):\n self.active = False\n effect = 1 / sel... | [
"0.7619489",
"0.7287964",
"0.58440965",
"0.5589851",
"0.55477417",
"0.55023503",
"0.5478167",
"0.5450696",
"0.5412159",
"0.54077435",
"0.5368345",
"0.53519577",
"0.53423446",
"0.53316164",
"0.5300739",
"0.52776587",
"0.5277615",
"0.52698386",
"0.52678233",
"0.52527237",
"0.52... | 0.7389867 | 1 |
Helper function for update_return_factor > deactivates. | def _deactivate_return_factor(self):
self.active = False
effect = 1 / self.factor
return effect | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def update_return_factor(self):\n if not self.active:\n effect = self._activate_return_factor()\n elif self.active:\n effect = self._deactivate_return_factor()\n return effect",
"def _activate_return_factor(self):\n self.active = True\n effect = self.facto... | [
"0.72491986",
"0.7173555",
"0.565905",
"0.548633",
"0.52655476",
"0.52646697",
"0.5120895",
"0.5088434",
"0.50735825",
"0.5009577",
"0.49936694",
"0.4986948",
"0.49810562",
"0.49603486",
"0.49565622",
"0.4942595",
"0.49352244",
"0.4931155",
"0.4921053",
"0.49184957",
"0.49069... | 0.8133898 | 0 |
Generates JSON for all categories | def allCategoriesJSON():
categories = db_session.query(Category).all()
return jsonify(categories=[c.serialize for c in categories]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def all_categories_handler():\n categories = getAllCategories()\n return jsonify(categories=[i.serialize for i in categories])",
"def get_all_categories():\n return jsonify({\n \"success\": True,\n \"categories\": _read_all_categories()\n })",
"def categoriesJSON():\n ... | [
"0.80022",
"0.7764015",
"0.7749237",
"0.7702453",
"0.7634991",
"0.76159203",
"0.75909245",
"0.7532368",
"0.74889326",
"0.7449233",
"0.74030787",
"0.7385225",
"0.7377895",
"0.7366753",
"0.73639095",
"0.7266052",
"0.721529",
"0.714454",
"0.7087932",
"0.7027108",
"0.6994505",
... | 0.78281426 | 1 |
Generates JSON for a book specified by the book_id | def bookJSON(book_id):
book = db_session.query(Book).filter_by(id=book_id).one()
return jsonify(book=book.serialize) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def book_info(book_id):\n\n book = data_manager.get_book(book_id)\n if not book:\n return jsonify({\"error\": \"No book found\"})\n if book.has_records == False:\n return jsonify({'book_id': book_id,\n 'title': book.title,\n 'author': book.author... | [
"0.68194824",
"0.66605145",
"0.6633134",
"0.6563902",
"0.65159816",
"0.65159816",
"0.6498835",
"0.6487234",
"0.64699584",
"0.6375881",
"0.6324021",
"0.63159806",
"0.63023865",
"0.6282721",
"0.62815195",
"0.6272364",
"0.60790217",
"0.59934425",
"0.5943473",
"0.5903797",
"0.589... | 0.8344132 | 0 |
Generates JSON for a category with all books in the category Specified by the category_id | def categoryWithBooksJSON(category_id):
books = db_session.query(Book).filter_by(category_id=category_id).all()
return jsonify(Category=[b.serialize for b in books]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def api_category_items(category_id):\n items = session.query(Item).filter(Item.category_id == category_id)\n return jsonify(json_list=[i.to_json() for i in items.all()])",
"def categoryItemJSON(category_id):\n category = db.getByCategory(category_id)\n items = db.getItemsByCategory(category_id)\n ... | [
"0.7138958",
"0.7041303",
"0.69258463",
"0.6853855",
"0.67250127",
"0.6645023",
"0.66027105",
"0.65864533",
"0.6539297",
"0.6498139",
"0.642202",
"0.6416721",
"0.63761836",
"0.6373455",
"0.6321239",
"0.62749213",
"0.62676096",
"0.6253013",
"0.62493944",
"0.6240726",
"0.622268... | 0.8570861 | 0 |
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